bluuebunny/crossref_metadata_2025 huggingface dataset:
The Antifouling and Drag-Reduction Performance of Alumina Reinforced Polydimethylsiloxane Coatings Containing Phenylmethylsilicone Oil
Authors: Yang Qiang , Zhang Zhanping , Qi Yuhong , Zhang Hongyang
Published: September 2021

Fouling-release coatings reinforced with micro-alumina and nano-alumina were prepared based on polydimethylsiloxane (PDMS) containing phenylmethylsilicone oil. The surface properties, mechanical properties, leaching behavior of silicone oil, anti-fouling and drag-reduction performance of the coating were studied. The results show that the addition of alumina can significantly improve the tensile strength, elastic modulus and Shore’s hardness of the coating. The adhesion experiments of marine bacteria and Navicula Tenera show that the addition of alumina can reduce the antifouling performance of the coating, which is related to the stripping mode of fouling organisms. The fouling organisms leave the coating surface by shearing, and the energy required for shearing is proportional to the elastic modulus of the coating. At 800–1400 rpm, the addition of alumina will reduce the drag reduction performance of the coating, which is related to the drag reduction mechanism of PDMS. PDMS counteracts part of the resistance by surface deformation. The larger the elastic modulus is, the more difficult the surface deformation is. The experiment of silicone oil leaching shows that the increase of alumina addition amount and the decrease of particle size will inhibit the leaching of silicone oil.
Alginic Acid Polymer-Hydroxyapatite Composites for Bone Tissue Engineering
Authors: Sikkema Rebecca , Keohan Blanca , Zhitomirsky Igor
Published: September 2021

Natural bone is a composite organic-inorganic material, containing hydroxyapatite (HAP) as an inorganic phase. In this review, applications of natural alginic acid (ALGH) polymer for the fabrication of composites containing HAP are described. ALGH is used as a biocompatible structure directing, capping and dispersing agent for the synthesis of HAP. Many advanced techniques for the fabrication of ALGH-HAP composites are attributed to the ability of ALGH to promote biomineralization. Gel-forming and film-forming properties of ALGH are key factors for the development of colloidal manufacturing techniques. Electrochemical fabrication techniques are based on strong ALGH adsorption on HAP, pH-dependent charge and solubility of ALGH. Functional properties of advanced composite ALGH-HAP films and coatings, scaffolds, biocements, gels and beads are described. The composites are loaded with other functional materials, such as antimicrobial agents, drugs, proteins and enzymes. Moreover, the composites provided a platform for their loading with cells for the fabrication of composites with enhanced properties for various biomedical applications. This review summarizes manufacturing strategies, mechanisms and outlines future trends in the development of functional biocomposites.
Experimental Study on Angular Flexural Performance of Multiaxis Three Dimensional (3D) Polymeric Carbon Fiber/Cementitious Concretes
Authors: Ozdemir Huseyin , Bilisik Kadir
Published: September 2021

Multiaxis three-dimensional (3D) continuous polymeric carbon fiber/cementitious concretes were introduced. Their angular (off-axis) flexural properties were experimentally studied. It was found that the placement of the continuous carbon fibers and their in-plane angular orientations in the pristine concrete noticeably influenced the angular flexural strength and the energy absorption behavior of the multiaxis 3D concrete composite. The off-axis flexural strength of the uniaxial (C-1D-(0°)), biaxial (C-2D-(0°), and C-2D-(90°)), and multiaxial (C-4D-(0°), C-4D-(+45°) and C-4D-(−45°)) concrete composites were outstandingly higher (from 36.84 to 272.43%) than the neat concrete. Their energy absorption capacities were superior compared to the neat concrete. Fractured four directional polymeric carbon fiber/cementitious matrix concretes limited brittle matrix failure and a broom-like fracture phenomenon on the filament bundles, filament-matrix debonding and splitting, and minor filament entanglement. Multiaxis 3D polymeric carbon fiber concrete, especially the C-4D structure, controlled the crack phenomena and was considered a damage-tolerant material compared to the neat concrete.
A Combination Extract of Gardeniae Fructus and Perillae Folium Exerts Anti-Inflammatory Effects on LPS-Activated RAW 264.7 Mouse Macrophages via an ER Stress-Induced CHOP Pathway
Authors: Park Wansu
Published: September 2021

The aim of this study is to investigate the effects of a combination extract of Gardeniae Fructus and Perillae Folium (GP) on inflammatory reactions in lipopolysaccharide (LPS)-activated mouse macrophages RAW 264.7 cells. Multiplex cytokine assay, Fluo-4 calcium assay, Flow cytometry assay for phospho-P38 MAPK, and quantitative PCR were carried out. GP significantly reduced LPS-induced productions of macrophage inflammatory protein (MIP)-1α and monokine induced by gamma interferon (MIG) and release of intracellular calcium in LPS-activated RAW 264.7 cells. GP also significantly inhibited P38 MAPK phosphorylation and mRNA levels of Chop, Camk2a, Stat1, Stat3, Jak2, Fas, Nos2, and Ptgs2 in LPS-activated RAW 264.7 cells. Taken together, this study represents that GP exerts anti-inflammatory effects on LPS-activated RAW 264.7 cells via ER stress-induced CHOP pathway.
Fabrication Process for On-Board Geometries Using a Polymer Composite-Based Selective Metallization for Next-Generation Electronics Packaging
Authors: Acharya Sarthak , Chouhan Shailesh Singh , Delsing Jerker
Published: September 2021

Advancements in production techniques in PCB manufacturing industries are still required as compared to silicon-ICs fabrications. One of the concerned areas in PCBs fabrication is the use of conventional methodologies for metallization. Most of the manufacturers are still using the traditional Copper (Cu) laminates on the base substrate and patterning the structures using lithography processes. As a result, significant amounts of metallic parts are etched away during any mass production process, causing unnecessary disposables leading to pollution. In this work, a new approach for Cu metallization is demonstrated with considerable step-reducing pattern-transfer mechanism. In the fabrication steps, a seed layer of covalent bonded metallization (CBM) chemistry on top of a dielectric epoxy resin is polymerized using actinic radiation intensity of a 375 nm UV laser source. The proposed method is capable of patterning any desirable geometries using the above-mentioned surface modification followed by metallization. To metallize the patterns, a proprietary electroless bath has been used. The metallic layer grows only on the selective polymer-activated locations and thus is called selective metallization. The highlight of this production technique is its occurrence at a low temperature (20–45 °C). In this paper, FR-4 as a base substrate and polyurethane (PU) as epoxy resin were used to achieve various geometries, useful in electronics packaging. In addition, analysis of the process parameters and some challenges witnessed during the process development are also outlined. As a use case, a planar inductor is fabricated to demonstrate the application of the proposed technique.
The Long-Term Mitigating Effect of Horizontal Ground-Source Heat Exchangers on Permafrost Thaw Settlement
Authors: Fatollahzadeh Gheisari Amir , Maghoul Pooneh , Holländer Hartmut M. , Kenyon Rob , Sinclair Rob , Saaly Maryam
Published: September 2021

This study investigated the long-term effect of horizontal Ground-Source Heat Exchangers (GSHEs) on mitigating permafrost thaw settlement. In the conceptual system, a fan coil was used to chill the recirculating fluid in the linear High-Density Polyethylene (HDPE) ground loop system. A fully coupled thermo-hydro-mechanical finite element framework was employed to analyze multiphysics processes involved in the thaw settlement phenomenon. To investigate the sustainability of the system, a period of 50 years was simulated. Two operational modes were defined: one without and the other with HDPE. Different heat carrier velocities and inlet temperatures, and heat exchanger depths were examined to explore their effects on the thaw settlement rate. It was concluded that the proposed system can effectively alleviate the predicted permafrost thaw settlement over the study period. Moreover, the heat carrier temperature was found to have a prominent impact on the thaw settlement rate amongst other parameters.
Novel Correlations between Spectroscopic and Morphological Properties of Activated Carbons from Waste Coffee Grounds
Authors: Rosson Egle , Sgarbossa Paolo , Mozzon Mirto , Venturino Federico , Bogialli Sara , Glisenti Antonella , Talon Aldo , Moretti Elisa , Carturan Sara Maria , Tamburini Sergio , Famengo Alessia , da Costa Ribeiro Ana Paula , Benhabiles Sadjia , Kamel Rida , Zorzi Federico , Bertani Roberta
Published: September 2021

Massive quantities of spent coffee grounds (SCGs) are generated by users around the world. Different processes have been proposed for SCG valorization, including pyrolytic processes to achieve carbonaceous materials. Here, we report the preparation of activated carbons through pyrolytic processes carried out under different experimental conditions and in the presence of various porosity activators. Textural and chemical characterization of the obtained carbons have been achieved through Brunauer–Emmett–Teller (BET), ESEM, 13C solid state NMR, XPS, XRD, thermogravimetric and spectroscopic determinations. The aim of the paper is to relate these data to the preparation method, evaluating the correlation between the spectroscopic data and the physical and textural properties, also in comparison with the corresponding data obtained for three commercial activated carbons used in industrial adsorption processes. Some correlations have been observed between the Raman and XPS data.
Feasibility of the Hybrid Use of Chlorella vulgaris Culture with the Conventional Biological Treatment in Urban Wastewater Treatment Plants
Authors: Belaiba Amani , Bouharat Dorsaf , Malvis Ana , Hodaifa Gassan
Published: September 2021

Currently, most wastewater treatment plants do not meet the legal requirements, especially regarding phosphorus and nitrogen contents. In this work, real primary urban wastewater (P-UW) was used as culture medium for the growth of Chlorella vulgaris. Experiments were carried out in batch photobioreactors at laboratory scale. To determine the maximum nutrient removal levels and the optimal pH value for C. vulgaris growth, the following pH values were studied: 5, 6, 7, 8, 9, 10, and 11. Additionally, two control experiments were conducted using UW and tap water at the same conditions but without microalgae inoculation. The operational conditions were agitation rate = 200 rpm, T = 25 °C, aeration rate = 0.5 L/min, and continuous light with illumination intensity = 359 µE m−2 s−1. Significant higher growth was obtained at pH = 7. The direct use of C. vulgaris for P-UW treatment demonstrated high removal percentages of organic (COD and BOD5 removal = 63.4% and 92.3%, respectively) and inorganic compounds (inorganic carbon removal = 99.6%). The final biomass was characterized by an accumulation of high energetic compounds, mainly carbohydrates, which ranged between 63.3% (pH = 5) and 82.8% (pH = 11) and represent a source of biofuels. These new achievements open up the possibility of new horizons in urban wastewater treatment.
Robust Chi-Square in Extreme and Boundary Conditions: Comments on Jak et al. (2021)
Authors: Asparouhov Tihomir , Muthén Bengt
Published: September 2021

In this article we describe a modification of the robust chi-square test of fit that yields more accurate type I error rates when the estimated model is at the boundary of the admissible space.
The Measure Aspect of Quantum Uncertainty, of Entanglement, and the Associated Entropies
Authors: Horváth Ivan
Published: September 2021

Indeterminacy associated with the probing of a quantum state is commonly expressed through spectral distances (metric) featured in the outcomes of repeated experiments. Here, we express it as an effective amount (measure) of distinct outcomes instead. The resulting μ-uncertainties are described by the effective number theory whose central result, the existence of a minimal amount, leads to a well-defined notion of intrinsic irremovable uncertainty. We derive μ-uncertainty formulas for arbitrary set of commuting operators, including the cases with continuous spectra. The associated entropy-like characteristics, the μ-entropies, convey how many degrees of freedom are effectively involved in a given measurement process. In order to construct quantum μ-entropies, we are led to quantum effective numbers designed to count independent, mutually orthogonal states effectively comprising a density matrix. This concept is basis-independent and leads to a measure-based characterization of entanglement.
Crossover Hinduism and Other Possible Categories When Dealing with Hinduism in Europe
Authors: Fibiger Marianne Qvortrup
Published: September 2021

This article will discuss the difficulties in analysing and mapping Hinduism in Europe, and will present a suggestion for a working typology. The point of departure is Denmark as a geographically limited space. The aim of this article is to use the findings in Denmark and the suggested categories for groups to form a heuristic framework, or present some overall patterns that apply to other European countries. This is despite the special history of Denmark, which of course has to be taken into account—not least when it comes to its Hindu migration patterns, relations with the Hindu tradition among the public, and relations with religion in general (Denmark and the other Scandinavian countries are regarded as prototypically secular societies). The following categories or typologies will be presented from a polythetic point of view: (a) people or groups who understand themselves as Hindus by birth and by descent; (b) people or groups that can be understood as Hindu related; (c) people or groups that can be understood as being inspired by the Hindu religion. There is also a fourth category or better tendency, which will be called Crossover Hinduism. Crossover Hinduism can be understood as a form of dialectical network and entanglement, but also as a mutual reference point between the different groups. This representation of Hinduism will be the main focus of this article. The concept of Crossover Hinduism not only challenges concepts such as syncretism and eclecticism, but can also give an idea of how concepts flow and generate new meaning(s) that will have some kind of impact on the way the different groups can be associated with the Hindu tradition.
Theology, Martyrdom and Female Agency in Reformation Prague
Authors: Fudge Thomas A.
Published: September 2021

The Hussite tradition historically has been excluded by the mainstream of Reformation historiography. Czech-language scholarship treating Hussite history have made few significant advances in the study of women and there has been limited attention given to the role women played in the Hussite tradition. The gap in Anglophone historiography is even more apparent. This essay considers Klára, a sixteenth-century Prague housekeeper, Marta, a learned figure contemporary with Klára who withstood civil and ecclesiastical officials, and Anna Marie Trejtlarová, an early seventeen-century educated laywoman. Their names are almost completely unknown outside Czech historiography. An examination of their lives and faith by means of the surviving primary sources and relevant historiography provides a window through which to observe the nature of religious reform in the Prague context in the world of Reformations. What is striking is the role of theology and the nature of female agency in the examination of these women. The essay endeavours to use these case studies to present a preliminary answer to the question: What do women tell us about Reformation? This study reveals the world of religious reform more fully by situating women and female agency in an active capacity.
Improving the Accuracy of Water Storage Anomaly Trends Based on a New Statistical Correction Hydrological Model Weighting Method
Authors: Wang Qingqing , Zheng Wei , Yin Wenjie , Kang Guohua , Zhang Gangqiang , Zhang Dasheng
Published: September 2021

The Gravity Recovery and Climate Experiment (GRACE) satellite solutions have been considerably applied to assess the reliability of hydrological models on a global scale. However, no single hydrological model can be suitable for all regions. Here, a New Statistical Correction Hydrological Model Weighting (NSCHMW) method is developed based on the root mean square error and correlation coefficient between hydrological models and GRACE mass concentration (mascon) data. The NSCHMW method can highlight the advantages of good models compared with the previous average method. Additionally, to verify the effect of the NSCHMW method, taking the Haihe River Basin (HRB) as an example, the spatiotemporal patterns of Terrestrial Water Storage Anomalies (TWSA) in HRB are analyzed through a comprehensive comparison of decadal trends (2003–2014) from GRACE and different hydrological models (Noah from GLDAS-2.1, VIC from GLDAS-2.1, CLSM from GLDAS-2.1, CLSM from GLDAS-2.0, WGHM, PCR-GLOBWB, and CLM-4.5). Besides, the NSCHMW method is applied to estimate TWSA trends in the HRB. Results demonstrate that (1) the NSCHMW method can improve the accuracy of TWSA estimation by hydrological models; (2) the TWSA trends continue to decrease through the study period at a rate of 15.7 mm/year; (3) the WGHM and PCR-GLOBWB have positive reliability with respect to GRACE with r > 0.9, while all the other models underestimate TWSA trends; (4) the NSCHMW method can effectively improve RMSE, NES, and r with 3–96%, 35–282%, 1–255%, respectively, by weighting the WGHM and PCR-GLOBWB. Indeed, groundwater depletion in HRB also proves the necessity of the South-North Water Diversion Project, which has already contributed to groundwater recovery.
Evaluation of Deep Learning Segmentation Models for Detection of Pine Wilt Disease in Unmanned Aerial Vehicle Images
Authors: Xia Lang , Zhang Ruirui , Chen Liping , Li Longlong , Yi Tongchuan , Wen Yao , Ding Chenchen , Xie Chunchun
Published: September 2021

Pine wilt disease (PWD) is a serious threat to pine forests. Combining unmanned aerial vehicle (UAV) images and deep learning (DL) techniques to identify infected pines is the most efficient method to determine the potential spread of PWD over a large area. In particular, image segmentation using DL obtains the detailed shape and size of infected pines to assess the disease’s degree of damage. However, the performance of such segmentation models has not been thoroughly studied. We used a fixed-wing UAV to collect images from a pine forest in Laoshan, Qingdao, China, and conducted a ground survey to collect samples of infected pines and construct prior knowledge to interpret the images. Then, training and test sets were annotated on selected images, and we obtained 2352 samples of infected pines annotated over different backgrounds. Finally, high-performance DL models (e.g., fully convolutional networks for semantic segmentation, DeepLabv3+, and PSPNet) were trained and evaluated. The results demonstrated that focal loss provided a higher accuracy and a finer boundary than Dice loss, with the average intersection over union (IoU) for all models increasing from 0.656 to 0.701. From the evaluated models, DeepLLabv3+ achieved the highest IoU and an F1 score of 0.720 and 0.832, respectively. Also, an atrous spatial pyramid pooling module encoded multiscale context information, and the encoder–decoder architecture recovered location/spatial information, being the best architecture for segmenting trees infected by the PWD. Furthermore, segmentation accuracy did not improve as the depth of the backbone network increased, and neither ResNet34 nor ResNet50 was the appropriate backbone for most segmentation models.
UAV Remote Sensing Image Automatic Registration Based on Deep Residual Features
Authors: Luo Xin , Lai Guangling , Wang Xiao , Jin Yuwei , He Xixu , Xu Wenbo , Hou Weimin
Published: September 2021

With the rapid development of unmanned aerial vehicle (UAV) technology, UAV remote sensing images are increasing sharply. However, due to the limitation of the perspective of UAV remote sensing, the UAV images obtained from different viewpoints of a same scene need to be stitched together for further applications. Therefore, an automatic registration method of UAV remote sensing images based on deep residual features is proposed in this work. It needs no additional training and does not depend on image features, such as points, lines and shapes, or on specific image contents. This registration framework is built as follows: Aimed at the problem that most of traditional registration methods only use low-level features for registration, we adopted deep residual neural network features extracted by an excellent deep neural network, ResNet-50. Then, a tensor product was employed to construct feature description vectors through exacted high-level abstract features. At last, the progressive consistency algorithm (PROSAC) was exploited to remove false matches and fit a geometric transform model so as to enhance registration accuracy. The experimental results for different typical scene images with different resolutions acquired by different UAV image sensors indicate that the improved algorithm can achieve higher registration accuracy than a state-of-the-art deep learning registration algorithm and other popular registration algorithms.
Learning Adjustable Reduced Downsampling Network for Small Object Detection in Urban Environments
Authors: Zhang Huijie , An Li , Chu Vena W. , Stow Douglas A. , Liu Xiaobai , Ding Qinghua
Published: September 2021

Detecting small objects (e.g., manhole covers, license plates, and roadside milestones) in urban images is a long-standing challenge mainly due to the scale of small object and background clutter. Although convolution neural network (CNN)-based methods have made significant progress and achieved impressive results in generic object detection, the problem of small object detection remains unsolved. To address this challenge, in this study we developed an end-to-end network architecture that has three significant characteristics compared to previous works. First, we designed a backbone network module, namely Reduced Downsampling Network (RD-Net), to extract informative feature representations with high spatial resolutions and preserve local information for small objects. Second, we introduced an Adjustable Sample Selection (ADSS) module which frees the Intersection-over-Union (IoU) threshold hyperparameters and defines positive and negative training samples based on statistical characteristics between generated anchors and ground reference bounding boxes. Third, we incorporated the generalized Intersection-over-Union (GIoU) loss for bounding box regression, which efficiently bridges the gap between distance-based optimization loss and area-based evaluation metrics. We demonstrated the effectiveness of our method by performing extensive experiments on the public Urban Element Detection (UED) dataset acquired by Mobile Mapping Systems (MMS). The Average Precision (AP) of the proposed method was 81.71%, representing an improvement of 1.2% compared with the popular detection framework Faster R-CNN.
Identification of Silvicultural Practices in Mediterranean Forests Integrating Landsat Time Series and a Single Coverage of ALS Data
Authors: Esteban Jessica , Fernández-Landa Alfredo , Tomé José Luis , Gómez Cristina , Marchamalo Miguel
Published: September 2021

Understanding forest dynamics at the stand level is crucial for sustainable management. Landsat time series have been shown to be effective for identification of drastic changes, such as natural disturbances or clear-cuts, but detecting subtle changes requires further research. Time series of six Landsat-derived vegetation indexes (VIs) were analyzed with the BFAST (Breaks for Additive Season and Trend) algorithm aiming to characterize the changes resulting from harvesting practices of different intensities (clear-cutting, cutting with seed-trees, and thinning) in a Mediterranean forest area of Spain. To assess the contribution of airborne laser scanner (ALS) data and the potential implications of it being after or before the detected changes, two scenarios were defined (based on the year in which ALS data were acquired (2010), and thereby detecting changes from 2005 to 2010 (before ALS data) and from 2011 to 2016 (after ALS data). Pixels identified as change by BFAST were attributed with change in VI intensity and ALS-derived statistics (99th height percentile and forest canopy cover) for classification with random forests, and derivation of change maps. Fusion techniques were applied to leverage the potential of each individual VI change map and to reduce mapping errors. The Tasseled Cap Brightness (TCB) and Normalized Burn Ratio (NBR) indexes provided the most accurate results, the latter being more precise for thinning detection. Our results demonstrate the suitability of Landsat time series and ALS data to characterize forest stand changes caused by harvesting practices of different intensity, with improved accuracy when ALS data is acquired after the change occurs. Clear-cuttings were more readily detectable compared to cutting with seed-trees and thinning, detection of which required fusion approaches. This methodology could be implemented to produce annual cartography of harvesting practices, enabling more accurate statistics and spatially explicit identification of forest operations.
Early Detection of Powdery Mildew Disease and Accurate Quantification of Its Severity Using Hyperspectral Images in Wheat
Authors: Khan Imran Haider , Liu Haiyan , Li Wei , Cao Aizhong , Wang Xue , Liu Hongyan , Cheng Tao , Tian Yongchao , Zhu Yan , Cao Weixing , Yao Xia
Published: September 2021

Early detection of the crop disease using agricultural remote sensing is crucial as a precaution against its spread. However, the traditional method, relying on the disease symptoms, is lagging. Here, an early detection model using machine learning with hyperspectral images is presented. This study first extracted the normalized difference texture indices (NDTIs) and vegetation indices (VIs) to enhance the difference between healthy and powdery mildew wheat. Then, a partial least-squares linear discrimination analysis was applied to detect powdery mildew with the combined optimal features (i.e., VIs & NDTIs). Further, a regression model on the partial least-squares regression was developed to estimate disease severity (DS). The results show that the discriminant model with the combined VIs & NDTIs improved the ability for early identification of the infected leaves, with an overall accuracy value and Kappa coefficient over 82.35% and 0.56 respectively, and with inconspicuous symptoms which were difficult to identify as symptoms of the disease using the traditional method. Furthermore, the calibrated and validated DS estimation model reached good performance as the coefficient of determination (R2) was over 0.748 and 0.722, respectively. Therefore, this methodology for detection, as well as the quantification model, is promising for early disease detection in crops.
Rapid glacier Shrinkage and Glacial Lake Expansion of a China-Nepal Transboundary Catchment in the Central Himalayas, between 1964 and 2020
Authors: Zhong Yan , Liu Qiao , Sapkota Liladhar , Luo Yunyi , Wang Han , Liao Haijun , Wu Yanhong
Published: September 2021

Climate warming and concomitant glacier recession in the High Mountain Asia (HMA) have led to widespread development and expansion of glacial lakes, which reserved the freshwater resource, but also may increase risks of glacial lake outburst floods (GLOFs) or debris floods. Using 46 moderate- and high-resolution satellite images, including declassified Keyhole and Landsat missions between 1964 and 2020, we provide a comprehensive area mapping of glaciers and glacial lakes in the Tama Koshi (Rongxer) basin, a highly glacierized China-Nepal transnational catchment in the central Himalayas with high potential risks of glacier-related hazards. Results show that the 329.2 ± 1.9 km2 total area of 271 glaciers in the region has decreased by 26.2 ± 3.2 km2 in the past 56 years. During 2000–2016, remarkable ice mass loss caused the mean glacier surface elevation to decrease with a rate of −0.63 m a−1, and the mean glacier surface velocity slowed by ~25% between 1999 and 2015. The total area of glacial lakes increased by 9.2 ± 0.4 km2 (~180%) from 5.1 ± 0.1 km2 in 1964 to 14.4 ± 0.3 km2 in 2020, while ice-contacted proglacial lakes have a much higher expansion rate (~204%). Large-scale glacial lakes are developed preferentially and experienced rapid expansion on the east side of the basin, suggesting that in addition to climate warming, the glacial geomorphological characters (aspect and slope) are also key controlling factors of the lake growing process. We hypothesize that lake expansion will continue in some cases until critical local topography (i.e., steepening icefall) is reached, but the lake number may not necessarily increase. Further monitoring should be focused on eight rapidly expanding proglacial lakes due to their high potential risks of failure and relatively high lake volumes.
Learning Rotated Inscribed Ellipse for Oriented Object Detection in Remote Sensing Images
Authors: He Xu , Ma Shiping , He Linyuan , Ru Le , Wang Chen
Published: September 2021

Oriented object detection in remote sensing images (RSIs) is a significant yet challenging Earth Vision task, as the objects in RSIs usually emerge with complicated backgrounds, arbitrary orientations, multi-scale distributions, and dramatic aspect ratio variations. Existing oriented object detectors are mostly inherited from the anchor-based paradigm. However, the prominent performance of high-precision and real-time detection with anchor-based detectors is overshadowed by the design limitations of tediously rotated anchors. By using the simplicity and efficiency of keypoint-based detection, in this work, we extend a keypoint-based detector to the task of oriented object detection in RSIs. Specifically, we first simplify the oriented bounding box (OBB) as a center-based rotated inscribed ellipse (RIE), and then employ six parameters to represent the RIE inside each OBB: the center point position of the RIE, the offsets of the long half axis, the length of the short half axis, and an orientation label. In addition, to resolve the influence of complex backgrounds and large-scale variations, a high-resolution gated aggregation network (HRGANet) is designed to identify the targets of interest from complex backgrounds and fuse multi-scale features by using a gated aggregation model (GAM). Furthermore, by analyzing the influence of eccentricity on orientation error, eccentricity-wise orientation loss (ewoLoss) is proposed to assign the penalties on the orientation loss based on the eccentricity of the RIE, which effectively improves the accuracy of the detection of oriented objects with a large aspect ratio. Extensive experimental results on the DOTA and HRSC2016 datasets demonstrate the effectiveness of the proposed method.
Integrating Landslide Typology with Weighted Frequency Ratio Model for Landslide Susceptibility Mapping: A Case Study from Lanzhou City of Northwestern China
Authors: Shu Heping , Guo Zizheng , Qi Shi , Song Danqing , Pourghasemi Hamid , Ma Jiacheng
Published: September 2021

Although numerous models have been employed to address the issue of landslide susceptibility at regional scale, few have incorporated landslide typology into a model application. Thus, the aim of the present study is to perform landslide susceptibility zonation taking landslide classification into account using a data-driven model. The specific objective is to answer the question: how to select reasonable influencing factors for different types of landslides so that the accuracy of susceptibility assessment can be improved? The Qilihe District in Lanzhou City of northwestern China was undertaken as the test area, and a total of 12 influencing factors were set as the predictive variables. An inventory map containing 227 landslides was created first, which was divided into shallow landslides and debris flows based on the geological features, distribution, and formation mechanisms. A weighted frequency ratio model was proposed to calculate the landslide susceptibility. The weights of influencing factors were calculated by the integrated model of logistic regression and fuzzy analytical hierarchy process, whereas the rating among the classes within each factor was obtained by a frequency ratio algorithm. The landslide susceptibility index of each cell was subsequently calculated in GIS environment to create landslide susceptibility maps of different types of landslide. The analysis and assessment process were separately performed for each type of landslide, and the final landslide susceptibility map for the entire region was produced by combining them. The results showed that 73.3% of landslide pixels were classified into “very high” or “high” susceptibility zones, while “very low” or “low” susceptibility zones covered only 3.6% of landslide pixels. The accuracy of the model represented by receiver operating characteristic curve was satisfactory, with a success rate of 70.4%. When the landslide typology was not considered, the accuracy of resulted maps decreased by 1.5~5.4%.
New Channel Errors Estimation Method for Multichannel SAR Based on Virtual Calibration Source
Authors: Liang Zhen , Fu Xikai , Lv Xiaolei
Published: September 2021

The multichannel synthetic aperture radar (SAR) system can effectively overcome the fundamental limitation between high-resolution and wide-swath. However, the unavoidable channel errors will result in a mismatch of the reconstruction filter and false targets in pairs. To address this issue, a novel channel errors calibration method is proposed based on the idea of minimizing the mean square error (MMSE) between the signal subspace and the space spanned by the practical steering vectors. The practical steering matrix of each Doppler bin can be constructed according to the Doppler spectrum. Compared with the time-domain correlation method, the proposed method no longer depends on the accuracy of the Doppler centroid estimation. Besides, compared with the orthogonal subspace method, the proposed method has the advantage of robustness under the condition of large samples by using the diagonal loading technique. To evaluate the performance, the results of simulation data and the real data acquired by the GF-3 dual-channel SAR system demonstrate that the proposed method has higher accuracy and more robustness than the conventional methods, especially in the case of low SNRs and high non-uniformity.
Feature Selection and Mislabeled Waveform Correction for Water–Land Discrimination Using Airborne Infrared Laser
Authors: Liang Gang , Zhao Xinglei , Zhao Jianhu , Zhou Fengnian
Published: September 2021

The discrimination of water–land waveforms is a critical step in the processing of airborne topobathy LiDAR data. Waveform features, such as the amplitudes of the infrared (IR) laser waveforms of airborne LiDAR, have been used in identifying water–land interfaces in coastal waters through waveform clustering. However, water–land discrimination using other IR waveform features, such as full width at half maximum, area, width, and combinations of different features, has not been evaluated and compared with other methods. Furthermore, false alarms often occur when water–land discrimination in coastal areas is conducted using IR laser waveforms because of environmental factors. This study provides an optimal feature for water–land discrimination using an IR laser by comparing the performance of different waveform features and proposes a dual-clustering method integrating K-means and density-based spatial clustering applications with noise algorithms to improve the accuracy of water–land discrimination through the clustering of waveform features and positions of IR laser spot centers. The proposed method is used for practical measurement with Optech Coastal Zone Mapping and Imaging LiDAR. Results show that waveform amplitude is the optimal feature for water–land discrimination using IR laser waveforms among the researched features. The proposed dual-clustering method can correct mislabeled water or land waveforms and reduce the number of mislabeled waveforms by 48% with respect to the number obtained through traditional K-means clustering. Water–land discrimination using IR waveform amplitude and the proposed dual-clustering method can reach an overall accuracy of 99.730%. The amplitudes of IR laser waveform and the proposed dual-clustering method are recommended for water–land discrimination in coastal and inland waters because of the high accuracy, resolution, and automation of the methods.
Automated Water Level Monitoring at the Continental Scale from ICESat-2 Photons
Authors: Madson Austin , Sheng Yongwei
Published: September 2021

Of the approximately 6700 lakes and reservoirs larger than 1 km2 in the Contiguous United States (CONUS), only ~430 (~6%) are actively gaged by the United States Geological Survey (USGS) or their partners and are available for download through the National Water Information System database. Remote sensing analysis provides a means to fill in these data gaps in order to glean a better understanding of the spatiotemporal water level changes across the CONUS. This study takes advantage of two-plus years of NASA’s ICESat-2 (IS-2) ATLAS photon data (ATL03 products) in order to derive water level changes for ~6200 overlapping lakes and reservoirs (>1 km2) in the CONUS. Interactive visualizations of large spatial datasets are becoming more commonplace as data volumes for new Earth observing sensors have markedly increased in recent years. We present such a visualization created from an automated cluster computing workflow that utilizes tens of billions of ATLAS photons which derives water level changes for all of the overlapping lakes and reservoirs in the CONUS. Furthermore, users of this interactive website can download segmented and clustered IS-2 ATL03 photons for each individual waterbody so that they may run their own analysis. We examine ~19,000 IS-2 derived water level changes that are spatially and temporally coincident with water level changes from USGS gages and find high agreement with our results as compared to the in situ gage data. The mean squared error (MSE) and the mean absolute error (MAE) between these two products are 1 cm and 6 cm, respectively.
Depth Estimation from Light Field Geometry Using Convolutional Neural Networks
Authors: Han Lei , Huang Xiaohua , Shi Zhan , Zheng Shengnan
Published: September 2021

Depth estimation based on light field imaging is a new methodology that has succeeded the traditional binocular stereo matching and depth from monocular images. Significant progress has been made in light-field depth estimation. Nevertheless, the balance between computational time and the accuracy of depth estimation is still worth exploring. The geometry in light field imaging is the basis of depth estimation, and the abundant light-field data provides convenience for applying deep learning algorithms. The Epipolar Plane Image (EPI) generated from the light-field data has a line texture containing geometric information. The slope of the line is proportional to the depth of the corresponding object. Considering the light field depth estimation as a spatial density prediction task, we design a convolutional neural network (ESTNet) to estimate the accurate depth quickly. Inspired by the strong image feature extraction ability of convolutional neural networks, especially for texture images, we propose to generate EPI synthetic images from light field data as the input of ESTNet to improve the effect of feature extraction and depth estimation. The architecture of ESTNet is characterized by three input streams, encoding-decoding structure, and skipconnections. The three input streams receive horizontal EPI synthetic image (EPIh), vertical EPI synthetic image (EPIv), and central view image (CV), respectively. EPIh and EPIv contain rich texture and depth cues, while CV provides pixel position association information. ESTNet consists of two stages: encoding and decoding. The encoding stage includes several convolution modules, and correspondingly, the decoding stage embodies some transposed convolution modules. In addition to the forward propagation of the network ESTNet, some skip-connections are added between the convolution module and the corresponding transposed convolution module to fuse the shallow local and deep semantic features. ESTNet is trained on one part of a synthetic light-field dataset and then tested on another part of the synthetic light-field dataset and real light-field dataset. Ablation experiments show that our ESTNet structure is reasonable. Experiments on the synthetic light-field dataset and real light-field dataset show that our ESTNet can balance the accuracy of depth estimation and computational time.
Application Combining VMD and ResNet101 in Intelligent Diagnosis of Motor Faults
Authors: Lin Shih-Lin
Published: September 2021

Motor failure is one of the biggest problems in the safe and reliable operation of large mechanical equipment such as wind power equipment, electric vehicles, and computer numerical control machines. Fault diagnosis is a method to ensure the safe operation of motor equipment. This research proposes an automatic fault diagnosis system combined with variational mode decomposition (VMD) and residual neural network 101 (ResNet101). This method unifies the pre-analysis, feature extraction, and health status recognition of motor fault signals under one framework to realize end-to-end intelligent fault diagnosis. Research data are used to compare the performance of the three models through a data set released by the Federal University of Rio de Janeiro (UFRJ). VMD is a non-recursive adaptive signal decomposition method that is suitable for processing the vibration signals of motor equipment under variable working conditions. Applied to bearing fault diagnosis, high-dimensional fault features are extracted. Deep learning shows an absolute advantage in the field of fault diagnosis with its powerful feature extraction capabilities. ResNet101 is used to build a model of motor fault diagnosis. The method of using ResNet101 for image feature learning can extract features for each image block of the image and give full play to the advantages of deep learning to obtain accurate results. Through the three links of signal acquisition, feature extraction, and fault identification and prediction, a mechanical intelligent fault diagnosis system is established to identify the healthy or faulty state of a motor. The experimental results show that this method can accurately identify six common motor faults, and the prediction accuracy rate is 94%. Thus, this work provides a more effective method for motor fault diagnosis that has a wide range of application prospects in fault diagnosis engineering.
Day-to-Day Variability and Year-to-Year Reproducibility of Accelerometer-Measured Free-Living Sit-to-Stand Transitions Volume and Intensity among Community-Dwelling Older Adults
Authors: Löppönen Antti , Karavirta Laura , Portegijs Erja , Koivunen Kaisa , Rantanen Taina , Finni Taija , Delecluse Christophe , Roie Evelien Van , Rantalainen Timo
Published: September 2021

(1) Background: The purpose of this study was to evaluate the day-to-day variability and year-to-year reproducibility of an accelerometer-based algorithm for sit-to-stand (STS) transitions in a free-living environment among community-dwelling older adults. (2) Methods: Free-living thigh-worn accelerometry was recorded for three to seven days in 86 (women n = 55) community-dwelling older adults, on two occasions separated by one year, to evaluate the long-term consistency of free-living behavior. (3) Results: Year-to-year intraclass correlation coefficients (ICC) for the number of STS transitions were 0.79 (95% confidence interval, 0.70–0.86, p < 0.001), for mean angular velocity—0.81 (95% ci, 0.72–0.87, p < 0.001), and maximal angular velocity—0.73 (95% ci, 0.61–0.82, p < 0.001), respectively. Day-to-day ICCs were 0.63–0.72 for number of STS transitions (95% ci, 0.49–0.81, p < 0.001) and for mean angular velocity—0.75–0.80 (95% ci, 0.64–0.87, p < 0.001). Minimum detectable change (MDC) was 20.1 transitions/day for volume, 9.7°/s for mean intensity, and 31.7°/s for maximal intensity. (4) Conclusions: The volume and intensity of STS transitions monitored by a thigh-worn accelerometer and a sit-to-stand transitions algorithm are reproducible from day to day and year to year. The accelerometer can be used to reliably study STS transitions in free-living environments, which could add value to identifying individuals at increased risk for functional disability.
Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals
Authors: Nieto-del-Amor Félix , Beskhani Raja , Ye-Lin Yiyao , Garcia-Casado Javier , Diaz-Martinez Alba , Monfort-Ortiz Rogelio , Diago-Almela Vicente Jose , Hao Dongmei , Prats-Boluda Gema
Published: September 2021

One of the remaining challenges for the scientific-technical community is predicting preterm births, for which electrohysterography (EHG) has emerged as a highly sensitive prediction technique. Sample and fuzzy entropy have been used to characterize EHG signals, although they require optimizing many internal parameters. Both bubble entropy, which only requires one internal parameter, and dispersion entropy, which can detect any changes in frequency and amplitude, have been proposed to characterize biomedical signals. In this work, we attempted to determine the clinical value of these entropy measures for predicting preterm birth by analyzing their discriminatory capacity as an individual feature and their complementarity to other EHG characteristics by developing six prediction models using obstetrical data, linear and non-linear EHG features, and linear discriminant analysis using a genetic algorithm to select the features. Both dispersion and bubble entropy better discriminated between the preterm and term groups than sample, spectral, and fuzzy entropy. Entropy metrics provided complementary information to linear features, and indeed, the improvement in model performance by including other non-linear features was negligible. The best model performance obtained an F1-score of 90.1 ± 2% for testing the dataset. This model can easily be adapted to real-time applications, thereby contributing to the transferability of the EHG technique to clinical practice.