bluuebunny/crossref_metadata_2025 huggingface dataset:
Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI
Authors: Li Lele , Chen Haihua , Guan Lei
Published: April 2021

Given their high albedo and low thermal conductivity, snow and sea ice are considered key reasons for amplified warming in the Arctic. Snow-covered sea ice is a more effective insulator, which greatly limits the energy and momentum exchange between the atmosphere and surface, and further controls the thermal dynamic processes of snow and ice. In this study, using the Microwave Emission Model of Layered Snowpacks (MEMLS), the sensitivities of the brightness temperatures (TBs) from the FengYun-3B/MicroWave Radiometer Imager (FY3B/MWRI) to changes in snow depth were simulated, on both first-year and multiyear ice in the Arctic. Further, the correlation coefficients between the TBs and snow depths in different atmospheric and sea ice environments were investigated. Based on the simulation results, the most sensitive factors to snow depth, including channels of MWRI and their combination form, were determined for snow depth retrieval. Finally, using the 2012–2013 Operational IceBridge (OIB) snow depth data, retrieval algorithms of snow depth were developed for the Arctic on first-year and multiyear ice, separately. Validation using the 2011 OIB data indicates that the bias and standard deviation (Std) of the algorithm are 2.89 cm and 2.6 cm on first-year ice (FYI), respectively, and 1.44 cm and 4.53 cm on multiyear ice (MYI), respectively.
Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data
Authors: Guo Zengkun , Kurban Alishir , Ablekim Abdimijit , Wu Shupu , Van de Voorde Tim , Azadi Hossein , Maeyer Philippe De , Dufatanye Umwali Edovia
Published: April 2021

Estimating the fractional coverage of the photosynthetic vegetation (fPV) and non-photosynthetic vegetation (fNPV) is essential for assessing the growth conditions of vegetation growth in arid areas and for monitoring environmental changes and desertification. The aim of this study was to estimate the fPV, fNPV and the fractional coverage of the bare soil (fBS) in the lower reaches of Tarim River quantitatively. The study acquired field data during September 2020 for obtaining the fPV, fNPV and fBS. Firstly, six photosynthetic vegetation indices (PVIs) and six non-photosynthetic vegetation indices (NPVIs) were calculated from Sentinel-2A image data. The PVIs include normalized difference vegetation index (NDVI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), modified soil adjusted vegetation index (MSAVI), reduced simple ratio index (RSR) and global environment monitoring index (GEMI). Meanwhile, normalized difference index (NDI), normalized difference tillage index (NDTI), normalized difference senescent vegetation index (NDSVI), soil tillage index (STI), shortwave infrared ratio (SWIR32) and dead fuel index (DFI) constitutes the NPVIs. We then established linear regression model of different PVIs and fPV, and NPVIs and fNPV, respectively. Finally, we applied the GEMI-DFI model to analyze the spatial and seasonal variation of fPV and fNPV in the study area in 2020. The results showed that the GEMI and fPV revealed the best correlation coefficient (R2) of 0.59, while DFI and fNPV had the best correlation of R2 = 0.45. The accuracy of fPV, fNPV and fBS based on the determined PVIs and NPVIs as calculated by GEMI-DFI model are 0.69, 0.58 and 0.43, respectively. The fPV and fNPV are consistent with the vegetation phonological development characteristics in the study area. The study concluded that the application of the GEMI-DFI model in the fPV and fNPV estimation was sufficiently significant for monitoring the spatial and seasonal variation of vegetation and its ecological functions in arid areas.
Towards Including Dynamic Vegetation Parameters in the EUMETSAT H SAF ASCAT Soil Moisture Products
Authors: Steele-Dunne Susan C. , Hahn Sebastian , Wagner Wolfgang , Vreugdenhil Mariette
Published: April 2021

The TU Wien Soil Moisture Retrieval (TUW SMR) approach is used to produce several operational soil moisture products from the Advanced Scatterometer (ASCAT) on the Metop series of satellites as part of the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The incidence angle dependence of backscatter is described by a second-order Taylor polynomial, the coefficients of which are used to normalize ASCAT observations to the reference incidence angle of 40∘ and for correcting vegetation effects. Recently, a kernel smoother was developed to estimate the coefficients dynamically, in order to account for interannual variability. In this study, we used the kernel smoother for estimating these coefficients, where we distinguished for the first time between their two uses, meaning that we used a short and fixed window width for the backscatter normalisation while we tested different window widths for optimizing the vegetation correction. In particular, we investigated the impact of using the dynamic vegetation parameters on soil moisture retrieval. We compared soil moisture retrievals based on the dynamic vegetation parameters to those estimated using the current operational approach by examining their agreement, in terms of the Pearson correlation coefficient, unbiased RMSE and bias with respect to in situ soil moisture. Data from the United States Climate Research Network were used to study the influence of climate class and land cover type on performance. The sensitivity to the kernel smoother half-width was also investigated. Results show that estimating the vegetation parameters with the kernel smoother can yield an improvement when there is interannual variability in vegetation due to a trend or a change in the amplitude or timing of the seasonal cycle. However, using the kernel smoother introduces high-frequency variability in the dynamic vegetation parameters, particularly for shorter kernel half-widths.
Semantic Image Segmentation Based Cable Vibration Frequency Visual Monitoring Using Modified Convolutional Neural Network with Pixel-wise Weighting Strategy
Authors: Yang Han , Xu Hong-Cheng , Jiao Shuang-Jian , Yin Feng-De
Published: April 2021

Attributed to the explosive adoption of large-span spatial structures and infrastructures as a critical damage-sensitive element, there is a pressing need to monitor cable vibration frequency to inspect the structural health. Neither existing acceleration sensor-utilized contact methods nor conventional computer vision-based photogrammetry methods have, to date, addressed the defects of lack in cost-effectiveness and compatibility with real-world situations. In this study, a state-of-the-art method based on modified convolutional neural network semantic image segmentation, which is compatible with extensively varying real-world backgrounds, is presented for cable vibration frequency remote and visual monitoring. Modifications of the underlying network framework lie in adopting simpler feature extractors and introducing class weights to loss function by pixel-wise weighting strategies. Nine convolutional neural networks were established and modified. Discrete images with varying real-world backgrounds were captured to train and validate network models. Continuous videos with different cable pixel-to-total pixel (C-T) ratios were captured to test the networks and derive vibration frequencies. Various metrics were leveraged to evaluate the effectiveness of network models. The optimal C-T ratio was also studied to provide guidelines for the parameter setting of monitoring systems in further research and practical application. Training and validation accuracies of nine networks were all reported higher than 90%. A network model with ResNet-50 as feature extractor and uniform prior weighting showed the most superior learning and generalization ability, of which the Precision reached 0.9973, F1 reached 0.9685, and intersection over union (IoU) reached 0.8226 when utilizing images with the optimal C-T ratio of 0.04 as testing set. Contrasted with that sampled by acceleration sensor, the first two order vibration frequencies derived by the most superior network from video with the optimal C-T ratio had merely ignorable absolute percentage errors of 0.41% and 0.36%, substantiating the effectiveness of the proposed method.
Urban Thermal Characteristics of Local Climate Zones and Their Mitigation Measures across Cities in Different Climate Zones of China
Authors: Li Nana , Yang Jun , Qiao Zhi , Wang Yongwei , Miao Shiguang
Published: April 2021

Understanding the urban thermal environment is vital for improving urban planning and strategy development when mitigating urban heat islands. However, urban thermal characteristics of local climate zones (LCZ) are different within cities and most studies lack regional perspective. This study explored surface thermal performances of cities in three urban agglomerations (Jing-Jin-Ji, Yangtze River Delta and Pearl River Delta) in China using MODIS land surface temperature (LST). Besides that, the diurnal and seasonal LST variations of LCZs are also studied. Moreover, the optimal LCZs for better urban cooling are also investigated in this study. Although the thermal distributions of LCZs are different in China, there are still some similar features. Our four key findings were as follows. (1) LCZs in China are well classified, with average overall accuracy of 82% being higher than that in some previous studies. (2) The LST of mid-rise (LCZ 2, 5) is higher than that of high- and low-rise buildings (LCZ 1, 3, 4, 6); and compact buildings are warmer than open buildings (LCZ 1–3 > LST 4–6) in summer of China. That shows both mid-rise and compact buildings are not beneficial to cool urban. In addition, LST variations at daytime and in summer are more significant than nighttime and other seasons. (3) LST differences within LCZs are significant at p < 0.05, and are most significant in Jing-Jin-Ji (JJJ). The LST difference within built types (LCZ 1–10) is more significant than within natural types (LCZ A–G), showing that built types alteration will be more effective for thermal environmental improvement. (4) Under the current population and urban area, increasing greenness and water area in compact high-rise buildings are the most effective strategies for urban cooling in all three urban agglomerations, with the largest reduction in LST of 4.11 K in JJJ. These findings will provide support for thermal environment mitigation, urban planning and sustainable urban development.
Automated Global Shallow Water Bathymetry Mapping Using Google Earth Engine
Authors: Li Jiwei , Knapp David E. , Lyons Mitchell , Roelfsema Chris , Phinn Stuart , Schill Steven R. , Asner Gregory P.
Published: April 2021

Global shallow water bathymetry maps offer critical information to inform activities such as scientific research, environment protection, and marine transportation. Methods that employ satellite-based bathymetric modeling provide an alternative to conventional shipborne measurements, offering high spatial resolution combined with extensive coverage. We developed an automated bathymetry mapping approach based on the Sentinel-2 surface reflectance dataset in Google Earth Engine. We created a new method for generating a clean-water mosaic and a tailored automatic bathymetric estimation algorithm. We then evaluated the performance of the models at six globally diverse sites (Heron Island, Australia; West Coast of Hawaiʻi Island, Hawaiʻi; Saona Island, Dominican Republic; Punta Cana, Dominican Republic; St. Croix, United States Virgin Islands; and The Grenadines) using 113,520 field bathymetry sampling points. Our approach derived accurate bathymetry maps in shallow waters, with Root Mean Square Error (RMSE) values ranging from 1.2 to 1.9 m. This automatic, efficient, and robust method was applied to map shallow water bathymetry at the global scale, especially in areas which have high biodiversity (i.e., coral reefs).
Remotely Piloted Aircraft and Random Forest in the Evaluation of the Spatial Variability of Foliar Nitrogen in Coffee Crop
Authors: Marin Diego Bedin , Ferraz Gabriel Araújo e Silva , Guimarães Paulo Henrique Sales , Schwerz Felipe , Santana Lucas Santos , Barbosa Brenon Dienevam Souza , Barata Rafael Alexandre Pena , Faria Rafael de Oliveira , Dias Jessica Ellen Lima , Conti Leonardo , Rossi Giuseppe
Published: April 2021

The development of approaches to determine the spatial variability of nitrogen (N) into coffee leaves is essential to increase productivity and reduce production costs and environmental impacts associated with excessive N applications. Thus, this study aimed to assess the potential of the Random Forest (RF) machine learning method applied to vegetation indices (VI) obtained from Remotely Piloted Aircraft (RPA) images to measure the N content in coffee plants. A total of 10 VI were obtained from multispectral images by a camera attached to a rotary-wing RPA. The RGB orthomosaic was used to determine sampling points at the crop area, which were ranked by N levels in the plants as deficient, critical, or sufficient. The chemical analysis of N content in the coffee leaves, as well as the VI values in sample points, were used as input parameters for the image training and its classification by the RF. The suggested model has shown global accuracy and a kappa coefficient of up to 0.91 and 0.86, respectively. The best results were achieved using the Green Normalized Difference Vegetation (GNDVI) and Green Optimized Soil Adjusted Vegetation Index (GOSAVI). In addition, the model enabled the evaluation of the spatial distribution of N in the coffee trees, as well as quantification of N deficiency in the crop for the whole area. The GNDVI and GOSAVI allowed the verification that 22% of the entire crop area had plants with N deficiency symptoms, which would result in a reduction of 78% in the amount of N applied by the producer.
GPR Monitoring of Artificial Debonded Pavement Structures throughout Its Life Cycle during Accelerated Pavement Testing
Authors: Dérobert Xavier , Baltazart Vincent , Simonin Jean-Michel , Todkar Shreedhar Savant , Norgeot Christophe , Hui Ho-Yan
Published: April 2021

The paper gives an overview of a ground penetrating radar (GPR) experiment to survey debonding areas within pavement structure during accelerated pavement tests (APT) conducted on the university Gustave Eiffel’s fatigue carrousel. Thirteen artificial defect sections composed of three types of defects (Tack-free, Geotextile, and Sand-based) were embedded during the construction phase between the top and the base layers. The data were collected in two stages covering the entire life cycle of the pavement structure using four GPR systems: An air-coupled ultra-wideband GPR (SF-GPR), two wideband 2D ground coupled GPRs (a SIR-4000 with a 1.5 GHz antenna and a 2.6 GHz-StructureScan from GSSI manufacturer), and a wideband 3D GPR (from 3D-radar manufacturer). The first stage of the experiments took place in 2012–2013 and lasted up to 300 K loadings. During this stage, the pavement structure presented no clear degradation. The second stage of experiments was conducted in 2019 and continued until the pavement surface demonstrated a strong degradation, which was observed at 800 K loadings. At the end of the GPR experiments, several trenches were cut at various sections to get the ground truth of the pavement structure. Finally, the GPR data are processed using the conventional amplitude ratio test to study the evolution of the echoes coming from the debonded areas.
A Multiscale Method for Road Network Extraction from High-Resolution SAR Images Based on Directional Decomposition and Regional Quality Evaluation
Authors: He Wenjing , Song Hongjun , Yao Yuanyuan , Jia Xinlin
Published: April 2021

Road network is an important part of modern transportation. For the demands of accurate road information in practical applications such as urban planning and disaster assessment, we propose a multiscale method to extract road network from high-resolution synthetic aperture radar (SAR) images, which consists of three stages: potential road area segmentation, preliminary network generation, and road network refinement. Multiscale analysis is implemented using an image pyramid framework together with a fixed-size filter. First, a directional road detector is designed to highlight road targets in feature response maps. Subsequently, adaptive fusion is performed independently at each image scale, followed by a threshold method to produce potential road maps. Then, binary maps are decomposed according to the obtained direction information. For each connected component (CC), quality evaluation is conducted to further distinguish road segments and polynomial curve fitting is adopted as a thinning method. Multiscale information fusion is realized through the weighted sum of road curves. Finally, tensor voting and spatial regularization are employed to generate the final road network. Experiments on three TerraSAR images demonstrate the effectiveness of the proposed algorithm to extract road network completely and correctly.
Performance and Evaluation of GNSS Receiver Vector Tracking Loop Based on Adaptive Cascade Filter
Authors: Yang Haotian , Zhou Bin , Wang Lixin , Wei Qi , Ji Feng , Zhang Rong
Published: April 2021

In the scenario of high dynamics and low C/N0, the discriminator output of a GNSS tracking loop is noisy and nonlinear. The traditional method uses a fixed-gain loop filter for error estimation, which is prone to lose lock and causes inaccurate navigation and positioning. This paper proposes a cascaded adaptive vector tracking method based on the KF+EKF architecture through the GNSS Software defined receiver in the signal tracking module and the navigation solution module. The linear relationships between the pseudo-range error and the code phase error, the pseudo-range rate error and the carrier frequency error are obtained as the measurement, and the navigation filter estimation is performed. The signal C/N0 ratio and innovation sequence are used to adjust the measurement noise covariance matrix and the process noise covariance matrix, respectively. Then, the estimated error value is used to correct the navigation parameters and fed back to the local code/carrier NCO. The field vehicle test results show that, in the case of sufficient satellite signals, the positioning error of the proposed method has a slight advantage compared with the traditional method. When there is signal occlusion or interference, the traditional method cannot achieve accurate positioning. However, the proposed method can maintain the same accuracy for the positioning results.
Planetary Gearbox Dynamic Modeling Considering Bearing Clearance and Sun Gear Tooth Crack
Authors: Chen Xianhua , Yang Xingkai , Zuo Ming J. , Tian Zhigang
Published: April 2021

Planetary gearbox systems are critical mechanical components in heavy machinery such as wind turbines. They may suffer from various failure modes, due to the harsh working environment. Dynamic modeling is a useful method to support early fault detection for enhancing reliability and reducing maintenance costs. However, reported studies have not considered the sun gear tooth crack and bearing clearance simultaneously to analyze their combined effect on vibration characteristics of planetary gearboxes. In this paper, a dynamic model is developed for planetary gearboxes considering the clearance of planet gear, sun gear, and carrier bearings, as well as sun gear tooth crack levels. Bearing forces are calculated considering bearing clearance, and the dynamic model equations are updated accordingly. The results reveal that the combination of bearing clearances can affect the vibration response with sun gear tooth crack by increasing the kurtosis. It is found that the effect of planet gear bearing clearance is very small, while the sun gear and carrier bearing clearance has clear impact on the vibration responses. These findings suggest that the incorporation of bearing clearance is important for planetary gearbox dynamic modeling.
UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks
Authors: Choi Daegyun , Bell William , Kim Donghoon , Kim Jichul
Published: April 2021

Structural cracks are a vital feature in evaluating the health of aging structures. Inspectors regularly monitor structures’ health using visual information because early detection of cracks on highly trafficked structures is critical for maintaining the public’s safety. In this work, a framework for detecting cracks along with their locations is proposed. Image data provided by an unmanned aerial vehicle (UAV) is stitched using image processing techniques to overcome limitations in the resolution of cameras. This stitched image is analyzed to identify cracks using a deep learning model that makes judgements regarding the presence of cracks in the image. Moreover, cracks’ locations are determined using data from UAV sensors. To validate the system, cracks forming on an actual building are captured by a UAV, and these images are analyzed to detect and locate cracks. The proposed framework is proven as an effective way to detect cracks and to represent the cracks’ locations.
SEMPANet: A Modified Path Aggregation Network with Squeeze-Excitation for Scene Text Detection
Authors: Li Shuangshuang , Cao Wenming
Published: April 2021

Recently, various object detection frameworks have been applied to text detection tasks and have achieved good performance in the final detection. With the further expansion of text detection application scenarios, the research value of text detection topics has gradually increased. Text detection in natural scenes is more challenging for horizontal text based on a quadrilateral detection box and for curved text of any shape. Most networks have a good effect on the balancing of target samples in text detection, but it is challenging to deal with small targets and solve extremely unbalanced data. We continued to use PSENet to deal with such problems in this work. On the other hand, we studied the problem that most of the existing scene text detection methods use ResNet and FPN as the backbone of feature extraction, and improved the ResNet and FPN network parts of PSENet to make it more conducive to the combination of feature extraction in the early stage. A SEMPANet framework without an anchor and in one stage is proposed to implement a lightweight model, which is embodied in the training time of about 24 h. Finally, we selected the two most representative datasets for oriented text and curved text to conduct experiments. On ICDAR2015, the improved network’s latest results further verify its effectiveness; it reached 1.01% in F-measure compared with PSENet-1s. On CTW1500, the improved network performed better than the original network on average.
Towards Tracking of Deep Brain Stimulation Electrodes Using an Integrated Magnetometer
Authors: Quirin Thomas , Féry Corentin , Vogel Dorian , Vergne Céline , Sarracanie Mathieu , Salameh Najat , Madec Morgan , Hemm Simone , Hébrard Luc , Pascal Joris
Published: April 2021

This paper presents a tracking system using magnetometers, possibly integrable in a deep brain stimulation (DBS) electrode. DBS is a treatment for movement disorders where the position of the implant is of prime importance. Positioning challenges during the surgery could be addressed thanks to a magnetic tracking. The system proposed in this paper, complementary to existing procedures, has been designed to bridge preoperative clinical imaging with DBS surgery, allowing the surgeon to increase his/her control on the implantation trajectory. Here the magnetic source required for tracking consists of three coils, and is experimentally mapped. This mapping has been performed with an in-house three-dimensional magnetic camera. The system demonstrates how magnetometers integrated directly at the tip of a DBS electrode, might improve treatment by monitoring the position during and after the surgery. The three-dimensional operation without line of sight has been demonstrated using a reference obtained with magnetic resonance imaging (MRI) of a simplified brain model. We observed experimentally a mean absolute error of 1.35 mm and an Euclidean error of 3.07 mm. Several areas of improvement to target errors below 1 mm are also discussed.
Design and Analysis of an Intelligent Toilet Wheelchair Based on Planar 2DOF Parallel Mechanism with Coupling Branch Chains
Authors: Shi Xiaohua , Lu Hao , Chen Ziming
Published: April 2021

Due to the fixed size of the structure or the possibility of only simple manual adjustment, the traditional toilet wheelchair cannot easily be adapted to the size of the user or the toilet. In this paper, a planar two-degree-of-freedom parallel mechanism with coupling branch chains is proposed to enable both seat height adjustment and body posture adjustment of a toilet chair, solving the problems of posture adaptability between the user and the machine, and height matching in the process of using the wheelchair-assisted toilet. The model of the parallel mechanism was designed after analyzing the general rules of posture transformation in the human body before and after the toilet process, and the dimensions of each linkage were then determined according to the constraint conditions. By analyzing the degree of freedom, kinematics, workspace, singularity and position of the center of gravity, the rationality of the design was ensured. The weighted average function was used to find the optimal fixed point of the horizontal moving slider, and the actual trajectory at the end of the single driving mode was close to the ideal trajectory. The experimental results show that the adjustable seat height range is 290~550 mm and the adjustable angle range is 0~90°, which can enable disabled people to use the toilet independently.
Real-Time and Efficient Traffic Information Acquisition via Pavement Vibration IoT Monitoring System
Authors: Ye Zhoujing , Yan Guannan , Wei Ya , Zhou Bin , Li Ning , Shen Shihui , Wang Linbing
Published: April 2021

Traditional road-embedded monitoring systems for traffic monitoring have the disadvantages of a short life, high energy consumption and data redundancy, resulting in insufficient durability and high cost. In order to improve the durability and efficiency of the road-embedded monitoring system, a pavement vibration monitoring system is developed based on the Internet of things (IoT). The system includes multi-acceleration sensing nodes, a gateway, and a cloud platform. The key design principles and technologies of each part of the system are proposed, which provides valuable experience for the application of IoT monitoring technology in road infrastructures. Characterized by low power consumption, distributed computing, and high extensibility properties, the pavement vibration IoT monitoring system can realize the monitoring, transmission, and analysis of pavement vibration signal, and acquires the real-time traffic information. This road-embedded system improves the intellectual capacity of road infrastructure and is conducive to the construction of a new generation of smart roads.
Classification of Cross-Country Ski Skating Sub-Technique Can Be Automated Using Carrier-Phase Differential GNSS Measurements of the Head’s Position
Authors: Gløersen Øyvind , Gilgien Matthias
Published: April 2021

Position–time tracking of athletes during a race can provide useful information about tactics and performance. However, carrier-phase differential global navigation satellite system (dGNSS)-based tracking, which is accurate to about 5 cm, might also allow for the extraction of variables reflecting an athlete’s technique. Such variables include cycle length, cycle frequency, and choice of sub-technique. The aim of this study was to develop a dGNSS-based method for automated determination of sub-technique and cycle characteristics in cross-country ski skating. Sub-technique classification was achieved using a combination of hard decision rules and a neural network classifier (NNC) on position measurements from a head-mounted dGNSS antenna. The NNC was trained to classify the three main sub-techniques (G2–G4) using optical marker motion data of the head trajectory of six subjects during treadmill skiing. Hard decision rules, based on the head’s sideways and vertical movement, were used to identify phases of turning, tucked position and G5 (skating without poles). Cycle length and duration were derived from the components of the head velocity vector. The classifier’s performance was evaluated on two subjects during an in-field roller skiing test race by comparison with manual classification from video recordings. Classification accuracy was 92–97% for G2–G4, 32% for G5, 75% for turning, and 88% for tucked position. Cycle duration and cycle length had a root mean square (RMS) deviation of 2–3%, which was reduced to <1% when cycle duration and length were averaged over five cycles. In conclusion, accurate dGNSS measurements of the head’s trajectory during cross-country skiing contain sufficient information to classify the three main skating sub-techniques and characterize cycle length and duration.
Nonlinear T-Wave Time Warping-Based Sensing Model for Non-Invasive Personalised Blood Potassium Monitoring in Hemodialysis Patients: A Pilot Study
Authors: Palmieri Flavio , Gomis Pedro , Ruiz José Esteban , Ferreira Dina , Martín-Yebra Alba , Pueyo Esther , Martínez Juan Pablo , Ramírez Julia , Laguna Pablo
Published: April 2021

Background: End-stage renal disease patients undergoing hemodialysis (ESRD-HD) therapy are highly susceptible to malignant ventricular arrhythmias caused by undetected potassium concentration ([K+]) variations (Δ[K+]) out of normal ranges. Therefore, a reliable method for continuous, noninvasive monitoring of [K+] is crucial. The morphology of the T-wave in the electrocardiogram (ECG) reflects Δ[K+] and two time-warping-based T-wave morphological parameters, dw and its heart-rate corrected version dw,c, have been shown to reliably track Δ[K+] from the ECG. The aim of this study is to derive polynomial models relating dw and dw,c with Δ[K+], and to test their ability to reliably sense and quantify Δ[K+] values. Methods: 48-hour Holter ECGs and [K+] values from six blood samples were collected from 29 ESRD-HD patients. For every patient, dw and dw,c were computed, and linear, quadratic, and cubic fitting models were derived from them. Then, Spearman’s (ρ) and Pearson’s (r) correlation coefficients, and the estimation error (ed) between Δ[K+] and the corresponding model-estimated values (Δ^[K+]) were calculated. Results and Discussions: Nonlinear models were the most suitable for Δ[K+] estimation, rendering higher Pearson’s correlation (median 0.77 ≤r≤ 0.92) and smaller estimation error (median 0.20 ≤ed≤ 0.43) than the linear model (median 0.76 ≤r≤ 0.86 and 0.30 ≤ed≤ 0.40), even if similar Spearman’s ρ were found across models (median 0.77 ≤ρ≤ 0.83). Conclusion: Results support the use of nonlinear T-wave-based models as Δ[K+] sensors in ESRD-HD patients.
Increasing the Efficiency of Enterprises in Tourism Sector Using Innovative Management Methods and Tools
Authors: Gallo Peter , Dobrovič Ján , Čabinová Veronika , Pártlová Petra , Straková Jarmila , Mihalčová Bohuslava
Published: April 2021

The paper deals with the issue of increasing the efficiency of business management using modern management methods and tools in tourism enterprises. The basis of the paper form hypotheses focused on the use of the concept of Balanced Scorecard, controlling, benchmarking, and talent management within the tourism enterprises. Based on the p-value (0.576) in the case of first hypothesis, we can confirm that there is no statistically significant relationship between the openness of hotels to change, innovation, and the willingness to introduce controlling into management. The p-value (0.004) achieved in the case of the second hypothesis confirmed a statistically significant relationship between the hotel classification category and the use of the modern methods in plan design. In the third hypothesis, we examined whether hotels focusing on the sales maximization tend to use their capital for modernization. Based on the validity of the inequality p-value (0.080), we confirm that there is no statistically significant relationship between the pricing approach and the use of capital. Searching strategic tool in management of a business in tourism is still an actual issue. Businesses are especially managed by financial indicators, but modern methods show the growing significance of non-financial indicators.
European Muslim Youth and Gender (in)Equality Discourse: Towards a More Critical Academic Inquiry
Authors: Boland Colleen
Published: April 2021

In Europe, gender equality can be framed as a secular value, juxtaposed against affiliation with and practice of Islam. Academic and public debate has either given special attention to the spread of religious fundamentalism in Europe, or to the way Muslim women dress, citing how both purportedly jeopardize gender equality. This is despite findings that a link between gender equality and religiosity or practice of Islam is neither inherent nor circumscribed. Moreover, it is possible to demonstrate that such discourse rests on implicitly racialized conceptualizations of the Muslim “other”. Meanwhile, Muslim youth in particular are benchmarked against these imagined standards of gender equality, as compared with non-Muslim peers. This work examines ways in which normative secular frameworks and discourses, taking ownership of gender equality narratives, have shaped Europe’s academic inquiry regarding Muslim youth. It notes what is absent in this inquiry, including intersections of race and class, which remain divorced from the limited conversation on gender and religious difference. A reflexive, intersectional approach to this discussion, conscious of the importance of embedded racial or structural inequality and what is absent in current inquiry, better serves in understanding and navigating power relations that ultimately contribute to multiple exclusion of these youth.
Experimental Study on the Thermal Behavior of Exterior Coating Textures of Building in Hot and Arid Climates
Authors: Boukhelkhal Islam , Bourbia Fatiha
Published: April 2021

The building envelope is the barrier between the interior and exterior environments. It has many important functions, including protecting the interior space from the climatic variations through its envelope materials and design elements, as well as reduction of energy consumption and improving indoor thermal comfort. Furthermore, exterior building sidings, in addition to their aesthetic appearance, can have useful textures for reducing solar gains and providing good thermal insulation performance. This research examined and evaluated the effect of external siding texture and geometry on energy performance. For this objective, a field in situ testing and investigation of surface temperature was carried out on four samples (test boxes) with different exterior textures and different orientations, under the climate zone of Constantine–Algeria during the summer period. The results indicated significant dependability between the exterior texture geometry, the percentage of shadow projected, and external surface temperature. The second part of the research involved a similar approach, exploring the effect of three types of particles with the same appearance but with different thermal characteristics. It was concluded that the natural plant aggregates “palm particles” had the best performance, which contributed to a significant reduction of external surface temperature reaching 4.3 °C, which meant decreasing the energy consumption.
Traffic Light Priority for Trams in Warsaw as a Tool for Transport Policy and Reduction of Energy Consumption
Authors: Czerepicki Andrzej , Krukowicz Tomasz , Górka Anna , Szustek Jarosław
Published: April 2021

The article presents an analysis of priority solutions for trams at a selected sequence of intersections in Warsaw (Poland). An analysis of the literature has shown the topicality of this issue. A computer simulation model of a coordinated sequence of intersections was constructed. Three test scenarios were designed: the existing control system, the new coordinated fixed-time control system, and the adaptive control system with active priority. In the simulation process, detailed travel characteristics of trams and other traffic participants in a selected section were obtained for the three varying scenarios. Electric energy consumption for traction needs and pollutant emissions was then estimated for each of the variants. It was concluded that for the analyzed configuration, implementation of the adaptive priority will result in a reduction of tram time losses by up to 25%, a reduction in energy consumption by up to 23%, and a reduction in the emission of pollutants from individual vehicles by up to 3% in relation to the original variant. The conducted research may be the basis for a comprehensive method of assessing the effectiveness of applying the adaptative priority when designing new tramway lines and modernizing the existing ones.
Microplastics in Glaciers: First Results from the Vatnajökull Ice Cap
Authors: Stefánsson Hlynur , Peternell Mark , Konrad-Schmolke Matthias , Hannesdóttir Hrafnhildur , Ásbjörnsson Einar Jón , Sturkell Erik
Published: April 2021

Microplastic particles, as a second-phase material in ice, may contribute to the effect such particles have on the melting and rheological behaviour of glaciers, and thus influence the future meltwater contribution to the oceans and rising sea levels. Hence, it is of the utmost importance to map and understand the presence and dispersal of microplastics on a global scale. In this work, we identified microplastic particles in snow cores collected in a remote and pristine location on the Vatnajökull ice cap in Iceland. Utilising optical microscopy and µ-Raman spectroscopy, we visualised and identified microplastic particles of various sizes and materials. Our findings support that atmospheric transport of microplastic particles is one of the important pathways for microplastic pollution.
The Efficacy of Whole Oyster Shells for Removing Copper, Zinc, Chromium, and Cadmium Heavy Metal Ions from Stormwater
Authors: Xu Zhiying , Valeo Caterina , Chu Angus , Zhao Yao
Published: April 2021

This research investigates the use of a common food waste product for removing four different types of metals typically found in stormwater. Whole, unprocessed oyster shells are explored for use in stormwater management infrastructure that addresses water quality concerns. The role of the shells’ surface area, exposure time, and the solution’s initial concentration on the removal efficiency were examined. Beaker scale experimental results demonstrated very good efficiency by the oyster shells for removing copper ions (80–95%), cadmium ions (50–90%), and zinc ions (30–80%) but the shells were not as effective in removing hexavalent chromium (20–60%). There was a positive relationship between initial concentration and removal efficiency for copper and zinc ions, a negative relationship for hexavalent chromium, and no relationship was found for cadmium ions. There was also a positive relationship between surface area and removal efficiency, and exposure time and removal efficiency. However, after a certain exposure time, the increase in removal efficiency was negligible and desorption was occasionally observed. A mid-scale experiment to mimic real-world conditions was conducted in which continuous inflow based on a 6-h design storm was applied to 2.7 kg of whole, unprocessed oyster shells. The shells provided an 86% and an 84% removal efficiency of cadmium and copper ions, respectively, in one day of hydraulic retention time. No removal was observed for hexavalent chromium, and zinc ion removal was only observed after initial leaching. This work has significant implications for sustainable stormwater infrastructure design using a material commonly found in municipal food waste.
Does Foreign Institutional Equity Participation Instigate Sustainable Corporate Investment Efficiency? Evidence from Emerging Economies
Authors: Riaz Sabahat , Hanifa Mohamed Hisham , Zainir Fauzi
Published: April 2021

This study examines the impact of overall foreign institutional equity participation and its two types—foreign institutional pressure-resistant and pressure-sensitive—on firm sustainable investment efficiency for non-financial listed domestic firms of three emerging economies over the period of 2009–2018, using an unbalanced panel of 733 firms with 4468 firm-year observations. It also investigates the impact of varying levels of foreign equity participation on investment efficiency. We used the regression estimation technique with robust standard errors clustered at the firm level. We also used the second-stage instrumental variable (IV) method to control potential endogeneity. Empirical findings reveal that overall foreign institutional equity participation and foreign institutional pressure-resistant ownership have a positive and significant impact on corporate investment efficiency, whereas foreign institutional pressure-sensitive ownership has a positive but insignificant impact. When we divided the overall institutional foreign equity ownership and its two types into five levels, we found a positive and significant impact of overall foreign institutional ownership at all levels. The foreign institutional pressure-resistant ownership has a positive and significant impact on investment efficiency when it is greater than 10%. However, we found a weak relationship of foreign institutional pressure-sensitive equity ownership with investment efficiency at all varying levels of investments. These results are robust when we controlled for endogeneity. Our results have implications for policymakers, regulators, academicians, and potential foreign equity participants. These results can be generalized to those emerging economies that have the potentials for attracting foreign equity inflows.
Influence Range and Traffic Risk Analysis of Moving Work Zones on Urban Roads
Authors: Fang Song , Ma Jianxiao
Published: April 2021

There is a body of literature on the influence range and traffic risk of fixed work zones. However, relatively few studies have examined the effect of ubiquitous moving operating vehicles, such as road cleaners, on urban roads. The influence of low speed moving work zones on road traffic flow and traffic risk is still unclear. In this work, we used simulations to establish an urban expressway three lanes VISSIM model, and selected the road traffic volume and speed of the moving work zone as the independent variables. We analyzed the range of influence of the moving work zone on the rear vehicles in the left, middle and right lanes of the urban expressway and the traffic risk variation law caused by the moving work zone. The results show that the left lane was indirectly affected by the moving work zone when the traffic volume reached 2000 pcu/h. The influence of the moving work zone on the middle lane was controlled by the traffic volume and the speed of the moving work zone. Both the left and middle lanes were mainly impacted by vehicles changing lane from the right lane. Regardless of the traffic volume and the speed of the moving work zone change, the vehicles 200 m behind a moving work zone will be directly affected in the right lane. Furthermore, the average traffic risk is the highest within 50 m of the moving work zone in the right lane. When the traffic volume decreases and the speed of the moving work zone increases, the average traffic risk decreases gradually. These results provide a scientific basis for the operation and management of moving working vehicles on urban roads.
Economizing the Uneconomic: Markets for Reliable, Sustainable, and Price Efficient Electricity
Authors: Rasouli Mohammad , Teneketzis Demosthenis
Published: April 2021

Current electricity markets do not efficiently achieve policy targets i.e., sustainability, reliability, and price efficiency. Thus, there are debates on how to achieve these targets by using either market mechanisms e.g., carbon and capacity markets, or non-market mechanisms such as offer-caps, price-caps, and market-monitoring. At the same time, major industry changes including demand response management technologies and large scale batteries bring more elasticity to demand; such changes will impact the methodology needed to achieve the above mentioned targets. This work provides market solutions that capture all three policy targets simultaneously and take into account the above-mentioned industry changes. The proposed solutions are based on: (i) a model of electricity markets that captures all the above mentioned electricity policy targets; (ii) mechanism design and the development of a framework for design of efficient auctions with constraints (individual, joint homogeneous, and joint non-homogeneous). The results show that, within the context of the proposed model, all policy targets can be achieved efficiently by separate capacity and carbon markets in addition to efficient spot markets. The results also highlight that all three policy targets can be achieved without any offer-cap, price-cap, or market monitoring. Thus, within the context of the proposed model, they provide clear answers to the above-mentioned policy debates.
Moderating Factors in University Students’ Self-Evaluation for Sustainability
Authors: León Samuel P. , Augusto-Landa José María , García-Martínez Inmaculada
Published: April 2021

Background: Self-evaluation is a multidimensional construct that has raised increasing interest within educational research at different educational stages. Different studies have pointed out the important role that Student Self-Assessment plays in improving student learning and ensuring the sustainability in instructional and evaluation processes. Method: The aim of this study with 630 university students is to analyze how engagement profiles and study strategies (measured by questionnaire) can predict the accuracy of students’ self-assessment of their achievements. For this purpose, the UWE-9 questionnaire was used to evaluate engagement, the Study Techniques Questionnaire scale to measure study strategies and a content-based test to evaluate performance, along with a self-assessment test in which the student had to estimate the level of achievement obtained in the content-based test, once the test had been completed. Results: The results show that both the academic engagement and the study strategies undertaken by students can be important factors that may influence different aspects of learning in the educational context. Students with higher performance and more engagement tend to show greater skills with student self-assessment (SSA) and students with better study habits tend to have better scores, greater confidence in the SSAs delivered and better skills for self-assessment. Conclusions: Findings suggests that providing opportunities for students to have a greater involvement in the construction of their learning and in its evaluation raises positive attitudes, which results in increased performance in order to achieve greater sustainability in the learning process assessment.