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The study was designed to explore the corrosion prevention capabilities of Citrullus colocynthis seeds alkaloid-rich extract (CSEA) on MS in a 1 M HCl environment by use of electrochemical and theoretical methods. Notably, Electrochemical Impedance Spectroscopy (EIS) and potentiodynamic polarization were used to probe the impact of immersion time and temperature. Energy-dispersive X-ray spectroscopy (EDX) and spanning electron microscopy (SEM) were used to confirm the presence of a protective layer on the substrate surface. Density functional theory (DFT) method was used to optimize the investigated species by use of B3LYP/6–31 + G(d, p) level of theory. The global and local quantum chemical reactivity descriptors were calculated to explore the inhibition corrosion efficiency and to identify the most favorable sites for electrophilic and nucleophilic attacks. Monte Carlo (MC) and molecular dynamics simulation (MDS) methods were used to study the interactions between corrosion inhibitor and metal surface. Noteworthy, results showed that CSEA exhibited an impressive inhibition efficiency, which reached 94.3% with a concentration of 2 g/L at 298 K. Potentiodynamic polarization revealed that the extract acted as a mixed-type inhibitor. Nyquist graphs showed that charge-transfer resistance and dæouble-layer capacitance both rised with increasing CSEA concentration, suggesting better inhibition efficiency. Notably, the Langmuir adsorption isotherm is well-aligned with the adsorption of inhibitor compounds. Importantly, all aforementioned theoretical methods were in agreement with the experimental findings. The outcome of the present work supported using Citrullus colocynthis seeds alkaloid-rich extract as ecofriendly agents to prevent corrosion.

Waste glass is hugely present in Morocco, and can be recycled for many geotechnical purposes, including road construction. In contrast, earthworks often produce significant amounts of clay waste that lack the necessary technical criteria for use as barriers. The present work aimed to study the influence of the addition of glass waste on the evolution of the mechanical characteristics of clays stabilized with crushed glass (particles less than 63 μm). The work consists of carrying out CBR, Proctor, and shear tests on natural clay taken as a reference and mixtures (clay-crushed glass) at different percentages. Results showed that the addition of glass to clay decreases the swelling and compaction indices along with modifying the intrinsic characteristics of the clay.

The distribution and composition of the vegetation are greatly affected by the edaphology and topography of an area. The current study explores the vegetation structure of the herbaceous layer at various habitats of district Kohat for the first time. A survey was conducted during the spring seasons of 2021, 2022 and 2023 selecting 40 sites on the basis of edaphology, topography, altitude, aspect and status. Data was collected via quadrat approach to establish plant communities by species Importance Value (IV), point out dominant species by Total IV (TIV) and dominant families via Total Family IV (TFIV). The quantitative biological spectrum was also calculated. Communities’ phytosociological characteristics were analyzed via various diversity indices (Shannon’s Index ( H ), Simpson’s Index ( D ), Species Richness (SR), Evenness (E) and Maturity index (Mi)) while similarity between the communities was calculated by using Sorensen’s Index. The findings revealed a total of 253 species belonging to 57 families having the dominant species Cynodon dactylon (L.) Pers. (TIV, 484.3) followed by Saussuria heteromalla (D. Don) Hand. (TIV, 360.4), Anagallis arvensis L. (TIV, 353.2) and Aristida adscensionis L. (TIV, 349.65). Among 40 plant communities, Poaceae (TFIV, 2706.6), Asteraceae (TFIV, 2018.8), Fabaceae (TFIV, 1071.5) and Brassicaceae (TFIV, 825.9) were the dominant families. Therophytes (TIV, 7882) class was the dominant life form class followed by hemicryptophytes (TIV, 2517) while microphylls (TIV, 4669) class was the dominant leaf size class followed by nanophylls (TIV, 5469). Environmental factors i.e. topography and edaphic characteristics, showed significant effects on the diversity of the communities. The study concludes in a diverse pattern of distribution with a rich flora in the area warranting its documentation which will preserve the valuable species opening vistas for future research.

Curcuma longa L., is recognized worldwide as a medicinally and economically important plant species due to its curcumin content which is an industrially important compound. In this study, a total of 329 accessions were collected from four states of India and planted in the experimental farm of CSIR-NEIST, Jorhat, India, in augmented design. Among these, 152 high curcumin (> 1.50%) accessions were screened for molecular divergence study using 39 SSR primers. The primers showed the most efficient outcome with 2–8 allele/ loci and a total 163 number of alleles with 100% polymorphism. Cluster analysis revealed the construction of three clusters, out of which one cluster was geographically dependent, and germplasm was particularly from Assam state. Jaccard's pairwise coefficient showed maximum genetic dissimilarity of (0.75) between accession RRLJCL 3 and RRLJCL 126, indicating high variation as it was from two different states viz Arunachal Pradesh and Nagaland respectively and minimum genetic dissimilarity of (0.09) between RRLJCL 58 and RRLJCL 59 indicating significantly less variation as the two accessions were from same state, i.e., Arunachal Pradesh. Analysis of Molecular Variance (AMOVA) revealed high molecular variation within the population (87%) and significantly less variation among the population (13%). Additionally, Neighbour Joining dendrogram, Principal Component Analysis (PCA), and bar plot structure revealed similar clustering of germplasm. This diversity assessment will help in selecting the trait-specific genotypes, crop improvement program, conservation of gene pool, marker-assisted breeding, and quantitative trait loci identification. Moreover, to the best of our knowledge, it is the first molecular diversity report among 152 high curcumin lines of C. longa from North East India using 39 SSR primers.

Water stress is an important factor that substantially impacts crop production. As a result, there is a need for various strategies that can mitigate these negative effects. One such strategy is the application of potassium humate (Kh) and cobalt (Co), which have been reported to enhance the resistance of crop plants. Therefore, the present experiment was designed to investigate whether the application of Kh and Co could positively affect proline, chlorophyll and mineral elements contents, and antioxidant defense systems which in turn will mitigate the negative impact of water stress under different irrigation strategies. In 2021 and 2022, an open-field experiments were conducted by using a split-plot design. The main plots were divided to represent different irrigation strategies (ST), with additional control of full irrigation requirements (ST1). Four STs were implemented, with ST1, followed by the application of 75%, 50%, and 25% irrigation strategies in ST2, ST3, and ST4 respectively, in the next irrigation, followed by the full requirements, and so on. In the subplots, peanut plants were treated with tap water (Control), Kh at 2 g l −1 and 3 g l −1 , Co, Co + Kh 2 g l −1 and Co + Kh 3 g l −1 . The yield was negatively affected by the implementation of ST4, despite the increase in proline contents. Furthermore, there was a decrease in relative water content, chlorophyll content, antioxidant enzymes, protein, and mineral nutrient elements. However, the application of Kh or Co showed better improvements in most of the studied parameters. It is worth noting that there was an antagonistic relationship between Co and iron/manganese, and the intensity of this relationship was found to depend on the STs implemented. The highest mineral nutrient accumulation, chlorophyll content, relative water content, protein content, oil content, seed yield, and water productivity were observed when peanut plants were treated with Kh 3 g l −1  + Co under the ST2 water strategy.

The decision support system for agro-technology transfer (DSSAT) is a worldwide crop modeling platform used for crops growth, yield, leaf area index (LAI), and biomass estimation under varying climatic, soil and management conditions. This study integrates DSSAT with satellite remote sensing (RS) data to estimates canopy state variables like LAI and biomass. For LAI estimation, Moderate Resolution Imaging Spectroradiometer (MODIS) product (MCD15A3H for LAI and MOD17A2 / MOD17A3 products for biomass) are used. Field data for Sheikhupura district is provided by National Agriculture Research Council (NARC) and used for the calibration and validation of the model. The results indicate strong agreement between the DSSAT and RS derived estimates. Correlation coefficients (R²) for LAI varied from 0.82 to 0.90, while for biomass ranged from 0.92 to 0.99 over two farms and two growing seasons (2012–2014). The index of agreement (D-index) ranged from 0.79 to 0.96 across the two farms and two growing seasons (2012–2014) affirming the model’s durability. However, the biomass estimated from RS data is underestimated due to saturation phenomenon in the optical RS. The performance metrics, comprising the coefficient of residual mass (CRM) and normalized root mean square error (nRMSE), further substantiate the approach utilized. This study will help decision and policymakers and researchers to apply geospatial techniques for the sustainable agriculture practices.

Addressing the growing need for methods for ecofriendly dye removal from aqueous media, this study explores the potential of rice husks coated with iron oxide (Fe2O3@RH composites) for efficient Acid Blue 25 decontamination. The adsorption potential of Acid Blue 25 is analyzed using raw rice husks and Fe2O3 nanoparticles in the literature, but their enhanced removal capacity by means of Fe2O3@RH composites is reported for the first time in this study. Fe2O3@RH composites were analyzed by using analytical techniques such as TGA, SEM, FTIR, BET, and the point of zero charge (pH(PZC)). The Acid Blue 25 adsorption experiment using Fe2O3@RH composites showed maximum adsorption at an initial concentration of Acid Blue 25 of 80 ppm, a contact time of 50 min, a temperature of 313 K, 0.25 g of Fe2O3@RH composites, and a pH of 2. The maximum percentage removal of Acid Blue 25 was found to be 91%. Various linear and nonlinear kinetic and isothermal models were used in this study to emphasize the importance and necessity of the adsorption process. Adsorption isotherms such as the Freundlich, Temkin, Langmuir, and Dubinin–Radushkevich (D–R) models were applied. The results showed that all the isotherms were best fitted on the data, except the linear form of the D–R isotherm. Adsorption kinetics such as the intraparticle kinetic model, the Elovich kinetic model, and the pseudo-first-order and pseudo-second-order models were applied. All the kinetic models were found to be best fitted on the data, except the PSO model (types II, III, and IV). Thermodynamic parameters such as ΔG° (KJ/mol), ΔH° (KJ/mol), and ΔS° (J/K*mol) were studied, and the reaction was found to be exothermic in nature with an increase in the entropy of the system, which supported the adsorption phenomenon. The current study contributes to a comprehensive understanding of the adsorption process and its underlying mechanisms through characterization, the optimization of the conditions, and the application of various models. The findings of the present study suggest practical applications of this method in wastewater treatment and environmental remediation.

Application of machine learning in plant breeding is a recent concept, that has to be optimized for precise utilization in the breeding program of high yielding crop plants. Identification and efficient utilization of heterotic grouping pattern aided with machine learning approaches is of utmost importance in hybrid cultivar breeding as it can save time and resources required to breed a new plant hybrid/variety. In the present study, 109 genotypes of sunflower were investigated at morphological, biochemical (SDS-PAGE) and molecular levels (through micro-satellites (SSR) markers) for heterotic grouping. All the three datasets were combined, scaled, and subjected to unsupervised machine learning algorithms, i.e., Hierarchical clustering, K-means clustering and hybrid clustering algorithm (hierarchical + K-means) for assessment of efficiency and resolution power of these algorithms in practical plant breeding for heterotic grouping identification. Following the application of machine learning unsupervised clustering approach, two major groups were identified in the studied sunflower germplasm, and further classification revealed six smaller classes in each major group through hierarchical and hybrid clustering approach. Due to high resolution, obtained in hierarchical clustering, classification achieved through this algorithm was further used for selection of potential parents. One genotype from each smaller group was selected based on the maximum seed yield potential and hybridized in a line  ×  tester mating design producing 36 F 1 cross combinations. These F 1 s along with their parents were studied in open field conditions for validating the efficacy of identified heterotic groups in sunflowers genetic material under study. Data for 11 agronomic and qualitative traits were recorded. These 36 F 1 combinations were tested for their combining ability (General/Specific), heterosis, genotypic and phenotypic correlation and path analysis. Results suggested that F 1 hybrids performed better for all the traits under investigation than their respective parents. Findings of the study validated the use of machine learning approaches in practical plant breeding; however, more accurate and robust clustering algorithms need to be developed to handle the data noisiness of open field experiments.

This study evaluates the antioxidant and antimicrobial activities of HPLC-characterized extracts from Juniperus thurifera (L.) leaf extract (ELJT), bark extract (EBJT), and fruit extract (ESJT). The HPLC analysis of the hydroethanol extract of EBJT identified several key constituents, notably urocanic acid. In terms of antioxidant potential, the DPPH assay showed that both the EBJT and ESJT extracts had significant free radical scavenging activity. The IC 50 values for EBJT and ESJT were 43 µg/mL and 77 µg/mL, respectively. These values indicate that EBJT has a stronger capacity to neutralize free radicals compared to ESJT. For comparison, the positive control (BHT) showed a significantly lower IC 50 , underscoring the fact that while the extracts exhibit antioxidant activity, their effectiveness is still relatively weaker than that of BHT. In the FRAP assay, the EC 50 values for EBJT and ESJT were 256 µg/mL and 261 µg/mL, respectively, indicating similar antioxidant efficacy between the two extracts. Again, both extracts show antioxidant potential, but still fall short of the control’s activity. The extracts exhibited significant antibacterial activity against S. aureus , E. coli , B. subtilis , and P. mirabilis , with ELJT displaying the strongest effect, characterized by large inhibition zones and low MIC values. This highlights the superior antibacterial potential of the leaf extract compared to the bark and fruit extracts. Regarding antifungal activity, EBJT demonstrated notable efficacy against A. niger , A. flavus , and F. oxysporum , with substantial inhibition zones and relatively low MIC values. Against C. albicans , all extracts showed significant inhibition, with EBJT exhibiting the highest inhibition zone (29.5 mm) and a MIC of 65.29 µL/mL. The J. thurifera extracts, especially ELJT, show promising antioxidant and antimicrobial activities, though less effective than positive controls. Despite this, they remain valuable sources of bioactive compounds for further study and potential applications.

Nanoparticles play a vital role in environmental remediation on a global scale. In recent years, there has been an increasing demand to utilize nanoparticles in wastewater treatment due to their remarkable physiochemical properties. In the current study, manganese oxide nanoparticles (MnO-NPs) were synthesized from the strain and characterized by UV/Vis spectroscopy, X-ray diffraction, scanning electron microscopy, and Fourier transform infrared spectroscopy. The objective of this study was to evaluate the potential of biosynthesized MnO-NPs to treat wastewater. Results showed the photocatalytic degradation and adsorption potential of MnO-NPs for chemical oxygen demand, sulfate, and phosphate were 79%, 64%, and 64.5%, respectively, depicting the potential of MnO-NPs to effectively reduce pollutants in wastewater. The treated wastewater was further utilized for the cultivation of wheat seedlings through a pot experiment. It was observed that the application of treated wastewater showed a significant increase in growth, physiological, and antioxidant attributes. However, the application of treated wastewater led to a significant decrease in oxidative stress by 40%. It can be concluded that the application of MnO-NPs is a promising choice to treat wastewater as it has the potential to enhance the growth, physiological, and antioxidant activities of wheat seedlings.