Determination of the Antibacterial Effect of Sponge Gourd (Luffa Cylindrica) Seeds Extract on Escherichia Coli and Staphylococcus Aureus.

The upsurge in the prevalence of side effects of many synthetic agents and incidence of multi-resistant bacteria has spurred the search for plant based antimicrobials of therapeutic potentials for effective control of infections. This study was aimed at the determination of the antibacterial effect of sponge gourd (Luffa cyclindrica) seeds extracts on Escherichia Coli and Staphylococcus aureus. Antimicrobial properties of ethanolic and aqueous extracts of Luffa cylindrica seeds was investigated by testing them against the two clinical important pathogens. Agar disc diffusion method was employed in determining the susceptibility pattern of the seeds extracts concentrations of 50mg/ml, 100mg/ml, 200mml/ and 3000mg/ml on the test organisms. From the results, the zone of inhibition of the ethanolic extract at varying concentrations on Staphylococcus aureus ranged from 8mm to 12mm and Escherichia Coli 9mm to 15mm. The growth of the organisms was inhibited though to varying degrees with E.Coli being more susceptible than S. aureus. The result obtained from the research study is an indication that sponge gourd seed can be developed into a new broad spectrum antimicrobials. However, further research should be carried out and mechanism of action of the chemical component of sponge gourd.

Page :  204-210

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Multi-objective Strategy for Autonomous Cloud Service Containers

Virtualization technology is the bedrock of cloud computing in which the instance of almost all physical infrastructure – RAM, CPU, networking and disk storage - can be virtually produced and provisioned to users on demand in a pay-per-use manner. The technology has made it possible to create virtual machines and virtual containers. Virtual container technology has offered cloud application developers avenues to create several containers as and when needed. Despite light-weight nature and scalability of containers, however, containers under the management of a single user exhibit limited tendency to collaborate when it comes to the nature of tasks to be run on the containers. This paper is focused on the possibility of integrating some intelligence into the containers to be able to optimally provision resources among containers under the management of a user through the use of some multi-objective optimization (MOO) strategy. The paper considered two provisioning technique called contiguous provisioning (CP) and non-contiguous provisioning technique (NCP) observed its scalability under varying conditions of increasing population size. The MOO used in the experiment are the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and the Multi-Objective Cellular Genetic Algorithm (MOCell). Results show that the provisioning algorithms are based on a trade-off between acceptance, and future scalability. While CP performs well in guaranteeing future scalability it however performs poorly in generating revenue and immediate lower interaction delay. Parameters tested are rate of acceptance and scalability coefficient.

Page :  211-219

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Private Sector Credit and Economic Growth in Nigeria

The paper determines empirically the local conditions and policy environment that influence the absorptive capacity of credit in the Nigerian economy for the period 1990 – 2021 using fully modified least squares. Findings show that credit is growth-enhancing, even when trade openness, monetary policy, investment climate and infrastructure are low. Also, the composite local condition index analysis revealed that private sector credit increased economic growth when domestic or local conditions were favourable and the absorptive capacity of the domestic economy for credit was estimated at 29% of the GDP in 2021. These results suggest that there is ample room for growth- enhancing credit expansion in Nigeria.

Page :  220-231

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Computational Approach for Cooling Load Forecasting of Air-Conditioning System: A Survey and Open Challenges

Due to the high energy consumption in buildings, cooling load forecasting plays a crucial role in the planning, control and operation of heating, ventilating and air-conditioning systems. The building sector is a major consumer of energy worldwide and a large amount of energy is used for Heating, Ventilation and Air Conditioning (HVAC). One cause of high consumption in HVAC systems lies in their frequent failure to operate as intended after a period of operation, even with correct commissioning. To curtail the gap between demand and supply, new paradigms have to be employed that will use automated methods to dynamically forecast the buildings energy consumption. Many influencing factors make the prediction complex including: occupancy behaviors, weather conditions, parameter and model selection, missing data or incomplete information, and computation complexity of some models. Neural networks are recently applied for solving load forecasting due to their wide application. Deep neural network models provide a practical approach to energy consumption prediction. This paper offers a review of deep neural network for building energy consumption prediction that utilize machine learning algorithm. Based on this Review, existing research gaps are identified and future research directions are highlighted in the context of building energy consumption prediction.

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Simulation of PWM DC-DC Boost Converter Using Fuzzy Logic Controller

Boost converter is one of the most important non-isolated step-up converters. A boost converter is a power converter with an output dc voltage greater than its input dc voltage. It is a class of switching-mode power supply (SMPS) containing at least two semiconductor and at least one energy storage element. Boost converter is a dc-to-dc converter that steps up the dc voltage from its fixed low level to a desired high level. Crucial with these demands, many researchers or designers have been struggling to find the most economical, reliable and stable controller to meet these demands. This paper describes the simulation of Pulse Width Modulation (PWM) DC-DC Boost Converter using Fuzzy Logic control. The output voltage was feedback to the input to significantly improve the dynamic performance of boost dc-dc converter. The objective of this proposed methodology is to develop fuzzy logic controller to control boost DC-DC converter using MATLAB Simulink software. The fuzzy logic controller has been implemented to the system by developing fuzzy logic control algorithm. The objective of the work has been achieved successfully. The proposed converter was implemented where the voltage was stepped up from 24v to 48v and controlled by the fuzzy logic controller.

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