Relationship between feed characteristics and histomorphometry of small intestines of growing pigs

The use of agricultural by-products has become the central focus in reducing feed costs in pig production. However, there is a need to determine how the gastrointestinal tract of pigs responds when subjected to fibrous ingredients. The objective of the study was to predict villi height and apparent villi surface area from physicochemical measurements of maize cob-based diets. Eighteen growing male pigs (initial bodyweight 14.3 ± 1.20 kg) were used in the experiment. The pigs were penned individually and subjected to diets containing 0 g, 80 g, 160 g, 240 g, 320 g, and 400 g maize cob meal/kg diet. Feed and water were provided ad libitum. Using stepwise regression, bulk density (BD) and neutral detergent fibre (NDF) were the best predictor variables influencing villi height (VH) and apparent villi surface area (AVSA). VH produced quadratic and linear responses with BD and NDF, respectively. The equations are VH = 211.3(BD)2 – 591.0(BD) + 442.4; and VH = 0.03(NDF) + 22.8. Conversely, AVSA produced quadratic and linear responses with NDF and BD, respectively. The equations are AVSA = 0.00036(NDF)2 – 0.012(NDF) +7.25 and AVSA = - 47.12(BD) + 45.03. In conclusion, the BD and NDF of a feed could be used to predict VH and AVSA of growing pigs fed maize cobs.Keywords: maize cob meal, mucosa lining, physicochemical properties, visceral organ

Toward Understanding Personalities Working on Computer: A Preliminary Study Focusing on Collusion/Plagiarism

Ample research has been carried out in the area of collusion, plagiarism and e-learning. Collusion is a form of active cheating where two or more parties secretly or illegally corporate. Collusion is at the root of common knowledge plagiarism. While plagiarism requires two or more entities to compare, collusion can be determined in isolation. It is also possible that collusion do not lead to positive plagiarism checks. It is therefore the aims of this preliminary study to: (i) identify the factors responsible for collusion in e-learning (ii) determine the prominent factor that is representative of collusion and (iii) through user behaviour including, but not limited to, application switching time, determine collusion. We claim that user computer activities and application processes can help understand user behaviour during assessment task. It is on this premise that we develop a machine learning model to predict collusion through user behaviour during assessment tas

Performance evaluation of 200W solar photovoltaic panel considering Bauchi microclimatic conditions

Measurement and modeling of broadband and spectral terrestrial solar radiation is important for the evaluation and deployment of solar renewable energy systems. This paper focuses on optimizing the performance of 200W solar module taking into consideration the local climatic conditions of Bauchi locality. The uncertainty in life cycle savings for solar thermal and photovoltaic (PV) systems as linearly correlated with uncertainty in solar resource data. These uncertainties paved way for the need to conduct a critical assessment of the resource. Assessment of the solar resource for these technologies rely upon measured data, where available. In this paper, we present the development of mathematical model of photovoltaic solar cells based on their detailed single diode equivalent circuit representation. Pertinent simulation models for PV solar module both for an ideal weather situation and for taking into consideration the effects of microclimatic conditions that prevail in Bauchi as evaluated and compared with benchmarks available. The complete model of the PV system was implemented using MATLAB/Simulink platform. The standard characteristic curves for the 200W solar panel are as presented. The simulation of the ideal PV system made use of standard test conditions (STC) to facilitate comparison with the existing benchmark results in the literature. The analysis of the characteristics performance curves returned an average VOC = 42.9v and ISC = 4.21A. The simulation results further revealed that the power delivered by the 200W monocrystalline solar module of 144.3W @620W/m2, 35ºC as recorded for Bauchi under all climatic conditions as evaluated. The benchmark values obtained in the laboratory are VOC = 45.5V, ISC = 5.92A and 200-W under the Standard test condition (STC) conditions of cell temperature 25ºC, solar irradiance of 1000W/m2 and air mass (AM) of 1.5. The average conversion efficiency and fill factor as evaluated are 0.77 and 16% respectively. This result agrees with the benchmark of module efficiency of >15.66% recorded at STC. The results conclusively reveal that the microclimate of a locality essentially affects the performances of solar PV systems deployed to each location on the globe. Therefore, utilization of these parameters is essential for consideration in the design of solar systems in all localities

Computational Enhancement of Genetic Algorithm Via Control Device Pre-Selection Mechanism for Power System Reactive Power/Voltage Control

In this paper, the application of a novel and computationally enhances genetic algorithm (GA) for solving the reactive power dispatch problem is presented. In order to attain a significant reduction in the computational time of GA, a systematic procedure of reactive power control device pre-selection mechanism is herein proposed to choose a-priori subsets of the available control devices, which maximally influence buses experiencing voltage limit violations. The GA reactive power dispatch module then accesses such judiciously pre-selected control device candidates to determine their optimal settings. A pragmatic scheme aimed at further curtailing the number of the final control actions entertained is also set forth. The far-reaching simulation results obtained for two case study scenarios using the proposed algorithmic procedures on a German utility network of Duisburg, replicated on an operator-training simulator, are presented and fully discussed in depth

Differential Evolution Approach for Reactive Power Optimization of Nigerian Grid System

The goal of reactive power dispatch is to minimize the system losses and improve the system voltage profiles at all times. This is achieved by adjusting various generating units\u27 excitation systems continuously, discrete tap positions of on-load tap changers of transformers as well as switching of correct doses of inductors or capacitors. This is a mixed integer non-linear optimization problem. In this paper, the differential evolution (DE), a novel evolutionary computation technique which was originally designed for continuous problems is applied to solve this problem. DE appears to ally qualities of established computational intelligence (CI) techniques with a more striking computational performance, thus suggesting the possibility of having the potential for on line applications in the control center; comparison work with other techniques is presently conducted. The developed tool was demonstrated on the Nigerian power system grid for three case scenarios preset on the power world simulator which was linked with DE for power flow calculation (fitness check of solutions). The results achieved revealed that DE procured a significant reduction of real power losses while simultaneously keeping the voltage profiles within the acceptable limits

Comparison of PSO and GA for K-Node Set Reliability Optimization of a Distributed System

Particle Swarm Optimization (PSO), as a novel evolutionary computing technique, has succeeded in many continuous problems, but quite a little research on discrete problem especially combinatorial optimization problem has been reported. In this paper, a discrete PSO algorithm is proposed to solve a typical combinatorial optimization problem: K-Node Set Reliability (KNR) optimization of a distributed computing system (DCS) which is a well-known NP-hard problem is presented. It computes the reliability of a subset of network nodes of a DCS such that the reliability is maximized and specified capacity constraint is satisfied. The feasibility of the proposed algorithm is demonstrated on 8 nodes 11 links DCS topology. The test results are compared with those obtained by the genetic algorithm (GA) method in terms of solution quality and convergence characteristics. Experimental study shows that the proposed PSO algorithm can achieve good results

Reactive Power and Voltage Control of the Nigerian Grid System using Micro-Genetic Algorithm

In this paper, a micro-genetic based approach to the optimization of reactive power and voltage profiles improvement and real power loss minimization is presented. The reactive power control devices such as generators, tap positions of on-load tap changer of transformers, shunt reactors are used to correct voltage limits violations while simultaneously reducing the system real power losses. Genetic algorithms (GAs) are well-known global search techniques anchored on the mechanisms of natural selection and genetics. Because of the time intensive nature of the conventional GA, the micro-GA is proposed as a more time efficient alternative. The feasibility and effectiveness of the developed algorithm is tested and verified on the Nigerian grid power system for three case studies scenarios preset in the power world simulator. The far-reaching simulation results that validate the effectiveness of the developed tool are presented and discussed in depth

Comparative Application of Differential Evolution and Particle Swarm Techniques to Reactive Power and Voltage Control

This paper presents the comparative application of two metaheuristic approaches: Differential Evolution (DE) and Particle Swarm Optimization (PSO) to the solution of the reactive power and voltage control problem. Efficient distribution of reactive power in an electric network leads to minimization of the system losses and improvement of the system voltage profile. It can be achieved by varying the excitation of generators or the on-load tap changer positions of transformers as well as by switching of discrete portions of inductors or capacitors etc. This constitutes a typical mixed integer non-linear optimization problem for the solution of which metaheuristic techniques have proven well suited in principle. The feasibility, effectiveness and generic nature of both DE and PSO approaches investigated are exemplarily demonstrated on the Nigerian grid system and the New England power system. Comparisons were made between the two approaches in terms of the solution quality and convergence characteristics. The simulation results revealed that both approaches were able to remove the voltage limit violations, but PSO procured in some instances slightly higher power loss reduction as compared with DE; on the other hand DE required a lower number of function evaluations as compared with PSO. Consideration of computational effort is relevant for potential real time on line application

Swarm Intelligence and Evolutionary Approaches for Reactive Power and Voltage Control

This paper presents a comparison of swarm intelligence and evolutionary techniques based approaches for minimization of system losses and improvement of voltage profiles in a power network. Efficient distribution of reactive power in an electric network can be achieved by adjusting the excitation on generators, the on-load tap changer positions of transformers, and proper switching of discrete portions of inductors or capacitors. This is a mixed integer non-linear optimization problem where metaheuristics techniques have proven suitable for providing optimal solutions. Four algorithms explored in this paper include differential evolution (DE), particle swarm optimization (PSO), a hybrid combination of DE and PSO, and a mutated PSO (MPSO) algorithm. The effectiveness of these algorithms is evaluated based on their solution quality and convergence characteristic. Simulation studies on the Nigerian power system show that a PSO based solution is more effective than a DE approach in reducing real power losses while keeping the voltage profiles within acceptable limits. The results also show that MPSO allows for further reduction of the real power losses while maintaining a satisfactory voltage profile

Optimal economie dispatch for the nigerian grid system considering voltage and line flow constraints

The electric power industries worldwide have undergone considerable changes especially from vertical structure to full deregulated entities. These changes are now introducing new problems in terms of operations, controls and planning of the entire grid systems. This calls for a more reliable analytical tool ever than before. One feasible solution is to perform the Optimal Economic Dispatch (OED) paradigm on this restructured power system so as to provide fairness to all operators. In this paper, the economic dispatch problem with voltage and line flow constraints has been formulated for the hydro-thermal generating units feeding the Nigerian power system. In order to solve the arising power flow problem a MATLAB based simulation package, MATPOWER version 3.0 has been suitably modified to obtain feasible solutions for different loading system scenarios. The results obtained showed that the OED offered a better optimal power schedules, power loss minimization and reduced total fuel cost than earlier work based on Micro-Genetic Algorithm, (MGA) and Conventional Genetic Algorithm (CGA)