Search Results (3,625)

Background and Objectives: Quality of life (QoL) is an important issue for breast cancer (BC) survivors. The objective of this study was to assess health-related QoL (HRQoL) of BC patients and investigate the impact of different demographic and clinical factors on physical and social functioning and BC-related symptoms. Materials and Methods: In this cross-sectional study, 107 BC patients undergoing chemotherapy in Greece completed a questionnaire collecting sociodemographic and clinical information and the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire–Core 30 (EORTC QLQ-C30) in order to assess HRQoL. Descriptive statistics and multiple linear regression analyses were used to identify factors linked to HRQoL outcomes. Results: Overall, participants reported moderate HRQoL, with high physical and social functioning and moderate emotional, cognitive, and role functioning. Fatigue was the most common symptom, whereas other symptoms were generally uncommon. Multiple regression analyses showed that marital status, place of residence, time since diagnosis, and type of surgery were significantly associated with the global QLQ-C30 score (R² = 0.337, p < 0.001). Physical functioning was associated with comorbidity burden, time since diagnosis, and employment status (R² = 0.155, p = 0.035), and social functioning with marital status and type of surgery (R² = 0.171, p = 0.011). Emotional functioning showed exploratory associations with place of residence and type of surgery; however, the overall regression model for emotional functioning did not reach statistical significance. No symptom model reached overall significance, but time since diagnosis, treatment type, and surgery were linked to distinct symptoms. Conclusions: BC patients undergoing chemotherapy in Greece report an overall moderate level of HRQoL, which is significantly influenced by a combination of demographic and clinical factors; physical and social functioning were high, with moderate emotional, cognitive, and role functioning. These findings highlight the importance of individualized supportive care strategies in order to improve QoL of BC patients. Full article

The rapid emergence of generative AI tools is transforming software development. Consequently, software engineering education must adapt to ensure that students not only learn traditional development methods but also understand how to use these new technologies effectively and responsibly. In particular, project-based courses provide an effective setting in which to explore and evaluate the integration of AI assistance into real-world development practices. This paper presents our approach and a user study conducted in the context of a university programming project in which students collaboratively developed computer games. The study investigates how participants used generative AI tools across different phases of the software development process, identifies the tasks for which these tools were perceived as most useful, and analyzes the challenges students encountered. Building on these insights, we further examine a repository-aware, locally deployed large language model (LLM) assistant designed to provide project-contextualized support. The system employs retrieval-augmented generation (RAG) to ground its responses in relevant documentation and source code, thereby enabling a qualitative analysis of model behavior, parameter sensitivity, and common failure modes. These findings deepen our understanding of context-aware AI support in educational software projects and inform the future integration of AI-based assistance into software engineering curricula. Full article

Object detection in complex industrial environments is prone to being affected by insufficient dynamic weighting of local and global features, as well as illumination variations and impurities. Moreover, existing models suffer from excessive model complexity, which directly impairs computational efficiency. To more accurately distinguish Chinese herbal materials with diverse morphologies, this paper proposes the MobileAttn module. Drawing on the idea of token representation in the Transformer architecture, this module extracts contextual information through global feature compression, fuses it with tokens to generate a spatial attention map, and realizes dynamic recalibration of convolutional features. This process enhances the feature weights of key semantic regions, suppresses redundant background information, and improves feature discriminability. To address illumination interference, brightness-aware weights are combined with dual-path (channel and spatial) attention for global control, dynamically reducing the impact of illumination; this component is named LightAttn. When Chinese herbal materials contain common industrial unknown impurities (e.g., small stones and weeds), an impurity detection auxiliary module, a post-processing step independent of the main detection network, is proposed. This module refines Non-Maximum Suppression (NMS) logic to distinguish target Chinese herbal materials from interfering impurities. Subsequently, it accurately locates and marks impurities on the conveyor belt, thereby achieving effective unknown impurity detection. Experimental results demonstrate that, compared with the original YOLOv11 on the Chinese herbal materials detection task, the optimized model achieves a 1.7% improvement in the overall mean Average Precision (mAP@0.5:0.95). On a per-class basis, gains are particularly pronounced for certain challenging high-aspect-ratio Chinese herbal materials. Prunella vulgaris and orange peel achieve respective AP improvements of 5.8% and 4.1%. Meanwhile, the model parameter count is reduced by 23.1% and the computational complexity by 20.3%. The F1-Score of the impurity detection results is 86.38%, verifying the effectiveness of the impurity detection auxiliary module. Full article

In artificial breeding of forest musk deer (Moschus berezovskii), common diseases such as abscess, enteritis, pneumonia, and parasitic infections exhibit persistently high morbidity rates. The early symptoms of certain diseases are often insidious and difficult to discern. Conventional manual inspection routines not only fail to achieve accurate diagnosis but also frequently disturb the animals, induce stress responses, and consequently delay optimal treatment windows. To address this practical challenge, this study employs an improved BRW-GPLinker joint entity-relationship extraction approach to perform integrated extraction and structural organization of disease entities, symptom manifestations, etiological associations, and preventive and therapeutic measures from farming literature and clinical records, thereby constructing a disease knowledge graph for forest musk deer. Through the introduction of a Boundary-Aware Module for refined entity boundary detection, a Relative Distance Bias Module to mitigate pairing errors in dense contexts, and a Weighted Sparse Multi-label Cross-Entropy loss function to enhance recall for infrequent relations, the proposed model achieves an F1 score of 0.887 on a self-constructed dataset and demonstrates favorable generalization capability on medical-domain datasets. By transforming fragmented clinical logs and manuals into structured medical associations, this knowledge graph facilitates rapid retrieval of forest musk deer disease information, thereby enhancing veterinary decision-making efficiency and assisting forest musk deer health management. Full article

The growth of Intelligent Transportation Systems (ITS) has made high-quality perception data from multi-sensor setups essential. Pairing millimeter-wave (mmW) radar with a monocular camera is a common way to recover three-dimensional information about the environment, but aligning the two is difficult because sparse radar point clouds and dense camera images differ sharply in how they sense a scene. The problem grows more severe in roadside infrastructure, where the high mounting elevation introduces perspective distortion that vehicle-mounted systems rarely face. This paper presents a systematic review, conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, of radar-camera extrinsic calibration for fixed roadside infrastructure, organizing existing work into a taxonomy that separates traditional two-stage pipelines from recent end-to-end learning frameworks. Because methods designed specifically for roadside units remain scarce, the review also covers vehicle- and robot-mounted methods whose static-sensor formulation carries over to fixed roadside deployment. For the two-stage pipeline, the analysis covers target-based and targetless correspondence registration along with the optimization techniques and algorithmic assumptions behind parameter estimation. The end-to-end learning literature shows a clear shift toward self-supervised and fusion-based models, some of which report real-time performance. The review also compares the metrics and procedures used to quantify calibration accuracy. Progress is evident, but robustness in cluttered urban environments remains an open challenge, and the paper closes by outlining future directions, arguing that standardized roadside benchmarks are needed before scalable, targetless calibration can mature. Full article

Accurate reconstruction of complete tree point clouds is essential for estimating ecosystem structural characteristics from LiDAR data. In urban forestry environments, however, terrestrial laser scanning (TLS) and mobile laser scanning (MLS) frequently produce incomplete observations. Occlusion caused by neighboring trees, together with interference from surrounding urban objects such as buildings and vehicles, often leads to missing regions within scanned point clouds. These defects may further affect the reliability of tree structural analysis and parameter estimation. Although recent learning-based point cloud completion methods have improved reconstruction performance, several limitations remain when they are applied to complex tree structures. Many existing networks depend on farthest point sampling (FPS) for feature extraction, which can result in the loss of fine-scale branching information. Furthermore, local feature aggregation methods based on the traditional k-nearest neighbor (KNN) strategy are highly sensitive to regions with uneven point cloud distribution, such as the canopy region where density variations are significant in tree point clouds. To alleviate these issues, this study proposes GS-TreeAttn, an attention-guided framework specifically for tree point cloud completion. This network models density and structural representation as a coupled problem and employs a structure-guided density-adaptive attention mechanism to jointly capture global structural dependencies and local geometric features. We comprehensively evaluate the proposed method using publicly available datasets and urban forestry data collected under real-world scanning conditions. Experimental results show that even in complex scenarios with severe occlusion and uneven sampling density, GS-TreeAttn generates more complete reconstruction results. This improvement is particularly evident in regions where the canopy and branches mutually occlude each other, where information loss is very common in real-world urban forestry. Full article

Preserving cultural heritage sites requires an interoperable digital framework capable of integrating heterogeneous spatial data and supporting immersive interaction for inspection and management. This study investigates the integration of multiple heritage data representations—including IFC-based Heritage Building Information Modeling (HBIM), terrestrial and UAV LiDAR point clouds, and 3D Gaussian Splatting reconstructions—into a unified digital management environment for the East Hall (Aula Timur) heritage site within the Bandung Institute of Technology (ITB) campus. A semantic–spatial interoperability workflow is proposed to harmonize BIM, point cloud, and landscape-scale data within a common georeferenced context, supported by a CityGML-based base map of the surrounding site. An immersive virtual environment was implemented using a head-mounted display to enable walkthrough-based inspection and damage annotation. All datasets were georeferenced within a unified coordinate system, allowing spatial registration between digital objects and the physical heritage site. The results demonstrate that multi-source heritage datasets can be integrated with high geometric accuracy, achieving TLS registration errors of approximately 2 mm and georeferencing residuals within 11.1 cm (horizontal) and 0.95 cm (vertical), while preserving semantic information and ensuring spatial coherence across HBIM, GIS, and immersive environments. The system is implemented in VR, with an architecture designed to support future MR-based on-site annotation and visualization. The proposed framework establishes a foundation for future heritage digital twin deployments and supports informed conservation decisions. Full article

Background: Seasonal influenza prevention in young adults is influenced by access, trust, and vaccine information exposure, but local evidence linking vaccination uptake with illness and economic burden is limited. Methods: We conducted a non-probability, cross-sectional electronic survey of adults aged 18–49 years who lived, worked, or studied in the San Francisco Bay Area during the 2025 to 2026 influenza season. Measures included vaccination uptake, influenza-like illness, recovery, functional and economic burden, vaccination sites, and vaccine information exposure. Multivariable logistic regression examined factors associated with vaccination uptake; Kaplan–Meier and Cox models examined time to recovery. Results: Of 554 responses, 463 were included. Vaccination uptake was 86.2% (n = 399; 95% confidence interval [CI], 82.7–89.2%), likely reflecting a health-engaged convenience sample. Influenza-like illness was reported by 38.4%; median recovery time was 5 days, median missed work or school was 2 days, and median direct out-of-pocket cost was US$20. Prior season vaccination (adjusted odds ratio [aOR], 2.24; 95% CI, 1.15–4.34) and greater trust in Centers for Disease Control and Prevention (CDC) or public health agencies (aOR, 1.46; 95% CI, 1.05–2.02) were associated with vaccination. Pharmacies were the second most common vaccination site and preferred future site. Conclusions: Influenza prevention for young adults may benefit from pharmacy-inclusive, multichannel access paired with trusted communication. Findings should be interpreted in light of non-probability recruitment and likely overrepresentation of health-engaged respondents. Full article

Class imbalance in datasets is a common issue across various fields, including banking, medicine, and information security. Data augmentation is a frequently used approach to address this problem by generating additional samples of the minority class to rebalance the dataset. Other studies have employed methods such as Generative Adversarial Networks (GAN) and Synthetic Minority Over-sampling Technique (SMOTE)for this purpose. Therefore, this study aims to compare the differences between the two oversampling techniques, GAN and SMOTE, in handling class imbalance problems. The results of this study show the accuracy of distinguishing between real and generated data to determine which method offers a greater advantage. The method demonstrates better performance in multi-class classification tasks. The GAN model can be effectively applied to both binary classification and the generation of diverse samples from minority and majority classes, even in extreme cases where the number of minority samples is tiny. Moreover, in terms of classification accuracy and the quality of generated samples, GAN outperforms SMOTE in data augmentation and oversampling. It maintains strong performance even when the number of instances in the minority class is limited. Full article

This paper considers a multi-period two-sided asymmetric information model with infinitely long-lived sellers and short-lived buyers. I assume that two exogenously given qualities are offered in the market. Each period, a consumer, who is uncertain about the quality of the offered product, observes her pairwise matched seller’s price and a noisy signal of quality that cannot be manipulated by the seller. Prices are fixed and it is common knowledge that consumers are not willing to pay a high price for the low-quality product. A matched seller with a low-quality good can choose to be either honest (by charging the lower market price) or dishonest (by charging the higher price). Sellers’ incentives to misrepresent quality depend on how current trade outcomes affect future access to consumer traffic. I show that the strength of the informational role of prices is non-decreasing in the intensity of competition for future consumer traffic in equilibrium and that consumers do not benefit from more intense competition. Full article

Background: Sports injuries impose a substantial burden on athletes. Machine learning (ML) and deep learning (DL) methods, collectively referred to as artificial intelligence (AI), are increasingly applied to develop predictive models and targeted prevention strategies. Objective: This scoping review aimed to map contemporary trends in AI applications for sports injury prediction and personalised prevention strategies, critically appraising the existing methodological approaches and identifying future research directions. Methods: Following PRISMA-ScR guidelines, we systematically searched five electronic databases, i.e., PubMed, Web of Science, Institute of Electrical and Electronics Engineers Xplore, Scopus, and Google Scholar, for peer-reviewed studies published up to February 2026 that applied AI methods for injury prediction and/or prevention in athletic populations. Results: Thirty-nine studies were included. Tree-based ML algorithms were the most common (59% of studies) methods used, with reported area under the curve values ranging from 0.82 to 0.95. DL was used in 18% of studies, with one hybrid model reporting 92% accuracy. Integrating multi-modal data was associated with improved model performance in 37% of studies. Among included studies, AI-informed prevention strategies were associated with injury reductions ranging from 23% to 42%, derived from synthesis-level and single-centre intervention evidence, respectively. The key challenges identified were heterogeneous injury definitions, small sample sizes, and data privacy concerns. Conclusions: AI models can inform personalised injury prevention, but their clinical use is limited by methodological issues. Key limitations include heterogeneous injury definitions, small sample sizes, and a lack of external validation. Standardised protocols are needed to improve the reliability and application of these models in practice. Full article

Genetically encoded biosensors are now central tools, deployed either as intracellular reporters to advance basic research, or as whole-cell reagents that detect analytes in diverse sample-types. Across the diversity of molecular scaffolds and modes of operation, biosensors serve a common functional purpose: translating ligand presence into a readable signal. Despite this shared logic, biosensor development as a field of practice remains fragmented: different scaffolds and modalities are advanced in separate, often lab-specific pipelines with diverse assays, metrics, and design practices. Moreover, libraries, selection histories and performance data generated during routine campaigns rarely outlive the projects that produced them. In this perspective, we focus on this fragmentation as a field-level bottleneck and argue that it deserves explicit attention in its own right. We discuss how modest, incremental steps—such as structured development records, adherence to high-information screening formats, library annotation, and community-level deposition infrastructure—could make biosensor development more reproducible, more comparable, and easier to build on across projects and laboratories. We further argue that such infrastructure will become increasingly valuable as computational protein design matures—not as a competing approach, but as the source of diverse, comparable, and context-annotated experimental data that sequence-function models and design benchmarks ultimately depend on. Full article

University common spaces are increasingly recognized as critical environments for social interaction and informal learning; yet empirical frameworks that integrate biophilic design, placemaking, and affective post-occupancy evaluation remain limited in educational contexts. This research adopts a post-occupancy evaluation (POE) design to assess how spatial configuration and biophilic placemaking strategies influence emotional experience, social interaction, and perceived inclusion in a redesigned university lobby serving five colleges. A structured questionnaire was administered to 212 users using the Pleasure–Arousal–Dominance (PAD) model, triangulated with systematic behavioral observations and spatial analysis. The results demonstrate that integrating biophilic elements, improving spatial organization, and introducing student-led activity areas yielded high perceived comfort (M = 3.75), balanced stimulation (M = 3.10), and a stronger sense of spatial control (M = 3.16), with significant positive correlations between biophilic integration scores and all three PAD dimensions. These findings introduce and empirically validate the concept of Socio-Biophilic Synergy and propose the Biophilic Placemaking Framework (BPF) as a unified evaluative structure, demonstrating that the intentional spatial design of the university spaces can meaningfully enhance social sustainability and emotional well-being in university environments. Full article

The intensification of the global ecosocial crisis has exposed the structural incompatibility between continuous economic growth and the biophysical limits of the planet, prompting increasing interest in degrowth as a framework for ecological sustainability and social justice. Despite the growing development of degrowth theory within ecological economics and political ecology, its educational implications remain underexplored. This article examines the role of education in the transition toward post-growth societies through a critical review of the literature and a conceptual analysis informed by critical pedagogy, ecofeminism, environmental education, and degrowth scholarship. The study identifies how contemporary educational systems reproduce growth-oriented subjectivities through human capital theory, neoliberal governance, competitiveness, and productivist curricular frameworks. The analysis demonstrates that dominant models of sustainability education frequently remain embedded within the assumptions of green growth and fail to address the structural drivers of ecological degradation and social inequality. As a result, the article develops an integrated framework for a pedagogy of degrowth structured around ecosocial literacy, democratic participation, care ethics, cooperation, critical civic engagement, curriculum transformation, technological sovereignty, and commitment to the commons. The main contribution of the study lies in articulating a comprehensive educational model that connects pedagogical transformation with broader processes of post-growth social change, positioning education not merely as a tool for environmental awareness but as a strategic arena for cultivating the values, capacities, and collective agency required for ecosocial justice. The findings suggest that a transition toward sustainable and equitable societies requires a profound reorientation of educational aims, contents, institutions, and practices beyond the paradigm of economic growth. Full article

Background/Objectives: Autistic traits are distributed dimensionally across psychiatric populations, yet their systematic assessment in mood and psychotic spectrum disorders remains limited. While elevated autistic traits have been documented in schizophrenia spectrum disorders, evidence in bipolar disorder (BD) and major depressive disorder (MDD) is scarce, and no studies have applied the clinician-rated PANSS Autism Severity Score (PAUSS) to mood disorder populations. This study aims to investigate the presence and severity of autistic traits across psychotic spectrum disorder (PSD), BD, and MDD in an outpatient sample using the PAUSS. Methods: In this cross-sectional naturalistic outpatient study, clinically stable adult patients with MDD, BD, or PSD, without autism spectrum disorder, were assessed with the Brief Psychiatric Rating Scale (BPRS) and PAUSS. Group comparisons, adjusted models, correlation analyses, principal component analysis, and multinomial logistic regression were performed. Results: A total of 165 patients were included (MDD, n = 84, BD, n = 45, PSD, n = 36). Compared with the mood disorder groups, PSD patients were younger and showed higher BPRS scores. PSD was also characterized by significantly higher PAUSS total, social, and communication scores, whereas PAUSS RRB did not differ in univariate analyses. In the overall sample, BPRS severity correlated positively with all PAUSS dimensions, while age showed only weak or non-significant associations. Diagnosis-stratified analyses revealed that the association between psychopathology and autistic traits was present in MDD and BD, but not in PSD. PCA showed that autistic trait dimensions converged on a broad common profile and differed across diagnostic groups, with PSD showing the most distinct pattern. In multinomial logistic regression, higher BPRS, higher PAUSS social and communication scores, and younger age independently distinguished PSD from MDD and BD; PAUSS RRB showed an inverse association only in the multivariable model. Conclusions: This study supports a transdiagnostic perspective on autistic traits in adult psychiatric populations, highlighting disorder-specific differences across diagnostic categories. Social and communication impairments emerged as key dimensions distinguishing PSD from mood disorders. Assessing autistic traits in psychiatric settings may improve diagnostic precision and inform personalized, stratified treatment approaches. Full article