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1.
Global Environmental Change ; 78:102622, 2023.
Article in English | ScienceDirect | ID: covidwho-2149756

ABSTRACT

The Finite Pool of Worry (FPW) hypothesis states that humans have finite emotional resources for worry, so that when we become more worried about one threat, worry about other threats decreases. Despite its relevance, no conclusive empirical evidence for the hypothesis exists. We leverage the sudden onset of new worries introduced by the COVID-19 pandemic as a natural experiment to test the FPW hypothesis and a related hypothesis, the Finite Pool of Attention (FPA) hypothesis. The FPA hypothesis proposes that when we pay more attention to one threat, our attention to other threats decreases. To test these two hypotheses, we assessed self-reported attention, self-reported worries, and Twitter/news attention to various threats (climate change, terrorism, economic problems, and others) throughout the pandemic in three countries (USA, Italy, and China). We find that as attention to and worry about COVID-19 increases, attention to climate change decreases, but worry does not. Our results are confirmed by further analysis of a large, longitudinal U.S. sample. We find that public perceptions that COVID-19 and climate change are related do not fully explain the positive relationship in worry between the two hazards. In summary, our findings suggest that while there may be a Finite Pool of Attention to threats, there is limited evidence for a Finite Pool of Worry.

2.
13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2120774

ABSTRACT

In power grids, short-term load forecasting (STLF) is crucial as it contributes to the optimization of their reliability, emissions, and costs, while it enables the participation of energy companies in the energy market. STLF is a challenging task, due to the complex demand of active and reactive power from multiple types of electrical loads and their dependence on numerous exogenous variables. Amongst them, special circumstances-such as the COVID-19 pandemic-can often be the reason behind distribution shifts of load series. This work conducts a comparative study of Deep Learning (DL) architectures-namely Neural Basis Expansion Analysis Time Series Forecasting (N-BEATS), Long Short-Term Memory (LSTM), and Temporal Convolutional Networks (TCN)-with respect to forecasting accuracy and training sustainability, meanwhile examining their out-of-distribution generalization capabilities during the COVID-19 pandemic era. A Pattern Sequence Forecasting (PSF) model is used as baseline. The case study focuses on day-ahead forecasts for the Portuguese nationa115-minute resolution net load time series. The results can be leveraged by energy companies and network operators (i) to reinforce their forecasting toolkit with state-of-the-art DL models;(ii) to become aware of the serious consequences of crisis events on model performance;(iii) as a high-level model evaluation, deployment, and sustainability guide within a smart grid context. © 2022 IEEE.

3.
Korean Journal of Cognitive Science ; 33(3):135-154, 2022.
Article in Korean | Academic Search Complete | ID: covidwho-2056696

ABSTRACT

The purpose of this study was to confirm that the property generalization to social categories with low coherence is stronger when stress due to COVID-19 is perceived as high, compared to when stress is perceived as low. To this end, this study selected categories with high coherence(nun, soldier, flight attendant) and categories with low coherence(wedding planner, interpreter, florist), and recruited 336 participants to perform a category-based inductive generalization task(inferring how many properties repeatedly observed by some category members would appear across all category members), and measured their perceived COVID-19 stress. As a result, this study showed that when the cohesion of social categories is high, the effect of property generalization is stronger than when it is low, and the effect of property generalization is stronger in those who perceive stress due to Corona 19 higher than those who perceive it as low. In addition, this study confirmed that people who perceive COVID-19 stress strongly tend to generalize strongly to properties that are repeatedly observed in the low coherence category. This study is important in that it shows that there is a cognitive mechanism that is at the root of the phenomenon that stereotypes and prejudices deepen and discriminatory behaviors increase after the outbreak of COVID-19, such as COVID-19 stress and the resulting increase in attribute generalization tendency. [ FROM AUTHOR] Copyright of Korean Journal of Cognitive Science is the property of Korean Society for Cognitive 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.)

4.
10th IEEE Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2022 ; 2022-June:1004-1009, 2022.
Article in English | Scopus | ID: covidwho-2018923

ABSTRACT

Coronavirus pandemics have influenced people's daily life seriously since 2019. Authorized organizations suggested people wear a mask in public areas can significantly reduce the probability of getting infected. Thus, we proposed a method based on a simple convolutional neural network (CNN) to perform mask detection. The whole developing process was divided into two stages and mainly used three datasets (dataset_l, dataset_2 and dataset_3). Dataset_1 has images of people with and without masks. Dataset_2 and dataset_3 have one more category-images of people wearing masks incorrectly. The first stage was to train the model based on dataset_1 and it achieved 100% accuracy on validation set. It could also be applied to another two similar datasets without any training on them with accuracy 73.55% and 66.80% respectively. In the second stage, to detect people wearing masks incorrectly, the same model was trained based on dataset_2. The accuracy of this model reached 99.34%. However, when applying it directly to dataset_3, only 44.50% accuracy was achieved. To improve the accuracy, the distribution of dataset_2 and dataset_3 was rearranged. Finally, the accuracy of the model for dataset_3 was nearly 80%. We concluded that generally deep learning models would have better generalization on mask-detection tasks and our model was good at handling two-label mask dataset. © 2022 IEEE.

5.
Biomedical Signal Processing and Control ; 79, 2023.
Article in English | Web of Science | ID: covidwho-2014960

ABSTRACT

Tubes and catheters are medical devices introduced into the human body to help ill patients in critical health conditions. However, several positioning errors occur during or after the placement of such devices (Endotracheal tubes mispositioned in 10 to 20% of intubations). In addition, the delay of X-ray diagnosis after surgery can cause serious complications. Such delays are caused by the hospitals' resourcelessness or due to workload in intensive care units. The X-rays images availability (Most used diagnosis modality in intensive care units, 40% to 50%) and the presence of tubes in those images (lines are present on 33% of X-ray images) present a fertile ground to feed DCNNs training on tube error detection tasks and reduce complications. However, training and tuning one DCNN learner to resolve tube detection is time-consuming. Therefore, we propose a custom stacked generalization framework to combine wake learners with a proposed meta learner neural network architecture to resolve tube error detection tasks. The proposed framework AUC (93.84%) outperforms other related work methods with the input size of (380pixel*380pixel). Furthermore, we demonstrated the sensibility of stacked generalization to the number of base learners. Moreover, we validated the utility of input cross-validation used to form level1-metadata for the stacked generalization. Our framework can be adapted to be integrated with a CAD (computer aid decision system) for tubes error detection. The CAD can detect errors immediately after patient screening and notify radiologists to prioritize diagnosis of cases with positioning errors to adjust tubes and reduce risks significantly.

6.
International Journal of Early Childhood Special Education ; 14(5):1895-1905, 2022.
Article in English | Web of Science | ID: covidwho-1998030

ABSTRACT

Respiratory diseases are one of the leading causes of death and disability in the world. Integration of AI with existing Chest X-Ray (CXR) diagnostics is currently a hot research topic. On similar lines, we propose a technique termed "Swasta-shwasa" for multi-class classification that associates CXR with one among Tuberculosis, COVID-19, Viral pneumonia, Bacteria Pneumonia, Normal and Lung Opacity ailments based on Deep Learning. The proposed technique which has accomplished an overall 98% test accuracy, 0.9991 AUROC, average Specificity of 99.82% and average Sensitivity of 98.51% involves four stages: Pre-processing, Segmentation, Classification and Saliency map visualization. Further, the trained model is used to predict on unseen real life data of COVID-19 cases from India and a cross-population generalization accuracy of 85% is witnessed. XAI is augmented for model interpretability. We also explore why CLAHE may not be suitable choice for pre-processing of CXRs.

7.
Behav Anal Pract ; : 1-16, 2021 Jul 12.
Article in English | MEDLINE | ID: covidwho-1312328

ABSTRACT

The field of applied behavior analysis (ABA) has utilized telehealth for clinical supervision and caregiver guidance with research supporting the use of both modalities. Research demonstrating effectiveness is crucial, as behavior analysts must ensure the services they provide are effective in order to be ethical. With the increased need for patients to access more services via telehealth, due to the novel coronavirus (COVID-19) pandemic, the current study evaluated the efficacy of telehealth direct therapy to teach new skills to individuals with autism spectrum disorder (ASD). This study examined the utility of natural environment teaching and discrete trial training strategies provided over a videoconferencing platform to teach new skills directly to seven individuals with varying ASD severity levels. The targeted skills were taught solely through telehealth direct therapy with varying levels of caregiver support across participants and included skills in the language, adaptive, and social domains. In a multiple baseline design, all seven participants demonstrated mastery and maintenance for all targets; in addition, generalization to family members was assessed for some targets. The evidence suggests that telehealth is a modality that is effective and can be considered for all patients when assessing the appropriate location of treatment.

8.
J Psychiatr Res ; 155: 90-99, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-1977560

ABSTRACT

The COVID-19 pandemic greatly disrupted our daily lives. Worldwide, people were confronted with health, financial, and existential fears or trauma-like experiences. Recent studies have identified an increase in stress, anxiety, and fear symptoms in connection with the pandemic. Furthermore, fear learning processes are central mechanisms in the development and maintenance of anxiety disorders. Patients commonly show impairments not only in fear learning but also in its generalization. Thus, pandemic-related anxiety may constitute a risk factor for both enhanced fear acquisition and generalization. In a pre-registered online study with a final sample of 220 healthy university students, we investigated whether participants with higher COVID-19-related anxiety (COVID-Anxiety) show impaired fear learning and generalization. For this purpose, we used a differential fear conditioning paradigm with a traumatic film clip as the unconditioned stimulus (US) and collected US-expectancy as the main measure of interest. Participants with high COVID-Anxiety show a tendency toward poorer discrimination between the reinforced conditioned stimulus (CS+) and the unreinforced conditioned stimulus (CS-) during acquisition and significantly poorer discrimination patterns during generalization. Furthermore, participants with high COVID-Anxiety show greater general fear throughout the whole experiment. Our results show that the subjective effects of the COVID-19 pandemic on psychological well-being are associated with impairments in both fear learning and fear generalization. As expected, high COVID-Anxiety leads to poorer performance in stimulus discrimination and greater levels of fear, which might contribute to a higher risk of anxiety disorders. GERMAN CLINICAL TRIAL REGISTER: DRKS00022761.


Subject(s)
COVID-19 , Pandemics , Humans , Anxiety/epidemiology , Anxiety Disorders/psychology , Fear/psychology
9.
Cell Rep Med ; 3(7): 100680, 2022 07 19.
Article in English | MEDLINE | ID: covidwho-1907870

ABSTRACT

The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC]training = 0.799, p = 4.2e-6; multi-class AUCvalidation = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.


Subject(s)
COVID-19 , Humans , NF-kappa B/metabolism , Proteomics , SARS-CoV-2 , Signal Transduction
10.
19th IEEE International Symposium on Biomedical Imaging, ISBI 2022 ; 2022-March, 2022.
Article in English | Scopus | ID: covidwho-1846120

ABSTRACT

AI models have become ubiquitous tools of choice for different medical imaging problems like enhancement, work-flow acceleration, etc.. While availability of large amounts of diverse data and reliable annotations continue to be a challenge, development cycles of these models have shrunk. This necessitates a reliable recipe for improving generalization of AI models that fare well during deployment on unseen data. In this paper, we investigate generalization through the lens of sharpness-aware optimizers. We study two representative problems in medical imaging: (a) a difficult task of cardiac view classification on ultrasound images and (b) COVID-19 detection from chest X-ray images and demonstrate high efficacy of flat minima solutions. Further, we perform extensive Hessian analysis that reveals the impact of the geometry of loss landscape towards generalization. Our empirical studies suggest that sharpness aware minimization improves generalization by 5-10%, over and above the gain obtained by other methods - on both in-domain and out-of-domain test data. © 2022 IEEE.

11.
Learning and Motivation ; : 101813, 2022.
Article in English | ScienceDirect | ID: covidwho-1821406

ABSTRACT

Intradimensional discrimination training may cause peak shift, in which participants respond more frequently to a novel stimulus presented during a generalization test than the positive exemplar used in training. Previous research has shown that peak shift is most likely to occur in participants who have achieved an intermediate level of proficiency with the discrimination. We sought to examine whether discrimination learning and peak shift could be altered through variations in stimulus discriminability. An international sample of 117 adults were trained to discriminate one visual representation of risk from COVID-19 (S+) from a second level of risk (S-) that was either lesser or greater. In a high discriminability condition, a single indicator of risk (a bar length) was presented on each trial. In a moderate discriminability condition, participants were required to estimate a person’s risk from multiple risk indicators. In a low discriminability condition, participants were additionally required to consider risk mitigation factors when estimating a person’s risk. Peak shift was absent in the high discriminability condition but present in the moderate condition. The low discriminability condition produced either flat or monotonic generalization gradients. The results additionally demonstrate how presenting health risk information to people in relatively simple or relatively complex ways affects their ability to judge that information correctly.

12.
Math Biosci Eng ; 18(6): 9264-9293, 2021 10 27.
Article in English | MEDLINE | ID: covidwho-1512792

ABSTRACT

The COVID-19 pandemic has inspired unprecedented data collection and computer vision modelling efforts worldwide, focused on the diagnosis of COVID-19 from medical images. However, these models have found limited, if any, clinical application due in part to unproven generalization to data sets beyond their source training corpus. This study investigates the generalizability of deep learning models using publicly available COVID-19 Computed Tomography data through cross dataset validation. The predictive ability of these models for COVID-19 severity is assessed using an independent dataset that is stratified for COVID-19 lung involvement. Each inter-dataset study is performed using histogram equalization, and contrast limited adaptive histogram equalization with and without a learning Gabor filter. We show that under certain conditions, deep learning models can generalize well to an external dataset with F1 scores up to 86%. The best performing model shows predictive accuracy of between 75% and 96% for lung involvement scoring against an external expertly stratified dataset. From these results we identify key factors promoting deep learning generalization, being primarily the uniform acquisition of training images, and secondly diversity in CT slice position.


Subject(s)
COVID-19 , Deep Learning , Humans , Pandemics , SARS-CoV-2 , Tomography, X-Ray Computed
13.
Contemp Clin Trials ; 109: 106497, 2021 10.
Article in English | MEDLINE | ID: covidwho-1347515

ABSTRACT

Family-based behavioral treatment (FBT) is an evidence-based treatment for pediatric obesity. FBT has primarily been implemented in specialty clinics, with highly trained interventionists. The goal of this study is to assess effectiveness of FBT implemented in pediatric primary care settings using newly trained interventionists who might implement FBT in pediatric practices. The goal is to randomize 528 families with a child with overweight/obesity (≥85th BMI percentile) and parent with overweight/obesity (BMI ≥ 25) across four sites (Buffalo and Rochester, New York; Columbus, Ohio; St. Louis, Missouri) to FBT or usual care and obtain assessments at 6-month intervals over 24 months of treatment. FBT is implemented using a mastery model, which provides quantity of treatment tailored to family progress and following the United States Preventive Services Task Force recommendations for effective dose and duration of treatment. The primary outcome of the trial is change in relative weight for children, and secondarily, for parents and siblings who are overweight/obese. Between group differences in the tendency to prefer small immediate rewards over larger, delayed rewards (delay discounting) and how this is related to treatment outcome is also evaluated. Challenges in translation of group-based interventions to individualized treatments in primary care settings, and in study implementation that arose due to the COVID-19 pandemic are discussed. It is hypothesized that the FBT intervention will be associated with better changes in relative weight for children, parents, and siblings than usual care. The results of this study can inform future dissemination and implementation of FBT into primary care settings.


Subject(s)
Family Therapy , Pediatric Obesity , Primary Health Care , COVID-19 , Child , Family Therapy/organization & administration , Humans , Pandemics , Parents , Pediatric Obesity/therapy
14.
Concurr Comput ; : e6331, 2021 Apr 22.
Article in English | MEDLINE | ID: covidwho-1201885

ABSTRACT

Head pose classification is an important part of the preprocessing process of face recognition, which can independently solve application problems related to multi-angle. But, due to the impact of the COVID-19 coronavirus pandemic, more and more people wear masks to protect themselves, which covering most areas of the face. This greatly affects the performance of head pose classification. Therefore, this article proposes a method to classify the head pose with wearing a mask. This method focuses on the information that is helpful for head pose classification. First, the H-channel image of the HSV color space is extracted through the conversion of the color space. Then use the line portrait to extract the contour lines of the face, and train the convolutional neural networks to extract features in combination with the grayscale image. Finally, stacked generalization technology is used to fuse the output of the three classifiers to obtain the final classification result. The results on the MAFA dataset show that compared with the current advanced algorithm, the accuracy of our method is 94.14% on the front, 86.58% on the more side, and 90.93% on the side, which has better performance.

15.
Assessment ; 29(5): 940-948, 2022 07.
Article in English | MEDLINE | ID: covidwho-1097076

ABSTRACT

A reliability generalization meta-analysis was carried out to estimate the average reliability of the seven-item, 5-point Likert-type Fear of COVID-19 Scale (FCV-19S), one of the most widespread scales developed around the COVID-19 pandemic. Different reliability coefficients from classical test theory and the Rasch Measurement Model were meta-analyzed, heterogeneity among the most reported reliability estimates was examined by searching for moderators, and a predictive model to estimate the expected reliability was proposed. At least one reliability estimate was available for a total of 44 independent samples out of 42 studies, being that Cronbach's alpha was most frequently reported. The coefficients exhibited pooled estimates ranging from .85 to .90. The moderator analyses led to a predictive model in which the standard deviation of scores explained 36.7% of the total variability among alpha coefficients. The FCV-19S has been shown to be consistently reliable regardless of the moderator variables examined.


Subject(s)
COVID-19 , Fear , Humans , Pandemics , Psychometrics , Reproducibility of Results , SARS-CoV-2
16.
Med Phys ; 48(3): 1197-1210, 2021 Mar.
Article in English | MEDLINE | ID: covidwho-1070776

ABSTRACT

PURPOSE: Accurate segmentation of lung and infection in COVID-19 computed tomography (CT) scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID-19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. METHODS: To promote the development of data-efficient deep learning methods, in this paper, we built three benchmarks for lung and infection segmentation based on 70 annotated COVID-19 cases, which contain current active research areas, for example, few-shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide standard training, validation and testing splits, evaluation metrics and, the corresponding code. RESULTS: Based on the state-of-the-art network, we provide more than 40 pretrained baseline models, which not only serve as out-of-the-box segmentation tools but also save computational time for researchers who are interested in COVID-19 lung and infection segmentation. We achieve average dice similarity coefficient (DSC) scores of 97.3%, 97.7%, and 67.3% and average normalized surface dice (NSD) scores of 90.6%, 91.4%, and 70.0% for left lung, right lung, and infection, respectively. CONCLUSIONS: To the best of our knowledge, this work presents the first data-efficient learning benchmark for medical image segmentation, and the largest number of pretrained models up to now. All these resources are publicly available, and our work lays the foundation for promoting the development of deep learning methods for efficient COVID-19 CT segmentation with limited data.


Subject(s)
COVID-19/diagnostic imaging , Image Processing, Computer-Assisted/methods , Lung/diagnostic imaging , Machine Learning , Tomography, X-Ray Computed , Benchmarking , Humans
17.
Phys Eng Sci Med ; 43(4): 1399-1414, 2020 Dec.
Article in English | MEDLINE | ID: covidwho-956816

ABSTRACT

The highly contagious nature of Coronavirus disease 2019 (Covid-19) resulted in a global pandemic. Due to the relatively slow and taxing nature of conventional testing for Covid-19, a faster method needs to be in place. The current researches have suggested that visible irregularities found in the chest X-ray of Covid-19 positive patients are indicative of the presence of the disease. Hence, Deep Learning and Image Classification techniques can be employed to learn from these irregularities, and classify accordingly with high accuracy. This research presents an approach to create a classifier model named StackNet-DenVIS which is designed to act as a screening process before conducting the existing swab tests. Using a novel approach, which incorporates Transfer Learning and Stacked Generalization, the model aims to lower the False Negative rate of classification compensating for the 30% False Negative rate of the swab tests. A dataset gathered from multiple reliable sources consisting of 9953 Chest X-rays (868 Covid and 9085 Non-Covid) was used. Also, this research demonstrates handling data imbalance using various techniques involving Generative Adversarial Networks and sampling techniques. The accuracy, sensitivity, and specificity obtained on our proposed model were 95.07%, 99.40% and 94.61% respectively. To the best of our knowledge, the combination of accuracy and false negative rate obtained by this paper outperforms the current implementations. We must also highlight that our proposed architecture also considers other types of viral pneumonia. Given the unprecedented sensitivity of our model we are optimistic it contributes to a better Covid-19 detection.


Subject(s)
Algorithms , COVID-19 Testing , COVID-19/diagnostic imaging , COVID-19/diagnosis , Neural Networks, Computer , Artifacts , COVID-19/virology , Databases, Factual , Humans , Image Processing, Computer-Assisted , Lung/diagnostic imaging , Models, Theoretical , ROC Curve , SARS-CoV-2/physiology , Time Factors , X-Rays
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