bluuebunny/crossref_metadata_2025 huggingface dataset:
Spring Frost Damage to Tea Plants Can Be Identified with Daily Minimum Air Temperatures Estimated by MODIS Land Surface Temperature Products
Authors: Wang Peijuan , Ma Yuping , Tang Junxian , Wu Dingrong , Chen Hui , Jin Zhifeng , Huo Zhiguo
Published: March 2021

Tea (Camellia sinensis) is one of the most dominant economic plants in China and plays an important role in agricultural economic benefits. Spring tea is the most popular drink due to Chinese drinking habits. Although the global temperature is generally warming, spring frost damage (SFD) to tea plants still occurs from time to time, and severely restricts the production and quality of spring tea. Therefore, monitoring and evaluating the impact of SFD to tea plants in a timely and precise manner is a significant and urgent task for scientists and tea producers in China. The region designated as the Middle and Lower Reaches of the Yangtze River (MLRYR) in China is a major tea plantation area producing small tea leaves and low shrubs. This region was selected to study SFD to tea plants using meteorological observations and remotely sensed products. Comparative analysis between minimum air temperature (Tmin) and two MODIS nighttime land surface temperature (LST) products at six pixel-window scales was used to determine the best suitable product and spatial scale. Results showed that the LST nighttime product derived from MYD11A1 data at the 3 × 3 pixel window resolution was the best proxy for daily minimum air temperature. A Tmin estimation model was established using this dataset and digital elevation model (DEM) data, employing the standard lapse rate of air temperature with elevation. Model validation with 145,210 ground-based Tmin observations showed that the accuracy of estimated Tmin was acceptable with a relatively high coefficient of determination (R2 = 0.841), low root mean square error (RMSE = 2.15 °C) and mean absolute error (MAE = 1.66 °C), and reasonable normalized RMSE (NRMSE = 25.4%) and Nash–Sutcliffe model efficiency (EF = 0.12), with significantly improved consistency of LST and Tmin estimation. Based on the Tmin estimation model, three major cooling episodes recorded in the "Yearbook of Meteorological Disasters in China" in spring 2006 were accurately identified, and several highlighted regions in the first two cooling episodes were also precisely captured. This study confirmed that estimating Tmin based on MYD11A1 nighttime products and DEM is a useful method for monitoring and evaluating SFD to tea plants in the MLRYR. Furthermore, this method precisely identified the spatial characteristics and distribution of SFD and will therefore be helpful for taking effective preventative measures to mitigate the economic losses resulting from frost damage.
Airborne Hyperspectral Data Acquisition and Processing in the Arctic: A Pilot Study Using the Hyspex Imaging Spectrometer for Wetland Mapping
Authors: Cristóbal Jordi , Graham Patrick , Prakash Anupma , Buchhorn Marcel , Gens Rudi , Guldager Nikki , Bertram Mark
Published: March 2021

A pilot study for mapping the Arctic wetlands was conducted in the Yukon Flats National Wildlife Refuge (Refuge), Alaska. It included commissioning the HySpex VNIR-1800 and the HySpex SWIR-384 imaging spectrometers in a single-engine Found Bush Hawk aircraft, planning the flight times, direction, and speed to minimize the strong bidirectional reflectance distribution function (BRDF) effects present at high latitudes and establishing improved data processing workflows for the high-latitude environments. Hyperspectral images were acquired on two clear-sky days in early September, 2018, over three pilot study areas that together represented a wide variety of vegetation and wetland environments. Steps to further minimize BRDF effects and achieve a higher geometric accuracy were added to adapt and improve the Hyspex data processing workflow, developed by the German Aerospace Center (DLR), for high-latitude environments. One-meter spatial resolution hyperspectral images, that included a subset of only 120 selected spectral bands, were used for wetland mapping. A six-category legend was established based on previous U.S. Geological Survey (USGS) and U.S. Fish and Wildlife Service (USFWS) information and maps, and three different classification methods—hybrid classification, spectral angle mapper, and maximum likelihood—were used at two selected sites. The best classification performance occurred when using the maximum likelihood classifier with an averaged Kappa index of 0.95; followed by the spectral angle mapper (SAM) classifier with a Kappa index of 0.62; and, lastly, by the hybrid classifier showing lower performance with a Kappa index of 0.51. Recommendations for improvements of future work include the concurrent acquisition of LiDAR or RGB photo-derived digital surface models as well as detailed spectra collection for Alaska wetland cover to improve classification efforts.
Correction: Schlembach, F., et al. Round Robin Assessment of Radar Altimeter Low Resolution Mode and Delay-Doppler Retracking Algorithms for Significant Wave Height. Remote Sens. 2020, 12, 1254
Authors: Schlembach Florian , Passaro Marcello , Quartly Graham D. , Kurekin Andrey , Nencioli Francesco , Dodet Guillaume , Piollé Jean-François , Ardhuin Fabrice , Bidlot Jean , Schwatke Christian , Seitz Florian , Cipollini Paolo , Donlon Craig
Published: March 2021

Our earlier work on assessment of altimeter significant wave height (SWH) algorithms [...]
Experimental Study on the Thermal Infrared Spectral Variation of Fractured Rock
Authors: Huang Jianwei , Liu Shanjun , Liu Wenfang , Zhang Chunju , Li Shuiping , Yu Min , Wu Lixin
Published: March 2021

Previous studies have shown that thermal infrared radiation (TIR) anomalies occur in the vicinity of fractures that form when a rock is loaded to failure. Different types of fracturing modes correspond to different TIR anomaly trends. However, the spectral features and the mechanisms responsible for the TIR changes in the fracturing stage remain poorly understood. In this paper, experiments involving observations of the thermal infrared spectrum (8.0–13.0 μm) of loaded sandstone during the fracturing stage were conducted under outdoor conditions. The experiment yielded the following results: (1) Different fracturing modes can lead to different trends in the spectral radiance variation; (2) when an extensional fissure appeared on the rock surface, the radiance increased with a local peak in the 8.0–9.7 μm range; (3) when local bulging formed at the surface, the radiance decreased, with a local valley in the 8.0–9.7 μm range. The radiance variation caused by morphologic changes is the combined result of changes in both the temperature and the emissivity. The characteristic waveband corresponding to the reststrahlen features (RF) of quartz was mainly related to the emissivity change. This study provides a preliminary experimental foundation for the detection of crustal surface fractures via satellite-based remote sensing technology.
Earthquake Damage Region Detection by Multitemporal Coherence Map Analysis of Radar and Multispectral Imagery
Authors: Hasanlou Mahdi , Shah-Hosseini Reza , Seydi Seyd Teymoor , Karimzadeh Sadra , Matsuoka Masashi
Published: March 2021

Earth, as humans’ habitat, is constantly affected by natural events, such as floods, earthquakes, thunder, and drought among which earthquakes are considered one of the deadliest and most catastrophic natural disasters. The Iran-Iraq earthquake occurred in Kermanshah Province, Iran in November 2017. It was a 7.4-magnitude seismic event that caused immense damages and loss of life. The rapid detection of damages caused by earthquakes is of great importance for disaster management. Thanks to their wide coverage, high resolution, and low cost, remote-sensing images play an important role in environmental monitoring. This study presents a new damage detection method at the unsupervised level, using multitemporal optical and radar images acquired through Sentinel imagery. The proposed method is applied in two main phases: (1) automatic built-up extraction using spectral indices and active learning framework on Sentinel-2 imagery; (2) damage detection based on the multitemporal coherence map clustering and similarity measure analysis using Sentinel-1 imagery. The main advantage of the proposed method is that it is an unsupervised method with simple usage, a low computing burden, and using medium spatial resolution imagery that has good temporal resolution and is operative at any time and in any atmospheric conditions, with high accuracy for detecting deformations in buildings. The accuracy analysis of the proposed method found it visually and numerically comparable to other state-of-the-art methods for built-up area detection. The proposed method is capable of detecting built-up areas with an accuracy of more than 96% and a kappa of about 0.89 in overall comparison to other methods. Furthermore, the proposed method is also able to detect damaged regions compared to other state-of-the-art damage detection methods with an accuracy of more than 70%.
The Annual Cycling of Nighttime Lights in India
Authors: Hsu Fengchi , Zhizhin Mikhail , Ghosh Tilottama , Elvidge Christopher , Taneja Jay
Published: March 2021

India is known to have unstable power supply, and many locations show an annual cycle in VIIRS Nighttime Light (VNL). In this study, autocorrelation function (ACF) analysis is used to identify the annual cycling in VNL. Two fundamentally different classification techniques are proposed to classify the ACF profile into one of the three arch types, i.e., acyclic, single peak, and dual peak. The results from the two classification techniques are closely compared to verify their output. This analysis is carried out for the entire territory of India in 15 arc second grid cells. The power stability data acquired from the India Human Development Survey (IHDS) and the Electricity Supply Monitoring Initiative (ESMI) are used to verify their relationship to the annual cycling of VNL. To further aide the analysis, land use/land class are accounted for by data from the India National Remote Sensing Center (NRSC). As a result, the contribution of power stability to VNL annual cycling in India is inconclusive due to the limitation of power stability data. Furthermore, other potential factors should be further examined.
A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges
Authors: Delavarpour Nadia , Koparan Cengiz , Nowatzki John , Bajwa Sreekala , Sun Xin
Published: March 2021

The incorporation of advanced technologies into Unmanned Aerial Vehicles (UAVs) platforms have enabled many practical applications in Precision Agriculture (PA) over the past decade. These PA tools offer capabilities that increase agricultural productivity and inputs’ efficiency and minimize operational costs simultaneously. However, these platforms also have some constraints that limit the application of UAVs in agricultural operations. The constraints include limitations in providing imagery of adequate spatial and temporal resolutions, dependency on weather conditions, and geometric and radiometric correction requirements. In this paper, a practical guide on technical characterizations of common types of UAVs used in PA is presented. This paper helps select the most suitable UAVs and on-board sensors for different agricultural operations by considering all the possible constraints. Over a hundred research studies were reviewed on UAVs applications in PA and practical challenges in monitoring and mapping field crops. We concluded by providing suggestions and future directions to overcome challenges in optimizing operational proficiency.
Panchromatic Image Super-Resolution Via Self Attention-Augmented Wasserstein Generative Adversarial Network
Authors: Du Juan , Cheng Kuanhong , Yu Yue , Wang Dabao , Zhou Huixin
Published: March 2021

Panchromatic (PAN) images contain abundant spatial information that is useful for earth observation, but always suffer from low-resolution ( LR) due to the sensor limitation and large-scale view field. The current super-resolution (SR) methods based on traditional attention mechanism have shown remarkable advantages but remain imperfect to reconstruct the edge details of SR images. To address this problem, an improved SR model which involves the self-attention augmented Wasserstein generative adversarial network ( SAA-WGAN) is designed to dig out the reference information among multiple features for detail enhancement. We use an encoder-decoder network followed by a fully convolutional network (FCN) as the backbone to extract multi-scale features and reconstruct the High-resolution (HR) results. To exploit the relevance between multi-layer feature maps, we first integrate a convolutional block attention module (CBAM) into each skip-connection of the encoder-decoder subnet, generating weighted maps to enhance both channel-wise and spatial-wise feature representation automatically. Besides, considering that the HR results and LR inputs are highly similar in structure, yet cannot be fully reflected in traditional attention mechanism, we, therefore, designed a self augmented attention (SAA) module, where the attention weights are produced dynamically via a similarity function between hidden features; this design allows the network to flexibly adjust the fraction relevance among multi-layer features and keep the long-range inter information, which is helpful to preserve details. In addition, the pixel-wise loss is combined with perceptual and gradient loss to achieve comprehensive supervision. Experiments on benchmark datasets demonstrate that the proposed method outperforms other SR methods in terms of both objective evaluation and visual effect.
Super-Resolution Enhancement Method Based on Generative Adversarial Network for Integral Imaging Microscopy
Authors: Alam Md. Shahinur , Kwon Ki-Chul , Erdenebat Munkh-Uchral , Y. Abbass Mohammed , Alam Md. Ashraful , Kim Nam
Published: March 2021

The integral imaging microscopy system provides a three-dimensional visualization of a microscopic object. However, it has a low-resolution problem due to the fundamental limitation of the F-number (the aperture stops) by using micro lens array (MLA) and a poor illumination environment. In this paper, a generative adversarial network (GAN)-based super-resolution algorithm is proposed to enhance the resolution where the directional view image is directly fed as input. In a GAN network, the generator regresses the high-resolution output from the low-resolution input image, whereas the discriminator distinguishes between the original and generated image. In the generator part, we use consecutive residual blocks with the content loss to retrieve the photo-realistic original image. It can restore the edges and enhance the resolution by ×2, ×4, and even ×8 times without seriously hampering the image quality. The model is tested with a variety of low-resolution microscopic sample images and successfully generates high-resolution directional view images with better illumination. The quantitative analysis shows that the proposed model performs better for microscopic images than the existing algorithms.
Spatial Location in Integrated Circuits through Infrared Microscopy
Authors: Abelé Raphaël , Damoiseaux Jean-Luc , Moubtahij Redouane El , Boi Jean-Marc , Fronte Daniele , Liardet Pierre-Yvan , Merad Djamal
Published: March 2021

In this paper, we present an infrared microscopy based approach for structures’ location in integrated circuits, to automate their secure characterization. The use of an infrared sensor is the key device for internal integrated circuit inspection. Two main issues are addressed. The first concerns the scan of integrated circuits using a motorized optical system composed of an infrared uncooled camera combined with an optical microscope. An automated system is required to focus the conductive tracks under the silicon layer. It is solved by an autofocus system analyzing the infrared images through a discrete polynomial image transform which allows an accurate features detection to build a focus metric robust against specific image degradation inherent to the acquisition context. The second issue concerns the location of structures to be characterized on the conductive tracks. Dealing with a large amount of redundancy and noise, a graph-matching method is presented—discriminating graph labels are developed to overcome the redundancy, while a flexible assignment optimizer solves the inexact matching arising from noises on graphs. The resulting automated location system brings reproducibility for secure characterization of integrated systems, besides accuracy and time speed increase.
Printed Textile-Based Ag2O–Zn Battery for Body Conformal Wearable Sensors
Authors: Kota Akash , Gogia Ashish , Neidhard-Doll Amy T. , Chodavarapu Vamsy P.
Published: March 2021

Wearable electronics are playing an important role in the health care industry. Wearable sensors are either directly attached to the body surface or embedded into worn garments. Textile-based batteries can help towards development of body conformal wearable sensors. In this letter, we demonstrate a 2D planar textile-based primary Ag2O–Zn battery fabricated using the stencil printing method. A synthetic polyester woven fabric is used as the textile substrate and polyethylene oxide material is used as the separator. The demonstrated battery achieves an areal capacity of 0.6 mAh/cm2 with an active electrode area of 0.5 cm × 1 cm.
Extracting the Relationship and Evolutionary Rule Connecting Residents’ Travel Demand and Traffic Supply Using Multisource Data
Authors: Wang Zijia , Chen Zhixiang , Shi Youyin , Huang Liping
Published: March 2021

Urban rail transit (URT) systems are often regarded as the backbone of their respective city. The evolutionary features of URT systems have attracted much attention in recent years, but their evolution and their distinct function in contrast to other transit modes have seldom been investigated, especially quantitatively from the perspective of work–residence separation. Accordingly, we propose a framework for exploring the evolution of URT topological networks and demand-weighted networks, comparing the different impacts of all transit modes on work–residence separation. In this study, a URT passenger flow assignment model was formulated on the basis of travel cost function and an improved logit model was proposed that takes into account the heterogeneity of passengers. This model was used to generate a section load, which is regarded as a weight and able to reflect the residents’ demand for travel by URT. Then, the fractal dimensions for a non-weighted network and demand-weighted network are proposed and their indications for transportation explained. Finally, the Beijing Subway System (BSS) is used as a case study by employing fifty years of network data and ten years of smart card data. Using fractal approaches, the different characteristics illustrated by the two networks were investigated and the reasons behind the observed patterns explained. In addition, the spatial features of the rail network, in terms of fractal indictors, were compared with population distribution and urban mobility for all modes, extracted from phone data as a proxy. Thus, the relationship between the residents’ travel demand and traffic supply can be revealed to some extent. The main finding of this work is that demand must be taken into account when analyzing the fractal features of a transport network, lest the demand side be separated from the supply and important issues missed such as inconsistencies between demand and supply. Additionally, the role of rail transit in work–home imbalance can be investigated in the context of urban mobility for an entire city.
Real-Time Human Detection and Gesture Recognition for On-Board UAV Rescue
Authors: Liu Chang , Szirányi Tamás
Published: March 2021

Unmanned aerial vehicles (UAVs) play an important role in numerous technical and scientific fields, especially in wilderness rescue. This paper carries out work on real-time UAV human detection and recognition of body and hand rescue gestures. We use body-featuring solutions to establish biometric communications, like yolo3-tiny for human detection. When the presence of a person is detected, the system will enter the gesture recognition phase, where the user and the drone can communicate briefly and effectively, avoiding the drawbacks of speech communication. A data-set of ten body rescue gestures (i.e., Kick, Punch, Squat, Stand, Attention, Cancel, Walk, Sit, Direction, and PhoneCall) has been created by a UAV on-board camera. The two most important gestures are the novel dynamic Attention and Cancel which represent the set and reset functions respectively. When the rescue gesture of the human body is recognized as Attention, the drone will gradually approach the user with a larger resolution for hand gesture recognition. The system achieves 99.80% accuracy on testing data in body gesture data-set and 94.71% accuracy on testing data in hand gesture data-set by using the deep learning method. Experiments conducted on real-time UAV cameras confirm our solution can achieve our expected UAV rescue purpose.
Tdnn-Based Engine In-Cylinder Pressure Estimation from Shaft Velocity Spectral Representation
Authors: Valencia-Duque Andrés F. , Cárdenas-Peña David A. , Álvarez-Meza Andrés M. , Orozco-Gutiérrez Álvaro A. , Quintero-Riaza Héctor F.
Published: March 2021

Pressure is one of the essential variables to give information about engine condition and monitoring. Direct recording of this signal is complex and invasive, while angular velocity can be measured. Nonetheless, the challenge is to predict the cylinder pressure using the shaft kinematics accurately. In this paper, a time-delay neural network (TDNN), interpreted as a finite pulse response (FIR) filter, is proposed to estimate the in-cylinder pressure of a single-cylinder internal combustion engine (ICE) from fluctuations in shaft angular velocity. The experiments are conducted over data obtained from an ICE operating in 12 different states by changing the angular velocity and load. The TDNN’s delay is adjusted to get the highest possible correlation-based score. Our methodology can predict pressure with an R2 >0.9, avoiding complicated pre-processing steps.
Research on Panoramic Stitching Algorithm of Lateral Cranial Sequence Images in Dental Multifunctional Cone Beam Computed Tomography
Authors: Liu Junyuan , Li Xi , Shen Siwan , Jiang Xiaoming , Chen Wang , Li Zhangyong
Published: March 2021

In the design of dental multifunctional Cone Beam Computed Tomography, the linear scanning strategy not only saves equipment cost, but also avoids the demand for patients to be repositioned when acquiring lateral cranial sequence images. In order to obtain panoramic images, we propose a local normalized cross-correlation stitching algorithm based on Gaussian Mixture Model. Firstly, the Block-Matching and 3D filtering algorithm is used to remove quantum and impulse noises according to the characteristics of X-ray images; Then, the segmentation of the irrelevant region and the extraction of the region of interest are performed by Gaussian Mixture Model; The locally normalized cross-relation is used to complete the registration with the multi-resolution strategy based on wavelet transform and Particle Swarm Optimization algorithm; Finally, image fusion is achieved by the weighted smoothing fusion algorithm. The experimental results show that the panoramic image obtained by this method has significant performance in both subjective vision and objective quality evaluation and can be applied to preoperative diagnosis of clinical dental deformity and postoperative effect evaluation.
Optical Navigation Sensor for Runway Relative Positioning of Aircraft during Final Approach
Authors: Hiba Antal , Gáti Attila , Manecy Augustin
Published: March 2021

Precise navigation is often performed by sensor fusion of different sensors. Among these sensors, optical sensors use image features to obtain the position and attitude of the camera. Runway relative navigation during final approach is a special case where robust and continuous detection of the runway is required. This paper presents a robust threshold marker detection method for monocular cameras and introduces an on-board real-time implementation with flight test results. Results with narrow and wide field-of-view optics are compared. The image processing approach is also evaluated on image data captured by a different on-board system. The pure optical approach of this paper increases sensor redundancy because it does not require input from an inertial sensor as most of the robust runway detectors.
How to Interact with a Fully Autonomous Vehicle: Naturalistic Ways for Drivers to Intervene in the Vehicle System While Performing Non-Driving Related Tasks
Authors: Ataya Aya , Kim Won , Elsharkawy Ahmed , Kim SeungJun
Published: March 2021

Autonomous vehicle technology increasingly allows drivers to turn their primary attention to secondary tasks (e.g., eating or working). This dramatic behavior change thus requires new input modalities to support driver–vehicle interaction, which must match the driver’s in-vehicle activities and the interaction situation. Prior studies that addressed this question did not consider how acceptance for inputs was affected by the physical and cognitive levels experienced by drivers engaged in Non-driving Related Tasks (NDRTs) or how their acceptance varies according to the interaction situation. This study investigates naturalistic interactions with a fully autonomous vehicle system in different intervention scenarios while drivers perform NDRTs. We presented an online methodology to 360 participants showing four NDRTs with different physical and cognitive engagement levels, and tested the six most common intervention scenarios (24 cases). Participants evaluated our proposed seven natural input interactions for each case: touch, voice, hand gesture, and their combinations. Results show that NDRTs influence the driver’s input interaction more than intervention scenario categories. In contrast, variation of physical load has more influence on input selection than variation of cognitive load. We also present a decision-making model of driver preferences to determine the most natural inputs and help User Experience designers better meet drivers’ needs.
Determination of the Influence of Mechanical Properties of Capsules and Seeds on the Susceptibility to Feeding of Mononychus pubctumalbum in Endangered Plant Species Iris aphylla L. and Iris sibirica L.
Authors: Śmigała Magdalena , Winiarczyk Krystyna , Dąbrowska Agnieszka , Domaciuk Marcin , Gancarz Marek
Published: March 2021

The aim of the study was to determine the correlation between the mechanical resistance of iris seed capsules and seeds to Mononychus punctumalbum foraging. The principal component analysis (PCA) demonstrated that the first main component referred to the variety type in 68%, and the second main component described the stage of the ontogenetic development of the plant in 26%. As indicated by the values of each parameter measured, all the parameters were found to exert a strong impact on the variability of the analyzed system. The occurrence of weevil infestation was also strongly but negatively correlated with seed wall thickness and capsule wall thickness. There was a correlation of seed max load and seed mass with the occurrence of the weevil. The analysis of the mechanical resistance of iris seed capsules (in June 9.28 N and September 6.27 N for I. sibirica and in June 6.59 N and September 2.94 N for I. aphylla) and seeds (in June 15.97 N and September 344.90 N for I. sibirica and in June 16.60 N and September 174.46 N for I. aphylla) showed significant differences between the terms and species. The PCA analysis revealed that the first variable was correlated with the occurrence of weevil foraging.
A Few-Shot U-Net Deep Learning Model for COVID-19 Infected Area Segmentation in CT Images
Authors: Voulodimos Athanasios , Protopapadakis Eftychios , Katsamenis Iason , Doulamis Anastasios , Doulamis Nikolaos
Published: March 2021

Recent studies indicate that detecting radiographic patterns on CT chest scans can yield high sensitivity and specificity for COVID-19 identification. In this paper, we scrutinize the effectiveness of deep learning models for semantic segmentation of pneumonia-infected area segmentation in CT images for the detection of COVID-19. Traditional methods for CT scan segmentation exploit a supervised learning paradigm, so they (a) require large volumes of data for their training, and (b) assume fixed (static) network weights once the training procedure has been completed. Recently, to overcome these difficulties, few-shot learning (FSL) has been introduced as a general concept of network model training using a very small amount of samples. In this paper, we explore the efficacy of few-shot learning in U-Net architectures, allowing for a dynamic fine-tuning of the network weights as new few samples are being fed into the U-Net. Experimental results indicate improvement in the segmentation accuracy of identifying COVID-19 infected regions. In particular, using 4-fold cross-validation results of the different classifiers, we observed an improvement of 5.388 ± 3.046% for all test data regarding the IoU metric and a similar increment of 5.394 ± 3.015% for the F1 score. Moreover, the statistical significance of the improvement obtained using our proposed few-shot U-Net architecture compared with the traditional U-Net model was confirmed by applying the Kruskal-Wallis test (p-value = 0.026).
“Reading Pictures Instead of Looking”: RGB-D Image-Based Action Recognition via Capsule Network and Kalman Filter
Authors: Zhao Botong , Wang Yanjie , Su Keke , Ren Hong , Sun Haichao
Published: March 2021

This paper proposes an action recognition algorithm based on the capsule network and Kalman filter called “Reading Pictures Instead of Looking” (RPIL). This method resolves the convolutional neural network’s over sensitivity to rotation and scaling and increases the interpretability of the model as per the spatial coordinates in graphics. The capsule network is first used to obtain the components of the target human body. The detected parts and their attribute parameters (e.g., spatial coordinates, color) are then analyzed by Bert. A Kalman filter analyzes the predicted capsules and filters out any misinformation to prevent the action recognition results from being affected by incorrectly predicted capsules. The parameters between neuron layers are evaluated, then the structure is pruned into a dendritic network to enhance the computational efficiency of the algorithm. This minimizes the dependence of in-depth learning on the random features extracted by the CNN without sacrificing the model’s accuracy. The association between hidden layers of the neural network is also explained. With a 90% observation rate, the OAD dataset test precision is 83.3%, the ChaLearn Gesture dataset test precision is 72.2%, and the G3D dataset test precision is 86.5%. The RPILNet also satisfies real-time operation requirements (>30 fps).
Virtual Reality Customized 360-Degree Experiences for Stress Relief
Authors: Vaquero-Blasco Miguel A. , Perez-Valero Eduardo , Morillas Christian , Lopez-Gordo Miguel A.
Published: March 2021

The latest studies in virtual reality (VR) have evidenced the potential of this technology to reproduce environments from multiple domains in an immersive way. For instance, in stress relief research, VR has been presented as a portable and inexpensive alternative to chromotherapy rooms, which require an adapted space and are expensive. In this work, we propose a portable and versatile alternative to the traditional chromotherapy color-loop treatment through four different 360-degree virtual experiences. A group of 23 healthy participants (mean age 22.65 ± 5.48) were conducted through a single-session experience divided into four phases while their electroencephalography (EEG) was recorded. First, they were stressed via the Montreal imaging stress task (MIST), and then relaxed using our VR proposal. We applied the Wilcoxon test to evaluate the relaxation effect in terms of the EEG relative gamma and self-perceived stress surveys. The results that we obtained validate the effectiveness of our 360-degree proposal to significantly reduce stress (p-value = 0.0001). Furthermore, the participants deemed our proposal comfortable and immersive (score above 3.5 out of 5). These results suggest that 360-degree VR experiences can mitigate stress, reduce costs, and bring stress relief assistance closer to the general public, like in workplaces or homes.
Integrated Geochemical Assessment of Soils and Stream Sediments to Evaluate Source-Sink Relationships and Background Variations in the Parauapebas River Basin, Eastern Amazon
Authors: Salomão Gabriel Negreiros , Farias Danielle de Lima , Sahoo Prafulla Kumar , Dall’Agnol Roberto , Sarkar Dibyendu
Published: March 2021

This study aims to handle an integrated evaluation of soil and stream sediment geochemical data to evaluate source apportionment and to establish geochemical threshold variations for Fe, Al, and 20 selected Potentially Toxic Elements (PTE) in the Parauapebas River Basin (PB), Eastern Amazon. The data set used in this study is from the Itacaiúnas Geochemical Mapping and Background Project (ItacGMBP), which collected 364 surface soil (0–10 cm) samples and 189 stream sediments samples in the entire PB. The <0.177 mm fraction of these samples were analyzed for 51 elements by ICP-MS and ICP-AES, following an aqua regia digestion. The geochemical maps of many elements revealed substantial differences between the north (NPB) and the south (SPB) of PB, mainly due to the geological setting. The new statistically derived threshold values of the NPB and SPB regions were compared to the threshold of the whole PB, reported in previous studies, and to quality guidelines proposed by Brazilian environmental agencies. The natural variation of geochemical background in soils and stream sediments of PB should be considered prior to defining new guideline values. At the regional scale, the local anomalies are mostly influenced by the predominant lithology rather than any anthropogenic impact.
Developing a Thermally Stable Ester-Based Drilling Fluid for Offshore Drilling Operations by Using Aluminum Oxide Nanorods
Authors: Ahmed Alaa , Sharifi Haddad Amin , Rafati Roozbeh , Bashir Ahmed , AlSabagh Ahmed M. , Aboulrous Amany A.
Published: March 2021

Esters were found to be promising alternatives to oil, as a constituent of drilling fluids, due to their biodegradability and bioaccumulation attributes. In this study, we used ethyl octanoate ester (EO) as a low molecular weight synthetic oil for formulating an ester-based drilling fluid (EBDF). Aluminum oxide nanorods (nanoparticles) were introduced as a Pickering emulsion stabilizer. Like the commercial emulsifiers, they showed that they stabilized the invert emulsion drilling fluid in our study. The rheological and filtration properties of the EBDF were tested at normal pressure and three temperatures: low temperature deepwater (LT) conditions of 2.6 °C, normal pressure and normal temperature (NPNT) conditions of 26.8 °C, and elevated temperature conditions of 70 °C. To enhance the stability and filtration properties of the drilling fluid, aluminum oxide nanoparticles (NPs) were used. An optimum concentration of 1 wt% was found to provide superior rheological performance and higher stability than samples without NPs at NPNT, LT, and elevated temperature conditions. Steadier gel rheology was exhibited at elevated temperature conditions, and a slow rate of an increasing trend occurred at the lower temperatures, with increasing NP concentrations up to 1.5 wt%. Filtration loss tests presented a reduction of fluid loss with increasing the NP concentration. The results demonstrate that a reduction of up to 45% was achieved with the addition of 1 wt% NP. These results show that nano-enhancement of ethyl octanoate drilling fluids would suffice to provide a wider range of operational temperatures for deepwater drilling operations by providing better thermal stability at elevated temperatures and maintaining stability at lower temperatures.
Research on Multi-Timescale Coordinated Method for Source-Grid-Load with Uncertain Renewable Energy Considering Demand Response
Authors: Ning Jia , Hao Sipeng , Zeng Aidong , Chen Bin , Tang Yi
Published: March 2021

The high penetration of renewable energy brings great challenges to power system operation and scheduling. In this paper, a multi-timescale coordinated method for source-grid-load is proposed. First, the multi-timescale characteristics of wind forecasting power and demand response (DR) resources are described, and the coordinated framework of source-grid-load is presented under multi-timescale. Next, economic scheduling models of source-grid-load based on multi-timescale DR under network constraints are established in the process of day-ahead scheduling, intraday scheduling, and real-time scheduling. The loads are classified into three types in terms of different timescale. The security constraints of grid side and time-varying DR potential are considered. Three-stage stochastic programming is employed to schedule resources of source side and load side in day-ahead, intraday, and real-time markets. The simulations are performed in a modified Institute of Electrical and Electronics Engineers (IEEE) 24-node system, which shows a notable reduction in total cost of source-grid-load scheduling and an increase in wind accommodation, and their results are proposed and discussed against under merely two timescales, which demonstrates the superiority of the proposed multi-timescale models in terms of cost and demand response quantity reduction.
Laboratory Evaluation on Performance of Recycled Asphalt Binder and Mixtures under Short-Term Aging Conditions
Authors: Zhu Yuefeng , Zhang Jiawei , Si Chundi , Yan Tao , Li Yanwei
Published: March 2021

As asphalt materials are exposed to very high temperatures before construction, such as in the transportation stage or the storage stage, short-term aging of asphalt material occurs. At these stages, diffusion or blending between RAP (reclaimed asphalt pavement) binder and virgin binder may occur. In this study, recycled blends, incorporating SBS modified binder, RAP binder and recycling agents, were prepared with incremental RAP binders of up to 40%, and RTFO (Rolling Thin-Film Oven) tests in condition times of 300 and 600 min were conducted on the recycled blends. Characterization tests included ΔTcr, complex modulus master curve, a G-R (Glover-Rowe) parameter on recycled blends, and dynamic modulus, fracture test, and midpoint bending fatigue tests on mixtures. The ΔTcr and the G-R parameter results showed that aging time significantly affected the cracking resistance of the recycled blends. Compared to the virgin SBS modified asphalt binder, the recycled blends tended to be more sensitive to the aging process. The complex modulus master curve of binders and the dynamic modulus and phase angle results of mixtures show that the binder/mixtures appear to be stiffer with an increase in the RAP binder dosage. Generally, the low temperature cracking and fatigue cracking resistance of virgin mixtures is better than that of RAP mixtures, especially for high RAP binder dosage mixtures, and longer aging times have a negative impact on the cracking resistance of mixture. However, when we extend RTFO aging time, the higher dosage of RAP mixtures show better cracking resistance than the lower dosage of RAP mixtures. The reason for this could be that the chemical process may occur between the virgin SBS modified asphalt binder and the RAP binder at high temperatures.
Applying Social Learning to Climate Communications—Visualising ‘People Like Me’ in Air Pollution and Climate Change Data
Authors: Fogg-Rogers Laura , Hayes Enda , Vanherle Kris , Pápics Péter I. , Chatterton Tim , Barnes Jo , Slingerland Stephan , Boushel Corra , Laggan Sophie , Longhurst James
Published: March 2021

Technological approaches to carbon emission and air pollution data modelling consider where the issues are located and what is creating emissions. This paper argues that more focus should be paid to people—the drivers of vehicles or households burning fossil fuels (‘Who’) and the reasons for doing so at those times (‘Why’). We applied insights from social psychology (social identity theory and social cognitive theory) to better understand and communicate how people’s everyday activities are a cause of climate change and air pollution. A new method for citizen-focused source apportionment modelling and communication was developed in the ClairCity project and applied to travel data from Bristol, U.K. This approach enables understanding of the human dimension of vehicle use to improve policymaking, accounting for demographics (gender or age groups), socio-economic factors (income/car ownership) and motives for specific behaviours (e.g., commuting to work, leisure, shopping, etc.). Tailored communications for segmented in-groups were trialled, aiming to connect with group lived experiences and day-to-day behaviours. This citizen-centred approach aims to make groups more aware that ‘people like me’ create emissions, and equally, ‘people like me’ can take action to reduce emissions.
Adapting Social Impact Assessment to Flood Risk Management
Authors: Aznar-Crespo Pablo , Aledo Antonio , Melgarejo-Moreno Joaquín , Vallejos-Romero Arturo
Published: March 2021

In the context of climate change, a significant increase in the flood risk is expected, which may lead to an intensification of the social impacts of disasters. Social impacts significantly affect the recovery processes of individuals, social groups, and institutions in the medium and long term. Hence, the management of such impacts throughout the disaster life cycle is essential. International institutions and frameworks for disaster risk reduction have claimed the need to generate tools for the systematic assessment and management of social impacts of floods. Recently, an innovative line of research has emerged aimed at adapting social impact assessment (SIA), usually directed at the evaluation of planned interventions (programs, plans and projects), to the field of environmental disasters. In order to contribute to academic efforts in this emerging field, this paper puts forward, through a systematic literature review based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria, an SIA methodological proposal for the identification, assessment and systematic management of the social impacts of flood events. This methodological proposal covers the three phases of the disaster cycle: (1) pre-event (preparedness), allowing the anticipation of potential impacts and supporting the proposal of preventive measures; (2) event (response), facilitating a strategic mobilization of resources and technical support towards previously identified critical disaster areas; and (3) post-event (recovery), evaluating the evolutionary dynamics of impacts, proposing measures to avoid their socio-territorial embedding and accelerating recovery processes. This tool is designed for strategic use by policy makers and managers responsible for flood risk management and regional development.
Trajectories of Victimization and Bullying at University: Prevention for a Healthy and Sustainable Educational Environment
Authors: Gómez-Galán José , Lázaro-Pérez Cristina , Martínez-López José Ángel
Published: March 2021

The bullying phenomenon is both an uncomfortable reality and a serious social problem for educational community institutions. Its presence is incompatible with a healthy and sustainable education environment. The educational, psychological, and social consequences of bullying transcend the personal sphere and reach the family and work environment in adulthood. Traditionally, bullying has been studied in the compulsory educational stages. However, at present, this problem is also being addressed in higher education. The present research, which is of a transversal nature, aims to explore bullying in the Spanish university setting, along with its typology, scope, and predictive factors, from both socio-demographic and family perspectives. In this sense, we set ourselves the following objectives: (GO1) to see if university students are exposed to bullying, as well as to identify the profiles of the subjects of bullying from three dimensions (physical, relational, and verbal victimization) and (GO2) to determine whether there are trajectories of bullying in university students from the compulsory education stage to the present. Based on a descriptive quantitative methodology, this study was conducted in 10 Spanish universities. The research’s major results show that the persistence of bullying in university classrooms is of a relational and verbal nature, but is not physical. Victimization occurs mainly in women who carry out studies linked to social and legal sciences or art and the humanities. Additionally, it is observed that a spiral of relational violence is produced, perpetuating this type of aggression over time. Among other predictive factors in university students is that they have suffered relational violence during their compulsory education. These data should alert educational and health institutions about the persistence of bullying in university students to prevent it and to facilitate its early detection and treatment to eradicate this problem from higher education classrooms.
Skilled Migrants in the Swedish Labour Market: An Analysis of Employment, Income and Occupational Status
Authors: Irastorza Nahikari , Bevelander Pieter
Published: March 2021

In a globalised world with an increasing division of labour, the competition for highly skilled individuals—regardless of their origin—is growing, as is the value of such individuals for national economies. Yet the majority of studies analysing the economic integration of immigrants shows that those who are highly skilled also have substantial hurdles to overcome: their employment rates and salaries are lower and they face a higher education-to-occupation mismatch compared to highly skilled natives. This paper contributes to the paucity of studies on the employment patterns of highly skilled immigrants to Sweden by providing an overview of the socio-demographic characteristics, labour-market participation and occupational mobility of highly educated migrants in Sweden. Based on a statistical analysis of register data, we compare their employment rates, salaries and occupational skill level and mobility to those of immigrants with lower education and with natives. The descriptive analysis of the data shows that, while highly skilled immigrants perform better than those with a lower educational level, they never catch up with their native counterparts. Our regression analyses confirm these patterns for highly skilled migrants. Furthermore, we find that reasons for migration matter for highly skilled migrants’ employment outcomes, with labour migrants having better employment rates, income and qualification-matched employment than family reunion migrants and refugees.