Search Results (2,432)

Concrete surface crack detection is a critical problem in the health monitoring and maintenance of engineering structures. The existence and development of cracks may lead to the deterioration of structural performance, potentially causing serious safety accidents. However, detecting cracks accurately remains challenging due to various factors such as uneven lighting, noise interference, and complex backgrounds, which often lead to incomplete or false detections. Traditional manual inspection methods are subjective, inefficient, and costly, while existing deep learning-based approaches still have the problem of insufficient precision and completeness. Therefore, this paper proposes a new crack detection model based on an improved TransUNet: AG-TransUNet, an adaptive multi-head self-attention mechanism, and a gated mechanism-based decoding module (GRU-T) is introduced to improve the accuracy and completeness of crack detection. Experimental results show that the AG-TransUNet outperforms the original TransUNet with a 4.05% increase in precision, a 2.59% improvement in F1-score, and a 0.36% enhancement in IoU on the CFD dataset. The AG-TransUNet achieves a 2.21% increase in precision, a 5.63% improvement in F1-score, and a 9.07% enhancement in IoU on the concrete crack dataset. In addition, in order to further quantitatively analyze the crack width, the orthogonal skeleton method is used to calculate the maximum width of a single crack to provide a reference for engineering maintenance. Experiments show that the maximum error between the real values and detection results is about 5%. Therefore, the proposed method better meets the needs of crack detection in practical engineering applications and provides a solution for improving the efficiency of crack detection. Full article

Objectives: To study the prevalence of injuries among padel players in Madinah and investigate potential causes. This study makes an attempt to add to the gaps in the literature regarding injury risks and preventive strategies in this fast-growing sport. Methods: Retrospective cross-sectional study on 305 padel players who come from Madinah, consisting of 193 men and 112 women aged 18–40 years. Data were collected using an online Google Forms questionnaire, including descriptive statistics and non-parametric tests, among which the chi-square test was performed, aiming for the assessment of demographic and injury-related variables. Results: There were significantly different incidences of injuries with regard to gender at the p = 0.001 level. A 44.6% prevalence was recorded among women, while men had a prevalence of 8.2%. With respect to the severity of injuries from moderate to severe, there are higher percentages in women that comprised 40.4% and 5.6%, respectively. The most frequent types of upper body injuries among women were ligament sprains and muscle strains. Stress and poor warm-up practices were some of the lifestyle factors identified to increase the risk of sustaining an injury. Conclusions: This study highlights gender-specific injury patterns among padel players in Madinah, emphasizing the need for targeted injury prevention programs, including structured warm-ups and strength training. The findings contribute valuable insights for enhancing player safety and aligning with public health objectives under Saudi Vision 2030. Full article

Gliomas within the insular region represent one of the most challenging problems in neurosurgical oncology. There are two main surgical approaches to address the complex vascular network and functional areas around the insula: the transsylvian approach and the transcortical approach. In the literature, there is not a clear consensus on the best approach in terms of safety and efficacy. The purpose of this study is to evaluate the effectiveness of these approaches and to analyze prognostic factors on the natural history of insular gliomas. Patients with newly diagnosed high-grade insular gliomas who underwent surgery between January 2019 and June 2024 were analyzed. The series was analyzed according to the classification of Berger–Sanai and Yaşargil. The Karnofsky performance score (KPS), extent of resection (EOR), progression-free survival (PFS), and overall survival (OS) were considered the outcome measures. A total of 58 primary high-grade insular glioma patients were enrolled in this study. The IDH mutation was found in 13/58 (22.4%); specifically, 3/13 (23.1%) were grade 4, and 10/13 (76.9%) were grade 3. Furthermore, 40/58 patients (69%) underwent gross total resection (GTR), 15 patients (26%) subtotal resection, and 3 patients (5%) partial resection. Middle cerebral artery encasement negatively affected the OS. GTR, radiotherapy, KPS, and autonomous deambulation at a month after surgery positively affected the OS. The surgical approach used was transsylvian and transcortical in 11 and 47 cases, respectively. The comparison between the two different approaches did not display differences in terms of neurological deficits and OS (p > 0.05). The transcortical approach was related to the greater achievement of GTR (p = 0.031). According to the Berger–Sanai classification, the transcortical approach has higher EOR and postoperative KPS when the lesion is in zone III-IV (p = 0.029). Greater resection of insular gliomas can be achieved with an acceptable morbidity profile and is predictive of improved OS. Both the transsylvian and transcortical corridors to the insula are associated with low morbidity profiles. The transcortical approach with intraoperative mapping is more favorable for achieving greater EOR, particularly in gliomas within the inferior border of the Sylvian fissure. Full article

Composite materials are increasingly used in the aviation industry for various aircraft components due to their lightweight and mechanical performances. However, these materials are susceptible to degradation due to environmental factors such as hot–wet environments and freeze–thaw cycles, which can compromise their performance and safety over time. This study develops an innovative Fiber Bragg Grating (FBG) sensor system capable of not only measuring but also decoupling the simultaneous effects of temperature, humidity and strain. Unlike existing FBG systems, our approach integrates a novel theoretical framework and sensor configuration that accurately isolates these parameters in an epoxy resin material. The system incorporates three FBG sensors: one for temperature, one for temperature and humidity and a third one for all three factors. A theoretical framework based on linear strain superposition and constitutive laws was developed to isolate the individual contributions of each factor. Experimental validation in controlled hygrothermal conditions demonstrated the system’s ability to accurately detect and decouple these effects, enabling the monitoring of moisture absorption and composite degradation over time. The proposed system provides a reliable, lightweight and efficient solution for the long-term monitoring of composite structures in extreme conditions. Additionally, it enhances predictive maintenance by improving the accuracy of Health and Usage Monitoring Systems (HUMSs) and provides a method to correct data inconsistencies in already installed sensors, further extending their operational value. Full article

Traffic safety remains a pressing global concern, with traffic signs playing a vital role in regulating and guiding drivers. However, environmental factors like lighting and weather often compromise their visibility, impacting human drivers and autonomous vehicle (AV) systems. This study addresses critical traffic sign detection (TSD) and classification (TSC) gaps by leveraging the YOLOv8 algorithm to evaluate the detection accuracy and sign quality under diverse lighting conditions. The model achieved robust performance metrics across day and night scenarios using the novel ZND dataset, comprising 16,500 labeled images sourced from the GTSRB, GitHub repositories, and real-world own photographs. Complementary retroreflectivity assessments using handheld retroreflectometers revealed correlations between the material properties of the signs and their detection performance, emphasizing the importance of the retroreflective quality, especially under night-time conditions. Additionally, video analysis highlighted the influence of sharpness, brightness, and contrast on detection rates. Human evaluations further provided insights into subjective perceptions of visibility and their relationship with algorithmic detection, underscoring areas for potential improvement. The findings emphasize the need for using various assessment methods, advanced algorithms, enhanced sign materials, and regular maintenance to improve detection reliability and road safety. This research bridges the theoretical and practical aspects of TSD, offering recommendations that could advance AV systems and inform future traffic sign design and evaluation standards. Full article

The digital maturity of Human Resource Management (HRM) is a critical determinant of organisational success in today’s digital age. This paper aims to contribute to the limited literature on the “digital maturity” of HRM by identifying emerging themes and success factors of HRM in the digital age. Drawing on data from 190 journal articles for 2017–2024, this paper identifies three motor themes shaping contemporary HRM: (1) Digital Transformation and Competition, (2) Innovation and Performance Management, and (3) COVID-19 Adaptive Human Resource Management. These findings indicate the multidimensionality of HR digital maturity—from focusing on technology and people to fostering innovation and crisis management. Several factors require attention to improve the digital maturity of HR, including HR strategy and governance; talent management, diversity, and safety; employee adoption and competencies; conflict resolution and stakeholder engagement; and HR practitioners’ competencies. Strategic investment in these pillars is necessary not only to facilitate organisational adaptation to digital transformation but also for harnessing the benefits of emerging technologies to drive innovation and long-term success in the post-COVID era. Full article

Carbon source limitation is a critical factor restricting the treatment efficiency of domestic wastewater by algae–bacteria consortia. Using agricultural waste as an external carbon source to enhance purification performance holds significant potential. This study investigated the effects of peanut shell powder (PSP) on wastewater treatment in algae–bacteria consortia. The results demonstrated that the optimal PSP dosage (2 mg/L) improved the removal efficiencies of TN, TP, and COD by 29.6%, 40.9%, and 18.7%, respectively. In contrast, excessive PSP reduced the removal performance. The primary mechanism by which PSP influenced the algae–bacteria consortia involved changes in microbial biomass and community structure. An optimal PSP dosage promoted the proliferation of the dominant algal species, Chlorella, enhanced photosynthetic activity, and increased the relative abundance of Rhodanobacter, known for its effective degradation of benzene compounds. Conversely, excessive PSP caused microbial cell rupture, inhibited Chlorella growth and photosynthesis, and elevated the abundance of Microcystis and Brevundimonas, which pose significant health risks. In conclusion, PSP can improve effluent quality and safety in algae–bacteria consortia, which represents a green, economical pathway for optimizing wastewater treatment processes. Full article

In the process of unconventional oil and gas production, a large number of microseismic signals are generated. These signals are received by geophones deployed on the ground or in wells and used for safety monitoring. The moving-coil geophone is a commonly used geophone, which is widely used for collecting vibration signals. However, the current conventional moving-coil geophones have certain limitations in terms of frequency band range and cannot fully meet the low-frequency requirements of microseismic signals. We studied the structure and material properties of moving-coil geophones to understand the factors that affect their frequency band. In this paper, we use finite element analysis method to perform structural analysis on a 10 Hz moving-coil geophone, and we combine modal analysis and excitation response analysis to obtain its operating frequency range of 10.63–200.68 Hz. We then discuss the effect of the vibrating components of a moving-coil geophone on its operating frequency range. The material properties of the spring sheet mainly affect the natural frequency of the first-order mode (natural frequency, the lower limit of the operating frequency of the geophone), and the material properties of the lead spring mainly affect the natural frequency of the second-order mode (spurious frequency, the upper limit of the operating frequency of the geophone). By analyzing the sensitivity of the material properties of the vibration system parts and selecting more suitable spring sheets and lead spring materials, a lower natural frequency and a higher spurious frequency can be obtained, thereby achieving the purpose of broadening the operating frequency range of the geophone, which is expected to provide help in actual production. Full article

This study aims to investigate the bending load-bearing capacity of precast hollow composite slabs composed of ultra-high-performance concrete (UHPC) and Normal Concrete (NC). Through finite element numerical analysis and verification, this study analyzes various key factors influencing the performance of the composite slab, including the wall thickness of the square steel tube, the diameter of transverse reinforcing bars, the thickness of the precast bottom slab, and the strength grade of the concrete. The results indicate that the use of UHPC significantly enhances the bending performance of the composite slab. As the wall thickness of the square steel tube and the strength of UHPC increase, both the yield load and ultimate load capacity of the composite slab show considerable improvement. By conducting an in-depth analysis, this study identifies different stages of the composite slab during the loading process, including the cracking stage, yielding stage, and ultimate stage, thereby providing important foundations for optimizing structural design. Furthermore, a set of bending load-bearing capacity calculation formulas applicable to UHPC-NC precast hollow composite slabs is proposed, offering practical tools and theoretical support for engineering design and analysis. The innovation of this study lies not only in providing a profound understanding of the application of composite materials in architectural design but also in offering feasible solutions to the energy efficiency and safety challenges faced by the construction industry in the future. This research demonstrates the tremendous potential of ultra-high-performance concrete and its combinations in modern architecture, contributing to the sustainable development of construction technology. Full article

Background/Objectives: The concept of patient participation is increasingly recognized as an important component in many areas, such as redesigning healthcare processes, improving patient safety, increasing satisfaction, and managing chronic diseases. In this context, measuring the level of patient participation in healthcare services is an important factor. The “Patient Participation Questionnaire” is a tool used to assess patients’ evaluations of their participation in their in-hospital care. The absence of a scale in the Turkish literature that measures this concept reveals the importance of this research. Methods: In this study conducted in a tertiary public hospital in Turkey, the final scale translated into Turkish was applied to 355 people using the convenience sampling method. In addition to the “Patient Participation Scale”, the “Patient Satisfaction Scale” was used for context validity in the study. Data were analyzed with SPSS 27 and AMOS programs. Results: As a result of the confirmatory factor analysis, the scale, which originally consisted of 16 questions and four dimensions, was adapted to Turkish as 14 questions and four dimensions. As a result of confirmatory factor analysis, the goodness of fit values of the scale were found to be x²/sd = 2.53, GFI = 0.93, AGFI = 0.90, CFI = 0.93, RMSEA = 0.066, RMR = 0.041, and NFI = 0.90. These values are within the acceptable and good fit level ranges. As a result of the correlation analysis performed for context validity, it was determined that there was a positive significant relationship between the adapted patient participation scale and the patient satisfaction scale (r = 0.692, p < 0.001). In addition, the internal consistency coefficient of the scale was examined to determine the reliability of the scale, and it was revealed that the scale was reliable at a good level (α = 0.86). Conclusions: Based on the findings, it was revealed that the “Patient Participation Scale” developed in English is a valid and reliable measurement tool in Turkish culture. Full article

Aircraft-missed approaches pose significant safety challenges, particularly under adverse weather conditions like wind shear. This study examines the critical factors influencing wind-shear-related missed approaches at Hong Kong International Airport (HKIA) using Pilot Report (PIREP) data from 2015 to 2023. A Binary Logistic Model (BLM) with L1 (Lasso) and L2 (Ridge) regularization was applied to both balanced and imbalanced datasets, with the balanced dataset created using the Synthetic Minority Oversampling Technique (SMOTE). The performance of the BLM on the balanced data demonstrated a good model fit, with Hosmer–Lemeshow statistics of 5.91 (L1) and 5.90 (L2). The Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) were slightly lower for L1 regularization, at 1528.77 and 1574.35, respectively, compared to 1528.86 and 1574.66 for L2. Cohen’s Kappa values were 0.266 for L1 and 0.253 for L2, reflecting moderate agreement between observed and predicted outcomes and improved performance compared to the imbalanced data. The analysis identified designated-approach runway, aircraft classification, wind shear source, and vertical proximity of wind shear to runway as the most influential factors. Runways 07R and 07C, gust fronts as wind shear sources, and wind shear occurring within 400 ft of the runway posed the highest risk for missed approaches. Narrow-body aircrafts also demonstrated greater susceptibility to turbulence-induced missed approaches. These findings show the importance of addressing these risk factors and enhancing safety protocols for adverse weather conditions. Full article

Background/Objectives: Voglibose, an α-glucosidase inhibitor commonly prescribed to manage postprandial hyperglycemia in diabetes mellitus, demonstrates potential for repurposing as an anti-melanogenic agent. This study aims to explore the inhibitory effects of voglibose on melanogenesis and elucidate its molecular mechanisms, highlighting its possible applications in treating hyperpigmentation disorders. Methods: The anti-melanogenic effects of voglibose were investigated using B16F10 melanoma cells. Cell viability, melanin content, and tyrosinase activity were assessed following voglibose treatment. Western blot analysis was performed to examine changes in melanogenic proteins and transcription factors. The role of signaling pathways, including PKA/CREB, MAPK, PI3K/AKT, and GSK3β/β-Catenin, was analyzed. Primary human skin irritation tests were conducted to evaluate the topical safety of voglibose. Results: Voglibose significantly reduced melanin synthesis and tyrosinase activity in B16F10 cells in a dose-dependent manner. Western blot analysis revealed decreased expression of MITF, TRP-1, and TRP-2, indicating the inhibition of melanogenesis. Voglibose modulated key signaling pathways, including the suppression of PKA/CREB, MAPK, and AKT activation, while restoring GSK3β activity to inhibit β-catenin stabilization. Human skin irritation tests confirmed voglibose’s safety for topical application, showing no adverse reactions at 50 and 100 μM concentrations. Conclusions: Voglibose demonstrates anti-melanogenic properties through the modulation of multiple signaling pathways and the inhibition of melanin biosynthesis. Its safety profile and efficacy suggest its potential as a repurposed drug for managing hyperpigmentation and advancing cosmeceutical applications. Full article

Construction projects’ unsatisfactory performance has been linked to factors influencing individuals’ well-being and mental alertness on projects. Drowsiness is a significant indicator of sleep deprivation and fatigue, so being able to identify the cognitive and physical preparedness of workers on site to engage in construction tasks is important. As a consequence of the strenuous nature of the work involved in construction, long work hours, and environmental conditions, drowsiness is commonplace and has received less attention despite being a leading cause of accidents occurring on-site. Detecting drowsiness is essential for determining the safety and well-being of site workers. This study presents a vision-based approach using an improved version of the You Only Look Once (YOLOv8) algorithm for real-time drowsiness exposure among construction workers. The proposed method leverages computer vision techniques to analyze facial and eye features, enabling the early detection of signs of drowsiness, effectively preventing accidents, and enhancing on-site safety. The model showed significant precision and efficiency in detecting drowsiness from the given dataset, accomplishing a drowsiness class with a mean average precision (mAP) of 92%. However, it also exhibited difficulties handling imbalanced classes, particularly the underrepresented ‘Awake with PPE’ class, which was detected with high precision but comparatively lower recall and mAP. This highlighted the necessity of balanced datasets for optimal deep learning performance. The YOLOv8 model’s average mAP of 78% in drowsiness detection compared favorably with other studies employing different methodologies. The system improves productivity and reduces costs by preventing accidents and enhancing worker safety. However, limitations, such as sensitivity to lighting conditions and occlusions, must be addressed in future iterations. Full article

There has been growing interest in understanding the injury profiles of young female soccer players due to their increasing participation in the sport and the unique risk factors they face. This narrative review examines the incidence, mechanisms, and location of injuries in this population, alongside the primary risk factors and effective preventive strategies. Injury incidence is higher during matches than in training sessions, with contact injuries dominating in games and non-contact injuries prevailing in practice. Knee and ankle injuries are the most prevalent, with anterior cruciate ligament (ACL) injuries being particularly concerning due to their frequency and long-term impact. The interplay of intrinsic factors, such as hormonal fluctuations, anatomical characteristics, and biomechanics, with extrinsic factors like training load, surface type, and footwear significantly influences injury risk. Prevention programs, particularly those combining neuromuscular, balance, and strength training, demonstrate high efficacy, provided that adherence is maintained. Moreover, fostering awareness among players, coaches, and stakeholders about psychosocial factors and menstrual health further enhances injury prevention. Tailored strategies addressing the specific needs of young female soccer players are crucial to ensuring their safety, optimizing performance, and supporting their long-term athletic development. Full article

Regular aircraft engine inspections play a crucial role in aviation safety. However, traditional inspections are often performed manually, relying heavily on the judgment and experience of operators. This paper presents a data-driven deep learning framework capable of automatically detecting defects on reactor blades. Specifically, this study develops Deep Neural Network models to detect defects in borescope images using various datasets, based on Computer Vision and YOLOv8n object detection techniques. Firstly, reactor blade images are collected from public resources and then annotated and preprocessed into different groups based on Computer Vision techniques. In addition, synthetic images are generated using Deep Convolutional Generative Adversarial Networks and a manual data augmentation approach by randomly pasting defects onto reactor blade images. YOLOv8n-based deep learning models are subsequently fine-tuned and trained on these dataset groups. The results indicate that the model trained on wide-shot blade images performs better overall at detecting defects on blades compared to the model trained on zoomed-in images. The comparison of multiple models’ results reveals inherent uncertainties in model performance that while some models trained on data enhanced by Computer Vision techniques may appear more reliable in some types of defect detection, the relationship between these techniques and subsequent results cannot be generalized. The impact of epochs and optimizers on the model’s performance indicates that incorporating rotated images and selecting an appropriate optimizer are key factors for effective model training. Furthermore, models trained solely on artificially generated images from collages perform poorly at detecting defects in real images. A potential solution is to train the model on both synthetic and real images. Future work will focus on improving the framework’s performance and conducting a more comprehensive uncertainty analysis by utilizing larger and more diverse datasets, supported by enhanced computational power. Full article