Search Results (8,523)

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

The growing demand for reliable long-term traffic forecasting has become increasingly critical in the development of intelligent transportation systems (ITS). However, capturing both strong periodic patterns and long-range temporal dependencies presents a significant challenge, and existing approaches often fail to balance these factors effectively, resulting in unstable or suboptimal predictions. To address this issue, we propose MixModel , a novel hybrid framework that integrates TimesNet and Informer to leverage their complementary strengths. Specifically, the TimesNet branch extracts periodic variations through frequency-domain decomposition and multi-scale convolution, while the Informer branch employs ProbSparse attention to efficiently capture long-range dependencies across extended horizons. By unifying these capabilities, MixModel achieves enhanced forecasting accuracy, robustness, and stability compared with state-of-the-art baselines. Extensive experiments on real-world highway datasets demonstrate the effectiveness of our model, highlighting its potential for advancing large-scale urban traffic management and planning. To the best of our knowledge, MixModel is the first hybrid framework that explicitly bridges frequency-domain periodic modeling and efficient long-range dependency learning for long-term traffic forecasting, establishing a new benchmark for future research in Intelligent Transportation Systems. 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

Clarifying the role of density regulation in different stand types of Chinese fir (Cunninghamia lanceolata) is beneficial for sustainable management. Stand density management diagrams (SDMDs) can help in simulating thinning, regulating stand structure, and balancing timber yield. This study, conducted in Ganzhou City, a mid-subtropical region of China, used second-class forest resource survey plots dominated by Chinese fir, including 541 Chinese fir pure stands, 232 Chinese fir-conifer mixed stands, and 351 Chinese fir-broadleaf mixed stands. Equations for self-thinning, dominant height, and stand volume were constructed, and the SDMDs were subsequently developed to simulate two management scenarios: self-thinning and thinning. The results indicate that self-thinning relationships differ among Chinese fir stand types and that appropriate thinning can improve stand growth. Mixed stands, particularly Chinese fir–broadleaf mixed stands, showed greater growth potential at later stages, highlighting the role of species mixing in reducing competition and enhancing resource-use efficiency. The SDMDs developed in this study provide a practical tool for density regulation and silvicultural planning in Chinese fir plantations. However, being based on regional-scale growth models, the results mainly reflect regional conditions and should be further validated with long-term experiments. Full article

This study investigates iron acetylacetonate (Fe(acac)₃) as a catalyst for enhancing high-temperature oxidation (HTO) during in situ combustion (ISC) of heavy oil. Thermal analysis revealed that Fe(acac)₃ decomposes at 360 °C to form crystalline magnetite (Fe₃O₄). This transformation precedes the HTO regime. Differential scanning calorimetry demonstrated significantly intensified HTO reactions in catalytic systems, as peak temperatures were lower than those in non-catalytic reactions. Kinetic analysis showed that the catalyst reduces HTO activation energy by 15.6%, substantially increasing reaction rates across the HTO temperature range. X-ray powder diffraction confirmed that the mixed-valence Fe²⁺/Fe³⁺ configuration in the magnetite structure facilitates electron transfer during oxidation, enabling more complete combustion at lower temperatures. These findings represent a novel approach to catalyst design, from general activity to temperature-specific activation for a more stable and efficient in situ combustion process. Full article

The rapid growth of 5G base stations (BSs) and electric vehicles (EVs) introduces significant challenges for distribution network operation due to high energy consumption and variable loads. This paper proposes a coordinated day-ahead scheduling framework that integrates 5G BS task migration, storage utilization, and EV charging or discharging with mobility constraints. A mixed-integer second-order cone programming (MISOCP) model is formulated to optimize network efficiency while ensuring reliable power supply and maintaining service quality. The proposed approach enables dynamic load adjustment via 5G computing task migration and coordinated operation between 5G BSs and EVs. Case studies demonstrate that the proposed method can effectively generate an optimal day-ahead scheduling strategy for the distribution network. By employing the task migration strategy, the computational workloads of heavily loaded 5G BSs are dynamically redistributed to neighboring stations, thereby alleviating computational stress and reducing their associated power consumption. These results highlight the potential of leveraging the joint flexibility of 5G infrastructures and EVs to support more efficient and reliable distribution network operation. Full article

Static mixers are commonly used for process intensification in a wide range of industrial applications. For the design and selection of a static mixer, an accurate prediction of the hydraulic performance, particularly the pressure drop, is essential. This experimental study examines the pressure drop for turbulent single-phase and gas–liquid two-phase flows through a Komax triple-action static mixer placed on a horizontal pipeline. New values of friction factor and z-factor are reported for fully turbulent liquid single-phase flow (11,700 ≤ Re_L ≤ 18,700). For two-phase flow, the pressure drop for stratified and intermittent flows (0.07 m/s ≤ U_L ≤ 0.28 m/s and 0.46 m/s ≤ U_G ≤ 3.05 m/s) is modeled using the Lockhart–Martinelli approach, with a coefficient, C, correlated to the homogenous void fraction. Conversely, the analysis of power dissipation reveals a dependence on both liquid and gas superficial velocities. For conditions corresponding to intermittent flow upstream of the mixer, flow visualization revealed the emergence of a swirling flow in the Komax static mixer. It is interesting to note that an increase in slug frequency leads to an increase, followed by stabilization of the pressure drop. The results offer valuable insights for improving the design and optimization of Komax static mixers operating under single-phase and two-phase flow conditions. In particular, the reported correlations can serve as practical tools for predicting hydraulic losses during the design and scale-up. Moreover, the observed influence of the slug frequency on the pressure drop provides guidance for selecting operating conditions that minimize energy consumption while ensuring efficient mixing. Full article

Accurate prediction of reinforced concrete shear strength is essential for structural safety, yet datasets often contain a mix of raw geometric and material properties alongside physics-informed engineered features, making optimal feature selection challenging. This study introduces a statistically principled framework that advances feature reduction for neural networks in three novel ways. First, it extends the artificial neural network-based signal-to-noise ratio (ANN-SNR) method, previously limited to classification, into regression tasks for the first time. Second, it couples ANN-SNR with a confidence-interval (CI)-based stopping rule, using the lower bound of the baseline ANN’s R² confidence interval as a rigorous statistical threshold for determining when feature elimination should cease. Third, it systematically evaluates both raw experimental variables and physics-informed engineered features, showing how their combination enhances both robustness and interpretability. Applied to experimental concrete shear strength data, the framework revealed that many low-SNR features in conventional formulations contribute little to predictive performance and can be safely removed. In contrast, hybrid models that combined key raw and engineered features consistently yielded the strongest performance. Overall, the proposed method reduced the input feature set by approximately 45% while maintaining results statistically indistinguishable from baseline and fully optimized models (R² ≈ 0.85). These findings demonstrate that ANN-SNR with CI-based stopping provides a defensible and interpretable pathway for reducing model complexity in reinforced concrete shear strength prediction, offering practical benefits for design efficiency without compromising reliability. Full article

Maximum likelihood estimation (MLE) in infinite mixture distributions often lacks closed-form solutions, requiring numerical methods such as the Newton–Raphson algorithm. Selecting appropriate initial values is a critical challenge in these procedures. This study introduces a bootstrap-based approach to determine initial parameter values for MLE, employing both nonparametric and parametric bootstrap methods to generate the mixing distribution. Monte Carlo simulations across multiple cases demonstrate that the bootstrap-based approaches, especially the nonparametric bootstrap, provide reliable and efficient initialization and yield consistent maximum likelihood estimates even when raw moments are undefined. The practical applicability of the method is illustrated using three empirical datasets: third-party liability claims in Indonesia, automobile insurance claim frequency in Australia, and total car accident costs in Spain. The results indicate stable convergence, accurate parameter estimation, and improved reliability for actuarial applications, including premium calculation and risk assessment. The proposed approach offers a robust and versatile tool both for research and in practice in complex or nonstandard mixture distributions. 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

Accurate detection and counting of wheat spikes are crucial for yield estimation and variety selection in precision agriculture. However, challenges such as complex field environments, morphological variations, and small target sizes hinder the performance of existing models in real-world applications. This study proposes FEWheat-YOLO, a lightweight and efficient detection framework optimized for deployment on agricultural edge devices. The architecture integrates four key modules: (1) FEMANet, a mixed aggregation feature enhancement network with Efficient Multi-scale Attention (EMA) for improved small-target representation; (2) BiAFA-FPN, a bidirectional asymmetric feature pyramid network for efficient multi-scale feature fusion; (3) ADown, an adaptive downsampling module that preserves structural details during resolution reduction; and (4) GSCDHead, a grouped shared convolution detection head for reduced parameters and computational cost. Evaluated on a hybrid dataset combining GWHD2021 and a self-collected field dataset, FEWheat-YOLO achieved a COCO-style AP of 51.11%, AP@50 of 89.8%, and AP scores of 18.1%, 50.5%, and 61.2% for small, medium, and large targets, respectively, with an average recall (AR) of 58.1%. In wheat spike counting tasks, the model achieved an R² of 0.941, MAE of 3.46, and RMSE of 6.25, demonstrating high counting accuracy and robustness. The proposed model requires only 0.67 M parameters, 5.3 GFLOPs, and 1.6 MB of storage, while achieving an inference speed of 54 FPS. Compared to YOLOv11n, FEWheat-YOLO improved AP@50, AP_s, AP_m, AP_l, and AR by 0.53%, 0.7%, 0.7%, 0.4%, and 0.3%, respectively, while reducing parameters by 74%, computation by 15.9%, and model size by 69.2%. These results indicate that FEWheat-YOLO provides an effective balance between detection accuracy, counting performance, and model efficiency, offering strong potential for real-time agricultural applications on resource-limited platforms. Full article

Fungal laccases are promising oxidative enzymes for bioremediation applications, particularly in the degradation of synthetic dyes present in industrial effluents. Here, we evaluated the inhibitory effects of sodium chloride (NaCl) and sodium sulfate (Na₂SO₄) on the activity of Trametes versicolor laccase and its ability to decolorize Congo Red (CR), Malachite Green (MG), and Remazol Brilliant Blue R (RBBR). Enzyme assays revealed concentration-dependent inhibition, with IC₅₀ values of 0.22 ± 0.04 M for NaCl and 1.00 ± 0.09 M for Na₂SO₄, indicating stronger inhibition by chloride. Kinetic modeling showed mixed-type inhibition for both salts. Despite this effect, the enzyme maintained significant activity: after 12 h, decolorization efficiencies reached 95 ± 4.0% for MG, 88 ± 3.0% for RBBR, and 75 ± 3.0% for CR, even in the presence of 0.5 M salts. When applied to a mixture of the three dyes, decolorization decreased only slightly in saline medium (94.04 ± 4.0% to 83.43 ± 5.1%). FTIR spectra revealed minor structural changes, but toxicity assays confirmed marked detoxification, with radicle length in lettuce seeds increasing from 20–38 mm (untreated dyes) to 41–48 mm after enzymatic treatment. Fungal growth assays corroborated reduced toxicity of treated dyes. These findings demonstrate that T. versicolor laccase retains functional robustness under ionic stress, supporting its potential application in saline textile wastewater remediation. Full article

This study investigates the influence of heavy-layer thickness on shock-accelerated interfacial instabilities in single-mode stratifications using high-order discontinuous Galerkin simulations at a fixed shock Mach number ($M_s=1.22$). By systematically varying the layer thickness, we quantify how acoustic transit time, shock attenuation, and phase synchronization modulate vorticity deposition, circulation growth, and interface deformation. The results show that thin layers ($d=2.5$–5 mm) generate strong and early baroclinic vorticity due to frequent reverberations, leading to rapid circulation growth, vigorous Kelvin–Helmholtz roll-up, and early jet pairing. In contrast, thick layers ($d=20$–40 mm) attenuate and dephase shock returns, producing weaker baroclinic reinforcement, delayed shear-layer growth, and smoother interfaces with reduced small-scale activity, while the intermediate case ($d=10$ mm) exhibits transitional behavior. Integral diagnostics reveal that thin layers amplify dilatational, baroclinic, and viscous vorticity production; sustain stronger circulation and enstrophy growth; and transfer bulk kinetic energy more efficiently into interface deformation and small-scale mixing. Full article

Electrical grids are currently facing new demands due to increased power consumption, growing interconnections, and limitations regarding transmission capacity. These factors introduce considerable challenges for the dispatch and operation of large-scale power systems, often resulting in congestion, energy losses, and high operating costs. To address these issues, this study presents a transmission line switching strategy, which is formulated as an optimal power flow problem with binary variables and solved via mixed-integer nonlinear programming. The proposed methodology was tested using MATLAB’s MATPOWER toolbox version 8.1, focusing on power systems with five and 3374 nodes. The results demonstrate that operating costs can be reduced by redistributing power generation while observing the system’s reliability constraints. In particular, disconnecting line 6 in the 5-bus system yielded a 13.61% cost reduction, and removing line 1116 in the 3374-bus system yielded cost savings of 0.0729%. These findings underscore the potential of transmission line switching in enhancing the operational efficiency and sustainability of large-scale power systems. Full article