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1.
Entropy (Basel) ; 24(11)2022 Nov 04.
Article in English | MEDLINE | ID: covidwho-2099401

ABSTRACT

In the context of the COVID-19 global epidemic, it is particularly important to use limited medical resources to improve the systemic control of infectious diseases. There is a situation where a shortage of medical resources and an uneven distribution of resources in China exist. Therefore, it is important to have an accurate understanding of the current status of the healthcare system in China and to improve the efficiency of their infectious disease control methods. In this study, the MP-SBM-Shannon entropy model (modified panel slacks-based measure Shannon entropy model) was proposed and applied to measure the disposal efficiency of the medical institutions responding to public health emergencies (disposal efficiency) in China from 2012 to 2018. First, a P-SBM (panel slacks-based measure) model, with undesirable outputs based on panel data, is given in this paper. This model measures the efficiency of all DMUs based on the same technical frontier and can be used for the dynamic efficiency analysis of panel data. Then, the MP-SBM model is applied to solve the specific efficiency paradox of the P-SBM model caused by the objective data structure. Finally, based on the MP-SBM model, undesirable outputs are considered in the original efficiency matrix alignment combination for the deficiencies of the existing Shannon entropy-DEA model. The comparative analysis shows that the MP-SBM-Shannon model not only solves the problem of the efficiency paradox of the P-SBM model but also improves the MP-SBM model identification ability and provides a complete ranking with certain advantages. The results of the study show that the disposal efficiency of the medical institutions responding to public health emergencies in China shows an upward trend, but the average combined efficiency is less than 0.47. Therefore, there is still much room for improvement in the efficiency of infectious disease prevention and control in China. It is found that the staffing problem within the Center for Disease Control and the health supervision office are two stumbling blocks.

2.
Expert Systems with Applications ; JOUR: 119095,
Article in English | ScienceDirect | ID: covidwho-2082973

ABSTRACT

COVID-19 is pervasive and threatens the safety of people around the world. Therefore, now, a method is needed to diagnose COVID-19 accurately. The identification of COVID-19 by X-ray images is a common method. The target area is extracted from the X-ray images by image segmentation to improve classification efficiency and help doctors make a diagnosis. In this paper, we propose an improved crow search algorithm (CSA) based on variable neighborhood descent (VND) and information exchange mutation (IEM) strategies, called VMCSA. The original CSA quickly falls into the local optimum, and the possibility of finding the best solution is significantly reduced. Therefore, to help the algorithm avoid falling into local optimality and improve the global search capability of the algorithm, we introduce VND and IEM into CSA. Comparative experiments are conducted at CEC2014 and CEC’21 to demonstrate the better performance of the proposed algorithm in optimization. We also apply the proposed algorithm to multi-level thresholding image segmentation using Renyi’s entropy as the objective function to find the optimal threshold, where we construct 2-D histograms with grayscale images and non-local mean images and maximize the Renyi’s entropy on top of the 2-D histogram. The proposed segmentation method is evaluated on X-ray images of COVID-19 and compared with some algorithms. VMCSA has a significant advantage in segmentation results and obtains better robustness than other algorithms. The available codes can be found at https://github.com/1234zsw/VMCSA.

3.
Studies in Business and Economics ; 17(2):141-159, 2022.
Article in English | Web of Science | ID: covidwho-2071044

ABSTRACT

Recessions and natural disasters continually slow down the economy. The scale of the effects depends on the origin of the crisis, the response capacity, among other factors. The objective of this article was to study the impact of the recession due to covid-19 on business creation. Using indicators of related and unrelated variety, the industrial diversity of 16 states of Mexico was measured. The main source of information was administrative data. The results show that a region with higher related industrial diversity has greater resilience and more firms. In times of crisis, these results could be used to assess the loss of businesses, given the type and scale of industrial variety.

4.
Journal of Cleaner Production ; : 134697, 2022.
Article in English | ScienceDirect | ID: covidwho-2069279

ABSTRACT

– This research is motivated by the challenges a ventilator remanufacturer encountered during the COVID-19 pandemic: (i) three refurbishing steps, namely, disassembling, sterilising, and reconditioning, which reduce the yield rates of reused components and thus complicating the remanufacturing process, are required to satisfy the compulsory hygienic regulations;and (ii) the lead time to procuring new components become rather variable because of the paralysed global logistics, thereby prolonging the remanufacturing time. To minimise the total remanufacturing costs, mathematical models are built to derive the optimal remanufacturing lead time analytically for one- and two-component cases and numerical studies are conducted to investigate the behaviour of the remanufacturing process. Four managerial insights are provided to improve the remanufacturing performance: (i) The minimum relative entropy method could approximate the optimal remanufacturing lead time with higher precision because the remanufacturing time might be multi-modal distributed. (ii) Increasing the yield rates at all refurbishing steps could shorten the remanufacturing lead time but does not lower the total cost necessarily. (iii) Investment in reducing the refurbishing lead times might not be economically efficient, whereas shortening the procurement lead time could lower the cost dramatically. (iv) Stock-based strategy for the components with low holding cost could help simplify the remanufacturing process and save the multi-skilled labour cost.

5.
Int J Environ Res Public Health ; 19(19)2022 Oct 02.
Article in English | MEDLINE | ID: covidwho-2066016

ABSTRACT

The occurrence of major health events can have a significant impact on public mood and mental health. In this study, we selected Shanghai during the 2019 novel coronavirus pandemic as a case study and Weibo texts as the data source. The ERNIE pre-training model was used to classify the text data into five emotional categories: gratitude, confidence, sadness, anger, and no emotion. The changes in public sentiment and potential influencing factors were analyzed with the emotional sequence diagram method. We also examined the causal relationship between the epidemic and public sentiment, as well as positive and negative emotions. The study found: (1) public sentiment during the epidemic was primarily affected by public behavior, government behavior, and the severity of the epidemic. (2) From the perspective of time series changes, the changes in public emotions during the epidemic were divided into emotional fermentation, emotional climax, and emotional chaos periods. (3) There was a clear causal relationship between the epidemic and the changes in public emotions, and the impact on negative emotions was greater than that of positive emotions. Additionally, positive emotions had a certain inhibitory effect on negative emotions.


Subject(s)
COVID-19 , Social Media , Attitude , COVID-19/epidemiology , China/epidemiology , Emergencies , Emotions , Humans , Pandemics
6.
International Journal of Nonlinear Sciences & Numerical Simulation ; : 1, 2022.
Article in English | Academic Search Complete | ID: covidwho-2065196

ABSTRACT

In this research, we intended to employ the Pearson correlation and a multiscale generalized Shannon-based entropy to trace the transition and type of inherent mutual information as well as correlation structures simultaneously. An optimal value for scale is found to prevent over smoothing, which leads to the removal of useful information. The lowest Singular Value Decomposition Multiscale Generalized Cumulative Residual Entropy (SVDMWGCRE), or SVD Entropy (SVDE), is obtained for periodic–chaotic series, generated by logistic map;hence, the different dynamic, correlation structures, and intrinsic mutual information have been characterized correctly. It is found out that the mutual information between emerging markets entails higher sensitivity, and moreover emerging markets have demonstrated the highest uncertainty among investigated markets. Additionally, the fractional order has synergistic effects on the enhancement of sensitivity with the multiscale feature. According to the logistic map and financial time series results, it can be inferred that the logistic map can be utilized as a financial time series. Further investigations can be performed in other fields through this financial simulation. The temporal evolutions of financial markets are also investigated. Although the results demonstrated higher noisy information for emerging markets, it was illustrated that emerging markets are getting more efficient over time. Additionally, the temporal investigations have demonstrated long-term lag and synchronous phases between developed and emerging markets. We also focused on the COVID-19 pandemic and compared the reactions of developing and emerging markets. It is ascertained that emerging markets have demonstrated higher uncertainty and overreaction to this pandemic. [ FROM AUTHOR] Copyright of International Journal of Nonlinear Sciences & Numerical Simulation is the property of De Gruyter 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.)

7.
2022 IEEE International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2063236

ABSTRACT

At the end of 2021, a 4th wave of Corona Virus 2019 (Covid-19 in short) pandemic has emerged at Germany against the expectations after a vaccination program that could have reached a 3/4 of German population (to date). It is actually interesting that the peak of infections at the third week of November is twice than the second wave as seen at data one year ago despite that at that times the vaccination scheme was still modest. This paper focuses at Germany and its ongoing wave that is perceived as a consequence of a type of entropy because the mobility of virus and infections. In addition the consequences of this entropy and the possible correlation at the neighbors countries such as Austria and Czech are analyzed. © 2022 IEEE.

8.
2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022 ; 2022-July, 2022.
Article in English | Scopus | ID: covidwho-2063229

ABSTRACT

In the Covid-19 era, it is important to have an edge detector for X-ray (XR) images affected by uncertainties with low computational load but with high performance. So, here, a new version of a well-known fuzzy edge detector, in which a new image fuzzification procedure has been formulated, is proposed. The performance were qualitatively/ quantitatively compared with those obtained by Canny's edge detector (gold standard for this type of problem). In addition, an evolution of the deep fuzzy-neural model named CovNNet, recently proposed by the authors to discriminate chest XR (CXR) images of patients with Covid-19 pneumonia from images of patients with interstitial pneumonias not related to Covid-19 (No-Covid-19), is presented and referred as to Enhanced-CovNNet (ECovNNet). Here, the generalization ability of it is also improved by introducing a regularization based on dropping out some nodes of the network in a random way. ECovNNet processes input CXR images and the corresponding fuzzy CXR images (processed through the proposed enhanced-fuzzy edge detector) and extracts relevant CXR/fuzzy features, subsequently combined in a single array named CXR and fuzzy features vector. The latter is used as input to an Autoencoder-(AE)-based classifier to perform the binary classification: Covid-19 and No-Covid-19, reporting accuracy rate up to 81%. Finally, the work is completed with some interesting physico-mathematical results. © 2022 IEEE.

9.
Journal of International Financial Markets, Institutions and Money ; 81, 2022.
Article in English | Scopus | ID: covidwho-2061291

ABSTRACT

Motivated by the severe impacts of the Covid 19 outbreak on the global trade and capital flows, which can shift the forex market structure, this paper aims to examine the equicorrelation and causal association across major currency markets during Covid 19 pandemic using novel approaches: DECO-GARCH and Transfer Entropy. We find that major exchange rate markets have a positive equicorrelation, and these trends have been more pronounced during the Covid-19 crisis, uncovering the existence of contagion effects. The results also show the causal associations between the currency markets, depicted by three categories: no effect, mono-direction, and bi-direction. Such connections unveil the shock sender and receiver in the examined exchange rate markets, supporting that there is contagion risk across currency markets. Our findings suggest important implications for investors, firms, and policymakers in risk management during crisis periods. © 2022 Elsevier B.V.

10.
Embase; 2021.
Preprint in English | EMBASE | ID: ppcovidwho-344418

ABSTRACT

Sampling for prevalence estimation of infection is subject to bias by both oversampling of symptomatic individuals and error-prone tests. This results in naive estimators that can be very far from the truth. In this work, we present a method of prevalence estimation that removes the effect of testing errors and reduces the effect of oversampling symptomatic individuals. Moreover, this procedure considers stratified errors in which tests have different error rate profiles for symptomatic and asymptomatic individuals. The result is an easily implementable algorithm (for which code is provided) that produces better prevalence estimates than other methods, as demonstrated by simulation and on Covid-19 data from the Israeli Ministry of Health. Copyright The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.

11.
15th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation Conference, SBP-BRiMS 2022 ; 13558 LNCS:46-56, 2022.
Article in English | Scopus | ID: covidwho-2059739

ABSTRACT

Focal Structures are key sets of individuals who may be responsible for coordinating events, protests, or leading citizen engagement efforts on social media networks. Discovering focal structures that can promote online social campaigns is important but complex. Unlike influential individuals, focal structures can effect large-scale complex social processes. In our prior work, we applied a greedy algorithm and bi-level decomposition optimization solution to identify focal structures in social media networks. However, the outcomes lacked a contextual representation of the focal structures that affected interpretability. In this research, we present a novel Contextual Focal Structure Analysis (CFSA) model to enhance the discovery and the interpretability of the focal structures to provide the context in terms of the content shared by individuals in the focal structures through their communication network. The CFSA model utilizes multiplex networks, where the first layer is the users-users network based on mentions, replies, friends, and followers, and the second layer is the hashtag co-occurrence network. The two layers have interconnections based on the user hashtag relations. The model's performance was evaluated on real-world datasets from Twitter related to domestic extremist groups spreading information about COVID-19 and the Black Lives Matter (BLM) social movement during the 2020–2021 time. The model identified Contextual Focal Structure (CFS) sets revealing the context regarding individuals’ interests. We then evaluated the model's efficacy by measuring the influence of the CFS sets in the network using various network structural measures such as the modularity method, network stability, and average clustering coefficient values. The ranking Correlation Coefficient (RCC) was used to conduct a comparative evaluation with real-world scenarios. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

12.
15th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation Conference, SBP-BRiMS 2022 ; 13558 LNCS:24-34, 2022.
Article in English | Scopus | ID: covidwho-2059737

ABSTRACT

Online disinformation actors are those individuals or bots who disseminate false or misleading information over social media, with the intent to sway public opinion in the information domain towards harmful social outcomes. Quantification of the degree to which users post or respond intentionally versus under social influence, remains a challenge, as individuals or organizations operating the profile are foreshadowed by their online persona. However, social influence has been shown to be measurable in the paradigm of information theory. In this paper, we introduce an information theoretic measure to quantify social media user intent, and then investigate the corroboration of intent with evolution of the social network and detection of disinformation actors related to COVID-19 discussions on Twitter. Our measurement of user intent utilizes an existing time series analysis technique for estimation of social influence using transfer entropy among the considered users. We have analyzed 4.7 million tweets originating from several countries of interest, during a 5 month period when the arrival of the first dose of COVID vaccinations were announced. Our key findings include evidence that: (i) a significant correspondence between intent and social influence;(ii) ranking over users by intent and social influence is unstable over time with evidence of shifts in the hierarchical structure;and (iii) both user intent and social influence are important when distinguishing disinformation actors from non-disinformation actors. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

13.
Mathematical Biosciences and Engineering ; 19(12):12518-12531, 2022.
Article in English | Scopus | ID: covidwho-2055533

ABSTRACT

The world is facing the pandemic situation due to a beta corona virus named Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The disease caused by this virus known as Corona Virus Disease 2019 (COVID-19) has affected the entire world. The current diagnosis methods are laboratory based and require specialized testing kits for performing the test. Therefore, to overcome the limitations of testing kits a diagnosis method from chest X-ray images is proposed in this paper. Chest X-ray images can be easily obtained by X-ray machines that are readily available at medical centres. The radiological examinations augmented with chest X-ray images is an effective way of disease diagnosis. The automated analysis of the chest X-ray images requires a highly efficient method for identifying COVID-19 from these images. Thus, a novel deep convolution neural network (CNN) optimized using Grasshopper Optimization Algorithm (GOA) is proposed. The deep learning model comprises depth wise separable convolutions that independently look at cross channel and spatial correlations. The optimization of deep learning models is a complex task due the multiple layers and their non-linearities. In image classification problems optimizers like Adam, SGD etc. get stuck in local minima. Thus, in this paper a metaheuristic optimization algorithm is used to optimize the network. Grasshoper Optimization Algorithm (GOA) is a metaheuristic algorithm that mimics the behaviour of grasshoppers for food search. This algorithm is a fast converging and is capable of exploration and exploitation of large search spaces. Maximum Probability Based Cross Entropy Loss (MPCE) loss function is used as it minimizes the back propogation error of cross entropy and improves the training. The experimental results show that the proposed method gives high classification accuracy. The interpretation of results is augmented with class activation maps. Grad-CAM visualization algorithm is used for class activation maps. © 2022 American Institute of Mathematical Sciences. All rights reserved.

14.
5th International Conference on Big Data and Artificial Intelligence, BDAI 2022 ; : 176-183, 2022.
Article in English | Scopus | ID: covidwho-2051933

ABSTRACT

The danmaku text is a popular interactive mode of instantaneous video comment. Websites with danmaku texts have been widely used recently, and this vast number of texts can be regarded as a short text mining resource. This paper uses Perplexity and Renyi entropy to evaluate the BTM (Biterm Topic Model), by extracting the topic from the danmaku texts to explore the evolution of danmaku text topics in videos relevant to COVID-19. The results show that Renyi entropy is an effective way to decide the optimal number of topics, and the topics captured by BTM indicate that video viewers showed positive attitudes in the face of this public health emergency. © 2022 IEEE.

15.
Intelligent Systems Conference, IntelliSys 2022 ; 542 LNNS:505-513, 2023.
Article in English | Scopus | ID: covidwho-2048136

ABSTRACT

Between the end of second semester of 2020 and along the first semester of 2021, Covid-19 has had a strong impact on United States and India as seen at the official statistics exhibiting a big number of new infections as well as fatalities, particularly India that have had sharp peaks at March 2021. The present paper addresses the question if there is a entropic nature in these cases from an intuitive model based at simple geometries that adjust well the histograms of new infections versus time. Although the geometry-based models might not be satisfactory in all, it provides a view that would lead to answer intrinsic questions related to the highest peaks of pandemic if these have a nature cause or are strongly related to disorder as dictated by Shannon’s entropy for instance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

16.
Sustainable Energy Technologies and Assessments ; 53:102776, 2022.
Article in English | ScienceDirect | ID: covidwho-2042134

ABSTRACT

Air conditioning (AC) systems for tropical countries like India account for sixty percent of the total energy needs of a building. With the onset of COVID-19, the increase of fresh air ventilation rate has been recommended by various guidelines for indoor spaces which increase the load on the AC system. The present study attempts to reduce this burden through retrofitting a phase change material (PCM) embedded pin fin heat exchanger into an air-conditioning system. The heat exchanger is designed to cater to the peak load fluctuations for cities in three hot climatic zones of India, viz., Jaisalmer, Kolkata, and Delhi. Dodecanol with a melting temperature of 24 °C, is chosen as the appropriate PCM material for these locations. The optimal pin fin diameters are estimated through an entropy generation minimization analysis for the three locations. A heat transfer analysis of the PCM embedded heat exchanger is further presented through an analytical approach to estimate the PCM mass requirement and energy savings potential. The masses of the PCM estimated for Jaisalmer, Kolkata, and Delhi are 11.36 kg, 22.42 kg, and 19.35 kg, respectively for their respective peak load fluctuations of 0.25 kW, 0.28 kW and 0.48 kW. Energy savings of up to 4.7 % for Delhi, 2 % for Kolkata, and 2.75 % for Jaisalmer are identified with the PCM embedded heat exchanger incorporation. The results show the potential of such PCM thermal storage in reducing the peak energy demands of buildings amidst various environmental and health concerns.

17.
Lebensm Wiss Technol ; 169: 114032, 2022 Nov 01.
Article in English | MEDLINE | ID: covidwho-2042006

ABSTRACT

SARS-CoV-2 isolation from cold-chain food products confirms the possibility of outbreaks through cold-chain food products. RNA extraction combined with RT-PCR is the primary method currently utilized for the detection of SARS-CoV-2. However, the requirement of hours of analytical time and the high price of RT-PCR hinder its worldwide implementation in food supervision. Here, we report a fluorescence biosensor for detection of SARS-CoV-2 N protein. The fluorescence biosensor was fabricated by aptamer-based conformational entropy-driven circuit where molecular beacon strands were labeled with graphitic carbon nitrides quantum dots@Zn-metal-organic framework (g-CNQDs@Zn-MOF) and Dabcyl. The detection of the N protein was achieved via swabbing followed by competitive assay using a fixed amount of N-48 aptamers in the analytical system. A fluorescence emission spectrum was employed for the detection. The detection limit of our fluorescence biosensor was 1.0 pg/mL for SARS-CoV-2 N protein, indicating very excellent sensitivity. The fluorescence biosensor did not exhibit significant cross-reactivity with other N proteins. Finally, the biosensor was successfully applied for the detection of SARS-CoV-2 N protein in actual cold-chain food products showing same excellent accuracy as RT-PCR method. Thus, our fluorescence biosensor is a promising analytical tool for rapid and sensitive detection of SARS-CoV-2 N protein.

18.
Turkish Journal of Public Health ; 20(2):235-243, 2022.
Article in English | CAB Abstracts | ID: covidwho-2040552

ABSTRACT

Objective: Currently the Covid-19 pandemic is studied with great expectations by several epidemiological models with the aim of predicting the future behaviour of the pandemic. Determining the level of disorder in the pandemic can give us insight into the societal reactions to the pandemic the socio-economic structures and health systems in different countries.

19.
Applied Psychological Measurement ; : 1, 2022.
Article in English | Academic Search Complete | ID: covidwho-2038495

ABSTRACT

In response to the closures of test centers worldwide due to the COVID-19 pandemic, several testing programs offered large-scale standardized assessments to examinees remotely. However, due to the varying quality of the performance of personal devices and internet connections, more at-home examinees likely suffered “disruptions” or an interruption in the connectivity to their testing session compared to typical test-center administrations. Disruptions have the potential to adversely affect examinees and lead to fairness or validity issues. The goal of this study was to investigate the extent to which disruptions impacted performance of at-home examinees using data from a large-scale admissions test. Specifically, the study involved comparing the average test scores of the disrupted examinees with those of the non-disrupted examinees after weighting the non-disrupted examinees to resemble the disrupted examinees along baseline characteristics. The results show that disruptions had a small negative impact on test scores on average. However, there was little difference in performance between the disrupted and non-disrupted examinees after removing records of the disrupted examinees who were unable to complete the test. [ FROM AUTHOR] Copyright of Applied Psychological Measurement is the property of Sage Publications Inc. 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.)

20.
Informatics and Automation ; 21(4):659-677, 2022.
Article in Russian | Scopus | ID: covidwho-2025816

ABSTRACT

We propose an approach to the estimation of the parameters of non-linear dynamic models using the concept of Randomized Machine Learning (RML), based on the transition from deterministic models to random ones (with random parameters), followed by estimation of the probability distributions of parameters and noises on real data. The main feature of this method is its efficiency in conditions of a small amount of real data. The paper considers models formulated in terms of ordinary differential equations, which are converted to a discrete form for setting and solving the problem of entropy optimization. The application of the proposed approach is demonstrated on the problem of predicting the total number of infected COVID-19 using a dynamic SIR epidemiological model. To do this, we construct a randomized SIR model (R-SIR) with one parameter, the entropy-optimal estimate of which is realized by its probability density function, as well as the probability density functions of the measurement noise at the points where training is performed. Next, the technique of randomized prediction with noise filtering is applied, based on the generation of the corresponding distributions and the construction of an ensemble of predictive trajectories with the calculation of the trajectory averaged over the ensemble. The paper implements a computational experiment using real operational data on the infection cases in the form of a comparative study with a well-known method for estimating model parameters based on the least squares method. The results obtained in the experiment demonstrate a significant decrease in the mean absolute percentage error (MAPE) with respect to real observations in the forecast interval, which shows the efficiency of the proposed method and its effectiveness in problems of the type considered in the work. © 2022 by the Author(s).

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