Language Anxiety of Older Adults in an Online and In-class EFL Course: Results of a Pilot Study

As recent meta-analyses reveal, language anxiety (LA) is one of the most important determinants of success in foreign language (FL) learning, next to aptitude, motivation, and working memory. Although studies on LA have been carried out for a few decades, exploring its nature, causes, correlates and influence on FL achievements, there are still several questions to be answered. One of such under-researched areas is LA experienced by older adults (60+), who nowadays constitute an important group of FL learners in Poland. A few years ago, their attempts to master English as a FL were affected by the Covid-19 pandemic, which brought several limitations and changes, forcing them to switch from learning in a classroom setting to an online mode. The main objective of this chapter is to report the outcomes of a mixed-method pilot study, aimed at measuring the level of LA of older adults (OAs) in the in-class and online modes, exploring its causes, and identifying the most anxiety-provoking skills. Data were collected among six OAs via a questionnaire, draw-a-picture technique, and semi-structured interviews. The results indicate that there is a complex interrelated network of external and internal factors of LA, which should be taken into account when teaching this age group. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Blockchain-Based Trusted Federated Learning with Pre-Trained Models for COVID-19 Detection

COVID-19 is a serious epidemic that not only endangers human health, but also wreaks havoc on the development of society. Recently, there has been research on using artificial intelligence (AI) techniques for COVID-19 detection. As AI has entered the era of big models, deep learning methods based on pre-trained models (PTMs) have become a focus of industrial applications. Federated learning (FL) enables the union of geographically isolated data, which can address the demands of big data for PTMs. However, the incompleteness of the healthcare system and the untrusted distribution of medical data make FL participants unreliable, and medical data also has strong privacy protection requirements. Our research aims to improve training efficiency and global model accuracy using PTMs for training in FL, reducing computation and communication. Meanwhile, we provide a secure aggregation rule using differential privacy and fully homomorphic encryption to achieve a privacy-preserving Byzantine robust federal learning scheme. In addition, we use blockchain to record the training process and we integrate a Byzantine fault tolerance consensus to further improve robustness. Finally, we conduct experiments on a publicly available dataset, and the experimental results show that our scheme is effective with privacy-preserving and robustness. The final trained models achieve better performance on the positive prediction and severe prediction tasks, with an accuracy of 85.00% and 85.06%, respectively. Thus, this indicates that our study is able to provide reliable results for COVID-19 detection.

FedCare: Federated Learning for Resource-Constrained Healthcare Devices in IoMT System

In social IoMT systems, resource-constrained devices face the challenges of limited computation, bandwidth, and privacy in the deployment of deep learning models. Federated learning (FL) is one of the solutions to user privacy and provides distributed training among several local devices. In addition, it reduces the computation and bandwidth of transferring videos to the central server in camera-based IoMT devices. In this work, we design an edge-based federated framework for such devices. In contrast to traditional methods that drop the resource-constrained stragglers in a federated round, our system provides a methodology to incorporate them. We propose a new phase in the FL algorithm, known as split learning. The stragglers train collaboratively with the nearest edge node using split learning. We test the implementation using heterogeneous computing devices that extract vital signs from videos. The results show a reduction of 3.6 h in the training time of videos using the split learning phase with respect to the traditional approach. We also evaluate the performance of the devices and system with key parameters, CPU utilization, memory consumption, and data rate. Furthermore, we achieve 87.29% and 60.26% test accuracy at the nonstragglers and stragglers, respectively, with a global accuracy of 90.32% at the server. Therefore, FedCare provides a straggler-resistant federated method for a heterogeneous system for social IoMT devices.

Effectiveness of Home Bowel Preparation for Children Living Long Distances from Endoscopy Centre

Introduction The North of Scotland Paediatric Gastroenterology, Hepatology and Nutrition Network (NoSPGHANN) manages children over an area of 53,000 km2. Travel distances to Royal Aberdeen Children's Hospital (RACH) were previously felt to preclude the adoption of home bowel preparation (HBP) for elective colonoscopies but a trial period of HBP commenced in March 2020. The same drugs (senna and Picolax) were used for inpatient bowel preparation (IPBP) or HBP but the timings were changed for HBP to complete all doses on the day prior to procedure to allow travel to RACH. This audit evaluates the impact of this change of practice. Methods All children undergoing elective colonoscopy at RACH between December 2019 and November 2020 were identified. Electronic were records reviewed to determine IPBP vs HBP, distance to RACH from patient's home, bowel preparation score, morning or afternoon list, requirement for intravenous (IV) fluids during the procedure, day case procedure and length of stay. Bowel preparation score was derived from the Aronchick Scale and converted as follows: 0 (unacceptable), 1 (poor), 2 (fair), 3 (good) and 4 (excellent). Results Summary The high standard of bowel preparation achieved with IPBP was maintained when delivered at home, despite some children travelling >100 miles and having travelling times of >3 hours. Delivering all doses of drugs on the day before procedure did not affect the quality of bowel preparation for afternoon lists. There is a trend to a higher proportion of children with HBP receiving IV fluids during anaesthetic which may suggest that some are dehydrated. The proportion of day case procedures has increased from 0% to 72%, which since March 2020, has saved NHS Grampian 18,000. Conclusion Home bowel preparation delivered on day prior to procedure is well tolerated and as effective as inpatient delivered, even for children with long travelling times to hospital. Covid-19 distancing measures have reduced the number of available inpatient beds so HBP has aided bed management in addition to providing a cost saving. The risk of dehydration may be higher for HBP and guidance will be changed to increase the emphasis on oral fluid intake, including during travelling time, on day of procedure.

Mononucleosis: Peculiarity of Blood Count Scattergram Performed on Abbott Alinity Hq

Introduction: Mononucleosis is an infectious disease caused by Epstein-Barr virus (EBV, human herpes virus type 4, HHV-4) and is characterized by asthenia, fever pharyngitis, and lymphadenopathy. In our laboratory diagnosis is made by rapid test and Epstein-Barr virus antibody assay. The presence of Epstein-Barr virus (VCA) specific IgM antibodies indicates primary infection. A marked lymphocytosis with inversion of the formula can be seen on the blood count. The smear shows numerous activated lymphocytic elements By examining the complete scattergram of patients with confirmed primary infection we noticed a peculiar arch arrangement of the lymphocytes in the FL1 x ALL specific leukocyte scatters. Method(s): In this study Vircell's Virapid mono M&G is used, an immunotest for the qualitative determination of 4 serological markers of EBV: two IgM, VCA and heterophile, and two IgG, VCA and EBNA. The presence of anti-VCA IgM antibodies and the absence of anti-EBNA antibodies are indicative of primary infection. CBC was performed on Abbott Alinity hq, which uses a combination of photometry optical counting and fluorescence analysis in order to enumerate cells and cellular constituens. The instrument utilizes eight light scatter detectors which include ALL (axial light loss), IAS (intermediate angles of light scatter), PSS (polarized side scatter), DSS (depolarized side scatter) and FL1 ( fluorescent channel). Result(s): The sixteen cases examined, all of which resulted positive for primary infection on the rapid test, showed a peculiar FL1 x ALL scattergram (see Fig.1). In the lymphocytes' scattergram cloud, we observed an archshaped trend going upwards and rightwards, thus highlighting cells with greater fluorescence and size Often these lymphocytes are identified as monocytes In cases of lymphocytosis from other causes (CLL lymphomas) we can see how the lymphocytes' scattergram cloud is totally different. In such a case the cloud seems like a short bar due to lymphocytes with increased fluorescence signal though with small size (see Fig.2). Conclusion(s): In the 16 cases of primary EBV infection examined, the blood count shows a peculiar FL1 x ALL scattergram, which compared with the scattergram of other causes of lymphocytosis highlights a substantial difference that could support the laboratory technician in the diagnostic differentiation of a lymphocytosis: lymphocytic response to viral infection (EBV, SARS-Cov19, ecc) or monoclonal lymphocyte proliferation.

Quality control strategy of remote heart rate monitoring devices in a health center in Peru

Cardiovascular disease (CVD) is a broad term used to refer to heart diseases and blood vessels. According to the World Health Organization (WHO), the number of deaths from heart diseases between the years 2000-2019 has ranged between 2 million and almost 9 million people. After analyzing the need to implement and validate a monitoring system for the PUCP health center, we chose the FL10 device. This device has passed the design specifications under the following standards: IEC 60601-1:2005, IEC 60601-1-2:2014 and ISO 60601-2-47:2012, therefore it meets the safety, sensitivity and efficacy parameters but its design does not favor maintenance and periodic quality evaluations with commercial measurement equipment. An adapter was made by implementing digital manufacturing by 3D design using Inventor professional 2021 software with the educational license granted by the university. The electrical safety test showed that the leakage current value of all the electrodes was 0.3 uA which is less than the standard (0.1 mA) and the efficacy test showed that all measurements were acceptable. In the case of reproducibility and repeatability tests, the measurements were acceptable as well;in the value of 180 bpm, we observe an attenuation (179 bpm), however it is within the margin of error of 1 bpm. In summary, the FL 10 device is electrical safety and efficient in a health center in Peru. © 2022 IEEE.

Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation

Federated learning (FL) is a distributed machine learning technique that enables collaborative model training while avoiding explicit data sharing. The inherent privacy-preserving property of FL algorithms makes them especially attractive to the medical field. However, in case of heterogeneous client data distributions, standard FL methods are unstable and require intensive hyperparameter tuning to achieve optimal performance. Conventional hyperparameter optimization algorithms are impractical in real-world FL applications as they involve numerous training trials, which are often not affordable with limited compute budgets. In this work, we propose an efficient reinforcement learning (RL)-based federated hyperparameter optimization algorithm, termed Auto-FedRL, in which an online RL agent can dynamically adjust hyperparameters of each client based on the current training progress. Extensive experiments are conducted to investigate different search strategies and RL agents. The effectiveness of the proposed method is validated on a heterogeneous data split of the CIFAR-10 dataset as well as two real-world medical image segmentation datasets for COVID-19 lesion segmentation in chest CT and pancreas segmentation in abdominal CT. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

FLOSSC: A Computer-Aided Tool for Detecting Corona Virus Infections through Chest X-Ray Images

Artificial intelligence has radically altered the world, and it continues to progress at an alarming rate as time passes. AI applications include healthcare and medical solutions, illness diagnostics, agriculture, constructing security infrastructures, autonomous cars, intelligent systems, industrial production, robotics, and much more. COVID19 is a deadly virus that first appeared in China in 2019 and soon spread over the world. By 2020, the globe had witnessed a tremendous epidemic, with countless lives lost as a result of this dreadful virus, which has now become a severe health danger. Furthermore, in 2021, several nations will be infected with new Covid19 forms that are more deadly and spread quicker. The research describes the proposed methodology for diagnosing covid-19 and pneumonia from human chest X-ray images using transfer learning with Resnet-18 and VGG-16 neural networks. The focal loss function was also used to homogenize the imbalanced dataset, which included X-ray images of normal, pneumonia, and Covid-19 patients. The purpose is to assess the performance and accuracy of fine-tuned neural networks after including Binary Cross-Entropy (BCE) and Focal Loss (FL) functions. However, when we used our Resnet-18 and VGG-16 neural networks with BCE and FL functions, the VGG-16 with FL function outperformed all other models, with training and validation accuracy of 98.37 percent and 97.37 percent, correspondingly.

Bioluminescent and Fluorescent Reporter-Expressing Recombinant SARS-CoV-2.

Reporter-expressing recombinant severe acute respiratory syndrome coronavirus 2 (rSARS-CoV-2) represents an excellent tool to understand the biology of and ease studying viral infections in vitro and in vivo. The broad range of applications of reporter-expressing recombinant viruses is due to the facilitated expression of fluorescence or bioluminescence readouts. In this chapter, we describe a detailed protocol on the generation of rSARS-CoV-2 expressing Venus, mCherry, and NLuc that represents a valid surrogate to track viral infections.

Growth and Gastrointestinal Tolerance in Healthy Term Infants Fed Milk-Based Infant Formula Supplemented with Five Human Milk Oligosaccharides (HMOs): A Randomized Multicenter Trial.

Background: Five of the most abundant human milk oligosaccharides (HMOs) in human milk are 2'-fucosyllactose (2'-FL), 3-fucosyllactose (3-FL), lacto-N-tetraose (LNT), 3'-sialyllactose (3'-SL) and 6'-sialyllactose (6'-SL). Methods: A randomized, double-blind, controlled parallel feeding trial evaluated growth in healthy term infants fed a control milk-based formula (CF; n = 129), experimental milk-based formula (EF; n = 130) containing five HMOs (5.75 g/L; 2'-FL, 3-FL, LNT, 3'-SL and 6'-SL) or human milk (HM; n = 104). Results: No significant differences (all p ≥ 0.337, protocol evaluable cohort) were observed among the three groups for weight gain per day from 14 to 119 days (D) of age, irrespective of COVID-19 or combined non-COVID-19 and COVID-19 periods. There were no differences (p ≥ 0.05) among the three groups for gains in weight and length from D14 to D119. Compared to the CF group, the EF group had more stools that were soft, frequent and yellow and were similar to the HM group. Serious and non-serious adverse events were not different among groups, but more CF-fed infants were seen by health care professionals for illness from study entry to D56 (p = 0.044) and D84 (p = 0.028) compared to EF-fed infants. Conclusions: The study demonstrated that the EF containing five HMOs supported normal growth, gastrointestinal (GI) tolerance and safe use in healthy term infants.

MediSecFed: Private and Secure Medical Image Classification in the Presence of Malicious Clients

Deep learning demonstrates its efficacy and potential to solve challenging computer vision problems in medical and other industrial applications. Federated learning is a learning paradigm that facilitates collaborative learning in a federation of users without exchanging actual data with a single authority like a server. However, federated learning provides only a basic level of privacy and robustness and is vulnerable to model poisoning and model inversion attacks in hostile training environments. Hence, in this article, we propose MediSecFed-a secure framework for federated learning in a hostile environment. Compared to the widely used FedAvg, our method relies on simple and practical ideas from knowledge distillation and model inversion to ensure additional security and privacy features. Our approach achieves knowledge exchange among participating entities without sharing model parameters as FedAvg does, thus protecting the privacy of the local data from the server and significantly reducing communication costs. We evaluate our method on two chest X-ray datasets. Our method outperforms FedAvg by 15% on both datasets in a hostile environment. Our method will also continue to maintain good performance even if the number of malicious participating entities increases. Robustness to learn in a malicious environment while preserving privacy with reduced communication costs makes our method more desirable and efficient than that of FedAvg.

PODCASTS AS A MEANS OF DEVELOPING ENGLISH LISTENING AND SPEAKING SKILLS FOR STUDENTS OF TECHNICAL UNIVERSITIES

In 2020, the world was swept by the COVID-19 pandemic, which made significant changes to all areas of human activity, including the educational process. In many countries, including Russia, universities were transferred to distance learning, so teachers had to change the work format. The closing of educational institutions and their urgent transition to online training are fraught with obvious problems that pose a challenge to the entire education system. However, along with challenges and problems, the new learning format provides a wide range of opportunities and prospects for changing and improving education systems. Since it became impossible to give studies in classrooms, university teachers had to use original methods and principles to organize the learning process. One of the didactic principles in teaching is the principle of visibility and, in terms of the implementation of this principle, the Internet has extensive opportunities for teachers of foreign languages. Increasingly, the term “Web 2.0 technologies” appears in scientific and pedagogical literature. It is a complete rethink of building the learning process. There are many Web 2.0 platforms such as blogs, social bookmarking service, podcast, LearningApps, Mindmeister, Quizlet, etc. The purpose of the study is to consider the possibility of using such Web 2.0 technology as a podcast to develop listening and speaking skills in English. The urgency of this problem is due to the increased focus on the integration of computer technologies into pedagogical practice. The article provides “podcast” definition, describes classification, the linguodidactic potential, and the main characteristics of podcast as a method of foreign language teaching. The novelty of the work lies in the description of the experience in using podcasts in teaching English listening and speaking to students of Nosov Magnitogorsk State Technical University. The authors come to the conclusion that the informativeness of visual and oral series, the image dynamism, wide didactic possibilities, accessibility at a convenient for the user time, as well as ease of use, make podcasting promising in the practice of teaching foreign languages. Analysis, synthesis, and generalization methods are used in the research. © 2021, National Defence University - Carol I Printing House. All rights reserved.

Flipped Classroom Methodology to Support Students’ Learning During COVID-19 Crisis

Teaching online against the backdrop of the COVID-19 pandemic required flexible and innovative approaches to foster learning properly. Implementing flipped learning methodology in online instruction helps students to sustain their learning and remain engaged and motivated while learning. In this study, we described how FL methodology can support students’ learning. We conducted a qualitative approach through three different tools: reviewing the literature on FL in 2020 during COVID-19 pandemic, conducting a focus group of 10 school teachers with different teaching disciplines, and semi-structured interviews with 4 school teachers. We found that FL methodology supports learning in many aspects like promoting interaction, engagement, supporting them emotionally, socially, and cognitively. Some challenges were found like technical issues that needed to be addressed and taken care of. This study suggested a novel model based on the findings of this study that could benefit the teachers who implement online FL to support students’ learning. We do recommend future research should also employ controlled studies that investigate the learning analytics of instructional materials that are delivered to students to assure engagement and interactions using online FL. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Federated Transfer Learning-Based IDS for the Internet of Medical Things (IoMT)

The Internet of Medical Things (IoMT) is a set of medical devices and applications that connect to healthcare systems through the Internet. Those devices are equipped with communication technologies that allow them to communicate with each other and the Internet. Reliance on the IoMT is increasing with the increase in epidemics and chronic diseases such as COVID-19 and diabetes;with the increase in the number of IoMT users and the need for electronic data sharing and virtual services, cyberattacks in the healthcare sector for accessing confidential patient data has been increasing in the recent years. The healthcare applications and their infrastructures have special requirements for handling sensitive users' data and the need for high availability. Therefore, securing healthcare applications and data has attracted special attention from both industry and researchers. In this paper, we propose a Federated Transfer Learning-based Intrusion Detection System (IDS) to secure the patient's healthcare-connected devices. The model uses Deep Neural Network (DNN) algorithm for training the network and transferring the knowledge from the connected edge models to build an aggregated global model and customizing it for each one of the connected edge devices without exposing data privacy. CICIDS2017 dataset has been used to evaluate the performance in terms of accuracy, detection rate, and average training time. In addition to preserving data privacy of edge devices and achieving better performance, our comparison indicates that the proposed model can be generalized better and learns incrementally compared to other baseline ML/DL algorithms used in the traditional centralized learning schemes. © 2021 IEEE.

Covid-19's nomadic war machine: Aestheticscapes of the epidemic-capitalism

The coronavirus emerged in a space where both the power that opresses and the opressed coincide in the desire to watch and be watched, by the action of social networks, which generate superfluous beings that exchange their intimacy for a like. Hence, we come to a capitalism where epidemiology precedes demography (epidemic-capitalism): the population is organized according to the ultra-individual logic of pandemic control (immunological passports, monitoring of infections). With the concept of a nomadic war machine by Deleuze et Guattari, the covid-19 is analyzed as an aesthetic landscape in which the territorialities are defined from the edges that pollute and not from the coordinates that delimit them. Therefore, it is concluded that this perfect totalitarianism, which is called al que se denomina net-(fl)asc(ix)smo, can move towards forms of dissent.

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review.

The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.

The value of federated learning during and post-COVID-19.

Federated learning (FL) as a distributed machine learning (ML) technique has lately attracted increasing attention of healthcare stakeholders as FL is perceived as a promising decentralized approach to address data privacy and security concerns. The FL approach stores and maintains the privacy-sensitive data locally while allows multiple sites to train ML models collaboratively. We aim to describe the most recent real-world cases using the FL in both COVID-19 and non-COVID-19 scenarios and also highlight current limitations and practical challenges of FL.