In the new century, the traditional model of enterprise human resource management is facing the challenge of the times, improving the human resource management of enterprises, and must innovate the concept of enterprise human resource management. After the 1950s, some economists established the theory of human capital, not only can more effectively explain the problems of modern social economic growth, but also on the enterprise's human resources management contribution to a positive impact. This paper introduces the concept of human capital and human capital investment into enterprise human resource management, which opens up a new perspective for enterprise human resource management. In this paper, we will first define the characteristics of human capital and the main body of human capital investment, and then analyze the meaning of various human resource management behaviors from the perspective of capital investment, estimate their benefits, costs and risks, and finally use scientific means to establish investment decision model and risk control mechanism, to maximize the effectiveness of human resources, so that the management behavior of enterprise's human can bring more revenue for the enterprises, thereby enhancing the competitiveness of enterprises. At present, the scientific operation of human resources is the key to the healthy development of enterprises.

Creating a crop type map is a dominant yet complicated model to produce. This study aims to determine the best model to identify the wheat crop in the Haridwar district, Uttarakhand, India, by presenting a novel approach using machine learning techniques for time series data derived from the Sentinel-2 satellite spanned from mid-November to April. The proposed methodology combines the Normalized Difference Vegetation Index (NDVI), satellite bands like red, green, blue, and NIR, feature extraction, and classification algorithms to capture crop growth's temporal dynamics effectively. Three models, Random Forest, Convolutional Neural Networks, and Support Vector Machine, were compared to obtain the start of season (SOS). It is validated and evaluated using the performance metrics. Further, Random Forest stood out as the best model statistically and spatially for phenology parameter extraction with the least RMSE value at 19 days. CNN and Random Forest models were used to classify wheat crops by combining SOS, blue, green, red, NIR bands, and NDVI. Random Forest produces a more accurate wheat map with an accuracy of 69% and 0.5 MeanIoU. It was observed that CNN is not able to distinguish between wheat and other crops. The result revealed that incorporating the Sentinel-2 satellite data bearing a high spatial and temporal resolution with supervised machine-learning models and crop phenology metrics can empower the crop type classification process.

In Nigeria, deforestation has led to an unimaginable loss of genetic variation within tree populations. Regrettably, little is known about the genetic variation of many important indigenous timber species in Nigeria. More so, the specific tools to evaluate the genetic diversity of these timber species are scarce. Therefore, this study developed species-specific markers for Pterygota macrocarpa using state-of-the-art equipment. Leaf samples were collected from Akure Forest Reserve, Ondo State, Nigeria. DNA isolation, quantification, PCR amplification, gel electrophoresis, post-PCR purification, and sequencing were done following a standardized protocol. The melting temperatures (TM) of the DNA fragments range from 57.5 ℃to 60.1 ℃ for primers developed from the MatK gene and 58.7 ℃ to 60.5 ℃ for primers developed from the RuBisCo gene. The characteristics of the ten primers developed are within the range appropriate for genetic diversity assessment. These species-specific primers are therefore recommended for population evaluation of Pterygota macrocarpa in Nigeria.

The demography of Saudi Arabia has been discussed many times but its conflict with the theories of transition and associated structural changes is unexplained. This research explains the demographic differentials stated as lag - real from theoretical – separately for the native and total population. This research developed demographic indicators revealing trends and patterns by adopting a secondary data analysis method, utilizing the General Authority for Statistics census data and other online data. The demographic transition of Saudi Arabia is in line with the theoretical contentions of pretransition and transition (early, mid, and late) stages but at definite time intervals. The absolute size, percentage change, and annual growth rate are explanatory for natives and are considered separately. Moreover, the structural population changes reveal transition stages from expansive to near expansive and constricting and stabilizing. Furthermore, broad age groups indicate rapid declines in the percentage of children, rapid increases in young adults, slow increases in older adults, and no changes in older persons. Even the sex ratio of natives is at par with other populations in transition (slightly above 100). Thus, it could be concluded that a demographic transition with structural changes as per theories: flawless growth rates with an expanding demographic dividend. At this juncture, the integration of migrants into society by endorsing family life and enabling social and demographic balance appears as imperative to improving the labor sector, productivity, and the image of the country in the international spheres for comparisons and benchmarking.

We present an innovative enthalpy method for determining the thermal properties of phase change materials (PCM). The enthalpy-temperature relation in the “mushy” zone is modelled by means of a fifth order Obreshkov polynomial with continuous first and second order derivatives at the zone boundaries. The partial differential equation (PDE) for the conduction of heat is rewritten so that the enthalpy variable is not explicitly present, rendering the equation nonlinear. The thermal conductivity of the PCM is assumed to be temperature dependent and is modelled by a fifth order Obreshkov polynomial as well. The method has been applied to lauric acid, a standard prototype. The latent heat and the conductivity coefficient, being the model parameters, were retrieved by fitting the measurements obtained through a simple experimental procedure. Therefore, our proposal may be profitably used for the study of materials intended for heat-storage applications.