The Effect of Funding on the Educational System

The effective running of the educational system requires influx of enough resources. These resources are generated as funds for the overall administration of affairs in the sector. In this paper, a review of the effects of funding on the educational system was done – its benefits to the society and the challenges hindering it. Also, a discussion on what should be done to implement these benefits on the Nigeria economy was done

Impact of Effective Teaching and Learning Process in Schools through Information and Communication Technology (ICT)

Technology is developed to solve problems associated with human need in more productive ways, if there is no problem to solve. The technology is not developed and or not adopted. Applying this principle to educational technology would mean that the educators should create and adopt technologies that address educational problems, of which there are many. Further, technology will not be adopted by educators where there is no perceived need or productivity gain. This is what LANSHEAR and SNYDER(2000) refer to as the WORKABILITY PRINCIPLE. Therefore when discussing applications of computer technology to education , the question must always be asked, what educational problems(s) needs to be addressed. This questions needs to be asked at all level of decision making, from the teacher planning a programme, to a school administrator purchasing hardware and software, to an educational system officer developing policy and strategic plans

IT on teaching and learning process of visually impaired students

info:eu-repo/semantics/publishedVersio

Artificial intelligence for photovoltaic systems

Photovoltaic systems have gained an extraordinary popularity in the energy generation industry. Despite the benefits, photovoltaic systems still suffer from four main drawbacks, which include low conversion efficiency, intermittent power supply, high fabrication costs and the nonlinearity of the PV system output power. To overcome these issues, various optimization and control techniques have been proposed. However, many authors relied on classical techniques, which were based on intuitive, numerical or analytical methods. More efficient optimization strategies would enhance the performance of the PV systems and decrease the cost of the energy generated. In this chapter, we provide an overview of how Artificial Intelligence (AI) techniques can provide value to photovoltaic systems. Particular attention is devoted to three main areas: (1) Forecasting and modelling of meteorological data, (2) Basic modelling of solar cells and (3) Sizing of photovoltaic systems. This chapter will aim to provide a comparison between conventional techniques and the added benefits of using machine learning methods

Optimization of Hardness Strengths Response of Plantain Fibres Reinforced Polyester Matrix Composites (PFRP) Applying Taguchi Robust Resign

Volume fraction of fibres (A), aspect ratio of fibres (B) and fibres orientation (C) are considered as control factors in the determination of hardness strength, hardness strength of plantain fiber reinforced polyester composites (PFR P). These properties were determined for plantain empty fruit bunch (PEFB) and plantain pseudo stem (PPS). Hardness tests were conducted on the replicated samples of PEFB fiber reinforced polyester composite and PPS fiber reinforced polyester respectively using Archimedes principles in each case to determine the volume fraction of fibers. To obtain the optimum properties being investigated a Monsanto tensometer were used to establish the control factor levels quality characteristics needed to optimize the mechanical properties being investigated. Taguchi robust design technique was applied for the greater the better to obtain the highest signal to noise ratio (SN ratio) for the quality characteristics being investigated employing Minitab 15 software. The optimum values of the control factors are established for empty fruit bunch composites and for pseudo stem fiber composite. The empty fruit bunch fiber reinforced polyester matrix composite has the maximum hardness strength of 19.062N/mm2 and a mean design strength of 17.978N/mm2, while the pseudo stem plantain fiber reinforced matrix composite has the maximum hardness strength of 18.655 N/mm2 and a mean design strength of 18.0385N/mm2. The properties studied depend greatly on the reinforcement combinations of control factors