A Spatiotemporal Analysis of the COVID-19 Pandemic in North Africa.

Spatial panel-data models are estimated to identify the factors of the prevalence of the coronavirus outbreak in North Africa. Using daily data on the number of cases collected between March 2020 and December 2021, three types of general models are investigated, and they include spatial spillovers between the neighboring countries of the region. In one model the spatial dependence is accounted for by adding a spatial lag of the dependent variable (SAR model).

The impact of insurance institutional investors on corporate value from selection and creation perspective.

The majority of insurance investment funds are derived from policy liability debt funds. It differs from other institutional investors in a number of ways, including investment size, horizon, duration, risk, and so on. However, only a small portion of the extant literature focuses on in-depth and extensive analysis of Insurance Institutional Investors' holdings (IIIs).

DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites.

Background: Addressing the laborious nature of traditional biological experiments by using an efficient computational approach to analyze RNA-binding proteins (RBPs) binding sites has always been a challenging task. RBPs play a vital role in post-transcriptional control. Identification of RBPs binding sites is a key step for the anatomy of the essential mechanism of gene regulation by controlling splicing, stability, localization and translation.

Prognostic Impact of Blood Pressure Change Patterns on Patients With Aortic Dissection After Admission.

Objectives: Hypertension is a predominant risk factor for aortic dissection (AD), and blood pressure (BP) control plays a vital role in the management of AD. However, the correlation between BP change and the prognosis for AD remains unclear. This study aims to demonstrate the impact of BP change patterns on AD prognosis.

Dairy Cattle Density and Temporal Patterns of Human Campylobacteriosis and Cryptosporidiosis in New Zealand.

Public health risks associated with the intensification of dairy farming are an emerging concern. Dairy cattle are a reservoir for a number of pathogens that can cause human illness. This study examined the spatial distribution of dairy cattle density and explored temporal patterns of human campylobacteriosis and cryptosporidiosis notifications in New Zealand from 1997 to 2015.

Robust Devaney chaos in the two-dimensional border-collision normal form.

The collection of all non-degenerate, continuous, two-piece, piecewise-linear maps on R can be reduced to a four-parameter family known as the two-dimensional border-collision normal form. We prove that throughout an open region of parameter space, this family has an attractor satisfying Devaney's definition of chaos. This strengthens the existing results on the robustness of chaos in piecewise-linear maps.

Regressive Class Modelling for Predicting Trajectories of COVID-19 Fatalities Using Statistical and Machine Learning Models.

The COVID-19 (SARS-CoV-2 virus) pandemic has led to a substantial loss of human life worldwide by providing an unparalleled challenge to the public health system. The economic, psychological, and social disarray generated by the COVID-19 pandemic is devastating. Public health experts and epidemiologists worldwide are struggling to formulate policies on how to control this pandemic as there is no effective vaccine or treatment available which provide long-term immunity against different variants of COVID-19 and to eradicate this virus completely.

COVID-19 case-fatality variations with application to the Middle East countries.

During a pandemic outbreak, it is important for health officials to know the proportions of deaths among infected individuals and to understand how these proportions change overtime, to accurately predict the impact of the pandemic and to implement effectively new intervention policies and health protocols and to adjust them accordingly. However, most studies where efforts have been made to estimate accurately the case fatality rates did not address the issue of measuring the dynamics of the pandemic deadliness during its course. Daily data on COVID-19 cases and deaths were collected from selected MENA countries.

Learning analytics dashboard: a tool for providing actionable insights to learners.

This study investigates current approaches to learning analytics (LA) dashboarding while highlighting challenges faced by education providers in their operationalization. We analyze recent dashboards for their ability to provide actionable insights which promote informed responses by learners in making adjustments to their learning habits. Our study finds that most LA dashboards merely employ surface-level descriptive analytics, while only few go beyond and use predictive analytics.

Longitudinal study of the effect of sporidesmin toxicity on lamb production and serum biochemistry in a flock of 46 Romney ewes using a standardised measure of liver damage.

Aims: To evaluate the effect of sporidesmin toxicity on production outcomes and serum biochemistry analytes in mixed age Romney ewes, using a standardised measure of liver damage.

Methods: This was a prospective longitudinal study following 46 mixed age Romney ewes from sporidesmin intoxication in April 2019, to slaughter 8 months later. The ewes were blood-sampled up to eight times, with a panel of serum biochemistry tests performed on the final six samples.

Delta Chem: A New Geometric Approach of Porosity for Symmetric Porous Materials.

Porous materials, such as zeolites and metal-organic frameworks (MOFs), and zeolitic-organic frameworks (ZIFs), are frequently considered for shape-selective separations, molecular storage, and catalysis applications, mainly due to their hollow structures. The amount and chemical nature of sorbate molecules that may (or may not) be fitted inside their cavities, and hence the bulk of their applications, depend on their internal structure, that is, on their surface areas, available volumes, and shapes of their porosities. However, experimentally, the access to such strucutral information is somewhat limited and computationally can be expensive to calculate for structures of more than 100 atoms.

D155Y substitution of SARS-CoV-2 ORF3a weakens binding with Caveolin-1.

The clinical manifestation of the recent pandemic COVID-19, caused by the novel SARS-CoV-2 virus, varies from mild to severe respiratory illness. Although environmental, demographic and co-morbidity factors have an impact on the severity of the disease, contribution of the mutations in each of the viral genes towards the degree of severity needs a deeper understanding for designing a better therapeutic approach against COVID-19. Open Reading Frame-3a (ORF3a) protein has been found to be mutated at several positions.

Development of a Risk Prediction Model for Assessing Dental Readiness in the Canadian Armed Forces.

Introduction: The establishment and sustainment of a high state of dental readiness in the Canadian Armed Forces (CAF) are the primary missions of the Royal Canadian Dental Corps. The objective of this study was to develop a risk prediction tool to estimate dental readiness in active CAF personnel.

Materials And Methods: The prediction model was developed to predict the classification of non-deployable (yes/no) within 12 months (primary) and 18 months (secondary) using both dental history data (including dental attendance, restorations, root canals, and third molar status) and demographic information.

Forecasting COVID-19 infections in the Arabian Gulf region.

In this paper, an empirical analysis of linear state space models and long short-term memory neural networks is performed to compare the statistical performance of these models in predicting the spread of COVID-19 infections. Data on the pandemic daily infections from the Arabian Gulf countries from 2020/03/24 to 2021/05/20 are fitted to each model and a statistical analysis is conducted to assess their short-term prediction accuracy. The results show that state space model predictions are more accurate with notably smaller root mean square errors than the deep learning forecasting method.

Asymptotic iteration method for solving Hahn difference equations.

Hahn's difference operator , , , is used to unify the recently established difference and -asymptotic iteration methods (DAIM, AIM). The technique is applied to solve the second-order linear Hahn difference equations. The necessary and sufficient conditions for polynomial solutions are derived and examined for the -hypergeometric equation.

A novel application of a data mining technique to study intersections in the social determinants of mental health among young Canadians.

Objectives: Adolescent mental health is an emergent clinical and public health priority in Canada. Gender-based differences in mental health are well established. The objective of this study was to evaluate a new data mining technique to identify social locations of young Canadians where differences in mental health between adolescent males and females were most pronounced.

Theoretical study of gas-phase detoxication of DMMP and DMPT using ammonia-borane and its analogous compound.

The detoxication of DMMP (Dimethyl methylphosphonate) and DMPT (O, S-dimethyl methylphosphonothiolate) via hydrogenation have been investigated computationally employing density functional theory (DFT). In this present study, we aim to explore the direct molecular H assisted as well as ammonia-borane (NHBH) and 3-methyl-1,2-BN-cyclopentane (denoted as cy-AB) assisted hydrogenation pathways of DMMP and DMPT in order to detoxify them. The detoxication of DMMP has been carried out by successive elimination of two -OMe groups.

Addressing the Data Acquisition Paradigm in the Early Detection of Pediatric Foot Deformities.

The analysis of plantar pressure through podometry has allowed analyzing and detecting different types of disorders and treatments in child patients. Early detection of an inadequate distribution of the patient's weight can prevent serious injuries to the knees and lower spine. In this paper, an embedded system capable of detecting the presence of normal, flat, or arched footprints using resistive pressure sensors was proposed.

Incidence of avian malaria in hummingbirds in humid premontane forests of Pichincha Province, Ecuador: A pilot study.

Background And Aim: Avian malaria is a tropical disease caused by protozoans of the genera and . As a nonlethal disease, avian malaria can affect the lifespan and reproductive rate of birds. If there is a differential effect depending on bird species, then this disease might have a significant effect on avian biodiversity.

The impact of image resolution on power, bias, and confounding: A simulation study of ambient light at night exposure.

Unlabelled: Studies of the impact of environmental pollutants on health outcomes can be compromised by mismeasured exposures or unmeasured confounding with other environmental exposures. Both problems can be exacerbated by measuring exposure from data sources with low spatial resolution. Artificial light at night, for example, is often estimated from low-resolution satellite images, which may result in substantial measurement error and increased correlation with air or noise pollution.

Prescreening and treatment of aortic dissection through an analysis of infinite-dimension data.

Background: Aortic dissection (AD) is one of the most catastrophic aortic diseases associated with a high mortality rate. In contrast to the advances in most cardiovascular diseases, both the incidence and in-hospital mortality rate of AD have experienced deviant increases over the past 20 years, highlighting the need for fresh prospects on the prescreening and in-hospital treatment strategies.

Methods: Through two cross-sectional studies, we adopt image recognition techniques to identify pre-disease aortic morphology for prior diagnoses; assuming that AD has occurred, we employ functional data analysis to determine the optimal timing for BP and HR interventions to offer the highest possible survival rate.

Extensive Phylogenetic Analysis of Piscine Orthoreovirus Genomic Sequences Shows the Robustness of Subgenotype Classification.

(PRV) belongs to the family and has been described mainly in association with salmonid infections. The genome of PRV consists of about 23,600 bp, with 10 segments of double-stranded RNA, classified as small (S1 to S4), medium (M1, M2 and M3) and large (L1, L2 and L3); these range approximately from 1000 bp (segment S4) to 4000 bp (segment L1). How the genetic variation among PRV strains affects the virulence for salmonids is still poorly understood.

A protein folding robot driven by a self-taught agent.

This paper presents a computer simulation of a virtual robot that behaves as a peptide chain of the Hemagglutinin-Esterase protein (HEs) from human coronavirus. The robot can learn efficient protein folding policies by itself and then use them to solve HEs folding episodes. The proposed robotic unfolded structure inhabits a dynamic environment and is driven by a self-taught neural agent.

Dynamics of a diffusive vaccination model with therapeutic impact and non-linear incidence in epidemiology.

In this paper, we study a more general diffusive spatially dependent vaccination model for infectious disease. In our diffusive vaccination model, we consider both therapeutic impact and nonlinear incidence rate. Also, in this model, the number of compartments of susceptible, vaccinated and infectious individuals are considered to be functions of both time and location, where the set of locations (equivalently, spatial habitats) is a subset of with a smooth boundary.

Environment Monitoring of Rose Crops Greenhouse Based on Autonomous Vehicles with a WSN and Data Analysis.

This work presents a monitoring system for the environmental conditions of rose flower-cultivation in greenhouses. Its main objective is to improve the quality of the crops while regulating the production time. To this end, a system consisting of autonomous quadruped vehicles connected with a wireless sensor network (WSN) is developed, which supports the decision-making on type of action to be carried out in a greenhouse to maintain the appropriate environmental conditions for rose cultivation.