Texture Mapping Research Articles

A deep neural network for predicting soil texture using airborne radiometric data

The ternary nature of soil texture, defined by its proportions of clay, silt, and sand, makes it challenging to predict through linear regression models from other soil attributes and auxiliary variables. The most promising results in this field have been recently achieved by Machine Learning methods which are able to derive non-linear, non-site-specific models to predict soil texture. In this paper we present a method of constructing a pair of Deep Neural Network (DNN) algorithms that can predict clay and sand soil contents from Airborne Gamma Ray Spectrometry data of K and Th ground abundances.We tested the algorithm's hyperparameters through various configurations to optimize the DNNs' performance, effectively avoiding underfitting and overfitting of the models. This led to the creation of a high-resolution 20 m × 20 m soil texture map from dense AGRS data, significantly refining the previous map's granularity. The application of the obtained DNN models to unseen sites can be supported by future training on additional textural classes.

Modelling and protection of grotto temples based on iterative closest point and Poisson

As a non-renewable cultural heritage, grotto temples face many value threats and the crises of rapid extinction. Due to the unique environment, diverse volume and complex geometric structure of grotto temples, cultural relic protection faces huge challenges. Digitisation has become the most important and effective preventive protection method. This study proposes a comprehensive modeling method, which uses three-dimensional (3D) laser scanning technology to obtain the point cloud data of grotto ruins, close range photogrammetry to obtain multi-angle and high-resolution two-dimensional (2D) image data, and through the fusion modelling of the laser point cloud and image dense matching point cloud, a complete geometric model of grotto is obtained. The registration method of the improved algorithm is based on Euclidean distance residual. The performance is better, with residuals mainly distributed in the range of 0.02 to 0.03 mm. In terms of texture mapping, the accuracy of model mapping is adjusted by adjusting the constraints of control points. The gray values of 50 pixels around the control points show significant gradient changes in the X direction, while the changes in the Y direction are relatively small. These methods and technologies provide practical and effective means for the protection and preservation of cultural relics in grotto temples.

Direct imprint of optical skyrmions in azopolymers as photoinduced relief structures

Skyrmions, topologically stable configurations of a three-component vector field with sophisticated textures, have been considered in many contexts, including atomic physics, Bose–Einstein condensates, liquid crystals, and magnetic materials. Although optical counterparts of skyrmions have extensively been studied theoretically and recently demonstrated in the laboratory experiments, their experimental mapping is challenging due to the fine, three-dimensional, and complicated structure of their polarization distributions. Here, we propose and demonstrate a straightforward mapping of the polarization textures of optical Néel-, Bloch-, and anti-skyrmions based on the radiation pressure and direct imprinting of the skyrmion textures on azopolymers. These results not only elucidate the exotic interaction that occurs between topologically protected quasiparticles of light and matter but also provide a simple approach for generation and characterization of optical skyrmions, based on a dual-path polarization shaping configuration with a single spatial light modulator, and their measurements based on the radiation pressure.

Digital Mapping of Key Static Soil Attributes of Tamil Nadu, India using Legacy Soil Information

Abstract Acquiring spatial soil information is pivotal for land resource management, environmental and soil modelling. Digital soil mapping approach helps in prediction of spatial soil properties by establishing the relationship between soil and environmental covariates. In the current study, we attempted to predict and map spatial distribution of soil depth, coarse fragments (CF), and soil textural classes over 0.13 million sq km area of Tamil Nadu, India. About 2105 samples were used for the prediction of soil attributes viz., soil depth and coarse fragments using random forest (RF) regression model, multiple linear regression (MLR), and support vector machine (SVM), while the same set of soil data was used to predict the soil textural classes as categorical variables using Random Forest classifier. Different environmental covariates such as derivatives of digital elevation models, IRS LISS-III data and bioclimatic variables were related for predicting the soil properties. The predicted soil depth and CF ranged from 46-200 cm and 1-42 per cent, respectively. The RF model outperformed by explaining the variability (R2) of 43% for soil depth and 21% for coarse fragments with root mean square error (RMSE) of 38 cm and 13%, respectively, whereas, MLR and SVM could achieve the maximum variability of R2 of 0.20 and 0.25 for soil depth and R2 of 0.07 and 0.09 for CF. With respect to soil textural classes, RF classifier performed well with overall accuracy of 63.8% and kappa index of 0.43. Variable importance ranking of Random Forest model showed that elevation, multiresolution valley bottom flatness (MrVBF), multiresolution ridge top flatness (MrRTF) and remote sensing variables (NDVI & EVI) displayed decisive role in prediction of the soil depth, coarse fragments and soil textural classes. In this study, 250 m resolution detailed soil depth, CF and textural class maps were prepared which will be useful for different environmental modelling and proper agricultural management purposes.

Exploring the mechanism of amylose/amylopectin improving formation of yeast-soy protein high-moisture extrudates based on small and large amplitude oscillatory shear rheology

Yeast protein (YP) has high production efficiency and comprehensive nutritional value compared to soy protein isolate (SPI). However, relatively higher proportion of YP replaces SPI seriously affect the structure of extrudates in the production of meat analogues. This study investigated starch addition (amylose/amylopectin) on the structure of YP-SPI extrudates with higher YP proportion and formation mechanism by using closed cavity rheometer simulated the high-moisture extrusion process (mixing, melting, cooling, and extrudates). The proportions of YP:SPI:amylose:amylopectin were set as 5:5:0:0, 5:4:1:0, 5:4:0:1, and 5:4:0.5:0.5. Starch addition could improve the structure formation of extrudes, but the improvement effect varied depending on the type of starch. Compared to amylose, incorporating amylopectin significantly enhanced the fibrous degree of extrudates and improved their tensile resistance. Furthermore, small amplitude oscillatory shear (SAOS) was used to establish texture map. In the mixing and melting zones, amylose decreased the miscibility of protein and starch phase, while amylopectin had positive effect on the integrity and uniformity of protein network. Amylopectin could transform the extrudates into a “mushy” state, contributing to reconstruct protein network. Large amplitude oscillatory shear (LAOS) demonstrated amylopectin had higher energy dissipation ratio in the cooling zone, indicating the uniform “phase separation” facilitated the formation of fiber structure of extrudates. This study provides technical reference for the preparation of high-moisture extrudates using YP as a substitute for SPI based on starch type addition.

Least-Squares Estimation of Tangent Space for a Triangular Mesh

This paper presents a novel method for computing an orthonormal tangent space at every vertex of a triangular mesh provided with texture coordinates, which is essential in normal mapping. Our method employs least-squares estimation of rotation between two sets of corresponding points: one set from the positions of the 1-ring neighbors of a vertex in texture space and the other set from their corresponding positions in world space. This optimal rotation represents a local space transformation that defines the tangent space at the vertex. Consequently, our method ensures that the resulting tangent space is inherently orthonormal, eliminating the need for intricate operations such as averaging and orthonormalization, which are required in the previous methods. In addition, we introduce texture space alignment of triangles to address vertices on seams, which are inevitable in texture mapping to a closed surface mesh. Experimental results demonstrate the effectiveness of the proposed methods in various experiments.

Digital 3D Reconstruction of Ancient Chinese Great Wild Goose Pagoda by TLS Point Cloud Hierarchical Registration

Digital reconstruction of ancient buildings is very challenging due to its architectural complexity and structural delicacy. Therefore, how to apply new Earth Observation (EO) technology to digitally reconstruct the complete and real model of ancient buildings without damage has become an urgent issue. This article proposes a multi-scan point cloud hierarchical registration method for 3D reconstruction of the ancient Chinese Great Wild Goose Pagoda based on Terrestrial Laser Scanner (TLS) data. First, a multi-scan point cloud hierarchical registration method is elaborately designed for 3D reconstruction. Then, the outline feature of the ancient Chinese Great Wild Goose Pagoda is automatically extracted by point cloud slicing. Finally, fine modeling is carried out according to the outline feature extracted from the reconstructed point cloud, and after that block combination and texture mapping are conducted for 3D digitalization and documentation. The experiments indicate that the Root Mean Square Error (RMSE) of multi-scan hierarchical registration of the translation and rotation are less than 3 cm and 0.862 ∘ , respectively, and the complete and real model of the Great Wild Goose Pagoda is obtained by fine modeling and texture mapping, which provides an important reference for 3D digital protection of ancient buildings.

A semantic SLAM-based method for navigation and landing of UAVs in indoor environments

Autonomous navigation and landing of Unmanned aerial vehicles (UAVs) are critical functions towards full intelligence in unknown environments. However, current methods heavily rely on positioning devices or geometric maps, leading to two limitations: (1) UAVs fail to work normally indoors when signals are blocked, and (2) Navigation systems are not capable of achieving high-level decision-making missions. In this paper, we propose a novel and complete framework to realize the autonomous landing of UAVs in unknown indoor scenes based on visual SLAM, semantic segmentation, terrain estimation, and a decision-making model. Firstly, our 3D map is built on top of the visual SLAM system with semantic features, and then multiple types of maps (e.g., texture map, octo-map, grid-map, and semantic topology) are created for different function requirements. To achieve high-level scene understanding, we design a data association rule to fuse semantic features extracted by a deep learning model into the mapping process of topology structure. Next, a terrain estimation strategy is performed in the lightweight grid-map, which is modeled only via low-level elevation representation. Through multiple terrain constraints factor, a few optimized landing sites are selected in safe regions via clustering algorithm. Finally, based on the guidance of semantic topology, we construct a decision-making model for UAV landing that effectively integrates terrain safety, environment perception, and path planning. We perform extensive autonomous landing experiments on multiple indoor scenes from the TUM RGB-D dataset, and the full unknown environment maps are created simultaneously. The experiment proves that the proposed framework can select an optimal landing site for the advanced mission of UAVs effectively.

Editorial

The Alpha Centauri Scientific and Technological Research Journal is a peer-reviewed journal produced internationally, it is published QUARTERLY in online version ISSN: 2709-4502. Alpha Centauri publishes scientific articles specializing in Applied Social Sciences and Social Sciences in Information Technologies with great impact at an international level. We are currently trending in more than 24 countries disseminating Open Access scientific knowledge. IMAGES: Still images, audio recordings, video and related computer files for non-commercial use. NASA content (images, audio, video, and computer files used in the reproduction of three-dimensional models, such as texture maps and polygonal data in any format).

Lesion segmentation using 3D scan and deep learning for the evaluation of facial portwine stain birthmarks

BackgroundPortwine stain (PWS) birthmarks are congenital vascular malformations. The quantification of PWS area is an important step in lesion classification and treatment evaluation. AimsThe aim of this study was to evaluate the combination of 3D scan with deep learning for automated PWS area quantization. Materials and methodsPWS color was measured using a portable spectrophotometer. PWS patches (29.26–45.82 cm2) of different color and shape were generated for 2D and 3D PWS model. 3D images were acquired by a handheld 3D scanner to create texture maps. For semantic segmentation, an improved DeepLabV3+ network was developed for PWS lesion extraction from texture mapping of 3D images. In order to achieve accurate extraction of lesion regions, the convolutional block attention module (CBAM) and DENSE were introduced and the network was trained under Ranger optimizer. The performance of different backbone networks for PWS lesion extraction were also compared. ResultsIDeepLabV3+ (Xception) showed the best results in PWS lesion extraction and area quantification. Its mean Intersection over Union (MIou) was 0.9797, Mean Pixel Accuracy (MPA) 0.9908, Accuracy 0.9989, Recall 0.9886 and F1-score 0.9897, respectively. In PWS area quantization, the mean value of the area error rate of this scheme was 2.61 ± 2.33. ConclusionsThe new 3D method developed in this study was able to achieve accurate quantification of PWS lesion area and has potentials for clinical applications.

Hybrid Method of Connection Evaluation and Framework Optimization for Building Surface Reconstruction

The three-dimensional (3D) reconstruction of buildings using photogrammetric point clouds is important for many applications, ranging from digital city construction to urban energy consumption analysis. However, problems such as building complexity and point cloud flaws may lead to incorrect modeling, which will affect subsequent steps such as texture mapping. This paper introduces a pipeline for building surface reconstruction from photogrammetric point clouds, employing a hybrid method that combines connection evaluation and framework optimization. Firstly, the plane segmentation method divides building point clouds into several pieces, which is complemented by a proposed candidate plane generation method aimed at removing redundancies and merging similarities. Secondly, the improved connection evaluation method detects potential skeleton lines from different planes. Subsequently, a framework optimization method is introduced to select suitable undirected polygonal boundaries from planes, forming the basis for plane primitives. Finally, by triangulating all plane primitives and filling holes, a building surface polygonal model is generated. Experiments conducted on various building examples provide both qualitative and quantitative evidence that the proposed hybrid method outperforms many existing methods, including traditional methods and deep learning methods. Notably, the proposed method successfully reconstructs the main building structures and intricate details, which can be further used to generate textural models and semantic models. Experimental results validate that the proposed method can be used for the surface reconstruction from photogrammetric point clouds of planar buildings.

Digital Mapping of Soil Particle Size Fractions in the Loess Plateau, China, Using Environmental Variables and Multivariate Random Forest

Soil particle size fractions (PSFs) are important properties for understanding the physical and chemical processes in soil systems. Knowledge about the distribution of soil PSFs is critical for sustainable soil management. Although log-ratio transformations have been widely applied to soil PSFs prediction, the statistical distribution of original data and the transformed data given by log-ratio transformations is different, resulting in biased estimates of soil PSFs. Therefore, multivariate random forest (MRF) was utilized for the simultaneous prediction of soil PSFs, as it is able to capture dependencies and internal relations among the three components. Specifically, 243 soil samples collected across the Loess Plateau were used. Meanwhile, Landsat data, terrain attributes, and climatic variables were employed as environmental variables for spatial prediction of soil PSFs. The results depicted that MRF gave satisfactory soil PSF prediction performance, where the R2 values were 0.62, 0.53, and 0.73 for sand, silt, and clay, respectively. Among the environmental variables, nighttime land surface temperature (LST_N) presented the highest importance in predicting soil PSFs in the Loess Plateau, China. Maps of soil PSFs and texture were generated at a 30 m resolution, which can be utilized as alternative data for soil erosion management and ecosystem conservation.

Triple line-scan camera measurement for efficient and accurate narrow-space 3-D scanning with motion estimation.

Vision-based three-dimensional (3-D) shape measurement plays a crucial role in the inspection of vehicles and trains in the field of transportation. Consequently, the demand for more comprehensive narrow-space inspection has become an inevitable necessity and presents a great challenge to conventional vision methods. We propose the following efficient and accurate narrow-space 3-D scanning method based on triple line-scan cameras. First, the structure of coplanar dual line-scan cameras with a narrow reflector is proposed, making it easy for the optical path to enter a narrow space and obtain 3-D shape information. Efficient in-motion measurement with a large field of view is thereby achieved without the worries that often accompany narrow-space constraints. Secondly, an additional line-scan camera is attached to the coplanar dual cameras and creates a time-space constraint in point cloud stitching direction through the triple line-scan camera structure. With an efficient algorithm template including matching and motion estimation, accurate point cloud stitching is ensured. Lastly, point cloud texture mapping and light source optimization are realized. Our experimental results prove that the method realizes low-distortion in-motion reconstruction in narrow space despite motion variation.

FLATTENING COMPLEX ARCHITECTURAL SURFACES: PHOTOGRAMMETRIC 3D MODELS CONVERTED INTO 2D MAPS

Abstract. The paper describes a workflow to flatten 3D photogrammetric models of undevelopable surfaces into unfragmented 2D texture maps. The aim is to create a texture map with reduced fragmentation compared to typical photogrammetric texture files associated with 3D models. Geometric reformatting of the mesh is required to achieve an unfragmented final texture image with enough visual quality to allow for its use in 2D editing software (e.g., Photoshop, Illustrator, etc.). With a lighter model and defragmented texture, graphic documentation of conditions, treatments, or other relevant information can be performed directly. The approach considers both simple and complex architectural surfaces, with particular attention to elements that cannot be developed without introducing distortions. In the case of historic buildings, such surfaces constitute the majority, especially in the case of decorative elements and irregular vaulted systems. The approach extends to full 3D models, particularly those where orthomosaics would result in stretched details. We will discuss two methods: the first is particularly suitable when the photogrammetric project (oriented images) is still available, and the second applies to generic 3D models without the availability of the original images.

Exploring reliable photogrammetry techniques for 3D modeling in anatomical research and education.

In anatomical research and education, three-dimensional visualization of anatomical structures is crucial for understanding spatial relationships in diagnostics, surgical planning, and teaching. While computed tomography (CT) and magnetic resonance imaging (MRI) offer valuable insights, they are often expensive and require specialized resources. This study explores photogrammetry as an affordable and accessible approach for 3D modeling in anatomical contexts. Two photogrammetry methods were compared: conventional open-source software (Colmap) and Apple's RealityKit Object Capture. Human C3 vertebrae were imaged with a 24 MP camera, with and without a cross-polarization filter. Reconstruction times, vertex distances, surface area, and volume measurements were compared to CT scans. Results revealed that the Object Capture method surpassed the conventional approach in reconstruction speed and user-friendliness. Both methods exhibited similar vertex distance from reference mesh and volume measurements, although the conventional approach produced larger surface areas compared to CT-based models. Cross-polarization filters eliminated the need for pre-processing and improved outcomes in challenging lighting conditions. This study demonstrates that photogrammetry, especially Object Capture, as a reliable and time-efficient tool for 3D modeling in anatomical research and education. It offers accessible alternatives to traditional techniques with advantages in texture mapping. While further validation of various anatomical structures is required, the accessibility and cost-effectiveness of photogrammetry make it a valuable asset for the field. In summary, photogrammetry would have the potential to revolutionize anatomical research and education by providing cost-effective, accessible, and accurate 3D modeling. The study underscores the promise of advancing anatomical research and education through the integration of photogrammetry with ongoing improvements in user-friendliness and accessibility.

Soil Loss Estimation by Water Erosion in Agricultural Areas Introducing Artificial Intelligence Geospatial Layers into the RUSLE Model

The existing digital soil maps are mainly characterized by coarse spatial resolution and are not up to date; thus, they are unable to support the physical process-based models for improved predictions. The overarching objective of this work is oriented toward a data-driven approach and datacube-based tools (Soil Data Cube), leveraging Sentinel-2 imagery data, open access databases, ground truth soil data and Artificial Intelligence (AI) architectures to provide enhanced geospatial layers into the Revised Universal Soil Loss Equation (RUSLE) model, improving both the reliability and the spatial resolution of the final map. The proposed methodology was implemented in the agricultural area of the Imathia Regional Unit (northern Greece), which consists of both mountainous areas and lowlands. Enhanced soil maps of Soil Organic Carbon (SOC) and soil texture were generated at 10 m resolution through a time-series analysis of satellite data and an XGBoost (eXtrene Gradinent Boosting) model. The model was trained by 84 ground truth soil samples (collected from agricultural fields) taking into account also additional environmental covariates (including the digital elevation model and climatic data) and following a Digital Soil Mapping (DSM) approach. The enhanced layers were introduced into the RUSLE’s soil erodibility factor (K-factor), producing a soil erosion layer with high spatial resolution. Notable prediction accuracy was achieved by the AI model with R2 0.61 for SOC and 0.73, 0.67 and 0.63 for clay, sand, and silt, respectively. The average annual soil loss of the unit was found to be 1.76 ton/ha/yr with 6% of the total agricultural area suffering from severe erosion (>11 ton/ha/yr), which was mainly found in the mountainous border regions, showing the strong influence of the mountains in the agricultural fields. The overall methodology could strongly support regional decision making and planning and environmental policies such as the European Common Agricultural Policy (CAP) and the Sustainable Development Goals (SDGs).

Rendering of 3D scenes in analytical polygon-based computer holography with texture mapping.

A computer-generated hologram (CGH) is a technique that generates an object light field by superimposing elementary holograms. Unlike traditional holography, this technique does not require the generation of an additional reference light to interfere with the calculated object light field. Texture mapping is a method that enhances the realism of 3D scenes. A fast method is presented that allows users to render holograms of 3D scenes consisting of triangular meshes with texture mapping. All calculations are performed with analytical expressions to ensure that the holograms generated by this method are fast and can reconstruct three-dimensional scenes with high quality. Using this method, a hologram of a three-dimensional scene consisting of thousands of triangles is generated. Our algorithm generates the same reconstruction results as those of Kim et al. [Appl. Opt.47, D117 (2008)APOPAI0003-693510.1364/AO.47.00D117], but significantly reduces the computation time (the computation time of our algorithm is only one-third of that of Kim et al.'s algorithm). The results show that the proposed method is computationally efficient as compared to a previous work. The proposed method is verified by simulations and optical experiments.

Exfoliation of an extruded Mg-4Li-1Ca alloy in presence of glucose and bovine serum albumin

Exfoliation corrosion (EFC) on extruded Mg-4Li-1Ca alloys as bone implant materials has not been reported. This study aimed to elucidate the degradation mechanism of a corrosion type-exfoliation occurred on an extruded Mg-4Li-1Ca alloy under the interaction of glucose (Glu) and bovine serum albumin (BSA) in Hank’s solution. The alloy microstructure, chemical composition, texture, stress distribution and Volta potential maps were characterized using metallographic microscopy, scanning electron microscopy and electron backscattered diffraction, and scanning Kelvin Probe. The alloy corrosion behavior was investigated via electrochemical testing and hydrogen evolution rate methods. X-ray diffraction, Fourier transform infrared spectrometry, X-ray photoelectron spectrometer, and fluorescence microscopy were used to analyze the corrosion products and the adsorption of the organic molecules. The experimental results indicated that the anodic second phase (Mg2Ca) were distributed in lamellar and rod-like patterns along the extrusion direction of the alloy, a lower Volta potential for Mg2Ca, texture and residual stresses also promote EFC. An increase in Glu concentration exacerbated the EFC, whereas BSA inhibited the EFC of the alloy. Higher Glu concentrations in BSA in turn inhibited EFC since Glu can complex with amino acids in proteins to form Glu-amino acid amides, which promoted protein adsorption and thereby forming a dense protective film on the alloy surface. The preferential dissolution of the Mg2Ca phase particles led to the complexation and adsorption of Glu and protein. This study enriches the understanding of the corrosion types in biomedical Mg-4Li-1Ca alloy and paving the way for enhancing the EFC resistance of the alloy.

Editorial

The Alpha Centauri Scientific and Technological Research Journal is a peer-reviewed journal produced internationally, it is published QUARTERLY in online version ISSN: 2709-4502. Alpha Centauri publishes scientific articles specializing in Applied Social Sciences and Social Sciences in Information Technologies with great impact at an international level. We are currently trending in more than 24 countries disseminating Open Access scientific knowledge. IMAGES: Still images, audio recordings, video and related computer files for non-commercial use. NASA content (images, audio, video, and computer files used in the reproduction of three-dimensional models, such as texture maps and polygonal data in any format).

Brain texture as a marker of transdiagnostic clinical profiles in patients with recent-onset psychosis and depression

The inter-relationships of voxels can be captured by the radiomics texture features across multiple spatial scales. Prediction models of brain texture changes captured by the contrast texture feature in recent-onset psychosis (ROP) and recent-onset depression (ROD) have recently been proposed, although the validation of these models transdiagnostically at the individual level and the investigation of the variability in clinical profiles are lacking. Established prevention and treatment approaches focus on specific diagnoses and do not address the heterogeneity and manifold potential outcomes of patients. Here we aimed to investigate the cross-sectional utility of brain texture changes for (1) identification of the psychopathological state (ROP and ROD) and (2) the association of individualized brain texture maps with clinical symptom severity and outcome profiles. We developed transdiagnostic models based on structural magnetic resonance imaging data for 116 patients with ROD, 122 patients with ROP and 197 healthy control participants from the PRONIA (Personalized pROgNostic tools for early psychosIs mAnagement) study by applying a set of tools and frameworks to explain the classification decisions of the deep-learning algorithm (named explainable artificial intelligence) and clustering analysis. We investigated the contrast texture feature as the key feature for the identification of a general psychopathological state. The discrimination power of the trained prediction model was >72% and was validated in a second independent age- and sex-matched sample of 137 ROP, 94 ROD and 159 healthy control participants. Clustering analysis was implemented to map the changes in texture brain produced from an explainable artificial intelligence algorithm, in a group fashion. The explained individualized brain contrast map grouped into eight homogeneous clusters. In the clinical group, we investigated the association between the explained brain contrast texture map and clinical symptom severity as well as outcome profiles. Different patterns in the explained brain contrast texture map showed unique associations of brain alterations with clinical symptom severity and clinical outcomes, that is, age, positive, negative and depressive symptoms, and functionality. In some clusters, the mean explained brain contrast texture map values and/or brain contrast texture voxels that contributed significantly to the classification decision predicted accurately the PANSS (positive and negative symptom scale) scores, functionality and change in functionality over time. In conclusion, we created homogeneous clusters which predict the clinical severity and outcome profile in ROP and ROD patients.