Stakeholder perspectives of adaptations of a burnout intervention for special education teachers

The high attrition and turnover rates of qualified special education teachers (SETs) is a significant concern exacerbated by COVID-19. Unfortunately, there are limited studies available on research-based interventions to decrease burnout. The purpose of this study was to describe our processes and results for adaptations and modifications of BREATHE, a burnout intervention originally developed for community mental health workers, into Burnout Reduction: Enhanced Awareness, Tools, Handouts, and Education: Evidence-based Activities for Stress for Educators (BREATHE-EASE) for special educators with guidance from the Framework for Reporting Adaptation and Modifications to Evidence-Based Interventions (FRAME). We applied the FRAME within a hybrid Type 1 trial for characterizing our approach. Four focus groups (N = 30;83% female) were conducted separately according to job title (SETs;school administrators), with semi-structured questions tailored to each group. Emergent thematic analysis was used to identify core themes related to adaptations, and results were presented to a subset of focus group members. Modifications involved content, context, and implementation changes for the adapted intervention, with most changes identified for content. FRAME was helpful for providing a systematic approach to integrate stakeholder-informed adaptations of a burnout intervention, addressing significant concerns of SET stress, burnout, and attrition.

Vito Project: Subsea System Goes Back to Basics

In 2009, the Vito field was discovered in more than 4,000 ft of water approximately 150 miles offshore from New Orleans, Louisiana. The project produces from reservoirs nearly 30,000 feet below sea level. The project underwent major redesign to remain competitive, and this paper describes changes within the subsea system. This paper is part of a Vito Project series at OTC 2023, and the other papers are listed in the references. As the industry and market began to change in 2015, the project faced significant financial hurdles, and the project team decided to refresh the field development concept to reduce cost and simplify. This paper focuses on the subsea production system and some of the key decisions leading to the selected design and the approach the team used for making these decisions. It also discusses how the project execution model was established, and the modifications made during project execution to react to schedule challenges, the unprecedented impacts of COVID-19, and a tightening offshore market. © 2023, Offshore Technology Conference.

INFLUENCE OF THE COVID-19 PANDEMIC ON REDUCING THE INCOME OF WORKERS

In Vietnam, the impact of COVID-19 on the economy is also huge, the economy was severely affected with the gross domestic product (GDP) growth at the lowest level in a decade, and most important industries saw a decline in growth, employment, and growing income is seriously affected (Dat, 2020). The COVID-19 pandemic has been affecting many aspects of the economy and society;many enterprises, business households, and cooperatives had to suspend operations, reduce production scale or rotate production, directly affecting the income of employees. This article uses the employment survey data of the General Statistics Office in 2021 and uses labor law (National Assembly, 2019) to analyze the influence of the COVID-19 pandemic on reducing the income of workers in Vietnam. The model estimation results show that the influence of the COVID-19 pandemic on income reduction does not differ between male and female workers, and has a strong influence on the group without professional and technical qualifications. Based on the findings, several suggestions are proposed to improve workers' income in the context of COVID-19. © 2023 The Authors.

Optimizing computational costs of Spark for SARS-CoV-2 sequences comparisons on a commercial cloud

Cloud computing is currently one of the prime choices in the computing infrastructure landscape. In addition to advantages such as the pay-per-use bill model and resource elasticity, there are technical benefits regarding heterogeneity and large-scale configuration. Alongside the classical need for performance, for example, time, space, and energy, there is an interest in the financial cost that might come from budget constraints. Based on scalability considerations and the pricing model of traditional public clouds, a reasonable optimization strategy output could be the most suitable configuration of virtual machines to run a specific workload. From the perspective of runtime and monetary cost optimizations, we provide the adaptation of a Hadoop applications execution cost model extracted from the literature aiming at Spark applications modeled with the MapReduce paradigm. We evaluate our optimizer model executing an improved version of the Diff Sequences Spark application to perform SARS-CoV-2 coronavirus pairwise sequence comparisons using the AWS EC2's virtual machine instances. The experimental results with our model outperformed 80% of the random resource selection scenarios. By only employing spot worker nodes exposed to revocation scenarios rather than on-demand workers, we obtained an average monetary cost reduction of 35.66% with a slight runtime increase of 3.36%. © 2023 John Wiley & Sons, Ltd.

Segmenting with big data analytics and Python: A quantitative exploratory analysis of household savings

According to the national balance sheets of the most advanced economies, despite a recent sharp decline in per capita net wealth, Italian private households present a higher rate among the wealthiest and least indebted in Europe. Recently, the COVID-19 outbreak caused a new leap in households' savings worldwide, particularly in advanced economies and Italy. This study underlines that using advanced analytics tools, household saving behaviour information, and big data analytics may support data-driven decision approaches addressing the management of complex relationships in the financial arena. More specifically, using exploratory and predictive analyses based on big data analytics and machine learning, this study aims to provide extensive customer profiling in the household saving sector in Italy, supporting a data-driven decision-making approach. A profiling of household savings has been defined using the information provided by big data analysis. To proceed in this direction, the hardware and software requirements necessary to perform data processing were considered in the first phase of the study. Data collection was performed according to the so-called extract, transform, load (ETL) process. The contribution of this study lies in the results obtained in terms of data analytics over a dataset that accounts for the purchasing behaviour of almost 20 million postal savers. The clustering algorithm is highly efficient and scales well for large datasets. K-means clustering can be implemented within the MapReduce computational framework. Therefore, the overall procedure proposed here can be easily extended to big data using parallel computing and software implementing MapReduce, such as Hadoop and Spark. © 2023 Elsevier Inc.

Applying Reuse and Reduce in an Undergraduate Organic Laboratory: Sustainable Practices

In this section of Resonance, we invite readers to pose questions likely to be raised in a classroom situation. We may suggest strategies for dealing with them, or invite responses, or both. "Classroom " is equally a forum for raising broader issues and sharing personal experiences and viewpoints on matters related to teaching and learning science.Amidst the Covid-19 pandemic, we have planned a strategy for our institution which aims towards reuse and reduce principles of Green Chemistry. Organic preparations in the undergraduate curriculum can be utilized for other sister laboratory experiments such as recrystallization, determination of physical constants (m.pt) and detection of extra elements, detection of functional group and in qualitative analysis. The product of preparation can also be subjected to a second synthesis. This approach will reduce the amount of chemicals needed for carrying out experiments other than organic preparations. This paper illustrates a few organic preparations which can be reused for other companion laboratory exercises. This approach may set a model towards sustainability for other undergraduate laboratories.

Restaurant customers' food leftover reduction intention derived from nature connection and biospheric values: A comparison between men and women.

As the COVID-19 pandemic extends over a long period of time, the World Food Programme (WFP) estimated that food insecurity would take place in the near future. Previous studies focused on various kinds of interventions for food waste prevention. Surprisingly, however, research tackling consumer attitudes and behaviors as a way to reduce food waste is still rare. To fill this gap in the literature, this study examined the antecedent roles of restaurant customers' nature connection and biospheric values in fostering their food leftover reduction intention through environmental self-identity and sense of obligation to reduce food leftover. In addition, the moderating effects of gender were tested on all the relationships in our conceptual model. A quantitative approach with an online survey for restaurant customers was adopted. Structural equation modeling was adopted to analyze the data. Through confirmatory factor analyses, the adequate reliability and validity of the measures were established. All the relationships between the constructs were found to be significant, supporting the hypotheses. In other words, the restaurant customers' nature connection and biospheric values were found to eventually induce the customers' food leftover reduction intention. In addition, in terms of the moderating effect, the male customers' nature connection more strongly increased their biospheric values than the female customers' case. The findings of this study revealed how restaurant customers' food leftover reduction intention is formed through their feeling of oneness with nature and biospheric values. Given that consumer behavior has been recognized as a major driver of restaurant food waste, the findings of this study provide useful insights to restauranteurs and policymakers for the health of society and people in it. It was especially true for men in that their feeling of oneness with nature significantly influences their biospheric values more than women's.

A comprehensive indoor hygiene concept for infection prevention and control within built environments

The COVID-19 pandemic has shown that actions related to infection prevention and control (IPC) need to be made more efficient, especially in indoor public spaces. Many standalone technologies and solutions are available to increase the hygiene levels of indoor environments. However, it is not clear how these technologies and solutions can be combined and adapted to building processes such that they cover the entire indoor environment and life cycle of a building-from its design to its use and maintenance. The construction industry faces challenges in this regard because many actors are involved, and interactions at multiple levels can hinder the implementation of innovations. Therefore, the aim of this article is to establish a framework for IPC within built environments by introducing a new indoor hygiene concept (IHC). It provides a tool for implementing necessary IPC actions during a building's life cycle to construct or renovate hygienic indoor environments. The IHC is based on the idea that all the elements of an indoor environment need to be considered to create a hygienic building. In addition, hygiene objectives need to be set at an early stage of the construction process and monitored throughout all the phases of a building's life cycle. This comprehensive approach enables designers, engineers, and other actors involved in different stages of a building's life cycle to see their roles in the IPC of shared public spaces. Adopting this approach can result in fewer infection transmissions via indoor environments and, in turn, cost benefits for society.

Vitamin D status can affect COVID-19 outcomes also in pediatric population.

Background: vitamin D influences the immune system and the inflammatory response. It is known that vitamin D supplementation reduces the risk of acute respiratory tract infection. In the last two years, many researchers have investigated vitamin D's role in the pathophysiology of COVID-19 disease. Results: the findings obtained from clinical trials and systematic reviews highlight that most patients with COVID-19 have decreased vitamin D levels and low levels of vitamin D increase the risk of severe disease. This evidence seems to be also confirmed in the pediatric population. Conclusions: further studies (systematic review and meta-analysis) conducted on children are needed to confirm that vitamin D affects COVID-19 outcomes and to determine the effectiveness of supplementation and the appropriate dose, duration and mode of administration.

Analysis of regional differences and decomposition of carbon emissions in China based on generalized divisia index method

The achievement of China's carbon dioxide (CO2) emission reduction target is of great significance in the face of global climate change. Accurate identification of key factors that affect CO2 emissions can provide theoretical support to policymakers when designing related policies. Compared to the traditional method, the generalized Divisia index method (GDIM) can capture the influence of multiple scale factors on carbon emissions, providing new tools for studying the decomposition of carbon emissions. The article proposed a GDIM-based decomposition method to analyze the drivers that influence CO2 emissions in China from 2000 to 2017. The results indicate that investment activity is the primary element in promoting China's carbon emissions, followed by energy use and economic activities. On the contrary, investment carbon intensity is the vital inhibitory factor, followed by GDP carbon intensity. Specifically, the positive driving force of investment and energy use is gradually weakening, while the contribution of economic activities is continuously strengthening. The effectiveness of carbon emission reduction in the Northeast, East, and Southwest is actively promoting China's carbon emission reduction, while the effectiveness of CO2 emission reduction in the Northwest is not performing well. The findings provide support and reference for carbon emission control in China. (C) 2022 Elsevier Ltd. All rights reserved.

Unraveling the mystery of efficacy in Chinese medicine formula: New approaches and technologies for research on pharmacodynamic substances.

Traditional Chinese medicine (TCM) is the key to unlock treasures of Chinese civilization. TCM and its compound play a beneficial role in medical activities to cure diseases, especially in major public health events such as novel coronavirus epidemics across the globe. The chemical composition in Chinese medicine formula is complex and diverse, but their effective substances resemble "mystery boxes". Revealing their active ingredients and their mechanisms of action has become focal point and difficulty of research for herbalists. Although the existing research methods are numerous and constantly updated iteratively, there is remain a lack of prospective reviews. Hence, this paper provides a comprehensive account of existing new approaches and technologies based on previous studies with an in vitro to in vivo perspective. In addition, the bottlenecks of studies on Chinese medicine formula effective substances are also revealed. Especially, we look ahead to new perspectives, technologies and applications for its future development. This work reviews based on new perspectives to open horizons for the future research. Consequently, herbal compounding pharmaceutical substances study should carry on the essence of TCM while pursuing innovations in the field.

Docker Container based Crowd Control Analysis Using Dask Hadoop Framework

Crowd control is a public policy technique in which massive crowds are handled in order to avoid the emergence of possible issues or threats caused by COVID-19 and over-crowding. In this pandemic, social distancing is critical as there is a high chance of being infected in a crowd. With mounting fears about public disease transmission, the significance of crowd monitoring is crucial in these testing times. In the existing system, the model takes more time and resources to process the data from the crowd control application thus resulting in delayed prediction. Early prediction of the crowd level will help people and other government agencies to control and monitor the crowd. Hence, the main goal of the proposed system is to process a large amount of input from the crowd control application in minimal time using Dynamic Task Scheduling (Dask) based Hadoop framework in a multi-node docker cluster. The multi-node cluster processes the input data in different clusters. Each cluster data is fed to model for prediction and forecasting the count of crowd at a location. The models considered for evaluation are RNN_LSTM and ARIMA. The results shown that RNN_LSTM model has provided better accuracy of 97% compared to the ARIMA of 89%. The results show that the prediction performance of RNN_LSTM has shown 40% decrease in Mean Absolute Error (MAE) and 30% decrease in Root Mean Squared Error (RMSE) over the existing ARIMA model. The proposed system is available as an application to the public and enable them to decide whether to visit a particular place or not. © 2022 ACM.

AISAR: Artificial Intelligence-Based Student Assessment and Recommendation System for E-Learning in Big Data

Educational systems have advanced with the use of electronic learning (e-learning), which is a promising solution for long-distance learners. Students who engage in e-learning can access tests and exams online, making education more flexible and accessible. This work reports on the design of an e-learning system that makes recommendations to students to improve their learning. This artificial intelligence-based student assessment and recommendation (AISAR) system consists of score estimation, clustering, performance prediction, and recommendation. In addition, the importance of student authentication is recognised in situations in which students must authenticate themselves prior to using the e-learning system using their identity, password, and personal identification number. Individual scores are determined using a recurrent neural network (RNN) based on student engagement and examination scores. Then, a density-based spatial clustering algorithm (DBSCAN) using Mahalanobis distance clustering is implemented to group students based on their obtained score values. The constructed clusters are validated by estimating purity and entropy. Student performance is predicted using a threshold-based MapReduce (TMR) procedure from the score-based cluster. When predicting student performance, students are classified into two groups: average and poor, with the former being divided into below- and above-average students and the latter into poor and very poor students. This categorisation aims to provide useful recommendations for learning. A recommendation reinforcement learning algorithm, the rule-based state–action–reward–state–action (R-SARSA) algorithm, is incorporated for evaluation. Students were required to work on their subjects according to the provided recommendations. This e-learning recommendation system achieves better performance in terms of true-positives, false-positives, true-negatives, false-negatives, precision, recall, and accuracy.

Dynamic Analysis of Demographic Sentiment

There is no doubt that big data analysis has a very positive impact on economics, security, and other aspects for countries and enterprises alike. Where we have recently noticed the frantic competition between companies to increase their profits by analyzing the largest amount of data as quickly as possible. Especially analyzing data related to Covid-19 to make the most of information in all areas. Covid-19 has drastically affected many lives in recent years but, even in these hard times, businesses can leverage the current pandemic to make a profit. In this paper, we investigate a variety of tweets using MapReduce, Spark, and Machine Learning methods to determine the sentiment of a given tweet based on the information provided by the dataset. With this information, businesses could learn how to present Covid-19 and pandemic related goods and information in a way that will be well received by its audience. To take this a step further, we will investigate trends in sentiment across demographics tweeting about the virus. This information in sentiment is dynamically useful to understand how specific audiences feel about the pandemic. We explore which Machine Learning methods produce the best results such as Multi-Layer Perceptron neural networks and Logistic Regression. © 2022 IEEE.

Applying Digital Twin Technology in Higher Education: An Automation Line Case Study

Production systems are being expanded to include Digital Twins (DTs) as part of increased industrial digitalization. DTs can bring benefits e.g., increase visibility, safety, and accessibility of the system. Further, digital experimentation can reduce time and cost. Though, application of DT technologies involves challenges i.e., model accuracy or errors in transferring data or codes between the DT and the physical twin. Many studies on DTs focus on industrial applications. However, DT technology has potential for implementation of digital labs in education. This aspect of DTs is of rising importance as distance education has increased over the last decade and access to physical laboratories can be restricted due to factors such as the Covid-19 pandemic. Thus, there is a need to study the use of DT technology in higher education. To address this, we investigate possibilities and challenges of applying DT technology in education to conduct industrial-like labs virtually. A case of an automation line, with full scale industrial equipment, based at a research center, is focused. Results emphasize that the application of DT technologies require multi-domain expertise to understand the consequences of every single decision in the design process on every piece of equipment involved, making the modelling process complex and time consuming. Thus, when applied in education, test procedures need to be designed to focus on students' motivation, improved learning and understanding of production systems. DTs are considered enabling technologies supporting the concept of Industry 5.0, thus stressing the human-centric aspects of advancing Industry 4.0. The predicted application of DTs emphasizes the need for educational curricula that include laboratory applications and theoretic understanding of DT technologies. This study focusses the application of DT technologies in higher education curricula, but the result of the study can contribute to other areas such as automation and virtual commissioning towards smarter manufacturing. © 2022 The authors and IOS Press.

Procyclicality of loan-loss provisions and competitive environment - a global perspective

Purpose Despite the extensive debate on the impact of bank competition on risk-taking, there is no evidence of its role in procyclicality of loan-loss provisions (LLPs). The purpose of this study is to find out what is the role of competition in the procyclicality of LLPs. Design/methodology/approach Using over 70,000 bank-level observations in 103 countries in 2004-2015 and the LLPs model, this study interacts competition with business cycle to check what is the effect of competition on procyclicality of LLPs. Findings This study finds that intense competition is associated with more procyclicality of LLPs. Increased procyclicality of LLPs in a more competitive environment is binding for high-income countries. The opposite effect is shown for low-income countries. Research limitations/implications Future research can be extended by testing the role of additional factors - such as regulations, supervision or institutional protection of shareholders' rights, in the association between procyclicality and competition. Practical implications The main message of this paper is that the competitive environment changes the procyclicality of LLPs. The results are important from the point of view of the COVID-19 pandemic because government interventions during lockdowns will affect competition in the banking industry and in other industries of the economy. Originality/value This paper contributes to the extant research in three dimensions. First, it shows that competition is an important factor behind procyclicality of LLPs. Second, it adds to the research on the links between competition and financial stability. Third, it shows that the link between competition and procyclicality of LLPs depends on the economic development of the country in which the banks are located.

Development and Prospect of Launch Vehicle Ground Testing Technology

As world space launch activities have entered an intensive stage, how to effectively improve efficiency, reduce costs, and enhance the ability to go into space while ensuring reliability and safety has become an important factor in measuring space capabilities. The launch vehicle must fly reliably and stably, and send the satellite into the predetermined orbit accurately. Not only is the important role of the systems on the vehicle, but ground testing and launch control also play a vital role in ensuring the success of the launch vehicle mission. The emergence of COVID-19 in early 2020 also challenged the personnel-intensive industrial model. Intelligent, unmanned, efficient, and system will be the dominant model in the future. This paper reviews the development status of the world's launch vehicle test launch technology, analyzes the capabilities and shortcomings of existing test launch technology, and proposes the development trend of future launch vehicle test launch technology based on new technologies emerging from the new round of scientific and technological revolution. The outlook for next-generation test launch system is also presented. Future test launch technologies will highlight the three characteristics of digitalization, networking and intelligence. Digitization lays the foundation for test launch informationization. Its development trend is big data analysis and application, replacing the existing software tools to extract, store, search, share, analyze, and process massive and complex data sets to achieve depth test launch data mining and maximum value. Networking provides a physical carrier for information dissemination. Its development trend is the adoption of Cyber-Physical Systems (CPS), integrated computing, communication, and control. Through networking, ground test transmitting equipment has computing, communication, precise control, remote coordination, autonomy and other functions. Intelligence reflects the level of information application. Its development trend is a new generation of artificial intelligence. According to the requirements of vehicle launch, it could quickly generate data and upload binding. Through intelligent detection methods, it could complete the required operations, inspections and tests before launching, and achieve autonomous vehicle launching. In the future, intelligent cyber-physical fusion system based on big data will become the mainstream direction of rocket vehicle test launch technology, which will further simplify operations, improve efficiency, reduce costs, and achieve the goal of "launch during transport". © 2021 by the International Astronautical Federation (IAF). All rights reserved.

Digitally Distanced Inspection & Maintenance at Drilling Rigs: Applied Augmented Reality

For drilling contractors, the moment of truth is the operations at the site. If the technician at the site encounters a problem he can't solve, then everything stops. The team has to wait for a subject matter expert (SME) to arrive at the site to diagnose rectify the problem. Such process of SME mobilization and till that time Non-Productive Time (NPT) results in loss of hundreds of thousands of dollars. Hence the key challenge is converting the Sparse to Adequate availability of Right Knowledge at Right Time at Right Place, for the support of technicians. This paper is focused on the approach of moving from Hand Held devices to Hands-Free environment at sites and connecting local/global support to site support systems, to reduce cost, improve HSE and enhance operational performance. The augmented reality technology-enabled, smart glass laced headsets are rugged, zone 1 certified, and are voice-operated which are better than smart tablets which were considered during Technology Qualification Process. Evaluation criteria were: 1. Availability and follow up of the digital work instruction while operating. Moreover, not missing a single step of work instruction while inspection or maintenance continues was noted carefully. 2. Reduced travel/accommodation cost: Normally at the time of shutdown, the rig crew contacts subject matter experts (SME) and (at times) in turn the SME contacts the OEM support team to mobilize service engineers globally. 3. Response time improvement-Availability of support by SME right at the time of need results from better response time to diagnose and fix the issue at hand. Call logging till final resolution process improvement is considered an important metric. Travel restrictions imposed by Covid-19, are also being addressed through the distanced inspection. A hands-free environment is compared vis a vis handheld device. Better training and knowledge transfer are achieved through better communication methods and this goes better with learning by doing. Subsequent text (NLP-speech to text) analysis is planned through deep learning models to derive related predictions. Sparse to Adequate availability of support to rig staff with Right Knowledge at Right Place at Right Time is the key outcome of this Proof of Value project. © Copyright 2021, Society of Petroleum Engineers

Performance Comparison of Apache Hadoop and Apache Spark for COVID-19 data sets

Nowadays the world is fighting against a global pandemic Covid-19 that has resulted in more than 5 million deaths and badly impacted world economy. The global spread of COVID-19 has triggered innovative research in the field of distributed computing using Big Data management tools. Big data analytics tools are used to better understand virus spread, to detect and track Covid-19 symptoms, to estimate risk factors, symptoms, diagnostics and other vital information and to control its spread. This paper presents a review of big data solutions that has been adopted to solve research issues in healthcare by performing distributed computing on massive datasets. In the proposed work, Apache Hadoop with MapReduce framework and Spark is used to perform analytics on Covid-19 datasets in parallel and distributive manner. Both frameworks have configuration parameters which can be modified to facilitate job performance and efficiency. This paper compares the performance of two major Bigdata platforms Hadoop and Spark. The execution time and throughput of both frameworks are analyzed with different input data size. The results shows that both platforms can be used to effectively to process huge amount of data in parallel and distributed computing and the performance depends on size of input data and configuration parameters. The results show that Spark has significantly faster computation time than Hadoop for smaller data sets. © 2022 IEEE

Preventing Against Virus Transmission by Detecting Facemask for Gateway Operation System

The COVID-19 epidemic compelled régimes around the globe to enact quarantine in order to protect the virus from transmitting. According to the documentation, wearing a face mask at work minimizes the chance of spreading. Use of AI to provide an innocuous milieu in a production setup that is both efficient and cost-effective. Face mask detection will be enabled utilizing a mixed model combining machine learning and deep learning. We will utilize Open CV to do real-time face detection from a live feed through our camera using a face mask detection library that comprises of photos with and without a mask. In this work, this dataset is utilized to create a COVID-19 face mask detector with CV utilizing Open CV, Python, Tensor Flow, and other tools. The mail aim of the work is to find whether the being on the image/cinematic rivulet is exhausting a face mask or not through the aid of deep learning and computer revelation. By using this face mask detection, we are going to make a gateway system. This system allows people in only if they wear a face mask. We use Raspberry pi to make this system and an a4899 driver module to control the stepper motor. The gateway is controlled by the motor which is connected to the driver module. © 2021 IEEE.