Nangarhar University

Accurate prediction of the Klinkenberg slippage factor (KSF) is crucial for characterizing low permeability gas reservoirs, but conventional laboratory determination methods are prohibitively costly and time-consuming, constituting a major research gap. This study addresses this limitation by presenting a novel, experimentally validated workflow that seamlessly integrates comprehensive laboratory measurements with advanced machine learning (ML) models to achieve rapid and accurate KSF prediction. A robust dataset of 253 limestone core samples was utilized, employing air permeability, cation exchange capacity (Qv), porosity, tortuosity and grain density as input features. After a systematic comparative evaluation of ten algorithms on the capacity of ML to model the complex, non-linear relationships in KSF estimation are demonstrated. The key distinction and novelty reside in the superior performance of the AdaBoost algorithm, which achieved the highest predictive accuracy (R2=0.998, AARE%=1.276). This research offers a robust, efficient, and cost-effective alternative to traditional permeability correction techniques, establishing an innovative, data-driven framework that significantly enhances predictive modeling for reservoir evaluation in tight gas formations.

Accurate estimation of energy requirements for biomass pyrolysis is essential for designing cost‑efficient and sustainable thermochemical conversion systems. This study addresses the challenge of predicting pyrolysis energy requirement by integrating comprehensive feedstock compositional data with process operational parameters, analyzed through a machine learning (ML)‑based framework. A curated dataset of 633 experimentally validated records from peer‑reviewed publications was compiled, encompassing elemental composition (C, H, N, S, O, and ash content), biochemical composition (protein, lipid, and carbohydrate), and operational parameters. Models were trained and validated using a 9:1 split with five-fold cross‑validation to ensure robust generalization. Eight algorithms, including decision tree, adaptive boosting (AdaBoost), random forest, K‑nearest neighbors (KNN), ensemble learning, convolutional neural network (CNN), support vector regression (SVR), and multilayer perceptron (MLP), were optimized via hyperparameter tuning and evaluated through the coefficient of determination (R2), mean squared error (EMS), and average absolute relative error (EAAR). Results demonstrated that AdaBoost and random forest achieved superior generalization on unseen data (test R2≥0.893 and test EAAR≤7.18

The primary objective of this study is to develop and validate robust data-driven models for accurately predicting bacterial growth inhibition induced by cerium oxide nanoparticles across different bacterial strains and experimental conditions. This study aims to develop and validate data-driven predictive models to quantify bacterial growth inhibition induced by cerium oxide nanoparticles under diverse experimental conditions, with the goal of supporting antibacterial nanotechnology research. To this end, sophisticated AI methods, including Convolutional Neural Networks (CNN), Multi-layer Perceptron Artificial Neural Networks (MLP-ANN), Random Forest (RF), Adaptive Boosting (AdaBoost), and Ensemble Learning (EL), were employed to model bacterial cell concentration (OD600) with high precision. Model hyperparameters were optimized using the Coupled Simulated Annealing (CSA) technique to enhance predictive performance. A comprehensive dataset comprising 484 experimental observations was compiled, with 387 samples allocated for training and 97 for validation. The study considers two bacterial strains, Escherichia coli and Bacillus subtilis, cultivated in media containing cerium oxide nanoparticles with nominal sizes of 6 ± 3.5 nm, 15 ± 4.3 nm, 22 ± 5.7 nm, and 40 ± 10 nm (Samples A–D). Input features included bacterial type, nanoparticle size (medium type), nanoparticle concentration, and exposure time. Monte Carlo sensitivity analysis revealed that exposure time is the dominant factor governing bacterial cell concentration, followed by nanoparticle concentration, nanoparticle size, and bacterial strain. Among the evaluated models, MLP-ANN exhibited the highest predictive accuracy, achieving the greatest R2 values and the lowest RMSE and AARE

The Himalayan alpine (Kullu Valley) ecosystem is increasingly vulnerable to ecological shifts driven by climatic variability and anthropogenic pressures. Despite widespread recognition of this vulnerability, systematic long-term assessments quantifying land use and land cover (LULC) transformations and their broader ecological impacts remain scarce. Addressing this critical gap, the present study investigates two decades (2004–2024) of LULC changes and projects future trajectories up to 2044. The research uniquely integrates LULC dynamics with terrestrial carbon stock assessment and ecosystem service valuation (ESV), offering novel insights into the ecological and economic consequences of landscape transformation in the high-altitude Himalayan (Kullu Valley) region. LULC classification was conducted using high-resolution satellite imagery processed through a Random Forest classifier, achieving 93

Land Use and Land Cover (LULC) transformations driven by human activities significantly influence the thermal behavior and ecological function of urban environments. This study investigates the spatiotemporal dynamics of LULC, land surface temperature (LST), and key spectral indices—including the Normalized Difference Built-up Index (NDBI, for mapping impervious surfaces), the Normalized Difference Vegetation Index (NDVI, for assessing vegetation health and density), and the Normalized Difference Water Index (NDWI, for detecting surface water and moisture)—in Burewala City, Pakistan. Using multi-temporal Landsat imagery (2003, 2014, 2023), supervised classification and transition matrix analysis revealed quantitatively modest but environmentally significant urban expansion, with built-up areas increasing from 11.44