Integration of Renewable Energy Resources in Microgrid

Microgrid is a new concept in power generation. The Microgrid concept assumes a cluster of loads and micro sources operating as a single controllable system that provides both power and heat to its local area. Not much is known about Microgrid behavior as a whole system. Some models exist which describe the components of a Microgrid. In this paper, model of Microgrids with steady state and their transient responses to changing inputs are presented. Current models of a fuel cell, microturbines, wind turbine and solar cell have been discussed. Finally a complete model built of Microgrid including the power sources, their power electronics, and a load and mains model in MATLAB/Simulink is presented

Implementation of Nanogrids for Future Power System

Microgrid is a new technology in power generation and this system is used to provide power and heat to its local area, such as cogeneration systems and renewable energy (wind turbines, photovoltaic cells, etc.). They are preferred for medium or high power applications. Nanogrid most likely to be used in small local loads for rural area as they will be more economic then the normal grid power system. Nano grids can operate independently or be connected to the mains and most likely the internal voltage can be utilized as ac or dc. In this research paper a small scale microgrid system is proposed for smart homes called "Nanogrid". Each houses have small electrical power system from them can be shared among houses. If it uses a DC system instead of a general AC system, it can reduce energy loss of inverter because each generator doesn’t need an inverter. Furthermore, it can continue to provide a power supply when blackout occurs in the bulk power system. A model of a nanogrid is developed to simulate the operation of the centralized power control. Finally a Simulink model is presented for small houses power range 90-285 KW

Inferring epidemic dynamics using Gaussian process emulation of agent-based simulations

Computational models help decision makers understand epidemic dynamics to optimize public health interventions. Agent-based simulation of disease spread in synthetic populations allows us to compare and contrast different effects across identical populations or to investigate the effect of interventions keeping every other factor constant between ``digital twins''. FRED (A Framework for Reconstructing Epidemiological Dynamics) is an agent-based modeling system with a geo-spatial perspective using a synthetic population that is constructed based on the U.S. census data. In this paper, we show how Gaussian process regression can be used on FRED-synthesized data to infer the differing spatial dispersion of the epidemic dynamics for two disease conditions that start from the same initial conditions and spread among identical populations. Our results showcase the utility of agent-based simulation frameworks such as FRED for inferring differences between conditions where controlling for all confounding factors for such comparisons is next to impossible without synthetic data.Comment: To be presented in Winter Simulation Conference 2023, repository link: https://github.com/abdulrahmanfci/gpr-ab

Estimating Treatment Effects Using Costly Simulation Samples from a Population-Scale Model of Opioid Use Disorder

Large-scale models require substantial computational resources for analysis and studying treatment conditions. Specifically, estimating treatment effects using simulations may require a lot of infeasible resources to allocate at every treatment condition. Therefore, it is essential to develop efficient methods to allocate computational resources for estimating treatment effects. Agent-based simulation allows us to generate highly realistic simulation samples. FRED (A Framework for Reconstructing Epidemiological Dynamics) is an agent-based modeling system with a geospatial perspective using a synthetic population constructed based on the U.S. census data. Given its synthetic population, FRED simulations present a baseline for comparable results from different treatment conditions and treatment conditions. In this paper, we show three other methods for estimating treatment effects. In the first method, we resort to brute-force allocation, where all treatment conditions have an equal number of samples with a relatively large number of simulation runs. In the second method, we try to reduce the number of simulation runs by customizing individual samples required for each treatment effect based on the width of confidence intervals around the mean estimates. In the third method, we use a regression model, which allows us to learn across the treatment conditions such that simulation samples allocated for a treatment condition will help better estimate treatment effects in other conditions. We show that the regression-based methods result in a comparable estimate of treatment effects with less computational resources. The reduced variability and faster convergence of model-based estimates come at the cost of increased bias, and the bias-variance trade-off can be controlled by adjusting the number of model parameters (e.g., including higher-order interaction terms in the regression model).Comment: To be presented in IEEE International Conference on Biomedical and Health Informatics 2023, repository link: https://github.com/abdulrahmanfci/intervention-estimatio

Determination of caffeine in roasted and irradiated coffee beans with gamma rays by high performance liquid chromatography

The present study was carried out to investigate a simple, quick and organic solvent saving procedure for the high performance liquid chromatography determination of caffeine in two different coffee beans (Indonesian and Brazilian) which roasted at two different temperatures (150 & 220 ºC) and irradiated at 3, 6, and 9 kGy doses by gamma rays. A linear calibration curve was generated with caffeine concentration ranging from 0.005 to 0.25 mg/g with correlation coefficient (R2= 0.9995, n=4) and relative standard deviation ? 2.1 %. The developed procedure provided a 7.3 x 10-9 mg/g and 2.2 x 10-8 mg/g limit of detection and limit of quantification, respectively. The developed method was repeatable and could be applied to determine trace amounts of caffeine in popular irradiated coffee beans with three different irradiation doses. Moreover, irradiation treatments at doses up to 9 kGy showed no significant effect on the caffeine content. Keywords: Caffeine determination; High performance liquid chromatography;  Coffee bean; Roasting; Gamma rays; Statistical analysi

Detection & Distinction of Colors using Color Sorting Robotic Arm in a Pick & Place Mechanism

Color sorting Robot is one of the useful, costless and fastest systems in Industrial applications to reduce manual working time and provides less human mistake when manual system is undertaken. The objective of this project is to design an efficient, microcontroller based system that pick up right color of objects and put it down at right place to optimize the productivity, minimizing the cost of the products and decreasing human mistakes. The controller used is a PIC Microcontroller (18F452) having high speed performance, low cost and 32 K bytes program memory. It communicates with color sensor TCS 3200 and various motor modules in real time to detect the right color object and to control the arm movement. Designed system can pick objects of 1kG weight and arm can rotate up to 3600 . Also, the use of easily available components reduces the manufacturing and maintenance costs. The design is quite flexible as the software can be changed according to specific requirements of the user. This makes the proposed system to be an economical, portable and a low maintenance solution for industrial applications

Maternal mortality: a tertiary care hospital experience in Upper Egypt

Background: Maternal mortality is one of the major challenges which face the developing countries throughout the world. The aim of the study is to assess the causes of maternal mortality at Women Health Hospital, Assiut University, Egypt, and to identify the avoidable ones.Methods: Data were collected from records of patients who presented to and/or delivered at Women Health Hospital between 2009 and 2014. Only cases of maternal mortality were included in this study. In our study, we found 213 maternal deaths at our hospital between 2009 and 2014.Results: The maternal mortality ratio decreased progressively from 2009 to 2014 (228 and 89 per 100000 live birth respectively). Moreover, we found that the indirect causes of maternal mortality accounted for 24.9 % of all mortalities. As regards the direct causes of maternal mortality, preeclampsia remained the primary cause and represented 27.7 % of the avoidable causes. The second most frequent cause of direct maternal mortality was postpartum hemorrhage (PPH), which represented 26.8 %.Conclusions: Preeclampsia and PPH, as well as their complications are the leading causes of death in one of the biggest tertiary care university hospitals in Egypt. However, there are other important avoidable predisposing factors that should be dealt with including lack of patient education, delayed transfer from other hospitals, and substandard practice

Stevens Johnson syndrome in Pakistan: a ten-year survey

Objective: A pre-tested questionnaire-based, retrospective study to highlight the causative factors, mode of presentation, complications and outcome of patients with Stevens Johnson syndrome.SETTING: Aga Khan University Hospital over a 10 year period.Methods: All case records with a diagnosis of Steven Johnson Syndrome in the period 1990 to 2000 were retrospectively reviewed. Data was retrieved on a comprehensive questionnaire. The demographic variatbles and drugs taken within the previous 21 days were noted. Date analysis was done by Epi-Info Version 6.0.Results: Of the 101 studied patient files, the most common offender was found to be the Penicillins as a group and Sulfadoxine-Pyrimethamine (Fansidar) when considering all drugs individually. Most common complications included electrolyte disturbances (13.9%) and congestive heart failure (6.9%). Mortality rate was high at 10.1%.CONCLUSION: SJS was found to be a rare condition but having a mortality rate of 10.1%. As it can be induced by a large number of drugs, caution should be practiced while prescribing