bluuebunny/crossref_metadata_2025 huggingface dataset:
Food Involvement, Eating Restrictions and Dietary Patterns in Polish Adults: Expected Effects of Their Relationships (LifeStyle Study)
Authors: Jezewska-Zychowicz Marzena , Gębski Jerzy , Kobylińska Milena
Published: April 2020

Understanding the factors that coexist with healthy and unhealthy eating behaviors is prevalent and important for public health. The aim of this study was to investigate the associations between food involvement, eating restrictions, and dietary patterns in a representative sample of Polish adults. The study was conducted among a group of 1007 adults. Questions with the answers yes or no were used to obtain the data regarding eating restrictions. Data relating to food involvement were obtained with the Food Involvement Scale (FIS). Questions from the Beliefs and Eating Habits questionnaire were used to measure the frequency of consumption of different food groups. Five dietary patterns (DPs) were derived using principal component analysis (PCA), i.e., ‘Fruit and vegetables’, ‘Wholemeal food’, ‘Fast foods and sweets’, ‘Fruit and vegetable juices’ and “Meat and meat products’. In each of the DPs, three groups of participants were identified based on tertile distribution with the upper tertile denoting the most frequent consumption. Nearly two-thirds of the study sample declared some restrictions in food consumption. The probability of implementing restrictions in consumption of foods high in sugar, fat and high-fat foods increased in the upper tertile of ‘Fruit and vegetables’ and ‘Wholemeal’ DPs. Moreover, the probability of implementing restrictions in consumption of meat and high-starch products increased in ‘Wholemeal’ DP. The probability of using eating restrictions decreased in the upper tertile of ‘Fast foods and sweets’ and Meat and meat products’ DPs. In conclusion, individuals characterized by high food involvement were more inclined to use eating restrictions than individuals with lower food involvement. Their DPs were also healthier compared to those of individuals manifesting low food involvement. Therefore, promoting personal commitment to learning about and experiencing food may be an effective way of inducing a change of eating habits, and therefore a healthier diet.
Evidence of the Extrahepatic Replication of Hepatitis E Virus in Human Endometrial Stromal Cells
Authors: El-Mokhtar Mohamed A. , Othman Essam R. , Khashbah Maha Y. , Ismael Ali , Ghaliony Mohamed AA , Seddik Mohamed Ismail , Sayed Ibrahim M.
Published: April 2020

Hepatitis E virus (HEV) is the most common cause of acute viral hepatitis worldwide. The tropism of HEV is not restricted to the liver, and the virus replicates in other organs. Not all the extrahepatic targets for HEV are identified. Herein, we found that non-decidualized primary human endometrial stromal cells (PHESCs), which are precursors for the decidua and placenta, are susceptible to HEV infection. PHESCs, isolated from healthy non-pregnant women (n = 5), were challenged with stool-derived HEV-1 and HEV-3. HEV RNA was measured by qPCR, and HEV capsid protein was assessed by flow cytometry, immunofluorescence (IF), and ELISA. HEV infection was successfully established in PHESCs. Intracellular and extracellular HEV RNA loads were increased over time, indicating efficient replication in vitro. In addition, HEV capsid protein was detected intracellularly in the HEV-infected PHESCs and accumulated extracellularly over time, confirming the viral assembly and release from the infected cells. HEV-1 replicated more efficiently in PHESCs than HEV-3 and induced more inflammatory responses. Ribavirin (RBV) treatment abolished the replication of HEV in PHESCs. In conclusion, PHESCs are permissive to HEV infection and these cells could be an endogenous source of HEV infection during pregnancy and mediate HEV vertical transmission.
Ethylicin Prevents Potato Late Blight by Disrupting Protein Biosynthesis of Phytophthora infestans
Authors: Zhang Shumin , Zhang Meiquan , Khalid A. Rehman , Li Linxuan , Chen Yang , Dong Pan , Wang Hanyan , Ren Maozhi
Published: April 2020

Phytophthora infestans, the causal agent of potato late blight, triggered the devastating Great Irish Famine that lasted from 1845 to 1852. Today, it is still the greatest threat to the potato yield. Ethylicin is a broad-spectrum biomimetic-fungicide. However, its application in the control of Phytophthora infestans is still unknown. In this study, we investigated the effects of ethylicin on Phytophthora infestans. We found that ethylicin inhibited the mycelial growth, sporulation capacity, spore germination and virulence of Phytophthora infestans. Furthermore, the integrated analysis of proteomics and metabolomics indicates that ethylicin may inhibit peptide or protein biosynthesis by suppressing both the ribosomal function and amino acid metabolism, causing an inhibitory effect on Phytophthora infestans. These observations indicate that ethylicin may be an anti-oomycete agent that can be used to control Phytophthora infestans.
GoPrime: Development of an In Silico Framework to Predict the Performance of Real-Time PCR Primers and Probes Using Foot-and-Mouth Disease Virus as a Model
Authors: Howson Emma L A , Orton Richard J , Mioulet Valerie , Lembo Tiziana , King Donald P , Fowler Veronica L
Published: April 2020

Real-time PCR (rPCR) is a widely accepted diagnostic tool for the detection and quantification of nucleic acid targets. In order for these assays to achieve high sensitivity and specificity, primer and probe-template complementarity is essential; however, mismatches are often unavoidable and can result in false-negative results and errors in quantifying target sequences. Primer and probe sequences therefore require continual evaluation to ensure they remain fit for purpose. This paper describes the development of a linear model and associated computational tool (GoPrime) designed to predict the performance of rPCR primers and probes across multiple sequence data. Empirical data were generated using DNA oligonucleotides (n = 90) that systematically introduced variation in the primer and probe target regions of a diagnostic assay routinely used to detect foot-and-mouth disease virus (FMDV); an animal virus that exhibits a high degree of sequence variability. These assays revealed consistent impacts of patterns of substitutions in primer and probe-sites on rPCR cycle threshold (CT) and limit of detection (LOD). These data were used to populate GoPrime, which was subsequently used to predict rPCR results for DNA templates (n = 7) representing the natural sequence variability within FMDV. GoPrime was also applicable to other areas of the FMDV genome, with predictions for the likely targets of a FMDV-typing assay consistent with published experimental data. Although further work is required to improve these tools, including assessing the impact of primer-template mismatches in the reverse transcription step and the broader impact of mismatches for other assays, these data support the use of mathematical models for rapidly predicting the performance of rPCR primers and probes in silico.
Pharmacists’ Confidence in Managing Patients with Inflammatory Bowel Disease
Authors: Prasad Sharmila S. , Keely Simon , Talley Nicholas J. , Kairuz Therése , Walker Marjorie M.
Published: April 2020

Background and aim: Managing patients with a chronic condition such as inflammatory bowel disease (IBD), requires a multidiscipline approach. The pharmacist might be the first point of contact for patients with initial symptoms or relapsing flares, yet there is no available literature on the role of pharmacists in IBD management. We conducted a survey to explore pharmacists’ confidence in and potential barriers to managing IBD and assess the impact of IBD education on their confidence in IBD management. Methods: Surveys assessing confidence levels in managing IBD, additional learning opportunities about IBD and barriers to their learning of IBD management were provided to pharmacists for completion before and after attending an IBD-specific education session at a national conference. Results: Of the 195 attendees, 125 participants completed the survey (64%). Most respondents reported a low to mid-range level of confidence with managing IBD. Specifically, they were only slightly confident in decision making on patient care, addressing patient needs and providing additional support for IBD patients; and somewhat confident with understanding, management and providing relevant information on IBD. Whist the education session improved pharmacists perceived level of confidence, most respondents indicated a need to learn more about IBD. Areas of additional learning included science, drug therapy, treatments (includes non-pharmacological options as well) and guidelines. A majority of pharmacists identified time constraints as a key barrier to learning. Conclusion: Pharmacists lack sufficient confidence about managing inflammatory bowel disease. These data indicate support within the pharmacy profession to play a more active role in the management of IBD.
Resemblance and Difference of Seedling Metabolic and Transporter Gene Expression in High Tolerance Wheat and Barley Cultivars in Response to Salinity Stress
Authors: Zeeshan Muhammad , Lu Meiqin , Naz Shama , Sehar Shafaque , Cao Fangbin , Wu Feibo
Published: April 2020

To elucidate inter-specific similarity and difference of tolerance mechanism against salinity stress between wheat and barley, high tolerant wheat cv. Suntop and sensitive cv. Sunmate and tolerant barley cv. CM72 were hydroponically grown in a greenhouse with 100 mM NaCl. Glutathione, secondary metabolites, and genes associated with Na+ transport, defense, and detoxification were examined to discriminate the species/cultivar difference in response to salinity stress. Suntop and CM72 displayed damage to a lesser extent than in Sunmate. Compared to Sunmate, both Suntop and CM72 recorded lower electrolyte leakage and reactive oxygen species (ROS) production, higher leaf relative water content, and higher activity of PAL (phenylalanine ammonia-lyase), CAD (cinnamyl alcohol dehydrogenase), PPO (polyphenol oxidase), SKDH (shikimate dehydrogenase), and more abundance of their mRNA under salinity stress. The expression of HKT1, HKT2, salt overly sensitive (SOS)1, AKT1, and NHX1 was upregulated in CM72 and Suntop, while downregulated in Sunmate. The transcription factor WRKY 10 was significantly induced in Suntop but suppressed in CM72 and Sunmate. Higher oxidized glutathione (GSSG) content was accumulated in cv. CM72 and Sunmate, but increased glutathione (GSH) content and the ratio of GSH/GSSG were observed in leaves and roots of Suntop under salinity stress. In conclusion, glutathione homeostasis and upregulation of the TaWRKY10 transcription factor played a more important role in wheat salt-tolerant cv. Suntop, which was different from barley cv. CM72 tolerance to salinity stress. This new finding could help in developing salinity tolerance in wheat and barley cultivars.
Low-Copy Genes in Terpenoid Metabolism: The Evolution and Expression of MVK and DXR Genes in Angiosperms
Authors: Silva Natacha , Ivamoto-Suzuki Suzana Tiemi , Camargo Paula Oliveira , Rosa Raíssa Scalzoni , Pereira Luiz Filipe Protasio , Domingues Douglas Silva
Published: April 2020

Terpenoids are a diverse class of metabolites that impact plant metabolism in response to environmental cues. They are synthesized either via a predominantly cytosolic (MVA) pathway or a plastidic pathway (MEP). In Arabidopsis, several enzymes from the MVA and MEP pathways are encoded by gene families, excluding MVK and DXR, which are single-copy genes. In this study, we assess the diversity, evolution and expression of DXR and MVK genes in selected angiosperms and Coffea arabica in particular. Evolutionary analysis revealed that DXR and MVK underwent purifying selection, but the selection effect for DXR was stronger than it was for MVK. Digital gene expression (DGE) profile analysis of six species revealed that expression levels of MVK in flowers and roots were high, whereas for DXR peak values were observed in leaves. In C. arabica, both genes were highly expressed in flowers, and CaDXR was upregulated in response to methyl jasmonate. C. arabica DGE data were validated by assessing gene expression in selected organs, and by plants treated with hexanoic acid (Hx) using RT-qPCR. MVK expression was upregulated in roots treated with Hx. CaDXR was downregulated in leaves by Hx treatment in a genotype-specific manner, indicating a differential response to priming.
Allelochemical Activity of Eugenol-Derived Coumarins on Lactuca sativa L.
Authors: Govêa Kamilla Pacheco , Pereira Rafaella Sueko Tomita , de Assis Mateus Donizetti Oliveira , Alves Pâmela Ingrid , Brancaglion Guilherme Andrade , Toyota André Eidi , Machado José Vaz Cardoso , Carvalho Diogo Teixeira , de Souza Thiago Corrêa , Beijo Luiz Alberto , Trindade Luciene de Oliveira Ribeiro , Barbosa Sandro
Published: April 2020

Coumarins are widely distributed substances in plant species that promote phytotoxic effects, allowing them to be exploited as herbicides less harmful to the environment, since many invasive species have demonstrated resistance to commercially available products. The derived coumarins used in this study had not been tested in plant models and their effect on plants was unknown. The objective of this study was to evaluate the phytotoxic action of these coumarins in bioassays with Lactuca sativa L., in order to select the most responsive substance whose toxicity was best elucidated by chromosomal complement and enzymatic antioxidant metabolism studies. From the phytotoxicity assays, coumarin 8-methoxy-2-oxo-6-(prop-2-en-1-yl)-2H-chromene-3-carboxylic acid (A1), reported here for the first time, was selected as the most responsive and caused a reduction in the following parameters: number of normal seedlings, fresh biomass, root length and shoot length. Subsequent studies demonstrated that this coumarin is cytogenotoxic due to damage caused to the cell cycle and the occurrence of chromosomal abnormalities. However, it did not interfere with antioxidant enzyme activity and did not cause lipid peroxidation. The changes caused by coumarin A1 described herein can contribute to better understanding the allelochemical actions of coumarins and the potential use of these substances in the production of natural herbicides.
Erratum: Thermodynamic and Transport Properties of Tetrabutylphosphonium Hydroxide and Tetrabutylphosphonium Chloride–Water Mixtures via Molecular Dynamics Simulation
Authors: Crawford Brad , Ismail Ahmed E.
Published: April 2020

The authors wish to make the following changes to the published paper as listed below [...]
Deep Arbitrage-Free Learning in a Generalized HJM Framework via Arbitrage-Regularization
Authors: Kratsios Anastasis , Hyndman Cody
Published: April 2020

A regularization approach to model selection, within a generalized HJM framework, is introduced, which learns the closest arbitrage-free model to a prespecified factor model. This optimization problem is represented as the limit of a one-parameter family of computationally tractable penalized model selection tasks. General theoretical results are derived and then specialized to affine term-structure models where new types of arbitrage-free machine learning models for the forward-rate curve are estimated numerically and compared to classical short-rate and the dynamic Nelson-Siegel factor models.
An Optimized Object-Based Random Forest Algorithm for Marsh Vegetation Mapping Using High-Spatial-Resolution GF-1 and ZY-3 Data
Authors: Lou Peiqing , Fu Bolin , He Hongchang , Li Ying , Tang Tingyuan , Lin Xingchen , Fan Donglin , Gao Ertao
Published: April 2020

Discriminating marsh vegetation is critical for the rapid assessment and management of wetlands. The study area, Honghe National Nature Reserve (HNNR), a typical freshwater wetland, is located in Northeast China. This study optimized the parameters (mtry and ntrees) of an object-based random forest (RF) algorithm to improve the applicability of marsh vegetation classification. Multidimensional datasets were used as the input variables for model training, then variable selection was performed on the variables to eliminate redundancy, which improved classification efficiency and overall accuracy. Finally, the performance of a new generation of Chinese high-spatial-resolution Gaofen-1 (GF-1) and Ziyuan-3 (ZY-3) satellite images for marsh vegetation classification was evaluated using the improved object-based RF algorithm with accuracy assessment. The specific conclusions of this study are as follows: (1) Optimized object-based RF classifications consistently produced more than 70.26% overall accuracy for all scenarios of GF-1 and ZY-3 at the 95% confidence interval. The performance of ZY-3 imagery applied to marsh vegetation mapping is lower than that of GF-1 imagery due to the coarse spatial resolution. (2) Parameter optimization of the object-based RF algorithm effectively improved the stability and classification accuracy of the algorithm. After parameter adjustment, scenario 3 for GF-1 data had the highest classification accuracy of 84% (ZY-3 is 74.72%) at the 95% confidence interval. (3) The introduction of multidimensional datasets improved the overall accuracy of marsh vegetation mapping, but with many redundant variables. Using three variable selection algorithms to remove redundant variables from the multidimensional datasets effectively improved the classification efficiency and overall accuracy. The recursive feature elimination (RFE)-based variable selection algorithm had the best performance. (4) Optical spectral bands, spectral indices, mean value of green and NIR bands in textural information, DEM, TWI, compactness, max difference, and shape index are valuable variables for marsh vegetation mapping. (5) GF-1 and ZY-3 images had higher classification accuracy for forest, cropland, shrubs, and open water.
Monitoring Woody Cover Dynamics in Tropical Dry Forest Ecosystems Using Sentinel-2 Satellite Imagery
Authors: Van Passel Johanna , De Keersmaecker Wanda , Somers Ben
Published: April 2020

Dry forests in Sub-Saharan Africa are of critical importance for the livelihood of the local population given their strong dependence on forest products. Yet these forests are threatened due to rapid population growth and predicted changes in rainfall patterns. As such, large-scale woody cover monitoring of tropical dry forests is urgently required. Although promising, remote sensing-based estimation of woody cover in tropical dry forest ecosystems is challenging due to the heterogeneous woody and herbaceous vegetation structure and the large intra-annual variability in the vegetation due to the seasonal rainfall. To test the capability of Sentinel-2 satellite imagery for producing accurate woody cover estimations, two contrasting study sites in Ethiopia and Tanzania were used. The estimation accuracy of a linear regression model using the Normalised Difference Vegetation Index (NDVI), a Partial Least Squares Regression (PLSR), and a Random Forest regression model using both single-date and multi-temporal Sentinel-2 images were compared. Additionally, the robustness and site transferability of these methods were tested. Overall, the multi-temporal PLSR model achieved the most accurate and transferable estimations (R2 = 0.70, RMSE = 4.12%). This model was then used to monitor the potential increase in woody coverage within several reforestation projects in the Degua Tembien district. In six of these projects, a significant increase in woody cover could be measured since the start of the project, which could be linked to their initial vegetation, location and shape. It can be concluded that a PLSR model combined with Sentinel-2 satellite imagery is capable of monitoring woody cover in these tropical dry forest regions, which can be used in support of reforestation efforts.
Spatial Perspectives toward the Recommendation of Remote Sensing Images Using the INDEX Indicator, Based on Principal Component Analysis
Authors: Hong Jung-Hong , Su Zeal Li-Tse , Lu Eric Hsueh-Chan
Published: April 2020

Progress in the development of sensor technology has increased the speed and convenience of remote sensing (RS) image acquisition. As the volume of RS images steadily increases, the challenge is no longer in producing and acquiring an RS image, but in finding a particular image from numerous RS images that precisely meets user application needs. Some spatial measuring methods specific to the recommendation of RS images have been proposed and could be used to score and sort RS images according to users’ requests. Our previous study introduced two measuring methods, namely, available space (AS) and image extension (IE), which have similar results but complementary effects for spatially ranking recommended images. The AS indicator could cover the inadequacies of the IE indicator in some cases and vice versa. The current study combines these two indicators using principal component analysis and produced a new indicator called INDEX, which we used in the RS image spatial recommendation. The ranking results were measured using a normalized discounted cumulative gain (NDCG) and several other statistic criteria. The results indicate that users are more satisfied with the recommendations of the INDEX indicator than those of AS, IE and Hausdorff distance for single RS image type selections which is the most common scenario for RS image applications. When dealing with hybrid RS image types, the INDEX indicator performs very closely to the dominant IE indicator, yet maintaining the characteristics of the AS indicator.
CERES Energy Balanced and Filled (EBAF) from Afternoon-Only Satellite Orbits
Authors: Loeb Norman G. , Doelling David R.
Published: April 2020

The Clouds and the Earth’s Radiant Energy System (CERES) Energy Balanced and Filled (EBAF) data product uses a diurnal correction methodology to produce a shortwave (SW) top-of-atmosphere (TOA) radiative flux time series that accounts for diurnal cycle changes between CERES observation times while ensuring that the stability of the EBAF record is tied as closely as possible to CERES instrument calibration stability. The current EBAF Ed4.1 data product combines observations from Terra and Aqua after July 2002. However, the Terra satellite will start to drift in Mean Local Time (MLT) in early 2021, and Aqua’s MLT will start to drift in 2022. To ensure the EBAF record remains temporally stable, we explore the feasibility of using only CERES instruments from afternoon satellite orbits with a tight 1330 MLT after July 2002. We test this approach by directly comparing SW TOA fluxes generated after applying diurnal corrections to Aqua-only and to Terra + Aqua for 07/2002–06/2019. We find that global climatological mean SW TOA fluxes for these two cases are within 0.01 Wm−2 and the trend of the difference is < is 0.03 Wm−2 per decade.
An Interaction Methodology to Collect and Assess User-Driven Requirements to Define Potential Opportunities of Future Hyperspectral Imaging Sentinel Mission
Authors: Taramelli Andrea , Tornato Antonella , Magliozzi Maria Lucia , Mariani Stefano , Valentini Emiliana , Zavagli Massimo , Costantini Mario , Nieke Jens , Adams Jennifer , Rast Michael
Published: April 2020

Evolution in the Copernicus Space Component is foreseen in the mid-2020s to meet priority user needs not addressed by the existing infrastructure, and/or to reinforce existing services. In this context, the European Commission is intending to evaluate the overall potential utility of a complementary Copernicus hyperspectral mission to be added to the Copernicus Sentinels fleet. Hyperspectral imaging is a powerful remote sensing technology that, allowing the characterization and quantification of Earth surface materials, has the potential to deliver significant enhancements in quantitative value-added products. This study aims to illustrate the interaction methodology that was set up to collect and assess user-driven requirements in different thematic areas to demonstrate the potential benefit of a future Copernicus hyperspectral mission. Therefore, an ad hoc interaction matrix was circulated among several user communities to gather preferences about hyperspectral-based products and services. The results show how the involvement of several user communities strengthens the identification of these user requirements. Moreover, the requirement evaluation is used to identify potential opportunities of hyperspectral imaging in addressing operational needs associated with policy obligations at European, national, and local levels. The frequency distribution of spectral range classes and spatial and temporal resolutions are also derived from the preference expressed by the user communities in each thematic area investigated.
Train Hunting Related Fast Degradation of a Railway Crossing—Condition Monitoring and Numerical Verification
Authors: Liu Xiangming , Markine Valéri L.
Published: April 2020

This paper presents the investigation of the root causes of the fast degradation of a railway crossing. The dynamic performance of the crossing was assessed using the sensor-based crossing instrumentation, and the measurement results were verified using the multi-body system (MBS) vehicle-crossing model. Together with the field inspections, the measurement and simulation results indicate that the fast crossing degradation was caused by the high wheel-rail impact forces related to the hunting motion of the passing trains. Additionally, it was shown that the train hunting was activated by the track geometry misalignment in front of the crossing. The obtained results have not only explained the extreme values in the measured responses, but also shown that crossing degradation is not always caused by the problems in the crossing itself, but can also be caused by problems in the adjacent track structures. The findings of this study were implemented in the condition monitoring system for railway crossings, using which timely and correctly aimed maintenance actions can be performed.
An Image Registration Method for Multisource High-Resolution Remote Sensing Images for Earthquake Disaster Assessment
Authors: Zhao Xin , Li Hui , Wang Ping , Jing Linhai
Published: April 2020

For earthquake disaster assessment using remote sensing (RS), multisource image registration is an important step. However, severe earthquakes will increase the deformation between the remote sensing images acquired before and after the earthquakes on different platforms. Traditional image registration methods can hardly meet the requirements of accuracy and efficiency of image registration of post-earthquake RS images used for disaster assessment. Therefore, an improved image registration method was proposed for the registration of multisource high-resolution remote sensing images. The proposed method used the combination of the Shi_Tomasi corner detection algorithm and scale-invariant feature transform (SIFT) to detect tie points from image patches obtained by an image partition strategy considering geographic information constraints. Then, the random sample consensus (RANSAC) and greedy algorithms were employed to remove outliers and redundant matched tie points. Additionally, a pre-earthquake RS image database was constructed using pre-earthquake high-resolution RS images and used as the references for image registration. The performance of the proposed method was evaluated using three image pairs covering regions affected by severe earthquakes. It was shown that the proposed method provided higher accuracy, less running time, and more tie points with a more even distribution than the classic SIFT method and the SIFT method using the same image partitioning strategy.
Strain Conditions Monitoring on Corroded Prestressed Steel Strands in Beams Based on Fiber Bragg Grating Sensors
Authors: Fan Guo-Xi , Lin Fan-Tong , Li Peng , Han Ji-Gang , Shang Huai-Shuai , Wang Ye , Zheng Han
Published: April 2020

Fiber Bragg Grating (FBG) sensors, with excellent properties, have been widely adopted to monitor the mechanical parameters in civil engineering in recent years. On the other hand, the current study on fatigue performance of corroded prestressed steel strands is still limited, and this is mainly because the long-term strain conditions monitoring is difficult to conduct. Based on the aforementioned considerations, a total of six beam specimens were fabricated in this study. The loading mode of four points bending was adopted in the form of sinusoidal waves in the experiments. On basis of the experimental results, it can be concluded that the fatigue life of the beam decreases sharply with the increase of the corrosion rate of steel strands. Besides, with the increase of the maximum fatigue load, the fatigue life of the beam will decrease significantly. Furthermore, the existing fatigue damage of steel strand inside the beam before corrosion may further accelerate its fatigue failure. As a result, the fatigue life of the beam is reduced because of the stress concentration. Under the same external load, the strain increment and the residual strain of steel strands in the stages of loading and unloading after corrosion increase significantly compared with other stages, while the existing residual strain always shows an increasing trend at various static loading stages. Therefore, the corrosion of steel strand seriously affects not only its mechanical properties, but also its fatigue performance. Finally, the FBG sensors are capable of measuring the steel strand strain, as well as the long-term strain conditions.
Design, Implementation and Data Analysis of an Embedded System for Measuring Environmental Quantities
Authors: Pieš Martin , Hájovský Radovan , Velička Jan
Published: April 2020

The article describes the development and implementation of a complex monitoring system for measuring the concentration of carbon dioxide, ambient temperature, relative humidity and atmospheric pressure. The presented system was installed at two locations. The first was in the rooms at the Department of Cybernetics and Biomedical Engineering, Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava. The second was in the classrooms of the Grammar School and Secondary School of Electrical Engineering and Computer Science in Frenštát pod Radhoštěm. The article contains a detailed description of the entire measurement network, whose basic component was a device for measuring carbon dioxide concentration, temperature and relative humidity in ambient air and atmospheric pressure via wireless data transmission using IQRF® technology. Measurements were conducted continuously for several months. The data were archived in a database. The article also describes the methods for processing the data with statistical analysis. Carbon dioxide concentration was selected for data analysis. Data were selected from at least two different rooms at each location. The processed results represent the time periods for the given carbon dioxide concentrations. The graphs display in percent how much of the time students or employees spent exposed to safe or dangerous concentrations of carbon dioxide. The collected data were used for the future improvement of air quality in the rooms.
The uulmMAC Database—A Multimodal Affective Corpus for Affective Computing in Human-Computer Interaction
Authors: Hazer-Rau Dilana , Meudt Sascha , Daucher Andreas , Spohrs Jennifer , Hoffmann Holger , Schwenker Friedhelm , Traue Harald C.
Published: April 2020

In this paper, we present a multimodal dataset for affective computing research acquired in a human-computer interaction (HCI) setting. An experimental mobile and interactive scenario was designed and implemented based on a gamified generic paradigm for the induction of dialog-based HCI relevant emotional and cognitive load states. It consists of six experimental sequences, inducing Interest, Overload, Normal, Easy, Underload, and Frustration. Each sequence is followed by subjective feedbacks to validate the induction, a respiration baseline to level off the physiological reactions, and a summary of results. Further, prior to the experiment, three questionnaires related to emotion regulation (ERQ), emotional control (TEIQue-SF), and personality traits (TIPI) were collected from each subject to evaluate the stability of the induction paradigm. Based on this HCI scenario, the University of Ulm Multimodal Affective Corpus (uulmMAC), consisting of two homogenous samples of 60 participants and 100 recording sessions was generated. We recorded 16 sensor modalities including 4 × video, 3 × audio, and 7 × biophysiological, depth, and pose streams. Further, additional labels and annotations were also collected. After recording, all data were post-processed and checked for technical and signal quality, resulting in the final uulmMAC dataset of 57 subjects and 95 recording sessions. The evaluation of the reported subjective feedbacks shows significant differences between the sequences, well consistent with the induced states, and the analysis of the questionnaires shows stable results. In summary, our uulmMAC database is a valuable contribution for the field of affective computing and multimodal data analysis: Acquired in a mobile interactive scenario close to real HCI, it consists of a large number of subjects and allows transtemporal investigations. Validated via subjective feedbacks and checked for quality issues, it can be used for affective computing and machine learning applications.
A Low-Cost Surge Current Detection Sensor with Predictive Lifetime Display Function for Maintenance of Surge Protective Devices
Authors: Lee Youngjun , Lee Young Sam
Published: April 2020

In this study, a low-cost surge current detection sensor (SCDS) that can measure high current surges using a low-current toroidal coil is proposed for maintenance of a surge protective device (SPD). The proposed SCDS is designed to display the predicted lifetime of the SPD based on the magnitude of surge current and number of surges. In addition, a method for measuring high surge current using a toroidal coil that can usually measure only low current is proposed. A lightning strike counter consists of a microcontroller with a low-power liquid crystal display (LCD) driver, 3 VDC lithium battery, and signal conditioning circuit that converts amplitude information of the surge current into duration information of a negative pulse to facilitate processing in the microcontroller. In this paper, we propose a software algorithm that can calculate the remaining lifetime of SPD based on the amplitude and number of surge currents. There is also an option to select the capacity of the surge protective device and the number of phase lines, allowing it to assess the predicted lifetime for various types of Class II SPDs. The proposed SCDS is measured as 7.2 μA from the battery power consumption test, and the service life is calculated to be 11.1 years. It meets the International Standard IEC62561-6 test conditions of the lightning strike counter and is expected to be useful in the maintenance of SPDs and lightning protection systems.
Selective Detection of Folic Acid Using 3D Polymeric Structures of 3-Carboxylic Polypyrrole
Authors: Melinte Gheorghe , Cernat Andreea , Irimes Maria-Bianca , Györfi Szabolcs János , Tertiș Mihaela , Suciu Maria , Anicăi Liana , Săndulescu Robert , Cristea Cecilia
Published: April 2020

The detection of folic acid in biological samples or pharmaceutical products is of great importance due to its implications in the biological functions of the human body, along with the development and growth of the fetus. The deficiency of folic acid can be reversed by the intake of different pharmaceutical formulations or alimentary products fortified with this molecule. The elaboration of sensing platforms represents a continuous work in progress, a task in which the use of conductive polymers modified with different functionalities represents one of the outcoming strategies. The possibility of manipulating their morphology with the use of templates or surfactants represents another advantage. A sensing platform based on carboxylic functionalized polypyrrole was synthesized via the electrochemical approach in the presence of a polymeric surfactant on a graphite-based surface. The sensor was able to detect the folic acid from 2.5 μM to 200 μM with a calculated limited of detection of 0.8 μM. It was employed for the detection of the analyte from commercial human serum and pharmaceutical products with excellent recovery rates. The results were double checked using an optimized spectrophotometric procedure that confirmed furthermore the performances of the sensor related to real samples assessment.
Big Data Analytics and Structural Health Monitoring: A Statistical Pattern Recognition-Based Approach
Authors: Entezami Alireza , Sarmadi Hassan , Behkamal Behshid , Mariani Stefano
Published: April 2020

Recent advances in sensor technologies and data acquisition systems opened up the era of big data in the field of structural health monitoring (SHM). Data-driven methods based on statistical pattern recognition provide outstanding opportunities to implement a long-term SHM strategy, by exploiting measured vibration data. However, their main limitation, due to big data or high-dimensional features, is linked to the complex and time-consuming procedures for feature extraction and/or statistical decision-making. To cope with this issue, in this article we propose a strategy based on autoregressive moving average (ARMA) modeling for feature extraction, and on an innovative hybrid divergence-based method for feature classification. Data relevant to a cable-stayed bridge are accounted for to assess the effectiveness and efficiency of the proposed method. The results show that the offered hybrid divergence-based method, in conjunction with ARMA modeling, succeeds in detecting damage in cases strongly characterized by big data.
Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model
Authors: Naji Hasan A.H. , Xue Qingji , Zheng Ke , Lyu Nengchao
Published: April 2020

Driving risk varies substantially according to many factors related to the driven vehicle, environmental conditions, and drivers. This study explores the contributing historical factors of driving risk with hierarchical clustering analysis and the quasi-Poisson regression model. The dataset of the study was collected from two sources: naturalistic driving experiments and self-reports. The drivers who participated in the naturalistic driving experiment were categorized into four risk groups according to their near-crash frequency with the hierarchical clustering method. Moreover, a quasi-Poisson model was used to identify the essential factors of individual driving risk. The findings of this study indicated that historical driving factors have substantial impacts on individual risk of drivers. These factors include the total number of miles driven, the driver’s age, the number of illegal parking (past three years), the number of over-speeding (past three years) and passing red lights (past three years). The outcome of the study can help transportation officials, educators, and researchers to consider the influencing factors on individual driving risk and can give insights and provide suggestions to improve driving safety.
Mobility Control of Unmanned Aerial Vehicle as Communication Relay to Optimize Ground-to-Air Uplinks
Authors: Wu Gaofeng , Gao Xiaoguang , Wan Kaifang
Published: April 2020

In recent years, unmanned aerial vehicles (UAVs) have been considered an ideal relay platform for enhancing the communication between ground agents, because they fly at high altitudes and are easy to deploy with strong adaptabilities. Their maneuvering allows them to adjust their location to optimize the performance of links, which brings out the relay UAV autonomous mobility control problem. This work addressed the problem in a novel scene with mobile agents and completely unknown wireless channel properties, using only online measured information of received signal strength (RSS) and agent positions. The problem is challenging because of the unknown and dynamic radio frequency (RF) environment cause by agents and UAV maneuvering. We present a framework for both end-to-end communication and multi-agent-inter communication applications, and focus on proposing: (1) least square estimation-based channel approximation with consideration of environment effects and, (2) gradient-based optimal relay position seeking. Simulation results show that considering the environmental effects on channel parameters is meaningful and beneficial in using UAV as relays for the communication of multiple ground agents, and validate that the proposed algorithms optimizes the network performance by controlling the heading of the UAV.
Internet of Multimedia Things (IoMT): Opportunities, Challenges and Solutions
Authors: Zikria Yousaf Bin , Afzal Muhammad Khalil , Kim Sung Won
Published: April 2020

With the immersive growth of the Internet of Things (IoT) and real-time adaptability, quality of life for people is improving. IoT applications are diverse in nature and one crucial aspect of it is multimedia sensors and devices. These IoT multimedia devices form the Internet of Multimedia Things (IoMT). It generates a massive volume of data with different characteristics and requirements than the IoT. The real-time deployment scenarios vary from smart traffic monitoring to smart hospitals. Hence, Timely delivery of IoMT data and decision making is critical as it directly involves the safety of human beings. In this paper, we present a brief overview of IoMT and future research directions. Afterward, we provide an overview of the accepted articles in our special issue on the IoMT: Opportunities, Challenges, and Solutions.
Real-Time Queue Length Detection with Roadside LiDAR Data
Authors: Wu Jianqing , Xu Hao , Zhang Yongsheng , Tian Yuan , Song Xiuguang
Published: April 2020

Real-time queue length information is an important input for many traffic applications. This paper presents a novel method for real-time queue length detection with roadside LiDAR data. Vehicles on the road were continuously tracked with the LiDAR data processing procedures (including background filtering, point clustering, object classification, lane identification and object association). A detailed method to identify the vehicle at the end of the queue considering the occlusion issue and package loss issue was documented in this study. The proposed method can provide real-time queue length information. The performance of the proposed queue length detection method was evaluated with the ground-truth data collected from three sites in Reno, Nevada. Results show the proposed method can achieve an average of 98% accuracy at the six investigated sites. The errors in the queue length detection were also diagnosed.
Modeling and Forecasting the GPS Zenith Troposphere Delay in West Antarctica Based on Different Blind Source Separation Methods and Deep Learning
Authors: Zhang Qingchuan , Li Fei , Zhang Shengkai , Li Wenhao
Published: April 2020

Tropospheric delay is an important error source in global positioning systems (GPS), and the water vapor retrieved from the tropospheric delay is widely used in meteorological research such as climate analysis and weather forecasting. Most zenith tropospheric delay (ZTD) models are presently used as positioning corrections, and few models are used for the estimation of water vapor, especially in Antarctica. Through two blind source separation algorithms (principal component analysis (PCA) and independent component analysis (ICA)), a back-propagation (BP) neural network and a deep learning technique (long short-term memory (LSTM) network), we establish an hourly high-accuracy ZTD model for GPS meteorology using the GPS-ZTD from 52 GPS stations in West Antarctica. Our results show that under the condition in which the principal components (PCs) and independent components (ICs) remain fixed after decomposition, the mean accuracy of the models for West Antarctica using PCA or ICA are better than 10 mm. Compared with the ZTDs from the nonmodeling stations, the mean root mean square (RMS) of the PCA and ICA models are 9.3 and 8.9 mm, respectively, and the correlation coefficients between the GPS-ZTD and model-ZTDs all exceed 90%. The accuracy of the ICA model is slightly higher than that of the PCA model, and the ICs of the ICA model show more consistent spatial responses. The six-hour forecast is the best among the forecast results, with a mean correlation coefficient of 90.6% and a mean RMS of 7.2 mm using GPS-ZTD. The long-term forecast result is significantly inaccurate, as the correlation coefficient between the 24-h forecast and GPS-ZTD is only 63.2%. Generally modest results have been achieved (HSS ≤ 0.38). Furthermore, the forecast accuracy in coastal areas is lower than that in inland areas. Our study confirms that the combined use of ICA and deep learning in ZTD modeling can effectively restore the original signals, and short-term forecasting can be effectively used in GPS meteorology. However, further development of the technology is necessary.
Human Interaction Recognition Based on Whole-Individual Detection
Authors: Ye Qing , Zhong Haoxin , Qu Chang , Zhang Yongmei
Published: April 2020

Human interaction recognition technology is a hot topic in the field of computer vision, and its application prospects are very extensive. At present, there are many difficulties in human interaction recognition such as the spatial complexity of human interaction, the differences in action characteristics at different time periods, and the complexity of interactive action features. The existence of these problems restricts the improvement of recognition accuracy. To investigate the differences in the action characteristics at different time periods, we propose an improved fusion time-phase feature of the Gaussian model to obtain video keyframes and remove the influence of a large amount of redundant information. Regarding the complexity of interactive action features, we propose a multi-feature fusion network algorithm based on parallel Inception and ResNet. This multi-feature fusion network not only reduces the network parameter quantity, but also improves the network performance; it alleviates the network degradation caused by the increase in network depth and obtains higher classification accuracy. For the spatial complexity of human interaction, we combined the whole video features with the individual video features, making full use of the feature information of the interactive video. A human interaction recognition algorithm based on whole–individual detection is proposed, where the whole video contains the global features of both sides of action, and the individual video contains the individual detail features of a single person. Making full use of the feature information of the whole video and individual videos is the main contribution of this paper to the field of human interaction recognition and the experimental results in the UT dataset (UT–interaction dataset) showed that the accuracy of this method was 91.7%.