Search Results (4,567)

Effectively modeling software failure behavior is crucial for reliability assessment and planning of releases. However, many current software reliability growth models assume that failures are independent and fault detection mechanisms are simplified. However, these assumptions may not accurately represent real-world testing environments. This study introduces a novel Nonhomogeneous Poisson Process (NHPP)-based Software Reliability Growth Model (SRGM) that includes dependent failure behavior and exponentially decaying fault detection rates to better reflect the software debugging process. The proposed model was validated using real failure datasets and compared with 17 existing models. The performance of the model was assessed using various goodness-of-fit criteria, such as errors, prediction accuracy, and metrics based on information theory. To provide a more thorough evaluation, a multi-criteria decision-making approach was used to rank the competing models based on their overall performance. Furthermore, a one-at-a-time sensitivity analysis was conducted to examine how the initial values of the parameters affected the model’s behavior. These findings indicate that the sensitivity of the model to this parameter varies depending on the dataset used. The results indicate that the proposed model achieved superior performance across multiple evaluation criteria and consistently obtained the best overall ranking under the integrated multi-criteria framework. In Dataset 1, the proposed model achieved the best performance in most goodness-of-fit criteria, whereas in Dataset 2 it produced the best results across all twelve evaluation criteria. The results show that the proposed model offers improved or competitive performance compared to existing models and provides greater flexibility in capturing complex failure processes within software systems. Full article

We model the cardiorespiratory interaction as arising within a collection of coupled, non-autonomous, nonlinear oscillators with explicitly time-dependent frequency modulation. The resulting system is analysed in terms of phase tracking and stability using finite-time Lyapunov exponents. We show that synchronisation emerges from the interplay between coupling strength, intrinsic frequency mismatch, and modulation amplitude, giving rise to regimes of stable entrainment, intermittent synchronisation, and desynchronised dynamics. The transitions between these regimes are governed by the system’s ability to track time-dependent attractors rather than by fixed phase-locking conditions. Numerical simulations, together with physiological recordings, demonstrate that time-varying modulation and interaction structure are both essential to reproduce observed cardiorespiratory behaviour. In particular, the data indicate that coupling is not stationary but evolves over time, contributing significantly to the observed variability in synchronisation patterns. These results suggest that the cardiorespiratory interaction is more naturally interpreted as an emergent property of a non-autonomous dynamical system with evolving interaction geometry and moving attractors, rather than as a stationary coupling process between autonomous oscillators. Full article

This paper develops a unified asymptotic theory for conditional single-index U-statistics and the associated conditional U-processes in the setting of locally stationary functional time series subject to missing-at-random response mechanisms. The proposed framework addresses, within a single nonparametric inferential architecture, three major sources of complexity in modern functional data analysis: infinite-dimensional covariates, smoothly time-varying stochastic dynamics, and incomplete response observations. The methodology is based on a class of kernel-type estimators combining temporal localization, functional single-index smoothing, and inverse-propensity correction. Temporal localization captures the gradual evolution of the underlying regression structure, the single-index projection provides an effective dimension-reduction mechanism for functional covariates, and the propensity adjustment restores the target conditional functional under the MAR sampling scheme. The principal contribution of the paper is the establishment of weak convergence, in a suitable space of bounded functions, for the resulting propensity-adjusted conditional U-process indexed by a general class of measurable kernels. Under absolute regularity conditions, local stationarity assumptions, small-ball probability requirements, entropy restrictions of VC type, and uniform consistency of the propensity-score estimator, the normalized process is shown to converge weakly to a tight centered Gaussian process. The limiting covariance structure explicitly reflects the interaction between temporal smoothing, functional concentration, dependence, and the random loss of responses. In parallel, uniform convergence rates are derived for the associated conditional single-index U-statistic estimators, thereby quantifying the respective contributions of smoothing bias, stochastic fluctuation, local-stationarity approximation error, and missingness-induced variance inflation. A substantial part of the analysis is devoted to the technical difficulties created by the simultaneous presence of dependence, nonstationarity, functional covariates, and incomplete observations. The proofs combine Hoeffding-type decompositions adapted to weighted incomplete data, blocking and coupling arguments for absolutely regular triangular arrays, refined entropy bounds for kernel-indexed function classes, and small-ball probability techniques for functional covariates. The MAR mechanism is incorporated via inverse-propensity weighting, and its effects on the effective sample size, asymptotic variance, and bias structure are made explicit. The theory also provides a rigorous foundation for bandwidth selection through blocked, propensity-adjusted cross-validation and clarifies its relation to the corresponding oracle risk. The proposed framework encompasses a broad class of statistical learning and inference problems involving pairwise or higher-order functionals of functional time series. In particular, it applies to conditional Kendall-type functionals, discrimination problems, metric learning with incomplete labels, and conditional independence testing under local stationarity. A simulation study illustrates the finite-sample behavior of the proposed estimators and supports the theoretical findings across varying regimes of temporal nonstationarity, serial dependence, functional concentration, and response missingness. Overall, the results provide a mathematically rigorous and methodologically flexible foundation for inference from evolving functional data when dependence, infinite dimensionality, and incomplete observation are present simultaneously. Full article

Accurate fault detection is essential for the safe and reliable operation of grid-connected photovoltaic (PV) systems under complex and dynamically varying conditions. However, existing data-driven approaches are often hindered by the scarcity of labeled fault data and by their limited ability to model complex multivariate temporal dependencies. To address these challenges, this paper first develops a realistic simulation of a grid-connected PV system to generate a large volume of labeled multivariate time-series fault data spanning diverse fault scenarios under varying operating conditions. The simulated data augment the limited real-world measurements, improving fault coverage and model generalization. On this basis, a dual-channel enhanced Mamba model is proposed for PV fault detection. The model decouples temporal modeling and variable-wise modeling into two dedicated channels, enabling complementary extraction of global temporal dependencies and intra-variable dynamics. Extensive experiments show that the proposed approach consistently outperforms several mainstream time-series classification methods in accuracy, precision, recall, and F1-score, demonstrating that it provides an effective and scalable solution for data-driven fault detection in grid-connected PV systems. Full article

Geopolitical risks—including export controls, entity listings, and end-use restrictions—have become a major source of disruptions in semiconductor supply chains. The impact of such disruptions depends not only on the policy trigger itself but also on the vulnerability of cross-regional partnerships between supply chain partners. Specifically, under the same policy regime, firms with weak partnerships suffer far greater disruption than those with strong partnerships. Apart from risk propagation, this vulnerability also propagates through the supply chain: when an upstream supply channel has weak partnerships, its downstream stages also become more exposed to disruptions. We call this phenomenon vulnerability propagation. Existing Bayesian Network (BN) frameworks portray risk propagation through fixed parameters that do not reflect partnership vulnerability and cannot capture vulnerability propagation. To fill this gap, we propose a Dependency-Robust Bayesian Network (DeRBN) that conditions risk propagation parameters on the partnership vulnerability. A robust worst-case oriented evaluation method is developed to assess the disruption risk under data scarcity. Computational experiments on a typical semiconductor supply chain network show that (i) moving from all-strong to all-weak partnerships increases the worst-case risk by approximately 24%, (ii) the dependency-induced risk amplification is unevenly distributed across supply channels, with the most influential channel contributing approximately 2.2 times the marginal risk of the least influential one, and (iii) the relative ranking of vulnerability profiles remains perfectly stable under varying levels of data uncertainty. These results suggest that DeRBN has the potential to serve not only as a risk assessment tool but also as a diagnostic instrument for identifying and prioritizing the most vulnerable supply channels for targeted risk mitigation. Full article

The present research, using finite element method simulation, studies the heat dissipation of a fin-type passive cooling system installed on monocrystalline photovoltaic panels under different environmental conditions associated with altitude. For this purpose, three scenarios at different altitudes were analyzed: Manta (14 m.a.s.l.), Puyo (926 m.a.s.l.), and Ambato (2724 m.a.s.l.). A model simulated using the finite element method, validated in a previous investigation, was used to simulate these three cases. The model was meshed, and the boundary conditions used were obtained from meteorological data averaged over one year. The variables used in this stage were irradiance, ambient temperature, and wind speed in the time range from 08:00 to 17:00. The numerical model used in the simulation considered the mechanisms of conduction in the panel layers, mixed convection toward the surrounding air, and thermal radiation from the exposed surfaces. The results show that, in the city of Ambato, the heat sink presents its best thermal performance. Under conditions of minimum ambient temperature and solar irradiance, a maximum percentage reduction of 3.11% in the photovoltaic panel temperature was obtained, while under conditions of maximum ambient temperature and solar irradiance, the reduction reached 11.11%. This reveals that, when higher panel temperatures occur, the heat sink exhibits better performance. In general, the results showed a reduction in temperature when this heat dissipation mechanism was used. It is evident that the effectiveness of these systems depends not only on geometry or materials, but also on the atmospheric conditions associated with altitude. It is concluded that the heat dissipation capacity of passive cooling mechanisms is influenced by the meteorological conditions of the area, such as ambient temperature, solar irradiance, and wind speed, which may vary according to the altitude at which the system is located. Full article

To address the strong coupling between trolley motion and payload swing, as well as the difficulty of determining optimal braking timing during emergency operations of overhead cranes in complex environments, a model-predictive braking control method integrated with the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm is proposed. Within the Model Predictive Control (MPC) framework, payload swing angle constraints are explicitly incorporated, and an adaptive braking reference trajectory is constructed to achieve rapid and stable stopping while effectively suppressing load oscillations. Furthermore, the TD3 algorithm is employed for online adaptive optimization of key MPC parameters, enabling a dynamic trade-off between braking performance and swing suppression under varying operating conditions. In addition, a minimum braking distance prediction model based on Support Vector Regression (SVR) is developed, and a state-dependent safety-critical region prediction model is established to quantitatively determine optimal braking timing. Simulation results across multiple operating conditions demonstrate that the proposed TD3–MPC method outperforms conventional MPC in terms of braking efficiency, swing suppression capability, and system stability while satisfying swing angle constraints. Moreover, real-crane experimental results demonstrate the effectiveness of the proposed safety-critical region prediction method in determining appropriate braking trigger timing and achieving safe and smooth stopping of the overhead crane under obstacle-avoidance conditions. Full article

Endocrine-disrupting chemicals (EDCs) are a heterogeneous group of exogenous compounds capable of interfering with hormonal homeostasis and endocrine-regulated physiological processes. Their widespread occurrence in food, water, air, consumer products and industrial materials has raised increasing concern regarding their contribution to chronic disease burden. This review synthesizes current evidence on the exposure characteristics, molecular mechanisms, health effects, and prevention strategies related to major EDC classes, including bisphenol A and phthalates, dioxins and polychlorinated biphenyls, per- and polyfluoroalkyl substances, pesticides, and brominated flame retardants. Evidence indicates that EDCs may act through receptor-mediated signaling, altered hormone synthesis and metabolism, oxidative stress, mitochondrial dysfunction, immune modulation, and epigenetic mechanisms, with effects that may vary according to dose, timing, sex, age, and developmental susceptibility. Reported health outcomes include metabolic and cardiovascular disorders, reproductive dysfunction, hormone-dependent cancers, thyroid disruption, immune dysregulation, and adverse developmental effects. Although complete avoidance is unrealistic, exposure reduction and risk mitigation can be achieved through coordinated individual, clinical, environmental, and regulatory interventions. A life-course approach is essential to limit the health burden associated with endocrine disruption. Full article

Accurate traffic flow prediction is fundamental to Intelligent Transportation Systems (ITSs), critical for transportation management and logistics. Despite advances in spatio-temporal prediction methods, existing approaches suffer from two key limitations: (i) multi-scale fusion methods inadequately capture hierarchical constraints between cross-scale features, and (ii) models rely on single spatio-temporal views, neglecting multi-source relationship complementarity. To address these issues, we propose ST-MAFNet, a spatio-temporal multi-scale adaptive fusion network comprising three key components, specifically, a Cross-Scale Hierarchical Anchoring strategy (CSHA) that anchors short-term predictions with multi-scale temporal patterns to mitigate noise; a Dual Spatial Perception Module (DSPM) that learns node heterogeneity and dynamic correlations through node embeddings and adaptive graph attention; and a Spatio-Temporal Adaptive Fusion Module (STAFM) that captures time-varying connectivity by integrating multi-scale temporal features with multi-source spatial relationships. Experiments on four real-world datasets demonstrate that ST-MAFNet is particularly effective for short-term traffic forecasting. Compared with the best previously reported MAE results, ST-MAFNet reduces MAE by 2.95%, 1.43%, 1.25%, and 0.37% on PEMS03, PEMS04, PEMS07, and PEMS08, respectively, and achieves the best or second-best performance on most evaluation metrics. Full article

Background: Uncontrolled hemorrhage, especially at non-compressible sites, remains a major cause of preventable trauma deaths. This study reports the development of fibrinogen-loaded calcium carbonate (CaCO₃) microparticles that combine hemostatic activity with self-propelling capability for targeted delivery against blood flow, with a focus on understanding formulation-dependent trade-offs among particle yield, protein loading, clotting performance, and transport behavior. Methods: Microparticles were synthesized via a precipitation method using different carbonate sources and characterized for yield, morphology, size, and fibrinogen encapsulation. Hemostatic function was assessed using rotational thromboelastometry (ROTEM) in fibrinogen-deficient plasma. Propulsion behavior was evaluated following exposure to protonated tranexamic acid (TXA⁺), which triggers CO₂ generation. Particle size and encapsulation were examined by microscopy and fluorescence imaging. Results: The precipitation method produced spherical micrometer-sized particles, with fibrinogen inclusion reducing yield and particle size relative to unload controls. Fluorescence microscopy confirmed successful encapsulation. Encapsulation efficiency varied with formulation, with sodium carbonate-based particles showing higher relative fibrinogen loading. ROTEM analysis demonstrated that fibrinogen-loaded particles significantly improved clot formation, increasing maximum clot firmness compared to fibrinogen-free particles, although performance remained formulation-dependent. TXA⁺-triggered propulsion achieved maximum speeds up to 4.221 cm/s. Fibrinogen-loaded particles exhibited longer activation lag times than unloaded particles, indicating a trade-off between hemostatic functionality and propulsion kinetics. Conclusions: Fibrinogen-loaded CaCO₃ microparticles exhibit both hemostatic activity and chemically triggered motion in vitro. The study identifies key formulation-dependent trade-offs between particle yield, fibrinogen loading, clotting performance, and propulsion behavior. While these findings support the feasibility of combining localization and clot stabilization mechanisms, further studies under physiologically relevant flow conditions and in vivo models are required to evaluate their potential for active delivery in non-compressible hemorrhage. Full article

Background/Objectives: The oscillatory dynamics underlying stage-specific processing in working memory (WM) remain incompletely characterized, particularly under varying memory loads. We examined the load-dependent modulation of theta (4–7 Hz), lower alpha (8–10 Hz), and upper alpha (11–13 Hz) absolute power during encoding, maintenance, and retrieval using quantitative EEG in a modified Sternberg task that temporally dissociates these stages. Methods: Forty-five healthy young adults performed trials with memory sets of three, five, or six uppercase consonants, followed by a lowercase probe. EEG data were analyzed using cluster-based permutation testing, and brain–behavior relationships were assessed using regression models. Results: Fronto-central theta power increased with memory load and was significantly higher during retrieval than during encoding or maintenance. Greater theta power during retrieval predicted faster reaction times in the three-letter condition. Alpha oscillations showed robust stage effects. Lower alpha power was higher during maintenance than retrieval across loads and exhibited a load effect during maintenance (three > six letters) in occipital regions. Upper alpha power was consistently maximal during maintenance across all loads, involving bilateral fronto-central, parietal, and occipital regions. Critically, under moderate load (five letters), higher upper alpha power predicted a greater probability of correct responses across task stages. Conclusions: These findings demonstrate a functional dissociation between oscillatory bands across temporally separated WM stages: theta activity was retrieval-dominant and associated with response speed, whereas alpha, particularly upper alpha, was maintenance-dominant and supported accuracy under increased mnemonic demand. Full article

Microwave-based remote sensing possesses the capability to penetrate through atmospheric obstructions such as cloud layers and fog, making it extensively utilized for estimating parameters including soil water content, atmospheric moisture levels, and terrestrial surface temperatures. Extended temporal datasets serve as fundamental requirements for climatological investigations; however, individual satellite operational lifespans remain constrained and prove inadequate for establishing multi-decade temporal sequences. Consequently, conducting comparative analyses and implementing cross-calibration procedures across measurements obtained from distinct sensors exhibiting comparable operational features becomes imperative. The FengYun (FY)-3G spacecraft, deployed into orbit during April 2023, hosts China’s most recent orbiting microwave radiometric instrument, designated as the Microwave Radiation Imager–Rainfall Mission (MWRI-RM). The FY-3G satellite’s unique drifting equator crossing time orbit plays a critical role in the calibration behavior of the MWRI-RM instrument, representing a key novelty of this study. The reliability of its brightness temperature (TB) observations has attracted considerable attention. Within this investigation, we conduct comparative assessments of orbital TB observations acquired from FY-3G/MWRI-RM against corresponding measurements obtained from the Advanced Microwave Scanning Radiometer 2 (AMSR2) installed on the Global Change Observation Mission–Water 1 (GCOM-W1) platform, and establish a straightforward linear inter-calibration methodology. Both sensing systems show strong consistency, with correlation coefficients exceeding 0.9 for all corresponding channels and systematic biases ranging from −1.40 K to −0.14 K. FY-3G/MWRI-RM generally reports lower TB values than GCOM-W1/AMSR2. The inter-sensor differences vary with frequency, land cover type, and TB range. Larger negative biases are mainly observed at 23.8 GHz and over water bodies, whereas the biases at 89 GHz are generally close to zero for most surface types. Latitude-dependent TB biases are most evident at 10.65 and 18.7 GHz, especially for vertical polarization at high latitudes, while orbit-dependent differences are more pronounced for vertically polarized low- and mid-frequency channels. After applying an inter-calibration procedure using AMSR2 as the reference, the agreement between FY-3G/MWRI-RM and GCOM-W1/AMSR2 is improved substantially, with mean biases below 0.25 K and RMSE values below 2 K for all channels. Validation using independent datasets further supports the stability of the calibration. The calibrated FY-3G/MWRI-RM TB data provide a basis for constructing long-term passive microwave brightness temperature records and for retrieving land and atmospheric parameters. Full article

This study introduces a spatiotemporal framework at the district level in Indonesia to examine and forecast stunting prevalence. The empirical analysis draws on data from 514 districts observed over 2022–2024, with short-term projections extended to 2025–2027 in line with the SDG 2.2 agenda. The modeling methodology is based on a Bayesian spatiotemporal formulation with the SPDE-INLA method. Instead of handling spatial and temporal lags separately, the model simultaneously incorporates them to reflect dependencies that change across both dimensions. This structure facilitates a more flexible representation of underlying risk dynamics. To improve prediction performance, we augment the baseline model with a hybrid component. Specifically, residual variation from the Bayesian specification is further explored using machine learning methods, providing an additional layer of adjustment. Spatial dependence is assessed through three alternative weighting schemes—KNN, Queen contiguity, and distance-based matrices—which are compared prior to selecting the final specification. The empirical specification includes nine key predictors within a semi-parametric framework. Several covariates are allowed to depart from strict linearity by accommodating time-varying effects. Three algorithms were evaluated during the prediction process to determine their abilities to capture the residual structure: XGBoost, Random Forest, and Elastic Net. Spatiotemporal clustering is examined through exceedance probabilities, resulting in the identification of seven unique cluster patterns. The findings consistently indicate that poverty is the main factor influencing stunting dynamics, with evident regional spillovers and temporal variations. Persistent hotspots are primarily located in eastern Indonesia. From a predictive standpoint, the hybrid specification—particularly the variant based on XGBoost—delivers the most stable performance. The forecast results indicate a gradual reduction in stunting prevalence throughout the forecast period. This study establishes persistent geographic inequalities in child nutrition risk and translates them into district-specific intervention priorities, providing decision-support information to further SDG Target 2.2 and its relationships with SDGs 1, 3, 4, and 6. Full article

Portable force platforms are increasingly used for neuromuscular performance assessment in field-based environments; however, their validity may vary depending on the analyzed variable and the experimental configuration. The present study investigated the concurrent validity and test–retest reliability of a novel portable force platform (Fitforce) during commonly used static and dynamic performance assessments under field-based conditions. Thirty recreationally active male university students (age: 24.5 ± 4.1 years; height: 177.1 ± 6.18 cm; body mass: 75.38 ± 4.62 kg) performed the isometric mid-thigh pull (IMTP) and countermovement jump (CMJ) on two force platforms positioned in a stacked configuration, with the Fitforce system placed on top of a laboratory-grade reference platform (ForceDecks). Concurrent validity was evaluated using paired comparisons, intraclass correlation coefficients (ICC), coefficients of determination (R²), and Bland–Altman analyses. Test–retest reliability of the Fitforce system was assessed across two testing sessions conducted 24 h apart. Very high agreement was observed between systems for IMTP-derived variables (ICC = 0.95–0.98) and for CMJ propulsion-related variables, including jump height, flight time, and peak take-off force (ICC = 0.92–0.96). In contrast, peak landing force showed poor agreement across systems (ICC = −0.88, R² = 0.19), with substantial systematic bias, whereas braking phase duration showed only moderate agreement (ICC = 0.50). Excellent test–retest reliability was observed across all IMTP (ICC > 0.96; CV% < 3.59) and CMJ (ICC > 0.97; CV% < 3.42) variables. Bland–Altman analyses demonstrated narrow limits of agreement for IMTP and propulsion-related CMJ variables but wide limits for landing-related force measurements. The Fitforce platform demonstrates strong concurrent agreement and excellent between-day reliability for selected IMTP and CMJ propulsion-related force–time variables under field-based conditions. However, landing-related variables should be interpreted cautiously under stacked measurement configurations due to their sensitivity to rapid impact transients and force transmission characteristics. Full article

Unmanned aerial vehicles (UAVs) equipped with multispectral sensors have become valuable tools in precision agriculture, enabling the monitoring of crop health, biomass estimation, and stress detection. However, the effectiveness of these measurements depends on several factors, including repeatability, sensitivity, and accuracy. Understanding these factors is crucial to ensure reliable data collection, particularly in regions with fluctuating weather patterns. This study evaluated the sensitivity of multispectral data collected within a short time frame and its impact on vegetation indices in normal field conditions. Measurements were taken over three days, with three UAV flights performed each day. Multispectral data were analyzed to identify statistically significant differences in vegetation indices, with calculations performed independently for each measurement day. The repeatability of vegetation indices varied between measurement days. When all measurement days were analyzed together, GARI, GNDVI, NDRE, and NDVI were the only indices that did not show statistically significant differences between flights. However, the magnitude of differences varied depending on the index, with some indices showing only minor variations between flights. Full article