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.

We studied the role of industry-academic collaboration (IAC) in the enhancement of educational opportunities and outcomes under the digital driven Industry 4.0 using research and development, the patenting of products/knowledge, curriculum development, and artificial intelligence as proxies for IAC. Relevant conceptual, theoretical, and empirical literature were reviewed to provide a background for this research. The investigator used mainly principal (primary) data from a sample of 230 respondents. The primary statistics were acquired through a questionnaire. The statistics were evaluated using the structural equation model (SEM) and Stata version 13.0 as the statistical software. The findings indicate that the direct total effect of Artificial intelligence (Aint) on educational opportunities (EduOp) is substantial (Coef. 0.2519916) and statistically significant (p < 0.05), implying that changes in Aint have a pronounced influence on EduOp. Additionally, considering the indirect effects through intermediate variables, Research and Development (Res_dev) and Product Patenting (Patenting) play crucial roles, exhibiting significant indirect effects on EduOp. Res_dev exhibits a negative indirect effect (Coef = −0.009969, p = 0.000) suggesting that increased research and development may dampen the impact of Aint on EduOp against a priori expectation while Patenting has a positive indirect effect (Coef = 0.146621, p = 0.000), indicating that innovation, as reflected by patenting, amplifies the effect of Aint on EduOp. Notably, Curriculum development (Curr_dev) demonstrates a remarkable positive indirect effect (Coef = 0.8079605, p = 0.000) underscoring the strong role of current development activities in enhancing the influence of Aint on EduOp. The study contributes to knowledge on the effective deployment of artificial intelligence, which has been shown to enhance educational opportunities and outcomes under the digital driven Industry 4.0 in the study area.

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%.

Afforestation is a main tool for preventing desertification and soil erosion in arid and semiarid regions of Iran. Large-scale afforestation, however, has poorly understood consequences for the future ecosystems in the term of ecosystems protection. The objective of the present study is to identify changes in soil properties following different intervals of planting of Ailanthus altissima (tree of heaven) in semiarid afforestation of Iran (Chitgar Forest Park, Tehran). For this purpose, sand, silt and clay ratios, bulk density, soil moisture, pH, electrical conductivity, phosphorus, potassium, magnesium, calcium, sodium, total soil N, and total carbon was measured. Our study highlighted the potential of the invasive trees by A. altissima, to alter soil properties along chronosequence. Almost all soil quality attributes showed a declining trend with stand age. A continuous decline in soil quality indicated that the present land management may not be sustainable. Therefore, an improved management practice is imperative to sustain soil quality and maintain long-term productivity of plantation forests. Thinning activity will be required to reduce the number of trees competing for the same nutrients especially in a older stand to protect forest soils.

Machine analysis of detection of the face is an active research topic in Human-Computer Interaction today. Most of the existing studies show that discovering the portion and scale of the face region is difficult due to significant illumination variation, noise and appearance variation in unconstrained scenarios. To overcome these problems, we present a method based on Extended Semi-Local Binary Patterns. For each frame, an aggregation of the pixel values over a neighborhood is considered and a local binary pattern is obtained. From these a binary code is obtained for each pixel and then histogram features is computed. Adaboost algorithm is used to learn and classify these discriminative features with the help of exemplar face and non-face signature of the images for detecting the location of face region in the frame. This Extended Semi Local Binary Pattern is sturdy to variations in illumination and noisy images. The developed methods are deployed on the real time YouTube video face databases and found to exhibit significant performance improvement owing to the novel features when compared to the existing techniques.

Given the increasing demand for sustainable energy sources and the challenges associated with the limited efficiency of solar cells, this review focuses on the application of gold quantum dots (AuQDs) in enhancing solar cell performance. Gold quantum dots, with their unique properties such as the ability to absorb ultraviolet light and convert it into visible light expand the utilization of the solar spectrum in solar cells. Additionally, these quantum dots, through plasmonic effects and the enhancement of localized electric fields, improve light absorption, charge carrier generation (electrons and holes), and their transfer. This study investigates the integration of quantum dots with gold plasmonic nanoparticles into the structure of solar cells. Experimental results demonstrate that using green quantum dots and gold plasmonic nanoparticles as intermediate layers leads to an increase in power conversion efficiency. This improvement highlights the significant impact of this technology on solar cell performance. Furthermore, the reduction in charge transfer resistance and the increase in short-circuit current are additional advantages of utilizing this technology. The findings of this research emphasize the high potential of gold quantum dots in advancing next-generation solar cell technology.