Search Results (5,026)

Achieving rapid and accurate obstacle avoidance in complex and dynamic environments remains a significant challenge for robots. To enhance the adaptability and flexibility of humanoid arms for obstacle avoidance, a brain-inspired multi-pathway motion decision-making method is proposed to modulate rational planning and habitual actions of humanoid arms. Firstly, a novel framework integrating both a slow and a fast pathway is designed for motion decision-making tasks. Imitating the rational planning function of the prefrontal cortex, the slow pathway employs an improved planning approach based on Real-Time Rapidly exploring Random Tree Star (RT-RRT${}^{*}$) to execute deliberate decisions, along with an improvement in planning via the Smart technique and the high-efficiency neighbor searching method. Meanwhile, mimicking the habitual responses governed by the striatum, the fast pathway utilizes an action model trained by Soft Actor-Critic to make quick and habitual motions. The model in the fast pathway is also used to guide the sampling strategy in the slow pathway. Moreover, to facilitate the integration and smooth transition between the two pathways, an emotional neural network is designed as the modulation module with inspiration from the structure and function of the amygdala. Based on body and obstacle information, the network generates emotional signals to modulate the involvement degree of the two pathways before each decision-making process. Experimental results demonstrate that the proposed multi-pathway framework achieves a higher obstacle-avoidance success rate than existing methods while generating motion characteristics that are consistent with certain aspects of human obstacle-avoidance behavior. Full article

The strapdown inertial navigation system (SINS) suffers from cumulative error growth, where velocity and acceleration drifts cause position errors to grow quadratically. While multi-sensor fusion using Doppler Velocity Sensors (DVS) can correct these drifts, traditional methods often assume a uniform radial velocity across the solar disk, thereby ignoring the differential surface velocities caused by solar rotation. This mismatch introduces a significant unmodeled system bias with a mean value of approximately 1.45 km/s and an upper bound of up to 2.07 km/s, which is orders of magnitude larger than the standard measurement error (~1 m/s). Such a dominant deterministic error leads to severe filter divergence, undermining the reliability of the entire integrated navigation system. This mismatch introduces unmodeled system bias, degrading filter performance. To address this issue, this paper proposes a solar disk differential velocity method within a multi-source fusion framework. By constructing a velocity measurement model that reflects the Sun’s actual geometric and rotational characteristics, this method effectively eliminates the deterministic system bias. Combined with dual-star Doppler measurements, the method enhances the observability of 3D velocity errors. Simulation results show that the proposed method significantly improves navigation accuracy compared to the traditional method. Position errors are reduced from kilometer-level magnitudes to the range of tens to hundreds of meters, with velocity errors remaining within 10⁻²–10⁻¹ m/s. Full article

Suspension docking systems offer significant application potential in autonomous underwater docking operations because of their deployment and recovery convenience. This study investigated the interaction between an axisymmetric, underactuated autonomous underwater vehicle (AUV) and a suspended guiding hood docking device (DOCK). The effects of collision velocity, collision location, collision angle, mass, and moment of inertia on the post-collision kinematic states of both bodies are analyzed. Previous studies have typically determined AUV parameters using empirical formulas, whereas few have clearly described the calibration procedure for the hydrodynamic drag coefficients of a suspended guiding hood DOCK. In this study, the hydrodynamic coefficients of both the AUV and the DOCK were determined using STAR-CCM+ and embedded into the ADAMS built-in functions to construct a physically more realistic simulation model. Subsequently, water tank experiments were conducted for suspension docking collisions. The validity of the simulation model was verified by comparing the kinematic states of the DOCK and AUV observed from the simulations and experiments. Based on the established model, the docking dynamics under various operating conditions were simulated. The simulation results indicate that the AUV mass should not exceed twice the mass of the DOCK, and the moment of inertia of the DOCK should be maximized. The risk of suspension docking failure increases significantly when the mooring line length exceeds 40 m, and the negative buoyancy of the DOCK should be at least 300 N. These findings provide critical guidance for improving the success rate of suspension docking operations. Full article

In window tracking mode, stray light and detector readout noise can submerge star spot signals in star sensor images. The resulting degradation reduces centroid extraction accuracy and may even cause extraction failure, thereby preventing precise attitude determination. This study uses the self-supervised spatiotemporal denoising model ASTERIS as the baseline. ASTERIS integrates 3D spatiotemporal inputs with a global attention mechanism for joint noise modeling, thereby providing stronger denoising and restoration capability than conventional methods such as multi-frame stacking. However, ASTERIS lacks adaptive compensation for subpixel jitter in on-orbit star images and has difficulty preserving the high-frequency morphology of star spots, affecting denoising performance and centroiding accuracy. To address these limitations, this study introduces two improvements: First, frame-by-frame spatial deformable convolution is incorporated into the decoder upsampling stage to adaptively compensate for subpixel offsets, actively suppress background noise, and lower the parameter count. Second, a complex-valued frequency domain loss with a high-frequency weighted mask is designed to jointly constrain the amplitude and phase spectra, thereby preserving high-frequency star spot details. Experimental results show that, for star images with extremely low signal-to-noise ratios, the proposed method improves the peak signal-to-noise ratio by approximately 17.8 dB and reduces the centroid localization error to approximately 0.1 pixels. This performance is substantially better than that of the original ASTERIS model, which improves the peak signal-to-noise ratio by approximately 9.5 dB and yields an error of approximately 0.4 pixels, and the multi-frame stacking method, which improves the peak signal-to-noise ratio by approximately 6.0 dB and yields an error of approximately 0.5 pixels. Under the simulated strong noise conditions considered in this study, the proposed method achieves effective centroid extraction, demonstrating its potential for on-orbit star sensor data processing. Future work will further address its engineering deployment. Full article

This paper proposes a vibration-based approach for real-time condition monitoring of Friction Stir Welding (FSW) tools, which are widely used in the marine and automotive industries. Conventional inspection techniques such as visual examination and endoscopy are not practicable during active welding operations. The Locally Weighted Learning (LWL) algorithm, a lazy learning method, is used to address this limitation. Vibration signals are collected from a PLC-controlled FSW machine under five tool conditions, statistical features are extracted from the raw data, and a J48 decision tree is applied for feature selection to reduce computational overhead. Classification performance is evaluated using three lazy learning algorithms K-star (K*), LWL, and k-Nearest Neighbour (kNN) with LWL yielding the best result. The previously reported best accuracy for the same FSW setup was 73.16% at 1400 rpm using Random Forest; the proposed LWL-based approach achieves 92% accuracy under identical conditions, enabling earlier detection of tool faults before they result in weld defects or component failures. Full article

Simultaneous transmit-and-receive (STAR) antennas are important components of in-band full-duplex wireless front ends. However, transmit-to-receive leakage through the antenna limits the achievable antenna-domain isolation. This paper presents a dual-circularly polarized patch antenna incorporating a decoupling feeding network (DFN) to suppress the residual coupling between the transmit and receive ports. A stacked patch radiator with cross-aperture coupling generates right-hand and left-hand circularly polarized radiation from two separate ports. The DFN introduces an additional coupling path whose magnitude and electrical phase are adjusted to produce destructive interference near the target frequency. The fabricated prototype exhibits overlapping −10 dB impedance and 3 dB axial ratio bandwidths from 4.1 to 4.4 GHz, a minimum measured S₂₁ of −50 dB, isolation higher than 30 dB from 4.25 to 4.28 GHz, and a peak realized gain of 7 dBic. The measured high-isolation range has an absolute bandwidth of 30 MHz and a fractional bandwidth of approximately 0.70% around 4.265 GHz. Therefore, the proposed DFN provides narrowband antenna-domain isolation enhancement around the designed frequency without requiring additional patterned decoupling elements on the radiating aperture. The proposed antenna is primarily intended for fixed-frequency or narrow-channel STAR front ends rather than broadband high-isolation operation. Full article

Cell segmentation plays a key role in a wide range of biomedical imaging applications, from single-cell analysis to pathology assessment. While classical deep learning architectures such as U-Net, StarDist, and HoVer-Net have set strong baselines, their reliance on domain-specific training limits generalization across diverse microscopy modalities. The emergence of foundation models, particularly the Segment Anything Model (SAM) and its derivatives, has introduced a paradigm shift toward more universal and adaptable segmentation frameworks. In this review, we summarize key advances in microscopy cell segmentation, highlighting both traditional methods and recent foundation model-based approaches. Beyond surveying the literature, we present an experimental comparison of four representative models—our proposed YOLO-SAM, along with CellSAM, Cellpose-SAM, and StarDist—tested on both fluorescence and brightfield microscopy spanning diverse cell populations and shapes. Our findings illustrate trade-offs between accuracy, robustness, and adaptability, with foundation-based models showing particular promise for cross-domain performance. By combining a comprehensive review with systematic benchmarking, this work provides practical guidance for researchers and outlines current challenges and future opportunities in developing robust, generalizable cell segmentation methods for microscopy. Full article

We establish a new fixed point result for measurable-selection-valued correspondences with nonconvex and possibly disconnected values arising from the composition of Caratheodory functions with an upper Caratheodory ($uC$) correspondence. Using Caratheodory approximation methods, we show that for any such upper Caratheodory composition correspondence, if in each state, the upper semicontinuous part of the underlying upper Caratheodory correspondence contains an upper semicontinuous sub-correspondence taking contractible values, then the underlying upper Caratheodory correspondence is Caratheodory approximable, further implying that the induced measurable-selection-valued correspondence has fixed points—all accomplished without the induced selection correspondence being convex-valued or upper semicontinuous in the appropriate topologies (in the case the weak star topologies). An excellent example of such a composition correspondence is provided by discounted stochastic games ($DSG$). In particular, the Nash payoff selection correspondence of the parameterized collection of state-contingent one-shot games underlying a discounted stochastic game is gotten by composing players’ parameterized collection of state-contingent Caratheodory payoff functions with the upper Caratheodory Nash equilibrium correspondence (i.e., the $uC$ Nash correspondence). We are able to conclude via our fixed point result that if the $uC$ Nash correspondence has an upper semicontinuous part containing a contractibly valued upper semicontinuous sub-correspondence, implying that the $uC$ Nash correspondence is Caratheodory approximable, then the Nash payoff selection correspondence induced by the $uC$ Nash correspondence has fixed points. It then follows from Blackwell’s Theorem (1965–extended to games) that the $DSG$ to which the selection correspondence belongs has stationary Markov perfect equilibria. Full article

Accurate attitude estimation for highly dynamic spacecraft relies on robust fusion of star-tracker and inertial measurements. However, asynchronous sensing, motion blur in star images, and delayed star-tracker outputs can significantly degrade estimation accuracy and temporal consistency. To address these challenges, this paper proposes a dual-layer factor graph optimization framework for asynchronous star-tracker/IMU fusion under highly dynamic conditions. At the lower layer, high-rate IMU measurements are combined with motion-blurred star streak observations to construct a local factor graph over the exposure interval. The proposed local fusion process reconstructs discrete star-trail points, estimates angular velocity, and selects IMU-aligned representative observations for temporally consistent association of blurred star measurements. At the upper layer, delayed attitude constraints, propagated star-vector information, and inertial rotational constraints are jointly incorporated to refine the attitude trajectory. Simulation and semi-physical experimental results demonstrate that the proposed framework achieves higher estimation accuracy, stronger robustness, and better tolerance to delayed or intermittent star-tracker observations than the comparison methods, while maintaining practical computational efficiency for near-real-time onboard implementation. Full article

This study investigates the impact of green leadership on financial performance in the hospitality sector, focusing on the mediating roles of resource management and market positioning. Drawing upon the Resource-Based View and Stakeholder Theory, data were collected from 390 employees working in five-star hotels in Egypt and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4.0. The findings reveal that green leadership positively influences financial performance both directly and indirectly. Specifically, green leadership enhances resource management practices and strengthens market positioning, which in turn contribute to improved financial outcomes. The mediation analysis confirms that both resource management and market positioning serve as significant mechanisms through which green leadership translates sustainability-oriented strategies into economic benefits. These findings extend current knowledge on sustainable leadership by identifying key organizational pathways linking environmental responsibility with financial success. This study also provides practical implications for hospitality managers seeking to integrate sustainability initiatives into strategic and operational decision-making to achieve long-term competitive advantage. Full article

The control of Dual-Star Induction Machines (DSIMs) with high performance remains a challenging task, particularly in the presence of parameter variations and under sensorless operation. In practice, widely used controllers such as Proportional–Integral (PI) and classical sliding mode (SM) often reach their limits, especially in terms of dynamic responses, sensitivity to disturbances, and chattering, which can negatively affect system stability and efficiency. In this work, an improved Rotor Flux-Oriented Control (RFOC) strategy is proposed. It combines a super-twisting sliding mode (STSM) speed controller with a Model Reference Adaptive System (MRAS) observer. The STSM controller ensures faster convergence and enhanced robustness while significantly reducing chattering. Meanwhile, the MRAS observer enables accurate rotor speed estimation without mechanical sensors, thereby simplifying the system and improving reliability. The control scheme is developed using the Xilinx System Generator (XSG) in a fixed-point environment, providing an FPGA-oriented and compatible simulation framework. To assess its effectiveness, the proposed method is evaluated through several simulation scenarios and compared with conventional RFOC-PI and RFOC-SM approaches. The results demonstrate clear improvements in dynamic performance, disturbance rejection capability, and steady-state accuracy. Overall, the proposed approach provides a practical and efficient solution for DSIM drive systems operating under demanding conditions. Full article

Stellar-aberration-based navigation requires angular measurements at the milliarcsecond (mas) level. While random sensor noise can be reduced by temporal integration, plate-solution uncertainty and residual geometric distortion may set a practical astrometric error floor. Here, we quantify the plate-model contribution to this error budget and examine its impact on the feasibility of stellar-aberration-based navigation. Using Gaia DR3 stars, HEALPix all-sky sampling, and covariance propagation to epoch J2026.0, we evaluate nine polynomial plate models while accounting for reference-star density and spatial distribution. We identify a bias–variance trade-off between model complexity, distortion-correction capability, and numerical stability. For the adopted ∼1° sparse-field configuration, the four-parameter linear model gives the lowest plate-constant variance, with a median of 0.95 mas and a 95th percentile of 1.7 mas. Using the first-order scaling of $\delta v\sim c\delta \theta$, this uncertainty corresponds to an approximate velocity-error scale of 0.9–2.5 m/s. These results show that plate-model errors can contribute at the meter-per-second level and must be included explicitly in StarNAV filter design. Full article

Background: This study aimed to explore the relationships among different indices and the underlying latent structure of dynamic postural control in young female volleyball players. It used factor analysis of indices from stabilogram diffusion analysis (SDA), the modified Star Excursion Balance Test (mSEBT), and the Weight-Bearing Lunge Test (WBLT) to generate hypotheses regarding the relationships among these measures. Methods: In total, 34 female middle- and high-school volleyball players participated in this study. The SDA was performed using center-of-pressure (COP) data obtained during a single-leg vertical jump landing task, and the critical point (CP) was calculated. Lower-limb reach distances in the anterior (ANT), posteromedial (PM), and posterolateral (PL) directions were measured using the modified mSEBT. In addition, hallux-to-wall distance (HWD) was measured using the WBLT. Exploratory factor analysis was conducted to examine the latent factor structure among these indices. Results: The Kaiser–Meyer–Olkin value was 0.63, and Bartlett’s test of sphericity had significant results (p < 0.001). Considering the factor retention decision and the study’s theoretical framework, a two-factor solution was adopted for factor analysis, yielding a cumulative explained variance of 79.77%. Factor 1 had high factor loadings for the ANT, PM, and PL directions. Meanwhile, factor 2 showed high factor loadings for CP and HWD. Conclusions: Factor 1 reflected the spatial dynamic balance ability associated with the mSEBT and may be related to it. Moreover, factor 2 may indicate the temporal characteristics of COP variability assessed via the SDA and based on ankle mobility-related characteristics. However, because this was an exploratory pilot study with a small sample size, the findings should be considered as preliminary hypotheses. Full article

Physical tempering is widely used to enhance the mechanical strength and thermal stability of glassware. Traditional numerical studies commonly adopt the uniform heat transfer coefficient assumption, which significantly deviates from the actual non-uniform jet cooling conditions, especially for glassware with complex three-dimensional curved surfaces. In this work, a fluid–thermal–structure sequential coupling numerical model for low-borosilicate glassware was developed using STAR-CCM+. The Realizable k-ε turbulence model, temperature-dependent thermophysical properties of glass and air, and transient non-uniform convective heat transfer boundaries were employed. Flow characteristics, heat transfer behavior, and residual stress distribution during air cooling were systematically investigated. The simulation results were verified using a polarizing stress instrument. Results indicate that obvious flow separation and vortices occur at the curved regions, resulting in highly non-uniform heat transfer. Temperature uniformity first decreases and then rebounds, while stress uniformity finally stabilizes above 90%. The through-thickness stress exhibits a parabolic profile with surface compression and internal tension. The maximum relative error between simulation and experiment is below 6%, demonstrating the reasonable engineering accuracy of the sequential coupling framework. Ultimately, these numerical observations quantify the fluid–thermal–structural interactions and underscore the critical importance of integrating realistic non-uniform aerodynamic boundaries. Full article

Total RNA was isolated from sections of healthy testes and Leydig cell tumors of mixed-breed dogs using TMA Master II device. The RNA-seq libraries were sequenced on the Illumina platform. Following differential expression analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were applied with quality control obtained using FastQC and Trimmomatic. This analysis revealed 1500 transcripts, including 982 upregulated and 168 downregulated genes. The results demonstrated that a significant proportion of these differentially expressed genes are directly involved in the control of sex steroid production (CYP11A1, STAR, and 3β-HSD3B1) or tube formation, angiogenesis, and extracellular matrix remodeling in interstitial cells (ESM1, FGG, and VEGFA). Moreover, we identified the upregulation of transcripts responsible for neurotransmitter or neuroendocrine signaling (SLC6A4, GRIN2C, GABRB3) and cholesterol metabolism and its regulation (GPX3, MSMO1, DHCR24). These genes were strongly associated with the phosphatidylinositol-3-kinase (PI3K)-Protein Kinase B (Akt) cascade and extracellular matrix interactions, features shared with various malignancies. Alterations in estrogen and relaxin signaling appear to be distinctive, understudied mechanisms specific to canine Leydig cell tumors. Concurrently, downregulated genes (e.g., DMRTC2, SEMA3C, ALOX12) were linked with cell differentiation, signaling and immunoregulatory pathway suppression involved in tumorigenesis. A complex transcriptomic profile of canine Leydig cell tumors was developed, revealing a conserved oncogenic core shared in some aspects with human malignancies alongside unique species-specific alterations. Findings seem to be useful for identifying novel diagnostic biomarkers and targeted therapies in veterinary oncology, establishing canine reproductive tissues as a valuable comparative biomedical model for research in human. Full article