Review on Network Function Virtualization in Information-Centric Networking

Network function virtualization (NFV / VNF) and information-centric networking (ICN) are two trending technologies that have attracted expert's attention. NFV is a technique in which network functions (NF) are decoupling from commodity hardware to run on to create virtual communication services. The virtualized class nodes can bring several advantages such as reduce Operating Expenses (OPEX) and Capital Expenses (CAPEX). On the other hand, ICN is a technique that breaks the host-centric paradigm and shifts the focus to 'named information' or content-centric. ICN provides highly efficient content retrieval network architecture where popular contents are cached to minimize duplicate transmissions and allow mobile users to access popular contents from caches of network gateways. This paper investigates the implementation of NFV in ICN. Besides, reviewing and discussing the weaknesses and strengths of each architecture in a critical analysis manner of both network architectures. Eventually, highlighted the current issues and future challenges of both architectures. © 2021 IEEE

Intelligent Optimization Systems for MaintenanceScheduling of Power Plant Generators

This paper presents a Genetic Algorithm (GA) and Ant-Colony (AC) optimization model for power plant generators’ maintenance scheduling. Maintenance scheduling of power plant generators is essential for ensuring the reliability and economic operation of a power system. Proper maintenance scheduling prolongs the shelf life of the generators and prevents unexpected failures. To reduce the cost and duration of generator maintenance, these models are built with various constants, fitness functions, and objective functions. The Analytical Hierarchy Process (AHP), a decision-making tool, is implemented to aid the researcher in prioritizing and re-ranking the maintenance activities from the most important to the least. The intelligent optimization models are developed using MATLAB and the developed intelligent algorithms are tested on a case study in a coal power plant located at minjung, Perak, Malaysia. The power plant is owned and operated by Tenaga Nasional Berhad (TNB), the electric utility company in peninsular Malaysia. The results show that GA outperforms ACO since it reduces maintenance costs by 39.78% and maintenance duration by 60%. The study demonstrates that the proposed optimization method is effective in reducing maintenance time and cost while also optimizing power plant operation

Univariate and Multivariate Regression Models for Short-Term Wind Energy Forecasting

Wind energy resource is a never-ending resource that is categorized under renewable energy. Electricity generated from the wind when the wind blows across the wind turbine system produces high kinetic energy once it goes through the wind blades, rotating and turning it into useful mechanical energy. That motion of the generator produces electricity. However, in Malaysia, the inconsistency in terms of wind speed required for wind turbines to operate efficiently and generate a suitable amount of electrical power is a major problem. Different locations have different weather parameters that affect wind speed and wind energy production. Wind energy forecasting is performed in this paper using linear, nonlinear, and deep learning models for a small-scale wind turbine. The paper focuses on comparing and correlating the performance of univariate and multivariate input parameters with wind speed as its primary feature using short-term forecasting with a time horizon of 1 hour ahead. The set location is at Mersing, Johor, where it is prominently one of the locations in Malaysia with a constant and high amount of wind speed. It is found that Huber Regressor, Gradient Boosting, and Convolutional Neural Network (CNN) are shown to be powerful in prediction. Huber Regressor has the best Mean Absolute Error (MAE) of 0.597 and Root Mean Square Error (RMSE) of 0.797, while Gradient Boosting has the best learning rate (R2) at 0.637. CNN has the best MAPE at 30.861 and is shown to be the most optimum forecasting model for a univariate parameter. The results show that the outcome of the evaluation does not vary significantly depending on the criteria chosen in the data selection

A survey on short-range WBAN communication; technical overview of several standard wireless technologies

In a healthy environment, a WBAN system is the key component or aspect of the patient monitoring system. WBAN systems allow for easy networking with other devices and networks so that healthcare professionals can easily access critical and non-critical patient data. One of the main advantages of WBAN is the remote monitoring of patients using an Intranet or the Internet. There are two main components to the type of communication technology used in WBAN. This page shows an insight of a variety of short-range standardized wireless devices, as well as a taxonomy of short-range technologies. These are proposed as intra-BAN communication candidates for communication within and between body area network (BAN) entities. This paper also highlights the advantages and disadvantages of the WBAN perspective. Finally, a side-by-side comparison of the basic principles of using MICS frequency bands and preparatory technologies

Univariate and Multivariate Regression Models for Short-Term Wind Energy Forecasting

Wind energy resource is a never-ending resource that is categorized under renewable energy. Electricity generated from the wind when the wind blows across the wind turbine system produces high kinetic energy once it goes through the wind blades, rotating and turning it into useful mechanical energy. That motion of the generator produces electricity. However, in Malaysia, the inconsistency in terms of wind speed required for wind turbines to operate efficiently and generate a suitable amount of electrical power is a major problem. Different locations have different weather parameters that affect wind speed and wind energy production. Wind energy forecasting is performed in this paper using linear, nonlinear, and deep learning models for a small-scale wind turbine. The paper focuses on comparing and correlating the performance of univariate and multivariate input parameters with wind speed as its primary feature using short-term forecasting with a time horizon of 1 hour ahead. The set location is at Mersing, Johor, where it is prominently one of the locations in Malaysia with a constant and high amount of wind speed. It is found that Huber Regressor, Gradient Boosting, and Convolutional Neural Network (CNN) are shown to be powerful in prediction. Huber Regressor has the best Mean Absolute Error (MAE) of 0.597 and Root Mean Square Error (RMSE) of 0.797, while Gradient Boosting has the best learning rate (R2) at 0.637. CNN has the best MAPE at 30.861 and is shown to be the most optimum forecasting model for a univariate parameter. The results show that the outcome of the evaluation does not vary significantly depending on the criteria chosen in the data selection

Assessing the Impact of Spectral Irradiance on the Performance of Different Photovoltaic Technologies

The performance of photovoltaic (PV) solar cells is influenced by solar irradiance as well as temperature. Particularly, the average photon energy of the solar spectrum is different for low and high light intensity, which influences the photocurrent generation by the PV cells. Even if the irradiance level and the operating temperature remain constant, the efficiency will still depend on the technological parameters of the PV cell, which in turn depends on the used PV material’s absorption quality and the spectral responsivity and cell structure. This study is devoted to the review of different commercially available technologies of PV cells include crystalline silicon (c-Si), polycrystalline silicon (pc-Si), cadmium telluride (CdTe), and copper indium gallium selenide (CIGS). We tried to correlate the spectral response or the photocurrent of different PV cells with the variations of the solar spectrum, environmental conditions, and the material properties and construction of PV cells

Delamination-and electromigration-related failures in solar panels—a review

The reliability of photovoltaic (PV) modules operating under various weather conditions attracts the manufacturer’s concern since several studies reveal a degradation rate higher than 0.8% per year for the silicon-based technology and reached up to 2.76% per year in a harsh climate. The lifetime of the PV modules is decreased because of numerous degradation modes. Electromigration and delamination are two failure modes that play a significant role in PV modules’ output power losses. The correlations of these two phenomena are not sufficiently explained and understood like other failures such as corrosion and potential-induced degradation. Therefore, in this review, we attempt to elaborate on the correlation and the influence of delamination and electromigration on PV module components such as metallization and organic materials to ensure the reliability of the PV modules. Moreover, the effects, causes, and the sites that tend to face these failures, particularly the silicon solar cells, are explained in detail. Elsewhere, the factors of aging vary as the temperature and humidity change from one country to another. Hence, accelerated tests and the standards used to perform the aging test for PV modules have been covered in this review

A Novel Quinazoline Inhibits Hsp90 Protein, EGFR and Induces Apoptosis in Leukemia Cells

The objective of the first part of this study was to investigate the Hsp90 protein possible activ ity of a novel quinazoline Her2/ EGFR inhibitor (Co mpound No. 1: 4-(2-(4-Oxo-2-thio xo-1,4-d ihydroquinazolin-3(2H)yl)ethyl)benzenesulfonamide) p reviously synthesized by a collaborating group. Heat shock protein 90 (Hsp90) has a central ro le in regulation of several client proteins involved in cancers [1,2]. Several Hsp90 inhibitors of the natural or synthetic origin d isplayed potent anticancer activity [3,4]. Accordingly, Hsp90 emerged as an attractive target in the design of anticancer agents. To evaluate the binding mode of compound No. 1 into the ATPase site of Hsp90, a co mparative mo lecular docking study was performed using AutoDock 4.2. The results of this studywas compared with that of the co-crystallized ligand (ATI-13387X, Onalespib). The energy minimization process of the chemical structures of No. 1 was done following our previous report [5]. The results of the docking study revealed that No. 1 fit n icely into the ATPase site, and it displayed a binding free energy (Gb) of-7.21 kcal/ mo l and inhibition constant (Ki) of 5.19 µM to Hsp90, co mpared to Gb of-7.90 kcal/ mol and Ki of 1.62 µM for ATI-13387X. Furthermore, to confirm this result, the surface plasmon resonance (SPR) was devised to test the Hsp90 inhibition activity of No.1, wh ich was 51 nM co mpared to Rad icico l and 17AA G (1.8 nM, and 360 nM; respectively). Overall, co mpound No. 1 exh ibited pro mising Hsp90 inhib iting activity. The second part of the study focused on the effect of No. 1, Dinaciclib and their co mbinationsin HL-60 leukemia cells. The comb ination showed synergistic EGFR inhib ition effect in HL-60 cells. Moreover, No. 1, Dinaciclib and their combination caused a significant increase in the Sub-G1 co mpared to control and doxorubicin (24h), at the expense of S and G2/M cell cycle phases. Cyclin D3, was consequently inhibited by each of the two drugs, and synergistically by their comb ination in HL-60 cells. Furthermore, each of the two drugs downregulated Survivin, wh ich was synergistically inhib ited by the co mbination. In conclusion, co mpound No.1, Dinaciclib and their comb inations showed synergestic EGFR inhibit ion; and pro-apoptoticeffect in HL-60 cells.This project was funded by the deanship of scientific research, Umm Alqura University, KSA (DSR: 15-M ED-3-1-0060). Keywords: Novel quinazoline EGFR inhi bi tor, Hs p90 protein, Leukemi a cells

The approach of value innovation towards superior performance, competitive advantage, and sustainable growth: A systematic literature review

The value innovation strategy of pursuing differentiation and low cost has diverged and correlated with various notions and perspectives, which adds complexity and ambiguity to the current knowledge of value innovation. Thus, this study uses a systematic literature review methodology to identify key scientific contributions to the field of value innovation by providing a structured reliable overview of the current knowledge. This study aims to integrate the findings of previous research on value innovation to identify where conclusions converge and diverge and highlight emerging trends and gaps in the literature. This study seeks to answer the research question, “How can value innovation be an approach for superior performance, competitive advantage, or sustainable growth?” In this context, results are achieved through analyzing and synthesizing 73 empirical articles on value innovation literature published from 1997 to January 2021. Particularly, this study contributes to the extant literature by providing an integrative framework that summarizes the literature findings and addressing thematic classifications of the value innovation process. This study also helps further improve research on value innovation by identifying gaps and suggesting a conceptual model to mitigate those gaps

Techno-economic analysis of a wind-energy-based charging station for electric vehicles in high-rise buildings in Malaysia

Renewable energy sources have become necessary for long-term energy sustainability due to the increased demand for electric cars and worrisome rises in carbon dioxide emissions from traditional energy sources. Furthermore, transportation is one of the sectors that uses the most energy on the planet, accounting for 24% of overall consumption. Fossil fuels are still the dominant energy source for balancing global demand/supply dynamics. Supporting laws and regulations have enhanced the first phase of environmentally friendly energy-resource consumption. This has spurred the development of new solutions that cut greenhouse-gas emissions and reduce the air pollution produced by internal combustion engines that are fuelled by fossil fuels. Wind energy is one of the clean energy sources that may be utilised for this purpose. Wind energy has been used to power electric-car-charging infrastructure, generally in a hybrid mode with another renewable source. This research examines the possibility of using wind energy as a standalone energy source to support electric-vehicle-charging infrastructure. Using data from Malacca, Malaysia, and HOMER software, the project will build and optimise a standalone wind-powered charging station. An RC-5K-A wind turbine coupled to a battery and converter is the appropriate choice for the system. The findings demonstrate that the turbine can produce 214,272 kWh per year at the cost of USD 0.081/kWh, confirming wind’s future feasibility as an energy-infrastructure support source