Sustainable concrete: investigating the synergistic effects of coconut fiber, wheat straw ash, and silica fume on RAC strength and durability.

As concrete production accounts for a large percentage of worldwide CO emissions, there is a need for alternative sustainable construction materials. Simultaneously, the increasing generation of construction and demolition waste has led to the growing interest in using recycled aggregates (RA) in concrete. However, recycling aggregate (RAC) tends to demonstrate poor mechanical and durability performance because of relatively high porosity and weak interfacial transition zone existing in RA.

Exploring the dynamics of a single vesicle induced by Fe₃O₄ nanoparticles using micropipette manipulation.

Magnetite nanoparticles (MNPs, Fe3O4) have gained substantial interest for different biomedical and biochemical applications. Therefore, it is important to understand the mechanism of interaction between MNPs and cell membranes. As a model for cells, giant unilamellar vesicles (GUVs) are used in various research studies, providing valuable insights into the behavior of lipid bilayers and their interactions with MNPs.

Quantifying features from X-ray images to assess early stage knee osteoarthritis.

Knee osteoarthritis (KOA) is a progressive degenerative joint disease and a leading cause of disability worldwide. Manual diagnosis of KOA from X-ray images is subjective and prone to inter- and intra-observer variability, making early detection challenging. While deep learning (DL)-based models offer automation, they often require large labeled datasets, lack interpretability, and do not provide quantitative feature measurements.

Risk factors associated with low back pain in rowers: a systematic review and meta-analysis.

Background: Low back pain (LBP) is a prevalent injury among rowers, impacting their careers and potentially causing disability. However, the risk factors for LBP in rowers are still unclear, with most existing studies focusing on individual factors and lacking comprehensive meta-analyses.

Methods: This systematic review and meta-analysis, following PRISMA guidelines, conducted comprehensive searches in PubMed, Web of Science, Embase, and Medrxiv without publication date restrictions.

Efficient synthesis of N-(ethylcarbamothioyl)-1-naphthamide: X-ray structure, Hirshfeld surface analysis, DFTs, and molecular modelling investigations as selective inhibitor of alkaline phosphatase.

Naphthyl Thiourea based derivative N-(ethylcaramothbioyl)-1-naphthamide (NA-MT) was synthesized by freshly prepared 1-naphthoyl isothiocyanate with ethyl amine to afford the products (NA-MT) high purity and characterized via spectroscopic techniques including FTIR, H-NMR, C-NMR, elemental and HRMS analysis and single crystal X-ray diffraction. In-vitro analysis showed that the compound (NA-MT) possesses potent inhibitory effect with IC = 9.875 ± 0.

Selective recovery of nanoscale copper particles from mobile phone waste printed circuit boards through acid leaching and low temperature electrowinning.

Waste printed circuit boards were collected and shredded by a shredder in the current work. Shredded boards were treated by NaOH solution to remove or loosen up the polymer coating painted on the boards. As copper was the target element in this experiment two step leaching process was adopted.

Eff-ReLU-Net: a deep learning framework for multiclass wound classification.

Chronic wounds have emerged as a significant medical challenge due to their adverse effects, including infections leading to amputations. Over the past few years, the prevalence of chronic wounds has grown, thus posing significant health hazards. It is now becoming necessary to automate the wound assessment mechanism to limit the dependence of healthcare practitioners on manual methods.

Optimal control analysis to reduce the health complexity of co-infection with COVID-19 and kidney disease.

The COVID-19 pandemic remains a serious health risk, especially with diseases like kidney disease. There is no information in the literature on co-infection of kidney disease with COVID-19. Therefore, the current study introduces a deterministic mathematical model to explore the co-infection dynamics between COVID-19 and kidney disease, intending to offer insightful guidance for effective control strategies.

Synthesis, in vitro evaluation and computational modelling of benzene sulfonamide derivatives as Dickkopf 1 inhibitors for anticancer drug development.

In the modern age of drug discovery sulfanilamide derivatives are known to have great anti-cancerous potential, the current study aimed to synthesize these derivatives in order to evaluate their biological properties against carbonic anhydrase II (CA-II) and Dickkopf - 1(Dkk1) protein which are highly expressed in many cancers including lung cancer. A series of 10 sulfanilamide derivatives was synthesized under controlled conditions using reflux condensation method. Among all the synthesized derivatives (5a-5j), the compound 5d was found to possess highest antioxidant activity (90.

DeepLASD countermeasure for logical access audio spoofing.

Voice-based authentication systems have become increasingly vulnerable to logical access (LA) spoofing through sophisticated voice conversion (VC) and text-to-speech (TTS) attacks. This paper proposes an end-to-end deep learning approach DeepLASD, that processes raw waveforms to detect spoofed speech without relying on handcrafted features. The model incorporates a SincConv layer for interpretable spectral processing, along with residual convolutional blocks that integrate attention for improved feature extraction.

Machine learning prediction and explainability analysis of high strength glass powder concrete using SHAP PDP and ICE.

Achieving high-strength concrete (HSC) with sustainable supplementary cementitious materials (SCMs) remains a significant challenge in the construction industry. Although glass powder has shown promise as a partial cement substitute, its specific impact on HSC growth is still unclear. This study aims to evaluate the compressive strength (CS) of high strength glass-powder concrete (HSGPC) using machine learning (ML) models and enhance predictive accuracy through hybrid optimization techniques.

Human and ecological health risks from heavy metal contamination in groundwater aquifers.

Landfills are the most commonly used Municipal Solid Waste (MSW) disposal method in the world. However, poorly engineered open landfill sites pose significant environmental threats, particularly groundwater contamination from leachate infiltration. In alignment with Sustainable Development Goals (SDGs), SDG 3- Good Health and well-being, Goal 6- Clean water and sanitation (SDG#5), this study conducts a comprehensive hydro-chemical assessment of twenty-four (24) groundwater samples extracted across four georeferenced zones surrounding Sialkot landfill sites, Punjab, Pakistan.

Highly selective catalytic pathway utilizing metal oxide nanoparticles to produce formic acid through methanol oxidation.

An emerging alternative energy source is formic acid, which has low toxicity and high hydrogen-carrying capacity. Metal-containing nanoparticles are very attractive for many applications, allowing large-scale and environmentally friendly production. This study proposes liquid-state synthesis for clean and facile formic acid production methanol oxidation over metal oxide nanoparticles.

GraphAge: Unleashing the power of graph neural network to decode epigenetic aging.

DNA methylation is a crucial epigenetic marker used in various clocks to predict epigenetic age. However, many existing clocks fail to account for crucial information about CpG sites and their interrelationships, such as co-methylation patterns. We present a novel approach to represent methylation data as a graph, using methylation values and relevant information about CpG sites as nodes, and relationships like co-methylation, same gene, and same chromosome as edges.

TASCI: transformers for aspect-based sentiment analysis with contextual intent integration.

In this article, we present a novel Transformer-Based Aspect-Level Sentiment Classification with Intent (TASCI) model, designed to enhance sentiment analysis by integrating aspect-level sentiment classification with intent analysis. Traditional sentiment analysis methods often overlook the nuanced relationship between the intent behind a statement and the sentiment expressed toward specific aspects of an entity. TASCI addresses this gap by first extracting aspects using a self-attention mechanism and then employing a Transformer-based model to infer the speaker's intent from preceding sentences.

SODU2-NET: a novel deep learning-based approach for salient object detection utilizing U-NET.

Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. To address this challenge posed by complex backgrounds in salient object detection is crucial for advancing the field. This article proposes a novel deep learning-based architecture called SODU2-NET (Salient object detection U2-Net) for salient object detection that utilizes the U-NET base structure.

Corrigendum to "Unveiling the role of supply chain parameters approved by blockchain technology towards firm performance through trust: The moderating role of government support" [Heliyon Volume 9, Issue 11, November 2023, Article e21831].

[This corrects the article DOI: 10.1016/j.heliyon.

Corrigendum to "Environmental impact assessment of tobacco farming in northern Bangladesh" [Heliyon Volume 9, Issue 3, March 2023, Article e14505].

[This corrects the article DOI: 10.1016/j.heliyon.

High sensitivity terahertz metamaterial sensor for trace pesticide detection.

Pesticides play a vital role in agriculture by protecting crops from pests and diseases and ensuring high yields. However, improper use poses severe risks to human health and the environment, emphasizing the need for the precise detection of pesticide residues. Methods like chromatography and biosensors are commonly used, but each has limitations in stability, selectivity, or applicability.

The impact of green human resource management on corporate social responsibility: The mediating role of pro-environmental psychological climate and behavior.

BackgroundEmerging environmental concerns have prompted organizations to adopt Corporate Social Responsibility (CSR) initiatives. Traditional business practices alone are no longer sufficient for CSR success, requiring organizations to revisit their business processes and practices.ObjectiveThis study examines the role of Green Human Resource Management (GHRM) practices in enhancing CSR performance, focusing on the mediating roles of pro-environmental psychological climate and pro-environmental behavior.

Advancing breast cancer prediction: Comparative analysis of ML models and deep learning-based multi-model ensembles on original and synthetic datasets.

Breast cancer is a significant global health concern with rising incidence and mortality rates. Current diagnostic methods face challenges, necessitating improved approaches. This study employs various machine learning (ML) algorithms, including KNN, SVM, ANN, RF, XGBoost, ensemble models, AutoML, and deep learning (DL) techniques, to enhance breast cancer diagnosis.

Dichotomous behavior of water in binary mixtures with a turning point between 40 and 60 °C.

Water is known for its anomalous properties; however, changes in its properties between the 40-60 °C temperature range remain poorly understood, particularly in complex aqueous systems. In this study, α-cyclodextrin, urea, poly(vinyl alcohol), and an inclusion complex of β-cyclodextrin with an ionic liquid, 1-butyl-3-methylimidazolium hexafluorophosphate ([Cmim]PF), were found to exhibit dichotomous behavior, rather than just an anomaly or a progressive shift in water and its binary mixtures. Using temperature-dependent dynamic light scattering (DLS) analysis, physicochemical measurements, and near-infrared (NIR) spectroscopy, a sharp and additive-independent transition in hydrodynamic particle size was observed, which was centered around 50 °C.

An effective statistical moment-based feature extraction technique to identify the phosphoglycerylation sites from protein sequences.

A kind of covalent modification known as post-translational modification (PTM) happens following the biosynthesis process, which is important in cell biology research. A reversible PTM called Lysine phosphoglycerylation alters glycolytic enzyme activity and is linked to several disorders, including heart failure, arthritis, and nervous system deterioration. Identification of phosphoglycerylation has been improved using a variety of feature extraction approaches with machine learning technologies.