Baba Guru Nanak University

Arsenic (As) accumulation is a critical environmental concern in many developing countries due to its high toxicity and wide distribution.In the current investigation, As contents were estimated in soil, forage and cow's milk samples collected from rain-fed and groundwater-irrigated areas of Chakwal, Pakistan. The samples were subjected to wet digestion and examined for As contents using an atomic absorption spectrophotometer. As concentrations ranged from 2.28 to 10.57mgkg-1 in soil, 0.663 to 2.40mgkg-1 in forages, and 0.012 to 0.017mgkg-1 in milk. Among the forages Chenopodium album demonstrated the highest As contents at the groundwater-irrigated site, while Tribulus terresteris had the lowest As contents at the rain-fed site. All samples exhibited As levels below the permissible limits set by WHO. This study also found a significant positive correlation between As levels in soil and forages at both sites, while the correlation between forages and milk was negative and non-significant. All estimated pollution indices were below the threshold limit, indicating insignificant As contamination across study sites. However,Chenopodium album exhibited significantly higher pollution indices than other forage species (p < 0.05), signaling its enhanced bioconcentration potential.

The main objective of this research paper is to explore further generalization of confluent hypergeometric and Whittaker functions by introducing a new parameter $k&gt;0$, in generalized extended confluent hypergeometric and Whittaker functions defined by Khan \emph{et al.} \cite{m7}. We also investigate some properties such as integral representations, Mallin transforms, Hankel, Laplace transformations and derivative of these new generalized extended confluent hypergeometric and Whittaker $k$-functions. We also obtain Riemann-Liouville fractional integral and Riemann-Liouville $k$-fractional integral of these new generalized extended Whittaker $k$-function.

Abstract This study explores the mechanism of heat and mass transfer in Maxwell hybrid nanofluid past a moving thin needle with non‐uniform heat rise and fall effects. The Darcy–Forchheimer porous medium is saturated with non‐Newtonian liquid containing AA7072‐AA7075 nanoparticles. The flow model assumes the effects of the inclined Lorentz force and linear constructive chemical reaction. The methyl alcohol as a base fluid in the current work is considered. The variational effects of volume fractions of nanoparticles, thickness of thin needle, Maxwell fluid parameter, chemical reaction parameter, irregular heat rise and fall parameter, Forchheimer number, porosity parameter, Schmid number, and Prandtl number on the heat and mass transportation, along with boundary layer flow of assumed fluid, are computed. It has been noted that an increase in chemical reaction causes an improvement in concentration boundary layers for both nano and hybrid nanofluids. The symmetry in the results for velocity, temperature, and mass concentration under the influential parameters at inclined Lorentz force angle was secured. Results indicate that increasing the Maxwell fluid parameter, porosity parameter, Forchheimer number, and thickness of a needle causes a reasonable reduction in the velocity field. The reverse trend in temperature and concentration profiles is seen for the same parametric variations. The validation of the current results is ensured by the comparison of available results shown in tabular and graphical form. The accuracy of the results is secured by the satisfaction of the boundary condition by the graphs.

The untreated disposal of industrial effluents that contain hazardous toxic metal elements such as cadmium (Cd), chromium (Cr), lead (Pb), nickel (Ni), manganese (Mn) and iron (Fe), causes major threats to the ecosystem. The present study aims to focus on the health of water bodies and riparian vegetation in the Sargodha district. Industrial effluents from sugar mill industries drain the Jhelum River and eventually pose a major threat to the riparian vegetation of the area as it does in other parts of the world. The results suggested the presence of a high level of pollution in drains carrying the industrial effluents and inside the river in the form of increased salinity, metal toxicity, and the acidic and alkaline nature of industrial drainage. Additionally, a few acclimated species such as Eclipta alba, Desmostachya bipinnata, Cynodon dactylon, Paspalum distichum, and Cyperus rotundus were identified and examined for their physiological, proximate, and morpho-anatomical attributes. Most of the species exhibit a high degrees of anatomical modification, such as sclerification and aerenchyma formation which support their survival in such a polluted environment. These plants presented increased production of crude fibers, carbohydrates, and proteins, as well as enhanced photosynthetic pigments, allowing them to cope with stress conditions. This study highlights the need for urgent measures to mitigate the impact of industrial pollution on the environment and ecosystem, and underscores the importance of identifying and conserving plant species that can tolerate and adapt to polluted conditions.

Improper disposal of municipal solid waste in metropolitan and semi-urban regions presents a significant global challenge, contributing to heavy metal contamination and associated health risks. This study assessed the ecological and anthropogenic health risks of manganese contamination resulting from the dietary intake of vegetables cultivated using municipal solid waste amendments in Sargodha, Pakistan. Commonly consumed vegetables (n = 15) were grown in pots over two years (2022-2023) with soil and municipal solid waste mixtures (100/0, 75/25, 50/50, and 25/75; w/w), designated as T0, T1, T2, and T3, respectively. Manganese concentrations were analyzed in soil (n = 600), vegetables (n = 600), and residents' blood serum (n = 240) using an atomic absorption spectrophotometer. Manganese levels ranged from 15.49 to 18.97mgkg⁻1 in soil, 0.508-5.483mgkg⁻1 in vegetables, and 0.0015-0.0043mg L⁻1 in serum. The highest manganese concentration was detected in S. oleracea during T3 in Year 2, while the lowest was observed in C. sativus during T0 in Year 1. Although these concentrations were within FAO/WHO permissible limits, the elevated manganese levels in S. oleracea may still pose a moderate risk if consumed frequently, whereas C. sativus presents a negligible risk. A strong positive correlation was observed between vegetable samples from Year 1 and Year 2 (r = 0.93, p < 0.01), indicating a significant relationship across the two years. The PCA results indicate that S. oleracea (PC1 = 3.9645) and C. sativum (PC1 = 2.6912) exhibit strong positive associations, while C. sativus (PC1 = - 1.3125) and C. vulgaris (PC1 = - 1.0466) show strong negative correlations. PC2 values were relatively lower, indicating weaker associations across species. The computed indices (BCF, PLI, EF, EF%, HRI, DIM, Igeo, EDI, EDD, and THQ) were consistently below the threshold of 1, indicating negligible concentrations in both soil and vegetables, thereby confirming the safety of the local population upon ingestion. To mitigate the potential risk of manganese accumulation from MSW, implementing regulated composting practices, adopting soil amendments to minimize metal uptake, and promoting public awareness regarding safe vegetable consumption are essential. Furthermore, continuous monitoring and the establishment of strict regulatory guidelines are crucial to prevent excessive heavy metal buildup in the food chain.

In this article, we have taken the molecular graph of indium phosphide and line graph of subdivision graph of naphthalenic naphtalenicnano-sheet. Irregularity indices play an important role to describe the quantitative characterization of the non-regular graphs. In various problems and applications, particularly in the subject of chemistry and material engineering irregular indices have so many uses, thus it is very important to know about the irregularity of a molecular structure. Moreover, the evaluation of the irregularity of graphs is an important not only for QSPR and QSAR but also very effective for measuring the entropy, melting and boiling points, enthalpy of vaporization, and toxicity. We have also discussed the graphical behaviors of the above indicated structures.

The scientific community is actively developing innovative nanomaterials with broad-spectrum antimicrobial properties. In this study, moringa gum-derived polymeric carbon dots (MGCDs) were synthesized via a rapid and eco-friendly microwave irradiation technique using aqueous moringa gum as the precursor. The resulting MGCDs exhibited strong green fluorescence under UV light, with a UV-Vis absorption peak at ~290 nm and excitation-dependent fluorescence at 360 nm. They demonstrated significant antioxidant activity, achieving 87% DPPH scavenging efficiency at 0.9 mg/mL, comparable to ascorbic acid. Zeta potential analysis confirmed high colloidal stability, with values of 29 ± 0.9 mV (DI water), 30 ± 0.8 mV (SBB), 28 ± 9 mV (PBS), and 23 ± 0.8 mV (DMEM). Hydrodynamic sizes ranged from 86 ± 3 nm to 135 ± 4 nm, indicating solvent-dependent dispersion. TGA showed high thermal stability, while XRD confirmed an amorphous carbon structure with a broad peak at 22°. MGCDs demonstrated the antibacterial activity against Staphylococcus aureus and Bacillus subtilis, resulting in inhibition zones of 17.4 ± 0.8 mm and 15.2 ± 0.6 mm, respectively, at 40 mg/mL. Their multifunctionality, simple synthesis, and cost-effectiveness highlight their potential in bioimaging, antimicrobial applications, and fluorescent materials.

The purpose of this study is to investigate the financial performance of merging banks in Pakistan in a pre and post-scenario to identify the impact of key indicators on the performance of acquirer company. For this purpose, data for 4 banking companies have been taken from financial year 2005 to 2022. Banking companies include Meezan Bank Limited (MBL) Muslim Commercial Bank Limited (MCB) Al Baraka Bank (Pakistan) Limited (ABPL) and Bank Islami Pakistan Limited (BIPL). Paired sample t-tests and regression analysis was used to identify significant differences and impact on pre and post-merger. Results revealed that there were significant positive differences in specific financial indicators as Return on Assets on MCB only. However, results indicated in pair sample t-test that Net Interest Margin was significant for MCB and ABPL, whereas Debt to Equity was significant for all banks. Further Capital Adequacy Ratio revealed significant results for MBL and ABPL but Total Loan to Total Deposit has significant outcome for BIPL only. It is important that results for Non-Performing Loans to Total Loans were significant for MBL, MCB and BIPL but Earning per Share has been found significant for MBL. The key important indicators of Market Price per Share is found significant for all banks. Dividend per share has significant outcome for ABPL only. Regression results revealed that in post-merger capital adequacy ratio, earning per share has positive but dividend per share and market price per share has significance negative impact on return on assets at (p&lt;0.05) but no significant impact was found pre-merger context. The findings provide valuable insights for policymakers to strategically focus on future corporate restructuring and to ensure financial stability.

Skin diseases impact millions of people around the world and pose a severe risk to public health. These diseases have a wide range of effects on the skin’s structure, functionality, and appearance. Identifying and predicting skin diseases are laborious processes that require a complete physical examination, a review of the patient’s medical history, and proper laboratory diagnostic testing. Additionally, it necessitates a significant number of histological and clinical characteristics for examination and subsequent treatment. As a disease’s complexity and quantity of features grow, identifying and predicting it becomes more challenging. This research proposes a deep learning (DL) model utilizing transfer learning (TL) to quickly identify skin diseases like chickenpox, measles, and monkeypox. A pre-trained VGG16 is used for transfer learning. The VGG16 can identify and predict diseases more quickly by learning symptom patterns. Images of the skin from the four classes of chickenpox, measles, monkeypox, and normal are included in the dataset. The dataset is separated into training and testing. The experimental results performed on the dataset demonstrate that the VGG16 model can identify and predict skin diseases with 93.29% testing accuracy. However, the VGG16 model does not explain why and how the system operates because deep learning models are black boxes. Deep learning models’ opacity stands in the way of their widespread application in the healthcare sector. In order to make this a valuable system for the health sector, this article employs layer-wise relevance propagation (LRP) to determine the relevance scores of each input. The identified symptoms provide valuable insights that could support timely diagnosis and treatment decisions for skin diseases.