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Passive Human Sensing (PHS) is an approach to collecting data on human presence, motion or activities that does not require the sensed human to carry devices or participate actively in the sensing process. In the literature, PHS is generally performed by exploiting the Channel State Information variations of dedicated WiFi, affected by human bodies obstructing the WiFi signal propagation path. However, the adoption of WiFi for PHS has some drawbacks, related to power consumption, large-scale deployment costs and interference with other networks in nearby areas. Bluetooth technology and, in particular, its low-energy version Bluetooth Low Energy (BLE), represents a valid candidate solution to the drawbacks of WiFi, thanks to its Adaptive Frequency Hopping (AFH) mechanism. This work proposes the application of a Deep Convolutional Neural Network (DNN) to improve the analysis and classification of the BLE signal deformations for PHS using commercial standard BLE devices. The proposed approach was applied to reliably detect the presence of human occupants in a large and articulated room with only a few transmitters and receivers and in conditions where the occupants do not directly occlude the Line of Sight between transmitters and receivers. This paper shows that the proposed approach significantly outperforms the most accurate technique found in the literature when applied to the same experimental data.

Passive human sensing approaches based on the analysis of the radio signals emitted by the most common wireless communication technologies have been steadily gaining momentum during the last decade. In this context, the Bluetooth technology, despite its widespread adoption in mobile and IoT applications, so far has not received all the attention it deserves. However, the introduction of the Bluetooth direction finding feature and the application of Artificial Intelligence techniques to the processing and analysis of the wireless signal for passive human sensing pave the way for novel Bluetooth-based passive human sensing applications, which will leverage Bluetooth Low Energy features, such as low power consumption, noise resilience, wide diffusion, and relatively low deployment cost. This paper provides a reasoned analysis of the data preprocessing and classification techniques proposed in the literature on Bluetooth-based remote passive human sensing, which is supported by a comparison of the reported accuracy results. Building on such results, the paper also identifies and discusses the multiple factors and operating conditions that explain the different accuracy values achieved by the considered techniques, and it draws the main research directions for the near future.

The aim of this study is to compare the performances of patients with Rett syndrome that were undergoing advanced telerehabilitation (ATR) and patients that were undergoing basic telerehabilitation (BTR). It was hypothesized that patients that were undergoing ATR training would have better improvement in nearly all the motor and cognitive scale scoring activities that were administered, thus showing reduced disability. A total of 20 young girls and women with a diagnosis of RTT, ranging from age 4 to 31 years old (Median: 12.50; IQR: 9.50-17.25) underwent a pre-test, treatment post-test 1, treatment, and post-test 2 procedure. The treatment consisted of either ATR or BTR, lasting 10 weeks with three sessions a week of about an hour. The results showed that the group with advanced telerehabilitation improved their performance better than the control group only in some neuropsychological measurements. The results are discussed in the light of critical factors of telerehabilitation.

Background: The closure of all educational institutions and most rehabilitation centres represents a precautionary measure to face the COVID-19 pandemic, but the isolation and social distancing may be particularly challenging for children with special needs and disabilities (SEND), such as Rett Syndrome (RTT). The main aim of this study was to promote cognitive and social interactions among children with RTT through an interactive school program. Methods: The Interactive School palimpsest was composed of moments in which a teacher spoke directly to children with RTT and expected a response through eye gaze, and moments in which storeys-cartoon were presented while tracking the eye gaze of children. We investigated behavioural, social and cognitive parameters. Results: Children participated in both social and cognitive tasks with the spontaneous reduction of stereotypies and with increase in attention. They recalled more significant indexes when music or a song was presented together with a cartoon or a cognitive task. Conclusions: This study provides initial insights in promoting cognitive and social interactions and in the support needs of families with a child with RTT during the COVID-19 pandemic., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Dovigo, Caprì, Iannizzotto, Nucita, Semino, Giannatiempo, Zocca and Fabio.)

Background: Since subjects with Rett syndrome (RTT) focus their attention mainly on the faces of people with whom they interact, in this study the role of a human-like smart interactive agent (an avatar) in enhancing cognitive processes is examined. More in depth, this study aimed to understand if, and to what extent, the use of an avatar can improve attention and memory abilities in subjects with RTT., Method: Thirty-six subjects with RTT participated in the study. All participants performed over-selectivity and memory tasks, for a total of six trials. These trials were randomly presented in two different conditions: with and without virtual avatar., Results: The results indicated that the participants improved their attention and memory abilities when they performed the tasks with the avatar. There were no improvements when they performed the tasks without the avatar., Discussion: The results were discussed considering the relationship between motivation, attention and memory in RTT.

Objective: Several studies agree on the link between attention and eye movements during reading. It has been well established that attention and working memory (WM) interact. A question that could be addressed to better understand these relationships is: to what extent can an attention deficit affect eye movements and, consequently, remembering a word? The main aims of the present study were (1) to compare visual patterns of word stimuli between children with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing (TD) children, during a visual task on word stimuli; (2) to examine the WM accuracy of the word stimuli; and (3) to compare the dynamic of visual scan path in both groups., Method: A total of 49 children with ADHD, age and sex matched with 32 TD children, were recruited. We used eye-tracking technology in which the Word Memory Test was implemented. To highlight the scan path of participants, two measures were used: the ordered direction of reading and the entropy index., Results: ADHD groups showed a poorer WM than TD group. They did not follow a typical scan path across the words compared with TD children, but their visual scanning was discontinuous, uncoordinated, and chaotic. ADHD groups showed an index of entropy among the four categories of saccades higher than TD group., Conclusions: The findings were discussed in light of two directions: the relationship between atypical visual scan path and WM and the training implications related to the necessity of redirecting the dynamic of visual scan path in ADHD to improve WM.

This study examined both children with attention deficit hyperactivity disorder (ADHD) and typically developing students in dynamic measures of intelligence through the use of a virtual avatar. Three conditions were compared: in the first condition, the avatar simply gave the instructions; in the second condition, the avatar presented the instructions and gave feedback on the attention of the learner; in the third condition, the avatar was not presented. Results indicated that ADHD subtypes do not differ in problem solving and the interactive avatar improved the performance of groups with ADHD in the dynamic intelligence test. Overall, these results support the hypothesis that the function of regulation and feedback of the avatar improve the attention process and, consequently, boosts performance.

Given the close connection between eye movement and frontal lobe functions and some evidence supporting the effect of eye-tracking training on enhancing cognitive performance mediated by the frontal lobe, this study aimed to explore if after-school eye-tracking training can improve the visuospatial working memory (VSWM) and cognitive flexibility performance in children with attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). This study is a non-randomized cluster trial. Forty children from eight primary schools were selected, half receiving eye-tracking training for 20 sessions over 9 months, while the other half served as a waitlist control. They were matched on demographic characteristics and baseline cognitive performance. Their VSWM and cognitive flexibility were assessed at the beginning and end of the study. Results showed that children who received eye-tracking training, but not those on a waitlist, exhibited significant improvements in the total score and working memory span of the VSWM tests, and the correct responses in cognitive flexibility tests. Specifically, VSWM performance at higher span levels (5 or above) yielded a greater improvement. The findings suggest that eye-tracking training can be a feasible and effective after-school program for improving working memory and cognitive flexibility performance in children with ADHD and ASD. This study was prospectively registered at ClinicalTrials.gov (https://clinicaltrials.gov/, trial number: NCT05428657)., (© 2024 The Author(s). Autism Research published by International Society for Autism Research and Wiley Periodicals LLC.)

Introduction: Autism spectrum disorder (ASD) is a sensory processing, social communication, and occupational performance disorder. Sensory diets can have positive effects on these fields. Current research compared the effectiveness of the sensory diet program with and without teleoccupational therapy on sensory processing patterns, social competency and occupational performance in children with ASD. Materials and Methods: The study sample comprised 32 children with ASD 4-10 years old. They were placed in the control and intervention groups. The number of sessions for both groups was 20 30-minute sessions administered over 8 weeks. Through photos, videos, and explanations, sensory problems were described in both groups, the mother presented and implemented activities and environmental adaptations, and the therapist supervised the implementation. This study used three tools--sensory profile 2, social responsive scale 2 and Canadian occupational performance measure. Results: According to the sensory profile 2, social responsiveness scale, and Canadian occupational performance measure, processing in the areas of auditory, visual, touch, movement, body position, oral, conduct, social-emotional, attentional, sensory seeking, sensory avoiding, sensory sensitive, low registration, social awareness, social cognition, social communication, social motivation, limited interests, repetitive behaviors, and occupational performance in both groups have a significant difference (P<0.05). Conclusion: It seems that providing a sensory diet program through teleoccupational therapy on sensory processing patterns, social competency, and occupational performance is applicable, as well as face-to-face occupational therapy in children with ASD. [ABSTRACT FROM AUTHOR]

Recently, with advancements in Deep Learning (DL) technology, Radio Frequency (RF) sensing has seen substantial improvements, particularly in outdoor applications. Motivated by these developments, this survey presents a comprehensive review of state-of-the-art RF sensing techniques in challenging outdoor scenarios with practical issues such as fading, interference, and environmental dynamics. We first investigate the characteristics of outdoor environments and explore potential wireless technologies. Then, we study the current trends in applying DL to RF-based systems and highlight its advantages in dealing with large-scale and dynamic outdoor environments. Furthermore, this paper provides a detailed comparison between discriminative and generative DL models in support of RF sensing, offering insights into both the theoretical underpinnings and practical applications of these technologies. Finally, we discuss the research challenges and present future directions of leveraging DL in outdoor RF sensing. [ABSTRACT FROM AUTHOR]

Nomophobia is one of the problems of the overuse of mobile phones and the fear of missing out, which has become prevalent among university students and interferes with their daily activities. This study aimed to investigate the extent and potential predictors of nomophobia among university students, including the role of smartphone use and demographic factors. The study surveyed 636 students from various academic levels using the Nomophobia Questionnaire (NMP-Q) through an online cross-sectional survey. The results showed that nomophobia varied in severity from mild to severe. Females experienced nomophobia more frequently than males (p <.001). Gender, phone-checking frequency, number of calls made and received per day, and number of texts received per day were all predictors of nomophobia. With such knowledge, targeted interventions and support systems can be developed to cater to these students' unique requirements and struggles regarding smartphone use and nomophobia. [ABSTRACT FROM AUTHOR]

This study investigates the impact of social networks and influencers on family functioning, with a particular focus on how these external influences affect family communication and economic stability. We explore the associations between family members' purchasing behaviors--prompted by influencers--and the communication patterns within families, specifically between parents and children. A quantitative questionnaire was used to examine how products and services promoted by influencers affect family dynamics and communication patterns concerning social networks. Family functioning was assessed using the McMaster Family Functioning Scale, supplemented by the Economic Strain Scale to evaluate financial stress within families. The sample comprised 93 respondents, with significant correlations found between parent-child communication about social networks and family roles. Economic strain emerged as a key factor influencing both social network usage and interactions with influencers. The results underscore that increased family communication predicts higher social network usage within families, while economic strain plays a pivotal role in influencing both the consumption of social network content and interactions with influencers. The findings suggest that social networks and influencers have a profound effect on family functioning, often impairing family dynamics and overall well-being. Economic strain and diminished communication appear to exacerbate the negative impacts, highlighting the need for greater awareness of the potential risks posed by these external influences on families. [ABSTRACT FROM AUTHOR]

The collection of personal protective equipment (PPE) violation data is crucial for assessing safety risks in behavior-based safety (BBS) management on construction sites. Owing to the limitations of manual inspection methods, many studies have employed computer vision–based methods for PPE violation detection. However, limitations exist in implementing this in actual construction projects due to the costs associated with the acquisition and labeling of a large number of images, and the accuracy and efficiency of recording PPE violation data. Therefore, this study introduces semisupervised object detection (SS-OD) and data augmentation for training PPE detection models to reduce the use of labeled data without reducing the performance, and proposes a framework for recording the PPE violation data by extracting PPE violation videos from real-time surveillance footage by considering the role of the worker and their position, thereby enhancing practical construction management and serving as input for BBS-based safety risk assessments on construction sites. The results show that (1) the labeled data demand for training PPE detection models can be reduced through semisupervised learning, image augmentation, and transfer learning, without reducing the performance of PPE detection, thereby reducing the cost of application on actual construction sites; (2) SS-OD methods are better equipped to handle changes in construction scenarios by making full use of unlabeled data, and thus are suitable for construction scenarios; and (3) recording PPE violation data using the video clip method in a real construction project achieved an average precision of 91.76% and an average F1 score of 92.79%. Using the PPE detection model trained with SS-OD effectively records PPE violation data for BBS-based safety risk assessment. This study significantly enhances the efficiency of real construction site PPE violation inspections and provides a valuable method for the automated and real-time collection of violation data in BBS-based management. Practical Applications: Computer vision–based personal protective equipment violation detection methods from past studies have been difficult to implement widely in real projects due to cost and effectiveness limitations. This study effectively improves upon these limitations using semisupervised learning and video clipping methods. Experimental results demonstrate that the object detection model trained with the semisupervised learning method not only reduces the need for labeled data in personal protective equipment detection model training but also exhibits improved generalization capabilities in complex and dynamic construction scenarios. Using the personal protective equipment violation video clipping method proposed in this study allows for efficient editing of violation video clips, facilitating rapid identification and documentation of construction workers' personal protective equipment violation behaviors. This enhances the safety culture at construction sites and aids construction managers in developing safety management measures and educational plans. The real-time-collected personal protective equipment violation data can be utilized further as input for a behavior-based safety risk assessment model, enhancing the automation of unsafe behavior data collection. [ABSTRACT FROM AUTHOR]

The purpose of this study is to assess the efficacy and the tolerability of a new vision-based non-intrusive eye tracker in a population composed of normal controls and in patients affected by nonadvanced Parkinson's disease (PD). PD patients characteristically have difficulty in sustaining repetitive motor actions. Previous studies showed a progressive bradykinesia and hypokinesia of pursuit ocular movements (POM) in advanced PD. We found that the values of POM were lower in PD patients than in normal controls (p < 0.001). In PD patients, the values correlated closely with Hoehn and Yahr stage and Unified Parkinson Disease Rating Scale motor subscore (p < 0.001, for both). Our data suggest that deficit in POM occurs also in nonadvanced PD patients and it is closely correlated with clinical scores. Thus, this vision-based system can be considered a new method to provide, noninvasively, measures of POM dysfunctions and can be used as reliable indices of disease severity in PD patients., ((c) 2007 S. Karger AG, Basel.)

Prefabricated prefinished volumetric construction (PPVC) is a relatively new technique that has recently gained popularity for its ability to improve flexibility in scheduling and resource management. Given the modular nature of PPVC assembly and the large amounts of visual data amassed throughout a construction project today, PPVC building construction progress monitoring can be conducted by quantifying assembled PPVC modules within images or videos. As manually processing high volumes of visual data can be extremely time consuming and tedious, building construction progress monitoring can be automated to be more efficient and reliable. However, the complex nature of construction sites and the presence of nearby infrastructure could occlude or distort visual data. Furthermore, imaging constraints can also result in incomplete visual data. Therefore, it is hard to apply existing purely data-driven object detectors to automate building progress monitoring at construction sites. In this paper, we propose a novel 2D window-based automated visual building construction progress monitoring (WAVBCPM) system to overcome these issues by mimicking human decision making during manual progress monitoring with a primary focus on PPVC building construction. WAVBCPM is segregated into three modules. A detection module first conducts detection of windows on the target building. This is achieved by detecting windows within the input image at two scales by using YOLOv5 as a backbone network for object detection before using a window detection filtering process to omit irrelevant detections from the surrounding areas. Next, a rectification module is developed to account for missing windows in the mid-section and near-ground regions of the constructed building that may be caused by occlusion and poor detection. Lastly, a progress estimation module checks the processed detections for missing or excess information before performing building construction progress estimation. The proposed method is tested on images from actual construction sites, and the experimental results demonstrate that WAVBCPM effectively addresses real-world challenges. By mimicking human inference, it overcomes imperfections in visual data, achieving higher accuracy in progress monitoring compared to purely data-driven object detectors. [ABSTRACT FROM AUTHOR]

As the world undergoes its fourth industrial revolution, digital media are becoming more prevalent in both rural and urban communities. This shift has had a profound impact on our daily lives, transforming the way we live, work, and communicate. Although technology now plays a crucial role in our daily routine, it can lead to interference in human relationships, a phenomenon referred to as technoference. This interference has significant consequences, particularly in parent–child relationships, as it can adversely affect children's emotional and behavioral development and the mental health and overall functioning of parents. Thus, for children to grow up healthy and for families to be harmoniously balanced, all the family subsystems need to be aligned in a healthy manner. This study conceptualizes how family health is affected when technological devices are brought as a subsystem into a family system. The researchers developed a conceptual multidimensional framework based on several interrelated theoretical concepts, and this framework is presented in three sequential frames for better comprehension, to identify how the interference, in particular, impacts the various dimensions of a healthy family system and contributes to dysfunctional family functioning and mental health issues in both parents and children. [ABSTRACT FROM AUTHOR]

Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The non-extensive parameter q contained in this entropy plays an important role in various adaptive algorithms and has been successfully applied in bi-level image thresholding. In this paper, the relationships between parameter q and pixels' long-range correlations have been further studied within multi-threshold image segmentation. It is found that the pixels' correlations are remarkable and stable for images generated by a known physical principle, such as infrared images, medical CT images, and color satellite remote sensing images. The corresponding non-extensive parameter q can be evaluated by using the self-adaptive Tsallis entropy algorithm. The results of this algorithm are compared with those of the Shannon entropy algorithm and the original Tsallis entropy algorithm in terms of quantitative image quality evaluation metrics PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). Furthermore, we observed that for image series with the same background, the q values determined by the adaptive algorithm are consistently kept in a narrow range. Therefore, similar or identical scenes during imaging would produce similar strength of long-range correlations, which provides potential applications for unsupervised image processing. [ABSTRACT FROM AUTHOR]

Background/Objectives: Rett syndrome is a rare neurodevelopmental disorder that can severely affect motor functioning, particularly walking. Previous training programs proposed treadmills as tools to increase walking endurance of patients with Rett syndrome, but these trainings did not include virtual reality (VR). The aim of this study was to assess the feasibility of a short treadmill training coupled to VR in girls with Rett syndrome. Methods: Nine patients with Rett syndrome underwent a 3-day treadmill walking program performed in semi-immersive VR. During the training, the happiness index and performance metrics were collected. At the end of the training parents filled out the Suitability Evaluation Questionnaire (SEQ) and, when feasible, patients underwent a gait assessment. Results: All the subjects recruited performed the three GRAIL sessions and parents showed a good satisfaction and considered the integration of treadmill and VR a good possibility for future rehabilitative programs. Participants showed greater satisfaction in environments requiring walking and their attention increased during training sessions, hypothesizing the feasibility of longer trainings with treadmill and VR. Data collected from gait analysis provided insights, although preliminary, concerning differences in gait pattern amongst the recruited subjects. Conclusions: Despite the small sample size and limited training duration, the paper suggests that a walking training with a treadmill combined with VR can represent a new strategy for Rett rehabilitation. [ABSTRACT FROM AUTHOR]

Today, Smart Assistants (SAs) are supported by significantly improved Natural Language Processing (NLP) and Natural Language Understanding (NLU) engines as well as AI-enabled decision support, enabling efficient information communication, easy appliance/device control, and seamless access to entertainment services, among others. In fact, an increasing number of modern households are being equipped with SAs, which promise to enhance user experience in the context of smart environments through verbal interaction. Currently, the market in SAs is dominated by products manufactured by technology giants that provide well designed off-the-shelf solutions. However, their simple setup and ease of use come with trade-offs, as these SAs abide by proprietary and/or closed-source architectures and offer limited functionality. Their enforced vendor lock-in does not provide (power) users with the ability to build custom conversational applications through their SAs. On the other hand, employing an open-source approach for building and deploying an SA (which comes with a significant overhead) necessitates expertise in multiple domains and fluency in the multimodal technologies used to build the envisioned applications. In this context, this paper proposes a methodology for developing and deploying conversational applications on the edge on top of an open-source software and hardware infrastructure via a multilayer architecture that simplifies low-level complexity and reduces learning overhead. The proposed approach facilitates the rapid development of applications by third-party developers, thereby enabling the establishment of a marketplace of customized applications aimed at the smart assisted living domain, among others. The supporting framework supports application developers, device owners, and ecosystem administrators in building, testing, uploading, and deploying applications, remotely controlling devices, and monitoring device performance. A demonstration of this methodology is presented and discussed focusing on health and assisted living applications for the elderly. [ABSTRACT FROM AUTHOR]

Background: Hybrid models that integrate both in-person and remote health services are increasingly recognized as a promising approach. Nevertheless, research that defines and characterizes these models in children and young people is scarce and essential for establishing guidelines for implementation of hybrid allied health services. This scoping review evaluates four key aspects of hybrid allied health services in children and young people: 1. definitions, 2. service characteristics, 3. outcome measures, and 4. results of hybrid allied health services. Methods: Six databases were searched: Medline (Ovid), Embase, CINHAL, Psycinfo, Cochrane CENTRAL, and Web of Science. Of the 9,868 studies potentially meeting the inclusion criteria, 49 studies focused on children and young people. Following full-text review, n = 21 studies were included. Results: Terminology used for hybrid allied health services varied across studies which targeted diverse clinical populations and varied in study design, type and frequency of remote and in-person treatments. Over 75% of cases used custom-written software, limiting scalability. All interventions started in-person, possibly to establish a therapeutic alliance and solve technological issues. Most hybrid allied health services (67%) were in mental health, while only a minority involved physical, occupational or speech therapy. The most common outcomes were feasibility and satisfaction, but tools used to measure them were inconsistent. Although 57% of studies demonstrated effectiveness of hybrid allied health services, none measured cost-effectiveness. Discussion: Despite the potential of hybrid allied health services for children and young people, the literature remains at a preliminary stage. Standardization of definitions and outcome measures, and clearer reporting of service characteristics and results would likely promote consolidation of hybrid allied health services in children and young people into clinical practice. [ABSTRACT FROM AUTHOR]

Serious games play a key role in the medical field, particularly in enhancing cognitive abilities in the elderly. However, the sensory organs of the elderly decline over time, and the intervention effect of traditional serious games for older adults. The objective of this study is to identify the evolution and current problems of serious game technology for the elderly by using bibliometric analysis. We selected 319 relevant documents from 2013 to 2024 from the Web of Science (WOS) database. This study uses Publish or Perish (Windows GUl Edition) and VOSviewer (1.6.20) for performance analysis and scientific charting. We deeply analyze the early trends, emerging technologies, and publication trends, including citations and journals, subject areas, and regional and institutional. Here, we identified serious games for older adults rely heavily on visual presentation, often utilizing screens for screening, rehabilitation, and therapeutic interventions. This may cause further visual impairment in older adults who are experiencing visual decline. In addition, we proposed the combination of rich tactile feedback and external devices as one of the effective solutions to the current problems for future research. [ABSTRACT FROM AUTHOR]

The study determined the self-reported Zoom exhaustion and fatigue (ZEF) levels of physical education teacher education (PETE) students in the Philippines who are attending a flexible learning setup (i.e., synchronous and asynchronous learning) offered by their university as a solution to continue classes during the coronavirus disease (COVID)-19 pandemic. Utilizing a cross-sectional comparative research design, the study indicates that PETE students were very tired and exhausted both in general and visually after a series of video conferencing within a semester. Furthermore, they were socially-, motivationally-, and emotionally- moderately tired and exhausted. It was observed that students in the PETE program experienced moderate to very tired and exhausted levels of fatigue whenever they participated in synchronous online classes, such as attendance at a video teleconferencing platform. Exploration of how specific mental aspects relate to their general health with regard to their culture and habits is worth exploring, either for students, teachers, or professionals in general. [ABSTRACT FROM AUTHOR]

While the COVID-19 has brought severe challenges to university students' mental health, there is inadequate research on the related protective factors from different ecological systems and the underlying mechanisms. Guided by the ecological systems theory and the positive youth development approach, this study investigated the associations between two protective factors in the ecological systems (i.e., positive family functioning in the microsystem and Chinese cultural beliefs of adversity in the macrosystem) and students' mental health in Hong Kong higher education during the period of the pandemic, with resilience (i.e., an important positive youth development quality) proposed as a mediating factor. This study was based on data collected in a large-scale survey of 978 Hong Kong Chinese undergraduate students (mean age = 20.69 with 62.9% being female) in the summer of 2022. Validated measures were used to assess students' mental health problems (anxiety and depression), the ecological protective factors (positive family functioning and Chinese cultural beliefs of adversity), and resilience. Structural equation modelling was conducted to examine the associations between ecological protective factors and mental health problems, as well as the mediating effects of resilience in the associations. Structural equation modelling revealed that both positive family functioning and Chinese cultural beliefs of adversity negatively predicted anxiety and depression, with resilience partially mediating all paths. The study contributes significantly to the understanding of different ecological protective factors in higher education students' mental health and the mediating role of resilience. It also provides practical implications for intervention and prevention. [ABSTRACT FROM AUTHOR]

Vascular dementia, the second most common type of dementia, currently lacks a definitive cure. In the pursuit of therapies aimed at slowing its progression and alleviating symptoms, transcranial direct current stimulation (tDCS) emerges as a promising approach, characterized by its non-invasive nature and the ability to promote brain plasticity. In this study, the primary objective was to investigate the effects of a two-week cycle of tDCS on the dorsolateral prefrontal cortex (DLPFC) and neurophysiological functioning in thirty patients diagnosed with vascular dementia. Each participant was assigned to one of two groups: the experimental group, which received anodal tDCS to stimulate DPCFL, and the control group, which received sham tDCS. Neurophysiological functions were assessed before and after tDCS using P300 event-related potentials (ERPs), while neuropsychological function was evaluated through a Mini-Mental State Examination (MMSE). The results showed a reduction in P300 latency, indicating a faster cognitive process; an increase in P300 amplitude, suggesting a stronger neural response to cognitive stimuli; and a significant improvement in MMSE scores compared to the control group, indicating an overall enhancement in cognitive functions. These findings suggest that tDCS could represent a promising therapeutic option for improving both neurophysiological and cognitive aspects in patients with vascular dementia. [ABSTRACT FROM AUTHOR]