Search Results (107,279)

Age estimation is an important component of forensic investigation, with applications in criminal casework, immigration assessments, and disaster victim identification. Determining whether an individual is a minor or an adult, or estimating the age at death of unidentified remains, can have significant legal and humanitarian implications. Traditional forensic age estimation methods rely primarily on anthropological and radiological assessment of skeletal development and degeneration; however, these approaches may be limited by subjectivity, population-specific reference standards, and reduced precision in adult age estimation. In recent years, molecular biomarkers have emerged as promising complementary tools for age prediction. Molecular approaches, including DNA methylation profiling, Y-chromosome-associated markers, RNA-based biomarkers, mitochondrial DNA alterations, proteomic signatures, and telomere length analysis, reflect biological processes associated with aging and may provide objective indicators that can be measured from biological samples. Among these methods, DNA methylation-based models currently demonstrate the strongest predictive performance and represent the most extensively studied molecular strategy for forensic age estimation. Nevertheless, several challenges remain before widespread forensic implementation can be achieved, including tissue specificity, environmental influences on biomarker stability, population variability, and the need for robust validation across laboratories and forensic sample types. This review summarises the current molecular approaches investigated for forensic age estimation, evaluates their biological basis and methodological limitations, and discusses their potential integration into forensic workflows. While molecular techniques offer promising avenues for improving age estimation, further standardisation, validation, and careful interpretation are required before they can be routinely applied in forensic practice. Full article

Background: Necrotizing pneumonia is a severe complication of Mycoplasma pneumoniae pneumonia (MPP) in children. Early recognition remains challenging because initial clinical manifestations are often non-specific, highlighting the need for a practical tool for early risk stratification. Methods: We conducted a single-center retrospective study of hospitalized children with MPP. Data from 2015–2023 were used for model development, and patients enrolled in 2024 were reserved for temporal validation. We compared candidate machine-learning algorithms and selected a parsimonious random forest model using routinely available variables obtained during the early hospitalization period. Model performance was evaluated using discrimination, calibration, and decision curve analysis, and model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results: The random forest model showed good discriminatory performance in internal validation and retained acceptable performance in the 2024 temporal cohort. Calibration indicated reasonable agreement between predicted and observed risks. Decision curve analysis suggested potential clinical value as a supportive tool for early risk stratification. SHAP analysis highlighted fever duration, C-reactive protein, pleural effusion, alanine aminotransferase, and gamma-glutamyl transferase as the main contributors to model prediction. Conclusions: We developed and temporally validated a clinical prediction model for necrotizing pneumonia in children hospitalized with MPP. The model may support early risk stratification using routinely available clinical data, but it is intended to complement rather than replace clinical judgment. External prospective validation is required before routine clinical implementation. Full article

Conventional control strategies for battery energy storage systems (BESSs) fail to achieve symmetrical and coordinated control between the DC/DC and DC/AC conversion stages, resulting in unsatisfactory DC capacitor voltage fluctuation suppression and threatening the safe and stable operation of the system. To address this issue, this study proposes an improved model predictive control (MPC)-based control method that explicitly considers DC capacitor voltage fluctuation suppression. First, a dynamic mathematical model of the BESS is established by jointly considering its DC/DC and DC/AC energy conversion stages. The model is then discretized using the first-order forward Euler method to facilitate controller implementation. Second, the cost function of the proposed MPC-based control method is designed to simultaneously incorporate DC capacitor voltage fluctuation suppression and output current tracking errors on both the DC and AC sides. Finally, the switching states of the DC and AC converters are selected as the control set, and the optimal switching signals for the BESS are determined by optimizing the aforementioned cost function. Verification results demonstrate that, compared with traditional control strategies, the proposed strategy achieves more symmetrical stable and dynamic performance and reduces DC side capacitor voltage fluctuation by approximately 80%, thereby effectively ensuring the safe and stable operation of the system. Full article

Plant-mediated CH₄ transport can enhance ecosystem CH₄ emission by transporting soil-produced CH₄. This pathway can exceed diffusion and ebullition as the dominant CH₄ emission route. However, limited studies have investigated the morphological and anatomical factors influencing CH₄ transport in plants. Through a series of manipulative experiments on the shoots and roots, this study examines the role of root and shoot structures in CH₄ transport and release in six widespread wetland species: Carex rostrata Stokes, Carex lasiocarpa Ehrh., Carex aquatilis Wahlenb., Iris pseudacorus L., Juncus effusus L., and Alocasia odora (Lodd.) Spach. CH₄ flux from all investigated species dropped significantly after clipping fine roots, while it did not change significantly after removing coarse roots. Shoot clipping and sealing significantly decreased CH₄ flux from the investigated Carex species, but not from the other species. Our results demonstrate the important role of fine roots in controlling CH₄ flux, whereas coarse roots play a minor role. Leaf blades are the major release site of CH₄ from Carex species, while micropores at the shoot base are the primary release site of CH₄ from the other species. Our study suggests that integrating plant-specific anatomical and morphological characteristics into global methane models is crucial to better predict and mitigate climate change impacts. Full article

Gaze estimation plays a crucial role in human-computer interaction and behavior analysis. However, in dynamic scenes, rigid head movements and rapid gaze shifts pose significant challenges to accurate gaze prediction. Most existing methods either process single-frame images independently or rely on long video sequences, making it difficult to simultaneously achieve strong performance and high computational efficiency. To address this issue, we propose DGAGaze, a gaze estimation framework based on a difference-driven spatiotemporal attention mechanism. This framework uses a geometry-aware temporal alignment module to mitigate interference from rigid head movements, compensating for them through pose estimation and affine feature warping, thereby achieving explicit decoupling between global head motion and local eye motion. Based on the aligned features, inter-frame differences are used to adjust spatial and channel attention weights, enhancing motion-sensitive representations without introducing an additional temporal modeling layer. Extensive experiments on the EyeDiap and Gaze360 datasets demonstrate the effectiveness of the proposed approach. DGAGaze achieves improved gaze estimation accuracy while maintaining a lightweight architecture based on a ResNet-18 backbone, outperforming existing state-of-the-art methods. Full article

Background and Objectives: Optic disc drusen (ODD) are deposits in the optic nerve head that can look like true swelling, and in some patients, slowly damage the optic nerve and cause visual field loss. We aimed to identify which eyes are most likely to worsen over time using common clinic tests. Methods: We studied 131 adults with OCT-confirmed ODD who also had OCT-angiography (a scan that measures small blood vessels around the optic nerve) and repeated visual field tests over at least 18 months. We measured (1) the size of the drusen (maximum drusen height), (2) blood vessel density around and inside the optic nerve, and (3) change in visual field performance over time. “Fast progression” was defined as visual field worsening of ≥0.5 dB per year. Results: Eyes with superficial ODD had larger drusen than buried ODD (382.6 ± 110.9 vs. 247.2 ± 92.8 µm; p < 0.001) and more frequent visual field defects (78.6% vs. 58.7%; p = 0.02). When blood vessel density around the optic nerve was low, fast progression was much more common (52.3%) than in the middle (16.3%) or highest groups (13.6%; p < 0.001). In the adjusted model, fast progression was more likely with superficial ODD (OR 6.3) and larger drusen (OR 2.0 per 100 µm), and less likely when the vessel density was higher (OR 0.8 per 1% increase). Adding the vessel measurements improved the prediction accuracy (AUC 0.8 → 0.9; p = 0.011). Conclusions: Combining drusen size and blood vessel measurements helps identify ODD patients at higher risk of faster visual field loss and may guide closer follow-up. Full article

The removal efficiency of wet scrubbers is governed by complex nonlinear interactions among operating parameters such as liquid level, airflow velocity, and dust concentration, making accurate real-time prediction challenging, which in turn leads to operational instability, increased energy consumption, and excessive emissions. To address this bottleneck, we first introduce multi-gene genetic programming (MGGP) to develop interpretable models quantifying multi-parameter coupling and predicting removal efficiency for PM₁, PM_2.5, PM₁₀, and TSP. Key input variables, including liquid level height, inlet airflow velocity, system pressure, and inlet dust concentration, were identified via correlation analysis. Explicit mathematical models were derived. Global sensitivity analysis using the elementary effect test (EET) identified inlet airflow velocity as most influential. Uncertainty quantification via quantile regression (QR) confirmed the model’s reliability with narrow prediction intervals and high coverage probabilities. MGGP offers a favorable balance of accuracy, generalization, and interpretability compared to extreme gradient boosting (XGBoost) and multiple nonlinear regression (MNR). Its explicit form quantifies parameter interactions, enabling efficient on-site monitoring with low computational cost. This study provides an interpretable prediction tool for intelligent wet scrubber operation, supporting cleaner production and refined control in complex industrial processes. Full article

Background/Objectives: Urine samples are the most frequently analyzed specimens in clinical microbiology laboratories. Although urine culture remains the gold standard for diagnosing urinary tract infections, it is time-consuming and resource-intensive. Therefore, reliable screening methods capable of predicting urine culture positivity are needed to optimize laboratory workflow. Automated urine analysis based on flow cytometry enables efficient screening and identification of samples with a low probability of bacterial infection, thereby rationalizing microbiological testing. This study evaluated the usefulness of a multivariable approach to support interpretation of flow cytometry results following the implementation of the Sysmex UF-4000 urine flow cytometer. Methods: Routinely collected urine samples from outpatients and hospitalized patients were analyzed using the UF-4000 flow cytometer, with a positivity threshold of ≥100 leukocytes/µL. Urinary parameters were compared between samples with positive and negative cultures. Multivariable logistic regression was applied to identify independent predictors of a positive urine culture. Urinary sediment parameters, including leukocyte, bacterial, fungal, and squamous epithelial cell counts, were assessed as covariates. Results: Urine samples with positive cultures showed significantly higher leukocyte counts (median 355.0, IQR 146.5–1429.4) and bacterial counts (median 9805.2, IQR 1134.3–45,011.5). Fungal and squamous epithelial cell counts differed only slightly between groups, although the differences were statistically significant (p < 0.001). Leukocyte counts were higher in urine samples from which Gram-negative bacteria were isolated compared with samples containing Gram-positive bacterial isolates (p < 0.001). The multivariable model demonstrated the most favorable overall performance, combining high sensitivity with improved specificity and the highest negative predictive value (AUC = 0.927). Optimal cut-off values were 70 leukocytes/µL and 105 bacteria/µL. Conclusions: Leukocyte and bacterial counts were the strongest predictors of positive urine culture results. A multivariable model including only these two parameters demonstrated high diagnostic accuracy and may serve as a practical screening tool to identify urine samples with a low probability of bacterial infection. The implementation of this approach could support more efficient use of urine cultures and help optimize laboratory workflow. Full article

Background/Objectives: Excessive smartphone use has emerged as an important behavioral health concern during adolescence, a developmental period characterized by heightened psychosocial vulnerability. This study aimed to identify psychosocial and behavioral factors associated with excessive smartphone use among Korean adolescents using nationally representative data. Methods: Data were obtained from the 2024 Korean Youth Risk Behavior Web-based Survey (KYRBS), including 54,653 adolescents. Excessive smartphone use was operationally defined as average daily smartphone use of ≥300 min. Multivariable logistic regression was conducted to examine associated factors. An exploratory machine learning analysis using a Light Gradient Boosting Machine included 52,450 participants with complete predictor data. Results: Female sex, higher grade level, lower perceived socioeconomic status, higher perceived daily stress, higher anxiety symptoms, poorer sleep-related recovery, suicidal ideation, and more frequent vigorous physical activity were associated with higher odds of excessive smartphone use. The supplementary modeling approach showed patterns consistent with the regression findings, with grade level, socioeconomic status, and sex contributing prominently. Vigorous physical activity demonstrated a nonlinear association with predicted risk. Conclusions: Excessive smartphone use among adolescents appears to be shaped by developmental stage, socioeconomic context, and psychological vulnerability. These findings support prevention strategies that address emotional well-being and sleep health alongside broader structural and school-based approaches. Full article

Forecasting financial time series (FTS) is essential for analyzing and understanding the dynamics of financial markets. Traditional recurrent neural network (RNN) models often suffer from low prediction accuracy on non-stationary and abruptly changing data, as their gating mechanisms struggle to capture evolving trends in FTS. This paper introduces variational mode decomposition (VMD) and multifractal analysis to enhance the gating mechanism of the gated recurrent unit (GRU). By quantifying the changing characteristics of FTS, the proposed model dynamically adjusts the gating weights. In addition, a state fusion strategy is employed to improve the utilization efficiency of historical information. Experiments are conducted using daily data of the SSE 50, CSI 300, and CSI 1000 indices, spanning from 4 January 2002, to 26 December 2025. The results demonstrate that, compared to traditional models, the proposed model better captures the evolving characteristics of FTS and achieves higher prediction accuracy. Full article

Hydropower remains one of the primary sources of power generation. It can be operated as either a base-load or peak-load plant due to its rapid, easy start-up and stop-down capability. However, power plants, old or new, need to be operated and maintained optimally to meet energy demand and maximise economic returns. While the older plants without digital controls such as the Supervisory Control and Data Acquisition (SCADA) system are unable to leverage the evolving technology including big data and Artificial Intelligence (AI), the newer plants or plants that already have some form of data acquisition system have the advantage of leveraging the newer platforms for efficient operation, monitoring and fault diagnosis. Thus, an Artificial Neural Network (ANN), a machine learning (ML) algorithm, was chosen for this case study to predict the generator’s operational stator temperature by selecting six parameters that could potentially affect it. Real data from the 336 MW Chhukha Hydropower Plant (CHP) in Bhutan were used to train the ANN. The prediction of temperature using an ANN in MATLAB^® yielded an R² (correlation coefficient) of 96.8%, which is impressive but can be further improved through various optimisation and tuning methods with increased data volume and complexity. The performance of ANN prediction was validated against other regression models, and the ANN was found to outperform them. This demonstrated its capability to predict and detect generator temperature faults before failures, thereby enhancing hydropower operation and maintenance (O&M) efficiency. The model’s interpretation was also done through Shapley Additive ExPlanations (SHAP). Full article

Financial time series forecasting remains a challenging task due to the high non-stationarity and concept drift inherent to market data. Existing deep learning models, such as LSTMs and transformers, typically employ static weights after training, limiting their ability to adapt to rapid market regime shifts (e.g., from trends to reversals). To bridge this gap between static parameters and dynamic environments, we propose a novel framework named Game-Theoretic HyperNetwork (GTH-Net), which introduces a context-aware meta-learning mechanism to achieve adaptive forecasting. Specifically, we first introduce an Evolutionary Game-Theoretic Correction Module (E-GTCM) to explicitly extract latent buying and selling pressure based on market microstructure priors through an iterative gated evolution process. Subsequently, we propose a HyperNetwork-based fusion mechanism that treats the extracted game state as a meta-context to dynamically generate the weights of the forecasting head. This allows the model to automatically switch its prediction rules in response to shifting market regimes. Extensive experiments on real-world stock datasets demonstrate that GTH-Net significantly outperforms baselines in terms of machine learning predictive accuracy and simulated financial profitability. Furthermore, ablation studies and parameter analysis confirm that the dynamic weight generation mechanism effectively captures market reversals caused by overcrowded trades. Full article

The Epi2SensA method is a method similar to the validated EpiSensA assay for assessing the skin sensitization potential of chemicals. The Epi2SensA protocol includes adaptation (changes to exposure conditions and the controls) for using an alternative reconstructed human epidermis (RhE) model, the EpiDerm™ model. The interlaboratory validation study evaluated the reliability and predictive capacity of Epi2SensA according to OECD Performance Standards. Four laboratories (Mattek, Now Part of Sartorius, Eurofins Munich, Burleson Research Technologies, Inc., and Food and Drug Safety Center) conducted blinded testing of 20 coded reference substances representing various chemical categories and sensitization potencies. Statistical analysis using modified acceptance criteria (a 60% cell viability threshold) and a modified prediction model (requiring at least two positive gene markers) demonstrated substantially improved performance compared to the original EpiSensA criteria. The between-laboratory reproducibility (BLR) was 85%, the average within-laboratory reproducibility (WLR) was 83.3%, and the average predictivity parameters were 88.1% for sensitivity, 88.9% for specificity, and 88.3% for accuracy. Epi2SensA achieved performance metrics comparable to the validated reference method (EpiSensA), supporting regulatory acceptance of the Epi2SensA assay using the EpiDerm™ model (Mattek Corporation, Now Part of Sartorius, Ashland, MA, USA) as an alternative RhE source for OECD TG 442D skin sensitization testing. Full article

Adenosine triphosphate (ATP) serves as a central energy currency and signaling molecule in cellular processes, with ATP-binding sites in proteins playing critical roles in enzymatic catalysis, signal transduction, and gene regulation. The accurate identification of ATP-binding sites is essential for understanding protein function mechanisms and facilitating drug discovery, enzyme engineering, and disease pathway analysis. In this study, we present a novel hybrid deep learning framework that synergizes heterogeneous learning paradigms based on protein sequence information for accurate ATP-binding site prediction. Our approach integrates two complementary base classifiers. One is a Transformer-based model, which leverages high-level contextual embeddings generated by Evolutionary Scale Modeling 2 (ESM-2), a state-of-the-art protein language model, combined with a local–global dual-attention mechanism that enables the model to simultaneously characterize short-segment and long-range contextual dependencies across the entire protein sequence. The other is a deep Q-network (DQN)-inspired classifier that achieves residue-level prediction as a sequential decision-making process. The final predictions are generated using a weighted ensemble strategy, where optimal weights are determined via cross-validations to leverage the strengths of both models. The prediction results on benchmark independent testing sets indicate that our method achieves satisfactory performance on key metrics. Beyond predictive efficacy, this work uncovers the intrinsic biological mechanisms underlying protein–ATP interactions, including the synergistic roles of local structural motifs and global conformational constraints, as well as family-specific binding patterns, endowing the research with substantial biological significance. The research in this work offers a deeper understanding of the protein–ligand recognition mechanisms and supportive efforts on large-scale functional annotations that are critical for system biology and drug target discovery. Full article

Esophageal cancer is a highly aggressive malignancy with a poor prognosis. More precise prognostic biomarkers are therefore needed. Disulfidptosis is a recently identified form of regulated cell death driven by disulfide stress. It has been implicated in tumor progression. However, its prognostic role in esophageal cancer remains largely unexplored. This study aimed to develop a disulfidptosis-related gene signature for risk stratification and outcome prediction in esophageal cancer patients. Based on 23 disulfidptosis-related genes, consensus clustering was performed to identify molecular subtypes. Differentially expressed genes (DEGs) between subtypes were subjected to functional enrichment, immune microenvironment, and drug sensitivity analyses. Univariate and multivariate Cox regression were used to construct a prognostic risk model, which was evaluated using time-dependent receiver operating characteristic (ROC) curve and Kaplan–Meier analysis. A clinical nomogram integrating the risk score and clinicopathological factors was developed and validated. Two distinct disulfidptosis-related subtypes were identified, showing significant differences in gene expression, immune infiltration, and stromal scores. A total of 1080 DEGs were enriched in pathways related to epidermal differentiation, NRF2 signaling, and glucocorticoid receptor activity. A five-gene prognostic signature was established and effectively stratified patients into high- and low-risk groups. The risk model exhibited strong discrimination for 1-, 3-, and 5-year overall survival outcomes. The predictive accuracy was further maximized through an integrated clinical nomogram, which achieved an outstanding area under the curve (AUC) of 0.94 for 5-year survival predictions. Drug sensitivity analysis revealed subtype-specific therapeutic vulnerabilities, supporting potential precision treatment strategies. This study proposes a novel disulfidptosis-related five-gene signature and nomogram that robustly predict prognosis in esophageal cancer. The findings highlight the clinical relevance of disulfidptosis in tumor biology and offer a potential tool for risk stratification and personalized therapeutic decision-making. Full article