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"Anwar, Muhammad"
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Mitigation of Salinity-Induced Oxidative Damage, Growth, and Yield Reduction in Fine Rice by Sugarcane Press Mud Application
Salinity stress is one of the major global problems that negatively affect crop growth and productivity. Therefore, ecofriendly and sustainable strategies for mitigating salinity stress in agricultural production and global food security are highly demandable. Sugarcane press mud (PM) is an excellent source of the organic amendment, and the role of PM in mitigating salinity stress is not well understood. Therefore, this study was aimed to investigate how the PM mitigates salinity stress through the regulation of rice growth, yield, physiological properties, and antioxidant enzyme activities in fine rice grown under different salinity stress conditions. In this study, different levels of salinity (6 and 12 dS m –1 ) with or without different levels of 3, 6, and 9% of SPM, respectively were tested. Salinity stress significantly increased malondialdehyde (MDA, 38%), hydrogen peroxide (H 2 O 2 , 74.39%), Na + (61.5%), electrolyte leakage (40.32%), decreased chlorophyll content (32.64%), leaf water content (107.77%), total soluble protein (TSP, 72.28%), and free amino acids (FAA, 75.27%). However, these negative effects of salinity stress were reversed mainly in rice plants after PM application. PM application (9%) remained the most effective and significantly increased growth, yield, TSP, FAA, accumulation of soluble sugars, proline, K + , and activity of antioxidant enzymes, namely, ascorbate peroxidase (APX), catalase (CAT), and peroxidase (POD). Thus, these findings suggest a PM-mediated eco-friendly strategy for salinity alleviation in agricultural soil could be useful for plant growth and productivity in saline soils.
Journal Article
Medical Image Analysis using Convolutional Neural Networks: A Review
The science of solving clinical problems by analyzing images generated in clinical practice is known as medical image analysis. The aim is to extract information in an affective and efficient manner for improved clinical diagnosis. The recent advances in the field of biomedical engineering have made medical image analysis one of the top research and development area. One of the reasons for this advancement is the application of machine learning techniques for the analysis of medical images. Deep learning is successfully used as a tool for machine learning, where a neural network is capable of automatically learning features. This is in contrast to those methods where traditionally hand crafted features are used. The selection and calculation of these features is a challenging task. Among deep learning techniques, deep convolutional networks are actively used for the purpose of medical image analysis. This includes application areas such as segmentation, abnormality detection, disease classification, computer aided diagnosis and retrieval. In this study, a comprehensive review of the current state-of-the-art in medical image analysis using deep convolutional networks is presented. The challenges and potential of these techniques are also highlighted.
Journal Article
Growth and nutrient removal efficiency of duckweed (lemna minor) from synthetic and dumpsite leachate under artificial and natural conditions
Sustainable management of leachate produced from the dumpsite is one of the major concerns in developing countries Aquatic plants such as duckweed have the potential to remove pollutants from wastewater which can also be cost-effective and feasible options for leachate treatment. Therefore, the objective of our present study was to examine the growth and nutrient removal efficiency of duckweed (Lemna minor) on leachate. Three tests were performed each by growing lemna minor on synthetic leachate under controlled conditions and on dumpsite leachate under natural conditions. During each test, duckweed was grown in 300 ml plastic containers with a surface area of 25.8 cm2. About 60 mg of fresh mass of duckweed was grown on 250 ml leachate at an internal depth of 9.5 cm. Results revealed that, in comparison to synthetic leachate, duckweed removed Chemical Oxygen Demand (COD), nitrogen (N), and phosphorous (P) more efficiently from dumpsite leachate under natural climatic conditions. However, the amounts of N and P absorbed into duckweed body mass were about 16% and 35% respectively more at synthetic leachate under controlled conditions. Maximum growth rate of duckweed (7.03 g m-2 day-1) was also observed for synthetic leachate in comparison to the growth rate of 4.87 g m-2 day-1 at dumpsite leachate. Results of this study provide a useful interpretation of duckweed growth and nutrient removal dynamics from leachate under natural and laboratory conditions.
Journal Article
Synthesis, characterization and heavy metal removal efficiency of nickel ferrite nanoparticles (NFN’s)
The heavy metals, such as Cr(VI), Pb(II) and Cd(II), in aqueous solutions are toxic even at trace levels and have caused adverse health impacts on human beings. Hence the removal of these heavy metals from the aqueous environment is important to protect biodiversity, hydrosphere ecosystems, and human beings. In this study, magnetic Nickel-Ferrite Nanoparticles (NFNs) were synthesized by co-precipitation method and characterized using X-Ray Diffraction (XRD), Energy Dispersive Spectroscopy (EDS) and Field Emission Scanning Electronic Microscopy (FE-SEM) techniques in order to confirm the crystalline structure, composition and morphology of the NFN’s, these were then used as adsorbent for the removal of Cr(VI), Pb(II) and Cd(II) from wastewater. The adsorption parameters under study were pH, dose and contact time. The values for optimum removal through batch-adsorption were investigated at different parameters (pH 3–7, dose: 10, 20, 30, 40 and 50 mg and contact time: 30, 60, 90, and 120 min). Removal efficiencies of Cr(VI), Pb(II) and Cd(II) were obtained 89%, 79% and 87% respectively under optimal conditions. It was found that the kinetics followed the pseudo second order model for the removal of heavy metals using Nickel ferrite nanoparticles.
Journal Article
Optimised knowledge distillation for efficient social media emotion recognition using DistilBERT and ALBERT
Accurate emotion recognition in social media text is critical for applications such as sentiment analysis, mental health monitoring, and human-computer interaction. However, existing approaches face challenges like computational complexity and class imbalance, limiting their deployment in resource-constrained environments. While transformer-based models achieve state-of-the-art performance, their size and latency hinder real-time applications. To address these issues, we propose a novel knowledge distillation framework that transfers knowledge from a fine-tuned BERT-base teacher model to lightweight DistilBERT and ALBERT student models, optimised for efficient emotion recognition. Our approach integrates a hybrid loss function combining focal loss and Kullback-Leibler (KL) divergence to enhance minority class recognition, attention-head alignment for effective contextual knowledge transfer, and semantic-preserving data augmentation to mitigate class imbalance. Experiments on two datasets, Twitter Emotions 416 K samples, six classes, and Social Media Emotion 75 K samples, five classes, show that our distilled models achieve near-teacher performance 97.35% and 73.86% accuracy, respectively. with only a < 1% and < 6% accuracy drop, while reducing model size by 40% and inference latency by 3.2×. Notably, our method significantly improves F1-scores for minority classes. Our work sets a new state-of-the-art in efficient emotion recognition, enabling practical deployment in edge computing and mobile applications.
Journal Article
The Role of Government Support in Sustainable Competitive Position and Firm Performance
Achievement of sustainable competitive position and superior performance is the first priority of business organizations. However, small firms, due to fairly known reasons; lack of resources, financial capabilities and lack of managerial skills are often unable to succeed in their mission. Hence, they often look for less risky and convenience sources to compete in the market. A variety of factors has been tested towards a firm competitive position and performance but the role of government support in this perspective has received minor attention. The present study examines the influence of government financial support and nonfinancial support on firm performance with mediating role of the sustainable competitive position. Hypotheses were tested using structural equation modeling in Analysis Moment of Structure (AMOS) on a data set of 326 Pakistani Small and Medium Size Enterprises (SMEs). The results indicate that government financial and nonfinancial support have a significant influence on sustainable competitive position and firm performance. Additionally, a sustainable competitive position partially mediates the relationship between government support and firm performance. Government bodies and policy makers are advised to provide financial and nonfinancial support to SMEs which in turn can upsurge economic growth and sustainability.
Journal Article
Colorectal Cancer and Alcohol Consumption—Populations to Molecules
Colorectal cancer (CRC) is a major cause of morbidity and mortality, being the third most common cancer diagnosed in both men and women in the world. Several environmental and habitual factors have been associated with the CRC risk. Alcohol intake, a common and rising habit of modern society, is one of the major risk factors for development of CRC. Here, we will summarize the evidence linking alcohol with colon carcinogenesis and possible underlying mechanisms. Some epidemiologic studies suggest that even moderate drinking increases the CRC risk. Metabolism of alcohol involves ethanol conversion to its metabolites that could exert carcinogenic effects in the colon. Production of ethanol metabolites can be affected by the colon microbiota, another recently recognized mediating factor to colon carcinogenesis. The generation of acetaldehyde and alcohol’s other metabolites leads to activation of cancer promoting cascades, such as DNA-adduct formation, oxidative stress and lipid peroxidation, epigenetic alterations, epithelial barrier dysfunction, and immune modulatory effects. Not only does alcohol induce its toxic effect through carcinogenic metabolites, but alcoholics themselves are predisposed to a poor diet, low in folate and fiber, and circadian disruption, which could further augment alcohol-induced colon carcinogenesis.
Journal Article
Mixed convective flow of CNTs nanofluid subject to varying viscosity and reactions
The addressed work explains SWCNTs (Single walled carbon nanotubnes) and MWCNTs (Multi walled carbon nanotubnes) nanofluid flow under the influences of temperature dependent viscosity and mixed convection. Comparative study of SWCNTs and MWCNTs suspended in base liquid is presented. Further heat and mass transfer are addressed for nanofluid effected by radiation, heat generation/absorption and diffusion species. Mathematical development of problem is taken in cylindrical coordinates. System of highly nonlinear differential equations are constructed via appropriate transformations. The system of equations are tackled numerically by bvp4c MATLAB solver. The findings of the study show that larger volume fraction
ϕ
contributes to enhance the nanoliquid flow. The velocity by submerging MWCNTs is noted higher than SWCNTs. Furthermore, the relationship between the viscosity variable
θ
r
and the temperature is such that the temperature near the surface decreases with increase in
θ
r
, while at the same time the temperature away from the surface increases. Subsequently, higher temperature is observed in SWCNTs-liquid compared to the MWCNTs-liquid to the similar values of
θ
r
. Further, heat transfer is an increasing function of varying viscosity variable
θ
r
.
Journal Article