Search Results (29)

Surfactants are often used in the process of coal dust suppression, and the wetting effect is greatly affected by the surfactant hydrophilic group structures. In order to explore the influence of hydrophilic groups of surfactants on their adsorption states and wetting effect on coal dust, three surfactants with similar hydrophilic groups were selected, namely, anionic surfactant sodium dodecyl sulfate (SDS), anionic-nonionic surfactant alkyl ether sulfate (AES), and nonionic surfactant alkyl polyoxyethylene ether-3 (AEO-3). To assess surfactant efficiency, surface tension, wetting time, infrared spectra, and wetting heat were analyzed. These parameters provide insights into molecular adsorption, interfacial behavior, and energy changes during wetting. The different adsorption states of surfactants on the coal dust surface due to EO and SO₄²⁻ hydrophilic groups were analyzed. Results show that both anionic surfactant SDS and nonionic surfactant AEO-3 form the monolayer adsorption structure on the coal dust surface. Due to the electrostatic repulsion of SO₄²⁻ groups, the adsorption density of SDS is lower than that of AEO-3, which results in the higher wetting heat of AEO-3 compared to SDS. In addition, the EO groups without electrostatic repulsion make AEO-3 molecules more tightly adsorbed at the air–liquid interface, causing the minimal surface tension. Therefore, the wetting time of AEO-3 is shorter than that of SDS. The anionic-nonionic surfactant AES has both EO and SO₄²⁻ groups. Because the EO groups in the inner surfactant adsorption layer can attract Na⁺ ions to distribute around them, the free AES molecules further form the outer adsorption layer under the electrostatic attraction between SO₄²⁻ groups and Na⁺ ions. The double-layer adsorption structure causes the hydrophobic groups of the outer AES molecules to face outward, the hydrophobic sites on the coal dust surface are not completely transformed into hydrophilic sites. Although AES exhibits the highest adsorption density, it has the lowest wetting heat and the longest wetting time. The research results can provide theoretical guidance for the selection of suitable surfactants for coal dust suppression. Full article

The digital twin model, which serves as a virtual counterpart symmetric to the physical entity, enables high-fidelity simulation and real-time monitoring. However, digital twin implementation for marine steam turbines (MSTs) faces dual multi-domain simulation fidelity and computational efficiency challenges. This study establishes a MST digital twin modeling methodology through two interconnected innovations: (1) a Modelica-based modular architecture enabling cross-domain coupling across mechanical, thermodynamic, and hydrodynamic systems via hierarchical decomposition, ensuring bidirectional symmetry between physical components and their virtual representations; and (2) a hybrid support vector regression-bidirectional long short-term memory (SVR-BiLSTM) surrogate model combining Gaussian radial basis function-supported SVR for steady-state mapping with Bi-LSTM networks for dynamic error compensation. Experimental validation demonstrates: (a) the SVR component achieves <1.57% absolute error under step-load conditions with 85% computational time reduction versus physics-based models; and (b) Bi-LSTM integration improves transient prediction accuracy by 14.85% in maximum absolute error compared to standalone SVR, effectively resolving static–dynamic discrepancies in telemetry simulation. This dual-approach innovation successfully bridges the critical trade-off between real-time computation and predictive accuracy while maintaining symmetric consistency between the physical turbine and its digital counterpart, providing a validated technical foundation for the intelligent operation and maintenance of MSTs. Full article

As one of the most important urban public spaces, the design and management of streets have shifted from “two-dimensional plan” to “three-dimensional space”, and higher requirements have been put forward for the scale and precision of urban design. The core research question of this research is how to refine street spatial quality measurement and evaluation based on multitemporal street view images, while providing basic data and corresponding decision support for updates and renovations. “One Garden and Twelve Fangs” in Jinan old city is the core area of the Jinan Commercial Port District. It integrates diverse cultural elements of tradition and modernity, local and foreign, and is of great significance to the cultural inheritance and urban development of Jinan. Nowadays, there is a lack of vitality, lagging development, and shorting of high-quality living service facilities here. How to enhance the overall vitality of the region and drive regional social value is an urgent problem that needs to be solved at present. This research takes the old city area of Jinan as the research scope, constructs a street space quality evaluation model through street view images and machine learning, and establishes the connection between quantitative research on street space quality and urban renewal practice. In this research, the standard system will be supplemented and improved, and the practicality of the application will be enhanced through more refined evaluation models. The evaluation indicators include walkability, green visibility, enclosure, openness, imaginability, coordination, extreme boundary area, and interface transparency. This article provides a feasible framework and paradigm for measuring the quality of large-scale and high-precision street spaces through the combination of big data and artificial intelligence, effectively bridging the gap between spatial quantification research and urban renewal practices. Full article

To address the issue of volatile organic compound (VOC) emissions during crude oil storage and transportation, this study proposes a sandwich-structured zeolite molecular sieve (SMZ) fabricated via a pressing-sintering process integrating ZSM-5 powder and granules. The resulting monolithic zeolite exhibits enhanced mechanical strength and optimized pore architecture. Systematic investigations revealed that sintering at 600 °C with 10% carboxymethyl cellulose (CMC) yielded SMZ with a specific surface area of 349.51 m²/g and pore volume of 0.37 cm³/g. Its hierarchical pore system—micropores (0.495 nm) coupled with mesopores (2–10 nm)—significantly improved adsorption kinetics. Dynamic adsorption tests demonstrated superior performance: SMZ achieved saturation capacities of 127.6 mg/g for propane and 118.2 mg/g for n-butane in liquefied petroleum gas (LPG), with a breakthrough time of 41 min and a 106% increase in adsorption capacity compared to conventional monolithic zeolite (MZ) (90.2 mg/g vs. 43.8 mg/g). Regeneration studies confirmed that combined thermal desorption (250 °C) and nitrogen purging maintained > 95% capacity retention over five cycles, attributed to the high thermal stability of the MFI topology framework (≤600 °C) and crack-resistant ceramic-like interfaces. Additionally, SMZ exhibited exceptional hydrophobicity, with a selectivity coefficient of 20.9 for propane under 60% relative humidity. This work provides theoretical and technical foundations for developing efficient and durable adsorbents for industrial VOC mitigation. Full article

The rheological properties of concrete paste significantly influence its tensile creep behavior. In this study, the tensile creep behavior of high-volume fly ash concrete (HVFAC) employing the same cementitious pastes was experimentally investigated, and the rheological properties of the paste containing a high volume of fly ash using the nanoindentation (NI) technique was investigated in order to explore the influence of the paste’s rheological properties (such as micro-mechanical properties and microscopic creep) on the early-age tensile creep of HVFAC. The results demonstrated that the micro-strain of paste containing a high volume of fly ash (HVFA) showed a larger value than that without fly ash. As the test age extends, a decreasing trend in microscopic creep was observed which could be attributed to the growth of the content of HD C–S–H (high density C–S–H) gel. Moreover, within the same age period, the experimental data revealed that the incorporation of fly ash resulted in the reduction of the values of the creep modulus C and characteristic time τ. The effects of fly ash dosages and loading age on the creep properties of concrete was consistent with the micro-creep properties of the cementitious paste. The tensile specific creep values derived from the ZC (“ZC” are initials for the word ‘‘self-developed” in Chinese) model based on nanoindentation data closely match those obtained from experiments. Full article

Background: The segmentation of electroencephalography (EEG) signals into a limited number of microstates is of significant importance in the field of cognitive neuroscience. Currently, the microstate analysis algorithm based on global field power has demonstrated its efficacy in clustering resting-state EEG. The task-related EEG was extensively analyzed in the field of brain–computer interfaces (BCIs); however, its primary objective is classification rather than segmentation. Methods: We propose an innovative algorithm for analyzing task-related EEG microstates based on spatial patterns, Riemannian distance, and a modified deep autoencoder. The objective of this algorithm is to achieve unsupervised segmentation and clustering of task-related EEG signals. Results: The proposed algorithm was validated through experiments conducted on simulated EEG data and two publicly available cognitive task datasets. The evaluation results and statistical tests demonstrate its robustness and efficiency in clustering task-related EEG microstates. Conclusions: The proposed unsupervised algorithm can autonomously discretize EEG signals into a finite number of microstates, thereby facilitating investigations into the temporal structures underlying cognitive processes. Full article

The gob-side entry driving in deep mines with soft rock exhibits a complex deformation and instability mechanism. This complexity leads to challenges in roadway stability control which greatly affects the coal mine production succession and safe and efficient mining. This paper takes the gob-side entry in Liuzhuang Coal Mine as the background. By adopting the method of theoretical analysis, a dynamic model of the roof subsidence in the goaf is established. The calculation indicates that achieving the stable subsidence of the basic roof and the equilibrium of the lateral abutment stress within the goaf requires a minimum of 108.9 days, offering a theoretical foundation for selecting an optimal driving time for the gob-side entry. The control technologies and methods of gob-side entry through grouting modification and high-strength support are proposed. Enhancing the length of anchor ropes and the density of bolt (cable) support to improve the role of the roadway support components can be better utilized, so the role of the support components of the roadway can be better exerted. The method of grouting and the reinforcement of coal pillars can effectively improve the carrying capacity of coal pillars. The numerical simulation is used to analyze the deformation law of gob-side entry. The study reveals significant deformation in the coal pillar and substantial roof subsidence, highlighting that maintaining the stability of the coal pillar is crucial for ensuring roadway safety. Following the grouting process, the deformation of the coal pillar and roof subsidence decreased by 16.7% and 7.1%, respectively. This demonstrates that coal pillar grouting not only mitigates pillar deformation but also provides effective control over roof subsidence. This study offers a quantitative calculation method to ascertain the excavation time of gob-side entry, and suggests that the application of high-strength support and the practice of coal pillar grouting can effectively maintain the steadiness of gob-side entry in deep mines with soft rock. Full article

Driven by global carbon neutrality goals, bamboo fiber-reinforced PP composites have shown significant potential for automotive applications due to their renewability, low carbon emissions, and superior mechanical properties. However, the environmental complexities associated with compression molding process parameters, which impact material properties and carbon emissions, pose challenges for large-scale adoption. This study systematically optimized the compression molding process of bamboo fiber-reinforced PP composites through a three-factor, five-level experimental design, focusing on preheating temperature, preheating time, and holding time. Additionally, an innovative life cycle assessment (LCA) was conducted to evaluate the environmental impact. The results indicated that at a preheating temperature of 220 °C, preheating time of 210–240 s, and holding time of 40–50 s, the material achieved a tensile strength of 35 MPa and a flexural strength of 45 MPa, with a 15% reduction in water absorption. The LCA further highlighted energy consumption, the compression molding process, and material composition as the primary contributors to carbon emissions and environmental impacts, identifying key areas for future optimization. This study provides an optimized framework for compression molding bamboo fiber-reinforced PP composites and establishes a theoretical foundation for their low-carbon application in the automotive industry. Future work will explore the optimization of bamboo fiber content and process parameters to further enhance material performance and reduce environmental impact. Full article

Wind speed prediction (WSP) provides future wind information and is crucial for ensuring the safety of high-speed railway systems (HSRs). However, the accurate prediction of wind speed (WS) remains a challenge due to the nonstationary and nonlinearity of wind patterns. To address this issue, a novel artificial-intelligence-based WSP model (EE-VMD-TCGRU) is proposed in this paper. EE-VMD-TCGRU combines energy-entropy-guided variational mode decomposition (EE-VMD) with a customized hybrid network, TCGRU, that incorporates a novel loss function: the Gaussian kernel mean square error (GMSE). Initially, the raw WS sequence is decomposed into various frequency-band components using EE-VMD. TCGRU is then applied for each decomposed component to capture both long-term trends and short-term fluctuations. Furthermore, a novel loss function, GMSE, is introduced to the training of TCGRU to analyze the WS’s nonlinear patterns and improve prediction accuracy. Experiments conducted on real-world WS data from the Beijing–Baotou railway demonstrate that EE-VMD-TCGRU outperforms benchmark models, achieving a mean absolute error (MAE) of 0.4986, a mean square error (MSE) of 0.4962, a root mean square error (RMSE) of 0.7044, and a coefficient of determination ($R^2$) of 94.58%. These results prove the efficacy of EE-VMD-TCGRU in ensuring train operation safety under strong wind environments. Full article

Noise reduction is a key process in digital image detection technology for concrete cracks and bug-holes. In this study, the threshold range of the flood-filling noise reduction algorithm was investigated experimentally. Surface cracks and bug-holes in RC bridge concrete were detected using mobile terminal images based on the flood-filling noise reduction algorithm. The results showed that the error range was within 10% when threshold range Θ was confined in [60, 80] as the crack width was from 0.1 mm to 2 mm. It is suitable that the threshold range Θ was selected as 70 while the measured crack width range was 0.2 mm to 2 mm. However, by reducing the values of the threshold range Θ to 50, the miscalculation was obviously eliminated. The influences of reducing values of the threshold range on bug-holes of the equivalent diameter and area were not significant. It is suitable that the threshold range Θ was elected on 50 to detect bug-holes in the concrete surface. The threshold range can be selected as a suitable value for the detection of cracks and bug-holes in order to reduce noise. Full article

Shaker potassium channel proteins are a class of voltage-gated ion channels responsible for K⁺ uptake and translocation, playing a crucial role in plant growth and salt tolerance. In this study, bioinformatic analysis was performed to identify the members within the Shaker gene family. Moreover, the expression patterns of rice Shaker(OsShaker) K⁺ channel genes were analyzed in different tissues and salt treatment by RT–qPCR. The results revealed that there were eight OsShaker K⁺ channel genes distributed on chromosomes 1, 2, 5, 6 and 7 in rice, and their promoters contained a variety of cis-regulatory elements, including hormone-responsive, light-responsive, and stress-responsive elements, etc. Most of the OsShaker K⁺ channel genes were expressed in all tissues of rice, but at different levels in different tissues. In addition, the expression of OsShaker K⁺ channel genes differed in the timing, organization and intensity of response to salt and chilling stress. In conclusion, our findings provide a reference for the understanding of OsShaker K⁺ channel genes, as well as their potential functions in response to salt and chilling stress in rice. Full article

This research investigates the crucial role of transportation infrastructure in influencing economic activity, thus employing advanced econometric methods including Moran’s I index, LM, Hausman, and LR tests to ensure analytical accuracy and select the appropriate spatial model. Our findings reveal that freight volumes across road, waterway, and civil aviation significantly enhance economic activity by bolstering domestic trade, industrial production, and supply chains. Conversely, the impact of passenger turnover is comparatively minor, although it still contributes to labor mobility and urban accessibility. This study highlights the need for strategic investment in transportation infrastructure and efficient public transport systems to foster economic growth and sustainable development. We recommend that policymakers focus on optimizing transportation networks and integrating intelligent transport technologies to boost economic competitiveness and societal well-being. This analysis not only sheds light on the direct economic impacts of transportation but also underscores the broader social implications, thus advocating for a holistic approach to transportation planning and policymaking. Full article

Lithium-ion batteries are currently widely employed in a variety of applications. Precise estimation of the remaining useful life (RUL) of lithium-ion batteries holds significant function in intelligent battery management systems (BMS). Therefore, in order to increase the fidelity and stabilization of predicting the RUL of lithium-ion batteries, in this paper, an innovative strategy for RUL prediction is proposed by integrating a one-dimensional convolutional neural network (1D CNN) and a bilayer long short-term memory (BLSTM) neural network. Feature extraction is carried out through the input capacity data of the model using 1D CNN, and these deep features are used as the input of the BLSTM. The memory function of the BLSTM is applied to retain key information in the database and to better understand the coupling relationship among consecutive time series data along the time axis, thereby effectively predicting the RUL trends of lithium-ion batteries. Two different types of lithium-ion battery datasets from NASA and CALCE were used to verify the effectiveness of the proposed method. The results show that the proposed method achieves higher prediction accuracy, demonstrates stronger generalization capabilities, and effectively reduces prediction errors compared to other methods. Full article

The spatial organization within ancient settlements offers valuable insights into the evolution of social complexity. This paper examines spatially and chronologically contextualized architectural structures and artifacts uncovered at the Late Bronze Age Shirenzigou site to explore the relationship between the use of space and underlying social dynamics in the Eastern Tianshan Mountains of Xinjiang (China). Central to our findings is a distinctive centripetal compound structure, consisting of a larger non-domestic building surrounded by smaller dwellings. This arrangement, along with the variety and distribution of the artifacts, reveals a complex interplay between private and communal spaces at the site, reflecting a growing complexity within the social fabric of the community. The formation of conglomerates of houses around a central communal structure which occurs across the Tianshan Mountains appears to be a strategic adaptation in response to environmental challenges and socio-political transformations across this region at the end of the second millennium BCE. Full article

The digital image method of monitoring structural displacement is receiving more attention today, especially in non-contact structure health monitoring. Some obvious advantages of this method, such as economy and convenience, were shown while it was used to monitor the deformation of the bridge structure during the service period. The image processing technology was used to extract structural deformation feature information from surveillance video images containing structural displacement in order to realize a new non-contact online monitoring method in this paper. The influence of different imaging distances and angles on the conversion coefficient (η) that converts the pixel coordinates to the actual displacement was first studied experimentally. Then, the measuring and tracking of bridge structural displacement based on surveillance video images was investigated by laboratory-scale experiments under idealized conditions. The results showed that the video imaging accuracy can be affected by changes in the relative position of the imaging device and measured structure, which is embodied in the change in η (actual size of individual pixel) on the structured image. The increase in distance between the measured structure and the monitoring equipment will have a significant effect on the change in the η value. The value of η varies linearly with the change in shooting distance. The value of η will be affected by the changes in shooting angle. The millimeter-level online monitoring of the structure displacement can be realized using images based on surveillance video images. The feasibility of measuring and tracking structural displacement based on surveillance video images was confirmed by a laboratory-scale experiment. Full article