Zero-valent iron is a moderately reducing reagent that is both non-toxic and affordable. In the present work, iron nanoparticles were synthesized using bitter guard leaf extract (Momordica charantia L.) (BGL-Fe NP). Using leaf samples from bitter protectant extract, iron nanoparticles were synthesized with secondary metabolites such as flavonoids and polyphenols acting as capping and reducing agents. Polyphenols reduce Fe²⁺/Fe³⁺ to nanovalent iron or iron nanoparticles. Iron nanoparticles were synthesized by reducing iron chloride as a precursor with bitter protective leaf extract in an alkaline environment. The obtained BGL-Fe NPs were calcined for 4 h at various temperatures of 400 °C, 500 °C, and 600 °C. The obtained samples were coded as BGL-Fe NPs-4, BGL-Fe NPs-5, and BGL-Fe NPs-6, respectively. The synthesized BGL-Fe NPs were systematically characterized by XRD, SEM, FTIR, UV-Vis and TG-DTA analysis. The obtained BGL-Fe NPs were then used as an adsorbent to remove the aqueous solution of basic methylene blue (MB) dye. MB concentration was monitored using UV-Vis spectroscopy.

City planning is becoming more and more crucial as modernization and urbanization progress quickly. Making maps is an essential and helpful way in the city planning process for gathering data about the layout of a city and its elements, including the roads, traffic, buildings, and environment. Thanks to advancements in technology, computer software is now used to create maps, yielding more accurate and varied results. As a result, cartography is now closely related to and plays a crucial part in city planning. This brief essay will discuss the value of cartography in urban development and planning, as well as the connection between the two.

In recent years, the pathological diagnosis of glomerular diseases typically involves the study of glomerular his-to pathology by specialized pathologists, who analyze tissue sections stained with Periodic Acid-Schiff (PAS) to assess tissue and cellular abnormalities. In recent years, the rapid development of generative adversarial networks composed of generators and discriminators has led to further developments in image colorization tasks. In this paper, we present a generative adversarial network by Spectral Normalization colorization designed for color restoration of grayscale images depicting glomerular cell tissue elements. The network consists of two structures: the generator and the discriminator. The generator incorporates a U-shaped decoder and encoder network to extract feature information from input images, extract features from Lab color space images, and predict color distribution. The discriminator network is responsible for optimizing the generated colorized images by comparing them with real stained images. On the Human Biomolecular Atlas Program (HubMAP)—Hacking the Kidney FTU segmentation challenge dataset, we achieved a peak signal-to-noise ratio of 29.802 dB, along with high structural similarity results as other colorization methods. This colorization method offers an approach to add color to grayscale images of glomerular cell tissue units. It facilitates the observation of physiological information in pathological images by doctors and patients, enabling better pathological-assisted diagnosis of certain kidney diseases.

In higher eukaryotes, the genes’ architecture has become an essential determinant of the variation in the number of transcripts (expression level) and the specificity of gene expression in plant tissue under stress conditions. The modern rise in genome-wide analysis accounts for summarizing the essential factors through the translocation of gene networks in a regulatory manner. Stress tolerance genes are in two groups: structural genes, which code for proteins and enzymes that directly protect cells from stress (such as genes for transporters, osmo-protectants, detoxifying enzymes, etc.), and the genes expressed in regulation and signal transduction (such as transcriptional factors (TFs) and protein kinases). The genetic regulation and protein activity arising from plants’ interaction with minerals and abiotic and biotic stresses utilize high-efficiency molecular profiling. Collecting gene expression data concerning gene regulation in plants towards focus predicts an acceptable model for efficient genomic tools. Thus, this review brings insights into modifying the expression study, providing a valuable source for assisting the involvement of genes in plant growth and metabolism-generating gene databases. The manuscript significantly contributes to understanding gene expression and regulation in plants, particularly under stress conditions. Its insights into stress tolerance mechanisms have substantial implications for crop improvement, making it highly relevant and valuable to the field.

In the present work, a series of butyl methacrylate/1-hexene copolymers were synthesized, and their efficiency as viscosity index improvers, pour point depressants, and shear stabilizers of lube oil was investigated. The effect of 1-hexene molar ratio, type, and concentration of Lewis acids on the incorporation of 1-hexene into the copolymer backbone was investigated. The successful synthesis of the copolymers was confirmed through FTIR and 1H NMR spectroscopy. Results obtained from quantitative 1H NMR and GPC revealed that an increase in the molar ratio of 1-hexene to butyl methacrylate, along with concentration of Lewis acids led to an increase in 1-hexene incorporation and a reduction in Mn and Ð. Similar trends were observed when the Lewis acid changed from AlCl₃ to organometallic acids. The maximum 1-hexene incorporation (26.4%) was achieved for sample BHY3, with a [1-hexene/BMA] ratio of 4 mol% and a [Yb(OTf)₃/BMA] ratio of 2.5 mol%. Evaluation of the synthesized copolymers as lube oil additives demonstrated that the viscosity index was more significantly influenced by samples with higher molecular weight. Sample BHA13 represents maximum VI of 137. The copolymer containing Yb(OTf)₃ as a catalyst exhibited superior efficiency as a pour point depressant. Furthermore, sample BHY3 showed the lowest shear stability index (6.4).

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.