ABSTRACT
Recently, and particularly after the Covid19 pandemic period and during teaching different courses, it has been noticed that most of the undergraduate engineering students have rising the type of questions such as ''Why we are learning this particular course?'' and ''What are the main benefits and direct impacts of such course on our future carrier? Also as a direct impact of the new available job requirements, it becomes most importance to prepare future engineers to thrive in recent dynamic changing in employment landscape. Hence for students who want to compete and involved in promising working opportunities, it is important to bridging the gap between teaching courses and the industry requirements by focusing on the concept of ''Industry Ready Engineers Since most of recent jobs concentrate on specific required competencies, the author believes that it is important now to give more focusing on the skill-based learning methodology. This paper introduces an approach focusing on group categorization for the recent specific required skills of electrical engineers;then how to involve these skills in specific teaching courses. The main objectives of such approach is to intentionally improve such group skills (one by one) throughout the all program courses in order to introduce a final graduated engineer with great working readiness skills. The approach is validated and evaluated on teaching the power electronics course 1 as a case study. © 2023 IEEE.
ABSTRACT
One of the main interventions against the spread of COVID-19 is so called social distancing, which has rather large consequence on activities held in the building interior. The direct impact can be seen in the limitations of the number of people present in an enclosed space at the same time in order to fulfil the necessary distance between persons. However, these rather unpleasant restrictions with dramatical impact on functioning of the so-called HORECA (hotel-restaurant-café) services, turned out to have also a positive impact on the indoor acoustic comfort. The main difference can be seen in the decreased noise levels in restaurants. This article compares acoustic performance of one space in two situations (before and during pandemic) and shows to what extent a simulation can be used for prediction of noise from quasi dynamic sound sources (i.e. talking people). © International Building Performance Simulation Association, 2022
ABSTRACT
After the changes in globally traded forest products patterns and supply chains caused by the COVID 19 pandemic, currently, there is another geopolitical event that has a direct impact on a whole spectrum of markets, not least the timber market. The current situation in Ukraine resulted in many sanctions imposed by the global community and regional groups on Russia, some of them directly targeting the trade with forest products. EU is a significant global player on forest products market. In 2020 it contributed to 42% of the world total export and 32% of the world total import of forest products. As the EU member states to a large extent depends on international trade there is a need to examine what may be the implications of such sanctions on the EU wood trade patterns and consequently on the supplies for forest based industries. Therefore, the aim of the paper is to quantify the EU's dependence on timber from Russia and to indicate the possible impacts on international timber trade. © 2022 15th International Scientific Conference WoodEMA 2022 - Crisis Management and Safety Foresight in Forest-Based Sector and SMES Operating in the Global Environment. All rights reserved.
ABSTRACT
The Oil and Gas (O&G) industry is used to cycles of lows and highs due to different challenging economic and political situations. Yet the challenges caused by the sanitary crisis due to the covid-19 pandemic are certainly like no others. The shutdown of a large number of social activities had a direct impact on energy consumption. Many studies [1], [2] and [3] have been published at the beginning of the covid-19 pandemic to predict impacts of the restrictions imposed on a global scale: decline in global oil demand, saturation of storage capacities and delay of exploration and production projects. Companies facing this unprecedented crisis had no option but to adopt innovative ways of driving costs lower and maximizing operational efficiency. As a consequence, the pace at which Data Science (DS) is finding its way to O&G applications has been noticeably accelerated although the O&G industry is one of the latecomers to digitalization [4]. The adoption of DS and data-driven solutions has moved from gaining acceptance in the industry to becoming a necessity to many companies. According to a Gartner survey [5], the O&G industry commitment to investment in digital transformation in general had become the first priority in 2021 while it was third-highest priority in 2019 and not even funded in 2014. This involves investments in data acquisition techniques through innovative sensing technologies but also investments in advanced data aggregation and analytics platforms. AI/ML/analytics are listed in the same survey [5] as "top game-changing technologies in 2021". The 2021 survey also states that 50% of the O&G companies have plans to increase their investments in AI/ML and related fields such as cloud-computing. But adoption and operationalization of DS does not come with no challenges. Acceptance and reliance on data-driven models need a favorable cultural and technical environment that is not necessarily compatible with the conventional corporate-like outlook of O&G companies: Data privacy and ownership regulations can diminish DS efforts. Security restrictions can prevent deployment of ML models to end users. All of these challenges are accentuated by the absence of a clear process model to implement and manage DS projects. In this paper, we survey the actual challenges the O&G industry is facing and present a number of corresponding solutions. The paper is structured as follows. The first section explores the state of the art of data-driven models in the O&G industry. The second section lists the challenges DS is facing within the O&G industry and proposes a classification of these challenges into three main classes, namely: human, data and infrastructure related challenges. The paper also proposes an O&G specific framework for DS projects to overcome these identified challenges. Copyright © 2022, International Petroleum Technology Conference.
ABSTRACT
Beginning in 2020, the new coronavirus began to expand globally. Due to Covid-19, millions of individuals are infected. Initially, the availability of corona test kits was problematic. Researchers examined the present scenario and developed the Covid-19 X-ray scan detection system. In terms of Covid-19 detection, artificial intelligence (AI)-based solutions give superior outcomes. Many AI-based models can not provide optimum results because of the issue of overfitting, which has a direct impact on model efficiency. In this work, we developed the CNN-based classification method based on the pre-trained Inception-v3 for normal, viral pneumonia, lung opacity, and Covid-19 samples. In the suggested model, we employed transfer learning to produce promising results for binary class classification. The presented model attained impressive outcomes with an accuracy of 99.42% for Covid-19 vs. Normal, 99.01% for Covid-19 vs. Lung Opacity, and 99.8% for Covid-19 vs. Viral Pneumonia, and 99.93% for Lung Opacity vs. Viral Pneumonia. Comparing the suggested model to existing deep learning-based systems indicated that ours was better. © 2022 IEEE.
ABSTRACT
Due to the COVID-19 pandemic, to control pandemic situations and its spread, the government took a decision to shut all the educational institutions, which in turn creating a direct impact on many people by causing stress and mental illness. We propose a solution for organizations where they can know the levels of stress faced by the students and could calculate percentage of stress. So for this to be done, students can take up the survey through a google form which consist of the parameters which are helpful in collecting information about mental distress and many other psychological factors faced by the students. The data which is collected from the students is inputted into the model with results the stress levels of the students. © 2022 IEEE.
ABSTRACT
Changes in everyday activities, such as adapting to the new online format due to lockdowns during the COVID-19 pandemic and being far away from family and friends, greatly influenced the emotions and feelings of students and their parents. Assessing the emotions of students' parents at the higher education level is necessary since their emotional well-being has a direct impact on the emotional well-being of students throughout their distant learning experience. In this article, we held a quantitative study over 8 subsequent weeks from the onset of the COVID-19 pandemic in students and parents from the Mexican institution Tecnologico de Monterrey. Using a questionnaire from Inteligencia Audiencias (Intelligence Hearings), students and their parents could register their feelings and their valence from April 13th to July 20th, 2020. The results indicate that the most predominant emotions in both groups were very unpleasant and unpleasant in nature, being "worried"and "tired"the most common ones. The current study also provides some approaches for addressing the negative repercussions of the COVID-19 pandemic. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic situation significantly affected public transport. This paper focuses on assessing the impact of the epidemiological situation on urban public transport. The effects of the pandemic and the ordered measures had a direct impact on passenger numbers as well as mileage. Reduced mobility and anti-pandemic measures in place had an impact on the operation of urban public transport services. The questionnaire survey evaluated changes in the transport habits of the traveling public during the COVID-19 pandemic. The article aims to evaluate the situation of urban public transport before and after the outbreak of the pandemic in the case study and to point out the impact of measures on mobility in urban public transport. © 2022 Kaunas University of Technology. All rights reserved.
ABSTRACT
Nowadays, an average of 2 kg of waste per person are generated in Spain. Furthermore, the household consumption is rising and, as a consequence, the waste production is also increasing. This trend presents a direct impact in the environment. Moreover, after two years of COVID-19 pandemic, it has been detected a stronger rise in consumption per person, while consumption through professional commercial channels for hospitality industry has been lower. This paper analizes the waste generation and product shrinkage in a potato bagging plant, which addresses its production to both final consumers and retailers. The raw materials washing line, as well as the production line, are taken into consideration in the analysis, while new uses to the produced waste are proposed, aiming at providing new useful life, such as the production of bioplastics or the production of biodiesel. As a consequence, the environment impact is minimized and new products are obtained. © 2022 The Authors.
ABSTRACT
The main sources which spread communicable diseases are polluted air and vex bugs. Various kinds of microorganisms such as bacteria, viruses, fungus, and toxic particulate matter are the main pollutants in the air. Insects such as mosquitos and flies, who are called vex bugs are the carriers of those microorganisms. Industrialization and Urbanization have led to environmental pollution, and this has a direct impact on the spreading of infectious diseases. Recently, the biggest pandemic outbreak is the coronavirus. This virus is spread by polluted air. Hence this work has implemented a method using technologies such as IoT and auto-controlling, to mitigate these issues through air purification, air refreshing, and vex bugs controlling. © 2022 IEEE.
ABSTRACT
Oil stock estimation has a direct impact on the oil price and is a critical asset in the global economy. The COVID-19 pandemic and the subsequent lockdowns in multiple countries had a big impact on the oil inventories. In this paper, a methodology to monitor oil inventories using Sentinel-1 data is presented. It exploits the differences in the backscatter response with respect to the roof level in floating roof tanks. The methodology doesn't imply advanced processing techniques (such as interferogram or coherence estimation) and it can be complemented with other data sources (e.g. Sentinel-2, VHR) for a more comprehensive foundation of the oil stock estimation. © 2021 IEEE
ABSTRACT
The control room acts as a central nervous system facility. This is where important decisions, using complex systems, are made every day. The actions of control room operators have a direct impact on uptime, production yields, quality, and industrial plant safety. In addition, long working hours per shift result in fatigue, irregularity of circadian rhythms and sleep cycles, and decreased cognitive performance at the end of day and night shifts. Fatigue causes decreased alertness, attention span, poor memory, and concentration and affect other mental factors. ADNOC Gas Processing established Fatigue Risk Management Taskforce (FRMT) to adapt practices to the specific conditions and create a safer working environment, leading to happier and healthier employees and an overall community. In industries that run continuous and heavy-duty plants such as Oil, gas, and petrochemical, shift work ensures production flow. After the outbreak of Covid-19, business needs to adapt quickly so that their activities can run. The finding suggests that the workers' cognitive performance is reduced, shown by the increase of triggered alarm by the average of 14.39% higher than before the outbreak of Covid-19. However, with the ability to adapt and implement control and monitoring measures, the number of alarm rate gradually decreased. The study framework was proven to be a valuable tool that decision-makers can use, especially to measure the performance of control room workers and their psychological fatigue affected by the Covid-19 pandemic. © Copyright 2021, Society of Petroleum Engineers
ABSTRACT
Because of the growing number of hospitals in the country like the United Arab Emirates, huge medical wastes are generated in the hospitals, and managing this medical waste is considered a big challenge. In recent days, COVID 19 pandemic has paved the way for the generation of relatively huge amounts of infectious and hazardous waste in healthcare hospitals, and proper disposal of this heterogeneous mixture of medical waste is the biggest challenge. Improper waste management developed in health care units causes a direct impact on the workers, waste handlers, patients, caregivers, and the community. Also, it is important to manage the medical waste properly so that the environment will not get affected. In order to overcome this problem, both the manufacturer and the medical practitioner should take utmost care in managing the medical waste properly in all stages, starting from collection to the final disposal. The main aim of this research is to understand the different types of medical waste in the hospital and identify the barriers that impede the effective management of medical waste. For analyzing the interactions among the barriers, Interpretive Structural Modelling (ISM) approach is proposed as a solution methodology in this research work. By analysing the interaction among the barriers using the ISM model, we may extract the most influencing barrier that challenges both hospital management and government in managing medical waste safely and effectively. © 2021 IEEE.
ABSTRACT
Covid-19 pandemics have made innovations even more crucial and used them to take market power over competitors considering challenges that the world and global economy face. To achieve this goal, organizations need competent and high-expertise human capital as a workforce. That is one of the key reasons organizations increase their investment in developing, re-skilling and up-skilling their workforce via various learning and development programs and solutions compared to previous years. Given the direct impact of this process on the company's revenues, the following graph demonstrates the value flow generated (Figure 1): Organizations aim to ensure minimum time and efficient expenditure structures to achieve and build a learning system that delivers sustainable developmental solutions and interventions. Knowledge sustainability is a purpose, which focuses on various learning methods and solutions to make knowledge last and kept longer. A learning management system (shortly, LMS) is a platform that gathers all the learning solutions in one place and automates the process of learning to present development opportunities to end-users - learners/ employees. Digital learning enables users/employees to develop their competencies quickly, no matter the place and time and makes knowledge and information accessible for all, and gives an unlimited option to relearn, repeat and refresh anything already completed unlimitedly. Copyright 2021, Society of Petroleum Engineers
ABSTRACT
Emotional states are social and highly influenced by city attributes. Population, for example, has a direct impact on parameters such as connections, communication, and social interaction based on urban scaling laws. It is likely that population also influences emotional experiences, as emotions are regulated by social activity. To analyze emotional states in social interaction, that is, emotion interaction, urban scaling properties are applied to the Twitter activity and emotion interactions of major cities in the USA and UK. To our knowledge, emotion interaction has yet to be explored through urban scaling laws. Furthermore, the world is facing the COVID-19 pandemic, which has severely affected the physical and emotional well-being of humans. This study compares emotion interaction during COVID-19 and before it started to analyze the impact of population on deviating emotional states. The findings suggest that emotion interaction follows superlinear scaling, i.e., there is an increase in emotion interaction with an increase in population. However, negative emotion interaction tends to increase more in response to population. The statistics on emotional interaction in cities reflect cognitive experiences of cities as well as an understanding of human behavior in expanding urban environments, which can be useful in defining the narratives of cities and developing citizen-centric sustainable and resilient city plans. © 2021 IEEE.
ABSTRACT
The COVID-19 pandemic impacts are deep and pervasive;the far-reaching consequences are only beginning to be understood. In addition to the tragic direct impacts such as deaths and hospitalizations, indirect impacts have devastated the global economy. Supply chain disruptions, quarantine restrictions, and travel bans, as well as other factors, have resulted in a projected 5-8% contraction of the global GDP for 2020 [1]. Disaster planning and risk management are mature fields with extensive literature, yet businesses across the globe were largely unprepared to respond to this crisis due to high levels of emergent conditions. This research examines pandemic-induced crises and argues that the unique nature of these crises makes existing theories and models insufficient to help business practitioners before or during such an event. A confirmatory approach was used to develop a model of key attributes of the more successful companies. This model is supported by empirical performance data that was collected and analyzed using structural equation modeling (SEM). The relatively new concept of “transiliency”, or “the ability to simultaneously restore some processes and change-often radically-others” [2] is also discussed as a characteristic possessed by companies exhibiting better performance during the COVID-19 crisis. This research can be harnessed by engineering managers to create actionable strategies for disaster response and recovery. © 2021 IISE Annual Conference and Expo 2021. All rights reserved.
ABSTRACT
The purpose of this study is to analyze how service quality and customer satisfaction impact behavioral intention, an indicator of hospital choice, in Shanghai during the COVID-19 pandemic. A total of 311 responses were collected through a convenience sampling on social media;however, only 281 responses are accepted due to some responses not being relevant in the context of Shanghai hospital choice. Factor analysis and structural equation model were then conducted to analyze how service quality and customer satisfaction impact behavioral intention. While service quality has a direct impact on both customer satisfaction and behavioral intention, there is no significant impact of customer satisfaction on behavioral intention. Findings can help hospital administrators better adjust prevention policies and healthcare practices during the COVID-19 pandemic to improve service quality and increase hospital visits. © 2021 ACM. Copyright held by the owner/author(s). Publication rights licensed to ACM.