The present research is on the propagation of Rayleigh waves in a homogenous thermoelastic solid half-space by considering the compact form of six different theories of thermoelasticity. The medium is subjected to an insulated boundary surface that is free from normal stress, tangential stress, and a temperature gradient normal to the surface. After developing a mathematical model, a dispersion equation is obtained with irrational terms. To apply the algebraic method, this equation must be converted into a rational polynomial equation. From this, only those roots are filtered out, which has satisfied both of the above equations for the propagation of waves decaying with depth. With the help of these roots, different characteristics are computed numerically, like phase velocity, attenuation coefficient, and path of particles. Various particular cases are compared graphically by using phase velocity and attenuation coefficient. The elliptic path of surface particles in Rayleigh wave propagation is also presented for the different theories using physical constants of copper material for different depths and thermal conductivity.

This study employs logistic regression to investigate determinants influencing active living among elderly individuals, with “Active Living” (1 = Active, 0 = Inactive) as the dependent variable. Analysing data from 500 participants, findings reveal significant associations between active living and variables such as chronic conditions (OR = 0.29, p < 0.001), mental well-being (OR = 1.57, p < 0.001), social support (OR = 5.75, p < 0.001), access to parks/recreational facilities (OR = 2.59, p < 0.001), income levels (OR = 1.82, p = 0.003), cultural attitudes (OR = 2.72, p < 0.001), and self-efficacy (OR = 2.01, p < 0.001). These findings highlight the complex interplay of factors influencing active living among elderly populations. Recommendations include implementing targeted interventions to manage chronic conditions, enhance mental well-being, strengthen social networks, improve access to recreational spaces, provide economic support for fitness activities, promote positive cultural attitudes towards aging, and empower older adults through self-efficacy programs. Such interventions are crucial for promoting healthier aging and fostering sustained engagement in physical activity among older adults.

Integrated Resource Management plays a crucial role in sustainable development by ensuring efficient allocation and utilization of natural resources. Remote Sensing (RS) and Geographic Information System (GIS) have emerged as powerful tools for collecting, analyzing, and managing spatial data, enabling comprehensive and integrated decision-making processes. This review article uniquely focuses on Integrated Resource Management (IRM) and its role in sustainable development. It specifically examines the application of RS and GIS in IRM across various resource management domains. The article stands out for its comprehensive coverage of the benefits, challenges, and future directions of this integrated approach.

Digital labor, as a new theoretical form of "audience commodity theory" in the digital media era, represents a new form of production and labor. This paper explores the unique features of digital labor in labor form, labor products and labor time, and combining Marx's theory, it further reveals the alienation and exploitation of human social relations, emotional value and social class in the process of digital labor, and finally makes suggestions on the unequal relationship between platform and workers in the process of digital labor.

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.

The xanthorrhiza species of the genus Arracacia belongs to the Apiaceae family and is known for its ability to generate tuberous reservoir roots that are harvested annually and marketed fresh in South American countries such as Colombia, Brazil, Venezuela, Peru, Bolivia and Ecuador. In Colombia, arracacha is planted mainly in 15 departments and the regional cultivars are differentiated by the color of the leaves, petiole and tuberous root, the best known being amarilla común or paliverde, yema de huevo, and cartagenera. There are studies that have characterized regional materials by applying a limited number of descriptors, but they do not allow knowing the morphology and phenotypic differentiation of each one; therefore, their definition and characterization constitute a support in breeding programs that allow the efficient use of the genetic potential and increase the knowledge about the diversity of cultivars. Phenotypic characterization and description of three cultivars was performed during two production cycles (2016 and 2018) in two phases (vegetative and productive) applying 74 morphological variables (42 qualitative and 32 quantitative) organized in seven groups of variables: plant, leaf, leaflet, petiole, propagule, stock and tuberous root. A factorial analysis for mixed data (FAMD) was performed, which incorporated a multivariate analysis with all variables and identified 11 discriminant variables, 8 qualitative and 3 quantitative, which can be used in processes of characterization of arracacha materials. A morphological description of each cultivar was made, which means that this is the first complete characterization study of regional arracacha materials in Colombia.