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Background: The current diagnostics of fish allergy lack sufficient accuracy such that more reliable tests such as component-resolved diagnosis (CRD) are urgently needed. This study aimed at identifying fish allergens of salmon and grass carp and evaluating the sensitization pattern in fish allergic subjects from two distinct populations in Asia. Methods: One hundred and three fish allergic subjects were recruited from Hong Kong (67 subjects) and Japan (46 subjects). Western blot and mass spectrometry were used to identify allergens from salmon and grass carp. Fish allergens were purified and tested against 96 sera on ELISA to analyze patients’ sensitization pattern. The protein profiles of salmon meat prepared under different cooking methods until core temperature reached 80 °C were evaluated by SDS-PAGE and mass spectrometry. Results: Three common allergens between salmon and grass carp, namely enolase, glycerldehyde-3-phosphate dehydrogenase (GAPDH) and parvalbumin, and two salmon-specific allergens collagen and aldolase were identified. Parvalbumin was the major allergen for both fishes showing an overall sensitization rate of 74.7%, followed by collagen (38.9%), aldolase (38.5%) and enolase (17.8%). Japanese subjects showed more diverse allergen sensitization pattern and more frequent IgE-binding to heat-labile salmon allergens. Compared with steaming and boiling, cooking by baking and frying retained more fish proteins inclusive of heat-labile allergens. Conclusions: Fish allergic patients from different Asian populations show varying fish allergen sensitization profiles. The relevant extracts and components for diagnosis are population-dependent but parvalbumin and collagen are important biomarkers. Cooking methods modify allergen composition of salmon and appear to influence patients’ allergic manifestations.

In this study, we examined the clinical and electrophysiological outcomes of adolescents in Hong Kong who developed myocarditis or pericarditis following BNT162b2 vaccination for COVID-19, and followed-up for 60–180 days after their initial diagnosis. Clinical assessments included electrocardiogram (ECG) and echocardiogram at the initial admission and follow-up were compared. Treadmill testing was also performed in some cases. Between 14 June 2021 and 16 February 2022, 53 subjects were approached to participate in this follow-up study, of which 28 patients were followed up for >60 days with a median follow-up period of 100 days (range, 61–178 days) and were included in this study. On admission, 23 patients had ECG abnormalities but no high-grade atrioventricular block. Six patients had echocardiogram abnormalities, including reduced contractility, small rim pericardial effusions, and hyperechoic ventricular walls. All patients achieved complete recovery on follow-up. After discharge, 10 patients (35.7%) reported symptoms, including occasional chest pain, shortness of breath, reduced exercise tolerance, and recurrent vasovagal near-syncope. At follow-up, assessments, including ECGs, were almost all normal. Among the three patients with possible ECG abnormalities, all their echocardiograms or treadmill testings were normal. Sixteen patients (57.1%) underwent treadmill testing at a median of 117 days post-admission, which were also normal. However, at follow-up, there was a significant mean bodyweight increase of 1.81 kg (95%CI 0.47-3.1 kg, p = 0.01), possibly due to exercise restriction. In conclusion, most adolescents experiencing myocarditis and pericarditis following BNT162b2 vaccination achieved complete recovery. Some patients developed non-specific persistent symptoms, and bodyweight changes shall be monitored.

The antibody response of children to SARS-CoV-2 is less well studied than in adults. Here Hachim et al. show that children have reduced antibody levels to structural proteins and suggest that the predominance of antibody responses to non-structural proteins can be used to discriminate infection and vaccination.

IntroductionControversies surround the issue if chronic consumption of a high-sugar diet is detrimental to health or not. This study investigates whether lifelong consumption of a higher sucrose diet will induce overeating, and obesity, and cause metabolic dysfunctions such as hyperglycemia and dyslipidaemia in C57BL/6N mice, compared to a lower sucrose diet.MethodsMale C57BL/6N mice at 3 weeks of age were randomized into consuming a diet with 25 or 10% kcal from sucrose for the rest of their lives. Body weight, food and water intake, fasting blood glucose, insulin, and lipid levels were measured at regular intervals. At the end of the study, organs and tissues were collected and gene expression was measured.ResultsThere was no discernible difference in the impact on food intake, body composition, glucose and lipid homeostasis, liver triglyceride content, life expectancy, as well as gene expression related to intermediary metabolism between mice fed a diet with 10 vs. 25% kcal as sucrose over their lifespan. We also showed that switching from a 25% kcal diet to a 10% kcal diet at different life stages, or vice versa, did not appear to affect these outcomes of interest.DiscussionThe results from our study suggest that lifelong consumption of a higher sugar diet generally did not induce overeating and obesity, disrupt carbohydrate metabolism and lipid homeostasis, and reduce life expectancy compared with a lower sugar diet. Our unorthodox findings disagreed with the popular belief that higher sugar consumption is detrimental to health, which should be confirmed in future studies.

Chronic tophaceous gout is a common disease but rarely found in the head and neck region. Here we report a case of a middle aged male who presented with a mass over the right side of the nasal bridge. CT of the paranasal sinuses revealed a tophi lesion in the right nasal bridge with erosion of the underlying nasal bone. The mass was excised and histologically confirmed to be a gouty tophus. This case report illustrates how a common disease presented in an uncommon location could pose a diagnostic challenge.

Why children are generally less susceptible than adults to COVID-19 is unclear and has not extensively been examined longitudinally. Here the authors compare antibodies, cytokines and immune cell responses in adults and children over 6 months post-infection showing, among other things, a reduced CD4+ and CD8+ T cell response in children.

Background Pediatric COVID-19 studies exploring the relationships between NPS and saliva viral loads, clinical and immunological profiles are lacking.Methods Demographics, immunological profiles, nasopharyngeal swab (NPS), and saliva samples collected on admission, and hospital length of stay (LOS) were assessed in children below 18 years with COVID-19.Findings 91 patients were included between March and August 20 20. NPS and saliva viral loads were correlated (r = 0.315, p = 0.01). Symptomatic patients had significantly higher NPS and saliva viral loads than asymptomatic patients. Serial NPS and saliva viral load measurements showed that the log10 NPS (r = −0.532, p

Projective non-negative matrix factorization (PNMF) as a variant of NMF has received considerable attention. However, the existing PNMF methods can be further improved from two aspects. On the one hand, the square loss function that is intended to measure the reconstruction error is sensitive to noise. On the other hand, it is non-trivial to estimate the intrinsic manifold of the feature space in a principal manner. So current paper is an attempt that has proposed a new method named as robust ensemble manifold projective non-negative matrix factorization (REPNMF) for image representation. Specifically, REPNMF not only assesses the influence of noise by imposing a spare noise matrix for image reconstruction, but it also assumes that the intrinsic manifold exists in a convex hull of certain pre-given manifold candidates. We aim to remove noise from the data and find the optimized combination of candidate manifolds to approximate the intrinsic manifold simultaneously. We develop iterative multiplicative updating rules for the optimization of REPNMF along with its convergence proof. The experimental results on four image datasets verify that REPNMF is superior as compare to other related state-of-the-art methods.

Microservices is a new paradigm in cloud computing that separates traditional monolithic applications into groups of services. These individual services may correlate or cross multi-clouds. Compared to a monolithic architecture, microservices are faster to develop, easier to deploy, and maintain by leveraging modern containers or other lightweight virtualization. To satisfy the requirements of end-users and preferences, appropriate microservices must be selected to compose complicated workflows or processes from within a large space of candidate services. The microservice composition should consider several factors, such as user preference, correlation effects, and fuzziness. Due to this problem being NP-hard, an efficient metaheuristic algorithm to solve large-scale microservice compositions is essential. We describe a microservice composition problem for multi-cloud environments that considers service grouping relations and corresponding correlation effects of the service providers within intra- or inter-clouds. We use the triangular fuzzy number to describe the uncertainty of QoS attributes, the improved fuzzy analytic hierarchy process to calculate multi-attribute QoS, construct fuzzy weights related to user preferences, and transform the multi-optimal problem into a single-optimal problem. We propose a new artificial immune algorithm based on the immune memory clone and clone selection algorithms. We also introduce several optimal strategies and conduct numerical experiments to verify effects and efficiencies. Our proposed method combines the advantages of monoclone, multi-clone, and co-evolution, which are suitable for the large-scale problems addressed in this paper.

This study aimed to compare the effect of consuming decaffeinated coffee at different times around a high glycemic index (GI) meal on postprandial glycemic responses. Ten healthy adults completed this randomized, crossover trial. In each session, overnight-fasted participants consumed the test drink, which was a cup of decaffeinated coffee with low-fat milk and white sugar added, at different timepoints before or after a high GI meal. Postprandial levels of glucose, insulin, and nitrotyrosine at individual timepoints, as well as the area-under-curves (AUC), were compared among the treatments using mixed-effect linear model. Results showed that consuming the test drink 30 and 60 min before the high GI meal (glucose AUC ± SE: 862.6 ± 24.8 and 861.4 ± 24.8 mmol * 120 min/L, respectively) led to significantly reduced glycemic responses when compared with consuming it after the meal (952.7 ± 24.8 mmol * 120 min/L, p

Fatigue driving is the main cause of traffic accidents. Analysis of electroencephalogram (EEG) signals has attracted wide attention for identifying fatigue driving. With the development of the Internet of Vehicles (IoV), we hope to establish an EEG-based IoV traffic management system to improve traffic safety. In the proposed system, real-time diagnosis is a significant factor, and improvement of the detection speed is our main concern. EEG signals generate a large amount of spatially oriented data over a relatively short duration; hence, their dimension needs to be reduced effectively before being analysed. We proposes a feature reduction method, based on a novel weighted principal component analysis (WPCA) algorithm for EEG signals. First, the EEG features are extracted by an autoregressive (AR) model. Second, we calculate the influence of different features on the classified performance of fatigue state. The accuracy reduction values of different features are normalised as the weights of the features. Finally, these weights are assigned to the WPCA to reduce the EEG features. To verify the effectiveness of the algorithm, we carried out a simulated driving experiment involving eight participants. For comparison, power spectral density and differential entropy models were also introduced to extract EEG features. Support Vector Machine was adopted as a classifier to establish a fatigue driving classification experiment. The experimental results show that the WPCA method can effectively reduce the feature dimension of different EEG feature extraction methods, speed up calculations, and achieve a much higher classification accuracy of fatigue driving.

Aiming at the demand for rapid detection of highway pavement damage, many deep learning methods based on convolutional neural networks (CNNs) have been developed. However, CNN methods with raw image data require a high-performance hardware configuration and cost machine time. To reduce machine time and to apply the detection methods in common scenarios, the CNN structure with preprocessed image data needs to be simplified. In this work, a detection method based on a CNN and the combination of the grayscale and histogram of oriented gradients (HOG) features is proposed. First, the Gamma correction was employed to highlight the grayscale distribution of the damage area, which compresses the space of normal pavement. The preprocessed image was then divided into several unit cells, whose grayscale and HOG were calculated, respectively. The grayscale and HOG of each unit cell were combined to construct the grayscale-weighted HOG (GHOG) feature patterns. These feature patterns were input to the CNN with a specific structure and parameters. The trained indices suggested that the performance of the GHOG-based method was significantly improved, compared with the traditional HOG-based method. Furthermore, the GHOG-feature-based CNN technique exhibited flexibility and effectiveness under the same accuracy, in comparison to those deep learning techniques that directly deal with raw data. Since the grayscale has a definite physical meaning, the present detection method possesses a potential application for the further detection of damage details in the future.

Various space missions, including the Russian and Chinese interplanetary exploration collaboration in 2011 and the Phobos-Grunt space project to be relaunched by the Chinese in 2025, carry a soil preparation system (SOPSYS), which is an instrument used for scientific experiments. The design and manufacture of this precision instrument require stringent manufacturing processes and workflow of the highest quality, with every process in the project carefully monitored and controlled. All processes should be completed within the deadline so that the space project can be launched at the scheduled time. The colored Petri net (CPN) modeling method can describe a variety of resource types and execution logic, and it can be formally verified. Based on these advantages, we clearly describe the complex structure of the SPOSYS unit production process. In addition, we use critical time and the 6 sigma system to evaluate the availability and reliability of workflows, and we use elimination and simplification (ECRS) methods and constraint theory to improve the manufacturing process of the SOPSYS unit. We further provide optimization theories, methods, and insights for workflow management in time-sensitive and independent manufacturing systems.

Background/Purpose: To assess the efficacy of low-energy extracorporeal shockwave therapy (ESWT) for the treatment of plantar fasciitis. Methods: This was a prospective case series study that was performed at the Department of Orthopaedics, Queen Mary Hospital, Hong Kong. Twenty-one symptomatic feet in 16 patients with persistent symptoms of plantar fasciitis despite 3 months of conservative treatment were recruited in November 2008. All patients received five sessions of low energy ESWT and their corresponding 10-point visual analogue scale scores were recorded before and after each treatment sessions for each symptomatic foot. The patients were assessed for up to 6 months post-treatment. Results: The mean visual analogue scale scores reduction for pain on first step in the morning, daily activities, and heel compression test were 2.62 (44.3%), 3 (38.3%), and 1.6 (36.8%), respectively, post-treatment. The analgesic effect was maintained in 52.3% (n = 11) of the patients at 6 months post treatment. Conclusion: Low energy ESWT was shown to be an effective outpatient treatment option for patients with plantar fasciitis.

Research has demonstrated the positive impact of active learning on students’ learning outcomes, particularly in science, technology, engineering and mathematics (STEM) fields. However, few studies have explored the impact of active learning via problem-based collaborative games in large mathematics classes in the context of Asian tertiary education. This study assesses the effects of active learning on students’ learning outcomes using class test scores and the calculus concept inventory (CCI) to test the conceptual understanding of the basic principles of differential calculus, in a first year calculus course in Hong Kong. Three hypotheses were tested to determine the effects of game-based collaborative learning on learning mathematics among students. Active learning through a problem-based collaborative learning methodology was employed, using Kahoot!, a game-based learning platform. Results supported all three hypotheses, demonstrating a statistically significant increase in students’ conceptual understanding and examination performance, based upon their individual perceptions of active engagement and time spent in active learning. Our results indicated that active learning “levels the playing field”, in the sense that students with less pre-requisite background knowledge, using a problem-based collaborative learning methodology, were relatively more inclined to catch up or even exceed the performance of students with a stronger prerequisite background knowledge, at the end of the course.

In digital and green city initiatives, smart mobility is a key aspect of developing smart cities and it is important for built-up areas worldwide. Double-parking and busy roadside activities such as frequent loading and unloading of trucks, have a negative impact on traffic situations, especially in cities with high transportation density. Hence, a real-time internet of things (IoT)-based system for surveillance of roadside loading and unloading bays is needed. In this paper, a fully integrated solution is developed by equipping high-definition smart cameras with wireless communication for traffic surveillance. Henceforth, this system is referred to as a computer vision-based roadside occupation surveillance system (CVROSS). Through a vision-based network, real-time roadside traffic images, such as images of loading or unloading activities, are captured automatically. By making use of the collected data, decision support on roadside occupancy and vacancy can be evaluated by means of fuzzy logic and visualized for users, thus enhancing the transparency of roadside activities. The CVROSS was designed and tested in Hong Kong to validate the accuracy of parking-gap estimation and system performance, aiming at facilitating traffic and fleet management for smart mobility.

Background: Whether low-dose azathioprine (AZA) is effective in maintaining remission in patients with steroid-dependent ulcerative colitis (UC) remains unclear. We assessed the efficacy and safety of low-dose AZA in a Chinese population with UC. Methods: We identified steroid-dependent UC patients in clinical remission on AZA maintenance therapy from a territory-wide IBD Registry. Standard- and low-dose AZA were defined as at least 2 mg/kg/day and less than 2 mg/kg/day, respectively. Relapse rates were analyzed by Kaplan–Meier analysis and compared using log-rank test. Results: Among 1226 UC patients, 128 (53% male, median duration on AZA 44 months) were included. Median maintenance AZA dose was 1.3 mg/kg/day. 97.7% of the patients were on concomitant oral 5-aminosalicylic acid. Cumulative relapse-free rates in patients on standard-dose and low-dose AZA were 71.2%, 52.8% and 45.2%, and 71.8%, 55.3% and 46.2% at 12, 24 and 36 months, respectively ( p = 0.871). Relapse rate within 12 months was higher in patients who withdrew compared with those who maintained on AZA (52.6% versus 29.4%; p = 0.045). Mean corpuscular volume increased after AZA therapy in both of the low-dose [median (interquartile range, IQR): 88.2 (81.4–92.2) versus 95.1 (90.1–100.9) fl, p < 0.001] and standard-dose subgroups [median (IQR) 86.8 (76.9–89.9) versus 94.7 (85.9–99.7) fl, p < 0.001]. Leukopenia occurred in 21.1% of the patients. Patients on standard dose had a higher risk for leukopenia than those on low-dose AZA [odds ratio (OR) 3.9, 95% CI 1.9–8.2, p < 0.001]. Conclusions: In the Chinese population, low-dose AZA is effective for maintaining remission in steroid-dependent UC patients. Standard-dose AZA was associated with more than threefold increased risk of leukopenia.

Today cloud computing is widely used in various industries. While benefiting from the services provided by the cloud, users are also faced with some security issues, such as information leakage and data tampering. Utilizing trusted computing technology to enhance the security mechanism, defined as trusted cloud, has become a hot research topic in cloud security. Currently, virtual TPM (vTPM) is commonly used in a trusted cloud to protect the integrity of the cloud environment. However, the existing vTPM scheme lacks protections of vTPM itself at a runtime environment. This paper proposed a novel scheme, which designed a new trusted cloud platform security component, ‘enclave TPM (eTPM)’ to protect cloud and employed Intel SGX to enhance the security of eTPM. The eTPM is a software component that emulates TPM functions which build trust and security in cloud and runs in ‘enclave’, an isolation memory zone introduced by SGX. eTPM can ensure its security at runtime, and protect the integrity of Virtual Machines (VM) according to user-specific policies. Finally, a prototype for the eTPM scheme was implemented, and experiment manifested its effectiveness, security, and availability.

Cataract is the most common cause of visual loss in humans. A spontaneously occurred, autosomal dominant mouse mutant Secc, which displayed combined features of small eye, cataract and closed eyelid was discovered in our laboratory. In this study, we identified the mutation and characterized the cataract phenotype of this novel Secc mutant. The Secc mutant mice have eyelids that remain half-closed throughout their life. The mutant lens has a significant reduction in size and with opaque spots clustered in the centre. Histological analysis showed that in the core region of the mutant lens, the fiber cells were disorganized and clefts and vacuoles were observed. The cataract phenotype was evident from new born stage. We identified the Secc mutation by linkage analysis using whole genome microsatellite markers and SNP markers. The Secc locus was mapped at chromosome 1 flanked by SNPs rs3158129 and rs13475900. Based on the chromosomal position, the candidate cataract locus γ-crystallin gene cluster (Cryg) was investigated by sequencing. A single base deletion (299delG) in exon 3 of Cryga which led to a frame-shift of amino acid sequence from position 91 was identified. As a result of this mutation, the sequences of the 3rd and 4th Greek-key motifs of the γA-crystallin are replaced with an unrelated C-terminal peptide of 75 residues long. Coincidentally, the point mutation generated a HindIII restriction site, allowing the identification of the CrygaSecc mutant allele by RFLP. Western blot analysis of 3-week old lenses showed that the expression of γ-crystallins was reduced in the CrygaSecc mutant. Furthermore, in cell transfection assays using CrygaSecc mutant cDNA expression constructs in 293T, COS-7 and human lens epithelial B3 cell lines, the mutant γA-crystallins were enriched in the insoluble fractions and appeared as insoluble aggregates in the transfected cells. In conclusion, we have demonstrated that the Secc mutation leads to the generation of CrygaSecc proteins with reduced solubility and prone to form aggregates within lens cells. Accumulation of mutant proteins in the lens fibers would lead to cataract formation in the Secc mutant.

The objective of this study is to perform a meta-analysis of published data on the effects of active learning on Asian students' performance in science, technology, engineering, and mathematics (STEM) subjects. This comprehensive meta-analysis study includes a systematic review of papers related to various active learning approaches and instructional methods such as collaborative learning, experiential learning, discovery-based, group-inquiry-based, problem-based and activity-based learning, specifically the effects these approaches have on Asian students' performance in STEM-related subjects. The Scopus electronic bibliographic database was searched with search terms and dates to identify and extract the relevant studies that met pre-stated inclusion criteria. A main criterion for inclusion of studies in this meta-analysis were Asian students' exposure to any active learning intervention in STEM-related subjects. Studies that fulfilled the inclusion criteria were processed for data extraction. A total of 2810 full-text peer reviewed papers published from 2000 to 2020 were analyzed and 38 papers met the inclusion criteria established for the meta-analysis. Effect sizes between experimental (n = 2230) and control groups (n = 2510) were calculated using means and standard deviations which were collected and summarized for a comprehensive and systematic review based on the compiled data. Using means and standard deviations from the studies, a moderately large effect size (ES = 0.6596) was detected. Analysis of the pre-post effect sizes demonstrated a significantly higher mean effect size thereby indicating a positive effect of active learning on Asian students' performance in STEM subjects. Findings from the meta-analysis in this study provide a comprehensive understanding of the effects of active learning on Asian students' performance in STEM subjects. Finally, the significance and relevance of these findings for future research directions are discussed.

Embryo cryopreservation is an indispensable technique in reproductive programs and in animal facilities where genetically modified mice are used extensively. Here we report the use of a vitrification spatula (VS) that can be readily homemade and has a large holding capacity to vitrify preimplantation mammalian embryos in a micro-drop employing ultra-rapid cooling in liquid nitrogen (LN2). Vitrified one-cell embryos and morulae have high survival rates after thawing, and the fertility of the derived progeny is comparable to that of the control unvitrified group. The large holding capacity (up to 50 embryos per VS) does not only allow rapid expansion of storage capacity for additional mouse strains but also opens up the possibility to streamline transgenic mice generation procedures in transgenic facilities.

This study describes the development and validation of a psychometrically-sound instrument, the Active Learning Strategies Inventory (ALSI), designed to measure learners' perceptions of their active learning strategies within an active learning context. Active learning encompasses a broad range of pedagogical practices and instructional methods that connect with an individual learner's active learning strategies. In order to fulfill the study's goals, a conceptual framework on learners' active learning strategies was developed and proposed, drawing upon the research literature on active learning. The development and construct validation of the Active Learning Strategies Inventory (ALSI), based on the conceptual and methodological underpinnings, involved identifying five scales of learners' active learning strategies: engagement, cognitive processing, orientation to learning, readiness to learn and motivational orientation. An item pool of 20 items was generated following an extensive review of the literature, standardized card sorting procedures including confirmatory factor analysis and scale validation of a pilot (n = 407) survey. The ALSI scale demonstrated strong internal consistency and reliability with a Cronbach's alpha ranging from 0.81 to 0.87. High item loading scores from the factor analysis provided initial support for the instrument's construct validity of the five-factor model. The ALSI scale provides a reliable and valid method for researchers and academicians who wish to measure learners' perceptions of their active learning strategies within an active learning context. Finally, we discuss the implications and address the limitations and directions for future research.

This study attempted to conceptualise and measure learners' perceptions of their collaborative problem-based learning and peer assessment strategies in a technology-enabled context. Drawing on the extant literature, we integrate collaborative, problem-based and peer assessment learning strategies and propose a new model, the collaborative problem-based learning and peer assessment (Co-PBLa-PA) conceptual framework, which forms the basis of a new psychometrically sound and conceptually based scale, the collaborative problem-based learning and peer assessment strategies inventory (CO-PBLaPA-SI). The development and validation of the CO-PBLa-PA-SI, based on the methodological and conceptual insights gained from prior research, involved identifying the following four scales: capacity to collaborate, readiness to engage, task-based interest and peer feedback usefulness. An item pool comprising of 16 items was established and verified by two panels of judges using a formalised card sorting procedure. Confirmatory factor analysis was conducted to validate the instrument of a small-scale (N = 164) study. The CO-PBLa-PA-SI scale showed strong construct validity and reliability with a Cronbach's coefficient alpha ranging from 0.828 to 0.880, which suggested strong internal consistency. The resultant instrument is intended as a tool to reliably measure learners' perceptions of their collaborative problem-based learning and peer assessment strategies in a technology-enabled context.

Research has demonstrated the positive impact of active learning on students' learning outcomes, particularly in science, technology, engineering and mathematics (STEM) fields. However, few studies have explored the impact of active learning via problem-based collaborative games in large mathematics classes in the context of Asian tertiary education. This study assesses the effects of active learning on students' learning outcomes using class test scores and the calculus concept inventory (CCI) to test the conceptual understanding of the basic principles of differential calculus, in a first year calculus course in Hong Kong. Three hypotheses were tested to determine the effects of game-based collaborative learning on learning mathematics among students. Active learning through a problem-based collaborative learning methodology was employed, using Kahoot!, a game-based learning platform. Results supported all three hypotheses, demonstrating a statistically significant increase in students' conceptual understanding and examination performance, based upon their individual perceptions of active engagement and time spent in active learning. Our results indicated that active learning "levels the playing field", in the sense that students with less pre-requisite background knowledge, using a problem-based collaborative learning methodology, were relatively more inclined to catch up or even exceed the performance of students with a stronger prerequisite background knowledge, at the end of the course.

We are living in an era when the world's aging population is rapidly expanding. The importance of family caregivers has been receiving increasing attention since last decade (Gitlin et al., [...], By applying the Stress Process Model to examine the characteristics of people with dementia and their spousal caregivers, this study aims to identify the potential risk and mitigating factors of psychosocial adjustment among the spousal caregivers. We recruited 80 care recipient-caregiver couples in Hong Kong and examined the relationships of socio-economic, care recipient&apos;s, and caregiver&apos;s factors with spousal caregivers&apos; psychosocial adjustment. It was found that care recipients&apos; cognitive functions were associated with caregivers&apos; reported frequencies of their behavioral and psychological symptoms of dementia (BPSD) (r = .30, p = .008), and caregivers&apos; perceived burden of caregiving (r = -.54, p = .008). Caregivers&apos; quality of life was associated with their perceived caregiving burden (r = -.82, p = .001) and self-efficacy (r = .32, p = .001). Upon further examining the caregiving model with these parameters, a significant multivariate general linear model was found with (F (1, 12) = 13.06, p = .001, partial eta square = .70, observed power = .99). Moreover, female caregivers reported higher sense of caregiving stress and poorer quality of life than male caregivers. This study found that the cognitive functions and BPSD of care recipients with dementia and perceived level of caregiving burden are strongly associated with degrees of psychosocial adjustment among their spousal caregivers. The self-perceived caregiving role in a family is also a possible confounding factor contributing to the perceived caregiving burden. To support in-home caregiving of people with dementia, strategies to empower spousal caregivers to execute their caregiving roles are recommended. Keywords: caregivers, dementia, psychosocial adjustment, quality of life

Dementia is an umbrella term used for several diseases affecting memory, other cognitive abilities, and behaviour that interferes significantly with a person's ability to maintain their daily living activities (WHO, [...], Dementia is a worldwide public health challenge. Caring for a person with dementia affects different caregivers in diverse ways. Using an integration of the Model of Human Occupation (MOHO) and Bronfenbrenner&apos;s ecological systems theory (EST), this study aimed at documenting the dynamic interaction among the five specific stages of caregiving (seeking understanding, stabilization, preparation, implementation, and adaptation) through individuals&apos; occupations and receive input and output; and to analyze how the social environments are synergistic with each other in the present medical-social collaboration model. Using the purposeful sampling strategy and a combination of closed-and-open-ended questions, four focus groups were conducted in four different districts. A phenomenological explanation approach was used to examine the participants&apos; caring experiences and their needs. The research team assessed the cognitive functions of care-recipients by the Hong Kong version of Montreal Cognitive Assessment (HK-MoCA) and all recruited caregivers were given the Chinese Version of the Revised Caregiving Self-Efficacy Scale (C-RCSES) to determine their level of caregiver self-efficacy. Eighty family caregivers (FCGs) of people with dementia were recruited. Seeking understanding, stabilization, preparation, implementation, and adaptation were the themes identified for analysis. The results indicated caregiving is a demanding task, both mentally and physically. Being older, having a lower socioeconomic status and lower levels of informal support were related to higher levels of stress and poorer coping strategies in FCGs. Moreover, active service provision can be started in the initial stages of caring and should be accompanied with the active involvement of service in the volitional aspects of the FCGs.. Keywords: needs of caregivers, older adults with dementia, focus group, qualitative research

Nowadays, patients' safety is the top priority for medical services around the world. However, it is believed that many of the adverse events in hospitals are preventable. Type and screen (T&S) procedures require intense practical training by each medical practitioner in each hospital. This study applied an interactive Virtual Reality (VR) technology to supplement the traditional approach to facilitate procedural training. The VR system made use of the Unity3D for application development. To investigate the reliability and validity of the conceptual medical training model, a survey was conducted to measure the content, motivation and enhanced readiness of practitioners. The partial least squares (PLS) modelling was carried out to investigate the correlation between each pair of measured variables. The study results indicated that the learning model has good reliability for each measurement factor and validates the survey study. The PLS modelling also indicated a significant correlation between each pair of measured variables. The project developed a VR training program for training in T&S procedures. The study provides important implications on the development of a practical VR training program for medical practitioners, as well as valuable insights for the development of similar VR training programs in the future.

Information and communication technologies play a crucial role in the development of tourism and service industries. Continuous innovation and the extension of technological knowledge are essential for adoption. This study critically evaluates the evolving structure and latest trends of the Technology Acceptance Model (TAM) by reviewing relevant studies in tourism-related and mainstream service industries. Sixteen SSCI journals were selected, encompassing all TAM-related articles. The research employs descriptive statistical analysis using CiteSpace and content analysis to identify similarities and differences between the two fields. The longitudinal review spans five decades, identifying shared moderators, mediators, and variables in existing TAM research. Findings highlight the integration of TAM in notable studies using the 4Es (Engagement, Efficacy, Enjoyment, and Experience) attributes. Additionally, the study reveals differences between the tourism-culture-museum area, which focuses on Extended Reality (XR), and the mainstream service area, which explores diverse innovations. This study quantifies innovation applications, presents TAM thrusts and generations, and itemizes all TAM attributes. A novel TAM variant is proposed to refine the framework, embedding the 4Es and obstructive factors, and emphasizing emotional, social, and control factors. [ABSTRACT FROM AUTHOR]