Search Results (427)

Despite the improvements in enhanced child protection, there is an increasing concern about the vulnerability and cases of child trafficking in Spain, the southern gateway to Europe from the African continent. Analyzing 23 interviews with professionals in the field, this article identifies the factors that contribute to high levels of child trafficking in Spain. This study identifies three primary results: (1) The dangers of residential childcare as places of recruitment; (2) The southern European and Spanish border as a place of elevated risk for the recruitment of children; (3) The stereotypes regarding child trafficking make invisible male victims, other types of trafficking for non-sexual purposes, domestic trafficking, and individual trafficking. Therefore, it remains imperative to advance a set of policies that: (i) invest in specific residential childcare resources for child victims either alone or with family members; (ii) invest in smaller residential childcare to prevent abuse; (iii) invest in mentoring programs for children previously under state guardianship; (iv) improve the working conditions and the training of residential childcare staff; (v) increase the visibility and diversity of child trafficking while avoiding stereotypes; (vi) improve the regional coordination; (vii) invest in campaigns to inform children about the dangers involved in running away, exploitation and abuse. Full article

Body and anthropometric profiles of football players vary depending on the physiological and technical demands imposed by different positions. The aim of this study was to evaluate the body composition of professional soccer players in relation to their position on the field during a training macrocycle. The Direct Segmental Multi-Frequency Bioelectrical Impedance Analysis method was used to analyze 58 players at six key moments of the macrocycle. The results show that body profiles are adjusted to the specific demands of each position. Midfielders showed the lowest muscle mass, while defenders showed many notable changes. In general, as the season progressed, all field players experienced an increase in trunk body fat. Fat and lean mass values of goalkeepers differed greatly from the rest. The greatest variations in body composition were observed during pre-season and transition in relation to variations in training load and competitive intensity. The results suggest that the phase of the macrocycle has a greater influence on these variations, although the physical characteristics of each position are relevant. Understanding these dynamics allows for the design of more personalized and efficient training programs to optimize the performance of footballers according to their roles and each stage of the season. Full article

The field of neuroeducation, which integrates neuroscience findings into educational practice, has gained significant attention in recent years. Establishing research priorities in neuroeducation is crucial for guiding future studies and ensuring that the field benefits both neuroscience and education. This study aimed to address the need for collaboration between neuroscientists and educators by conducting a priority-setting exercise with early career professionals from both fields. Using the nominal group technique (NGT) with interquartile range (IQR) analysis, we identified seven key priorities in neuroeducation and assessed the level of consensus on these priorities. The top-ranked priorities were “Emotional and Mental Well-being”, “Neurodiversity and Special Education Needs”, and “Active and Inclusive Teaching Methods”, though IQR analysis revealed varying levels of consensus. Lower-ranked priorities, such as “Role of Technology on Learning and the Brain”, showed a higher consensus. This discrepancy between ranking and consensus highlights the complex nature of neuroeducation, reflecting differing perspectives between neuroscientists and educators. These findings suggest the need for interdisciplinary collaboration to bridge these gaps and foster evidence-based practices. We recommend that future research focuses on the specific neural mechanisms underlying emotional well-being, strategies for supporting neurodivergent learners, and practical approaches to integrating inclusive teaching methods in diverse educational contexts. Full article

Background/Objectives. Burnout and occupational stress are significant issues among forensic professionals, impacting their well-being and job performance. This systematic review aims to provide an up-to-date overview of the occupational stress and burnout experienced by forensic personnel, exploring the profound and multifaceted impact on their physical, mental, professional, and interpersonal well-being. Methods. A systematic review was conducted following PRISMA guidelines using Scopus and WOS databases to search for articles published from 1 January 2000 to 31 August 2024. The search used keywords related to burnout and forensic professions. Inclusion criteria were original articles in English and French, while reviews, book chapters, editorials, and notes were excluded. A total of 10 studies were included after eliminating duplicates and excluding irrelevant articles. Results. The review identified seven key findings. (1) High levels of occupational stress and burnout among forensic personnel necessitate effective stress management strategies and resilience training; (2) autopsy technicians in Romania experience burnout and alexithymia, particularly related to traumatic events involving children, highlighting the need for specialized support systems; (3) disparities in burnout and post-traumatic stress disorder (PTSD) symptoms were observed in autopsy technicians and resident doctors, suggesting tailored mental health resources; (4) organizational factors, such as peer support and compensation satisfaction, significantly impact burnout and secondary traumatic stress (STS) among sexual assault nurse examiners; (5) burnout among forensic physicians, both in Romania and Egypt, is linked to personality traits, job satisfaction, and socio-demographic factors; (6) pathologists face a range of health issues, including musculoskeletal problems and psychological disorders, underscoring the need for industry-specific health measures; and (7) the lack of wellness resources for forensic professionals calls for improved mental health support and training. Conclusions. The findings highlight the pervasive issue of burnout and stress among forensic professionals globally. Addressing these challenges requires comprehensive stress management programs, tailored mental health resources, and organizational support. Future research should focus on developing and implementing effective interventions to enhance resilience and job satisfaction within this high-stress field. Full article

Over the past few decades, machine learning (ML) has demonstrated significant advancements in all areas of human existence. Machine learning and deep learning models rely heavily on data. Typically, basic machine learning (ML) and deep learning (DL) models receive input data and its matching output. Within the model, these models generate rules. In a physics-guided model, input and output rules are provided to optimize the model’s learning, hence enhancing the model’s loss optimization. The concept of the physics-guided neural network (PGNN) is becoming increasingly popular among researchers and industry professionals. It has been applied in numerous fields such as healthcare, medicine, environmental science, and control systems. This review was conducted using four specific research questions. We obtained papers from six different sources and reviewed a total of 81 papers, based on the selected keywords. In addition, we have specifically addressed the difficulties and potential advantages of the PGNN. Our intention is for this review to provide guidance for aspiring researchers seeking to obtain a deeper understanding of the PGNN. Full article

Despite technological growth and worldwide advancements in various fields, the agriculture sector continues to face numerous challenges such as desertification, environmental pollution, resource scarcity, and the excessive use of pesticides and inorganic fertilizers. These unsustainable problems in agricultural field can lead to land degradation, threaten food security, affect the economy, and put human health at risk. To mitigate these global issues, it is essential for researchers and agricultural professionals to promote advancements in smart agriculture by integrating modern technologies such as Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), Wireless Sensor Networks (WSNs), and more. Among these technologies, this paper focuses on UAVs, particularly quadcopters, which can assist in each phase of the agricultural cycle and improve productivity, quality, and sustainability. With their diverse capabilities, quadcopters have become the most widely used UAVs in smart agriculture and are frequently utilized by researchers in various projects. To explore the different aspects of quadcopters’ use in smart agriculture, this paper focuses on the following: (a) the unique advantages of quadcopters over other UAVs, including an examination of the quadcopter types particularly used in smart agriculture; (b) various agricultural missions where quadcopters are deployed, with examples highlighting their indispensable role; (c) the modelling of quadcopters, from configurations to the derivation of mathematical equations, to create a well-modelled system that closely represents real-world conditions; and (d) the challenges that must be addressed, along with suggestions for future research to ensure sustainable development. Although the use of UAVs in smart agriculture has been discussed in other papers, to the best of our knowledge, none have specifically examined the most popular among them, “quadcopters”, and their particular use in smart agriculture in terms of types, applications, and modelling techniques. Therefore, this paper provides a comprehensive survey of quadcopters’ use in smart agriculture and offers researchers and engineers valuable insights into this evolving field, presenting a roadmap for future enhancements and developments. Full article

Digital solutions in the field of restorative neurology offer significant assistance, enabling patients to engage in rehabilitation activities remotely. This research introduces ReMoVES, an Internet of Medical Things (IoMT) system delivering telemedicine services specifically tailored for multiple sclerosis rehabilitation, within the overarching framework of the STORMS project. The ReMoVES platform facilitates the provision of a rehabilitative exercise protocol, seamlessly integrated into the Individual Rehabilitation Project, curated by a multidimensional medical team operating remotely. This manuscript delves into the second phase of the STORMS pilot feasibility study, elucidating the technology employed, the outcomes achieved, and the practical, professional, and academic implications. The STORMS initiative, as the genesis of digital telerehabilitation solutions, aims to enhance the quality of life for multiple sclerosis patients. Full article

Most of the current machine learning algorithms are applied to predict the jacking force required in micro-tunneling; in contrast, few studies about long-distance, large-section jacking projects have been reported in the literature. In this study, an intelligent framework, consisting of a differential evolution (DE), a bidirectional gated re-current unit (BiGRU), and attention mechanisms was developed to automatically identify the optimal hyperparameters and assign weights to the information features, as well as capture the bidirectional temporal features of sequential data. Based on field data from a pipe jacking project crossing underneath a canal, the model’s performance was compared with those of four conventional models (RNN, GRU, BiGRU, and DE–BiGRU). The results indicated that the DE–BiGRU–attention model performed best among these models. Then, the generalization performance of the proposed model in predicting jacking forces was evaluated with the aid of a similar case at the site. It was found that fine-tuning parameters for specific projects is essential for improving the model’s generalization performance. More generally, the proposed prediction model was found to be practically useful to professionals and engineers in making real-time adjustments to jacking parameters, predicting jacking force, and carrying out performance evaluations. Full article

Motion perception is crucial in competitive sports like dance, basketball, and diving. However, evaluations in these sports heavily rely on professionals, posing two main challenges: subjective assessments are uncertain and can be influenced by experience, making it hard to guarantee timeliness and accuracy, and increasing labor costs with multi-expert voting. While video analysis methods have alleviated some pressure, challenges remain in extracting key points/frames from videos and constructing a suitable, quantifiable evaluation method that aligns with the static–dynamic nature of movements for accurate assessment. Therefore, this study proposes an innovative intelligent evaluation method aimed at enhancing the accuracy and processing speed of complex video analysis tasks. Firstly, by constructing a keyframe extraction method based on musical beat detection, coupled with prior knowledge, the beat detection is optimized through a perceptually weighted window to accurately extract keyframes that are highly correlated with dance movement changes. Secondly, OpenPose is employed to detect human joint points in the keyframes, quantifying human movements into a series of numerically expressed nodes and their relationships (i.e., pose descriptions). Combined with the positions of keyframes in the time sequence, a standard pose description sequence is formed, serving as the foundational data for subsequent quantitative evaluations. Lastly, an Action Sequence Evaluation method (ASCS) is established based on all action features within a single action frame to precisely assess the overall performance of individual actions. Furthermore, drawing inspiration from the Rouge-L evaluation method in natural language processing, a Similarity Measure Approach based on Contextual Relationships (SMACR) is constructed, focusing on evaluating the coherence of actions. By integrating ASCS and SMACR, a comprehensive evaluation of dancers is conducted from both the static and dynamic dimensions. During the method validation phase, the research team judiciously selected 12 representative samples from the popular dance game Just Dance, meticulously classifying them according to the complexity of dance moves and physical exertion levels. The experimental results demonstrate the outstanding performance of the constructed automated evaluation method. Specifically, this method not only achieves the precise assessments of dance movements at the individual keyframe level but also significantly enhances the evaluation of action coherence and completeness through the innovative SMACR. Across all 12 test samples, the method accurately selects 2 to 5 keyframes per second from the videos, reducing the computational load to 4.1–10.3% compared to traditional full-frame matching methods, while the overall evaluation accuracy only slightly decreases by 3%, fully demonstrating the method’s combination of efficiency and precision. Through precise musical beat alignment, efficient keyframe extraction, and the introduction of intelligent dance motion analysis technology, this study significantly improves upon the subjectivity and inefficiency of traditional manual evaluations, enhancing the scientificity and accuracy of assessments. It provides robust tool support for fields such as dance education and competition evaluations, showcasing broad application prospects. Full article

The presented work provides a comprehensive evaluation of the current availability of education programs and courses related to of AI the field of Information Technologies and Computer Science in higher education institutions (HIEs) in the Republic of Bulgaria. More specifically, this study examines 163 bachelor’s and 239 master’s degree programs from 28 HEIs available during the 2023/24 academic year in four professional fields: (1) Electrical Engineering, Electronics, and Automation; (2) Communication and Computer Technologies; (3) Informatics and Computer Science; and (4) Mathematics. The conducted evaluation shows that 41.1% of evaluated BSc programs and 26.4% of MSc programs include at least one AI-dedicated course. Results indicate a significant presence of AI-focused education, particularly in degrees related to Informatics and Computer Science, where 47.8% of AI courses are concentrated. However, a notable disparity exists in the inclusion of AI subjects across other technical fields, particularly in Electrical Engineering and related degrees, which contain only 8% of the identified AI courses for BSc degree programs. The findings highlight the need for a broader and more accelerated integration of AI education to meet the evolving demands of both students and the labor market. This work underscores the importance of strategic curriculum adaptation to enhance the readiness of Bulgarian HEIs to support the development and application of AI technologies, addressing the skills gap and fostering a workforce capable of navigating the AI-driven future. Full article

In the field of sports performance, sports medicine, and physical rehabilitation, there is a great interest in the development of protocols and reliable techniques and instruments for the evaluation of strength produced by athletes. In the last ten years, women’s football has increased its popularity and participation in numerous countries, which has contributed to players developing more professionally and requiring more specific muscle strength training to improve their performance. The aim of this study was to analyze the absolute and relative test–retest reliabilities of peak muscle strength in knee flexion (FLE) and extension (EXT) controlled using a functional electromechanical dynamometer (FEMD) in a group of seventeen professional female soccer players (age = 18.64 ± 0.62 years; weight = 54.72 ± 7.03 kg; height = 1.58 ± 0.04 m; BMI = 21.62 ± 2.70 kg/m²). Peak muscle strength was measured with knee flexion (FLE) and extension (EXT) movements at a speed of 0.4 m·s⁻¹ unilaterally in a concentric phase (CON) and an eccentric phase (ECC). No significant mean differences were found in the test–retest analysis (p > 0.05; effect size < 0.14), and high reliability was reported for peak muscle strength assessments in both the CON (ICC) = 0.90–0.95) and the ECC (ICC = 0.85–0.97). Furthermore, stable repeatability was presented for extension in the CON (CV = 7.39–9.91%) and ECC (CV = 8.65–13.64). The main findings of this study show that peak muscle strength in knee flexion and extension in CON and ECC is a measure with acceptable absolute reliability and extremely high relative reliability using the FEMD in professional female soccer players. Full article

Illegal waste dumping not only encroaches on land resources but also threatens the health of the surrounding residents. The traditional artificial waste monitoring solution requires professional workers to conduct field investigations. This solution not only requires high labor resources and economic costs but also demands a prolonged cycle for updating the monitoring status. Therefore, some scholars use deep learning to achieve automatic waste detection from satellite imagery. However, relevant models cannot effectively capture multi-scale features and enhance key information. To further bolster the monitoring efficiency of urban solid waste, we propose a novel multi-scale context fusion network for solid waste detection in remote sensing images, which can quickly collect waste distribution information in a large-scale range. Specifically, it introduces a new guidance fusion module that leverages spatial attention mechanisms alongside the use of large kernel convolutions. This module helps guide shallow features to retain useful details and adaptively adjust multi-scale spatial receptive fields. Meanwhile, it proposes a novel context awareness module based on heterogeneous convolutions and gating mechanisms. This module can effectively capture richer context information and provide anisotropic features for waste localization. In addition, it also designs an effective multi-scale interaction module based on cross-guidance and coordinate perception. This module not only enhances critical information but also fuses multi-scale semantic features. To substantiate the effectiveness of our approach, we conducted a series of comprehensive experiments on two representative urban waste detection datasets. The outcomes of relevant experiments indicate that our methodology surpasses other deep learning models. As plug-and-play components, these modules can be flexibly integrated into existing object detection frameworks, thereby delivering consistent enhancements in performance. Overall, we provide an efficient solution for monitoring illegal waste dumping, which contributes to promoting eco-friendly development. Full article

The study has as purpose to identify, analyze, and validate challenge constructs of Industry 4.0, which can affect the promotion of sustainability within the industry. A systematic literature review was conducted to identify challenges to the promotion of Industry 4.0 sustainability. A set of seventy challenges were grouped into the following eight constructs: finances, technology, organizational, human resources, legislation, geopolitical and economic factors, and both internal and external factors. Subsequently, the same constructs were validated using a survey involving industry professionals. The data were analyzed using the Lawshe method. Five constructs within the eight constructs were considered relevant to industry sustainability according to the experts’ opinion. The validated set of constructs included: finance, technology, organizational, human resources, and internal factors. This study contributes to the literature in the field by addressing a research gap of constructs identification based on expert’s opinions that impact Industry 4.0 in promotion of sustainable development. This study delivers theoretical and practical implications. From a theoretical standpoint, it contributes to expanding knowledge by providing valuable insights into the adoption of Industry 4.0 and its specific challenges concerning the pursuit of more sustainable practices. These implications extend to diverse research areas given the multidisciplinary nature of Industry 4.0. Full article

Continuous changes in society and the need for sustainable development demand updates in designing better built environments to respond to the variety of user needs. Notwithstanding the growing interest of research and the introduction of guidelines and standards on inclusion, diversity, equity, and accessibility, there are still several limitations in effectively and efficiently embedding such principles for the design of buildings and neighborhoods. Previous research demonstrated the critical need for innovative tools and methods to support professionals in designing responsive, inclusive spaces for an extended range of users. This article reports the results of a pilot study using the new IDEA Audit Tool for assessing how inclusion, diversity, equity, and accessibility are perceived by building occupants in a specific facility. The analysis of significant data provided by this study shows the challenges and highlights the benefits of the tool, including fostering an evidence-based decision-making process, speeding up the prioritization of critical design improvements, demonstrated through a six-month trial with a London-based inclusive design firm. The research-driven outcomes showcase the huge potential that the tool offers to improve the company strategy while evolving towards more inclusive, accessible spaces that foster diversity and equity, and has the potential to be replicated in several fields of action to raise awareness and improve the application of IDEA principles in all phases of the design process. Full article

The velocity and acceleration of a soccer player varies depending on the specific demands of the field position as well as individual characteristics, establishing the need to determine relative profiles by position. A cross-sectional study was conducted in 18 semi-professional soccer players to determine (i) the specific demands of external load according to playing position, (ii) distances covered at different intensities and the number of sprints, and (iii) the number of accelerations at moderate and high intensity. GPS tracking systems were used to collect data, and the relative acceleration profiles were analyzed based on initial velocity (0–7 km/h; 7.1–14.3 km/h; >14.3 km/h), intensity (moderate 50–75% and high > 75% of maximal acceleration), number of sprints/accelerations, and distance covered. Additionally, relative speed profiles were evaluated through the distance covered at moderate intensity (40–60% Vmax), high intensity (60–75.5% Vmax), very high intensity (>75.5% Vmax), total distance, and number of sprints. Statistically significant differences were observed in the distance covered at moderate and high intensity (midfielders), distance covered sprinting (center backs), and acceleration at moderate and high intensity in all positions (p < 0.05). These findings will enhance the monitoring of external loading strategies and prescription of specific training exercises for soccer players based on their respective playing position, ultimately contributing to optimized performance. Full article