Search Results (2,891)

The distributional specification in Markov-switching GARCH (MS-GARCH) models has historically been driven by empirical convention. This paper derives the regime-conditional Student-t innovation distribution from the Tsallis Maximum Entropy Principle, providing an information-theoretic foundation for the choice of heavy-tailed innovations. The GARCH variance dynamics and Markov-switching structure are standard modelling choices adopted independently of the MaxEnt derivation. The framework is applied to five major cryptocurrencies over January 2017 to March 2026, comprising 15,824 daily observations. Three principal findings emerge. First, Tsallis entropy maximisation under a variance constraint yields the q-Gaussian density, which coincides with the Student-$t_{{\nu }_k}$ distribution for $q_k=({\nu }_k+3)/({\nu }_k+1)$, with degrees of freedom determined endogenously from the empirical excess kurtosis. Second, calm-regime half-lives ${\tau }_C\in [1.21,2.37]$ days and stationary turbulent probabilities ${\pi }_T\in [0.254,0.437]$ confirm that both regimes are economically active across all assets; a Francq–Zakoïan stationarity verification confirms global ergodicity. Third, near-unity turbulent GARCH persistence suppresses the point-forecast advantage of regime-switching, consistent with a Fano-type Forecasting Irreversibility Bound; HAR-RV achieves the lowest QLIKE loss for three of five assets. Value-at-Risk backtests confirm adequate tail-risk calibration for four of five assets at the 1% and 5% levels, outperforming single-regime benchmarks. An empirical assessment of the VolShock extension identifies asset-class boundary conditions, motivating a proportional specification for future work. Full article

Selective labels create a support failure for prediction along dependent stochastic processes: alert-triggered events are observed, whereas silent periods are usually unlabeled. We model this mechanism as predictable inclusion on a filtered probability space and show that population risk is non-identifiable when any silent region has zero labeling probability. Selective-observation weighted risk control (SOWRC) combines alert labels with randomized audits through Horvitz–Thompson losses and a martingale-mixture boundary. It provides finite-sample calibration-population control under arbitrary temporal dependence subject to predictable design choices, conditional ignorability, positivity, bounded losses, and deterministic design envelopes, together with a prospective guarantee under an externally certified deployment-drift envelope and explicit error allocation. Extensions cover anytime monitoring, multiple losses, adaptive budgets, and estimated propensities. Synthetic maintenance and financial studies, a complete-log replay on a real dependent sensor series with 100 audit-mask replications, and 4000 selection-level validation runs demonstrate support recovery and conservative probabilistic risk control on deterministic threshold grids. Full article

During the early developmental stages of solid rocket motors (SRMs), particularly under extreme operating conditions, the scarcity of experimental data severely limits the predictive capabilities of conventional deep learning models. To address this challenge, this paper proposes a novel hybrid predictive framework, termed Shape-Regularized Meta-Variational Multi-Scale Network (SR-MVSNet), tailored for few-shot thrust prediction via shape-regularized meta-learning. First, a variational autoencoder (VAE) maps nine static design and operating parameters to a latent probability distribution and reconstructs the parameter vector. A parallel multi-scale convolutional neural network (MSCNN) then processes the reconstructed features to predict the complete thrust curve. Crucially, a joint loss function with shape regularization is integrated within the model-agnostic meta-learning (MAML) architecture, guiding the network to reproduce the measured thrust build-up and decay through supervised first- and second-order difference matching. Experimental results demonstrate that the proposed framework achieves the lowest mean squared error among the evaluated models in the low-temperature-to-ambient-temperature transfer task. Notably, during the critical steady-state combustion phase, the mean absolute percentage error is 1.71% under the combined-source task, supporting accurate steady-state thrust prediction for the rapid performance evaluation of SRMs in engineering applications. Full article

This study develops a techno-economic sizing and energy-management framework based on the Hippopotamus Optimization Algorithm (HOA) for hydrogen-integrated autonomous Hybrid Renewable Energy Systems (HRES) for rural electrification. A representative remote community in Tabuk, Saudi Arabia, is investigated using 11 years of NASA POWER satellite-derived meteorological data. The modelled community is constructed from a synthesised connected-load inventory representing approximately 600 households and 3000 residents; accordingly, the results represent a simulation-based planning case study rather than a validated design for a specific settlement. Seven configurations combining photovoltaic generation, wind turbines, battery storage, hydrogen production and storage, fuel cells, and diesel generation are evaluated considering Total Net Present Cost, CO₂ emissions, and Loss of Power Supply Probability (LPSP), with a Demand Response Management System (DRMS) incorporated into the framework. The three objectives are combined using a weighted-sum scalar formulation, complemented by a hard-constrained formulation for reliability. HOA is benchmarked against Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Walrus Optimizer (WO), and Osprey Optimization Algorithm (OOA) under a common budget of 10,000 objective-function evaluations per run and 10 independent runs. Under the constrained formulation, six of the seven configurations satisfy LPSP ≤ 5% within the investigated sizing bounds, with costs of energy (COE) ranging from $0.1046/kWh for PV/wind/battery to $0.1357/kWh for wind/battery/diesel; the fully renewable PV/wind/hydrogen configuration is feasible at $0.1195/kWh. Only the wind-free configuration fails to satisfy both imposed constraints because of the 30% diesel-energy limit rather than reliability. Evaluation over the eleven individual meteorological years shows that all designs violate the 5% reliability criterion in every year, reaching 2.0–2.9 times the design LPSP because hour-of-year averaging removes prolonged low-resource periods. Re-optimization against the worst observed year increases COE by 33–63% and storage capacity by factors of three to five, with hydrogen storage in the fully renewable configuration increasing from 10 to 75.4 kg. Sensitivity analysis identifies wind availability as the dominant economic parameter, with a 20% wind-speed reduction increasing COE by 36.1%. The DRMS reduces the peak-to-average ratio by 20.0% for the assumed evening-peaking profile, whereas no reduction is obtained for an afternoon-peaking profile consistent with measured Saudi residential demand. These findings demonstrate that meteorological and demand-profile representation materially affects autonomous HRES sizing and should be explicitly considered when interpreting techno-economic optimization results. Full article

Urban disaster screening requires transparent methods that can integrate heterogeneous open datasets without implying unsupported causal or probabilistic relationships. This study presents an open-data framework for screening flood-footprint population proxies and potential disruption to hospital accessibility in Bucharest, Romania. The analysis covers 104 archived hexagonal spatial units and combines a 100-year flood-depth scenario from the Joint Research Centre, WorldPop 2020 population estimates, hospital-routing outputs derived from OpenStreetMap, and a publicly available seismic screening surface from the European Facilities for Earthquake Hazard and Risk. Flood and seismic information are retained as distinct screening dimensions because the available data do not support modelling their causal interaction, temporal sequence, joint probability, earthquake-related infrastructure damage, or hospital capacity. For spatial units that remain connected to a hospital, a transparent two-domain service-priority index is calculated using flood-footprint population proxy and the potential change in hospital accessibility under the flood scenario. Units for which no hospital route is available are reported separately as binary service-disconnection alerts rather than being assigned an arbitrary numerical penalty. The robustness and interpretability of the framework are examined through network monotonicity, score boundedness, Pareto dominance, penalty-free rank invariance, and rank-acceptability analysis. The results are communicated using a Pareto frontier, rank trajectories across the full weight simplex, indicator-contribution decomposition, and an exact hypergeometric assessment of class overlap. The proposed framework provides a transparent first-order planning tool for identifying locations that may require more detailed investigation. It should be interpreted as a screening approach rather than as a probabilistic risk, cascading-hazard, infrastructure-damage, or service-loss model. Full article

Background and Objectives: Audiometric monitoring is recommended for patients receiving ototoxic (chemo)radiotherapy for head and neck cancer (HNC), yet real-world uptake is poorly characterized and potentially inequitable. We quantified the uptake and timing of audiometric testing and examined rural–urban residence and age as candidate determinants. Materials and Methods: We conducted a single-center retrospective cohort study of 70 consecutive patients with HNC treated with radiotherapy between November 2024 and September 2025, comparing rural (n = 32) and urban (n = 38) residents. Outcomes were audiometric testing at treatment initiation, repeat audiometry during follow-up, and post-treatment hearing change. Analyses included Fisher’s exact and Mann–Whitney U tests, Spearman correlations, Firth-penalized logistic regression, inverse probability of treatment weighting (IPTW), restricted cubic splines, and sensitivity analyses. Results: Audiometry at treatment initiation was performed in 31/70 patients (44.3%) and repeat audiometry in only 10/70 (14.3%); among tested patients with documented dates, just 19.4% underwent audiometry before the first radiotherapy fraction, so that a true pre-exposure baseline existed for only 6/70 patients (8.6%). Uptake at initiation was 53.7% with concurrent cisplatin, 14.3% with carboplatin, and 36.4% with radiotherapy alone (p = 0.114). Uptake did not differ by residence (rural 40.6% vs. urban 47.4%; odds ratio (OR) 0.76, 95% confidence interval (CI) 0.30–1.96, p = 0.634), with an IPTW-weighted risk difference of −9.9% (95% CI −30.9 to +11.8). Age was the dominant determinant: uptake fell from 90.5% below 65 years to 24.5% at ≥65 years (adjusted OR per decade 0.32, 95% CI 0.15–0.71, p = 0.005), with significant non-linearity (p = 0.015). Pre-existing hearing loss was associated with lower testing (OR 0.29, 95% CI 0.10–0.91, p = 0.032). Post-treatment hearing change occurred in 54.3% overall (rural 46.9% vs. urban 60.5%, p = 0.336), comprising a subjective hearing complaint in 51.4% and audiometric deterioration in six of the nine patients with paired audiograms. Conclusions: Audiometric monitoring was incomplete, rarely obtained before irradiation, and steeply age-patterned, whereas rural residence was not associated with lower uptake; structured, exposure-based referral pathways, triggered by planned cisplatin and clinically relevant cochlear dose and applied irrespective of age, are warranted. Full article

Dual-parameter photonic sensors are usually reported through a nominal sensitivity and one detection limit, without stating which statistic that limit is or whether it survives transfer between devices. This computational study supplies that evaluation for one structure, a 36-layer one-dimensional multilayer read in transmission, whose Zak-phase-distinct TiO₂/SiO₂ photonic-crystal sections enclose a 600 nm analyte cavity and a 500 nm thermo-optic reference cavity, each carrying a 5 nm ITO/5 nm TiO₂ nanolaminate insert. Two coupled interface resonances at 1517 and 1651 nm, with loaded Q of 232 and 208 and refractive-index (RI) sensitivities of 90.71 and 329.41 nm/RIU, are inverted by a bounded nonlinear calibration to $2.43\times {10}^{-5}$ RIU and $0.155$$°\mathrm{C}$; the temperature channel reports the device temperature. Probability-of-detection limits at 1% false alarm and 95% detection are $4.36\times {10}^{-5}$ RIU and $0.123$$°\mathrm{C}$; they are set by the calibration standards and the wavelength reference, not by the linewidth. Transferring one calibration between devices worsens them 81-fold and 203-fold; a three-point per-device correction removes 84–93% of that loss. A trivial control matched on wavelength, Q, transmission, thickness and RI sensitivity shows no topological robustness advantage. Applied to the design itself, the same evaluation shows that the modes are cavity-selected, that hyperbolicity brings no benefit, and that the nanolaminate-free stack is preferred. Full article

As climate change increases typhoon intensity and frequency, typhoon-induced high winds increasingly threaten nuclear power plant (NPP) safety by disrupting off-site power systems. Previous probabilistic safety assessments (PSAs) evaluated this risk using transmission tower fragility models derived from high-wind fragility equations developed for NPP structures, systems, and components. However, these models do not adequately reflect the structural characteristics of Korean transmission towers. In this study, a PSA of the off-site power system at the Kori NPP site under typhoon-induced high winds was performed using a universal voltage-class-based fragility model developed for Korean transmission towers. To isolate the effect of the fragility model on risk estimates, the typhoon hazard, network, and damage correlation conditions were set identically to those adopted in previous research. The Korean universal fragility model produced a higher median wind speed but a lower high-confidence-of-low-probability-of-failure capacity, resulting in an increase in the estimated annual risk. Because the lower HCLPF extends the probability of damage to lower wind speeds, which occur more frequently, the annual risk increases despite the greater median capacity. The risk-contribution analysis showed that the overall risk was governed by the overlap between the 0–50% range of the fragility curve and the hazard below its 100-year return period, rather than by the median failure wind speed. The analysis further demonstrated that the choice of logarithmic standard deviation shifts the governing wind-speed range. Therefore, realistic assessments require fragility models developed specifically for Korean transmission towers. These findings provide a quantitative basis for estimating the frequency of loss of off-site power events at NPPs, thereby contributing to resilient and sustainable infrastructure management. Full article

Rice leaf blast can develop rapidly under field conditions, creating a need for fast, non-destructive, and spatially explicit monitoring. UAV multispectral imagery and synchronized ground disease surveys were collected from artificially induced rice leaf blast experiments conducted in 2024 and 2025. After image registration, U-Net canopy segmentation, and ROI quality screening, 1801 curated 3 × 3-pixel canopy ROIs were retained. Each ROI was represented by four reflectance bands and 10 vegetation indices selected by Pearson correlation analysis, and the resulting dataset supported model development and spatial mapping. The improved 1DCNN-Transformer branch combined multi-scale Inception convolution, SE recalibration, and Focal Loss with RF and GBDT probability fusion. In the hold-out evaluation, the model achieved an overall accuracy of 98.90% and a weighted F1-score of 98.89%. Five repetitions of five-fold grouped cross-validation were performed, with identical 14-feature vectors constrained to the same fold. RF, GBDT, and equal RF-GBDT probability fusion achieved mean accuracies of 99.63%, 99.29%, and 99.33%, respectively, supporting strong class separability after duplicate-group isolation. The resulting severity and prescription maps provide an end-to-end digital workflow from canopy extraction to spatial decision support. Full article

Background/Objectives: Juvenile systemic sclerosis (jSSc) is a rare, chronic autoimmune disease with limited pediatric data. We aimed to characterize the demographic, clinical, immunological, and microvascular features of a Russian cohort of children with jSSc, and to evaluate treatment outcomes and predictors of escalation to biologic therapy. Methods: A retrospective single-center study of 40 children with jSSc (36 girls, 4 boys) followed over a 20-year period (2004–2024) was conducted. Clinical assessment included the modified Rodnan Skin Score (mRSS) and the Juvenile Systemic Sclerosis Severity Score (J4S). Nailfold capillaroscopy (NFC) was performed in 12 patients. Autoantibody profiling was available for 30 patients. Two first-line regimens were compared: glucocorticosteroids with penicillamine (PA, n = 19) versus glucocorticosteroids with DMARDs (methotrexate, mycophenolate mofetil, or cyclophosphamide; n = 21). Predictors of switching to biologic therapy were identified using binary logistic regression. Results: Median age at onset was 9.0 years (IQR 6.0–10.0), and diagnostic delay was 12.0 months (IQR 4.0–24.0). Diffuse cutaneous jSSc predominated (82.5%). Gastrointestinal involvement was detected in 82.5%, Raynaud’s phenomenon in 87.5%, and ILD in 37.5%. NFC revealed reduced capillary density (5.26 ± 1.33/mm). Giant capillaries were observed exclusively in males (4/4 vs. 0/8, p = 0.002). DMARD-based regimens were associated with better outcomes than PA, particularly for joint involvement. Eleven patients (27.5%) were switched to biologic therapy, primarily rituximab. ILD and J4S > 15 were the strongest predictors of switching (probability 60–61% vs. 8–15%, p < 0.001). The 5-year survival was 100%; the estimated 10-year survival was 71.4%, although the latter estimate should be interpreted cautiously because of the small number of events and loss to follow-up. Conclusions: In our cohort, children with jSSc present predominantly with diffuse cutaneous involvement. Male patients may be at risk for more severe microvascular changes, possibly related to diagnostic delay. ILD and J4S > 15 were associated with escalation to biologic therapy, and earlier switching to rituximab may be beneficial in refractory patients. Future prospective multicenter studies are needed to validate these findings. Full article

An empirical model of the TU Muscae binary star system has been developed by a study of 23 high resolution SWP spectrophotometric images that were obtained with the International Ultraviolet Explorer (IUE) satellite telescope including some that were downloaded from the NASA/MAST IUE Archive. The images are well distributed in Keplerian orbital phase thereby permitting a simultaneous fitting of the C IV wind-line profile by the SEI method and the light curve for the blanketed continuum (1450–1490 Å) bandpass by means of a program developed by the author. The result is a set of parameters characterizing the physical and geometric properties of the wind envelopes surrounding the stars. Surprisingly, there is no evidence for a P Cygni profile or strong, distinguishable shock front in the system, as has been found for similar investigations of EM Carinae and HD 159176. This is probably a result of the contact nature of the binary and the high-temperature environment of such a shock. That is, most of the carbon ions in the shock are more highly ionized. Based on the parameters for the SEI fit to the C IV profile, the value for the ionization fraction of C IV in the wind was calculated to be 10⁻⁴. With this value, the mass loss rate, Ṁ, calculated from two independent equations, was found to be about 10⁻⁶ solar masses per year (M_ʘ/yr). The UV line blanketing in the 1500 to 1600 Ångstrom bandpass was found to be erratically variable with orbital phase and time, indicating a variable amount of fast moving, dense clouds in the winds and/or a great amount of turbulence. The meaning of rotational velocities for the stars is problematic and depends on what point on the photospheres one is considering. Full article

Reinforcement learning (RL) can guide autonomous underwater vehicle (AUV) maneuvering to improve the relative source–target–receiver geometry for bistatic sonar target tracking. However, learning a reliable RL policy typically requires substantial interactions with the environment. This paper proposes a risk-aware hybrid decision-making framework that combines an RL policy with selective receding horizon control (RHC) to reduce policy training requirements. Specifically, soft actor-critic (SAC) serves as the nominal decision maker, while a tracking-risk detector assesses target existence probability and estimation uncertainty. When a high-risk belief state is identified, RHC temporarily overrides the SAC action and performs finite-horizon planning based on predicted tracking uncertainty and acoustic detectability. Monte Carlo tree search is employed to efficiently solve the resulting planning problem. Numerical simulations show that the proposed framework improves tracking performance and reduces target-loss events under limited SAC training budgets. In particular, the hybrid framework using a SAC policy trained for 160,000 interaction steps achieves tracking performance comparable to that of pure SAC trained for 500,000 steps, while requiring only sparse RHC intervention. These results demonstrate that selective online planning provides an effective trade-off among policy training requirements, online computational cost, and target tracking performance. Full article

Mineral prospectivity mapping of weathering-crust-type ilmenite placer deposits is challenged by the limited number of known mineral occurrences, the lack of reliable barren-site labels, and spatial autocorrelation. This study focuses on the Wuding–Luquan area of Yunnan Province, China, and develops an eight-predictor framework comprising elevation, slope, fault kernel density, an interpolated Ti-concentration predictor, FeO_x, Al–OH and Mg/Fe–OH mineral anomalies, and distance to mafic rocks. Forty-three known ilmenite occurrences were used as positive samples, and 86 pseudo-absence/background samples were generated under spatial constraints. Random forest, XGBoost, and a multilayer perceptron, together with an equal-weight soft-voting ensemble, were used for mineral prospectivity mapping. Model performance and predictor contributions were evaluated using nested spatial cross-validation, SHapley Additive exPlanations (SHAP), permutation importance, and spatial-block bootstrap resampling. The equal-weight ensemble achieved the highest out-of-fold ROC-AUC (0.9202) and average precision (0.8580), while yielding the lowest log loss and Brier score. Ti was the most important predictor across all four modeling schemes, whereas Al–OH mineral anomalies, slope, and Mg/Fe–OH mineral anomalies also showed relatively high contributions in the equal-weight ensemble. A total of 379 measured TiO₂ records showed weak positive and scale-dependent rank associations with predicted prospectivity, with median Spearman coefficients ranging from 0.133 to 0.325 across aggregation scales of 250–2000 m. This comparison was treated as an exploratory geochemical comparison rather than as independent validation of model performance. By integrating model probabilities with the distributions of mafic rocks, known mineral occurrences, faults, and measured TiO₂, eight prospective areas (T1–T8) were delineated. The results provide a quantitative basis for regional mineral prospectivity assessment and exploration-target prioritization in the Wuding–Luquan area. Full article

Ice storms threaten distribution network resilience through combined mechanical loading and secondary failures, increasing the risk of prolonged power interruptions. This paper proposes a two-stage robust energy storage planning strategy for evolving ice storm conditions. A line failure probability model combines wind, ice, and gravity loads with fuzzy inference of secondary hazard effects to generate time-varying failure scenarios. Overall and important load resilience indices characterize system performance and the restoration of essential electricity services. The optimization model coordinates energy storage siting, sizing, and scheduling while accounting for investment, operation, electricity purchase, and load-loss costs. The model is solved using column-and-constraint generation and evaluated on a modified IEEE 33-bus distribution network. In the reported worst-case scenario, total energy not supplied decreases from 18.4 to 10.9 MWh with energy storage, a reduction of 40.8%. Energy not supplied to important loads decreases from 1.33 to 0.24 MW—a reduction of 82.0%. These reductions quantify unserved energy rather than changes in the absolute resilience indices. The results indicate that coordinated storage planning and scheduling can reduce outage consequences and prioritize important loads within the evaluated network, scenarios, and benchmark parameter settings. Full article

Distribution matching (DM) transforms a stream of equally likely bits into digital symbols that correspond to a specific digital modulation scheme with probabilities of occurrence following a specified statistical distribution. This technique is referred to as probabilistic constellation shaping (PCS), and using the Maxwell–Boltzmann (MB) distribution improves spectral and energy efficiency of digital modulation schemes by enabling more frequent transmissions of lower energy digital symbols. The background behind PCS is presented in the paper and the two main DM methods widely used in practice are discussed, outlining scenarios in which one DM method may be preferable over the other. For illustration, a 64-QAM digital constellation is considered as a case study for which the two DM scenarios presented are simulated and compared in terms the average signal power, entropy, and rate loss. Full article