Rebalancing UK regional and industrial policy post-Brexit and post-Covid-19: lessons learned and priorities for the future

In this paper, we examine the challenges and opportunities facing the UK's industrial and regional policy in the context of the policy decisions made over recent decades. We argue that the overly centralized and sectoral logic of the UK governance systems has led to a lack of clarity in thinking through place-based issues. This, in turn, has resulted in policy ambiguity, confusion and contradictions, and successfully moving industrial policy and regional policy forward post-Brexit can only take place if conceptual and operational clarity is brought to these matters.

Application of Ultrasonic Sensor and IR Sensor in Automatic Alcohol Hand Sanitizer

Hygiene is necessary to maintain human health. Hygiene keeps the body clean and free from germs, preventing the spread of diseases, which has been especially important during the COVID-19 pandemic. For this reason, we designed automatic alcohol hand sanitizers (AAHSs): one with an IR sensor and one with an ultrasonic sensor. The sanitizers will help prevent germs from spreading via the hands of people because no part of each device need be touched during its use. The AAHS with the ultrasonic sensor has various advantages over that with the IR sensor: it is 32% cheaper to produce, easier to configure and maintain, has a higher average score for user satisfaction, is smaller and more portable, and can use rechargeable batteries. In addition, its low cost makes it more suitable for commercialization. It can also be installed both outdoors and indoors. In an outdoor test, it was demonstrated to operate flawlessly. This paper includes useful information on the components of the AAHSs with the two types of sensor and an evaluation of their performance using confusion matrices. © 2023 M Y U Scientific Publishing Division. All rights reserved.

Psychological Impact of E-Learning among Students: A Survey

Aim: This study is conducted to know the psychological impact of e learning among the students. Background(s): From the time of very first beginning of civilization to modern days before corona pandemic situation, most of the students of India are very much used to with the offline mode of learning. But now the situation is changed totally. They are getting themselves adapted to the online mode of learning as per need of time. In this changed scenario they are totally disconnected from their usual life with frames schools teachers and society. This situation wreaks havoc to their psychology. Methodology: This study is conducted with primary data in form of online survey. It was conducted with a pre formed questionnaire. 428 responses were collected for the present study. With advanced Excel software statistical analysis done. Outcome(s): Results show that students have shown negative impression on online learning and still they are not ready totally psychologically. Still positive answers show neck to neck result, which signifies increasing interest towards e learning. More practices and awareness required before further implementation.Copyright © 2020 Ubiquity Press. All rights reserved.

Prognostic significance of chest CT severity score in mortality prediction of COVID-19 patients, a machine learning study

Background: The high mortality rate of COVID-19 makes it necessary to seek early identification of high-risk patients with poor prognoses. Although the association between CT-SS and mortality of COVID-19 patients was reported, its prognosis significance in combination with other prognostic parameters was not evaluated yet. Method(s): This retrospective single-center study reviewed a total of 6854 suspected patients referred to Imam Khomeini hospital, Ilam city, west of Iran, from February 9, 2020 to December 20, 2020. The prognostic performances of k-Nearest Neighbors (kNN), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and J48 decision tree algorithms were evaluated based on the most important and relevant predictors. The metrics derived from the confusion matrix were used to determine the performance of the ML models. Result(s): After applying exclusion criteria, 815 hospitalized cases were entered into the study. Of these, 447(54.85%) were male and the mean (+/- SD) age of participants was 57.22(+/- 16.76) years. The results showed that the performances of the ML algorithms were improved when they are fed by the dataset with CT-SS data. The kNN model with an accuracy of 94.1%, sensitivity of 100. 0%, precision of 89.5%, specificity of 88.3%, and AUC around 97.2% had the best performance among the other three ML techniques. Conclusion(s): The integration of CT-SS data with demographics, risk factors, clinical manifestations, and laboratory parameters improved the prognostic performances of the ML algorithms. An ML model with a comprehensive collection of predictors could identify high-risk patients more efficiently and lead to the optimal use of hospital resources.Copyright © 2023, The Author(s).

Superior Cerebellar Artery Dissection in a Patient Diagnosed with Reversible Cerebral Vasoconstriction Syndrome: A Case Report

Arterial dissection is an uncommon complication of reversible cerebral vasocon-striction syndrome (RCVS). We describe the case of a 35-year-old woman with a migraine history who presented with recurrent thunderclap headache and focal neurological signs, including right hemiataxia. She had been diagnosed with COVID-19 disease two weeks earlier. Neuroimaging revealed multifocal stenosis of the posterior circulation arteries and dissection of the right superior cerebellar artery. She improved significantly throughout her one-week hospitalization and maintained only mild ataxia. The interplay between COVID-19 disease, RCVS, and arterial dissection requires further investigation.Copyright © Author(s) (or their employer(s)) and Sinapse 2022.

Glial fibrillary acidic protein astrocytopathy in a patient with recent mRNA SARS-CoV-2 vaccination: COVERSHEET

Background: Literature describing triggers of GFAP astrocytopathy (GFAP-A) is limited. We report a case of GFAP-A in a patient with recent messenger ribonucleic acid (mRNA) severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) vaccination and discuss the possible pathogenesis. Case description: A 45-year-old gentleman presented with features of meningoencephalitis 31 days after the first dose and 4 days after the second dose of mRNA SARS-CoV-2 vaccination. He sequentially developed brainstem/cerebellar, autonomic and cord dysfunction. Cerebrospinal fluid was positive for GFAP autoantibody. Clinical improvement occurred after intravenous methylprednisolone and immunoglobulins. Conclusion(s): Although we are uncertain of a causal link of GFAP-A to mRNA vaccine, indirect activation of an underlying dysregulated immune milieu is plausible.Copyright © 2021 The Author(s)

La povertà energetica come privazione delle capacità

La pandemia da COVID-19 e l'attuale crisi energetica hanno portato all'attenzione dei policy maker la necessità di affrontare la povertà energetica. Per contrastare efficacemente la sua crescente diffusione, è fondamentale conoscere la sua entità e i suoi determinanti. La piena comprensione è stata però ostacolata dalla confusione terminologica tra fuel poverty ed energy poverty, nonché dal ricorso a indici unidimensionali. Dopo aver descritto i limiti degli approcci tradizionali, l'articolo sostiene come il capability approach di Sen rappresenti il quadro normativo più adeguato a valorizzare la natura multidimensionale della povertà energetica dal punto di vista teorico ed empirico.Alternate :The COVID-19 pandemic and the current energy crisis have brought to the attention of policymakers the need to tackle energy poverty. To effectively counter its growing diffusion, it is essential to know its extent and its determinants. However, the full understanding was hindered by the terminological confusion between fuel poverty and energy poverty, as well as by the use of unidimensional indexes. After describing the limits of traditional approaches, the paper argues that Sen's capability approach represents the most appropriate normative framework to enhance the multidimensional nature of energy poverty.

The Relationship between Wearing Protective Masks and Poor Oral Health

Since the beginning of the COVID-19 pandemic, the number of people wearing masks in everyday life has increased. At the same time, there has been a noticeable rise in the amount of patients with bad breath (foe-tor ex ore), gingivitis, caries, and xerostomia. The appearance of these symptoms and diseases caused by wearing a mask is designated by the term mask mouth. The aim of this article is to establish the link between wearing protective masks and deteriorating oral health. From the conducted research, it has been es-tablished that wearing a surgical mask over a long period of time leads to reduced air exchange in the mask and "recycling" of exhaled air. This leads to inhalation of air with increased CO2 content and increase in pCO2 in the blood, which is subsequently compensated by rapid and deep breathing in most cases through the mouth. The goal is to exhale the accumulated CO2. As the mask reduces air exchange, the level of CO2 in the mask remains relatively high. Prolonged breathing through the mouth often leads to xerostomia. Saliva is known to have protective functions against the development of bacteria in the oral cavity through its an-tibacterial properties. Xerostomia can be a prerequisite for the development of various diseases of bacterial origin, such as gingivitis. Furthermore, oral respiration leads to an increase in temperature and CO2 in the air in the mask and a decrease in pH in the oral cavity, which are optimal conditions for biofilm formation, plaque buildup, development of most bacteria, e.g., S. mutans, which is the main cause of caries.Copyright © 2022, Bulgarian-American Center. All rights reserved.

Analysis of Covid-19 CT chest image classification using Dl4jMlp classifier and Multilayer Perceptron in WEKA Environment.

INTRODUCTION: In recent years, various deep learning algorithms have exhibited remarkable performance in various data-rich applications, like health care, medical imaging, as well as in computer vision. Covid-19, which is a rapidly spreading virus, has affected people of all ages both socially and economically. Early detection of this virus is therefore important in order to prevent its further spread. METHOD: Covid-19 crisis has also galvanized researchers to adopt various machine learning as well as deep learning techniques in order to combat the pandemic. Lung images can be used in the diagnosis of Covid-19. RESULT: In this paper, we have analysed the Covid-19 chest CT image classification efficiency using multilayer perceptron with different imaging filters, like edge histogram filter, colour histogram equalization filter, color-layout filter, and Garbo filter in the WEKA environment. CONCLUSION: The performance of CT image classification has also been compared comprehensively with the deep learning classifier Dl4jMlp. It was observed that the multilayer perceptron with edge histogram filter outperformed other classifiers compared in this paper with 89.6% of correctly classified instances.

Acute Diarrhea in Adults

Acute diarrheal disease accounts for 179 million outpatient visits annually in the United States. Diarrhea can be categorized as inflammatory or noninflammatory, and both types have infectious and noninfectious causes. Infectious noninflammatory diarrhea is often viral in etiology and is the most common presentation;however, bacterial causes are also common and may be related to travel or foodborne illness. History for patients with acute diarrhea should include onset and frequency of symptoms, stool character, a focused review of systems including fever and other symptoms, and evaluation of exposures and risk factors. The physical examination should include evaluation for signs of dehydration, sepsis, or potential surgical processes. Most episodes of acute diarrhea in countries with adequate food and water sanitation are uncomplicated and self-limited, requiring only an initial evaluation and supportive treatment. Additional diagnostic evaluation and management may be warranted when diarrhea is bloody or mucoid or when risk factors are present, including immunocompromise or recent hospitalization. Unless an outbreak is suspected, molecular studies are preferred over traditional stool cultures. In all cases, management begins with replacing water, electrolytes, and nutrients. Oral rehydration is preferred;however, signs of severe dehydration or sepsis warrant intravenous rehydration. Antidiarrheal agents can be symptomatic therapy for acute watery diarrhea and can help decrease inappropriate antibiotic use. Empiric antibiotics are rarely warranted, except in sepsis and some cases of travelers' or inflammatory diarrhea. Targeted antibiotic therapy may be appropriate following microbiologic stool assessment. Hand hygiene, personal protective equipment, and food and water safety measures are integral to preventing infectious diarrheal illnesses.Copyright © 2022 American Academy of Family Physicians.

Initial Stage Identification of COVID-19 Using Capsule Networks

Coronavirus is a pandemic that has kept us in great grief for the past few months. These days have created a devastating effect all through the world. As coronavirus has lot of similarities with other lung diseases, it becomes a challenging task for medical practitioners to identify the virus. A fast and robust system to identify the disease has been the need of the hour. In this chapter, we have used convolutional CapsNet for detecting COVID-19 disease using chest X-ray images. This design aims at obtaining fast and accurate diagnostic results. The proposed technique with less trainable parameters, COVID-CAPS, produced an accuracy of 87.5%, a sensitivity of 90%, a specificity of 95.8%, and an area under the curve (AUC) of 0.97. The main advantage of using CapsNet is that it can capture affine transformation in data that is a common scenario while dealing with real-world X-ray images. The CapsNet model is trained with normal data and tested with affine transformed data. The accuracy level obtained in the proposed method is comparatively much better along with having less learnable parameters and computational speed as compared to standard architectures such as ResNet, MobileNet, etc. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Encephalitis or Stroke? A Case Report of Thalamic Stroke in COVID Era

Introduction: Since 2019, COVID-19 pneumonia caused by SARS-CoV-2 virus has led to a worldwide pandemic. Since then, various neurological manifestations of COVID-19 pneumonia have been reported. Neurological manifestations include headache, anosmia, seizures, and altered mental status. In some cases, it presents as stroke, encephalitis, and neuropathy. Artery of Percheron (AOP) is a variant in the posterior circulation. Here, a single artery arises from the posterior cerebral artery p1 segment. It supplies bilateral thalamus with or without midbrain. Thrombosis in this artery leads to clinical symptoms like reduced level of consciousness, altered mental status, and memory impairment. Case Report: Here, we present a case who presented with fever and altered sensorium without any focal neurological deficits and without known risk factors for stroke. His COVID-19 PCR was positive. He was initially diagnosed as COVID-19 pneumonia with encephalitis and was started on treatment for the same. His initial CT brain and lumbar puncture were normal. The next day, when MRI brain with and without contrast was done, the thalamic stroke due to AOP infarction was diagnosed and appropriate treatment for stroke was initiated. Discussion(s): Many patients miss the window for thrombolysis because of variable presentation in clinical symptoms with negative imaging. It is also difficult to assess the time of onset of stroke in this varied presentation. Our patient had fever and cough for 2 days and had altered mental status since the morning of admission. During hospital stay, he developed bilateral third nerve palsy. This case also highlights the importance of detailed evaluation in COVID-19 patients with neurological complaints. This helps to avoid delays in treatment and to improve clinical outcomes. As our knowledge of COVID-19 and its varied neurological manifestations evolve, we need to be prepared for more atypical presentation to facilitate timely interventions.Copyright © 2022 The Author(s). Published by S. Karger AG, Basel.

Confusion of circumstances: observations on the decision of the Constitutional Council of March 26, 2020 on the health emergency organic law

The article aims to analyze the decision of the French Constitutional Council of March 26, 2020 on the health emergency organic law, promulgated in France to deal with the COVID-19 epidemic, in order to verify whether in this case the Council acknowledges deciding less in law than in pure opportunity. © Pontificia Universidade Catolica do Parana. All rights reserved.

Semantic Regulation of mental states of the individuals during the SARS-COV-2 Pandemic

The first ever case of Corona Virus Pneumonia was reported on 8th December 2019 in Hubei Province of Wuhan China. The virus was believed to be transferred from seafood market and subsequently the causative agent was identified as SARS-COV-2. In this study, we conducted a study aimed at identifying the nature and characteristics of the influence of the cognitive assessment of the situation associated with the SARS-COV-2 pandemic, its semantic perception on the mental states of people of working age.The main semantic education of a person during the SARS-COV-2 pandemic is situational semantic attitudes-the primary ways of responding to signs of situational uncertainty, which carry out actual management of mental states, exerting a stabilizing or conversely destabilizing effect on them. Situational semantic attitudes of the personality play an indirect role in the interaction of the situation and mental states. It is not the situation itself that becomes the source of the emergence and development of certain mental states, but those situational semantic attitudes that, as a result of conscious and unconscious thought processes, enhance or weaken the signs of uncertainty of the situation that has arisen and determines the selective control of the mental activity of the individual. When organizing psychological assistance to the population during periods of pandemics, it is necessary to take into account the results of the study, which may affect the development of targeted programs for the formation of an adequate perception of the situation that has arisen and the development of conscious mechanisms for self-regulation of mental states.Copyright © RJPT. All right reserved.

COVID and Cognitive Warfare in Taiwan

In this study, we examine China's cognitive warfare coordinated with military air operations during the COVID pandemic in Taiwan. In May 2021, Taiwan experienced its first novel coronavirus outbreak with up to 500 daily cases. The Chinese government launched a series of coordinated "cognitive warfare” campaigns targeting Taiwan in addition to the People's Liberation Army (PLA) frequent air force incursions into Taiwan's air zone. Meanwhile, through manipulation of the vaccine supply, China turned COVID vaccine into a political issue in Taiwan involving multiple players including pharmaceutical developers, tech giants, and local politicians. Combining multiple sources of data, we analyze the Chinese Government's orchestrated cognitive and information warfare (IW) efforts targeted at influencing the Taiwan public's trust in the Democratic Progressive Party (DPP) government as well as its home-developed vaccine. Identifying the patterns of influencing using cognitive and IW, we found China's ultimate goal was to instill skepticism and confusion in Taiwan's public about the President Tsai Ing-wen's health policy and more generally undermine the creditability of the DPP government.

Multiversal Methods in Observational Studies: The Case of COVID-19

In the present study, 13 covariates have been selected as potentially associated with 3 metrics of the spread of COVID-19 in 20 European countries. Robustness of the linear correlations between 10 of the 13 covariates as main regressors and the 3 COVID-19 metrics as dependent variables have been tested through a methodology for sensitivity analysis that falls under the name of "Multiverse”. Under this methodology, thousands of alternative estimates are generated by a single hypothesis of regression. The capacity of identification of a robust causal claim for the 10 variables has been measured through 3 indicators over a Janus Confusion Matrix, which is a confusion matrix that assumes the likelihood to observe a True claim as the ratio between the absolute difference of estimates with a different sign and the total of estimates. This methodology provides the opportunity to evaluate the outcomes of a shift from the common level of significance to the alternative. According to the results of the study, in the dataset the benefits of the shifts come at a very high cost in terms of false negatives. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Inferior vena cava and pulmonary artery diameters for prognosis of Coronavirus disease Pulmonary vascular diameters in Coronavirus disease

Aim: In this study, we aimed to analyze the relationship between pulmonary artery (PA) and inferior vena cava (IVC) diameters in non-contrast chest computerized tomography (CT) images of patients with coronavirus disease 2019 (COVID-19) and overall survival. Material(s) and Method(s): This retrospective study consisted of 404 consecutive patients who underwent chest CT after admission to the emergency department between May 1 and June 31, 2021. CT measurements were performed by two radiologists. The prognostic value of PA and IVC diameters, the computerized tomography severity score (CT-SS), quick sequential organ failure assessment (qSOFA), and confusion, urea, respiratory rate, blood pressure, and age >=65 years (CURB-65) score on overall survival were examined. Result(s): The median age of the participants was 62 years (49-72), and 196 (48.5%) were male. Of the 404 patients, 61 died after admission. While main-PA, left-PA, right-PA (p < 0.001) and IVC-transverse (IVC-Tr) (p = 0.045) diameters were larger and statistically significant in the patients who died (AUC;0.686, 0.722, 0.746, and 0.581, respectively), a statistically significant difference was not detected in terms of IVC anteroposterior diameter (IVC-AP) (p = 0.053) and the IVC-Tr/AP (p = 0.754) ratio. There was a statistical difference in mortality in qSOFA, CURB-65, and CT-SS values (AUC;0.727, 0.798, and 0.708 p < 0.001, respectively). Discussion(s): PA diameters measured from chest CT images at admission (main-PA >= 26.5 mm, right-PA >= 22.9 mm, and left-PA >= 21.6 mm) and the IVC-Tr diameter (>=34.5 mm) can be used as mortality predictors for COVID-19, along with other prognostic scores.Copyright © 2022, Derman Medical Publishing. All rights reserved.

Grayscale Image Statistical Attributes Effectively Distinguish the Severity of Lung Abnormalities in CT Scan Slices of COVID-19 Patients.

Grayscale statistical attributes analysed for 513 extract images taken from pulmonary computed tomography (CT) scan slices of 57 individuals (49 confirmed COVID-19 positive; eight confirmed COVID-19 negative) are able to accurately predict a visual score (VS from 0 to 4) used by a clinician to assess the severity of lung abnormalities in the patients. Some of these attributes can be used graphically to distinguish useful but overlapping distributions for the VS classes. Using machine and deep learning (ML/DL) algorithms with twelve grayscale image attributes as inputs enables the VS classes to be accurately distinguished. A convolutional neural network achieves this with better than 96% accuracy (only 18 images misclassified out of 513) on a supervised learning basis. Analysis of confusion matrices enables the VS prediction performance of ML/DL algorithms to be explored in detail. Those matrices demonstrate that the best performing ML/DL algorithms successfully distinguish between VS classes 0 and 1, which clinicians cannot readily do with the naked eye. Just five image grayscale attributes can also be used to generate an algorithmically defined scoring system (AS) that can also graphically distinguish the degree of pulmonary impacts in the dataset evaluated. The AS classification illustrated involves less overlap between its classes than the VS system and could be exploited as an automated expert system. The best-performing ML/DL models are able to predict the AS classes with better than 99% accuracy using twelve grayscale attributes as inputs. The decision tree and random forest algorithms accomplish that distinction with just one classification error in the 513 images tested.