Journal of Applied Science and Emerging Technology

Advanced 3D-Printing Technologies for Transformative Radio Frequency and Optical Devices

2024-12-27T13:34:12+07:00

Determining the Distinctiveness of Learners with Outlier Detection Ensembles

2024-09-26T18:43:57+07:00

Gamification is the application of game design principles and elements to non-game contexts. It has been increasingly used to engage and motivate learners. A clear understanding of learners’ characteristics is a key success of gamification. Understanding the distinctiveness of the learners' behaviors enables gamification design to encompass all types of participants. This paper then proposes a methodology to assist gamification design in identifying distinctive individual learners within a group. The proposed methodology adopts ensembles of outlier detection techniques to examine how much individual learners differ quantitatively from the group, and utilizes decision tree classifiers to identify the factors contributing to the distinctiveness. The outcomes of these methods are presented in the if-then rules, which assist the interpretability of the discovered insights. This method enables educators and gamification designers to personalize gamified learning environments by focusing on unique learner characteristics.

Evaluation of the effects of processing on physicochemical properties of the freshly extracted crude groundnut oil

2023-12-06T12:54:06+07:00

The industrial processing of groundnut oil involves hydrothermal and chemical treatments that could affect its lipid constituents. The effect of this processing on physicochemical properties of freshly extracted crude groundnut oil was determined. Crude groundnut oil was extracted following the traditional method and half of it was processed as done industrially. Chemical properties [acid value, free fatty acid, peroxide value, saponification value, ester value, iodine value, total phenolic content (TPC), p-Anisidine value, total tocopherol (TT), total antioxidant capacity (TAC) and thiobarbituric acid reactive species (TBARS)] and physical properties (refractive index, surface tension, smoke point, flash point, viscosity and specific gravity) were determined following the AOAC and other standard methods. The results indicated that the processing of the crude groundnut oil, although improved the physical appearance and reduced free fatty acid by 56% consequently, could improve its stability but it also compromised the levels of antioxidant compounds such as tocopherols in the oil that reduced by 16.4%. TAC, an antioxidant parameter also reduced by 92% while TBARS increased by 15.1%. It can be concluded that processing of groundnut oil could either improve its stability or compromise it, therefore, there is a need for the stability study of processed groundnut oil.

Evaluation of Various Starches as Binders and Properties for Ya-Hom Thepphachit Tablet

2023-11-04T09:26:54+07:00

Ya-Hom Thepphachit formula are Thai traditional medicine listed in the National List of Essential Medicine (NLEM), which wasthe powder infusedwith hot water. The development into tablet form often problem about flowability of herbal powder in direct compression. Therefore, solid dosage form the granulation preparation technique is commonly used.The purpose of this research was to the evaluation of various starches as binders of Ya-Hom Thepphachit in tablet formulaby usingthewet granulation technique. The following physical properties of Thepphachit powders have been studied: flow property, bulk density, tapped density, compressibility, hausner ratio, estimation of mixing uniformity, dissolution, moisture content etc., including the antioxidant activity of formula by DPPH, ABTS andFRAP methods. When, devided the extraction 3 methods: infusion by water, maceration by 95% ethanol and dispersion inkasaiya water from flowers.The tablets were quanlity control evaluate followed by United states pharmacopeia 40 (USP 40) such as weight variation, hardness, thickness, diameter, friability and disintegration time, when various binders 7 types using: tapioca starch (TS), glutinous rice flour (GRF), dark rye flour (DRF), finest rice flour (FRF), corn flour (CF), pure corn flour (PCF) and corn starch (CS). The study found that the Thepphachit powders has low flow properties, high moisture content because it contains fiber and essential oil of herbs, powder is fluffy and moisture. Thus, the direct compression wasdifficult and powders prepared in wet granulation. The 95% ethanol extract had the highestantioxidant activity in DPPH assay (IC50= 27.95± 4.78g/ml), ABTS assay (VEAC= 0.42 ± 0.01mM/gandTEAC= 0.28 ± 0.01mM/g)andFRAP assay (FRAP value= 0.72 ± 0.01mM/g). From evaluating the quality of tabletsfound that the CS is thebest binder by highest hardness (2.82 ± 0.07 kp), low friability (0.0550 ± 0.02 %) and fastest disintegration time (17.91 ± 0.12 min). While, PCF and CF were the second most effective, respectively. At the same time, PCF (0.0081 ± 0.00 %) gave abetter friability value than CS. Alternative, PCF is thebinder for instead CS for Ya-Hom Thepphachit formula because the quality of PCFisthe same as CS tablets andgives alower friability.

The relationship and impact of pH, ORP, and elemental contents on arsenic remediation by Cyperus papyrus (L.) under submerged soil conditions

2023-06-23T14:23:34+07:00

Arsenic remediation in wetlands is very important to the ecosystem. Phytoremediation efficiency is high and environment-friendly. Cyperus papyrus (L.) can highly selective accumulation arsenic from soil in large quantities. This objective study aimed to investigate variation of pH, redox potential, available phosphorus, extractable iron, exchangeable aluminum, exchangeable calcium and exchangeable magnesium for arsenic remediation in submerged soil by C. papyrus (L.). Experimental design was a 3 × 4 factorial design in a completely randomized experiment (CRD) included two factors. The first factor was the arsenic speciation consisting of arsenite and arsenate. The second factor was the cultivation periods of C. papyrus (L.) including 15, 30, 45 and 60 days. The result indicated that the mean comparison using DMRT of redox potential, total arsenic accumulation of C. papyrus (L.), exchangeable calcium and extractable iron were significantly different among all soil treatment groups (P-value < 0.01). The pH mean and exchangeable magnesium content were significantly different between arsenite and arsenate treated soil with control soil (P-value < 0.01 and 0.05). The amounts of available phosphorus and exchangeable aluminum were not significantly different among all soil types. The variation in group of three soil types during cultivation period 60 days showed that pH, redox potential, available phosphorus, extractable iron, exchangeable aluminum, exchangeable calcium and exchangeable magnesium were statistically significant (P-value < 0.01). The results of comparison among three treatment soils at each cultivating period demonstrated that redox potential, total arsenic accumulation, extractable iron, exchangeable aluminum, exchangeable calcium and exchangeable magnesium were significantly different among all cultivation periods (P-value < 0.01). pH was no significant difference on 15 days and available phosphorus was significant difference on 15 and 30 days (P-value < 0.01 and 0.05). Multiple regression analysis indicated that predictive factors of total arsenic accumulation of C. papyrus (L.) were arsenic speciation, redox potential, pH, and cultivation period. The model explained 85.2% in the regression model. The regression model was -339.542 + 20.249 (arsenic speciation) - 0.322 (Eh) + 52.681 (pH) – 0.987 (cultivating day).

Influence of Fruit Juices and Optimum of Ammonium Sulfate on the Bacterial Cellulose Production

2022-01-24T18:16:45+07:00

Bacterial cellulose is a product obtained from the synthesis of cellulose outside the cell, produced by bacteria. The purpose of this research is to study the influence of fruit juices and the optimum of ammonium sulfate on bacterial cellulose production from Acetobacter xylinum. The experiments were conducted by a 2 × 3 factorial experiment using a completely randomized design with five replications. There were two types of fruit juice, including coconut water and pineapple juice. Three levels of ammonium sulfate were used at 0.25%, 0.50% and 0.75%. Wet weight and thickness of bacterial cellulose sheet were collected. The results showed that there was a significant difference (p < 0.05) between the main effects of fruit juices and the amount of ammonium sulfate on the wet weight and thickness of bacterial cellulose. However, there was no interaction effect between them. The treatments that gave the highest average of wet weight and thickness were coconut water (wet weight 37.671.94 g., thickness 2.740.18 cm.) and the concentration levels 0.25% or 0.50% of ammonium sulfate (wet weight 29.689.46 and 30.009.08 g. respectively, thickness 2.150.73 and 2.180.71 cm. respectively)

Self-checkout system using vision-based object detection and subcategory recognition

2024-01-19T16:48:56+07:00

The purpose of this research was to study and develop a conceptual framework for self-checkout systems to use only cameras at the checkout station to detect goods from customers. This framework is designed to use deep learning models and computer vision to classify similar products or identify sub-category products with similar appearances but different sizes. The framework is flexible to use with no retraining whenever a new product or package is introduced by using feature point extraction and feature point matching. The researcher uses the YOLO model to detect and identify the shape of the products and also defines the bounding boxes in the detected image, and then defines the feature point of the product using feature point algorithms (e.g. SIFT, ORB, and BRISK) and feature matching with reference images. The results from the experiment on the Thai products dataset that was collected by the researcher found that this framework can be applied to effectively detect and recognize retail products in a self-checkout system. The YOLO model can predict the shape and position of the product with an average mAP of 0.727, and the best feature point algorithm for product classification from this experiment is SIFT, which has an accuracy of 82.96%. In summary, SIFT can improve the accuracy of YOLO. Because of SIFT, the system can accurately predict the product class, although the YOLO bounding box does not cover an entire region occupied by a product. However, this experiment has a limitation because SIFT has low accuracy when predicting products in bottle shape. This is expected to be a result of the light and shadows reflected from the bottles. Therefore, the environment at the checkout area should be controlled. The product images should be clear, have no obstructions, and be free from disturbing light and shadows.