bluuebunny/crossref_metadata_2025 huggingface dataset:
Sterilization Induced Changes in Polypropylene-Based Ffp2 Masks
Authors: Richaud Emmanuel , Ferry Muriel , Carpentier Floriane , Rouif Sophie , Cortella Laurent , Esnouf Stéphane
Published: December 2021

In the context of the SARS-CoV2 pandemic and because of the surgical and FFP2 mask (equivalent to the American N95 masks) shortages, studies on efficient sterilization protocols were initiated. As sterilization using irradiation is commonly used in the medical field, this method was among those that were evaluated. In this work, we tested irradiation under vacuum and under air (under both γ-rays and e-beams), but also, for acceptance purposes, undertook washing prior to the e-beam irradiation sterilization process. This article deals with the modifications induced by the sterilization processes at the molecular and the macromolecular scales on an FFP2 mask. Fourier transform infrared spectroscopy in attenuated total reflectance mode, size-exclusion chromatography and thermal-desorption–gas chromatography–mass spectrometry were used to characterize possible damage to the materials. It appeared that the modifications induced by the different sterilization processes under vacuum were relatively tenuous and became more significant when irradiation was performed using γ-rays under air.
Clinical Evaluation of Reduced-Thickness Monolithic Lithium-Disilicate Crowns: One-Year Follow-Up Results
Authors: Špehar Davor , Jakovac Marko
Published: December 2021

Purpose: The purpose of this in vivo study was to investigate whether the less invasive approach (reduced thickness of the restoration) will result in a comparable risk of failure and similar aesthetic results, compared to conventional layered full porcelain crowns, and can, therefore, be used as a good alternative. Material and Methods: The tested ceramic was lithium-disilicate ceramic (IPS e.max). Forty-four patients with endodontically treated premolars or molars were randomized into two groups and provided with single crowns. One group received conventional all-ceramic crowns made from a lithium-disilicate core and hand-veneered aesthetic ceramic, while another group received full-contoured lithium-disilicate ceramic crowns with reduced wall thickness than manufactures recommendations. The teeth for conventional crowns were prepared with 1 mm rounded shoulder and 2 mm occlusal reduction, while teeth for monolithic crowns were prepared with 0.6 mm wide rounded shoulder and 1 mm occlusal reduction. All crowns were prepared by the same clinician and manufactured in the same laboratory by the same technician. The survival and aesthetics of the crowns were assessed by the independent clinician. Apart from this, patients’ aesthetic satisfaction was evaluated. The assessment was double blind as both the examiner and the patients did not know which type of crown was provided. The observation period was 36 months. Survival of the crowns was assessed using the modified United States Public Health Service (USPHS) criteria and aesthetics and participants’ aesthetic satisfaction with the crowns was evaluated using a visual analogue scale. Results: The one-year survival rate for layered crowns was 100% and for monolithic crowns 95.5%. The median patients’ aesthetic satisfaction with both crowns was 100%. Conclusions: The results indicate similar one-year survival rate of reduced-thickness monolithic lithium-disilicate crowns and conventional veneered crowns. Differences with patients’ satisfaction with the aesthetics of both crowns were not statistically significant and it can be said that the patients’ aesthetic satisfaction was the same for both crowns.
Casimir Effect between Superconducting Plates in the Mixed State
Authors: Inui Norio
Published: December 2021

The Casimir effect between type-II superconducting plates in the coexisting phase of a superconducting phase and a normal phase is investigated. The dependence of the optical conductivity of the superconducting plates on the external magnetic field is described in terms of the penetration depth of the incident electromagnetic field, and the permittivity along the imaginary axis is represented by a linear combination of the permittivities for the plasma model and Drude models. The characteristic frequency in each model is determined using the force parameters for the motion of the magnetic field vortices. The Casimir force between parallel YBCO plates in the mixed state is calculated, and the dependence on the applied magnetic field and temperature is considered.
Introduction to Special Issue, Music in World Religions: A Response to Isabel Laack
Authors: MacLachlan Heather
Published: December 2021

This article serves to introduce a special issue of Religions, titled Music in World Religions. A 2015 article by religion scholar Isabel Laack claimed that the study of music and religion has been neglected by Laack’s peers in the field of religions. Responding to Laack, I argue that scholars of music have been making important contributions to the study of music and religion and, indeed, have been addressing the twelve specific topics she highlights for decades. After summarizing academic works which respond to Laack’s twelve categories of inquiry, I introduce each of the articles in this special issue, showing that each of these also address the gap in the literature that Laack perceived. Ultimately, I argue that transdisciplinarity in the study of music and religion is alive and well, and is exemplified both by historic writings and by those contained in Music in World Religions.
The Influence of Dietary Intervention in Connective Tissue Diseases: Evidence from Randomized Clinical Trials
Authors: Oliviero Francesca , Galozzi Paola , Zanatta Elisabetta , Gatto Mariele , Spinella Paolo , Doria Andrea
Published: December 2021

The aim of this review is to identify and discuss randomized clinical trials conducted in patients with connective tissue diseases, including systemic lupus erythematosus, idiopathic inflammatory myopathies, vasculitis, Sjögren’s syndrome, and systemic sclerosis. Although limited, the results obtained with bioactive compounds, namely n-3 polyunsaturated and short-chain fatty acids, demonstrate that dietary intervention and nutritional counseling might have an important role as adjuvant therapy in patients with connective tissue diseases, particularly in the light of the comorbidities which characterize these conditions.
An Unusual Case of “Conjugal” Polymyalgia Rheumatica after SARS-CoV-2 Vaccination
Authors: Vanni Elena , Ciaffi Jacopo , Mancarella Luana , Ursini Francesco
Published: December 2021

The rare occurrence of polymyalgia rheumatica (PMR) in married couples has been reported in the literature. Susceptibility to PMR is contributed by genetic and environmental factors and cases of PMR developing after influenza vaccine have also been described, in a debated phenomenon known as ‘ASIA’ syndrome. We report the case of two cohabitating married patients developing PMR few weeks after the first dose of ChAdOx1-S SARS-CoV-2 vaccine. Both patients presented with typical symptoms suggestive of PMR. Laboratory findings and ultrasound examination confirmed the diagnosis. Glucocorticoid therapy led to rapid improvment of symptoms. Anti-receptor-binding domain IgG titre was tested and, eight weeks after vaccination, both patients showed no antibody response. It has been suggested that vaccines might trigger autoimmune or inflammatory states in predisposed individuals and various hypotheses have been made regarding the pathogenesis of PMR. Although the causative effect of vaccines cannot be determined, the close temporal correlation observed in our case supports the potential role of environmental factors in triggering the onset of PMR. However, the literature indicates that post-COVID19 vaccination immune-mediated or inflammatory adverse events are extremely rare and vaccination should be encouraged since the benefit largely outweighs possible risks.
A Robust Rigid Registration Framework of 3D Indoor Scene Point Clouds Based on RGB-D Information
Authors: Zhong Saishang , Guo Mingqiang , Lv Ruina , Chen Jianguo , Xie Zhong , Liu Zheng
Published: December 2021

Rigid registration of 3D indoor scenes is a fundamental yet vital task in various fields that include remote sensing (e.g., 3D reconstruction of indoor scenes), photogrammetry measurement, geometry modeling, etc. Nevertheless, state-of-the-art registration approaches still have defects when dealing with low-quality indoor scene point clouds derived from consumer-grade RGB-D sensors. The major challenge is accurately extracting correspondences between a pair of low-quality point clouds when they contain considerable noise, outliers, or weak texture features. To solve the problem, we present a point cloud registration framework in view of RGB-D information. First, we propose a point normal filter for effectively removing noise and simultaneously maintaining sharp geometric features and smooth transition regions. Second, we design a correspondence extraction scheme based on a novel descriptor encoding textural and geometry information, which can robustly establish dense correspondences between a pair of low-quality point clouds. Finally, we propose a point-to-plane registration technology via a nonconvex regularizer, which can further diminish the influence of those false correspondences and produce an exact rigid transformation between a pair of point clouds. Compared to existing state-of-the-art techniques, intensive experimental results demonstrate that our registration framework is excellent visually and numerically, especially for dealing with low-quality indoor scenes.
Pose Normalization of Indoor Mapping Datasets Partially Compliant with the Manhattan World Assumption
Authors: Hübner Patrick , Weinmann Martin , Wursthorn Sven , Hinz Stefan
Published: December 2021

Due to their great potential for a variety of applications, digital building models are well established in all phases of building projects. Older stock buildings however frequently lack digital representations, and creating these manually is a tedious and time-consuming endeavor. For this reason, the automated reconstruction of building models from indoor mapping data has arisen as an active field of research. In this context, many approaches rely on simplifying suppositions about the structure of buildings to be reconstructed such as, e.g., the well-known Manhattan World assumption. This however not only presupposes that a given building structure itself is compliant with this assumption, but also that the respective indoor mapping dataset is aligned with the coordinate axes. Indoor mapping systems, on the other hand, typically initialize the coordinate system arbitrarily by the sensor pose at the beginning of the mapping process. Thus, indoor mapping data need to be transformed from the local coordinate system, resulting from the mapping process, to a local coordinate system where the coordinate axes are aligned with the Manhattan World structure of the building. This necessary preprocessing step for many indoor reconstruction approaches is also frequently known as pose normalization. In this paper, we present a novel pose-normalization method for indoor mapping point clouds and triangle meshes that is robust against large portions of the indoor mapping geometries deviating from an ideal Manhattan World structure. In the case of building structures that contain multiple Manhattan World systems, the dominant Manhattan World structure supported by the largest fraction of geometries was determined and used for alignment. In a first step, a vertical alignment orienting a chosen axis to be orthogonal to horizontal floor and ceiling surfaces was conducted. Subsequently, a rotation around the resulting vertical axis was determined that aligned the dataset horizontally with the axes of the local coordinate system. The performance of the proposed method was evaluated quantitatively on several publicly available indoor mapping datasets of different complexity. The achieved results clearly revealed that our method is able to consistently produce correct poses for the considered datasets for different input rotations with high accuracy. The implementation of our method along with the code for reproducing the evaluation is made available to the public.
Detection of Bark Beetle Disturbance at Tree Level Using UAS Multispectral Imagery and Deep Learning
Authors: Minařík Robert , Langhammer Jakub , Lendzioch Theodora
Published: December 2021

This study aimed to examine the potential of convolutional neural networks (CNNs) for the detection of individual trees infested by bark beetles in a multispectral high-resolution dataset acquired by an unmanned aerial system (UAS). We compared the performance of three CNN architectures and the random forest (RF) model to classify the trees into four categories: pines, sbbd (longer infested trees when needles turn yellow), sbbg (trees under green attack) and non-infested trees (sh). The best performance was achieved by the Nez4c3b CNN (kappa 0.80) and Safaugu4c3b CNN (kappa 0.76) using only RGB bands. The main misclassifications were between sbbd and sbbg because of the similar spectral responses. Merging sbbd and sbbg into a more general class of infested trees made the selection of model type less important. All tested model types, including RF, were able to detect infested trees with an F-score of the class over 0.90. Nevertheless, the best overall metrics were achieved again by the Safaugu3c3b model (kappa 0.92) and Nez3cb model (kappa 0.87) using only RGB bands. The performance of both models is comparable, but the Nez model has a higher learning rate for this task. Based on our findings, we conclude that the Nez and Safaugu CNN models are superior to the RF models and transfer learning models for the identification of infested trees and for distinguishing between different infestation stages. Therefore, these models can be used not only for basic identification of infested trees but also for monitoring the development of bark beetle disturbance.
Arctic Tundra Land Cover Classification on the Beaufort Coast Using the Kennaugh Element Framework on Dual-Polarimetric TerraSAR-X Imagery
Authors: A’Campo Willeke , Bartsch Annett , Roth Achim , Wendleder Anna , Martin Victoria S. , Durstewitz Luca , Lodi Rachele , Wagner Julia , Hugelius Gustaf
Published: December 2021

Arctic tundra landscapes are highly complex and are rapidly changing due to the warming climate. Datasets that document the spatial and temporal variability of the landscape are needed to monitor the rapid changes. Synthetic Aperture Radar (SAR) imagery is specifically suitable for monitoring the Arctic, as SAR, unlike optical remote sensing, can provide time series regardless of weather and illumination conditions. This study examines the potential of seasonal backscatter mechanisms in Arctic tundra environments for improving land cover classification purposes by using a time series of HH/HV TerraSAR-X (TSX) imagery. A Random Forest (RF) classification was applied on multi-temporal Sigma Nought intensity and multi-temporal Kennaugh matrix element data. The backscatter analysis revealed clear differences in the polarimetric response of water, soil, and vegetation, while backscatter signal variations within different vegetation classes were more nuanced. The RF models showed that land cover classes could be distinguished with 92.4% accuracy for the Kennaugh element data, compared to 57.7% accuracy for the Sigma Nought intensity data. Texture predictors, while improving the classification accuracy on the one hand, degraded the spatial resolution of the land cover product. The Kennaugh elements derived from TSX winter acquisitions were most important for the RF model, followed by the Kennaugh elements derived from summer and autumn acquisitions. The results of this study demonstrate that multi-temporal Kennaugh elements derived from dual-polarized X-band imagery are a powerful tool for Arctic tundra land cover mapping.
The Austrian Semantic EO Data Cube Infrastructure
Authors: Sudmanns Martin , Augustin Hannah , van der Meer Lucas , Baraldi Andrea , Tiede Dirk
Published: December 2021

Big optical Earth observation (EO) data analytics usually start from numerical, sub-symbolic reflectance values that lack inherent semantic information (meaning) and require interpretation. However, interpretation is an ill-posed problem that is difficult for many users to solve. Our semantic EO data cube architecture aims to implement computer vision in EO data cubes as an explainable artificial intelligence approach. Automatic semantic enrichment provides semi-symbolic spectral categories for all observations as an initial interpretation of color information. Users graphically create knowledge-based semantic models in a convergence-of-evidence approach, where color information is modelled a-priori as one property of semantic concepts, such as land cover entities. This differs from other approaches that do not use a-priori knowledge and assume a direct 1:1 relationship between reflectance values and land cover. The semantic models are explainable, transferable, reusable, and users can share them in a knowledgebase. We provide insights into our web-based architecture, called Sen2Cube.at, including semantic enrichment, data models, knowledge engineering, semantic querying, and the graphical user interface. Our implemented prototype uses all Sentinel-2 MSI images covering Austria; however, the approach is transferable to other geographical regions and sensors. We demonstrate that explainable, knowledge-based big EO data analysis is possible via graphical semantic querying in EO data cubes.
Spatiotemporal Evolution of Wetland Eco-Hydrological Connectivity in the Poyang Lake Area Based on Long Time-Series Remote Sensing Images
Authors: Xia Yang , Fang Chaoyang , Lin Hui , Li Huizhong , Wu Bobo
Published: December 2021

Hydrological connectivity is important for maintaining the stability and function of wetland ecosystems. Small-scale hydrological connectivity restricts large-scale hydrological cycle processes. However, long-term evolutionary studies and quantitative evaluation of the hydrological connectivity of wetlands in the Poyang Lake area have not been sufficiently conducted. In this study, we collected 21 Landsat remote sensing images and extracted land use data from 1989 to 2020, introducing a morphological spatial pattern analysis model to assess the wetland hydrological connectivity. A comprehensive method for evaluating the hydrological connectivity of wetlands was established and applied to the Poyang Lake area. The results showed that, over the course of 31 years, the wetland landscape in the Poyang Lake area changed dramatically, and the wetland area has generally shown a decreasing and then increasing trend, among which the core wetland plays a dominant role in the hydrological connectivity of the Poyang Lake area. In addition, the hydrological connectivity decreases as the core wetland area decreases. From 1989 to 2005, the landscape in the Poyang Lake area focused mainly on the transition from wetland to non-wetland. From 2005 to 2020, the conversion of wetland landscape types shows a clear reversal compared to the previous period, showing a predominant shift from non-wetland to wetland landscapes. The eco-hydrological connectivity of the wetlands in the Poyang Lake area from 1989 to 2020 first decreased, and then increased after 2005. In the early stage of the study (1989−2005), we found that the connectivity of 0.3444 in 2005 was the lowest value in the study period. A resolution of 30 m and an edge effect width of 60 m were optimal for studying the hydrological connectivity of wetlands in the Poyang Lake area. The main drivers of the changes in hydrological connectivity were precipitation and the construction of large-scale water conservation projects, as well as changes in land use. This study provides a good basis for assessing hydrological connectivity in a meaningful way, and is expected to provide new insights for maintaining and restoring biodiversity and related ecosystem services in the Poyang Lake area.
Parasitic Surveillance Potentialities Based on a GEO-SAR Illuminator
Authors: Santi Fabrizio , Blasone Giovanni Paolo , Pastina Debora , Colone Fabiola , Lombardo Pierfrancesco
Published: December 2021

Synthetic aperture radar systems operating with satellites in geosynchronous orbits (GEO-SAR) can provide a permanent coverage of wide specific areas of the Earth’s surface. As well as for primary applications in remote sensing areas such as soil moisture and deformation monitoring, the wide availability of the signal emitted by a GEO-SAR on a regional scale makes it an appealing illuminator of opportunity for bistatic radars. Different types of receiving-only devices located on or near the Earth could exploit the same signal source, noticeably already conceived for radar purposes, for applications in the framework of both military and civil surveillance. This paper provides an overview of possible parasitic applications enabled by a GEO-SAR illuminator in different operative scenarios, including aerial, ground and maritime surveillance. For each selected scenario, different receiver configurations are proposed, providing an assessment of the achievable performance with discussions about the expected potentialities and challenges. This research aims at serving as a roadmap for designing parasitic systems relying on GEO-SAR signals, and also aims at extending the net of potential users interested in investing in GEO-SAR missions.
Attention-Guided Multispectral and Panchromatic Image Classification
Authors: Shi Cheng , Dang Yenan , Fang Li , Lv Zhiyong , Shen Huifang
Published: December 2021

Multi-sensor image can provide supplementary information, usually leading to better performance in classification tasks. However, the general deep neural network-based multi-sensor classification method learns each sensor image separately, followed by a stacked concentrate for feature fusion. This way requires a large time cost for network training, and insufficient feature fusion may cause. Considering efficient multi-sensor feature extraction and fusion with a lightweight network, this paper proposes an attention-guided classification method (AGCNet), especially for multispectral (MS) and panchromatic (PAN) image classification. In the proposed method, a share-split network (SSNet) including a shared branch and multiple split branches performs feature extraction for each sensor image, where the shared branch learns basis features of MS and PAN images with fewer learn-able parameters, and the split branch extracts the privileged features of each sensor image via multiple task-specific attention units. Furthermore, a selective classification network (SCNet) with a selective kernel unit is used for adaptive feature fusion. The proposed AGCNet can be trained by an end-to-end fashion without manual intervention. The experimental results are reported on four MS and PAN datasets, and compared with state-of-the-art methods. The classification maps and accuracies show the superiority of the proposed AGCNet model.
Ground Observations and Environmental Covariates Integration for Mapping of Soil Salinity: A Machine Learning-Based Approach
Authors: Naimi Salman , Ayoubi Shamsollah , Zeraatpisheh Mojtaba , Dematte Jose Alexandre Melo
Published: December 2021

Soil salinization is a severe danger to agricultural activity in arid and semi-arid areas, reducing crop production and contributing to land destruction. This investigation aimed to utilize machine learning algorithms to predict spatial soil salinity (dS m−1) by combining environmental covariates derived from remotely sensed (RS) data, a digital elevation model (DEM), and proximal sensing (PS). The study is located in an arid region, southern Iran (52°51′–53°02′E; 28°16′–28°29′N), in which we collected 300 surface soil samples and acquired the spectral data with RS (Sentinel-2) and PS (electromagnetic induction instrument (EMI) and portable X-ray fluorescence (pXRF)). Afterward, we analyzed the data using five machine learning methods as follows: random forest—RF, k-nearest neighbors—kNN, support vector machines—SVM, partial least squares regression—PLSR, artificial neural networks—ANN, and the ensemble of individual models. To estimate the electrical conductivity of the saturated paste extract (ECe), we built three scenarios, including Scenario (1): Synthetic Soil Image (SySI) bands and salinity indices derived from it; Scenario (2): RS data, PS data, topographic attributes, and geology and geomorphology maps; and Scenario (3): the combination of Scenarios (1) and (2). The best prediction accuracy was obtained for the RF model in Scenario (3) (R2 = 0.48 and RMSE = 2.49), followed by Scenario (2) (RF model, R2 = 0.47 and RMSE = 2.50) and Scenario (1) for the SVM model (R2 = 0.26 and RMSE = 2.97). According to ensemble modeling, a combined strategy with the five models exceeded the performance of all the single ones and predicted soil salinity in all scenarios. The results revealed that the ensemble modeling method had higher reliability and more accurate predictive soil salinity than the individual approach. Relative improvement (RI%) showed that the R2 index in the ensemble model improved compared to the most precise prediction for the Scenarios (1), (2), and (3) with 120.95%, 56.82%, and 66.71%, respectively. We applied the best model in each scenario for mapping the soil salinity in the selected area, which indicated that ECe tended to increase from the northwestern to south and southeastern regions. The area with high ECe was located in the regions that mainly had low elevations and playa. The areas with low ECe were located in the higher elevations with steeper slopes and alluvial fans, and thus, relief had great importance. This study provides a precise, cost-effective, and scientific base prediction for decision-making purposes to map soil salinity in arid regions.
An Automated Snow Mapper Powered by Machine Learning
Authors: Wang Haojie , Zhang Limin , Wang Lin , He Jian , Luo Hongyu
Published: December 2021

Snow preserves fresh water and impacts regional climate and the environment. Enabled by modern satellite Earth observations, fast and accurate automated snow mapping is now possible. In this study, we developed the Automated Snow Mapper Powered by Machine Learning (AutoSMILE), which is the first machine learning-based open-source system for snow mapping. It is built in a Python environment based on object-based analysis. AutoSMILE was first applied in a mountainous area of 1002 km2 in Bome County, eastern Tibetan Plateau. A multispectral image from Sentinel-2B, a digital elevation model, and machine learning algorithms such as random forest and convolutional neural network, were utilized. Taking only 5% of the study area as the training zone, AutoSMILE yielded an extraordinarily satisfactory result over the rest of the study area: the producer’s accuracy, user’s accuracy, intersection over union and overall accuracy reached 99.42%, 98.78%, 98.21% and 98.76%, respectively, at object level, corresponding to 98.84%, 98.35%, 97.23% and 98.07%, respectively, at pixel level. The model trained in Bome County was subsequently used to map snow at the Qimantag Mountain region in the northern Tibetan Plateau, and a high overall accuracy of 97.22% was achieved. AutoSMILE outperformed threshold-based methods at both sites and exhibited superior performance especially in handling complex land covers. The outstanding performance and robustness of AutoSMILE in the case studies suggest that AutoSMILE is a fast and reliable tool for large-scale high-accuracy snow mapping and monitoring.
Landsat-Derived Annual Maps of Agricultural Greenhouse in Shandong Province, China from 1989 to 2018
Authors: Ou Cong , Yang Jianyu , Du Zhenrong , Zhang Tingting , Niu Bowen , Feng Quanlong , Liu Yiming , Zhu Dehai
Published: December 2021

Agricultural greenhouse (AG), one of the fastest-growing technology-based approaches worldwide in terms of controlling the environmental conditions of crops, plays an essential role in food production, resource conservation and the rural economy, but has also caused environmental and socio-economic problems due to policy promotion and market demand. Therefore, long-term monitoring of AG is of utmost importance for the sustainable management of protected agriculture, and previous efforts have verified the effectiveness of remote sensing-based techniques for mono-temporal AG mapping in a relatively small area. However, currently, a continuous annual AG remote sensing-based dataset at large-scale is generally unavailable. In this study, an annual AG mapping method oriented to the provincial area and long-term period was developed to produce the first Landsat-derived annual AG dataset in Shandong province, China from 1989 to 2018 on the Google Earth Engine (GEE) platform. The mapping window for each year was selected based on the vegetation growth and the phenological information, which was critical in distinguishing AG from other misclassified categories. Classification for each year was carried out initially based on the random forest classifier after the feature optimization. A temporal consistency correction algorithm based on classification probability was then proposed to the classified AG maps for further improvement. Finally, the average User’s Accuracy, Producer’s Accuracy and F1-score of AG based on visually-interpreted samples over 30 years reached 96.56%, 86.64% and 0.911, respectively. Furthermore, we also found that the ranked features via calculating the importance of each tested feature resulted in the highest accuracy and the strongest stability in the initial classification stage, and the proposed temporal consistency correction algorithm improved the final products by approximately five percent on average. In general, the resultant AG sequence dataset from our study has revealed the expansion of this typical object of “Human–Nature” interaction in agriculture and has a potential application in use of greenhouse-related technology and the scientific planning of protected agriculture.
PM2.5 Concentration Forecasting over the Central Area of the Yangtze River Delta Based on Deep Learning Considering the Spatial Diffusion Process
Authors: Lu Mingyue , Lao Tengfei , Yu Manzhu , Zhang Yadong , Zheng Jianqin , Li Yuchen
Published: December 2021

Precise PM2.5 concentration forecasting is significant to environmental management and human health. Researchers currently add various parameters to deep learning models for PM2.5 concentration forecasting, but most of them ignore the problem of PM2.5 concentration diffusion. To address this issue, a deep learning model-based PM2.5 concentration forecasting method considering the diffusion process is proposed in this paper. We designed a spatial diffuser to express the diffusion process of gaseous pollutants; that is, the concentration of PM2.5 in four surrounding directions was taken as the explanatory variable. The information from the target and associated stations was then employed as inputs and fed into the model, together with meteorological features and other pollutant parameters. The hourly data from 1 January 2019 to 31 December 2019, and the central area of the Yangtze River Delta, were used to conduct the experiment. The results showed that the forecasting performance of the method we proposed is superior to that of ignoring diffusion, with an average RMSE = 8.247 μg/m3 and average R2 = 0.922 in three different deep learning models, RNN, LSTM, and GRU, in which RMSE decreased by 10.52% and R2 increased by 2.22%. Our PM2.5 concentration forecasting method, which was based on an understanding of basic physical laws and conformed to the characteristics of data-driven models, achieved excellent performance.
Desertification Extraction Based on a Microwave Backscattering Contribution Decomposition Model at the Dry Bottom of the Aral Sea
Authors: Song Yubin , Zheng Hongwei , Chen Xi , Bao Anming , Lei Jiaqiang , Xu Wenqiang , Luo Geping , Guan Qing
Published: December 2021

The fine particles produced during the desertification process provide a rich material source for sand and dust activities. Accurately locating the desertified areas is a prerequisite for human intervention in sand and dust activities. In arid and semi-arid regions, due to very sparse vegetation coverage, the microwave surface scattering model is very suitable for describing the variation of topsoil property during the process of desertification. However, the microwave backscattering coefficient (MBC) trend of the soil during the desertification process is still unclear now. Moreover, the MBC of a resolution unit usually involves the contribution of soil and vegetation. These problems seriously limit the application of microwave remote sensing technology in desertification identification. In this paper, we studied the soil MBC change trend during the desertification process and proposed a microwave backscattering contribution decomposition (MBCD) model to estimate the soil MBC of a resolution unit. Furthermore, a simple microwave backscattering threshold (SMSBT) model was established to describe the severity of desertification. The MBCD and SMSBT models were verified qualitatively through landscape photos of sampling points from a field survey in November 2018. The results showed that the MBC would gradually decline with the deepening degree of desertification. The MBCD model and the corresponding least squares method can be used to estimate the soil MBC accurately, and the SMSBT model can accurately distinguish different degrees of desertification. The results of desertification classification showed that more than 68% of the dry bottom of the Aral Sea is suffering from different degrees of desertification.
UAV-Assisted Privacy-Preserving Online Computation Offloading for Internet of Things
Authors: Wei Dawei , Xi Ning , Ma Jianfeng , He Lei
Published: December 2021

Unmanned aerial vehicle (UAV) plays a more and more important role in Internet of Things (IoT) for remote sensing and device interconnecting. Due to the limitation of computing capacity and energy, the UAV cannot handle complex tasks. Recently, computation offloading provides a promising way for the UAV to handle complex tasks by deep reinforcement learning (DRL)-based methods. However, existing DRL-based computation offloading methods merely protect usage pattern privacy and location privacy. In this paper, we consider a new privacy issue in UAV-assisted IoT, namely computation offloading preference leakage, which lacks through study. To cope with this issue, we propose a novel privacy-preserving online computation offloading method for UAV-assisted IoT. Our method integrates the differential privacy mechanism into deep reinforcement learning (DRL), which can protect UAV’s offloading preference. We provide the formal analysis on security and utility loss of our method. Extensive real-world experiments are conducted. Results demonstrate that, compared with baseline methods, our method can learn cost-efficient computation offloading policy without preference leakage and a priori knowledge of the wireless channel model.
Associations between Circulating IGF-1 Concentrations, Disease Status and the Leukocyte Transcriptome in Early Lactation Dairy Cows
Authors: Wathes D. Claire , Becker Frank , Buggiotti Laura , Crowe Mark A. , Ferris Conrad , Foldager Leslie , Grelet Clément , Hostens Miel , Ingvartsen Klaus L. , Marchitelli Cinzia , Salavati Mazdak , Signorelli Federica , Takeda Haruko , Tang Lijing , Cheng Zhangrui ,   the GplusE Consortium
Published: December 2021

Peripartum dairy cows commonly experience negative energy balance (EB) and immunosuppression together with high incidences of infectious and metabolic disease. This study investigated mechanisms linking EB status with immune defense in early lactation. Data were collected from multiparous Holstein cows from six herds and leukocyte transcriptomes were analyzed using RNA sequencing. Global gene expression was related to circulating IGF-1 (as a biomarker for EB) by subdividing animals into three groups, defined as IGF-1 LOW (<35 ng/mL, n = 35), MODERATE (35–100 ng/mL, n = 92) or HIGH (>100 ng/mL, n = 43) at 14 ± 4 days in milk (DIM). Differentially expressed genes between groups were identified using CLC Genomics Workbench V21, followed by cluster and KEGG pathway analysis, focusing on the comparison between LOW and HIGH IGF-1 cows. LOW cows were older and had significantly lower dry matter intakes and EB values, whereas HIGH cows produced more milk. During the first 35 DIM, 63% of LOW cows had more than one health problem vs. 26% HIGH cows, including more with clinical mastitis and uterine infections. Gene expression analysis indicated that leukocytes in LOW cows switched energy metabolism from oxidative phosphorylation to aerobic glycolysis (PGM, LDH, and PDK4). Many antimicrobial peptides were up-regulated in LOW cows (e.g., PTX3, DMBT1, S100A8, and S100A9) together with genes associated with inflammation, platelet activation and the complement cascade. HIGH cows had greater expression of genes regulating T and B cell function and the cytoskeleton. Overall, results suggested an ongoing cycle of poor EB and higher infection rates in LOW IGF-1 cows which was reflected in altered leukocyte functionality and reduced milk production.
Design and Performance Verification of a Space Radiation Detection Sensor Based on Graphene
Authors: An Heng , Li Detian , Yang Shengsheng , Wen Xuan , Zhang Chenguang , Cao Zhou , Wang Jun
Published: December 2021

In order to verify the performance of a graphene-based space radiation detection sensor, the radiation detection principle based on two-dimensional graphene material was analyzed according to the band structure and electric field effect of graphene. The method of space radiation detection based on graphene was studied and then a new type of space radiation sensor samples with small volume, high resolution, and radiation-resistance was formed. Using protons and electrons, the electrical performance of GFET radiation sensor was verified. The designed graphene space radiation detection sensor is expected to be applied in the radiation environment monitoring of the space station and the moon, and can also achieve technological breakthroughs in pulsar navigation and other fields.
A Methodology for the Multi-Point Characterization of Short-Term Temperature Fluctuations in Complex Microclimates Based on the European Standard EN 15757:2010: Application to the Archaeological Museum of L’Almoina (Valencia, Spain)
Authors: Díaz-Arellano Ignacio , Zarzo Manuel , García-Diego Fernando-Juan , Perles Angel
Published: December 2021

The monitoring and control of thermo-hygrometric indoor conditions is necessary for an adequate preservation of cultural heritage. The European standard EN 15757:2010 specifies a procedure for determining if seasonal patterns of relative humidity (RH) and temperature are adequate for the long-term preservation of hygroscopic materials on display at museums, archives, libraries or heritage buildings. This procedure is based on the characterization of the seasonal patterns and the calculation of certain control limits, so that it is possible to assess whether certain changes in the microclimate can be harmful for the preventive conservation of artworks, which would lead to the implementation of corrective actions. In order to discuss the application of this standard, 27 autonomous data-loggers were located in different points at the Archaeological Museum of l’Almoina (Valencia). The HVAC system (heating, ventilation and air conditioning) at the museum tries to reach certain homogeneous environment, which becomes a challenge because parts of the ruins are covered by a skylight that produces a greenhouse effect in summer, resulting in severe thermo-hygrometric gradients. Based on the analysis of temperatures recorded during 16 months, the air conditions in this museum are discussed according to the standard EN 15757:2010, and some corrective measures are proposed to improve the conservation conditions. Although this standard is basically intended for data recorded from a single sensor, an alternative approach proposed in this work is to find zones inside the museum with a homogeneous microclimate and to discuss next the average values collected in each area. A methodology is presented to optimize the application of this standard in places with a complex microclimate like this case, when multiple sensors are located at different positions.
Lensless Multispectral Camera Based on a Coded Aperture Array
Authors: Wang Jianwei , Zhao Yan
Published: December 2021

Multispectral imaging can be applied to water quality monitoring, medical diagnosis, and other applications, but the principle of multispectral imaging is different from the principle of hyper-spectral imaging. Multispectral imaging is generally achieved through filters, so multiple photos are required to obtain spectral information. Using multiple detectors to take pictures at the same time increases the complexity and cost of the system. This paper proposes a simple multispectral camera based on lensless imaging, which does not require multiple lenses. The core of the system is the multispectral coding aperture. The coding aperture is divided into different regions and each region transmits the light of one wavelength, such that the spectral information of the target can be coded. By solving the inverse problem of sparse constraints, the multispectral information of the target is inverted. Herein, we analyzed the characteristics of this multispectral camera and developed a principle prototype to obtain experimental results.
Dual Properties of Polyvinyl Alcohol-Based Magnetorheological Plastomer with Different Ratio of DMSO/Water
Authors: Hapipi Norhiwani Mohd , Mazlan Saiful Amri , Ubaidillah Ubaidillah , Abdul Aziz Siti Aishah , Choi Seung-Bok , Nordin Nur Azmah , Nazmi Nurhazimah , Pang Zhengbin , Mohd Yusuf Shahir
Published: December 2021

Polyvinyl alcohol (PVA)-based magnetorheological plastomer (MRP) possesses excellent magnetically dependent mechanical properties such as the magnetorheological effect (MR effect) when exposed to an external magnetic field. PVA-based MRP also shows a shear stiffening (ST) effect, which is very beneficial in fabricating pressure sensor. Thus, it can automatically respond to external stimuli such as shear force without the magnetic field. The dual properties of PVA-based MRP mainly on the ST and MR effect are rarely reported. Therefore, this work empirically investigates the dual properties of this smart material under the influence of different solvent compositions (20:80, 40:60, 60:40, and 80:20) by varying the ratios of binary solvent mixture (dimethyl sulfoxide (DMSO) to water). Upon applying a shear stress with excitation frequencies from 0.01 to 10 Hz, the storage modulus (G′) for PVA-based MRP with DMSO to water ratio of 20:40 increases from 6.62 × 10−5 to 0.035 MPa. This result demonstrates an excellent ST effect with the relative shear stiffening effect (RSTE) up to 52,827%. In addition, both the ST and MR effect show a downward trend with increasing DMSO content to water. Notably, the physical state of hydrogel MRP could be changed with different solvent ratios either in the liquid-like or solid-like state. On the other hand, a transient stepwise experiment showed that the solvent’s composition had a positive effect on the arrangement of CIPs within the matrix as a function of the external magnetic field. Therefore, the solvent ratio (DMSO/water) can influence both ST and MR effects of hydrogel MRP, which need to be emphasized in the fabrication of hydrogel MRP for appropriate applications primarily with soft sensors and actuators for dynamic motion control.
A New Bearing Fault Diagnosis Method Based on Capsule Network and Markov Transition Field/Gramian Angular Field
Authors: Han Bin , Zhang Hui , Sun Ming , Wu Fengtong
Published: December 2021

Compared to time-consuming and unreliable manual analysis, intelligent fault diagnosis techniques using deep learning models can improve the accuracy of intelligent fault diagnosis with their multi-layer nonlinear mapping capabilities. This paper proposes a model to perform fault diagnosis and classification by using a time series of vibration sensor data as the input. The model encodes the raw vibration signal into a two-dimensional image and performs feature extraction and classification by a deep convolutional neural network or improved capsule network. A fault diagnosis technique based on the Gramian Angular Field (GAF), the Markov Transition Field (MTF), and the Capsule Network is proposed. Experiments conducted on a bearing failure dataset from Case Western Reserve University investigated the impact of two coding methods and different network structures on the diagnosis accuracy. The results show that the GAF technique retains more complete fault characteristics, while the MTF technique contains a small number of fault characteristics but more dynamic characteristics. Therefore, the proposed method incorporates GAF images and MTF images as a dual-channel image input to the capsule network, enabling the network to obtain a more complete fault signature. Multiple sets of experiments were conducted on the bearing fault dataset at Case Western Reserve University, and the Capsule Network in the proposed model has an advantage over other convolutional neural networks and performs well in the comparison of fault diagnosis methods proposed by other researchers.
Implementation of Thermal Camera for Non-Contact Physiological Measurement: A Systematic Review
Authors: Manullang Martin Clinton Tosima , Lin Yuan-Hsiang , Lai Sheng-Jie , Chou Nai-Kuan
Published: December 2021

Non-contact physiological measurements based on image sensors have developed rapidly in recent years. Among them, thermal cameras have the advantage of measuring temperature in the environment without light and have potential to develop physiological measurement applications. Various studies have used thermal camera to measure the physiological signals such as respiratory rate, heart rate, and body temperature. In this paper, we provided a general overview of the existing studies by examining the physiological signals of measurement, the used platforms, the thermal camera models and specifications, the use of camera fusion, the image and signal processing step (including the algorithms and tools used), and the performance evaluation. The advantages and challenges of thermal camera-based physiological measurement were also discussed. Several suggestions and prospects such as healthcare applications, machine learning, multi-parameter, and image fusion, have been proposed to improve the physiological measurement of thermal camera in the future.
Low-Complexity Robust Adaptive Beamforming Based on INCM Reconstruction via Subspace Projection
Authors: Duan Yanliang , Yu Xinhua , Mei Lirong , Cao Weiping
Published: December 2021

Adaptive beamforming is sensitive to steering vector (SV) and covariance matrix mismatches, especially when the signal of interest (SOI) component exists in the training sequence. In this paper, we present a low-complexity robust adaptive beamforming (RAB) method based on an interference–noise covariance matrix (INCM) reconstruction and SOI SV estimation. First, the proposed method employs the minimum mean square error criterion to construct the blocking matrix. Then, the projection matrix is obtained by projecting the blocking matrix onto the signal subspace of the sample covariance matrix (SCM). The INCM is reconstructed by replacing part of the eigenvector columns of the SCM with the corresponding eigenvectors of the projection matrix. On the other hand, the SOI SV is estimated via the iterative mismatch approximation method. The proposed method only needs to know the priori-knowledge of the array geometry and angular region where the SOI is located. The simulation results showed that the proposed method can deal with multiple types of mismatches, while taking into account both low complexity and high robustness.