Science & Technology Asia

Efficacy and Safety of a Facial Mask Containing Aquilaria crassna Leaf Extract: a Double-Blind Randomized Controlled Trial

Chaniata Pearnakorn — 2024-06-26

Aquilaria crassna Pierre ex Lecomte (Agarwood) is a common plant in Southeast Asia. Both A. crassna leaf extract and its component, mangiferin, have been reported as having antioxidant, anti-inflammatory and antiglycation activities. The objectives of this study were, first, to test the standardized aqueous extract of A. crassna leaves (eAC) and formulations containing eAC, for skin irritation, and second, to test the cosmetic efficacy of facial masks containing eAC via a double-blind, randomized, placebo-controlled trial. For the skin irritation tests, patches containing either 1% or 5% eAC in water, or 0.1% and 0.2% eAC formulation were applied to the skin and the extent of skin irritation was measured by the Mean Cumulative Irritation Index. To test the cosmetic efficacy, facial masks containing a formulation of 0.2% eAC, or a placebo formulation, were randomly applied to either the left or the right half of the face of the 33 participants for 8 weeks. The result showed that the formulae containing 0.1% and 0.2% eAC caused no skin irritation. However, higher eAC doses of 1% and 5% in water caused mild irritation. The efficacy study showed that the formulation of 0.2% eAC significantly increased skin hydration from 30.9±1.3 (baseline) to 39.6±1.4 at week 4 and 45.9±1.3 at week 8 (p=0.001), while the effect of the placebo facial mask was negligible. Skin elasticity was not affected by either treatment. In conclusion, eAC at appropriate concentrations improves skin hydration while having no adverse or toxic reactions, thus showing promise as a bioactive ingredient in cosmetic products.

Assessment of Genetic Diversity, Correlation and Path Coefficients Analysis in Sesame (Sesamum indicum L.)

Khin Mu Aye — 2024-06-26

Knowledge of variability, correlation and path coefficient analysis is essential for crop improvement procedures. In this study, 54 sesame genotypes were planted in a randomized complete block design with three replications to analyze genetic diversity, correlation and path analysis in sesame. Eleven characters were collected and analyzed. The analysis of variance indicated a significant variation among the genotypes for 11 characters. Days to 50% flowering, days to maturity, height of first capsule bearing node, number of primary branches per plant, capsule length, capsule width, number of seeds per capsule and 1000 seed weight revealed high heritability values. Moderate heritability values were observed for plant height, number of capsules per plant and seed yield per plant. The number of primary branches per plant, number of capsules per plant and number of seeds per capsule were strongly correlated with seed yield per plant. Moreover, path coefficient analysis demonstrated that number of primary branches per plant (0.493), number of capsules per plant (1.833) and number of seeds per capsule (0.448) had a positive direct effect on seed yield. These traits may serve as selection criteria for increasing the yield of sesame.

Some New Results on Fixed Points for 𝜛-Distances in Complex-Valued Metric Spaces

Wongvisarut Khuangsatung — 2024-06-25

In this paper, we introduce the notion of a 𝜛-distance in complete complex-valued metric spaces and prove some fixed point theorems for mappings satisfying some appropriate inequalities in complete complex-valued metric spaces. Moreover, we deduce new fixed point results in complete complex-valued metric spaces and provide some examples to illustrate the usability of the obtained results.

Fixed Point of Total Asymptotically Nonexpansive Mappings in Banach Spaces

Kannika Khomphurngson — 2024-06-25

In this paper, we present a strong convergence theorem for total asymptotically nonexpansive mappings in a real uniformly convex Banach space.

The Bivariate Length Biased - Power Garima Distribution Derived from Copula: Properties and Application to Environmental Data

Sirinapa Aryuyuen — 2024-06-25

Bivariate distributions calculate the probabilities for simultaneous outcomes of two random variables. They are essential for understanding the relationship between two variables. This study proposed a new bivariate distribution called the bivariate length biased power Garima (BLBPG) distribution, created by combining the Farlie-Gumbel-Morgenstern (FGM) copula with a length-biased power Garima distribution. The BLBPG distribution describes lifetime bivariate data with a weak correlation between variables as a flexible alternative to bivariate lifetime distributions for modeling real-valued data in applications. The proposed distribution yielded various properties including joint conditional probability functions, reliability (survival) and hazard functions, and generating a random variable. The maximum likelihood estimation was presented to estimate the parameters of the proposed distribution and a Monte Carlo simulation study was conducted to evaluate the performance of the estimators. A practical application of the proposed bivariate distribution to analyze environmental data was also demonstrated.

Effect of Chemical Fertilizers on the Efficiency of Biochar in Reducing Lead Mobility in Soil

Chompoonut Chaiyaraksa — 2024-06-25

The objective of this research was to investigate the impact of ten different fertilizers on the mobility of lead in soils that had been treated with biochar. The soil used in this study was collected from Chanthaburi Province. To simulate the experimental conditions, this soil was artificially enriched with 550 mg/kg of lead. The synthetic soil was prepared by mixing it with 10% biochar and 0.04% of various chemical fertilizers. To assess the bioavailability of lead in the soil to plants, an extraction process using diethylenetriamine pentaacetate was performed. This allowed researchers to determine how these fertilizers affected the movement and availability of lead in the soil for plant uptake. In the study, it was observed that among the fertilizers tested, urea was the only one that increased the bioavailability of lead in the soil, making it more accessible to plants. Sequential extraction techniques were employed to analyze six different forms of lead in the soil. Interestingly, all fertilizers, except for urea, caused a transformation of lead from less stable forms to more stable forms in the soil. To further investigate the relationship between fertilizer variables and heavy metal uptake, a stepwise linear regression analysis was applied. The results indicated that the mobility of lead in the soil was primarily influenced by the nitrogen content, potassium levels, and sulfate ion concentration in the fertilizers.

Integrated Optimization Strategies for Enhanced Coffee Retail Store Efficiency with DEA Variants, Taguchi Signal to Noise, and Randomized Block Design

Pongchanun Luangpaiboon — 2024-06-25

By integrating Data Envelopment Analysis (DEA) variants, Taguchi, and randomized block design techniques, this research addresses a substantial deficiency in the existing body of knowledge by evaluating the collective performance of coffee retail establishments situated on university campuses. The research considers undesirable output such as customer complaints by conducting an analysis of five operational inputs of OPEX, CAPEX, staff count, sitting capacity, and shop size with two desirable outputs of cup production and total income. DEA and its derivatives, super efficiency CCR and BCC were extremely efficient. The BCC infeasible efficiency score was improved by incorporating a modified super-efficiency BCC. The investigation further enhances the methodology for determining the optimal DecisionMaking Units (DMUs) among various DEA variations by incorporating Taguchi signal-to-noise and randomized block design as additional components. The most effective decision-making units (12, 9, and 5) demonstrated consistent and outstanding performance in all three DEA variants, as indicated by the results that succinctly outline the key results. Conversely, DMUs 2, 10, and 14 have been identified as prospective improvement areas.

Enhancing Warehouse Management Efficiency for Precast Concrete Product Business

Pannita Chaitien — 2024-06-25

Nowadays, technology plays a significant role in businesses, whether small or large. Companies leverage technology to gain a competitive edge in the business world and to adapt the era of information technology. This research aims to enhance warehouse management efficiency, using precast concrete product manufacturing as a case study. The company intends to implement a web based application to enhance warehouse management efficiency. The decision is prompted by issues encountered in the case study where the quantity of products in the system does not match the actual quantity in the warehouse or stock diff, and the cost of warehouse management is high. This led to the proposition of applying the Lean concept. The Lean concept has been utilized through the use of the Value Stream Mapping (VSM) tool to analyze the workflow and categorize activities in the process using the 8 wastes. The analysis revealed that non-value activities include checking stock, document issuing, and managing transport schedules. Subsequently, improvement directions were identified to reduce waste following the principles of ECRS. The next step involved designing and developing a web-based application prototype tailored to the specific operational needs. From testing and evaluation, it was found that the inventory accuracy rate increased. Before implementing the web-based application, the average percentage of stock diff was 107.54%. After experimenting with the web application in process, the average percentage of stock diff decreased to 18.48% and the cost of the warehouse per sale, measured by the salary or wages of warehouse staff, decreased from 60,500 baht per year to 44,000 baht per year.

A Bilevel QP-PLP Approach to Demand Response Modulation between Consumers and a Single Electricity Seller

Printaporn Sanguansuttigul — 2024-06-25

During the peak hours, electricity becomes extremely expensive to produce and deliver and this causes a negative effect both to the suppliers and consumers. From the producer point-of-view, real-time pricing may be implemented to incentivize the consumers to shift their load usage to off-peak hours. That is, to adjust for a lower price during off-peak hours and for a higher price during the on peak hours. On the other hand, the consumers would then respond optimally to the given strategic prices by means of flexible load scheduling and incorporation of smart energy management systems. We present in this paper an intermediate model that maximizes the profit of the supplier while maintaining the low expenses of the consumers by using a bilevel program of the form QP-PLP, where the upper-level problem is quadratic and the lower-level problem is a parametric linear program.

Real Time Production Status Monitoring System

Hathaipree Ngukhiew — 2024-06-25

This research aims to develop a monitoring system to show production data and analyze machine status in real-time. It uses the Internet of Things (IoT) and the LoRa communication system. The researcher uses a tension sensor to receive tension variablesin the beaming process. A tension sensor connects to the LoRa sender. The first LoRa device is the one that takes data from a tension sensor and sends it to the second LoRa receiver. Then the ESP32 board sends the tension data to the database server. It uses an API to take the data into the Google Sheet database. Then, the tension data will be extracted and analyzed. The researcher assumes a hypothesis about the machine's operation from the data obtained to make a decision whether the machine is working or stops working. If the tension value is 0 for more than a specified time, the machine has stopped running. It can use data to calculate the length of time the machine has stopped running. It can also calculate the length of time the machine has been running. If the tension value is not the standard, the machine will be damaged. The Line application also sends notifications. Maintenance departments and related agencies can then hurry and fix the issues in time. Moreover, the researcher used the length of time the machine is running and the length of time the machine is stopped to calculate the MTBF, MTTR, and availability. These variable values are displayed in a report on a Looker Studio dashboard. This allows production status to be tracked and analyzed to solve problems in time.

An Integrated Approach for Designing Healthcare Facilities with a Location-Inventory Model

Hathairat Poomisirisawat — 2024-06-25

Getting services at public hospitals in Thailand requires outpatients to visit doctors and receive medicines and medical supplies at the dispensary as the final step of the treatment process. This often leads to congestion issues in hospitals. Therefore, the workload should be distributed outside the hospital to alleviate congestion. Presently, outpatients of public hospitals can participate in the “Taking Medicine Nearby House” project to increase convenience and relieve congestion in the hospital. This study addresses the Location Inventory (LIP) problem, specifically focusing on “pharmacies,” by determining the optimal number and locations to meet patient needs. This approach aims to increase patients' accessibility. Additionally, the right inventory level for each pharmacy must be considered to reduce operating costs. Therefore, a mathematical model is developed to simultaneously solve the location and inventory problems using an exact method. The model is processed using the Lingo program to facilitate efficient problem-solving. This study benefits congestion mitigation in public hospitals and ensures greater patient access and proper pharmacy inventory management by the integrated solution. The results in actual implementation within the healthcare system will improve operational efficiency and raise the standard of patient care.

Using Digital Image Correlation (DIC) in MATLAB Monitoring Number and Size of Speckle Granules

Siripon Kaoroptham — 2024-06-25

This research aims to study the effect of changing the number and size of speckle granules to compare the result of the material properties between using the Digital Image Correction method (DIC) in MATLAB and the standard average of material properties. This research used stainless steel - grade 304 (UNS S30400) by testing 3 sets of experiments with an unequal number and size of speckle granules in each test to compare the effect of the number and size of speckle granules in calculations using Digital Image Correlation (DIC). The results show that the errors of changing the number of speckle granules from Digital Image Correlation (DIC) in Young’s Modulus (𝑬) around 0.017238407-0.063998425% and Poisson’s ratio (𝝑) around 0.072674419-1.251840943% and the errors of changing the size of speckle granules from Digital Image Correlation (DIC) in Young’s Modulus (𝑬) around 0.030729470-0.316238141% and Poisson’s ratio (𝝑) around 0.069043207-0.531703259%.

Cost Reduction by Fleet Planning for Parcel Delivery Service

Nicharee Oonchokdee — 2024-06-25

We consider the fleet planning problem for one of the leading Thai parcel delivery couriers, emphasizing the synchronization of fleet sizes with cyclical parcel volumes. The formulation of an integer programming problem aims to minimize fleet costs by leveraging historical parcel volumes. The model is applied to a single last-mile hub, resulting in a 19% reduction in the average monthly number of riders, a 22% decrease in the number of drivers, and an overall 19% reduction in fleet costs. Additionally, the model enhances fleet earnings, demonstrating a remarkable 36% increase in rider incentive payments and a 22% rise for drivers. In our model, we establish conditions that define the minimum number of fleets for each type of fleet on a daily basis. Furthermore, we guarantee that the total number of monthly shift fleets aligns with the daily count of shift fleets in each month. Encouraged by these positive outcomes, the recommendation is to extend the application of this model to all 95 last-mile hubs. The findings underscore the efficacy of strategic fleet planning in achieving cost reduction, operational optimization, and revenue increase across the organization.

Alternative Method for the Estimation of Parameters for the Normal Inverse Gaussian Distribution

Hussaya Nookaew — 2024-06-25

This article presents a study on the parameter estimation of the Normal Inverse Gaussian distribution, which is a specialized instance of the generalized hyperbolic distribution that is extensively utilized in the analysis of financial time series. Conventionally, the maximum likelihood method and the method of moment are used to estimate the parameters; however, these methods have a restriction on the feasible domain of possible skewness and excess kurtosis values. Therefore, we propose an alternative parameter estimation method for the Normal Inverse Gaussian distribution based on the Metropolis Hasting exponential maximum likelihood method. Moreover, the performance of this method will be compared with the maximum likelihood estimator, the epsilon maximum likelihood estimator, the exponential maximum likelihood estimator, and the Metropolis Hasting maximum likelihood estimator using both simulated and real-world datasets. For simulation, we use the smallest root mean square error and provide descriptive statistics, including means and standard deviations to evaluate the performance of the model. For real data application, the selection of the model is guided by a goodness-of-fit test using the Anderson-Darling test statistics criterion. Furthermore, the model selection should demonstrate the smallest AD value alongside the highest p-value.

Exploring Augmented Reality Design for Robot Maintenance Training through the Kano Model

Rattawut Vongvit — 2024-06-25

Augmented Reality (AR) is a technology that allows users to access the real world while embedding it with a layer of virtual objects designed to coexist seamlessly in real time. Recently, researchers and developers have found a growing number of AR uses. The effectiveness of staff and student training has improved due to the improvement of AR technologies. However, since the field of AR is still in its early stages, there needs to be more literature on AR user requirements. As a result, implementing customer insights is critical. This research aims to identify consumer requirements for designing AR-based robot maintenance training. The Kano model determines consumer requirements and classifies the factors influencing customer intentions to use AR-based robot maintenance training. The framework and results from this study can be used to prioritize and comprehend customer needs for designing AR-based robot maintenance training according to the user requirements.

Plus-Size Clothing Recommendation System Based on Sales Transaction Data Using FP-Growth Algorithm

Peeraphol Kamolnawin — 2024-06-25

The recommendation systems for the fashion industry have been widely studied. However, a study on the recommendation systems for the plus-size fashion market is limited. This study investigates the plus-size clothing sales recommendation system on an e commerce platform in Thailand using the FP-Growth algorithm. A total of 26,993 transaction records were collected during January 2021 to December 2022. The significance of the discovered rules is also examined to gain implicit knowledge about customer preferences in the plus-size clothing domain. The findings show the lists of association rules of the items that customers frequently purchase together. The model has a precision value of 7.88%, a recall of 4.23%, and an F1-score of 5.50%. The relatively low performance of the FP-Growth algorithm indicated the challenges of developing a recommendation system in this domain due to the limitation of the dataset and the diverse purchasing behavior of the plus-size customers. This study fills a research gap and provides a foundation for future research in recommendation systems for plus size clothing.

The Slope-Circuit Hybrid Method for Solving Degenerate Two-Dimensional Linear Programs

Panthira Jamrunroj — 2024-06-25

Traditional linear programming (LP) methods, like the simplex algorithm, often struggle with the efficiency of solving degenerate LP problems. This study introduces the slopecircuit hybrid method, an innovative interior search technique designed to overcome these
challenges by strategically combining slope-based analysis and circuit direction search. This method accelerates convergence, yielding optimal solutions. Focusing on degenerate constraints, the algorithm intelligently selects an initial circuit direction using slope information. The circuit direction search adeptly navigates the next direction to improve a solution, resulting in a significant reduction in iterations. Rigorous termination at an optimal solution is guaranteed through the computation of associated dual variables. Empirical testing on degenerate 2D linear programs supports substantial performance enhancements over simplex, interior point, and slope algorithms, evident in reduced iterations and improved running time. The slope-circuit hybrid method emerges as a promising solution for optimizing resource allocation in industrial settings, especially those constrained by limited computational resources. Its potential extends to streamlining decision-making processes and enhancing efficiency across various real-world applications.

Developing Multi-Label Classification Model for Improving Text Categorizing Problems a Case of Traffy* Fondue Platform

Phopthorn Kaewvichit — 2024-06-25

This paper develops an integrated deep-learning-based model for a multi-label text classification problem in order to enhance the efficiency of Traffy* Fondue which is a comprehensive platform for diverse issues of citizen’s complaints over the Bangkok metropolitan area. The dataset of Traffy* Fondue has been found to have inaccuracy problems of label categorization, especially in the 'Others' category, which results in delaying of coordination and problem-solving to address the complaint on time. To overcome this problem, this study develops an integrated deep-learning-based model for multi-label text classification problem. Five main methods have been applied to model text classification, namely, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Bi-LSTM, fastText and WangchanBERTa. The performance of developed models has been tested against traditional algorithms. The result of modeling in the 'Others' dataset shows that the Bi-LSTM-, CNN+Bi-LSTM- and WangchanBERTa-based approaches are the best three models that outperform others with higher precision, recall, and F1 score. Our approach offers a promising solution to expedite issue resolution and improve coordination within Bangkok's civic management framework.

Game Theory Analysis of Agricultural Land Fragmentation in Thailand

Natthawan Sriphong — 2024-06-25

Agricultural land fragmentation (ALF) is a common issue in rural areas in developing countries, impacting agricultural productivity and future food security. Thailand is one of the countries having an ALF problem. To analyze this problem, we use a game theory model having two players: landowners (L) and the government (G). Landowners have two strategies: fragment (F) and non-fragment (N) while the government has two strategies: encouragement (E) and implementation of punishment (I). In this model, we determine four variables that affect the landowners' decision-making: the expected value of fragmented land (VF), the expected value of non fragmented land (VN), the implement punishment value associated with land fragmentation (IV), and the encourage value of maintaining non-fragmented land (EV). We find that in an ideal model suggests landowners often choose N; however, real data indicates that landowners prefer F over N. We also determine all possibilities of the ALF problem. To address this problem, the government should increase IV and EV or decrease VF – VN, but both scenarios seem quite hard in Thailand.

Implementing the Taguchi-Statistical Learning-DEAR Methodology in a MultiCriteria Decision Making Approach to Balance Trade-offs in Evolutionary Algorithm Performance

Pongchanun Luangpaiboon — 2024-06-25

This work presents a new approach to improving the efficiency of Evolutionary Algorithms (EA) in extremely noisy function landscapes. Statistical Learning, Taguchi analysis and normalization, and Data Envelopment Analysis based Ranking (DEAR) are used to provide a hybrid technique that provides a complete framework for EA parameter adjustment. The study examines the effects of four important EA parameters on function yield and computing time: convergence, mutation rate, population size, and random seed. Taguchi analysis and normalization is used to generate an efficient experimental design that covers different combinations of parameter values, allowing a methodical exploration of the parameter space. Subsequently, the DEAR approach is employed to prioritize each set of parameters according to certain optimization criteria. To further complicate matters, the optimization goals and EA parameters are both modeled using Statistical Learning approaches. There has been a lot of testing with noisy functions of artificial landscapes with three different types: single-peak, curved-ridge, and multi-peak. Assuming a normally distributed distribution with a mean of 0 and standard deviations of 0.05 and 0.2, noise presents practical obstacles to optimization. When compared to more traditional approaches of parameter tuning, the suggested hybrid strategy clearly outperforms the competition in terms of computing time, function yield, and mean and standard deviation of both metrics. The technique shows improved resilience and adaptability across varied noisy environments and more successfully finds optimal parameter configurations, according to the results. By demonstrating its flexibility to meet evolving optimization needs, sensitivity assessments provide more evidence of the suggested methodology's dependability. Finally, the research presents a state-of-the-art hybrid method for tweaking evolutionary algorithm parameters, which considerably improves upon previous efforts. In particular, when it comes to dealing with complicated and noisy optimization scenarios, the offered technique stands out due to its capacity to continuously produce greater performance, making a vital addition to the optimization community.