Navigation System Research Articles

One-shot entorhinal maps enable flexible navigation in novel environments.

Animals must navigate changing environments to find food, shelter or mates. In mammals, grid cells in the medial entorhinal cortex construct a neural spatial map of the external environment1-5. However, how grid cell firing patterns rapidly adapt to novel or changing environmental features on a timescale relevant to behaviour remains unknown. Here, by recording over 15,000 grid cells in mice navigating virtual environments, we tracked the real-time state of the grid cell network. This allowed us to observe and predict how altering environmental features influenced grid cell firing patterns on a nearly instantaneous timescale. We found evidence that visual landmarks provide inputs to fixed points in the grid cell network. This resulted in stable grid cell firing patterns in novel and altered environments after a single exposure. Fixed visual landmark inputs also influenced the grid cell network such that altering landmarks induced distortions in grid cell firing patterns. Such distortions could be predicted by a computational model with a fixed landmark to grid cell network architecture. Finally, a medial entorhinal cortex-dependent task revealed that although grid cell firing patterns are distorted by landmark changes, behaviour can adapt via a downstream region implementing behavioural timescale synaptic plasticity6. Overall, our findings reveal how the navigational system of the brain constructs spatial maps that balance rapidity and accuracy. Fixed connections between landmarks and grid cells enable the brain to quickly generate stable spatial maps, essential for navigation in novel or changing environments. Conversely, plasticity in regions downstream from grid cells allows the spatial maps of the brain to more accurately mirror the external spatial environment. More generally, these findings raise the possibility of a broader neural principle: by allocating fixed and plastic connectivity across different networks, the brain can solve problems requiring both rapidity and representational accuracy.

Visual Navigation Systems for Maritime Smart Ships: A Survey

The rapid development of artificial intelligence has greatly ensured maritime safety and made outstanding contributions to the protection of the marine environment. However, improving maritime safety still faces many challenges. In this paper, the development background and industry needs of smart ships are first studied. Then, it analyzes the development of smart ships for navigation from various fields such as the technology industry and regulation. Then, the importance of navigation technology is analyzed, and the current status of key technologies of navigation systems is deeply analyzed. Meanwhile, this paper also focuses on single perception technology and integrated perception technology based on single perception technology. As the development of artificial intelligence means that intelligent shipping is inevitably the trend for future shipping, this paper analyzes the future development trend of smart ships and visual navigation systems, providing a clear perspective on the future direction of visual navigation technology for smart ships.

Development status and challenges of anti-spoofing technology of GNSS/INS integrated navigation

The threat of spoofing interference has posed a severe challenge to the security application of Global Navigation Satellite System (GNSS). It is particularly urgent and critical to carry out in-depth defense research on spoofing interference. When combined with the inertial navigation system (INS), the GNSS/INS integrated navigation system offers distinct advantages in the field of anti-spoofing technology research, which has garnered significant attention in recent years. To summarize the current research achievements of GNSS/INS integrated navigation anti-spoofing technology, it is necessary to provide an overview of the three core technical aspects of spoofing attack principles and implementation strategies, spoofing detection, and spoofing mitigation. First, the principles and implementation strategies of spoofing interference attacks are introduced, and different classifications of spoofing interference attacks are given. Then, the performance characteristics and technical points of different spoofing detection and spoofing mitigation methods are compared and analyzed, and the shortcomings and challenges in the current development of GNSS/INS anti-spoofing technology are pointed out. Finally, based on the summary and shortcomings of the existing technology, a prospect for the future development of GNSS/INS integrated navigation anti-spoofing technology is discussed.

Innovation Adaptive UKF Train Location Method Based on Kinematic Constraints

To address the issue of reduced positioning accuracy caused by satellite signal interruptions when trains pass through long tunnels, a novel train positioning method based on an innovative adaptive unscented Kalman filter (UKF) under kinematic constraints is proposed. This method aims to improve the accuracy of the location of trains during operation. By considering the dynamic characteristics of the train, a dynamic kinematic-constrained inertial navigation system (INS)/odometer (ODO) combination positioning system is established. This system utilizes kinematic constraints to correct the accumulated errors of the INS. Additionally, the algorithm incorporates real-time estimation of the measurement noise covariance using innovation sequences. The updated adaptive estimation algorithm is applied within the UKF framework for nonlinear filtering, forming the innovative adaptive UKF algorithm. At each time step, the difference between the ODO sensor data and the INS output is used as the measurement input for the innovative adaptive UKF algorithm, enabling global estimation. This process ultimately yields the actual positioning result for the train. Simulation results demonstrate that the innovative adaptive UKF train positioning method, incorporating kinematic constraints, effectively mitigates the impact of satellite signal interruptions. Compared with the traditional INS/ODO positioning method, the innovative adaptive UKF method reduces position errors by 34.35% and speed errors by 36.33%. Overall, this method enhances navigation accuracy, minimizes train positioning errors, and meets the requirements of modern train positioning systems.

Seal Pipeline: Enhancing Dynamic Object Detection and Tracking for Autonomous Unmanned Surface Vehicles in Maritime Environments

This study addresses the dynamic object detection problem for Unmanned Surface Vehicles (USVs) in marine environments, which is complicated by boat tilting and camera illumination sensitivity. A novel pipeline named “Seal” is proposed to enhance detection accuracy and reliability. The approach begins with an innovative preprocessing stage that integrates data from the Inertial Measurement Unit (IMU) with LiDAR sensors to correct tilt-induced distortions in LiDAR point cloud data and reduce ripple effects around objects. The adjusted data are grouped using clustering algorithms and bounding boxes for precise object localization. Additionally, a specialized Kalman filter tailored for maritime environments mitigates object discontinuities between successive frames and addresses data sparsity caused by boat tilting. The methodology was evaluated using the VRX simulator, with experiments conducted on the Volga River using real USVs. The preprocessing effectiveness was assessed using the Root Mean Square Error (RMSE) and tracking accuracy was evaluated through detection rate metrics. The results demonstrate a 25% to 30% improvement in detection accuracy and show that the pipeline can aid industry even with sparse object representation across different frames. This study highlights the potential of integrating sensor fusion with specialized tracking for accurate dynamic object detection in maritime settings, establishing a new benchmark for USV navigation systems’ accuracy and reliability.

Tunable Acoustic Tweezer System for Precise Three-Dimensional Particle Manipulation

This study introduces a tunable acoustic tweezer system designed for precise three-dimensional particle trapping and manipulation. The system utilizes a dual-liquid-layer acoustic lens, which enables the dynamic control of the focal length through the adjustable curvature of a latex membrane. This tunability is essential for generating the acoustic forces necessary for effective manipulation of particles, particularly along the direction of acoustic wave propagation (z-axis). Experiments conducted with spherical particles as small as 1.5 mm in diameter demonstrated the system’s capability for stable trapping and manipulation. Performance was rigorously evaluated through both z-axis and 3D manipulation tests. In the z-axis experiments, the system achieved a manipulation range of 33.4–53.4 mm, with a root-mean-square error and standard deviation of 0.044 ± 0.045 mm, which highlights its precision. Further, the 3D manipulation experiments showed that particles could be accurately guided along complex paths, including multilayer rectangular and helical trajectories, with minimal deviation. A visual feedback-based particle navigation system significantly enhanced positional accuracy, reducing errors relative to open-loop control. These results confirm that the tunable acoustic tweezer system is a robust tool for applications requiring precise control of particles with diameter of 1.5 mm in three-dimensional environments. Considering its ability to dynamically adjust the focal point and maintain stable trapping, this system is well suited for tasks demanding high precision, such as targeted particle delivery and other applications involving advanced material manipulation.

Advanced Edge AI Navigation and Assistance System for Blind User

- Despite substantial advancements in visual assistance technology, many existing systems are limited by sensor capabilities, computational resources, and power consumption. Traditional computer vision algorithms struggle to perform complex tasks required for real-time navigation. This paper presents the design and implementation of an advanced computer vision-based navigation system specifically tailored for Blender users, aimed at facilitating independent navigation in diverse environments .By leveraging edge Artificial Intelligence (AI) and deep learning methodologies, the proposed system achieves real-time object detection, person recognition, and environmental awareness, addressing the critical challenges posed by conventional systems. The use of cost-effective, low-power mobile computing platforms, such as smart depth sensors like the OpenCV AI Kit-Depth (OAK-D), enables efficient processing while ensuring portability. This system not only enhances the ability to identify and navigate obstacles but also incorporates additional functionalities, including reading written notices aloud and responding to traffic signals. Key design considerations, such as training data collection, computational efficiency, and portability, have been meticulously addressed to ensure reliable performance in real-world scenarios. The incorporation of an AI-driven voice interface facilitates user-friendly interaction, making this system an innovative and unobtrusive visual assistance device.

GNSS/IMU/LO integration with a new LO error model and lateral constraint for navigation in urban areas

The quest for reliable vehicle navigation in urban environments has led the integration of Light Detection and Ranging (LiDAR) Odometry (LO) with Global Navigation Satellite Systems (GNSS) and Inertial Measurement Units (IMU). However, the performance of the integrated system is limited by a lack of accurate LO error modeling. In this paper, we propose a weighted GNSS/IMU/LO integration-based navigation system with a novel LO error model. The Squared Exponential Gaussian Progress Regression (SE-GPR) based LO error model is developed by considering the vehicle velocity and number of point cloud features. Based on error prediction for GNSS positioning and LO, a weighting strategy is designed for integration in an Extended Kalman Filter (EKF). Furthermore, error accumulation of the navigation state, especially in GNSS-challenging scenarios, is restrained by the LiDAR-Aided Lateral Constraint (LALC) and Non-Holonomic Constraint (NHC). An experiment was conducted in a deep urban area to test the proposed algorithm. The results show that the proposed algorithm delivers horizontal and three-dimensional (3D) positioning Root Mean Square Errors (RMSEs) of 3.669 m and 5.216 m, respectively. The corresponding accuracy improvements are 35.9% and 50.0% compared to the basic EKF based GNSS/IMU/LO integration.

Analysis of Alignment Accuracy due to Velocity/Attitude Error of Master Inertial Navigation System in Velocity/Attitude Matching Transfer Alignment

This paper theoretically analyzes the effect of the velocity and attitude errors of Master Inertial Navigation System(MINS) on the accuracy of Slave Inertial Navigation System(SINS) transfer alignment in velocity and attitude matching, and validates the analysis through simulation. Theoretical analysis involves deriving a new state equation that considers the velocity and attitude errors of MINS from the state equation of the transfer alignment filter, and deriving the state estimation equation of the Kalman filter based on this. The analysis confirms that MINS's velocity and attitude errors induce the same level of velocity and attitude errors in SINS. A reference inertial navigation system model is added to the simulation model, and the transfer alignment accuracy is analyzed by comparing the navigation information of MINS and SINS with the reference inertial navigation system. It is confirmed that the accuracy analysis results through simulation are consistent with the theoretically analyzed results, and through this, the validity of the theoretically analysis in this paper is verified. The research findings indicate that when performing transfer alignment using MINS, which is likely to be operated for prolonged periods in pure inertial navigation mode, the navigation errors of MINS are transferred to SINS. This implies that initial correction navigation is necessary to be considered for SINS

Autonomous Underwater Vehicle Navigation Enhancement by Optimized Side-Scan Sonar Registration and Improved Post-Processing Model Based on Factor Graph Optimization

Autonomous Underwater Vehicles (AUVs) equipped with Side-Scan Sonar (SSS) play a critical role in seabed mapping, where precise navigation data are essential for mosaicking sonar images to delineate the seafloor’s topography and feature locations. However, the accuracy of AUV navigation, based on Strapdown Inertial Navigation System (SINS)/Doppler Velocity Log (DVL) systems, tends to degrade over long-term mapping, which compromises the quality of sonar image mosaics. This study addresses the challenge by introducing a post-processing navigation method for AUV SSS surveys, utilizing Factor Graph Optimization (FGO). Specifically, the method utilizes an improved Fourier-based image registration algorithm to generate more robust relative position measurements. Then, through the integration of these measurements with data from SINS, DVL, and surface Global Navigation Satellite System (GNSS) within the FGO framework, the approach notably enhances the accuracy of the complete trajectory for AUV missions. Finally, the proposed method has been validated through both the simulation and AUV marine experiments.

Acceptance and feasibility of an augmented reality-based navigation system with optical tracking for percutaneous procedures in interventional radiology - a simulation-based phantom study.

Augmented reality (AR) projects additional information into the user's field of view during interventions. The aim was to evaluate the acceptance and clinical feasibility of an AR system and to compare users with different levels of experience. A system was examined that projects a CT-generated 3D model of a phantom into the field of view using a HoloLens 2, whereby the tracked needle is displayed and navigated live. A projected ultrasound image is used for live control of the needle positioning. This should minimize radiation exposure and improve orientation.The acceptance and usability of the AR navigation system was evaluated by 10 physicians and medical students with different levels of experience by performing punctures with the system in a phantom. The required time was then compared and a questionnaire was completed to assess clinical acceptance and feasibility. For statistical analysis, frequencies for qualitative characteristics, location and dispersion measures for quantitative characteristics and Spearman rank correlations for correlations were calculated.9 out of 10 subjects hit all 5 target regions in the first attempt, taking an average of 29:39 minutes for all punctures. There was a significant correlation between previous experience in interventional radiology, years in the profession, and the time required. Overall, the time varied from an average of 43:00 min. for medical students to 15:00 min. for chief physicians. All test subjects showed high acceptance of the system and rated especially the potential clinical feasibility, the simplification of the puncture, and the image quality positively. However, the majority require further training for sufficient safety in use.The system offers distinct advantages for navigation and orientation, facilitates percutaneous interventions during training and enables professionally experienced physicians to achieve short intervention times. In addition, the system improves ergonomics during the procedure by making important information always directly available in the field of view and has the potential to reduce the radiation exposure of staff in particular by combining AR and sonography and thus shortening CT-fluoroscopy times. · AR navigation offers advantages for orientation during percutaneous radiological interventions.. · The subjects would like to use the AR system in everyday clinical practice on patients.. · AR improves ergonomics by making important information directly available in the field of view.. · The combination of AR and sonography can significantly reduce radiation exposure for staff.. · Rohmer K, Becker M, Georgiades M et al. Acceptance and feasibility of an augmented reality-based navigation system with optical tracking for percutaneous procedures in interventional radiology - a simulation-based phantom study. Fortschr Röntgenstr 2024; DOI 10.1055/a-2416-1080.

Orbit Determination Method for BDS-3 MEO Satellites Based on Multi-Source Observation Links

Research on augmentation and supplement systems for navigation systems has become a significant aspect in comprehensive positioning, navigation and timing (PNT) studies. The BeiDou-3 navigation satellite system (BDS-3) has constructed a dynamic inter-satellite network to gain more observation data than ground monitoring stations. Low Earth orbit (LEO) satellites have advantages in their kinematic velocity and information carrying rate and can be used as satellite-based monitoring stations for navigation satellites to make up for the distribution limitation of ground monitoring stations. This study constructs multi-source observation links with satellite-to-ground, inter-satellite and satellite-based observation data, proposes an orbit synchronization method for navigation satellites and LEO satellites and verifies the influence thereof on orbit accuracy with different observation data. The experimental results under conditions of real and simulated observation data showed the following: (1) With the support of satellite-based observation links, the orbit accuracy of the BDS-3 MEO satellites could be improved significantly, with a 78% improvement with the simulation data and a 76% improvement with the real data. When the navigation satellites leave the monitoring area of the ground monitoring stations, the accuracy reduction tendency of the orbit prediction could also be slowed down with the support of the LEO satellites and the accuracy could be maintained within centimeters. (2) Comparing the orbit accuracy with the support of the satellite-to-ground observation links, the orbit accuracy of the MEO satellites could be improved by 65.5%, 73.7% and 79.4% with the support of the 6, 12 and 60 LEO satellites, respectively. When the observation geometry and the covering multiplicity meet the basic requirement of orbit determination, the improvements to the orbit accuracy decrease with the growth of LEO satellite numbers. (3) The accuracy of orbit determination with the support of the LEO satellites or the inter-satellite links was at the centimeter level for both, verifying that inter-satellite links and satellite-based links can be used as each other’s backups for navigation satellites. (4) The accuracy of orbit determination with the multi-source observation links was also at the centimeter level, which was not better than the results with the support of the satellite-to-ground and inter-satellite links or the satellite-to-ground and satellite-based links.

Accuracy of robotic arm-assisted versus computed tomography-based navigation in total hip arthroplasty using the direct anterior approach: a retrospective study

BackgroundA robotic arm-assisted and a computed tomography (CT)- based navigation system have been reported to improve the accuracy of component positioning in total hip arthroplasty (THA). However, no study has compared robotic arm-assisted THA (rTHA) to CT-based navigated THA (nTHA) concerning accuracy of cup placement and acetabular fractures using the direct anterior approach (DAA). This study aimed to compare the accuracy of cup placement and the presence of intraoperative acetabular fractures between rTHA and nTHA using DAA in the supine position.MethodsWe retrospectively investigated 209 hips of 188 patients who underwent rTHA or nTHA using DAA (rTHA using the Mako system: 85 hips of 79 patients; nTHA: 124 hips of 109 patients). After propensity score matching for age and sex, each group consisted of 73 hips. We evaluated clinical and radiographic outcomes, comparing postoperative cup orientation and position, measured using a three-dimensional templating software, to preoperative CT planning. Additionally, we investigated the prevalence of occult acetabular fracture.ResultsClinical outcomes were not significantly different between the groups at 1 year postoperatively. The mean absolute error of cup orientation was significantly smaller in the rTHA group than in nTHA (inclination: 1.4° ± 1.2° vs. 2.7° ± 2.2°, respectively; p = 0.0001, anteversion: 1.5° ± 1.3° vs. 2.2° ± 1.7°, respectively; p = 0.007). The cases within an absolute error of 5 degrees in both RI and RA were significantly higher in the rTHA (97.3%) than in nTHA group (82.2%) (p = 0.003). The absolute error of the cup position was not significantly different between the two groups. The prevalence of occult acetabular fracture did not differ significantly between the two groups (rTHA: n = 0 [0%] vs. nTHA: n = 1 [1.4%]).ConclusionCup placement using DAA in the supine position in rTHA was more accurate with fewer outliers compared to nTHA. Therefore, rTHA performed via DAA in a supine position would be useful for accurate cup placement.

Multiple and Gyro-Free Inertial Datasets

An inertial navigation system (INS) utilizes three orthogonal accelerometers and gyroscopes to determine platform position, velocity, and orientation. There are countless applications for INS, including robotics, autonomous platforms, and the internet of things. Recent research explores the integration of data-driven methods with INS, highlighting significant innovations, improving accuracy and efficiency. Despite the growing interest in this field and the availability of INS datasets, no datasets are available for gyro-free INS (GFINS) and multiple inertial measurement unit (MIMU) architectures. To fill this gap and to stimulate further research in this field, we designed and recorded GFINS and MIMU datasets using 54 inertial sensors grouped in nine inertial measurement units. These sensors can be used to define and evaluate different types of MIMU and GFINS architectures. The inertial sensors were arranged in three different sensor configurations and mounted on a mobile robot, a passenger car and a turntable. In total, the dataset contains 45 hours of inertial data and corresponding ground truth trajectories. The data is freely accessible through our figshare repository.

Connectomic reconstruction predicts visual features used for navigation

Many animals use visual information to navigate1–4, but how such information is encoded and integrated by the navigation system remains incompletely understood. In Drosophila melanogaster, EPG neurons in the central complex compute the heading direction5 by integrating visual input from ER neurons6–12, which are part of the anterior visual pathway (AVP)10,13–16. Here we densely reconstruct all neurons in the AVP using electron-microscopy data17. The AVP comprises four neuropils, sequentially linked by three major classes of neurons: MeTu neurons10,14,15, which connect the medulla in the optic lobe to the small unit of the anterior optic tubercle (AOTUsu) in the central brain; TuBu neurons9,16, which connect the AOTUsu to the bulb neuropil; and ER neurons6–12, which connect the bulb to the EPG neurons. On the basis of morphologies, connectivity between neural classes and the locations of synapses, we identify distinct information channels that originate from four types of MeTu neurons, and we further divide these into ten subtypes according to the presynaptic connections in the medulla and the postsynaptic connections in the AOTUsu. Using the connectivity of the entire AVP and the dendritic fields of the MeTu neurons in the optic lobes, we infer potential visual features and the visual area from which any ER neuron receives input. We confirm some of these predictions physiologically. These results provide a strong foundation for understanding how distinct sensory features can be extracted and transformed across multiple processing stages to construct higher-order cognitive representations.

LIDAR adaptive parameters selection and target pointing control for close-proximity space operations

This paper deals with advanced Guidance, Navigation and Control (GNC) functions required to enable autonomous and safe operations of a chaser spacecraft in close-proximity to an uncooperative space target, as in Active Debris Removal or On-Orbit servicing scenarios. Specifically, it presents an original approach to autonomously and adaptively select the field of view and resolution of a scanning LIDAR to improve both state estimation accuracy and computational efficiency of a LIDAR-based relative navigation system. In general, the correct operation of such system is also determined by the capability to keep the boresight axis of the relative navigation sensor aligned with the target geometric center. To address this task, an original control technique, based on the sliding-mode formulation and relying on a reduced attitude representation, is proposed. This control scheme is also compared to state-of-the-art PD and PID approaches in terms of pointing accuracy and control effort. The proposed techniques have been numerically validated in a simulation environment integrating the chaser attitude control and the LIDAR-based relative navigation functions in a closed-loop architecture. The simulation environment realistically reproduces the generation of LIDAR-based point clouds, and the spacecraft relative dynamics in close proximity. Results show that the adaptive selection of the LIDAR operational parameters improves the relative navigation performance, while the proposed sliding-mode control guarantees higher pointing accuracy than PD and PID control approaches.

A Short-term clinical and radiologic outcomes of reverse total shoulder arthroplasty with navigation system in Asian population: A retrospective comparative study

BackgroundIn reverse total arthroplasty (rTSA), glenoid component positioning is a critical factor for outcomes especially in Asian population with smaller glenoid. The purpose of this study was to compare the clinical and radiologic outcomes of rTSA with and without the navigation system with a minimum follow-up of two years in Asian population. MethodsThis was a retrospective comparative study of 33 rTSA with the navigation system (NAV group) and 40 conventional rTSA (CON group). Radiologic measurements regarding the position of the glenoid component, glenoid vault perforation by the central cage, and scapular notching, as well as clinical outcomes including range of motion (ROM), functional scores and complications were compared. Number, length, and angulation of screws were assessed. ResultsThe mean age was 73.9 ± 5.9 years with a mean follow-up of 30.1 ± 6.4 months. The NAV group more frequently utilized augmented baseplate (P < 0.001), showed less superior inclination (P = 0.030) and had lower incidence of glenoid vault perforation (P = 0.040). The length of superior (P = 0.001) and inferior screws (P = 0.045) was longer in the NAV group. In the NAV group compared to the CON group, more inferior orientation of superior screws (P < .001), more anterior orientation for inferior screws (P = 0.031), and anterior screws (P = 0.003) were observed. The NAV group showed significantly less penetration into the suprascapular fossa by a superior screw (P = 0.007). Final ROM, functional scores, and complications showed no significant differences between two groups. ConclusionIn short-term follow-up, the use of a navigation system in rTSA showed no significant difference in clinical outcomes and complications compared to conventional implantation. However, it enabled a lower superior inclination and a reduced glenoid vault perforation by the central cage, simultaneously allowing for the insertion of longer peripheral screws in a safer direction compared to conventional implantation.

Healthcare system navigation difficulties among informal caregivers of older adults: a logistic regression analysis of social capital, caregiving support and utilization factors

BackgroundInformal caregivers of older adults play a vital role in improving the degree to which older adults access community and healthcare services in a seamless and timely manner. They are fulfilling important navigation and support roles for their older care recipients. However, there is still little knowledge of the most significant facilitators and barriers to effective and efficient system navigation among caregivers. This paper aims to fill these knowledge gaps through investigation of the key factors (i.e., social capital/cohesion, caregiving supports, and utilization factors) affecting navigation difficulties faced by informal caregivers of older adults.MethodsThe Behavioural-Ecological Framework of Healthcare Access and Navigation (BEAN) model is used to frame the study. Using the General Social Survey on Caregiving and Care Receiving 2018, we analyzed 2,733 informal caregivers whose primary care recipients were aged 65 or older. Hierarchical logistic regression was conducted to identify the relationship between system navigation difficulties among informal caregivers and four sequentially ordered blocks of predictors: (1) sociodemographic (2), social capital/cohesion (3), caregiving supports, and (4) healthcare demand.ResultsThe fully adjusted model showed that the probability of reporting navigation difficulties was lower for caregivers with social capital/cohesion compared to those without social capital/cohesion. In comparison, the probability of reporting navigation difficulties was higher among caregivers with caregiving support and among caregivers whose care receivers use a higher amount of health service use. Several sociodemographic covariates were also identified.ConclusionOur findings support certain aspects of the BEAN model. This study extends our understanding of potential facilitators and barriers that informal caregivers of older adults face while navigating complex community and health systems. There is a need to implement coordinated schemes and health policies especially for older adults with mental/neurological issues to address the challenges of their caregivers given the specific vulnerability identified in this study. The need for further research using different approaches to examine the disproportionate impact of COVID-19 on caregivers’ system navigation experience is crucial.

Medial center of rotation and 90-degree lateral laxity improve patient-reported outcomes in posterior cruciate retaining total knee arthroplasty.

Physiologic knee kinematics are crucial for successful total knee arthroplasty (TKA) but are often not replicated. Using a medial stabilizing technique (MST) minimizes bone resection but results in lateral laxity. This study aimed to investigate the effects of lateral laxity on knee kinematics and symptoms after TKA. Mobile-bearing cruciate-retaining MST-TKA was performed on 40 knees using a navigation system. In the kinematic analysis, the anteroposterior (AP) translations of the medial femoral condyle (MFC) and lateral femoral condyle (LFC), femoral rotation angles, and medial and lateral component gaps were recorded every 0.1 s. These data were extracted from the software from 0° to 120° flexion in 10° increments. Kinematics were classified as the medial center of rotation (MCR) or non-MCR between 0° to 90° of flexion. Lateral laxity was calculated by subtracting the medial component gap from the lateral component gap. The final follow-up Knee Injury and Osteoarthritis Outcome Scores (KOOS) were evaluated. The relationships between the pre and postoperative kinematics and between postoperative lateral laxity and kinematics were assessed using Spearman's correlation coefficients. Finally, the correlation between postoperative lateral laxity and KOOS symptoms was evaluated using linear regression analysis. Preoperative kinematics, including AP translation of the MFC and LFC and femoral rotation, correlated with postoperative kinematics (all P < 0.001). Additionally, postoperative lateral laxity correlated with postoperative AP translation of the MFC, LFC, and femoral rotation (all P < 0.001). Furthermore, the receiver operating characteristic analysis indicated a cutoff value of 0.9 mm on postoperative lateral laxity at 90° flexion for postoperative MCR (P < 0.001). Postoperative lateral laxity at 90° flexion was significantly correlated with KOOS symptoms (β = 0.465, P = 0.025). Preoperative kinematics and postoperative lateral laxity correlated with postoperative kinematics after MST-TKA. Postoperative lateral laxity greater than 0.9mm at 90° flexion was associated with physiological kinematic motion, leading to fewer knee symptoms in the PROMs. The key to successful TKA was considered to be keeping the asymmetric gap balance with physiological lateral laxity, rather than the conventional symmetrical gap balance. Retrospective study.

Marine remote target signal extraction based on 128 line-array single photon LiDAR

LiDAR technology has garnered significant attention in recent years due to its superior directivity, high resolution, and precise 3D information acquisition capabilities, making it indispensable in navigation systems. Among its variants, single-photon LiDAR stands out for maritime applications, owing to its reduced power consumption and extended detection range. However, the high sensitivity of single-photon detectors often results in substantial noise, necessitating effective denoising before data can be utilized for identification, tracking, and other purposes. In this study, we present a novel 128-line, 1550 nm shipborne long-range single-photon LiDAR system, with data collected and analyzed in maritime environments. This system contends with challenges such as a large dynamic range, abundant noise photons, and the complexity of sea surface conditions. To address these issues, we propose an efficient and adaptive denoising algorithm based on the k-th nearest neighbor (KNN) methodology. By examining the distribution characteristics of signal and noise photons, our approach enables target extraction even under conditions of intense noise and sparse signals. Our method exhibits robust adaptability across various detection scenarios. Experimental evaluations demonstrate its efficacy, accurately identifying targets at distances of 3.2 km in clear weather and 1.6 km in foggy conditions.