Search Results (2,634)

Simultaneous Localization and Mapping (SLAM) is a critical technology for robot intelligence. Compared to cameras, Light Detection and Ranging (LiDAR) sensors achieve higher accuracy and stability in indoor environments. However, LiDAR can only capture the geometric structure of the environment, and LiDAR-based SLAM often fails in scenarios with insufficient geometric features or highly similar structures. Furthermore, low-cost mechanical LiDARs, constrained by sparse point cloud density, are particularly prone to odometry drift along the Z-axis, especially in environments such as tunnels or long corridors. To address the localization issues in such scenarios, we propose a forward-enhanced SLAM algorithm. Utilizing a 16-line LiDAR and a monocular camera, we construct a dense colored point cloud input and apply an efficient multi-modal feature extraction algorithm based on centroid distance to extract a set of feature points with significant geometric and color features. These points are then optimized in the back end based on constraints from points, lines, and planes. We compare our method with several classic SLAM algorithms in terms of feature extraction, localization, and elevation constraint. Experimental results demonstrate that our method achieves high-precision real-time operation and exhibits excellent adaptability to indoor environments with similar structures. Full article

Mitochondrial fission and fusion appear to be relatively infrequent in cardiac cells compared to other cell types; however, the proteins involved in these events are highly expressed in adult cardiomyocytes (ACM). Therefore, these proteins likely have additional non-canonical roles. We have previously shown that DRP1 not only participates in mitochondrial fission processes but also regulates mitochondrial bioenergetics in cardiac tissue. However, it is still unknown where the DRP1 that does not participate in mitochondrial fission is located and what its role is at those non-fission spots. Therefore, this manuscript will clarify whether oligomeric DRP1 is located at the SR–mitochondria interface, a specific region that harbors the Ca²⁺ microdomains created by Ca²⁺ release from the SR through the RyR2. The high Ca²⁺ microdomains and the subsequent Ca²⁺ uptake by mitochondria through the mitochondrial Ca²⁺ uniporter complex (MCUC) are essential to regulate mitochondrial bioenergetics during excitation–contraction (EC) coupling. Herein, we aimed to test the hypothesis that mitochondria-bound DRP1 preferentially accumulates at the mitochondria–SR contacts to deploy its function on regulating mitochondrial bioenergetics and that this strategic position is modulated by calcium in a beat-to-beat manner. In addition, the mechanism responsible for such a biased distribution and its functional implications was investigated. High-resolution imaging approaches, cell fractionation, Western blot, 2D blue native gel electrophoresis, and immunoprecipitations were applied to both electrically paced ACM and Langendorff-perfused beating hearts to elucidate the mechanisms of the strategic DRP1 localization. Our data show that in ACM, mitochondria-bound DRP1 clusters in high molecular weight protein complexes at mitochondria-associated membrane (MAM). This clustering requires DRP1 interaction with β-ACTIN and is fortified by EC coupling-mediated Ca²⁺ transients. In ACM, DRP1 is anchored at the mitochondria–SR contacts through interactions with β-ACTIN and Ca²⁺ transients, playing a fundamental role in regulating mitochondrial physiology. Full article

High-altitude lakes are sensitive indicators of climate change, reflecting the hydrological impacts of global warming in alpine regions. This study investigates the long-term dynamics of the water level and surface area of Lake Karakul on the eastern Pamir Plateau from 1991 to 2020 using integrated satellite altimetry data from ERS-1/2, ICESat, and CryoSat-2. A multi-source fusion approach was applied to generate a continuous time series, overcoming the temporal limitations of individual missions. The results show a significant upward trend in both water level and area, with an average lake level rise of 8 cm per year and a surface area increase of approximately 13.2 km² per decade. The two variables exhibit a strong positive correlation (r = 0.84), and the Mann–Kendall test confirms the significance of the trends at the 95% confidence level. The satellite-derived water levels show high reliability, with an RMSE of 0.15 m when compared to reference data. These changes are primarily attributed to increased glacial meltwater inflow, driven by regional warming and accelerated glacier retreat, with glacier area shrinking by over 10% from 1978 to 2001 in the eastern Pamir. This study highlights the value of integrating multi-sensor satellite data for monitoring inland waters and provides critical insights into the climatic drivers of hydrological change in high-altitude endorheic basins. Full article

This prospective pilot study aimed to develop and evaluate a VR–based screening tool for assessing distance fusional vergence amplitude in healthy orthotropic young adults aged 18 to 30 years. A VR–based balloon-hitting game was used to measure hitting deviation angles and total vergence amplitudes under five conditions: control (0 prism diopter [PD]), inward image rotation for 10 and 20 PD (negative fusional vergence [NFV] 10/20 groups), and outward image rotation for 10 and 20 PD (positive fusional vergence [PFV] 10/20 groups). Of the 20 subjects recruited, one was excluded due to esotropia, leaving 19 participants (mean age: 22.2 ± 2.2 years; 13 wore glasses and 3 were female). In the control group, the mean hitting deviation was 0.65 ± 0.25 PD. The PFV 10 PD group showed similar deviation (0.67 ± 0.25 PD, p = 0.67), while the PFV 20 PD group had a significant increase (1.71 ± 2.0 PD, p = 0.04). NFV groups demonstrated greater deviations (NFV 10 PD: 3.40 ± 2.05 PD; NFV 20 PD: 9.9 ± 2.40 PD, both p < 0.01). Total vergence amplitudes were 8.65, 16.48, 6.60, and 10.05 PD for PFV 10, PFV 20, NFV 10, and NFV 20 PD, respectively. The VR–based tool enables standardized, efficient assessment of fusional vergence and shows promise for large-scale screening. Full article

Measurement of grasp configurations (position, orientation, and width) in unstructured environments is critical for robotic systems. Accurate and robust prediction relies on rich multi-scale object representations; however, detail loss and fusion conflicts in multi-scale processing often cause measurement errors, particularly for complex objects. This study proposes a multi-scale and cross-layer fusion grasp detection network (MCFG-Net) based on a skip-connected encoder–decoder architecture. The sampling module in the encoder–decoder is optimized, and the multi-scale fusion strategy is improved, enabling pixel-level grasp rectangles to be generated in real time. A multi-scale spatial feature enhancement module (MSFEM) addresses spatial detail loss in traditional feature pyramids and preserves spatial consistency by capturing contextual information within the same scale. In addition, a cascaded fusion attention module (CFAM) is designed to assist skip connections and mitigate redundant information and semantic mismatch during feature fusion. Experimental results show that MCFG-Net achieves grasp detection accuracies of 99.62% ± 0.11% on the Cornell dataset and 94.46% ± 0.22% on the Jacquard dataset. Real-world tests on an AUBO i5 robot yield success rates of 98.5% for single-target and 95% for multi-target grasping tasks, demonstrating practical applicability in unstructured environments. Full article

The primary challenge that robots face in disaster rescue is to precisely and efficiently construct disaster maps and achieve autonomous navigation. This paper proposes a method for air–ground collaborative map construction. It utilizes the flight capability of an unmanned aerial vehicle (UAV) to achieve rapid three-dimensional space coverage and complex terrain crossing for rapid and efficient map construction. Meanwhile, it utilizes the stable operation capability of an unmanned ground vehicle (UGV) and the ground detail survey capability to achieve precise map construction. The maps constructed by the two are accurately integrated to obtain precise disaster environment maps. Among them, the map construction and positioning technology is based on the FAST LiDAR–inertial odometry 2 (FAST-LIO2) framework, enabling the robot to achieve precise positioning even in complex environments, thereby obtaining more accurate point cloud maps. Before conducting map fusion, the point cloud is preprocessed first to reduce the density of the point cloud and also minimize the interference of noise and outliers. Subsequently, the coarse and fine registrations of the point clouds are carried out in sequence. The coarse registration is used to reduce the initial pose difference of the two point clouds, which is conducive to the subsequent rapid and efficient fine registration. The coarse registration uses the improved sample consensus initial alignment (SAC-IA) algorithm, which significantly reduces the registration time compared with the traditional SAC-IA algorithm. The precise registration uses the voxelized generalized iterative closest point (VGICP) algorithm. It has a faster registration speed compared with the generalized iterative closest point (GICP) algorithm while ensuring accuracy. In reinforcement learning navigation, we adopted the deep deterministic policy gradient (DDPG) path planning algorithm. Compared with the deep Q-network (DQN) algorithm and the A* algorithm, the DDPG algorithm is more conducive to the robot choosing a better route in a complex and unknown environment, and at the same time, the motion trajectory is smoother. This paper adopts Gazebo simulation. Compared with physical robot operation, it provides a safe, controllable, and cost-effective environment, supports efficient large-scale experiments and algorithm debugging, and also supports flexible sensor simulation and automated verification, thereby optimizing the overall testing process. Full article

The visual–inertial odometry (VIO) system is not robust enough in long time operation. Especially, the visual–inertial and Global Navigation Satellite System (GNSS) coupled system is prone to dispersion of system position information in case of failure of visual information or GNSS information. To address the above problems, this paper proposes a tightly coupled nonlinear optimized localization system of RGBD visual, inertial measurement unit (IMU), and global position (GDVI-Fusion) to solve the problems of insufficient robustness of carrier position estimation and inaccurate localization information in environments where visual information or GNSS information fails. The preprocessing of depth information in the initialization process is proposed to solve the influence of an RGBD camera by lighting and physical structure and to improve the accuracy of the depth information of image feature points so as to improve the robustness of the localization system. Based on the K-Nearest-Neighbors (KNN) algorithm, to process the feature points, the matching points construct the best geometric constraints and eliminate the feature matching points with an abnormal length and slope of the matching line, which improves the rapidity and accuracy of the feature point matching, resulting in the improvement of the system’s localization accuracy. The lightweight monocular GDVI-Fusion system proposed in this paper achieves a 54.2% improvement in operational efficiency and a 37.1% improvement in positioning accuracy compared with the GVINS system. We have verified the system’s operational efficiency and positioning accuracy using a public dataset and on a prototype. Full article

Position localization of wideband source over multipath environment is addressed in this paper. Traditional methods generally estimate intermediate parameters first and then use these parameters to construct equations for determining the source position. However, the localization accuracy of such methods deteriorates significantly in the presence of multipath effects. In this paper, a direct position determination method combining Taylor expansion and subspace data fusion in the cross-spectrum domain is proposed. The method constructs the data model based on the cross-spectrum of the received signals from arbitrary sensor pairs, effectively avoiding the loss of the available information. Subsequently, forward spatial smoothing is used to address the rank-deficiency problem caused by the multipath effect. Finally, a cost function using subspace data fusion is constructed, and the optimal value is derived via first-order Taylor expansion to compensate for the position estimation bias. The proposed method shows higher localization accuracy compared to state-of-the-art methods. The numerical and experimental results validate the superior localization performance of the proposed algorithm. Full article

This study investigates the thermal control effect of a water mist fire-extinguishing system in road tunnels under both single-source and double-source fire scenarios. A total of eight full-scale fire tests were executed in a physical tunnel, and the double-source fire scenarios were further subdivided into two spatial configurations, including fire sources close together and fire sources with a center-to-center distance of 2 m. During the fire tests, the evolution of fire, temporal and spatial temperature distributions of the tunnel ceiling, longitudinal and vertical temperature gradients, and smoke behavior within the tunnel were systematically recorded and interpreted. The results demonstrate that early activation of the water mist system optimizes its physicochemical mechanisms by preventing the transition from the growth phase of fire to a stable phase. In single-source fire scenarios, the water mist directly suppresses the flame and eliminates the high-temperature core, leading to a significant alteration in the vertical temperature gradient. For double-source fire scenarios, the high-temperature region on the ceiling is reduced upon the application of the water mist. However, when the fire sources are positioned in close proximity, they tend to merge into a larger fire source, with the water mist proving insufficient to prevent this fusion. Conversely, when the center-to-center distance between the fire sources is 2 m, the water mist effectively separates the sources, blocking thermal feedback between them and forcing the flames to develop vertically. This, in turn, accelerates the attenuation of the fire and the recovery of the ambient temperature. Additionally, within the effective coverage of the water mist, the longitudinal temperature distribution on the tunnel ceiling still follows an exponential attenuation pattern, with a significantly high rate of temperature decline. Full article

Background/Objectives: Anterior cervical discectomy and fusion (ACDF) is a common procedure for treating cervical spondylotic myelopathy. Limited research exists on the predictors of subsidence following ACDF. Subsidence can compromise surgical outcomes, alter alignment, and predispose patients to further complications, making it essential to prevent and understand it. This study aims to identify key risk factors for clinically significant subsidence and evaluate its impact on cervical alignment parameters in a large, diverse patient population. Methods: We conducted a retrospective review of patients who underwent ACDF between 2013 and 2022 at a single institution. Subsidence was calculated as the mean change in anterior and posterior disc height, with clinically significant subsidence being defined as three millimeters or more. Univariate analysis was followed by regression modeling to identify subsidence predictors and analyze patterns. Subgroup analyses stratified patients by implant type, number of levels fused, and cage material. Results: A total of 96 patients with 141 levels of ACDF met the inclusion criteria. Patients with significant subsidence were younger on average (52.44 vs. 55.94 years; p = 0.074). Those with less postoperative lordosis were more likely to experience significant subsidence (79.5% vs. 90.2%; p = 0.088). Patients with significant subsidence were more likely to have standalone implants (38.5% vs. 16.7%; p < 0.01), taller cages (6.62 mm vs. 6.18 mm; p < 0.05), and greater loss of segmental lordosis (7.33 degrees vs. 3.31 degrees; p < 0.01). Multivariate analysis confirmed that standalone implants were a significant independent predictor of subsidence (OR 2.679; p < 0.05), and greater subsidence was positively associated with loss of segmental lordosis (OR 1.089; p < 0.01). Subgroup analysis revealed that multi-level procedures had a higher incidence of subsidence (35.7% vs. 28.1%; p = 0.156), and PEEK cages demonstrated similar subsidence rates compared to titanium constructs (28.1% vs. 29.4%; p = 0.897). Conclusions: Standalone implants are the strongest independent predictor of significant subsidence, and those that experience subsidence also show greater loss of segmental lordosis, although not overall lordosis. These findings have implications for surgical planning, particularly in patients with borderline bone quality or requiring multi-level fusions. The results support the use of plated constructs in high-risk patients and emphasize the importance of individualized surgical planning based on patient-specific factors. Further research is needed to explore these findings and determine how they can be applied to improve ACDF outcomes. Full article

In GNSS-deprived settings, such as indoor and underground environments, research on simultaneous localization and mapping (SLAM) technology remains a focal point. Addressing the influence of dynamic variables on positional precision and constructing a persistent map comprising solely static elements are pivotal objectives in visual SLAM for dynamic scenes. This paper introduces optical flow motion segmentation-based SLAM(OS-SLAM), a dynamic environment SLAM system that incorporates optical flow motion segmentation for enhanced robustness. Initially, a lightweight multi-scale optical flow network is developed and optimized using multi-scale feature extraction and update modules to enhance motion segmentation accuracy with rigid masks while maintaining real-time performance. Subsequently, a novel fusion approach combining the YOLO-fastest method and Rigidmask fusion is proposed to mitigate mis-segmentation errors of static backgrounds caused by non-rigid moving objects. Finally, a static dense point cloud map is generated by filtering out abnormal point clouds. OS-SLAM integrates optical flow estimation with motion segmentation to effectively reduce the impact of dynamic objects. Experimental findings from the Technical University of Munich (TUM) dataset demonstrate that the proposed method significantly outperforms ORB-SLAM3 in handling high dynamic sequences, achieving a reduction of 91.2% in absolute position error (APE) and 45.1% in relative position error (RPE) on average. Full article

Background: Subacromial impingement or pain syndrome (SAPS) is the most common diagnosis for chronic shoulder pain. Current surgeries do not reduce long-term pain, suggesting they miss the root etiology. Previously, we described the Human Disharmony Loop (HDL), where the unique lower trunk innervation to the pectoralis minor (PM) causes scapular dyskinesis and deforms its connections, including tugging the acromion down and impinging the subacromial structures. We hypothesize that SAPS patients who meet HDL criteria would benefit significantly from PM tenotomy with infraclavicular brachial plexus neurolysis (PM + ICN) alone. Methods: SAPS patients who met HDL diagnostic criteria were treated with PM + ICN, with secondary distal neurolysis if needed. Outcomes included pain and shoulder abduction ROM. Six-month follow-up minimum was required. Results: N = 140 patients were included. Median age was 49. Prior surgeries included 27% subacromial decompression/acromioplasty, 21% rotator cuff repair, 16% biceps tenodesis, 4% SLAP repair, 2% labral repair, 7% distal clavicle resection, 10% reverse total shoulder arthroplasty (rTSA), 1% rib resection with scalenectomy, 16% cervical spine fusion, 28% distal neurolysis. Median pain decreased from 8 to 2 and median shoulder ROM increased from 90 to 180 degrees. Positive impingement signs on exam decreased from 100% to 11%. (p < 0.01) Conclusions: In a large series of SAPS patients, evaluation and treatment for the HDL significantly reduced pain and restored motion. These findings suggest that in many patients SAPS may be a subset of the HDL: the ventral PM disturbing the scapula constitutes the anatomic basis and optimal surgical target behind SAPS. Full article

Background/Objectives: Clinicians routinely rely on periapical radiographs to identify root-end disease, but interpretation errors and inconsistent readings compromise diagnostic accuracy. We, therefore, developed an explainable, multimodal AI framework that (i) fuses two data modalities, deep CNN embeddings and radiomic texture descriptors that are extracted only from lesion-relevant pixels selected by Grad-CAM, and (ii) makes every prediction transparent through dual-layer explainability (pixel-level Grad-CAM heatmaps + feature-level SHAP values). Methods: A dataset of 2285 periapical radiographs was processed using six CNN architectures (EfficientNet-B1/B4/V2M/V2S, ResNet-50, Xception). For each image, a Grad-CAM heatmap generated from the penultimate layer of the CNN was thresholded to create a binary mask that delineated the region most responsible for the network’s decision. Radiomic features (first-order, GLCM, GLRLM, GLDM, NGTDM, and shape2D) were then computed only within that mask, ensuring that handcrafted descriptors and learned embeddings referred to the same anatomic focus. The two feature streams were concatenated, optionally reduced by principal component analysis or SelectKBest, and fed to random forest or XGBoost classifiers; five-view test-time augmentation (TTA) was applied at inference. Pixel-level interpretability was provided by the original Grad-CAM, while SHAP quantified the contribution of each radiomic and deep feature to the final vote. Results: Raw CNNs achieved a ca. 52% accuracy and AUC values near 0.60. The multimodal fusion raised performance dramatically; the Xception + radiomics + random forest model achieved a 95.4% accuracy and an AUC of 0.9867, and adding TTA increased these to 96.3% and 0.9917, respectively. The top ensemble, Xception and EfficientNet-V2S fusion vectors classified with XGBoost under five-view TTA, reached a 97.16% accuracy and an AUC of 0.9914, with false-positive and false-negative rates of 4.6% and 0.9%, respectively. Grad-CAM heatmaps consistently highlighted periapical regions, while SHAP plots revealed that radiomic texture heterogeneity and high-level CNN features jointly contributed to correct classifications. Conclusions: By tightly integrating CNN embeddings, mask-targeted radiomics, and a two-tiered explainability stack (Grad-CAM + SHAP), the proposed system delivers state-of-the-art lesion detection and a transparent technique, addressing both accuracy and trust. Full article

In precision livestock farming, synchronous and high-precision instance segmentation of multiple key body parts of sika deer serves as the core visual foundation for achieving automated health monitoring, behavior analysis, and automated antler collection. However, in real-world breeding environments, factors such as lighting changes, severe individual occlusion, pose diversity, and small targets pose severe challenges to the accuracy and robustness of existing segmentation models. To address these challenges, this study proposes an improved model, MPDF-DetSeg, based on YOLO11-seg. The model reconstructs its neck network, and designs the multipath diversion feature fusion pyramid network (MPDFPN). The multipath feature fusion and cross-scale interaction mechanism are used to solve the segmentation ambiguity problem of deer body occlusion and complex illumination. The design depth separable extended residual module (DWEResBlock) improves the ability to express details such as texture in specific parts of sika deer. Moreover, we adopt the MPDIoU loss function based on vertex geometry constraints to optimize the positioning accuracy of tilted targets. In this study, a dataset consisting of 1036 sika deer images was constructed, covering five categories, including antlers, heads (front/side views), and legs (front/rear legs), and used for method validation. Compared with the original YOLO11-seg model, the improved model made significant progress in several indicators: the mAP50 and mAP50-95 under the bounding-box metrics increased by 2.1% and 4.9% respectively; the mAP50 and mAP50-95 under the mask metrics increased by 2.4% and 5.3%, respectively. In addition, in the mIoU index of image segmentation, the model reached 70.1%, showing the superiority of this method in the accurate detection and segmentation of specific parts of sika deer, this provides an effective and robust technical solution for realizing the multidimensional intelligent perception and automated applications of sika deer. Full article

This study proposes CEL-DL-Bagging (Cross-Entropy Loss-optimized Deep Learning Bagging), a multi-model fusion framework that integrates cross-entropy loss-weighted voting with Bootstrap Aggregating (Bagging). First, we develop a lightweight recognition architecture by embedding a salient position attention (SPA) mechanism into four base networks (YOLOv5s-cls, EfficientNet-B0, MobileNetV3, and ShuffleNetV2), significantly enhancing discriminative feature extraction for disease patterns. Our experiments show that these SPA-enhanced models achieve consistent accuracy gains of 0.8–1.7 percentage points, peaking at 97.86%. Building on this, we introduce DB-CEWSV—an ensemble framework combining Deep Bootstrap Aggregating (DB) with adaptive Cross-Entropy Weighted Soft Voting (CEWSV). The system dynamically optimizes model weights based on their cross-entropy performance, using SPA-augmented networks as base learners. The final integrated model attains 98.33% accuracy, outperforming the strongest individual base learner by 0.48 percentage points. Compared with single models, the ensemble learning algorithm proposed in this study led to better generalization and robustness of the ensemble learning model and better identification of rice diseases in the natural background. It provides a technical reference for applying rice disease identification in practical engineering. Full article