This research project investigated the perceptions of learner autonomy (LA) in the context of EFL tertiary education in Vietnam as well as the factors that may influence the development of LA among non-English-major students. Questionnaires and interviews were used to collect data. A total of 1,565 students completed the survey questionnaires and 13 of whom participated in the interviews. Two types of data were collected at the same time before the mixed data analysis with the support of SPSS version 24.0, SmartPLS3, and SPSS AMOS for quantitative data and ATLAS.ti for qualitative data. The findings from two strands were compared, contrasted, and mixed to elucidate the research questions. The conceptualization of LA is consistently both theoretically from the literature and empirically. LA defined in this study consisted of four related facets including beliefs about teacher’s role, motivation and desire to learn English, metacognition in language learning, and freedom. In general, the participating students described proactive autonmoy and showed the positivity to LA; however, as they perceived, they appeared to lack LA in several ways. The students demonstrated high levels of motivation and desire to learn English and sound metacognitive knowledge about the self and the learning context. Still, they believed that the teachers were really of importance to their learning process and they held high expectations for their teachers. Besides, there was a lack of metacognitive skills (i.e., planning, monitoring, and evaluating) and metacognive knowledge about language matters and learning process. Moreover, communicating with the teachers with regard to learning issuse was not undertaken as it should be. The out-of-class activities were not frequently engaged in, except for two entertaining activities. Their exercise of LA, from their persepctive, was affected by two types of factors, namely internal (e.g., desire, motivation, metacognition, and language proficiency) and external (e.g., teacher’s activities, social environment, curriculum, and peers). The former was believed to exert a greater influence on LA than the latter. On the basis of the students’ understandings of LA and the LA-influencing factors, the study offered fundamental implications about how to cultivate the students’ LA, which is considered an important capacity to promote in tertiary education, both inside and outside classrooms, as one way to nurture lifelong learning (Dam, 2012; Yurdakul, 2017). Therefore, the involvement of many different stakeholders in the educational system is really necessary. The research brought about insights into LA and the factors that influence LA, as perceived by non-English-major students in Vietnam. Besides, the study contributed to the literature on LA and language learning in an Asian context from the learners’ perspective. Furthermore, the findings practically informed the relevant stakeholders such as lecturers, educators, curriculum planners, and policy makers through implications to foster LA especially in the hard times of COVID 19 pandemic.
Abstract Bioethics has expanded considerably over the last few decades in the academic enterprise and policy arena. However, despite the progress, the history of bioethics exhibits methodological controversies among contributors in the field. Generally, the contention is related to bioethics' complex and contested relationship with philosophical theory, contributors' perspectivism, and a "reliance upon high-flying ethical theory," as well as "skepticism of the applied nature of bioethics," which further point to differences in interpretation of the logic and epistemology of morality and moral judgments. On the other hand, it is claimed that pragmatic ethics, mainly on the grounds of incorporating the components of different understandings of ethics, its interdisciplinarity, and its practical focus, avoid the controversies over the methods and goals of bioethics through a consideration of the context in ethical inquiry and serves as a method. In this dissertation, I focus on investigating the methodological dimensions of bioethics while emphasizing topical issues in the field, including gestational surrogacy, healthcare allocation, rationing, and organ trade and trafficking in Africa. On the whole, I look at the methodology and goals of bioethics mainly, from the point of view of pragmatist ethics, following the line of John Dewey's ethics. I also investigate specific moral problems in bioethics to further illuminate the methods of pragmatic bioethics and show the practical usefulness for solving specific moral dilemmas arising in a particular context. The dissertation is devided into seven chapters. In Chapter One, I discuss the background of the study and locate the problems of the study by showing the contested nature of the methodological terrain of bioethics. Further, I discus the disagreements about the logic and epistemology of morality, moral judgment and decision making, the nature of moral issues, and the practical goals of bioethics. Finally, I also look at how pragmatist bioethics avoids methodological disagreements in bioethics. In Chapter Two, I examine the methodological dimensions of bioethics and show how a pragmatist approach and consideration of context are relevant in bioethical investigations. I also provide an overview of the recently introduced context-sensitive methodologies, theories, and principles of bioethics in the global South and East and show the relevance of context-based bioethical research and bioethical deliberations. Finally, discusing the epistemic ground of morality and the nature of bioethics, I argue that a pragmatist-empirical turn in bioethics can help us think about and make decisions about specific bioethical dilemmas. In Chapter Three, I further discuss the meta-method of bioethics by examining Dewey's inquiry ethics and the case of gestational surrogacy. First, I mainly revisited Dewey's ethical inquiry method and pragmatist bioethics and then identified steps of pragmatist bioethical inquiry. Using these steps, I discuss the moral dilemma of gestational surrogacy at the level of a public issue that needs social policy and suggest pragmatic ways to come up with solutions. In the last part of this chapter, I undeline the significance of Dewey's emphasis on education, deliberative democracy, and institutions as the basis for solving bioethical issues arising in different societal contexts. Next, in Chapter Four, I examine the ethical dilemma of healthcare allocation and rationing from a pragmatist ethics perspective, again mainly following Dewey's work. The moral dilemma of distribution always entails rationing: denying service to someone to benefit others. Such aspects of allocation and rationing and the normative-relational aspect of disease and health render the problem morally controversial. It is not easy to reach on agreed upon principles of healthcare resource allocation and rationing applicable across different contexts. Hence, in this chapter, I argue that the moral challenges of healthcare rationing ought not to be addressed through an appeal to principles but rather through deliberation that embraces a more pragmatic and democratic approach to thinking with sensitivity to context. However, this does not mean that moral principles and values are unnecessary when allocating healthcare resources. In Chapter Five, I further illuminate the methods of pragmatist bioethics and moral challeges of healthcare allocation and rationing by using the context of African healthcare systems and the COVID-19 pandemic. In the first part of this chapter, I critically review the African healthcare crisis's factors and suggest pragmatist means to address justice issues in healthcare allocation in the region. In the second part, I present the worldwide and Sub-Saharan African situations during the COVID-19 pandemic and examine the place of moral principles in the allocation and rationing of healthcare resources. In this chapter, I mainly argue for the relevance of go
Autophagy is an intracellular catabolic process that controls infections both directly and indirectly via its multifaceted effects on the innate and adaptive immune responses. It has been reported that LPS stimulates this cellular process, whereas the effect of IL-36α on autophagy remains largely unknown. We, therefore, investigated how IL-36α modulates the endogenous and LPS-induced autophagy in THP-1 cells. The levels of LC3B-II and autophagic flux were determined by western blotting. The intracellular localization of LC3B was measured by immunofluorescence assay. The activation levels of signaling pathways implicated in autophagy regulation were evaluated by using a phosphokinase array. Our results showed that combined IL-36α and LPS treatment cooperatively increased the levels of LC3B-II and Beclin-1, stimulated the autophagic flux, facilitated intracellular redistribution of LC3B, and increased the average number of autophagosomes per cell. The IL36α/LPS combined treatment increased phosphorylation of STAT5a/b, had minimal effect on the Akt/PRAS40/mTOR pathway, and reduced the levels of phospho-Yes, phospho-FAK, and phospho-WNK1. Thus, this cytokine/PAMP combination triggers pro-autophagic biased signaling by several mechanisms and thus cooperatively stimulates the autophagic cascade. An increased autophagic activity of innate immune cells simultaneously exposed to IL-36α and LPS may play an important role in the pathogenesis of Gram-negative bacterial infections. SARS-CoV-2 can infect and replicate in esophageal cells and enterocytes, leading to direct damage to the intestinal epithelium. The infection decreases the level of angiotensin converting enzyme 2 receptors, thereby altering the composition of the gut microbiota. SARS-CoV-2 elicits a cytokine storm, which contributes to gastrointestinal inflammation. The direct cytopathic effects of SARS-CoV-2, gut dysbiosis, and aberrant immune response result in increased intestinal permeability, which may exacerbate existing symptoms and worsen the prognosis. By exploring the elements of pathogenesis, several therapeutic options have emerged for the treatment of COVID-19 patients, such as biologics and biotherapeutic agents. However, the presence of SARS-CoV-2 in the feces may facilitate the spread of COVID-19 through fecal-oral transmission and contaminate the environment. Thus, gastrointestinal SARS-CoV-2 infection has important epidemiological significance. The development of new therapeutic and preventive options is necessary to treat and restrict the spread of this severe and widespread infection more effectively.
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.
Nzimande Ntombifuthi P.; El Tantawi Maha; Zuñiga Roberto Ariel Abeldaño; Opoku-Sarkodie Richmond; Brown Brandon; Ezechi Oliver C.; Uzochukwu Benjamin S. C.; Ellakany Passent; Aly Nourhan M.; Nguyen Annie Lu; +1 more
Nzimande Ntombifuthi P.; El Tantawi Maha; Zuñiga Roberto Ariel Abeldaño; Opoku-Sarkodie Richmond; Brown Brandon; Ezechi Oliver C.; Uzochukwu Benjamin S. C.; Ellakany Passent; Aly Nourhan M.; Nguyen Annie Lu; Folayan Morenike Oluwatoyin;
Agarwal Mohit; Agarwal Sushant; Saba Luca; Chabert Gian Luca; Gupta Suneet; Carriero Alessandro; Pasche Alessio; Danna Pietro; Mehmedovic Armin; Faa Gavino; +6 more
Agarwal Mohit; Agarwal Sushant; Saba Luca; Chabert Gian Luca; Gupta Suneet; Carriero Alessandro; Pasche Alessio; Danna Pietro; Mehmedovic Armin; Faa Gavino; Shrivastava Saurabh; Jain Kanishka; Jain Harsh; Nagy Ferenc; Kincses Zsigmond Tamás; Ruzsa Zoltán;
COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization.ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted.Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions.Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings.
Suri Jasjit S.; Agarwal Sushant; Chabert Gian Luca; Carriero Alessandro; Paschè Alessio; Danna Pietro S. C.; Saba Luca; Mehmedovic Armin; Faa Gavino; Singh Inder M.; +5 more
Suri Jasjit S.; Agarwal Sushant; Chabert Gian Luca; Carriero Alessandro; Paschè Alessio; Danna Pietro S. C.; Saba Luca; Mehmedovic Armin; Faa Gavino; Singh Inder M.; Turk Monika; Chadha Paramjit S.; Johri Amer M.; Nagy Ferenc Tamás; Ruzsa Zoltán;
The previous COVID-19 lung diagnosis system lacks both scientific validation and the role of explainable artificial intelligence (AI) for understanding lesion localization. This study presents a cloud-based explainable AI, the "COVLIAS 2.0-cXAI" system using four kinds of class activation maps (CAM) models.Our cohort consisted of ~6000 CT slices from two sources (Croatia, 80 COVID-19 patients and Italy, 15 control patients). COVLIAS 2.0-cXAI design consisted of three stages: (i) automated lung segmentation using hybrid deep learning ResNet-UNet model by automatic adjustment of Hounsfield units, hyperparameter optimization, and parallel and distributed training, (ii) classification using three kinds of DenseNet (DN) models (DN-121, DN-169, DN-201), and (iii) validation using four kinds of CAM visualization techniques: gradient-weighted class activation mapping (Grad-CAM), Grad-CAM++, score-weighted CAM (Score-CAM), and FasterScore-CAM. The COVLIAS 2.0-cXAI was validated by three trained senior radiologists for its stability and reliability. The Friedman test was also performed on the scores of the three radiologists.The ResNet-UNet segmentation model resulted in dice similarity of 0.96, Jaccard index of 0.93, a correlation coefficient of 0.99, with a figure-of-merit of 95.99%, while the classifier accuracies for the three DN nets (DN-121, DN-169, and DN-201) were 98%, 98%, and 99% with a loss of ~0.003, ~0.0025, and ~0.002 using 50 epochs, respectively. The mean AUC for all three DN models was 0.99 (p < 0.0001). The COVLIAS 2.0-cXAI showed 80% scans for mean alignment index (MAI) between heatmaps and gold standard, a score of four out of five, establishing the system for clinical settings.The COVLIAS 2.0-cXAI successfully showed a cloud-based explainable AI system for lesion localization in lung CT scans.