Progress in Applied Science and Technology

Uncovering the Potential of Nitrogen and Salt Stress for Enhanced ꞵ-Carotene Production and Antioxidant Capacity in Plant Pathogenic Alga Cephaleuros

2024-07-15T18:23:25+07:00

A pure strain of Cephaleuros alga, designated Cephaleuros Cp.1, was successfully isolated directly from a citrus leaf lesion. This study investigated factors influencing both biomass and carotenoid accumulation in this green filamentous alga. Different nitrogen sources, NaCl stress, and trace elements in HSM, BBM, and Bristol media were compared. The autotrophic condition with HSM medium clearly offered the highest green biomass. Interestingly, Cephaleuros Cp.1 remained green in HSM using NH₄Cl as the nitrogen source but visibly changed to an orange hue due to the accumulation of β-carotene in BBM containing NaNO₃. This color change, along with the lower biomass and more intense yellow color when using nitrate, was the first reported in Cephaleuros, implying that nitrate may cause stress in the alga. Similar phenomena were clearly observed when NaCl was applied to HSM and BBM; on the other hand, Hutner’s trace elements and trace metal solution had no significant effect. These findings suggest, for the first time, a link between stress conditions and the accumulation of β-carotene in Cephaleuros Cp.1. TLC revealed β-carotene as the main carotenoid accumulated by this alga. The accumulation was further enhanced by both nitrogen deficiency and salt stress. However, these stresses also led to a decrease in algal biomass. This study is the first to report free radical scavenging activity linked to β-carotene in Cephaleuros. Among the tested cultures, BBM exhibited the strongest activity (EC₅₀ 1.40 mg/mL). These findings hold promise for future applications of Cephaleuros as a source of natural β-carotene with antioxidant properties.

Comparison of Physical Properties of Coatings on Paper Substrate after Curing by UV and Electron Beam

2024-06-21T09:28:13+07:00

This study aimed to assess the curing efficacy of ultraviolet (UV) radiation and electron beam (EB) curing techniques and the physical attributes of coated overprint varnish (OPV) applied on paper substrates. Coating formulations with and without photo-initiator (PI) underwent UV irradiation at doses ranging from 0.561 to 1.612 J/cm² and EB at doses ranging from 30 to 100 J/g. The investigation utilized Fourier-transform infrared (FTIR) spectroscopy to analyze the degree of conversion of double bonds in the cured samples, with a notable absorption peak observed at 810 cm^-1 corresponding to C=C bonds in the acrylate group. Furthermore, EB curing exhibited polymer curing percentages ranging from 79% to 83%, similar to UV curing. Notably, the yellowness index of EB-cured coating films (ranging from 0.5 to 10) was lower than that of those cured with UV (ranging from 10 to 28). The study suggests that EB curing could serve as an alternative process, potentially reducing health hazards associated with photo-initiator migration and food contamination within packaging materials, thereby offering a promising future for the field.

Analysis of Student Learning Behavior using Process Mining and Spectrogram

2024-05-15T11:42:20+07:00

This study aims to present an analysis using Spectrogram Analysis, Correlation Analysis, and Multiple Linear Regression Analysis to examine factors affecting the efficiency of teaching management and the relationship between the frequency of attendance and the duration of attendance on academic achievement. The tools employed in this study include event log data analyzed using Process Mining techniques with the Fuzzy Miner algorithm and Spectrogram Analysis through the Matplotlib Library of Python. The sample group consisted of 247 undergraduate students from a private university in Thailand, selected using a purposive sampling method. The results of the Spectrogram Analysis reveal a clear distinction in the continuity of the learning process between groups with academic achievement above 70% and below 70%. The frequency of attendance is positively correlated with the duration of attendance at a statistical significance level of 0.01, and the frequency of attendance (Beta = 0.537) is a significant factor affecting the efficiency of teaching management at a statistical significance level of 0.01. Therefore, it is possible to integrate techniques of Process Mining, Spectrogram Analysis, Correlation Analysis, and Multiple Linear Regression Analysis to discover and confirm methods for developing teaching processes, improving teaching quality, enhancing students' learning experiences, and driving e-learning systems to achieve better academic outcomes, promoting continuous awareness and learning.

Video Analytic for Human Management and Security and FPGA Accelerated High Concurrency

2024-06-12T13:52:41+07:00

This paper explores the use of video analytics by leveraging accelerated FPGA technology in combination with high-performance computing, specifically utilizing two Xilinx Alveo U50Lv cards and one U55C card. While many applications exist for motion analysis and detection in videos, the use of FPGAs in this context remains relatively scarce. FPGAs offer significant advantages in terms of energy efficiency and throughput. We present results demonstrating the parallelism capabilities in terms of the number of threads within a single Docker container that shares stack memory, as well as across multiple Docker containers. When operating within a single Docker process, the application shares the same memory space and resources, making it ideal for tasks that require efficient communication or data sharing. In contrast, running in multiple containers isolates processes, each with its own environment, and can significantly increase the number of threads. Our findings show that the combination of these techniques offers optimal performance for video analytics.

Predicting Prices of Airbnb Accommodations in Thailand by SVM and XGBoost Methods

2024-06-06T13:19:11+07:00

In this study, our objective was to predict accommodation prices in Bangkok utilizing Airbnb data. The data went through necessary preparation procedures and was split into training and test sets. Both support vector machines and extreme gradient boosting methodologies were employed and optimized through hyperparameter tuning. However, the detection of overfitting necessitated a reassessment of feature selection. Several features were identified as having high importance values in both models, including the number of bedrooms, proximity to tourist destinations and landmarks in Bangkok, maximum property capacity, and the number of host listings. Additionally, support vector machines with the top 10 features outperformed other models, exhibiting the lowest mean absolute error (385.37) and root mean squared error (526.16) values. Crucially, features such as the number of bedrooms, proximity to tourist destinations, maximum property capacity, private room type, and provision of safety and facility information played significant roles. In conclusion, this study emphasizes the significance of machine learning in comprehending accommodation prices. The results highlight the importance of considering specific features, such as those identified, when setting accommodation prices.

Some Properties of Subspaces over Residue Class Rings

2024-06-14T15:57:27+07:00

Let denote the residue class ring where is a prime number and is a positive integer. For , a free submodule of the -module that has a basis is called a subspace of . In this paper, we present some properties of subspaces regarding their dimensions and the joins of subspaces of .