Search Results (3,953)

With the rapid growth of transportation electrification and smart energy systems, the reliable operation of electric vehicle (EV) charging infrastructure has become an important issue for sustainable transport, since charging faults may interrupt service and shorten equipment lifetime. However, practical charging environments are often characterized by heterogeneous operating conditions and severely imbalanced fault distributions, which limit the effectiveness of conventional fault diagnosis methods. To address these challenges, this study proposes a lightweight Proto-Contrastive Discriminative Learning (PCDL) framework for intelligent fault diagnosis in EV charging systems. The proposed method combines supervised contrastive learning with a prototype-distance discrimination mechanism to improve the identification of rare abnormal states under long-tailed data conditions. Heterogeneous charging features, including discrete control signals and continuous total harmonic distortion (THD) indicators, are projected into a discriminative embedding space, while anomaly detection is performed according to the relative distances between samples and class prototypes. Experimental results on a publicly available EV charging-pile monitoring dataset, containing 122,144 samples with four discrete control/safety features and two THD-based power-quality features, demonstrate that the proposed framework maintains stable detection performance under imbalance ratios of 1:1, 1:10, and 1:100. Under the most challenging 1:100 condition, the proposed method achieves an F1-score of 84.21%, representing a 29.08% improvement over the strongest baseline method. In addition, the framework requires only approximately 11 KB of memory and maintains CPU inference latency below 6.3 ms, demonstrating strong potential for real-time deployment on resource-constrained edge devices. These results suggest that the proposed framework can provide a lightweight diagnostic tool for practical charging stations and support safer and more reliable EV charging operation. Full article

Symmetry plays a fundamental role in the structural analysis of lattice-based systems, particularly in graphene-like molecular structures. In chemical graph theory, counting independent sets is equivalent to computing the Merrifield–Simmons (M–S) index, a key descriptor of molecular stability in conjugated systems. Most existing exact counting methods rely on regular lattice symmetry, where structural uniformity simplifies computation; however, these approaches are difficult to extend to irregular graphs, where symmetry breaking introduces non-local dependencies and increases computational complexity. This paper proposes an asymmetry-aware algorithmic framework based on Hamiltonian traversals and a traversal-induced pathwidth parameter $w\left(G\right)$, defined through backward dependencies. Our method organizes non-local adjacencies into a bounded set of structured constraints, enabling a dynamic programming scheme over a reduced state space. The resulting algorithm runs in time $\mathcal{O}\left(2^{w\left(G\right)}·\mathrm{poly}\left(n\right)\right)$ and is fixed-parameter tractable with respect to $w\left(G\right)$. The results demonstrate that asymmetry-aware traversal strategies enable efficient exact enumeration in irregular mesh graph families, providing a robust computational framework for analyzing molecular descriptors in graphene-based structures with topological defects such as Stone–Wales transformations. Full article

This paper presents detailed small-signal modeling and modal analysis of a 1.5 MW grid-connected doubly fed induction generator (DFIG) wind turbine. A full nonlinear model capturing stator, rotor, and grid-side converter dynamics, DC-link voltage behavior, and the wind-turbine electromechanical subsystem is developed in the synchronously rotating d-q frame and linearized around a realistic steady-state operating point. The resulting state-space representation is utilized to investigate the intrinsic dynamic characteristics of the DFIG through eigenvalue analysis, modal classification, and participation factor evaluation. The results show that the open-loop DFIG contains a weakly damped electrical mode, a slowly growing unstable mode, and a near-integrator mode linked to the DC-link voltage, all of which strongly influence system behavior under disturbances. Parameter-sensitivity studies reveal how rotor speed, stator voltage, and rotor resistance affect the dominant modes, highlighting significant deterioration under low-voltage and low-speed operating conditions. Time-domain small-signal responses to temporary voltage sags further expose the vulnerability of DC-link voltage and power outputs when no coordinated control is applied. Overall, the study establishes a rigorous dynamic baseline for DFIG systems and provides the foundational insight needed for a follow-up paper focused on advanced damping and robustness-enhancing controllers. Full article

Beer is one of the oldest and most widely consumed fermented beverages in the world. However, barley production is increasingly vulnerable to agricultural and socioeconomic pressures, particularly in temperate growing regions where rising temperatures threaten yield stability. In contrast, rice is projected to experience comparatively smaller yield declines, highlighting its potential as a more climate-resilient starch source for brewing. This opportunity is especially relevant in the United States, where Arkansas produces approximately half of the nation’s rice supply. Large commercial breweries have successfully incorporated rice through the use of cereal cookers, but these systems are often impractical for smaller operations because of their cost and space requirements. In addition, rice supplied to the brewing industry is often sourced as a byproduct of the edible rice market, where multiple cultivars may be blended, reducing consistency and obscuring cultivar-specific effects that influence brewing performance. This manuscript reviews variation among rice cultivars in the physical, chemical, and agronomic properties relevant to brewing and examines how these differences affect extract yield and processability. Particular emphasis is placed on practical strategies to overcome technical barriers, including alternative mashing approaches and the use of heat-stable exogenous enzymes to facilitate the use of milled rice without dedicated cereal-cooking infrastructure. Full article

This paper establishes a rigorous mathematical foundation for modeling scientific research design as a dynamic, decision-centric system. We introduce the Scientific Decision Architecture for Complex Systems (SDA-CS), formalizing research configurations as trajectories within a complete neutrosophic metric space $\mathcal{D}$. By employing the Banach Fixed-Point Theorem, we prove that the research evolution operator $\Psi$ acts as a contraction mapping, ensuring convergence toward a unique, stable methodological state ${\mathrm{V}}_{\mathrm{d}}^{\ast }$ even under conditions of high initial indeterminacy. The framework integrates neutrosophic logic to explicitly characterize indeterminacy ($I$), and local stability is analyzed through the spectral radius of the methodological Jacobian matrix $J_{\Psi }$. Furthermore, we examine the system through information theory, demonstrating that the SDA-CS architecture acts as an entropy-reduction mechanism that promotes information gain by pruning inconsistent decision paths. These theoretical results provide a cybernetic basis for ensuring reproducibility and structural robustness in complex scientific investigations. Full article

The rapid growth of the low-altitude economy and urban air mobility (UAM) is reshaping urban transport and infrastructure systems. However, current planning practices still tend to treat green spaces, stormwater facilities, and drone infrastructure as separate subsystems. This paper proposes a Garden Hydrology UAV collaborative infrastructure framework for resilient urban low-altitude logistics and inspection. Pocket parks and sponge city facilities (rain gardens, detention basins) are redesigned as multi-functional UAV bases that integrate take-off/landing and charging with stormwater retention and recreation. A SWMM-based hydrological model provides time-varying inundation and storage states, which are mapped into dynamic node availability constraints for UAV operations, using EPA SWMM 5.2. A multi-objective optimization model is formulated to minimize logistics operation cost, hydrological risk exposure and noise impact on sensitive receptors, while respecting airspace and battery constraints. A stylized 4 km² high-density district is used to evaluate three scenarios: depot-only operations, garden–UAV integration without hydrological coupling, and the full collaborative framework with SWMM-based node availability and high-precision navigation. Simulation results show that the integrated design reduces makespan by up to 19.7%, energy use by 22.3%, and hydrological risk exposure by 63.4%, while lowering noise exposure by 21.3%, relative to the baseline. The study suggests that garden and sponge city infrastructures can become key physical supports of smart low-altitude networks under the low-altitude economy. Full article

Background: Glaucoma is a progressive, age-related optic neuropathy and a leading cause of irreversible blindness worldwide. Ensuring timely diagnosis and equitable access to surgical care is therefore essential to prevent avoidable vision loss. Methods: Nationwide registry-based data on hospital-based glaucoma diagnoses and surgical procedures over an 11-year period were analyzed and stratified by treatment region and area of residence. Population data were age-stratified to allow calculation of standardized diagnosis and surgery rates per 10,000 population aged ≥ 60 years. Regional comparisons were performed, and demographic projections for populations aged ≥ 60 years were generated using an exponential smoothing state space model. Results: Geographic variation in glaucoma care was observed, despite broadly similar demographic profiles across regions. Surgery-to-diagnosis ratios among individuals aged ≥ 60 years differed markedly between regions. Relevant for future healthcare planning, age forecasting suggests an increase in people aged ≥ 60 years over the coming years. Conclusions: Geographic disparities in glaucoma surgical care may persist even in well-resourced healthcare systems. Centralization of surgical services may contribute to differences, although explanations such as variation in referral patterns and clinical decision-making cannot be excluded. These findings highlight a broader, internationally relevant challenge: aligning glaucoma care delivery with shifting demographic needs. Full article

With more satellites, richer payload resources, and more diverse service functions, satellite systems are increasingly operated as large space–ground networks. These networks must schedule arriving missions under changing topology, gateway access, beam availability, weather-affected links, spectrum compatibility, and mission time windows. Offline optimization can compute high-quality schedules when the mission set, satellite visibility windows, and resource states are known before execution, but repeated replanning is costly for asynchronous arrivals. Online heuristics make faster decisions from local route rules, but they do not evaluate how an accepted service path changes the capacity left for later requests. Reinforcement-learning schedulers can adapt from delayed scheduling outcomes. However, many generic policies rely on fixed-step state updates or flat compound-action scores, whereas online satellite scheduling makes decisions at irregular arrivals over continuously evolving topology and capacity-coupled service paths. We propose TopoAgent, an online reinforcement-learning agent for heterogeneous satellite mission scheduling. TopoAgent models each request as a service-path decision, propagates compound feasibility through the satellite–gateway–beam hierarchy, and uses a capacity-aware policy to choose among feasible paths. A deterministic constraint manager places the selected path in time, while SRV guides the policy toward assignments that preserve reusable beam capacity. In a high-fidelity simulator, TopoAgent achieves a 74.7% mission completion rate and a 75.5% high-priority completion ratio over five seeds. Full article

Underground space is increasingly positioned in contemporary urban discourse as a strategic resource for sustainable spatial and territorial development, particularly under conditions of limited surface capacity, growing infrastructural demand, and the need for long-term urban resilience. However, its implementation remains constrained by insufficient institutional, planning, and governance integration. Starting from this problem, this paper assesses the institutional readiness of Serbia’s spatial and urban planning system for the integration of underground planning into the territorial development system. The methodological approach is based on the development of an analytical framework for institutional readiness, structured around three key dimensions: regulatory–institutional, spatial–infrastructural, and governance–coordination. This research is conducted through a qualitative analysis of legislative, strategic, planning, and supplementary sources, using stratified criteria—normative, operational, and integrative levels—which enables a structured, document-based diagnostic assessment of the current state of the system. The results indicate that institutional readiness in Serbia is at a low to medium-low level. Although a partially developed normative framework and certain technical-informational capacities exist, underground space is not clearly recognised as a distinct planning category or as an integrated three-dimensional spatial resource. The spatial–infrastructural dimension reveals the existence of relevant cadastral, geospatial, and infrastructural foundations, but without their sufficient integration into a unified 3D planning and governance system. The key limitation is identified in the governance–coordination dimension, where fragmented competences, uneven local capacities, and the absence of dedicated coordination mechanisms hinder the systematic application of underground planning. The paper concludes that the integration of underground planning in Serbia requires gradual institutional transformation toward an integrated, three-dimensional, and long-term-oriented model of spatial governance. Its contribution lies in formulating an initial diagnostic framework that connects debates on planning systems, institutional fragmentation, spatial data integration, and territorial governance, and may serve as a basis for further research and policy development in the field of integrated territorial development. Full article

Underwater images often suffer from color cast, low contrast, scattering-induced haze, and texture degradation, which limit the performance of underwater visual perception systems. To address these problems, this study proposes FreqMambaGAN, a frequency-decoupled selective state-space cycle-adversarial network for underwater image enhancement. The proposed method is built upon a CycleGAN-style bidirectional translation framework and introduces a frequency-decoupled Mamba generator to separately model low-frequency color and illumination information and high-frequency texture and edge details. In addition, Efficient Mamba Blocks are embedded into the generator and discriminator to enhance long-range dependency modeling with linear computational complexity. Skip-attention connections are further adopted to preserve shallow spatial details during reconstruction. To improve training stability and imaging plausibility, a multi-stage training strategy is designed by combining supervised warm-up, unpaired cycle-adversarial learning, perceptual regularization, total variation smoothing, and a lightweight physics-inspired consistency constraint based on dark-channel and underwater image-formation priors. Experiments on public underwater image enhancement datasets demonstrate that FreqMambaGAN achieves competitive quantitative performance and visually improved enhancement results in terms of color correction, contrast restoration, haze suppression, and structural preservation. These results indicate that integrating frequency-domain decomposition with selective state-space modeling is effective for underwater image enhancement. Full article

Mental health interventions involve complex and evolving situations that require careful reasoning and transparency. This paper presents a knowledge-based system designed to simulate and analyze intervention strategies for student depression in a controlled and explainable setting. The system combines reinforcement learning with formal ontological modeling. A simulation environment, grounded in large-scale integrated student mental health datasets containing questionnaire-derived indicators, represents the evolution of students’ psychological states under a set of clinically informed intervention actions. The proposed framework is evaluated using a composite dataset constructed by integrating multiple publicly available student mental health datasets from Kaggle and Figshare, incorporating integrated student mental health datasets containing questionnaire-derived indicator measures such as depression, anxiety, and lifestyle indicators. A learning agent explores alternative intervention strategies through interaction with this environment. All states, actions, and outcomes are formalized within an OWL ontology, making the decision structure explicit. By embedding learned policies into a structured knowledge representation, the system allows intervention dynamics to be inspected, queried, and analyzed independently of the underlying learning mechanism. Reinforcement learning is used to generate and refine candidate strategies, while ontology provides a stable and interpretable model of the decision space. Experimental results show that the approach can identify coherent intervention strategies within the simulation environment while preserving transparency. The study demonstrates how adaptive learning and symbolic knowledge representation can be integrated within a single knowledge-based system, offering a structured and explainable approach to sequential decision analysis in sensitive domains. Full article

This paper develops a finite-state mathematical framework for structural stability and regime classification in discrete-time state–event–response systems whose effective transition structure is generated endogenously by state-dependent response rules. Unlike classical structural stability theory, which focuses on qualitative persistence in smooth dynamical systems, and unlike Markov-chain analysis, which typically assumes a fixed transition kernel, the proposed framework treats the transition graph as an induced object. The model specifies a finite state space, an event-generation law, an elasticity-dependent attenuation function, and a deterministic transition mapping. Structural regimes are classified by adjacency relations, communicating components, absorbing organization, and long-run occupancy support. A Monte Carlo verification layer is used only to examine whether the analytically defined topological regimes are visible in finite-sample occupancy signatures. The results indicate that, within the finite-state setting considered here, admissible disturbance scaling changes traversal frequency without changing graph identity, whereas elasticity variation can activate or deactivate effective edges and thereby generate structurally distinct regimes. Full article

Thin-film solar cells (TFSCs) remain a cornerstone of the global transition toward renewable energy, characterized by consistent reductions in manufacturing costs and steady gains in power conversion efficiency. In addition to electricity generation, TFSCs play an important role in advanced solar thermal cooling, heating, and energy storage systems, where their tunable optical absorption, low thermal mass, and flexibility enable integration with photovoltaic–thermal (PV/T) collectors, thermally driven cooling cycles, and hybrid thermal–electrical storage architectures. This paper provides a comprehensive review of prominent TFSC technologies, including copper indium gallium selenide (CIGS), cadmium telluride (CdTe/CdS), amorphous silicon (a-Si), copper zinc tin sulfide (CZTS), organic photovoltaics (OPVs), and metal halide perovskite solar cells (PSCs), with a focus on their material structures, performance specifications, and current efficiency benchmarks. Compared to state-of-the-art reviews, this article distinguishes itself by addressing next-generation innovations, cross-domain solar thermal–photovoltaic applications, and economic analysis. Specifically, the integration of machine learning and simulation-based material dynamics is examined to accelerate material discovery, process optimization, and the characterization of novel TFPV components relevant to coupled thermal–electrical energy systems. Furthermore, the study explores how additive manufacturing is transforming the industry through the development of high-efficiency electrodes, electrohydrodynamic atomization for thin-film deposition, and the fabrication of flexible solar arrays suitable for thermally integrated and building-scale energy systems, including space applications. By integrating advancements in module efficiency, scalable manufacturing approaches, and techno-economic analysis, this paper positions TFSCs as sustainable, resource-abundant technologies essential for next-generation solar thermal cooling, heating, and energy storage infrastructures. Full article

This paper proposes an improved metaheuristic algorithm named LHBBO, which incorporates legal and moral constraints into a human behavior-based optimization framework to tackle the limitations of conventional methods in high-dimensional and multimodal problem spaces. Three key innovations are introduced: a dual-layer normative audit mechanism that enforces hard legal and soft moral constraints during candidate evaluation; a jury-guided collaborative consultation strategy that diversifies search direction references; and a directional migration mechanism triggered by population diversity and stagnation metrics. The proposed LHBBO is evaluated on the CEC2017 and CEC2022 benchmark suites, where it demonstrates significantly better convergence behavior and solution quality compared to several state-of-the-art algorithms. Notably, in 100-dimensional tests, LHBBO improves optimization precision by over 97% relative to the standard HBBO. When applied to unsupervised visual anomaly detection in industrial settings using the M2AD dataset, LHBBO effectively optimizes key parameters of a collaborative discrepancy-based model. The resulting system achieves a 9.05% increase in pixel-level localization accuracy (AUPRO) compared to the baseline CDO model, confirming its practical utility in complex, non-convex industrial optimization tasks. Full article

Modern manufacturing systems operate under substantial uncertainty because automation modes, machine states, and workforce capabilities evolve across different time scales. Classical deterministic models often fail to capture rare disruptions, rapid switching between automation states, and stochastic variability in production systems. To address these limitations, we extend a Lotka–Volterra interaction model by incorporating fast Markov-switching for automation-mode transitions and Poisson-jump perturbations for rare operational shocks. Using phase-space aggregation and ergodic averaging, we derive a reduced limit system that preserves the main long-term behavior of the original multiscale process while remaining suitable for simulation and statistical analysis. Numerical experiments show that the aggregated model reproduces cyclical workforce-automation interactions, switching-dependent variability, and sensitivity to automation intensity and shock frequency. The proposed framework provides a tractable stochastic model for studying uncertainty, automation dynamics, and disruption effects in manufacturing systems. Full article