Does Revolution Work? Evidence from Nepal’s People’s War

In 2015, after a decade-long conflict and nine years of negotiation, Nepal promulgated a constitution that replaced its 240-year-old monarchy by a federal republic. The subsequent 2017 local elections ushered more than 30,000 first-time politicians into office. Using a census of 3.68 million Nepalis (2.56 million of whom are of voting age) covering eleven districts, party nomination lists and party candidate selection committee surveys, electoral data and information on conflict incidence, we document that castes that were historically excluded from political representation achieved representation without a significant representation-ability trade-off: improved social representation among politicians is accompanied by positive selection on education and income. Triangulating across multiple data sources, we show that the entry of the revolutionary Maoist group as a post-conflict mainstream party played an important role. Finally, political representation of non-elite castes improved their policy inclusion as measured by individual access to earthquake reconstruction transfers. These gains, however, vary with the extent of social connections to the elected mayor and point to a continuing need to balance power by supporting institutions that provide all citizens political voice

Intelligent wireless network selection

WiFi network selection is typically based on the received signal strength of an access point (AP). However, the strongest signal does not necessarily lead to good user experience. For example, a strong signal or SSID may simultaneously attract many WiFi clients, causing congestion. This disclosure utilizes machine learning models trained to intelligently select a wireless access point or an SSID based on multiple factors, e.g., neighboring APs, neighboring clients, historical service information, signal and interference levels, ping jitter, time-of-day, day-of-week, etc. Per the techniques, the selected AP is associated with a best overall score as determined by the machine learning model based on several factors. The selected AP therefore is not necessarily the AP with the strongest signal or geographically nearest to the client device making the selection. User experience is improved by selecting the AP in this manner

Evaluation of the KA24 (Knowledge Access 24) service for health- and social-care staff in London and the south-east of England. Part 2: qualitative

AIMS AND OBJECTIVES: The aim of this two-part paper is to identify the main transferable lessons learned from both the quantitative and qualitative evaluations of the KA24 (Knowledge Access 24) service of online databases and selected full text journals for health and social care staff in London and the South-East of England. The objectives of the qualitative evaluation were to assess the enablers and barriers to usage, and to assess the impact of the service on patient care. METHODS: Telephone interviews (n=65) and a questionnaire survey (n=296) were conducted with various types of user, in various Trust settings. Some non-users were also contacted. Selection of interviewees and questionnaire recipients was not random, and aimed to cover all groups of users representatively. RESULTS: Results show that policy goals were being delivered, with indications of changes to clinical practice, and improved clinical governance. Promotion, training and support needs to be extensive, and tailored to needs, but users are not always aware they need training. The sharing of passwords cast doubts on the reliability of some usage data. CONCLUSIONS: Digital health library services, delivered at the point of care, are changing the way some clinicians practise. A combination of qualitative and quantitative evaluation methods are needed to assess digital library services

Quality of Service Oriented Access Point Selection Framework for Large Wi-Fi Networks

This paper addresses the problem of Access Point (AP) selection in large Wi-Fi networks. Unlike current solutions that rely on Received Signal Strength (RSS) to determine the best AP that could serve a wireless user’s request, we propose a novel framework that considers the Quality of Service (QoS) requirements of the user’s data flow. The proposed framework relies on a function reflecting the suitability of a Wi-Fi AP to satisfy the QoS requirements of the data flow. The framework takes advantage of the flexibility and centralised nature of Software Defined Networking (SDN). A performance comparison of this algorithm developed through an SDN-based simulator shows significant achievements against other state of the art solutions in terms of provided QoS and improved wireless network capacity

Factors influencing the adoption of improved vegetable production practices among women farmers in Aguata-Agricultural Zone, Anambra State, Nigeria

The study examined the level and determinants of adoption of improved vegetable production practices in the study area. A three-stage sampling procedure was used in the selection of 160 respondents. A structured questionnaire was used for data collection. Data collected were analysed using descriptive statistics and Probit regression model. Results on adoption level of the selected improved vegetable production practices indicated that improved seeds had a grand mean of ̅ = 3.17, field preparation (̅ = 3.19), planting distance (̅ = 2.99), water management (̅ = 3.43), fertilizer/organic manure application (̅ = 3.55), pesticides (̅ = 2.57), harvesting ( ̅= 5.00) and storage procedure (̅ = 4.89) based on 5-point Likert scale adoption level. On factors influencing adoption of improved vegetable production practices, the result revealed that farm size (1.00188***), credit access (4.704902**), on-farm demonstration (2.900749**) and farm labour (1.295902***) had a positive and significant influence on improved vegetable production practices by the respondents. The result further indicated that the age (0.3135258***) and the off-farm income (0.0870768) of the farmers had a negative influence on the adoption of improved vegetable production practices. Based on these findings, the study revealed that the women farmers could have full adoption of the improved production practices if the factors are adequately addressed. The study therefore recommends that before the introduction of a new technology, the ADPs should ensure that maximum audience analysis is carried out to address some of the factors influencing adoption. Besides, introduction of new technologies to farmers should go hand-in-hand with on-farm demonstration since it is by that they would develop confidence and allay their fears associated with improved practices

The poverty impacts of improved cowpea varieties in Nigeria: a counterfactual analysis

Open Access Article; Published online: 12 June 2019Adoption of improved agricultural technologies has long been recognized as critical for reducing poverty through increased productivity, incomes, and asset accumulation. Using a nationally representative survey data from a sample of over 1500 households in Nigeria, this paper evaluates the impacts of adoption of improved cowpea varieties on income and asset poverty reduction using an endogenous switching regression model. The results showed that adoption of improved cowpea varieties increased per capita household income and asset ownership by 17 and 24 percentage points, respectively. The results based on the observed and counterfactual income and asset distributions further showed that adoption reduced both income poverty and asset poverty by 5 percentage points. The paper concludes with a discussion of the policy options for increasing adoption and impacts of improved cowpea varieties in Nigeria

Fecal contamination of drinking-water in low- and middle-income countries: a systematic review and meta-analysis

Background: access to safe drinking-water is a fundamental requirement for good health and is also a human right. Global access to safe drinking-water is monitored by WHO and UNICEF using as an indicator “use of an improved source,” which does not account for water quality measurements. Our objectives were to determine whether water from “improved” sources is less likely to contain fecal contamination than “unimproved” sources and to assess the extent to which contamination varies by source type and setting.Methods and findings: studies in Chinese, English, French, Portuguese, and Spanish were identified from online databases, including PubMed and Web of Science, and grey literature. Studies in low- and middle-income countries published between 1990 and August 2013 that assessed drinking-water for the presence of Escherichia coli or thermotolerant coliforms (TTC) were included provided they associated results with a particular source type. In total 319 studies were included, reporting on 96,737 water samples. The odds of contamination within a given study were considerably lower for “improved” sources than “unimproved” sources (odds ratio [OR] = 0.15 [0.10–0.21], I2 = 80.3% [72.9–85.6]). However over a quarter of samples from improved sources contained fecal contamination in 38% of 191 studies. Water sources in low-income countries (OR = 2.37 [1.52–3.71]; p<0.001) and rural areas (OR = 2.37 [1.47–3.81] p<0.001) were more likely to be contaminated. Studies rarely reported stored water quality or sanitary risks and few achieved robust random selection. Safety may be overestimated due to infrequent water sampling and deterioration in quality prior to consumption.Conclusion: access to an “improved source” provides a measure of sanitary protection but does not ensure water is free of fecal contamination nor is it consistent between source types or settings. International estimates therefore greatly overstate use of safe drinking-water and do not fully reflect disparities in access. An enhanced monitoring strategy would combine indicators of sanitary protection with measures of water qualit

Improved detection of Probe Request Attacks : Using Neural Networks and Genetic Algorithm

The Media Access Control (MAC) layer of the wireless protocol, Institute of Electrical and Electronics Engineers (IEEE) 802.11, is based on the exchange of request and response messages. Probe Request Flooding Attacks (PRFA) are devised based on this design flaw to reduce network performance or prevent legitimate users from accessing network resources. The vulnerability is amplified due to clear beacon, probe request and probe response frames. The research is to detect PRFA of Wireless Local Area Networks (WLAN) using a Supervised Feedforward Neural Network (NN). The NN converged outstandingly with train, valid, test sample percentages 70, 15, 15 and hidden neurons 20. The effectiveness of an Intruder Detection System depends on its prediction accuracy. This paper presents optimisation of the NN using Genetic Algorithms (GA). GAs sought to maximise the performance of the model based on Linear Regression (R) and generated R > 0.95. Novelty of this research lies in the fact that the NN accepts user and attacker training data captured separately. Hence, security administrators do not have to perform the painstaking task of manually identifying individual frames for labelling prior training. The GA provides a reliable NN model and recognises the behaviour of the NN for diverse configurations