The COVID-19, novel coronavirus or SARS-Cov-2, has claimed hundreds of thousands of lives and affected millions of people all around the world with the number of deaths and infections growing exponentially. Deep convolutional neural network (DCNN) has been a huge milestone for image classification task including medical images. Transfer learning of state-of-the-art models have proven to be an efficient method of overcoming deficient data problem. In this paper, a thorough evaluation of eight pre-trained models is presented. Training, validating, and testing of these models were performed on chest X-ray (CXR) images belonging to five distinct classes, containing a total of 760 images. Fine-tuned models, pre-trained in ImageNet dataset, were computationally efficient and accurate. Fine-tuned DenseNet121 achieved a test accuracy of 98.69% and macro f1-score of 0.99 for four classes classification containing healthy, bacterial pneumonia, COVID-19, and viral pneumonia, and fine-tuned models achieved higher test accuracy for three-class classification containing healthy, COVID-19, and SARS images. The experimental results show that only 62% of total parameters were retrained to achieve such accuracy.One of the basic emotions generated by the COVID-19 pandemic is the fear of contacting this disease. The main aim of this study was to examine the psychometric properties of the Romanian version of the Fear of COVID-19 Scale (FCV-19S), based on classical test theory and item response theory, namely, graded response model. The FCV-19S was translated into Romanian following a forward-backward translation procedure. The reliability and validity of the instrument were assessed in a sample of 809 adults (34.6% males; M age = 32.61; SD  ±11.25; age range from 18 to 68 years). Results showed that the Romanian FCV-19S had very good internal consistency (Cronbach’s alpha = .88; McDonald’s omega = .89; composite reliability = .89). The confirmatory factor analysis for one-factor FCV-19S based on the maximum likelihood estimation method with Satorra-Bentler correction for non-normality proved that the model fitted well (CFI = .99, TLI = .97, RMSEA = .06, 90% CI [.05, .09], SRMR = .01). As for criterion-related validity, the fear of COVID-19 score correlated with depression (r = .25, p  less then  .01), stress (r = .45, p  less then  .01), resilience (r = - .22, p  less then  .01) and happiness (r = -.33, p  less then  .01). The heterotrait-monotrait criteria less than .85 certified the discriminant validity of the FCV-19S-RO. The GRM analysis highlighted robust psychometric properties of the scale and measurement invariance across gender. These findings emphasized validity for the use of Romanian version of FCV-19S and expanding the existing body of research on the fear of COVID-19. Overall, the current research contributes to the literature not only by validating the FCV-19S-RO but also by considering the positive psychology approach in the study of fear of COVID-19, emphasizing a negative relationship among resilience, happiness and fear in the context of the COVID-19 pandemic.There is no information in Peru on the prevalence of mental health problems associated with COVID-19 in older adults. In this sense, the aim of the study was to gather evidence on the factor structure, criterion-related validity, and reliability of the Spanish version of the Fear of COVID-19 Scale (FCV-19S) in this population. The participants were 400 older adults (mean age = 68.04, SD = 6.41), who were administered the Fear of COVID-19 Scale, Revised Mental Health Inventory-5, Patient Health Questionnaire-2 items, and Generalized Anxiety Disorder Scale 2 items. Structural equation models were estimated, specifically confirmatory factor analysis (CFA), bifactor CFA, and structural models with latent variables (SEM). Internal consistency was estimated with composite reliability indexes (CRI) and omega coefficients. A bifactor model with both a general factor underlying all items plus a specific factor underlying items 1, 2, 4, and 5 representing the emotional response to COVID better represents the factor structure of the scale. This structure had adequate fit and good reliability, and additionally fear of COVID had a large effect on mental health. In general, women had more fear than men, having more information on COVID was associated to more fear, while having family or friends affected by COVID did not related to fear of the virus. The Spanish version of the Fear of COVID-19 Scale presents evidence of validity and reliability to assess fear of COVID-19 in the Peruvian older adult population.In the modern era of computing, the news ecosystem has transformed from old traditional print media to social media outlets. Social media platforms allow us to consume news much faster, with less restricted editing results in the spread of fake news at an incredible pace and scale. In recent researches, many useful methods for fake news detection employ sequential neural networks to encode news content and social context-level information where the text sequence was analyzed in a unidirectional way. Therefore, a bidirectional training approach is a priority for modelling the relevant information of fake news that is capable of improving the classification performance with the ability to capture semantic and long-distance dependencies in sentences. In this paper, we propose a BERT-based (Bidirectional Encoder Representations from Transformers) deep learning approach (FakeBERT) by combining different parallel blocks of the single-layer deep Convolutional Neural Network (CNN) having different kernel sizes and filters with the BERT. Such a combination is useful to handle ambiguity, which is the greatest challenge to natural language understanding. Classification results demonstrate that our proposed model (FakeBERT) outperforms the existing models with an accuracy of 98.90%.

Susceptibility studies keep farmers, managers and household users informed and enhance breeding program’s testing against infestation and damage by storage insect pests. Therefore, laboratory tests were carried out to examine the susceptibility of ten rice brands to rice weevil,

L. momordin-Ic in vitro (Coleoptera Curculionidae), infestation under temperature and relative humidity of 25 ± 2°C and 75 ± 5%, respectively. The specific objectives of the study were to identify some commercially available rice brands with resistance to

.

, by determining whether brand difference influences insect body weight at emergence and whether infestation is related to brand palatability and appearance. The ten brands used for the study were royale stallion, Mama royale, parboiled rice, Mama gold, white rice, Super eagle, Indian rice, champion rice, Abakiliki rice and Mama Africa, and standard methods were used to achieve the objectives. The indices measured were F

progeny emergence, grain weight loss and frass accumulation.

The results showed that Abakiliki rice was poor in both palatability and appearance, whereas Super eagle was the most palatable and white rice was visually excellent.