University of Lahore

The purpose of the study was to find out how teachers' job satisfaction in Lahore's public secondary schools related to their working environment. The study employed a correlation research design. The random sampling approach was used to select 500 teachers (250 male and 250 female) from 25 boys and 25 girls secondary schools. The School-Level Environment Questionnaire (SLEQ), the Teacher Job Satisfaction Questionnaire (TJSQ), and demographic performance (age, gender, and qualification) were the tools employed in this study. Both inferential (Pearson-product moment correlation, Independent sample t-test, and ANOVA) and descriptive (mean, standard deviation, and frequency) statistics were employed. The main finding, which was based on descriptive and inferential statistics, showed that there was a substantial correlation between teachers' job satisfaction and the working conditions in public secondary schools. The result of the study indicated that there was a significant difference between male and female teachers' perceptions of their working conditions in schools and their level of job satisfaction. The study also revealed that the working school environment and job satisfaction of public secondary school teachers were not significantly correlated with their age or level of degrees.

In this study, a generalization of the Estevez–Mansfield–Clarkson equation which considers the presence of conformable time-fractional derivatives is investigated analytically. The integer-order model finds applications in mathematical physics, optics and the investigation of shape developing in liquid drops. In the present manuscript, the Sardar sub-equation method is employed to solve the generalized Estevez–Mansfield–Clarkson equation. From the Sardar sub-equation method a broad range of soliton solutions, including dark-bright, combined dark-singular and periodic singular solitons, have been obtained. Some of the results derived in the present manuscript are plotted to illustrate that the solutions are solitary waves, indeed.

Glucoraphenin (GRE), a glucosinolate in Raphanus sativus L. seeds and roots, can degrade into isothiocyanates through myrosinase. However, myrosinase in R. sativus roots and seeds is inactivated during cooking, allowing GRE to enter the body in its unmodified form and exert bioactivity. Therefore, investigating the biotransformation of GRE by intestinal flora and the activity of GRE and its metabolites is essential. In this study, fresh fecal samples from healthy SD rats were collected to prepare an intestinal flora culture medium, which was incubated with GRE under anaerobic conditions. GRE metabolite was isolated through Sephadex LH‐20 column chromatography, and the structure was identified using HPLC coupled with time‐of‐flight mass spectrometry (HPLC‐TOF/MS) and NMR. Additionally, fluorescence labeling and the number of intestinal peristalsis were employed to assess the effect of GRE and its metabolite on intestinal motility in zebrafish models. Results indicated that GRE can be metabolized in vitro by rat intestinal flora, producing glucoraphasatin (GRH). NMR and MS analysis confirmed GRH’s structure as 4‐methylthio‐3‐butenyl glucosinolate. Both GRE and GRH were found to enhance intestinal peristalsis in zebrafish. This study elucidates GRE’s metabolic pathway in intestinal flora and suggests that GRE and GRH may be functional components to promote intestinal motility.

The function of supercapacitor electrodes was enhanced using Cadmium Oxide (CdO) nanorods synthesized at different calcination temperatures via a wet chemical technique and characterized. Structural analysis revealed changes in crystalline properties and size with varying calcination temperatures. The morphology of CdO nanorods, which exhibits uniform size, is suitable for application as supercapacitors. Temperature-dependent changes in crystalline characteristics were revealed by structural investigations. Galvanostatic charge-discharge (GCD) and cyclic voltammetry investigations support the pseudo-capacitive charge storage mechanism of CdO. A 169 F g⁻¹ specific capacitance was obtained for the CdO nanorods electrode material from the GCD profile, showing excellent capacitive retention of 84% for 100 cycles. This shows that pure CdO has high electrical conductivity, making it a better electrode material for supercapacitor application without doping. As scan rate increased, the specific capacitance dropped, suggesting less ion diffusion. Measured energy and power densities show promising results, with maximum values of 164 Wh kg⁻¹ and 25 kW kg⁻¹, respectively, at 1 A g⁻¹. Electrochemical impedance spectroscopy demonstrates low equivalent series resistance values (98 Ω after CV, 195 Ω after GCD), highlighting CdO nanorods’ suitability for supercapacitor applications. CdO nanorods show promising capacitive behavior, suggesting that they have the potential to be useful and affordable materials for energy storage.

The challenge of feeding the world's growing population is impaired by declining arable land, water quality and erratic weather patterns due to climate change. Abiotic stresses such as drought, heat, salinity and cold disrupt plant growth, reducing crop yields and quality. Modern biotechnological tools including high-throughput sequencing and bioinformatics have enabled the characterization of plant stress responses through advanced "omics" technologies. Genomics, transcriptomics, proteomics, metabolo-mics and epigenomics describe molecular mechanisms underlying plant stress tolerance. Integrating multi-omics approaches provides a deeper understanding of these mechanisms, addressing the limitations of single-omics studies. The combination of multi-omics data (genomics, transcriptomics, proteomics and metabolomics) identifies important biomarkers, regulatory networks and genetic targets that enhance plant stress resilience. This multi-omics information regarding plants is crucial for genome-assisted breeding (GAB) to improve crop traits and the development of climate-resilient crops to withstand environmental challenges. Therefore, researchers use multi-omics pipelines to enhance productive crops, quality and stress tolerance, solving global food security challenges caused by climate change and environmental stressors. This review discusses the role of omics technologies in describing the genetic mechanisms of plant stress responses and explores how this information is applied to enhance crop resilience and productivity, which leads to improved crops. The application of combining omics approaches to develop next-generation crops that are capable of thriving under adverse environmental conditions, ensuring reliable and safe food supply for the future under stress conditions.

Background Moral sensitivity is essential for nurses to recognize and appropriately respond to ethical issues, understanding the impact of their actions on patient well-being. It improves care quality, ethical decision-making, and equips nurses with skills to handle moral dilemmas in clinical settings. Various studies in Iran have reported different results. Accordingly, this study was conducted to estimate the pooled standardized moral sensitivity score among Iranian nurses. Methods A systematic search of national and international databases was conducted up until June 2023, yielding 563 articles. After screening by two independent researchers, 52 articles with a total sample size of 11,621 participants were included in the analysis. Any discrepancies were resolved through discussion. Heterogeneity was assessed using the I² index. Subgroup analyses were performed based on the language of the articles, sample size, and country region. Meta-regression analysis was conducted to explore the relationship between the moral sensitivity score and variables such as age, year of publication, and sample size. All analyses were performed using Stata software, version 17. Results The pooled moral sensitivity score was 69% (95% CI: 66–72, I² = 94.03%). The highest overall ethical sensitivity score was observed in region 5 of the country (73.6%, 95% CI: 67-80.1), in articles published in Farsi (70.2%, 95% CI: 65.8–74.7), and in articles with a sample size of less than 200 participants (70%, 95% CI: 66.3–73.6). No significant relationship was found between the overall ethical sensitivity score and variables such as age, year of publication, or sample size. Publication bias was significant (p = 0.001). Conclusion This meta-analysis indicates a moderate level of moral sensitivity among Iranian nurses. While regional and study-related differences were observed, no significant relationship was found between the moral sensitivity score and variables like age, year of publication, or sample size. Publication bias suggests the need for more representative studies to fully understand the factors influencing moral sensitivity in nursing. Clinical trial number This study is a systematic review and meta-analysis, and not a clinical trial.

In this paper, we developed a pose-aware facial expression recognition technique. The proposed technique employed K nearest neighbor for pose detection and a neural network-based extended stacking ensemble model for pose-aware facial expression recognition. For pose-aware facial expression classification, we have extended the stacking ensemble technique from a two-level ensemble model to three-level ensemble model: base-level, meta-level and predictor. The base-level classifier is the binary neural network. The meta-level classifier is a pool of binary neural networks. The outputs of binary neural networks are combined using probability distribution to build the neural network ensemble. A pool of neural network ensembles is trained to learn the similarity between multi-pose facial expressions, where each neural network ensemble represents the presence or absence of a facial expression. The predictor is the Naive Bayes classifier, it takes the binary output of stacked neural network ensembles and classifies the unknown facial image as one of the facial expressions. The facial concentration region was detected using the Voila-Jones face detector. The Radboud faces database was used for stacked ensembles’ training and testing purpose. The experimental results demonstrate that the proposed technique achieved 90% accuracy using Eigen features with 160 stacked neural network ensembles and Naive Bayes classifier. It demonstrates that the proposed techniques performed significantly as compare to state of the art pose-ware facial expression recognition techniques.

Data retrieval and feature extraction by means of different computational techniques has played a vital role in the field of artificial intelligence as it has been successfully employed for the extraction of useful parameters from the input data. However, successful implementation of a relatively simplified approach for the experimental parameter extraction from the visual data is yet to be explored. In this work, we implement a computer vision (CV)-based automatic video analysis technique for the estimation of time period of a simple pendulum from the visual data of the performed experiment. We adopt various direct and indirect methods including real-time measurement of oscillation time, visual estimation through recorded video clips, and through CV-generated time series graphs of oscillations. The analysis of the time series plots by using fast Fourier transform helps in determining the frequency and consequently the time period of the harmonic oscillations. The comparison of the resulting values estimated by all of the implemented methodologies with the one theoretically calculated, shows that the CV based technique performs equally well with a reasonable level of accuracy and tops the list of adopted methods in terms of the least human intervention. These findings may help in paving the avenue for future implementation of the CV-based strategies in parameter extraction from the visual data of experiments in the field of natural sciences.

This study uses the Xue model to explore how well a nanofluid transfers heat in a steady oblique stagnation-point flow. It examines the impact of nonlinear thermal radiation on a mixture of three different nanoparticles as the fluid moves along a stretching surface. This intended comparison model is unique and still scarce in the literature. Trihybrid nanofluids or composites have, therefore, been created to enhance heat transfer efficiency. Three different types of nanoparticles (Fe3O4, Cu, and TiO2) are exploring circumstances where ethylene glycol is the base medium. A mathematical framework is developed. Using the appropriate transformations, the system of partial differential equations (PDEs) is transformed into an ordinary differential system of three equations (ODEs), which is evaluated numerically using the bvp4c method. This integrated technique facilitates the convergence process effectively. A detailed analysis is conducted of the graphical representation and the physical behavior of important factors. On temperature and velocity profiles, the impacts of several variables, including a thermal radiation, surface heating parameter, stretching ratio, and particle volume fraction, are investigated thoroughly. The results show that the Fe3O4+Cu+TiO2/ethylene glycol nanofluid outperforms with a high particle volume fraction of TiO2. It has been demonstrated that Fe3O4+Cu+TiO2/ethylene glycol nanofluid with a high particle volume fraction of TiO2 has considerably greater thermal radiation than other nanoparticles.The inclusion of nanofluids significantly improves heat transfer compared with conventional fluids due to their higher thermal conductivity, which is crucial for enhancing heat dissipation at stagnation points in solar systems.

The behavior of second-grade nanofluid is investigated in this work using entropy formation, thermal radiation, and changing thermal conductivity. The objective of this study is to provide deeper insights into how these variables influence fluid flow characteristics and heat transfer in nanofluid. To assess their impact on fluid dynamics and thermal behavior, the Tomson–Troian velocity slip condition and temperature slip boundary conditions are incorporated to examine mass and heat transport. The governing partial differential equations are simplified and effectively analyzed by transforming them into a collection of ordinary differential equations employing stream functions and similarity transformations. The shooting approach is used to produce numerical solutions for the physical phenomena, with the addition of the Newton–Raphson and Keller-box scheme for improved accuracy and convergence. This method also assesses the impact of physical parameters on temperature, velocity, and mass transfer sketches graphically for a clear understanding of their behavior. These parameters include heat production, variable thermal conductivity, the second-grade fluid parameter, the Eckert number, the Brownian motion, the Prandtl number, thermophoresis, and the Lewis number. This study found that the raising parameter for variable thermal conductivity enhances both temperature and velocity profiles. For the maximum second-grade fluid parameter, the temperature profile diminishes, while the velocity profile exhibits an upward trend. The Eckert number enhances the concentration and temperature profiles. The velocity profile of second-grade nanofluid decreases with increasing Prandtl numbers. Higher temperature-dependent density results in the greatest fluid temperature and concentration values. Greater Brownian motion results in improved mass and heat transmission magnitudes. The Sherwood number, Nusselt number, and skin friction coefficient decrease as the Prandtl number rises, but increase when the Lewis number rises.

Chalcogenide perovskites have gained popularity in optoelectronic research due to their earth-abundant, stable and Pb-free composition. However, theoretical studies on excitonic and polaronic features have not been well investigated due to high processing costs. We use state-of-the-art density functional theory to study the excitonic and polaronic effects in a sequence of chalcogenide perovskites ATlS3, where A = Sc, Y, La and Lu. The structure is stable in the orthorhombic phase. The electronic bandgap is designed with TB-mBJ potential, and the exciton binding energy is estimated with the help of band structure. The optical characteristics containing the complex dielectric constant, refractive index, absorption coefficient and their derivatives are studied deeply. The studied compounds have low $\:{\boldsymbol{E}}_{\mathbf{b}}^{\mathbf{e}\mathbf{x}}$ due to high $\:{\boldsymbol{\epsilon\:}}_{1}\left(0\right)$. Thermoelectric and thermodynamic properties are also examined with BoltzTraP and Gibbs2 codes. The mechanical and molecular dynamic investigation shows the compounds are stable. The computed properties suggest that these compounds can be used in optoelectronic devices.

Terminalia arjuna, known as Arjuna, is a medicinal plant native to the Indian subcontinent. It has a rich history of traditional use and contains a wide range of phytoconstituents that contribute to its potential health benefits. The key phytoconstituents in Terminalia arjuna include polyphenols, triterpenoids, flavonoids, and tannins. The plant's bark is rich in polyphenols, particularly gallic acid and ellagic acid derivatives, which are powerful antioxidants. These antioxidants can protect cells from oxidative stress and may help prevent degenerative diseases. Additionally, Terminalia arjuna contains triterpenoids like arjunolic acid and arjunic acid, which have various therapeutic properties, including cardioprotective, anti‐allergic, anti‐cancer, and antibacterial effects. Flavonoids found in Terminalia arjuna, such as luteolin and quercetin, contribute to its potential cardiovascular benefits. These compounds have been studied for their positive effects on heart health. Tannins, including pyrocatechols and punicalagin, are also present in the bark and are known for their astringent properties, wound‐healing abilities, and possible antimicrobial activity. This review highlights the Terminalia arjuna potential health benefits include cardioprotection, antioxidant effects, anti‐inflammatory and analgesic properties, hypolipidemic (lipid‐lowering) effects, and potential anti‐cancer and antibacterial actions.

Rheumatoid arthritis (RA) is a chronic inflammatory disease that primarily affects the synovial joint linings, resulting in progressive disability, increased mortality, and considerable economic costs. Early treatment with disease-modifying antirheumatic medications (DMARDs) can significantly improve the overall outlook for people with RA. Contemporary pharmaceutical interventions, encompassing standard, biological, and emerging small molecule disease- modifying anti-rheumatic medications continue to be the cornerstone of RA management, with substantial advancements made in the pursuit of achieving remission from the disease and preventing joint deformities. Nevertheless, a substantial segment of individuals with RA do not experience a satisfactory response to existing treatments, underscoring the pressing need for novel therapeutic options. Biologic DMARDs are among the therapy choices. Non-tumor necrosis factor inhibitors (Non-TNFi) such as abatacept, rituximab, tocilizumab, and sarilumab are examples, as are anti-tumor necrosis factor (TNF) medications such as infliximab, adalimumab, etanercept, golimumab, and certolizumab pegol. More recent biomarkers have emerged and showed usefulness in the early detection of RA. These biomarkers, often referred to simply as “biomarkers”, are quantifiable indicators of normal or pathologic processes, and they can also gauge treatment response. The assessment of RA treatment response typically combines patient-reported outcomes, physical evaluations, and laboratory findings, as there isn’t a single biomarker that has proven sufficient for measuring disease activity. This review explores the usage of biologic DMARDs as a therapeutic approach for RA, as well as the biomarkers typically used for RA early diagnosis, prognosis prediction, and disease activity evaluation.

This article explores the (3+1)-dimensional Bateman–Burgers equation using the bilinear neural network technique. Single and double layers of neural networks are built to construct different bilinear neural network models such as “4-3-1” and “4-2-2-1” by using specific activation functions. The interaction solution and periodic type-l solutions were extracted for this equation. The (3+1)-dimensional Bateman–Burgers equation has many applications in traffic flow, fluid mechanics, gas dynamics, and nonlinear acoustics. For enhancing the graphical representation of the dynamic behavior and the physical attributes of particular solutions, the computing tool Mathematica 13.1 was used for generating 3D visualizations, 2D graphical representations, and density mappings. The methodology used in this article improves the study of nonlinear partial differential equations that arise in different complex phenomena. In the end, we believe that these solutions will play a role in the understanding of some high-order equation nonlinear phenomena.

Spiruchostatin A also referred to as YM753 and OBP801, a cyclic peptide-based natural product derived from Pseudomonas sp., is distinguished by its potent inhibition of Class I histone deacetylases (HDACs). The modulation of epigenetic mechanisms by HDAC inhibitors is fundamental for altering gene expression related to cell growth, apoptosis, and differentiation, highlighting their potential in oncologic therapies. This updated review assesses the antitumor efficacy of Spiruchostatin A across diverse cellular and animal models, scrutinizing its viability as a therapeutic agent against various cancers. A systematic literature review was executed by searching databases such as PubMed/MedLine, Scopus, and Web of Science from October 2022 to February 2023. The inclusion criteria focused on studies involving Spiruchostatin A in the context of cancer treatment, including in vitro and in vivo models. The review concentrated on the compound’s mechanistic action, biological activity, and clinical applicability. Spiruchostatin A has demonstrated significant antitumor activities, including inducing apoptosis and inhibiting tumor growth effectively in multiple models. Its therapeutic potential is particularly noted in synergistic applications with other anticancer agents, enhancing its efficacy. Mechanistically, the compound facilitates chromatin relaxation and transcriptional activation of key tumor suppressor genes through increased histone acetylation. Spiruchostatin A exhibits substantial potential as an anticancer agent through effective HDAC inhibition and subsequent epigenetic modifications of cancer cell biology. However, comprehensive clinical trials are imperative to validate its efficacy and safety profiles comprehensively. Future research is warranted to elucidate detailed molecular mechanisms and to develop biomarkers for predicting treatment response. Comprehensive longitudinal clinical studies are also critical to establish Spiruchostatin A’s role within the broader oncological therapeutic regimen, along with the exploration of its analogs for improved therapeutic outcomes.

Topological indices have emerged as a vital tool in designing pharmacological compounds, offering a cost-effective alternative to biological testing. This study computes and analyzes the fuzzy topological indices of Tetracene, a polycyclic aromatic hydrocarbon. Specifically, we calculate the fuzzy Randic, fuzzy harmonic, fuzzy first Zagreb, and fuzzy second Zagreb indices of Tetracene’s chemical structure. A graphical analysis of our results reveals a positive correlation between the size of the graph and the values of these indices. Our findings contribute to the growing body of research on the application of topological indices in chemistry and pharmacology, highlighting their potential in streamlining the drug discovery process.

Chemical graph theory stands as a crucial field within mathematical chemistry, boasting diverse applications. Within this discipline, a molecular graph is identified by a numerical measure known as a topological index. Topological indices are one of the main types which can be classified into many categories, with degree-based being most important in chemical graph theory. We investigate the first Kulli-Basava indice (KB Index I) and second Kulli-Basava indices (KB Index II), geometric-arithmetic Kulli-Basava indices (GAKB), hyper Kulli-Basava indices (HKB), as well as certain connectivity Kulli-Basava indices and reciprocal Kulli-Basava indices (RKB) of the Quadrilateral carbon nanocone graph $\text{CNC}_4[n]$. A comparative statistical analysis reveals that the quadratic regression model demonstrates the highest accuracy in predicting topological properties. Among the examined indices, PKBE (Probably the Best Kulli-Basava Index) exhibits the lowest error values across multiple statistical measures, establishing itself as the most reliable topological descriptor. These findings contribute to the advancement of chemical graph theory by enhancing predictive models for molecular properties and refining structural analysis methodologies, thereby improving the understanding of molecular interactions and material properties.

The latest progress summary of sustainable development goals (SDGs) in 2023 reveals that the ongoing growth pattern of Asian economies is inadequate. Asian countries are facing several challenges in securing the targets of SDGs, and environmental degradation is one of the major issues among them. Hence, urgent actions are required to attain SDG targets. Several studies in the available literature have considered multiple determinants of environmental degradation. However, the/home/eae impact of green production practices (GPP) and geopolitical risk (GPR) is relatively ignored particularly in the framework of Asian economies. Therefore, it is needed to propose an extensive policy framework for attaining the objectives of SDGs and raising environmental quality. Moreover, this study is a pioneering attempt that scrutinizes the eclectic influence of green production practices and geopolitical risk on carbon emissions. The study follows the model based on the environmental Kuznets curve (EKC) hypothesis for selected Asian countries. The study has utilized the Panel Quantile Regression (PQR) technique to analyze the facts from selected Asian economies from 1990 to 2020. The long-run evaluations reveal the EKC hypothesis proves valid in the preferred Asian economies. Moreover, green production practices play a crucial role in controlling the rising levels of carbon emissions in the selected Asian countries whereas, geopol-itical risk and foreign direct investments are proven constructive elements in raising carbon emissions. Lastly, based on the empirical outcomes, this study provides policy implications for achieving the targets of SDG 07, SDG 09, SDG 12, SDG 13, SDG 15, SDG 16, and SDG 17.

Early and precise diagnosis of cancer is pivotal for effective therapeutic intervention. Traditional diagnostic methods, despite their reliability, often face limitations such as invasiveness, high costs, labor-intensive procedures, extended processing times, and reduced sensitivity for early-stage detection. Electrochemical biosensing is a revolutionary method that provides rapid, cost-effective, and highly sensitive detection of cancer biomarkers. This review discusses the use of electrochemical detection in biosensors to provide real-time insights into disease-specific molecular interactions, focusing on target recognition and signal generation mechanisms. Furthermore, the superior efficacy of electrochemical biosensors compared to conventional techniques is explored, particularly in their ability to detect cancer biomarkers with enhanced specificity and sensitivity. Advancements in electrode materials and nanostructured designs, integrating nanotechnology, microfluidics, and artificial intelligence, have the potential to overcome biological interferences and scale for clinical use. Research and innovation in oncology diagnostics hold potential for personalized medicine, despite challenges in commercial viability and real-world application.