Silymarin, a bioactive compound derived primarily from the seeds and fruit of the milk thistle (Silybum marianum) plant, has garnered increasing attention in recent years due to its potential applications in agriculture. This comprehensive review explores the multifaceted role of silymarin in agricultural practices, shedding light on its chemistry, biological activities, and diverse applications. The chemical structure and properties of silymarin are elucidated, emphasizing its unique solubility, stability, and bioavailability, which render it suitable for agricultural use. A significant portion of the review is dedicated to examining the biological activities of silymarin, which encompasses its antioxidant properties. The underlying mechanisms responsible for these activities are explored, highlighting their potential as a natural solution for mitigating environmental stressors that adversely affect crop health and productivity. Illustrative examples from research studies and practical applications underscore its effectiveness in safeguarding agricultural yields and ensuring food security. Furthermore, the review delves into the potential of silymarin to enhance crop growth, yield, and quality. Mechanisms through which silymarin influences plant physiology and metabolism are examined, providing valuable insights into its role as a growth-promoting agent in agriculture. The review concludes with a forward-looking examination of the prospects of silymarin in agriculture, highlighting emerging trends and areas of innovation that hold promise for sustainable and resilient farming systems. In summary, this review consolidates the current body of knowledge surrounding silymarin’s potential in agriculture. It underscores the versatility of silymarin as a natural tool for crop protection, growth enhancement, and environmental sustainability, offering valuable insights for researchers, practitioners, and policymakers seeking innovative approaches to address the challenges of modern agriculture.

The semiclassical boron–boron interatomic pair potential is constructed in an integral form allowing its converting into the analytical one. It is an ab initio B–B potential free of any semiempirical adjusting parameters, which would serve as an effective tool for the theoretical characterization of all-boron and boron-rich nanomaterials.

Monitoring marine biodiversity is a challenge in some vulnerable and difficult-to-access habitats, such as underwater caves. Underwater caves are a great focus of biodiversity, concentrating a large number of species in their environment. However, most of the sessile species that live on the rocky walls are very vulnerable, and they are often threatened by different pressures. The use of these spaces as a destination for recreational divers can cause different impacts on the benthic habitat. In this work, we propose a methodology based on video recordings of cave walls and image analysis with deep learning algorithms to estimate the spatial density of structuring species in a study area. We propose a combination of automatic frame overlap detection, estimation of the actual extent of surface cover, and semantic segmentation of the main 10 species of corals and sponges to obtain species density maps. These maps can be the data source for monitoring biodiversity over time. In this paper, we analyzed the performance of three different semantic segmentation algorithms and backbones for this task and found that the Mask R-CNN model with the Xception101 backbone achieves the best accuracy, with an average segmentation accuracy of 82%.

Implementing green retrofitting can save 50–90% of energy use in buildings built worldwide. Government policies in several developed countries have begun to increase the implementation of green retrofitting buildings in those countries, which must rise by up to 2.5% of the lifespan of buildings by 2030. By 2050, it is hoped that more than 85% of all buildings will have been retrofitted. The high costs of implementing green retrofitting amounting to 20% of the total initial construction costs, as well as the uncertainty of costs due to cost overruns are one of the main problems in achieving the implementation target in 2050. Therefore, increasing the accuracy of the costs of implementing green retrofitting is the best solution to overcome this. This research is limited to analyzing the factors that influence increasing the accuracy of green retrofitting costs based on WBS, BIM, and Information Systems. The results show that there are 10 factors affecting the cost accuracy of retrofitting or customizing high-rise office buildings, namely Energy Use Efficiency, Water Use Efficiency, Use of Environmentally Friendly Materials, Maintenance of Green Building Performance during the Use Period, Initial Survey, Project Information Documents, Cost Estimation Process, Resources, Legal, and Quantity Extraction applied. These factors are shown to increase the accuracy of green retrofitting costs.

The semi-arid is a climate characterized by precipitation that is. insufficient to maintain crops and where evaporation often exceeds rainfall. Vegetation is one of the most sensitive indicators of environmental changes understanding the patterns of biodiversity distribution and what influences them is a fundamental pre-requisite for effective conservation and sustainable utilization of biodiversity. In this study. our focus was on examining the vegetation diversity in the semi-arid region of Tebessa. which falls within the Eastern Saharan Atlas domain in North Africa’s semi-arid zone. Plants were sampled at 15 sites distributed across the study area. The quadrat method was used to conduct floral surveys. The sampling area of each sample was 100 square meters 10 m × 10 m (quadrat). Each quadrat was measured for species richness (number of species). abundance (number of individuals). and Richness generic (plant cover). Based on the floristic research. 48 species were found. classified into 21 families. with Asteraceae accounting for 34.69% of the species and Poaceae accounting for 14.28%.

Heat conduction theory stipulates that two thermo-physical properties of materials: the thermal conductivity “k” and the thermal diffusivity “α” influence the temperature evolution in regular and irregular bodies as a response to various cooling/heating conditions. The traditional statement involving the two thermo-physical properties is examined at length in the present study for the case of a semi-infinite region. The primary objective of the present study is to investigate the influence of the less known thermo-physical property called the thermal effusivity “e” on the incipient surface temperature rise in a semi-infinite body affected by uniform surface heat flux. The secondary objective of the study is to identify a key figure of merit named the dimensionless threshold time that separates the incipient temperature elevation in a semi-infinite region from the incipient temperature elevation in a large wall of finite thickness under the same uniform surface heat flux. The outcome of the methodical analysis suggests that the accurate estimate for the dimensionless threshold time τ_th in the semi-infinite region should be 0.10.