Comprehensive review of polymer-based concrete: properties, sustainability, and challenges.

Concrete production significantly contributes to CO emissions and depletion of natural resources, leading to substantial environmental concerns. The integration of polymers into concrete has emerged as a promising innovative solution aimed at overcoming inherent limitations of traditional concrete, including brittleness, susceptibility to tracking, environmental degradation, and substantial ecological impacts. This systematic review thoroughly investigates the properties, sustainability implications, and practical challenges associated with polymer-based concrete (PBC), particularly focusing on polymer concrete composites (PCC) and polymer-modified concrete (PMC) detailing their composition, mechanical behavior, and durability.

Advanced Materials for CO Capture: A Critical Review of Emerging Adsorbents and Technologies.

Carbon dioxide (CO) capture technology (CCT) is a critical step toward reducing the environmental impact of fossil fuel combustion, which contributes significantly to global climate change. This review examines the current state of CCT, focusing on its efficiency, limitations, and scalability. Advanced technologies such as postcombustion, precombustion, oxyfuel combustion, and direct air capture are examined, with an emphasis on their suitability for industrial-scale applications.

Change point detection to analyze air pollution and its economic effects: an exponentially weighted moving average perspective.

Air pollution has a direct impact on every society, leading to consequential effects on the economy of a nation. Poor air quality adversely affects human health, resulting in various economic outcomes such as rising healthcare costs, diminished labor productivity, negative impacts on tourism and living standards, increased regulatory expenses for businesses, and heightened economic disparities. Effective control methods are essential to monitor factors influencing the economy, including air quality.

An integrated blockchain and IPFS-based solution for secure and efficient source code repository hosting using middleman approach.

Centralized version control systems (VCS) are vital for software development but pose risks of data loss and ownership disputes. While blockchain offers a decentralized alternative, existing solutions are often hindered by high latency, compromising the real-time collaboration essential for modern workflows. This study introduces a novel hybrid architecture combining the security of the Ethereum blockchain and the InterPlanetary File System (IPFS) with two key contributions: 1) Shamir's Secret Sharing (SSS) to create a trust-minimized model for key distribution, and 2) an authoritative-first, optimistic-fallback retrieval protocol utilizing a temporary middleware to decouple the user experience from blockchain confirmation delays.

Pickering Emulsified Acid Systems (PEAS): A Promising Approach for Reduced Drag in Matrix Acidizing.

Matrix acidizing is a widely employed stimulation technique that enhances well productivity in carbonate reservoirs by dissolving formation damage and creating conductive channels known as wormholes. Conventional hydrochloric acid (HCl) systems, while effective, suffer from limitations such as high corrosivity, excessive reactivity, and poor thermal stability. Emulsified acid systems (EAS) have emerged to address these drawbacks by introducing an oil phase that slows the acid-rock reaction and facilitates deeper penetration.

Investigating the performance of asphalt pavements modified with reclaimed asphalt and crumb rubber.

This research provides useful insights into sustainable and cost-effective pavement rehabilitation by evaluating the combined effects of both Reclaimed Asphalt Pavement (RAP) and Crumb Rubber (CR) modification on flexible pavement performance using actual motorway sections. Pavement rehabilitation and maintenance can enhance the design and serviceable life of the pavement. Additionally, modification of asphalt with Crumb Rubber (CR) and Reclaimed Asphalt Pavement (RAP) not only proves to be economical but can also increase the resistance of flexible pavement concerning rutting, fatigue, and moisture damage.

Machine learning-based prediction of unconfined compressive strength of organic-rich clay shales using hybrid destructive and non-destructive inputs.

The unconfined compressive strength of organic-rich clay shale is a fundamental parameter in geotechnical and energy applications, influencing drilling efficiency, wellbore stability, and excavation design. This study presents machine learning-based predictive models for unconfined compressive strength estimation, trained on a comprehensive dataset of 1217 samples that integrate non-destructive indicators such as ultrasonic pulse velocity, shale fabric metrics, wettability potential and destructive field-derived parameters. A dual-model framework was implemented using Support Vector Machine, Decision Tree, K-Nearest Neighbor, and Extreme Gradient Boosting (XGBoost) algorithms.

Detection and Recognition of Bilingual Urdu and English Text in Natural Scene Images Using a Convolutional Neural Network-Recurrent Neural Network Combination with a Connectionist Temporal Classification Decoder.

Urdu and English are widely used for visual text communications worldwide in public spaces such as signboards and navigation boards. Text in such natural scenes contains useful information for modern-era applications such as language translation for foreign visitors, robot navigation, and autonomous vehicles, highlighting the importance of extracting these texts. Previous studies focused on Urdu alone or printed text pasted manually on images and lacked sufficiently large datasets for effective model training.

Tribological Characterization of an Epoxy Composite Coating for Enhanced Wear Resistance in Oil Well Casing Applications.

The tribological performance of a novel nonmetallic composite casing coating is investigated under dry wear conditions and different side loads and rotational speeds. The coating is composed of a short-glass-fiber-reinforced epoxy matrix with silicon carbide, aluminum oxide, and calcium carbonate nanofillers to provide a protective barrier against contact with hardened drill pipe tool joints. The results revealed that the wear behavior was highly dependent on the applied side load and rotational speed.

Self-Assembled Vanadium Carbide MXene-Modified Ceramic Membranes with High Permeability for Efficient Oil-in-Water Emulsion Separation.

Developing high-performance membranes for efficient oil-water emulsion separation is essential for advancing sustainable and cost-effective wastewater treatment technologies. MXenes, as emerging two-dimensional materials, offer distinct advantages due to their inherent hydrophilicity, chemical stability, and mechanical durability. In this work, ceramic membranes were surface-modified with vanadium carbide (VC) MXene, a relatively underexplored member of the MXene family, and systematically characterized and evaluated for oil-in-water emulsion separation.

Interfacial Engineering to Construct Two-Dimensional Boron-Doped Iron Disulfide/Metal-Organic Framework Heterostructures for Selective Anodic Reaction in Seawater.

Hydrogen production from seawater is promising; however, the complex chemistry and corrosive nature of seawater are a huge bottleneck. Therefore, it is imperative to design catalysts that provide highly active and stable catalytic sites for preferential seawater catalysis. Here, we constructed an interface by heterostructuring boron-doped iron disulfide (B-FeS) sheets with metal-organic framework (MOF) sheets to achieve higher activity and longer life, confirmed through theoretical and experimental results.

The Crucial Role of Solvent and Bound Oxygen on the Interplay of Cerium (III) and Cerium (IV) Chloride in a Photocatalytic Cycle for Organic Transformations.

Here, we investigate the fundamental interactions between Ce (III) chloride, molecular oxygen, and various solvent environments to elucidate their collective impact on photocatalytic efficiency. Through comprehensive spectroscopic analyses and computational modeling, we discovered that in acetonitrile (MeCN), O molecules directly bind to the metal complex using the sphere of action model with a separation distance of 2.35 Å.

Transforming petroleum coke into greener and sustainable carbon nanomaterials: Synthesis, structure and applications.

The exceptional capabilities of carbon nanomaterials (CNMs) have led to a notable surge in research on various dimensionalities i.e. (0D, 1D, 2D), which hold promise for use in a range of scientific and technological domains.

Ultrahigh-porosity peony-like magnesium oxide for the superior adsorption of arsenic (III) and arsenic (V) from aqueous solutions.

Solidification of soluble arsenic from water has been one of the major challenges in global water management, with the urgent need for new adsorbent development. Therefore, ultrahigh-porosity MgO adsorbent (i.e.

Integrative bioinformatics and drug repurposing for metastatic prostate cancer: identifying novel therapeutic targets by transcriptional profiling and molecular Modeling.

Metastasis is one of the leading factors of cancer-related deaths worldwide. New potential targets and treatment strategies are needed to extend survival and enhance the quality of life for these patients. We performed an in-depth bioinformatics analysis to identify potential genes and associated potential therapeutic compounds for metastasis of prostate adenocarcinoma.

Automatic margin line extraction using 3D deep learning on digital surface models of prepared teeth for crown generation.

We propose a personalized digital solution to create margin lines on prepared teeth automatically and consistently during a dental crown design. Using artificial intelligence (AI), the proposed framework disrupts the current nonrepeatable manual margin line design procedure. We used supervised deep learning on 3D scanned prepared teeth to segment them in two regions such that margin lines coincide with the boundaries separating the two regions.

Feature fusion ensemble classification approach for epileptic seizure prediction using electroencephalographic bio-signals.

Introduction: Epilepsy is a neurological disorder in which patients experience recurrent seizures, with the frequency of occurrence more than twice a day, which highly affects a patient's life. In recent years, multiple researchers have proposed multiple machine learning and deep learning-based methods to predict the onset of seizures using electroencephalogram (EEG) signals before they occur; however, robust preprocessing to mitigate the effect of noise, channel selection to reduce dimensionality, and feature extraction remain challenges in accurate prediction.

Methods: This study proposes a novel method for accurately predicting epileptic seizures.

CAKL: Commutative algebra k-mer learning of genomics.

Despite the availability of various sequence analysis models, comparative genomic analysis remains a challenge in genomics, genetics, and phylogenetics. Commutative algebra, a fundamental tool in algebraic geometry and number theory, has rarely been used in data and biological sciences. In this study, we introduce commutative algebra k-mer learning (CAKL) as the first-ever nonlinear algebraic framework for analyzing genomic sequences.

Contact morphology of sand particles in dunes.

This study presents an analysis of the morphology of sand particles and their contacts using high-resolution microcomputer tomography (CT) and scanning electron microscopy (SEM). Raw 3D images are filtered, binarized, and smoothed to distinguish the granular space from the void space. The 3D granular space is segmented via an advanced adaptive watershed algorithm.

Unlocking the Potential of ,,','‑Tetraphenylbenzidine Based on Conjugated Microporous Polymers for Rhodamine B Adsorption: A Synergistic Experimental and Density Functional Theory Perspective.

This study presents the synthesis and construction of two innovative conjugated microporous polymers (CMPs), TPBZ-PyT CMP and TPBZ-TPET CMP, which incorporate pyrene (Py) and tetraphenylethene (TPE) subunits, respectively. These subunits are selected for their complementary properties, with the TPE moiety offering enhanced flexibility compared to the more rigid Py structure. The flexibility of TPE is hypothesized to improve the adsorption performance of the CMPs for removing rhodamine B (RhB) dye from aqueous solutions.

Advances in adsorbent materials for heavy metals remediation: a roadmap for sustainability.

The pervasive presence of heavy metal contaminants in the environment has become a pressing ecological and public health concern due to their toxicity, persistence, and bioaccumulative behavior. Heavy metals, primarily introduced through anthropogenic activities, disrupt natural ecosystems and threaten human health. This review examines recent advancements in adsorbent materials for heavy metal remediation with an emphasis on sustainability, commercial viability, and enhanced performance metrics.

A Post-Quantum Public-Key Signcryption Scheme over Scalar Integers Based on a Modified LWE Structure.

To ensure confidentiality and integrity in the era of quantum computing, most post-quantum cryptographic schemes are designed to achieve either encryption or digital signature functionalities separately. Although a few signcryption schemes exist that combine these operations into a single, more efficient process, they typically rely on complex vector, matrix, or polynomial-based structures. In this work, a novel post-quantum public-key encryption and signature (PQES) scheme based entirely on scalar integer operations is presented.

Entanglement distribution in lossy quantum networks.

Entanglement distribution is essential for unlocking the potential of distributed quantum information processing. We consider an N-partite network where entanglement is distributed via a central source over lossy channels, and network participants cooperate to establish entanglement between any two chosen parties under local operations and classical communication (LOCC). We develop a general mathematical framework to assess the average bipartite entanglement shared in a lossy distribution, and introduce a tractable lower bound by optimizing over a subset of single-parameter LOCC transformations.

Electromagnetic induction heating of polymer nanocomposites: a computational study on design parameters.

Electromagnetic induction technology enables rapid, noncontact heating of conductive polymer nanocomposites, yet uncontrolled localized heating during this process can induce significant thermomechanical damage. Key influencing factors include nanoparticle dispersion, agglomeration, magnetic field frequency, and coil geometry. This study presents a multiphysics computational model to simulate the induction heating of acrylonitrile butadiene styrene reinforced with iron oxide (FeO) nanoparticles, assessing the impact of these variables on heating efficiency.

Organically Functionalized Magnesium Phyllosilicates: Surface Engineering and Antibacterial Performance.

Synthetic clay analogues (SCAs) of a new organosilicate layered material family, in contrast to common clays, are produced via an in situ room-temperature sol-gel route, providing the possibility for the design and synthesis of diverse, tailor-made functional groups on the surface and interior of the synthetic clay sheets. In this work, we introduce organophyllosilicates bearing different functional end groups, which are synthesized by a magnesium metal salt precursor and organosilanes such as (3-aminopropyl)-triethoxysilane (APTEOS), -[3-(trimethoxysilyl)-propyl]-ethylenediamine (EDAPTEOS), -(3-trimethoxysilylpropyl)-diethylenetriamine (TAPTMOS), 1,4-bis-(triethoxysilyl)-benzene (BTB), tetraethyl orthosilicate (TEOS), 3-glycidoxypropyltrimethoxysilane (GLYMO), and (3-chloropropyl)-trimethoxysilane (CPTMOS). The surface free energy for various organosynthetic clay analogues lies in the 29-252 mJ/m range.