Game on! Lessons learned from joint development and production of health games

In September 2010, Hanze University of Applied Sciences in Groningen (the Netherlands) started a 20-week international program titled GameOn! The object of this program is for students to develop serious games, that aim to help the youth become aware of social and/or health related issues. Since the start of GameOn! students have worked on a number of different projects, all related to education through the use of interactive media. Topics were malaria, hiv/aids and personal hygiene. In all these projects, specific knowledge about the target region, domain knowledge of the subject of the game, and the target group was brought in by specialists and local representatives. The lessons drawn, in development and production, from these projects are: 1. The importance of an agile game development method that allows for regular testing, feedback moments and changes. 2. The importance of a user/player centred design and the context of playful experiences. 3. Cultural awareness in game design and development: consider and adapt to the values and beliefs of the target audience. 4. Collaboration and co-creation with local representatives in game development adds to game acceptance. 5. A very positive attitude towards the use of computers in education in the targeted areas. Addressing and incorporating these aspects into projects may contribute in more effective and adequate (social) health games or, in a broader sense, more effective interactive media applications aimed at facilitating educational learning

Evidence for conservation in antigen gene sequences combined with extensive polymorphism at VNTR loci

Theileria parva is a tick‐transmitted apicomplexan protozoan parasite that infects lymphocytes of cattle and African Cape buffalo (Syncerus caffer), causing a frequently fatal disease of cattle in eastern, central and southern Africa. A live vaccination procedure, known as infection and treatment method (ITM), the most frequently used version of which comprises the Muguga, Serengeti‐transformed and Kiambu 5 stocks of T. parva, delivered as a trivalent cocktail, is generally effective. However, it does not always induce 100% protection against heterologous parasite challenge. Knowledge of the genetic diversity of T. parva in target cattle populations is therefore important prior to extensive vaccine deployment. This study investigated the extent of genetic diversity within T. parva field isolates derived from Ankole (Bos taurus) cattle in south‐western Uganda using 14 variable number tandem repeat (VNTR) satellite loci and the sequences of two antigen‐encoding genes that are targets of CD8+T‐cell responses induced by ITM, designated Tp1 and Tp2. The findings revealed a T. parva prevalence of 51% confirming endemicity of the parasite in south‐western Uganda. Cattle‐derived T. parva VNTR genotypes revealed a high degree of polymorphism. However, all of the T. parva Tp1 and Tp2 alleles identified in this study have been reported previously, indicating that they are widespread geographically in East Africa and highly conserved

A Multinomial DGA Classifier for Incipient Fault Detection in Oil-Impregnated Power Transformers

This study investigates the use of machine-learning approaches to interpret Dissolved Gas Analysis (DGA) data to find incipient faults early in oil-impregnated transformers. Transformers are critical pieces of equipment in transmitting and distributing electrical energy. The failure of a single unit disturbs a huge number of consumers and suppresses economic activities in the vicinity. Because of this, it is important that power utility companies accord high priority to condition monitoring of critical assets. The analysis of dissolved gases is a technique popularly used for monitoring the condition of transformers dipped in oil. The interpretation of DGA data is however inconclusive as far as the determination of incipient faults is concerned and depends largely on the expertise of technical personnel. To have a coherent, accurate, and clear interpretation of DGA, this study proposes a novel multinomial classification model christened KosaNet that is based on decision trees. Actual DGA data with 2912 entries was used to compute the performance of KosaNet against other algorithms with multiclass classification ability namely the decision tree, k-NN, Random Forest, Naïve Bayes, and Gradient Boost. Investigative results show that KosaNet demonstrated an improved DGA classification ability particularly when classifying multinomial data

Game on! Lessons learned from joint development and production of health games

In September 2010, Hanze University of Applied Sciences in Groningen (the Netherlands) started a 20-week international program titled GameOn! The object of this program is for students to develop serious games, that aim to help the youth become aware of social and/or health related issues. Since the start of GameOn! students have worked on a number of different projects, all related to education through the use of interactive media. Topics were malaria, hiv/aids and personal hygiene. In all these projects, specific knowledge about the target region, domain knowledge of the subject of the game, and the target group was brought in by specialists and local representatives. The lessons drawn, in development and production, from these projects are: 1. The importance of an agile game development method that allows for regular testing, feedback moments and changes. 2. The importance of a user/player centred design and the context of playful experiences. 3. Cultural awareness in game design and development: consider and adapt to the values and beliefs of the target audience. 4. Collaboration and co-creation with local representatives in game development adds to game acceptance. 5. A very positive attitude towards the use of computers in education in the targeted areas. Addressing and incorporating these aspects into projects may contribute in more effective and adequate (social) health games or, in a broader sense, more effective interactive media applications aimed at facilitating educational learning

An Efficient LoRa-Enabled Smart Fault Detection and Monitoring Platform for the Power Distribution System Using Self-Powered IoT Devices

Transient stability and supply disturbances are common yet unwelcome phenomena in power distribution systems, particularly in sub-Saharan Africa. The growing demand for greater reliability and dependability in power delivery has aroused the interest of researchers and renewed the pursuit of advanced technological solutions for fault detection and location determination at medium and low-voltage levels. The length of the distribution network typically ranges from hundreds to thousands of kilometers. In this regard, the management of distribution networks, including the identification of faulty segments, is a significant recurrent challenge facing power-system operators. With the ever-expanding distribution network and regulatory demands for service reliability, the challenge for network operators is daunting. However, the deployment of IoT technologies in the energy distribution infrastructure would significantly accelerate the detection and location of faults, thus transforming the electricity delivery service into one that is responsive, communicative, attractive, and robust. This study proposes, designs, and implements a reasonably priced LoRaWAN-based IoT platform for monitoring distribution networks. The study was conducted in Nakuru County, Kenya on an actual and active distribution network owned and managed by Kenya Power Company. Experimental results showed that a trigger is generated at the network-monitoring center in about 100 ms of the occurrence of a fault on the distribution network, thus enabling quick, prompt, and immediate commencement of reparative action. Furthermore, practical evaluation has shown that this platform is well adapted for the context of developing countries where budgetary constraints and cost prohibitions hinder the upgrade of the legacy grid into fully-fledged smart entities

Factors associated with physical violence by a sexual partner among girls and women in rural Kenya

Intimate partner physical violence increases women's risk for negative health outcomes and is an important public health concern. The purpose of the present study was to determine 1) the proportion of girls (≤18 years) and women (>18 years) who experienced physical violence by a sexual partner, and 2) factors (including self-reported HIV infection) associated with girls and women who experienced physical violence by a sexual partner. Cross-sectional surveys conducted in the Gem Health and Demographic Surveillance System (HDSS) area in Siaya County, western Kenya in 2011-2012 (Round 1) and 2013-2014 (Round 2). Among 8003 unique participants (582 girls and 7421 women), 11.6% reported physical violence by a sexual partner in the last 12 months (girls: 8.4%, women: 11.8%). Three factors were associated with physical violence by a sexual partner among girls: being married or cohabiting (nearly 5-fold higher risk), low education, and reporting forced sex in the last 12 months (both with an approximate 2-fold higher risk). Predictive factors were similar for women, with the addition of partner alcohol/drug use and deliberately terminating a pregnancy. Self-reported HIV status was not associated with recent physical violence by a sexual partner among girls or women. Gender-based physical violence is prevalent in this rural setting and has a strong relationship with marital status, low education level, and forced sex among girls and women. Concerted efforts to prevent child marriage and retain girls in school as well as implementation of school and community-based anti-violence programs may help mitigate this risk