Principal Perspectives on School Staffing Struggles: Finding from the 2022 Learn Together Survey. American Educator Panels. Research Report. RR-A827-14

In this report, a nationally representative sample of kindergarten through 12th grade (K-12) public school principals were asked about their experiences with covering classrooms and hiring staff. In the spring of the 2021-2022 school year, which coincided with the coronavirus disease 2019 (COVID-19) omicron variant surge, most principals struggled to keep classrooms consistently staffed and many reported that hiring had become more challenging since the previous school year. Principals indicated that a lack of substitute teachers -- not an increase in open teaching positions -- was the main reason for classroom coverage shortages. In addition to day-to-day coverage issues, most principals reported that teacher vacancies were on the rise. Most of these principals believed that vacancies had grown more difficult to fill than in the prior school year, largely because of declining applicant counts. Principals' preferences when hiring teachers lend further insight into potential drivers of hiring challenges. A large majority of principals expressed strong preferences for like-minded teachers whose mindsets aligned with the vision and culture of the schools. Few principals prioritized the diversity of the educator workforce at their schools.

Nonstationary Gaussian Process Discriminant Analysis With Variable Selection for High-Dimensional Functional Data

High-dimensional classification and feature selection tasks are ubiquitous with the recent advancement in data acquisition technology. In several application areas such as biology, genomics, and proteomics, the data are often functional in their nature and exhibit a degree of roughness and nonstationarity. These structures pose additional challenges to commonly used methods that rely mainly on a two-stage approach performing variable selection and classification separately. We propose in this work a novel Gaussian process discriminant analysis (GPDA) that combines these steps in a unified framework. Our model is a two-layer nonstationary Gaussian process coupled with an Ising prior to identify differentially-distributed locations. Scalable inference is achieved via developing a variational scheme that exploits advances in the use of sparse inverse covariance matrices. We demonstrate the performance of our methodology on simulated datasets and two proteomics datasets: breast cancer and SARS-CoV-2. Our approach distinguishes itself by offering explainability as well as uncertainty quantification in addition to low computational cost, which are crucial to increase trust and social acceptance of data-driven tools. Supplementary materials for this article are available online.

Impact of the COVID-19 pandemic on patients with osteogenesis imperfecta

OBJECTIVES: On March 11, 2020, the WHO classified COVID-19 as a global pandemic. Measures to quell the pandemic included limiting non-essential activities including clinic visits and procedures. It is unclear if individuals with OI had disruptions in their access to healthcare or medications, and if such disruptions affected patients' symptoms. METHOD(S): A REDCap survey was distributed through the OI Foundation on August 31. Surveys completed through September 11 by individuals with OI or their caregiver are included in this analysis. Participants were asked to compare their symptoms and access to healthcare during the first 4 months of the pandemic to the 4 months before the pandemic. RESULT(S): 85 surveys were completed, and 6 were partially completed. The median age of participants was 40 years;35% were children. 32% of participants self-identified as having severe OI. Although most reported no changes in bone pain or fractures, 46% reported they were less likely to seek emergency medical care to treat a fracture, while 33% reported they were more likely to treat fractures at home (Fig 1A). There were delays in accessing all services, with greatest delays accessing dentistry (74%) and aquatic therapy (84%) (Fig 1B). 36% of participants receiving bisphosphonate infusions had delayed infusions because of the pandemic (Fig 1C). Of note, 50% of planned surgeries were delayed. CONCLUSION(S): Although many individuals with OI and their caregivers reported delays in accessing bone-related services/clinics during this 4-month period, there was not a concomitant increase in reported symptoms. This may have related to shelter-in-place restrictions and decreased activity. Limitations of this study include small sample size and potential selection bias because responses were obtained only from OIF members. To address these limitations, we are distributing the survey through healthcare providers of individuals with OI across major regions of the US from a variety of practice types including endocrine, orthopedics and multidisciplinary clinics. Furthermore, as the COVID-19 pandemic continues, we hope that this survey will provide information to address what aspects of healthcare may be in greatest need, as well as the modality through which services may be met. (Figure Presented).

Impacts of COVID-19 on proton therapy - Shaping the future of patient selection

Objectives: This scoping review aimed to determine whether the COVID-19 pandemic influenced any modifications to patient selection methods or prioritisation and services provided by proton therapy centres. Method(s): This review was conducted based on the PRISMA methodology and Joanna Briggs Institute scoping review guidelines.1,2 A literature search was performed in Medline, Embase, Web Of Science and Scopus as well as grey literature. Keywords including "COVID-19" and "Proton Therapy" were used. Articles published from 1 January 2020 in English were included. In total, 138 studies were identified of which 14 articles met the inclusion criteria. A scoping review design was chosen to capture the full extent of information published relating to the aim. Result(s): Six of 14 articles included statements regarding treatment of COVID-19 patients. Three publications recommended deferred or alternative treatment, two indicated to treat urgent/emergency patients and one reported continuous treatment for infectious patients. Recurring impacts on PT provision included more frequent use of alternative therapies, reduced referrals, delayed treatment starts and CT simulation, change in treatment volume and staffing limitations due to pandemic restrictions. Consequently, telehealth consults, remote work, reduction in patient visitors, screening procedures and rigorous cleaning protocols were recommended. Discussion/Conclusion: Few publications detailed patient selection or workflow methods used during the pandemic. Further research is needed to obtain more detailed information regarding current global patient selection methods in proton therapy, collecting this data could aid in future planning for proton therapy in Australia.

Optimal selection of cruise ship waste disposal ports

As cruise ships call at many ports and passengers come from all over the world, it is very easy to carry viruses on cruise ships. Under the control of the epidemic situation on board, the solid waste generated by them should be scientifically treated to prevent the spread of infectious diseases such as COVID-19 pneumonia. Therefore, Reasonable selection of waste disposal ports and formulation of unloading plans are directly related to the resumption of cruise operations. This study considers the cost and risk of waste disposal, uses robust optimization to deal with waste volume, increases the scenarios of port service interruption due to epidemics and other reasons, and proposes a variety of emergency strategies. Finally, the relevant strategies are selected according to the decision-maker's preference for cost and risk;By solving the relevant examples, the optimal choice of the cruise ship waste disposal port under the epidemic situation is given, which verifies the validity and feasibility of the model. The research helps to improve the management of cruise waste during the post-epidemic period, and has practical value and guiding significance for the normal operation and development of the global cruise market. © 2023 SPIE.

Explainable Mortality Prediction Model for Congestive Heart Failure with Nature-Based Feature Selection Method

A mortality prediction model can be a great tool to assist physicians in decision making in the intensive care unit (ICU) in order to ensure optimal allocation of ICU resources according to the patient's health conditions. The entire world witnessed a severe ICU patient capacity crisis a few years ago during the COVID-19 pandemic. Various widely utilized machine learning (ML) models in this research field can provide poor performance due to a lack of proper feature selection. Despite the fact that nature-based algorithms in other sectors perform well for feature selection, no comparative study on the performance of nature-based algorithms in feature selection has been conducted in the ICU mortality prediction field. Therefore, in this research, a comparison of the performance of ML models with and without feature selection was performed. In addition, explainable artificial intelligence (AI) was used to examine the contribution of features to the decision-making process. Explainable AI focuses on establishing transparency and traceability for statistical black-box machine learning techniques. Explainable AI is essential in the medical industry to foster public confidence and trust in machine learning model predictions. Three nature-based algorithms, namely the flower pollination algorithm (FPA), particle swarm algorithm (PSO), and genetic algorithm (GA), were used in this study. For the classification job, the most widely used and diversified classifiers from the literature were used, including logistic regression (LR), decision tree (DT) classifier, the gradient boosting (GB) algorithm, and the random forest (RF) algorithm. The Medical Information Mart for Intensive Care III (MIMIC-III) dataset was used to collect data on heart failure patients. On the MIMIC-III dataset, it was discovered that feature selection significantly improved the performance of the described ML models. Without applying any feature selection process on the MIMIC-III heart failure patient dataset, the accuracy of the four mentioned ML models, namely LR, DT, RF, and GB was 69.9%, 82.5%, 90.6%, and 91.0%, respectively, whereas with feature selection in combination with the FPA, the accuracy increased to 71.6%, 84.8%, 92.8%, and 91.1%, respectively, for the same dataset. Again, the FPA showed the highest area under the receiver operating characteristic (AUROC) value of 83.0% with the RF algorithm among all other algorithms utilized in this study. Thus, it can be concluded that the use of feature selection with FPA has a profound impact on the outcome of ML models. Shapley additive explanation (SHAP) was used in this study to interpret the ML models. SHAP was used in this study because it offers mathematical assurances for the precision and consistency of explanations. It is trustworthy and suitable for both local and global explanations. It was found that the features that were selected by SHAP as most important were also most common with the features selected by the FPA. Therefore, we hope that this study will help physicians to predict ICU mortality for heart failure patients with a limited number of features and with high accuracy.

An Integrated Approach to Evaluating Eco-Innovation Strategies from the Perspective of Strategic Green Transformation: A Case of the Lithuanian Furniture Industry

Evaluation and selection of eco-innovation strategies is a significant and complex strategic decision, and despite the relevance and interest in the field of eco-innovation, the area of eco-innovation strategies has not been explored in depth in the scientific literature. Therefore, in this study, we propose an integrated approach to evaluating eco-innovation strategies from the perspective of strategic green transformation that helps decision-makers evaluate and select eco-innovation strategy aiming to achieve a competitive advantage. For this study, we adopted a validated multi-criteria decision-making methodology (MCDM) by combining Analytical Hierarchy Process (AHP) and The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The reliability of the proposed framework was tested and applied in the context of the Lithuanian furniture industry. This study offers three contributions and provides a comprehensive and profound insights into eco-innovation strategies. First, this study conceptualizes eco-innovation strategy from the perspective of strategic green transformation and proposed a novel definition and classification of eco-innovation strategies leading to competitive advantage. Second, this study proposes a novel approach to the evaluation of eco-innovation strategies taking into account micro-, meso-, and macro-level environmental factors. Third, the findings of this study provide implications for scholars and decision-makers in the field of eco-innovation strategy and set an agenda for future research.

Q&A

In this interview, Sujay Jadhav, global vice president, study start-up, Oracle Health Sciences, touches on how COVID has affected study start-up and what new perspectives it has forced the industry to have on its own challenges. [...]assessing site ability to leverage telehealth will be a factor in site selection. Andy Studna is an Assistant Editor for Applied Clinical Trials Sujay Jadhav Global Vice President, Study Start-Up, Oracle Health Sciences Problems with startup, more than any other phase of a clinical trial, have the greatest potential to increase timelines and budgets.

Cardiac manifestations of post-COVID syndrome in children and adolescents.

The aim of the research. To study the features of cardiovascular system disorders in post-covid syndrome (PCS) in children and adolescents after a mild form of coronavirus infection (COVID-19). Material and methods. From 260 children and adolescents after a mild form of COVID-19, a total of 30 patients aged 7-17 years with cardiac manifestations of PCS were selected. Therewith, 32 patients with an uncomplicated form of the disease were selected to form a comparison group. In 3 and 6 months after disease onset, a comprehensive examination of patients was performed with a questionnaire on the subjective scale for MFI-20 assessment asthenia (Multidimensional Fatigue Inventory-20), electrocardiography (ECG), echocardiography;daily monitoring of ECG and blood pressure. The biochemical blood test included assay of creatine phosphokinase-MB (CPK-MB), troponin I and lactate dehydrogenase (LDH). Results. The incidence of PCS with cardiac manifestations amounted to 11.5 %. After 3 months from the disease onset, complaints of pain and discomfort in the chest, palpitations, fatigue, and poor exercise tolerance persisted. Asthenic syndrome was diagnosed in 70 % of patients. The "general asthenia" indicator totalled14 [12;16] points (p<0.001) and was associated with the age of patients (r=+0.5;p<0.05). Arrhythmic syndrome and conduction disorders were detected in 67% of children. Labile arterial hypertension and hypotension occurred in 23 % of the adolescents. The increase in CPK-MB remained in 17% of the children, LDH - in 10%. In the sixth month after the onset of the disease, there were no significant differences in the results of the examination in the observation groups. However, a decrease in the level of resistance within 6 months was recorded in 43.3% of the schoolchildren with PCS (p<0.001). Conclusion. The data obtained indicate the need for early verification of cardiopathies in children with COVID-19, determination of a set of therapeutic and rehabilitation measures as well as ECG monitoring.Copyright © 2022, Krasnoyarsk State Medical University. All rights reserved.

The analysis of risk assessment for the transmission of COVID-19 by using PROMETHEE and ELECTRE methods

Multi-Criteria Decision Making (MCDM) methods help researchers in solving many prob-lems in terms of numerical analysis. However, MCDM methods have not been very popular in the health sector. In this study, five ones of Turkey's most intense and highly populated cities were selected and the risk of the spread of Covid-19 disease was evaluated on the basis of seven criteria. The PROMETHEE and the ELECTRE methods were conducted to rank the cities in terms of the spread of Covid-19. The PROMETHEE method correctly ranked the most risky city as Istanbul, but ELECTRE ranked Istanbul the second most risky. The results of the meth-ods are compared with real data. PROMETHEE gave more convenient results than ELECTRE. Also, this paper offers a new field of study to the literature.Cite this article as: Pekel ozmen E, Demir B. The analysis of risk assessment for the trans-mission of COVID-19 by using PROMETHEE and ELECTRE methods. Sigma J Eng Nat Sci 2023;41:2:232-242.

A data-driven model for sustainable and resilient supplier selection and order allocation problem in a responsive supply chain: A case study of healthcare system

This research attempts to study the Supplier Selection and Order Allocation Problem (SSOAP) considering three crucial concepts, namely responsiveness, sustainability, and resilience. To do so, the current research develops a Multi-Stage Decision-Making Framework (MSDMF) to select potential suppliers and determine the quantity of orders. The first stage aims at computing the scores of the suppliers based on several indicators. To do this, a novel decision-making approach named the Stochastic Fuzzy Best–Worst Method (SFBWM) is developed. Then, in the second stage, a Multi-Objective Model (MOM) is suggested to deal with supplier selection and order allocation decisions. In the next step, a data-driven Fuzzy Robust Stochastic (FRS) optimization approach, based on the fuzzy robust stochastic method and the Seasonal Autoregressive Integrated Moving Average (SARIMA) methods, is employed to efficiently treat the hybrid uncertainty of the problem. Afterwards, a novel solution method named the developed Chebyshev Multi-Choice Goal Programming with Utility Function (CMCGP-UF) is developed to obtain the optimal solution. Moreover, given the crucial role of the Medical Equipment (ME) industry in society's health, especially during the recent Coronavirus disease, this important industry is taken into account. The outcomes of the first stage demonstrate that agility, cost, GHG emission, quality, robustness, and Waste Management (WM), respectively, are the most important criteria. The outcomes of the second stage determine the selected suppliers, utilized transportation systems, and established sites. It is also revealed that demand directly affects all the objective functions while increasing the rate of disruptions has a negative effect on the sustainability measures.

Exploring crucial social media marketing factors for improving customer satisfaction and customer loyalty in bed and breakfast sectors in Taiwan

PurposeThe Bed and Breakfast (B&B) enterprises generally lack sufficient human resources and time to conduct research on important social media marketing factors for visitors' satisfaction and visitors' intentions. Therefore, this study aims to provide crucial social media marketing and factors and service quality elements for improving customer satisfaction and customer loyalty in B&B sectors. This study also provides some recommendations for attracting more visitors and increasing customer satisfaction and customer loyalty through social media.Design/methodology/approachFirst, social media marketing factors and service quality elements were identified through the systematic literature review. Then these identified factors and elements were used to design a survey questionnaire for collecting data. The research data included responses of 64 B&B enterprises and 625 customers. The collected data was analyzed by feature selection approaches including Decision Tree algorithm and Information Gain to identify the key factors for improving customer satisfaction and customer loyalty.FindingsThe findings of this study determined that featured choice is an important social media marketing factor, and assurance is the common service quality element for both B&B enterprises and their customers in terms of satisfaction and loyalty.Originality/valueThis study adds a value to the growing literature on customer satisfaction and loyalty in B&B sectors by exploring key social media marketing factors and service quality elements. The study reveals several implications for theories and practices. The findings hopefully help B&B enterprises better social media marketing with less workforce and budget.

A Hybrid Grey Wolf-Whale Optimization Algorithm for Classification of Corona Virus Genome Sequences using Deep Learning

Genome sequence data is widely accepted as complex data and is still growing in an exponential rate. Classification of genome sequences plays a crucial role as it finds its applications in the area of biology, medical and forensics etc. For classification, Genome sequences can be represented in terms of features. More number of less significant features leads to lower accuracy in classification task. Feature selection addresses this issue by selecting the most important features which aids to improve the accuracy and lessens the computational complexity. In this research, Hybrid Grey Wolf-Whale Optimization Algorithm (HGWWOA) is proposed for Genome sequence classification. The proposed algorithm is evaluated using 23 benchmark objective functions along with Convolutional Neural Network classifier and its efficiency is verified using a novel metric namely "Feature Reduction Rate”. The proposed optimization algorithm can be applied for any optimization problems. In this research work, the proposed algorithm is used for classification of Corona Virus genome sequences. Performance comparison of the proposed and existing algorithms was carried out and it is evident that the performance of proposed algorithm exceeds the previous algorithms with an accuracy of 98.2%. © 2023, Zarka Private University. All rights reserved.

A survival genome-wide association study to identify variants associated with COVID-19 risk of death

Background/Objectives: SARS-CoV-2 infection clinical manifestations hugely vary among patients, ranging from no symptoms, to life-threatening conditions. This variability is also due to host genetics: COVID-19 Host Genetics Initiative identified six loci associated with COVID-19 severity in a previous case-control genome-wide association study. A different approach to investigate the genetics of COVID-19 severity is looking for variants associated with mortality, e.g. by analyzing the association between genotypes and time-to-event data. Method(s): Here we perform a case-only genome-wide survival analysis, of 1,777 COVID-19 patients from the GEN-COVID cohort, 60 days after infection/hospitalization. Case-only studies has the advantage of eliminating selection biases and confounding related to control subjects. Patients were genotyped using Illumina Infinium Global Screening Arrays. PLINK software was used for data quality check and principal component analysis. GeneAbel R package was used for survival analysis and age, sex and the first four principal components were used as covariates in the Cox proportional hazard model. Result(s): We found four variants associated with COVID-19 patient survival at a nominal P < 1.0 x 10-6. Their minor alleles were associated with a higher mortality risk (i.e. hazard ratios (HR)>1). In detail, we observed: HR=1.03 for rs28416079 on chromosome 19 (P=1.34 x 10-7), HR=1.15 for rs72815354 on chromosome 10 (P=1.66 x 10-7), HR=2.12 for rs2785631 on chromosome 1 (P=5.14 x 10-7), and HR=2.27 for rs2785631 on chromosome 5 (P=6.65 x 10-7). Conclusion(s): The present results suggest that germline variants are COVID-19 prognostic factors. Replication in the remaining HGI COVID-19 patient cohort (EGAS00001005304) is ongoing at the time of submission.

Towards Timeline Generation with Abstract Meaning Representation

Timeline summarization (TLS) is a challenging research task that requires researchers to distill extensive and intricate temporal data into a concise and easily comprehensible representation. This paper proposes a novel approach to timeline summarization using Meaning Representations (AMRs), a graphical representation of the text where the nodes are semantic concepts and the edges denote relationships between concepts. With AMR, sentences with different wordings, but similar semantics, have similar representations. To make use of this feature for timeline summarization, a two-step sentence selection method that leverages features extracted from both AMRs and the text is proposed. First, AMRs are generated for each sentence. Sentences are then filtered out by removing those with no named-entities and keeping the ones with the highest number of named-entities. In the next step, sentences to appear in the timeline are selected based on two scores: Inverse Document Frequency (IDF) of AMR nodes combined with the score obtained by applying a keyword extraction method to the text. Our experimental results on the TLS-Covid19 test collection demonstrate the potential of the proposed approach. © 2023 ACM.

Evolutionary game analysis of crowding risk control strategies during the COVID-19 Pandemic

To discuss the decision-making scheme of crowding risk management during the COVID-19 pandemic, this paper constructs an evolutionary game model based on the changes of pedestrian and government strategies, and simulates the strategy selection under different states. The results show that under the condition of pedestrian rationality, when the difference between the benefits and costs of the government's active response strategy is less than the benefits of inaction, the government will choose the strategy of inaction. If the benefit of rational action is less than the additional benefit of irrational action, pedestrians will choose irrational action. By establishing the replication dynamic equations of governments and pedestrians, the stability strategy of the system is analyzed. It is found that the values of R1, R2, R3, R4, R5, C1, C2, C3, C4, C5, C6, C7 will affect the strategy choices of the players, and how to measure the benefits and costs under different circumstances becomes the key to the problem. These findings provide a theoretical basis for the risk control decision of human crowding during the COVID-19 epidemic. © 2023 SPIE.

Examining labour market hierarchies in Slovakia from the perspective of intra-EU migration and return

This paper applies the concept of hierarchised mobility to study return migration in Slovakia in the context of the country's EU accession. The analysis is based on the national Labour Force Survey dataset, covering a decade of labour migration and return between the 2008/2009 financial crisis and the Covid pandemic, concentrating in particular on the short-term labour market outcomes for less skilled return migrants. It is found that even under improved economic conditions, patterns of labour mobility set in the aftermath of the EU's Eastern enlargement continued to persist, together with structural inequalities in the Slovak labour market. Returnees in Slovakia face a markedly higher unemployment rate relative to stayers, and are less likely to be self-employed shortly after their return to Slovakia, compared to stayers or migrants. Returnees were also more exposed to instability in their jobs than migrants and stayers. From this perspective, return migration itself is a reflection of hierarchised mobility, as returnees clearly occupy the least stable jobs, and are the most exposed to instability in their employment. It appears that migration patterns from and to Slovakia are ingrained within the broader functioning of the European labour market.

Fitting Percentage of Body Fat to Simple Body Measurements: College Women

Percentage of body fat, age, weight, height, and 14 circumference measurements (e.g., waist) are given for 184 women aged 18–25. Body fat, one measure of health, was accurately determined by an underwater weighing technique which requires special equipment and training of the individuals conducting the process. Modeling body fat percentage using multiple regression provides a convenient method of estimating body fat percentage using measures collected using only a measuring tape and a scale. This dataset can be used to show students the utility of multiple regression and to provide practice in model building.

Integration AHP and MOORA for Sustainable Supplier Selection During the COVID-19 Pandemic Era: A Case Study

The global COVID-19 pandemic had a complex impact on the supply chain system. Manufacturing companies always strive to be able to face corporate competition and become superior with one of them through selecting the right supplier. Suppliers have the highest risk in a company, especially during the COVID-19 pandemic era, but with the correct selection of suppliers, the company can provide strength in global competition. The purpose of this research is to be able to solve the problem of sustainable supplier selection in a garment industry in Indonesia during the COVID-19 pandemic through the integration method between AHP and MOORA. AHP as a method that has been proven in many studies, in this study is used to determine the weight of each criterion. Furthermore, MOORA as a method that has good selectivity in choosing the best alternative will be used in the selection process. 12 criteria with 5 alternatives are used to determine the best supplier. The contribution of this research is the integration of the AHP and MOORA methods and the determination of important criteria in the era of the COVID-19 Pandemic. The results show that the criteria for the area with the level impact of COVID-19 (C12) have the greatest weight and supplier 3 becomes the first ranked supplier or the best supplier. The integration method between AHP and MOORA is easy to use and can choose the right sustainable supplier during the COVID-19 pandemic. © 2023 American Institute of Physics Inc.. All rights reserved.