Explore the vast range of titles available.

The comprehensive benefit evaluation of LID based on multi-criteria decision-making methods faces technical issues such as the uncertainties and vagueness in hybrid information sources, which can affect the overall evaluation results and ranking of alternatives. This study introduces a multi-indicator fuzzy comprehensive benefit evaluation approach for the selection of LID measures, aiming to provide a robust and holistic framework for evaluating their benefits at the community level. The proposed methodology integrates quantitative environmental and economic indicators with qualitative social benefit indicators, combining the use of the Storm Water Management Model (SWMM) and ArcGIS for scenario-based analysis, and the use of hesitant fuzzy language sets and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for decision-making. The framework’s novelty lies in the integration of the hesitant fuzzy weighted average algorithm to handle subjective uncertainties in expert judgment and the incorporation of multi-return period scenarios to enhance the robustness of the evaluation. The comprehensive benefits of 26 LID configurations were conducted in Chenglong Road Subdistrict under five rainfall return period scenarios of 5, 10, 20, 50, and 100 years. The results show that LID measures, particularly combinations of sunken green spaces and permeable paving, offer significant reductions in runoff and peak flow, along with improved flood mitigation across multiple return periods. Additionally, this study identifies practical LID implementation priorities for local decision-makers. The relative closeness is influenced by the indicators and non-calibrated parameters. However, it overall does not affect the main trends and key insights derived. The robustness of the proposed approach is reinforced by four key aspects: the impact of the Thiessen polygon method in ArcGIS, the influence of composite runoff coefficient and iterative optimization in SWMM, the effect of hesitant fuzzy linguistic sets and TOPSIS on weight calculation, and the contribution of simulations under different return periods to stability analysis.

The installation of arterial stents refers to the use of stents (also known as vascular stents) to maintain the patency of arteries during the treatment of arterial stenosis or blockage. Arterial stents are typically made of metal or polymer materials and are structured as a mesh that provides support within the blood vessel, preventing it from collapsing again after interventional treatment. The installation of arterial stents is an effective interventional therapy that can significantly improve symptoms caused by arterial stenosis or blockage and enhance the quality of life for patients. Endovascular therapy has become increasingly important for treating both thoracic and abdominal aortic diseases. A critical aspect of this procedure is the precise positioning of stents and complete isolation of the pathology. To enhance stent placement accuracy, we propose a deep learning model called the Double Branch Medical Image Detector (DBMedDet), which offers real-time guidance for stent placement during implantation surgeries. The DBMedDet model features a parallel dual-branch edge feature extraction network, a bidirectional feedback feature fusion neck sub-network, as well as a position detection head and a classification head specifically designed for thoracic and abdominal aortic stents. The model has achieved a detection Mean Average Precision (mAP) of 0.841 (mAP@0.5) and a real-time detection speed of 127 Frames Per Second (FPS). For mAP@0.5, when employing 5-fold cross-validation, DBMedDet demonstrates superior performance compared to several YOLO models, achieving improvements of 4.88% over YOLOv8l, 4.61% over YOLOv8m, 3.20% over YOLOv8s, 6.23% over YOLOv8n, 6.09% over YOLOv10s, 3.92% over YOLOv9s, 3.20% over YOLOv8s, 3.00% over YOLOv7tiny, and 5.01% over YOLOv5s. This study presents a precise and easily implementable method for the automatic detection of stent placement limits in the thoracic and abdominal aorta. The model can be applied in various areas such as coronary intervention therapy, peripheral vascular intervention therapy, cerebrovascular intervention therapy, postoperative monitoring and follow-up, and medical training and education. By utilizing real-time imaging guidance and deep learning models (such as DBMedDet), stent placement procedures in these application areas can be performed with greater precision and safety, thereby enhancing patient treatment outcomes and quality of life.

This study investigates the effects of chromium (0.4~1.2) Cr content and temperature (35–80 °C) on the corrosion behavior and mechanisms of steels in a water-saturated supercritical CO2 (S-CO2) environment, aiming to provide theoretical foundations for material selection and corrosion management in S-CO2 pipeline systems. Results indicate that increasing Cr content promotes the formation of granular bainite as the dominant microstructure, accompanied by refined martensite–austenite (MA) constituents with increased population and reduced dimensions, leading to enhanced strength at the expense of toughness. In the S-CO2/H2O environment, Cr reacts with CO2 to form a dense Cr2O3 layer, significantly suppressing the corrosion rate. Temperature critically governs corrosion kinetics: at 35 °C, where S-CO2 exhibits maximum density and CO2 solubility in water peaks, electrochemical corrosion dominates, resulting in the highest corrosion rate. As temperature rises, the corrosion mechanism transitions to chemical corrosion, while accelerated formation of protective corrosion product films further reduces corrosion rates. Mechanistic analysis reveals that uniform corrosion arises from carbonic acid generated by water dissolution in S-CO2, whereas localized corrosion intensifies upon direct contact between precipitated aqueous phases and the steel surface. These findings offer critical theoretical foundations for optimizing material design, operational parameters, and corrosion mitigation strategies in S-CO2 transportation infrastructure.

Recently, the transformer model has progressed from the field of visual classification to target tracking. Its primary method replaces the cross-correlation operation in the Siamese tracker. The backbone of the network is still a convolutional neural network (CNN). However, the existing transformer-based tracker simply deforms the features extracted by the CNN into patches and feeds them into the transformer encoder. Each patch contains a single element of the spatial dimension of the extracted features and inputs into the transformer structure to use cross-attention instead of cross-correlation operations. This paper proposes a reconstruction patch strategy which combines the extracted features with multiple elements of the spatial dimension into a new patch. The reconstruction operation has the following advantages: (1) the correlation between adjacent elements combines well, and the features extracted by the CNN are usable for classification and regression; (2) using the performer operation reduces the amount of network computation and the dimension of the patch sent to the transformer, thereby sharply reducing the network parameters and improving the model-tracking speed.

Landslides are a prevalent geological hazard in China, posing significant threats to life and property. Landslide susceptibility assessment is essential for disaster prevention, and the quality of non-landslide samples critically affects model accuracy. This study takes Yongxin County, Jiangxi Province, as a case, selecting ten susceptibility factors and applying the Random Forest (RF) model with six non-landslide sampling methods for comparison. Results indicate that non-landslide sample selection substantially influences model performance, with the RF model using the IV method achieving the highest accuracy (AUC = 0.9878). SHAP analysis identifies NDVI, slope, lithology, land cover, and elevation as the primary contributing factors. Statistical results show that RF_IV non-landslide sample predictions are lowest, mainly below 0.18, with a median of 0.18, confirming that the IV method effectively excludes landslide-prone areas and accurately represents non-landslide regions. These findings provide practical guidance for landslide risk managers, local authorities, and policymakers, and offer methodological insights for researchers in geological hazard modeling.

This study systematically evaluates the influence of copper (Cu) addition in gas-shielded solid wires on the microstructure and cryogenic toughness of X80 pipeline steel welds. Welds were fabricated using solid wires with varying Cu contents (0.13–0.34 wt.%) under identical gas metal arc welding (GMAW) parameters. The mechanical capacities were assessed via tensile testing, Charpy V-notch impact tests at −20 °C and Vickers hardness measurements. Microstructural evolution was characterized through optical microscopy (OM), scanning electron microscopy (SEM) and electron backscatter diffraction (EBSD). Key findings reveal that increasing the Cu content from 0.13 wt.% to 0.34 wt.% reduces the volume percentage of acicular ferrite (AF) in the weld metal by approximately 20%, accompanied by a significant decline in cryogenic toughness, with the average impact energy decreasing from 221.08 J to 151.59 J. Mechanistic analysis demonstrates that the trace increase in the Cu element. The phase transition temperature and inclusions is not significant but can refine the prior austenite grain size of the weld, so that the total surface area of the grain boundary increases, and the surface area of the inclusions within the grain is relatively small, resulting in the nucleation of acicular ferrite within the grain being weak. This microstructural transition lowers the critical crack size and diminishes the density for high-angle grain boundaries (HAGBs > 45°), which weakens crack deflection capability. Consequently, the crack propagation angle decreases from 54.73° to 45°, substantially reducing the energy required for stable crack growth and deteriorating low-temperature toughness.

Permanent magnet linear synchronous motors (PMLSMs) with stator segmented structures are widely used in the design of high-power propulsion systems. However, due to the inherent delay and segmented structure of the systems, there are parameter disturbances in the inductance and flux linkage of the motors. This makes the deadbeat predictive current control (DPCC) algorithm for a current loop less robust in the control system, leading to a decrease in control performance. Compensation methods such as compensation by observer and online estimation of parameters, are problematic to apply in practice due to the difficulty of parameter adjustment and the high complexity of the algorithm. In this paper, a robustness-improved incremental DPCC (RII-DPCC) method—which uses incremental DPCC (I-DPCC) to eliminate flux linkage parameters—is proposed. The stability of the current loop was evaluated through zero-pole analysis of the discrete transfer function. Current feedforward was introduced to improve the stability of I-DPCC. The inductance stability range of I-DPCC was increased from 0.8–1.25 times to 0–2 times the actual value, and the theoretical stability range was increased more than 4 times, effectively improving the robustness of the predictive model to flux linkage and inductance parameters. Finally, the effectiveness of the proposed method was verified through numerical simulation and experiment.

The suspension gap of the electromagnetic suspension maglev train is around 8 mm. In practice, it is found that the system gap fluctuations are amplified due to the inner coupling of the suspension module system in the maglev train. In addition, maglev trains are affected by load disturbances and parameter perturbations during operation. These uncertainties reduce the ride comfort. Therefore, it is necessary to propose a novel control strategy to suppress inner coupling while reducing the influence of uncertainties on the system. In this paper, a control strategy based on feedback linearization and extended state observer (ESO) is proposed to address this challenge. Firstly, the suspension module system model is established with parameter uncertainties and external disturbances. Additionally, the inner coupling of the suspension module is represented in this model. Subsequently, the feedback linearization method based on differential geometry theory is applied to reduce the effect of inner coupling. Meanwhile, the system uncertainties are transformed into equivalent disturbances by this method. Afterward, a linear ESO is designed to estimate the equivalent disturbances. Finally, a state feedback controller is used to achieve stable suspension and compensate for the disturbances. Simulation and experimental results show that the proposed decoupled control strategy significantly suppresses the influence of inner coupling and uncertainties on the system compared with the traditional PID control strategy.

In order to analyze the dynamic response of medium-speed maglev train in the speed range of 0–200 km/h, the suspension performance, suspended energy consumption, and riding comfort of the train stimulated by random track irregularities are discussed in this paper. Firstly, the model of medium-speed maglev train including car body, air spring vibration isolation system, and the suspension system is established. Then, a controller based on flux inner feedback loop and PID outer feedback loop is designed for the suspension system. The established model is stimulated by the actual track power spectrum in full speed range. The simulation results show that the fluctuation of suspension gap is less than ±4 mm. Furthermore, thanks to the adding of permanent magnet, the power consumption is significantly reduced, which is of benefit to the electromagnet heating problem and on-board levitation power supply system. The riding comfort of the train moving on the irregular track using Sperling index is assessed. The experimental results validate the effectiveness of the proposed analytical calculation model of medium-speed maglev train. It is shown that medium-speed maglev train achieved good performance, significant power reduction, and satisfactory riding comfort.

PurposeElevated CO2 contributes greatly to global warming, playing a pivotal role in terrestrial ecosystem processes, in particular microbially regulated C and N cycling. However, the responses of microbial C and N anabolic and metabolic processes to elevated CO2 are unclear.MethodsThis study used a meta-analysis based on a global dataset (i.e., 312 observations from 66 studies) to calculate the effect size (i.e., natural log response ratio) of soil microbial C and N metabolic processes and relevant soil C and N concentrations under elevated CO2.ResultsResults showed that elevated CO2 increased soil total C concentrations by 5.3% and total N concentrations by 4.8%, and decreased soil dissolved organic N and NO3− concentrations by 4.4% and 9.4%, respectively, but did not affect dissolved organic C or C:N ratios across global terrestrial ecosystems. Elevated CO2 significantly increased soil CO2 emissions and microbial biomass C by 19.3% and 13.3%, respectively, indicating that elevated CO2 increased both microbial anabolic and catabolic processes in soil. Similarly, elevated CO2 significantly increased soil N2O emissions and microbial biomass N by 18.7% and 9.0%, respectively. Microbial C cycling processes were associated with microbial N cycling processes under elevated CO2. Specifically, CO2 and N2O emissions were highest in soils with moisture contents of 40–60% and 60–80%, respectively, and microbial biomass C was largest in soils with pH values of 6.5–7.5.ConclusionOur findings demonstrated the profound impacts of elevated CO2 on microbially regulated C and N metabolic processes and the close linkage between soil microbial C and N cycling under global warming.