Search Results (19,802)

Background: The aim of this study was to examine the relationship between anxiety levels, emotional intelligence, and stress coping strategies in individuals diagnosed with personality disorders. According to Lazarus and Folkman’s transactional model of stress, the appraisal of stressors and available psychological resources determines the selection of coping strategies—whether adaptive or maladaptive. Material and Methods: This observational case series study involved 30 individuals diagnosed with personality disorders (ICD-10 codes F60 and F61). Psychological assessments were conducted at two time points: upon admission to a day-care psychiatric unit and after three months of standard therapeutic intervention. The following standardized instruments were administered: the State-Trait Anxiety Inventory (STAI), the Emotional Intelligence Questionnaire (INTE), and the Mini-COPE Inventory for Coping with Stress. Results: Elevated levels of anxiety—particularly trait anxiety—were significantly associated with maladaptive coping strategies, including denial and self-blame. Conversely, higher emotional intelligence was positively correlated with the use of adaptive coping mechanisms, such as planning and proactive problem-solving. Conclusions: The findings support the hypothesis that both anxiety and emotional intelligence are significant predictors of stress coping styles in individuals with personality disorders. The results underscore the importance of considering these psychological variables in the design and implementation of therapeutic programs. Enhancing emotional intelligence may substantially improve treatment outcomes and overall psychological functioning in this clinical population. However, further studies with larger sample sizes are needed. Full article

Background: The aim of this study was to develop an artificial intelligence (AI)-based decision support model for evaluating the extraction indication of permanent first molars in pediatric patients using panoramic radiographs, and to investigate the potential contribution of this model to the clinical decision-making process. Methods: This retrospective observational study analyzed 1000 panoramic radiographs obtained from children aged 8–10 years who attended the Clinics of Batman University Faculty of Dentistry for routine dental examination. Among the radiographs meeting the inclusion criteria, a total of 176 panoramic images were selected based on dental maturation assessment using the Demirjian tooth development staging system. Cases in which the permanent second molar was classified as Demirjian stages E–F were labeled as “extraction indication present”, while the remaining stages were labeled as “extraction indication absent”. A balanced dataset was created, consisting of 88 cases in each group. Image features were extracted using Gabor filters and Histogram of Oriented Gradients (HOG). The selected features were analyzed using a Support Vector Machine (SVM) classifier with a radial basis function (RBF) kernel. Model performance was evaluated using accuracy, sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve (ROC–AUC). Results: The proposed Gabor–HOG–SVM-based AI model achieved an overall classification accuracy of 77.78% with an AUC value of 0.77 in distinguishing between “extraction indication present” and “extraction indication absent” cases. For the extraction-indicated group, the sensitivity was 0.81 and the F1-score was 0.79, whereas for the non-indicated group, the sensitivity and F1-score were 0.74 and 0.77, respectively. No statistically significant differences were observed between the groups in terms of age or sex distribution (p > 0.05). Conclusions: This study demonstrates that artificial intelligence-based analysis of panoramic radiographic images can provide an objective and reproducible decision support approach for evaluating extraction indications of permanent first molars in pediatric patients. The proposed model should be considered as an adjunctive tool to reduce observer-dependent variability rather than a replacement for clinical judgment, and its clinical applicability should be further validated through multicenter and multi-parametric studies. Full article

In the internal force analysis of plane frames, traditional mechanics solutions require the cumbersome derivation of equations and complex numerical calculations, a process that is both time-consuming and error-prone. While general-purpose Finite Element Analysis (FEA) software offers rapid and precise calculations, it is limited by tedious modeling pre-processing and a steep learning curve, making it difficult to meet the demand for rapid and intelligent solutions. To address these challenges, this paper proposes a deep learning-based automatic solution method for plane frames, enabling the extraction of structural information from printed plane structural schematics and automatically completing the internal force analysis and visualization. First, images of printed plane frame schematics are captured using a smartphone, followed by image pre-processing steps such as rectification and enhancement. Second, the YOLOv8 algorithm is utilized to detect and recognize the plane frame, obtaining structural information including node coordinates, load parameters, and boundary constraints. Finally, the extracted data is input into a static analysis program based on the Matrix Displacement Method to calculate the internal forces of nodes and elements, and to generate the internal force diagrams of the frame. This workflow was validated using structural mechanics problem sets and the analysis of a double-span portal frame structure. Experimental results demonstrate that the detection accuracy of structural primitives reached 99.1%, and the overall solution accuracy of mechanical problems in the final test set exceeded 90%, providing a more convenient and efficient computational method for the analysis of plane frames. Full article

With the rapid advancement of Artificial Intelligence (AI) technologies, Machine Learning (ML) and Deep Learning (DL) have become pivotal methods for driving the digital documentation, restoration, preservation, and preventive conservation of Cultural Heritage (CH). This paper constructs an integrated “data + technology + task” framework tailored for CH scenarios. It employs a combination of bibliometric analysis and systematic content study based on relevant literature published between 2011 and 2025. First, publication trends, sources of publication, global collaboration networks, and topic modeling reveal the overall landscape and evolutionary path of research on the digitization and intelligent transformation of CH. Subsequently, beginning with ML and DL systems, it summarizes classic workflows and outlines their applications in CH conservation. Concurrently, integrating topic modeling, existing research is categorized into three themes based on task attributes: Recognition, Reconstruction and Virtual Restoration, and Monitoring and Prediction. Representative literature, typical tasks, and technological trends within each theme are systematically outlined. Distinct from existing reviews, this study introduces a unified data technology task framework that explicitly links AI model paradigms to heritage-specific constraints. Moving forward, by constructing high-quality heritage datasets, enhancing model interpretability, and exploring cross-model fusion approaches, AI technologies hold promise to play a more reliable and sustainable role in CH conservation, risk management, and digital dissemination. Full article

Background: Artificial intelligence (AI) is increasingly integrated into healthcare and public services, making user acceptance a key prerequisite for safe and effective implementation. The Artificially Intelligent Device Use Acceptance (AIDUA) model provides a multidimensional framework for evaluating acceptance of intelligent systems, yet no validated Italian instrument is currently available. Objectives: This study aimed to translate, culturally adapt, and preliminarily validate the Italian version of the AIDUA scale (AIDUA-IT) following COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) and Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) recommendations. Methods: A two-phase cross-sectional design was used. Phase one included forward–backward translation, expert review (n = 7), and cognitive debriefing (n = 8). Phase two assessed structural validity, internal consistency, convergent and discriminant validity, and short-term test–retest reliability in a convenience sample of Italian-speaking adults (N = 140), with a subsample completing the test–retest assessment (n = 32). Results: The hypothesized eight-factor measurement model demonstrated excellent fit (Comparative Fit Index [CFI] = 0.984; Tucker–Lewis Index [TLI] = 0.981; Root Mean Square Error of Approximation [RMSEA] = 0.041; Standardized Root Mean Square Residual [SRMR] = 0.056), with strong standardized loadings (β range: 0.64–0.96) and good internal consistency (Cronbach’s α and McDonald’s ω range: 0.82–0.90). Convergent and discriminant validity were supported, and test–retest reliability was good to excellent across subscales (Intraclass Correlation Coefficient [ICC] range: 0.81–0.90). Conclusions: These findings provide initial evidence that the AIDUA-IT is a reliable and valid instrument for assessing acceptance of AI-enabled services in Italy. Further validation in larger and more diverse samples is recommended. Full article

We propose a novel Expert-Driven Conditional Auxiliary Classifier Generative Adversarial Network (AC-GAN) framework tailored for heterogeneous multi-modal federated learning at edge AI devices such as the NVIDIA Jetson Orin Nano. Unlike prior works that assume idealized distributions or rely on centralized data, our approach jointly addresses statistical non-IID data, model heterogeneity, privacy protection, and resource constraints through an expert-guided training pipeline and hierarchical model updates. Specifically, we introduce a collaborative synthesis and aggregation mechanism where local experts guide conditional data generation, enabling realistic data augmentation on resource-constrained edge nodes and enhancing global model generalization without sharing raw data. Through hierarchical updates between client and server levels, our method mitigates bias from skewed local distributions and significantly reduces communication overhead compared to classical federated averaging baselines. We demonstrate that while “perfect precision” is theoretically unattainable under non-IID and real-world conditions, our framework achieves substantially improved precision and false positive trade-offs (e.g., precision 0.89) relative to benchmarks, validating robustness in practical multi-modal settings. Extensive experiments across synthetic and real datasets show that the proposed AC-GAN approach consistently outperforms federated baselines in accuracy, convergence stability, and privacy preservation. Our results suggest that expert-guided conditional generative modeling is a promising direction for scalable, privacy-aware edge intelligence. Full article

Background: Periapical pathosis in periapical radiographs must be properly diagnosed for the success of endodontic treatment but is often muddled by 2D imaging limitations and subjective interpretation. Artificial intelligence (AI) offers a solution, but whether the diagnostic granularity of AI versus human clinicians in everyday clinical practice has been adequately explored remains to be addressed. The purpose of this study was to evaluate the diagnostic accuracy of ChatGPT-5 in detecting periapical radiographic abnormalities compared with the three-expert consensus reference standard. Methods: In this diagnostic accuracy retrospective study, 270 periapical radiographs were independently read by a large language model (ChatGPT-5) and a three-board-certified oral radiologist consensus. The AI was given a standardized prompt to label radiographic features, like the presence of periapical radiolucency, border, shape, and integrity of lamina dura. Diagnostic accuracy, agreement (Cohen’s κ), and predictors of correct AI classification were compared with the expert consensus reference standard. Results: ChatGPT-5 demonstrated high sensitivity (87.5%) but low specificity (12.5%), resulting in an overall diagnostic accuracy of 50.0%. This performance profile reflects a tendency toward over-identification of pathology, with the model classifying 87.5% of radiographs as abnormal compared with 50.0% by expert consensus. Agreement was almost perfect for anatomical localization (arch, κ = 0.857) but poor for binary abnormality detection (κ = 0.000). For morphological descriptors, statistically significant disagreement was observed for lesion border characterization (κ = 0.127; p < 0.001), whereas lesion shape demonstrated only descriptive divergence without reaching statistical significance (κ = 0.359). Root resorption assessment also differed significantly between evaluators (p = 0.046). Regression analysis showed that well-defined corticated borders (OR = 60.25, p < 0.001) and first molar-associated lesions (OR = 32.55, p < 0.001) were significant predictors of correct AI classification. Conclusions: This study demonstrates that while ChatGPT-5 Vision can visually interpret periapical radiographs with high sensitivity, limited specificity and inconsistent morphological feature characterization restrict its reliability for independent clinical diagnosis. The AI system tends to over-diagnose systematically and categorizes lesions more structurally and defined compared to dental experts. AI has the potential for being optimized as a sensitive first-screening test, but its findings must be validated by dental professionals to avoid false positives and ensure proper characterization. Full article

As intelligent construction technology advances, new projects have become more technology-intensive, collaborative, and multi-objective. Traditional team formation methods based on people’s experience can no longer meet their complex management needs. This study reframes team formation as a multi-objective optimization problem to maximize person–job fit and team collaboration. By introducing a hierarchical penalty mechanism for structured resumes and performing semantic feature extraction on unstructured text via the BERT-base-Chinese model, we develop a job competency model, quantify person–job fit with cosine similarity, and assess team collaboration through MBTI theory and a project-specific scoring framework. An improved algorithm, CSCD-NSGA-II, is proposed, which combines K-means clustering and a modified crowding distance, to maintain solution diversity under constraints. It improves HV by 1.55% and reduces SP by 10.81% compared to the standard NSGA-II. Validation using real projects, simulated data, and algorithm comparisons demonstrates that CSCD-NSGA-II generates teams more efficiently than manual methods. Survey results indicate improved role diversity and the feasibility of collaboration, along with similar task adaptability. The algorithm also outperforms NSGA-II, MOPSO, and SPEA2, supporting intelligent team formation in modern construction. Full article

Background/Objectives: Alzheimer’s disease (AD), amyotrophic lateral sclerosis (ALS), and frontotemporal dementia (FTD) share molecular features yet differ clinically, suggesting underlying systems-level commonalities. We aimed to characterize shared and disease-specific multimorbidity architectures across AD, ALS, and FTD using an artificial intelligence–driven literature-based semantic network. Methods: We applied SemNet 2.0, constructed from over 35 million PubMed abstracts, to analyze disease and syndrome (DSYN) and pharmacological substance (PHSU) nodes. Nodes were ranked using HeteSim and mapped to a harmonized 13-category mechanistic ontology. We quantified pairwise disease intersections, ontology-level enrichment, rank similarity, and intersection–disease alignment, and constructed an integrated multimorbidity priority landscape integrating disease-specific and intersection-level hierarchies. Results: Across AD, ALS, and FTD, a convergent multimorbidity architecture centered on a shared metabolic and immune core was identified, accompanied by prominent neurobehavioral processes and intermediate systems including gastrointestinal, endocrine, hematological, hepatic, and sensory pathways. Disease-specific signatures shaped distinct vulnerability profiles within this shared structure, including cardiovascular enrichment in AD, neuromuscular and toxin-related pathways in ALS, and coupled neurobehavioral–metabolic features in FTD. PHSU patterns reinforced these findings, with centrally positioned compounds predominantly targeting inflammatory, metabolic, or neuromodulatory processes. Conclusions: These findings position AD, ALS, and FTD within a unified, AI-derived multimorbidity framework. This ontology-guided approach provides a computational, hypothesis-generating foundation for multimorbidity-aware biomarker discovery, risk stratification, and cross-disease therapeutic exploration in neurodegenerative disease. Full article

People counting and occupancy monitoring play a vital role in applications such as intelligent building management, safety control, and resource optimization in future smart cities. Conventional camera and infrared-based methods often suffer from privacy risks, lighting dependency, and limited robustness in complex indoor environments. In this paper, we present a 60 GHz millimeter-wave (mmWave) radar-based occupancy monitoring system that enables accurate and privacy-preserving people counting. The proposed system leverages echo signals processed through Doppler and range spectrogram and analyzed by an enhanced ResNet-50 deep learning model to classify motion states and count individuals. Experimental results collected in a typical indoor environment demonstrate that the system achieves 95.45% accuracy across 6 classes of movements and 98.86% accuracy for people counting (0–3 persons). The method also shows strong adaptability under limited data and robustness to Gaussian blur interference, providing an efficient and reliable solution for intelligent indoor occupancy monitoring. Full article

The deployment volume of urban surveillance cameras has reached hundreds of thousands or even millions with the advancement of intelligent transportation systems (ITSs), indicating an enormous scale. However, the number of small-field-of-view surveillance cameras in large-scale traffic areas is insufficient to achieve full coverage of urban traffic zones. In the fields of ITSs, this study proposes a traffic information-based driving route inference method to clarify target vehicles’ paths in zones with monitoring blind spots and enhance the collaborative capability between surveillance cameras and traffic networks. First, this study maps traffic roads containing monitoring blind spots and their topologies into Bayesian network (BN) structures. The influencing factors of the target vehicle path can be analyzed, extracted, and quantified by the known data in a traffic network. A weight analysis method is utilized to estimate the weight coefficients of the influencing factors on the basis of the traditional BN model, thereby realizing the driving routes based on traffic networks. This study conducted experiments in Xinbei District, Changzhou City, and Jiangsu Province, China. Experimental results verify that the proposed method can accurately infer and reconstruct driving routes with monitoring blind zones. This method can provide theoretical support for analyzing driving directions at complex traffic intersections and enabling driving route inference in traffic network areas with monitoring blind spots. Full article

Scalable video coding plays an essential role in supporting heterogeneous devices, network conditions, and application requirements in modern video streaming systems. However, most existing scalable coding approaches primarily optimize human perceptual quality and provide limited support for data privacy, as well as for machine analyses and the integration of heterogeneous sensor data. This limitation motivated the development of adaptive scalable video coding frameworks. The proposed approach is designed to serve both human viewers and automated analysis systems while ensuring high security and compression efficiency. The method adaptively encrypts selected layers during transmission to protect sensitive content without degrading decoding or analysis performance. Experimental evaluations on benchmark datasets demonstrate that the proposed framework achieves superior rate distortion efficiency and reconstruction quality, while also improving machine analysis accuracy compared to existing traditional and learning-based codes. In video surveillance scenarios, where the base layer is preserved for analysis, the proposed scalable human machine coding (SHMC) method outperforms scalable extensions of H.265/High Efficiency Video Coding (HEVC), Scalable High Efficiency Video Coding (SHVC), reducing the average bit-per-pixel (bpp) by 26.38%, 30.76%, and 60.29% at equivalent mean Average Precision (mAP), Peak Signal-to-Noise Ratio (PSNR), and Multi-Scale Structural Similarity (MS-SSIM) levels. These results confirm the effectiveness of integrating scalable video coding with intelligent encryption for secure and efficient video transmission. Full article

Temperature prediction for partially shaded photovoltaic (PV) modules is essential for ensuring the stability and safety of PV systems. However, existing methods suffer from high computational complexity, limiting their applicability in engineering practice. Aimed at a real-time and portable algorithm that can be embedded in mobile devices for intelligent monitoring of PV stations, a simple and fast method is designed in this work for estimating the thermal behavior of PV modules under partial shading conditions. To the best of our knowledge, this is the first work in this field that achieves computational simplicity without relying on professional commercial software. The experimental results validate the accuracy of the proposed method in comparison with the multiphysics model (which is widely regarded as the benchmark in this field) while significantly improving computational efficiency. Simulations are conducted to explore the effects of shading proportions and environmental conditions. Shading proportions ranging from 6% to 90% are prone to promoting the development of hotspots under conditions that involve partial shading of an individual cell. Higher irradiance, a higher ambient temperature and a lower wind speed result in a higher temperature of the PV module. Full article

Background/objectives: Emotional intelligence (EI) plays an important role in nursing education by supporting competencies such as communication, leadership, resilience, and clinical performance. In contemporary nursing education, students face increasing academic, clinical, and emotional demands, highlighting the need to identify modifiable factors that may be associated with EI and can inform student support strategies. Despite extensive EI research, evidence remains limited and inconsistent regarding how specific health-promoting lifestyle domains and sleep quality relate to EI among prelicensure nursing students. This study aimed to examine factors associated with EI and its relationship with health behaviors among prelicensure nursing students. Methods: A cross-sectional quantitative design was used. A convenience sample of 287 prelicensure nursing students from a local nursing school completed self-report questionnaires: the Schutte Self-report Emotional Intelligence Scale (SSEIS), the Health-Promoting Lifestyle Profile II (HPLP-II), and the Pittsburgh Sleep Quality Index (PSQI). Results: In structured multiphase regression, HPLP-II interpersonal relations (B = 4.42, 95% CI = 1.44 to 7.50, p = 0.004) and spiritual growth (B = 6.59, 95% CI = 3.81 to 9.37, p < 0.001) were positively associated with EI. Poor sleep quality (PSQI > 5) was negatively associated with EI (B = −1.95, 95% CI = −3.88 to −0.01, p = 0.049). Conclusions: Interpersonal relations, spiritual growth, and sleep quality were associated with EI among prelicensure nursing students. These factors may be relevant to consider when designing student support and EI-related educational initiatives; however, longitudinal and intervention studies are needed to clarify directionality and causality. Full article

Electroencephalography (EEG) has transitioned from a subjective observational method into a data-intensive analytical field that utilises sophisticated algorithms and mathematical models. This review provides a holistic foundation by detailing the neurophysiological basis, recording techniques, and applications of EEG before providing a rigorous examination of traditional and modern analytical pillars. Statistical and Time-Series Analysis, Spectral and Time-Frequency Analysis, Spatial Analysis and Source Modelling, Connectivity and Network Analysis, and Nonlinear and Chaotic Analysis are explored. Afterwards, while acknowledging the historical role of Machine Learning (ML) and Deep Learning (DL) architectures, such as Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs), this review shifts the primary focus toward current state-of-the-art Artificial Intelligence (AI) trends. We place emphasis on the emergence of Foundation Models, including Large Language Models (LLMs) and Large Vision Models (LVMs), adapted for high-dimensional neural sequences. Finally, we explore the integration of Generative AI for data augmentation and review Explainable AI (XAI) frameworks designed to bridge the gap between “black-box” decoding and clinical interpretability. We conclude that the next generation of EEG analysis will likely converge into Neuro-Symbolic architectures, synergising the massive generative power of foundation models with the rigorous, rule-based interpretability of classical signal theory. Full article