Government College Women University Faisalabad

The seventh-order boundary value problems (BVPs), which are important because of their complexity and prevalence in many scientific and engineering fields, are the subject of this paper’s study. These high-order boundary value problems appear in fields such as fluid dynamics, where they are used to model fluid flow, and in elasticity theory, where they help describe the deformation of materials. Unfortunately, the precision and stability required to solve these high-order problems consistently are frequently lacking from current numerical techniques. Consequently, the advancement of theoretical research as well as practical applications in these disciplines depends on the development of a reliable and accurate method for solving seventh-order boundary value problems. In order to improve the accuracy and stability of solutions for these challenging issues, we propose novel numerical strategies that involves non-polynomial and polynomial cubic splines. For both methods, the domain [0,1] is divided into sub-intervals with step sizes of h=1/10 and h=1/5. This method involves initially transforming the seventh-order boundary value problems into a system of second-order. These second-order boundary value problems are then discretized using finite difference approximations, incorporating essential boundary conditions, and ultimately converted into a set of linear algebraic equations. The employed methods are rigorously assessed through experimentation on three distinct test problems. The outcomes attained showcase an exceptional level of accuracy, extending up to 7 decimal places. These commendable results are vividly depicted in both the tabulated data and accompanying graphs. Such a high degree of precision substantiates the dependability and efficiency of the proposed method. Comparisons, presented in tables and graphs, highlight the precision and reliability of our methods. These comparisons confirm that our approaches are valuable tools for addressing the challenges associated with seventh-order boundary value problems, marking a notable contribution to the field of numerical analysis. While lower-order boundary value problems have been extensively studied, applying these splines methods to seventh-order boundary value problems presents new challenges and insights. The novelty of this work involves non-polynomial and polynomial cubic spline techniques to solve seventh-order boundary value problems, offering improved accuracy and stability over existing numerical methods.

Breast cancer is a major issue of investigation in drug discovery due to its rising frequency and global dominance. Plants are significant natural sources for the development of novel medications and therapies. Medicinal mushrooms have many biological response modifiers and are used for the treatment of many physical illnesses. In this research, a database of 89 macro-molecules with anti-breast cancer activity, which were previously isolated from the mushrooms in literature, has been selected for the three-dimensional quantitative structure–activity relationships (3D-QSAR) studies. The 3D-QSAR model was necessarily used in Pharmacopoeia virtual evaluation of the database to develop novel MCF-7 inhibitors. With the known potential targets of breast cancer, the docking studies were achieved. Using molecular dynamics simulations, the targets’ stability with the best-chosen natural product molecule was found. Furthermore, the absorption, distribution, metabolism, excretion, and toxicity of three compounds, resulting after the docking study, were predicted. The compound C1 (Pseudonocardian A) showed the features of effective compounds because it has bioavailability from different coral species and is toxicity-free for the prevention of many dermatological illnesses. C1 is chemically active and possesses charge transfer inside the monomer, as seen by the band gaps of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) electrons. The reactivity descriptors ionization potential, electron affinity, chemical potential (μ), hardness (η), softness (S), electronegativity (χ), and electrophilicity index (ω) have been estimated using the energies of frontier molecular orbitals (HOMO–LUMO). Additionally, molecular electrostatic potential maps were created to show that the C1 is reactive.

Agricultural production faces significant losses due to salinity, drought, pests, insects, and weeds, particularly in nutrient- and fertilizer-deficient soils. This review focuses on enhancing the productivity of crops grown in dry and saline environments. Silicon nanoparticles (Si NPs) and silicon compounds (SiO₂/SiO₃²⁻) have shown potential to improve crop yields while mitigating the effects of biotic and abiotic stresses. As an eco-friendly alternative to chemical fertilizers, herbicides, and pesticides, Si NPs stimulate germination, plant growth, biomass accumulation, and nutrient absorption due to their small size, large surface area, and ease of cellular penetration. These nanoparticles reduce salinity stress by modulating gene expression, leading to the activation of antioxidant enzymes such as SOD, CAT, and APX, which help combat reactive oxygen species (ROS). Treatment with low concentrations of nano-silica (100–300 mg/L) significantly enhances plants' tolerance to salinity. Si NPs, when combined with soluble polymeric materials and rhizobacteria, provide a sustainable impact due to their slow-release properties, offering prolonged protection against bacterial and viral infections under saline stress conditions. Graphical abstract

The global distribution of population density and freshwater resources is uneven, compelling farmers to use wastewater for irrigating food crops. While wastewater can supply essential nutrients to plants, its use poses significant environmental, sanitary, and health risks due to the presence of harmful pollutants and pathogens. Contaminated irrigation water can elevate hazardous metal levels in agricultural soil, impacting soil fertility, crop quality, and human health. Arsenic, lead, cadmium, and chromium are notable examples of such heavy metals. Enhancing wastewater treatment efficiency requires the proper implementation of primary, secondary, and tertiary treatment stages. This review examines both conventional methods and advanced treatment strategies, highlighting the potential of reclaimed water as an alternative supply to address these challenges. Key considerations include urban planning, sanitation, remediation technologies, community awareness (especially among farmers), and government involvement. Furthermore, integrating artificial intelligence (AI) and computational techniques has shown promise in optimizing wastewater treatment processes, improving efficiency, and ensuring better water quality management. This review presents recent findings from the literature and offers recommendations for future research.

Amylase (α-AMY) is a key biomarker associated with the development and progression of dental caries due to its ability to bind its alpha portion to the Streptococcus mutans. Herein, we design a colorimetric sensor for monitoring salivary α-AMY levels by using graphitic carbon nitride-cadmium tungsten oxide (GCN-CdWO4) nanocomposite as a nanozyme probe and 3,3′,5,5′-tetramethylbenzidine (TMB) as its substrate. The GCN-CdWO4 nanocomposite is responsible for the catalytic conversion of TMB to oxidized TMB (oxi-TMB) without using exogenous hydrogen peroxide (H2O2). The optical response of GCN-CdWO4 nanocomposite is quenched when coated with starch (ST), as the ST layer blocks TMB access to nanocomposite. However, upon α-AMY binding, the optical response is restored due to the hydrolysis of ST. Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), and dynamic light scattering (DLS) were performed to characterize the synthesized graphitic carbon nitride (GCN), tungsten oxide (WO3) and GCN-CdWO4 nanocomposite. The sensor fabrication process was examined using FESEM and optical microscopy. The designed sensor exhibited a broad linear range (0.18–24 U/mL) with a lower limit of detection (LOD) of 0.01 U/mL under optimized conditions. It demonstrated exceptional sensitivity and improved reproducibility and stability over 4 weeks. Practical validation was performed by monitoring α-AMY levels in spiked artificial saliva, yielding recovery (96 to 104%), and clinical samples of patients with mild to acute dental caries of different age groups, demonstrating its effectiveness in real-world applications.

Organic nanomaterials play an important role in scientific research due to their small size, biodegradable nature, and better encapsulation of materials. Organic nanomaterials are made up of organic compounds having sizes in the range of 10 nm–1 µm. Liposomes, dendrimers, micelles, ferritin, nanogels, and polymeric nanoparticles fall under the category of organic nanomaterials. These nanomaterials have wide applications in various fields such as the pharmaceutical, food, and cosmetic industries. Different methods are used for the synthesis of organic nanomaterials such as top-down synthesis and bottom-up synthesis. Other techniques contributing to green synthesis are microwave-assisted synthesis, ultrasound-assisted synthesis, enzyme catalysis, and solvent-free synthesis. In addition to this, the use of renewable resources, plant extract, green solvents such as ethanol and water, and energy-efficient procedures (sonochemistry and mechanochemistry) also play an important role in sustainable approaches for the synthesis of nanomaterials. This chapter addressed the green chemistry approaches like reverse phase evaporation, emulsification, self-assembly and using biological processes for the synthesis of organic nanomaterials. The green synthesis of organic nanomaterials offers possible advantages, i.e., increasing product performance with novel structures and morphologies and preventing harm to the environment and human health. These developments highlight the potential of green synthetic methods to contribute to sustainable development within material science.

Engineered nanomaterials (ENMs) have aroused extensive interest in agricultural, industrial, and medical applications. The integration of ENMs into the agricultural systems aligns with the principles of United Nations’ sustainable development goals (SDGs), circular economy (CE) and bio-economy (BE) principles. This approach offers excellent opportunities to enhance productivity and address global climate change challenges. The revelation of the adverse effects of nanomaterials (NMs) on various organisms and ecosystems, however, has fueled the debate on ‘Nano-paradox’ leading to emergence of a new research domain ‘Nanotoxicology’. ENMs have shown different interactions with biological and environmental systems as compared to their bulk counterparts. They bioaccumulate in organisms, soils, and other environmental matrices, move through food chains and reach higher trophic levels including humans ultimately resulting in oxidative stress and cellular damage. Understanding nano-bio interactions, the mechanism of gene- and cytotoxicity, and associated potential hazards, is therefore, essential to mitigate their toxicological outputs. This review comprehensively examines the cyto- and genotoxicity mechanisms of ENMs in biological systems, covering aspects such as their entry, uptake, cellular responses, dynamic interactions in biological environments their long-term effects and environmental risk assessment (ERA). It also discusses toxicological assessment methods, regulatory policies, strategies for toxicity management/mitigation and future research directions in nanotechnology, all within the context of SDGs, CE, promoting resource efficiency and sustainability. Navigating the nano-paradox involves balancing the benefits of nanomaterials with concerns about nanotoxicity. Prioritizing thorough research on above facets can ensure sustainability and safety, enabling responsible harnessing of nanotechnology’s transformative potential in various applications including mitigating global climate change and enhancing agricultural productivity. Graphical abstract

Rainfall-induced geological disasters are widespread in the Jianghuai region of China, endangering human lives and socioeconomic activities. Anhui Province, a hotspot for these disasters, warrants a thorough analysis of the temporal and spatial distribution of geological disasters and their correlation with rainfall for effective forecasting and warning. This study divides Anhui Province into the Dabie Mountains, southern Anhui Mountains, and other areas based on different background conditions, and establishes effective rainfall threshold warning models for each. We reconstructed the collection of geological disaster precipitation records and rainfall data in Anhui from 2008 to 2023. Using binary logistic regression, we analyzed the correlation between rainfall factors and geological disasters, selected the optimal effective rainfall attenuation parameters for the study area, and determined the critical effective rainfall for different warning levels. Results show: (1) Landslides and collapses are the main types, mostly occurring in high altitude areas like the Dabie and southern Anhui Mountains, and are concentrated in the rainy season of June - July each year; (2) Rainfall is the main inducer, with both single heavy rainfall processes and sustained rainfall influencing geological disaster occurrence, mostly through their combined effect; (3) Effective rainfall is significantly correlated with the day of and previous 8 days rainfall. The optimal attenuation coefficients in the Dabie Mountains, southern Anhui Mountains, and other regions are 0.60, 0.66, and 0.61, respectively. The study shows that setting fine tuned critical rainfall threshold models for different regions is better than a province wide threshold. With a 79% forecast accuracy, it can provide a scientific basis for geological disaster meteorological risk forecasting and warning in Anhui Province.

This study provides a comprehensive theoretical and experimental analysis of the interactions between 3-(4-{9-[4-(3, 4-dicyanophenoxy) phenyl]-9 H-fluoren-9-yl}phenoxy)phthalonitrile and metal chlorides, focusing specifically on nickel chloride (NiCl₂) and cobalt chloride (CoCl₂). Combining Density Functional Theory (DFT) calculations with various spectroscopic techniques and electrochemical methods, the research explores the binding mechanisms and the electronic changes induced by the metal chlorides. DFT calculations, using the B3LYP functional and a 6-31G basis set for ground-state optimization, revealed significant modifications in the HOMO-LUMO energy gap upon coordination with the metal chlorides, indicating notable shifts in the compound’s electronic properties. Experimental data from UV-visible and fluorescence spectroscopy confirmed the formation of metal-ligand complexes, with observed shifts in absorption and emission spectra that corresponded with theoretical predictions. Further electrochemical insights were obtained through cyclic voltammetry, demonstrating an enhanced electron transfer process due to the metal ion interactions. These findings highlight how the inclusion of NiCl₂ and CoCl₂ significantly influences the electronic and optical characteristics of the phthalonitrile compound, suggesting their potential applications in optoelectronic devices and catalytic systems. This multidisciplinary approach provides a deeper understanding of metal-organic interactions and emphasizes the compound’s promise in material science applications. Graphical abstract

Malware presents a significant threat to computer networks and devices that lack robust defense mechanisms, despite the widespread use of anti-malware solutions. The rapid growth of the Internet has led to an increase in malicious code attacks, making them one of the most critical challenges in network security. Accurate identification and classification of malware variants are crucial for preventing data theft, security breaches, and other cyber risks. However, existing malware detection methods are often inefficient or inaccurate. Prior research has explored converting malicious code into grayscale images, but these approaches are often computationally intensive, especially in binary form. To address these challenges, we propose the Malware Variants Detection System (MVDS), a novel technique that transforms malicious code into color images, enhancing malware detection capabilities compared to traditional methods. Our approach leverages the richer information in color images to achieve higher classification accuracy than grayscale-based methods. We further improve the detection process by employing transfer learning to automatically identify and classify malware images based on their distinctive features. Empirical results demonstrate that MVDS achieves 97.98% accuracy with high detection speed, highlighting its potential for practical implementation in strengthening network security.

Background Plant species of the genus Daphne clasps a historical background with a potential source of bioactive phytochemicals such as flavonoids and daphnodorins. These compounds manifest a significant chemotaxonomic value in drug discovery. Their flair comprehensive pharmacological, phytochemical, biological, catalytic, and clinical utilities make them exclusively unique. This study was conducted to investigate the optimization and structure-based virtual screening of these peculiar analogs. The majority of the active constituents of medicines are obtained from natural products. Previously, before the invention of virtual screening methods or techniques, almost 80% of drugs were obtained from natural resources. Comparing reported data to drug discovery from 1981 to 2007 signifies that half of the FDA-approved drugs are obtained from natural resources. It has been reported that structures of natural products that have particularities of structural diversity, biochemical specification, and molecular properties make them suitable products for drug discovery. These products basically have unique chiral centers which increase their structural complexity than the synthesized drugs. Background Daphnodorins and their analogues are contemplated as pharmacologically significant scaffolds that have demonstrated numerous biological activities such as antifungal and insecticidal activities [17] antibacterial, cytotoxic, antiviral, anti-HIV activity, antioxidant activity [18], inhibitory activity against ∝-glucosidase and angiotensin II formation [19]. Recent pharmacological studies have manifested their efficient and versatile anti-inflammatory, anti-oxidant and protein glycation inhibition activities [20]. Method This work aimed to probe the use of daphnodorins analogs for the first time as antidiabetic inhibitors based on significant features and to determine the potential of daphnodorin analogs as antidiabetic inhibitors through computational analysis and structure-based virtual screening. A dataset of 38 compounds was selected from different databases, including PubChem and ZINC, for computational analysis, and optimized compounds were docked against various co-crystallized structures of inhibitors, antagonists, and receptors which were downloaded from PDB by using AutoDock Vina (by employing Broyden-Fletcher-Goldfarb-Shanno method), Discovery studio visualizer 2020, PYMOL (Schrodinger). Docking results were further validated by Molecular dynamic simulation and MM-GBSA calculation. Quantitative structure-activity relationship (QSAR) was reported by using Gaussian 09W by intimating Density Functional Theory (DFT). Using this combination of multi-approach computational strategy, 14 compounds were selected as potential exclusive lead compounds, which were analyzed through ADMET studies to pin down their druglike properties and toxicity. Objective Optimized compounds were docked against various co-crystallized structures of antidiabetic drug targets using AutoDock Vina and Discovery studio. Using this approach, 14 compounds were selected as potential exclusive lead compounds, which were analyzed through ADMET studies to pin down their drugs-like properties and toxicity and subjected to measure significant parameters such as dipole moment, electronic energy, frontier molecular orbitals (FMO) energy, and the hardness of molecules by performing DFT. Result At significant phases of drug design approaches regular use of molecular docking has helped to promote the separation of important representatives from 38 pharmaceutically active compounds by setting a threshold docking score of -9.0 kcal/mol which was used for their exposition. Subsequently, by employing a threshold it was recognized that 14 compounds proclaimed this threshold for antidiabetic activity. Further, molecular dynamic simulation, MM-GBSA, ADMET, and DFT results screened out daphnegiralin B4 (36) as a potential lead compound for developing antidiabetic agents. Method Molecular docking, DFT, MD Simulation, ADMET Conclusion Our analysis took us to the conclusion that daphnegiralin B4 (36) among all ligands comes out to be a lead compound having drug-like properties among 38 ligands being non-carcinogenic and non-cytotoxic which would benefit the medical community by providing significant drugs against diabetes. Pragmatic laboratory investigations identified a new precursor to open new doors for new drug discovery. Result Hit to lead, out of 38 daphnodorins, 14 compounds were exclusively selected as lead compounds, and from these leads, one compound was chosen as a pre-eminent antidiabetic agent. Conclusion Hit to lead, out of 38 daphnodorins, 14 compounds were exclusively selected as lead compounds, and from these leads, one compound was chosen as a pre-eminent antidiabetic agent.

Unemployment is one of the major issues in Pakistan. It causes psychological distress among individuals irrespective of their educational status. However, there is scarcity of research with reference to graduates from the discipline of Psychology and Sociology. This study aims to fill that gap by examining the association between perceived stress and anxiety among unemployed individuals with degrees in Psychology and Sociology. A cross-sectional correlational design was adopted, and stratified simple random sampling was used to collect data from 384 Pakistani men and women who hold Bachelor’s, Master’s, or PhD degrees in Psychology or Sociology from public and private universities recognized by the Higher Education Commission of Pakistan (HEC). Perceived Stress was measured through the Perceived Stress Scale (PSS), whereas anxiety level of the respondents was measured using the Beck Anxiety Inventory (Second Edition). The findings revealed a significant and positive association between perceived stress and anxiety (r=.83, p<.01). Moreover, perceived stress was found to significantly and positively predict anxiety (β=.83, R2= .69, p<.001), accounting for 69% of the variance in anxiety scores. The study offers several important implications, including encouraging help-seeking behaviour from mental health professionals, promoting relaxation exercises, providing psychoeducation to reduce societal pressure on unemployed individuals, and urging the government to take firm action against nepotism, lack of merit, and bias in both public and private sectors. Ensuring transparency in the employment selection process is also essential.

Metal nanoparticles synthesized by the green method show remarkably different properties from bulk materials due to their size, especially in biological applications. The study's objective is to lessen the adverse effects of synthesis processes, the chemicals they use, and the derivative substances that come from them. One practical approach in green nanotechnology is the use of various biomaterials for the synthesis of nanoparticles. In the present study, chromium nanoparticles were fabricated using Fagonia indica (LEFI) leaf extract as a reducing agent. This technique produced 46 nm-sized nanoparticles that are not only highly stable but also hold promise for a range of applications. The synthesized nanoparticles were characterized by X-ray diffraction spectroscopy, scanning electron microscopy, energy dispersive X-ray spectroscopy, dynamic light scattering, Fourier transform infrared spectroscopy, and ultraviolet-visible spectroscopy. An alpha-amylase assay was used to determine the antidiabetic potential of the nanoparticles. The antioxidant activity of plant extract and chromium nano-particles was evaluated using 2,2-Diphenyl-1-picrylhydrazyl scavenging activity. The agar diffusion method was used to test how well chromium nanoparticles killed Staphylococcus aureus and Escherichia coli. The study's findings indicate that Cr 2 O 3 nanoparticles have potential as physiologically active agents for bio-medicinal uses, providing reassurance for their future use.

Organic solar cells (OSCs) hold significant promise for sustainable energy solutions due to their lightweight design, low cost, and mechanical flexibility. In this study, novel small molecule donor materials (SMDs) for OSCs are designed and optimized using density functional theory (DFT) and time-dependent DFT (TD-DFT). All computational calculations were carried out using the Gaussian 09 program package to design donor molecules for OSCs. Density functional theory (DFT) functionals, namely B3YLP, CAM-B3LYP, MPW1PW91, and WB97XD, with 6-31G(d,p) levels of theory in suitable solvents were employed to compute different parameters such as frontier molecular orbital, binding energy, and open circuit voltage (Voc). The designed SMDs exhibited reduced band gaps (1.27–1.86 eV) and extended absorption spectra, with λmax values reaching 600.8 nm in chloroform solvent. Among the molecules, S1 demonstrated the highest light-harvesting efficiency (LHE = 0.89) and optimal reorganization energy for charge mobility. The results provide insights into the development of efficient and stable donor materials, facilitating advancements in OSC technology.