Search Results (1,239)

To address large fluctuations in fertilizer flow and the difficulty of real-time quantitative blockage monitoring during fertilization by an integrated tillage, land preparation, and seeding machine, a fertilization monitoring system combining real-time detection and intelligent alarm functions was designed and developed. The system uses an STC32G12K128 microcontroller as the core control unit and integrates fiber-optic sensors, fiber-optic amplifiers, and associated peripheral hardware. Supporting host computer software was also developed on the Python3.13 platform. Based on the light-blocking principle, the optical signal generated by fertilizer particles passing through the sensing area is converted into a digital signal by the fiber-optic amplifier. A quantitative correlation model between the amount of blocked light and the fertilizer discharge rate was then established, enabling indirect and non-contact measurement of the fertilizer discharge rate. Indoor bench tests demonstrated a highly significant positive linear correlation between the amount of blocked light and the fertilizer discharge rate. The overall mean absolute percentage error of the fitted model was below 10%. Based on this detection system, a fertilization monitoring and alarm module was further developed for indoor bench conditions, together with discrimination logic for fertilizer blockage and fertilizer shortage. At rotational speeds of 30–50 r/min, the system achieved an average blockage detection rate of 98%, an average false alarm rate of 4.4%, and an alarm response time of no more than 3 s. Full article

Oriented object detection (OOD) in remote sensing images (RSIs) suffers from insufficient feature representation caused by arbitrary rotation angles and small spatial resolutions. Existing spatial–frequency fusion paradigms merely implement single-granularity feature interaction, either global image-level frequency compensation or local instance-level feature refinement, and fail to simultaneously capture global scene semantic consistency and local object fine-grained discriminability. In this paper, we propose a unified dual-level spatial–frequency collaborative detector (DSCDet) for remote sensing OOD tasks. Different from previous decoupled designs, the proposed DSCDet constructs a complete spatial–frequency collaborative fusion paradigm that shares a generic wavelet-based frequency extraction mechanism and cross-feature fusion module, which is adaptively deployed at both image-level and instance-level granularities. Specifically, our method introduces Haar wavelet transform to extract multi-scale frequency mutation features. On this basis, a generic cross-domain attention fusion (GCDAF) is constructed with granularity-dependent positional encoding constraints. The core difference between dual granularity fusion lies in geometric positional encoding, where image-level fusion adopts global scene positional embedding to maintain overall semantic stability, and instance-level fusion leverages local pairwise instance positional embedding to optimize fine-grained target feature interaction. The unified dual-level fusion architecture comprehensively integrates global semantic integrity and local target specificity, forming a robust and universal spatial–frequency feature representation system. Extensive experiments on three public remote sensing datasets, including DOTA-v1.0, DOTA-v1.5 and DIOR-R, demonstrate that the proposed DSCDet achieves competitive and superior performance against state-of-the-art OOD detectors. Full article

Rolling bearings are critical components in rotating machinery, and their failures may cause unexpected downtime and safety risks. However, conventional deep diagnostic models are often difficult to deploy on resource-constrained edge devices because of their high computational cost and memory consumption. This paper proposes a lightweight time–frequency attention network (LTFANet) for multi-fault diagnosis of rolling bearings on an edge platform. The proposed model directly processes one-dimensional vibration signals and employs multi-scale depthwise separable convolutions to capture impact and periodic fault features with low computational complexity. A lightweight frequency branch is introduced to enhance fault-frequency representation, while an efficient channel attention module adaptively emphasizes fault-sensitive features. Moreover, a severity-aware multi-task extension is introduced to jointly identify the fault location and degradation level. To further improve edge inference efficiency, knowledge distillation, structured pruning, and TensorRT-based acceleration are integrated into the deployment pipeline. Experiments on CWRU-10 and Paderborn achieve 97.20% and 90.25% accuracy, respectively, while LTFANet contains only 0.020 M parameters and requires 0.610 M FLOPs. Knowledge distillation increases the CWRU-10 accuracy to 98.50%, and the severity-aware extension achieves 95.18% severity accuracy. On the NVIDIA Jetson Nano, the pruned TensorRT FP16 implementation achieves an average inference latency of 0.520 ms and a throughput of 1923.08 samples/s. The framework provides an effective solution for real-time and low-cost bearing condition monitoring at the edge. Full article

The Luzon Strait is a critical generation site for global internal tides. Their generation and propagation are significantly modulated by background currents, including the Kuroshio Current and mesoscale eddies. This study investigates nonlinear effects of these background currents on low-mode (modes 1–3) internal tides using a high-resolution numerical simulation. We apply the Taylor–Goldstein equation considering the Earth’s rotation and background currents to perform modal decomposition, and utilize a nonlinear internal tidal energy equation to quantify three crucial energy pathways: inter-modal energy conversion, nonlinear energy exchange with background currents, and nonlinear advection effects. Results demonstrate that while stationary mode-1 internal tides dominate in the generation region of the Luzon Strait, non-stationary energy increases significantly in the western and eastern propagation regions, driven largely by seasonal variability of the Kuroshio Current. Inter-modal energy conversion follows a cascade from lower to higher modes, with conversion efficiency increasing with mode number. Nonlinear exchanges between background currents and internal tides are one order of magnitude smaller than inter-modal conversions but exhibit a bidirectional transfer, where advection redistributes internal tidal energy within the eddy structures. This study provides a quantitative framework for understanding multiscale energy pathways of internal tides under complex ocean dynamics. Full article

Long-term crop rotation can improve soil function and crop performance, but it remains unclear whether rotation history can simultaneously alleviate drought-induced oxidative injury in spring wheat leaves and roots. To address this gap, we used a long-term field rotation experiment established in 2016 in the western foothills of the Greater Khingan Mountains. Four cropping systems were selected: spring wheat–potato rotation (R1), spring wheat–potato–rape rotation (R2), spring wheat–rape rotation (R3), and continuous spring wheat cropping (C1). In 2022, wheat occurred naturally in all rotation sequences, and all plots were planted with the same spring wheat cultivar (‘Longmai 36’) to enable comparison among different rotation histories. Drought stress was imposed from late jointing, and at anthesis, ROS-related levels, malondialdehyde (MDA), glutathione (GSH), and proline (Pro), as well as the activities of superoxide dismutase (SOD) and peroxidase (POD), were determined in the flag leaves and roots of spring wheat under normal-water (NC) and drought-stress (HC) conditions. Under drought stress, rotation significantly increased yield (R1 and R2 by 73.3% and 77.7% vs. C1, respectively, partly reflecting the low C1 baseline associated with continuous cropping) and reduced drought-induced accumulation of ROS-related levels and MDA, along with excessive antioxidant and osmotic responses. R1 better protected leaves, while R2 optimized root osmotic regulation. Two-way ANOVA revealed significant drought × rotation interactions for most leaf traits (p < 0.01) but not for root ROS-related levels, SOD, or POD, indicating organ-specific regulation. R1 and R2 show strong drought resilience potential, warranting multi-year, multi-site validation. Full article

Remote sensing image super-resolution aims to reconstruct high-resolution images from low-resolution observations and is important for image interpretation. Existing fixed-scale methods achieve good performance at predefined integer scales, but their dedicated upsampling modules limit their application to arbitrary-scale scenarios such as interactive GIS and multi-source image fusion. Continuous implicit methods provide scale flexibility but often exhibit spectral bias, resulting in over-smoothed textures and blurred object boundaries. To overcome these limitations, we propose an Arbitrary-scale Equivariant Resolution Operator (AERO) for remote sensing image super-resolution. AERO consists of three components. The Omnidirectional Feature Extractor enhances feature representation under orientation variations. The Wavelet–Arnold Residual Group models low- and high-frequency information in the wavelet domain to preserve textures and geographic boundaries. The Local Implicit Terrain Operator employs relative sub-pixel coordinates for continuous arbitrary-scale reconstruction. Experiments on AID, NWPU-RESISC45, UCMerced, and WHU-RS19 demonstrate that AERO achieves the best performance in the ×4 fixed-scale task. On WHU-RS19, AERO reaches a PSNR of 31.02 dB, exceeding FMSR by 0.64 dB. In rotational robustness experiments, the maximum PSNR fluctuation is reduced from 0.0181 dB to 0.0010 dB. The results show that AERO provides a practical approach for arbitrary-scale remote sensing image super-resolution. Full article

Conventional pinhole point-diffraction interferometers (PPDIs) owe their popularity to a simple configuration, but that same configuration limits both the usable measurement aperture and the load the phase shifter can carry. Here, we propose a heterodyne phase-shifting point-diffraction interferometer (HPDI) based on dual-cascaded acousto-optic modulators (AOMs), which removes both limitations while keeping the configuration compact. The dual-cascaded AOM module integrates the functions of frequency shifting, beam splitting, and contrast modulation. By modulating its input RF power, the interference contrast can be flexibly adjusted. Simulation and experimental results verify the feasibility of the proposed HPDI, which achieves a measurement repeatability of 0.045 nm (RMS, with piston, tilt, and defocus removed) and agrees with an absolute shift-rotation calibration to within 3.3 nm (RMS). Full article

Radio frequency identification (RFID) tags are now included in the plastic wrap used to protect seed cotton formed into cylindrical or “round” modules on modern cotton harvesters. In this paper, the development of a new work tool system for handling round modules with articulated wheel loaders or telehandlers is described. The work tool system reads the module-specific identification number from the RFID tags in the wrap and associates the module’s weight, seed cotton moisture content, GPS location, cotton ownership, and load information with the module serial number. Finite element analysis of critical components indicated that the system was capable of processing modules weighing 3178 kg (7000 lb.) Module weight was determined on the loader using measurements of the hydraulic pressure in the lift arm circuit. Seed cotton moisture content was measured using a custom-designed resistance-based probe. To help reduce the potential for lint bale contamination from module wrap plastic, the work tool system was designed to rotate modules so that the wrap can be cut within the manufacturer-recommended cut zone before the wrap is removed at the gin. The total cost for the system configured for fully automated data collection and module rotation control was $28,909. Full article

In the era of intelligence, accurate remaining useful life (RUL) prediction is essential to ensure the reliable operation of smart equipment, particularly for rolling bearings—critical components that are highly susceptible to degradation in rotating machinery. However, as faults progressively develop, the vibration signals of rolling bearings exhibit strong non-stationarity and complex degradation patterns. Existing RUL prediction methods, particularly standard Transformer-based models, often struggle to capture local transient features within non-stationary signals and fail to effectively decouple long-term degradation trends from periodic variations. To overcome these limitations, a novel RUL prediction method that integrates time-series analysis techniques with the FEDformer architecture is proposed, termed TE-FEDformer. Firstly, a feature enhancement module is employed at the input stage to reconstruct and strengthen the original sequence, aiming to strengthen the representation of weak fault features that are often overlooked by global attention mechanisms. Then, deep time-series representations are extracted via the encoder. In the decoding stage, a frequency enhancement mechanism and a sequence decomposition mechanism are jointly utilized to explicitly model the coupling between degradation trends and periodic variations, thus resolving the spectral interference commonly encountered in complex degradation processes. Comparative experimental results on the PHM2012 and XJTU-SY datasets demonstrate that TE-FEDformer outperforms other benchmark models. Ablation studies further validate that each module contributes positively to the overall performance, confirming the effectiveness of the proposed approach for RUL prediction. Full article

Single-UAV dense visual SLAM is often limited by long trajectory accumulation, incomplete local observations, redundant map growth, and onboard computation constraints. Collaborative mapping can distribute a large mission across several local submaps, but dense 3D Gaussian Splatting (3DGS) maps are expensive to exchange and individual Gaussian primitives are not reliable cross-agent matching units. This paper proposes G³M-SLAM, an anchor-guided Gaussian memory framework for UAV-oriented dense SLAM with representation-level submap fusion. Stable geometric anchors organize local Gaussian primitives and form a compact structural interface for submap exchange, overlap recognition, and correction. A hybrid feature-render tracking strategy combines sparse geometric constraints with Gaussian rendering residuals. A generative completion module predicts candidate Gaussians in weakly observed regions, while multi-view geometric verification and an evidence-aware lifecycle mechanism reject unsupported candidates and control redundant map growth. A dual-graph loop bundle adjustment couples the camera pose graph and the anchor memory graph so that corrections can be propagated to anchor-associated Gaussian structures. Experiments on Replica, ScanNet, TUM RGB-D, and EuRoC MAV evaluate local tracking, dense rendering, runtime, and a split-agent fusion protocol. In the latter protocol, fusion reduces ATE from 0.045 m to 0.031 m, translational RPE from 0.030 m to 0.017 m, and rotational RPE from $1.{56}^{°}$ to $0.{93}^{°}$. The serialized anchor packet is 0.66 MB, approximately $101.2\times$ smaller than the complete Gaussian submap. On Jetson AGX Orin, the full system processes EuRoC V101 and V103 at 1.49 FPS and 1.42 FPS, respectively, while ATE increases by only 0.001 m relative to the RTX 3090 Ti workstation results. These gains represent recovery from split-agent degradation and compact representation exchange rather than an improvement over full-sequence single-agent processing. The present study therefore evaluates a representation-level fusion interface and does not reproduce the full conditions of a field-deployed decentralized multi-UAV system. Full article

Background/Objectives: Photobiomodulation therapy (PBMT) has shown promising clinical results for a variety of musculoskeletal disorders, yet many of the cellular mechanisms underlying its anti-inflammatory effects remain incompletely understood in the context of tenocytes and bursa-derived cells. This study evaluated the effects of a 1064 nm laser on inflammatory gene expression with an in vitro model of IL-1β-induced inflammation. Methods: Human tenocytes and subacromial bursa-derived cells were isolated following rotator cuff repair and cultured under standard conditions. An inflammatory model was established by stimulating cells with IL-1β prior to PBMT treatment, to evaluate whether PBMT alters inflammatory gene expression. Cells then received PBMT at fluences of 1.5 and 4.5 J/cm² using a 1064 nm laser once daily for three days. Relative markers of TNF-α, IL-6, MMP-1, MMP-3, PTGS2 (COX-2), and NF-κB were quantified using RT-qPCR. Statistical analysis was performed with Dunnett-adjusted comparisons. Results: PBMT showed no significant effects on gene expression compared with IL-1β-treated controls after correction for multiple comparisons. An isolated significant increase in IL-6 expression was observed in bursa-derived cells treated at 1.5 J/cm² (p = 0.024), whereas no other significant differences were detected. Bursa-derived cells nevertheless showed greater variability and directional responsiveness. Conclusions: PBMT at 1.5 and 4.5 J/cm² did not modulate inflammatory gene expression in this in vitro model. Further studies with larger sample sizes and additional time points are needed to determine the cellular effects of PBMT during inflammation. Full article

This study investigates active geometric modulation in a variable-length pendulum energy harvester combining radial electromagnetic, rotational electromagnetic, and piezoelectric transduction. A reciprocal seven-state electromechanical model is formulated and analysed using phase-aligned continuation, transverse Floquet stability, Lyapunov diagnostics, physical load variation, and paired control-on/control-off energy accounting. A minimal threshold-based shift of the radial spring equilibrium serves to reveal the branch-support mechanism rather than to provide a final control strategy. The results show that an established finite-amplitude branch may persist below the local instability boundary of the inactive response, while piezoelectric loading can modify this boundary through electromechanical back-action. All three transduction channels contribute to gross electrical output, and physical load matching increases that output. Nevertheless, the continuous modulation remains energetically unfavourable after actuator, power-conditioning, and auxiliary demands are included and does not robustly retain the branch under the tested nonstationary excitations. The results therefore define requirements for phase-aware, adaptive, latching, or regenerative implementations with substantially lower actuation work. Full article

Early hidden cracks in magnetic encoder rings induce only slight magnetic perturbations at the incipient stage, yet they may evolve into missing-pole, demagnetization, or severe waveform-distortion faults that degrade angular-displacement measurement and closed-loop control. This study establishes a physical mapping between crack-induced magnetization nonuniformity, pole-pitch deviation, and the amplitude-modulated and frequency-modulated components of the measured magnetic signal. A precision-controlled experimental platform equipped with a self-developed tunnel magnetoresistance read head, a precision rotary stage, multi-axis positioning stages, and laser displacement sensing was built to measure the spatial magnetic field during shaft rotation. Fast Fourier transform-based band-pass filtering, Hilbert transform-based demodulation, and sixth-order Butterworth low-pass filtering were used to extract the instantaneous amplitude $A_{\mathrm{i}}$ and instantaneous angular frequency ${\omega }_{\mathrm{i}}$; coefficient-of-variation analysis was then used to construct the diagnostic indicators. Experiments on intact, hidden-crack, and visible-crack states show that the CV of $A_{\mathrm{i}}$ and the CV of ${\omega }_{\mathrm{i}}$ sensitively capture weak crack-related fluctuations and increase monotonically with damage severity. The method does not require a high-accuracy external reference and shows promise for online monitoring of magnetic encoder rings and related electromagnetic sensing elements. Full article

This study examines the relationship between large-scale atmospheric circulation and precipitation variability over Greece, with a focus on both mean and extreme conditions. Seasonal mean sea level pressure anomalies over the Mediterranean are analysed using Principal Component Analysis with varimax rotation, followed by k-means clustering to identify dominant circulation patterns. The analysis is performed separately for each season over the period 1980–2025 using ERA5 reanalysis data. The identified circulation types are linked to precipitation over Greece through spatial composites and quantitative metrics, including seasonal precipitation totals and the maximum daily precipitation. The results reveal distinct spatial precipitation patterns associated with each circulation type, with cyclonic conditions favouring enhanced and widespread precipitation, particularly over western Greece, and anticyclonic types leading to suppressed rainfall. Despite relatively small differences in mean extreme precipitation intensity, substantial variations are observed in the upper tail of the distribution, with specific circulation types associated with a higher potential for intense daily rainfall events. This highlights differences between widespread precipitation and locally intense extremes, reflecting the role of circulation in modulating both the spatial extent and intensity of rainfall. The findings show that large-scale atmospheric circulation plays an important role in precipitation variability and extremes over Greece, particularly during the wet seasons, while summer precipitation appears to be more strongly influenced by local and convective processes. Full article

With the rapid development of Industrial Internet-oriented smart energy systems, hydro-turbine generator units are increasingly monitored through networked sensors, industrial communication infrastructures, and edge/cloud-based condition-monitoring platforms. These Internet-connected monitoring environments provide abundant vibration data for intelligent operation and maintenance (O&M) but also introduce a challenging unsupervised cross-scenario diagnosis problem. Specifically, diagnostic models trained on labeled historical data may suffer severe performance degradation when deployed to unlabeled online data collected under different hydraulic conditions, rotational speeds, or operating conditions. Furthermore, existing domain adaptation methods, in their pursuit of distribution alignment, frequently overlook a critical bottleneck that limits generalization performance: inter-class entanglement. Specifically, under intense hydraulic background noise and cross-condition distribution shifts, features belonging to distinct fault types are highly susceptible to aliasing within the feature space. To overcome these issues, this paper proposes a Conditional Wasserstein Adversarial Network with Bi-level Prototype Disentanglement Regularization (CWAN-BPDR). First, a Conditional Wasserstein Adversarial Network (CWAN) is constructed by combining the smooth-gradient property of Wasserstein distance with conditional adversarial alignment, thereby achieving stable and fine-grained category-level domain adaptation. Furthermore, to alleviate the inter-class entanglement problem that may arise during cross-domain alignment, a Bi-level Prototype Disentanglement Regularization (BPDR) term is designed. By jointly implementing source–target prototype alignment and prototype–feature bidirectional alignment, BPDR explicitly suppresses inter-class confusion and enhances intra-class compactness and inter-class separability in the feature space. Experimental results on the JNU and NEFU datasets demonstrate that CWAN-BPDR achieves average diagnostic accuracies of 97.82% and 98.99%, respectively, while significantly mitigating label entanglement in challenging cross-operating-condition tasks. These results indicate that the proposed method can effectively transfer diagnostic knowledge acquired from labeled historical operating conditions to unlabeled online monitoring data. It can therefore serve as an offline-trained diagnostic module for Industrial Internet of Things-based condition-monitoring platforms in hydropower systems. Full article