The increasing emphasis on clean emission-free transportation has stressed upon the high adoption and penetration of electric vehicles worldwide. However, challenges like range anxiety and long charging time hinder the widespread acceptance. Among various solution the usage of battery swapping station seems more promising as it provide quick battery refueling within a very short time period. The battery swapping station's progress is limited due to the associated investment and operational cost which needs to be addressed to ensure the global acceptance. In this paper, an optimal battery swapping station operation is proposed based on a multi-objective optimization which combines the generation mix of grid, solar PV, and biogas generation along with the battery arrival using mixed integer programming and orderly charging of discharged batteries to allow the swapping station to operate in battery-to-grid mode using time-dependent dynamic programming. The former reduced the cost of charging while the later increases the swapping station revenue. The combined multi-objective optimization increases the daily net profit by almost 20 times as compared to the base case and by 8% in the optimal operation when the batteries are allowed to be discharged in battery-to-grid mode. The results show that the proposed strategy has potential to significantly boost the daily net profit of the swapping station by increasing revenue and cut cost and can contribute towards the acceptance of swapping station in the current electric vehicle market.
This paper reports analysis on an automatic intelligent controller use to drive a prototype fuel cell electric vehicle while maintaining maximum efficiency and completing a course with in a specific period of time. The objective is to reduce driving error while minimizing energy usage for the Shell Eco-marathon Asia 2014 race. The vehicle is equipped with a proton exchange membrane (PEM) fuel cell system, brush DC motor and DC/DC converter. This prototype vehicle is a single seater with streamline body shape that is designed for energy-efficiency race with the objective of
driving the archive furthest distance with the least amount of fuel within the specific time given. In the design’s process, the car’s fuel cell efficiency test, energy demand,
track behavior, motor efficiency analysis, and driving control strategy must be conducted. Experiment on automated intelligent controller was conduct to analyze the performance of powertrain system for certain time given. This powertrain system for automated intelligent controller analysis is part of energy efficiency study for electric
vehicle. It forms the basis of knowledge for the energy efficiency analysis.
Growing power demand and carbon emissions is motivating utility providers to introduce smart power systems. One of the most promising technology to deliver cheaper and smarter electricity is demand side management. A DSM solution controls the devices at user premises in order to achieve overall goals of lower cost for consumer and utility. To achieve this various technologies from different domains come in to play from power electronics to sensor networks to machine learning and distributed systems design. The eventual system is a large, distributed software system over a heterogeneous environment and systems. Whereas various algorithms to plan the DSM schedule have been proposed, no concerted effort has been made to propose models and architectures to develop such a complex software system. This lack of models provides for a haphazard landscape for researchers and practitioners leading to confused requirements and overlapping concerns of domains. This was observed by the authors in developing a DSM system for their lab and faculty housing. To this end in this paper we present a model to develop software systems to deliver DSM. In addition to the model, we present a road map of software engineering research to aid development of future DSM systems. This is based on our observations and insights of the developed DSM systems.
Background: Chronic kidney disease (CKD) patients, especially those on hemodialysis, are at increased risk of developing hepatitis B virus (HBV) infection. Guidelines suggest that all patients with CKD should be vaccinated against HBV, but these guidelines are usually not followed. We conducted this studyto know the status of vaccination against HBV in CKD patients on regular hemodialysis.Methods: This observational descriptive study was conducted at the Department of Medicine, Sheikh Khalifa Bin Zayed Teaching Hospital, Poonch Medical College Rawalakot , and POF Teaching Hospital, Wah Medical College Wah Cantt, from March to July 2019. Patients reporting to the dialysis center of both hospitals on regular dialysis were included in the study. Patient information (HBV vaccination status, age, gender, education, socioeconomic status, duration of CKD and duration of dialysis) were collected on a specially designed questionnaire. The statistical analysis of data was done in SPSS for Windows, version 20.Results: A total 149 patients were included in the study, 63.1% were male and 36.9% were female. Out of these 24.2% were uneducated, 33.6% had 1-10 years school education, 38.2% had 10-14 years education, and 4% had more than 14 years education. About 35% patients were from low socioeconomic class, 54% from middle and 11% from higher class. Only 45.6% (n=68) of patients were vaccinated and 54.4% (n=81) were not vaccinated against HBV. Vaccination status was significantly associated with education (p=0.004) and socioeconomic status (p=0.008).Conclusion: TheHBV status of patients on regular hemodialysis is not satisfactory at the two centers observed. It is associated with education and socioeconomic status of the patient.
Accurate short-term load forecasting (STLF) is essential for the efficient operation of the power sector. Due to heightened volatility and intrinsic stochasticity, forecasting load at a fine resolution, such as weekly load, is difficult. Existing STLF techniques only rely on temporal data and auto-regressive processes to forecast load. However, the power grid has a graphical structure that provides spatial information too. This paper proposes an innovative STLF method fusing both spatial and temporal information. We propose a creative way to convert load data into graphical form, which is fed into graph convolutional networks (GCN) to learn spatial embeddings. The GCN embeddings are used along with temporal features to predict the load. We perform extensive experiments using state-of-the-art machine learning and deep learning techniques to validate our approach. The results demonstrate that by using spatial information, we can sub-stantially improve the forecasting performance.
Reliable telecommunication tower operation is paramount for sustainable cities, as it ensures uninterrupted communication, supports economic growth, facilitates smart city applications, and enables emergency response. This study evaluates the reliability and economic aspects of three hybrid system configurations aimed at providing uninterrupted power supply to base transceiver stations (BTS) during power outages. A framework is developed to optimize power operation and assess the operational costs of these configurations. A case study is conducted to examine the effectiveness of the optimization framework. The study evaluates the system size and costs of solar PV, hydrogen fuel cell, and battery energy storage system. The results demonstrate that system architecture combining a utility grid with battery energy storage and solar PV offers the most cost-effective option. The system architecture incorporating utility grid with battery energy storage and hydrogen fuel cell provides the highest reliability. Per-day operating cost of the solar PV-based architecture is 40.3% lower than that of the architecture with hydrogen fuel cell system, and 35.8% lower than the system architecture of utility grid and battery storage. This study contributes to the integration of renewable power sources and optimization framework, enhancing the sustainability of energy supply and promoting the long-term well-being of society.
Many studies have shown that students often face difficulty in applying programming concepts to design a program that solves a given task. To impart better problem solving skills a number of pedagogical approaches have been presented in the literature. However, most of these approaches provide a general strategy of problem solving. But in reality problem solving is a skill that is developed with experience over a period of time. In this paper, we present a pedagogical approach to teach problem solving using think-alouds. In a think-aloud problem solving approach students learn the skill of problem solving by closely observing an 'experienced programmer. We used this approach in a CS2 class and our evaluation results show that think-aloud problem solving is an extremely effective pedagogical technique, particularly for female students.
Background: The discipline of otorhinolaryngology specialty is a very promising field all over the globe. This field is equipped with modified lifestyle as compared to general surgery field. However, otolaryngology is a flexible, promising and important field but still its trend is decreasing all over the world across the undergraduate’s professionals. Aim: To determine the frequency of otolaryngology specialty choice being taken by 4th year MBBS students. Methods: A descriptive cross-sectional study was conducted in otorhinolaryngology department of Islamabad Medical and Dental College, Islamabad from January to December 2020. The answers were obtained through the Likert scale-5 having the following components, as strongly agree, agree, not sure, disagree, strongly disagree. Results: A 100 undergraduate students participated in this survey. All students were of 4th year MBBS. 20% students were agreed and 40% not sure about the question regarding taking up otorhinolaryngology as a career choice and 15% strongly agreed for this. Conclusion: The study concluded that the graduates are not sure whether they want to choose otorhinolaryngology as their final career choice. We suggest that it should be one of the compulsory rotations during the internship. Keywords: Otorhinolaryngology; Specialty; Undergraduate medical education; Postgraduate; Career choice.
Background Chronic kidney disease (CKD) patients, especially those on hemodialysis, are at increased risk of developing hepatitis B virus (HBV) infection. Guidelines suggest that all patients with CKD should be vaccinated against HBV, but these guidelines are usually not followed. We conducted this study to know the status of vaccination against HBV in CKD patients on regular hemodialysis. Methods This observational descriptive study was conducted at the Department of Medicine, Sheikh Khalifa Bin Zayed Teaching Hospital, Poonch Medical College Rawalakot , and POF Teaching Hospital, Wah Medical College Wah Cantt, from March to July 2019. Patients reporting to the dialysis center of both hospitals on regular dialysis were included in the study. Patient information (HBV vaccination status, age, gender, education, socioeconomic status, duration of CKD and duration of dialysis) were collected on a specially designed questionnaire. The statistical analysis of data was done in SPSS for Windows, version 20. Results A total 149 patients were included in the study, 63.1% were male and 36.9% were female. Out of these 24.2% were uneducated, 33.6% had 1–10 years school education, 38.2% had 10–14 years education, and 4% had more than 14 years education. About 35% patients were from low socioeconomic class, 54% from middle and 11% from higher class. Only 45.6% (n=68) of patients were vaccinated and 54.4% (n=81) were not vaccinated against HBV. Vaccination status was significantly associated with education (p=0.004) and socioeconomic status (p=0.008). Conclusion The HBV status of patients on regular hemodialysis is not satisfactory at the two centers observed. It is associated with education and socioeconomic status of the patient.
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