Lecturers’ perspectives on using virtual classrooms in education: Challenges and Opportunities for the Post-COVID-19 Era

This study aims at examining the perspectives of university lecturers on using virtual classrooms in education in Palestine. This study used quantitative and qualitative data collection tools and analysis methods. The present cross-sectional study was carried out from January 10 to February 13, 2023, during the second semester of 2022/2023. An online questionnaire with 33 closed-ended questions was used to collect data. The study group consisted of teachers and lecturers (n = 311) who teach in Palestinian universities. The R cran program was employed for the statistical analysis of quantitative data. The Mann-Whitney U-test and Kruskal-Wallis test were used To determine whether demographic characteristics had a significant impact on teachers' perspective of virtual classrooms in education. The results showed that most of the participants (71.4%) prefer to employ virtual classrooms in education since they are just like in-person education as it is very organized with deadlines. There is a significant difference in the mean scores of the preference toward employing virtual classrooms among lecturers who have experience in the usage of technological tools and those who have no experience (p = 0.07). Lecturers aged 25-30 years were the highest in mean scores of preference (3.96 ± 0.32) and have a higher satisfaction toward using virtual classrooms (p = 0.01). In addition, teachers who attended courses respecting employing virtual classrooms have a higher satisfaction (3.6 ± 0.78) towards using it in education compared to those who did not attend any course related to virtual classrooms (2.91 ± 1.06) (p <0.001). Only 28.6% of teachers experience problems during distance teaching through virtual classrooms. Teachers face many obstacles related to the curriculum, students, and electronic environment. These obstacles might be contributed to the stress experienced by 35.70% of teachers during distance teaching. &nbsp

Graphical models for classification and time series

Dans cette thèse nous nous intéressons aux méthodes de classifications supervisées utilisant les réseaux bayésiens. L'avantage majeur de ces méthodes est qu'elles peuvent prendre en compte les interactions entre les variables explicatives. Dans une première partie nous proposons une procédure de discrétisation spécifique et une procédure de sélection de variables qui permettent d'améliorer considérablement les classifieurs basés sur des réseaux bayésiens. Cette procédure a montré de très bonnes performances empiriques sur un grand choix de jeux de données connus de l’entrepôt d'apprentissage automatique (UCI Machine Learning repository). Une application pour la prévision de type d’épilepsie à partir de de caractéristiques des patients extraites des images de Tomographie par émission de positrons (TEP) confirme l’efficacité de notre approche comparé à des approches communes de classifications supervisées. Dans la deuxième partie de cette thèse nous nous intéressons à la modélisation des interactions entre des variables dans le contexte de séries chronologiques en grande dimension. Nous avons proposé deux nouvelles approches. La première, similaire à la technique "neighborhood Lasso" remplace la technique Lasso par des machines à vecteurs de supports. La deuxième approche est un réseau bayésien restreint: les variables observées à chaque instant et à l’instant précédent sont utilisées dans un réseau dont la structure est restreinte. Nous montrons l’efficacité de ces approches par des simulations utilisant des donnés simulées issues de modèles linéaires, non-linéaires et un mélange des deux.First, in this dissertation, we will show that Bayesian networks classifiers are very accurate models when compared to other classical machine learning methods. Discretising input variables often increase the performance of Bayesian networks classifiers, as does a feature selection procedure. Different types of Bayesian networks may be used for supervised classification. We combine such approaches together with feature selection and discretisation to show that such a combination gives rise to powerful classifiers. A large choice of data sets from the UCI machine learning repository are used in our experiments, and the application to Epilepsy type prediction based on PET scan data confirms the efficiency of our approach. Second, in this dissertation we also consider modelling interaction between a set of variables in the context of time series and high dimension. We suggest two approaches; the first is similar to the neighbourhood lasso where the lasso model is replaced by Support Vector Machines (SVMs); the second is a restricted Bayesian network for time series. We demonstrate the efficiency of our approaches simulations using linear and nonlinear data set and a mixture of both

Bayesian Network Classification: Application to Epilepsy Type Prediction Using PET Scan Data

International audienc

Inferring linear and nonlinear Interaction networks using neighborhood support vector machines

International audienc

CNVmap: a method and software to detect and map copy number variants from segregation data

Single nucleotide polymorphisms (SNPs) are used widely for detecting quantitative trait loci, or for searching for causal variants of diseases. Nevertheless, structural variations such as copy-number variants (CNVs) represent a large part of natural genetic diversity, and contribute significantly to trait variation. Numerous methods and softwares based on different technologies (amplicons, CGH, tiling, or SNP arrays, or sequencing) have already been developed to detect CNVs, but they bypass a wealth of information such as genotyping data from segregating populations, produced, e.g., for QTL mapping. Here, we propose an original method to both detect and genetically map CNVs using mapping panels. Specifically, we exploit the apparent heterozygous state of duplicated loci: peaks in appropriately defined genome-wide allelic profiles provide highly specific signatures that identify the nature and position of the CNVs. Our original method and software can detect and map automatically up to 33 different predefined types of CNVs based on segregation data only. We validate this approach on simulated and experimental biparental mapping panels in two maize populations and one wheat population. Most of the events found correspond to having just one extra copy in one of the parental lines, but the corresponding allelic value can be that of either parent. We also find cases with two or more additional copies, especially in wheat, where these copies locate to homeologues. More generally, our computational tool can be used to give additional value, at no cost, to many datasets produced over the past decade from genetic mapping panels

Study of eccentricity based topological indices for benzenoid structure

Topological indices play paramount role in defining chemical and structural properties of different compounds. Chemical graph theory is useful for predicting the bioactivity and physicochemical properties through numerical invariants. A broad range of topological indices are studied and used in theoretical chemistry. The eccentricity of a graph is maximum distance from one vertex a to another vertex b. The connectivity based topological indices depict vital role in mathematical chemistry. In this paper, we discuss Benzenoid Hourglass Network and calculate its total eccentricity, average eccentricity, atom-bond connectivity index, eccentricity based Zagreb index and geometric-arithmetic index. Some new helpful closed formulas are developed

Measuring the energy for the molecular graphs of antiviral agents: Hydroxychloroquine, Chloroquine and Remdesivir

We consider the energy for the molecular graphs of antiviral agents like Hydroxychloroquine, Remdesivir and Chloroquine. These drugs play a vital role in the treatment of COVID-19. Let Ѓ1,Ѓ2 and Ѓ3 be the n-dimensional graphs of the molecular structures of antiviral agents Hydroxychloroquine, Chloroquine and Remdesivir, respectively. We define their energies as E′(Ѓ1)=∑|λi′|, E′(Ѓ2)=∑|λj′| and E′(Ѓ3)=∑|λk′|, respectively. Where the sets {λ1′(Ѓ1),λ2′(Ѓ1),λ3′(Ѓ1),...,λn′(Ѓ1)}, {λ1′(Ѓ2),λ2′(Ѓ2),λ3′(Ѓ2),...,λn′(Ѓ2)} and {λ1′(Ѓ3),λ2′(Ѓ3),λ3′(Ѓ3),...,λn′(Ѓ3)} depict the eigenvalues for the adjacency matrices of Ѓ1,Ѓ2 and Ѓ3, respectively. We have developed some basic ideas and properties in order to measure the energies for the antiviral agents Hydroxychloroquine, Chloroquine and Remdesivir

Eccentric Harmonic Index for the Cartesian Product of Graphs

Suppose ρ is a simple graph, then its eccentric harmonic index is defined as the sum of the terms 2/ea+eb for the edges vavb, where ea is the eccentricity of the ath vertex of the graph ρ. We symbolize the eccentric harmonic index (EHI) as He=Heρ. In this article, we determine He for the Cartesian product (CP) of particularly chosen graphs. Lower bounds for He of the CP of the two graphs are established. The formulas of EHI for the Hamming and Hypercube graphs are obtained. These obtained formulas can be used in QSAR and QSPR studies to get a better understanding of their applications in mathematical chemistry

Glucocorticoids with low-dose anti-IL1 anakinra rescue in severe non-ICU COVID-19 infection: A cohort study.

BackgroundThe optimal treatment for patients with severe coronavirus-19 disease (COVID-19) and hyper-inflammation remains debated.Material and methodsA cohort study was designed to evaluate whether a therapeutic algorithm using steroids with or without interleukin-1 antagonist (anakinra) could prevent death/invasive ventilation. Patients with a ≥5-day evolution since symptoms onset, with hyper-inflammation (CRP≥50mg/L), requiring 3-5 L/min oxygen, received methylprednisolone alone. Patients needing ≥6 L/min received methylprednisolone + subcutaneous anakinra daily either frontline or in case clinical deterioration upon corticosteroids alone. Death rate and death or intensive care unit (ICU) invasive ventilation rate at Day 15, with Odds Ratio (OR) and 95% CIs, were determined according to logistic regression and propensity scores. A Bayesian analysis estimated the treatment effects.ResultsOf 108 consecutive patients, 70 patients received glucocorticoids alone. The control group comprised 63 patients receiving standard of care. In the corticosteroid±stanakinra group (n = 108), death rate was 20.4%, versus 30.2% in the controls, indicating a 30% relative decrease in death risk and a number of 10 patients to treat to avoid a death (p = 0.15). Using propensity scores a per-protocol analysis showed an OR for COVID-19-related death of 0.9 (95%CI [0.80-1.01], p = 0.067). On Bayesian analysis, the posterior probability of any mortality benefit with corticosteroids+/-anakinra was 87.5%, with a 7.8% probability of treatment-related harm. Pre-existing diabetes exacerbation occurred in 29 of 108 patients (26.9%).ConclusionIn COVID-19 non-ICU inpatients at the cytokine release phase, corticosteroids with or without anakinra were associated with a 30% decrease of death risk on Day 15