Wind energy is one of the supremely renewable energy sources and has been widely established worldwide. Due to strong seasonal variations in the wind resource, accurate predictions of wind resource assessment and appropriate wind speed distribution models (for any location) are the significant facets for planning and commissioning wind farms. In this work, the wind characteristics and wind potential assessment of onshore, offshore, and nearshore locations of India—particularly Kayathar in Tamilnadu, the Gulf of Khambhat, and Jafrabad in Gujarat—are statistically analyzed with wind distribution methods. Further, the resource assessments are carried out using Weibull, Rayleigh, gamma, Nakagami, generalized extreme value (GEV), lognormal, inverse Gaussian, Rician, Birnbaum–Sandras, and Bimodal–Weibull distribution methods. Additionally, the advent of artificial intelligence and soft computing techniques with the moth flame optimization (MFO) method leads to superior results in solving complex problems and parameter estimations. The data analytics are carried out in the MATLAB platform, with in-house coding developed for MFO parameters estimated through optimization and other wind distribution parameters using the maximum likelihood method. The observed outcomes show that the MFO method performed well on parameter estimation. Correspondingly, wind power generation was shown to peak at the South West Monsoon periods from June to September, with mean wind speeds ranging from 9 to 12 m/s. Furthermore, the wind speed distribution method of mixed Weibull, Nakagami, and Rician methods performed well in calculating potential assessments for the targeted locations. Likewise, the Gulf of Khambhat (offshore) area has steady wind speeds ranging from 7 to 10 m/s with less turbulence intensity and the highest wind power density of 431 watts/m2. The proposed optimization method proves its potential for accurate assessment of Indian wind conditions in selected locations.

The rapid increase in soil salinization has impacted agricultural output and poses a threat to food security. There is an urgent need to focus on improving soil fertility and agricultural yield, both of which are severely influenced by abiotic variables such as soil salinity and sodicity. Abiotic forces have rendered one-third of the overall land unproductive. Microbes are the primary answer to the majority of agricultural production’s above- and below-ground problems. In stressful conditions, proper communication between plants and beneficial microbes is critical for avoiding plant cell damage. Many chemical substances such as proteins and metabolites synthesized by bacteria and plants mediate communication and stress reduction. Metabolites such as amino acids, fatty acids, carbohydrates, vitamins, and lipids as well as proteins such as aquaporins and antioxidant enzymes play important roles in plant stress tolerance. Plant beneficial bacteria have an important role in stress reduction through protein and metabolite synthesis under salt stress. Proper genomic, proteomic and metabolomics characterization of proteins and metabolites’ roles in salt stress mitigation aids scientists in discovering a profitable avenue for increasing crop output. This review critically examines recent findings on proteins and metabolites produced during plant-bacteria interaction essential for the development of plant salt stress tolerance.

This study aims to increase Bacillus and Streptomyces antagonistic activity against the root rot and wilt diseases of pulses caused by Macrophomina phaseolina and Fusarium oxysporum f. sp. udum, respectively. To increase antagonistic action, Bacillus subtilis BRBac4, Bacillus siamensis BRBac21, and Streptomyces cavourensis BRAcB10 were subjected to random mutagenesis using varying doses of gamma irradiation (0.5–3.0 kGy). Following the irradiation, 250 bacterial colonies were chosen at random for each antagonistic strain and their effects against pathogens were evaluated in a plate assay. The ERIC, BOX, and random amplified polymorphic studies demonstrated a clear distinction between mutant and wild-type strains. When mutants were compared to wild-type strains, they showed improved plant growth-promoting characteristics and hydrolytic enzyme activity. The disease suppression potential of the selected mutants, B. subtilis BRBac4-M6, B. siamensisi BRBac21-M10, and S. cavourensis BRAcB10-M2, was tested in green gram, black gram, and red gram. The combined inoculation of B. siamensis BRBac21-M10 and S. cavourensis BRAcB10-M2 reduced the incidence of root rot and wilt disease. The same treatment also increased the activity of the defensive enzymes peroxidase, polyphenol oxidase, and phenylalanine ammonia-lyase. These findings suggested that gamma-induced mutation can be exploited effectively to improve the biocontrol characteristics of Bacillus and Streptomyces. Following the field testing, a combined bio-formulation of these two bacteria may be utilised to address wilt and root-rot pathogens in pulses.

BackgroundThe Internet has greatly enhanced health care, helping patients stay up-to-date on medical issues and general knowledge. Many cancer patients use the Internet for cancer diagnosis and related information. Recently, cloud computing has emerged as a new way of delivering health services but currently, there is no generic and fully automated cloud-based self-management intervention for breast cancer patients, as practical guidelines are lacking.ObjectiveWe investigated the prevalence and predictors of cloud use for medical diagnosis among women with breast cancer to gain insight into meaningful usage parameters to evaluate the use of generic, fully automated cloud-based self-intervention, by assessing how breast cancer survivors use a generic self-management model. The goal of this study was implemented and evaluated with a new prototype called “CIMIDx”, based on representative association rules that support the diagnosis of medical images (mammograms).MethodsThe proposed Cloud-Based System Support Intelligent Medical Image Diagnosis (CIMIDx) prototype includes two modules. The first is the design and development of the CIMIDx training and test cloud services. Deployed in the cloud, the prototype can be used for diagnosis and screening mammography by assessing the cancers detected, tumor sizes, histology, and stage of classification accuracy. To analyze the prototype’s classification accuracy, we conducted an experiment with data provided by clients. Second, by monitoring cloud server requests, the CIMIDx usage statistics were recorded for the cloud-based self-intervention groups. We conducted an evaluation of the CIMIDx cloud service usage, in which browsing functionalities were evaluated from the end-user’s perspective.ResultsWe performed several experiments to validate the CIMIDx prototype for breast health issues. The first set of experiments evaluated the diagnostic performance of the CIMIDx framework. We collected medical information from 150 breast cancer survivors from hospitals and health centers. The CIMIDx prototype achieved high sensitivity of up to 99.29%, and accuracy of up to 98%. The second set of experiments evaluated CIMIDx use for breast health issues, using t tests and Pearson chi-square tests to assess differences, and binary logistic regression to estimate the odds ratio (OR) for the predictors’ use of CIMIDx. For the prototype usage statistics for the same 150 breast cancer survivors, we interviewed 114 (76.0%), through self-report questionnaires from CIMIDx blogs. The frequency of log-ins/person ranged from 0 to 30, total duration/person from 0 to 1500 minutes (25 hours). The 114 participants continued logging in to all phases, resulting in an intervention adherence rate of 44.3% (95% CI 33.2-55.9). The overall performance of the prototype for the good category, reported usefulness of the prototype (P=.77), overall satisfaction of the prototype (P=.31), ease of navigation (P=.89), user friendliness evaluation (P=.31), and overall satisfaction (P=.31). Positive evaluations given by 1...

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