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

Khanh Han Nguyen, Tin Van Vo

<i>Background</i>: Amid global sustainability imperatives, the logistics sector serves as a key economic enabler while remaining a major contributor to greenhouse gas emissions. This study investigates the causal relationships between green logistics practices and sustainable business performance in Vietnamese small- and medium-sized enterprises, mediated by competitiveness, and forecasts future trends to inform transitions aligned with net-zero goals. <i>Methods</i>: A mixed-methods design integrates structural equation modeling with the gray model. Primary data were collected via Likert-scale questionnaires administered to 350 managers to measure latent variables. Secondary financial metrics (revenue, costs, assets, profits) from 15 firms spanning 2021–2024 enabled forecasting. <i>Results</i>: SEM, employing bootstrapping for path estimation, revealed positive direct effects, with the strongest effects for green transportation and weaker effects for technology, packaging, and warehousing. Mediation via competitiveness yielded mixed indirect effects: positive for warehousing and transportation, but negative for technology. GM(1,1) projected moderate performance growth under conditions of data uncertainty. <i>Conclusions</i>: The hybrid framework advances the resource-based view in emerging market contexts, recommending prioritization of transportation and technology initiatives alongside policy incentives to align with sustainable development goals and enhance resilience in Vietnam’s logistics sector.

Naomi Sasaya, Shigefumi Kishida, Ryo Kikuchi et al.

Digital traces of daily activities, such as e-commerce (EC) purchase histories, provide scalable signals for public health surveillance, yet their epidemiological validity remains unclear. This study validates a behavioral proxy for disease onset, defined as transitions from regular to therapeutic diets, by comparing large-scale EC data (N=55,645) against independent insurance-derived clinical records. Using feline lower urinary tract disease (FLUTD) as a case study, the proxy showed strong agreement with clinical data for ingredient-level risk patterns (r=0.74) and seasonal dynamics (r=0.82). Furthermore, analysis using EC data alone reproduced the established protective association of wet food consumption. These results demonstrate that validated behavioral signals from EC data can serve as cost-effective complements to traditional surveillance, with potential applicability to monitoring lifestyle-related diseases in human populations.

Benyu Zhang, Qiang Zhang, Jianpeng Cheng et al.

Large Language Models (LLMs) represent a promising frontier for recommender systems, yet their development has been impeded by the absence of predictable scaling laws, which are crucial for guiding research and optimizing resource allocation. We hypothesize that this may be attributed to the inherent noise, bias, and incompleteness of raw user interaction data in prior continual pre-training (CPT) efforts. This paper introduces a novel, layered framework for generating high-quality synthetic data that circumvents such issues by creating a curated, pedagogical curriculum for the LLM. We provide powerful, direct evidence for the utility of our curriculum by showing that standard sequential models trained on our principled synthetic data significantly outperform ($+130\%$ on recall@100 for SasRec) models trained on real data in downstream ranking tasks, demonstrating its superiority for learning generalizable user preference patterns. Building on this, we empirically demonstrate, for the first time, robust power-law scaling for an LLM that is continually pre-trained on our high-quality, recommendation-specific data. Our experiments reveal consistent and predictable perplexity reduction across multiple synthetic data modalities. These findings establish a foundational methodology for reliable scaling LLM capabilities in the recommendation domain, thereby shifting the research focus from mitigating data deficiencies to leveraging high-quality, structured information.

Shuang Liu, Sarah Scheffler

The Supreme Court decision in Free Speech Coalition v. Paxton upheld the constitutionality of Texas H.B. 1181, one of the most constitutionally vulnerable of these age verification laws, holding that it was subject to and satisfied intermediate scrutiny and the requirement that age verification regulations be "adequately tailored". However, the decision leaves unresolved practical challenges. What is the current state of age verification legislation in the United States? How can "adequate tailoring" be interpreted in a way that is accessible to non-legal experts, particularly those in technical and engineering domains? What age verification approaches are used today, what infrastructures and standards support them, and what tradeoffs do they introduce? This paper addresses those questions by proposing an analytical model to interpret "adequate tailoring" from multiple perspectives with associated governmental goals and interests, and by applying that model to evaluate both current state laws and widely used verification methods. This paper's major contributions include: (1) we mapped the current U.S. age-verification legislative landscape; (2) we introduce an analytical model to analyze "adequate tailoring" for age verification and potential application to other online regulatory policies; and (3) we analyze the main technical approaches to age verification, highlighting the practical challenges and tradeoffs from a technical perspective. Further, while we focus on U.S. State laws, the principles underlying our framework are applicable to age-verification debates and methods worldwide.

Marie Legg, Reginald A. Silver, Sungjune Park

<i>Background</i>: Despite growing interest in supply chain resilience (SCRes), theoretical overlap between dynamic capabilities (DC) and supply chain agility (SCA) has complicated empirical analysis of their distinct roles. Additionally, the contextual role of information asymmetry in shaping resilience remains underexplored. This study addresses both issues by modeling DC hierarchically and examining IA as a moderator. <i>Methods</i>: Data were collected through a cross-sectional survey of 157 U.S.-based supply chain professionals. Partial least squares structural equation modeling (PLS-SEM) was used to examine the relationships among DC, SCA, IA, and SCRes. <i>Results</i>: SCA was a strong, direct predictor of SCRes. In contrast, DC showed no direct effect in the full model; however, in a hierarchical component model (HCM), DC, a higher-order construct, emerged as significant predictor of SCRes. IA exerted a dual negative influence: it directly weakened SCRes and negatively moderated the relationship between DC and SCRes. <i>Conclusions</i>: This study makes two novel contributions. First, it resolves ambiguity between DC and SCA by empirically modeling DC as a higher-order construct that encompasses but remains distinct from SCA. Second, it introduces IA as a multidimensional barrier to resilience, demonstrating its direct and interactive effects. These findings provide new insight into capability design and contextual adaptation for SCRes in uncertain, information-constrained environments.

Martin Holubčík, Lukáš Falát, Jakub Soviar et al.

<i>Background</i>: Governments face critical decisions regarding road remediation projects, requiring careful economic evaluation, especially in countries like Slovakia where road infrastructure is crucial for attracting foreign investment. These decisions are complex, involving short-term and long-term costs and revenues, along with inherent uncertainty about future outcomes. Traditional economic assessments often fail to capture the full scope of these factors, potentially leading to suboptimal choices. <i>Methods</i>: This study proposes four probability-based models: the Short-term Model (SM), Long-term-Short-term Model (LSM), Social Long-term-Short-term Model (SLSM), and Long-term-Short-term Model with a Time Aspect (TLSM). These models incorporate probabilistic functions to calculate expected costs and profits, considering various factors such as reparation costs, financial compensations, social costs, and time-related costs, as well as long-term benefits like increased investment and lives saved. <i>Results</i>: The proposed models were partially validated through an ex post analysis of a past road remediation project on road 1/18 (E50) under the Strecno castle cliff in Slovakia. The analysis demonstrated the models’ utility for multi-criteria decision-making in transportation problems, highlighting their ability to capture the complex interplay of economic and societal factors. <i>Conclusions</i>: The models enable governments to maximize societal benefit while mitigating potential risks, contributing to a more sustainable and efficient transportation sector. Future research could focus on refining the models and adapting them to other sectors beyond transportation.

Yunus Lutz, Timo Wilm, Philipp Duwe

In e-commerce recommender and search systems, tree-based models, such as LambdaMART, have set a strong baseline for Learning-to-Rank (LTR) tasks. Despite their effectiveness and widespread adoption in industry, the debate continues whether deep neural networks (DNNs) can outperform traditional tree-based models in this domain. To contribute to this discussion, we systematically benchmark DNNs against our production-grade LambdaMART model. We evaluate multiple DNN architectures and loss functions on a proprietary dataset from OTTO and validate our findings through an 8-week online A/B test. The results show that a simple DNN architecture outperforms a strong tree-based baseline in terms of total clicks and revenue, while achieving parity in total units sold.

Lujun Li, Lama Sleem, Niccolo' Gentile et al.

With the emergence of ChatGPT, Transformer models have significantly advanced text classification and related tasks. Decoder-only models such as Llama exhibit strong performance and flexibility, yet they suffer from inefficiency on inference due to token-by-token generation, and their effectiveness in text classification tasks heavily depends on prompt quality. Moreover, their substantial GPU resource requirements often limit widespread adoption. Thus, the question of whether smaller language models are capable of effectively handling text classification tasks emerges as a topic of significant interest. However, the selection of appropriate models and methodologies remains largely underexplored. In this paper, we conduct a comprehensive evaluation of prompt engineering and supervised fine-tuning methods for transformer-based text classification. Specifically, we focus on practical industrial scenarios, including email classification, legal document categorization, and the classification of extremely long academic texts. We examine the strengths and limitations of smaller models, with particular attention to both their performance and their efficiency in Video Random-Access Memory (VRAM) utilization, thereby providing valuable insights for the local deployment and application of compact models in industrial settings.

Peter Klimek, Sophia Baum, Markus Gerschberger et al.

Rare earth elements (REEs) are critical to a wide range of clean and high-tech applications, yet global trade dependencies expose countries to vulnerabilities across production networks. Here, we construct a multi-tiered input-output trade network spanning 168 REE-related product codes from 2007-2023 using a novel AI-augmented statistical framework. We identify significant differences between dependencies in upstream and intermediate (input) products, revealing that exposure and supplier concentration are systematically higher in input products, while systemic trade risk is lower, suggesting localized vulnerabilities. By computing network-based dependency indicators across countries and over time, we classify economies into five distinct clusters that capture structural differences in rare-earth reliance. China dominates the low-risk, high-influence cluster, while the EU and US remain vulnerable at intermediate tiers. Regression analyses show that high exposure across all products predicts future export strength, consistent with import substitution. However, high systemic trade risk in input products like magnets, advanced ceramics or phosphors, significantly impedes the development of comparative advantage. These results demonstrate that the structure of strategic dependencies is tier-specific, with critical implications for industrial resilience and policy design. Effective mitigation strategies must move beyond raw material access and directly address country-specific chokepoints in midstream processing and critical input production.

Arthur Plaud, Olivier Bénichou

Occupation times quantify how long a stochastic process remains in a region, and their single-time statistics are famously given by the arcsine law for Brownian and Lévy processes. By contrast, two-time occupation statistics, which directly probe temporal correlations and aging, have resisted exact characterization beyond renewal processes. In this Letter we derive exact results for generic one-dimensional jump processes, a central framework for intermittent and discretely sampled dynamics. Using generalized Wiener-Hopf methods, we obtain the joint distribution of occupation time and position, the aged occupation-time law, and the autocorrelation function. In the continuous-time scaling limit, universal features emerge that depend only on the tail of the jump distribution, providing a starting point for exploring aging transport in complex environments.

Anthony Boyd Stevenson, Julia Rieck

<i>Background</i>: The systematic literature review with additional descriptive analysis at hand focuses on analysing returns management in e-commerce, which is an increasingly critical issue as the volume of online shopping is rising. <i>Methods</i>: Drawing from a comprehensive search of academic databases and a manual review of Google Scholar, 54 articles dating from 2007 onwards were collected and fully read. <i>Results</i>: The review reveals a main research effort emerging mainly from Germany and other countries, with a notable focus on fashion retail. The bulk of these studies aim to understand and reduce the frequency of customer returns, addressing a substantial operational challenge for online retailers. The findings provide multiple research streams extracted from the collected literature and combined to an overview. <i>Conclusions</i>: Through this, there are tendencies which can be interpreted to derive the evolution of the research field. The illustrated results in this review paint a detailed picture of the existing research landscape. This highlights the importance of ongoing research, which, e.g., holds potential benefits for customer satisfaction and environmental sustainability. The review also lists future research directions, recommending the continued investigation of areas such as predictive analytics and customer behaviour to further refine returns management practices.

Otsason Riina, Hilmola Olli-Pekka, Tapaninen Ulla et al.

The need to decarbonise and reduce pollutant emissions from maritime transport is facilitating the studies of ground effect vehicles. Technical development in recent decade concerning unmanned flights in drones has supported this development. These vehicles could have much higher speed than sea vessels and they are estimated to be less costly compared to air transport. Unmanned operations without passengers enable wider range of transport connections (even in difficult conditions). In this research we analyse prototype vehicle called Airship and its possible use in different routes of intra Canary Islands’ transport. We suggest the most lucrative routes and cargo groups. Initial cost and revenue considerations are made over the life-cycle of Airship. As a result, we can point that there are three main factors determine the success of the transport operations. They are: the number of journeys per day, business days operating per year and freight price.

Hisatoshi Naganawa, Enna Hirata, Nailah Firdausiyah et al.

<i>Background:</i> The global logistics industry is facing looming challenges related to labor shortages and low-efficiency problems due to the lack of logistics facilities and resources, resulting in increased logistics delays. The Physical Internet is seen as a way to take logistics into the next generation of transformation. This research proposes a Physical Internet-enabled system that allows multiple companies to efficiently share warehouses and trucks to achieve operational efficiency and reduce CO₂ emissions. <i>Methods:</i> We propose a novel demography-weighted combinatorial optimization model utilizing a genetic algorithm and the Lin–Kernighan heuristic. The model is tested with real data simulations to evaluate its performance. <i>Results:</i> The results show that compared to the existing model presented in a previous study, our proposed model improves location optimality and distributive routing efficiency and reduces CO₂ emissions by 54%. <i>Conclusions:</i> By providing a well-founded novel model, this research makes an important contribution to the implementation of the Physical Internet by computing optimal logistics hubs and routes as well as providing a solution to cut CO₂ emissions by half.

Aramesh Shahbazi, Sanaz Shabani Kolahi

ith the formation and development of the Internet of Things, the smart city has been manifested as an emerging concept that monitors various aspects of human life and intelligent information processing and control systems. A smart city can monitor the physical world and provide smart services for citizens, transform services such as healthcare, transportation, entertainment and energy and make them available to the government in a new way. The technologies used in smart cities raise some concerns and challenges regarding the security and privacy of citizens because applications in smart cities provide a wide range of information related to citizens' privacy. They collect to meet the interests and benefits of the public, but they also have comprehensive control over the city, and naturally, the lives of citizens will be affected by serious supervision and restrictions. In this article, we identified the international and domestic laws and regulations by investigating books and articles and using the available sources. We examined the laws and regulations in international law and Iranian law regarding privacy in smart cities. The study's most important finding is identifying gaps related to this area in Iranian laws and the need for more speed of updating laws at both the international and Iranian levels with the speed of advancement of technologies used in smart cities.

Mehari Beyene Teshome, Faisal Rasool, Guido Orzes

<i>Background</i>: The sustainable development of mountain areas, which have fragile ecosystems, has increasingly attracted the attention of researchers and practitioners. Logistics systems are crucial in supporting these regions and addressing mountainous terrain’s unique challenges. While many studies have examined aspects of mountain logistics, a comprehensive and systematic review of the field is still lacking. <i>Design/Methodology/Approach</i>: This paper aims to fill the gap by systematically reviewing the existing literature on mountain logistics using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology. <i>Results/Conclusions</i>: We identify four main research foci: design of logistics infrastructure or vector, optimization of logistics systems, safety in logistics systems, and impact of logistics systems on mountain communities. In addition to categorizing these themes, we conduct a detailed descriptive analysis of published studies in this domain. Our findings highlight significant research gaps, particularly in integrating digital technologies, sustainable mass transportation solutions, and logistics systems’ socioeconomic and environmental impacts. We propose targeted directions for future research to advance sustainable logistics practices in mountain regions.

Behzad Maleki Vishkaei, Pietro De Giovanni

<i>Background</i>: Business continuity entails the potential negative consequences of uncertainty on a firm’s ability to achieve strategic objectives. The COVID-19 pandemic significantly impacted business continuity due to lockdowns, travel restrictions, and social distancing measures. Consequently, firms adopted specific supply chain (SC) practices to effectively navigate this global crisis. <i>Methods</i>: This research adopted a stochastic approach based on Bayesian Networks to evaluate the implications of business continuity on firms’ decisions to embrace SC practices, focusing on omnichannel strategies, SC coordination, and technologies such as artificial intelligence systems, big data and machine learning, and mobile applications. <i>Results</i>: Our findings revealed that firms facing disruption in a single performance area can apply specific strategies to maintain resilience. However, multiple areas of underperformance necessitate a varied approach. <i>Conclusions</i>: According to our empirical analysis, omnichannel strategies are critical when disruptions simultaneously impact quality, inventory, sales, and ROI, particularly during major disruptions such as the COVID-19 pandemic. AI and big data become vital when multiple risks coalesce, enhancing areas such as customer service and supply chain visibility. Moreover, supply chain coordination and mobile app adoption are effective against individual performance risks, proving crucial in mitigating disruption impacts across various business aspects. These findings help policy-makers and business owners to have a better understanding of how business continuity based on performance resistance to disruptions pushes companies to adopt different practices including new technologies and supply chain coordination. Accordingly, they can use the outputs of this study to devise strategies for improving resilience considering their supply chain vulnerabilities.

Wenjia Jing, Jack Xin, Yifeng Yu

Using formal renormalization theory, Yakhot derived in ([32], 1988) an $O\left(\frac{A}{\sqrt{\log A}}\right)$ growth law of the turbulent flame speed with respect to large flow intensity $A$ based on the inviscid G-equation. Although this growth law is widely cited in combustion literature, there has been no rigorous mathematical discussion to date about its validity. As a first step towards unveiling the mystery, we prove that there is no intermediate growth law between $O\left(\frac{A}{\log A}\right)$ and $O(A)$ for two dimensional incompressible Lipschitz continuous periodic flows with bounded swirl sizes. In particular, we do not assume the non-degeneracy of critical points. Additionally, other examples of flows with lower regularity, Lagrangian chaos, and related phenomena are also discussed.

Eoin M. Kenny, Julie A. Shah

Regulation is increasingly cited as the most important and pressing concern in machine learning. However, it is currently unknown how to implement this, and perhaps more importantly, how it would effect model performance alongside human collaboration if actually realized. In this paper, we attempt to answer these questions by building a regulatable large-language model (LLM), and then quantifying how the additional constraints involved affect (1) model performance, alongside (2) human collaboration. Our empirical results reveal that it is possible to force an LLM to use human-defined features in a transparent way, but a "regulation performance trade-off" previously not considered reveals itself in the form of a 7.34% classification performance drop. Surprisingly however, we show that despite this, such systems actually improve human task performance speed and appropriate confidence in a realistic deployment setting compared to no AI assistance, thus paving a way for fair, regulatable AI, which benefits users.

Takamichi Sano, Otmar Venjakob

We extend the interpolation property of the Lubin-Tate regulator map from [SV24] to Artin characters and show a reciprocity law in the sense of Cherbonnier-Colmez. This allows us to provide a new proof of Kato's explicit reciprocity law for Lubin-Tate formal groups.

mokhtar bidarvand, Babak pourghahramani , Jamal Beigi

During the COVID-19 epidemic, due to the increasing influx of users to cyberspace and its use, the need to teach the components of social media capital or media literacy to control and reduce cybercrime is an essential requirement for users, especially young audiences doubled. The primary purpose of writing this article is to investigate the impact of the components of social media capital from the perspective of academics in reducing crimes and harms of cyberspace and socio-cultural factors in the context of new media. The research was conducted by survey method, and the data collection method was a questionnaire. The statistical population includes students studying in the academic year 1401-1400, of whom 250 have been selected for the research sample using Cochran's formula. Findings show that increasing the ability of users in society, based on promoting literacy based on culture, ethics, justice, and conscious and critical understanding of the nature of mass media and techniques used by media producers on the individual and society to reduce cybercrime is effective. The study's findings show a direct relationship between understanding the content of messages, awareness of the hidden purposes of messages, conscious selection of messages, analysis of media messages, rationality and critical view of messages as components of social media capital with reducing cybercrime statistics.