The University of Education (Urdu: یونیورسٹی آف ایجوکیشن) (initials: UE), is a public research university located in a residential area of Lahore, Punjab, Pakistan. It is a multi–campus university whose institutions and campuses are located in different metropolitan cities of Punjab province of Pakistan. Its main campus is in Township, Lahore.Established in 2002, it offers undergraduate, post-graduate and doctoral programmes in various academic disciplines including arts and science. Approximately 13,000 students attend the university. It was ranked as one of the top institutions of higher learning in Pakistan by Higher Education Commission (HEC) in 2010.
Heavy metal (HM) toxicity is a major concern in aquaculture due to bioaccumulation, persistence, and toxicological nature of these pollutants. These substances enter the aquatic environment and pose a serious threat to species. Although traditional remediation methods are used to eliminate HMs, they have significant drawbacks, including low processing speed, operational challenges, high costs associated with nanoparticle production, and regulatory issues. To address these limitations, nano-bioremediation – the integration of nanoparticles with bioremediation – has emerged as a rapid, sustainable, and eco-friendly solution that mitigates HM effects. Nano-bioremediation enhances the effectiveness of HM removal by harnessing the combined properties of nanoparticles and microorganisms, reducing ecological concerns and economic costs. Furthermore, nano-bioremediation improves contaminant degradation, increases stability, and lessens environmental and aquaculture impacts. Biogenic nanoparticles are preferred due to their large-scale synthesis, scalability, rapid production, eco-friendliness, and absence of harmful contaminants. Overall, this review critically compares conventional techniques with nano-bioremediation, highlighting its advantages, applications, significance, and optimisation of nano-microbe interactions for the future.
Abstract This research examined the association between artificial intelligence (AI) literacy and work performance (WP) through the mediation of employee engagement (EE) among academicians in the Sargodha region, Pakistan. Theoretically grounded in the Technology Acceptance Model (TAM), Human Capital Theory (HCT), and the Job Demands–Resources (JD-R) model, this study utilized a cross-sectional survey of 300 academicians from three public universities in Pakistan. The data were analyzed using a multi-method approach, including Partial Least Squares Structural Equation Modeling (PLS-SEM), Fuzzy-Set Qualitative Comparative Analysis (fsQCA), and Shapley Additive exPlanations (SHAP), to provide a robust understanding of linear, configurational, and non-linear relationships. The PLS-SEM results showed that AI literacy is positively associated with WP (β = 0.301) and that EE significantly mediated this association (β = 0.25), with the model explaining 39.6% of the variance in WP. The fsQCA analysis found that a combination of high AI literacy and high EE was a sufficient condition for high WP (consistency = 0.875). Additionally, SHAP analysis confirmed that EE was the most significantly associated factor with WP (mean |SHAP| = 0.430). The findings provide empirical insights for academicians and policymakers on the importance of AI education in the workplace to enhance EE and WP.
Exopolysaccharides are classified as high molecular weight polymers of carbohydrates. The aim of the present study was the isolation of exopolysaccharides (EPS) producing strain from fruits and vegetables indigenously and optimization of physical parameters for the hyperproduction of EPS. Yeast Industrial wastewater from AB Mauri (Associated British Foods) was used as substrate for the production of EPS. Fruits and vegetables source were used for isolation of Lactobacillus strain. The morphological examination and 16 S RNA sequence analysis were used to identify the isolated Lactobacillus strain. The screening of nitrogen (organic, inorganic) was carried out for the production of EPS. A statistical approach of optimization the Response Surface Methodology (RSM) was applied to optimize the physiochemical parameters such as nitrogen concentration, pH, temperature, and incubation period in order to maximize the production. Further the effect of various parameters were studied at lab scale bioreactor to enhance the production. Isolated strain was gram positive rods and identified as Lactobacillus fermentum-MH473306.1. It was determined that urea as the best source of nitrogen. The optimized condition for maximum production of EPS the ideal pH, temperature, nitrogen supply, and incubation period were 7, 35 °C, 1.5
This study assessed airborne atrazine concentrations in selected urban and farming communities and the non-carcinogenic inhalation risks for residents. The main research question examined whether ambient atrazine concentrations pose potential health risks under different exposure settings. A total of 40 airborne particulate matter samples were collected using pre-mounted filter papers and a Sibata Low Volume Air Sampler (Model LV- 40) at a flow rate of 30 L/min over 8 h periods. Atrazine was extracted using acetonitrile and ultrasonication and analyzed with High-Performance Liquid Chromatography with UV detection (HPLC-UV), using an autosampler (model 410 Varian, USA). Atrazine concentrations in urban areas were generally low, ranging from 0.28 ± 0.55 pg/m3 in Suame and Gyinyase, 0.29 ± 0.55 pg/m3 in Buokrom Estate, 0.53 ± 1.05 pg/m3 in Tanoso, to 1.05 ± 2.10 pg/m3 in Kejetia. In contrast, relatively higher concentrations were observed in the farming areas: 12.72 ± 7.69 pg/m3 in Nkawie-Toase, 13.34 ± 10.70 pg/m3 in Afari, and 16.48 ± 17.17 pg/m3 in Mpasatia. All measured concentrations were below the acceptable daily intake of 2.00 E + 07 pg/m3 for inhalation. Risk quotient values were also below the threshold of concern, suggesting minimal non-carcinogenic health effects. Although atrazine levels are within the regulatory limits, continuous exposure may pose potential risks to children in farming communities. Although limited by a small sample size and consideration of only the inhalation pathway, this study provides valuable baseline data on airborne atrazine exposure in Ghana. It emphasizes the need for improved pesticide management practices and regular monitoring of airborne contaminants in agricultural settings.
Artisanal and Small-Scale Mining (ASM) is a critical driver of soil contamination in West Africa, yet quantifying the relative contributions of natural and anthropogenic sources remains a major challenge. This study presents an integrated framework combining contamination indices, receptor modelling, and Machine Learning (ML) to evaluate Potentially Toxic Element (PTE) dynamics in soils from the Nangodi area, northern Ghana. A total of 552 grid-based soil samples were analysed for Cr, Co, Cu, Pb, V, and Zn using ED-XRF. Descriptive statistics revealed highly skewed distributions and elevated coefficients of variation for Cr, Co, and Cu, reflecting heterogeneous contamination patterns typical of ASM terrains. Pollution indices indicated divergent outcomes: the ecological Risk Index (RI) classified all samples as low risk (RI < 150), whereas the Nemerow Integrated Pollution Index (NIPI) identified 35.5
