Development and Application of the Just Employment Policy Assessment in American Higher Education Institutions

The COVID-19 pandemic has heightened the visibility of economic inequality and the inadequacy of current minimum wage laws in the United States. Changes in the minimum wage, a living wage, or just employment practices may be compelled by law or voluntarily enacted by employers. A literature search failed to yield a concise and practical tool to comprehensively assess existing just employment policies or practices in higher education institutions. This article describes the development of a concise and practical assessment based on the "Model Just Employment Policy" from the Kalmanovitz Initiative for Labor and the Working Poor at Georgetown University. The resulting Just Employment Policy Assessment is used to evaluate the publicly available policies of four disparate higher education institutions in the United States. The article concludes with a discussion of implications for future research and administrative practice.

A regional clinical audit of the provision of adjuvant systemic anti-cancer treatment following lung cancer resection in eligible patients across Greater Manchester to support national benchmarking

Introduction: Adjuvant anti-cancer systemic therapy (SACT) following lung resection improves overall survival in stage II/II non-small cell lung cancer (NSCLC). The Getting It Right First Time (GIRFT) National Specialty Report for Lung Cancer recommends centres publish adjuvant SACT rates for National benchmarking and proposes a target of >40% of eligible patients undergo SACT. We report a regional audit into the uptake of adjuvant SACT in Greater Manchester (GM). Method(s): A retrospective case review of all patients undergoing curative-intent NSCLC surgery with a pathological stage of II/III from 01/01/21 to 30/04/21. Data collected included patient demographics, uptake of adjuvant SACT, reasons for no adjuvant SACT and tolerance and complications of SACT. Result(s): 58 patients underwent surgical resection within the audit period and were eligible for adjuvant SACT. Median age was 70 years (range 45 - 81) and 60% were female. 47% (27/58) commenced adjuvant SACT;41% (24/58) were treated with chemotherapy and 7% (4/58) were treated with tyrosine kinase inhibitors. 58% (14/24) of patients that commenced adjuvant chemotherapy completed 4 cycles. Carboplatin/Vinorelbine was the commonest regimen (82%, 18/22). There were no grade III-V complications and no chemotherapy-related deaths. Dose reduction due to toxicity was required in 14% (3/22). The reasons adjuvant systemic therapy was not given were patient choice in 32% (10/31), poor physical health such that risks outweighed benefits in 42% (13/31), and other reasons (e.g. need to treat synchronous primary tumours) in 26% (8/31). COVID-19 was not recorded as a cause for adjuvant omission/ dose reduction. Conclusion(s): This data provides national benchmarking information for adjuvant SACT in NSCLC and suggests the target of >40% is achievable and appropriate. Interventions that improve patient fitness pre- and post-operatively might increase adjuvant SACT uptake. This regional audit will be extended to review all eligible patients in 2021 and further data will be presented. Disclosure: No significant relationships.Copyright © 2023 Elsevier B.V.

What's Left in Your List? An Analysis of Business Database Holdings, Funding Sources, Cancelations, and the Impact of COVID-19 at Top Ranking U.S. Universities

Business and economics related databases and data sets are the most vital resources for supporting scholarly research and the curriculum at colleges and schools of business, and these resources evolve rapidly and are subject to significant price fluctuations. In this study, the top-ranking U.S. universities according to the U.S. News and World Report with Association to Advance Collegiate Schools of Business (AACSB) accreditation were surveyed about their subscriptions to databases, WRDS data sets, and Bloomberg Terminals to create a benchmark. In this survey, these institutions were also asked to provide information about funding source or cost-sharing changes and cancelations brought forth by budgetary pressures from COVID-19. The intent was to provide a snapshot of the impact of COVID-19 but to also provide guidance to institutions facing similar budgetary pressures in a future financial crisis. [ FROM AUTHOR] Copyright of Journal of Business & Finance Librarianship is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Multi-Joint Unit Network for COVID-19 Diagnosis using X-Ray and CT Images

The global COVID-19 pandemic has caused a health crisis globally. Automated diagnostic methods can control the spread of the pandemic, as well as assists physicians to tackle high workload conditions through the quick treatment of affected patients. Owing to the scarcity of medical images and from different resources, the present image heterogeneity has raised challenges for achieving effective approaches to network training and effectively learning robust features. We propose a multi-joint unit network for the diagnosis of COVID-19 using the joint unit module, which leverages the receptive fields from multiple resolutions for learning rich representations. Existing approaches usually employ a large number of layers to learn the features, which consequently requires more computational power and increases the network complexity. To compensate, our joint unit module extracts low-, same-, and high-resolution feature maps simultaneously using different phases. Later, these learned feature maps are fused and utilized for classification layers. We observed that our model helps to learn sufficient information for classification without a performance loss and with faster convergence. We used three public benchmark datasets to demonstrate the performance of our network. Our proposed network consistently outperforms existing state-of-the-art approaches by demonstrating better accuracy, sensitivity, and specificity and F1-score across all datasets. © 2022 ACM.

Diagnosis of COVID-19 from the X-Ray images using BAT Algorithm with Deep Convolutional Neural Network

The COVID-19 widespread has posed a chief contest to the scientific community around the world. For patients with COVID-19 illness, the international community is working to uncover, implement, or invent new approaches for diagnosis and action. A opposite transcription-polymerase chain reaction is currently a reliable tactic for diagnosing infected people. This is a time- and money-consuming procedure. Consequently, the development of new methods is critical. Using X-ray images of the lungs, this research article developed three stages for detecting and diagnosing COVID-19 patients. The median filtering is used to remove the unwanted noised during pre-processing stage. Then, Otsu thresholding technique is used for segmenting the affected regions, where Spider Monkey Optimization (SMO) is used to select the optimal threshold. Finally, the optimized Deep Convolutional Neural Network (DCNN) is used for final classification. The benchmark COVID dataset and balanced COVIDcxr dataset are used to test projected model's performance in this study. Classification of the results shows that the optimized DCNN architecture outperforms the other pre-trained techniques with an accuracy of 95.69% and a specificity of 96.24% and sensitivity of 94.76%. To identify infected lung tissue in images, here SMO-Otsu thresholding technique is used during the segmentation stage and achieved 95.60% of sensitivity and 95.8% of specificity. © 2023 IEEE.

FlexiTeam: Flexible Team and Work Organization using Process-Oriented Case-Based Reasoning

During crises such as COVID-19, there is a need to adapt existing work processes and teams to the changing environment in a very flexible and dynamic way in many business and healthcare organizations. In this paper, we conceptualize the advances required for Process-Oriented Case-Based Reasoning to flexibly and dynamically organize human resources in a team and work processes. The novel contributions of this paper include an extended case representation to represent resources, profiles, and key performance indicators (KPIs) of processes, a query definition which covers the "context”, and an overall process to flexibly and dynamically organize work processes and human resources. We evaluate the FlexiTeam process using a cooking recipe casebase and analyze the quality of the retrieval using a quality measure. We also derive the research questions that need to be addressed to fully explore this approach and the specific difficulties involved in solving this problem. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (CEUR-WS.org)

Thirty years of ANZICS CORE: A clinical quality success story

In 2023, the Australian and New Zealand Intensive Care Society (ANZICS) Registry run by the Centre for Outcomes and Resources Evaluation (CORE) turns 30 years old. It began with the Adult Patient Database, the Australian and New Zealand Paediatric Intensive Care Registry, and the Critical Care Resources Registry, and it now includes Central Line Associated Bloodstream Infections Registry, the Extra-Corporeal Membrane Oxygenation Database, and the Critical Health Resources Information System. The ANZICS Registry provides comparative case-mix reports, risk-adjusted clinical outcomes, process measures, and quality of care indicators to over 200 intensive care units describing more than 200 000 adult and paediatric admissions annually. The ANZICS CORE outlier management program has been a major contributor to the improved patient outcomes and provided significant cost savings to the healthcare sector. Over 200 peer-reviewed papers have been published using ANZICS Registry data. The ANZICS Registry was a vital source of information during the COVID-19 pandemic. Upcoming developments include reporting of long-term survival and patient-reported outcome and experience measures.

Benchmarking staffing in paediatric respiratory laboratories in Australia

Introduction/Aim: TSANZ Accreditation recommends sufficient staff to adequately meet service needs. There are no specific benchmarking FTE guidelines for both Scientific and Administrative staff. The aim of this project was to benchmark scientific and administrative staff in paediatric laboratories in Australia and NZ. Method(s): Eight paediatric laboratories from tertiary hospitals in Australia (7) and New Zealand (1) were invited to participate by providing the number and range of tests performed for years 2019, 2020, 2021. An activity study previously performed by 2 hospitals with input from a third was used to apply time weighting to each test. The hours taken for each test and Scientific FTE were analysed and results were evaluated via the program Tableau. Result(s): Two laboratories were unable to provide testing data and one laboratory had incomplete data sets making it difficult to determine FTE. Data was collated from five laboratories. Due to COVID-19 lockdowns in 2020-2021, only data from 2019 was used for benchmarking purposes. Total hours of testing per year were calculated and divided to give weekly amounts. This was then compared to the total amount of available weekly FTE hours. In 2019, scientific staff spent 26% (range 17%-36%) of their week in patient testing. During COVID-19 (2020 and 2021) the average testing time was 20% (range 14%-26% and 11%-28%). Laboratories with administrative staff spent more of their week in patient testing than those without (average: 28% vs. 23%). Conclusion(s): The raw number of tests a laboratory performs does not accurately define staffing FTE in paediatric laboratories in tertiary hospitals. Time weighting for specific tests should be considered when assessing staffing requirements. Laboratories with the most dedicated administrative FTE scientific staff were able to spend a greater portion of their week performing tests. Scientific staffs are required to undertake mandatory duties such as equipment calibration, meetings, professional development and research. Limitations to this study include differences in record keeping between laboratories. A more thorough time in motion study to include complex testing could be performed.

Global trends of the research on COVID-19 risks effect in sustainable facility management fields: a bibliometric analysis

This study used bibliometric analysis to investigate global research trends regarding the effect of COVID-19 risks in sustainable facility management fields. Between 2019 and 2021, the Scopus database published 208 studies regarding the effect of COVID-19 risks on sustainable facility control fields. VOSviewer software was used to analyse the co-occurrence of all keywords, and Biblioshiny software allowed getting the most relevant affiliation using the three-field plot. The results show the contribution by authors from 51 countries, and 73 keywords were identified and organised into six clusters, such as the effect of COVID-19 risks on human health, supply chain in construction projects and industry, disaster risk management in a changing climate, sustainable supply chain benchmarking, facility management and quality control, and, finally, sensitivity analysis & decision-making. © 2023 Khaled Jameel Aladayleh et al., published by Sciendo.

Benchmarking for effective services in Nigeria

This chapter assesses the practice of benchmarking in Nigerian libraries, with a focus on academic libraries. Participant observation, questionnaires, and focus group interviews were methods used to establish the situation. Results show that standards are not fully developed and implemented among libraries in the country. Benchmarks are hardly even mentioned. However, there is interest among university librarians in knowing more and implementing benchmarks which can improve the delivery of library services. The inadequate response to COVID-19 is a clear indication of the preparedness of libraries in the country. Prevention of dire effect of future pandemics or disruptions will require intentional preparedness. Nigerian librarians, especially the academic librarians as leaders, must take responsibility for improving on the present situation by taking advantage of their global networking. The regulatory body, Librarians' Registration Council of Nigeria, must also step up in preparing and monitoring the implementation of benchmarks in libraries. © 2023 Elsevier Ltd. All rights reserved.

Benchmarking on the fly and new ways of working: leveraging benefits from the pandemic experience

The University of Queensland (UQ) Library was a participant in the international benchmarking exercise reported in Part Two of this book. This paper is inspired by a question raised during the workshop "will our COVID-19 responses and experience remain relevant in the future?” This paper presents a case study of the UQ Library experience 2020–21. We position qualitative benchmarking as a social and immediate practice and share our new ways of working born in response to the pandemic. We believe that these new ways of working can persist. We share our reflections so that others can consider leveraging benefits from the pandemic experience. © 2023 Elsevier Ltd. All rights reserved.

Benchmarking of COVID-19 testing facilities: a case in the Philippines

PurposeWith the aggressive movement towards testing for COVID-19 across the globe, this study aims to shed light on how testing facilities perform in an operational perspective.Design/methodology/approachWith 102 testing facilities in the Philippines, the relative efficiencies of each facility are quantified using a data envelopment analysis technique. Afterwards, a best-worst method was conducted to assign priority weights to each testing facility.FindingsResults show that the proposed approach effectively prioritizes testing facilities that most likely have high utilization.Research limitations/implicationsThe findings in this study would be significant to the literature in a number of respects. For one, it reveals results that would stimulate the interest among scholars in a wide variety of disciplines such as management, data mining, policymaking, decision science and epidemiology, among others.Originality/valueThis study differs from previous works in a number of respects, particularly, in that to the best of the authors' knowledge, this is the first study to examine the relative efficiencies of COVID-19 testing facilities.

Evaluating a Covid-19 Vaccine Centre in the UK Using a DES Model

Efficient management of a Covid-19 vaccine centre (VC) is necessary for proper-functioning of a mass vaccination programme. This study reports on an evaluation of the operational performance of a VC. There are two key considerations: the VC capacity (patients per hour) and the patient flow-time (total time patients spent in the centre). In this paper, Witness Horizon a simulation model tool that can be used to enhance the effectiveness of vaccination facilities is introduced. The model is developed using discrete event simulation. The model utilises animation whilst dynamically displaying key performance indicators. The uniqueness of this approach is the ability to simulate and analyse VC scenarios stochastically by varying hourly arrivals, walk-ins to drive-in ratios, staffing levels, registration, immunization, and observation capacities. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Towards Safer Work Environments During the COVID-19 Crisis: A Study of Different Floor Plan Layouts and Ventilation Strategies Coupling Open FOAM and Airborne Pathogen Data for Actionable, Simulation-based Feedback in Design

As work environments struggle to reopen during the current COVID-19 pandemic, it is crucial to establish practical decision-aiding tools. While a strong emphasis has been placed on determining generic guidelines to reduce the risk of airborne viral spread, there is a lack of free and easy-to-use simulation workflows to quantify indoor air quality and the risk of airborne pathogens indoors at a spatial resolution that can take into account floor-plan layouts, furniture, and ventilation inlet-outlet positions. This paper describes the development of a new, free, early design tool that allows designers and other stakeholders to simulate and compare airborne viral concentrations under different indoor conditions. The tool leverages OpenFOAM-based Computational Fluid Dynamics (CFD) and a passive scalar simulation approach to allow architects and interior designers to quantify airborne pathogens' exposure. The tool is integrated into the popular Rhino3d & Grasshopper CAD environment to facilitate its application in fast-paced design processes. We demonstrate good agreement compared to a CFD benchmark test. Further, we validate newly developed COVID-19 capabilities by comparing our results to an existing restaurant case study that included tracer gas measurements and validation using Fluent (Ansys). We demonstrate applications of the tool in a comparative study of a restaurant that investigates how plan and furniture layout interventions, ventilation strategies can impact the movement of airborne pathogens in indoor environments. © International Building Performance Simulation Association, 2022

Hand-breathe: Non-Contact Monitoring of Breathing Abnormalities from Hand Palm

In post-covid19 world, radio frequency (RF)-based non-contact methods, e.g., software-defined radios (SDR)-based methods have emerged as promising candidates for intelligent remote sensing of human vitals, and could help in containment of contagious viruses like covid19. To this end, this work utilizes the universal software radio peripherals (USRP)-based SDRs along with classical machine learning (ML) methods to design a non-contact method to monitor different breathing abnormalities. Under our proposed method, a subject rests his/her hand on a table in between the transmit and receive antennas, while an orthogonal frequency division multiplexing (OFDM) signal passes through the hand. Subsequently, the receiver extracts the channel frequency response (basically, fine-grained wireless channel state information), and feeds it to various ML algorithms which eventually classify between different breathing abnormalities. Among all classifiers, linear SVM classifier resulted in a maximum accuracy of 88.1%. To train the ML classifiers in a supervised manner, data was collected by doing real-time experiments on 4 subjects in a lab environment. For label generation purpose, the breathing of the subjects was classified into three classes: normal, fast, and slow breathing. Furthermore, in addition to our proposed method (where only a hand is exposed to RF signals), we also implemented and tested the state-of-the-art method (where full chest is exposed to RF radiation). The performance comparison of the two methods reveals a trade-off, i.e., the accuracy of our proposed method is slightly inferior but our method results in minimal body exposure to RF radiation, compared to the benchmark method. IEEE

Evaluation of motor coordination in 9-year-old Colombian infants postconfinement by COVID-19: gender related, sociodemographic context and sport

The objective of the present research was to evaluate the level of motor coordination in 9-year-old infants belonging to sports training schools in Bogotá and Chía-Cundinamarca in a post-confinement context by means of the 3JS test in relation to gender, sociodemographic context, sport, confinement training and days of training. The study included 307 infants, of which: 187 were male and 120 were female. Likewise, they were grouped by sport: soccer n:113, skating n:91, basketball n:52 and tennis n:51 and, finally, by sociodemographic context 102 were evaluated in Chia-Cundinamarca and 205 in Bogotá. The study has a quantitative approach, descriptive-transversal type and with a non-probabilistic sampling. The statistical treatment was carried out using the statistical software ® version 4.1.0. The results indicate that the significant differences were established in response to gender p=0.00 better in male infants, sociodemographic context p=0.04 better in Bogotá, days of training p=0.006 better in equal or greater than four days of weekly training, training in confinement during Covid-19 p=0.02 better in those who trained and sport p=0.00 evidencing significant differences between them, having as reference soccer as the highest and tennis the lowest level correspondingly. These findings show that normal levels of motor coordination were found for all the infants evaluated according to the intervals proposed by the 3JS test, likewise, the differences were established in turn, between locomotor coordination and object control coordination according to the variables object of this study. © Federación Española de Asociaciones de Docentes de Educación Física (FEADEF).

Enviro-Health Dimensions of Environment Impact Assessment (EIA) Draft Notification 2020 and COVID-19 Pandemic

The enviro-health dimensions of the Environment Impact Assessment (EIA) Draft Notification 2020 and the COVID-19 pandemic in India needs dispassionate reading despite its criticism of being medically motivated and relaxing public scrutiny of B2 projects. The EIA law began in 1994 has finished 25 years of its authorisation and work in 2019. The EIA law encouraged excellent administration and ecological equity. The EIA law is in the constant deluge of experimentation, as evident from the 55-time changes and 230 government circulars from 2006-2021. The EIA Notification, 2006, has intensified the disarray and alteration in EIA law arrangement. The salubrious enactment made statutory requirements on the continuous relaxing mode. The worldwide rankings on the working of EIA law in 2020 by Yale University's Environmental Performance Index positioned India as 168 out of 180 nations. The fundamental qualities of benchmarking contain 32 markers and decade execution patterns. The EIA Notification, 2020 subsumes these concerns by providing public participation, ex-post-facto clearances, and speedy authorisations of environmental projects. The EIA law gets promissory to ecological improvement, contamination control standards and health protection. © 2023 - IOS Press. All rights reserved.

Digital Transformation for Promoting Renewable Energy & Sustainability: A Systematic Approach for Carbon Footprint Reduction in Well Construction

As the industry recovers from the recent downturn in petroleum commodity prices and the economic impacts from coronavirus (COVID-19), governing authorities in most countries are imposing methodological measures to promote the reduction of carbon footprint. This affects every industry including the petroleum sector. Therefore, most investors and stakeholders have increased their focus on Environmental, Social, and Corporate Governance (ESG) policies. During the well construction phase, a transition from a hydraulic to an electric tong is achieved, resulting in carbon footprint reduction. Achieving carbon neutrality or carbon emission reduction while producing hydrocarbons is one of the topmost key performance indicators (KPIs) in the industry. With the implementation of digital technologies in the tubular and casing connection make-up process, a hydraulic tong is substituted with an electric tong of an equivalent specification. The energy consumption for both systems are calculated and compared. Other important KPIs on tracking operational cost are also assessed and the results are then compared to determine the benefits of implementing the upgraded digitalized tong solution. The electric tong digitalized solution, commercially available in the petroleum industry, is a key enabler for carbon emission reduction while running tubulars in/out of the wellbore. This solution is one of the milestones that serve as foundation to advocate carbon reduction. Eventually, this will lead to establishing carbon neutrality during hydrocarbon extraction and production. The results concluded that a digitalized solution eventually reduced personnel on board working in the "red zone," which eventually leads to carbon emission reductions caused by a decrease in fuel consumption. The decrease of 43% in CO2 emission is observed while performing tubular connection process. Moreover, an overall comparison between a legacy system with the digitalized electric system displayed more than 59% reduction in CO2 during the tubular running services. In addition to carbon reduction, this electric power and control solution allows for more precise torque control, leading to enhanced system integrity and increased reliability achieved by cleaner energy. With this digital solution, not only is the safety and well-being of rig personnel enhanced to avoid any recordable incidents, the reduction of carbon emission is also achieved, aligning to the objectives of current ESG regulatory authorities. This paper will provide comprehensive details on the novelty of this technology and solution offered to the industry. © 2022, Offshore Technology Conference. All rights reserved.

Raspberry-based Low-resolution Thermal image system using a Smoothing Filter-based Kalman

Since the emergence of global epidemics such as SARS-CoV-2, H1N1, SARS and MERS, a wide range of systems for measuring temperature have been developed based on computer vision to reduce and prevent the virus contagious. By implementing a Raspberry-based Low-resolution embedded system based and a FLIR Lepton® sensor human body temperature is measured and improved by four different algorithms implemented. Firstly, three traditional time-series processes solving such as, Simple Mean (SM), Simple Moving Average (SMA), and Multi Lineal Regression (MLR), and secondly, and online filter-based Kalman predictor were implemented to increase the signal to noise ratio of the acquired temperature magnitude. Results of average prediction for different benchmarks demonstrate the best performance of Kalman Filter upon traditional processes. In addition, this algorithm achieves to smooth output temperature with fewer samples (∼10% of total samples) in comparison MLR and SMA. Finally, Raspberry-based Low-resolution Thermal image system is a feasible tool as a high-speed temperature estimator, by implementation of algorithms codified in Python language. © 2022 IEEE.