Hybrid Energy-Based Chilling System for Food Preservation in Remote Areas

The milk processing and preservation is a fast growing business in developing countries and it is facing problems due to high energy cost and environmental concerns in using conventional energy sources. Since the selection of feedstock and conversion technologies, appropriate research must implement renewable energy-based technologies to promote a constant flow of energy services. In this chapter, the focus is on implementing cooling technologies, using locally available energy sources such as biomass, biogas, gobar gas, which is going to be popular in the near future. The renewable energy sources can be used alone or in combination to run the generator of the vapor absorption system. Sufficient study is not available for hybrid energy systems, with the combination of locally available energy sources, focused in this study. Therefore a systematic analysis is needed to find the appropriate mixing of various renewable energy sources to meet the cooling requirements in any region to implement the complete renewable energy-based cooling system. The effect of variations in the combination of renewable energy sources on the overall system COP has been studied. Based on the maximum system performance and best economic performance, suitable combinations that can be preferred in various regions are predicted

Prevalence of Anemia among Adolescent Girls in Rural Area of a District of Maharashtra

Introduction: Nutritional anemia is one of India’s major public health problems. Adolescence is a vulnerable period in the human life cycle for the development of nutritional anemia. Anemia in adolescent girls contributes to maternal and foetal mortality and morbidity in future. Aim and Objectives: To estimate the prevalence of anemia among adolescent girls and to study the sociodemographic factors associated with anemia. Method: It was a community based cross sectional study in 10 villages of a district. 420 adolescent girls were interviewed using a predesigned, pretested questionnaire, and their anemic status was assessed by hemoglobin estimation. Results were analyzed by using percentage, proportion and Chi-square test, with the help of Microsoft Excel 2007 and SPSS version 20.0 statistical software. Result: Mean age of the study sample was 14.01 ± 2.57 years. The majority (64.8%) of the girls were Hindu by religion and belonged to a nuclear family (53.6%). 45.2 % were educated up to high school level. Most of the girls belonged to socioeconomic class IV (46.0%). The prevalence of anemia in this study was found to be 65.7%. The prevalence of mild and moderate anemia among study participants was 32.6 and 29.8%, respectively. A statistically significant association was found between the prevalence of anemia with age group, educational status of both father and mother, and status of attainment of menarche (p<0.05). Conclusion and Recommendation: The prevalence of anemia among adolescent girls was very high; therefore, attempts must be made to sensitize adolescents and their parents through health and nutrition education, information, education, and communication (IEC), and appropriate behavioral change communication (BCC) activities

A Robust Deep Model for Improved Categorization of Legal Documents for Predictive Analytics

Predictive legal analytics is a technology used to predict the chances of successful and unsuccessful outcomes in a particular case. Predictive legal analytics is performed through automated document classification for facilitating legal experts in their classification of court documents to retrieve and understand the details of specific legal factors from legal judgments for accurate document analysis. However, extracting these factors from legal texts document is a time-consuming process. In order to facilitate the task of classifying documents, a robust method namely Distributed Stochastic Keyword Extraction based Ensemble Theil-Sen Regressive Deep Belief Reweight Boost Classification (DSKE-TRDBRBC) is proposed. The DSKE-TRDBRBC technique consists of two major processes namely Keyword Extraction and Classification. At first, the t-distributed stochastic neighbor embedding technique is applied to DSKE-TRDBRBC for keyword extraction. This in turn minimizes the time consumption for document classification. After that, the Ensemble Theil-Sen Regressive Deep Belief Reweight Boosting technique is applied for document classification. The Ensemble boosting algorithm initially constructs’ set of Theil-Sen Regressive Deep Belief neural networks to classify the input legal documents. Then the results of the Deep Belief neural network are combined to built a strong classifier by reducing the error. This aids in improving the classification accuracy. The proposed method is experimentally evaluated with various metrics such as F-measure , recall, accuracy, precision, , and computational time. The experimental results quantitatively confirm that the proposed DSKE-TRDBRBC technique achieves better accuracy with lowest computation time as compared to the conventional approaches

Development of an efficient trap for lobster fishing

A modified trap was developed for fishing spiny lobsters. Experimental fishing was conducted using this trap along with traditional trap (as control) to assess the comparative efficiency. Design details and comparative efficiency of the modified trap is reported in this paper. From the analysis of variance, the difference in average catches between the modified trap and the control is found to be highly significant establishing the high efficiency of new trap

Network Traffic Behavioral Analytics for Detection of DDoS Attacks

As more organizations and businesses in different sectors are moving to a digital transformation, there is a steady increase in malware, facing data theft or service interruptions caused by cyberattacks on network or application that impact their customer experience. Bot and Distributed Denial of Service (DDoS) attacks consistently challenge every industry relying on the internet. In this paper, we focus on Machine Learning techniques to detect DDoS attack in network communication flows using continuous learning algorithm that learns the normal pattern of network traffic, behavior of the network protocols and identify a compromised network flow. Detection of DDoS attack will help the network administrators to take immediate action and mitigate the impact of such attacks. DDoS attacks are costing enterprises anywhere between $50,000 to$2.3 million per year. We performed experiments with Intrusion Detection Evaluation Dataset (CICIDS2017) available from Canadian Institute for Cybersecurity to detect anomalies in network traffic. We use flow based traffic characteristics to analyze the difference in pattern between normal vs anomaly packet.We evaluate several supervised classification algorithms using metrics like maximum detection accuracy, lowest false negatives prediction, time taken to train and run. We prove that decision tree based Random Forest is the most promising algorithm whereas Dense Neural network performs equally well on certain DDoS types but require more samples to improve the accuracy of low sampled attacks

CHARM, a gender equity and family planning intervention for men and couples in rural India: protocol for the cluster randomized controlled trial evaluation.

BackgroundGlobally, 41% of all pregnancies are unintended, increasing risk for unsafe abortion, miscarriage and maternal and child morbidities and mortality. One in four pregnancies in India (3.3 million pregnancies, annually) are unintended; 2/3 of these occur in the context of no modern contraceptive use. In addition, no contraceptive use until desired number and sex composition of children is achieved remains a norm in India. Research shows that globally and in India, the youngest and most newly married wives are least likely to use contraception and most likely to report husband's exclusive family planning decision-making control, suggesting that male engagement and family planning support is important for this group. Thus, the Counseling Husbands to Achieve Reproductive Health and Marital Equity (CHARM) intervention was developed in recognition of the need for more male engagement family planning models that include gender equity counseling and focus on spacing contraception use in rural India.Methods/designFor this study, a multi-session intervention delivered to men but inclusive of their wives was developed and evaluated as a two-armed cluster randomized controlled design study conducted across 50 mapped clusters in rural Maharashtra, India. Eligible rural young husbands and their wives (N = 1081) participated in a three session gender-equity focused family planning program delivered to the men (Sessions 1 and 2) and their wives (Session 3) by village health providers in rural India. Survey assessments were conducted at baseline and 9&18 month follow-ups with eligible men and their wives, and pregnancy tests were obtained from wives at baseline and 18-month follow-up. Additional in-depth understanding of how intervention impact occurred was assessed via in-depth interviews at 18 month follow-up with VHPs and a subsample of couples (n = 50, 2 couples per intervention cluster). Process evaluation was conducted to collect feedback from husbands, wives, and VHPs on program quality and to ascertain whether program elements were implemented according to curriculum protocols. Fidelity to intervention protocol was assessed via review of clinical records.DiscussionAll study procedures were completed in February 2015. Findings from this work offer important contributions to the growing field of male engagement in family planning, globally.Trial registrationClinicalTrial.gov, NCT01593943