Mobile cloud computing and edge computing-based solutions provide means to offload tasks for resource-limited mobile devices. Mobile cloud computing provides remote cloud solutions while edge computing provides closer proximity-based solutions. Remote cloud solutions suffer from network latency and limited bandwidth challenges due to distance and dependency on the Internet. However, these challenges are addressed by edge-based solutions since the edge node is available in the same network. The use of Internet of Things-based solutions considering future Information Communication Technology infrastructure is on the rise resulting in the massive growth of digital equipment increasing the load at edge devices. Hence, some load balancing mechanism is required at the edge level to avoid resource congestion. The load balancing at the edge must consider the user’s preferences about edge resources such as personal computers or mobile devices. A user must declare which resources can be spared for other devices to avoid overprovisioning essential resources. We present Load Balancing for Resource Optimization (LBRO), a collaborative cloudlet platform to address load balancing challenges in edge computing considering users’ preferences. A comparative analysis of the proposed approach with the conventional edge-based approach yields that the proposed approach provides significantly improved results in terms of CPU, memory, and disk utilization.

Climate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food security. Reliable crop type maps can play an essential role in monitoring crops, estimating yields, and maintaining smooth food supplies. However, these maps are not available for developing countries until crops have matured and are about to be harvested. The use of remote sensing for accurate crop-type mapping in the first few weeks of sowing remains challenging. Smallholder farming systems and diverse crop types further complicate the challenge. For this study, a ground-based survey is carried out to map fields by recording the coordinates and planted crops in respective fields. The time-series images of the mapped fields are acquired from the Sentinel-2 satellite. A deep learning-based long short-term memory network is used for the accurate mapping of crops at an early growth stage. Results show that staple crops, including rice, wheat, and sugarcane, are classified with 93.77% accuracy as early as the first four weeks of sowing. The proposed method can be applied on a large scale to effectively map crop types for smallholder farms at an early stage, allowing the authorities to plan a seamless availability of food.

CYP11B2 gene is located over the upper layer of the kidney. It produces aldosterone synthase enzyme and thereby has an essential role to balance salt and mineral level in the body. A mutation in this gene can deregulate the production of aldosterone hormone in the body which may lead to many diseases including hypertension and cardiac diseases. To control the excess production of this aldosterone an inhibitor “ Fadrozole ” is being used which is associated with an active site cavity of CYP11B2. This study has been divided into two parts. In the first part, the four computational tools (SIFT, Polyphen-2, I-Mutant, ConSurf) were used to identify 29 deleterious SNPs out of 1600 CYP11B2 SNPs. In the second part, five residues (R448G, R141P, W260R, F130S, and F445S) were identified in the active site cavity (out of 29 deleterious CYP11B2 SNPs) at the distance of 5A°. Binding free energy calculation as well as Dynamics simulation techniques were applied to determine the effect of these mutations on the CYP11B2-Fadrozole compound. The results showed that Fadrozole binding with CYP11B2 became stronger which proved the efficiency of this drug inhibitor with these highly damaging mutations. Our study will be useful for selecting the high priority CYP11B2 mutations, which could be further, investigated in this gene-associated study, for better understanding of the structural and functional aspects of the observed (CYP11B2) protein.