Search Results (10,944)

Global population aging has emerged as a major driver of the growing burden of neurological diseases, highlighting the urgent demand for advances in early diagnosis, prevention, and rehabilitation. These conditions are typically characterized by insidious onset and irreversible progression, yet their clinical management remains critically compromised by substantial diagnostic delays, representing an intractable bottleneck for existing detection technologies. Therefore, the development of precise, early-stage detection technologies is crucial for expanding the therapeutic window and improving long-term clinical outcomes, addressing a critical unmet clinical need. Herein, we review and compare precision detection strategies for neurological diseases, focusing on the types and mechanisms of mainstream biosensing platforms. Based on the classification of detection substrates and signal transduction mechanisms, four major bio-detection branches are analyzed, including liquid, exosomal, imaging, and digital biomarker detection, with representative studies demonstrating detection limits reaching femtomolar concentrations, clinical diagnostic sensitivities exceeding 90%, and classification accuracies comparable to or surpassing conventional imaging modalities. The inherent advantages and limitations of each biosensing technology are also comprehensively discussed. This review underscores that future research on neurological biomarker sensing is trending toward multimodal integration, which enables the construction of more robust early warning and prognostic assessment systems. This work aims to provide valuable theoretical insights for clinical translation of relevant sensing technologies and integrated diagnostic and treatment strategies, thereby facilitating the progress of early intervention and rehabilitation for common neurological diseases. Full article

Digital rural construction is a key component of China’s agricultural and rural transformation, as well as a means to improve rural productivity and strengthen the coordinated development of urban and rural areas in the Yangtze River Delta, which requires the backing of an efficient policy framework. Based on this, the research object is the 145 digital village policies issued by the Yangtze River Delta, and 16 of them are selected for evaluation after content mining. The findings are as follows: (1) Sixteen policies have an average PMC index of 8.42; four policies are excellent, 12 are good, and the quality of policies is generally good. (2) At the administrative level, there is a characteristic that the quality of provincial policies is superior to that of municipal policies. Among regions, the policy quality advantages represented by Zhejiang are obvious, presenting a pattern of “Zhejiang leading, Jiangsu steady, Anhui catching up, and Shanghai waiting for improvement”. (3) Except for the “policy function”, although the absolute scores of some indicators (such as policy field, policy content, policy evaluation) are at a high level, there is still a significant gap compared to the outstanding performance of the policy function (0.98). Moreover, from the perspective of the requirement for comprehensive and coordinated development of policies, the attention and investment in these dimensions are slightly insufficient, resulting in policies not fully exerting their expected comprehensive effectiveness, which is the main reason restricting the overall quality of policies. (4) It is recommended to increase the regulatory and advisory nature of the policy; Expand the scope of policy audiences; Take into account the forward-looking and long-term nature of policy formulation; Refine the execution plan to ensure the implementation of policies; The content of the construction of fiscal taxation, laws and regulations will be increased to provide a scientific basis for the rural transformation; Encourage local exploration and innovation, combine with local conditions, and form replicable and promotable development models. Full article

Background/Objectives: This study aimed to apply morphometric and multivariate analytical techniques to footprint evidence for forensic identity determination, focusing on sex estimation and the reconstruction of body proportions as components of the biological profile. By integrating detailed footprint metrics with body measurements, the research sought to develop discriminant and regression models to evaluate the predictive value of footprint metrics for sex estimation and selected somatic dimensions. Methods: Static bilateral footprints were obtained using charcoal powder impressions and digitized using ImageJ. Eleven footprint parameters (F1–F11) and eleven body measurements (B1–B11) were recorded. Sex-based differences were examined using appropriate parametric or non-parametric tests with effect sizes. Sex estimation was evaluated using discriminant function analysis and internally validated using leave-one-out and stratified 10-fold cross-validation. Regression models for stature and body dimension estimation were assessed with multicollinearity diagnostics and repeated 10-fold cross-validation, including RMSE, MAE, and cross-validated R². Results: The apparent discriminant classification accuracies were 74.0% for the right foot and 71.0% for the left foot. After internal validation, classification performance decreased to approximately 64–67%, indicating moderate discriminative ability. Reduced regression models showed the most stable validated performance for stature and arm span, although cross-validated R² values remained weak. Conclusions: Static footprint morphometry may provide supportive information for sex estimation and selected somatic dimensions in this Turkish adult sample. However, the validated performance indicates that these models should be interpreted as ancillary and exploratory tools rather than standalone forensic identification methods. Full article

Supply chain digitalization has increased the need for reliable systems that support transparency, traceability, trust, and structured interorganizational information exchange. Electronic Data Interchange (EDI) remains widely used for standardized business transactions, while blockchain offers decentralized verification, data immutability, and stronger data governance. However, blockchain–EDI integration depends not only on technical compatibility but also on organizational capacity, partner alignment, financial resources, and regulatory preparedness. Existing studies mainly examine blockchain benefits and adoption barriers, with limited attention to readiness assessment before implementation. This study develops an analytical framework for evaluating organizational preparedness for blockchain–EDI integration in supply chains. Five readiness dimensions are identified from the literature: technological, organizational, partner, financial, and regulatory readiness. These dimensions are measured using a 0–5 scoring system, combined into a weighted readiness score, and linked to a logistic function that estimates integration success under different complexity levels. Deterministic simulation, Monte Carlo simulation, and sensitivity analysis are used to examine the model. The results show a nonlinear readiness-success relationship under assumed parameter values: low readiness is associated with limited estimated success, medium readiness forms a transition zone, and high readiness supports more stable estimated outcomes. The framework is positioned as a methodological readiness assessment model rather than an empirically validated predictive system. It provides a basis for future empirical calibration, pilot testing, and validation using organizational implementation data. Full article

The roughness coefficient is a vital parameter in river dynamics calculations, and its accuracy is crucial for simulating water flow. Various factors contribute to channel roughness, and the underlying mechanisms are quite complex. There is a strong spatiotemporal correlation, which complicates the calculations, particularly when hydrological data is lacking or insufficient. In this study, we solved the two-dimensional shallow-water equations using the Population Simplex Evolution (PSE) with the Finite Volume Method (FVM). This approach allowed us to obtain samples for calibrating channel roughness coefficients. To enhance the analysis, we introduced a Convolutional Neural Network (CNN) to reduce the dimensionality of input parameters and extract the temporal characteristics of the flow series. Notably, we integrated a Transformer to capture the spatial characteristics of the time series. By combining the PSE-FVM with the CNN-Transformer, we effectively calibrated the roughness coefficients. Our findings indicated that the integrated PSE-FVM and CNN-Transformer model achieved high accuracy and efficiency in this calibration process. Specifically, the cross-correlation coefficients exceeded 0.90 for calibration results from September to December 2020. We recorded an average absolute deviation of 7 cm between the calculated and measured maximum water levels, and the average calibration runtime ratio was approximately 0.19% when comparing the CNN-Transformer to the PSE-FVM. Importantly, this approach could be used for rivers with incomplete hydrological data. Our work highlighted spatiotemporal correlations between roughness coefficients and their influencing factors, thereby facilitating the integration of river dynamics models with intelligent algorithms. Therefore, these findings may serve as a valuable reference for river numerical analysis, flood impact assessment, and the development of digital twins and information systems for water-related engineering projects. Full article

Background: Cardiovascular diseases (CVDs) are the leading cause of death in Saudi Arabia, and public knowledge of risk factors and warning signs supports early detection and prevention. This study aimed to assess CVD knowledge and its demographic predictors among adults in the Jazan region. Methods: A cross-sectional study was conducted among 382 adults (≥18 years) between February and April 2025. A questionnaire adapted from prior validated instruments assessed CVD awareness, knowledge of 11 risk factors and 10 warning signs, perceptions, and practices. Total knowledge scores (0–21) were dichotomized as adequate (≥8) versus inadequate (<8). Mann–Whitney U and Kruskal–Wallis tests were used for bivariate analysis, followed by binary logistic regression. Results: Most participants (89.5%) had heard of CVD, yet 53.7% had inadequate knowledge, and only 9.9% demonstrated good knowledge (≥15). The median total knowledge score was 7 (IQR 2–11) out of 21, with warning-sign knowledge (2.96/10) lower than risk-factor knowledge (3.95/11). Overweight/obesity (52.6%), hypertension (51.3%), and smoking (49.5%) were the most recognized risk factors; chest pain (47.6%) and shortness of breath (46.1%) were the most recognized warning signs. University education (aOR = 2.44, 95% CI 1.23–4.85, p = 0.011) and family history of chronic disease (aOR = 2.26, 95% CI 1.32–3.85, p = 0.003) were the only independent predictors of adequate knowledge. Conclusions: More than half of the surveyed adults in the Jazan region had inadequate CVD knowledge despite high general awareness. These findings suggest that targeted education using digital platforms and primary care providers may help improve knowledge of risk factors and warning signs in the region. Full article

Digital terrain models (DTMs) are essential elevation datasets that represent the morphology of the Earth’s surface and play a critical role in applications, such as urban planning, civil engineering, infrastructure design, and environmental assessment. However, the excessive cost remains the major challenge in obtaining accurate terrain models. Recent advancements in low-cost inertial navigation and motion-sensing technologies offer significant potential to enhance the cost-effectiveness of surveying projects. This study investigates the vertical accuracy and operational usability of a handheld inertial measurement unit (IMU) device (Moasure 2) for DTM generation in urban environments through the comparison with traditional total station and digital levels procedures. It also assesses the device compliance with The American Society for Photogrammetry and Remote Sensing (ASPRS) Positional Accuracy Standards. For this purpose, a comprehensive field survey was conducted in a small urban area characterized by varied terrain morphology. The vertical accuracy of the Moasure 2 was acceptable for many urban mapping applications based on a rigorous analysis of checkpoint data and error patterns, which were quantitatively assessed relative to reference surfaces. Profile-based validation showed that the elevation differences between similar terrain types were mainly within ±25 cm, with minimal bias and symmetric error distributions. The findings indicate that Moasure 2 can be a viable alternative tool for fast DTM generation in low-cost urban projects. It offers significant advantages in terms of portability, ease of use, and reduced fieldwork time compared to conventional methodologies. Furthermore, this study addresses the critical gap in the validation of the new IMU-based surveying technology and provides evidence for choosing appropriate equipment for urban terrain modeling. Full article

The large-scale connection of distributed energy resources, electric vehicles, and flexible loads, together with expanding low-voltage monitoring and edge sensing, is turning distribution networks into active cyber-physical systems. Conventional offline simulation cannot fully support the online state tracking, short-term scenario analysis, operational risk assessment, and closed-loop decision support now expected in network operation. Digital twins offer a way to address this gap by linking network models to operational data and revising those models as system conditions change. After systematically searching Scopus and the Web of Science, six application areas for digital twin applications in distribution network simulations are summarized: model construction, simulation and validation platforms; asset, equipment and spatial digitalization; DER (distributed energy resource), PV, EV (electric vehicle) and prosumer integration; operation, monitoring and situational awareness; protection, fault diagnosis and resilience; and optimization, control and planning. The review examines the architectures, enabling technologies, and applications reported across this evidence base. The literature indicates a gradual shift from conceptual digital representations toward real-time simulation, hardware-in-the-loop validation, data-driven model updating, and distribution-side decision support. Persistent gaps concern low-voltage observability, data governance, model credibility assessment, standardized interfaces, cybersecurity, and closed-loop control. Full article

Clinical prediction rules often suffer from a translation gap, balancing high-dimensional statistical accuracy against practical bedside interpretability. This study presents the Surgical Complication Risk Index (S-CRI), an explainable, data-decoupled risk-stratification framework designed to predict post-operative complications using multi-center electronic health registry records (N = 19,965). To ensure strict validation integrity, data partitioning (70% development, n = 13,975; 30% independent holdout testing, n = 5990) was executed before any engineering or risk-tier group isolation. A parsimonious multivariate logistic regression model was fitted within the development cohort, utilizing five predictors: length of stay (LOS) accrued up to the morning of assessment, two institutional categorical groupings, and two historical entry-diagnosis empirical risk tiers. To bridge the translational gap, all fractional regression coefficients were scaled by the baseline anchor and rounded to the nearest whole integer, yielding a simple bedside scorecard where 1 point = 1 inpatient day. On the completely blinded independent holdout cohort, the whole-integer S-CRI demonstrated robust discriminative performance with an Area Under the Receiver Operating Characteristic curve (AUC) of 0.8741 (95% CI: 0.864–0.884) and a Precision–Recall AUC of 0.5785. Setting a baseline operational threshold ≥ 0 yielded an accuracy of 88.18%, a specificity of 96.43%, and a sensitivity of 35.43%, while an optimized integer screening cutoff score of ≥−4 maximized screening capacity (sensitivity: 63.95%; specificity: 91.68%). By enforcing strict temporal landmark constraints to eliminate reverse causality and removing all out-of-sample data leakage, the S-CRI provides an objective, transparent, and interpretable clinical decision support mechanism for early inpatient risk stratification, designed as a supplementary clinical decision-support aid, rather than as a definitive diagnostic replacement for independent clinical judgment. Full article

Background/Objectives: To develop and evaluate a deep learning-based quality control system for Lumbar Spinal Digital Radiographs (LSDR), designed to automate and improve their evaluation and reduce reliance on manual reviews. Methods: This retrospective study utilized a deep learning workflow comprising image segmentation, feature extraction, and a classification model. The dataset, including anteroposterior (AP) and lateral (LAT) X-ray images, was expanded through data augmentation techniques. Four U-Net-based models were assessed: standard U-Net, Swin-UNet, Attention U-Net, and Attention U-Net with the weight map, with the latter selected for its superior performance. Extracted features, such as brightness, contrast, and anatomical positioning, were used in an XGBoost classifier, which was evaluated using mean intersection over union (mIoU), accuracy, sensitivity, specificity, and AUC. Results: The Attention U-Net with weighted attention outperformed the other models, achieving high mIoU scores in both AP and LAT views. The XGBoost classifier achieved the best performance in classifying images as “qualified” or “unqualified,” with an AUC of approximately 0.9, high accuracy, and balanced sensitivity and specificity. This approach effectively addressed class imbalances and improved model accuracy compared to traditional machine learning models such as MLP and SVM. Conclusions: The developed automated quality control system demonstrated potential for enhancing image quality, enhancing diagnostic reliability, and optimizing clinical workflow efficiency. Full article

This paper combines the application of AI technology in the field of architectural design to construct a “three-stage model” (imitation, exploration, and creation) centered on cultivating students’ creative thinking and innovative ability, with the goals of AI literacy cultivation, digital twin practice, and interdisciplinary collaboration. By integrating the theoretical model with the latest practical cases, the effectiveness of the new generation of AI-driven innovative teaching modes is verified. Taking library architectural design and old building renovation teaching as examples, the teaching process and evaluation system with real-time feedback, intelligent assessment, and full-process traceability are designed to achieve the dual improvement of teaching efficiency and students’ practical innovation ability. The research shows that the characteristics of artificial intelligence, including multimodal generation, immersive interaction, and full-cycle simulation, are reconstructing the core logic of architectural design education, promoting the in-depth transformation of the teaching mode from “imitation” to “creation”, building a talent cultivation system adapted to the future development of the construction industry, and providing a feasible reference path for the innovation of education modes. Full article

Background and Objectives: The fourth heart sound (S4) is a recognized marker of left ventricular (LV) diastolic dysfunction, suggesting a potential risk of congestive heart failure (CHF). However, low-frequency sounds are often audible during the S4-corresponding phase in the general population without symptoms or a history of CHF. The present retrospective cross-sectional exploratory study examined the acoustic features of the S4 phase sound in patients with stage A or B heart failure in association with echocardiographic markers. Materials and Methods: Sixty asymptomatic male patients underwent simultaneous phonocardiographic and electrocardiographic recordings using Cardio-EGG (AMI Inc.). The amplitude of acoustic signals during the S4 phase was quantified across ten frequency bands using a continuous wavelet transform. Echocardiographic parameters of LV diastolic function were also assessed. Results: Several lower-frequency bands demonstrated significant correlations with LV diastolic indices. After adjustment for age, systolic blood pressure, HbA1c, interventricular septal thickness, and coronary artery disease, strong associations were observed at the fourth left sternal border signals in the [7.81, 15.63) Hz band and septal E/e′ (r = 0.41, p = 0.0012). The [15.63, 31.25) Hz band was associated with septal E/e′, septal and lateral e′ velocities, and the E/A ratio. This frequency range at the apex also correlated with A-wave velocity (p = 0.0011). Conclusions: Specific low-frequency acoustic components observed during the S4 phase are closely associated with echocardiographic markers of left ventricular diastolic function in asymptomatic individuals. These characteristics resemble those of the standard S4, indicating that digital phonocardiography could facilitate the identification of early cardiac dysfunction before the onset of heart failure. Full article

Digital dentistry increasingly relies on artificial intelligence (AI) to automate restorative design. However, the ability of generative networks to produce multiple geometrically distinct outputs for the same prosthetic field remains insufficiently evaluated. This study assessed repeated-output geometric variability in a previously developed three-dimensional generative adversarial network (3DGAN) for screw-retained implant crown design as a preliminary indicator of potential generative diversity. Nine AI-generated implant crown designs were analyzed, consisting of three independently generated crowns for each of three different prosthetic fields. Within each set, the crowns were superimposed and compared using “MeshLab”. Mean Hausdorff distance (HD), maximum HD, and root mean square (RMS) values were recorded, with 0.05 model units used as the threshold for identifying insufficient morphological variation. The overall mean HD was 3.32 model units, the mean maximum HD was 16.18 model units, and the mean RMS value was 4.40 model units. No pairwise comparison showed values equal to or below 0.05 model units. In conclusion, the investigated 3DGAN demonstrated preliminary evidence of geometric output variability compatible with potential generative diversity. Full article

Canine cutaneous neoplasms are common and morphologically heterogeneous lesions whose diagnosis relies on integrating gross examination, cytology, and histopathology. This retrospective pilot study assessed the feasibility of a multimodal GPT-based large language model as an assistive, not autonomous, tool for standardized description, differential diagnosis generation, and classification support across this diagnostic workflow. Fifty-one histologically confirmed canine cutaneous tumors were retrospectively selected from the laboratory information system of the Veterinary Pathology Laboratory, University of Naples Federico II. For each case, de-identified gross photographs, digitized cytology, and representative histologic images were provided to the model using templated prompts. Model outputs were independently reviewed by two veterinary pathologists, who reached consensus on descriptive quality and diagnostic concordance with the histologic reference diagnosis. Final diagnostic outputs were classified as correct, partially correct, or incorrect. Strict accuracy was defined as the proportion of fully correct diagnoses, whereas broad accuracy combined correct and partially correct outputs considered diagnostically informative. Overall, the model achieved a strict diagnostic accuracy of 66.7% (34/51; 95% CI: 53.0–78.0) and a broad diagnostic accuracy of 90.2% (46/51; 95% CI: 79.0–95.7). Performance was highest in epithelial tumors and lower in mesenchymal and melanocytic tumors, in which the model more often identified broader diagnostic categories than specific histotypes. These findings suggest that GPT-based systems may support report standardization, descriptive consistency, and morphology-driven reasoning in veterinary pathology. However, reduced entity-level specificity, variable descriptive quality, and the risk of plausible but non-concordant outputs require strict human supervision and further validation before routine implementation. Full article

Autonomous Haulage Systems (AHS) have significantly transformed surface mining operations by improving safety, productivity, and operational consistency. Currently, AHS predominantly rely on vehicle-centric perception architectures. Onboard LiDAR, radar, cameras, and Global Navigation Satellite Systems (GNSS) perform sensing, interpretation, and decision-making within individual systems. These processes enable collision avoidance and path tracking. However, they are limited in their ability to consider the broader, dynamic mining environment characterized by dust, terrain degradation, geotechnical instability, heterogeneous traffic, and rapidly evolving operational conditions. This paper presents a systematic review of dynamic vision systems of AHS in surface mining. It critically analyzes the transition from autonomy to interconnected, ecosystem-aware intelligence. The review synthesizes literature from mining automation, robotics, intelligent transportation systems, and multi-agent perception. It assesses sensing technologies, perception algorithms, sensor fusion strategies, and environmental robustness techniques. Attention is focused on the limitations of egocentric perception models in complex surface mining ecosystems. Building on identified gaps, the paper proposes a conceptual framework for Ecosystem-Centric Dynamic Vision (ECDV). Perception is enhanced through integration with fleet communication networks, dispatch systems, digital twins, geotechnical monitoring platforms, and environmental sensing infrastructure. The framework outlines a multi-layer architecture enabling cooperative perception, predictive hazard modeling, and risk-aware decision support at the mine-wide level. The review concludes by outlining a research agenda to transition from vehicle autonomy to ecosystem intelligence in surface mining. It highlights opportunities in cooperative perception, adaptive sensor fusion under degraded visibility, and digital-twin-integrated predictive safety systems. Full article