bluuebunny/crossref_metadata_2025 huggingface dataset:
Expanding the CRISPR Toolbox in P. patens Using SpCas9-NG Variant and Application for Gene and Base Editing in Solanaceae Crops
Authors: Veillet Florian , Perrot Laura , Guyon-Debast Anouchka , Kermarrec Marie-Paule , Chauvin Laura , Chauvin Jean-Eric , Gallois Jean-Luc , Mazier Marianne , Nogué Fabien
Published: February 2020

Genome editing has become a major tool for both functional studies and plant breeding in several species. Besides generating knockouts through the classical CRISPR-Cas9 system, recent development of CRISPR base editing holds great and exciting opportunities for the production of gain-of-function mutants. The PAM requirement is a strong limitation for CRISPR technologies such as base editing, because the base substitution mainly occurs in a small edition window. As precise single amino-acid substitution can be responsible for functions associated to some domains or agronomic traits, development of Cas9 variants with relaxed PAM recognition is of upmost importance for gene function analysis and plant breeding. Recently, the SpCas9-NG variant that recognizes the NGN PAM has been successfully tested in plants, mainly in monocotyledon species. In this work, we studied the efficiency of SpCas9-NG in the model moss Physcomitrella patens and two Solanaceae crops (Solanum lycopersicum and Solanum tuberosum) for both classical CRISPR-generated gene knock-out and cytosine base editing. We showed that the SpCas9-NG greatly expands the scope of genome editing by allowing the targeting of non-canonical NGT and NGA PAMs. The CRISPR toolbox developed in our study opens up new gene function analysis and plant breeding perspectives for model and crop plants.
How p53 Molecules Solve the Target DNA Search Problem: A Review
Authors: Kamagata Kiyoto , Itoh Yuji , Subekti Dwiky Rendra Graha
Published: February 2020

Interactions between DNA and DNA-binding proteins play an important role in many essential cellular processes. A key function of the DNA-binding protein p53 is to search for and bind to target sites incorporated in genomic DNA, which triggers transcriptional regulation. How do p53 molecules achieve “rapid” and “accurate” target search in living cells? The search dynamics of p53 were expected to include 3D diffusion in solution, 1D diffusion along DNA, and intersegmental transfer between two different DNA strands. Single-molecule fluorescence microscopy enabled the tracking of p53 molecules on DNA and the characterization of these dynamics quantitatively. Recent intensive single-molecule studies of p53 succeeded in revealing each of these search dynamics. Here, we review these studies and discuss the target search mechanisms of p53.
Vehicle Location Prediction Based on Spatiotemporal Feature Transformation and Hybrid LSTM Neural Network
Authors: Xiao Yuelei , Nian Qing
Published: February 2020

Location prediction has attracted much attention due to its important role in many location-based services. The existing location prediction methods have large trajectory information loss and low prediction accuracy. Hence, they are unsuitable for vehicle location prediction of the intelligent transportation system, which needs small trajectory information loss and high prediction accuracy. To solve the problem, a vehicle location prediction algorithm was proposed in this paper, which is based on a spatiotemporal feature transformation method and a hybrid long short-term memory (LSTM) neural network model. In the algorithm, the transformation method is used to convert a vehicle trajectory into an appropriate input of the neural network model, and then the vehicle location at the next time is predicted by the neural network model. The experimental results show that the trajectory information of an original taxi trajectory is basically reserved by its shadowed taxi trajectory, and the trajectory points of the predicted taxi trajectory are close to those of the shadowed taxi trajectory. It proves that our proposed algorithm effectively reduces the information loss of vehicle trajectory and improves the accuracy of vehicle location prediction. Furthermore, the experimental results also show that the algorithm has a higher distance percentage and a shorter average distance than the other predication models. Therefore, our proposed algorithm is better than the other prediction models in the accuracy of vehicle location predication.
Dynamic Performances of Foil Bearing Supporting a Jeffcot Flexible Rotor System Using FEM
Authors: Bou-Saïd Benyebka , Lahmar Mustapha , Mouassa Ahcène , Bouchehit Bachir
Published: February 2020

Aerodynamic bearings have received considerable attention in recent decades and are increasingly being used in applications where high speed, low loads and high precision are required. Aerodynamic applications mainly concern auxiliary power units (APU) and air-conditioning machines (ACM). From the industrial point of view, the static and dynamic characteristics of these bearings rotating at very high speed must be determined. According to the literature, studies carried out on this type of bearing consider the elastic deformations of the foils due to the pressure generated in the air film. The linear approach is from time to time adopted for the prediction of the dynamic behavior of these bearings, which is not always justified. This paper aims to present a step towards a better mastery of the non-linear dynamic behavior of a flexible rotor-air bearing system. We will focus on finite element modeling (FEM) of the non-linear isothermal elasto-aerodynamic lubrication problem in the case of a radial bearing operating in a dynamic regime. We will present the effects of rotational speed, unbalance eccentricity, and rotor mass on the non-linear response of rigid and compliant bearings. We use a partitioned approach which treats fluid and structure as two computation domains solved separately; reducing the development time needed for a monolithic code which is difficult to manage when the geometries or the physical properties of the problem to be treated become complex.
Zirconia Reduced Graphene Oxide Nano-Hybrid Structure Fabricated by the Hydrothermal Reaction Method
Authors: Smirnov Anton , Solís Pinargote Nestor Washington , Peretyagin Nikita , Pristinskiy Yuri , Peretyagin Pavel , Bartolomé José F.
Published: February 2020

In this work, we report an available technique for the effective reduction of graphene oxide (GO) and the fabrication of nanostructured zirconia reduced graphene oxide powder via a hydrothermal method. Characterization of the obtained nano-hybrid structure materials was carried out using a scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS) and Fourier-transform infrared spectroscopy (FTIR). The confirmation that GO was reduced and the uniform distribution of zirconia nanoparticles on graphene oxide sheets during synthesis was obtained due to these techniques. This has presented new opportunities and prospects to use this uncomplicated and inexpensive technique for the development of zirconia/graphene nanocomposite powders.
Development of Smart Textile Materials with Shape Memory Alloys for Application in Protective Clothing
Authors: Bartkowiak Grażyna , Dąbrowska Anna , Greszta Agnieszka
Published: February 2020

The latest directions of research on the design of protective clothing concern the implementation of smart materials, in order to increase its protective performance. This paper presents results on the resistance to thermal factors such as flames, radiant heat, and molten metals, which were obtained for the developed smart textile material with shape memory alloys (SMAs). The laboratory tests performed indicated that the application of the designed SMA elements in the selected textile material system caused more than a twofold increase in the resistance to radiant heat (RHTI24 = 224 s) with an increase of thickness of 13 mm (sample located vertically with a load), while in the case of tests on the resistance to flames, it was equal to 41 mm (sample located vertically without a load) and in the case of tests on the resistance to molten metal, it was 17 mm (sample located horizontally).
Hesitant Fuzzy Topological Spaces
Authors: Lee Jeong-Gon , Hur Kul
Published: February 2020

In this study, we define a hesitant fuzzy topology and base, obtain some of their properties, respectively, and give some examples. Next, we introduce the concepts of a hesitant fuzzy neighborhood, Q-neighborhood, closure, and interior and obtain some of their properties, respectively. Furthermore, we define a hesitant fuzzy continuous mapping and investigate some of its properties. Furthermore, we define a hesitant fuzzy subspace and obtain some of its properties. In particular, we obtain the Pasting lemma. We investigate the concept of hesitant fuzzy product space and study some of its properties.
Truth-Teller–Liar Puzzles with Self-Reference
Authors: Alzboon Laith , Nagy Benedek
Published: February 2020

In this paper, we use commonsense reasoning and graph representation to study logical puzzles with three types of people. Strong Truth-Tellers say only true atomic statements, Strong Liars say only false atomic statements, and Strong Crazy people say only self-contradicting statements. Self-contradicting statements are connected to the Liar paradox, i.e., no Truth-Teller or a Liar could say “I am a Liar”. A puzzle is clear if it only contains its given statements to solve it, and a puzzle is good if it has exactly one solution. It is known that there is no clear and good Strong Truth-Teller–Strong Liar (also called SS) puzzle. However, as we prove here, there are good and clear Strong Truth-Teller, Strong Liar and Strong Crazy puzzles (SSS-puzzles). The newly investigated type ‘Crazy’ drastically changes the scenario. Some properties of the new types of puzzles are analyzed, and some statistics are also given.
A Truncated Galectin-3 Isolated from Skin Mucus of Atlantic Salmon Salmo salar Binds to and Modulates the Proteome of the Gram-Negative Bacteria Moritella viscosa
Authors: Patel Deepti Manjari , Kitani Yoichiro , Korsnes Kjetil , Iversen Martin Haugmo , Brinchmann Monica Fengsrud
Published: February 2020

The mucus of fish skin plays a vital role in innate immune defense. Some mucus proteins have the potential to incapacitate pathogens and/or inhibit their passage through the skin. In this study the aim was to isolate and characterize galectin(s), β-galactosides binding proteins, present in skin mucus. A novel short form of galectin-3 was isolated from Atlantic salmon skin mucus by α-lactose agarose based affinity chromatography followed by Sephadex G-15 gel filtration. Mass spectrometric analysis showed that the isolated protein was the C-terminal half of galectin-3 (galectin-3C). Galectin-3C showed calcium independent and lactose inhabitable hemagglutination, and agglutinated the Gram-negative pathogenic bacteria Moritella viscosa. Galectin-3 mRNA was highly expressed in skin and gill, followed by muscle, hindgut, spleen, stomach, foregut, head kidney, and liver. Moritella viscosa incubated with galectin-3C had a modified proteome. Proteins with changed abundance included multidrug transporter and three ribosomal proteins L7/12, S2, and S13. Overall, this study shows the isolation and characterization of a novel galectin-3 short form involved in pathogen recognition and modulation, and hence in immune defense of Atlantic salmon.
Discharge Planning and Home Care Needs Assessment for Older Patients in a Nursing Hospital
Authors: Urbietė Lina , Lesauskaitė Vita , Macijauskienė Jūratė
Published: February 2020

Background and objectives: Following the accumulation of a sufficient amount of scientific evidence, it is now possible to appeal for changes in the organization of nursing services. Our aims are to assess the health status of patients discharged from nursing hospitals and to identify their home care needs by applying the international InterRAI Home Care (HC) assessment form. Material and methods: 152 geriatric patients (older than 65 years of age) discharged after a 90–120-day stay at a nursing hospital were examined using face-to-face interviews. The data from the medical records were also assessed. The capacities of patients were discussed with the patients themselves, nursing personnel, and relatives of the patients. Results: The analysis revealed that 45.4% of the respondents had severely impaired cognitive skills, while 27.6% had moderately impaired cognitive skills for decision making in daily living. People with greater cognitive difficulties were more dependent during daily instrumental activities and ordinary daily activities. The strongest relationship was established among the cognitive skills and management of medications, management of finances, and ordinary housework. For the greater part of respondents, a special need for permanent nursing (57.9%) or assistance (25.7%) was determined, i.e., official, state-funded nursing at home was appointed. The remaining respondents (16.4%) were not appointed further state-funded nursing or assistance at home, but an assessment of the independence of these patients based on the InterRai Activities of Daily Living Hierarchy Scale indicated that these skills varied from moderate independence (decision making was difficult only in new situations) to severely impaired skills (made no independent decisions or they were scarce). Despite the low independence of respondents, the majority of them would prefer nursing services at home to institutional nursing. Conclusions: The low independence observed in all participants, as well as their limited capacities, prove the need for nursing services at home and the necessity of their continuity. Despite the low independence of respondents, the majority of them would prefer nursing services at home to institutional nursing.
A Review on the Mechanism, Impacts and Control Methods of Membrane Fouling in MBR System
Authors: Du Xianjun , Shi Yaoke , Jegatheesan Veeriah , Haq Izaz Ul
Published: February 2020

Compared with the traditional activated sludge process, a membrane bioreactor (MBR) has many advantages, such as good effluent quality, small floor space, low residual sludge yield and easy automatic control. It has a promising prospect in wastewater treatment and reuse. However, membrane fouling is the biggest obstacle to the wide application of MBR. This paper aims at summarizing the new research progress of membrane fouling mechanism, control, prediction and detection in the MBR systems. Classification, mechanism, influencing factors and control of membrane fouling, membrane life prediction and online monitoring of membrane fouling are discussed. The research trends of relevant research areas in MBR membrane fouling are prospected.
Physical and Structural Characterization of Monocrystalline Cu-13.7% Al-4.2% Ni Alloy Submitted to Thermo-Cyclical Treatments under Applied Loads
Authors: Matlakhova Lioudmila A. , Pereira Elaine C. , Pulnev Serguey A. , Shigue Carlos Y. , Palii Natalia A.
Published: February 2020

Monocrystalline alloy with a nominal composition of Cu-13.7% Al-4.2% Ni (wt.%) that shows reversible martensitic transformations (RMTs) was studied. The alloy, manufactured by the “Memory Crystals Group” in Russia, was subjected to thermo-cyclical treatment (TCT) under tension within a range that included critical RMT temperatures. A special device was developed to perform TCTs (up to 500 cycles) and three different loads were applied: 0.11, 0.26, and 0.53 MPa. X-ray diffraction analysis, optical microscopy, differential calorimetry, and Vickers microhardness were involved in the alloy’s characterization. Under TCTs, the alloy displayed complex structural transformation, revealing the sequence of RMT, β1 ↔ R ↔ β′1 + γ′1; the involved phases were coherently precipitated but very sensitive to the experimental conditions. It was found that during TCTs (from 300 cycles) performed under optimum load (0.26 MPa), the processes of martensite reorientation, hardening, and stabilization of the structure were the most intensive thus leading to a reduction of RMT critical intervals and increased microhardness.
Green Technology for Remediation of Water Polluted with Petroleum Crude Oil: Using of Eichhornia crassipes (Mart.) Solms Combined with Magnetic Nanoparticles Capped with Myrrh Resources of Saudi Arabia
Authors: Atta Ayman M. , Mohamed Nermen H. , Hegazy Ahmad K. , Moustafa Yasser M. , Mohamed Rodina R. , Safwat Gehan , Diab Ayman A.
Published: February 2020

Crude oil pollution of water bodies is a worldwide problem that affects water ecosystems and is detrimental to human health and the diversity of living organisms. The objective of this study was to assess the ability of water hyacinth (Eichhornia crassipes (Mart.) Solms) combined with the presence of magnetic nanoparticles capped with natural products based on Myrrh to treat fresh water contaminated by crude petroleum oil. Magnetic nanoparticles based on magnetite capped with Myrrh extracts were prepared, characterized, and used to adsorb heavy components of the crude oil. The hydrophobic hexane and ether Myrrh extracts were isolated and used as capping for magnetite nanoparticles. The chemical structures, morphologies, particle sizes, and magnetic characteristics of the magnetic nanoparticles were investigated. The adsorption efficiencies of the magnetic nanoparticles show a greater efficiency to adsorb more than 95% of the heavy crude oil components. Offsets of Water hyacinth were raised in bowls containing Nile River fresh water under open greenhouse conditions, and subjected to varying crude oil contamination treatments of 0.5, 1, 2, 3, and 5 mL/L for one month. Plants were harvested and separated into shoots and roots, oven dried at 65 °C, and grounded into powder for further analysis of sulphur and total aromatic and saturated hydrocarbons, as well as individual aromatic constituents. The pigments of chlorophylls and carotenoids were measured spectrophotometrically in fresh plant leaves. The results indicated that the bioaccumulation of sulphur in plant tissues increased with the increased level of oil contamination. Water analysis showed significant reduction in polyaromatic hydrocarbons. The increase of crude oil contamination resulted in a decrease of chlorophylls and carotenoid content of the plant tissues. The results indicate that the water hyacinth can be used for remediation of water slightly polluted by crude petroleum oil. The presence of magnetite nanoparticles capped with Myrrh resources improved the remediation of water highly polluted by petroleum crude oil.
Map3k14 as a Regulator of Innate and Adaptive Immune Response during Acute Viral Infection
Authors: Hamdan Thamer A. , Bhat Hilal , Cham Lamin B. , Adomati Tom , Lang Judith , Li Fanghui , Murtaza Ali , Hardt Cornelia , Lang Philipp A. , Duhan Vikas , Lang Karl S.
Published: February 2020

The replication of virus in secondary lymphoid organs is crucial for the activation of antigen-presenting cells. Balanced viral replication ensures the sufficient availability of antigens and production of cytokines, and both of which are needed for virus-specific immune activation and viral elimination. Host factors that regulate coordinated viral replication are not fully understood. In the study reported here, we identified Map3k14 as an important regulator of enforced viral replication in the spleen while performing genome-wide association studies of various inbred mouse lines in a model of lymphocytic choriomeningitis virus (LCMV) infection. When alymphoplasia mice (aly/aly, Map3k14aly/aly, or Nikaly/aly), which carry a mutation in Map3k14, were infected with LCMV or vesicular stomatitis virus (VSV), they display early reductions in early viral replication in the spleen, reduced innate and adaptive immune activation, and lack of viral control. Histologically, scant B cells and the lack of CD169+ macrophages correlated with reduced immune activation in Map3k14aly/aly mice. The transfer of wildtype B cells into Map3k14aly/aly mice repopulated CD169+ macrophages, restored enforced viral replication, and resulted in enhanced immune activation and faster viral control.
A New Deep Learning Network for Automatic Bridge Detection from SAR Images Based on Balanced and Attention Mechanism
Authors: Chen Lifu , Weng Ting , Xing Jin , Pan Zhouhao , Yuan Zhihui , Xing Xuemin , Zhang Peng
Published: February 2020

Bridge detection from Synthetic Aperture Radar (SAR) images has very important strategic significance and practical value, but there are still many challenges in end-to-end bridge detection. In this paper, a new deep learning-based network is proposed to identify bridges from SAR images, namely, multi-resolution attention and balance network (MABN). It mainly includes three parts, the attention and balanced feature pyramid (ABFP) network, the region proposal network (RPN), and the classification and regression. First, the ABFP network extracts various features from SAR images, which integrates the ResNeXt backbone network, balanced feature pyramid, and the attention mechanism. Second, extracted features are used by RPN to generate candidate boxes of different resolutions and fused. Furthermore, the candidate boxes are combined with the features extracted by the ABFP network through the region of interest (ROI) pooling strategy. Finally, the detection results of the bridges are produced by the classification and regression module. In addition, intersection over union (IOU) balanced sampling and balanced L1 loss functions are introduced for optimal training of the classification and regression network. In the experiment, TerraSAR data with 3-m resolution and Gaofen-3 data with 1-m resolution are used, and the results are compared with faster R-CNN and SSD. The proposed network has achieved the highest detection precision (P) and average precision (AP) among the three networks, as 0.877 and 0.896, respectively, with the recall rate (RR) as 0.917. Compared with the other two networks, the false alarm targets and missed targets of the proposed network in this paper are greatly reduced, so the precision is greatly improved.
The New Potential of Deep Convective Clouds as a Calibration Target for a Geostationary UV/VIS Hyperspectral Spectrometer
Authors: Lee Yeeun , Ahn Myoung-Hwan , Kang Mina
Published: February 2020

As one of geostationary earth orbit constellation for environmental monitoring over the next decade, the Geostationary Environment Monitoring Spectrometer (GEMS) has been designed to observe the Asia-Pacific region to provide information on atmospheric chemicals, aerosols, and cloud properties. In order to continuously monitor sensor performance after its launch in early 2020, we suggest in this paper deep convective clouds (DCCs) as a possible target for the vicarious calibration of the GEMS, the first ultraviolet and visible hyperspectral sensor onboard a geostationary satellite. The Tropospheric Monitoring Instrument (TROPOMI) and the Ozone Monitoring Instrument (OMI) are used as a proxy for GEMS, and a conventional DCC-detection approach applying a thermal threshold test is used for DCC detection based on collocations with the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. DCCs are frequently detected over the GEMS observation area at an average of over 200 pixels within a single observation scene. Considering the spatial resolution of the GEMS (3.5 × 8 km2), which is similar to the TROPOMI and its temporal resolution (eight times a day), the availability of DCCs is expected to be sufficient for the vicarious calibration of the GEMS. Inspection of the DCC reflectivity spectra estimated from OMI and TROPOMI data also shows promising results. The estimated DCC spectra are in good agreement within a known uncertainty range with comparable spectral features even with different observation geometries and sensor characteristics. When DCC detection is improved further by applying both visible and infrared tests, the variability of DCC reflectivity from TROPOMI data is reduced from 10% to 5%. This is mainly due to the efficient screening out of cold, thin cirrus clouds in the visible test and of bright, warm clouds in the infrared test. Precise DCC detection is also expected to contribute to the accurate characterization of cloud reflectivity, which will be investigated further in future research.
Land Subsidence Response to Different Land Use Types and Water Resource Utilization in Beijing-Tianjin-Hebei, China
Authors: Zhou Chaofan , Gong Huili , Chen Beibei , Gao Mingliang , Cao Qun , Cao Jin , Duan Li , Zuo Junjie , Shi Min
Published: February 2020

The long-term overexploitation of groundwater leads to serious land subsidence and threatens the safety of Beijing-Tianjin-Hebei (BTH). In this paper, an interferometric point target analysis (IPTA) with small baseline subset InSAR (SBAS-InSAR) technique was used to derive the land subsidence in a typical BTH area from 2012 to 2018 with 126 Radarsat-2 and 184 Sentinel-1 images. The analysis reveals that the average subsidence rate reached 118 mm/year from 2012 to 2018. Eleven subsidence features were identified: Shangzhuang, Beijing Airport, Jinzhan and Heizhuanghu in Beijing, Guangyang and Shengfang in Langfang, Wangqingtuo in Tianjin, Dongguang in Cangzhou, Jingxian and Zaoqiang in Hengshui and Julu in Xingtai. Comparing the different types of land use in subsidence feature areas, the results show that when the land-use type is relatively more complex and superimposed with residential, industrial and agricultural land, the land subsidence is relatively more significant. Moreover, the land subsidence development patterns are different in the BTH areas because of the different methods adopted for their water resource development and utilization, with an imbalance in their economic development levels. Finally, we found that the subsidence changes are consistent with groundwater level changes and there is a lag period between land subsidence and groundwater level changes of approximately two months in Beijing Airport, Jinzhan, Jingxian and Zaoqiang, of three months in Shangzhuang, Heizhuanghu, Guangyang, Wangqingtuo and Dongguang and of four months in Shengfang.
High-Resolution Reconstruction of the Maximum Snow Water Equivalent Based on Remote Sensing Data in a Mountainous Area
Authors: Liu Mingyu , Xiong Chuan , Pan Jinmei , Wang Tianxing , Shi Jiancheng , Wang Ninglian
Published: February 2020

Currently, the accurate estimation of the maximum snow water equivalent (SWE) in mountainous areas is an important topic. In this study, in order to improve the accuracy and spatial resolution of SWE reconstruction in alpine regions, the Sentinel-2(MSI) and Landsat 8(OLI) satellite data with the spatial resolution of tens of meters are used instead of the Moderate-resolution Imaging Spectroradiometer (MODIS) data so that the pixel mixing problem is avoided. Meanwhile, geostationary satellite-based and topographic-corrected incoming shortwave radiation is used in the restricted degree-day model to improve the accuracy of radiation inputs. The seasonal maximum SWE accumulation of a river basin in the winter season of 2017–2018 is estimated. The spatial and temporal characteristics of SWE at a fine spatial and temporal resolution are then analyzed. And the results of reconstruction model with different input parameters are compared. The results showed that the average maximum SWE of the study area in 2017–2018 was 377.83 mm and the accuracy of snow cover, air temperature and the radiation parameters all affects the maximum SWE distribution on magnitude, elevation and aspect. Although the accuracy of other forcing parameters still needs to be improved, the estimation of the local maximum snow water equivalent in mountainous areas benefits from the application of high-resolution Sentinel-2 and Landsat 8 data. The joint usage of high-resolution remote sensing data from different satellites can greatly improve the temporal and spatial resolution of snow cover and the spatial resolution of SWE estimation. This method can provide more accurate and detailed SWE for hydrological models, which is of great significance to hydrology and water resources research.
Merge-Swap Optimization Framework for Supervoxel Generation from Three-Dimensional Point Clouds
Authors: Xiao Yanyang , Chen Zhonggui , Lin Zhengtao , Cao Juan , Zhang Yongjie Jessica , Lin Yangbin , Wang Cheng
Published: February 2020

Surpervoxels are becoming increasingly popular in many point cloud processing applications. However, few methods have been devised specifically for generating compact supervoxels from unstructured three-dimensional (3D) point clouds. In this study, we aimed to generate high quality over-segmentation of point clouds. We propose a merge-swap optimization framework that solves any supervoxel generation problem formulated in energy minimization. In particular, we tailored an energy function that explicitly encourages regular and compact supervoxels with adaptive size control considering local geometric information of point clouds. We also provide two acceleration techniques to reduce the computational overhead. The performance of the proposed merge-swap optimization approach is superior to that of previous work in terms of thorough optimization, computational efficiency, and practical applicability to incorporating control of other properties of supervoxels. The experiments show that our approach produces supervoxels with better segmentation quality than two state-of-the-art methods on three public datasets.
A Refined SNR Based Stochastic Model to Reduce Site-Dependent Effects
Authors: Xi Ruijie , Meng Xiaolin , Jiang Weiping , An Xiangdong , He Qiyi , Chen Qusen
Published: February 2020

Site-dependent effects are now the key factors that restrict the high accuracy applications of Global Navigation Satellite System (GNSS) technology, such as deformation monitoring. To reduce the effects of non-line-of-sight (NLOS) signal and multipath, methods and models applied to both of the function model and stochastic model of least-squares (LS) have been proposed. However, the existing methods and models may not be convenient to use and not be appropriate to all GNSS satellites. In this study, the SNR features of GPS and GLONASS are analyzed first, and a refined SNR based stochastic model is proposed, in which the links between carrier phase precision and SNR observation have been reasonably established. Compared with the existing models, the refined model in this paper could be used in real-time and the carrier phase precision could be reasonably shown with the SNR data. More importantly, it is applicable to all GNSS satellite systems. Based on this model, the site observation environment can be assessed in advance to show the obstruction area. With a bridge deformation monitoring platform, the performance of this model was tested in the aspect of integer ambiguity resolution and data processing. The results show that, compared with the existing stochastic models, this model could have the highest integer ambiguity resolution success rate and the lowest noise level in the data processing time series with obvious obstruction beside the site.
Elevation-Dependent Removal of Cirrus Clouds in Satellite Imagery
Authors: Schläpfer Daniel , Richter Rudolf , Reinartz Peter
Published: February 2020

Masking of cirrus clouds in optical satellite imagery is an important step in automated processing chains. Firstly, it is a prerequisite to a subsequent removal of cirrus effects, and secondly, it affects the atmospheric correction, i.e., aerosol and surface reflectance retrievals. Cirrus clouds can be detected with a narrow bandwidth channel near 1.38 μ m and operational detection algorithms have been developed for Landsat-8 and Sentinel-2 images. However, concerning cirrus removal in the case of elevated surfaces, current methods do not separate the ground reflected signal from the cirrus signal in the 1.38 μ m channel when performing an atmospheric correction, often resulting in an overcorrection of the cirrus influence. We propose a new operational algorithm using a Digital Elevation Model (DEM) to estimate the surface and cirrus cloud contributions in the 1.38 μ m channel and to remove cirrus effects during the surface reflectance retrieval. Due to the highly variable nature of cirrus clouds and terrain conditions, no generic quantitative results could be derived. However, results for typical cases and the achieved improvement in cirrus removal are given for selected scenes and critical issues and limitations of the approach are discussed.
Modifying an Image Fusion Approach for High Spatiotemporal LST Retrieval in Surface Dryness and Evapotranspiration Estimations
Authors: Januar Tri Wandi , Lin Tang-Huang , Huang Chih-Yuan , Chang Kuo-En
Published: February 2020

Thermal infrared (TIR) satellite images are generally employed to retrieve land surface temperature (LST) data in remote sensing. LST data have been widely used in evapotranspiration (ET) estimation based on satellite observations over broad regions, as well as the surface dryness associated with vegetation index. Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) can provide LST data with a 30-m spatial resolution. However, rapid changes in environmental factors, such as temperature, humidity, wind speed, and soil moisture, will affect the dynamics of ET. Therefore, ET estimation needs a high temporal resolution as well as a high spatial resolution for daily, diurnal, or even hourly analysis. A challenge with satellite observations is that higher-spatial-resolution sensors have a lower temporal resolution, and vice versa. Previous studies solved this limitation by developing a spatial and temporal adaptive reflectance fusion model (STARFM) for visible images. In this study, with the primary mechanism (thermal emission) of TIRS, surface emissivity is used in the proposed spatial and temporal adaptive emissivity fusion model (STAEFM) as a modification of the original STARFM for fusing TIR images instead of reflectance. For high a temporal resolution, the advanced Himawari imager (AHI) onboard the Himawari-8 satellite is explored. Thus, Landsat-like TIR images with a 10-minute temporal resolution can be synthesized by fusing TIR images of Himawari-8 AHI and Landsat-8 TIRS. The performance of the STAEFM to retrieve LST was compared with the STARFM and enhanced STARFM (ESTARFM) based on the similarity to the observed Landsat image and differences with air temperature. The peak signal-to-noise ratio (PSNR) value of the STAEFM image is more than 42 dB, while the values for STARFM and ESTARFM images are around 31 and 38 dB, respectively. The differences of LST and air temperature data collected from five meteorological stations are 1.53 °C to 4.93 °C, which are smaller compared with STARFM’s and ESATRFM’s. The examination of the case study showed reasonable results of hourly LST, dryness index, and ET retrieval, indicating significant potential for the proposed STAEFM to provide very-high-spatiotemporal-resolution (30 m every 10 min) TIR images for surface dryness and ET monitoring.
A Three-dimensional Finger Motion Measurement System of a Thumb and an Index Finger Without a Calibration Process
Authors: Park Yeongyu , Bae Joonbum
Published: February 2020

Various wearable systems have been investigated to measure hand motion, but some challenges remain. Many systems require a calibration process to map sensor signals to actual finger joint angles by the principle of measuring the length change of the finger, or bending sensors. Also, few studies have investigated how to measure thumb motion accurately using the wearable systems. This paper proposes an exoskeleton system with linear Hall sensors to measure three-dimensional hand motion without a calibration process. The calibration process is avoided by measuring finger joint angles through an absolute rotation measurement. A new wearing method with lower parts underneath the hand joints and rubber bands is proposed to fix the structure to the hand and adapt it for various hand sizes. As the thumb has a complex biomechanical feature at carpometacarpal (CMC) joint, a new measuring method of the CMC joint is proposed to directly calculate the orientation of the metacarpal. The prototype of the thumb and index finger was manufactured, and the performance was verified experimentally by using an optical motion capture system.
Experimentally Derived Feasibility of Optical Camera Communications under Turbulence and Fog Conditions
Authors: Matus Vicente , Eso Elizabeth , Teli Shivani Rajendra , Perez-Jimenez Rafael , Zvanovec Stanislav
Published: February 2020

Optical camera communications (OCC) research field has grown recently, aided by ubiquitous digital cameras; however, atmospheric conditions can restrict their feasibility in outdoor scenarios. In this work, we studied an experimental OCC system under environmental phenomena emulated in a laboratory chamber. We found that the heat-induced turbulence does not affect our system significantly, while the attenuation caused by fog does decrease the signal quality. For this reason, a novel strategy is proposed, using the camera’s built-in amplifier to overcome the optical power loss and to decrease the quantization noise induced by the analog-digital converter of the camera. The signal quality has been evaluated using the Pearson’s correlation coefficient with respect to a reference template signal, along with the signal-to-noise ratio that has been empirically evaluated. The amplification mechanism introduced allows our system to receive the OCC signal under heavy fog by gradually increasing the camera gain up to 16 dB, for meteorological visibility values down to 10 m, with a correlation coefficient of 0.9 with respect to clear conditions.
On the Self-Structuring Antenna
Authors: Jagodzińska Katarzyna
Published: February 2020

This paper shows the results of an investigation on the self-structuring antenna. This antenna consists of a combination of planar paths interconnected by controllable switches. A numerical electromagnetics code (NEC) environment and application with implemented genetic algorithms were used in this research. As a result of the investigation, an antenna template was built and measured.
Thermal Stability of Type II Modifications by IR Femtosecond Laser in Silica-based Glasses
Authors: Wei Shu-En , Wang Yitao , Yao Heng , Cavillon Maxime , Poumellec Bertrand , Peng Gang-Ding , Lancry Matthieu
Published: February 2020

Femtosecond (fs) laser written fiber Bragg gratings (FBGs) are excellent candidates for ultra-high temperature (>800 °C) monitoring. More specifically, Type II modifications in silicate glass fibers, characterized by the formation of self-organized birefringent nanostructures, are known to exhibit remarkable thermal stability around 1000 °C for several hours. However, to date there is no clear understanding on how both laser writing parameters and glass composition impact the overall thermal stability of these fiber-based sensors. In this context, this work investigates thermal stability of Type II modifications in various conventional glass systems (including pure silica glasses with various Cl and OH contents, GeO2-SiO2 binary glasses, TiO2- and B2O3-doped commercial glasses) and with varying laser parameters (writing speed, pulse energy). In order to monitor thermal stability, isochronal annealing experiments (Δt⁓ 30 min, ΔT⁓ 50 °C) up to 1400 °C were performed on the irradiated samples, along with quantitative retardance measurements. Among the findings to highlight, it was established that ppm levels of Cl and OH can drastically reduce thermal stability (by about 200 °C in this study). Moreover, GeO2 doping up to 17 mole% only has a limited impact on thermal stability. Finally, the relationships between glass viscosity, dopants/impurities, and thermal stability, are discussed.
Geo-Social Top-k and Skyline Keyword Queries on Road Networks
Authors: Attique Muhammad , Afzal Muhammad , Ali Farman , Mehmood Irfan , Ijaz Muhammad Fazal , Cho Hyung-Ju
Published: February 2020

The rapid growth of GPS-enabled mobile devices has popularized many location-based applications. Spatial keyword search which finds objects of interest by considering both spatial locations and textual descriptions has become very useful in these applications. The recent integration of social data with spatial keyword search opens a new service horizon for users. Few previous studies have proposed methods to combine spatial keyword queries with social data in Euclidean space. However, most real-world applications constrain the distance between query location and data objects by a road network, where distance between two points is defined by the shortest connecting path. This paper proposes geo-social top-k keyword queries and geo-social skyline keyword queries on road networks. Both queries enrich traditional spatial keyword query semantics by incorporating social relevance component. We formalize the proposed query types and appropriate indexing frameworks and algorithms to efficiently process them. The effectiveness and efficiency of the proposed approaches are evaluated using real datasets.
A Stackelberg Security Game for Adversarial Outbreak Detection in the Internet of Things
Authors: Chen Lili , Wang Zhen , Li Fenghua , Guo Yunchuan , Geng Kui
Published: February 2020

With limited computing resources and a lack of physical lines of defense, the Internet of Things (IoT) has become a focus of cyberattacks. In recent years, outbreak propagation attacks against the IoT have occurred frequently, and these attacks are often strategical. In order to detect the outbreak propagation as soon as possible, t embedded Intrusion Detection Systems (IDSs) are widely deployed in the IoT. This paper tackles the problem of outbreak detection in adversarial environment in the IoT. A dynamic scheduling strategy based on specific IDSs monitoring of IoT devices is proposed to avoid strategic attacks. Firstly, we formulate the interaction between the defender and attacker as a Stackelberg game in which the defender first chooses a set of device nodes to activate, and then the attacker selects one seed (one device node) to spread the worms. This yields an extremely complex bilevel optimization problem. Our approach is to build a modified Column Generation framework for computing the optimal strategy effectively. The optimal response of the defender’s problem is expressed as mixed-integer linear programming (MILPs). It is proved that the solution of the defender’s optimal response is a NP-hard problem. Moreover, the optimal response of defenders is improved by an approximate algorithm--a greedy algorithm. Finally, the proposed scheme is tested on some randomly generated instances. The experimental results show that the scheme is effective for monitoring optimal scheduling.
Non-Enzymatic Amperometric Glucose Sensor Based on Carbon Nanodots and Copper Oxide Nanocomposites Electrode
Authors: Sridara Tharinee , Upan Jantima , Saianand Gopalan , Tuantranont Adisorn , Karuwan Chanpen , Jakmunee Jaroon
Published: February 2020

In this research work, a non-enzymatic amperometric sensor for the determination of glucose was designed based on carbon nanodots (C-dots) and copper oxide (CuO) nanocomposites (CuO-C-dots). The CuO-C-dots nanocomposites were modified on the surface of a screen-printed carbon electrode (SPCE) to increase the sensitivity and selectivity of the glucose sensor. The as-synthesized materials were further analyzed for physico-chemical properties through characterization tools such as transmission electron microscopy (TEM) and Fourier-transform infrared spectroscopy (FTIR); and their electrochemical performance was also studied. The SPCE modified with CuO-C-dots possess desirable electrocatalytic properties for glucose oxidation in alkaline solutions. Moreover, the proposed sensing platform exhibited a linear range of 0.5 to 2 and 2 to 5 mM for glucose detection with high sensitivity (110 and 63.3 µA mM−1cm−2), and good selectivity and stability; and could potentially serve as an effective alternative method of glucose detection.
Depth Density Achieves a Better Result for Semantic Segmentation with the Kinect System
Authors: Deng Hanbing , Xu Tongyu , Zhou Yuncheng , Miao Teng
Published: February 2020

Image segmentation is one of the most important methods for animal phenome research. Since the advent of deep learning, many researchers have looked at multilayer convolutional neural networks to solve the problems of image segmentation. A network simplifies the task of image segmentation with automatic feature extraction. Many networks struggle to output accurate details when dealing with pixel-level segmentation. In this paper, we propose a new concept: Depth density. Based on a depth image, produced by a Kinect system, we design a new function to calculate the depth density value of each pixel and bring this value back to the result of semantic segmentation for improving the accuracy. In the experiment, we choose Simmental cattle as the target of image segmentation and fully convolutional networks (FCN) as the verification networks. We proved that depth density can improve four metrics of semantic segmentation (pixel accuracy, mean accuracy, mean intersection over union, and frequency weight intersection over union) by 2.9%, 0.3%, 11.4%, and 5.02%, respectively. The result shows that depth information produced by Kinect can improve the accuracy of the semantic segmentation of FCN. This provides a new way of analyzing the phenotype information of animals.
Immunosensor Based on Long-Period Fiber Gratings for Detection of Viruses Causing Gastroenteritis
Authors: Janczuk-Richter Marta , Gromadzka Beata , Richter Łukasz , Panasiuk Mirosława , Zimmer Karolina , Mikulic Predrag , Bock Wojtek J. , Maćkowski Sebastian , Śmietana Mateusz , Niedziółka Jönsson Joanna
Published: February 2020

Since the norovirus is the main cause of acute gastroenteritis all over the world, its fast detection is crucial in medical diagnostics. In this work, a rapid, sensitive, and selective optical fiber biosensor for the detection of norovirus virus-like particles (VLPs) is reported. The sensor is based on highly sensitive long-period fiber gratings (LPFGs) coated with antibodies against the main coat protein of the norovirus. Several modification methods were verified to obtain reliable immobilization of protein receptors on the LPFG surface. We were able to detect 1 ng/mL norovirus VLPs in a 40-min assay in a label-free manner. Thanks to the application of an optical fiber as the sensor, there is a possibility to increase the user’s safety by separating the measurement point from the signal processing setup. Moreover, our sensor is small and light, and the proposed assay is straightforward. The designed LPFG-based biosensor could be applied in both fast norovirus detection and in vaccine testing.