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Environmental factors may operate differently when relations are measured across different geographical locations, a phenomenon known as spatial non-stationarity. This study investigates the spatial non-stationarity effect of unhealthy food environments and green spaces on the T2DM prevalence rate at the neighborhood level in Toronto. This study also compares how the results vary between age groups, classified as all adults (20 and above), young adults (from 20 to 44), middle adulthood (from 45 to 64), and seniors (65 and above). The geographically weighted regression model is utilized to explore the impacts of spatial non-stationarity effects on the research results, which may lead to biased conclusions, which have often been ignored in past studies. The results from this study reveal that environmental variables dissimilarly affect T2DM prevalence rates among different age groups and neighborhoods in Toronto after controlling for socioeconomic factors. For example, the green space density yields positive associations with diabetes prevalence rates for elder generations but negative relationships for younger age groups in twenty-two and four neighborhoods, respectively, around Toronto East. The observed associations will provide beneficial suggestions to support government and public health authorities in designing education, prevention, and intervention programs targeting different neighborhoods to control the burden of diabetes.

The lockdown and social distancing policies to reduce COVID-19 spread and perceived safety threats of COVID-19 significantly affected people’s travel behavior. Greece has been suffering from the COVID-19 pandemic, and people’s mobility behavior has been greatly affected. This study aims at: (1) exploring the variations in individuals’ trip frequencies by mode and purpose before and after the COVID-19 outbreak; (2) understanding the effects of individual differences (i.e., sociodemographic details) and perceptions towards COVID-19 (i.e., the perceived threats of COVID-19) on people’s mobility behavior changes after the outbreak; (3) underlining the individuals’ perceptions of the COVID-19 threat on the willingness of public transportation usage. Overall, 403 responses were collected in late 2020. A series of random parameter Probit modeling results reveal multiple individual and perception factors affecting the changes in mobility behavior in Greece. The results from structural equation modeling indicate that perceived COVID-19 threats affect the attitudes and subjective norms towards people’s intentions to use public transportation. The results from this study provide valuable insights for transportation authorities to develop effective strategies to manage traffic during the spread of disease for a possible future epidemic.

The prevailing need for a more sustainable management of natural resources depends not only on the decisions made by governments and the will of the population, but also on the knowledge of the role of energy in our society and the relevance of preserving natural resources. In this sense, critical work is being done to instill key concepts—such as the circular economy and sustainable energy—in higher education institutions. In this way, it is expected that future professionals and managers will be aware of the importance of energy optimization, and will learn a series of computational methods that can support the decision-making process. In the context of higher education, this paper reviews the main trends and challenges related to the concepts of circular economy and sustainable energy. Besides, we analyze the role of simulation and serious games as a learning tool for the aforementioned concepts. Finally, the paper provides insights and discusses open research opportunities regarding the use of these computational tools to incorporate circular economy concepts in higher education degrees. Our findings show that, while efforts are being made to include these concepts in current programs, there is still much work to be done, especially from the point of view of university management. In addition, the analysis of the teaching methodologies analyzed shows that, although their implementation has been successful in favoring the active learning of students, their use (especially that of serious games) is not yet widespread.

Background: HIV-1 protease inhibitor (PIs) is a good choice for AIDS patients. Nevertheless, for PIs, there are several bugs in clinical application, like drug resistance, the large dose, the high costs and so on, among which, the poor pharmacokinetics property is one of the important reasons that leads to the failure of its clinical application. Objective: We aimed to build computational models for studying the relationship between PIs structure and its pharmacological activities. Methods: We collected experimental values of koff/Ki and structures of 50 PIs through a careful literature and database search. Quantitative structure activity/pharmacokinetics relationship (QSAR/QSPR) models were constructed by support vector machine (SVM), partial-least squares regression (PLSR) and back-propagation neural network (BPNN). Results: For QSAR models, SVM, PLSR and BPNN all generated reliable prediction models with the r2 of 0.688, 0.768 and 0.787, respectively, and r2pred of 0.748, 0.696 and 0.640, respectively. For QSPR models, the optimum models of SVM, PLSR and BPNN obtained the r2 of 0.952, 0.869 and 0.960, respectively, and the r2pred of 0.852, 0.628 and 0.814, respectively. Conclusion: Among these three modelling methods, SVM showed superior ability than PLSR and BPNN both in QSAR/QSPR modelling of PIs, thus, we suspected that SVM was more suitable for predicting activities of PIs. In addition, 3D-MoRSE descriptors may have a tight relationship with the Ki values of PIs, and the GETAWAY descriptors have significant influence on both koff and Ki in PLSR equations.

AbstractBackground and aimsCannabis and alcohol are frequently detected in fatal and injury motor vehicle crashes. While epidemiological meta‐analyses of cannabis and alcohol have found associations with an increase in crash risk, convergent evidence from driving performance measures is insufficiently quantitatively characterized. Our objectives were to quantify the magnitude of the effect of cannabis and alcohol—alone and in combination—on driving performance and behaviour.MethodsSystematic review and meta‐analysis. We systematically searched Academic Search Complete, CINAHL, Embase, Scopus, Google Scholar, MEDLINE, PsycINFO, SPORTDiscus and TRID. Of the 616 studies that underwent full‐text review, this meta‐analysis represents 57 studies and 1725 participants. We extracted data for hazard response time, lateral position variability, lane deviations or excursions, time out of lane, driving speed, driving speed variability, speed violations, time speeding, headway, headway variability and crashes from experimental driving studies (i.e. driving simulator, closed‐course, on‐road) involving cannabis and/or alcohol administration. We reported meta‐analyses of effect sizes using Hedges’ g and r.ResultsCannabis alone was associated with impaired lateral control [e.g. g = 0.331, 95% confidence interval (CI) = 0.212–0.451 for lateral position variability; g = 0.198, 95% CI = 0.001–0.395 for lane excursions) and decreased driving speed (g = –0.176, 95% CI = –0.298 to –0.053]. The combination of cannabis and alcohol was associated with greater driving performance decrements than either drug in isolation [e.g. g = 0.480, 95% CI = 0.096–0.865 for lateral position variability (combination versus alcohol); g = 0.525, 95% CI = 0.049–1.002 for time out of lane (versus alcohol); g = 0.336, 95% CI = 0.036–0.636 for lateral position variability (combination versus cannabis; g = 0.475, 95% CI = 0.002–0.949 for time out of lane (combination versus cannabis)]. Subgroup analyses indicated that the effects of cannabis on driving performance measures were similar to low blood alcohol concentrations. A scarcity of data and study heterogeneity limited the interpretation of some measures.ConclusionsThis meta‐analysis indicates that cannabis, like alcohol, impairs driving, and the combination of the two drugs is more detrimental to driving performance than either in isolation.

The European Universities initiative, launched by the European Commission in 2018, has its origin in the concept of Civic Universities (CivUs) and consists of transnational higher education alliances throughout the European Union that share long-term strategies. They are expected to become universities of the future, to promote European ideals and character, and to revolutionize the competitiveness and excellence of European higher education. European universities add 41 alliances, involving 31 different countries. This article presents an early quantitative evaluation of this initiative. This paper addresses the coverage of the 41 alliances and selects five of the most advanced for a deeper evaluation of their best practices and their contribution to the realization of CivUs. This paper also outlines the criteria for evaluating the extent to which good practices implemented by these alliances are aligned and can contribute to the attributes of CivUs, based upon state-of-the-art educational standards. A quantitative framework, based on application of the analytical hierarchy process (AHP), is also provided to rank the good practices developed by these alliances against the previous evaluation criteria. Furthermore, by applying a sensitivity analysis, this paper also addresses the robustness of this approach.

Computational thinking (CT) skills are now a key part of everyday life and work, and CT has been incorporated into K-12 curricula worldwide. Combining the fundamental concepts of CT with English writing constitutes an innovative and sustainable learning strategy. However, few academic studies have examined the incorporation of CT into English writing. English writing frequently generates excessive stress and anxiety among students, yet motivation can mitigate the negative effect of anxiety. This study investigated the effects of the fundamental concepts of CT on reducing writing anxiety and increasing motivation toward English writing. A quasi-experimental design was applied, and data were collected from experimental and control groups through writing anxiety and motivation questionnaires. The results indicated that the fundamental concepts of CT exerted a more significant influence on the dimensions of writing anxiety and motivation than did a conventional learning method. In conclusion, the fundamental concepts of CT promoted organized and structured English writing, increased students’ writing motivation, and reduced their writing anxiety.

Abstract: COVID-19, caused by the SARS-CoV-2 virus, has been expanding. SARS-CoV caused an outbreak in early 2000, while MERS-CoV had a similar expansion of illness in early 2010. Nanotechnology has been employed for nasal delivery of drugs to conquer a variety of challenges that emerge during mucosal administration. The role of nanotechnology is highly relevant to counter this “virus” nano enemy. This technique directs the safe and effective distribution of accessible therapeutic choices using tailored nanocarriers, as well as the interruption of virion assembly, by preventing the early contact of viral spike glycoprotein with host cell surface receptors. This study summarises what we know about earlier SARS-CoV and MERS-CoV illnesses, with the goal of better understanding the recently discovered SARS-CoV-2 virus. It also explains the progress made so far in creating COVID-19 vaccines/ treatments using existing methods. Furthermore, we studied nanotechnology- based vaccinations and therapeutic medications that are now undergoing clinical trials and other alternatives.