Search Results (1,387)

This study reported the development of a novel Al-Si-Mg-based composite reinforced by micron-sized Cu-Mn binary solid solution phases and submicron-sized α-Al(Mn,Fe)Si dispersoids. The Cu-Mn binary solid solution phases were added to the melt in the form of an Al-3%CuMn master alloy, whereas α-Al(Mn,Fe)Si dispersoids were obtained via heat treatment. The microstructure analysis confirmed the presence of micron-sized Cu-Mn binary, eutectic Mg₂Si, and Al₁₅(FeMn)₃Si₂ intermetallic phases, submicron-sized α-Al(Mn,Fe)Si dispersoids, and nano-sized precipitates in the Al-based composite. At room temperature, tensile results represented a yield strength of 287 MPa and a tensile strength of 306 MPa, with an elongation of 17%. Moreover, the Al-based composite maintained a yield strength of 277 MPa up to 250 °C, with a slight increase in elongation. The composite also exhibited excellent high-temperature high-cycle fatigue properties and showed a high-cycle fatigue limit of 140 MPa at 130 °C, which is ~2.3 times higher than that of the commercial A319 alloy. A fractography study revealed that the secondary particles hindered the movement of dislocations, thus delaying crack initiation under cyclic loading at high temperatures. Additionally, Cu-Mn binary solid solutions and Al₁₅(FeMn)₃Si₂ phases were found to be effective in reducing the crack propagation rate by hindering the movement of the propagated crack. Full article

With the continuous growth of network threats, intrusion detection systems need to have robustness and adaptability to effectively identify malicious behaviors. However, factors such as noise interference, class imbalance, and complex attack pattern recognition have posed significant challenges to traditional systems. To address these issues, this paper proposes a dynamic preprocessing-enhanced DyP-CNX framework. The framework designs a sliding window dynamic interquartile range (IQR) standardization mechanism to effectively suppress the temporal non-stationarity interference of network traffic. It also combines a random undersampling strategy to mitigate the class imbalance problem. The model architecture adopts a CNN-XGBoost collaborative learning framework, combining a dual-channel convolutional neural network (CNN) and two-stage extreme gradient boosting (XGBoost) to integrate the original statistical features and deep semantic features. On the UNSW-NB15 and CSE-CIC-IDS2018 datasets, the method achieved F1 values of 91.57% and 99.34%, respectively. The experimental results show that the DyP-CNX method has the potential to handle the feature drift and pattern confusion problems in complex network environments, providing a new technical solution for adaptive intrusion detection systems. Full article

Road surface cracks are the most common and significant diseases in concrete pavement inspection. However, the presence of crack-like edges on objects such as water stains, fallen leaves, and ruts often result in the false detection of concrete pavement cracks. To better recognize pseudo-cracks, we first construct a novel dataset containing real pseudo-crack images for training and evaluation. To distinguish pseudo-cracks within images, a gradient prior is introduced to enhance the network’s perception of the detailed changes in crack edges, thereby improving its crack localization capability. Next, a self-attention mechanism is employed to focus on the extraction of global crack features, effectively mitigating interference from pseudo-crack features. Subsequently, deep global semantic features are fused with shallow detail features through dense connections, enriching feature extraction while circumventing the issue of edge gradient disappearance often encountered in deeper networks. Finally, the concatenation of deep global features with shallow detail features enhances the utilization of effective features, enabling robust pseudo-crack removal and preserving the continuity and integrity of the detected cracks. To validate the effectiveness of the proposed approach, we conduct comparative experiments with several crack detection methods across multiple datasets. The results demonstrate that our method achieves superior performance in both quantitative indicators and visual effects. Full article

As a critical technology of autonomous mining trucks, object detection directly determines system safety and operational reliability. However, autonomous mining trucks often work in dusty open-pit environments, in which dusty interference significantly degrades the accuracy of object detection. To overcome the problem mentioned above, a multi-branch feature interaction and location detection network (MBFILNet) is proposed in this study, consisting of multi-branch feature interaction with differential operation (MBFI-DO) and depthwise separable convolution-enhanced non-local attention (DSC-NLA). On one hand, MBFI-DO not only strengthens the extraction of channel-wise semantic features but also improves the representation of salient features of images with dusty interference. On the other hand, DSC-NLA is used to capture long-range spatial dependencies to focus on target-object structural information. Furthermore, a custom dataset called Dusty Open-pit Mining (DOM) is constructed, which is augmented using a cycle-consistent generative adversarial network (CycleGAN). Finally, a large number of experiments based on DOM are conducted to evaluate the performance of MBFILNet in dusty open-pit environments. The results show that MBFILNet achieves a mean Average Precision (mAP) of 72.0% based on the DOM dataset, representing a 1.3% increase compared to the Featenhancer model. Moreover, in comparison with YOLOv8, there is an astounding 2% increase in the mAP based on MBFILNet, demonstrating detection accuracy in dusty open-pit environments can be effectively improved with the method proposed in this paper. Full article

To address the detection of stealthy False Data Injection Attacks (FDIA) that evade traditional detection mechanisms in smart grids, this paper proposes an unsupervised learning framework named SHAP-LUNAR (SHapley Additive ExPlanations-Learnable Unified Neighborhood-based Anomaly Ranking). This framework overcomes the limitations of existing methods, including parameter sensitivity, inefficiency in high-dimensional spaces, dependency on labeled data, and poor interpretability. Key contributions include (1) constructing a lightweight k-nearest neighbor graph through learnable graph aggregation to unify local anomaly detection, significantly reducing sensitivity to core parameters; (2) generating negative samples via boundary uniform sampling to eliminate dependency on real attack labels; (3) integrating SHAP for quantifying feature contributions to achieve feature-level model interpretation. Experimental results on IEEE 14-bus and IEEE 118-bus systems demonstrate F1 scores of 99.40% and 96.79%, respectively, outperforming state-of-the-art baselines. The method combines high precision, strong robustness, and interpretability. Full article

In recent years, digital twin technology has demonstrated remarkable potential in intelligent transportation systems, leveraging its capabilities of high-precision virtual mapping and real-time dynamic simulation of physical entities. By integrating multi-source data, it constructs virtual replicas of vehicles, roads, and infrastructure, enabling in-depth analysis and optimal decision-making for traffic scenarios. In vehicular networks, existing information caching and transmission systems suffer from low real-time information update and serious transmission delay accumulation due to outdated storage mechanism and insufficient interference coordination, thus leading to a high age of information (AoI). In response to this issue, we focus on pairwise road side unit (RSU) collaboration and propose a digital twin-integrated framework to jointly optimize information caching and communication power allocation. We model the tradeoff between information freshness and resource utilization to formulate an AoI-minimization problem with energy consumption and communication rate constraints, which is solved through deep reinforcement learning within digital twin systems. Simulation results show that our approach reduces the AoI by more than 12 percent compared with baseline methods, validating its effectiveness in balancing information freshness and communication efficiency. Full article

Against the backdrop of green and sustainable development, green innovation has become a central issue of concern for both society and academia. Based on regional innovation system and network theories, this study conceptualizes the urban knowledge base as a network structure rather than a simple collection of isolated knowledge elements. Using green patent licensing data, a multi-layer network is constructed, and the Exponential Random Graph Model (ERGM) is employed to examine the impact of urban knowledge network structures on city-level innovation diffusion. The study finds that in the green ICT field, cities’ deep embedding in knowledge networks weakens their ability to absorb external innovations, while broad embedding facilitates the introduction of external innovations. In the green transportation field, deep embedding in knowledge networks enhances the absorption of external innovations, whereas broad embedding has no significant effect. In both fields, knowledge combination potential and knowledge uniqueness promote the outward diffusion of local innovations but weaken the inflow of external innovations. This study not only offers theoretical insights into innovation diffusion at the city level but also provides guidance for policymakers in developing targeted urban sustainable development strategies. Full article

Cobalt-based bulk metallic glasses (Co-based BMGs) offer a combination of high strength, corrosion resistance, and soft magnetic properties, yet their limited glass-forming ability (GFA) and poor room-temperature ductility restrict broader application. In this study, a microalloying strategy was applied to the Co₆₁Nb₈B₃₁ base composition to develop Co-Nb-B-Si and Co-Fe-Nb-B-Si systems. The effects of Si addition and Fe substitution on GFA, thermal stability, and mechanical properties were systematically investigated. Si doping combined with Co/B ratio tuning broadened the supercooled liquid region and increased the critical glass-forming diameter from 1 mm to 3 mm. Further addition of 5 at.% Fe expanded the supercooled liquid region and enabled the fabrication of a fully amorphous plate with 1 mm thickness. The optimized Co₆₃Nb₈B₂₇Si₂ alloy exhibited a compressive strength of 5.18 GPa and a plastic strain of 3.81%. Fracture surface analysis revealed ductile fracture features in the Si-containing alloy and brittle characteristics in Fe-rich compositions. These results demonstrate that microalloying is effective in optimizing the balance between GFA and mechanical performance of Co-based BMGs, offering guidance for composition and processing design. Full article

Geometrically frustrated magnets exhibit exotic excitations due to competing interactions between spins. The spinel compound MgCr₂O₄, a three-dimensional Heisenberg antiferromagnet, hosts both spin-wave and spin-resonance modes, but the origin of its resonant excitations remains debated. Suppressing magnetic order via non-magnetic doping can help isolate these modes in neutron scattering studies. We synthesized Ga³⁺ and Cd²⁺-doped MgCr₂O₄ via solid-state reaction and analyzed their structure and magnetism. Ga³⁺ doping (0–20%) causes anomalous lattice shrinkage due to site disorder from Ga³⁺ occupying both Mg²⁺ and Cr³⁺ sites. Magnetically, Ga³⁺ doping drives the system from the antiferromagnetic order to a spin-glass state, fully suppressing magnetic ordering at 20% doping. In contrast, Cd²⁺ replaces only Mg²⁺, expanding the lattice and meantime inducing strong spin-glass behavior. At 10% Cd²⁺, long-range antiferromagnetic order is entirely suppressed. Thus, 10% Cd-doped MgCr₂O₄ offers an ideal platform to study the resonant magnetic excitations without any spin-wave interference. Full article

This study developed an inertial measurement unit (IMU)-enhanced bidirectional reflectance distribution function (BRDF) imaging system to address angular errors in multi-angle polarimetric measurements. The system integrates IMU-based closed-loop feedback, motorized motion, and image calibration, achieving zenith angle error reduction of up to 1.2° and angular control precision of approximately 0.05°. With a modular and lightweight structure, it supports rapid deployment in field scenarios, while the 2000 mm rail span enables detection of large-scale targets and three-dimensional reconstruction beyond the capability of conventional tabletop devices. Experimental evaluations on six representative materials show that compared with mark-based reference angles, IMU feedback consistently improves polarimetric accuracy. Specifically, the degree of linear polarization (DoLP) mean deviations are reduced by about 5–12%, while standard deviation fluctuations are suppressed by 20–40%, enhancing measurement repeatability. For the angle of polarization (AoP), IMU feedback decreases mean errors by 10–45% and lowers standard deviations by 10–37%, ensuring greater spatial phase continuity even under high-reflection conditions. These results confirm that the proposed system not only eliminates systematic angular errors but also achieves robust stability in global measurements, providing a reliable technical foundation for material characterization, machine vision, and volumetric reconstruction. Full article

Conventional commuting mode split models are characterized by inherent limitations in dynamic adaptability, primarily due to persistent dependence on periodic survey data with significant temporal gaps. A dominant transportation distance-based modeling framework for commuting mode choice is proposed, formalizing a generalized cost function. Through the application of random utility theory, probability density curves are generated to quantify mode-specific dominant distance ranges across three demographic groups: car-owning households, non-car households, and collective households. Empirical validation was conducted using Dongguan as a case study, with model parameters calibrated against 2015 resident travel survey data. Parameter updates are dynamically executed through the integration of big data sources (e.g., mobile signaling and LBS). Successful implementation has been achieved in maintaining Dongguan’s transportation models during the 2021 and 2023 iterations. Full article

Diffuse correlation tomography (DCT) is an emerging technique for detecting diseases associated with localized abnormal perfusion from near-infrared light intensity temporal autocorrelation functions (g₂(τ)). However, a critical drawback of traditional reconstruction methods is the imbalance between optical measurements and the voxels to be reconstructed. To address this issue, this paper proposes Conv-TransNet, a convolutional neural network (CNN)–Transformer hybrid model that directly maps g₂(τ) data to blood flow index (BFI) images. For model training and testing, we constructed a dataset of 18,000 pairs of noise-free and noisy g₂(τ) data with their corresponding BFI images. In simulation validation, the root mean squared error (RMSE) for the five types of anomalies with noisy data are 2.13%, 4.43%, 2.15%, 4.05%, and 4.39%, respectively. The MJR (misjudgment ratio)of them are close to zero. In the phantom experiments, the CONTRAST of the quasi-solid cross-shaped anomaly reached 0.59, with an MJR of 2.21%. Compared with the traditional Nth-order linearization (NL) algorithm, the average CONTRAST of the speed-varied liquid tubular anomaly increased by 0.55. These metrics also demonstrate the superior performance of our method over traditional CNN-based approaches. The experimental results indicate that the Conv-TransNet model would achieve more accurate and robust reconstruction, suggesting its potential as an alternative for blood flow imaging. Full article

Lithium–selenium batteries (LSeBs) have potential applications in mobile electronic devices and electric vehicles due to their high theoretical volume specific capacity (3253 mAh cm⁻³). However, their cycling performance is poor because of the serve shuttle effect. Porous carbon can restrict the shuttle effect. However, past porous carbon is cumbersome, expensive, and unsuitable for large-scale production. In this work, we develop an annealing/etching method to convert biowaste (Rapeseed meal) to a N, S co-doped three-dimensional porous carbon (NSPC) which is then used as the Se host for LSeBs. The Se/NSPC composite delivers a specific capacity of 496.5 mAh g⁻¹ for 200 cycles at 0.2 C, corresponding to a high-capacity retention of 91.8%. Moreover, the Se/NSPC composite maintains a high capacity over 200 mAh g⁻¹ after 1000 cycles at a high current density of 2 C. Our work provides an efficient approach to addressing biowaste issues while simultaneously facilitating the mass production of economical Se hosts for LSeBs. Full article

Icing represents a significant hazard to the flight safety of unmanned aerial vehicles (UAVs), particularly affecting critical aerodynamic surfaces such as air intakes, wings, and empennages. While conventional adhesive electrothermal de-icing systems are straightforward to operate, they present safety concerns, including a 15–25% increase in system weight, elevated anti-/de-icing power consumption, and the risk of interlayer interface delamination. To address the objectives of reducing weight and power consumption, this study introduces an innovative electrothermal–structural–durability co-design strategy. This approach successfully led to the development of a glass fiber-reinforced polymer (GFRP) component that integrates anti-icing functionality with structural load-bearing capacity, achieved through an embedded hot-pressing process. A stress-damage cohesive zone model was utilized to accurately quantify the threshold of mechanical performance degradation under electrothermal cycling conditions, elucidating the evolution of interfacial stress and the mechanism underlying interlayer failure. Experimental data indicate that this novel component significantly enhances heating performance compared to traditional designs. Specifically, the heating rate increased by approximately 202%, electrothermal efficiency improved by about 13.8% at −30 °C, and interlayer shear strength was enhanced by approximately 30.5%. This research offers essential technical support for the structural optimization, strength assessment, and service life prediction of UAV anti-icing and de-icing systems in the aerospace field. Full article