Search Results (4,833)

The objects in high-resolution remote sensing images usually exhibit arbitrary orientations, multi-scale variations, dense distributions, and complex background interference, posing significant challenges to oriented object detection. Although existing DETR-style end-to-end detectors eliminate the need for anchor design and non-maximum suppression, they still suffer from insufficient orientation priors in object queries, limited orientation consistency in decoder feature interaction, and unstable set matching for oriented bounding boxes. To address these issues, this paper proposes an end-to-end Transformer framework, termed OPT-Net (Orientation-Preserving Transformer Network), for oriented object detection in remote sensing images. OPT-Net treats orientation information as a structured geometric prior and propagates it through query initialization, feature interaction, and matching optimization. Specifically, an Orientation-Aware Query Initialization (OAQI) module is designed to generate initial queries using center confidence and orientation priors. An Orientation-Consistent Cross-Attention (OCCA) mechanism is proposed to perform orientation-conditioned modulation on Value features while keeping the standard Query–Key attention computation unchanged. Furthermore, an Uncertainty-aware Matching Loss (UML) is introduced to incorporate instance-level geometric uncertainty into Hungarian matching and regression optimization. Experimental results on the DOTA-v1.0 and HRSC2016 datasets show that OPT-Net achieves 76.83% and 90.58% mAP, respectively, demonstrating competitive detection accuracy and adaptability to complex remote sensing scenarios. Ablation studies and visualization results further validate the effectiveness of each proposed module. Full article

Pressure pulsations generated by pumps impair noise behaviour, increase mechanical loading, and reduce control performance in hydraulic systems. This study investigates the use of a rotary mechanical pulsation compensator integrated into the drivetrain of an external gear pump. The aim is to attenuate pulsations directly at their source without modifying the hydraulic layout. This is accomplished by using the torque induced flow rate pulsation to cancel the external flow rate excitation, which leads to destructive interference between flow rate induced and torque induced pressure pulsations. An analytical frequency domain model of the coupled mechanical–hydraulic system is derived to determine the required stiffness and damping conditions. The theoretical results are validated experimentally at mean pressure levels of 100 bar and 170 bar, both for two different hydraulic layouts. With a resonator pipeline at the pump outlet, the first harmonic of the pressure pulsation at the compensation frequency is reduced by 10.9 bar and 18.4 bar, respectively, which corresponds to reduction rates of 93% and 98%. The required damping value depends on the operating conditions, but it is independent of the hydraulic layout. While insufficient damping increases pressure pulsations around the compensation frequency, slightly higher damping flattens the frequency characteristics of pressure pulsation and reduces the maxima around the compensation frequency. In the neighbourhood of this frequency, the proposed concept enables effective reduction of the first pressure pulsation harmonic through a structural modification of the drivetrain. Full article

Energy pile groups present a dual-functional solution for structural support and geothermal energy utilization, yet their thermo-mechanical interactions with conventional piles remain insufficiently understood. This study establishes a 3D transient finite element model incorporating thermo-hydro-mechanical coupling to investigate thermal interference and differential settlement in hybrid pile groups under seasonal thermal loading. Systematic parametric analyses of pile length (10–30 m), diameter (1–2 m), and spacing (2D–3D) reveal two key findings: (1) Thermal perturbations in adjacent conventional piles exhibit distance-dependent attenuation characteristics, with measurable temperature variations (1–4 °C) observed within 4D spacing distances; (2) Differential settlement patterns demonstrate significant dependence on thermal operation modes, where heating cycles induce upward thermal stresses while cooling enhances consolidation settlement. The numerical framework is validated against field monitoring data and benchmarked with COMSOL 5.6/ABAQUS 6.14 simulations. Through optimized pile arrangements and spacing configurations, we demonstrate effective mitigation strategies for thermal interference and structural deformation, providing key guidance for the design of geothermal-energy-integrated foundation systems. Full article

Ion channels are validated drug targets, but they remain difficult to study as their pharmacology is influenced by rapid gating, conformational state transitions, cell-type-specific expression, and narrow safety margins. Recent advances in cryo-electron microscopy, structure-based in silico screening, machine-learning-guided prioritization, optical high-throughput screening, automated patch-clamp electrophysiology, and human-relevant organoid or microphysiological system (MPS) models are transforming this field. In this expanded review, we examine how these modalities can be integrated into a hybrid discovery pipeline that begins with computational triage, proceeds through scalable functional screening and state-aware electrophysiological validation, and concludes with multi-channel safety de-risking and translational analysis in complex human models. We also discuss disease-associated channel remodeling in cancer and inflammatory disorders, with an emphasis on transient receptor potential channels, voltage-gated potassium channel 1.3 (Kv1.3), Piezo channels, transmembrane protein 16A/anoctamin-1 (TMEM16A/ANO1), chloride channels, and proarrhythmic safety risks. Additionally, we highlight unresolved challenges, including bias in artificial intelligence models, incomplete conformational sampling, assay interference, organoid heterogeneity, and regulatory acceptance of MPS platforms. This review proposes a staged decision framework in which computational prioritization, scalable functional screening, direct electrophysiological confirmation, safety pharmacology, DMPK assessment, and disease-relevant human models serve as complementary filters rather than competing platforms for the identification of selective and translatable ion-channel therapeutics. Full article

This paper investigates the performance of cooperative multiple-input multiple-output (MIMO) systems under practical operating impairments, with particular emphasis on imperfect channel state information (CSI) and relay decoding errors. Although Decode-and-Forward (DF) relaying can provide diversity gains under ideal assumptions, these gains may significantly degrade in practical wireless environments affected by channel uncertainty. The analysis demonstrates that imperfect CSI introduces residual interference, leading to SINR saturation and BER error floors in the high-SNR regime. In cooperative systems, this degradation becomes more severe due to relay error propagation, where erroneously detected relay symbols introduce additional structured interference at the destination. Consequently, cooperative transmission may underperform direct MIMO communication within specific SNR operating regimes. To characterize this behavior, the concept of a harmful cooperation region is introduced, describing the operating regime in which the combined effects of CSI uncertainty and relay decoding errors render cooperation detrimental. To mitigate these limitations, a reliability-aware relay activation mechanism is proposed, enabling selective relay participation according to channel quality. By suppressing unreliable relay transmissions, the proposed approach significantly reduces error propagation and improves BER performance, particularly in the medium-to-high SNR regime. In addition, the impact of antenna scaling is investigated. The results reveal a robustness transition in which larger MIMO configurations exhibit improved resilience to CSI imperfections due to increased spatial diversity and improved channel conditioning. Overall, the findings demonstrate that cooperative transmission under imperfect CSI is inherently SNR-dependent and that robust system design requires the joint consideration of channel uncertainty, relay reliability, and system dimension. Full article

Two-dimensional (2D) material terahertz (THz) detectors offer a promising platform for compact, room-temperature detection, yet their performance is fundamentally constrained by weak absorption in atomically thin layers. Here, we demonstrate a graphene plasmon polariton atomic cavity (PPAC) THz detector in which intrinsic graphene plasmon absorption is enhanced through vertical cavity-assisted field redistribution. By incorporating a metallic back reflector beneath a silicon substrate of designed thickness, a Fabry–Pérot (FP) interference cavity is formed that positions the standing-wave antinode near the graphene plasmonic layer. Electromagnetic simulations reveal that the Fabry–Pérot cavity itself primarily redistributes the vertical electromagnetic field, thereby enhancing the local in-plane driving field responsible for intrinsic graphene plasmon excitation. Experimental measurements at the optimized cavity condition confirm a pronounced increase in plasmon-induced photothermoelectric response, consistent with the predicted absorption enhancement. As a result, the detector exhibits an approximately 30-fold increase in responsivity compared with the corresponding structure without the cavity, while maintaining a fast response time below 130 μs. The detector further enables discrimination of concealed polar and nonpolar liquids through continuous-wave THz imaging at 2.52 THz, achieving a discrimination speed 30-fold faster than that of conventional time-domain spectroscopy. This result highlights the potential of cavity-enhanced intrinsic plasmon absorption for compact, high-sensitivity, and high-speed THz photodetection. Full article

Real-time kinematic (RTK) navigation, which enables centimeter-level positioning accuracy through carrier-phase differential correction, provides high-accuracy positioning for orchard robots, but short-term outages caused by canopy occlusion and signal interference may interrupt path guidance and increase lateral drift. To address this issue, this study proposes a vision-based takeover navigation method for orchard robots under short-term RTK failure conditions. First, an improved YOLOv11-based road segmentation and completion model, termed YOLOv11-VF, was developed. By introducing a Squeeze-and-Excitation (SE) channel attention mechanism, the model jointly perceives visible road regions and occluded road completion regions, thereby producing continuous and complete road semantic representations. Second, a structured geometric road representation was constructed from the segmentation results to extract the navigation reference line, and a joint direction-position constraint mechanism was established by integrating the reference line with the robot reference view axis. A hierarchical constraint strategy based on a travel corridor and a deadband region was further designed to jointly determine heading deviation and lateral drift. Finally, road segmentation, navigation-line extraction, parameter analysis, and vision-based takeover experiments were conducted in a standardized orchard environment. The results showed that YOLOv11-VF achieved Precision, Recall, AP₅₀, mAP_@0.5:0.95, and F1 values of 92.31%, 88.56%, 94.40%, 67.41%, and 90.40, respectively, showing the best overall segmentation performance among all compared models while maintaining good real-time performance. The proposed method also demonstrated high consistency in navigation-line extraction and maintained mean absolute deviations of 0.0176 ± 0.0041 m to 0.0718 ± 0.0138 m during RTK outage intervals over 10 repeated trials, indicating good path-following capability and operational stability. Full article

Distinguishing between human-made interferences and natural background disturbances is of great significance for the safe operation of terrestrial optical cables because human-caused damage can be halted through timely intervention. To address the problem of small-sample disturbance recognition in distributed acoustic sensing (DAS) systems, this paper proposes a fused CNN–SVM classification model based on hybrid features. A convolutional neural network is employed to extract the high-level spatiotemporal features of disturbance signals, which are subsequently fused with statistical features and fed into a support vector machine for classification. Evaluated on open-source data, the proposed model achieves accuracy improvements of 9.1%, 8.7%, and 2.7% over the conventional CNN, the statistical-feature-based SVM, and the conventional CNN-SVM model, respectively. Furthermore, based on field-measured data, a dataset comprising 5664 samples was constructed, covering four typical disturbance-event types: background noise, drilling, knocking, and digging. The field classification results demonstrate that the three-layer convolutional structure of the model achieves a recognition accuracy of 98.5%. Both the ROC curves and multiple evaluation metrics indicate that the proposed three-layer fused CNN–SVM model delivers better classification performance and more balanced category recognition, offering a feasible reference for similar fiber disturbance engineering tasks. Full article

Accurate prediction of multivariate effluent quality is essential for achieving reliable operation and sustainable management of wastewater treatment processes (WWTPs). However, the strong nonlinearity, coupling relationships, and non-prioritized multi-input multi-output (MIMO) characteristics of WWTP pose significant challenges to conventional prediction methods. To address these issues, a vectorized evidential reasoning-based multivariate effluent quality (VER-MEQ) prediction method is proposed. First, a VER model is developed, in which the nonlinear mapping between multiple process variables and multiple effluent quality indicators is established through a vector evidence matrix (VEM), enabling simultaneous online prediction of multiple outputs within a unified inference framework. Subsequently, a structured hybrid initialization (SHI) strategy is introduced to improve the initialization quality of the genetic algorithm, and the VER inference process is incorporated into parameter optimization to enable online model parameter updating, thereby improving prediction performance. The proposed method is validated under sunny, rainy, and stormy operating scenarios. Experimental results demonstrate that VER-MEQ achieves competitive prediction accuracy, provides a transparent belief-based inference process, and maintains preliminary anti-interference performance under the tested conditions. By providing transparent and credible prediction results for effluent ammonia nitrogen (NH₃-N_e) and total nitrogen (TN_e), the proposed framework can support proactive operational decision-making, improve effluent compliance, reduce the risk of nutrient discharge, and contribute to the sustainable operation of WWTPs. Full article

Since its inception in 2017, temporal interference stimulation (TIS) has attracted increasing attention as a novel neuromodulation approach with the potential to non-invasively target deep brain structures. As the field moves from initial biophysical validation toward broader experimental and translational applications, a macroscopic understanding of its developmental trajectory and thematic evolution is needed. In this study, we systematically mapped the scientific landscape of TIS research using bibliometric methods to characterize its knowledge structure, core themes, and emerging frontiers. The analysis shows that TIS research has expanded rapidly from foundational animal studies and biophysical mechanism validation toward computational head modeling, individualized electric field optimization, and early human applications. Current research is increasingly focused on cross-species scaling, stimulation dosimetry, comparative advantages over other neuromodulation techniques, precise targeting strategies, and potential physiological risks such as high-frequency conduction block. Overall, TIS is evolving from an exploratory biophysical concept into a promising but technically and physiologically complex neuromodulation tool. Overcoming current engineering and translational barriers, particularly through individualized modeling, rigorous optimization, and well-designed human studies, will be essential for establishing TIS as a reliable therapeutic intervention. Full article

We present a theoretical study focused on the photoelectron spectrum of near-infrared (NIR) laser-driven ionization of hydrogen atoms by attosecond pulse trains composed of several HHs of the former. We analyze the effects of increasing the intensity of the NIR probe laser to account for the interference of multiple quantum pathways arising from mainbands formed in ionization by the attosecond pulse train within the strong-field approximation (SFA) beyond the commonly used first-order perturbative (in the NIR laser intensity) reconstruction of attosecond beating by interference of two-photon transitions (RABBIT). The structure of the energy bands formed in the photoelectron spectrum is governed by quantum interferences of the photoelectron wave packet released within one optical cycle of the NIR probe laser field—intracycle interference—and by the number of active high harmonic components, leading to higher-order Fourier contributions as a function of the NIR–XUV relative phase delay. We show that Fourier terms can be interpreted in terms of well-defined semiclassical trajectories. Our results demonstrate a significant departure from the standard two-path quantum-interference RABBIT picture, showing that both the phase-dependent oscillations of mainbands and sidebands and the extracted phase delays depend strongly on the probing laser intensity. The predictions of the SFA reveal that the above-threshold ionization bands exhibit systematic splitting and oscillation patterns as a function of the NIR intensity. SFA predictions are compared with results obtained within ab initio solutions of the time-dependent Schrödinger equation (TDSE), showing an excellent agreement, which evidences the minor effect of the Coulomb potential of the remaining ion on the escaping photoelectron for high energy above-threshold ionization. The precise study of the SFA reference phases is essential for the determination of the effect of the Coulomb potential on the escaping photoelectron for what these findings provide new insights into attosecond chronoscopy in the strong-field regime. Full article

In response to the challenges in UAV-oriented ground small-object localization and detection, including the easy loss of tiny target features, insufficient scale adaptability, severe interference from complex backgrounds, as well as high missed and false detection rates and the inadequate localization accuracy of the conventional YOLOv11n model in such scenarios, this paper takes YOLOv11n as the basic framework and performs systematic optimization from three aspects, network structure, core modules, and feature enhancement, proposing a lightweight small-object-enhanced detection algorithm named YOLOSO for UAV applications. By introducing a P2 high-resolution feature branch with a stride of 4, a four-scale detection structure consisting of P2-P3-P4-P5 is constructed, which reduces the minimum detection stride from 8 to 4 and alleviates the loss of detailed feature information for ultra-tiny targets. A bidirectional “top-down + bottom-up” multi-scale feature fusion strategy is utilized to improve the complementation between deep semantic information and shallow detailed features, while the core modules C3k2SO and C2PSASO are optimized and redesigned, respectively; by adjusting the channel compression ratio (0.25 for shallow modules and 0.75 for deep modules in C3k2SO; 0.25 in C2PSASO), optimizing the convolution kernel configuration (combining 1 × 3 and 3 × 1 convolutions), increasing the number of attention heads (from 4 to 8), and introducing residual connections with a 1 × 1 convolutional branch, the refinement and focusing ability of small-object feature extraction are improved. Additionally, an Enhanced Dual-branch Convolutional Block Attention Module (ED-CBAM) is proposed to further suppress background interference. Experimental results on the VisDrone2019-DET dataset demonstrate that the proposed YOLOSO contains 3.56M parameters and maintains a lightweight structure, attaining P, R, and mAP50 values of 47.2%, 36.8%, and 37.3% in the test set, which are 4.5 percentage points, 4.8 percentage points, and 3.7 percentage points higher than those of the baseline YOLOv11n (42.7%, 32.0% and 33.6%), respectively. Meanwhile, the medium-to-large version YOLOSO-S (14.85M parameters, 45.3% mAP50) reduces the number of parameters by 53.6% compared with the same-scale Rtdetr-L (32.0M) while achieving significantly better performance (37.8% mAP50). Experiments on the DOTAv1 dataset further confirm the generalization of YOLOSO, achieving 62.2% precision and 27.3% mAP50, outperforming all compared YOLO models. Evaluated on the DOTA-v1 dataset, YOLOSO achieves a feasible FPS of 20.53. Although slightly slower than mainstream lightweight YOLO models, the substantial accuracy gains fully offset the minor inference speed loss, and such performance trade-off is acceptable for practical UAV deployment. Ablation experiments verify that structural optimization (2.8 percentage points mAP50 improvement, from 33.6% to 36.4%) and the proposed C2PSASO (0.7 percentage points mAP50 improvement to 34.3%) and C3k2SO (1.4 percentage points mAP50 improvement to 35.0%) modules all contribute positive performance gains with favorable complementarity. While retaining lightweight characteristics, the model effectively enhances the detection accuracy of small objects in unmanned aerial vehicle scenarios and can provide technical references for practical applications such as remote sensing monitoring and security patrolling. Full article

Juvenile hormone (JH) analog insecticides are widely used in pest management because of their ability to disrupt insect growth and metamorphosis; however, the molecular mechanisms linking endocrine disruption to metabolic dysregulation remain incompletely understood. In addition to their established roles in diapause and developmental regulation, JH signaling pathways have also been implicated in carbohydrate and lipid metabolism. In the present study, we investigated the effects of two JH analogs, pyriproxyfen and hydroprene, on the migratory locust, Locusta migratoria, with particular emphasis on lipid metabolic regulation and the function of midgut-enriched fatty acid-binding protein gene (Mg-FABP). Bioassays were performed to evaluate insecticidal activity, and transcriptomic analyses were conducted to identify differentially expressed genes associated with endocrine signaling and lipid metabolism. Functional characterization of Mg-FABP was further performed using RNA interference (RNAi) and Oil Red O staining assays. In addition, the tertiary structure of LmMg-FABP was predicted using AlphaFold 3, and molecular docking analyses were carried out to investigate its interactions with fatty acid ligands. Both pyriproxyfen and hydroprene caused approximately 70% mortality in locust nymphs and induced significant transcriptional changes in pathways related to hormone signaling and lipid metabolism. Transcriptomic analysis revealed pronounced downregulation of Mg-FABP following JH analog exposure. RNAi-mediated silencing of Mg-FABP significantly reduced lipid droplet accumulation in the fat body, indicating that Mg-FABP plays an essential role in lipid transport and metabolic homeostasis in L. migratoria. Structural analyses further demonstrated that LmMg-FABP possesses a conserved tertiary structure highly similar to FABP homologs from other insect species. Molecular docking identified key amino acid residues involved in fatty acid binding and suggested that hydrophobic interactions are critical for ligand stabilization within the binding cavity. Collectively, our findings demonstrate that pyriproxyfen and hydroprene disrupt insect development not only through endocrine imbalance but also through perturbation of Mg-FABP-associated lipid metabolic pathways. This study provides new mechanistic insight into the coordinated interaction between hormonal signaling and lipid metabolism during JH analog exposure and identifies FABP-mediated lipid transport as a potential molecular target for the development of more selective insect growth regulators. Full article

“Interbedded water in thin coal seams” is characterized by its high degree of concealment and complex hydraulic connections. However, due to the confined space of underground mine tunnels and severe electromagnetic interference from metal structures, traditional geophysical methods struggle to accurately delineate the boundaries of water accumulation, making this a major and challenging water hazard in coal mines. Taking the Qinhua Coal Mine in Xinjiang, China, as the engineering context, this paper investigates the detection of water accumulation in interbedded coal seams within goaf areas using the cross-hole resistivity method. It proposes a cross-hole resistivity tomography scanning approach characterized by “progressive depth penetration and layer-by-layer traversal,” and employs an inversion method based on inequality constraints to obtain relatively detailed and reliable imaging results. Through resistivity imaging analysis, low-resistivity water accumulation anomalies were successfully delineated, and water accumulation dead zones were identified. Based on the detection results, effective drainage was carried out beneath the water-filled zones. Subsequent follow-up surveys confirmed the disappearance of the low-resistivity anomalies, thereby validating the reliability and engineering practicality of the cross-hole resistivity tomography method for precisely detecting water body boundaries under complex geological conditions in coal seams. Full article

To satisfy the demands of 5G communication and reliable data connectivity for unmanned aerial vehicles (UAVs), a novel two-element dual-band MIMO antenna with an inherent self-decoupling property based on orthogonal linear polarization diversity is proposed. Distinct from conventional designs relying on extra decoupling components, the antenna realizes isolation enhancement via coupled currents between annular strips and S-shaped strips without additional decoupling structures, representing the core design novelty. Fabricated on a low-cost 1.6 mm thick FR4 substrate, the antenna features compact overall dimensions of 60 mm × 30 mm × 1.6 mm, covering the 2.40–2.73 GHz ISM band and 3.38–3.63 GHz 5G Sub-6 GHz band. Measured results demonstrate that the reflection coefficient remains below −10 dB across the entire operating bands, with port isolation exceeding 27 dB for the 2.4 GHz band and 20 dB for the 3.5 GHz 5G band. The measured realized gain is 0.7–1.5 dB in the lower band and 2.3–2.9 dB in the upper band. The radiation efficiency, which is obtained exclusively from ANSYS HFSS 2025 R1 simulation, is higher than 90% for the lower band and over 80% for the upper band. The calculated envelope correlation coefficient (ECC) is less than 0.15 throughout the working bandwidth, which effectively suppresses inter-channel electromagnetic interference and mitigates channel fading caused by varying UAV attitudes to improve system channel capacity. Further verifications via epoxy encapsulation and co-simulation on an eight-rotor UAV platform prove slight frequency drift after packaging and installation, whereas its bandwidth and isolation still meet practical engineering requirements. Benefiting from a compact layout and omnidirectional radiation performance, the proposed low-cost MIMO antenna is convenient for conformal integration into a UAV fuselage, improving the practicability of UAV-aided emergency communication, equipment inspection and 5G network coverage. Full article