<i>Background</i>: Cold chain distribution in Agri-Biotech supply chains faces serious challenges due to strict time windows, high temperature sensitivity, and conflict between different operational objectives, and conventional static approaches are unable to address these complexities. <i>Methods</i>: In this study, an integrated decision support framework is presented that combines multi-objective fuzzy modeling and an adaptive digital twin to simultaneously manage logistics costs, product quality degradation, and service time compliance under operational uncertainty. Key uncertain parameters are modeled using triangular fuzzy numbers, and the digital twin dynamically updates the decision parameters based on operational information. The proposed framework is evaluated using real industrial data and comprehensive computational experiments. <i>Results</i>: The results show that the proposed approach is able to produce stable and balanced solutions, provides near-optimal performance in benchmark cases, and is highly robust to demand fluctuations and temperature deviations. Digital twin activation significantly improves the convergence behavior and stability of the solutions. <i>Conclusions</i>: The proposed framework provides a reliable and practical tool for adaptive planning of cold chain distribution in Agri-Biotech industries and effectively reduces the gap between advanced optimization models and real-world operational requirements.
Transportation and communication, Management. Industrial management
<i>Background</i>: This study evaluates an additive manufacturing (AM) network designed to balance economic performance, lead time, and environmental impact within the healthcare logistics and supply chain. <i>Methods</i>: An integrated framework is proposed that identifies optimal AM facility locations using spatial K-means clustering and optimizes delivery routes through a multi-objective vehicle routing problem with time windows (MOVRPTW). This framework was applied to a case study in Phra Nakhon Si Ayutthaya, Thailand, utilizing hospital geocoordinates, demand profiles, and CO<sub>2</sub> emission factors to evaluate centralized versus decentralized network configurations. <i>Results</i>: Findings demonstrate that hub structures derived from K-means clustering achieve the highest economic efficiency, reducing the AM part cost per unit to 698.51 Baht. In contrast, a fully centralized network resulted in a significantly higher unit cost of 4759.79 Baht, while clustering based on hospital types yielded a unit cost of 959.34 Baht. Quantitative results indicate that the multi-objective approach provides a superior trade-off, achieving lead time requirements while maintaining operational costs and emissions. <i>Conclusions</i>: The results indicate that the proposed framework, particularly through spatial clustering, offers a practical decision-support tool for designing AM networks that achieve a balance between operational efficiency and sustainability objectives in healthcare logistics.
Transportation and communication, Management. Industrial management
Paria Mahmoudi, Mohammad Hori Najafabadi, Bernd Noche
et al.
<i>Background</i>: Modern supply chains (SC) are increasingly difficult to manage as they become more complex and interconnected. This encourages companies to rely more on real-time data analysis and analytical tools on operational processes. This study aims to develop and evaluate a Supply Chain Wave Report for a non-food retail that represents goods movement across logistics stages as a continuous analytical flow. <i>Methods</i>: Proposed framework integrates multiple operational phases—Booked Orders, Main Transit, On-Carriage, Warehouse Operations, Store Delivery, and Sales—into a unified monitoring structure. This model can combine operational data with advanced analytics, including Artificial Intelligence-, cloud computing-, and Internet of Things-based technologies. Through cloud-based data infrastructures, System enables data integration and near real-time visibility across organizational functions, allowing continuous monitoring through key performance indicators and predictive simulations. <i>Results</i>: This framework enables dynamic performance of supply chain management and generates real-time signals as goods move across logistics network. This enables managers to detect irregularities earlier and respond before operational deviations propagate further along the chain. Wave-based monitoring approach highlights interdependence between SC stages and illustrates how small disruptions may propagate over time, potentially contributing to effects like bullwhip effect. <i>Conclusions</i>: Findings suggest that a cloud-enabled wave analytics framework can enhance coordination, reduce information gaps, and support informed decision-making in retail.
Transportation and communication, Management. Industrial management
This article argues that security is not enough to fully capture what is at stake in government exceptional access to encrypted data. A conception of privacy as security has little to say about ``lawful-surveillance protocols'' -- an active research agenda in cryptography that aims to enable government exceptional access without compromising systemic security. But the limitations are not contingent on the success of this agenda. The normative landscape today cannot be explained if security is all there is to privacy. And fundamental objections to Apple's abandoned client-side scanning system gesture beyond security. This article's contribution is modest: to show that there must be more to privacy than the security mold it has taken. A richer understanding is needed both to assess policy and to guide research on lawful-surveillance protocols.
<i>Background</i>: Ongoing challenges such as geopolitical conflicts, trade disruptions, economic sanctions, and political instability have underscored the urgent need for large manufacturing enterprises to improve resilience and reduce dependence on global supply chains. Integrating regional and local Small- and Medium-Sized Enterprises (SMEs) has been proposed as a strategic approach to enhance supply chain localization, yet barriers such as limited visibility, qualification hurdles, and integration difficulties persist. <i>Methods</i>: This study proposes a comprehensive knowledge graph driven framework for representing and discovering SMEs, implemented as a proof-of-concept in the U.S. BioPharma sector. The framework constructs a curated knowledge graph in Neo4j, converts it to Resource Description Framework (RDF) format, and aligns it with the Schema.org vocabulary to enable semantic interoperability and enhance the discoverability of SMEs. <i>Results</i>: The developed knowledge graph, consisting of 488 nodes and 11,520 edges, enabled accurate multi-hop SME discovery with query response times under 10 milliseconds. RDF serialization produced 16,086 triples, validated across platforms to confirm interoperability and semantic consistency. <i>Conclusions</i>: The proposed framework provides a scalable, adaptable, and generalizable solution for SME discovery and supply chain localization, offering a practical pathway to strengthen resilience in diverse manufacturing industries.
Transportation and communication, Management. Industrial management
W aktualnym stanie prawnym inwestorzy mogą domagać się od przedsiębiorców, którzy prowadzą ruch zakładu górniczego, zwrotu uzasadnionych i koniecznych nakładów poniesionych na zabezpieczenie obiektu budowlanego przed szkodliwym wpływem ruchu takiego zakładu. Realizacja tego roszczenia wymaga spełnienia określonych prawnie przesłanek, uwarunkowanych okolicznościami faktycznymi konkretnego przypadku. Opracowanie wyjaśnia więc zarówno podstawy prawne, jak i okoliczności faktyczne, które decydują o rozstrzygnięciu w sprawie roszczenia o zwrot kosztów zabezpieczenia obiektu budowlanego przed przewidywanymi szkodami spowodowanymi ruchem zakładu górniczego. Autor analizuje orzecznictwo sądów powszechnych i Sądu Najwyższego, uwzględnia także literaturę przedmiotu dotyczącą aktualnego oraz historycznego stanu prawnego.
Environmental law, Regulation of industry, trade, and commerce. Occupational law
Rafael R. Maciel, Adler Diniz de Souza, Rodrigo M. A. Almeida
et al.
<i>Background</i>: Waste collection is a critical logistical challenge in urban management, and while Internet of Things (IoT) technologies are increasingly used to optimize collection routes, a systematic, quantitative synthesis of their impact is lacking. This study aims to bridge this gap by quantifying the effect of IoT-enabled routing optimization on waste collection distances. <i>Methods</i>: We conducted a systematic review and meta-analysis following the PRISMA protocol, searching the Scopus, IEEE Xplore, and ACM Digital Library databases. This process yielded 11 eligible studies, providing 21 distinct samples for quantitative synthesis. <i>Results</i>: The analysis reveals that IoT-enabled routing optimization reduces collection distance by a combined average of 21.51%. A significant disparity was found between study types, with simulation-based approaches reporting higher reductions (−39.79%) compared to real-world deployments (−12.37%). No statistically significant performance differences were observed across different routing algorithm categories or Vehicle Routing Problem (VRP) variants. <i>Conclusions</i>: These findings provide robust quantitative evidence of the significant efficiency gains from implementing IoT-based smart waste management systems. The gap between simulated and real-world results underscores the need for practitioners to set realistic expectations, while our analysis supports the adoption of these technologies for more sustainable urban logistics.
Transportation and communication, Management. Industrial management
Amid the surge of intellectual property (IP) disputes surrounding non-fungible tokens (NFTs), some scholars have advocated for the application of personal property or sales law to regulate NFT minting and transactions, contending that IP laws unduly hinder the development of the NFT market. This Article counters these proposals and argues that the existing IP system stands as the most suitable regulatory framework for governing the evolving NFT market. Compared to personal property or sales law, IP laws can more effectively address challenges such as tragedies of the commons and anticommons in the NFT market. NFT communities have also developed their own norms and licensing agreements upon existing IP laws to regulate shared resources. Moreover, the IP regimes, with both static and dynamic institutional designs, can effectively balance various policy concerns, such as innovation, fair competition, and consumer protection, which alternative proposals struggle to provide.
Luis Antonio Gutiérrez Guanilo, Mir Tafseer Nayeem, Cristian López
et al.
Large Language Models (LLMs) have demonstrated exceptional versatility across diverse domains, yet their application in e-commerce remains underexplored due to a lack of domain-specific datasets. To address this gap, we introduce eC-Tab2Text, a novel dataset designed to capture the intricacies of e-commerce, including detailed product attributes and user-specific queries. Leveraging eC-Tab2Text, we focus on text generation from product tables, enabling LLMs to produce high-quality, attribute-specific product reviews from structured tabular data. Fine-tuned models were rigorously evaluated using standard Table2Text metrics, alongside correctness, faithfulness, and fluency assessments. Our results demonstrate substantial improvements in generating contextually accurate reviews, highlighting the transformative potential of tailored datasets and fine-tuning methodologies in optimizing e-commerce workflows. This work highlights the potential of LLMs in e-commerce workflows and the essential role of domain-specific datasets in tailoring them to industry-specific challenges.
Computer science research sometimes brushes with the law, from red-team exercises that probe the boundaries of authentication mechanisms, to AI research processing copyrighted material, to platform research measuring the behavior of algorithms and users. U.S.-based computer security research is no stranger to the Computer Fraud and Abuse Act (CFAA) and the Digital Millennium Copyright Act (DMCA) in a relationship that is still evolving through case law, research practices, changing policies, and legislation. Amid the landscape computer scientists, lawyers, and policymakers have learned to navigate, anti-fraud laws are a surprisingly under-examined challenge for computer science research. Fraud brings separate issues that are not addressed by the methods for navigating CFAA, DMCA, and Terms of Service that are more familiar in the computer security literature. Although anti-fraud laws have been discussed to a limited extent in older research on phishing attacks, modern computer science researchers are left with little guidance when it comes to navigating issues of deception outside the context of pure laboratory research. In this paper, we analyze and taxonomize the anti-fraud and deception issues that arise in several areas of computer science research. We find that, despite the lack of attention to these issues in the legal and computer science literature, issues of misrepresented identity or false information that could implicate anti-fraud laws are actually relevant to many methodologies used in computer science research, including penetration testing, web scraping, user studies, sock puppets, social engineering, auditing AI or socio-technical systems, and attacks on artificial intelligence. We especially highlight the importance of anti-fraud laws in two research fields of great policy importance: attacking or auditing AI systems, and research involving legal identification.
While artificial intelligence (AI) holds enormous promise, many experts in the field are warning that there is a non-trivial chance that the development of AI poses an existential threat to humanity. Existing regulatory initiative do not address this threat but merely instead focus on discrete AI-related risks such as consumer safety, cybersecurity, data protection, and privacy. In the absence of regulatory action to address the possible risk of human extinction by AI, the question arises: What legal obligations, if any, does public international law impose on states to regulate its development. Grounded in the precautionary principle, we argue that there exists an international obligation to mitigate the threat of human extinction by AI. Often invoked in relation to environmental regulation and the regulation of potentially harmful technologies, the principle holds that in situations where there is the potential for significant harm, even in the absence of full scientific certainty, preventive measures should not be postponed if delayed action may result in irreversible consequences. We argue that the precautionary principle is a general principle of international law and, therefore, that there is a positive obligation on states under the right to life within international human rights law to proactively take regulatory action to mitigate the potential existential risk of AI. This is significant because, if an international obligation to regulate the development of AI can be established under international law, then the basic legal framework would be in place to address this evolving threat.
The evolution of Large Language Models (LLMs) has significantly advanced multi-turn conversation systems, emphasizing the need for proactive guidance to enhance users' interactions. However, these systems face challenges in dynamically adapting to shifts in users' goals and maintaining low latency for real-time interactions. In the Baidu Search AI assistant, an industrial-scale multi-turn search system, we propose a novel two-phase framework to provide proactive guidance. The first phase, Goal-adaptive Supervised Fine-Tuning (G-SFT), employs a goal adaptation agent that dynamically adapts to user goal shifts and provides goal-relevant contextual information. G-SFT also incorporates scalable knowledge transfer to distill insights from LLMs into a lightweight model for real-time interaction. The second phase, Click-oriented Reinforcement Learning (C-RL), adopts a generate-rank paradigm, systematically constructs preference pairs from user click signals, and proactively improves click-through rates through more engaging guidance. This dual-phase architecture achieves complementary objectives: G-SFT ensures accurate goal tracking, while C-RL optimizes interaction quality through click signal-driven reinforcement learning. Extensive experiments demonstrate that our framework achieves 86.10% accuracy in offline evaluation (+23.95% over baseline) and 25.28% CTR in online deployment (149.06% relative improvement), while reducing inference latency by 69.55% through scalable knowledge distillation.
<i>Background</i>: This publication presents a review, multiple criteria optimization models, and a practical example pertaining to the integration of automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. This publication addresses challenges in logistics and transportation, aiming to enhance efficiency, reduce costs and improve customer satisfaction. This study integrates automated smart locker systems, capillary distribution networks, crowdshipping, last-mile delivery and supply chain management. <i>Methods</i>: A review of the existing literature synthesizes key concepts, such as facility location problems, vehicle routing problems and the mathematical programming approach, to optimize supply chain operations. Conceptual optimization models are formulated to solve the complex decision-making process involved in last-mile delivery, considering multiple objectives, including cost minimization, delivery time optimization, service level minimization, capacity optimization, vehicle minimization and resource utilization. <i>Results</i>: The multiple criteria approaches combine the vehicle routing problem and facility location problem, demonstrating the practical applicability of the proposed methodology in a real-world case study within a logistics company. <i>Conclusions</i>: The execution of multi-criteria models optimizes automated smart locker deployment, capillary distribution design, crowdshipping and last-mile delivery strategies, showcasing its effectiveness in the logistics sector.
Transportation and communication, Management. Industrial management
Paula Fraga-Lamas, Tiago M Fernandez-Carames, Oscar Blanco-Novoa
et al.
Shipbuilding companies are upgrading their inner workings in order to create Shipyards 4.0, where the principles of Industry 4.0 are paving the way to further digitalized and optimized processes in an integrated network. Among the different Industry 4.0 technologies, this article focuses on Augmented Reality, whose application in the industrial field has led to the concept of Industrial Augmented Reality (IAR). This article first describes the basics of IAR and then carries out a thorough analysis of the latest IAR systems for industrial and shipbuilding applications. Then, in order to build a practical IAR system for shipyard workers, the main hardware and software solutions are compared. Finally, as a conclusion after reviewing all the aspects related to IAR for shipbuilding, it is proposed an IAR system architecture that combines Cloudlets and Fog Computing, which reduce latency response and accelerate rendering tasks while offloading compute intensive tasks from the Cloud.
Recently, generative AI (GAI), with their emerging capabilities, have presented unique opportunities for augmenting and revolutionizing industrial recommender systems (Recsys). Despite growing research efforts at the intersection of these fields, the integration of GAI into industrial Recsys remains in its infancy, largely due to the intricate nature of modern industrial Recsys infrastructure, operations, and product sophistication. Drawing upon our experiences in successfully integrating GAI into several major social and e-commerce platforms, this survey aims to comprehensively examine the underlying system and AI foundations, solution frameworks, connections to key research advancements, as well as summarize the practical insights and challenges encountered in the endeavor to integrate GAI into industrial Recsys. As pioneering work in this domain, we hope outline the representative developments of relevant fields, shed lights on practical GAI adoptions in the industry, and motivate future research.
Idoia Berges, Víctor Julio Ramírez-Durán, Arantza Illarramendi
The growing trends in automation, Internet of Things, big data and cloud computing technologies have led to the fourth industrial revolution (Industry 4.0), where it is possible to visualize and identify patterns and insights, which results in a better understanding of the data and can improve the manufacturing process. However, many times, the task of data exploration results difficult for manufacturing experts because they might be interested in analyzing also data that does not appear in pre-designed visualizations and therefore they must be assisted by Information Technology experts. In this paper, we present a proposal materialized in a semantic-based visual query system developed for a real Industry 4.0 scenario that allows domain experts to explore and visualize data in a friendly way. The main novelty of the system is the combined use that it makes of captured data that are semantically annotated first, and a 2D customized digital representation of a machine that is also linked with semantic descriptions. Those descriptions are expressed using terms of an ontology, where, among others, the sensors that are used to capture indicators about the performance of a machine that belongs to a Industry 4.0 scenario have been modeled. Moreover, this semantic description allows to: formulate queries at a higher level of abstraction, provide customized graphical visualizations of the results based on the format and nature of the data, and download enriched data enabling further types of analysis.
Defect Triage is a time-sensitive and critical process in a large-scale agile software development lifecycle for e-commerce. Inefficiencies arising from human and process dependencies in this domain have motivated research in automated approaches using machine learning to accurately assign defects to qualified teams. This work proposes a novel framework for automated defect triage (DEFTri) using fine-tuned state-of-the-art pre-trained BERT on labels fused text embeddings to improve contextual representations from human-generated product defects. For our multi-label text classification defect triage task, we also introduce a Walmart proprietary dataset of product defects using weak supervision and adversarial learning, in a few-shot setting.
This paper presents an efficient approach for building occupancy modeling to reduce energy consumption. In this work, a novel approach to occupancy modeling based on the posture and comfort level of the occupant is developed, and subsequently, we report a new and efficient framework for detecting posture and emotion from skeleton joints and face points data respectively obtained from the Kinect sensor. The proposed approach is tested in terms of accuracy, region of convergence, and confusion matrix using several machine learning techniques. Out of all the techniques, random forest classifier gave the maximum blind test accuracy for multi-class classification of posture detection. Deep learning is used for emotion detection using several optimizers out of which Adadelta gave the maximum blind test accuracy for multi-class classification. Along with the Kinect sensor, several other sensors such as the magnetic door sensor, pyroelectric sensors, and illumination sensors are connected through a wireless network using Raspberry Pi Zero W. Thus creating an unmanned technique for illumination regulation.
We discuss model-checking problems as formal models of algorithmic law. Specifically, we ask for an algorithmically tractable general purpose model-checking problem that naturally models the European transport Regulation 561, and discuss the reaches and limits of a version of discrete time stopwatch automata.