Predicting the climate impact of aviation for en-route emissions: the algorithmic climate change function submodel ACCF 1.0 of EMAC 2.53

Using climate-optimized flight trajectories is one essential measure to reduce aviation's climate impact. Detailed knowledge of temporal and spatial climate sensitivity for aviation emissions in the atmosphere is required to realize such a climate mitigation measure. The algorithmic Climate Change Functions (aCCFs) represent the basis for such purposes. This paper presents the first version of the Algorithmic Climate Change Function submodel (ACCF 1.0) within the European Centre HAMburg general circulation model (ECHAM) and Modular Earth Submodel System (MESSy) Atmospheric Chemistry (EMAC) model framework. In the ACCF 1.0, we implement a set of aCCFs (version 1.0) to estimate the average temperature response over 20 years (ATR20) resulting from aviation CO2 emissions and non-CO2 impacts, such as NOx emissions (via ozone production and methane destruction), water vapour emissions, and contrail cirrus. While the aCCF concept has been introduced in previous research, here, we publish a consistent set of aCCF formulas in terms of fuel scenario, metric, and efficacy for the first time. In particular, this paper elaborates on contrail aCCF development, which has not been published before. ACCF 1.0 uses the simulated atmospheric conditions at the emission location as input to calculate the ATR20 per unit of fuel burned, per NOx emitted, or per flown kilometre.In this research, we perform quality checks of the ACCF 1.0 outputs in two aspects. Firstly, we compare climatological values calculated by ACCF 1.0 to previous studies. The comparison confirms that in the Northern Hemisphere between 150–300 hPa altitude (flight corridor), the vertical and latitudinal structure of NOx-induced ozone and H2O effects are well represented by the ACCF model output. The NOx-induced methane effects increase towards lower altitudes and higher latitudes, which behaves differently from the existing literature. For contrail cirrus, the climatological pattern of the ACCF model output corresponds with the literature, except that contrail-cirrus aCCF generates values at low altitudes near polar regions, which is caused by the conditions set up for contrail formation. Secondly, we evaluate the reduction of NOx-induced ozone effects through trajectory optimization, employing the tagging chemistry approach (contribution approach to tag species according to their emission categories and to inherit these tags to other species during the subsequent chemical reactions). The simulation results show that climate-optimized trajectories reduce the radiative forcing contribution from aviation NOx-induced ozone compared to cost-optimized trajectories. Finally, we couple the ACCF 1.0 to the air traffic simulation submodel AirTraf version 2.0 and demonstrate the variability of the flight trajectories when the efficacy of individual effects is considered. Based on the 1 d simulation results of a subset of European flights, the total ATR20 of the climate-optimized flights is significantly lower (roughly 50 % less) than that of the cost-optimized flights, with the most considerable contribution from contrail cirrus. The CO2 contribution observed in this study is low compared with the non-CO2 effects, which requires further diagnosis.

An Empirical Analysis of Efficiency in Accommodation Industry in Australian Tourism Regions

A body of empirical literature exists which sets out how the accommodation industry performs across a range of locations. However, research on tourism regions in terms of its accommodation industry remains underdeveloped, especially in the Covid-19 pandemic when tourism faced unprecedented adversity and need to find a way to move forward. In an attempt to address this and take the Australian accommodation industry as a case study, this paper sought to investigate the efficiency of Australian tourism regions in the accommodation industry for the period of 2014/15–2017/18. The findings clearly showed that Australian tourism regions had seen significant growth in terms of their efficiency in the accommodation industry over the surveyed period. The Australian commercial large cities, namely Sydney, Melbourne, Brisbane, and the Goal Coast, represent perhaps the best example, having obtained a higher efficiency than all other tourism regions. Exogenous factors, such as the occupancy rate, the average daily rate, the number of international visitors and the number of domestic visitors overnight were identified as influencing the technical efficiency score of tourism regions, with policy formulation and implementation identified as being key to improving the efficiency of the accommodation industry at the regional level for a post-Covid-19 period. [ FROM AUTHOR] Copyright of International Journal of Hospitality & Tourism Administration is the property of Taylor & Francis Ltd 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.)

Masked Face Recognition Model with Explainable AI

Due to the recent COVID-19 pandemic, people tend to wear masks indoors and outdoors. Therefore, systems with face recognition, such as FaceID, showed a tendency of decline in accuracy. Consequently, many studies and research were held to improve the accuracy of the recognition system between masked faces. Most of them targeted to enhance dataset and restrained the models to get reasonable accuracies. However, not much research was held to explain the reasons for the enhancement of the accuracy. Therefore, we focused on finding an explainable reason for the improvement of the model's accuracy. First, we could see that the accuracy has actually increased after training with a masked dataset by 12.86%. Then we applied Explainable AI (XAI) to see whether the model has really focused on the regions of interest. Our approach showed through the generated heatmaps that difference in the data of the training models make difference in range of focus. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

THE RELATIONSHIP BETWEEN THE FUNCTIONALITY OF CHILDREN WITH DISABILITIES AND ITS EFFECTS ON PARENTS DURING THE COVID-19 PANDEMIC PROCESS.

Purpose: The aim of this study was to investigate the relationship between the functionality of disabled children and its effects on parents during the Covid-19 pandemic. Method(s): A total of 168 people, including 84 disabled children and 84 mothers, were included in the study. The Pediatric Disability Assessment Inventory (PEDI) and Gross Motor Function Classification System (GMFCS) were used for children with disabilities. The Zarit Burden Scale (ZBS), Fatigue Severity Scale (FSS) and The Nordic Musculoskeletal Questionnaire (NMQ) were applied to the mothers to question musculoskeletal disorders. Result(s): There was no correlation between care burden score and PEDI, total score, self-care and mobility scores (p>0.05). A moderately negative (r=-0.306;p<0.01) significant linear relationship was found between care burden score and social function score. There was no significant linear relationship between the fatigue severity score and PEDI total score, self-care, mobility and social function scores (p>0.05). No correlation was found between care burden score and fatigue severity score (p>0.05). For the last 12 months, only the pain in the lumbar region of the parents prevented them from doing their usual work. It was determined that the most aching body parts of the parents who complained of musculoskeletal pain during the last 12 months were in the waist, neck, shoulder, back, and knee regions. Conclusion(s): As a result, no relationship was found between the functionality of disabled children and their parents' influences during the Covid-19 pandemic.Copyright © 2022 Turkish Physiotherapy Association. All rights reserved.

Covid-19 disruption, resilience and industrial policy: the automotive sector in the West Midlands

The Covid-19 pandemic and Brexit have focused attention on the resilience of key sectors and firms. This paper explores the financial resilience of the 50 largest automotive firms in the West Midlands region of the UK in their response to disruption and economic shocks. The findings demonstrate that 22 firms are at high risk due to poor current liquidity ratios, with Coventry and Birmingham emerging as locations most susceptible to firm closures. High-risk firms include key flagship original equipment manufacturers operating at the downstream end of supply chains. If these firms were to fail, there would be a significant destructive impact on both the industry and the local economy. We assert an effective subnational industrial policy is required in order to support economic resilience in regions such as the West Midlands where a few firms account for a disproportionate share of employment and value-added.

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.

Improving Healthcare Facilities in Remote Areas Using Cutting-Edge Technologies

Healthcare is a critical field of research and equally important for all nations. Providing secure healthcare facilities to citizens is the primary concern of each nation. However, people living in remote areas do not get timely and sufficient healthcare facilities, even in developed countries. During the recent COVID-19 pandemic, many fatalities occurred due to the inaccessibility of healthcare facilities on time. Therefore, there is a need to propose a solution that may help citizens living in remote areas with proper and secure healthcare facilities without moving to other places. The revolution in ICT technologies, especially IoT, 5G, and cloud computing, has made access to healthcare facilities easy and approachable. There is a need to benefit from these technologies so that everyone can get secure healthcare facilities from anywhere. This research proposes a framework that will ensure 24/7 accessibility of healthcare facilities by anyone from anywhere, especially in rural areas with fewer healthcare facilities. In the proposed approach, the patients will receive doorstep treatment from the remote doctor in rural areas or the nearby local clinic. Healthcare resources (doctor, treatment, patient counseling, diagnosis, etc.) will be shared remotely with people far from these facilities. The proposed approach is tested using mathematical modeling and a case study, and the findings confirm that the proposed approach helps improve healthcare facilities for remote patients.

From the Editors

Since 2022, the journal Izvestiya Rossiiskoi Akademii Nauk. Seriya Geograficheskaya is publishing special issues on important topics in geography. The comprehensive special section offered to readers' attention is another innovation of the journal. This collection has been prepared for the centennial of the International Geographical Union and its congress in Paris (July 2022). It includes seven review articles. Their English translations are simultaneously published in the journal Regional Research of Russia, no. 1 for 2022. These circumstances determine articles' genre and fundamental features. The topics of the articles were chosen so that the special section reflects areas in which the most striking results have been achieved and which are specific to Russia. The authors are leading Russian experts in their respective fields. They have tried, as far as possible, to compare the topics, methodological approaches, and research results with the world mainstream. The main focus is on the last decade, 2010–2021;when necessary, the authors also have referred to earlier publications. The authors of most of the articles pay attention to the peculiarities of spatial development that arose in 2020–2021 due to the COVID-19 pandemic. © 2022 Izvestiya Rossiiskoi Akademii Nauk. Seriya Geograficheskaya. All rights reserved.

Technological Devices and Digital Competences: A Look into the Digital Divides for University Continuity during the COVID-19 Pandemic

Virtual education has gained great relevance in recent years, due to the pandemic. The access to electronic devices and services represents an urgent necessity and thus the concern for acquiring digital competences, which allow a proper interaction within the teaching–learning process. Recent studies have demonstrated the importance of having digital resources and the adaptability of their use from the university students' homes during the pandemic crisis. This research intends to identify the relevant challenges regarding the accessibility to technological devices and digital competences that university students had to face to obtain suitable learning during the lockdown, due to the pandemic. The sample information consisted of 9326 Peruvian university students. The data was obtained from the National Homes Survey from the Statistics and Information National Institute, and it was distributed in twenty-five regions (in groups of five macro-regions) over a period of three years (2019–2021). The results showed significant differences in the number of students with internet access from home: between 40% and 60% access classes with a desktop or laptop, and digital competences have improved in the last year. This is evidence that digital divides set limits on the opportunities for a quality education.

Decision region analysis to deconstruct the subgroup influence on AI/ML predictions

Assessing the generalizability of deep learning algorithms based on the size and diversity of the training data is not trivial. This study uses the mapping of samples in the image data space to the decision regions in the prediction space to understand how different subgroups in the data impact the neural network learning process and affect model generalizability. Using vicinal distribution-based linear interpolation, a plane of the decision region space spanned by the random 'triplet' of three images can be constructed. Analyzing these decision regions for many random triplets can provide insight into the relationships between distinct subgroups. In this study, a contrastive self-supervised approach is used to develop a 'base' classification model trained on a large chest x-ray (CXR) dataset. The base model is fine-tuned on COVID-19 CXR data to predict image acquisition technology (computed radiography (CR) or digital radiography (DX) and patient sex (male (M) or female (F)). Decision region analysis shows that the model's image acquisition technology decision space is dominated by CR, regardless of the acquisition technology for the base images. Similarly, the Female class dominates the decision space. This study shows that decision region analysis has the potential to provide insights into subgroup diversity, sources of imbalances in the data, and model generalizability. © 2023 SPIE.

Dominant Modes of Agricultural Production Helped Structure Initial COVID-19 Spread in the US Midwest

The spread of COVID-19 is geographically uneven in agricultural regions. Explanations proposed include differences in occupational risks, access to healthcare, racial inequalities, and approaches to public health. Here, we additionally explore the impacts of coexisting modes of agricultural production across counties from twelve midwestern U.S. states. In modeling COVID-19 spread before vaccine authorization, we employed and extended spatial statistical methods that make different assumptions about the natures and scales of underlying sociospatial processes. In the process, we also develop a novel approach to visualizing the results of geographically weighted regressions that allows us to identify distinctive regional regimes of epidemiological processes. Our approaches allowed for models using spatial weights (e.g., inverse-squared distances) to be meaningfully improved by also integrating process-specific relations (e.g., the geographical relations of the food system or of commuting). We thus contribute in several ways to methods in health geography and epidemiology for identifying contextually sensitive public engagements in socio-eco-epidemiological issues. Our results further show that agricultural modes of production are associated with the spread of COVID-19, with counties more engaged in modes of regenerative agricultural production having lower COVID-19 rates than those dominated by modes of conventional agricultural production, even when accounting for other factors.

Quantification of oil and gas methane emissions in the Delaware and Marcellus basins using a network of continuous tower-based measurements

According to the United States Environmental Protection Agency (US EPA), emissions from oil and gas infrastructure contribute 30 % of all anthropogenic methane (CH4) emissions in the US. Studies in the last decade have shown emissions from this sector to be substantially larger than bottom-up assessments, including the EPA inventory, highlighting both the increased importance of methane emissions from the oil and gas sector in terms of their overall climatological impact and the need for independent monitoring of these emissions. In this study we present continuous monitoring of regional methane emissions from two oil and gas basins using tower-based observing networks. Continuous methane measurements were taken at four tower sites in the northeastern Marcellus basin from May 2015 through December 2016 and five tower sites in the Delaware basin in the western Permian from March 2020 through April 2022. These measurements, an atmospheric transport model, and prior emission fields are combined using an atmospheric inversion to estimate monthly methane emissions in the two regions. This study finds the mean overall emission rate from the Delaware basin during the measurement period to be 146–210 Mg CH4 h-1 (energy-normalized loss rate of 1.1 %–1.5 %, gas-normalized rate of 2.5 %–3.5 %). Strong temporal variability in the emissions was present, with the lowest emission rates occurring during the onset of the COVID-19 pandemic. Additionally, a synthetic model–data experiment performed using the Delaware tower network shows that the presence of intermittent sources is not a significant source of uncertainty in monthly quantification of the mean emission rate. In the Marcellus, this study finds the overall mean emission rate to be 19–28 Mg CH4 h-1 (gas-normalized loss rate of 0.30 %–0.45 %), with relative consistency in the emission rate over time. These totals align with aircraft top-down estimates from the same time periods. In both basins, the tower network was able to constrain monthly flux estimates within ±20 % uncertainty in the Delaware and ±24 % uncertainty in the Marcellus. The results from this study demonstrate the ability to monitor emissions continuously and detect changes in the emissions field, even in a basin with relatively low emissions and complex background conditions.

Shocks, resilience and regional industry policy: Brexit and the automotive sector in two Midlands regions

This article draws upon novel survey evidence to examine the possible regional impacts of Brexit as a ‘disruptive process' to manufacturing operations and logistics in the automotive industry, in the context of the regional resilience literature. The current Brexit (and Covid-19) context, along with the sector's need to re-orientate towards electrification, provides renewed urgency to reconsider industrial policy in spatial terms. The findings have salience not only in the context of anticipating and reacting to Brexit-induced economic shocks at a regional level, but also over the role of decentralized regional bodies. In this regard, the UK government's agenda of ‘levelling up' will be challenging, especially in the context of the place-based shocks likely to arise from Brexit as well as the impact of Covid-19. The article concludes that a more place-based regional industrial policy is required both to anticipate and to respond to shocks and also to reposition the sector in the region going forward.

An observer comparison study to evaluate a machine learning model to quantify the infected pneumonia on lung CT images

Quantification of infected lung volume using computed tomography (CT) images can play a critical role in predicting the severity of pulmonary infectious disease. Manual segmentation of infected areas from several CT image slices, however, is not efficient and viable in clinical practice. To assist clinicians in overcoming this challenge, we developed a new method to automatically segment and quantify the percentage of the infected lung volume. First, we used a public dataset of 20 COVID-19 patients, which consists of manually annotated lung and infection masks, to train a new joint deep learning (DL) model for lung and infection segmentation. As for lung segmentation, a Mask-RCNN model was applied to the lung volume with a novel postprocessing technique. Following that, an ensemble model with a customized residual attention UNet model and feature pyramid network (FPN) models was employed for infection segmentation. Next, we assembled another set of 80 CT scans of Covid-19 patients. Two chest radiologists manually evaluated each CT scan and reported the infected lung volume percentage using a customized graphical user interface (GUI). The developed DL-model was also employed to process these CT images. Then, we compared the agreement between the radiologist (manual) and model-based (automated) percentages of diseased regions. Additionally, the GUI was used to let radiologists rate acceptance of the DL-model generated segmentation results. Analyzing the results demonstrate that the agreement between manual and automated segmentation is >95% in 28 testing cases. Furthermore, >53% of testing cases received the top assessment rating scores from two radiologists (between four-five- score). Thus, this study illustrates the feasibility of developing a DL-model based automated tool to effectively provide quantitative evaluation of infected lung regions to assist in improving the efficiency of radiologists in infection diagnosis. © COPYRIGHT SPIE. Downloading of the is permitted for personal use only.

Cochlear Function in Adults Exposed to Severe Acute Respiratory Syndrome Coronavirus 2.

INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a viral infection with a wide variety of symptoms, including fever, coughing, sneezing, fatigue, and a loss of taste and smell. Moreover, there are some recent studies that investigate the effects of SARS-CoV-2 on hearing and auditory performance. With this current study, we investigate the early effects of the coronavirus disease on hair cells in the cochlea. METHODS: In the current study, there were 25 subjects (17 females, 8 males) who tested positive for polymerase chain reaction on nasopharyngeal swabs. They had reported normal auditory functions and no history of otology before SARS-CoV-2. To determine auditory functions, pure-tone audiometry, otoacoustic emissions (OAE) tests, and threshold equalizing noise (TEN) tests were used. RESULTS: Although the hearing thresholds increased at higher frequencies, they were within normal limits according to four-frequency pure-tone averages. All participants had normal OAE, and there were no detected dead regions for any of the subjects. Even so, there were significant increases in hearing thresholds in TEN. CONCLUSION: There is no cochlear dysfunction discovered by OAE and TEN in SARS-CoV-2-affected individuals. Nonetheless, the increase in hearing thresholds at higher frequencies, other than the pure-tone average frequencies detected by TEN, and the decrease in the presence of detected OAE could be related to deterioration in the basal part of the cochlea.

Ecosystem degradation and the spread of Covid-19.

The linkages between the emergence of zoonotic diseases and ecosystem degradation have been widely acknowledged by the scientific community and policy makers. In this paper we investigate the relationship between human overexploitation of natural resources, represented by the Human Appropriation of Net Primary Production Index (HANPP) and the spread of Covid-19 cases during the first pandemic wave in 730 regions of 63 countries worldwide. Using a Bayesian estimation technique, we highlight the significant role of HANPP as a driver of Covid-19 diffusion, besides confirming the well-known impact of population size and the effects of other socio-economic variables. We believe that these findings could be relevant for policy makers in their effort towards a more sustainable intensive agriculture and responsible urbanisation.

EXITitis in the UK: Gravity Estimates in the Aftermath of Brexit.

The withdrawal of the United Kingdom from the European Union has had disruptive effects on international trade. As part of its 'Global Britain' strategy in the wake of Brexit, the UK is pursuing a series of Free Trade Agreements with countries around the world, including Canada, Japan, Korea, Mexico, Norway, Switzerland, Turkey and possibly the United States. Closer to home, the UK is under mounting pressure to dissuade Scotland, Northern Ireland and Wales from seeking independence to regain the severed ties with the EU. We analyze the economic consequences of these scenarios with a state-of-the-art structural gravity model for major economies around the world. We find that 'Global Britain' yields insufficient trade creation to compensate for Brexit-induced trade losses. Our results also reveal that secession from the UK in itself would inflict greater post-Brexit economic harm on the devolved nations of Great Britain. Nevertheless, these effects could be offset when secession from the UK is combined with regained EU membership.

Socio-economic Dimension of the Sustainable Development of Polish Provinces

The aim of this article was to conduct research and present the results of a ratio analysis assessing the implementation of the concept of sustainable development in Polish provinces. It suggests a set of indicators tracking two of the areas of sustainable development: economic and social, in two separate periods: before the 2019 pandemic and during the 2020 pandemic. In addition to determining the performance of Lubusz Province compared to other provinces, the authors' assumption is also to identify the impact of the COVID-19 pandemic on the implementation of economic and social sustainability goals in Poland as a whole. The key issues concerning the concept of sustainable development and the state of research on sustainability indicators at a regional level are presented with the help of literature review. On the basis ratio analysis was rated economic development of 16 Polish provinces was assessed and then benchmarked to determine the position of Lubusz Province. The proposed tracking indicators included the following aspects: economic potential, innovation of the economy, sustainable production patterns, demographic changes, job market, and social integration. The obtained results answered the question concerning the socio-economic development of Polish provinces, in particular that of Lubusz Province, in the context of sustainability before and during the pandemic.

The effect of the healthcare system's structure on COVID-19 dynamics in Ecuador

To manage the COVID-19 pandemic, governments established certain stringency measures, such as lockdowns and traffic light systems. However, the response to the pandemic depended on the regions' preexisting capacities and underlying conditions. This study aims to determine the relationship between the COVID-19 pandemic dynamics and the underlying regional healthcare system's structure in Ecuador, using three dependent variables-the number of COVID-19 cases, the COVID-19 prevalence rate at the cantonal level, and the daily deaths resulting from COVID-19 at the provincial level-for three phases: isolation, social distancing, and contingency. Using daily COVID-19 data and cantonal (provincial) level variables, a negative binomial model and a tobit model were estimated to analyze the determinants of the number of COVID-19 cases and the prevalence rate, respectively. The results show a positive relationship between primary and secondary health centers and the number of infected people, implying that it is possible to make more diagnoses when the health infrastructure is more developed. As for COVID-19 deaths, primary health centers are associated with a low number of daily deaths whereas secondary health centers are associated with a higher number of daily deaths, implying the transfer of severe COVID-19 cases to cantons with secondary health centers. More affluent cantons had more COVID-19 cases and deaths. Stringency measures, such as the traffic light system, were effective in managing the pandemic. The geographical proximity between cantons and the nature of economic activities (essential versus non-essential) also impacted the spread of the virus.

The geography of breakthrough invention in the United States over the 20th century

The geography of breakthrough invention in the U.S. – defined as the spatial distribution of the production of patents that are both novel and impactful – underwent three broad changes during the 20th century. At the start of the century, breakthrough invention was concentrated in populous metropolitan areas with high levels of local knowledge variety. By the 1930s, breakthroughs were created less frequently across the entire country and so their invention had a less distinct geography. The substantial creation of breakthroughs resumed in the 1960s, and while their invention was once again concentrated in major metropolitan areas with high knowledge variety, they frequently involved long-distance collaboration. In this article, I document these changes and propose a theory to interpret why they occurred. The theory emphasizes how changes in inventors' institutional and communication technology environments influence the geographical locations that are advantageous for breakthrough invention. In support of the model, I find that the disruptiveness of the regime of technological change, the knowledge intensity of breakthroughs, the distance-based frictions incurred by collaboration technologies, and the distance-based frictions incurred by knowledge-sourcing technologies help to predict the spatial distribution of breakthrough invention. To conclude the article, I discuss lessons that the 20th century's geography of breakthrough innovation provide for anticipating the geography of innovation in the 21st century, including in the years beyond COVID-19.