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

Mateusz Zajac, Tomislav Rožić, Justyna Swieboda-Kutera et al.

<i>Background</i>: The increasing complexity of global supply chains and environmental expectations has highlighted the strategic importance of digital transformation in the transport, forwarding, and logistics (TFL) sector. Despite a growing portfolio of available tools, adoption rates—particularly among small and medium-sized enterprises (SMEs) in Central and Eastern Europe—remain low. This study investigates the barriers and motivations related to the implementation of digital planning tools supporting intermodal transport planning. <i>Methods</i>: A structured online survey was conducted among 80 Polish TFL enterprises, targeting decision-makers responsible for operational and digital strategies. The questionnaire included 17 closed and semi-open questions grouped into three thematic sections: tool usage, implementation barriers, and digital readiness. <i>Results</i>: The findings indicate that only 20% of respondents use dedicated route planning tools, and merely 10% report satisfaction with their performance. Key barriers include lack of awareness, organizational inertia, and the prioritization of other initiatives, with financial cost cited less frequently. While environmental sustainability is declared as a priority by most enterprises, digital support for emission tracking is limited. The results highlight the need for targeted education, integration support, and differentiated platform functionalities for SMEs and larger firms. <i>Conclusions</i>: This study offers evidence-based recommendations for developers, policymakers, and logistics managers aiming to accelerate digital adoption in the intermodal logistics landscape.

Zahra Shakeri, Mohammad Matin Miladi qomi

Chatbots are a modern manifestation of artificial intelligence that are now placed for public interaction and conversation with individuals. They can engage in a two-way interaction with a user, responding to their questions and even offering suggestions. This system is based on the capabilities arising from the development of artificial intelligence and is continuously advancing. Among these, the legal issue concerns whether chatbots are responsible for what they respondand whether what they provide is their intellectual property. Essentially, can personalities be attributed to chatbots? The present article finally concludes, with the analytical-descriptive approach, that chatbots may potentially possess a degree of personality in the future, but in current circumstances, the effects and content provided by chatbots are attributed to their owners and creators. Although their development and the reinforcement of chatbots' autonomy can pose numerous challenges to classical legal theories.

H. A. Elhamy, A. B. Eltawil

<i>Background</i>: Defining the optimal fleet portfolio is a crucial process in airline planning. The published efforts in literature provide ways to anticipate the disruption effects on the passenger demand; however, the proposed solution in this paper provides visibility on the impact of sustainable disruption and the way an airline can resist it. <i>Methods</i>: This paper proposes a two-stage methodology to find the best portfolio for airline operational requirements under the impact of disruption. The first stage considers optimization for normal airline operations under a specific fleet portfolio using an Integer Linear Programming (ILP) model. The second stage of the analysis is a mapping for the scenario-based methodology to find a way out for an airline subjected to some given disruption in operations. <i>Results</i>: The result of the two-stage analysis shall define the best fleet portfolio to withstand sustained disruptions by mapping the results in a disruption funnel and showing the impact of the supply and demand gap on the airline’s sustainable profitability. <i>Conclusions</i>: This paper provides a novel, practical way of evaluating strategic decisions to choose the best fleet portfolio and make airlines rely on the mapping of the disruption funnel to modify their network while increasing supply chain resilience.

Andrew Balthrop, Justin T. Kistler, Yemisi Bolumole et al.

<i>Background:</i> Carbon pricing in the form of fuel taxes is an important tool for abating climate change. This study examines the impact and pass-through of fuel taxes in the truckload freight market. <i>Methods:</i> State-level truckload market data, integrated with retail diesel prices, are analyzed using fixed-effects regression modeling. <i>Results:</i> Taxes and fuel costs are not only passed on by diesel retailers to motor carriers; the results reveal the overshifting of diesel taxes from motor carriers to shippers. <i>Conclusions:</i> The findings are consistent with inelastic short-term demand for long-haul carriage, indicating that relatively large price increases will be necessary to reduce diesel consumption in the trucking industry.

William Machado Emiliano, Thalyta Cristina Mansano Schlosser, Vitor Eduardo Molina Júnior et al.

<i>Background</i>: This study aims to compare the logistical challenges of Home Health Care (HHC) services in Portugal and Brazil, highlighting the structural and operational differences between both systems. <i>Methods</i>: Guided by an abductive research approach, data were collected using a semi-structured survey with open-ended questions, applied to 13 HHC teams in Portugal and 18 in Brazil, selected based on national coordination recommendations. The data collection process was conducted in person, and responses were analyzed using descriptive statistics and qualitative content analysis. <i>Results</i>: The results reveal that Portugal demonstrates higher productivity, stronger territorial coverage, and a more integrated inventory management system, while Brazil presents greater multidisciplinary team integration, more flexible fleet logistics, and more advanced digital health records. Despite these strengths, both countries continue to address key logistical aspects, such as scheduling, supply distribution, and data management, largely through empirical strategies. <i>Conclusions</i>: This research contributes to the theoretical understanding of international HHC logistics by emphasizing strategic and systemic aspects often overlooked in operational studies. In practical terms, it offers insights for public health managers to improve resource allocation, fleet coordination, and digital integration in aging societies.

Mari Ashiga, Vardan Voskanyan, Fateme Dinmohammadi et al.

Recent advancements in Large Language Models (LLMs) for code optimization have enabled industrial platforms to automate software performance engineering at unprecedented scale and speed. Yet, organizations in regulated industries face strict constraints on which LLMs they can use - many cannot utilize commercial models due to data privacy regulations and compliance requirements, creating a significant challenge for achieving high-quality code optimization while maintaining cost-effectiveness. We address this by implementing a Mixture-of-Agents (MoA) approach that directly synthesizes code from multiple specialized LLMs, comparing it against TurinTech AI's vanilla Genetic Algorithm (GA)-based ensemble system and individual LLM optimizers using real-world industrial codebases. Our key contributions include: (1) First MoA application to industrial code optimization using real-world codebases; (2) Empirical evidence that MoA excels with open-source models, achieving 14.3% to 22.2% cost savings and 28.6% to 32.2% faster optimization times for regulated environments; (3) Deployment guidelines demonstrating GA's advantage with commercial models while both ensembles outperform individual LLMs; and (4) Real-world validation across 50 code snippets and seven LLM combinations, generating over 8,700 variants, addresses gaps in industrial LLM ensemble evaluation. This provides actionable guidance for organizations balancing regulatory compliance with optimization performance in production environments.

Georg Rottenwalter, Marcel Tilly, Victor Owolabi

Machine learning is an essential tool for optimizing industrial quality control processes. However, the complexity of machine learning models often limits their practical applicability due to a lack of interpretability. Additionally, many industrial machines lack comprehensive sensor technology, making data acquisition incomplete and challenging. Explainable Artificial Intelligence offers a solution by providing insights into model decision-making and identifying the most relevant features for classification. In this paper, we investigate the impact of feature reduction using XAI techniques on the quality classification of injection-molded parts. We apply SHAP, Grad-CAM, and LIME to analyze feature importance in a Long Short-Term Memory model trained on real production data. By reducing the original 19 input features to 9 and 6, we evaluate the trade-off between model accuracy, inference speed, and interpretability. Our results show that reducing features can improve generalization while maintaining high classification performance, with an small increase in inference speed. This approach enhances the feasibility of AI-driven quality control, particularly for industrial settings with limited sensor capabilities, and paves the way for more efficient and interpretable machine learning applications in manufacturing.

Cary Coglianese, Colton R. Crum

Foundation models and generative artificial intelligence (AI) exacerbate a core regulatory challenge associated with AI: its heterogeneity. By their very nature, foundation models and generative AI can perform multiple functions for their users, thus presenting a vast array of different risks. This multifunctionality means that prescriptive, one-size-fits-all regulation will not be a viable option. Even performance standards and ex post liability - regulatory approaches that usually afford flexibility - are unlikely to be strong candidates for responding to multifunctional AI's risks, given challenges in monitoring and enforcement. Regulators will do well instead to promote proactive risk management on the part of developers and users by using management-based regulation, an approach that has proven effective in other contexts of heterogeneity. Regulators will also need to maintain ongoing vigilance and agility. More than in other contexts, regulators of multifunctional AI will need sufficient resources, top human talent and leadership, and organizational cultures committed to regulatory excellence.

Avinash Agarwal, Manisha J. Nene

The integration of artificial intelligence (AI) into telecommunications infrastructure introduces novel risks, such as algorithmic bias and unpredictable system behavior, that fall outside the scope of traditional cybersecurity and data protection frameworks. This paper introduces a precise definition and a detailed typology of telecommunications AI incidents, establishing them as a distinct category of risk that extends beyond conventional cybersecurity and data protection breaches. It argues for their recognition as a distinct regulatory concern. Using India as a case study for jurisdictions that lack a horizontal AI law, the paper analyzes the country's key digital regulations. The analysis reveals that India's existing legal instruments, including the Telecommunications Act, 2023, the CERT-In Rules, and the Digital Personal Data Protection Act, 2023, focus on cybersecurity and data breaches, creating a significant regulatory gap for AI-specific operational incidents, such as performance degradation and algorithmic bias. The paper also examines structural barriers to disclosure and the limitations of existing AI incident repositories. Based on these findings, the paper proposes targeted policy recommendations centered on integrating AI incident reporting into India's existing telecom governance. Key proposals include mandating reporting for high-risk AI failures, designating an existing government body as a nodal agency to manage incident data, and developing standardized reporting frameworks. These recommendations aim to enhance regulatory clarity and strengthen long-term resilience, offering a pragmatic and replicable blueprint for other nations seeking to govern AI risks within their existing sectoral frameworks.

Rogério Gesta Leal

O presente trabalho objetiva abordar os cenários e riscos inerentes à segurança pública e à inteligência artificial, assim como iniciativas para controlá-los. Para tanto, elegemos como objetivos específicos: (i) demarcar as relações entre sociedade do conhecimento e sociedade da vigilância; (ii) as reações institucionais e políticas à sociedade de vigilância; (iii) propor premissas viabilizadoras de politicas de segurança pública democráticas para o uso de novas tecnologias com o uso de IA no âmbito da segurança pública. Pretendemos utilizar neste trabalho o método dedutivo, testando nossas hipóteses com os fundamentos que passam a ser declinados. Utilizaremos para tanto técnica de pesquisa com documentação indireta, nomeadamente bibliográfica.

Oscar Expósito-López

Technological advancements in the field of artificial intelligence are increasingly bringing us closer to the possibility of incorporating automation in administrative decision-making. This tool would yield significant benefits in key areas such as efficiency and improvement of public services. However, it also poses risks, such as the potential loss of empathy that public workers contribute to decision-making and even the displacement of administrative personnel engaged in the processing of files. This study aims to delve into the aspects where the implementation of automated administration would be feasible, distinguishing between rule-based and discretionary decisions. Administrative mediation and artificial intelligence have distinct but teleologically complementary scopes of applicability within these powers. Consequently, we will explore how the role of the administrative mediator can represent a new administrative employment opportunity resulting from these new technological advancements, in search of the empathy and humanity compromised by the purely objective and amoral actions of any form of artificial intelligence.

Muhammad Yasir Siddiqui, Alam Gir

The e-commerce systems are being tackled from commerce behavior and internet technologies. Therefore, trust aspect between buyer-seller transactions is a potential element which needs to be addressed in competitive e-commerce industry. The e-commerce industry is currently handling two different trust approaches. First approach consists on centralized mechanism where digital credentials/set of rules assembled, called Policy based trust mechanisms . Second approach consists on decentralized trust mechanisms where reputation, points assembled and shared, called Reputation based trust mechanisms. The difference between reputation and policy based trust mechanism will be analyzed and recommendations would be proposed to increase trust between buyer and seller in e-commerce industry. The integration of trust mechanism is proposed through mapping process, strength of one mechanism with the weakness of other. The proposed model for integrated mechanism will be presented and illustrated how the proposed model will be used in real world e-commerce industry.

Eric Guiffo Kaigom

As a digital environment of interconnected virtual ecosystems driven by measured and synthesized data, the Metaverse has so far been mostly considered from its gaming perspective that closely aligns with online edutainment. Although it is still in its infancy and more research as well as standardization efforts remain to be done, the Metaverse could provide considerable advantages for smart robotized applications in the industry.Workflow efficiency, collective decision enrichment even for executives, as well as a natural, resilient, and sustainable robotized assistance for the workforce are potential advantages. Hence, the Metaverse could consolidate the connection between Industry 4.0 and Industry 5.0. This paper identifies and puts forward potential advantages of the Metaverse for robotized applications and highlights how these advantages support goals pursued by the Industry 4.0 and Industry 5.0 visions. Keywords: Robotics, Metaverse, Digital Twin, VR/AR, AI/ML, Foundation Model;

Adrienne Heritier

Purpose – This paper aims to conceptualize and empirically illustrate the challenges that financial market regulation presents to politicians and the organization tasked with specifying regulations and supervising their implementation in the interest of users and consumers of financial instruments. It analyses the problem from the viewpoint of the governor's dilemma and the control/competence conflict, the linked problem of the rent-seeking of agents/intermediators and consumers of financial instruments. Political accountability problems are enhanced by the materiality of the technologies used, i.e. algo trading. Design/methodology/approach – The paper theoretically conceptualizes and empirically illustrates the argument. Findings – The paper finds that regulators of digitalized financial markets are faced with considerable problems and depend on private agents when regulating financial transactions. However, the new technological instruments also offer new possibilities for securing compliance. Research limitations/implications – Further research should focus more in-depth on the cooperation between public and private actors in the specification and implementation of regulatory details. It should further investigate the conditions which allow regulators to use RegTech in the surveillance of financial firms. Practical implications – Since financial market transactions are opaque for most users, the creation of more transparency is crucial to hold regulators accountable in their activity of surveillance of financial firms. New algorithm-based technologies may lend important support in doing so. Originality/value – By linking the different analytical perspectives, i.e. the governor's dilemma vis-à-vis the intermediator or agent and the possible rent-seeking of intermediators, under the condition of a highly developed technology of financial transactions as well as the market structure, the paper offers new insights into the limits as well as new opportunities of regulating financial markets allowing for political accountability of regulators and financial firms.

Slimani Ibtissam, Zaarane Abdelmoghit, Atouf Issam

In this work, a day and night time vehicle detection system for traffic surveillance is proposed. Our system is composed of two main processes, day time and night time processes. In the night time, the vehicles are detected based on their taillights and headlights. First of all, the 2D-DWT (Two Dimensional Discrete Wavelet Transform) and the background subtraction are applied to the input image. Then, the connected component technique is used to extract the region of interest. If it is the daytime, the connected component candidates are taken as potential vehicles after applying a pre-processing algorithm to improve the result. If it is the night-time, a filtering operation is used to keep only the bright white and red connected component candidates (which represent potential headlights and taillights, respectively). Finally, potential lamp sets are formed by grouping the extracted components on the basis of their positions, sizes, and colours. The potential extracted vehicles are classified as a vehicle or non-vehicle by using a pre-trained CNN (Convolutional Neural Network) classifier. The proposed system was tested and evaluated using different works from the literature. The experiments show that our proposed system has reached a high accuracy in terms of vehicle detection process whether in day or night time. The experiments were performed using four different videos and were implemented using the C++ language, which facilitates mathematical computation, and its OpenCV library, which is used to run the image processing algorithms used, as well as the TensorFlow library, which facilitates transfer learning of pre-trained CNN models.

Aritra Mandal, Daniel Tunkelang, Zhe Wu

Search query variation poses a challenge in e-commerce search, as equivalent search intents can be expressed through different queries with surface-level differences. This paper introduces a framework to recognize and leverage query equivalence to enhance searcher and business outcomes. The proposed approach addresses three key problems: mapping queries to vector representations of search intent, identifying nearest neighbor queries expressing equivalent or similar intent, and optimizing for user or business objectives. The framework utilizes both surface similarity and behavioral similarity to determine query equivalence. Surface similarity involves canonicalizing queries based on word inflection, word order, compounding, and noise words. Behavioral similarity leverages historical search behavior to generate vector representations of query intent. An offline process is used to train a sentence similarity model, while an online nearest neighbor approach supports processing of unseen queries. Experimental evaluations demonstrate the effectiveness of the proposed approach, outperforming popular sentence transformer models and achieving a Pearson correlation of 0.85 for query similarity. The results highlight the potential of leveraging historical behavior data and training models to recognize and utilize query equivalence in e-commerce search, leading to improved user experiences and business outcomes. Further advancements and benchmark datasets are encouraged to facilitate the development of solutions for this critical problem in the e-commerce domain.

Binoy Debnath, Md Shihab Shakur, Fahmida Tanjum et al.

<i>Background:</i> Additive manufacturing (AM) applications in producing spare parts are increasing day by day. AM is bridging the digital and physical world as a 3D computer-aided manufacturing (CAM) method. The usage of AM has made the supply chain of the aviation spare parts industry simpler, more effective, and efficient. <i>Methods:</i> This paper demonstrates the impacts of AM on the supply chain of the aircraft spare parts industry following a systematic literature review. Hence, centralized and decentralized structures of AM supply chains have been evaluated. Additionally, the attention has been oriented towards the supply chain with AM technologies and industry 4.0, which can support maintenance tasks and the production of spare parts in the aerospace industry. <i>Results:</i> This review article summarizes the interconnection of the industry findings on spare parts. It evaluates the potentiality and capability of AM in conceptualizing the overall supply chain. Moreover, MROs can adopt the proposed framework technologies to assist decision-makers in deciding whether the logistics hub with AM facilities is centralized or decentralized. <i>Conclusions:</i> Finally, this review provides an overall view to make critical decisions on the supply chain design of spare parts driven by new and disruptive technologies of industry 4.0. The next-generation supply chain may replace the logistics barriers by reducing waste and improving capability and sustainability by implementing AM technologies.

Licheng Yu, Jun Chen, Animesh Sinha et al.

We introduce CommerceMM - a multimodal model capable of providing a diverse and granular understanding of commerce topics associated to the given piece of content (image, text, image+text), and having the capability to generalize to a wide range of tasks, including Multimodal Categorization, Image-Text Retrieval, Query-to-Product Retrieval, Image-to-Product Retrieval, etc. We follow the pre-training + fine-tuning training regime and present 5 effective pre-training tasks on image-text pairs. To embrace more common and diverse commerce data with text-to-multimodal, image-to-multimodal, and multimodal-to-multimodal mapping, we propose another 9 novel cross-modal and cross-pair retrieval tasks, called Omni-Retrieval pre-training. The pre-training is conducted in an efficient manner with only two forward/backward updates for the combined 14 tasks. Extensive experiments and analysis show the effectiveness of each task. When combining all pre-training tasks, our model achieves state-of-the-art performance on 7 commerce-related downstream tasks after fine-tuning. Additionally, we propose a novel approach of modality randomization to dynamically adjust our model under different efficiency constraints.

Cássio Castro Souza, Justo Reyna

The Brazilian Public Administration is a repeat player and, often, predatory and strategic player. The behavior of the Public Administration is oriented towards the litigation and contributes to the increase in the congestion rate of the Judiciary, limiting access to justice. In this article, it was reflected whether a more adequate choice architecture could make the Public Administration start to show a more consensual and less litigious behavior. It was found that an architecture of choices appropriate to the greater promotion of access to Justice must create an administrative consensus by default, implemented based on an online dispute resolution system that presents an architecture of choices that makes the standard choice of individuals who wish to resolve a conflict with the Public Administration is self-composition.

Anselm Busse, Benno Gerlach, Joel Cedric Lengeling et al.

Both modern multi- and intermodal supply chains pose a significant challenge to control and maintain while offering numerous optimization potential. Digital Twins have been proposed to improve supply chains. However, as of today, they are only used for certain parts of the entire supply chain. This paper presents an initial framework for a holistic Digital Supply Chain Twin (DSCT) capable of including an entire multimodal supply chain. Such a DSCT promises to enable several improvements all across the supply chain while also be capable of simulating and evaluate several different scenarios for the supply chain. Therefore, the DSCT will not only be able to optimize multi- and intermodal supply chains but also makes them potentially more robust by identifying possible issues early on. This paper discusses the major requirements that such a DSCT must fulfil to be useful and how several information technologies that matured in recent years or are about the mature are the key enablers to fulfil these requirements. Finally, a suggested high-level architecture for such a DSCT is presented as a first step towards the realization of a DSCT, as presented in this work