Search Results (27,332)

Prolonged exposure to landfill leachate can weaken the impermeability of liner systems, leading to leachate leakage and the contamination of surrounding soil and water. To improve loess impermeability to enable its use as a liner material, this study uses synthetic landfill leachate to investigate its effects on loess permeability via a series of laboratory tests. This study focused on the influence of varying dosages of calcium lignosulfonate (CLS) on loess permeability, along with its capacity to adsorb and immobilize heavy metal ions. Microscale characterization techniques, including Zeta potential analysis, X-ray fluorescence spectroscopy (XRF), and scanning electron microscopy (SEM), were employed to investigate the impermeability mechanisms of CLS-modified loess and its adsorption behavior toward heavy metals. The results indicate that the permeability coefficient of loess decreases significantly with increasing compaction, while higher leachate concentrations lead to a notable increase in permeability. At a compaction degree of 0.90, the permeability coefficient was reduced to 8 × 10⁻⁸ cm/s. In contrast, under conditions of maximum leachate concentration, the permeability coefficient rose markedly to 1.5 × 10⁻⁴ cm/s. Additionally, increasing the dosage of the compacted loess stabilizer (CLS) effectively reduced the permeability coefficient of the modified loess to 7.1 × 10⁻⁵ cm/s, indicating improved impermeability and enhanced resistance to contaminant migration. With the prolonged infiltration time of landfill leachate, the removal efficiency of Pb²⁺ gradually decreases and stabilizes, while the Pb²⁺ removal efficiency of the modified loess increased by approximately 40%. CLS-modified loess, through multiple mechanisms, reduces the fluid flow pathways and enhances its adsorption capacity for Pb²⁺, thereby improving the soil’s protection against heavy metal contamination. While these results demonstrate the potential of CLS-modified loess as a sustainable landfill liner material, the findings are based on controlled laboratory conditions with Pb²⁺ as the sole target contaminant. Future work should evaluate long-term performance under field conditions, including seasonal wetting–drying and freeze–thaw cycles, and investigate multi-metal systems to validate the broader applicability of this modification technique. Full article

To enhance the utilization efficiency of coal gangue aggregate, coarse aggregates are chemically modified with 5% sodium silicate solution. The effects of this modification on the compressive strength and microstructural characteristics of concrete are systematically investigated through integrated macro-testing and micro-characterization. By evaluating the compressive performance of modified coal gangue concrete blocks, the optimal mix ratio of each strength grade of blocks is determined. Experimental results indicate that the apparent density, water absorption, and crushing index of the modified coal gangue coarse aggregate exhibit better mechanical properties than the control group. The modified coal gangue coarse aggregate demonstrates improved mechanical performance, with the compressive strength of 28-day concrete showing a 15.3% increase relative to the control group. Furthermore, using a sodium silicate solution effectively enhances the interface transition zone’s performance between coal gangue coarse aggregate and cement mortar, improving the compactness of this interface. The modified coal gangue concrete blocks exhibit higher compressive strength than the original material. When the substitution rate remains constant, the compressive strength of modified coal gangue concrete decreases with increasing water–cement ratio. Similarly, at a constant water–binder ratio, compressive strength decreases with higher modified gangue aggregate replacement. Finally, compressive tests are conducted on masonry constructed with hollow blocks of strength grades MU7.5, MU10, and MU15. Then, a calculation model for the average compressive strength of modified coal gangue concrete hollow block masonry is proposed, providing theoretical support for its engineering application. Full article

The lacustrine horizon (thickness of 8.75 cm thick) of the Qin and Han dynasties (221 BC–220 AD) was determined based on AMS-¹⁴C analysis conducted by the Beta Analytic Radiocarbon Dating Laboratory on the Dishaogouwan section (37°43′ N, 108°31′ E) in the Salawusu River Basin, Mu Us Desert, located in the temperate zone of China. The identification results of the ostracod and charophyta fossils from the four samples at this horizon show the following results: 1. All the samples contain 458 ostracod fossil valves, belonging to six genera and eight species. Their quantity (valves) and percentage, in descending order of abundance, are Candoniella albicans (Brady), 255/55.68%, Ilyocypris bradyi Sars, 73/15.94%, Eucypris inflata Sars, 46/10.04%, Cyclocypris serena Koch, 26/5.68%, Candona kirgizica Mandelstam, 18/3.93%, Ilyocypris biplicata (Koch), 17/3.71%, Candoniella mirabilis Schneider14/3.06% and Leucocytherella sinensis Huang, 6/1.31%. 2. All the samples contain 99 fossil charophyte gyrogonites, belonging to one genera and four species. In terms of quantity/percentage, the Chara sp. is the most abundant, with 41 pieces (41.41%), followed by Chara braunii Gemlin, with 26 pieces (26.26%); Chara leptosperma Braun and Chara canescens Loiseleur account for 19 pieces (19.19%) and 13 pieces (13.13%), respectively. Based on the analysis of the ecological environment of the existing species of these ostracods and charophytes, combined with the fossilized Ilyocypris brady, Ilyocypris biplicata, and Gyraulus convexiusculus Hutton found in all the samples—which indicate very warm, even subtropical climates then—it can be concluded that during the Qin and Han Dynasties, the Salawusu River Basin was primarily characterized by a freshwater lake environment under a warm climate, with the average annual temperature and precipitation in this area approximately 2.1 °C and 100 mm higher than they are currently. The prevailing East Asian summer monsoon pushed the warm temperate climate at least 110 km northwestward from this basin. During this period, there were at least four episodes of brief subtropical climate fluctuations, occurring approximately every 110 years. Full article

Bearings are vital to rotating machinery, where undetected faults can cause severe failures. Conventional fault diagnosis methods depend on manual feature engineering and labeled data, struggling with complex industrial conditions. This study introduces an innovative unsupervised framework combining masked self-supervised learning with the Swin Transformer for bearing fault diagnosis. The novel integration leverages masked Auto Encoders to learn robust features from unlabeled vibration signals through reconstruction-based pretraining, while the Swin Transformer’s shifted window attention mechanism enhances efficient capture of fault-related patterns in long-sequence signals. This approach eliminates reliance on labeled data, enabling precise detection of unknown faults. The proposed method achieves 99.53% accuracy on the Paderborn dataset and 100% accuracy on the CWRU dataset significantly, surpassing other unsupervised Auto Encoder-based methods. This method’s innovative design offers high adaptability and substantial potential for predictive maintenance in industrial applications. Full article

The typical structure of an oblique detonation wave (ODW) consists of a leading shock wave followed by a coupled shock-flame complex. The distance from the leading shock’s originating point to the ignition onset is referred to as the induction length. This work numerically studies the induction length effect using a two-step induction-reaction kinetic model. Results reveal that the induction length governs the transition pattern of ODWs. By testing four distinct induction lengths, four ODW regimes are identified, including a prompt ODW, a delayed smooth ODW, a delayed abrupt ODW, and a delayed abrupt ODW with an upstream triple point in oscillatory motion. The mechanisms behind these regimes are analyzed in detail. Additionally, hysteresis is observed when the induction length decreases from a larger value, demonstrating that this phenomenon can be influenced by the kinetic process. Full article

Ultra-high-performance concrete (UHPC) is increasingly employed in long-span and heavily loaded structural applications; however, the accurate prediction of its flexural capacity remains a significant challenge because of the complex interactions among geometric parameters, reinforcement details, and advanced material properties. Existing design codes and single-architecture machine learning models often struggle to capture these nonlinear relationships, particularly when experimental datasets are limited in size and diversity. This study proposes a compact hybrid CNN–Transformer model that combines convolutional layers for local feature extraction with self-attention mechanisms for modeling long-range dependencies, enabling robust learning from a database of 120 UHPC beam tests drawn from 13 laboratories worldwide. The model’s predictive performance is benchmarked against conventional design codes, analytical and semi-empirical formulations, and alternative machine learning approaches including Convolutional Neural Networks (CNN), eXtreme Gradient Boosting (XGBoost), and K-Nearest Neighbors (KNN). Results show that the proposed architecture achieves the highest accuracy with an R² of 0.943, an RMSE of 41.310, and a 25% reduction in RMSE compared with the best-performing baseline, while maintaining strong generalization across varying fiber dosages, reinforcement ratios, and shear-span ratios. Model interpretation via SHapley Additive exPlanations (SHAP) analysis identifies key parameters influencing capacity, providing actionable design insights. The findings demonstrate the potential of hybrid deep-learning frameworks to improve structural performance prediction for UHPC beams and lay the groundwork for future integration into reliability-based design codes. Full article

Accurate prediction of shear strength at the interface between new and old concrete is vital for the structural performance of repaired and composite systems. However, the underlying shear transfer mechanism is highly nonlinear and influenced by multiple interdependent factors, which limit the applicability of conventional empirical models. To address this challenge, an interpretable machine-learning (ML) framework is proposed. The latest database of 247 push-off specimens was compiled from the recent literature, incorporating diverse interface types and design parameters. The hyperparameters of the adopted ML models were optimized via a grid search to ensure the predictive performance on the updated database. Among the evaluated algorithms, eXtreme Gradient Boosting (XGBoost) demonstrated the best predictive performance, with R² = 0.933, RMSE = 0.663, MAE = 0.486, and MAPE = 12.937% on the testing set, outperforming Support Vector Regression (SVR), Random Forest (RF), and adaptive boosting (AdaBoost). Compared with the best empirical model (AASHTO, R² = 0.939), XGBoost achieved significantly lower prediction errors (e.g., RMSE was reduced by 67.8%), enhanced robustness (COV = 0.176 vs. 0.384), and a more balanced mean ratio (1.054 vs. 1.514). The SHapley Additive exPlanations (SHAP) method was employed to interpret the model predictions, identifying the shear reinforcement ratio as the most influential factor, followed by interface type, interface width, and concrete strength. These results confirm the superior accuracy, generalizability, and explainability of XGBoost in modeling the shear behaviors of new–old concrete interfaces. Full article

Remote sensing objects often exhibit significant scale variations, high aspect ratios, and diverse orientations. The anisotropic spatial distribution of such objects’ features leads to the conflict between feature representation and boundary regression caused by the coupling of different attribute parameters: previous detection methods based on square-kernel convolution lack the overall perception of large-scale or slender objects due to the limited receptive field; if the receptive field is simply expanded, although more context information can be captured to help object perception, a large amount of background noise will be introduced, resulting in inaccurate feature extraction of remote sensing objects. Additionally, the extracted features face issues of feature conflict and discontinuous loss during parameter regression. Existing methods often neglect the holistic optimization of these aspects. To address these challenges, this paper proposes SODE-Net as a systematic solution. Specifically, we first design a multi-scale fusion and spatially orthogonal convolution (MSSO) module in the backbone network. Its multiple shapes of receptive fields can naturally capture the long-range dependence of the object without introducing too much background noise, thereby extracting more accurate target features. Secondly, we design a multi-level decoupled detection head, which decouples target classification, bounding-box position regression and bounding-box angle regression into three subtasks, effectively avoiding the coupling problem in parameter regression. At the same time, the phase-continuous encoding module is used in the angle regression branch, which converts the periodic angle value into a continuous cosine value, thus ensuring the stability of the loss value. Extensive experiments demonstrate that, compared to existing detection networks, our method achieves superior performance on four widely used remote sensing object datasets: DOTAv1.0, HRSC2016, UCAS-AOD, and DIOR-R. Full article

From September 8th to 12th, 2024, the 19th SDEWES Conference on Sustainable Development of Energy, Water, and Environment Systems was successfully held in Rome. This event drew 700 researchers, scientists, and practitioners from 62 nations across six continents, with 570 participating in person and another 130 joining virtually. A total of seven papers were selected to be published in Energies, and the corresponding literature published in the most recent year is here reviewed. The main topics of the selected papers regard the adoption of district heating and cooling and their integration with renewable energies (such as geothermal or solar, the use of innovative bifacial PV panels, the use of biomass energy for the bio-synthetic natural gas production, the short-term electric load forecasting for industrial applications, and others. The reviewed papers show that several energy measures can be addressed to reach the decarbonization goals of 2050 and that the scientific community continues to find novel, sustainable, and efficient methods for the reduction in energy consumption and related CO₂ emissions. Full article

This study investigated safety management performance in small- and medium-sized private coal mining enterprises (SMPCMEs) through an integrated application of the 24Model accident causation theory and fuzzy-set qualitative comparative analysis (fsQCA). Analyzing 40 sudden incidents (2013–2023), we examined six key factors—organizational, individual, and external dimensions—to identify nonlinear risk pathways. Results revealed four critical failure types—Internally Balanced (cultural–behavioral–environmental collapse), Safety Culture–Deficient (institutional hollowing), Cultural–External Environment (policy-implementation paradox), and External Environment–Integrated (technological-regulatory failure)—that collectively explained 83% of performance variance. Tailored strategies, including IoT-based real-time monitoring and AI-driven inspections, are proposed to transition from fragmented interventions to systemic governance. These findings provide actionable insights for enhancing safety resilience in high-risk mining sectors. Full article

The present study aims to optimize the extraction process and systematically investigate the bioactivity of polysaccharides derived from Peristrophe roxburghiana (Schult.) Brem. (CPPRs). To this end, the Box–Behnken design–response surface methodology was employed to optimize the extraction parameters of polysaccharides. The optimal extraction conditions were as follows: extraction temperature, 84 °C; extraction duration, 208 min; liquid-to-material ratio, 1:27 g/mL; extraction times, 4 times. The maximum extraction yield reached 17.89%, and the yield under non-optimal extraction conditions is 11–16%. This study systematically investigated the polysaccharides’ physicochemical, structural, and morphological properties using multiple advanced techniques (FTIR, SEM, XRD, HPLC, rheology, and TGA). CPPRs are primarily composed of arabinose, galactose and glucose as the main monosaccharides, amorphous, and capable of low-viscosity gels at low shear rates. Furthermore, CPPRs displayed notable antioxidant activity in vitro, scavenging ABTS^•+ and DPPH^• and reducing Fe³⁺ (with scavenging/reducing rates exceeding 40% at a concentration of 1 mg/mL). Meanwhile, 3 mg/mL CPPRs reduced oxidative damage of red blood cells induced by AAPH, scavenging more than 50% of ROS, and reducing the hemolysis rate by 94.5%. Additionally, CPPRs significantly promoted secretion of cytokines (including TNF-α, IL-6, and IL-10) and NO in RAW264.7 macrophages in vitro compared with the untreated control group. These findings collectively highlight the potential of CPPRs—possessing both antioxidant and immune-enhancing properties—as promising functional ingredients for application in the food and pharmaceutical industries. Full article

The efficient and high-quality welding for joining Invar alloy parts is imperative for the fabrication of composite material forming molds. The residual stress distributions and microstructural evolution during oscillating welding of Invar alloy remain inadequately characterized in the current literature, necessitating further comprehensive investigation. In this paper, laser oscillating welding with circle mode is carried out for 5 mm thick plates of Invar alloy. A finite element model for the laser oscillation welding process of Invar alloy has been established. The numerical simulations and experimental methodologies are synthetically carried out to investigate the influence of oscillating parameters on temperature field, residual stress field, and microstructure characteristics. Furthermore, the microstructural evolution of laser oscillating-welded Invar alloy is elucidated by correlating it with the characteristic distribution of the temperature field. Simulation results showed that the residual stress significantly decreases under the action of the oscillating laser. The increasing of the oscillation frequency and amplitude results in a more uniform distribution of the residual stress, and the stress peak shows a downward trend. It is indicated that the oscillation of the beam resulted in the formation of numerous fragmented fine crystals within the weld seam. Consequently, the tensile strength and elongation of the oscillating welded joint exhibit respective enhancements of 15.0% and 36.6% compared to the non-oscillating condition. Full article

Background: Epilepsy is a cluster of central nervous system (CNS) disorders identified by recurrent seizures, which affects about 60 million people around the world. In this research, a total of 40 types of 3,4,5-trimethoxycinnamic acid (TMCA) piperazine amide derivatives were designed and synthesized, inspired by the traditional Chinese medicine (TCM) herb pair drugs Polygala tenuifolia and Acori tatarinowii, followed by determination of their anticonvulsant potency. Methods: All the TMCA analogues were tested for their anticonvulsant potential through two acute models of seizures induced in mice: the maximal electroshock (MES) and sc-pentylenetetrazole (PTZ) models. In addition, the lactate dehydrogenase (LDH) inhibitory activity was determined in vitro. Results: The results showed that compounds A3, A9, A12, A14, B9, and B12 exhibited preferable anticonvulsant activity in the primary evaluation. In addition, the molecular docking results predicted good interactions of screened analogues with the LDH. Molecular dynamic simulation was used to reveal the consensual binding affinity between the most promising compound (B9) and active site interactions with LDH. Electroencephalogram (EEG) analysis and silver and immunofluorescence staining were performed to illustrate the anti-epilepsy potential of compound B9. Conclusions: Novel derivatives in this study provide new cores for the further design and optimization inspired by TCM herb pair drugs P. tenuifolia and A. tatarinowii, with the aim to explore new anticonvulsant agents. Full article

Compound 221s (2,9) is a novel antihypertensive drug candidate synthesized utilizing danshensu, borneol, and proline by using the strategy of combinatorial molecular chemistry. This study aimed to systematically identify the safety of danshensu-derived compound 221s (2,9) by conducting an acute toxicity test and long-term toxicity study and to elucidate the in vivo metabolic pathways of 221s (2,9) in order to provide critical insights into the observed toxicity. In the acute toxicity study, a single oral dose of 221s (2,9) at 3000 mg/kg in mice produced no clinical signs of toxicity or mortality, indicating an MTD of 3000 mg/kg. In a subsequent 12-week repeated-dose toxicity study in rats, doses of 20, 40, and 80 mg/kg were well tolerated, with no adverse clinical observations or deaths. Notably, organ coefficient analysis revealed transient lung injury, which resolved following a 4-week recovery period. The metabolite identification study indicated that metabolism in rats is predominated by Phase II metabolites, potentially contributing to the low toxicity of 221s (2,9). Further investigation into the impact of the drug metabolic enzyme–transporter interplay on the in vivo disposition of 221s (2,9) is warranted. Full article