Search Results (1,073)

Local Lorentz Invariance is one of the fundamental postulates of General Relativity, making its experimental verification of paramount importance. Given that various frontier theoretical models predict potential symmetry breaking, the Standard Model Extension framework has been established to systematically study such phenomena. Within the Standard Model Extension gravitational sector, the high-order Lorentz-violating terms with mass dimension $d=6$ exhibit a rapid signal decay with distance, providing a distinct detection advantage in short-range gravity experiments. This work is dedicated to optimizing the testing schemes for $d=6$ Lorentz-violating coefficients. Based on a high-precision torsion balance platform, we propose a novel scheme featuring a comb-stripe design. The improvements are twofold: first, the spatial orientation of the experimental apparatus is optimized to leverage the modulation effects of the Earth’s rotation, thereby enhancing the capability to distinguish and constrain different violation parameters; second, the test and source masses are reconfigured into specifically designed stripe patterns to significantly amplify the fringe-field signals sensitive to Lorentz-violating effects. This paper systematically elaborates on the theoretical foundation and design principles of the new scheme. By performing a detailed comparison of the constraint potentials of various stripe configurations, the five-stripe geometry is identified as the optimal experimental configuration. This study provides a new experimental methodology for exploring physics beyond the Standard Model at higher levels of precision. Full article

Motor vehicle encroachment into non-motorized lanes is a common but hard-to-verify violation in urban intersections, especially when monitored from unmanned aerial vehicles (UAVs) or high-mounted overhead views. Existing rule-based solutions built on horizontal bounding boxes and center-point/line-crossing criteria are sensitive to perspective distortion, occlusion, and frame-to-frame jitter, resulting in unstable decisions and low evidential value. This paper presents a cascaded UAV-view system that closes the loop from perception to evidence output through detection–segmentation–recognition–decision. First, we adopt a two-stage detection cascade: a lightweight vehicle detector localizes vehicles using axis-aligned bounding boxes, and a dedicated YOLOv5n-based oriented bounding box (OBB) license plate detector, constructed via architecture grafting and weight transfer, is then applied within each vehicle region of interest (ROI) to localize rotated license plates under large pose variation and small-target conditions. Second, a U-Net lane region segmentation module provides pixel-level spatial constraints to define an enforceable lane occupancy region. Third, a perspective rectification step is integrated with the PP-OCRv4 optical character recognition (OCR) framework to improve license plate recognition reliability for tilted plates. Finally, an area ratio criterion and an N-frame temporal counter are used to suppress transient misdetections and stabilize alarms. On a representative 100-sample controlled encroachment benchmark, the proposed system improves detection accuracy from 67.0% to 92.0% and reduces the false positive rate from 32.35% to 5.88% compared with a baseline horizontal bounding box (HBB)-based rule. The system outputs both violation alarms and license plate evidence, supporting practical deployment for multi-view traffic governance. Full article

Synthetic aperture radar (SAR) imagery plays a vital role in critical applications such as military reconnaissance and disaster monitoring. These applications require high detection accuracy. Therefore, rotated object detection has gained increasing attention. By predicting an object orientation angle, it offers advantages over horizontal bounding boxes, especially for elongated structures such as ships and bridges in SAR scenes. However, challenges such as speckle noise and complex backgrounds in SAR imagery still hinder high-precision detection. To address this, we propose WE-KAN, a novel rotated object detection framework based on wavelet features and Kolmogorov–Arnold network (KAN) prediction. First, we enhance the backbone by incorporating wavelet domain features from SAR grayscale images. The extracted wavelet domain features and image features are fused by a proposed attention module. Second, considering the sensitivity to angle prediction, we design a angle predictor based on KAN. This architecture provides a powerful and dedicated solution for accurate angle regression. Finally, for precise rotated bounding box regression, we employ a joint loss function combining a rotated intersection over union (RIoU) with a Gaussian distance loss function. These designs improve the model’s robustness to noise and its perception of fine object structures. When evaluated on the large-scale public RSAR dataset, our method achieves an AP50 of 70.1 and a mAP of 35.9 under the same training schedule and backbone network, significantly outperforming existing baselines. This demonstrates the effectiveness and robustness of our method for dense, small, and highly oriented objects in complex SAR scenes. Full article

This study presents a novel self-adaptive marine current power generation system capable of operating efficiently across a wide range of flow velocities. The key innovations include an adaptive variable-solidity rotor and a non-contact magnetic speed-increasing dynamic seal. The rotor employs foldable blades that enable passive solidity regulation in response to varying inflow conditions. At low flow velocities, the blades remain deployed, increasing rotor solidity and reducing the required startup flow velocity. Water tank experiments indicate that the minimum startup velocity of the variable-solidity rotor is 0.217 m/s, which represents a 38% reduction compared to a conventional rotor. At high flow velocities, the blades fold under increased hydrodynamic loading, thereby reducing the effective solidity and suppressing torque growth to provide overload protection. The power transmission module incorporates a non-contact magnetic speed-increasing dynamic seal, which ensures underwater dynamic sealing of the main shaft while simultaneously increasing the rotational speed of the driven shaft. Motor-driven bench tests demonstrate that when the active shaft speed remains below the cut-off threshold, a stable speed-increasing ratio of 2:1 is maintained, enabling effective speed amplification and torque transmission. Once the active shaft speed exceeds the cut-off threshold, the driven shaft automatically stalls, thereby preventing motor overload. Overall, this work provides an effective solution for enhancing the operational adaptability and transmission reliability of marine current energy conversion systems under variable flow conditions. Full article

Indoor pedestrian navigation tasks, as a key part of smart cities and navigation services, face dual challenges of accuracy and cost under complex building environments. Currently, neural inertial navigation is at the vanguard of current research in indoor pedestrian navigation, and existing related studies have achieved positive results. However, the exploration of deep learning solutions is still not sufficient, mainly reflected in the lack of explorations of model training configurations. Based on testing results under different deep learning schemes, this paper proposes EDIN, an enhanced deep inertial navigation approach. This method benefits from a proprietary neural network based on ResNeXt with Convolutional Block Attention Module (CBAM) to predict the relationship between inertial data and motion trajectory. Compared to existing projects, this paper also makes improvements in the model training process, thereby improving the predictive effect of the trained model. Specifically, this paper innovatively uses Logcosh as the loss function and combines data rotation and additional noise as data augment methods. To assess EDIN’s performance, extensive tests were conducted using three publicly available datasets: RoNIN, OXIOD, and RIDI. The results clearly indicate EDIN’s superior performance relative to other neural inertial navigation systems. Notably, localization accuracy improved significantly, with an average enhancement of 16.06% compared to the RoNIN-ResNet method. Full article

To address the problem of fault diagnosis for rotating machinery under complex operating conditions in real industrial systems, most existing domain generalization methods fail to sufficiently consider inter-class feature structures when learning domain-invariant representations. This limitation often leads to degraded diagnostic performance in cross-domain scenarios, particularly under class imbalance or significant operating condition variations. Moreover, existing feature extraction networks specifically designed for rotating machinery are often inadequate for fault diagnosis tasks under variable operating conditions. To overcome these challenges, this paper proposes a domain-invariant fault feature representation learning framework for multi-source domain generalization. Specifically, we design a mechanism-aware multi-branch feature extraction network inspired by excitation–modulation mechanisms of fault generation, which captures fault-sensitive characteristics from both time-domain and frequency-domain perspectives. In addition, a class-conditional feature alignment strategy based on ICM (Independent Causal Mechanism) mixing is introduced to enhance cross-domain consistency. Through feature structure regularization, discriminative information across categories is effectively preserved under domain shifts. Extensive experimental results demonstrate that the proposed method significantly improves diagnostic performance and generalization ability on the CWRU bearing dataset as well as the HUST bearing and gearbox datasets. Notably, when the number of source domains increases, the proposed framework exhibits superior training efficiency. Full article

Exosomes are nanoscale extracellular vesicles that carry disease-associated microRNAs (miRNAs) and represent promising biomarkers for cancer diagnosis. Triple-negative breast cancer (TNBC) lacks well-defined molecular markers, necessitating sensitive and integrable analytical approaches for TNBC-related exosomal miRNAs. In this study, exosomes were isolated from MDA-MB-231 TNBC cells using a paddle screw-based system designed to enhance mass transfer through active rotation, providing a mechanically driven isolation strategy that is compatible with miniaturized and microfluidic platforms. This dynamic isolation process enabled rapid and efficient exosome recovery within a short processing time. Three TNBC-associated miRNAs encapsulated in the isolated exosomes were quantitatively analyzed using polyadenylation tailing (poly(A) tailing) and specific bidirectional extension sequence-based assays combined with reverse transcription quantitative real-time PCR (RT-qPCR). The bidirectional extension (BDE) assay generated highly specific PCR templates, leading to improved amplification specificity and reduced background signals. The RT-qPCR analysis exhibited high sensitivity, wide dynamic range, and good reproducibility for all target miRNAs. Overall, these results demonstrate that the integration of a paddle screw-based exosome isolation module with an extension-based nucleic acid detection strategy provides a scalable and biosensor-compatible analytical framework for profiling TNBC-associated exosomal miRNAs, with potential applications in microfluidic liquid biopsy platforms and exosome-based cancer diagnostics. Full article

Over the past two decades, numerous studies have examined the etiological significance of DNA fragmentation in human sperm using methods such as the comet assay (CA), the sperm chromatin structure assay, the sperm chromatin dispersion assay, and the TUNEL assay. We developed single-cell pulsed-field gel electrophoresis techniques, including one-dimensional (1D-SCPFGE) and angle-modulated two-dimensional (2D-SCPFGE), to detect early signs of naturally occurring DNA fragmentation. Comparative studies using purified human sperm with and without DNA fragmentation revealed some technical limitations in the conventional methods. This technical review outlines the procedures to ensure the quantitative performance of SCPFGE: (1) The mass of naked DNA was prepared through simultaneous in-gel swelling and proteolysis, which are highly sensitive to chemical and physical factors. Notably, these processes are vulnerable to reactive oxygen species (ROS). We developed the anti-ROS SCPFGE system to prevent artifactual cleavages. (2) 1D-SCPFGE discharges long-chain fibers from the origin, separating fibrous and granular segments beyond the tips of the fibers. (3) During continuous electrophoresis after 150° rotation (2D-SCPFGE-0-150), long-chain fibers unexpectedly extended diagonally backward from the origin, with long fibrous segments pulled out from a bundle that extended during the first electrophoresis, indicating some fibrous segments were embedded within the long-chain fibers. Even when SCPFGE was employed, one-directional current led to false negatives. (4) 2D-SCPFGE with angle rotation is currently the most sensitive imaging method for single-nuclear DNA fibers. However, without knowing the size of DNA fragments, it remains a semi-quantitative analysis. (5) To prevent artifactual DNA cleavage caused by ice crystals, low-temperature liquid storage is recommended. (6) The in-gel proteolyzed naked DNA is suitable as a substrate for chemical and enzymatic DNA cleavage analyses. Full article

We investigate the optical response properties of an atom-assisted spinning optomechanical system, in which a spinning optical resonator is coupled simultaneously to a two-level atomic ensemble and a mechanical resonator driven by a weak pump field. Remarkably, we demonstrate that by simply reversing the rotation direction, the system can be switched between a low-absorption electromagnetic and optomechanically induced transparency state and a high-absorption state, constituting a form of non-reciprocal optical control at the quantum level. Furthermore, by tuning the phase difference between the mechanical pump and the probe field, direction-dependent switching between absorption and gain is achieved. These non-reciprocal effects originate from the Sagnac-induced frequency shift in the optical mode, which leads to distinct optomechanical and atom–cavity couplings for opposite spinning directions. We also show that the absorption spectrum can be modulated by the angular velocity and the atomic number. Our results indicate that the optical properties of the hybrid system can be manipulated via the angular velocity, phase difference, and atom number, with potential applications in chiral photonic communications. Full article

Rotational modulation improves strapdown inertial navigation system (SINS) by periodically reorienting the inertial measurement unit (IMU) to convert slowly varying sensor errors into manageable, cancelable components. However, existing dual-axis schemes may accumulate large total rotation angles and introduce delayed error balancing, which results in non-negligible residual attitude errors and degrades real-time navigation accuracy. To overcome these limitations, we propose an odd-symmetric dual-axis rotation strategy that jointly optimizes the rotation order and dwell positions to maximize error cancellation on each axis and across axes while constraining cumulative rotation. Based on this principle, we design a 64-position rotation scheme and derive its IMU error modulation/suppression characteristics, including gyroscope drift, accelerometer bias, scale-factor errors, and misalignment (installation) errors, and we quantify their effects on attitude and velocity. Simulations show that the proposed scheme reduces position and velocity errors by more than $60\%$ compared to a 16-position scheme, and decreases longitude error, east-velocity error, and yaw error by more than $30\%$ relative to a 32-position scheme. Experiments further validate consistent improvements in position, velocity, and attitude accuracy, demonstrating the effectiveness of the proposed rotational design for dual-axis SINS. Full article

Pennycress (Thlaspi arvense L.), a representative and economically valuable cover crop, supports and enhances key ecological processes throughout its life cycle via its root system. It is hypothesized that pennycress selectively modulates its rhizosphere microbial community through root-derived metabolites, which may influence both the crop’s growth and the subsequent crops in rotation. However, systematic investigations comparing the rhizosphere microbiomes and metabolomes among different pennycress lines remain limited. This study employed metagenomic and metabolomic approaches to examine the dynamic changes in the rhizosphere microbial community and metabolite profiles of three pennycress lines with significantly different total alkaloid contents. The goal was to elucidate the interactions between microbes and metabolites. Results indicated significant differences in microbial community structure across the cultivars. JiL67 maintained stable community diversity, while LiN54 (with the lowest alkaloid content) showed reduced diversity. HeL43 (with the highest alkaloid content) exhibited increased diversity but also potential community homogenization, accompanied by the significant enrichment of microbial taxa capable of alkaloid tolerance. Metabolomic analysis identified metabolites such as Portulacaxanthin II, Oleanolic acid, and Soraphen A as significantly enriched in the rhizosphere soil of pennycress. This study reveals the shifts in rhizosphere microbial communities and metabolites linked to different pennycress lines and uncovers their interactive mechanisms, providing a scientific foundation for developing more economically efficient pennycress cultivation strategies. Full article

Accurate identification of anatomical landmarks on three-dimensional (3D) pelvic surface scans is essential for musculoskeletal assessment, yet manual procedures remain limited by operator dependence and soft tissue variability. This study presents a fully automated deep learning pipeline for localizing anatomical landmarks on the posterior pelvic region from raw 3D point cloud data. The pipeline integrates three modules: PelvicROINet for extracting the region of interest, PelvicAlignNet for rotation correction to standardize posture, and PelvicLandmarkNet for localizing six anatomical landmarks including the bilateral posterior superior iliac spines, bilateral iliac crests, L1, and L4. The models were trained independently with task-specific annotations and combined sequentially during inference. Under a subject-level split evaluation setting, the fully integrated system achieved a median error of 11.25 mm, demonstrating consistent localization performance across unseen subjects. Compared with manual landmark marking, the automated measurements showed improved within-visit repeatability, with reduced variability and higher intraclass correlation coefficients. The entire inference process required approximately three seconds per scan, supporting near real-time clinical applicability. These results indicate that the proposed modular framework enhances numerical consistency and robustness in surface-based pelvic landmark assessment and provides a scalable foundation for AI-assisted musculoskeletal evaluation and longitudinal monitoring. Full article

Slow lateral wandering and trochoidal-like motion are commonly observed in intense atmospheric vortices, yet most reduced-order vortex models assume a fixed axis or represent centre motion as purely advective. In this work, we propose a minimal reduced-order framework in which slow gyroscopic precession is introduced as an explicit degree of freedom superimposed on a rapidly rotating vortex core. The vortex is represented by a Burgers–Rott-type velocity field with time-dependent stretching rate and circulation, while the vortex centre undergoes a slow precessional motion governed by a time-dependent rate ${\Omega }_p\left(t\right)$. The evolution of the vortex parameters is coupled to environmental variability through simple relaxation laws driven by standard large-scale diagnostics, including convective available potential energy, vertical shear, and background vorticity. A tracker-only analysis of tropical cyclone best-track data is used to constrain the appropriate dynamical regime at the track scale, indicating that observed centre wandering typically occurs in a slow-precession limit P = ${\Omega }_p/{\omega }_c\ll 1$. Numerical demonstrations in cyclone-like configurations show that, despite the smallness of the precession number, cumulative lateral displacement and enhanced Lagrangian dispersion can develop over the vortex lifetime. The proposed framework is intended as a proof-of-concept reduced-order model that isolates the role of weak, environmentally forced precession in modulating vortex wandering and transport, and complements more detailed numerical and observational studies. Full article

In modern industrial systems, the fault diagnosis of rotating machinery is crucial for ensuring safe equipment operation. However, practical fault data are often contaminated by noise, and the scarcity of samples across fault conditions makes effective feature extraction challenging. Moreover, single-sensor measurements provide limited and incomplete information, further degrading the accuracy and reliability of diagnostic models. To address these challenges, this paper proposes an explainable intelligent fault diagnosis for rotating machinery via multi-source information fusion under noisy environments and small sample conditions. Firstly, a multi-sensor data intelligent fusion module (MSDIFM) is developed. It converts multi-sensor vibration signals into time–frequency maps via continuous wavelet transform (CWT). Pixel-level cross-channel fusion is then performed using a variance-driven dynamic weighting strategy to generate a unified fusion map, adaptively highlighting high information channels. Secondly, a multi-dimensional adaptive asymmetric soft-threshold residual shrinkage block (MASRSB) is proposed to implement differentiated and dynamic threshold control for positive and negative features, enhancing representation and discrimination capabilities. Thirdly, the multi-scale Swin Transformer (MSSwin-T) is designed. This module significantly enhances the model’s feature extraction capability by expanding multi-level receptive fields, strengthening key channel representations, and reinforcing cross-window feature interactions. Finally, to validate the effectiveness of the proposed method, experiments are conducted on both the Case Western Reserve University (CWRU) dataset and the self-created PT890 dataset. Results demonstrate that the proposed method exhibits outstanding diagnostic performance and robustness under noisy conditions and with small sample sizes. Full article

Accurate fault diagnosis of gear transmission systems is crucial for ensuring mechanical reliability and preventing catastrophic failures. However, existing research predominantly focuses on single-gear crack faults, often overlooking the complex coupling effects when cracks occur simultaneously on meshing gears in practical engineering scenarios. To address this research gap, a multi-degree-of-freedom dynamic model incorporating time-varying mesh stiffness under normal, single-crack, and coupled-crack conditions is established. Experimental validation is conducted based on an FZG closed test rig for power flow. The results indicate that the mesh stiffness under coupled-crack conditions is generally lower than that under single-crack conditions. In the time-domain vibration response, the periodic impact amplitudes induced by coupled cracks are significantly amplified, with the impact period jointly influenced by the rotational speeds of both the driving and driven gears. According to frequency-domain analysis, coupled cracks result in a notable increase in harmonic peaks of the mesh frequency, enhanced sideband amplitudes, and a modulation period that is between the rotational frequencies of the driving and driven gears. The simulation results from the dynamic model show high consistency with the experimental signals in terms of time-frequency characteristic trends and time-domain indicators such as the crest factor, thereby validating the effectiveness of the dynamic model. This study elucidates the unique influence mechanism of coupled cracks on the dynamic behavior of gear systems and can provide theoretical guidance for the accurate diagnosis and condition assessment of multi-tooth faults in subsequent research. Full article