International University of Business Agriculture and Technology

Biochar has emerged as an effective and sustainable amendment for improving soil fertility and mitigating environmental pollution in soil–plant systems. This review synthesizes recent advances in biochar production, characteristics, and applications in soil restoration and contaminant remediation. Biochar is typically produced through slow pyrolysis of biomass feedstocks such as crop residues, wood waste, and animal manure at temperatures ranging from 350–700 °C, producing a stable carbon-rich material with high surface area and porous structure. Most effective applications involve fine to medium particle sizes (< 2 mm) and soil application rates of approximately 5–30 t ha–1, depending on soil properties and management goals. Evidence indicates that biochar is particularly beneficial in degraded, acidic, and sandy soils, where it significantly improves soil physical properties (aggregation, porosity, bulk density, and water-holding capacity), chemical properties (pH buffering, cation exchange capacity, and nutrient retention), and biological properties (microbial biomass, enzyme activity, and rhizosphere interactions). In polluted soils, biochar plays a critical role in mitigating contaminants through mechanisms including adsorption, ion exchange, surface complexation, precipitation, and redox reactions, which reduce the mobility and bioavailability of heavy metals and organic pollutants. Its highly porous structure and functional surface groups also facilitate microbial colonization and phytoremediation processes, enhancing pollutant degradation and improving plant tolerance under stress conditions. Overall, biochar-based strategies provide a multifunctional approach for soil fertility restoration, pollution mitigation, and climate-resilient agriculture, although further long-term field studies are required to optimize biochar properties, application rates, and soil-specific management practices for large-scale implementation.

Introduction:This systematic review provides the first comprehensive synthesis of empirical studies on Artificial Intelligence (AI) integration in nursing education, offering actionable insights for nurse educators and clinical leaders. It highlights how AI transforms learning environments by enhancing personalization, feedback, and instructional efficiency. Aims:To examine how AI is applied across nursing education settings and its impact on learning outcomes. Methods:A systematic search of PubMed, CINAHL, IEEE Xplore, and Scopus identified peer-reviewed studies published from January 2010 to April 2025. Eligible studies focused on empirical AI applications in academic, clinical, or hybrid nursing education contexts. Studies were appraised using the Critical Appraisal Skills Programme (CASP) checklist, and findings were synthesized thematically. Results:Twenty-eight studies met the inclusion criteria. AI-enhanced nursing education in four main areas: (a) personalized learning systems tailored content to individual needs, (b) simulation-based training improved decision-making in high-acuity scenarios,(c) automated assessment tools provided immediate, unbiased feedback, and (d) at the institutional level, AI supported curriculum management and predictive analytics. Common risks included technological inequities, faculty preparedness gaps, and ethical concerns around privacy and bias. Conclusion:To support implementation, this study recommends: (a) integrating AI-powered simulation into emergency care training, (b) deploying adaptive platforms to support at-risk learners, and (c) using automated tools for real-time formative feedback. Diagnostic accuracy is proposed as a measurable outcome to assess impact. The next step for educators is to initiate multi-site pilot programs over 6-12 months, evaluating improvements in learning outcomes, trust, and system integration.

Colon cancer continues to be a major contributor to cancer-related deaths worldwide, highlighting the critical need for reliable and early detection methods. In response, this research introduces an advanced deep learning framework for the automated identification of colon cancer through histopathological image analysis. The framework integrates Convolutional Neural Networks (CNNs) with Bayesian optimization to efficiently fine-tune hyperparameters, enhancing classification accuracy while minimizing overfitting. The model was trained and tested using a publicly accessible dataset that merges data from Kaggle and the Kaggle Cancer Data Portal (KCDP), covering nine distinct tissue types: Normal, Tumor, Stroma, Lympho, Complex, Debris, Mucosa, Adipose, and Background. The optimized CNN demonstrated strong performance, achieving an accuracy of 96.84%, a precision of 97.02%, a recall of 96.50%, and an F1-score of 96.71%. Additionally, the model attained an AUC (Area Under Curve) of 0.97, indicating high discriminative capability. Compared to baseline CNN and ResNet architectures, the proposed method demonstrated superior robustness and generalization due to effective data augmentation and stain normalization techniques. These findings suggest that the model offers substantial promise as a computer-aided diagnosis (CAD) tool to assist pathologists in clinical decision-making, and can be extended to other cancer types through transfer learning and model adaptation. While the framework demonstrates strong within-dataset performance, external validation on independent, multi-institutional cohorts is required before clinical deployment.

Water insecurity is a growing sustainability challenge in arid regions, where climate variability and groundwater overexploitation threaten ecological stability and socio-economic development. In Eastern Saudi Arabia, groundwater is the primary freshwater source, yet integrated frameworks linking surface hydroclimatic stress with groundwater availability under future climate scenarios remain limited. This study aims to develop a climate-adaptive GeoAI framework to assess seasonal water risk and resilience by jointly evaluating surface drought indicators and groundwater potential. The framework integrates satellite-based Earth observation data, physically based hydrological variables, and a hybrid artificial neural network and long short-term memory model to predict reference evapotranspiration, actual evapotranspiration, and land surface temperature, and to analyze their relationship with groundwater potential zones. Applied across the Eastern Province using historical data and CMIP6 climate projections under SSP1-2.6, SSP2-4.5, and SSP5-8.5, the model demonstrates strong predictive performance with R² exceeding 0.95. Results indicate higher seasonal water risk during winter and spring, with resilience patterns more closely associated with groundwater potential than surface drought indicators. These findings highlight the critical role of groundwater resilience in sustainable water and land management under climate change.

This study investigates the thermo-fluidic dynamics of a magnetised hybrid Ag-TiO₂/EG-water nanofluid over a bi-directionally stretching/shrinking surface, a configuration relevant to advanced thermal management in biomedical, aerospace, and electronics cooling. To address the limitations of traditional numerical approaches in multi-parameter optimization, novel Artificial Neural Network (ANN) optimized with the Levenberg–Marquardt algorithm (LMA) is developed. The model incorporates realistic physical effects, including temperature-dependent viscosity and thermal conductivity, surface suction, and Joule heating. A high-fidelity dataset was generated by solving the transformed governing equations using MATLAB's bvp4c solver, covering the parameter ranges: magnetic parameter (1 ≤ M ≤ 5), mixed convection (−1 ≤ λ ≤ 6), variable viscosity (0.1 ≤ a ≤ 1.5), thermal conductivity (0.1 ≤ b ≤ 0.5), stretching ratio(0.1 ≤ ε ≤1), suction parameter (−1 ≤ S ≤ 1), and nanoparticle volume fraction (0.01 ≤ φ ≤ 0.1). The velocity and temperature data, varying with M, ε, λ, a, and b, were divided into 70% training, 15% validation, and 15% testing sets for ANN-LMA modelling. The framework achieved absolute 10−3–10−9 and MSE between 10−10–10−7, demonstrating high predictive accuracy. Results reveal that the magnetic field enhances vertical velocity in shrinking flows but reduces it in stretching flows, while horizontal velocity is suppressed in both cases. Temperature rises with magnetic and mixed convection effects, and variable conductivity causes hybrid nanofluids to exhibit up to 45% higher thermal elevation than mono nanofluids. Notably, a 10% Ag + TiO₂ mixture enhances heat transfer by 45%, compared to 18.6% for 10% Ag alone. The novelty of this work lies in its integrated AI-driven framework that accurately captures coupled multiphysics interactions, providing a rapid and reliable predictive tool for the design of advanced thermal-MHD systems.