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

Sahibpreet Singh

The study investigates the juridico-technological architecture of international public health instruments, focusing on their implementation across India, the European Union, the United States and low- and middle-income countries (LMICs), particularly in Sub-Saharan Africa. It addresses a research lacuna: the insufficient harmonisation between normative health law and algorithmic public health infrastructures in resource-constrained jurisdictions. The principal objective is to assess how artificial intelligence augments implementation of instruments grounded in IHR 2005 and the WHO FCTC while identifying doctrinal and infrastructural bottlenecks. Using comparative doctrinal analysis and legal-normative mapping, the study triangulates legislative instruments, WHO monitoring frameworks, AI systems including BlueDot, Aarogya Setu and EIOS, and compliance metrics. Preliminary results show that AI has improved early detection, surveillance precision and responsiveness in high-capacity jurisdictions, whereas LMICs face infrastructural deficits, data privacy gaps and fragmented legal scaffolding. The findings highlight the relevance of the EU Artificial Intelligence Act and GDPR as regulatory prototypes for health-oriented algorithmic governance and contrast them with embryonic AI integration and limited internet penetration in many LMICs. The study argues for embedding AI within a rights-compliant, supranationally coordinated regulatory framework to secure equitable health outcomes and stronger compliance. It proposes a model for algorithmic treaty-making inspired by FCTC architecture and calls for WHO-led compliance mechanisms modelled on the WTO Dispute Settlement Body to enhance pandemic preparedness, surveillance equity and transnational governance resilience.

Mehadji Djamil Abdelhak, Kaddour Mejdi

This paper addresses the problem of determining bandwidth allocation and traffic routes in fixed microwave networks such that overall bandwidth cost is minimized while traffic demands are satisfied with a required reliability level. These networks exhibit high variability in link throughput as modulations schemes are adapted dynamically to ensure acceptable bit-error rate at the receivers according to external conditions such as the weather. First, we formulate an optimal optimization approach based on mixed-integer linear programming, which is subsequently reinforced by inserting problem-specific valid inequalities based on global network capacity to reduce the search space and eliminate the unfeasible bandwidth/modulation combinations, thus reducing the number of decision variables. Then, we introduce a Lagrangian-based heuristic that provides near optimal solutions while reducing drastically the computation time. In comparison to previous work, our experimental results show that our approaches are capable to solve large real-world network instances in an effective manner. Furthermore, the results evaluate the impact of reliability and transported traffic demands on bandwidth cost.

Siyavash Filom, Satrya Dewantara, Mahnam Saeednia et al.

<i>Background</i>: Disruptions in freight transportation—such as service delays, infrastructure failures, and labor strikes—pose significant challenges to the reliability and efficiency of intermodal networks. To address these challenges, this study introduces Adaptive Intermodal Transportation (AIT), a resilient and flexible planning framework that enhances Synchromodal Freight Transport (SFT) by integrating real-time disruption management. <i>Methods</i>: Building on recent advances, we propose two novel strategies: (1) Reassign with Delay Buffer, which enables dynamic rerouting of shipments within a user-defined delay tolerance, and (2) (De)Consolidation, which allows splitting or merging of shipments across services depending on available capacity. These strategies are incorporated into a re-planning module that complements a baseline optimization model and a continuous disruption-monitoring system. Numerical experiments conducted on a Great Lakes-based case study evaluate the performance of the proposed strategies against a benchmark approach. <i>Results</i>: Results show that under moderate and high-disruption conditions, the proposed strategies reduce delay and disruption-incurred costs while increasing the percentage of matched shipments. The Reassign with Delay Buffer strategy offers controlled flexibility, while (De)Consolidation improves resource utilization in constrained environments. <i>Conclusions</i>: Overall, the AIT framework demonstrates strong potential for improving operational resilience in intermodal freight systems by enabling adaptive, disruption-aware planning decisions.

Zsolt Toth, Alexandru-Silviu Goga, Mircea Boșcoianu

<i>Background</i>: Despite significant technological advances, many logistics organizations in emerging markets struggle to realize the transformative potential of artificial intelligence, with reported success rates below 65% and limited theoretical understanding of the organizational capabilities. This study develops and proposes an integrated theoretical framework examining how knowledge process capabilities and dynamic capabilities interact to enable successful artificial intelligence adoption in logistics organizations within emerging market contexts. <i>Methods</i>: Through comprehensive literature review and theoretical synthesis, we propose a hybrid capability framework that integrates knowledge-based view perspectives with dynamic capabilities theory. <i>Results</i>: Theoretical analysis suggests that knowledge combination capabilities may be the strongest predictor of artificial intelligence implementation success, while dynamic reconfiguring capabilities could mediate the relationship between artificial intelligence adoption and performance outcomes. The proposed framework indicates that organizations with hybrid capability architecture may achieve superior implementation success compared to traditional approaches. Environmental uncertainty is theorized to strengthen the knowledge process capabilities—artificial intelligence adoption relationship. <i>Conclusions</i>: The framework suggests that successful artificial intelligence integration requires simultaneous development of knowledge-based and adaptive capabilities rather than sequential capability building. The hybrid capability framework provides theoretical guidance for managers in emerging markets, while highlighting the critical role of environmental context in shaping transformation strategies.

Raiyn Jamal

Livable cities involve such quality-of-life factors as transportation, convenience of daily life, education, and a safe and stable built and natural environment. The livability of a city also has social and psychological dimensions, such as emotion and perception. This paper explores how the advantages of new transportation technology can be realized under mixed-traffic conditions, while meeting the requirements for safety, convenience, and high educational standards. One of the key challenges to realizing the advantages of a new technology, such as autonomous shuttle buses, under mixed-traffic conditions and under adverse weather conditions is how to strike a balance between innovation and adherence to essential urban demands. Ensuring that the introduction of advanced technologies like autonomous shuttle buses does not compromise safety is of the utmost importance. Under mixed-traffic conditions, autonomous shuttle buses must negotiate interactions with human-driven vehicles, pedestrians, and bicyclists. Predictable and safe driving behavior is vital to gaining the confidence and acceptance of all road users. Additionally, adopting vehicle-to-everything (V2X) communication enables real-time information exchange with other vehicles and infrastructure, which enhance safety. Autonomous shuttle buses also face crucial challenges in adverse weather conditions. Robust sensor systems are required to accurately perceive the environment despite rain, snow, fog, and other weather-related constraints. Continual learning and software updates ensure the system’s ability to adapt to changing weather conditions. To overcome these challenges, collaboration with regulatory bodies and traffic management authorities is essential. Furthermore, public awareness campaigns and educational initiatives can inform the public about the safety measures and benefits of autonomous shuttle buses, fostering acceptance and reducing concerns during the transition.

Xuanyu Chen, Nan Yang, Shuai Wang et al.

The recent success of large language models (LLMs) has sparked a growing interest in training large-scale models. As the model size continues to scale, concerns are growing about the depletion of high-quality, well-curated training data. This has led practitioners to explore training approaches like Federated Learning (FL), which can leverage the abundant data on edge devices while maintaining privacy. However, the decentralization of training datasets in FL introduces challenges to scaling large models, a topic that remains under-explored. This paper fills this gap and provides qualitative insights on generalizing the previous model scaling experience to federated learning scenarios. Specifically, we derive a PAC-Bayes (Probably Approximately Correct Bayesian) upper bound for the generalization error of models trained with stochastic algorithms in federated settings and quantify the impact of distributed training data on the optimal model size by finding the analytic solution of model size that minimizes this bound. Our theoretical results demonstrate that the optimal model size has a negative power law relationship with the number of clients if the total training compute is unchanged. Besides, we also find that switching to FL with the same training compute will inevitably reduce the upper bound of generalization performance that the model can achieve through training, and that estimating the optimal model size in federated scenarios should depend on the average training compute across clients. Furthermore, we also empirically validate the correctness of our results with extensive training runs on different models, network settings, and datasets.

Maobin Lu, Martin Guay, Telema Harry et al.

This paper investigates the robust output regulation problem of second-order nonlinear uncertain systems with an unknown exosystem. Instead of the adaptive control approach, this paper resorts to a robust control methodology to solve the problem and thus avoid the bursting phenomenon. In particular, this paper constructs generic internal models for the steady-state state and input variables of the system. By introducing a coordinate transformation, this paper converts the robust output regulation problem into a nonadaptive stabilization problem of an augmented system composed of the second-order nonlinear uncertain system and the generic internal models. Then, we design the stabilization control law and construct a strict Lyapunov function that guarantees the robustness with respect to unmodeled disturbances. The analysis shows that the output zeroing manifold of the augmented system can be made attractive by the proposed nonadaptive control law, which solves the robust output regulation problem. Finally, we demonstrate the effectiveness of the proposed nonadaptive internal model approach by its application to the control of the Duffing system.

Chengqing Li, Junjie Zhou

We study price regulation for a monopolist operating in networked markets with demand spillovers. Achieving efficiency requires price reductions proportional to consumers' Katz-Bonacich centralities, which generally cannot be implemented by commonly used price regulations. Moreover, these regulations become asymptotically welfare neutral as spillovers grow. Nevertheless, some price regulations may still benefit consumers. In particular, average-price regulation robustly increases consumer surplus. By contrast, banning price discrimination increases consumer surplus only when more central consumers have higher intrinsic willingness to pay.

Mohammad Hosein Vakili Moghadam

The expansion of technology and innovative methods in financial services has created opportunities and challenges that make its correct legal planning a necessary structure. The diversity of financial services and products, the speed of technological developments and the unknown processes used in the field of financial technologies are the reasons for the inactivity or slowness of legal systems. This is despite the fact that financial technologies, as a global phenomenon, by ignoring legal and financial restrictions, have taken the citizens' financial relationships beyond the traditional legal formats and the limits of the political borders of countries without the need for governmental infrastructures, or the approvals of the central institutions. They all make a new financial ecosystem in contemporary societies. The current research seeks to explain and evaluate the existing approaches in dealing with this complex financial structure using an analytical-comparative study. It then puts forward suggestions that can be proposed in Iran's legal and financial system. The research results show that the effectiveness of regulatory methods depends on minimal intervention and recognition of the independence of financial technology standards. Therefore, having the advantage from the benefits of financial technologies and managing the risks requires a review of the related criteria at both the content and form (instrumental) levels.

Francisca Santana-Robles, Eva Selene Hernández-Gress, Ricardo Martínez-López et al.

<i>Background</i>: In the context of humanitarian logistics, efficiently evacuating people from disaster-stricken areas is a complex challenge. This study focuses on the Tula River region in Hidalgo, Mexico, exploring the evacuation and support of individuals in temporary shelters. Despite the fact that the topic has been addressed in the literature, it is necessary to have quick response methods that can be used by decision-makers to adapt and utilize existing spaces as temporary shelters, in addition to knowing how to evacuate people. <i>Methods</i>: Addressing this void, a methodology to minimize evacuation and aid distribution costs is introduced. Leveraging existing algorithms, particularly Integer Linear Programming, the model determines shelter activation and utilizes the Vehicle Routing Problem to assess aid delivery strategies. <i>Results</i>: The research identifies optimal evacuation routes from 13 affected areas to 34 shelters and analyzes aid distribution costs under various demand scenarios: original, increased, and decreased by 10%, based on the number of transport units allocated and Google Maps distances. It also evaluates the costs associated with humanitarian aid distribution under varying collection strategies, involving state and municipal governments. <i>Conclusion</i>: This approach provides a decision-making foundation and can be adapted for similar analyses in other communities during extreme events.

Pyatin Ilya, Boiko Juliy, Eromenko Oleksander

The article explores a recursive algorithm for determining Bhattacharyya parameters, which is a measure of the degree of channel polarization for telecommunications with polar coding. A method is given for determining the positions of information and fixed bits in a polar code. The principles of constructing a polar encoder and a polar decoder successive cancellation are considered. The Field-Programmable Gate Array implementation of the successive cancellation list decoder is considered. Experimental studies of the construction of a polar code using Gaussian approximation have been carried out. The influence of the design signal-to-noise ratio on the bit error rate of telecommunications with polar codes has been studied. The use of multi-position modulation in telecommunications with polar codes has been studied. It is expected that the results obtained will be useful in hardware and software implementation of 5G telecommunications with polar coding.

Danilo Abril, Carlos D. Paternina-Arboleda, Jesus Velasquez-Bermudez

The ongoing issues in global supply chain disruptions have raised many concerns of port productivity, among which port congestion is a key issue. This article implements an integrated tactical–operational optimization framework which raises the capabilities of port information systems to deliver smarter decision-making processes in ports through a decision support system. To this end, we developed a library of multiple smart models for the optimization of port operations, independently engaged in parallel but mathematically coordinated to achieve autonomous real-time distributed optimization, using a novel event-driven structure to enable future implementations using digital twins. The framework was tested to benchmark different commercial solvers on several real instances for the port under study. The results show a strong improvement in port operational planning.

Susan Athey, Herman Brunborg, Tianyu Du et al.

This paper builds an empirical model that predicts a worker's next occupation as a function of the worker's occupational history. Because histories are sequences of occupations, the covariate space is high-dimensional, and further, the outcome (the next occupation) is a discrete choice that can take on many values. To estimate the parameters of the model, we leverage an approach from generative artificial intelligence. Estimation begins from a ``foundation model'' trained on non-representative data and then ``fine-tunes'' the estimation using data about careers from a representative survey. We convert tabular data from the survey into text files that resemble resumes and fine-tune the parameters of the foundation model, a large language model (LLM), using these text files with the objective of predicting the next token (word). The resulting fine-tuned LLM is used to calculate estimates of worker transition probabilities. Its predictive performance surpasses all prior models, both for the task of granularly predicting the next occupation as well as for specific tasks such as predicting whether the worker changes occupations or stays in the labor force. We quantify the value of fine-tuning and further show that by adding more career data from a different population, fine-tuning smaller LLMs (fewer parameters) surpasses the performance of fine-tuning larger models. When we omit the English language occupational title and replace it with a unique code, predictive performance declines.

Peter Sasitharan Gandhi Maniam, Catherine Prentice, Anne-Marie Sassenberg et al.

<i>Background</i>: Blockchain adoption in agriculture is facing challenges. Some of its key challenges include lack of regulatory framework and unclear policies. <i>Methods</i>: This quantitative research based on a survey aims to examine the factors that influence blockchain implementation in the agricultural sector. Several theories including the technology acceptance model (TAM), the theory of planned behaviour (TPB), and the technology readiness index (TRI) were drawn upon to determine the factors influencing Blockchain adoption. The study was undertaken in Australia with 358 agricultural supply chain experts. <i>Results</i>: A range of novel findings were generated. While perceived usefulness, perceived ease of use, and attitude positively influenced Blockchain adoption, discomfort and insecurity were considered deterrents. <i>Conclusions</i>: The results can be used by relevant practitioners to improve the supply chain management for agriculture business. The findings can also inform a new direction for the research on the agricultural supply chain and the literature on logistics.

Barzilai Orly, Rika Havana, Voloch Nadav et al.

Traffic lights monitoring that considers only traffic volumes is not necessarily the optimal way to time the green/red allocation in a junction. A “smart” allocation should also consider the necessities of the vehicle’s passengers and the needs of the people those passengers ought to serve.

Sheila Stela Matusse, Xi Xi, Isaque Manteiga Joaquim

Purpose – The purpose of the present paper was to explore the best practices of destination management in promoting tourist destinations through the Mozambique government website (INATUR) and identify strategies that enhance its visibility and online presence. This was only possible by (1) exploring if people are aware of the government website’s existence; (2) examining the existence of indicators of the engagement behaviors for the web-users (visitors) in their searching process on the government website; (3) exploring if the engagement behavior and website features have influence on the government website visitors’ satisfaction and (4) providing measures to enhance the popularity of the government website at INATUR. Design/methodology/approach – The study combined a qualitative and quantitative methodological approach from the primary data collected via an online questionnaire survey of 269 random respondents, and the selected data was analyzed and processed using Stata 13 with the descriptive statistic and ANOVA [Analysis of Variance (an econometric model)] technique. The data was collected from secondary sources and from the interview, a strengths, weaknesses, opportunities, and threats (SWOT) analysis was applied with an interpretive approach. Findings – The government website presents the minimum of relevant information to respond to the users’ needs and expectations. There is little knowledge regarding the existence of the government website for tourism destination marketing. Few respondents were surprised about this website’s existence. The optimistic side of responses came from those peple who used the website and it helped their expectation. The correlation analysis showed a significant positive relationship between the government website features and the visitors’ searching satisfaction; the interview outputs noted that the shortage of staff at INATUR with knowledge of digital marketing engagement plays a role in solving the problem of the visibility and online presence of the website. Research limitations/implications – One of the apparent limitations of this research was the world pandemic situation (Covid-19), which influenced to make abrupt arrangements in conducting the questionnaire survey and interview compared to the planned schedule. The interview was supposed to be a field research to have direct contact with her respondents and collect nonverbal information through the respondents’ body language, but unfortunately, it was not possible. Improvising was one of the solutions and had to design an online questionnaire survey for national and international tourist respondents and an emailed interview with INATUR director. Because of that, the results showed a very significant gap between African nationals and international respondents in number of 264 and 5, respectively (about in 98,14%) caused by the lockdown and traveling limitation. Practical implications – The adoption of the contents in “Recommendations for policy and decision-making” can help in synergizing an integrative marketing communication strategy that enables all actors to maximize local economic benefits without spending many financial resources, and support sustainability, different tourist destination suppliers, authorities and local communities’ development. Ensuring effective and efficient communication, and above all, enhancing the provision of reliable information. Reinforce the importance of the practical teaching and learning of digital platforms in tourism schools and universities; offer a thematic tool to serve as an analytical basis in future studies, encouraging continuous scientific research on the subject under study. Social implications – Raising the awareness of the government website among tourist consumers; promoting Mozambique as a reference destination and its tourist diversity through the use of the government website; capitalizing tourists’ enterprises for communities’ development; improving the competitiveness of destinations through greater exposure of tourism products and services on the government website boosts the economic gains for the development of the tourism sector in the country. Accessibility to the information channel of Mozambican tourist destinations via the government website, and stimulating the desire to visit; improve and enrich the visitor’s experience quality on the government website in the tourist information consultation. Originality/value – It is the first research in the country about tourism destination marketing using indicators like customers’ behavioral engagement levels based on social interactions such as likes, shares and comments on the government website, as well as its awareness and performance aspects, to analyze if the INATUR government website is being successful on its tourism destination marketer role. The research was also done to bring solutions to the current trends of the Covid-19 pandemic that has affected and disrupted the tourism industry.

Ebrahim Javanmard Farkhani, Hamid Miri , Saleh Yamrali

Artificial cloud fertilisation technology has recently affected various aspects of human life. This technology affects air, water, and land and has potential implications for economic development, production, and the environment. Cloud fertility technology can have both positive and negative effects on the environment. The application of this technology will increase rainfall, develop required water reserves, reduce drought, and improve human health and well-being. However, silver iodide particles and other substances used in cloud fertilisation may have harmful environmental effects. Based on this, this research was conducted with jurisprudence and legal investigation of the environmental aspects of cloud fertility and with a descriptive-analytical method. The research findings show that the reasons for opposing this technology are not real, and this matter's complications and biological risks are more imaginary and caused by a pessimistic attitude. The extraction of water using cloud fertility technology does not pose environmental risks. Therefore, it is possible to use this technology from a jurisprudential and legal point of view.

Rafaella Queiroz Del Rei Conversani, Tais Macêdo de Brito Cunha, Rod Daniel Gomes

O presente trabalho tem por objetivo analisar se o Código de Defesa do Consumidor é aplicável nos contratos administrativos. Para este propósito, são identificados os requisitos que o CDC exige para configuração da relação consumerista, para na sequência analisar se a Administração Pública atende estes requisitos no bojo do contrato administrativo, analisando-se em quais situações a Administração Pública é destinatária final para fins de aplicação do CDC, bem assim se os entes públicos podem ser parte vulnerável nos contratos administrativos. A pesquisa é documental e adota abordagem dedutiva, identificando as premissas fixadas na legislação, na doutrina e na jurisprudência, para analisar se a Administração Pública pode ser beneficiada com as prerrogativas que o CDC confere aos consumidores. Sem deixar de reconhecer que o tema é controvertido, o trabalho conclui pela aplicabilidade do CDC nos contratos administrativos, alertando para necessidade da advocacia pública evidenciar a vulnerabilidade do ente público no caso concreto

David Higgins, Christian Johner

Purpose: The introduction of artificial intelligence / machine learning (AI/ML) products to the regulated fields of pharmaceutical research and development (R&D) and drug manufacture, and medical devices (MD) and in-vitro diagnostics (IVD), poses new regulatory problems: a lack of a common terminology and understanding leads to confusion, delays and product failures. Validation as a key step in product development, common to each of these sectors including computerized systems and AI/ML development, offers an opportune point of comparison for aligning people and processes for cross-sectoral product development. Methods: A comparative approach, built upon workshops and a subsequent written sequence of exchanges, summarized in a look-up table suitable for mixed-teams work. Results: 1. A bottom-up, definitions led, approach which leads to a distinction between broad vs narrow validation, and their relationship to regulatory regimes. 2. Common basis introduction to the primary methodologies for AI-containing software validation. 3. Pharmaceutical drug development and MD/IVD specific perspectives on compliant AI software development, as a basis for collaboration. Conclusions: Alignment of the terms and methodologies used in validation of software products containing artificial intelligence / machine learning (AI/ML) components across the regulated industries of human health is a vital first step in streamlining processes and improving workflows.