Search Results (9,628)

Due to the increasing volume of traffic on the world’s highways, researchers have been searching for new composite techniques and methods to develop durable and cost-effective pavement structures. The semi-rigid pavement is a composite pavement consisting of a porous asphalt mix with air voids between 25 and 30% and a high-fluidity cementitious grout. In this study, different cementitious grout mixes were prepared. Then a porous asphalt mix with almost 30% air void content was designed. After evaluating the workability, mechanical strength, and volume stability of the prepared grout mixes, the most suitable mix is proposed to fill the voids in the porous asphalt mix. Finally, the prepared semi-rigid pavement specimens were subjected to various tests to evaluate the performance characteristics of the designed pavement. The research concludes that the grout mixture ratio proposed in this study has good grouting ability and the semi-rigid pavement has superior performance characteristics. Full article

The persistent conflict between strength and electrical conductivity in copper-based materials presents a fundamental limitation for next-generation high-performance applications. Graphene, with its unique two-dimensional architecture and exceptional intrinsic characteristics, has become a promising reinforcement phase for copper matrices. This comprehensive review synthesizes recent advancements in graphene–copper composites (CGCs), focusing particularly on structural design innovations and scalable manufacturing approaches such as powder metallurgy, molecular-level mixing, electrochemical deposition, and chemical vapor deposition. The analysis examines pathways for optimizing key properties—including mechanical strength, thermal conduction, and electrical performance—while investigating the fundamental reinforcement mechanisms and charge/heat transport phenomena. Special consideration is given to how graphene morphology, concentration, structural quality, interfacial chemistry, and processing conditions collectively determine composite behavior. Significant emphasis is placed on interface engineering strategies, graphene alignment, consolidation control, and defect management to minimize electron and phonon scattering while improving stress transfer efficiency. The review concludes by proposing research directions to resolve the strength–conductivity paradox and broaden practical implementation domains, thereby offering both methodological frameworks and theoretical foundations to support the industrial adoption of high-performance CGCs. Full article

A student’s active thinking state directly affects their learning experience in the classroom. To help teachers understand students’ active thinking states in real-time, this study aims to construct a model which characterizes their active thinking states. The main research objectives are as follows: (1) to achieve accurate classification of the cognitive levels of in-class exercises; (2) to effectively quantify the active thinking state of students through analyzing the correlation between student cognitive levels and exercise cognitive levels. The research methods used in this study to achieve these objectives are as follows: First, LSTM and Chinese-RoBERTa-wwm models are integrated to extract sequential and semantic information from plain text while TBCC is used to extract the semantic features of code text, allowing for comprehensive determination of the cognitive level of exercises. Second, a cognitive diagnosis model—namely, the QRCDM—is adopted to evaluate students’ real-time cognitive levels with respect to knowledge points. Finally, the cognitive levels of exercises and students are input into a self-attention mechanism network, their correlation is analyzed, and the thinking activity state is generated as a state representation. The proposed text classification model outperforms baseline models regarding ACC, micro-F1, and macro-F1 scores on two sets of exercise datasets in Chinese containing mixed code texts, with the highest ACC, micro-F1, and macro-F1 values reaching 0.7004, 0.6941, and 0.6912, respectively. This proves the proposed model’s effectiveness in classifying the cognitive level of exercises. The accuracy of the thinking activity state characterization model reaches 61.54%. In particular, this is higher than the random baseline, thus verifying the model’s feasibility. Full article

The primary aim of this study is to predict the Marshall stability and flow values of hot-mix asphalt samples prepared according to the Marshall design method using regression-based machine learning algorithms. To overcome the limited number of experimental observations, synthetic data generation was applied using the Conditional Tabular Generative Adversarial Network (CTGAN), while the structural consistency of the generated data was validated through Principal Component Analysis (PCA). Two datasets containing 17 physical and mechanical input variables were analyzed, and multiple regression models were compared, including Extra Trees, Random Forest, Gradient Boosting, AdaBoost, and K-Nearest Neighbors. Among these, the Extra Trees Regressor consistently achieved the best results with near-perfect accuracy in flow predictions (MAE ≈ 4.06 × 10⁻¹⁵, RMSE ≈ 4.97 × 10⁻¹⁵, Accuracy ≈ 99.99%) and high performance in stability predictions (MAE = 109.52, RMSE = 150.67, accuracy = 90.45%). Furthermore, model interpretability was ensured by applying SHapley Additive Explanations (SHAP), which revealed that parameters such as softening point, VMA, penetration, and void ratios were the most influential features. These findings demonstrate that regression-based ensemble models, combined with synthetic data augmentation and explainable AI methods, can serve as reliable and interpretable tools in asphalt mixture design. Full article

Forest fires significantly impact the global climate through carbon emissions, yet the multi-scale coupling mechanisms among meteorological factors, fire behavior, and emissions remain uncertain. Focusing on tropical Asia, this study integrated satellite-based fire behavior products, meteorological datasets, and emission factors, and employed machine learning together with structural equation modeling (SEM) to explore the mediating role of fire behavior in the meteorological regulation of carbon emissions. The results revealed significant differences among vegetation types in both carbon emission intensity and sensitivity to meteorological drivers. For example, average gas emissions (GEs) and particle emissions (PEs) in mixed forests (MF, 323.68 g/m²/year for GE and 0.73 g/m²/year for PE) were approximately 172% and 151% higher, respectively, than those in evergreen broadleaf forests (EBF, 118.92 g/m²/year for GE and 0.29 g/m²/year for PE), which exhibited the lowest emission intensity. Mixed forests and deciduous broadleaf forests exhibited stronger meteorological regulation effects, whereas evergreen broadleaf forests were comparatively stable. Temperature and vapor pressure deficit emerged as the core drivers of fire behavior and carbon emissions, exerting indirect control through fire behavior. Overall, the findings highlight fire behavior as a critical link between meteorological conditions and carbon emissions, with ecosystem-specific differences determining the responsiveness of carbon emissions to meteorological drivers. These insights provide theoretical support for improving the accuracy of wildfire emission simulations in climate models and for developing vegetation-specific fire management and climate adaptation strategies. Full article

Breast cancer (BC) is one of the most common malignancies among women worldwide. Despite advances in early detection and treatment, prognosis remains highly variable. Molecular biomarkers, such as microRNAs (miRNAs), have emerged as promising tools to refine prognostic assessment. Among them, miR-21 is consistently overexpressed in solid tumors and implicated in key oncogenic pathways. This systematic review and meta-analysis aimed to clarify the prognostic significance of miR-21 in BC and explore its molecular mechanisms through bioinformatic analyses. A systematic search of PubMed, Scopus, and Web of Science up to April 2025 identified 18 eligible observational studies. Pooled analyses showed that high miR-21 expression was significantly associated with poorer overall survival (OS) (HR = 2.37, 95% CI: 1.42–3.98) and recurrence-related outcomes (DFS/RFS) (HR = 2.10, 95% CI: 1.32–3.34). Subgroup analyses confirmed robust associations across different cut-off definitions and revealed particularly strong effects in triple-negative BC (HR = 5.69) and mixed subtypes (HR = 2.55), but no significant association in HER2-positive BC. Bioinformatic analysis identified target genes such as PTEN, BCL2, STAT3, and MYC, involved in apoptosis regulation, proliferation, NF-κB signaling, and immune modulation. These findings provide consistent evidence that miR-21 is a promising minimally invasive prognostic biomarker in BC, particularly in aggressive subtypes, and support its integration into future multimodal prognostic models. Full article

The search for viable alternative resources is essential for advancing sustainable development in the construction industry. A significant global concern is the substantial generation of industrial waste, particularly coal ash byproducts such as fly ash (FA) and coal bottom ash (CBA) from thermal power plants (TPPs). India ranks as the third-largest producer of coal ash globally and the second-largest in Asia, generating approximately 105 million metric tonnes annually. While TPP-derived wastes have been extensively studied in masonry mortars, the potential of CBA as a partial or complete replacement for natural fine aggregates (NFA) in high-strength mortar (HSM) remains significantly underexplored. This study investigates the fresh, mechanical, and microstructural properties of mortar incorporating CBA as a substitute for NFA, specifically up to a 100% replacement level Flow table tests revealed improved workability with increasing CBA content, which is attributed to its porous microstructure; however, significant bleeding was observed at higher replacement levels (≥75%). The dry density consistently decreased with the addition of CBA with a reduction of up to 19.27% at full replacement. Ultrasonic pulse velocity (UPV) values declined with higher levels of CBA but improved with curing age. The mortar incorporating up to 100% CBA retains appreciable mechanical properties despite a progressive reduction in compressive strength (C_S) with increasing CBA content. The observed compressive strengths for the different mixes were as follows: control mix (CM) at 36.72 MPa, mix with 25% CBA (CBA25) at 25.56 MPa, mix with 50% CBA (CBA50) at 19.69 MPa, mix with 75% CBA (CBA75) at 16 MPa, and mix with 100% CBA (CBA100) at 9.93 MPa. All mixes exceeded the minimum strength criteria, confirming their classification as HSMs at all replacement levels. These results highlight the potential of CBA as a sustainable alternative in construction materials, supporting efforts toward resource efficiency and environmental sustainability in the industry. Full article

Currently, the customs inspection of wood-boring pests in timber still primarily relies on manual visual inspection, which involves observing insect holes on the timber surface and splitting the timber for confirmation. However, this method has significant drawbacks such as long detection time, high labor cost, and accuracy relying on human experience, making it difficult to meet the practical needs of efficient and intelligent customs quarantine. To address this issue, this paper develops a rapid identification system based on the peristaltic signals of wood-boring pests through the PyQt framework. The system employs a deep learning model with multi-attention mechanisms, namely the Residual Denoising Vision Network (RDVNet). Firstly, a LabVIEW-based hardware–software system is used to collect pest peristaltic signals in an environment free of vibration interference. Subsequently, the original signals are clipped, converted to audio format, and mixed with external noise. Then signal features are extracted through three cepstral feature extraction methods Mel-Frequency Cepstral Coefficients (MFCC), Power-Normalized Cepstral Coefficients (PNCC), and RelAtive SpecTrAl-Perceptual Linear Prediction (RASTA-PLP) and input into the model. In the experimental stage, this paper compares the denoising module of RDVNet (de-RDVNet) with four classic denoising models under five noise intensity conditions. Finally, it evaluates the performance of RDVNet and four other noise reduction classification models in classification tasks. The results show that PNCC has the most comprehensive feature extraction capability. When PNCC is used as the model input, de-RDVNet achieves an average peak signal-to-noise ratio (PSNR) of 29.8 and a Structural Similarity Index Measure (SSIM) of 0.820 in denoising experiments, both being the best among the comparative models. In classification experiments, RDVNet has an average F1 score of 0.878 and an accuracy of 92.8%, demonstrating the most excellent performance. Overall, the application of this system in customs timber quarantine can effectively improve detection efficiency and reduce labor costs and has significant practical value and promotion prospects. Full article

This study valorizes onion peel, an agro-industrial by-product rich in phenolic compounds and structural carbohydrates, for the development of cassava starch-based biodegradable films. The films were prepared using the solution casting method; a cassava starch matrix was mixed with a 2.5% glycerol solution and heated to 85 °C for 30 min. A separate solution of onion peel powder (OPP) in distilled water was prepared at 25 °C. The two solutions were then combined and stirred for an additional 2 min before 25 mL of the final mixture was cast to form the films. Onion peel powder (OPP) incorporation produced darker and more opaque films, suitable for packaging light-sensitive foods. Film thickness increased with OPP content (0.138–0.218 mm), while moisture content (19.2–32.6%) and solubility (24.0–25.2%) decreased. Conversely, water vapor permeability (WVP) significantly increased (1.69 × 10⁻⁹–2.77 × 10⁻⁹ g·m⁻¹·s⁻¹·Pa⁻¹; p < 0.0001), reflecting the hydrophilic nature of OPP. Thermal analysis (TGA/DSC) indicated stability up to 245 °C, supporting applications as food coatings. Morphological analysis (SEM) revealed OPP microparticles embedded in the starch matrix, with FTIR and XRD suggesting electrostatic and hydrogen–bond interactions. Mechanically, tensile strength improved (up to 2.71 MPa) while elongation decreased (14.1%), indicating stronger but less flexible films. Biodegradability assays showed slightly reduced degradation (29.0–31.8%) compared with the control (38.4%), likely due to antimicrobial phenolics inhibiting soil microbiota. Overall, OPP and cassava starch represent low-cost, abundant raw materials for the formulation of functional biopolymer films with potential in sustainable food packaging. Full article

To address the bottleneck issues of traditional concrete T-beams, such as excessive self-weight, susceptibility to cracking, and insufficient durability, this study investigates the flexural performance of Ultra-High-Performance Concrete (UHPC) T-beams. Through systematic experiments, the combined effects of three UHPC material ratios and three rebar schemes were analyzed. Six UHPC T-beam specimens were designed, and flexural performance tests were conducted using a staged loading approach, focusing on crack propagation, failure modes, and load-deflection curves to reveal their mechanical behavior and failure mechanisms. The results indicate that steel fibers significantly enhance UHPC toughness. At a fiber content of 1.5%, the specimens exhibited a yield load of 395–418 kN, with an ultimate load increase of 93% compared to the fiber-free specimens. The failure mode transitioned from brittle shear to ductile flexural. Increasing the rebar ratio improved load-bearing capacity, with a 4.58% rebar ratio yielding an ultimate load of 543 kN (51% higher than B1-02), but reduced ductility by 36%. Steel fibers restricted crack widths to 0.1 mm via crack-bridging effects, raising the cracking load by 53% and the shear capacity by 2.8 times. UHPC mix ratio adjustments had a limited impact on beam performance at the same fiber content. Overall, UHPC T-beams exhibited a compressive concrete crushing-dominated failure mode, with load-deflection curves showing a 42% gentler slope than conventional concrete. The ductility coefficient ranged from 3.8 to 5.2. For engineering applications, it is recommended to maintain a steel fiber content of at least 1.5% and a rebar ratio of 2.5–4.0% to strike a balance between strength and ductility. Full article

Introduction: Noggin encoding (NOG) gene plays a critical role in early embryogenesis and development of bones, joints, cartilage, eyes, and neural tissue. The NOG gene encodes the noggin protein. Noggin is the only secreted inhibitor of bone morphogenetic protein (BMP) that is associated with abnormal phenotypes in humans. The most commonly observed manifestations of NOG gene mutations include bilateral conductive hearing loss, proximal symphalangism, broad thumbs, hyperopia, and a distinct facial appearance. This genetic disorder was first reported in 1990 by Teunissen and Cremers. Since then, various phenotypic presentations of NOG mutation have been reported, leading to the introduction of the term NOG-related symphalangism spectrum disorder (NOG-SSD). Case report: In this report, we describe a family (mother and daughter) with bilateral mixed hearing loss. Both patients had hyperopia, distinct facial appearance with hemicylindrical nose, broad thumbs, and syndactyly of the second and third toes. Genetic testing confirmed a NOG gene mutation. Bilateral stapedotomy was successfully performed, resulting in significant hearing improvement. However, due to sensorineural component of hearing loss, complete hearing recovery was only achieved with the use of hearing aids. Discussion: The etiology of the sensorineural component of hearing loss in NOG-SSD remains unclear. In animal models, the NOG gene is essential for inner ear development, while in humans, only middle ear malformations have been reported. The phenotypic variability observed in individuals with NOG mutations is very wide, suggesting that the sensorineural component of hearing loss could represent one of the possible manifestations. Conclusions: Conductive hearing loss is the primary manifestation of the NOG-SSD, and all previously reported cases of NOG gene mutations have presented exclusively with conductive hearing loss. It is possible that additional genetic factors, not necessarily directly related to the NOG gene but present in this family, contribute to the development of the sensorineural component of hearing loss, although thorough genetic testing did not reveal any additional mutation. This is, to our knowledge, the first report of mixed hearing loss associated with a NOG mutation confirmed preoperatively. Further studies are needed to determine whether the sensorineural component represents a primary manifestation or arises from secondary mechanisms. Full article

Spinal cord injury (SCI) often results in long-term locomotor impairments, and strategies to enhance functional recovery remain limited. While epidural electrical stimulation (EES) has shown clinical promise, our understanding of the mechanisms by which it improves function remains incomplete. Here, we use genetic tools in an animal model to perform neuromodulation and treadmill rehabilitation in a manner similar to EES, but with the benefit of the genetic tools and animal model allowing for targeted manipulation, precise quantification of the cells and circuits that were manipulated, and the gathering of extensive kinematic data. We used a viral construct that selectively transduces large diameter afferent fibers (LDAFs) with a designer receptor exclusively activated by a designer drug (hM3Dq DREADD; a chemogenetic construct) to increase the excitability of large fibers specifically, in the rat contusion SCI model. As changes in locomotion with afferent stimulation can be subtle, we carried out a detailed characterization of the kinematics of locomotor recovery over time. Adult Long-Evans rats received contusion injuries and direct intraganglionic injections containing AAV2-hSyn-hM3Dq-mCherry, a viral vector that has been shown to preferentially transduce LDAFs, or a control with tracer only (AAV2-hSyn-mCherry). These neurons then had their activity increased by application of the designer drug Clozapine-N-oxide (CNO), inducing tonic excitation during treadmill training in the recovery phase. Kinematic data were collected during treadmill locomotion across a range of speeds over nine weeks post-injury. Data were analyzed using a mixed effects model chosen from amongst several models using information criteria. That model included fixed effects for treatment (DREADDs vs. control injection), time (weeks post injury), and speed, with random intercepts for rat and time point nested within rat. Significant effects of treatment and treatment interactions were found in many parameters, with a sometimes complicated dependence on speed. Generally, DREADDs activation resulted in shorter stance duration, but less reduction in swing duration with speed, yielding lower duty factors. Interestingly, our finding of shorter stance durations with DREADDs activation mimics a past study in the hemi-section injury model, but other changes, including the variability of anterior superior iliac spine (ASIS) height, showed an opposite trend. These may reflect differences in injury severity and laterality (i.e., in the hemi-section injury the contralateral limb is expected to be largely functional). Furthermore, as with that study, withdrawal of DREADDs activation in week seven did not cause significant changes in kinematics, suggesting that activation may have dwindling effects at this later stage. This study highlights the utility of high-resolution kinematics for detecting subtle changes during recovery, and will enable the refinement of neuromechanical models that predict how locomotion changes with afferent neuromodulation, injury, and recovery, suggesting new directions for treatment of SCI. Full article

Artificial Intelligence (AI) holds significant potential to enhance sustainable non-chemical agricultural methods (NCAM) by optimising resource management, automating precision farming practices, and strengthening climate resilience. However, its widespread adoption among farmers’ remains limited due to socio-economic, infrastructural, and justice-related challenges. This study investigates AI adoption among NCAM farmers using an Integrated Mechanism for Sustainable Practices (IMSP) conceptual framework which combines the Technology Acceptance Model (TAM) with a justice-centred approach. A mixed-methods design was employed, incorporating Fuzzy-Set Qualitative Comparative Analysis (fsQCA) of AI adoption pathways based on survey data, alongside critical discourse analysis of thematic farmers narrative through a justice-centred lens. The study was conducted in Tamil Nadu between 30 September and 25 October 2024. Using purposive sampling, 57 NCAM farmers were organised into three focus groups: marginal farmers, active NCAM practitioners, and farmers from 18 districts interested in agricultural technologies and AI. This enabled an in-depth exploration of practices, adoption, and perceptions. The findings indicates that while factors such as labour shortages, mobile technology use, and cost efficiencies are necessary for AI adoption, they are insufficient without supportive extension services and inclusive communication strategies. The study refines the TAM framework by embedding economic, cultural, and political justice considerations, thereby offering a more holistic understanding of technology acceptance in sustainable agriculture. By bridging discourse analysis and fsQCA, this research underscores the need for justice-centred AI solutions tailored to diverse farming contexts. The study contributes to advancing sustainable agriculture, digital inclusion, and resilience, thereby supporting the United Nations’ Sustainable Development Goals (SDGs). Full article

Terrestrial cross-boundary inputs of leaf litter serve as a critical foundation for secondary productivity in freshwater ecosystems. The regulatory mechanisms of tannins in leaf litter on degradation rates under submerged conditions remain unclear. This study employed leaf litter from low-tannin plants Osmanthus fragrans (A) and Canna glauca (B) as decomposition substrates, with the high-tannin species Myriophyllum verticillatum (C) incorporated to adjust tannin levels. A 140-day hydroponic degradation experiment was conducted under controlled temperature and dark conditions, which included four mixed litter treatments with a gradient of tannin additions (AB as the control, 0 g; ABC1: 0.5 g; ABC2: 2.5 g; ABC3: 4.5 g) along with two single-species treatments (A and B). The following results were found: (1) Low tannin levels (ABC1) promoted degradation rates of A and B (increased by 1.33–12.70%), whereas high tannin (ABC3) inhibited decomposition (decreased by 6.21–6.82%). (2) Tannin–protein complexes reduce nitrogen bioavailability and inhibit nitrification, thereby disrupting the nitrogen cycle in aquatic systems. In ABC3, total nitrogen content in A and B litter increased by 17.69–26.46% compared to AB, with concurrent 59.29% elevation in water NH₄⁺-N concentration. (3) High tannin induced dominance of oligotrophic stress-resistant bacterial communities (e.g., Treponema) through nutrient limitation and toxicity stress; however, their low metabolic efficiency reduced overall decomposition efficiency. Research reveals that the ecological benefits of plant secondary metabolites outweigh their nutritional quality attributes. Full article

Compression of clayey sediments not only causes land subsidence but also results in geogenic high fluoride groundwater. The distribution characteristics and enrichment mechanisms of fluoride in alluvial−lacustrine facies clayey sediments in the land subsidence area of Cangzhou Plain, China, were investigated using sample collection, mineralogical research, and hydrogeochemical and isotopic analysis. The results show that F⁻ concentration of groundwater samples ranged from 0.31 to 5.54 mg/L in aquifers. The total fluoride content of clayey sediments ranged from 440 to 792 mg/kg and porewater F⁻ concentration ranged from 0.77 to 4.18 mg/L. Clay minerals containing fine particles, such as muscovite, facilitate the enrichment of fluoride in clayey sediments, resulting in higher total fluoride levels than those in sandy sediments. The clay porewater F⁻ predominantly originated from the dissolution of water-soluble F and the desorption of exchangeable F from sediments. The F⁻ concentration in porewater was further influenced by ionic interactions such as cation exchange. The stable sedimentary environment and intense compression promoted the dissolution of F–bearing minerals and the desorption of adsorbed F in deep clayey sediments. The similar composition feature of δ²H−δ¹⁸O in deep groundwater and clay porewater samples suggests a significant mixing effect. These findings highlight the joint effects of hydrogeochemical and mineralogical processes on F behavior in clayey sediments. Full article