Search Results (3,445)

Magnesium phosphate cement (MPC) has gained attention in specialized construction applications due to its rapid setting and high early strength, though its inherent brittleness limits structural performance. This study developed an innovative toughening strategy through synergistic reinforcement using hybrid fibers and carbon fiber-reinforced polymer (CFRP) fabric capable of multi-scale crack control. The experimental program systematically evaluated the hybrid fiber system, dosage, and CFRP positioning effects through mechanical testing of 7-day cured specimens. The results indicated that 3.5% fiber dosage optimized flexural–compressive balance (45% flexural gain with <20% compressive reduction), while CFRP integration at 19 mm displacement enhanced flexural capacity via multi-scale reinforcement. Fracture analysis revealed that the combined system increases post-cracking strength by 60% through coordinated crack bridging at micro (fiber) and macro (CFRP) scales. These findings elucidated the mechanisms by which fiber–CFRP interaction mitigates MPC’s brittleness through hierarchical crack control while maintaining its rapid hardening advantages. The study established quantitative design guidelines, showing the fiber composition of CF/WSF/CPS15 = 1/1/1 with 19 mm CFRP placement achieves optimal toughness–flexural balance (f_f/f_c > 0.38). The developed composite system reduced brittleness through effective crack suppression across scales, confirming its capability to transform fracture behavior from brittle to quasi-ductile. This work advances MPC’s engineering applicability by resolving its mechanical limitations through rationally designed composite systems, with particular relevance to rapid repair scenarios requiring both early strength and damage tolerance, expanding its potential in specialized construction where conventional cement proves inadequate. Full article

Thorium has emerged as a promising alternative to uranium in nuclear energy systems due to its higher natural abundance, favorable conversion to fissile ²³³U, and reduced generation of long-lived transuranic waste. This review provides a comprehensive overview of advanced techniques for thorium recovery from primary ores and secondary resources. The main mineralogical carriers—including monazite, thorianite, thorite, and cheralite as well as industrial by-products such as rare-earth processing tailings—are critically examined with respect to their occurrence and processing potential. Physical enrichment methods (gravity, magnetic, and electrostatic separation) and hydrometallurgical approaches (acidic and alkaline leaching) are analyzed in detail, highlighting their efficiencies, limitations, and environmental implications. Particular emphasis is placed on modern separation strategies such as solvent extraction with organophosphorus reagents, diglycolamides, and ionic liquids, as well as extraction chromatography, nanocomposite sorbents, ion-imprinted polymers, and electrosorption on carbon-based electrodes. These techniques demonstrate significant progress in enhancing selectivity, reducing reagent consumption, and enabling recovery from low-grade and secondary feedstocks. Environmental and radiological aspects, including waste minimization, immobilization, and regulatory frameworks, are discussed as integral components of sustainable thorium management. Finally, perspectives on hybrid technologies, digital process optimization, and economic feasibility are outlined, underscoring the need for interdisciplinary approaches that combine chemistry, materials science, and environmental engineering. Collectively, the analysis highlights the transition from conventional practices to integrated, scalable, and environmentally responsible technologies for thorium recovery. Full article

Google Earth Engine (GEE) has become one of the most widely used platforms for Land Use and Land Cover (LULC) research, offering cloud-based access to petabyte-scale datasets and scalable analytical tools. While earlier reviews provided valuable overviews of data and applications, this study synthesizes 72 selected articles published between 2016 and February 2025 to examine the evolution of GEE–LULC research. Results show exponential growth in publications, with Landsat and Sentinel imagery dominating datasets and Random Forest (RF) and Support Vector Machine (SVM) remaining the most common classifiers. Geographically, output is concentrated in China and India, reflecting regional leadership in GEE adoption. Despite its strengths, GEE faces persistent challenges, including memory limits, restricted support for advanced Deep Learning (DL), and reliance on labeled data. Promising directions include integrating few-shot semantic segmentation and hybrid workflows combining GEE scalability with local Graphics Processing Unit (GPU) computing. By bridging platform-focused and application-focused studies, this review provides a comprehensive synthesis of GEE–LULC research and outlines actionable pathways for advancing scalable and Artificial Intelligence (AI)-enabled geospatial analysis. Full article

It is known that geopolymer mortar exhibits high compressive strength but relatively low flexural strength, high brittleness, and poor toughness. Engineering practices for cement-based materials have demonstrated that incorporating fibers can effectively prevent the expansion of existing cracks and the formation of new ones in the materials. Adding polypropylene fibers to geopolymer mortar can, on the one hand, improve the crack resistance of the mortar, and on the other hand, enhance the impact resistance of the geopolymer mortar. In this paper, slag, metakaolin, and fly ash are utilized as silico-aluminous raw materials, standard sand is employed as aggregate, and a mixture of water glass and NaOH in a specific proportion is used as the alkali activator to prepare geopolymer mortar. Polypropylene fibers are incorporated to improve its mechanical properties. The effects of fiber length and mixing method on the mechanical properties of geopolymer mortar are studied to determine the optimal fiber length and mixing method. The mechanism of the mechanical properties of fiber-reinforced geopolymer mortar is analyzed by combining SEM. The research results indicate that the geopolymer mortar with 15 mm single-doped fibers exhibits the best flexural strength and toughness. In contrast, the geopolymer mortar with 12 mm single-doped fibers demonstrates the best compressive strength. The geopolymer with 9 mm and 18 mm hybrid-doped fibers has the best mechanical properties and is superior to the geopolymer mortar with single-doped fibers. Full article

Accurate prediction of the ultimate bearing capacity (UBC) of single piles is essential for safe and economical foundation design, as it directly impacts construction safety and resource efficiency. This study aims to develop a hybrid prediction framework integrating Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to optimize a Backpropagation Neural Network (BPNN). GA performs global exploration to generate diverse initial solutions, while PSO accelerates convergence through adaptive parameter updates, balancing exploration and exploitation. The primary objective of this study is to enhance the accuracy and reliability of UBC prediction, which is crucial for informed decision-making in geotechnical engineering. A dataset consisting of 282 high-strain dynamic load tests was employed to assess the performance of the proposed GA-PSO-BPNN model in comparison with CNN, XGBoost, and traditional dynamic formulas (Hiley, Danish, and Winkler). The GA-PSO-BPNN achieved an R² of 0.951 and an RMSE of 660.13, outperforming other AI models and traditional approaches. Furthermore, SHAP (SHapley Additive exPlanations) analysis was conducted to evaluate the relative importance of input variables, where SHAP values were used to explain the contribution of each feature to the model’s predictions. The findings indicate that the GA-PSO-BPNN model provides a robust, cost-efficient, and interpretable approach for UBC prediction, which aligns with current sustainability goals by optimizing resource usage in foundation design. This model shows significant potential for practical use across various geotechnical settings, contributing to safer, more sustainable infrastructure projects. Full article

Coagulation–flocculation, historically reliant on simple inorganic salts, has evolved into a technically sophisticated process that is central to the removal of turbidity, suspended solids, organic matter, and an expanding array of micropollutants from complex wastewaters. This review synthesizes six decades of research, charting the transition from classical aluminum and iron salts to high-performance polymeric, biosourced, and hybrid coagulants, and examines their comparative efficiency across multiple performance indicators—turbidity removal (>95%), COD/BOD reduction (up to 90%), and heavy metal abatement (>90%). Emphasis is placed on recent innovations, including magnetic composites, bio–mineral hybrids, and functionalized nanostructures, which integrate multiple mechanisms—charge neutralization, sweep flocculation, polymer bridging, and targeted adsorption—within a single formulation. Beyond performance, the review highlights persistent scientific gaps: incomplete understanding of molecular-scale interactions between coagulants and emerging contaminants such as microplastics, per- and polyfluoroalkyl substances (PFAS), and engineered nanoparticles; limited real-time analysis of flocculation kinetics and floc structural evolution; and the absence of predictive, mechanistically grounded models linking influent chemistry, coagulant properties, and operational parameters. Addressing these knowledge gaps is essential for transitioning from empirical dosing strategies to fully optimized, data-driven control. The integration of advanced coagulation into modular treatment trains, coupled with IoT-enabled sensors, zeta potential monitoring, and AI-based control algorithms, offers the potential to create “Coagulation 4.0” systems—adaptive, efficient, and embedded within circular economy frameworks. In this paradigm, treatment objectives extend beyond regulatory compliance to include resource recovery from coagulation sludge (nutrients, rare metals, construction materials) and substantial reductions in chemical and energy footprints. By uniting advances in material science, process engineering, and real-time control, coagulation–flocculation can retain its central role in water treatment while redefining its contribution to sustainability. In the systems envisioned here, every floc becomes both a vehicle for contaminant removal and a functional carrier in the broader water–energy–resource nexus. Full article

Urban water infrastructure faces increasing deterioration, necessitating accurate, cost-effective condition assessment. Traditional inspection techniques are intrusive and inefficient, creating demand for scalable machine learning (ML) solutions. This study develops a hybrid ML meta-model to predict underground pipe conditions using a comprehensive dataset of 11,544 records. The objective is to enhance multi-class classification performance while preserving interpretability. A stacked hybrid architecture was employed, integrating Random Forest, LightGBM, and CatBoost models. Following data preprocessing, feature engineering, and correlation analysis, the neural network-based stacking meta-model achieves 96.67% accuracy, surpassing individual base learners while delivering enhanced robustness through model diversity, improved probability calibration, and consistent performance on challenging intermediate condition classes, which are essential for condition prioritization. Age emerged as the most influential feature, followed by length, material type, and diameter. ROC-AUC scores ranged from 0.894 to 0.998 across all models and classes, confirming high discriminative capability. This work demonstrates hybrid architectures for infrastructure diagnostics. Full article

Detecting underwater structural defects is vital for hydraulic engineering safety. Diverse patterns of underwater structural defects, i.e., the morphology and scale characteristics, pose difficulties on feature representability during detection. Any single feature morphology is insufficient to fully characterize diverse types of underwater defect patterns. This paper proposes a novel hybrid neural network to enhance feature representation of underwater structural multi-defects, which in turn improves the accuracy and adaptability of underwater detection. Three types of convolution operations are combined to build Hybrid Aggregation Network (HanNet), enhancing the morphological representation for diverse defects. Considering the scale difference of diverse defects, the Multi-Scale Shared Feature Pyramid (MSFP) is proposed, facilitating adaptive representation for diverse sizes of structural defects. The defect detection module leverages an Adaptive Spatial-Aware Attention (ASAA) at the backend, enabling selective enhancement of salient defect features. For model training and evaluation, we, for the first time, build an underwater structural multi-defects sonar image dataset containing a wide range of typical defect types. Experimental results demonstrate that the proposed model outperforms state-of-the-art methods, significantly improving defect detection accuracy, and provides an effective solution for detecting diverse structural defects in complex underwater environments. Full article

Polyoxometalates (POMs) are nanoscale anionic clusters constructed from transition-metal oxide units with well-defined architectures and tunable electronic structures, offering abundant reversible redox sites and adjustable energy levels. Their diverse valence states and compositional flexibility of molecular architectures render them promising candidates for electrochemical energy storage. Rational molecular design and nano-structural engineering can significantly enhance the electrical conductivity, structural stability, and ion transport kinetics of POM-based materials, thus improving device performance. In solar cells, the tunable energy levels and light-harvesting capabilities contribute to enhanced photoconversion efficiency. In secondary batteries, the dense redox centers provide additional capacity. For supercapacitors, the rapid electron transfer supports high power density storage. This review systematically summarizes recent advances in POM-based functional nanomaterials, with an emphasis on material design strategies, energy storage mechanisms, performance optimization approaches, and structure–property relationships. Fundamental structures and properties of POMs are outlined, followed by synthesis and functionalization approaches. Key challenges such as dissolution, poor conductivity, and interfacial instability are discussed, together with progress in batteries and hybrid capacitors. Finally, future challenges and development directions are outlined to inspire further advancement in POM-based energy storage materials. Full article

Neurodegenerative diseases, including Alzheimer’s disease (AD) and Parkinson’s disease (PD), present a growing public health challenge globally. Recent advancements in neurotechnology and neuroengineering have significantly enhanced brain–computer interfaces, artificial intelligence, and organoid technologies, making them pivotal instruments for diagnosis, monitoring, disease modeling, treatment development, and rehabilitation of various diseases. Nonetheless, the majority of neural interface platforms focus on unidirectional control paradigms, neglecting the need for co-adaptive systems where both the human user and the interface continually learn and adapt. This selected review consolidates information from neuroscience, artificial intelligence, and organoid engineering to identify the conceptual underpinnings of co-adaptive and symbiotic human–machine interaction. We emphasize significant shortcomings in the advancement of long-term AI-facilitated co-adaptation, which permits individualized diagnostics and progression tracking in Alzheimer’s disease and Parkinson’s disease. We concentrate on incorporating deep learning for adaptive decoding, reinforcement learning for bidirectional feedback, and hybrid organoid–brain–computer interface platforms to mimic disease dynamics and expedite therapy discoveries. This study outlines the trends and limitations of the topics at hand, proposing a research framework for next-generation AI-enhanced neural interfaces targeting neurodegenerative diseases and neurological disorders that are both technologically sophisticated and clinically viable, while adhering to ethical standards. Full article

Chloroplast codon usage bias (CUB) records both maternal phylogeny and selection intensity. Characterizing CUB in the synthetic cereal × Triticosecale and its Triticum and Secale parents is therefore a prerequisite for plastid-based engineering and for tracing the evolutionary consequences of recent allopolyploidy. Complete plastome sequences of five taxa—Triticum monococcum, T. turgidum, T. aestivum, Secale cereale and × Triticosecale sp.—were downloaded. Protein-coding genes were extracted to calculate overall GC, GC1–GC3, SCUO, RSCU, ENC-GC3s, neutrality, and PR2 plots. Optimal codons were defined as RSCU ≥ 1 and △RSCU ≥ 0.8. The results showed that the chloroplast genomes of these five species are low in GC content for the third base of codons, suggesting an end preference for A or U bases. The SCUO values ranged from 0.22 to 0.23, suggesting no significant codon usage bias. GC content was relatively low (38.78–39.16%), with the order GC1 > GC2 > GC3. RSCU analysis indicated that codons ending with A/T are more commonly used. Neutral mapping, ENC-GC3s, and the PR2 plot all showed that the preference of codon usage for the majority of functional genes was influenced by a combination of mutation and natural selection pressure, and the influence of natural selection was predominant. RSCU clustering recovers the expected maternal tree (Triticum clade + triticale). All optimal codons terminate with A or U, yielding identical plastid translation tables for the five species. Despite its recent hybrid origin, triticale plastid CUB is indistinguishable from its wheat maternal ancestor and is governed mainly by selection. The compiled optimal codon set provides an immediate reference for chloroplast transformation and for dissecting selection relaxation in newly synthesized triticale combinations. Full article

Zinc oxide (ZnO) nanomaterials have emerged as promising candidates for gas sensing applications due to their high sensitivity, fast response–recovery cycles, thermal and chemical stability, and low fabrication cost. However, the performance of pristine ZnO remains limited by high operating temperatures, poor selectivity, and suboptimal detection at low gas concentrations. To address these limitations, significant research efforts have focused on dopant incorporation and polymer hybridization. This review summarizes recent advances in dopant engineering using elements such as Al, Ga, Mg, In, Sn, and transition metals (Co, Ni, Cu), which modulate ZnO’s crystal structure, defect density, carrier concentration, and surface activity—resulting in enhanced gas adsorption and electron transport. Furthermore, ZnO–polymer nanocomposites (e.g., with polyaniline, polypyrrole, PEG, and chitosan) exhibit improved flexibility, surface functionality, and room-temperature responsiveness due to the presence of active functional groups and tunable porosity. The synergistic combination of dopants and polymers facilitates enhanced charge transfer, increased surface area, and stronger gas–molecule interactions. Where applicable, sol–gel-based studies are explicitly highlighted and contrasted with non-sol–gel routes to show how synthesis controls defect chemistry, morphology, and sensing metrics. This review provides a comprehensive understanding of the structure–function relationships in doped ZnO and ZnO–polymer hybrids and offers guidelines for the rational design of next-generation, low-power, and selective gas sensors for environmental and industrial applications. Full article

Electrification of transport is advancing, yet debate continues over whether battery electric vehicles (EVs) are a like-for-like and affordable alternative to internal-combustion engine (ICE) cars. Positioned in a rapidly evolving mainstream market, this study examines structural similarity and relative pricing of EVs versus ICE models available in Poland in 2025. Data on 373 base passenger-car models (excluding hybrids) were analyzed using two clustering methods: k-means and k-medoids. The optimal number of clusters was determined by 23 validity indices, identifying three clusters. The significance of mean price differences between EVs and non-EVs within the specified clusters was tested using a permutation test. Results indicate no statistically meaningful EV price premium within clusters: no EV price exceeded two standard deviations above its cluster mean, and no cluster consisted exclusively of EVs, which points to strong technical similarity across powertrains. Additionally, permutation tests indicated no differences within clusters, except in the cluster with the best technical parameters, where non-EV cars were more expensive, which suggests that the premium segment of the market continues to be dominated by combustion cars. These findings, which show that electric vehicles are price-comparable to non-EVs, challenge the perception that EVs are systematically more expensive and demonstrate that, within market segments defined by technical characteristics. Therefore, the evidence suggests that EVs are becoming a genuine competitive alternative to ICE cars in the Polish market. Full article

Recent advances in nanotechnology and optical imaging have transformed molecular diagnostics, enabling the detection and analysis of individual biomolecules with unprecedented precision. Nanobiosensors provide ultrasensitive molecular detection, and super-resolution microscopy (SRM) exceeds the diffraction limit of conventional optics to achieve nanometer-scale resolution. Although their integration remains in its infancy, with only a handful of proof-of-concept studies reported, the convergence of nanobiosensors and SRM holds significant promise for next-generation diagnostics. In this review, we first outline nanobiosensor-based single-molecule detection strategies and highlight representative implementations. These include plasmonic–SRM hybrids, electrochemical–optical correlatives, and SRM-enabled immunoassays, with a focus on their applications in oncology, infectious diseases, and neurodegenerative disorders. Then, we discuss emerging studies at the interface of nanobiosensors and SRM, including nanostructure-assisted SRM. Despite not being true biosensing approaches, these studies provide valuable insights into how engineered nanomaterials can improve imaging performance. Finally, we evaluate current challenges, including reproducibility, multiplexing, and clinical translation, and outline future opportunities, such as the development of photostable probes, artificial intelligence-assisted image reconstruction, microfluidic integration, and regulatory strategies. This review highlights the synergistic potential of nanobiosensors and SRM, outlining a roadmap toward clinically translatable next-generation single-molecule diagnostic platforms. Full article

Modern crop breeding requires efficient tools for genetic identity and parentage verification to manage large-scale programs. To address this, we present GIPA (Genomic Identity and Parentage Analysis), a high-performance toolkit designed for these tasks. GIPA integrates key innovations: a sliding-window algorithm enhances accuracy by correcting genotyping errors, an intelligent system classifies samples by heterozygosity to streamline parentage analysis, and an integrated engine generates intuitive chromosome-level heatmaps. We demonstrate its utility in a soybean backcrossing scenario, where it identified a donor line with 98.02% genomic identity to the recipient, providing a strategy to significantly shorten the breeding program. In maize, its parentage module accurately identified the known parents of commercial hybrids with match scores exceeding 97%, validating its use for variety authentication and quality control. By transforming complex SNP data into clear, quantitative, and visual insights, GIPA provides a robust solution that accelerates data-driven decision-making in plant breeding. Full article