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Automated essay scoring (AES) has become a valuable tool in educational settings, providing efficient and objective evaluations of student essays. However, the majority of AES systems have primarily focused on native English speakers, leaving a critical gap in the evaluation of non-native speakers' writing skills. This research addresses this gap by exploring the effectiveness of automated essay-scoring methods specifically designed for non-native speakers. The study acknowledges the unique challenges posed by variations in language proficiency, cultural differences, and linguistic complexities when assessing non-native speakers' writing abilities. This work focuses on the automated student assessment prize and Khon Kaen University academic English language test dataset and presents an approach that leverages variants of the long short-term memory network model to learn features and compare results with the Kappa coefficient. The findings demonstrate that the proposed framework and approach, which involve joint learning of different essay representations, yield significant benefits and achieve results comparable to state-of-the-art deep learning models. These results suggest that the novel text representation proposed in this paper holds promise as a new and effective choice for assessing the writing tasks of non-native speakers. The result of this study can apply to advance educational assessment practices and promote equitable opportunities for language learners worldwide by enhancing the evaluation process for non-native speakers.

This paper focuses on the cases of Malaysia and Thailand and investigates the possibility of upgrading these countries from the middle-income trap and the possible roles of the 4th Industrial Revolution (4IR) in this process. The 4IR may induce firms to introduce factory automation to facilitate upgrade into higher segments with local spillovers. A key factor for this positive scenario is local institutions facilitating the training and upskilling of their local workforce. By contrast, increased problems are expected for other countries with the mid-range level of wages and mid-level of educated population, and firms in these economies mostly execute labor-intensive production and are likely to be substituted by additional factory automations or their multinational firms may move to neighboring countries for low wages. [ABSTRACT FROM AUTHOR]

Background: Thailand has a shortage of community health nurses for supporting the self‐management of type 2 diabetes, which is prevalent and poorly controlled. Aim: This study examined the feasibility and acceptability of a self‐care assistance programme for poorly controlled type 2 diabetes mellitus. The SukapapNet programme consisted of automated interactive voice response calls to patients and automated follow‐up email notifications to their nurses. Design: Single‐arm pre‐post trial. Methods: Six nurses and 35 type 2 diabetes patients were recruited from primary care settings in suburban provinces in Thailand. The study was conducted from June 2017 to November 2017. We assessed patients before and after 12 weeks of the SukapapNet intervention. Results: Mean glycated haemoglobin decreased by 0.9%. Patients reported reduced carbohydrate consumption, increased physical activity, increased medication adherence, improved sleep quality, and more frequent foot care. Patients and nurses both recommended using the intervention, although nurses expressed concerns regarding increased workload. Conclusions: The study programme could improve outcomes in Thai type 2 diabetes patients. Further study of the impact of technology upon nurses' workload is warranted. SUMMARY STATEMENT: What is already known about this topic? Prevalence of type 2 diabetes is increasing in Thailand, and nurses who play an important role in health care at the community level are limited.Mobile health strategies including short message service, automated telephonic monitoring, and internet‐based education have been successfully applied in Thailand with prenatal care, depression, Parkinson disease, cardiac rehabilitation, and diabetes.Although mobile health is a promising way to bridge gaps in managing type 2 diabetes, an effect of automated mobile health with nurse follow‐up has never been investigated in primary care settings. What this paper adds? Automated interactive voice response monitoring of diabetes patients with emailed updates to nurses is acceptable to Thai patients with poorly controlled type 2 diabetes.Mobile health strategies could also improve Thai nurses' ability to help diabetes patients with glycaemic control.To reduce burden for nurses, summaries of patients' responses should be emailed on a less‐than‐daily basis. The implications of this paper: This study contributes to the knowledge of how mobile health strategies could help Thai patients with type 2 diabetes achieve glycaemic control and increase nurses' engagement with these patients.Innovative use of automated interactive voice response with feedback to community health nurses should be further developed in Thailand.Future research should investigate a mobile health benefit to diabetes glycaemic control through a large‐scale randomized controlled trial. [ABSTRACT FROM AUTHOR]

The article focuses on the design of an automated warehouse for the Thai Airways Cargo Terminal at Don Muang International Airport in Bangkok, Thailand. The facilities of Thai Cargo have been extended due to the increase increment in tonnage handled. Space utilization is of critical issue for the airport planners because of the airport's location in a suburban area. In this regard, the management left with no options but to introduce an automated storage and retrieval (AS/R) system. However, the installation of such system is very expensive. To counteract the expensive cost of the system, two steps were taken. First, is the used of an analytical approach to determine an optimal AS/R system design configuration minimizing cost. Then, the design configuration was further tested by discrete event simulation.