Search Results (3,587)

Context: Modern quantum-computing experimentation generates heterogeneous, context-dependent execution data whose scientific value depends on preserving calibration state, compilation decisions, and run outcomes in a traceable and repository-ready form. In the NISQ era, probabilistic outputs, time-varying hardware conditions, and opaque transpilation pipelines create a data-management problem that directly affects reproducibility, traceability, and long-term reuse of experimental records. Goal: This paper aims to address this gap by proposing a specialized metadata and schema model for managing quantum-circuit execution data as governed, machine-interpretable, and evolvable repository artifacts. Proposal: We propose QC-MM, a platform-agnostic metadata model for capturing, validating, and relating contextual evidence of quantum-circuit experiments. The model integrates time-indexed calibration binding, transpilation traceability, lightweight provenance links, validation rules, and controlled schema evolution through a JSON Schema specification. Results: The evaluation follows a multi-scenario protocol and shows that QC-MM captures dynamic calibration context in IBM Quantum Cloud, remains interoperable through a local SpinQ NMR device, and makes transpilation effects traceable through structured records. It also supports repeated-run statistical reporting and links compilation decisions to execution outcomes, including circuit-depth reductions and changes in an estimated fidelity proxy under different optimization settings. Conclusions: QC-MM provides a specialized data-modeling and schema-governance foundation for traceable quantum-experiment repositories. Beyond improving reproducibility-oriented reporting, the proposal contributes to metadata validation, controlled schema evolution, and repository-oriented management of contextual experimental data. Full article

Intelligent Transportation Systems (ITS) play a central role in the development of more efficient, adaptive, and resilient road networks. Traffic control strategies have progressively evolved from traditional approaches toward more intelligent and adaptive frameworks. This paper presents a taxonomy-based perspective on intelligent traffic control strategies for road networks, organizing existing approaches according to three complementary dimensions: control scope, decision-making mechanism, and control architecture. Based on this framework, the paper discusses representative methodologies, including rule-based control, model-based methods, simulation-based optimization, data-driven and artificial intelligence-based methods, and emerging cooperative strategies enabled by connected and automated vehicles (CAVs). The analysis also examines key application domains, such as traffic signal control, ramp metering, CAV-based traffic management, and simulation platforms, highlighting their operational principles, advantages, limitations, and implementation challenges. Particular attention is given to the transition from local and reactive control toward coordinated, predictive, and learning-based traffic management systems. The paper identifies major challenges related to scalability, robustness, interpretability, safety, real-world deployment, and the gap between simulation performance and practical implementation. The proposed taxonomy also supports practical comparison and preliminary selection of context-specific strategies. Future directions point toward integrated and hybrid frameworks combining data-driven adaptability, vehicle–infrastructure cooperation, and digital twin technologies. Full article

Multi-terminal voltage-source-converter-based HVDC (VSC-MTDC) systems are increasingly used to integrate renewable energy and interconnect asynchronous AC grids, but conventional fixed-coefficient droop control cannot simultaneously limit DC-voltage deviations, reduce operating losses, and preserve converter power margins during operating-point switching. This paper hypothesizes that a rule-based fuzzy adjustment of the droop slope can provide smooth multi-objective coordination without inter-station communication. A dual Mamdani fuzzy controller is developed: one controller adjusts the weighting between loss-oriented and power-margin-oriented droop coefficients according to converter power margin, while the other introduces a voltage-deviation correction according to DC-bus voltage. The controller is implemented and verified in a five-terminal MMC-based VSC-MTDC model built in PSCAD/EMTDC, where simulation data are generated under heavy-load, light-load, and power-reference switching scenarios using specified line and converter parameters. Compared with conventional droop control, the proposed strategy improves power-margin utilization, reduces operating-point discontinuities, and raises the minimum DC voltage from 370.2 kV to 381.4 kV in the severe switching case. The results confirm that fuzzy-slope droop control can achieve smoother operating-point switching and better coordinated optimization among voltage stability, operating loss, and converter reserve margin. Full article

Film posters serve as front-end visual communication media that shape viewers’ initial judgments of film genre, emotional tone, and viewing appeal. However, whether the optimal overall color configuration follows a universal rule or varies across poster types remains insufficiently examined. This study investigated how overall lightness and chroma influence the communication effects of film posters and identified type-specific optimal color intervals. Based on a cross-type poster sample library, film posters were classified into four visual grammar types: affable-entertaining, relational-emotional, spectacle-dynamic, and threat-suspenseful. Type-specific quantile thresholds for lightness and chroma were established within each category. Eye-tracking data, subjective ratings, mixed-effects response surface modeling, and constrained desirability optimization were combined to identify optimal regions of overall color configuration. The results show that no single optimal lightness–chroma interval applies across all poster types. The dominant optimal interval was low lightness–high chroma for affable-entertaining and relational-emotional posters, high lightness–low chroma for spectacle-dynamic posters, and medium lightness–high chroma for threat-suspenseful posters. These findings indicate that overall color optimization varies across poster types within the present experimental context and provide practical support for evidence-based, type-specific poster color design. Full article

A massive number of early-constructed small-to-medium-span bridges are collectively entering an “aging” phase in China. Meanwhile, vast amounts of unstructured bottom-level inspection texts remain underutilized. To address them, this paper proposes a data governance method. Large Language Models were leveraged to process unstructured defect data from 18,238 real-world bridges nationwide. The data were structurally cleaned and mapped into discrete features, revealing multidimensional vulnerabilities. On this basis, the Stable Contrastive Pattern Risk (SCPR) intelligent decision-making model was developed. The results demonstrate that, following robust filtration, 6 nationwide common risk rules were extracted from 2064 initial candidate combinations. These rules converge into three core risk patterns: the heavy-duty aging pattern, the substructure-dominated pattern, and the over-water small-span low-seismic-design pattern. Guided by these robust rules and specific damage enrichment characteristics, risk stratification and differentiated management strategies were further formulated for Class III bridges. This research facilitates a paradigm shift in bridge maintenance. It moves from reactive, post-event symptom characterization toward data-driven, proactive early warnings. This shift provides a substantive scientific foundation for optimizing resource allocation and enabling precise investment decisions at the road network level. Full article

Lost circulation in fractured formations is a major challenge during drilling operations, while conventional plugging evaluation methods relying solely on pressure-bearing curves and fluid-loss data often fail to accurately distinguish effective internal plugging from ineffective plugging behavior. To address this issue, a visualized plugging evaluation apparatus with high pressure-bearing capacity and large-window observation capability was developed to directly observe the plugging process and evaluate plugging performance under different fracture conditions. Based on the Ideal Packing Theory and the D₉₀ rule, plugging formulations were systematically evaluated under different fracture-width coefficients, slurry concentrations, and fracture-width conditions. The results showed that excessively large fracture-width coefficients or excessively high slurry concentrations could lead to premature “external plugging,” in which plugging materials accumulated near the fracture entrance without forming effective internal plugging structures. Although such cases exhibited rapid pressure buildup, visual observations confirmed that the fracture itself remained insufficiently sealed. Under the present experimental conditions, the optimized formulation with a fracture-width coefficient of 0.8 W and a slurry concentration of 25% exhibited the best overall plugging performance. The formulation reached 10 MPa in approximately 2650 s and successfully formed stable internal plugging structures under different fracture-width conditions, with the maximum variation in plugging time remaining within 7%. Field applications in Well BD-X further validated the effectiveness of the proposed method and optimized formulations under real drilling conditions. The developed apparatus and evaluation method provide a reliable experimental approach for optimizing plugging formulations and preventing lost circulation in fractured formations. Full article

Three-dimensional UAV operations require path planning methods that can jointly maintain route efficiency, threat avoidance, and trajectory smoothness under spatially distributed and time-varying constraints. To address this problem, this paper develops an integrated Dual-Layer Adaptive T-perturbation and Opposition-based Multi-Objective Particle Swarm Optimization framework, termed DATO-MOPSO, for 3D UAV path planning in complex threat environments. The method integrates a dual-layer adaptive inertia-weight and velocity-regulation mechanism with symmetric T-perturbation, an elite quasi-opposition-based learning strategy for diversity recovery and feasible local exploitation, and an archive-driven simulated annealing rule for stagnation-aware personal-best updating. A three-objective model minimizing path length, threat exposure, and path smoothness is established, and comparative experiments against MOPSO, ZAMOPSO, NSGA-II, and SPEA2 are conducted in both static and dynamic environments, together with statistical and ablation analyses. In the static scenario, DATO-MOPSO achieved the highest mean HV and stable repeated-run performance, but its IGD was comparable to ZAMOPSO with higher computational cost. In the dynamic scenario, DATO-MOPSO showed its main advantage, achieving the highest mean HV and the lowest mean IGD with statistically significant HV and IGD improvements over all baselines. Overall, DATO-MOPSO is most advantageous in time-varying complex threat environments, whereas its static-scenario advantages are accompanied by higher computational cost. Full article

Background: Patients with mild traumatic brain injury (mTBI) have a small but clinically relevant risk of intracranial injury (ICI), requiring timely detection. Computed tomography (CT) remains the diagnostic gold standard but is costly and exposes patients to ionising radiation. Combining blood-based biomarkers, glial fibrillary acidic protein (GFAP) and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1), with clinical decision rules may allow safe exclusion of ICI without CT, reducing unnecessary imaging, radiation exposure, and resource use. Methods: A systematic review of clinical and economic studies in patients with mTBI was registered in PROSPERO (CRD420251051158). Searches were conducted in January 2025 and updated in May 2025 in MEDLINE, Embase, and the Cochrane Library. The aim was to assess the diagnostic accuracy and economic value of the combination of GFAP and UCH-L1 compared with CT scanning to rule out ICI in both adults and children with mTBI. Where available, studies directly comparing GFAP and UCH-L1 with S100β were also analysed descriptively. The quality of the clinical evidence was assessed with QUADAS-2 and GRADE. Meta-analyses used a bivariate random-effects model, with heterogeneity and sensitivity analyses explored. Results: Overall, 21 studies were considered in our review. Moderate- to high-quality evidence indicates that GFAP and UCH-L1, when used together with clinical assessment, have very high sensitivity and can reliably rule out ICI in adults with mTBI presenting within 12 h to the emergency department. Evidence for paediatric populations shows promise but remains very limited. Specificity is low, particularly in older adults, which limits the ability to reduce CT use in this high-risk group. Research on age-adjusted cut-offs is ongoing and may help to reduce the proportion of false positive tests without compromising sensitivity. Few studies directly compared GFAP and UCH-L1 with S100β, with slightly higher to equivalent sensitivity for GFAP and UCH-L1. Economic evaluations suggest possible cost savings and reduced CT utilisation, but these analyses rely on assumptions unsupported by robust data and are highly context-dependent. There is a lack of clarity in the included studies regarding whether existing clinical head rules were used to define the study populations (i.e., to determine which patients would be recommended for CT scanning) and, if so, which specific rules were applied. Conclusions: Evidence shows that GFAP and UCH-L1 can safely exclude ICI in adults with mTBI in whom a CT scan would otherwise be considered based on clinical assessment or decision rules. Nevertheless, real-world evidence and cost-effectiveness data are scarce. Further prospective studies, including paediatric and elderly populations, and integration with clinical decision rules will be informative to ensure optimal use in clinical practice. Full article

Background/Objectives: We investigated the association of serum ferritin and procalcitonin (PCT) with Sepsis-related Organ Failure Assessment (SOFA)-2 score-based organ dysfunction severity in intensive care patients with COVID-19-associated sepsis. Methods: Patients were stratified by day 5 ferritin (ng/mL) and PCT (μg/L) levels; associations were analysed across severity groups defined by an SOFA-2 score of <5 (mild) or ≥5 (severe). Results: Day 5 PCT did not predict the SOFA-2 score (p > 0.05). The optimal day 5 ferritin cut-off was >1191 ng/mL (35.78% sensitivity, 82.38% specificity; area under the curve (AUC) = 0.608). Day 5 ferritin was associated with SOFA-2 severity in the univariable analysis but did not remain an independent correlate after adjustment for C-reactive protein (CRP) and lactate dehydrogenase (LDH); in a mortality model, neither ferritin nor PCT independently predicted intensive care unit (ICU) death. PCT provided no predictive value beyond existing inflammatory markers, consistent with its suppression during viral infections. Conclusions: Day 5 ferritin reflects, rather than independently predicts, organ dysfunction severity and may complement, rather than replace, established multi-parameter scoring. Relative to the independent determinants of severity and mortality (PaO₂/FiO₂ ratio, LDH, CRP, and age), day 5 ferritin is a specific, rule-in adjunctive marker of concurrent organ dysfunction rather than a standalone prognostic tool. Whether these associations extend to non-COVID sepsis populations requires prospective study. Full article

Background: Spinal cord stimulation (SCS) is an established intervention for refractory chronic neuropathic pain, but response to trial stimulation and long-term benefit remain heterogeneous. Clinicians need practical tools to document patient-selection domains discussed in the neuromodulation literature without overstating the precision of currently available evidence. Methods: We conducted a narrative synthesis of randomized trials, cohort studies, registry analyses, systematic reviews, and consensus recommendations addressing SCS outcomes and candidate selection. The objective was not to derive or validate a multivariable prediction model, but to construct a transparent, bedside-oriented framework organizing clinically accessible domains relevant to SCS trial candidacy. Results: Six domains were incorporated into the proposed SCS Trial Success Score (STSS): primary indication, psychological status, smoking status, opioid burden, body mass index, and pain duration. The resulting 0 to 12 point score is presented as an evidence-informed clinical profile rather than a validated prognostic instrument. Three descriptive categories are proposed: more favorable profile, optimization-sensitive profile, and less favorable profile. These categories are intended to guide documentation, shared decision-making, and optimization of modifiable factors, not to determine eligibility automatically. Conclusions: Pending prospective validation, checklist-mode use is the preferred interim application of the STSS. The framework may support structured pre-trial assessment, identification of modifiable factors, and shared decision-making. It should not be used as a standalone numerical decision rule, to deny access to neuromodulation, or to replace multidisciplinary judgment. Prospective derivation, calibration, external validation, and decision-curve analysis are required before the STSS can be considered a clinical prediction rule. Full article

As the global energy transition accelerates, distribution systems are integrating increasing shares of inverter-interfaced renewables, making reliable voltage support a key operational requirement. In grid-connected microgrids, especially weak radial feeders in rural and remote areas, voltage-reactive power (Volt/Var) control must coordinate multiple inverters under uncertainty from photovoltaic (PV) intermittency, load volatility, and point-of-common-coupling (PCC) disturbances. Existing droop, model-based optimization, and non-graph reinforcement learning (RL) approaches often rely on fixed rules or do not explicitly exploit electrical topology, which limits adaptive coordination. To address this gap, we propose a topology-aware graph reinforcement learning framework for voltage-reactive power control in grid-connected microgrids under uncertainty. The method encodes node states with a graph convolutional network (GCN) and learns coordinated PV/storage reactive-power actions via proximal policy optimization (PPO) with a multi-objective reward balancing voltage quality, control effort, and action smoothness. In a controlled comparison against a multilayer perceptron (MLP)-PPO baseline with identical action space, reward, and PPO objective, our method reduces voltage violation rate (VVR) from 0.0316 ± 0.0086 to 0.0048 ± 0.0019. Additional validation on a modified IEEE 33-bus feeder further reduces VVR from 0.00726 for MLP-PPO and 0.02999 for Droop control to 0.00095, supporting the effectiveness of topology-aware state representation on a larger radial benchmark feeder. Full article

This paper addresses the bidirectional multi-chamber lock scheduling problem by formulating a multi-objective mixed-integer linear programming (MILP) model that simultaneously minimizes average ship waiting time and maximizes chamber utilization. A tailored adaptive large neighborhood search (ALNS) algorithm is developed specifically based on the principle of the destruction and reconstruction of solutions. The algorithm efficacy is validated using the real-word data from Huai’an Lock of the Subei canal. The scheduling rules and parameters are defined from practical operation records. Simulation results demonstrate that the ALNS-based optimization significantly improves lock performance with average chamber utilization increasing by 12.98% and waiting time decreasing by 44.40%. Sensitivity analyses on objective weights further confirm the robustness of the proposed method. Benchmark comparisons with a greedy heuristic, genetic algorithm (GA), and particle swarm optimization (PSO) highlight the effectiveness and computational efficiency of ALNS. This study further explores a threshold-based directional control strategy, showing that relaxing strict alternating-direction rules under asymmetric traffic demand can improve efficiency. The findings provide practical insights for lock scheduling, offering decision support for lock authorities in designing adaptive scheduling and directional control policies. Full article

Background/Objectives: Limited research has evaluated the association between the triglyceride-to-high-density lipoprotein (TG/HDL) ratio and premature coronary artery disease (PCAD), particularly in Saudi Arabia. Therefore, this study aimed to investigate the association of the TG/HDL ratio with PCAD and to assess its sensitivity and specificity in a young Saudi population. Methods: This comparative retrospective case–control study utilized data collected from patients’ electronic medical records at King Saud University Medical City (KSUMC) between 2015 and 2023. The vessel score and Gensini score were used to evaluate the severity of coronary occlusion. The study population was divided into two groups: (1) a healthy control group consisting of blood bank donors, selected to exclude individuals with chronic diseases such as metabolic disorders and hypertension, with no evidence of coronary artery disease and aged ≤50 years (as confirmed by a cardiologist to rule out cardiovascular disease); and (2) patients with PCAD, aged ≤51 years, who underwent selective coronary angiography using the standard hospital procedure (right femoral artery approach). Coronary angiographic images were evaluated using right and left oblique views with cranial and caudal angulations. Results: A total of 898 subjects were included in the study, comprising 440 healthy controls and 458 patients with PCAD. Higher HbA1c levels were significantly associated with PCAD (adjusted OR = 13.03, 95% CI [7.32, 23.18], p < 0.001). Importantly, the TG/HDL ratio, the primary biomarker of interest, remained significantly associated with PCAD after full adjustment. Each unit increase in the TG/HDL ratio was associated with more than a threefold increase in the odds of PCAD (adjusted OR = 3.39, 95% CI [2.22, 5.16], p < 0.001), independent of age, sex, BMI, HbA1c, smoking, and total cholesterol levels. Among females, the TG/HDL ratio demonstrated an area under the curve (AUC) of 0.796, with an optimal cut-off value of 0.91, yielding 77.8% sensitivity and 71.4% specificity. Among males, the TG/HDL ratio yielded an AUC of 0.786, with a higher optimal cut-off value of 1.09 providing 73.4% sensitivity and 65.4% specificity. Conclusions: Our study indicates that the TG/HDL ratio and HbA1c are significantly associated with PCAD in young Saudi male and female populations, demonstrating good sensitivity and specificity. Females exhibited a lower cut-off value than males. Smoking and elevated cholesterol levels were also identified as prominent risk factors. However, the TG/HDL ratio did not distinguish between moderate and severe coronary stenosis, as assessed by the Gensini score. Full article

Energy communities (ECs) are becoming a key institutional instrument for decentralizing the European energy transition, yet their implementation remains constrained by fragmented legal interpretation, uneven national transposition, and unresolved socio-technical coordination problems. This review synthesizes the peer-reviewed literature, EU primary legal texts, and national legislation to clarify the distinction between Renewable Energy Communities (RECs) and Citizen Energy Communities (CECs), alongside the amendment relationship between the RED II and RED III directives. The analysis demonstrates that the scalability of these initiatives depends less on theoretical legal recognition and more on aligning operational frameworks, including metering, settlement, cybersecurity, and equitable allocation rules. The Romanian case illustrates this challenge clearly: rapid prosumer growth creates valuable distributed generation but also exposes physical grid constraints, asymmetric socio-economic participation capacity, and weak experience with cooperative energy governance. To address these vulnerabilities, this paper contributes a focused analytical framework linking energy justice, peer-to-peer game-theoretic modeling, and the strategic integration of “anchor-prosumers.” The study argues that larger renewable self-consumers can act as stabilizing community anchors when internal energy prices are designed between wholesale export values and retail import prices, thereby improving both producer incentives and consumer affordability. Future research developments, including targeted surveys and longitudinal empirical validations, will sustain this claim and optimize the socio-economic resilience of decentralized energy markets. Full article

Empirical logic (EL) is a bio-inspired soft computing approach to rule-based decision-making that emphasizes intuitive, experience-based reasoning. While its theoretical foundations have been established in previous work, its practical applicability and accessibility have so far received less attention. This paper addresses this gap by providing two representative application examples from distinct domains: control engineering and cluster analysis. The first example demonstrates the use of EL for the speed control of a DC drive, highlighting its ability to achieve competitive dynamic performance with a small number of intuitive rules. The second example introduces a novel approach to cluster analysis, where cluster structures emerge from the collective interaction of EL rules rather than from the optimization of a predefined objective function. In addition, the paper emphasizes the availability of publicly accessible software realizations of EL, including a Maple-based prototype and a Python framework, which enable direct experimentation and practical use. By combining illustrative applications with executable tools, the paper aims to facilitate the transition from conceptual understanding to practical deployment and to support further exploration of EL in applied soft computing contexts. Full article