bluuebunny/crossref_metadata_2025 huggingface dataset:
Autophagic Proteome in Two Saccharomyces cerevisiae Strains during Second Fermentation for Sparkling Wine Elaboration
Authors: Porras-Agüera Juan Antonio , Moreno-García Jaime , González-Jiménez María del Carmen , Mauricio Juan Carlos , Moreno Juan , García-Martínez Teresa
Published: April 2020

A correlation between autophagy and autolysis has been proposed in order to accelerate the acquisition of wine organoleptic properties during sparkling wine elaboration. In this context, a proteomic analysis was carried out in two industrial Saccharomyces cerevisiae strains (P29, conventional sparkling wine strain and G1, implicated in sherry wine elaboration) with the aim of studying the autophagy-related proteome and comparing the effect of CO2 overpressure during sparkling wine elaboration. In general, a detrimental effect of pressure and second fermentation development on autophagy-related proteome was observed in both strains, although it was more pronounced in flor yeast strain G1. Proteins mainly involved in autophagy regulation and autophagosome formation in flor yeast G1, and those required for vesicle nucleation and expansion in P29 strain, highlighted in sealed bottle. Proteins Sec2 and Sec18 were detected 3-fold under pressure conditions in P29 and G1 strains, respectively. Moreover, ‘fingerprinting’ obtained from multivariate data analysis established differences in autophagy-related proteome between strains and conditions. Further research is needed to achieve more solid conclusions and design strategies to promote autophagy for an accelerated autolysis, thus reducing cost and time production, as well as acquisition of good organoleptic properties.
The Effect of Vacancies on Grain Boundary Segregation in Ferromagnetic fcc Ni
Authors: Mazalová Martina , Všianská Monika , Pavlů Jana , Šob Mojmír
Published: April 2020

This work presents a comprehensive and detailed ab initio study of interactions between the tilt Σ5(210) grain boundary (GB), impurities X (X = Al, Si) and vacancies (Va) in ferromagnetic fcc nickel. To obtain reliable results, two methods of structure relaxation were employed: the automatic full relaxation and the finding of the minimum energy with respect to the lattice dimensions perpendicular to the GB plane and positions of atoms. Both methods provide comparable results. The analyses of the following phenomena are provided: the influence of the lattice defects on structural properties of material such as lattice parameters, the volume per atom, interlayer distances and atomic positions; the energies of formation of particular structures with respect to the standard element reference states; the stabilization/destabilization effects of impurities (in substitutional (s) as well as in tetragonal (iT) and octahedral (iO) interstitial positions) and of vacancies in both the bulk material and material with GBs; a possibility of recombination of Si(i)+Va defect to Si(s) one with respect to the Va position; the total energy of formation of GB and Va; the binding energies between the lattice defects and their combinations; impurity segregation energies and the effect of Va on them; magnetic characteristics in the presence of impurities, vacancies and GBs. As there is very little experimental information on the interaction between impurities, vacancies and GBs in fcc nickel, most of the present results are theoretical predictions, which may motivate future experimental work.
3D Porous Ti3C2 MXene/NiCo-MOF Composites for Enhanced Lithium Storage
Authors: Liu Yijun , He Ying , Vargun Elif , Plachy Tomas , Saha Petr , Cheng Qilin
Published: April 2020

To improve Li storage capacity and the structural stability of Ti3C2 MXene-based electrode materials for lithium-ion batteries (LIBs), a facile strategy is developed to construct three-dimensional (3D) hierarchical porous Ti3C2/bimetal-organic framework (NiCo-MOF) nanoarchitectures as anodes for high-performance LIBs. 2D Ti3C2 nanosheets are coupled with NiCo-MOF nanoflakes induced by hydrogen bonds to form 3D Ti3C2/NiCo-MOF composite films through vacuum-assisted filtration technology. The morphology and electrochemical properties of Ti3C2/NiCo-MOF are influenced by the mass ratio of MOF to Ti3C2. Owing to the interconnected porous structures with a high specific surface area, rapid charge transfer process, and Li+ diffusion rate, the Ti3C2/NiCo-MOF-0.4 electrode delivers a high reversible capacity of 402 mAh g−1 at 0.1 A g−1 after 300 cycles; excellent rate performance (256 mAh g−1 at 1 A g−1); and long-term stability with a capacity retention of 85.7% even after 400 cycles at a high current density, much higher than pristine Ti3C2 MXene. The results highlight that Ti3C2/NiCo-MOF have great potential in the development of high-performance energy storage devices.
Prediction of Thermo-Physical Properties of TiO2-Al2O3/Water Nanoparticles by Using Artificial Neural Network
Authors: Sadeghzadeh Milad , Maddah Heydar , Ahmadi Mohammad Hossein , Khadang Amirhosein , Ghazvini Mahyar , Mosavi Amirhosein , Nabipour Narjes
Published: April 2020

In this paper, an artificial neural network is implemented for the sake of predicting the thermal conductivity ratio of TiO2-Al2O3/water nanofluid. TiO2-Al2O3/water in the role of an innovative type of nanofluid was synthesized by the sol–gel method. The results indicated that 1.5 vol.% of nanofluids enhanced the thermal conductivity by up to 25%. It was shown that the heat transfer coefficient was linearly augmented with increasing nanoparticle concentration, but its variation with temperature was nonlinear. It should be noted that the increase in concentration may cause the particles to agglomerate, and then the thermal conductivity is reduced. The increase in temperature also increases the thermal conductivity, due to an increase in the Brownian motion and collision of particles. In this research, for the sake of predicting the thermal conductivity of TiO2-Al2O3/water nanofluid based on volumetric concentration and temperature functions, an artificial neural network is implemented. In this way, for predicting thermal conductivity, SOM (self-organizing map) and BP-LM (Back Propagation-Levenberq-Marquardt) algorithms were used. Based on the results obtained, these algorithms can be considered as an exceptional tool for predicting thermal conductivity. Additionally, the correlation coefficient values were equal to 0.938 and 0.98 when implementing the SOM and BP-LM algorithms, respectively, which is highly acceptable.
Levels of the Novel Endogenous Antagonist of Ghrelin Receptor, Liver-Enriched Antimicrobial Peptide-2, in Patients with Rheumatoid Arthritis
Authors: Francisco Vera , Tovar Sulay , Conde Javier , Pino Jesús , Mera Antonio , Lago Francisca , González-Gay Miguel Angel , Dieguez Carlos , Gualillo Oreste
Published: April 2020

Rheumatoid arthritis (RA) is a debilitating, chronic, inflammatory, autoimmune disease associated with cachexia. The substitutive therapy of gut hormone ghrelin has been pointed at as a potential countermeasure for the management of metabolic and inflammatory complications in RA. The recent discovery of liver-expressed antimicrobial peptide 2 (LEAP2) as an endogenous inverse agonist/antagonist of the ghrelin receptor makes feasible the development of a more rational pharmacological approach. This work aimed to assess the serum LEAP2 levels, in a cohort of RA patients, in comparison with healthy individuals and determine its correlation with inflammatory parameters. LEAP2 levels were determined by a commercial ELISA kit, plasma C-reactive protein (CRP) levels were evaluated using immunoturbidimetry, and serum levels of inflammatory mediators, namely IL-6, IL-8, IL-1β, MIP1α, MCP1, and LCN2, were measured by XMap multiplex assay. LEAP2 serum levels were significantly increased in RA patients (n = 101) compared with control subjects (n = 26). Furthermore, the LEAP2 levels significantly correlated with CRP and inflammatory cytokines, but not with BMI. These data reveal LEAP2 as a new potential RA biomarker and indicated the pharmacological control of LEAP2 levels as a novel approach for the treatment of diseases with alterations on the ghrelin levels, such as rheumatoid cachexia.
Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification
Authors: Feng Jie , Feng Xueliang , Chen Jiantong , Cao Xianghai , Zhang Xiangrong , Jiao Licheng , Yu Tao
Published: April 2020

Classifying hyperspectral images (HSIs) with limited samples is a challenging issue. The generative adversarial network (GAN) is a promising technique to mitigate the small sample size problem. GAN can generate samples by the competition between a generator and a discriminator. However, it is difficult to generate high-quality samples for HSIs with complex spatial–spectral distribution, which may further degrade the performance of the discriminator. To address this problem, a symmetric convolutional GAN based on collaborative learning and attention mechanism (CA-GAN) is proposed. In CA-GAN, the generator and the discriminator not only compete but also collaborate. The shallow to deep features of real multiclass samples in the discriminator assist the sample generation in the generator. In the generator, a joint spatial–spectral hard attention module is devised by defining a dynamic activation function based on a multi-branch convolutional network. It impels the distribution of generated samples to approximate the distribution of real HSIs both in spectral and spatial dimensions, and it discards misleading and confounding information. In the discriminator, a convolutional LSTM layer is merged to extract spatial contextual features and capture long-term spectral dependencies simultaneously. Finally, the classification performance of the discriminator is improved by enforcing competitive and collaborative learning between the discriminator and generator. Experiments on HSI datasets show that CA-GAN obtains satisfactory classification results compared with advanced methods, especially when the number of training samples is limited.
Vegetation Fraction Images Derived from PROBA-V Data for Rapid Assessment of Annual Croplands in Brazil
Authors: Arai Egidio , Eyji Sano Edson , Dutra Andeise Cerqueira , Cassol Henrique Luis Godinho , Hoffmann Tânia Beatriz , Shimabukuro Yosio Edemir
Published: April 2020

This paper presents a new method for rapid assessment of the extent of annual croplands in Brazil. The proposed method applies a linear spectral mixing model (LSMM) to PROBA-V time series images to derive vegetation, soil, and shade fraction images for regional analysis. We used S10-TOC (10 days synthesis, 1 km spatial resolution, and top-of-canopy) products for Brazil and S5-TOC (five days synthesis, 100 m spatial resolution, and top-of-canopy) products for Mato Grosso State (Brazilian Legal Amazon). Using the time series of the vegetation fraction images of the whole year (2015 in this case), only one mosaic composed with maximum values of vegetation fraction was generated, allowing detecting and mapping semi-automatically the areas occupied by annual crops during the year. The results (100 m spatial resolution map) for the Mato Grosso State were compared with existing global datasets (Finer Resolution Observation and Monitoring—Global Land Cover (FROM-GLC) and Global Food Security—Support Analyses Data (GFSAD30)). Visually those maps present a good agreement, but the area estimated are not comparable since the agricultural class definition are different for those maps. In addition, we found 11.8 million ha of agricultural areas in the entire Brazilian territory. The area estimation for the Mato Grosso State was 3.4 million ha for 1 km dataset and 5.3 million ha for 100 m dataset. This difference is due to the spatial resolution of the PROBA-V datasets used. A coefficient of determination of 0.82 was found between PROBA-V 100 m and Landsat-8 OLI area estimations for the Mato Grosso State. Therefore, the proposed method is suitable for detecting and mapping annual croplands distribution operationally using PROBA-V datasets for regional analysis.
Synergy between Ocean Variables: Remotely Sensed Surface Temperature and Chlorophyll Concentration Coherence
Authors: Umbert Marta , Guimbard Sebastien , Ballabrera Poy Joaquim , Turiel Antonio
Published: April 2020

The similarity of mesoscale and submesoscale features observed in different ocean scalars indicates that they undergo some common non-linear processes. As a result of quasi-2D turbulence, complicated patterns of filaments, meanders, and eddies are recognized in remote sensing images. A data fusion method used to improve the quality of one ocean variable using another variable as a template is used here as an extrapolation technique to improve the coverage of daily Aqua MODIS Level-3 chlorophyll maps by using MODIS SST maps as a template. The local correspondence of SST and Chl-a multifractal singularities is granted due to the existence of a common cascade process which makes it possible to use SST data to infer Chl-a concentration where data are lacking. The quality of the inference of Level-4 Chl-a maps is assessed by simulating artificial clouds and comparing reconstructed and original data.
Subpixel Mapping of Surface Water in the Tibetan Plateau with MODIS Data
Authors: Liu Chenzhou , Shi Jiancheng , Liu Xiuying , Shi Zhaoyong , Zhu Ji
Published: April 2020

This article presents a comprehensive subpixel water mapping algorithm to automatically produce routinely open water fraction maps in the Tibetan Plateau (TP) with the Moderate Resolution Imaging Spectroradiometer (MODIS). A multi-index threshold endmember extraction method was applied to select the endmembers from MODIS images. To incorporate endmember variability, an endmember selection strategy, called the combined use of typical and neighboring endmembers, was adopted in multiple endmember spectral mixture analysis (MESMA), which can assure a robust subpixel water fractions estimation. The accuracy of the algorithm was assessed at both the local scale and regional scale. At the local scale, a comparison using the eight pairs of MODIS/Landsat 8 Operational Land Imager (OLI) water maps demonstrated that subpixels water fractions were well retrieved with a root mean square error (RMSE) of 7.86% and determination coefficient (R2) of 0.98. At the regional scale, the MODIS water fraction map in October 2014 matches well with the TP lake data set and the Global Lake and Wetland Database (GLWD) in both latitudinal and longitudinal distribution. The lake area estimation is more consistent with the reference TP lake data set (difference of −3.15%) than the MODIS Land Water Mask (MOD44W) (difference of −6.39%).
Minimax Bridgeness-Based Clustering for Hyperspectral Data
Authors: Le Moan Steven , Cariou Claude
Published: April 2020

Hyperspectral (HS) imaging has been used extensively in remote sensing applications like agriculture, forestry, geology and marine science. HS pixel classification is an important task to help identify different classes of materials within a scene, such as different types of crops on a farm. However, this task is significantly hindered by the fact that HS pixels typically form high-dimensional clusters of arbitrary sizes and shapes in the feature space spanned by all spectral channels. This is even more of a challenge when ground truth data is difficult to obtain and when there is no reliable prior information about these clusters (e.g., number, typical shape, intrinsic dimensionality). In this letter, we present a new graph-based clustering approach for hyperspectral data mining that does not require ground truth data nor parameter tuning. It is based on the minimax distance, a measure of similarity between vertices on a graph. Using the silhouette index, we demonstrate that the minimax distance is more suitable to identify clusters in raw hyperspectral data than two other graph-based similarity measures: mutual proximity and shared nearest neighbours. We then introduce the minimax bridgeness-based clustering approach, and we demonstrate that it can discover clusters of interest in hyperspectral data better than comparable approaches.
Land Cover Mapping in Cloud-Prone Tropical Areas Using Sentinel-2 Data: Integrating Spectral Features with Ndvi Temporal Dynamics
Authors: Huang Chong , Zhang Chenchen , He Yun , Liu Qingsheng , Li He , Su Fenzhen , Liu Gaohuan , Bridhikitti Arika
Published: April 2020

Accurate remote sensing and mapping of land cover in the tropics remain difficult tasks since data gaps and a heterogenic landscape make it challenging to perform land cover classification. In this paper, we proposed a multi-feature classification method to integrate temporal statistical features with spectral and textural features. This method is designed to improve the accuracy of land cover classification in cloud-prone tropical regions. Sentinel-2 images were used to construct an NDVI stack for a time-series statistical analysis to characterize the temporal variance of land cover. Two statistical indices were calculated and used to represent the variation in annual vegetation. These indices included the mean (NDVI_mean) and coefficient of variation (NDVI_cv) for the NDVI time series. The temporal statistical features were then integrated with spectral and textural features extracted from high-quality Sentinel-2 imagery for Random Forest classification. The performance and contribution of different combinations were assessed based on their classification accuracies. Our results show that the time-series statistical analysis is an effective way to represent land cover category information contained in annual NDVI variance. The method uses clear pixels from dense low-quality images to obtain the NDVI statistical characteristics, thus, to reduce the influence of random factors such as weather conditions on single-date image. The addition of NDVI_mean and NDVI_cv can improve the separability among most types of land cover. The overall accuracy and the kappa coefficient reached values of 0.8913 and 0.8514 when NDVI_mean and NDVI_cv were integrated. Furthermore, the time-series statistical analysis has less stringent requirements regarding image quality and features a high computational efficiency, which shows its great potential to improve the overall accuracy of land cover classification at regional scales in cloud-prone tropical regions.
Scale Impact of Soil Moisture Observations to Noah-MP Land Surface Model Simulations
Authors: Yin Jifu , Zhan Xiwu
Published: April 2020

Due to the limitations of satellite antenna technology, current operational microwave soil moisture (SM) data products are typically at tens of kilometers spatial resolutions. Many approaches have thus been proposed to generate finer resolution SM data using ancillary information, but it is still unknown if assimilation of the finer spatial resolution SM data has beneficial impacts on model skills. In this paper, a synthetic experiment is thus conducted to identify the benefits of SM observations at a finer spatial resolution on the Noah-MP land surface model. Results of this study show that the performance of the Noah-MP model is significantly improved with the benefits of assimilating 1 km SM observations in comparison with the assimilation of SM data at coarser resolutions. Downscaling satellite microwave SM observations from coarse spatial resolution to 1 km resolution is recommended, and the assimilation of 1 km remotely sensed SM retrievals is suggested for NOAA National Weather Service and National Water Center.
The Value of Distributed High-Resolution UAV-Borne Observations of Water Surface Elevation for River Management and Hydrodynamic Modeling
Authors: Jiang Liguang , Bandini Filippo , Smith Ole , Klint Jensen Inger , Bauer-Gottwein Peter
Published: April 2020

Water level or water surface elevation (WSE) is an important state variable of rivers, lakes, and wetlands. Hydrodynamic models of rivers and streams simulate WSE and can benefit from spatially distributed WSE observations, to increase model reliability and predictive skill. This has been partially addressed by satellite radar altimetry, but satellite altimetry is unable to deliver useful data for small rivers. To overcome such limitations, we deployed a radar altimetry system on an unmanned aerial vehicle (UAV), to map spatially distributed WSE. We showed that UAV altimetry can provide observations of WSE with a very high spatial resolution (ca. 0.5 m) and accuracy (ca. 3 cm), in a time-saving and cost-effective way. Furthermore, we investigated the value of this dataset for the calibration and validation of hydrodynamic models. Specifically, we introduced spatially distributed roughness parameters in a hydrodynamic model and estimated these parameters, using the observed WSE profiles along the stream as input. A case study was conducted in the Åmose stream, Denmark. The results showed that UAV-borne WSE can identify significant variations of the Manning–Strickler coefficients, along this small and highly vegetated stream and over time. Moreover, the model performed extremely well using distributed roughness coefficients, but it could not reproduce WSE satisfactorily using uniform roughness. We concluded that distributed roughness coefficients should be considered, especially for small vegetated rivers, to improve model performance, both locally and globally. Spatially distributed parameterizations of the effective channel roughness could be constrained with UAV-borne WSE. This study demonstrated for the first time that UAV-borne WSE can help to understand the variations of hydraulic roughness, and can support efficient river management and maintenance.
Integrity Monitoring for Horizontal RTK Positioning: New Weighting Model and Overbounding CDF in Open-Sky and Suburban Scenarios
Authors: Wang Kan , El-Mowafy Ahmed , Rizos Chris , Wang Jinling
Published: April 2020

Integrity monitoring is an essential task for ensuring the safety of positioning services. Under a selected probability of hazardous misleading information, the protection levels (PLs) are computed according to a considered threat model to bound the positioning errors. A warning message is sent to users when the PL exceeds a pre-set alert limit (AL). In the short-baseline real-time relative kinematic positioning, the spatially correlated errors, such as the the orbital errors and the atmospheric delays are significantly reduced. However, the remaining atmospheric residuals and the multipath that are not considered in the observation model could directly bias the positioning results. In this contribution, these biases are analysed with the focus put on the multipath effects in different measurement environments. A new observation weighting model considering both the elevation angle and the signal-to-noise ratios is developed and their impacts on the positional results are investigated. The coefficients of the proposed weighting model are determined for the open-sky and the suburban scenarios with the positional benefits maximised. Next, the overbounding excess-mass cumulative distribution functions (EMCs) are searched on the between-receiver level for the weighted phase and code observations in these two scenarios. Based on the mean and standard deviations of these EMCs, horizontal protection levels (HPLs) are computed for the ambiguity-fixed solutions of real experiments. The HPLs are compared with the horizontal positioning errors (HPEs) and the horizontal ALs (HALs). Using the sequential exclusion algorithm developed for the ambiguity resolution in this contribution, the full ambiguity resolution can be achieved in around 100% and 95% of the time for the open-sky and the suburban scenarios, respectively. The corresponding HPLs of the ambiguity-fixed solutions are at the sub-dm to dm-level for both scenarios, and all the valid ambiguity-fixed HPLs are below a HAL of 0.5 m. For the suburban scenario with more complicated multipath environments, the HPLs increase by considering extra biases to account for multipath under a certain elevation threshold. In complicated multipath environments, when this elevation threshold is set to 30 degrees, the availability of the ambiguity-fixed solutions could decrease to below 50% for applications requiring HAL as low as 0.1 m.
Spatiotemporal Distribution and Risk Assessment of Heat Waves Based on Apparent Temperature in the One Belt and One Road Region
Authors: Yin Cong , Yang Fei , Wang Juanle , Ye Yexing
Published: April 2020

Heat waves seriously affect the productivity and daily life of human beings. Therefore, they bring great risks and uncertainties for the further development of countries in the One Belt and One Road (OBOR) region. In this study, we used daily meteorological monitoring data to calculate the daily apparent temperature and annual heat wave dataset for 1989–2018 in the OBOR region. Then, we studied their spatiotemporal distribution patterns. Additionally, multi-source data were used to assess heat wave risk in the OBOR region. The main results are as follows: (1) The daily apparent temperature dataset and annual heat wave dataset for 1989–2018 in the OBOR region at 0.1° × 0.1° gridded resolution were calculated. China, South Asia and Southeast Asia are suffering the most serious heat waves in the OBOR region, with an average of more than six heat waves, lasting for more than 60 days and the extreme apparent temperature has reached over 40 °C. Additionally, the frequency, duration and intensity of heat waves have been confirmed to increase continuously. (2) The heat wave risk in the OBOR region was assessed. Results show that the high heat wave risk areas are distributed in eastern China, northern South Asia and some cities. The main conclusion is that the heat wave risk in most areas along the OBOR route is relatively high. In the process of deepening the development of countries in the OBOR region, heat wave risk should be fully considered.
Continuous Monitoring of Cotton Stem Water Potential using Sentinel-2 Imagery
Authors: Lin Yukun , Zhu Zhe , Guo Wenxuan , Sun Yazhou , Yang Xiaoyuan , Kovalskyy Valeriy
Published: April 2020

Monitoring cotton status during the growing season is critical in increasing production efficiency. The water status in cotton is a key factor for yield and cotton quality. Stem water potential (SWP) is a precise indicator for assessing cotton water status. Satellite remote sensing is an effective approach for monitoring cotton growth at a large scale. The aim of this study is to estimate cotton water stress at a high temporal frequency and at a large scale. In this study, we measured midday SWP samples according to the acquisition dates of Sentinel-2 images and used them to build linear-regression-based and machine-learning-based models to estimate cotton water stress during the growing season (June to August, 2018). For the linear-regression-based method, we estimated SWP based on different Sentinel-2 spectral bands and vegetation indices, where the normalized difference index 45 (NDI45) achieved the best performance (R2 = 0.6269; RMSE = 3.6802 (-1*swp (bars))). For the machine-learning-based method, we used random forest regression to estimate SWP and received even better results (R2 = 0.6709; RMSE = 3.3742 (-1*swp (bars))). To find the best selection of input variables for the machine-learning-based approach, we tried three different data input datasets, including (1) 9 original spectral bands (e.g., blue, green, red, red edge, near infrared (NIR), and shortwave infrared (SWIR)), (2) 21 vegetation indices, and (3) a combination of original Sentinel-2 spectral bands and vegetation indices. The highest accuracy was achieved when only the original spectral bands were used. We also found the SWIR and red edge band were the most important spectral bands, and the vegetation indices based on red edge and NIR bands were particularly helpful. Finally, we applied the best approach for the linear-regression-based and the machine-learning-based methods to generate cotton water potential maps at a large scale and high temporal frequency. Results suggests that the methods developed here has the potential for continuous monitoring of SWP at large scales and the machine-learning-based method is preferred.
Nanostructured MOS Sensor for the Detection, Follow up, and Threshold Pursuing of Campylobacter Jejuni Development in Milk Samples
Authors: Núñez-Carmona Estefanía , Abbatangelo Marco , Zappa Dario , Comini Elisabetta , Sberveglieri Giorgio , Sberveglieri Veronica
Published: April 2020

Food poisoning is still the first cause of hospitalization worldwide and the most common microbial agent, Campylobacter jejuni, is the most commonly reported gastrointestinal disease in humans in the EU (European Union) as is reported by the European Union One Health 2018 Zoonoses Report styled by the EFSA (European Food Safety Authority) and ECDC (European Center for Disease Prevention and Control). One of the vehicles of transmission of this disease is milk. Nanostructured MOS (Metal Oxide Semiconductor) sensors have extensively demonstrated their ability to reveal the presence and follow the development of microbial species. The main objective of this work was to find a set up for the detection and development follow up of C. jejuni in milk samples. The work was structured in two different studies, the first one was a feasibility survey and the second one was to follow up the development of the bacteria inside milk samples. The obtained results of the first study demonstrate the ability of the sensor array to differentiate the contaminated samples from the control ones. Thanks to the second study, it has been possible to find the limit of microbial safety of the contaminated milk samples.
Healthy Operator 4.0: A Human Cyber–Physical System Architecture for Smart Workplaces
Authors: Sun Shengjing , Zheng Xiaochen , Gong Bing , García Paredes Jorge , Ordieres-Meré Joaquín
Published: April 2020

Recent advances in technology have empowered the widespread application of cyber–physical systems in manufacturing and fostered the Industry 4.0 paradigm. In the factories of the future, it is possible that all items, including operators, will be equipped with integrated communication and data processing capabilities. Operators can become part of the smart manufacturing systems, and this fosters a paradigm shift from independent automated and human activities to human–cyber–physical systems (HCPSs). In this context, a Healthy Operator 4.0 (HO4.0) concept was proposed, based on a systemic view of the Industrial Internet of Things (IIoT) and wearable technology. For the implementation of this relatively new concept, we constructed a unified architecture to support the integration of different enabling technologies. We designed an implementation model to facilitate the practical application of this concept in industry. The main enabling technologies of the model are introduced afterward. In addition, a prototype system was developed, and relevant experiments were conducted to demonstrate the feasibility of the proposed system architecture and the implementation framework, as well as some of the derived benefits.
Antibody-Electroactive Probe Conjugates Based Electrochemical Immunosensors
Authors: Kondzior Mateusz , Grabowska Iwona
Published: April 2020

Suitable immobilization of a biorecognition element, such as an antigen or antibody, on a transducer surface is essential for development of sensitive and analytically reliable immunosensors. In this review, we report on (1) methods of antibody prefunctionalization using electroactive probes, (2) methods for immobilization of such conjugates on the surfaces of electrodes in electrochemical immunosensor construction and (3) the use of antibody-electroactive probe conjugates as bioreceptors and sensor signal generators. We focus on different strategies of antibody functionalization using the redox active probes ferrocene (Fc), anthraquinone (AQ), thionine (Thi), cobalt(III) bipyridine (Co(bpy)33+), Ru(bpy)32+ and horseradish peroxidase (HRP). In addition, new possibilities for antibody functionalization based on bioconjugation techniques are presented. We discuss strategies of specific, quantitative antigen detection based on (i) a sandwich format and (ii) a direct signal generation scheme. Further, the integration of different nanomaterials in the construction of these immunosensors is presented. Lastly, we report the use of a redox probe strategy in multiplexed analyte detection.
Single Channel Source Separation with ICA-Based Time-Frequency Decomposition
Authors: Mika Dariusz , Budzik Grzegorz , Józwik Jerzy
Published: April 2020

This paper relates to the separation of single channel source signals from a single mixed signal by means of independent component analysis (ICA). The proposed idea lies in a time-frequency representation of the mixed signal and the use of ICA on spectral rows corresponding to different time intervals. In our approach, in order to reconstruct true sources, we proposed a novelty idea of grouping statistically independent time-frequency domain (TFD) components of the mixed signal obtained by ICA. The TFD components are grouped by hierarchical clustering and k-mean partitional clustering. The distance between TFD components is measured with the classical Euclidean distance and the β distance of Gaussian distribution introduced by as. In addition, the TFD components are grouped by minimizing the negentropy of reconstructed constituent signals. The proposed method was used to separate source signals from single audio mixes of two- and three-component signals. The separation was performed using algorithms written by the authors in Matlab. The quality of obtained separation results was evaluated by perceptual tests. The tests showed that the automated separation requires qualitative information about time-frequency characteristics of constituent signals. The best separation results were obtained with the use of the β distance of Gaussian distribution, a distance measure based on the knowledge of the statistical nature of spectra of original constituent signals of the mixed signal.
Wireless Sensors System for Stress Detection by Means of ECG and EDA Acquisition
Authors: Affanni Antonio
Published: April 2020

This paper describes the design of a two channels electrodermal activity (EDA) sensor and two channels electrocardiogram (ECG) sensor. The EDA sensors acquire data on the hands and transmit them to the ECG sensor with wireless WiFi communication for increased wearability. The sensors system acquires two EDA channels to improve the removal of motion artifacts that take place if EDA is measured on individuals who need to move their hands in their activities. The ECG channels are acquired on the chest and the ECG sensor is responsible for aligning the two ECG traces with the received packets from EDA sensors; the ECG sensor sends via WiFi the aligned packets to a laptop for real time plot and data storage. The metrological characterization showed high-level performances in terms of linearity and jitter; the delays introduced by the wireless transmission from EDA to ECG sensor have been proved to be negligible for the present application.
Effect of Deep-Level Defects on the Performance of CdZnTe Photon Counting Detectors
Authors: Li Yingrui , Zha Gangqiang , Wei Dengke , Yang Fan , Dong Jiangpeng , Xi Shouzhi , Xu Lingyan , Jie Wanqi
Published: April 2020

The effect of deep-level defects is a key issue for the applications of CdZnTe high-flux photon counting devices of X-ray irradiations. However, the major trap energy levels and their quantitive relationship with the device’s performance are not yet clearly understood. In this study, a 16-pixel CdZnTe X-ray photon counting detector with a non-uniform counting performance is investigated. The deep-level defect characteristics of each pixel region are analyzed by the current–voltage curves (I–V), infrared (IR) optical microscope photography, photoluminescence (PL) and thermally stimulated current (TSC) measurements, which indicate that the difference in counting performance is caused by the non-uniformly distributed deep-level defects in the CdZnTe crystals. Based on these results, we conclude that the CdZnTe detectors with a good photon counting performance should have a larger Te cd 2 + and Cd vacancy-related defect concentration and a lower A-center and Tei concentration. We consider the deep hole trap Tei, with the activation energy of 0.638–0.642 eV, to be the key deep-level trap affecting the photon counting performance. In addition, a theoretical model of the native defect reaction is proposed to understand the underlying relationships of resistivity, deep-level defect characteristics and photon counting performance.
Wearable Cardiorespiratory Monitoring Employing a Multimodal Digital Patch Stethoscope: Estimation of ECG, PEP, LVET and Respiration Using a 55 mm Single-Lead ECG and Phonocardiogram
Authors: Klum Michael , Urban Mike , Tigges Timo , Pielmus Alexandru-Gabriel , Feldheiser Aarne , Schmitt Theresa , Orglmeister Reinhold
Published: April 2020

Cardiovascular diseases are the main cause of death worldwide, with sleep disordered breathing being a further aggravating factor. Respiratory illnesses are the third leading cause of death amongst the noncommunicable diseases. The current COVID-19 pandemic, however, also highlights the impact of communicable respiratory syndromes. In the clinical routine, prolonged postanesthetic respiratory instability worsens the patient outcome. Even though early and continuous, long-term cardiorespiratory monitoring has been proposed or even proven to be beneficial in several situations, implementations thereof are sparse. We employed our recently presented, multimodal patch stethoscope to estimate Einthoven electrocardiogram (ECG) Lead I and II from a single 55 mm ECG lead. Using the stethoscope and ECG subsystems, the pre-ejection period (PEP) and left ventricular ejection time (LVET) were estimated. ECG-derived respiration techniques were used in conjunction with a novel, phonocardiogram-derived respiration approach to extract respiratory parameters. Medical-grade references were the SOMNOmedics SOMNO HDTM and Osypka ICON-CoreTM. In a study including 10 healthy subjects, we analyzed the performances in the supine, lateral, and prone position. Einthoven I and II estimations yielded correlations exceeding 0.97. LVET and PEP estimation errors were 10% and 21%, respectively. Respiratory rates were estimated with mean absolute errors below 1.2 bpm, and the respiratory signal yielded a correlation of 0.66. We conclude that the estimation of ECG, PEP, LVET, and respiratory parameters is feasible using a wearable, multimodal acquisition device and encourage further research in multimodal signal fusion for respiratory signal estimation.
An Efficient Key Management Technique for the Internet of Things
Authors: Tabassum Tamanna , Hossain SK Alamgir , Rahman Md. Anisur , Alhamid Mohammed F. , Hossain M. Anwar
Published: April 2020

The Internet of Things (IoT) has changed our lives drastically. Customers, regulatory bodies, and industrial partners are driving us to use IoT. Although IoT provides new opportunities, security remains a key concern while providing various services. It is especially challenging how the data generated from IoT devices can be protected from potential security attacks and how to safeguard the exchange of these data while transiting through different nodes and gateways. In this research, we aim to ensure a safe IoT environment by proposing an efficient key management technique that uses a combination of symmetric and asymmetric cryptosystem to obtain the speed of the former as well as the security benefits of the latter. Our proposal considers a set of Smart Objects (SO) capable of key registration, generation and distribution for IoT data transmission. We used the open-source Message Queuing Telemetry Transport (MQTT) protocol to facilitate communications between the source and the destination nodes. The suitability of the proposed approach is measured experimentally and the results are comparable to existing works with respect to key conversion time, algorithm execution time, number of reuse connections, and bandwidth utilization.
Output-Only Damage Detection of Shear Building Structures Using an Autoregressive Model-Enhanced Optimal Subpattern Assignment Metric
Authors: Mei Liu , Li Huaguan , Zhou Yunlai , Li Dawang , Long Wujian , Xing Feng
Published: April 2020

This paper proposes a novel output-only structural damage indicator by incorporating the pole-based optimal subpattern assignment distance with autoregressive models to localize and relatively assess the severity of damages for sheared structures. Autoregressive models can model dynamic systems well, while their model poles can represent the state of the dynamic systems. Structural damage generally causes changes in the dynamic characteristics (especially the natural frequency, mode shapes and damping ratio) of structures. Since the poles of the autoregressive models can solve the modal parameters of the structure, the poles have a close relationship with the modal parameters so that the changes in the poles of its autoregressive model reflect structural damages. Therefore, we can identify the damage by tracking the shifts in the dynamic system poles. The optimal subpattern assignment distance, which is the performance evaluator in multi-target tracking algorithms to measure the metric between true and estimated tracks, enables the construction of damage sensitive indicator from system poles using the Hungarian algorithm. The proposed approach has been validated with a five-story shear-building using numerical simulations and experimental verifications, which are subjected to excitations of white noise, El Centro earthquake and sinusoidal wave with frequencies sweeping, respectively; the results indicate that this approach can localize and quantify structural damages effectively in an output-only and data-driven way.
Development of a Solar-Powered IoT-Based Instrument for Automatic Measurement of Water Clarity
Authors: Pham Tuan Ngoc , Ho Anh Pham Huy , Nguyen Tuong Van , Nguyen Ha Minh , Truong Nhu Huynh , Huynh Nguyen Duc , Nguyen Tung Huy , Dung Le The
Published: April 2020

Water clarity is the most common indicator of water quality. The purpose of the study was to develop an instrument which can automatically measure water clarity in place of manual measurement by Secchi disk. The instrument is suspended by buoys at the water surface and uses solar energy to measure the light intensity of LED bulbs after passing through a water column; the result is then converted to Secchi depth by using a regression function. Measurement data are stored in a cloud server so that mobile users can access via an Internet connection. Three experiments were conducted to examine the instrument performance: (i) to ensure light intensity of the LED bulbs is strong enough to pass through the water column; (ii) to determine the regression relationship between the measured light intensity of the instrument and Secchi depth; and (iii) to evaluate the coefficient of variation (CV) of the measured water clarity when using our instrument and a conventional Secchi disk. Experiment results show that the measured values of light intensity are stable with the average CV = 5.25%. Moreover, although there are slight differences between the Secchi depth measured by our instrument and those measured by Secchi disk, the measurements by our instrument can efficiently replace the measurements by conventional Secchi disk, which can be affected by weather conditions as well as by human subjectivity.
Application of Multi-Branch Cauer Circuits in the Analysis of Electromagnetic Transducers Used in Wireless Transfer Power Systems
Authors: Kurzawa Milena , Jędryczka Cezary , Wojciechowski Rafał M.
Published: April 2020

In this paper, the feasibility of applying a multi-branch equivalent model employing first- and second-order Cauer circuits for the analysis of electromagnetic transducers used in systems of wireless power transfer is discussed. A method of formulating an equivalent model (EqM) is presented, and an example is shown for a wireless power transfer system (WPTS) consisting of an air transformer with field concentrators. A method is proposed to synthesize the EqM of the considered transducer based on the time-harmonic field model, an optimization algorithm employing the evolution strategy (ES) and the equivalent Cauer circuits. A comparative analysis of the performance of the considered WPTS under high-frequency voltage supply calculated using the proposed EqM and a 3D field model in the time domain using the finite element method (FEM) was carried out. The selected results of the conducted analysis are presented and discussed.
Game Theory in Mobile CrowdSensing: A Comprehensive Survey
Authors: Dasari Venkat Surya , Kantarci Burak , Pouryazdan Maryam , Foschini Luca , Girolami Michele
Published: April 2020

Mobile CrowdSensing (MCS) is an emerging paradigm in the distributed acquisition of smart city and Internet of Things (IoT) data. MCS requires large number of users to enable access to the built-in sensors in their mobile devices and share sensed data to ensure high value and high veracity of big sensed data. Improving user participation in MCS campaigns requires to boost users effectively, which is a key concern for the success of MCS platforms. As MCS builds on non-dedicated sensors, data trustworthiness cannot be guaranteed as every user attains an individual strategy to benefit from participation. At the same time, MCS platforms endeavor to acquire highly dependable crowd-sensed data at lower cost. This phenomenon introduces a game between users that form the participant pool, as well as between the participant pool and the MCS platform. Research on various game theoretic approaches aims to provide a stable solution to this problem. This article presents a comprehensive review of different game theoretic solutions that address the following issues in MCS such as sensing cost, quality of data, optimal price determination between data requesters and providers, and incentives. We propose a taxonomy of game theory-based solutions for MCS platforms in which problems are mainly formulated based on Stackelberg, Bayesian and Evolutionary games. We present the methods used by each game to reach an equilibrium where the solution for the problem ensures that every participant of the game is satisfied with their utility with no requirement of change in their strategies. The initial criterion to categorize the game theoretic solutions for MCS is based on co-operation and information available among participants whereas a participant could be either a requester or provider. Following a thorough qualitative comparison of the surveyed approaches, we provide insights concerning open areas and possible directions in this active field of research.
A Trajectory Collaboration Based Map Matching Approach for Low-Sampling-Rate GPS Trajectories
Authors: Bian Wentao , Cui Ge , Wang Xin
Published: April 2020

GPS (Global Positioning System) trajectories with low sampling rates are prevalent in many applications. However, current map matching methods do not perform well for low-sampling-rate GPS trajectories due to the large uncertainty between consecutive GPS points. In this paper, a collaborative map matching method (CMM) is proposed for low-sampling-rate GPS trajectories. CMM processes GPS trajectories in batches. First, it groups similar GPS trajectories into clusters and then supplements the missing information by resampling. A collaborative GPS trajectory is then extracted for each cluster and matched to the road network, based on longest common subsequence (LCSS) distance. Experiments are conducted on a real GPS trajectory dataset and a simulated GPS trajectory dataset. The results show that the proposed CMM outperforms the baseline methods in both, effectiveness and efficiency.