Miracle-dz/UniFAR-FTUs huggingface dataset:
A virtual geographic environment for dynamic simulation and analysis of tailings dam failure

ABSTRACT It is of great significance for disaster prevention and mitigation to carry out disaster simulations for dam failure accidents in advance, but at present, there are few professional systems for disaster simulations of tailings dams. In this paper, we focused on the construction of a virtual geographic environment (VGE) system that provides an effective tool for visualizing the dam-break process of a tailings pond. The dam-break numerical model of the tailings dam based on computational fluid dynamics (CFD) was integrated into the VGE system. The infrastructure of the VGE was supported by a 3-D geographic information system (GIS) with a user-friendly interface for the initiation, visualization, and analysis of the dynamic process of tailings dam failure. Key technologies, including the integration of numerical models, rendering of large-scale scenes, and optimizations of disaster simulation and visualization, were discussed in detail. In the prototype system, information on the run-out path, travel distance, etc. can be obtained to visually describe the flow motion released by two dam failure cases. The simulation results showed that the VGE can be used for the multidimensional, dynamic and interactive visualization of dam-break disasters, and can also be useful for assessing the risk associated with tailings dams.
Teacher–student relationships and academic achievement in Confucian educational countries/systems from PISA 2012 perspectives

Abstract Classroom climate in seven Confucian Asian countries/systems was examined with respect to three dimensions: teachers’ affective qualities, behavioural-classroom management style and cognitive-pedagogical approaches. The results based on PISA 2012 data showed that there were substantial variations in classroom climate across the seven Confucian countries/systems, with Shanghai and Singapore showing the most positive climate; Korea and Japan showing the least positive; and Hong Kong, Macao and Taipei being in-between. The latent profile analysis further revealed the existence of four classes: ‘Low Cognitive-Pedagogical Approaches’ (Class 1), ‘The OECD Averages’ (Class 2), ‘High Affective Qualities’ (Class 3) and ‘High Cognitive-Pedagogical Approaches’ (Class 4). While disciplinary climate was the strongest predictor of students’ mathematics performance, consistently across all countries/system, Class 3 characterised by high affective qualities showed the highest mathematics performance. The findings suggest that both discipline and teacher support are the important and meaningful dimensions of classroom climate for Confucian Asian students.
Chemical changes in curing and ageing oil paints

A chemical and physical description is given of different processes in the development of an oil paint film: curing, maturing, and degradation. A transition from the initial polyester to an ionometric system during the maturation process as a result of hydrolysis is proposed. Analytical results describe to what extent the transition from a polyester to apolyanionic ionomer has occurred for a number of aged paint samples. High degrees of hydrolysis (65) are found in samples from 100-250-year-old paintings.
Inertial normal S-type Tseng's extragradient algorithm for solution of variational inequality problems

In this paper, we introduce inertial Tseng’s extragradient algorithms combined with normal-S iteration process for solving variational inequality problems involving pseudo-monotone and Lipschitz continuous operators. Under mild conditions, we establish the weak convergence results in Hilbert spaces. Numerical examples are also presented to show that faster behaviour of the proposed method.
Trust mechanisms underlying the self-efficacy-rumour use relationship

Purpose Journalism students, a special user group with the dual perspective of both social media general users and online journalists, and their trust in rumours is a valued but understudied topic in relation to preparing rational information users and professionals for rumour control. To reveal these trust mechanisms, this paper aims to identify salient psychological and behavioural factors related to journalism students’ different levels of trust. Design/methodology/approach Using structural equation modelling to analyse the survey data of 234 journalism students, this paper tested a theoretical model that considers self-efficacy and the expressive and consumptive use of social media rumours as the antecedents and consequences of trust belief and trust action, respectively. Findings Self-efficacy has a positive effect on trust belief but a negative effect on trust action. Trust belief positively affects expressive use of rumours, whereas trust action negatively affects consumptive use. Practical implications This study contributes to the cultivation of future online news gatekeepers. Originality/value This paper distinguishes journalism students’ trust mechanisms from those of general users and online journalists. The integration of dual process theories provides insights into trust-building processes related to rumours and advances the understanding of the anchoring and adjustment effects of self-efficacy on trust.
Water Ice Detection Research in Utopia Planitia Based on Simulation of Mars Rover Full-Polarimetric Subsurface Penetrating Radar

The probe of China’s first Mars exploration mission, Tianwen-1, has been successfully launched. It will carry out scientific exploration on the topography, soil characteristics, water ice, climate, ionosphere, and physical fields of Mars. Different from other rovers landing on the moon and Mars, the Zhurong rover is equipped with a full polarimetric subsurface penetrating radar (FP-SPR) system for the first time. The radar’s mission is to depict the shallow subsurface structure of Mars and search for possible water ice. Therefore, in this paper, a 3D realistic structure model is established and numerically simulated based on the possible subsurface structure of Utopia Planitia (the landing area). Influencing factors such as topographical fluctuations, rocks, water ice, and the variation of dielectric constant of different layers are added to the model. The analysis of the acquired FP-SPR data set shows that the two-dimensional principal component analysis (2D-PCA) method can extract effective reflected signals from the radar data with noise interference and improve the data quality. These clearly imaged targets may be water ice blocks, so the application of 2D-PCA to FP-SPR data increases the imaging quality of suspected water ice targets. The results of this paper are the basis for future processing of the measured FP-SPR data on Mars, which will help to identify more details of subsurface structures.
Change Capsule Network for Optical Remote Sensing Image Change Detection

Change detection based on deep learning has made great progress recently, but there are still some challenges, such as the small data size in open-labeled datasets, the different viewpoints in image pairs, and the poor similarity measures in feature pairs. To alleviate these problems, this paper presents a novel change capsule network by taking advantage of a capsule network that can better deal with the different viewpoints and can achieve satisfactory performance with small training data for optical remote sensing image change detection. First, two identical non-shared weight capsule networks are designed to extract the vector-based features of image pairs. Second, the unchanged region reconstruction module is adopted to keep the feature space of the unchanged region more consistent. Third, vector cosine and vector difference are utilized to compare the vector-based features in a capsule network efficiently, which can enlarge the separability between the changed pixels and the unchanged pixels. Finally, a binary change map can be produced by analyzing both the vector cosine and vector difference. From the unchanged region reconstruction module and the vector cosine and vector difference module, the extracted feature pairs in a change capsule network are more comparable and separable. Moreover, to test the effectiveness of the proposed change capsule network in dealing with the different viewpoints in multi-temporal images, we collect a new change detection dataset from a taken-over Al Udeid Air Basee (AUAB) using Google Earth. The results of the experiments carried out on the AUAB dataset show that a change capsule network can better deal with the different viewpoints and can improve the comparability and separability of feature pairs. Furthermore, a comparison of the experimental results carried out on the AUAB dataset and SZTAKI AirChange Benchmark Set demonstrates the effectiveness and superiority of the proposed method.
Brain, cognition and physically active lifestyle in healthy ageing

Strömmer, Juho Brain, cognition and physically active lifestyle in healthy ageing Jyväskylä: University of Jyväskylä, 2018, 59 p. (Jyväskylä Studies in Education, Psychology and Social Research ISSN 0075-4625; 615) ISBN 978-951-39-7445-9 (nid.) ISBN 978-951-39-7446-6 (PDF) Normal ageing is accompanied by profound changes in brain structure and declines in the automatic processing of sensory stimuli, which in turn lead to altered cognitive performance. However, behaviour and lifestyle factors, such as physical activity, may help to maintain the brain’s structural connectivity and function, as well as cognitive functioning in old age. Study I examined the effects of age on the brain’s capability to detect changes in somatosensory stimuli by recording event-related potentials (ERPs) in response to electrical stimulation of different fingers in young and older adults. The results of Study I show that somatosensory mismatch response (sMMR) was attenuated in amplitude in older subjects as compared to young adults, indicating age-related decline in somatosensory change detection. Study II investigated whether agerelated decline in the change detection mechanism in somatosensory and auditory modalities is associated with age-related alterations in cognitive performance, and whether physical fitness modulates this relationship. The results of Study II showed that the higher the sMMR amplitudes were the better executive functions older adults had. In addition, better aerobic fitness was linked to higher somatosensory ERP amplitudes and to better executive functions. Study III examined whether physical activity mediates the effect of age on the brain’s white matter integrity, and whether, in tracts sensitive to physical activity, this integrity mediates age-related decline in cognitive speed and fluid cognitive capabilities. The results of Study III show that overall daily physical activity mitigates age-related decline of white matter integrity. In addition, physical activity that benefits white matter integrity in the genu of the corpus callosum is associated with reduced ageing-related slowing of reaction times. Overall, these results indicate that age-related changes in the brain’s electrophysiological responses are linked to changes in cognitive performance, and that a physically active lifestyle protects against age-related structural disconnection and cognitive slowing, as good aerobic fitness helps to preserve physiological and executive functions in ageing.
DSDet: A Lightweight Densely Connected Sparsely Activated Detector for Ship Target Detection in High-Resolution SAR Images

Traditional constant false alarm rate (CFAR) based ship target detection methods do not work well in complex conditions, such as multi-scale situations or inshore ship detection. With the development of deep learning techniques, methods based on convolutional neural networks (CNN) have been applied to solve such issues and have demonstrated good performance. However, compared with optical datasets, the number of samples in SAR datasets is much smaller, thus limiting the detection performance. Moreover, most state-of-the-art CNN-based ship target detectors that focus on the detection performance ignore the computation complexity. To solve these issues, this paper proposes a lightweight densely connected sparsely activated detector (DSDet) for ship target detection. First, a style embedded ship sample data augmentation network (SEA) is constructed to augment the dataset. Then, a lightweight backbone utilizing a densely connected sparsely activated network (DSNet) is constructed, which achieves a balance between the performance and the computation complexity. Furthermore, based on the proposed backbone, a low-cost one-stage anchor-free detector is presented. Extensive experiments demonstrate that the proposed data augmentation approach can create hard SAR samples artificially. Moreover, utilizing the proposed data augmentation approach is shown to effectively improves the detection accuracy. Furthermore, the conducted experiments show that the proposed detector outperforms the state-of-the-art methods with the least parameters (0.7 M) and lowest computation complexity (3.7 GFLOPs).
Performance Comparison of Filtering Algorithms for High-Density Airborne LiDAR Point Clouds over Complex LandScapes

Airborne light detection and ranging (LiDAR) technology has become the mainstream data source in geosciences and environmental sciences. Point cloud filtering is a prerequisite for almost all LiDAR-based applications. However, it is challenging to select a suitable filtering algorithm for handling high-density point clouds over complex landscapes. Therefore, to determine an appropriate filter on a specific environment, this paper comparatively assessed the performance of five representative filtering algorithms on six study sites with different terrain characteristics, where three plots are located in urban areas and three in forest areas. The representative filtering methods include simple morphological filter (SMRF), multiresolution hierarchical filter (MHF), slope-based filter (SBF), progressive TIN densification (PTD) and segmentation-based filter (SegBF). Results demonstrate that SMRF performs the best in urban areas, and compared to MHF, SBF, PTD and SegBF, the total error of SMRF is reduced by 1.38%, 48.21%, 48.25% and 31.03%, respectively. MHF outperforms the others in forest areas, and compared to SMRF, SBF, PTD and SegBF, the total error of MHF is reduced by 1.98%, 35.87%, 45.11% and 9.42%, respectively. Moreover, both SMRF and MHF keep a good balance between type I and II errors, which makes the produced DEMs much similar to the references. Overall, SMRF and MHF are recommended for urban and forest areas, respectively, and MHF averagely performs slightly better than SMRF on all areas with respect to kappa coefficient.
Extraction of Sunflower Lodging Information Based on UAV Multi-Spectral Remote Sensing and Deep Learning

The rapid and accurate identification of sunflower lodging is important for the assessment of damage to sunflower crops. To develop a fast and accurate method of extraction of information on sunflower lodging, this study improves the inputs to SegNet and U-Net to render them suitable for multi-band image processing. Random forest and two improved deep learning methods are combined with RGB, RGB + NIR, RGB + red-edge, and RGB + NIR + red-edge bands of multi-spectral images captured by a UAV (unmanned aerial vehicle) to construct 12 models to extract information on sunflower lodging. These models are then combined with the method used to ignore edge-related information to predict sunflower lodging. The results of experiments show that the deep learning methods were superior to the random forest method in terms of the obtained lodging information and accuracy. The predictive accuracy of the model constructed by using a combination of SegNet and RGB + NIR had the highest overall accuracy of 88.23%. Adding NIR to RGB improved the accuracy of extraction of the lodging information whereas adding red-edge reduced it. An overlay analysis of the results for the lodging area shows that the extraction error was mainly caused by the failure of the model to recognize lodging in mixed areas and low-coverage areas. The predictive accuracy of information on sunflower lodging when edge-related information was ignored was about 2% higher than that obtained by using the direct splicing method.
Optimal Solar Zenith Angle Definition for Combined Landsat-8 and Sentinel-2A/2B Data Angular Normalization Using Machine Learning Methods

Data from Landsat-8 and Sentinel-2A/2B are often combined for terrestrial monitoring because of their similar spectral bands. The bidirectional reflectance distribution function (BRDF) effect has been observed in both Landsat-8 and Sentinel-2A/2B reflectance data. However, there is currently no definition of solar zenith angle (θsz) that is suitable for the normalization of the BRDF-adjusted reflectance from the three sensors’ combined data. This paper describes the use of four machine learning (ML) models to predict a global θsz that is suitable for the normalization of bidirectional reflectance from the combined data in 2018. The observed θsz collected globally, and the three locations in the Democratic Republic of Congo (26.622°E, 0.356°N), Texas in the USA (99.406°W 30.751°N), and Finland (25.194°E, 61.653°N), are chosen to compare the performance of the ML models. At a global scale, the ML models of Support Vector Regression (SVR), Multi-Layer Perception (MLP), and Gaussian Process Regression (GPR) exhibit comparably good performance to that of polynomial regression, considering center latitude as the input to predict the global θsz. GPR achieves the best overall performance considering the center latitude and acquisition time as inputs, with a root mean square error (RMSE) of 1.390°, a mean absolute error (MAE) of 0.689°, and a coefficient of determination (R2) of 0.994. SVR shows an RMSE of 1.396°, an MAE of 0.638°, and an R2 of 0.994, following GPR. For a specific location, the SVR and GPR models have higher accuracy than the polynomial regression, with GPR exhibiting the best performance, when center latitude and acquisition time are considered as inputs. GPR is recommended for predicting the global θsz using the three sensors’ combined data.
MAT: GIS-Based Morphometry Assessment Tools for Concave Landforms

The rapid development of remote sensing technology for obtaining high-resolution digital elevation models (DEMs) in recent years has made them more and more widely available and has allowed them to be used for morphometric assessment of concave landforms, such as valleys, gullies, glacial cirques, sinkholes, craters, and others. The aim of this study was to develop a geographic information systems (GIS) toolbox for the automatic extraction of 26 morphometric characteristics, which include the geometry, hypsometry, and volume of concave landforms. The Morphometry Assessment Tools (MAT) toolbox in the ArcGIS software was developed. The required input data are a digital elevation model and the form boundary as a vector layer. The method was successfully tested on an example of 21 erosion-denudation valleys located in the young glacial area of northwest Poland. Calculations were based on elevation data collected in the field and LiDAR data. The results obtained with the tool showed differences in the assessment of the volume parameter at the average level of 12%, when comparing the field data and LiDAR data. The algorithm can also be applied to other types of concave forms, as well as being based on other DEM data sources, which makes it a universal tool for morphometric evaluation.
Modeling of Environmental Impacts on Aerial Hyperspectral Images for Corn Plant Phenotyping

Aerial imaging technologies have been widely applied in agricultural plant remote sensing. However, an as yet unexplored challenge with field imaging is that the environmental conditions, such as sun angle, cloud coverage, temperature, and so on, can significantly alter plant appearance and thus affect the imaging sensor’s accuracy toward extracting plant feature measurements. These image alterations result from the complicated interaction between the real-time environments and plants. Analysis of these impacts requires continuous monitoring of the changes through various environmental conditions, which has been difficult with current aerial remote sensing systems. This paper aimed to propose a modeling method to comprehensively understand and model the environmental influences on hyperspectral imaging data. In 2019, a fixed hyperspectral imaging gantry was constructed in Purdue University’s research farm, and over 8000 repetitive images of the same corn field were taken with a 2.5 min interval for 31 days. Time-tagged local environment data, including solar zenith angle, solar irradiation, temperature, wind speed, and so on, were also recorded during the imaging time. The images were processed for phenotyping data, and the time series decomposition method was applied to extract the phenotyping data variation caused by the changing environments. An artificial neural network (ANN) was then built to model the relationship between the phenotyping data variation and environmental changes. The ANN model was able to accurately predict the environmental effects in remote sensing results, and thus could be used to effectively eliminate the environment-induced variation in the phenotyping features. The test of the normalized difference vegetation index (NDVI) calculated from the hyperspectral images showed that variance in NDVI was reduced by 79%. A similar performance was confirmed with the relative water content (RWC) predictions. Therefore, this modeling method shows great potential for application in aerial remote sensing applications in agriculture, to significantly improve the imaging quality by effectively eliminating the effects from the changing environmental conditions.
Program Optimization for a Pipelined Machine: A Case Study

The Amdahl 580 processor is a pipelined processor whose performance can be affected by characteristics of the instructions it executes. This paper describes certain optimizations made to a set of system software routines during their development. The optimization effort was driven by the execution frequencies of common paths through the programs in question, and by the execution characteristics of those paths, as shown by a processor simulator. Path optimization itself was done with both general program optimization techniques and with techniques specific to the particular characteristics of the 580's pipeline. Overall, the average execution time for these routines was reduced by over 50%.
Rapid Crystallization of Poly(lactic acid) by Using Tailor-Made Oxalamide Derivatives as Novel Soluble-Type Nucleating Agents

The crystallization rate and crystallinity of poly(lactic acid) (PLA) was significantly increased by the incorporation of 0.25–1.0 wt % of tailor-made oxalamide derivatives (NAs). The nucleation effect and nucleation mechanisms of the NAs were studied via diferential scanning calorimetry (DSC), polarized optical microscopy (POM), and wide-angle X-ray diffraction (WAXD) techniques. The experimental results convincingly revealed that the NA molecules are soluble in a PLA melt and are capable of self-organizing into fibrils upon cooling. The fibrils as efficient nucleation sites induced rapid growth of α-form PLA crystal along the fibrils, forming shish-kebab-like structures. In isothermal crystallization, very fine PLA sperrulites with high density were obtained in the presence of NAs. The high nucleation efficiency and the simple synthetic routes of the NAs make them promising to be a new generation of nucleating agents for (bio)polymers, e.g., PLA.