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

Alaa Fouad Momena, Kamal Hossain Gazi, Sankar Prasad Mondal

<i>Background:</i> The supply chain refers to the full process of creating and providing a good or service, starting with the raw materials and ending with the final customer. It requires cooperation and coordination between many parties, including the suppliers, manufacturers, distributors, retailers, and customers. <i>Methods:</i> In the medicinal supply chain (MSC), the critical nature of these processes becomes more complicated. It requires strict regulation, quality control, and traceability to ensure patient safety and compliance with regulatory standards. This study is conducted to suggest a smooth channel to deal with the challenges and adaptability of the MSC. Different MSC challenges are considered as criteria which deal with various adaptation plans. Multi-criteria decision-making (MCDM) methodologies are taken as optimization tools and probabilistic linguistic term sets (PLTSs) are considered for express uncertainty. <i>Results:</i> The subscript degree function (SDF) and deviation degree function (DDF) are introduced to evaluate the crisp value of the PLTSs. An MSC model is constructed to optimize the sustainable medicinal supply chain and overcome various barriers to MSC problems. <i>Conclusions:</i> Additionally, sensitivity analysis and comparative analysis were conducted to check the robustness and flexibility of the system. Finally, the conclusion section determines the optimal weighted criteria for the MSC problem and identifies the best possible solutions for MSC using PLTS-based MCDM methodologies.

Emmanuel Ahatsi, Oludolapo Akanni Olanrewaju

<i>Background:</i> This study examines the application of Artificial Intelligence (AI) and Big Data Analytics (BDA) in enhancing humanitarian supply chain resilience, focusing on Ghana and South Africa. Despite their potential, AI-BDA applications are underexplored in disaster response, particularly in developing economies. <i>Methods:</i> An explanatory research design using a quantitative approach was employed, analyzing data from 200 supply chain professionals in both nations. Structured questionnaires assessed the implementation of four key AI-BDA techniques: Time-Series Forecasting (TSF), Early Warning Systems (EWS), Logistics Optimization (LO), and Real-time Monitoring (RTM). Exploratory factor analysis and regression analysis were conducted to evaluate the relationship between these techniques and supply chain resilience, controlling for organizational size and technological readiness. <i>Results:</i> The findings indicate that AI-BDA techniques significantly improve humanitarian supply chain resilience, with TSF and LO demonstrating the highest predictive power. Additionally, technological readiness facilitates the adoption of these techniques. <i>Conclusions:</i> While AI-BDA offers substantial benefits, opportunities for greater adoption remain, particularly in real-time monitoring and predictive analytics. Humanitarian organizations should invest in capacity-building initiatives, enhance data quality, and foster multi-stakeholder partnerships to maximize the impact of AI-BDA.

Ilias Alexandros Parmaksizoglou, Alessandro Bombelli, Alexei Sharpanskykh

<i>Background:</i> The rapid growth of international maritime trade has intensified operational challenges at marine terminals due to increased interaction between vessels, trucks, and trains. Key issues include berth congestion, inefficient truck arrivals, and underutilization of terminal resources. Ensuring coordinated planning among transport modes and fostering collaboration between stakeholders such as vessel operators, logistics providers, and terminal managers is critical to mitigating these inefficiencies. <i>Methods:</i> This study proposes a multi-agent, multi-objective coordination model that synchronizes vessel berth allocation with truck appointment scheduling. A solution method combining prioritized planning with a neighborhood search heuristic is introduced to explore Pareto-optimal trade-offs. The performance of this approach is benchmarked against well-established multi-objective evolutionary algorithms (MOEAs), including NSGA-II and SPEA2. <i>Results:</i> Numerical experiments demonstrate that the proposed method generates a greater number of Pareto-optimal solutions and achieves higher hypervolume indicators compared to MOEAs. These results show improved balance among objectives such as minimizing vessel waiting times, reducing truck congestion, and optimizing terminal resource usage. <i>Conclusions:</i> By integrating berth allocation and truck scheduling through a transparent, multi-agent approach, this work provides decision-makers with better tools to evaluate trade-offs in port terminal operations. The proposed strategy supports more efficient, fair, and informed coordination in complex multimodal environments.

Jianhua Zhou, Haifei Wu, Hailong Weng et al.

<i>Background:</i> As a labor-, capital-, and technology-intensive sector, shipbuilding supports water transportation, international trade, and marine development, driving economic growth and employment. Yet rising raw material/labor costs now bottleneck enterprise performance, making cost reduction and efficiency improvement urgent for shipbuilding and repair firms. It is an effective way to improve logistics transportation efficiency for reducing the cost of shipbuilding and repair firms. However, there are still few methods specifically designed for logistics transportation scheduling in shipbuilding and repair firms. <i>Methods:</i> In this paper, a “dual-cycle” strategy is proposed to optimize material transportation and cut logistics vehicles’ empty-load rate in the shipbuilding and repair process. A mixed-integer programming model is built to minimize total empty travel time, considering task priorities and time windows. A genetic algorithm-based scheduling method is proposed to solve this complex scheduling model. <i>Results</i>: Simulation with real shipyard logistics data shows the proposed model and algorithm can effectively address the shipbuilding logistics vehicle scheduling problem. In addition, the proposed algorithm performs better than two other compared algorithms in handling the studied problem. <i>Conclusions:</i> This study aids shipbuilding and repair logistics managers in making scheduling plans and determining optimal vehicle numbers, supporting cost-efficiency improvement.

Jingzhi Gong, Rafail Giavrimis, Paul Brookes et al.

There is a growing interest in leveraging multiple large language models (LLMs) for automated code optimization. However, industrial platforms deploying multiple LLMs face a critical challenge: prompts optimized for one LLM often fail with others, requiring expensive model-specific prompt engineering. This cross-model prompt engineering bottleneck severely limits the practical deployment of multi-LLM systems in production environments. We introduce Meta-Prompted Code Optimization (MPCO), a framework that automatically generates high-quality, task-specific prompts across diverse LLMs while maintaining industrial efficiency requirements. MPCO leverages metaprompting to dynamically synthesize context-aware optimization prompts by integrating project metadata, task requirements, and LLM-specific contexts. It is an essential part of the ARTEMIS code optimization platform for automated validation and scaling. Our comprehensive evaluation on five real-world codebases with 366 hours of runtime benchmarking demonstrates MPCO's effectiveness: it achieves overall performance improvements up to 19.06% with the best statistical rank across all systems compared to baseline methods. Analysis shows that 96% of the top-performing optimizations stem from meaningful edits. Through systematic ablation studies and meta-prompter sensitivity analysis, we identify that comprehensive context integration is essential for effective meta-prompting and that major LLMs can serve effectively as meta-prompters, providing actionable insights for industrial practitioners.

Hyewon Lee, Junghyun Oh, Minkyung Song et al.

This study presents the multilingual e-commerce search system developed by the DILAB team, which achieved 5th place on the final leaderboard with a competitive overall score of 0.8819, demonstrating stable and high-performing results across evaluation metrics. To address challenges in multilingual query-item understanding, we designed a multi-stage pipeline integrating data refinement, lightweight preprocessing, and adaptive modeling. The data refinement stage enhanced dataset consistency and category coverage, while language tagging and noise filtering improved input quality. In the modeling phase, multiple architectures and fine-tuning strategies were explored, and hyperparameters optimized using curated validation sets to balance performance across query-category (QC) and query-item (QI) tasks. The proposed framework exhibited robustness and adaptability across languages and domains, highlighting the effectiveness of systematic data curation and iterative evaluation for multilingual search systems. The source code is available at https://github.com/2noweyh/DILAB-Alibaba-Ecommerce-Search.

Qinwen Chen, Wenbiao Tao, Zhiwei Zhu et al.

Community Question Answering (CQA) platforms can be deemed as important knowledge bases in community, but effectively leveraging historical interactions and domain knowledge in real-time remains a challenge. Existing methods often underutilize external knowledge, fail to incorporate dynamic historical QA context, or lack memory mechanisms suited for industrial deployment. We propose ComRAG, a retrieval-augmented generation framework for real-time industrial CQA that integrates static knowledge with dynamic historical QA pairs via a centroid-based memory mechanism designed for retrieval, generation, and efficient storage. Evaluated on three industrial CQA datasets, ComRAG consistently outperforms all baselines--achieving up to 25.9% improvement in vector similarity, reducing latency by 8.7% to 23.3%, and lowering chunk growth from 20.23% to 2.06% over iterations.

Vidya Venkatesan, Aomawa L. Shields, Russell Deitrick et al.

Eccentric planets may spend a significant portion of their orbits at large distances from their host stars, where low temperatures can cause atmospheric CO2 to condense out onto the surface, similar to the polar ice caps on Mars. The radiative effects on the climates of these planets throughout their orbits would depend on the wavelength-dependent albedo of surface CO2 ice that may accumulate at or near apoastron and vary according to the spectral energy distribution of the host star. To explore these possible effects, we incorporated a CO2 ice-albedo parameterization into a one-dimensional energy balance climate model. With the inclusion of this parameterization, our simulations demonstrated that F-dwarf planets require 29% more orbit-averaged flux to thaw out of global water ice cover compared with simulations that solely use a traditional pure water ice-albedo parameterization. When no eccentricity is assumed, and host stars are varied, F-dwarf planets with higher bond albedos relative to their M-dwarf planet counterparts require 30% more orbit-averaged flux to exit a water snowball state. Additionally, the intense heat experienced at periastron aids eccentric planets in exiting a snowball state with a smaller increase in instellation compared with planets on circular orbits; this enables eccentric planets to exhibit warmer conditions along a broad range of instellation. This study emphasizes the significance of incorporating an albedo parameterization for the formation of CO2 ice into climate models to accurately assess the habitability of eccentric planets, as we show that, even at moderate eccentricities, planets with Earth-like atmospheres can reach surface temperatures cold enough for the condensation of CO2 onto their surfaces, as can planets receiving low amounts of instellation on circular orbits.

Prause Gunnar, Gerlitz Laima

The classical pathway of mass production followed a linear model with trashed products and wasted remaining materials at the final stage of their life cycle. Smart approaches of manufacturing and product life cycle management aim for Circular Economy (CE) models to implement sustainable business models to overcome imbalances between resource supply and demand of goods. Non-Fungible Token (NFT) solutions together with smart contracts seem to have the potential to realise such new sustainable business models in the context of CE. The study demonstrates how NFT technology can become an integral part of smart product life cycle management for batteries of e-cars. The research highlights how circular business models can be developed and implemented in the e-car sector around the life cycle management of batteries as well as how NFT technology can contribute to sustainable conceptualisation for battery recycling.

Ana Isabel Bento, Carla Cruz, Gabriela Fernandes et al.

<i>Background</i>: Supply chains, characterized by complexity and sensitivity, require continuous mapping to address challenges, particularly disruptions like the COVID-19 pandemic. In this context, Social Network Analysis (SNA) has proven valuable in analyzing how actors in a network connect and create interdependencies. However, some studies suggest that the SNA literature needs to embrace new fields of application and develop innovative metrics. <i>Methods</i>: The aim of this study is to clarify the role and contribution of SNA when characterizing and understanding the challenges of contemporary supply chains. A literature review was conducted to achieve this. <i>Results</i>: The results reveal that SNA has been applied in a wide variety of areas (e.g., manufacturing and construction sectors), with an emerging application in the tertiary sector. Furthermore, the findings demonstrate that metrics related to the network and to nodes have been used repeatedly, highlighting the need for new supply-chain-related metrics, such as the novel concept of semi-directedness. <i>Conclusions</i>: Despite the versatility of SNA, some aspects may limit its application to supply chain management, including shortcomings in data acquisition and the fact that SNA only allows for the visualization of network configurations, thus preventing the capture of nuances that characterize the relationships between the actors involved.

Robert Law, David Chandler, Phillip Voigt et al.

Glacier and ice-sheet motion is fundamental to glaciology. However, we still lack a consensus for the optimal way to relate basal velocity to basal traction for large-scale glacier and ice-sheet models (the 'sliding relationship'). Typically, a single tunable coefficient loosely connected to one or a limited number of physical processes is varied spatially to reconcile model output with observations. Yet, process-agnostic studies indicate that the suitability of a given sliding relationship depends on the setting. Here, we suggest that this arises from myriad overlapping setting- and scale-dependent sliding sub-processes, including complicated near-basal stress states not captured by large-scale models, reviewed here as comprising a basal 'sliding layer'. A corresponding 'bulk layer' then accounts for ice deformation only minimally influenced by bed properties. We provide a framework for incorporating arbitrarily many sub-processes within a given region -- separated into normal ('form drag') and tangential ('slip') resistance at the ice-bed interface, stressing that the maximum scale of cavitation is an important contributor to the division between the two. Under reasonable assumptions, our framework implies that sliding relationships should fall within a sum of regularised-Coulomb and power-law components, with a rough-smooth distinction proving more consequential in dictating sliding behaviour than a traditional hard-soft transition.

C-Y Law, Jonathan C. Tan, Raphael Skalidis et al.

Magnetic fields may play a crucial role in setting the initial conditions of massive star and star cluster formation. To investigate this, we report SOFIA-HAWC+ $214\:μ$m observations of polarized thermal dust emission and high-resolution GBT-Argus C$^{18}$O(1-0) observations toward the massive Infrared Dark Cloud (IRDC) G28.37+0.07. Considering the local dispersion of $B$-field orientations, we produce a map of $B$-field strength of the IRDC, which exhibits values between $\sim0.03 - 1\:$mG based on a refined Davis-Chandrasekhar-Fermi (r-DCF) method proposed by Skalidis \& Tassis. Comparing to a map of inferred density, the IRDC exhibits a $B-n$ relation with a power law index of $0.51\pm0.02$, which is consistent with a scenario of magnetically-regulated anisotropic collapse. Consideration of the mass-to-flux ratio map indicates that magnetic fields are dynamically important in most regions of the IRDC. A virial analysis of a sample of massive, dense cores in the IRDC, including evaluation of magnetic and kinetic internal and surface terms, indicates consistency with virial equilibrium, sub-Alfvénic conditions and a dominant role for $B-$fields in regulating collapse. A clear alignment of magnetic field morphology with direction of steepest column density gradient is also detected. However, there is no preferred orientation of protostellar outflow directions with the $B-$field. Overall, these results indicate that magnetic fields play a crucial role in regulating massive star and star cluster formation and so need to be accounted for in theoretical models of these processes.

Nivedita Singh, Yejin Do, Yongsang Yu. Imane Fouad et al.

Despite stringent data protection regulations such as the General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and other country-specific regulations, many websites continue to use cookies to track user activities. Recent studies have revealed several data protection violations, resulting in significant penalties, especially for multinational corporations. Motivated by the question of why these data protection violations continue to occur despite strong data protection regulations, we examined 360 popular e-commerce websites in multiple countries to analyze whether they comply with regulations to protect user privacy from a cookie perspective.

mehdi omani , Jamal Beigi , Babak pourghahramani

s a new technology, HAARP has several adverse effects on the international community, even though its owners say it is intended to study ionospheres to develop new technologies, facilitate radio communication, and counteract the negative effects of atomic explosions. This was not the case in principle, and its abuses and deviations have been observed, turning it into a technology against humanity. This article aims to examine the abuses of HAARP 's technology and how they violate fundamental human rights. Research methods are descriptive and analytical, and data collection is done through collection and filing. In this qualitative study, researchers found that Haarp's new technology violates fundamental human rights, including the right to life, freedom of thought, and future generations' rights.

Andrii Voshchepynets, Oleksiy Agapitov, Lynn Wilson et al.

We present the results of processing the effects of the powerful Gamma Ray Burst GRB221009A captured by the charged particle detectors (electrostatic analyzers and solid-state detectors) onboard spacecraft at different points in the heliosphere on October 9, 2022. To follow the GRB221009A propagation through the heliosphere we used the electron and proton flux measurements from solar missions Solar Orbiter and STEREO-A; Earth magnetosphere and the solar wind missions THEMIS and Wind; meteorological satellites POES15, POES19, MetOp3; and MAVEN - a NASA mission orbiting Mars. GRB221009A had a structure of four bursts: less intense Pulse 1 - the triggering impulse - was detected by gamma-ray observatories at 131659 UT (near the Earth); the most intense Pulses 2 and 3 were detected on board all the spacecraft from the list, and Pulse 4 detected in more than 500 s after Pulse 1. Due to their different scientific objectives, the spacecraft, which data was used in this study, were separated by more than 1 AU (Solar Orbiter and MAVEN). This enabled tracking GRB221009A as it was propagating across the heliosphere. STEREO-A was the first to register Pulse 2 and 3 of the GRB, almost 100 seconds before their detection by spacecraft in the vicinity of Earth. MAVEN detected GRB221009A Pulses 2, 3, and 4 at the orbit of Mars about 237 seconds after their detection near Earth. By processing the time delays observed we show that the source location of the GRB221009A was at RA 288.5 degrees, Dec 18.5 degrees (J2000) with an error cone of 2 degrees

Zilong Zhang, Zhibin Zhao, Xingwu Zhang et al.

Industrial anomaly detection (IAD) is crucial for automating industrial quality inspection. The diversity of the datasets is the foundation for developing comprehensive IAD algorithms. Existing IAD datasets focus on the diversity of data categories, overlooking the diversity of domains within the same data category. In this paper, to bridge this gap, we propose the Aero-engine Blade Anomaly Detection (AeBAD) dataset, consisting of two sub-datasets: the single-blade dataset and the video anomaly detection dataset of blades. Compared to existing datasets, AeBAD has the following two characteristics: 1.) The target samples are not aligned and at different scales. 2.) There is a domain shift between the distribution of normal samples in the test set and the training set, where the domain shifts are mainly caused by the changes in illumination and view. Based on this dataset, we observe that current state-of-the-art (SOTA) IAD methods exhibit limitations when the domain of normal samples in the test set undergoes a shift. To address this issue, we propose a novel method called masked multi-scale reconstruction (MMR), which enhances the model's capacity to deduce causality among patches in normal samples by a masked reconstruction task. MMR achieves superior performance compared to SOTA methods on the AeBAD dataset. Furthermore, MMR achieves competitive performance with SOTA methods to detect the anomalies of different types on the MVTec AD dataset. Code and dataset are available at https://github.com/zhangzilongc/MMR.

Jacek Krystek

Obecnie obserwuje się praktykę zwiększania szczegółowości i objętości kart informacyjnych przedsięwzięcia, w literaturze przedmiotu brak jednak rozważań na temat tego, co taki dokument powinien zawierać. W artykule przeanalizowano, jakie dane powinny się znaleźć w karcie informacyjnej. Zwrócono też uwagę na żądania dotyczące podania informacji, których nie przewiduje ustawa, wysuwane niekiedy przez organy administracji.

Desta Robel, Tóth János

One of the primary causes of poor public transport performance is delays at intersections. Among the efficient and sustainable solutions to boost mass transportation performance, Bus Rapid Transit (BRT) consists of infrastructures integrating dedicated bus lanes and smart operational service with different ITS technologies like Transit Signal Priority (TSP). This research studies the competence of buses operating on junctions of the BRT corridor where they have Signal Priority on the dedicated lane. The studied intersection is located around the center of the Addis Ababa BRT-B2 line, which is relatively gentle grade and characterized by the high traffic and pedestrian volume. Microscopic models were created for the chosen intersection, along with possible calibration and validation; moreover, a statistical comparison was performed to evaluate different scenarios with the goal of displaying the deployment benefits. To assess the performance of BRT buses and their overall influence on general traffic, scenarios with and without TSP were evaluated. PTV VISSIM and the VisVAP add-on simulation program were used to examine TSP alternatives. Incorporating TSP reduced the travel time by up to 4.78% in the priority direction, the average travel speed increased by 7.25%, and the queue length also reduced by a maximum of 6%, whereas in the non-priority direction, the queue length increased by a maximum of 2.5%. Moreover, the overall average passenger delay has reduced by an average amount of 15%. One of the simplest ways to improve transit performance could be signal priority strategies, which has a minor influence on the general traffic.

Baofeng Huo, Mengqiu Guo

As the modern aviation-oriented business model (aerotropolis), the Airport Economy Zone (AEZ) accumulates capital, technology, workforce, and other production factors. The AEZ always has a large number of infrastructure investments. Still, it has not yet achieved the expected effect in integrating and driving other regional resource endowments in the short term in China. Therefore, governments and AEZ organizations must utilize these investments and create values. This paper demonstrates how the Zhengzhou AEZ (ZAEZ) in China integrates its resources and stakeholders to overcome the remaining issues in its airport stage and gain competitive advantages. We classify three integrations: integrations from contents, including strategic alliance, information sharing, and process coordination; integrations from objectives, including internal integration and external integration; and integrations from objects, including integrating stakeholder, financial resource, and material resource. This paper presents the value creation and competitive advantages of the economy zone from the analysis of different integrations.

Reza Maboudi Neishabouri , SeyedAlireza Rezaee

One of the most important developments in dispute resolution law is the emergence of online dispute resolution methods. There are ambiguities and legal issues regarding said methods due to their specific characteristics and occurrence in cyberspace. One of the challenges of these methods is how to enforce the results because the ordinary or traditional methods of enforcing final dispute resolution documents are not helpful in this field. Due to the high importance of the enforcement of final documents in the validity and acceptability of each dispute resolution method, the legal doctrine and e-commerce activists: predicted and implemented solutions about automatic enforcement of results of online dispute resolution methods without the need for the recourse to courts or other authorities. The present study has examined the strategies of automatic enforcement of ODR final documents in the following topics: "Monetary Enforcement Mechanisms", "Domain Name Resolution Enforcement Strategies", and "Automatic Enforcement Based on Blockchain Technology". The present article concludes that supporting and promoting online dispute resolution methods and strategies for automatic enforcement of ODR final documents is the most critical and fundamental step in reducing the workload of the courts and other legal authorities. At the same time, such a goal is not achievable without the legislature's support and the approval of the corresponding laws. In this regard, the Iranian Legislature is advised to draft and enact integrated and comprehensive rules in the area.