Search Results (5,169)

Background: Adolescents in low- and middle-income countries face a disproportionate burden of dietary inadequacy, yet evidence on the determinants of overall diet quality remains scarce. This study assessed the prevalence of macro- and micronutrient inadequate intake and identified factors associated with dietary adequacy among adolescents in rural Pakistan. Methods: A cross-sectional survey was conducted among adolescents using multistage cluster sampling. Dietary intake was assessed through a single 24-hour dietary recall; nutrient adequacy was determined using age-and sex-specific Dietary Reference Intake (DRI) standards, including Estimated Average Requirements (EARs), Adequate Intakes (AIs), Estimated Energy Requirements (EERs), and Acceptable Macronutrient Distribution Ranges (AMDRs), as appropriate. Overall dietary adequacy was quantified using the Mean Adequacy Ratio (MAR), calculated as the unweighted mean of nutrient adequacy ratios for fourteen nutrients, including protein, fiber, calcium, iron, zinc, thiamin, riboflavin, niacin, vitamins A, B6, C, D, E, and folate, scaled from 0 to 100. Multivariable linear regression incorporating survey weights and clustering was used to identify independent predictors of MAR using STATA. Results: A total of 1132 adolescents were included. Most of the energy was derived from carbohydrates, and a high proportion of adolescents had energy intakes below age- and sex-specific Estimated Energy Requirement (EER) values, while very high levels of inadequacies were observed for protein, fiber, calcium, vitamin D, and vitamin A. The mean MAR score was 54.04 ± 16.51, indicating overall poor dietary adequacy. In multivariable analysis, male sex (β = 3.26; 95% CI: 1.76, 4.75), belonging to the richest wealth quintile (β = 5.81; 95% CI: 1.76, 9.86), always eating between meals (β = 6.70; 95% CI: 3.93, 9.47), and use of bar soap for handwashing (β = 4.97; 95% CI: 2.00, 7.94) were significantly associated with higher dietary adequacy. Adolescents who were underweight (β = −2.40; 95% CI: −4.81, −0.11) and those with moderate physical activity levels (β = −3.96; 95% CI: −7.62, −0.29) had significantly lower dietary adequacy scores. Conclusions: Dietary inadequacy among rural adolescents in Pakistan was highly prevalent and was associated with socioeconomic, behavioral, and nutritional factors. These findings highlight the need for interventions that address food insecurity, promote equitable food distribution, and strengthen nutrition education to support adolescent health and nutrition. However, these findings should be interpreted in light of the cross-sectional design and the use of a single 24-hour dietary recall, which may not reflect habitual dietary intake. Full article

The rapid expansion of make-on-demand and public chemical libraries has made exhaustive docking-based structure-based virtual screening increasingly difficult. This study introduces SurroDock, a lightweight deep-learning surrogate designed to approximate AutoDock Vina docking scores from low-cost two-dimensional molecular features, serving as a practical pre-filter for docking. SurroDock was evaluated for estrogen receptor alpha using two distinct conformations: an agonist-bound (PDB ID: 1GWR) and an antagonist/SERM-bound (PDB ID: 3ERT). The dataset comprised approximately 334,000 unique compounds curated from the NCI Open Database, PubChem, and BindingDB, all docked using a standardized AutoDock Vina workflow. The model was trained on concatenated 2D molecular representations comprising Morgan fingerprints, MACCS keys, RDKit physicochemical descriptors, Vina-inspired ligand descriptors, atom-pair fingerprints, and 2D pharmacophore fingerprints. The docking-score distributions differed substantially between receptor states, with 3ERT exhibiting more favorable scores than 1GWR and weak inter-state score correlation supporting state-specific modeling. Using the integrated Unified-200k training set (200,000 compounds randomly sampled per receptor from the three docked sources), SurroDock achieved strong held-out validation performance, with R² values of approximately 0.88 for 1GWR and 0.93 for 3ERT. In retrospective screening-style evaluation, SurroDock recovered substantial fractions of Vina’s top-ranked compounds at the top-1% recall (Recall@1%) of approximately 0.57 and 0.61 for 1GWR and 3ERT, respectively, yielding corresponding enrichment factors (EF@1%) of approximately 57-fold and 61-fold relative to random selection. Overall, the results indicate that 2D-based docking-score surrogate modeling can provide a reproducible and retrainable strategy for large-scale structure-based virtual screening by concentrating docking resources on a smaller, enriched subset of compounds. Because SurroDock emulates a docking scoring function rather than experimental binding affinity, its predictions should be used as prioritization aids and complemented by confirmatory docking, pose inspection, and experimental validation. Full article

Virtual power plants (VPPs) are emerging as flexible resources for automatic generation control (AGC) frequency regulation by coordinating geographically dispersed distributed energy resources. However, the timely execution of AGC commands is highly sensitive to communication latency and reliability, and conventional cellular networks may fail to provide stable service guarantees under high concurrency regulation scenarios. To address these issues, this paper proposes 5G radio access network (RAN) slicing technology to provide dedicated communication resources for VPP frequency regulation command transmission. First, the resulting communication performance is further embedded into the AGC performance score, establishing an explicit mapping from network slicing resources to VPP regulation revenue. Next, a Stackelberg game model between the telecom operator and the VPP is constructed to achieve coordinated optimization of network slice resource pricing and allocation. Simulation results show that the proposed method can significantly improve AGC command transmission reliability and frequency regulation tracking performance, while achieving a coordinated enhancement of both VPP regulation profit and telecom operator revenue. Full article

Deep neural networks (DNNs) have shown outstanding performance in visual recognition tasks within vision sensor networks; however, they are still vulnerable to adversarial manipulations and imperceptible perturbations that can lead to erroneous predictions. To address that, this paper presents AEGIS, a semantic aware and uncertainty guided adversarial detection framework designed for robust image classification in vision sensors pipelines. At its core, a SemantiGAN module functions as a multi-class semantic discriminator, identifying and filtering visually inconsistent adversarial inputs before they propagate further in the pipeline. For inputs that pass this stage, a stochastic augmentation process generates test time variations, from which handcrafted instability metrics FlipScore, Prediction Inconsistency, Layerwise Cosine Similarity (early and mid layers), and Entropy are computed. These features are aggregated into a compact five dimensional vector and processed by an Evidential Deep Learning (EDL) classifier, which models output evidence using a Dirichlet distribution to yield both class predictions and calibrated uncertainty estimates. Evaluations on the Tiny ImageNet dataset across six categories clean, FGSM, PGD, patch-based, functional, and geometric attacks demonstrate the effectiveness of AEGIS. The proposed framework achieves an AUROC of 92.1%, an AUPRC of 90.2%, and an accuracy of 90.7%, outperforming conventional softmax-based detectors in terms of detection performance, robustness, interpretability, and uncertainty calibration. Full article

Background/Objectives: Plant-mediated silver nanoparticles (AgNPs) are promising components for oral biomaterials because of their antimicrobial potential; however, their biological behavior depends strongly on the phytochemical matrix, physicochemical characteristics, and exposure concentration. This study aimed to evaluate silver nanoparticles formulations synthesized using turmeric powder-derived Curcuma longa ethanolic and aqueous extracts, with emphasis on physicochemical characterization, antibacterial activity against oral-relevant Gram-positive bacteria, cytocompatibility toward human gingival fibroblasts (HGF-1), and acute in ovo vascular compatibility. Methods: AgCUR-EtOH NPs and AgCUR-H₂O NPs were synthesized using CUR-EtOH and CUR-H₂O extracts as reducing and stabilizing matrices. The resulting formulations were characterized by UV–visible spectroscopy (UV-Vis), dynamic light scattering (DLS), zeta-potential analysis, X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), transmission electron microscopy (TEM), and energy-dispersive X-ray spectroscopy (EDX). Minimum inhibitory concentrations (MICs) and minimum bactericidal concentrations (MBCs) were determined against Streptococcus mutans, Streptococcus oralis, and Staphylococcus aureus. Cytocompatibility was evaluated in HGF-1 human gingival fibroblasts after 24 h exposure to 1–10 µg/mL using complementary viability, lysosomal, mitochondrial, and fluorescence-based assays. Acute vascular irritation was assessed using the hen’s egg test–chorioallantoic membrane (HET-CAM) assay. Results: Both formulations exhibited broad, polydisperse hydrodynamic distributions and negative apparent zeta potentials. AgCUR-H₂O NPs showed a lower Z-average diameter than AgCUR-EtOH NPs under their respective solvent-specific measurement conditions. XRD pattern revealed heterogeneous crystalline compositions dominated by residual AgNO₃, together with weaker contributions consistent with metallic Ag and a possible minor oxidized silver phase. FTIR spectra demonstrated extract-derived organic functional groups and prominent nitrate-associated bands. TEM/EDX confirmed Ag-containing nanostructures with approximate size ranges of 15–175 nm for AgCUR-EtOH NPs and 15–150 nm for AgCUR-H₂O NPs. S. mutans was the most susceptible microorganism, with MIC values of 9 and 7 µg/mL and MBC values of 88 and 62 µg/mL for AgCUR-EtOH NPs and AgCUR-H₂O NPs, respectively. AgCUR-H₂O NPs consistently showed lower MIC and MBC values against all tested strains, but also produced a more pronounced concentration-dependent reduction in HGF-1 viability. At 10 µg/mL, cell viability was 71.88% for AgCUR-EtOH NPs and 52.14% for AgCUR-H₂O NPs. Both formulations showed low acute irritation potential in ovo, with irritation scores of 1.06 and 0.69, respectively. Conclusions: The two CUR-AgNP formulations exhibited distinct physicochemical, antibacterial, and cellular response profiles under the tested conditions. At equivalent concentrations expressed as total dried formulation mass, AgCUR-H₂O NPs yielded lower MIC and MBC values against the tested bacterial strains, whereas AgCUR-EtOH NPs produced a less pronounced reduction in HGF-1 viability. Because the powders were not quantitatively normalized for total silver, extract-derived organic fraction, or residual precursor content, these differences cannot be attributed exclusively to nanoparticle properties or to the extraction solvent and should not be interpreted as evidence of the intrinsic superiority of either formulation. Both formulations showed low acute vascular irritation. Further quantitative compositional, silver-release, and biofilm assessments are required before incorporation into oral biomaterial platforms. Full article

Background/Objectives: Compression therapy serves as first-line conservative management for low-flow vascular malformations and Klippel–Trénaunay syndrome (KTS). However, ready-made garments are frequently ill-fitting for patients with limb overgrowth, asymmetry, deformity, or heterogeneous body habitus. Custom-made compression elastic garments offer an individualized solution, yet systematic data across diverse clinical entities remain scarce. This study evaluated objective and subjective outcomes of custom-made compression garments across vascular anomalies and edematous disorders in a multi-institutional real-world setting. Methods: A retrospective observational case series was conducted between May 2023 and September 2025 at four institutions: a tertiary medical center, a pediatric specialty hospital, a corporate hospital, and a corporate clinic. Patients who received custom-made compression elastic garments for vascular anomalies, edema, varicose veins, post-traumatic or post-burn venous stasis, or postoperative conditions were included. Objective outcome (limb circumference change) and subjective outcome (patient satisfaction) were analyzed. Results: A total of 191 patients who received custom-made garments were described (117 females, 61.3%; 74 males, 38.7%; mean age 36.3 years; median 22 years; range 1–96 years). The age distribution was bimodal, with 91 patients (47.6%) aged 0–19 years and 50 patients (26.2%) aged ≥70 years. The tertiary center and pediatric hospital treated vascular anomaly patients exclusively (n = 102), while the corporate hospital and clinic primarily served adult and elderly edematous disorder patients (n = 88; one additional vascular anomaly case was managed at the corporate hospital). Quantitative paired outcome analysis was feasible in the subgroups with complete paired data: 20 vascular anomaly patients (objective outcome) and 20 edematous disorder patients (subjective outcome). In the vascular anomaly subgroup (n = 20), the affected limb showed a significantly greater circumference reduction at the ankle’s narrowest point (median −2 mm, IQR −4.2 to 0.0) compared with the unaffected contralateral limb (median +0.5 mm, IQR −0.2 to +1.0; Wilcoxon signed-rank test T = 4; p < 0.001; effect size r = 0.83). In the edematous disorder subgroup (n = 20), patient satisfaction scores improved significantly from a median of 3 (IQR 2.0–4.0) to 4 (IQR 3.0–6.0) after garment use (Wilcoxon signed-rank test T = 0; p < 0.001; effect size r = 0.83). Representative cases illustrated pediatric vascular anomaly (Case A, vascular malformation; Case C, KTS) and adult edematous disorder (Case B, chronic lower limb edema) presentations and outcomes. Conclusions: Custom-made compression elastic garments were feasible and well tolerated across a broad real-world spectrum of vascular anomalies and edematous disorders. In the analyzed subgroups, a measurable reduction in affected-limb circumference was observed in pediatric vascular anomaly patients, and patient satisfaction improved in adult and elderly edematous disorder patients. Because each subgroup was evaluated with only one outcome domain and no head-to-head comparison of measures was performed, these findings should be regarded as hypothesis-generating. We propose that outcome measures may need to be tailored to disease background and age, a hypothesis that warrants testing in prospective studies capturing both objective and subjective endpoints across all patient groups. Full article

This paper presents a numerical-computational analysis of ${\mathsf{\ell }}_2$-regularized logistic learning for binary intrusion detection under heterogeneous datasets, class imbalance, and cross-dataset shift. Rather than proposing a new intrusion detection architecture, the study examines how numerical conditioning, feature scaling, feature set design, threshold selection, false negative behavior, false alarm behavior, and distribution shift affect operational detection performance. Experiments were conducted on CICIDS2017, UNSW-NB15, and CIRA-CIC-DoHBrw-2020 using reproducible train–validation–test protocols over five fixed random seeds. The numerical audit showed that standard scaling reduced the spectral condition number of traffic feature matrices by several orders of magnitude across datasets and feature configurations. However, scaling did not produce uniformly monotonic predictive gains: in some cases, raw feature optimization achieved comparable or higher F1-score, whereas scaled preprocessing produced more controlled false alarm behavior. In-domain experiments showed that dataset-specific features may improve ranking metrics such as area under the receiver-operating-characteristic curve (AUROC) or area under the precision–recall curve (AUPR) without necessarily improving thresholded operational metrics. Cross-dataset transfer experiments revealed strong source–target asymmetry, with transferred thresholds producing either near-zero positive detection or excessive false alarms. Additional robustness experiments with Random Forest and XGBoost improved in-domain F1-score and false negative rate (FNR), but did not eliminate off-domain degradation, with high FNR persisting under direct cross-dataset transfer. Finally, a Kolmogorov–Smirnov-based distribution shift analysis showed that in-domain discrepancies were small, whereas cross-dataset discrepancies were consistently large under common standardized traffic features. These findings suggest that numerical stability, ranking quality, thresholded detection performance, false negative and false alarm behavior, and distribution shift should be analyzed jointly when evaluating intrusion detection models. Full article

Predicting the cytotoxicity of engineered nanoparticles remains a significant challenge due to the vast combinatorial diversity of their physicochemical properties. In this study, we developed a multimodal generative framework to synthesize high-fidelity nanoparticle candidates with predefined toxicity indices. We used a large language model to extract heterogeneous data from scientific articles and utilized SciBERT-based embeddings to encode unstructured textual toxicity summaries. Four generative architectures—CTGAN, TVAE, WGAN-GP, and TabDDPM—were benchmarked using the Synthetic Data Vault quality score. The TabDDPM demonstrated superior performance in capturing complex structure–activity relationships, achieving an SDV quality score of 0.78. The case study validation and feature evolution analysis prove the practical efficacy of the TabDDPM. To validate the physical plausibility of the best generated model, we conducted coarse-grained molecular dynamics simulations in the GROMACS 2026.0 engine using the Martini 3.0.0 force field. Comparative analysis of safe and toxic nanoparticles candidates revealed that the toxic variant induced 2.4 times higher electrostatic stress (88.76 kJ/mol) and significantly prolonged membrane equilibration times. The safe candidates had a lower center-of-mass distance between the nanoparticle and the hydrophobic core of the lipid bilayer compared to the toxic counterpart. These results confirm that the proposed generative approach not only replicates statistical distributions but also captures the underlying biophysical mechanisms of membrane disruption, providing a potentially robust tool for the in silico design of biocompatible nanomaterials. Full article

Federated learning (FL) has emerged as an effective paradigm for collaborative model training in Industrial Internet of Things (IIoT) environments by enabling distributed devices to learn shared models without exchanging raw data. However, existing FL defence mechanisms predominantly rely on either behavioural analysis of client updates or semantic validation of model performance, limiting their ability to detect sophisticated poisoning and stealthy backdoor attacks that evade single-dimensional trust assessment. This paper proposes DBST-FL, a dynamic behavioural and semantic trust framework for robust federated learning in the Industrial IoT. The proposed framework evaluates each client through two complementary trust dimensions: a behavioural trust layer that measures gradient alignment, historical consistency, and collective deviation and a semantic trust layer that assesses benign utility and template-free semantic stress validation using server-side data. The two trust scores are integrated through a non-compensatory multiplicative trust fusion mechanism, ensuring that weaknesses in one trust dimension cannot be masked by strengths in the other. The resulting trust score guides a trust-aware aggregation strategy that reduces the influence of malicious participants while preserving the contributions of reliable clients. Extensive experiments are conducted on the Edge-IIoTset and UNSW-NB15 datasets using ANN, 1D-CNN, and LSTM models under multiple poisoning and backdoor attack scenarios. The proposed framework achieves overall classification performance competitive with the strongest robust aggregation baselines while consistently delivering stronger resilience against adversarial attacks and lower backdoor attack success rates than representative trust-based and Byzantine-robust aggregation methods, all while maintaining linear per-round computational complexity suitable for large-scale IIoT deployments. The results demonstrate that integrating behavioural and semantic trust within a unified aggregation framework provides an effective and scalable defence against advanced adversarial threats in federated learning. Full article

Camera and LiDAR sensors in safety-critical autonomous systems suffer undetected distributional shifts that silently corrupt downstream perception. Out-of-distribution (OOD) detection is the established sensor-data-integrity primitive, but no single post hoc detector covers every shift type, and existing detectors lack guarantees on their false-positive rate (FPR). This paper asks when calibrated score fusion helps and provides a distribution-free finite-sample FPR certificate. Four post hoc scores—Maximum Softmax Probability (MSP), Energy, Mahalanobis distance and k-nearest-neighbour (KNN) distance—are calibrated to p-values by the empirical cumulative distribution function (ECDF) and combined by Fisher’s method or cross-backbone z-score averaging, then wrapped in a conformal predictor with Hoeffding-based Probably Approximately Correct (PAC) bounds. On the full-split PUG camera benchmark (215,040 images), uniform same-backbone p-value fusion does not beat the best single detector (Mahalanobis); the gain comes from cross-backbone diversity: a z-score average of Mahalanobis distances over ResNet-50 and frozen DINOv2 reaches a mean area-under-the-ROC-curve (AUROC) of $0.9258$ ($+0.0199$), rising to $0.9292$ ($+0.0233$) with added spectral and dropout signals (DeLong $p<{10}^{-9}$). On the nuScenes LiDAR sensor (256,873 frames), uniform fusion yields only a small, calibration-sensitive gain over the best single detector (MSP), so the substantial fusion gain is confined to cross-backbone averaging on the camera. The distribution-free PAC certificate, by contrast, transfers across both sensors with margins below $1.5\%$ ($0.96\%$ camera, $0.25\%$ LiDAR), giving evidence usable in ISO 26262 and EASA CoDANN safety cases. Full article

Background and Objectives: Carpal tunnel release (CTR) has evolved toward increasingly less invasive techniques, with the aim of reducing procedural trauma and accelerating recovery. While ultrasound-guided and microinvasive approaches have demonstrated safety and technical precision, prospective data describing Return-to-Work (RTW) and early functional recovery remain limited. Materials and Methods: This prospective cohort study included adults undergoing office-based ultrasound-guided microinvasive CTR (Micro-CTR) under local anaesthesia using a retractable needle-mounted blade system. Patient-reported outcomes were collected using the Comprehensive Carpal Tunnel Recovery Questionnaire. The primary outcome was time to RTW. Secondary outcomes included change in pain score, analgesic duration, and time to return to driving and household tasks. Continuous outcomes were non-normally distributed and are reported as medians with interquartile ranges (IQRs). Recovery outcomes were compared by structured self-reported postoperative-event status using non-parametric and time-to-event analyses, and an exploratory distributional analysis assessed the influence of longer self-reported RTW intervals on the overall recovery distribution. Results: Sixty-seven patients were included. Median RTW was 9 days (IQR, 30.5; n = 48), with a median return to driving at 5 days (IQR 6) and household tasks at 7 days (IQR 7). Median pain decreased from 8/10 preoperatively to 0/10 postoperatively (p < 0.0001). Participants reporting at least one structured postoperative event had longer RTW intervals than those selecting no structured event (median 35 vs. 5 days; HR 0.42, 95% CI 0.22–0.80; p = 0.0079). Exploratory distributional analysis showed that longer RTW intervals had a substantial influence on the overall RTW distribution; the full RTW responder cohort remained the primary analysis. Conclusions: Ultrasound-guided Micro-CTR was associated with rapid early functional recovery in most patients in this prospective cohort. Recovery was heterogeneous, with longer RTW intervals observed among participants reporting structured postoperative events. These findings support RTW as a pragmatic functional endpoint in evaluating emerging CTR techniques. Full article

Machine learning techniques for network-based intrusion detection systems (NIDS) have advanced considerably over the past decade. Still, improvements are inhibited by handcrafted feature pipelines, isolated public benchmark data, and evaluation procedures that do not reflect real-life deployment. AutoML, a branch of ML automating model selection, automated architecture search, and the creation of model pipelines, may help overcome these shortcomings. While numerous NIDS applications employing automated ML techniques have been proposed, and recent surveys have mapped the AutoML framework landscape for network intrusion detection, no existing review critically audits the evaluation practice of this literature: the quality of its benchmark datasets, the reproducibility of its reported results, and the realism of its deployment assumptions. This paper critically reviews 26 research works published between January 2023 and June 2026, collected via a two-phase structured search: a documented keyword search across five databases (Scopus, IEEE Xplore, Web of Science, ACM Digital Library, and Google Scholar), followed by full-text eligibility screening, citation chaining, and expert evaluation. Findings drawn from this collection capture trends observed among the selected studies, rather than reflecting the broader state of the field. Analysis of the corpus reveals that 88% of dataset-verified studies evaluate exclusively or partly on the legacy benchmark family (KDD-derived, CICIDS, UNSW-NB15, CIDDS), 21% evaluate on a single dataset only, and among attribute-verified studies only 32% release source code, 40% report statistical significance testing, and 36% include variance analysis, findings that collectively motivate the four contributions of this study. First, a recommended evaluation framework is proposed, addressing baseline parity, transparent search-space and budget reporting, nested cross-validation for selection-bias control, and stability reporting across multiple random seeds. Second, a dataset quality scoring framework is introduced, assessing five dimensions: overlap rate, duplication rate, label correctness, attack-type representativeness, and coverage of benign, IoT, and IIoT traffic. Third, a cross-domain justification is provided for neural architecture search (NAS) and meta-learning in NIDS, grounded in advances in federated NAS, out-of-distribution robustness, edge-constrained search cost reduction, and few-shot adaptation. Fourth, a structured research roadmap is outlined, targeting real-world validation, standardized benchmarks, curated datasets, resource-aware AutoML, and privacy-preserving federated NAS. In contrast to prior surveys of AutoML for network intrusion detection, which map frameworks and computational paradigms, this review contributes a formalized evaluation checklist, an explicit and partially empirically validated dataset quality scoring scheme, and evidence-based methodological guidance grounded in a transparent, fully enumerated study corpus. Full article

Time-reversed Young (TRY) interferometry reconstructs interference from a fixed detector by reading out a programmable source-label distribution. This work formulates the architecture as a source-coded Bayesian response sensor. For a perturbation parameter $\theta$, the detected source-label histogram is a posterior distribution determined by a programmed source prior, an optical likelihood for a fixed-detector click, and an evidence factor equal to the click probability. The key point is not the Bayesian identity itself, but its physical implementation: in TRY the prior is imposed before propagation and can therefore reshape the response ensemble actually sampled by the detector. The normalized posterior is shown to respond through a centered likelihood score, and the detected-event Fisher information is the posterior variance of this score. This identifies posterior-weighted score contrast, rather than local response magnitude alone, as the relevant sensing resource. The framework separates posterior-shape information from evidence information, giving a resource-aware way to judge near-null response enhancement. It also yields practical design rules: a two-label source code converts a weak perturbation into a fixed-detector label imbalance, while the multiparameter score covariance provides a route to nuisance rejection and gives a minimal-label rank condition for sensing multiple perturbations. A passive double-slit implementation with weak one-slit phase and loss perturbations is proposed, requiring only fixed-detector source scans before and after calibrated perturbations. Practical tolerances associated with source-programming error, drift, background, imperfect coherence, and polarization mismatch are analyzed, and extensions to multi-aperture and integrated photonic systems are formulated. The results position TRY as a source-programmable Bayesian sensing architecture complementary to conventional detector-plane Young interferometry. Full article

Accurate short-term precipitation nowcasting remains challenging in complex terrain regions, where storm displacement, evolution, and orographic enhancement strongly affect precipitation distribution. This study evaluates three precipitation nowcasting schemes initialized from LAPS analysis fields: first-order advection (Control), advection–diffusion (AD), and advection–diffusion coupled with the linear theory of orographic precipitation (ADLOP). The schemes are tested over the Attica region of Greece using three high-impact precipitation events representing different synoptic weather regimes and verified against high-resolution weather radar observations. Forecast performance is assessed using continuous, categorical, and neighborhood-based spatial verification metrics. Results show that the Control performs competitively for light precipitation and at larger neighborhood sizes in localized events. The inclusion of diffusion in the AD scheme generally reduces random errors. In this limited three-case sample, aggregated results show that ADLOP reduces systematic bias, with reductions reaching approximately 33% at longer lead times and showing higher detection scores. However, its added value is strongly dependent on the terrain-influenced precipitation regime and may be accompanied by increased error at longer lead times. Overall, the benefits of incorporating diffusion and simplified linear orographic forcing depend on precipitation regime, lead time, and verification metric; therefore, the results should be interpreted as diagnostic case-study evidence rather than as a general assessment of ADLOP performance. Full article

The high rate of growth of misinformation on decentralized platforms causes a risk to public confidence and the integrity of decisions and requires a system of verification that is not only accurate but can be causally informed and interpreted via proxy causal metrics, reliable, and privacy-safe as well. The state-of-the-art federated learning (FL)-based fact-verification models mainly use correlation-driven patterns and do not provide ways to deal with causal reasoning, measuring formal reliability, or being resilient to Byzantine adversaries. This paper proposes a unified framework called CORE-FACT (causal optimization and reliability-enhanced fact-tracking) that can be used to conduct interpretable and robust misinformation detection in a distributed environment by combining causally informed optimization with reliability-weighted federated optimization. The proposed three-tier architecture includes: (1) a causal graph construction module, where variational attention is utilized to learn directed relationships of claims and evidence; (2) a reliability-weighted federated optimization module, where Byzantine-resilient aggregation (adaptive trust scoring) is achieved; and (3) an adaptive fact-tracking module, which is used to achieve fusion of multi-source evidence by combining hierarchical consistency verification with knowledge-graph embeddings. Empirical testing on the LIAR and FEVER datasets shows that CORE-FACT achieves 94.7% and 96.3% accuracy, respectively, outperforming state-of-the-art baselines by 3.5 to 4.8 percentage points in accuracy, with 17% lower latency than GEAR and 31% lower latency than DAGNN, and 98.2% robustness against 30% Byzantine attacks. Under differential-privacy guarantees verified at ε = 10.96, δ = 10⁻⁵ (Renyi DP composition, empirical membership inference validation committed for revision), CORE-FACT achieves a 31% reduction in false positives through explicit causally informed reasoning. These findings make CORE-FACT a scalable and interpretable framework that consolidates causal optimization, trustworthiness evaluation, and secure aggregation for next-generation federated misinformation detection. Full article