Search

A new image analysis method, called contiguous volume analysis, has been developed to automatically extract 3D information from emission images. The method considers volumes of activity and display,s data about them in a format which allows quantitative image comparison. This method of numerical analysis enables us to show, for example, whether or not information has been gained, lost or changed through the use of different filters and different attenuation and scatter correction, and reconstruction algorithms. Since the analysis method is consistent with a visual inspection of the data, intuitive insights into the meaning of the data are possible, allowing a better understanding of the effects of the different image processing techniques on the images. The method can be used to find patterns of activity in sets of images, and may be used to quantify noise, allowing an objective determination of which volumes in an image are meaningful.

To segment brain tissues in magnetic resonance images of the brain, we have implemented a stochastic relaxation method which utilizes partial volume analysis for every brain voxel, and operates on fully three-dimensional (3-D) data. However, there are still problems with automatically or semi-automatically segmenting thick magnetic resonance (MR) slices, particularly when trying to segment the small lesions present in MR images of multiple sclerosis patients. To improve lesion segmentation we have extended our method of stochastic relaxation by both pre- and post-processing the MR images. The preprocessing step involves image enhancement using homomorphic filtering to correct for nonhomogeneities in the coil and magnet. Because approximately 95% of all multiple sclerosis lesions occur in the white matter of the brain, the post-processing step involves application of morphological processing and thresholding techniques to the intermediate segmentation in order to develop a mask image containing only white matter and Multiple Sclerosis (MS) lesion. This white/lesion masked image is then segmented by again applying our stochastic relaxation technique. The process has been applied to multispectral MRI scans of multiple sclerosis patients and the results compare favorably to manual segmentations of the same scans obtained independently by radiology health professionals.

We describe two methods to correct for motion artifacts in head images obtained by positron emission tomography (PET). The methods are based on six-dimensional motion data of the head that have to be acquired simultaneously during scanning. The data are supposed to represent the rotational and translational deviations of the head as a function of time, with respect to the initial head position. The first compensation method is a rebinning procedure by which the lines of response are geometrically transformed according to the current values of the motion data, assuming a cylindrical scanner geometry. An approximation of the rebinning transformations by use of large look-up tables, having the potential of on-line event processing, is presented. The second method comprises post-processing of the reconstructed images by unconstrained or constrained de-convolution of the image or image segments with kernels that are generated from the motion data. We use motion data that were acquired with a volunteer in supine position, immobilized by a thermoplastic head holder, to demonstrate the effects of the compensation methods. Preliminary results obtained with test data indicate that the methods have the potential to improve the resolution of Positron Emission Tomography images in cases where significant head motion has occurred, provided that the head position and orientation can be accurately measured.

Background: The care of dependent people is eminently family-oriented, and often, there is a high level of dedication to this family care. Constant and continuous care leads to a series of negative psychological consequences. Social support has been related to improved mental health in family caregivers. We found heterogeneous results regarding the relationship between the types of social support received and the perceived level of support. In addition, to our knowledge, no reviews analyse this relationship among family caregivers. Objectives: Therefore, we objective to systematically synthesise the relationships between perceived and received social support in informal caregivers. Methodology: We have carried out a quantitative systematic review with a meta-analysis, registered in PROSPERO (id: CRD42023470047); the systematic search was carried out in the following databases: PubMed, CINAHL, PsycINFO, and Scopus, until November 2023. After the selection and review of the results, twelve studies were obtained, two of which were eliminated due to a high risk of classification bias. Results: Regarding the results, a medium-size positive statistical association was found (r = 0.43). The results were consistent, accurate, and robust. The Trim and Fill test showed a variation of 7%. Subgroup analysis indicated no differences in the age group of the people cared for (adults or children), selection bias, and confounding bias. Conclusions: In conclusions, perceived social support is related to more social support received by family caregivers. [ABSTRACT FROM AUTHOR]

Background/Objectives: Mental health concerns among youth represent a critical global public health issue. Research has found that youth with mental health concerns are often reliant on their caregivers while being isolated from peers. Guided by the recovery model of mental health care, this study investigates the often-overlooked role of caregivers in fostering 'connectedness' in youth; Methods: Semi-structured interviews were conducted with nine caregivers of youth with mental health issue; Results: Thematic analysis underscored three tasks in the role of caregivers in promoting connectedness in youth: (a) understanding the nature and quality of social networks, (b) supporting readiness to engage in social relationships, and (c) promoting their youth's social problem solving and self-efficacy; Discussion: These tasks highlight the complex dialectics faced by caregivers in supporting youth recovery. The findings hold key implications for developing interventions, resources and policies designed for caregivers. [ABSTRACT FROM AUTHOR]

Skin cancer represents a significant global public health concern, with over five million new cases diagnosed annually. If not diagnosed at an early stage, skin diseases have the potential to pose a significant threat to human life. In recent years, deep learning has increasingly been used in dermatological diagnosis. In this paper, a multiclassification model based on the Inception-v2 network and the focal loss function is proposed on the basis of deep learning, and the ISIC 2019 dataset is optimised using data augmentation and hair removal to achieve seven classifications of dermatological images and generate heat maps to visualise the predictions of the model. The results show that the model has an average accuracy of 89.04%, a precision of 87.37%, recall of 90.15%, and an F1-score of 88.76%, The accuracy rates of ResNext101, MobileNetv2, Vgg19, and ConvNet are 88.50%, 85.30%, 88.57%, and 86.90%, respectively. These results show that our proposed model performs better than the above models and performs well in classifying dermatological images, which has significant application value. [ABSTRACT FROM AUTHOR]

(1) Background. The post-COVID-19 era has imposed unique challenges on educators, significantly impacting their psychological and physical well-being. This study examines the interrelationships among psychological stress, sleep quality, and somatization in a sample of teachers, elucidating the impact of these factors during the ongoing recovery from the pandemic. (2) Methods. Using validated instruments such as the Pittsburgh Sleep Quality Index (PSQI) and the Mesure du Stress Psychologique (MSP), this research investigates how stress and sleep disturbances correlate with somatization among teachers. The study also considers the influence of demographic factors such as age, gender, and years of experience. (3) Results. The results indicated that sleep quality significantly correlates with both psychological stress and somatic pain, emphasizing the crucial role of sleep in managing stress-induced physical symptoms. Additionally, the fear of COVID-19 significantly exacerbates these effects, illustrating the complex interplay of psychological and physical health factors during the pandemic. Contrary to initial hypotheses, demographic factors such as gender, age, and years of experience did not significantly influence these primary relationships. (4) Conclusions. The findings emphasize the necessity of addressing both psychological stress and sleep quality to mitigate their combined effects on somatization. Educational institutions and policymakers are urged to develop targeted interventions that address these issues to support teachers' health and well-being in a post-pandemic landscape. [ABSTRACT FROM AUTHOR]

Air Traffic Controllers' (ATCos) mental workload is likely to remain the specific greatest functional limitation on the capacity of the Air Traffic Management (ATM) system. Developing computational models to monitor mental workload and task complexity is essential for enabling ATCOs and ATM systems to adapt to varying task demands. Most methodologies have computed task complexity based on basic parameters such as air-traffic density; however, literature research has shown that it also depends on many other factors. In this paper, we present a study in which we explored the possibility of predicting task complexity and performance through mental workload measurements of participants performing an ATM task in an air-traffic control simulator. Our findings suggest that mental workload measurements better predict poor performance and high task complexity peaks than other established factors. This underscores their potential for research into how different ATM factors affect task complexity. Understanding the role and the weight of these factors in the overall task complexity confronted by ATCos constitutes one of the biggest challenges currently faced by the ATM sphere and would significantly contribute to the safety of our sky. [ABSTRACT FROM AUTHOR]

The onset of the COVID-19 pandemic prompted a rapid shift to Emergency Remote Teaching (ERT). Social networks had a key role in supporting the educational community in facing challenges and opportunities. A quantitative study was conducted to assess the Greek teachers' perceptions of social network support. Findings indicated that teachers turned to universities, educational institutions, the Ministry of Education, school support groups, and virtual communities for support. Additionally, the study revealed the barriers faced by teachers, including infrastructure limitations, technical difficulties, skill deficiencies, problems with students' engagement, and school policies. Teachers' evaluation of support regarding ERT provided fruitful insight. The results illustrate teachers' perspectives on ERT, contributing to the ongoing discourse on educational resilience to unpredictable disruptions. In conclusion, the role of social networks was considered as critical for the teachers to overcome barriers during ERT with the formation of social communities for support and the sharing of common experiences. Expertise in internet use and social networking played a significant role in readiness for the abrupt shift to distance education. The present study uniquely contributes to the educational field by emphasizing the role of teachers' support as an innovative approach to holistically enhance teachers' performance in ERT. [ABSTRACT FROM AUTHOR]

Improving healthcare requires appropriate community involvement supported by appropriate partner engagement methods. This research aims to develop a complex tool for evaluating the social responsibility of health facilities regarding community involvement and development. We developed areas of a new reference framework for the sustainability of healthcare organizations, which includes the area of community involvement and expansion. It is made up of nine indicators. These were designed using the most representative activities reported by hospitals around the world. Their testing was conducted in an orthopedic emergency hospital. The designed indicators are community engagement actions; the interventions' content adapted to the community; partnership and networking; the involvement of volunteers and training networks; the involvement and participation of professional associations; community-involved local opinion leaders; satisfaction with partnerships; initiatives together with the community; and educational visits. The testing and validation of health practices of the indicators highlighted their adequacy with the proposed purpose of the research and the promotion of sustainable development. We have also verified their compatibility with the requirements of national hospital accreditation legislation and the European framework for quality assurance in hospitals. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) into medical practice has become a critical focus in contemporary medical research. This bibliometric analysis examined the scope of AI utilization across the healthcare spectrum by analyzing a significant body of publications from the Scopus and PubMed databases. After removing duplicates and reviews, a total of 2061 articles were assessed using VOSviewer software (version 1.6.20). The results were organized into two main sections: influential factors and thematic directions of AI integration in healthcare. The first section highlights the most productive countries, authors, and institutions in terms of publications. The second section explores the keywords used in the relevant literature, and identifies the main thematic areas where AI has a significant impact in medical sector. The findings of this study aimed not only to assess AI's current contributions to medicine in general but also to highlight specific technological advancements across medical departments, offering a comprehensive overview. [ABSTRACT FROM AUTHOR]

Understanding human actions often requires in-depth detection and interpretation of bio-signals. Early eye disengagement from the target (EEDT) represents a significant eye behavior that involves the proactive disengagement of the gazes from the target to gather information on the anticipated pathway, thereby enabling rapid reactions to the environment. It remains unknown how task difficulty and task repetition affect EEDT. We aim to provide direct evidence of how these factors influence EEDT. We developed a visual tracking task in which participants viewed arrow movement videos while their eye movements were tracked. The task complexity was increased by increasing movement steps. Every movement pattern was performed twice to assess the effect of repetition on eye movement. Participants were required to recall the movement patterns for recall accuracy evaluation and complete cognitive load assessment. EEDT was quantified by the fixation duration and frequency within the areas of eye before arrow. When task difficulty increased, we found the recall accuracy score decreased, the cognitive load increased, and EEDT decreased significantly. The EEDT was higher in the second trial, but significance only existed in tasks with lower complexity. EEDT was positively correlated with recall accuracy and negatively correlated with cognitive load. Performing EEDT was reduced by task complexity and increased by task repetition. EEDT may be a promising sensory measure for assessing task performance and cognitive load and can be used for the future development of eye-tracking-based sensors. [ABSTRACT FROM AUTHOR]

(1) Background: Early interventions may effectively reduce the risk of mental disorders in individuals with ultra-high risk. Specifying the health needs of individuals with ultra-high risk is crucial before the implementation of successful early intervention. This study aimed to explore the differences in lifestyles, mental risks, and physical indices among individuals with ultra-high risk, patients with schizophrenia, and healthy subjects. (2) Methods: A cross-section design applying seven questionnaires with physical examinations for 144 participants aged 13–45 years old was conducted in this study. The questionnaires included one about personal data, four on mental risks, and two for lifestyles. (3) Results: The individuals with ultra-high risk scored similarly in many dimensions as the patients with schizophrenia, but they displayed lower positive symptoms, lower negative symptoms, lower prodromal symptoms, higher interpersonal deficits, lower nutrition intake, and higher levels of exercise than the patients with schizophrenia. Female individuals with ultra-high risk had lower self-esteem, higher positive symptoms, lower nutrition intake, and higher exercise levels than male ones. (4) Conclusions: The study pinpointed specific health needs with interpersonal deficits, nutrition intake, and physical activity for the individuals with ultra-high risk. Future interventions targeted on improving social function, dietary pattern, and exercise will be beneficial. [ABSTRACT FROM AUTHOR]

The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human–robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions—categorized as accurate performance or inaccurate performance due to high/low task load—are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot's speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human–robot collaboration. [ABSTRACT FROM AUTHOR]

Workload (WL) measurement is a crucial foundation for human–machine collaboration, particularly in high-stress multitasking environments such as manned vehicle operations during emergencies, where operators often experience High Workload (HWL) levels, increasing the risk of human error. To address this challenge, this study introduces a novel WL measurement method that combines Task Demand Load (TDL) and Subject Load Capacity (SLC) to quantitatively assess operator workload. This method was validated through experiments with 45 subjects using the Environmental Control and Atmospheric Regeneration (ECAR) system. The statistical results showed that as the designed WL levels increased, the Average Workload (AWL), the NASA-TLX score, and the work time percentage increased significantly, while the task accuracy and the fixation duration decreased significantly. These results also revealed the impact of WL levels on human responses (such as subjective feeling, work performance, and eye movement). In addition, very strong correlations were found between AWL measurements and NASA-TLX scores (r = 0.75, p < 0.01), task accuracy (r = −0.73, p < 0.01), and work time percentage (r = 0.97, p < 0.01). Overall, these results proved the effectiveness of the proposed method for measuring WL. On this basis, this study defined WL thresholds by integrating task accuracy with AWL calculations, providing a framework for the dynamic management of task allocation between humans and machines to maintain operators within optimal WL ranges. [ABSTRACT FROM AUTHOR]

A positive child–teacher relationship is a crucial means of addressing problem behaviors in young children. In recent years, there has been an increase in factors triggering problem behaviors in young children. It is particularly important to employ universally applicable and scientifically effective strategies to improve child behavior. Banking Time, as an emerging variant of play therapy, aims to enhance child behavior by establishing an intimate child–teacher relationship. This study conducted a multiple-baseline experiment involving eight four-year-old children and their teachers from China, exploring the effectiveness of Banking Time in enhancing child–teacher relationships and subsequently improving child behavior from dual perspectives, utilizing tools such as the Student–Teacher Relationship Scale and Conners' Comprehensive Behavior Rating Scales-Teacher Assessment Report. Visual analysis and statistical analysis results indicate a strong positive impact of Banking Time on child–teacher relationships and a slight inhibitory effect on child problem behaviors. The implementation of Banking Time provides valuable insights into specific paths and strategies for promoting teachers' professional development. [ABSTRACT FROM AUTHOR]

Due to increased exposure to severe environmental elements and an aging population, the prevalence of skin cancer has been progressively rising in recent decades. When the type of skin infection is not identified in the early stages, it can lead to skin cancer and even death. Low-level visual characteristics directly connected to color and shape have traditionally been employed in studies on skin-lesion identification. The performance features of deep-based models profoundly made them applicable in diverse domains. Indeed, balancing the size of the deep networks with limited and unbalanced training data to meet the real-time demand in a computationally limited platform is an ongoing challenge. Often fine-tuning the last layers is performed to tackle unbalanced and shortage of data. However in the current study, layers like global average pooling, drop-block, and batch normalization are incorporated to the base models to classify unbalanced skin-lesion data. This boosts the performance of the model compared to fine-tuning with drop-out. The effectiveness of the incorporated layers are evaluated on three popular representative models having different depths. Comparisons are provided with the results obtained with conventional fine-tuning and a four-layer CNN scratch model. The best classification accuracy obtained with the proposed approach on DenseNet-121 model is 91.54%, and the average macro-F1 score obtained is 0.774. [ABSTRACT FROM AUTHOR]

Working on a moving platform can significantly impede human performance. Previous studies on moving vehicles have often focused on the overall impact on general task performance, whereas our study's emphasis is on precise hand movements, exploring the interaction between body motion and the escalation of task difficulty. We recruited 28 participants to engage in reciprocal aiming tasks, following Paul Fitts's setting, under both in-motion and stationary conditions. The task index of difficulty (ID) was manipulated by varying the width of the targets and the distance between the targets. We measured participants' movement time (MT), performance errors, and monitored their eye movements using an eye-tracking device, heart rate (HR), and respiration rate (RR) during the tasks. The measured parameters were compared across two experimental conditions and three ID levels. Compared to the stationary conditions, the in-motion conditions degraded human aiming performance, resulting in significantly prolonged MT, increased errors, and longer durations of eye fixations and saccades. Furthermore, HR and RR increased under the in-motion conditions. Linear relationships between MT and ID exhibited steeper slopes under the in-motion conditions compared to the stationary conditions. This study builds a foundation for us to explore the control mechanisms of individuals working in dynamic and demanding environments, such as pilots in airplanes and paramedics in ambulances. [ABSTRACT FROM AUTHOR]

Measuring oculomotor abnormalities in human subjects is challenging due to the delicate spatio-temporal nature of the oculometric measures (OMs) used to assess eye movement abilities. Some OMs require a gaze estimation accuracy of less than 2 degrees and a sample rate that enables the detection of movements lasting less than 100 ms. While past studies and applications have used dedicated and limiting eye tracking devices to extract OMs, recent advances in imaging sensors and computer vision have enabled video-based gaze detection. Here, we present a self-calibrating neural network model for gaze detection that is suitable for oculomotor abnormality measurement applications. The model considers stimuli target locations while the examined subjects perform visual tasks and calibrate its gaze estimation output in real time. The model was validated in a clinical trial and achieved an axial accuracy of 0.93 degrees and 1.31 degrees for horizontal and vertical gaze estimation locations, respectively, as well as an absolute accuracy of 1.80 degrees. The performance of the proposed model enables the extraction of OMs using affordable and accessible setups—such as desktop computers and laptops—without the need to restrain the patient's head or to use dedicated equipment. This newly introduced approach may significantly ease patient burden and improve clinical results in any medical field that requires eye movement measurements. [ABSTRACT FROM AUTHOR]

Purpose: The study seeks to understand what teachers know about students' friend networks and how they use that information for instruction. Design/methodology/approach: This qualitative study relied on interviews and sociograms that teachers drew of students' friend networks. Findings: Our data suggest that teachers' awareness of their students' friend networks varies by their experience and their exposure to students. Also, their use of this information for instruction coalesces around dimensions of grouping and social support. Research limitations/implications: This study took place at one school. To more deeply understand what teachers perceive about their students' friend networks and how they use that information for instruction, more studies could be done with teachers in more schools. Practical implications: Implications might suggest developing teachers' social competence to support their students' learning and development. Originality/value: While studies cited in this paper have explored teachers' knowledge of students' social networks, this study builds on this work by exploring how that information can be useful for instruction. In addition, this study explores the use of teachers drawing sociograms as representations of what they know about students' friend networks. [ABSTRACT FROM AUTHOR]

Stroke is a medical condition that affects around 15 million people annually. Patients and their families can face severe financial and emotional challenges as it can cause motor, speech, cognitive, and emotional impairments. Stroke lesion segmentation identifies the stroke lesion visually while providing useful anatomical information. Though different computer-aided software are available for manual segmentation, state-of-the-art deep learning makes the job much easier. This review paper explores the different deep-learning-based lesion segmentation models and the impact of different pre-processing techniques on their performance. It aims to provide a comprehensive overview of the state-of-the-art models and aims to guide future research and contribute to the development of more robust and effective stroke lesion segmentation models. [ABSTRACT FROM AUTHOR]

Screen time refers to the amount of time a child is exposed to a screen, that is, television, computer, smartphone, or any other digital medium. Prolonged screen time in the first years of life may affect a child's cognitive abilities, especially language acquisition. A systematic review was conducted, following the PRISMA-P guidelines, with the aim to explore the available literature relating to the impact of screen time on children's language development. This review identified 18 articles. The articles reviewed showed that prolonged screen time and exposure to screens in the first 2 years of life can negatively affect language development and communication skills, in terms of comprehension and vocabulary range. In addition, overexposure to screens in the early years can affect overall cognitive development, especially attention to environmental stimuli, social experiences, problem solving, and communication with others, e.g., the alternance of rhythms and roles in a conversation. In conclusion, our systematic review supports the idea that preschool screen time has negative effects on children's cognitive and language development. Television seems to be the medium most detrimental to children's skills, as it is used in a passive manner and is often characterised by language and content that do not suit the child's processing mode. Future studies should increasingly focus on the digital media that children possess at an early age, such as mobile phones and tablets, and on how children relate to the online world, such as social networks. [ABSTRACT FROM AUTHOR]