The Impact of COVID-19 Lockdown on Ambient Air Quality in Shanghai, 2022

The COVID-19 lockdown contributes to the improvement of air quality. Most previous studies have attributed this to the reduction of human activity while ignoring the meteorological changes, this may lead to an overestimation or underestimation of the impact of COVID-19 lockdown measures on air pollution levels. To investigate this issue, we propose an XGBoost-based model to predict the concentrations of PM2.5 and PM10 during the COVID-19 lockdown period in 2022, Shanghai, and thus explore the limits of anthropogenic emission on air pollution levels by comprehensively employing the meteorological factors and the concentrations of other air pollutants. Results demonstrate that actual observations of PM2.5 and PM10 during the COVID-19 lockdown period were reduced by 60.81% and 43.12% compared with the predicted values (regarded as the period without the lockdown measures). In addition, by comparing with the time series prediction results without considering meteorological factors, the actual observations of PM2.5 and PM10 during the lockdown period were reduced by 50.20% and 19.06%, respectively, against the predicted values during the non-lockdown period. The analysis results indicate that ignoring meteorological factors will underestimate the positive impact of COVID-19 lockdown measures on air quality. © 2023 by the authors.

Travel behavior adjustment based epidemic spreading model and prediction for COVID-19

Owing to the continuous variant of the COVID-19 virus, the present epidemic may persist for a long time, and each breakout displays strongly region/time-dependent characteristics. Predicting each specific burst is the basic task for the corresponding strategies. However, the refinement of prevention and control measures usually means the limitation of the existing records of the evolution of the spread, which leads to a special difficulty in making predictions. Taking into account the interdependence of people' s travel behaviors and the epidemic spreading, we propose a modified logistic model to mimic the COVID-19 epidemic spreading, in order to predict the evolutionary behaviors for a specific bursting in a megacity with limited epidemic related records. It continuously reproduced the COVID-19 infected records in Shanghai, China in the period from March 1 to June 28, 2022. From December 7, 2022 when Mainland China adopted new detailed prevention and control measures, the COVID-19 epidemic broke out nationwide, and the infected people themselves took "ibuprofen” widely to relieve the symptoms of fever. A reasonable assumption is that the total number of searches for the word "ibuprofen” is a good representation of the number of infected people. By using the number of searching for the word "ibuprofen” provided on Baidu, a famous searching platform in Mainland China, we estimate the parameters in the modified logistic model and predict subsequently the epidemic spreading behavior in Shanghai, China starting from December 1, 2022. This situation lasted for 72 days. The number of the infected people increased exponentially in the period from the beginning to the 24th day, reached a summit on the 31st day, and decreased exponentially in the period from the 38th day to the end. Within the two weeks centered at the summit, the increasing and decreasing speeds are both significantly small, but the increased number of infected people each day was significantly large. The characteristic for this prediction matches very well with that for the number of metro passengers in Shanghai. It is suggested that the relevant departments should establish a monitoring system composed of some communities, hospitals, etc. according to the sampling principle in statistics to provide reliable prediction records for researchers. © 2023 Chinese Physical Society.

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.

Predicting the climate impact of aviation for en-route emissions: the algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53

Using climate-optimized flight trajectories is one essential measure to reduce aviation's climate impact. Detailed knowledge of temporal and spatial climate sensitivity for aviation emissions in the atmosphere is required to realize such a climate mitigation measure. The algorithmic Climate Change Functions (aCCFs) represent the basis for such purposes. This paper presents the first version of the Algorithmic Climate Change Function submodel (ACCF 1.0) within the European Centre HAMburg general circulation model (ECHAM) and Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model framework. In the ACCF 1.0, we implement a set of aCCFs (version 1.0) to estimate the average temperature response over 20 years (ATR20) resulting from aviation CO2 emissions and non-CO2 impacts, such as NOx emissions (via ozone production and methane destruction), water vapour emissions, and contrail cirrus. While the aCCF concept has been introduced in previous research, here, we publish a consistent set of aCCF formulas in terms of fuel scenario, metric, and efficacy for the first time. In particular, this paper elaborates on contrail aCCF development, which has not been published before. ACCF 1.0 uses the simulated atmospheric conditions at the emission location as input to calculate the ATR20 per unit of fuel burned, per NOx emitted, or per flown kilometre.In this research, we perform quality checks of the ACCF 1.0 outputs in two aspects. Firstly, we compare climatological values calculated by ACCF 1.0 to previous studies. The comparison confirms that in the Northern Hemisphere between 150–300 hPa altitude (flight corridor), the vertical and latitudinal structure of NOx-induced ozone and H2O effects are well represented by the ACCF model output. The NOx-induced methane effects increase towards lower altitudes and higher latitudes, which behaves differently from the existing literature. For contrail cirrus, the climatological pattern of the ACCF model output corresponds with the literature, except that contrail-cirrus aCCF generates values at low altitudes near polar regions, which is caused by the conditions set up for contrail formation. Secondly, we evaluate the reduction of NOx-induced ozone effects through trajectory optimization, employing the tagging chemistry approach (contribution approach to tag species according to their emission categories and to inherit these tags to other species during the subsequent chemical reactions). The simulation results show that climate-optimized trajectories reduce the radiative forcing contribution from aviation NOx-induced ozone compared to cost-optimized trajectories. Finally, we couple the ACCF 1.0 to the air traffic simulation submodel AirTraf version 2.0 and demonstrate the variability of the flight trajectories when the efficacy of individual effects is considered. Based on the 1 d simulation results of a subset of European flights, the total ATR20 of the climate-optimized flights is significantly lower (roughly 50 % less) than that of the cost-optimized flights, with the most considerable contribution from contrail cirrus. The CO2 contribution observed in this study is low compared with the non-CO2 effects, which requires further diagnosis.

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.)

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.

Exploring Social Media for Early Detection of Depression in COVID-19 Patients

The COVID-19 pandemic has caused substantial damage to global health. Even though three years have passed, the world continues to struggle with the virus. Concerns are growing about the impact of COVID-19 on the mental health of infected individuals, who are more likely to experience depression, which can have long-lasting consequences for both the affected individuals and the world. Detection and intervention at an early stage can reduce the risk of depression in COVID-19 patients. In this paper, we investigated the relationship between COVID-19 infection and depression through social media analysis. Firstly, we managed a dataset of COVID-19 patients that contains information about their social media activity both before and after infection. Secondly, We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression. Thirdly, we proposed a deep neural network for early prediction of depression risk. This model considers daily mood swings as a psychiatric signal and incorporates textual and emotional characteristics via knowledge distillation. Experimental results demonstrate that our proposed framework outperforms baselines in detecting depression risk, with an AUROC of 0.9317 and an AUPRC of 0.8116. Our model has the potential to enable public health organizations to initiate prompt intervention with high-risk patients. © 2023 ACM.

Fast de novo antibody structure prediction with atomic accuracy

Background: In the field of antibody engineering, an essential task is to design a novel antibody whose paratopes bind to a specific antigen with correct epitopes. Understanding antibody structure and its paratope can facilitate a mechanistic understanding of its function. Therefore, antibody structure prediction from its sequence alone has always been a highly valuable problem for de novo antibody design. AlphaFold2 (AF2), a breakthrough in the field of structural biology, provides a solution to this protein structure prediction problem by learning a deep learning model. However, the computational efficiency and undesirable prediction accuracy on antibody, especially on the complementarity-determining regions limit its applications in de novo antibody design. Method(s): To learn informative representation of antibodies, we trained a deep antibody language model (ALM) on curated sequences from observed antibody space database via a well-designed transformer model. We also developed a novel model named xTrimoABFold++ to predict antibody structure from antibody sequence only based on the pretrained ALM as well as efficient evoformers and structural modules. The model was trained end-to-end on the antibody structures in PDB by minimizing the ensemble loss of domain-specific focal loss on CDR and the frame aligned point loss. Result(s): xTrimoABFold++ outperforms AF2 and OmegaFold, HelixFold-Single with 30+% improvement on RMSD. Also, it is 151 times faster than AF2 and predicts antibody structure in atomic accuracy within 20 seconds. In recently released antibodies, for example, cemiplimab of PD1 (PDB: 7WVM) and cross-neutralizing antibody 6D6 of SARS-CoV-2 (PDB: 7EAN), the RMSD of xTrimoABFold++ are 0.344 and 0.389 respectively. Conclusion(s): To the best of our knowledge, xTrimoABFold++ achieved the state-of-the-art in antibody structure prediction. Its improvement on both accuracy and efficiency makes it a valuable tool for de novo antibody design, and could make further improvement in immuno-theory.

Readwell: A Book Recommender Android Application with a Point-of-Sales System

The COVID-19 pandemic has given people much free time. With this, the researchers want to encourage these people to read instead of scrolling through social media. A barrier to reading for many people is not knowing what to read and disinterest in popular books that they would find when they search online. The existing websites that encourage book reading rely on social networking for their recommendations, while the collaborative filtering algorithms applied to books do not exist in the mobile application form. Readwell is a book recommender Android app with a Point-of-Sales System created using Java, Python, and SQLite databases. The information regarding the books was web scraped from the Goodreads website. It aims to apply the more efficient collaborative filtering algorithm to an accessible mobile application that allows users to directly buy the books they are interested in, thus encouraging the reading and buying of books. The researchers created unit test cases to validate the different functionalities of the application. © 2022 IEEE.

DEEP LEARNING BASED AN EFFICIENT HYBRID PREDICTION MODEL FOR COVID-19 CROSS-COUNTRY SPREAD AMONG E7 AND G7 COUNTRIES

The COVID-19 pandemic has caused the death of many people around the world and has also caused economic problems for all countries in the world. In the literature, there are many studies to analyze and predict the spread of COVID-19 in cities and countries. However, there is no study to predict and analyze the cross-country spread in the world. In this study, a deep learning based hybrid model was developed to predict and analysis of COVID-19 cross-country spread and a case study was carried out for Emerging Seven (E7) and Group of Seven (G7) countries. It is aimed to reduce the workload of healthcare professionals and to make health plans by predicting the daily number of COVID-19 cases and deaths. Developed model was tested extensively using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R Squared (R2). The experimental results showed that the developed model was more successful to predict and analysis of COVID-19 cross-country spread in E7 and G7 countries than Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM). The developed model has R2 value close to 0.9 in predicting the number of daily cases and deaths in the majority of E7 and G7 countries. © 2023 by the authors.

Intelligent Methods and Models for Assessing Level of Student Adaptation to Online Learning

The problem of introducing online learning is becoming more and more popular in our society. Due to COVID-19 and the war in Ukraine, there is an urgent need for the transition of educational institutions to online learning, so this paper will help people not make mistakes in the process and afterward. The paper's primary purpose is to investigate the effectiveness of machine learning tools that can solve the problem of assessing student adaptation to online learning. These tools include intelligent methods and models, such as classification techniques and neural networks. This work uses data from an online survey of students at different levels: school, college, and university. The survey consists of questions such as gender, age, level of education, whether the student is in the city, class duration, quality of Internet connection, government/non-government educational institution, availability of virtual learning environment, whether the student is familiar with IT, financial conditions, type of Internet connection, a device used for studying, etc. To obtain the results on the effectiveness of online education were used the following machine learning algorithms and models: Random Forest (RF), Extra Trees (ET), Extreme, Light, and Simple Gradient Boosting (GB), Decision Trees (DT), K-neighbors (K-mean), Logistic Regression (LR), Support Vector Machine (SVM), Naїve Bayes (NB) classifier and others. An intelligent neural network model (NNM) was built to address the main issue. © 2023 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)

Graph-Based Fusion of Imaging and Non-Imaging Data for Disease Trajectory Prediction

This study proposes a graph convolutional neural networks (GCN) architecture for fusion of radiological imaging and non-imaging tabular electronic health records (EHR) for the purpose of clinical event prediction. We focused on a cohort of hospitalized patients with positive RT-PCR test for COVID-19 and developed GCN based models to predict three dependent clinical events (discharge from hospital, admission into ICU, and mortality) using demographics, billing codes for procedures and diagnoses and chest X-rays. We hypothesized that the two-fold learning opportunity provided by the GCN is ideal for fusion of imaging information and tabular data as node and edge features, respectively. Our experiments indicate the validity of our hypothesis where GCN based predictive models outperform single modality and traditional fusion models. We compared the proposed models against two variations of imaging-based models, including DenseNet-121 architecture with learnable classification layers and Random Forest classifiers using disease severity score estimated by pre-trained convolutional neural network. GCN based model outperforms both imaging-only methods. We also validated our models on an external dataset where GCN showed valuable generalization capabilities. We noticed that edge-formation function can be adapted even after training the GCN model without limiting application scope of the model. Our models take advantage of this fact for generalization to external data. © 2023 IEEE.

Identification Symptoms and Underlying Diseases Related to COVID-19 And Prediction of Death Status Using Artificial Neural Network and Logistic Regression: A Data Mining Approach.

Background and Objectives: Due to the high prevalence of COVID-19 disease and its high mortality rate, it is necessary to identify the symptoms, demographic information and underlying diseases that effectively predict COVID-19 death. Therefore, in this study, we aimed to predict the mortality behavior due to COVID-19 in Khorasan Razavi province. Method(s): This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19. Result(s): Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively. Conclusion(s): Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.Copyright © 2022 The Authors.

Self-Directed Learning Predicts Online Learning Engagement in Higher Education Mediated by Perceived Value of Knowing Learning Goals

Online education has made it possible to implement the "classes suspended but learning continues" policy during the COVID-19 outbreak. However, the intangible sense of the online educational setting requires self-directed learning (SDL) and may force students to know the goals of learning that may impact their engagement. To understand the effect, based on situated expectancy-value theory, this study considered SDL as attitude and approach and constructed a research model to explore the mediating power of perceived value of knowing learning goals (PVKLG) related to participants' online learning engagement during the COVID-19 lockdown. Data were collected from 497 higher education students in China. After the confirmatory factor analysis with structural equation modeling, the results reported that SDL attitude and approach positively predicted learning engagement mediated by PVKLG. The results suggest that only when students have a high level of PVKLG will they be able to regulate their learning process through the two types of SDL and enhance their engagement in online learning contexts during the COVID-19 lockdown.

IRF2BP2 mutation with autoimmune enteropathy, immune deficiency, and respiratory failure

Case history: We present the case of a 31-year-old Hispanic male with history of recurrent bronchiectasis, invasive aspergillosis, and severe persistent asthma, who is now status post lung transplant for end-stage lung disease. He initially presented at 7 years of age with diarrhea, failure to thrive, and nearly absent immunoglobulin levels (IgG < 33 mg/dL, IgA < 7 mg/dL, IgM = 11 mg/dL, IgE = 4 IU/dL) necessitating IVIG treatment. Small intestinal biopsy showed villous atrophy consistent with autoimmune enteropathy. Sweat chloride was reported as indeterminate (44 me/dL). Initial WBC, platelet, and T- and NK-cell counts were within normal range, and B-cell count and percentage were borderline low. Most recently, he was found to have increased immature B-cell count (CD21low), decreased memory B-cells, and poor pneumococcal vaccine antibody response. Patient has been hospitalized numerous times with increasingly severe bronchiectasis, pneumonitis, and COVID-19 infections twice despite vaccination, leading to respiratory failure and lung transplantation. Family history is negative for immune deficiency and lung diseases. Discussion(s): Of these 3 VUSs (see the table), the one in IRF2BP2 has the most pathogenic potential due to its autosomal dominant inheritance, its location in a conserved domain (Ring), and previous case reports of pathogenic variants at the same or adjacent alleles 1-3. Baxter et al reported a de novo truncating mutation in IRF2BP2 at codon 536 (c.1606CinsTTT), which is similar to our patient's mutation. This patient was noted to have an IPEX-like presentation, with chronic diarrhea, hypogammaglobulinemia, and recurrent infections. Variant Functional Prediction Score for our variant predicts a potentially high damage effect. There are 2 other case reports of heterozygous mutations in loci adjacent to this allele;one (c.1652G>A)2 with a similar clinical phenotype to our patient and the other (C.625-665 del)3 with primarily inflammatory features and few infections. Impact: This case highlights a variant in IRF2BP2 associated with severe hypogammaglobulinemia, recurrent pulmonary infections, and autoimmune enteropathy. [Table presented]Copyright © 2023 Elsevier Inc.

Unraveling the Impact of COVID-19 Pandemic Dynamics on Commercial Water-Use Variation

Water use was impacted significantly by the COVID-19 pandemic. Although previous studies quantitatively investigated the effects of COVID-19 on water use, the relationship between water-use variation and COVID-19 dynamics (i.e., the spatial-temporal characteristics of COVID-19 cases) has received less attention. This study developed a two-step methodology to unravel the impact of COVID-19 pandemic dynamics on water-use variation. First, using a water-use prediction model, the water-use change percentage (WUCP) indicator, which was calculated as the relative difference between modeled and observed water use, i.e., water-use variation, was used to quantify the COVID-19 effects on water use. Second, two indicators, i.e., the number of existing confirmed cases (NECC) and the spatial risk index (SRI), were applied to characterize pandemic dynamics, and the quantitative relationship between WUCP and pandemic dynamics was examined by means of regression analysis. We collected and analyzed 6-year commercial water-use data from smart meters of Zhongshan District in Dalian City, Northeast China. The results indicate that commercial water use decreased significantly, with an average WUCP of 59.4%, 54.4%, and 45.7%during the three pandemic waves, respectively, in Dalian. Regression analysis showed that there was a positive linear relationship between water-use changes (i.e., WUCP) and pandemic dynamics (i.e., NECC and SRI). Both the number of COVID-19 cases and their spatial distribution impacted commercial water use, and the effects were weakened by restriction strategy improvement, and the accumulation of experience and knowledge about COVID-19. This study provides an in-depth understanding of the impact of COVID-19 dynamics on commercial water use. The results can be used to help predict water demand under during future pandemic periods or other types of natural and human-made disturbance.

An Alternative System for Assessing Pavement Condition in the Event of an Epidemic: A Case of COVID-19

Maintaining the efficiency of road pavement is essential to achieving the highest road performance and comfort for road users. Pavement monitoring plays a significant role in maintaining the sustainability of road networks. Additionally, assessments have been performed using different equipment and devices or through visual inspections to determine the type and severity of pavement degradation. However, some obstacles may affect the sustainability of road networks by preventing the regular monitoring and maintenance of pavements, such as the COVID-19 pandemic. Due to the COVID-19 pandemic, the construction and management of transportation systems have been affected by economic shut-downs and imposed social restrictions. Road networks have also suffered from neglect and a lack of monitoring and maintenance due to the government's lockdowns in addition to strict regulations that limit movement on roads and any form of construction, monitoring, inspection, and evaluation to improve road pavement conditions. This research introduces a safe pavement monitoring system using an e-bike to evaluate and predict pavement degradation. An accelerometer sensor and line-scan camera were used to collect pavement vibration data during the e-bike's movement. The results of the proposed monitoring method showed reliable evaluation outcomes. Moreover, the SVM model showed a significant contribution to detecting and classifying pavement distress.

Using the SIR Model to Analyze the Epidemiological Situation in Kazakhstan and Neighboring Countries

In this paper, we study the epidemic situation in Kazakhstan and neighboring countries, taking into account territorial features in emergency situations. As you know, the excessive concentration of the population in large cities and the transition to a world without borders created ideal conditions for a global pandemic. The article also provides the results of a detailed analysis of the solution approaches to modeling the development of epidemics by types of models (basic SIR model, modified SEIR models) and the practical application of the SIR model using an example (Kazakhstan, Russia, Kyrgyzstan, Uzbekistan and other neighboring countries). The obtained processing results are based on statistical data from open sources on the development of the COVID-19 epidemic. The result obtained is a general solution of the SIR-model of the spread of the epidemic according to the fourth-order Runge-Kutta method. The parameters β, γ, which are indicators of infection, recovery, respectively, were calculated using data at the initial phase of the Covid 2019 epidemic. An analysis of anti-epidemic measures in neighboring countries is given. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

The space between stress and reaction: A three-way interaction of active coping, psychological stress, and applied mindfulness in the prediction of sustainable resilience

Amid a global pandemic, data was collected to explore the extent to which resilience practices (active coping and applied mindfulness) under varying degrees of stress levels can promote sustainable resilience, defined as the ability to move through challenges in a way that leads to increased positive adaptation to meet present and future challenges. Results did not support the proposed three-way interaction;however, post-hoc analyses indicated that active coping (r = .316) and applied mindfulness (r = .250) were independently predictive of sustained resilience and, when combined, predicted approximately 20 percent (R2 = .203) of sustained resilience one month later. Furthermore, the results suggest a significant quadratic two-way moderation between mindfulness and sustained resilience at different stress levels suggesting that at high stress levels, moderate levels of mindfulness are most predictive of resilience. Implications for theory, practice, and future research are discussed. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Liver biomarkers for prognosis in COVID-19

The novel coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2).Mortality attributable to COVID-19 remains considerably high, with case fatality rates as high as 8-11%. Early medical intervention in patients who are seriously and critically ill with COVID-19 reduces fatal outcomes. Thus, there is an urgent need to identify biomarkers that could help clinicians determine which patients with SARS-CoV-2 infection are at a higher risk of developing the most adverse outcomes, which include intensive care unit (ICU) admission, invasive ventilation, and death. In COVID-19 patients experiencing the most severe form of the disease, tests of liver function are frequently abnormal and liver enzymes are found to be elevated. For this reason, we examine the most promising liver biomarkers for COVID-19 prognosis in an effort to help clinicians predict the risk of ARDS, ICU admission, and death at hospital admission. In patients meeting hospitalization criteria for COVID-19, serum albumin < 36 g/L is an independent risk factor for ICU admission, with an AUC of 0.989, whereas lactate dehydrogenase (LDH) values > 365 U/L accurately predict death with an AUC of 0.943.The clinical scores COVID-GRAM and SOFA that include measures of liver function such as albumin, LDH, and total bilirubin are also good predictors of pneumonia development, ICU admission, and death, with AUC values ranging from 0.88 to 0.978.Thus, serum albumin and LDH, together with clinical risk scores such as COVID-GRAM and SOFA, are the most accurate biomarkers in the prognosis of COVID-19.Copyright © 2021 Sociedad Medica del Hospital General de Mexico. Published by Permanyer.