Search Results (4,613)

The traditional monitoring of forest musk deer behavior primarily relies on direct human observation or the post hoc playback analysis of ordinary surveillance videos. This approach is not only time-consuming and labor-intensive but also highly subjective, easily leading to missing or misjudged critical behavioral information. Moreover, it is difficult to achieve real-time monitoring and anomaly warning. These limitations severely constrain the efficiency of the large-scale artificial breeding of forest musk deer and the effective advancement of wild population conservation. Thus, this study proposes a forest musk deer behavioral recognition method based on an improved YOLOv8-Pose. A forest musk deer behavior image dataset covering four typical behaviors was constructed, and 18 keypoints were systematically annotated. This study designs a Dilated Spatial Pyramid Pooling-Fast (DILATED-SPPF) module and a Multi-scale Depthwise Separable Context Mixer (MDSC-Mixer) module, and integrates them into YOLOv8-Pose. Experimental results show that the improved model outperforms the original YOLOv8-Pose and comparison models such as YOLOv11/v12-Pose on key metrics of object detection (Box-mAP50 0.929, Box-mAP50-95 0.814) and pose estimation (Pose-mAP50 0.879, Pose-mAP50-95 0.565). This study further develops a visual interactive interface that intuitively presents detection results and skeleton structures. This work provides a high-precision, low-cost automated behavior analysis tool for the artificial breeding and wild conservation of forest musk deer with significant application value for enhancing the intelligence level of endangered species protection. Full article

Material selection in architectural design necessitates a multifaceted evaluation of economic, technical, esthetic, and cultural variables. Beyond fundamental requirements such as cost, structural integrity, and transparency, architects must synthesize subjective attributes, including warmth and formality, with objective constraints like multifunctionality and cultural heritage. Despite the strategic impact of material choice on project performance, empirical research systematically categorizing these governing criteria remains sparse. Furthermore, existing methodologies often overlook the psychophysical principles of human perception essential for construction material evaluation. Thus, this study identifies the fundamental factors influencing material selection and establishes a hierarchical framework to prioritize their relative significance within the design process. The research employs a weighted Analytic Hierarchy Process integrated with the Weber–Fechner law (W-AHP) to structure and quantify selection criteria. By incorporating perceptual scaling principles into the AHP framework, the methodology accounts for variations in judgment sensitivity across different evaluation scales. A hierarchical decision model was developed to categorize criteria and sub-criteria, followed by pairwise comparisons to derive priority weights. Results reveal a distinct priority hierarchy among the identified criteria and confirm that judgment sensitivity varies significantly across evaluation scales. The W-AHP method produced differentiated weightings that accurately reflect the psychological intensity of professional decision-making, offering a structured mechanism to balance functional performance with complex design intentions. This study contributes to the field of construction management by introducing the W-AHP method as a novel decision-support tool. The integration of Weber–Fechner perceptual principles enhances weight differentiation and addresses the inherent subjectivity of architectural evaluation, providing a transparent methodology to justify material procurement within a rigorous engineering management context. Full article

Visual explainable artificial intelligence (XAI) is an important mechanism for connecting analytically complex decision models with practitioners who must interpret and act upon their outputs in industrial supply chains. In facility localization problems, wafer foundries and other capital-intensive manufacturers must evaluate geographically dispersed candidate sites against multiple uncertain criteria. The ability to communicate fuzzy group decision-making (FGDM) outcomes in a transparent, interpretable form has direct operational relevance. The literature has introduced hanging gradient bar charts, gradient bidirectional scatterplots, and traceable aggregation charts as visual XAI instruments for semiconductor supply chain localization that show substantial reductions in interpretation error versus conventional plots. However, the quantitative assessment of explanation quality itself remains underdeveloped. To address such a gap, this research proposes a quantitative explainability quality index (XQI) that formalizes visual explanation quality in FGDM as a composite measurable construct. XQI integrates two complementary layers: (1) An objective explainability layer (OEI), consisting of normalized fuzzy interpretation deviation, response time, ranking fidelity, and interpretation accuracy, and (2) a subjective explainability layer (SEI), consisting of perceived understanding, perceived transparency, decision confidence, and cognitive load. Trust, acceptance, and decision quality are downstream outcome constructs rather than components of the index. A weighted linear combination of OEI and SEI produces a single index for systematic, reproducible comparison across competing visualization designs. A structural equation model is specified as a planned validation mechanism for examining how explanation quality may relate to trust, acceptance, and downstream decision quality. The proposed validation framework includes a semiconductor facility localization scenario, three visualization conditions, and a planned participant pool of 150–240 supply chain managers, engineers, and graduate students. The XQI framework transforms visual XAI from a descriptive communication aid into a testable decision-support construct, thereby addressing a key evaluation gap in the FGDM visualization literature. Full article

Objective and reproducible diagnosis of attention-deficit/hyperactivity disorder (ADHD) remains challenging because of the limited availability of reliable electroencephalography (EEG) biomarkers and the high variability of neural signals. This study proposes a computational framework for ADHD detection based on dynamic functional connectivity and network-index modeling. Multichannel EEG recordings were transformed into temporal connectivity graphs using sliding-window correlations of band-limited amplitude envelopes. Several network-index models were evaluated, including linear, graph-based, recurrent, and hybrid spatio-temporal approaches. The proposed Hybrid Spatio-Temporal Graph Transformer demonstrated moderate, yet reproducible, subject-level classification performance. On the independent test set, the model achieved an accuracy of 63.16%, a balanced accuracy of 62.22%, a sensitivity of 80.00%, a specificity of 44.44%, an F1-score of 69.57%, and an AUC-ROC of 0.7444. Additional analysis of the derived network index demonstrated moderate intergroup separability, with a mean index shift of 1.16, Cohen’s d = 0.73, Pearson’s r = 0.36, and distribution overlap = 0.72. These findings suggest that the proposed framework captures informative spatio-temporal EEG connectivity patterns associated with ADHD; however, the model’s diagnostic applicability should be considered preliminary and requires validation in larger independent cohorts. Full article

Background/Objectives: Propranolol (PROP) is a non-selective β-blocker widely prescribed for cardiovascular and neurological disorders. Its pharmacokinetics (PK) are highly variable, and co-administration with omeprazole (OME), a CYP2C19 substrate and inhibitor, may alter systemic exposure. Herein, this study aimed to investigate factors influencing PROP PK variability and evaluate the effect of OME coadministration using physiologically based pharmacokinetic (PBPK) modeling and population PK (popPK) analysis. Methods: PBPK models for PROP and OME were developed and validated against published data. DDI simulations were conducted across clinically relevant dosing regimens. A two-period fixed-sequence virtual trial of 125 subjects was simulated with PROP alone and PROP combined with OME. Population PK (popPK) analysis was performed on simulated plasma concentration data to identify covariates affecting PROP disposition and quantify DDI magnitude. Results: PBPK models were successfully developed and validated. PROP disposition was best described by a two-compartment model with linear elimination. Health status was found to influence clearance, and body surface area (BSA) affected the central volume of distribution. Co-administration with OME increased PROP exposure, with larger effects in patients with renal impairment. Simulated plasma concentrations remained below established toxicity thresholds. Conclusions: Virtual clinical trials integrating PBPK and popPK modeling provide a robust approach to identifying key determinants of PK variability and DDI risk. Although these findings were not directly translated to clinical observations, this helps identify sources of PK variability in PROP treatment settings and factors that may intensify its interaction with OME, thereby supporting model-informed precision dosing to enhance safety and efficacy. Full article

Background/Objectives: Epiretinal membrane (ERM) is a common vitreoretinal disorder characterized by fibrocellular proliferation on the inner retinal surface, often leading to progressive visual impairment. Accurate grading of ERM severity using optical coherence tomography (OCT) is critical for treatment planning and surgical decision-making; however, manual grading is labor-intensive and subjective. This study aims to develop an automated and reliable deep learning-based method for ERM severity classification. Methods: We propose DenseViT-OCT, a hybrid deep learning model that integrates dense convolutional neural networks (CNN) and vision transformers (ViT). The model introduces three key modules: Multi-Scale Dense Feature Aggregation (MDFA) for capturing hierarchical features across multiple spatial scales, Adaptive Feature Calibration (AFC) for enhancing feature discrimination through channel and spatial attention, and Cross-Attention Feature Fusion (CAFF) for enabling bidirectional interaction between convolutional and transformer representations. The model was trained and evaluated on 2195 OCT B-scan images obtained from 397 patients. Results: DenseViT-OCT achieved an overall accuracy of 94.76% on the internal four-class test set, outperforming 19 benchmark models, including ConvNeXt, EfficientNet, ViT, and Swin Transformers. The model demonstrated balanced performance with a macro-averaged precision of 93.76%, recall of 93.22%, F1-score of 93.47%, Cohen’s kappa of 92.62%, and macro-Area Under the Curve (AUC) of 98.95%. Ablation experiments confirmed the contribution of the proposed MDFA, AFC, CAFF, and deep supervision components, with the full model consistently outperforming reduced variants and standalone DenseNet121 and ViT-B/16 backbones. In repeated experiments across five random seeds, DenseViT-OCT also achieved the best mean accuracy (0.9399 ± 0.0052). External validation on the public multicenter OCTDL dataset, performed as binary ERM-versus-normal classification because of label availability, yielded 90.76% accuracy and 97.61% AUC, indicating promising generalization beyond the development cohort. Conclusions: DenseViT-OCT provides a robust framework for automated ERM severity classification from OCT B-scans. The combination of local CNN features, global transformer context, and dedicated fusion modules improves classification performance and yields clinically meaningful error patterns. Although further stage-wise multicenter validation, volumetric OCT analysis, and prospective clinical assessment are required, the proposed method shows promise as a research-oriented decision-support framework for B-scan-level ERM assessment. Full article

The basic renovation of old urban communities is an important livelihood project for urban renewal, but there are many problems in the decision-making of renovation schemes, such as strong dependence on experience, lack of quantitative basis for multi-objective trade-off, and difficulty in describing residents’ risk attitude. Combining Case-Based Reasoning (CBR) and utility theory, this paper constructs a set of intelligent decision support models driven by data and knowledge. First of all, through literature analysis and expert investigation, a decision-making index system is established, which includes four dimensions and 16 quantitative indicators: policy and financial support, residential conditions and needs, residents’ consensus and social coordination, and implementation management and long-term maintenance. Secondly, the framework representation method is used to describe the reconstruction case, a hybrid retrieval strategy combining inductive retrieval and nearest-neighbor retrieval is designed, and the subjective and objective data combination weights are calculated by using AHP and the entropy method. On this basis, a loss utility function and risk aversion coefficient based on accident and public opinion data (a = 0.02) are introduced to modify the similarity calculation results to describe the risk avoidance behavior of decision-makers. Through 40 real renovation projects, a case base is built, and two types of target cases, “typical inclusive” (F5) and “key renovation” (F35), are selected for empirical verification. The results show that the model can effectively retrieve similar cases, and the similarity ranking changes in line with risk aversion expectations after utility correction. Taking F5 as an example, by reusing and revising the reconstruction scheme of a similar case, targeted suggestions are generated, which give consideration to safety, economy and operability. This model provides a new quantifiable and reusable method for scientific decision-making in basic renovation of old residential areas. Full article

Engineering education is increasingly shifting toward data-driven and creativity-centred pedagogies that foster innovation, communication, ethical awareness, and teamwork. However, traditional Problem-Based Learning and Design Thinking approaches rely heavily on subjective evaluation and lack scalable mechanisms for monitoring learning progression and creativity development. These pedagogical limitations highlight the need for data-driven approaches that can support iterative learning processes, continuous feedback, and objective evaluation of creativity and performance. This study proposes a Generative Artificial Intelligence (GenAI)-driven predictive analytics framework for modelling student performance and creativity in engineering design systems. The framework integrates deep learning architectures, including Long Short-Term Memory (LSTM) networks and Transformer-based multimodal fusion, to analyze temporal and heterogeneous learning data. The novel Creativity Index (CI) is introduced to quantify design innovation by combining novelty and feasibility metrics derived from AI-assisted interactions and project milestones. The model was evaluated on a longitudinal dataset comprising 450 students across 10 semesters (~5400 time-series observations). Experimental results demonstrate strong predictive performance, achieving 89% classification accuracy and RMSE of 3.8. Comparative analysis shows significant improvements in engineering design (+15%), communication (+16%), ethical awareness (+17%), and teamwork (+16%) (p < 0.01). The proposed framework enables real-time feedback, early risk detection, and adaptive learning optimization. These findings highlight the potential of integrating generative AI and predictive analytics to develop scalable, data-driven intelligent learning systems. Full article

In tennis, where performance is governed by complex kinetic chain interactions, objective skill classification is vital for coaching and talent identification. This study presents a hierarchical deep learning framework leveraging synchronized bilateral Inertial Measurement Unit (IMU) data from 39 participants (11 elite, 28 amateur). The proposed system successfully distinguishes expertise levels across a total of 4594 strokes, including augmented samples.. A hybrid Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) architecture was developed to autonomously extract spatiotemporal features from the raw kinematic signals of forehand, backhand, service, and volley strokes. The proposed model achieved an accuracy of 95.54%, significantly outperforming both traditional machine learning and state-of-the-art deep learning benchmarks. Qualitative t-distributed Stochastic Neighbor Embedding (t-SNE) analyses revealed that elite athletes form highly homogeneous clusters in the feature space. Furthermore, quantitative Asymmetry Index assessments confirmed that professionals exhibit superior bilateral coordination stability. These findings demonstrate that the proposed end-to-end system offers a robust, field-applicable solution for identifying technical excellence. It provides coaches with reliable digital biomarkers, thereby overcoming the limitations of subjective visual observation. Full article

As urban areas expand, the sustainable management of municipal water becomes a critical challenge, especially in arid and semi-arid regions facing severe water scarcity. Accurate assessment of urban plant water requirements (PWR) is essential for developing sustainable landscape architecture and resilient green infrastructure. In this study, a new quantitative equation (PWR_q) was developed as a regional proof of concept to adjust reference evapotranspiration estimates for hyper-arid conditions. A Tree Morphology Coefficient (K_tm) is introduced to combine canopy features (form, height) and leaf traits (size, density) with an updated drought-resistance coefficient (K_dr). Field measurements of 277 mature trees, representing 27 native and introduced species in Riyadh and Jeddah, Saudi Arabia, were analyzed. The framework explicitly includes an empirical multiplier to account for extreme urban heat island (UHI) effects and aerodynamic canopy scaling. Instead of direct empirical validation, the PWR_q model was benchmarked against established reference indices: Water Use Classification of Landscape Species (WUCOLS) and Simplified Landscape Irrigation Demand Estimation (SLIDE), showing strong alignment with established categorical indices and structural traits. The results confirm that the morphology-based method effectively makes previously subjective classifications objective. Notably, the quantitative assessment found that the dominant introduced species require about 3.5 times more water than native species. As a proof of concept, future research should empirically validate these findings against direct physical measurements, such as sap flow sensors or lysimeters. The proposed framework presents a practical, objective decision-support tool for municipal policymakers and landscape architects to optimize species selection, implement nature-based solutions (NBS), and achieve long-term sustainability in urban greening. Full article

Background/Objectives: Heart failure (HF) remains a major cause of global mortality and morbidity; it is, therefore, of paramount importance that diagnosis and prognostication are made timely in order to better improve outcomes and reduce healthcare expenditure. This research presents a novel predictive model of heart failure that combines clinical criteria with demographic factors in order to maximize predictive performance and act as a reliable tool for individualized healthcare intervention. Methods: Complex machine learning techniques, including decision trees, random forest, and deep learning, are applied in analyzing a large dataset of subjects with heart failure. We collected a diverse dataset comprising clinical indicators such as echocardiographic data, biomarkers, electrocardiogram (ECG) features, and demographic information. Data preprocessing techniques, such as feature normalization and handling of missing values, were applied to ensure the integrity and reliability of the dataset. Results: The results indicate that integrating both clinical indicators and demographic characteristics significantly improves the predictive power of the model, compared to models based on clinical indicators alone. Specifically, the hybrid model demonstrated a superior ability to predict short- and long-term outcomes in heart failure patients, offering enhanced accuracy in risk stratification and prognosis prediction. Conclusions: This research highlights the potential of artificial intelligence (AI) and machine learning in revolutionizing heart failure care by providing healthcare professionals with more accurate, data-driven decision support tools. The proposed model not only holds promise for clinical applications but also offers insights for future research into personalized medicine. Full article

Most artistic expressions are conveyed through images (e.g., painting) and audio (e.g., music), and deep learning has been successfully applied to neural style transfer within each of these modalities. However, there is still a lack of deep models that explicitly learn to transfer style between images and audio. Motivated by synesthesia, which reflects intrinsic connections between vision and hearing in the human brain, we propose a deep hybrid synesthesia model for audio–image style transfer. Our framework consists of two main components: (1) a component conversion module that learns cross-modal mappings between audio rhythm/spectrum and image color/shape in a continuous valence–arousal (VA) emotion space; and (2) a style conversion module that transfers high-level artistic styles between Eastern (ink-wash, shui-mo) and Western painting and their corresponding musical counterparts. We first learn emotion-aware feature networks that align low-level audio and visual components based on shared affective representations, and then model long-term stylistic structures for cross-modal style transfer. Experiments include “seeing the sound” (audio-to-image generation with controllable components) and full audio–image style transformations. Both objective analyses and subjective evaluations suggest that our model can produce cross-modal artworks whose perceived style and emotional content are consistent with human synesthetic impressions. Full article

Background/Objectives: Weight loss and hypermetabolism are negative prognostic factors in amyotrophic lateral sclerosis (ALS). Ketone bodies (β-hydroxybutyrate, βHB) as high-energy substrates may compensate for this energy deficit, since a ketogenic diet (KD) has been shown to increase survival and stabilize body weight in the SOD1 mouse model. In this case series, we tested exogenous ketone salts (KS), ketone esters (KE), and a KD, in patients with ALS and in healthy subjects to identify novel therapeutic interventions for subsequent clinical studies. Methods: KS (KetoForce^® (KetoSports, Frisco, TX, USA)) were tested in healthy subjects (11.7 g and 15.6 g βHB) and patients (15.6 g βHB 3×/day over 3 days). KE (KE4^® (KetoneAid, Falls Church, VA, USA)) containing 10.0 g βHB were applied in healthy subjects (once) and in patients (3×/day over 2 days). For the KD, KetoCal^® 2.5:1 LQ MCT MF Vanilla (Nutricia, Frankfurt, Germany) was applied via percutaneous endoscopic gastrostomy over four weeks. Regular capillary βHB measurements were conducted, and adverse events were recorded. Results: Between January 2021 and March 2025, we treated nine patients with ALS and two healthy subjects at the Department of Neurology of Ulm University, Germany. KE and KS increased βHB temporarily. However, the elevation was more pronounced following KE (maximum 2.2–2.7 mmol/L vs. 0.8–1.2 mmol/L). The KD increased βHB levels continuously with nighttime fluctuations. No adverse events occurred under KE. KS caused diarrhea in 3/5 patients and 1/2 healthy subjects. The KD was well tolerated, with mild gastrointestinal symptoms occurring in all patients. Conclusions: All ketogenic approaches increased βHB blood levels. While the KD and KE provided good tolerability, KS caused significant gastrointestinal side effects. KD seems to be an interesting candidate for future clinical studies, as it prompted a long-term increase in βHB while providing satisfying tolerability. Since maintaining a KD long-term is difficult for oral-feeding patients, KE may constitute a feasible alternative. Full article

Background/Objectives: The present study aims to test three different types of PMMA (Fotodent Guide—3D printed (M1), Aidite Temp—milled (M2), Duracryl—self-polymerized (M3) on HFIB-G and on OECM-1. Methods: The two cell types (HFIB-G and OECM-1) were kept in contact with the materials, Fotodent Guide, Aidite Temp, and Duracryl (n = 6), for 24 and 48 h, and subsequently subjected to the following tests: MTT, LDH, NO (according to ISO 10993-5:2009), and immunofluorescent detection of proteins associated with autophagy and apoptosis (mitochondria and caspases 3/7; detection of autophagosomes). Statistical interpretation was made using t-test and ANOVA (* p < 0.05; ** p < 0.01; *** p < 0.001). Results: The MTT assay revealed a reduction in cell viability for all tested materials on gingival fibroblasts compared to control cells, with the most pronounced decrease observed for the 3D-printed material (M1 viability 66.77% for 24 and 52.45% 48 h—p < 0.001), while the self-polymerizing resin (M3 viability 85.92% for 24 h and 85.63% for 48 h) showed the highest level of cellular tolerance (p < 0.001 at 24 h and p < 0.01 at 48 h). Regarding OECM-1 cells, all materials reduced cell viability, particularly M3 after 48 h of incubation (viability 61.79%—p < 0.001). LDH levels generally indicated low membrane damage for all materials. Statistically significant increases in NO levels were recorded for both cell types, suggesting a mild proinflammatory response, especially for M2 OECM-1 48 h—p < 0.05 and M3 (HFIB-G 48 h—p < 0.05, OECM-1 48 h p < 0.05). For both 24 and 48 h, fluorescence analysis demonstrated a significant increase in mitochondrial activity in gingival fibroblasts (p < 0.001), whereas tumor cells exhibited a significantly decreased mitochondrial activity (p < 0.001), particularly for the 3D-printed material M1 (p < 0.001). Caspase-3/7 expression increased in gingival fibroblasts incubated with materials for 24 and 48 h (p < 0.001), while tumor cells showed reduced caspase activity both after 24 and 48 h (p < 0.001). Autophagosome formation decreased initially in fibroblasts at 24 h (p < 0.001) but increased significantly after 48 h (p < 0.001), while tumor cells generally showed enhanced autophagic activity under most experimental conditions (p < 0.001). Conclusions: Our results suggest that all three PMMA-based materials exhibit acceptable biocompatibility profiles, of more than 70%, according to ISO 10993-5:2009, although cellular responses vary depending on the manufacturing technique and the cellular model used. In our study conditions, self-polymerized resin (M3) was the most compatible with gingival fibroblasts, while the 3D-printed and CAD/CAM milled materials (M1 and M2) had a more pronounced impact on cells’ viability and metabolic activity. Full article

Underwater image enhancement is essential for marine visual perception tasks. However, the highly heterogeneous optical degradations in real-world waters, the scarcity of paired training data, and the inherent dilemma for existing models in balancing long-range dependency modeling with computational overhead pose significant challenges. To address these issues, this paper proposes a prior-guided, self-supervised underwater image enhancement framework called Scene-Aware Retinex Mamba (SARM). This framework seamlessly integrates Retinex theoretical priors with state space models (SSMs) and operates without paired supervision by employing a prior-guided pseudo-labeling strategy to guide network optimization. Architecturally, SARM deeply couples the physical Retinex prior with SSM. Its core module integrates multi-color space features and leverages a 2D selective scan mechanism to achieve global context modeling with linear complexity $\mathcal{O}\left(HW\right)$, effectively removing complex color casts and suppressing non-uniform scattering noise. To further overcome the generalization bottlenecks in cross-domain underwater testing, this paper introduces a Scene-Aware Adapter (SAA), which facilitates dynamic loss scheduling and adaptive feature gating by quantifying scene-specific degradation characteristics. Comprehensive evaluations on multiple benchmark datasets, including UIEB, EUVP, and UCCS, demonstrate that SARM achieves state-of-the-art subjective and objective enhancement quality (e.g., yielding a URanker score of 2.491 and a CCF score of 35.76), while maintaining an ultra-fast inference speed of 136.52 FPS on the UIEB dataset. Furthermore, extended experiments reveal that SARM can significantly boost the performance of downstream vision tasks, validating its potential as a robust preprocessing module for various practical marine vision applications. Full article