bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of the Surface Modification of Calcium Carbonate on Polyamide 12 Composites
Authors: Ippolito Fabio , Hübner Gunter , Claypole Tim , Gane Patrick
Published: June 2020

In previous investigations, it was found that the thermal properties of a polyamide 12 compound can be manipulated, using a designed filler, to improve the melting as well as crystallization behavior, determined for selective laser sintering. A common downside of the introduction of a non-flexing mineral filler is the reduction of the mechanical properties, such as ductility. This paper investigates the influence of content and surface modification of limestone on the mechanical properties. The aim is to understand the effect of an optimized coupling agent on the properties of a compound, containing polyamide 12 filled with 10 wt % of surface modified calcium carbonate. A range of four mineral filler modifications was chosen to investigate their coupling effect, namely 6-amino hexanoic acid, ε-caprolactam, l-arginine or glutamic acid. The in advance surface modified fillers were then each used in combination with the polyamide 12 in a twin-screw extrusion process. With an optimized surface modifying agent, the tensile strength as well as elongation at break can be improved in comparison with uncoated filler implementation, such that up to 60% of the loss of ductility and toughness of a final part when using an untreated filler could be regained using an optimized surface modifier at a correct amount. With the tested filler grade and the specific tested filler amount, the optimized amount of 6-amino hexanoic acid was approx. 2.5 mmol of treatment agent per 100 m2 of CaCO3. These found improvements in a twin-screw extruded polyamide 12 compound show the possible usage of modified calcium carbonate as a functional filler in additive manufacturing and can potentially be transferred in a subsequent investigation in the selective laser sintering process.
Synthesis and Comparative Study of Nanoparticles Derived from Bovine and Human Serum Albumins
Authors: Tazhbayev Yerkeblan , Mukashev Olzhas , Burkeyev Meiram , Lozinsky Vladimir I.
Published: June 2020

This study describes the preparation of nanoparticles derived from bovine serum albumin (BSA) in comparison with the formation of nanoparticles composed of human serum albumin (HSA), when the same preparation procedure was used in both cases. To obtain protein nanoparticles, the method of desolvation with ethanol was employed, followed by the stabilization with urea and cysteine. It was shown that, upon transition from HSA to BSA, the particles with smaller sizes and with a narrower polydispersity were formed. The possibility of the immobilization of the antitumor drug hydroxyurea in such protein nanoparticles by adsorption and inclusion methods has been shown. The drug release profile from the polymer matrix was established.
An Exploration of Relationships among Thermal Insulation, Area Factor and Air Gap of Male Chinese Ethnic Costumes
Authors: Ke Ying , Wang Faming
Published: June 2020

The present study investigated total and local thermal insulations of 39 sets of male Chinese ethnic costumes. Total and local clothing area factor, air gap size and air volume were determined by a 3D body scanner. Relationships between thermal insulation and air gap for the whole body, as well as local body parts, were explored. Correlations of both the total and local clothing area factor with the intrinsic insulation were also developed. Results demonstrated that the clothing total thermal insulation first increased with the increasing air gap size/air volume, followed by a decrease when the air gap size/air volume exceeded 37.8 mm/55.8 dm3. Similarly, it was also found that parabolic relationships widely existed between the local thermal insulation and local air gap at each body part. Our research findings provide a comprehensive database for predicting both global and local thermal comfort of male Chinese minority groups.
Mechanical Behavior of Melt-Mixed 3D Hierarchical Graphene/Polypropylene Nanocomposites
Authors: Gaska Karolina , Manika Georgia C. , Gkourmpis Thomas , Tranchida Davide , Gitsas Antonis , Kádár Roland
Published: June 2020

The mechanical properties of novel low percolation melt-mixed 3D hierarchical graphene/polypropylene nanocomposites are analyzed in this study. The analysis spans a broad range of techniques and time scales, from impact to tensile, dynamic mechanical behavior, and creep. The applicability of the time–temperature superposition principle and its limitations in the construction of the master curve for the isotactic polypropylene (iPP)-based graphene nanocomposites has been verified and presented. The Williams–Landel–Ferry method has been used to evaluate the dynamics and also Cole–Cole curves were presented to verify the thermorheological character of the nanocomposites. Short term (quasi-static) tensile tests, creep, and impact strength measurements were used to evaluate the load transfer efficiency. A significant increase of Young’s modulus with increasing filler content indicates reasonably good dispersion and adhesion between the iPP and the filler. The Young’s modulus results were compared with predicted modulus values using Halpin–Tsai model. An increase in brittleness resulting in lower impact strength values has also been recorded.
Characterization and Performance Evaluation of Cellulose Acetate–Polyurethane Film for Lead II Ion Removal
Authors: Iqhrammullah M. , Marlina Marlina , Khalil H. P. S. Abdul , Kurniawan K. H. , Suyanto H. , Hedwig R. , Karnadi I. , Olaiya N. G. , Abdullah C. K. , Abdulmadjid S. N.
Published: June 2020

Global pollution from toxic metal waste has resulted in increased research on toxic metal adsorption. A cellulose acetate–polyurethane (CA–PU) film adsorbent was successfully prepared in this research. The cellulose acetate–polyurethane film adsorbent was prepared with a polycondensation reaction between cellulose acetate and methylene diphenyl diisocyanate. The CA–PU bond formation was confirmed by functional group analysis obtained from Fourier transform infrared (FTIR) spectroscopy. The obtained film was characterized for improved tensile and thermal properties with the addition of methylene diphenyl diisocyanate (MDI). The adsorption ability of the obtained film was evaluated with laser-induced breakdown spectroscopy (LIBS). The best film adsorbent from the LIBS was selected and studied for adsorption isotherm. The FTIR analysis confirmed the formation of the CA–PU bond from the polycondensation between cellulose acetate and the methylene diphenyl diisocyanate. The result showed that the addition of methylene diphenyl diisocyanate (MDI) resulted in the urethane network’s growth. The characterization result showed an improvement in the morphology, thermal stability, and tensile strength of the film. The LIBS studies showed improvement in the adsorption of Pb2+ with CA–PU compared with the neat CA. The isotherm studies revealed that Pb2+ adsorption by cellulose acetate–polyurethane film adsorbent was heterogeneously dependent on the Freundlich isotherm model (R2 = 0.97044). Overall, the polycondensation method proposed by this study enhanced the Pb2+ removal, and was comparable to those reported in previous studies.
Superordinate Ties, Value Orientations, and Congregations’ Organizational Cultures
Authors: May Matthew
Published: June 2020

In this paper, I examine how clergy’s value orientations and congregations’ relationships to the superordinate organizations in their institutional environment are reflected in congregations’ organizational cultures. My analysis of nearly 50 qualitative interviews with clergy, members, and former members of four Southern Baptist Convention congregations and one Independent Christian megachurch indicates organizational cultures are (1) reflections of their leaders’ value orientations and the congregation’s engagement with superordinate organizations and (2) an important indicator of how congregations establish legitimacy. I describe three unique organizational cultures and their relationship to clergy’s value orientations and the congregations’ ties to the superordinate organizations in their institutional environment. In the discussion, I argue there is a need to focus on specific components of the institutional environment beyond superordinate organizations, and I consider the role the three organizational cultures described in the text play in congregational growth and decline and church conflict.
Pathophysiological Basis of Endometriosis-Linked Stress Associated with Pain and Infertility: A Conceptual Review
Authors: Ghosh Debabrata , Filaretova Ludmila , Bharti Juhi , Roy Kallol K. , Sharma Jai B. , Sengupta Jayasree
Published: June 2020

Women with endometriosis are often under stress due to the associated pain, infertility, inflammation-related and other comorbidities including cancer. Additionally, these women are also under stress due to taboos, myths, inter-personal troubles surrounding infertility and pain of the disease as well as due to frequent incidences of missed diagnosis and treatment recurrence. Often these women suffer from frustration and loss of valuable time in the prime phase of life. All these complexities integral to endometriosis posit a hyperstructure of integrative stress physiology with overt differentials in effective allostatic state in women with disease compared with disease-free women. In the present review, we aim to critically examine various aspects of pathophysiological basis of stress surrounding endometriosis with special emphasis on pain and subfertility that are known to affect the overall health and quality of life of women with the disease and promising pathophysiological basis for its effective management.
Monitoring Vegetation Change in the Presence of High Cloud Cover with Sentinel-2 in a Lowland Tropical Forest Region in Brazil
Authors: Nazarova Tatiana , Martin Pascal , Giuliani Gregory
Published: June 2020

Forests play major roles in climate regulation, ecosystem services, carbon storage, biodiversity, terrain stabilization, and water retention, as well as in the economy of numerous countries. Nevertheless, deforestation and forest degradation are rampant in many parts of the world. In particular, the Amazonian rainforest faces the constant threats posed by logging, mining, and burning for agricultural expansion. In Brazil, the “Sete de Setembro Indigenous Land”, a protected area located in a lowland tropical forest region at the border between the Mato Grosso and Rondônia states, is subject to illegal deforestation and therefore necessitates effective vegetation monitoring tools. Optical satellite imagery, while extensively used for landcover assessment and monitoring, is vulnerable to high cloud cover percentages, as these can preclude analysis and strongly limit the temporal resolution. We propose a cloud computing-based coupled detection strategy using (i) cloud and cloud shadow/vegetation detection systems with Sentinel-2 data analyzed on the Google Earth Engine with deep neural network classification models, with (ii) a classification error correction and vegetation loss and gain analysis tool that dynamically compares and updates the classification in a time series. The initial results demonstrate that such a detection system can constitute a powerful monitoring tool to assist in the prevention, early warning, and assessment of deforestation and forest degradation in cloudy tropical regions. Owing to the integrated cloud detection system, the temporal resolution is significantly improved. The limitations of the model in its present state include classification issues during the forest fire period, and a lack of distinction between natural vegetation loss and anthropogenic deforestation. Two possible solutions to the latter problem are proposed, namely, the mapping of known agricultural and bare areas and its subsequent removal from the analyzed data, or the inclusion of radar data, which would allow a large amount of finetuning of the detection processes.
The Moon as a Climate-Quality Radiometric Calibration Reference
Authors: Stone Thomas C. , Kieffer Hugh , Lukashin Constantine , Turpie Kevin
Published: June 2020

On-orbit calibration requirements for a space-based climate observing system include long-term sensor response stability and reliable inter-calibration of multiple sensors, both contemporaneous and in succession. The difficulties with achieving these for reflected solar wavelength instruments are well known. The Moon can be considered a diffuse reflector of sunlight, and its exceptional photometric stability has enabled development of a lunar radiometric reference, manifest as a model that is queried for the specific conditions of Moon observations. The lunar irradiance model developed by the Robotic Lunar Observatory (ROLO) project has adequate precision for sensor response temporal trending, but a climate-quality lunar reference will require at least an order of magnitude improvement in absolute accuracy. To redevelop the lunar calibration reference with sub-percent uncertainty and SI traceability requires collecting new, high-accuracy Moon characterization measurements. This paper describes specifications for such measurements, along with a conceptual framework for reconstructing the lunar reference using them. Three currently active NASA-sponsored projects have objectives to acquire measurements that can support a climate-quality lunar reference: air-LUSI, dedicated lunar spectral irradiance measurements from the NASA ER-2 high altitude aircraft; ARCSTONE, dedicated lunar spectral reflectance measurements from a small satellite; and Moon viewing opportunities by CLARREO Pathfinder from the International Space Station.
Wheat Lodging Detection from UAS Imagery Using Machine Learning Algorithms
Authors: Zhang Zhao , Flores Paulo , Igathinathane C. , L. Naik Dayakar , Kiran Ravi , Ransom Joel K.
Published: June 2020

The current mainstream approach of using manual measurements and visual inspections for crop lodging detection is inefficient, time-consuming, and subjective. An innovative method for wheat lodging detection that can overcome or alleviate these shortcomings would be welcomed. This study proposed a systematic approach for wheat lodging detection in research plots (372 experimental plots), which consisted of using unmanned aerial systems (UAS) for aerial imagery acquisition, manual field evaluation, and machine learning algorithms to detect the occurrence or not of lodging. UAS imagery was collected on three different dates (23 and 30 July 2019, and 8 August 2019) after lodging occurred. Traditional machine learning and deep learning were evaluated and compared in this study in terms of classification accuracy and standard deviation. For traditional machine learning, five types of features (i.e. gray level co-occurrence matrix, local binary pattern, Gabor, intensity, and Hu-moment) were extracted and fed into three traditional machine learning algorithms (i.e., random forest (RF), neural network, and support vector machine) for detecting lodged plots. For the datasets on each imagery collection date, the accuracies of the three algorithms were not significantly different from each other. For any of the three algorithms, accuracies on the first and last date datasets had the lowest and highest values, respectively. Incorporating standard deviation as a measurement of performance robustness, RF was determined as the most satisfactory. Regarding deep learning, three different convolutional neural networks (simple convolutional neural network, VGG-16, and GoogLeNet) were tested. For any of the single date datasets, GoogLeNet consistently had superior performance over the other two methods. Further comparisons between RF and GoogLeNet demonstrated that the detection accuracies of the two methods were not significantly different from each other (p > 0.05); hence, the choice of any of the two would not affect the final detection accuracies. However, considering the fact that the average accuracy of GoogLeNet (93%) was larger than RF (91%), it was recommended to use GoogLeNet for wheat lodging detection. This research demonstrated that UAS RGB imagery, coupled with the GoogLeNet machine learning algorithm, can be a novel, reliable, objective, simple, low-cost, and effective (accuracy > 90%) tool for wheat lodging detection.
Quantifying Uncertainty and Bridging the Scaling Gap in the Retrieval of Leaf Area Index by Coupling Sentinel-2 and UAV Observations
Authors: Revill Andrew , Florence Anna , MacArthur Alasdair , Hoad Stephen , Rees Robert , Williams Mathew
Published: June 2020

Leaf area index (LAI) estimates can inform decision-making in crop management. The European Space Agency’s Sentinel-2 satellite, with observations in the red-edge spectral region, can monitor crops globally at sub-field spatial resolutions (10–20 m). However, satellite LAI estimates require calibration with ground measurements. Calibration is challenged by spatial heterogeneity and scale mismatches between field and satellite measurements. Unmanned Aerial Vehicles (UAVs), generating high-resolution (cm-scale) LAI estimates, provide intermediary observations that we use here to characterise uncertainty and reduce spatial scaling discrepancies between Sentinel-2 observations and field surveys. We use a novel UAV multispectral sensor that matches Sentinel-2 spectral bands, flown in conjunction with LAI ground measurements. UAV and field surveys were conducted on multiple dates—coinciding with different wheat growth stages—that corresponded to Sentinel-2 overpasses. We compared chlorophyll red-edge index (CIred-edge) maps, derived from the Sentinel-2 and UAV platforms. We used Gaussian processes regression machine learning to calibrate a UAV model for LAI, based on ground data. Using the UAV LAI, we evaluated a two-stage calibration approach for generating robust LAI estimates from Sentinel-2. The agreement between Sentinel-2 and UAV CIred-edge values increased with growth stage—R2 ranged from 0.32 (stem elongation) to 0.75 (milk development). The CIred-edge variance between the two platforms was more comparable later in the growing season due to a more homogeneous and closed wheat canopy. The single-stage Sentinel-2 LAI calibration (i.e., direct calibration from ground measurements) performed poorly (mean R2 = 0.29, mean NRMSE = 17%) when compared to the two-stage calibration using the UAV data (mean R2 = 0.88, mean NRMSE = 8%). The two-stage approach reduced both errors and biases by >50%. By upscaling ground measurements and providing more representative model training samples, UAV observations provide an effective and viable means of enhancing Sentinel-2 wheat LAI retrievals. We anticipate that our UAV calibration approach to resolving spatial heterogeneity would enhance the retrieval accuracy of LAI and additional biophysical variables for other arable crop types and a broader range of vegetation cover types.
A Tropospheric Tomography Method with a Novel Height Factor Model Including Two Parts: Isotropic and Anisotropic Height Factors
Authors: Zhang Wenyuan , Zhang Shubi , Ding Nan , Zhao Qingzhi
Published: June 2020

Global Navigation Satellite System (GNSS) tomography has developed into an efficient tool for sensing the high spatiotemporal variability of atmospheric water vapor. The integration of GNSS top signals and side rays for tropospheric tomography systems using a novel height factor model (HFM) is proposed and discussed in this paper. Within the HFM, the sectional slant wet delay (SWD) of inside signals (the part of the side signal inside the tomography area), which is considered a key factor for modeling side rays, is separated into isotropic and anisotropic components. Correspondingly, two height factors are defined to calculate the isotropic and anisotropic part of tropospheric delays in the HFM. In addition, the dynamic tomography top boundary is first analyzed and determined based on 30-year radiosonde data to reasonably divide signals into top and side rays. Four special experimental schemes based on different tomography regions of Hong Kong are performed to assess the proposed HFM method, the results of which show increases of 33.42% in the mean utilization of rays, as well as decreases of 0.46 g/m3 in the average root mean square error (RMSE), compared to the traditional approach, revealing the improvement of tomography solutions when the side signals are included in the modeling. Furthermore, compared with the existing correction model for modeling side rays, the water vapor profiles retrieved from the proposed improved model are closer to the radiosonde data, which highlights the advantages of the proposed HFM for optimizing the GNSS tomography model.
Evaluation of Grid-Based Rainfall Products and Water Balances over the Mekong River Basin
Authors: Dinh Kha Dang , Anh Tran Ngoc , Nguyen Nhu Y , Bui Du Duong , Srinivasan Raghavan
Published: June 2020

Gridded precipitation products (GPPs) with wide spatial coverage and easy accessibility are well recognized as a supplement to ground-based observations for various hydrological applications. The error properties of satellite rainfall products vary as a function of rainfall intensity, climate region, altitude, and land surface conditions—all factors that must be addressed prior to any application. Therefore, this study aims to evaluate four commonly used GPPs: the Climate Prediction Center (CPC) Unified Gauge-Based Analysis of Global Daily Precipitation, the Climate Prediction Center Morphing (CMORPH) technique, the Tropical Rainfall Measuring Mission (TRMM) 3B42, and the Global Satellite Mapping of Precipitation (GSMaP), using data collected in the period 1998–2006 at different spatial and temporal scales. Furthermore, this study investigates the hydrological performance of these products against the 175 rain gauges placed across the whole Mekong River Basin (MRB) using a set of statistical indicators, along with the Soil and Water Assessment Tool (SWAT) model. The results from the analysis indicate that TRMM has the best performance at the annual, seasonal, and monthly scales, but at the daily scale, CPC and GSMaP are revealed to be the more accurate option for the Upper MRB. The hydrological evaluation results at the daily scale further suggest that the TRMM is the more accurate option for hydrological performance in the Lower MRB, and CPC shows the best performance in the Upper MRB. Our study is the first attempt to use distinct suggested GPPs for each individual sub-region to evaluate the water balance components in order to provide better references for the assessment and management of basin water resources in data-scarce regions, suggesting strong capabilities for utilizing publicly available GPPs in hydrological applications.
Novel Air Temperature Measurement Using Midwave Hyperspectral Fourier Transform Infrared Imaging in the Carbon Dioxide Absorption Band
Authors: Kim Sungho
Published: June 2020

Accurate visualization of air temperature distribution can be useful for various thermal analyses in fields such as human health and heat transfer of local area. This paper presents a novel approach to measuring air temperature from midwave hyperspectral Fourier transform infrared (FTIR) imaging in the carbon dioxide absorption band (between 4.25–4.35 μm). In this study, the proposed visual air temperature (VisualAT) measurement is based on the observation that the carbon dioxide band shows zero transmissivity at short distances. Based on analysis of the radiative transfer equation in this band, only the path radiance by air temperature survives. Brightness temperature of the received radiance can provide the raw air temperature and spectral average, followed by a spatial median-mean filter that can produce final air temperature images. Experiment results tested on a database obtained by a midwave extended FTIR system (Telops, Quebec City, QC, Canada) from February to July 2018 show a mean absolute error of 1.25 ∘K for temperature range of 2.6−26.4 ∘C.
Agreement Index for Burned Area Mapping: Integration of Multiple Spectral Indices Using Sentinel-2 Satellite Images
Authors: Smiraglia Daniela , Filipponi Federico , Mandrone Stefania , Tornato Antonella , Taramelli Andrea
Published: June 2020

Identifying fire-affected areas is of key importance to support post-fire management strategies and account for the environmental impact of fires. The availability of high spatial and temporal resolution optical satellite data enables the development of procedures for detailed and prompt post-fire mapping. This study proposes a novel approach for integrating multiple spectral indices to generate more accurate burned area maps by exploiting Sentinel-2 images. This approach aims to develop a procedure to combine multiple spectral indices using an adaptive thresholding method and proposes an agreement index to map the burned areas by optimizing omission and commission errors. The approach has been tested for the burned area classification of four study areas in Italy. The proposed agreement index combines multiple spectral indices to select the actual burned pixels, to balance the omission and commission errors, and to optimize the overall accuracy. The results showed the spectral indices singularly performed differently in the four study areas and that high levels of commission errors were achieved, especially for wildfires which occurred during the fall season (up to 0.93) Furthermore, the agreement index showed a good level of accuracy (minimum 0.65, maximum 0.96) for all the study areas, improving the performance compared to assessing the indices individually. This suggests the possibility of testing the methodology on a large set of wildfire cases in different environmental conditions to support the decision-making process. Exploiting the high resolution of optical satellite data, this work contributes to improving the production of detailed burned area maps, which could be integrated into operational services based on the use of Earth Observation products for burned area mapping to support the decision-making process.
35 Years of Vegetation and Lake Dynamics in the Pechora Catchment, Russian European Arctic
Authors: Cheţan Marinela-Adriana , Dornik Andrei , Ardelean Florina , Georgievski Goran , Hagemann Stefan , Romanovsky Vladimir E. , Onaca Alexandru , Drozdov Dmitry S.
Published: June 2020

High-latitude regions are a hot spot of global warming, but the scarce availability of observations often limits the investigation of climate change impacts over these regions. However, the utilization of satellite-based remote sensing data offers new possibilities for such investigations. In the present study, vegetation greening, vegetation moisture and lake distribution derived from medium-resolution satellite imagery were analyzed over the Pechora catchment for the last 35 years. Here, we considered the entire Pechora catchment and the Pechora Delta region, located in the northern part of European Russia, and we investigated the vegetation and lake dynamics over different permafrost zones and across the two major biomes, taiga, and tundra. We also evaluated climate data records from meteorological stations and re-analysis data to find relations between these dynamics and climatic behavior. Considering the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI) in the summer, we found a general greening and moistening of the vegetation. While vegetation greenness follows the evolution of summer air temperature with a delay of one year, the vegetation moisture dynamics seems to better concur with annual total precipitation rather than summer precipitation, and also with annual snow water equivalent without lag. Both NDVI and NDMI show a much higher variability across discontinuous permafrost terrain compared to other types. Moreover, the analyses yielded an overall decrease in the area of permanent lakes and a noticeable increase in the area of seasonal lakes. While the first might be related to permafrost thawing, the latter seems to be connected to an increase of annual snow water equivalent. The general consistency between the indices of vegetation greenness and moisture based on satellite imagery and the climate data highlights the efficacy and reliability of combining Landsat satellite data, ERA-Interim reanalysis and meteorological data to monitor temporal dynamics of the land surface in Arctic areas.
Angular-Based Radiometric Slope Correction for Sentinel-1 on Google Earth Engine
Authors: Vollrath Andreas , Mullissa Adugna , Reiche Johannes
Published: June 2020

This article provides an angular-based radiometric slope correction routine for Sentinel-1 SAR imagery on the Google Earth Engine platform. Two established physical reference models are implemented. The first model is optimised for vegetation applications by assuming volume scattering on the ground. The second model is optimised for surface scattering, and therefore targeted at urban environments or analysis of soil characteristics. The framework of both models is extended to simultaneously generate masks of invalid data in active layover and shadow affected areas. A case study, using openly available and reproducible code, exemplarily demonstrates the improvement of the backscatter signal in a mountainous area of the Austrian Alps. Furthermore, suggestions for specific use cases are discussed and drawbacks of the method with respect to pixel-area based methods are highlighted. The radiometrically corrected radar backscatter products are overcoming current limitations and are compliant with recent CEOS specifications for SAR backscatter over land. This improves a wide range of potential usage scenarios of the Google Earth Engine platform in mapping various land surface parameters with Sentinel-1 on a large scale and in a rapid manner. The provision of an openly accessible Earth Engine module allows users a smooth integration of the routine into their own workflows.
Temperature and Humidity Profile Retrieval from FY4-GIIRS Hyperspectral Data Using Artificial Neural Networks
Authors: Cai Xi , Bao Yansong , Petropoulos George P. , Lu Feng , Lu Qifeng , Zhu Liuhua , Wu Ying
Published: June 2020

This study proposes a new technique for retrieving temperature and humidity profiles based on Artificial Neural Networks (ANNs) using data acquired from the GIIRS (Geosynchronous Interferometric Infrared Sounder) L1 and ERA-Interim (European Centre for Medium-Range Weather Forecasts Reanalysis). The approach is also compared against another method that uses simulated data from the radiative transfer model to construct the retrieval network. Furthermore, the two methods of network construction are evaluated in the North China Plain for July and August 2018, for which ground validated observations concurrent to the satellite data were available. In summary, the results showed that: (1) the ANN built with the GIIRS L1 and the EC data is superior to that built with the forward simulation and EC data in retrieval accuracy; (2) the retrieval accuracy for the troposphere exceeds that for the stratosphere; (3) the root mean square errors (RMSEs) of the relative humidity in the troposphere as retrieved by the two ANNs are 6.003% and 10.608%, respectively; (4) a relatively low correlation (R) between the simulated and observed radiance of the GIIRS is found, ranging between 720 and 736.875 cm−1, and the correlation between the simulated and observed radiance of the water vapor channels exceeds that between the temperature channels; (5) compared with Atmospheric Infrared Sounder’s (AIRS’) products, our retrieved temperature profiles exhibit preferable consistency and the humidity retrievals also show an acceptable accuracy. Our study offers important insights towards improving our ability to retrieve atmospheric temperature and humidity profiles from the most sophisticated Earth Observation instruments such as the GIIRS of the FY-4 satellite, which could assist in expanding the use of those products globally.
Evaluation of the ERA5 Sea Surface Skin Temperature with Remotely-Sensed Shipborne Marine-Atmospheric Emitted Radiance Interferometer Data
Authors: Luo Bingkun , Minnett Peter J.
Published: June 2020

Sea surface temperature is very important in weather and ocean forecasting, and studying the ocean, atmosphere and climate system. Measuring the sea surface skin temperature (SSTskin) with infrared radiometers onboard earth observation satellites and shipboard instruments is a mature subject spanning several decades. Reanalysis model output SSTskin, such as from the newly released ERA5, is very widely used and has been applied for monitoring climate change, weather prediction research, and other commercial applications. The ERA5 output SSTskin data must be rigorously evaluated to meet the stringent accuracy requirements for climate research. This study aims to estimate the accuracy of the ERA5 SSTskin fields and provide an associated error estimate by using measurements from accurate shipboard infrared radiometers: the Marine-Atmosphere Emitted Radiance Interferometers (M-AERIs). Overall, the ERA5 SSTskin has high correlation with ship-based radiometric measurements, with an average difference of~0.2 K with a Pearson correlation coefficient (R) of 0.993. Parts of the discrepancies are related to dust aerosols and variability in air-sea temperature differences. The downward radiative flux due to dust aerosols leads to significant SSTskin differences for ERA5. The SSTskin differences are greater with the large, positive air–sea temperature differences. This study provides suggestions for the applicability of ERA5 SSTskin fields in a selection of research applications.
Implementation of High-Performance Blockchain Network Based on Cross-Chain Technology for IoT Applications
Authors: Lin Ting , Yang Xu , Wang Taoyi , Peng Tu , Xu Feng , Lao Shengxiong , Ma Siyuan , Wang Hanfeng , Hao Wenjiang
Published: June 2020

With the development of technology, the network structure has changed a lot. Many people regard the Internet of Things as the next-generation network structure, which means all the embedded devices can communicate with each other directly. However, some problems remain in IoT before it can be applied in a large scale. Blockchain, which has become a hot research topic in recent years, may be one of the solutions. However, currently, the transaction speed of blockchain is still a disadvantage compared to traditional transaction methods. This paper focuses on to implement a high-performance blockchain platform. After investigation of the current blockchain consensus algorithm and blockchain architecture, we propose: (1) an improved blockchain consensus algorithm, which is implemented based on the mortgage model instead of probability model; (2) a cross-chain protocol with transverse expansion capacity, which would support the message transmission among chains; (3) a high-performance cross-chain blockchain network structure, which could handle more than 1000 transactions per second per chain by verification. Experiments have been carried out, and shown that the cross-chain blockchain network structure we provided is feasible to meet the requirement of large-scale distributed IoT applications.
Compressed-Domain ECG-Based Biometric User Identification Using Compressive Analysis
Authors: Chou Ching-Yao , Pua Yo-Woei , Sun Ting-Wei , Wu An-Yeu (Andy)
Published: June 2020

Nowadays, user identification plays a more and more important role for authorized machine access and remote personal data usage. For reasons of privacy and convenience, biometrics-based user identification, such as iris, fingerprint, and face ID, has become mainstream methods in our daily lives. However, most of the biometric methods can be easily imitated or artificially cracked. New types of biometrics, such as electrocardiography (ECG), are based on physiological signals rather than traditional biological traits. Recently, compressive sensing (CS) technology that combines both sampling and compression has been widely applied to reduce the power of data acquisition and transmission. However, prior CS-based frameworks suffer from high reconstruction overhead and cannot directly align compressed ECG signals. In this paper, in order to solve the above two problems, we propose a compressed alignment-aided compressive analysis (CA-CA) algorithm for ECG-based biometric user identification. With CA-CA, it can avoid reconstruction and extract information directly from CS-based compressed ECG signals to reduce overall complexity and power. Besides, CA-CA can also align the compressed ECG signals in the eigenspace-domain, which can further enhance the precision of identifications and reduce the total training time. The experimental result shows that our proposed algorithm has a 94.16% accuracy based on a public database of 22 people.
Assessment of Mental Burden of Family Caregivers of Persons with Health Disabilities in the Czech Republic
Authors: Truhlarova Zuzana , Stech Ondrej , Voseckova Alena , Klimova Blanka , Kuca Kamil
Published: June 2020

The purpose of this study was to analyse the subjective perception of stress burden in family caregivers, especially those caring for disabled and elderly persons. The tool for quantifying the burden was the Meister questionnaire, which describes the basic dimensions of overload in ten items. These were divided into three factors: overload, monotony and the non-specific factor. The research tool was a standardised questionnaire distributed via a snowball method to a group of 484 family caregivers who cared for a person with disability. The findings show that they have to largely rely on the support of social services. Their burden is characterised by the prevalence of the overload factor. The findings also indicate that there is an urgent call among health professionals to identify and treat caregivers’ psychological distress by applying relevant intervention strategies, which could reduce this distress and prevent caregivers’ burnout. Future research should concentrate on the efficacy of intervention strategies which would reduce the overall burden of family caregivers and nurture the family as a whole.
Impact of Gaps in the Educational Levels between Married Partners on Health and a Sustainable Lifestyle: Evidence from 32 Countries
Authors: Piao Xiangdan , Ma Xinxin , Zhang Chi , Managi Shunsuke
Published: June 2020

Using original cross-sectional internet survey data from 32 countries covering six continents, we investigated the impact of education gaps between married partners on their health status and sustainable lifestyles using the instrumental variable method. A self-rated health status index, mental health index, and an objective health status index were utilized to assess the health statuses of individuals, and six unique indices were used to investigate the sustainable lifestyles. According to the main findings, work-family conflicts may be severe for both wives and husbands with high education levels, and the hypothesis regarding the positive effect of income was not supported. Two major conclusions were derived. First, in general, as opposed to couples with equal education levels, the probability of reporting a worse health status was higher, and the activities related to sustainable development such as improving environmental sustainability were less for couples with education gaps. Second, a comparison of the effects of education gaps on the health status of couples in various groups reveals that highly educated groups, women, and people in Asian or middle-income countries had a higher negative effect on their health status.
Input-Output Efficiency of Economic Growth: A Multielement System Perspective
Authors: Kang Lei , Song Zhouying
Published: June 2020

Achieving sustainable and efficient economic development involves the pursuit of a model with low input, low emissions, and high yield. One approach to this is by considering input-output efficiency, which has been studied by many previous studies. However, existing literature mainly tend only to give an overall evaluation of regional input-output efficiency, which is unable to reveal the structure and components within the input-output system. This paper aims to overcome this problem by a systematic examination and measuring the resource efficiency, socio-economic efficiency, and environmental efficiency of separate subsystems using the Super-DEA model. The overall efficiency of 30 Chinese provinces from 2000 to 2015 is analyzed, along with each subsystem’s efficiency. The results show: (i) The overall input-output efficiency, resource efficiency, and socio-economic efficiency of the eastern region are relatively high. The efficiency of the northeastern region has performed poorly. Although the efficiency of the central and western regions is not high, their resource efficiency and socio-economic efficiency have risen in the last decade; (ii) Environmental efficiencies are markedly lower than the levels of the other two subsystems. Most western and northeastern provinces increased in rank, while most eastern and central provinces fell. (iii) Provinces can be divided into three categories, such as resource, socio-economic, and environmental efficiency-constrained provinces. Finally, we discuss the reasons driving the spatiotemporal pattern of China’s input-output efficiency and improvement policies.
Equal Opportunities in an Inclusive and Sustainable Education System: An Explanatory Model
Authors: Medina-García Marta , Doña-Toledo Luis , Higueras-Rodríguez Lina
Published: June 2020

Equal opportunities is an objective to be achieved in a sustainable society, as formulated by various sustainable development objectives. Inclusive education refers to the right of all people to education, guaranteeing the presence, participation, and progress of all students and, above all, equal opportunities. However, today, it is a dual and controversial issue, as it appears among the strategies and objectives planned at international and European levels, but its application and real development is still far from being a right with guarantees. Moreover, the concept of integration remains in most areas and many education policies. Therefore, the objective of this work is to establish which integration and inclusion measures favor equal opportunities. The study offers, as a major novelty, the results of empirical research, which provides a scientific framework to this process of equal opportunities. It is approached from the perspective of teaching staff with a sample of 133 professionals. The results are analyzed through factorial analysis and multiple linear regression. The results show that the aspects related to inclusion (measures of attention to diversity, high knowledge about inclusion, and adapting the system to inclusion) have a significant positive effect. The integration of students reduces equal opportunities in a statistically significant way. The results are of interest for educational policies and for decision-making and strategies to achieve sustainability and inclusion in the school environment.
The Return on Investment in Social Farming: A Strategy for Sustainable Rural Development in Rural Catalonia
Authors: Tulla Antoni F. , Vera Ana , Guirado Carles , Valldeperas Natàlia
Published: June 2020

Although social farming (SF) has been a successful endeavor in Catalonia over the last 30 years, it has not been visible for part of society, which is largely unaware of its existence. Nevertheless, this study has demonstrated after studying five cases that the social return on investment (SROI) has been significant in rural areas. In this study, five in-depth interviews were carried out, and a general questionnaire was sent to 161 SF projects in Catalonia with a view to analyzing the participation of stakeholders and the returns they have achieved. The SROI method and the benefits to different stakeholders are concisely described. Our main foci are people at risk of social exclusion, their families, and the project promoters, but we also believe it is essential to analyze the administration of projects and the settings in which they take place. It is calculated that there is a social, economic, and environmental return on investment in terms of the benefits to the territory’s sustainable local development at an approximate proportion of three euros for every euro invested. Without a doubt, SF can help to support proximity agriculture, thus favoring its development and viability.
Vulnerability Assessment and Adaptation Strategies for the Impact of Climate Change on Agricultural Land in Southern Taiwan
Authors: Huang Kuo-Ching , Lee Chen-Jai , Chan Shih-Liang , Tai Cheng-Hsin
Published: June 2020

Maintaining a certain amount of agricultural land and promoting its agricultural land utilization efficiency is essential in a country. Many innovative strategies for adapting to climate change have been implemented in developed countries. To achieve the goal of climate change adaptation for agricultural land, a vulnerability assessment of farmland is indispensable. Based on the research framework of the Intergovernmental Panel on Climate Change, this study applied the structure of exposure, sensitivity, and adaptation to build criteria and conduct an evaluation of a designated area in Southern Taiwan. We identified the key factors of the vulnerability of farmland, through mapping with spatial analysis, and by using geographic information system tools. The main purpose of the application of a vulnerability assessment is not to explicitly describe the status of agricultural land to climate change, but to help local government and farmers to identify the critical area, and to discuss the appropriated adaptive policies. According to the results of the vulnerability assessment of agricultural land, the entire study region can be divided into three patterns: Pattern 1, located in the western coastal zone, filled with various attributes of high vulnerability; Pattern 2, distributed on the central plain region in the east, with complete blocks of agricultural land and low vulnerability; and Pattern 3, located in the central plain region to the west, a region in which areas with various vulnerability levels. The following three types of adaptation strategies for climate change for farmland were established: (1) the enhancement of agricultural production, (2) the maintenance of agricultural production, and (3) the conservation of the agricultural environment. The current results can serve as valuable guidelines for governments to implement feasible local adaptation strategies in the future.
The Role of Smart Technology in Sustainable Agriculture: A Case Study of Wangree Plant Factory
Authors: Santiteerakul Salinee , Sopadang Apichat , Yaibuathet Tippayawong Korrakot , Tamvimol Krisana
Published: June 2020

Sustainable development is of growing importance to the agriculture sector because the current lacking utilization of resources and energy usage, together with the pollution generated from toxic chemicals, cannot continue at present rates. Sustainability in agriculture can be achieved through using less (or no) poisonous chemicals, saving natural resources, and reducing greenhouse gas emissions. Technology applications could help farmers to use proper data in decision-making, which leads to low-input agriculture. This work focuses on the role of smart technology implementation in sustainable agriculture. The effects of smart technology implementation are analyzed by using a case study approach. The results show that the plant factory using intelligence technology enhances sustainability performance by increasing production productivity, product quality, crop per year, resource use efficiency, and food safety, as well as improving employees’ quality of life.