Out of 575 survey participants in the U.S. who delayed purchasing a new vehicle during COVID-19 restrictions in 2020, nearly half of the participants claimed that they would feel comfortable buying a vehicle from a dealership within ** days of the restrictions being lifted. Only ***** percent of respondents said that they would wait at least six months after restrictions have been lifted. Restrictions in the U.S. Like many countries worldwide, measures to slow down and control the spread of COVID-19 on a national scale were implemented across several U.S. states. Such measures included the temporary closure of schools, bars, restaurants, and movie theaters, along with the cancellation or postponement of several large public events. While online activity in the U.S. has steadily increased during the pandemic, e-tailers in the automotive industry are predicting a decrease in sales: projected auto sales growth for 2020 in the U.S. are anticipated to be **** percent below the level *** year earlier. Post-lockdown behavior Respondents in this survey were also asked whether they would feel comfortable performing other activities after COVID-19 restrictions were lifted. A total of ** percent of respondents stated that they were comfortable buying a vehicle from a dealership within a month of restrictions being lifted, ** percent claimed that they would feel comfortable returning to work, ** percent would dine in at a restaurant, and only ** percent would travel via airplane.

This graph shows the impact of coronavirus (COVID-19) on the daily number of new car registrations in France during **********. During that month, daily registrations peaked at ***** new cars on ********, before plummeting to around *** daily registrations from ******** onwards. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

In 2020, following the corona virus pandemic, the new forecasts for passenger car sales in Saudi Arabia was approximately *** thousand units. The forecasts of passenger car sales for that year previous to the pandemic was about *** thousand units.

In early May of 2020, **** percent of respondents reported using car sharing at least weekly with the arrival of the COVID-19 pandemic. The number of respondents with the same opinion has since remained unchanged as per the fifth wave survey findings.

IntroductionThe development of robust predictive models for high-grade cytokine release syndrome (CRS) in CAR-T recipients remains limited by sparse clinical trial data.MethodsWe analyzed of 496 COVID-19 patients revealed that CRS plays a pivotal role in disease progression and serves as a valuable data source for understanding CRS progression. Building on this insight, we evaluated and compared the predictive performance of three machine learning models, with the ultimate goal of developing a predictive model for high-grade CRS in patients receiving CAR-T therapy.ResultsAmong evaluated algorithms (XGBoost, Random Forest, Logistic Regression), XGBoost demonstrated superior performance in high-grade CRS prediction. Feature importance analysis identified SpO2, D-dimer, diastolic blood pressure, and INR as key predictors, enabling development of a validated riskassessment algorithm. In an independent CAR-T cohort (n=45), the algorithm achieved impressive predictive performance for high-grade CRS prediction.DiscussionUsing machine learning, we identified key clinical biomarkers strongly associated with high-grade CRS. This tool efficiently predicts progression to high-grade CRS post-onset and shows significant potential for clinical deployment in CAR-T therapy.

Respondents in the U.S. revealed various incentives behind purchasing vehicles in the midst of the coronavirus pandemic in a survey conducted between ******** and **, 2020. The most common triggers included the number of cases declining, finding very attractive deals and offers, and governments starting to relax quarantine restrictions.

Targeted therapeutics for the treatment of coronavirus disease 2019 (COVID-19), especially severe cases, are currently lacking. As macrophages have unique effector functions as a first-line defense against invading pathogens, we genetically armed human macrophages with chimeric antigen receptors (CARs) to reprogram their phagocytic activity against SARS-CoV-2. After investigation of CAR constructs with different intracellular receptor domains, we found that although cytosolic domains from MERTK (CARMERTK) did not trigger antigen-specific cellular phagocytosis or killing effects, unlike those from MEGF10, FcRγ and CD3ζ did, these CARs all mediated similar SARS-CoV-2 clearance in vitro. Notably, we showed that CARMERTK macrophages reduced the virion load without upregulation of proinflammatory cytokine expression. These results suggest that CARMERTK drives an ‘immunologically silent’ scavenger effect in macrophages and pave the way for further investigation of CARs for the treatment of individuals with COVID-19, particularly those with severe cases at a high risk of hyperinflammation.

As of January 2021, around 40 percent of respondents were not influenced by the Covid-19 pandemic in their attitude toward self-driving vehicles. Some 20 percent, however, said that the pandemic made them see self-driving vehicles as an alternative to public transportation or a ride-hailing service. Between 18 and 19 percent of respondents stated they were less willing to use a self-driving vehicle then than before the pandemic.

The global economy is seeing significant differences in commercial vehicle activity due to the COVID-19 pandemic. The COVID-19 Mobility Impact Dataset offers insight into changes in commercial vehicle mobility and plotting its course toward recovery. Discover trends that illustrate recovery to pre-pandemic norms by industry and region. Further dive into the impact that has been felt in commercial vehicle activity surrounding airports, seaports, fuel stations, and international borders (including US/Canada and US/Mexico). These mobility changes have had an impact on the flow and transport of goods and services within cities -- peruse datasets that look at city-wide congestion changes and how they are evolving with time. For private and public sector organizations, this dataset supports critical evidence-based decision-making to inform everything from public policy, benchmarking, process optimization, and more. The data is available in BigQuery's EU and US regions: US region EU region This public dataset is hosted in Google BigQuery. Each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch thisto get started quickly using BigQuery What is BigQuery? This dataset is created and owned by Geotab. It has significant public interest in light of the COVID-19 crisis. All bytes processed in queries against this dataset will be zeroed out, making this part of the query free. Data joined with the dataset will be billed at the normal rate to prevent abuse. After September 15, queries over these datasets will revert to normal billing rates.

The dataset includes all data collected and analysed for the study on "Physical distancing and risk of COVID-19 in small-scale fisheries: A remote sensing assessment in coastal Ghana." The novel coronavirus is predicted to have dire implications on global food systems including fisheries value chains due to restrictions imposed on human movements in many countries. In Ghana, food production, both agriculture and fisheries, is exempted from restrictions as an essential service. We employed an Unmanned Aerial Vehicle (UAV) in assessing the risk of artisanal fishers to the pandemic using physical distancing as a proxy. From analysis of cumulative distribution function (G-function) of the nearest-neighbour distances (NND), this study underscored crowding at all surveyed fish landing beaches and identified potential “hotspots” for disease transmission. Aerial images were obtined. The locations of people in orthomosaic images were manually extracted as point data in ESRI ArcMap v.10.3 using the editor tool. From the point data, the distance from each point to the nearest other point, that is the nearest-neighbour distance (NND), was measured for all individuals presents in each of the six landing beaches in this study. The median distances were compared to the World Health Organisation (WHO) and Centre for Disease Control (CDC) standards on physical (social) distancing.

Context

Although numerous studies have shown the capability of CNNs in effective identification of COVID-19 from CXR images, none of these studies investigated local phase CXR image features as multi-feature input to a CNN architecture for improved diagnosis of COVID-19 disease.

• Study-1: We incorporated datasets [1]-[6] for evaluate our proposed multi-feature CNNs (Paper link) and Github repo is here for reproducing our study. CXR _ ijcar _ mix and Enh _ ijcar _ mix are original CXR images and the corresponding enhanced images were used in our study. covid _ metadata _ ijcar, normal _ metadata _ ijcar, and pneumonia _ metadata _ ijcar are the corresponding metadata files.

• Study-2: In our second study, we incorporated all listing datasets [1]-[8] for proposed multi-feature semi-supervised learning. The used datasets and metadata files are CXR, Enh, covid _ metadata, normal _ metadata, and pneumonia _ metadata.

• Additional COVID-19 dataset: A new COVID-19 dataset [9] was added. It includes 243 scans from 71 subjects.

Thus, the COVID-Ti Dataset has 4038 COVID-19 scans from 2006 subjects in total after merging with the additional COVID-19 dataset. | | Normal |Pneumonia|COVID-19 | --- | --- | | No. imgs | 8851 | 6045 | 4038 | No. subs | 8851 | 6031 | 2006

Please cite our study if you are using this dataset: Qi, X., Brown, L.G., Foran, D.J. et al. Chest X-ray image phase features for improved diagnosis of COVID-19 using convolutional neural network. Int J CARS 16, 197–206 (2021). https://doi.org/10.1007/s11548-020-02305-w (Paper link)

Content

• Data Distribution_ Study-1 (IJCAR): Image size is 299 by 299. Five fold validation was used in our study. In each fold, dataset were split into train: val: test = 60%: 20%: 20% based on the number of subjects | | Normal |Pneumonia|COVID-19 | --- | --- | | No. imgs | 2567 | 2567 | 2567 | No. subs | 2567 | 2567 | 1484

• Data Distribution_ Study-2 To the best of our knowledge, COVID-Ti is the largest COVID-19 CXR dataset, including 3795 scans from 1935 patients. Image size is 299 by 299. | | Normal |Pneumonia|COVID-19 | --- | --- | | No. imgs | 8851 | 6045 | 3795 | No. subs | 8851 | 6031 | 1935

• Data Distribution_ Additional COVID-19 dataset [9] Image size is 299 by 299 | | Normal |Pneumonia|COVID-19 | --- | --- | | No. imgs | 0 | 0 | 243 | No. subs | 0 | 0 | 71

• Uploaded datasets Two types of images are uploaded: the original CXR and the corresponding enhanced CXR.

  • CXR and Enh are datasets for Study-2. Metadatas are covid_ metadata, normal_ metadata, and pneumonia_ metadata.
  • CXR_ ijcar_ mix and Enh_ ijcar_ mix are datasets for Study-1 (IJCAR). Metadatas are covid_ metadata_ ijcar, normal_ metadata_ ijcar, and pneumonia_ metadata_ ijcar.
  • COVID-19_ CXR and COVID-19_ Enh are datesets for the additional COVID-19 dataset. Metadata is additional_ covid_ metadata.

• metadata Each metadata file has four columns: sub_name, img_name, class, dataset. Enhanced images have same images with original CXR image. In the dataset column, rsna corresponding to [1], cohen corresponding to [2], sirm corresponding to [3], fig1 corresponding to [4], actmed corresponding to [5], BIMCV corresponding to [6], TCIA-1 _Rual corresponding to [7], TCIA-4 _rsna corresponding to [8], and Germany corresponding to [9]

Acknowledgements

Thanks to the following every organization and individual's effort for providing the valuable COVID-19 CXR images: [1] RSNA Pneumonia Detection Challenge dataset (https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data) [2] COVID-19 image data collection(covid-chestxray-dataset) collected by J.P. Cohen (https://github.com/ieee8023/covid-chestxray-dataset) [3] The Italian Society of Medical and Interventional Radiology (SIRM) (https://sirm.org/category/senza-categoria/covid-19/) [4] Figure 1 COVID-19 Chest X-ray Dataset Initiative (https://github.com/agchung/Figure1-COVID-chestxray-dataset) [5] ActualMed COVID-19 Chest X-ray Dataset Initiative (https://github.com/agchung/Actualmed-COVID-chestxray-dataset) [6] BIMCV-COVID19 (https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/#1590858128006-9e640421-6711) [7] COVID-19-AR (https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70226443) [8] MIDRC-RICORD-1c (https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=70230281) [9] COVID-19 Image Repository (https://github.com/ml-workgroup/covid-19-image-repository)

Inspiration

We hope our dataset and image enhancement technique could be used as many as possible in a varsity of studies to facilitate the development of the more effective COVID-19 diagnosis method. Hope the pandemic end soon.

Inspiration

With the covid 19 impact in the market, we have seen lot of changes in the car market. Now some cars are in demand hence making them costly and some are not in demand hence cheaper. With the change in market due to covid 19 impact, small traders are facing problems with their previous car price valuation machine learning models. So, they are looking for new machine learning models from new data.

According to Cognitive Market Research, the global Car Smart Key market size is USD 12581.6 million in 2024 and will expand at a compound annual growth rate (CAGR) of 9.00% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 5032.64 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.2% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 3774.48 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 2893.77 million in 2024 and will grow at a compound annual growth rate (CAGR) of 11.0% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 629.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.4% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 251.63 million in 2024 and will grow at a compound annual growth rate (CAGR) of 8.7% from 2024 to 2031.
The OEM held the highest Car Smart Key market revenue share in 2024.

Market Dynamics of Car Smart Key Market

Key Drivers for Car Smart Key Market

Emergence of Hybrid and Electric Vehicle Drives Market Growth

The trend of electric cars (EVs) is currently spreading throughout the world as consumers continue to look for alternatives to internal combustion engines due to a variety of issues, including fluctuating oil prices and environmental concerns. It is difficult for drivers in the 19th century to envision driving an electric car today. These automobiles can offer the safety and convenience that modern consumers desire, from active services and remote vehicle access to smart driving. Electric vehicles have made great progress toward actively improving the environment we live in. Cars release a lot of carbon dioxide into the atmosphere, which increases our susceptibility to pollutants and other greenhouse gasses. The automobile industry's future lies with electric vehicles or EVs. While some automakers design all of their models with proactive and electric usage in mind, others also sell hybrid cars that combine the use of natural gas and electricity. Thus, electric and hybrid cars with cutting-edge safety and security features are the way of the future for the automobile industry. The global market for car smart keys will shift as a result of the rising demand for these keys.

Rising Consumer Demand for Advanced Vehicle Access to Propel Market Growth

Modern consumers demand seamless, simple-to-use experiences in everything from their everyday encounters with cars to other facets of their lives. In keeping with this expectation, smart keys that offer push-button ignition, keyless entry, and remote capabilities are becoming more and more common in automobiles. Consumer demand for cars with cutting-edge features that not only make daily duties easier but also give off an impression of beauty and modernity is driving the need for advanced vehicle access solutions. How technology is evolving demonstrates this trend.

Restraint Factor for the Car Smart Key Market

High Initial Cost to Limit the Sales

The integration of cutting-edge technology into cars to enable smart key systems comes with significant upfront expenditures, which could deter buyers on a tight budget and limit market penetration. The broad implementation of these technologies in various vehicle sectors is hindered by the manufacturers' incapacity to offer reasonably priced solutions. Consequently, the perceived financial barrier impedes the smooth adoption of smart key technologies in the automotive industry, hindering the transition from traditional key systems to more sophisticated and advanced access solutions, despite the potential long-term benefits.

Impact of Covid-19 on the Car Smart Key Market

The COVID-19 pandemic has caused significant disruptions to the automotive sector. As a result, manufacturing sites have closed, and sales volume has decreased. Additionally, in 2020 there was less of a need for passenger and commercial automobiles. Since the manufacturing of cars is directly correlated with the demand for car smart keys, the anticipated decline will have a negative impact on the market for car smart keys. It's possible that R&D funding would be cut, which will impede smart vital innovation. Nonetheless, bus...

Data used in the article "Fear or price? Vulnerability of the interest in green transport to COVID dynamics and fuel prices in V4 economies" (DOI: 10.14254/2071-789X.2025/18-1/4). The data covers the period from January 2020 to the end of February 2023. It includes weekly data on consumer prices of petroleum products (PB95) inclusive of duties and taxes (data source: weekly oil bulletin prepared by the European Commission) and the results of Google search terms related to public transport, alternative transport and electric cars in Czechia, Germany, Hungary, Poland, Slovakia and Sweden. We considered the total Google search volume related to a given means of transport for various phrases in national languages. Lastly, we include the data on the number of confirmed COVID-19 cases per week cases provided by World Health Organization.The dataset consists of 6 files: Czechia, Germany, Hungary, Poland, Slovakia and Slovenia. Each file stores the analogous data collected for each country. Column names:PB - price (in Euro) of the 95-PB petrol;ecar - number of Google searches for the keywords related to electric cars;pubTR - number of Google searches for the keywords related to public transport;susTR - number of Google searches for the keywords related to alternative transport;pubTR_sa - de-seasoned number of searches for the keywords related to public transport;susTR_sa - de-seasoned number of searches for the keywords related to alternative transport;COV - number of COVID deaths.

In 2022, the aftermarket revenue of light vehicles in the Gulf Cooperation Council (GCC) region was about *** billion U.S. dollars. The aftermarket was expected to gradually recover from the pandemic reaching approximately ** billion U.S. dollars by 2027.

Here, we described the case of a B cell-deficient patient after CD19 CAR-T cell therapy for refractory B cell Non-Hodgkin Lymphoma with protracted coronavirus disease 2019 (COVID-19). For weeks, this patient only inefficiently contained the virus while convalescent plasma transfusion correlated with virus clearance. Interestingly, following convalescent plasma therapy natural killer cells matured and virus-specific T cells expanded, presumably allowing virus clearance and recovery from the disease. Our findings, thus, suggest that convalescent plasma therapy can activate cellular immune responses to clear SARS-CoV-2 infections. If confirmed in larger clinical studies, these data could be of general importance for the treatment of COVID-19 patients.

Motor Vehicle Production: Car data was reported at 391,300.000 Unit in Mar 2025. This records an increase from the previous number of 361,500.000 Unit for Feb 2025. Motor Vehicle Production: Car data is updated monthly, averaging 437,671.000 Unit from Jul 2000 (Median) to Mar 2025, with 297 observations. The data reached an all-time high of 583,399.000 Unit in Mar 2011 and a record low of 11,287.000 Unit in Apr 2020. Motor Vehicle Production: Car data remains active status in CEIC and is reported by Federal Motor Transport Authority. The data is categorized under Global Database’s Germany – Table DE.RA002: Motor Vehicle Production. [COVID-19-IMPACT]

Car Rental (hiring of a passenger vehicle for self drive, which includes cars and small vans, by both business and leisure travelers for short term duration; excluding leasing and long term rentals) market has evolved intensely in the very recent years and is also expected to evolve in similar fashion in the near future. The report Car Rentals (Self Drive) Market in Chile to 2024: Fleet Size, Rental Occasion and Days, Utilization Rate and Average Revenue Analytics provides deep dive data analytics on wide ranging Car Rental market aspects including overall market value by customer type – Business and Leisure, by point of rental – Airport and Non-Airport, Insurance / Temporary Replacement Revenue, Car Rental Occasion, Days and Length for the period 2015 to 2019. Read More