Distance Coefficient Research Articles

Impact of coil envelope on the coupling coefficient in magnetic induction wireless power transmission systems

Abstract In recent years, research in the field of wireless power transmission by magnetic induction has increased due to the diversity of applications, such as medicine, electronics, and transport, which require this technology. However, despite its maturity to date, this technology has had difficulty gaining acceptance due to the short transmission distance and low coupling coefficient between the transmitting and receiving coils. Generally, these coils need to be protected by an envelope. However, the impact of the nature of the envelope on the coupling coefficient has not studied. This work focuses on the impact of the nature of the coil protection envelope on the coupling coefficient. Three-dimensional flat spiral coils and the protective envelope are modelled and subjected to parametric analysis with variable air gaps and frequencies using ANSYS-Electronics Maxwell 2022 R1 software with the finite element method (FEM). Simulation results show that a coil protected by an insulating envelope has a higher coupling coefficient compared to one protected by a conducting material. It is also shown that the coupling coefficient decreases as the operating frequencies increases. The ohmic losses in coils protected by an insulating envelope are greater than those in coils protected by a conductive material.

Litter Removal Counteracts the Effects of Warming on Soil Bacterial Communities in the Qinghai–Tibet Plateau

Climate warming and high-intensity human activities threaten the stability of alpine meadow ecosystems. The stability of the soil microbial community is crucial for maintaining ecological service function. However, the effects of warming and litter removal on microbial interactions, community-building processes, and species coexistence strategies remain unclear. In this study, we used a fiberglass open-top chamber to simulate global change, and moderate grazing in winter was simulated by removing above-ground litter from all plants in the Qinghai–Tibet Plateau, China, to investigate the effects of warming, litter removal, and interactions on soil microbial communities. The treatments included (1) warming treatment (W); (2) litter removal treatment (L); (3) the combined treatment (WL); and (4) control (CK). The results show that compared with the control treatment, warming, litter removal, and the combined treatments increased bacterial Shannon diversity but reduced fungal Shannon diversity, and warming treatment significantly changed the bacterial community composition. Warming, litter removal, and the combined treatments reduced the colinear network connectivity among microorganisms but increased the modularity of the network, and the average path distance and average clustering coefficient were higher than those in the control group. Stochastic processes played a more important role in shaping the microbial community composition, and soil–available phosphorus and soil ammonium contributed more to the βNTI of the bacterial community, while total phosphorus and NAG enzyme in the soil contributed more to the βNTI of the fungal community. Notably, litter removal counteracts the effects of warming on bacterial communities. These results suggest that litter removal may enhance bacterial community stability under warming conditions, providing insights for managing alpine meadow ecosystems in the context of climate change.

Investigation of intrafractional spinal cord and spinal canal movement during stereotactic MR-guided online adaptive radiotherapy for kidney cancer.

This study aimed to investigate the intrafractional movement of the spinal cord and spinal canal during MR-guided online adaptive radiotherapy (MRgART) for kidney cancer. All patients who received stereotactic MRgART for kidney cancer between February 2022 and February 2024 were included in this study. Patients received 30-42 Gy in 3-fraction MRgART for kidney cancer using the Elekta Unity, which is equipped with a linear accelerator and a 1.5 Tesla MRI. MRI scans were performed at three points during each fraction: for online planning, position verification, and posttreatment assessment. The spinal cord was contoured from the upper edge of Th12 to the medullary cone, and the spinal canal was contoured from Th12 to L3, using the first MRI. These contours were adjusted to the second and third MR images via deformable image registration, and movements were measured. Margins were determined via the formula "1.3×Σ+0.5×σ" and 95% prediction intervals. A total of 22 patients (66 fractions) were analyzed. The median interval between the first and third MRI scans were 38 minutes. The mean ± standard deviation of the spinal cord movements after this interval were -0.01 ± 0.06 for the x-axis (right-left), 0.01 ± 0.14 for the y-axis (caudal-cranial), 0.07 ± 0.05 for the z-axis (posterior-anterior), and 0.15 ± 0.08 for the 3D distance, respectively. The correlation coefficients of the 3D distance between the spinal cord and the spinal canal was high (0.92). The calculated planning organ at risk volume margin for all directions was 0.11 cm for spinal cord. The 95% prediction intervals for the x-axis, y-axis, and z-axis were -0.11-0.09 cm, -0.23-0.25 cm and -0.14-0.03 cm, respectively. Margins are necessary in MRgART to compensate for intrafractional movement and ensure safe treatment delivery.

Design of Positive Pressure Re-Acceleration Assisted Seeding Mechanism for Corn Based on CFD-EDEM Gas-Solid Coupling Simulation

This study proposes a positive pressure re-acceleration assisted seeding mechanism and analyzes the motion mechanism of corn seeds during the seeding process. By employing the CFD-EDEM gas-solid coupling simulation analysis method, the fluid characteristics, initial ejection velocity of seeds, seed dropping time difference, and sowing position difference in the seeding mechanism under different structural parameters of the air pressure valve body were investigated. The optimal structural parameters of the air pressure valve body were determined (nozzle gap c = 0.6 mm, throat constriction diameter d = 16 mm, and throat constriction length l = 44 mm). A multi-factor experimental method was used to explore the effects of airflow pressure, forward speed during sowing, and sowing distance on sowing performance, aiming to identify the optimal working parameters for the positive pressure re-acceleration seeding mechanism. High-speed camera technology was used to record and analyze the seed movement process. The results indicate that an increase in positive pressure within the seed guide tube shortens the sowing time of corn seeds, reduces the coefficient of variation of seed dropping time difference, and effectively improves the consistency of sowing distance. The optimal parameters are a forward speed of 8 km/h, sowing distance of 20 cm, airflow pressure of 10 kPa, with a sowing distance coefficient of variation of 7.56%, and a seed dropping time difference coefficient of variation of 5.35%.

Mapping 18F-FDG PET uptake in the aortic wall and thrombus

Abstract Background Positron Emission Tomography (PET) uptake allows for the assessment of molecular activity, being 18F-FDG uptake indicative of glucose metabolism. PET uptake is commonly quantified manually by drawing regions of interest, which is subjective, poorly-reproducible and may miss relevant information. Purpose To develop and test an image analysis method to quantify and map 18F-FDG uptake on the whole aortic wall and thrombus. Methods PET-MRI was acquired in patients with thoracic or abdominal aortic aneurysms. The aorta was semi-automatically segmented in the MR angiography using 3DSlicer, generating a surface mesh that was expanded by 3 mm inward and outward. PET uptake was interpolated over this volume with a uniform spatial resolution of 1 mm. By means of 9 reference points, the aortic wall was discretized into 52 (thoracic) or 80 (thoraco-abdominal) standardized regions (i.e. wall patches). For each aortic patch and for the whole thrombus volume, the target to background Ratio (TBR) was calculated by dividing the uptake by a reference uptake in the left atrium. Two observers segmented the aorta and thrombus, located the anatomical reference points and quantified TBR. Inter-observer agreement for segmentation and aortic wall discretization was evaluated in terms of the 95% Hausdorff Distance (HD) and Dice Score Coefficient (DSC), while both median and 95% TBR in each region were tested via intra-class correlation coefficient (absolute value, single measure). Results Data from 20 patients with thoracic or abdominal aneurysms were analysed, 10 of them presented thrombus. The reproducibility of the segmentation of the aorta (median HD 3.16 IQR [2.42 - 3.94] mm, DSC 0.90 [0.89 - 0.92]) and of the thrombus (HD 4.87 [3.51 - 8.45] mm, DSC 0.85 [0.80 - 0.87]) were excellent. The inter-observer agreement in the localization of reference points was good (HD 5.10 [3.29 - 8.73] mm), resulting in excellent overlapping of the standardized aortic wall patches (HD 3.80 [2.44 - 5.08] mm). Inter-observer agreement of median and 95% TBR in each aortic patch were excellent (ICC of 0.90 and 0.93, respectively, Figure 1), with minimal bias (Figure 2). Similarly, excellent interobserver reproducibility was observed for median and 95% TBR in the thrombus (ICC of 0.97 and 0.96, respectively, Figure 2). Conclusions A robust and reproducible image analysis method to map PET uptake along the aortic wall was developed, extending the possibilities to study aortic diseases molecular activity and its association with local aortic characteristics.

Intelligent Fault Diagnosis for Aero-Engine Lubrication Systems Using Knowledge Graph and Deep Learning Techniques

Due to the complex structure and function of aero-engine lubrication system, fault diagnosis based on the existing health management system is not explicable and highly dependent on experts' experience. A method for constructing aeroengine lubrication system fault knowledge graph was proposed in this paper. Based on the experts' knowledge, the concept of lubrication system fault knowledge graph ontology was designed. With the help of deep learning techniques such as BiLSTM and CRF, we achieved the automatic extraction of unstructured knowledge. Next, based on the Cosine Distance and Jaccard coefficient, multi-source heterogeneous fault knowledge fusion was realized. In the end, intelligent fault knowledge question answering and fault attribution analysis are realized based on the building of areo-engine lubrication system fault knowledge graph. The application results show that the knowledge graph technology can realize the utilization of prior knowledge of lubrication system faults and the explanation of fault causes. And the knowledge graph technology has a good application prospect in the field of intelligent fault diagnosis.

Cultivation Strategies of English Thinking Ability in the Environment of Internet of Things

The study aims to broaden the horizons of English learners and solve the problem of insufficient cultivation of English thinking. With the widespread use of the Internet of Things (IoT) and from the perspective of deep learning, the Local Similar Convolutional Neural Network (LSNN) recommendation model is designed by adding adjustment layers to the Convolutional Neural Network (CNN). The LSNN model alleviates the sparsity of data. Through comparative experiments on related data, the data sparsity is 0.7-0.9. The LSNN prediction is higher than that of Euclidean Distance and Pearson correlation coefficient, which proves that LSNN can alleviate data sparsity. The LSNN model has the lowest mean absolute error (MAE) of 0.83, which is smaller than the previous CNN’s MAE. The LSNN model recommends the expansion books they need most for English learners, and then improves the vision of English learners, thereby strengthening the cultivation of English learners’ thinking ability.

Spatial non-stationarity effects of the driving factors on landscape ecological risk: A case of the Xiamen-Zhangzhou-Quanzhou Urban Agglomeration, China

Increasing construction land demands and population growth within urban agglomerations have led to increasingly prominent landscape ecological risk (LER) issues. However, the multi-scale nature and spatial non-stationarity characteristics of the driving patterns have not been fully investigated. In this study, taking the Xiamen-Zhangzhou-Quanzhou Urban Agglomeration (XZQUA) as a case, we analyzed the spatio-temporal evolution characteristics of LER utilizing the long time series land use data from 1980 to 2020; then, the Exploratory Regression Analysis (ERA) was employed to investigate the optimal combination of key influential elements affecting LER. Finally, the Multiscale Geographically Weighted regression (MGWR) model was applied to explore the spatial non-stationarity effects of the key driving elements. The results reveal that: (1) The LER of the XZQUA has shown a declining trend from 1980 to 2020, characterized with significantly and increasingly spatial autocorrelation as well. (2) The optimal variable combination is the temperature, distance from roads, elevation, and construction land area. (3) The MGWR model results reveal that the impact of various factors on LER demonstrates significant spatial non-stationarity. The mechanisms and intensity of these influences change depending on geographical location. The negative proportion of regression coefficients for construction land area (−0.203 to −0.002) and elevation (−0.046 to −0.001) all exceeded 80%, indicating a high negative effect on LER; The positive proportion of the regression coefficient for distance from roads (−0.026 to 0.042) exceeded 60%, indicating a predominantly positive effect on LER. Our findings provide guidance for regional policymakers to adopt targeted ecological risk intervention measures, thus reducing LER in the urban agglomeration.

Deep learning-based segmentation for high-dose-rate brachytherapy in cervical cancer using 3D Prompt-ResUNet

Objective.To develop and evaluate a 3D Prompt-ResUNet module that utilized the prompt-based model combined with 3D nnUNet for rapid and consistent autosegmentation of high-risk clinical target volume (HRCTV) and organ at risk (OAR) in high-dose-rate brachytherapy for cervical cancer patients.Approach.We used 73 computed tomography scans and 62 magnetic resonance imaging scans from 135 (103 for training, 16 for validation, and 16 for testing) cervical cancer patients across two hospitals for HRCTV and OAR segmentation. A novel comparison of the deep learning neural networks 3D Prompt-ResUNet, nnUNet, and segment anything model-Med3D was applied for the segmentation. Evaluation was conducted in two parts: geometric and clinical assessments. Quantitative metrics included the Dice similarity coefficient (DSC), 95th percentile Hausdorff distance (HD95%), Jaccard index (JI), and Matthews correlation coefficient (MCC). Clinical evaluation involved interobserver comparison, 4-grade expert scoring, and a double-blinded Turing test.Main results.The Prompt-ResUNet model performed most similarly to experienced radiation oncologists, outperforming less experienced ones. During testing, the DSC, HD95% (mm), JI, and MCC value (mean ± SD) for HRCTV were 0.92 ± 0.03, 2.91 ± 0.69, 0.85 ± 0.04, and 0.92 ± 0.02, respectively. For the bladder, these values were 0.93 ± 0.05, 3.07 ± 1.05, 0.87 ± 0.08, and 0.93 ± 0.05, respectively. For the rectum, they were 0.87 ± 0.03, 3.54 ± 1.46, 0.78 ± 0.05, and 0.87 ± 0.03, respectively. For the sigmoid, they were 0.76 ± 0.11, 7.54 ± 5.54, 0.63 ± 0.14, and 0.78 ± 0.09, respectively. The Prompt-ResUNet achieved a clinical viability score of at least 2 in all evaluation cases (100%) for both HRCTV and bladder and exceeded the 30% positive rate benchmark for all evaluated structures in the Turing test.Significance.The Prompt-ResUNet architecture demonstrated high consistency with ground truth in autosegmentation of HRCTV and OARs, reducing interobserver variability and shortening treatment times.

Evaluation of the geometric and dosimetric accuracies of deformable image registration of targets and critical organs in prostate CBCT-guided adaptive radiotherapy.

Kilovoltage cone beam computed tomography (kVCBCT)-guided adaptive radiation therapy (ART) uses daily deformed CT (dCT), which is generated automatically through deformable registration methods. These registration methods may perform poorly in reproducing volumes of the target organ, rectum, and bladder during treatment. We analyzed the registration errors between the daily kVCBCTs and corresponding dCTs for these organs using the default optical flow algorithm and two registration procedures. We validated the effectiveness of these registration methods in replicating the geometry for dose calculation on kVCBCT for ART. We evaluated three deformable image registration (DIR) methods to assess their registration accuracy and dose calculation effeciency in mapping target and critical organs. The DIR methods include (1) default intensity-based deformable registration, (2) hybrid deformable registration, and (3) a two-step deformable registration process. Each technique was applied to a computerized imaging reference system (CIRS) phantom (Model 062M) and to five patients who received volumetric modulated arc therapy to the prostate. Registration accuracy was assessed using the 95% Hausdorff distance (HD95) and Dice similarity coefficient (DSC), and each method was compared with the intensity-based registration method. The improvement in the dCT image quality of the CIRS phantom and five patients was assessed by comparing dCT with kVCBCT. Image quality quantitative metrics for the phantom included the signal-to-noise ratio (SNR), uniformity, and contrast-to-noise ratio (CNR), whereas those for the patients included the mean absolute error (MAE), mean error, peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM). To determine dose metric differences, we used a dose-volume histogram (DVH) and 3.0%/0.3mm gamma analysis to compare planning computed tomography (pCT) and kVCBCT recalculations with restimulated CT images used as a reference. The dCT images generated by the hybrid (dCTH) and two-step (dCTC) registration methods resulted in significant improvements compared to kVCBCT in the phantom model. Specifically, the SNR improved by 107% and 107.2%, the uniformity improved by 90% and 75%, and the CNR improved by 212.2% and 225.6 for dCTH and dCTC methods, respectively. For the patient images, the MAEs improved by 98% and 94%, the PSNRs improved by 16.3% and 22.9%, and the SSIMs improved by 1% and 1% in the dCTH and dCTC methods, respectively. For the geometric evaluation, only the two-step registration method improved registration accuracy. The dCTH method yielded an average HD95 of 12mm and average DSC of 0.73, whereas dCTC yielded an average HD95 of 2.9mm and average DSC of 0.902. The DVH showed that the dCTC-based dose calculations differed by <2% from the expected results for treatment targets and volumes of organs at risk. Additionally, gamma indices for dCTC-based treatment plans were >95% at all points, whereas they were <95% for kVCBCT-based treatment plans. The two-step registration method outperforms the intensity-based and hybrid registration methods. While the hybrid and two-step-based methods improved the image quality of kVCBCT in a linear accelerator, only the two-step method improved the registration accuracy of the corresponding structures among the pCT and kVCBCT datasets. A two-step registration process is recommended for applying kVCBCT to ART, which achieves better registration accuracy for local and global image structures. This method appears to be beneficial for radiotherapy dose calculation in patients with pelvic cancer.

Impact of geometric correction on echo-planar imaging-based apparent diffusion coefficient maps for abdominal radiotherapy

Objective. The apparent diffusion coefficient (ADC) extracted from diffusion-weighted magnetic resonance imaging (DWI) is a potential biomarker in radiotherapy (RT). DWI is often implemented with an echo-planar imaging (EPI) read-out due to speed, but unfortunately low geometric accuracy follows. This study aimed to investigate the influence of geometric distortions on the ADCs extracted from the gross tumor volume (GTV) and on the shape of the GTV in abdominal EPI-DWI. Approach. Twenty-one patients had EPI-DWI scans on a 1.5 T MRI sim before treatment and on a 1.5 T MRI-Linac at one of the first treatment fractions. Off-resonance correction with and without eddy current correction were applied to ADC maps. The clinical GTVs were deformed based on the same (but inverted) corrections to assess the local-regional geometric influence of distortions. Mean surface distance (MSD), Hausdorff distance (HD), and Dice similarity coefficient (DSC) were calculated to compare the original and distorted GTVs, and ADC values were calculated based on a mono-exponential model. Phantom measurements were performed to validate the applied correction method. Main results. The median (range) ADC change within the GTV after full distortion correction was 1.3% (0.02%–6.9%) for MRI-Sim and 1.5% (0.1%–6.4%) for MRI-Linac. The additional effect of the eddy current correction was small in both systems. The median (range) MSD, HD, and DSC comparing the original and off-resonance distorted GTVs for all patients were 0.43 mm (0.11–0.94 mm), 4.00 mm (1.00–7.81 mm) and 0.93 (0.82–0.99), respectively. Significance. Overall effect of distortion correction was small in terms of derived ADC values, indicating that distortion correction is unimportant for prediction of outcomes based on ADC. However, large local geometric changes occurred after off-resonance distortion correction for some patients, suggesting that if the spatial information from ADC maps is to be used for dose painting strategies, corrections should be applied.

Characteristics of hollow micron zero valent iron and its transport properties in groundwater: Effect of key engineering parameters and retention mechanism

In this study, a hollow micron zero-valent iron (H-mZVI) was synthesized, and its transport and retention property in saturated porous media was determined via a series of column experiments. Furthermore, the maximum migration distance (Lmax) and sedimentation rate coefficient (Kdep) models of H-mZVI in saturated porous media were established using statistical methods. The results revealed a distinct hollow structure in H-mZVI, with a density of 1.03±0.03 g/cm3, significantly lower than solid micron zero-valent iron (4.57±0.15 g/cm3). FTIR and XRD analyses indicated no formation of new functional groups on H-mZVI's surface, with iron being the main component. The column experiment demonstrated that the Lmax of H-mZVI in saturated porous media was 4.15 times that of solid micron zero-valent iron (mZVI) under the same conditions. The prediction model of Lmax aligned with the linear model, where Lmax correlated positively with particle size, injection velocity, and H-mZVI concentration, but inversely with ionic strength. Medium particle size and injection velocity were the main engineering parameters to control H-mZVI. The prediction model of Kdep accorded with the quadratic model, and an interaction was observed between medium particle size and injection velocity, which jointly affected the deposition rate of H-mZVI. Moreover, the single particle capture coefficient (η0) was hereby calculated and analyzed using the T-E theory. Interception primarily governed the precipitation of H-mZVI in saturated porous media, with gravity sedimentation contributing minimally to η0.

Automatic Segmentation of Heschl Gyrus and Planum Temporale by MRICloud.

This study used a cloud-based program, MRICloud, which parcellates T1 MRI brain scans using a probabilistic classification based on manually labeled multi-atlas, to create a tool to segment Heschl gyrus (HG) and the planum temporale (PT). MRICloud is an online platform that can automatically segment structural MRIs into 287 labeled brain regions. A 31-brain multi-atlas was manually resegmented to include tags for the HG and PT. This modified atlas set with additional manually labeled regions of interest acted as a new multi-atlas set and was uploaded to MRICloud. This new method of automated segmentation of HG and PT was then compared to manual segmentation of HG and PT in MRIs of 10 healthy adults using Dice similarity coefficient (DSC), Hausdorff distance (HD), and intraclass correlation coefficient (ICC). This multi-atlas set was uploaded to MRICloud for public use. When compared to reference manual segmentations of the HG and PT, there was an average DSC for HG and PT of 0.62 ± 0.07, HD of 8.10 ± 3.47 mm, and an ICC for these regions of 0.83 (0.68-0.91), consistent with an appropriate automatic segmentation accuracy. This multi-atlas can alleviate the manual segmentation effort and the difficulty in choosing an HG and PT anatomical definition. This protocol is limited by the morphology of the MRI scans needed to make the MRICloud atlas set. Future work will apply this multi-atlas to observe MRI changes in hearing-associated disorders.

Identification of generator coherency in power systems with wind farm

In the context of the "dual carbon" goal, the penetration rate of new energy represented by wind power is gradually increasing. The large-scale grid connection of wind power makes the power system more complex and uncertain. The output of wind farms changes the system flow, indirectly affecting the power angle characteristics of synchronous generators, and thereby changing the coherence between generators. Affects synchronous generator homology identification. This paper proposes a generator homology identification method for power systems containing wind farms, in response to the problem that the existing methods for identifying unit homology have not taken into account the adverse effects of wind farms on identification results. Translate the influence of wind farms on the homology of synchronous generators into the contraction admittance matrix as a static electrical distance indicator; Select dynamic data reflecting the homology of synchronous generators after disturbance, and form four dynamic indicators to measure the power angle increment curve between each generator: Euclidean distance, Chebyshev distance, grey correlation, and correlation coefficient; By using the combination weighting method to determine the weights of each indicator, a comprehensive similarity matrix is formed, and the optimal clustering results are determined using fuzzy system clustering and F-statistical values. Finally, the effectiveness of the proposed method was verified using EPRI-9 node system, EPRI-36 node system, and IEEE68 node system as examples.© 2017 Elsevier Inc. All rights reserved.

Performance Evaluation of Ultrasound Images Using Non-Local Means Algorithm with Adaptive Isotropic Search Window for Improved Detection of Salivary Gland Diseases: A Pilot Study.

Speckle noise in ultrasound images (UIs) significantly reduces the accuracy of disease diagnosis. The aim of this study was to quantitatively evaluate its feasibility in salivary gland ultrasound imaging by modeling the adaptive non-local means (NLM) algorithm. UIs were obtained using an open-source device provided by SonoSkills and FUJIFILM Healthcare Europe. The adaptive NLM algorithm automates optimization by modeling the isotropic search window, eliminating the need for manual configuration in conventional NLM methods. The coefficient of variation (COV), contrast-to-noise ratio (CNR), and edge rise distance (ERD) were used as quantitative evaluation parameters. UIs of the salivary glands revealed evident visualization of the internal echo shape of the malignant tumor and calcification line using the adaptive NLM algorithm. Improved COV and CNR results (approximately 4.62 and 2.15 times, respectively) compared with noisy images were achieved. Additionally, when the adaptive NLM algorithm was applied to the UIs of patients with salivary gland sialolithiasis, the noisy images and ERD values were calculated almost similarly. In conclusion, this study demonstrated the applicability of the adaptive NLM algorithm in optimizing search window parameters for salivary gland UIs.

MultiFuzzTOPS: A Fuzzy Multi-Criteria Decision-Making Model Using Type-2 Soft Sets and TOPSIS

Effective and optimal decision-making can enhance system performance, potentially leading to a positive reputation and financial gains. Multi-criteria decision-making (MCDM) is an important research topic widely applied to practical decision-making problems. Using the basic idea of symmetry to balance the arrangement where elements or features have an equality or similarity in distribution, MCDM provides robust decisions in such multi-dimensional complex issues. This study proposes MultiFuzzTOPS, a decision-making model to deal with complexity of multi-criteria decision-making. The proposed MultiFuzzTOPS leverages the fuzzy logic and soft sets such as type-2 soft sets (T2SS) and technique for order preference by similarity to ideal solution (TOPSIS) for decision-making. We validate the proposed model by implementing it to solve the pesticide selection problem in food science by considering various criteria for the selection of pesticides. Our proposed MultiFuzzTOPS recommends the best pesticide compared with its counterparts because it covers the maximum information for the selection of the best alternative. Results are ranked on the basis of the Hamming distance and similarity coefficient. We also validate the effectiveness by performing the sensitivity analysis, and the validation shows the reliability and effectiveness of our proposed model.

Logarithmic moments of B-meson quasidistribution amplitude

It was demonstrated that the lattice simulation of B-meson light-cone distribution amplitude (LCDA) is feasible via the quasi-distribution amplitude (quasi-DA) in large momentum effective theory (LaMET). The structures of logarithmic moments (LMs) of B-meson quasi-DA are explored in this work. The one-loop results indicate mixing in the matching: the n-th LM would be not only factorized into the n-th LM of LCDA, but also other moments with different power, accompanied by short distance coefficients. These results supply the understanding of the matching in LaMET and may provide guidance to the lattice study of LMs or other parameters of B-meson LCDA.

Hydrodynamic and bearing behaviors of two typical deepwater dynamically installed anchors: torpedo anchor and fish anchor

Dynamically installed anchors, such as torpedo and fish anchors, are innovative and cost-effective anchorage foundations. In this study, the advantages and disadvantages of these two types of dynamically installed anchors are discussed by comparing the hydrodynamic and bearing characteristics of torpedo and fish anchors commonly used in practical engineering. First, based on OpenFOAM, 3D numerical models were established to investigate the directional stability and simulate the underwater free-fall process of anchors using static and dynamic model analysis methods. Using the validated model, the hydrodynamic performances (i.e., inclination angle, installation offset distance, penetration velocity, and drag coefficient) of torpedo and fish anchors were explored and compared. Based on these findings, the installation height of each anchor was recommended. Finally, the bearing performances of the torpedo and fish anchors were investigated using ABAQUS, and the selection principles of these two types of dynamically installed anchors were presented based on their hydrodynamic and bearing behaviors.

A Deep Learning Framework for Analysis of the Eustachian Tube and the Internal Carotid Artery.

Obtaining automated, objective3-dimensional(3D) models of the Eustachian tube (ET) and the internal carotid artery (ICA) from computed tomography (CT) scans could provide useful navigational and diagnostic information for ET pathologies and interventions. We aim to develop a deep learning (DL) pipeline to automatically segment the ET and ICA and use these segmentations to compute distances between these structures. Retrospective cohort. Tertiary referral center. From a database of 30 CT scans, 60 ET and ICA pairs were manually segmented and used to train an nnU-Net model, a DL segmentation framework. These segmentations were also used to develop a quantitative tool to capture the magnitude and location of the minimum distance point (MDP) between ET and ICA. Performance metrics for the nnU-Net automated segmentations were calculated via the average Hausdorff distance (AHD) and dice similarity coefficient (DSC). The AHD for the ET and ICA were 0.922 and 0.246 mm, respectively. Similarly, the DSC values for the ET and ICA were 0.578 and 0.884. The mean MDP from ET to ICA in the cartilaginous region was 2.6 mm (0.7-5.3 mm)and was located on average 1.9 mm caudal from the bony cartilaginous junction. This study describes the first end-to-end DL pipeline for automated ET and ICA segmentation and analyzes distances between these structures. In addition to helping to ensure the safe selection of patients for ET dilation, this method can facilitate large-scale studies exploring the relationship between ET pathologies and the 3D shape of the ET.

An improved dynamic window approach algorithm for dynamic obstacle avoidance in mobile robot formation

Dynamic Window Approach (DWA) algorithm is a commonly used choice in dynamic obstacle avoidance. However, the traditional DWA algorithm evaluation function is poorly adapted to dynamic obstacle avoidance and has defects in efficiency and safety. In this paper, we consider the speed and heading of the mobile robot formation and the speed and heading of the obstacles and design the speed-varying obstacle avoidance and safety distance evaluation coefficients. The objective is to design an improved DWA algorithm to improve the obstacle avoidance ability of mobile robot formations when encountering dynamic obstacle interference. First, the obstacle environment in which the mobile robot formation is located is analyzed to determine the obstacles that threaten the formation and those that are not. Second, the obstacle velocity space is evaluated and analyzed using the velocity change evaluation coefficient so that the robot formation’s obstacle avoidance behavior after the combination of angular and linear travel velocities has high robustness in the face of dynamic obstacle interference. Finally, the evaluation coefficient of safe obstacle avoidance distance is designed to accurately judge the positional relationship between the robot formation and dynamic obstacles at the moment of obstacle avoidance, which maximizes the safety of the robot formation traveling. The experimental results show that the improved algorithm shortens the obstacle avoidance time by 37.3% and saves 16.8% of the distance traveled than the traditional algorithm. It also achieves good results in terms of robustness and safety.