bluuebunny/crossref_metadata_2025 huggingface dataset:
Dendritic Macrosurfactant Assembly for Physical Functionalization of HIPE-Templated Polymers
Authors: Li Chenhui , Weng Shiqi , Jin Ming , Wan Decheng
Published: April 2020

High-internal-phase emulsion-templated macroporous polymers (polyHIPEs) have attracted much interest, but their surface functionalization remains a primary concern. Thus, competitive surface functionalization via physical self-assembly of macrosurfactants was reviewed. Dendritic and diblock-copolymer macrosurfactants were tested, and the former appeared to be more topologically competitive in terms of solubility, viscosity, and versatility. In particular, hyperbranched polyethyleneimine (PEI) was transformed into dendritic PEI macrosurfactants through click-like N-alkylation with epoxy compounds. Free-standing PEI macrosurfactants were used as molecular nanocapsules for charge-selective guest encapsulation and robustly dictated the surface of a macroporous polymer through the HIPE technique, in which the macroporous polymer could act as a well-recoverable adsorbent. Metal nanoparticle-loaded PEI macrosurfactants could similarly lead to polyHIPE, whose surface was dictated by its catalytic component. Unlike conventional Pickering stabilizer, PEI macrosurfactant-based metal nanocomposite resulted in open-cellular polyHIPE, rendering the catalytic sites well accessible. The active amino groups on the polyHIPE could also be transformed into functional groups of aminopolycarboxylic acids, which could efficiently eliminate trace and heavy metal species in water.
Ag NPs-Assisted Synthesis of Stable Cu NPs on PET Fabrics for Antibacterial and Electromagnetic Shielding Performance
Authors: Wang Ke , Ma Qian , Zhang Yuanming , Wang Shudong , Han Guangting
Published: April 2020

In this study, Cu/Ag/polydopamine (PDA)/polyester (PET) fabrics were fabricated for multi-functional textiles. The PET fabrics were firstly modified by dopamine to form a polydopamine (PDA) layer on the fiber surface, then Ag nanoparticles (Ag NPs) were anchored on fiber surface through chelation between PDA and Ag+ ions, and the Ag NPs were further used as catalytic seeds for in situ reduction of Cu nanoparticles (Cu NPs). The surface morphology, chemistry, and crystalline structure of the prepared PET fabrics were characterized by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDS), and X-ray diffraction (XRD). As expected, Cu NPs were evenly dispersed on the surface of fibers. The Cu/Ag/PDA/PET fabrics showed good antibacterial property against Escherichia coli and exhibited excellent electromagnetic interference (EMI) shielding ability. The Cu/Ag/PDA/PET fabrics with high performance antibacterial and EMI shielding properties can be applied as functional protective textiles.
A Mini-Review on Anion Exchange and Chelating Polymers for Applications in Hydrometallurgy, Environmental Protection, and Biomedicine
Authors: Cyganowski Piotr , Dzimitrowicz Anna
Published: April 2020

The rapidly increasing demand for technologies aiming to resolve challenges of separations and environmental protection causes a sharp increase in the demand for ion exchange (IX) and chelating polymers. These unique materials can offer target-selective adsorption properties vital for the removal or recovery of harmful and precious materials, where trace concentrations thereof make other techniques insufficient. Hence, recent achievements in syntheses of IX and chelating resins designed and developed in our research group are discussed within this mini-review. The aim of the present work is to reveal that, due to the diversified and unique physiochemical characteristics of the proposed materials, they are not limited to traditional separation techniques and could be used in multifunctional areas of applications, including catalysis, heat management, and biomedicine.
Surface Modification of FeCoNiCr Medium-Entropy Alloy (MEA) Using Octadecyltrichlorosilane and Atmospheric-Pressure Plasma Jet
Authors: Cheng Pei-Yu , Lu Nian-Hu , Lu Yi-Sheng , Chen Chih-Hsuan , Lee Yueh-Lien , Chen Jian-Zhang
Published: April 2020

Surface condition and corrosion resistance are major concerns when metallic materials are going to be utilized for applications. In this study, FeCoNiCr medium-entropy alloy (MEA) is first treated with a nitrogen atmospheric-pressure plasma jet (APPJ) and then coated with octadecyltrichlorosilane (OTS) for the surface modification. The hydrophobicity of the FeCoNiCr MEA was effectively improved by OTS-coating treatment, APPJ treatment, or the combination of both treatments (OTS-coated APPJ-treated), which increased the water contact angle from 54.49° of the bare MEA to 70.56°, 93.94°, and 88.42°, respectively. Potentiodynamic polarization and electrochemical impedance spectroscopy tests demonstrate that the APPJ-treated FeCoNiCr MEA exhibits the best anti-corrosion properties. X-ray photoelectron spectroscopy reveals that APPJ treatment at 700 °C oxidizes all the alloying elements in the FeCoNiCr MEA, which demonstrates that a short APPJ treatment of two-minute is effective in forming a metal oxide layer on the surface to improve the corrosion resistance of FeCoNiCr MEA. These results provide a convenient and rapid method for improving surface properties of FeCoNiCr MEA.
Self-Assembly of Block Copolymers
Authors: Abetz Volker
Published: April 2020

Block copolymers and block-copolymer-containing blends represent a fascinating class of soft matter and can self-assemble in a variety of ordered structures on the mesoscale [...]
High-Yield Production of Lignin-Derived Functional Carbon Nanosheet for Dye Adsorption
Authors: Chen Fenggui , Hu Xi , Tu Xiaohan , Chen Linfei , Liu Xi , Tan Linli , Mao Yulin , Shi Jianwei , Teng Xiaoxu , He Shuhua , Qin Zonghui , Xu Jianhua , Wu Jian
Published: April 2020

In this article, we report the preparation of lignin-derived carbon nanosheet (L-CNS) by direct thermal treatment of lignin without activation operation and the functions of the L-CNS as an adsorbent for rhodamine dye. The L-CNSs are fabricated by freeze-drying (FD) methods of lignin followed by high-temperature carbonization. It is found that lower frozen temperature in FD or lower concentration of lignin aqueous solution renders L-CNSs’ more porous morphology and higher specific surface area (SSA), allowing a promising application of the L-CNSs as an efficient adsorbent for organic pollutants. In particular, the alkaline hydroxide catalyst helps to increase the SSA of carbon products, leading to a further improved adsorption capacity. On the other hand, p-toluenesulfonic acid (TsOH) catalyzed pyrolysis, which dramatically increased the L-CNS product yield, and provided a high-yield approach for the production of pollutant absorbent.
Morphological Transitions of Photoresponsive Vesicles from Amphiphilic Polypeptoid Copolymers for Controlled Release
Authors: Yang Xu , Wang Zhiwei , Sun Jing
Published: April 2020

Photoresponsive polymers have attracted increasing interest for a variety of applications. Here, we report a family of photoresponsive polypeptoid-based copolymer poly(ethylene glycol)-b-poly(N-(S-(o-nitrobenzyl)-thioethyl) glycine)-co-poly(N-(2-phenylethyl) glycine) (PEG-b-PNSN-co-PNPE) synthesized by the controlled ring-opening polymerization (ROP) technique. The key feature of the design is to incorporate both o-nitrobenzyl group moiety to offer the photoresponsive property and phenethyl residues to tune the structural and amphiphilic property of the system. We demonstrate that the cleavage degree of the o-nitrobenzyl group can reach to 100% upon UV-irradiation. With delicate design, a photoresponsive vesicle-to-sphere transition has been observed that facilitates the release of the encapsulants. This work provides a facile approach to prepare a type of photoresponsive polymers with tunable properties for drug delivery.
In Memoriam of Professor Roger W.H. Sargent, the Founder of “Process Systems Engineering”
Authors: Gani Rafiqul , Cameron Ian
Published: April 2020

In September 2018, the global chemical engineering community lost a true pioneer in the field [...]
Residual Stress Distribution in Water Jet Peened Type 304 Stainless Steel
Authors: Hayashi Makoto , Okido Shinobu , Suzuki Hiroshi
Published: April 2020

In materials with a surface treatment such as shot peening, the residual stress gradient in the surface layer is severe. When measuring the residual stress distribution near the surface with a severe stress gradient by the neutron diffraction method, the gauge volume must be removed from the measurement sample. However, when the gauge volume deviates from the sample, a pseudo peak shift occurs and accurate stress distribution cannot be evaluated. Therefore, it is necessary to evaluate the pseudo peak shift in advance under the same conditions, as in the case of actual residual stress measurement, using a sample in an unstressed state. In this study, the stress distributions in the surface layer of a type 304 stainless steel plate and bar with simulated stress-corrosion cracks which were subjected to water jet peening—giving a surface layer residual stress equivalent better than that of normal shot peening—were evaluated considering the pseudo peak shift. As a result, the residual stress distributions in the surface layer were measured in good agreement with the measurement result obtained by the sequential polishing X-ray diffraction method. It was clarified that the residual stress distribution in the near surface with steep stress gradient can be evaluated by the neutron diffraction method.
Numerical Investigations on Wave Remote Sensing from Synthetic X-Band Radar Sea Clutter Images by Using Deep Convolutional Neural Networks
Authors: Duan Wenyang , Yang Ke , Huang Limin , Ma Xuewen
Published: April 2020

X-band marine radar is an effective tool for sea wave remote sensing. Conventional physical-based methods for acquiring wave parameters from radar sea clutter images use three-dimensional Fourier transform and spectral analysis. They are limited by some assumptions, empirical formulas and the calibration process while obtaining the modulation transfer function (MTF) and signal-to-noise ratio (SNR). Therefore, further improvement of wave inversion accuracy by using the physical-based method presents a challenge. Inspired by the capability of convolutional neural networks (CNN) in image characteristic processing, a deep-learning inversion method based on deep CNN is proposed. No intermediate step or parameter is needed in the CNN-based method, therefore fewer errors are introduced. Wave parameter inversion models were constructed based on CNN to inverse the wave’s spectral peak period and significant wave height. In the present paper, the numerically simulated X-band radar image data were used for a numerical investigation of wave parameters. Results of the conventional spectral analysis and CNN-based methods were compared and the CNN-based method had a higher accuracy on the same data set. The influence of training strategy on CNN-based inversion models was studied to analyze the dependence of a deep-learning inversion model on training data. Additionally, the effects of target parameters on the inversion accuracy of CNN-based models was also studied.
Developing a Gap-Filling Algorithm Using DNN for the Ts-VI Triangle Model to Obtain Temporally Continuous Daily Actual Evapotranspiration in an Arid Area of China
Authors: Cui Yaokui , Ma Shihao , Yao Zhaoyuan , Chen Xi , Luo Zengliang , Fan Wenjie , Hong Yang
Published: April 2020

Temporally continuous daily actual evapotranspiration (ET) data play a critical role in water resource management in arid areas. As a typical remotely sensed land surface temperature (LST)-based ET model, the surface temperature-vegetation index (Ts-VI) triangle model provides direct monitoring of ET, but these estimates are temporally discontinuous due to cloud contamination. In this work, we present a gap-filling algorithm (TSVI_DNN) using a deep neural network (DNN) with the Ts-VI triangle model to obtain temporally continuous daily actual ET at regional scale. The TSVI_DNN model is evaluated against in situ measurements in an arid area of China during 2009–2011 and shows good agreement with eddy covariance (EC) observations. The temporal coverage was improved from 16.1% with the original Ts-VI tringle model to 67.1% with the TSVI_DNN model. The correlation coefficient (R), root mean square error (RMSE), bias, and mean absolute difference (MAD) are 0.9, 0.86 mm d−1, −0.16 mm d−1, and 0.65 mm d−1, respectively. When compared with the National Aeronautics and Space Administration (NASA) official MOD16 version 6 ET product, estimates of ET using TSVI_DNN are improved by approximately 49.2%. The method presented here can potentially contribute to enhanced water resource management in arid areas, especially under climate change.
Tracing Leaf Photosynthetic Parameters Using Hyperspectral Indices in an Alpine Deciduous Forest
Authors: Jin Jia , Arief Pratama Bayu , Wang Quan
Published: April 2020

Leaf photosynthetic parameters are important in understanding the role of photosynthesis in the carbon cycle. Conventional approaches to obtain information on the parameters usually involve long-term field work, even for one leaf sample, and are, thus, only applicable to a small area. The utilization of hyperspectral remote sensing especially of various vegetation indices is a promising approach that has been attracting increasing attention recently. However, most hyperspectral indices are only applicable to a specific area and specific forest stands, depending heavily on the conditions from which the indices are developed. In this study, we tried to develop new hyperspectral indices for tracing the two critical photosynthetic parameters (the maximum rate of carboxylation, Vcmax and the maximum rate of electron transport, Jmax) that are at least generally applicable for alpine deciduous forests, based on original hyperspectral reflectance, first-order derivatives, and apparent absorption spectra. In total, ten types of hyperspectral indices were screened to identify the best indices, and their robustness was determined using the ratio of performance to deviation (RPD) and Akaike’s Information Criterion corrected (AICc). The result revealed that the double differences (DDn) type of indices using the short-wave infrared (SWIR) region based on the first-order derivatives spectra performed best among all indices. The specific DDn type of indices obtained the RPD values of 1.43 (R2 = 0.51) for Vcmax and 1.68 (R2 = 0.64) for Jmax, respectively. These indices have also been tested using the downscaled dataset to examine the possibilities of using hyperspectral data derived from satellite-based information. These findings highlight the possibilities of tracing photosynthetic capacity using hyperspectral indices.
Enhanced 3D Point Cloud from a Light Field Image
Authors: Farhood Helia , Perry Stuart , Cheng Eva , Kim Juno
Published: April 2020

The importance of three-dimensional (3D) point cloud technologies in the field of agriculture environmental research has increased in recent years. Obtaining dense and accurate 3D reconstructions of plants and urban areas provide useful information for remote sensing. In this paper, we propose a novel strategy for the enhancement of 3D point clouds from a single 4D light field (LF) image. Using a light field camera in this way creates an easy way for obtaining 3D point clouds from one snapshot and enabling diversity in monitoring and modelling applications for remote sensing. Considering an LF image and associated depth map as an input, we first apply histogram equalization and histogram stretching to enhance the separation between depth planes. We then apply multi-modal edge detection by using feature matching and fuzzy logic from the central sub-aperture LF image and the depth map. These two steps of depth map enhancement are significant parts of our novelty for this work. After combing the two previous steps and transforming the point–plane correspondence, we can obtain the 3D point cloud. We tested our method with synthetic and real world image databases. To verify the accuracy of our method, we compared our results with two different state-of-the-art algorithms. The results showed that our method can reliably mitigate noise and had the highest level of detail compared to other existing methods.
Substantially Greater Carbon Emissions Estimated Based on Annual Land-Use Transition Data
Authors: Diao Jiaojiao , Liu Jinxun , Zhu Zhiliang , Li Mingshi , Sleeter Benjamin M.
Published: April 2020

Quantifying land-use and land-cover change (LULCC) effects on carbon sources and sinks has been very challenging because of the availability and quality of LULCC data. As the largest estuary in the United States, Chesapeake Bay is a rapidly changing region and is affected by human activities. A new annual land-use and land-cover (LULC) data product developed by the U.S. Geological Survey Land Change Monitoring and Analysis Program (LCMAP) from 2001 to 2011 was analyzed for transitions between agricultural land, developed land, grassland, forest land and wetland. The Land Use and Carbon Scenario Simulator was used to simulate effects of LULCC and ecosystem disturbance in the south of the Chesapeake Bay Watershed (CBW) on carbon storage and fluxes, with carbon parameters derived from the Integrated Biosphere Simulator. We found that during the study period: (1) areas of forest land, disturbed land, agricultural land and wetland decreased by 90, 82, 57, and 65 km2, respectively, but developed lands gained 293 km2 (29 km2 annually); (2) total ecosystem carbon stock in the CBW increased by 13 Tg C from 2001 to 2011, mainly due to carbon sequestration of the forest ecosystem; (3) carbon loss was primarily attributed to urbanization (0.224 Tg C·yr−1) and agricultural expansion (0.046 Tg C·yr−1); and (4) estimated carbon emissions and harvest wood products were greater when estimated with the annual LULC input. We conclude that a dense time series of LULCC, such as that of the LCMAP program, may provide a more accurate accounting of the effects of land use change on ecosystem carbon, which is critical to understanding long-term ecosystem carbon dynamics.
Global Registration of Terrestrial Laser Scanner Point Clouds Using Plane-to-Plane Correspondences
Authors: Pavan Nadisson Luis , dos Santos Daniel Rodrigues , Khoshelham Kourosh
Published: April 2020

Registration of point clouds is a central problem in many mapping and monitoring applications, such as outdoor and indoor mapping, high-speed railway track inspection, heritage documentation, building information modeling, and others. However, ensuring the global consistency of the registration is still a challenging task when there are multiple point clouds because the different scans should be transformed into a common coordinate frame. The aim of this paper is the registration of multiple terrestrial laser scanner point clouds. We present a plane-based matching algorithm to find plane-to-plane correspondences using a new parametrization based on complex numbers. The multiplication of complex numbers is based on analysis of the quadrants to avoid the ambiguity in the calculation of the rotation angle formed between normal vectors of adjacent planes. As a matching step may contain several matrix operations, our strategy is applied to reduce the number of mathematical operations. We also design a novel method for global refinement of terrestrial laser scanner data based on plane-to-plane correspondences. The rotation parameters are globally refined using operations of quaternion multiplication, while the translation parameters are refined using the parameters of planes. The global refinement is done non-iteratively. The experimental results show that the proposed plane-based matching algorithm efficiently finds plane correspondences in partial overlapping scans providing approximate values for the global registration, and indicate that an accuracy better than 8 cm can be achieved by using our global fine plane-to-plane registration method.
Multitemporal Analysis of Soil Sealing and Land Use Changes Linked to Urban Expansion of Salamanca (Spain) Using Landsat Images and Soil Carbon Management as a Mitigating Tool for Climate Change
Authors: Criado Marco , Santos-Francés Fernando , Martínez-Graña Antonio , Sánchez Yolanda , Merchán Leticia
Published: April 2020

The lack of urban sustainability is a widespread deficiency in urban agglomerations. To achieve adequate land use, we present a methodology that allows for: 1) the identification of the impacts caused by urban expansion since 1956 to the present in Salamanca (Spain); and 2) the promotion of a more sustainable future in urban development. A multi-temporal assessment of land use was made by remote sensing, while sustainability criteria were analyzed using the multicriteria analysis (MCA) with Geographical Information Systems (GIS). In addition, we established recommendations for soil carbon management in semi-arid ecosystem soils that contribute to climate change mitigation. The results show an increase of the urbanized area from 3.8% to 22.3% in the studied period, identifying up to 15% of buildings in zones with some type of restriction. In 71% of the cases, urbanization caused the sealing of productive agricultural soils (2519 Ha), almost 20% of which were of the highest quality. In last few decades, an excessive increase of built-up areas in comparison to population dynamics was identified, which causes unnecessary soil sealing that affects the food production and the capacity to mitigate climate change by managing the carbon cycle in the soil.
Variations in Winter Surface Temperature of the Purog Kangri Ice Field, Qinghai–Tibetan Plateau, 2001–2018, Using MODIS Data
Authors: Qie Yufan , Wang Ninglian , Wu Yuwei , Chen An’an
Published: April 2020

In the context of global warming, the land surface temperature (LST) from remote sensing data is one of the most useful indicators to directly quantify the degree of climate warming in high-altitude mountainous areas where meteorological observations are sparse. Using the daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (MOD11A1 V6) after eliminating pixels that might be contaminated by clouds, this paper analyzes temporal and spatial variations in the mean LST on the Purog Kangri ice field, Qinghai–Tibetan Plateau, in winter from 2001 to 2018. There was a large increasing trend in LST (0.116 ± 0.05 °C·a−1) on the Purog Kangri ice field during December, while there was no evident LST rising trend in January and February. In December, both the significantly decreased albedo (−0.002 a−1, based on the MOD10A1 V6 albedo product) on the ice field surface and the significantly increased number of clear days (0.322 d·a−1) may be the main reason for the significant warming trend in the ice field. In addition, although the two highest LST of December were observed in 2017 and 2018, a longer data set is needed to determine whether this is an anomaly or a hint of a warmer phase of the Purog Kangri ice field in December.
Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review
Authors: Talukdar Swapan , Singha Pankaj , Mahato Susanta , Shahfahad  , Pal Swades , Liou Yuei-An , Rahman Atiqur
Published: April 2020

Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.
Estimating Plant Nitrogen Concentration of Maize Using a Leaf Fluorescence Sensor across Growth Stages
Authors: Dong Rui , Miao Yuxin , Wang Xinbing , Chen Zhichao , Yuan Fei , Zhang Weina , Li Haigang
Published: April 2020

Nitrogen (N) is one of the most essential nutrients that can significantly affect crop grain yield and quality. The implementation of proximal and remote sensing technologies in precision agriculture has provided new opportunities for non-destructive and real-time diagnosis of crop N status and precision N management. Notably, leaf fluorescence sensors have shown high potential in the accurate estimation of plant N status. However, most studies using leaf fluorescence sensors have mainly focused on the estimation of leaf N concentration (LNC) rather than plant N concentration (PNC). The objectives of this study were to (1) determine the relationship of maize (Zea mays L.) LNC and PNC, (2) evaluate the main factors influencing the variations of leaf fluorescence sensor parameters, and (3) establish a general model to estimate PNC directly across growth stages. A leaf fluorescence sensor, Dualex 4, was used to test maize leaves with three different positions across four growth stages in two fields with different soil types, planting densities, and N application rates in Northeast China in 2016 and 2017. The results indicated that the total leaf N concentration (TLNC) and PNC had a strong correlation (R2 = 0.91 to 0.98) with the single leaf N concentration (SLNC). The TLNC and PNC were affected by maize growth stage and N application rate but not the soil type. When used in combination with the days after sowing (DAS) parameter, modified Dualex 4 indices showed strong relationships with TLNC and PNC across growth stages. Both modified chlorophyll concentration (mChl) and modified N balance index (mNBI) were reliable predictors of PNC. Good results could be achieved by using information obtained only from the newly fully expanded leaves before the tasseling stage (VT) and the leaves above panicle at the VT stage to estimate PNC. It is concluded that when used together with DAS, the leaf fluorescence sensor (Dualex 4) can be used to reliably estimate maize PNC across growth stages.
An Evaluation of Autonomous In Situ Temperature Loggers in a Coastal Region of the Eastern Mediterranean Sea for Use in the Validation of Near-Shore Satellite Sea Surface Temperature Measurements
Authors: Androulakis Dimitrios N. , Banks Andrew Clive , Dounas Costas , Margaris Dionissios P.
Published: April 2020

The coastal ocean is one of the most important environments on our planet, home to some of the most bio-diverse and productive ecosystems and providing key input to the livelihood of the majority of human society. It is also a highly dynamic and sensitive environment, particularly susceptible to damage from anthropogenic influences such as pollution and over-exploitation as well as the effects of climate change. These have the added potential to exacerbate other anthropogenic effects and the recent change in sea temperature can be considered as the most pervasive and severe cause of impact in coastal ecosystems worldwide. In addition to open ocean measurements, satellite observations of sea surface temperature (SST) have the potential to provide accurate synoptic coverage of this essential climate variable for the near-shore coastal ocean. However, this potential has not been fully realized, mainly because of a lack of reliable in situ validation data, and the contamination of near-shore measurements by the land. The underwater biotechnological park of Crete (UBPC) has been taking near surface temperature readings autonomously since 2014. Therefore, this study investigated the potential for this infrastructure to be used to validate SST measurements of the near-shore coastal ocean. A comparison between in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua and Terra SST data is presented for a four year (2014–2018) in situ time series recorded from the UBPC. For matchups between in situ and satellite SST data, only nighttime in situ extrapolated to the sea surface (SSTskin) data within ±1 h from the satellite’s overpass are selected and averaged. A close correlation between the in situ data and the MODIS SST was found (squared Pearson correlation coefficient-r2 > 0.9689, mean absolute error-Δ < 0.51 both for Aqua and Terra products). Moreover, close correlation was found between the satellite data and their adjacent satellite pixel’s data further from the shore (r2 > 0.9945, Δ < 0.23 for both Aqua and Terra products, daytime and nighttime satellite SST). However, there was also a consistent positive systematic difference in the satellite against satellite mean biases indicating a thermal adjacency effect from the land (e.g., mean bias between daytime Aqua satellite SST from the UBPC cell minus the respective adjacent cell’s data is δ = 0.02). Nevertheless, if improvements are made in the in situ sensors and their calibration and uncertainty evaluation, these initial results indicate that near-shore autonomous coastal underwater temperature arrays, such as the one at UBPC, could in the future provide valuable in situ data for the validation of satellite coastal SST measurements.
Head-Mounted Display-Based Microscopic Imaging System with Customizable Field Size and Viewpoint
Authors: Aoyama Tadayoshi , Takeno Sarau , Takeuchi Masaru , Hasegawa Yasuhisa
Published: April 2020

In recent years, the use of microinjections has increased in life science and biotechnology fields; specific examples include artificial insemination and gene manipulation. Microinjections are mainly performed based on visual information; thus, the operator needs high-level skill because of the narrowness of the visual field. Additionally, microinjections are performed as the operator views a microscopic image on a display; the position of the display requires the operator to maintain an awkward posture throughout the procedure. In this study, we developed a microscopic image display apparatus for microinjections based on a view-expansive microscope. The prototype of the view-expansive microscope has problems related to the variations in brightness and focal blur that accompany changes in the optical path length and amount of reflected light. Therefore, we propose the use of a variable-focus device to expand the visual field and thus circumvent the above-mentioned problems. We evaluated the observable area of the system using this variable-focus device. We confirmed that the observable area is 261.4 and 13.9 times larger than that of a normal microscope and conventional view-expansive microscopic system, respectively. Finally, observations of mouse embryos were carried out by using the developed system. We confirmed that the microscopic images can be displayed on a head-mounted display in real time with the desired point and field sizes.
Ideal Oscillation of a Hydrogenated Deformable Rotor in a Gigahertz Rotation–Translation Nanoconverter at Low Temperatures
Authors: Song Bo , Shi Jiao , Wang Jinbao , Shen Jianhu , Cai Kun
Published: April 2020

It was discovered that large-amplitude axial oscillation can occur on a rotor with an internally hydrogenated deformable part (HDP) in a rotation–translation nanoconverter. The dynamic outputs of the system were investigated using molecular dynamics simulations. When an input rotational frequency (100 GHz > ω > 20 GHz) was applied at one end of the rotor, the HDP deformed under the centrifugal and van der Waals forces, which simultaneously led to the axial translation of the other end of the rotor. Except at too high an input rotational frequency (e.g., >100 GHz), which led to eccentric rotation and even collapse of the system, the present system could generate a periodic axial oscillation with an amplitude above 0.5 nm at a temperature below 50 K. In other ranges of temperature and amplitude, the oscillation dampened quickly due to the drastic thermal vibrations of the atoms. Furthermore, the effects of the hydrogenation scheme and the length of HDP on the equilibrium position, amplitude, and frequency of oscillation were investigated. The conclusions can be applied to the design of an ideal nano-oscillator based on the present rotation–translation converter model.
Secure Key Establishment Mechanism for Smart Sensing System Based Robots Network
Authors: Xiao Qi , Qin Yunchuan , Xu Cheng , Li Kenli
Published: April 2020

The smart robot is playing an increasingly important role in the social economy, and multi-robot systems will be an important development in robotics. With smart sensing systems, the communications between sensors, actuators, and edge computing systems and robots are prone to be attacked due to the highly dynamic and distributed environment. Since smart robots are often distributed in open environments, as well as due to their limited hardware resources and security protection capabilities, the security requirements of their keys cannot be met with traditional key distribution algorithms. In this paper, we propose a new mechanism of key establishment based on high-order polynomials to ensure the safe key generation and key distribution. Experiments show that the key establishment mechanism proposed in this paper guarantees the security of keys; its storage cost and communication cost are smaller than state-of-the-art mechanisms; and it allows robot components to join and leave the network dynamically, which is more suitable for multi-robot systems.
A Comprehensive Review: Materials for the Fabrication of Optical Fiber Refractometers Based on Lossy Mode Resonance
Authors: Ozcariz Aritz , Ruiz-Zamarreño Carlos , Arregui Francisco J.
Published: April 2020

Lossy mode resonance based sensors have been extensively studied in recent years. The versatility of the lossy mode resonance phenomenon has led to the development of sensors based on different configurations that make use of a wide range of materials. The coating material is one of the key elements in the performance of a refractometer. This review paper intends to provide a global view of the wide range of coating materials available for the development of lossy mode resonance based refractometers.
Learning Hierarchical Representations of Stories by Using Multi-Layered Structures in Narrative Multimedia
Authors: Lee O-Joun , Jung Jason J. , Kim Jin-Taek
Published: April 2020

Narrative works (e.g., novels and movies) consist of various utterances (e.g., scenes and episodes) with multi-layered structures. However, the existing studies aimed to embed only stories in a narrative work. By covering other granularity levels, we can easily compare narrative utterances that are coarser (e.g., movie series) or finer (e.g., scenes) than a narrative work. We apply the multi-layered structures on learning hierarchical representations of the narrative utterances. To represent coarser utterances, we consider adjacency and appearance of finer utterances in the coarser ones. For the movies, we suppose a four-layered structure (character roles ∈ characters ∈ scenes ∈ movies) and propose three learning methods bridging the layers: Char2Vec, Scene2Vec, and Hierarchical Story2Vec. Char2Vec represents a character by using dynamic changes in the character’s roles. To find the character roles, we use substructures of character networks (i.e., dynamic social networks of characters). A scene describes an event. Interactions between characters in the scene are designed to describe the event. Scene2Vec learns representations of a scene from interactions between characters in the scene. A story is a series of events. Meanings of the story are affected by order of the events as well as their content. Hierarchical Story2Vec uses sequential order of scenes to represent stories. The proposed model has been evaluated by estimating the similarity between narrative utterances in real movies.
A Noise Tolerant Spread Spectrum Sound-Based Local Positioning System for Operating a Quadcopter in a Greenhouse
Authors: Huang Zichen , Tsay Lok Wai Jacky , Shiigi Tomoo , Zhao Xunyue , Nakanishi Hiroaki , Suzuki Tetsuhito , Ogawa Yuichi , Kondo Naoshi
Published: April 2020

Quadcopters are beginning to play an important role in precision agriculture. In order to localize and operate the quadcopter automatically in complex agricultural settings, such as a greenhouse, a robust positioning system is needed. In previous research, we developed a spread spectrum sound-based local positioning system (SSSLPS) with a 20 mm accuracy within a 30 × 30 m greenhouse area. In this research, a noise tolerant SSSLPS was developed and evaluated. First, the acoustic noise spectrum emitted by the quadcopter was documented, and then the noise tolerance properties of SSSounds were examined and tested. This was done in a greenhouse with a fixed quadcopter (9.75 N thrust) with the positioning system mounted on it. The recorded quadcopter noise had a broadband noise compared to the SSSound. Taking these SSSound properties into account, the noise tolerance of the SSSLPS was improved, achieving a positioning accuracy of 23.2 mm and 31.6 mm accuracy within 12 × 6 m for both Time-division Multiple Access (TDMA) and Frequency-division Multiple Access (FDMA) modulation. The results demonstrate that the SSSLPS is an accurate, robust positioning system that is noise tolerant and can used for quadcopter operation even within a small greenhouse.
The Effect of a Diverse Dataset for Transfer Learning in Thermal Person Detection
Authors: Huda Noor Ul , Hansen Bolette D. , Gade Rikke , Moeslund Thomas B.
Published: April 2020

Thermal cameras are popular in detection for their precision in surveillance in the dark and for privacy preservation. In the era of data driven problem solving approaches, manually finding and annotating a large amount of data is inefficient in terms of cost and effort. With the introduction of transfer learning, rather than having large datasets, a dataset covering all characteristics and aspects of the target place is more important. In this work, we studied a large thermal dataset recorded for 20 weeks and identified nine phenomena in it. Moreover, we investigated the impact of each phenomenon for model adaptation in transfer learning. Each phenomenon was investigated separately and in combination. the performance was analyzed by computing the F1 score, precision, recall, true negative rate, and false negative rate. Furthermore, to underline our investigation, the trained model with our dataset was further tested on publicly available datasets, and encouraging results were obtained. Finally, our dataset was also made publicly available.
Caching Transient Contents in Vehicular Named Data Networking: A Performance Analysis
Authors: Amadeo Marica , Campolo Claudia , Ruggeri Giuseppe , Lia Gianmarco , Molinaro Antonella
Published: April 2020

Named Data Networking (NDN) is a promising communication paradigm for the challenging vehicular ad hoc environment. In particular, the built-in pervasive caching capability was shown to be essential for effective data delivery in presence of short-lived and intermittent connectivity. Existing studies have however not considered the fact that multiple vehicular contents can be transient, i.e., they expire after a certain time period since they were generated, the so-called FreshnessPeriod in NDN. In this paper, we study the effects of caching transient contents in Vehicular NDN and present a simple yet effective freshness-driven caching decision strategy that vehicles can implement autonomously. Performance evaluation in ndnSIM shows that the FreshnessPeriod is a crucial parameter that deeply influences the cache hit ratio and, consequently, the data dissemination performance.
An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data
Authors: Huang Junsheng , Mao Baohua , Bai Yun , Zhang Tong , Miao Changjun
Published: April 2020

Various traffic-sensing technologies have been employed to facilitate traffic control. Due to certain factors, e.g., malfunctioning devices and artificial mistakes, missing values typically occur in the Intelligent Transportation System (ITS) sensing datasets, resulting in a decrease in the data quality. In this study, an integrated imputation algorithm based on fuzzy C-means (FCM) and the genetic algorithm (GA) is proposed to improve the accuracy of the estimated values. The GA is applied to optimize the parameter of the membership degree and the number of cluster centroids in the FCM model. An experimental test of the taxi global positioning system (GPS) data in Manhattan, New York City, is employed to demonstrate the effectiveness of the integrated imputation approach. Three evaluation criteria, the root mean squared error (RMSE), correlation coefficient (R), and relative accuracy (RA), are used to verify the experimental results. Under the ±5% and ±10% thresholds, the average RAs obtained by the integrated imputation method are 0.576 and 0.785, which remain the highest among different methods, indicating that the integrated imputation method outperforms the history imputation method and the conventional FCM method. On the other hand, the clustering imputation performance with the Euclidean distance is better than that with the Manhattan distance. Thus, our proposed integrated imputation method can be employed to estimate the missing values in the daily traffic management.
Concentric Array of Printed Strain Sensors for Structural Health Monitoring
Authors: Zymelka Daniel , Togashi Kazuyoshi , Kobayashi Takeshi
Published: April 2020

Civil infrastructure is expanding around the world. The ever-growing trend toward urbanization drives the demand for new investments. However, the new constructions and gradual deterioration of those already existing, especially bridges, give rise to concerns about their proper maintenance. To improve safety and drive down maintenance costs of civil structures, there is a need for inexpensive sensing systems capable of reliable and automated monitoring. In this study, we present a new concept of thin-film strain sensors arranged in an array with a concentric layout that is incorporated into a flexible substrate sheet. The designed sensor array is intended to analyze strains in the proximity of round holes made at the crack tips, found in the investigated construction elements of civil structures. In this study, the performance of the sensor array was demonstrated using measurements taken on a highway bridge in one of the largest cities in Japan. We show that it can measure local strain distribution and indicate a region with risk for crack formation. The demonstrated results show new area of potential applications for the printed strain sensors in monitoring civil structures.
An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification
Authors: Yu Donghang , Xu Qing , Guo Haitao , Zhao Chuan , Lin Yuzhun , Li Daoji
Published: April 2020

Classifying remote sensing images is vital for interpreting image content. Presently, remote sensing image scene classification methods using convolutional neural networks have drawbacks, including excessive parameters and heavy calculation costs. More efficient and lightweight CNNs have fewer parameters and calculations, but their classification performance is generally weaker. We propose a more efficient and lightweight convolutional neural network method to improve classification accuracy with a small training dataset. Inspired by fine-grained visual recognition, this study introduces a bilinear convolutional neural network model for scene classification. First, the lightweight convolutional neural network, MobileNetv2, is used to extract deep and abstract image features. Each feature is then transformed into two features with two different convolutional layers. The transformed features are subjected to Hadamard product operation to obtain an enhanced bilinear feature. Finally, the bilinear feature after pooling and normalization is used for classification. Experiments are performed on three widely used datasets: UC Merced, AID, and NWPU-RESISC45. Compared with other state-of-art methods, the proposed method has fewer parameters and calculations, while achieving higher accuracy. By including feature fusion with bilinear pooling, performance and accuracy for remote scene classification can greatly improve. This could be applied to any remote sensing image classification task.
Optical Fiber Vibration Sensor Using Least Mean Square Error Algorithm
Authors: Wang Yu , Zou Jie , Xu Yuelin , Chen Yu , Liu Xin , Bai Qing , Jin Baoquan
Published: April 2020

In order to enhance the signal-to-noise ratio (SNR) of a distributed optical fiber vibration sensor based on coherent optical time domain reflectometry (COTDR), a high extinction ratio cascade structure of an acousto-optic modulator and semiconductor optical amplifier is applied. The prior time-frequency analysis and least mean square error algorithm are adopted in the COTDR system for amplitude demodulation and phase demodulation, in order to improve the SNR by noise elimination. The experimental results show that the adaptive filter based on the least mean square error algorithm could realize the extraction of a three-order sinusoidal harmonic signal from strong background noise along the optical fiber and the SNR improvement from 10.4 dB to 42.2 dB. The proposed demodulation algorithm is suitable for the detection of vibration signals with characteristic frequencies in the application of acoustic fault diagnosis for electromechanical devices.
Doppler-Spectrum Feature-Based Human–Vehicle Classification Scheme Using Machine Learning for an FMCW Radar Sensor
Authors: Hyun Eugin , Jin YoungSeok
Published: April 2020

In this paper, we propose a Doppler-spectrum feature-based human–vehicle classification scheme for an FMCW (frequency-modulated continuous wave) radar sensor. We introduce three novel features referred to as the scattering point count, scattering point difference, and magnitude difference rate features based on the characteristics of the Doppler spectrum in two successive frames. We also use an SVM (support vector machine) and BDT (binary decision tree) for training and validation of the three aforementioned features. We measured the signals using a 24-GHz FMCW radar front-end module and a real-time data acquisition module and extracted three features from a walking human and a moving vehicle in the field. We then repeatedly measured the classification decision rate of the proposed algorithm using the SVM and BDT, finding that the average performance exceeded 99% and 96% for the walking human and the moving vehicle, respectively.