Search Results (92,390)

Sustainable energy systems increasingly rely on digital auxiliary processes that convert telemetry, forecasts, diagnostic evidence, and operating rules into reviewable information for authorized human decision-makers. This targeted review develops a transparent and traceable conceptual evidence-to-review framework for photovoltaic, wind, storage, fuel-cell, and converter-coupled systems. The coded review pool comprised 100 sources, including 75 analytical and 25 contextual sources; 10 additional references supported methodological, technical, and case-specific context and were not included in thematic coding. A six-stage thematic analysis, supported by a revision-stage coding audit, identified five analytical layers: data-quality control, forecasting and uncertainty, explanation diagnostics, constrained evidence briefing, and accountable human review. These layers were translated into an eight-stage advisory architecture that separates evidence preparation, validation, briefing, and review from the authorized control plane. No direct large-language-model-to-controller or model-to-asset path is permitted. An illustrative Jeju scenario traces one evidence record through the architecture and shows how safety conditions, evidence admissibility, and procedural authority lead to CHECK, HOLD, or ESCALATE outcomes. The review concludes that trustworthy digital support requires traceable evidence, deterministic safety and rule boundaries, and accountable human authorization. Full article

Background: Ovarian cancer is the most lethal gynecologic malignancy, and CA-125 has limited early-stage sensitivity. We developed and evaluated Foretell Ovary, an ensemble machine-learning classifier that integrates tumor-educated-platelet RNA markers and routine hematologic parameters for non-invasive ovarian cancer detection, and compared it head-to-head with CA-125. Methods: A multi-site prospective cohort study enrolled 460 South Korean participants (67 ovarian cancer, 15 borderline ovarian tumors, 197 benign gynecological conditions, and 181 asymptomatic controls) at six clinical sites between September 2024 and March 2026. The cohort was split into training (n = 279) and independent test (n = 181) sets under a pre-specified ACTB cycle threshold ≤ 28 per-protocol rule (test n = 169). The classifier blended a random forest and two calibrated logistic-regression sub-models built on eight platelet RNA markers, 24 complete blood count parameters, and 30 marker-by-hematology combination features. Performance was assessed at a pre-frozen cutoff of 0.44 and compared against CA-125 (35 U/mL cutoff) in a matched subset (n = 100). Results: Foretell Ovary achieved an area under the receiver operating characteristic curve (AUC) of 0.923, sensitivity of 87.5% (100.0% for Stage II–IV), specificity of 89.0%, and negative predictive value of 97.7% in the test set. In the matched subset, Foretell Ovary outperformed CA-125 on every metric (AUC 0.839 vs. 0.723; sensitivity 85.0% vs. 65.0%), with the largest gap in pre-menopausal participants (+25 percentage points in sensitivity). A three-tier risk-grading scheme retained no ovarian cancer in the low-risk grade. Conclusions: Foretell Ovary offers more accurate non-invasive detection of ovarian cancer than CA-125, particularly in pre-menopausal women, offering the flexibility to be used alone or combined with CA-125. Prospective validation studies are warranted. Full article

Smart textiles require reliable physiological sensing despite signal degradation caused by fabric deformation, material fatigue, and unstable textile–skin interfaces. This study presents a multi-modal GAN-based anomaly detection framework for signal reliability assessment using acceleration (ACC), electrodermal activity (EDA), and heart rate (HR). Controlled injection of baseline drift, amplitude scaling, and signal dropout generates normal/anomalous labels for supervised training. Temporal encoding and cross-modal attention distinguish textile-mimicking anomalies from physiologically plausible variations. On the 36-subject PhysioNet dataset, the framework achieves an F1-score of 0.9197 and exceeds the evaluated single-modal models by more than 35%. Using PhysioNet-trained weights without fine-tuning, zero-shot evaluation on the textile-integrated WWBS Metrics dataset achieves an F1-score of 0.8723 with ACC and derived HR. These results demonstrate cross-dataset transfer under the controlled synthetic-fault protocol; validation using physically induced textile faults remains necessary. Full article

During vacuum induction melting (VIM) of superalloys, oxygen and nitrogen control involves interfacial processes at both the melt free surface and the crucible–melt interface, where melt flow is an important factor affecting reaction kinetics. A coupled electromagnetic and fluid flow model with a deformable free surface was developed and validated for a 3 t industrial VIM furnace with two electromagnetic excitation modes. The melt flow under the Heating and Stirring modes is compared, with particular attention to the role of free-surface deformation, and the effects of input power and filling ratio are further examined. The results show that at an input power of 190 kW and a filling ratio of 85%, the Heating mode produces two counter-rotating vortices separated by a low-velocity mid-region, while the Stirring mode generates a dominant upper vortex covering most of the melt volume with a smaller counter-rotating vortex at the bottom. The Stirring mode achieves approximately 1.3 times the surface velocity, 1.7 times the wall friction velocity, and half the mixing time of the Heating mode. Free-surface deformation significantly affects the predicted flow structure, particularly under the Heating mode. Parametric results further show that input power mainly changes the flow intensity without altering the flow structure under either mode. By contrast, the filling ratio strongly affects the flow structure under the Heating mode, while that under the Stirring mode is largely preserved. These findings provide insight into the melt flow conditions relevant to oxygen and nitrogen removal during VIM processing. Full article

The intensification of poultry production, associated with climate change, has increased the occurrence of heat stress, compromising animal welfare and productive efficiency. Despite recent advances, studies quantifying heat exchange in quail under different environmental and dietary conditions from an integrated bioenergetic perspective remain scarce, particularly regarding the shift between sensible and latent heat dissipation mechanisms. In this context, this study aimed to quantify and model sensible and latent heat exchange, together with associated physiological responses, in Japanese quail (Coturnix coturnix japonica) subjected to different air temperatures and dietary inclusion levels of the macroalga Gracilaria birdiae. A total of 864 quail were distributed in a completely randomized design, arranged in a 4 × 3 factorial design with four macroalgae inclusion levels (0.00, 3.00, 6.00, and 9.00%) and three air temperature levels (25.00, 29.00, and 33.00 °C), and maintained in climate-controlled chambers. Heat exchange was estimated using biophysical models integrating convective, radiative, and evaporative heat fluxes. Increasing air temperature reduced sensible heat exchange and intensified latent heat losses (p < 0.0001). During the growing phase, approximately 73.18% of sensible heat exchange was dissipated through radiation. In the laying phase, reductions of up to 59.96% in sensible heat exchange were observed, along with increases exceeding 50.00% in latent heat losses and reductions of up to 26.00% in total heat exchange. Increasing air temperature promoted higher respiratory rate (p < 0.0001), whereas surface and cloacal temperatures remained within the physiological range required to maintain homeothermy. Dietary inclusion of up to 9.00% G. birdiae exerted only limited effects on the quantified heat exchange pathways and did not impair physiological thermoregulation under the experimental conditions evaluated. No significant interaction between air temperature and dietary supplementation was observed for the heat exchange variables (p > 0.05). These findings show that heat stress was the primary determinant of bioenergetic heat exchange, whereas dietary supplementation with G. birdiae exerted only limited effects. Full article

Imaging underpins the diagnosis and structural staging of osteonecrosis of the femoral head (ONFH) but does not directly quantify functional impairment during weight-bearing walking. We developed a subject-level ONFH classification framework using multilevel gait features acquired with wearable inertial measurement units (IMUs). Thirty healthy controls and 21 participants with imaging-confirmed ONFH completed self-paced walking trials recorded at 100 Hz. Gait cycles were segmented from bilateral foot-contact events, normalized to 120 points, and represented as 17-channel kinematic waveforms, 22-dimensional cycle-level scalar features, and 7-channel dynamic absolute asymmetry waveforms. These inputs were encoded by CNN–CBAM–BiLSTM, multilayer perceptron, and one-dimensional convolutional branches, respectively, and fused at the feature level. Evaluation used 51-fold leave-one-subject-out cross-validation, training-fold-only preprocessing, within-subject probability averaging, and five predefined random seeds. The five-seed ensemble achieved an accuracy of 0.9412, sensitivity of 0.8571, specificity of 1.0000, F1-score of 0.9231, and area under the receiver operating characteristic curve of 0.9556. Ablation analysis identified the scalar-feature vector as the principal source of incremental performance; the dynamic asymmetry branch contributed complementary information only in the complete model. These findings provide preliminary evidence for further evaluation of wearable gait-based ONFH classification in independent cohorts and objective functional assessment. Full article

The increasing penetration of renewable energy has made the transient stability of new power systems a critical concern. Grid-forming (GFM) energy storage can provide voltage and frequency support for renewable energy stations. However, existing studies on the transient stability of GFM converters predominantly consider only the synchronization of a GFM converter with an infinite bus and do not fully account for the effects of renewable-energy injection and LVRT control in remote-grid-connected renewable energy stations. To fill this gap, this paper establishes a transient power-angle stability analysis model for a GFM energy storage system in renewable energy stations connected to a remote grid. Based on the equivalent swing equation and the equal-area criterion, the transient instability mechanisms under different renewable energy source LVRT depths are investigated. The results demonstrate that increasing renewable energy output reduces the transient stability margin of the GFM converter. Furthermore, the system exhibits two distinct transient response modes depending on the renewable energy source LVRT depth: under shallow LVRT depth, the GFM converter accelerates first and then decelerates, whereas under deep LVRT depth, it decelerates first and then exhibits a swing-back oscillation. These findings, validated through time-domain simulations, provide a theoretical basis for understanding the effects of renewable energy output, LVRT control, virtual inertia, and virtual damping on the transient stability of GFM-integrated renewable energy systems. Full article

The rapid adoption of small drones for applications such as surveillance, disaster response, and infrastructure inspection has increased demand for coordinated multi-drone operations, in which information exchange is essential. However, effective collaboration across different drones remains challenging due to differences in information exchange and communication protocols. This paper proposes a Networked Drone Information Exchange Model (NDIEM), an XML-based model that enables interoperability of the structural information. NDIEM defines five essential element categories: Identification, Telemetry, Command and Control, Sensor, and Mission. NDIEM can be used with protocol-specific adapters for exchanging information in multi-drone operations. The proposed model has been implemented and evaluated on three different drones (an ArduPilot-based Hexacopter, a Crazyflie 2.1, and a Tello EDU) using real sensor telemetry captured under controlled conditions, in which each platform’s onboard sensors were manipulated to generate representative telemetry variation. Experimental results demonstrate information interoperability with 0.013–0.019 ms processing overhead, 0.049–0.073 ms transformation latency per message, and XSD schema validation compliance, achieving 69.7% transformation completeness for telemetry data. These findings show that NDIEM can provide a practical and scalable foundation for software development for drone collaboration and interoperability. Full article

Controlled bridge simulation and laboratory damage identification require models that can simulate structural responses and infer localized stiffness loss from limited measurements. Existing Fourier Neural Operator (FNO)-based vehicle–bridge interaction (VBI) models provide efficient surrogates for these mappings, but the global Fourier representation can smooth localized damage transitions and introduce boundary-related errors for finite-span bridge responses. This study adapts the Wavelet Neural Operator (WNO) to the VBI setting and develops the Vehicle–Bridge Interaction Wavelet Neural Operator (VIWNO), an application-oriented framework for wavelet-domain operator learning between structural response fields and damage fields. VIWNO is pre-trained on a numerical VBI finite-element dataset (VBI-FE) and fine-tuned using only healthy-state measurements from a scaled VBI experimental dataset (VBI-EXP), before being evaluated on unseen laboratory damage scenarios. Under the controlled VBI-FE setting, where bridge, vehicle, speed, and measured road-profile parameters are fixed and the main variation is the damage field, VIWNO reduces forward response errors by 20–30% and inverse damage-estimation errors by 26–32% relative to the FNO-based Vehicle–Bridge Interaction Neural Operator (VINO) baseline. Additional morphology and operating-condition stress tests show that the error increases under sharper damage fields and perturbed VBI conditions, but VIWNO remains more accurate than VINO and the added convolutional or frequency-domain baselines in the tested cases. On VBI-EXP, projection-only healthy-state fine-tuning reduces intact false-damage levels and yields sharper damage estimates than VINO under both displacement and acceleration inputs. Stability checks over five initializations and repeated vehicle passages show limited variation in the reported inverse metrics. These results support the feasibility of wavelet-domain neural operators for calibrated VBI simulation and scaled laboratory damage identification, while field-scale bridge health monitoring still requires validation under broader traffic, environmental, support, and damage-morphology variability. Full article

Immune-mediated inflammatory diseases (IMIDs) arise from interactions among genetic susceptibility, epithelial barrier function, microbiota, diet, and other environmental exposures. Modern diets influence mucosal immunity not only through fibre intake, food processing, and microbiota composition, but also through a less explored exposure layer: plant-derived molecules with potential immune activity. Crop breeding, intensive agriculture, global trade, gluten-free substitutes, and plant-based food technologies have changed the spectrum, dose, concentration, and matrix in which plant defence proteins, antinutritional factors, endogenous toxicants, and novel plant antigens reach the intestinal surface. In this structured, hypothesis-generating narrative review, we propose dietary adjuvanticity as a mechanistic framework for considering how selected food-derived molecules may amplify mucosal immune responsiveness, modify antigen presentation, disturb barrier function, or lower tolerance thresholds without necessarily acting as classical autoantigens. The framework differs from general food-derived immunomodulation, nutritional exposomics, and diet-microbiota-host interaction models by focusing specifically on adjuvant-like immune amplification at the intestinal mucosa. The ASIA concept is used only in Shoenfeld’s functional sense, as an analogy for exogenous immune amplification through innate activation, danger signalling, bystander activation, epitope spreading, and loss of tolerance in susceptible hosts; it is not applied as a dietary diagnosis. Wheat amylase-trypsin inhibitors, gluten epitopes, lectins, potato glycoalkaloids, saponins, quinoa prolamins, emerging legume proteins, L-canavanine, and tolerance-promoting plant substrates are discussed with explicit separation of established clinical evidence, strong mechanistic evidence, preclinical/ex vivo evidence, and speculative disease-modifier hypotheses. Overall, plant-derived exposures are best interpreted as potential modifiers within the IMID exposome, not as primary causes of autoimmunity. Testing this model will require defined exposures, food-matrix and processing studies, biomarkers of barrier and immune activation, patient stratification, and controlled human studies. Full article

This study investigates the flexural performance of prefabricated reinforced concrete (RC) beams with a novel UHPC–cogging–grouted sleeve composite joint. Five beam specimens—one cast-in-place reference and four prefabricated examples—were tested under four-point bending. The key parameters included the presence of interface shear keys and the configuration of middle connecting reinforcement. The results demonstrate that the proposed joint is generally consistent with the “strong joint, weak member” design philosophy. The specimen with shear keys and dedicated connecting bars (W2) achieved the highest peak load of 224 kN, 7.7% higher than the CIP beam, with the joint crack width limited to 0.2 mm at failure. Replacing dedicated bars with two bent-up bottom bars (W4) maintained acceptable performance (219 kN, 5.3% above CIP), while bending up four bars (W5) reduced the capacity to 200 kN, below the CIP level, inducing a near-under-reinforced flexural failure. The UHPC joint and shear keys effectively delayed cracking and suppressed joint damage but shifted the failure mode from flexural to shear-compression. The stress development in the rebars was fully consistent with the failure modes: the midspan rebar dominated in flexure-dominated specimens (580–596 MPa), whereas the loading-point rebar dominated in shear-compression-dominated ones (585–590 MPa). Plane section assumption was validated before cracking. Finite element models developed in ABAQUS reproduced the experimental behavior with deviations within 10%, and the stiffness degradation distributions accurately captured the damage patterns. Preliminary design recommendations are provided, suggesting that the proportion of bent-up bars should be conservatively controlled, subject to further experimental verification. Full article

Multi-modal sentiment analysis integrates linguistic, acoustic, and visual evidence, yet the reliability of these streams varies across samples because of missing observations, masking, measurement noise, and feature corruption. This paper presents a trainable reliability-aware evidential fusion framework that estimates not only sentiment predictions but also modality-specific evidence, predictive uncertainty, observable input quality, cross-modal disagreement, and normalized sample-dependent fusion weights. Each available modality is independently encoded and processed by an evidential classification head and a quality estimation head. Availability masks enforce exact exclusion of missing streams, while estimated quality, Dirichlet uncertainty, and Jensen–Shannon disagreement jointly regulate the contribution of each observed stream. The model is optimized end-to-end using fused classification, evidential regularization, clean–corrupted consistency, reliability-calibrated cross-modal alignment, and quality regression objectives. Experiments are conducted on both CMU-MOSI and CMU-MOSEI using their official speaker-independent splits. Binary classification follows the standard non-zero protocol, in which samples with sentiment score zero are excluded from Acc-2 and binary F1 evaluation; all labeled samples are retained for seven-class accuracy, mean absolute error, and correlation. The evaluation covers complete-input, every single- and double-modality missing pattern, graded and unseen corruption, combined missing-plus-corrupted conditions, calibration, selective prediction, statistical testing, and computational efficiency. All comparative values in the main tables are identified as local controlled adaptations under the common pipeline, while selected published reference values are reported separately to prevent provenance mixing. Across both datasets, the empirical results show that the proposed method preserves competitive complete-input performance while providing larger and more consistent gains as modality availability or integrity deteriorates. Full article

The evaluation of the ultimate bearing capacity (UBC) of foundation systems remains a fundamental challenge in geotechnical engineering because of the complex interactions among soil properties, foundation geometry, loading conditions, embedment depth, and soil–foundation behavior. In recent years, artificial intelligence (AI) and deep learning (DL) techniques have emerged as powerful data-driven tools for modeling nonlinear geotechnical systems and improving bearing-capacity prediction. This study presents a comprehensive state-of-the-art review of AI- and DL-based approaches for foundation systems, including shallow foundations, deep foundations, pile foundations, and other geotechnical applications. Major models, including Artificial Neural Networks (ANNs), Deep Neural Networks (DNNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Transformer models, Graph Neural Networks (GNNs), hybrid AI frameworks, and physics-informed deep learning approaches, are critically reviewed and compared. Particular attention is given to the integration of AI models with numerical methods, including the finite element method (FEM) and finite element limit analysis (FELA). The reviewed studies frequently report lower prediction errors than conventional empirical, numerical, and machine-learning approaches within the evaluated datasets. However, many of the highest reported accuracies are based on laboratory-scale experiments, simulation-generated data, or random train–test partitions of a single database. Consequently, these results may demonstrate effective interpolation within controlled data distributions rather than reliable performance under independent field conditions. Model performance is strongly influenced by dataset origin and diversity, feature selection, validation strategy, overfitting control, and generalization capability. Hybrid datasets combining field, laboratory, and numerical data offer a promising route toward more reliable prediction, but genuine external validation using independent sites, projects, or institutions remains uncommon. Moreover, architectural suitability should reflect the physical structure of the problem: CNNs are appropriate for spatial heterogeneity, LSTMs for time-dependent behavior, Transformers for long-range interactions, and GNNs for mechanically connected systems. Limited field-scale datasets, weak external validation, limited model interpretability, inadequate uncertainty quantification, and persistent data scarcity continue to restrict widespread engineering implementation. Future research should prioritize explainable AI, physics-informed learning, transfer learning, hybrid data frameworks, open benchmark datasets, and multi-site field validation to improve the robustness, transparency, and practical applicability of intelligent bearing-capacity prediction for diverse foundation systems. Full article

Background: Tibial bone stress injury (TBSI) with associated bone marrow edema (BME) is a common and clinically challenging condition in athletes, often requiring prolonged load restriction and delayed return to sport. Evidence for adjunctive interventions that improve both symptoms and MRI-derived recovery remains limited. The aim of this study was to investigate whether adding Functional Magnetic Stimulation (FMS) to standardized rehabilitation improves pain, function, and MRI-derived outcomes over 16 weeks in athletes with MRI-confirmed TBSI. Materials and Methods: Forty athletes with Fredericson grade 2–3 TBSI were randomized to FMS plus rehabilitation (n = 20) or rehabilitation alone (n = 20). Both groups completed a 4-week load-based rehabilitation program, while the FMS group additionally received eight 30 min FMS sessions at 40 Hz. Outcomes were assessed at baseline, 4 weeks, and 16 weeks. The primary outcome was activity-related pain (NPRS), while secondary outcomes included lower-limb function (LEFS-GR), BME extent, and Fredericson grade. Continuous outcomes were analyzed using two-way mixed ANOVA, whereas Fredericson grade was analyzed using an ordinal generalized estimating equation model. The level of statistical significance was set at p < 0.05. Results: Groups were comparable at baseline. Significant group × time interactions favored FMS for NPRS, F(2, 76) = 12.46, p < 0.001, η²p = 0.247; LEFS-GR, F(1.08, 41.09) = 81.56, p < 0.001, η²p = 0.682; and BME extent, F(1.44, 54.85) = 49.33, p < 0.001, η²p = 0.565. At 16 weeks, the FMS group showed lower NPRS scores, higher LEFS-GR scores, and lower BME extent than controls. Fredericson grade also showed a significant group × time effect, Wald χ²(2) = 20.38, p < 0.001. The FMS group had significantly lower cumulative odds of classification in a higher Fredericson grade at 4 weeks (OR = 0.045, 95% CI: 0.012–0.163, p < 0.001), whereas the corresponding difference at 16 weeks remained directionally favorable but did not reach statistical significance (OR = 0.033, 95% CI: 0.001–1.029, p = 0.052). Conclusions: The addition of FMS to load-based rehabilitation was associated with greater clinical and functional improvements, greater reductions in BME extent, and a more favorable longitudinal trajectory in MRI severity classification than rehabilitation alone in athletes with TBSI. However, because the study did not include a sham-FMS condition and participants could not be blinded, the self-reported pain and functional outcomes should be interpreted cautiously. FMS may represent a useful adjunct to structured rehabilitation, although sham-controlled and longer-term studies should determine its effects on return-to-sport progression and recurrence risk. Full article

Branch prediction errors can reduce superscalar throughput by more than the error frequency alone suggests because a single misprediction can trigger redirect, squash, refetch, refill, and window recovery, which collectively disrupt sustained instruction-level parallelism. This paper presents a quantitative framework that relates prediction accuracy to realized parallelism loss in out-of-order superscalar processors. The framework separates prediction-error frequency, effective recovery cost, and unrealized issue capacity using prediction accuracy (Acc), misprediction rate (MR), effective branch penalty in cycles per misprediction (BP), parallelism loss ratio (PLR), the ratio-based branch sensitivity factor $BSF=PLR/MR$, and the slope-based branch sensitivity factor $\mathit{S-BSF}=\mathit{\partial }PLR/\mathit{\partial }MR$. BSF measures how strongly a particular processor configuration and workload convert prediction errors into lost issue capacity, whereas S-BSF provides a more stable sensitivity estimate when MR approaches zero. The framework is evaluated using timing-detailed gem5 O3 simulations on RV64GC workloads. The evaluation includes controlled branch microbenchmarks and six GAPBS graph workloads, allowing the proposed metrics to be examined under both mechanism-isolating and complex workload conditions. Two complementary controlled sweeps are used. At a fixed processor structure, predictor family and predictor level are varied to determine whether changing the predictor strengthens or weakens the relationship between MR and IPC/PLR. At a fixed predictor configuration, issue width and an effective front-end-depth proxy are varied to measure how the microarchitecture amplifies the performance cost of the remaining prediction errors. Thus, issue width is treated as an amplification variable for branch-prediction failures rather than as an independent performance topic. At the fixed structural point, Tournament and BiMode predictors show strong monotonic MR–PLR relationships on the high-branch benchmark, with Spearman coefficients of 1.00 and 0.98, whereas the Local predictor exhibits nearly unchanged MR but materially different IPC and PLR across levels. This demonstrates that the mapping from MR to throughput depends on predictor family and configuration rather than being invariant. In the controlled structural sweep, increasing issue width from 4 to 8 raises PLR by 37.6% and BSF by 55.0% on the high-branch benchmark, even though MR remains in the same order of magnitude. On GAPBS workloads, the lowest-MR configuration is not always the highest-IPC configuration, confirming that effective branch penalty and parallelism loss must be considered together with prediction frequency. These numerical findings are conditional on the evaluated single-thread gem5 DerivO3CPU model, RV64GC binaries, predictor implementations, memory hierarchy, and workload set. They characterize predictor–microarchitecture interactions in this controlled simulation environment and should not be interpreted as universal constants for all processors or applications. Full article