In its effort to modernize its public service delivery, Malaysia is actively leveraging on big data analytics. To support this ambitious initiative, a new framework addressing the needs of Big Data Quality for Malaysia is imperative, as big data analytics requires high quality data in order for it to be useful. Unfortunately, a proper big data quality framework, particularly one which focuses on the specific context and needs of Malaysia’s Public Sector Open Data initiative is missing. This paper thus focuses on the proposed development of the Big Data Quality Framework for Malaysia’s Public Sector Open Data Initiative (MyPS-ODI). Using Systematic Literature Review (SLR) approach, we conceptualize and propose a framework for big data quality that can contribute in enabling the sharing of quality data widely, so as to increase the transparency of the Malaysian government's services, and provide people and the business community the opportunity to increase creativity and innovation in developing new products and services through high quality data. The proposed framework will benefit IS managers in government sectors and help them better understand and meet their consumers’ data quality needs, as well as help them to facilitate big data analytics readiness of the public sector in the country. It will also assist in providing a high-quality platform to the citizens to get quality information from official government sources. Finally, the framework will help in saving the time and effort needed in correcting the results of data analysis due to poor data quality, by providing quality data from the data preparation stage.

Deforestation is the long-term or permanent conversion of forest land to other uses, such as agriculture, mining, and urban development. As a result, deforestation has catastrophic consequences for the environment, including the loss of biodiversity, disruption of clean water supplies, and the acceleration of climate change. According to statistics, the deforestation trend in developing countries is at an alarming rate including Malaysia where plantation activities are the primary cause of forest loss. Recent anecdotal studies have demonstrated the effectiveness of the deep learning-based (DL) approach in producing deforestation maps. However, there are limited studies concentrating on DL approach for synthetic aperture radar (SAR) imaging due to complexity of the computational concepts of the method. The SAR imagery can be challenging to interpret but its all-weather and all-day capability can be critical in forest monitoring compared to optical imagery. Thus, in this study, we propose to map deforestation areas in Permanent Forest Reserve (HSK) using multi-temporal Sentinel-1 SAR data. Deep learning-based U-Net was employed to classify the SAR imagery as forest and non-forest due to its semantic segmentation capabilities. The experiment results showed that the proposed deep learning-based technique successfully achieved 0.993 of intersection over union (IoU) and 0.980 of overall accuracy (OA). Also, we explained the entire procedure from beginning to end as simple as possible for beginners to comprehend. In brief, the findings of this study have the potential to improve monitoring of damaged HSK areas, prioritize the restoration of the affected forest areas and protecting the forest lands from illegal deforestation activities.

Visual analytics fundamentally influences the analytical process. Without proper guidance, the use of visual analytics in complex domain can become an obstacle that hides the usefulness of analytical data. Since evaluation is the only way to identify the effectiveness of visual analytics to represent analytical outcomes, misconception of the evaluation process will bury the relevancy of visualization to support valuable decision. Recently, the nature domain of data has changed and we are now dealing with data that is massive, ambiguous, and dynamic, is often processed in real time. Hence, the data is more complex and cognitive activities that visual analytics facilitates are also getting more complex. Thus, this research revisits the way to evaluate visual analytics in complex cognitive conditions that are natural, uncertain and context dependent. Governed by the Design Science Research Methodology (DSRM), there are three phases involve during the evaluation process; i). demonstration, ii). evaluation and iii). communication. The design process is embedded with the human-activity centered design approach to gain better understanding on the visual analytics users and the complex cognitive activities involved. Thus, the research proposes Focus Group Observation method in conducting the evaluation in authentic setting. By offering a set of evaluation recommendations, this research aims to enhance visual analytics among users. It also recommends the evaluation criteria, sampling strategy and participation, focus group tasks

Despite the effectiveness of text messaging therapy in improving mental health conditions, limited attention has been paid to how the text messages are constructed. Thus, this study questions whether there is a need to develop a model of text message construction for mental health therapy. In this backdrop, this study reviews how a text message for mental health therapy is constructed, specifically focused on the process and guidelines. This study also aims to identify the research gap regarding the guideline of text message construction for mental health therapy and to identify mental health professionals’ practices in text messaging therapy. In addition, the opinions of mental health professionals on the need to develop a text message construction guideline were also gathered. The findings from the literature review confirmed that there are still limited guidelines explaining the process of constructing text messages for mental health therapy. Meanwhile, results from the online survey found that mental health professionals expressed a high need to explore and develop a model of text message construction for mental health therapy. With this research gap addressed, this study proposes further research into the development of a text message construction model for mental health therapy.

Isu dan maklumat mengenai masalah kesihatan mental kerap dibincangkan menerusi saluran media. Namun, kesedaran masyarakat terhadap masalah kesihatan mental masih lagi rendah. Tambahan lagi, sesetengah individu yang mengalami masalah kesihatan mental enggan tampil menerima rawatan. Bagi mengatasi masalah ini, agensiagensi kesihatan awam professional di seluruh dunia telah mewujudkan terapi masalah kesihatan mental secara digital. Terapi masalah kesihatan mental secara digital telah memberikan focus terhadap penyampaia rawatan pemulihan psikologi dan kesihatan fzikal. Walaupun secara ke seluruhannya, kaedah terapis ecara digital telah dibuktikan berkesan dalam memperbaiki tahap kesihatan mental seseorang, namunjenis platform digital yang sesuai digunakan untuk menyampaikan terapi bagi sesuatu jenis masalah kesihatan mental masih belum diteliti. Oleh itu artikel ini memberi tumpuan terhadap jenis platform digital yang digunakan dalam terapi masalah kesihatan mental dan keberkesanannya ke atas pelbagai aspek kesihatan serta pemulihan. Dalam hal ini, analisa kandungan ke atas kajiankajian lepas berkenaan dengan jenis platform digital dan keberkesanannya diberi penelitian. Hasil analisa kandungan dari tinjauan kajian lepas menunjukkan sistem pesanan ringkas (SMS) adalah yang paling banyak digunakan sebagai platform untuk menyampai terapi masalah kesihatan mental diikuti aplikasi telefon pintar, laman sesawang dan ruang perbualan dalam talian. Secara keseluruhan, dapatan dari tinjauan kajian lepas membuktikan terdapat perubahan positif terhadap emosi, pemikiran dan tingkah laku. Dengan ini menunjukkan bahawa terapi masalah kesihatan mental secara dalam talian amat berpotensi untuk dilaksanakan terutamanya bagi individu yang mempunyai kekangan untuk mendapatkan perkhidmatan rawatan secara bersemuka atau semasa situasi setempat berada pada tahap kritikal seperti situasi pandemik COVID19 yang membataskan pergerakan seluruh populasi. Kata kunci: Terapi digital; terapi mobil; kesihatan mental; pesanan teks; psikoterapi
Abstract
Issues and information on mental health problems are frequently discussed through media channels. However, public awareness on mental health problems is still low. In addition, individuals with a mental health problem refuses to receive treatment. To overcome these problems, professional public health agencies around the world have designed digital mental health therapy. Digital mental health therapy has given a focus to the delivery of psychological and physical health rehabilitation treatments. Although digital mental health therapy has been shown to be effective in improving mental health conditions, however the appropriate type of digital platform used to provide the therapy for a particular type of mental health problem has yet to be studied. Therefore, this article focuses on the types of digital platforms used in therapy of mental health problems and its effectiveness on various aspects of health as well as rehabilitation. In this regard, content analysis of previous studies related to the type of digital platform and its effectiveness are done. The results of the content analysis showed that SMS the most widely used platform to deliver mental health problem therapy followed by smartphone applications, websites and online chat. Overall, the fndings from the results of previous studies show positive changes in emotions, thoughts and behaviours. This suggests that mobile therapy for mental health has great potential to be implemented for individuals who have constraints to access in-person therapy or during critical times such as COVID19 pandemic situations which limiting population movement. Keyword: Digital therapy; mobile therapy; mental health; text message; psychotherapy

Background: China has the largest number of patients with type 2 diabetes (T2D) worldwide, and the chronic complications and economic burden associated with T2D are becoming increasingly severe. Developing accurate and widely applicable risk prediction models is of great significance for the early identification of and intervention in high-risk populations. However, current Chinese models still have many shortcomings in terms of methodological design and clinical application. Objective: This study conducts a systematic review and narrative synthesis of existing risk prediction models for type 2 diabetes in China, aiming to identify issues with existing models and provide references with which Chinese scholars can develop higher-quality risk prediction models. Methods: This study followed the PRISMA guidelines to conduct a systematic search of the literature related to T2D risk prediction models in China published in English journals from October 2019 to October 2024. The databases included PubMed, CNKI and Web of Science. Included studies had to meet criteria such as clear modeling objectives, detailed model development and validation processes, and a focus on non-diabetic populations in China. A total of 20 studies were ultimately selected and comprehensively analyzed based on model type, variable selection, validation methods, and performance metrics. Results: The 20 included studies employed various modeling methods, including statistical and machine learning approaches. The AUC values of the models ranged from 0.728 to 0.977, indicating overall good predictive capability. However, only one study conducted external validation, and 45% (9/20) of the studies binned continuous variables, which may have reduced the models’ generalization ability and predictive performance. Additionally, most models did not include key variables such as lifestyle, socioeconomic factors, and cultural background, resulting in limited data representativeness and adaptability. Conclusions: Chinese T2DM risk prediction models remain in the developmental stage, with issues such as insufficient validation, inconsistent variable handling, and incomplete coverage of key influencing factors. Future research should focus on strengthening multicenter external validation, standardizing modeling processes, and incorporating multidimensional social and behavioral variables to enhance the clinical utility and cross-population applicability of these models. Registration ID: CRD420251072143.