Search Results (12)

Optical coordinate measurement is a universal technique that aligns with the rapid development of industrial technologies and new materials. Nevertheless, can this technique be consistently effective when applied to the precise measurement of all types of materials? As shown in this article, an analysis of optical measurement systems reveals that some materials cause difficulties during the scanning process. This article details the matting process, resulting, as demonstrated, in lower measurement uncertainty values compared to the pre-matting state, and identifies materials for which applying a matting spray significantly improves the measurement quality. The authors propose a classification of materials into easy-to-scan and hard-to-scan groups, along with specific procedures to improve measurements, especially for the latter. Tests were conducted in an accredited Laboratory of Coordinate Metrology using an articulated arm with a laser probe. Measured objects included spheres made of ceramic, tungsten carbide (including a matte finish), aluminum oxide, titanium nitride-coated steel, and photopolymer resin, with reference diameters established by a high-precision Leitz PMM 12106 coordinate measuring machine. Diameters were determined from point clouds obtained via optical measurements using the best-fit method, both before and after matting. Color measurements using a spectrocolorimeter supplemented this study to assess the effect of matting on surface color. The results revealed correlations between the material type and measurement accuracy. Full article

This study investigates the excavation of the cataclastic loose rock slope at the mixing plant on the right bank of the BDa Hydropower Station, which is situated in the upper reaches of Lancang River. The dominant structural plane of the cataclastic loose rock mass was obtained using unmanned aerial vehicle tilt photography and 3D point cloud technology. The actual 3D numerical model of the study area was developed using the 3DEC discrete element numerical simulation software. The excavation response characteristics and overall stability of the cataclastic loose rock slope were analyzed. The support effect was evaluated considering the preliminary shaft micropile and Macintosh reinforced mat as slope support measures, and the stability was assessed by applying seismic waves. The results showed the main deformation and failure area after slope cleaning excavation at the junction of the cataclastic loose rock mass and Q^edl deposits in the shallow surface of the excavation face. Moreover, the maximum total displacement could reach 18.3 cm. Subsequently, the overall displacement of the slope was significantly reduced, and the maximum total displacement decreased to 2.78 cm. The support effect was significant. Under an earthquake load, the slope with support exhibited considerable displacement in the shallow surface of the excavation slope, with collapse deformation primarily occurring through shear failure. Full article

Mg₃Bi_2−vSb_v (0 ≤ v ≤ 2) is a class of promising thermoelectric materials that have a high thermoelectric performance around room temperatures, whereas their thermoelectric properties under pressures and temperatures are still illusive. In this study, we examined the influence of pressure, temperature, and carrier concentration on the thermoelectric properties of Mg₃Bi_2−vSb_v using first-principle calculations accompanied with Boltzmann transport equations method. There is a decrease in the lattice thermal conductivity of Mg₃Sb₂ (i.e., v = 2) with increasing pressure. For a general Mg₃Bi_2−vSb_v system, power factors are more effectively improved by n-type doping where electrons are the primary carriers over holes in n-type doping, and can be further enhanced by applied pressure. The figure of merit (zT) exhibits a positive correlation with temperature. A high zT value of 1.53 can be achieved by synergistically tuning the temperature, pressure, and carrier concentration in Mg₃Sb₂. This study offers valuable insights into the tailoring and optimization of the thermoelectric properties of Mg₃Bi_2−vSb_v. Full article

Material research and development driven by data analysis necessitates a substantial volume of data. However, conventional material data sharing platforms encounter challenges in sharing and integrating data across multiple platforms. This article proposes a blockchain-based materials data discovery and sharing infrastructure—InterMat, which is a material big data management and sharing framework model integrating cloud platforms and blockchain. It could support the full lifecycle of materials data sharing, including data generation, management, discovery, sharing, traceability, and valuation. The architecture of the InterMat, its unique method of constructing a consortium chain, and the protocol for data discovery are presented in this paper. Additionally, the method for materials data identifier and blockchain certification is established, which allows for a unified identifier on the blockchain and cloud-based data addressing from various organizations. InterMat has data discovery algorithms for various materials to achieve the discovery of similar materials data from different nodes. Furthermore, we have designed some blockchain smart contracts for InterMat to encourage data sharing across nodes. These contracts include a proof smart contract that records data sharing activities, ensuring transparency and traceability in the materials data flow. The other contract is a value-estimating contract to encourage high-quality data sharing. Finally, this article introduces the application case of InterMat, using steel materials as an example to demonstrate its applications in data management, data discovery, data valuation, etc. This study successfully addresses various challenges associated with the cross-platform sharing of materials data, such as issues related to data discovery, data rights and control, and willingness to share. InterMat can assist material researchers in discovering and accessing more data, which would create a new ecology for sharing data. Full article

Clouds are the major source of clutter in optical remote sensing (RS) images. Approximately 60% of the Earth’s surface is covered by clouds, with the equatorial and Tibetan Plateau regions being the most affected. Although the implementation of techniques for cloud removal can significantly improve the efficiency of remote sensing imagery, its use is severely restricted due to the poor timeliness of time-series cloud removal techniques and the distortion-prone nature of single-frame cloud removal techniques. To thoroughly remove thin clouds from remote sensing imagery, we propose the Saliency Cloud Matting Convolutional Neural Network (SCM-CNN) from an image fusion perspective. This network can automatically balance multiple loss functions, extract the cloud opacity and cloud top reflectance intensity from cloudy remote sensing images, and recover ground surface information under thin cloud cover through inverse operations. The SCM-CNN was trained on simulated samples and validated on both simulated samples and Sentinel-2 images, achieving average peak signal-to-noise ratios (PSNRs) of 30.04 and 25.32, respectively. Comparative studies demonstrate that the SCM-CNN model is more effective in performing cloud removal on individual remote sensing images, is robust, and can recover ground surface information under thin cloud cover without compromising the original image. The method proposed in this article can be widely promoted in regions with year-round cloud cover, providing data support for geological hazard, vegetation, and frozen area studies, among others. Full article

Clouds seriously limit the application of optical remote sensing images. In this paper, we remove clouds from satellite images using a novel method that considers ground surface reflections and cloud top reflections as a linear mixture of image elements from the perspective of image superposition. We use a two-step convolutional neural network to extract the transparency information of clouds and then recover the ground surface information of thin cloud regions. Given the poor balance of the generated samples, this paper also improves the binary Tversky loss function and applies it on multi-classification tasks. The model was validated on the simulated dataset and ALCD dataset, respectively. The results show that this model outperformed other control group experiments in cloud detection and removal. The model better locates the clouds in images with cloud matting, which is built based on cloud detection. In addition, the model successfully recovers the surface information of the thin cloud region when thick and thin clouds coexist, and it does not damage the original image’s information. Full article

Conducting realistic interactions while communicating efficiently in online conferences is highly desired but challenging. In this work, we propose a novel pixel-style virtual avatar for interacting with virtual objects in virtual conferences that can be generated in real-time. It consists of a 2D segmented head video stream for real-time facial expressions and a 3D point cloud body for realistic interactions, both of which are generated from RGB video input of a monocular webcam. We obtain a human-only video stream with a human matting method and generate the 3D avatar’s arms with a 3D pose estimation method, which improves the stereoscopic realism and sense of interaction of conference participants while interacting with virtual objects. Our approach fills the gap between 2D video conferences and 3D virtual avatars and combines the advantages of both. We evaluated our pixel-style avatar by conducting a user study; the result proved that the efficiency of our method is superior to other various existing avatar types. Full article

Quercus litseoides, an endangered montane cloud forest species, is endemic to southern China. To understand the genomic features, phylogenetic relationships, and molecular evolution of Q. litseoides, the complete chloroplast (cp) genome was analyzed and compared in Quercus section Cyclobalanopsis. The cp genome of Q. litseoides was 160,782 bp in length, with an overall guanine and cytosine (GC) content of 36.9%. It contained 131 genes, including 86 protein-coding genes, eight ribosomal RNA genes, and 37 transfer RNA genes. A total of 165 simple sequence repeats (SSRs) and 48 long sequence repeats with A/T bias were identified in the Q. litseoides cp genome, which were mainly distributed in the large single copy region (LSC) and intergenic spacer regions. The Q. litseoides cp genome was similar in size, gene composition, and linearity of the structural region to those of Quercus species. The non-coding regions were more divergent than the coding regions, and the LSC region and small single copy region (SSC) were more divergent than the inverted repeat regions (IRs). Among the 13 divergent regions, 11 were in the LSC region, and only two were in the SSC region. Moreover, the coding sequence (CDS) of the six protein-coding genes (rps12, matK, atpF, rpoC2, rpoC1, and ndhK) were subjected to positive selection pressure when pairwise comparison of 16 species of Quercus section Cyclobalanopsis. A close relationship between Q. litseoides and Quercus edithiae was found in the phylogenetic analysis of cp genomes. Our study provided highly effective molecular markers for subsequent phylogenetic analysis, species identification, and biogeographic analysis of Quercus. Full article

Zr₂Fe alloys have been widely used in fusion energy and hydrogen energy for hydrogen storage. However, disproportionation reactions occur easily in Zr-based alloys at medium and high temperatures, which greatly reduces the storage capacity of the alloys, and is not conducive to repeated cycle applications. The doping of Zr-based alloys with appropriate transition metal elements has been found to significantly improve their H storage properties and prevent hydrogen disproportionation. A convenient approach is required to efficiently predict the desirable doped structures that are physically stable with optimal properties. In this paper, based on the MatCloud High-Throughput Material Integrated Computing Platform (MatCloud), an automated process algorithm was established to solve the disproportionation reaction of Zr₂Fe. Rather than testing the doping materials one by one, such high-throughput material screening is effective in reducing the computational time. The structural stability of modified Zr₂Fe alloys, with different doping elements and doping concentrations, is systematically studied. The results indicate that the maximum doping concentration of Ni-doped Zr₂Fe is 33 at%, and beyond this doping concentration, Zr₂(Fe_1−xNi_x) phases become unstable. While Ti doping Zr₂Fe will form a new phase, the overall hydrogen absorption capacity may have been affected by the decrease in the phase content of Zr₂Fe in the main phase. The present study can shed valuable light on the design of high-performance Zr-based alloys for fusion energy and hydrogen storage. Full article

The primary purpose of this paper is to present a system intended for student education based on a microcontroller platform and a cloud system where data will be stored. Obtained research results will be based on the application of The NodeMcu platform for data collection with sensor systems. MatLab, ThingSpeak cloud system, and the Virtuino platform will be used for data presentation. Quantitative data are applied to determine conditions for agriculture system performance management. By analyzing the literature and the current situation in monitoring and managing in the agriculture and ecology field, it can be concluded that there are no special education courses regarding these issues. This knowledge was a good starting point for the research presented in this paper. Accordingly, the findings include developing a monitoring and management system intended for student education in mentioned fields. In addition, the practical implications of this research includes the acquisition of information related to student satisfaction with this method of education in the courses of engineering, agriculture, and ecology. The presented system may enable benchmarking, simulation, and verification of different scenarios to improve students’ knowledge regarding sensors’ application in natural conditions. The originality of the research lies in the presented software solution that can be made available to educational institutions and other scientific institutions to serve as a basis in the overall monitoring and management of agricultural and ecology parameters. Full article

In this work, we studied the stability of matrices with temperature-dependent solubility and their interactions with water at physiological temperature for their application in cell culture in vitro. Gradient copolymers of 2-isopropyl- with 2-n-propyl-2-oxazoline (P(iPrOx-nPrOx)) were used to prepare the matrices. The comonomer ratio during polymerization was chosen such that the cloud point temperature (T_CP) of the copolymer was below 37 °C while the glass transition (T_g) was above 37 °C. The role of the support for matrices in the context of their stability in aqueous solution was examined. Therefore, matrices in the form of both self-supported bulk polymer materials (fibrillar mats and molds) and polymer films supported on the silica slides were examined. All of the matrices remained undissolved when incubated in water at a temperature above T_CP. For the self-supported mats and molds, we observed the loss of shape stability, but, in the case of films supported on silica slides, only slight changes in morphology were observed. For a more in-depth investigation of the origin of the shape deformation of self-supported matrices, we analyzed the wettability, thickness, and water uptake of films on silica support because the matrices remained undeformed under these conditions. It was found that, above the T_CP of P(iPrOx-nPrOx), the wettability of the films decreased, but at the same time the films absorbed water and swelled. We examined how this specific behavior of the supported films influenced the culture of fibroblasts. The temperature-dependent solubility of the matrices and the possibility of noninvasive cell separation were also examined. Full article

For near real-time water applications, the Moderate Resolution Imaging Spectroradiometers (MODIS) on Terra and Aqua are currently the only satellite instruments that can provide well-calibrated top-of-atmosphere (TOA) radiance data over the global aquatic environments. However, TOA radiance data in the MODIS ocean bands over turbid atmosphere in east China often saturate, leaving only four land bands to use. In this study, an approach based on Empirical Orthogonal Function (EOF) analysis has been developed and validated to estimate chlorophyll a concentrations (Chla, μg/L) in surface waters of Taihu Lake, the third largest freshwater lake in China. The EOF approach analyzed the spectral variance of normalized Rayleigh-corrected reflectance (Rrc) data at 469, 555, 645, and 859 nm, and subsequently related that variance to Chla using 28 concurrent MODIS and field measurements. This empirical algorithm was then validated using another 30 independent concurrent MODIS and field measurements. Image analysis and radiative transfer simulations indicated that the algorithm appeared to be tolerant to aerosol perturbations, with unbiased RMS uncertainties of <80% for Chla ranging between 3 and 100 μg/L. Application of the algorithm to a total of 853 MODIS images between 2000 and 2013 under cloud-free conditions revealed spatial distribution patterns and seasonal changes that are consistent to previous findings based on floating algae mats. The current study can provide additional quantitative estimates of Chla that can be assimilated in an existing forecast model, which showed improved performance over the use of a previous Chla algorithm. However, the empirical nature, relatively large uncertainties, and limited number of spectral bands all point to the need of further improvement in data availability and accuracy with future satellite sensors. Full article