Search Results (758)

Velocity saturation constraints are a significant issue for wheeled mobile robots (WMRs) when designing kinematics-based control laws. To handle the problem of velocity saturation constraints, a novel monocular visual servoing controller is developed for WMRs to solve tracking problems and enable unknown depth estimation. By analyzing the kinematic model of the robot system and employing the homography decomposition technique, measurable signals are obtained to develop a visual tracking error model for non-holonomic mobile robots. To ensure that the velocity commands are consistently constrained within the allowed limits, a saturation function is employed in the designed visual servoing control law. Furthermore, an adaptive updating law is designed to estimate the unknown depth information. The boundedness of the velocity commands is analyzed to evaluate the saturation performance of the developed visual servoing controller. With the aid of Lyapunov techniques and Barbalat’s lemma, the stability of this scheme is demonstrated. The simulation and experiment verify the performance of the proposed method. Full article

Cervical cancer screening through computer-aided diagnosis often faces challenges like inaccurate segmentation and incomplete boundary detection in colposcopic images. This study proposes a lightweight segmentation model to improve accuracy and computational efficiency. The architecture integrates dual encoder backbones (ResNet50 and MobileNetV2) for high-level and efficient feature extraction. While a lightweight atrous spatial pyramid pooling (ASPP) module records multi-scale contextual information, a novel attention module improves feature details by concentrating on relevant locations. The decoder employs advanced upsampling and feature fusion for refined segmentation boundaries. The experimental results show exceptional performance: training accuracy of 97.56%, validation accuracy of 96.04%, 97.00% specificity, 96.78% sensitivity, 98.71% Dice coefficient, and 97.56% IoU, outperforming the existing methods. In collaboration with the MNJ Institute of Oncology Regional Center, Hyderabad, this work demonstrates potential for real-world clinical applications, delivering precise and reliable colposcopic image segmentation. This research advances efficient, accurate tools for cervical cancer diagnosis, improving diagnostic workflows and patient outcomes. Full article

Background: Dental caries is a significant public health challenge globally, particularly acute in Saudi Arabia’s remote areas with limited healthcare access. Traditional paper-based methods for recording epidemiological data have limitations in data collection, storage, and sharing, highlighting the need for mobile solutions to enhance dental surveillance in resource-limited settings. Objective: To develop and evaluate the Saudi Electronic Caries Assessment Tool (SECAT), a mobile application designed for collecting dental caries data in remote locations, following a user-centered design approach. Methods: This mixed-methods feasibility study was conducted in three stages: (1) requirement gathering through semi-structured interviews with 12 dental professionals to explore experiences and needs; (2) development of the SECAT application using Flutter frontend and Supabase backend; and (3) evaluation through usability testing with 18 clinicians and heuristic study with five domain experts. Results: The usability study revealed an 82% overall satisfaction rate among clinicians, with 78% strongly endorsing the application’s user-friendly features. Expert evaluation highlighted the application’s utility for remote areas and offline functionality. Key improvements implemented based on feedback included an automated tooth recognition system, enhanced visualization protocol, and integration of clinical metrics. Primary limitations identified included the need for improved color contrast and individual-level calculations of dental indices. Conclusions: The SECAT application demonstrated high satisfaction and acceptability among dental health providers for collecting examination data in remote locations. The preliminary evaluation identified both limitations and positive aspects, particularly regarding utility in areas with limited internet connectivity. The SECAT mobile application could be valuable aid for caries assessment in remote places and also in school and community-based dental health programs. Full article

The value of cultural heritage for sustainable development has been acknowledged since the mid-20th century. Built heritage plays a key role in preserving identity, memory, and, currently, aiding education through mobile technology. The integration of education, heritage, and technology presents opportunities for innovation and originality, which are crucial for engaging students in sustainability. This paper focuses on the development of the “Art Nouveau Path” mobile game within the “EduCITY” app, which has been designed to promote Education for Sustainability. The game explores Aveiro’s Art Nouveau heritage and its alignment with nature and environmental concerns. Full article

Neck and lower back pain, often caused by spinal disorders such as scoliosis and degenerative disc disease, affects over 80% of the global population, with an estimated from 250,000 to 500,000 spinal cord injuries occurring annually according to the WHO. As the demand for spinal procedures continues to rise, advancements in implant materials have become essential. Orthopedic implants play a vital role in restoring mobility and improving the quality of life of patients with musculoskeletal disorders. Metallic implants, such as stainless steel, titanium, and its alloys, are commonly used to make fixation devices for spinal fusion surgery due to their excellent mechanical properties. However, complications such as stress shielding have been recorded. Polymeric materials offer new prospects as an alternative to metal-based materials such as those based on Polyaryletherketone (PEAK). Among the advanced materials used in these implants, PAEK has emerged as the preferred choice due to its exceptional mechanical strength, thermal stability, and chemical resistance. Polyetheretherketone (PEEK) and Polyetherketoneketone (PEKK) offer notable advantages, such as radiolucency and mechanical properties resembling those of natural bone, reducing stress shielding and facilitating postoperative imaging. Although PEEK and PEKK are considered as bioinert, it has been demonstrated that adding bioactive agents such as hydroxyapatite (HA) into the matrix to make composites solves this problem and can help with aiding direct bone apposition. Furthermore, PAEK’s compatibility with 3DP enables the creation of patient-specific implants with intricate geometries, enhancing the surgical outcomes. In addition, the lattice structures of orthopedic implants can alleviate stress shielding, provide an enhanced surface area for the release of bioactive agents (or antimicrobial materials), and eliminate more imaging artifacts compared to that of simple, solid metal implants. PAEK/HA composite implants represent a transformative solution, addressing the psychological, social, and economic burdens of spinal disorders, while enhancing the surgical outcomes. With continuous technological evolution, PAEK/HA composites are poised to play a pivotal role in modern spinal care. Full article

Background: Research shows that media-based dietary and behavioral strategies can aid weight loss, but limited studies have been conducted among Black/African American adults. Objective: This review examines the literature on dietary and behavioral strategies for weight loss and maintenance among Black/African American adults, identifying the types of media used alongside these strategies. Methods: The PubMed, Web of Science, CINAHL, and Communication & Mass Media databases were searched for peer-reviewed articles with no restrictions on the publication date. Two reviewers conducted the screening. Studies were included if they had >75% Black/African American adult participants (18 years and older), included behavioral or dietary strategies, had a media component, weight loss or maintenance as an outcome, and published in English language. Results: Nine studies (randomized control trials (n = 5), mixed-method studies (n = 2) and qualitative studies (n = 2)) were included. Behavioral strategies used to lose and maintain weight included goal setting (n = 4), self-monitoring (n = 5), and weekly self-weighing (n = 2). Limiting sugary drinks (n = 3), limiting junk and high fat foods (n = 5), aiming for a set number of calories per day (n = 3), portion control (n = 4), and increasing fruits and vegetable intake (n = 3) were the most common dietary strategies used to lose and maintain weight. Media used in the intervention studies included mHealth text messaging (n = 2), Facebook (n = 2), a website (n = 1), television (n = 1) and a mobile app (n = 1). Conclusions: The findings highlight the limited research on the utilization of media for behavioral and dietary weight loss strategies among Black/African American adults, indicating a need for future studies to explore and optimize media-based strategies for this population. Full article

This study presents a novel method combining photoelectrochemical etching with ultrasonic vibration for the formation of nanocrystalline porous silicon (NC-PS). This combined process enhances the band gap energy absorption (BEA) by reducing bubble accumulation in the etching area. It is found that laser irradiation can decrease the etching rate, while ultrasonic vibration aids with bubble expulsion, preventing accumulation in the etching area, resulting in more uniform etching and increasing the porosity of the porous silicon (PS). High porosity in NC-PS structures enhances the surface area, thereby increasing electron mobility and improving the electron energy distribution. Our experiments demonstrate that this combined process leads to more uniform and deeper etching and the creation of well-defined porous structures. The more uniform PS size distribution (8–14 nm) achieved by photoelectrochemical etching combined with ultrasonic vibration enhances the optical properties of the material due to quantum confinement effects. Porosity measurements provide essential surface characterization information that is crucial for determining the performance of PS diode components in various applications. Our findings demonstrate that this combination technique improves the uniformity, efficiency, and precision of porous silicon etching, producing material for high-performance applications, including sensors, catalysts, and photonic devices. Full article

This study examines the relationship between gender-related aid and legal frameworks governing women’s economic opportunities. Using data from 116 countries (2009–2022), we analyze how significant (SGRA) and principal (PGRA) gender-related aid influence the Women, Business, and Law (WBL) Index, which measures women’s access to employment, credit, and entrepreneurship. Results from fixed-effects models show that SGRA consistently improves WBL Index components, while PGRA significantly impacts marriage, parenthood, and mobility regulations, with modest effects on workplace and entrepreneurship measures. PGRA substantially affects marriage, parenthood, and mobility regulations while demonstrating more modest impacts on workplace conditions and entrepreneurship measures. These observations underscore the complexity of addressing gender inequality and the necessity of targeted, multifaceted approaches to overcome legal restrictions, entrenched social norms, and economic barriers. The research offers valuable insights for policymakers and donors on the transformative potential of gender-mainstreamed aid initiatives in fostering a more equitable world. Full article

Background/Objectives: Respiratory diseases are common and result in high mortality, especially in the elderly, with pneumonia and chronic obstructive pulmonary disease (COPD). Auscultation of lung sounds using a stethoscope is a crucial method for diagnosis, but it may require specialized training and the involvement of pulmonologists. This study aims to assist medical professionals who are non-pulmonologist doctors in early screening for pneumonia and COPD by developing a smart stethoscope with cloud server-embedded machine learning to diagnose lung sounds. Methods: The smart stethoscope was developed using a Micro-Electro-Mechanical system (MEMS) microphone to record lung sounds in the mobile application and then send them wirelessly to a cloud server for real-time machine learning classification. Results: The model of the smart stethoscope classifies lung sounds into four categories: normal, pneumonia, COPD, and other respiratory diseases. It achieved an accuracy of 89%, a sensitivity of 89.75%, and a specificity of 95%. In addition, testing with healthy volunteers yielded an accuracy of 80% in distinguishing normal and diseased lungs. Moreover, the performance comparison between the smart stethoscope and two commercial auscultation stethoscopes showed comparable sound quality and loudness results. Conclusions: The smart stethoscope holds great promise for improving healthcare delivery in the post-COVID-19 era, offering the probability of the most likely respiratory conditions for early diagnosis of pneumonia, COPD, and other respiratory diseases. Its user-friendly design and machine learning capabilities provide a valuable resource for non-pulmonologist doctors by delivering timely, evidence-based diagnoses, aiding treatment decisions, and paving the way for more accessible respiratory care. Full article

The automated and precise classification of lung and colon cancer from histopathological photos continues to pose a significant challenge in medical diagnosis, as current computer-aided diagnosis (CAD) systems are frequently constrained by their dependence on singular deep learning architectures, elevated computational complexity, and their ineffectiveness in utilising multiscale features. To this end, the present research introduces a CAD system that integrates several lightweight convolutional neural networks (CNNs) with dual-layer feature extraction and feature selection to overcome the aforementioned constraints. Initially, it extracts deep attributes from two separate layers (pooling and fully connected) of three pre-trained CNNs (MobileNet, ResNet-18, and EfficientNetB0). Second, the system uses the benefits of canonical correlation analysis for dimensionality reduction in pooling layer attributes to reduce complexity. In addition, it integrates the dual-layer features to encapsulate both high- and low-level representations. Finally, to benefit from multiple deep network architectures while reducing classification complexity, the proposed CAD merges dual deep layer variables of the three CNNs and then applies the analysis of variance (ANOVA) and Chi-Squared for the selection of the most discriminative features from the integrated CNN architectures. The CAD is assessed on the LC25000 dataset leveraging eight distinct classifiers, encompassing various Support Vector Machine (SVM) variants, Decision Trees, Linear Discriminant Analysis, and k-nearest neighbours. The experimental results exhibited outstanding performance, attaining 99.8% classification accuracy with cubic SVM classifiers employing merely 50 ANOVA-selected features, exceeding the performance of individual CNNs while markedly diminishing computational complexity. The framework’s capacity to sustain exceptional accuracy with a limited feature set renders it especially advantageous for clinical applications where diagnostic precision and efficiency are critical. These findings confirm the efficacy of the multi-CNN, multi-layer methodology in enhancing cancer classification precision while mitigating the computational constraints of current systems. Full article

Despite inclusive design focusing on improving environmental accessibility for mobility aid (MA) users, it often fails to ensure true inclusivity due to mismatches between interventions and user perceptions. Therefore, understanding MA users’ perceptions of inclusivity is essential for advancing disability studies and design, as it highlights key interventions for more effective practices. This study aims to explore perceived inclusivity in MAs’ usage and identifies gaps in users’ needs, classifying their needs and offering recommendations to meet them. First, the DARE-Inclusive Design Framework was used to develop interview guidelines and interpret results. Next, using a qualitative descriptive research, semi-structured in-depth interviews were conducted with 12 experienced physiotherapists in Iran. Finally, an inductive thematic analysis was applied to identify and present the emerging themes. Four themes were identified: 1. Perceived Financial Value: Assessing Worth, 2. Objective Enhancements: Optimizing Environments and MAs, 3. Subjective Enhancements: Trustworthiness, Support, and Hope, and 4. Contextual Factors: Interpretations and Representations. The four interconnected themes provide guidelines for inclusivity-oriented interventions, emphasizing financial assessment, high-tech integration, and aesthetic and symbolic considerations in MA design. Physiotherapists can also mediate emotional responses and enhance inclusion during rehabilitation. Additionally, social context and disability etiology impact users’ acceptance and use of MAs. Full article

This paper aims to evaluate the accessibility to urban mobility services with high spatial resolution. The methodology identifies the percentage of the population living within walking distance of the different public transport and shared mobility systems. The motivation for this study stems from the importance of understanding and improving sustainable urban mobility, as well as the need to evaluate the effectiveness of transportation infrastructure. The study involved creating buffers from each station following the road network according to urban design to identify areas within a 5-min walk, then calculating the affected population by age range within these zones to ensure equity. This method determined the percentage of the population with access to available mobility services: bike renting, urban bus, intercity bus, metro, taxi, tram and train. The application of the methodology to the Valencia case study underscores the overall strong accessibility of mobility services. The main results indicate access to mobility services for 99.98% of the total population of the city. The accessibility percentages for specific mobility services vary, with urban bus and bike renting showing high accessibility, while train and tram services exhibit lower accessibility rates. Additionally, the study extends its assessment to the peri-urban area, revealing a global access percentage of 46.33%. In conclusion, this study allows us to identify areas with lower accessibility and help to improve the sustainability of urban mobility. These findings can aid urban planners and policymakers by providing valuable insights into the city’s mobility network’s strengths and areas for improvement. Full article

Aircraft maintenance is a complex, multifaceted process that greatly benefits from IT systems designed to improve supervision, record keeping, and task management. This study focuses on the role of a dedicated mobile application, integrated into the broader IT Aircraft Maintenance Support System, which supports maintenance operations for the M-346 BIELIK training aircraft. Aircraft maintenance is a highly intricate and multifaceted process that significantly benefits from advanced IT systems designed to enhance supervision, streamline record keeping, and optimize task management. This study explores the pivotal role of a dedicated mobile application integrated into the broader IT Aircraft Maintenance Support System, specifically tailored to support the maintenance operations of the M-346 BIELIK training aircraft. By focusing on the analysis of Intelligent Transportation Systems (ITSs), the research highlights how the application contributes to maintenance reliability and operational efficiency, with sustainability considerations in mind. The ITS-based approach assesses maintenance scheduling, tracking, and resource optimization, thereby enhancing the reliability of aircraft operations while reducing unnecessary resource consumption. This alignment with sustainable practices not only improves reliability characteristics and exploitation rates but also positively impacts the efficiency and effectiveness of aviation training. By accurately estimating the time requirements of specific maintenance tasks during periodic inspections, the application aids in identifying and addressing organizational bottlenecks, ultimately supporting both operational sustainability and improved task reliability across maintenance activities. Full article

Background/Objectives: An Extensor Pollicis Longus (EPL) subcutaneous rupture is a substantial complication in post-traumatic or degenerative wrist and tendinous lesions. The diagnosis is essentially dictated by a clinical evaluation; in fact, it is characterized by the inability to extend the thumb interphalangeal joint and to retropose the thumb while the hand is resting on a surface. The tendinous transposition using the Extensor Indicis Proprius (EIP) as a donor tendon is a well-known surgical technique performed to restore functional activity to the thumb, and it is preferred for the closer cerebellar network with the thumb itself. However, there is a dearth of clinical results and scientific evidence in the literature. The aim of this study is to evaluate the return-to-work eligibility after an inveterate EPL subcutaneous rupture repaired with a transposition of the EIP. Methods: Patients who reported a subcutaneous rupture of the EPL due to rheumatic diseases or who had undergone previous hand or wrist surgery were excluded; however, all patients tested positive for traumatic wrist hypertension. The surgical technique involves three small incisions to achieve tenorrhaphy of the EIP at the distal head of the EPL. Dynamic tests are carried out intraoperatively to verify the tightness and sufficient rigidity of the suture. The objective evaluation involves the range of motion, pinch strength, and power extension of the thumb and the index finger. Patient-reported outcome measures for pain and patient satisfaction include the Numeric Pain Rating Scale and the Disabilities of the Arm, Shoulder, and Hand questionnaire. Results: A total of 12 patients were eligible (7 W, 5 M, mean age 56.3 years) and were followed for at least one year. There were no surgery-related complications. After the cast was removed 3 weeks after surgery, patients could extend their thumbs, put them back, and use their index fingers. An immediate improvement in objective and subjective assessments was reported. At 10 weeks, nine patients (75%) returned to full work with no pain and without the aid of rehabilitation; two patients (17%) returned to full work with no symptoms 2 weeks later; and only one patient (8%) with a neurological disease required physical therapy, achieving pain control and restoration of full mobility approximately six months after surgery. Conclusions: This surgical technique seems to address satisfactory results in terms of skill recovery and rapid return to work. A tailored rehabilitation program should be implemented for selected patients with neurological conditions that may prolong the adaptation process of the transposed tendon, the coordination, and the independent function of the thumb and index finger. Full article

The “Four Illegal Activities”, which involve occupation, extraction, and construction along shorelines, have become significant challenges in river and lake management in recent years. Due to the diverse and scattered nature of monitoring targets, coupled with the large volumes of data involved, traditional manual inspection methods are no longer sufficient to meet regulatory demands. Late fusion change detection methods in deep learning are particularly effective for monitoring river and lake occupation due to their straightforward principles and processes. However, research on this topic remains limited. To fill this gap, we selected eight popular deep learning networks—VGGNet, ResNet, MobileNet, EfficientNet, DenseNet, Inception-ResNet, SeNet, and DPN—and used the Jinshui River Basin in Wuhan as a case study to explore the application of Siamese networks to monitor river and lake occupation. Our results indicate that the Siamese network based on EfficientNet outperforms all other models. It can be reasonably concluded that the combination of the SE module and residual connections provides an effective approach for improving the performance of deep learning models in monitoring river and lake occupation. Our findings contribute to improving the efficiency of monitoring river and lake occupation, thereby enhancing the effectiveness of water resource and ecological environment protection. In addition, they aid in the development and implementation of efficient strategies for promoting sustainable development. Full article