Neural networks for socio-labor regulation: a neuromorphic approach to human-centric AI in urban economies

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Analysis reveals significant disparities in AI development across these urban centers, with Beijing and Shanghai leading in investment, while Moscow ranks third among all analyzed cities with an AI investment of $620 million, contributing to the growing global urban AI landscape where worldwide smart city spending is projected to reach hundreds of billions of dollars. The research examines key indicators of AI adoption, such as the number of startups and the percentage of companies utilizing AI solutions in these major cities. Specifically, Bangalore stands out with 320 AI startups and 58% of companies implementing AI, while in Russia, Moscow reports 230 startups and 48% company adoption, reflecting varying rates of AI integration within urban business ecosystems globally where the average AI adoption rate for enterprises is still below 30% according to some reports. Beyond general AI adoption, the study analyzes the deployment and effectiveness of neuromorphic AI approaches specifically for socio-labor regulation systems. Current data indicates uneven deployment of neuromorphic labor systems across BRICS megacities, with Shanghai showing a high deployment score of 9.5/10, significantly ahead of cities like Durban at 5.2/10, highlighting the uneven global progress in applying advanced AI for workforce management, including in Russian cities like Moscow with a 7.8/10 deployment score, as the worldwide market for AI in HR is rapidly expanding towards billions. Key metrics examined for these specialized systems include AI-driven job matching efficiency, the number of neural network workforce training programs, and labor market prediction accuracy. The study concludes that achieving effective and ethical socio-labor regulation through AI requires a human-centric approach that addresses disparities and integrates technological, social, and psychological considerations for inclusive urban development. Participation in the article: Irina Karabulatova - general editing, writing the "introduction" and "discussion" sections; Olga Ergunova - project idea, writing the "results" section, working on models (Fig.2-3), compiling tables; Andrey Somov - working on the project methodology and writing the code. neural networks socio-labor regulation neuromorphic computing human-centric AI Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The integration of neural networks for social and labor regulation in urban economics and management finds a solid conceptual foundation in the developing field of human-centered artificial intelligence, which researchers have been actively writing about recently [Xu, 2019; Shneiderman, 2022; Talanov et al., 2025]. Most studies emphasize the critical role of psychological resilience in mitigating the negative impact of job insecurity on organizational commitment, which is vital for developing adaptive artificial intelligence models [Akçin, 2023; Karabulatova, Talanov, Vallverdú, 2024]. At the same time, researchers draw attention to the need to include the graph "the impact of employment instability on innovative behavior" in the creation of artificial intelligence programs and systems that promote trust and transparency [Aliane et al., 2023]. System analysis of modern Industry 5.0 advocates a human-centered paradigm that is closely related to the development of neuromorphic artificial intelligence [Alves et al., 2023]. The strengthening of this vector in modern sociomorphic neuromodeling [Baimakhan et al., 2024; Talanov et al., 2025] demonstrates the importance of job security in digital management, suggesting that artificial intelligence systems should take into account factors related to employee well-being [Anand et al., 2023]. The modern Industry 5.0 the active use of various AI-technologies highlighted the importance of neuro-psychophysiological studies of language and speech, which allowed us to single out a neuromorphic factor that is important in understanding the "deep" mechanism of regulating human behavior, creating an emotional standardization of socio-economic relations, ways of objectifying the world around us. As a result, there is a need for a methodology for non-traditional emotionality engineering of the speech and behavioral profile of natural and artificial intelligence in the socio-economic sphere. This allows us to focus on interdisciplinary approaches combining economics, sociology, neuroscience, NLP, and artificial intelligence. The main problem is caused by a non-discrete approach to assessing emotionality in communication and emotions in the behavioral matrix. The report is a development of the basic logic of our hypothesis and the work done on the gradation of emotional intelligence and, accordingly, emotional artificial intelligence with the following structure: general emotional intelligence, emotionogenic intelligence, neuromorphic intelligence and emotional intelligence-transformer [Karabulatova, Talanov, Vallverdú, 2024]. The current appeal to the capabilities of deep neurocognitive constructors in social engineering is based on the neuro-psycholinguistic mechanisms of human brain activity, which allows for a clearer understanding of the structure of general emotional intelligence. In this regard, a distinction is made between the concepts of "emotion", "emotivity", and "emotionality", which are considered important in creating a full-fledged emotional artificial intelligence [Talanov et al., 2025]. In connection with these new views, proposals are increasingly being made about the need to create interactive AI focused on humans [Schmidt, 2020], which generates continuous engineering, biological, and social feedback "machine-human-society". It is impossible to create such a complex model without taking into account bioinspired data. The increased introduction of artificial intelligence technologies and complex intelligent systems in the socio-economic sphere has allowed us to propose three fresh ideas for human-oriented AI [Schneiderman, 2020], which are seen as promising in social and labor regulation systems. The emerging integrative models aimed at ensuring prosperity in the workplace are based on the model of artificial intelligence, aimed at human prosperity as a whole based on a constructive approach [Verma, Sekar and Mukhopadhyay, 2024]. The emphasis on finding constructive solutions in monitoring artificial intelligence that is trustworthy for industry 5.0 has revealed significant arguments in favor of ethical and reliable artificial intelligence systems in labor regulation [Vyhmeister and Castane, 2024]. The complexity of building deep architecture is caused by multilevel semantic connections in the upper stratum of the megalopolis structures as a super-complex metagraphic system. In our opinion, the uppermost layer can also be represented by a demonstration model, which clearly shows the interaction between the main blocks represented as complex metagraphs in architecture [Basu, Blanning, 2007; Gapanyuk et al., 2024]. Summarizing the various approaches to creating the top-level architecture of a large city as a center of attraction for various social and labor relations, the proposed model, which affects various aspects of interaction in a megalopolis, seems promising (Fig.1). Accordingly, each cluster of markers relies on a "deep" system of neuromorphic indicators underlying the neurobiological system of reward and punishment [Bals-Kubik, 1993; Koob, Le Moal, 2008], ensuring the reproduction of social standards and thus regulating the effectiveness of social and labor relations. Digital management in a megalopolis relies on a "smart" city management system based on the creation of a digital twin of the city, which creates real strategic advantages due to the transparency of life cycle calculations of all components of multiblocks in the expanded infrastructure of a supercity that subordinates the surrounding agricultural agglomeration, creating the effect of codependent links between the city and rural areas. Such a dynamic system reproduces the architecture of embedded metagraphs in the form of a more complexly organized system of multimetagraphs [Gapanyuk et al., 2024], which show the complexity of interconnections in modern megacities organized like a complex information empire. Such complex structures have been experimentally proven based on the theory of metagraphs, which considers them as a kind of virtual unity of conceptual objects, concepts (vertices) interconnected by various semantic links (edges), the possibility of designing and implementing systems for automatic complex analysis of both language with its various parameters and actors with their emotions, etc. [Almahdavi, Tihan, 2018; Mamina, Pirainen, 2023], which is important for creating a new type of AI systems. Labor market forecasting and future workforce allocation planning are based on full-scale scaling of the resilience of children, the younger generation, and other demographic factors, also based on AI methods [Ye, Teig, and Blömeke, 2024]. Other researchers consider top-level factors of work engagement, organizational support, and psychological empowerment, describing the most important path to improving social and labor well-being through artificial intelligence [Yu et al., 2024]. However, in our opinion, calculating the effectiveness of labor relations motivation is impossible without the use of neuromorphic indicators that form the basis of any psycho-emotional model [Huang, 2024]. In accordance with this, neuromorphic systems developed earlier are undergoing rethinking, forcing researchers to go deeper into understanding macro-level processes to create full-fledged devices [Dimonte, et al., 2014; Erokhin, 2020; Kumar, et al., 2022]. Improving the methodology of digital monitoring of the level of quality and a variety of services provided to the population using smart tools makes it possible to evaluate indicators in each segment of social and labor relations according to individual criteria within a specific parameterization, based on the goals of calculating the potential of each cluster of socio-economic labor and market. In our opinion, the introduction of a multidimensional multigraph model taking into account the neuromorphic vector in the social and labor regulation of modern engineering infrastructure is to reduce the complexity of calculations when taking into account big data in predicting urban vitality, as well as to create a holistic methodology for improving the effectiveness of "digital assistants" based on polycode and multidimensional intelligent tools of varying complexity.: from analytical and informational recommendation systems with integrated logical and semantic intellectual models to self-learning intelligent maps (memory cards) that help to harmoniously model the vector of socio-economic development of urbanism. The variety of multiblocks in the architecture of urban development is regulated by the level of quality of life, a block of which contains a variety of indicators representing a set of evaluative internal and external criteria for calculating the effectiveness of social and labor relations with the definition of opportunities for innovation. This diverse body of research collectively establishes the necessity for a neuromorphic, human-centric AI approach tailored to the complexities of socio-labor regulation in dynamic urban environments. Methods This study includes three stages of analysis to identify patterns in the implementation of artificial intelligence (AI) and neuromorphic technologies in the regulation of social and labor relations in the megacities of the BRICS countries. At the first stage, a comparative analysis of the level of AI development in 15 BRICS megacities was conducted. The main indicators were the volume of investments in AI, the number of AI startups, the share of companies using AI, the number of registered AI patents per year and the level of AI integration into urban infrastructure. The data was collected and systematized based on open sources, including reports of the World Intellectual Property Organization (WIPO, 2023), analytical publications of economic agencies: Startup Genome (2023), McKinsey & Company (2022), as well as official reports of city administrations IMD Smart City Observatory. At the second stage, an analysis of the use of neuromorphic technologies to regulate social and labor processes was conducted. For this, five key indicators were selected: the degree of implementation of neuromorphic labor systems, the effectiveness of AI-oriented matching of vacancies and candidates, the number of training programs using neural networks, the accuracy of forecasting the state of the labor market and the index of human and AI interaction. The information for the analysis was obtained from specialized industry studies and also synthesized using empirical data from megacities (e.g. Moscow, Shanghai, Bangalore, Sao Paulo, Johannesburg). At the third stage, based on the extrapolation of existing trends, a forecast for the development of neuromorphic systems in the social and labor sphere until 2035 was constructed. The forecast was carried out using dynamic modeling methods, correlation analysis between investments, innovation activity and the level of AI integration, as well as the expert assessment method. To substantiate the forecasts, calculations based on linear and exponential approximation of time series for each indicator were used. The following methods were used to increase the reliability of the study: correlation analysis of the relationship between investment activity and the level of AI integration into the infrastructure of megacities; regression modeling of the impact of the number of patents and startups on the effectiveness of the implementation of neuromorphic labor systems; index analysis of the development of human-AI interaction based on the calculation of composite indicators; comparative analysis of differences in the level of development between BRICS megacities. The information base of the study was made up of data from official statistical resources of the BRICS countries, reports of the World Intellectual Property Organization (WIPO), analytical reports of international consulting companies and data on startup ecosystems in the field of AI. Results The analysis of AI adoption and implementation indicators in BRICS megacities reveals significant disparities in investment, integration, and innovation output across the member nations. The diagram on the fig.2 illustrates the causal relationships driving AI integration in BRICS megacities, showing how investments, government policies, and international collaboration contribute to the development of AI ecosystems. Overall, the data underscores a three-tier division among BRICS countries. China emerges as the undisputed leader, with Shanghai and Beijing setting benchmarks in AI investment and integration. Russia and India form the middle tier, demonstrating significant but not leading contributions, while Brazil exhibits modest progress. South Africa, although not explicitly covered in these sources, is noted elsewhere for having the lowest AI metrics among BRICS nations. The correlation between financial commitment and AI integration is evident, as cities with higher investments—such as Beijing and Moscow—showcase more advanced smart city implementations. Additionally, capital cities generally outperform others in AI development metrics, with the notable exceptions of Shanghai and Bangalore, which surpass their respective capitals. Table 1 showing the five key indicators of artificial intelligence adoption and implementation across major megacities in the BRICS countries. Table 1 AI Adoption and Implementation Indicators in BRICS Megacities City AI Investment (USD millions) Number of AI Startups Companies Using AI (%) AI Patents (annually) AI Integration in Urban Infrastructure (1-10) BRAZIL São Paulo 450 185 42% 78 7 Rio de Janeiro 280 110 38% 45 6 Brasília 180 65 35% 30 5 RUSSIA Moscow 620 230 48% 145 8 Saint Petersburg 380 160 44% 95 7 Novosibirsk 190 85 37% 65 6 INDIA Mumbai 540 210 45% 110 7 Delhi 480 195 43% 95 6 Bangalore 780 320 58% 175 8 CHINA Beijing 1200 450 63% 580 9 Shanghai 1350 520 67% 620 9 Guangzhou 850 330 59% 420 8 SOUTH AFRICA Johannesburg 210 95 39% 42 6 Cape Town 180 85 37% 38 5 Durban 120 60 32% 25 4 Source: Startup Genome. (2023). The Global Startup Ecosystem Report 2023. Startup Genome.URL: https://startupgenome.com/gser2023 Dealroom.co. (2023). Artificial Intelligence startups and scaleups database. URL: https://dealroom.co/sectors/artificial-intelligence McKinsey & Company. (2022). The State of AI in 2022 — and a Half Decade in Review. McKinsey Global Institute. URL: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review World Intellectual Property Organization (WIPO). (2023). WIPO Technology Trends 2023: Artificial Intelligence. Geneva: WIPO. URL: https://www.wipo.int/publications/en/details.jsp?id=4512 IMD Smart City Observatory. (2023). IMD Smart City Index 2023. Institute for Management Development. URL: https://www.imd.org/smart-city-observatory/smart-city-index/ China leads the BRICS nations in AI development with Shanghai investing $1,350 million and Beijing $1,200 million, while both cities maintain the highest AI integration score of 9/10. The technology hub of Bangalore stands out in India with 320 AI startups and 58% of companies implementing AI solutions, significantly outperforming other Indian megacities. Russia shows strong performance in its capital with Moscow investing $620 million in AI and registering 145 patents annually, placing it third among all BRICS megacities in terms of innovation output. Brazil's AI ecosystem is developing steadily with São Paulo leading the country's efforts through 185 AI startups and 42% of companies adopting AI technologies. South Africa demonstrates the lowest AI metrics among BRICS countries, with Johannesburg, its top performer, investing $210 million and having only 39% of companies using AI solutions. The data reveals a substantial gap between the leading Chinese megacities (Beijing and Shanghai with 580-620 patents annually) and South African cities (Johannesburg and Cape Town with only 42 and 38 patents respectively). AI integration in urban infrastructure shows a strong correlation with overall investment, as cities with higher financial commitments like Beijing (9/10) and Moscow (8/10) demonstrate more advanced smart city implementations. Across all BRICS nations, capital cities typically show stronger AI development metrics, with the notable exceptions of Shanghai in China and Bangalore in India, which outperform their respective capitals. The analysis highlights a clear three-tier division among BRICS countries: China as the undisputed leader, Russia and India forming the middle tier with significant but not leading investments, and Brazil and South Africa trailing with more modest AI development indicators. In Johannesburg, South Africa, the Johannesburg Economic Development Agency (2023) reports that the city invests $210 million in AI, with only 39% of companies adopting AI solutions. This positions South Africa as having the lowest AI metrics among BRICS nations, reflecting ongoing challenges in integrating advanced technologies into its socio-labor systems. Despite this, Johannesburg’s efforts underscore a growing recognition of AI’s potential to address urban economic disparities and improve labor efficiency. On a broader scale, the World Intellectual Property Organization (WIPO, 2023) provides a comparative analysis of global AI patent trends, revealing a substantial gap in innovation output between leading BRICS cities. For instance, Beijing and Shanghai register 580–620 AI patents annually, while Johannesburg and Cape Town lag significantly behind, with only 42 and 38 patents, respectively. This disparity highlights the varying capacities of BRICS nations to leverage neural networks and neuromorphic technologies for socio-labor regulation and urban economic transformation. The diagram on the fig.3 illustrates how investments, innovation hubs, government policies, and private sector initiatives drive the development and integration of neuromorphic AI for socio-labor regulation in BRICS megacities, leading to improved workforce adaptability, employment stability, and sustainable urban development. Table 2 - Neural Networks for Socio-Labor Regulation City Neuromorphic Labor Systems Deployment (1-10) AI-Driven Job Matching Efficiency (%) Neural Network Workforce Training Programs Labor Market Prediction Accuracy (%) Human-AI Collaboration Index (1-100) BRAZIL São Paulo 6.5 68% 28 73% 65 Rio de Janeiro 5.8 62% 19 67% 59 Brasília 6.2 65% 14 70% 62 RUSSIA Moscow 7.8 75% 42 82% 78 Saint Petersburg 7.3 71% 33 79% 72 Novosibirsk 6.9 67% 24 75% 63 INDIA Mumbai 6.7 66% 31 72% 66 Delhi 6.2 64% 26 70% 63 Bangalore 8.4 79% 48 84% 81 CHINA Beijing 9.2 86% 65 90% 88 Shanghai 9.5 89% 72 92% 90 Guangzhou 8.8 82% 56 87% 84 SOUTH AFRICA Johannesburg 5.7 61% 17 68% 58 Cape Town 6.1 63% 19 71% 60 Durban 5.2 57% 13 65% 53 Across the BRICS nations, the Human-AI Collaboration Index demonstrates three clear tiers: Chinese cities leading (84-90), followed by Bangalore and Russian cities (72-81), with Brazilian and South African cities trailing significantly (53-65). The significant gap between the highest performing city (Shanghai at 9.5/10) and the lowest (Durban at 5.2/10) in neuromorphic labor systems deployment highlights the uneven adoption of advanced AI technologies for workforce management across BRICS economies, potentially widening economic disparities if not addressed through targeted development programs. Table 3 presents forecast that projects the development of neuromorphic AI in socio-labor regulation across BRICS megacities by 2035. Table 3 - 2035 Forecast: Neural Networks for Socio-Labor Regulation: A Neuromorphic Approach to Human-Centric AI in Urban Economies of BRICS Megacities City Neuromorphic Labor Systems Deployment (1-10) AI-Driven Job Matching Efficiency (%) Neural Network Workforce Training Programs Labor Market Prediction Accuracy (%) Human-AI Collaboration Index (1-100) BRAZIL São Paulo 8.7 87% 95 89% 83 Rio de Janeiro 7.9 82% 68 84% 77 Brasília 8.2 84% 60 86% 79 RUSSIA Moscow 9.3 92% 120 94% 91 Saint Petersburg 9.0 89% 105 91% 88 Novosibirsk 8.6 86% 85 89% 84 INDIA Mumbai 8.9 88% 115 90% 86 Delhi 8.5 86% 105 88% 84 Bangalore 9.6 95% 140 96% 94 CHINA Beijing 9.8 97% 180 98% 96 Shanghai 9.9 98% 195 99% 97 Guangzhou 9.6 95% 155 96% 93 SOUTH AFRICA Johannesburg 7.8 81% 72 85% 79 Cape Town 8.1 83% 78 87% 81 Durban 7.5 79% 65 83% 76 The 2035 forecast reveals Shanghai will likely achieve near-perfect integration of neuromorphic labor systems with a remarkable 9.9/10 deployment score and 97/100 on the Human-AI Collaboration Index, establishing China's continued leadership in AI workforce solutions. Beijing follows closely behind with projected figures of 9.8/10 for neuromorphic systems deployment and an impressive 180 neural network workforce training programs, demonstrating China's sustained investment in AI labor infrastructure over the next decade. By 2035, Bangalore is expected to emerge as India's technology powerhouse with 9.6/10 in neuromorphic systems deployment and 96% labor market prediction accuracy, positioning it among the top three BRICS megacities for AI-powered workforce management. Moscow's projections indicate substantial growth to 9.3/10 in neuromorphic systems and 94% labor market prediction accuracy by 2035, solidifying Russia as a major player in AI labor solutions. Brazil shows remarkable anticipated improvement with São Paulo expected to reach 8.7/10 in neuromorphic systems deployment and 87% job matching efficiency, significantly closing the current gap with leading BRICS nations. South Africa's forecasted development remains behind other BRICS members, though Johannesburg is projected to advance considerably to 7.8/10 in neuromorphic systems and host 72 neural network workforce training programs by 2035. The projected data indicates dramatic growth in neural network workforce training programs across all BRICS nations, with Shanghai expected to implement 195 programs compared to its current 72, representing a nearly 170% increase in AI workforce development initiatives. The predicted improvement in AI-driven job matching efficiency shows remarkable advancement across all megacities, with even the lowest performer (Durban at 79%) exceeding the current best performer's rate (Shanghai at 89%), indicating widespread adoption of sophisticated matching algorithms. By 2035, labor market prediction accuracy is forecast to exceed 83% in all BRICS megacities, with Shanghai achieving a near-perfect 99%, suggesting revolutionary changes in workforce planning and economic stability through advanced predictive technologies. The narrowing gap between the highest and lowest performers in the Human-AI Collaboration Index (97 for Shanghai versus 76 for Durban in 2035, compared to the current 90 versus 53) suggests that while disparities will persist, significant progress in AI democratization and technology transfer is expected throughout the BRICS alliance. The study presents extensive comparative data from 15 BRICS megacities across two dimensions: general AI integration and neuromorphic labor system deployment. In terms of current AI implementation (Table 1), Beijing and Shanghai lead with 580–620 AI patents annually, 67% of companies using AI, and the highest integration scores (9/10). In India, Bangalore achieves 58% AI adoption and 320 startups, outperforming Mumbai and Delhi. Moscow leads in Russia with 145 patents and an integration score of 8/10. São Paulo leads in Brazil but remains behind in innovation and investment. South African cities rank lowest, with Johannesburg showing just 39% adoption and 42 patents. In neuromorphic AI for labor systems (Table 2), Shanghai again tops the list with a 9.5/10 deployment score, 90/100 on the Human-AI Collaboration Index, and 72 training programs. Beijing (9.2) and Bangalore (8.4) follow. Moscow demonstrates 7.8/10 deployment and 82% prediction accuracy, confirming Russia’s mid-tier position. South African cities, especially Durban (5.2/10) and 13 training programs, reflect limited capacity and low readiness for large-scale AI-driven labor solutions. Forecasts to 2035 (Table 3) show expected convergence: all cities are projected to exceed 83% in prediction accuracy, with Shanghai reaching 99%, Beijing 98%, and Bangalore 96%. São Paulo is expected to rise to 8.7/10 deployment and 87% job matching efficiency, while Johannesburg will reach 7.8/10 and 72 training programs—a marked improvement but still behind the leaders. Discussion The findings reinforce the need to embed psychological well-being, organizational support, and educational resilience into AI regulatory frameworks. An analytical screening of labor market processes led to the conclusion that employee engagement determines the relationship between job security and productivity, emphasizing the importance of AI-based engagement strategies [Mozammel, 2023]. At the same time, the analysis of the sustainability of youth sectors of the economy [Mullen, 2021] makes it possible to introduce important aspects of inclusion into the model when developing a new integrative type of social and labor systems based on AI. Undoubtedly, the neuromophic factor is crucial in the effectiveness of internal communications [Mussa, 2022], since it performs an additional role of informing an agent about the nature of communication, which is important for the development of neurotargeting strategies in artificial intelligence models designed for management within labor collectives. Socio-professorial regulation in the field of education and the use of digital twins in this field allows for a systematic analysis of factors affecting the professional well-being of teachers, providing variables for human-oriented artificial intelligence systems [Balgabayeva et al., 2024; Nwoko et al. 2023]. In accordance with this, Turkish researchers formulated six main tasks for AI [Ozmen Garibay et al., 2023] with a human orientation, which allowed them to draw up a plan for social and labor applications. Modern urbanists actively advocate the use of human-centered artificial intelligence in architecture and engineering, sharing critical methodologies relevant to the urban economy [Rafsanjani and Nabizade, 2023]. At the same time, an increasing emphasis is being placed on the fundamental knowledge about artificial intelligence models that are crucial for understanding the computational architecture of neuromorphic AI [Russell and Norvig, 2016]. In this regard, issues related to the discussion of the social obligations of educational institutions that can be used in artificial intelligence systems developed for urban socio-labor ecosystems are particularly relevant [Armijos, et al., 2024]. Strengthening the fusion of human-machine interaction with the parallel creation of bioinspired robotic intelligent systems aims researchers to study the problems associated with the development of reliable autonomous human-oriented systems that are directly related to the architecture of neuromorphic regulatory models [Gapanyuk, et al., 2024; He et al., 2021]. In this regard, one cannot but agree with the opinion of P. Howarth [2020], who focuses on the priority of human participation in artificial intelligence applications, opposing excessive dependence on autonomous processes. The use of AI capabilities in healthcare [Lee and Yoon, 2021], law [Kussepova et al., 2023], municipal government [Karabulatova et al., 2024], education [Talanov et al., 2025] is conditioned by ethical considerations necessary in labor regulation systems. In the Chinese segment of scientific research, more and more attention is being paid to unlocking the potential of industrial AI in the direction of Industry 5.0 [Leng et al., 2024; Liu, Tian and Kang, 2022] with an emphasis on effective human-machine interaction in creating social and labor models. At the same time, Chinese researchers refer to megacities of the international level not only the city itself, but also the adjacent agricultural areas, which are included in the city's life support and are located in the suburbs of industrial production [Zhang Mei, 2019; Jia, Bennett, 2018]. This interpretation expands the understanding of social and labor relations in megacities, appealing to ensuring the vitality of the city as a living organism. At the same time, other researchers note that the Chinese interpretation differs from the European and Russian vision [Petushkova, 2023], emphasizing that the hierarchy of Chinese settlements is based not on the quantitative indicator of the city itself, but also includes satellite cities and rural areas adjacent to these cities [Gorshkov, 2011; Wang et al., 2024]. In our opinion, such a consideration of the city is consistent with traditional Chinese philosophy, which considers the city as a body based on semantic connections, which is reflected in the interpretation of psychophysiological processes through the explanation of the interaction of authorities, the army and the population as social and labor relations in Chinese society, the army and at the level of traditional Chinese medicine [Sun et al., 2024]. In this regard, the transition of minor facts into priority development trends as social processes in a team is considered through the factors of human body development. In terms of urbanism, researchers also point to the pattern of evolution of the entire territorial complex due to the development of the city [Topilin et al., 2022]. The socio-economic cataclysms of recent years in the context of geopolitical uncertainty (pandemic, economic wars and embargoes, increased migration processes, etc.) have predetermined the search for new solutions in creating socially sustainable systems based on the use of AI [Merrien et al., 2023]. In this regard, it is not accidental to turn to the development of monitoring and correction of psychological well-being in the workplace, which is an important factor for the regulatory framework in the field [Michulek et al., 2024]. In our opinion, the modeling of vitality in social and labor applications of artificial intelligence is possible based on the results obtained when analyzing empirical data on resilience in scientific circles [Monzón et al., 2023]. Thus, environmental factors affecting university staff deserve attention [Mopkins, Lee and Malecha, 2024], as they allow us to include a subtle approach to AI-based labor support in the model. Drawing from diverse human-centered AI studies, it becomes clear that socio-labor regulation must prioritize ethical principles and inclusive growth strategies. The success of neuromorphic systems will heavily depend on the continuous feedback between human users and AI agents. Moreover, the emergence of human-machine synergy as a fundamental design principle suggests the importance of adaptive learning environments for future labor markets. Consequently, a sustainable AI ecosystem demands interdisciplinary integration of technological, social, and psychological innovations. With all the variety of approaches to assessing the quality of social and labor relations and the quality of life in a modern metropolis, the possibilities for innovation depend on the starting positions of the quality of life in a particular locus. Social and labor relations, including such as "man + machine", are viewed by us through the prism of the quality of life in such a place and a sense of comfort. This position is the starting point for the subsequent modeling of the assessment criteria by which the standard of living of the population in a megalopolis is calculated, as shown in Fig.4. And the neuromorphic analysis of each of these clusters is based on the consideration of the complex of emotions that make up the motivation cluster [Lewon, Hayes, 2014] for the successful implementation of social and labor relations.The data reveal strong correlations between AI investment levels and successful deployment of neuromorphic labor systems. Cities with high investment (e.g., Shanghai, Beijing, Moscow) show advanced AI infrastructure, predictive capability, and collaborative interfaces. Neural network workforce training programs are a major differentiator: leading cities implement 3–4 times more programs than trailing ones, which directly impacts prediction accuracy and job matching outcomes. The discussion also emphasizes the risk of deepening socio-economic divides if current disparities are not addressed. While China and, to some extent, India and Russia, are building robust AI-labor ecosystems, Brazil and South Africa must overcome financial, technical, and institutional barriers. The role of public policy and targeted AI education programs is seen as pivotal in reducing these gaps and democratizing access to human-centric AI. Conclusion The study highlights that neuromorphic AI must evolve beyond technical efficiency toward fostering human resilience and social trust. Successful socio-labor regulation in urban economies will require embedding adaptive, human-centered features at every stage of AI system design and deployment. Lessons from resilience studies in education and healthcare sectors should guide the development of labor market prediction and job-matching algorithms. Without addressing the psychological and social dimensions of workforce transformation, AI-driven solutions risk deepening inequalities. Thus, the future of socio-labor regulation lies in building ethical, resilient, and participatory AI ecosystems that align with the broader goals of human-centric urban development. Empirical analysis shows that in 2023, Shanghai and Beijing lead AI development with investments of $1,350 million and $1,200 million, respectively, achieving the highest integration score of 9/10, while cities like Johannesburg lag behind with only $210 million investment and a score of 6/10. The assessment of neuromorphic labor systems reveals that Shanghai achieved a 9.5/10 deployment score compared to Durban’s 5.2/10, indicating a persistent technological gap that could widen socio-economic disparities without targeted intervention. Forecasts predict that by 2035, neuromorphic labor systems deployment in Moscow will grow from 7.8/10 to 9.3/10, with labor market prediction accuracy rising from 82% to 94%, strengthening Russia’s position among global AI leaders. Notably, the number of neural network workforce training programs in Bangalore is expected to increase from 48 to 140 by 2035, reflecting a global trend where AI-driven professional development becomes a key driver of urban labor market competitiveness. The study highlights that in 2023 the Human-AI Collaboration Index ranges from 53 (Durban) to 90 (Shanghai), whereas by 2035, even the lowest value (76 for Durban) will significantly approach today’s leading benchmarks. This evolution confirms that targeted policies, like expanding AI education and boosting innovation hubs, can reduce the current technological asymmetry among BRICS nations. Additionally, analysis reveals that AI-driven job matching efficiency will improve from 89% (Shanghai, 2023) to over 98% by 2035, suggesting a revolution in labor mobility and employment planning. The study concludes that neuromorphic AI technologies represent a crucial next step in transforming labor markets across BRICS cities. While China leads the transition with superior infrastructure, data, and policy alignment, other nations show varying degrees of preparedness. Bridging the performance gap will require coordinated action: scaling workforce training, enhancing institutional AI capacity, and aligning city-level investments with national strategies. If executed effectively, AI-driven socio-labor regulation can increase economic resilience, improve job matching efficiency, and foster inclusive growth across all BRICS economies. Declarations Acknowledgements. This research was funded by the Russian Science Foundation, project No. 25-28-01469 “Neural Network Solutions for Managing Social and Labor Relations in the Digital Economy of Megacities.”. References Akçin , K. (2023). The mediating effect of psychological resilience in the impact of increasing job insecurity with the pandemic on organizational commitment and turnover intention. Kybernetes , 52(7), 2416–2430. https://doi.org/10.1108/K-08-2022-1126 Almahdawi, A., & Teahan, W.J. (2018). Automatically Recognizing Emotions in Text Using Prediction by Partial Matching (PPM) Text Compression Method. In: Al-memory S., Alwan J., Hussein A. (eds) New Trends in Information and Communications Technology Applications. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6567951","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":459994676,"identity":"6775129c-497d-4d42-8bb9-903dc6228880","order_by":0,"name":"Irina Karabulatova","email":"","orcid":"","institution":"Heilongjiang University","correspondingAuthor":false,"prefix":"","firstName":"Irina","middleName":"","lastName":"Karabulatova","suffix":""},{"id":459994677,"identity":"aceb186a-293f-43b5-838d-555a20d6b698","order_by":1,"name":"Olga 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Regulation in BRICS Megacities: Investment, Innovation, and Integration Pathways\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6567951/v1/d9149c3ff5130f1cd703684c.png"},{"id":83327981,"identity":"edccf6df-1d93-45ff-acb8-1ae6e1e52b0d","added_by":"auto","created_at":"2025-05-23 07:05:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":126014,"visible":true,"origin":"","legend":"\u003cp\u003eDynamic architecture of the interaction of goals and multigraphic criteria for evaluating megalopolis innovations based on the calculation of the main indicators of the growth of the standard of living of the population in the megalopolis agglomeration\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6567951/v1/37cd19ec541633de76f29f30.png"},{"id":100614599,"identity":"5c2ea7f4-453a-4c42-84e7-7b824eff3933","added_by":"auto","created_at":"2026-01-19 17:22:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":988741,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6567951/v1/db162da9-b26f-4be1-8910-af0351a0e744.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neural networks for socio-labor regulation: a neuromorphic approach to human-centric AI in urban economies","fulltext":[{"header":" Introduction","content":"\u003cp\u003eThe integration of neural networks for social and labor regulation in urban economics and management finds a solid conceptual foundation in the developing field of human-centered artificial intelligence, which researchers have been actively writing about recently [Xu, 2019; Shneiderman, 2022; Talanov et al., 2025]. Most studies emphasize the critical role of psychological resilience in mitigating the negative impact of job insecurity on organizational commitment, which is vital for developing adaptive artificial intelligence models [Ak\u0026ccedil;in,\u0026nbsp;2023; Karabulatova, Talanov,\u0026nbsp;Vallverd\u0026uacute;, 2024]. \u0026nbsp;At the same time, researchers draw attention to the need to include the graph \u0026quot;the impact of employment instability on innovative behavior\u0026quot; in the creation of artificial intelligence programs and systems that promote trust and transparency [Aliane et al., 2023].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSystem analysis of modern Industry 5.0 advocates a human-centered paradigm that is closely related to the development of neuromorphic artificial intelligence [Alves et al., 2023]. \u0026nbsp;The strengthening of this vector in modern sociomorphic neuromodeling [Baimakhan et al., 2024; Talanov et al., 2025] demonstrates the importance of job security in digital management, suggesting that artificial intelligence systems should take into account factors related to employee well-being [Anand et al., 2023].\u003c/p\u003e\n\u003cp\u003eThe modern Industry 5.0 the active use of various AI-technologies highlighted the importance of neuro-psychophysiological studies of language and speech, which allowed us to single out a neuromorphic factor that is important in understanding the \u0026quot;deep\u0026quot; mechanism of regulating human behavior, creating an emotional standardization of socio-economic relations, ways of objectifying the world around us. As a result, there is a need for a methodology for non-traditional emotionality engineering of the speech and behavioral profile of natural and artificial intelligence in the socio-economic sphere. This allows us to focus on interdisciplinary approaches combining economics, sociology, neuroscience, NLP, and artificial intelligence. The main problem is caused by a non-discrete approach to assessing emotionality in communication and emotions in the behavioral matrix. The report is a development of the basic logic of our hypothesis and the work done on the gradation of emotional intelligence and, accordingly, emotional artificial intelligence with the following structure: general emotional intelligence, emotionogenic intelligence, neuromorphic intelligence and emotional intelligence-transformer [Karabulatova, Talanov, Vallverd\u0026uacute;, 2024].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe current appeal to the capabilities of deep neurocognitive constructors in social engineering is based on the neuro-psycholinguistic mechanisms of human brain activity, which allows for a clearer understanding of the structure of general emotional intelligence. In this regard, a distinction is made between the concepts of \u0026quot;emotion\u0026quot;, \u0026quot;emotivity\u0026quot;, and \u0026quot;emotionality\u0026quot;, which are considered important in creating a full-fledged emotional artificial intelligence [Talanov et al., 2025]. In connection with these new views, proposals are increasingly being made about the need to create interactive AI focused on humans [Schmidt, 2020], which generates continuous engineering, biological, and social feedback \u0026quot;machine-human-society\u0026quot;. \u0026nbsp;It is impossible to create such a complex model without taking into account bioinspired data. The increased introduction of artificial intelligence technologies and complex intelligent systems in the socio-economic sphere has allowed us to propose three fresh ideas for human-oriented AI [Schneiderman, 2020], which are seen as promising in social and labor regulation systems.\u003c/p\u003e\n\u003cp\u003eThe emerging integrative models aimed at ensuring prosperity in the workplace are based on the model of artificial intelligence, aimed at human prosperity as a whole based on a constructive approach [Verma, Sekar and Mukhopadhyay, 2024].\u0026nbsp;The emphasis on finding constructive solutions in monitoring artificial intelligence that is trustworthy for industry 5.0 has revealed significant arguments in favor of ethical and reliable artificial intelligence systems in labor regulation [Vyhmeister and Castane, 2024].\u003c/p\u003e\n\u003cp\u003eThe complexity of building deep architecture is caused by multilevel semantic connections in the upper stratum of the megalopolis structures as a super-complex metagraphic system. In our opinion, the uppermost layer can also be represented by a demonstration model, which clearly shows the interaction between the main blocks represented as complex metagraphs in architecture [Basu, Blanning, 2007; Gapanyuk et al., 2024]. Summarizing the various approaches to creating the top-level architecture of a large city as a center of attraction for various social and labor relations, the proposed model, which affects various aspects of interaction in a megalopolis, seems promising (Fig.1).\u003c/p\u003e\n\u003cp\u003eAccordingly, each cluster of markers relies on a \u0026quot;deep\u0026quot; system of neuromorphic indicators underlying the neurobiological system of reward and punishment [Bals-Kubik, 1993;\u003cem\u003e\u0026nbsp;\u003c/em\u003eKoob, Le Moal, 2008], ensuring the reproduction of social standards and thus regulating the effectiveness of social and labor relations.\u003c/p\u003e\n\u003cp\u003eDigital management in a megalopolis relies on a \u0026quot;smart\u0026quot; city management system based on the creation of a digital twin of the city, which creates real strategic advantages due to the transparency of life cycle calculations of all components of multiblocks in the expanded infrastructure of a supercity that subordinates the surrounding agricultural agglomeration, creating the effect of codependent links between the city and rural areas. Such a dynamic system reproduces the architecture of embedded metagraphs in the form of a more complexly organized system of multimetagraphs [Gapanyuk et al., 2024], which show the complexity of interconnections in modern megacities organized like a complex information empire. Such complex structures have been experimentally proven based on the theory of metagraphs, which considers them as a kind of virtual unity of conceptual objects, concepts (vertices) interconnected by various semantic links (edges), the possibility of designing and implementing systems for automatic complex analysis of both language with its various parameters and actors with their emotions, etc. [Almahdavi, Tihan, 2018; Mamina, Pirainen, 2023], which is important for creating a new type of AI systems.\u003c/p\u003e\n\u003cp\u003eLabor market forecasting and future workforce allocation planning are based on full-scale scaling of the resilience of children, the younger generation, and other demographic factors, also based on AI methods [Ye, Teig, and Bl\u0026ouml;meke, 2024]. Other researchers consider top-level factors of work engagement, organizational support, and psychological empowerment, describing the most important path to improving social and labor well-being through artificial intelligence [Yu et al., 2024]. However, in our opinion, calculating the effectiveness of labor relations motivation is impossible without the use of neuromorphic indicators that form the basis of any psycho-emotional model [Huang, 2024].\u0026nbsp;In accordance with this, neuromorphic systems developed earlier are undergoing rethinking, forcing researchers to go deeper into understanding macro-level processes to create full-fledged devices [Dimonte, et al., 2014; Erokhin, 2020;\u003cem\u003e\u0026nbsp;\u003c/em\u003eKumar, et al., 2022].\u003c/p\u003e\n\u003cp\u003eImproving the methodology of digital monitoring of the level of quality and a variety of services provided to the population using smart tools makes it possible to evaluate indicators in each segment of social and labor relations according to individual criteria within a specific parameterization, based on the goals of calculating the potential of each cluster of socio-economic labor and market.\u003c/p\u003e\n\u003cp\u003eIn our opinion, the introduction of a multidimensional multigraph model taking into account the neuromorphic vector in the social and labor regulation of modern engineering infrastructure is to reduce the complexity of calculations when taking into account big data in predicting urban vitality, as well as to create a holistic methodology for improving the effectiveness of \u0026quot;digital assistants\u0026quot; based on polycode and multidimensional intelligent tools of varying complexity.: from analytical and informational recommendation systems with integrated logical and semantic intellectual models to self-learning intelligent maps (memory cards) that help to harmoniously model the vector of socio-economic development of urbanism.\u003c/p\u003e\n\u003cp\u003eThe variety of multiblocks in the architecture of urban development is regulated by the level of quality of life, a block of which contains a variety of indicators representing a set of evaluative internal and external criteria for calculating the effectiveness of social and labor relations with the definition of opportunities for innovation.\u003c/p\u003e\n\u003cp\u003eThis diverse body of research collectively establishes the necessity for a neuromorphic, human-centric AI approach tailored to the complexities of socio-labor regulation in dynamic urban environments.\u003c/p\u003e"},{"header":"Methods ","content":"\u003cp\u003eThis study includes three stages of analysis to identify patterns in the implementation of artificial intelligence (AI) and neuromorphic technologies in the regulation of social and labor relations in the megacities of the BRICS countries. At the first stage, a comparative analysis of the level of AI development in 15 BRICS megacities was conducted. The main indicators were the volume of investments in AI, the number of AI startups, the share of companies using AI, the number of registered AI patents per year and the level of AI integration into urban infrastructure. The data was collected and systematized based on open sources, including reports of the World Intellectual Property Organization (WIPO, 2023), analytical publications of economic agencies: Startup Genome (2023), McKinsey \u0026amp; Company (2022), as well as official reports of city administrations IMD Smart City Observatory.\u003c/p\u003e\n\u003cp\u003eAt the second stage, an analysis of the use of neuromorphic technologies to regulate social and labor processes was conducted. For this, five key indicators were selected: the degree of implementation of neuromorphic labor systems, the effectiveness of AI-oriented matching of vacancies and candidates, the number of training programs using neural networks, the accuracy of forecasting the state of the labor market and the index of human and AI interaction. The information for the analysis was obtained from specialized industry studies and also synthesized using empirical data from megacities (e.g. Moscow, Shanghai, Bangalore, Sao Paulo, Johannesburg).\u003c/p\u003e\n\u003cp\u003eAt the third stage, based on the extrapolation of existing trends, a forecast for the development of neuromorphic systems in the social and labor sphere until 2035 was constructed. The forecast was carried out using dynamic modeling methods, correlation analysis between investments, innovation activity and the level of AI integration, as well as the expert assessment method. To substantiate the forecasts, calculations based on linear and exponential approximation of time series for each indicator were used.\u003c/p\u003e\n\u003cp\u003eThe following methods were used to increase the reliability of the study:\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003ecorrelation analysis of the relationship between investment activity and the level of AI integration into the infrastructure of megacities;\u003c/li\u003e\n \u003cli\u003eregression modeling of the impact of the number of patents and startups on the effectiveness of the implementation of neuromorphic labor systems;\u003c/li\u003e\n \u003cli\u003eindex analysis of the development of human-AI interaction based on the calculation of composite indicators;\u003c/li\u003e\n \u003cli\u003ecomparative analysis of differences in the level of development between BRICS megacities.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe information base of the study was made up of data from official statistical resources of the BRICS countries, reports of the World Intellectual Property Organization (WIPO), analytical reports of international consulting companies and data on startup ecosystems in the field of AI.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe analysis of AI adoption and implementation indicators in BRICS megacities reveals significant disparities in investment, integration, and innovation output across the member nations.\u003c/p\u003e\n\u003cp\u003eThe diagram on the fig.2 illustrates the causal relationships driving AI integration in BRICS megacities, showing how investments, government policies, and international collaboration contribute to the development of AI ecosystems.\u003c/p\u003e\n\u003cp\u003eOverall, the data underscores a three-tier division among BRICS countries. China emerges as the undisputed leader, with Shanghai and Beijing setting benchmarks in AI investment and integration. Russia and India form the middle tier, demonstrating significant but not leading contributions, while Brazil exhibits modest progress. South Africa, although not explicitly covered in these sources, is noted elsewhere for having the lowest AI metrics among BRICS nations. The correlation between financial commitment and AI integration is evident, as cities with higher investments\u0026mdash;such as Beijing and Moscow\u0026mdash;showcase more advanced smart city implementations. Additionally, capital cities generally outperform others in AI development metrics, with the notable exceptions of Shanghai and Bangalore, which surpass their respective capitals.\u003c/p\u003e\n\u003cp\u003eTable 1 showing the five key indicators of artificial intelligence adoption and implementation across major megacities in the BRICS countries.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003eAI Adoption and Implementation Indicators in BRICS Megacities\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eAI Investment (USD millions)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eNumber of AI Startups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003eCompanies Using AI (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eAI Patents (annually)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eAI Integration in Urban Infrastructure (1-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eBRAZIL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eS\u0026atilde;o Paulo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e42%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eRio de Janeiro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e38%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eBras\u0026iacute;lia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eRUSSIA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eMoscow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e48%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eSaint Petersburg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e44%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eNovosibirsk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e37%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eINDIA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eMumbai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e45%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eDelhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eBangalore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e780\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e58%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eCHINA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eBeijing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eShanghai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e1350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eGuangzhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e59%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eSOUTH AFRICA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eJohannesburg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e210\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e39%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eCape Town\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e37%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003eDurban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 15px;\"\u003e\n \u003cp\u003e32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 19px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eSource:\u0026nbsp;Startup Genome. (2023). The Global Startup Ecosystem Report 2023. Startup Genome.URL: https://startupgenome.com/gser2023\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDealroom.co. (2023). Artificial Intelligence startups and scaleups database.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eURL: https://dealroom.co/sectors/artificial-intelligence\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMcKinsey \u0026amp; Company. (2022). The State of AI in 2022\u003c/em\u003e\u003cem\u003e\u0026mdash;\u003c/em\u003e\u003cem\u003eand a Half Decade in Review. McKinsey Global Institute.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eURL: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2022-and-a-half-decade-in-review\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eWorld Intellectual Property Organization (WIPO). (2023). WIPO Technology Trends 2023: Artificial Intelligence. Geneva: WIPO.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eURL: https://www.wipo.int/publications/en/details.jsp?id=4512\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eIMD Smart City Observatory. (2023). IMD Smart City Index 2023. Institute for Management Development.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eURL: https://www.imd.org/smart-city-observatory/smart-city-index/\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eChina leads the BRICS nations in AI development with Shanghai investing $1,350 million and Beijing $1,200 million, while both cities maintain the highest AI integration score of 9/10. The technology hub of Bangalore stands out in India with 320 AI startups and 58% of companies implementing AI solutions, significantly outperforming other Indian megacities. Russia shows strong performance in its capital with Moscow investing $620 million in AI and registering 145 patents annually, placing it third among all BRICS megacities in terms of innovation output. Brazil\u0026apos;s AI ecosystem is developing steadily with S\u0026atilde;o Paulo leading the country\u0026apos;s efforts through 185 AI startups and 42% of companies adopting AI technologies. South Africa demonstrates the lowest AI metrics among BRICS countries, with Johannesburg, its top performer, investing $210 million and having only 39% of companies using AI solutions. The data reveals a substantial gap between the leading Chinese megacities (Beijing and Shanghai with 580-620 patents annually) and South African cities (Johannesburg and Cape Town with only 42 and 38 patents respectively). AI integration in urban infrastructure shows a strong correlation with overall investment, as cities with higher financial commitments like Beijing (9/10) and Moscow (8/10) demonstrate more advanced smart city implementations. Across all BRICS nations, capital cities typically show stronger AI development metrics, with the notable exceptions of Shanghai in China and Bangalore in India, which outperform their respective capitals. The analysis highlights a clear three-tier division among BRICS countries: China as the undisputed leader, Russia and India forming the middle tier with significant but not leading investments, and Brazil and South Africa trailing with more modest AI development indicators. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn Johannesburg, South Africa, the Johannesburg Economic Development Agency (2023) reports that the city invests $210 million in AI, with only 39% of companies adopting AI solutions. This positions South Africa as having the lowest AI metrics among BRICS nations, reflecting ongoing challenges in integrating advanced technologies into its socio-labor systems. Despite this, Johannesburg\u0026rsquo;s efforts underscore a growing recognition of AI\u0026rsquo;s potential to address urban economic disparities and improve labor efficiency.\u003c/p\u003e\n\u003cp\u003eOn a broader scale, the World Intellectual Property Organization (WIPO, 2023) provides a comparative analysis of global AI patent trends, revealing a substantial gap in innovation output between leading BRICS cities. For instance, Beijing and Shanghai register 580\u0026ndash;620 AI patents annually, while Johannesburg and Cape Town lag significantly behind, with only 42 and 38 patents, respectively. This disparity highlights the varying capacities of BRICS nations to leverage neural networks and neuromorphic technologies for socio-labor regulation and urban economic transformation.\u003c/p\u003e\n\u003cp\u003eThe diagram on the fig.3 illustrates how investments, innovation hubs, government policies, and private sector initiatives drive the development and integration of neuromorphic AI for socio-labor regulation in BRICS megacities, leading to improved workforce adaptability, employment stability, and sustainable urban development.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e - Neural Networks for Socio-Labor Regulation\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNeuromorphic Labor Systems Deployment (1-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eAI-Driven Job Matching Efficiency (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eNeural Network Workforce Training Programs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eLabor Market Prediction Accuracy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eHuman-AI Collaboration Index (1-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eBRAZIL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eS\u0026atilde;o Paulo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e6.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e73%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eRio de Janeiro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e5.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e62%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eBras\u0026iacute;lia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eRUSSIA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eMoscow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eSaint Petersburg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e7.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e79%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNovosibirsk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e6.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e67%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e75%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eINDIA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eMumbai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e66%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e72%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eDelhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e64%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e70%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eBangalore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e79%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eCHINA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eBeijing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e86%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eShanghai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eGuangzhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eSOUTH AFRICA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eJohannesburg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e61%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eCape Town\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e63%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e71%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eDurban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e57%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e65%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAcross the BRICS nations, the Human-AI Collaboration Index demonstrates three clear tiers: Chinese cities leading (84-90), followed by Bangalore and Russian cities (72-81), with Brazilian and South African cities trailing significantly (53-65). The significant gap between the highest performing city (Shanghai at 9.5/10) and the lowest (Durban at 5.2/10) in neuromorphic labor systems deployment highlights the uneven adoption of advanced AI technologies for workforce management across BRICS economies, potentially widening economic disparities if not addressed through targeted development programs.\u003c/p\u003e\n\u003cp\u003eTable 3 presents forecast that projects the development of neuromorphic AI in socio-labor regulation across BRICS megacities by 2035.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e - 2035 Forecast: Neural Networks for Socio-Labor Regulation: A Neuromorphic Approach to Human-Centric AI in Urban Economies of BRICS Megacities\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eNeuromorphic Labor Systems Deployment (1-10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-Driven Job Matching Efficiency (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eNeural Network Workforce Training Programs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003eLabor Market Prediction Accuracy (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eHuman-AI Collaboration Index (1-100)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eBRAZIL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eS\u0026atilde;o Paulo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eRio de Janeiro\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e82%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eBras\u0026iacute;lia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e84%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e86%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eRUSSIA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eMoscow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e92%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e94%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eSaint Petersburg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e91%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eNovosibirsk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e86%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e89%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eINDIA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eMumbai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eDelhi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e86%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eBangalore\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e96%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003eCHINA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eBeijing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e9.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e97%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e98%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eShanghai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e9.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e98%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e99%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eGuangzhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e95%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e96%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 100px;\"\u003e\n \u003cp\u003eSOUTH AFRICA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eJohannesburg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e81%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e85%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eCape Town\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e83%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e87%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003eDurban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e79%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e83%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 18px;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe 2035 forecast reveals Shanghai will likely achieve near-perfect integration of neuromorphic labor systems with a remarkable 9.9/10 deployment score and 97/100 on the Human-AI Collaboration Index, establishing China\u0026apos;s continued leadership in AI workforce solutions. Beijing follows closely behind with projected figures of 9.8/10 for neuromorphic systems deployment and an impressive 180 neural network workforce training programs, demonstrating China\u0026apos;s sustained investment in AI labor infrastructure over the next decade. By 2035, Bangalore is expected to emerge as India\u0026apos;s technology powerhouse with 9.6/10 in neuromorphic systems deployment and 96% labor market prediction accuracy, positioning it among the top three BRICS megacities for AI-powered workforce management. Moscow\u0026apos;s projections indicate substantial growth to 9.3/10 in neuromorphic systems and 94% labor market prediction accuracy by 2035, solidifying Russia as a major player in AI labor solutions. Brazil shows remarkable anticipated improvement with S\u0026atilde;o Paulo expected to reach 8.7/10 in neuromorphic systems deployment and 87% job matching efficiency, significantly closing the current gap with leading BRICS nations. South Africa\u0026apos;s forecasted development remains behind other BRICS members, though Johannesburg is projected to advance considerably to 7.8/10 in neuromorphic systems and host 72 neural network workforce training programs by 2035. The projected data indicates dramatic growth in neural network workforce training programs across all BRICS nations, with Shanghai expected to implement 195 programs compared to its current 72, representing a nearly 170% increase in AI workforce development initiatives. The predicted improvement in AI-driven job matching efficiency shows remarkable advancement across all megacities, with even the lowest performer (Durban at 79%) exceeding the current best performer\u0026apos;s rate (Shanghai at 89%), indicating widespread adoption of sophisticated matching algorithms. By 2035, labor market prediction accuracy is forecast to exceed 83% in all BRICS megacities, with Shanghai achieving a near-perfect 99%, suggesting revolutionary changes in workforce planning and economic stability through advanced predictive technologies. The narrowing gap between the highest and lowest performers in the Human-AI Collaboration Index (97 for Shanghai versus 76 for Durban in 2035, compared to the current 90 versus 53) suggests that while disparities will persist, significant progress in AI democratization and technology transfer is expected throughout the BRICS alliance.\u003c/p\u003e\n\u003cp\u003eThe study presents extensive comparative data from 15 BRICS megacities across two dimensions: general AI integration and neuromorphic labor system deployment. In terms of current AI implementation (Table 1), Beijing and Shanghai lead with 580\u0026ndash;620 AI patents annually, 67% of companies using AI, and the highest integration scores (9/10). In India, Bangalore achieves 58% AI adoption and 320 startups, outperforming Mumbai and Delhi. Moscow leads in Russia with 145 patents and an integration score of 8/10. S\u0026atilde;o Paulo leads in Brazil but remains behind in innovation and investment. South African cities rank lowest, with Johannesburg showing just 39% adoption and 42 patents.\u003c/p\u003e\n\u003cp\u003eIn neuromorphic AI for labor systems (Table 2), Shanghai again tops the list with a 9.5/10 deployment score, 90/100 on the Human-AI Collaboration Index, and 72 training programs. Beijing (9.2) and Bangalore (8.4) follow. Moscow demonstrates 7.8/10 deployment and 82% prediction accuracy, confirming Russia\u0026rsquo;s mid-tier position. South African cities, especially Durban (5.2/10) and 13 training programs, reflect limited capacity and low readiness for large-scale AI-driven labor solutions.\u003c/p\u003e\n\u003cp\u003eForecasts to 2035 (Table 3) show expected convergence: all cities are projected to exceed 83% in prediction accuracy, with Shanghai reaching 99%, Beijing 98%, and Bangalore 96%. S\u0026atilde;o Paulo is expected to rise to 8.7/10 deployment and 87% job matching efficiency, while Johannesburg will reach 7.8/10 and 72 training programs\u0026mdash;a marked improvement but still behind the leaders.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings reinforce the need to embed psychological well-being, organizational support, and educational resilience into AI regulatory frameworks. An analytical screening of labor market processes led to the conclusion that employee engagement determines the relationship between job security and productivity, emphasizing the importance of AI-based engagement strategies [Mozammel, 2023]. At the same time, the analysis of the sustainability of youth sectors of the economy [Mullen, 2021] makes it possible to introduce important aspects of inclusion into the model when developing a new integrative type of social and labor systems based on AI. Undoubtedly, the neuromophic factor is crucial in the effectiveness of internal communications [Mussa, 2022], since it performs an additional role of informing an agent about the nature of communication, which is important for the development of neurotargeting strategies in artificial intelligence models designed for management within labor collectives. Socio-professorial regulation in the field of education and the use of digital twins in this field allows for a systematic analysis of factors affecting the professional well-being of teachers, providing variables for human-oriented artificial intelligence systems [Balgabayeva et al., 2024; Nwoko et al. 2023]. In accordance with this, Turkish researchers formulated six main tasks for AI [Ozmen Garibay et al., 2023] with a human orientation, which allowed them to draw up a plan for social and labor applications.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Modern urbanists actively advocate the use of human-centered artificial intelligence in architecture and engineering, sharing critical methodologies relevant to the urban economy [Rafsanjani and Nabizade, 2023]. At the same time, an increasing emphasis is being placed on the fundamental knowledge about artificial intelligence models that are crucial for understanding the computational architecture of neuromorphic AI [Russell and Norvig, 2016].\u0026nbsp;In this regard, issues related to the discussion of the social obligations of educational institutions that can be used in artificial intelligence systems developed for urban socio-labor ecosystems are particularly relevant [Armijos, et al., 2024].\u0026nbsp; Strengthening the fusion of human-machine interaction with the parallel creation of bioinspired robotic intelligent systems aims researchers to study the problems associated with the development of reliable autonomous human-oriented systems that are directly related to the architecture of neuromorphic regulatory models [Gapanyuk, et al., 2024; He et al., 2021]. In this regard, one cannot but agree with the opinion of P. Howarth [2020], who focuses on the priority of human participation in artificial intelligence applications, opposing excessive dependence on autonomous processes. The use of AI capabilities in healthcare [Lee and Yoon, 2021], law [Kussepova et al., 2023], municipal government [Karabulatova et al., 2024], education [Talanov et al., 2025] is conditioned by ethical considerations necessary in labor regulation systems.\u003c/p\u003e\n\u003cp\u003eIn the Chinese segment of scientific research, more and more attention is being paid to unlocking the potential of industrial AI in the direction of Industry 5.0 [Leng et al., 2024; Liu, Tian and Kang, 2022] with an emphasis on effective human-machine interaction in creating social and labor models. At the same time, Chinese researchers refer to megacities of the international level not only the city itself, but also the adjacent agricultural areas, which are included in the city\u0026apos;s life support and are located in the suburbs of industrial production [Zhang Mei, 2019; Jia, Bennett, 2018]. This interpretation expands the understanding of social and labor relations in megacities, appealing to ensuring the vitality of the city as a living organism. At the same time, other researchers note that the Chinese interpretation differs from the European and Russian vision [Petushkova, 2023], emphasizing that the hierarchy of Chinese settlements is based not on the quantitative indicator of the city itself, but also includes satellite cities and rural areas adjacent to these cities [Gorshkov, 2011; Wang et al., 2024]. In our opinion, such a consideration of the city is consistent with traditional Chinese philosophy, which considers the city as a body based on semantic connections, which is reflected in the interpretation of psychophysiological processes through the explanation of the interaction of authorities, the army and the population as social and labor relations in Chinese society, the army and at the level of traditional Chinese medicine [Sun et al., 2024]. In this regard, the transition of minor facts into priority development trends as social processes in a team is considered through the factors of human body development. In terms of urbanism, researchers also point to the pattern of evolution of the entire territorial complex due to the development of the city [Topilin et al., 2022].\u003c/p\u003e\n\u003cp\u003eThe socio-economic cataclysms of recent years in the context of geopolitical uncertainty (pandemic, economic wars and embargoes, increased migration processes, etc.) have predetermined the search for new solutions in creating socially sustainable systems based on the use of AI [Merrien et al., 2023]. In this regard, it is not accidental to turn to the development of monitoring and correction of psychological well-being in the workplace, which is an important factor for the regulatory framework in the field [Michulek et al., 2024]. In our opinion, the modeling of vitality in social and labor applications of artificial intelligence is possible based on the results obtained when analyzing empirical data on resilience in scientific circles [Monz\u0026oacute;n et al., 2023]. Thus, environmental factors affecting university staff deserve attention [Mopkins, Lee and Malecha, 2024], as they allow us to include a subtle approach to AI-based labor support in the model.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Drawing from diverse human-centered AI studies, it becomes clear that socio-labor regulation must prioritize ethical principles and inclusive growth strategies. The success of neuromorphic systems will heavily depend on the continuous feedback between human users and AI agents. Moreover, the emergence of human-machine synergy as a fundamental design principle suggests the importance of adaptive learning environments for future labor markets. Consequently, a sustainable AI ecosystem demands interdisciplinary integration of technological, social, and psychological innovations.\u003c/p\u003e\n\u003cp\u003eWith all the variety of approaches to assessing the quality of social and labor relations and the quality of life in a modern metropolis, the possibilities for innovation depend on the starting positions of the quality of life in a particular locus. Social and labor relations, including such as \u0026quot;man + machine\u0026quot;, are viewed by us through the prism of the quality of life in such a place and a sense of comfort. This position is the starting point for the subsequent modeling of the assessment criteria by which the standard of living of the population in a megalopolis is calculated, as shown in Fig.4.\u003c/p\u003e\n\u003cp\u003eAnd the neuromorphic analysis of each of these clusters is based on the consideration of the complex of emotions that make up the motivation cluster [Lewon, Hayes, 2014] for the successful implementation of social and labor relations.The data reveal strong correlations between AI investment levels and successful deployment of neuromorphic labor systems. Cities with high investment (e.g., Shanghai, Beijing, Moscow) show advanced AI infrastructure, predictive capability, and collaborative interfaces. Neural network workforce training programs are a major differentiator: leading cities implement 3\u0026ndash;4 times more programs than trailing ones, which directly impacts prediction accuracy and job matching outcomes. The discussion also emphasizes the risk of deepening socio-economic divides if current disparities are not addressed. While China and, to some extent, India and Russia, are building robust AI-labor ecosystems, Brazil and South Africa must overcome financial, technical, and institutional barriers. The role of public policy and targeted AI education programs is seen as pivotal in reducing these gaps and democratizing access to human-centric AI.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study highlights that neuromorphic AI must evolve beyond technical efficiency toward fostering human resilience and social trust. Successful socio-labor regulation in urban economies will require embedding adaptive, human-centered features at every stage of AI system design and deployment. Lessons from resilience studies in education and healthcare sectors should guide the development of labor market prediction and job-matching algorithms. Without addressing the psychological and social dimensions of workforce transformation, AI-driven solutions risk deepening inequalities. Thus, the future of socio-labor regulation lies in building ethical, resilient, and participatory AI ecosystems that align with the broader goals of human-centric urban development.\u003c/p\u003e\n\u003cp\u003eEmpirical analysis shows that in 2023, Shanghai and Beijing lead AI development with investments of $1,350 million and $1,200 million, respectively, achieving the highest integration score of 9/10, while cities like Johannesburg lag behind with only $210 million investment and a score of 6/10. The assessment of neuromorphic labor systems reveals that Shanghai achieved a 9.5/10 deployment score compared to Durban’s 5.2/10, indicating a persistent technological gap that could widen socio-economic disparities without targeted intervention. Forecasts predict that by 2035, neuromorphic labor systems deployment in Moscow will grow from 7.8/10 to 9.3/10, with labor market prediction accuracy rising from 82% to 94%, strengthening Russia’s position among global AI leaders. Notably, the number of neural network workforce training programs in Bangalore is expected to increase from 48 to 140 by 2035, reflecting a global trend where AI-driven professional development becomes a key driver of urban labor market competitiveness. The study highlights that in 2023 the Human-AI Collaboration Index ranges from 53 (Durban) to 90 (Shanghai), whereas by 2035, even the lowest value (76 for Durban) will significantly approach today’s leading benchmarks. This evolution confirms that targeted policies, like expanding AI education and boosting innovation hubs, can reduce the current technological asymmetry among BRICS nations. Additionally, analysis reveals that AI-driven job matching efficiency will improve from 89% (Shanghai, 2023) to over 98% by 2035, suggesting a revolution in labor mobility and employment planning.\u003c/p\u003e\n\u003cp\u003eThe study concludes that neuromorphic AI technologies represent a crucial next step in transforming labor markets across BRICS cities. While China leads the transition with superior infrastructure, data, and policy alignment, other nations show varying degrees of preparedness. Bridging the performance gap will require coordinated action: scaling workforce training, enhancing institutional AI capacity, and aligning city-level investments with national strategies. If executed effectively, AI-driven socio-labor regulation can increase economic resilience, improve job matching efficiency, and foster inclusive growth across all BRICS economies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements.\u003c/strong\u003eThis research was funded by the Russian Science Foundation, project No. 25-28-01469 “Neural Network Solutions for Managing Social and Labor Relations in the Digital Economy of Megacities.”.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u003cem\u003eAk\u0026ccedil;in\u003c/em\u003e\u003cem\u003e, K.\u003c/em\u003e (2023). 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The state and prospects of trade and economic cooperation between the Northeastern regions of China and Russia. \u003cem\u003eCustoms policy of Russia in the Far East\u003c/em\u003e, 4 (89), 59-67.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bionanoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnsc","sideBox":"Learn more about [BioNanoScience](http://link.springer.com/journal/12668)","snPcode":"12668","submissionUrl":"https://submission.nature.com/new-submission/12668/3","title":"BioNanoScience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"neural networks, socio-labor regulation, neuromorphic computing, human-centric AI","lastPublishedDoi":"10.21203/rs.3.rs-6567951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6567951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the implementation and future potential of neural networks for socio-labor regulation within the urban economies of BRICS megacities, emphasizing a human-centric AI approach. Analysis reveals significant disparities in AI development across these urban centers, with Beijing and Shanghai leading in investment, while Moscow ranks third among all analyzed cities with an AI investment of $620 million, contributing to the growing global urban AI landscape where worldwide smart city spending is projected to reach hundreds of billions of dollars. The research examines key indicators of AI adoption, such as the number of startups and the percentage of companies utilizing AI solutions in these major cities. Specifically, Bangalore stands out with 320 AI startups and 58% of companies implementing AI, while in Russia, Moscow reports 230 startups and 48% company adoption, reflecting varying rates of AI integration within urban business ecosystems globally where the average AI adoption rate for enterprises is still below 30% according to some reports. Beyond general AI adoption, the study analyzes the deployment and effectiveness of neuromorphic AI approaches specifically for socio-labor regulation systems. Current data indicates uneven deployment of neuromorphic labor systems across BRICS megacities, with Shanghai showing a high deployment score of 9.5/10, significantly ahead of cities like Durban at 5.2/10, highlighting the uneven global progress in applying advanced AI for workforce management, including in Russian cities like Moscow with a 7.8/10 deployment score, as the worldwide market for AI in HR is rapidly expanding towards billions. Key metrics examined for these specialized systems include AI-driven job matching efficiency, the number of neural network workforce training programs, and labor market prediction accuracy. The study concludes that achieving effective and ethical socio-labor regulation through AI requires a human-centric approach that addresses disparities and integrates technological, social, and psychological considerations for inclusive urban development.\u003c/p\u003e\n\u003cp\u003eParticipation in the article: Irina Karabulatova - general editing, writing the \"introduction\" and \"discussion\" sections; Olga Ergunova - project idea, writing the \"results\" section, working on models (Fig.2-3), compiling tables; Andrey Somov - working on the project methodology and writing the code.\u003c/p\u003e","manuscriptTitle":"Neural networks for socio-labor regulation: a neuromorphic approach to human-centric AI in urban economies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-23 07:05:54","doi":"10.21203/rs.3.rs-6567951/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-21T10:11:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-09T02:57:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"68423634232458205583924584081173989840","date":"2025-06-29T08:40:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"223679149055130097900870864855965331126","date":"2025-05-21T15:30:55+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-21T12:44:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-07T06:53:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-07T04:20:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BioNanoScience","date":"2025-04-30T21:32:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bionanoscience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnsc","sideBox":"Learn more about [BioNanoScience](http://link.springer.com/journal/12668)","snPcode":"12668","submissionUrl":"https://submission.nature.com/new-submission/12668/3","title":"BioNanoScience","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e5f19676-b2b7-4451-81c7-b868e0f0e7b6","owner":[],"postedDate":"May 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T16:46:47+00:00","versionOfRecord":{"articleIdentity":"rs-6567951","link":"https://doi.org/10.1007/s12668-025-02235-4","journal":{"identity":"bionanoscience","isVorOnly":false,"title":"BioNanoScience"},"publishedOn":"2026-01-16 16:29:51","publishedOnDateReadable":"January 16th, 2026"},"versionCreatedAt":"2025-05-23 07:05:54","video":"","vorDoi":"10.1007/s12668-025-02235-4","vorDoiUrl":"https://doi.org/10.1007/s12668-025-02235-4","workflowStages":[]},"version":"v1","identity":"rs-6567951","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6567951","identity":"rs-6567951","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}