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Data centers are a major source of carbon emissions and are subsequently contributing to global carbon footprints. Keeping in view of providing a sustainable solution to society, we have analyzed various factors that can help to achieve carbon neutrality and maximum sustainability. Our study pointed towards the need to follow a sustainable approach for incoming workflow throughout the life-cycle of Data centers. We analyzed that workloads need to be segregated before assigning them to the data centers so that energy-efficient resource allocation could be done. This paper demonstrates unsupervised learning techniques to cluster the incoming cloud workloads. The heterogeneous workloads were characterized using machine learning approaches and appropriate clusters were crafted. For analysis, Google Cluster Dataset is used. In order to improve the accuracy, data were normalized, and random samples of data were selected for clustering. The machine learning algorithms applied were able to successfully determine the appropriate clusters that can further be used for energy-efficient resource scheduling. [ABSTRACT FROM AUTHOR]

When it comes to software development, precise planning, proper documentation and proper process control, some errors are inevitable in the software environment. These software flaws can lead to quality deterioration, which can be the main reason behind system failure. As the whole world especially developing countries is dependent upon software systems, it is very important to focus on its reliability aspect. Nowadays sophisticated systems require concerted efforts for managing and reducing the shortcomings in software engineering. But, these efforts require more cost, more money and more time. Software error prediction is the most helpful step in the testing stage of the software development life cycle. It identifies components or parts of the code where an error may occur and requires broad testing, so the test resources can be efficiently used. Software error assessment reduces efforts of testing the software by helping the software testers locate the actual problem and classify different classes of errors in the system. Error estimators are majorly used in various organizations to evaluate the software to save time, improve the quality of software and testing and optimize resources to meet timelines. Machine learning provides support in fault projection by collecting the training data from various edge devices and thus helps in escalating the reliability of the software available on Kaggle. The multilayer perceptron shows better results in precision, recall, F1 score and accuracy as compared to decision tree and Gaussian Naive Bayes as it achieves an accuracy of 96.8%. [ABSTRACT FROM AUTHOR]

A new technology that is gaining popularity today is the Wireless Sensor Network. Smart sensors are being used in a variety of wireless network applications, including intruder detection, transportation, the Internet of Things, smart cities, the military, industrial, agricultural, and health monitoring, as a result of their rapid expansion. Sensor network technologies improve social advancement and life quality while having little to no negative impact on the environment or natural resources of the planet are examined in sensor networks for sustainable development. Real-world applications face challenges ensuring Quality of Service (QoS) due to dynamic network topology changes, resource constraints, and heterogeneous traffic flow. By enhancing its properties, such as maintainability, packet error ratio, reliability, scalability, availability, latency, jitter, throughput, priority, periodicity, deadline, security, and packet loss ratio, the optimized QoS may be attained. Real-world high performance is difficult to attain since sensors are spread out in a hostile environment. The performance parameters are divided into four categories: network-specific, deployment phase, layered WSN architecture, and measurability. Integrity, secrecy, safety, and security are among the privacy and security levels. This article leads emphasis on the trustworthiness of the routes as well as the nodes involved in those routes from where the data has to pass from source to destination. First of all, the nodes are deployed and cluster head selection is done by considering the total number of nodes and the distance from the base station. The proposed work uses AODV architecture for computing QoS parameters that are throughput, PDR and delay. K-means clustering algorithm is used to divide the aggregated data into three possible segments viz. good, moderate and bad as this process does not involve the labelling of aggregated data due to its supervised behavior. The proposed trust model works in two phases. In first phase, data is divided into 3 segments and labelling is done. In second phase, uses generated class objects are to be applied viz. the route records to publicize the rank of the routes followed by the rank of nodes. The proposed technique employed the statistical machine learning and swarm intelligence strategy with dragon fly algorithm in order to address the issues related effective rank generation of nodes and improving the network lifetime. Deep learning concepts can be combined with fuzzy logics approach for resolving issues like secure data transmission, trustworthiness of ranking nodes and efficient route discovery.Article Highlights: This paper implies certain techniques for the lifetime enhancement of WSN. A modified version of dragonfly algorithm is used for achieving the trustworthiness of nodes and routes in the network. Trustnet is compared with existing techniques and it showed improvement in the results thereby attaining the trustworthiness of the network. [ABSTRACT FROM AUTHOR]

Pomology, also known as fruticulture, is a significant contributor to the economies of many nations worldwide. While vertical farming methods are not well-suited for fruit cultivation, substrate-based cultivation is commonly practiced. Vertical farming methods use no soil for cultivation of the plants, and the cultivation is done in vertically stacked layers. Therefore, smaller herbs are best suited for such cultivation, whereas, the majority of the fruit trees are big and woody. Therefore, vertical farming methods are not well suited for fruit trees. However, to maximize fruit production, smarter substrate cultivation methods are needed. Utilizing remote sensing techniques, such as Internet of Things (IoT) devices, agriculture sensors, and cloud computing, allows for precision agriculture and smart farming in autonomous systems. Nevertheless, a lack of understanding of fruit nutrient requirements, growing conditions, and soil health conditions can result in reduced fruit production. To address these challenges, this paper proposes an intelligent model based on machine learning that recommends the best fruit to grow based on prevailing soil and climatic conditions. The system is trained on a dataset that includes details on eleven different fruits, such as Nitrogen (N), Phosphorous (P), Potassium (K), temperature, humidity, pH, and rainfall. The model takes into account the soil type and nutrient contents to recommend the most suitable fruit to grow in the prevailing climate. To enhance the model's efficiency, two novel techniques, Gradient-based Side Sampling (GOSS) and Exclusive Feature Bundling (EFB), have been incorporated. The results show that the proposed system has achieved 99% accuracy in recommending the right fruit based on the given environmental conditions. As a result, this system has the potential to significantly improve the profitability of the pomology industry and boost national economies.Article Highlights: This article aims at the creation of an efficient recommendation model for fruit cultivation in soil medium by analyzing the soil nutrient contents and the existing climatic conditions. The most suitable fruit plant corresponding to the existing conditions and soil type is recommended for an enhanced yield of the plant. Three climatic parameters, i.e., temperature, humidity, and rainfall; along with four soil-based parameters, i.e., pH, N content, P content, and K content are considered as the required growing condition for eleven varieties of fruits. To ensure enhanced accuracy, a hundred entries for each fruit type is entered in the dataset. The created dataset is then divided in the proportion of 7:3 as training data: testing data and Light Gradient Boosting Machine (Light GBM) model is applied to the created dataset. The correlation of all the parameters is checked for an efficient recommendation of fruits. Finally, the model is evaluated and its efficiency is checked. [ABSTRACT FROM AUTHOR]

Since the emergence of the first case of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2), the viral genome has constantly undergone rapid mutations for better adaptation in the host system. These newer mutations have given rise to several lineages/ variants of the virus that have resulted in high transmission and virulence rates compared to the previously circulating variants. Owing to this, the overall caseload and related mortality have tremendously increased globally to > 233 million infections and > 4.7 million deaths as of Sept. 28th, 2021. SARS-CoV-2, Spike (S) protein binds to host cells by recognizing human angiotensin-converting enzyme 2 (hACE2) receptor. The viral S protein contains S1 and S2 domains that constitute the binding and fusion machinery, respectively. Structural analysis of viral S protein reveals that the virus undergoes conformational flexibility and dynamicity to interact with the hACE2 receptor. The SARS-CoV-2 variants and mutations might be associated with affecting the conformational plasticity of S protein, potentially linked to its altered affinity, infectivity, and immunogenicity. This review focuses on the current circulating variants of SARS-CoV-2 and the structure–function analysis of key S protein mutations linked with increased affinity, higher infectivity, enhanced transmission rates, and immune escape against this infection. [ABSTRACT FROM AUTHOR]

The essentiality of DNA Gyrase in basic cellular processes in bacterial pathogens makes it an ideal drug target. Though the Gyrase has a conserved mechanism of action, the complete DNA wrapping and binding process is still unknown. In this study, we have identified six arginine residues R556, R612, R667, R716, R766, and R817 in the DNA GyraseA – C-terminal domain from Salmonella enterica serovar Typhi (StGyrA-CTD) to be essential for DNA wrapping and sliding by a sequence and structure analysis. Through site-directed mutagenesis and EMSA studies, we observed that the substitution of R667 (blade 3) and R716 (blade 4) in StGyrA-CTD led to loss of DNA binding. Whereas, upon mutation of residue R612 (blade2), R766 (blade5) and R817 (blade6) along with supporting residue R712 (blade 4) a decrease in binding affinity was seen. Our results indicate that R667 and R716 act as a pivot point in DNA wrapping and sliding during gyrase catalytic activity. In this study, we propose that the DNA wrapping mechanism commences with DNA binding at blade3 and blade4 followed by other blades to facilitate the DNA sliding during supercoiling activity. This study provides a better understanding of the DNA binding and wrapping mechanism of GyrA-CTD in DNA Gyrase. [ABSTRACT FROM AUTHOR]

Aim:To investigate the antidepressant-like effects of a novel 5-HT3 receptor antagonist N-(benzo[d]thiazol-2-yl)-3-methoxyquinoxalin-2-carboxamide (6z) in acute and chronic murine models of depression.Methods:5-HT3 receptor antagonism was examined in guinea pig ileum in vitro. A tail suspension test (TST) was used as acute depression model to evaluate the antidepressant-like behavior in mice treated with 6z (0.5-2 mg/kg, ip). In chronic depression model, mice were exposed to a 4-week chronic unpredictable stress (CUS) protocol, and treated with 6z (0.5-2 mg·kg−1·d−1, po) or a positive drug fluoxetine (10 mg·kg−1·d−1, po) in the last 2 weeks, followed by behavioral and biochemical assessments.Results:The 5-HT3 receptor antagonism of 6z (pA2=7.4) in guinea pig ileum was more potent than that of a standard 5-HT3 receptor antagonist ondansetron (pA2=6.9). In acute depression model, 6z administration significantly decreased the immobility duration. In chronic depression model, 6z administration reversed CUS-induced depressive-like behavior, as evidenced by increased immobility duration in the forced swim test and sucrose preference in the sucrose preference test. Furthermore, chronic administration of 6z prevented CUS-induced brain oxidative stress, with significant reduction of pro-oxidant markers and elevation of antioxidant enzyme activity. Moreover, chronic administration of 6z attenuated CUS-induced hypothalamic-pituitary-adrenal axis hyperactivity, as shown by reduced plasma corticosterone levels. Similar results were observed in the fluoxetine-treated group.Conclusion:6z is a novel 5-HT3 receptor antagonist with potential antidepressant-like activities, which may be related to modulating hypothalamic-pituitary-adrenal axis and attenuating brain oxidative damage. [ABSTRACT FROM AUTHOR]