Evolutionary dynamics of Pinus taeda L. in the Late Quaternary: An interdisciplinary approach

Pinus taeda L. dynamics, migration patterns and genetic structure were investigated over geological time scale (the past 21,000 years), historical time scale (the past 500 years) and recent time scale (the past 50 years ago) using multi-source data and an interdisciplinary approach. Population genetics, microsatellite markers, DNA fingerprinting, fossil records, geological history, historical records, aerial photographs, soil maps, weather data, remote sensing and geographic information systems (GIS) were used to assess the dynamics of P. taeda populations especially for the Lost Pines (LP), a disjunct population at the westernmost edge of the species range. Pinus taeda populations east and west of the Mississippi River Valley are genetically differentiated. Eastern populations had higher allelic diversity and diagnostic alleles than western populations. Gene flow estimates are high. Allelic diversity and diagnostic alleles patterns are attributed to the prevailing wind direction. Differentiation east and west of the MRV was attributed to separation to two refugia during the Pleistocene. The Lost Pines population is believed to have undergone one or more bottleneck events with loss of rare alleles. Despite the bottleneck, allelic richness was similar for the LP and the control population from the Western Gulf (WG) population. Population size contraction of the LP was attributed to climate change in central Texas over geological time scale. The natural origin of the Lost Pines was investigated. Multivariate and clustering techniques and assignment and exclusion methods using DNA markers show that the LP population shared ancestry with the WG populations with no evidence for admixture from other sources. Historical records parallel this conclusion. With the absence of logging within Bastrop and Buescher State Parks, P. taeda area and patch size increased from 1949 to 1995. Thirty six percent of the pine patches observed in 1949 had disappeared by 1995 by merging. Landscape pattern analysis shows significant dynamics. The distribution of P. taeda in Bastrop County was associated with sandy light topsoils, clayey heavy sub-soils and high permeable soils. Pinus taeda grow on various soil types as well. Growing on these soils under current climatic conditions may compensate for the precipitation regime in this area

Mathematical Modelling for Predicting Thermal Properties of Selected Limestone

Due to a lack of geotechnical and geothermal studies on Jordanian limestone, this paper aims to provide the thermal properties, including thermal conductivity, thermal diffusivity, and specific heat, using the Hot Disk Transient Plane Source (TPS) 2200 method. It also aims to provide a set of mathematical models through which the thermal properties can be indirectly predicted from the rocks’ physical and engineering properties. One hundred cylindrical rock specimens with a height of 20 cm and a diameter of 10 cm were extracted and prepared. The results showed that the thermal conductivity values ranged between (1.931–3.468) (W/(m × k)), thermal diffusivity (1.032–1.81) (mm2/s), and specific heat (1.57–2.563) ((MJ)/(m3 × K)). The results also suggest a direct relationship between conductivity and diffusivity and an inverse relationship between conductivity and specific heat. On the other hand, the results indicate the direct relationship between the conductivity and diffusivity, and the inverse relationship between the specific heat and density, hardness, sound velocity, and rock strength; the opposite happens when the rock’s porosity is considered. Simple regression, multivariate regression, and the backpropagation–artificial neural network (BP–ANN) approach were utilized to predict the thermal properties of limestone. Results indicated that the ANN model provided superior prediction performance compared to other models

Enhancing of uniaxial compressive strength of travertine rock prediction through machine learning and multivariate analysis

Indirect methods for predicting material properties in rock engineering are vital for assessing elastic mechanical properties. Accurately predicting material properties holds significant importance in rock and geotechnical engineering, as it strongly influences decisions about the design and construction of infrastructure projects. Uniaxial compressive strength (UCS) is one of the most important elastic mechanical properties for understanding how rocks and geological formations respond to stress and deformation. However, the standard UCS test faces several challenges, including its destructive nature, high costs, time-consuming procedures, and the requirement for high-quality samples. Therefore, there is a growing demand for indirect methods to estimate UCS, which are invaluable tools for evaluating the elastic mechanical properties of materials. The study aimed to comprehensively analyze the relationships between UCS of travertine rock samples collected from the Dead Sea and Jordan Valley formations and seven different rock indices by utilizing parametric and non-parametric methods. The laboratory results indicate that the study area's travertine rock possesses high-quality and desirable properties. The results reveal that certain rock indices, such as Schmidt hammer, Leeb rebound hardness, and Point Load, strongly correlate with Uniaxial Compressive Strength (UCS). Conversely, other indices, specifically dry density, absorption, pulse velocity, and porosity, exhibit a considerably weaker or very weak relationship with UCS. The paper employs three machine learning techniques, namely the Tree model, k-nearest neighbors (KNN), and Artificial Neural Networks (ANN), to develop predictive models for rock strength. The models were trained on a dataset of rock properties and corresponding mechanical strength values. The study's results revealed that the M5 tree model is the most suitable method for predicting UCS. It demonstrates robust performance across a spectrum of metrics and boasts low prediction errors. Following the M5 tree model are the KNN, ANN, and regression methods in descending order of performance

Impediments of Using E-Learning Platforms for Teaching English: A Case Study in Jordan

E-learning platforms are essential tools used widely for teaching and learning English, especially since the COVID-19 pandemic. They are also used to communicate and interact with students, assess progress, evaluate assignments, and provide feedback. However, teachers of English face potential barriers when they use such platforms. This study examines the use of e-learning platforms in teaching English as a foreign language in Jordan. The study employed a quantitative research method. The findings revealed that using e-learning platforms for educational purposes is beneficial regarding accessibility when attending courses. E-learning enabled the students to practise more and to be more engaged in the learning process, which improved their language skills. Effective e-learning platform strategies significantly broaden students' perceptions and increase the opportunity to exchange information with their classmates. Nonetheless, several impediments may hinder the application of e-learning platforms, including teacher-related, technical, and technological factors. The study recommends that teachers use interactive methods, including images, sounds, videos, and multimedia, to engage learners with various needs and abilities. The study also suggests building codified standards when designing e-learning to develop students' skills at all levels and training teachers on using modern technological strategies in e-learning

Nucleotide diversity and linkage disequilibrium in loblolly pine

Outbreeding species with large, stable population sizes, such as widely distributed conifers, are expected to harbor relatively more DNA sequence polymorphism. Under the neutral theory of molecular evolution, the expected heterozygosity is a function of the product 4N(e)μ, where N(e) is the effective population size and μ is the per-generation mutation rate, and the genomic scale of linkage disequilibrium is determined by 4N(e)r, where r is the per-generation recombination rate between adjacent sites. These parameters were estimated in the long-lived, outcrossing gymnosperm loblolly pine (Pinus taeda L.) from a survey of single nucleotide polymorphisms across ≈18 kb of DNA distributed among 19 loci from a common set of 32 haploid genomes. Estimates of 4N(e)μ at silent and nonsynonymous sites were 0.00658 and 0.00108, respectively, and both were statistically heterogeneous among loci. By Tajima's D statistic, the site frequency spectrum of no locus was observed to deviate from that predicted by neutral theory. Substantial recombination in the history of the sampled alleles was observed and linkage disequilibrium declined within several kilobases. The composite likelihood estimate of 4N(e)r based on all two-site sample configurations equaled 0.00175. When geological dating, an assumed generation time (25 years), and an estimated divergence from Pinus pinaster Ait. are used, the effective population size of loblolly pine should be 5.6 × 10(5). The emerging narrow range of estimated silent site heterozygosities (relative to the vast range of population sizes) for humans, Drosophila, maize, and pine parallels the paradox described earlier for allozyme polymorphism and challenges simple equilibrium models of molecular evolution