An Attitude-Behavioral Model to Understand People's Behavior Towards Tourism during COVID-19 Pandemic

The impact of pandemics on the tourism industry should be explored from the perspective of those who will travel, go to the tourist places on vacation, and avail services from tourism and hospitality-related organizations. This study has aimed to identify the reasons for the changed human psychology towards tourism during the COVID-19 Pandemic to develop an attitude-behavioral model. This investigation thus conducted an extensive empirical study among tourists to capture their social, emotional, and financial beliefs. The research then examined the measurement model through confirmatory factor analysis (CFA) before investigating the cause-effect relationship through the structural model. Analysis revealed that the negative effect of attitude on behavioral intention toward this new equilibrium is controlled by the emotional aspect of attitude. Furthermore this paper made several contributions to the literature on human psychology, crisis management, human behavior, marketing, and tourism.

Multi-head deep learning framework for pulmonary disease detection and severity scoring with modified progressive learning.

Chest X-rays (CXR) are the most commonly used imaging methodology in radiology to diagnose pulmonary diseases with close to 2 billion CXRs taken every year. The recent upsurge of COVID-19 and its variants accompanied by pneumonia and tuberculosis can be fatal in some cases and lives could be saved through early detection and appropriate intervention for the advanced cases. Thus CXRs can be used for an automated severity grading of pulmonary diseases that can aid radiologists in making better and informed diagnoses. In this article, we propose a single framework for disease classification and severity scoring produced by segmenting the lungs into six regions. We present a modified progressive learning technique in which the amount of augmentations at each step is capped. Our base network in the framework is first trained using modified progressive learning and can then be tweaked for new data sets. Furthermore, the segmentation task makes use of an attention map generated within and by the network itself. This attention mechanism allows to achieve segmentation results that are on par with networks having an order of magnitude or more parameters. We also propose severity score grading for 4 thoracic diseases that can provide a single-digit score corresponding to the spread of opacity in different lung segments with the help of radiologists. The proposed framework is evaluated using the BRAX data set for segmentation and classification into six classes with severity grading for a subset of the classes. On the BRAX validation data set, we achieve F1 scores of 0.924 and 0.939 without and with fine-tuning, respectively. A mean matching score of 80.8% is obtained for severity score grading while an average area under receiver operating characteristic curve of 0.88 is achieved for classification.

The Role of Religion and Spirituality to Cope with COVID-19 Infections Among People of Lower Socioeconomic Status in Pakistan: An Exploratory Qualitative Study.

Religion and spirituality have been key coping mechanisms of Pakistani Muslims amidst natural calamities such as the COVID-19 pandemic. This study aimed to identify and explore the role of religion and spirituality in the recovery of COVID-19 patients in lower socioeconomics. The data for this qualitative research study were collected from 13 people in Pakistan who survived COVID-19 infection during the wave of the Omicron variant. The participants of this study referenced four key themes about their story of getting infected by COVID-19 and recovering from it and referenced religion and spirituality as an overarching aspect of that story. The patients who recovered believed that COVID-19 was a punishment from God for sinful humanity, which was unavoidable. Amidst such a belief, the studied patients tried to avoid hospitalization but prayed to God for mercy, forgiveness, and help in their recovery. A few who took medical treatment also established and/or strengthened their spiritual connections seeking quick recovery from the infection. The participants of this study believed that their religion or spirituality played a medicinal role in their recovery from COVID-19 infection.

An attitude-behavioral model to understand people's behavior towards tourism during COVID-19 pandemic.

The impact of pandemics on the tourism industry should be explored from the perspective of those who will travel, go to the tourist places on vacation, and avail services from tourism and hospitality-related organizations. This study has aimed to identify the reasons for the changed human psychology towards tourism during the COVID-19 Pandemic to develop an attitude-behavioral model. This investigation thus conducted an extensive empirical study among tourists to capture their social, emotional, and financial beliefs. The research then examined the measurement model through confirmatory factor analysis (CFA) before investigating the cause-effect relationship through the structural model. Analysis revealed that the negative effect of attitude on behavioral intention toward this new equilibrium is controlled by the emotional aspect of attitude. Furthermore this paper made several contributions to the literature on human psychology, crisis management, human behavior, marketing, and tourism.

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks.

Artificial Intelligence (AI) techniques of deep learning have revolutionized the disease diagnosis with their outstanding image classification performance. In spite of the outstanding results, the widespread adoption of these techniques in clinical practice is still taking place at a moderate pace. One of the major hindrance is that a trained Deep Neural Networks (DNN) model provides a prediction, but questions about why and how that prediction was made remain unanswered. This linkage is of utmost importance for the regulated healthcare domain to increase the trust in the automated diagnosis system by the practitioners, patients and other stakeholders. The application of deep learning for medical imaging has to be interpreted with caution due to the health and safety concerns similar to blame attribution in the case of an accident involving autonomous cars. The consequences of both a false positive and false negative cases are far reaching for patients' welfare and cannot be ignored. This is exacerbated by the fact that the state-of-the-art deep learning algorithms comprise of complex interconnected structures, millions of parameters, and a 'black box' nature, offering little understanding of their inner working unlike the traditional machine learning algorithms. Explainable AI (XAI) techniques help to understand model predictions which help develop trust in the system, accelerate the disease diagnosis, and meet adherence to regulatory requirements. This survey provides a comprehensive review of the promising field of XAI for biomedical imaging diagnostics. We also provide a categorization of the XAI techniques, discuss the open challenges, and provide future directions for XAI which would be of interest to clinicians, regulators and model developers.

Honey and Nigella sativa against COVID-19 in Pakistan (HNS-COVID-PK): A multicenter placebo-controlled randomized clinical trial.

Until now, no specific and effective treatment exists for coronavirus disease 2019 (COVID-19). Since honey and Nigella sativa (HNS) have established antiviral, antibacterial, antiinflammatory, antioxidant, and immunomodulatory properties, we tested their efficacy for this disease in a multicenter, placebo-controlled, and randomized clinical trial at four medical care facilities in Pakistan. RT-PCR confirmed COVID-19 adults showing moderate or severe disease were enrolled in the trial. Patients were randomly assigned in a 1:1 ratio to receive either honey (1 g kg-1 day-1 ) and Nigella sativa seeds (80 mg kg-1 day-1 ) or a placebo for up to 13 days along with standard care. The outcomes included symptoms' alleviation, viral clearance, and 30-day mortality in the intention-to-treat population. Three hundred and thirteen patients, 210 with moderate and 103 with severe disease, underwent randomization from April 30 to July 29, 2020. Among the moderate cases, 107 were assigned to HNS, whereas 103 were assigned to the placebo group. Among the severe cases, 50 were given HNS, and 53 were given the placebo. HNS resulted in ~50% reduction in time taken to alleviate symptoms as compared to placebo (moderate cases: 4 vs. 7 days, Hazard Ratio [HR]: 6.11; 95% Confidence Interval [CI]: 4.23-8.84, p < 0.0001 and for severe cases: 6 vs. 13 days, HR: 4.04; 95% CI: 2.46-6.64; p < 0.0001). HNS also cleared the virus earlier than placebo in both moderate cases (6 vs. 10 days, HR: 5.53; 95% CI: 3.76-8.14, p < 0.0001) and severe cases (8.5 vs. 12 days, HR: 4.32; 95% CI: 2.62-7.13, p < 0.0001). HNS further led to a better clinical score on day 6 with normal activity resumption in 63.6% vs. 10.9% among moderate cases (OR: 0.07; 95% CI: 0.03-0.13, p < 0.0001) and hospital discharge in 50% versus 2.8% in severe cases (OR: 0.03; 95% CI: 0.01-0.09, p < 0.0001). In severe cases, the mortality rate was less than 1/4th in the HNS group than in placebo (4% vs. 18.87%, OR: 0.18; 95% CI: 0.02-0.92, p = 0.029). No HNS-related adverse effects were observed. HNS, compared with placebo, significantly improved symptoms, expedited viral load clearance, and reduced mortality in COVID-19 patients. This trial was registered on April 15, 2020 with ClinicalTrials.gov Identifier: NCT04347382.

A novel group decision-making framework under Pythagorean fuzzy N-soft expert knowledge

Many scholars have been challenged by multi-attribute group decision-making problems that have stimulated the appearance of increasingly general models. Pythagorean fuzzy sets were a reaction by Yager who in 2013, suggested this model to improve the performance of intuitionistic fuzzy sets. Another hybrid model –soft expert sets– deals with uncertain parameterized information. It considers opinions of different experts, improving the single-agent experience of soft sets. N-soft expert sets and their fuzzy version, namely, fuzzy N-soft expert sets, consider the ratings given to objects by more than one expert with respect to relevant parameters. The arguments supporting the need for independent allocation of membership and non-membership degrees apply to the fuzzy expressions imposed on top of the benefits of the N-soft expert environment. These challenges converge on the formulation of a new hybrid model called Pythagorean fuzzy N-soft expert sets that improves upon Pythagorean fuzzy sets with the benefits of N-soft expert sets. We study their scope of application with practical examples. Afterwards we discuss certain basic operators (subsethood, complement, union and intersection), prove some of their remarkable properties, and provide the concepts of equal, agree, and disagree-Pythagorean fuzzy N-soft expert sets. We present an algorithm for group decision-making problems in this framework and we explore three applications of this methodology, namely, to the analysis of wheat varieties, employee selection, and recovery order of patients suffering COVID-19. In the end, we provide a sensitivity analysis comparing the proposed model with some existing models to guarantee its cogency and feasibility.

Automated grading of chest x-ray images for viral pneumonia with convolutional neural networks ensemble and region of interest localization.

Following its initial identification on December 31, 2019, COVID-19 quickly spread around the world as a pandemic claiming more than six million lives. An early diagnosis with appropriate intervention can help prevent deaths and serious illness as the distinguishing symptoms that set COVID-19 apart from pneumonia and influenza frequently don't show up until after the patient has already suffered significant damage. A chest X-ray (CXR), one of many imaging modalities that are useful for detection and one of the most used, offers a non-invasive method of detection. The CXR image analysis can also reveal additional disorders, such as pneumonia, which show up as anomalies in the lungs. Thus these CXRs can be used for automated grading aiding the doctors in making a better diagnosis. In order to classify a CXR image into the Negative for Pneumonia, Typical, Indeterminate, and Atypical, we used the publicly available CXR image competition dataset SIIM-FISABIO-RSNA COVID-19 from Kaggle. The suggested architecture employed an ensemble of EfficientNetv2-L for classification, which was trained via transfer learning from the initialised weights of ImageNet21K on various subsets of data (Code for the proposed methodology is available at: https://github.com/asadkhan1221/siim-covid19.git). To identify and localise opacities, an ensemble of YOLO was combined using Weighted Boxes Fusion (WBF). Significant generalisability gains were made possible by the suggested technique's addition of classification auxiliary heads to the CNN backbone. The suggested method improved further by utilising test time augmentation for both classifiers and localizers. The results for Mean Average Precision score show that the proposed deep learning model achieves 0.617 and 0.609 on public and private sets respectively and these are comparable to other techniques for the Kaggle dataset.

Evaluation of clinical laboratory parameters in COVID-19 positive cases admitted in Islamabad

Objective: To assess the variation of laboratory parameters in COVID-19 positive patients with different genders and age groups and to clarify the consequences of COVID-19 infection on different patients. Study Design: Prospective study. Setting: IHITC (Isolation Hospital & Infectious Treatment Center), Islamabad. Period: 20th May, 2021 to 25th July, 2021. Material & Methods: Study was conducted With 222 participants among them 119 were COVID positive serve as Case and 103 were COVID negative considered as control. Blood samples were drawn from all participants of study to measure biochemical and hematological laboratory parameters with demographic characteristics. Mean ± standard deviation (SD) of different lab parameters analyzed by using IBM SPSS Statistics 20. Results: Total 222 participants were analyzed having 115 (50.7%) male and 107 (49.3%) female having mean age 60±13.8. No significant variation has been seen in ALP, total bilirubin, creatinine and uric acid having mean values with in normal range. In 119 positive patients, ALT (p=0.001) (t=2.031), urea (p=0.001) (t=7.590), Ferritin (p=0.001) (t=7.13), CRP (p=0.001) (t=9.90) and D-dimer (p=0.001) (t=5.962) were elevated and good predictor of poor prognosis of disease. Pathological impacts of COVID-19 were also represented by hematological parameters including WBC count (p=0.001) (t=7.126), Neutrophil to Lymphocyte Ratio (p=0.001) (t=9.042) and Lymphocyte count (p=0.001) (t=-12.707). Conclusion: According to this research, males and old age population is more susceptible to SARS-2. Our study suggests that laboratory biomarkers including ALT, Urea, Ferritin, CRP, D-dimer and WBC count are significantly associated with poor prognosis in Covid-19 patients. [ FROM AUTHOR]

A PROMETHEE based outranking approach for the construction of Fangcang shelter hospital using spherical fuzzy sets.

This study mainly aims to develop two effective and practical multi-criteria group decision-making approaches by taking advantage of the ground-breaking theory of PROMETHEE family of outranking methods. The presented variants of Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method are acknowledged to address the complex decision-making problems carrying the ambiguous information, expressible in terms of yes, no, abstinence and refusal, owing to the preeminent condition and wider structure of spherical fuzzy sets. Both of the proposed approaches seek help from the Shannon's entropy formula to evaluate the object weights of the decision criteria. The proposed techniques operate by taking into account the deviation between each pair of potential alternatives in accordance to different types of preference functions to determine the preference indices. The proposed technique of spherical fuzzy PROMETHEE I method carefully compares the positive and negative outranking flows of the alternative to get partial rankings. In contrast, the spherical fuzzy PROMETHEE II method has the edge to eliminate the incomparable pair by employing the net outranking flow to derive the final ranking. The application of proposed approaches is explained via a case study in the field of medical concerning the selection of appropriate site to establish Fangcang shelter hospital in Wuhan to treat COVID-19 patients. The convincing comparisons of the proposed methodologies with q-rung orthopair fuzzy PROMETHEE and spherical fuzzy TOPSIS methods are also included to verify the aptitude of the proposed methodology.

Editorial introduction to the special issue: Social customer journey – behavioural and social implications of a digitally disruptive environment

This editorial provides an overview of this Special Issue of Technological Forecasting & Social Change on “Social Customer Journey - Behavioural and Social Implications of a Digitally Disruptive Environment”, bringing together insights from the papers accepted for inclusion in this special issue. Fifteen articles on a variety of topics relevant to this special issue have been accepted for publication. These articles examine the behavioural and social implications of a digitally disruptive environment from a variety of theoretical, contextual and methodological perspectives.

Solving Pythagorean fuzzy partial fractional diffusion model using the Laplace and Fourier transforms

Many mathematical models describe the Corona virus disease 2019 (COVID-19) outbreak;however, they require advance mathematical skills. The need for this study is to determine the diffusion of the COVID-19 vaccine in humans. To this end, we first establish a Pythagorean fuzzy partial fractional differential equation using the Pythagorean fuzzy integral transforms to express the effects of COVID-19 vaccination on humans under the generalized Hukuhara partial differential conditions. We extract the analytical solution of the Pythagorean fuzzy partial fractional differential equation using the Pythagorean fuzzy Laplace transform under the generalized Hukuhara partial differential and the Pythagorean fuzzy Fourier transform using the Caputo generalized Hukuhara partial differential. Moreover, we present some essential postulates and results related to the Pythagorean fuzzy Laplace transform and the Pythagorean fuzzy Fourier transform. Furthermore, we develop the solution procedure to extract the solution of the Pythagorean fuzzy partial fractional differential equation. To grasp the considered approach, a mathematical model for the diffusion of the COVID-19 vaccination in the human body is provided and analyzed the behavior to visualize and support the proposed model. Our proposed method is efficient and has a great worth to discuss the bio-mathematical models in various fields of science and medicines.

Neonatal RSV positive bronchiolitis with fulminant viral septic shock

AimsMain purpose of presenting this clinical case is that RSV BRONCHIOLITIS can present with lobar pneumonia,Fulminant viral septic shock with DIC,pulmonory haemorrhage and asystole. Viral VS Bacterial sepsis- clinically difficult to differentiate.Methods1 month old girl,unwell for 2 days with cough,decrease oral intake, seen by GP in the morning and diagnosed as BRONCHIOLITIS,same day evening presented to the hospital with apnoea in the car arrived at PAU within 3 mins of apnoea.O/E-no HR or breathing,bleeding from nose and mouth,pale looking,mottled, CRT 5 sec.CPR started and connected to monitor showed asystole.Immediate cardiac arrest call was activated.Intubated, cannula inserted, 2 doses of adrenaline given IV,Bolus of normal saline 10mls/kg thrice,partial septic screening done and covered with triple antibiotics amoxycillin,gentamycin and cefotaxime.After 10 mins of resuscitation baby responded. Given vitamin K and transfused with O negative blood and FFP.Blood gas showed mixed metabolic and respiratory acidosis and hence connected to ventilator started on morphine,maintenance fluids,ionotropes,morphine infusion and transferred to tertiary centre. In tertiary centre admitted for 11 days,extubated to CPAP on day 5, weaned to high flow on day 6, RA on day 9. Ionotropes for 1 day,acylovir, vitamin k for 9 and 6 days respectively.Neuroprotective measures followed.ResultsNPA for RSV positive, covid 19 PCR negative, blood c/s,CSF c/s and CSF PCR for bacteria and viruses negative, X ray chest consolidation upper lobes bilateral,CT Angiogram subsegmental consolidation and possible intraparenchymal haemorrhage. Initial Echo pulmonory hypertension and repeat Echo normal.MRI Brain -hypersensitivity in posterior putamina. Deranged coagulation profile.APTT more than 180, PT 16.2, INR 1.4ConclusionRSV positive bronchiolitis with all complications can mimic bacterial sepsis and its clinically difficult to differentiate between viral and bacterial septic shock.As this baby’s blood C/S was negative only positive thing was RSV in NPA, We have to consider this case as RSV BRONCHIOLITIS with fulminant septic shock with pneumonia, DIC, Pulmonory Haemorrhage leading to Asystole.Management of bacterial and viral Septic shock is pretty much the same except in certain cases we may have to use antivirals drugs when indicated.

Impact of the coronavirus pandemic on mental prosperity of medical care laborers in tertiary consideration hospital: A case report.

Since the outbreak of the Corona pandemic in December 2019, many people affected, especially medical care laborers, who deal with the treated cases. Coronavirus disease 2019 not only affects the body parts, but also extends to the psychological symptoms. The purpose of this research is to explore the impact of the pandemic on the mental prosperity of the laborers. Clinical staff members from the administration emergency clinic, Lahore, were enlisted. A poll was used to collect data on the segment information, a sleeping disorder, despondency and stress manifestations. Correlation of the segment information and the mental factors were done among the sleeping and non-sleeping disorder samples. All 356 medical service laborers were selected for this investigation. There were manifestations of misery in 222 (62.35%), nervousness in 227 (64.76%), stress in 197 (55.33%) and sleep deprivation in 190 (53.37%) of members. Gentle to extreme side effects of melancholy (91.65% vs 28.9%), nervousness (83.1% vs 41.6%) and stress (84.26% vs 22.22%) were seen predominately in the sleep deprivation gathering (P < .001). Insomnia was more pronounced in the members with low training levels (78.08%) versus post-advanced education (30.9%). Paramedics, attendants, and medical service laborers in confinement/serious consideration units were more inclined to the sleep deprivation (P < .001). Mental prosperity of medical care laborers was influenced because of Coronavirus pandemic. Attendants, paramedics, and those working in the detachment unit showed a critical sleeping disorder. The results and indicators have proven that there is a relationship between the infection with the Corona pandemic and occurrence of disorders in psychological behavior. Therefore, the psychological rehabilitation sessions must be conducted for those infected and those in contact with the Corona cases to relieve the burden of that patients to raise their psychological conditions and support the immune system such that resist against the infection.

Modified EDAS Method for MAGDM Based on MSM Operators with 2-Tuple Linguistic T-Spherical Fuzzy Sets

The coronavirus (COVID-19) pandemic, which began in China and is fast spreading around the world, has increased the number of cases and deaths. Governments have suffered substantial damage and losses not only in the health sector but also in a variety of other areas. In this situation, it is critical to determine the most crucial vaccine that doctors and specialists should implement. In order to evaluate the many vaccines to control the COVID-19 epidemic, a decision problem based on the decisions of many experts, with some contradicting and multiple criteria, should be taken into account. This decision process is characterized as a multiattribute group decision-making (MAGDM) problem that includes uncertainty in this study. T -spherical fuzzy sets are utilized for this, allowing decision-experts to make evaluations over a larger area and better deal with complicated data. The T -spherical fuzzy set is a useful tool for dealing with uncertainty and ambiguity, especially where additional answers of the type "yes," "no," "abstain," and "refusal" are required, and the 2-tuple linguistic terms are useful for the qualitative evaluation of uncertain data. From the perspective of the uncertainty surrounding the problems of MAGDM, we propose the notion of 2-tuple linguistic T -spherical fuzzy numbers (2TL T -SFNs) generated with the integration of T -spherical fuzzy numbers and 2-tuple linguistic terms. Then, the assessment based on distance from average solution (EDAS) for the ranking of alternatives based on the 2TL T -SFNs is investigated as a new decision-making strategy. This study provides the following significant contributions: (1) the procedure for constructing a 2TL T -SFNs is described, together with their aggregation operators, ranking criteria, relevant attributes, and some operational laws. (2) The traditional Maclaurin symmetric mean (MSM) operator is useful for modeling attribute interrelationships and aggregating 2TL T -SF information to tackle the MAGDM problems. A few recent MSM and dual MSM operators are being built to evaluate the 2TL T -SF information. Thus, 2-tuple linguistic T -spherical fuzzy Maclaurin symmetric mean (2TL T -SFMSM) operator, 2-tuple linguistic T -spherical fuzzy weighted Maclaurin symmetric mean (2TL T -SFWMSM) operator, 2-tuple linguistic T -spherical fuzzy dual Maclaurin symmetric mean (2TL T -SFDMSM) operator, and 2-tuple linguistic T -spherical fuzzy weighted dual Maclaurin symmetric mean (2TL T -SFWDMSM) operator are proposed. (3) We incorporate the 2TL T -SFNs into the EDAS approach and develop a new 2TL T -SF-EDAS method for solving the MAGDM problems based on the proposed aggregation operators in a 2TL T -SF environment. A case study for the selection of an optimal vaccine to control the outbreak of the COVID-19 epidemic is also presented to show the validity of the proposed methodology. Furthermore, the comparative analysis with existing approaches shows the advantages and superiority of the proposed framework. [ FROM AUTHOR] Copyright of Mathematical Problems in Engineering is the property of Hindawi Limited 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.)

Dexamethasone : a corticosteroid drug for the treatment of coronavirus disease 2019

The corticosteroid drug “dexamethasone” has been in use since 1960s for reducing inflammation in a variety of conditions such as certain cancers and other inflammatory disorders. It is an affordable agent and currently off-patent in most countries and listed in multiple formulations since 1977 in the World Health Organization model list of essential medicines. The cytokines production and damaging effect has been limited through the use of dexamethasone and this will also block B cells from antibodies production and inhibit the T cell's protective function potential leading to elevated viral load in the plasma that persists for longer time after a patient survives SARS. In addition, dexamethasone would chunk the macrophages from clearing the resultant nosocomial infections. Thus, dexamethasone may be valuable for the immediate relief in severe cases of COVID-19, but could be dangerous on the long run as the body will be barred from producing protective antibodies in addition to the persistence of the virus.

Pathogenesis and mutagenesis of SARS-CoV-2 : cellular attachment, entry, and infection

Coronavirus disease 2019 (COVID-19) is a highly infectious disease characterized by higher leukocyte numbers, acute respiratory distress, and elevated levels of plasma proinflammatory cytokines. Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), the causative agent of COVID-19, begins its pathogenesis by the binding of the virus to the host's angiotensin-converting enzyme 2 (ACE-2) receptor and then replication. The various replicated viruses then reinfect other cells and organs with ACE-2 receptor and further wreak havoc and could later result in multisystem organ failure. Presently, efforts are on the way to develop vaccines and drugs for this virus. But the current spike in COVID-19 cases linked to mutation in the virus genome and those of its enzymes is a cause of concern. Studies conducted by some authors have identified 6 major clads (basal, D614G, L84S, L3606F, D448del, and G392D), out of which D614G (a G-to-A base change at position 23403 in the Wuhan reference strain) was found to be the most reoccurring clad. This chapter examines all of these.

Models for MAGDM with dual hesitant q-rung orthopair fuzzy 2-tuple linguistic MSM operators and their application to COVID-19 pandemic.

In this article, we introduce dual hesitant q -rung orthopair fuzzy 2-tuple linguistic set (DHq-ROFTLS), a new strategy for dealing with uncertainty that incorporates a 2-tuple linguistic term into dual hesitant q -rung orthopair fuzzy set (DHq-ROFS). DHq-ROFTLS is a better way to deal with uncertain and imprecise information in the decision-making environment. We elaborate the operational rules, based on which, the DHq-ROFTL weighted averaging (DHq-ROFTLWA) operator and the DHq-ROFTL weighted geometric (DHq-ROFTLWG) operator are presented to fuse the DHq-ROFTL numbers (DHq-ROFTLNs). As Maclaurin symmetric mean (MSM) aggregation operator is a useful tool to model the interrelationship between multi-input arguments, we generalize the traditional MSM to aggregate DHq-ROFTL information. Firstly, the DHq-ROFTL Maclaurin symmetric mean (DHq-ROFTLMSM) and the DHq-ROFTL weighted Maclaurin symmetric mean (DHq-ROFTLWMSM) operators are proposed along with some of their desirable properties and some special cases. Further, the DHq-ROFTL dual Maclaurin symmetric mean (DHq-ROFTLDMSM) and weighted dual Maclaurin symmetric mean (DHq-ROFTLWDMSM) operators with some properties and cases are presented. Moreover, the assessment and prioritizing of the most important aspects in multiple attribute group decision-making (MAGDM) problems is analysed by an extended novel approach based on the proposed aggregation operators under DHq-ROFTL framework. At long last, a numerical model is provided for the selection of adequate medication to control COVID-19 outbreaks to demonstrate the use of the generated technique and exhibit its adequacy. Finally, to analyse the advantages of the proposed method, a comparison analysis is conducted and the superiorities are illustrated.

A scalable approach to topographically mediated antimicrobial surfaces based on diamond.

Bio-inspired Topographically Mediated Surfaces (TMSs) based on high aspect ratio nanostructures have recently been attracting significant attention due to their pronounced antimicrobial properties by mechanically disrupting cellular processes. However, scalability of such surfaces is often greatly limited, as most of them rely on micro/nanoscale fabrication techniques. In this report, a cost-effective, scalable, and versatile approach of utilizing diamond nanotechnology for producing TMSs, and using them for limiting the spread of emerging infectious diseases, is introduced. Specifically, diamond-based nanostructured coatings are synthesized in a single-step fabrication process with a densely packed, needle- or spike-like morphology. The antimicrobial proprieties of the diamond nanospike surface are qualitatively and quantitatively analyzed and compared to other surfaces including copper, silicon, and even other diamond surfaces without the nanostructuring. This surface is found to have superior biocidal activity, which is confirmed via scanning electron microscopy images showing definite and widespread destruction of E. coli cells on the diamond nanospike surface. Consistent antimicrobial behavior is also observed on a sample prepared seven years prior to testing date.

A quadruple blinded placebo controlled randomised trial to evaluate the effectiveness of an Iodine complex for patients with mild to moderate COVID-19 in Pakistan (I-COVID-PK): A structured summary of a study protocol for a randomised controlled trial.

OBJECTIVES: The objective of the study is to measure the efficacy of ionic-iodine polymer complex [1] for clinical and radiological improvement in coronavirus disease 2019 (COVID-19) patients. TRIAL DESIGN: The trial will be closed label, randomized and placebo-controlled with a 1:1:1:1 allocation ratio and superiority framework. PARTICIPANTS: All PCR confirmed COVID-19 adult patients including non-pregnant females, with mild to moderate disease, will be enrolled from Shaikh Zayed Post-Graduate Medical Complex, Ali Clinic and Doctors Lounge in Lahore (Pakistan). Patients with any pre-existing chronic illness will be excluded from the study. INTERVENTION AND COMPARATOR: In this multi-armed study ionic-iodine polymer complex with 200 mg of elemental iodine will be given using three formulations to evaluate efficacy. Patients will be receiving either encapsulated iodine complex of 200 mg (arm A), iodine complex syrup form 40 ml (arm B), iodine complex throat spray of 2 puffs (arm C) or empty capsule (arm D) as placebo; all three times a day. All the 4 arms will be receiving standard care as per version 3.0 of the clinical management guidelines for COVID-19 established by the Ministry of National Health Services of Pakistan. MAIN OUTCOMES: Primary outcomes will be viral clearance with radiological and clinical improvement. SARS-CoV-2 RT-PCR and HRCT chest scans will be done on the admission day and then after every fourth day for 12 days or till the symptoms are resolved. RT-PCR will only be shown as positive or negative while HRCT chest scoring will be done depending on the area and severity of lung involvement [2]. Time taken for the alleviation of symptoms will be calculated by the number of days the patient remained symptomatic. 30-day mortality will be considered as a secondary outcome. RANDOMISATION: Stratification for initial COVID-19 status (or days from initial symptoms as a proxy), age groups, gender, baseline severity of symptoms and co-morbidities will be used to ensure that the study arms remain balanced in size for the 1:1:1:1 allocation ratio. Randomization will be done using the lottery method. As patients are being admitted at different times, they will be recruited after obtaining their voluntary written informed consent following all standard protocols of the infection, control and disinfection. BLINDING (MASKING): This is a quadruple (participants, care providers, investigators and outcomes assessors) blinded study where only the study's Primary Investigator will have information about the arms and their interventions. NUMBERS TO BE RANDOMISED (SAMPLE SIZE): 200 patients will be randomized into four groups with three experimental and one placebo arm. TRIAL STATUS: Protocol Version Number is 2.3 and it is approved from IRB Shaikh Zayed Hospital with ID SZMC/IRB/Internal0056/2020 on July 14th, 2020. The recruitment is in progress. It was started on July 30, 2020, and the estimated end date for the trial is August 15, 2021. TRIAL REGISTRATION: Clinical Trial has been retrospectively registered on www.clinicaltrials.gov with registration ID NCT04473261 dated July 16, 2020. FULL PROTOCOL: The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). With the intention of expediting dissemination of this trial, the conventional formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol. The study protocol has been reported in accordance with the Standard Protocol Items: Recommendations for Clinical Interventional Trials (SPIRIT) guidelines.