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Toward low-energy-consumption spintronic devices, magnetic multilayers that host chiral spin textures, as well as efficient spin-torques, are highly promising. As compared with resistive materials, superconducting materials are optimal for constructing dissipationless electronic devices, in which the electricity is conducted without producing Joule heating. In this regard, magnetic multilayers containing superconductors are well suited for building ultra-low power spintronic devices. Following this motivation, we study the possible existence of chiral spin textures in the two-dimensional (2D) high temperature superconductor/ferrimagnet heterostructures of stacking order Bi2Sr2CaCu2O8+δ/Fe1−xTbx and stacking order Bi2Sr2CaCu2O8+δ/Co1−xTbx. Through x-ray photon-emission electron microscopy, we observe bubble-like spin textures in a wide temperature range. Based on the first-principles calculations, the important role of spin–orbit interaction from the BiO termination layer is discussed, which induces a very large interfacial Dzyaloshinskii–Moriya interaction and results in the possible existence of chiral spin textures on top of superconductors. Our work suggests that the layered high-temperature superconductor could be incorporated for stabilizing chiral spin textures and for building dissipationless spin-orbitronic devices. [ABSTRACT FROM AUTHOR]

Bridge expansion joints (BEJs) in service are susceptible to damage from various factors such as fatigue, impact, and environmental conditions. While visual inspection is the most common approach for inspecting BEJs, it is subjective and labor-intensive. In this paper, we propose a novel methodology for detecting the fault status of BEJs, inspired by voiceprint recognition (VPR) based on audio signals. We establish an Artificial Neural Network to filter nonevent segments from low signal-to-noise ratio signals, achieving an AuC value of 0.981. We design and improve ConFormer VPR models with a multifeature aggregation strategy and cascade them to realize fault detection of BEJs. For three successive tasks in classifying environment sound types, vehicle impact types, and faults, the ConFormer VPR models achieve AuC values of 0.975, 0.925, and 0.886, respectively, demonstrating the feasibility of our methods for unmanned inspection of BEJs. In future research, the introduction of multiple types of damage and the implementation of benchmarking tests are planned to further enhance the capabilities of the system. [ABSTRACT FROM AUTHOR]

Timely wear evaluation is crucial in maintaining the functionality of bridge expansion joints (BEJs), ultimately ensuring the safety of bridges. Despite the significance of traffic load simulation (TLS) in simulation-based evaluation methods, existing TLS approaches face challenges in accurately modeling in situ traffic flow at a high fidelity. This paper presents a novel methodology and its application for evaluating the wear performance of BEJs, employing a Transformer-enhanced TLS approach. Initially, a tailored dataset is crafted for data-driven car-following modeling, leveraging an established spatial-temporal traffic load monitoring system. High-fidelity TLS with a mean absolute error (MAE) of 0.1738 m/s is then achieved using Transformer modules equipped with an attention mechanism. To evaluate the final wear life of BEJs, transient dynamic analysis and a calibrated finite element model of the bridge are employed to extract cumulative displacement. Additionally, a surrogate model is developed to depict the relationship between the hourly traffic weight on the entire bridge deck and the cumulative displacement of BEJs, yielding an impressive R-squared value of 0.96619. Comparative results demonstrate the superior performance of our proposed TLS approach over other data-driven approaches, with the linear model derived from our TLS approach outperforming the model generated by the conventional Monte Carlo-based TLS approach. To conclude, our proposed TLS emerges as a comprehensive and precise methodology for the wear evaluation of BEJs. [ABSTRACT FROM AUTHOR]

Suspenders play a crucial role in transmitting loads from the bridge deck to the main cable in a suspension bridge. They are susceptible to fatigue due to repeated dynamic loads, particularly traffic loads. Traffic Load Models (TLMs), typically created using Monte–Carlo simulation and Weigh-In-Motion (WIM) data, are employed to evaluate this fatigue. However, these models often overlook practical vehicle trajectories and spatio-temporal distribution, which compromises the precision of fatigue assessments. In this study, we introduce a novel 2D Intelligent Driver Model (2D-IDM) that incorporates actual vehicle trajectories, with a particular focus on transverse vehicle movement. This enhancement aims to improve the fidelity of existing TLMs. To provide a clear, qualitative, and quantitative understanding of the effects of fatigue evaluation with or without actual trajectory characteristics, we have structured this paper as a comparative study. We compare our proposed model, denoted as TLM S-3, with two observation-based models (O-1 and O-2) and two simulation-based models (S-1 and S-2). We conducted an experimental case study on a long-span suspension bridge, where the actual traffic load trajectory was obtained using a WIM-Vision integrated system. To calculate fatigue damage considering both longitudinal and transverse directions, we established a multi-scale Finite Element Model (FEM) using solid element types to simulate the bridge girder. This model can generate the stress influence surface of the bridge and has been verified in both static and dynamic aspects. Our detailed comparative analysis demonstrates the consistency of the proposed 2D-IDM with the actual measured traffic load trajectories. This indicates that our approach can enhance the fidelity and precision of fatigue evaluations for bridge suspenders. [ABSTRACT FROM AUTHOR]

The nondestructive characterization of citrus varieties (Egyptian sweet orange, Lane Late navel orange, Australian orange, and Blood orange) was developed based on near-infrared diffuse reflectance spectroscopy (NIRDRS) together with principal component analysis (PCA) and Fisher linear discriminant analysis (FLDA). An experiment for the penetration of NIRDRS into the peel was designed and the effects of different spectral acquisition points were investigated. Pretreatments were used to eliminate the spectral interferences. As an unsupervised pattern recognition method, PCA was used to establish the characterization models. Furthermore, supervised pattern recognition based on PCA and FLDA was employed to enhance the accuracy. The results of the penetration experiments show that near-infrared light enters the citrus peel and is able to characterize the internal composition. Even with the optimized spectral pretreatment, accurate characterization of citrus varieties was not achieved by PCA. However, the accurate characterization of citrus varieties was provided by PCA-FLDA. The accuracies of four spectral acquisition points are 95%, while the characterization accuracies of six spectral acquisition points are 100% combined with optimized spectral pretreatment. Therefore, NIRDRS with PCA-FLDA is suitable for the rapid and nondestructive characterization of citrus varieties. [ABSTRACT FROM AUTHOR]

An accurate and nondestructive prediction method of lime acidity was established based on near infrared (NIR) spectroscopy and ensemble learning strategy. Dual-band spectra were obtained nondestructively with a single scan using a desk-top Fourier transform spectrometer and a grating portable spectrometer. Spectral preprocessing methods were used to eliminate the interferences in the spectra. The quantification models of available acidity (pH and 10−pH) and total acidity (TA) were developed with the ensemble learning strategy compared with partial least squares (PLS) and variable selection methods. The results indicated that due to the high-energy light source, the models of the grating portable spectrometer were much better than those of the Fourier transform spectrometer. Short-wave NIR (SWNIR) was more suitable for quantitative analysis of available acidity, while long-wave NIR (LWNIR) was more effective for quantitative analysis of TA. Besides, the models of available acidity were ahead of those of TA. Compared with PLS and variables selection methods, the ensemble learning strategy can produce models with higher prediction accuracy and better robustness. In the optimized models, the correlation coefficients of pH, 10−pH and TA for the prediction set were 0.84, 0.82 and 0.66, respectively. The experiment results show that accurate and nondestructive prediction of lime acidity can be achieved with the grating portable NIR spectrometer and ensemble learning strategy. [Display omitted] • Simultaneous and nondestructive detection of pH and TA in lime was achieved. • Dual-band spectra were obtained with a single scan using two spectrometers. • Models of the grating portable spectrometer can satisfy practical application. • Ensemble learning strategy can produce models with higher prediction accuracy. • SWNIR was more suitable for pH, while LWNIR was more effective for TA. [ABSTRACT FROM AUTHOR]

Spin valve effect driven by current-induced spin–orbit torques can help to realize electrically controllable and miniaturized two terminal spintronic devices. Here, we show that in a perpendicularly magnetized Fe 1 − x 1 Tb x 1 /Pt/ Fe 1 − x 2 Tb x 2 spin-valve multilayer, spin currents from dual surfaces of the inserted Pt layer can be used to generate spin torques that can separately switch the adjacent ferrimagnetic Fe1−xTbx layers. In a two-terminal device, we further show that the accompanied parallel and anti-parallel magnetization configurations between the two Fe1−xTbx layers can lead to the observation of the current-in-plane giant magnetoresistance (CIP-GMR). Our results demonstrate that the current-induced spin–orbit torques can be implemented into two-terminal spin-torque devices, which can be electrically read out by the CIP-GMR. The present study could be important for designing miniaturized spintronic devices. [ABSTRACT FROM AUTHOR]

The spin–orbit torques (SOTs) induced by spin currents in the heavy metal/ferromagnet (HM/FM) bilayer are of great importance for enabling nonvolatile, low power consumption spintronics. In the standard HM/FM bilayers, only the spin current at the interface is employed for implementing SOTs. Another part of the spin current with the opposite spin polarization, which is generated at the opposite side of the HM layer, is supposably not put into action. Here, we will show the experimental feasibility of utilizing spin currents from the dual surfaces of the HM Pt layer. In particular, through synthesizing perpendicularly magnetized Fe 1 − x 1 Tb x 1 /Pt/ Fe 1 − x 2 Tb x 2 trilayers, we demonstrate that spin currents at the dual surfaces of the Pt layer can be simultaneously used to deterministically switch the perpendicular magnetization vectors of the neighboring Fe1-xTbx layers, through the current-induced SOTs. Based on the second harmonic measurement, we further quantify the enhanced efficiency of the current-induced SOTs. A three-dimensional spintronic device that can host an exponentially increased magnetization states is also demonstrated. Our results could be important for enabling more energy efficient spin-orbitronics that could be used in multistate memory and spin logic devices. [ABSTRACT FROM AUTHOR]

The explosive demands of storage capacity and the von Neumann bottleneck of modern computer architectures trigger many innovations in information technology. Amongst them, nonvolatile spintronics attract considerable attentions for which can embed the computation capability into memory, enable neuromorphic, and probabilistic computing. These exciting progresses typically rely on the manipulation of the relative magnetization orientations of two magnetic layers. By extending to 3D spintronic architectures made of multiple magnetic layers (n), the exponentially increased 2n magnetic states can provide ample opportunities for implementing novel spintronic functionalities. Here, through building perpendicularly magnetized 3D spin‐orbitronic architectures – [Pt/Fe1−xTbx/Si3N4]n multilayers, it is demonstrated the electrical programing of 2n memory states via current‐induced spin–orbit torques (SOTs), and the accompanied reconfigurable multifunction in‐memory logic features in a single four‐terminal Hall device. Further, an electrical readout of these 2n states, together with the implementation of Boolean logic gates and digital circuitry such as 2–4 and 3–8 decoders, are successfully conducted. More complex logic circuits are also envisioned. The experiments thus substantiate 3D spin‐orbitronic structures as a promising platform for exponentially boosting the storage capacity and accommodating in‐memory computing that can be important for promoting the emerging 3D nanospintronics. [ABSTRACT FROM AUTHOR]

In this paper, the penetration ability of near infrared (NIR) diffuse reflectance light to peel was studied with grating NIR spectrometer. 15 kinds of fruit, including 3 kinds of thin-peel non-citrus fruit, 9 kinds of medium-peel citrus and 3 kinds of thick-peel citrus were investigated. A new strategy to evaluate the NIR penetration ability was developed with a designed penetration experiment and chemometric methods. The penetration abilities of different integration time, peel thickness and wavelength bands were compared. The results show that NIR light can easily penetrate the peel of thin-peel fruit, while it is not easy to penetrate the peel of citrus due to the thick peel. Both the thickness and composition of the peel have relationship with the penetration ability. Increase of integration time can improve the penetration ability to some extent. However, as the integration time lengthens, noise interference and spectral distortion may appear in long wave near infrared (LWNIR) region. The optimal integration time is 65 ms for citrus. Besides, the penetration ability of short wave near infrared (SWNIR) light is better than that of LWNIR light, and the absorption peaks of peel are mainly located in LWNIR region, indicating that SWNIR is more suitable for fruit internal quality detection. [Display omitted] • A new method to evaluate the NIR penetration ability of peel was developed. • Penetrability of integration time, peel thickness and wavelength was compared. • It is not easy for NIR light to penetrate peel of citrus due to the thick peel. • Increase of integration time can improve NIR penetration ability to some extent. • Penetration ability of SWNIR light is better than those of LWNIR light. [ABSTRACT FROM AUTHOR]

To test whether sonographically determined fecal width (SDFW) correlates with symptom improvement in a population of children with bladder and bowel dysfunction (BBD) managed with standard urotherapy (SU), even for those patients lacking initial bowel complaints. We retrospectively analyzed 200 pediatric BBD patients managed with SU for at least 3 months. Self-reported symptom improvement (complete, partial, no response) following International Children's Continence Society guidelines was tabulated. Patients with complex urologic diagnoses other than vesicoureteral reflux (VUR) were excluded. Pharmacotherapy choice, physical therapy (PT), urinary tract infection (UTI) occurrence, and VUR status were tabulated. SDFW was recorded. Non-parametric analysis of variants (ANOVA) and parametric/non-parametric t testing were used for analysis. Patients had a mean age of 9.5 years (4-12). Forty-eight patients had no gastrointestinal complaints at presentation. Urotherapy yielded complete, partial, and no responses in 14% (n = 27), 33% (n = 67), and 53% (n = 106) of patients, respectively. The average SDFW for those patients with complete response (2.6 cm) was smaller than the SDFW of those with a partial response (3.1 cm) or no response (3.3 cm) (P =.0001). Non-compliance led to greater SDFW compared to compliant patients (3.7 cm and 3.1 cm, respectively, P =.0001). Fecal width was unaffected by VUR, UTI, PT, or pharmacotherapy. SDFW correlates well with symptom improvement in pediatric patients managed for BBD, confirming our hypothesis. SDFW is reasonable as single objective parameter to identify successful management in patients with BBD, extending to those without bowel complaints at presentation. [ABSTRACT FROM AUTHOR]

Vehicle Load Monitoring (VLM) on entire long-span bridge decks presents significant challenges due to the spatial and temporal randomness of vehicles. Existing VLM systems often suffer from limited viewing coverage and poor continuity of multi-vehicle tracking methods. This paper proposes a VLM system consisting of multi-vision image pre-processing, modified YOLO-v4 model, kinematics-enhanced vehicle tracking algorithm, and data fusion method between vision and Weigh-In-Motion sub-systems. The system was tested on a long-span bridge using six cameras, achieving vehicle monitoring of entire deck. The precision of multi-vehicle tracking achieved 99.28% built upon YOLO-v4 model with 96.2% mean Average Precision (mAP). Comparative results demonstrate that the modified YOLO-v4 model outperforms state-of-the-art approaches, and our proposed tracking method surpasses other methods. Our proposed system offers a comprehensive solution for VLM on entire bridge deck, overcoming the limitations of existing methods. Future work could extend the system's capability to include complex traffic patterns. [Display omitted] • Multi-view video data are seamlessly integrated to broaden limited monitoring field. • Original YOLO-v4 is modified, achieving SOTA performance in vehicle detection. • Kinematics-based strategies are exploited to achieve high-precision vehicle tracking. • Intersection principle is proposed, fusing vision-WIM data accurately. [ABSTRACT FROM AUTHOR]

Cable replacement has been commonly implemented to avoid its insufficient bearing capacity after long-term service. To guide better the cable maintenance and life assessment, the experimental studies on replaced cables are significant. As the fundamental step of replaced cable assessment, the corrosion examination of steel wires is required. The existing methods and systems mainly focus on the qualitative estimation and localization of corrosion, which is hard to fulfill the requirement of precise analyses. In this paper, a multi-vision scanning system is proposed to detect and represent the real corrosion and its distribution on the surface of a steel wire. Applied with a novel high-definition image scanning hardware and advanced panoramic image stitching processing algorithms, the surface 2D texture of a single steel wire can be scanned automatically. Afterward, by using the deep learning methods, the corrosion is precisely identified. Furthermore, non-uniformity indicators are proposed for the description of the apparent corrosion. The system has been verified and utilized in the inspection and evaluation of several steel wires from a replaced suspender. Due to it is effective and economical, the system is promising and serves as an instrument for both corrosion researches and structural assessment. [Display omitted] • Design and develop a novel flexibly extensible multi-vision system for visual examination of steel wires. • Deduct and accomplish the image processing algorithms for standard panoramic image acquisition of steel wires. • Propose the evaluation indicators reflecting the non-uniformity of corrosion by image identification of the panoramic image of steel wires. [ABSTRACT FROM AUTHOR]

Spectral data of the outer wall and inner capsule of Pericarpium Citri Reticulatae Viride from different regions (Anhui, Guangdong, and Sichuan) were collected with portable near infrared spectrometer. Multiple interferences in the spectra were eliminated using single and combined pretreatment methods. Pattern recognition methods such as principal component analysis (PCA), cluster independent soft pattern classification (SIMCA) and Fisher linear discriminant analysis (FLDA) were used to establish traceability models of Pericarpium Citri Reticulatae Viride origin. The results showed that spectral pretreatment could eliminate the interferences of baseline drift, background, and spectral peak overlap to a certain extent. However, the traceability analysis of the origin couldn't be achieved. Among the three pattern recognition methods, accurate traceability analysis of Pericarpium Citri Reticulatae Virid couldn't be achieved with PCA method. The whole identification rate of SIMCA model with the outer wall and inner capsule original spectra were 99.14% and 98.28%, respectively. The whole identification rates of FLDA models were both 99.57%, better than that of SIMCA models. The identification rates of SIMCA and FLDA models with appropriate spectral pretreatment were 100%. Therefore, portable near infrared spectroscopy technology combined with the supervised pattern recognition methods can achieve non-destructive traceability analysis of Pericarpium Citri Reticulatae Viride origin, expanding a new way for the traceability analysis of food and drug homologous substances origin. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) digital technology is essential to the maintenance and monitoring of cultural heritage sites. In the field of bridge engineering, 3D models generated from point clouds of existing bridges is drawing increasing attention. Currently, the widespread use of the unmanned aerial vehicle (UAV) provides a practical solution for generating 3D point clouds as well as models, which can drastically reduce the manual effort and cost involved. In this study, we present a semi-automated framework for generating structural surface models of heritage bridges. To be specific, we propose to tackle this challenge via a novel top-down method for segmenting main bridge components, combined with rule-based classification, to produce labeled 3D models from UAV photogrammetric point clouds. The point clouds of the heritage bridge are generated from the captured UAV images through the structure-from-motion workflow. A segmentation method is developed based on the supervoxel structure and global graph optimization, which can effectively separate bridge components based on geometric features. Then, recognition by the use of a classification tree and bridge geometry is utilized to recognize different structural elements from the obtained segments. Finally, surface modeling is conducted to generate surface models of the recognized elements. Experiments using two bridges in China demonstrate the potential of the presented structural model reconstruction method using UAV photogrammetry and point cloud processing in 3D digital documentation of heritage bridges. By using given markers, the reconstruction error of point clouds can be as small as 0.4%. Moreover, the precision and recall of segmentation results using testing date are better than 0.8, and a recognition accuracy better than 0.8 is achieved. [ABSTRACT FROM AUTHOR]

Interfacially asymmetric magnetic multilayers made of heavy metal/ferromagnet have attracted considerable attention in the spintronics community for accommodating spin‐orbit torques (SOTs) and meanwhile for hosting chiral spin textures. In these multilayers, the accompanied interfacial Dzyaloshinskii–Moriya interaction (iDMI) permits the formation of Néel‐type spin textures. While significant progresses have been made in Co, CoFeB, Co2FeAl, CoFeGd based multilayers, it would be intriguing to identify new magnetic multilayers that could enable spin‐torque controllability and meanwhile host nanoscale skyrmions. In this report, first, thin films made of permanent magnet SmCo5 with perpendicular magnetic anisotropy are synthesized, in which the deterministic SOT switching, enabled by the spin Hall effect, in Pt/SmCo5/Ta trilayer is demonstrated. Further, the stabilization of room‐temperature skyrmions with diameters ≈100 nm in [Pt/SmCo5/Ta]15, together with a skyrmionium‐like spin texture in [Pt/SmCo5/Ir]15 multilayers is shown. Based on the material specific parameters, micromagnetic simulations are also carried out. The results confirm the presence of chiral spin textures in this new material family. Through interfacial engineering, the results thus demonstrate that rare earth permanent magnets could be a new platform for studying interfacial chiral spintronics. [ABSTRACT FROM AUTHOR]

Van der Waals (vdW) ferromagnetic materials have attracted considerable attention in the nanomaterial community, which could provide a unique platform to study magnetism at the nanoscale. Along this direction, many interesting results have been reported, including the electric field control of magnetism and topological spin textures. In this report, we present a rapid and spatially resolved imaging method to study the dimensionality-dependent magnetic properties of Fe3GeTe2 (FGT) nanoflakes. Our method is named as polar magneto-optical Kerr imaging microscopy magnetometry (p-MIMM), which is made possible by analyzing the intensity evolution of wide-field polar magneto-optical Kerr effect (MOKE) images that were collected by varying magnetic fields, thicknesses, and temperatures. In particular, spatially resolved MOKE hysteresis loops can be acquired in the FGT nanoflakes with a submicrometer resolution. By analyzing the evolution of the relative (saturated) MOKE intensity as a function of temperature, we further study the critical exponent and universality class and its dependence on the FGT nanoflake thickness. Combining the polar MOKE images with the calculated MOKE hysteresis loops, a detailed magnetic phase diagram summarizing an evolution of the stripe domain, single domain, and paramagnetic state is further validated. Our results suggest that the wide-field p-MIMM can be conveniently used for rapidly examining the magnetic properties of versatile vdW magnetic materials. [ABSTRACT FROM AUTHOR]

Vehicle detection and classification have become important tasks for traffic monitoring, transportation management and pavement evaluation. Nowadays there are sensors to detect and classify the vehicles on road. However, on one hand, most sensors rely on direct contact measurement to detect the vehicles, which have to interrupt the traffic. On the other hand, complex road scenes produce much noise to consider when to process the signals. In this paper, a data-driven methodology for the detection and classification of vehicles using strain data is proposed. The sensors are well arranged under the bridge deck without traffic interruption. Next, a cascade pre-processing method is applied for vehicle detection to eliminate in-situ noise. Then, a neural network model is trained to identify the close-range following vehicles and separate them by Non-Maximum Suppression. Finally, a deep convolutional neural network is designed and trained to identify the vehicle types based on the axle group. The methodology was applied in a long-span bridge. Three strain sensors were installed beneath the bridge deck for a week. High robustness and accuracy were obtained by these algorithms. The methodology proposed in this paper is an adaptive and promising method for vehicle detection and classification under complex noise. It would serve as a supplement to current transportation systems and provide reliable data for management and decision-making. [ABSTRACT FROM AUTHOR]

An accurate and nondestructive detection method of hidden mold infection in citrus was established based on portable near infrared diffuse reflectance spectroscopy and chemometric methods. [Display omitted] • Nondestructive detection of hidden mold infection in citrus was achieved. • NIRDRS light was demonstrated to penetrate the citrus peel. • Models were validated by the validation and external validation sets. • Identification models of LWNIR were much better than those of SWNIR. • Identification accuracy of LWNIR-DT-PLS-DA model was 100%. An accurate and nondestructive detection method of hidden mold infection in citrus was established based on portable near infrared diffuse reflectance spectroscopy (NIRDRS) and chemometric methods. Penetrability of NIRDRS light on the peel of Chunjian hybrid citrus was studied. The results show that NIRDRS light can penetrate the peel to a certain extent, while the penetrability of short-wave near infrared (SWNIR) was better than that of long-wave near infrared (LWNIR). The identification models of hidden mold infection were established with five pattern recognition methods combined with different wavelength bands. The results show that the identification models of LWNIR were much better than those of SWNIR. 100% identification accuracies of LWNIR were obtained with soft independent pattern classification (SIMCA), support vector machine (SVM), partial least squares discriminant analysis (PLS-DA) and the optimized pretreatment methods. In addition, the developed models were further validated by the external validation set collected one month later. [ABSTRACT FROM AUTHOR]