bluuebunny/crossref_metadata_2025 huggingface dataset:
Reduction of Tetrachloroaurate(III) Ions With Bioligands: Role of the Thiol and Amine Functional Groups on the Structure and Optical Features of Gold Nanohybrid Systems
Authors: Ungor Ditta , Dékány Imre , Csapó Edit
Published: August 2019

In this review, the presentation of the synthetic routes of plasmonic gold nanoparticles (Au NPs), fluorescent gold nanoclusters (Au NCs), as well as self-assembled Au-containing thiolated coordination polymers (Au CPs) was highlighted. We exclusively emphasize the gold products that are synthesized by the spontaneous interaction of tetrachloroaurate(III) ions (AuCl4¯) with bioligands using amine and thiolate derivatives, including mainly amino acids. The dominant role of the nature of the applied reducing molecules as well as the experimental conditions (concentration of the precursor metal ion, molar ratio of the AuCl4¯ ions and biomolecules; pH, temperature, etc.) of the syntheses on the size and structure-dependent optical properties of these gold nanohybrid materials have been summarized. While using the same reducing and stabilizing biomolecules, the main differences on the preparation conditions of Au NPs, Au NCs, and Au CPs have been interpreted and the reducing capabilities of various amino acids and thiolates have been compared. Moreover, various fabrication routes of thiol-stabilized plasmonic Au NPs, as well as fluorescent Au NCs and self-assembled Au CPs have been presented via the formation of –(Au(I)-SR)n– periodic structures as intermediates.
Experimental Investigation of Thermal and Pressure Performance in Computer Cooling Systems Using Different Types of Nanofluids
Authors: Alfaryjat Altayyeb , Miron Lucian , Pop Horatiu , Apostol Valentin , Stefanescu Mariana-Florentina , Dobrovicescu Alexandru
Published: August 2019

A modern computer generates a great amount of heat while working. In order to secure appropriate working conditions by extracting the heat, a specific mechanism should be used. This research paper presents the effect of nanofluids on the microchannel heat sink performance of computer cooling systems experimentally. CeO2, Al2O3 and ZrO2 nanoparticles suspended in 20% ethylene glycol and 80% distilled water are used as working fluids in the experiment. The concentration of the nanoparticles ranges from 0.5% to 2%, mass flow rate ranges from 0.028 kg/s to 0.084 kg/s, and the ambient temperature ranges from 25 °C to 40 °C. Regarding the thermal component, parameters such as thermophysical properties of the nanofluids and base fluids, central processing unit (CPU) temperature, heat transfer coefficient, pressure drop, and pumping power have been experimentally investigated. The results show that CeO2-EG/DW, at a concentration of 2% and a mass flow rate of 0.084 kg/s, has with 8% a lower temperature than the other nanofluids and with 29% a higher heat transfer coefficient compared with the base fluid. The Al2O3-EG/DW shows the lowest pressure drop and pumping power, while the CeO2-EG/DW and ZrO2-EG/DW show the highest. However, a slight increase of pumping power and pressure drop can be accepted, considering the high improvement that the nanofluid brings in computer cooling performance compared to the base fluid.
Breast Milk Content of Vitamin A and E from Early- to Mid-Lactation Is Affected by Inadequate Dietary Intake in Brazilian Adult Women
Authors: Machado Michele R. , Kamp Fernanda , Nunes Juliana C. , El-Bacha Tatiana , Torres Alexandre G.
Published: August 2019

Our aims were to investigate vitamin A and E status during lactation and the determinants of breast milk content for the appropriate nutrition of the infant in a study with nursing Brazilian women. We hypothesized that both inadequate intake and the lipoprotein distribution of vitamin A and E during lactation could have an impact on their breast milk levels from early- to mid-lactation. Nineteen adult lactating women participated in this longitudinal observational study, in which dietary records, blood and mature breast milk samples were collected for the analysis of vitamin A and E, and carotenoids in early- (2nd to 4th week) and mid-lactation (12th to 14th week). Nutrient intake was balanced by the Multiple Source Method (MSM), and the intake of vitamin A and E was inadequate in 74 and 100% of the women, respectively. However, these results were not reflected in low serum concentrations of retinol and only 37% of the volunteers were vitamin E deficient according to the blood biomarker. As lactation progressed, vitamin A and E status worsened, and this was clearly observed by the decrease in their content in breast milk. The reduced content of vitamin A and E in the breast milk was not related to their distribution in lipoproteins. Taken together, the contents of vitamin A and E in breast milk seemed to be more sensitive markers of maternal nutrition status than respective blood concentrations, and dietary assessment by the MSM in early lactation was sensitive to indicate later risks of deficiency and should support maternal dietary guidance to improve the infant’s nutrition.
Spectroscopic Characterization of Bovine, Avian and Johnin Purified Protein Derivative (PPD) with High-Throughput Fourier Transform InfraRed-Based Method
Authors: Corneli Sara , Corte Laura , Roscini Luca , Di Paolo Antonella , Colabella Claudia , Petrucci Linda , Severi Giulio , Cagiola Monica , Mazzone Piera
Published: August 2019

Tuberculins purified protein derivatives (PPDs) are obtained by precipitation from heat treated mycobacteria. PPDs are used in diagnosis of mycobacterial infections in humans and animals. Bovine PPD (PPDB) is obtained from Mycobacterium bovis (Mycobacterium tuberculosis complex), while Avian PPD (PPDA) and Johnin PPD (PPDJ) are extracted, respectively, from Mycobacterium avium and M. avium subsp. paratuberculosis (M. avium complex). PPDB and PPDA are used for bovine tuberculosis diagnosis, while PPDJ is experimentally used in the immunodiagnosis of paratuberculosis. Although PPDs date back to the 19th Century, limited knowledge about their composition is currently available. The goal of our study was to evaluate Fourier Transform InfraRed (FTIR) spectroscopy as a tool to differentiate PPDB, PPDA, and three PPDJs. The results highlighted that the three PPDs have specific profiles, correlated with phylogenetic characteristics of mycobacteria used for their production. This analysis is eligible as a specific tool for different PPDs batches characterization and for the assessment of their composition. The entire PPD production may be efficiently controlled, since the N content of each preparation is related to IR spectra, with a reference spectrum for each PPD and a standardized analysis protocol.
Multilevel Impacts of Iron in the Brain: The Cross Talk between Neurophysiological Mechanisms, Cognition, and Social Behavior
Authors: Ferreira Ana , Neves Pedro , Gozzelino Raffaella
Published: August 2019

Iron is a critical element for most organisms, which plays a fundamental role in the great majority of physiological processes. So much so, that disruption of iron homeostasis has severe multi-organ impacts with the brain being particularly sensitive to such modifications. More specifically, disruption of iron homeostasis in the brain can affect neurophysiological mechanisms, cognition, and social behavior, which eventually contributes to the development of a diverse set of neuro-pathologies. This article starts by exploring the mechanisms of iron action in the brain and follows with a discussion on cognitive and behavioral implications of iron deficiency and overload and how these are framed by the social context. Subsequently, we scrutinize the implications of the disruption of iron homeostasis for the onset and progression of psychosocial disorders. Lastly, we discuss the links between biological, psychological, and social dimensions and outline potential avenues of research. The study of these interactions could ultimately contribute to a broader understanding of how individuals think and act under physiological and pathophysiological conditions.
Chemical Structure of EVA Films Obtained by Pulsed Electron Beam and Pulse Laser Ablation
Authors: Niemczyk Agata , Moszyński Dariusz , Jędrzejewski Roman , Kwiatkowski Konrad , Piwowarczyk Joanna , Baranowska Jolanta
Published: August 2019

Poly(ethylene-co-vinyl acetate) (EVA) films were deposited for the first time using physical methods. The chemical structure of the films obtained using two techniques, pulsed electron beam deposition (PED) and pulsed laser deposition (PLD), was studied by attenuated total reflection Fourier infrared spectroscopy (ATR-FTIR) and X-ray photoelectron spectroscopy (XPS). Whilst significant molecular degradation of the EVA films was observed for the PLD method, the original macromolecular structure was only partially degraded when the PED technique was used, emphasizing the superiority of the PED method over PLD for structurally complex polymers such as EVA. Optical and scanning electron microscopic observations revealed compact and smooth EVA films deposited by pulsed electron beam ablation as opposed to heterogeneous films with many different sized particulates obtained by PLD.
Intercomparison of AVHRR GIMMS3g, Terra MODIS, and SPOT-VGT NDVI Products over the Mongolian Plateau
Authors: Bai Yongqing , Yang Yaping , Jiang Hou
Published: August 2019

The rapid development of remote sensing technology has promoted the generation of different vegetation index products, resulting in substantive accomplishment in comprehensive economic development and monitoring of natural environmental changes. The results of scientific experiments based on various vegetation index products are also different with the variation of time and space. In this work, the consistency characteristics among three global normalized difference vegetation index (NDVI) products, namely, GIMMS3g NDVI, MOD13A3 NDVI, and SPOT-VGT NDVI, are intercompared and validated based on Landsat 8 NDVI at biome and regional scale over the Mongolian Plateau (MP) from 2000 to 2014 by decomposing time series datasets. The agreement coefficient (AC) and statistical scores such as Pearson correlation coefficient, root mean square error (RMSE), mean bias error (MBE), and standard deviation (STD) are used to evaluate the consistency between three NDVI datasets. Intercomparison results reveal that GIMMS3g NDVI has the highest values basically over the MP, while SPOT-VGT NDVI has the lowest values. The spatial distribution of AC values between various NDVI products indicates that the three NDVI datasets are highly consistent with each other in the northern regions of the MP, and MOD13A3 NDVI and SPOT-VGT NDVI have better consistency in expressing vegetation cover and change trends due to the highest proportions of pixels with AC values greater than 0.6. However, the trend components of decomposed NDVI sequences show that SPOT-VGT NDVI values are about 0.02 lower than the other two datasets in the whole variation periods. The zonal characteristics show that GIMMS3g NDVI in January 2013 is significantly higher than those of the other two datasets. However, in July 2013, the three datasets are remarkably consistent because of the greater vegetation coverage. Consistency validation results show that values of SPOT-VGT NDVI agree more with Landsat 8 NDVI than GIMMS3g NDVI and MOD13A3 NDVI, and the consistencies in the northeast of the MP are higher than northwest regions.
Improving SWE Estimation by Fusion of Snow Models with Topographic and Remotely Sensed Data
Authors: De Gregorio Ludovica , Günther Daniel , Callegari Mattia , Strasser Ulrich , Zebisch Marc , Bruzzone Lorenzo , Notarnicola Claudia
Published: August 2019

This paper presents a new concept to derive the snow water equivalent (SWE) based on the joint use of snow model (AMUNDSEN) simulation, ground data, and auxiliary products derived from remote sensing. The main objective is to characterize the spatial-temporal distribution of the model-derived SWE deviation with respect to the real SWE values derived from ground measurements. This deviation is due to the intrinsic uncertainty of any theoretical model, related to the approximations in the analytical formulation. The method, based on the k-NN algorithm, computes the deviation for some labeled samples, i.e., samples for which ground measurements are available, in order to characterize and model the deviations associated to unlabeled samples (no ground measurements available), by assuming that the deviations of samples vary depending on the location within the feature space. Obtained results indicate an improved performance with respect to AMUNDSEN model, by decreasing the RMSE and the MAE with ground data, on average, from 154 to 75 mm and from 99 to 45 mm, respectively. Furthermore, the slope of regression line between estimated SWE and ground reference samples reaches 0.9 from 0.6 of AMUNDSEN simulations, by reducing the data spread and the number of outliers.
Investigating Fold-River Interactions for Major Rivers Using a Scheme of Remotely Sensed Characteristics of River and Fold Geomorphology
Authors: Woodbridge Kevin P. , Pirasteh Saied , Parsons Daniel R.
Published: August 2019

There are frequently interactions between active folds and major rivers (mean annual water discharges > 70 m3s−1). The major river may incise across the fold, to produce a water gap across the fold, or a bevelling (or lateral planation) of the top of the fold. Alternatively, the major river may be defeated to produce a diversion of the river around the fold, with wind gaps forming across the fold in some cases, or ponding of the river behind the fold. Why a river incises or diverts is often unclear, though influential characteristics and processes have been identified. A new scheme for investigating fold-river interactions has been devised, involving a short description of the major river, climate, and structural geology, and 13 characteristics of river and fold geomorphology: (1) Channel width at location of fold axis, w, (2) Channel-belt width at location of fold axis, cbw, (3) Floodplain width at location of fold axis, fpw, (4) Channel sinuosity, Sc, (5) Braiding index, BI, (6) General river course direction, RCD, (7) Distance from fold core to location of river crossing, C-RC, (8) Distance from fold core to river basin margin, C-BM, (9) Width of geological structure at location of river crossing, Wgs, (10) Estimate of erosion resistance of surface sediments/rocks and deeper sediments/rocks in fold, ERs, ERd, (11) Channel water surface slope at location of fold axis, s, (12) Average channel migration rate, Rm, (13) Estimate of fold total uplift rate, TUR. The first 10 geomorphological characteristics should be readily determinable for almost all major rivers using widely available satellite imagery and fine scale geological maps. This use of remote sensing allows a large number of major rivers to be investigated relatively easily, including those in remote or inaccessible areas, without recourse to expensive fieldwork. The last three geomorphological characteristics should be determinable for most major rivers where other data sources are available. This study demonstrates the methodology of this scheme, using the example of the major rivers Karun and Dez interacting with active folds in the foreland basin tectonic setting of lowland south-west Iran. For the rivers Karun and Dez (mean annual water discharges 575 m3s−1 and 230 m3s−1, respectively), it was found that geomorphological characteristics Nos. 2, 3 and 7 had statistically significant differences (p-value ≤ 0.05) between the categories of river incision across a fold and river diversion around a fold. This scheme should be used to investigate a variety of major rivers from across the globe. By comparing the same parameters for different major rivers, a better understanding of fold-river interactions will be achieved.
Development of an Operational Algorithm for Automated Deforestation Mapping via the Bayesian Integration of Long-Term Optical and Microwave Satellite Data
Authors: Mizuochi Hiroki , Hayashi Masato , Tadono Takeo
Published: August 2019

The frequent fine-scale monitoring of deforestation using satellite sensors is important for the sustainable management of forests. Traditional optical satellite sensors suffer from cloud interruption, particularly in tropical regions, and recent active microwave sensors (i.e., synthetic aperture radar) demonstrate the difficulty in data interpretation owing to their inherent sensor noise and complicated backscatter features of forests. Although the sensor integration of optical and microwave sensors is of compelling research interest, particularly in the conduct of deforestation monitoring, this topic has not been widely studied. In this paper, we introduce an operational algorithm for automated deforestation mapping using long-term optical and L-band SAR data, including a simple time-series analysis of Landsat stacks and a multilayered neural network with Advanced Spaceborne Thermal Emission and Reflection Radiometer and Phased Array-type L-band Synthetic Aperture Radar-2, followed by sensor integration based on the Bayesian Updating of Land-Cover. We applied the algorithm over a deciduous tropical forest in Cambodia in 2003–2018 for validation, and the algorithm demonstrated better accuracy than existing approaches, which only depend on optical data or SAR data. Owing to the cloud penetration ability of SAR, observation gaps of optical data under cloudy conditions were filled, resulting in a prompter detection of deforestation even in the tropical rainy season. We also investigated the effect of posterior probability constraints in the Bayesian approach. The land-cover maps (forest/deforestation) created by the well-tuned Bayesian approach achieved 94.0% ± 4.5%, 80.0% ± 10.1%, and 96.4% ± 1.9% for the user’s accuracy, producer’s accuracy, and overall accuracy, respectively. In the future, small-scale commission errors in the resultant maps should be improved by using more sophisticated machine-learning approaches and considering the reforestation effects in the algorithm. The application of the algorithm to other landscapes with other sensor combinations is also desirable.
Analysis of Changes and Potential Characteristics of Cultivated Land Productivity Based on MODIS EVI: A Case Study of Jiangsu Province, China
Authors: Xu Weiyi , Jin Jiaxin , Jin Xiaobin , Xiao Yuanyuan , Ren Jie , Liu Jing , Sun Rui , Zhou Yinkang
Published: August 2019

Cultivated land productivity is a basic guarantee of food security. This study extracted the multiple cropping index (MCI) and most active days (MAD, i.e., days when the EVI exceeded a threshold) based on crop growth EVI curves to analyse the changes and potential characteristics of cultivated land productivity in Jiangsu Province during 2001–2017. The results are as follows: (1) The MCI of 83.8% of cultivated land remained unchanged in Jiangsu, the cultivated land with changed MCI (16.2%) was mainly concentrated in the southern and eastern coastal areas of Jiangsu, and the main cropping systems were single and double seasons. (2) The changes in cultivated land productivity were significant and had an obvious spatial distribution. The areas where the productivity of single cropping system changed occupied 67.8% of the total cultivated land of single cropping system, and the decreased areas (46.5%) were concentrated in southern Jiangsu. (3) For double cropping systems, the percentages of the changed productivity areas accounting for cultivated land were 82.7% and 73.3%. The decreased areas were distributed in central Jiangsu. In addition, the productivity of the first crop showed an overall (72%) increasing trend and increased areas (40.8%) of the second crop were found in northern Jiangsu. (4) During 2001–2017, cultivated land productivity greatly improved in Jiangsu. In the areas where productivity increased, the proportions of cultivated land with productivity potential space greater than 20% in single and double cropping systems were greater than 60% and 90%, respectively. In the areas where productivity decreased, greater than 25% and 75% of cultivated land had potential space in greater than 80% of the single and double cropping systems, respectively. This result shows that productivity still has much room for development in Jiangsu. This study provides new insight for studying cultivated land productivity and provides references for guiding agricultural production.
A New Vegetation Index to Detect Periodically Submerged Mangrove Forest Using Single-Tide Sentinel-2 Imagery
Authors: Jia Mingming , Wang Zongming , Wang Chao , Mao Dehua , Zhang Yuanzhi
Published: August 2019

Mangrove forests are tropical trees and shrubs that grow in sheltered intertidal zones. Accurate mapping of mangrove forests is a great challenge for remote sensing because mangroves are periodically submerged by tidal floods. Traditionally, multi-tides images were needed to remove the influence of water; however, such images are often unavailable due to rainy climates and uncertain local tidal conditions. Therefore, extracting mangrove forests from a single-tide imagery is of great importance. In this study, reflectance of red-edge bands in Sentinel-2 imagery were utilized to establish a new vegetation index that is sensitive to submerged mangrove forests. Specifically, red and short-wave near infrared bands were used to build a linear baseline; the average reflectance value of four red-edge bands above the baseline is defined as the Mangrove Forest Index (MFI). To evaluate MFI, capabilities of detecting mangrove forests were quantitatively assessed between MFI and four widely used vegetation indices (VIs). Additionally, the practical roles of MFI were validated by applying it to three mangrove forest sites globally. Results showed that: (1) theoretically, Jensen–Shannon divergence demonstrated that a submerged mangrove forest and water pixels have the largest distance in MFI compared to other VIs. In addition, the boxplot showed that all submerged mangrove forests could be separated from the water background in the MFI image. Furthermore, in the MFI image, to separate mangrove forests and water, the threshold is a constant that is equal to zero. (2) Practically, after applying the MFI to three global sites, 99–102% of submerged mangrove forests were successfully extracted by MFI. Although there are still some uncertainties and limitations, the MFI offers great benefits in accurately mapping mangrove forests as well as other coastal and aquatic vegetation worldwide.
A Fast and Robust Deep Convolutional Neural Networks for Complex Human Activity Recognition Using Smartphone
Authors: Qi Wen , Su Hang , Yang Chenguang , Ferrigno Giancarlo , De Momi Elena , Aliverti Andrea
Published: August 2019

As a significant role in healthcare and sports applications, human activity recognition (HAR) techniques are capable of monitoring humans’ daily behavior. It has spurred the demand for intelligent sensors and has been giving rise to the explosive growth of wearable and mobile devices. They provide the most availability of human activity data (big data). Powerful algorithms are required to analyze these heterogeneous and high-dimension streaming data efficiently. This paper proposes a novel fast and robust deep convolutional neural network structure (FR-DCNN) for human activity recognition (HAR) using a smartphone. It enhances the effectiveness and extends the information of the collected raw data from the inertial measurement unit (IMU) sensors by integrating a series of signal processing algorithms and a signal selection module. It enables a fast computational method for building the DCNN classifier by adding a data compression module. Experimental results on the sampled 12 complex activities dataset show that the proposed FR-DCNN model is the best method for fast computation and high accuracy recognition. The FR-DCNN model only needs 0.0029 s to predict activity in an online way with 95.27% accuracy. Meanwhile, it only takes 88 s (average) to establish the DCNN classifier on the compressed dataset with less precision loss 94.18%.
Multi-feature Fusion and Damage Identification of Large Generator Stator Insulation Based on Lamb Wave Detection and SVM Method
Authors: Li Ruihua , Gu Haojie , Hu Bo , She Zhifeng
Published: August 2019

Due to the merits of Lamb wave to Structural Health Monitoring (SHM) of composite, the Lamb wave-based damage detection and identification technology show a potential solution for the insulation condition evaluation of large generator stator. This was performed in order to overcome the problem that it is difficult to effectively identify the stator insulation damage the using single feature of Lamb wave. In this paper, a damage identification method of stator insulation based on Lamb wave multi-feature fusion is presented. Firstly, the different damage features were extracted from time domain, frequency domain, and fractal dimension of lamb wave signals, respectively. The features of Lamb wave signals were extracted by Hilbert transform (HT), power spectral density (PSD), fast Fourier transform (FFT), and wavelet fractal dimension (WFD). Then, a machine learning method based on support vector machine (SVM) was used to fuse and reconstruct the multi-features of Lamb wave and furtherly identify damage type of stator insulation. Finally, the effect of typical stator insulation damage identification is verified by simulation and experiment.
A Semi-Automatic Coupling Geophone for Tunnel Seismic Detection
Authors: Wang Yao , Fu Nengyi , Fu Zhihong , Lu Xinglin , Liao Xian , Wang Haowen , Qin Shanqiang
Published: August 2019

The tunnel seismic method allows for the detection of the geology in front of a tunnel face for the safety of tunnel construction. Conventional geophones have problems such as a narrow spectral width, low sensitivity, and poor coupling with the tunnel wall. To tackle issues above, we propose a semi-automatic coupling geophone equipped with a piezoelectric sensor with a spectral range of 10–5000 Hz and a sensitivity of 2.8 V/g. After the geophone was manually pushed into the borehole, it automatically coupled with the tunnel wall under the pressure of the springs within the device. A comparative experiment showed that the data spectrum acquired by the semi-automatic coupling geophone was much higher than that of the conventional geophone equipped with the same piezoelectric sensor. The seismic data were processed in combination with forward modeling. The imaging results also show that the data acquired by the semi-automatic coupling geophone were more in line with the actual geological conditions. In addition, the semi-automatic coupling geophone’s installation requires a lower amount of time and cost. In summary, the semi-automatic coupling geophone is able to efficiently acquire seismic data with high fidelity, which can provide a reference for tunnel construction safety.
Evaluating a Spoken Dialogue System for Recording Systems of Nursing Care
Authors: Mairittha Tittaya , Mairittha Nattaya , Inoue Sozo
Published: August 2019

Integrating speech recondition technology into an electronic health record (EHR) has been studied in recent years. However, the full adoption of the system still faces challenges such as handling speech errors, transforming raw data into an understandable format and controlling the transition from one field to the next field with speech commands. To reduce errors, cost, and documentation time, we propose a dialogue system care record (DSCR) based on a smartphone for nursing documentation. We describe the effects of DSCR on (1) documentation speed, (2) document accuracy and (3) user satisfaction. We tested the application with 12 participants to examine the usability and feasibility of DSCR. The evaluation shows that DSCR can collect data efficiently by achieving 96% of documentation accuracy. Average documentation speed was increased by 15% (P = 0.012) compared to traditional electronic forms (e-forms). The participants’ average satisfaction rating was 4.8 using DSCR compared to 3.6 using e-forms on a scale of 1–5 (P = 0.032).
Compensation Method for Pipeline Centerline Measurement of in-Line Inspection during Odometer Slips Based on Multi-Sensor Fusion and LSTM Network
Authors: Liu Shucong , Zheng Dezhi , Li Rui
Published: August 2019

The accurate measurement of pipeline centerline coordinates is of great significance to the management of oil and gas pipelines and energy transportation security. The main method for pipeline centerline measurement is in-line inspection technology based on multi-sensor data fusion, which combines the inertial measurement unit (IMU), above-ground marker, and odometer. However, the observation of velocity is not accurate because the odometer often slips in the actual inspection, which greatly affects the accuracy of centerline measurement. In this paper, we propose a new compensation method for oil and gas pipeline centerline measurement based on a long short-term memory (LSTM) network during the occurrence of odometer slip. The field test results indicated that the mean of absolute position errors reduced from 8.75 to 2.02 m. The proposed method could effectively reduce the errors and improve the accuracy of pipeline centerline measurement during odometer slips.
Screen-Printed Soft-Nitrided Carbon Electrodes for Detection of Hydrogen Peroxide
Authors: Ogbu Chidiebere I. , Feng Xu , Dada Samson N. , Bishop Gregory W.
Published: August 2019

Nitrogen-doped carbon materials have garnered much interest due to their electrocatalytic activity towards important reactions such as the reduction of hydrogen peroxide. N-doped carbon materials are typically prepared and deposited on solid conductive supports, which can sometimes involve time-consuming, complex, and/or costly procedures. Here, nitrogen-doped screen-printed carbon electrodes (N-SPCEs) were fabricated directly from a lab-formulated ink composed of graphite that was modified with surface nitrogen groups by a simple soft nitriding technique. N-SPCEs prepared from inexpensive starting materials (graphite powder and urea) demonstrated good electrocatalytic activity towards hydrogen peroxide reduction. Amperometric detection of H2O2 using N-SPCEs with an applied potential of −0.4 V (vs. Ag/AgCl) exhibited good reproducibility and stability as well as a reasonable limit of detection (2.5 µM) and wide linear range (0.020 to 5.3 mM).
Fabric Circuit Board Connecting to Flexible Sensors or Rigid Components for Wearable Applications
Authors: Li Qiao , Ran Ziyuan , Ding Xin , Wang Xi
Published: August 2019

Electronic textiles demand a new family of flexible circuit boards in the construction of fiber or fiber assemblies. This paper presents a stretchable woven fabric circuit board (FCB) with permanent as well as detachable electrical connections to sensors or other wearable electronics components. The woven FCB was created by integrating conductive yarns into an elastic woven fabric. Permanent connection was designed between the conductive tracks and flexible sensors; detachable connection was achieved by the helical structure of conductive yarns wrapping around the rigid component electrode encapsulated within elastomeric layer. The developed FCB, with its connections to flexible sensors or rigid components, is porous, flexible, and capable of stretching to 30% strain. The woven FCB with permanent connection to temperature sensors has a large fatigue life of more than 10,000 cycles while maintaining constant electrical resistance due to crimped configurations of the conductive track in the elastic fabric substrate and stable contact resistance. A prototype of the FCB assembly, with independent light-emitting diodes electrically linked and mechanically supported by the woven FCB, is also demonstrated for wearable applications.
Robust Stereo Visual-Inertial Odometry Using Nonlinear Optimization
Authors: Ma Shujun , Bai Xinhui , Wang Yinglei , Fang Rui
Published: August 2019

The fusion of visual and inertial odometry has matured greatly due to the complementarity of the two sensors. However, the use of high-quality sensors and powerful processors in some applications is difficult due to size and cost limitations, and there are also many challenges in terms of robustness of the algorithm and computational efficiency. In this work, we present VIO-Stereo, a stereo visual-inertial odometry (VIO), which jointly combines the measurements of the stereo cameras and an inexpensive inertial measurement unit (IMU). We use nonlinear optimization to integrate visual measurements with IMU readings in VIO tightly. To decrease the cost of computation, we use the FAST feature detector to improve its efficiency and track features by the KLT sparse optical flow algorithm. We also incorporate accelerometer bias into the measurement model and optimize it together with other variables. Additionally, we perform circular matching between the previous and current stereo image pairs in order to remove outliers in the stereo matching and feature tracking steps, thus reducing the mismatch of feature points and improving the robustness and accuracy of the system. Finally, this work contributes to the experimental comparison of monocular visual-inertial odometry and stereo visual-inertial odometry by evaluating our method using the public EuRoC dataset. Experimental results demonstrate that our method exhibits competitive performance with the most advanced techniques.
Iron Redox Reactions Can Drive Microtopographic Variation in Upland Soil Carbon Dioxide and Nitrous Oxide Emissions
Authors: Krichels Alexander H. , Sipic Emina , Yang Wendy H.
Published: August 2019

Topographic depressions in upland soils experience anaerobic conditions conducive for iron (Fe) reduction following heavy rainfall. These depressional areas can also accumulate reactive Fe compounds, carbon (C), and nitrate, creating potential hot spots of Fe-mediated carbon dioxide (CO2) and nitrous oxide (N2O) production. While there are multiple mechanisms by which Fe redox reactions can facilitate CO2 and N2O production, it is unclear what their cumulative effect is on CO2 and N2O emissions in depressional soils under dynamic redox. We hypothesized that Fe reduction and oxidation facilitate greater CO2 and N2O emissions in depressional compared to upslope soils in response to flooding. To test this, we amended upslope and depressional soils with Fe(II), Fe(III), or labile C and measured CO2 and N2O emissions in response to flooding. We found that depressional soils have greater Fe reduction potential, which can contribute to soil CO2 emissions during flooded conditions when C is not limiting. Additionally, Fe(II) addition stimulated N2O production, suggesting that chemodenitrification may be an important pathway of N2O production in depressions that accumulate Fe(II). As rainfall intensification results in more frequent flooding of depressional upland soils, Fe-mediated CO2 and N2O production may become increasingly important pathways of soil greenhouse gas emissions.
Use of GIS to Evaluate Minor Rural Buildings Distribution Compared to the Communication Routes in a Part of the Apulian Territory (Southern Italy)
Authors: Parlavecchia Marco , Pascuzzi Simone , Anifantis Alexandros Sotirios , Santoro Francesco , Ruggiero Giuseppe
Published: August 2019

The aim of this paper is to analyze the relationship between minor rural buildings and the most relevant communication routes of the regional area made up of six municipalities which, until 2016, formed the Local Action Group Sud Est Barese (LAG SEB): Acquaviva delle Fonti, Casamassima, Conversano, Mola di Bari, and Noicattaro e Rutigliano (Apulia, Italy). Information on the territorial distribution and typological classification of buildings was obtained by the official cartography of Military Geographic Institute (IGM, 1:25,000) and the Regional Technical Map (CTR, 1:5000) using the Geographic Information System (GIS) software ArcMap ArcGis 10.1. IGM cartography was chosen due to the greater amount of information pertaining to typological classification as well as to toponymy identification. CTR cartography, used as a second cartographic source, has been useful for acquiring more up-to-date territorial information compared to the IGM, in particular concerning the infrastructures of the territory. Moreover, the use of the CTR has allowed us to verify whether buildings listed on IGM cartography still exist. The cartographic calculations have allowed us to investigate possible relationships between the typology and distribution of rural buildings and the network of public roads and urban centers. The study of the connection between building types, roads and urban centers has allowed us to better understand the spatial distribution criteria, thus acquiring useful information to outline intervention policies, the implementation of which would be used to attempt to recover and improve building structures.
Household Waste Sorting and Engagement in Everyday Life Occupations After Migration—A Scoping Review
Authors: Hellwig Coralie , Häggblom-Kronlöf Greta , Bolton Kim , Rousta Kamran
Published: August 2019

The aim of this scoping review was to gain an overview of the current state of the literature on the engagement in waste sorting post migration from an occupational perspective, in the light of two aspects sustainability efforts currently face: Increased human migration and environmental degradation. Both the resource recovery and occupational science literature were reviewed and analyzed. However, despite the current lack on studies on how migrants’ transition into waste sorting schemes at the household level, this scoping review was able to provide a broad picture of the engagement in daily activities that support sustainability, such as household waste sorting. Given the current initiatives to develop efficient resource recovery from waste, such knowledge contributes to efforts to engage households with different cultures and experiences in waste sorting. The results highlight the importance of future research to better understand how people who are new to waste management schemes experience these, and study the way that engagement in waste sorting shifts and transforms. This is because providing such knowledge can contribute to raising awareness of the environmental impact of waste sorting, and inform policies aimed at sustainable waste management.
Erratum: Sheng, J. Effect of Uncertainties in Estimated Carbon Reduction from Deforestation and Forest Degradation on Required Incentive Payments in Developing Countries. Sustainability 2017, 9, 1608
Authors: Sheng Jichuan
Published: August 2019

The authors would like to make the following corrections about the published paper [...]
Characteristics of the Digital Content about Energy-Saving in Different Countries around the World
Authors: Valerio-Ureña Gabriel , Rogers Richard
Published: August 2019

Environmental sustainability is fundamental for human well-being, and energy-saving behaviors are fundamental for promoting environmental sustainability. This paper explores the types of information consumed on the Internet concerning the topic of energy-saving. The qualitative study used digital methods to analyze, from 17 different countries, one-year of Google searches, 170 webpages, and 6800 images. The results demonstrated that (a) most topics related to energy-saving involved commercial products; (b) in countries from North America, Europe, and Oceania, Google’s highest-ranked webpages were of a commercial nature, and in countries from Latin America, Africa, and Asia, webpages were of an educational nature; and (c) most images found in Google with the “energy-saving” query were related to lighting products. By and large, commercial interests were found to dominate the digital rhetoric around energy-saving, regardless of the countries’ region.
Forecasting Hotel Accommodation Demand Based on LSTM Model Incorporating Internet Search Index
Authors: Zhang Binru , Pu Yulian , Wang Yuanyuan , Li Jueyou
Published: August 2019

Accurate forecasting of the hotel accommodation demands is extremely critical to the sustainable development of tourism-related industries. In view of the ever-increasing tourism data, this paper constructs a deep learning framework to handle the prediction problem in the hotel accommodation demands. Taking China’s Hainan province as an empirical example, the internet search index is used from August 2008 to May 2019 to forecast the overnight passenger flows for hotels accommodation in Hainan Province, China. Forecasting results indicate that compared to benchmark models, the constructed forecasting method can effectively simulate dynamic characteristics of the overnight passenger flows for the hotel accommodation and significantly improve the forecasting performance of the model. Forecasting results can provide necessary references for decision-making in tourism-related industries, and this forecasting framework can also be extended to other similar complex time series forecasting problems.
Examining the Factors Influencing Transport Sector CO2 Emissions and Their Efficiency in Central China
Authors: Sun Huali , Li Mengzhen , Xue Yaofeng
Published: August 2019

The fast development of the transport sector has resulted in high energy consumption and carbon dioxide (CO2) emissions in China. Though existing studies are concerned with the factors influencing transport sector CO2 emissions at the national level (or in megacities), little attention has been paid to the comprehensive impact of socio-economic, urban form, and transportation development on transport sector carbon emissions and emissions efficiency in central China. This paper examines the comprehensive impact of the transport sector’s carbon emissions from six provinces in central China, during the period from 2005 to 2016, based on the panel data model. The dynamic change of CO2 emissions efficiency is then analyzed using the Global Malmquist Luenberger Index. The results indicate that, firstly, economic growth, road density, the number of private vehicles, and the number of public vehicles have caused greater CO2 emissions during the study period, while the freight turnover, urbanization level, and urban population density had repressing effects on CO2 emissions. Secondly, an uneven distribution of CO2 emissions and CO2 emissions efficiency was found among different provinces in central China. Thirdly, changes in CO2 emissions efficiency were mainly due to technical changes. Finally, we present some policy suggestions to mitigate transport sector CO2 emissions in central China.
Comparative Analysis between Hydrous Ethanol and Gasoline C Pricing in Brazilian Retail Market
Authors: Murari Thiago B. , Nascimento Filho Aloisio S. , Pereira Eder J.A.L. , Ferreira Paulo , Pitombo Sergio , Pereira Hernane B.B. , Santos Alex A.B. , Moret Marcelo A.
Published: August 2019

The global energy landscape is rapidly changing, including the transition to a low carbon economy and the use of liquid biofuel. The production of liquid biofuel has emerged as an alternative to the use of fossil fuels for purposes of energy conservation, carbon emission mitigation and agricultural development. In this article we study the co-movements between hydrous ethanol and gasoline C in the Brazilian retail market. A multi-scale cross correlation analysis was applied to the Average Retail Margin time series of hydrous ethanol for fifteen relevant retail markets in Brazil to analyze the competitiveness of hydrous ethanol towards gasoline C. The empirical results showed a remarkable different behavior between hydrous ethanol and gasoline C, for any time scale, regardless of geographical distance or regional differences.
Government Intervention and Automobile Industry Structure: Theory and Evidence from China
Authors: Ma Lin , Du Qinchuan , Wu Tian
Published: August 2019

The development of the automobile maintenance industry less developed to satisfy the increasing demand for automobile maintenance service as the automobile manufacturing industry increased rapidly in China. This is not conducive to the sustainable development of the automobile industry. Besides the factors of market behavior that can affect the automobile industry structure, like an investment, operation structure or economic development stage, the structure is also influenced by government intervention. We investigated the unbalanced development of automobile structure from the perspective of government incentives, and provide a logical framework for analyzing the industrial policies on the automobile industry. We first established a two-sector theoretical model with government intervention, and we found that the governments’ GDP incentive induced the biased intervention policy. More preferential policies are given to enterprises of automobile manufacturing industries as they contribute more to intermediate goods and capital. The greater the government’s GDP incentive, the more biased the intervention will be. Then we test the differential impact of GDP incentive on tax avoidance of the two kinds of firms empirically. The empirical results show that GDP incentive of the government induced more preferential treatment to automobile manufacturing enterprises, and thus, increased their tax avoidance. This phenomenon is more significant in SOEs, larger firms and firms belong to local governments. Understanding the incentive and implementation of industrial policy can help us know the evolution of automobile industrial structure better, and then improve industrial policy better to promote the transformation and upgrading of automobile industrial structure.
Pattern and Influencing Factors of Foreign Direct Investment Networks between Countries along the “Belt and Road” Regions
Authors: He Quan , Cao Xishen
Published: August 2019

With the in-depth implementation of the “Belt and Road” initiative (BRI), the investment patterns between Belt and Road countries have also become more complicated. The impact of this complex investment network on regional economic development is also growing. To reveal the complexity of this investment pattern, and to better promote the sustainable development of the region’s economy, this paper used the complex network method to study the foreign direct investment (FDI) network of 50 countries along the Belt and Road from 2003 to 2017, revealing its structural and behavioral characteristics and evolution process. The results showed that the imbalance of the investment network structure is outstanding, and preferential selection behavior is obvious. The Central and Eastern European countries show significant clustering behavior. In addition, the network evolved slowly and followed the “Pareto rule” in the early stages of its evolution. The BRI was a turning point in the evolution process. On this basis, the quadratic assignment procedure (QAP) regression analysis method was used to further study the factors affecting the formation process of this investment pattern. It found that economic development level, geographical distance, and bilateral trade were the main influencing factors. Among them, bilateral trade had the greatest impact on the pattern of network.