AWSQ: an approximated web server queuing algorithm for heterogeneous web server cluster

With the rising popularity of web-based applications, the primary and consistent resource in the infrastructure of World Wide Web are cluster-based web servers. Overtly in dynamic contents and database driven applications, especially at heavy load circumstances, the performance handling of clusters is a solemn task. Without using efficient mechanisms, an overloaded web server cannot provide great performance. In clusters, this overloaded condition can be avoided using load balancing mechanisms by sharing the load among available web servers. The existing load balancing mechanisms which were intended to handle static contents will grieve from substantial performance deprivation under database-driven and dynamic contents. The most serviceable load balancing approaches are Web Server Queuing (WSQ), Server Content based Queue (QSC) and Remaining Capacity (RC) under specific conditions to provide better results. By Considering this, we have proposed an approximated web server Queuing mechanism for web server clusters and also proposed an analytical model for calculating the load of a web server. The requests are classified based on the service time and keep tracking the number of outstanding requests at each webserver to achieve better performance. The approximated load of each web server is used for load balancing. The investigational results illustrate the effectiveness of the proposed mechanism by improving the mean response time, throughput and drop rate of the server cluster

Is agricultural engagement associated with lower incidence or prevalence of cardiovascular diseases and cardiovascular disease risk factors? A systematic review of observational studies from low- and middle-income countries

Non-communicable diseases, such as cardiovascular diseases (CVDs), diabetes and cancer account for more than half of the global disease burden, and 75% of related deaths occur in low- and middle-income countries (LMICs). Despite large regional variations in CVD incidence and prevalence, CVDs remain the leading causes of death worldwide. With urbanisation, developing nations are undergoing unprecedented labour-force transitions out of agriculture and into types of non-agricultural employment, mainly in the industry and service sectors. There are few studies on the effect of these transitions on CVDs and CVD risk factors in LMICs. We systematically searched MEDLINE, PubMed, EMBASE and the Cochrane Library from January 1950 to January 2017 to assess the association of engaging in agriculture compared to types of non-agricultural employment (e.g. services and manufacturing) with CVD incidence, prevalence and risk factors. Studies were included if they: included participants who engaged in agriculture and participants who did not engage in agriculture; measured atherosclerotic CVDs or their modifiable risk factors; and involved adults from LMICs. We assessed the quality of evidence in seven domains of each study. Prevalence ratios with 95% confidence intervals were calculated and compared in forest plots across studies. Study heterogeneity did not permit formal meta-analyses with pooled results. There was a lack of publications on the primary outcomes, atherosclerotic CVDs (n = 2). Limited evidence of varying consistency from 13 studies in five countries reported that compared with non-agricultural workers, mainly living in urban areas, rural agriculture workers had a lower prevalence of hypertension, overweight and obesity; and a higher prevalence of underweight and smoking. High quality evidence is lacking on the associations of engaging in and transitioning out of agriculture with atherosclerotic CVDs and their modifiable risk factors in LMICs. There is a need for interdisciplinary longitudinal studies to understand associations of types of employment and labour-force transitions with CVD burdens in LMICs

An Intrusion Detection Using Machine Learning Algorithm Multi-Layer Perceptron (MlP): A Classification Enhancement in Wireless Sensor Network (WSN)

During several decades, there has been a meteoric rise in the development and use of cutting-edge technology. The Wireless Sensor Network (WSN) is a groundbreaking innovation that relies on a vast network of individual sensor nodes. The sensor nodes in the network are responsible for collecting data and uploading it to the cloud. When networks with little resources are deployed harshly and without regulation, security risks occur. Since the rate at which new information is being generated is increasing at an exponential rate, WSN communication has become the most challenging and complex aspect of the field. Therefore, WSNs are insecure because of this. With so much riding on WSN applications, accuracy in replies is paramount. Technology that can swiftly and continually analyse internet data streams is essential for spotting breaches and assaults. Without categorization, it is hard to simultaneously reduce processing time while maintaining a high level of detection accuracy. This paper proposed using a Multi-Layer Perceptron (MLP) to enhance the classification accuracy of a system. The proposed method utilises a feed-forward ANN model to generate a mapping for the training and testing datasets using backpropagation. Experiments are performed to determine how well the proposed MLP works. Then, the results are compared to those obtained by using the Hoeffding adaptive tree method and the Restricted Boltzmann Machine-based Clustered-Introduction Detection System. The proposed MLP achieves 98% accuracy, which is higher than the 96.33% achieved by the RBMC-IDS and the 97% accuracy achieved by the Hoeffding adaptive tree

A Process for Co-Designing Educational Technology Systems for Refugee Children

There is a growing interest in the potential for technology to facilitate emergency education of refugee children. However, designing in this space requires knowledge of the displaced population and the contextual dynamics surrounding it. Design should therefore be informed by both existing research across relevant disciplines, and from the practical experience of those who are on the ground facing the problem in real life. This paper describes a process for designing appropriate technology for these settings. The process draws on literature from emergency education, student engagement and motivation, educational technology, and participatory design. We emphasise a thorough understanding of the problem definition, the nature of the emergency, and of socio-cultural aspects that can inform the design process. We describe how this process was implemented leading to the design of a digital learning space for children living in a refugee camp in Greece. This drew on involving different groups of participants such as social-workers, parents, and children

Pilot studies on GP Crop yield estimation using Technology (Kharif 2019) using SENTINEL- 2 satellite data (in Andhra Pradesh, Telangana and Odisha States (Five Districts)) for Groundnut, Chickpea, Maize and Rice

The Government of India plans to optimize Crop Cutting Experiments (CCEs) using different technologies including satellite derived metrics on crop performance and spatial variability to guide the selection and number of ground data sites. This requires the development of an approach for different crops for the different agro-climatic regions of India. The present study plans to develop an approach for following crops viz., Groundnut, Chickpea, Rice and Maize. The above crops will be studied in five districts of three states viz. Andhra Pradesh, Telangana and Odisha. The study will use comprehensive and existing environmental, weather and management data along with satellite derived crop spatial data. This information will be modelled using statistical optimization techniques to assess the optimal numbers of CCE’s that can be undertaken

Understanding Pathways Between Agriculture, Food Systems, and Nutrition: An Evidence and Gap Map of Research Tools, Metrics, and Methods in the Last 10 Years.

New tools, metrics, and methods in agriculture, food systems, and nutrition (A&N) research proliferated in the decade following the 2007-2008 food price crisis. We map these developments across themes derived from conceptual A&N pathways and expert consultations. We created an interactive Evidence and Gap Map (EGM) from a systematic search of published and gray literature since 2008, following Campbell Collaboration guidelines. We retrieved over 30,000 reports from published literature databases, and individually searched 20 online repositories. We systematically screened 24,359 reports by title and/or abstract, 1577 by full report, and included 904 eligible reports. The EGM consists of rows of thematic domains and columns of types of tools, metrics, and methods, as well as extensive coding applied as filters. Each cell of the map represents research surrounding a type of tool, metric, or method within a given theme. Reports in each cell are grouped by stage of development, which expand to a corresponding bibliography. Users can filter EGM reports by various characteristics. The 4 most populated domains were: diets, nutrition, and health; primary food production; water, sanitation, and hygiene; and environment and sustainability. The 4 most common types of metrics, methods, and tools were: diet metrics; footprint analysis (especially water); technology applications; and network or Bayesian analysis. Gaps represent areas of few or no reports of innovation between 2008 and 2018. There were gaps in reports and innovations related to: power or conflicts of interest; food environments; markets; private sector engagement; food loss and waste; conflict; study design and system-level tools, metrics, and methods. The EGM is a comprehensive tool to navigate advances in measurement in A&N research: to highlight trends and gaps, conduct further synthesis and development, and prioritize the agenda for future work. This narrative synthesis accompanies the EGM, which can be found at https://www.anh-academy.org/evidence-and-gap-map

Assessment of climate change and vulnerability in Indian state of Telangana for better agricultural planning

Climate variability and change pose ever-growing challenges in the semiarid tropics, where majority of the population depend on climate-dependent activities such as agriculture. This has rendered these countries more vulnerable to climate change–induced variability. In spite of the uncertainties about anticipated magnitude of climate change on regional scale, an assessment of the possible changes in key climatic elements to identify most vulnerable locations becomes important for formulating adaptation strategies. This study compiles the existing knowledge about observed climate and projections of future change in Telangana state of India. The agriculture in this semiarid state has to adapt to changes in mean climate variables to increased variability with greater risk of extreme weather events, such as prolonged dry spells. Based on climatic vulnerability assessment, we found that the number of vulnerable mandals (currently 28%) will be increased to 45% during early century and to 59% by mid-century. As per the climate exposure index scores, Jogulamba-Gadwal district was found to be most sensitive. Overall, vulnerability index scores indicated that Adilabad, Nagarkurnool, Nalgonda, Peddapalli, Suryapet, Wanaparthy, and Yadadri are extremely vulnerable districts in the state. The ranking of vulnerable mandals in each district envisages the need for a holistic approach for each mandal or a group of mandals to reduce their sensitivity though implementation of site-specific adaptation strategies to minimize climate-related shocks not only in agriculture but also in other sectors

Identifying irrigation and nitrogen best management practices foraerobic rice–maize cropping system for semi-arid tropics using CERES-rice and maize models

Research based development of best management options for aerobic rice–maize cropping systems must be developed to improve water and nitrogen use efficiency. The main objective of this study was to identify water saving rice production technology for rice grown in sandy loam soils in semi-arid conditions using the calibrated CERES-Rice and Maize models of the Decision Support System for Agro Technology Transfer (DSSAT). A two-year experiment with two different crop establishment methods viz., aerobic rice and flooded rice with four nitrogen rates followed by maize under zero tilled conditions was used to calibrate and evaluate DSSAT CERES-Rice and CERES-Maize models. The calibrated models were used to develop best management options for an aerobic rice–maize sequence which can produce similar yields with water savings relative to that of traditional flooded rice–maize system. The results showed that application of 180 kg N ha−1 in four splits and automatic irrigation with 40 mm, when soil available water (ASW) in top 30 cm fell below to 60% was the best management combination for aerobic rice, saving 41% of water while producing 96% of the yield attainable under flooded conditions. Similarly for maize, application of 120 kg N ha−1 and irrigation with 30 mm of water at 40% ASW in the top 30 cm soil was the most dominant management option. Further, application of 180 kg N ha−1 with rice followed by 120 kg N ha−1 in maize provided stable yield for both aerobic and flooded rice systems over time as simulated by the model. The results illustrate that DSSAT model is a useful tool for evaluating alternative management options aimed at maintaining yields and saving water in rice–maize systems in semi-arid regions