穆尔纳·阿扎德国家理工学院

Trip generation modelling is the foundational step in travel demand forecasting. Traditional approaches such as multiple linear regression (MLR) and cross-classification face limitations in capturing nonlinear relationships among socioeconomic, built environment, and travel behaviour variables. This review systematically evaluates both traditional and machine learning (ML) techniques for household trip generation modelling. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) compliant methodology was adopted, screening 3,845 records from four academic databases, resulting in 70 studies included in the final analysis. The reviewed ML techniques include Artificial neural networks (ANNs), ensemble methods, Support vector machines, deep learning architectures, and hybrid fuzzy-neural systems across diverse geographic contexts. The findings indicate that the superiority of ML over traditional methods is context-dependent rather than universal. ANNs outperform MLR primarily in single-city, household-level studies with moderate sample sizes in developing-country contexts. However, ensemble methods have shown near-equivalence with linear regression in geographically heterogeneous, multi-context datasets. Deep learning architectures such as Graph Neural Networks and Convolutional Neural Network–Multidimensional Long Short-Term Memory (CNN-MDLSTM) models achieve high accuracy for spatial trip generation but are limited to scenarios with large-scale datasets and substantial computational resources. Classical MLR remains appropriate where institutional accountability, policy transparency, or resource constraints preclude complex models. The review identifies a complexity–transferability trade-off, where simpler models transfer more reliably across cities. Model selection should therefore be guided by the planning context rather than algorithmic sophistication alone.

A silicon-based refractory compound (SiRC) derived from rice husk was utilized as a sustainable reinforcement for fabricating AlSi10Mg composites through the powder metallurgy route. XRD analysis confirmed the presence of Al, Si, MgO, and SiO2 phases, while SEM-EDS revealed a uniform distribution of SiRC particles and strong interfacial bonding. The composite density decreased from 2.61 g/cm3 to 2.32 g/cm3 at 9 wt.% SiRC, accompanied by a 26% increase in hardness attributed to the hard ceramic phases and grain refinement. Response Surface Methodology (RSM) was applied to model and optimize erosive wear performance by considering impingement angle, impact velocity, and reinforcement content. The quadratic regression model exhibited an excellent fit (R2 = 0.9976), and ANOVA identified impact velocity as the most influential factor. The optimized parameters 90 degrees impingement angle, 40 m/s velocity, and 6 wt.% SiRC yielded a minimum erosion rate of 14.13 mg/kg with a deviation of only 0.91%, validating the model's accuracy and demonstrating the enhanced erosion resistance of the developed composites.

Accurate energy load forecasting is essential for optimizing power grid management, reducing operational costs, and enhancing energy efficiency. Traditional forecasting models rely heavily on real-world historical data, which often contains noise, irregular fluctuations, and privacy constraints, limiting their generalization and robustness. This study explores the impact of synthetic data augmentation on load forecasting models by comparing real data, synthetic data (generated by utilizing an autoencoder-based generative adversarial networks (GAN)), and blended-data training approaches. A comprehensive quantitative and qualitative evaluation was conducted across multiple forecasting models, including autoregressive integrated moving average (ARIMA), convolutional neural network-long short-term memory (CNN-LSTM), gated recurrent units (GRU), neural basis expansion analysis for time series (N-BEATS), and temporal fusion transformer (TFT), with a particular focus on the 70

The energy efficiency of a building is largely affected by occupant behaviour. This review article deals with cases where energy efficiency is improved by reducing energy consumption depending on occupant behaviour, building characteristics and characteristics of the appliances. Here, the primary focus is Human Building Interaction (HBI) affecting the energy usage. The data related to energy consumption by citizens of different countries is collected from the literature published between 2010 to 2024 describing various approaches like survey, empirical study, field experiment, case study energy reports. A statistical analysis of variables in different cases of HBI using Partial Least Square-Structural Equation Modelling method shows that attitude, knowledge and income are the strongest elements in determining the energy-saving behaviour when combined with intervention technique like monetary incentives.

This study investigates the feasibility of using red mud, an industrial byproduct of the Bayer process, as a sustainable partial replacement for commercial grade silica sand in green and dry sand molds for A-356 aluminum alloy casting. The motivation for this work arises from the increasing scarcity and environmental concerns associated with silica sand mining, while red mud disposal poses significant environmental challenges. The experimental investigation involved replacing silica sand with 0–15 wt