University of Sahiwal

Abstract Due to urbanization and industrialization, there has been an increase in solid waste generation and has become a global concern and leakage of leachate from landfills contaminate the soil and groundwater and hence can have a severe impact on human health. The present study aimed to determine the composition of toxic metals (Cr, Mn, Cu, As) and heavy metals (Cd, Ba, Hg, Pb) in soil and water by an inductively coupled plasma optical emission spectrometer (ICP-OES). To ensure accuracy during the analysis of Cr, Mn, Cu, As, Cd, Ba, Hg, and Pb in real samples, certified reference material (CRM, SRM 2709a) of San Joaquin soil and water (SRM 1640a) were analyzed and results were presented in terms of % recovery studies. The mean concentration of all the metals in soil and water did not exceed the limit set by the European Community (EU), WHO, and US EPA except Cu where the permissible limit defined by the EU is 50–140 mg/kg in soil. The soil is uncontaminated to moderately contaminated with respect to all metals except the Cu and Pb. Among the average daily dose (ADD) of soil, ADD ing and ADD inh for children had the maximum dose for all metals than adults while ADD derm was higher in adults. Hazard quotient (HQ) trend in both adults and children was found in order HQ ing > HQ derm > HQ inh of soil for all metals except Ba which followed HQ ing > HQ inh > HQ derm . Hazard index (HI) values of soil for Cr and Pb in children were 7 and 7.5 times higher than adults respectively. Lifetime cancer risk (LCR) value for Cr by different exposure pathways of soil was 5.361 × 10 −4 for children which are at the lower borderline of risk for cancer.

This study examines the role of ChatGPT as a writing assistant in academia through a systematic literature review of the 30 most relevant articles. Since its release in November 2022, ChatGPT has become the most debated topic among scholars and is also being used by many users from different fields. Many articles, reviews, blogs, and opinion essays have been published in which the potential role of ChatGPT as a writing assistant is discussed. For this systematic review, 550 articles published six months after ChatGPT’s release (December 2022 to May 2023) were collected based on specific keywords, and the final 30 most relevant articles were finalized through PRISMA flowchart. The analyzed literature identifies different opinions and scenarios associated with using ChatGPT as a writing assistant and how to interact with it. Findings show that artificial intelligence (AI) in education is a part of the ongoing development process, and its latest chatbot, ChatGPT is a part of it. Therefore, the education process, particularly academic writing, has both opportunities and challenges in adopting ChatGPT as a writing assistant. The need is to understand its role as an aid and facilitator for both the learners and instructors, as chatbots are relatively beneficial devices to facilitate, create ease and support the academic process. However, academia should revisit and update students’ and teachers’ training, policies, and assessment ways in writing courses for academic integrity and originality, like plagiarism issues, AI-generated assignments, online/home-based exams, and auto-correction challenges.

This research paper aims to understand the impact of corporate governance (CG) on economic, social, and environmental sustainability disclosures. This paper adopted an explanatory sequential mixed methods approach. The data regarding corporate governance and sustainability disclosure were collected from top 100 companies listed on the Pakistan Stock Exchange (PSE) for the period ranging from 2012 to 2015. In addition to the quantitative data, we collected qualitative data through interviews with five board members of different companies. Overall, our results indicate that CG elements enhance sustainability disclosures. This study concludes that a large board size consisting of a female director and a CSR committee (CSRC) is better able to check and control management decisions regarding sustainability issues (be they economic, environment, or social) and resulted in better sustainability disclosure. This paper, through quantitative and qualitative analysis, provides a methodological and empirical contribution to the literature on corporate governance and sustainability reporting in emerging and developing countries.

High Resolution Image Download MS PowerPoint Slide Titanium dioxide (TiO 2 ) is one of the most widely used photocatalysts due to its physical and chemical properties. In this study, hydrogen energy production using TiO 2 - and titanate-based photocatalysts is discussed along with the pros and cons. The mechanism of the photocatalysis has been elaborated to pinpoint the photocatalyst for better performance. The chief characteristics and limitations of the TiO 2 photocatalysts have been assessed. Further, TiO 2 -based photocatalysts modified with a transition metal, transition metal oxide, noble metal, graphitic carbon nitride, graphene, etc. have been reviewed. This study will provide a basic understanding to beginners and detailed knowledge to experts in the field to optimize the TiO 2 -based photocatalysts for hydrogen production.

Introduction: Multicomponent reactions (MCR) has been utilized to synthesize a vast range of analogs belonging to diverse classes of heterocyclic compounds offering multidimensional pharmaceutical applications. The unique feature of MCR includes the synthesis of highly functionalized molecules in a single pot to build quick libraries of compounds of biological interest to identify new leads as potential therapeutic agents.Area covered: The current review article covers the patents published in the last decade in order to highlight the importance of multicomponent reactions for synthesizing complex-functionalized molecules of high biological significance.Expert opinion: Easily automated one-pot multicomponent reactions (MCRs) has demonstrated successful impact at different stages of the lead discovery, lead optimization, and pre-clinical process development arenas. Application of MCRs is the recent advancement in the field of drug design and discovery which will expectedly lead to the development of medicinally important heterocyclic compounds with a vast range of biological activities.

The degradation of dyes is a difficult task due to their persistent and stable nature; therefore, developing materials with desirable properties to degrade dyes is an important area of research. In the present study, we propose a simple, one-pot mechanochemical approach to synthesize CuO nanoparticles (NPs) using the leaf extract of Seriphidium oliverianum, as a reducing and stabilizing agent. The CuO NPs were characterized via X-ray diffraction (XRD), scanning electron microscopy (SEM), photoluminescence (PL) and Fourier-transform infrared spectroscopy (FTIR). The photocatalytic activity of CuO NPs was monitored using ultraviolet-visible (UV-Vis) spectroscopy. The CuO NPs exhibited high potential for the degradation of water-soluble industrial dyes. The degradation rates for methyl green (MG) and methyl orange (MO) were 65.231% ± 0.242 and 65.078% ± 0.392, respectively. Bio-mechanochemically synthesized CuO NPs proved to be good candidates for efficiently removing dyes from water.

reduction, and nitrogen fixation. Perovskite photocatalyst materials are gained special attention due to their exceptional properties because of their flexibility in chemical composition, structure, bandgap, oxidation states, and valence states. The current review is focused on perovskite materials and their applications in photocatalysis. Special attention has been given to the structural, stoichiometric, and compositional flexibility of perovskite photocatalyst materials. The photocatalytic activity of perovskite materials in different photocatalysis applications is also discussed. Various mechanisms involved in photocatalysis application from wastewater treatment to hydrogen production are also provided. The key objective of this review is to encapsulate the role of perovskite materials in photocatalysis along with their fundamental properties to provide valuable insight for addressing future environmental challenges.

Sonogashira coupling involves coupling of vinyl/aryl halides with terminal acetylenes catalyzed by transition metals, especially palladium and copper. This is a well known reaction in organic synthesis and plays a role in sp2-sp C-C bond formations. This cross coupling was used in synthesis of natural products, biologically active molecules, heterocycles, dendrimers, conjugated polymers and organic complexes. This review paper focuses on developments in the palladium and copper catalyzed Sonogashira cross coupling achieved in recent years concerning substrates, different catalyst systems and reaction conditions.

Adsorption is one of the promising techniques for the remediation of wastewater and it also offers advantages such as low cost, availability of the adsorbent and ease in operation. The wastewater treatment using smart materials gained much attention and the present investigation evaluated the adsorption efficiencies of ZnO, MgO and FeO for the removal of Direct Sky Blue (DSB) dye. The adsorption affecting parameters, i.e., effect of adsorbent dose, pH, concentration of dye, temperature and contact time were studied in association with dye adsorption. The effect of electrolytes and surfactants was also studied on dye adsorption. The dye adsorption data was analysed using various kinetics parameters, isotherms and thermodynamics models. The maximum adsorption capacities of MgO, FeO and ZnO were recorded to be 46.7, 42.9 and 40.9 mg/g, respectively at the pH 2, adsorbent dose 0.05 g, contact time 75 min, 40 °C and 70 mg/L initial concentration of the dye. The dye adsorption onto MgO followed a Langmuir isotherm, while ZnO followed Temkin isotherm. Langmuir and Temkin isotherms were satisfactory in the case of FeO. Pseudo second order best explained the dye adsorption kinetics. The thermodynamics studies revealed non-spontaneous adsorption of DSB dye onto ZnO, MgO and FeO. In view of promising efficiency, the ZnO, MgO and FeO have potential to apply for dyes adsorption from textile wastewater.

Water purification by eco-friendly and cost-effective method is a challenge for scientists in the current era. The present study reports a facile and cost-effective green synthesis method of zinc oxide nanoparticles (ZnO-NPs) from leaves extract of Syzygium cumini plant. UV–visible spectroscopy verified the synthesis of nano-catalyst ZnO-NPs. X-ray diffraction technique and scanning electron microscopy explored the formation of pure crystalline hexagonal and spherical NPs. Fourier-transform infrared spectroscopy analysis ascertains the existence of flavonoids, phenolic acids, enzymes and steroids in leaves extract of S. cumini to reduce zinc slat into ions during synthesis and hence capping of the NPs. The ZnO-NPs were analysed for seed germination activity which showed enhancement in seed germination of Pennisetum glaucum seeds. The synthesised NPs were likewise utilised as nano-catalyst for degradation and removal of Rhodamine B (RhB) dye. Moreover, the effect of light irradiation time, temperature and pH was also investigated for optimisation of best conditions for degradation of dye. The green synthesised ZnO-NPs showed 98% degradation of RhB dye and purified the dye polluted water and, therefore, is an emerging catalyst for dye degradation.

The degradation of disperse V-26 in solution using UV; UV/H2O2, UV/H2O2/TiO2 and UV/H2O2/ZnO were investigated. The impacts of various key parameters i.e. initial pH, concentration of hydrogen peroxide (H2O2) dose and the dye concentration effect on degradation were studied. The maximum degradation of 93 % was optimized using UV/H2O2/TiO2 at pH 3 in 60 min. Fourier transform infrared spectroscopy (FTIR) and gas chromatography-mass spectrometry (GCMS) were applied to check the products obtained after complete degradation. The removal of peaks of certain groups present in dye molecule assured the maximum degradation of dispersive V-26. In biological treatment, cytotoxicity reduction and Ames test were used to check the toxicity level of products. Certain water parameters i.e. dissolved oxygen (DO), chemical oxygen demand (COD) and biological oxygen demand (BOD) were also performed to ensure the maximum degradation of DV-26. DO was increased up to 82 %. The COD and BOD were reduced considerably owing to the treatment of disperse dye Violet 26 at optimum settings of process variables. The degradation of disperse dye V-26 with UV/H2O2/ZnO was 90.1 % which increased up to 93 % with the alternative photo-catalyst UV/H2O2/TiO2. Keywords: Disperse V-26, UV, H2O2, TiO2, ZnO, FTIR, GC–MS, Toxicity

Increasing waste generation has become a significant issue over the globe due to the rapid increase in urbanization and industrialization. In the literature, many issues that have a direct impact on the increase of waste and the improper disposal of waste have been investigated. Most of the existing work in the literature has focused on providing a cost-efficient solution for the monitoring of garbage collection system using the Internet of Things (IoT). Though an IoT-based solution provides the real-time monitoring of a garbage collection system, it is limited to control the spreading of overspill and bad odor blowout gasses. The poor and inadequate disposal of waste produces toxic gases, and radiation in the environment has adverse effects on human health, the greenhouse system, and global warming. While considering the importance of air pollutants, it is imperative to monitor and forecast the concentration of air pollutants in addition to the management of the waste. In this paper, we present and IoT-based smart bin using a machine and deep learning model to manage the disposal of garbage and to forecast the air pollutant present in the surrounding bin environment. The smart bin is connected to an IoT-based server, the Google Cloud Server (GCP), which performs the computation necessary for predicting the status of the bin and for forecasting air quality based on real-time data. We experimented with a traditional model (k-nearest neighbors algorithm (k-NN) and logistic reg) and a non-traditional (long short term memory (LSTM) network-based deep learning) algorithm for the creation of alert messages regarding bin status and forecasting the amount of air pollutant carbon monoxide (CO) present in the air at a specific instance. The recalls of logistic regression and k-NN algorithm is 79% and 83%, respectively, in a real-time testing environment for predicting the status of the bin. The accuracy of modified LSTM and simple LSTM models is 90% and 88%, respectively, to predict the future concentration of gases present in the air. The system resulted in a delay of 4 s in the creation and transmission of the alert message to a sanitary worker. The system provided the real-time monitoring of garbage levels along with notifications from the alert mechanism. The proposed works provide improved accuracy by utilizing machine learning as compared to existing solutions based on simple approaches.

Metal-organic frameworks (MOFs) are a fascinating class of porous crystalline materials constructed by organic ligands and inorganic connectors. Owing to their noteworthy catalytic chemistry, and matching or compatible coordination with numerous materials, MOFs offer potential applications in diverse fields such as catalysis, proton conduction, gas storage, drug delivery, sensing, separation and other related biotechnological and biomedical applications. Moreover, their designable structural topologies, high surface area, ultrahigh porosity, and tunable functionalities all make them excellent materials of interests for nanoscale applications. Herein, an effort has been to summarize the current advancement of MOF-based materials (i.e., pristine MOFs, MOF derivatives, or MOF composites) for electrocatalysis, photocatalysis, and biocatalysis. In the first part, we discussed the electrocatalytic behavior of various MOFs, such as oxidation and reduction candidates for different types of chemical reactions. The second section emphasizes on the photocatalytic performance of various MOFs as potential candidates for light-driven reactions, including photocatalytic degradation of various contaminants, CO2 reduction, and water splitting. Applications of MOFs-based porous materials in the biomedical sector, such as drug delivery, sensing and biosensing, antibacterial agents, and biomimetic systems for various biological species is discussed in the third part. Finally, the concluding points, challenges, and future prospects regarding MOFs or MOF-based materials for catalytic applications are also highlighted.

Nano-hydroxyapatite is being investigated as vital components of implants and dental and tissue engineering devices. It is found as a bone replacement due to its non-toxicity and cytocompatibility with dental tissues and bone. The reality that nanocrystalline hydroxyapatite can be made of porous granules and scaffolds. Additionally, it has a massive loading potential indicating its use as a transporter for drugs or a regulated drug release mechanism in pharmaceutical research. This review aims to present existing nano-hydroxyapatite research developments as a drug carrier employed in bone tissue disorders locally and deliver poorly soluble drugs with reduced bioavailability. We have discussed the nano-hydroxyapatite role in the delivery of drugs (i.e. anti-resorptive, anti-cancer, and antibiotics), proteins, genetic material, and radionuclides.

Traffic congestion is one of the most notable urban transport problems, as it causes high energy consumption and air pollution. Unavailability of free parking spaces is one of the major reasons for traffic jams. Congestion and parking are interrelated because searching for a free parking spot creates additional delays and increase local circulation. In the center of large cities, 10% of the traffic circulation is due to cruising, as drivers nearly spend 20 min searching for free parking space. Therefore, it is necessary to develop a parking space availability prediction system that can inform the drivers in advance about the location-wise, day-wise, and hour-wise occupancy of parking lots. In this paper, we proposed a framework based on a deep long short term memory network to predict the availability of parking space with the integration of Internet of Things (IoT), cloud technology, and sensor networks. We use the Birmingham parking sensors dataset to evaluate the performance of deep long short term memory networks. Three types of experiments are performed to predict the availability of free parking space which is based on location, days of a week, and working hours of a day. The experimental results show that the proposed model outperforms the state-of-the-art prediction models.

Rapid industrial development, vehicles, domestic activities and mishandling of garbage are the main sources of pollutants, which are destroying the atmosphere. There is a need to continuously monitor these pollutants for the safety of the environment and human beings. Conventional instruments for monitoring of toxic gases are expensive, bigger in size and time-consuming. Hybrid materials containing organic and inorganic components are considered potential candidates for diverse applications, including gas sensing. Gas sensors convert the information regarding the analyte into signals. Various polymeric/inorganic nanohybrids have been used for the sensing of toxic gases. Composites of different polymeric materials like polyaniline (PANI), poly (4-styrene sulfonate) (PSS), poly (3,4-ethylene dioxythiophene) (PEDOT), etc. with various metal/metal oxide nanoparticles have been reported as sensing materials for gas sensors because of their unique redox features, conductivity and facile operation at room temperature. Polymeric nanohybrids showed better performance because of the larger surface area of nanohybrids and the synergistic effect between polymeric and inorganic materials. This review article focuses on the recent developments of emerging polymeric/inorganic nanohybrids for sensing various toxic gases including ammonia, hydrogen, nitrogen dioxide, carbon oxides and liquefied petroleum gas. Advantages, disadvantages, operating conditions and prospects of hybrid composites have also been discussed.

The current study examines the change in environment performance through green human resource management in a developing country’s higher education institutes. The data were collected by survey using a reliable and valid instrument adopted from the literature. The unit of analysis in the current study is an individual consisting of employees working in higher educational institutions of Pakistan. Three hundred questionnaires were distributed while 220 questionnaires were found completely filled for statistical analysis. The current study utilizes the multiple regression techniques through structural equation modelling using second-generation software SmartPLSv3.0. The results indicate the positive influence of green human resource policies on environmental performance and provide significant insights on the partial mediating effect of employee eco-friendly behavior between green human resource management and environmental performance. The present study provides numerous theoretical and practical implications through the extension of Ability-Motivation-Opportunity theory by constituting the employee behaviors for the implementation of environmental strategies in the organization context. The findings of the present study suggest guidelines for human resource managers and management of educational institutes to implement green human resource policies that are likely to improve institutes environmental performance.

Tax payments stimulate business enterprises to choose tax management through tax avoidance activities, which is the legal practice to reduce the amount of tax payable. In developing economies, taxation is considered more critical for budget and revenues of a country. This paper investigates whether various business strategies influence corporate tax avoidance decisions of firms by adopting business strategies. Besides, it explores how gender diversity can ease this relationship. This study has chosen a sample of organizations from non-financial sector in Pakistan. The time frame is 5 years, including once a year. The present model employed a generalized moment method (GMM) and tested the proposed hypothesis to draw the results. The study has taken the size, leverage, and business profitability as control variables of firms. The study outcomes by using the GMM method demonstrate that the presence of female directors reduces tax avoidance behavior in prospector companies. This study provides insight into future research for stakeholders, government officials, tax authorities, and policymakers. The findings offer valuable recommendations and practical insights and implications. The findings provide future directions for research to test different frameworks to attain beneficial results to promote the responsibility of tax payment culture.

Textile sector is one of the major industries responsible for water pollution and the remediation of dyes is necessary to safe guard the water resources. Adsorption using nanoadsorbents have been emerged as one of viable techniques to eliminate the dyes from wastewater. Iron oxide was prepared via green route using Fan palm (F.P), Dombeya wallichii (D.W) and Pyrus comminis (P.C) as a reducing, capping and stabilizing agents. The adsorption of anionic dye using nanoadsorbents was studied and pH, adsorbent dose, contact time, temperature and concentration of dye were optimized. The maximum dye adsorption was achieved at pH 3, 0.01 g/50 mL adsorbent dose, 200 mg/L initial concentration at 65 °C. Thermodynamic study revealed that the adsorption on to nanoadsorbents was an endothermic process. Langmuir model and pseudo-second-order fitted well to the dye adsorption data. The presence of detergents and surfactants affected the dye adsorption on to nanoadsorbents. Efficient desorption was attained using 0.5 N sodium hydroxide. Results revealed that iron oxide prepared via green route is an efficient adsorbent for the removal of anionic dye and could possibly be used for the remediation of textile effluents.

COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseased patients can facilitate early intervention and avoid disease succession. This article intends to develop a hybrid deep neural networks (HDNNs), using computed tomography (CT) and X-ray imaging, to predict the risk of the onset of disease in patients suffering from COVID-19. To be precise, the subjects were classified into 3 categories namely normal, Pneumonia, and COVID-19. Initially, the CT and chest X-ray images, denoted as 'hybrid images' (with resolution 1080 × 1080) were collected from different sources, including GitHub, COVID-19 radiography database, Kaggle, COVID-19 image data collection, and Actual Med COVID-19 Chest X-ray Dataset, which are open source and publicly available data repositories. The 80% hybrid images were used to train the hybrid deep neural network model and the remaining 20% were used for the testing purpose. The capability and prediction accuracy of the HDNNs were calculated using the confusion matrix. The hybrid deep neural network showed a 99% classification accuracy on the test set data.