We report a laboratory scale combined absorption and adsorption chemical process to remove contaminants from anaerobically produced biogas using cafeteria (food), vegetable, fruit, and cattle manure wastes. Iron oxide (Fe2O3), zero valent iron (Feo), and iron chloride (FeCl2) react with hydrogen sulfide (H2S) to deposit colloidal sulfur. Silica gel, sodium sulfate (Na2SO4), and calcium oxide (CaO) reduce the water vapour (H2O) and carbon dioxide (CO2). It is possible to upgrade methane (CH4) above 95% in biogas using chemical or physical absorption or adsorption process. The removal efficiency of CO2, H2S, and H2O depends on the mass of removing agent and system pH. The results showed that Ca(OH)2solutions are capable of reducing CO2below 6%. The H2S concentration was reduced to 89%, 90%, 86%, 85%, and 96% for treating with 10 g of FeCl2,Feo(with pH), Fe2O3,Feo, and activated carbon, respectively. The H2O concentration was reduced to 0.2%, 0.7%, 0.2%, 0.2%, and 0.3% for treating raw biogas with 10 g of silica gel and Na2SO4for runs R1, R2, R3, R4, and R5, respectively. Thus, given the successful contaminant elimination, the combined absorption and adsorption process is a feasible system for biogas purification.

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University of Michigan, where he studied the solidification and oxidization in reactor using the experimental method and numerical simulation. In 1993, he became the associate professor at Kagoshima University, where he studied the thermal fluid flow transport phenomena for rotating machinery and combustion and the development of turbulence model. Since 2003, he became a professor of Department of Mechanical Engineering at Kumamoto University. His research interest on production and development of clean energy and renewable energy, thermal fluid flow transport phenomena using nanofluids, advanced cooling device development with the use of nanofluids and development of new clean fuel with the aid of shock-wave.

Reasonable use of agricultural machinery has an extraordinary potential for poverty alleviation by increasing land and labor productivity in Thailand, Vietnam, and even in Bangladesh. This study was conducted under a program entitled “Agriculture Mechanization, Agro-Processing, Value addition and Export Market Development in Thailand and Vietnam from 1–14 November, 20I9” from the Ministry of Agriculture, Bangladesh. In all three distinct nations, farming activities represent a significant area of activity and remains the biggest wellspring of agricultural business. About 10.5% of Thailand’s, 21.5% of Vietnam’s, and 14.23% of Bangladesh’s GDP come from agriculture. For sustainable development, it is essential to modernize agriculture through the mechanization of its operations, which is therefore inevitable in the studied countries. Thailand’s government started mechanization in 1891 with the import of steam-powered tractor and rotary hoes. Since then the country has witnessed several milestones in the course of mechanization development. The focal plain agro-ecological zone of the state is the maximum and almost fully modernized area. As of now, there are two methods of practicing farming apparatus use: as a proprietor and/or through custom renting provision which coincides with Vietnam and Bangladesh. Historically, mechanization patterns in Vietnam can been described by tillage machinery with associated implement equipment use preceding 1975. This was non-linear, followed by a decreasing trend during the 80s prior to recovery during the 90s, with significant disparities in implementation status across the areas. In 2018, the number of tillage implements and harvesters was boosted about 1.6 and 25.6 times, respectively compared with 2006. The percentage of machinery use in soil tillage operation is 80% of the whole territory of cultivable land in Vietnam, compared to about 90% in Bangladesh and 100% in Thailand. Mechanization in Bangladesh started before independence with the importation of 2-wheel tractors and irrigation pumps in the last part of the 1960s as part of ‘Green Revolution’ activities. To continue this momentum, the Bangladesh Government permitted the continuation of agricultural machinery importation after later autonomy. Machinery use in different agricultural activities has increased in recent years in the areas of irrigation, land preparation, intercultural operation, and threshing. Though its degree of advancement is by and large still quite low contrasted with other South Asian nations, it is noticeable that the most recent two decades, the pace of mechanization has increased rapidly with the increase of mechanical power use in farm activities. The use of farm machinery in rice cultivation has been the most amazing when contrasted with different crops in these three nations. A clear comparison has been given in the paper, which aims to help researchers and policymakers take necessary measures.

A reliable and accurate diagnosis and identification system is required to prevent and manage tea leaf diseases. Tea leaf diseases are detected manually, increasing time and affecting yield quality and productivity. This study aims to present an artificial intelligence-based solution to the problem of tea leaf disease detection by training the fastest single-stage object detection model, YOLOv7, on the diseased tea leaf dataset collected from four prominent tea gardens in Bangladesh. 4000 digital images of five types of leaf diseases are collected from these tea gardens, generating a manually annotated, data-augmented leaf disease image dataset. This study incorporates data augmentation approaches to solve the issue of insufficient sample sizes. The detection and identification results for the YOLOv7 approach are validated by prominent statistical metrics like detection accuracy, precision, recall, mAP value, and F1-score, which resulted in 97.3%, 96.7%, 96.4%, 98.2%, and 0.965, respectively. Experimental results demonstrate that YOLOv7 for tea leaf diseases in natural scene images is superior to existing target detection and identification networks, including CNN, Deep CNN, DNN, AX-Retina Net, improved DCNN, YOLOv5, and Multi-objective image segmentation. Hence, this study is expected to minimize the workload of entomologists and aid in the rapid identification and detection of tea leaf diseases, thus minimizing economic losses.