Search Results (1,125)

Satellite communication systems experience significant signal degradation during rain events, a phenomenon that can be leveraged for meteorological applications. This study introduces a novel hybrid machine learning framework combining unsupervised clustering with cluster-specific supervised deep learning models to transform satellite signal attenuation into a predictive tool for rainfall prediction. Unlike conventional single-model approaches treating all atmospheric conditions uniformly, our methodology employs K-Means Clustering with the Elbow Method to identify four distinct atmospheric regimes based on Signal-to-Noise Ratio (SNR) patterns from a 12-m Ku-band satellite ground station at King Mongkut’s Institute of Technology Ladkrabang (KMITL), Bangkok, Thailand, combined with absolute pressure and hourly rainfall measurements. The dataset comprises 98,483 observations collected with 30-s temporal resolutions, providing comprehensive coverage of diverse tropical atmospheric conditions. The experimental platform integrates three subsystems: a receiver chain featuring a Low-Noise Block (LNB) converter and Software-Defined Radio (SDR) platform for real-time data acquisition; a control system with two-axis motorized pointing incorporating dual-encoder feedback; and a preprocessing workflow implementing data cleaning, K-Means Clustering (k = 4), Synthetic Minority Over-Sampling Technique (SMOTE) for balanced representation, and standardization. Specialized Long Short-Term Memory (LSTM) networks trained for each identified cluster enable capture of regime-specific temporal dynamics. Experimental validation demonstrates substantial performance improvements, with cluster-specific LSTM models achieving R² values exceeding 0.92 across all atmospheric regimes. Comparative analysis confirms LSTM superiority over RNN and GRU. Classification performance evaluation reveals exceptional detection capabilities with Probability of Detection ranging from 0.75 to 0.99 and False Alarm Ratios below 0.23. This work presents a scalable approach to weather radar systems for tropical regions with limited ground-based infrastructure, particularly during rapid meteorological transitions characteristic of tropical climates. Full article

Condition-based maintenance strategies using AI-driven health monitoring have emerged as valuable tools for industrial reliability, yet their implementation remains challenging in industries with limited operational data. Class I induction motors (≤15 kW), which power critical equipment in industries such as grain handling facilities, represent a significant portion of industrial assets but lack established healthy vibration baselines for effective monitoring. A fundamental challenge exists in deploying AI-based health monitoring systems when no historical performance data is available, creating a ’cold-start’ problem that prevents industries from adopting predictive maintenance strategies without costly pilot programs or prolonged data collection periods. This study developed a data-driven health monitoring framework for Class I induction motors that eliminates the dependency on long-term historical trends. Through extensive experimental testing of 98 configurations on new motors, a correlation between vibration amplitude at rotational frequency and motor power rating was established, enabling the creation of a synthetic signal generation algorithm. A robust Health Index (HI) model with integrated diagnostic capabilities was developed using the JPCCED-HI framework, trained on both experimental and synthetically generated healthy vibration data to detect degradation and diagnose common failure modes. The regression analysis revealed a statistically significant relationship between motor power rating and healthy vibration signatures, enabling synthetic generation of baseline data for any Class I motor within the rated range. When implemented at an operational grain silo facility, the HI model successfully detected faulty behavior and accurately diagnosed probable failure modes in equipment with no prior monitoring history, demonstrating that maintenance decisions could be made based on condition data rather than reactive responses to failures. This framework enables immediate deployment of AI-based condition monitoring in industries lacking historical data, eliminating a major barrier to adopting predictive maintenance strategies. The synthetic data generation approach provides a cost-effective solution to the data scarcity problem identified as a critical challenge in industrial AI applications, while the successful industrial implementation validates the feasibility of this approach for small-to-medium industrial facilities. Full article

This paper presents the simultaneous maximization of speed and sensitivity in the Optimal Coordination of Directional Over-Current Protections (OC-DOCP), considering that maximum selectivity is maintained in all solutions. Only these three desirable features of the protection system were considered in the multi-objective approach; thus, the problem can be simply formulated as the weighted sum of speed and sensitivity as goals to be maximized, and the Pareto frontiers correlating speed and sensitivity are easily found in this way. These Pareto frontiers had not been shown in the literature about this topic, and they properly show the compromise solutions for the optimal solutions (i.e., speed improvements imply sensitivity deterioration while sensitivity improvements imply speed degradation). The simplest OC-DOCP formulation, applied to a well-known sample system, is taken as an example to show the Pareto frontiers for different time–current curve types. Another OC-DOCP formulation, which considers different topologies and their probability of occurrence, is also solved and the corresponding Pareto frontiers are also shown. The main findings of this work are the following: (a) in general, the results show that the variation in the speed in the Pareto frontier is more notorious for the less inverse curve types, whose optimal solutions are slower; (b) in the case of extremely inverse curves, the optimal solutions are faster and the effect of changes in sensitivity on the protection speed is very low in the Pareto frontiers; (c) it is also herein shown that the knowledge of this topic is also useful to solve some possible cases of unfeasibility related to the upper bound of time dial settings. Full article

Natural recruitment of Manila clam (Ruditapes philippinarum) often persists in degraded estuaries, yet the environmental thresholds enabling this resilience remain quantitatively undefined. We employed binomial generalized additive model (GAM) coupled with field surveys (n = 168) in the Xiaoqing River estuary (Laizhou Bay, China) to identify critical limits for adult occurrence, which served as a field-based proxy for recruitment potential. Sediment median grain size (D₅₀), salinity (Sal) and dissolved inorganic nitrogen (DIN) were identified as the key factors, collectively explaining 79.30% of the deviance (AUC = 0.98). The probability of occurrence decreased sharply beyond two distinct thresholds: D₅₀ > 95 μm and salinity < 17.50‰. While DIN had a positive effect, it did not offset the strong negative associations with coarse sediment or low salinity. These field-validated thresholds provide quantifiable criteria to guide habitat suitability mapping, activation of early-warning systems against salinity-driven mortality, and site prioritization for ecological restoration in the Xiaoqing River estuary. Our findings offer a framework for developing management strategies to support clam resilience under environmental stress. Full article

In this paper, we propose FedENLC, an end-to-end noisy label correction model that performs model training and label correction simultaneously to fundamentally mitigate the label noise problem of federated learning (FL). FedENLC consists of two stages. In the first stage, the proposed model employs Symmetric Cross Entropy (SCE), a robust loss function for noisy labels, and label smoothing to prevent the model from being biased by incorrect information in noisy environments. Subsequently, a Bayesian Gaussian Mixture Model (BGMM) is utilized to detect noisy clients. BGMM mitigates extreme parameter bias through its prior distribution, enabling stable and reliable detection in FL environments where data heterogeneity and noisy labels coexist. In the second stage, only the top noisy clients with high noise ratios are selectively included in the label correction process. The selection of top noisy clients is determined dynamically by considering the number of classes, posterior probabilities, and the degree of data heterogeneity. Through this approach, the proposed model prevents performance degradation caused by incorrect detection, while improving both computational efficiency and training stability. Experimental results show that FedENLC achieves significantly improved performance over existing models on the CIFAR-10 and CIFAR-100 datasets under data heterogeneity settings along with four noise settings. Full article

Zero inflation is pervasive across text mining, event log, and sensor analytics, and it often degrades the predictive performance of analytical models. Classical approaches, most notably the zero-inflated Poisson (ZIP) and zero-inflated negative binomial (ZINB) models, address excess zeros but rely on rigid parametric assumptions and fixed model structures, which can limit flexibility in high-dimensional, sparse settings. We propose a Bayesian neural network (BNN) with regularization for sparse zero-inflated data modeling. The method separately parameterizes the zero inflation probability and the count intensity under ZIP/ZINB likelihoods, while employing Bayesian regularization to induce sparsity and control overfitting. Posterior inference is performed using variational inference. We evaluate the approach through controlled simulations with varying zero ratios and a real-world dataset, and we compare it against Poisson generalized linear models, ZIP, and ZINB baselines. The present study focuses on predictive performance measured by mean squared error (MSE). Across all settings, the proposed method achieves consistently lower prediction error and improved uncertainty problems, with ablation studies confirming the contribution of the regularization components. These results demonstrate that a regularized BNN provides a flexible and robust framework for sparse zero-inflated data analysis in information-rich environments. Full article

Background: Postmortem DNA identification of highly decomposed human remains is often limited by the availability and quality of conventional biological samples. Keratinized tissues, such as fingernails, represent a potentially valuable alternative due to their anatomical resistance to environmental degradation, however, their use as primary biological material for DNA profiling remains underreported in forensic practice. Case presentation: We report a case involving the recovery of a highly decomposed body of a missing woman, in which DNA samples were collected from a fingernail and a tooth. DNA extraction was performed using the PrepFiler Forensic DNA Extraction Kit for the fingernail sample and PrepFiler BTA Forensic DNA Extraction Kit for the tooth sample. No usable DNA profile was obtained from the tooth sample; however, the fingernail sample yielded a complete and high-quality STR profile with successful amplification across all 24 loci (GlobalFiler PCR Amplification Kit). Reference buccal swabs from the presumed biological parents were collected for subsequent kinship analysis. Discussion: Kinship analysis based on allele frequencies in the Croatian population resulted in a combined paternity index (CPI) corresponding to a probability of paternity of 99.99999812%, providing strong genetic support for the proposed identity of the deceased. Notably, this is the first documented forensic case in Croatia in which nail material served as the primary—and ultimately successful—biological sample for postmortem identification. Conclusions: This case highlights the evidentiary value of fingernails as a robust, accessible, and forensically valid DNA source in postmortem identification, particularly in cases of advanced decomposition where conventional biological materials are unavailable or degraded. Further studies involving larger sample sets and diverse postmortem conditions are needed to support the broader implementation of nail material in routine forensic identification workflows, particularly within the Croatian medico-legal context. Full article

Ecosystem health assessment is essential for informing ecological protection and sustainable management, yet current evaluation frameworks often overlook the foundational role of natural background conditions and struggle with methodological uncertainties in indicator weighting, particularly in ecologically fragile regions. To address these dual challenges, this study proposes a novel Base–Pressure–State–Response (BPSR) framework that systematically integrates key natural background factors as a fundamental “Base” layer. Focusing on the Qinling Mountains—a critical ecological barrier in China—we implemented this framework at the county scale using multi-source data (2000–2023) and introduced a Monte Carlo simulation with triangular probability distributions to quantify and synthesize weight uncertainties from multiple methods, thereby enhancing assessment robustness. Furthermore, the Geodetector method was employed to quantitatively identify the driving forces behind the spatiotemporal heterogeneity of ecosystem health. Supported by 3S technology, our analysis demonstrates a sustained improvement in ecosystem health: the composite index rose from 0.723 to 0.916, healthy areas expanded from 60.17% to 68.48%, and nearly half of the region achieved a higher health grade. Spatially, a persistent “low–south, high–north” pattern was observed, shaped by human disturbance gradients, while temporally, the region evolved from localized improvement (2000–2010) to broad-scale recovery (2010–2023), despite lingering degradation in human-dominated zones. Driving force analysis revealed a shift from early dominance by natural and land use factors to a later complex interplay where urbanization pressure and climatic conditions jointly shaped the health pattern. The BPSR framework, combined with probabilistic weight optimization and driving force quantification, offers a methodologically robust and spatially explicit tool that advances ecosystem health evaluation and supports targeted ecological governance, policy formulation, and sustainable management in fragile mountain ecosystems, with transferable insights for similar regions globally. Full article

The search for innovative solutions in the field of construction materials used in aircraft manufacturing has led to the development of composite materials, particularly CFRP polymer composites. Composite airframe components, which are required to have high strength, are joined using mechanical fasteners. Considering that the composite consists of a polymer matrix, which is a material susceptible to rheological phenomena occurring rapidly at elevated temperature, there is a high probability of significant changes in the strength and performance properties. Coupled thermal and mechanical loads on composite material joints occur in everyday aircraft operation. Experimental tests were conducted using a quasi-isotropic CFRP on an epoxy resin matrix with aerospace certification. The assessment of changes in the strength parameters of the material itself showed a decrease of approx. 40% in its short-term strength at 80 °C compared to the ambient temperature and a decrease in the load-bearing capacity of single-lap bolted joints of over 25%. Even more rapid changes were observed when assessing the fatigue life of the joints assessed at ambient and elevated temperature. In addition, the actual glass transition temperature of the resin was determined using the DSC technique. Analysis of the damage mechanisms showed that at 80 °C, the main degradation mechanisms of the material are accelerated creep processes of the CFRP and softening of the matrix, increasing its susceptibility to damage in the joint area. Full article

A condition-based maintenance modeling approach is proposed for a multi-state system under competing failures and imperfect repairs. The system experiences three states (normal, defective and failed) over its lifecycle. Two competing failure processes, i.e., natural degradation and external shocks, cause these state changes. If the system becomes defective, an imperfect repair is adopted to restore it to a normal state. Imperfect repairs addressing defects are mathematically characterized. Based on this, two system renewal scenarios and their occurrence probabilities are simulated and derived. The cost of downtime caused by hidden failures is then deduced. A maintenance model of the expected cost rate is constructed, and the optimal inspection period that minimizes the expected cost rate is determined. Finally, a numerical example verifies the correctness and effectiveness of the maintenance model. Full article

Due to low thermal conductivity and high specific strength, nickel-based superalloys are prone to service performance degradation caused by thermal damage during traditional high-efficiency grinding processes. Although the heat pipe grinding wheel with minimum quantity lubrication (HPGW-MQL) technology can reduce the probability of thermal damage to a certain extent, further breakthroughs are still needed. Therefore, this study proposes a new integrated process of ultrasonic vibration-assisted grinding by heat pipe grinding wheel with minimum quantity lubrication (UVAG-HPGW-MQL), aiming to balance the requirements of green grinding and the optimization of grinding performance for nickel-based superalloys. However, the mechanism of action of ultrasonic vibration on the cooling and lubrication performance of the proposed process remains unclear. Given that, comparative experiments between UVAG-HPGW-MQL and HPGW-MQL were conducted, focusing on exploring the influence of ultrasonic vibration on their cooling and lubrication performance. The experimental results, obtained when the grinding speed, workpiece feed rate, and grinding depth were set at 15–35 m/s, 40–120 mm/min, and 0.05–0.25 mm, respectively, indicate that, compared with HPGW-MQL, ultrasonic vibration causes periodic “contact-separation” between grains and workpiece. This dynamic process shortens the contact length between grains and workpiece, leading to maximum reductions of 43.85%, 22.15%, 34.16%, and 30.77% in grinding force, grinding force ratio, grinding temperature, and specific grinding energy, respectively. On the other hand, the ultrasonic cavitation effect causes atomization of the lubricating oil film adsorbed on the workpiece surface, leading to a decrease in lubrication performance and resulting in a maximum increase of 27.27% in the friction coefficient. This study provides new theoretical support and technical approaches for the green grinding of nickel-based superalloys. Full article

In complex energy storage operating scenarios, batteries seldom undergo complete charge–discharge cycles required for periodic capacity calibration. Methods based on accelerated aging experiments can indicate possible aging paths; however, due to uncertainties like changing operating conditions, environmental variations, and manufacturing inconsistencies, the degradation information obtained from such experiments may not be applicable to the entire lifecycle. To address this, we developed a stage-wise state-of-health (SOH) prediction approach that combined offline training with online updating. During the offline training phase, multiple single-cell experiments were conducted under various combinations of depth of discharge (DOD) and C-rate. Multi-dimensional health features (HFs) were extracted, and an accelerated aging probability $p_{AA}$ was defined. Based on the correlation statistics between HFs, $k_{HF}$, the SOH, and $p_{AA}$, all cells in the dataset were divided into general early, middle, and late aging stages. For each stage, cells were further classified by their longevity (long, medium, and short), and multiple models were trained offline for each category. The results show that models trained on cells following similar aging paths achieve significantly better performance than a model trained on all data combined. Meanwhile, HF optimization was performed via a three-step process: an initial screening based on expert knowledge, a second screening using Spearman correlation coefficients, and an automatic feature importance ranking using a random forest regression (RFR) model. The proposed method is innovative in the following ways: (1) The stage-wise multi-model strategy significantly improves the SOH prediction accuracy across the entire lifecycle, maintaining the mean absolute percentage error (MAPE) within 1%. (2) The improved model provides uncertainty quantification, issuing a warning signal at least 50 cycles before the onset of accelerated aging. (3) The analysis of feature importance from the model outputs allows the indirect identification of the primary aging mechanisms at different stages. (4) The model is robust against missing or low-quality HFs. If certain features cannot be obtained or are of poor quality, the prediction process does not fail. Full article

This study examines global econo-environmental capability for 118 countries over 1995 to 2024 using a five-lens framework covering productive capacity (PC), developmental momentum (DM), resource efficiency (RE), degradation and depletion ratio (DDR), and remaining development potential (RDP). Using pooled k-means, a stable four archetype typology is identified and shown to persist over time. The analysis assesses how archetypes characterize country–year outcomes (RQ1), whether cross-sectional fairness is changing and relates to frontier slowdown (RQ2), and how archetypes, distance, and regional context shape transition probabilities and club convergence (RQ3). Inequality in five-dimensional capability declines slightly over the period (Gini from 0.109 to 0.092 and Palma from 1.563 to 1.464), implying modest convergence rather than increasing polarization. Average capability also improves, with larger gains for initially distant countries and smaller gains near the frontier, which is consistent with mild club convergence. Regionally, high capability cases are concentrated in Western Europe and North America, while sustained upgrading is observed in parts of Eastern Europe, mixed stability is observed in East and Central Asia, and selective advances are observed in ASEAN. Policy implications should be based on a country’s archetype and its distance to the capability ideal. Lagging countries should prioritize diffusion of proven high efficiency options and basic capability building, while frontier countries should priorities innovation, structural change, and deeper decarbonization. Policy emphasis should be updated as countries move within the capability space over time. Full article

Background. Glucocorticoid-induced osteoporosis represents a well-known type of secondary osteoporosis (SOp). While the most prevalent sub-category includes corticotherapy, another important contributor is represented by Cushing’s syndrome. In this traditional landscape, adrenal incidentalomas do not involve a standard cause of SOp, since most of them are non-functioning adrenal tumours (NFATs). Yet, 30–40% of them are not entirely “non-functioning”, due to mild autonomous cortisol secretion (MACS). Despite not being a guideline-based diagnosis, a lower ACTH might point to various NFATs/MACS complications. Objective. This study aimed to determine the relationship between the bone health status of post-menopausal women with NFATs/MACS and their baseline morning ACTH level. The bone health indicators were DXA, FRAX, and bone remodelling markers. Methods. This was a retrospective, real-life, transversal study in adult females who were hospitalized in a single tertiary centre of endocrinology. They were all anti-osteoporotic drug-naïve. The subjects underwent CT and DXA scanning and a 1 mg dexamethasone suppression test (DST). Results. The cohort (sample size of N = 84 patients, 61.49 ± 7.86 years) had a type 2 diabetes rate of 18%, arterial hypertension rate of 75%, and a dyslipidemia rate of 78%. Median ACTH was 11.89 pg/mL. The prevalence of MACS was 30.95%. The mean largest tumour diameter (LTD) was 2.25 ± 0.99 cm. ACTH correlated with second-day cortisol after the 1 mg DST (r = −0.301, p = 0.024), and LTD (r = −0.434, p < 0.001). ROC analysis for the bone resorption marker CrossLaps showed an AUC of 0.647 (p = 0.05), with the highest Youden index for the cut-off at 0.32 ng/mL (sensitivity 87.50%, specificity 39.50%). Bone impairment (osteoporosis + osteopenia) was found in 65% of patients, with an osteoporotic fracture prevalence of 4.76%. The lowest mean T-score (−1.12 ± 1.00) showed osteopenia, and the median trabecular bone score pointed a partially degraded microarchitecture [median (interquartile interval): 1.320 (1.230, 1.392)]. FRAX and FRAXplus estimations correlated with bone mineral density (BMD) at all three central DXA sites, regardless of the ACTH cut-off. Patients with a low ACTH (<10 pg/mL) displayed similar bone/adrenal features when compared to those with normal ACTH, except forbut they had a higher MACS rate (45.45% versus 21.57%, p = 0.021) and a larger LTD (2.67 ± 0.98 versus 1.98 ± 0.92 cm, p = 0.003). Fracture estimation showed that only in patients with a low ACTH, the 10-year fracture risk for major osteoporotic fractures (MOF) adjusted for lumbar BMD was lower than the risk for MOF adjusted for diabetes (p = 0.036), and the 10-year hip fracture risk was lower when adjusted for lumbar BMD (p = 0.007). ACTH correlated with lumbar BMD (r = 0.591, p = 0.002) only in the group with an ACTH < 10 pg/mL, suggesting its potential usefulness as a bone biomarker in these cases. On the other hand, MACS-negative subjects with a low ACTH versus those with a normal ACTH showed higher CrossLaps (0.60 ± 0.27 versus 0.42 ± 0.21 ng/mL, p = 0.022), indicating an elevated bone resorption even in patients with tumours that are regarded as true non-secretors. Conclusions. A subgroup of patients diagnosed with NFATs/MACS might be prone to skeletal damage, and biomarkers such as ACTH (specifically, suppressed ACTH) might serve as a surrogate pointer to help refine this higher risk in daily practice. Further research to address other ACTH cut-offs will place ACTH assays in the overall bone status evaluation in these patients, most probably not as a single biomarker, but in addition to other assays. Full article

Background: Genetic mutations have proven to be the epicenters of cancer and disease progression. Traditional WXS sequencing and BLOSUM scoring can be used to infer the evolutionary conservation of amino acid substitutions, though these approaches are not informed by probable base pair sequence changes. Within gene mutation analysis, most tools focus on amino acid conservation or codon switching independently, limiting their ability to contextualize observed mutations against stochastic mutational processes. In the clinical setting, variants of unspecified significance remain difficult to interpret, as clinicians are often unable to determine whether observed mutations arise from oncogenic selection or from stochastic mutational degradation. Methods: We analyzed mutation sequences from the TCGA BRCA cohort for TP53 and PIK3CA and developed a model that integrates BLOSUM scoring with statistical modeling of base pair changes to evaluate deviation from codon-aware neutral expectations. Observed mutational distributions were compared against a stochastic neutral model to assess statistical significance. Results: Within the TCGA BRCA cohort, TP53 mutations were significantly more evolutionarily radical than expected under the codon-aware neutral model, while PIK3CA mutations were significantly more evolutionarily conservative, as determined using chi-square testing. These opposing patterns are consistent with the distinct functional roles of TP53 and PIK3CA in oncogenesis, where TP53 is inhibited through disruptive loss-of-function mutations, whereas PIK3CA is recurrently mutated in a manner that preserves protein structure and promotes constitutive pathway activation. This contrast reflects selective pressure toward disabling tumor suppressor function while maintaining persistent oncogenic signaling. Conclusions: Codon-aware neutral modeling provides a statistical framework for distinguishing mutations that deviate from stochastic expectations and may aid in the interpretation of variants of unspecified significance. By contextualizing mutational severity relative to neutral processes, this approach offers insight into tumor evolution and may support prognostic assessment without relying on predefined gene-level neutrality. Full article