ISA QAMBER - Profile on Academia.edu (original) (raw)

Papers by ISA QAMBER

Electricity power plants are the primary source of carbon dioxide (CO2) production. The combustio... more Electricity power plants are the primary source of carbon dioxide (CO2) production. The combustion of fossil fuels in these power plants releases CO2 and other greenhouse gases (GHGs) into the atmosphere. Emissions of CO2 and other GHGs occur throughout the life cycle of electricity generation technologies, including extraction, processing, transportation, and combustion of fossil fuels. Nowadays, the development of smart cities plays a crucial role in reducing CO2 emissions. By integrating technology and sustainable practices into urban infrastructure, smart cities aim to minimize energy consumption and emissions. This research focuses on creating models that correlate CO2 emissions with the installed capacity of power plants and the energy they produce. Polynomial quadratic regression is applied to establish these correlations, enabling the assessment of both present and future CO2 emission values. The construction of new infrastructure for smart cities aligns with the broader goal of fostering a low-carbon economy. The study also considers the potential contribution of thermal energy to CO2 reduction efforts. Researchers aim to identify strategies for meeting emission reduction goals by analyzing power generation models. This involves implementing policies and technologies aimed at reducing GHG emissions and mitigating climate change. CO2 emissions contribute significantly to global warming, leading to climate change and patterns of extreme weather. The data on CO2 emissions encompasses gases generated from burning fossil fuels, highlighting the importance of reducing reliance on these energy sources.

Academia Green Energy, May 24, 2024

Electricity power stations are the primary source of carbon dioxide production. The combustion o... more Electricity power stations are the primary source of carbon dioxide production. The combustion of fossil fuels in these power stations releases CO2 and other greenhouse gases into the atmosphere. Emissions of CO2 and other greenhouse gases occur throughout the life cycle of electricity generation technologies, including extraction, processing, transportation, and combustion of fossil fuels. Nowadays, the development of smart cities plays a crucial role in reducing CO2 emissions. By integrating technology and sustainable practices into urban infrastructure, smart cities aim to minimize energy consumption and emissions. The research mentioned focuses on creating models that correlate CO2 emissions with the installed capacity of power stations and the energy they produce. Polynomial quadratic regression is applied to establish these correlations, enabling the assessment of both present and future CO2 emission values. The construction of new infrastructure for smart cities aligns with the goal of fostering a low-carbon economy. The study also considers the potential contribution of thermal energy to CO2 reduction efforts. By analysing power generation models, researcher aims to identify strategies to meet emission reduction goals. The construction of new infrastructure for smart cities aligns with the broader goal of fostering a low-carbon economy. This involves implementing policies and technologies aimed at reducing greenhouse gas emissions and mitigating climate change. Carbon dioxide emissions contribute significantly to global warming, leading to climate change and patterns of extreme weather. The data on CO2 emissions encompasses gases generated from the burning of fossil fuels, highlighting the importance of reducing reliance on these sources of energy.

Journal paper., 2024

Electricity power plants are the primary source of carbon dioxide production. The combustion of f... more Electricity power plants are the primary source of carbon dioxide production. The combustion of fossil fuels in these power plants releases CO2 and other greenhouse gases into the atmosphere. Emissions of CO2 and other greenhouse gases occur throughout the life cycle of electricity generation technologies, including extraction, processing, transportation, and combustion of fossil fuels. Nowadays, the development of smart cities plays a crucial role in reducing CO2 emissions. By integrating technology and sustainable practices into urban infrastructure, smart cities aim to minimize energy consumption and emissions. The research mentioned focuses on creating models that correlate CO2 emissions with the installed capacity of power plants and the energy they produce. Polynomial quadratic regression is applied to establish these correlations, enabling the assessment of both present and future CO2 emission values. The construction of new infrastructure for smart cities aligns with the goal of fostering a low-carbon economy. The study also considers the potential contribution of thermal energy to CO2 reduction efforts. By analysing power generation models, researcher aims to identify strategies to meet emission reduction goals. The construction of new infrastructure for smart cities aligns with the broader goal of fostering a low-carbon economy. This involves implementing policies and technologies aimed at reducing greenhouse gas emissions and mitigating climate change. Carbon dioxide emissions contribute significantly to global warming, leading to climate change and patterns of extreme weather. The data on CO2 emissions encompasses gases generated from the burning of fossil fuels, highlighting the importance of reducing reliance on these sources of energy.

Future Energy Consumption Estimation for the Kingdom of Bahrain

2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), Nov 20, 2022

International Journal of Computing and Digital Systems, Mar 16, 2023

Any power system needs a comprehensive study. The reliability of the system is a must, where the ... more Any power system needs a comprehensive study. The reliability of the system is a must, where the appropriate loss term is one of the required terms. In the present study, the Loss of Load Expectation (LOLE) is targeted. In addition, the LOLE is estimated using the advanced Adaptive Neuro-Fuzzy Inference System (ANFIS) method. The LOLE and the Capacity Margin Probabilities (CMP) are calculated. The main target of the present study is to avoid the blackout of the system which is the novelty of the study for the considered power system which helps for a high reliable system also. Furthermore, the satisfaction of the economic development for the countries resulted using the developed model and the demand energy will be reached for different sectors at a required period. The results obtained for any considered country are reducing the capital investment and helping to limit the installed equipment plus the expectation of the load. Finally, the Neuro-Fuzzy is one of the most accurate methods. It is applied in the planning and development of electric power systems.

Power Systems Control and Reliability: Electric Power Design and Enhancement

Investigation of a cleaned PV panels for two regions

4th Smart Cities Symposium (SCS 2021)

Estimation of the Maximum Annual Loads Modeling for Kingdom of Bahrain

The present paper proposes the impact of the air temperature on electricity demand as expected. I... more The present paper proposes the impact of the air temperature on electricity demand as expected. It is clear that the annual maximum load is recorded versus the years starting by the year 2009 up to 2012. At present, the graph fitting technique is applied with some mathematical and computational tools based on the actual values of the years 2009 up to 2012 considering the lower values, the higher values and the average values of the annual maximum loads for Kingdom of Bahrain. For the three scenarios, the models are obtained by curve fitting technique. As well, the model of actual loads is obtained finally which has mostly the closest values obtained. For technical operations and control of power systems, the knowledge of the evolution of electric power load is important. The reason behind that is the electric demand which must meet secure and reliable operation at all times. Load forecasting is adaptive in the sense that the model parameters are automatically corrected to keep track...

Journal of Energy and Power Engineering, 2015

In the deregulated economy, the maximum load forecasting is important for the electric industry. ... more In the deregulated economy, the maximum load forecasting is important for the electric industry. Many applications are included such as the energy generation and purchasing. The aim of the present study is to find the most suitable models for the peak load of the Kingdom of Bahrain. Many mathematical methods have been developing for maximum load forecasting. In the present paper, the modeling of the maximum load, population and GDP (gross domestic product) versus years obtained. The curve fitting technique used to find that models, where Graph 4.4.2 as a tool used to find the models. As well, Neuro-Fuzzy used to find the three models. Therefore, three techniques are used. These three are exponential, linear modeling and Neuro-Fuzzy. It is found that, the Neuro-Fuzzy is the most suitable and realistic one. Then, the linear modeling is the next suitable one.

Development of Solar Panel Models in Different Countries/Regions

Artificial Intelligence for Solar Photovoltaic Systems, Jun 2, 2022

International Journal of Computing and Digital Systems, 2020

Two important terms, the LOLP and LOLE, are helping to plan, design, operation and maintenance of... more Two important terms, the LOLP and LOLE, are helping to plan, design, operation and maintenance of the power system is the reliability study. Therefore, the future expectation needs the electric load historical data. The present study concentrates on these two terms used in the reliability. These terms are the Loss of Load Probability (LOLP) and Loss of Load Expectation (LOLE). These terms followed, when the loss of load occurred as the electric load exceeds the available generating capacity of the system. As a measure of the LOLP and LOLE, mean how much in time the load exceeds the available capacity of the system using the MATLAB (R2014b). A power plant has six power-generated units combined and considered to form one power system plant. Then, the LOLP and LOLE calculated for the system under different loads. This type of consideration is helping to plan the future of the power plant and finding the effect of load on power system reliability. At the same time, the LOLP-FOR and LOLE-FOR models investigated.

Energy consumption prediction using Petri Nets-ANFIS development technique

Arab Journal of Basic and Applied Sciences

Accurate formula for the conduction angle of single phase voltage controllers and controlled rectifiers with inductive loads

MODELL MEAS CONTROL A, 1995

A simple and accurate formula has been developed for the conduction angle of single phase half-wa... more A simple and accurate formula has been developed for the conduction angle of single phase half-wave or full-wave controlled rectifiers and ac voltage controllers with inductive loads under discontinuous current mode of operation. It is shown that the conduction angle calculated from developed formula agrees very well with the exact solution obtained from interactive solution of the associated non-linear equation. The formula could be useful in computer aided or manual analysis and design problems. Also it facilitates the ...

The established mega watt linear programming-based optimal power flow model applied to the real power 56-bus system in eastern province of Saudi Arabia

Energy, 2008

... In this first case study; the model is applied to the actual eastern province power system in... more ... In this first case study; the model is applied to the actual eastern province power system in Saudi Arabia as it is without any changes as formulated by LINDO ... Qaisomah power plant (QAIS) was under maintenance which explains why the actual is much less than the model. ...

Spark plug failure detection using Z-freq and machine learning

TELKOMNIKA (Telecommunication Computing Electronics and Control), 2021

Preprogrammed monitoring of engine failure due to spark plug misfire can be traced using a method... more Preprogrammed monitoring of engine failure due to spark plug misfire can be traced using a method called machine learning. Unluckily, a challenge to get a high-efficiency rate because of a massive volume of training data is required. During the study, these failure-generated were enhanced with a novel statistical signal-based analysis called Z-freq to improve the exploration. This study is an exploration of the time and frequency content attained from the engine after it goes under a specific situation. Throughout the trial, the misfire was formed by cutting the voltage supplied to simulate the actual outcome of the worn-out spark plug. The failure produced by fault signals from the spark plug misfire were collected using great sensitivity, space-saving and a robust piezo-based sensor named accelerometer. The achieved result and analysis indicated a significant pattern in the coefficient value and scattering of Z-freq data for spark plug misfire. Lastly, the simulation and experimen...

Energies, 2009

Current limiting fuses are widely used to protect the thyristors in DC drive systems. One very im... more Current limiting fuses are widely used to protect the thyristors in DC drive systems. One very important problem is the choice of the correct voltage rating for fuses protecting regenerative DC drives, where many types of fault may occur, which makes fuse protection difficult. In the event of a commutation failure while regenerating, the fuses need to interrupt the loop supplied by the AC and DC voltages acting in series, which is the most difficult case for protection by fuses. In this paper a detailed study of the complete interruption process has been investigated by modeling of arcing process of the fuse protection against the regenerative circuit internal commutation fault. The effect of varying the motor time constant, supply impedance, number of fuses used to clear the fault and DC machine rating on the total transient response is studied. The model of a 200 A fuse is employed in this study. Fuses in series with both the semiconductor devices (F1) and fuses in AC lines (F2) a...

International Journal of Molecular Medicine

Autophagy is a lysosome-mediated cell contentdependent degradation pathway that leads to enhanced... more Autophagy is a lysosome-mediated cell contentdependent degradation pathway that leads to enhanced inflammation in an uncontrolled state. This study examined the role of autophagy in lipopolysaccharide (LPS)-induced brain inflammation and the effects of the traditional chinese medicine ligustrazine on LPS-induced neurocognitive impairment in rats. Furthermore, the molecular mechanisms by which ligustrazine influences neurocognitive impairments were explored. The production of the inflammatory mediators interleukin (IL)-1β and tumor necrosis factor (TNF)-α was analyzed using ELISAs, and the expression levels of the autophagy marker microtubule-associated protein light chain 3 (Lc3) II/I were analyzed using western blotting. LPS exposure upregulated the expression of IL-1β and TNF-α and downregulated the expression of Lc3 II/I. Ligustrazine activated autophagy by preventing the expression of phosphoinositide 3-kinase (PI3K), phosphorylated protein kinase B (p-AKT), and phosphorylated mammalian target of rapamycin (p-mTOR). The present results suggest that ligustrazine improved LPS-induced neurocognitive impairments by activating autophagy and ameliorated neuronal injury by regulating the PI3K/AKT/mTOR signaling pathway. These findings provide an important reference for the prevention and treatment of neuroinflammation.

International Journal of Instruction

This study aimed to determine the effectiveness of critical thinking aspects on the module to enh... more This study aimed to determine the effectiveness of critical thinking aspects on the module to enhance students' academic achievement of human circulatory system. Research participants were 8th grade of junior high school students, which assigned into experimental and control groups (each group consists of 27 students) based on One-Way ANOVA result (0.255; p-value > 0.05). This quasiexperimental study applied a pre-test and post-test non-equivalent control group design. An analysis of covariance (ANCOVA) was used to analyse pre-test and post-test data, with pre-test as a covariate. Analysis result showed that: 1) students' academic achievement in the experimental group is significantly different with the control group (F=90.562; p-value < 0.05); 2) students' academic achievement gap in the experimental group is lower than the control group; and 3) applying critical thinking aspects on the module have an effective contribution of 64% in students' academic achievement enhancement of the experimental group. Therefore, the use of critical thinking aspects on the module has a good impact on students' academic achievement.

Development of statistical theory in reliability engineering: A survey

Microelectronics and reliability, 1994

... In Reliability and Fault Tree Analysis, RE Barlow et al. Eds., SIAM Philadelphia, PA Barlow, ... more ... In Reliability and Fault Tree Analysis, RE Barlow et al. Eds., SIAM Philadelphia, PA Barlow, RE and Proschan, F. (1981). ... J. Amer. Statist. ... Monotonicity of ratios of means and other applications of majorization. In Inequalities, ed. O. Shisha, Academic Press, New York, 177190. ...