Analysis of optimum combination of integrated crop-livestock enterprise in North-West, Nigeria

The study was conducted to determine the optimum combination of integrated crop-livestock enterprises in north-west, Nigeria. Primary data were obtained through structured questionnaire and interview schedule. A multi-stage sampling procedure was employed to select 3 states, 3 zones, 21 LGAs, 84 villages, and 428 crop-livestock farmers made up of 178, 128 and 122 farmers in Kaduna, Kano and Katsina states respectively. Descriptive statistics and Data Envelopment Analysis (DEA) was used to achieve the objective of the study. The results of socio-economic characteristics showed that about 89% of the pooled farmers were male with mean age of 48 years and household size of 10 persons per farmer. The findings from DEA revealed the mean total efficiency, pure efficiency and scale efficiency of 0.79, 0.91 and 0.86 respectively. DEA results further indicated that farmers can reduce the quantity of farm size, labour, seed, fertilizer, manure and agrochemical inputs by 0.2, 12.9, 17.6, 6.6, 35.9 and 26.4 %, respectively. Results further specified that 17.3, 26.25 and 56.5 % of farmers operated at optimal, sub-optimal and super-optimal scale, respectively. Tobit regression model used to determine factors influencing technical efficiency established that coefficients of age (0.0210), marital status (0.0016), household size (0.0616), education level (-0.1247), farming experience (0.1412), extension contact (-0.2548) and cooperative membership (-0.1102) were statistically significant variables at different level of probability. There should be synergy between crop and animal scientists; extension agents and agricultural economists to bring into bearing the needs for farmers to imbibe integrated crop-livestock farming to achieve optimum level of efficiency

A heuristic crossover enhanced evolutionary algorithm for clustering wireless sensor network

© Springer International Publishing Switzerland 2016.In this paper, a Heuristic-Crossover Enhanced Evolutionary Algorithm for Cluster Head Selection is proposed. The algorithm uses a novel heuristic crossover operator to combine two different solutions in order to achieve a high quality solution that distributes the energy load evenly among the sensor nodes and enhances the distribution of cluster head nodes in a network. Additionally, we propose the Stochastic Selection of Inactive Nodes, a mechanism inspired by the Boltzmann Selection process in genetic algorithms. This mechanism stochastically considers coverage effect in the selection of nodes that are required to go into sleep mode in order to conserve energy of sensor nodes. The proposed selection of inactive node mechanisms and cluster head selections protocol are performed sequentially at every round and are part of the main algorithm proposed, namely the Heuristic Algorithm for Clustering Hierarchy (HACH). The main goal of HACH is to extend network lifetime of wireless sensor networks by reducing and balancing the energy consumption among sensor nodes during communication processes. Our protocol shows improved performance compared with state-of-the-art protocols like LEACH, TCAC and SEECH in terms of improved network lifetime for wireless sensor networks deployments

Iterated Local Search Algorithm for Clustering Wireless Sensor Networks.

In this paper, a new clustering protocol employing an iterated local search (ILS) to solve cluster head selection problem is proposed. ILS uses a perturbation operator to change an initial random solution to produce a new point in the vicinity of the solution. Using a combination operator, this new point is mated with the random solution producing a new solution. A move from the current solution to the new solution is considered acceptable only for higher fitness value. If a move is rejected after a predetermined search length, the change rate of the current solution is increased in order to explore a wider search space for quality solutions. In each round, this search process continues until good solution that ensures balanced energy consumption is obtained for the network. Furthermore, we propose a sleep scheduling scheme inspired by the Boltzmann Selection process in genetic algorithms. This mechanism stochastically considers coverage effect in the selection of nodes that are required to go into sleep mode in order to conserve energy of sensor nodes. The proposed mechanism of inactive node and cluster head selection protocols are performed sequentially at every round and they form part of the main algorithm proposed, namely the Dynamic Local Search-Based Algorithm for Clustering Hierarchy (DLSACH). The ultimate goal of the DLSACH protocol is to extends the network lifetime of wireless sensor networks by reducing and balancing the energy consumption among sensor nodes during communication processes. Our protocol shows an improved performance compared to state-of-the-art protocols such as LEACH, TCAC and SEECH in terms of improved network lifetime for wireless sensor networks deployment

Modeling of Criteria Air Pollutant Emissions from Selected Nigeria Petroleum Refineries

The dispersion models were used to estimate or predict the concentration of air pollutants or toxins emitted from sources such as industrial plants, vehicular traffic or accidental chemical releases. In this study, the Industrial Source Complex Short Term (ISCST3) emission dispersion model was used to measure the ground level concentration of criteria air pollutants within 50 km radius of location. This model considered emissions from major point sources of pollutants in four existing and twenty-three proposed Nigeria petroleum refineries. The obtained ground level concentration for 24-hr averaging periods of the criteria air pollutants at sensitive receptor around each of the refineries was compared with the National Ambient Air Quality Standards (NAAQS) of Nigeria, World Bank and World Health Organization (WHO) to determine their level of compliance. The highest ground level concentration predicted to be 450 - 1875 μg/m3 for 24-h averaging period was obtained at Tonwei Oil Refinery, Ekeremor Local Government, Bayelsa State, while the lowest ground level concentration predicted to be 0.0099 - 0.1 μg/m3 for 24-h averaging period was obtained at Amakpe International Refinery, Eket Local Government, Akwa Ibom State. Percentage set limits of criteria air pollutants ranging from 0.02% to 94.5% are within the set standard limits and no health risk is associated with areas around the plant’s locations while percentage set limits of criteria pollutants ranging from 1.1 to 55.6 folds of the standards exceed the maximum permitted limits, hence affecting areas around the plants. The air quality standards guiding petroleum refinery emissions must be strictly considered, in order to ensure that the ground level concentrations do not exceed the required standard limits and prevent the adverse effects of air pollution in the Nigeria airshed

Environmental Impact Analysis of the Emission from Petroleum Refineries in Nigeria

Health and environmental hazards, a thing of global concern have been the major characteristics of the petroleum refinery areas worldwide, Nigeria inclusive. This is as a result of the emissions from petroleum refineries which resulted into air quality degradation of the host environment. This problem which has equally affected the climatic conditions of the petroleum producing areas is more pronounced in Nigeria due to lack of implementing adequate policies to protect the host environment. This study is carried out to investigate the atmospheric conditions of the petroleum refineries and identify the environmental impact of emissions of criteria pollutants from the proposed project in the area of influence. Emission inventory of criteria pollutants was carried out on the four existing and twenty-three proposed petroleum refineries in Nigeria. Using no control-measure option, the estimated annual criteria air pollutants emissions from point sources in the existing refineries are 1,217 tons/annum for PM10, 45,124 tons/annum for SO2, 167,570 tons/annum for NOx, 3,842 tons/annum for VOC and 242,469 tons/annum for CO. An additional 1,082 tons/annum of PM10, 168,944 tons/annum of SO2, 688,687 tons/annum of NOx, 9,122 tons/annum of VOC and 569,975 tons/annum of CO were predicted to be added into the Nigeria airshed by the proposed petroleum refineries. The highest pollutant emitting state was predicted to be Rivers State with the highest number of refineries while the least pollutant emitting states were predicted to be Kaduna, Edo, Lagos and Anambra States with only one refinery in each of the state. The ability to adopt appropriate control measures will determine the rate of emission of criteria pollutants released into the country’s airshed

Compositional Analysis of Lignocellulosic Materials: Evaluation of an Economically Viable Method Suitable for Woody and Non-woody Biomass

The determination of the composition of lignocellulosic substrate is a crucial step in order to determine the overall efficiency of the processes designed to convert lignocelluloses to ethanol. Standard methods as gravimetric, chromatography, and spectroscopic are routinely explored in the scientific literature. This paper details our investigations in the application of economically viable gravimetric methods particularly suitable for developing countries. The methods were proven to be reproducible and representative for the analysis of biomass as sugarcane bagasse, siam weed, shea tree sawdust

Acid Hydrolysis of Lignocellulosic Content of Sawdust to Fermentable Sugars for Ethanol Production

This study evaluates the yield of glucose from acid hydrolysis of cellulosic content of sawdust, at ambient temperature and atmospheric pressure, and the effect of yeast concentration on its subsequent fermentation to ethanol. The method used involves acid hydrolysis of sawdust, with varying acid molarities of 18M, 15M, 10M, 5M and 1M. The product, consisting mainly of simple sugars, was subsequently fermented with varied concentrations of yeast of 0.5g/20ml, 1g/20ml, 3g/20ml, 5g/20ml and 7g/20ml in order to obtain ethanol. The result obtained shows that there is a gradual increase in the glucose yield with increasing acid molarity from 1M until a critical optimum point is obtained at a high acid concentration of 15M. Beyond the molarity of 15M up to the 18M limit, there exists a decline in the ethanol yield, from the optimum point. The ethanol yield from the fermentation of the resulting fermentable sugars gave the same pattern as the glucose yield irrespective of the yeast concentration used for fermentation. The evaluation of the concentration of yeast on the fermentation of hydrolsed lignocellulosic contents shows that the optimum ethanol yield is obtained at a yeast concentration of 3g/20ml for all the varying acid concentrations. A combination of acid concentration of 15M and yeast concentration of 3g/20ml therefore gives the optimum conditions, at moderate temperature and pressure, for the acid hydrolysis of sawdust’s lignocellulosic content and the fermentation of the resulting product

A new approach for event detection using k-means clustering and neural networks.

In composite event detection systems such as fire alarms, the two foremost goals are speed and accuracy. One way to achieve these goals is by performing data aggregation at central nodes. This helps reduce energy consumption and redundancy. In this paper we present a new hybrid approach that involves the use of k-means algorithm with neural networks, an efficient supervised learning algorithm that extracts patterns and detects trends that are hidden in complex data. Previous research on event detection concentrates majorly on the use of feed forward neural network and other classifiers such as naive Bayes and decision tree alone for modern fire detection applications. In our approach presented here, we combine k-means with neural networks and other classifiers in order to improve the detection rate of event detection applications. To demonstrate our approach, we perform data aggregation on normalized multi-dimensional fire datasets in order to remove redundant data. The aggregated data forms two clusters which represent the two class labels (actual outputs) with the aid of k-means clustering. The resulting data outputs are trained by the Feed Forward Neural Network, Naive Bayes, and Decision Trees. This approach was found to significantly improve fire detection performance