Search Results (1,436)

A hybrid control architecture is proposed for enhancing the stability and energy management of DC microgrids (DCMGs) integrating photovoltaic generation, batteries, and supercapacitors. The approach combines nonlinear Sliding Mode Control (SMC) for fast and robust DC bus voltage regulation with a Deep Q-Learning (DQL) agent that learns optimal high-level policies for charging, discharging, and load management. This dual-layer design leverages the real-time precision of SMC and the adaptive decision-making capability of DQL to achieve dynamic power sharing and balanced state-of-charge levels across storage units, thereby reducing asymmetric wear. Simulation results under variable operating scenarios showed that the proposed method significantly improvedvoltage stability, loweredthe occurrence of deep battery discharges, and decreased load shedding compared to conventional fuzzy-logic-based energymanagement, highlighting its effectiveness and resilience in the presence of renewable generation variability and fluctuating load demands. Full article

Programmable Logic Controllers (PLCs), widely used in industrial automation, are often programmed in IEC 61131-3 Structured Text (ST), which is prone to subtle logic vulnerabilities. Traditional tools like static analysis and fuzzing struggle with the complexity and domain-specific semantics of ST. This work explores Large Language Models (LLMs) for PLC vulnerability detection, supported by both theoretical insights and empirical validation. Theoretically, we prove that control flow features carry the most vulnerability-relevant information, establish a feature informativeness hierarchy, and derive sample complexity bounds. We also propose an optimal synthetic data mixing strategy to improve learning with limited supervision. Empirically, we build a dataset combining real-world and synthetic ST code with five vulnerability types. We fine-tune open-source LLMs (CodeLlama, Qwen2.5-Coder, Starcoder2) using LoRA, demonstrating significant gains in binary and multi-class classification. The results confirm our theoretical predictions and highlight the promise of LLMs for PLC security. Our work provides a principled and practical foundation for LLM-based analysis of cyber-physical systems, emphasizing the role of domain knowledge, efficient adaptation, and formal guarantees. Full article

Cycle slips, abrupt discontinuities in carrier-phase measurements, pose a significant challenge for single-frequency GNSS receivers, particularly in real-time applications where rapid detection is critical. Unlike dual-frequency approaches, these receivers cannot rely on redundant combinations to isolate slips from other errors. This study proposes a real-time cycle slip detection algorithm for single-frequency GNSS receivers based solely on short-term differencing of pseudorange and carrier-phase observables. The method employs a two-step logic: first-order differencing of code-minus-carrier and second-order differencing of carrier phase. Both steps employ satellite elevation-dependent adaptive thresholds, enabling robust detection under diverse signal conditions. The method requires no user position, receiver-generated tracking flags, or additional sensor data. Experimental results reveal that the algorithm achieves over 98% detection accuracy for slips exceeding 10 cycles, with no false positives in artificial slip testing, and 87.93% agreement with Loss of Lock Indicators (LLI) during periods in which the receiver indicated signal instability. Full article

Management of smart greenhouses represents a transformative advancement in precision agriculture, enabling sustainable intensification of food production through the integration of multi-sensor networks, intelligent control, and sophisticated data filtering techniques. Unlike conventional greenhouses that rely on manual monitoring, smart greenhouses combine environmental sensors, Internet of Things (IoT) platforms, and artificial intelligence (AI)-driven decision making to optimize microclimates, improve yields, and enhance resource efficiency. This review systematically investigates three key technological pillars, multi-sensor monitoring, intelligent control, and data filtering techniques, for smart greenhouse environment management. A structured literature screening of 114 peer-reviewed studies was conducted across major databases to ensure methodological rigor. The analysis compared sensor technologies such as temperature, humidity, carbon dioxide (CO₂), light, and energy to evaluate the control strategies such as IoT-based automation, fuzzy logic, model predictive control, and reinforcement learning, along with filtering methods like time- and frequency-domain, Kalman, AI-based, and hybrid models. Major findings revealed that multi-sensor integration enhanced precision and resilience but faced changes in calibration and interoperability. Intelligent control improved energy and water efficiency yet required robust datasets and computational resources. Advanced filtering strengthens data integrity but raises concerns of scalability and computational cost. The distinct contribution of this review was an integrated synthesis by linking technical performance to implementation feasibility, highlighting pathways towards affordable, scalable, and resilient smart greenhouse systems. Full article

In recent years, Christian language and symbols have played an increasingly prominent role in right-wing populist rhetoric across many western countries. The form of religious expression in right-wing populist rhetoric corresponds to the kind of religiousness that characterizes the contextual factors under which rhetorical communication occurs. In making this case, this article analyzes salient themes found in speeches, interviews, and manifesto content to uncover dynamic similarities and dissimilarities between right-wing populist parties in two religiously different contexts: the Alternative für Deutschland (“Alternative for Germany”) and Fratelli d’Italia (“Brothers of Italy”). First, I discuss how the vertical and horizontal tensions within the populist framework combine with notions of civilizational identity and show the extent to which positive references to Christianity are combined with negative references to Islam. Next, I demonstrate how these parties differ in their treatment of the transcendent and doctrinal qualities of religious commitment. Lastly, I show the ways in which religion is used to help brighten symbolic boundaries, as well as the functions served by the dramatic and emotional elements that are embedded in the process of boundary formation. In light of the respective contextual factors that mediate the nature of religious expression, I discuss how understanding the social logic of this rhetoric can grant valuable insight into what has become such a critical feature of populism’s social character. Full article

In response to the problems of rough modeling and insufficient coverage of edge interaction scenarios in autonomous driving tests, this paper proposes a scene generation method based on interaction coding. The method constructs a hierarchical parameter system of function–logic–specific scene, uses the time difference of arrival at interaction points (TTC_diff) to determine the critical state of interaction, and realizes the efficient generation and iterative optimization of high-risk scenes. Taking the unprotected left turn at the signal intersection of urban roads as an example, the interaction coding combination is determined in combination with real traffic data, the test scene compatible with OpenSCENARIO is generated, and CARLA0.9.15 is called for test verification. The results show that the interaction intensity is significantly negatively correlated with the trustworthiness score (−0.815), the generated scene has high coverage, and both safety and challenge are taken into account. Compared with the simulated annealing method, the method in this paper performs better in terms of iteration efficiency, scene difficulty control, and score stability, which provides an efficient and reliable test strategy for the trustworthiness evaluation of autonomous driving. Full article

One of the limiting factors in the implementation of water resource management is the absence of tools that help water programs evaluate processes and progress. This is because, until now, the indicators that have been developed have not addressed specific local characteristics and issues. Therefore, in this research, a set of indicators has been proposed, with the purpose of developing a management index for urban public water supply, which will consider the Drinking Water and Sewer System of León (SAPAL), in the Mexican state of Guanajuato, as case study. This index will be useful to measure progress toward sustainable development, monitor the impact of public policies, and foster citizen participation. In order to propose a methodology that aligns with the changing environments, where proper decision-making is key to the current water management requirements, the combination of the Analytic Hierarchy Process (AHP) and Fuzzy Logic (FL) methodologies will be helpful for proper decision-making. All this will foster a paradigm shift towards appropriate water management actions that allow for the conditions and availability of human and natural resources, which the municipality has control of, for a long-term improvement that guarantees the well-being of the population. Full article

A probabilistic version of geometry is introduced. The fifth postulate of Euclid (Playfair’s axiom) is adopted in the following probabilistic form: consider a line and a point not on the line—there is exactly one line through the point with probability P, where $0\le P\le 1$. Playfair’s axiom is logically independent of the rest of the Hilbert system of axioms of the Euclidian geometry. Thus, the probabilistic version of the Playfair axiom may be combined with other Hilbert axioms. $P=1$ corresponds to the standard Euclidean geometry; $P=0$ corresponds to the elliptic- and hyperbolic-like geometries. $0<P<1$ corresponds to the introduced probabilistic geometry. Parallel constructions in this case are Bernoulli trials. Theorems of the probabilistic geometry are discussed. Given a triangle and a line drawn from a vertex parallel to the opposite side, the event that this line is actually parallel occurs with probability P. Otherwise, the line may intersect the side or diverge. Parallelism is not transitive in the probabilistic geometry. Probabilistic geometry occurs on the surface with a stochastically variable Gaussian curvature. Alternative geometries adopting various versions of the probabilistic Playfair axiom are introduced. Probabilistic non-Archimedean geometry is addressed. Applications of the probabilistic geometry are discussed. Full article

Recipes available on the web often lack nutritional transparency and clear indicators of dietary suitability. While searching by title is straightforward, exploring recipes that meet combined dietary needs, nutritional goals, and ingredient-level preferences remains challenging. Most existing recipe search systems do not effectively support flexible multi-dietary reasoning in combination with user preferences and restrictions. For example, users may seek gluten-free and dairy-free dinners with suitable substitutions, or compound goals such as vegan and low-fat desserts. Recent systematic reviews report that most food recommender systems are content-based and often non-personalized, with limited support for dietary restrictions, ingredient-level exclusions, and multi-criteria nutrition goals. This paper introduces DietQA, an end-to-end, language-adaptable chatbot system that integrates a Knowledge Graph (KG), Retrieval-Augmented Generation (RAG), and a Large Language Model (LLM) to support personalized, dietary-aware recipe search and question answering. DietQA crawls Greek-language recipe websites to extract structured information such as titles, ingredients, and quantities. Nutritional values are calculated using validated food composition databases, and dietary tags are inferred automatically based on ingredient composition. All information is stored in a Neo4j-based knowledge graph, enabling flexible querying via Cypher. Users interact with the system through a natural language chatbot friendly interface, where they can express preferences for ingredients, nutrients, dishes, and diets, and filter recipes based on multiple factors such as ingredient availability, exclusions, and nutritional goals. DietQA supports multi-diet recipe search by retrieving both compliant recipes and those adaptable via ingredient substitutions, explaining how each result aligns with user preferences and constraints. An LLM extracts intents and entities from user queries to support rule-based Cypher retrieval, while the RAG pipeline generates contextualized responses using the user query and preferences, retrieved recipes, statistical summaries, and substitution logic. The system integrates real-time updates of recipe and nutritional data, supporting up-to-date, relevant, and personalized recommendations. It is designed for language-adaptable deployment and has been developed and evaluated using Greek-language content. DietQA provides a scalable framework for transparent and adaptive dietary recommendation systems powered by conversational AI. Full article

This article presents a gear selection methodology for electric vehicle powertrains employing two identical Permanent Magnet Synchronous Machines (PMSMs) arranged in a twin-drive configuration. Both machines are coupled through a shared output shaft and operate with coordinated torque–speed characteristics, enabling efficient utilization of the available gear stages. The proposed approach establishes a control-oriented drivetrain framework that incorporates mechanical dynamics together with real-time thermal states and loss mechanisms. Unlike conventional strategies, which rely mainly on static or speed-based shifting rules, the method integrates detailed thermal and electromagnetic loss modeling directly into the gear-shifting logic. By accounting for the dynamic thermal behavior of PMSMs under variable load conditions, the strategy aims to reduce cumulative drivetrain losses, including electromagnetic, thermal, and mechanical, while maintaining high efficiency. The methodology is implemented in a MATLAB/Simulink R2024a and LabVIEW 2024Q2 co-simulation environment, where thermal feedback and instantaneous efficiency metrics dynamically guide gear selection. Simulation results demonstrate measurable improvements in energy utilization, particularly under transient operating conditions. The resulting efficiency maps are broader and flatter, as the motors’ operating points are continuously shifted toward zones of optimal performance through adaptive gear ratio control. The novelty of this work lies in combining real-time loss modeling, thermal feedback, and coordinated gear management in a twin-motor system, validated through experimentally motivated efficiency maps. The findings highlight a scalable and dynamic control framework suitable for advanced electric vehicle architectures, supporting intelligent efficiency-oriented drivetrain strategies that enhance sustainability, thermal management, and system performance across diverse operating conditions. Full article

Recent studies on detecting hallucinations in summaries follow a method of decomposing summaries into atomic content units (ACUs) and then determining whether each unit logically matches the document text based on natural language inference. However, this fails to consider discourse link relations such as temporal order, causality, and purpose, leading to the inability to detect conflicts in semantic connections between individual summary ACUs, even when the conflicts are present in the document. To overcome this limitation, this study proposes a method of extracting Discourse Link-Aware Content Unit (DL-ACU) by converting the summary into an Abstract Meaning Representation (AMR) graph and structuring the discourse link relations between ACUs. Additionally, to align summary ACUs with corresponding document information in a fine-grained manner, we propose a Selective Document-Atomic Content Unit (SD-ACU). For each summary ACU, the SD-ACU retrieves only the most relevant document sentences and then decomposes them into document ACUs. Applying the DL-ACU module to existing hallucination detection systems such as FIZZ and FENICE reduces the error rate of discourse link errors on FRANK. When both modules are combined, the system improves balanced accuracy and ROC-AUC across major benchmarks. This suggests the proposed method effectively captures discourse link errors while enabling ACU-to-ACU alignment. Full article

Following the significant disruption of traditional teaching by the COVID-19 pandemic, gamified education—an approach integrating technology and cognitive strategies—has gained widespread attention and use among educators and learners. This study explores how game-based learning, supported by situated learning theory and game design elements, can boost learner motivation and knowledge construction. Using 20,293 user comments from the Chinese video platform Bilibili, the study applies sentiment analysis and LDA to uncover users’ sentimental tendencies and cognitive themes. The analysis identifies four core themes: (1) The application of contextual strategies in language learning, (2) Autonomous exploration and active participation in gamified learning, (3) Progressive enhancement of logical thinking in gamified environments, and (4) Teaching innovation in promoting knowledge construction and deepening. Building on these findings, the study further develops a cognitive scaffolding model integrating “contextualization–procedural logic–active construction” to explain the mechanisms of motivation–cognition interaction in gamified learning. Methodologically, this study innovatively combines LDA topic modeling with sentiment analysis, offering a new approach for multidimensional measurement of learner attitudes in gamified education. Theoretically, it extends the application of situated learning theory to digital education, providing systematic support for instructional design and meaning-making. Findings enrich empirical research on gamified learning and offer practical insights for optimizing educational platforms and personalized learning support. Full article

This paper addresses the trajectory tracking control problem of Autonomous Underwater Vehicles (AUVs). A comprehensive mathematical model is first established based on Newtonian mechanics, incorporating both kinematic and dynamic equations. By reasonably neglecting the minor influence of roll motion, a five-degree-of-freedom (5-DOF) underactuated AUV model is derived. Considering the strong nonlinearities, high coupling, and time-varying hydrodynamic parameters typical of underwater environments, a fuzzy adaptive PID controller is proposed. This controller combines the adaptability of fuzzy logic with the structural simplicity and reliability of PID control, making it well-suited to the demanding requirements of AUV motion control. Extensive simulation experiments are conducted to evaluate the controller’s performance under various operating conditions. The results show that the fuzzy adaptive PID controller significantly outperforms conventional PID and standalone fuzzy logic controllers in terms of convergence speed and oscillation suppression. Furthermore, a theoretical stability analysis is provided to ensure that the proposed control system remains stable under time-varying fuzzy gain scheduling, confirming its effectiveness and potential for practical application in underwater vehicle control. Full article

Asset maintenance management is critical in industries such as petrochemicals and oil and gas (O&G), where complex, interdependent systems heighten failure risks. Maintenance costs represent a significant portion of operational expenditures, emphasizing the need for effective risk-based strategies. A considerable gap exists in integrating uncertainty modelling into both criticality assessment and maintenance planning. Existing approaches often neglect combining expert-driven assessments with optimization models, limiting their applicability in real-world scenarios where cost-effective and risk-informed decision-making is crucial. Maintenance inefficiencies due to suboptimal asset selection result in substantial financial and safety-related consequences in asset-intensive industries. This study presents a framework integrating Reliability-Centered Maintenance (RCM) principles with fuzzy logic and decision-support methodologies to optimise maintenance portfolios for offshore O&G assets, particularly focusing on corrosion management. The framework evaluates asset criticality through comprehensive FMEA, employing MCDM and fuzzy logic to enhance maintenance planning and extend asset lifespan. A case study on offshore asset corrosion management demonstrates the framework’s effectiveness, selecting 60% of highly critical assets for maintenance, compared to 10% by current industry practices. This highlights the potential risk reduction and prevention of critical failures that might otherwise go unnoticed, providing actionable insights for asset integrity managers in the O&G sector. Full article

In complex testing scenarios of large-scale information systems, communication networks, and the Internet of Things, exhaustive testing is always prohibitively expensive and time-consuming. T-way combinatorial testing has emerged as a cost-effective solution. To address the problem of generating test suites for t-way combinatorial testing, a Logical Combination Index Table (LCIT) is proposed. Utilizing the LCIT, the t-way combinatorial coverage model (t-wCCM) is constructed to guide the test case generation process. Multi-start Construction Procedure (MsCP) algorithm is employed to generate an initial solution set, and then local optimization is performed using a low-complexity Balanced Local Search (BLS) algorithm. Further, Evolutionary Path Relinking combined with the BLS (EvPR + BLS) algorithm is proposed to accelerate the convergence process. Experiments show that the proposed Refined Evolutionary Heuristic (REH) algorithm performs best on 50% of the classic test instances, and performs superior to the average on 66% of the test instances, with a relative improvement in the maximum computation time of approximately 33.33%. Full article