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In December 2019, a novel coronavirus broke out in Wuhan City, Hubei Province, and, as the center of the coronavirus disease 2019 (COVID-19) epidemic, the economy and production throughout Hubei Province suffered huge temporary impacts. Based on the input–output and industrial pollution emissions data of 33 industrial industries in Hubei from 2010 to 2019, this article uses the non-parametric frontier analysis method to calculate the potential production losses and compliance costs caused by environmental regulations in Hubei's industrial sector by year and industry. Research has found that the environmental technology efficiency of the industrial sector in Hubei is showing a trend of increasing year-on-year, but the overall efficiency level is still not high, and there is great room for improvement. The calculation results with and without environmental regulatory constraints indicate that, generally, production losses and compliance costs may be encountered in the industrial sector in Hubei, and there are significant differences by industry. The potential production losses and compliance costs in pollution-intensive industries are higher than those in clean production industries. On this basis, we propose relevant policy recommendations to improve the technological efficiency of Hubei's industrial environment, in order to promote the high-quality development of Hubei's industry in the post-epidemic era.
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Covid-19 has a significant risk of spreading in urban areas because of the aglomeration of built-up areas and people. It frequently contains a mix of land uses and is accessible to urban amenities. Due to the area's extensive usage of mixed land uses, it is better able to provide internal urban services on its own. Greater use of area lockdown and social separation strategies could result from this situation. The most populous city in the province of Central Java, Surakarta, has a significant risk of contracting COVID-19. The purpose of this study is to evaluate the impact of density and levels of mixed land use on the Covid-19 distribution in Surakarta City.Population density is used to calculate density. The entropy index approach was used to measure the amount of mixed land use. It is a method for calculating the balance between each form of land use. The availability of current land use data being processed by the spatial analysis with the Arc GIS application provided help for the analysis. Additionally, it makes use of information on Covid-19 cases in relation to the general populace that is supplied by the Surakarta Municipality. The relationship between mixed land use and Covid-19 risk was analyzed using a linear regression approach. The study's findings indicated a minor influence between density and the spread of COVID-19. Meanwhile, the level of mixed land use does not influence the spread of the Covid-19 virus in Surakarta City.
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One of the leading causes of the increase in the intensity of dengue fever transmission is thought to be climate change. Examining panel data from January 2000 to December 2021, this study discovered the nonlinear relationship between climate variables and dengue fever cases in Bangladesh. To determine this relationship, in this study, the monthly total rainfall in different years has been divided into two thresholds: (90 to 360 mm) and (<90 or >360 mm), and the daily average temperature in different months of the different years has been divided into four thresholds: (16degreeC to <=20degreeC), (>20degreeC to <=25degreeC), (>25degreeC to <=28degreeC), and (>28degreeC to <=30degreeC). Then, quasi-Poisson and zero-inflated Poisson regression models were applied to assess the relationship. This study found a positive correlation between temperature and dengue incidence and furthermore discovered that, among those four average temperature thresholds, the total number of dengue cases is maximum if the average temperature falls into the threshold (>28degreeC to <=30degreeC) and minimum if the average temperature falls into the threshold (16degreeC to <=20degreeC). This study also discovered that between the two thresholds of monthly total rainfall, the risk of a dengue fever outbreak is approximately two times higher when the monthly total rainfall falls into the thresholds (90 mm to 360 mm) compared to the other threshold. This study concluded that dengue fever incidence rates would be significantly more affected by climate change in regions with warmer temperatures. The number of dengue cases rises rapidly when the temperature rises in the context of moderate to low rainfall. This study highlights the significance of establishing potential temperature and rainfall thresholds for using risk prediction and public health programs to prevent and control dengue fever.Copyright © 2023 Shamima Hossain.
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The traditional lecture-based model of teaching and learning has led to the exploration of innovative approaches including digital escape rooms. Digital escape rooms offer an immersive and engaging experience that promotes critical thinking, problem-solving, and teamwork, making them a unique opportunity to address the challenges of STEM education, which is often perceived as difficult, boring, and intimidating. In this study, the goal is to explore the application of digital escape rooms as an innovative practice in STEAM (science, technology, engineering, arts, and mathematics) education in Europe. More specifically, the study aims to evaluate the influence of digital escape rooms on student engagement and learning outcomes in mathematics education as well as to provide valuable insights into the efficacy of this approach as a means of teaching mathematics and fostering active and experiential learning in STEAM education. In order to investigate the potential of digital escape rooms as a metaphor for breaking down barriers and escaping from the ”box” in STEM education, this paper proposes a conceptual framework for understanding the pedagogical value of digital escape rooms in STEM education. It outlines the design process, including learning paths and scenarios, storyline, puzzles, challenges, and feedback mechanisms, and presents a concept of escape room design patterns. An example case study of a digital escape room designed to teach mathematics to university students is also presented, providing insights into the effectiveness of this approach. By using digital escape rooms as a metaphor for breaking down barriers in STEM education, a more inclusive, engaging, and impactful learning environment can be created to prepare students for the challenges and opportunities of the 21st century.
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PurposeDecision-making problems in emergency plan selection for epidemic prevention and control (EPAC) are generally characterized by risky and uncertainty due to multiple possible emergency states and vagueness of decision information. In the process of emergency plan selection for EPAC, it is necessary to consider several obvious features, i.e. different states of epidemics, dynamic evolvement process of epidemics and decision-makers' (DMs') psychological factors such as risk preference and loss aversion.Design/methodology/approachIn this paper, a novel decision-making method based on cumulative prospect theory (CPT) is proposed to solve emergency plan selection of an epidemic problem, which is generally regarded as hybrid-information multi-attribute decision-making (HI-MADM) problems in major epidemics. Initially, considering the psychological factors of DMs, the expectations of DMs are chosen as reference points to normalize the expectation vectors and decision information with three different formats. Subsequently, the matrix of gains and losses is established according to the reference points. Furthermore, the prospect value of each alternative is obtained and the comprehensive prospect values of alternatives under different states are calculated. Accordingly, the ranking of alternatives can be obtained.FindingsThe validity and robustness of the proposed method are demonstrated by a case calculation of emergency plan selection. Meanwhile, sensitivity analysis and comparison analysis with fuzzy similarity to ideal solution (FTOPSIS) method and TODIM (an acronym in Portuguese for interactive and MADM) method illustrate the effectiveness and superiority of the proposed method.Originality/valueAn emergency plan selection method is proposed for EPAC based on CPT, taking into account the psychological factors of DMs.HighlightsThis paper proposes a new CPT-based EDM method for EPAC under a major epidemic considering the psychological factorsof DMs, such as risk preference, loss aversion and so on.The authors' work gives approaches of normalization, comparison and distance measurement for dealing with the integration of three hybrid formats of attributes.This article gives some guidance, which contributes to solve the problems of risk-based hybrid multi-attribute EDM.The authors illustrate the advantages of the proposed method by a sensitivity analysis and comparison analysis with existing FTOPSIS method and TODIM method.
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Larson discusses the mathematical equation of the Covid-19 pandemic. He offers a mathematical formula to explain the spread, transmission and infection of the Covid-19. He also explains how measures such as social isolation can help protect the most vulnerable population such as the senior citizens and those with chronic medical conditions and asserts that with the significant number of re-openings, if done with meticulous care, will not increase the current value of infections. The author also emphasizes the importance of masks, social distancing and temperature checks and until vaccine is available, everything will be back to normal.
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Covid19 has infected many individuals around the world, this virus is spreading rapidly. In the context of controlling and handling the spread of COVID-19, appropriate strategies and policies are needed, mathematics will play a very important role in this problem, especially to provide information about this with an understanding of the dynamics of the transmission of this covid virus. To suppress the spread of the Covid-19 virus which is currently hitting, several countries have implemented large-scale social restrictions. To identify the best approach to reduce of this Covid-19 disease spreading at minimal cost, we developed a mathematical model of the covid 19 virus by implementing large-scale social restrictions and applying optimal control theory. We provide two types of control: the first is in the form of an education campaign about covid-19 and an awareness program, and the second is in the form of a quarantine program. Compared with no optimal control, giving optimal control can provide a more significant reduction in the number of populations S, Sr, O, P, I and increase the number of individual populations that recover more significantly.
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Two coordination complexes, a cobalt(II) complex tris(1,10-phenanthroline)-cobalt perchlorate hydrate, [Co(phen)3]·(ClO4)2·H2O(1), and a copper(II) complex tris(1,10-phenanthroline)-copper perchlorate 4-bromo-2-{[(naphthalene-1-yl)imino]methyl}phenol hydrate, [Cu(phen)3]·(ClO4)2·HL·[O] (2), [where, phen = 1,10-phenathroline as aromatic heterocyclic ligand, HL = 4-bromo-2-((Z)-(naphthalene-4-ylimino) methyl) phenol] have been synthesized and structurally characterized. Single crystal X-ray analysis of both complexes has revealed the presence of a distorted octahedral geometry around cobalt(II) and copper(II) ions. density functional theory (DFT)-based quantum chemical calculations were performed on the cationic complex [Co(phen)3]2+ and copper(II) complex [Cu(phen)3]2+ to get the structure property relationship. Hirshfeld surface and 2-D fingerprint plots have been explored in the crystal structure of both the metal complexes. To find potential SARS-CoV-2 drug candidates, both the complexes were subjected to molecular docking calculations with SARS-CoV-2 virus (PDB ID: 7BQY and 7C2Q). We have found stable docked structures where docked metal chelates could readily bound to the SARS-CoV-2 Mpro. The molecular docking calculations of the complex (1) into the 7C2Q-main protease of SARS-CoV-2 virus revealed the binding energy of −9.4 kcal/mol with a good inhibition constant of 1.834 µM, while complex (2) exhibited the binding energy of −9.0 kcal/mol, and the inhibition constant of 1.365 µM at the inhibition binding site of receptor protein. Overall, our in silico studies explored the potential role of cobalt(II) complex (1), and copper(II) complex (2) complex as the viable and alternative therapeutic solution for SARS-CoV-2.
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This article formulates and analyzes a discrete-time Human immunodeficiency virus type 1 (HIV-1) and human T-lymphotropic virus type I (HTLV-I) coinfection model with latent reservoirs. We consider that the HTLV-I infect the CD4(+)T cells, while HIV-1 has two classes of target cells-CD4(+)T cells and macrophages. The discrete-time model is obtained by discretizing the original continuous-time by the non-standard finite difference (NSFD) approach. We establish that NSFD maintains the positivity and boundedness of the model's solutions. We derived four threshold parameters that determine the existence and stability of the four equilibria of the model. The Lyapunov method is used to examine the global stability of all equilibria. The analytical findings are supported via numerical simulation. The impact of latent reservoirs on the HIV-1 and HTLV-I co-dynamics is discussed. We show that incorporating the latent reservoirs into the HIV-1 and HTLV-I coinfection model will reduce the basic HIV-1 single-infection and HTLV-I single-infection reproductive numbers. We establish that neglecting the latent reservoirs will lead to overestimation of the required HIV-1 antiviral drugs. Moreover, we show that lengthening of the latent phase can suppress the progression of viral coinfection. This may draw the attention of scientists and pharmaceutical companies to create new treatments that prolong the latency period.
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Amoebiasis is a parasitic intestinal infection caused by the highly pathogenic amoeba Entamoeba histolytica. It is spread through person-to -person contact or by eating or drinking food or water contaminated with feces. Its transmission rate depends on the number of cysts present in the environment. The traditional models assumed a homogeneous and contra-dictory transmission with reality. The heterogeneity of its transmission rate is a significant factor when modeling disease dynamics. The heterogeneity of disease transmission can be described mathematically by introducing fuzzy theory. In this context, a fuzzy SEIR Amoebiasis disease model is consid-ered in this study. The equilibrium analysis and reproductive number are studied with fuzziness. Two numerical schemes forward Euler method and a nonstandard finite difference (NSFD) approach, are developed for the learned model, and the results of numerical simulations are presented. The numerical and simulation results reveal that the proposed NSFD method provides an adequate representation of the dynamics of the disease despite the uncertainty and heterogeneity. Moreover, the obtained method generates plausible predictions that regulators can use to support decision-making to design and develop control strategies.
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Artificially prepared microbial spores have excellent electromagnetic attenuation properties due to their special composition and structure. At present, studies on the optical properties of microbial spores have mainly focused on those with a single band or a single germplasm, which has limitations and cannot reveal the optical properties comprehensively. In this paper, 3 kinds of laboratory-prepared microbial spores were selected for compounding, and the spectral reflectivities of single-germplasm biospores and compound biospores were measured in the wavebands of 0.25–2.4 and 3–15 μm. The complex refractive indices (CRIs) were calculated in combination with the Kramers–Kronig (K-K) algorithm. Relying on the smoke box broadband test system, the transmittance of single-germplasm bioaerosols and compound bioaerosols from the ultraviolet (UV) band to the far-infrared (FIR) band was measured, and the mass extinction coefficients were calculated. The results indicate that the trend of the complex refractive indices of the compound spores is consistent with that of the single-germplasm spores with a larger particle size. For the single-germplasm bioaerosols, the lowest transmittance values were 2.21, 5.70 and 6.27% in the visible (VIS), near-infrared (NIR) and middle-infrared (FIR) bands, and the mass extinction coefficients reached 1.15, 0.87 and 0.84 m2/g, respectively. When AO and BB spores were compounded at 4:1, the extinction performance of the bioaerosols somewhat improved in all wavebands. These results can help to comprehensively analyze the optical properties of bioaerosols and provide ideas for the development of new extinction materials.
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3rd International Conference on Physics and Engineering Mathematics (ICPEM2022) (https://www.icpem.cn/), Due to the influence of COVID-19, the Online International Conference was held on December 16-17, 2022. The Conference covers a broad range of areas from Physics and Engineering Mathematics. Applied Physics, Engineering Mathematics, Modeling and simulation are just a few subjects covered by the Conference. Aiming at providing an excellent international academic forum for all the researchers and practitioners, the conference enjoys a wide spread participation among all over the universities and research institutes.Organization is led by Hubei Zhongke Research Institute of Nature Science, China. We have 6 keynote speakers who will be talking on a diversity of interesting topics during the Conference. We rigorously assessed 77 submitted papers of which 42 were accepted. All the accepted papers have been submitted to strict peer-review by 2-3 international expert referees, and selected based on originality, significance and clarity for the purpose of the conference. We are grateful to our 66 reviewers who dedicated their valuable time and put sincere effort in assessing submissions. We thank to all contributors to ICPEM2022 whether their submissions were accepted or not. We wish that their invaluable contributions will continue in ICPEM2023.Finally, we hope this collection can enhance the understanding of physics sciences and encourage more applications and creations. We appreciate all participants and speakers who presented at ICPEM Online conference at this special year;meanwhile, we look forward to a more successful conference next year.The Organizing Committee of ICPEM2022List of Committee, Conference Chairs, Editors, Program Chairs, Technical Program Committee are available in this pdf.
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Background: Air travel restrictions to prevent the spread of coronavirus disease 2019 (COVID-19) impacted air travel to and from and within South Africa significantly. The duration of the pandemic was more protracted than initially expected as new variants of the pandemic (in ‘waves') resulted in additional restrictions. Objectives: To determine the nature of COVID-19-related air travel restrictions, their impact on annual passenger demand, the number of flights operated (supply of services), the related average passenger loads carried as well as on tourism indicators of the direct contribution of travel and tourism, the total contribution to Gross Domestic Product (GDP) and employment. Method: The study identifies the number of passengers carried and flights operated and calculates the average passenger load per flight and trends. The impact on tourism indicators is based on the unit values of metrics published by the World Travel and Tourism Council for the 2019 calendar year, adapted for the reduction in passengers in the first and second years following the COVID-19 lockdown. Results: Significant decreases in the annual number of passengers carried, flights operated (in the three geographic areas), and their impact on tourism and employment indicators were identified. Conclusion: The decline in passengers exceeded the decrease in flights operated, which resulted in a decline in the average load of passengers carried per flight. The significance of COVID-19 restrictions on tourism indicators and employment was also calculated. Contribution: The study identifies the impact of COVID-19 air travel restrictions on both air transport and tourism indicators for South Africa.
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Air Quality Index (AQI) is an indicator of the pollution level of our surroundings and household. Prediction of the AQI values from the historical values can help us analyze and mitigate the pollution levels. The AQI values can be classified into predetermined categories and machine learning algorithms can be made use of to improve the classification accuracy of the Air Quality Index value calculated. The main objective of the paper is to provide the potential researchers, with the importance of various Machine Learning approaches used for the forecast of the Air Quality Index. This paper analyzes various strategies used for the prediction, classification of AQI incorporating machine learning techniques. The air quality index can be calculated using Machine learning-based methods. Some of the methods to be considered are logistic regression, decision tree, support vector regression, support vector classifier, random forest tree, Naive Bayes classifier, and K-nearest neighbor. Application of these methods on the Air Quality Index datasets may yield different Accuracy, Recall, and F1 Score. Different algorithms that can be used for the said purpose with their strengths are summarized in a comparison table.
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Research focus on optimal control problems brought on by fractional differential equations has been extensively applied in practice. However, because they are still open ended and challenging, a number of problems with fractional mathematical modeling and problems with optimal control require additional study. Using fractional-order derivatives defined in the Atangana–Baleanu–Caputo sense, we alter the integer-order model that has been proposed in the literature. We prove the solution's existence, uniqueness, equilibrium points, fundamental reproduction number, and local stability of the equilibrium points. The operator's numerical approach was put into practice to obtain a numerical simulation to back up the analytical conclusions. Fractional optimum controls were incorporated into the model to identify the most efficient intervention strategies for controlling the disease. Utilizing actual data from Ghana for the months of March 2020 to March 2021, the model is validated. The simulation's results show that the fractional operator significantly affected each compartment and that the incidence rate of the population rose when v≥0.6. The examination of the most effective control technique discovered that social exclusion and vaccination were both very effective methods for halting the development of the illness.
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Objetivo: Estimar la seroincidencia acumulada de inmunoglobulinas (Ig) clase G (IgG) anti-SARS-CoV-2 en trabajadores de la salud asintomáticos y su asociación epidemiológica dentro de las áreas funcionales del Hospital Departamental de Villavicencio (HDV). Metodología: Se llevó a cabo un estudio observacional analítico longitudinal de una cohorte de trabajadores, donde cada 21 días, en tres oportunidades, se midieron IgG anti-SARS-CoV-2 en suero sanguíneo, a través de ELISA indirecto, en una muestra representativa aleatoria (n = 105) de trabajadores sanitarios del hospital (N = 756). Como instrumento de recolección de datos se utilizó una encuesta, donde cada trabajador sanitario declaró no haber sido diagnosticado con COVID-19, e igualmente registró la información sobre las variables independientes: sexo, edad, condición laboral, área funcional y comorbilidades. Resultados: La prevalencia inicial para SARS-CoV-2 entre los trabajadores sanitarios asintomáticos del HDV fue de 9,52 % (IC 95 % 5,25-16,65). La seroincidencia acumulada durante 42 días fue de 12,38 % (IC 95 % 7,38-20,04). El riesgo relativo (RR) se utilizó para establecer los factores de riesgo asociados a las variables independientes. El sexo masculino (RR ajustado = 3,34, IC 95 % 1,98-5,86), obesidad (RR ajustado = 10,98, IC 95 % 1,41-85,98) y sexo femenino (RR ajustado = 2,15, IC 95 % 1,12-4,31) en las áreas funcionales de Hospitalización, Medicina Crítica y Urgencias, respectivamente, son factores de riesgo en el HDV. Conclusión: Un total de 13 de 105 trabajadores sanitarios del hospital seroconvirtieron positivamente para SARS-CoV-2 y fueron asintomáticos durante 42 días de seguimiento epidemiológico. Además, existen factores de riesgo importantes en su exposición a este virus en el HDV.Alternate : Objetivo: Estimar a incidência zero acumulada de imunoglobulinas (Ig) classe G (IgG) anti-SARS-CoV-2 em profissionais de saúde assintomáticos e sua associação epidemiológica dentro das áreas funcionais do Hospital Estadual de Villavicencio (HDV). Metodologia: Foi realizado um estudo observacional analítico longitudinal de uma coorte de profissionais, no qual a cada 21 dias, em três ocasiões mediram-se IgG anti-SARS-CoV-2 em soro sanguíneo, através de ELISA indireto, em uma amostra representativa aleatória (n = 105) de profissionais de saúde do hospital (N =756). Como instrumento de recolecção de dados foi usada uma pesquisa, onde cada profissional de saúde declarou não ter sido diagnosticado com COVID-19, e igualmente registrou a informação sobre as variáveis independentes: sexo, idade, condições de trabalho, área de atuação e comorbidades. Resultados: A prevalência inicial para SARS-CoV-2 entre os profissionais de saúde assintomáticos do HDV foi de 9,52% (IC 95% 5,25-16,65). A incidência zero acumulada durante 42 dias foi de 12,38% (IC 95% 7,38-20,04). O risco relativo (RR) foi utilizado para estabelecer os fatores de risco associados às variáveis independentes. O sexo masculino (RR ajustado 3,34, IC 95% 1,98-5,86), obesidade (RR ajustado 10,98, IC 95% 1,41-85,98) e sexo feminino (RR ajustado 2,15, IC 95% 1,12-4,31) nas áreas funcionais de Internação, Unidade de Terapia Intensiva e Urgências, respectivamente, são fatores de risco no HDV. Conclusão: Um total de 13 de 105 profissionais de saúde do hospital foram detectados positivamente para SARS-CoV-2 e foram assintomáticos durante 42 dias de seguimento epidemiológico. Além disso, existem importantes fatores de risco na sua exposição a este vírus no HDV.Alternate : Objective: To estimate the cumulative seroincidence of anti-sars-CoV-2 immunoglobulin (Ig) class G (IgG) in asymptomatic health care workers and its epidemiological association within the functional areas of the Villavicencio Departmental Hospital (HDV). Methodology: A longitudinal analytical observational study of a cohort of workers was conducted in which anti-SARS-CoV-2 IgG levels in blood serum were measured every 21 days on three occasions using an in irect ELISA in a random representative sample (n = 105) of hospital health workers (N = 756). The data collection tool was a survey in which each healthcare worker indicated that they had not been diagnosed with COVID-19 and provided information on the independent variables: sex, age, job status, functional area, and comorbidities. Results: The baseline prevalence for SARS-CoV-2 among asymptomatic HDV healthcare workers was 9.52% (CI 95% 5.25-16.65). Cumulative seroincidence over 42 days was 12.38% (CI 95% 7.38-20.04). Relative risk (RR) was used to establish the risk factors associated with the independent variables. Male sex (adjusted RR 3.34, CI 95% 1.98-5.86), obesity (adjusted RR 10.98, CI 95% 1.41-85.98) and female sex (adjusted RR 2.15, CI 95% 1.12-4.31) in the functional areas of Hospitalization, Critical Medicine and Emergency, respectively, are risk factors in the HDV. Conclusion: During 42 days of epidemiological follow-up, 13 out of 105 hospital healthcare workers seroconverted positively for SARS-CoV-2 and remained asymptomatic. Additionally, significant risk factors are associated with their exposure to this virus in the HDV.
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Online teaching has come to play an increasingly important role in higher education, especially during the Covid-19 pandemic. Being unable to be on campus with face-to-face teaching can be challenging. This article provides a possible option of how first-year engineering students at NTNU (Norwegian University of Science and Technology) adapt to mastering higher mathematics. The purpose of the research is to see whether a more ‘face to face' experienced online teaching technological method will have a positive impact on student engagement, as well as exam performance. We refer to this new method as ‘TeachUs' in this paper for the sake of convenience. This method gives eye contact experience for the students, which has been shown to improve engagement. The surveys of the student groups confirmed the preference of this teaching method as compared to conventional screen sharing methods.
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Lung ultrasound is used to detect various artifacts in the lungs that support the diagnosis of different conditions. There is ongoing research to support the automatic detection of such artifacts using machine learning. We propose a solution that uses analytical computer vision methods to detect two types of lung artifacts, namely A- and B-lines. We evaluate the proposed approach on the POCUS dataset and data acquired from a hospital. We show that by using the Fourier transform, we can analyze lung ultrasound images in real-time and classify videos with an accuracy above 70%. We also evaluate the method's applicability for segmentation, showcasing its high success rate for B-lines (89% accuracy) and its shortcomings for A-line detection. We then propose a hybrid solution that uses a combination of neural networks and analytical methods to increase accuracy in horizontal line detection, emphasizing the pleura.
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The study investigated the challenges encountered by mathematics postgraduate students' learning courses via online platforms. An online survey included 31 postgraduate mathematics students. Mean responses and regression analysis were used for data evaluation. The results indicate social-related challenges are the most important factor to learn mathematics in an online platform, although the challenges including accessibility of online learning resource, course nature, online learning skill, and instructor-related challenges are confirmed with a higher mean response. Moreover, a multiple regression analysis has indicated that social and instructor related challenges are significant predictors of students' overall expectation of learning mathematics courses on the online platform. The study concluded that students prefer learning mathematics courses via face-to-face approach over online platforms, and students do not want to extend semester courses with an online approach. From the finding, adapting a blended approach is recommended for a similar situation of the current study.
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Aerosol pollution in urban areas is highly variable due to numerous single emission sources such as automobiles, industrial and commercial activities as well as domestic heating, but also due to complex building structures redirecting air mass flows, producing leeward and windward turbulences and resuspension effects. In this publication, it is shown that one or even few aerosol monitoring sites are not able to reflect these complex patterns. In summer 2019, aerosol pollution was recorded in high spatial resolution during six night and daytime tours with a mobile sensor platform on a trailer pulled by a bicycle. Particle mass loadings showed a high variability with PM10 values ranging from 1.3 to 221 µg m-3 and PM2.5 values from 0.7 to 69.0 µg m-3. Geostatistics were used to calculate respective models of the spatial distributions of PM2.5 and PM10. The resulting maps depict the variability of aerosol concentrations within the urban space. These spatial distribution models delineate the distributions without cutting out the built-up structures. Elsewise, the overall spatial patterns do not become visible because of being sharply interrupted by those outcuts in the resulting maps. Thus, the spatial maps allow to identify most affected urban areas and are not restricted to the street space. Furthermore, this method provides an insight to potentially affected areas, and thus can be used to develop counter measures. It is evident that the spatial aerosol patterns cannot be directly derived from the main wind direction, but result far more from an interplay between main wind direction, built-up patterns and distribution of pollution sources. Not all pollution sources are directly obvious and more research has to be carried out to explain the micro-scale variations of spatial aerosol distribution patterns. In addition, since aerosol load in the atmosphere is a severe issue for health and well-being of city residents more attention has to be paid to these local inhomogeneities.