Search Results (9)

Fault-tolerant control systems deployed in manufacturing and process industries must maintain output regulation under actuator degradation and parametric drift while producing operator-readable intervention records. This paper proposes a three-layer cognitive supervisory control framework: a continuously synchronized nominal plant model serves as a lightweight digital twin, driving residual-based anomaly detection that responds to model-mismatch faults before tracking error accumulates; an event-triggered supervisory layer applies composite anomaly scoring, risk estimation, and dwell-time-constrained mode switching over a bank of pre-verified stable controllers; and an optional bounded LLM reasoning agent forming Layer 3 of the cognitive architecture produces real-time natural-language audit records at each supervisory event. The framework is functionally non-intrusive—the closed-loop response is numerically identical to standalone LQI in three of four benchmark scenarios and on an unstable plant variant, with zero supervisory interventions. Under abrupt 75% actuator loss, a single targeted intervention achieves lower overshoot and better tracking than all baselines, including MPC whose feasibility-degradation under reduced actuator authority is confirmed empirically. On near-undamped plants representative of compressor and pipeline dynamics, integral absolute error improves significantly relative to standalone LQI; on a distributed thermal process representative of laser welding and induction heating, IAE improves substantially under abrupt heater power loss. Parametric sensitivity analysis across fault severity, detection threshold, onset timing, measurement noise, score weights, and dwell-time confirms robustness of the detection architecture. The LLM layer’s contribution is regulatory rather than performative: its value is the causally connected audit record generated at the moment of each decision, not IAE improvement—a form of real-time explainability that after-the-fact attribution methods such as SHAP and LIME cannot provide by construction. Full article

The research explores the application of the General Data Protection Regulation (GDPR) within the context of Greek public administration to see whether its legislative transposition has had real impacts on enhancing good governance, accountability, and the protection of data subjects’ rights. Following a doctrinal and comparative approach to law, based on principles of public and administrative law, it analyses the rules governing data protection both at the EU level, as well as the corresponding national implementing legislation (Law 4624/2019), and the practices of supervision authorities, focusing especially on the procedural aspect of GDPR transposition, supported by an empirical examination of selected decisions of the Hellenic Data Protection Authority (HDPA) (2025–2026). Within such a framework, compliance with GDPR becomes one of the main aspects of the European administrative governance system, being associated not with mere legislative requirements, but also with other important elements, such as transparency, proportionality, institutional trust, and efficiency of public services. Greece provides a relevant context for examining the implementation of the GDPR through the high standards of HDPA within public administration. The findings indicate that GDPR compliance in public administration is primarily a matter of governance rather than merely legal alignment. The comparative analysis with recent European Data Protection Board (EDPB) opinions and coordinated supervisory activities further demonstrates that the governance challenges identified in Greece are broadly consistent with those observed across the European Union. The findings suggest that the Greek supervisory framework operates within the wider European system of GDPR governance and exhibits a level of institutional maturity and regulatory alignment comparable to contemporary European supervisory practice. Full article

Article 5 GDPR sets out the core principles governing the lawful processing of personal data and occupies a central place in the EU data protection framework. However, empirical evidence on how Article 5 is reflected in supervisory practice across the European Economic Area remains limited. This article addresses that gap through an empirical analysis of 790 national data protection authority decisions involving infringements related to Article 5 GDPR, drawn from an initial GDPRhub retrieval pool of 1660 publicly available decisions issued between 25 May 2018 and 15 September 2025. Using structured content analysis, the article identifies recurring infringement patterns, examines the co-occurrence of Article 5 with other GDPR provisions, and analyses selected sectoral, contextual, and outcome-related dimensions of supervisory practice. The findings show that Article 5-related infringements most frequently concern lawfulness, fairness and transparency, data minimisation, integrity and confidentiality, and accountability, and that they often appear alongside infringements of Article 6, Articles 12 to 14, and Article 32 GDPR. The analysis further points to meaningful sectoral variation, while organisation-size findings remain exploratory. Although only a limited subset of the analysed decisions was explicitly AI-related, the recurring legal weaknesses identified in the published decision record remain highly relevant for automated and data-intensive processing environments. Full article

Engineering consultants and design firms are central to the success of construction projects. However, the systematic evaluation of their performance in the Saudi Arabian context remains methodologically fragmented and empirically underdeveloped. Existing prequalification frameworks rely predominantly on administrative criteria and single-method ranking approaches that cannot adequately differentiate between high- and low-performing firms. To address this gap, the study proceeds in two distinct parts. Part I—Literature Review: A PRISMA-compliant systematic literature review across five major academic databases was conducted to map the existing evidence base, identify three substantive gaps in the Saudi and GCC engineering firm evaluation literature, and derive a consensus-based set of 29 performance criteria grouped into seven dimensions. This review constitutes an independent contribution: it establishes the gap that motivates the empirical work and provides the criterion framework on which that work is built. Part II—Practical Application: A structured questionnaire was administered to 288 construction professionals in Saudi Arabia (Cronbach’s α = 0.936), and the collected data were analyzed through a hybrid RII–Shannon Entropy Weighting (EWM)–TOPSIS pipeline that produced a Composite Priority Index (CPI) for each criterion, enabling a stable and discriminating ranking that integrates subjective expert consensus with objective distributional information. The main finding revealed that five criteria attained Very High Priority status (CPI > 0.70): Supervisory Experience (CPI = 0.740), Engineers’ Capability Index (CPI = 0.717), License Class (CPI = 0.709), Client Satisfaction Index (CPI = 0.708), and Average Delay Time (CPI = 0.705). These top-ranked criteria collectively center on technical leadership, regulatory standing, client-reported outcomes, and schedule reliability, indicating that procurement decisions should prioritize demonstrable competence over structural size or geographic footprint. The consistently lower importance of physical branch networks and headquarters location further suggests that remote management capabilities and digital coordination tools are reshaping performance expectations under Saudi Vision 2030. The Quality Indicators dimension achieved the highest mean CPI across all seven dimensions. The findings provide actionable evidence for procurement authorities, regulatory bodies, and engineering firms seeking to strengthen performance-evaluation practices in the Saudi construction sector. Full article

Ship lock zones represent bottlenecks and a particular challenge for authorities managing vessel traffic. Traditionally, the control strategy of such systems has relied heavily on the subjective judgment, experience, and tacit knowledge of ship lock operators. To address the inherent uncertainty and imprecision associated with these subjective assessments, fuzzy logic and fuzzy set theory have been adopted as appropriate mathematical frameworks. In this work, the control strategy and the Fuzzy Decision Support System (FDSS) of a single-chamber ship lock designed for two vessels on a two-way waterway are analyzed and modeled. The input data is generated based on a synthesized dataset reflecting the annual schedule of vessel arrivals. The software is based on proposals and suggestions of experienced ship lock operators, and it is further validated through vessel traffic simulations. Moreover, the development of an appropriate Supervisory Control and Data Acquisition (SCADA) system integrated with a Programmable Logic Controller (PLC) is detailed, providing the necessary infrastructure for real-time deployment of the fuzzy control algorithm. The proposed control system represents an original contribution and offers practical applications both as a decision-support tool for real-time lock management and as a training platform for novice or less experienced operators. Full article

This study aimed to provide an overview of the most frequent animal welfare cases documented by the authorities involved in sheep farming in Germany. A questionnaire with 26 questions was developed and sent to all German veterinary offices (supervisory authorities). Furthermore, publicly available court decisions on animal welfare cases over the last 30 years were consulted and analyzed. Approximately 18.1% (n = 71) of all veterinary authorities participated in the survey. The most frequently reported animal welfare cases included inadequate water supply (n = 63) and a lack of weather protection (n = 60). Other common issues were lameness (n = 56) and poor shearing management (n = 52). A total of 52 animal welfare cases presented in courts were found in the online search. The most common problems were nutrition (50%), water supply (34.6%), weather protection (32.7%), and shearing management (21.2%). These results provide information on the areas of sheep farming in which animal welfare violations occur most frequently. Based on these results, conclusions can be drawn regarding the development of preventive measures. Full article

This study determines to what extent Jordanian banks refer to and use artificial intelligence (AI) technologies in their operation process and examines the impact of AI-related terms disclosure on financial performance. Content analysis is used to analyze the spread of AI and related information in the annual report textual data. Based on content analysis and regression analysis of data from 115 annual reports for 15 Jordanian banks listed in the Amman Stock Exchange for the period 2014 to 2021, the study reveals a consistent increase in the mention of AI-related terms disclosure since 2014. However, the level of AI-related disclosure remains weak for some banks, suggesting that Jordanian banks are still in the early stages of adopting and implementing AI technologies. The results indicate that AI-related keywords disclosure has an influence on banks’ financial performance. AI has a positive effect on accounting performance in terms of ROA and ROE and a negative impact on total expenses, which supports the dominant view that AI improves revenue and reduces cost and is also consistent with past literature findings. This study contributes to the growing body of AI literature, specifically the literature on AI voluntary disclosure, in several aspects. First, it provides an objective measure of the uses of AI by formulating an AI disclosure index that captures the status of AI adoption in practice. Second, it provides insights into the relationship between AI disclosure and financial performance. Third, it supports policymakers’, international authorities’, and supervisory organizations’ efforts to address AI disclosure issues and highlights the need for disclosure guidance requirements. Finally, it provides a contribution to banking sector practitioners who are transforming their operations using AI mechanisms and supports the need for more AI disclosure and informed decision making in a manner that aligns with the objectives of financial institutions. Full article

This article presents an approach for the construction of an expert system, which helps to make the decisions when parrying failures during information exchange in real-time systems based on a deterministic optical network. The authors analyze the requirements for deterministic data exchange environments and consider the trends for the development of software and hardware for deterministic networks. The main goal of this article is to propose a concept for the implementation of a deterministic control unit based on an all-optical network with spectral division multiplexing. Two main steps for the implementation of such a system are described. The first is the implementation of the DOS protocol stack and its model, which was used in several protocol development stages. This model is a part of a proposed concept for an automation design and development system for information exchanges. The second presented step is the development of an expert decision support system, based on the principles of the supervisory approach. This article presents a neurocontroller, which provides hardware support for the implementation of individual modes of operation of an expert system. Full article

In order to enable urban economic development, the use of the right value and asset value of rural collective construction land (RCCL) is increasingly becoming apparent and this market is experiencing rapid development. However, the arrangement of the governance structure of rural shareholding cooperatives (RSCs) can seriously affect the efficiency of collective construction land market transactions, since the governance of RSCs is related to the interests of farmers. Protecting the rights and interests of farmers while improving the governance efficiency of RSCs is a considerable challenge worldwide. To better deal with this challenge, this study used a field survey in Nanhai District, Guangdong Province, China, to estimate how the governance structure of RSCs affect the efficiency of RCCL market transactions. Tobit models were constructed, and the results show that (1) most of the governance functions of RSCs were not separate from the administrative management of the village committees, which leads to low efficiency of RSCs’ governance; (2) leaders of rural collective economic organizations played a key role in governance efficiency; (3) from the perspective of collective land property rights, most village shareholders did not have decision-making power or supervisory authority in the RCCL transfers because they could not complete access to transaction information. Furthermore, most villagers felt that the amount of income distributed was unreasonable, and the rights and interests of farmers and village shareholders were not guaranteed by the RSCs. Therefore, we suggest that the Chinese authorities should strengthen their current efforts to construct a more open and fair governance structure of the RSCs and thus improve their market transaction efficiency. Our work provides some insights into ways to improve the governance structure and market transaction efficiency of RSCs, which can further contribute to the development of the RCCL market in other areas of China and worldwide. Full article