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Solid-state shear milling (S3M) equipment is an evolution from traditional stone mills, enabling the processing of polymer materials and fillers through crushing, mixing, and mechanochemical reactions at ambient temperature. Due to the complex structure of the mill-pan, empirical data alone are insufficient to give a comprehensive understanding of the physicochemical interactions during the milling process. To provide an in-depth insight of the working effect and mechanism of S3M equipment, finite element method (FEM) analysis is employed to simulate the milling dynamics, which substantiates the correlation between numerical outcomes and experimental observations. A model simplification strategy is proposed to optimize calculation time without compromising accuracy. The findings in this work demonstrate the S-S bond breakage mechanism behind stress-induced devulcanization and suggest the structural optimizations for enhancing the devulcanization and pulverization efficiency of S3M equipment, thereby providing a theoretical foundation for its application in material processing. [ABSTRACT FROM AUTHOR]

The hairpin structure is a common and fundamental secondary structure in macromolecules. In this work, the process of the translocation of a model polymer chain with a hairpin structure is studied using Langevin dynamics simulations. The simulation results show that the dynamics of hairpin polymer translocation through a nanopore are influenced by the hairpin structure. Hairpin polymers can be classified into three categories, namely, linear-like, unsteady hairpin, and steady hairpin, according to the interaction with the stem structure. The translocation behavior of linear-like polymers is similar to that of a linear polymer chain. The time taken for the translocation of unsteady hairpin polymers is longer than that for a linear chain because it takes a long time to unfold the hairpin structure, and this time increases with stem interaction and decreases with the driving force. The translocation of steady hairpin polymers is distinct, especially under a weak driving force; the difficulty of unfolding the hairpin structure leads to a low translocation probability and a short translocation time. The translocation behavior of hairpin polymers can be explained by the theory of the free-energy landscape. [ABSTRACT FROM AUTHOR]

Euphorbia dentata Michx. is an invasive plant species in China, known for its toxicity and potential to reduce crop yields, posing numerous threats. To gain a deeper understanding of this invasive plant, phytochemical methods were employed to isolate 13 terpenoids (1–11, 19, 20) and 7 sterols (12–18) from the ethanol extract of E. dentata, identifying one new compound and 19 known compounds. Within spectroscopic methods such as NMR, HR-ESI-MS, and ECD, the structures and absolute configurations of these compounds were established. Among them, dentatacid A (11) possesses an unprecedented 2, 3-seco-arbor-2, 3-dioic skeleton within the potential biosynthetic pathway proposed. Dentatacid A also exhibited excellent anti-proliferative activity against the HT-29 (human colorectal adenocarcinoma) cell line, with an IC50 value of 2.64 ± 0.78 μM, which was further confirmed through network pharmacology and molecular docking. This study significantly expands the chemical diversity of E. dentata and offers new insights into the resource utilization and management of this invasive plant from the perspective of natural product discovery. [ABSTRACT FROM AUTHOR]

Fish swim bladder (FSB) is a type of traditional nutraceutical, but the lack of high-quality drying methods limits its premium market development. In order to obtain optimal-quality dried FSBs from Chinese longsnout catfish, the effects of liquid nitrogen pre-freezing (LNF) and drying on the physical properties and flavor of FSB were evaluated. Four methods were used for FSB drying, including natural air-drying (ND), hot-air-drying (HD), LNF combined with freeze-drying (LN-FD), and LNF combined with HD (LN-HD). Color, collagen content, rehydration ratio, textural properties, and flavor characteristics (by GC-IMS, E-nose, and E-tongue) were measured to clarify the differences among four dried FSBs. The results showed that ND cannot effectively remove moisture from FSB as the final product showed a stronger sourness in taste. HD led to a decrease in the collagen content and the collapse of the fiber structure in FSB. Compared to HD, LN-HD showed a higher collagen content (0.56 g/g) and a different flavor fingerprint. FSB treated by LN-FD had better physical qualities in terms of an attractive color, a high collagen content (0.79 g/g), low shrinkage, a higher rehydration ratio (2.85), and a soft texture, while also possessing richer characteristic flavors. The application of LN-FD may help the optimization of the nutrition level, rehydration ability, mouthfeel, and flavor of dried FSB. [ABSTRACT FROM AUTHOR]

As mining engineering progresses into the deep excavation phase, the intensification of high pressure, high temperature, strong disturbances, and complex geological conditions becomes increasingly prominent. Researchers perform stability analysis on the excavation area to reduce potential safety hazards during the extraction process. Developing a detailed numerical calculation model that accurately reflects the true geological structure is essential for numerical simulation analysis in mining engineering. Based on the excellent 3D geological modeling capabilities of 3D Mine software, this paper introduces a new 3D geological and numerical unified modeling method (3DMine-Rhino-HyperMesh) involving multi-software coupling and details the specific steps and concepts of this modeling approach. Subsequently, using a certain open-pit mine in Panzhihua as a backdrop, a detailed geological and numerical unified model is established, reflecting the true geological structure of the mining area, and the potential failure mechanisms of the mine slope are analyzed. The results indicate that the modeling method aligns well with the actual geological conditions, enhancing the grid quality of the numerical model and offering a new modeling approach for simulating and analyzing large complex geological entities in mining operations. [ABSTRACT FROM AUTHOR]

Automatic classification of buildings within port areas from synthetic aperture radar (SAR) images is crucial for effective port monitoring and planning. Yet, the unique challenges of SAR imaging, such as side-looking geometry, multi-bouncing scattering, and the compact arrangement of structures, often lead to incomplete building structures and blurred boundaries in classification results. To address these issues, this paper introduces SPformer, an efficient and lightweight pyramid transformer model tailored for semantic segmentation. The SPformer utilizes a pyramid transformer encoder with spatially separable self-attention (SSSA) to refine both local and global spatial information and to process multi-scale features, enhancing the accuracy of building structure delineation. It also integrates a lightweight all multi-layer perceptron (ALL-MLP) decoder to consolidate multi-scale information across various depths and attention scopes, refining detail processing. Experimental results on the Gaofen-3 (GF-3) 1 m port building classification dataset demonstrate the effectiveness of SPformer, achieving competitive performance compared to state-of-the-art models, with mean intersection over union (mIoU) and mean F1-score (mF1) reaching 77.14% and 87.04%, respectively, while maintaining a compact model size and lower computational requirements. Experiments conducted on the entire scene of SAR images covering port area also show the good capabilities of the proposed method. [ABSTRACT FROM AUTHOR]

The assessment of soil erosion in a region can provide an effective reference for local ecological environment management. The Yunnan–Guizhou Plateau54 is an important ecological security barrier in southwest China, owing to its unique climatic and environmental characteristics and superior natural resource endowment. The current research focus is the spatial analysis of a certain area. In this study, soil erosion in the Yunnan–Guizhou Plateau during 2000–2030 was analyzed and predicted from two aspects of structure and spatial layout by coupling several models. The report also analyzes the shift in the center of gravity of land use and analyzes the drivers of soil erosion, analyzing soil erosion by land use type. The study shows a decreasing trend in the soil erosion modulus from 2000 to 2020 from 1183.69 to 704.58 t·hm−2·a−1, but it is expected to have an increasing trend in the future and will increase to 877.72 t·hm−2·a−1. Analyzing the drivers of soil erosion allows for testing whether the factor affects the spatial distribution of the independent variable and to what extent it explains that dependent variable. This study showed that elevation had the highest explanatory power for soil erosion. Relatively high mountainous areas are often subject to greater soil erosion due to their steep topography, resulting in poorer vegetation cover. The north–south offset distance is greater than the east–west offset distance for forested land, water and unutilized land, and the east–west offset distance is greater than the north–south offset distance for cropland, grassland and built-up land in the Yunnan–Guizhou Plateau. The purpose of this study is to identify areas of serious soil erosion vulnerability in the Yunnan–Guizhou Plateau, and to analyze the driving factors affecting soil erosion vulnerability, so as to provide a basis for regional soil erosion management, and, at the same time, to provide a reference for the government to formulate soil and water conservation measures. [ABSTRACT FROM AUTHOR]

An integrated construction station for an expressway is characterized by complex carbon emission sources and high carbon emission intensity. Conducting carbon emissions accounting makes possible a comprehensive understanding of these characteristics, enabling targeted and guided carbon reduction efforts, which is crucial for advancing the low-carbon development of expressway construction. This paper, based on an in-depth analysis of the carbon emission structure during the operational period of an integrated expressway construction station identifies, as calculation boundaries, eight categories: residential areas, station transportation, mixing stations, precast beams, steel bar yards, artificial carbon emissions, chemical reactions during construction, and construction conditions. The study adopts a "bottom-up" approach to carbon emission measurement and constructs a carbon emission model for the production and operational period of the integrated construction station, based on the carbon emission factor method. Using the SG-2 section of the Fengqiu to Xiuwu stretch of the Changxiu Expressway as an engineering case, carbon emissions accounting for each component of the integrated construction station's operational period was conducted, and the results were compared with the station's monitoring system. High-precision characterization and calculation of total carbon emissions, as well as emissions from each process and piece of equipment during the operational period, were achieved. The results indicate that: (1) the relative error between the overall calculation results and actual monitoring is 3.6%, verifying the model's accuracy; (2) the monthly carbon emissions of the integrated construction station during the operational period reached 72.15 tons; (3) there is a significant difference in carbon emissions among the different processes, with the highest emissions coming from transportation and residential areas, accounting for 43.4% and 23.7%, respectively. Therefore, electrification of transportation equipment could significantly reduce the overall carbon emissions of the integrated construction station. [ABSTRACT FROM AUTHOR]

The impact of global population aging on older adults' health and emotional well-being is examined in this study, emphasizing innovative technological solutions to address their diverse needs. Changes in physical and mental functions due to aging, along with emotional challenges that necessitate attention, are highlighted. Gaze estimation and interactive art are utilized to develop an interactive system tailored for elderly users, where interaction is simplified through eye movements to reduce technological barriers and provide a soothing art experience. By employing multi-sensory stimulation, the system aims to evoke positive emotions and facilitate meaningful activities, promoting active aging. Named "Natural Rhythm through Eyes", it allows for users to interact with nature-themed environments via eye movements. User feedback via questionnaires and expert interviews was collected during public demonstrations in elderly settings to validate the system's effectiveness in providing usability, pleasure, and interactive experience for the elderly. Key findings include the following: (1) Enhanced usability of the gaze estimation interface for elderly users. (2) Increased enjoyment and engagement through nature-themed interactive art. (3) Positive influence on active aging through the integration of gaze estimation and interactive art. These findings underscore technology's potential to enhance well-being and quality of life for older adults navigating aging challenges. [ABSTRACT FROM AUTHOR]

SLAM (simultaneous localization and mapping) is essential for accurate positioning and reasonable path planning in outdoor mobile robots. LiDAR SLAM is currently the dominant method for creating outdoor environment maps. However, the mainstream LiDAR SLAM algorithms have a single point cloud feature extraction process at the front end, and most of the loop closure detection at the back end is based on RNN (radius nearest neighbor). This results in low mapping accuracy and poor real-time performance. To solve this problem, we integrated the functions of point cloud segmentation and Scan Context loop closure detection based on the advanced LiDAR-inertial SLAM algorithm (LIO-SAM). First, we employed range images to extract ground points from raw LiDAR data, followed by the BFS (breadth-first search) algorithm to cluster non-ground points and downsample outliers. Then, we calculated the curvature to extract planar points from ground points and corner points from clustered segmented non-ground points. Finally, we used the Scan Context method for loop closure detection to improve back-end mapping speed and reduce odometry drift. Experimental validation with the KITTI dataset verified the advantages of the proposed method, and combined with Walking, Park, and other datasets comprehensively verified that the proposed method had good accuracy and real-time performance. [ABSTRACT FROM AUTHOR]

Steroid-resistant asthma (SRA), resisting glucocorticoids such as dexamethasone (DEX), is a bottleneck in the treatment of asthma. It is characterized by a predominantly neutrophilic inflammatory subtype and is prone to developing into severe refractory asthma and fatal asthma. Currently, there is a lack of universally effective treatments for SRA. Moreover, since cold stimulation does increase the risk of asthma development and exacerbate asthma symptoms, the treatment of cold-stimulated SRA (CSRA) will face greater challenges. To find effective new methods to ameliorate CSRA, this study established a CSRA mouse model of allergic airway inflammation mimicking human asthma for the first time and evaluated the alleviating effects of 80% ethanol extract of mountain-cultivated ginseng (MCG) based on multi-omics analysis. The results indicate that cold stimulation indeed exacerbated the SRA-related symptoms in mice; the DEX individual treatment did not show a satisfactory effect; while the combination treatment of DEX and MCG could dose-dependently significantly enhance the lung function; reduce neutrophil aggregation; decrease the levels of LPS, IFN-γ, IL-1β, CXCL8, and IL-17; increase the level of IL-10; alleviate the inflammatory infiltration; and decrease the mucus secretion and the expression of MUC5AC. Moreover, the combination of DEX and high-dose (200 mg/kg) MCG could significantly increase the levels of tight junction proteins (TJs), regulate the disordered intestinal flora, increase the content of short-chain fatty acids (SCFAs), and regulate the abnormal gene profile and metabolic profile. Multi-omics integrated analysis showed that 7 gut microbes, 34 genes, 6 metabolites, and the involved 15 metabolic/signaling pathways were closely related to the pharmacological effects of combination therapy. In conclusion, integrated multi-omics profiling highlighted the benefits of MCG for CSRA mice by modulating the interactions of microbiota, genes, and metabolites. MCG shows great potential as a functional food in the adjuvant treatment of CSRA. [ABSTRACT FROM AUTHOR]

Electric bicycles offer convenient short-distance travel, but improper battery charging poses a fire risk, especially indoors, potentially causing significant accidents, property damage, and even threats to life. Recognizing the charging state of electric bicycle batteries is crucial for safety. This paper proposes a novel method to identify the charging process of lithium batteries in electric bicycles. Methods that do not require physical alterations to the equipment are used to acquire users' electricity consumption data, with current signals preprocessed and input into a combined model integrating convolutional neural networks (CNN) and bidirectional long short-term memory (BiLSTM) networks. The proposed model captures complex patterns and features in the charging data, effectively identifying the charging characteristics of lithium batteries. Validation using NASA's lithium battery dataset and real experimental data shows that the combined model achieves recognition accuracy of 96% and 97% on training data and 93% and 94% on validation data. Further validation under multiple device loads and comparison with other models indicate that the proposed method is highly accurate, outperforming traditional CNN and LSTM models by 4–9%. This research enhances the safety and regulation of electric bicycle battery charging and provides a reliable method for non-intrusive load identification in smart monitoring systems, contributing to improved safety measures and energy management in residential environments. [ABSTRACT FROM AUTHOR]

In the context of global warming, the accelerated degradation of circum-Arctic permafrost is releasing a significant amount of carbon. InSAR can indirectly reflect the degradation of permafrost by monitoring its deformation. This study selected three typical permafrost regions in North America: Alaskan North Slope, Northern Great Bear Lake, and Southern Angikuni Lake. These regions encompass a range of permafrost landscapes, from tundra to needleleaf forests and lichen-moss, and we used Sentinel-1 SAR data from 2018 to 2021 to determine their deformation. In the InSAR process, due to the prolonged snow cover in the circum-Arctic permafrost, we used only SAR data collected during the summer and applied a two-stage interferogram selection strategy to mitigate the resulting temporal decorrelation. The Alaskan North Slope showed pronounced subsidence along the coastal alluvial plains and uplift in areas with drained thermokarst lake basins. Northern Great Bear Lake, which was impacted by wildfires, exhibited accelerated subsidence rates, revealing the profound and lasting impact of wildfires on permafrost degradation. Southern Angikuni Lake's lichen and moss terrains displayed mild subsidence. Our InSAR results indicate that more than one-third of the permafrost in the North American study area is degrading and that permafrost in diverse landscapes has different deformation patterns. When monitoring the degradation of large-scale permafrost, it is crucial to consider the unique characteristics of each landscape. [ABSTRACT FROM AUTHOR]

Crop models are instrumental in simulating resource utilization in agriculture, yet their complexity necessitates extensive calibration, which can impact the accuracy of yield predictions. Machine learning shows promise for enhancing yield estimations but relies on vast amounts of training data. This study aims to improve the pakchoi yield prediction accuracy of simulation models. We developed a stacking ensemble learning model that integrates three base models—EU-Rotate_N, Random Forest Regression and Support Vector Regression—with a Multi-layer Perceptron as the meta-model for the pakchoi dry matter yield prediction. To enhance the training dataset and bolster machine learning performance, we employed the EU-Rotate_N model to simulate daily dry matter yields for unsampled data. The test results revealed that the stacking model outperformed each base model. The stacking model achieved an R² value of 0.834, which was approximately 0.1 higher than that of the EU-Rotate_N model. The RMSE and MAE were 0.283 t/ha and 0.196 t/ha, respectively, both approximately 0.6 t/ha lower than those of the EU-Rotate_N model. The performance of the stacking model, developed with the expanded dataset, showed a significant improvement over the model based on the original dataset. [ABSTRACT FROM AUTHOR]

Utilization of large aggregates can promote energy conservation and emissions reductions, and large aggregates have been widely used in hydraulic concrete. The failure criterion for concrete material utilizing large aggregates forms the basis for constitutive models and structural design. However, the concrete failure criterion with respect to large aggregates has never been researched. To this end, the authors first conducted a series of triaxial compressive tests on concrete specimens with scaled aggregates. On this basis, several 3D mesoscopic numerical models were established with different aggregate gradations and used to simulate the triaxial compressive behaviors of hydraulic concrete after the models had been verified by experimental results. The results showed a pronounced aggregate-gradation effect on triaxial compressive behaviors, and concrete mixes with larger aggregates usually have higher compressive strength, especially under conditions of higher confinement. The normalized peak strength can increase by up to 23.49%. Finally, based on the available testing data, the strength criterion in different constitutive models is discussed and modified to allow more accurate simulation of the dynamic responses of and damage to fully graded concrete structures. This result can provide a theoretical basis on which construction entities can optimize the mix proportions of fully graded concrete and detect the failure modes of concrete structures. [ABSTRACT FROM AUTHOR]

Laser bending forming, as a flexible and die-less forming approach, facilitates the three-dimensional shaping of sheets through the generation of thermal stress via laser-material interaction. In this study, the bending forming characteristics of CoCrFeMnNi high-entropy alloy sheets induced by nanosecond pulse laser irradiation were systematically investigated. The effects of parameters including laser power, scanning speed, number of scans, scanning interval, and sheet size on the bending angle, cross-sectional morphology, and hardness were studied in detail under both the laser single-line and multi-line scanning modes. The experimental results confirmed the effectiveness of nanosecond pulse laser irradiation for achieving accurate formation of CoCrFeMnNi sheets, with the successful fabrication of J, L, and U-shaped metal components. Apart from the forming ability, the cross-sectional hardness was significantly increased due to the grain refinement effect of nanosecond pulse laser irradiation. Furthermore, employing the laser single-line scanning mode enabled the effective rectification of overbending parts, showcasing complete recovery for small-angle overbending, and a remarkable 91% recovery for larger-angle overbending. This study provides an important basis for the bendability of CoCrFeMnNi sheets by laser forming and elucidates the evolution of the microstructure and mechanical properties in the bending region. [ABSTRACT FROM AUTHOR]

By constructing a text quality analysis system and company quality analysis system based on a prospectus, the intelligent analysis method of investment banking IPO business risk is proposed based on the machine learning method and text analysis technology. Taking the Sci-Tech Innovation Board in China as a sample, the empirical analysis results show that the text quality and the company quality disclosed in the prospectus can affect the withdrawal rate of investment banking IPO business. By carrying out a text analysis and machine learning on the text quality and company quality, the risk of investment banking IPO business can be predicted intelligently and effectively. The research results can not only improve the business efficiency of investment banking IPO, and save resource cost, but also improve the standardization and authenticity of investment banking IPO business. [ABSTRACT FROM AUTHOR]

Urban areas with parks tend to have the best outdoor thermal comfort in regions with high urban heat island effects during summer. This study analyzed the synergistic cooling effects of 94 urban parks and the adjacent built-up areas in six districts of Xi'an City using four cooling indicators: park cooling intensity (PCI), park cooling area (PCA), park cooling effect (PCE), and park cooling gradient (PCG). The results showed that 84 out of 94 parks exhibited significant cooling effects, with an average PCI of 1.98 °C, PCA of 51.7 ha, PCE of 6.6, and PCG of 8.2 °C/km. Correlation analyses indicated that the intrinsic park attributes, external buffer zone building height, and building density were the main factors affecting the cooling effect. The park landscape configuration, building height, and density significantly influenced the PCI and PCG, while the park shape and size were crucial for the PCA (positive) and PCE (negative). The optimal park areas for improving the thermal environment were identified as 26 ha (cooling area focus, building density <13%) and 15 ha (cooling intensity focus, building height <21 m, density >32%). This study provides theoretical guidance for planning urban parks and the surrounding areas based on cooling effects, offering insights for future climate resilience planning. [ABSTRACT FROM AUTHOR]

The materials charged into a converter comprise molten iron and scrap steel. Adjusting the ratio by increasing scrap steel and decreasing molten iron is a steelmaking raw material strategy designed specifically for China's unique circumstances, with the goal of lowering carbon emissions. To maintain the converter tapping temperature, scrap must be preheated to provide additional heat. Current scrap preheating predominantly utilizes horizontal tunnel furnaces, resulting in high energy consumption and low efficiency. To address these issues, a three-stage shaft furnace for scrap preheating was designed, and Fluent software was used to compare and study the preheating efficiency of the new three-stage furnace against the traditional horizontal furnace under various operational conditions. Initially, a three-dimensional transient multi-field coupling model was developed for two scrap preheating scenarios, examining the effects of both furnaces on scrap surface and core temperatures across varying preheating durations and gas velocities. Simulation results indicate that, under identical gas heat consumption conditions, scrap achieves markedly higher final temperatures in the shaft furnace compared to the horizontal furnace, with scrap surface and core temperatures increasing notably with extended preheating times and higher gas velocities, albeit with a gradual decrease in heating rate as the scrap temperature rises. At a gas velocity of 9 m/s and a preheating time of 600 s, the shaft furnace achieves the highest waste heat utilization rate for scrap, with scrap averaging 325 °C higher than in the horizontal furnace, absorbing an additional 202 MJ of heat per ton. In the horizontal preheating furnace, scrap steel exhibits a heat absorption efficiency of 35%, whereas in the vertical furnace, this efficiency increases notably to 63%. In the vertical furnace, the waste heat recovery rate of scrap steel reaches 57%. [ABSTRACT FROM AUTHOR]

The effect of Al or Cu content on the microstructure and mechanical properties of continuous casting and extrusion Zn alloys has been studied by a room temperature tensile test, X-ray diffraction, and scanning electron microscope. With the increase in Al content, the microstructure of continuous casting and extrusion Zn alloys slightly coarsens, and the lamellar eutectic structure increases. The changes in the above structural factors result in a slight decrease in strength and a significant increase in the elongation of Zn-Al alloys. The strength of Zn alloys increases as the Cu content increases due to the increased content and size of the second phase in the Zn alloys. This means that the mechanical properties of Zn alloys can be adjusted by a continuous casting and extrusion process, and the improvement of equipment capacity can improve the structure and morphology of the alloys. [ABSTRACT FROM AUTHOR]

The incorporation of antioxidants in food products is essential to prevent or delay deterioration, thereby addressing food spoilage. Thiol compounds, recognized for their natural antioxidant properties, are widely used in various foods; however, their antioxidant capacity is often limited. This study investigates the potential enhancement of thiol antioxidant capacity through the addition of a soluble, low-toxic inorganic Sm-cluster. Our findings demonstrate that the Sm-cluster significantly bolsters the antioxidant efficacy of thiol compounds. We explored, for the first time, the in vitro antioxidant activities of an Sm-oxo/hydroxy cluster combined with a cysteine derivative for potential food applications. The composition exhibited a robust inhibition of aromatic aldehyde flavor compound oxidation and displayed strong, dose-dependent DPPH (2,2-diphenyl-1-picrylhydrazine) radical scavenging activity. Notably, the antioxidant activity of the Sm-cluster/cysteine derivative was further enhanced under strong visible light conditions, which typically increased the likelihood of oxidation. These results suggest that the combination of inorganic cluster and thiol compounds presents a promising natural alternative to traditional antioxidants in the food industry. [ABSTRACT FROM AUTHOR]

The benchmarking of CO2 emissions serves as the foundation for the accurate assessment of the environmental impact of air traffic. To calculate the environmental benchmarks of arrival traffic flows with Point Merge System (PMS) patterns, this study proposes a 4D trajectory optimization method that combines data-driven and optimal control models. First, the predominant arrival routes of traffic flows are identified using the trajectory spectral clustering method, which provides the horizontal reference for 4D trajectory optimization. Second, an optimal control model for vertical profiles with point merging topology is established, with the objective of minimizing the fuel–time cost. Finally, considering the complex structure of the PMS, a flexible and adaptable genetic algorithm-based vertical profile nonlinear optimization model is created. The experimental results demonstrate that the proposed method is adaptable to variations in aircraft type and cost index parameters, enabling the generation of different 4D trajectories. The results also indicate an environmental efficiency gap of approximately 10% between the actual CO2 emissions of the arrival traffic flow example and the obtained benchmark. With this benchmark trajectory generation methodology, the environmental performance of PMSs and associated arrival aircraft scheduling designs can be assessed on the basis of reliable data. [ABSTRACT FROM AUTHOR]

Polarimetric imaging technology plays a crucial role in de-scattering, particularly in the field of underwater image restoration. Circularly polarized light (or the underlying circular polarization memory effect) has been proven to better preserve the polarization characteristics of detected light. Utilizing circularly polarized light as illumination can further enhance the effectiveness of polarization de-scattering techniques. After rethinking the advantages of circularly polarized light, this paper proposes a new method for underwater polarimetric imaging restoration that leverages the pre-processing of polarized sub-images and the correlation of polarization characteristics (i.e., the angle of polarization and degree of polarization). Additionally, to address the challenge of selecting scattering light parameters due to uneven light fields in target scenes, an intensity adjustment factor search algorithm is designed. This algorithm eliminates the need for the manual selection of scattering light parameters, effectively solving the problem of uneven illumination in restoration results. A series of experiments demonstrate that, compared to traditional algorithms, the proposed method offers superior detail restoration and higher robustness. [ABSTRACT FROM AUTHOR]

This study aimed to determine the optimal growth stage and additives for producing high quality millet silage through two experiments. Experiment 1: Whole-plant millet from the same field and under uniform management was harvested at the heading, sizing, milking, dough, and full-maturity stages. Then, it was chopped into 2–3 cm segments, vacuum-sealed in plastic bags without any further treatment, stored at 20 °C, and opened after 60 days. The results indicated that the dough stage had the highest water-soluble carbohydrate (WSC) and crude protein (CP) contents. The lactic acid (LA) and acetic acid (AA) contents during the dough and maturity stages were significantly higher than other stages, with the lowest pH observed during the dough stage. Experiment 2: The whole-plant millet was harvested at the dough stage. It was then chopped into 2–3 cm segments using a forage chopper, mixed thoroughly, and subjected to different treatments—inoculation with 106 CFU/g FM of Lactiplantibacillus plantarum (LP), adding of 1% FM sucrose (S), and a combination of Lactiplantibacillus plantarum and sucrose (MIX)—with a control group (CK) receiving an equivalent amount of water. The MIX treatment significantly enhanced the WSC content compared to other treatments (p < 0.05), and both the LP and MIX treatments showed superior LA and AA contents and lactic acid bacteria counts. These additives significantly altered the bacterial community, shifting dominance from Proteobacteria in the CK and raw materials to Firmicutes. Klebsiella dominated the CK group but was significantly reduced in the additive treatments, where Lentilactobacillus became the dominant genus. Therefore, we recommend harvesting millet at the dough stage and adding a mixture of Lactiplantibacillus plantarum and sugar to improve fermentation quality and aerobic stability. [ABSTRACT FROM AUTHOR]

The toxicity of cadmium (Cd) not only affects the growth and development of plants but also has an impact on human health. In this study, high-throughput sequencing and LC-MS were conducted to analyze the effect of CdCl2 treatment on the microbial community and soil metabolomics of rhizosphere soil in Rhododendron decorum subsp. diaprepes. The results showed that CdCl2 treatment reduced the quality of the rhizosphere soil by significantly decreasing the soil organic carbon (SOC) content, urease, and invertase activities, increasing the percentage of the exchangeable Cd fraction. CdCl2 treatment did not significantly change the Chao1 and Shannon indices of bacterial and fungal communities in the rhizosphere soil. R. decorum was more likely to recruit Cd-resistant bacteria (e.g., Proteobacteria, Chloroflexi) and increase the abundance of Cd-resistant fungi (e.g., Basidiomycota, Rozellomycota). Moreover, CdCl2 treatment decreased the content of secondary metabolites associated with plants' resistance to Cd. Rhizosphere soil urease, invertase activities, alkaline phosphatase (ALP), SOC, total potassium (TK), Cd, and nitrate nitrogen (NN) were the main drivers of the composition of rhizosphere bacterial and fungal communities. CdCl2 treatment weakened the relationships among bacterial/fungi, differential metabolites, and physicochemical properties in rhizosphere soil. [ABSTRACT FROM AUTHOR]

Scrap steel serves as the primary alternative raw material to iron ore, exerting a significant impact on production costs for steel enterprises. With the annual growth in scrap resources, concerns regarding traditional manual inspection methods, including issues of fairness and safety, gain increasing prominence. Enhancing scrap inspection processes through digital technology is imperative. In response to these concerns, we developed CNIL-Net, a scrap-quality inspection network model based on object detection, and trained and validated it using images obtained during the scrap inspection process. Initially, we deployed a multi-camera integrated system at a steel plant for acquiring scrap images of diverse types, which were subsequently annotated and employed for constructing an enhanced scrap dataset. Then, we enhanced the YOLOv5 model to improve the detection of small-target scraps in inspection scenarios. This was achieved by adding a small-object detection layer (P2) and streamlining the model through the removal of detection layer P5, resulting in the development of a novel three-layer detection network structure termed the Improved Layer (IL) model. A Coordinate Attention mechanism was incorporated into the network to dynamically learn feature weights from various positions, thereby improving the discernment of scrap features. Substituting the traditional non-maximum suppression algorithm (NMS) with Soft-NMS enhanced detection accuracy in dense and overlapping scrap scenarios, thereby mitigating instances of missed detections. Finally, the model underwent training and validation utilizing the augmented dataset of scraps. Throughout this phase, assessments encompassed metrics like mAP, number of network layers, parameters, and inference duration. Experimental findings illustrate that the developed CNIL-Net scrap-quality inspection network model boosted the average precision across all categories from 88.8% to 96.5%. Compared to manual inspection, it demonstrates notable advantages in accuracy and detection speed, rendering it well suited for real-world deployment and addressing issues in scrap inspection like real-time processing and fairness. [ABSTRACT FROM AUTHOR]

Some high-specific-speed centrifugal pumps exhibit instability in terms of hydraulic performance and cavitation characteristics, and there's a lack of reliable numerical models to guide the optimization of cavitation instability. This paper, by conducting a study on mesh independence, analyzes the cavitation curves and cavitation counters for various mesh combinations in the numerical model, The findings indicate that the boundary layer grid not only influences the location of peak points but also the size of the peak. To achieve a stable NPSH peak position, the y+ at the blade leading edge of high-specific-speed centrifugal pumps needs to be controlled between 20–80. The turbulence model, evaporation coefficient, and condensation coefficient were simulated using the orthogonal experimental design method, analyzing the impact of these parameters on the NPSH peak. A visual high-speed photography test rig was established, and rotating cavitation and sheet cavitation is found at part-load. By comparing the cavitation and pressure counters with high-speed photography images, a numerical model was obtained that closely mirrors the experimental cavitation characteristics. [ABSTRACT FROM AUTHOR]

In recent years, wastewater reuse has become crucial for addressing global freshwater scarcity and promoting sustainable water resource development. Accurate inflow volume predictions are essential for enhancing operational efficiency in water treatment facilities and effective wastewater utilization. Traditional and decomposition integration models often struggle with non-stationary time series, particularly in peak and anomaly sensitivity. To address this challenge, a differential decomposition integration model based on real-time rolling forecasts has been developed. This model uses an initial prediction with a machine learning (ML) model, followed by differential decomposition using Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN). A Time-Aware Outlier-Sensitive Transformer (TS-Transformer) is then applied for integrated predictions. The ML-CEEMDAN-TSTF model demonstrated superior accuracy compared to basic ML models, decomposition integration models, and other Transformer-based models. This hybrid model explicitly incorporates time-scale differentiated information as a feature, improving the model's adaptability to complex environmental data and predictive performance. The TS-Transformer was designed to make the model more sensitive to anomalies and peaks in time series, addressing issues such as anomalous data, uncertainty in water volume data, and suboptimal forecasting accuracy. The results indicated that: (1) the introduction of time-scale differentiated information significantly enhanced model accuracy; (2) ML-CEEMDAN-TSTF demonstrated higher accuracy compared to ML-CEEMDAN-Transformer; (3) the TS-Transformer-based decomposition integration model consistently outperformed those based on LSTM and eXtreme Gradient Boosting (XGBoost). Consequently, this research provides a precise and robust method for predicting reclaimed water volumes, which holds significant implications for research on clean water and water environment management. [ABSTRACT FROM AUTHOR]

The distribution of forest-dominant tree species is crucial for ecosystem assessment. Remote sensing monitoring requires annual ground sample data, but consistent field surveys are challenging. This study addresses this by combining sample migration learning and machine learning for multi-year tree species classification in the Three Gorges Reservoir area in China. Using the continuous change detection and classification (CCDC) algorithm, sample data from 2023 were successfully migrated to 2018–2022, achieving high migration accuracy ( R 2 = 0.8303, RMSE = 4.64). Based on migrated samples, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB) algorithms classified forest tree species with overall accuracies above 70% and Kappa coefficients above 0.6. XGB. They outperformed other algorithms, with classification accuracy of over 80% and Kappa above 0.75 in almost all years. The final map indicates stable distribution from 2018 to 2023, with eucalyptus covering over 40% of the forest area, followed by horsetail pine, fir, cypress, and wetland pine. [ABSTRACT FROM AUTHOR]

Background: Heart failure (HF) remains a leading cause of morbidity and mortality globally, necessitating the identification of reliable prognostic biomarkers to guide therapeutic interventions. Recent clinical observations have underscored phenylalanine (PHE) as a prognostic marker in HF, although the mechanisms involving inter-organ crosstalk remain understood. Methods: This study adopted a dull approach, with a retrospective analysis of 550 HF patients to establish the prognostic value of pre-discharge PHE levels and a study on the inter-organ crosstalk of PHE among 24 patients. We analyzed the correlations between PHE concentrations and clinical outcomes, alongside a comprehensive examination of PHE metabolism across the skeletal muscle, liver, heart, kidney, and lung. Results: In the clinical prognostic analysis of 550 patients hospitalized for acute decompensated HF, elevated PHE levels (≥65.6 μM) were significantly and independently associated with increased all-cause mortality during a median follow-up of 4.5 years (log rank = 36.7, p < 0.001), underscoring its value as a prognostic marker in HF. The inter-organic crosstalk study elucidated the mechanism associated with PHE elevation in patients with HF, characterized by an increase in PHE output in skeletal muscle and a decrease in hepatic and cardiac PHE uptakes. Notably, PHE concentration gradients across these organs were correlated with HF severity, such as the NYHA functional class, B-type natriuretic peptide levels, and the presence of acute HF. Conclusions: Our findings confirm the prognostic significance of PHE in patients with HF and unveil the complex metabolic interplay among key organs that contribute to PHE dysregulation. These insights not only reinforce the importance of metabolic monitoring in HF management but also open avenues for therapeutic targets. [ABSTRACT FROM AUTHOR]

Optimizing lanthanide catalyst performance with organic ligands often encounters significant challenges, including susceptibility to water or oxygen and complex synthesis pathways. To address these issues, our research focuses on developing inorganic lanthanide clusters with enhanced stability and functionality. In this study, we introduce the [Sm6O(OH)8(H2O)24]I8(H2O)8 cluster (Sm-OC) as a sustainable and efficient catalyst for the aerobic oxidation of thiols under heating conditions. The Sm-OC catalyst demonstrated remarkable stability, outstanding recyclability, and excellent chemoselectivity across a diverse range of functional groups in 38 different tests. Notably, it enables efficient unsymmetrical disulfide synthesis and prevents the formation of over-oxidized by-products, highlighting its superior performance. This Sm-OC catalyst provides a practical and robust tool for the precise construction of versatile disulfides, thus establishing a template for the broader use of lanthanide clusters in organic synthesis. [ABSTRACT FROM AUTHOR]

The multimode underwater acoustic communication network is a novel form of underwater acoustic communication that adjusts its communication mode to enhance overall performance. Current performance analysis methods are primarily applied to single-mode networks and assume uniform communication capability across all nodes, making them unsuitable for multimode networks. This paper investigates the multimode communication of the physical layer, considering factors such as the marine environment, the node transmitting sound source level, and the transmitting distance. A decoding conflict model is proposed to support multimode concurrent transmission scenarios. The communication mode is designed to be compatible with the channel and node characteristics. Additionally, using a Markov decision process, this paper establishes a performance evaluation and analysis model for multimode underwater acoustic networks to determine throughput performance limits in real underwater environments. Simulations across various scenarios validate that the throughput performance limits obtained by this method are more accurate under multimode networks, with an improvement in accuracy of over 67.5% compared to existing methods. [ABSTRACT FROM AUTHOR]

The present investigations on tandem wing configurations primarily revolve around the effects of the spacing L and the phase difference φ between the forewing and the hindwing on aerodynamic performance. However, in nature, organisms employing biplane flight, such as dragonflies, demonstrate the ability to achieve superior aerodynamic performance by flexibly adjusting their flapping trajectories. Therefore, this study focuses on the effects of φ , as well as the trajectory of the hindwing, on aerodynamic performance. By summarizing four patterns of wake–wing interaction processes, it is indicated that φ = − 90 ∘ and 0 ∘ enhances the thrust of the hindwing, while φ = 90 ∘ and 180 ∘ result in reductions. Furthermore, the wake–wing interactions and shedding modes are summarized corresponding to three kinds of trajectories, including elliptical trajectories, figure-eight trajectories, and double figure-eight trajectories. The results show that the aerodynamic performance of the elliptical trajectory is similar to that of the straight trajectory, while the figure-eight trajectory with positive surging motion significantly enhances the aerodynamic performance of the hindwing. Conversely, the double-figure-eight trajectory degrades the aerodynamic performance of the hindwing. [ABSTRACT FROM AUTHOR]

Heat treatment, particularly solution heat treatment, is a critical process in the preparation of powder metallurgy superalloys, where the cooling process significantly impacts the microstructure. This study, based on thermodynamic and kinetic databases as well as the precipitation mechanism of strengthening phases, delves into the influence of cooling process, especially the cooling path, on the material's microstructure. The results indicate that under slow cooling rates, the precipitated phases are more likely to exhibit a multimodal size distribution, while under rapid cooling rates, a unimodal distribution may form. The average cooling rate does not consistently accurately reflect the growth of the precipitated phases; even with the same average cooling rate, different cooling paths can lead to significant differences in the size of the precipitates. To accurately predict the size of the precipitates, it is necessary to consider the specific cooling process. Constant cooling rate experiments designed for the study and the dissection testing of full-size turbine discs produced in manufacturing validated the calculated results of the precipitates. Therefore, optimizing cooling through simulation calculations can effectively and accurately control the precipitates, thereby obtaining a microstructure that can meet performance requirements. [ABSTRACT FROM AUTHOR]

The power battery is a key component of electric vehicles and its performance is greatly affected by temperature. Battery thermal management systems based on phase change materials can effectively control the battery temperature and at the same time have the advantages of simple structures, energy savings, and good temperature uniformity, and has broad development prospects. In this paper, expanded graphite–paraffin composite phase change materials were prepared, phase change material cooling experiments were carried out, and a phase change material cooling simulation model was also established using the Fluent software to study the influence of phase change material thermophysical parameters on thermal management performance. The results show that the phase change material thermal management method has excellent cooling performance. The best thermal management performance is achieved at the 3C discharge rate, with a phase change material filling thickness of 4 mm, a melting point of 40 °C above ambient temperature, and a thermal conductivity of 3 W/(m·K). When the phase change latent heat was increased from 150 J/g to 250 J/g, the liquid phase ratio decreased from 0.84 to 0.51, and the subsequent cooling performance was greatly improved, so the phase change latent heat should be increased as much as possible. [ABSTRACT FROM AUTHOR]

Taxus chinensis var. mairei is an endangered tree species endemic to China; it has important ornamental, timber, and medicinal value. In this work, based on a MaxEnt model, the Jiangnan hilly region was used as the study area, and geographic, climatic, soil, and vegetation data were synthesized to simulate the present area of suitable habitat for T. chinensis; the key environmental factors that constrain its habitat expansion were also explored. Additionally, the potential future distribution of this species under different climate-change scenarios was predicted. The results showed that the six variables making the highest contribution to T. chinensis habitat suitability were the precipitation of the warmest quarter (14.2%), precipitation seasonality variation coefficient (9.1%), aspect (8.2%), altitude (8%), maximum temperature of the warmest month (7.4%), and base saturation (6.6%). Ideal areas have middle elevations, northeastern or northwestern slopes, warmest quarterly precipitation of 508.3–629.2 mm, maximum temperature in the warmest month of 34.6–35.9 °C, and relatively moist soil. The current area of suitable habitat is 6.09 × 105 km2, of which the area of high suitability is 7.56 × 104 km2; this is mainly concentrated in the southwestern part of Hunan, the southwestern part of Jiangxi Province, and the northern part of Zhejiang. Under the SSP2-4.5 climate scenario, the area of high habitat suitability increases; under both the SSP1-2.6 and SSP5-8.5 climate scenarios, the suitable habitat area expands similarly. The direction of the center-of-mass migration of T. chinensis under different climate scenarios is somewhat different from that caused by the uncertainty of human activities and climate warming. This paper clarifies the distribution of suitable habitat and future potential for T. chinensis in the Jiangnan hilly region, providing a theoretical basis for habitat management of this species. [ABSTRACT FROM AUTHOR]

Sports image classification has gained increasing significance in various domains, including athlete training, event management, and broadcasting. Nonetheless, accurately classifying these images remains a formidable challenge due to factors such as variations in lighting conditions, pose, and attire. This paper proposes an advanced deep learning approach to address this issue. The proposed method uses a modified Inception-V4 (IV4) architecture, which has exhibited remarkable success in image classification tasks. To further enhance the performance of the IV4 model, an advanced version of the seasons optimizer (ASO), a potent optimization algorithm, has been introduced, which adjusts the learning rate during training. This modified optimizer enables the IV4 model to converge more rapidly and achieve higher accuracy. An evaluation of the proposed method has been conducted using a comprehensive dataset comprising Olympic sports images. The experimental results demonstrate significant enhancements over existing approaches, attaining superior performance across multiple evaluation metrics. These findings underscore the potential of combining cutting-edge deep neural networks with advanced optimization techniques for the precise and efficient classification of sports images. [ABSTRACT FROM AUTHOR]

When there are dynamic obstacles in the environment, it is difficult for traditional path-generation algorithms to achieve desired obstacle-avoidance results. To solve this problem, we propose a robot navigation control method based on SAC (Soft Actor-Critic) Deep Reinforcement Learning. Firstly, we use a fast path-generation algorithm to control the robot to generate expert trajectories when the robot encounters danger as well as when it approaches a target, and we combine SAC reinforcement learning with imitation learning based on expert trajectories to improve the safety of training. Then, for the hybrid data consisting of agent data and expert data, we use an improved prioritized experience replay method to improve the learning efficiency of the policies. Finally, we introduce RNN (Recurrent Neural Network) units into the network structure of the SAC Deep Reinforcement-Learning navigation policy to improve the agent's transfer inference ability in a new environment and obstacle-avoidance ability in dynamic environments. Through simulation and practical experiments, it is fully verified that our method has a higher training efficiency and navigation success rate compared to state-of-the-art reinforcement-learning algorithms, which further enhances the obstacle-avoidance capability of the robot system. [ABSTRACT FROM AUTHOR]

Mycobacterium tuberculosis causes 6.4 million cases of tuberculosis and claims 1.6 million lives annually. Mycobacterial adhesion, invasion of host cells, and subsequent intracellular survival are crucial for the infection and dissemination process, yet the cellular mechanisms underlying these phenomena remain poorly understood. This study created a Bacillus Calmette–Guérin (BCG) transposon library using a MycomarT7 phage carrying a Himar1 Mariner transposon to identify genes related to mycobacteria adhesion and invasion. Using adhesion and invasion model screening, we found that the mutant strain B2909 lacked adhesion and invasion abilities because of an inactive fadD18 gene, which encodes a fatty-acyl CoA ligase, although the specific function of this gene remains unclear. To investigate the role of FadD18, we constructed a complementary strain and observed that fadD18 expression enhanced the colony size and promoted the formation of a stronger cord-like structure; FadD18 expression also inhibited BCG growth and reduced BCG intracellular survival in macrophages. Furthermore, FadD18 expression elevated levels of the proinflammatory cytokines IL-6, IL-1β, and TNF-α in infected macrophages by stimulating the NF-κB and MAPK signaling pathways. Overall, the FadD18 plays a key role in the adhesion and invasion abilities of mycobacteria while modulating the intracellular survival of BCG by influencing the production of proinflammatory cytokines. [ABSTRACT FROM AUTHOR]

Lithium cobalt oxide (LCO) has been widely used as a leading cathode material for lithium-ion batteries in consumer electronics. However, unstable cathode electrolyte interphase (CEI) and undesired phase transitions during fast Li+ diffusivity always incur an inferior stability of the high-voltage LCO (HV-LCO). Here, an ultra-thin amorphous titanium dioxide (TiO2) coating layer engineered on LCO by an atomic layer deposition (ALD) strategy is demonstrated to improve the high-rate and long-cycling properties of the HV-LCO cathode. Benefitting from the uniform TiO2 protective layer, the Li+ storage properties of the modified LCO obtained after 50 ALD cycles (LCO-ALD50) are significantly improved. The results show that the average Li+ diffusion coefficient is nearly tripled with a high-rate capability of 125 mAh g−1 at 5C. An improved cycling stability with a high-capacity retention (86.7%) after 300 cycles at 1C is also achieved, far outperforming the bare LCO (37.9%). The in situ XRD and ex situ XPS results demonstrate that the dense and stable CEI induced by the surface TiO2 coating layer buffers heterogenous lithium flux insertion during cycling and prevents electrolyte, which contributes to the excellent cycling stability of LCO-ALD50. This work reveals the mechanism of surface protection by transition metal oxides coating and facilitates the development of long-life HV-LCO electrodes. [ABSTRACT FROM AUTHOR]

Bioretention can be an efficient measure of stormwater treatment. The bioretention system has been globally adapted for controlling rainfall-runoff volume and removing pollutants. However, there is a lack of systematic analysis of some hydrological processes in the bioretention facility, such as the transportation of rainfall-runoff (event water) and soil water (pre-event water). This research uniquely applied stable isotope tracing to a bioretention system to investigate the transport of rainfall-runoff and the movement of soil water in a bioretention column. The stable isotope tracing method is helpful for determining the transport process of water and is expected to enhance our understanding of bioretention systems. The ratio of the event rainfall-runoff and the pre-event soil water in the bottom outflow of bioretention columns was calculated by the stable isotope bivariate mixing model based on the oxygen and hydrogen stable isotopic data from continuous testing in the discharge of bioretention column. The time-varying proportion of event water and pre-event water in the hydrographic curve reveals that the outflow process of soil water is divided into three stages. In the early stage, all the outflow was soil water, accounting for about 6–13% of the total outflow. In the middle stage, the proportion of soil water rapidly decreased from 100% to 20% within a few minutes. In the later stage, the soil water proportion decreased slowly from 20% to 0%. The outflow of soil water accounted for more than 36% of the total outflow and decreased with the extension of the drought period. Soil water is a critical part of the bottom outflow of bioretention columns, and the influence of soil water on the hydrological effect of bioretention columns cannot be ignored. [ABSTRACT FROM AUTHOR]

The smart city concept has taken center stage as a paradigm shift in urban governance, promising innovation, sustainability, and strategic upgrades, and drawing the attention of researchers globally. However, existing frameworks for assessing smart cities remain incomplete and simplistic. This paper aims to address the following question: what is the process and current situation of 30 capitals in China after the implementation of smart city construction, especially from the new perspective of social, physical, and informational space development? To this end, we focus on 30 national and provincial capitals in China, proposing a novel, tri-dimensional SPI model—Social, Physical, and Information space—for smart city spatial development assessment. Employing a robust methodological approach, including entropy weighting, coupled coordination degree models, and the Dagum Gini coefficient, we conduct a spatial development and coordination analysis of these cities from 2011 to 2021. In addition, we utilize BP neural networks to evaluate the contribution of each indicator to the spatial coupled coordination. Our findings indicate a steady increase in the spatial coupled coordination development level of smart capitals in China, alongside a narrowing disparity in development speeds across regions, resulting in a staggered spatial distribution pattern. Specifically, the Information space subsystem represents the most influential driver of coupled coordination. The significance of this research lies in its unique tri-dimensional spatial perspective, offering valuable insights into the spatial development and coordination discrepancies in the smart city concept. These insights offer evidence-based support for regional planning and optimization strategies in China. [ABSTRACT FROM AUTHOR]

Thanks to the line-scanning camera, the measurement method based on line-scanning stereo vision has high optical accuracy, data transmission efficiency, and a wide field of vision. It is more suitable for continuous operation and high-speed transmission of industrial product detection sites. However, the one-dimensional imaging characteristics of the line-scanning camera cause motion distortion during image data acquisition, which directly affects the accuracy of detection. Effectively reducing the influence of motion distortion is the primary problem to ensure detection accuracy. To obtain the two-dimensional color image and three-dimensional contour data of the heavy rail surface at the same time, a binocular color line-scanning stereo vision system is designed to collect the heavy rail surface data combined with the bright field illumination of the symmetrical linear light source. Aiming at the image motion distortion caused by system installation error and collaborative acquisition frame rate mismatch, this paper uses the checkerboard target and two-step cubature Kalman filter algorithm to solve the nonlinear parameters in the motion distortion model, estimate the real motion, and correct the image information. The experiments show that the accuracy of the data contained in the image is improved by 57.3% after correction. [ABSTRACT FROM AUTHOR]

Binary transition metal selenides (BTMSs) are more promising than single transition metal selenides (TMS) as anode materials of sodium-ion batteries (SIBs). However, it is still very challenging to prepare high-performance BTMSs in the pure phase, instead of a mixture of two TMSs. In this study, a binary metal center-based MOF derived selenization strategy was developed to prepare iron–cobalt selenide (Fe2CoSe4@NC) and iron–nickel selenide (Fe2NiSe4@NC) nanocomposites in the single phase and when wrapped with carbon layers. As the anode material of SIBs, Fe2CoSe4@NC exhibits higher long-term cycling performance than Fe2NiSe4@NC, maintaining a capacity of 352 mAh g−1 after 2100 cycles at 1.0 A g−1, which is ascribed to the higher percentage of the nanopores, larger lattice spacing, and faster Na+ diffusion rate in the electrode materials of the former rather than the latter. [ABSTRACT FROM AUTHOR]

Analyzing and breaking down nonstationary signals into their primary components is significant in various optical applications. In this work, we design a direct, localized, and mathematically rigorous method for nonstationary signals by employing a modulated short-time Fourier transform (MSTFT) that can be implemented efficiently using fast Fourier transform, subsequently isolating energy-concentrated sets through an approximate threshold process, allowing us to directly retrieve instantaneous frequencies and signal components by determining the maximum frequency within each set. MSTFT provides a new insight into the time-frequency analysis in multicomponent signal separation and can be extended to other time-frequency transforms. Beyond the analysis of the synthetic, we also perform real dual-comb ranging signals under turbid water, and the results show an approximate 1.5 dB improvement in peak signal-to-noise ratio, further demonstrating the effectiveness of our method in challenging conditions. [ABSTRACT FROM AUTHOR]

This study examines the use of SrAl2O4: Eu2+, Dy3+ long-afterglow materials doped into g-C3N4/TiO2 coatings for photodegradation. The prepared sample was tested for the purification of automotive exhaust fumes, with the optimal mass ratio of g-C3N4/TiO2 and SrAl2O4: Eu2+, Dy3+ determined to be 1:1. Characterization tests, including XRD, FT-IR, XPS, and TG-DSC, were conducted to evaluate the microstructure and properties of the samples. Under poor lighting conditions, g-C3N4/TiO2 reduced CH and NOx by 59 ppm and 13 ppm within 4 h, respectively, while g-C3N4/TiO2/SrAl2O4: Eu2+, Dy3+ decreased CH and NOx by 98ppm and 34ppm, respectively, resulting in a significant improvement in degradation efficiency. The addition of long-afterglow materials significantly improves the efficiency of photocatalysts in purifying exhaust fumes in low-light environments, providing potential value for all-weather exhaust treatment in the future. [ABSTRACT FROM AUTHOR]

During the operation of hypersonic vehicles, a reciprocal coupling effect is manifested between the inlet and the combustion chamber. This results in an unavoidable non-uniformity of conditions at the combustion chamber's entrance, which, in turn, influences the fuel mixing within the chamber. The present study employed the Reynolds-averaged Navier–Stokes (RANS) equations to perform a numerical simulation of an X-51-like vehicle, with a focus on examining the impact of isolation section length and multi-injection strategies on the fuel mixing characteristics within the combustion chamber under conditions of non-uniform inflow. The findings indicated that a supersonic non-uniform inlet triggers incident shock waves, leading to a non-uniform pressure distribution across the flow section. Moreover, the position of injection was found to be pivotal in regulating penetration depth and mixing efficiency. The incident shock wave, bow shock, and boundary layer separation shock interacted with each other to increase local pressure. The coupling of high and low pressures generated an adverse pressure gradient that led to boundary layer separation, which further enhanced fuel penetration depth. [ABSTRACT FROM AUTHOR]

Fault structures can cause a bad mining environment and increase the stress of surrounding coal pillar faults. The study investigates the stress evolution characteristics within fault structure groups and their surrounding coal pillars and explores the extent to which these fault structure groups influence the stress distribution in coal pillars. Based on three-dimensional modeling technology, a transparent geological model of the geological environment of fault structure groups was constructed and finite difference software was used to generate a numerical simulation model. Two survey lines and four survey points were arranged to analyze the stress distribution of a coal pillar fault. The results show that the fault structure groups have obvious stress barrier effects. There is a 35 m stress reduction zone in the hanging wall of the fault and a 30 m stress increase zone in the footwall of the fault. Both FL-1 and FL-3 faults have a stress barrier effect in the hanging wall. The obvious stress increases in the footwall of the fault are 37.7 MPa and 33.5 MPa, respectively. The stress of the FL-2 fault as a whole appears to be a more obvious superposition at the end of mining, and the peak stress reaches 41.5 MPa. [ABSTRACT FROM AUTHOR]

The Kaladawan iron deposit is located in the North Altyn Tagh and exhibits occurrences of iron ore bodies at the contact zone between Ordovician magmatic rocks (basalts, rhyolite, and granodiorite) and marble. However, controversy persists regarding the genetic classification and metallogenic mechanism of this deposit. Through a field investigation, single mineral in situ geochemical analysis, whole-rock geochemical analysis, and Fe isotope determination, the following conclusions are made: (1) Ti-(Ni/Cr) and (V/Ti)-Fe diagrams indicate that the magnetite from all studied rocks underwent hydrothermal metasomatism, while (Ni/(Cr + Mn))-(Ti + V) and (Ca + Al + Mn)-(Ti + V) diagrams suggest a skarn origin for these magnetites. Therefore, it can be inferred that the Kaladawan iron deposit is skarn-type. (2) The iron ore exhibits similar rare-earth-element characteristics to the altered basalt. Additionally, the altered basalts (δ56Fe = 0.024~0.100‰) are more enriched in light Fe isotopes than the unaltered basalts (δ56Fe = 0.129~0.197‰) at the same location, indicating that the ore-forming materials of the Kaladawan iron ore are mainly derived from basaltic rocks. (3) According to the law of mass conservation and the intermediate Fe isotopic composition of the iron ore between the granodiorite and basalt, the hydrothermal fluid for the formation of iron ores was inferred to be derived from the late intrusive granodiorite. [ABSTRACT FROM AUTHOR]