Search Results (1,520)

The early detection of Alzheimer’s disease (AD) in cognitively healthy individuals remains a major preclinical challenge. EEG is a promising tool that has shown effectiveness in detecting AD risk. Task-related EEG has been rarely used in Alzheimer’s disease research, as most studies have focused on resting-state EEG. An interpretable deep learning framework—Interpretable Convolutional Neural Network (InterpretableCNN)—was utilized to identify AD-related EEG features. EEG data were recorded during three cognitive task conditions, and samples were labeled based on APOE genotype and polygenic risk scores. A 100-fold leave-p%-subjects-out cross-validation (LPSO-CV) was used to evaluate model performance and generalizability. The model achieved an ROC AUC of 60.84% across the tasks and subjects, with a Kappa value of 0.22, indicating fair agreement. Interpretation revealed a consistent focus on theta and alpha activity in the parietal and temporal regions—areas commonly associated with AD pathology. Task-related EEG combined with interpretable deep learning can reveal early AD risk signatures in healthy individuals. InterpretableCNN enhances transparency in feature identification, offering a valuable tool for preclinical screening. Full article

This study examined the effect of executive function training targeting both updating and inhibition in children. The training included both single training (i.e., number 2-back training) and combined training (i.e., number 2-back and fish flanker training). Event-related potentials were also recorded. In Experiment 1, we employed both single-training and combined-training groups, which were contrasted with each other and with an active control group. In Experiment 2, the control group and the combined-training group were recruited to perform training tasks identical to those used in Experiment 1, and their EEG data were collected during the pretest and posttest stage. Experiment 1 found that the single group showed clear evidence for transfer to letter 2-back task compared with the active control group. The combined group showed significant transfer to the letter 2-back and arrow flanker task. Both groups found no transfer to fluid intelligence or shifting. Experiment 2 revealed that the participants who received updating and inhibition training showed a significant reduction in N2 amplitude and a significant increase in P300 amplitude after training in comparison to the active control group. Importantly, there was a significant positive correlation between reduced N2 amplitude and decreased response time in conflict effects. Additionally, there was a strong positive trend toward a relationship between behavioral performance improvement and an increase in P300 amplitude. From the perspective of the near-transfer effect, combined training is more effective than single training. Our results showed that the extent of transfer depends on the cognitive component overlap between the training and transfer tasks. Full article

Mitochondria are currently of great interest to scientists. The role of mitochondrial DNA (mtDNA) mutations has been proven in the genesis of more than 200 pathologies, which are called mitochondrial disorders. Therefore, the study of mitochondria and mitochondrial DNA is of great interest not only for understanding cell biology but also for the treatment and prevention of many mitochondria-related pathologies. There are two main trends of mitochondrial therapy: mitochondrial replacement therapy (MRT) and mitochondrial transplantation therapy (MTT). Also, there are two main categories of MRT based on the source of mitochondria. The heterologous approach includes the following methods: pronuclear transfer technique (PNT), maternal spindle transfer (MST), Polar body genome transfer (PBT) and germinal vesicle transfer (GVT). An alternative approach is the autologous method. One promising autologous technique was the autologous germline mitochondrial energy transfer (AUGMENT), which involved isolating oogonial precursor cells from the patient, extracting their mitochondria, and then injecting them during ICSI. Transmission of defective mtDNA to the next generation can also be prevented by using these approaches. The development of a healthy child, free from genetic disorders, and the prevention of the occurrence of lethal mitochondrial disorders are the main tasks of this method. However, a number of moral, social, and cultural objections have restricted its exploration, since humanity first encountered the appearance of a three-parent baby. Therefore, this review summarizes the causes of mitochondrial diseases, the various methods involved in MRT and the results of their application. In addition, a new technology, mitochondrial transplantation therapy (MTT), is currently being actively studied. MTT is an innovative approach that involves the introduction of healthy mitochondria into damaged tissues, leading to the replacement of defective mitochondria and the restoration of their function. This technology is being actively studied in animals, but there are also reports of its use in humans. A bibliographic review in PubMed and Web of Science databases and a search for relevant clinical trials and news articles were performed. A total of 83 publications were selected for analysis. Methods of MRT procedures were reviewed, their risks described, and the results of their use presented. Results of animal studies of the MTT procedure and attempts to apply this therapy in humans were reviewed. MRT is an effective way to minimize the risk of transmission of mtDNA-related diseases, but it does not eliminate it completely. There is a need for global legal regulation of MRT. MTT is a new and promising method of treating damaged tissues by injecting the body’s own mitochondria. The considered methods are extremely good in theory, but their clinical application in humans and the success of such therapy remain a question for further study. Full article

Background: While some evidence supports the benefits of food-related tasks, research examining their association with psychosocial health in adolescents remains scarce. The aim of this study was to examine the association between Spanish adolescents’ involvement in food-related household tasks and their psychosocial health. Methods: This cross-sectional study used secondary data from the original Eating Healthy and Daily Life Activities (EHDLA) study. The final sample comprised 273 boys (43.0%) and 361 girls (57.0%). Adolescents self-reported their weekly frequency of involvement in two food-related tasks: meal preparation and grocery shopping, with responses ranging from ‘never’ to ‘seven times’. Psychosocial health was assessed using the 25-item self-report version of the Strengths and Difficulties Questionnaire (SDQ), comprising five subscales: emotional problems, conduct problems, hyperactivity, peer problems, and prosocial behavior. A total difficulties score was calculated by summing the first four subscales. Generalized linear models were used to evaluate associations between the frequency of food task involvement (categorized into five levels) and SDQ outcomes. All models were adjusted for age, sex, socioeconomic status, body mass index, sleep duration, physical activity, sedentary behavior, and energy intake. Results: Concerning to the frequency of helping to prepare food for dinner, an inverse association was observed between food preparation involvement and several psychosocial problems. Adolescents who helped seven times per week reported significantly lower scores in conduct problems (B = −2.00; 95% CI −3.30 to −0.69; p = 0.003), peer problems (B = −2.83; 95% CI −4.29 to −1.38; p < 0.001), internalizing problems (B = −3.90; 95% CI −7.03 to −0.77; p = 0.015), and total psychosocial difficulties (B = −5.74; 95% CI −10.68 to −0.80; p = 0.023), compared to those who never helped. Conversely, those who helped seven times per week had higher prosocial behavior than their counterparts who never helped (B = 1.69; 95% CI: 0.14 to 3.24; p = 0.033). Regarding the frequency of helping to shop for food, similar patterns were found, with lower conduct problems (B = −2.11; 95% CI −3.42 to −0.81; p = 0.002), peer problems (B = −2.88; 95% CI −4.34 to −1.42; p < 0.001), internalizing problems (B = −4.16; 95% CI −7.28 to −1.04; p = 0.009), and total psychosocial difficulties (B = −6.31; 95% CI −11.24 to −1.39; p = 0.012) associated with more frequent involvement, especially among those who helped five or more times per week. Conversely, adolescents who helped seven times per week had higher prosocial behavior than their peers who never helped (B = 1.56; 95% CI: 0.01 to 3.11; p = 0.049). Conclusions: Although adolescent psychosocial health is influenced by multiple factors, our findings suggest that regular involvement in food-related household tasks may serve as a protective factor against conduct problems, peer problems, internalizing problems, and total difficulties, while also enhancing prosocial behavior. However, given the cross-sectional design, conclusions regarding causality should be made cautiously, and further longitudinal research is needed to confirm these associations and assess their long-term impact. These results highlight the relevance of daily structured routines, such as meal preparation and grocery shopping, as potential support for mental well-being during adolescence. Full article

Poor officer fitness can lead to decreased occupational task performance, injuries, increased absenteeism, and a variety of negative health sequalae further adding to the challenges of staffing law enforcement agencies. Optimizing the physical fitness for both serving officers and new recruits is critical as their loss is, and will increasingly be, difficult to replace. However, maintaining and recruiting a physically fit workforce faces several challenges. For serving officers, shiftwork is known to decrease motivation to exercise and negatively impact sleep and diet. Additional factors impacting their fitness includes age-related declines in fitness, increasing obesity, long periods of sedentarism, and negative COVID-19 effects. Concurrently, recruiting physically fit recruits is challenged by declining levels of fitness, reduced physical activity, and increasing obesity in community youth. Ability-based training (ABT), individualizing physical conditioning training based on the existing fitness levels of individuals within a group, offers a potential solution for delivering physical conditioning to groups of applicants, recruits, and officers with a range of physical fitness capabilities. Law enforcement agencies should consider implementing ABT during academy training and ongoing fitness maintenance to minimize injury risk and optimize task performance. Full article

Background: Multiple-object tracking (MOT) is a cognitively demanding task involving sustained attention and interhemispheric integration. While previous studies have revealed that gamma-band coherence mediates interhemispheric integration in MOT tasks with visible internal boundaries, the neural mechanisms supporting integration without such boundaries remain unclear. This study investigated brain functional connectivity during a boundary-free MOT task. Methods: Thirty-eight healthy participants completed the task under four experimental conditions, defined by two load levels (two and four targets) and two movement configurations (within hemifield and between hemifield). Electroencephalography (EEG) activity was recorded in both the task and resting states. The phase locking value (PLV) and network properties were analyzed. Results: The behavioral results demonstrated greater accuracy under the two-target conditions than under the four-target conditions and significantly worse performance under the four-target between-hemifield condition. EEG analyses revealed increased theta-band PLV under the four-target between-hemifield condition, reflecting enhanced interhemispheric synchronization. The PLV difference between the four-target within-hemifield and between-hemifield conditions was positively correlated with the accuracy difference, suggesting that increased theta-band phase synchronization is associated with better task performance. Moreover, sex-related differences were observed, with males showing better performance, shorter click times, and higher theta-band PLV than females. Conclusions: Our study provides evidence that theta-band phase synchronization plays a critical role in interhemispheric integration during boundary-free MOT, extending previous findings on gamma-band coherence under visible-boundary conditions and offering new insights into the neural mechanisms of interhemispheric coordination. Full article

At the stage of the final processing of surfaces, the quality indicators of the surfaces of parts—size, shape in cross-section and longitudinal section, mutual arrangement of surfaces, and roughness—are obtained. This includes technical and organizational measures and activities that are laid down or taken into account in the applied technology. This publication constructs cause-and-effect diagrams of the factors influencing the achievement of each of these accuracy indicators. Ways to reduce the negative impact of some factors are indicated. Errors related to the components of the technological system are analyzed and grouped. Tasks related to accurate process design are defined. Guidelines related to structural accuracy design are given. The technological conditions for ensuring the accuracy of finishing operations when processing parts by turning are formulated. Full article

This study explores how Speech and Elocution Training (SET) activates language potential and fosters career-oriented development among higher vocational students through self-efficacy mechanisms. Through qualitative interviews with four vocational graduates who participated in SET 5 to 10 years ago, the research identifies three key findings. First, SET comprises curriculum content (e.g., workplace communication modules such as hosting, storytelling, and sales pitching) and classroom training using multimodal TED resources and Toastmasters International-simulated practices, which spark language potential through skill-focused, realistic exercises. Second, these pedagogies facilitate a progression where initial language potential evolves from nascent career interests into concrete job-seeking intentions and long-term career plans: completing workplace-related speech tasks boosts confidence in career choices, planning, and job competencies, enabling adaptability to professional challenges. Third, SET aligns with Bandura’s four self-efficacy determinants; these are successful experiences (including personalized and virtual skill acquisition and certified affirmation), vicarious experiences (via observation platforms and constructive peer modeling), verbal persuasion (direct instructional feedback and indirect emotional support), and the arousal of optimistic emotions (the cognitive reframing of challenges and direct desensitization to anxieties). These mechanisms collectively create a positive cycle that enhances self-efficacy, amplifies language potential, and clarifies career intentions. While highlighting SET’s efficacy, this study notes a small sample size limitation, urging future mixed-methods studies with diverse samples to validate these mechanisms across broader vocational contexts and refine understanding of language training’s role in fostering linguistic competence and career readiness. Full article

This academic article seeks to clarify the state of the literature on a very pertinent topic that is based on how digital innovation, considering emerging technologies and how they could be used in business management and marketing, could increase sustainable development. The sustainable economy, which should maintain long-term development through efficient resource management, has as allies emerging technologies such as artificial intelligence, blockchain, and the Internet of Things that can help reduce waste, reduce the carbon footprint, and automate tasks. Additionally, they could present themselves as a solution to improve aspects of digital communication between companies and their consumers in remote training, distribution chain, e-commerce, and process optimization in different sectors of activity. These advances will, on the one hand, allow the possibility of conducting a greater amount of professional training, increasing the number of qualified professionals and, on the other hand, facilitate trade exchanges, promoting the economy. Based on a systematic bibliometric review of the literature using the PRISMA framework, this study investigates how digital tools catalyze transformative changes in different sectors of activity. The results indicate that, overall, the academic articles analyzed in this literature review present studies focused on digitalization and sustainability (approximately 50%). In second place are topics related to digitalization and other topics such as: smart cities; Sustainable Development Goals; academia; the digital economy; government policies; academic education; and sustainable communication (29%). Finally, in third place, there are academic articles closely linked to digitalization and the environment, more specifically to sustainable practices and the management of natural resources (21%). The article concludes that digital development, when used wisely, serves as a crucial lever to address the world’s most pressing sustainability imperatives. Future research should emphasize interdisciplinary collaboration and adaptive governance to ensure that these digital changes produce lasting impacts for people and the planet. Full article

This study explores the influence of team communication (TC) and shared mental models (SMMs) on entrepreneurial team efficacy (ETE) within the context of Chinese higher education, introducing a dual-path model to reconcile the discrepancy between policy expectations and practical outcomes in entrepreneurship education. Utilizing partial least squares structural equation modeling (PLS-SEM) on data from 475 university-based questionnaires from March to May in 2024 in China, the research reveals that structured internal communication significantly enhances the alignment of learning goals, teammate cognition, and activity synchronization, thereby fostering SMMs as a pivotal psychological infrastructure. The findings indicate that shared learning goals and cognitive convergence are primary drivers of task performance, whereas coordinated activity states are more influential in strengthening relational cohesion. The study challenges the conventional “communication frequency–efficacy paradox” by demonstrating distinct pathways through which internal and external communication mechanisms differentiated impact task and relational outcomes. Additionally, demographic analyses highlight that team maturity and age diversity positively correlate with task efficacy, while gender and disciplinary heterogeneity show no significant association. Theoretically, this research advances the understanding of team collaboration dynamics and contextualizes Western entrepreneurship theories within China’s collectivist framework. Practically, it provides robust, evidence-based strategies for refining communication protocols and enhancing both collaborative efficiency and innovation in entrepreneurial education settings. Full article

Rehabilitation robotic systems have been developed to perform therapy with minimal supervision from a specialist. Hence, they require algorithms to assess and support patients’ motions. Artificial intelligence brings an opportunity to implement new exercises based on previously modelled ones. This study focuses on analysing the similarities in upper extremity movements during activities of daily living (ADLs). This research aimed to model ADLs by registering and segmenting real-life movements and dividing them into sub-tasks based on joint motions. The investigation used IMU sensors placed on the body to capture upper extremity motion. Angular measurements were converted into joint variables using Matlab computations. Then, these were divided into segments assigned to the sub-functionalities of the tasks. Further analysis involved calculating mathematical measures to evaluate the similarity between the different movements. This approach allows the system to distinguish between similar motions, which is critical for assessing rehabilitation scenarios and anatomical correctness. Twenty-two ADLs were recorded, and their segments were analysed to build a database of typical motion patterns. The results include a discussion on the ranges of motion for different ADLs and gender-related differences. Moreover, the similarities and general trends for different motions are presented. The system’s control algorithm will use these results to improve the effectiveness of robotic-assisted physiotherapy. Full article

Situation awareness (SA) involves analyzing sensory data, such as audio signals, to identify anomalies. While acoustic features are widely used in audio analysis, existing methods face critical limitations; they often overlook the relevance of SA audio segments, failing to capture the complex relational patterns in audio data that are essential for SA. In this study, we first propose a graph neural network (GNN) with an attention mechanism that models SA audio features through graph structures, capturing both node attributes and their relationships for richer representations than traditional methods. Our analysis identifies suitable audio feature combinations and graph constructions for SA tasks. Building on this, we introduce a situation awareness gated-attention GNN (SAGA-GNN), which dynamically filters irrelevant nodes through max-relevance neighbor sampling to reduce redundant connections, and a learnable edge gated-attention mechanism that suppresses noise while amplifying critical events. The proposed method employs sigmoid-activated attention weights conditioned on both node features and temporal relationships, enabling adaptive node emphasizing for different acoustic environments. Experiments reveal that the proposed graph-based audio features demonstrate superior representation capacity compared to traditional methods. Additionally, both proposed graph-based methods outperform existing approaches. Specifically, owing to the combination of graph-based audio features and dynamic selection of audio nodes based on gated-attention, SAGA-GNN achieved superior results on two real datasets. This work underscores the importance and potential value of graph-based audio features and attention mechanism-based GNNs, particularly in situational awareness applications. Full article

With the advent of Industry 4.0, sensor-based human activity recognition has become increasingly vital for improving worker safety, enhancing operational efficiency, and optimizing workflows in Internet-of-Things (IoT) environments. This study introduces a novel deep learning-based framework for construction worker activity recognition, employing a deep residual convolutional neural network (ResNet) architecture integrated with multi-sensor fusion techniques. The proposed system processes data from multiple inertial measurement unit sensors strategically positioned on workers’ bodies to identify and classify construction-related activities accurately. A comprehensive pre-processing pipeline is implemented, incorporating Butterworth filtering for noise suppression, data normalization, and an adaptive sliding window mechanism for temporal segmentation. Experimental validation is conducted using the publicly available VTT-ConIoT dataset, which includes recordings of 16 construction activities performed by 13 participants in a controlled laboratory setting. The results demonstrate that the ResNet-based sensor fusion approach outperforms traditional single-sensor models and other deep learning methods. The system achieves classification accuracies of 97.32% for binary discrimination between recommended and non-recommended activities, 97.14% for categorizing six core task types, and 98.68% for detailed classification across sixteen individual activities. Optimal performance is consistently obtained with a 4-second window size, balancing recognition accuracy with computational efficiency. Although the hand-mounted sensor proved to be the most effective as a standalone unit, multi-sensor configurations delivered significantly higher accuracy, particularly in complex classification tasks. The proposed approach demonstrates strong potential for real-world applications, offering robust performance across diverse working conditions while maintaining computational feasibility for IoT deployment. This work advances the field of innovative construction by presenting a practical solution for real-time worker activity monitoring, which can be seamlessly integrated into existing IoT infrastructures to promote workplace safety, streamline construction processes, and support data-driven management decisions. Full article

Functional near-infrared spectroscopy (fNIRS) is a non-invasive imaging technique that measures brain hemodynamic activity by detecting changes in oxyhemoglobin and deoxyhemoglobin concentrations using light in the near-infrared spectrum. This study aims to provide a comprehensive characterization of fNIRS signals acquired with a prototypal continuous-wave fNIRS device during a breath-holding task, to evaluate the impact of respiratory activity on scalp hemodynamics within the framework of Network Physiology. To this end, information-theoretic and spectral analysis methods were applied to characterize the dynamics of fNIRS signals. In the time domain, time-resolved information-theoretic measures, including entropy, conditional entropy and, information storage, were employed to assess the complexity and predictability of the fNIRS signals. These measures highlighted distinct informational dynamics across the breathing and apnea phases, with conditional entropy showing a significant modulation driven by respiratory activity. In the frequency domain, power spectral density was estimated using a parametric method, allowing the identification of distinct frequency bands related to vascular and respiratory components. The analysis revealed significant modulations in both the amplitude and frequency of oscillations during the task, particularly in the high-frequency band associated with respiratory activity. Our observations demonstrate that the proposed analysis provides novel insights into the characterization of fNIRS signals, enhancing the understanding of the impact of task-induced peripheral cardiovascular responses on NIRS hemodynamics. Full article

Human activity recognition (HAR) using wearable sensors has advanced through various machine learning paradigms, each with inherent trade-offs between performance and labeling requirements. While fully supervised techniques achieve high accuracy, they demand extensive labeled datasets that are costly to obtain. Conversely, unsupervised methods eliminate labeling needs but often deliver suboptimal performance. This paper presents a comprehensive investigation across the supervision spectrum for wearable-based HAR, with particular focus on novel approaches that minimize labeling requirements while maintaining competitive accuracy. We develop and empirically compare: (1) traditional fully supervised learning, (2) basic unsupervised learning, (3) a weakly supervised learning approach with constraints, (4) a multi-task learning approach with knowledge sharing, (5) a self-supervised approach based on domain expertise, and (6) a novel weakly self-supervised learning framework that leverages domain knowledge and minimal labeled data. Experiments across benchmark datasets demonstrate that: (i) our weakly supervised methods achieve performance comparable to fully supervised approaches while significantly reducing supervision requirements; (ii) the proposed multi-task framework enhances performance through knowledge sharing between related tasks; (iii) our weakly self-supervised approach demonstrates remarkable efficiency with just 10% of labeled data. These results not only highlight the complementary strengths of different learning paradigms, offering insights into tailoring HAR solutions based on the availability of labeled data, but also establish that our novel weakly self-supervised framework offers a promising solution for practical HAR applications where labeled data are limited. Full article