Accurate Angle Research Articles

Impact of Atomic Defects on Water Contact Angle of 2D Molybdenum Disulfide Surfaces.

Interfacial dynamics within nanofluidic systems are crucial for applications like water desalination and osmotic energy harvesting. Understanding these dynamics can inform the rational optimization of two-dimensional (2D) materials and devices for such applications. This study explores the wetting behavior of realistic 2D MoS2 surfaces incorporating vacancies and atomic steps, known as atomic defects. We employ a combined density functional theory (DFT) and molecular dynamics (MD) computational approach to elucidate the influence of atomic defects on the MoS2-water interface. DFT calculations are utilized to determine the charge distribution within MoS2. Subsequently, free energy calculations are obtained through MD simulations of the MoS2-water interface. Our findings underscore the importance of incorporating atomic defects into MoS2 surfaces for accurate water contact angle (WCA) predictions in nanofluidic simulations, particularly when using Abal et al. force field parameters. However, the force field developed by Liu et al. yielded more accurate results for pristine MoS2 surfaces. While these parameters provide reliable outcomes for pristine MoS2 surfaces, their application to surfaces with defects may lead to underestimation of WCA. This highlights the critical need for realistic surface representations in nanofluidic modeling to accurately capture the complex interactions between water and MoS2 materials.

R-C-D-F machine learning method to measure for geological structures in 3D point cloud of rock tunnel face

This study introduces an innovative Roughness-CANUPO-Dip-Facet (R-C-D-F) methodology for the measurement of dip angle and direction in geological rock facets. The R-C-D-F method is distinguished by its comprehensive four-step approach, encompassing filtration through roughness analysis, CANUPO analysis, and dip angle filtration, followed by facet segmentation as the measurement step. To achieve precise and efficient results, the method specifically focuses on isolating joint embedment, achieved by systematically filtering out joint bands. This selective filtration process ensures that measurements are conducted exclusively on relevant joint embedment points. The novelty of this methodology lies in its capability to automatically eliminate joint bands while retaining the joint embedment points, facilitating precise measurements without manual intervention. Three site models were evaluated using the R-C-D-F method, alongside four different techniques for measuring dip angle and direction: plane fitting, normal vector conversion, facet segmentation, and compass measurements. The results demonstrated that all methods accurately calculated the dip angle, with an accuracy ranging from 97 % to 99.4 %. The facet segmentation method was selected as the optimal measurement tool due to its automatic nature and capacity to provide accurate results without manual intervention. Furthermore, the optimal local neighbour radius (LNR) for calculating normal vectors was determined, with findings indicating that a larger LNR value enhances accuracy but also increases computational time. A verification was conducted to estimate the dip angle used for filtering and discarding additional points representing joint rock bands, with the optimal value being 45, 30, and 45 degrees for the respective sites.The R-C-D-F method effectively detected and eliminated 100 % of joint band points while retaining 81 % of joint embedment points, and the facet segmentation method provided accurate dip angle and direction measurements for each joint embedment segment. These outcomes underscore the robustness and precision of the R-C-D-F method in geological engineering and rock stability studies.

Validity and Reliability of OpenPose-Based Motion Analysis in Measuring Knee Valgus during Drop Vertical Jump Test.

OpenPose-based motion analysis (OpenPose-MA), utilizing deep learning methods, has emerged as a compelling technique for estimating human motion. It addresses the drawbacks associated with conventional three-dimensional motion analysis (3D-MA) and human visual detection-based motion analysis (Human-MA), including costly equipment, time-consuming analysis, and restricted experimental settings. This study aims to assess the precision of OpenPose-MA in comparison to Human-MA, using 3D-MA as the reference standard. The study involved a cohort of 21 young and healthy adults. OpenPose-MA employed the OpenPose algorithm, a deep learning-based open-source two-dimensional (2D) pose estimation method. Human-MA was conducted by a skilled physiotherapist. The knee valgus angle during a drop vertical jump task was computed by OpenPose-MA and Human-MA using the same frontal-plane video image, with 3D-MA serving as the reference standard. Various metrics were utilized to assess the reproducibility, accuracy and similarity of the knee valgus angle between the different methods, including the intraclass correlation coefficient (ICC) (1, 3), mean absolute error (MAE), coefficient of multiple correlation (CMC) for waveform pattern similarity, and Pearson's correlation coefficients (OpenPose-MA vs. 3D-MA, Human-MA vs. 3D-MA). Unpaired t-tests were conducted to compare MAEs and CMCs between OpenPose-MA and Human-MA. The ICCs (1,3) for OpenPose-MA, Human-MA, and 3D-MA demonstrated excellent reproducibility in the DVJ trial. No significant difference between OpenPose-MA and Human-MA was observed in terms of the MAEs (OpenPose: 2.4° [95%CI: 1.9-3.0°], Human: 3.2° [95%CI: 2.1-4.4°]) or CMCs (OpenPose: 0.83 [range: 0.99-0.53], Human: 0.87 [range: 0.24-0.98]) of knee valgus angles. The Pearson's correlation coefficients of OpenPose-MA and Human-MA relative to that of 3D-MA were 0.97 and 0.98, respectively. This study demonstrated that OpenPose-MA achieved satisfactory reproducibility, accuracy and exhibited waveform similarity comparable to 3D-MA, similar to Human-MA. Both OpenPose-MA and Human-MA showed a strong correlation with 3D-MA in terms of knee valgus angle excursion.

Angle Accuracy of Intramedullary Bone Resection Guides for Total Knee Arthroplasty.

The importance of proper prosthetic placement has been confirmed in numerous studies. The objective of this study was to compare the planned resection angles to the verified intraoperative angles of femoral and tibial varus/valgus, tibial slope, and femoral flexion for each total knee performed using intramedullary (IM) cut guides for both distal femur and proximal tibia cuts. A total of 1,000 total knee arthroplasties (TKAs) were evaluated for this study. Intraoperative cut-check technology was used to show real-time validation of these resection angles. Assuming an acceptable range of within 2° of the planned cuts, results show the femoral varus/valgus angles were 75% accurate, the femoral flexion angles were 50.8% accurate, the tibial cuts were 95.2% accurate in the coronal plane, and the tibial slope was the least accurate with only 50.3% within the acceptable range. This showed that IM guides are reasonably accurate in producing desired angles in the coronal plane but less accurate in the sagittal plane, with a greater number of outliers in femoral flexion and posterior slope. Surgeons need to be aware of potential cutting errors when using IM guides as they affect the overall alignment of the implant, and real-time verification technology is available to verify the accuracy of the cuts.

The Predictive Accuracy of Anogenital Distance and Genital Tubercle Angle for First-Trimester Fetal Sex Determination.

Early identification of fetal gender is crucial for managing gender-linked genetic disorders. This study aimed to evaluate the predictive performance of anogenital distance (AGD) and genital tubercle angle (GTA) for fetal sex determination during the first trimester. A multicenter retrospective cohort study was conducted on 312 fetal cases between 11 and 13 + 6 weeks of gestation from two tertiary hospitals. AGD and GTA measurements were taken from midsagittal plane images using ultrasound, with intra- and inter-reader reproducibility assessed. Binomial logistic regression and ROC curve analysis were employed to determine the diagnostic performance and optimal cutoff points. AGD had a mean of 7.16 mm in male fetuses and 4.42 mm in female fetuses, with a sensitivity of 88.8%, specificity of 94.4%, and an area under the ROC curve (AUC) of 0.931 (95% CI: 0.899-0.962) using 5.74 mm as a cutoff point. For GTA, the mean was 35.90 degrees in males and 21.57 degrees in females, with a sensitivity of 92%, specificity of 84.7%, and an AUC of 0.932 (95% CI: 0.904-0.961) using 28.32 degrees as a cutoff point. The reproducibility results were excellent for AGD (intra-operator ICC = 0.938, inter-operator ICC = 0.871) and moderate for GTA (intra-operator ICC = 0.895, inter-operator ICC = 0.695). The findings suggest that AGD and GTA are reliable markers for early fetal sex determination, with AGD showing higher reproducibility. The findings highlight the feasibility and accuracy of these non-invasive sonographic markers and their potential usefulness in guiding timely interventions and enhancing the management of gender-linked genetic conditions.

Tightly Coupled Visual–Inertial Fusion for Attitude Estimation of Spacecraft

The star sensor boasts the highest accuracy in spacecraft attitude measurement. However, it is vulnerable to disturbances, including high-dynamic motion, stray light, and various in-orbit environmental factors. These disruptions may lead to a significant decline in attitude accuracy or even abnormal output, potentially inducing a state of disorientation in the spacecraft. Thus, it is usually coupled with a high-frequency gyroscope to compensate for this limitation. Nevertheless, the accuracy of long-term attitude estimation using a gyroscope decreases due to the presence of bias. We propose an optimization-based tightly coupled scheme to enhance attitude estimation accuracy under dynamic conditions as well as to bolster the star sensor’s robustness in cases like lost-in-space. Our approach commenced with visual–inertial measurement preprocessing and estimator initialization. Subsequently, the enhancement of attitude and bias estimation precision was achieved by minimizing visual and inertial constraints. Additionally, a keyframe-based sliding window approach was employed to mitigate potential failures in visual sensor measurements. Numerical tests were performed to validate that, under identical dynamic conditions, the proposed method achieves a 50% improvement in the accuracy of yaw, pitch, and roll angles in comparison to the star sensor only.

Enhancing Automatic Inertial Sensor Calibration Algorithm for Accurate Joint Angle Estimation in High Flexion Postures

Enhancing Automatic Inertial Sensor Calibration Algorithm for Accurate Joint Angle Estimation in High Flexion Postures

Reliability and accuracy of scoliotic parameters on using a wireless handheld 3D ultrasound for children with adolescent idiopathic scoliosis: a pilot study.

To report the accuracy and reliability of Cobb angle (CA), axial vertebral rotation (AVR), kyphotic and lordotic angles (KA and LA) measurements on using a new 3D ultrasound (US) system. Forty participants (34F, 6M, aged 14.0 ± 2.3 years) were recruited. The first 20 participants were scanned by the validated US system and the new US system. The other 20 participants were scanned with the new US system only. Two raters (R1 and R2) performed the measurements: R1 has 10 years of experience in radiology but is new in ultrasound scoliosis, while R2 has 30 years of scoliosis experience. All US images were measured twice by R1, and once by R2. Forty posteroanterior and 30 lateral standing radiographs were obtained and measured once by R1. Statistical analysis consisted of mean absolute difference (MAD), intraclass correlation coefficient (ICC(2,1)), and Bland-Altman plots. R1 showed excellent intra-rater and inter-rater reliability for US measurements with ICCs(2,1) ≥ 0.91. The inter-method reliability was good between the two US systems for all parameters with ICCs(2,1) ≥ 0.85 and maximum MAD of 3.4°. The new US showed good reliability and accuracy compared to radiographs for CA, AVR and KA with ICCs(2,1) ≥ 0.81 and maximum MAD of 5.8°, but poor results for LA with ICCs(2,1) of 0.27-0.35 and MADs of 14.0°-15.4°. The new 3D US system showed good reliability and accuracy for CA, AVR and KA measurements, but a large measurement discrepancy on LA. A new measurement method for US LA may need to investigate.

Automated Cobb Angle Measurements for Scoliosis Diagnosis and Assessment: AI Applications and Accuracy Enhancement Through Image Processing Techniques.

Introduction Scoliosis is characterized by an abnormal curvature of the spine in the coronal plane. Idiopathic scoliosis is the most prevalent type, though specific causes are sometimes identifiable. Genetic factors significantly influence adolescent idiopathic scoliosis (AIS), which is diagnosed through clinical and radiographic evaluations, primarily using the Cobb angle to measure curvature severity. The classification of scoliosis severity ranges from mild scoliosis, where sometimes the absence of pain is encountered, to moderate and severe, which is usually associated with lancinating pain. Early onset and high progression rates in idiopathic scoliosis are indicative of poorer prognoses. Methods The study analyzed 197 radiographic images from a private clinic database between December 2023 and April 2024. Inclusion criteria focused on anteroposterior images of the thorax and abdomen, excluding unclear and non-spinal images. Manual Cobb angle measurements were performed using RadiAnt DICOM Viewer 2020.2, followed by automated measurements using the Cobb Angle Calculator software. Discrepancies led to further image processing with enhanced color contrast for improved visualization. Data were analyzed using GraphPad InStat to assess error margins between manual and automated measurements. Results Initial results indicated discrepancies between manual and automated Cobb angle measurements. Enhanced image processing improved accuracy, demonstrating the efficacy of both manual and automated techniques in evaluating spinal deformities. Statistical analysis revealed significant error margins, prompting a refined approach for minimizing measurement errors. Discussion The study highlights the importance of accurate Cobb angle measurement in diagnosing and classifying scoliosis. Manual measurements, while reliable, are time-consuming and prone to human error. Automated methods, particularly those enhanced by machine learning algorithms, offer promising accuracy and efficiency. The integration of image processing techniques further enhances the reliability of scoliosis evaluation. Conclusion Accurate assessment of scoliosis through Cobb angle measurement is crucial for effective diagnosis and treatment planning. The study demonstrates that combining manual techniques with advanced automated methods and image processing significantly improves measurement accuracy. Such an approach is intended to support better clinical outcomes. Future research should focus on refining these technologies for broader clinical applications.

Predictor-corrector reentry guidance for hypersonic glide vehicles based on high-precision analytical solutions

To address the challenge of improving downrange prediction accuracy while reducing unnecessary bank reversals, we propose a predictor-corrector guidance method based on high-precision analytical solutions. First, precise analytical solutions for downrange and crossrange are derived to establish the foundation for the guidance law, and we use a neural network model for accurate flight-path angle prediction. Next, we compensate for Earth's rotation effects in the downrange prediction by employing the Taylor expansion. Aiming to eliminate phugoid oscillations, we add the flight-path angle feedback into the command of the bank angle and angle of attack. Subsequently, a lateral guidance law that combines the heading angle error corridor with predicting bank reversals is introduced. The sign of the bank angle is determined by the corridor during the initial flight phase. Following the vehicle travels a specified downrange distance, we use the crossrange analytical solution to regulate subsequent bank reversals. Simulations under disturbance conditions demonstrate that the proposed guidance method has high accuracy and strong robustness, effectively meeting various constraints.

Accurate and Robust Static Hydrophobic Contact Angle Measurements Using Machine Learning.

We present a machine learning (ML) approach to static contact angle measurement, trained on a large data set (>7.2 million) of half drop contours based on solutions to the Young-Laplace equation where the contact angle is known a priori (removing all sources of error from human input). The data set included the effects of surface roughness, gravity, the size of drop relative to the image, and reflections of the drop on the surface. The presented ML model (valid for contact angles >110°), in combination with a new automated image and contour processing approach, is shown to be more accurate than other methods when benchmarked against an experimental data set, with an estimated error of 1°. The ML model is also 2 orders of magnitude faster at predicting contact angles than Young-Laplace fitting (the current best practice approach). The accuracy and speed of the presented approach provides a viable pathway toward robust and reproducible high-throughput contact angle analysis. This approach, Conan-ML, is open-source and provided for the use and development of new approaches to goniometry.

Study of the contact angle and roughness effects on mercury intrusion porosimetry (MIP) analysis in natural/real carbonate rocks using massive pure minerals and synthetic carbonate rocks

Studying pore networks in rocks is critical for understanding reservoir properties, with mercury intrusion porosimetry (MIP) being a key laboratory method. MIP involves capillary intrusion of mercury, with intrusion pressure (P) related to capillary diameter (d) by the Washburn equation, incorporating mercury properties like contact angle (θ) and surface tension (γ). While a constant contact angle of 140° is typically assumed for MIP, it can vary based on rock mineralogy and surface roughness. This study aims to analyze how contact angle variations and surface roughness affect MIP analysis of carbonate rocks. Initially, contact angle data was obtained for calcite, dolomite, and quartz with varying roughness levels. Synthetic rocks were then constructed using these minerals. MIP was performed using the typical 140° contact angle and a calculated contact angle from the Cassie model, which considers surface composition fraction. Natural rock MIP analysis followed, with contact angles varying due to mineral composition and roughness. The study found higher contact angles than the standard 140°, affecting pore size distributions up to 30%. Natural rock pore network curves were adjusted based on synthetic rock data analysis. These findings emphasize the need for accurate contact angle selection in MIP for precise pore distribution analysis and contribute to the further study of carbonate rocks.

The application of B1 inhomogeneity-corrected variable flip angle T1 mapping for assessing liver fibrosis

PurposeThe aim of this study was to evaluate the diagnostic accuracy of the B1 inhomogeneity-corrected variable flip angle (VFA) method using native T1 values in the staging of liver fibrosis. MethodsEighty-three patients who presented for liver biopsy due to varying degrees of liver damage, underwent MR examinations and had T1-mapping images of the liver acquired using the B1 inhomogeneity-corrected VFA VIBE method. Among them, 65 patients underwent Fibroscan, and their results were used to evaluate the elasticity of liver tissue. Additionally, T1-mapping images were collected from 19 normal control patients. Independent sample t-tests were used to analyze the correlation between T1 mapping and Fibroscan. The diagnostic efficacy of T1 mapping in patients with different stages of liver fibrosis was evaluated using receiver operating characteristic (ROC) curves. ResultsThe consistency between different observer groups was intraclass correlation coefficient (ICC) =0.802. T1 mapping demonstrated significant differences between mid-stage liver fibrosis (S = 2) and late-stage liver fibrosis (S = 3), as well as moderate inflammation (G = 2) and severe inflammation (G = 3), P < 0.05. The Area Under Curve(AUC) values of T1 mapping for early liver fibrosis (S ≥ 1), significant liver fibrosis (S ≥ 2), advanced liver fibrosis (S ≥ 3), and end-stage liver fibrosis (S = 4) were 0.760, 0.709, 0.790, and 0.768, respectively. T1 mapping combined with Fibroscan had an AUC value of 0.860. ConclusionsThe B1 inhomogeneity-corrected VFA T1 mapping may be useful for the staging of liver fibrosis. It has a superior diagnostic efficiency for diagnosing advanced fibrosis (≥S3), while native T1 values combined with Fibroscan have potential value for the staging of liver fibrosis.

Hybridized deep learning goniometry for improved precision in Ehlers-Danlos Syndrome (EDS) evaluation

BackgroundGeneralized Joint Hyper-mobility (GJH) can aid in the diagnosis of Ehlers-Danlos Syndrome (EDS), a complex genetic connective tissue disorder with clinical features that can mimic other disease processes. Our study focuses on developing a unique image-based goniometry system, the HybridPoseNet, which utilizes a hybrid deep learning model.ObjectiveThe proposed model is designed to provide the most accurate joint angle measurements in EDS appraisals. Using a hybrid of CNNs and HyperLSTMs in the pose estimation module of HybridPoseNet offers superior generalization and time consistency properties, setting it apart from existing complex libraries.MethodologyHybridPoseNet integrates the spatial pattern recognition prowess of MobileNet-V2 with the sequential data processing capability of HyperLSTM units. The system captures the dynamic nature of joint motion by creating a model that learns from individual frames and the sequence of movements. The CNN module of HybridPoseNet was trained on a large and diverse data set before the fine-tuning of video data involving 50 individuals visiting the EDS clinic, focusing on joints that can hyperextend. HyperLSTMs have been incorporated in video frames to avoid any time breakage in joint angle estimation in consecutive frames. The model performance was evaluated using Spearman’s coefficient correlation versus manual goniometry measurements, as well as by the human labeling of joint position, the second validation step.OutcomePreliminary findings demonstrate HybridPoseNet achieving a remarkable correlation with manual Goniometric measurements: thumb (rho = 0.847), elbows (rho = 0.822), knees (rho = 0.839), and fifth fingers (rho = 0.896), indicating that the newest model is considerably better. The model manifested a consistent performance in all joint assessments, hence not requiring selecting a variety of pose-measuring libraries for every joint. The presentation of HybridPoseNet contributes to achieving a combined and normalized approach to reviewing the mobility of joints, which has an overall enhancement of approximately 20% in accuracy compared to the regular pose estimation libraries. This innovation is very valuable to the field of medical diagnostics of connective tissue diseases and a vast improvement to its understanding.

An Adjustment Strategy for Tilted Moiré Fringes via Deep Q-Network

Overlay accuracy, one of the three fundamental indicators of lithography, is directly influenced by alignment precision. During the alignment process based on the Moiré fringe method, a slight angular misalignment between the mask and wafer will cause the Moiré fringes to tilt, thereby affecting the alignment accuracy. This paper proposes a leveling strategy based on the DQN (Deep Q-Network) algorithm. This strategy involves using four consecutive frames of wafer tilt images as the input values for a convolutional neural network (CNN), which serves as the environment model. The environment model is divided into two groups: the horizontal plane tilt environment model and the vertical plane tilt environment model. After convolution through the CNN and training with the pooling operation, the Q-value consisting of n discrete actions is output. In the DQN algorithm, the main contributions of this paper lie in three points: the adaptive application of environmental model input, parameter optimization of the loss function, and the possibility of application in the actual environment to provide some ideas. The environment model input interface can be applied to different tilt models and more complex scenes. The optimization of the loss function can match the leveling of different tilt models. Considering the application of this strategy in actual scenarios, motion calibration and detection between the mask and the wafer provide some ideas. To verify the reliability of the algorithm, simulations were conducted to generate tilted Moiré fringes resulting from tilt angles of the wafer plate, and the phase of the tilted Moiré fringes was subsequently calculated. The angle of the wafer was automatically adjusted using the DQN algorithm, and then various angles were measured. Repeated measurements were also conducted at the same angle. The angle deviation accuracy of the horizontal plane tilt environment model reached 0.0011 degrees, and the accuracy of repeated measurements reached 0.00025 degrees. The angle deviation accuracy of the vertical plane tilt environment model reached 0.0043 degrees, and repeated measurements achieved a precision of 0.00027 degrees. Moreover, in practical applications, it also provides corresponding ideas to ensure the determination of the relative position between the mask and wafer and the detection of movement, offering the potential for its application in the industry.

Uterocervical angle and cervical length measurements for spontaneous preterm birth prediction in low-risk singleton pregnant women: a prospective cohort study.

Preterm birth is the leading cause of early neonatal morbidity and mortality. Strategies to predict preterm birth risk can help improve pregnancy outcomes. Even pregnant women without known risk factors for preterm birth can also experience it. This study aimed to evaluate the ability of the uterocervical angle and cervical length to predict spontaneous preterm birth in low-risk singleton pregnant women. A prospective study on 1107 singleton pregnant women between 16+0 and 23+6weeks gestation at low risk for spontaneous preterm birth who were treated at the Haiphong Hospital of Obstetrics and Gynecology, Vietnam, between September 2020 and September 2021 was conducted. A single sonographer assessed the cervical length and the uterocervical angle using transvaginal ultrasonography. The patients were followed up until delivery to determine the main pregnancy outcome (spontaneous preterm birth before 37weeks gestation). The cut-off points for the uterocervical angle and cervical length were established by analyzing the receiver operating characteristic curve. The sensitivity, specificity, likelihood ratio, positive and negative predictive values, and accuracy of the uterocervical angle and cervical length for predicting spontaneous preterm birth were determined. A uterocervical angle ≥ 99° predicted spontaneous preterm birth at < 37weeks, with a sensitivity and specificity of 91% and 76%, respectively. A cervical length ≤ 33.8mm predicted preterm birth at < 37weeks with a sensitivity and specificity of 25% and 66%, respectively. A uterocervical angle ≥ 99° combined with a cervical length ≤ 33.8mm yielded the sensitivity, specificity, positive predictive value, likelihood ratio, and accuracy of spontaneous preterm birth prediction of 66%, 93%, 36%, 9, and 91%, respectively; thus provided a significant increase of specificity with an acceptable reduction of sensitivity as compared to cervical length alone. Besides the cervical length, the uterocervical angle can be considered a valuable ultrasound parameter for predicting spontaneous preterm birth in low-risk singleton pregnant women. Combining the uterocervical angle and cervical length yielded stronger spontaneous preterm birth prediction values.

Supporter-Type Anterior Cruciate Ligament Prevention System Based on Estimation of Knee Joint Valgus Angle Using Stretch Sensors

Anterior cruciate ligament (ACL) injuries are common in sports involving jumping and rapid direction changes, often occurring in non-contact situations. The risk of ACL injury is evaluated by knee flexion and valgus angles; a small knee flexion angle combined with a large valgus angle increases the risk. Monitoring these angles during activities can help athletes recognize their ACL injury risk and adjust their movements. Traditional 3D motion analysis, used for measuring knee angles, is costly and impractical for daily practice. This study proposes a knee supporter with stretch sensors to estimate knee flexion and valgus angles in practice settings, evaluating ACL injury risk and notifying athletes of high-risk movements. The proposed device wirelessly transmits data from three stretch sensors placed on the device to a PC and uses machine learning to estimate the knee angles. The results of the evaluation experiments, conducted with data from five healthy male and female participants in their twenties, indicate that the estimation accuracy for the knee flexion angle, achieved by a model trained using a Random Forest Regressor (RFR) with data from individuals other than the target user, resulted in a Mean Absolute Error (MAE) of 8.86 degrees. For the knee valgus angle, a model trained with the user’s own data using the RFR achieved a MAE of 0.81 degrees.

Combining high-throughput deep learning phenotyping and GWAS to reveal genetic variants of fruit branch angle in upland cotton

Cotton (Gossypium spp.), an economically and strategically significant crop in China, faces challenges such as rising cultivation costs and conflicts between grain and cotton cultivation. These challenges underscore the need for enhancing yields per unit area. In response, this study employs deep learning techniques, combined with high-throughput angle detection, to conduct genome-wide association studies (GWAS) on 355 upland cotton accessions, identify key SNPs and candidate genes for plant architecture by fruit branch angle (FBA) influences planting density, yield, and mechanized harvesting. A convolutional neural network (CNN)-based software was developed for rapid and accurate branch angle detection, showing high correlation with both AutoCAD and manual measurements. Significant phenotypic variation in FBA was observed across various cotton planting regions, with the Northwest Inland Region (NIR) exhibiting notably smaller angles. In total, 107 significant Single Nucleotide Polymorphisms (SNPs) were detected across 45 quantitative trait loci (QTL), and three potential candidate genes (Ghir_A11G034910, Ghir_D05G007790, and Ghir_D05G031350) were identified, providing insights into the genetic basis of FBA and presenting valuable genetic resources for cotton breeding programs.

Evaluating the accuracy of a new robotically assisted system in cadaveric total knee arthroplasty procedures

BackgroundRobot-assisted total knee arthroplasty (TKA) has been shown to facilitate high-precision bone resection, which is an important goal in TKA. The aim of this cadaveric study was to analyze the accuracy of the target angle and bone resection thickness of a recently introduced robotic TKA system.MethodsThis study used 4 frozen cadaveric specimens (8 knees), 2 different implant designs, navigation, and a robotic system. The 4 surgeons who participated in this study were trained and familiar with the basic principles and operating procedures of this system. The angle of the bone cuts performed using the robotic system was compared with the target angles from the intraoperative plan. For each bone cut, the resection thickness was recorded and compared with the planned resection thickness.ResultsThe mean angular difference for all specimens was less than 1°, and the standard deviation was less than 2°. The mean difference between the planned and measured angles was close to 0 and not significantly different from 0 except for the difference in the frontal tibial component angle, which was 0.88°. The mean difference in the hip-knee-ankle axis angle was − 0.21°± 1.06°. The mean bone resection difference for all specimens was less than 1 mm, and the standard deviation was less than 0.5 mm.ConclusionsThe results of the cadaveric experimental study showed that the new TKA system can realize highly accurate bone cuts and achieve planned angles and resection thicknesses. Despite the limitations of small sample sizes and large differences between cadaveric and clinical patients, the accuracy of cadaveric experiments provides strong support for subsequent clinical trials.

The Kinematic Models of the SINS and Its Errors on the SE(3) Group in the Earth-Centered Inertial Coordinate System.

In this paper, the kinematic models of the Strapdown Inertial Navigation System (SINS) and its errors on the SE(3) group in the Earth-Centered Inertial frame (ECI) are established. On the one hand, with the ECI frame being regarded as the reference, based on the joint representation of attitude and velocity on the SE(3) group, the dynamic of the local geographic coordinate system (n-frame) and the body coordinate system (b-frame) evolve on the differentiable manifold, respectively, and the high-order expansion of the Baker-Campbell-Haussdorff equation compensates for the non-commutative motion errors stimulated by strong maneuverability. On the other hand, the kinematics of the left- and right-invariant errors of the n-frame and the b-frame on the SE(3) group are separately derived, where the errors of the b-frame completely depend on inertial sensor errors, while the errors of the n-frame rely on position errors and velocity errors. In this way, the errors brought by the inconsistency of the reference coordinate system are tackled, and a novel attitude error definition is introduced to separate and decouple the factors affecting the dynamic of the n-frame errors and the b-frame errors for better attitude estimation. Through a turntable experiment and a car-mounted field experiment, the effectiveness of the proposed kinematic models in estimating attitude has been verified, with a remarkable improvement in yaw angle accuracy in the case of large initial misalignment angles, and the models developed have better robustness compared to the traditional SE(3) group-based model.