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363 Research products, page 1 of 37

  • COVID-19
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  • 2013-2022
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  • COVID-19

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  • Open Access English
    Authors: 
    Bakhtawar Chaudhry; Saiza Azhar; Shazia Jamshed; Jahanzaib Ahmed; Laiq-ur-Rehman Khan; Zahid Saeed; Melinda Madléna; Márió Gajdács; Abdur Rasheed;
    Country: Hungary

    Self-medication (SM) is characterized by the procurement and use of medicines by bypassing primary healthcare services and without consulting a physician, usually to manage acute symptoms of self-diagnosed illnesses. Due to the limited availability of primary healthcare services and the anxiety associated with the COVID-19 pandemic, the compulsion to SM by the public has increased considerably. The study aimed to assess the characteristics, practices, and associated factors of SM by the public during the COVID-19 pandemic in Sargodha, Pakistan. χ2-tests and univariable analyses were conducted to explore the identification of characteristics and the potential contributing factors for SM during COVID-19, while multivariable logistic regression models were run to study the effect of variables that maintained a significant association. The study was performed during July–September 2021, with n = 460 questionnaires returned overall (response rate: 99.5%). The majority of respondents were males (58.7%, n = 270) who live in the periphery of the town (63.9%, n = 294), and most of the respondents belonged to the age group of 18–28 years (73.3%, n = 339). A large number, 46.1% (n = 212), of the participants were tested for COVID-19 during the pandemic, and among them, 34.3% (n = 158) practiced SM during the pandemic; the most common source of obtaining medicines was requesting them directly from a pharmacy (25.0%; n = 127). The chances of practicing SM for medical health professionals were 1.482 (p-value = 0.046) times greater than for non-medical health personnel. The likelihood of practicing SM in participants whose COVID-19 test was positive was 7.688 (p-value < 0.001) times more than who did not test for COVID-19. Allopathic medicines, acetaminophen (23.6%), azithromycin (14,9%), and cough syrups (13%), and over the counter (OTC) pharmaceuticals, vitamin oral supplements, such as Vitamin C (39.1%), folic acid (23.5%), and calcium (22.6%), were the most commonly consumed medicines and supplements, respectively; being a healthcare professional or having a COVID-test prior showed a significant association with the usage of Vitamin C (p < 0.05 in all cases). Respondents who mentioned unavailability of the physician and difficulty in travelling/reaching healthcare professionals were found 2.062-times (p-value = 0.004) and 1.862-times (p-value = 0.021) more likely to practice SM, respectively; SM due to fear of COVID was more common in individuals who had received COVID-tests prior (p = 0.004). Practices of SM were observed at alarming levels among our participants. Consciousness and understanding about the possible adverse effects of SM must be established and validated on a continuous level; in addition, on a commercial level, collaboration from pharmacists not to sell products (especially prescription-only medicines) without a certified prescription must be developed and implemented.

  • Open Access English
    Authors: 
    Lőrincz, Noémi Szilvia;
    Country: Hungary

    The purpose of the thesis is to analyze how the automotive manufacturing companies being active in Hungary operate in global value chains, with a particular focus on suppliers. Although the topic of GVC is widespread and discussed in international literature, there is a gap in relation to the Hungarian automotive manufacturing industry, especially in the current situation when the COVID-19 pandemic affects the operation of the multinational enterprises. The main identified research question is the following: What is the value creation of the automotive manufacturing industry in Hungary within global value chain? The research process started with a comprehensive literature review and theoretical background analysis about the GVC concept (including the introduction of ‘Smile-curve’) and FDI investment in Central and Eastern Europe (including the characterization of near-shoring activities) and continued with conducting a sample survey and semi-structured interviews with the key car parts suppliers. Executive board, managerial level and engineers were the target persons both for the survey and for interviews. Based on the literature review, I formulated two hypotheses: 1. The theory of ‘Smile curve’ is also valid in case of the Hungarian automotive manufacturing industry, typically low value-added production processes take place in the country. 2. In addition to the central location, the cheap and skilled Hungarian labour was the most important factor in the near-shoring activities of multinational companies expanding to Hungary. In order to be able to accept or reject the first hypothesis about the relevance of the so called ‘Smile curve’ in the Hungarian automotive manufacturing industry, to define position of the automotive manufacturer companies being active in Hungary in the global automotive manufacturing value chain and to create an in-depth understanding about investment incentives of the Western European firms in the country, I prepared an online survey. To test my second hypothesis about the reasons of near-shoring activity in Hungary, I conducted 3 interviews with industry experts from TIER 1 companies of different size. The targeted automotive parts manufacturers are all suppliers of the 5 OEMs present in Hungary (Audi, BMW, Mercedes, Opel and Suzuki) among others. The new results of the doctoral dissertation are the following: I can reject the first hypothesis about the relevance of ‘Smile curve’ in the Hungarian automotive manufacturing industry, because beside manufacturing activities with low added value typically, also research and development activities take place at bigger multinational companies with higher added value. I can accept the second hypothesis about near-shoring in Hungary, because beside the ‘proximity to export markets’, the cheap but skilled labour was decisive when multinationals decided to invest in the country. The ‘positive support system’, ‘favourable tax conditions’, ‘government policy’ and ‘proximity to HQ’ were aspects that companies used, but they are rather neutral factors. The ‘good infrastructure’ is not so good in the real life and the ‘cheap raw material’ is not cheap, because firms have to deal with world market prices, thus, these were not attractive to investors. Further results about the business operations of the analyzed supplier companies: The purchasing decisions for the Hungarian production happens locally decisively, either independently or with involving the headquarter. The manufactured products are typically drive chains, body parts and electric sensors and the proportion of products designated by OEMs is rather high. Western Europe is the biggest export market of the companies analysed, followed by China, North-America and the Central Eastern European region. Relocation processes are not characteristic of the firms. If so, only from other country to Hungary and it is also determined by OEMs providing new opportunities for them. In some cases, wage costs and logistics also play a role in the relocation process. Electromobility and autonomous driving are the most affecting trends in the automotive manufacturing industry. The semiconductor shortage as a serious downside risk is also the result of the pandemic. The effects of COVID-19 are becoming less pronounced today, but the semiconductor crisis is continuing. Favourable tax conditions and higher value added are the success criteria that will help the Hungarian automotive manufacturing industry to remain competitive in the future. Professional trainings, more support for SMEs and favourable legal conditions are also important aspects. Today, the CEE region, including Hungary is a net exporter of knowledge-intensive goods. To improve its global competitiveness and to be able to move into higher-value-added goods and services, the region should invest more in R&D, infrastructure, education and collaboration between companies and universities. The key players in the automotive part manufacturing has realized that value added is a very important factor in the success of an industry and it can be increased due to investment in research and development and innovation. As revealed by the research, they have already established R&D centers and joint projects with universities (e.g. departments), so companies are well on their way to producing higher added value.

  • Other research product . 2022
    Open Access English
    Authors: 
    Dobránszky-Bartus, Katalin;
    Country: Hungary

    In our research, we aim to shed light on the role of overdue debt in reinforcing poverty. This not only helps to better understand the dynamics of poverty trap induced by overdue debt but also enhances the rediscussing of current policy tools. Our research is based on data collected with targeted questionnaires in March and April 2019 by the Soreco Research Kft. Data were recorded with a personal question and answer method, by a so-called multi-stage stratified random sampling procedure. The data collection was anonymised and focused on the financial and liquidity decisions of households in small settlements in one of the most disadvantaged counties of Hungary, Borsod-Abaúj-Zemplén (BAZ) county. The sample is representative on the level of households living in small settlements. After cleaning the raw data, we have information from 504 households and 1794 individuals. 1196 individuals are of legal age, from whom 179 had overdue debt. We develop a theoretical model inspired by Akerlof (1978), Tirole (2006), and Mukherjee, Subramanian, Tantri (2019) to derive a feasibility condition for market-based debt relief programs. Our empirical analysis aims at investigating the role of overdue debt in creating poverty trap. With the help of statistical analysis and linear probability models we examine the impact of overdue debt on employment, on having a bank account, and on mental- and physical- health based on targeted questionnaires and in-depth interviews in the most disadvantaged regions of Hungary. We controlled for socioeconomic factors (e.g., gender, age, education level, ability to pay) and for settlement and county development indicators. In these regions, a significant part of the society has been the victim of financial exclusion well before the Covid 19 crisis, even under prospering economic conditions. Results: § The theoretical model shows that lenders have no interest to offer payment reductions if non-performing borrowers are few, have small debts, and are difficult to reach. In this situation, poor debtors serve better as deterrents, similarly if we put them into a pillory. § Calibrating model parameters to poor households struggling with overdue debts, we show that this might be the case on our sample, too. § As, in normal economic circumstances, private debt relief programs are typically not feasible, a state subsidy would be needed to consolidate the debts of the poor. State intervention can be justified both by positive externalities and moral considerations. § We find that many borrowers hide from debt collection as a consequence of overdue debt that has escalated to an unbearable level due to penalty rates. These borrowers are following the hiding strategy and take their decisions accordingly: to avoid deductions, they do not apply for registered jobs, do not open bank accounts and consequently, they are forced to live under constant stress. § To sum up the impact of overdue debt on social inclusion factors and according to our estimations, overdue debts reduce the likelihood of having a registered job by nearly 14 percentage points. Not having a registered job reduces the probability of owning a bank account by 22 percentage points and, in addition, overdue debts further decrease the probability by 5 percentage points. In addition, overdue debt also has a negative effect on the health of those living in the same household as the debtor, and this negative effect is greater than what a combined high school diploma and diploma could compensate for (0.4 versus 1.08-0.72 = 0.36). § Overdue debt, therefore, leads to a certain type of debt-trap mechanism resulting in significant loss for both the individual and the society. In this light, policy makers should pay more attention to addressing credit cycles and resolving non-performing debt obligations, especially in this fragile part of the society.

  • Open Access English
    Authors: 
    Dániel Honfi; Nikolett Gémes; Enikő Szabó; Patrícia Neuperger; József Á. Balog; Lajos I. Nagy; Gergely Toldi; László G. Puskás; Gábor J. Szebeni; Attila Balog;
    Publisher: Multidisciplinary Digital Publishing Institute
    Country: Hungary

    Vaccination against SARS-CoV-2 to prevent COVID-19 is highly recommended for immunocompromised patients with autoimmune rheumatic and musculoskeletal diseases (aiRMDs). Little is known about the effect of booster vaccination or infection followed by previously completed two-dose vaccination in aiRMDs. We determined neutralizing anti-SARS-CoV-2 antibody levels and applied flow cytometric immunophenotyping to quantify the SARS-CoV-2 reactive B- and T-cell mediated immunity in aiRMDs receiving homologous or heterologous boosters or acquired infection following vaccination. Patients receiving a heterologous booster had a higher proportion of IgM+ SARS-CoV-2 S+ CD19+CD27+ peripheral memory B-cells in comparison to those who acquired infection. Biologic therapy decreased the number of S+CD19+; S+CD19+CD27+IgG+; and S+CD19+CD27+IgM+ B-cells. The response rate to a booster event in cellular immunity was the highest in the S-, M-, and N-reactive CD4+CD40L+ T-cell subset. Patients with a disease duration of more than 10 years had higher proportions of CD8+TNF-α+ and CD8+IFN-γ+ T-cells in comparison to patients who were diagnosed less than 10 years ago. We detected neutralizing antibodies, S+ reactive peripheral memory B-cells, and five S-, M-, and N-reactive T-cells subsets in our patient cohort showing the importance of booster events. Biologic therapy and <10 years disease duration may confound anti-SARS-CoV-2 specific immunity in aiRMDs.

  • Open Access English
    Authors: 
    Hao-Chun Hu; Szu-Yin Yu; Xiao-Shan Hung; Chun-Han Su; Yu-Liang Yang; Chien-Kei Wei; Yuan-Bin Cheng; Yang-Chang Wu; Chia-Hung Yen; Tsong-Long Hwang; +5 more
    Country: Hungary
  • Publication . Article . Other literature type . 2022
    Open Access English
    Authors: 
    Jolien Anne van Breen; Maja Kutlaca; Yasin Koc; Bertus F. Jeronimus; Anne Margit Reitsema; Veljko Jovanović; Maximilian Agostini; Jocelyn J. Bélanger; Ben Gützkow; Jannis Kreienkamp; +91 more
    Countries: Croatia, Serbia, Germany, Netherlands, United Kingdom, Italy

    We examine how social contacts and feelings of solidarity shape experiences of loneliness during the COVID-19 lockdown in early 2020. From the PsyCorona database, we obtained longitudinal data from 23 countries, collected between March and May 2020. The results demonstrated that although online contacts help to reduce feelings of loneliness, people who feel more lonely are less likely to use that strategy. Solidarity played only a small role in shaping feelings of loneliness during lockdown. Thus, it seems we must look beyond the current focus on online contact and solidarity to help people address feelings of loneliness during lockdown. Finally, online contacts did not function as a substitute for face-to-face contacts outside the home—in fact, more frequent online contact in earlier weeks predicted more frequent face-to-face contacts in later weeks. As such, this work provides relevant insights into how individuals manage the impact of restrictions on their social lives.

  • Open Access English
    Authors: 
    Beáta Farkas; Andor Máté; Tamás Rácz;
    Country: Hungary

    Abstract Since the eastern enlargement of the European Union (EU), the movement from east to west has become the main driver of intra-EU mobility. Recently, the free movement of labour has been contested not only in the debates around Brexit, but also in other receiving countries. It is not on the political agenda, but several studies have highlighted the economic and demographic effects of massive emigration in eastern EU Member States. More recently, the COVID-19 pandemic has disrupted the functioning of free movement. Economic integration theory assumes that migration continues until wages are equalized in the receiving and sending countries. This paper analyses the perception of intra-EU mobility in the literature and empirically tests whether there is a relationship between the dynamism of income growth in the receiving (Germany, Austria and Spain) and sending (Central and Eastern European) countries, and the dynamism of migration. The empirical results do not support the neoclassical assumption that an equalization mechanism can function, even in the long run. To cope with recent challenges, this paper argues that free movement should not be considered as an element of a spontaneous market mechanism, but as an economic-political product, based on a constitutional order.

  • Open Access English
    Authors: 
    Jasjit S. Suri; Sushant Agarwal; Luca Saba; Gian Luca Chabert; Alessandro Carriero; Alessio Paschè; Pietro Danna; Armin Mehmedović; Gavino Faa; Tanay Jujaray; +12 more
    Countries: Hungary, Croatia

    Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, "COVLIAS 1.0-Unseen" proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. In a multicenter study, 10, 000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations-two Unseen sets: (i) train-CRO:test- ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Two trained, blinded senior radiologists conducted ground truth annotations. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web- based system. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. HDL was found to be superior to SDL.

  • Open Access English
    Authors: 
    Narendra N. Khanna; Mahesh Maindarkar; Anudeep Puvvula; Sudip Paul; Mrinalini Bhagawati; Puneet Ahluwalia; Zoltan Ruzsa; Aditya Sharma; Smiksha Munjral; Raghu Kolluri; +32 more
    Countries: Hungary, Croatia

    The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

  • Open Access English
    Authors: 
    Jasjit S. Suri; Mahesh A. Maindarkar; Sudip Paul; Puneet Ahluwalia; Mrinalini Bhagawati; Luca Saba; Gavino Faa; Sanjay Saxena; Inder M. Singh; Paramjit S. Chadha; +33 more
    Countries: Croatia, Hungary

    Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID‐19 causes the ML systems to be-come severely non‐linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well‐explained ML paradigms. Deep neural networks are powerful learning machines that generalize non‐linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID‐19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID‐19 framework. We study the hypothesis that PD in the presence of COVID‐19 can cause more harm to the heart and brain than in non‐ COVID‐19 conditions. COVID‐19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID‐19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID‐19 lesions, office and laboratory arterial atherosclerotic image‐based biomarkers, and medicine usage for the PD patients for the design of DL point‐based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID‐ 19 environment and this was also verified. DL architectures like long short‐term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID‐19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID‐19. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

Advanced search in Research products
Research products
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Searching FieldsTerms
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The following results are related to COVID-19. Are you interested to view more results? Visit OpenAIRE - Explore.
363 Research products, page 1 of 37
  • Open Access English
    Authors: 
    Bakhtawar Chaudhry; Saiza Azhar; Shazia Jamshed; Jahanzaib Ahmed; Laiq-ur-Rehman Khan; Zahid Saeed; Melinda Madléna; Márió Gajdács; Abdur Rasheed;
    Country: Hungary

    Self-medication (SM) is characterized by the procurement and use of medicines by bypassing primary healthcare services and without consulting a physician, usually to manage acute symptoms of self-diagnosed illnesses. Due to the limited availability of primary healthcare services and the anxiety associated with the COVID-19 pandemic, the compulsion to SM by the public has increased considerably. The study aimed to assess the characteristics, practices, and associated factors of SM by the public during the COVID-19 pandemic in Sargodha, Pakistan. χ2-tests and univariable analyses were conducted to explore the identification of characteristics and the potential contributing factors for SM during COVID-19, while multivariable logistic regression models were run to study the effect of variables that maintained a significant association. The study was performed during July–September 2021, with n = 460 questionnaires returned overall (response rate: 99.5%). The majority of respondents were males (58.7%, n = 270) who live in the periphery of the town (63.9%, n = 294), and most of the respondents belonged to the age group of 18–28 years (73.3%, n = 339). A large number, 46.1% (n = 212), of the participants were tested for COVID-19 during the pandemic, and among them, 34.3% (n = 158) practiced SM during the pandemic; the most common source of obtaining medicines was requesting them directly from a pharmacy (25.0%; n = 127). The chances of practicing SM for medical health professionals were 1.482 (p-value = 0.046) times greater than for non-medical health personnel. The likelihood of practicing SM in participants whose COVID-19 test was positive was 7.688 (p-value < 0.001) times more than who did not test for COVID-19. Allopathic medicines, acetaminophen (23.6%), azithromycin (14,9%), and cough syrups (13%), and over the counter (OTC) pharmaceuticals, vitamin oral supplements, such as Vitamin C (39.1%), folic acid (23.5%), and calcium (22.6%), were the most commonly consumed medicines and supplements, respectively; being a healthcare professional or having a COVID-test prior showed a significant association with the usage of Vitamin C (p < 0.05 in all cases). Respondents who mentioned unavailability of the physician and difficulty in travelling/reaching healthcare professionals were found 2.062-times (p-value = 0.004) and 1.862-times (p-value = 0.021) more likely to practice SM, respectively; SM due to fear of COVID was more common in individuals who had received COVID-tests prior (p = 0.004). Practices of SM were observed at alarming levels among our participants. Consciousness and understanding about the possible adverse effects of SM must be established and validated on a continuous level; in addition, on a commercial level, collaboration from pharmacists not to sell products (especially prescription-only medicines) without a certified prescription must be developed and implemented.

  • Open Access English
    Authors: 
    Lőrincz, Noémi Szilvia;
    Country: Hungary

    The purpose of the thesis is to analyze how the automotive manufacturing companies being active in Hungary operate in global value chains, with a particular focus on suppliers. Although the topic of GVC is widespread and discussed in international literature, there is a gap in relation to the Hungarian automotive manufacturing industry, especially in the current situation when the COVID-19 pandemic affects the operation of the multinational enterprises. The main identified research question is the following: What is the value creation of the automotive manufacturing industry in Hungary within global value chain? The research process started with a comprehensive literature review and theoretical background analysis about the GVC concept (including the introduction of ‘Smile-curve’) and FDI investment in Central and Eastern Europe (including the characterization of near-shoring activities) and continued with conducting a sample survey and semi-structured interviews with the key car parts suppliers. Executive board, managerial level and engineers were the target persons both for the survey and for interviews. Based on the literature review, I formulated two hypotheses: 1. The theory of ‘Smile curve’ is also valid in case of the Hungarian automotive manufacturing industry, typically low value-added production processes take place in the country. 2. In addition to the central location, the cheap and skilled Hungarian labour was the most important factor in the near-shoring activities of multinational companies expanding to Hungary. In order to be able to accept or reject the first hypothesis about the relevance of the so called ‘Smile curve’ in the Hungarian automotive manufacturing industry, to define position of the automotive manufacturer companies being active in Hungary in the global automotive manufacturing value chain and to create an in-depth understanding about investment incentives of the Western European firms in the country, I prepared an online survey. To test my second hypothesis about the reasons of near-shoring activity in Hungary, I conducted 3 interviews with industry experts from TIER 1 companies of different size. The targeted automotive parts manufacturers are all suppliers of the 5 OEMs present in Hungary (Audi, BMW, Mercedes, Opel and Suzuki) among others. The new results of the doctoral dissertation are the following: I can reject the first hypothesis about the relevance of ‘Smile curve’ in the Hungarian automotive manufacturing industry, because beside manufacturing activities with low added value typically, also research and development activities take place at bigger multinational companies with higher added value. I can accept the second hypothesis about near-shoring in Hungary, because beside the ‘proximity to export markets’, the cheap but skilled labour was decisive when multinationals decided to invest in the country. The ‘positive support system’, ‘favourable tax conditions’, ‘government policy’ and ‘proximity to HQ’ were aspects that companies used, but they are rather neutral factors. The ‘good infrastructure’ is not so good in the real life and the ‘cheap raw material’ is not cheap, because firms have to deal with world market prices, thus, these were not attractive to investors. Further results about the business operations of the analyzed supplier companies: The purchasing decisions for the Hungarian production happens locally decisively, either independently or with involving the headquarter. The manufactured products are typically drive chains, body parts and electric sensors and the proportion of products designated by OEMs is rather high. Western Europe is the biggest export market of the companies analysed, followed by China, North-America and the Central Eastern European region. Relocation processes are not characteristic of the firms. If so, only from other country to Hungary and it is also determined by OEMs providing new opportunities for them. In some cases, wage costs and logistics also play a role in the relocation process. Electromobility and autonomous driving are the most affecting trends in the automotive manufacturing industry. The semiconductor shortage as a serious downside risk is also the result of the pandemic. The effects of COVID-19 are becoming less pronounced today, but the semiconductor crisis is continuing. Favourable tax conditions and higher value added are the success criteria that will help the Hungarian automotive manufacturing industry to remain competitive in the future. Professional trainings, more support for SMEs and favourable legal conditions are also important aspects. Today, the CEE region, including Hungary is a net exporter of knowledge-intensive goods. To improve its global competitiveness and to be able to move into higher-value-added goods and services, the region should invest more in R&D, infrastructure, education and collaboration between companies and universities. The key players in the automotive part manufacturing has realized that value added is a very important factor in the success of an industry and it can be increased due to investment in research and development and innovation. As revealed by the research, they have already established R&D centers and joint projects with universities (e.g. departments), so companies are well on their way to producing higher added value.

  • Other research product . 2022
    Open Access English
    Authors: 
    Dobránszky-Bartus, Katalin;
    Country: Hungary

    In our research, we aim to shed light on the role of overdue debt in reinforcing poverty. This not only helps to better understand the dynamics of poverty trap induced by overdue debt but also enhances the rediscussing of current policy tools. Our research is based on data collected with targeted questionnaires in March and April 2019 by the Soreco Research Kft. Data were recorded with a personal question and answer method, by a so-called multi-stage stratified random sampling procedure. The data collection was anonymised and focused on the financial and liquidity decisions of households in small settlements in one of the most disadvantaged counties of Hungary, Borsod-Abaúj-Zemplén (BAZ) county. The sample is representative on the level of households living in small settlements. After cleaning the raw data, we have information from 504 households and 1794 individuals. 1196 individuals are of legal age, from whom 179 had overdue debt. We develop a theoretical model inspired by Akerlof (1978), Tirole (2006), and Mukherjee, Subramanian, Tantri (2019) to derive a feasibility condition for market-based debt relief programs. Our empirical analysis aims at investigating the role of overdue debt in creating poverty trap. With the help of statistical analysis and linear probability models we examine the impact of overdue debt on employment, on having a bank account, and on mental- and physical- health based on targeted questionnaires and in-depth interviews in the most disadvantaged regions of Hungary. We controlled for socioeconomic factors (e.g., gender, age, education level, ability to pay) and for settlement and county development indicators. In these regions, a significant part of the society has been the victim of financial exclusion well before the Covid 19 crisis, even under prospering economic conditions. Results: § The theoretical model shows that lenders have no interest to offer payment reductions if non-performing borrowers are few, have small debts, and are difficult to reach. In this situation, poor debtors serve better as deterrents, similarly if we put them into a pillory. § Calibrating model parameters to poor households struggling with overdue debts, we show that this might be the case on our sample, too. § As, in normal economic circumstances, private debt relief programs are typically not feasible, a state subsidy would be needed to consolidate the debts of the poor. State intervention can be justified both by positive externalities and moral considerations. § We find that many borrowers hide from debt collection as a consequence of overdue debt that has escalated to an unbearable level due to penalty rates. These borrowers are following the hiding strategy and take their decisions accordingly: to avoid deductions, they do not apply for registered jobs, do not open bank accounts and consequently, they are forced to live under constant stress. § To sum up the impact of overdue debt on social inclusion factors and according to our estimations, overdue debts reduce the likelihood of having a registered job by nearly 14 percentage points. Not having a registered job reduces the probability of owning a bank account by 22 percentage points and, in addition, overdue debts further decrease the probability by 5 percentage points. In addition, overdue debt also has a negative effect on the health of those living in the same household as the debtor, and this negative effect is greater than what a combined high school diploma and diploma could compensate for (0.4 versus 1.08-0.72 = 0.36). § Overdue debt, therefore, leads to a certain type of debt-trap mechanism resulting in significant loss for both the individual and the society. In this light, policy makers should pay more attention to addressing credit cycles and resolving non-performing debt obligations, especially in this fragile part of the society.

  • Open Access English
    Authors: 
    Dániel Honfi; Nikolett Gémes; Enikő Szabó; Patrícia Neuperger; József Á. Balog; Lajos I. Nagy; Gergely Toldi; László G. Puskás; Gábor J. Szebeni; Attila Balog;
    Publisher: Multidisciplinary Digital Publishing Institute
    Country: Hungary

    Vaccination against SARS-CoV-2 to prevent COVID-19 is highly recommended for immunocompromised patients with autoimmune rheumatic and musculoskeletal diseases (aiRMDs). Little is known about the effect of booster vaccination or infection followed by previously completed two-dose vaccination in aiRMDs. We determined neutralizing anti-SARS-CoV-2 antibody levels and applied flow cytometric immunophenotyping to quantify the SARS-CoV-2 reactive B- and T-cell mediated immunity in aiRMDs receiving homologous or heterologous boosters or acquired infection following vaccination. Patients receiving a heterologous booster had a higher proportion of IgM+ SARS-CoV-2 S+ CD19+CD27+ peripheral memory B-cells in comparison to those who acquired infection. Biologic therapy decreased the number of S+CD19+; S+CD19+CD27+IgG+; and S+CD19+CD27+IgM+ B-cells. The response rate to a booster event in cellular immunity was the highest in the S-, M-, and N-reactive CD4+CD40L+ T-cell subset. Patients with a disease duration of more than 10 years had higher proportions of CD8+TNF-α+ and CD8+IFN-γ+ T-cells in comparison to patients who were diagnosed less than 10 years ago. We detected neutralizing antibodies, S+ reactive peripheral memory B-cells, and five S-, M-, and N-reactive T-cells subsets in our patient cohort showing the importance of booster events. Biologic therapy and <10 years disease duration may confound anti-SARS-CoV-2 specific immunity in aiRMDs.

  • Open Access English
    Authors: 
    Hao-Chun Hu; Szu-Yin Yu; Xiao-Shan Hung; Chun-Han Su; Yu-Liang Yang; Chien-Kei Wei; Yuan-Bin Cheng; Yang-Chang Wu; Chia-Hung Yen; Tsong-Long Hwang; +5 more
    Country: Hungary
  • Publication . Article . Other literature type . 2022
    Open Access English
    Authors: 
    Jolien Anne van Breen; Maja Kutlaca; Yasin Koc; Bertus F. Jeronimus; Anne Margit Reitsema; Veljko Jovanović; Maximilian Agostini; Jocelyn J. Bélanger; Ben Gützkow; Jannis Kreienkamp; +91 more
    Countries: Croatia, Serbia, Germany, Netherlands, United Kingdom, Italy

    We examine how social contacts and feelings of solidarity shape experiences of loneliness during the COVID-19 lockdown in early 2020. From the PsyCorona database, we obtained longitudinal data from 23 countries, collected between March and May 2020. The results demonstrated that although online contacts help to reduce feelings of loneliness, people who feel more lonely are less likely to use that strategy. Solidarity played only a small role in shaping feelings of loneliness during lockdown. Thus, it seems we must look beyond the current focus on online contact and solidarity to help people address feelings of loneliness during lockdown. Finally, online contacts did not function as a substitute for face-to-face contacts outside the home—in fact, more frequent online contact in earlier weeks predicted more frequent face-to-face contacts in later weeks. As such, this work provides relevant insights into how individuals manage the impact of restrictions on their social lives.

  • Open Access English
    Authors: 
    Beáta Farkas; Andor Máté; Tamás Rácz;
    Country: Hungary

    Abstract Since the eastern enlargement of the European Union (EU), the movement from east to west has become the main driver of intra-EU mobility. Recently, the free movement of labour has been contested not only in the debates around Brexit, but also in other receiving countries. It is not on the political agenda, but several studies have highlighted the economic and demographic effects of massive emigration in eastern EU Member States. More recently, the COVID-19 pandemic has disrupted the functioning of free movement. Economic integration theory assumes that migration continues until wages are equalized in the receiving and sending countries. This paper analyses the perception of intra-EU mobility in the literature and empirically tests whether there is a relationship between the dynamism of income growth in the receiving (Germany, Austria and Spain) and sending (Central and Eastern European) countries, and the dynamism of migration. The empirical results do not support the neoclassical assumption that an equalization mechanism can function, even in the long run. To cope with recent challenges, this paper argues that free movement should not be considered as an element of a spontaneous market mechanism, but as an economic-political product, based on a constitutional order.

  • Open Access English
    Authors: 
    Jasjit S. Suri; Sushant Agarwal; Luca Saba; Gian Luca Chabert; Alessandro Carriero; Alessio Paschè; Pietro Danna; Armin Mehmedović; Gavino Faa; Tanay Jujaray; +12 more
    Countries: Hungary, Croatia

    Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, "COVLIAS 1.0-Unseen" proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. In a multicenter study, 10, 000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations-two Unseen sets: (i) train-CRO:test- ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Two trained, blinded senior radiologists conducted ground truth annotations. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web- based system. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. HDL was found to be superior to SDL.

  • Open Access English
    Authors: 
    Narendra N. Khanna; Mahesh Maindarkar; Anudeep Puvvula; Sudip Paul; Mrinalini Bhagawati; Puneet Ahluwalia; Zoltan Ruzsa; Aditya Sharma; Smiksha Munjral; Raghu Kolluri; +32 more
    Countries: Hungary, Croatia

    The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

  • Open Access English
    Authors: 
    Jasjit S. Suri; Mahesh A. Maindarkar; Sudip Paul; Puneet Ahluwalia; Mrinalini Bhagawati; Luca Saba; Gavino Faa; Sanjay Saxena; Inder M. Singh; Paramjit S. Chadha; +33 more
    Countries: Croatia, Hungary

    Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID‐19 causes the ML systems to be-come severely non‐linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well‐explained ML paradigms. Deep neural networks are powerful learning machines that generalize non‐linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID‐19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID‐19 framework. We study the hypothesis that PD in the presence of COVID‐19 can cause more harm to the heart and brain than in non‐ COVID‐19 conditions. COVID‐19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID‐19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID‐19 lesions, office and laboratory arterial atherosclerotic image‐based biomarkers, and medicine usage for the PD patients for the design of DL point‐based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID‐ 19 environment and this was also verified. DL architectures like long short‐term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID‐19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID‐19. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.