Pterygium is an eye condition that causes the fibrovascular tissues to grow towards the corneal region. At the early stage, it is not a harmful condition, except for slight discomfort for the patients. However, it will start to affect the eyesight of the patient once the tissues encroach towards the corneal region, with a more serious impact if it has grown into the pupil region. Therefore, this condition needs to be identified as early as possible to halt its growth, with the use of simple eye drops and sunglasses. One of the associated risk factors for this condition is a low educational level, which explains the reason that the majority of the patients are not aware of this condition. Hence, it is important to develop an automated pterygium screening system based on simple imaging modalities such as a mobile phone camera so that it can be assessed by many people. During the early stage of automated pterygium screening system development, conventional machine learning techniques such as support vector machines and artificial neural networks are the de facto algorithms to detect the presence of pterygium tissues. However, with the arrival of the deep learning era, coupled with the availability of large training data, deep learning networks have replaced the conventional networks in screening for the pterygium condition. The deep learning networks have been successfully implemented for three major purposes, which are to classify an image regarding whether there is the presence of pterygium tissues or not, to localize the lesion tissues through object detection methodology, and to semantically segment the lesion tissues at the pixel level. This review paper summarizes the type, severity, risk factors, and existing state-of-the-art technology in automated pterygium screening systems. A few available datasets are also discussed in this paper for both classification and segmentation tasks. In conclusion, a computer-assisted pterygium screening system will benefit many people all over the world, especially in alerting them to the possibility of having this condition so that preventive actions can be advised at an early stage.

Since the start of the COVID-19 pandemic at the end of 2019, more than 170 million patients have been infected with the virus that has resulted in more than 3.8 million deaths all over the world. This disease is easily spreadable from one person to another even with minimal contact, even more for the latest mutations that are more deadly than its predecessor. Hence, COVID-19 needs to be diagnosed as early as possible to minimize the risk of spreading among the community. However, the laboratory results on the approved diagnosis method by the World Health Organization, the reverse transcription-polymerase chain reaction test, takes around a day to be processed, where a longer period is observed in the developing countries. Therefore, a fast screening method that is based on existing facilities should be developed to complement this diagnosis test, so that a suspected patient can be isolated in a quarantine center. In line with this motivation, deep learning techniques were explored to provide an automated COVID-19 screening system based on X-ray imaging. This imaging modality is chosen because of its low-cost procedures that are widely available even in many small clinics. A new convolutional neural network (CNN) model is proposed instead of utilizing pre-trained networks of the existing models. The proposed network, Residual-Shuffle-Net, comprises four stacks of the residual-shuffle unit followed by a spatial pyramid pooling (SPP) unit. The architecture of the residual-shuffle unit follows an hourglass design with reduced convolution filter size in the middle layer, where a shuffle operation is performed right after the split branches have been concatenated back. Shuffle operation forces the network to learn multiple sets of features relationship across various channels instead of a set of global features. The SPP unit, which is placed at the end of the network, allows the model to learn multi-scale features that are crucial to distinguish between the COVID-19 and other types of pneumonia cases. The proposed network is benchmarked with 12 other state-of-the-art CNN models that have been designed and tuned specially for COVID-19 detection. The experimental results show that the Residual-Shuffle-Net produced the best performance in terms of accuracy and specificity metrics with 0.97390 and 0.98695, respectively. The model is also considered as a lightweight model with slightly more than 2 million parameters, which makes it suitable for mobile-based applications. For future work, an attention mechanism can be integrated to target certain regions of interest in the X-ray images that are deemed to be more informative for COVID-19 diagnosis.

Interconnected components of holistic development, such as being thankful, addressing basic psychological needs, and acting effectively toward others, should be a priority for college athletes. Athletes at the College level need all-encompassing support systems to ensure their health, happiness, and success because of the special difficulties they have juggling their academic, athletic, and personal schedules. Problems with work-life balance, stress, and performance expectations all impede College Student Athletes’ holistic development. A thorough plan that considers all of the social, emotional, and psychological aspects impacting athlete development is necessary to overcome these obstacles. An Integrated Holistic Development Program for College Athletes (IHDP-CA) is suggested in this paper as a method that incorporates various aspects of positive psychology, mindfulness, resilience training, and the enhancement of interpersonal skills. Athletes at the College level can benefit from this all-encompassing program’s emphasis on helping others, developing an attitude of gratitude, and meeting basic psychological requirements. Sports counseling services, schools, and College athletic teams can all benefit from the IHDP-CA. A more positive and supportive sporting environment can be achieved when the program takes a more holistic approach to athletes’ needs, improving their mental health, social connections, and overall performance. The possible effect of the IHDP-CA on the holistic development outcomes of College Student-Athletes will be predicted through simulation analysis. To gain a better understanding of the program’s long-term viability, efficacy, and scalability, this analysis will run simulations of different situations and tweak program settings.

The economic valuation is crucial to estimate non-marketed goods, which refer to public willingness to pay (WTP) for conservation efforts in the tourism area. Both local communities and visitors play an important role in developing sustainable tourism and well-being. However, the information on economic valuation based on their preferences and well-being is still limited in the world heritage site Gunung Mulu National Park (GMNP). Thus, the study attempts to elucidate the WTP on how the local community and visitors value biodiversity conservation in GMNP through park entrance fees and how it relates to conservation awareness and community well-being in the area. The validated questionnaire was distributed to the visitors (n = 87) and the local community (n = 99) through an online and face-to-face approach, respectively. From the study, the estimated mean WTP among the public was MYR40.61 (USD9.60) per entry, which is highly influenced by their level of attitude and experience. The level of attitude among the respondents has influenced the WTP for the existing park entrance fee. Meanwhile, there is no awareness construct that affects the WTP for increased fees among visitors and the local community. The low level of knowledge and experience of the respondents but the high level of attitude has influenced the good WTP for the cheaper park entrance fee. It is noted that certain socio-demographic factors had influenced their WTP for the offered and maximum price. It was found that the environment and social factors have significantly influenced the WTP among the public. However, the economic factor did not predict their WTP. The findings enhance the relationship between crucial social value determinants and the economy, particularly on WTP in a protected area.

Background: In recent years, advances in wearable sensor technology and artificial intelligence (AI) have provided new possibilities for detecting and monitoring depression. Objective: This study systematically reviewed and meta-analyzed the diagnostic and predictive performance of wearable device–based AI models for detecting depression and predicting depressive episodes and explored factors influencing outcomes. Methods: Following PRISMA-DTA (Preferred Reporting Items for a Systematic Review and Meta-Analysis of Diagnostic Test Accuracy) guidelines, the PubMed, Embase, Web of Science, and PsycINFO databases were searched from inception to May 27, 2025. Eligible studies used AI algorithms on wearable device data for depression detection or episode prediction. Sensitivity, specificity, diagnostic odds ratio, and area under the curve (AUC) were pooled using a bivariate random effects model. Risk of bias was assessed using Prediction Model Risk of Bias Assessment Tool plus artificial intelligence (PROBAST+ AI), and certainty of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) tool. Results: We included 16 studies (32 datasets) with 1189 patients and 13,593 samples. For depression detection, pooled sensitivity and specificity were 0.89 (95% CI 0.83‐0.93) and 0.93 (95% CI 0.87‐0.96), with a diagnostic odds ratio of 110.47 (95% CI 33.33‐366.17) and AUC of 0.96 (95% CI 0.94‐0.98). Random forest models showed the best performance (sensitivity=0.89, specificity=0.91, AUC=0.97). Subgroup analyses indicated that study design, AI method, reference standard, and input type significantly affected diagnostic accuracy ( P <.05). For depressive episode prediction (3 datasets), pooled sensitivity was 0.86 (95% CI 0.80‐0.91), and pooled specificity was 0.65 (95% CI 0.59‐0.71). The overall risk of bias was low to moderate, with no evidence of publication bias. Conclusions: Wearable device–based AI models achieved high accuracy for detecting depression and moderate utility in predicting episodes. However, heterogeneity, reliance on retrospective and public datasets, and lack of standardized methods limited generalizability.