bluuebunny/crossref_metadata_2025 huggingface dataset:
Coastal Turbidity Derived From PROBA-V Global Vegetation Satellite
Authors: De Keukelaere Liesbeth , Sterckx Sindy , Adriaensen Stefan , Bhatia Nitin , Monbaliu Jaak , Toorman Erik , Cattrijsse André , Lebreton Carole , Van der Zande Dimitry , Knaeps Els
Published: February 2020

PROBA-V (Project for On-Board Autonomy-Vegetation) is a global vegetation monitoring satellite. The spectral quality of the data and the coverage of PROBA-V over coastal waters provide opportunities to expand its use to other applications. This study tests PROBA-V data for the retrieval of turbidity in the North Sea region. In the first step, clouds were masked and an atmospheric correction, using an adapted version of iCOR, was performed. The resulted water leaving radiance reflectance was validated against AERONET-OC stations, yielding a coefficient of determination of 0.884 in the RED band. Next, turbidity values were retrieved using the RED band. The PROBA-V retrieved turbidity data was compared with turbidity data from CEFAS Smartbuoys and ad-hoc measurement campaigns. This resulted in a coefficient of determination of 0.69. Finally, a time series of 1.5 year of PROBA-V derived turbidity data was plotted over MODIS data to check consistencies in both datasets. Seasonal dynamics were noted with high turbidity in autumn and winter and low values in spring and summer. For low values, PROBA-V and MODIS yielded similar results, but while MODIS seems to saturate around 50 FNU, PROBA-V can reach values up till almost 80 FNU.
An Improved Mapping with Super-Resolved Multispectral Images for Geostationary Satellites
Authors: Yang Xue , Li Feng , Xin Lei , Lu Xiaotian , Lu Ming , Zhang Nan
Published: February 2020

Super-resolution (SR) technology has shown great potential for improving the performance of the mapping and classification of multispectral satellite images. However, it is very challenging to solve ill-conditioned problems such as mapping for remote sensing images due to the presence of complicated ground features. In this paper, we address this problem by proposing a super-resolution reconstruction (SRR) mapping method called the mixed sparse representation non-convex high-order total variation (MSR-NCHOTV) method in order to accurately classify multispectral images and refine object classes. Firstly, MSR-NCHOTV is employed to reconstruct high-resolution images from low-resolution time-series images obtained from the Gaofen-4 (GF-4) geostationary orbit satellite. Secondly, a support vector machine (SVM) method was used to classify the results of SRR using the GF-4 geostationary orbit satellite images. Two sets of GF-4 satellite image data were used for experiments, and the MSR-NCHOTV SRR result obtained using these data was compared with the SRR results obtained using the bilinear interpolation (BI), projection onto convex sets (POCS), and iterative back projection (IBP) methods. The sharpness of the SRR results was evaluated using the gray-level variation between adjacent pixels, and the signal-to-noise ratio (SNR) of the SRR results was evaluated by using the measurement of high spatial resolution remote sensing images. For example, compared with the values obtained using the BI method, the average sharpness and SNR of the five bands obtained using the MSR-NCHOTV method were higher by 39.54% and 51.52%, respectively, and the overall accuracy (OA) and Kappa coefficient of the classification results obtained using the MSR-NCHOTV method were higher by 32.20% and 46.14%, respectively. These results showed that the MSR-NCHOTV method can effectively improve image clarity, enrich image texture details, enhance image quality, and improve image classification accuracy. Thus, the effectiveness and feasibility of using the proposed SRR method to improve the classification accuracy of remote sensing images was verified.
Retrieval of Fine-Resolution Aerosol Optical Depth (AOD) in Semiarid Urban Areas Using Landsat Data: A Case Study in Urumqi, NW China
Authors: Chen Xiangyue , Ding Jianli , Wang Jingzhe , Ge Xiangyu , Raxidin Mayira , Liang Jing , Chen Xiaoxiao , Zhang Zipeng , Cao Xiaoyi , Ding Yue
Published: February 2020

The aerosol optical depth (AOD) represents the light attenuation by aerosols and is an important threat to urban air quality, production activities, human health, and sustainable urban development in arid and semiarid regions. To some extent, the AOD reflects the extent of regional air pollution and is often characterized by significant spatiotemporal dynamics. However, detailed local AOD information is ambiguous at best due to limited monitoring techniques. Currently, the availability of abundant satellite data and constantly updated AOD extraction algorithms offer unprecedented perspectives for high-resolution AOD extraction and long-time series analysis. This study, based on the long-term sequence MOD09A1 data from 2010 to 2018 and lookup table generation, uses the improved deep blue algorithm (DB) to conduct fine-resolution (500 m) AOD (at 550 nm wavelength) remote sensing (RS) estimation on Landsat TM/OLI data from the Urumqi region, analyzes the spatiotemporal AOD variation characteristics in Urumqi and combines gray relational analysis (GRA) and the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model to analyze AOD influence factors and simulate pollutant propagation trajectories in representative periods. The results demonstrate that the improved DB algorithm has a high inversion accuracy for continuous AOD inversion at a high spatial resolution in urban areas. The spatial AOD distribution in Urumqi declines from urban to suburban areas, and higher AODs are concentrated in cities and along roads. Among these areas, Xinshi District has the highest AOD, and Urumqi County has the lowest AOD. The seasonal AOD variation characteristics are distinct, and the AOD order is spring (0.411) > summer (0.285) > autumn (0.203), with the largest variation in spring. The average AOD in Urumqi is 0.187, and the interannual variation generally shows an upward trend. However, from 2010 to 2018, AOD first declined gradually and then declined significantly. Thereafter, AOD reached its lowest value in 2015 (0.076), followed by a significant AOD increase, reaching a peak in 2016 (0.354). This shows that coal to natural gas (NG) project implementation in Urumqi promoted the improvement of Urumqi’s atmospheric environment. According to GRA, the temperature has the largest impact on the AOD in Urumqi (0.699). Combined with the HYSPLIT model, it was found that the aerosols observed over Urumqi were associated with long-range transport from Central Asia, and these aerosols can affect the entire northern part of China through long-distance transport.
Preliminary Evaluation of the Error Budgets in the TALIS Measurements and Their Impact on the Retrievals
Authors: Wang Wenyu , Wang Zhenzhan , Duan Yongqiang
Published: February 2020

The THz Atmospheric Limb Sounder (TALIS) is a Chinese sub-millimeter limb sounder being designed by National Space Science Center of the Chinese Academy of Sciences to measure the temperature and chemical constituents vertically in the middle and upper atmosphere, with good precision and vertical resolution. This paper presents a simulation study that assesses the measurement errors and their impacts on the retrievals. Three error sources, including instrument uncertainties, calibration errors and a priori errors, are considered. The sideband weight uncertainty, the local oscillator, the pointing angle offsets and the measurement noise (NEDT), are considered as instrument uncertainties. Calibration errors consist of the hot target offset, the nonlinearity residual of the two-point calibration, use of the Rayleigh–Jeans (R–J) approximation and the choice of the antenna pattern. A priori profile errors of temperature, pressure and species are also considered. The results suggest that the antenna pattern mainly affects the retrievals in the troposphere. The NEDT is a major error source affecting all of the retrievals. The R–J approximation has a great impact upon the retrievals at 643 GHz, and should not be used. The local oscillator offset leads to an obvious error above 50 km. The effect of nonlinearity residuals cannot be neglected above 70 km. The impact of the sideband weight uncertainty and the hot target offset are relatively small. The pointing and the a priori errors can be neglected in most observation regions.
Do Protected Areas Improve Ecosystem Services? A Case Study of Hoh Xil Nature Reserve in Qinghai-Tibetan Plateau
Authors: Zeng Jie , Chen Tianyang , Yao Xiaowei , Chen Wanxu
Published: February 2020

Although there is a consensus that protected areas (PAs) can provide various ecosystem services, it is unclear whether protected areas effectively contribute to the preservation and enhancement of ecosystem services. We conducted a case study of the Hoh Xil Nature Reserve (HXNR) in Qinghai-Tibetan Plateau, China, in order to examine the effectiveness of PA in the conservation of ecosystem services. First, the dynamics of land use/land cover (LULC) were analyzed based on remotely sensed data sets. Then, the ecosystem service value (ESV) in the PA and non-PA were evaluated using a modified benefit transfer method that had been adjusted using normalized difference vegetation index (NDVI). Finally, hotspot analysis was implemented to reveal the ESV changes for the different districts of the PA. The results of the comparison experiment indicate that: (1) The ESV of the HXNR has considerably increased after it was designated as protected, which had been in decline in the previous stage. The ESVs in a near-by non-PA showed opposite results where the values initially increased but then dropped due to urban expansion and desertification. (2) The areas in HXNR with increased ESV significantly outnumbered the areas that had declining values from 1980 to 2018. For the non-PA, the areas that had increased ESV in 1980–1995 saw a decline in value in 1995–2008; moreover, new areas with decreasing ESV emerged in 2008–2018. (3) The HXNR was found to be more effective than non-PA in improving ecosystem services. (4) The core zone of the nature reserve demonstrated better effectiveness in ecosystem service preservation.
Object-Based Mapping of Gullies Using Optical Images: A Case Study in the Black Soil Region, Northeast of China
Authors: Wang Biwei , Zhang Zengxiang , Wang Xiao , Zhao Xiaoli , Yi Ling , Hu Shunguang
Published: February 2020

Gully erosion is a widespread natural hazard. Gully mapping is critical to erosion monitoring and the control of degraded areas. The analysis of high-resolution remote sensing images (HRI) and terrain data mixed with developed object-based methods and field verification has been certified as a good solution for automatic gully mapping. Considering the availability of data, we used only open-source optical images (Google Earth images) to identify gully erosion through image feature modeling based on OBIA (Object-Based Image Analysis) in this paper. A two-end extrusion method using the optimal machine learning algorithm (Light Gradient Boosting Machine (LightGBM)) and eCognition software was applied for the automatic extraction of gullies at a regional scale in the black soil region of Northeast China. Due to the characteristics of optical images and the design of the method, unmanaged gullies and gullies harnessed in non-forest areas were the objects of extraction. Moderate success was achieved in the absence of terrain data. According to independent validation, the true overestimation ranged from 20% to 30% and was mainly caused by land use types with high erosion risks, such as bare land and farm lanes being falsely classified as gullies. An underestimation of less than 40% was adjacent to the correctly extracted gullied areas. The results of extraction in regions with geographical object categories of a low complexity were usually more satisfactory. The overall performance demonstrates that the present method is feasible for gully mapping at a regional scale, with high automation, low cost, and acceptable accuracy.
Monitoring Three-Decade Expansion of China’s Major Cities Based on Satellite Remote Sensing Images
Authors: Shen Yao , Shen Huanfeng , Cheng Qing , Huang Liwen , Zhang Liangpei
Published: February 2020

As the largest developing country, China has experienced dramatic urban expansion since the “reform and opening-up” policy started at the end of the 1970s. In this paper, we monitor three decades of urban expansion in China’s 36 major cities, based on the spectral mixture analysis of remotely sensed satellite images. The results demonstrated that these major cities have expanded by 5.85 times from 1986 to 2015, with 15.51 km2 average expansion area per city per year. We found the urban expansion trajectories showed three different modes, i.e., exponential, linear and s-shaped, which were closely related to the city development level. In the old city zones, however, there was an interesting common tendency of the impervious surface area (ISA) first increasing and then decreasing, which could be largely attributed to the phenomenon of urban village reconstruction in China’s cities. Based on the Granger Causality Test (GCT), the interaction between urban ISA and Gross Domestic Product (GDP) per capita (GDPPC) suggested that the former was the driver of the latter. Meanwhile, taking the Yangtze River as the division between north and south China, there exists a north–south territorial differentiation for the interaction between ISA and total population at the year-end (TP).
An Assessment of Using Remote Sensing-based Models to Estimate Ground Surface Soil Heat Flux on the Tibetan Plateau during the Freeze-thaw Process
Authors: Yang Cheng , Wu Tonghua , Yao Jimin , Li Ren , Xie Changwei , Hu Guojie , Zhu Xiaofan , Zhang Yinghui , Ni Jie , Hao Junming , Li Xiangfei , Ma Wensi , Wen Amin
Published: February 2020

The ground surface soil heat flux (G0) is very important to simulate the changes of frozen ground and the active layer thickness; in addition, the freeze-thaw cycle will also affect G0 on the Tibetan Plateau (TP). As G0 could not be measured directly and soil heat flux is difficult to be observed on the TP in situ due to its high altitude and cold environment, most of previous studies have directly applied existing remote sensing-based models to estimate G0 without assessing whether the selected model is the best one of those models for those study regions. We use in-situ observation data collected at 12 sites combined with Moderate Resolution Imaging Spectroradiometer (MODIS) data (MOD13Q1, MODLT1D, MOD09CMG, and MCD15A2H) and the China meteorological forcing dataset (CMFD-SRad and CMFD-LRad) to validate the main models during the freeze-thaw process. The results show that during the three stages (complete freezing (CF), daily freeze-thaw cycle (DFT), and complete thawing (CT)) of the freeze-thaw cycle, the root mean square error (RMSE) between the models' G0 simulated value and the corresponding G0 "measured value" is the largest in the CT phase and smallest in the CF phase. The simulated results of the second group schemes (SEBAL, Ma, SEBALadj, and Maadj) were slightly underestimated, more stable, and closer to the measured values than the first group schemes (Choudhury, Clawson, SEBS, Choudhuryadj, Clawsonadj, and SEBSadj). The Maadj scheme is the one with the smallest RMSE among all the schemes and could be directly applied across the entire TP. Then, four possible reasons leading to the errors of the main schemes were analyzed. The soil moisture affecting the ratio G0/Rn and the phase shift between G0 and net radiation Rn are not considered in the schemes directly; the scheme cannot completely and correctly capture the direction of G0; and the input data of the schemes to estimate the regional G0 maybe bring some errors into the simulated results. The results are expected to provide a basis for selecting remote sensing-based models to simulate G0 in frozen ground dynamics and to calculate evapotranspiration on the TP during the freeze-thaw process. The scheme Maadj suitable for the TP was also offered in the study. We proposed several improvement directions of remote sensing-based models in order to enhance understanding of the energy exchange between the ground surface and the atmosphere.
Landslide Susceptibility Prediction Based on Remote Sensing Images and GIS: Comparisons of Supervised and Unsupervised Machine Learning Models
Authors: Chang Zhilu , Du Zhen , Zhang Fan , Huang Faming , Chen Jiawu , Li Wenbin , Guo Zizheng
Published: February 2020

Landslide susceptibility prediction (LSP) has been widely and effectively implemented by machine learning (ML) models based on remote sensing (RS) images and Geographic Information System (GIS). However, comparisons of the applications of ML models for LSP from the perspectives of supervised machine learning (SML) and unsupervised machine learning (USML) have not been explored. Hence, this study aims to compare the LSP performance of these SML and USML models, thus further to explore the advantages and disadvantages of these ML models and to realize a more accurate and reliable LSP result. Two representative SML models (support vector machine (SVM) and CHi-squared Automatic Interaction Detection (CHAID)) and two representative USML models (K-means and Kohonen models) are respectively used to scientifically predict the landslide susceptibility indexes, and then these prediction results are discussed. Ningdu County with 446 recorded landslides obtained through field investigations is introduced as case study. A total of 12 conditioning factors are obtained through procession of Landsat TM 8 images and high-resolution aerial images, topographical and hydrological spatial analysis of Digital Elevation Modeling in GIS software, and government reports. The area value under the curve of receiver operating features (AUC) is applied for evaluating the prediction accuracy of SML models, and the frequency ratio (FR) accuracy is then introduced to compare the remarkable prediction performance differences between SML and USML models. Overall, the receiver operation curve (ROC) results show that the AUC of the SVM is 0.892 and is slightly greater than the AUC of the CHAID model (0.872). The FR accuracy results show that the SVM model has the highest accuracy for LSP (77.80%), followed by the CHAID model (74.50%), the Kohonen model (72.8%) and the K-means model (69.7%), which indicates that the SML models can reach considerably better prediction capability than the USML models. It can be concluded that selecting recorded landslides as prior knowledge to train and test the LSP models is the key reason for the higher prediction accuracy of the SML models, while the lack of a priori knowledge and target guidance is an important reason for the low LSP accuracy of the USML models. Nevertheless, the USML models can also be used to implement LSP due to their advantages of efficient modeling processes, dimensionality reduction and strong scalability.
Multi-Spectral Image Classification Based on an Object-Based Active Learning Approach
Authors: Su Tengfei , Zhang Shengwei , Liu Tingxi
Published: February 2020

In remote sensing, active learning (AL) is considered to be an effective solution to the problem of producing sufficient classification accuracy with a limited number of training samples. Though this field has been extensively studied, most papers exist in the pixel-based paradigm. In object-based image analysis (OBIA), AL has been comparatively less studied. This paper aims to propose a new AL method for selecting object-based samples. The proposed AL method solves the problem of how to identify the most informative segment-samples so that classification performance can be optimized. The advantage of this algorithm is that informativeness can be estimated by using various object-based features. The new approach has three key steps. First, a series of one-against-one binary random forest (RF) classifiers are initialized by using a small initial training set. This strategy allows for the estimation of the classification uncertainty in great detail. Second, each tested sample is processed by using the binary RFs, and a classification uncertainty value that can reflect informativeness is derived. Third, the samples with high uncertainty values are selected and then labeled by a supervisor. They are subsequently added into the training set, based on which the binary RFs are re-trained for the next iteration. The whole procedure is iterated until a stopping criterion is met. To validate the proposed method, three pairs of multi-spectral remote sensing images with different landscape patterns were used in this experiment. The results indicate that the proposed method can outperform other state-of-the-art AL methods. To be more specific, the highest overall accuracies for the three datasets were all obtained by using the proposed AL method, and the values were 88.32%, 85.77%, and 93.12% for “T1,” “T2,” and “T3,” respectively. Furthermore, since object-based features have a serious impact on the performance of AL, eight combinations of four feature types are investigated. The results show that the best feature combination is different for the three datasets due to the variation of the feature separability.
Characterizing Uncertainty in Forest Remote Sensing Studies
Authors: Persson Henrik Jan , Ståhl Göran
Published: February 2020

This discussion paper addresses (1) the challenge of concisely reporting uncertainties in forest remote sensing (RS) studies, primarily conducted at plot and stand level, and (2) the influence of reference data errors and how corrections for such errors can be made. Different common ways of reporting uncertainties are discussed, and a parametric error model is proposed as a core part of a comprehensive approach for reporting uncertainties (compared to, e.g., conventional reporting of root mean square error (RMSE)). The importance of handling reference data errors is currently increasing since estimates derived from RS data are becoming increasingly accurate; in extreme cases the accuracies of RS- and field-based estimates are of equal magnitude and there is a risk that reported RS accuracies are severely misjudged due to inclusion of errors from the field reference data. Novel methods for correcting for some types of reference data errors are proposed, both for the conventional RMSE uncertainty metric and for the case when a parametric error model is applied. The theoretical framework proposed in this paper is demonstrated using real data from a typical RS study where airborne laser scanning and synthetic aperture radar (SAR) data are applied for estimating biomass at the level of forest stands. With the proposed correction method, the RMSE for the RS-based estimates from laser scanning was reduced from 50.5 to 49.5 tons/ha when errors in the field references were properly accounted for. The RMSE for the estimates from SAR data was reduced from 28.5 to 26.1 tons/ha.
Recent Advances in the Development of Biosensors for Malaria Diagnosis
Authors: Krampa Francis D. , Aniweh Yaw , Kanyong Prosper , Awandare Gordon A.
Published: February 2020

The impact of malaria on global health has continually prompted the need to develop more effective diagnostic strategies that could overcome deficiencies in accurate and early detection. In this review, we examine the various biosensor-based methods for malaria diagnostic biomarkers, namely; Plasmodium falciparum histidine-rich protein 2 (PfHRP-2), parasite lactate dehydrogenase (pLDH), aldolase, glutamate dehydrogenase (GDH), and the biocrystal hemozoin. The models that demonstrate a potential for field application have been discussed, looking at the fabrication and analytical performance characteristics, including (but not exclusively limited to): response time, sensitivity, detection limit, linear range, and storage stability, which are first summarized in a tabular form and then described in detail. The conclusion summarizes the state-of-the-art technologies applied in the field, the current challenges and the emerging prospects for malaria biosensors.
A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots
Authors: Odry Ákos , Kecskes Istvan , Sarcevic Peter , Vizvari Zoltan , Toth Attila , Odry Péter
Published: February 2020

This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy.
Incorporating Worker Awareness in the Generation of Hazard Proximity Warnings
Authors: Chan Kelsey , Louis Joseph , Albert Alex
Published: February 2020

Proximity warning systems for construction sites do not consider whether workers are already aware of the hazard prior to issuing warnings. This can generate redundant and distracting alarms that interfere with worker ability to adopt timely and appropriate avoidance measures; and cause alarm fatigue, which instigates workers to habitually disable the system or ignore the alarms; thereby increasing the risk of injury. Thus, this paper integrates the field-of-view of workers as a proxy for hazard awareness to develop an improved hazard proximity warning system for construction sites. The research first developed a rule-based model for the warning generation, which was followed by a virtual experiment to evaluate the integration of worker field-of-view in alarm generation. Based on these findings, an improved hazard proximity warning system incorporating worker field-of-view was developed for field applications that utilizes wearable inertial measurement units and localization sensors. The system’s effectiveness is illustrated through several case studies. This research provides a fresh perspective to the growing adoption of wearable sensors by incorporating the awareness of workers into the generation of hazard alarms. The proposed system is anticipated to reduce unnecessary and distracting alarms which can potentially lead to superior safety performance in construction.
The Solar-Induced Chlorophyll Fluorescence Imaging Spectrometer (SIFIS) Onboard the First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1): Specifications and Prospects
Authors: Du Shanshan , Liu Liangyun , Liu Xinjie , Zhang Xinwei , Gao Xianlian , Wang Weigang
Published: February 2020

The global monitoring of solar-induced chlorophyll fluorescence (SIF) using satellite-based observations provides a new way of monitoring the status of terrestrial vegetation photosynthesis on a global scale. Several global SIF products that make use of atmospheric satellite data have been successfully developed in recent decades. The Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1), the first Chinese terrestrial ecosystem carbon inventory satellite, which is due to be launched in 2021, will carry an imaging spectrometer specifically designed for SIF monitoring. Here, we use an extensive set of simulated data derived from the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) and Soil Canopy Observation Photosynthesis and Energy (SCOPE) models to evaluate and optimize the specifications of the SIF Imaging Spectrometer (SIFIS) onboard TECIS for accurate SIF retrievals. The wide spectral range of 670−780 nm was recommended to obtain the SIF at both the red and far-red bands. The results illustrate that the combination of a spectral resolution (SR) of 0.1 nm and a signal-to-noise ratio (SNR) of 127 performs better than an SR of 0.3 nm and SNR of 322 or an SR of 0.5 nm and SNR of 472 nm. The resulting SIF retrievals have a root-mean-squared (RMS) diff* value of 0.15 mW m−2 sr−1 nm−1 at the far-red band and 0.43 mW m−2 sr−1 nm−1 at the red band. This compares with 0.20 and 0.26 mW m−2 sr−1 nm−1 at the far-red band and 0.62 and 1.30 mW m−2 sr−1 nm−1 at the red band for the other two configurations described above. Given an SR of 0.3 nm, the increase in the SNR can also improve the SIF retrieval at both bands. If the SNR is improved to 450, the RMS diff* will be 0.17 mW m−2 sr−1 nm−1 at the far-red band and 0.47 mW m−2 sr−1 nm−1 at the red band. Therefore, the SIFIS onboard TECIS-1 will provide another set of observations dedicated to monitoring SIF at the global scale, which will benefit investigations of terrestrial vegetation photosynthesis from space.
Fluorescence Sensing of Caffeine in Tea Beverages with 3,5-diaminobenzoic Acid
Authors: Du Chenxu , Ma Chaoqun , Gu Jiao , Li Lei , Chen Guoqing
Published: February 2020

A rapid, selective and sensitive method for the detection of caffeine in tea infusion and tea beverages are proposed by using 3,5-diaminobenzoic acid as a fluorescent probe. The 3,5-diaminobenzoic acid emits strong fluorescence around 410 nm under the excitation of light at 280 nm. Both the molecular electrostatic potential analysis and fluorescent lifetime measurement proved that the existence of caffeine can quench the fluorescence of 3,5-diaminobenzoic acid. Under the optimal experimental parameters, the 3,5-diaminobenzoic acid was used as a fluorescent probe to detect the caffeine aqueous solution. There exists a good linear relationship between the fluorescence quenching of the fluorescent probe and the concentration of caffeine in the range of 0.1–100 μM, with recovery within 96.0 to 106.2%, while the limit of detection of caffeine is 0.03 μM. This method shows a high selectivity for caffeine. The caffeine content in different tea infusions and tea beverages has been determined and compared with the results from HPLC measurement.
Potentiometric E-Tongue System for Geosmin/Isoborneol Presence Monitoring in Drinkable Water
Authors: Lvova Larisa , Jahatspanian Igor , Mattoso Luiz H.C. , Correa Daniel S. , Oleneva Ekaterina , Legin Andrey , Di Natale Corrado , Paolesse Roberto
Published: February 2020

A potentiometric E-tongue system based on low-selective polymeric membrane and chalcogenide-glass electrodes is employed to monitor the taste-and-odor-causing pollutants, geosmin (GE) and 2-methyl-isoborneol (MIB), in drinkable water. The developed approach may permit a low-cost monitoring of these compounds in concentrations near the odor threshold concentrations (OTCs) of 20 ng/L. The experiments demonstrate the success of the E-tongue in combination with partial least squares (PLS) regression technique for the GE/MIB concentration prediction, showing also the possibility to discriminate tap water samples containing these compounds at two concentration levels: the same OTC order from 20 to 100 ng/L and at higher concentrations from 0.25 to 10 mg/L by means of PLS-discriminant analysis (DA) method. Based on the results, developed multisensory system can be considered a promising easy-to-handle tool for express evaluation of GE/MIB species and to provide a timely detection of alarm situations in case of extreme pollution before the drinkable water is delivered to end users.
Review of Structural Health Monitoring Methods Regarding a Multi-Sensor Approach for Damage Assessment of Metal and Composite Structures
Authors: Kralovec Christoph , Schagerl Martin
Published: February 2020

Structural health monitoring (SHM) is the continuous on-board monitoring of a structure’s condition during operation by integrated systems of sensors. SHM is believed to have the potential to increase the safety of the structure while reducing its deadweight and downtime. Numerous SHM methods exist that allow the observation and assessment of different damages of different kinds of structures. Recently data fusion on different levels has been getting attention for joint damage evaluation by different SHM methods to achieve increased assessment accuracy and reliability. However, little attention is given to the question of which SHM methods are promising to combine. The current article addresses this issue by demonstrating the theoretical capabilities of a number of prominent SHM methods by comparing their fundamental physical models to the actual effects of damage on metal and composite structures. Furthermore, an overview of the state-of-the-art damage assessment concepts for different levels of SHM is given. As a result, dynamic SHM methods using ultrasonic waves and vibrations appear to be very powerful but suffer from their sensitivity to environmental influences. Combining such dynamic methods with static strain-based or conductivity-based methods and with additional sensors for environmental entities might yield a robust multi-sensor SHM approach. For demonstration, a potent system of sensors is defined and a possible joint data evaluation scheme for a multi-sensor SHM approach is presented.
Two-Stream Attention Network for Pain Recognition from Video Sequences
Authors: Thiam Patrick , Kestler Hans A. , Schwenker Friedhelm
Published: February 2020

Several approaches have been proposed for the analysis of pain-related facial expressions. These approaches range from common classification architectures based on a set of carefully designed handcrafted features, to deep neural networks characterised by an autonomous extraction of relevant facial descriptors and simultaneous optimisation of a classification architecture. In the current work, an end-to-end approach based on attention networks for the analysis and recognition of pain-related facial expressions is proposed. The method combines both spatial and temporal aspects of facial expressions through a weighted aggregation of attention-based neural networks’ outputs, based on sequences of Motion History Images (MHIs) and Optical Flow Images (OFIs). Each input stream is fed into a specific attention network consisting of a Convolutional Neural Network (CNN) coupled to a Bidirectional Long Short-Term Memory (BiLSTM) Recurrent Neural Network (RNN). An attention mechanism generates a single weighted representation of each input stream (MHI sequence and OFI sequence), which is subsequently used to perform specific classification tasks. Simultaneously, a weighted aggregation of the classification scores specific to each input stream is performed to generate a final classification output. The assessment conducted on both the BioVid Heat Pain Database (Part A) and SenseEmotion Database points at the relevance of the proposed approach, as its classification performance is on par with state-of-the-art classification approaches proposed in the literature.
GC-FID Analysis and Antibacterial Activity of the Calyptranthes concinna Essential Oil against MDR Bacterial Strains
Authors: Costa Maria do Socorro , Araújo Nara Juliana Santos , Freitas Thiago Sampaio de , Cunha Francisco Assis Bezerra da , Amaral Wanderlei do , Deschamps Cícero , Confortin Camila , Everson da Silva Luiz , Coutinho Henrique Douglas Melo
Published: February 2020

Presently, results from a study carried out in this area using the essential oil from the Calyptranthes concinna species, a representative from the Myrtaceae family, are reported. The essential oil was obtained by hydrodistillation and gas chromatography coupled to mass spectrometry was used to identify its chemical constituents. Antibacterial activity was determined using the broth microdilution method, thus obtaining the Minimal Inhibitory Concentration (MIC) value, from which the subinhibitory concentration (MIC/8) was derived. The C. concinna essential oil presented antibacterial activity against both standard and multiresistant bacteria. In addition, the oil demonstrated an antibiotic activity potentiation against Staphylococcus aureus and Escherichia coli when in combination with the antibiotic gentamicin, reducing the MIC from 141.38 μg/mL and 208.63 μg/mL to 64 μg/mL and 128 μg/mL, respectively. Conclusions: Findings from the present study suggest this oil is promising in terms of its antimicrobial activity.
Corporate Social Responsibility: Findings from the Vietnamese Paint Industry
Authors: Tran Khoa T. , Nguyen Phuong V.
Published: February 2020

In contemporary society, customers tend to spend money on goods and services they trust not only in terms of their quality but also because of their ethical production standards; therefore, thanks to integrating corporate social responsibility (CSR) into business activities, a company can ensure that their business success is in line with high moral principles and social expectations. Considering the paint business in the Vietnamese context, this study aims to investigate the effects of CSR related to the environmental management practices of companies in the paint industry. By analyzing data collected from 269 project managers and the purchasing managers of construction companies located in Ho Chi Minh City, Vietnam, the findings show that CSR programs generally have a positive relationship with perceived reputation, organizational customer satisfaction, and environmental management practices (EMPs). Furthermore, the results also indicate that EMPs have no significant impact on organizational customer satisfaction, and perceived reputation is not associated with either customer satisfaction or commitment in the business-to-business (B2B) context. It is worth noting that the results provide some managerial recommendations for paint companies.
The Evolutionary Game of Stakeholders’ Coordination Mechanism of New Energy Power Construction PPP Project: A China Case
Authors: Gao Lei , Zhao Zhen-Yu
Published: February 2020

The government, investors, and the public have formed a dynamic multi-game relationship on the Public-Private-Partnership (PPP) project, but few studies include them in a system to study their win–win solutions. Firstly, we constructed a tripartite game model of the government, investors, and the public based on evolutionary game theory. Secondly, the evolutionary process of tripartite strategy behaviors was studied with the system dynamics (SD) model. Finally, the impact of changes in key factors on behavior strategies was studied through sensitivity analysis. The results show the following: (1) In the outsourcing of new energy and power construction PPP projects, the three parties will eventually reach the equilibrium state of {government supervision, public participation, investors effort}, the three parties achieve a win–win situation and the project benefits are the highest at this time, and the public participation will play an important role in promoting the smooth outsourcing of PPP projects. (2) The strategic choices of the government, investors, and the public are sensitive to changes in the corresponding exogenous variables. (3) The security factor plays a crucial role in the choice of public strategy. The public’s choice is not only affected by its own income and cost, but also by the amount of compensation promised by the government and the estimated damage caused by the investor.
Flexible Pavements and Climate Change: A Comprehensive Review and Implications
Authors: Qiao Yaning , Dawson Andrew R. , Parry Tony , Flintsch Gerardo , Wang Wenshun
Published: February 2020

Flexible pavements and climate are interactive. Pavements are climate sensitive infrastructure, where climate can impact their deterioration rate, subsequent maintenance, and life-cycle costs. Meanwhile, climate mitigation measures are urgently needed to reduce the environmental impacts of pavements and related transportation on the macroclimate and microclimate. Current pavement design and life cycle management practices may need to be modified to adapt to changing climates and to reduce environmental impacts. This paper reports an extensive literature search on qualitative and quantitative pavement research related to climate change in recent years. The topics cover climate stressors, sensitivity of pavement performance to climatic factors, impacts of climate change on pavement systems, and, most importantly, discussions of climate change adaptation, mitigation, and their interactions. This paper is useful for those who aim to understand or research the climate resilience of flexible pavements.
How to Coordinate Economic, Logistics and Ecological Environment? Evidences from 30 Provinces and Cities in China
Authors: Zhang Wei , Zhang Xinxin , Zhang Mingyang , Li Woyuan
Published: February 2020

With the rapid development of economy, the scale of the logistics industry is also expanding rapidly, which brings great convenience to economy and trade, and becomes one of the pillar industries of national economy. However, with the development of economy and logistics, the problem of ecological environment is becoming more and more prominent. Through the design of economic development, logistics development, and the ecological environment index system, the economic development, logistics development and eco-environment development level of 30 provinces and cities in China from 2008 to 2017 are analyzed by using the entropy method and coupling coordination degree model, and the spatial characteristics of regional economic development, logistics development, and ecological environment are analyzed by using ArcGIS software. The results show that the coupling coordination of economic development, logistics development and ecological environment in most provinces and cities in China is at the mediate coupling level, and only Shanghai, Anhui, and Fujian in the Eastern region have reached the high-quality coupling level; there are significant temporal and spatial differences in the coupling and coordinated development between economic development, logistics development and ecological environment. The level of coupling coordination in the western region has always been at a low level, while the level of coupling coordination in most of the central and eastern regions is relatively high. There are situations where the level of coupling coordination is not high; the coordinated growth of economic development, logistics development, and ecological environment is mainly driven by economic development and logistics development. However, the level of ecological environment has been lagging behind the level of economic and logistics development. In the future development, it is necessary to give full play to the role of the logistics industry in economic development, weigh the relationship between the development of the logistics industry and ecological environmental protection, actively develop green logistics, and the level of coordinated development among economic development, logistics development and ecological environment.
On the Sustainable Choice of Alloying Elements for Strength of Aluminum-Based Alloys
Authors: Jarfors Anders E.W. , Du Andong , Yu Gegan , Zheng Jinchuan , Wang Kaikun
Published: February 2020

Aluminum alloys are today entirely recyclable and are seen as a sustainable material. However, there are limitations in the use of aluminum from material strength and cost perspective. Nickel, copper and rare earth metals are alloying elements that may provide strength at room and elevated temperatures. These are, however, often seen as harmful from a sustainability viewpoint. Additionally, these alloying elements are commonly costly. The current paper makes an analysis of the sustainability–strength dimension of alloying, together with a cost perspective, to guide alloy producers and alloy users in making an educated choice of direction for future materials and material development.
Masterplanning at the Port of Dover: The Use of Discrete-Event Simulation in Managing Road Traffic
Authors: Preston Geoffrey C. , Horne Phillip , Scaparra Maria Paola , O’Hanley Jesse R.
Published: February 2020

The Port of Dover is Europe’s busiest ferry port, handling £119 billion or 17% of the UK’s annual trade in goods. The Port is constrained geographically to a small area and faces multiple challenges, both short- and long-term, with managing the flow of five million vehicles per year to/from mainland Europe. This article describes some of the work that the Port is doing to minimize the impact of port road traffic on the local community and environment using discrete-event simulation modeling. Modeling is particularly valuable in identifying where future bottlenecks are likely to form within the Port due to projected growth in freight traffic and comparing the effectiveness of different interventions to cope with growth. One of our key findings is that space which can be used flexibly is far more valuable than dedicated space. This is supported by the much greater reduction in traffic congestion that is expected to be achieved given a 10% increase in freight traffic by reallocating space at the front of the system to temporarily hold vehicles waiting to pass through border control and check-in compared to extending the amount of space for ferry embarkation at the rear of the system. The importance of flexible space has implications for port design that can be applied more broadly. Modeling is also useful in identifying critical thresholds for vehicle processing times that would cause the system to become overwhelmed. Increasing the check-in time by just three to five minutes, for example, would completely exceed the Port’s capacity and produce indefinite queueing. This finding has important implications for Brexit planning. From a wider context, the research presented here nicely illustrates how simulation can be used to instill more evidence-based thinking into port masterplanning and support “green port” and other corporate sustainability initiatives.
SROI Methodology for Public Administration Decisions about Financing with Social Criteria. A Case Study
Authors: Ruiz-Lozano Mercedes , Tirado-Valencia Pilar , Sianes Antonio , Ariza-Montes Antonio , Fernández-Rodríguez Vicente , López-Martín Mª Carmen
Published: February 2020

The measurement of impacts has been considered one of the best methodologies to evaluate the level of achievement of social entities’ objectives as well as of their contribution to resolving social problems. Those methodologies can guide public policies and subsidies granting, as they help to identify the organizations producing a higher social value, and the effects of their projects. Our research focused on the effectiveness and the efficiency of social entities, measured through their capacity to generate impacts on their stakeholders. The research was realized through the analysis of a case study: the special education center for disabled youths, CEE-SA, in Spain. The social return on investment (SROI) methodology has allowed us to monetarize the social value created for stakeholders through the activity carried out by CEE-SA, and it provides information about the whole value creation process that is generated, for which the analysis and follow-up through the indicators offers a contribution to its management system. This case study can serve as a reference in assessing the management processes of similar entities and can also highlight SROI usefulness for public administrations as an assessment tool for subsidies granted on social criteria. The originality of this research relies on the new SROI methodology provided for the assessment of public financing decisions, especially in a field that remains as under-researched as special education schools.