Gene–diet interactions on metabolic disease-related outcomes in Southeast Asian populations: a systematic review

Diabetes and obesity are chronic diseases that are a burden to low- and middle-income countries. We conducted this systematic review to understand gene–diet interactions affecting the Southeast Asian population’s risk of obesity and diabetes. The literature search was performed on Google Scholar and MEDLINE (PubMed) search engines independently by four reviewers who evaluated the eligibility of articles based on inclusion criteria. Out of 19,031 articles, 20 articles examining gene–diet interactions on obesity and/or diabetes-related traits met the inclusion criteria. Three (Malaysia, Indonesia, and Singapore) out of eleven Association of Southeast Asian Nations (ASEAN) countries have conducted studies on gene–diet interactions on obesity and diabetes. From the 20 selected articles, the most common interactions were observed between macronutrients and genetic risk score (GRS) on metabolic disease-related traits in the Malay, Chinese, and Indian ethnicities. Overall, we identified 29 significant gene–diet interactions in the Southeast Asian population. The results of this systematic review demonstrate ethnic-specific gene–nutrient interactions on metabolic-disease-related traits in the Southeast Asian population. This is the first systematic review to explore gene–diet interactions on obesity and diabetes in the Southeast Asian population and further research using larger sample sizes is required for better understanding and framing nutrigenetic approaches for personalized nutrition

Target Object Detection from Unmanned Aerial Vehicle (UAV) Images Based on Improved YOLO Algorithm

Aerial image-based target object detection has several glitches such as low accuracy in multi-scale target detection locations, slow detection, missed targets, and misprediction of targets. To solve this problem, this paper proposes an improved You Only Look Once (YOLO) algorithm from the viewpoint of model efficiency using target box dimension clustering, classification of the pre-trained network, multi-scale detection training, and changing the screening rules of the candidate box. This modified approach has the potential to be better adapted to the positioning task. The aerial image of the unmanned aerial vehicle (UAV) can be positioned to the target area in real-time, and the projection relation can convert the latitude and longitude of the UAV. The results proved to be more effective; notably, the average accuracy of the detection network in the aerial image of the target area detection tasks increased to 79.5%. The aerial images containing the target area are considered to experiment with the flight simulation to verify its network positioning accuracy rate and were found to be greater than 84%. This proposed model can be effectively used for real-time target detection for multi-scale targets with reduced misprediction rate due to its superior accuracy

Abstracts of the International Conference on Business, Accounting and Finance 2023: Embracing New Business Paradigm Shifts

This book presents the abstracts of the selected contributions to the second International Academic Conference 2023, held on 25-26 February 2023 by the International University of Malaya-Wales (IUMW), Kuala Lumpur, Malaysia. IAC 2023 is the coming together of researchers and industry. It’s a place to gather and share groundbreaking ideas, discoveries, and experiences on a variety of thought leadership topics covered under this year’s conference theme, “Embracing New Business Paradigm Shifts". Conference Title: International Academic Conference 2023Conference Acronym: IAC 2023Conference Theme: Embracing New Business Paradigm ShiftsConference Date: 25-26 February 2023Conference Venue: IUMW, MalaysiaConference Organizer: International University of Malaya-Wales, Kuala Lumpur, Malaysi