Multi-class waste segregation using computer vision and robotic arm.

Waste segregation is an essential aspect of a smoothly functioning waste management system. Usually, various recyclable waste types are disposed of together at the source, and this brings in the necessity to segregate them into their categories. Dry waste needs to be separated into its own categories to ensure that the proper procedures are implemented to treat and process it, which leads to an overall increased recycling rate and reduced landfill impact. Paper, plastics, metals, and glass are just a few examples of the many dry waste materials that can be recycled or recovered to create new goods or energy. Over the past years, much research has been conducted to devise effective and productive ways to achieve proper segregation for the waste that is being produced at an ever-increasing rate. This article introduces a multi-class garbage segregation system employing the YOLOv5 object detection model. Our final prototype demonstrates the capability of classifying dry waste categories and segregating them into their respective bins using a 3D-printed robotic arm. Within our controlled test environment, the system correctly segregated waste classes, mainly paper, plastic, metal, and glass, eight out of 10 times successfully. By integrating the principles of artificial intelligence and robotics, our approach simplifies and optimizes the traditional waste segregation process.

Immunophenotyping of normal individuals classified on the basis of human dosha prakriti.

BACKGROUND: Human variations related to immune response and disease susceptibility is well-documented in Ayurveda. Prakriti (body constitution) is the basic constitution of an individual established at the time of birth and distinguishes variations, into three broad phenotype categories such as vata, pitta and kapha. Variation in immune response is often attributed to and measured from the difference in cluster differentiation (CD) markers expressed in lymphocytes. Currently, there are no reports available on the expression of CD markers related to prakriti. OBJECTIVE: This is a pilot study performed to evaluate a panel of lymphocyte subset CD markers in dominant prakriti individuals. MATERIALS AND METHODS: Immunophenotyping was carried out using whole blood from a total of healthy 222 subjects, who are grouped into kapha (n = 95), pitta (n = 57) and vata (n = 70) prakritis. CD markers such as CD3, CD4, CD8, CD14, CD25, CD56, CD69, CD71 and HLA-DR were analyzed using flow cytometry method. Differences between groups were analyzed using one-way ANOVA or Kruskal-Wallis analysis of variance (ANOVA) and multiple comparisons between groups were performed by Bonferroni or Mann-Whitney U test with corrections for type I error respectively. Significance was evaluated by ANOVA and Pearson's correlation. RESULTS: We observed a significant difference (P < 0.05) in the expression of CD markers such as CD14 (monocytes), CD25 (activated B cells) and CD56 (Natural killer cells) between different prakriti groups. CD25 and CD56 expression was significantly higher in kapha prakriti samples than other prakriti groups. Similarly, slightly higher levels of CD14 were observed in pitta prakriti samples. CONCLUSION: Significant difference in the expression of CD14, CD25 and CD56 markers between three different prakriti is demonstrated. The increased level of CD25 and CD56 in kapha prakriti may indicate ability to elicit better immune response, which is in conformity with textual references in Ayurveda.