Multi2Claim: Generating Scientific Claims from Multi-Choice Questions for Scientific Fact-Checking

In automated scientific fact-checking, machine learning models are trained to verify scientific claims given evidence. A major bottleneck of this task is the availability of large-scale training datasets on different domains, due to the required domain expertise for data annotation. However, multiple-choice question-answering datasets are readily available across many different domains, thanks to the modern online education and assessment systems. As one of the first steps towards addressing the fact-checking dataset scarcity problem in scientific domains, we propose a pipeline for automatically converting multiple-choice questions into fact-checking data, which we call Multi2Claim. By applying the proposed pipeline, we generated two large-scale datasets for scientific-fact-checking: Med-Fact and Gsci-Fact for the medical and general science domains, respectively. These two datasets are among the first examples of large-scale scientific-fact-checking datasets. We developed baseline models for the verdict prediction task using each dataset. Additionally, we demonstrated that the datasets could be used to improve performance measured by weighted F1 on existing fact-checking datasets such as SciFact, HEALTHVER, COVID-Fact, and CLIMATE-FEVER. In some cases, the improvement in performance was up to a 26% increase. The generated datasets are publicly available. © 2023 Association for Computational Linguistics.

Structural implications of SARS-CoV-2 Surface Glycoprotein N501Y mutation within receptor-binding domain [499-505]–computational analysis of the most frequent Asn501 polar uncharged amino acid mutations

The aim of this study was to evaluate the impact of the most frequent Asn501 polar uncharged amino acid mutations upon important structural properties of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) Surface Glycoprotein RBD–hACE2 (human angiotensin-converting enzyme 2) heterodimer. Mutations N501Y, N501T and N501S were considered and their impact upon complex solubility, secondary motifs formation and intermolecular hydrogen bonding interface was analyzed. Results and findings are reported based on 50 ns run in Gromacs molecular dynamics simulation software. Special attention is paid on the biomechanical shifts in the receptor-binding domain (RBD) [499-505]: ProThrAsn(Tyr)GlyValGlyTyr, having substituted Asparagine to Tyrosine at position 501. The main findings indicate that the N501S mutation increases SARS-CoV-2 S-protein RBD–hACE2 solubility over N501T, N501 (wild type): (Formula presented.), (Formula presented.). The N501Y mutation shifts (Formula presented.) -helix S-protein RBD [366-370]: SerValLeuTyrAsn into π-helix for t > 38.5 ns. An S-protein RBD [503-505]: ValGlyTyr shift from (Formula presented.) -helix into a turn is observed due to the N501Y mutation in t > 33 ns. An empirical proof for the presence of a Y501-binding pocket, based on RBD [499-505]: PTYGVGY (Formula presented.) 's RMSF peak formation is presented. There is enhanced electrostatic interaction between Tyr505 (RBD) phenolic -OH group and Glu37 (hACE2) side chain oxygen atoms due to the N501Y mutation. The N501Y mutation shifts the (Formula presented.) hydrogen bond into permanent polar contact;(Formula presented.);(Formula presented.). © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Early detection of COVID-19 using deep learning architectures: CNN and resnet-101

The main purpose of this paper was to classify if subject has a COVID-19 or not base on CT scan. CNN and resNet-101 neural network architectures are used to identify the coronavirus. The experimental results showed that the two models CNN and resNet-101 can identify accurately the patients have COVID-19 from others with an excellent accuracy of 83.97 % and 90.05 % respectively. The results demonstrates the best ability of the used models in the current application domain. © 2022 IEEE.

Detection of Semantic Duplicates in Temporal Domains Using Directed Acyclic Graphs

We demonstrate the feasibility of a generalized technique for semantic deduplication in temporal data domains using graph-based representations of data records. Structured data records with multiple timestamp attributes per record may be represented as a directed graph where the nodes represent the events and the edges represent event sequences. Edge weights are based on elapsed time between connecting nodes. In comparing two records, we may merge these directed graphs and determine a representative directed acyclic graph (DAG) inclusive of a subset of nodes and edges that maintain the transitive weights of the original graphs. This DAG may then be evaluated by weighting elapsed time equivalences between records at each node and measuring the fraction of nodes represented in the DAG versus the union of nodes between the records being compared. With this information, we establish a duplication score and use a specified threshold requirement to assert duplication. This method is referred to as Temporal Deduplication using Directed Acyclic Graphs (TD:DAG). TD:DAG significantly outperformed established ASNM and ASNM+LCS methods for datasets rep-resenting two disparate domains, COVID-19 government policy data and PlayStation Network (PSN) trophy data. TD:DAG produced highly effective and comparable F1 scores of 0.960 and 0.972 for the two datasets, respectively, versus 0.864/0.938 for ASNM+LCS and 0.817/0.708 for ASNM. © 2023 IEEE.

The Detection of Abnormal Behavior in Healthcare IoT Using IDS, CNN, and SVM

Health care is always a top priority, and that has not changed no matter how far we have come in terms of technology. Since the coronavirus epidemic broke out, almost every country has made health care a top priority. Therefore, the best way to deal with the coronavirus pandemic and other urgent health problems is through the use of IoHT. The tremendous growth of IoT devices and networks especially in the healthcare domain generates massive amounts of data, necessitating careful authentication and security. Other domains include agriculture, smart homes, industry, etc. These massive data streams can be evaluated to determine undesirable patterns. It has the potential to reduce functional risks, avoid problems that are not visible, and eliminate system downtime. Past systematic and comprehensive reviews have significantly aided the field of cybersecurity. However, this research focuses on IoT issues relating to the medical or healthcare domain, using the systematic literature review method. The current literature in health care is not enough to analyze the anomaly of IoHT. This research has revealed that fact. In our subsequent work, we will discuss the architecture of IoHT and use AI techniques such as CNN and SVM to detect intrusions in IoHT. In the interest of advancing scientific knowledge, this study identifies and suggests potential new lines of inquiry that may be pursued in this area of study. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

COVID-19 Instance Segmentation Network Based on Semantic Information Generating Proposals

The CT images of lungs of COVID-19 patients have distinct pathological features, segmenting the lesion area accurately by the method of deep learning, which is of great significance for the diagnosis and treatment of COVID-19 patients. Instance segmentation has higher sensitivity and can output the Bounding Boxes of the lesion region, however, the traditional instance segmentation method is weak in the segmentation of small lesions, and there is still room for improvement in the segmentation accuracy. We propose a instance segmentation network which is called as Semantic R-CNN. Firstly, a semantic segmentation branch is added on the basis of Mask-RCNN, and utilizing the image processing tool Skimage in Python to label the connected domain for the result of semantic segmentation, extracting the rectangular boundaries of connected domain and using them as Proposals, which will replace the Regional Proposal Network in the instance segmentation. Secondly, the Atrous Spatial Pyramid Pooling is introduced into the Feature Pyramid Network, then improving the feature fusion method in FPN. Finally, the cascade method is introduced into the detection branch of the network to optimize the Proposals. Segmentation experiments were carried out on the pathological lesion segmentation data set of CC-CCII, the average accuracy of the semantic segmentation is 40.56mAP, and compared with the Mask-RCNN, it has improved by 9.98mAP. After fusing the results of semantic segmentation and instance segmentation, the Dice coefficient is 80.7%, the sensitivity is 85.8%, and compared with the Inf-Net, it has increased by 1.6% and 8.06% respectively. The proposed network has improved the segmentation accuracy and reduced the false-negatives. © 2022 ACM.

Internet of Things (IoT) in Smart Cities: A Statistical Survey

Internet of things is progressing very rapidly and involving multiple domains of everyday life including environment, governance, healthcare system, transportation system, energy management system, etc. smart city is a platform for collecting and storing the information that is accessed through various sensor-based IoT devices and make their information available in required and authorized domains. This interoperability can be achieved by semantic web technology. In this paper, I have reviewed multiple papers related to IoT in Smart Cities and presented a comparison among the semantic parameters. Moreover, I've presented my future domain of research which is about delivering the COVID-19 patients report to the concerned domains by the healthcare system domain. © 2023 IEEE.

Modeling the influence of the information domain on countermeasure effectiveness in case of COVID-19

A common way to model an epidemic — restricted to contagion aspects only — is a modification of the Kermack-McKendrick SIR Epidemic model (SIR model) with differential equations. (Mis-)Information about epidemics may influence the behavior of the people and thus the course of epidemics as well. We have thus coupled an extended SIR model of the COVID-19 pandemic with a compartment model of the (mis-)information-based attitude of the population towards epidemic countermeasures. The resulting combined model is checked concerning basic plausibility properties like positivity and boundedness. It is calibrated using COVID-19 data from RKI and attitude data provided by the COVID-19 Snapshot Monitoring (COSMO) study. The values of parameters without corresponding observation data have been determined using an L2-fit under mild additional assumptions. The predictions of the calibrated model are essentially in accordance with observations. An uncertainty analysis of the model shows, that our results are in principle stable under measurement errors. We also assessed the scale, at which specific parameters can influence the evolution of epidemics. Another result of the paper is that in a multi-domain epidemic model, the notion of controlled reproduction number has to be redefined when being used as an indicator of the future evolution of epidemics.

Burnout syndrome and remote learning strategies during the pandemic of COVID-19: a longitudinal study of Agrarian Sciences students

Purpose:We aimed to evaluate the levels of Burnout Syndrome (BS) in Agricultural Sciences students, both before and during the COVID-19 pandemic period.Design/methodology/approach:We accessed 77 students for Burnout Syndrome using the Maslach Burnout Inventory-Student Survey (MBI-SS) on two occasions, the first in February and the second in October 2020. Sixty-three students completed both phases. The data were analyzed in a mixed factorial scheme (Three-way ANOVA;p < 0.05).Findings:Academic efficacy improved over time without interference of sex or physical activity frequency (p < 0.001). The emotional exhaustion domain significantly reduced over time in women who declared to practice physical activity (p = 0.037). A similar effect was observed in both men and women for the cynicism domain (p < 0.001). Online teaching strategies and the regular practice of exercise were associated to reduced levels of BS during the pandemic.Practical implications:The adoption of the emergency remote education system (ERE) associated with the practice of exercise can mitigate the harmful effects of the COVID-19 quarantine on the mental health of students.Theoretical implications:The ERE can be an effective strategy to mitigate BS levels in Agricultural Science students in the post-pandemic period.Originality/Value:This is the first study that presents results of BS in students of Agricultural Sciences, comparing before and during the COVID-19 pandemic.

Tourist Behavior in the Cruise Industry Post-COVID-19: An Examination of Service Quality, Corporate Image, and Intentions to Pay and Revisit

The COVID-19 outbreak has had detrimental consequences on the cruise industry due to the suspension of commercial cruise trips, and these effects remain apparent in Saudi Arabia. The offered service quality (SQ) in the post-COVID-19 era seems to be a critical element for improving customer experiences and satisfaction, enhancing destination attractiveness, increasing revenue, and maintaining repeat business. The current study aimed to assess the impact of service quality on tourists' satisfaction and corporate image as well as the intention to pay for cruise trips and revisit the destination among 315 tourists in Saudi Arabia. Service quality was measured using five subscales of the SERVQUAL scale, including reliability, tangibles, responsiveness, assurance, and empathy. Tourists' satisfaction was significantly influenced by four domains of SQ, whereas the intention to pay more, intention to revisit the destination, and corporate image were significantly predicted by ≤3 domains of SQ. The study's findings can help the cruise industry to improve its offerings and create more personalized and engaging experiences that meet the changing needs of customers in the recovery period after the COVID-19 outbreak.

Addressing Issues of Cross-Linguality in Open-Retrieval Question Answering Systems For Emergent Domains

Open-retrieval question answering systems are generally trained and tested on large datasets in well-established domains. However, low-resource settings such as new and emerging domains would especially benefit from reliable question answering systems. Furthermore, multilingual and cross-lingual resources in emergent domains are scarce, leading to few or no such systems. In this paper, we demonstrate a cross-lingual open-retrieval question answering system for the emergent domain of COVID-19. Our system adopts a corpus of scientific articles to ensure that retrieved documents are reliable. To address the scarcity of cross-lingual training data in emergent domains, we present a method utilizing automatic translation, alignment, and filtering to produce English-to-all datasets. We show that a deep semantic retriever greatly benefits from training on our English-to-all data and significantly outperforms a BM25 baseline in the cross-lingual setting. We illustrate the capabilities of our system with examples and release all code necessary to train and deploy such a system1 © 2023 Association for Computational Linguistics.

COVID QA Network: A Specific Case of Biomedical Question Answering

COVID-19 crisis has led to an outburst of information that needs to be organized, validated, and made available to the seekers. Despite the rapid growth and success of BERT models in the last 3 years, COVID QA is a difficult task due to the lack of applicable datasets and a relevant language representation. Therefore, this study proposes a transformer-based Question Answering (QA) model for COVID-19 questions from the biomedical domain. Further, explored several datasets, and models required for question type prediction, no-Answer prediction, and answer extraction and transfer learning strategies. It has been demonstrated that the exact match score can be significantly improved with limited amounts of training data from the biomedical domain. Finally, the findings of the study have been summarized as Factoid QA Finetuning Framework (FQFF), which can provide initial direction for domain-specific QA tasks with a limited amount of data. © 2023 IEEE.

BEsafe - Validating URLs and Domains with the aid of Indicator of Compromise

The advancement of information technology has stimulated the conversion of physical interactions to online activities, especially during the Covid-19 pandemic. Thus, users' awareness and cyber hygiene need to be emphasized when they are involved in the cyber world. A browser extension named 'BEsafe' is developed to validate the websites and promote a safe browsing environment. It prevents users from falling prey to network-based attacks and raises their security awareness. To ensure users' privacy, the permissions needed for BEsafe are listed on the permission tab. Moreover, BEsafe will not be working on Incognito mode by default to promise that the private mode leaves no tracks. However, the user can still enable the extension to be functioning on Incognito mode by navigating to the Extension Details and turning on the relevant toggle. © 2023 IEEE.

How COVID-19 Changed Mobility: A Life-Oriented Approach to Travel Behavior Change in Flanders, Belgium

This research regards the COVID-19 pandemic as a major life event with the ability to affect daily activity-travel behavior, and investigates if specific activity participation (work/study, shopping, social contact, free time) is associated with different travel modes (walk, cycle, car, public transportation), with attention paid to residential neighborhood using survey data (n = 854) in Flanders, Belgium. Through mean-comparison tests and regression analyses, evidence was found of (1) compensation for changed working/studying time with walking time, (2) compensation for changed social contact with cycling, and (3) similarly affected travel behavior regardless of residential neighborhood, though suburban residents may have more mode-resilience and less reliance on public transportation. Further evidence indicate that those working/studying may have taken advantage of decreased traffic and congestion with an increase in car and public transportation use and that older respondents may be more likely to hold flexible, teleworkable jobs and treat the pandemic with greater caution. Some travel behavior changes are expected to persist post-pandemic, therefore understanding which life domains are associated with which travel modes can inform policy aiming to decrease motorized and increase active mode use (e.g., for health or sustainability goals). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Molecular Docking and Simulation Analysis of Cyclopeptides as Anticancer Agents

Background: Cancer is a leading cause of death for people worldwide, in addition to the rise in mortality rates attributed to the Covid epidemic. This allows scientists to do additional research. Here, we have selected Integerrimide A, cordy heptapeptide, and Oligotetrapeptide as the three cyclic proteins that will be further studied and investigated in this context.Methods: Docking research was carried out using the protein complexes 1FKB and 1YET, downloaded from the PDB database and used in the docking investigations. Cyclopeptides have been reported to bind molecularly to human HSP90 (Heat shock protein) and FK506. It was possible to locate HSP90 in Protein Data Banks 1YET and 1FKB. HSP90 was retrieved from Protein Data Bank 1YET and 1FKB. Based on these findings, it is possible that the anticancer effects of Int A, Cordy, and Oligo substances could be due to their ability to inhibit the mTOR rapamycin binding domain and the HSP90 Geldanamycin binding domain via the mTOR and mTOR chaperone pathways. During the calculation, there were three stages: system development, energy reduction, and molecular dynamics (also known as molecular dynamics). Each of the three compounds demonstrated a binding affinity for mTOR's Rapamycin binding site that ranged from -6.80 to -9.20 Kcal/mol (FKB12).Results: An inhibition constant Ki of 181.05 nM characterized Cordy A with the highest binding affinity (-9.20 Kcal/mol). Among the three tested compounds, Cordy A was selected for MD simulation. HCT116 and B16F10 cell lines were used to test each compound's anticancer efficacy. Doxorubicin was used as a standard drug. The cytotoxic activity of substances Int A, Cordy A, and Oligo on HCT116 cell lines was found to be 77.65 μM, 145.36 μM, and 175.54 μM when compared to Doxorubicin 48.63 μM, similarly utilizing B16F10 cell lines was found to be 68.63 μM, 127.63 μM, and 139.11 μM to Doxorubicin 45.25 μM.Conclusion: Compound Cordy A was more effective than any other cyclic peptides tested in this investigation.

Metaverse: An Innovative Model for Healthcare Domain

Present situation after the Coronavirus has made every one of us understand the deficiencies and the impediments of India's medical services area. There was an intense shortage of clinical staff, beds, and other such essential things, which made us believe this is the future to be lived with, and provided that this is true, then, at that point, it is a significant eye-opener for specialists, designers, government and each capable individual to think of an answer for this. This occasion touched off the inclination for the tracking down the arrangement or possibly a stage towards settling or, in any event, restricting this destruction. Metaverse, and its ground-breaking capacities are the same old thing to the world. It's been anticipated that it will revolutionize gaming, association with companions, shopping, and whatnot. But this paper is kept to spotlight the most deserving space, the healthcare sector. Metaverse can change the fortunes of the medical care area. This paper will examine all the potential ways this innovation can be valuable. It can work on obsolete facilities for treatment and educational purposes, and numerous such up-sides have been highlighted beneath. © 2023 IEEE.

Positive and negative impacts of COVID-19, an analysis with special reference to challenges on the supply chain in South Asian countries.

The Coronavirus or COVID-19 is a disease based on an unknown virus. It seems that it started in China and has widely spread in almost all countries in the world. This pandemic situation is one of the widely spread diseases in recent history. However, there was an influenza pandemic in 1918 with the exact number of deaths still unknown. Some believe that the death toll would have been about 50-100 million people. At the time of writing this article, COVID-19 has infected 5,306,928 persons worldwide (when the article was finalised for publication, the number has increased up to 15,947,291). The article is aimed at analysing the positive and negative impacts of COVID-19 in a sociological perspective. It is further focused on possible challenges to the supply chain in South Asia. South Asian countries are highly influenced by the pandemic situation, and the regional representation is about 4% in the later part of May 2020 with an increasing tendency. Also, the article has a proposal for the control of the disease as well as the entire socio-economic, environmental and political atmosphere in a country, whilst particularly giving more weight to South Asia. The proposed actions are analysed in short-term, mid-term and long-term basis, and any expert and social worker who is involved in the pandemic control process can gain an insight into what to do and how to perform their tasks. A sociological analysis on COVID-19 is very important because there is a wing comprising dominant medical experts in the control and management of the disease. The article emphasises the importance of a sociological analysis in a pandemic situation. Naturally, anyone would think of a pandemic situation in very negative terms due to its emotional, socio-economic, environmental, political and cultural factors. However, it is also positive due to certain factors that help to reintegrate and reorganise the social system as a whole.

The Core Rehabilitation Outcome Set for Single-Sided Deafness (CROSSSD) study: International consensus on outcome measures for trials of interventions for adults with single-sided deafness.

BACKGROUND: Single-sided deafness (SSD) has functional, psychological, and social consequences. Interventions for adults with SSD include hearing aids and auditory implants. Benefits and harms (outcome domains) of these interventions are until now reported inconsistently in clinical trials. Inconsistency in reporting outcome measures prevents meaningful comparisons or syntheses of trial results. The Core Rehabilitation Outcome Set for Single-Sided Deafness (CROSSSD) international initiative used structured communication techniques to achieve consensus among healthcare users and professionals working in the field of SSD. The novel contribution is a set of core outcome domains that experts agree are critically important to assess in all clinical trials of SSD interventions. METHODS: A long list of candidate outcome domains compiled from a systematic review and published qualitative data, informed the content of a two-round online Delphi survey. Overall, 308 participants from 29 countries were enrolled. Of those, 233 participants completed both rounds of the survey and scored each outcome domain on a 9-point scale. The set of core outcome domains was finalised via a web-based consensus meeting with 12 participants. Votes involved all stakeholder groups, with an approximate 2:1 ratio of professionals to healthcare users participating in the Delphi survey, and a 1:1 ratio participating in the consensus meeting. RESULTS: The first round of the survey listed 44 potential outcome domains, organised thematically. A further five outcome domains were included in Round 2 based on participant feedback. The structured voting at round 2 identified 17 candidate outcome domains which were voted on at the consensus meeting. Consensus was reached for a core outcome domain set including three outcome domains: spatial orientation, group conversations in noisy social situations, and impact on social situations. Seventy-seven percent of the remaining Delphi participants agreed with this core outcome domain set. CONCLUSIONS: Adoption of the internationally agreed core outcome domain set would promote consistent assessment and reporting of outcomes that are meaningful and important to all relevant stakeholders. This consistency will in turn enable comparison of outcomes reported across clinical trials comparing SSD interventions in adults and reduce research waste. Further research will determine how those outcome domains should best be measured.

Cholesterol and Melatonin Exert Opposite Effects on the Interaction of Model DMPC Membranes with the SARS-CoV-2 S-Protein: A SANS Study

An effect of receptor-binding domain (RBD) of SARS-CoV-2 S-protein on structural parameters of model lipid membranes presented by dimyristoylphosphatidylcholine (DMPC) systems with cholesterol and melatonin impurities is studied by small angle neutron scattering (SANS). It is shown that an increase in melatonin concentration in the lipid membrane leads to a decrease in the thickness of the lipid bilayer, while an increase in the concentration of cholesterol leads to an increase in its thickness. It is suggested that increasing the concentration of melatonin in a membrane prevents the interaction of coronaviral S-protein with a lipid membrane of a cell. In the presence of cholesterol in the system, the interaction of a lipid membrane with an active part of S-protein occurs depending on a phase state of the lipid: in the case of a gel phase, there is no changes in structural parameters, but at higher temperatures in the case of a liquid crystal phase, an addition of RBD SARS-CoV-2 to the system causes a reduce in the membrane thickness. © 2023 Wiley-VCH GmbH.

Unsupervised Domain Adaptation for COVID-19 Information Service with Contrastive Adversarial Domain Mixup

In the real-world application of COVID-19 misinformation detection, a fundamental challenge is the lack of the labeled COVID data to enable supervised end-to-end training of the models, especially at the early stage of the pandemic. To address this challenge, we propose an unsupervised domain adaptation framework using contrastive learning and adversarial domain mixup to transfer the knowledge from an existing source data domain to the target COVID-19 data domain. In particular, to bridge the gap between the source domain and the target domain, our method reduces a radial basis function (RBF) based discrepancy between these two domains. Moreover, we leverage the power of domain adversarial examples to establish an intermediate domain mixup, where the latent representations of the input text from both domains could be mixed during the training process. Extensive experiments on multiple real-world datasets suggest that our method can effectively adapt misinformation detection systems to the unseen COVID-19 target domain with significant improvements compared to the state-of-the-art baselines. © 2022 IEEE.