Trends in infection-related patient safety incident reporting before and during the COVID-19 pandemic in Korea.

Purpose: The purpose of this study was to analyze the trends and characteristics of infection-related patient safety incident reporting before and during the coronavirus disease 2019 (COVID-19) pandemic in Korea, and to provide basic data for preventing infection-related patient safety incidents and improving their management. Methods: A cross-sectional analysis of secondary national data (Patient Safety Reporting Data) was conducted. In total, 517 infection-related patient safety incidents reported from 2018 to 2021 were analyzed. Changes in the number of reports before and during the COVID-19 pandemic and differences in variables related to infection-related patient safety incidents were analyzed using the chi-square test and independent t-test in SPSS 29.0. Results: This study found that infection-related patient safety incidents decreased during the COVID-19 pandemic compared to before the pandemic. Furthermore, incident-related characteristics, such as the type of healthcare organization, severity of harm, and post-incident actions, changed during the COVID-19 pandemic. Conclusion: The many changes in the infection control system and practices during the COVID-19 pandemic may have contributed to a decrease in the reporting of infection-related patient safety incidents. It is hoped that longitudinal studies on patient safety incidents related to the pandemic and analytical studies on factors influencing patient safety incidents will continue to be conducted to prevent and improve patient safety incidents. [ FROM AUTHOR] Copyright of Journal of Korean Biological Nursing Science is the property of Korean Society of Biological Nursing Science 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.)

COVID-19, Digital Transformation of Banks, and Operational Capabilities of Commercial Banks

The development of financial technology has promoted the innovation and digital transformation of commercial banks. Through digital transformation, commercial banks can improve bank efficiency and operational capabilities. Through empirical analysis, this study explored the relationship between digital bank transformation and commercial bank operating capabilities and how COVID-19, bank categories, and enterprise life cycles affect the relationship between digital bank transformation and commercial bank operating capabilities. This study selected data from China's commercial banks from 2011 to 2021 and used the regression method of fixed effects to conduct an empirical analysis. The research results show that the digital transformation of banks has improved the operational capabilities of commercial banks. Further analysis showed that the emergence of COVID-19 has negatively affected their relationship. At the same time, compared with rural commercial banks and commercial banks in the recession and phase-out periods, non-rural commercial banks and commercial banks in the growth and maturity stages play a more vital moderating role in the impact of the digital transformation of banks on the financial performance of commercial banks. The main research object of this study is Chinese commercial banks, and this study examines the results of banks' digital transformation and enriches the research on digital transformation. At the same time, this study is helpful to investors who like investment banks and has good practical significance.

Role of noscomial infection informatics surveillance system in the prevention and control of multidrug-resistant organisms infections

OBJECTIVE: To analyze the role of nosocomial infection informatics surveillance system in the prevention and control of multidrug-resistant organisms(MDROs) infections. METHODS: The First Affiliated Hospital of Guangdong Pharmaceutical University was selected as the study subjects, which had adopted the nosocomial infection informatics surveillance system since Jan.2020. The period of Jan.to Dec.2020 were regarded as the study period, and Jan.to Dec.2019 were regarded as the control period. The situation of nosocomial infection and MDROs infections in the two periods were retrospectively analyzed. RESULTS: The incidence of nosocomial infections and underreporting of nosocomial infection cases in this hospital during the study period were 2.52%(1 325/52 624) and 1.74%(23/1 325), respectively, and the incidences of ventilator associated pneumonia(VAP), catheter related bloodstream infection(CRBSI), catheter related urinary tract infection(CAUTI)were 4.10(31/7 568), 2.11(14/6 634), and 2.50(25/9 993) respectively, which were lower than those during the control period(P< 0.05). The positive rate of pathogenic examination in the hospital during the study period was 77.95%(1 269/1 628), which was higher than that during the control period(P<0.05), the overall detection rate of MDROs was 15.77%(206/1 306), the detection rates of MDROs in Escherichia coli, Acinetobacter baumannii, Klebsiella pneumoniae, Staphylococcus epidermidis, Pseudomonas aeruginosa and Staphylococcus aureus were lower than those during the control period(P<0.05). CONCLUSION: The development and application of the informatics technology-based surveillance system of nosocomial infection could effectively reduce the incidence of nosocomial infections and device related infections, decrease the under-reporting of infection cases, and also reduce the detection rate of MDROs as well as the proportion of MDROs detected in common pathogenic species.

Mixup-Inf-Net: A data augmentation algorithm for segmentation of new coronary pneumonia infections

In 2020, the global spread of Coronavirus Disease 2019 exposed entire world to a severe health crisis. This has limited fast and accurate screening of suspected cases due to equipment shortages and and harsh testing environments. The current diagnosis of suspected cases has benefited greatly from the use of radiographic brain imaging, also including X-ray and scintigraphy, as a crucial addition to screening tests for new coronary pneumonia disease. However, it is impractical to gather enormous volumes of data quickly, which makes it difficult for depth models to be trained. To solve these problems, we obtained a new dataset by data augmentation Mixup method for the used chest CT slices. It uses lung infection segmentation (Inf-Net [1]) in a deep network and adds a learning framework with semi-supervised to form a Mixup-Inf-Net semi-supervised learning framework model to identify COVID-19 infection area from chest CT slices. The system depends primarily on unlabeled data and merely a minimal amount of annotated data is required;therefore, the unlabeled data generated by Mixup provides good assistance. Our framework can be used to improve improve learning and performance. The SemiSeg dataset and the actual 3D CT images that we produced are used in a variety of tests, and the analysis shows that Mixup-Inf-Net semi-supervised outperforms most SOTA segmentation models learning framework model in this study, which also enhances segmentation performance. © 2023 SPIE.

Coping Orientation of Academic Community in the Time of COVID-19 Pandemic: A Pilot Multi-Country Survey Study

In this paper, we have mapped the coping methods used to address the coronavirus pandemic by members of the academic community. We conducted an anonymous survey of a convenient sample of 674 faculty/staff members and students from September to December 2020. A modified version of the RCOPE scale was used for data collection. The results indicate that both religious and existential coping methods were used by respondents. The study also indicates that even though 71% of informants believed in God or another religious figure, 61% reported that they had tried to gain control of the situation directly without the help of God or another religious figure. The ranking of the coping strategies used indicates that the first five methods used by informants were all non-religious coping methods (i.e., secular existential coping methods): regarding life as a part of a greater whole, regarding nature as an important resource, listening to the sound of surrounding nature, being alone and contemplating, and walking/engaging in any activities outdoors giving a spiritual feeling. Our results contribute to the new area of research on academic community's coping with pandemic-related stress and challenges.

Within-group estimators for unbalanced-panel data regression model of the open unemployment rate data in east kalimantan province

The COVID-19 pandemic has hit hard the Indonesian economy. Many businesses had to close because they could not cover operational costs, and many workers were laid off creating an unemployment crisis. Unemployment causes people's productivity and income to decrease, leading to poverty and other social problems, making it a crucial problem and great concern for the nation. Economic conditions during this pandemic have also provided an unusual pattern in economic data, in which outliers may occur, leading to biased parameter estimation results. For that reason, it is necessary to deal with outliers in research data appropriately. This study aims to find within-group estimators for unbalanced panel data regression model of the Open Unemployment Rate (OUR) in East Kalimantan Province and the factors that influence it. The method used is the within transformation with mean centering and median centering processing methods. The results of this study may provide advice on factors that can increase and decrease the OUR of East Kalimantan Province. The results show that the best model for estimating OUR data in East Kalimantan Province is the within-transformation estimation method using median centering. According to the best model, the Human Development Index (HDI) and Gross Regional Domestic Product (GRDP) are two factors that influence the OUR of East Kalimantan Province (GRDP). © 2023, International Association of Engineers. All rights reserved.

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.

COVID-19 Data Prediction and Visualization in Mainland China

Contemporarily, COVID-19 shows a sign of recurrence in Mainland China. To better understand the situation, this paper investigates the growth pattern of COVID-19 based on the research of past data through regression models. The proposed work collects the data on COVID-19 in Mainland China from January 21st, 2020, to April 30th, 2020, including confirmed, recovered, and death cases. Based on polynomial regression and support vector machine regressor, it predicts the further trend of COVID-19. The paper uses root mean squared error to evaluate the performance of both models and concludes that there is no best model due to the high frequency of daily changes. According to the analysis, support vector machine regressors fit the growth of COVID-19 confirmed case better than polynomial regression does. The best solution is to utilize different types of models to generate a range of prediction result. These results shed light on guiding further exploration of the growth of COVID-19. © 2023 SPIE.

Effect of Distant Education on Children's Academic Achievement in Primary Schools During Corona Pandemic in Dubai Emirate- Field Study

This study aimed to examine the effect of distance education on the level of educational achievement of children during the Corona period in ten primary schools in the Emirate of Dubai. To achieve the objectives of the study the researchers adopted the descriptive analytical approach. The quantitative method of data collection had been applied using the electronic questionnaire tool consisted of four main axes for data collection and had been distributed to the study sample consisted of 190 students' parents and administrators selected by using simple random techniques. The results of the study indicated that the participation of students in the educational process, and in the establishment of appropriate educational programs and applications for the transmission to distance learning have contributed to reducing the negative effects of the process of shifting from traditional education / face-to-face education classroom teaching to virtual classroom (ZOOM).The study recommended the need for strengthening distance education mechanisms, which contribute in developing the student's interests, tendencies, attitudes, concentrating on the study material, and using of safe and secured electronic devices to increase the search for additional information to reach the correct knowledge. Also, the school administration should have good e-learning plan ahead with required financial credits that will help in overcoming the crisis and mange distance learning processes to reach future objectives successfully. © 2022 Owner/Author.

IMPAIRED HEALTH-RELATED QUALITY OF LIFE IN PATIENTS WITH IDIOPATHIC INFLAMMATORY MYOPATHIES: A CROSS-SECTIONAL ANALYSIS FROM AN INTERNATIONAL E-SURVEY

BackgroundComprehensive and large-scale assessment of health-related quality of life in patients with idiopathic inflammatory myopathies (IIMs) worldwide is lacking. The second COVID-19 vaccination in autoimmune disease (COVAD-2) study [1] is an international, multicentre, self-reported e-survey assessing several aspects of COVID-19 infection and vaccination as well as validated patient-reported outcome measures (PROMs) to outline patient experience in various autoimmune diseases (AIDs), with a particular focus on IIMs.ObjectivesTo investigate physical and mental health in a global cohort of IIM patients compared to those with non-IIM autoimmune inflammatory rheumatic diseases (AIRDs), non-rheumatic AIDs (NRAIDs), and those without AIDs (controls), using Patient-Reported Outcome Measurement Information System (PROMIS) global health data obtained from the COVAD-2 survey.MethodsDemographics, AID diagnoses, comorbidities, disease activity, treatments, and PROMs were extracted from the COVAD-2 database. The primary outcomes were PROMIS Global Physical Health (GPH) and Global Mental Health (GMH) scores. Secondary outcomes included PROMIS physical function short form-10a (PROMIS PF-10a), pain visual analogue scale (VAS), and PROMIS Fatigue-4a scores. Each outcome was compared between IIMs, non-IIM AIRDs, NRAIDs, and controls. Factors affecting GPH and GMH scores in IIMs were identified using multivariable regression analysis.ResultsA total of 10,502 complete responses from 1582 IIMs, 4700 non-IIM AIRDs, 545 NRAIDs, and 3675 controls, which accrued as of May 2022, were analysed. Patients with IIMs were older [59±14 (IIMs) vs. 48±14 (non-IIM AIRDs) vs. 45±14 (NRAIDs) vs. 40±14 (controls) years, p<0.001] and more likely to be Caucasian [82.7% (IIMs) vs. 53.2% (non-IIM AIRDs) vs. 62.4% (NRAIDs) vs. 34.5% (controls), p<0.001]. Among IIMs, dermatomyositis (DM) and juvenile DM were the most common (31.4%), followed by inclusion body myositis (IBM) (24.9%). Patients with IIMs were more likely to have comorbidities [68.1% (IIMs) vs. 45.7% (non-IIM AIRDs) vs. 45.1% (NRAIDs) vs. 26.3% (controls), p<0.001] including mental disorders [33.4% (IIMs) vs. 28.2% (non-IIM AIRDs) vs. 28.4% (NRAIDs) vs. 17.9% (controls), p<0.001].GPH median scores were lower in IIMs compared to NRAIDs or controls [13 (interquartile range 10–15) IIMs vs. 13 (11–15) non-IIM AIRDs vs. 15 (13–17) NRAIDs vs. 17 (15–18) controls, p<0.001] and PROMIS PF-10a median scores were the lowest in IIMs [34 (25–43) IIMs vs. 40 (34–46) non-IIM AIRDs vs. 47 (40–50) NRAIDs vs. 49 (45–50) controls, p<0.001]. GMH median scores were lower in AIDs including IIMs compared to controls [13 (10–15) IIMs vs. 13 (10–15) non-IIM AIRDs vs. 13 (11–16) NRAIDs vs. 15 (13–17) controls, p<0.001]. Pain VAS median scores were higher in AIDs compared to controls [3 (1–5) IIMs vs. 4 (2–6) non-IIM AIRDs vs. 2 (0–4) NRAIDs vs. 0 (0–2) controls, p<0.001]. Of note, PROMIS Fatigue-4a median scores were the highest in IIMs [11 (8–14) IIMs vs. 8 (10–14) non-IIM AIRDs vs. 9 (7–13) NRAIDs vs. 7 (4–10) controls, p<0.001].Multivariable regression analysis in IIMs identified older age, male sex, IBM, comorbidities including hypertension and diabetes, active disease, glucocorticoid use, increased pain and fatigue as the independent factors for lower GPH scores, whereas coexistence of interstitial lung disease, mental disorders including anxiety disorder and depression, active disease, increased pain and fatigue were the independent factors for lower GMH scores.ConclusionBoth physical and mental health are significantly impaired in patients with IIMs compared to those with non-IIM AIDs or those without AIDs. Our results call for greater attention to patient-reported experience and comorbidities including mental disorders to provide targeted approaches and optimise global well-being in patients with IIMs.Reference[1]Fazal ZZ, Sen P, Joshi M, et al. COVAD survey 2 long-term outcomes: unmet need and protocol. Rheumatol Int. 2022;42:2151–58.AcknowledgementsThe authors a e grateful to all respondents for completing the questionnaire. The authors also thank The Myositis Association, Myositis India, Myositis UK, the Myositis Global Network, Cure JM, Cure IBM, Sjögren's India Foundation, EULAR PARE for their contribution to the dissemination of the survey. Finally, the authors wish to thank all members of the COVAD study group for their invaluable role in the data collection.Disclosure of InterestsAkira Yoshida: None declared, Yuan Li: None declared, Vahed Maroufy: None declared, Masataka Kuwana Speakers bureau: Boehringer Ingelheim, Ono Pharmaceuticals, AbbVie, Janssen, Astellas, Bayer, Asahi Kasei Pharma, Chugai, Eisai, Mitsubishi Tanabe, Nippon Shinyaku, Pfizer, Consultant of: Corbus, Mochida, Grant/research support from: Boehringer Ingelheim, Ono Pharmaceuticals, Naveen Ravichandran: None declared, Ashima Makol Consultant of: Boehringer-Ingelheim, Parikshit Sen: None declared, James B. Lilleker: None declared, Vishwesh Agarwal: None declared, Sinan Kardes: None declared, Jessica Day Grant/research support from: CSL Limited, Marcin Milchert: None declared, Mrudula Joshi: None declared, Tamer A Gheita: None declared, Babur Salim: None declared, Tsvetelina Velikova: None declared, Abraham Edgar Gracia-Ramos: None declared, Ioannis Parodis Grant/research support from: Amgen, AstraZeneca, Aurinia Pharmaceuticals, Eli Lilly, Gilead Sciences, GlaxoSmithKline, Janssen Pharmaceuticals, Novartis, and F. Hoffmann-La Roche, Elena Nikiphorou Speakers bureau: Celltrion, Pfizer, Sanofi, Gilead, Galapagos, AbbVie, Eli Lilly, Consultant of: Celltrion, Pfizer, Sanofi, Gilead, Galapagos, AbbVie, Eli Lilly, Grant/research support from: Pfizer, Eli Lilly, Ai Lyn Tan Speakers bureau: AbbVie, Gilead, Janssen, Eli Lilly, Novartis, Pfizer, UCB, Consultant of: AbbVie, Gilead, Janssen, Eli Lilly, Novartis, Pfizer, UCB, Arvind Nune: None declared, Lorenzo Cavagna: None declared, Miguel A Saavedra Consultant of: AbbVie, GlaxoSmithKline, Samuel Katsuyuki Shinjo: None declared, Nelly Ziade Speakers bureau: AbbVie, Boehringer-Ingelheim, Eli Lilly, Janssen, Pfizer, Roche, Consultant of: AbbVie, Boehringer-Ingelheim, Eli Lilly, Janssen, Pfizer, Roche, Grant/research support from: AbbVie, Boehringer-Ingelheim, Eli Lilly, Janssen, Pfizer, Roche, Johannes Knitza: None declared, Oliver Distler Speakers bureau: AbbVie, Amgen, Bayer, Boehringer Ingelheim, Janssen, Medscape, Novartis, Consultant of: 4P-Pharma, AbbVie, Acceleron, Alcimed, Altavant, Amgen, AnaMar, Arxx, AstraZeneca, Baecon, Blade, Bayer, Boehringer Ingelheim, Corbus, CSL Behring, Galderma, Galapagos, Glenmark, Gossamer, iQvia, Horizon, Inventiva, Janssen, Kymera, Lupin, Medscape, Merck, Miltenyi Biotec, Mitsubishi Tanabe, Novartis, Prometheus, Redxpharma, Roivant, Sanofi, Topadur, Grant/research support from: AbbVie, Amgen, Boehringer Ingelheim, Kymera, Mitsubishi Tanabe, Novartis, Roche, Hector Chinoy Grant/research support from: Eli Lilly, UCB, Vikas Agarwal: None declared, Rohit Aggarwal Consultant of: Mallinckrodt, Octapharma, CSL Behring, Bristol Myers-Squibb, EMD Serono, Kezar, Pfizer, AstraZeneca, Alexion, Argenx, Boehringer Ingelheim (BI), Corbus, Janssen, Kyverna, Roivant, Merck, Galapagos, Actigraph, Abbvie, Scipher, Horizontal Therapeutics, Teva, Biogen, Beigene, ANI Pharmaceutical, Nuvig, Capella, CabalettaBio, Grant/research support from: Bristol Myers-Squibb, Pfizer, Mallinckrodt, Janssen, Q32, EMD Serono, Boehringer Ingelheim, Latika Gupta: None declared.

Analysis and contrast of Chadian-Senegalese Covid-19 outbreaks

As COVID-19 is an emerging pandemic, analysing its evolution is necessary to understand it in order to find appropriate answers. In this paper, we aim to observe and analyse it at the Chadian-Senegalese level. Thus, we collect public data in order to present via curves, histograms and tables the main characteristics of this pandemic. In this way, we implement a python program to construct these. We focus only on extracting long-term data without predictive models. We observed that there are mainly two waves (outbreak) per year with stable or even decreasing infection and death rates. We also identified moments of growth and relaxation of the disease. These results can be used to identify times when treatment or prevention should be intensified.

A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices

The ongoing Russia–Ukraine conflict has exacerbated the global crisis of natural gas supply, particularly in Europe. During the winter season, major importers of liquefied natural gas (LNG), such as South Korea and Japan, were directly affected by fluctuating spot LNG prices. This study aimed to use machine learning (ML) to predict the Japan Korea Marker (JKM), a spot LNG price index, to reduce price fluctuation risks for LNG importers such as the Korean Gas Corporation (KOGAS). Hence, price prediction models were developed based on long short-term memory (LSTM), artificial neural network (ANN), and support vector machine (SVM) algorithms, which were used for time series data prediction. Eighty-seven variables were collected for JKM prediction, of which eight were selected for modeling. Four scenarios (scenarios A, B, C, and D) were devised and tested to analyze the effect of each variable on the performance of the models. Among the eight variables, JKM, national balancing point (NBP), and Brent price indexes demonstrated the largest effects on the performance of the ML models. In contrast, the variable of LNG import volume in China had the least effect. The LSTM model showed a mean absolute error (MAE) of 0.195, making it the best-performing algorithm. However, the LSTM model demonstrated a decreased in performance of at least 57% during the COVID-19 period, which raises concerns regarding the reliability of the test results obtained during that time. The study compared the ML models' prediction performances with those of the traditional statistical model, autoregressive integrated moving averages (ARIMA), to verify their effectiveness. The comparison results showed that the LSTM model's performance deviated by an MAE of 15–22%, which can be attributed to the constraints of the small dataset size and conceptual structural differences between the ML and ARIMA models. However, if a sufficiently large dataset can be secured for training, the ML model is expected to perform better than the ARIMA. Additionally, separate tests were conducted to predict the trends of JKM fluctuations and comprehensively validate the practicality of the ML models. Based on the test results, LSTM model, identified as the optimal ML algorithm, achieved a performance of 53% during the regular period and 57% d during the abnormal period (i.e., COVID-19). Subject matter experts agreed that the performance of the ML models could be improved through additional studies, ultimately reducing the risk of price fluctuations when purchasing spot LNG. [ FROM AUTHOR] Copyright of Energies (19961073) is the property of MDPI 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.)

Optimizing mRNA vaccines and virusmediated CAR T-cell therapy for immuno-oncology: Synthetic tools for gene modification

With the success of mRNA vaccines during the COVID-19 pandemic and CAR T-cell therapies in clinical trials, there is growing opportunity for immunotherapies in the treatment of many types of cancers. Lentiviral vectors have proven effective at delivery of genetic material or gene editing technology for ex vivo processing, but the benefits and promise of Adeno-associated virus (AAV) and mRNA tools for in vivo immunotherapy have garnered recent interest. Here we describe complete synthetic solutions for immuno-oncology research programs using either mRNA-vaccines or virus-mediated cell and gene engineering. These solutions optimize workflows to minimize screening time while maximizing successful research results through: (1) Efficiency in lentiviral packaging with versatility in titer options for high-quality particles. (2) A highthroughput viral packaging process to enable rapid downstream screening. (3) Proprietary plasmid synthesis and preparation techniques to maintain ITR integrity through AAV packaging and improve gene delivery. (4) Rapid synthesis, in vitro transcription, and novel sequencing of mRNA constructs for complete characterization of critical components such as the polyA tail. The reported research demonstrates a streamlined approach that improves data quality through innovative synthesis and sequencing methodologies as compared to current standard practices.

Concept Drift Adaptation Methods under the Deep Learning Framework: A Literature Review

With the advent of the fourth industrial revolution, data-driven decision making has also become an integral part of decision making. At the same time, deep learning is one of the core technologies of the fourth industrial revolution that have become vital in decision making. However, in the era of epidemics and big data, the volume of data has increased dramatically while the sources have become progressively more complex, making data distribution highly susceptible to change. These situations can easily lead to concept drift, which directly affects the effectiveness of prediction models. How to cope with such complex situations and make timely and accurate decisions from multiple perspectives is a challenging research issue. To address this challenge, we summarize concept drift adaptation methods under the deep learning framework, which is beneficial to help decision makers make better decisions and analyze the causes of concept drift. First, we provide an overall introduction to concept drift, including the definition, causes, types, and process of concept drift adaptation methods under the deep learning framework. Second, we summarize concept drift adaptation methods in terms of discriminative learning, generative learning, hybrid learning, and others. For each aspect, we elaborate on the update modes, detection modes, and adaptation drift types of concept drift adaptation methods. In addition, we briefly describe the characteristics and application fields of deep learning algorithms using concept drift adaptation methods. Finally, we summarize common datasets and evaluation metrics and present future directions.

The Use of AI in Managing Big Data Analysis Demands: Status and Future updates Directions

In the modern age, the context of health, energy, environment, climate crisis, and global Covid-19 pandemic, managing Big Data demands via Sustainable Development Goals and disease mitigation supported by Artificial Intelligence, present significant challenges for a given territory or national boundaries' policies, legal systems, energy infrastructure, societal cohesion, internal and external national security. We look at policy, technical, and legal applications alongside ramifications of relevant policies and practices to highlight key challenges from a global and societal context. This review contributes to developing further awareness of the complexity and demands on policy and technology. In the long term due to these significant changes, inferences of multifaceted policy and data acquisition could present additional compounding challenges regarding civil liberties, data privacy law, and equitable health outcomes, whilst implementing continually evolving policies, practices, and techniques that can weaken infrastructure and present cyber-attack vulnerabilities. As a consequence of local, regional, and international paradigm shifts, Blockchain and Smart Contracts are suggested as part of a solution in providing data protection, transparency, and validity with transactional data to enable further trust between private and public sectors during times of crisis and technological transition processes. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

An Improved Granulated Convolutional Neural Network Data Analysis Model for COVID-19 Prediction

As COVID-19 poses a major threat to people's health and economy, there is an urgent need for forecasting methodologies that can anticipate its trajectory efficiently. In non-stationary time series forecasting jobs, there is frequently a hysteresis in the anticipated values relative to the real values. The multilayer deep-time convolutional network and a feature fusion network are combined in this paper's proposal of an enhanced Multilayer Deep Time Convolutional Neural Network (MDTCNet) for COVID-19 prediction to address this problem. In particular, it is possible to record the deep features and temporal dependencies in uncertain time series, and the features may then be combined using a feature fusion network and a multilayer perceptron. Last but not least, the experimental verification is conducted on the prediction task of COVID-19 real daily confirmed cases in the world and the United States with uncertainty, realizing the short-term and long-term prediction of COVID-19 daily confirmed cases, and verifying the effectiveness and accuracy of the suggested prediction method, as well as reducing the hysteresis of the prediction results.

Stress reactions and health problems in hospital personnel during the COVID-19 pandemic-A longitudinal, multicenter study

Background: The overarching aim of the study was to (1) investigate how working with COVID-19 patients has impacted work environment, and (2) to identify how factors in the work environment impact adverse health outcomes among hospital personnel (HP), throughout the four waves of the pandemic. Material(s) and Method(s): In a web-based survey altogether 2472 HP participated from four large university hospitals in Norway, whereof N = 680 in April-June 2020 (T1), N = 1073 in December-January 2020/2021 (T2), N = 818 in April-May 2021 (T3), and N = 972 in December 2021-February 2022 (T4). At each time point participants reported on pandemic related work tasks, work environment and adverse health outcomes. Somatic symptoms, psychological distress, posttraumatic stress symptoms and burnout served as outcomes of multivariable linear regression models. The percentage of responders involved in ICU treatment of COVID-19 patients varied between 21% and 40% from T1-T4. Result(s): Reported stressors altered in strength during the 4 waves. Preliminary results indicate that exposure to patients with COVID-19 was associated with more frequent experience of work environmental factors. Compared to colleagues not working with patients with COVID-19 HP reported challenges related to professional competency and training, predictability in teams and work environment, manageable workload, adequate help and support for work stress management, user-friendliness of Personal Protection Equipment and infection protection safety. Furthermore, these environmental factors were associated with symptoms of psychological unhealth on at least one timepoint. Conclusion(s): The results may help guide organizational efforts to maintain professional competency and to reduce stress more efficiently among hospital personnel at different stages in response to long-term crises.

EVALUATION OF RESPONSE TO ADJUVANTED RECOMBINANT ZOSTER VACCINATION IN PATIENTS WITH RHEUMATOID ARTHRITIS RECEIVING UPADACITINIB: RESULTS FROM A RANDOMIZED TRIAL SUB-STUDY

BackgroundUpadacitinib (UPA) is an oral JAK inhibitor (JAKi) approved for the treatment of RA. JAKi have been associated with an elevated risk of herpes zoster (HZ) in patients (pts) with RA. The adjuvanted recombinant zoster vaccine (RZV, Shingrix) was shown to be well-tolerated and effective in preventing HZ in adults aged ≥ 50 years.[1] The efficacy and safety of RZV have not been studied in pts with RA while on UPA in combination with MTX.ObjectivesTo assess the immunogenicity of RZV in pts with RA receiving UPA 15 mg once daily (QD) with background MTX.MethodsEligible adults aged ≥ 50 years with RA enrolled in the ongoing SELECT-COMPARE phase 3 trial (NCT02629159) received two RZV doses, administered at the baseline and week (wk) 12 visits. Pts should have been on stable doses of UPA 15 mg QD and background MTX for ≥ 8 wks before the first vaccination and ≥ 4 wks after the second vaccination. Antibody titers were collected pre-vaccination (baseline), 4 wks post-dose 1 vaccination (wk 4), and 4 wks post-dose 2 vaccination (wk 16). The primary endpoint was the proportion of pts with a humoral response to RZV defined as ≥ 4-fold increase in pre-vaccination concentration of anti-glycoprotein E [gE] titer levels at wk 16. Secondary endpoints included humoral response to RZV at wk 4 and the geometric mean fold rise (GMFR) in anti-gE antibody levels at wks 4 and 16. Cell-mediated immunogenicity to RZV was an exploratory endpoint evaluated by the frequencies of gE-specific CD4+ [2+] T cells (CD4+ T cells expressing ≥ 2 of 4 activation markers: IFN-γ, IL-2, TNF-α, and CD40 ligand) measured by flow cytometry at wks 4 and 16 in a sub-cohort of pts.ResultsOf the 95 pts who received ≥ 1 RZV dose, 93 (98%) received both RZV doses. Pts had a mean (standard deviation) age of 62.4 (7.5) years. The median (range) disease duration was 11.7 (4.9–41.6) years and duration of UPA exposure was 3.9 (2.9–5.8) years. At baseline, all but 2 pts were receiving concomitant MTX and half (50%) were taking an oral corticosteroid (CS) at a median daily dose of 5.0 mg. One pt discontinued UPA by wk 16. Blood samples were available from 90/93 pts. Satisfactory humoral responses to RZV occurred in 64% (95% confidence interval [CI]: 55–74) of pts at wk 4 and 88% (81–95) at wk 16 (Figure 1). Age (50–< 65 years: 85% [95% CI: 75–94];≥ 65 years: 94% [85–100]) and concomitant CS (yes: 87% [77–97];no: 89% [80–98]) use at baseline did not affect humoral responses at wk 16. GMFR in anti-gE antibody levels compared with baseline values were observed at wks 4 (10.2 [95% CI: 7.3–14.3]) and 16 (22.6 [15.9–32.2]). Among the sub-cohort of pts, nearly two-thirds achieved a cell-mediated immune response to RZV (wk 4: n = 21/34, 62% [95% CI: 45–78];wk 16: n = 25/38;66% [51–81]). Within 30 days post-vaccination of either RZV dose, no serious adverse events (AEs) (Table 1) or HZ were reported. AEs that were possibly related to RZV were reported in 17% of pts. One death occurred more than 30 days after wk 16 due to COVID-19 pneumonia.ConclusionMore than three-quarters (88%) of pts with RA receiving UPA 15 mg QD on background MTX achieved a satisfactory humoral response to RZV at wk 16. In a subgroup of pts, two-thirds (66%) achieved a cell-mediated immune response to RZV at wk 16. Age and concomitant CS use did not negatively affect RZV response.Reference[1]Syed YY. Drugs Aging. 2018;35:1031–40.Table 1. Safety Results Through 30-Days Post-RZV Vaccination in UPA-Treated PatientsEvent, n (%)UPA 15 mg QD (N = 95)Any AE38 (40%)AE with reasonable possibility of being related to UPAa13 (14%)AE with reasonable possibility of being related to RZVa16 (17%)Severe AEb1 (1%)Serious AE0AE leading to discontinuation of UPA0Death0AE, adverse event;QD, once daily;RZV, adjuvanted recombinant zoster vaccine;UPA, upadacitinib.aAs assessed by the investigator.bHypersensitivity.AcknowledgementsAbbVie funded this study and participated in the study design, research, analysis, data collection, interpretation of data, review, and approval of the . All authors had access to relevant data and participated in the drafting, review, and approval of this publication. No honoraria or payments were made for authorship. Medical writing support was provided by Julia Zolotarjova, MSc, MWC, of AbbVie.Disclosure of InterestsKevin Winthrop Consultant of: AbbVie, AstraZeneca, BMS, Eli Lilly, Galapagos, Gilead, GSK, Novartis, Pfizer, Regeneron, Roche, Sanofi, and UCB, Grant/research support from: AbbVie, AstraZeneca, BMS, Eli Lilly, Galapagos, Gilead, GSK, Novartis, Pfizer, Regeneron, Roche, Sanofi, and UCB, Justin Klaff Shareholder of: AbbVie, Employee of: AbbVie, Yanxi Liu Shareholder of: AbbVie, Employee of: AbbVie, CONRADO GARCIA GARCIA: None declared, Eduardo Mysler Speakers bureau: AbbVie, Amgen, AstraZeneca, BMS, Eli Lilly, GlaxoSmithKline, Pfizer, Roche, and Sandoz, Consultant of: AbbVie, Amgen, AstraZeneca, BMS, Eli Lilly, GlaxoSmithKline, Pfizer, Roche, and Sandoz, Alvin F. Wells Consultant of: AbbVie, Amgen, BMS, Eli Lilly, Novartis, Pfizer, and Sanofi, Xianwei Bu Shareholder of: AbbVie, Employee of: AbbVie, Nasser Khan Shareholder of: AbbVie, Employee of: AbbVie, Michael Chen Shareholder of: AbbVie, Employee of: AbbVie, Heidi Camp Shareholder of: AbbVie, Employee of: AbbVie, Anthony Cunningham Consultant of: GSK, Merck Sharp & Dohme, and BioCSL/Sequirus.

Port strategy and performance: empirical evidence from major container ports and implications for role of data analytics

Container ports operate in more challenging and volatile environments at present times. Events such as US-China trade tensions and the COVID-19 pandemic severely affect numerous container ports at various levels. Strategies pursued by container ports are key to port development and management amidst these challenges. Drawing on configuration theory, this research employs Fuzzy-set Qualitative Comparative Analysis to investigate the relation between port strategies and container throughput. The research contributes to the literature by proposing an approach to account for complexity of the port sector and offers insights into strategies adopted by major container ports. The research further identifies 10 port strategies and proposed indicators that can represent the essence of these strategies. Being able to represent strategies in a quantitative format is important for strategy analysis and performance evaluation. Results reveal that major container ports employ a combination of strategies which address both the supply and demand-side aspects of the port business. Growing digitalization and digitization coupled with advancements in information capture, diagnostics capabilities and predictive abilities means a greater role for data analytics to influence container port strategy and performance. Implications for port managers, policy makers and researchers from the perspective of port policy and management are proposed.

Persistent Topological Laplacian Analysis of SARS-CoV-2 Variants

Topological data analysis (TDA) is an emerging field in mathematics and data science. Its central technique, persistent homology, has had tremendous success in many science and engineering disciplines. However, persistent homology has limitations, including its inability to handle heterogeneous information, such as multiple types of geometric objects;being qualitative rather than quantitative, e.g., counting a 5-member ring the same as a 6-member ring, and a failure to describe nontopological changes, such as homotopic changes in protein–protein binding. Persistent topological Laplacians (PTLs), such as persistent Laplacian and persistent sheaf Laplacian, were proposed to overcome the limitations of persistent homology. In this work, we examine the modeling and analysis power of PTLs in the study of the protein structures of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike receptor binding domain (RBD). First, we employ PTLs to study how the RBD mutation-induced structural changes of RBD-angiotensin-converting enzyme 2 (ACE2) binding complexes are captured in the changes of spectra of the PTLs among SARS-CoV-2 variants. Additionally, we use PTLs to analyze the binding of RBD and ACE2-induced structural changes of various SARS-CoV-2 variants. Finally, we explore the impacts of computationally generated RBD structures on a topological deep learning paradigm and predictions of deep mutational scanning datasets for the SARS-CoV-2 Omicron BA.2 variant. Our results indicate that PTLs have advantages over persistent homology in analyzing protein structural changes and provide a powerful new TDA tool for data science. [ FROM AUTHOR] Copyright of Journal of Computational Biophysics & Chemistry is the property of World Scientific Publishing Company 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.)