Assessing the Impact of a Dam on the Livelihood of Surrounding Communities: A Case Study of Vea Dam in the Upper East Region of Ghana

River basins are well known as the origin of advanced human social development and cultural heritage which ancient and modern communities have depended on for livelihood, commerce and habitat. Dam is one of the many man-made alterations to river basins that have been built for centuries and without doubt have contributed to the development of many nations. However, their social, health and environmental impacts have in too many cases not been assessed most often in developing countries. This research sought to explore and understand the Vea Dam within the context of socio-economic and health impacts on the host communities. Secondary data were collected from Irrigation Company of Upper Region and Bongo District Assembly in Ghana whilst primary data were obtained through random and stratified sampling. The results revealed that 2.6% and 66% of the respondents are employed in the fishery sector and irrigation sector, respectively. The dam necessitated the relocation of about 34% of the communities and on the average two people are drown annually in the Dam. The prevalence of water borne diseases after the construction of the Dam was also perceived by the communities to have increased. The Dam has both positive and negative socio-economic and health impacts on the surrounding communities with the benefits outweighing the negative impacts. The availability of potable drinking water in the area has created development with inevitable rise in standard of living. The study also revealed that agricultural activities, freshwater fishery and availability of water for irrigation of farmlands have improved. However, an enhancement in the beneficial impacts and minimization of the adverse impacts would help better livelihood in the communities. Keywords: Bongo District, Impacts of a dam, Livelihood, Socio-economic, Vea Da

Selecting appropriate machine learning classifiers for DGA diagnosis

Dissolved gas analysis (DGA) is a common method of assessing transformer health. There are a number of machine learning classifiers reported to give a high accuracy on specific datasets, such as Artificial Neural Networks or Support Vector Machines. When these methods reach the same conclusion about the type of fault present, this can give an increased confidence in the veracity of the diagnosis. However, it is critical to analyze and quantify the strength of these classifiers in the presence of conflicting data to test their practicality for usage in the field. This paper investigates the adequacy of different machine learning based DGA diagnosis models in the presence of conflicting data. The proposed method will aid engineers with the selection of machine learning models so as to maximize the usability and accuracy in the presence of conflicting data

RELIGIOSITY AND CONSUMER BEHAVIOR: A STUDY OF CONSUMPTION PATTERNS FOR ALCOHOLIC AND NON-ALCOHOLIC BEVERAGES AMONG ANIMIST, CHRISTIAN AND MUSLIM CONSUMERS IN THE CONTEXT OF GHANA

Researchers' interest in consumer religiosity and behavior is explained by the fact that religion influences not only the social behavior of individuals, but also their consumption behavior. Most of the studies on the subject come from Western and Asian countries with a few of such studies been conducted in Africa and particularly in Ghana. The aim of this paper is to explore the concepts of religiosity and consumer behavior in Ghana, in order to consider the role of culture in the management and marketing of industrial products. Ghana is a country where religion plays an important role in shaping lives and ensuring community cohesion. However, a determined part of the believers contributes to increasing the consumption of industrial beverages, and the obliviousness in the marketing sector also seems to be a barrier that slows the production and consumption of non-alcoholic industrial beverages. The research approach is exploratory and qualitative. The collection of qualitative data is done with the aid of a SONY voice recorder through some semi-structured interviews. Then, the qualitative data are transcribed manually and verbatim analyzed. The results show that in the context of Ghana, religiosity of believers affects the behavior of the consumer and that consumer behavior towards non-alcoholic industrial beverages affects religiosity. Keywords: Religiosity, Consumer Behavior, Industrial Beverages, Consumption, Marketing, Ghana

Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing

Accurate diagnosis of power transformers is critical for the reliable and cost-effective operation of the power grid. Presently there are a range of methods and analytical models for transformer fault diagnosis based on dissolved gas analysis. However, these methods give conflicting results and they are not able to generate uncertainty information associated with the diagnostics outcome. In this situation it is not always clear which model is the most accurate. This paper presents a novel multiclass probabilistic diagnosis framework for dissolved gas analysis based on Bayesian networks and hypothesis testing. Bayesian network models embed expert knowledge, learn patterns from data and infer the uncertainty associated with the diagnostics outcome, and hypothesis testing aids in the data selection process. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset and is shown to have a maximum diagnosis accuracy of 88.9%

Determining appropriate data analytics for transformer health monitoring

Transformers are vital assets for the safe, reliable and cost-effective operation of nuclear power plants. The unexpected failure of a transformer can lead to different consequences ranging from a lack of export capability, with the corresponding economic penalties, to catastrophic failure, with the associated health, safety and economic effects. Condition monitoring techniques examine the health of the transformer periodically, with the aim to identify early indicators of anomalies. However, many transformer failures occur because diagnostic and monitoring models do not identify degraded conditions in time. Therefore, health monitoring is an essential component to transformer lifecycle management. Existing tools for transformer health monitoring use traditional dissolved gas analysis based diagnostics techniques. With the advance of prognostics and health management (PHM) applications, we can enhance traditional transformer health monitoring techniques using PHM analytics. The design of an appropriate data analytics system requires a multi-stage design process including: (i) specification of engineering requirements; (ii) characterization of existing data sources and analytics to identify complementary techniques; (iii) development of the functional specification of the analytics suite to formalize its behavior, and finally (iv) deployment, validation, and verification of the functional requirements in the final platform. Accordingly, in this paper we propose a transformer analytics suite which incorporates anomaly detection, diagnostics, and prognostics modules in order to complement existing tools for transformer health monitoring

Improving the accuracy of transformer DGA diagnosis in the presence of conflicting evidence

Transformers are critical assets for the reliable and cost-effective operation of the power grid. Transformers may fail if condition monitoring does not identify degraded conditions in time. Dissolved Gas Analysis (DGA) focuses on the examination of the dissolved gasses in the transformer oil and there exist different methods for transformer fault diagnosis based on different analyses of the gassing levels. However, these methods can give conflicting results, and it is not always clear which model is most accurate in a given situation. This paper presents a novel evidence combination framework for DGA based on Bayesian networks. Bayesian network models embed expert knowledge along with learned data patterns and evidence combination which aids in the consistency of analysis. The effectiveness of the proposed framework is validated using the IEC TC 10 dataset with a maximum diagnosis accuracy of 88.3%