Tiger Nut (Cyperus Esculentus): Composition, Products, Uses and Health Benefits - A Review

This paper is a review on little history and the composition of Tigernut ranging from proximate, mineral and amino acid  content. The paper further explains the kind of phytochemicals and antinutrients that can be found in tigernut .The kind of microorganisms which could be found on tigernut was also explained based on the previous works of researchers. Tigernut can also be eaten raw, processed in to flour and be used for different purposes such as bread and substitute in animal feed manufacture. Oil can also be obtained from tigernut, which is highly unsaturated and good for the health of humans. Tigernut can be used to produce drink/milk, which can serve as substitute of traditional cow milk, different types of tigernut milk are also produced, it can also be used to produce a local snack “Dakuwa”. Tigernut also contributes to the reduction of cholesterol, it reduces the risk of coronary heart disease, arteriosclerosis and is recommended for those who have heavy digestion, flatulence and dysentery.Keywords: Tigernut, Composition, Milk, Flour, Oi

Relevance of Sustainable Community Education for Entrepreneurship Development in Nigerian Rural Communities

Community education is an education process that enables members of a community to identify their needs/problems and proffer solutions using locally available resources. Rural communities in Nigeria remain the central base of natural resources and producers of raw materials. Most of the necessary prerequisite needed to set up a vibrant entrepreneurial skill acquisition centre for a small scale business is available in the rural communities – land, labour, manpower, resources etc. but the problem lies in lack of awareness and inadequate knowledge or information on how to harness and mobilise these potentials to boost entrepreneurial skills. This paper is a theoretical highlight on the relevance of community education for entrepreneurship development in rural communities. The paper adopts the descriptive research method. Data used were mostly generated from secondary sources such as journals, conference papers, articles, books, websites and other texts etc. The paper highlights the nature of Nigerian rural communities, relevance of community education in entrepreneurship development and the strategies through which entrepreneurship can be enhanced in rural communities. The paper finally recommends among others that entrepreneurship development should be a component of any community education programme especially in rural areas

Review-based collaborative recommender system using deep learning methods

Recommender systems have been widely adopted to assist users in purchasing and increasing sales. Collaborative filtering techniques have been identified to be the most popular methods used for the recommendation system. One major drawback of these approaches is the data sparsity problem, which generally leads to low performances of the recommender systems. Recent development has shown that user review texts can be exploited to tackle the issue of data sparsity thereby improving the accuracy of the recommender systems. However, the problem with existing methods for the review-based recommender system is the use of handcrafted features which makes the system less accurate. Thus, to address the above issue, this study proposed collaborative recommender system models that utilize user textual reviews based on deep learning methods for improving predictive performances of recommender systems. To extract the product aspects to mine users‟ opinion, an aspect extraction method was first developed using a Multi-Channel Convolutional Neural Network. An aspect-based recommender system was then designed by integrating the opinions of users based on the product aspects into the collaborative filtering method for the recommendation process. To further improve the predictive performance, the fine-grained user-item interaction based on the aspect-based collaborative method was studied and a sentiment-aware recommender system was also designed using a deep learning method. Extensive series of experiments were conducted on real-world datasets from the Semeval-014, Amazon, and Yelp reviews to evaluate the performances of the proposed models from both the aspect extraction and rating prediction. Experimental results showed that the proposed aspect extraction model performed better than compared methods such as rule-based and the neural network-based approaches, with average gains of 5.2%, 12.0%, and 7.5% in terms of Precision, Recall, and F1 score, respectively. Meanwhile, the proposed aspect-based collaborative methods demonstrated better performances compared to benchmark approaches such as topic modelling techniques with an average improvement of 6.5% and 8.0% in terms of the Root Means Squared Error (RMSE) and Mean Absolute Error (MAE), respectively. Statistical T-test was conducted and the results showed that all the performance improvements were significant at P<0.05. This result indicates the effectiveness of utilizing the multi-channel convolutional neural network for better extraction accuracy. The findings also demonstrate the advantage of utilizing user textual reviews and the deep learning methods for improving the predictive accuracy in recommendation systems

Aspect extraction on user textual reviews using multi-channel convolutional neural network

Aspect extraction is a subtask of sentiment analysis that deals with identifying opinion targets in an opinionated text. Existing approaches to aspect extraction typically rely on using handcrafted features, linear and integrated network architectures. Although these methods can achieve good performances, they are time-consuming and often very complicated. In real-life systems, a simple model with competitive results is generally more effective and preferable over complicated models. In this paper, we present a multichannel convolutional neural network for aspect extraction. The model consists of a deep convolutional neural network with two input channels: a word embedding channel which aims to encode semantic information of the words and a part of speech (POS) tag embedding channel to facilitate the sequential tagging process. To get the vector representation of words, we initialized the word embedding channel and the POS channel using pretrained word2vec and one-hot-vector of POS tags, respectively. Both the word embedding and the POS embedding vectors were fed into the convolutional layer and concatenated to a one-dimensional vector, which is finally pooled and processed using a Softmax function for sequence labeling. We finally conducted a series of experiments using four different datasets. The results indicated better performance compared to the baseline models

Assessment of the Effects of Flood on Agricultural Land Use in Doma Local Government Area, Nasarawa State, Nigeria

This study aimed at assessing the effects of flood on agricultural land use in Doma Local Government Area, Nasarawa state, Nigeria. In order to achieve the objectives of the study, information on the causes effects frequency and magnitude of flood on agricultural land use were needed from primary and secondary source of data. The primary data were obtained through the use of structured questionnaire, field observation and measurement and ten years rainfall data (2004-2014). The rainfall data were obtained from Nigeria meteorological agency (NIMET), Lafia, Nasarawa state. Rainfall data obtained were used for rainfall trend analysis for the study. The secondary data include information from relevant text such as journals, newspaper dictionary, encyclopaedia textbooks, internet and web and related past students dissertation and thesis. Basic statistical techniques such as the computation of totals, mean, and standard deviation were employed for the analysis of rainfall data. Descriptive statistics were adopted to analyze the result from the questionnaire and simply linear regression analysis were used to determined rainfall trend for this work. The study shows that there has an appreciable effect of flood in the    study area which is due to many factors such as human activities but is greatly influenced by climate. This evident in the study as about 44.1% and 21.8% of the total respondents have been engaged in farming for 21 years and above, and 16 to 20 years respectively and are thereby able to explain clearly the effects they home observed experienced over time. The finding also revealed that 1.76% of the respondents have lost a total 15 hectares of farm land, 30.59% have 416 hectare 23.53 have lost 480 hectares, 14.71% lost 500 histories and 7.06% 600 hectares respective. This indicates a great destruction farm lands in the area and invariably a decrease in food production. The state disaster management and other agencies charges with disaster management in the state level should be properly funded in order to respond to the challenges of sudden of occurrences of natural disaster in future. Keywords: Effect, Floods, Agriculture, Landuse, Devastation DOI: 10.7176/JEES/9-3-08 Publication date:March 31st 201

Evaluation of Environmental Effect of Shiroro Hydropower Dam on the Downstream Communities of Shiroro Local Government Area, Niger State, Nigeria

This study evaluates the environmental effect of Shiroro hydropower dam on the downstream communities. The main objective of this study is to investigate the environmental effects of Shiroro hydropower dam on the downstream communities. A suitable conceptual framework was formulated and obtained data on the environmental impacts of dam on downstream communities, followed by a comprehensive literature review for viable information on the study. Three communities were used as case studies and quantitative method was  used as an appropriate research paradigm such as structured questionnaire survey (with predominantly quantitative questions) and  relevant data was obtained from the study area. Subsequently the data was analysed using descriptive, factor analysis and Kruskal-Wallis test as well construct reliability and validity analysis. From a broad range of environmental impacts, core environmental impacts were determined. The core impacts include changes in riparian vegetation, changes in river water quality, changes to channel shape and changes in floodplains among others. Similarly, control techniques were identified to lessen the effect of the impact and the result revealed that watershed management, water pollution control, management of water releases, fishing regulation, fish hatcheries and fish passage facilities were the core control techniques. The result of Kruskal-Wallis test revealed that majority of the environmental impacts affecting the communities under study is significantly and statistically different while the Cronbach alpha for internal consistencies of the constructs of the questionnaire was 0.745, hence high enough for generalising the result. Keywords: Environment, Effects, Hydroelectric, Dam, Downstream DOI: 10.7176/JEES/9-3-11 Publication date:March 31st 201

Determining Temperature trends in the Granary Areas of Peninsular Malaysia using Mann-Kendall and Sen’s Slope Estimator

The spatiotemporal dynamics of temperature as well as rainfall have received greater attention from the scientific communities This study analysed temperature variability in the three granary areas of Peninsular Malaysia using descriptive statistics parametric least square regression and nonparametric Mann- Kendall and Sen s slope estimator The study identified significant warming trend in the annual mean maximum temperature in two of the study areas i e Subang Jaya and Kota Bharu Also significant warming trend was detected in the annual minimum temperature and significant increasing trend in some of the monthly maximum and minimum temperatures for all the three stations Also the result reveals spatial and temporal variation in both the maximum and minimum temperature at annual monthly and seasonal scales For the annual scale maximum temperature this study identified a warming trend for the two stations with about 0 014oC per year 1 4oC per 100 year

Transition from Land Use/Cover into Urban Expansion in Dutse Metropolis, Jigawa State, Nigeria

The purpose of this research is to analyse the effect of Urban Expansion on Land Use/Cover Change in Dutse, Jigawa State, Nigeria. Landsat TM and ETM+ satellite imageries of 1986, 2000 and 2014 respectively, were used. The imageries were processed using ERDAS IMAGINE 9.2 software. A supervised classification approach using maximum likelihood classifier and Transition Change Analysis were carried out using IDRISSI 17.0 Selva Edition version. Results from the analysis revealed that increase in built-up area resulted to changes in other land use/cover categories between 1986 and 2014. Built up area was 20.4% in 1986 and increased to 28.4% in 2000. Further increase was witnessed in built up area to about 38.2% in 2014. The results further revealed that about 7.2% bare surfaces changed into built-up from 1986-2014. About 14.8% of cultivated land changed into built-up from 1986-2014. Vegetation witnessed a remarkable changes into built-up of about 4.5% from 1986-2014. There is need for Jigawa State Government to equip the planning authorities and other ministries involved in decision making with adequate spatial data to ensure broad based decisions. Land use suitability analysis of the study area is also recommended

High pyrethroid/DDT resistance in major malaria vector Anopheles coluzzii from Niger-Delta of Nigeria is probably driven by metabolic resistance mechanisms

Entomological surveillance of local malaria vector populations is an important component of vector control and resistance management. In this study, the resistance profile and its possible mechanisms was characterised in a field population of the major malaria vector Anopheles coluzzii from Port Harcourt, the capital of Rivers state, in the Niger-Delta Region of Nigeria. Larvae collected in Port-Harcourt, were reared to adulthood and used for WHO bioassays. The population exhibited high resistance to permethrin, deltamethrin and DDT with mortalities of 6.7% ± 2.4, 37.5% ± 3.2 and 6.3% ± 4.1, respectively, but were fully susceptible to bendiocarb and malathion. Synergist bioassays with piperonylbutoxide (PBO) partially recovered susceptibility, with mortalities increasing to 53% ± 4, indicating probable role of CYP450s in permethrin resistance (χ2 = 29.48, P < 0.0001). Transcriptional profiling revealed five major resistance-associated genes overexpressed in the field samples compared to the fully susceptible laboratory colony, Ngoussou. Highest fold change (FC) was observed with GSTe2 (FC = 3.3 in permethrin exposed and 6.2 in unexposed) and CYP6Z3 (FC = 1.4 in exposed and 4.6 in unexposed). TaqMan genotyping of 32 F0 females detected the 1014F and 1575Y knockdown resistance (kdr) mutations with frequencies of 0.84 and 0.1, respectively, while 1014S mutation was not detected. Sequencing of a fragment of the voltage-gated sodium channel, spanning exon 20 from 13 deltamethrin-resistant and 9 susceptible females revealed only 2 distinct haplotypes with a low haplotype diversity of 0.33. The findings of high pyrethroid resistance but with a significant degree of recovery after PBO synergist assay suggests the need to move to PBO-based nets. This could be complemented with carbamate- or organophosphate-based indoor residual spraying in this area

Sentiment-aware deep recommender system with neural attention networks

With the advent of web technology, user-generated textual reviews are becoming increasingly accumulated on many e-commerce websites. These reviews contain not only the user comments on different aspects of the products but also the user sentiments associated with the aspects. Although these user sentiments serve as vital side information for improving the performance of recommender systems, most existing approaches ignore to fully exploit them in modeling the fine-grained user-item interaction for improving recommender system performance. Thus, this paper proposes a sentiment-aware deep recommender system with neural attention network (SDRA), which can capture both the aspects of products and the underlying user sentiments associated with the aspects for improving the recommendation system performance. Particularly, a semi-supervised topic model is designed to extract the aspects of the product and the associated sentiment lexicons from the user textual reviews, which are then incorporated into a long short term memory (LSTM) encoder via an interactive neural attention mechanism for better learning of the user and item sentiment-aware representation. Furthermore, a co-attention mechanism is introduced to better model the fine-grained user-item interaction for improving predictive performance. The extensive experiments on different datasets showed that our proposed SDRA model can achieve better performance over the baseline approaches