Water, soil, and food products contain pesticide residues. These residues result from excessive pesticides use, motivated by the fact that agricultural productivity can be increased by the use of these pesticides. The accumulation of these residues in the body can cause health problems, leading to food safety concerns. Cold plasma technology has been successfully employed in various applications, such as seed germination, bacterial inactivation, wound disinfection, surface sterilization, and pesticide degradation. In recent years, researchers have increasingly explored the effectiveness of cold plasma technology in the degradation of pesticide residues. Most studies have shown promising outcomes, encouraging further research and scaling-up for commercialization. This review summarizes the use of cold plasma as an emerging technology for pesticide degradation in terms of the plasma system and configuration. It also outlines the key findings in this area. The most frequently adopted plasma systems for each application are identified, and the mechanisms underlying pesticide degradation using cold plasma technology are discussed. The possible factors influencing pesticide degradation efficiency, challenges in research, and future trends are also discussed. This review demonstrates that despite the nascent nature of the technology, the use of cold plasma shows considerable potential in regards to pesticide residue degradation, particularly in food applications.

Microfluidics is proposed as a technique for efficient sperm sorting, to achieve the ultimate goal of resolving infertility problems in livestock industry. Our study aimed to design a microfluidic sperm-sorting device (SSD) through a high-efficacy and cost- and time-effective fabrication process, by using COMSOL Multiphysics simulation and modeling software, and the design of experiment (DOE) method. The eight factors affecting SSD performance were established. The simulation was then run, and statistically significant factors were analyzed. Minitab16 was used to optimize the design modulus factor. By setting the statistical significance at p < 0.05, the factors affecting experimental structure were analyzed. At a desirability of 97.99, the optimal parameters for the microfluidic chip were: angle between sperm and medium inlet chambers (A = 43°), sperm inlet flow rate (B = 0.24 µL min⁻¹), medium inlet flow rate (C = 0.34 µL min⁻¹), and inlet and outlet chamber lengths (D = 5000 µm). These optima were then applied to microfluidics device construction. The device was produced using soft lithographic microfabrication techniques and tested on Holstein–Friesian bull sperm. The highest bull sperm-sorting performance for this microfluidic device prototype was 96%. The error between the simulation and the actual microfluidic device was 2.72%. Fluid viscosity ranges analysis-based simulations revealed acceptable fluid viscosity tolerances for the SSD. The simulation results revealed that the acceptable tolerance range for fluid viscosity was 0.00001–0.003 kg m⁻¹ s⁻¹. This optimally designed microfluidic chip-based SSD may be integrated into sperm x/y separation micro devices.

Diabetic retinopathy (DR) is a potentially blinding complication affecting individuals with diabetes, where early diagnosis and treatment are crucial to preventing vision loss. Recent advances in deep learning have shown promise in automating DR diagnosis, offering faster, more reliable, and cost-effective solutions. Our study employed convolutional neural networks (CNNs) to classify the severity of DR using retinal images from the EyePACS dataset, which includes 35,155 images categorized into five classes. Building on previous research that often classified DR into two classes, such as no DR and varying levels of DR, we found that while these studies typically used models like Inception V3, VGGNet, and ResNet, they focused on simplifying the diagnostic process by reducing the number of classes. However, our approach utilized a smaller, more flexible CNN architecture, allowing for a more detailed classification into five stages of DR. We employed various image preprocessing techniques, including grayscale conversion, background removal, and data augmentation, with our findings indicating that background removal significantly enhanced model performance, achieving a validation accuracy of 90.60%. This underscores the importance of sophisticated data preprocessing in medical imaging, and our study contributes to the ongoing development of automated DR diagnosis, potentially easing the burden on healthcare systems and improving patient outcomes.

This study applied engineering techniques to develop a nurse scheduling model that, while maintaining the highest level of service, simultaneously minimized hospital‐staffing costs and equitably distributed overtime pay. In the mathematical model, the objective function was the sum of the overtime payment to all nurses and the standard deviation of the total overtime payment that each nurse received. Input data distributions were analyzed in order to formulate a simulation model to determine the optimal demand for nurses that met the hospital’s service standards. To obtain the optimal nurse schedule with the number of nurses acquired from the simulation model, we proposed a genetic algorithm (GA) with two‐point crossover and random mutation. After running the algorithm, we compared the expenses and number of nurses between the existing and our proposed nurse schedules. For January 2013, the nurse schedule obtained by GA could save 12% in staffing expenses per month and 13% in number of nurses when compare with the existing schedule, while more equitably distributing overtime pay between all nurses.

Plasma activated water (PAW) generated from pinhole plasma jet using gas mixtures of argon (Ar) and 2% oxygen (O₂) was evaluated for pesticide degradation and microorganism decontamination (i.e., Escherichia coli and Colletotrichum gloeosporioides) in chili (Capsicum annuum L.). A flow rate of 10 L/min produced the highest concentration of hydrogen peroxide (H₂O₂) at 369 mg/L. Results showed that PAW treatment for 30 min and 60 min effectively degrades carbendazim and chlorpyrifos by about 57% and 54% in solution, respectively. In chili, carbendazim and chlorpyrifos were also decreased, to a major extent, by 80% and 65% after PAW treatment for 30 min and 60 min, respectively. E. coli populations were reduced by 1.18 Log CFU/mL and 2.8 Log CFU/g with PAW treatment for 60 min in suspension and chili, respectively. Moreover, 100% of inhibition of fungal spore germination was achieved with PAW treatment. Additionally, PAW treatment demonstrated significantly higher efficiency (p < 0.05) in controlling Anthracnose in chili by about 83% compared to other treatments.