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

https://ijwr.usc.ac.ir/article_238486_867ac0c2144b34253c56f9f55b38944a.pdf, Mohadese Parvizib, Siamak Mohammadi

Driver drowsiness detection plays a critical role in improving road safety, as drowsiness substantially increases the likelihood of traffic accidents. In this study, we propose a novel multi-modal framework within Advanced Driver Assistance Systems (ADAS) that leverages an Attention-Guided Siamese Network coupled with temporal modeling to accurately capture both spatial and temporal patterns of driver fatigue. The Siamese network processes paired facial images, enabling the extraction of discriminative features that highlight subtle changes in driver state. The attention mechanism is explicitly applied to the spatial feature maps within each branch of the Siamese network, allowing the model to focus selectively on key facial regions—such as eyes and mouth—that are most indicative of drowsiness, while also weighting complementary sensor modalities dynamically. Temporal modeling is incorporated through a sequential module (e.g., LSTM or temporal convolution) that analyzes the extracted features over time, capturing gradual and evolving signs of drowsiness that static frame-based methods often overlook. Extensive evaluations on benchmark datasets (YawDD, NTHUDDD) and a novel real-world driving dataset demonstrate superior accuracy exceeding 98.8%, along with strong cross-subject generalization. Ablation studies confirm the critical contributions of the attention mechanism in improving feature discrimination, and the temporal modeling module in enhancing sensitivity to progressive drowsiness. The proposed method surpasses traditional approaches in temporal awareness, data efficiency, and resilience to inter-subject and environmental variations, offering a robust and interpretable solution for real-time driver drowsiness monitoring in intelligent vehicles.

Anjali Sonkariya, Anjali Awasthi

Ports are the major gateways of cities. <i>Background</i>: Sustainable growth requires ports to prioritize efficiency while balancing economic, social, and environmental goals. There is limited synthesized evidence on the sustainability evaluation of ports, including those of North America. In this paper, we propose a multi-step approach based on fuzzy DEA to evaluate the environmental performance of ports. <i>Methods</i>: In the first step, we identify indicators for environmental performance evaluation. The second step involves application of fuzzy DEA using the identified indicators to measure the environmental efficiency of ports. In the third step, a numerical illustration is provided using open data. The proposed model incorporates undesirable outputs and employs one set of constraints to make a production frontier. <i>Results</i>: The findings show wide differences in performance, ports reach higher scores when they use resources wisely plus keep emissions low, not merely when they expand. <i>Conclusions</i>: The proposed methodology provides a robust and comparable measurement of port environmental efficiency under uncertainty.

Mehul Goenka, Tejas Pathak, Siddharth Asthana

The global economy is entering the era of Agentic Commerce, where autonomous agents can discover services, negotiate prices, and transact value. However adoption towards agentic commerce faces a foundational trust gap: current systems are built for direct human interactions rather than agent-driven operations. It lacks core primitives across three critical stages of agentic transactions. First, Task Delegation lacks means to translate user intent into defined scopes, discover appropriate agents, and securely authorize actions. Second, Payment Settlement for tasks is processed before execution, lacking verifiable evidence to validate the agent's work. Third, Audit Mechanisms fail to capture the full transaction lifecycle, preventing clear accountability for disputes. While emerging standards address fragments of this trust gap, there still remains a critical need for a unified infrastructure that binds the entire transaction lifecycle. To resolve this gap, we introduce TessPay, a unified infrastructure that replaces implicit trust with a 'Verify-then-Pay' architecture. It is a two plane architecture separating control and verification from settlement. TessPay operationalizes trust across four distinct stages: Before execution, agents are anchored in a canonical registry and user intent is captured as verifiable mandates, enabling stakeholder accountability. During execution, funds are locked in escrow while the agent executes the task and generates cryptographic evidence (TLS Notary, TEE etc.) to support Proof of Task Execution (PoTE). At settlement, the system verifies this evidence and releases funds only when the PoTE satisfies verification predicates; modular rail adapters ensure this PoTE-gated escrow remains chain-agnostic across heterogeneous payment rails. After settlement, TessPay preserves a tamper-evident audit trail to enable clear accountability for dispute resolution.

Przemysław Osóbka

Artykuł jest poświęcony ochronie in situ oraz ex situ składników różnorodności biologicznej w warunkach zmian klimatycznych w świetle przepisów Konwencji o różnorodności biologicznej z 5 czerwca 1992 r. W tekście przedstawiono podstawy prawne ochrony różnorodności biologicznej oraz scharakteryzowano cel, strukturę i sposób funkcjonowania konwencji. Poruszono również problem znaczenia obszarów chronionych w ramach ochrony in situ i ex situ składników różnorodności biologicznej zgodnie z art. 8 i art. 9 analizowanej umowy międzynarodowej. Ponadto artykuł traktuje o relacjach między różnorodnością biologiczną a zmianami klimatu.

Amirhesam Taghipour, Jabbar Ali Zakeri, Ali Ghozat et al.

In recent years, collisions between over-height road vehicles and railway bridge superstructures have become increasingly common, especially in urban areas, compromising structural safety and traffic flow. For instance, in Beijing, approximately 50% of bridges are exposed to impact loads, with around 20% of these impacts resulting from truck tops striking bridge superstructures, leading to damage to girders and concrete decks. In Iran, similar incidents have caused severe structural damage to railway bridges, creating uncertainties in bridge serviceability, operational safety, and in some cases, line closures. This study investigates the dynamic behavior of a railway bridge located in the Badrud–Spidan region of Isfahan under the impact of a truck top colliding with its superstructure. A three-dimensional finite element model of the bridge and the impact load was developed using ABAQUS software. The collision force was simulated as a time-dependent impulsive load applied at mid-span. The dynamic responses analyzed include displacement and acceleration time-history diagrams of the main structural elements such as girders and flat concrete slabs. The results indicate that applying honeycomb-structured aluminum sheets along the impact zone on the concrete girders significantly mitigates impact severity. Moreover, using vibration-absorbing materials to dampen responses and reduce damage to the structural components is shown to be highly effective.

Wei Meng

This study addresses the structural complexity and semantic ambiguity in stakeholder interactions within the Education-Industry Integration (EII) system. The scarcity of real interview data, absence of structured variable modeling, and lack of interpretability in inference mechanisms have limited the analytical accuracy and policy responsiveness of EII research. To resolve these challenges, we propose a structural modeling paradigm based on the National Institute of Standards and Technology (NIST) synthetic data quality framework, focusing on consistency, authenticity, and traceability. We design a five-layer architecture that includes prompt-driven synthetic dialogue generation, a structured variable system covering skills, institutional, and emotional dimensions, dependency and causal path modeling, graph-based structure design, and an interactive inference engine. Empirical results demonstrate the effectiveness of the approach using a 15-segment synthetic corpus, with 41,597 tokens, 127 annotated variables, and 820 semantic relationship triples. The model exhibits strong structural consistency (Krippendorff alpha = 0.83), construct validity (RMSEA = 0.048, CFI = 0.93), and semantic alignment (mean cosine similarity > 0.78 via BERT). A key causal loop is identified: system mismatch leads to emotional frustration, reduced participation, skill gaps, and recurrence of mismatch, revealing a structural degradation cycle. This research introduces the first NIST-compliant AI modeling framework for stakeholder systems and provides a foundation for policy simulation, curriculum design, and collaborative strategy modeling.

Orfeas Menis Mastromichalakis, Giorgos Filandrianos, Maria Symeonaki et al.

Machine Translation (MT) systems frequently encounter gender-ambiguous occupational terms, where they must assign gender without explicit contextual cues. While individual translations in such cases may not be inherently biased, systematic patterns-such as consistently translating certain professions with specific genders-can emerge, reflecting and perpetuating societal stereotypes. This ambiguity challenges traditional instance-level single-answer evaluation approaches, as no single gold standard translation exists. To address this, we introduce GRAPE, a probability-based metric designed to evaluate gender bias by analyzing aggregated model responses. Alongside this, we present GAMBIT, a benchmarking dataset in English with gender-ambiguous occupational terms. Using GRAPE, we evaluate several MT systems and examine whether their gendered translations in Greek and French align with or diverge from societal stereotypes, real-world occupational gender distributions, and normative standards

Nathan Lane

I study the impact of industrial policies on industrial development by considering an important episode during the East Asian miracle: South Korea's heavy and chemical industry (HCI) drive, 1973--1979. Based on newly assembled data, I use the introduction and termination of industrial policies to study their impacts during and after the intervention period. (1) I reveal that heavy-chemical industrial policies promoted the expansion and dynamic comparative advantage of directly targeted industries. (2) Using variation in exposure to policies through the input-output network, I demonstrate that the policy indirectly benefited downstream users of targeted intermediates. (3) The benefits of HCI persisted even after the policy ended, as some results were slower to appear. The findings suggest that the temporary drive shifted Korean manufacturing into more advanced markets and supported durable change. This study helps clarify the lessons drawn from the East Asian growth miracle. JEL Codes: L5, O14, O25, N6. Keywords: industrial policy, East Asian miracle, economic history, industrial development, Heavy-Chemical Industry Drive, Heavy and Chemical Industry Drive.

saeid gohari

Preventing crimes related to counterfeit drugs, due to the technologies used in the production and distribution of these drugs, will not have a bright outlook with traditional methods such as field surveillance. Therefore, adopting appropriate preventive measures requires the use of innovative technologies capable of detecting these crimes on a large scale and with high accuracy. In this regard, artificial neural networks such as recurrent neural networks, generative adversarial networks, and convolutional neural networks, inspired by the structure of the human brain, are capable of detecting these crimes. However, each of these networks has its drawbacks, ignoring which makes the legal system face difficulties in preventing these crimes. Therefore, the present study, through a case study method, seeks to identify the most efficient neural network for preventing these crimes. The outcome of this research indicates that the legislature has paid special attention to the monitoring technique in the prevention domain but has not defined the tools for this monitoring. Nevertheless, the Food and Drug Administration, using the Titac system (tracking code), identifies the discovery of crimes in this area. However, due to the non-intelligence of the system, it will not be able to detect all forms of fraud. Therefore, simultaneous use of three networks (recurrent neural networks, generative adversarial networks, and convolutional neural networks) in the form of a composite neural network seems to improve the detection of drug crimes on a large scale.

Nattakorn Pinyanitikorn, Walailak Atthirawong, Wirachchaya Chanpuypetch

<i>Background</i>: The freight forwarding industry is undergoing digital transformation through the implementation of online platforms designed to enhance operational efficiency and transparency. Despite these benefits, the adoption of these platforms has been slower than anticipated due to customer concerns and industry-specific challenges. <i>Methods</i>: This study investigates the factors influencing the intention to adopt and the actual use of online platforms for freight forwarding services among business customers in Thailand. A modified Unified Theory for Acceptance and Use of Technology (UTAUT) model, incorporating perceived risk, serves as the theoretical framework. Survey data were collected from 400 respondents in managerial or higher-level positions involved in freight shipping within Thai firms and analyzed using a structural equation model (SEM). <i>Results</i>: The analysis reveals that performance expectancy, effort expectancy, social influence, and facilitating conditions positively influence adoption intention, while perceived risk negatively impacts it. Firm size moderates the effect of social influence, with a stronger impact observed in larger enterprises. <i>Conclusions</i>: The findings offer practical insights for Thai freight forwarders, suggesting strategies to improve customer acceptance and encourage the adoption of online platforms. Addressing the identified factors could lead to improved efficiency and greater integration of digital technologies in the logistics industry.

Andrew Koh, Sivakorn Sanguanmoo

We analyze how uncertain technologies should be robustly regulated and how regulation should evolve with new information. An adaptive sandbox comprising a zero marginal tax up to an evolving quantity limit is (i) robust: it delivers optimal payoff guarantees when the agent's learning process and/or preferences are chosen adversarially; (ii) dominant: it outperforms other robust and regular mechanisms across all agent learning processes and preferences; (iii) time-consistent: it is the only robust mechanism that can be implemented without commitment. Robustness is important: absent robust regulation, worst-case payoffs can be arbitrarily poor and are induced by weak but growing optimism that encourages excessive risk-taking. Our results offer optimality foundations for existing policy and speak directly to current debates around managing emerging technologies.

Anuschka Schmitt, Krzysztof Z. Gajos, Osnat Mokryn

The adoption of generative Artificial Intelligence (GAI) in organizational settings calls into question workers' roles, and relatedly, the implications for their long-term skill development and domain expertise. In our qualitative study in the software engineering domain, we build on the theoretical lenses of occupational identity and self-determination theory to understand how and why software engineers make sense of GAI for their work. We find that engineers' sense-making is contingent on domain expertise, as juniors and seniors felt their needs for competence, autonomy, and relatedness to be differently impacted by GAI. We shed light on the importance of the individual's role in preserving tacit domain knowledge as engineers engaged in sense-making that protected their occupational identity. We illustrate how organizations play an active role in shaping workers' sense-making process and propose design guidelines on how organizations and system designers can facilitate the impact of technological change on workers' occupational identity.

Claudio Novelli, Philipp Hacker, Jessica Morley et al.

Regulation is nothing without enforcement. This particularly holds for the dynamic field of emerging technologies. Hence, this article has two ambitions. First, it explains how the EU's new Artificial Intelligence Act (AIA) will be implemented and enforced by various institutional bodies, thus clarifying the governance framework of the AIA. Second, it proposes a normative model of governance, providing recommendations to ensure uniform and coordinated execution of the AIA and the fulfilment of the legislation. Taken together, the article explores how the AIA may be implemented by national and EU institutional bodies, encompassing longstanding bodies, such as the European Commission, and those newly established under the AIA, such as the AI Office. It investigates their roles across supranational and national levels, emphasizing how EU regulations influence institutional structures and operations. These regulations may not only directly dictate the structural design of institutions but also indirectly request administrative capacities needed to enforce the AIA.

François Bachoc, Nicolò Cesa-Bianchi, Tommaso Cesari et al.

In online bilateral trade, a platform posts prices to incoming pairs of buyers and sellers that have private valuations for a certain good. If the price is lower than the buyers' valuation and higher than the sellers' valuation, then a trade takes place. Previous work focused on the platform perspective, with the goal of setting prices maximizing the gain from trade (the sum of sellers' and buyers' utilities). Gain from trade is, however, potentially unfair to traders, as they may receive highly uneven shares of the total utility. In this work we enforce fairness by rewarding the platform with the fair gain from trade, defined as the minimum between sellers' and buyers' utilities. After showing that any no-regret learning algorithm designed to maximize the sum of the utilities may fail badly with fair gain from trade, we present our main contribution: a complete characterization of the regret regimes for fair gain from trade when, after each interaction, the platform only learns whether each trader accepted the current price. Specifically, we prove the following regret bounds: $Θ(\ln T)$ in the deterministic setting, $Ω(T)$ in the stochastic setting, and $\tildeΘ(T^{2/3})$ in the stochastic setting when sellers' and buyers' valuations are independent of each other. We conclude by providing tight regret bounds when, after each interaction, the platform is allowed to observe the true traders' valuations.

Ihsan Ullah, Muhammad Khan, Dilshodjon Alidjonovich Rakhmonov et al.

<i>Background:</i> A substantial portion of the world’s population owns and utilizes computers and mobile devices, contributing to the rapid expansion of digital advertising. Marketers swiftly recognized the communicative benefits of social media platforms like Facebook, YouTube, Twitter, Instagram, Snapchat, Pinterest, and LinkedIn. Considering the importance of social media platforms and digital modes of marketing, it is considered especially significant for small firms to integrate these platforms into their business strategies in order to improve performance. <i>Methods:</i> Based on this aim, this study collected data from 363 owners/managers of SMEs in Pakistan. Structural equation modeling is used to check the hypothesized model of the study. <i>Results:</i> The results show that compatibility, owner/manager support, employee IT skills, financial cost, government policies, and social influence significantly affect adoption of digital marketing by SMEs in Pakistan. <i>Conclusions:</i> Furthermore, digital marketing also positively affects SME performance. This paper discusses the study’s findings as well as managerial and academic implications, including its limitations and future research avenues.

Henriett Matyi, Péter Tamás

<i>Background</i>: As a result of the effort to satisfy unique customer needs, the complexity of production and service processes is constantly increasing. In this context, the requirements for packaging systems, essential for carrying out logistic tasks, are also diversifying, and various quality defects and problems are appearing more and more frequently. <i>Methods</i>: The research used an inductive method. While practical problems were being solved, the need for developing the concept of a packaging inspection framework arose, the lack of which was also supported by a systematic literature review. <i>Results</i>: During the concept’s development, packaging errors found in the literature were identified and methods for detection and solution were systematized. A general framework was developed to identify and eliminate these errors. The applicability of the developed method was demonstrated through a complex case study, and its accuracy was verified. <i>Conclusions</i>: This research is important because, instead of using “island” solutions, in the future, companies will have a general framework available to them for handling all packaging-related errors according to a predefined methodology. This can reduce the time required for problem-solving and increase efficiency, which is a significant competitive factor.

Philipp Hacker, Andreas Engel, Marco Mauer

Large generative AI models (LGAIMs), such as ChatGPT, GPT-4 or Stable Diffusion, are rapidly transforming the way we communicate, illustrate, and create. However, AI regulation, in the EU and beyond, has primarily focused on conventional AI models, not LGAIMs. This paper will situate these new generative models in the current debate on trustworthy AI regulation, and ask how the law can be tailored to their capabilities. After laying technical foundations, the legal part of the paper proceeds in four steps, covering (1) direct regulation, (2) data protection, (3) content moderation, and (4) policy proposals. It suggests a novel terminology to capture the AI value chain in LGAIM settings by differentiating between LGAIM developers, deployers, professional and non-professional users, as well as recipients of LGAIM output. We tailor regulatory duties to these different actors along the value chain and suggest strategies to ensure that LGAIMs are trustworthy and deployed for the benefit of society at large. Rules in the AI Act and other direct regulation must match the specificities of pre-trained models. The paper argues for three layers of obligations concerning LGAIMs (minimum standards for all LGAIMs; high-risk obligations for high-risk use cases; collaborations along the AI value chain). In general, regulation should focus on concrete high-risk applications, and not the pre-trained model itself, and should include (i) obligations regarding transparency and (ii) risk management. Non-discrimination provisions (iii) may, however, apply to LGAIM developers. Lastly, (iv) the core of the DSA content moderation rules should be expanded to cover LGAIMs. This includes notice and action mechanisms, and trusted flaggers. In all areas, regulators and lawmakers need to act fast to keep track with the dynamics of ChatGPT et al.

Hamed Petoft, Vahid Fakhari, Abbas Rahi

Hyperloop Transportation Technology (HTT) is a worldwide invention proposed by Elon Musk in the last decade. This system works based on moving an ultra-high-speed capsule-shaped vehicle called a “pod” into low-air pressure tubes. In this paper, we conceptually designed a largesized industrial pod equipped with an axial compressor. Also, we considered an unbalanced centrifugal force on the compressor blades. The novel-designed pod has two suspensions simultaneously, including magnetic levitation (EMS kind) and air cushion technology. We applied the air cushions to overcome the overall weight of the pod. Also, we used magnets for the motion stability of the pod. The present study proposes a 5-DOF dynamic model for the system containing the pod’s vertical and lateral displacements and the body pitching, rolling, and yawing angles. In this regard, the natural frequencies are verified using simulating the system in ADAMS software. Afterward, we analytically calculated the natural frequencies and system responses by applying the impedance matrix method. In the numerical results, we analyzed the pod responses, when the resonance phenomenon occurs for undamped and damping cases. Results showed oscillations increased by increasing the unbalancing parameter. We finally investigated the effect of two main design parameters containing the pod’s total mass and stiffness of the air cushions on the natural frequencies. Increasing the air cushion’s stiffness and decreasing the total mass generally increase the natural frequencies.

Kamble Shridevi Jeevan, Kounte Manjunath R.

Predicting the most favorable traveling routes for Vehicles plays an influential role in Intelligent Transportation Systems (ITS). Shortest Traveling Routes with high congestion grievously affect the driving comfort level of VANET users in populated cities. As a result, increase in journey time and traveling cost. Predicting the most favorable traveling routes with less congestion is imperative to minimize the driving inconveniences. A major downside of existing traveling route prediction models is to continuously learn the real-time road congestion data with static benchmarking datasets. However, learning the new information with already learned data is a cumbersome task. The main idea of this paper is to utilize incremental learning on the Hybrid Learning-based traffic Congestion and Timing Prediction (HL-CTP) to select realistic, congestion-free, and shortest traveling routes for the vehicles. The proposed HL-CTP model is decomposed into three steps: dataset construction, incremental and hybrid prediction model, and route selection. Firstly, the HL-CTP constructs a novel Traffic and Timing Dataset (TTD) using historical traffic congestion information. The incremental learning method updates the novel real-time data continuously with the TDD during prediction to optimize the performance efficiency of the hybrid prediction model closer to real-time. Secondly, the hybrid prediction model with various deep learning models performs better by taking the route prediction decision based on the best sub-predictor results. Finally, the HL-CTP selects the most favorable vehicle routes selected using traffic congestion, timing, and uncertain environmental information and enhances the comfort level of VANET users. In the simulation, the proposed HL-CTP demonstrates superior performance in terms of Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).

Ali Hirsa, Massoud Heidari

Individual trade orders are often bunched into a block order for processing efficiency, where in post execution, they are allocated into individual accounts. Since Regulators have not mandated any specific post trade allocation practice or methodology, entities try to rigorously follow internal policies and procedures to meet the minimum Regulatory ask of being procedurally fair and equitable. However, as many have found over the years, there is no simple solution for post trade allocation between accounts that results in a uniform distribution of returns. Furthermore, in many instances, the divergences between returns do not dissipate with more transactions, and tend to increase in some cases. This paper is the first systematic treatment of trade allocation risk. We shed light on the reasons for return divergence among accounts, and we present a solution that supports uniform allocation of return irrespective of number of accounts and trade sizes.