Journal of Applied Science and Emerging Technology

Emerging Technologies for Sustainable Food Innovation: Integrating Processing, Powder Engineering, Digitalisation, Circularity, and Sustainable Packaging

2026-08-31T08:43:49+07:00

The food sector is under increasing pressure to improve productivity and food security while reducing resource consumption, food loss, waste, and environmental impacts. Emerging technologies are providing new approaches to address these challenges, but their contribution to sustainability depends on their technical performance, economic feasibility, and integration within the wider food system. This review examines recent advances in sustainable food innovation, focusing on five complementary areas: advanced food processing, powder engineering, circular food systems, digital transformation, and sustainable packaging. Technologies including high-pressure processing, pulsed electric fields, cold plasma, ultrasound, electrostatic powder coating, artificial intelligence, food-waste valorisation, and biodegradable and active packaging are discussed in relation to their effects on processing efficiency, resource use, product quality, and waste reduction. The review also considers the main barriers to wider adoption, including scale-up, investment costs, regulatory requirements, process integration, data availability, and variability in raw materials and processing conditions. Particular attention is given to the need for life cycle, techno-economic, and social assessment to determine whether improvements at individual processing stages translate into benefits at the food-system level. Future research should focus on integrating complementary technologies, developing data-driven and resource-efficient manufacturing systems, and improving the commercial viability of circular and sustainable solutions. Such an integrated approach can help move promising technologies from laboratory research towards practical applications that support more resilient, efficient, and sustainable food systems.

Research and Development of a Prototype Student Backpack: Emphasis on Ergonomic Criteria

2026-01-29T09:13:42+07:00

This study investigates the ergonomic design of student backpacks, considering factors such as load distribution, proper fit, strapping, accessibility, and aesthetics. Through a comparative analysis of five sample backpacks and a developed backpack prototype, various performance metrics were evaluated. The analysis revealed notable variations in dimensions, weight, strap length, and compartmentalization among the sample backpacks. Results indicated that the developed backpack prototype exhibited favourable characteristics in load distribution, proper fit, and strapping, outperforming some of the sample backpacks. However, there were areas of improvement identified across all backpacks, particularly in accessibility and aesthetics. The findings underscore the importance of ergonomics in backpack design, highlighting the need for careful consideration of user comfort and functionality. Additionally, the study provides valuable insights for designers and manufacturers to enhance the design of student backpacks, aiming to mitigate the risk of musculoskeletal issues and improve overall user experience. Further research may explore additional design features and conduct empirical testing to validate the effectiveness of proposed ergonomic solutions. In conclusion, this study contributes to advancing the field of ergonomic backpack design, with potential implications for student well-being and academic performance.

Left and Right Transposition Regular $\Gamma$-TA-Groupoids

2026-04-06T00:24:04+07:00

This paper introduces an innovative algebraic concept called the transposition regular -TA-groupoid. The study focuses on investigating the fundamental properties and structural characteristics of the regular associative -TA-groupoid. The results demonstrate that every left transposition regular -TA-groupoid is always a -semigroup. Furthermore, it is proved that every left transposition regular -TA-groupoid is also a right transposition regular -TA-groupoid, and conversely, every right transposition regular -TA-groupoid is a left transposition regular -TA-groupoid. These findings reveal a relationship between the two forms of -TA-groupoids and provide deeper insight into the structural behavior of -TA-groupoids within the framework of algebraic regularity.

Effects of Ash Derived from Quality Improvement of Coconut Husk by Leaching with Water

2026-02-20T10:10:37+07:00

This study investigates the method of water washing coconut husk fuel to increase its quality. X-ray fluorescence (XRF) was used to study the fuel ash's composition and determine the degree of fouling and agglomeration issues using two parameters: the Bed Agglomeration Index (BAI) and the Base to Acid Ratio (Rb/a). The study revealed that the quantities of K₂O, Na₂O, and Cl were decreased by 40.4%, 50.4%, and 82.0%, respectively, after washing the coconut husk fuel with water at a flow rate of 197 milliliters per minute for 25 minutes. The time needed for bed particle defluidization was 32 minutes for the untreated sample and 78 minutes for the water-washed sample when untreated and water-washed coconut husk fuel were burned at 800 °C in a fluidized bed reactor.

Evaluating the Performance of Quantile Regression-Based Prediction Intervals

2026-07-02T08:29:58+07:00

This study compares the performance of prediction intervals from Ordinary Least Squares (OLS) and Quantile Regression (QR) in simple linear regression. Using Monte Carlo simulations, five error distributions were examined: the normal distribution (which satisfies the model assumptions) and four that violate the assumptions (skewed, heavy-tailed, extremely heavy-tailed, and heteroscedastic). Performance was evaluated using Coverage Probability (CP), Average Width (AW), and Average Interval Score (AIS).

The results showed that under the normal distribution, OLS achieved coverage close to the nominal level and provided lower AIS values across all conditions. In contrast, QR undercovered at small sample sizes but converged to the nominal level as the sample size increased. When assumptions were violated, QR outperformed OLS provided that the sample size was sufficiently large. Specifically, for skewed and extremely heavy-tailed errors, QR yielded a clearly lower AIS. Under heteroscedasticity, the coverage probability of OLS deviated significantly from the target level depending on the prediction point (x₀), whereas QR adapted more effectively.

The findings suggest that selecting the appropriate method should take into account both the error distribution and the sample size. QR is a suitable alternative when OLS assumptions are violated, and the sample size is adequate.

A Comparison of Time Series Forecasting Model for Predicting theNumber of Applicants for Graduate Level

2026-07-25T11:59:44+07:00

This research aimed to compare the performance of four time-series forecasting methods, namely the Decomposition Method, Time-Series Regression, Box–Jenkins Method (ARIMA), and Exponential Smoothing, for forecasting the number of graduate program applicants. The data consisted of doctoral and master's degree applicants at King Mongkut’s University of Technology North Bangkok during the academic years 2014-2025. Data were analyzed using Minitab and Microsoft Excel. Forecasting accuracy was evaluated using Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). The results revealed that the ARIMA model provided the highest forecasting accuracy for both doctoral and master's degree applicants. In conclusion the ARIMA model was found to be the most appropriate and effective method for forecasting the number of graduate program applicants. The forecasting results can support enrollment planning, educational resource management, and policy formulation for graduate education management.