Geospatial assessment of agricultural drought vulnerability using integrated three-dimensional model in the upper Dwarakeshwar river basin in West Bengal, India.

The amount of agricultural drought vulnerability in an underdeveloped rain-fed agro-based economy at the local, regional, and national level is most prominent factor for measurement. The desiccation of rain in agricultural sector becomes apprehensive to intercontinental food supply chain. So, adequate investigation and development of sustainable agricultural methodology are key factors to sustain the food security of a territory. In this research, delineation of agricultural drought vulnerability (ADV) status has been carried out by multidimensional mixed-method index approach using remote sensing and geographic information system. An integrated three-dimensional model is utilized to enrich this study. The three indices of this model include exposure index (EI), sensitivity index (SI), and adaptive capacity index (ACI). The ACI has been constructed by combining the environmental adaptive capacity (EAC), social adaptive capacity (SAC), and economic adaptive capacity (EcAC) index. The 40 parameters for ADV modeling are picked up by analyzing meteorological, geo-environmental, social, and remote sensing data. There are six exposure parameters, seven sensitivity parameters, twelve environmental adaptive capacity parameters, six social adaptive capacity parameters, and nine economic adaptive capacity parameters. Each index has been computed by assigning the weights based on their relative importance by using the analytic hierarchy process (AHP) approach. Final results were classified into five vulnerability zones, e.g., very low, low, moderate, high, and very high covering an area 362.32 km2, 186.68 km2, 568.69 km2, 547.05 km2, and 266.89 km2 respectively. Results have been validated with long-term Aman paddy yield data (2004 to 2014) through the yield anomaly index (YAI). Finally, the model ADV is a good model fit (R square = 0.894) and all the relationships were found significant, when SI, EI, and ACI are considered its predictors. While SI (B = 0.391, p < 0.001) and EI (B = 0.223, p < 0.001) are positively associated with ADV, ACI is negatively associated with ADV (B = - 0.721, p < 0.001). This regional agricultural drought vulnerability model can be useful to identify drought-responsive areas and improve drought mitigation measures.

A Joint Resource Allocation, Security with Efficient Task Scheduling in Cloud Computing Using Hybrid Machine Learning Techniques.

The rapid growth of cloud computing environment with many clients ranging from personal users to big corporate or business houses has become a challenge for cloud organizations to handle the massive volume of data and various resources in the cloud. Inefficient management of resources can degrade the performance of cloud computing. Therefore, resources must be evenly allocated to different stakeholders without compromising the organization's profit as well as users' satisfaction. A customer's request cannot be withheld indefinitely just because the fundamental resources are not free on the board. In this paper, a combined resource allocation security with efficient task scheduling in cloud computing using a hybrid machine learning (RATS-HM) technique is proposed to overcome those problems. The proposed RATS-HM techniques are given as follows: First, an improved cat swarm optimization algorithm-based short scheduler for task scheduling (ICS-TS) minimizes the make-span time and maximizes throughput. Second, a group optimization-based deep neural network (GO-DNN) for efficient resource allocation using different design constraints includes bandwidth and resource load. Third, a lightweight authentication scheme, i.e., NSUPREME is proposed for data encryption to provide security to data storage. Finally, the proposed RATS-HM technique is simulated with a different simulation setup, and the results are compared with state-of-art techniques to prove the effectiveness. The results regarding resource utilization, energy consumption, response time, etc., show that the proposed technique is superior to the existing one.

Association of high-density lipoprotein, triglycerides, and homocysteine with bone mineral density in young Indian tribal women.

This study investigated association between lipids and homocysteine (Hcy) with bone mineral density (BMD) in young women as opposed to previous studies on elderly women. HDL, triglyceride, and Hcy are significantly associated with BMD in young women and tobacco and alcohol consumption have no effect on this association. PURPOSE: The present study investigates whether the association of serum lipids and homocysteine (Hcy) with bone mineral density (BMD) reported mostly in elderly population can be generalized to young or premenopausal women, consequently suggesting screening of young women with low BMD for dyslipidemia or any cardiovascular events and vice versa. METHODS: Women (n = 293, aged 20-47 years) from Northeast India belonging to Tibeto-Burman origin were enrolled. Information about their physical and clinical attributes were collected by a structured questionnaire. Their BMDs at lumbar spine and femur were measured by dual-energy X-ray absorptiometry (DXA) and sera were profiled for lipid parameters and Hcy by auto-analyzer and ELISA, respectively. Women consuming tobacco and/or alcohol were grouped as consumers and others as non-consumers for the analysis. RESULTS: Positive correlation of BMD with HDL (spine and femur r = 0.38, p < 0.0001) and triglyceride (spine r = 0.534, p < 0.0001; femur r = 0.423, p < 0.0001) was observed, whereas Hcy correlated negatively with BMD (spine r = - 0.189, p = 0.0026; femur r = - 0.273, p < 0.0001). LDL showed a weak negative correlation with BMD (spine r = - 0.128, p = 0.0283; femur r = - 0.199, p = 0.0006). However, after adjusting for age, BMI, and consumption, HDL, triglyceride, and Hcy continued to show significant correlation with BMD at both the sites. Logistic regression analyses indicated that HDL, triglyceride, and Hcy were significant predictors of osteopenia and osteoporosis in our study cohort; however, consumption did not contribute to its prediction. CONCLUSION: Low levels of HDL and triglyceride and high levels of Hcy are significantly associated with osteopenia and osteoporosis in young Northeast Indian women.