Hussain Alsadiq

Enhancing thermal properties of concrete blocks in hot climates is crucial for energy efficiency. In this study, the thermal properties of wax-expanded polystyrene (EPS) composite insulation were investigated to assess its effectiveness in reducing heat transfer and improving heat capacitance. Four hollow concrete blocks, measuring 10 cm × 19 cm × 40 cm were examined: three blocks were filled with varying ratios of wax and Expanded Polystyrene, and one remained hollow as a control. The specific heat capacity of the wax-Expanded Polystyrene composite was measured to evaluate its thermal storage potential. Additionally, the thermal insulation abilities of the filled blocks were assessed using a heat source and a thermal camera. The results showed that the blocks with the wax-EPS composites exhibited reduced heat transfer compared to the control block, demonstrating improved thermal performance. The block with the higher wax content additionally displayed greater specific heat capacity, achieving a value of 1825.1 (J/kg.K), indicating enhanced thermal storage capabilities. These findings highlight the potential of wax-EPS concrete blocks in improving thermal regulation, contributing to a viable and energy-efficient building material for hot climates.

The scarcity and imbalance of labelled training data in real-world requirement engineering datasets constrain the effectiveness of automating software requirement classification using deep learning. This paper presents a suite of adaptive data augmentation techniques specifically designed to enhance deep learning performance for software requirement classification tasks. This study proposes novel augmentation strategies that use the class distribution and length of requirements to dynamically determine the number and proportion of synthetic samples to be generated. The proposed methods include weighted augmentation for one-label and multilabel scenarios, as well as semantic augmentation using domain-specific dictionaries and transformer-based language models. The techniques are integrated into deep learning pipeline based on Bidirectional Gated Recurrent Unit (BiGRU) and evaluated on two datasets PROMISE and EHR across binary, multiscale, and multilabel classification. The experimental results showed that the proposed method significantly improved classification performance, particularly for minorities and underrepresented classes where traditional models struggle. Specifically, adaptive augmentation proposed an improved F1-score of up to 9.3% for underrepresented classes and increased the macro-average F1 by 6.7% on PROMISE-10-class tasks. Transformer-based augmentation outperformed all baselines, achieving an F1-score of 84.1% in the PROMISE multilabel setting compared to 77.5% without augmentation. Similarly, dictionary-based augmentation yielded a 5.2% gain in the EHR multilabel classification. The proposed approach requires minimal manual intervention, which makes it practical and scalable for real-world applications.

Lipid-shelled microbubbles (MBs) and echogenic liposomes (ELIPs) have been proposed as acoustofluidic theranostic agents after having been proven to be efficient in diagnostics as ultrasonic contrast agents. Their mechanical properties—such as shell stiffness, friction, and resonance frequency—are critical to their performance, stability, oscillatory dynamics, and response to sonication. A precise characterization of these properties is essential for optimizing their biomedical applications, however the current methods vary significantly in their sensitivity and accuracy. This review examines the experimental and theoretical methodologies used to quantify the mechanical properties of MBs and ELIPs, discusses how each approach estimates shell stiffness and friction, and outlines the strengths and limitations inherent to each technique. Additionally, the effects of parameters such as temperature and lipid composition on MB and ELIP mechanical behavior are examined. Four characterization methods are analyzed, including frequency-dependent attenuation, optical observation, atomic force microscopy (AFM), and laser scattering, their advantages and limitations are critically assessed. Additionally, the factors that influence the mechanical properties of the MBs and ELIPs, such as temperature and lipid composition, are examined. Frequency-dependent attenuation was shown to provide reliable shell elasticity estimates but is influenced by nonlinear oscillations, AFM confirms that microbubble stiffness is size-dependent with smaller bubbles exhibiting higher shell stiffness, and theoretical models such as modified Rayleigh–Plesset equations increasingly incorporate viscoelastic shell properties to improve prediction accuracy. However, many of these models still assume radial symmetry and neglect inter-bubble interactions, which can lead to inaccurate elasticity values when applied to dense suspensions. In such cases, using modified frameworks like the Sarkar model, which incorporates damping and surface tension explicitly, may provide more reliable estimates under nonlinear conditions. Additionally, lipid composition and temperature significantly affect shell mechanics, with higher temperatures generally reducing stiffness. On the other hand, inconsistencies in experimental protocols hinder direct comparison across studies, highlighting the need for standardized characterization methods and improved computational modeling.