Search Results (105)

Background: As the social-emotional well-being of children as a whole and specifically during the transition to kindergarten is of paramount importance, it is important to continuously research this topic using a multi-informant approach. Moreover, a further contribution of this study lies in addressing the substantial gap in the existing literature within this important field. Objectives: Starting from the Ecological-Dynamic Transition Model and the Positive Development and Resilience in Kindergarten (PERIK) Model, the main aim of this research was to analyze parents’ and teachers’ perceptions of children’s social-emotional well-being during the transition and adjustment, and the quality of transition and adjustment. Methods: The study was conducted on a sample of parents (N = 154; 147 mothers) and teachers from 4 kindergartens (N = 12, all female) as raters of children’s (N = 202; 82 girls) social-emotional well-being, using PERIK scale and four questions on the quality of transition. Results: All PERIK-dimensions were rated as elevated based on parents’ ratings and moderate based on teachers’ ratings. Ratings of difficulties during transition decreased, and satisfaction with transition and adjustment and cooperation between parents and caregivers during transition increased (teachers’ ratings were lower than parents’ ratings). The average duration of adjustment in kindergarten was three weeks. Correlation analyses showed the expected significant correlations between the PERIK dimensions and the quality of transitions and adjustment of children. Inter-rater agreement analyses showed the effect sizes were predominantly large and poor to medium agreement between parent and teacher ratings was determined. Conclusions: Although the study found that there are significant differences in perceptions of the relationship between PERIK-dimensions and satisfaction with children’s transition between teachers and parents, which was expected due to the assessment of children in different contexts, it is important to consider them both in future research. Full article

Considering the problems of having insufficient fault identification from single information sources in actual industrial environments, and different information sensitivity in multi-information source data, and different sensitivity of artificial feature extraction, which can lead to difficulties of effective fusion of equipment information, insufficient state representation ability, low fault identification accuracy, and poor robustness, a multi-information fusion fault identification network model based on deep ensemble learning is proposed. The network is composed of multiple sub-feature extraction units and feature fusion units. Firstly, the fault feature mapping information of each information source is extracted and stored in different sub-models, and then, the features of each sub-model are fused by the feature fusion unit. Finally, the fault recognition results are obtained. The effectiveness of the proposed method is evaluated by using two gearbox datasets. Compared with the method of simple stacking fusion and single measuring point without fusion, the accuracy of each type of fault recognition of the proposed method is close to 100%. The results show that the proposed method is feasible and effective in the application of gearbox fault recognition. Full article

Background/Objectives: Executive function (EF) difficulties are increasingly recognized as closely linked to ADHD, particularly when assessed via rating scales. Methods: The present study investigated the nature of these associations, using the Conners 3 Rating Scales to assess ADHD symptoms and the Executive Function Questionnaire (EFQU) to assess EF impairments, in a sample of 1068 children (40.8% males, 38.8% females) aged 7–14 years (M = 10.7, SD = 1.74). Results: Both parent and teacher ratings revealed strong correlations, particularly between inattentive symptoms and EF difficulties, across multiple executive domains. To examine whether these associations stemmed from construct or phrasing overlap, exploratory and confirmatory factor analyses were conducted. The results demonstrate that the Conners 3 and the EFQU capture distinct latent dimensions of functioning, with virtually no overlap in item content. Conclusions: The strength and consistency of the associations between these latent factors support the interpretation that, although conceptually distinct, ADHD symptoms and EF impairments are empirically intertwined in everyday functioning, as consistently reported by both parents and teachers. Interestingly, teachers provided more integrated views of behavior, while parents tended to distinguish ADHD and EF traits more clearly. These findings underscore the importance of multi-informant assessment and contextual variability in understanding children’s functioning. Full article

Alexithymia is a trait-like deficit in the cognitive processing of emotions, characterized by difficulty identifying and describing feelings, externally oriented thinking, and limited imaginal capacity. It reflects a deficit in emotional intelligence, specifically in the intrapersonal ability to understand and manage one’s own emotional states and to similarly recognize how others might view them. Emotional intelligence has been conceptualized as a distinct form of intelligence that involves emotion-related mental abilities and meets standard psychometric criteria for inclusion within the broader taxonomy of human intelligences. Increasingly, alexithymia is also understood as a failure of affect-focused mentalization, or the ability to perceive emotions in oneself and others as intentional states. This study examined alexithymia using a multi-informant approach to assess intrapersonal and interpersonal emotional awareness. A sample of 211 university students and their informants completed the Toronto Alexithymia Scale (TAS-20), an informant version (TAS-20-IF), and a novel meta-perception version (TAS-20-Meta). Two hypotheses were tested and supported: (1) participants underestimated their alexithymia traits relative to informant ratings and (2) self- and meta-perception ratings were more strongly correlated than either was with informant ratings. These findings support the view that alexithymia reflects deficits in both affective mentalization and a specific domain of human intelligence. Full article

Airborne radar-based ground moving target tracking faces challenges such as low detection rates and high clutter density. While lowering the detection threshold can improve detection performance, it introduces significant false alarms, thereby degrading tracking performance. To address these challenges, this paper proposes a novel multi-information assisted Joint Detection and Tracking (JDT) framework for ground moving targets. This study enhances detection and tracking performance by integrating multi-source information, specifically echo information, road network data, and velocity limits, enabling bidirectional data exchange between the detector and tracker for multiple ground targets. An adaptive threshold detector is developed by incorporating a priori information and tracker feedback. Additionally, we innovatively propose an improved Variable Structure Interacting Multiple Model (VS-IMM) filter that leverages road network constraints and detector outputs for tracking, featuring an enhanced model probability calculation to significantly reduce computational time. Simulation results demonstrate that the proposed method significantly improves data association accuracy and tracking precision. Full article

This study aims to illustrate the impact of accumulated traumatic experiences in adolescence and to evaluate the potential of brief focal psychotherapy (BFP) as a treatment approach for complex trauma. We present the case of a 14-year-old boy who experienced vicarious gender-based violence, child abuse, early maternal separation without alternative secure attachment figures, and forced sudden migration. The patient exhibited symptoms consistent with post-traumatic stress disorder (PTSD) and complex trauma. The culturally sensitive intervention, delivered at a public child and adolescent mental health center, consisted of twenty weekly individual sessions of 45 min each, complemented by three 45 min psychoeducation sessions with the caregiver. The assessment was conducted using a multitrait and multi-informant approach, systematically gathering information across multiple domains of functioning (emotional–behavioral, physical, cognitive, self-perception, and relational) and from different sources (the adolescent, his mother, and the clinician) through clinical interviews, projective techniques, and parental feedback. The primary therapeutic focus was the establishment of a secure therapeutic alliance to facilitate emotional exploration and trauma processing. Following treatment, the patient demonstrated significant improvements in emotional regulation, family relationships, and school performance, as measured by both self-report and parental observations. This case highlights the potential of BFP in addressing complex trauma in adolescents, particularly during a developmental stage marked by increased vulnerability to the effects of chronic trauma exposure. The findings suggest that BFP can effectively reduce both acute symptomatology and broader psychosocial consequences associated with prolonged and cumulative trauma. Further research, particularly controlled studies and longitudinal follow-ups, is needed to refine and optimize the use of BFP by mental health professionals working with adolescents affected by complex trauma. Full article

Sweat analysis represents an emerging non-invasive approach for health monitoring, yet its practical application is hindered by challenges such as insufficient natural sweat secretion and inefficient collection. To overcome these limitations, this study develops a hydrogel sheet composed of agarose and glycerol, which efficiently facilitates resting sweat collection without external stimulation when integrated into the microfluidic channels of a sweat-sensing patch. The microfluidic sweat-sensing patch, fabricated with laser-cut technology, features a sandwich structure that enables the measurement of sweat rate and chloride ion concentration while minimizing interference from electrochemical reactions. Additionally, a colorimetric module utilizing glucose oxidase and peroxidase is also integrated into the platform for cost-effective and efficient glucose detection through a color change that can be quantified via RGB analysis. The hydrogel interface, characterized by its optimal thickness and water content, exhibits superior absorption capability for efficient sweat collection and retention, with a negligible effect on the dilution of sweat components. This hydrogel-interfaced microfluidic platform demonstrates high efficiency in sweat collection and multi-biomarker analysis, offering a non-invasive, real-time solution for health monitoring. Its low-cost and wearable design highlights its potential for broad applications in personalized healthcare. Full article

To address the precision challenges in upper-limb joint motion prediction, this study proposes a novel artificial neural network (COANN) enhanced by the Cheetah Optimization Algorithm (COA). The model integrates surface electromyography (sEMG) signals with joint angle data through multi-source information fusion, effectively resolving the local optima issue in neural network training and improving the accuracy limitations of single sEMG predictions. Experimental results demonstrate that the COANN achieves significant performance improvements: compared with RBF neural networks, it reduces the root mean square error (RMSE) by 24.32% (ΔR² + 18.75%) with a 22.6% shorter system runtime; relative to conventional ANNs, it decreases the RMSE by 31.59% (ΔR² + 12.15%) while reducing computational time by 35.1%; compared with CNN neural networks, it reduces the root mean square error (RMSE) by 14.9% (ΔR² + 3.84%); and relative to conventional LSTM, it decreases the RMSE by 15.31% (ΔR² + 4.86%). Multi-source integration enhanced elbow joint prediction accuracy by 5.7% and shoulder joint accuracy by 6.9% compared with single sEMG approaches. This methodology provides theoretical foundations for human–robot interaction systems in upper-limb rehabilitation robotics and motion-assistive devices. Full article

With the transformation of energy structure and the development of new power systems, higher requirements have been put forward for the performance and stability of microgrids. To adapt to the multi-information, multi-energy, and multi-business characteristics of microgrids, this paper proposes a cyber-physical system (CPS) based a microgrid simulation platform, which constructs an integration architecture composed of the physical system, main station system, and strategy simulation system. Through the interaction of information and energy businesses, real-time reproduction and state control of business scenarios are achieved. The platform innovatively introduces the theory of finite state machine (FSM) and designs a state transition strategy. Taking fault optimization as an example, the optimal path can be selected through state transition, and the system fault optimization effect is improved based on FSM. Compared to traditional methods, this platform reduces simulation time by 16% to 86.6%, significantly shortening scene reproduction time. In addition, the practical application value of the platform in fault optimization and operational efficiency improvement was verified by building a semi-physical simulation system based on a rapid control prototype (RCP) and hardware-in-the-loop testing (HIL). Full article

Neurotypical (NT) siblings of individuals with Autism Spectrum Disorder (ASD) experience complex emotional, psychological, behavioral, and social challenges. Understanding the factors that influence their well-being is essential for developing tailored interventions. This systematic review examines the psychological and social functioning of NT siblings and identifies protective and risk factors that impact their adaptation. A systematic search was conducted across EBSCO, PubMed, and Google Scholar, covering studies published between 2013 and 2024. Inclusion criteria focused on research investigating NT siblings’ emotional, psychological, behavioral, and social well-being. Thirty studies met the inclusion criteria and were synthesized narratively. Findings reveal heterogeneous experiences among NT siblings, ranging from increased empathy and resilience to heightened anxiety, depression, and social difficulties. The quality of sibling relationships and social support systems plays a pivotal role in moderating these outcomes. NT siblings represent a vulnerable group requiring family-centered interventions. Future research should adopt longitudinal and multi-informant approaches to explore long-term effects and culturally sensitive support strategies. Full article

Multi-object tracking (MOT) is a key intermediate task in many practical applications and theoretical fields, facing significant challenges due to complex scenarios, particularly in the context of drone-based air-to-ground military operations. During drone flight, factors such as high-altitude environments, small target proportions, irregular target movement, and frequent occlusions complicate the multi-object tracking task. This paper proposes a cross-scene multi-object tracking (CST) method to address these challenges. Firstly, a lightweight object detection framework is proposed to optimize key sub-tasks by integrating multi-dimensional temporal and spatial information. Secondly, trajectory prediction is achieved through the implementation of Model-Agnostic Meta-Learning, enhancing adaptability to dynamic environments. Thirdly, re-identification is facilitated using Dempster–Shafer Theory, which effectively manages uncertainties in target recognition by incorporating aircraft state information. Finally, a novel dataset, termed the Multi-Information Drone Detection and Tracking Dataset (MIDDTD), is introduced, containing rich drone-related information and diverse scenes, thereby providing a solid foundation for the validation of cross-scene multi-object tracking algorithms. Experimental results demonstrate that the proposed method improves the IDF1 tracking metric by 1.92% compared to existing state-of-the-art methods, showcasing strong cross-scene adaptability and offering an effective solution for multi-object tracking from a drone’s perspective, thereby advancing theoretical and technical support for related fields. Full article

While male youths with mild intellectual disabilities (MIDs) display similar sexual development as their peers without MIDs, they experience higher rates of sexual and dating violence (SDV) and sexual risk behavior. Yet, little is known about effective gender-specific prevention for this population. Therefore, we conducted a cluster-randomized controlled trial of a Dutch group-counseling program promoting psychosexual health and preventing SDV among male youths with MIDs aged 14–21 years—Make a Move+. The 120 participating male youths completed three questionnaires (baseline, post-test, 3-month follow-up; 77.5% retention). A subsample of 14 male youths and 5 trainers were interviewed pre- and post-program. With these multi-method, multi-informant data, we evaluated the program’s effectiveness on the six intended outcomes (attitudes (primary outcome), global self-esteem, skills, knowledge, SDV use and victimization, and sexual risk behavior) by (1) statistically comparing the self-reports of the intervention and control groups and (2) thematically analyzing interview data. We found limitations in the program’s integrity, and mixed evidence for the program’s effectiveness on attitudes, knowledge, skills, SDV use, and sexual risk behavior, and no evidence for effectiveness on global self-esteem or SDV victimization. We also found indications of adverse effects on SDV use and victimization. We offer suggestions for program refinement and future program evaluations. Full article

Adolescent sexual and dating violence (SDV) is a worldwide problem. Although male adolescents in vocational education or youth care may be at increased risk of perpetrating SDV, little is known about effective gender-specific prevention. Therefore, we conducted a quasi-experimental evaluation of a Dutch group counseling program promoting psychosexual health and preventing SDV among male adolescents aged 12–18 years: Make a Move. The 66 participating male adolescents completed three questionnaires (baseline, post-test, 3-month follow-up; 48.5% retention). We also conducted interviews with a subsample of four adolescents and two program trainers and performed observations in one group. With these multi-method, multi-informant data, we evaluated program effectiveness on the six intended outcomes (attitudes, social norms, self-efficacy, skills, intentions, and SDV perpetration) by (1) statistically comparing self-reports between the intervention and control groups; (2) thematically analyzing interview data; and (3) describing three individual male adolescent cases, triangulating questionnaire, interview, and observation data. We found limitations in program integrity, evidence for program effectiveness on skills, and mixed evidence for effects on attitudes, but no evidence for effects on socials norms, self-efficacy, or SDV perpetration. Yet our interviews indicated perceived effectiveness on self-efficacy and intentions. We also found indications of adverse effects on attitudes and intentions. We offer suggestions for program refinement and future program evaluations. Full article

The intelligent perception of moisture content (MC) for tea leaves during the black tea withering process is an unsolved task because of the acquisition of limited sample characteristic information. In this study, both the external and internal features of withering samples were simultaneously acquired based on near-infrared spectroscopy (NIRS) and machine vision (MV) technology. Different data fusion strategies, including low-, middle- and high-level strategies, were employed to integrate two types of heterogeneous information. Subsequently, the different fused features were combined with a support vector regression (SVR) algorithm to establish the moisture perception models of withering leaves. The middle-level-variable iterative space shrinkage approach (VISSA) displayed the best performance with 5.7705 for the relative percent deviation (RPD). Therefore, the proposed multi-information fusion strategy could achieve an intelligent perception of tea leaves in the black tea withering process. The integration of NIRS and MV technology overcomes the limitations of single-technology approaches in black tea withering assessment, providing a robust methodology for precision processing and targeted quality control of black tea. Full article

The global aging trend is becoming increasingly severe, and the demand for life assistance and medical rehabilitation for frail and disabled elderly people is growing. As the best solution for assisting limb movement, guiding limb rehabilitation, and enhancing limb strength, exoskeleton robots are becoming the focus of attention from all walks of life. This paper reviews the progress of research on upper limb exoskeleton robots, sEMG technology, and intention recognition technology. It analyzes the literature using keyword clustering analysis and comprehensively discusses the application of sEMG technology, deep learning methods, and machine learning methods in the process of human movement intention recognition by exoskeleton robots. It is proposed that the focus of current research is to find algorithms with strong adaptability and high classification accuracy. Finally, traditional machine learning and deep learning algorithms are discussed, and future research directions are proposed, such as using a deep learning algorithm based on multi-information fusion to fuse EEG signals, electromyographic signals, and basic reference signals. A model with stronger generalization ability is obtained after training, thereby improving the accuracy of human movement intention recognition based on sEMG technology, which provides important support for the realization of human–machine fusion-embodied intelligence of exoskeleton robots. Full article