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

Joseph Bak-Coleman, Jevin West, Cailin O'Connor et al.

To what extent is social media research independent from industry influence? Leveraging openly available data, we show that half of the research published in top journals has disclosable ties to industry in the form of prior funding, collaboration, or employment. However, the majority of these ties go undisclosed in the published research. These trends do not arise from broad scientific engagement with industry, but rather from a select group of scientists who maintain long-lasting relationships with industry. Undisclosed ties to industry are common not just among authors, but among reviewers and academic editors during manuscript evaluation. Further, industry-tied research garners more attention within the academy, among policymakers, on social media, and in the news. Finally, we find evidence that industry ties are associated with a topical focus away from impacts of platform-scale features. Together, these findings suggest industry influence in social media research is extensive, impactful, and often opaque. Going forward there is a need to strengthen disclosure norms and implement policies to ensure the visibility of independent research, and the integrity of industry supported research.

Alexander Zuepke, Ashutosh Pradhan, Daniele Ottaviano et al.

The Embedded Trace Macrocell (ETM) is a standard component of Arm's CoreSight architecture, present in a wide range of platforms and primarily designed for tracing and debugging. In this work, we demonstrate that it can be repurposed to implement a novel hardware-assisted memory bandwidth regulator, providing a portable and effective solution to mitigate memory interference in real-time multicore systems. ETM2 requires minimal software intervention and bridges the gap between the fine-grained microsecond resolution of MemPol and the portability and reaction time of interrupt-based solutions, such as MemGuard. We assess the effectiveness and portability of our design with an evaluation on a large number of 64-bit Arm boards, and we compare ETM2 with previous works using a setup based on the San Diego Vision Benchmark Suite on the AMD Zynq UltraScale+. Our results show the scalability of the approach and highlight the design trade-offs it enables. ETM2 is effective in enforcing per-core memory bandwidth regulation and unlocks new regulation options that were infeasible under MemGuard and MemPol.

Elmadina Nahla Nur, Saeed Rashid A., Saeid Elsadig et al.

Future 6th Generation (6G) networks will rely on Terahertz (THz) wireless communication as their main enabler for delivering both ultra-high data speed and minimal delay. THz wireless systems become crucial for upcoming communications by using Unmanned Aerial Vehicles (UAVs) together with Intelligent Reflecting Surfaces (IRS) while improving reliability and efficiency. In UAV-IRS-assisted networks, minimizing mission completion time and energy consumption is critical. However, achieving rapid mission execution often requires UAVs to operate at higher speeds, increasing energy usage and creating a trade-off that demands optimization. This paper addresses the challenge of optimizing UAV-IRS trajectories in THz networks to reduce mission time while adhering to energy constraints. Given the non-convex and NP-hard nature of the problem, traditional optimization methods are insufficient. To tackle this, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm, which provides an efficient, low-complexity solution for trajectory optimization. MADRL dynamically adapts UAV-IRS paths, balancing mission efficiency and energy savings. Simulation results demonstrate that the proposed MADRL-based approach outperforms existing benchmarks, achieving shorter mission times and near-optimal energy consumption across varying scenarios. By leveraging cooperative learning, the algorithm effectively handles complex environments with multiple users and IRS elements. This work highlights the potential of MADRL for UAV-IRS trajectory optimization, offering a scalable solution for energy-efficient and high-performance THz communication systems.

Po-Lin Lai, Xinchen Wang, Yiran Liu

PurposeThis study investigates the causal relationships among knowledge sharing, service capability (SC) and organizational performance within the framework of international logistics companies, highlighting the strategic importance of knowledge dynamics in supporting trade facilitation and policy compliance.Design/methodology/approachA theoretical framework was developed through a comprehensive review of literature on international logistics, trade-related service innovation and organizational knowledge practices. The model was empirically tested using structural equation modeling based on survey responses from 416 employees across multinational logistics firms engaged in international trade operations.FindingsThe results reveal that knowledge sharing significantly enhances both SC and organizational performance. Furthermore, SC mediates the impact of knowledge sharing on performance, indicating its strategic role in adapting to international regulatory environments, meeting global customer expectations and complying with evolving trade policies.Originality/valueThis study contributes theoretically by clarifying how knowledge sharing enhances logistics firms' SCs, which in turn mediate organizational performance. Empirically, it extends prior knowledge management (KM) studies by using survey data from Chinese logistics firms, thereby addressing a gap in emerging-market contexts.

Ali F. Dalain, Mohammad Alnadi, Mahmoud Izzat Allahham et al.

<i>Background</i>: This study examines the impact of technological innovations on digital supply chain management, with a focus on the mediating role of artificial intelligence. With global supply chains increasingly relying on digital platforms, the integration of advanced technologies has become essential for achieving efficiency and competitiveness. <i>Methods</i>: The research employs a mixed-methods approach, combining survey data and expert interviews with professionals from Jordan’s industrial sector. It investigates how emerging digital innovations influence supply chain performance and examines the extent to which artificial intelligence contributes to automation, predictive analytics, and data-driven decision-making. <i>Results</i>: The findings reveal that artificial intelligence plays a pivotal role in enhancing the effectiveness of technological innovations within digital supply chain systems. Specifically, AI improves adaptability to market fluctuations, increases operational efficiency, and strengthens strategic flexibility. These outcomes suggest that organizations adopting AI-enabled innovations are better equipped to respond to uncertainty and achieve superior supply chain performance. <i>Conclusions</i>: The study concludes that technological innovations significantly advance digital supply chain management when supported by artificial intelligence as a mediating factor. The integration of AI not only magnifies the value of digital innovations but also enables sustainable performance improvements and reinforces competitiveness in dynamic industrial environments.

Bohlouli Ramin, Varghese Ken Koshy, Gentile Guido et al.

Accurate mode-choice forecasts are vital for effective transportation planning. Transit agencies and city planners rely on precise predictions, but unreliable forecasts can misdirect even the most behaviorally grounded insights. For decades, discrete choice models (DCMs), notably Multinomial Logit (MNL) and Mixed Multinomial Logit (MMNL), have explained why travelers choose particular modes via interpretable parameters, yet they often underperform in forecast accuracy. More recently, machine learning methods (e.g., tree-based algorithms) have come to capture complex, nonlinear patterns, often outperforming DCMs in point-prediction accuracy. However, they lack built-in confidence measures, limiting their use in risk-aware decision making. In this work, we help narrow this gap by wrapping our best ML model in an Inductive Mondrian Conformal Prediction (IMCP) layer with per-mode calibration at 90% nominal coverage. We leverage a survey of approximately 8,000 Italian employees, capturing their socioeconomic attributes and travel habits. Using a tailored preprocessing pipeline, we compare XGBoost, Random Forest, and CatBoost, observing that XGBoost performs best on the test set with an overall accuracy of 89.7% and a macro-average F1 score of 83.6%. Our IMCP layer then produces distribution-free prediction sets that contain the true mode at least 90% of the time, both overall and within each individual mode category. Singleton prediction sets can be treated as high-confidence forecast for capacity planning, while multilabel sets (and the occasional empty sets for highly ambiguous cases) highlight where uncertainty is greatest and pinpoint exactly which individuals merit follow-up surveys or targeted incentives.

Konstantin V. Getman, Oleg Kochukhov, Joe P. Ninan et al.

We explore the empirical power-law relationship between X-ray luminosity (Lx) and total surface magnetic flux (Phi), established across solar magnetic elements, time- and disk-averaged emission from the Sun, older active stars, and pre-main-sequence (PMS) stars. Previous models of large PMS X-ray flares, lacking direct magnetic field measurements, showed discrepancies from this baseline law, which MHD simulations attribute to unusually strong magnetic fields during flares. To test this, we used nearly simultaneous Chandra X-ray and HET-HPF near-infrared observations of four young Orion stars, measuring surface magnetic fields during or just after powerful PMS X-ray flares. We also modeled these PMS X-ray flares, incorporating their measured magnetic field strengths. Our findings reveal magnetic field strengths at the stellar surface typical of non-flaring PMS stars, ruling out the need for abnormally strong fields during flares. Both PMS and solar flares deviate from the Lx-Phi law, with PMS flares exhibiting a more pronounced deviation, primarily due to their much larger active regions on the surface and larger flaring loop volumes above the surface compared to their solar counterparts. These deviations likely stem from the fact that powerful flares are driven by magnetic reconnection, while baseline X-ray emission may involve less efficient mechanisms like Alfven wave heating. Our results also indicate a preference for dipolar magnetic loops in PMS flares, consistent with Zeeman-Doppler imaging of fully convective stars. This requirement for giant dipolar loops aligns with MHD predictions of strong dipoles supported by polar magnetic surface active regions in fast-rotating, fully convective stars.

One Octadion, Bondan Sapta Prakoso, Nanang Yudi Setiawan et al.

In this study, we explore the fine-tuning of Large Language Models (LLMs) to better support policymakers in their crucial work of understanding, analyzing, and crafting legal regulations. To equip the model with a deep understanding of legal texts, we curated a supervised dataset tailored to the specific needs of the legal domain. Additionally, we integrated the Retrieval-Augmented Generation (RAG) method, enabling the LLM to access and incorporate up-to-date legal knowledge from external sources. This combination of fine-tuning and RAG-based augmentation results in a tool that not only processes legal information but actively assists policymakers in interpreting regulations and drafting new ones that align with current needs. The results demonstrate that this approach can significantly enhance the effectiveness of legal research and regulation development, offering a valuable resource in the ever-evolving field of law.

Dodi Sugianto

Changes in United States trade policy, especially protectionist policies, have had a significant impact on Indonesia's international trade, especially in the textile and agricultural sectors. The imposition of high tariffs on several commodities has disrupted the dynamics of global trade, as shown by the decline in Indonesian exports to the United States based on data from the Ministry of Trade of the Republic of Indonesia. This research aims to analyze the specific impact of United States trade policy on Indonesia's trade performance and provide strategic recommendations to reduce these negative impacts. The research method used is descriptive analysis with a case research approach using data from the World Bank and the Center for Strategic and International Studies. The results show that United States protectionist policies have the greatest impact on small and medium enterprises in Indonesia's textile industry. As an implication, this research recommends diversifying export markets and increasing trade agreements with non-US countries to strengthen Indonesia's trade resilience. It is hoped that these findings will serve as a reference for policy makers in formulating adaptive and sustainable trade strategies.

Ahmad Aburayya

<i>Background</i>: Despite the resurgence of interest in augmented reality (AR) due to Industry 4.0 and its ability to resolve several challenges faced by current business models, comprehensive research examining the capabilities of AR in supply chain management (SCM) and logistics remains limited. This article aims to investigate the potential effects of AR technology on organizational performance through the mediation role of SCM and logistics value chain functions to address the existing knowledge gap. <i>Methods</i>: This research employed a cross-sectional design and an explanatory survey as a deductive approach for hypothesis development. The primary data collection method involved the self-administration of a questionnaire to furniture suppliers located in the Gulf Cooperation Council (GCC), including six countries. Of the 656 questionnaires submitted to suppliers, 483 were considered usable, yielding a response rate of 73.6%. The research utilized partial least squares structural equation modelling (PLS-SEM) and artificial neural network (ANN) techniques to evaluate the gathered data. <i>Results</i>: The current paper’s statistical evidence demonstrates that AR implementation has a positive impact on the supply and logistics value chain activities and organizational performance of furniture suppliers in the GCC region. Moreover, it illustrates that the design and planning variable of supply chain value dominates as the primary predictor of organization performance. The results indicated that the ANN strategy provided a more comprehensive explanation of internally generated constructs compared to the PLS-SEM technique. <i>Conclusions</i>: This study demonstrates its usefulness by advising furniture industry decision-makers on what to avoid and what aspects to consider when creating plans and regulations. The report also suggests operations managers apply machine learning (ANN) for prediction and decision-making in supply and operations value chains. This essay looks at how the AR and resource-based supply value chain view may affect company performance across countries, firm sizes, and ages.

Jose Maria Pernas Alonso

El 18 de diciembre de 2023 la Comisión Europea inició una investigación contra Twitter (actual X), con base en la llamada Ley de Servicios Digitales de la Unión Europea (UE). En agosto de 2024 se ha conocido la detención por el Estado francés del fundador de Telegram, Pável Dúrov, por no aceptar la restricción de contenidos exigida en dicha red social. Todas estas acciones conducen a analizar si el valor de la libertad y el derecho de libertad de expresión están en peligro en la UE y su caracterización en la Carta de Derechos Fundamentales la UE (CDFUE) y en la jurisprudencia del Tribunal de Justicia de la UE (TJUE) y del Tribunal Europeo de Derechos Humanos (TEDH).

Małgorzata Bryk-Zwolska

Zdając sobie sprawę ze znaczenia, jakie ma usuwanie dwutlenku węgla dla realizacji globalnych i unijnych celów klimatycznych, Komisja Europejska we wniosku ogłoszonym 30 listopada 2022 r. zaproponowała ramy prawne certyfikacji dobrowolnego usuwania dwutlenku węgla (CDR). Starania Unii Europejskiej o stworzenie systemu certyfikacji CDR są ważną inicjatywą w tym zakresie, ale wiążą się z kluczowymi wyzwaniami, które powinny zostać pokonane w trakcie trwających procesów legislacyjnych. Cel opracowania stanowi ocena treści rzeczonego projektu rozporządzenia z punktu widzenia jego skuteczności jako narzędzia klimatycznego.

Mohammad Yaghini, Patty Liu, Franziska Boenisch et al.

Existing work on trustworthy machine learning (ML) often concentrates on individual aspects of trust, such as fairness or privacy. Additionally, many techniques overlook the distinction between those who train ML models and those responsible for assessing their trustworthiness. To address these issues, we propose a framework that views trustworthy ML as a multi-objective multi-agent optimization problem. This naturally lends itself to a game-theoretic formulation we call regulation games. We illustrate a particular game instance, the SpecGame in which we model the relationship between an ML model builder and fairness and privacy regulators. Regulators wish to design penalties that enforce compliance with their specification, but do not want to discourage builders from participation. Seeking such socially optimal (i.e., efficient for all agents) solutions to the game, we introduce ParetoPlay. This novel equilibrium search algorithm ensures that agents remain on the Pareto frontier of their objectives and avoids the inefficiencies of other equilibria. Simulating SpecGame through ParetoPlay can provide policy guidance for ML Regulation. For instance, we show that for a gender classification application, regulators can enforce a differential privacy budget that is on average 4.0 lower if they take the initiative to specify their desired guarantee first.

John Beverley, Robin McGill, Sam Smith et al.

The term credential encompasses educational certificates, degrees, certifications, and government-issued licenses. An occupational credential is a verification of an individuals qualification or competence issued by a third party with relevant authority. Job seekers often leverage such credentials as evidence that desired qualifications are satisfied by their holders. Many U.S. education and workforce development organizations have recognized the importance of credentials for employment and the challenges of understanding the value of credentials. In this study, we identified and ontologically defined credential and credential-related terms at the textual and semantic levels based on the Occupation Ontology (OccO), a BFO-based ontology. Different credential types and their authorization logic are modeled. We additionally defined a high-level hierarchy of credential related terms and relations among many terms, which were initiated in concert with the Alabama Talent Triad (ATT) program, which aims to connect learners, earners, employers and education/training providers through credentials and skills. To our knowledge, our research provides for the first time systematic ontological modeling of the important domain of credentials and related contents, supporting enhanced credential data and knowledge integration in the future.

So Sasaki, Cédric Langbort

Social media platforms have diverse content moderation policies, with many prominent actors hesitant to impose strict regulations. A key reason for this reluctance could be the competitive advantage that comes with lax regulation. A popular platform that starts enforcing content moderation rules may fear that it could lose users to less-regulated alternative platforms. Moreover, if users continue harmful activities on other platforms, regulation ends up being futile. This article examines the competitive aspect of content moderation by considering the motivations of all involved players (platformer, news source, and social media users), identifying the regulation policies sustained in equilibrium, and evaluating the information quality available on each platform. Applied to simple yet relevant social networks such as stochastic block models, our model reveals the conditions for a popular platform to enforce strict regulation without losing users. Effectiveness of regulation depends on the diffusive property of news posts, friend interaction qualities in social media, the sizes and cohesiveness of communities, and how much sympathizers appreciate surprising news from influencers.

Archana Ahlawat, Amy Winecoff, Jonathan Mayer

Across the technology industry, many companies have expressed their commitments to AI ethics and created dedicated roles responsible for translating high-level ethics principles into product. Yet it is unclear how effective this has been in leading to meaningful product changes. Through semi-structured interviews with 26 professionals working on AI ethics in industry, we uncover challenges and strategies of institutionalizing ethics work along with translation into product impact. We ultimately find that AI ethics professionals are highly agile and opportunistic, as they attempt to create standardized and reusable processes and tools in a corporate environment in which they have little traditional power. In negotiations with product teams, they face challenges rooted in their lack of authority and ownership over product, but can push forward ethics work by leveraging narratives of regulatory response and ethics as product quality assurance. However, this strategy leaves us with a minimum viable ethics, a narrowly scoped industry AI ethics that is limited in its capacity to address normative issues separate from compliance or product quality. Potential future regulation may help bridge this gap.

hamid kazemi

With the tremendous developments in space technologies in recent years, artificial intelligence is used instead of humans in decision-making. Artificial intelligence (AI) by the ability to think logically, manage its actions and correct its decisions when external conditions change without human interference. New intelligent and autonomous space technologies are being developed for various space activities. It uses for different applications such as processing of space data and information, debris removal, and exploration and extraction of natural resources in outer space. However, regulating the activities of space-faring and especially private actors in the use of AI in space technology and dispute settlement between the states have become one of new issues in international space law. Since the state's responsibility in space law is explained based on human behaviour, the issue arises that the existing international space regulations related to the state's responsibility to regulate space activities and their liability that is based on human behaviour can still be applied to the use of the new technologies or we need the new legal measures in international space law. It seems that with the extensive interpretation of Articles 6 and 7 of the Outer Space Treaty regarding state responsibility and liability, these provisions can still be considered applicable. Nevertheless, the new international space regulations can be an essential step in better determining and recognizing the state responsibility and liability of space actors that use space technologies that use AI.

Maksim Papenkov, Chris Meredith, Claire Noel et al.

Accurate industry classification is a critical tool for many asset management applications. While the current industry gold-standard GICS (Global Industry Classification Standard) has proven to be reliable and robust in many settings, it has limitations that cannot be ignored. Fundamentally, GICS is a single-industry model, in which every firm is assigned to exactly one group - regardless of how diversified that firm may be. This approach breaks down for large conglomerates like Amazon, which have risk exposure spread out across multiple sectors. We attempt to overcome these limitations by developing MIS (Multi-Industry Simplex), a probabilistic model that can flexibly assign a firm to as many industries as can be supported by the data. In particular, we utilize topic modeling, an natural language processing approach that utilizes business descriptions to extract and identify corresponding industries. Each identified industry comes with a relevance probability, allowing for high interpretability and easy auditing, circumventing the black-box nature of alternative machine learning approaches. We describe this model in detail and provide two use-cases that are relevant to asset management - thematic portfolios and nearest neighbor identification. While our approach has limitations of its own, we demonstrate the viability of probabilistic industry classification and hope to inspire future research in this field.

Yasaman Mashayekhy, Amir Babaei, Xue-Ming Yuan et al.

<i>Background</i>: The advancement of Industry 4.0 technologies has affected every aspect of supply chains. Recently, enterprises have tried to create more value for their businesses by tapping into these new technologies. Warehouses have been one of the most critical sections in a supply chain affected by Industry 4.0 technologies. <i>Methods</i>: By recognizing the role of inventory management in a supply chain and its importance, this paper aims to highlight the impact of IoT technologies on inventory management in supply chains and conducts a comprehensive study to identify the research gap of applying IoT to inventory management. The trend and potential opportunities of applying IoT to inventory management in the Industry 4.0 era are explored by analyzing the literature. <i>Results:</i> Our findings show that the research on this topic is growing in various industries. A broad range of journals is paying particular attention to this topic and publishing more articles in this research direction. <i>Conclusions</i>: Upgrading a supply chain into an integrated supply chain 4.0 is beneficial. Given the changes in fourth-generation technology compared to previous generations, the approach of conventional inventory replenishment policies seems not responsive enough to new technologies and is not able to cope with IoT systems well.

Mohammed Awad Alshahrani, Mohammad Asif Salam

Existing studies have predominantly explored the influence of supply chain resilience on the performance of firms in the context of developed economies. This study highlights the need for SMEs to be prepared to tackle uncertainties in business operations. <i>Background:</i> Small and Medium Enterprises (SMEs) play a critical role in every economy, and limited studies have highlighted the significance of resilience in the firms. Therefore, this study aims to determine the impact of supply chain resilience on SMEs’ performance in Saudi Arabia based on three dimensions of resilience, namely agility, robustness, and flexibility. It aims to investigate how they relate to the dimensions of SMEs’ performance, namely production performance and market/sales performance. This study also investigates the overall impact of supply chain resilience as a construct on SMEs’ performance. <i>Methods:</i> This study employed a quantitative research design to answer the research questions. A self-administered questionnaire was used to collect data. The study was based on 255 samples of managers in SMEs from Saudi Arabia. The hypothesized model has been tested using the SPSS/Amos 26. <i>Results:</i> Based on the findings, it has been found that supply chain agility and flexibility had a significant positive relationship with SMEs’ production and marketing/sales performances. Supply chain robustness demonstrated a significant positive relationship with SMEs’ production performance but not their marketing/sales performances. Overall, there was a significant positive relationship between supply chain resilience and SMEs’ performance. <i>Conclusions:</i> This study contributes to the body of literature on supply chain resilience by expounding knowledge on aspects such as agility, flexibility, and robustness. The study enhances our understanding of the role of supply chain resilience on SMEs’ performance in an emerging economy context.