Search Results (5,519)

Hailstorms frequently develop in Yun-Gui Plateau, Western China, which bring about significant economic damage. Due to the high terrain, these storms are typically shallow, rapidly evolving, and challenging to forecast. An X-band phased-array radar (XPAR) network is set up at Weining in Yun-Gui Plateau to study these storms. To explore these XPAR data for numerical prediction of hailstorms in this region, we implement the Weather Research and Forecast (WRF) model and Hydrometeor and Latent Heat Nudging (HLHN) method to assimilate the data and conduct prediction experiments. The XPAR data was evaluated along with the operational Severe Weather Automatic Nowcast (SWAN) system radar mosaic data. Furthermore, a humidity adjustment scheme is used to overcome inconsistency of the humidity field and related prediction errors. The model results show that in comparison to the SWAN data, assimilating XPAR data in 1-min intervals significantly reduces the model error, and improves the representation of rapid hail cloud evolution. Additionally, adjusting the model humidity based on vertically integrated liquid (VIL) derived from the radar data can effectively correct model analyses of humidity and temperatures, suppressing spurious convection, thus improving the hailstorm forecast. Overall, we recommend joint assimilation of the high spatiotemporal resolution XPAR data along with SWAN radar data with the improved WRF-HLHN for hailstorm prediction over the study region, and the algorithm can be promptly adapted to forecasting hailstorms in other regions. Full article

Smart sensors are increasingly used in agriculture to monitor environmental conditions and support data-driven decision-making. However, traditional sensor implementations face critical challenges related to power consumption, especially in remote farms—such as pitaya plantations—where access to electricity and ongoing maintenance is limited. This paper presents a smart energy management system for agricultural sensor nodes integrating a machine learning model for adaptive sampling and a batching strategy to optimize energy usage. A lightweight Stochastic Gradient Descent (SGD) regressor trained on temperature dynamics runs on-device to predict the sampling interval ($T_s$). In parallel, the node adjusts the number of buffered samples as the battery state of charge ($SOC$) decreases, reducing Long Range (LoRa) transmissions. Field experiments show that the proposed approach reduces energy consumption by 77.8% compared with fixed-interval sampling, while maintaining good temperature fidelity with Mean Absolute Error (MAE) of 0.537 °C for temperature reconstruction. Full article

Background: Gestational diabetes mellitus (GDM) affects many pregnancies worldwide and is associated with adverse maternal and fetal outcomes. Current screening at 24–28 weeks limits opportunities for early intervention. We evaluated whether machine learning (ML) models using first-trimester clinical and dietary data can predict GDM risk before the standard oral glucose tolerance test. Methods: We analyzed data from 797 pregnant women enrolled in the BORN2020 prospective cohort study (Thessaloniki, Greece). Ten ML algorithms were evaluated across five class-imbalance handling strategies using stratified 5-fold cross-validation, with final evaluation on an independent 20% held-out test set. Features included maternal demographics, obstetric history, lifestyle factors, and 22 dietary micronutrient intakes from the pre-pregnancy period assessed by Food Frequency Questionnaire. Results: The best-performing model (Logistic Regression without resampling) achieved an AUC-ROC of 0.664 (95% CI: 0.542–0.777), with sensitivity of 0.783 and NPV of 0.932 at the pre-specified threshold. The high NPV should be interpreted in the context of the low GDM prevalence (14.7%), as NPV is mathematically dependent on disease prevalence. A reduced nine-feature model using only routine clinical and demographic variables achieved a numerically higher AUC of 0.712 (95% CI: 0.589–0.825), with overlapping confidence intervals, indicating that detailed FFQ-derived micronutrient data did not improve prediction. Maternal age and pre-pregnancy BMI were the strongest individual predictors by SHAP analysis. No model reached the AUC >0.80 threshold for good discrimination. Substantial miscalibration was observed (slope: 0.56; intercept: −1.83), limiting use for absolute risk estimation. Conclusions: This exploratory study demonstrates that first-trimester ML models achieve modest discriminative ability for early GDM prediction, with routine clinical variables performing comparably to models incorporating detailed dietary assessment. These findings should be interpreted with caution, as no external validation cohort was available and the low events-per-variable ratio (~3.8) constrains the reliability of individual model estimates. Substantial miscalibration further limits use for absolute risk estimation. Accordingly, these models should be regarded as exploratory risk-ranking tools only and require external validation and recalibration before any clinical implementation. Full article

Environmental monitoring systems require robust uncertainty quantification for effective decision-making in complex ecological processes. Harmful algal blooms represent a critical challenge where prediction uncertainty directly impacts resource allocation and response timing, yet current remote sensing-based prediction systems provide only deterministic classifications without confidence measures. This gap between algorithmic predictions and actionable risk assessment limits operational utility for stakeholders managing water quality under varying risk tolerances. This study developed a transferable probabilistic forecasting framework integrating Sentinel-2 multispectral imagery with quantile regression and ensemble machine learning to generate continuous confidence indicators for cyanobacteria density prediction, demonstrated through its application to Lake Okeechobee, Florida. The methodology combines spectral indices extracted from Sentinel-2 data with XGBoost for quantile regression at 0.05, 0.50, and 0.95 probability levels, and LightGBM for multi-horizon temporal forecasting. Sentinel-2’s 13 spectral bands spanning visible to shortwave infrared wavelengths, combined with its 5-day revisit frequency provide a spectrally rich and temporally dense input space that is well-suited to gradient boosting methods such as XGBoost, which can exploit complex nonlinear interactions among spectral features to distinguish cyanobacterial signatures from background water constituents. LightGBM achieved mean absolute percentage errors of 2.9% for 10-day forecasts and 5.7% for 20-day forecasts, outperforming conventional regression models. The framework generates 90% prediction intervals that enable reliable risk classifications for operational bloom management. This approach bridges the gap between satellite-based algal bloom detection and actionable decision-making by quantifying predictive uncertainty, representing a shift from binary classifications to probability-based environmental monitoring systems that accommodate varying stakeholder risk tolerances in water quality management applications. Full article

Fusarium ear rot (FER), caused by Fusarium verticillioides, is a devastating disease that substantially reduces maize yield and compromises kernel quality. To investigate the genetic and molecular basis of resistance, an F₂ population derived from a cross between the resistant inbred line 3IBZ2 and the susceptible inbred line KW5G321 was analysed. By integrating bulked segregant analysis sequencing (BSA-Seq) with RNA sequencing (RNA-Seq), a major quantitative trait locus (QTL), designated qFER4, was identified on chromosome 4. Genetic analysis further demonstrated that qFER4 confers resistance through partial dominance. Transcriptome profiling of the resistant line revealed 7684 and 7906 differentially expressed genes (DEGs) at 36 and 72 h post inoculation (hpi), respectively. These DEGs were significantly enriched in defence-related biological processes and pathways, including phenylpropanoid biosynthesis, jasmonic acid signalling, MAPK cascades, and plant-pathogen interactions. By combining QTL mapping with transcriptome analyses, four candidate genes within the qFER4 interval were screened. Sequence analysis identified extensive structural variations in the promoter and coding regions of Zm00001d053393, including a premature stop codon predicted to lead to a gain-of-function mutation. In contrast, the other three genes exhibited only minor promoter polymorphisms with identical coding sequences between the parental lines. Overall, this study identifies a novel major-effect QTL and candidate gene associated with FER resistance, providing a foundation for gene function and a valuable genetic resource for breeding FER-resistant maize varieties. Full article

Background/Objectives: Acute severe ulcerative colitis (ASUC) affects up to one quarter of patients with ulcerative colitis and carries a substantial risk of colectomy. Early recognition of the need for escalation beyond intravenous (IV) corticosteroids is essential, yet most indices—such as the Oxford criteria—require reassessment on day 3, delaying rescue therapy. The ASUC score, based on admission albumin, C-reactive protein (CRP), endoscopic severity (Ulcerative Colitis Endoscopic Index of Severity, UCEIS), and pre-admission steroid use, was recently proposed to predict early escalation at admission. This study aimed to externally validate the ASUC score and compare its performance with established indices. Methods: We performed a single-center retrospective validation study including consecutive ASUC admissions (2015–2024). The primary outcome was escalation beyond IV steroids, defined as medical rescue therapy with infliximab or ciclosporin and/or colectomy during the index hospitalization. As a sensitivity analysis providing a more specific estimate of IV corticosteroid non-response, we repeated analyses restricting the outcome to medical rescue therapy alone. The model performance was assessed for discrimination (AUC and bootstrap-corrected 2000 resamples), calibration (intercept, slope, and Brier score), and clinical utility (decision-curve analysis). Comparator indices included Albumin-CRP-Endoscopy score (ACE), Admission Model for Acute Severe Colitis (ADMIT-ASC), Oxford Day 3, Lindgren, and Edinburgh. Predefined subgroup analyses (exploratory and underpowered) evaluated infection and biologic exposure. Results: Ninety-one admissions were included overall. The primary validation was performed in the infection-free cohort (n = 77), and infected cases (n = 14) were analyzed separately. In the infection-free cohort, 17/77 (22.1%) required escalation beyond IV steroids during the index hospitalization (medical rescue therapy and/or colectomy), and 5/91 (5.5%) underwent colectomy within 90 days. The ASUC score showed excellent discrimination (Area under the receiver-operating characteristic curve [AUC] 0.89, 95% Confidence Interval [CI] 0.81–0.95), good calibration (intercept 0.26, slope 1.29), and net clinical benefit across 30–50% thresholds. In the rescue-only sensitivity analysis, discrimination remained high (AUC 0.86, 95% CI 0.77–0.94). At a cut-off of ≥2, sensitivity 94% and specificity 78% outperformed other indices (AUC 0.62–0.83). Exploratory subgroup analyses were imprecise due to small sample sizes; discrimination was lower in the infected-only subgroup (AUC 0.71), and estimates in biologic-experienced patients were unstable because of severe imbalance. Conclusions: The ASUC score accurately identified patients likely to require escalation beyond IV steroids on the day of admission, outperforming or matching established day-3 indices. Its simplicity and reliability support its integration into early ASUC management to expedite rescue therapy and potentially improve outcomes. Full article

Many real-world screening tasks in venture capital must rank large start-up candidate pools under conditions of tight review capacity, time-varying information, and rare investment success outcomes. When datasets are constructed retrospectively, post-decision updates can leak into features and inflate performance, especially with random splits. This study proposes a leakage-aware, time-based evaluation framework for capacity-constrained screening formulated as a top-K ranking problem. Using a dataset of 117,141 early-stage firms as an empirical testbed, features were constructed strictly as of a reference time $t_0$, a 180-day temporal embargo was enforced around the train–test boundary, and generalization was assessed with time-ordered splits. Because venture capital decisions are made on a shortlist, evaluation emphasizes ranking quality using PR-AUC, Lift@K, Precision@K/Recall@K, and NDCG@K, reported with bootstrap confidence intervals. Under this leakage-aware protocol and with strong class imbalance, maturity-related signals achieve the strongest PR-AUC (0.0144), while team and combined signals yield the best top-50 shortlist concentration. Finally, probability calibration substantially improves reliability for threshold planning (Brier score reduced from 0.0972 to 0.0161 with sigmoid calibration) while leaving ranking essentially unchanged. Overall, the study provides a leakage-aware evaluation template and an interpretable baseline for time-dependent venture capital screening tasks involving start-up selection, investment success prediction, leakage risk, and limited review capacity. Full article

Craniopharyngioma (CP) is a rare benign tumor of the sellar and suprasellar region that often compresses the optic pathways, causing significant visual impairment in both children and adults. The early detection and monitoring of optic nerve involvement are essential for preserving visual function. Optical coherence tomography (OCT) and OCT angiography (OCTA) are noninvasive, high-resolution imaging modalities that provide quantitative assessment of retinal nerve fiber layer (RNFL) thickness, ganglion cell complex (GCC), and retinal microvasculature. Thinning of the RNFL and GCC correlates with visual field defects and reduced visual acuity and may also serve as a predictor of postoperative visual recovery. OCTA reveals microvascular alterations that may precede structural damage and, together with other imaging parameters, can be used to estimate the likelihood of visual improvement after neurosurgery. This review summarizes current evidence on the use of OCT and OCTA in CP, highlighting their applications in assessment of optic pathway involvement, preoperative evaluation, postoperative monitoring, and risk stratification. Based on our clinical experience, we propose a table with recommended OCT parameters and follow-up intervals. Importantly, OCT should be interpreted alongside the visual acuity, visual field testing, and fundus examination for comprehensive assessment. Future directions include the standardization of imaging protocols and prospective multicenter studies, and integration of OCTA metrics into predictive models of visual outcomes. OCT and OCTA provide objective, reproducible biomarkers that support individualized patient care and may improve visual prognosis in CP. Full article

Background/Objectives: Heart rate variability (HRV) is a non-invasive marker of autonomic nervous system function with established prognostic value after acute coronary syndrome (ACS). The clinical relevance of temporal changes in short-term HRV remains insufficiently defined. This study evaluated short-term HRV dynamics and their association with mortality after ACS. Methods: This retrospective–prospective study included 230 patients with acute myocardial infarction. Five-minute resting ECG recordings were obtained on day 1 and day 21. Time- and frequency-domain HRV parameters were analyzed, and delta values were calculated. The primary endpoint was overall mortality. Survival was assessed using Kaplan–Meier analysis and Cox regression. Results: Patients who died during follow-up had lower HRV values on day 21 and more pronounced declines in selected parameters. In multivariable analysis, decreased ΔLF and shorter RR intervals independently predicted overall mortality. Conclusions: Short-term HRV provides a practical bedside assessment of autonomic function after ACS. Unfavorable temporal changes likely reflect persistent autonomic imbalance and may offer additional prognostic insight. Larger contemporary studies are needed to confirm these findings. Full article

Background: Accurate estimation of the postmortem interval (PMI), the time elapsed between death and body discovery, is a critical challenge in forensic science due to the complex interplay of factors affecting decomposition. Traditional methods based on macroscopic changes often lack precision, especially in later postmortem stages. Methods: This study aimed to develop a novel PMI estimation framework by integrating the dynamics of endogenous small non-coding RNAs (sncRNAs) and exogenous bacterial-derived small RNAs (sRNAs) using sRNA transcriptomics and machine learning. Results: Cardiac RNA degradation strongly correlated with PMI, with a random forest (RF) model achieving high accuracy (coefficient of determination (R²) = 0.939, mean absolute error (MAE) = 2.987 h). Employing PANDORA-seq, we profiled temporal changes in sncRNAs (miRNAs, tsRNAs and piRNAs) in postmortem cardiac tissue within 30 h in a mouse model, while simultaneously assessing RNA integrity (RIN) across eight organs. PANDORA-seq revealed stable sncRNA landscapes with specific dynamic shifts, leading to the identification of seven novel biomarkers (four tsRNAs, three piRNAs) for PMI prediction (R² = 0.760, MAE = 158.990 min). Bacterial-derived sRNAs, predominantly from Staphylococcus aureus, were upregulated at 30 h postmortem, suggesting complementary biomarker potential. Bioinformatics analysis indicated that host miRNAs may target bacterial mRNAs, hinting at cross-kingdom interactions. Conclusion: These findings highlight the potential of integrated endogenous and exogenous sRNA analysis in PMI estimation, providing a high-precision, rapid diagnostic tool and revealing complex postmortem molecular processes. Full article

This study systematically examines the robustness of the Akaike Information Criterion (AIC) in determining the optimal order (p) of an autoregressive (AR) model applied to the RR interval time series of the PhysioNet healthy subject database. The AR approach is widely used to estimate the power spectral density (PSD) of heart rate variability (HRV), and accurate order selection is essential for model stability and reliable spectral estimation. Although the AIC is designed to balance model fit and complexity, it suffers from the problem of arbitrary model selection. This study provides a quantitative robustness analysis of information-criterion-based AR order selection under controlled expansion of the search space. Specifically, we investigated the behavior of the AIC using the PhysioNet database (N = 1257) under conditions where the maximum search order was set to an excessively high value (p = 50), far exceeding the commonly recommended range. Our analysis suggested that the AR model began to capture subtle noise and nonstationary components rather than the intrinsic HRV structure, leading to overfitting and excessive order selection, resulting in false peaks in the PSD and reduced robustness. In conclusion, order decisions based solely on information criteria such as the AIC become unstable when the search range is too large. To ensure robustness, it is recommended to complement the AIC with more stringent criteria such as the Bayesian Information Criterion (BIC) or Final Prediction Error (FPE), in addition to the traditional maximum order restriction. Full article

Japan is a super-aged society where community group multicomponent exercise is widely implemented, yet the age at which a fixed, low-frequency exercise dose no longer offsets functional decline is unclear. We examined 6-year trainability and explored “zero-change” ages in frail older women. Twenty community-dwelling frail women (80–86 years) participated in a once-weekly 90 min multicomponent exercise program for 6 years. Nine physical tests were assessed at baseline (Pre), 6 months, and annually. Overall time effects were tested using repeated-measures ANOVA and generalized estimating equations, with planned paired t-tests versus Pre. Age-specific annual percent changes (%/year) from Pre to each follow-up were annualized, grouped by age at follow-up (81–91 years), and tested against 0%/year. Separately, regression analyses related age to annual percent change across seven consecutive intervals to estimate “zero-change age” (predicted change = 0%). Time effects were significant for all nine measures (all p ≤ 0.032). Chair stand, 10 m fast/zigzag walk, supine-to-stand, maximal 5-step length, and 10-times knee lift generally improved in the early follow-up, whereas handgrip strength and sit-and-reach declined over time. In 6/9 tests, annual percent change diminished with advancing age; estimated zero-change ages ranged from ≈82 years (maximal 5-step length) to ≈88 years (chair stand and one-leg stance). Attendance remained high (≈90%). In this single-arm community program, several mobility-related functions improved or were maintained in frail women in their early 80s, whereas reduced trainability beyond the mid-80s may limit further protection. Threshold ages are exploratory statistical estimates; controlled trials are warranted. Full article

Type 2 diabetes mellitus is associated with major cardiovascular complications, including cardiac autonomic neuropathy, which contributes to sympathetic–parasympathetic imbalance and increases susceptibility to arrhythmias and sudden cardiac death. Heart rate variability, assessed through R–R intervals on electrocardiography and 24 h Holter monitoring, represents a sensitive, non-invasive marker of autonomic dysfunction and arrhythmogenic risk. In patients with type 2 diabetes mellitus, chronic hyperglycaemia, oxidative stress, and metabolic inflammation lead to early impairment of the autonomic nervous system, manifested by consistent reductions in SDNN, RMSSD, pNN50, total power, and the high-frequency component, indicating diminished parasympathetic tone and sympathetic predominance. Nonlinear HRV indices demonstrate a loss of complexity and fractal organisation, providing additional prognostic value beyond conventional time- and frequency-domain analyses. Reduced HRV correlates with the severity of cardiac autonomic neuropathy, duration of diabetes, and poor glycaemic control, identifying patients with increased arrhythmogenic vulnerability. HRV analysis enables prediction of arrhythmic risk, facilitating the identification of high-risk individuals and guiding personalised interventions. The integration of HRV assessment into routine clinical practice may improve the early detection of subclinical autonomic neuropathy and optimise cardiovascular risk stratification and management in patients with type 2 diabetes mellitus. Full article

Olive pomace biochar, obtained through the pyrolysis of lignocellulosic biomass, has emerged as a sustainable and multifunctional additive for polymer composites. Its physicochemical properties, including porosity, surface area, and electrical conductivity, can be tailored by controlling feedstock type and pyrolysis conditions. Although mechanical reinforcement and thermal stability improvements are well documented, the influence of biochar on surface-related properties such as wettability and contact angle remains insufficiently explored for environmentally relevant composite systems. In this study, epoxy-based composites containing biochar synthesized at 750 °C were evaluated in terms of their water interaction behavior by monitoring the evaporation dynamics of ultra-pure water droplets (10 μL, 0.055 mS/cm conductivity) at eight time intervals between 20 and 580 s using high-resolution digital microscopy. Image enhancement and segmentation were performed prior to Discrete Cosine Transform (DCT) analysis to describe droplet geometry in the frequency domain. Time-dependent variations in the standard deviations of DCT coefficients were optimized using the Bat Algorithm, resulting in mathematical models capable of accurately representing droplet evolution and surface–fluid interactions. The primary novelty of this study lies in the development of a hybrid experimental–computational framework that integrates droplet-based wettability measurements with signal-domain analysis and metaheuristic optimization. Unlike conventional studies focusing solely on material characterization, this approach establishes quantitative relationships between surface behavior and numerical descriptors derived from DCT and the Bat Algorithm. The proposed methodology provides a data-driven tool for predicting wettability trends in biochar-reinforced composites and supports the development of moisture-resistant materials for coatings, packaging, and thermal insulation applications within the context of sustainable composite design. Full article

Photovoltaic (PV) power forecasting is strongly influenced by volatility, randomness, and changing meteorological conditions, while conventional point forecasting provides limited uncertainty information for engineering use. This study proposes a hybrid interval forecasting framework for PV prediction. Similar-day clustering first segments weather data into distinct scenarios (sunny, cloudy and overcast) to reduce noise and redundant information within sequences, enhancing stability and thereby providing a more refined feature space for deep learning. A BiLSTM–Transformer model is then used as the core forecaster, taking multiple meteorological variables as multi-feature time-series inputs. BiLSTM captures bidirectional temporal dependencies, and the Transformer enhances long-range feature extraction via attention. To improve robustness and stability, the Alpha Evolution (AE) algorithm is applied for hyperparameter optimization, balancing global exploration and local refinement. For probabilistic forecasting, Adaptive Bandwidth Kernel Density Estimation (ABKDE) is employed to construct prediction intervals, where the local bandwidth is determined by minimizing a local error function to adapt to data density and error distribution. Case studies utilizing a full-year, 5 min high-resolution dataset from the DKASC station demonstrate that the proposed AE-BiLSTM–Transformer achieves highly accurate point forecasts across diverse weather conditions, reducing the RMSE by 81.85%, 76.99%, and 72.26% under sunny, cloudy, and overcast scenarios, respectively, compared to the baseline LSTM. ABKDE further produces reliable and compact intervals; at the 90% confidence level on sunny days, it achieves PICP = 0.921 with PINAW = 0.0378, reducing PINAW by 75.16% relative to conventional KDE while maintaining comparable coverage. Full article