Search Results (4,631)

Adolescent vaping has become a persistent health and behavioural challenge in schools, yet many institutions lack reliable tools to detect and respond to concealed e-cigarette use. This study addresses this problem by evaluating the real-world performance of a low-cost “Internet of Things” (IoT) vape detection system deployed across 37 high-risk restroom and change-room locations at a large Australian Independent school. The aim was to determine whether an IoT-based environmental monitoring platform could accurately identify vaping events, support timely staff intervention, and provide actionable insights into student behaviour patterns. A longitudinal case study design was used, collecting continuous particulate matter (PM_2.5 and PM₁₀) data at one-minute intervals over an 18-month period, where PM₂.₅ and PM₁₀ refer to particulate matter with aerodynamic diameters ≤ 2.5 µm and ≤ 10 µm, respectively, reported in micrograms per cubic metre (µg/m³). Threshold-based alerting, cloud-based data processing, and school-led Closed-circuit television (CCTV) verification were combined to assess detection accuracy, temporal trends, and operational responses. The system recorded more than 300 vaping-related incidents, with clusters aligned to predictable times of day and higher prevalence among senior students. Operational detection performance was high, with alert events characterised by rapid, concurrent PM_2.5 and PM₁₀ excursions consistent with vaping-related aerosol profiles, although staff responsiveness declined over time due to alert fatigue and competing priorities. A major environmental smoke event demonstrated the need for context-aware logic to reduce false positives. The findings demonstrate that real-time aerosol monitoring is not only technically reliable but also highly effective in detecting vaping within school environments. These perspectives help explain why user engagement, alert fatigue, and institutional follow-through are as critical as sensor accuracy itself. Ultimately, the effectiveness of vape detection relies on strong organisational commitment, well-defined response workflows, and alignment with broader wellbeing and policy strategies. When these elements are in place, such systems can evolve from simple detection tools into intelligent, integrated components of school health governance. Full article

Multimodal biomedical machine learning increasingly integrates heterogeneous data sources (including medical imaging, multi-omics profiles, electronic health records, and wearable sensor signals) to support clinical diagnosis, prognosis, and treatment response prediction. Despite strong empirical performance, most existing multimodal systems lack a principled theoretical foundation for understanding why fusion improves prediction, how information is distributed across modalities, and when models can be trusted under incomplete or shifting data. This paper develops a unified information-theoretic framework that formalizes multimodal biomedical learning as an information optimization problem. We formulate multimodal representation learning through the information bottleneck principle, deriving a variational objective that balances predictive sufficiency against informational compression in an architecture-agnostic manner. Building on this foundation, we introduce information-theoretic tools for decomposing modality contributions via conditional mutual information, quantifying redundancy and synergy, and diagnosing fusion collapse. We further show that robustness to missing modalities can be cast as an information consistency problem and extend the framework to longitudinal disease modeling through transfer entropy and sequential information bottleneck objectives. Applications to multimodal foundation models, uncertainty quantification, calibration, and out-of-distribution detection are developed. Empirical case studies across three biomedical datasets (TCGA breast cancer multi-omics, TCGA glioma clinical-plus-molecular data, and OASIS-2 longitudinal Alzheimer’s data) show that the framework’s key quantities are computable and interpretable on real data: MI decomposition identifies modality dominance and redundancy; the VMIB traces a compression–prediction tradeoff in the information plane; entropy-based selective prediction raises accuracy from 0.787 to 0.939 at 50% coverage; transfer entropy reveals stage-dependent modality influence in disease progression; and pretraining/adaptation diagnostics distinguish efficient from wasteful fine-tuning strategies. Together, these results develop entropy and mutual information as organizing principles for the design, analysis, and evaluation of multimodal biomedical AI systems. Full article

Background: Metabolic syndrome (MetS) leads to alterations in cardiac autonomic control that can be detected from electrocardiogram (ECG)-derived markers, particularly when the cardiovascular system is challenged during an oral glucose tolerance test (OGTT). Methods: In this paper, we present an automated framework for MetS identification using RR intervals and heart rate variability (HRV) features extracted from 12-lead ECG recordings acquired during the five OGTT stages in 40 male participants (15 with MetS, 10 controls, and 15 endurance-trained marathon runners). RR intervals were first derived using a multilead Pan-Tompkins approach with fusion-based validation. From these RR series, HRV descriptors were computed from time-domain statistics (RR mean, SDNN, rMSSD, pNN50), spectral indices (VLF, LF, HF, LF/HF), and nonlinear measures (SD1, SD2, SampEn, DFA-${\alpha }_1$). Conventional HRV analysis revealed pronounced physiological differences between groups: MetS subjects exhibited reduced parasympathetic activity, reflected by lower rMSSD and SD1, lower HF power, and higher LF/HF ratios, whereas marathoners showed greater vagal modulation, higher HF power, and increased signal complexity. Healthy controls showed an intermediate autonomic profile. Using RR sequences and HRV descriptors (256 samples per stage), we trained three multimodal classifiers: a CNN-MLP model with a softmax output, a CNN-MLP model with an SVM head, and a CNN + LSTM-MLP + SVM architecture. Results: All models achieved strong discriminative performance, with accuracies ranging from 0.92 to 0.95, F1-macro values from 0.92 to 0.95, and macro-AUC values from 0.96 to 0.97. The CNN-MLP model achieved the best overall performance, whereas the CNN + LSTM-MLP + SVM model showed strong class discrimination, particularly for endurance athletes, while maintaining competitive recall for MetS. Conclusions: These findings support the feasibility of ECG-based autonomic assessment as a complementary non-invasive approach for early metabolic risk detection in clinical and preventive cardiometabolic screening settings. Full article

Bloodstream infection (BSI) is a major cause of morbidity and mortality in patients with chronic kidney disease (CKD), particularly those receiving hemodialysis. Delayed identification of pathogens and their resistance profiles can lead to inappropriate therapy and adverse outcomes. This review evaluates rapid molecular diagnostic approaches for detecting pathogens and resistance markers in BSI, with emphasis on their application in CKD. These technologies provide faster microbiological information by enabling direct or accelerated detection of pathogens and selected resistance determinants. Clinical studies indicate that their use supports prompt adjustment of antimicrobial therapy, especially when combined with antimicrobial stewardship and applied after blood culture positivity. In CKD, identification of the causative organism facilitates treatment selection aligned with renal function and helps reduce unnecessary exposure to nephrotoxic agents. However, diagnostic accuracy differs among platforms, and detection of resistance genes does not consistently reflect phenotypic susceptibility. Furthermore, most evidence is derived from mixed hospital populations rather than CKD-specific cohorts. These factors require careful interpretation within the clinical context. Rapid molecular diagnostics can enhance antimicrobial decision-making in BSI, but their effectiveness depends on integration with conventional microbiology and structured care pathways. Further research in CKD populations is required to clarify their impact on clinical outcomes and to support implementation in nephrology practice. Full article

Managed pressure drilling (MPD) is the core technology for developing formations with high pressure and narrow density windows. It precisely maintains the bottomhole pressure (BHP) within the safe operating window defined by formation pore pressure and fracture pressure by actively regulating the wellbore pressure profile. If pressure control becomes unstable, it can easily trigger gas kicks or lost circulation, posing a severe threat to operational safety. However, existing model predictive control (MPC) schemes have significant limitations: pure data-driven models exhibit poor generalization under complex conditions, while control algorithms based on traditional mechanistic models struggle to meet the stringent real-time requirements of field control cycles due to high-complexity numerical iteration processes. To balance control precision and real-time performance, this paper proposes a physics-informed model predictive control framework (PINC-MPC). During the training phase, physical prior knowledge such as the law of mass conservation is embedded into the neural network as constraints to construct a physically consistent deep surrogate model, enabling it to characterize complex wellbore characteristics. In the control phase, this surrogate model replaces the time-consuming numerical solving process of the mechanistic model within the MPC loop, achieving near-real-time state prediction and rolling optimization while ensuring physical fidelity. Experimental results indicate that PINC-MPC demonstrates superior control performance. Its median single-step solving time is only 16.81 ms, achieving an 11.1-fold acceleration compared to the mechanistic model-based scheme (187.3 ms). In a 5000 s full-cycle closed-loop control experiment, the total time required for the former is only 1.68 s, while the latter reaches 18.73 s, representing an efficiency improvement of approximately 91%. In terms of control accuracy, the integrated absolute error (IAE), reflecting the total deviation of the control process, significantly decreased from 63.40 MPa·s for the industrial successive linearization MPC (SLMPC) to 12.90 MPa·s, an improvement of 79.7%. Especially in extreme dynamic conditions such as simulated pump shutdowns for pipe connections and sudden gas kicks, the framework demonstrates excellent predictive ability and response efficiency. It can proactively trigger compensation actions to keep BHP fluctuations within 0.30 MPa, significantly outperforming the traditional SLMPC method. The research results prove that PINC-MPC provides an efficient, precise, and robust nonlinear control strategy for MPD systems, offering important engineering reference value for enhancing the automation level of intelligent drilling systems. Full article

This study systematically investigated differences in volatile flavor profiles among fermented green plum wines by integrating gas chromatography–mass spectrometry (GC–MS), sensory evaluation, and odor activity value (OAV) analysis with machine learning and SHapley Additive exPlanations (SHAP) based feature interpretation. The primary objective was to evaluate the applicability of machine learning algorithms for flavor profiling of green plum wine. The results indicated that floral and fruity aromas were predominant in samples NG9, YM7, and YM9. Most green plum wines contained high levels of esters, with ethyl benzoate (up to 4820.53 μg/L), ethyl octanoate (up to 2640.83 μg/L), and benzenecarbaldehyde (up to 3432.96 μg/L) being the major contributors. Among the six classification algorithms compared, fuzzy c-means clustering provided the most distinct clustering structure, identifying three distinct flavor categories. Six machine learning models were subsequently established, of which the decision tree (DT) model exhibited the highest performance, with an accuracy of 95.13%. SHAP analysis further revealed that ethyl octanoate, benzyl ethanoate, and 2-phenylethyl ethanoate exerted the greatest influence on model predictions. Overall, these findings highlight the effectiveness of machine learning as a robust tool for the classification and interpretation of flavor characteristics in fermented fruit wines, with broad applicability in flavor science. Full article

Creating a dependable approach for identifying both the mass of a shuttle car and how material is distributed in it removes the need for equipment operators to manually engage the flight chain. The quantification of environmental and installation conditions and the extent of influence considering their combined contribution towards inaccurate or exclusive measurements are to that degree limited. This experimental study investigated how two different strain transducers—installed in a force-shunt configuration—respond to thermo-mechanical loads when used to determine load distribution and position. Initial observations indicated that thermal effects at the installation site contributed to measurement inaccuracies or exclusive readings. The investigation quantified the impact of environmental and installation variables on measurement accuracy and found this influence to be indirectly linked to the mechanical properties of the substrate to which the strain transducers were mounted. Mounting bolt torque was determined to exert a negligible effect on strain measurement accuracy for the custom-built strain transducers. Nonetheless, both transducers failed to consistently return to the selected baseline at the start of experiments since thermal dependence persisted at the balanced state following the first cycle of loading. The research indicated that the custom-built force-shunt strain transducers are an effective means for mapping the profile and location of coal in shuttle cars, provided that the systems are subjected to continuous and cyclic rebalancing to maintain accuracy. Full article

Lightweight intrusion detection systems (IDSs) are increasingly integrated into applied data science workflows for cybersecurity and process monitoring, where limited computational resources and asymmetric error costs constrain model design. This paper presents a comparative study of two lightweight deep learning IDS architectures: ForNet, a convolutional model optimized for feature-centric detection, and SigNet, a gated recurrent model designed for sequence-oriented modeling of ordered flow-feature representations. Both models are trained with Cost-Robust Focal Loss (CRF-Loss), a cost-aware objective that penalizes false positives and false negatives according to deployment-specific risk preferences. We evaluate the models on the UNSW-NB15 and CIC-IDS2017 benchmarks using six standard metrics (accuracy, precision, recall, F1-score, Matthews correlation coefficient (MCC), and the area under the receiver operating characteristic curve (AUROC)), complemented by an analysis of false-positive behavior. On CIC-IDS2017, ForNet achieves precision up to 0.95 and MCC up to 0.93 with AUROC above 0.94, while SigNet shows a stronger recall-oriented profile on UNSW-NB15. In an ablation study, replacing Binary Cross-Entropy with CRF-Loss reduces the false-positive rate by approximately 15–20% and improves robustness-oriented metrics such as MCC by up to 12% on CIC-IDS2017. Rather than claiming universal state-of-the-art performance, the study focuses on performance–risk trade-offs under realistic operational constraints. The results highlight how architectural bias and cost-aware optimisation jointly shape IDS behaviour and offer benchmark-based guidance for interpreting performance–risk trade-offs in lightweight intrusion detection. Full article

Background/Objectives: Minor neurocognitive disorder (minor NCD) represents a heterogeneous and potentially modifiable stage along the continuum from normal aging to dementia, offering a critical window for targeted health promotion interventions. Early identification of individuals at increased risk of progression is essential for implementing preventive strategies that may delay functional decline. This study developed a transparent machine learning (ML) framework to predict diagnostic change from minor to major NCD at 12 and 24 months using baseline demographic, clinical, and multidomain neuropsychological data. Methods: A retrospective cohort of 162 memory clinic patients was analyzed using a rigorously controlled pipeline incorporating nested stratified cross-validation, SMOTE-based imbalance correction, and sequential forward feature selection. Logistic regression, support vector machines (SVMs), and XGBoost were evaluated, with SHapley Additive exPlanations (SHAPs) applied to ensure interpretability. Results: SVM achieved the most balanced predictive performance at both 12 months (accuracy = 0.90) and 24 months (accuracy = 0.81). Short-term progression was primarily driven by subtle multidomain cognitive inefficiencies, while longer-term risk reflected continued cognitive vulnerability modulated by metabolic factors such as diabetes. Conclusions: These findings highlight the potential of explainable ML as a health promotion tool and suggest that explainable ML can uncover clinically meaningful cognitive risk signatures at the earliest stages of NCD. By identifying modifiable systemic contributors alongside cognitive risk profiles, this framework supports precision-oriented preventive strategies and proactive longitudinal monitoring in minor NCD. Full article

Machining stability in profile grinding directly affects surface quality and form accuracy, while the variation in local contact conditions induced by complex contour geometries makes its stability behavior more complicated than that of conventional grinding. This study investigates chatter stability under the coupled effects of contour geometric features and process parameters. A dynamic grinding force model is developed based on a tool nose micro-element method, explicitly considering the coupled effects of contour geometric parameters, wheel–workpiece contact, and regenerative effects. A chatter stability model is then established, and an iterative method is proposed to predict stability limits under different contour features. The results indicate that wheel speed and grinding depth dominate system stability. Under the same curvature radius, convex contours exhibit the highest stability, followed by straight and concave contours. As the curvature radius increases, the stability boundaries gradually converge toward that of the straight contour. Increasing the contour normal angle (CNA) significantly enhances stability and promotes the transition of the dominant unstable mode from single-direction to multi-directional coupling. Grinding experiments on a composite curved workpiece validate the model, showing strong agreement between predicted stability regions and measured chatter marks and spectra. The proposed model provides a basis for parameter selection and chatter suppression in complex profile grinding. Full article

Interpretability in corporate financial risk profiling must support not only predictive performance but also governance-oriented decision-making. This study proposes a three-class financial risk assessment workflow for B2B settings and introduces Risk-Weighted Uncertainty-Conditioned Feature Importance (RW-UCFI) as a post-explanation prioritization framework. RW-UCFI is not a new attribution method; rather, it reorganizes existing explanation outputs according to class sensitivity, predictive uncertainty, and asymmetric risk relevance. The empirical analysis uses a single cross-sectional dataset of 954 Indonesia Stock Exchange-listed firms with organizationally provided Low Risk, Medium Risk, and High Risk labels. A stacked ensemble model is used as the explanatory substrate, followed by calibration analysis, uncertainty analysis, and governance-oriented explainability aggregation. On the held-out validation set, the model achieved an accuracy of 0.7487 and a macro ROC-AUC of 0.8630. Repeated stratified validation indicated moderately stable aggregate performance, although class-level reliability remained uneven, with High Risk recall emerging as the weakest and most variable component. The original model showed the most favorable probability reliability among the evaluated variants, whereas temperature scaling and one-vs-rest isotonic regression did not improve calibration. Uncertainty analysis further showed that the most uncertain cases concentrated substantially more misclassifications and High Risk misses; the top 30% most uncertain cases captured 52.1% of all errors and 43.8% of High Risk misses. RW-UCFI produced a materially different feature-priority structure from standard global SHAP ranking, suggesting that explanation outputs may become more decision-relevant for governance-oriented review when contextualized by uncertainty and asymmetric risk conditions in the present setting. Full article

Real-world evidence for wearable noninvasive glucose monitoring (NIGM) remains limited. To evaluate the functional equivalence of a wearable NIGM device and explore its utility for T2DM and prediabetes screening. In this multicenter study, 12-h daytime glucose profiles obtained by a flexible reverse iontophoresis-based electrochemical sensor were compared with capillary glucose using functional equivalence. Subgroup analyses were conducted. Screening models of T2DM and prediabetes were developed using elastic net and Logistic regression. A total of 135 participants (mean age 35.3 years; 60.0% female) were included, and no serious device-related adverse events were reported. Compared to the capillary measurements, functional equivalence was confirmed (T = −6.537 < threshold = −2.081) in the general population but not in older adults or T2DM patients. The T2DM noninvasive screening model demonstrated discrimination and reclassification performance comparable to those of the capillary-based model (AUC: 0.906 vs. 0.850, NRI: 0.044, IDI: −0.078, p > 0.05). Functional principal component scores facilitated the identification of prediabetes (AUC = 0.760). The device demonstrated acceptable accuracy and functional equivalence with reference methods. Its capability to detect T2DM and early glycemic anomalies supports its feasibility as a wearable, interpretative adjunct tool for large-scale screening in free-living populations. Full article

Background: Meningiomas, the most common primary intracranial tumors, are predominantly benign, but high-grade variants show marked aggressiveness, histo-molecular heterogeneity, and treatment resistance. Although the 2021 WHO CNS classification integrates molecular and histopathologic criteria, substantial inter- and intratumoral variability still limits prognostic accuracy and treatment effectiveness. The goal was to provide insight regarding the histo-molecular intratumoral heterogeneity (ITH) of meningioma and examine its clinical implications. Methods: A narrative review was performed in accordance with PRISMA guidelines. PubMed and Google Scholar were screened for studies on “meningioma” and “intratumoral heterogeneity” published up to 28 July 2025. Eligible studies included original human research reporting histological or molecular heterogeneity with clinical relevance. Results: Eighteen studies comprising 2952 meningioma patients (mean age 59.4 ± 14.8 years, range 16–85) were included. Integrated cytogenetic, molecular, and spatial analyses, including FISH, karyotyping, scRNA-seq, CNV profiling, and spatial transcriptomics, revealed multilayered histo-molecular heterogeneity. Histologically, regional variations in morphology and proliferative index increased with tumor grade. Genomic diversity, marked by recurrent losses of 1p, 14q, and 22q and transcriptionally distinct subclones, defined a complex tumor architecture. Spatial and temporal analyses demonstrated subclonal expansion, stepwise clonal evolution, and therapy resistance, particularly in recurrent tumors. Functionally, SULT1E1⁺ subclones and COL6A3-mediated macrophage–tumor interactions emerged as potential key drivers of malignancy, recurrence, and radioresistance. Conclusions: Histo-molecular diversity underlies meningioma progression, recurrence, and therapeutic resistance. Standardization of ITH assessment, integration of AI-based spatial analytics, and the development of subclone-specific therapies are essential next steps toward advancing precision neuro-oncology. Full article

Background: Conventional analyses of physiological recovery often rely on discrete metrics that assume independence across time points, thereby ignoring intrinsic temporal continuity and masking substantial interindividual heterogeneity. This proof-of-concept study assesses the efficacy of Functional Data Analysis (FDA) as a promising framework for characterizing individual response dynamics following a functional threshold power (FTP) test. Methods: Physiological time-series data (including blood lactate, heart rate, blood pressure, and glucose levels) collected from 21 trained cyclists (10 professionals, 11 amateurs) were represented as functional objects using FDataGrid on the original sampling grid (0, 3, 5, 10, 20 min), without basis expansion or smoothing. We conducted unsupervised functional clustering (K-means; Fuzzy K-means) and supervised classification (Maximum Depth with Modified Band Depth, K-Nearest Neighbors, Nearest Centroid, functional QDA with parametric Gaussian covariance). Model performance was estimated via Repeated Stratified 5-Fold Cross-Validation with 10 repetitions (50 folds), reporting accuracy, balanced accuracy (mean ± SD), 95% CIs, permutation p-values, and sensitivity/specificity from aggregated confusion matrices. Results: Lactate (CL) and diastolic blood pressure (DBP) provided useful and statistically significant discrimination across several classifiers (e.g., KNN, Nearest Centroid, functional QDA), whereas heart rate showed modest discriminative value and glucose intermediate performance. Unsupervised analyses revealed distinct lactate recovery profiles and graded membership for hemodynamic/metabolic variables, supporting the value of FDA for resolving heterogeneity beyond group-average trends. Conclusions: FDA offers a feasible and informative approach for classifying recovery phenotypes while preserving temporal structure. Findings are promising but should be interpreted with caution due to the small sample size, sparse time points, and the need for external validation in larger, independent cohorts before translation into routine decision-making. Full article

Equipment wear and assembly clearances can change the longitudinal stiffness of hot strip mills and further affect roll-gap levelling accuracy and asymmetric strip profile control. In this study, the longitudinal stiffness of a 1580 mm four-high hot strip finishing mill was investigated by combining the analytical calculation of the hydraulic press-down system with a three-dimensional mill–strip finite element model. The effects of typical horizontal and vertical gap forms, including work-roll offset, same-side deflection, roll crossing, and unilateral vertical clearance caused by step-pad wear, on total longitudinal stiffness and stiffness difference between the two sides were analysed systematically. The results show that work-roll horizontal offset changes the longitudinal stiffness in a nonlinear manner, whereas work-roll rotation and roll crossing generally reduce the longitudinal stiffness and increase the stiffness asymmetry between the two sides. Unilateral vertical clearance also causes nonlinear variation in both total stiffness and side-to-side stiffness difference. The proposed method was further applied to the stiffness prediction module of the Guangxi BG 1700 mm hot strip mill production line, providing support for equipment maintenance, roll-gap levelling, and stable strip production. Full article