PKRU SciTech Journal

A Study of the Quality of Chan Rong Honey Products in Songkhla Province

Sun, 06 Oct 2024 00:00:00 +0700

This study aimed to assess the quality of Chan Rong honey products in Songkhla Province, focusing on honey produced by community enterprises in the region. Our research involved a sample group comprising 25 community enterprises in Songkhla Province. The study encompassed an evaluation of both physical properties and microbial contamination. The results revealed that the pH values of the Chan Rong honey products ranged from 3.21 to 4.45, while the moisture content varied between 14.48% to 20.07%. Microbial contamination was also analyzed, including yeast and mold, Staphylococcus aureus, Salmonella sp., Bacillus cereus, and Clostridium perfringens in honey samples collected from the 25 community enterprise groups. Specifically, the findings indicated that yeast and mold levels ranged from less than 1 colony-forming unit per gram (CFU/g) to 9.0x10⁴ CFU/g. Staphylococcus aureus was detected at less than 1 CFU/g. Bacillus cereus was found in amounts less than 1 CFU/g, and Clostridium perfringens was present at levels below 1 CFU/g, while Salmonella sp. was not detected in any of the samples. These research outcomes hold valuable insights for enhancing the capacity of local communities in the development of high-quality, safe Chan Rong honey for consumers.

Orbital Period Change and Evolution of the Binary System V1851 Orionids

Thitipong Unchai — Sun, 06 Oct 2024 00:00:00 +0700

The research objective was to study the orbital period change of the variable star V1851 Orionids using a 0.2-meter reflecting telescope with a DSLR camera in the visible light band. A photometric system was used to observe the light curve and the primary minimum eclipse. The results indicated that the orbital period of V1851 Orionids is 0.2702 (±0.0028) days. The O – C diagrams of the light curve showed that the orbital period decreased by 1.2340 ´10^-11 (±0.0001´10^-11) day/cycle, or 1.4062´10^-3 (±0.0001´10^-3) second/year, influenced by the thermal relaxation oscillation process in the closed binary system.

The Development of a Coffee Bean Moisture and Temperature Measuring Device by Using Internet of Things

Sun, 06 Oct 2024 00:00:00 +0700

This research aimed to develop a device for measuring the moisture and temperature of coffee beans and to test its efficiency. We developed a prototype of the device along with an information system to display the measurement results. The device comprised a moisture sensor and a temperature sensor, which measured the values, transferred the data to a cloud server, and presented the findings through the information system. The differences in test results between the humidity sensor and the EE-KU machine, as well as between the temperature sensor and the digital thermometer (TP101), were not statistically significant at the 0.01 level. The device accurately measured the moisture and temperature of coffee beans and displayed the results on a mobile application installed on mobile devices, as well as a web application accessible to users. This application allows users to create additional user accounts and includes a database for gathering seller information. In terms of cost, the developed device is less expensive than commercially available devices, which cannot be used online and only display the results on the machine’s screen. Moreover, the developed device is smaller, lighter, and more portable than commercially available alternatives. Maintenance is also feasible, as the equipment can be easily sourced domestically at a low cost.

Comparative Analysis of Total Heavy Metal Removal in Influent and Effluent of the Oxidation Pond System at Ayutthaya Municipal Landfill

Somkid Tangkan, Sirapassorn Phanthasa — Fri, 29 Nov 2024 00:00:00 +0700

The study analyzed and compared total heavy metal contamination (THMs)—arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn)—in influent and effluent during dry and wet seasons. THMs analysis utilized acid digestion, with determinations conducted according to USEPA methods 3005A and 6010D. Results indicated that THMs levels were below USEPA and Thai wastewater discharge standards in both seasons. The metals with the highest treatment efficiencies were As (dry season: Inf=0.07 ppm, Eff=BDL; wet season: Inf=0.065 ppm, Eff=BDL), Cd (dry season: Inf=0.005 ppm, Eff=BDL; wet season: Inf=0.005 ppm, Eff=BDL), and Cr (dry season: Inf=0.065 ppm, Eff=BDL; wet season: Inf=0.01 ppm, Eff=BDL), achieving 100% removal efficiency. In contrast, Ni showed the lowest efficiency, with removal rates of 28% (dry season: Inf=0.125 ppm, Eff=0.09 ppm) and 58.27% (wet season: Inf=0.115 ppm, Eff=0.055 ppm). Statistical analysis revealed significant differences (P<0.05) in contamination levels between influent and effluent, particularly for Cr, Mn, and Ni during the dry season, and Cr, Mn, Ni, and Pb during the wet season. Additionally, most influent metals, except Cu and Fe, showed significant statistical correlations (P<0.05). The findings highlight areas for improving heavy metal treatment in leachate and monitoring surrounding environmental conditions.

Characterization of Regularity on Elements in the Bicyclic Semigroup

Nares Sawatraksa, Chanya Lekjaroensri, Panthakan Wetchakorn, Patcharaporn Sitong — Fri, 29 Nov 2024 00:00:00 +0700

Development of a Simple DTT Assay for Determining the Oxidative Potential of PM2.5 Samples

Duangduean Thepnuan, Primruthai Dokkhamtai, Nuttipon Yabueng — Fri, 29 Nov 2024 00:00:00 +0700

The research aimed to determine the optimal conditions for a simplified dithiothreitol (DTT) assay using a microplate reader to assess the oxidative potential of PM_2.5 samples. The method is based on measuring the rate of DTT consumption as it reacts with reactive oxygen species (ROS) present in the particulate matter over different time intervals. The remaining DTT is quantified by its absorbance after reacting with 5,5'-dithio-bis-[2-nitrobenzoic acid] (DTNB). The optimal conditions were as follows: PM_2.5 samples, collected on quartz fiber filters (amount less than 0.5 mg), were extracted with 4 mL of phosphate buffer solution using a shaker at room temperature for 30 minutes. Subsequently, 100 µL of the extracted solution was aliquoted to react with 50 µL of 0.10 mM DTT solution at time intervals of 0, 5, 10, 15, 20, 25, and 30 minutes, respectively. Then, 100 µL of 0.40 mM DTNB was added, and the absorbance was measured at lmax 410 nm. The slope and intercept of the absorbance decline with reaction time were used to calculate the oxidative potential (OP). Twelve PM_2.5samples collected during the daytime in March–April 2022 in Chiang Mai Province (57.8 ± 14.4 µg/m³) were tested. The average oxidative potential of the particulate matter, calculated relative to air volume (OPv) and particulate mass (OPm), was 0.304 ± 0.133 nmol/min·m³ and 5.05 ± 1.65 pmol/min·µg, respectively, indicating low oxidative toxicity of the substances in the dust samples.

Developing Next-Generation Semantic Image Classification Model Through Generative Adversarial Networks (GANs)

Sooksawaddee Nattawuttisit — Fri, 29 Nov 2024 00:00:00 +0700

This research aims to develop an image classification model using Generative Adversarial Networks (GANs) to improve image retrieval and interpretation through natural language processing. This technology generates new content by learning from existing data and producing outputs similar to the original samples. The study's sample data is drawn from the Flickr 30K dataset, consisting of 158,915 entries of images and natural language descriptions. A sample size of 384 entries was determined using Cochran's formula with a 95% confidence level and a 5% margin of error. The data was split into training and testing sets at a ratio of 80/20 to optimize the model's performance in image interpretation. The model's performance was evaluated based on the similarity between the AI-predicted outcomes and the images with descriptions and validated by AI experts. The test results showed an accuracy of 82%, a recall of 78%, and a precision of 80%, indicating the model's effectiveness in interpreting images based on natural language descriptions. This research has commercial applications, such as automatic image categorization on social media or image retrieval in large-scale databases. Future model development should focus on improving recall to enhance completeness and better meet user needs.