Search Results (566)

The paper provides an analysis of the causes of accidents in oil and gas pipeline systems. As part of a comprehensive overview of the topic, it also presents the historical development of pipeline systems, from the first commercial oil pipelines in the United States to modern infrastructure projects, with a particular focus on the role of regulatory requirements and measures (prevention, detection, and mitigation) to improve transport efficiency and pipeline safety. The research uses historical accident data from various databases to identify the main causes of accidents and analyse trends. The focus is on factors such as corrosion, third-party interference, and natural disasters that can lead to accidents. A comparison of the various accident databases shows that there are different practises and approaches to operation and reporting. As each database differs in terms of inclusion criteria, the categories are divided into five main groups to allow systematic interpretation of the data and cross-comparison of accident causes. Regional differences in the causes of accidents involving oil and gas pipelines in Europe, the USA, and Canada are visible. However, an integrated analysis shows that the number of accidents is declining in almost all categories. The majority of all recorded accidents are in the “Human factors and Operational disruption” and “Corrosion and Material damage” groups. It is recommended to use the database as required, as each category has its own specifics. Full article

The development of big data analytics in healthcare has created a demand for privacy-conscious and scalable machine learning algorithms that can allow the use of patient information across different healthcare organizations. In this study, the difficulties that come with traditional federated learning frameworks in healthcare sectors, such as scalability, computational effectiveness, and preserving patient privacy for numerous healthcare systems, are discussed. In this work, a new conceptual model known as Hierarchical Federated Learning (HFL) for large, integrated healthcare organizations that include several institutions is proposed. The first level of aggregation forms regional centers where local updates are first collected and then sent to the second level of aggregation to form the global update, thus reducing the message-passing traffic and improving the scalability of the HFL architecture. Furthermore, the HFL framework leveraged more robust privacy characteristics such as Local Differential Privacy (LDP), Gaussian Differential Privacy (GDP), Secure Multi-Party Computation (SMPC) and Homomorphic Encryption (HE). In addition, a Novel Aggregated Gradient Perturbation Mechanism is presented to alleviate noise in model updates and maintain privacy and utility. The performance of the proposed HFL framework is evaluated on real-life healthcare datasets and an artificial dataset created using Generative Adversarial Networks (GANs), showing that the proposed HFL framework is better than other methods. Our approach provided an accuracy of around 97% and 30% less privacy leakage compared to the existing models of FLBM-IoT and PPFLB. The proposed HFL approach can help to find the optimal balance between privacy and model performance, which is crucial for healthcare applications and scalable and secure solutions. Full article

Private array equality comparison (PAEC) aims to evaluate whether two arrays are equal while maintaining the confidentiality of their elements. Current private comparison protocols predominantly focus on determining the relationships of secret integers, lacking exploration of array comparisons. To address this issue, we propose a swap test-based quantum protocol for PAEC, which satisfies both functionality and security requirements using the principles of quantum mechanics. This protocol introduces a semi-honest third party (TP) that acts as a medium for generating Bell states as quantum resources and distributes the first and second qubits of these Bell states to the respective participants. They encode their array elements into the received qubits by performing rotation operations. These encoded qubits are sent to TP to derive the comparison results. To verify the feasibility of the proposed protocol, we construct a quantum circuit and conduct simulations on the IBM quantum platform. Security analysis further indicates that our protocol is resistant to various quantum attacks from outsider eavesdroppers and attempts by curious participants. Full article

The article is aimed at building on the existing studies devoted to the last stage of the assimilation policy directed at the Muslim population in Communist Bulgaria during the second half of the 1980s. The 40th anniversary of the forced change of the given Turkish–Arabic and Persian names of this population is an occasion to revisit this dark period of the recent past. This study focuses on the short- and long-term consequences of the political measures, which became known as the ‘Revival process’ (1984/1985–1989). For the first time, the author presents new written sources, including analytical and field reports commissioned by the Central Committee of the Bulgarian Communist Party and prepared by Bulgarian scholars during the second half of the 1980s, as well as later collected biographical data related to Muslims affected by the events, derived through an (auto)ethnographic method of research among Turks, Crimean Tatars and Muslim Roma. Full article

The rapid advancement of artificial intelligence is transforming agriculture by enabling data-driven plant disease monitoring and decision support. Soil-borne mosaic wheat virus (SBWMV) is a soil-transmitted virus disease that poses a serious threat to wheat production across multiple ecological zones. Due to the regional variability in environmental conditions and symptom expressions, accurately evaluating the severity of wheat soil-borne mosaic (WSBM) infections remains a persistent challenge. To address this, the problem is formulated as large-scale group decision-making process (LSGDM), where each planting plot is treated as an independent virtual decision maker, providing its own severity assessments. This modeling approach reflects the spatial heterogeneity of the disease and enables a structured mechanism to reconcile divergent evaluations. First, for each site, field observation of infection symptoms are recorded and represented using intuitionistic fuzzy numbers (IFNs) to capture uncertainty in detection. Second, a Bayesian graph convolutional networks model (Bayesian-GCN) is used to construct a spatial trust propagation mechanism, inferring missing trust values and preserving regional dependencies. Third, an enhanced spectral clustering method is employed to group plots with similar symptoms and assessment behaviors. Fourth, a feedback mechanism is introduced to iteratively adjust plot-level evaluations based on a set of defined agricultural decision indicators sets using a multi-granulation rough set (ADISs-MGRS). Once consensus is reached, final rankings of candidate plots are generated from indicators, providing an interpretable and evidence-based foundation for targeted prevention strategies. By using the WSBM dataset collected in 2017–2018 from Walla Walla Valley, Oregon/Washington State border, the United States of America, and performing data augmentation for validation, along with comparative experiments and sensitivity analysis, this study demonstrates that the AI-driven LSGDM model integrating enhanced spectral clustering and ADISs-MGRS feedback mechanisms outperforms traditional models in terms of consensus efficiency and decision robustness. This provides valuable support for multi-party decision making in complex agricultural contexts. Full article

The management of transfer pricing operations is considered from the perspective of modeling financial and accounting processes for various organizations, using agricultural enterprises as an example. It is demonstrated that the execution of transfer pricing operations between related parties—which may function as responsibility centers within an organizational holding structure—serves as a managerial lever influencing the financial income and expenses of individual business units. It is revealed that the developed model of managerial accounting for transfer pricing operations, grounded in tax compliance and the balancing of stakeholder interests, is based on two key aspects: first, to ensure the balanced development of the company’s business units, a list of key performance indicators (KPIs) is developed and integrated into a balanced scorecard (BSC), promoting the sustainable and stable operation and growth of the company; second, with access to this list of KPIs, the manager of each business unit can exert indirect influence over a segment of the final product’s value chain by selecting transfer prices that adhere to the arm’s length principle. The practical application of the proposed model is illustrated using previously formed economic operations from the research base. Full article

Credit supervision of practitioners in the water conservancy construction market, a vital pillar of national infrastructure development, significantly impacts project safety and the maintenance of order in the industry. From the perspective of indirect supervision, this study constructs a tripartite evolutionary game model involving government departments, enterprises, and practitioners to analyze the dynamic evolution mechanism of credit supervision. By examining the strategic interactions among the three parties under different regulatory scenarios, we identify key factors influencing the stable equilibrium of evolution and verify the theoretical conclusions through numerical simulations. The study yields several key insights. First, while government regulation and social supervision can substantially increase the likelihood of practitioners’ integrity, relying solely on administrative regulation has an efficiency limit. Second, the effectiveness of the reward and punishment mechanism of the direct manager plays a crucial leveraging role in credit evolution. Lastly, under differentiated regulatory strategies, high-credit practitioners respond more strongly to long-term cost optimization, while low-credit practitioners are more effectively deterred by short-term, high-intensity disciplinary actions. Based on these findings, this study proposes a systematic governance framework of “regulatory model innovation–corporate responsibility enhancement–social supervision deepening.” Unlike previous studies, this framework adopts a comprehensive approach from three dimensions: regulatory model innovation, corporate responsibility enhancement, and social supervision deepening. It offers a more holistic and systematic solution for refining the credit system in the water conservancy construction market, providing both theoretical support and practical approaches. Full article

Trade-in programs have become a vital mechanism for promoting sustainable consumption and reducing negative impacts on the environment, gaining substantial support from branders, e-platforms, and consumers in recent years. Concurrently, the emergence of professional recyclers has provided firms with viable alternatives for the outsourcing of recycling processes. To investigate the optimal leadership and recycling model with respect to trade-in operations, this study examines the strategy selection in a platform-based supply chain under a resale model. A two-period game-theoretic framework is developed, encompassing four models: self-recycling and outsourcing models under the leadership of the brander or platform. The main findings are as follows: (1) In markets characterized by a low consumer price sensitivity, both branders and platforms tend to choose the self-recycling model to capture the closed-loop value. In contrast, in highly price-sensitive markets, both parties exhibit a preference for “free-riding” strategies. (2) Once the recycling leader is determined, adopting a self-recycling model can lead to a relative win–win outcome in high price sensitivity contexts. (3) With a short product iteration cycle, both the brander and platform should strategically lower their prices in the first period, sacrificing short-term profits to enhance trade-in incentives and maximize long-term gains. (4) When the brander leads the recycling process, they should consider reusing the resources derived from old products; however, in platform-led models, the brander can only consider reusing the recycled resources in a low price sensitivity market. This study provides strategic insights for the sustainable development of the supply chain through the analysis of a game between a brander and an e-commerce platform, enriching the literature on CLSCs through integrating trade-in leadership selection and the choice to outsource, offering theoretical support for dynamic pricing strategies over multi-period product lifecycles. Full article

This study addresses a critical deficiency in real estate management by examining how contractual arrangements between property owners and facility managers (FMs) can mitigate reputational damage arising from third-party liability incidents. While Spanish regulations impose comprehensive conservation and maintenance duties on property owners, current contractual frameworks inadequately protect owners from reputational risks when damages occur due to FMs’ negligence or operational failures. This conceptual study employs a systematic analysis of 16 Spanish regulations governing real estate conservation and maintenance duties, complemented by an examination of the statutory contract law and a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis to evaluate the proposed solutions. The analysis reveals four distinct liability attribution blocks, ranging from quasi-objective owner liability to full objective installation holder liability. Current service contracts between owners and FMs provide insufficient reputational protection, as civil liability remains with the property owners regardless of the FMs’ performance. This study identifies specific contractual mechanisms—combining statutory work contracts with representative mandates and installation ownership transfers—that effectively redirect tort liability from owners to FMs. While this study focuses on Spanish regulatory frameworks as a methodologically necessary foundation for theoretical development, the conceptual framework provides transferable mechanisms for adaptation to other civil law jurisdictions. This study constitutes the first comprehensive analysis bridging legal architecture and facility management to propose novel liability transfer mechanisms within established frameworks. Full article

With the continuous expansion of users, businesses, and services in electricity retail trading systems, the demand for personalized recommendations has grown significantly. To address the issue of reduced recommendation accuracy caused by insufficient data in standalone recommendation systems, the academic community has conducted in-depth research on distributed recommendation systems. However, this collaborative recommendation environment faces two critical challenges: first, how to effectively protect the privacy of data providers and power users during the recommendation process; second, how to handle the potential presence of malicious data providers who may supply false recommendation data, thereby compromising the system’s reliability. To tackle these challenges, a blockchain-based lightweight reputation-aware electricity retail trading service recommendation (BLR-ERTS) system is proposed, tailored for electricity retail trading scenarios. The system innovatively introduces a recommendation method based on Locality-Sensitive Hashing (LSH) to enhance user privacy protection. Additionally, a reputation management mechanism is designed to identify and mitigate malicious data providers, ensuring the quality and trustworthiness of the recommendations. Through theoretical analysis, the security characteristics and privacy-preserving capabilities of the proposed system are explored. Experimental results show that BLR-ERTS achieves an MAE of 0.52, MSE of 0.275, and RMSE of 0.52 in recommendation accuracy. Compared with existing baseline methods, BLR-ERTS improves MAE, MSE, and RMSE by approximately 13%, 14%, and 13%, respectively. Moreover, the system exhibits 94% efficiency, outperforming comparable approaches by 4–24%, and maintains robustness with only a 30% attack success rate under adversarial conditions. The findings demonstrate that BLR-ERTS not only meets privacy protection requirements but also significantly improves recommendation accuracy and system robustness, making it a highly effective solution in a multi-party collaborative environment. Full article

In the development process of the power industry, accurately assessing the level of development of the electricity business environment is of great significance. However, traditional evaluation systems have limitations, with the issue of “data silos” being prominent, and user privacy under federated learning is also at risk. This paper proposes a federated learning-based data protection method for the electricity business environment to address these challenges. Based on the World Bank’s B-READY framework, this paper constructs an electricity business environment evaluation system containing nine indicators, focusing on three aspects: electricity regulations, public services, and operational efficiency. The indicators are weighted using the Sequence Relation and Entropy Weight Method. To address the issue of sensitive data protection, we first use federated learning technology to build a distributed modeling framework, ensuring that raw data never leaves the local environment during the collaborative modeling process. Next, we embed a differential privacy mechanism in the model parameter transmission stage, encrypting the model parameters by adding controlled noise. Finally, an incentive mechanism based on contribution quantification is implemented to encourage participation from all parties. This paper conducts experiments using the data of Shenzhen City, Guangdong Province. Compared with the FNN model and the SVR model, the MLP model reduces MAE by 78.9% and 94.12%, respectively, and increases R² by 37.95% and 55.62%, respectively. The superiority of the method proposed in this paper has been proved. Full article

This study presents a system for automatic cookie collection using bots that simulate user browsing behavior. Five bots were deployed, one for each of the most commonly used university browsers, enabling comprehensive data collection across multiple platforms. The infrastructure included an Ubuntu server with PiHole and Tshark services, facilitating cookie classification and association with third-party advertising and tracking networks. The BotSoul algorithm automated navigation, analyzing 440,000 URLs over 10.9 days with uninterrupted bot operation. The collected data established relationships between visited domains, generated cookies, and captured traffic, providing a solid foundation for security and privacy analysis. Machine learning models were developed to classify suspicious web domains and predict their vulnerability to XSS attacks. Additionally, clustering algorithms enabled user segmentation based on cookie data, identification of behavioral patterns, enhanced personalized web recommendations, and browsing experience optimization. The results highlight the system’s effectiveness in detecting security threats and improving navigation through adaptive recommendations. This research marks a significant advancement in web security and privacy, laying the groundwork for future improvements in protecting user information. Full article

This paper proposes a multi-party verifiably collaborative system for encrypting the nonlinear and the non-stationary biomedical signals captured by biomedical sensors via the singular spectrum analysis (SSA)-based chaotic networks. In particular, the raw signals are first decomposed into the multiple components by the SSA. Then, these decomposed components are fed into the chaotic filter bank networks for performing the encryption. To perform the multi-party verifiably collaborative encryption, the window length of the SSA and the total number of the layers in the chaotic network are flexibly designed to match the total number of the collaborators. The computer numerical simulation results show that our proposed system achieves a good encryption performance. Full article

Establishing a partnership necessitates agreeing on how to divide future profits or losses. We consider parties who wish to contract on the division of uncertain future profits. We propose to divide profits according to the Raiffa–Kalai–Smorodinsky (K-S) solution, which is the intersection point of the feasible region’s boundary and the line connecting the disagreement and ideal points. It is the only function which satisfies invariance to linear transformations, symmetry, strong Pareto optimality, and monotonicity. We formulate the general problem of designing a contract which divides uncertain future profit between the partners and determines shares of each partner. We first focus on linear and, later, non-linear contracts between two partners, providing analytical and numerical solutions for various special cases in terms of the utility functions of the partners, their beliefs, and the disagreement point. We then generalize the analysis to any number of partners. We also consider a contract which is partially based on the parties’ financial contribution to the partnership, which have a positive impact on profit. Finally, we address asymmetric K-S solutions. K-S solutions are seen to be a useful predictor of the outcome of negotiations, similar to Nash’s bargaining solution. Full article

Background/Objectives: Although psychoeducation for relatives of individuals with a first episode psychosis is important for increasing understanding of psychosis, reducing relapse rates, decreasing hospitalization duration, and improving patient functionality, there is limited research on the specific experiences and needs of relatives of patients with a first episode psychosis. This study aims to explore the experiences and needs of relatives of young adults with first-episode psychosis regarding psychoeducation, with the goal of developing tailored psychoeducation (PE) that can be delivered by nurses. Methods: This qualitative study employed a descriptive, interpretative approach with a total sample of 23 participants, including semi-structured interviews (N = 16), two dyadic interviews (N = 4) and one triadic interview (N = 3). The dyadic interviews included two relatives and two patients, while the triadic interview involved two relatives and one patient. A topic list was utilized to guide the interviews. Thematic analysis was employed to analyse the data, supported by the use of ATLAS.ti. Results: During data analysis, five key themes were identified as relevant for the development of a psychoeducational program: experiences with first-episode psychosis and psychoeducation, the content of PE (what), timing (when), exchanging experiences (how) and joint PE versus separate groups (which format). Conclusions: This study highlights valuable insights and key components for an integrated psychoeducation program, focussing on the needs and experiences of relatives, for the development of the PE program. To optimize the benefits for both parties, future research should explore the potential of offering PE sessions that accommodate both individual and combined participant formats, allowing for a design tailored to the specific needs of the participants. Full article