Summary This paper explores the market participation decision of smallholder farmers in

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A lack of adaptive capacities for climate change prevents poor farmers from diversifying agricultural production in Bangladesh’s drought-resilient areas. Climate change adaptation strategies can reduce the production risk relating to unforeseen climatic shocks and increase farmers’ food, income, and livelihood security. This paper investigates rice farmers’ adaptive capacities to adapt climate change strategies to reduce the rice production risk. The study collected 400 farm-level micro-data of rice farmers with the direct cooperation of Rajshahi District. The survey was conducted during periods between June and July of 2020. Rice farmers’ adaptive capacities were estimated quantitatively by categorizing the farmers as high, moderate, and low level adapters to climate change adaptation strategies. In this study, a Cobb–Douglas production function was used to measure the effects of farmers’ adaptive capacities on rice production. The obtained results show that farmers are moderately adaptive in terms of adaptation strategies on climate change and the degree of adaptation capacities. Agronomic practices such as the quantity of fertilizer used, the amount of labor, the farm’s size, and extension contacts have a substantial impact on rice production. This study recommends that a farmer more significantly adjusts to adaptation strategies on climate change to reduce rice production. These strategies will help farmers to reduce the risk and produce higher quality rice. Consequently, rice farmers should facilitate better extension services and change the present agronomic practice to attain a higher adaptation status. It can be very clearly seen that low adaptability results in lower rice yields.

<span>This paper reflects on the implementation of IoT enabled Farming, especially for the people needed a smart way of agriculture. This research focuses on real-time observation with efficient use of cheapest security system. The features of this research including i) Sensor data monitoring using soil moisture sensor which is responsible for measuring moisture of the filed, water level sensor which is liable for detecting flooded water, pH sensor which is accountable for measuring pH of the soil and Temperature and humidity sensor which is responsible for tracking out the present temperature and humidity in the atmosphere ii) Live monitoring of sensor’s value using cloud and a Dashboard iii) Security issues of the farming using Laser shield and IP-Camera through Wi-Fi which is conducted by android application. This paper also assures the analysis of the experimented data through various sensor’s value and gives a momentous way for future application. Result and discussion ensures the contribution in the field of Internet of things</span>

This study was aimed to find out the suitable dose of fresh plantain (Plantago lanceolata L.) supplementation for optimum growth, serum antioxidants status, liver health, and meat quality in broilers. A total of 1152-days-old Cobb-500 broilers (average weight: 45 ± 0.7 g) were randomly assigned into four dietary treatments, including (i) control (CON): corn-soya based basal diet, and plantain (PL) supplemented groups (ii) PL40: CON þ 40 g fresh PL/kg diet; (iii) PL80: CON þ 80 g fresh PL/kg diet; and (iv) PL120: CON þ 120 g fresh PL/kg diet. Improved growth efficiency (p < .05) was observed in PL supplemented groups compared to CON, where PL80 and PL120 groups had the highest value. Serum superoxide dismutase and glutathione peroxidase concentrations were comparable in the PL80 and PL120 groups, but higher (p < .05) than other groups. The lowest concentrations of aspartate aminotransferase and alanine aminotransferase were found in the PL80 group, while alkaline phosphatase was the highest in the PL40 group. Furthermore, the PL80 group exhibited the lowest (p ¼ .001) abdominal fat content and the highest (p ¼ .002) breast meat yield. Meat linoleic acid content was nevertheless improved linearly with PL supplement levels, and the highest value was found in the PL120 group. Furthermore, the maximum meat redness (a à ) was observed in PL80 and PL120 groups, which was approximately twice that of the CON. Overall, the growth and health responses of both PL80 and PL120 groups were similar, while the latter had improved the meat fatty acid profile. HIGHLIGHTSSupplementation of 80 g plantain/kg diet showed optimum growth performance, health status, and plasma antioxidants level in broilers. 120 g plantain/kg diet might be supplemented with the purpose of producing value-added broiler meat.

The use of unmanned aerial vehicle (UAV) has been increasing rapidly in the civilian and military applications, because of UAV's high-performance communication with ground clients, especially for its intrinsic properties such as adaptive altitude, mobility, and flexibility. UAV deployment can be monitored and controlled through 5G wireless network as user equipment (UE) along with other devices. A highly directive microstrip patch antenna (MPA) could establish long-distance communication by overcoming air attenuation and reduce co-channel interference in the limited region if UAV uses a specifically dedicated band, which might enhance spatially reuse of the spectrum. Also, MPA is highly recommended for UAV because of its low weight, low cost, compact size, and flat shape. In this paper, we have designed a highly directive single-band 2×2 and 4×4 antenna array for 5.8 GHz and 28 GHz frequency respectively for UAV application in a focus to deploy UAV through 5G wireless network. Here, The Roger RT5880 (lossy) material utilize as a substrate due to its lower dielectric constant which achieves higher directivity and good mechanical stability. Inset feed technique used to feed antenna for lowering input impedance which provides higher antenna efficiency. The results show a wider bandwidth of 702 MHz and 1.596 GHz for 5.8 GHz and 28 GHz antenna array correspondingly with a compact size.

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COVID-19, caused by SARS-CoV-2, has been declared as a global pandemic by WHO. Early diagnosis of COVID-19 patients may reduce the impact of coronavirus using modern computational methods like deep learning. Various deep learning models based on CT and chest X-ray images are studied and compared in this study as an alternative solution to reverse transcription-polymerase chain reactions. This study consists of three stages: planning, conduction, and analysis/reporting. In the conduction stage, inclusion and exclusion criteria are applied to the literature searching and identification. Then, we have implemented quality assessment rules, where over 75 scored articles in the literature were included. Finally, in the analysis/reporting stage, all the papers are reviewed and analysed. After the quality assessment of the individual papers, this study adopted 57 articles for the systematic literature review. From these reviews, the critical analysis of each paper, including the represented matrix for the model evaluation, existing contributions, and motivation, has been tracked with suitable illustrations. We have also interpreted several insights of each paper with appropriate annotation. Further, a set of comparisons has been enumerated with suitable discussion. Convolutional neural networks are the most commonly used deep learning architecture for COVID-19 disease classification and identification from X-ray and CT images. Various prior studies did not include data from a hospital setting nor did they consider data preprocessing before training a deep learning model.