bluuebunny/crossref_metadata_2025 huggingface dataset:
A large-sample investigation into uncertain climate change impacts on high flows across Great Britain
Authors: Lane Rosanna , Coxon Gemma , Freer Jim , Seibert Jan , Wagener Thorsten
Published: June 2021

Abstract. Climate change may significantly increase flood risk across Great Britain (GB), but there are large uncertainties in both future climatic changes and how these propagate into changing river flows. Here, the impact of climate change on the magnitude and frequency of high flows is modelled for 346 larger (> 144 km2) catchments across GB using the latest UK Climate Projections (UKCP18) and the DECIPHeR hydrological modelling framework. This study provides the first spatially consistent GB projections including both climate ensembles and hydrological model parameter uncertainties. Generally, results indicated an increase in the magnitude and frequency of high flows (Q10, Q1 and annual maximum) along the west coast of GB in the future (2050–2075), with increases in annual maximum flows of up to 65 % for west Scotland. In contrast, median flows (Q50) were projected to decrease across GB. All flow projections contained large uncertainties, and while the RCMs were the largest source of uncertainty overall, hydrological modelling uncertainties were considerable in east and south-east England. Regional variation in flow projections were found to relate to i) differences in climatic change and ii) catchment conditions during the baseline period as characterised by the runoff coefficient (mean discharge divided by mean precipitation). Importantly, increased heavy-precipitation events (defined by an increase in 99th percentile precipitation) did not always result in increased flood flows for catchments with low runoff coefficients, highlighting the varying factors leading to changes in high flows. These results provide a national overview of climate change impacts on high flows across GB, which will inform climate change adaptation, while also highlighting the need to account for uncertainty sources when modelling climate change impact on high flows.
3D LEAST SQUARES MATCHING APPLIED TO MICRO-TOMOGRAPHY DATA
Authors: Liebold F. , Lorenzoni R. , Curosu I. , Léonard F. , Mechtcherine V. , Paciornik S. , Maas H.-G.
Published: June 2021

Abstract. The paper introduces 3D least squares matching as a technique to analyze multi-temporal micro-tomography data in civil engineering material testing. Time series of tomography voxel data sets are recorded during an in-situ tension test of a strain-hardening cement-based composite probe at consecutive load steps. 3D least squares matching is a technique to track cuboids in consecutive voxel data sets minimizing the sum of the squares of voxel value differences after a 12-parameter 3D affine transformation. For a regular grid of locations in each voxel data set of the deformed states, a subvoxel-precise 3D displacement vector field is computed. Discontinuities in these displacement vector fields indicate the occurrence of cracks in the probes during the load tests. These cracks are detected and quantitatively described by the computation of principal strains of tetrahedrons in a tetrahedral mesh, that is generated between the matching points. The subvoxel-accuracy potential of the technique allows the detection of very small cracks with a width much smaller than the actual voxel size.
ACCURATE SCALING AND LEVELLING IN UNDERWATER PHOTOGRAMMETRY WITH A PRESSURE SENSOR
Authors: Menna F. , Nocerino E. , Chemisky B. , Remondino F. , Drap P.
Published: June 2021

Abstract. Photogrammetry needs known geometric elements to provide metric traceable measurements. These known elements can be a distance between two three-dimensional object points or two camera stations, or a combination of known coordinates and/or angles to solve the seven degrees of freedom that lead to rank deficiency of the normal-equation matrix. In this paper we present a novel approach for scaling and levelling to the local vertical direction an underwater photogrammetric survey. The developed methodology is based on a portable low-cost device designed and realized by the authors that uses depth measurements from a high resolution pressure sensor. The prototype consists of a data logger featuring a pressure sensor synchronized with a digital camera in its underwater pressure housing. The modular design, with optical communication and synchronization, provides great flexibility not requiring the camera housing to undergo any hardware modifications. The proposed methodology allows for a full 3D levelling transformation comprising two angles, a vertical translation and a scale factor and can work for surveying scenes extending horizontally, vertically or both. The paper presents the theoretical principles, an overview of the developed system together with preliminary calibration results. Tests in a lake and at sea are reported. An accuracy better than 1:5000 on the length measurement was achieved in calm water conditions.
MRSSC: A BENCHMARK DATASET FOR MULTIMODAL REMOTE SENSING SCENE CLASSIFICATION
Authors: Liu K. , Wu A. , Wan X. , Li S.
Published: June 2021

Abstract. Scene classification based on multi-source remote sensing image is important for image interpretation, and has many applications, such as change detection, visual navigation and image retrieval. Deep learning has become a research hotspot in the field of remote sensing scene classification, and dataset is an important driving force to promote its development. Most of the remote sensing scene classification datasets are optical images, and multimodal datasets are relatively rare. Existing datasets that contain both optical and SAR data, such as SARptical and WHU-SEN-City, which mainly focused on urban area without wide variety of scene categories. This largely limits the development of domain adaptive algorithms in remote sensing scene classification. In this paper, we proposed a multi-modal remote sensing scene classification dataset (MRSSC) based on Tiangong-2, a Chinese manned spacecraft which can acquire optical and SAR images at the same time. The dataset contains 12167 images (optical 6155 and 6012 for optical and SAR, resp.) of seven typical scenes, namely city, farmland, mountain, desert, coast, lake and river. Our dataset is evaluated by state-of-theart domain adaptation methods to establish a baseline with average classification accuracy of 79.2%. The MRSSC dataset will be released freely for the educational purpose and can be found at China Manned Space Engineering data service website (http://www.msadc.cn). This dataset will fill the gap between remote sensing scene classification between different image sources, and paves the way for a generalized image classification model for multi-modal earth observation data.
DEFORMATION MONITORING AT EUROPEAN SCALE: THE COPERNICUS GROUND MOTION SERVICE
Authors: Crosetto M. , Solari L. , Balasis-Levinsen J. , Bateson L. , Casagli N. , Frei M. , Oyen A. , Moldestad D. A. , Mróz M.
Published: June 2021

Abstract. The Advanced Differential Interferometric SAR (A-DInSAR) technique is a class of powerful techniques to monitor ground motion. In the last two decades, the A-DInSAR technique has undergone an important development in terms of processing algorithms and the capability to monitor wide areas. This has been accompanied by an important increase of the Synthetic Aperture Radar (SAR) data acquisition capability by spaceborne sensors. An important step forward was the launch of the Copernicus Sentinel-1 constellation. The development of A-DInSAR based ground deformation services is now technically feasible. This paper describes some of the most important features of A-DInSAR. Then, it describes the European Ground Motion Service (EGMS), part of the Copernicus Land Monitoring Service, which represents a unique initiative for performing ground deformation monitoring on a European scale.
CONCEPT TO ANALYZE THE DISPLACEMENT TIME SERIES OF INDIVIDUAL PERSISTENT SCATTERERS
Authors: Evers M. , Thiele A. , Hammer H. , Cadario E. , Schulz K. , Hinz S.
Published: June 2021

Abstract. Persistent Scatterer Interferometry (PSInSAR) exploits a time series of Synthetic Aperture Radar (SAR) images to estimate the mean velocity with which the surface of the earth is deforming. However, most PSInSAR algorithms estimate the mean velocities using a linear regression model. Since some deformation phenomena can exhibit a more complex behavior over time, using a linear regression model leads to potentially wrong estimations for the mean velocity. For example, the velocity of a landslide moving down a steep slope can change depending on the water content of the material of the landslide, or an inactive landslide can reactivate due to an earthquake. Both scenarios would not result in a time series with a constant linear slope but in a piecewise linear time series.This paper presents a Matlab-based tool to analyze an individual Persistent Scatterer (PS) time series. The Persistent Scatterer Deformation Pattern Analysis Tool (PSDefoPAT) aims to build a mathematical model that sufficiently describes the time series trend and seasonal and noise components. The trend component is estimated using polynomial regression and piecewise linear models, while a sine function approximates the seasonal component. The goal is to identify the best fitting model for the displacement time series of a PS. PSDefoPAT is introduced by examine the time series of three different PS located in the region surrounding Patras, Greece. Based on the derived models, we discuss the nature of their deformation patterns.
A COMPARATIVE STUDY OF POINT CLOUDS SEMANTIC SEGMENTATION USING THREE DIFFERENT NEURAL NETWORKS ON THE RAILWAY STATION DATASET
Authors: Lumban-Gaol Y. A. , Chen Z. , Smit M. , Li X. , Erbaşu M. A. , Verbree E. , Balado J. , Meijers M. , van der Vaart N.
Published: June 2021

Abstract. Point cloud data have rich semantic representations and can benefit various applications towards a digital twin. However, they are unordered and anisotropically distributed, thus being unsuitable for a typical Convolutional Neural Networks (CNN) to handle. With the advance of deep learning, several neural networks claim to have solved the point cloud semantic segmentation problem. This paper evaluates three different neural networks for semantic segmentation of point clouds, namely PointNet++, PointCNN and DGCNN. A public indoor scene of the Amersfoort railway station is used as the study area. Unlike the typical indoor scenes and even more from the ubiquitous outdoor ones in currently available datasets, the station consists of objects such as the entrance gates, ticket machines, couches, and garbage cans. For the experiment, we use subsets from the data, remove the noise, evaluate the performance of the selected neural networks. The results indicate an overall accuracy of more than 90% for all the networks but vary in terms of mean class accuracy and mean Intersection over Union (IoU). The misclassification mainly occurs in the classes of couch and garbage can. Several factors that may contribute to the errors are analyzed, such as the quality of the data and the proportion of the number of points per class. The adaptability of the networks is also heavily dependent on the training location: the overall characteristics of the train station make a trained network for one location less suitable for another.
FUSING MULTI-MODAL DATA FOR SUPERVISED CHANGE DETECTION
Authors: Ebel P. , Saha S. , Zhu X. X.
Published: June 2021

Abstract. With the rapid development of remote sensing technology in the last decade, different modalities of remote sensing data recorded via a variety of sensors are now easily accessible. Different sensors often provide complementary information and thus a more detailed and accurate Earth observation is possible by integrating their joint information. While change detection methods have been traditionally proposed for homogeneous data, combining multi-sensor multi-temporal data with different characteristics and resolution may provide a more robust interpretation of spatio-temporal evolution. However, integration of multi-temporal information from disparate sensory sources is challenging. Moreover, research in this direction is often hindered by a lack of available multi-modal data sets. To resolve these current shortcomings we curate a novel data set for multi-modal change detection. We further propose a novel Siamese architecture for fusion of SAR and optical observations for multi-modal change detection, which underlines the value of our newly gathered data. An experimental validation on the aforementioned data set demonstrates the potentials of the proposed model, which outperforms common mono-modal methods compared against.
RESEARCH ON THE NETWORK MAP SERVICE TECHNOLOGY OF REMOTE SENSING IMAGE INTELLIGENT CONVERSION BASED ON GAN MODEL
Authors: Tang D. , Huang W. , Zha Z. , Yang J. , Zhou X. , Wang C.
Published: June 2021

Abstract. Based on an improved generative adversarial networks algorithm (CGAN), this paper explores a technical way to realize map transformation through autonomous learning and training of remote sensing images. Just skip the trial process vector data update and cumbersome process of mapping the basic map elements can be automatically transform, the image on the main streets and typical rules of construction material, can achieve automatic identification and transformation, greatly shorten the tile map production and update cycle, improve the efficiency of the network map service quality. The results of the test platform have proved that it can be applied to a certain extent and can basically meet the requirements of network map production.
ANALYSIS OF NO2 TROPOSPHERIC COLUMN AMOUNT AT AIRPORTS IN MALAYSIA BEFORE AND DURING COVID-19 PANDEMIC USING SENTINEL-5P TROPOMI DATA
Authors: Kanniah K. D. , Kamarul Zaman N. A. F. , Perumal K.
Published: June 2021

Abstract. Air pollution is a serious environmental and health issue in Malaysia due to the recent urbanization processes. The main sources of air pollutants are motorized vehicles in urban areas and airports and industrial activities. At the airports, NO2 is the main pollutant of concern besides aerosols particles, yet gap in data availability prevent studies to describe their patterns and quantify their effects on human health and climate change. In this study NO2 data from TROPOMI sensor on board Sentinel 5-P satellite was used to characterize the spatial and temporal patterns of NO2 tropospheric column amounts at major airports in Malaysia. The results demonstrate that NO2 amounts from aircrafts and ground traffic activities are generally higher and/or similar to the amounts found in urban areas. Total tropospheric column amounts of NO2 during the movement restriction imposed due to Covid-19 pandemic between March and April 2020 was approximately 50% lower the total emission during the same period in 2019 (representing a business as usual period). Assessing the spatial pattern and temporal variations in NO2 (both surface and total vertical profile) is important for monitoring the impact of air pollutants on climate change and human health in Malaysia.
UNMANNED AERIAL VEHICLE DERIVED 3D MODEL EVALUATION BASED ON ICESAT-2 FOR ICE SURFACE MICRO-TOPOGRAPHY ANALYSIS IN EAST ANTARCTICA
Authors: He Y. , Qiao G. , Li H. , Yuan X. , Li Y.
Published: June 2021

Abstract. Modelling of ice sheet micro-topography based on Unmanned Aerial Vehicle (UAV) is meaningful for the understanding of interactions between local ice mass and climate. 3D reconstruction based on UAV has advantages that satellite remote sensing cannot replace. Here, the surface micro-topography measurement was performed during the China's 36th Antarctic expedition (CHINARE) in 2019–2020, using an UAV platform composed of a DJI Phantom 4 and a D-RTK GNSS mobile station around Zhongshan Station of China. Then, four partly overlapped models were obtained by the SfM-MVS technology. Affected by the complex environment factors, the performance of this technology sometimes is challenged over the marginal Antarctic Ice Sheet. Satellite altimetry is one of the most essential technologies for land ice surface elevation measurements, widely used in regional or global ice mass balance estimations.We use the land ice surface heights with high accuracy derived from the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) to compare with the UAV-derived models. Combined with the high precision and space-time resolution of ICESat-2 satellite altimetry, the results of the model were evaluated under different terrain conditions. It has been certified that the derived models without extra GCPs were capable of detecting the surface micro-topographic features if considering the potential factors, which can be popularized and developed in polar research.
COMPARISON OF MULTI-IMAGES DEEP LEARNING SUPER RESOLUTION FOR PASSIVE MICROWAVE IMAGES OF ARCTIC SEA ICE
Authors: Shen X. , Liu X. , Yao Y. , Feng T.
Published: June 2021

Abstract. The observation of Arctic sea ice is of great significance to monitoring of the polar environment, research on global climate change and application of Arctic navigation. Compared to optical imagery and SAR imagery, passive microwave images can be obtained for all-sky conditions with high time resolution. However, the spatial resolution of passive microwave images is relatively low (6.25 km – 25 km) for the observation of detailed sea ice characteristics and small-scale sea ice geographical phenomena. Therefore, in this paper, considering the suitability of different alignment and fusion strategies to the characteristics of passive microwave images of sea ice, two multi-images deep learning super-resolution (SR) algorithms, Recurrent Back-Projection Network (RBPN) and network of Temporal Group Attention (TGA), are selected to test the effects of SR technique for passive microwave images of sea ice. Both qualitative and quantitative comparisons are provided for the SR results oriented from two algorithms. Overall, the SR performance of TGA algorithm outperforms RBPN algorithm for the passive microwave images of sea ice.
AN AUTOMATIC EXTRACTION METHOD FOR ANTARCTIC SUBGLACIAL LAKE BASED ON RADIO ECHO SOUNDING DATA
Authors: Wang D. , Feng T. , Hao T. , Li R.
Published: June 2021

Abstract. Detection of subglacial lakes and interpretation their hydrological connectivity is of great importance to understanding the mass balance of Antarctic ice sheet. Over the past five decades, a large number of Radio Echo Sounding (RES) data has been collected in Antarctica. However, the identification of subglacial lakes based on RES data mainly relies on visual interpretation due to the lack of quantitative indicators for subglacial lakes distinguishing. To solve this problem, an automatic subglacial lakes detection method based on the reflective characteristics of B-scan echogram is proposed in this paper. Firstly, the echo strength of the original radar echogram is corrected for the dielectric attenuation and geometric spreading in the ice. Secondly, the original radar echogram is binarized to preserve the bright subglacial lake region, and the thickness of bright pixels is measured along the direction of A-scan. Thirdly, the thickness, the variance of the thickness in the neighbourhood and the corrected echo strength are used to obtain the response value for the subglacial lake. Finally, a threshold for the response value is determined to detect subglacial lakes. It is found that the proposed method can determine the location of the subglacial lake in Antarctica's Gamburtsev Province (AGAP) region with high accuracy.
EXPLOITATION OF SPECTRAL AND TEMPORAL INFORMATION FOR MAPPING PLANT SPECIES IN A FORMER INDUSTRIAL SITE
Authors: Gimenez R. , Lassalle G. , Hédacq R. , Elger A. , Dubucq D. , Credoz A. , Jennet C. , Fabre S.
Published: June 2021

Abstract. Characterization and seasonal (periodic) monitoring of plant species distribution in the context of former industrial activity is crucial to assess long-term anthropogenic footprint on vegetated area. Species discrimination has shown promising results using both HyperSpectral (HS) and MultiSpectral (MS) images. Airborne HS instruments enable high spatial and spectral resolution imagery while time series of satellite MS images provide high temporal resolution and phenological information. This paper aims to compare supervised classification results obtained with non-parametric (Random Forest, RF, Support Vector Machine, SVM) and parametric methods (Regularized Logistic Regression, RLR) applied on both kinds of images acquired on an industrial brownfield. The studied site is a complex vegetated environment due to species diversity: 8 dominant species are retained. The performance obtained by preliminary feature selection based on principal component analysis and vegetation indices, to improve separability of spectral or temporal information according to species, is analysed. The best performance is obtained by RLR method applied on HS data without feature selection (global accuracy of 93 %). Feature selection is found to be a necessary step to perform classification with time series of MS images. Species that are difficult to distinguish from the HS image, namely Salix and Populus, are well separated using Sentinel-2 images (precision around 70%).
INDIVIDUAL BANANA TREE CROWN DELINEATION USING UNMANNED AERIAL VEHICLE (UAV) IMAGES
Authors: Kuikel S. , Upadhyay B. , Aryal D. , Bista S. , Awasthi B. , Shrestha S.
Published: June 2021

Abstract. Individual Tree Crown (ITC) delineation from aerial imageries plays an important role in forestry management and precision farming. Several conventional as well as machine learning and deep learning algorithms have been recently used in ITC detection purpose. In this paper, we present Convolutional Neural Network (CNN) and Support Vector Machine (SVM) as the deep learning and machine learning algorithms along with conventional methods of classification such as Object Based Image Analysis (OBIA) and Nearest Neighborhood (NN) classification for banana tree delineation. The comparison was done based by considering two cases; Firstly, every single classifier was compared by feeding the image with height information to see the effect of height in banana tree delineation. Secondly, individual classifiers were compared quantitatively and qualitatively based on five metrices i.e., Overall Accuracy, Recall, Precision, F-Score, and Intersection Over Union (IoU) and best classifier was determined. The result shows that there are no significant differences in the metrices when height information was fed as there were banana tree of almost similar height in the farm. The result as discussed in quantitative and qualitative analysis showed that the CNN algorithm out performed SVM, OBIA and NN techniques for crown delineation in term of performance measures.
LANDSLIDE DETECTION IN CENTRAL AMERICA USING THE DIFFERENTIAL BARE SOIL INDEX
Authors: Ariza A. , Davila N. A. , Kemper H. , Kemper G.
Published: June 2021

Abstract. The increasing availability of EO data from the Copernicus program through its Sentinel satellites of the medium spatial and spectral resolution has generated new applications for risk management and disaster management. The recent growth in the intensity and number of hurricanes and earthquakes has demanded an increase in the monitoring of landslides. It is necessary to monitor large areas at a detailed level, which has previously been unsatisfactory due to its reliance on the interpretation of aerial photographs and the cost of high-resolution images.Using the differential Bare Soil Index for optical imagery interpretation in combination with cloud-computing in Google Earth Engine is a novel approach. Applying this method on a recent landslide event in Oaxaca in Mexico around 62% of the landslides were detected automatically, however, there is a big potential for improvement. Including NDVI values and considering images with a higher spatial resolution could contribute to the enhancement of landslide detection, as the majority of missed events have a size smaller than half a pixel. Landslide detection in Google Earth Engine has become a promising approach for big data processing and landslide inventory creation.
DEVELOPMENT OF A GEO-ANALYTICS PLATFORM FOR POST-DISASTER GROUND ASSESSMENT
Authors: Atasoy K. , Kocaman S.
Published: June 2021

Abstract. Earthquake is a frequently encountered natural hazard and can cause losses of lives and assets. In order to understand the nature of seismic hazards and the risks sourced from them, careful investigations are needed by using diverse geoinformation types. Thanks to the availability of multi-platform and high-resolution geospatial datasets, vast amount of geodata can be collected and analyzed for obtaining timely information on land’s surface before and after an earthquake event; and to assist the disaster management authorities and first responders for supporting the mitigation efforts. On January 24, 2020, an earthquake with Mw 6.8 occurred in Elazig Province, Turkey; caused 41 deaths and damaged the buildings and infrastructure. Although various geodatasets could be collected and processed by the geomatics experts, their utilization by other stakeholders, such as geoscientists, local authorities, and citizens remained limited due to the accessibility issues, and high complexity in their visualization, processing and interpretation. In this study, a web-based platform called EQ4D was designed and implemented to present the multi-sensor and multi-platform 4D data to the stakeholders, and to allow for basic geo-analytical processes such as change detection, 3D surface measurements, etc. Cesium JS and Potree libraries were employed for 3D visualization. PostGIS spatial database management system was used for storing and managing the data, and for performing spatial queries. Currently EQ4D is suitable for the use of geoscientists, and can be further customized for generic use at similar geohazards and developed for the use of inexperienced users, such as citizen scientists.
DEEP LEARNING ALGORITHM FOR URBAN FEATURE EXTRACTION USING SAR DATA
Authors: Pithva N. , Vyas A. , Rawal D. , Nizalapur V. , Jain G. , Das A.
Published: June 2021

Abstract. This paper aims to discusses the extraction of urban features from airborne NISAR (NASA-ISRO SAR) data using deep learning algorithm for a part of Ahmedabad City. NISAR data is acquired in two wavelength bands (L and S) in hybrid polarization i.e., RH and RV. This study has used level two data viz., amplitude data. Pre-processing of NISAR data in L and S wavelength bands was carried out by using MIDAS, software developed and provided by the Space Applications Centre. Pre-processing viz., Speckle suppression using different filters in varying window sizes, radiometric and geometric calibration was performed. Variation of backscattering coefficient (Sigma- nought) in different wavelengths and polarizations for different land use features were analysed. NISAR data in conjunction with LISS 4 (5.8 m resolution) data is subjected to different fusion techniques. Qualitative and Quantitative analysis was carried out and Gram Schmidt technique was chosen for further analysis. Segmentation was performed to achieve better analysis of the fused image and the amplitude image. Lastly, a deep learning architecture was developed for the automatic classification of the image, and the Convolution Neural Network model was designed using mobile net and the regularization techniques. Deep learning architecture in conjunction with e-cognition developer was used for extracting urban features.
Dawn and dusk of Late Cretaceous basin inversion in central Europe
Authors: Voigt Thomas , Kley Jonas , Voigt Silke
Published: June 2021

Abstract. Central and western Europe were affected by a compressional
tectonic event in the Late Cretaceous, caused by the convergence of Iberia
and Europe. Basement uplifts, inverted graben structures, and newly formed
marginal troughs are the main expressions of crustal shortening. Although
the maximum activity occurred during a short period of time between 90 and 75 Ma, the
exact timing of this event is still unclear. Dating of the start and end of Late
Cretaceous basin inversion gives very different results depending on the
method applied. On the basis of borehole data, facies, and thickness maps,
the timing of basin reorganization was reconstructed for several basins in
central Europe. The obtained data point to a synchronous start of basin
inversion at 95 Ma (Cenomanian), 5 Myr earlier than commonly
assumed. The end of the Late Cretaceous compressional event is difficult to
pinpoint in central Europe, because regional uplift and salt migration
disturb the signal of shifting marginal troughs. Late Campanian to Paleogene
strata deposited unconformably on inverted structures indicate slowly
declining uplift rates during the latest Cretaceous. The differentiation of
separate Paleogene inversion phases in central Europe does not appear
possible at present.
Deep ice as a geochemical reactor: insights from iron speciation and mineralogy of dust in the Talos Dome ice core (East Antarctica)
Authors: Baccolo Giovanni , Delmonte Barbara , Di Stefano Elena , Cibin Giannantonio , Crotti Ilaria , Frezzotti Massimo , Hampai Dariush , Iizuka Yoshinori , Marcelli Augusto , Maggi Valter
Published: June 2021

Abstract. Thanks to its insolubility, mineral dust is considered a stable proxy in polar ice cores. With this study we show that below an ice-depth of 1000 m, the Talos Dome ice core (Ross Sea sector of East Antarctica) presents evident and progressive signs of post-depositional processes affecting the mineral dust records. We applied a suite of established and cutting edge techniques to investigate the properties of dust present in the Talos Dome ice core, ranging from concentration and grain-size to elemental-composition and Fe-mineralogy. Results show that through acidic/oxidative weathering, the conditions of deep ice at Talos Dome promote the dissolution of specific minerals and the englacial formation of others, deeply affecting dust primitive features. The expulsion of acidic atmospheric species from ice-grains and their concentration in localized environments is likely the main process responsible for englacial reactions and is related with ice re-crystallization. Deep ice can be seen as a "geochemical reactor" capable of fostering complex reactions which involve both soluble and insoluble impurities. Fe-bearing minerals can efficiently be used to explore such transformations.
Sosyal Medya Kullanımında Kullanıcı Motivasyonlarının ve Platformların Önemi: Kurumlar, İşletmeler ve Markalar için Strateji Kurgulamada Önemli Noktalar
Authors: KARAGEYİM Meral Ahu
Published: June 2021

Dijital platformlar ve sosyal medya teknolojik gelişmelerin de etkisiyle her geçen gün tüketicilerin hayatında daha fazla rol oynamaktadır. Tüketicilerin sosyal medya kullanımının artması ile beraber kurumlar, markalar ve işletmeler de tüketici ve kullanıcılara ulaşmak için sosyal medyayı daha aktif bir şekilde kullanmaya başlamışlardır. Bu çalışmada tüketicilerin sosyal medya kullanımı ile ilgili motivasyonlarını ve sosyal medya platformların özelliklerini incelenmekte ve kullanıcı motivasyonları ile platformların sınıflandırılmasının pazarlama stratejilerine olan etkisi tartışılmaktadır.
Effects of a novel dye containing toothpaste versus regular fluoridated toothpaste for supragingival biofilm reduction in patients with chronic gingivitis: A Randomized Controlled Trial
Authors: Rath Avita , Junning Chong , HR Priyadarshini , Teo Brandon Nicholas , Zhen Naik Hsian , Yin Ong Wey
Published: June 2021

AIM:To compare the effects of a plaque disclosing toothpaste with a regular fluoridated toothpaste for supragingival biofilm reduction in patients with chronic gingivitis.MATERIALS AND METHODS:This study was a randomized controlled trial with a parallel design. Forty patients, both males and females aged 18-25 years with chronic generalized gingivitis (N=40) were randomly allocated to two groups of twenty each (n=20), using lottery method. Participants in the experimental group brushed with a plaque disclosing toothpaste, while participants in the control group brushed with regular fluoridated toothpaste. Clinically, biofilm was measured using Silness & Loe Plaque Index, both at baseline and at follow up visits by two assessors who were blinded to group allocation. Adjunctly, intraoral photographs were taken for Digital Plaque Image Analysis of the remaining dental biofilm in the upper and lower anterior labial tooth surfaces, and calculated based on the area of stained pixels. Data was analyzed using SPSS version 22. Mean plaque scores between the groups were compared using independent sample t-test and p<0.05 was considered significant.RESULTS:Data of all the 40 participants were analyzed as they were no drop outs. No significant differences were found in the mean plaque scores between the two groups (p=0.059) at the end of the study. Similarly, Digital Plaque Image Analysis showed no significant difference in the mean plaque scores between the groups (p= 0.93).CONCLUSION:This study showed that there are no additional benefits of using a plaque disclosing toothpaste, in enhancing the removal of the supragingival dental biofilm during routine oral hygiene maintenance.
Collecting Pennsylvania Political Twitter Data
Authors: Dudash Andrew M. , Russell John E.
Published: June 2021

During the two most recent elections we have seen the importance of social media, and Twitter in particular, for political discourse. This paper describes the effort of an academic library to collect election-related Twitter data from Pennsylvania-specific organizational accounts and hashtags for 2018 and 2020 in the run-up and aftermath of both election cycles. Because of its importance to understanding contemporary politics and its historic value, libraries need to consider the opportunity to collect and make this data accessible to Pennsylvanians.
Affording Access: Pathways to Reducing Textbook Costs
Authors: Banyas Kelly , Cloutier Marleen
Published: June 2021

After piloting an Open Educational Resources (OER) grant program, librarians from the University of Scranton’s Weinberg Memorial Library OER Committee conducted a survey of librarians about the inclusion of library resources in OER initiatives. The survey examined if institutions defined library resources as OER, why libraries chose not to include library resources, and, if included, the types of library-licensed or purchased content. The survey found that most (18 of 27) respondents did not include OER and library resources together and, of the institutions that did, a majority (5 of 9) used licensed library resources in addition to open educational and open access resources. The conclusion of this article looks at the pilot grant program and the OER Committee’s decision to realign and rebrand the grant in light of the survey results.
From The Editor’s Desk-Post-Pandemic Edition
Authors: Chaiklin Martha
Published: June 2021

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Las redes informales de apoyo como recurso clave en la intervención social
Authors: Garcia-Moreno Cristina
Published: June 2021

Este trabajo muestra la importancia que adquieren las redes interpersonales en la intervención social y, sobre todo, la influencia que éstas tienen en la vida de las personas. Desde esta consideración, y utilizando la técnica del mapa de red social, se centra la atención en conocer las características y el funcionamiento de las redes familiares y sociales de apoyo de mujeres atendidas en servicios sociales de atención primaria (tanto del ámbito social como sanitario). Al respecto, se observa que estas mujeres perciben sus redes como recursos personales con los que pueden hacer frente a situaciones de necesidad. Así, se propone potenciar estas redes como recurso complementario en la intervención social.
Tensiones y soportes en el proceso de formación académica de los estudiantes de Trabajo Social en una universidad pública del suroccidente colombiano
Authors: Burbano Cerón Mary Hellen , Manco Lozano Bianca Alejandra , Bastidas Rivera Stefany
Published: June 2021

En este artículo se presentan los resultados de un estudio cualitativo cuyo propósito ha sido comprender los momentos del proceso de formación académica que han generado mayores tensiones en los/las estudiantes de un programa académico de Trabajo Social en una universidad pública de la ciudad de Cali, en el suroccidente colombiano. Asimismo, se identifican los principales soportes utilizados por los y las estudiantes para permanecer en el programa académico. Como resultado se revela que durante el proceso académico formativo —entendido como una prueba estructural (Martuccelli, 2006)— los momentos más tensos fueron: el ingreso y la adaptación a la universidad, el curso de algunas asignaturas y la práctica académica. Frente a estas tensiones los y las estudiantes universitarias/os apelaron al uso de soportes de carácter interno o individual y también externos, principalmente de carácter institucional, tales y como: el apoyo derivado del vínculo entre pares —compañeros de estudio— y la relación con los y las docentes de la institución. Los resultados del estudio sugieren la importancia de incluir estrategias que fortalezcan los vínculos entre pares y docentes en los programas de permanencia universitaria.
Current Understanding and Future Perspectives of Interstitial Cystitis/Bladder Pain Syndrome
Authors: Ueda Tomohiro , Hanno Philip M. , Saito Ryoichi , Meijlink Jane M. , Yoshimura Naoki
Published: June 2021

Interstitial cystitis/bladder pain syndrome (IC/BPS) is a chronic disease characterized by suprapubic pain and lower urinary tract symptoms. Perhaps because of the heterogeneous nature of this disease and its multifactorial etiology, clinical trials in allinclusive populations of IC/BPS patients without phenotyping in the last decade have mainly failed to discover new therapeutic modalities of IC/BPS. Thus, phenotyping IC/BPS, aimed at identifying bladder-centric and/or bladder-beyond pathologies, including cystoscopic observation of Hunner or non-Hunner lesions of the bladder mucosa, is particularly important for the future of IC/BPS management. Based on recent discussions at international conferences, including the International Consultation on IC, Japan, it has been proposed that Hunner-lesion IC should be separated from other non-Hunner IC/BPS because of its distinct inflammatory profiles and epithelial denudation compared with non-Hunner IC/BPS. However, there are still no standard criteria for the diagnosis of Hunner lesions other than typical lesions, while conventional cystoscopic observations may miss atypical or small Hunner lesions. Furthermore, diagnosis of the bladder-centric phenotype of IC/BPS requires confirmation that identified mucosal lesions are truly a cause of bladder pain in IC/BPS patients. This review article discusses the current status of IC/BPS pathophysiology and diagnosis, as well as future directions of the proper diagnosis of bladder-centric IC/BPS, in which pathophysiological mechanisms other than those in inflammatory pathways, such as angiogenic and immunogenic abnormalities, could also be involved in both Hunner-lesion IC and non-Hunner IC/BPS. It is hoped that this new paradigm in the pathophysiological evaluation and diagnosis of IC/BPS could lead to pathology-based phenotyping and new treatments for this heterogeneous disease.
STUDY EFFECTS OF QUINCE SEED MUCILAGE AND GUAR GUM ON THE SENSORY CHARACTERISTICS AND THE VOLATILE COMPOUND PROFILE OF DOOGH BY GAS CHROMATOGRAPHY/ NANO-SIZED POLYPYRROLE FIBER
Authors: PIRSA S. , DALILI R. , YAZDANI I.
Published: June 2021

Doogh as an acidified milk drink is produced by adding salt and water into yoghurt. In the present work the effects of some additives like; Quince seed mucilage (Qm) and guar gum (Gg) on the sensory properties of Doogh (odour, colour and overall acceptability) and its relation with the total peak area and total peak height of volatile compounds of doogh in gas chromatography (GC) were studied. Extraction of volatile compounds of doogh was done with polypyrrole fiber from a head space of doogh sample as head-space solid phase micro extraction method (HS-SPME). Polypyrrole (PPy) was chemically-deposited on the polyester fiber surface. The Electron Microscopy (SEM) technique showed that PPy particles are in the range of 50-150 nm and seed shape. Central composite design (CCD) and response surface methods were applied to design the experiments and find out the interaction among different factors and correlations among sensory properties and GC parameters (total peak area and total peak height) of compounds in doogh. The results showed that there is a positive relation between sensory properties and GC parameters of doogh and presented methods can be used for quality control of dairy products.
MODIFICATION OF THE RECOVERED LOW- GRADE FAT TO FORMULATE ECO-FRIENDLY LUBRICANT GREASE
Authors: HABIB M. A.
Published: June 2021

This study presents utilization of the inedible by-product fat of the municipal massacres to formulate bio-based lubricant grease. Inedible animal fat of acceptable quality has been recovered through the wet rendering process. The recovered fat was sterilized and bleached using 200 ppm chlorine dioxide solutions. The acceptable saponification and iodine values of the fat emphasize that it can be used in the chemical industry as an oleochemical resource. The fat was fractionalized into two fractions; stearic and oleic. The lithium soap of the stearic fraction was employed as a thickener and the oleic fraction has been adapted for use as base oil, depending on its proportion of combination compared to the thickener. It was found that the ideal composition of the formulated bio-grease is 15-20 % thickener to 85-80 % base oil. The performance testing shows that the formulated grease has NGLI ranges between 2- 3. The grease can be utilized as a lubricant agent in machinery and gears with an acceptable lubricating performance.
EFFECT OF DIFFERENT TEMPERATURE ON REHYDRATION KINETICS OF CHICKEN BREAST MEAT CUBES
Authors: Ozunlu Orhan , Ergezer Haluk , Demiray Engin , Gokce Ramazan
Published: June 2021

In the present research, it was aimed to understand the effect of different rehydration temperatures (80, 90 and 100°C) on rehydration kinetics of hot air dried chicken breast meat cubes. The rehydration rate increased with the increasing of temperature of rehydration water. ΔE and chroma values of the rehydrated samples at 90°C and 100°C samples were found statistically similar. To describe the rehydration kinetics, four different models, Peleg’s, Weibull, first order and exponential association, were considered. Between these four models proposed Peleg’s model gave a better fit for all rehydration conditions applied. The effective moisture diffusivity values of chicken meat increased as water rehydration temperature increased.
DRYING CHARACTERISTICS, MINERAL CONTENT, TEXTURE AND SENSORIAL PROPERTIES OF PUMPKIN FRUIT LEATHER
Authors: Özkan Karabacak Azime , Suna Senem , Dorak Saliha , Çopur Ömer Utku
Published: June 2021

The objective of this study was to investigate the influences of hot air (HAD: 50, 60, 70°C), vacuum (VD: 50, 60, 70°C at 300 mbar) and microwave methods (MWD: 90, 180W) on drying characteristics, effective moisture diffusivity (Deff), mineral content, texture, and sensorial properties of pumpkin pestils. MWD led to the highest drying rate and the lowest drying time in all methods. Page and Modified Page were ideally fitted to experimental results among seven thin layer drying models. Mineral content (Ca, K, Na, P, Mg, Fe, Zn, Cu, Mn) of the pestils showed higher values than non-dried (paste) mixture. Significant differences were determined between textural features of the pestils (p < 0.05). Furthermore, products dried with HAD and VD were preferred rather than MWD in terms of sensorial properties.
Storytelling to Enhance Speaking and Listening Skills for English Young Learners: A Case Study at Language Centers in Binh Duong Province
Authors: Tran Du Thanh
Published: June 2021

With the availability of English teaching and learning facilities in most schools as well as in English language centers in Vietnam nowadays, storytelling is believed to be one of the most suitable techniques to improve young learners’ communicative competences; therefore, it should be further investigated and promoted. With the aim of fully exploiting storytelling in Vietnamese English language teaching contexts, the research is designed to discuss and evaluate its effectiveness as a teaching technique to enhance young learners’ speaking and listening skills - a well-matched couple of oral competences. Conducted with mixed research methods through the use of questionnaires, interview and observation, the outcomes, conclusions, theories as well as evaluations and recommendations put forward in the study are of great validity and reliability. Based on the rationale of this teaching technique, some models and activities for teaching demonstration are designed and carried out in real language environments in order to evaluate its advantages as well as disadvantages. From what have been discovered, the study can serve as a reference for teachers who wish to enhance their students’ listening and speaking skills by means of storytelling.
Kiến thức, thái độ, thực hành của bố mẹ bệnh nhi về bệnh thalassemia điều trị tại Bệnh viện Đa khoa Trung ương Thái Nguyên năm 2020
Authors: Thị Phương Ngân Nguyễn , Huy Hoàng Nguyễn , Thị Thu Hiền Nguyễn , Văn Đạt Nguyễn , Thị Huyền Lê , Thị Ánh Nguyễn
Published: June 2021

Mục tiêu: Đánh giá kiến thức, thái độ, thực hành của bố mẹ bệnh nhi về bệnh thalassemia điều trị tại Bệnh viện Đa khoa Trung ương Thái Nguyên năm 2020. Đối tượng và phương pháp: Mô tả, thiết kế nghiên cứu cắt ngang. Kết quả: Tỷ lệ bố mẹ bệnh nhi có kiến thức khá, tốt về dự phòng bệnh thalassemia là 31,1%. Tỷ lệ bố mẹ bệnh nhi có thái độ khá, tốt về dự phòng bệnh thalassemia là 68,9%. Có 20% bố mẹ bệnh nhi tự nguyện xét nghiệm phát hiện bệnh thalassemia. Tỷ lệ bố mẹ bệnh nhi đã từng tư vấn cho người thân làm xét nghiệm phát hiện bệnh chiếm 36,7%, 74,4% bố mẹ bệnh nhi chủ động tìm kiếm các thông tin về bệnh; 65,0% bố mẹ bệnh nhi đã từng chia sẻ kiến thức về bệnh với người khác. Kết luận: Kiến thức, thái độ, thực hành dự phòng bệnh của bố mẹ bệnh nhi còn chưa tích cực.

Từ khóa: Kiến thức, thái độ, thực hành, thalassemia, bố mẹ bệnh nhi, Thái Nguyên.
Kết quả sớm sau phẫu thuật thay van động mạch chủ tại Bệnh viện Trung ương Quân đội 108
Authors: Tuấn Anh Ngô , Trường Giang Nguyễn
Published: June 2021

Mục tiêu: Đánh giá một số kết quả sớm sau phẫu thuật thay van động mạch chủ ở bệnh nhân hẹp chủ tại Bệnh viện Trung ương Quân đội 108. Đối tượng và phương pháp: 69 bệnh nhân hẹp chủ được thay van động mạch chủ từ tháng 05/2010 đến tháng 05/2020, với tuổi trung bình 63,3 ± 10,4 năm với nam và 67,2 ± 8,5 năm với nữ. Nghiên cứu can thiệp lâm sàng thu thập các chỉ số (trong 30 ngày sau mổ) gồm NYHA, chỉ số điện tim (nhịp tim, Sokolow Lyon), siêu âm tim (EF, Dd, Ds, Mean PG, LVMI, CSKTTR, diện tích lỗ van, hoạt động cua van nhân tạo), biến chứng sớm (xuất huyết). Kết quả: NYHA II-III chuyển về NYHA I-II với tỷ lệ là 92,8% (p<0,05). Chỉ số Sokolow Lyon trung bình là 31,1 ± 2,9mm (p<0,05). Chênh áp qua van giảm và diện tích lỗ van tăng, Dd và LVMI giảm có ý nghĩa thống kê (p<0,05). Chênh áp < 20mmHg chiếm 91,3%. Biến chứng xuất huyết sớm chiếm 2,9%. Kết luận: Kết quả sớm sau thay van động mạch chủ cải thiện rõ rệt triệu chứng, hình thái tim và ít biến chứng sớm sau phẫu thuật.

              Từ khóa: Hẹp chủ, thay van động mạch chủ.