The paper explores logistics service providers (LSPs) in the context of material flow coordination and examines the correlation between digitalization and the implementation of selected coordination features. It includes a literature review and interviews with 18 experts in LSP operations to assess how digitalization influences the adoption of key material flow coordination elements. The study aims to determine the impact of technologies used by LSPs on the implementation of these elements, thereby expanding the logistics coordination concept by integrating the role of digitalization. Findings indicate that while LSPs seek to implement basic material flow coordination elements, they lack sufficient knowledge about advanced technologies like AI bots and Digital Twins. A strong positive correlation was observed between the number of technological solutions implemented and experts’ perceptions of coordination capabilities. Different coordination elements require varying levels of technological adoption, with greater implementation leading to improved coordination possibilities. The study provides practical insights for LSP managers regarding the necessary technologies for easier integration of fundamental coordination elements. Additionally, it examines international LSPs to evaluate their potential for digital transformation. The paper highlights the significance of digital solutions in material flow coordination as a crucial mechanism for managing contemporary distribution networks.
Samrena Jabeen, Mudassar Khan, Sabeen Hussain Bhatti
et al.
<i>Background</i>: Digital supply chain transformation research exhibits a critical gap, examining technologies in isolation rather than as integrated ecosystems. <i>Methods</i>: This study addresses this limitation by developing a comprehensive orchestration frame-work through PRISMA-guided systematic review of 96 publications (2012–2024) using bibliometric analysis, structural topic modeling, and thematic synthesis across Scopus and Web of Science databases. <i>Results</i>: Analysis revealed three distinct research clusters: Supply Chain Management (centrality: 14.95), Digital Transformation (centrality: 9.50, density: 101.05), and Big Data Analytics (density: 113.22), with substantial negative correlations (−0.48 to −0.54) indicating organizational evolution from fragmented adoption toward integration. <i>Conclusions</i>: Publications increased 78% year-over-year during 2021–2022, while Supply Chain Management dominated topic prevalence (41%) and Big Data Analytics declined from 0.9 to 0.15 as practices normalized. The Digital Supply Chain Orchestration Framework conceptualizes transformation as multi-layered with hierarchical relationships between foundational domains, technological enablers, integration mechanisms, and value creation dimensions. This framework provides structured approaches for organizations to assess digital maturity, identify technological gaps, and develop strategic roadmaps aligned with Sustainable Development Goals, bridging theory and practice for integrated, value-driven digital transformation.
Transportation and communication, Management. Industrial management
Deepfake technology, with its capacity to generate manipulated and highly realistic multimedia content, poses significant legal, ethical, and security challenges. This research adopts an interdisciplinary approach, examining these challenges through the lens of Governmental Jurisprudence (Fiqh). The primary aim is to develop a coherent Fiqh-legal framework for regulating deepfakes and mitigating their potential harms. By examining Fiqh and legal sources, this study analyzes the foundations of Governmental Fiqh relevant to this topic, including the principles of justice ('Adl), no harm (La Dharar), public interest (Maslahat), and preservation of the social order (Hifz-e Nezam). These principles are considered the theoretical basis for formulating legal rules and regulations concerning deepfakes. Key challenges analyzed include the incompatibility of existing laws with the nature and technical complexities of deepfakes, ambiguity in determining instances of criminalization, potential conflicts with human rights principles such as freedom of expression, and the necessity of preserving public interests and national security. While examining the capacities of existing laws, such as the Computer Crimes Law, this research emphasizes the need for specific and comprehensive legislation for the effective regulation of deepfakes. The findings also suggest that by utilizing existing capacities within domestic laws and aligning them with Fiqh principles, effective steps can be taken towards criminalizing risky behaviors in this area. The ultimate goal is to provide a robust and efficient framework for addressing the legal and Fiqh-related challenges arising from this technology.. Introduction
The rapid advancement of artificial intelligence has led to the emergence of deepfake technology, capable of generating highly convincing manipulated multimedia content. This technology presents unprecedented challenges to legal systems, ethical norms, and societal trust. By blurring the line between reality and fabrication, deepfakes can be misused. This research addresses the urgent need for effective regulation of deepfakes by adopting an interdisciplinary approach that integrates the principles of Governmental Jurisprudence (Fiqh) with existing legal frameworks. The study focuses on the application of key Fiqh principles such as justice ('Adl), no harm (La Dharar), public interest (Maslahat), and preservation of the social order (Hifz-e Nezam), to the regulation of deepfakes. These principles offer a rich ethical and legal framework for addressing the complex challenges posed by this technology. The research analyzes also the shortcomings of existing legal frameworks in dealing with deepfakes and proposes a comprehensive Fiqh-legal model for their effective governance. This study aims to contribute to the ongoing discourse on deepfake regulation by providing a novel perspective based on Islamic legal principles
2. Methodology
This research employs a descriptive-analytical methodology, combining library research with doctrinal analysis. Fiqh sources, including the Quran, Hadith, and authoritative scholarly interpretations from various Islamic schools of thought (particularly the Ja'fari school), are analyzed to identify relevant principles applicable to the regulation of deepfakes. The research also examines classical and contemporary works on Governmental Fiqh to understand the scope and application of these principles in the context of governance and public policy. Legal sources, including existing Iranian laws and regulations (such as the Computer Crimes Law, the Press Law, and the E-Commerce Law), are examined to evaluate their effectiveness in addressing the challenges posed by deepfakes. Furthermore, the research considers comparative legal studies and international best practices related to deepfake regulation, including legislative efforts in other countries and international legal instruments. he analysis carefully examines the compatibility and potential conflicts between Fiqh principles and existing legal frameworks. This comparative approach helps identify best practices and potential solutions for developing a comprehensive regulatory framework.
3. Results and Discussion
The research findings demonstrate that the principles of Governmental Fiqh offer a robust and adaptable framework for regulating deepfakes. The principle of justice ('Adl) mandates fairness and equity in the digital realm, prohibiting the use of deepfakes for malicious purposes. The principle of no harm (La Dharar) prohibits causing harm to oneself or others which extends to the psychological, reputational, and economic damage that deepfakes can inflict. The principle of public interest (Maslahat) allows for the restriction of certain uses of deepfakes to protect societal well-being, national security, and public order. The principle of preservation of social order (Hifz-e Nezam) emphasizes the importance of maintaining social stability and preventing the spread of misinformation that could disrupt public trust and social cohesion.
The study identifies several key regulatory challenges:
Rapid Technological Evolution: The rapid pace of technological development makes it difficult for legal frameworks to keep pace with new deepfake techniques.
Detection Challenges: Detecting deepfakes can be technically challenging, especially as the technology becomes more sophisticated.
Distinguishing between Legitimate and Malicious Uses: Differentiating between legitimate uses of deepfakes (e.g., in art or satire) and malicious uses is crucial for effective regulation.
Freedom of Expression: Balancing the need to regulate deepfakes with the protection of freedom of expression is a delicate but essential task.
Jurisdictional Issues: The cross-border nature of the internet complicates the enforcement of deepfake regulations.
The research proposes a multi-layered regulatory approach based on the Fiqh principles:
Clear Definitions: Establishing clear legal definitions of deepfakes and related activities.
Criminalization of Harmful Conduct: Criminalizing the production and distribution of deepfakes with malicious intent.
Platform Accountability: Defining the responsibilities of online platforms in detecting and removing harmful deepfakes.
Transparency and Disclosure: Requiring disclosure when deepfakes are used for non-malicious purposes.
Public Awareness and Media Literacy: Promoting public awareness and media literacy to help individuals identify deepfakes.
4. Conclusions and Future Research
This research concludes that existing legal frameworks are insufficient to address the complex challenges posed by deepfakes effectively. A comprehensive approach, informed by the principles of Governmental Fiqh, is necessary to regulate this technology and mitigate its potential harms. The proposed Fiqh-legal framework offers a foundation for developing specific legislation that balances the need to prevent abuse with the protection of fundamental rights. This study highlights the need for a "Comprehensive Deepfake Law" that addresses the specific challenges identified. This law should include provisions for clear definitions, criminalization of harmful behaviors, platform accountability, transparency and disclosure requirements, and public awareness campaigns.
Future research could focus on several areas:
Technical Solutions: Developing robust technical solutions for deepfake detection and authentication.
International Cooperation: Exploring the potential for international cooperation in deepfake regulation and enforcement.
Ethical Guidelines: Developing ethical guidelines for the responsible use of deepfake technology.
Empirical Studies: Conducting empirical studies to assess the real-world impact of deepfakes and the effectiveness of different regulatory approaches.
Comparative Fiqh Analysis: Examining how other Islamic schools of thought address the issue of deepfakes.
Regulation of industry, trade, and commerce. Occupational law, Islamic law
Kappatos Vassilios, Spyrou Evangelos D., Aggelakakis Aggelos
et al.
Efficient truck service optimization in port areas is essential for minimizing congestion, reducing delays and improving overall logistics efficiency. This study presents a comparative analysis of Genetic Algorithm (GA) and Cuckoo Search Algorithm (CSA) for optimizing truck scheduling and resource allocation in dynamic port environments. GA explores a broad solution space using evolutionary operators, while CSA leverages Lévy flight-based search mechanisms to enhance solution quality and escape local optima. The comparison evaluates both algorithms based on key performance metrics, including waiting times and server utilization. Simulation results indicate that the GA produces smaller waiting times, while the CSA exhibits higher. Moreover, the server utilization of the GA is significantly lower than with the CSA. The findings highlight the strengths and limitations of each method, providing valuable insights into their applicability for real-time truck service optimization in smart port management systems.
Moqbel S. Jaffal, Amjad B. Abdulghafour, Omar Ayadi
et al.
<i>Background</i>: The Petroleum Products Distribution Company in Anbar Governorate is responsible for securing and distributing petroleum products to various sectors, including transportation, agriculture, industry, and households, through over 100 gas stations. The company has faced significant challenges due to the destruction of its infrastructure caused by past conflicts. These challenges have necessitated strategic decisions to design an efficient distribution network. <i>Methods:</i> This study aimed to assist the company in selecting the optimal location for a distribution center by evaluating four potential locations. Three of the proposed locations were suggested by the company: Ramadi, Habbaniyah, and Haqlaniyah. The fourth location, referred to as the GFA DC location, was determined through a greenfield analysis (GFA) experiment using AnyLogistix software (version 3.2.1. PLE) ALX. The simulation experiment in ALX was conducted using product data, fuel station locations, order quantities, distribution center data, and transportation and emissions data. <i>Results</i>: The simulation results, taking into account both practical and regulatory constraints, indicated that the Ramadi location was the most suitable for establishing the new distribution center. <i>Conclusions</i>: Based on the analysis, the study concluded that the Ramadi location was the optimal site for building the petroleum products distribution center in Anbar Governorate, offering a solution that aligns with the company’s goals of improving distribution efficiency and overcoming existing logistical challenges.
Transportation and communication, Management. Industrial management
Przedmiotem rozważań zawartych w opracowaniu jest próba ustalenia, czy przy formułowaniu treści art. 4 ustawy z dnia 27 kwietnia 2001 r. – Prawo ochrony środowiska wykorzystano jako wzorzec rozwiązania dotyczące korzystania z wód zawarte w ustawie Prawo wodne. Pogląd taki bowiem można dość często napotkać w literaturze przedmiotu. Przeprowadzona analiza dowodzi, że nie znajduje on dostatecznego uzasadnienia, na co wskazują przede wszystkim istotne różnice pod względem metody regulacji zastosowanej w art. 4 oraz w ustawie Prawo wodne.
Environmental law, Regulation of industry, trade, and commerce. Occupational law
Yi Loor Jiawey D., Espinal Albert, Sanchez Padilla V.
This work presents the employing of LoRaWAN (Long Range Wide Area Network) for location applications through a network simulation to determine a mobile node position. We rely on FLoRa (Framework for LoRa) and OMNeT++ (Objective Modular Network Testbed in C++) simulator, which uses Python feature tools, following the calculation of node placement using the trilateration technique. Our method differs from others in that we calculate the FLoRa power loss and determine different simulation settings using the shadowing feature of the log-distance path loss model. We approached RSSI (Received Signal Strength Indicator) to measure the distance between the LoRa gateways and the nodes, establishing a link between these parameters. Our work aims to promote the integration of open-source tools for verifying signal intensity values based on node distance from gateways. We consider it useful for engineers in predicting signal behaviors according to topology and settings variations. During the experimentation, the network underwent different performances according to the transmission parameters considered during the simulation. This was critical when increasing the number of mobile nodes, leading to consuming computer capacity and resources. Through repetition of tests, we confirmed the lower intensity of the received signal as the node moves to farther positions, reaching consistent power indicators and positioning accuracy. Overall, the results show that LoRaWAN integrated with trilateration techniques can be practical in providing adequate performance for node positioning accuracy and long-distance communication with low power consumption.
Supply chain management is essential for businesses to handle uncertainties, maintain efficiency, and stay competitive. Financial risks can arise from various internal and external sources, impacting different supply chain stages. Companies that effectively manage these risks gain a deeper understanding of their procurement activities and implement strategies to mitigate financial threats. This paper explores financial risk assessment in supply chain management using advanced deep learning techniques on big data. The Adaptive Serial Cascaded Autoencoder (ASCA), combined with Long Short-Term Memory (LSTM) and Multi-Layered Perceptron (MLP), is used to evaluate financial risks. A data transformation process is used to clean and prepare financial data for analysis. Additionally, Sandpiper Galactic Swarm Optimization (SGSO) is employed to optimize the deep learning model’s performance. The SGSO-ASCALSMLP-based financial risk prediction model demonstrated superior accuracy compared to traditional methods. It outperformed GRU (gated recurrent unit)-ASCALSMLP by 3.03%, MLP-ASCALSMLP by 7.22%, AE-LSTM-ASCALSMLP by 10.7%, and AE-LSTM-MLP-ASCALSMLP by 10.9% based on F1-score performance. The SGSO-ASCALSMLP model is highly efficient in predicting financial risks, outperforming conventional prediction techniques and heuristic algorithms, making it a promising approach for enhancing financial risk management in supply chain networks.
Transportation and communication, Management. Industrial management
Thanapong Champahom, Chamroeun Se, Wimon Laphrom
et al.
<i>Background</i>: The automotive industry is pivotal in advancing sustainability, with electric vehicles (EVs) essential for reducing emissions and promoting cleaner transport. This study examines the determinants of EV adoption intentions in Thailand, integrating demographic and psychographic factors from Environmental psychology and innovation diffusion theory; <i>Methods</i>: Data from a structured questionnaire, administered to 4003 respondents at gas stations with EV charging facilities across Thailand, were analyzed using a Correlated Mixed-Ordered Probit Model with Heterogeneity in Means (CMOPMHM); <i>Results</i>: Findings indicate that younger adults, particularly those aged 25–34 years old and 45–54 years old, are more likely to adopt EVs, whereas conventional or hybrid vehicle owners are less inclined. Rural residency or travel also hinders adoption. Individuals with strong environmental values and openness to new technologies are more likely to adopt EVs; <i>Conclusions</i>: The proposed model quantified the relative importance of these factors and uncovered heterogeneity in user preferences, offering reliable and valuable insights for policymakers, EV manufacturers, and researchers. The study suggests targeted policies and enhanced charging infrastructure, especially in rural areas, and recommends leveraging environmental values and trialability through communication campaigns and test drive events. These insights can guide the development of targeted incentives, infrastructure expansion, communication strategies, and trialability programs to effectively promote wider EV adoption in Thailand and similar markets.
Transportation and communication, Management. Industrial management
Previous studies have shown that the level of awareness of SDVs is a deciding factor that affects the public attitude towards this emerging technology; however, none of these studies focuses on understanding the relationship between these two variables. Thus, this study utilizes a questionnaire survey with the objective of drawing the relationship between the public attitude and level of knowledge. A total of 2447 complete responses were revised from participants from the US. The results show that people with prior knowledge about SDVs are more likely to travel on SDVs. However, participants who know a bit about SDVs were the most likely to travel on SDVs when compared with participants who had no knowledge and participants who know a lot about SDVs. In addition, the analysis shows that the relationship between the level of knowledge and the level of acceptance of SDVs is not linear but rather parabolic.
The right to benefit from scientific advances, including new technologies, has always been considered one of the fundamental human rights; one of these new technologies is artificial intelligence technology. Artificial intelligence is a type of intelligence that was born in the 1950s and is an integral part of the digital revolution. The progress of artificial intelligence and its application in many aspects of human life, especially human rights, has transformed people's way of life. This research investigates the impact of artificial intelligence on the international human rights system using the descriptive analytical research method and library tools. The results of this research indicate that the use of artificial intelligence on different examples of known rights in multiple generations of human rights (first, second and third generation) has positive and negative effects and can be one of the examples of the fourth generation of rights (doctrine of technology). Additionally, to address the issues and potential adverse impacts, legal actions have been implemented at both national and international levels with the goal of setting standards and enhancing cooperation.
Regulation of industry, trade, and commerce. Occupational law, Islamic law
<i>Background</i>: Safety is the very necessary issue that must be considered during human-robot collaboration in the same workspace or area. <i>Methods</i>: In this manuscript, a nonlinear autoregressive model with an exog-enous inputs neural network (NARXNN) is developed for the detection of collisions between a manipulator and human. The design of the NARXNN depends on the dynamics of the manipulator’s joints and considers only the signals of the position sensors that are intrinsic to the manipulator’s joints. Therefore, this network could be applied and used with any conventional robot. The data used for training the designed NARXNN are generated by two experiments considering the sinusoidal joint motion of the manipulator. The first experiment is executed using a free-of-contact motion, whereas in the second experiment, random collisions by human hands are performed with the robot. The training process of the NARXNN is carried out using the Levenberg–Marquardt algorithm in MATLAB. The evaluation and the effectiveness (%) of the developed method are investigated taking into account different data and conditions from the used data for training. The experiments are executed using the KUKA LWR IV manipulator. <i>Results</i>: The results prove that the trained method is efficient in estimating the external joint torque and in correctly detecting the collisions. <i>Conclusions</i>: Eventually, a comparison is presented between the proposed NARXNN and the other NN architectures presented in our previous work.
Transportation and communication, Management. Industrial management
The operating condition of bus transit system has not been efficient in most cities of Iran, and many management methods such as regular bus scheduling, assigning exclusive bus lanes, etc., which are necessary for increasing the efficiency of this system, were not regarded enough. Thus, achieving a method for locating the bus stops and optimizing the number of such stops based on a non-homogeneous spatial and temporal distribution of passengers as well as the local traffic patterns are important to be investigated. As such, the present study aims to investigate the modeling of a bus transit system corridor according to the non-homogeneous spatial and temporal distribution of passengers throughout the route aiming at optimization of the number of attracted passengers to the bus. For this purpose, the 8-km route from Vali-e-asr roundabout to Gas roundabout in the city of Rasht in the north of Iran is selected for modeling. Hammersley sampling method, as well as two heuristic optimization techniques, including a Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm, are used for generating a non-uniform population and solving the optimization model. Therefore, the results of this analysis are compared to the optimization results by using the probabilistic analysis without considering the reference uncertainty. Finally, the PSO is selected as the superior algorithm for modeling and locating the bus stops due to its results in less travel time, and the validity of robust optimization model is shown due to its higher accuracy and adaptation to the real-world environment. Overall, although the optimization results based on indeterminate analysis in comparison to determinate analysis brought about more average travel time, more population sets were covered by the new introduced stops during 18 active hours of the bus transit system.
During the recent decade a lot of research has been focused on identification of the importance of regional airport for the local economy and the measuring and predicting of the airport’s efficiency. With regard to individual airport planning it turns out that airports are not free in the optimization of infrastructure due the need to comply international aviation standards and recommended practices, one the one hand, and the tendency of air transportation system development including aircraft design and airlines’ fleet development, on the other. Furthermore, it is also important to take into account the specificity of each particular airport, including the traffic variability resulting from seasonality, with the airport’s mission geared to it. We found, tha t one of the reasons for the relatively low efficiency of regional airports, given the general trend in the growth in the number of seats for narrow-body aircraft. Likewise, it has been found, that the seasonality of air traffic at regional airports is comparatively high. In addition, this paper spotlights the ways the theoretical model of returns to scale affects the efficiency of the apron of the regional airport and briefly discusses the different interpretations of the definition of regional airport.
Power purchasing agreements (PPA) seek to protect and develop electricity production specifically some kinds like renewable and green electricity. They have been common after restructuring electricity industrial. They now broadly are used as a mean to conservation of environment, electricity security of supply and foreign investment protection around the world. In Iran after restructuring of electricity industrial according to article 25(b) of "fourth development program act,” ministry of power and its company (Tavanir) designed and concluded some type of power purchasing agreements (PPA). These contracts comprise some terms and conditions, which refer to general rules of contract and either technical restrictions. This article seeks to study these contracts terms and conditions in the framework of sale contract and general rules of contract under Iranian civil law. We will answer to this question that these contracts shall be assumed as a sale contract or a specific contract under article 10? It seems to there are not exactly sale contract that has been mentioned in the article 338 and deem to be an especially contract.
Regulation of industry, trade, and commerce. Occupational law, Islamic law
This research proposes a background subtraction method with the truncate threshold to improve the accuracy of vehicle detection and tracking in real-time video streams. In previous research, vehicle detection accuracy still needs to be optimized, so it needed to be improved. In the vehicle detection method, there are several parts that greatly affect, one of which is the thresholding technique. Different thresholding methods can affect the results of the background and foreground separation. Based on the results of testing the proposed method can improve accuracy by more than 20% compared to the previous method. The thresholding method has a considerable influence on the final result of vehicle object detection. The results of the average accuracy of the three types of time, i.e. morning, daytime, and afternoon reached 96.01%. These results indicate that the vehicle counting accuracy is very satisfying, moreover, the method has also been implemented in a real way and can run smoothly.
Late research has established the critical environmental, health and social impacts of traffic in highly populated urban regions. Apart from traffic monitoring, textual analysis of geo-located social media responses can provide an intelligent means in detecting and classifying traffic related events. This paper deals with the content analysis of Twitter textual data using an ensemble of supervised and unsupervised Machine Learning methods in order to cluster and properly classify traffic related events. Voluminous textual data was gathered using innovative Twitter APIs and managed by Big Data cloud methodologies via an Apache Spark system. Events were detected using a traffic related typology and the clustering K-Means model, where related event classification was achieved applying Support Vector Machines (SVM), Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM) networks. We provide experimental results for 2-class and 3-class classification examples indicating that the ensemble performs with accuracy and F-score reaching 98.5%.
International trade policy theorists have repeatedly focused their works on actors developing or implementing policies, rather than the actual policies themselves. Dani Rodrik, Chalmers Johnson and Peter Evans are amongst those renowned international trade and industrialization scholars who dedicated most of their research to human resource development in countries that delivered miraculous economic growth. Chile may be considered one of those miraculous states, in part due to the development of its salmon fishing industry. Tasmania, another southern hemisphere salmon producing region, has also procure a State-guided sectorial development. Both face common attributes and challenges. On one hand, Chile and Tasmania salmon development is highly explained due to the existence of a bureaucratic regime highly supportive of human capital development in the new sector. On the other hand, still both face challenges to find the right balance between profitability and environmental sustenance. Chile has shown record export growth, but it has raised questions on sanitary management; while Tasmania has enforced strict environmental compliance, it has hardly earned anything from exports of Salmon.
Regulation of industry, trade, and commerce. Occupational law, International relations
Travel times for simple trips and cycles are analyzed for a storage/retrieval machine working in a one-dimensional or two-dimensional zone with taxicab geometry. A semi-random trip is defined as one-way travel from a known to a random location or vice versa. A random trip is defined as one-way travel from a random to another random location. The probability density function (PDF) of the travelling time for a semi-random trip in a one-dimensional zone is expressed analytically for all possible locations of its starting point. The PDF of a random trip within the same zone is found as a marginal probability by considering all possible durations for such travel. Then the PDFs for the travel times of single command (SC) and dual command (DC) cycles are obtained by scaling the PDF for the travel time of a semi-random trip (for SC) and as the maximum travel time of two independent semi-random trips (for DC). PDFs for travel times in a two-dimensional service zone with taxicab geometry are calculated by considering the trip as a superposition of two one-dimensional trips. The PDFs for travel times of SC and DC cycles are calculated in the same way. Both the one-dimensional and the two-dimensional service zones are analyzed in the time domain without normalization. The PDFs for all travel times are expressed in an analytical form parameterized by the maximal possible travel time within the zone. The graphs of all PDFs are illustrated by numerical examples.
Transportation and communication, Management. Industrial management