PurposeThis study was designed to specifically explore confirmation and perceived usefulness associated with mobile food ordering apps (MFOAs) in consideration of their impacts upon attitudes, satisfaction and intention to continuously use.Design/methodology/approachThe research utilized the convenience sampling to gather data from 250 respondents having prior experience with MFOAs during COVID-19 pandemic period in Bangladesh. The Structural Equation Modeling technique was applied to analyze the data using SmartPLS 3 software.FindingsThis study's results showed that customers' perceived confirmation and usefulness were significant in determining their dinning attitudes. Besides, customers' dining attitudes were positively related to e-satisfaction. Finally, the customers’ continuance intention to use MFOAs was significantly influenced by their e-satisfaction.Research limitations/implicationsRestaurants managers should focus on online sales through MFOAs during the pandemic period since social distancing is a key strategy to manage COVID-19. Customers should be assured that the safety measures are undertaken while delivering the food.Originality/valueThis study incorporated the expectation-confirmation theory and technology acceptance model and tested it in the context of MFOAs.

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Mycotoxins are secondary metabolites of filamentous fungi that contaminate food products such as fruits, vegetables, cereals, beverages, and other agricultural commodities. Their occurrence in the food chain, especially in beverages, can pose a serious risk to human health, due to their toxicity, even at low concentrations. Mycotoxins, such as aflatoxins (AFs), ochratoxin A (OTA), patulin (PAT), fumonisins (FBs), trichothecenes (TCs), zearalenone (ZEN), and the alternaria toxins including alternariol, altenuene, and alternariol methyl ether have largely been identified in fruits and their derived products, such as beverages and drinks. The presence of mycotoxins in beverages is of high concern in some cases due to their levels being higher than the limits set by regulations. This review aims to summarize the toxicity of the major mycotoxins that occur in beverages, the methods available for their detection and quantification, and the strategies for their control. In addition, some novel techniques for controlling mycotoxins in the postharvest stage are highlighted.

In this era of COVID19, proper diagnosis and treatment for pneumonia are very important. Chest X-Ray (CXR) image analysis plays a vital role in the reliable diagnosis of pneumonia. An experienced radiologist is required for this. However, even for an experienced radiographer, it is quite difficult and timeconsuming to diagnose due to the fuzziness of CXR images. Also, identification can be erroneous due to the involvement of human judgment. Hence, an authentic and automated system can play an important role here. In this era of cutting-edge technology, deep learning (DL) is highly used in every sector. There are several existing methods to diagnose pneumonia but they have accuracy problems. In this study, an automatic pneumonia detection system has been proposed by applying the extreme learning machine (ELM) on the Kaggle CXR images (Pneumonia). Three models have been studied: classification using extreme learning machine (ELM), ELM with a hybrid convolutional neural network -principle component analysis (CNN-PCA) based feature extraction (ECP), and ECP with the CXR images which are contrast-enhanced by contrast limited adaptive histogram equalization (CLAHE). Among these three proposed methods, the final model provides an optimistic result. It achieves the recall score of 98% and accuracy score of 98.32% for multiclass pneumonia classification. On the other hand, a binary classification achieves 100% recall and 99.83% accuracy. The proposed method also outperforms the existing methods. The outcome has been compared using several benchmarks that include accuracy, precision, recall, etc.

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The COVID-19 is an infectious disease that primarily affects the lungs and leads to death in the severe stage. It also changes the lung CT scans of affected patients. For introducing a more convenient COVID-19 identification technique during this pandemic, we have implemented a simple convolution neural network (CNN) based model by using lung CT images. And finally, we have used LeNet-5 CNN architecture for this purpose.For training and testing purposes, we have obtained a dataset that contained 349 COVID-19 lung CT frames and 397 number of NON COVID-19 CT frames. We have introduced the data augmentation technique and got 1744 CT frames of COVID-19 and 1588 CT frames of NON COVID-19 patients. Among them, we have used 80% of lung CT frames for training purposes and 20% frames for testing purposes. The total number of trainable parameters of our LeNet-5 CNN architecture was 82,146. After completing the whole process, we got the accuracy of 86.06%, f1 score of 87%, the precision of 85%, and recall of 89%, and area under the ROC curve of 0.86 for COVID-19 detection.