The Practices of Solid Waste Utility and Thriving Conditions of Logistics (a Case of Tepi Town): A Study to Treat the Healthy Environment.

Unwanted remains, discarded residues, and byproduct materials that are not required by the initial user are known as wastes. In Ethiopia, improper solid waste management becomes endemic and it affects the health conditions, comforts, and freedom of town communities. Improper solid waste management can also adversely affect infrastructure damages, socioeconomic conditions, and environmental and health problems. So, awareness creation among the communities is necessary. The main objective of the study was to assess the management of existing solid waste activities and reverse logistic systems in Tepi town. The impacts of improper solid waste management were reduced through waste accumulation, transportation, recycling, and waste removal. Available pieces of information for the study were gathered from 450 near house places and 549 survivals. The collected data were analyzed by using Vensim system dynamics software, and the obtained results were modeled by a system dynamic cause and effect relationship diagram. Finally, the appropriate recommendations for communities, municipals, and institutions were provided.

Hyperspectral Image Analysis of Colon Tissue and Deep Learning for Characterization of Health care.

Colon cancer is a disease characterized by the unusual and uncontrolled development of cells that are found in the large intestine. If the tumour extends to the lower part of the colon (rectum), the cancer may be colorectal. Medical imaging is the denomination of methods used to create visual representations of the human body for clinical analysis, such as diagnosing, monitoring, and treating medical conditions. In this research, a computational proposal is presented to aid the diagnosis of colon cancer, which consists of using hyperspectral images obtained from slides with biopsy samples of colon tissue in paraffin, characterizing pixels so that, afterwards, imaging techniques can be applied. Using computer graphics augmenting conventional histological deep learning architecture, it can classify pixels in hyperspectral images as cancerous, inflammatory, or healthy. It is possible to find connections between histochemical characteristics and the absorbance of tissue under various conditions using infrared photons at various frequencies in hyperspectral imaging (HSI). Deep learning techniques were used to construct and implement a predictor to detect anomalies, as well as to develop a computer interface to assist pathologists in the diagnosis of colon cancer. An infrared absorbance spectrum of each of the pixels used in the developed classifier resulted in an accuracy level of 94% for these three classes.