Search Results (18,491)

Diagnosis in epilepsy depends on several distinct objectives—including interictal epileptiform discharge (IED) detection, source localisation of epileptiform activity, and seizure classification—each shaped by the underlying sensing technology. This review examines the opportunities that quantum magnetic sensing offers within this diagnostic framework, with particular emphasis on translational readiness and clinical potential. Seizure classification is included to situate the broader clinical context of epilepsy diagnostics, whereas the review of quantum sensing evidence is focused on IED detection and presurgical source localisation, the objectives for which such evidence currently exists. Magnetoencephalography (MEG) provides non-invasive source localisation that complements electroencephalography (EEG), and its clinical adoption is increasingly determined by advances in magnetic sensor technology. Optically pumped magnetometer (OPM)-based MEG represents the most clinically developed quantum sensing application, progressing from proof-of-concept in 2020 toward small-cohort clinical feasibility studies from 2022 onwards. By enabling on-scalp recordings at reduced sensor-to-brain distance, OPM-MEG improves signal amplitude and permits flexible sensor placement, with early evidence of benefit in paediatric populations and mesial temporal lobe epilepsy. However, current evidence derives from small, heterogeneous cohorts, and no prospective data yet link OPM-MEG to surgical outcomes. ⁴He-OPMs represent an early feasibility stage with IED detection comparable to superconducting quantum interference device (SQUID)-MEG in small case series; nitrogen-vacancy centres in diamond and silicon carbide divacancy magnetometry remain preclinical, constrained by a fundamental standoff-sensitivity trade-off. We conclude that OPM-MEG is best positioned as a complementary modality within established presurgical workflows, with prospective multicentre outcome studies representing the critical next step toward clinical translation. Full article

Background/Objective: Radiomics offers a powerful, non-invasive approach for extracting quantitative features to predict lesion phenotypes. This study performs a radiomic characterization of breast tissue using synchrotron radiation breast computed tomography (SR-bCT). Method: Four mastectomy samples were imaged at the Australian Synchrotron (ANSTO) using five energies (25, 32, 35, 40, and 60 keV). From 10 slices per energy for each sample, 1546 regions of interest (ROIs) were extracted across four tissue subtypes: fatty, glandular, fibrous, and microcalcified. Using Pyradiomics, 93 features were initially calculated and then reduced to 34, eliminating highly correlated variables to reduce redundancies. Seven linear regression models and a discriminant analysis evaluated subtype tissue separation and radiomic characterization across individuals and combined samples and energies with a good fit (0.81 ≤ R ≤ 0.98). Results: The study identified six possible robust exploratory candidate features independent of sample variability and energy levels, whose mean values are significantly different among the four tissue subtypes (clusters, p < 0.001). The selected candidate features were 90th Percentile, Kurtosis, Skewness, GLCM IDM, GLDM LDE, and NGTDM Coarseness. These metrics successfully differentiated all tissue pairs (microcalcifications/fat, microcalcifications/gland, microcalcifications/fiber, fat/gland, fat/fiber and gland/fiber), with p < 0.05 inside the most general regression model and p < 0.0001 with the linear discriminant separation in clusters. Conclusions: Findings indicate that some first-order and second-order texture metrics reflecting global dependencies remain stable across experimental conditions. Conversely, fine-texture metrics are highly sensitive to sample energy changes, limiting their generalizability. These results align with successful biomarkers in mammography and validate the potential of radiomics in SR-bCT characterization. Full article

Background/Objectives: Suicide-risk assessment during child and adolescent psychiatric hospitalization draws on patient report, clinical history, and professional observation. Whether nocturnal autonomic physiology is concurrently associated with a structured admission-window assessment after accounting for depressive symptoms and self-reported suicidal ideation remains uncertain. Methods: We analyzed 212 hospitalized children and adolescents receiving inpatient care for a major depressive episode. All had a Nurses’ Global Assessment of Suicide Risk (NGASR) rating, self-report measures, covariates, and first admission-night non-contact autonomic data. Principal component analysis was used to derive a nocturnal autonomic dysregulation factor from heart-rate and heart-rate-variability (HRV) summaries. Ordinary least squares regression with HC3 robust standard errors estimated its concurrent association with NGASR after adjustment for age, sex, BMI, Beck Depression Inventory score, and Chinese Beck Scale for Suicide Ideation score. Score-appropriate sensitivity analyses used Poisson, negative-binomial, and predefined NGASR-category models. Results: Higher autonomic dysregulation was associated with higher NGASR after adjustment for depressive symptoms and self-reported suicidal ideation (standardized beta = 0.182, 95% CI 0.067 to 0.297; p = 0.002; q = 0.005), accounting for an additional 3.2 percentage points of explained variance. The association was similar in a robust Poisson model (incidence-rate ratio = 1.059, 95% CI 1.022 to 1.097; p = 0.001) and remained stable after adjustment for subjective sleep quality, AHI, sleep efficiency, and monitoring timing. Separate component models showed larger associations for SDNN and RMSSD than for heart rate or LF/HF, but did not decompose the composite effect. Conclusions: Admission-window nocturnal autonomic dysregulation showed a modest concurrent association with clinical suicide-risk assessment after adjustment for depressive symptoms and self-reported suicidal ideation. The finding does not establish disclosure-independent assessment, temporal improvement of admission assessment, or prediction of future suicidal behavior. Full article

Histaglobulin (histamine/immunoglobulin complex) has been used for decades as a non-allergen-specific immunotherapy for allergic diseases including allergic rhinitis, chronic urticaria, and atopic dermatitis. Despite growing clinical interest, no meta-analysis has synthesized the available evidence. This systematic review and meta-analysis aimed to evaluate the efficacy and safety of histaglobulin across allergic diseases, with particular emphasis on implications for allergic rhinitis management. PubMed, Embase, Web of Science, and Cochrane Library were searched from inception to March 2026. Studies evaluating subcutaneous histaglobulin in patients with allergic diseases were included. For chronic urticaria studies reporting Urticaria Activity Score (UAS) as mean ± standard deviation, a random-effects meta-analysis was performed using the standardized mean difference (Hedges’ g). Allergic rhinitis studies were synthesized narratively. Risk of bias was assessed using the National Institutes of Health (NIH) Quality Assessment Tools. The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO). Fourteen studies (N = 754) met the inclusion criteria: six chronic urticaria studies with quantitative data (N = 190), three allergic rhinitis studies (N = 165), two atopic dermatitis/chronic spontaneous urticaria case series (N = 8), and three chronic urticaria studies with incomplete data. Meta-analysis of six chronic urticaria studies, all of which used an uncontrolled before–after design, demonstrated a large pooled within-group effect size (Hedges’ g = 2.82; 95% confidence interval [CI] 1.75–3.88; p < 0.00001), with high heterogeneity (I² = 98.2%). UAS reduction ranged from 44% to 89% across studies. Complete remission rates ranged from 30% to 89%. Sensitivity analysis excluding one study with reporting concerns yielded consistent results (g = 2.34; 95% CI 1.83–2.85; I² = 90.2%). All three allergic rhinitis studies reported significant symptom improvement (response rates 57–90%) with normalization of serum immunoglobulin E (IgE) and eosinophil counts. No serious adverse events were identified within the available literature; however, the cumulative sample size (N = 754) remains insufficient to exclude rare or long-term adverse effects. Available evidence suggests that histaglobulin may reduce disease activity in chronic urticaria; because the pooled estimate derives predominantly from uncontrolled before–after studies with substantial heterogeneity, it reflects within-group improvement following treatment rather than a controlled estimate of treatment efficacy, and confidence in the effect remains limited. Preliminary evidence suggests that histaglobulin may also provide benefit in patients with allergic rhinitis; however, the available data are limited and heterogeneous, and larger, well-designed controlled studies are needed to confirm its effectiveness. Well-designed randomized controlled trials are needed, particularly for allergic rhinitis, to establish histaglobulin’s role in evidence-based otorhinolaryngology practice. Full article

The Weibull distribution is one of the most versatile and widely applied continuous probability distributions in reliability engineering, survival analysis, wind-energy modelling, and extreme-value theory. Classical parameter estimation relies on maximum likelihood estimation or method-of-moments using integer-order moments, both of which may suffer from instability or high variance in small to moderate samples. This paper develops a rigorous framework for estimating Weibull parameters by combining the maximum-entropy principle with fractional-order moment constraints, i.e., constraints of the form $E\left[X^r\right]$ for non-integer $r>0$. A central theme of the paper is symmetry: we show that the maximum-entropy density subject to a finite set of fractional-moment constraints uniquely recovers the Weibull family, and that the underlying moment-matching system, while not symmetric in every sense considered in an earlier draft (see Remark ), possesses a precisely characterised duality under joint rescaling and relabelling of the exponent pair, together with a log-moment map whose local curvature is strictly positive and increasing with exponent spacing rather than symmetric about a fixed midpoint. We derive closed-form expressions relating the Lagrange multipliers to the shape and scale parameters, establish new sound theoretical results on the symmetric behaviour of the moment-ratio function and its sensitivity, and propose a numerically stable algorithm for solving the resulting moment-matching system. Extensive Monte Carlo experiments demonstrate that the proposed maximum-entropy fractional-moment estimator achieves a bias and root-mean-square error that are comparable to, and for small-to-moderate samples somewhat better than, maximum likelihood estimation, with the size of the advantage depending on how closely the chosen exponent pair tracks the true shape parameter. Applications to real wind-speed data and composite-material fatigue life data illustrate the practical utility of the method. The paper contributes both to the information-theoretic foundations of distribution fitting and to applied statistical methodology, with symmetry serving as both a diagnostic tool and a unifying structural principle throughout. Full article

Background: The “hygiene hypothesis” and “farm effect” suggest that early-life microbial exposure protects against allergic diseases. Maternal low-dose lipopolysaccharide (LPS) exposure during pregnancy attenuates allergic airway inflammation in offspring, yet the underlying mechanisms remain incompletely understood. Immunoglobulin G (IgG) N-glycosylation critically regulates antibody effector functions and maternal–fetal immune transfer, but its role in transgenerational allergy protection is unknown. Objective: To investigate whether maternal low-dose LPS exposure modulates serum IgG N-glycan profiles in offspring and whether these glycomic changes are associated with protection against house dust mite (HDM)-induced allergic airway inflammation. Methods: Pregnant C57BL/6J mice received intraperitoneal LPS (7 µg/kg) or phosphate-buffered saline (PBS) on gestational day 15. Offspring were sensitized with HDM extract or PBS at 6 weeks of age. Serum IgG was purified using Protein G affinity chromatography, and N-glycans were released with PNGase F, labeled with 2-aminobenzamide, and analyzed by ultra-performance liquid chromatography (UPLC) with fluorescence detection. Twelve glycan peaks (GP1-GP12) and five derived traits (galactosylation, sialylation, monogalactosylation, digalactosylation, and bisecting glycans) were quantified. Results: HDM challenge significantly altered offspring IgG glycosylation, characterized by decreased digalactosylation, total galactosylation, and sialylation, consistent with inflammatory glycan signatures reported in other inflammatory diseases. Maternal LPS exposure alone induced similar glycomic shifts in offspring (decreased digalactosylation, total galactosylation, and sialylation). In HDM-challenged offspring, maternal LPS pretreatment produced a trend toward reduced lung inflammation without statistical significance; however, IgG glycan differences between LPS-HDM and PBS-HDM groups were non-significant. Conclusions: Maternal low-dose LPS exposure reduced offspring IgG galactosylation and sialylation. HDM challenge produced similar glycomic changes. Despite a trend toward attenuation of HDM-induced lung inflammation, maternal LPS did not restore IgG glycosylation. These findings provide first evidence linking prenatal immune stimulation to transgenerational IgG glycomic remodeling. Full article

The characterization of plant-derived extracts is strongly influenced by the extraction procedure and solvent polarity, which determine the recovery of bioactive compounds and volatile constituents. In this study, an electronic nose (E-nose) based on a metal oxide semiconductor (MOS) sensor array was evaluated as a rapid analytical tool for the classification of Cistus extracts obtained using solvents of different polarity. Leaves of Cistus salviifolius, Cistus crispus, and Cistus ladanifer were sequentially extracted with hexane, diethyl ether, tetrahydrofuran (THF), chloroform, ethanol, and methanol. Extraction yield, total phenolic content, flavonoid content, and antioxidant activity were determined, and volatile fingerprints were acquired using the E-nose system. Chemometric analysis was performed using principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). Differences were observed among extraction solvents regarding extraction efficiency, phytochemical composition, and antioxidant activity. PCA successfully discriminated extracts according to solvent polarity, explaining between 70.96% and 90.22% of the total variance depending on the Cistus species analyzed. Furthermore, the PLS-DA model achieved a classification accuracy of 87.5%, with sensitivity and specificity values of 87.5% and 97.5%, respectively. These results demonstrate that the combination of E-nose technology and chemometric tools provides a rapid, non-destructive, and cost-effective approach for the classification and characterization of Cistus extracts according to extraction solvent, highlighting its potential application in quality control and process monitoring of plant-derived products. Full article

Background/Objectives: Digital self-triage tools may support patient-flow management in crowded emergency departments (EDs), but their agreement with established professional triage systems requires prospective validation. We evaluated the agreement and classification performance of a locally developed mobile self-triage application, structured according to Manchester Triage System (MTS)-related urgency logic, compared with professional MTS assessment in adult ED patients. Methods: This prospective, observational, single-center agreement study included 2274 consecutive, hemodynamically stable adult walk-in patients who used a tablet-based application covering 11 predefined presenting complaints and subsequently underwent a single, blinded professional MTS assessment, which served as the prespecified comparator. Of 3244 initial registrations, 970 (29.9%) were excluded because their complaints were not covered by the predefined pathways. MTS Categories 2–3 were classified as urgent. Results: Exact agreement occurred in 968 patients (42.6%); 1161 patients (51.1%) were overtriaged and 145 (6.3%) were undertriaged. The application showed 93.9% sensitivity, 28.7% specificity, 61.1% positive predictive value, and 79.8% negative predictive value for identifying professional MTS-defined urgent cases. Agreement was slight by unweighted kappa (κ = 0.198), fair by quadratically weighted kappa (κw = 0.278), and the prevalence- and bias-adjusted kappa for binary urgent/non-urgent classification was 0.28. Overtriage exceeded 70% for headache and low back pain, and these complaints were the strongest independent predictors of overtriage; higher self-reported pain intensity was strongly associated with overtriage. Conclusions: The application demonstrated a conservative classification profile with high sensitivity and a low undertriage rate but frequent overtriage that would inflate the apparent urgent caseload if the tool were used unsupervised; it should be considered an adjunct to, rather than a replacement for, professional triage. Findings apply to stable adult walk-in patients whose complaints matched the predefined categories and may not generalize to the broader ED population. Full article

This study develops a transparent and reproducible multi-criteria decision analysis (MCDA) framework for selecting the material and structural variant of floating navigation buoys on inland waterways. The decision problem was defined at the buoy-platform level and separated from external descriptive evidence on visual and radar performance. Three alternatives—galvanized steel, rotationally moulded polyethylene (PE), and mixed steel–PE—were assessed using the Weighted Sum Model (WSM) across eight non-overlapping technical, economic, and operational criteria. An eight-member purposive technical calibration panel screened the criteria, agreed their ordinal priority ranking, and validated the performance-class definitions—this panel was separate from a survey of 52 employees responsible for waterway maintenance. Technical performance classes were assigned from post-season condition assessments of 20 buoys per variant, whereas 36 repeated visual and radar observation series were treated as external descriptive evidence. In the baseline rank-sum model, steel buoys achieved 66.11 points, compared with 59.44 for PE and 54.44 for mixed buoys. Alternative rank-reciprocal and rank-order-centroid weight transformations retained steel as the highest-scoring alternative, while equal weights produced a tie between steel and PE (65.00 points each). One-factor sensitivity analysis showed that PE was preferred when the durability weight decreased from 22.22% to approximately 6.7% or when the corrosion-resistance weight increased from 19.44% to approximately 31%. Simultaneous perturbation of all weights retained steel as the highest-scoring alternative in 99.94% of simulations for ±30% perturbations and 95.17% for ±50%. The survey nevertheless showed greater practitioner preference for plastic or mixed solutions. The framework therefore provides an auditable, context-dependent decision process rather than a universal material recommendation and should be recalibrated and validated for other environmental and operational conditions. Full article

Vision-based traffic accident anticipation is critical for active vehicle safety systems, yet existing architectures frequently conflate performance gains with heavy parameter scaling optimized from scratch on compact domains. Consequently, during real-time inference, these frameworks suffer from severe prediction volatility and early triggering biases that induce dangerous control instability. To address these limitations, this paper shifts the research focus away from network modifications toward a highly controlled, strategy-driven training pipeline executed under a completely invariant spatial–temporal neural backbone. Our proposed paradigm establishes a robust framework through three decoupled milestones. First, an out-of-domain initialization strategy transferred generalized driving kinetics from a large-scale sequence domain (Mapillary) to serve as a stable temporal anchor. Second, a target-domain generative enrichment step injected synthetic nighttime scenes to decouple hazard features from low-light ambient noise. Third, progressive temporal supervision paradigm scaling targeted labels monotonically to align with continuous kinetic risk accumulation. Overall evaluations on the Car Crash Dataset (CCD) benchmark demonstrate that the fully integrated configuration (C4) pipeline achieves 69.89% in frame-level Mean Average Precision (mAP), which is an improvement of +22.81 percentage points over the baseline configuration. Continuous temporal measurements prove that our framework can adapt to tracking volatility, compressing Temporal Confidence Variance to 0.00328, and dropping the Prediction Instability Count to 0.66. While hyper-sensitive baselines report early raw latency averages driven by premature trigger noise, our model purposefully filters this early-frame variability to deliver a secure warning profile, achieving an absolute zero false alarm rate (FAR = 0.00%) across evaluated non-hazardous driving sequences, establishing the sequence-level trustworthiness required for practical autonomous deployment. Full article

Urban green spaces play an important role in supporting ecosystem functions, landscape connectivity, and urban resilience; however, their potential contribution may be constrained by fragmentation, inadequate maintenance, and limited integration among spatial planning instruments. This study develops a preliminary green infrastructure (GI) planning framework for the municipality of Noicattaro (Bari, Apulia, Italy) by integrating planning provisions with the observed condition and spatial distribution of existing public green spaces. A GIS-based spatial analysis, complemented by field surveys, assessed 89 public green spaces for compliance with planning requirements and maintenance status. The analysis also considered the spatial structure of the surrounding peri-urban landscape, including the karstic dry valleys (Lame), agricultural land uses, and relevant landscape and hydrogeological planning constraints. The results show that only 20.2% of the surveyed green spaces met both planning and maintenance criteria, whereas 78.4% fell into categories characterised by planning non-compliance, inadequate maintenance, or both. These findings highlight potential gaps between formal planning provisions and the observed condition and management of the municipal green system. The analysis of the Lame further revealed a predominance of agricultural land over natural vegetation, emphasising the importance of considering the wider agricultural and peri-urban landscape when identifying potential ecological connections. Based on the combined spatial and field assessment, a preliminary GI network framework was developed around core ecological areas, buffer zones, and strategically located green spaces, with the aim of identifying potential spatial connections and areas for further investigation. Overall, the study demonstrates the potential of integrating planning analysis, GIS-based assessment, and field observations as a screening and decision-support approach for more context-sensitive GI planning in peri-urban Mediterranean landscapes. The proposed framework should be regarded as a preliminary planning proposal requiring further validation through biodiversity, ecosystem-service, accessibility, and implementation assessments. Full article

This paper presents a systematic reward engineering methodology for training a Proximal Policy Optimization (PPO) quadrotor navigation policy in the Webots simulator, using a hierarchical architecture in which a PID controller handles low-level stabilization and a PPO policy issues velocity commands. We document the complete evolution of a composite ten-term reward function across seven versions (v5 through v11) and retrain the key versions with multiple independent training seeds. The multi-seed study revises the single-seed history: penalty-dominated configurations (v8, v10) fail across all seeds, while the strongest historical version proves seed-sensitive (v11: 32.2 +/− 15.8%). An ablation removing the continuous distance-shaping term from v11 yields 0% success across seven seeds, identifying that term as necessary for convergence. We further isolate a previously hidden co-factor: with the library-default exploration-noise initialization (sigma_0 = 1.0), sampled actions saturate the bounded action space, the exploration variance receives no learning gradient, and curriculum progression deadlocks regardless of reward design; initializing sigma_0 = 0.37 restores gradient flow. With this correction and a deterministic evaluation-gated curriculum, the final configuration is evaluated across the full curriculum rather than at a single operating point: across five independent training seeds under a deterministic protocol, it attains 95.0% ± 6.2% navigation success at Stage 0 conditions (2 m targets, no obstacles), 89.6% ± 6.9% at Stage 1 conditions (4 m, one obstacle), and 48.4% ± 10.8% at Stage 2 conditions (7 m, three obstacles). Reporting this difficulty curve, rather than a single headline value, exposes a substantial generalization gap whose dominant failure mode is obstacle collision (45–52% of episodes at Stage 2). Matched retraining of Soft Actor-Critic and TD3 baselines under identical reward and curriculum conditions yields one completed seed each both baselines show non-monotonic difficulty curves, and at Stage 2 conditions, TD3 (64.0%) exceeds PPO (48.4% ± 10.8%) while SAC (43.0%) falls just below it, whereas at Stage 0, PPO (95.0%) leads both, so the ranking is operating point-dependent on the current single-seed evidence. We conclude that a continuous shaping gradient and the exploration-noise initialization, interacting with the curriculum advancement criterion, determine convergence in continuous control deep reinforcement learning, and that reward polarity by itself does not. Full article

To address the issues of low mechanical rock-breaking efficiency and severe tool wear in hard rock, this study investigates the mechanical deterioration and abrasivity response of granite subjected to rapid flame-jet heating followed by liquid nitrogen cooling, with rock pre-treatment in thermal-assisted rock breaking as the background. Fine-grained granite specimens were heated by flame jet to temperatures ranging between 200 and 600 °C and then subjected to natural cooling or sudden liquid nitrogen cooling. Physical parameter measurements, uniaxial compression tests, Brazilian splitting tests, and CERCHAR abrasivity tests were conducted using national standards to investigate the evolution of thermal damage and the effect of cooling path. The results show that the damage degree of granite continuously increases with increasing heating temperature, and the damage induced by liquid nitrogen cooling is greater than that induced by natural cooling. Visible cracks begin to appear on the specimen surface after 300 °C, while crack propagation and structural deterioration become more pronounced at 500–600 °C. Among the measured physical parameters, P-wave velocity is the most sensitive to damage, with a maximum attenuation rate of 60.6%. The deterioration of the physical structure further reduces the load-bearing capacity and deformation performance of granite. After liquid nitrogen cooling at 600 °C, the uniaxial compressive strength, tensile strength, and elastic modulus decrease to 92.6 MPa, 1.42 MPa, and 17.4 GPa, respectively, corresponding to reductions of 56.0%, 87.7%, and 69.3% compared with the untreated specimens. The tensile strength is the most sensitive mechanical parameter to liquid nitrogen cooling. The CERCHAR abrasivity index decreases from 3.81 to 2.12. Liquid nitrogen cooling advances the transition of granite abrasivity from high to medium from 400–500 °C to 300–400 °C. The integrated analysis indicates that the non-uniform temperature field generated by rapid flame-jet heating and the differences in thermal deformation among minerals promote the initiation of initial cracks. The additional shrinkage stress induced by liquid nitrogen cooling further drives crack propagation and coalescence, eventually leading to the coupled reduction in the strength, stiffness, and abrasivity of granite. The results provide laboratory-scale evidence for evaluating granite pre-treatment under rapid flame-jet heating and liquid nitrogen cooling and offer a reference for thermal-assisted mechanical rock breaking and standardized abrasivity reduction. However, the relationship between CAI and actual tool wear still requires further verification. Full article

In this study, the vibration characteristics of microbeams subjected to thermal loads and electric field effects under non-ideal boundary conditions were investigated. The non-ideal boundary conditions are modeled as a linear combination of ideal fixed and simply supported boundary conditions. The weighting factor, k, is defined as a measure of deviation from the ideal boundary conditions. To determine the influence of the boundary conditions, both non-ideal fixed and non-ideal simply supported beams were examined. The equations of motion of the system are derived using Hamilton’s principle, and the Method of Multiple Scales, a perturbation technique, is applied to obtain approximate analytical solutions for both linear and nonlinear vibration responses. The effects of thermal loads, electric field influence, and non-ideal boundary conditions on the natural frequencies were investigated. The results demonstrate that the natural frequencies vary significantly with changes in the weighting factor, k. To address operational reliability, a formal probabilistic sensitivity and uncertainty analysis using Monte Carlo simulations is integrated, quantifying how uncertainties in geometrical and thermal parameters propagate to structural instability. Furthermore, a theoretical framework for calibrating the abstract boundary parameter via Finite Element Model updating is introduced. These results indicate that idealized boundary condition assumptions may lead to significant inaccuracies in the design and reliability assessment of micro-electromechanical systems (MEMSs) operating under the combined effects of thermal and electric fields. Relying solely on idealized support assumptions may result in significant failure (damage) predictions owing to the underestimation of thermally induced buckling and electrical pull-in risks arising from assembly imperfections. Furthermore, a probabilistic sensitivity analysis is implemented to quantify how parameter uncertainties propagate through the system, bridging the gap between pure deterministic modeling and operational reliability. Full article

The beneficial effects of dietary fiber (DF) and high-dietary-fiber diets (high-DF diets) on body weight (BW) regulation and lipid metabolism have been extensively investigated in the general population. However, due to the unique metabolic adaptations of athletes, exercise-specific nutritional requirements, and enhanced gastrointestinal sensitivity caused by intensive training and competition, the evidence for their application and in-depth mechanism remains limited. This review synthesizes current evidence on the effects of DF and high-DF diets on BW regulation and lipid metabolism in athletic populations. Emerging data indicate that athletes exhibit unique lipid metabolic phenotypes and gut microbial compositions relative to non-athletic individuals—factors that may modulate their physiological responses to DF interventions. Studies conducted specifically in athletes suggest that DF supplementation and high-DF diets—including the vegetarian diet and the Mediterranean diet—may support favorable changes in body composition and lipid-related biomarkers. Nevertheless, these benefits appear to be moderated by sport discipline, biological sex, and interindividual variability in metabolic and gastrointestinal physiology. Critically, excessive or poorly timed DF intake—particularly during intense training blocks or immediately before or during competition—may exacerbate gastrointestinal discomfort and impair energy availability, thereby potentially compromising performance. Consequently, the integration of DF into athletic nutrition strategies necessitates individualized consideration of fiber type, dosage, timing of ingestion, fermentability, and gastrointestinal tolerance. Future research should prioritize rigorously designed longitudinal studies and randomized controlled trials employing standardized DF interventions and outcome assessments. Comprehensive evaluation—including metabolic parameters, markers of energy availability, gastrointestinal symptomatology, and microbiome profiling—will be essential to inform evidence-based, personalized DF recommendations aligned with athletes’ training phases and metabolic profiles. Full article