Hasil untuk "Regulation of industry, trade, and commerce. Occupational law"

Rajali Maharjan, Hironori Kato, Sunkyung Choi

<i>Background</i>: The COVID-19 pandemic has underscored the critical importance of supply chain resilience. However, little is known about firms’ willingness to implement logistics and supply chain resilience strategies (SCRESTs), and how this willingness varies across contexts. This study investigates the willingness of Japanese manufacturing firms to implement SCRESTs and examines how the pandemic has influenced this willingness. <i>Methods:</i> Using survey data from 549 Japanese manufacturing firms collected from March to April 2022, we employed binary choice models and the average treatment effect on the treated (ATET) analysis to examine the factors influencing the willingness to implement SCRESTs before and during/after the pandemic. <i>Results:</i> Firms demonstrated significantly higher willingness to implement SCRESTs during/after the pandemic compared with before. Company size, industry sector, logistics strategy, implementation obstacles, and past SCREST implementation significantly influenced willingness across both periods. The ATET analysis confirmed that past SCREST implementation positively affects future willingness. <i>Conclusions:</i> The pandemic served as a catalyst for enhanced supply chain resilience awareness among Japanese manufacturers. Sector-specific interventions addressing both informational and structural barriers are essential to sustain and strengthen the willingness to implement SCRESTs, particularly in strategically important sectors where financial incentives alone may prove insufficient.

Sizhong Sun

This paper studies firms' optimal response to a trade liberalization shock in terms of export and product innovation both theoretically and empirically. We find that trade liberalization, namely China's WTO accession, reduces trade cost and promotes export, which in turn incentivizes firms to innovate as the marginal benefit of innovation for exporting firms is higher than that for non-exporting firms. In addition, as a firm starts to innovate, it predicts to have a higher probability of moving to a better productivity state and can save the entry cost of innovation in the future, resulting in additional dynamic benefits. Such an innovation-promotion effect is an unintended consequence of trade liberalization.

Anurag Singh, Julian Rodemann, Rajeev Verma et al.

While high-stakes ML applications demand strict regulations, strategic ML providers often evade them to lower development costs. To address this challenge, we cast AI regulation as a mechanism design problem under uncertainty and introduce regulation mechanisms: a framework that maps empirical evidence from models to a license for some market share. The providers can select from a set of licenses, effectively forcing them to bet on their model's ability to fulfil regulation. We aim at regulation mechanisms that achieve perfect market outcome, i.e. (a) drive non-compliant providers to self-exclude, and (b) ensure participation from compliant providers. We prove that a mechanism has perfect market outcome if and only if the set of non-compliant distributions forms a credal set, i.e., a closed, convex set of probability measures. This result connects mechanism design and imprecise probability by establishing a duality between regulation mechanisms and the set of non-compliant distributions. We also demonstrate these mechanisms in practice via experiments on regulating use of spurious features for prediction and fairness. Our framework provides new insights at the intersection of mechanism design and imprecise probability, offering a foundation for development of enforceable AI regulations.

Heidar Piri

With significant developments in technologies, artificial intelligence is used instead of humans in armed conflicts gradually. The use of AI weapons systems in armed conflicts has challenged not only our traditional concepts of responsibility but also attribution of international responsibility to states; because traditional concepts of responsibility and the principles of states responsibility has been based on human behavior and its adaptation to AI-based behavior requires a new review. Therefore, using the descriptive-analytical method, this article examines the consequences of the international responsibility of states in the use of military AI in armed conflicts and reparation for the damages resulting from their performance. To encounter monitoring the performance of AI technologies, the main question of this essay is that in what situations are states internationally responsible in relation to violations of international law when using military artificial intelligence? By examining the draft articles on responsibility of states for internationally wrongful acts, the author conclude that neither the contemporary applications of AI nor the truly autonomous visions of its future create a conceptual obstacle in the framework of responsibility laws, and not only the illegal behaviors of AI-based systems in the battlefield can be attributed to the states, but also the responsibility of the states can be stablished before the use of artificial intelligence (in the study, development, acquisition or adoption of a new weapons).1. Introduction daily lives, significantly impacting a wide range of sectors within the international community. One of which is the realm of armed conflicts. In fact, artificial intelligence and autonomous weapon systems represent an important step in the evolution of military conflicts. The advancement of AI technologies presents unprecedented opportunities for the execution of new forms of military operations in armed conflicts. By employing AI in the production of weapons and armaments, human presence on the battlefield has been minimized (Wood, 2023, p. 16). AI is used as a suppressive weapon to gain military advantage, capable of autonomously selecting and engaging targets in hostilities without human intervention (Lee, 2022, p. 177). Most states regard autonomous weapons systems as pivotal technologies in the struggle for global dominance. In 2017, Russian President Vladimir Putin described AI not only as the future of Russia but also of humanity. He also foresaw its threats, stating: "Whoever becomes the leader in this sphere will become the ruler of the world." In the absence of meaningful regulations concerning AI, international law faces new challenges. In this context, identifying and understanding the international legal cocepts arising from the rapid emergence and deployment of AI, as well as analyzing existing norms of international law from the perspective of this new phenomenon, is deeply concerning. Despite the various benefits and constructive applications of AI in human daily life, the world has witnessed its adverse effects when employed in armed conflicts an issue that may entail violations of fundamental human rights. Sooner or later, such weapons, like all others, will malfunction due to systemic flaws (Schmitt, 2013, p. 7), causing harm to civilians and damage to civilian targets and objects, thereby raising the question: Who is responsible? AI-based weapons operate in truly unpredictable ways and are inherently volatile and dangerous. Their use will have unintended and detrimental consequences for global stability, stemming from either the use or misuse of such systems. Lethal AI weapons are sparking a new arms race that endangers everyone yet on a far broader scale than the nuclear arms race, as they are cheaper and easier to develop independently. Advanced military AI dehumanizes warfare, as its capacity to strike targets thousands of kilometers away and autonomously select human targets complicates the attribution of responsibility a crucial element in holding war criminals accountable and, due to the evolving nature of this technology, makes accountability even more complex. On one hand, the inherent complexity of AI systems, particularly their autonomy and unpredictability, along with the fact that (like humans) they will never be entirely flawless, means that violations of international law are inevitable. Breaches of IHL through the use of AI entail both criminal and non-criminal liability. While individual and state responsibility are complementary and concurrent, the focus of this discussion is on state responsibility and the concept of "effective control" over the conduct of AI-based weapons. On the other hand, the near-endless list of potentially liable parties including software developers, military personnel or commander, weapons users, manufacturer, and political leaders creates difficulties in assigning responsibility. The global political landscape shows that a comprehensive ban on military AI technology is unlikely to be adopted in the near future. However, given the remarkable technological advancements in recent years, the continued integration of AI into military weaponry is inevitable. While it is widely accepted that IHL fully applies to the use of AI-based technologies, the issue of state responsibility for IHL violations stemming from such technologies remains highly contentious. Thus, the central question is: How does the international law of state responsibility apply to violations of international law arising from AI military technologies? Can international responsibility even be established for such incidents? Given the role of humans as accountable agents during the production or deployment stages, the very assumption of responsibility is undermined by the autonomous nature of AI. On what basis, wrongful act occurring during the use of AI weapons in the armed conflicts can be attributed to a state? To answer these questions, this paper first examines state obligations regarding the use of AI-based weapons in armed conflicts. It then analyzes the general rules of state responsibility under de lege lata in international law (the ILC’s 2001 Draft Articles) concerning military AI. Next, it explores the possibility of attributing wrongful act of military AI to states under de lege ferenda. Finally, it specifically addresses the dimensions of direct and indirect state responsibility in the development, acquisition, and use of military AI. The article also pays particular attention to the liability regime for compensation concerning acts not prohibited under international law. With significant developments in technologies, artificial intelligence is used instead of humans in armed conflicts gradually. Military AI technologies create new challenges in a variety of fields of international law. The use of AI weapons systems in armed conflicts has challenged not only our traditional concepts of responsibility but also attribution of international responsibility to states; Because the traditional concepts of responsibility and the principles of states responsibility has been based on human behavior and its adaptation to AI-based behavior requires a new review. Therefore, the goal of this analysis is to examines the consequences of the international responsibility of states in the use of military AI in armed conflicts and reparation for the damages resulting from their performance. To encounter monitoring the performance of AI technologies, the main question of this essay is that who is legally responsible for the effects of weapons equipped with artificial intelligence? In what situations are states responsible in relation to violations of international law when using military artificial intelligence? Regarding the research background and the innovative aspect of this paper, it must be noted that the systematic study of AI-based weapons in armed conflicts is relatively new in domestic scholarship. Recent Persian-language literature, whether original or translated, has addressed the use of AI-based weapons in armed conflicts from the perspectives of IHL and international criminal law (ICL), with key works cited in this article. However, the author has not encountered any specific study examining the application of AI-based weapons in armed conflicts within the framework of state responsibility under international law. At the international level, due to the significance of the issue, research has been conducted on the accountability gap concerning AI-based weapons and their use in armed conflicts. For instance, Gabriel Wood (2023) wrote an article titled "Autonomous Weapon Systems and Responsibility Gaps: A Taxonomy," which, contrary to its title, focused only on the challenges of such weapons under the law of armed conflict rather than state responsibility. Bernic Boutin (2022), in an article titled "State Responsibility in Relation to Military Applications of AI," discussed AI in military structures, state responsibility in the production and sale of such technologies, and their wrongful use. Damian Bielicki (2021), in "Regulating AI in Industry," edited a collection of essays by international law scholars, providing a relatively comprehensive overview of AI applications and their legal challenges. Additionally, Magdalena Pacholska (2020) examined ‘‘Military Artificial Intelligence and the Principle of Distinction: A State Responsibility Perspective’’, though her focus remained on IHL and individual responsibility rather than state responsibility, as she argued that no unique issues arise in this context. Thus, while the aforementioned works touch on the subject, none systematically address, defend, or refute the hypothesis of this paper. The primary hypothesis of the article is that existing international law provides a suitable legal framework to deal with the effects of AI-equipped weapons, but more clarity is needed on how to apply this existing legal framework to new technologies. . 2. Methodology This research aims to gather information from various sources, including books, articles, theses, research reports, The article has been performed based on the descriptive-analytical research method. The necessary data has been collected by library method. Following that, relevant data from legal and jurisprudence doctrine, academic commentaries and international jurisprudence are collected and analyzed from various perspectives adopting scientific evidence in order to answer the research question. Finally, the findings are discussed. . 3. Results and Discussion This article shows that although the use of AI technologies in armed conflicts is irrefutable, but the applicable international treaties did not explicitly address the issue of AI systems sand its used in armed conflicts, nor has any other international regulation expressly referenced the application of AI in hostilities. The absence of international norms governing AI creates complex and potential problems concerning the applicable law in resolving inter-state disputes over responsibility for the use of AI in armed conflicts. The author also believes that the lack of international rule (treaty or customary) regarding AI systems creates complex and potential problems regarding applicable law in resolving disputes between states to determine responsibility for the use of AI in armed conflicts. The inherent complexity of AI based systems and the multitude of different actors (software programmers, military personnel or commander, users, manufacturer, weapons inspectors and political leaders) involved in their construction, development and deployment in military operations in military operations, lead to ambiguities regarding the attribution of responsibility for violating international obligations. However, the author is of the opinion that neither contemporary employment of AI systems nor their future ‘truly autonomous’ incarnations create any major conceptual hurdles under the law of state responsibility. There is no doubt that the states can be held responsible for the wrongful applications of AI systems in armed conflicts, inattentive procurement and omission to respect for international law by other states and private actors who are developing or applying artificial intelligence. At the deployment stage, due to the characteristics of AI, the direct acts or omissions of human operators do not always provide sufficient grounds for attribution. However, attributing conduct involving AI may instead be based on human behavior and decision-making by other entities (i.e., developers, political and military decision-makers). At the development stage, existing obligations impose a duty to ensure compliance with international regulations—specifically, the obligation to embed applicable norms into AI design. This obligation also comes into play at the procurement and supply stage, where states must verify compliance. Thus, states must subject military AI to continuous oversight by their institutions to prevent violations of international obligations through preventive measures. 4. Conclusions and Future Research By examining the draft articles on responsibility of states for internationally wrongful acts and IHL rules, the author conclude that neither the contemporary applications of AI nor the truly autonomous visions of its future create a conceptual obstacle in the framework of responsibility laws, and not only the illegal behaviors of AI-based systems in the battlefield can be attributed to the states, but also the responsibility of the states can be stablished before the use of artificial intelligence (in the study, development, acquisition or adoption of a new weapons). However, better application of secondary rules in the face of military AI problems requires the adoption of a binding international treaty or the formation of customary rules to cover these weapons. The arguments presented in this article are intended solely to initiate a discussion on the necessity of state responsibility under international law for their tools and instruments, analogous to their responsibility for state organs. This does not mean that machines can never be held accountable. The scope of responsibility in the use of AI is only discernible if unlawful acts can be attributed to an identifiable person. However, the lack of human agency in autonomous weapon systems does not ipso facto negate responsibility. The existing framework of international law (de lege ferenda) applicable to military AI reflects that AI-based weapon systems regardless of their autonomy are ultimately the product of human behavior, social institutions, and decisions. Therefore, even with technological advancements and the increasing use of military AI, the essential causal link between AI-related malfunctions and state responsibility remains intact. While the framework of state responsibility plays a useful role in regulating AI and addressing accountability challenges specific to AI-based technologies, the mens rea (mental element) of violations in the law of armed conflict hinges on perpetrator intent, which is exceedingly difficult to prove in AI-driven systems. Consequently, the law of state responsibility cannot address all major challenges posed by such weaponry. To fill accountability gaps regarding military AI, state responsibility under international law serves a complementary role alongside other liability frameworks, collectively ensuring comprehensive accountability at all levels. Efforts to hold states accountable for AI underscore their unique position and primary role in regulating military technologies and supervising non-state and private actors. Specifically, state responsibility demonstrates that a viable approach exists to ensure military AI development aligns with applicable international norms. Any miscalculation by AI-based systems in armed conflicts may result in significant civilian casualties or severe damage to civilian property. Since blaming artificial or mechanical tools is futile, it follows that only humans are subjects of legal rules. Human conduct remains a critical factor in applying state responsibility for violations of jus ad bellum, IHL, and human rights law. The final decision on whether AI-based weapons are actually used in a specific operation rest with humans particularly military authorities responsible for operational planning. In this sense, when weighing potential collateral damage against operational advantages, the unpredictability inherent in autonomous systems must be factored in. Ultimately, to meet the demands of international law, states should adopt a binding international treaty establishing design standards for AI weapons, the degree and form of human control, permissible targets, and the scope of their use.

Joana Machado, Larissa Fonseca Toledo

O artigo explora possibilidades de responsabilização das plataformas digitais, tendo em vista seu impacto sobre a política e sobre minorias políticas, com ênfase na amplificação de desinformação, reprodução de violências e discursos de ódio no Brasil. Fundamentado em uma abordagem qualitativa de caráter exploratório, com análise documental e bibliográfica, o trabalho examina o Projeto de Lei 2630/2020, em tramitação paralisada no Congresso Nacional brasileiro, atento à possibilidade de reação do Poder Legislativo brasileiro ao julgamento em curso no Supremo Tribunal Federal sobre a constitucionalidade do Marco Civil da Internet e possíveis desdobramentos sobre a responsabilização das plataformas por conteúdos de terceiros. O trabalho discute a arquitetura das redes, o modelo de negócio em que operam, a transição do liberalismo para o constitucionalismo digital, e o papel do Poder Judiciário nesse processo. Discute modelos regulatórios para as redes sociais, ressalta as limitações da autorregulação e pontua insuficiências da autorregulação regulada quanto a riscos inaceitáveis sobre direitos fundamentais de minorias políticas. O trabalho procura contribuir para uma agenda crítica e mais substantiva de constitucionalismo digital.

Seungjun Baek

PurposeWe compare the macroeconomic and housing-market effects of (1) reducing mortgage subsidies, (2) tightening borrower-based tools (debt-service-to-income [DSTI]/loan-to-value [LTV]) and (3) standard monetary and external shocks.Design/methodology/approachWe develop a small open-economy (SOE) New Keynesian dynamic stochastic general equilibrium (DSGE) model that embeds a housing market featuring LTV and DSTI constraints. Borrower-based policy instruments and mortgage interest subsidies are calibrated to Mongolian institutions.FindingsHigh subsidies amplify house-price and consumption volatility and strengthen monetary transmission through the mortgage channel. Relative to tightening DSTI, cutting subsidies achieves comparable reductions in leverage and house-price pressures at lower output and consumption costs, suggesting a policy sequencing in which subsidies are scaled down before DSTI caps are tightened.Originality/valueWe develop the first Mongolia-calibrated small open-economy New Keynesian DSGE that embeds a mortgage block with explicit LTV and DSTI constraints and a government mortgage–subsidy wedge. By unifying monetary, macroprudential (DSTI/LTV) and fiscal (subsidy) instruments, the model quantifies policy sequencing and transmission under external shocks.

Mohammad Shamsuddoha, Ahamed Ismail Hossain, Irma Dewan et al.

<i>Background</i>: Jute, recognized as the ‘golden fiber’ of Bangladesh, produces a substantial amount of stick left over (waste), a byproduct of the fiber. Usually, unused jute sticks (JS) are thrown away or burned, since they are treated as landfill or unusable waste. Noteworthy research gaps exist in the farming process, infrastructure, [supply chains], unfavorable policies, government interference, and insufficient farmers’ knowledge of the export market. This research examines the potential of jute stick charcoal (JSC) as a sustainable and value-added product within the circular economy framework. <i>Methods</i>: This study employs a system dynamics (SD) modeling approach to examine how various factors, including agricultural output, supply chain process efficiency, trade flows, and relevant variables, influence JSC supply chain performance. Considering technologies, logistics, and policy variables, this study constructed a simulation model with three scenarios: current, worst-case, and improved, using Vensim DSS to identify system behavior under changing conditions. <i>Results:</i> The simulation indicates that optimizing idle jute resources, enhancing supply chain processes, and expanding markets can increase economic returns, reduce waste, and create more rural jobs, particularly for women. <i>Conclusions</i>: Enhanced coordination, technologies, and logistics can reduce carbon emissions, benefit farmers, support rural industries, and contribute to SDGs 8, 12, and 13.

Pao An Chen, Saeyeon Roh

PurposeThis study examines the role of corporate social responsibility (CSR) in sustainable supply chain management, specifically targeting the dimensions most valued by practitioners. By identifying and ranking key CSR dimensions–Environmental Management, Social Responsibility, Customer Management and Health, Safety and Risk Management–the research aims to establish a framework that enhances CSR implementation in supply chains.Design/methodology/approachThe study uses a quantitative survey of supply chain management practitioners, analyzed through the consistency fuzzy preference relations (CFPR). This methodology enables a pairwise comparison to determine the relative importance of CSR dimensions. The sample includes professionals with varying levels of experience and roles in supply chain management, ensuring diverse perspectives on CSR priorities.FindingsResults indicate that Health, Safety and Risk Management is considered the most critical CSR dimension, followed by Customer Management and Social Responsibility, with Environmental Management ranked last. The findings suggest that practitioners prioritize CSR elements that directly impact organizational safety and risk, highlighting a potential gap between broader CSR goals and operational focus areas in supply chains.Research limitations/implicationsThe study offers a scalable CSR assessment model for supply chains, though limited by a sample size focused on practitioners from a single geographic region. Further research could broaden the sample and explore industry-specific CSR priorities.Practical implicationsBy highlighting Health, Safety and Risk Management as the primary CSR focus, this study provides actionable insights for supply chain managers. The findings can guide companies in developing CSR policies that align with practitioner priorities, thus enhancing sustainability efforts and stakeholder trust in supply chain operations.Originality/valueThis research extends existing literature by employing a CFPR approach, which enables a more refined prioritization of CSR dimensions from the perspective of supply chain practitioners. Unlike previous studies that focus on general CSR criteria, this study provides an industry-specific evaluation, offering insights that bridge theoretical CSR frameworks with operational decision-making in sustainable supply chain management.

N. S. Gonchar, O. P. Dovzhyk, A. S. Zhokhin et al.

The paper analyses trade between the most developed economies of the world. The analysis is based on the previously proposed model of international trade. This model of international trade is based on the theory of general economic equilibrium. The demand for goods in this model is built on the import of goods by each of the countries participating in the trade. The structure of supply of goods in this model is determined by the structure of exports of each country. It is proved that in such a model, given a certain structure of supply and demand, there exists a so-called ideal equilibrium state in which the trade balance of each country is zero. Under certain conditions on the structure of supply and demand, there is an equilibrium state in which each country have a strictly positive trade balance. Among the equilibrium states under a certain structure of supply and demand, there are some that differ from the ones described above. Such states are characterized by the fact that there is an inequitable distribution of income between the participants in the trade. Such states are called degenerate. In this paper, based on the previously proposed model of international trade, an analysis of the dynamics of international trade of 8 of the world's most developed economies is made. It is shown that trade between these countries was not in a state of economic equilibrium. The found relative equilibrium price vector turned out to be very degenerate, which indicates the unequal exchange of goods on the market of the 8 studied countries. An analysis of the dynamics of supply to the market of the world's most developed economies showed an increase in China's share. The same applies to the share of demand.

Zhe-hui Ma, Hang-ye Zhang, Chuang Liu et al.

Superheated steam is widely employed in various energy systems, particularly in power plants, chemical industries, and other applications where high-temperature and high-pressure steam is essential for efficient energy conversion and process control. In these systems, regulation valves are crucial components that control the flow of steam, adjusting its pressure and temperature to ensure safe and efficient operation. Accurate understanding and prediction of temperature variations within regulation valves are essential for optimizing their performance and improving the overall system efficiency. This study investigates the temperature variations of superheated steam flowing through a regulation valve using computational fluid dynamics (CFD) simulations combined with Proper Orthogonal Decomposition (POD) techniques. The analysis begins with an examination of the internal flow field parameters, including temperature and pressure, to understand the overall fluid dynamics within the valve. POD is applied to reduce the dimensionality of the CFD results. Singular Value Decomposition (SVD) is employed to extract the dominant modes that capture the key flow structures responsible for heat transfer and temperature fluctuations. The POD analysis reveals that the most influential modes are associated with regions of high turbulence intensity and significant temperature gradients, which are critical to the thermal performance of the steam flow through the regulation valve. The application of POD to 3D CFD results represents a novel approach, particularly for complex fluid flow models such as steam flow through regulation valves. The insights gained from this study have practical implications for the design and optimization of temperature and pressure regulation valves in energy systems, providing a theoretical foundation for enhancing the efficiency and reliability of these systems.

Haniyeh Zakerinia, Zahra Gholampour

The discussion of civil liability arising from algorithmic losses - mostly investigated as civil liability of artificial intelligence or liability arising from the use of artificial intelligence is nascent /aborning in the legal literature. thinking algorithms require a special civil liability system in case of losses Due to the unique characteristics of self-learning, randomness, unpredictability and autonomy. The general theory of Opposability can be a guide in such new cases, too. However, having a transparent and pragmatic criterion and standard to compare the harmful algorithm with similar cases to determine its normal operation is associated with challenges. Determining the scope of the concept of reasonable algorithms, in terms of their unknown nature and inherent complexities, needs to be investigated. By examining the existing obstacles, this research pursues the re-examination of reasonable algorithms with a mixture criterion: A standard that creates incentives for technological and innovative spaces, compensates for the damage, improves the safety level of algorithms and realises the appropriate flexibility to face new generations of technology. Rereading the Opposability theory (causality customary) in the field of algorithms with conventional and reasonable efficiency, as well as adapting this general theory in dealing with the losses arising from the use of thinking algorithms, is the main result of ​​the authors' efforts in this article

Run-Xuan Tang

This paper presents an innovative Comprehensive Growth Dynamic Model (CGDM). CGDM is designed to simulate the temporal evolution of an event, incorporating economic and social factors. CGDM is a regression of logistic regression, power law regression, and Gaussian perturbation term. CGDM is comprised of logistic regression, power law regression, and Gaussian perturbation term. CGDM can effectively forecast the temporal evolution of an event, incorporating economic and social factors. The illicit trade in wildlife has a deleterious impact on the ecological environment. In this paper, we employ CGDM to forecast the trajectory of illegal wildlife trade from 2024 to 2034 in China. The mean square error is utilized as the loss function. The model illuminates the future trajectory of illegal wildlife trade, with a minimum point occurring in 2027 and a maximum point occurring in 2029. The stability of contemporary society can be inferred. CGDM's robust and generalizable nature is also evident.

Enrico Maria Fenoaltea, Dario Mazzilli, Aurelio Patelli et al.

The integration of artificial intelligence (AI) into the workplace is advancing rapidly, necessitating robust metrics to evaluate its tangible impact on the labour market. Existing measures of AI occupational exposure largely focus on AI's theoretical potential to substitute or complement human labour on the basis of technical feasibility, providing limited insight into actual adoption and offering inadequate guidance for policymakers. To address this gap, we introduce the AI Startup Exposure (AISE) index-a novel metric based on occupational descriptions from O*NET and AI applications developed by startups funded by the Y Combinator accelerator. Our findings indicate that while high-skilled professions are theoretically highly exposed according to conventional metrics, they are heterogeneously targeted by startups. Roles involving routine organizational tasks-such as data analysis and office management-display significant exposure, while occupations involving tasks that are less amenable to AI automation due to ethical or high-stakes, more than feasibility, considerations -- such as judges or surgeons -- present lower AISE scores. By focusing on venture-backed AI applications, our approach offers a nuanced perspective on how AI is reshaping the labour market. It challenges the conventional assumption that high-skilled jobs uniformly face high AI risks, highlighting instead the role of today's AI players' societal desirability-driven and market-oriented choices as critical determinants of AI exposure. Contrary to fears of widespread job displacement, our findings suggest that AI adoption will be gradual and shaped by social factors as much as by the technical feasibility of AI applications. This framework provides a dynamic, forward-looking tool for policymakers and stakeholders to monitor AI's evolving impact and navigate the changing labour landscape.

Liangyu Teng, Yang Liu, Jing Liu et al.

In recent years, the e-commerce industry has seen a rapid increase in the demand for advanced AI-driven customer service solutions. Traditional cloud-based models face limitations in terms of latency, personalized services, and privacy concerns. Furthermore, end devices often lack the computational resources to deploy large AI models effectively. In this paper, we propose an innovative End-Cloud Collaboration (ECC) framework for advanced AI customer service in e-commerce. This framework integrates the advantages of large cloud models and mid/small-sized end models by deeply exploring the generalization potential of cloud models and effectively utilizing the computing power resources of terminal chips, alleviating the strain on computing resources to some extent. Specifically, the large cloud model acts as a teacher, guiding and promoting the learning of the end model, which significantly reduces the end model's reliance on large-scale, high-quality data and thereby addresses the data bottleneck in traditional end model training, offering a new paradigm for the rapid deployment of industry applications. Additionally, we introduce an online evolutive learning strategy that enables the end model to continuously iterate and upgrade based on guidance from the cloud model and real-time user feedback. This strategy ensures that the model can flexibly adapt to the rapid changes in application scenarios while avoiding the uploading of sensitive information by performing local fine-tuning, achieving the dual goals of privacy protection and personalized service. %We make systematic contributions to the customized model fine-tuning methods in the e-commerce domain. To conclude, we implement in-depth corpus collection (e.g., data organization, cleaning, and preprocessing) and train an ECC-based industry-specific model for e-commerce customer service.

Soheil Ghaderi Talkhab, Roozbeh Asad

With the growing trend of electrification in the rail transportation industry, the control system of electric motors plays a crucial role. Typically, each metro train consists of multiple wagons, and each wagon is equipped with several electric motors. In the conventional transportation system, each electric motor of the train is powered by a three-phase inverter, which increases the cost of the drive system and requires more space. Alternatively, another method involves using a three-phase inverter to control multiple electric motors, but this approach cannot independently control each motor. This paper provides a comprehensive review along with a comparative analysis of single-input multiple-output inverter topologies, along with some suggestions for selecting suitable configurations for electric transportation applications, particularly electric railways, to achieve independent control of each electric motor. Modern railway systems utilize multiple electric motors/drives for various functions such as traction, braking, steering, and suspension. As the number of electric motors in a train increases, challenges and issues arise in terms of cost, space, reliability, control, and energy management. This paper presents various architectures for power inverters to reduce the number of components and achieve centralized control in train bogies, different methods of motor synchronization in multi-motor drive systems, control algorithms for single-motor drive systems, and their extensions to various multi-motor drive structures.

Fabio Ashtar Telarico

Studies built on dependency and world-system theory using network approaches have shown that international trade is structured into clusters of 'core' and 'peripheral' countries performing distinct functions. However, few have used these methods to investigate how sanctions affect the position of the countries involved in the capitalist world-economy. Yet, this topic has acquired pressing relevance due to the emergence of economic warfare as a key geopolitical weapon since the 1950s. And even more so in light of the preeminent role that sanctions have played in the US and their allies' response to the Russian-Ukrainian war. Applying several clustering techniques designed for complex and temporal networks, this paper shows that a shift in the pattern of commerce away from sanctioning countries and towards neutral or friendly ones. Additionally, there are suggestions that these shifts may lead to the creation of an alternative 'core' that interacts with the world-economy's periphery bypassing traditional 'core' countries such as EU member States and the US.

Ron Teichner, Naama Brenner, Ron Meir

Homeostasis, the ability to maintain a stable internal environment in the face of perturbations, is essential for the functioning of living systems. Given observations of a system, or even a detailed model of one, it is both valuable and extremely challenging to extract the control objectives of the homeostatic mechanisms. Lacking a clear separation between plant and controller, frameworks such as inverse optimal control and inverse reinforcement learning are unable to identify the homeostatic mechanisms. A recently developed data-driven algorithm, Identifying Regulation with Adversarial Surrogates (IRAS), detects highly regulated or conserved quantities as the solution of a min-max optimization scheme that automates classical surrogate data methods. Yet, the definition of homeostasis as regulation within narrow limits is too strict for biological systems which show sustained oscillations such as circadian rhythms. In this work, we introduce Identifying Dynamic Regulation with Adversarial Surrogates (IDRAS), a generalization of the IRAS algorithm, capable of identifying control objectives that are regulated with respect to a dynamical reference value. We test the algorithm on simulation data from realistic biological models and benchmark physical systems, demonstrating excellent empirical results.

Jay Lee, Hanqi Su

The recent emergence of large language models (LLMs) demonstrates the potential for artificial general intelligence, revealing new opportunities in Industry 4.0 and smart manufacturing. However, a notable gap exists in applying these LLMs in industry, primarily due to their training on general knowledge rather than domain-specific knowledge. Such specialized domain knowledge is vital for effectively addressing the complex needs of industrial applications. To bridge this gap, this paper proposes a unified industrial large knowledge model (ILKM) framework, emphasizing its potential to revolutionize future industries. In addition, ILKMs and LLMs are compared from eight perspectives. Finally, the "6S Principle" is proposed as the guideline for ILKM development, and several potential opportunities are highlighted for ILKM deployment in Industry 4.0 and smart manufacturing.

Shah J Miah

With the advancement in the marketing channel, the use of e-commerce has increased tremendously therefore the basic objective of this study is to analyze the impact of business analytics and decision support systems on e-commerce in small and medium enterprises. Small and medium enterprises are becoming a priority for economies as by implementing some policies and regulations these businesses could encourage gain development on an international level. The objective of this study is to analyze the impact of business analytics and decision support systems on e-commerce in small and medium enterprises that investigate the relationship between business analytics and decision support systems in e-commerce businesses. To evaluate the impact of both on e-commerce the, descriptive analysis approach is adopted that reviews the research of different scholars who adopted different plans and strategies to predict the relationship between e-commerce and business analytics. The study contributes to the literature by examining the impact of business analytics in SMEs and provides a comprehensive understanding of its relationship with the decision support system. After analyzing the impact of business analytics and decision support system in SMEs, the research also highlights some limitations and provide future recommendations that are helpful to overcome these limitations.

Wonguk Cho, Daekyung Lee, Beom Jun Kim

The international trade network is a complex system where multiple trade blocs with varying sizes coexist and overlap with each other. However, the resulting structures of community detection in trade networks are often inconsistent and fails to capture the complex landscape of international trade. To address these problems, we propose a multiresolution framework that aggregates all the configuration information from a range of resolutions. This allows us to consider trade communities of different sizes and illuminate the underlying hierarchical structure of trade networks and its constituting blocks. Furthermore, by measuring membership inconsistency (MeI) of each country and conducting multiple regression analysis with various economic and political indicators, we demonstrate that there exists a positive correlation between the external instability of countries and their structural inconsistency in terms of network topology.