This study aims to build upon the authors’ previous work investigating the complex connection relating energy consumption, emission reduction, and economic development. Specifically, it focuses on the Association of Southeast Asian Nations (ASEAN) within the context of post-pandemic economic recovery. The methodology includes implementation of regression modelling using the MATLAB Regression Learner program that utilizes World Energy Council (WEC) Trilemma ratings as input predictors. A range of regression models are utilized and undergo thorough assessment using established metrics, such as Mean Absolute Error (MAE), R-squared Coefficient of Determination, Root Mean Squared Error (RMSE), and Mean Squared Error (MSE). Additionally, practical metrics such as prediction time and training time are considered. Through a comparative analysis of the results achieved by the 2023 model in relation to its predecessor, an evaluation is conducted to determine the suitability of previous model. This assessment leads to the identification of relevant policy implications that may contribute to sustainable energy trajectory of the region. This academic pursuit hopes to enhance the scientific dialogue by integrating empirical research results with policy imperatives, in order to promote the development of ecologically sustainable and economically resilient energy frameworks in the ASEAN region. Based on the results, the key discovery of this study pertains to the ever-changing nature of energy dynamics and the significance of flexible modelling in influencing regional energy strategies. Hence, it is essential for policymakers to remain agile to the ever-changing factors that impact environmental sustainability, energy security and energy equity.

Diabetes mellitus is a disease incorporated with carbohydrate metabolism whereas the body becomes unable to generate or react with insulin which leads to abnormal levels of blood sugar (glucose). In a worldwide perspective, Diabetes mellitus is ranked as the 9th leading cause of death based on the records of the World Health Organization and according to the International Diabetes Federation, there are about 463 million diabetic people worldwide in 2019 which is projected to increase to 700 million diabetic people by year 2045. In a regional perspective, about 251 million (45%) diabetic people resides on the Western Pacific and Southeast Asian region, whereas about 140 million people are undiagnosed of the disease. In this study, a genetic algorithm-optimized neural network using MATLAB was developed based on the risk factors. The experimental results show that the best validation performance has a value of 0.014129 and with a regression model coefficient R2 value of 0.95864.

Due to the advent of the COVID-19 pandemic, the Philippine government encouraged enterprises and businesses to utilize flexible work arrangements such as work-from-home (WFH) or telecommuting setup. Nowadays, the key components necessary for a telecommuting include a WiFi-enabled IT equipment, secured work environment, and reliable internet connection, while research shows that type of work and computer literacy are also key factors for telework implementation. Multiple studies in relation to telework have already been conducted but some studies were deemed inconclusive and need further analysis. Therefore, in this study, a Mamdani fuzzy-based model was developed for telework capability assessment for Philippine government employees based on four significant factors namely: internet speed, IT equipment availability, computer literacy, and type of work, which are expressed in linguistic representations. The proposed fuzzy system can provide a feedback telework capability score based on the four input parameters which may also be characterized with the potential telecommuting cost requirement.

In medical imaging, an X-ray image generated using a flat panel detector (digital) typically has poor image quality, affecting the capability of successful medical diagnosis based on the images. The image enhancement process intends to provide better interpretability of the information contained in the images. The main problems considered for medical images include poor quality and low contrast. Therefore, the general objectives of image enhancement include contrast improvement and noise reduction. This study proposes an upgraded X-ray image enhancement hybrid algorithm that utilizes and consists of the Contrast Limited Adaptive Histogram Equalization (CLAHE) method combined with the Wiener filter. Based on the performance metrics results, including MSE, PSNR, and Entropy, as compared to the existing CLAHE method only, the proposed methodology has a lower MSE signifying lower error, a higher PSNR representing a lower amount of distortion, and higher information entropy which indicates higher obtained information. Furthermore, the implementation of the proposed approach is applied to 6000 X-ray images before deep learning classification modeling, which significantly improved from 50% to 78% validation accuracy. Therefore, the proposed method improves the image enhancement methodology and can substantially assist in diagnosing diseases.

The COVID-19 pandemic has caused disruption to the economy due to the increasing infection that affects the workforce in different sectors. The Philippine government has imposed lockdowns to control the spread of infection. This urged the different sectors to implement flexible work schedules or work from home setup. A work-from-home (WFH) setup burdens both the employee and employer by installing different equipment set-ups such as WiFi-equipped laptops, computers, tablets, or smartphones. However, the internet stability in some of the areas in the Philippines is not yet reliable. In this study, an application is used collect survey information and provide an estimate of the telework internet cost requirement of a given government employee or a given government employee implementing a work-from-home set up in their respective household. This involves survey results from different respondents who are currently on a work-from-home setup and significant factors from the survey have been analyzed using machine learning (ML) algorithms. Among the machine learning algorithms used, the ensemble bagged trees model outperformed the other ML models. This work can be extended by incorporating a wider scope of datasets from different industry doing work from home set-up. In addition, in terms of education, it is also recommended to determine the WFH set up not just with the government employee and employer but to also extend this into the education side.