Search Results (6,179)

Background/Objectives: The cardiometabolic index (CMI) is a simple anthropometric–metabolic indicator that has recently gained attention as a marker of cardiometabolic risk. This study compared the associations and predictive utility of CMI, body mass index (BMI), and waist circumference (WC) for cardiometabolic multimorbidity (CMM). Methods: Data were drawn from 3348 adults (mean age 63.5 years; 45.1% male) in the English Longitudinal Study of Ageing who were free of hypertension, coronary heart disease, diabetes, and stroke at wave 4 (2008–2009). CMI was calculated using the triglyceride-to-HDL-cholesterol ratio and the waist-to-height ratio. Incident CMM at wave 10 (2021–2023) was defined as the presence of ≥2 of these conditions: hypertension, cardiovascular disease, diabetes, or stroke. Odds ratios (ORs) with 95% confidence intervals (CIs) and measures of discrimination were estimated. Results: During 12–15 years of follow-up, 197 CMM cases were recorded. CMI, BMI, and WC were each linearly related to CMM. Higher CMI was associated with increased CMM risk (per 1-SD increase: OR 1.25, 95% CI 1.08–1.44; highest vs. lowest tertile: OR 1.88, 95% CI 1.09–3.25), with similar effect sizes for BMI. WC showed stronger associations (per 1-SD increase: OR 1.46, 95% CI 1.25–1.71; highest vs. lowest tertile: OR 2.16, 95% CI 1.35–3.44). Adding CMI to a base model resulted in a small, non-significant improvement in discrimination (ΔC-index = 0.0032; p = 0.55) but significantly improved model fit (−2 log-likelihood p = 0.004), with comparable effects for BMI and greater improvements for WC. Conclusions: In this older UK cohort, higher CMI levels were associated with increased long-term risk of CMM but did not outperform traditional adiposity measures such as BMI and WC. Full article

Early spring frost is a major meteorological hazard during the Apple Flowering period. To improve frost event prediction, this study proposes a hybrid 1D-CNN-BiLSTM-Attention model, with its core novelty lying in the integrated dual attention mechanism (Self-attention and Cross-variable Attention) and hybrid architecture. The 1D-CNN extracts extreme points and mutation features from meteorological factors, while BiLSTM captures long-term patterns such as cold wave accumulation. The dual attention mechanisms dynamically weight key frost precursors (low temperature, high humidity, calm wind), aiming to enhance the model’s focus on critical information. Using 1997–2016 data from Luochuan (four variables: Ground Surface Temperature (GST), Air Temperature (TEM), Wind Speed (WS), Relative Humidity (RH)), a segmented interpolation method increased temporal resolution to 4 h, and an adaptive Savitzky–Golay Filter reduced noise. For frost classification, Recall, Precision, and F1-score were higher than those of baseline models, and the model showed good agreement with the actual frost events in Luochuan on 6, 9, and 10 April 2013. The 4 h lead time could provide growers with timely guidance to take mitigation measures, alleviating potential losses. This research may offer modest technical references for frost prediction during the Apple Flowering period in similar regions. Full article

A cobalt-containing zeolitic imidazolate framework (ZIF-67) was carbonized by different routes to composite materials (cZIFs) composed of metallic Co, Co₃O₄, and N-doped carbonaceous phase. The effect of the carbonization procedure on the water pollutant removal properties of cZIFs was studied. Higher temperature and prolonged thermal treatment resulted in more uniform particle size distribution (as determined by nanoparticle tracking analysis, NTA) and surface charge lowering (as determined by zeta potential measurements). Surface-governed environmental applications of prepared cZIFs were tested using physical (adsorption) and electrochemical methods for dye degradation. Targeted dyes were methylene blue (MB) and methyl orange (MO), chosen as model compounds to establish the specificity of selected remediation procedures. Electrodegradation was initiated via an intermediate reactive oxygen species formed during oxygen reduction reaction (ORR) on cZIFs serving as electrocatalysts. The adsorption test showed relatively uniform adsorption sites at the surface of cZIFs, reaching a removal of over 70 mg/g for both dyes while governed by pseudo-first-order kinetics favored by higher mesoporosity. In the electro-assisted degradation process, cZIF samples demonstrated impressive efficiency, achieving almost complete degradation of MB and MO within 4.5 h. Detailed analysis of energy consumption in the degradation process enabled the calculation of the current conversion efficiency index and the amount of charge associated with O₂^•−/^•OH generation, normalized by the quantity of removed dye, for tested materials. Here, the proposed method will assist similar research studies on the removal of organic water pollutants to discriminate among electrode materials and procedures based on energy efficiency. Full article

Cone beam computed tomography (CBCT) allows for rapid and accessible acquisition of three-dimensional images with a lower radiation dose compared to conventional computed tomography (CT) scans. However, the quality of CBCT images is limited by a variety of artifacts. This systematic review attempts to explore different artificial intelligence-based solutions for enhancing the quality of CBCT scans and reducing different types of artifacts in these three-dimensional images. PubMed, Web of Science, Scopus, Embase, Cochrane, and Google Scholar were searched up to March 2025. Risk of bias of included studies was assessed using the QUADAS-II tool. Extracted data included bibliographic information, aim, imaging modality, anatomical site of interest, artificial intelligence modeling approach and details, data and dataset details, qualitative and quantitative performance metrics, and main findings. A total of 27 papers from 2018 to 2025 were included. These studies focused on five areas: metal artifact reduction, scatter correction, image reconstruction improvement, motion artifact reduction, and noise reduction. Artificial intelligence models mainly used U-Net variants, though hybrid and transformer-based models were also explored. The thoracic region was the most analyzed, and the structural similarity index measure and peak signal-to-noise-ratio were common performance metrics. Data availability was limited, with only 26% of studies providing public access and 15% sharing model source codes. Artificial intelligence-driven approaches have demonstrated promising results for CBCT artifact reduction. This review highlights a wide variability in performance assessments and that most studies have not received diagnostic validation, limiting conclusions on the true clinical impact of these artificial intelligence-based improvements. Full article

Background/Objectives: Obesity is a significant risk factor for obstructive sleep apnea (OSA); however, conventional anthropometric measures, such as body mass index (BMI), may not fully reflect the physiological burden associated with adiposity. The triponderal mass index (TMI) has been proposed as an alternative anthropometric indicator, while inflammation-related biomarkers have emerged as potential complementary tools for characterizing OSA severity. This study aimed to evaluate the relationships between BMI, TMI, hypoxemia, and systemic inflammation, and to assess whether combining anthropometric indices with inflammatory biomarkers improves the identification of severe OSA. Methods: In this retrospective cross-sectional study, 238 adults undergoing full-night polysomnography were classified into four groups: non-OSA, mild OSA, moderate OSA, and severe OSA, based on the apnea–hypopnea index (AHI). Anthropometric indices, polysomnographic parameters, and a comprehensive panel of laboratory biomarkers—including C-reactive protein (CRP), neutrophil- and platelet-derived inflammatory indices, prognostic nutritional index (PNI), CRP-to-albumin ratio (CAR), and CRP-to-lymphocyte ratio (CLR)—were analyzed. Associations were evaluated using Spearman correlation analyses, and diagnostic performance for severe OSA (AHI ≥ 30 events/h) was assessed using receiver operating characteristic (ROC) analyses, DeLong tests, and multivariable models. Results: Both BMI and TMI increased progressively with OSA severity (both p < 0.001) and showed comparable correlations with AHI and nocturnal oxygenation parameters. ROC analyses demonstrated similar discriminative performance for severe OSA (BMI AUC = 0.834; TMI AUC = 0.823; p = 0.229). Among inflammatory biomarkers, CRP, multi-inflammatory index (MII), CAR, and CLR showed moderate diagnostic accuracy. Among the evaluated markers, serum albumin (AUC = 0.836) and PNI demonstrated the highest diagnostic accuracy (AUC = 0.994). A combined model integrating BMI or TMI with PNI achieved near-perfect discrimination for severe OSA (BMI-based AUC = 0.9956; TMI-based AUC = 0.9969), while the addition of CRP-based inflammatory markers did not yield meaningful incremental benefit. Conclusions: BMI and TMI exhibit comparable performance in relation to OSA severity, hypoxemia, and systemic inflammation, with no clear superiority of TMI over BMI in adult patients. Inflammation-related biomarkers—particularly PNI—provide additional discriminatory value beyond anthropometric measures alone. Integrating simple biochemical markers with anthropometric and polysomnographic parameters may enhance risk stratification and identification of severe OSA phenotypes. Full article

This paper designs a hardware-implementable joint denoising and demosaicing acceleration system. Firstly, a lightweight network architecture with multi-scale feature extraction based on partial convolution is proposed at the algorithm level. The partial convolution scheme can reduce the redundancy of filters and feature maps, thereby reducing memory accesses, and achieve excellent visual effects with a smaller model complexity. In addition, multi-scale extraction can expand the receptive field while reducing model parameters. Then, we apply separable convolution and partial convolution to reduce the parameters of the model. Compared with the standard convolutional solution, the parameters and MACs are reduced by 83.38% and 77.71%, respectively. Moreover, different networks bring different memory access and complex computing methods; thus, we introduce a unified and flexibly configurable hardware acceleration processing platform and implement it on the Xilinx Zynq UltraScale + FPGA board. Finally, compared with the state-of-the-art neural network solution on the Kodak24 set, the peak signal-to-noise ratio and the structural similarity index measure are approximately improved by 2.36dB and 0.0806, respectively, and the computing efficiency is improved by 2.09×. Furthermore, the hardware architecture supports multi-parallelism and can adapt to the different edge-embedded scenarios. Overall, the image processing task solution proposed in this paper has positive advantages in the joint denoising and demosaicing system. Full article

This work addresses the issue of estimating air pollution maps for urban areas. Spatially dense maps of air pollution can be calculated using physical models, such as ADMS-Urban; however, due to the high computational cost of such models, maps are verified with low temporal resolution (such as monthly or yearly averages). We investigate the feasibility of using machine learning models to predict air pollution maps based on historical data and current measurements from a limited number of monitoring stations. The models are trained on spatially dense pollution maps generated by physical models, along with corresponding measurements from monitoring stations and selected meteorological data. We evaluate the performance of the models using real-world data from a central district in Wrocław, Poland, considering various pollutants such as $P{M}_{2.5}$, $P{M}_{10}$, CO, VOC, and NOx, presented on spatially dense pollution maps with ca. $2\times {10}^5$ points with a 10 × 10 m grid. The results demonstrate that the proposed method can effectively predict air pollution maps with high spatial resolution and a fast inference time, making it suitable for generating pollution maps with significantly higher temporal resolution (e.g., hourly) compared to physical models. We also experimentally demonstrated that PM10, CO, and VOC pollution models can be built based on measurements from $P{M}_{2.5}$ monitoring stations only with similar, and in the case of CO, higher, accuracy than using measurements from $P{M}_{10}$, CO, and VOC monitoring stations, respectively. Full article

Sex differences in stress response continue to be understudied in basic physiological and behavioral research. The current study aimed to investigate the sex-specific effects of chronic stress in wild-derived house mice subjected to chronic unpredictable stress (CUS). The use of wild-derived mice enhanced the ecological validity of our stress model. We applied CUS for 5 weeks based on protocols previously established in laboratory mice, with regular weighting and welfare checks. Control mice were not subjected to stress. After the 5-week exposure, behavioral tests were performed, blood and hair samples were collected for corticosterone measurement, and stress-sensitive organ weights were assessed. Stressed females, but not stressed males, gained significantly less body weight over the entire CUS period. After CUS, mice tended to have higher adrenal and thymus weights. In stressed females, we observed significantly prolonged grooming time in the open field test and fewer immobility episodes in the tail suspension test (TST). Stressed males displayed significantly shorter immobility time in TST. Stressed males, but not stressed females, had significantly higher levels of hair corticosterone, with a similar tendency in plasma. Our results indicate different CUS coping strategies in males and females and raise a question about the development of different protocols for the assessment of stress responses in males and females. Full article

The growing threat of unauthorized drones to ground-based critical infrastructure necessitates efficient ground-to-air surveillance systems. This paper proposes a lightweight framework for UAV detection and altitude measurement from a fixed ground perspective. We introduce Ghost-YOLOv11n, an optimized detector that integrates GhostConv modules into YOLOv11n, reducing computational complexity by 12.7% while achieving 98.8% mAP_0.5 on a comprehensive dataset of 8795 images. Deployed on a LuBanCat4 edge device with Rockchip RK3588S NPU acceleration, the model achieves 20 FPS. For stable altitude estimation, we employ an Extended Kalman Filter to refine measurements from a monocular ranging method based on similar-triangle geometry. Experimental results under ground monitoring scenarios show height measurement errors remain within 10% up to 30 m. This work provides a cost-effective, edge-deployable solution specifically for ground-based anti-drone applications. Full article

Background/Objectives: The USAID STOP Spillover project in Cambodia aimed to reduce the risk of zoonotic virus spillover from bats to humans in bat guano farming communities. Methods: Using participatory tools, such as Outcome Mapping and Trials of Improved Practices, a team of local experts and community members collaboratively designed, tested, and refined biosafety and hygiene practices that are acceptable and sustainable to mitigate the risk of bat-borne disease spillover. We tracked progress and rolled out interventions to promote the adoption of safe behaviors that strengthen the understanding of zoonotic disease and reinforce the adoption of safety practices among bat guano producers and their neighbors. The intervention’s effectiveness was evaluated after three-month trials. Results: An improvement in knowledge, attitudes, and risk reduction practices was observed among participants. The primary motivators for adopting these measures were fear of disease, families’ well-being, cost savings, and experience of the COVID-19 pandemic. Conclusions: The community-driven approach fostered a sense of ownership, enabling participants to find the best solutions for their circumstance for long-term sustainability of the intervention. The findings recommended continued community engagement, improved access to biosafety and hygiene resources, and reinforced routine zoonotic disease surveillance. This model can be applied to mitigate emerging infectious disease spillover risks in similar contexts. Full article

This study quantitatively verified the impact of applying accumulated data on the model’s prediction accuracy, overfitting, and adaptive learning ability by using a method that accumulates and retrains machine data of a TBM generated whenever excavation progresses at regular intervals. To achieve this, the performance of five machine learning algorithms was evaluated on two field datasets. The best-performing gradient boosting model was selected as the preliminary model. The performance results of the preliminary model and the cumulative model were then compared using another field dataset. The field data for the performance comparison were divided into 14 steps based on ground information, and the performance of the two models was compared sequentially at each step. The results showed that the preliminary and cumulative models exhibited similar predictive performance in the initial intervals. However, the cumulative model more closely matched actual measurements as new data was added than the preliminary model. Consequently, the preliminary model, based on past data, has clear limitations in adapting to the diverse variables encountered in real-world situations. On the other hand, cumulative models are essential for improving real-time prediction performance of processes with constantly changing environments, such as TBM, by continuously increasing relevant field data. Full article

With the rapid advancement of intelligent manufacturing and digital transformation, assessing the digital transformation and maturity of manufacturing enterprises enables firms to evaluate their digital achievements and establish appropriate transformation pathways. Existing maturity assessment models for manufacturing enterprises predominantly emphasize strategic-level evaluation and rely heavily on survey-based data, while paying limited attention to the business-function level. However, in practice, enterprises often initiate digital transformation by addressing specific business challenges and then gradually advance through concrete application scenarios. To address this gap, this paper proposes a scenario-based approach for measuring the digital transformation and maturity of manufacturing enterprises. With this approach, a differentiated weighting system is constructed based on “core keywords–extended keywords–negative keywords”, and a semantic similarity model is used to identify and quantify digital application scenarios in corporate annual reports. Building on this, a three-dimensional evaluation framework, comprising scenario coverage, scenario depth, and scenario consistency, is developed to comprehensively assess the extent of digital transformation from the perspective of application scenarios. With the proposed method, different business units can be evaluated independently, thereby capturing transformation progress across heterogeneous levels. Since it relies on publicly available corporate annual reports, the evaluation process is transparent and traceable and generates quantitative results. By shifting from survey-based, strategy-oriented assessments to function-oriented, data-driven, and modular evaluations, the method not only enhances accuracy and interpretability but also provides practical guidance for resource allocation, cross-functional complexity management, and the progressive expansion of digital transformation. Full article

Background/Objectives: Advancing information on the key physicochemical properties of biologically active substances enables the development of formulations with reduced dosing, lower toxicity, and minimal adverse effects. This work addresses the knowledge gap concerning the pharmacologically relevant properties of harmaline (HML), with a focus on thermodynamic and kinetic aspects. New data were obtained on the compound’s solubility and distribution coefficients across a wide temperature range. Specifically, solubility was measured in aqueous buffers (pH 2.0 and 7.4), 1-octanol (OctOH), n-hexane (Hex), and isopropyl myristate (IPM), while distribution coefficients were determined in OctOH/pH 7.4, Hex/pH 7.4, and IPM/pH 7.4 systems. Methods: Three membranes—regenerated cellulose (RC), PermeaPad (PP) and polydimethylsiloxane-polycarbonate (PDS)—were used as barriers in permeability studies using a Franz diffusion cell. Results: At 310.15 K, the molar solubility of HML in the solvents decreased in the following order: OctOH > pH 2.0 > pH 7.4 > IPM > Hex. The distribution coefficient of HML showed a strong dependence on the nature of the organic phase, correlating with its solubility in the respective solvents. The OctOH/pH 7.4 distribution coefficient ranged from 0.973 at 293.15 K to 1.345 at 313.15 K, falling within the optimal range for potential drug bioavailability. The transfer of HML into OctOH (from either pH 7.4 or hexane) is thermodynamically spontaneous, whereas its transfer into Hex is unfavorable. Conclusions: Based on its permeability across the PP barrier, HML was classified as highly permeable. The distribution and permeation profiles of HML showed similar trends over 5 h in both the OctOH/pH 7.4–PP and IPM/pH 7.4–PDS systems. These systems were therefore proposed as suitable models for studying HML transport in vitro. Full article

Accurate estimation of live weight is essential for efficient management and precision control in poultry production. This study evaluated the potential of supervised machine learning (ML) algorithms and digital image analysis for non-invasive prediction of live weight in Anadolu-T broilers, a locally developed genotype in Türkiye. A total of 4200 records were collected from 100 broilers (50 males and 50 females) over 42 days, including daily measurements of back length, back width, and live weight. Five ML algorithms—Random Forest (RF), k-Nearest Neighbors (KNN), Support Vector Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR)—were trained and validated to estimate live weight based on morphometric traits. Among all algorithms, KNN achieved the highest accuracy (R² = 0.982, RMSE = 111.509 g, MAPE = 8.205%), followed by RF and XGB, which also produced stable and reliable predictions. The image-based models using log-transformed regression between body surface pixel area and live weight yielded similar accuracy (R² = 0.989, RMSE = 101.197 g, MAPE = 7.266%), confirming that projected surface area can effectively represent growth progression. The results demonstrate that integrating ML algorithms with digital imaging offers a practical, cost-effective, and non-invasive approach for real-time broiler weight estimation. This approach supports the advancement of precision poultry farming through automated, data-driven growth monitoring. Full article

Quick visual balance perception in layouts is essential for a positive user experience. However, existing computational models often struggle to accurately capture this key aesthetic aspect, particularly in interfaces with asymmetric elements. This paper introduces Visual Moment Equilibrium (VME), a new cognitive model that redefines visual balance as a unified perceptual force field, similar to moment equilibrium in physical systems. Based on principles from Gestalt psychology, spatial cognition, and psychophysics, we incorporate three main innovations: (1) a Measured Balance index enhanced with psychophysical transformations to enable sensitive quantification of visual imbalance; (2) a nine-grid visual weighting system combined with Manhattan distance to reflect human attentional distribution and non-Euclidean spatial reasoning; and (3) a Shape Sparsity Ratio with a piecewise compensation function that formally operationalizes the Gestalt principle of closure, especially for irregular visual elements. Validation against human perceptual benchmarks from the Analytic Hierarchy Process shows that the VME model has a strong correlation with expert judgments regarding regular interfaces (Pearson’s r = 0.942, accounting for 88.8% of the variance), outperforming the widely used model (33.9%). VME also maintains high predictive accuracy for irregular interfaces (r = 0.890), emphasizing its wide applicability across various design configurations. Full article