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Title: Multi-Sensory in Online Shopping: Insights into User Engagement Keywords: Online Shopping, Multi-sensory, Consumer Awareness, User Experience, User Engagement Research questions: 1. What factors of digitally enhanced multi-sensory experiences affect consumers' awareness? Purpose: 2. How are multi-sensory experiences affecting consumer engagement in e-commerce? Purpose: The purpose of this study is to describe the level of consumer behaviour in multi-sensory online shopping, consisting of three stages: awareness, experience, and engagement, and to explore the factors that affect purchase intention. Method: To fulfill the purpose of the study, a quantitative research method is employed, utilizing an online questionnaire. Conclusion: Based on the research, consumer awareness, user experience, and user engagement with multi-sensory technology have a strong impact on purchase intention. Furthermore, gender, shopping frequency, level of interest in digital innovation, and country are related to consumer awareness and purchase intention.

Title: Multi-Sensory in Online Shopping: Insights into User Engagement Keywords: Online Shopping, Multi-sensory, Consumer Awareness, User Experience, User Engagement Research questions: 1. What factors of digitally enhanced multi-sensory experiences affect consumers' awareness? Purpose: 2. How are multi-sensory experiences affecting consumer engagement in e-commerce? Purpose: The purpose of this study is to describe the level of consumer behaviour in multi-sensory online shopping, consisting of three stages: awareness, experience, and engagement, and to explore the factors that affect purchase intention. Method: To fulfill the purpose of the study, a quantitative research method is employed, utilizing an online questionnaire. Conclusion: Based on the research, consumer awareness, user experience, and user engagement with multi-sensory technology have a strong impact on purchase intention. Furthermore, gender, shopping frequency, level of interest in digital innovation, and country are related to consumer awareness and purchase intention.

Constraint-based applications attempt to identify a solution that meets all defined user requirements. If the requirements are inconsistent with the underlying constraint set, algorithms that compute diagnoses for inconsistent constraints should be implemented to help users resolve the "no solution could be found" dilemma. FastDiag is a typical direct diagnosis algorithm that supports diagnosis calculation without predetermining conflicts. However, this approach faces runtime performance issues, especially when analyzing complex and large-scale knowledge bases. In this paper, we propose a novel algorithm, so-called FastDiagP, which is based on the idea of speculative programming. This algorithm extends FastDiag by integrating a parallelization mechanism that anticipates and pre-calculates consistency checks requested by FastDiag. This mechanism helps to provide consistency checks with fast answers and boosts the algorithm's runtime performance. The performance improvements of our proposed algorithm have been shown through empirical results using the Linux-2.6.3.33 configuration knowledge base., Comment: presented at The 37th AAAI Conference on Artificial Intelligence, AAAI'23, Washington DC, USA

Oxyfluoride glass-ceramics (OxGCs) with the molar composition 80SiO2-20(1.5Eu3+: NaGdF4) were prepared with sol-gel following the “pre-crystallised nanoparticles route” with promising optical results. The preparation of 1.5 mol % Eu3+-doped NaGdF4 nanoparticles, named 1.5Eu3+: NaGdF4, was optimised and characterised using XRD, FTIR and HRTEM. The structural characterisation of 80SiO2-20(1.5Eu3+: NaGdF4) OxGCs prepared from these nanoparticles’ suspension was performed by XRD and FTIR revealing the presence of hexagonal and orthorhombic NaGdF4 crystalline phases. The optical properties of both nanoparticles’ phases and the related OxGCs were studied by measuring the emission and excitation spectra together with the lifetimes of the 5D0 state. The emission spectra obtained by exciting the Eu3+-O2− charge transfer band showed similar features in both cases corresponding the higher emission intensity to the 5D0→7F2 transition that indicates a non-centrosymmetric site for Eu3+ ions. Moreover, time-resolved fluorescence line-narrowed emission spectra were performed at a low temperature in OxGCs to obtain information about the site symmetry of Eu3+ in this matrix. The results show that this processing method is promising for preparing transparent OxGCs coatings for photonic applications.

Sports recommender systems receive an increasing attention due to their potential of fostering healthy living, improving personal well-being, and increasing performances in sport. These systems support people in sports, for example, by the recommendation of healthy and performance boosting food items, the recommendation of training practices, talent and team recommendation, and the recommendation of specific tactics in competitions. With applications in the virtual world, for example, the recommendation of maps or opponents in e-sports, these systems already transcend conventional sports scenarios where physical presence is needed. On the basis of different working examples, we present an overview of sports recommender systems applications and techniques. Overall, we analyze the related state-of-the-art and discuss open research issues., Comment: Article under review in the Journal of Intelligent Information Systems (Springer JIIS)

Recommender systems assist users in decision-making, where the presentation of recommended items and their explanations are critical factors for enhancing the overall user experience. Although various methods for generating explanations have been proposed, there is still room for improvement, particularly for users who lack expertise in a specific item domain. In this study, we introduce the novel concept of \textit{consequence-based explanations}, a type of explanation that emphasizes the individual impact of consuming a recommended item on the user, which makes the effect of following recommendations clearer. We conducted an online user study to examine our assumption about the appreciation of consequence-based explanations and their impacts on different explanation aims in recommender systems. Our findings highlight the importance of consequence-based explanations, which were well-received by users and effectively improved user satisfaction in recommender systems. These results provide valuable insights for designing engaging explanations that can enhance the overall user experience in decision-making., Comment: The paper was presented at IntRS'23: Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, September 18, 2023, Singapore. and is published in the workshop proceedings: https://ceur-ws.org/Vol-3534

CD4+ conventional T cells (Tconvs) mediate adaptive immune responses, whereas regulatory T cells (Tregs) suppress those responses to safeguard the body from autoimmunity and inflammatory diseases. The opposing activities of Tconvs and Tregs depend on the stage of the immune response and their environment, with an orchestrating role for cytokine- and costimulatory receptors. Nutrient availability also impacts T-cell functionality via metabolic and biosynthetic processes that are largely unexplored. Many data argue that costimulation by Tumor Necrosis Factor Receptor 2 (TNFR2) favors support of Treg over Tconv responses and therefore TNFR2 is a key clinical target. Here, we review the pertinent literature on this topic and highlight the newly identified role of TNFR2 as a metabolic regulator for thymus-derived (t)Tregs. We present novel transcriptomic and metabolomic data that show the differential impact of TNFR2 on Tconv and tTreg gene expression and reveal distinct metabolic impact on both cell types.

Objectives. To determine how harm reduction should be applied in low-resource countries such as Vietnam by exploring the perspectives of people who use drugs (PWUD), health care professionals, and policymakers regarding methadone treatment and harm reduction strategies. Methods. We conducted 2 qualitative studies in Vietnam between 2016 and 2021. We interviewed 62 PWUD and 22 experts in drug policy development and drug treatment programs, conducted observations at methadone clinics and harm reduction program meetings, and analyzed drug policy documents. Results. PWUD considered methadone treatment only as a transition to a drug-free life. Policymakers deemed harm reduction ineffective and continued to enforce arrest and incarceration of PWUD. Drug intervention programs are not yet geared to providing specialized services. Effective communication strategies and information on evidence-based harm reduction models are inadequate to help policymakers make the right decisions. Conclusions. Harm reduction principles have not been fully adopted in Vietnam. A harm reduction strategy based on a more humanistic approach that goes beyond a biomedicalized approach is urgently needed in Vietnam and other countries in the Global South.

Introduction: The emergence of widespread amphetamine-type stimulants (ATSs) usage has created significant challenges for drug control and treatment policies in Southeast Asian countries. This study analyses the development of drug policies and examines current treatment program constraints in Vietnam to deal with ATS misuse. The aim was to gain insights that may be useful for national and international drug-related policy development and revision. Methods: A desk review of national policy documents and 22 in-depth key informant interviews were conducted from 2019 to 2021. Thematic content analysis was employed to identify key themes and their connections. Results: Analysis identified Vietnam’s 30-year history of developing policies and formulating strategies to reduce supply, demand, and harm from illicit drugs. With the increasing number of people who use ATS (PWUA), Vietnam has recently promoted harsh policy and law enforcement to deter drug use and supply. This policy trend prevails in many Asian countries. The three main constraints in dealing with ATS misuse emerged from punitive and restrictive drug policies. First, the general public believed that Centre-based compulsory treatment (CCT) is the only appropriate treatment for all types of illicit drug addiction despite its low-quality service provision. The rigid drug policy has led to social persuasion with impractical expectations for CCT effectiveness. Second, the emphasis on punishment and detention has hampered new drug treatment service development in Vietnam. CCT has become monopolistic in the context of impoverished services. Third, people who use drugs tend to hide their needs and avoid formal treatment and support services, resulting in declined social coherence. Conclusion: While new drugs are constantly evolving, the current law enforcement approach potentially constrains expertise to adopt effective treatment services. This study suggests that the top-down policing mecha

Importance: The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. Objective: To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. Evidence Review: The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). Findings: In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the

Background In estimating the global burden of cancer, adolescents and young adults with cancer are often overlooked, despite being a distinct subgroup with unique epidemiology, clinical care needs, and societal impact. Comprehensive estimates of the global cancer burden in adolescents and young adults (aged 15–39 years) are lacking. To address this gap, we analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, with a focus on the outcome of disability-adjusted life-years (DALYs), to inform global cancer control measures in adolescents and young adults. Methods Using the GBD 2019 methodology, international mortality data were collected from vital registration systems, verbal autopsies, and population-based cancer registry inputs modelled with mortality-to-incidence ratios (MIRs). Incidence was computed with mortality estimates and corresponding MIRs. Prevalence estimates were calculated using modelled survival and multiplied by disability weights to obtain years lived with disability (YLDs). Years of life lost (YLLs) were calculated as age-specific cancer deaths multiplied by the standard life expectancy at the age of death. The main outcome was DALYs (the sum of YLLs and YLDs). Estimates were presented globally and by Socio-demographic Index (SDI) quintiles (countries ranked and divided into five equal SDI groups), and all estimates were presented with corresponding 95% uncertainty intervals (UIs). For this analysis, we used the age range of 15–39 years to define adolescents and young adults. Findings There were 1·19 million (95% UI 1·11–1·28) incident cancer cases and 396 000 (370 000–425 000) deaths due to cancer among people aged 15–39 years worldwide in 2019. The highest age-standardised incidence rates occurred in high SDI (59·6 [54·5–65·7] per 100 000 person-years) and high-middle SDI countries (53·2 [48·8–57·9] per 100 000 person-years), while the highest age-standardised mortality rates were in l

In recent times, the Vietnamese e-commerce market is in a period of strong development, in addition, to the sharp increase in people’s demands to join e-commerce platforms after the epidemic. The research group started with an overview study, then research hypotheses and models were proposed. After conducting preliminary qualitative and quantitative research to adjust the appropriate scales, the research group created a questionnaire and collected data in online forms with a sample size of 350. Next, the data was entered into the software for SEM analysis. The results of the study indicate that the quality of the e-commerce platform has an indirect impact on the purchase intention through the positive impact on the trust and the negative impact on the perceived risk. Attitudes towards information, trust, perceived risk, and perceived usefulness have direct influences on purchase intention. Attitude towards information, trust, and perceived usefulness have positive effects on purchase intention, while perceived risk harms purchase intention. In addition, purchase intention is relatively strongly influenced by trust and perceived usefulness. Perceived risk and attitude towards information have little influence on purchase intention. Finally, the research team proposes some solutions for businesses to increase the purchase intention of consumers through e-commerce platforms. &nbsp

In recent times, the Vietnamese e-commerce market is in a period of strong development, in addition, to the sharp increase in people’s demands to join e-commerce platforms after the epidemic. The research group started with an overview study, then research hypotheses and models were proposed. After conducting preliminary qualitative and quantitative research to adjust the appropriate scales, the research group created a questionnaire and collected data in online forms with a sample size of 350. Next, the data was entered into the software for SEM analysis. The results of the study indicate that the quality of the e-commerce platform has an indirect impact on the purchase intention through the positive impact on the trust and the negative impact on the perceived risk. Attitudes towards information, trust, perceived risk, and perceived usefulness have direct influences on purchase intention. Attitude towards information, trust, and perceived usefulness have positive effects on purchase intention, while perceived risk harms purchase intention. In addition, purchase intention is relatively strongly influenced by trust and perceived usefulness. Perceived risk and attitude towards information have little influence on purchase intention. Finally, the research team proposes some solutions for businesses to increase the purchase intention of consumers through e-commerce platforms. &nbsp

In recent times, the Vietnamese e-commerce market is in a period of strong development, in addition, to the sharp increase in people’s demands to join e-commerce platforms after the epidemic. The research group started with an overview study, then research hypotheses and models were proposed. After conducting preliminary qualitative and quantitative research to adjust the appropriate scales, the research group created a questionnaire and collected data in online forms with a sample size of 350. Next, the data was entered into the software for SEM analysis. The results of the study indicate that the quality of the e-commerce platform has an indirect impact on the purchase intention through the positive impact on the trust and the negative impact on the perceived risk. Attitudes towards information, trust, perceived risk, and perceived usefulness have direct influences on purchase intention. Attitude towards information, trust, and perceived usefulness have positive effects on purchase intention, while perceived risk harms purchase intention. In addition, purchase intention is relatively strongly influenced by trust and perceived usefulness. Perceived risk and attitude towards information have little influence on purchase intention. Finally, the research team proposes some solutions for businesses to increase the purchase intention of consumers through e-commerce platforms. &nbsp

Complex and large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we propose the DirectDebug algorithm that supports the automated testing and debugging of variability models. Our approach assists software engineers in identifying an adaptation hint (diagnosis) that makes all test cases consistent with the knowledge base. We also develop the software package so-called d2bug_eval to evaluate the DirectDebug’s performance. The software package can be re-produced thoroughly to evaluate consistency-based algorithms.

Variability models (e.g., feature models) are a common way for the representation of variabilities and commonalities of software artifacts. Such models can be translated to a logical representation and thus allow different operations for quality assurance and other types of model property analysis. Specifically, complex and often large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we introduce DIRECTDEBUG which is a direct diagnosis approach to the automated testing and debugging of variability models. The algorithm helps software engineers by supporting an automated identification of faulty constraints responsible for an unintended behavior of a variability model. This approach can significantly decrease development and maintenance efforts for such models.

Complex and large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we propose the DirectDebug algorithm that supports the automated testing and debugging of variability models. Our approach assists software engineers in identifying an adaptation hint (diagnosis) that makes all test cases consistent with the knowledge base. We also develop the software package so-called d2bug_eval to evaluate the DirectDebug’s performance. The software package can be re-produced thoroughly to evaluate consistency-based algorithms.

Variability models (e.g., feature models) are a common way for the representation of variabilities and commonalities of software artifacts. Such models can be translated to a logical representation and thus allow different operations for quality assurance and other types of model property analysis. Specifically, complex and often large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we introduce DIRECTDEBUG which is a direct diagnosis approach to the automated testing and debugging of variability models. The algorithm helps software engineers by supporting an automated identification of faulty constraints responsible for an unintended behavior of a variability model. This approach can significantly decrease development and maintenance efforts for such models.

Complex and large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we propose the DirectDebug algorithm that supports the automated testing and debugging of variability models. Our approach assists software engineers in identifying an adaptation hint (diagnosis) that makes all test cases consistent with the knowledge base. We also develop the software package so-called d2bug_eval to evaluate the DirectDebug’s performance. The software package can be re-produced thoroughly to evaluate consistency-based algorithms.

Variability models (e.g., feature models) are a common way for the representation of variabilities and commonalities of software artifacts. Such models can be translated to a logical representation and thus allow different operations for quality assurance and other types of model property analysis. Specifically, complex and often large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we introduce DIRECTDEBUG which is a direct diagnosis approach to the automated testing and debugging of variability models. The algorithm helps software engineers by supporting an automated identification of faulty constraints responsible for an unintended behavior of a variability model. This approach can significantly decrease development and maintenance efforts for such models.

Variability models (e.g., feature models) are a common way for the representation of variabilities and commonalities of software artifacts. Such models can be translated to a logical representation and thus allow different operations for quality assurance and other types of model property analysis. Specifically, complex and often large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we introduce DIRECTDEBUG which is a direct diagnosis approach to the automated testing and debugging of variability models. The algorithm helps software engineers by supporting an automated identification of faulty constraints responsible for an unintended behavior of a variability model. This approach can significantly decrease development and maintenance efforts for such models.

Complex and large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we propose the DirectDebug algorithm that supports the automated testing and debugging of variability models. Our approach assists software engineers in identifying an adaptation hint (diagnosis) that makes all test cases consistent with the knowledge base. We also develop the software package so-called d2bug_eval to evaluate the DirectDebug’s performance. The software package can be re-produced thoroughly to evaluate consistency-based algorithms.

Complex and large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we propose the DirectDebug algorithm that supports the automated testing and debugging of variability models. Our approach assists software engineers in identifying an adaptation hint (diagnosis) that makes all test cases consistent with the knowledge base. We also develop the software package so-called d2bug_eval to evaluate the DirectDebug’s performance. The software package can be re-produced thoroughly to evaluate consistency-based algorithms.

Variability models (e.g., feature models) are a common way for the representation of variabilities and commonalities of software artifacts. Such models can be translated to a logical representation and thus allow different operations for quality assurance and other types of model property analysis. Specifically, complex and often large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we introduce DIRECTDEBUG which is a direct diagnosis approach to the automated testing and debugging of variability models. The algorithm helps software engineers by supporting an automated identification of faulty constraints responsible for an unintended behavior of a variability model. This approach can significantly decrease development and maintenance efforts for such models.

Configuration is a successful application area of Artificial Intelligence. In the majority of the cases, configuration systems focus on configuring one solution (configuration) that satisfies the preferences of a single user or a group of users. In this paper, we introduce a new configuration approach - multi-configuration - that focuses on scenarios where the outcome of a configuration process is a set of configurations. Example applications thereof are the configuration of personalized exams for individual students, the configuration of project teams, reviewer-to-paper assignment, and hotel room assignments including individualized city trips for tourist groups. For multi-configuration scenarios, we exemplify a constraint satisfaction problem representation in the context of configuring exams. The paper is concluded with a discussion of open issues for future work., Comment: Cite as: A. Felfernig, A. Popescu, M. Uta, V.M. Le, S.P. Erdeniz, M. Stettinger, M. Atas, and T.N.T. Tran. Configuring Multiple Instances with Multi-Configuration. 23rd International Configuration Workshop, Vienna, Austria, CEUR, vol. 2945, pp. 45-47, 2021

Recommender systems support decisions in various domains ranging from simple items such as books and movies to more complex items such as financial services, telecommunication equipment, and software systems. In this context, recommendations are determined, for example, on the basis of analyzing the preferences of similar users. In contrast to simple items which can be enumerated in an item catalog, complex items have to be represented on the basis of variability models (e.g., feature models) since a complete enumeration of all possible configurations is infeasible and would trigger significant performance issues. In this paper, we give an overview of a potential new line of research which is related to the application of recommender systems and machine learning techniques in feature modeling and configuration. In this context, we give examples of the application of recommender systems and machine learning and discuss future research issues., Comment: Proceedings of ACM Vamos 2021

Variability models (e.g., feature models) are a common way for the representation of variabilities and commonalities of software artifacts. Such models can be translated to a logical representation and thus allow different operations for quality assurance and other types of model property analysis. Specifically, complex and often large-scale feature models can become faulty, i.e., do not represent the expected variability properties of the underlying software artifact. In this paper, we introduce DirectDebug which is a direct diagnosis approach to the automated testing and debugging of variability models. The algorithm helps software engineers by supporting an automated identification of faulty constraints responsible for an unintended behavior of a variability model. This approach can significantly decrease development and maintenance efforts for such models., Comment: to appear in the ICSE'21 Proceedings, 5 pages, 2 figures

The induction of additional randomness in parallel and sequential ensemble methods has proven to be worthwhile in many aspects. In this manuscript, we propose and examine a novel random tree depth injection approach suitable for sequential and parallel tree-based approaches including Boosting and Random Forests. The resulting methods are called \emph{Random Boost} and \emph{Random$^2$ Forest}. Both approaches serve as valuable extensions to the existing literature on the gradient boosting framework and random forests. A Monte Carlo simulation, in which tree-shaped data sets with different numbers of final partitions are built, suggests that there are several scenarios where \emph{Random Boost} and \emph{Random$^2$ Forest} can improve the prediction performance of conventional hierarchical boosting and random forest approaches. The new algorithms appear to be especially successful in cases where there are merely a few high-order interactions in the generated data. In addition, our simulations suggest that our random tree depth injection approach can improve computation time by up to 40%, while at the same time the performance losses in terms of prediction accuracy turn out to be minor or even negligible in most cases.

Background: Considerable progress has been made in dengue management, however the lack of appropriate predictors of severity has led to huge number of unwanted admissions mostly decided on the grounds of warning signs. Apoptosis related mediators,among others, are known to correlate with severe dengue (SD) although no predictive validity is established. The objective of this study was to investigate the association of plasma cell-free DNA (cfDNA) with SD, and evaluate its prognostic value in SD prediction at acute phase. Methods: This was a hospital-based prospective cohort study conducted in Vietnam.All the recruited patients were required to be admitted to the hospital and were strictly monitored for various laboratory and clinical parameters(including progression to SD) until discharged. Plasma samples collected during acute phase (6-48 h before defervescence) were used to estimate the level of cfDNA. Results: Of the 61 dengue patients,SD patients (n = 8)developed shock syndrome in 4.8 days (95% CI 3.7-5.4) after the fever onset. Plasma cfDNA levels before the defervescence of SD patients were significantly higher than the non-SD group (p = 0.0493). From the receiver operating characteristic (ROC) curve analysis, a cut-off of > 36.9 ng/mL was able to predict SD with a good sensitivity (87.5%), specificity (54.7%), and area under the curve (AUC) (0.72, 95% CI 0.55-0.88; p = 0.0493).Conclusions: Taken together, these findings suggest that cfDNA could serve as a potential prognostic biomarker of SD. Studies with cfDNA kinetics and its combination with other biomarkers and clinical parameters would further improve the diagnostic ability for SD., Annals of Clinical Microbiology and Antimicrobials, 18(1), art.no.10; 2019

The wide spread usage of automated data-driven decision support systems has raised a lot of concerns regarding accountability and fairness of the employed models in the absence of human supervision. Existing fairness-aware approaches tackle fairness as a batch learning problem and aim at learning a fair model which can then be applied to future instances of the problem. In many applications, however, the data comes sequentially and its characteristics might evolve with time. In such a setting, it is counter-intuitive to "fix" a (fair) model over the data stream as changes in the data might incur changes in the underlying model therefore, affecting its fairness. In this work, we propose fairness-enhancing interventions that modify the input data so that the outcome of any stream classifier applied to that data will be fair. Experiments on real and synthetic data show that our approach achieves good predictive performance and low discrimination scores over the course of the stream., Comment: 15 pages, 7 figures. To appear in the proceedings of 30th International Conference on Database and Expert Systems Applications, Linz, Austria August 26 - 29, 2019

Les irrégularités financières en entreprise ne sont pas un nouveau phénomène. Cependant, depuis les années 90 avec l’omniprésence des médias, la diffusion médiatique des annonces de fraudes boursières semble avoir un impact significatif sur le comportement des investisseurs et, plus particulièrement, sur les cours boursiers. Toutefois, au-delà de son effet sur la performance financière, une autre question fondamentale se pose : est-ce que l’annonce médiatique d’irrégularités ou de fraudes financières influence le risque des entreprises ? Si oui, quels sont les types de risque affectés et quel est l’effet du traitement médiatique ? Afin de répondre à cette question, ce papier se base sur un modèle d’évaluation d’actif conditionnel et sur un modèle de variance conditionnelle GJR-GARCH. Ces modèles capturent, respectivement, la variation du risque systématique et la variation du risque total. Nous étudions ces variations de risque selon le traitement médiatique, le type de fraude, la victime de fraude ainsi que la décision juridique. Notre échantillon contient 491 événements-entreprise, concernant des fraudes et des irrégularités financières d’une entreprise (première diffusion et suivi) qui commence, au plus tôt, le 1er janvier 2000, et qui se termine, au plus tard, le 31 décembre 2011. En général, nos résultats ne permettent pas de conclure à une augmentation significative du risque d’une entreprise suite à une annonce d’irrégularité. Ces résultats sont cohérents avec la littérature existante sur l’analyse de risque à la suite d’une annonce de fraude. Plusieurs avenues sont proposées pour expliquer cette conclusion qui peut sembler, de prime abord, contre-intuitive. Ainsi, cette conclusion pourrait être expliquée par l’anticipation de l’annonce d’une irrégularité par les investisseurs, par une meilleure gestion anticipée de l’entreprise suite à la révélation d’une irrégularité, ou encore par la neutralité de l’irrégularité sur le risque de l’entreprise. Nos résultats

Les irrégularités financières en entreprise ne sont pas un nouveau phénomène. Cependant, depuis les années 90 avec l’omniprésence des médias, la diffusion médiatique des annonces de fraudes boursières semble avoir un impact significatif sur le comportement des investisseurs et, plus particulièrement, sur les cours boursiers. Toutefois, au-delà de son effet sur la performance financière, une autre question fondamentale se pose : est-ce que l’annonce médiatique d’irrégularités ou de fraudes financières influence le risque des entreprises ? Si oui, quels sont les types de risque affectés et quel est l’effet du traitement médiatique ? Afin de répondre à cette question, ce papier se base sur un modèle d’évaluation d’actif conditionnel et sur un modèle de variance conditionnelle GJR-GARCH. Ces modèles capturent, respectivement, la variation du risque systématique et la variation du risque total. Nous étudions ces variations de risque selon le traitement médiatique, le type de fraude, la victime de fraude ainsi que la décision juridique. Notre échantillon contient 491 événements-entreprise, concernant des fraudes et des irrégularités financières d’une entreprise (première diffusion et suivi) qui commence, au plus tôt, le 1er janvier 2000, et qui se termine, au plus tard, le 31 décembre 2011. En général, nos résultats ne permettent pas de conclure à une augmentation significative du risque d’une entreprise suite à une annonce d’irrégularité. Ces résultats sont cohérents avec la littérature existante sur l’analyse de risque à la suite d’une annonce de fraude. Plusieurs avenues sont proposées pour expliquer cette conclusion qui peut sembler, de prime abord, contre-intuitive. Ainsi, cette conclusion pourrait être expliquée par l’anticipation de l’annonce d’une irrégularité par les investisseurs, par une meilleure gestion anticipée de l’entreprise suite à la révélation d’une irrégularité, ou encore par la neutralité de l’irrégularité sur le risque de l’entreprise. Nos résultats

To document the viral zoonotic risks in Vietnam, fecal samples were systematically collected from a number of mammals in southern Vietnam and subjected to agnostic deep sequencing. We describe here novel Vietnamese bunyavirus sequences detected in bat feces. The complete L and S segments from 14 viruses were determined.

Background: Recent in-vitro studies have suggested that mast cells are involved in Dengue virus infection. To clarify the role of mast cells in the development of clinical Dengue fever, we compared the plasma levels of several mast cell-derived mediators (vascular endothelial cell growth factor [VEGF], soluble VEGF receptors [sVEGFRs], tryptase, and chymase) and -related cytokines (IL-4, -9, and -17) between patients with differing severity of Dengue fever and healthy controls. Methodology/Principal Findings: The study was performed at Children's Hospital No. 2, Ho Chi Minh City, and Vinh Long Province Hospital, Vietnam from 2002 to 2005. Study patients included 103 with Dengue fever (DF), Dengue hemorrhagic fever (DHF), and Dengue shock syndrome (DSS), as diagnosed by the World Health Organization criteria. There were 189 healthy subjects, and 19 febrile illness patients of the same Kinh ethnicity. The levels of mast cell-derived mediators and -related cytokines in plasma were measured by ELISA. VEGF and sVEGFR-1 levels were significantly increased in DHF and DSS compared with those of DF and controls, whereas sVEGFR-2 levels were significantly decreased in DHF and DSS. Significant increases in tryptase and chymase levels, which were accompanied by high IL-9 and -17 concentrations, were detected in DHF and DSS patients. By day 4 of admission, VEGF, sVEGFRs, and proteases levels had returned to similar levels as DF and controls. In-vitro VEGF production by mast cells was examined in KU812 and HMC-1 cells, and was found to be highest when the cells were inoculated with Dengue virus and human Dengue virus-immune serum in the presence of IL-9. Conclusions: As mast cells are an important source of VEGF, tryptase, and chymase, our findings suggest that mast cell activation and mast cell-derived mediators participate in the development of DHF. The two proteases, particularly chymase, might serve as good predictive markers of Dengue disease severity., PLoS Neglected Tropical Diseases, 6(2), e1505; 2012

Apoptosis is thought to play a role in the pathogenesis of severe dengue and the release of cell-free DNA into the circulatory system in several medical conditions. Therefore, we investigated circulating DNA as a potential biomarker for severe dengue., PLoS One, 6(10), e25969; 2011

Joint Research on Environmental Science and Technology for the Earth

This thesis examines the impact of director networks on corporate strategic decisions, and consists of three main studies. The first and the second investigate the influence of director networks on M&A short-term and long-term performance, respectively, using a sample of large Indian firms from the BSE 500 and CNX 500 indices from 2000 to 2014. Specifically, the key focus of the first two studies is whether the continuity of director networks affects the performance of bidding firms, and whether independent director networks matter to M&A short-term and long-term performance. Consistent with the market reaction to connected deals at the announcement date, the study shows a negative association between bidding firms’ operating performance following M&A transactions in which bidders and targets are connected through shared directors. The third study examines the relationship between busy independent boards and corporate external finance using an unbalanced panel data of 445 top firms in India from 2000 to 2014. The results show that there is a negative association between busy independent boards and external leverage. The results reveal the existence of an internal capital market in Indian business groups, and shows a negative relationship between net loan receivers and external finance. The study also reflects that, compared to non-group firms with busy independent boards, group firms with a greater connectedness of independent directors face more limited access to external finance.

Joint Research on Environmental Science and Technology for the Earth

Joint Research on Environmental Science and Technology for the Earth

Joint Research on Environmental Science and Technology for the Earth

Joint Research on Environmental Science and Technology for the Earth

Joint Research on Environmental Science and Technology for the Earth

Joint Research on Environmental Science and Technology for the Earth

Joint Research on Environmental Science and Technology for the Earth

Joint Research on Environmental Science and Technology for the Earth