Boundary Distance Research Articles

Spatial Predictive Modeling of the Burning of Sugarcane Plots in Northeast Thailand with Selection of Factor Sets Using a GWR Model and Machine Learning Based on an ANN-CA

The main purpose of the study is to apply symmetry principles to general mathematical modelling based on multi-criteria decision making (MCDM) approach for use in development in conjunction with geographic weighted regression (GWR) model and optimize the artificial neural network-cellular automaton (ANN-CA) model for forecasting the sugarcane plot burning area of Northeast Thailand. First, to calculate the service area boundaries of sugarcane transport that caused the burning of sugarcane with a fire radiative power (FRP) values using spatial correlation analysis approach. Second, the analysis of the spatial factors influencing sugarcane burning. The study uses the approach of symmetry in the design of algorithm for finding the optimal service boundary distance (called as cut-off) in the analysis of hot-spot clustering and uses calculations with the geographic information system (GIS) approach, and the final stage is the use of screened independent variable factors to predict the plots of burned sugarcane in 2031. The results showed that the positively related factors for the percentage of cane plot sintering in the sub-area units of each sugar plant’s service were the distance to transport sugarcane plots index and percentage of sugarcane plantations in service areas, while the negative coefficients were FRP differences and density of sugarcane yield factors, according to the analysis with a total of seven spatial variables. The best GWR models display local R2 values at levels of 0.902 to 0.961 in the service zones of Khonburi and Saikaw. An influential set of independent variables can increase the accuracy of the ANN-CA model in forecasting with kappa statistical estimates in the range of 0.81 to 0.85 The results of the study can be applied to other regions of Thailand, including countries with similar sugarcane harvesting industries, to formulate policies to reduce the exposure of sugarcane harvested by burning methods and to support the transportation of sugarcane within the appropriate scope of service so that particulate matter less than 2.5 microns (PM2.5) can be reduced.

Automatic Well Test Interpretation Method for Circular Reservoirs With Changing Wellbore Storage Using One-Dimensional Convolutional Neural Network

Abstract In order to develop reservoirs rationally, accurate reservoir parameters are usually obtained through well test analysis. However, a good deal of well test data with changing wellbore storage characteristics bring difficulties to the current well test interpretation, so it is important to find a valid interpretation method for changing well storage reserves data. This paper proposed an automatic well test interpretation method based on one-dimensional convolutional neural network (1D CNN) for circular reservoir with changing wellbore storage. Compared with two-dimensional convolutional neural network (2D CNN), 1D CNN significantly reduces the computational complexity and time cost. The CNN takes pressure change and pressure derivative data of the log–log plot as input and reservoir parameters as output of network. This method applies two 1D CNNs respectively to fit two types of reservoir parameters, one type includes CDe2s, CαD, and CϕD and the other type is boundary distance R. In addition, the training samples of the two networks are different according to different parameters. The two-network approach reduces the difficulty of extracting curve characteristics and improves interpretation ability. The effectiveness of this method is proved by the field data in Daqing oilfield. The method greatly improves the working efficiency of well test interpreters and can be widely used.

Effects of crystal orientation and twin boundary distance on mechanical properties of FeNiCrCoCu high-entropy alloy under nanoindentation

The effect of crystal orientation and twinning on the mechanical characteristics and microstructure development of the equiatomic FeNiCrCoCu high-entropy alloy is explored and quantified by nanoindentation using molecular dynamics. The substrate hardness and discontinuity in the loading curves are strongly influenced by various crystal orientations due to differences in microstructural evolution and dissimilar dislocation events. The Hall-Petch aspect representing the correlation between the twinning distance and the material strength is found with decreasing the twin boundary distance. The plastic deformation exposes that the propagation of local stress and shear strain into the substrate bulk is dependent on crystal orientation, characterized through the various directions of the slipping bands. Meanwhile, the growth of shear bands into the substrate is easier with the great twin boundary distance, which can enhance the ductility of the alloy. The microstructural evolution suggests that the sliding prismatic dislocation loops in the substrate play a significant role in deformation. The displacement of the dislocation loops into the substrate depends on the crystal orientation, and their propagation is inhibited by the twin boundary. The various surface morphologies of the substrate are dominated by the sliding mechanism of the atoms following the symmetry of <11‾0 >{111} primary sliding system. Besides, the pile-up height tends to increase with the decrease of the twinning distance because the interaction between the sliding system and the twinning occurs more for the small twin boundary distance.

Video segmentation via target objectness constraint and multi-head soft aggregation

Objectness is a notion of how a generic object looks like. Recently, matching-based methods have yielded impressive video object segmentation results. However, these methods suffer from mis-segmentation of distractors that are distant from the targets due to the lack of objectness constraint. The segmentation mask has a very low objectness when both the correct object and the distant distractor are considered as a target. Position information is also ignored in matching-based methods. In this paper, a video object segmentation method based on target objectness constraint and multi-head soft aggregation is proposed. The target objectness constraint is introduced by forcing the network to learn to predict object center and pixel-to-object boundary distances. The problem of mis-segmentation of objects or regions with similar appearance at a distance is alleviated. Besides, the target position consistency constraint is strengthened by introducing the target position information in the feature matching of adjacent frames. Furthermore, an instance attention mechanism is implemented using multi-head soft aggregation to re-calibrate channel importance. Multi-head soft aggregation enables better utilization of target feature information than global average pooling. Comprehensive experiments demonstrate that the proposed method obtains a competitive performance, surpassing many other existing methods.

Can Small Industrial Platforms Achieve Large Space Spillover? Identifying the Spatial Spillover Scope of Characteristic Towns Using the Gradient Difference Method

Characteristic towns represent relatively small industrial platforms, characterized by clear industrial positioning, cultural connotations, tourism, and certain community functions. Determining the spatial spillover scope of characteristic towns is of importance for both spatial decision-making and policy optimization. By using the gradient difference method, this paper aims to determine the spatial spillover scope of characteristic towns in 2014 and 2020. The research is based on the nighttime light data of the first three batches of Zhejiang characteristic towns. The results suggest that (1) there is a spatial spillover phenomenon in Zhejiang characteristic towns; namely, the results of 84 characteristic towns are either consistent or partially consistent with the expected results; (2) there is an inverse S-shaped decreasing trend from the boundary of characteristic towns; the trend rapidly decreases with an increase at the boundary distance of characteristic towns; and (3) characteristic towns are formed around the 1 km core spillover area and 2 km secondary spillover area. Provincial characteristic towns have relatively high maturity and account for 43.86% of the total. They are concentrated in the 1 km range. Compared to previous research, this study was able to accurately identify the spatial spillover scope of characteristic towns using the gradient difference method. Specifically, the spatial spillover scope is concentrated primarily in the core spillover area of 1 km. These findings serve as reference for the adjustment and optimization of characteristic town policies as well as of town’s spatial layouts.

Delineation of agricultural fields using multi-task BsiNet from high-resolution satellite images

This paper presents a new multi-task neural network, called BsiNet, to delineate agricultural fields from high-resolution satellite images. BsiNet is modified from a Psi-Net by structuring three parallel decoders into a single encoder to improve computational efficiency. BsiNet learns three tasks: a core task for agricultural field identification and two auxiliary tasks for field boundary prediction and distance estimation, corresponding to mask, boundary, and distance tasks, respectively. A spatial group-wise enhancement module is incorporated to improve the identification of small fields. We conducted experiments on a GaoFen1 and three GaoFen2 satellite images collected in Xinjiang, Fujian, Shandong, and Sichuan provinces in China, and compared BsiNet with 13 different neural networks. Our results show that the agricultural fields extracted by BsiNet have the lowest global over-classification (GOC) of 0.062, global under-classification (GUC) of 0.042, and global total errors (GTC) of 0.062 for the Xinjiang dataset. For the Fujian dataset with irregular and complex fields, BsiNet outperformed the second-best method from the Xinjiang dataset analysis, yielding the lowest GTC of 0.291. It also produced satisfactory results on the Shandong and Sichuan datasets. Moreover, BsiNet has fewer parameters and faster computation than existing multi-task models (i.e., Psi-Net and ResUNet-a D7). We conclude that BsiNet can be used successfully in extracting agricultural fields from high-resolution satellite images and can be applied to different field settings.

New Insights into the First Two PSP Solar Encounters Enabled by Modeling Analysis with ADAPT-WSA

Parker Solar Probe's (PSP's) unique orbital path allows us to observe the solar wind closer to the Sun than ever before. Essential to advancing our knowledge of solar wind and energetic particle formation is identifying the sources of PSP observations. We report on results for the first two PSP solar encounters derived using the Wang–Sheeley–Arge (WSA) model driven by Air Force Data Assimilative Photospheric Flux Transport (ADAPT) model maps. We derive the coronal magnetic field and the 1 R ⊙ source regions of the PSP-observed solar wind. We validate our results with the solar wind speed and magnetic polarity observed at PSP. When modeling results are very reliable, we derive time series of model-derived spacecraft separation from the heliospheric current sheet, magnetic expansion factor, coronal hole boundary distance, and photospheric field strength along the field lines estimated to be connected to the spacecraft. We present new results for Encounter 1, which show time evolution of the far-side mid-latitude coronal hole that PSP corotates with. We discuss how this evolution coincides with solar wind speed, density, and temperature observed at the spacecraft. During Encounter 2, a new active region emerges on the solar far side, making it difficult to model. We show that ADAPT-WSA output agrees well with PSP observations once this active region rotates onto the near side, allowing us to reliably estimate the solar wind sources retrospectively for most of the encounter. We close with ways in which coronal modeling enables scientific interpretation of these encounters that would otherwise not have been possible.

Multi-scale discriminative network for prostate cancer lesion segmentation in multiparametric MR images.

The accurate and reliable segmentation of prostate cancer (PCa) lesions using multiparametric magnetic resonance imaging (mpMRI) sequences, is crucial to the image-guided intervention and treatment of prostate disease. For PCa lesion segmentation, it is essential to reliably combine local and global information to retain the features of small targets at multiple scales. Therefore, this study proposes a multi-scale segmentation network with a cascading pyramid convolution module (CPCM) and a double-input channel attention module (DCAM) for the automated and accurate segmentation of PCa lesions using mpMRI. First, the region of interest was extracted from the data by clipping to enlarge the target region and reduce the background noise interference. Next, four CPCMs with large convolution kernels in their skip connection paths were designed to improve the feature extraction capability of the network for small targets. At the same time, a convolution decomposition was applied to reduce the computational complexity. Finally, the DCAM was adopted in the decoder to provide bottom-up semantic discriminative guidance; it can use the semantic information of the network's deep features to guide the shallow output of features with a higher discriminant ability. A residual refinement module (RRM) was also designed to strengthen the recognition ability of each stage. The feature maps of the skip connection and the decoder all go through the RRM. For the Initiative for Collaborative Computer Vision Benchmarking (I2CVB) dataset, our proposed model achieved a Dice similarity coefficient (DSC) of 79.31% and an average boundary distance (ABD) of 4.15mm. For the Prostate Multiparametric MRI (PROMM) dataset, our method greatly improved the DSC to 82.11% and obtained an ABD of 3.64mm. The experimental results of two different mpMRI prostate datasets demonstrate that our model is more accurate and reliable on small targets. In addition, it outperforms other state-of-the-art methods.

Combining natural and artificial intelligence for robust automatic anatomy segmentation: Application in neck and thorax auto-contouring.

Automatic segmentation of 3D objects in computed tomography (CT) is challenging. Current methods, based mainly on artificial intelligence (AI) and end-to-end deep learning (DL) networks, are weak in garnering high-level anatomic information, which leads to compromised efficiency and robustness. This can be overcome by incorporating natural intelligence (NI) into AI methods via computational models of human anatomic knowledge. We formulate a hybrid intelligence (HI) approach that integrates the complementary strengths of NI and AI for organ segmentation in CT images and illustrate performance in the application of radiation therapy (RT) planning via multisite clinical evaluation. The system employs five modules: (i) body region recognition, which automatically trims a given image to a precisely defined target body region; (ii) NI-based automatic anatomy recognition object recognition (AAR-R), which performs object recognition in the trimmed image without DL and outputs a localized fuzzy model for each object; (iii) DL-based recognition (DL-R), which refines the coarse recognition results of AAR-R and outputs a stack of 2D bounding boxes (BBs) for each object; (iv) model morphing (MM), which deforms the AAR-R fuzzy model of each object guided by the BBs output by DL-R; and (v) DL-based delineation (DL-D), which employs the object containment information provided by MM to delineate each object. NI from (ii), AI from (i), (iii), and (v), and their combination from (iv) facilitate the HI system. The HI system was tested on 26 organs in neck and thorax body regions on CT images obtained prospectively from 464 patients in a study involving four RT centers. Data sets from one separate independent institution involving 125 patients were employed in training/model building for each of the two body regions, whereas 104 and 110 data sets from the 4 RT centers were utilized for testing on neck and thorax, respectively. In the testing data sets, 83% of the images had limitations such as streak artifacts, poor contrast, shape distortion, pathology, or implants. The contours output by the HI system were compared to contours drawn in clinical practice at the four RT centers by utilizing an independently established ground-truth set of contours as reference. Three sets of measures were employed: accuracy via Dice coefficient (DC) and Hausdorff boundary distance (HD), subjective clinical acceptability via a blinded reader study, and efficiency by measuring human time saved in contouring by the HI system. Overall, the HI system achieved a mean DC of 0.78 and 0.87 and a mean HD of 2.22 and 4.53mm for neck and thorax, respectively. It significantly outperformed clinical contouring in accuracy and saved overall 70% of human time over clinical contouring time, whereas acceptability scores varied significantly from site to site for both auto-contours and clinically drawn contours. The HI system is observed to behave like an expert human in robustness in the contouring task but vastly more efficiently. It seems to use NI help where image information alone will not suffice to decide, first for the correct localization of the object and then for the precise delineation of the boundary.

Automatic esophagus Z‐line delineation in endoscopic images using a new boundary linking method

Due to the American cancer society, many people with esophageal adenocarcinoma are not survived. The treatment rate can be significant in the early detection of Barrett's esophagus (BE) as a premalignant stage for adenocarcinoma. An important landmark to detect BE is the Z-line. BE segmentation is already highly dependent upon the operator's knowledge and skill. The main aim of this study is automatic Z-line extraction using endoscopic images leading to segmentation of the early BE stage. To this end, a computer-aided detection method exploiting k-means clustering, image segmentation using the edge detector, and a novel boundary linking algorithm is proposed. For the evaluation, the gold standard is considered the average contours of Z-lines extracted by the three experts. The proposed method annotated the Z-line with the accuracy and precision of 0.92 and 0.87, respectively, and the value of the average boundary distance is 5.9 pixels. To the results and visual inspection, the presented method can be used for efficient and robust extraction of the Z-line at the early BE stage. Furthermore, it can be used in other medical imaging applications with complex boundaries and low contrast in the images, limiting the common automatic boundary detection methods.

Optimization of Laminar Boundary Layers in Flow over a Flat Plate Using Recent Metaheuristic Algorithms

Heat transfer is one of the most fundamental engineering subjects and is found in every moment of life. Heat transfer problems, such as heating and cooling, where the transfer of heat between regions is calculated, are problems that can give exact solutions with parametric equations, many of which were obtained by solving differential equations in the past. Today, the fact that heat transfer problems have a more complex structure has led to the emergence of multivariate models, and problems that are very difficult to solve with differential equations have emerged. Optimization techniques, which are also the subject of computer science, are frequently used to solve complex problems. In this study, laminar thermal boundary layers in flow over a flat plate, a sub-problem of heat transfer, is solved with recent metaheuristic algorithms. Teaching learning-based optimization (TLBO), sine cosine optimization (SCO), gray wolf optimization (GWO), whale optimization (WO), salp swarm optimization (SSO), and Harris hawk optimization (HHO) algorithms are used in the study. In the optimization problem, the laminar boundary layer thickness, heat flow, and distance from the leading edge are determined. These three models’ minimum, maximum, and target values are found under the specified design variables and constraints. In the study, 540 optimization models are run, and it is seen that HHO is the most suitable optimization technique for heat transfer problems. Additionally, SSO and WO algorithms gave results close to HHO. Other algorithms also set model targets with an average of less than 0.07% and acceptable error rates. In addition, the average problem solution time of all optimization algorithms and all models was 0.9 s. To conclude, the recent metaheuristic algorithms are found to be powerful and fast in solving heat transfer problems.

Recent advances of lab-based diffraction contrast tomography – reconstruction speed benchmark testing and validations

Lab-based diffraction contrast tomography (DCT) enables the user to reconstruct 3D grain maps of polycrystalline materials non-destructively. For each grain, the morphology and crystallographic orientation, as well as derived properties such as grain boundaries, can be determined. Through two application examples this paper demonstrates the data acquisition and reconstruction speed of the current implementation and validates the resulting grain maps. Firstly, for a conventional Laue focusing scan of an AlCu sample comprising 340 grains, major performance enhancements of the reconstruction algorithm have reduced the reconstruction time from half a day to half an hour. The second example highlights an advanced scan with projection geometry of an oriented electrical steel sheet containing 7,800 grains. While the data collection time is around a day for each of the data sets, the boosted reconstruction of the advanced acquisition data takes half a day and gives the full grain map without the need for stitching. After the major algorithm speed enhancements, grain map qualities are comparable with misorientations below 0.02° and grain boundary distances less than a voxel for both samples.

Flexibility of sections of nearly integrable Hamiltonian systems

Given any symplectomorphism on [Formula: see text] which is [Formula: see text] close to the identity, and any completely integrable Hamiltonian system [Formula: see text] in the proper dimension, we construct a [Formula: see text] perturbation of [Formula: see text] such that the resulting Hamiltonian flow contains a “local Poincaré section” that “realizes” the symplectomorphism. As a (motivating) application, we show that there are arbitrarily small perturbations of any completely integrable Hamiltonian system which are entropy non-expansive (and, in particular, exhibit hyperbolic behavior on a set of positive measure). We use some results in Berger–Turaev [On Herman’s positive entropy conjecture, Adv. Math. 349 (2019) 1234—1288], though in higher dimensions we could simply apply a construction from [D. Burago and S. Ivanov, Boundary distance, lens maps and entropy of geodesic ows of Finsler metrics, Geom. &amp; Topol. 20 (2016) 469–490].

On the Effects of Disc Deformation on the Tilting-Induced Vibration of a Spline-Guided Spinning Disc with an Axial-Fixed Boundary

This paper investigates the effects of disc deformation on the tilting-induced vibration of a splined spinning disc with axial-fixed boundaries.The purpose is to provide an intuitive interpretation of the vibration variance of the wet clutch system with different deformed discs. First, tilting models of flat and deformed discs are derived by introducing distinctive shape functions. Additionally, the inner spline interface is chosen as the friction boundary. Then, an impact model between friction pairs and the rigid boundary is established by adopting Hertz’s contact theory. Finally, the dynamic equations are solved via numerical methods, and the responses are analyzed in both time and frequency domains. The deformation can increase the nonlinearity of the dynamic response of the spinning disc. Moreover, the effects of increasing the impulse force and reducing the boundary distance are quite similar; they both increase the motion intensity.

MCAL: An Anatomical Knowledge Learning Model for Myocardial Segmentation in 2-D Echocardiography.

Segmentation of the left ventricular (LV) myocardium in 2-D echocardiography is essential for clinical decision making, especially in geometry measurement and index computation. However, segmenting the myocardium is a time-consuming process and challenging due to the fuzzy boundary caused by the low image quality. The ground-truth label is employed as pixel-level class associations or shape regulation in segmentation, which works limit for effective feature enhancement for 2-D echocardiography. We propose a training strategy named multiconstrained aggregate learning (referred to as MCAL), which leverages anatomical knowledge learned through ground-truth labels to infer segmented parts and discriminate boundary pixels. The new framework encourages the model to focus on the features in accordance with the learned anatomical representations, and the training objectives incorporate a boundary distance transform weight (BDTW) to enforce a higher weight value on the boundary region, which helps to improve the segmentation accuracy. The proposed method is built as an end-to-end framework with a top-down, bottom-up architecture with skip convolution fusion blocks and carried out on two datasets (our dataset and the public CAMUS dataset). The comparison study shows that the proposed network outperforms the other segmentation baseline models, indicating that our method is beneficial for boundary pixels discrimination in segmentation.

Pumping-Induced Non-Darcian Flow in a Finite Confined Aquifer

An analytic model for depicting non-Darcian flow caused by pumping in a finite confined aquifer with an outer barrier boundary is established. The model considers the wellbore storage and skin effect. And the semi-analytical solution is derived with the aid of the Izbash’s law-based non-Darcian flow and the method for linearization procedure combined with the Laplace transformation. The new presented solution can reduce to some available solutions for the confined aquifer of unlimited extension. The drawdowns in abstraction well and aquifer are explored. The results suggest that the presence of the no-flux outer boundary makes no difference to the early-time drawdowns, the late-time drawdowns in the unlimited confined aquifer are smaller than those in the finite confined aquifer with a zero-flux outer boundary, and the flow in this finite confined aquifer cannot approach quasi-steady state, especially to a smaller finite outer boundary distance radius. The change of power index n in the non-Darcian flow equation cannot affect the early-time drawdowns in abstraction well, while the drawdowns in aquifer are underestimated for Darcian flow case at early pumping time. The late-time drawdowns in the pumping well and aquifer are significantly overestimated under the assumption of Darcian flow. The early-time drawdowns in abstraction well and aquifer are significantly affected by wellbore storage, and a larger wellbore storage coefficient leads to a smaller drawdown. The skin factor can impact the intermediate-time drawdowns in abstraction well, while the early and intermediate-time drawdowns in aquifer are influenced by skin effect.

Dose prediction via distance-guided deep learning: Initial development for nasopharyngeal carcinoma radiotherapy

Background and purposeGeometric information such as distance information is essential for dose calculations in radiotherapy. However, state-of-the-art dose prediction methods use only binary masks without distance information. This study aims to develop a dose prediction deep learning method for nasopharyngeal carcinoma radiotherapy by taking advantage of the distance information as well as the mask information. Materials and methodsA novel transformation method based on boundary distance was proposed to facilitate the prediction of dose distributions. Radiotherapy datasets of 161 nasopharyngeal carcinoma patients were retrospectively collected, including binary masks of organs-at-risk (OARs) and targets, planning CT, and clinical plans. The patients were randomly divided into 130, 11 and 20 cases for training, validating, and testing the models, respectively. Furthermore, 40 patients from an external cohort were used to test the generalizability of the models. ResultsThe proposed method shows superior performance. The predicted dose error and dose–volume histogram (DVH) error of our method were 7.51% and 11.6% lower than the mask-based method, respectively. For the inverse planning, compared with mask-based methods, our method provided similar performances on the GTVnx and OARs and outperformed on the GTVnd and the CTV, the pass rates of which increased from 89.490% and 90.016% to 96.694% and 91.189%, respectively. ConclusionThe preliminary results on nasopharyngeal carcinoma radiotherapy cases showed that our proposed distance-guided method for dose prediction achieved better performance than mask-based methods. Further studies with more patients and on other cancer sites are warranted to fully validate the proposed method.

Extracting Buildings from Remote Sensing Images Using a Multitask Encoder-Decoder Network with Boundary Refinement

Extracting buildings from high-resolution remote sensing images is essential for many geospatial applications, such as building change detection, urban planning, and disaster emergency assessment. Due to the diversity of geometric shapes and the blurring of boundaries among buildings, it is still a challenging task to accurately generate building footprints from the complex scenes of remote sensing images. The rapid development of convolutional neural networks is presenting both new opportunities and challenges with respect to the extraction of buildings from high-resolution remote sensing images. To capture multilevel contextual information, most deep learning methods extract buildings by integrating multilevel features. However, the differential responses between such multilevel features are often ignored, leading to blurred contours in the extraction results. In this study, we propose an end-to-end multitask building extraction method to address these issues; this approach utilizes the rich contextual features of remote sensing images to assist with building segmentation while ensuring that the shape of the extraction results is preserved. By combining boundary classification and boundary distance regression, clear contour and distance transformation maps are generated to further improve the accuracy of building extraction. Subsequently, multiple refinement modules are used to refine each part of the network to minimize the loss of image feature information. Experimental comparisons conducted on the SpaceNet and Massachusetts building datasets show that the proposed method outperforms other deep learning methods in terms of building extraction results.

Enhanced Conductivity and Microstructure in Highly Textured TiN1-x /c-Al2O3 Thin Films.

Titanium nitride thin films are used as an electrode material in superconducting (SC) applications and in oxide electronics. By controlling the defect density in the TiN thin film, the electrical properties of the film can achieve low resistivities and a high critical temperature (Tc) close to bulk values. Generally, low defect densities are achieved by stoichiometric growth and a low grain boundary density. Due to the low lattice mismatch of 0.7%, the best performing TiN layers are grown epitaxially on MgO substrates. Here, we report for the first time a Tc of 4.9 K for ultrathin (23 nm), highly textured (111), and stoichiometric TiN films grown on 8.75% lattice mismatch c-cut Al2O3 (sapphire) substrates. We demonstrate that with the increasing nitrogen deficiency, the (111) lattice constant increases, which is accompanied by a decrease in Tc. For highly N deficient TiN thin films, no superconductivity could be observed. In addition, a dissociation of grain boundaries (GBs) by the emission of stacking faults could be observed, indicating a combination of two sources for electron scattering defects in the system: (a) volume defects created by nitrogen deficiency and (b) defects created by the presence of GBs. For all samples, the average grain boundary distance is kept constant by a miscut of the c-cut sapphire substrate, which allows us to distinguish the effect of nitrogen deficiency and grain boundary density. These properties and surface roughness govern the electrical performance of the films and influence the compatibility as an electrode material in the respective application. This study aims to provide detailed and scale-bridging insights into the structural and microstructural response to nitrogen deficiency in the c-Al2O3/TiN system, as it is a promising candidate for applications in state-of-the-art systems such as oxide electronic thin film stacks or SC applications.

Segmentation of Remote Sensing Images Based on U-Net Multi-Task Learning

In order to accurately segment architectural features in high-resolution remote sensing images, a semantic segmentation method based on U-net network multi-task learning is proposed. First, a boundary distance map was generated based on the remote sensing image of the ground truth map of the building. The remote sensing image and its truth map were used as the input in the U-net network, followed by the addition of the building ground prediction layer at the end of the U-net network. Based on the ResNet network, a multi-task network with the boundary distance prediction layer was built. Experiments involving the ISPRS aerial remote sensing image building and feature annotation data set show that compared with the full convolutional network combined with the multi-layer perceptron method, the intersection ratio of VGG16 network, VGG16 + boundary prediction, ResNet50 and the method in this paper were increased by 5.15%, 6.946%, 6.41% and 7.86%. The accuracy of the networks was increased to 94.71%, 95.39%, 95.30% and 96.10% respectively, which resulted in high-precision extraction of building features.