bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation on the Adsorption-Interaction Mechanism of Pb(II) at Surface of Silk Fibroin Protein-Derived Hybrid Nanoflower Adsorbent
Authors: Li Xiang , Xiong Yan , Duan Ming , Wan Haiqin , Li Jun , Zhang Can , Qin Sha , Fang Shenwen , Zhang Run
Published: March 2020

For further the understanding of the adsorption mechanism of heavy metal ions on the surface of protein-inorganic hybrid nanoflowers, a novel protein-derived hybrid nanoflower was prepared to investigate the adsorption behavior and reveal the function of organic and inorganic parts on the surface of nanoflowers in the adsorption process in this study. Silk fibroin (SF)-derived and copper-based protein-inorganic hybrid nanoflowers of SF@Cu-NFs were prepared through self-assembly. The product was characterized and applied to adsorption of heavy metal ion of Pb(II). With Chinese peony flower-like morphology, the prepared SF@Cu-NFs showed ordered three-dimensional structure and exhibited excellent efficiency for Pb(II) removal. On one hand, the adsorption performance of SF@Cu-HNFs for Pb(II) removal was evaluated through systematical thermodynamic and adsorption kinetics investigation. The good fittings of Langmuir and pseudo-second-order models indicated the monolayer adsorption and high capacity of about 2000 mg g−1 of Pb(II) on SF@Cu-NFs. Meanwhile, the negative values of Δ r G m ( T ) θ and Δ r H m θ proved the spontaneous and exothermic process of Pb(II) adsorption. On the other hand, the adsorption mechanism of SF@Cu-HNFs for Pb(II) removal was revealed with respect to its individual organic and inorganic component. Organic SF protein was designated as responsible ‘stamen’ adsorption site for fast adsorption of Pb(II), which was originated from multiple coordinative interaction by numerous amide groups; inorganic Cu3(PO4)2 crystal was designated as responsible ‘petal’ adsorption site for slow adsorption of Pb(II), which was restricted from weak coordinative interaction by strong ion bond of Cu(II). With only about 10% weight content, SF protein was proven to play a key factor for SF@Cu-HNFs formation and have a significant effect on Pb(II) treatment. By fabricating SF@Cu-HNFs hybrid nanoflowers derived from SF protein, this work not only successfully provides insights on its adsorption performance and interaction mechanism for Pb(II) removal, but also provides a new idea for the preparation of adsorption materials for heavy metal ions in environmental sewage in the future.
Gain-Enhanced Metamaterial Absorber-Loaded Monopole Antenna for Reduced Radar Cross-Section and Back Radiation
Authors: Jeong Heijun , Kim Yeonju , Tentzeris Manos M. , Lim Sungjoon
Published: March 2020

This paper proposes a gain-enhanced metamaterial (MM) absorber-loaded monopole antenna that reduces both radar cross-section and back radiation. To demonstrate the proposed idea, we designed a wire monopole antenna and an MM absorber. The MM absorber comprised lumped elements of subwavelength unit cells and achieved 90% absorbance bandwidth from 2.42–2.65 GHz. For low-profile configurations, the MM absorber was loaded parallel to and 10 mm from the monopole antenna, corresponding to 0.09 λ0 at 2.7 GHz. The monopole antenna resonated at 2.7 GHz with a 3.71 dBi peak gain and 2.65 GHz and 6.46 dBi peak gain, before and after loading the MM absorber, respectively. Therefore, including the MM absorber increased peak gain by 2.7 dB and reduced back radiation by 15 dB. The proposed antenna radar cross-section was reduced by 2 dB compared with a monopole antenna with an artificial magnetic conductor.
Study on the Structure-Property Dependences of Rigid PUR-PIR Foams Obtained from Marine Biomass-Based Biopolyol
Authors: Kosmela Paulina , Hejna Aleksander , Suchorzewski Jan , Piszczyk Łukasz , Haponiuk Józef Tadeusz
Published: March 2020

The paper describes the preparation and characterization of rigid polyurethane-polyisocyanurate (PUR-PIR) foams obtained with biopolyol synthesized in the process of liquefaction of biomass from the Baltic Sea. The obtained foams differed in the content of biopolyol in polyol mixture (0–30 wt%) and the isocyanate index (IISO = 200, 250, and 300). The prepared foams were characterized in terms of processing parameters (processing times, synthesis temperature), physical (sol fraction content, apparent density) and chemical structure (Fourier transform infrared spectroscopy), microstructure (computer microtomography), as well as mechanical (compressive strength, dynamic mechanical analysis), and thermal properties (thermogravimetric analysis, thermal conductivity coefficient). The influence of biopolyol and IISO content on the above properties was determined. The addition of up to 30 wt% of biopolyol increased the reactivity of the polyol mixture, and the obtained foams showed enhanced mechanical, thermal, and insulating properties compared to foams prepared solely with petrochemical polyol. The addition of up to 30 wt% of biopolyol did not significantly affect the chemical structure and average cell size. With the increase in IISO, a slight decrease in processing times and mechanical properties was observed. As expected, foams with higher IISO exhibited a higher relative concentration of polyisocyanurate groups in their chemical structure, which was confirmed using principal component analysis (PCA).
Effect of Terbium Ion Substitution in Inverse Spinel Nickel Ferrite: Structural and Magnetic Study
Authors: Guragain Deepa , Rai Binod Kumar , Yoon Sunghyun , Poudel Tej Prasad , Bhandari Subash Chandra , Mishra Sanjay R
Published: March 2020

Doping rare-earth ions into spinel ferrites can alter their electrical and magnetic properties. The present study delineates the structure–property relationship of the effect of rare-earth terbium doping in NiFe2O4 ferrite. X-ray diffraction analysis (XRD) showed unit cell lattice expansion with increased Tb3+ content. The Fourier transform infrared spectroscopy (FTIR) results indicate preferential occupancy of Tb3+ at the octahedral B site. The magnetic parameters derived from room temperature hysteresis loops where both the saturation magnetization, Ms, and coercivity, Hc, value decreased with the Tb3+ substitution and reached a minimum value of Ms ~30.6 emu/g at x = 0.1 and Hc ~102 Oe at x = 0.075. The temperature-dependent magnetocrystalline anisotropy derived from the magnetic isotherm was observed to be the highest for x = 0.1 at 5 K with the value K1 ~1.09 × 106 J/m3. The Tb3+ doping also resulted in the Curie temperature reduction from 938 K at x = 0.0 to 899 K at x = 0.1.
Finite Difference Method for the Multi-Asset Black–Scholes Equations
Authors: Kim Sangkwon , Jeong Darae , Lee Chaeyoung , Kim Junseok
Published: March 2020

In this paper, we briefly review the finite difference method (FDM) for the Black–Scholes (BS) equations for pricing derivative securities and provide the MATLAB codes in the Appendix for the one-, two-, and three-dimensional numerical implementation. The BS equation is discretized non-uniformly in space and implicitly in time. The two- and three-dimensional equations are solved using the operator splitting method. In the numerical tests, we show characteristic examples for option pricing. The computational results are in good agreement with the closed-form solutions to the BS equations.
Laminarin-Derived from Brown Algae Suppresses the Growth of Ovarian Cancer Cells via Mitochondrial Dysfunction and ER Stress
Authors: Bae Hyocheol , Song Gwonhwa , Lee Jin-Young , Hong Taeyeon , Chang Moon-Jeong , Lim Whasun
Published: March 2020

Ovarian cancer (OC) is difficult to diagnose at an early stage and leads to the high mortality rate reported in the United States. Standard treatment for OC includes maximal cytoreductive surgery followed by platinum-based chemotherapy. However, relapse due to chemoresistance is common in advanced OC patients. Therefore, it is necessary to develop new anticancer drugs to suppress OC progression. Recently, the anticancer effects of laminarin, a beta-1,3-glucan derived from brown algae, have been reported in hepatocellular carcinoma, colon cancer, leukemia, and melanoma. However, its effects in OC are not reported. We confirmed that laminarin decreases cell growth and cell cycle progression of OC cells through the regulation of intracellular signaling. Moreover, laminarin induced cell death through DNA fragmentation, reactive oxygen species generation, induction of apoptotic signals and endoplasmic reticulum (ER) stress, regulation of calcium levels, and alteration of the ER-mitochondria axis. Laminarin was not cytotoxic in a zebrafish model, while in a zebrafish xenograft model, it inhibited OC cell growth. These results suggest that laminarin may be successfully used as a novel OC suppressor.
Improvement of Psoriasis by Alteration of the Gut Environment by Oral Administration of Fucoidan from Cladosiphon Okamuranus
Authors: Takahashi Masanobu , Takahashi Kento , Abe Sunao , Yamada Kosuke , Suzuki Manami , Masahisa Mai , Endo Mari , Abe Keiko , Inoue Ryo , Hoshi Hiroko
Published: March 2020

Psoriasis is a chronic autoimmune inflammatory disease for which there is no cure; it results in skin lesions and has a strong negative impact on patients’ quality of life. Fucoidan from Cladosiphon okamuranus is a dietary seaweed fiber with immunostimulatory effects. The present study reports that the administration of fucoidan provided symptomatic relief of facial itching and altered the gut environment in the TNF receptor-associated factor 3-interacting protein 2 (Traf3ip2) mutant mice (m-Traf3ip2 mice); the Traf3ip2 mutation was responsible for psoriasis in the mouse model used in this study. A fucoidan diet ameliorated symptoms of psoriasis and decreased facial scratching. In fecal microbiota analysis, the fucoidan diet drastically altered the presence of major intestinal opportunistic microbiota. At the same time, the fucoidan diet increased mucin volume in ileum and feces, and IgA contents in cecum. These results suggest that dietary fucoidan may play a significant role in the prevention of dysfunctional immune diseases by improving the intestinal environment and increasing the production of substances that protect the immune system.
Hereditary Deficiency of the Second Component of Complement: Early Diagnosis and 21-Year Follow-Up of a Family
Authors: Dellepiane Rosa Maria , Baselli Lucia Augusta , Cazzaniga Marco , Lougaris Vassilios , Macor Paolo , Giordano Mara , Gualtierotti Roberta , Cugno Massimo
Published: March 2020

Complement deficiencies are rare and often underdiagnosed primary immunodeficiencies that may be associated with invasive bacterial diseases. Serious infections with encapsulated organisms (mainly Streptococcus pneumoniae, but also Neisseria meningitides and Haemophilus influenzae type B) are frequent in patients with a deficiency of the second component of complement (C2), but no data are available on long-term follow-up. This study aimed to evaluate the long-term clinical outcome and the importance of an early diagnosis and subsequent infection prophylaxis in C2 deficiency. Here, we report the 21-year follow-up of a whole family which was tested for complement parameters, genetic analysis and biochemical measurements, due to recurrent pneumococcal meningitis in the elder brother. The two sons were diagnosed with homozygous type 1 C2 deficiency, while their parents were heterozygous with normal complement parameters. For the two brothers, a recommended vaccination program and antibiotic prophylaxis were prescribed. During the long-term follow-up, no severe/invasive infections were observed in either patient. At the age of 16, the younger brother developed progressive hypogammaglobulinemia of all three classes, IgA, IgM and IgG. A next generation sequencing panel excluded the presence of gene defects related to primary antibody deficiencies. Our data show that early diagnosis, use of vaccinations and antibiotic prophylaxis may allow a normal life in hereditary C2 deficiency, which can be characterized using functional and genetic methods. Moreover, a periodical check of immunoglobulin serum levels could be useful to detect a possible hypogammaglobulinemia.
Evaluation of 2-Mercaptobenzimidazole Derivatives as Corrosion Inhibitors for Mild Steel in Hydrochloric Acid
Authors: Lgaz Hassane , Masroor Sheerin , Chafiq Maryam , Damej Mohamed , Brahmia Ameni , Salghi Rachid , Benmessaoud Mohammed , Ali Ismat H. , Alghamdi Majed M. , Chaouiki Abdelkarim , Chung Ill-Min
Published: March 2020

This research aimed to develop a better understanding of the corrosion inhibition of the mild steel in acidic medium by new organic molecules. For this purpose, two new compounds namely, 2,3-dihydrobenzo[4,5]imidazo[2,1-b]thiazole (2-BIT) and 3,4-dihydro-2H-benzo[4,5]imidazo[2,1-b]thiazole (3-BIT) were synthesized and evaluated for mild steel (MS) corrosion in HCl. Analyses were carried out using weight loss measurements, electrochemical techniques, and scanning electron microscope (SEM). The adsorption of inhibitors onto the steel surface follows the Langmuir adsorption model. Generally, results showed that the corrosion inhibition efficiency of the investigated molecules was found to increase with increased concentration of inhibitors. Electrochemical tests, i.e., electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization (PDP) techniques, showed that the addition of our investigated inhibitors decreases the dissolution of the metal and generally act as mixed-type inhibitors. In addition, the influence of temperature (from 303 to 333 K) on the corrosion inhibition was studied, and the results demonstrated that with an increase in temperature, the inhibition efficiency decrease. SEM results confirmed that the inhibition process is due to a protective film that prevents corrosion. Similarly, the results showed that the inhibitory efficiencies reach 93% at 5 × 10−3 M in the case of inhibitor 3-BIT. These results revealed that this compound could effectively control and reduce the corrosion rate of mild steel in the corrosion test solution.
Optimization of Industrial Casting Processes
Authors: Cockcroft Steve
Published: March 2020

Casting processes have seen continuous technological development for close to 5000 years, largely through a process of trial-and-error improvement [...]
High-Sensitivity Terahertz Refractive Index Sensor in a Multilayered Structure with Graphene
Authors: Tang Jiao , Ye Yunyang , Xu Jiao , Zheng Zhiwei , Jin Xiangliang , Jiang Leyong , Jiang Jie , Xiang Yuanjiang
Published: March 2020

In this paper, we propose a high-sensitivity optical sensor at terahertz frequencies based on a composite structure containing a one-dimensional photonic crystal (1D PC) coated with a layer of monolayer graphene. Between the 1D PC and the graphene there is a sensing medium. This high-sensitivity phenomenon originates from the excitation of optical resonance between the graphene and the 1D PC. The proposed sensor is highly sensitive to the Fermi energy of graphene, the thickness and refractive index of the sensing medium, and the number of graphene layers. By selecting appropriate parameters, the maximum sensitivity ( 407.36 ∘ / RIU ) is obtained. We believe the proposed configuration is promising for fabricating graphene-based biosensor- or gas-sensor devices and other related applications in the terahertz band.
University Students’ Preferences for Labour Conditions at a Mining Site: Evidence from Two Australian Universities
Authors: Ito Yutaka , Mikami Shuto , Jang Hyongdoo , Taheri Abbas , Tanaka Kenta , Kawamura Youhei
Published: March 2020

The mining industry makes up a large portion of the gross domestic product (GDP) in Australia, although securing human resources remains a problem in that field. The aim of this paper is to identify Australian university mining students’ preferences, considering it as potential employees’ preferences, for labour conditions at mining sites by means of a discrete choice experiment to promote efficient improvements in labour conditions in the mining industry. The data of 93 respondents analysed in this paper was collected by survey carried out in two universities in Australia. The result of the study showed that students have preferences on several factors such as wage, fatality rate, working position, commuting style, and company. Students having specific sociodemographic characters were found to show specific preferences on labour conditions. The results of this study indicate the potential average of appropriate monetary compensation for each factor.
Advances in Mechanical Systems Dynamics
Authors: Rosati Giulio , Boschetti Giovanni , Carbone Giuseppe
Published: March 2020

Nowadays, robotics is developing at a much faster pace than ever in the past, both inside and outside industrial environments [...]
Multi-scale Adaptive Feature Fusion Network for Semantic Segmentation in Remote Sensing Images
Authors: Shang Ronghua , Zhang Jiyu , Jiao Licheng , Li Yangyang , Marturi Naresh , Stolkin Rustam
Published: March 2020

Semantic segmentation of high-resolution remote sensing images is highly challenging due to the presence of a complicated background, irregular target shapes, and similarities in the appearance of multiple target categories. Most of the existing segmentation methods that rely only on simple fusion of the extracted multi-scale features often fail to provide satisfactory results when there is a large difference in the target sizes. Handling this problem through multi-scale context extraction and efficient fusion of multi-scale features, in this paper we present an end-to-end multi-scale adaptive feature fusion network (MANet) for semantic segmentation in remote sensing images. It is a coding and decoding structure that includes a multi-scale context extraction module (MCM) and an adaptive fusion module (AFM). The MCM employs two layers of atrous convolutions with different dilatation rates and global average pooling to extract context information at multiple scales in parallel. MANet embeds the channel attention mechanism to fuse semantic features. The high- and low-level semantic information are concatenated to generate global features via global average pooling. These global features are used as channel weights to acquire adaptive weight information of each channel by the fully connected layer. To accomplish an efficient fusion, these tuned weights are applied to the fused features. Performance of the proposed method has been evaluated by comparing it with six other state-of-the-art networks: fully convolutional networks (FCN), U-net, UZ1, Light-weight RefineNet, DeepLabv3+, and APPD. Experiments performed using the publicly available Potsdam and Vaihingen datasets show that the proposed MANet significantly outperforms the other existing networks, with overall accuracy reaching 89.4% and 88.2%, respectively and with average of F1 reaching 90.4% and 86.7% respectively.
Morphometric Analysis for Soil Erosion Susceptibility Mapping Using Novel GIS-Based Ensemble Model
Authors: Arabameri Alireza , Tiefenbacher John P. , Blaschke Thomas , Pradhan Biswajeet , Tien Bui Dieu
Published: March 2020

The morphometric characteristics of the Kalvārī basin were analyzed to prioritize sub-basins based on their susceptibility to erosion by water using a remote sensing-based data and a GIS. The morphometric parameters (MPs)—linear, relief, and shape—of the drainage network were calculated using data from the Advanced Land-observing Satellite (ALOS) phased-array L-type synthetic-aperture radar (PALSAR) digital elevation model (DEM) with a spatial resolution of 12.5 m. Interferometric synthetic aperture radar (InSAR) was used to generate the DEM. These parameters revealed the network’s texture, morpho-tectonics, geometry, and relief characteristics. A complex proportional assessment of alternatives (COPRAS)-analytical hierarchy process (AHP) novel-ensemble multiple-criteria decision-making (MCDM) model was used to rank sub-basins and to identify the major MPs that significantly influence erosion landforms of the Kalvārī drainage basin. The results show that in evolutionary terms this is a youthful landscape. Rejuvenation has influenced the erosional development of the basin, but lithology and relief, structure, and tectonics have determined the drainage patterns of the catchment. Results of the AHP model indicate that slope and drainage density influence erosion in the study area. The COPRAS-AHP ensemble model results reveal that sub-basin 1 is the most susceptible to soil erosion (SE) and that sub-basin 5 is least susceptible. The ensemble model was compared to the two individual models using the Spearman correlation coefficient test (SCCT) and the Kendall Tau correlation coefficient test (KTCCT). To evaluate the prediction accuracy of the ensemble model, its results were compared to results generated by the modified Pacific Southwest Inter-Agency Committee (MPSIAC) model in each sub-basin. Based on SCCT and KTCCT, the ensemble model was better at ranking sub-basins than the MPSIAC model, which indicated that sub-basins 1 and 4, with mean sediment yields of 943.7 and 456.3 m 3 km − 2   year − 1 , respectively, have the highest and lowest SE susceptibility in the study area. The sensitivity analysis revealed that the most sensitive parameters of the MPSIAC model are slope (R2 = 0.96), followed by runoff (R2 = 0.95). The MPSIAC shows that the ensemble model has a high prediction accuracy. The method tested here has been shown to be an effective tool to improve sustainable soil management.
Fusing China GF-5 Hyperspectral Data with GF-1, GF-2 and Sentinel-2A Multispectral Data: Which Methods Should Be Used?
Authors: Ren Kai , Sun Weiwei , Meng Xiangchao , Yang Gang , Du Qian
Published: March 2020

The China GaoFen-5 (GF-5) satellite sensor, which was launched in 2018, collects hyperspectral data with 330 spectral bands, a 30 m spatial resolution, and 60 km swath width. Its competitive advantages compared to other on-orbit or planned sensors are its number of bands, spectral resolution, and swath width. Unfortunately, its applications may be undermined by its relatively low spatial resolution. Therefore, the data fusion of GF-5 with high spatial resolution multispectral data is required to further enhance its spatial resolution while preserving its spectral fidelity. This paper conducted a comprehensive evaluation study of fusing GF-5 hyperspectral data with three typical multispectral data sources (i.e., GF-1, GF-2 and Sentinel-2A (S2A)), based on quantitative metrics, classification accuracy, and computational efficiency. Datasets on three study areas of China were utilized to design numerous experiments, and the performances of nine state-of-the-art fusion methods were compared. Experimental results show that LANARAS (this method was proposed by lanaras et al.), Adaptive Gram–Schmidt (GSA), and modulation transfer function (MTF)-generalized Laplacian pyramid (GLP) methods are more suitable for fusing GF-5 with GF-1 data, MTF-GLP and GSA methods are recommended for fusing GF-5 with GF-2 data, and GSA and smoothing filtered-based intensity modulation (SFIM) can be used to fuse GF-5 with S2A data.
Can Landsat-Derived Variables Related to Energy Balance Improve Understanding of Burn Severity From Current Operational Techniques?
Authors: Fernández-Manso Alfonso , Quintano Carmen , Roberts Dar A.
Published: March 2020

Forest managers rely on accurate burn severity estimates to evaluate post-fire damage and to establish revegetation policies. Burn severity estimates based on reflective data acquired from sensors onboard satellites are increasingly complementing field-based ones. However, fire not only induces changes in reflected and emitted radiation measured by the sensor, but also on energy balance. Evapotranspiration (ET), land surface temperature (LST) and land surface albedo (LSA) are greatly affected by wildfires. In this study, we examine the usefulness of these elements of energy balance as indicators of burn severity and compare the accuracy of burn severity estimates based on them to the accuracy of widely used approaches based on spectral indexes. We studied a mega-fire (more than 450 km2 burned) in Central Portugal, which occurred from 17 to 24 June 2017. The official burn severity map acted as a ground reference. Variations induced by fire during the first year following the fire event were evaluated through changes in ET, LST and LSA derived from Landsat data and related to burn severity. Fisher’s least significant difference test (ANOVA) revealed that ET and LST images could discriminate three burn severity levels with statistical significance (uni-temporal and multi-temporal approaches). Burn severity was estimated from ET, LST and LSA using thresholding. Accuracy of ET and LST based on burn severity estimates was adequate (κ = 0.63 and 0.57, respectively), similar to the accuracy of the estimate based on dNBR (κ = 0.66). We conclude that Landsat-derived surface energy balance variables, in particular ET and LST, in addition to acting as useful indicators of burn severity for mega-fires in Mediterranean ecosystems, may provide critical information about how energy balance changes due to fire.
Research on Post-Earthquake Landslide Extraction Algorithm Based on Improved U-Net Model
Authors: Liu Peng , Wei Yongming , Wang Qinjun , Chen Yu , Xie Jingjing
Published: March 2020

Seismic landslides are the most common and highly destructive earthquake-triggered geological hazards. They are large in scale and occur simultaneously in many places. Therefore, obtaining landslide information quickly after an earthquake is the key to disaster mitigation and relief. The survey results show that most of the landslide-information extraction methods involve too much manual participation, resulting in a low degree of automation and the inability to provide effective information for earthquake rescue in time. In order to solve the abovementioned problems and improve the efficiency of landslide identification, this paper proposes an automatic landslide identification method named improved U-Net model. The intelligent extraction of post-earthquake landslide information is realized through the automatic extraction of hierarchical features. The main innovations of this paper include the following: (1) On the basis of the three RGB bands, three new bands, DSM, slope, and aspect, with spatial information are added, and the number of feature parameters of the training samples is increased. (2) The U-Net model structure is rebuilt by adding residual learning units during the up-sampling and down-sampling processes, to solve the problem that the traditional U-Net model cannot fully extract the characteristics of the six-channel landslide for its shallow structure. At the end of the paper, the new method is used in Jiuzhaigou County, Sichuan Province, China. The results show that the accuracy of the new method is 91.3%, which is 13.8% higher than the traditional U-Net model. It is proved that the new method is effective and feasible for the automatic extraction of post-earthquake landslides.
Estimation of Hourly Rainfall during Typhoons Using Radar Mosaic-Based Convolutional Neural Networks
Authors: Wei Chih-Chiang , Hsieh Po-Yu
Published: March 2020

Taiwan is located at the junction of the tropical and subtropical climate zones adjacent to the Eurasian continent and Pacific Ocean. The island frequently experiences typhoons that engender severe natural disasters and damage. Therefore, efficiently estimating typhoon rainfall in Taiwan is essential. This study examined the efficacy of typhoon rainfall estimation. Radar images released by the Central Weather Bureau were used to estimate instantaneous rainfall. Additionally, two proposed neural network-based architectures, namely a radar mosaic-based convolutional neural network (RMCNN) and a radar mosaic-based multilayer perceptron (RMMLP), were used to estimate typhoon rainfall, and the commonly applied Marshall–Palmer Z-R relationship (Z-R_MP) and a reformulated Z-R relationship at each site (Z-R_station) were adopted to construct benchmark models. Monitoring stations in Hualien, Sun Moon Lake, and Taichung were selected as the experimental stations in Eastern, Central, and Western Taiwan, respectively. This study compared the performance of the models in predicting rainfall at the three stations, and the results are outlined as follows: at the Hualien station, the estimations of the RMCNN, RMMLP, Z-R_MP, and Z-R_station models were mostly identical to the observed rainfall, and all models estimated an increase during peak rainfall on the hyetographs, but the peak values were underestimated. At the Sun Moon Lake and Taichung stations, however, the estimations of the four models were considerably inconsistent in terms of overall rainfall rates, peak rainfall, and peak rainfall arrival times on the hyetographs. The relative root mean squared error for overall rainfall rates of all stations was smallest when computed using RMCNN (0.713), followed by those computed using RMMLP (0.848), Z-R_MP (1.030), and Z-R_station (1.392). Moreover, RMCNN yielded the smallest relative error for peak rainfall (0.316), followed by RMMLP (0.379), Z-R_MP (0.402), and Z-R_station (0.688). RMCNN computed the smallest relative error for the peak rainfall arrival time (1.507 h), followed by RMMLP (2.673 h), Z-R_MP (2.917 h), and Z-R_station (3.250 h). The results revealed that the RMCNN model in combination with radar images could efficiently estimate typhoon rainfall.
Effectiveness of Innovate Educational Practices with Flipped Learning and Remote Sensing in Earth and Environmental Sciences—An Exploratory Case Study
Authors: López Núñez Juan Antonio , López Belmonte Jesús , Moreno Guerrero Antonio José , Pozo Sánchez Santiago
Published: March 2020

The rapid advancements in the technological field, especially in the field of education, have led to the incorporation of remote sensing in learning spaces. This innovation requires active and effective teaching methods, among which is flipped learning. The objective of this research was to analyze the effectiveness of flipped learning on the traditional-expository methodology in the second year of high school. The research is part of a quantitative methodology based on a quasi-experimental design of descriptive and correlational type. Data collection was carried out through an ad hoc questionnaire applied in a sample of 59 students. The Student’s t-test was applied for independent samples, differentiating the means given between the experimental group and the control group. The results show that there was a better assessment of the teaching method through flipped learning than the traditional teaching method in all the variables analyzed, except in the academic results, where the difference was minimal. It is concluded that flipped learning provides improvements in instructional processes in high school students who have used remote sensing in training practices. Therefore, the combination of flipped learning and remote sensing is considered effective for the work of contents related to environmental sciences in said educational level.
An Adaptive Face Tracker with Application in Yawning Detection
Authors: Khurshid Aasim , Scharcanski Jacob
Published: March 2020

In this work, we propose an adaptive face tracking scheme that compensates for possible face tracking errors during its operation. The proposed scheme is equipped with a tracking divergence estimate, which allows to detect early and minimize the face tracking errors, so the tracked face is not missed indefinitely. When the estimated face tracking error increases, a resyncing mechanism based on Constrained Local Models (CLM) is activated to reduce the tracking errors by re-estimating the tracked facial features’ locations (e.g., facial landmarks). To improve the Constrained Local Model (CLM) feature search mechanism, a Weighted-CLM (W-CLM) is proposed and used in resyncing. The performance of the proposed face tracking method is evaluated in the challenging context of driver monitoring using yawning detection and talking video datasets. Furthermore, an improvement in a yawning detection scheme is proposed. Experiments suggest that our proposed face tracking scheme can obtain a better performance than comparable state-of-the-art face tracking methods and can be successfully applied in yawning detection.
CAPTCHA Image Generation: Two-Step Style-Transfer Learning in Deep Neural Networks
Authors: Kwon Hyun , Yoon Hyunsoo , Park Ki-Woong
Published: March 2020

Mobile devices such as sensors are used to connect to the Internet and provide services to users. Web services are vulnerable to automated attacks, which can restrict mobile devices from accessing websites. To prevent such automated attacks, CAPTCHAs are widely used as a security solution. However, when a high level of distortion has been applied to a CAPTCHA to make it resistant to automated attacks, the CAPTCHA becomes difficult for a human to recognize. In this work, we propose a method for generating a CAPTCHA image that will resist recognition by machines while maintaining its recognizability to humans. The method utilizes the style transfer method, and creates a new image, called a style-plugged-CAPTCHA image, by incorporating the styles of other images while keeping the content of the original CAPTCHA. In our experiment, we used the TensorFlow machine learning library and six CAPTCHA datasets in use on actual websites. The experimental results show that the proposed scheme reduces the rate of recognition by the DeCAPTCHA system to 3.5% and 3.2% using one style image and two style images, respectively, while maintaining recognizability by humans.
Optimal Design of Water Quality Monitoring Networks in Semi-Enclosed Estuaries
Authors: Kim Nam-Hoon , Hwang Jin Hwan
Published: March 2020

The semi-enclosed estuary is very susceptible to changes in the physical and environmental characteristics of the inflow from the land. Therefore, continuous and comprehensive monitoring of such changes is necessary for managing the estuary. Nevertheless, the procedure or framework has not been proposed appropriately to determine how many instruments are necessary and where they need to be monitored and standardized to detect critical changes. The present work proposes a systematical strategy for the deployments of the monitoring array by using the combination of graphical optimization with the objective mapping technique. In order to reflect the spatiotemporal characteristics of the bay, the representative variables and eigenvectors were determined by the Empirical Orthogonal Function (EOF), and the cosine angle among them calculated and used as a design index of optimization. At the recommended locations, the sampled representative variables were interpolated to reconstruct their spatiotemporal distribution and compared with the true distribution. The analysis confirmed that the selected locations, even with a minimal number of points, can be used for on-site monitoring. In addition, the present framework suggests how to determine installable regions for real-time monitoring stations, which reflect the global and local characteristics of the semi-enclosed estuary.
Towards IoT-Aided Human–Robot Interaction Using NEP and ROS: A Platform-Independent, Accessible and Distributed Approach
Authors: Coronado Enrique , Venture Gentiane
Published: March 2020

This article presents the novel Python, C# and JavaScript libraries of Node Primitives (NEP), a high-level, open, distributed, and component-based framework designed to enable easy development of cross-platform software architectures. NEP is built on top of low-level, high-performance and robust sockets libraries (ZeroMQ and Nanomsg) and robot middlewares (ROS 1 and ROS 2). This enables platform-independent development of Human–Robot Interaction (HRI) software architectures. We show minimal code examples for enabling Publish/Subscribe communication between Internet of Things (IoT) and Robotics modules. Two user cases performed outside laboratories are briefly described in order to prove the technological feasibility of NEP for developing real-world applications. The first user case briefly shows the potential of using NEP for enabling the creation of End-User Development (EUD) interfaces for IoT-aided Human–Robot Interaction. The second user case briefly describes a software architecture integrating state-of-art sensory devices, deep learning perceptual modules, and a ROS -based humanoid robot to enable IoT-aided HRI in a public space. Finally, a comparative study showed better latency results of NEP over a popular state-of-art tool (ROS using rosbridge) for connecting different nodes executed in local-host and local area network (LAN).
Experimental Investigation of the Dielectric Constants of Thin Noble Metallic Films Using a Surface Plasmon Resonance Sensor
Authors: Tao Longbiao , Deng Shuo , Gao Hongyun , Lv Haifei , Wen Xiaoyan , Li Min
Published: March 2020

Gold and silver have an extremely low refractive index value of about 0.04 in the visible to near infrared (NIR) regions, and this induces a relative error of about 50% in refractive index measurements. This can lead to a large uncertainty in the imaginary part of the dielectric constants. A large difference exists between the experimental results and the classic models. The surface plasmon resonance (SPR) sensors, which use tens of nanometer thick noble metal film as the sensing layer, show ultra-high sensitivity (reaching 10−8 RIU) in this spectral range. As the spectral sensitivity and amplitude of SPR curves depend on the thickness and the dielectric constant of the sensing layer, we obtained high precision optical constants of the noble metal film using a multi-wavelength angle-modulated SPR sensing technology. The dielectric constant inferred from the parameters of the SPR curves, rather than from the refractive index and absorption ratio of noble metals, introduced a relative error within 10% of the resonance angle measurement. The measurement results demonstrate that the dielectric constants of gold and silver nano-films are more consistent with the widely used experimental results than with the classical theoretical model and always fall in the upper half of the imaginary part of the uncertainty range in the spectra of 500–900 nm.
Electronic Skin Wearable Sensors for Detecting Lumbar–Pelvic Movements
Authors: Zhang Yuxin , Haghighi Pari Delir , Burstein Frada , Yap Lim Wei , Cheng Wenlong , Yao Lina , Cicuttini Flavia
Published: March 2020

Background: A nanomaterial-based electronic-skin (E-Skin) wearable sensor has been successfully used for detecting and measuring body movements such as finger movement and foot pressure. The ultrathin and highly sensitive characteristics of E-Skin sensor make it a suitable alternative for continuously out-of-hospital lumbar–pelvic movement (LPM) monitoring. Monitoring these movements can help medical experts better understand individuals’ low back pain experience. However, there is a lack of prior studies in this research area. Therefore, this paper explores the potential of E-Skin sensors to detect and measure the anatomical angles of lumbar–pelvic movements by building a linear relationship model to compare its performance to clinically validated inertial measurement unit (IMU)-based sensing system (ViMove). Methods: The paper first presents a review and classification of existing wireless sensing technologies for monitoring of body movements, and then it describes a series of experiments performed with E-Skin sensors for detecting five standard LPMs including flexion, extension, pelvic tilt, lateral flexion, and rotation, and measure their anatomical angles. The outputs of both E-Skin and ViMove sensors were recorded during each experiment and further analysed to build the comparative models to evaluate the performance of detecting and measuring LPMs. Results: E-Skin sensor outputs showed a persistently repeating pattern for each movement. Due to the ability to sense minor skin deformation by E-skin sensor, its reaction time in detecting lumbar–pelvic movement is quicker than ViMove by ~1 s. Conclusions: E-Skin sensors offer new capabilities for detecting and measuring lumbar–pelvic movements. They have lower cost compared to commercially available IMU-based systems and their non-invasive highly stretchable characteristic makes them more comfortable for long-term use. These features make them a suitable sensing technology for developing continuous, out-of-hospital real-time monitoring and management systems for individuals with low back pain.
Fast Approximations of Activation Functions in Deep Neural Networks when using Posit Arithmetic
Authors: Cococcioni Marco , Rossi Federico , Ruffaldi Emanuele , Saponara Sergio
Published: March 2020

With increasing real-time constraints being put on the use of Deep Neural Networks (DNNs) by real-time scenarios, there is the need to review information representation. A very challenging path is to employ an encoding that allows a fast processing and hardware-friendly representation of information. Among the proposed alternatives to the IEEE 754 standard regarding floating point representation of real numbers, the recently introduced Posit format has been theoretically proven to be really promising in satisfying the mentioned requirements. However, with the absence of proper hardware support for this novel type, this evaluation can be conducted only through a software emulation. While waiting for the widespread availability of the Posit Processing Units (the equivalent of the Floating Point Unit (FPU)), we can already exploit the Posit representation and the currently available Arithmetic-Logic Unit (ALU) to speed up DNNs by manipulating the low-level bit string representations of Posits. As a first step, in this paper, we present new arithmetic properties of the Posit number system with a focus on the configuration with 0 exponent bits. In particular, we propose a new class of Posit operators called L1 operators, which consists of fast and approximated versions of existing arithmetic operations or functions (e.g., hyperbolic tangent (TANH) and extended linear unit (ELU)) only using integer arithmetic. These operators introduce very interesting properties and results: (i) faster evaluation than the exact counterpart with a negligible accuracy degradation; (ii) an efficient ALU emulation of a number of Posits operations; and (iii) the possibility to vectorize operations in Posits, using existing ALU vectorized operations (such as the scalable vector extension of ARM CPUs or advanced vector extensions on Intel CPUs). As a second step, we test the proposed activation function on Posit-based DNNs, showing how 16-bit down to 10-bit Posits represent an exact replacement for 32-bit floats while 8-bit Posits could be an interesting alternative to 32-bit floats since their performances are a bit lower but their high speed and low storage properties are very appealing (leading to a lower bandwidth demand and more cache-friendly code). Finally, we point out how small Posits (i.e., up to 14 bits long) are very interesting while PPUs become widespread, since Posit operations can be tabulated in a very efficient way (see details in the text).
Liver Tumor Segmentation in CT Scans Using Modified SegNet
Authors: Almotairi Sultan , Kareem Ghada , Aouf Mohamed , Almutairi Badr , Salem Mohammed A.-M.
Published: March 2020

The main cause of death related to cancer worldwide is from hepatic cancer. Detection of hepatic cancer early using computed tomography (CT) could prevent millions of patients’ death every year. However, reading hundreds or even tens of those CT scans is an enormous burden for radiologists. Therefore, there is an immediate need is to read, detect, and evaluate CT scans automatically, quickly, and accurately. However, liver segmentation and extraction from the CT scans is a bottleneck for any system, and is still a challenging problem. In this work, a deep learning-based technique that was proposed for semantic pixel-wise classification of road scenes is adopted and modified to fit liver CT segmentation and classification. The architecture of the deep convolutional encoder–decoder is named SegNet, and consists of a hierarchical correspondence of encode–decoder layers. The proposed architecture was tested on a standard dataset for liver CT scans and achieved tumor accuracy of up to 99.9% in the training phase.
An Automatic Bearing Fault Diagnosis Method Based on Characteristics Frequency Ratio
Authors: Wu Dengyun , Wang Jianwen , Wang Hong , Liu Hongxing , Lai Lin , He Tian , Xie Tao
Published: March 2020

Bearing is a key component of satellite inertia actuators such as moment wheel assemblies (MWAs) and control moment gyros (CMGs), and its operating state is directly related to the performance and service life of satellites. However, because of the complexity of the vibration frequency components of satellite bearing assemblies and the small loading, normal running bearings normally present similar fault characteristics in long-term ground life experiments, which makes it difficult to judge the bearing fault status. This paper proposes an automatic fault diagnosis method for bearings based on a presented indicator called the characteristic frequency ratio. First, the vibration signals of various MWAs were picked up by the bearing vibration test. Then, the improved ensemble empirical mode decomposition (EEMD) method was introduced to demodulate the envelope of the bearing signals, and the fault characteristic frequencies of the vibration signals were acquired. Based on this, the characteristic frequency ratio for fault identification was defined, and a method for determining the threshold of fault judgment was further proposed. Finally, an automatic diagnosis process was proposed and verified by using different bearing fault data. The results show that the presented method is feasible and effective for automatic monitoring and diagnosis of bearing faults.
Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review
Authors: Zhang Qian , Liu Yeqi , Gong Chuanyang , Chen Yingyi , Yu Huihui
Published: March 2020

Deep Learning (DL) is the state-of-the-art machine learning technology, which shows superior performance in computer vision, bioinformatics, natural language processing, and other areas. Especially as a modern image processing technology, DL has been successfully applied in various tasks, such as object detection, semantic segmentation, and scene analysis. However, with the increase of dense scenes in reality, due to severe occlusions, and small size of objects, the analysis of dense scenes becomes particularly challenging. To overcome these problems, DL recently has been increasingly applied to dense scenes and has begun to be used in dense agricultural scenes. The purpose of this review is to explore the applications of DL for dense scenes analysis in agriculture. In order to better elaborate the topic, we first describe the types of dense scenes in agriculture, as well as the challenges. Next, we introduce various popular deep neural networks used in these dense scenes. Then, the applications of these structures in various agricultural tasks are comprehensively introduced in this review, including recognition and classification, detection, counting and yield estimation. Finally, the surveyed DL applications, limitations and the future work for analysis of dense images in agriculture are summarized.
Optimization of a Single Tube Practical Acoustic Thermometer
Authors: Tavčar Rok , Drnovšek Janko , Bojkovski Jovan , Beguš Samo
Published: March 2020

When designing a single tube practical acoustic thermometer (PAT), certain considerations should be addressed for optimal performance. This paper is concerned with the main issues involved in building a reliable PAT. It has to be emphasised that a PAT measures the ratio of the time delay between the single temperature calibration point (ice point) and any other temperature. Here, we present different models of the speed of sound in tubes, including the effects of real gases and an error analysis of the most accurate model with a Monte Carlo simulation. Additionally, we introduce the problem of acoustic signal overlap and some possible solutions, one of which is acoustic signal cancellation, which aims to eliminate the unwanted parts of an acoustic signal, and another is to optimize the tube length for the parameters of the gas used and specific temperature range.