Distance difference functions on nonconvex boundaries of Riemannian manifolds

It is shown that a complete Riemannian manifold with boundary is uniquely determined, up to isometry, by its distance difference representation on the boundary. Unlike previously known results, no restrictions on the boundary are imposed.

Accuracy in Determining Aircraft Position by Terrestrial Radio Navigation

The main objective of terrestrial radio navigation is position determination. In this study, the accuracy of the distance measurement, distance difference measurement, and integrated angle measurement/distance measurement terrestrial radio navigation methods is investigated. in order to calculate the position errors, simulations for the aircraft flight dynamics were carried out, and the obtained position values were compared with the actual values. The aircraft position determination methods were evaluated in the sense of accuracy. The position determination method with better accuracy was determined by comparing the absolute errors of the examined methods. Simulation and error analysis shows that the distance difference method is superior and gives more accurate position results. It was observed that the distance measurement method errors were smaller than the errors of the integrated angle measurement/distance measurement method.

Development of a fully automatic deep learning system for L3 selection and body composition assessment on computed tomography

As sarcopenia research has been gaining emphasis, the need for quantification of abdominal muscle on computed tomography (CT) is increasing. Thus, a fully automated system to select L3 slice and segment muscle in an end-to-end manner is demanded. We aimed to develop a deep learning model (DLM) to select the L3 slice with consideration of anatomic variations and to segment cross-sectional areas (CSAs) of abdominal muscle and fat. Our DLM, named L3SEG-net, was composed of a YOLOv3-based algorithm for selecting the L3 slice and a fully convolutional network (FCN)-based algorithm for segmentation. The YOLOv3-based algorithm was developed via supervised learning using a training dataset (n = 922), and the FCN-based algorithm was transferred from prior work. Our L3SEG-net was validated with internal (n = 496) and external validation (n = 586) datasets. Ground truth L3 level CT slice and anatomic variation were identified by a board-certified radiologist. L3 slice selection accuracy was evaluated by the distance difference between ground truths and DLM-derived results. Technical success for L3 slice selection was defined when the distance difference was < 10 mm. Overall segmentation accuracy was evaluated by CSA error and DSC value. The influence of anatomic variations on DLM performance was evaluated. In the internal and external validation datasets, the accuracy of automatic L3 slice selection was high, with mean distance differences of 3.7 ± 8.4 mm and 4.1 ± 8.3 mm, respectively, and with technical success rates of 93.1% and 92.3%, respectively. However, in the subgroup analysis of anatomic variations, the L3 slice selection accuracy decreased, with distance differences of 12.4 ± 15.4 mm and 12.1 ± 14.6 mm, respectively, and with technical success rates of 67.2% and 67.9%, respectively. The overall segmentation accuracy of abdominal muscle areas was excellent regardless of anatomic variation, with CSA errors of 1.38–3.10 cm2. A fully automatic system was developed for the selection of an exact axial CT slice at the L3 vertebral level and the segmentation of abdominal muscle areas.

Surgical Outcomes of Unilateral Lateral Rectus Recession Versus Recess-resect in Children With Small-to-medium Angle Convergence Insufficiency-type Intermittent Exotropia

To determine the efficacy of unilateral lateral rectus recession (ULR) for convergence insufficiency-type intermittent exotropia (CI-type IXT), we compared surgical outcomes following ULR and recess‒resect (RR) procedures for CI-type IXT. In this retrospective study, medical records of 57 children who underwent ULR (n = 30) or RR (n = 27) for CI-type IXT of less than 25 PD at distance with a postoperative follow-up of 6 months or more were reviewed. Surgical success was defined as an alignment between 10 PD exodeviation and 5 PD esodeviation at distance and near fixation. The postoperative exodeviation showed no significant difference between the two groups at the last follow-up. A significant reduction in the mean near-distance difference was achieved postoperatively in both groups: from 5.4 PD preoperatively to 2.5 at last follow-up after ULR, and from 8.2 to 2.4 after RR (both p = 0.001). However, this difference between ULR and RR was not statistically significant (p > 0.05). The success rate at the last follow-up was 63.3% for ULR and 70.4% for RR (p = 0.574). ULR was found to be an effective treatment for CI-type IXT, with similar surgical outcomes to RR.

A NOMA-Combined Hybrid Approach for SDMA Improvement in Wireless Powered Communication Networks

Wireless Powered Communication Network (WPCN) consists of Hybrid Access Point (HAP) that performs power transmission and data collection at the same time, and multiple nodes that can transmit data. In WPCN, depending on the wireless communication environment, the nodes cannot be able to transmit data because they can fail to receive power. Hence, increasing the transmission rate under a given resource is one of the very important issues. In ordinary mobile communications, a cell is divided into several sectors and the data is collected through multiple antennas to increase the transmission rate using SDMA. As a result, if the number of nodes in the one sector increases, the interference between nodes increases, and the transmission rate may decrease. Accordingly, in order to maximize performance, the number of nodes that can exist in a sector must be limited. The transmission rate between nodes according to the distance difference may not be fair because the nodes far from the HAP charge a small amount of power by attenuation of the signal, and the nodes close to the HAP charge a relatively large amount of power. Therefore, we propose Hybrid SDMA and Non-Orthogonal Multiple Access (NOMA) as a way to maximize the performance in term of both Sum-Throughput and Fairness. Also, we prove that there is a tradeoff between Sum-Throughput and Fairness according to the number of sectors. The simulation results show that the Hybrid SDMA and NOMA improves the performance substantially compared to the conventional SDMA.

The influence of multi-point roller-dies on the shape deviation differences of profile in flexible stretch-bending forming

In the process of flexible 3D stretch bending, the shape deviation difference between the contact zone and non-contact zone is studied. It is obvious that in the contact zone, the die regulates the deformation of the profile to make it conform to the target shape with small shape deviation; in the non-contact zone, the profile has no die restriction and deviates from the target shape with large shape deviation. When the dies are placed equidistantly along the x-axis, the shape deviation of the non-contact zone near the clamp side is greater than that near the middle of the profile. Arrange the distance between adjacent dies in equal ratio along the x-axis, so that the spacing near the clamp side is a little smaller, and the spacing near the middle of the profile is a bit larger. The difference between the shape deviation of the non-contact zone profile near the clamp side and the middle of the profile decreases, and the maximum shape deviation is reduced, which greatly improves the processing accuracy and quality. However, with the increase of the distance difference between adjacent dies, the shape deviation difference of the non-contact zone near the middle of the profile also increases greatly. Although the clamp side decreases, the maximum shape deviation has become the shape deviation of the profile in the non-contact zone near the middle of the profile.

Scoring Protein Sequence Alignments Using Deep Learning

Background: A high-quality sequence alignment (SA) is the most important input feature for accurate protein structure prediction. For a protein sequence, there are many methods to generate a SA. However, when given a choice of more than one SA for a protein sequence, there are no methods to predict which SA may lead to more accurate models without actually building the models. In this work, we describe a method to predict the quality of a protein's SA. Methods: We created our own dataset by generating a variety of SAs for a set of 1,351 representative proteins and investigated various deep learning architectures to predict the local distance difference test (lDDT) scores of distance maps predicted with SAs as the input. These lDDT scores serve as indicators of the quality of the SAs. Results: Using two independent test datasets consisting of CASP13 and CASP14 targets, we show that our method is effective for scoring and ranking SAs when a pool of SAs is available for a protein sequence. With an example, we further discuss that SA selection using our method can lead to improved structure prediction.

A fully automatic deep learning system for L3 slice selection and body composition assessment on abdominal computed tomography.

Background and aims: As sarcopenia research has been gaining emphasis, the need for quantification of abdominal muscle on computed tomography (CT) is increasing. Thus, a fully automated system to select L3 slice and segment muscle in an end-to-end manner is demanded. We aimed to develop a deep learning model (DLM) to select the L3 slice with consideration of anatomic variations and to segment cross-sectional areas (CSAs) of abdominal muscle and fat. Methods: Our DLM, named L3SEG-net, was composed of a YOLOv3-based algorithm for selecting the L3 slice and a fully convolutional network (FCN)-based algorithm for segmentation. The YOLOv3-based algorithm was developed via supervised learning using a training dataset (n=922), and the FCN-based algorithm was transferred from prior work. Our L3SEG-net was validated with internal (n=496) and external validation (n=586) datasets. L3 slice selection accuracy was evaluated by the distance difference between ground truths and DLM-derived results. Technical success for L3 slice selection was defined when the distance difference was <10 mm. Overall segmentation accuracy was evaluated by CSA error. The influence of anatomic variations on DLM performance was evaluated.Results: In the internal and external validation datasets, the accuracy of automatic L3 slice selection was high, with mean distance differences of 3.7±8.4 mm and 4.1±8.3 mm, respectively, and with technical success rates of 93.1% and 92.3%, respectively. However, in the subgroup analysis of anatomic variations, the L3 slice selection accuracy decreased, with distance differences of 12.4±15.4 mm and 12.1±14.6 mm, respectively, and with technical success rates of 67.2% and 67.9%, respectively. The overall segmentation accuracy of abdominal muscle areas was excellent regardless of anatomic variation, with the CSA errors of 1.38–3.10 cm2.Conclusions: A fully automatic system was developed for the selection of an exact axial CT slice at the L3 vertebral level and the segmentation of abdominal muscle areas.

Catalytic Descriptors to Investigate Catalytic Power in the Reaction of Haloalkane Dehalogenase Enzyme with 1,2-Dichloroethane

Enzymes play a fundamental role in many biological processes. We present a theoretical approach to investigate the catalytic power of the haloalkane dehalogenase reaction with 1,2-dichloroethane. By removing the three main active-site residues one by one from haloalkane dehalogenase, we found two reactive descriptors: one descriptor is the distance difference between the breaking bond and the forming bond, and the other is the charge difference between the transition state and the reactant complex. Both descriptors scale linearly with the reactive barriers, with the three-residue case having the smallest barrier and the zero-residue case having the largest. The results demonstrate that, as the number of residues increases, the catalytic power increases. The predicted free energy barriers using the two descriptors of this reaction in water are 23.1 and 24.2 kcal/mol, both larger than the ones with any residues, indicating that the water solvent hinders the reactivity. Both predicted barrier heights agree well with the calculated one at 25.2 kcal/mol using a quantum mechanics and molecular dynamics approach, and also agree well with the experimental result at 26.0 kcal/mol. This study shows that reactive descriptors can also be used to describe and predict the catalytic performance for enzyme catalysis.

Method of Using RealSense Camera to Estimate the Depth Map of Any Monocular Camera

Robot detection, recognition, positioning, and other applications require not only real-time video image information but also the distance from the target to the camera, that is, depth information. This paper proposes a method to automatically generate any monocular camera depth map based on RealSense camera data. By using this method, any current single-camera detection system can be upgraded online. Without changing the original system, the depth information of the original monocular camera can be obtained simply, and the transition from 2D detection to 3D detection can be realized. In order to verify the effectiveness of the proposed method, a hardware system was constructed using the Micro-vision RS-A14K-GC8 industrial camera and the Intel RealSense D415 depth camera, and the depth map fitting algorithm proposed in this paper was used to test the system. The results show that, except for a few depth-missing areas, the results of other areas with depth are still good, which can basically describe the distance difference between the target and the camera. In addition, in order to verify the scalability of the method, a new hardware system was constructed with different cameras, and images were collected in a complex farmland environment. The generated depth map was good, which could basically describe the distance difference between the target and the camera.