Wide field fluorescence microscopes (WFFM) are widely used due to their affordability and ease of use, but they suffer from a significant limitation: the images often appear blurry because light from above and below the focal plane contributes to the final image. Eliminating this out-of-focus light typically requires expensive equipment like confocal microscopes. This study aimed to address this issue by applying deconvolution algorithms to images obtained from conventional WFFM. MATLAB was used to implement adaptive histogram equalization, unsharp masking, and thresholding operations, while the Lucy Richardson algorithm was applied in Python. Both approaches successfully removed out-of-focus blur and enabled quantification of the percentage of β-catenin expression overlapping with nuclei at 0, 36, and 48 h post transfection of C33A cell lines with HPV16 & 18 E6/E7 oncogenes. Our results indicated that deconvoluted images revealed a higher percentage of β-catenin overlapping the nuclei compared to non-deconvoluted images: for example, in MATLAB, 13.63% vs. 9.23% at 0 h, 15.55% vs. 11.26% at 36 h, and 18.33% vs. 12.55% at 48 h. In Python, similar trends were observed, with the control showing 1.29% vs. 0.54%. This method is a computationally efficient and cost-effective solution for removing out-of-focus light from WFFM images, making it particularly useful for institutions with limited access to high-cost microscopy equipment.
Water stress is a critical factor affecting crop yield and quality in agriculture. Timely detection and management of water stress can significantly enhance crop productivity and resource efficiency. This study employs a custom-designed E-nose system to distinguish between four stages of water stress in Khasi mandarin orange plants. The research underscores the significant potential of machine learning in addressing water stress within agricultural practices, emphasizing the crucial role of integrating advanced technologies like E-nose and machine learning to tackle agricultural challenges. The optimized machine learning parameters demonstrate improved performance, achieving 99% accuracy in detecting water stress, enabling timely and precise interventions for potential crop damage mitigation and water usage optimization.
All-terrain vehicles (ATVs) are designed to navigate rough terrains, demanding robust and efficient powertrain systems. ATVs are used predominantly for recreational purposes and even in racing events. ATVs are popular for their rugged, compact, but lightweight structure. The powertrain system in an electric ATV is the heart of the vehicle. It is also responsible for providing power output, vehicle dynamics, maximum speed, and acceleration. The powertrain system, which is pivotal for performance, faces several challenges such as weight, dynamics, inertia, and heat management. These issues impact durability and efficiency of the powertrain system and the ATV. The present research attempted to address these issues with a compact and efficient powertrain in electric vehicles (EVs). Featuring a permanent magnet synchronous motor, motor controller, driveshaft, and optimized gearbox, the system aims to maximize power output while ensuring structural integrity through rigorous mechanical analysis. An EV-driven ATV was successfully designed, developed, tested and validated in competitive events like SAE BAJA2023 and MEGA ATV2023. Overall, the system demonstrates enhanced performance and reliability.
Understanding the drying kinetics of sprouted azuki red beans in a fluidized-bed dryer is essential for balancing energy efficiency with preservation of their nutritional and health benefits. This study investigated the effects of the fluidized-bed dryer temperature on sprouted azuki red beans using shade drying as a baseline. The fluidized-bed dryer reduced the moisture content to 11–13% (wet basis) within 35 min, compared with 4,200 min for shade drying. Although the fluidized-bed dryer produced a darker product, it yielded superior bioactive properties, including total phenolic and flavonoid contents, radical scavenging capacity, and ferric ion-reducing antioxidant power. The most favorable bioactive properties and the lowest specific energy consumption were achieved at 150 °C because of the rapid drying rate. Among tested models, the logarithmic model provided the best fit to the observed drying behavior. These results highlight the efficiency of fluidized-bed dryers and will help food processing companies improve the energy efficiency of their drying processes while maintaining the product quality and antioxidant properties of sprouted azuki red beans.
This study aimed to explore the factor structure of workplace bullying (WB) and depression (DS) and their relationship among 300 teachers in the Eastern Economic Corridor of Thailand. The sampling was conducted using a multi-stage sampling method. A questionnaire was used to collect data. Data were analyzed using exploratory factor analysis (EFA), the principal component method, the Kaiser-Meyer-Olkin measure of sampling adequacy (KMO-MSA), and Pearson’s correlation. Three experts evaluated the results of the 22-item WB and 11-item DS questionnaires. The index of item-objective congruence scores ranged between 0.67 and 1.00. The EFA results indicated that the 22-item WB questionnaire could be classified into four components. The KMO-MSA value was 0.8895, and Bartlett’s test of sphericity was statistically significant. The 11-item DS questionnaire could be classified into two components. The KMO-MSA value was 0.918, and Bartlett’s test of sphericity was statistically significant. Cronbach’s alpha coefficients for both the both 22-item WB and 11-item DS questionnaires were 0.904 and 0.918, respectively. In addition, WB and DS had a statistically significant positive relationship (r = 0.475, p < 0.001). This study concludes that the 22-item WB and 11-item DS questionnaires meet the quality criteria and can be used as measurements for teachers. However, the complete questionnaire should be re-evaluated or adapted to fit the organizational culture, and its reliability should be tested again.
