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

Huisheng Wang, H. Vicky Zhao

Herding, where investors imitate others' decisions rather than relying on their own analysis, is a prevalent phenomenon in financial markets. Excessive herding distorts rational decisions, amplifies volatility, and can be exploited by manipulators to harm the market. Traditional regulatory tools, such as information disclosure and transaction restrictions, are often imprecise and lack theoretical guarantees for effectiveness. This calls for a quantitative approach to regulating herding. We propose a regulator-leader-follower trilateral game framework based on optimal control theory to study the complex dynamics among them. The leader makes rational decisions, the follower maximizes utility while aligning with the leader's decisions, whereas the regulator designs a mechanism to maximize social welfare and minimize regulatory cost. We derive the follower's decisions and the regulator's mechanisms, theoretically analyze the impact of regulation on decisions, and investigate effective mechanisms to improve social welfare.

Bouzi Wissam, Bentaieb Samia, Ouamri Abdelaziz

An efficient traffic management system is essential to minimize traffic problems and ensure the rapid circulation of emergency vehicles. This research proposes a new single-agent deep reinforcement-learning model using a deep Q-Network (DQN) to optimize traffic lights, aiming to reduce waiting times and increase vehicle speed, with particular emphasis on emergency vehicles. Our method incorporates a new state representation, which captures variations in vehicle density and speed that directly influence the reward structure to prioritize both traffic flow and emergency vehicle response times. The decision of the agent is enhanced by a replay memory mechanism, which ensures that experiences are effectively used in learning. The model’s effectiveness was tested in a simulated environment using SUMO, showing significant improvements in traffic management compared to traditional methods. Experimental results show that our system significantly reduces average waiting times and improves emergency vehicle prioritization.

Luong Nguyen, Oscar Mayet, Salil Desai

<i>Background:</i> In a fast-changing sector, apparel distribution centers (DCs) are under increasing pressure to meet labor intensive operational requirements, short delivery windows, and variable demand in the rapidly changing apparel industry. Traditional labor forecasting methods often fail in dynamic environments, leading to inefficiencies, inadequate staffing, and reduced responsiveness. <i>Methods:</i> This comprehensive review discusses AI-enhanced labor forecasting tools that support flexible workforce planning in apparel DCs using a PRISMA methodology. To provide proactive, data-driven scheduling recommendations, the model combines machine learning algorithms with workforce metrics and real-time operational data. <i>Results:</i> Key performance indicators such as throughput per work hour, skill alignment among employees, and schedule adherence were used to assess performance. Apparel distribution centers can significantly benefit from real-time, adaptive decision-making made possible by AI technologies in contrast to traditional models that depend on static forecasts and human scheduling. These include LSTM for time-series prediction, XGBoost for performance-based staffing, and reinforcement learning for flexible task assignments. <i>Conclusions:</i> The paper demonstrates the potential of AI in workforce planning and provides useful guidance for the digitization of labor management in the clothing distribution industry for dynamic and responsive supply chains.

Mariem Mrad, Mohamed Amine Frikha, Younes Boujelbene

<i>Background</i>: Artificial intelligence (AI) and robotics are increasingly pivotal for reducing carbon emissions in supply chain management (SCM); however, research exploring their combined potential from a sustainability perspective remains fragmented. This study aims to systematically map the research landscape and synthesize evidence on the applications, benefits, and challenges. <i>Methods</i>: A systematic scoping review was conducted on 23 peer-reviewed studies from the Scopus database, published between 2013 and 2024. Data were systematically extracted and analyzed for publication trends, application domains (e.g., transportation, warehousing), specific AI and robotic technologies, emissions reduction strategies, and implementation challenges. <i>Results</i>: The analysis reveals that AI-driven logistics optimization is the most frequently reported strategy for reducing transportation emissions. At the same time, robotic automation is commonly associated with improved energy efficiency in warehousing. Despite these benefits, the reviewed literature consistently identifies significant barriers, including the high energy demands of AI computation and complexities in data integration. <i>Conclusions</i>: This review confirms the transformative potential of AI and robotics for developing low-carbon supply chains. An evidence-based framework is proposed to guide practical implementation and identify critical gaps, such as the need for standardized validation benchmarks, to direct future research and accelerate the transition to sustainable SCM.

Nicholas Andrey Monteiro Watzko, Lucas Bossoni Saikali, Ana Flávia Hadas

É objetivo fundamental da República Federativa do Brasil promover o bem de todos, sem preconceitos de origem, raça, sexo, cor, idade e quaisquer outras formas de discriminação, conforme assegurado pelo inciso IV, do art. 3º da Constituição Federal de 1988. Em paralelo, é notório o rápido desenvolvimento da inovação digital e do incremento das tecnologias disruptivas na vida das pessoas, à exemplo da inteligência artificial. Entretanto, problemas sociais e atentatórios aos direitos fundamentais são parte da experiência cotidiana no Brasil e as novas tecnologias podem impulsionar tais ameaças, estimulando a discriminação social por meio da incorporação de vieses discriminatórios. Diante dessa problemática, o presente trabalho realiza uma discussão acerca da necessária regulação da inteligência artificial no Brasil, empreendendo uma análise do PL 2.338/2023, aprovado pelo Senado Federal no dia 10 de dezembro de 2024, a fim de investigar se e como esse projeto de regulação da IA protege o direito fundamental à não discriminação, ameaçado pelo uso desordenado de sistemas de inteligência artificial. Ainda, este estudo também efetua um exame comparado com a pioneira legislação de regulação de IA aprovada pela União Europeia, verificando-se como lá se deu a disposição normativa voltada à proteção do direito fundamental à não discriminação.

Jurek Preker

We introduce a novel equilibrium concept that incorporates Knightian uncertainty into the cursed equilibrium (Eyster and Rabin, 2005). This concept is then applied to a two-player game in which agents can engage in trade or refuse to do so. While the Bayesian Nash equilibrium predicts that trade never happens, players do trade in a cursed equilibrium. The inclusion of uncertainty enhances this effect for cursed and uncertainty averse players. This contrasts general findings that uncertainty reduces trade but is consistent with behavior that has been observed in experiments.

Wenjie Liu, Yifei Li, Jian Sun et al.

Output regulation is a fundamental problem in control theory, extensively studied since the 1970s. Traditionally, research has primarily addressed scenarios where the system model is explicitly known, leaving the problem in the absence of a system model less explored. Leveraging the recent advancements in Willems et al.'s fundamental lemma, data-driven control has emerged as a powerful tool for stabilizing unknown systems. This paper tackles the output regulation problem for unknown single and multi-agent systems (MASs) using noisy data. Previous approaches have attempted to solve data-based output regulation equations (OREs), which are inadequate for achieving zero tracking error with noisy data. To circumvent the need for solving data-based OREs, we propose an internal model-based data-driven controller that reformulates the output regulation problem into a stabilization problem. This method is first applied to linear time-invariant (LTI) systems, demonstrating exact solution capabilities, i.e., zero tracking error, through solving a straightforward data-based linear matrix inequality (LMI). Furthermore, we extend our approach to solve the $k$th-order output regulation problem for nonlinear systems. Extensions to both linear and nonlinear MASs are discussed. Finally, numerical tests validate the effectiveness and correctness of the proposed controllers.

Hongjun Ma, Weichang Li

This paper investigates a framework (CATCH-FORM-3D) for the precise contact force control and surface deformation regulation in viscoelastic material manipulation. A partial differential equation (PDE) is proposed to model the spatiotemporal stress-strain dynamics, integrating 3D Kelvin-Voigt (stiffness-damping) and Maxwell (diffusion) effects to capture the material's viscoelastic behavior. Key mechanical parameters (stiffness, damping, diffusion coefficients) are estimated in real time via a PDE-driven observer. This observer fuses visual-tactile sensor data and experimentally validated forces to generate rich regressor signals. Then, an inner-outer loop control structure is built up. In the outer loop, the reference deformation is updated by a novel admittance control law, a proportional-derivative (PD) feedback law with contact force measurements, ensuring that the system responds adaptively to external interactions. In the inner loop, a reaction-diffusion PDE for the deformation tracking error is formulated and then exponentially stabilized by conforming the contact surface to analytical geometric configurations (i.e., defining Dirichlet boundary conditions). This dual-loop architecture enables the effective deformation regulation in dynamic contact environments. Experiments using a PaXini robotic hand demonstrate sub-millimeter deformation accuracy and stable force tracking. The framework advances compliant robotic interactions in applications like industrial assembly, polymer shaping, surgical treatment, and household service.

Sylvester Willys Namagwa

As life expectancy in Kenya increases, so does the need for efficient pension schemes that can secure a dignified retirement and protect members from old age poverty. Limited research, however, has explored the efficiency of these schemes under existing governance structures. This study addresses that gap by examining the combined effects of corporate governance, risk management, and industry regulation on pension scheme efficiency in Kenya. Using a quantitative design, we conducted a panel regression analysis on a seven-year secondary dataset of 128 Kenyan pension schemes, totaling 896 observations. Our results reveal significant insights That the presence of employee representatives on the board and effective risk management have a significant positive effect on efficiency. Conversely, independent board members exhibit a significant negative effect. Other factors, including top management representation, female board members, and industry regulation, showed no significant effect on efficiency in the joint model. These findings suggest that the impact of governance and risk management on efficiency is nuanced, with specific factors like employee representation playing a more prominent role. We propose that the electoral process for employee board members may introduce a Self Cleaning Mechanism that progressively enhances scheme efficiency. This mechanism offers a novel theoretical extension of Agency Theory, explaining the convergence of interests between elected trustees and scheme members.

Majid Mohammed Kunambi, Hongxing Zheng

<i>Background:</i> This research conducts a contextual comparative analysis between Dar es Salaam and Mombasa ports, employing a hybrid data envelopment analysis (DEA) model that integrates the contextual value-added approach (CVA). The assessment incorporates various inputs (quay length, number of cranes, and storage area) and outputs (number of ship calls and cargo throughput) to compute efficiency scores, offering nuanced insights into the strengths, weaknesses, and areas for improvement of both ports. <i>Methods</i>: The hybrid DEA model with CVA is applied to calculate efficiency scores, considering the diverse inputs and outputs. This approach allows for a comprehensive evaluation of the relative performance of Dar es Salaam and Mombasa ports. The study also explores the influence of trade-related externalities on port efficiency, providing a holistic understanding of the factors shaping the ports’ operational effectiveness. <i>Results:</i> The efficiency scores depict distinctive performance trends between Dar es Salaam and Mombasa ports. Notably, Dar es Salaam exhibits maximum efficiency (efficiency value of 1) in 2018 and 2021, while Mombasa attains optimal performance (efficiency value of 1) in 2021. However, efficiency values fluctuate for both ports in other years, ranging between 0.895 and 0.985 for Mombasa and 0.924 and 0.960 for Dar es Salaam. <i>Conclusions</i>: This study highlights the dynamic efficiency levels of Dar es Salaam and Mombasa ports over multiple years and identifies critical factors influencing their performance. The findings contribute valuable insights to the field of port analysis, offering guidance to port management and policymakers in optimizing the efficiency and competitiveness of these vital maritime hubs.

Jérémie Katembo Kavota, Luc Cassivi, Pierre-Majorique Léger

<i>Background</i>: This study provides a comprehensive overview of current supply chain challenges and how they are taught within university circles or among supply chain professionals to simulate reality. <i>Methods</i>: The study applied a systematic literature review, using bibliometric co-citation and concept-centered content analysis for a comprehensive review of 118 relevant articles, leading to the identification of critical challenges in modern supply chain management. <i>Results</i>: These challenges include supplier selection and quality, supply chain networks, and sustainable supply chains. Supply chain educators are encouraged to use games that mirror real-world scenarios to teach these challenges. Results from this review underscore that existing games covered supply chain concepts such as the bullwhip effect, collaboration, networks, supplier selection, quality management, humanitarian logistics, sustainability, lean supply chain, Supply Chain 4.0, and perishable goods supply. <i>Conclusions</i>: The study’s contribution is to assist in selecting games tailored to the supply chain specific aspects and to guide developers in creating realistic games that address recent challenges in supply chain management. It recommends a holistic approach to enhance new supply chain game development, drawing from methodologies such as problem-based learning and Lego Serious Play. This multifaceted approach imparts practical knowledge and comprehensive skills for addressing supply chain intricacies in modern business settings.

Tomasz Bojar-Fijałkowski

Na gruncie zdrowia publicznego wartym wyjaśnienia i szerszej analizy jest wątek zdrowia środowiskowego, co może być ciekawe dla prawników. Równolegle na gruncie prawa publicznego, co ufam może zainteresować przedstawicieli innych niż prawo dziedzin nauki, należy zdefiniować pojęcia w sferze prawnej ochrony elementów środowiska. Analiza taka doprowadzi nas bezspornie do pojęcia zdrowotnego prawa środowiska, które tworzy prawne instrumenty realizacji postulatów zdrowia środowiskowego. Część pierwsza tekstu przedstawia pojęcie i ewolucję zdrowia publicznego oraz wyodrębniającego się zeń koncepcję nowego zdrowia publicznego. Część druga skupia się na wskazaniu miejsca w zdrowiu publicznym wątków środowiskowych tworzących pojęcie zdrowia środowiskowego oraz na przedstawieniu jego cech. Część trzecia, zarazem ostatnia, przedstawia koncepcję zdrowotnego prawa środowiska, jako obszaru badawczego z pogranicza, zdefiniowanego w tejże części, prawa środowiskowego oraz, określonego wcześniej, zdrowia środowiskowego. Całość kończą wnioski oraz postulaty de lege lata i de lege ferenda. Pracę oparto na krajowej i międzynarodowej literaturze zdrowia publicznego oraz prawa i zarządzania środowiskowego. Stan prawny aktualny na dzień 30 kwietnia 2023 roku.

Valdemar Tang, Claudio Gomes, Daniel Lucani

We introduce a novel event-trigger threshold (ETT) regulation mechanism based on the quantitative semantics of propositional logic (PL). We exploit the expressiveness of the PL vocabulary to deliver a precise and flexible specification of ETT regulation based on system requirements and properties. Additionally, we present a modified ETT regulation mechanism that provides formal guarantees for satisfaction/violation detection of arbitrary PL properties. To validate our proposed method, we consider a convoy of vehicles in an adaptive cruise control scenario. In this scenario, the PL operators are used to encode safety properties and the ETTs are regulated accordingly, e.g., if our safety metric is high there can be a higher ETT threshold, while a smaller threshold is used when the system is approaching unsafe conditions. Under ideal ETT regulation conditions in this safety scenario, we show that reductions between 41.8 - 96.3% in the number of triggered events is possible compared to using a constant ETT while maintaining similar safety conditions.

Maanas Kumar Sharma

Many AI systems are deployed even when they do not work. Some AI will simply never be able to perform the task it claims to perform. We call such systems Impossible AI. This paper seeks to provide an integrated introduction to Impossible AI in the United States and guide advocates, both technical and policy, to push forward regulation of Impossible AI in the U.S. The paper tracks three examples of Impossible AI through their development, deployment, criticism, and government regulation (or lack thereof). We combine this with an analysis of the fundamental barriers in the way of current calls for Impossible AI regulation and then offer areas and directions in which to focus advocacy. In particular, we advance a functionality-first approach that centers the fundamental impossibility of these systems and caution against criti-hype. This work is part of a broader shift in the community to focus on validity challenges to AI, the decision not to deploy technical systems, and connecting technical work with advocacy.

Steven Arzt, Linda Schreiber, Dominik Appelt

Software security has been an important research topic over the years. The community has proposed processes and tools for secure software development and security analysis. However, a significant number of vulnerabilities remains in real-world software-driven systems and products. To alleviate this problem, legislation is being established to oblige manufacturers, for example, to comply with essential security requirements and to establish appropriate development practices. We argue that software engineering research needs to provide better tools and support that helps industry comply with the new standards while retaining effcient processes. We argue for a stronger cooperation between legal scholars and computer scientists, and for bridging the gap between higher-level regulation and code-level engineering.

Juan Sosa, Andrés Felipe Arévalo-Arévalo, Juan Pablo Torres-Clavijo

Globalization has rapidly advanced but exposed countries to supply chain disruptions, highlighted by the COVID-19 pandemic. This study exhaustively analyzes bilateral export data for 186 countries from 2018, 2020, and 2022, using Exponential Random Graph Models (ERGMs), to identify determinants of trade relationships, as well as Stochastic Block Models (SBMs), to characterize countries' roles in the trade network. Our findings show persistent, significant nodal characteristics driving bilateral trade and reveal no major structural changes in the trade network due to the pandemic.

Fotis Kitsios, Evangelia Nousopoulou, Maria Kamariotou

<i>Background:</i> The COVID-19 pandemic is a worldwide threat that has positioned micro-enterprises under enormous tension to persevere. As a result, these businesses are obligated to respond to the epidemic in an efficacious manner. In order to weather this economic storm, micro-enterprises have implemented a variety of digital technologies. <i>Methods:</i> The research investigates the connection between the communications technology of driving schools and the public crisis responses of those driving schools using a data set obtained from a survey administered to those schools. <i>Results:</i> The quantitative findings demonstrate that digitalization has made it possible for driving schools to efficiently and successfully respond to the public dilemma by utilizing their resilient functionality. In addition, digitalization can greatly enhance driving schools’ performance. <i>Conclusions:</i> This paper provides drawings for digitalization and crisis responses for driving schools.

Jonas Tallberg, Magnus Lundgren, Johannes Geith

As the development and use of artificial intelligence (AI) continues to grow, policymakers are increasingly grappling with the question of how to regulate this technology. The most far-reaching international initiative is the European Union (EU) AI Act, which aims to establish the first comprehensive, binding framework for regulating AI. In this article, we offer the first systematic analysis of non-state actor preferences toward international regulation of AI, focusing on the case of the EU AI Act. Theoretically, we develop an argument about the regulatory preferences of business actors and other non-state actors under varying conditions of AI sector competitiveness. Empirically, we test these expectations using data from public consultations on European AI regulation. Our findings are threefold. First, all types of non-state actors express concerns about AI and support regulation in some form. Second, there are nonetheless significant differences across actor types, with business actors being less concerned about the downsides of AI and more in favor of lax regulation than other non-state actors. Third, these differences are more pronounced in countries with stronger commercial AI sectors. Our findings shed new light on non-state actor preferences toward AI regulation and point to challenges for policymakers balancing competing interests in society.

Sumayya Babangida Sabo, Samuel C. Avemaria Utulu

The papers appraised the Nigeria Data Protection Regulation wit the aim of exposing the institutional propositions contained in the regulation. The aim of the paper is to address how the institutional propositions positions organizations in Nigeria to implement data protections regulations.

Tola Alamirew Mulugeta, Demissie Tamene Adugna, Saathoff Fokke et al.

Predicting the number of crashes that may occur as a result of specific highway features is critical in evaluating different treatment or design alternatives. Since different highway geometric characteristics can influence crash distribution datasets, Highway Safety Manual’s (HSM’s) predictive method encourages users to predict crashes based on their severity and collision type proportions. This study used crash data from rural two-way two-lane road segments in the Oromia region over seven years to develop Oromia’s fixed crash distribution dataset on Interactive Highway Safety Design Model (IHSDM) software. The crash distribution dataset has two parts; the crash severity proportions and the collision type percentages. The developed Oromia’s fixed crash distribution dataset was compared and validated against the default HSM crash configuration. As a result, the Crash Prediction Model (CPM) evaluation results confirmed that the developed crash severity proportion (the first part of the crash distribution dataset) estimates are more accurate and closer to the observed values. Furthermore, the findings show that crashes in the Oromia region are severer than in the states where the HSM crash configuration was developed. According to the second part of the crash distribution dataset evaluation (collision type percentage), the developed fixed crash distribution dataset outperforms the default HSM configuration in most collision type proportions, but not in all. For instance, from the ten collision type proportions developed, Right-Angle and sides-wipe collision proportions are predicted more precisely by the default HSM configuration. This points to the need for developing collision type proportion (the second part of the crash distribution dataset) as a function rather than a fixed configuration for a better result, based on the availability of complete crash data (i.e. crash location). In general, the study revealed that in order to exploit the full potential of HSM’s predictive approach, researchers must develop a jurisdiction crash distribution dataset using local crash data. The methodology demonstrated in this study to develop the jurisdiction’s crash distribution dataset has been validated as true thus, safety practitioners are encouraged to adopt it.