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

Thayane Ingrid Xavier de Andrade, Selma Maria da Fonseca Viegas, Gabriela Gonçalves Amaral et al.

<i>Background</i>: Immunobiologicals are thermolabile products that require strict storage and transportation conditions to maintain their immunogenic efficacy, particularly in countries where logistical and operational challenges are evident, such as Brazil. <i>Methods</i>: A holistic multiple case study, carried out in five regions of Brazil, in 2022, with 42 workers from different instances of the cold chain was conducted. As a source of evidence, data were collected through interviews and analysis of printed documents and analyzed using Thematic Content Analysis, using the analytical technique of cross-case synthesis. <i>Results</i>: The influence of geoclimatic diversity and transportation modes on immunobiological logistics was highlighted. Challenges and requirements were identified, as well as aspects of monitoring during transportation and distribution. Among the main challenges were long distances, poor road conditions, seasonality and the need to share vehicles due to the unavailability of exclusive transportation. Conversely, positive practices were highlighted, such as the use of air-conditioned vehicles, dataloggers and properly prepared thermal boxes. <i>Conclusions</i>: It is necessary to adopt mitigation strategies that consider regional inequalities and promote equity, through raising awareness among managers, investing in logistical infrastructure and expanding good practices in order to guarantee the universal and qualified distribution of immunobiologicals in the country.

Jan Sobuś

W artykule przeanalizowano wybrane problemy podatkowe związane z działalnością przedsiębiorstw górniczych w Polsce, zwłaszcza podatkowe aspekty szkód górniczych, a także wątpliwości dotyczące wydatków na realizację idei społecznej odpowiedzialności biznesu (CSR), dopłat publicznoprawnych oraz podatku od nieruchomości. Celem pracy jest identyfikacja sposobu rozwiązania owych problemów zaaprobowanego w orzecznictwie i praktyce interpretacyjnej wraz z oceną wewnętrznej spójności stanowisk Dyrektora Krajowej Informacji Skarbowej i sądów administracyjnych. Autor wykorzystuje metodę dogmatycznoprawną, by poddać analizie przepisy prawa, orzecznictwo sądowe oraz interpretacje indywidualne z lat 2020–2025. Wskazuje, że organy podatkowe często przyjmują restrykcyjne stanowisko, np. w kwestii kwalifikacji wydatków na CSR jako kosztów uzyskania przychodów, podczas gdy sądy skłaniają się ku elastyczniejszemu podejściu. W przypadku szkód górniczych autor potwierdza możliwość uznania kosztów ich naprawy za podatkowe koszty uzyskania przychodów, a w kontekście dopłat wykazuje różnice w kwalifikacji wydatków w zależności od formy wsparcia. Postuluje, aby z uwagi na rosnące znaczenie koncepcji CSR w prowadzeniu działalności gospodarczej opublikować objaśnienia w sprawie podatkowej oceny wydatków związanych z jej wdrażaniem. Podkreśla również rolę spójności wewnętrznej stanowisk organów podatkowych, gdy oceniają one tożsame zagadnienia.

Ionica Oncioiu, Diana Andreea Mândricel, Mihaela Hortensia Hojda

<i>Background</i>: Digital transformation is increasingly present in modern logistics, especially in the context of sustainability and circularity pressures. The integration of technologies such as Internet of Things (IoT), Radio Frequency Identification (RFID), and automated platforms involves not only infrastructure but also a strategic vision, a flexible organizational culture, and the ability to support decisions through artificial intelligence (AI)-based systems. <i>Methods</i>: This study proposes an extended conceptual model using structural equation modelling (SEM) to explore the relationships between five constructs: technological change, strategic and organizational readiness, transformation environment, AI-enabled decision configuration, and operational redesign. The model was validated based on a sample of 217 active logistics specialists, coming from sectors such as road transport, retail, 3PL logistics services, and manufacturing. The participants are involved in the digitization of processes, especially in activities related to operational decisions and sustainability. <i>Results</i>: The findings reveal that the analysis confirms statistically significant relationships between organizational readiness, transformation environment, AI-based decision processes, and operational redesign. <i>Conclusions</i>: The study highlights the importance of an integrated approach in which technology, organizational culture, and advanced decision support collectively contribute to the transition to digital and circular logistics chains.

Audai Al-Majali, Ahmad Alsarayreh, Huthaifa Alqaralleh

<i>Background</i>: Emerging Asian economies face a critical policy dilemma: macroeconomic and sustainability factors affect high-performing and struggling logistics exporters in fundamentally different ways. <i>Methods</i>: Analysing transportation trade data from China, South Korea, India, Vietnam, Malaysia, and Indonesia (2000–2023) using Panel Quantile Autoregressive Distributed Lag (P-QARDL) methodology, this study investigates asymmetric relationships between macroeconomic indicators (real GDP, inflation, real effective exchange rate), sustainability variables (energy intensity, energy prices, CO₂ emissions), and logistics performance measured through transportation trade flows. <i>Results</i>: The results reveal striking performance-dependent heterogeneities that conventional approaches overlook. Economic growth provides 55% larger benefits to high performers (0.345) versus strugglers (0.222), confirming scale advantages. Energy constraints intensify for successful exporters, with energy intensity penalties 12% larger in upper quantiles. CO₂ emissions correlate positively with logistics performance, with effects doubling from lower (0.142) to upper quantiles (0.341), highlighting an intensifying sustainability trade-off. Error correction operates 39% faster during high-performance periods. <i>Conclusions</i>: These asymmetric relationships challenge one-size-fits-all policies, necessitating targeted energy efficiency interventions for high performers and growth-enabling support for struggling exporters.

G. de Mathelin, C. Hartl-Nesic, A. Kugi

The performance of industrial robotic work cells depends on optimizing various hyperparameters referring to the cell layout, such as robot base placement, tool placement, and kinematic design. Achieving this requires a bilevel optimization approach, where the high-level optimization adjusts these hyperparameters, and the low-level optimization computes robot motions. However, computing the optimal robot motion is computationally infeasible, introducing trade-offs in motion planning to make the problem tractable. These trade-offs significantly impact the overall performance of the bilevel optimization, but their effects still need to be systematically evaluated. In this paper, we introduce metrics to assess these trade-offs regarding optimality, time gain, robustness, and consistency. Through extensive simulation studies, we investigate how simplifications in motion-level optimization affect the high-level optimization outcomes, balancing computational complexity with solution quality. The proposed algorithms are applied to find the time-optimal kinematic design for a modular robot in two palletization scenarios.

Benjamin Laufer, Jon Kleinberg, Hoda Heidari

Recent policy proposals aim to improve the safety of general-purpose AI, but there is little understanding of the efficacy of different regulatory approaches to AI safety. We present a strategic model that explores the interactions between safety regulation, the general-purpose AI creators, and domain specialists--those who adapt the technology for specific applications. Our analysis examines how different regulatory measures, targeting different parts of the AI development chain, affect the outcome of this game. In particular, we assume AI technology is characterized by two key attributes: safety and performance. The regulator first sets a minimum safety standard that applies to one or both players, with strict penalties for non-compliance. The general-purpose creator then invests in the technology, establishing its initial safety and performance levels. Next, domain specialists refine the AI for their specific use cases, updating the safety and performance levels and taking the product to market. The resulting revenue is then distributed between the specialist and generalist through a revenue-sharing parameter. Our analysis reveals two key insights: First, weak safety regulation imposed predominantly on domain specialists can backfire. While it might seem logical to regulate AI use cases, our analysis shows that weak regulations targeting domain specialists alone can unintentionally reduce safety. This effect persists across a wide range of settings. Second, in sharp contrast to the previous finding, we observe that stronger, well-placed regulation can in fact mutually benefit all players subjected to it. When regulators impose appropriate safety standards on both general-purpose AI creators and domain specialists, the regulation functions as a commitment device, leading to safety and performance gains, surpassing what is achieved under no regulation or regulating one player alone.

Generoso Immediato

In direct response to requests for a logico-mathematical test of the conjecture, we formally disprove a recently conjectured artificial intelligence trade-off between epistemic certainty and scope in its published universal hyperbolic product form, as introduced in Philosophy and Technology. Certainty is defined as the worst-case correctness probability over the input space, and scope as the sum of the Kolmogorov complexities of the input and output sets. Using standard facts from coding theory and algorithmic information theory, we show, first, that when the conjecture is instantiated with prefix (self-delimiting, prefix-free) Kolmogorov complexity, it leads to an internal inconsistency, and second, that when it is instantiated with plain Kolmogorov complexity, it is refuted by a constructive counterexample. These results establish a main theorem: contrary to the conjecture's claim, no universal "certainty-scope" hyperbola holds as a general bound under the published definitions. We further show that a subsequent "entropy-based" revision, replacing the Kolmogorov scope with Shannon joint entropy and redefining the epistemic certainty level accordingly, cannot restore universality either.

Andrew Crawley, Adam Daigneault, Jonathan Gendron

From 2000 to 2017, 64% of Maine's pulp and paper processing mills shut down; these closures resulted in harmful effects to communities in Maine and beyond. One question this research asks is how will key macroeconomic and related variables for Maine's forestry and logging industry change in the future? To answer this, we forecast key macroeconomic and related variables with a vector error correction (VEC model) to assess past and predict future economic contributions from Maine's forestry and logging industry. The forecasting results imply that although the contribution of the industry in Maine would likely remain stable due to level prices and a slight increase in output, local Maine communities could be worse off due to decreases in employment and firms. We then incorporated these forecasts into a 3-stage modeling process to analyze how a negative shock to exchange rates from an increase in tariffs could affect Maine's employment and output. Our results suggest that increased tariffs will reduce output and increase employment volatility in Maine. Rising uncertainty and costs of business operations suggest care should be taken when changing tariffs and trade restrictions, especially when changes to business operations can harm markets and communities.

Sacha Alanoca, Shira Gur-Arieh, Tom Zick et al.

AI governance has transitioned from soft law-such as national AI strategies and voluntary guidelines-to binding regulation at an unprecedented pace. This evolution has produced a complex legislative landscape: blurred definitions of "AI regulation" mislead the public and create a false sense of safety; divergent regulatory frameworks risk fragmenting international cooperation; and uneven access to key information heightens the danger of regulatory capture. Clarifying the scope and substance of AI regulation is vital to uphold democratic rights and align international AI efforts. We present a taxonomy to map the global landscape of AI regulation. Our framework targets essential metrics-technology or application-focused rules, horizontal or sectoral regulatory coverage, ex ante or ex post interventions, maturity of the digital legal landscape, enforcement mechanisms, and level of stakeholder participation-to classify the breadth and depth of AI regulation. We apply this framework to five early movers: the European Union's AI Act, the United States' Executive Order 14110, Canada's AI and Data Act, China's Interim Measures for Generative AI Services, and Brazil's AI Bill 2338/2023. We further offer an interactive visualization that distills these dense legal texts into accessible insights, highlighting both commonalities and differences. By delineating what qualifies as AI regulation and clarifying each jurisdiction's approach, our taxonomy reduces legal uncertainty, supports evidence-based policymaking, and lays the groundwork for more inclusive, globally coordinated AI governance.

Khalladi Sofiane Abdelkrim, Ouessai Asmâa, Benamara Nadir Kamel et al.

Due to rapid population growth, traffic congestion has become one of the major issues in urban areas. The utilization of technology may help to address this issue. This paper proposes a new Multi-head Self-attention Vision Transformer (MSViT) based macroscopic approach, for road traffic congestion classification. To evaluate this approach, we use the UCSD (University of California San Diego) dataset that includes different weather conditions (clear, overcast and rainy) and different traffic scenarios (light, medium and heavy). The classification accuracy reached a high level of 99.76% with this dataset and 99.37% when night-mode frames are added to it. The proposed MSViT based method outperforms the state-of-the-art macroscopic and microscopic methods that have been evaluated using the same UCSD dataset, which makes it an efficient solution for traffic congestion prediction.

Tadesse Kenea Amentae, Girma Gebresenbet, Nuredin Jemal Abdela

<i>Background</i>: Effective supply chain management (SCM) is widely considered vital for enhancing business sustainability, yet empirical evidence across industries and contexts remains limited. This paper aims to address this gap by presenting empirical findings specific to a particular industry, business size, and economic setting. <i>Methods</i>: The data are collected from small- and medium-sized water bottling companies in Ethiopia utilizing a Likert scale questionnaire and analyzed using SPPS version 29 using multi-variable regression analysis. <i>Results</i>: The findings reveal a statistically significant positive influence of supply chain management practices on economic, environmental, and social sustainability business performances. Accordingly, supply chain internal practices and customer and supplier integration impact business economic sustainability, while customer and supplier integration affect business environmental sustainability performance. Customer integration, supplier integration, and supply chain internal practices significantly influence business social sustainability performance. <i>Conclusions</i>: These results highlight the potential for businesses to achieve holistic sustainability goals through targeted improvements in SCM practices. The research results are consistent with most previous studies on this topic, except for a few variations that may need further investigation. The discussion highlighted the intricate links between supply chain management practices and business sustainability, underscoring the need for comprehensive further empirical studies in various contexts.

Sabir Muhammad, Akram Muhammad, Mian Rehman Uddin

Transportation and logistics play a crucial role in the overall supply chain of a country by ensuring the timely delivery of goods for production and consumption. The current study investigates the relationship between economic growth and logistics performance across countries. The study’s significant contribution is using logistics performance data from a panel of 107 countries covering the period from 2007 to 2019 and applying panel data-based time-series econometric models to investigate the link between economic growth and logistics performance. The study confirms a long-term relationship between economic growth and logistics performance and shows that short-term disequilibrium is adjusted back to steady-state conditions. The findings suggest that policymakers should focus on developing all aspects of logistics performance rather than physical infrastructure.

Kubra Sar, Pezhman Ghadimi

<i>Background:</i> The significant increase in home healthcare (HHC) driven by technological advancements, an ageing population, and heightened disease outbreaks—especially evident during the COVID-19 pandemic—has created an urgent need for improved medical waste management. <i>Methods</i>: This paper presents the development of a decision support system with a web-based interface designed for efficient medical waste collection in the HHC sector. <i>Results:</i> The system utilises Flask for backend operations, with HTML and CSS for the user interface, and manages data using JSON files. Its flexible design supports real-time adjustments for various vehicle types and changing waste production locations. It incorporates dynamic routing by employing two sophisticated metaheuristic algorithms: the Strength Pareto Evolutionary Algorithm (SPEA-2) and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). This setup supports different dataset sizes and vehicle fleets, including Internal Combustion Engine (ICE) vehicles and Electric Vehicles (EVs). <i>Conclusions:</i> The automation reduces uncertainties in waste collection by minimising human intervention. The system is built to be easily adaptable for other sectors with minor modifications and can be expanded to test various scenarios with new selectable parameters.

Davies K. Bett, Islam Ali, Mohamed Gheith et al.

<i>Background</i>: Container terminals (CTs) have constantly administered truck appointment systems (TASs) to effectively accomplish the planning and scheduling of drayage operations. However, since the operations in the gate and yard area of a CT are stochastic, there is a need to incorporate uncertainty during the development and execution of appointment schedules. Further, the situation is complicated by disruptions in the arrival of external trucks (ETs) during transport, which results in congestion at the port due to unbalanced arrivals. In the wake of Industry 4.0, simulation can be used to test and investigate the present CT configurations for possible improvements. <i>Methods</i>: This paper presents a simulation optimization (SO) and simulation-based optimization (SBO) iteration framework which adopts a dual transactions approach to minimize the gate operation costs and establish the relationship between productivity and service time while considering congestion in the yard area. It integrates the use of both the developed discrete event simulation (DES) and a mixed integer programming (MIP) model from the literature to iteratively generate an improved schedule. The key performance indicators considered include the truck turnaround time (TTT) and the average time the trucks spend at each yard block (YB). The proposed approach was verified using input parameters from the literature. <i>Results</i>: The findings from the SO experiments indicate that, at most, two gates were required to be opened at each time window (TW), yielding an average minimum operating cost of USD 335.31. Meanwhile, results from the SBO iteration experiment indicate an inverse relationship between productivity factor (PF) values and yard crane (YC) service time. <i>Conclusions</i>: Overall, the findings provided an informed understanding of the need for dynamic scheduling of available resources in the yard to cut down on the gate operating costs. Further, the presented two methodologies can be incorporated with Industry 4.0 technologies to design digital twins for use in conventional CT by planners at an operational level as a decision-support tool.

Yutao Sun, Mingshuai Chen, Tiancheng Zhao et al.

Artificial intelligence is rapidly encroaching on the field of service regulation. However, existing AI-based regulation techniques are often tailored to specific application domains and thus are difficult to generalize in an automated manner. This paper presents Horae, a unified specification language for modeling (multimodal) regulation rules across a diverse set of domains. We showcase how Horae facilitates an intelligent service regulation pipeline by further exploiting a fine-tuned large language model named RuleGPT that automates the Horae modeling process, thereby yielding an end-to-end framework for fully automated intelligent service regulation. The feasibility and effectiveness of our framework are demonstrated over a benchmark of various real-world regulation domains. In particular, we show that our open-sourced, fine-tuned RuleGPT with 7B parameters suffices to outperform GPT-3.5 and perform on par with GPT-4o.

Kaidi Chen, Ben Chen, Dehong Gao et al.

Existing Neural Machine Translation (NMT) models mainly handle translation in the general domain, while overlooking domains with special writing formulas, such as e-commerce and legal documents. Taking e-commerce as an example, the texts usually include amounts of domain-related words and have more grammar problems, which leads to inferior performances of current NMT methods. To address these problems, we collect two domain-related resources, including a set of term pairs (aligned Chinese-English bilingual terms) and a parallel corpus annotated for the e-commerce domain. Furthermore, we propose a two-step fine-tuning paradigm (named G2ST) with self-contrastive semantic enhancement to transfer one general NMT model to the specialized NMT model for e-commerce. The paradigm can be used for the NMT models based on Large language models (LLMs). Extensive evaluations on real e-commerce titles demonstrate the superior translation quality and robustness of our G2ST approach, as compared with state-of-the-art NMT models such as LLaMA, Qwen, GPT-3.5, and even GPT-4.

Zhiyu Chen, Jason Choi, Besnik Fetahu et al.

In e-commerce, high consideration search missions typically require careful and elaborate decision making, and involve a substantial research investment from customers. We consider the task of identifying High Consideration (HC) queries. Identifying such queries enables e-commerce sites to better serve user needs using targeted experiences such as curated QA widgets that help users reach purchase decisions. We explore the task by proposing an Engagement-based Query Ranking (EQR) approach, focusing on query ranking to indicate potential engagement levels with query-related shopping knowledge content during product search. Unlike previous studies on predicting trends, EQR prioritizes query-level features related to customer behavior, finance, and catalog information rather than popularity signals. We introduce an accurate and scalable method for EQR and present experimental results demonstrating its effectiveness. Offline experiments show strong ranking performance. Human evaluation shows a precision of 96% for HC queries identified by our model. The model was commercially deployed, and shown to outperform human-selected queries in terms of downstream customer impact, as measured through engagement.

Martin Hoefer, Carmine Ventre, Lisa Wilhelmi

The recent banking crisis has again emphasized the importance of understanding and mitigating systemic risk in financial networks. In this paper, we study a market-driven approach to rescue a bank in distress based on the idea of claims trading, a notion defined in Chapter 11 of the U.S. Bankruptcy Code. We formalize the idea in the context of financial networks by Eisenberg and Noe. For two given banks v and w, we consider the operation that w takes over some claims of v and in return gives liquidity to v to ultimately rescue v. We study the structural properties and computational complexity of decision and optimization problems for several variants of claims trading. When trading incoming edges of v, we show that there is no trade in which both banks v and w strictly improve their assets. We therefore consider creditor-positive trades, in which v profits strictly and w remains indifferent. For a given set C of incoming edges of v, we provide an efficient algorithm to compute payments by w that result in maximal assets of v. When the set C must also be chosen, the problem becomes weakly NP-hard. Our main result here is a bicriteria FPTAS to compute an approximate trade. The approximate trade results in nearly the optimal amount of assets of v in any exact trade. Our results extend to the case in which banks use general monotone payment functions and the emerging clearing state can be computed efficiently. In contrast, for trading outgoing edges of v, the goal is to maximize the increase in assets for the creditors of v. Notably, for these results the characteristics of the payment functions of the banks are essential. For payments ranking creditors one by one, we show NP-hardness of approximation within a factor polynomial in the network size, when the set of claims C is part of the input or not. Instead, for proportional payments, our results indicate more favorable conditions.

Carlos Góes

Can trade integration induce product innovation? I document that countries that joined the European Union (EU) started producing more product varieties, investing more in R&D, and trading more compared to candidate countries that did not join at a given horizon. Additionally, I show that a plausibly exogenous increase in market access increases the probability of a given country starting production of and exporting a given product. To rationalize this reduced-form evidence, I propose a new quantitative framework that integrates the forces of specialization and market size. This is a dynamic general equilibrium model of frictional trade and endogenous growth with arbitrarily many asymmetric countries that nests the Eaton-Kortum model of trade and the Romer growth model as special cases. The key result is an analytical expression to decompose gains from trade into dynamic and static components. In this framework, the product innovation growth rate increases with higher market access. Finally, a quantitative version of the model suggests that: (a) the EU enlargement increased its long-run yearly growth rate by about 0.10pp; and (b) dynamic gains can account for between 65-90% of total welfare gains from trade.

Vladimir Belyaev

Railway administrations used to have no requirements for the fatigue strength and endurance of electric trains’ couplings. However, mass decommissioning of ICE1 trains in Germany due to cracked couplings and two coupling breakages in an electric train in Russia happened. Forces transferred through couplings are low-levelled which are not compared with couplings’ yield stress. Consequently, the abovementioned problems arise from fatigue damage. The need for detection of forces in electric trains’ couplings, the development of fatigue strength requirements, and test methodologies became obvious. In 2022, VNIIZHT assessed forces into couplings of different electric trains under low and high temperatures; mileage was 9 600 km. Maximal recorded forces under compression and tension were +117/ 128 kN – far less than the yield stress of electric train couplings (+1500/-1000 kN). It verifies assumed fatigue damage.After data processing, the statistical distribution of peak-to-peak amplitudes of forces in couplings per run unit and per service life of an electric train (40 years) with the account of their average annual run (130 and 170 thousand km for electric trains with constructional speeds of 120 and 160 km/h, respectively) was derived. The impact of forces of different levels on fatigue strength was calculated with respect to fatigue curves, and the values of peak-to-peak amplitudes were set to 4 (normalized couplings) and 5 (quenched and tempered couplings). Endurance under cyclic loading depends not only on the quantity of amplitudes of forces at each level but also on the asymmetry coefficient of the load cycle R = Pmin/Pmax. The developed methodology contains specific quantities of load cycles depending on the force amplitude and asymmetry coefficient of load cycle R.