Search Results (6,520)

Underwater Object Detection (UOD) based on Synthetic Aperture Sonar (SAS) images is one of the core tasks of underwater intelligent perception systems. However, the existing UOD methods suffer from excessive model redundancy, high computational demands, and severe image quality degradation due to noise. To mitigate these issues, this paper proposes an ultra-lightweight and high-precision underwater object detection method for SAS images. Based on a single-stage detection framework, four efficient and representative lightweight modules are developed, focusing on three key stages: feature extraction, feature fusion, and feature enhancement. For feature extraction, the Dilated-Attention Aggregation Feature Module (DAAFM) is introduced, which leverages a multi-scale Dilated Attention mechanism for strengthening the model’s capability to perceive key information, thereby improving the expressiveness and spatial coverage of extracted features. For feature fusion, the Channel–Spatial Parallel Attention with Gated Enhancement (CSPA-Gate) module is proposed, which integrates channel–spatial parallel modeling and gated enhancement to achieve effective fusion of multi-level semantic features and dynamic response to salient regions. In terms of feature enhancement, the Spatial Gated Channel Attention Module (SGCAM) is introduced to strengthen the model’s ability to discriminate the importance of feature channels through spatial gating, thereby improving robustness to complex background interference. Furthermore, the Context-Aware Feature Enhancement Module (CAFEM) is designed to guide feature learning using contextual structural information, enhancing semantic consistency and feature stability from a global perspective. To alleviate the challenge of limited sample size of real sonar images, a diffusion generative model is employed to synthesize a set of pseudo-sonar images, which are then combined with the real sonar dataset to construct an augmented training set. A two-stage training strategy is proposed: the model is first trained on the real dataset and then fine-tuned on the synthetic dataset to enhance generalization and improve detection robustness. The SCTD dataset results confirm that the proposed technique achieves better precision than the baseline model with only 10% of its parameter size. Notably, on a hybrid dataset, the proposed method surpasses Faster R-CNN by 10.3% in mAP50 while using only 9% of its parameters. Full article

In the early stages of computer software information system development, requirements errors can lead to software system failure and performance degradation and even cause huge security incidents. Traditional requirements verification methods are inefficient and susceptible to human factors when dealing with complex software requirements. Rapid prototyping is an effective requirement validation method, but the generated prototype does not contain any data, and the traditional method requires domain experts to write the data manually, which is time consuming and complicated. In this study, an automatic software prototype data generation method, InitialGPT, is proposed, which automatically generates requirements-compliant prototype data by interacting with users through a requirements model to improve the efficiency and accuracy of requirements validation. We designed a framework containing a prompt generation template, a data generation model, a data evaluation model, and multiple prototype data tools, and validated it on four real-world software system cases. The results show that the approach improves the efficiency of requirements validation by a factor of 7.02, and generates data of similar quality to those written manually, but at a more advantageous cost and efficiency, demonstrating its potential for application in the computer software industry. Full article

During the last decade, artificial intelligence (AI) has enabled key technological innovations within the modern dementia and frailty healthcare and prevention landscape. This has boosted the impact of technology in the clinical setting, enabling earlier diagnosis with improved specificity and sensitivity, leading to accurate and time-efficient support that has driven the development of preventative interventions minimizing the risk and rate of progression. Background/Objectives: The rapid ageing of the European population places a substantial strain on the current healthcare system and imposes several challenges. COMFORTage is the joint effort of medical experts (i.e., neurologists, psychiatrists, neuropsychologists, nurses, and memory clinics), social scientists and humanists, technical experts (i.e., data scientists, AI experts, and robotic experts), digital innovation hubs (DIHs), and living labs (LLs) to establish a pan-European framework for community-based, integrated, and people-centric prevention, monitoring, and progression-managing solutions for dementia and frailty. Its main goal is to introduce an integrated and digitally enabled framework that will facilitate the provision of personalized and integrated care prevention and intervention strategies on dementia and frailty, by piloting novel technologies and producing quantified evidence on the impact to individuals’ wellbeing and quality of life. Methods: A robust and comprehensive design approach adopted through this framework provides the guidelines, tools, and methodologies necessary to empower stakeholders by enhancing their health and digital literacy. The integration of the initial information from 13 pilots across 8 European countries demonstrates the scalability and adaptability of this approach across diverse healthcare systems. Through a systematic analysis, it aims to streamline healthcare processes, reduce health inequalities in modern communities, and foster healthy and active ageing by leveraging evidence-based insights and real-world implementations across multiple regions. Results: Emerging technologies are integrated with societal and clinical innovations, as well as with advanced and evidence-based care models, toward the introduction of a comprehensive global coordination framework that: (a) improves individuals’ adherence to risk mitigation and prevention strategies; (b) delivers targeted and personalized recommendations; (c) supports societal, lifestyle, and behavioral changes; (d) empowers individuals toward their health and digital literacy; and (e) fosters inclusiveness and promotes equality of access to health and care services. Conclusions: The proposed framework is designed to enable earlier diagnosis and improved prognosis coupled with personalized prevention interventions. It capitalizes on the integration of technical, clinical, and social innovations and is deployed in 13 real-world pilots to empirically assess its potential impact, ensuring robust validation across diverse healthcare settings. Full article

E-commerce platforms offering regional fresh produce often face a trade-off between logistics costs and product quality. Due to limited use of cold chain logistics, consumers frequently receive damaged goods, resulting in negative post-purchase experiences. This study examines how logistics service encounters, as reflected in consumer reviews, influence subsequent purchase behaviour, and how the alignment between review images and text moderates this relationship. We analyse sales and review data from 694 fruit products on Tmall between February and April 2024. Latent Dirichlet Allocation (LDA) is applied to extract logistics-related review content. At the same time, image–text similarity is assessed using the Chinese-CLIP model. Regression analysis reveals that positive logistics service encounters significantly enhance purchase intention. However, high image–text similarity weakens this positive effect, suggesting that overly repetitive content may reduce informational value for prospective buyers. These findings advance understanding of consumer behaviour in online fresh produce markets by highlighting the interactive effects of logistics experiences and user-generated content. The results offer practical implications for improving logistics services, enhancing content diversity in review systems, and increasing consumer trust in e-commerce environments. Full article

Background: This systematic review examines the lived experiences of Black students in UK higher education (HE), focusing on their encounters with racism and racial disadvantage, and how institutional and social factors contribute to these experiences. Methods: We conducted a systematic search across seven databases (Academic Search Complete, Education Abstracts, PsycINFO, Race Relations Abstracts, Scopus, Web of Science, and SocINDEX) in April 2023, with periodic updates. The grey literature, which refers to research and information produced outside of traditional academic publishing and distribution channels, was reviewed. This includes reports, policy briefs, theses, conference proceedings, government documents, and materials from organisations, think tanks, or professional bodies that are not commercially published or peer-reviewed but can still offer valuable insights relevant to the topic. Hand searches were also included. Studies were included if they were peer-reviewed, published between 2012 and 2024, written in English, and focused on the experiences of Black students in UK higher education. Both qualitative and quantitative studies with a clear research design were eligible. Studies were excluded if they lacked methodological rigour, did not focus on the UK HE context, or did not disaggregate Black student experiences. Risk of bias was assessed using standard qualitative appraisal tools. Thematic analysis was used to synthesise findings. Results: Nineteen studies were included in the review. Two main themes emerged: (1) diverse challenges including academic barriers and difficulties with social integration, and (2) the impact of racism and institutional factors, such as microaggressions and biased assessments. These issues contributed to mental fatigue and reduced academic performance. Support systems and a sense of belonging helped mitigate some of the negative effects. Discussion: The evidence was limited by potential bias in reporting and variability in study quality. Findings reveal persistent racial inequalities in UK HE that affect Black students’ well-being and outcomes. Institutional reforms, increased representation, and equity-focused policies are needed. Future research should explore effective interventions to reduce the awarding gap and support Black student success Full article

The development of medical question-answering systems (QASs) encounters substantial challenges due to the complexities of medical terminologies and the lack of reliable datasets. The shortcomings of traditional artificial intelligence (AI) driven QAS lead to generating outcomes with a higher rate of hallucinations. In order to overcome these limitations, there is a demand for a reliable QAS to understand and process complex medical queries and validate the quality and relevance of its outcomes. In this study, we develop a medical QAS by integrating SPARQL, retrieval-augmented generation (RAG), and generative pre-trained transformer (GPT)-Neo models. Using this strategy, we generate a synthetic dataset to train and validate the proposed model, addressing the limitations of the existing QASs. The proposed QAS was generalized on the MEDQA dataset. The findings revealed that the model achieves a generalization accuracy of 87.26% with a minimal hallucination rate of 0.16. The model outperformed the existing models by leveraging deep learning techniques to handle complex medical queries. The dynamic responsive capability of the proposed model enables it to maintain the accuracy of medical information in a rapidly evolving healthcare environment. Employing advanced hallucination reduction and query refinement techniques can fine-tune the model’s performance. Full article

Background: Inherited epidermolysis bullosa (EB) includes a group of rare genetic disorders affecting the skin and mucous membranes. These disorders are characterized by extreme fragility and blister formation after minimal or no trauma. Oral and systemic manifestations vary by subtype; the more severe forms often present with extensive intra-oral blistering, scarring, microstomia, vestibular obliteration, ankyloglossia, and—in some cases—oral cancer. This study aims to collect data on oral-health practices and challenges in people with EB to inform preventive strategies and dental care. Methods: An international, structured online questionnaire with 31 items was distributed to individuals with a confirmed diagnosis of EB. The survey explored clinical and oral manifestations, home-care routines (oral hygiene and diet), experiences with dental professionals, and the impact of oral health on quality of life. Results: Eighty-two questionnaires were completed. Dystrophic EB was the most often reported subtype (69.5%). Most respondents (67.1%) experienced recurrent oral blisters and/or erosions. Many reported relying exclusively on soft foods and struggling with mechanical plaque removal because of microstomia and pseudo-syndactyly. Severe oral pain hindered effective brushing in 17% of participants. Hand contractures and microstomia interfered with oral hygiene in 74% and 31% of participants, respectively. Nearly 30% sought dental care only when in pain. Among those who did not attend regular check-ups or hygiene sessions (44.6%), the most cited reason was that dental clinics were inadequately equipped or trained to manage EB. Conclusions: Because dental procedures carry significant risks for patients with EB, preventive care should begin in early childhood. Yet many patients are still insufficiently informed about essential preventive measures and lack access to dental professionals trained in EB management. Full article

This study presents the implementation and evaluation of a high-resolution operational forecasting system for the Tagus estuary (Portugal), focusing on inundation and water quality predictions to support estuarine management. Developed using the relocatable Water Information Forecast Framework (WIFF), the system integrates two implementations of SCHISM: a 2D barotropic model including wave–current interactions for flood-prone areas, and a 3D baroclinic model simulating salinity, temperature, and biogeochemical variables. Forecasts were assessed over six months using in situ and satellite near real-time observations. Results show that the operational models represent well water levels, waves, salinity, temperature, and water quality dynamics. Compared to a regional model, the local forecast system generally offers improved accuracy within the estuary due to higher spatial resolution and better representation of local dynamics. Several challenges remain, including uncertainties in oceanic and riverine boundary conditions and limited high-resolution near real-time observations to continuously assess and improve operational models. Furthermore, the absence of operational two-way coupling between regional and local models limits cross-scale integration of physical and biogeochemical processes. The forecasting system for the Tagus estuary demonstrates the potential of local high-resolution operational models as reliable, user-oriented tools for managing transitional water systems, and as core elements for coastal management. Full article

The complex topography of China’s Tibetan Plateau mountainous roads, characterized by diverse curve types and frequent traffic accidents, significantly impacts the safety and sustainability of the transportation system. To enhance driving safety on these mountain roads and promote low-carbon, resilient transportation development, this study investigates the mechanisms through which different curve types affect driving safety and proposes optimization strategies based on interpretable machine learning methods. Focusing on three typical curve types in plateau regions, drone high-altitude photography was employed to capture footage of three specific curves along China’s National Highway G318. Oblique photography was utilized to acquire road environment information, from which 11 data indicators were extracted. Subsequently, 8 indicators, including cornering preference and vehicle type, were designated as explanatory variables, the curve type indicator was set as the dependent variable, and the remaining indicators were established as safety assessment indicators. Linear models (logistic regression, ridge regression) and non-linear models (Random Forest, LightGBM, XGBoost) were used to conduct model comparison and factor analysis. Ultimately, three non-linear models were selected, employing an explainability-oriented dynamic ensemble learning strategy (X-DEL) to evaluate the three curve types. The results indicate that non-linear models outperform linear models in terms of accuracy and scene adaptability. The explainability-oriented dynamic ensemble learning strategy (X-DEL) is beneficial for the construction of driving safety models and factor analysis on Tibetan Plateau mountainous roads. Furthermore, the contribution of indicators to driving safety varies across different curve types. This research not only deepens the scientific understanding of safety issues on plateau mountainous roads but, more importantly, its proposed solutions directly contribute to building safer, more efficient, and environmentally friendly transportation systems, thereby providing crucial impetus for sustainable transportation and high-quality regional development in the Tibetan Plateau. Full article

Data is needed to understand local air quality, reduce exposure, and mitigate the negative impacts on human health. Measuring local air quality often requires a hybrid monitoring approach consisting of the national air monitoring network and one or more networks of air sensors. However, it can be challenging to combine this data to produce a consistent picture of air quality, largely because sensor data is produced in a variety of formats. Users may have difficulty reformatting, performing basic quality control steps, and using the data for their intended purpose. We developed an R-Shiny application that allows users to import text-based air sensor data, describe the format, perform basic quality control, and export the data to standard formats through a user-friendly interface. Format information can be saved to speed up the processing of additional sensors of the same type. This tool can be used by air quality professionals (e.g., state, local, Tribal air agency staff, consultants, researchers) to more efficiently work with data and perform further analysis in the Air Sensor Network Analysis Tool (ASNAT), Google Earth or Geographic Information System (GIS) programs, the Real Time Geospatial Data Viewer (RETIGO), or other applications they already use for air quality analysis and management. Full article

Indoor wireless networks face increasing challenges in maintaining stable coverage and performance, particularly with the widespread use of high-frequency Wi-Fi and growing demands from smart home devices. Traditional methods to improve signal quality, such as adding access points, often fall short in dynamic environments where user movement and physical obstructions affect signal behavior. In this work, we propose a system that leverages existing Internet of Things (IoT) devices to perform real-time user localization and network adaptation using fine-grained Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) measurements. We deploy multiple ESP-32 microcontroller-based receivers in fixed positions to capture wireless signal characteristics and process them through a pipeline that includes filtering, segmentation, and feature extraction. Using supervised machine learning, we accurately predict the user’s location within a defined indoor grid. Our system achieves over $82\%$ accuracy in a realistic laboratory setting and shows improved performance when excluding redundant sensors. The results demonstrate the potential of communication-based sensing to enhance both user tracking and wireless connectivity without requiring additional infrastructure. Full article

Background/Objectives: Children, caregivers, and physicians may be insufficiently aware of the effect(s) of physical activity levels on the quality of life (QoL) of children with cerebral palsy (CP). This study aimed to understand the levels of physical activity of school-age children with CP compared with typically developing (TD) peers, and to examine the relationship between physical activity levels and the QoL of children with CP. Methods: We conducted a cross-sectional study of children with CP and TD children aged 6–12 years. Parents of children with CP completed a four-section survey: demographic information, parent-reported Gross Motor Functional Classification System, physical activity, and the CP-QoL questionnaire. Parents of TD children completed the demographic and physical activity sections. To account for the severity of motor impairment associated with CP, further analysis was conducted to compare QoL between the ambulant and non-ambulant groups of children with CP. Results: Eighty-two participants were included in the analysis: 42 children with CP and 40 TD children (8.29 ± 1.79 years; 8.35 ± 1.76 years). The lowest QoL domain scores were access to service, pain, and effect(s) of disability. Children with CP reported similar physical activity levels to those of the TD children. Physical activity levels were associated with the general QoL score, and feeling-social domains of QoL. Conclusion: Our findings support the positive prediction of high physical activity levels with QoL among school-aged children with CP. Full article

Comprehensive healthcare seeks to uphold the right to health by providing patient-centred care in both personal and work environments. However, the unequal distribution of healthcare services significantly restricts access in remote or underserved areas—a challenge that is particularly critical in mental health care within low-income countries. On average, there is only one psychiatrist for every 200,000 people, which severely limits early diagnosis and continuous monitoring in patients’ daily environments. In response to these challenges, this research explores the feasibility of implementing an information system that integrates cloud computing with an intelligent Facial Expression Recognition (FER) module to enable psychologists to remotely and periodically monitor patients’ emotional states. This approach enhances comprehensive clinical assessments, supporting early detection, ongoing management, and personalised treatment in mental health care. This applied research follows a descriptive and developmental approach, aiming to design, implement, and evaluate an intelligent cloud-based solution that enables remote monitoring of patients’ emotional states through Facial Expression Recognition (FER). The methodology integrates principles of user-centred design, software engineering best practices, and machine learning model development, ensuring a robust and scalable solution aligned with clinical and technological requirements. The development process followed the Software Development Life Cycle (SDLC) and included functional, performance, and integration testing. To assess overall system quality, we defined an evaluation framework based on ISO/IEC 25010 quality characteristics: functional suitability, performance efficiency, usability, and security. The intelligent FER model achieved strong validation results, with a loss of 0.1378 and an accuracy of 96%, as confirmed by the confusion matrix and associated performance metrics. Full article

Background: Dental professionals are particularly susceptible to occupational stress and burnout, which are amplified during armed conflicts. Civilian dentists continuing to provide care under wartime conditions face unique psychological challenges. This study aimed to evaluate their psychological wellbeing and professional quality of life during military conflict. Methods: A cross-sectional study was conducted using an anonymous online questionnaire distributed through the national dental association. The survey included the Professional Quality of Life Scale (ProQOL, version 5) to assess compassion satisfaction, burnout, and secondary traumatic stress; and the Generalized Anxiety Disorder 7-item scale (GAD-7) to measure anxiety severity. Additional items captured demographic information, professional experience, pre-conflict workload, current work status, family circumstances, and subjective financial impact. The final sample included 239 civilian dentists. Statistical analysis included descriptive statistics, Pearson correlations, chi-square tests for categorical variables, Mann-Whitney U and Kruskal-Wallis tests for between-group comparisons, and multiple regression to identify predictors of psychological outcomes. Results: High compassion satisfaction was reported by 38.9% of respondents, while 70.3% exhibited average burnout levels; only 0.4% had high burnout. Secondary traumatic stress was low in 85.4% of participants. Minimal anxiety was found in 54% of respondents. Significant correlations were found between professional satisfaction and lower anxiety (p < 0.001), lower burnout (p < 0.001), and higher compassion satisfaction (p < 0.001). Dentists with more years of experience and older age reported lower anxiety and burnout levels. Higher pre-conflict workloads were associated with increased anxiety during the conflict (p < 0.001). Dentists working in Health Maintenance Organizations (HMOs) reported significantly higher anxiety levels compared to their non-HMO counterparts (p = 0.022), although reported income loss was similar between groups. Conclusions: Civilian dentists demonstrated resilience and overall positive professional functioning during prolonged conflict. However, public sector dentists, especially those in HMOs, showed greater vulnerability to anxiety. These findings underscore the need for systemic strategies to support dental professionals’ mental health during national crises, with emphasis on those in the public health system. Full article

The presence of foreign matter in food poses food safety issues for consumers and directly threatens the food supply chain. In order to ensure food quality and hygiene, promote economic efficiency, and protect consumers’ health rights, the rapid, non-destructive detection of foreign matter in food is an urgent task that requires development. Hyperspectral imaging technology can obtain high-resolution spectral information of foreign matter in multiple wavelengths, and it is widely used in food safety testing. However, the cost and size of the system remain obstacles to further development. Additionally, there are currently no effective solutions for acquiring foreign matter samples or for storing and sharing hyperspectral data during production. This review introduces hyperspectral imaging systems, covering both the software and hardware, as well as a series of algorithms for processing spectral images. The focus is on cases of hyperspectral imaging used for foreign matter detection tasks, with an examination of future developments and challenges. Full article