bluuebunny/crossref_metadata_2025 huggingface dataset:
Dynamic Metabolic Response to Adriamycin-Induced Senescence in Breast Cancer Cells
Authors: You Rong , Dai Jin , Zhang Ping , Barding Gregory A. , Raftery Daniel
Published: December 2018

Cellular senescence displays a heterogeneous set of phenotypes linked to tumor suppression; however, after drug treatment, senescence may also be involved in stable or recurrent cancer. Metabolic changes during senescence can provide detailed information on cellular status and may also have implications for the development of effective treatment strategies. The metabolic response to Adriamycin (ADR) treatment, which causes senescence as well as cell death, was obtained with the aid of metabolic profiling and isotope tracing in two human breast cancer cell lines, MCF7 and MDA-MB-231. After 5 days of ADR treatment, more than 60% of remaining, intact cells entered into a senescent state, characterized by enlarged and flattened morphology and positive blue staining using SA-β-gal. Metabolic trajectory analysis showed that the two cell lines’ responses were significantly different and were divided into two distinct stages. The metabolic shift from the first stage to the second was reflected by a partial recovery of the TCA cycle, as well as amino acid and lipid metabolisms. Isotope tracing analysis indicated that the higher level of glutamine metabolism helped maintain senescence. The results suggest that the dynamic changes during senescence indicate a multi-step process involving important metabolic pathways which might allow breast cancer cells to adapt to persistent ADR treatment, while the higher level of anapleurosis may be important for maintaining the senescent state. Ultimately, a better understanding of metabolic changes during senescence might provide targets for cancer therapy and tumor eradication.
Role of Solid Wall Properties in the Interface Slip of Liquid in Nanochannels
Authors: Gao Wei , Zhang Xuan , Han Xiaotian , Shen Chaoqun
Published: December 2018

A two-dimensional molecular dynamics model of the liquid flow inside rough nanochannels is developed to investigate the effect of a solid wall on the interface slip of liquid in nanochannels with a surface roughness constructed by rectangular protrusions. The liquid structure, velocity profile, and confined scale on the boundary slip in a rough nanochannel are investigated, and the comparison of those with a smooth nanochannel are presented. The influence of solid wall properties, including the solid wall density, wall-fluid coupling strength, roughness height and spacing, on the interfacial velocity slip are all analyzed and discussed. It is indicated that the rough surface induces a smaller magnitude of the density oscillations and extra energy losses compared with the smooth solid surface, which reduce the interfacial slip of liquid in a nanochannel. In addition, once the roughness spacing is very small, the near-surface liquid flow dominates the momentum transfer at the interface between liquid and solid wall, causing the role of both the corrugation of wall potential and wall-fluid coupling strength to be less obvious. In particular, the slip length increases with increasing confined scales and shows no dependence on the confined scale once the confined scale reaches a critical value. The critical confined scale for the rough channel is larger than that of the smooth scale.
Modeling and Analysis of Upright Piezoelectric Energy Harvester under Aerodynamic Vortex-induced Vibration
Authors: Jia Jinda , Shan Xiaobiao , Upadrashta Deepesh , Xie Tao , Yang Yaowen , Song Rujun
Published: December 2018

This paper presents an upright piezoelectric energy harvester (UPEH) with cylinder extension along its longitudinal direction. The UPEH can generate energy from low-speed wind by bending deformation produced by vortex-induced vibrations (VIVs). The UPEH has the advantages of less working space and ease of setting up an array over conventional vortex-induced vibration harvesters. The nonlinear distributed modeling method is established based on Euler–Bernoulli beam theory and aerodynamic vortex-induced force of the cylinder is obtained by the van der Pol wake oscillator theory. The fluid–solid–electricity governing coupled equations are derived using Lagrange’s equation and solved through Galerkin discretization. The effect of cylinder gravity on the dynamic characteristics of the UPEH is also considered using the energy method. The influences of substrate dimension, piezoelectric dimension, the mass of cylinder extension, and electrical load resistance on the output performance of harvester are studied using the theoretical model. Experiments were carried out and the results were in good agreement with the numerical results. The results showed that a UPEH configuration achieves the maximum power of 635.04 μW at optimum resistance of 250 kΩ when tested at a wind speed of 4.20 m/s. The theoretical results show that the UPEH can get better energy harvesting output performance with a lighter tip mass of cylinder, and thicker and shorter substrate in its synchronization working region. This work will provide the theoretical guidance for studying the array of multiple upright energy harvesters.
The Process of the Intensification of Coal Fly Ash Flotation Using a Stirred Tank
Authors: Yang Lu , Zhu Zhenna , Qi Xin , Yan Xiaokang , Zhang Haijun
Published: December 2018

Pulp preconditioning using a stirred tank as a pretreatment process is vital to the flotation system, which can be used to improve the flotation efficiency of mineral particles. The kinetic energy that is dissipated in the stirred tank could strengthen the interaction process between mineral particles and flotation reagents to improve the flotation efficiency in the presence of the preconditioning. In this paper, the effect of the conditioning speed on the coal fly ash flotation was investigated using numerical simulations and conditioning-flotation tests. The large eddy simulation coupled with the Smagorinsky-Lilly subgrid model was employed to simulate the turbulence flow field in the stirred tank, which was equipped with a six blade Rushton turbine. The impeller rotation was modelled using the sliding mesh. The simulation results showed that the large eddy simulation (LES) well matched the previous experimental data. The turbulence characteristics, such as the mean velocity, turbulent kinetic energy, power consumption and instantaneous structures of trailing vortices were analysed in detail. The turbulent length scale (η) decreased as the rotation speed increased, and the minimum value of η was almost unchanged when the rotation speed was more than 1200 rpm. The conditioning-flotation tests of coal fly ash were conducted using different conditioning speeds. The results showed that the removal of unburned carbon was greatly improved due to the strengthened turbulence in the stirred tank, and the optimal results were obtained with an LOI of 3.32%, a yield of 78.69% and an RUC of 80.89% when the conditioning speed was 1200 rpm.
Synthesis of Cucurbitacin B Derivatives as Potential Anti-Hepatocellular Carcinoma Agents
Authors: Ge Weizhi , Chen Xinyi , Han Fangzhi , Liu Zhongquan , Wang Tianpeng , Wang Mengmeng , Chen Yue , Ding Yahui , Zhang Quan
Published: December 2018

Cucurbitacin B shows potent activity against tumor cells, but its high toxicity limits its application in the clinic. A series of cucurbitacin B derivatives was synthesized and evaluated for their anti-hepatocellular carcinoma (HCC) activities against the HepG-2 cell line. These compounds were also tested for their toxicity against the L-O2 normal cell line. The compound with the most potential, 10b, exhibited potent activity against the HepG-2 cell line with an IC50 value of 0.63 μM. Moreover, compound 10b showed the highest TI value (4.71), which is a 14.7-fold improvement compared to its parent compound cucurbitacin B. A preliminary molecular mechanism study of 10b indicated that 10b could inhibit P-STAT3 to induce the activation of mitochondrial apoptotic pathways. An in vivo acute toxicity study indicated that the compound 10b has preferable safety and tolerability compared with cucurbitacin B. These findings indicate that compound 10b might be considered as a lead compound for exploring effective anti-HCC drugs.
Inverse Molecular Docking as a Novel Approach to Study Anticarcinogenic and Anti-Neuroinflammatory Effects of Curcumin
Authors: Furlan Veronika , Konc Janez , Bren Urban
Published: December 2018

Research efforts are placing an ever increasing emphasis on identifying signal transduction pathways related to the chemopreventive activity of curcumin. Its anticarcinogenic effects are presumably mediated by the regulation of signaling cascades, including nuclear factor κB (NF-κB), activator protein 1 (AP-1), and mitogen-activated protein kinases (MAPK). By modulating signal transduction pathways, curcumin induces apoptosis in malignant cells, thus inhibiting cancer development and progression. Due to the lack of mechanistic insight in the scientific literature, we developed a novel inverse molecular docking protocol based on the CANDOCK algorithm. For the first time, we performed inverse molecular docking of curcumin into a collection of 13,553 available human protein structures from the Protein Data Bank resulting in prioritized target proteins of curcumin. Our predictions were in agreement with the scientific literature and confirmed that curcumin binds to folate receptor β, DNA (cytosine-5)-methyltransferase 3A, metalloproteinase-2, mitogen-activated protein kinase 9, epidermal growth factor receptor and apoptosis-inducing factor 1. We also identified new potential protein targets of curcumin, namely deoxycytidine kinase, NAD-dependent protein deacetylase sirtuin-1 and -2, ecto-5′-nucleotidase, core histone macro-H2A.1, tyrosine-protein phosphatase non-receptor type 11, macrophage colony-stimulating factor 1 receptor, GTPase HRas, aflatoxin B1 aldehyde reductase member 3, aldo-keto reductase family 1 member C3, amiloride-sensitive amine oxidase, death-associated protein kinase 2 and tryptophan-tRNA ligase, that may all play a crucial role in its observed anticancer effects. Moreover, our inverse docking results showed that curcumin potentially binds also to the proteins cAMP-specific 3′,5′-cyclic phosphodiesterase 4D and 17-β-hydroxysteroid dehydrogenase type 10, which provides a new explanation for its efficiency in the treatment of Alzheimer’s disease. We firmly believe that our computational results will complement and direct future experimental studies on curcumin’s anticancer activity as well as on its therapeutic effects against Alzheimer’s disease.
Deposition of Magnetite Nanofilms by Pulsed Injection MOCVD in a Magnetic Field
Authors: Zukova Anna , Teiserskis Arunas , Rohava Yuliya , Baranov Alexander V. , Van Dijken Sebastiaan , Gun’ko Yurii K.
Published: December 2018

This report is on the growth of Fe3O4 nanofilms on Al2O3(0001) and MgO(001) substrates with and without the presence of an external magnetic field using a pulsed injection metallorganic chemical vapour deposition (PI MOCVD) technique. The effects of growing magnetic oxide nanofilms in a 1 T field have been examined using various instrumental methods. It was found that the application of a magnetic field during PI MOCVD does not drastically alter the crystalline texture, surface morphology, and film thickness, but it significantly modifies the Fe3O4 film magnetisation and coercive field. Moreover, it was shown that the application of a 1 T field during the cooling of the sample also improves the magnetic properties. We believe that the large external field orients the magnetic spin structure at high temperatures (during growth or the initial stages of cool down) and that cooling through local magnetic ordering temperatures at Fe3O4 defect sites subsequently favours a ferromagnetic spin alignment. The control of magnetic properties of magnetite nanofilms by the application of magnetic fields during growth opens up new routes towards the fabrication and application of magnetic thin film devices.
Low Sucrose, Omega-3 Enriched Diet Has Region-Specific Effects on Neuroinflammation and Synaptic Function Markers in a Mouse Model of Doxorubicin-Based Chemotherapy
Authors: Orchard Tonya S. , Gaudier-Diaz Monica M. , Phuwamongkolwiwat-Chu Panchita , Andridge Rebecca , Lustberg Maryam B. , Bomser Joshua , Cole Rachel M. , Belury Martha A. , DeVries A. Courtney
Published: December 2018

Chemotherapeutic agents such as doxorubicin may negatively affect long-term brain functioning in cancer survivors; neuroinflammation may play a causal role. Dietary approaches that reduce inflammation, such as lowering sucrose and increasing eicosapentaenoic acid plus docosahexaenoic acid (EPA + DHA), may attenuate chemotherapy-induced neuroinflammation and synaptic damage, thereby improving quality of life. Ovariectomized, C57BL/6 mice were assigned to a chemotherapy (9 mg/kg doxorubicin + 90 mg/kg cyclophosphamide) or vehicle two-injection regimen, with injections two and four weeks after starting diets. In Study 1, mice received low sucrose diets with EPA + DHA or No EPA + DHA for four to six weeks; tissues were collected four, seven, or 14 days after the second injection. Compared to vehicle, chemotherapy increased pro-inflammatory cytokine IL-1β at day seven in the cortex and hippocampus, and reduced gene expression of synaptic marker Shank 3 at all timepoints in cortex, while EPA + DHA increased expression of Shank 3. In Study 2, high or low sucrose/EPA + DHA or No EPA + DHA diets were fed for five weeks; tissues were collected ten days after the second injection. Among chemotherapy-treated mice, brain DHA was higher with low sucrose feeding. Furthermore, low sucrose increased gene expression of Shank 1, while EPA + DHA increased expression of Shank 3 and reduced protein concentrations of pro-inflammatory markers IL-5, IL-6 and KC/GRO in the cortex, but not the hippocampus. Low sucrose, EPA + DHA diets may attenuate neuroinflammation and synaptic damage induced by doxorubicin-based chemotherapy in specific brain regions.
In Vitro Immune Organs-on-Chip for Drug Development: A Review
Authors: Shanti Aya , Teo Jeremy , Stefanini Cesare
Published: December 2018

The current drug development practice lacks reliable and sensitive techniques to evaluate the immunotoxicity of drug candidates, i.e., their effect on the human immune system. This, in part, has resulted in a high attrition rate for novel drugs candidates. Organ-on-chip devices have emerged as key tools that permit the study of human physiology in controlled in vivo simulating environments. Furthermore, there has been a growing interest in developing the so called “body-on-chip” devices to better predict the systemic effects of drug candidates. This review describes existing biomimetic immune organs-on-chip, highlights their physiological relevance to drug development and discovery and emphasizes the need for developing comprehensive immune system-on-chip models. Such immune models can enhance the performance of novel drug candidates during clinical trials and contribute to reducing the high attrition rate as well as the high cost associated with drug development.
HSA—Coated Magnetic Nanoparticles for MRI-Guided Photodynamic Cancer Therapy
Authors: Ostroverkhov Petr , Semkina Alevtina , Naumenko Victor , Plotnikova Ekaterina , Yakubovskaya Raisa , Vodopyanov Stepan , Abakumov Artem , Majouga Alexander , Grin Michael , Chekhonin Vladimir , Abakumov Maxim
Published: December 2018

Background: Photodynamic therapy (PDT) is a promising technique for cancer treatment; however, low tissue permeability for irradiating light and insufficient photosensitizer (PS) accumulation in tumors limit its clinical potential. Nanoparticles are engineered to improve selective drug delivery to tumor sites, but its accumulation is highly variable between tumors and patients. Identifying PS accumulation peak in a personalized manner is crucial for therapeutic outcome. Magnetic nanoparticles (MNPs) provide opportunity for tracking drug accumulation in dynamics using non-invasive magnetic resonance imaging (MRI). The purpose of the study was to evaluate MNP loaded with PS as a theranostic tool for treating cancer in mice xenograft colon cancer models. Methods: MNPs coated with human serum albumin (HSA) were loaded with bacteriochlorine a. MRI, atomic emission spectroscopy (AES) and fluorescent imaging were used to study MNP and drug accumulation rates and dynamics in CT26 tumors. Tumor growth curves were evaluated in animals that received PDT at different time points upon MNP systemic injection. Results: Peak MNP accumulation in tumors was detected by MRI 60 min post injection (pi) and the data were verified by AES and fluorescent imaging. Up to 17% of injected dose/g of tissue was delivered to malignant tissues 24 h after injection. Consistent with MRI predicted drug accumulation peak PDT performed 60 min after intravenous injection was more efficient in inhibiting tumor growth than treatment scheduled 30 min and 240 min pi. Conclusions: PS loading on HAS-coated MNPs is a perspective approach to increase drug delivery to tumor site. Tracking for MNP accumulation by MRI can be used to predict drug concentration peak in tumors and to adjust PDT time scheduling for improved antitumor response.
Confined Polymers as Self-Avoiding Random Walks on Restricted Lattices
Authors: Benito Javier , Karayiannis Nikos Ch. , Laso Manuel
Published: December 2018

Polymers in highly confined geometries can display complex morphologies including ordered phases. A basic component of a theoretical analysis of their phase behavior in confined geometries is the knowledge of the number of possible single-chain conformations compatible with the geometrical restrictions and the established crystalline morphology. While the statistical properties of unrestricted self-avoiding random walks (SAWs) both on and off-lattice are very well known, the same is not true for SAWs in confined geometries. The purpose of this contribution is (a) to enumerate the number of SAWs on the simple cubic (SC) and face-centered cubic (FCC) lattices under confinement for moderate SAW lengths, and (b) to obtain an approximate expression for their behavior as a function of chain length, type of lattice, and degree of confinement. This information is an essential requirement for the understanding and prediction of entropy-driven phase transitions of model polymer chains under confinement. In addition, a simple geometric argument is presented that explains, to first order, the dependence of the number of restricted SAWs on the type of SAW origin.
Accuracy Assessment on MODIS (V006), GLASS and MuSyQ Land-Surface Albedo Products: A Case Study in the Heihe River Basin, China
Authors: Wu Xiaodan , Wen Jianguang , Xiao Qing , You Dongqin , Dou Baocheng , Lin Xinwen , Hueni Andreas
Published: December 2018

This study assessed accuracies of MCD43A3, Global Land-Surface Satellite (GLASS) and forthcoming Multi-source Data Synergized Quantitative Remote Sensing Production system (MuSyQ) albedos using ground observations and Huan Jing (HJ) data over the Heihe River Basin. MCD43A3 and MuSyQ albedos show similar high accuracies with identical root mean square errors (RMSE). Nevertheless, MuSyQ albedo is better correlated with ground measurements when sufficient valid observations are available or snow-free. The opposite happens when less than seven valid observations are available. GLASS albedo presents a larger RMSE than MCD43A3 and MuSyQ albedos in comparison with ground measurements. Over surfaces with smaller seasonal variations, MCD43A3 and MuSyQ albedos show smaller RMSEs than GLASS albedo in comparison with HJ albedo. However, for surfaces with larger temporal variations, both RMSEs and R2 of GLASS albedo are comparable with MCD43A3 and MuSyQ. Generally, MCD43A3 and MuSyQ albedos featured the same RMSEs of 0.034 and similar R2 (0.920 and 0.903, respectively), which are better than GLASS albedo (RMSE = 0.043, R2 = 0.787). However, when it comes to comparison with aggregated HJ albedo, MuSyQ and GLASS albedos are with lower RMSEs of 0.027 and 0.032 and higher R2 of 0.900 and 0.898 respectively than MCD43A3 (RMSE = 0.038, R2 = 0.836). Despite the limited geographic region of the study area, they still provide an important insight into the accuracies of three albedo products.
The Use of Photogrammetry to Construct Time Series of Vegetation Permeability to Water and Seed Transport in Agricultural Waterways
Authors: Vinatier Fabrice , Dollinger Jeanne , Rudi Gabrielle , Feurer Denis , Belaud Gilles , Bailly Jean-Stéphane
Published: December 2018

Terrestrial vegetation has numerous positive effects on the main regulating services of agricultural channels, such as seed retention, pollutant mitigation, bank stabilization, and sedimentation, and this vegetation acts as a porous medium for the flow of matter through the channels. This vegetation also limits the water conveyance in channels, and consequently is frequently removed by farmers to increase its porosity. However, the temporal effects of these management practices remain poorly understood. Indeed, the vegetation porosity exhibits important temporal variations according to the maintenance schedule, and the water level also varies with time inside a given channel section according to rainfall events or irrigation practices. To maximise the impacts of vegetation on agricultural channels, it is now of primary importance to measure vegetation porosity according to water level over a long time period rather than at a particular time. Time series of such complex vegetation characteristics have never been studied using remote sensing methods. Here, we present a new approach using the Structure-from-Motion approach using a Multi-View Stereo algorithm (SfM-MVS) technique to construct time series of herbaceous vegetation porosity in a real agricultural channel managed by five different practices: control, dredging, mowing, burning, and chemical weeding. We post-processed the time series of point clouds to create an indicator of vegetation porosity for the whole section and of the surface of the channel. Mowing and chemical weeding are the practices presenting the most favorable temporal evolutions of the porosity indicators regarding flow events. Burning did not succeed in restoring the porosity of the channel due to quick recovery of the vegetation and dephasing of the maintenance calendar with the flow events. The high robustness of the technique and the automatization of the SfM-MVS calculation together with the post-processing of the point clouds should help in handling time series of SfM-MVS data for applications in ecohydrology or agroecology.
Convolutional Neural Network Based Multipath Detection Method for Static and Kinematic GPS High Precision Positioning
Authors: Quan Yiming , Lau Lawrence , Roberts Gethin Wyn , Meng Xiaolin , Zhang Chao
Published: December 2018

Global Positioning System (GPS) has been used in many aerial and terrestrial high precision positioning applications. Multipath affects positioning and navigation performance. This paper proposes a convolutional neural network based carrier-phase multipath detection method. The method is based on the fact that the features of multipath characteristics in multipath contaminated data can be learned and identified by a convolutional neural network. The proposed method is validated with simulated and real GPS data and compared with existing multipath mitigation methods in position domain. The results show the proposed method can detect about 80% multipath errors (i.e., recall) in both simulated and real data. The impact of the proposed method on positioning accuracy improvement is demonstrated with two datasets, 18–30% improvement is obtained by down-weighting the detected multipath measurements. The focus of this paper is on the development and test of the proposed convolutional neural network based multipath detection algorithm.
A Deep Convolution Neural Network Method for Land Cover Mapping: A Case Study of Qinhuangdao, China
Authors: Hu Yunfeng , Zhang Qianli , Zhang Yunzhi , Yan Huimin
Published: December 2018

Land cover and its dynamic information is the basis for characterizing surface conditions, supporting land resource management and optimization, and assessing the impacts of climate change and human activities. In land cover information extraction, the traditional convolutional neural network (CNN) method has several problems, such as the inability to be applied to multispectral and hyperspectral satellite imagery, the weak generalization ability of the model and the difficulty of automating the construction of a training database. To solve these problems, this study proposes a new type of deep convolutional neural network based on Landsat-8 Operational Land Imager (OLI) imagery. The network integrates cascaded cross-channel parametric pooling and average pooling layer, applies a hierarchical sampling strategy to realize the automatic construction of the training dataset, determines the technical scheme of model-related parameters, and finally performs the automatic classification of remote sensing images. This study used the new type of deep convolutional neural network to extract land cover information from Qinhuangdao City, Hebei Province, and compared the experimental results with those obtained by traditional methods. The results show that: (1) The proposed deep convolutional neural network (DCNN) model can automatically construct the training dataset and classify images. This model performs the classification of multispectral and hyperspectral satellite images using deep neural networks, which improves the generalization ability of the model and simplifies the application of the model. (2) The proposed DCNN model provides the best classification results in the Qinhuangdao area. The overall accuracy of the land cover data obtained is 82.0%, and the kappa coefficient is 0.76. The overall accuracy is improved by 5% and 14% compared to the support vector machine method and the maximum likelihood classification method, respectively.
Differing Responses to Rainfall Suggest More Than One Functional Type of Grassland in South Africa
Authors: Van den Hoof Catherine , Verstraete Michel , Scholes Robert J.
Published: December 2018

Grasslands, which represent around 40% of the terrestrial area, are mostly located in arid and semi-arid zones. Semiarid ecosystems in Africa have been identified as being particularly vulnerable to the impacts of increased human pressure on land, as well as enhanced climate variability. Grasslands are indeed very responsive to variations in precipitation. This study evaluates the sensitivity of the grassland ecosystem to precipitation variability in space and time, by identifying the factors controlling this response, based on monthly precipitation data from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) data from the Multi-angle Imaging SpectroRadiometer-High Resolution (MISR-HR) datasets, used as proxy for productivity, at 60 grassland sites in South Africa. Our results show that MISR-HR products adequately capture the spatial and temporal variability in productivity at scales that are relevant to this study, and they are therefore a good tool to study climate change impacts on ecosystem at small spatial scales over large spatial and temporal domains. We show that combining several determinants and accounting for legacies improves our ability to understand patterns, identify areas of vulnerability, and predict the future of grassland productivity. Mean annual precipitation is a good predictor of mean grassland productivity. The grasslands with a mean annual rainfall above about 530 mm have a different functional response to those receiving less than that amount of rain, on average. On the more arid and less fertile soils, large inter-annual variability reduces productivity. Our study suggests that grasslands on the more marginal soils are the most vulnerable to climate change.
Improving the Performance of 3-D Radiative Transfer Model FLIGHT to Simulate Optical Properties of a Tree-Grass Ecosystem
Authors: Melendo-Vega José , Martín M. , Pacheco-Labrador Javier , González-Cascón Rosario , Moreno Gerardo , Pérez Fernando , Migliavacca Mirco , García Mariano , North Peter , Riaño David
Published: December 2018

The 3-D Radiative Transfer Model (RTM) FLIGHT can represent scattering in open forest or savannas featuring underlying bare soils. However, FLIGHT might not be suitable for multilayered tree-grass ecosystems (TGE), where a grass understory can dominate the reflectance factor (RF) dynamics due to strong seasonal variability and low tree fractional cover. To address this issue, we coupled FLIGHT with the 1-D RTM PROSAIL. The model is evaluated against spectral observations of proximal and remote sensing sensors: the ASD Fieldspec® 3 spectroradiometer, the Airborne Spectrographic Imager (CASI) and the MultiSpectral Instrument (MSI) onboard Sentinel-2. We tested the capability of both PROSAIL and PROSAIL+FLIGHT to reproduce the variability of different phenological stages determined by 16-year time series analysis of Moderate Resolution Imaging Spectroradiometer-Normalized Difference Vegetation Index (MODIS-NDVI). Then, we combined concomitant observations of biophysical variables and RF to test the capability of the models to reproduce observed RF. PROSAIL achieved a Relative Root Mean Square Error (RRMSE) between 6% to 32% at proximal sensing scale. PROSAIL+FLIGHT RRMSE ranged between 7% to 31% at remote sensing scales. RRMSE increased in periods when large fractions of standing dead material mixed with emergent green grasses —especially in autumn—; suggesting that the model cannot represent the spectral features of this material. PROSAIL+FLIGHT improves RF simulation especially in summer and at mid-high view angles.
IoT Device Security: Challenging “A Lightweight RFID Mutual Authentication Protocol Based on Physical Unclonable Function”
Authors: Bendavid Ygal , Bagheri Nasour , Safkhani Masoumeh , Rostampour Samad
Published: December 2018

With the exponential increase of Internet of things (IoT) connected devices, important security risks are raised as any device could be used as an attack channel. This preoccupation is particularly important with devices featuring limited processing power and memory capabilities for security purposes. In line with this idea, Xu et al. (2018) proposed a lightweight Radio Frequency Identification (RFID) mutual authentication protocol based on Physical Unclonable Function (PUF)—ensuring mutual tag-reader verification and preventing clone attacks. While Xu et al. claim that their security protocol is efficient to protect RFID systems, we found it still vulnerable to a desynchronization attack and to a secret disclosure attack. Hence, guidelines for the improvements to the protocol are also suggested, for instance by changing the structure of the messages to avoid trivial attacks. In addition, we provide an explicit protocol for which our formal and informal security analysis have found no weaknesses.
Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 2—Neural Networks and K-Nearest Neighbor Classifier Approach
Authors: Junior Pedro , D’Addona Doriana M. , Aguiar Paulo , Teti Roberto
Published: December 2018

This paper presents an approach for impedance-based sensor monitoring of dressing tool condition in grinding by using the electromechanical impedance (EMI) technique. This method was introduced in Part 1 of this work and the purpose of this paper (Part 2) is to achieve an optimal selection of the excitation frequency band based on multi-layer neural networks (MLNN) and k-nearest neighbor classifier (k-NN). The proposed approach was validated on the basis of dressing tool condition information obtained from the monitoring of experimental dressing tests with two industrial stationary single-point dressing tools. Moreover, representative damage indices for diverse damage cases, obtained from impedance signatures at different frequency bands, were taken into account for MLNN data processing. The intelligent system was able to select the most damage-sensitive features based on optimal frequency band. The best models showed a general overall error lower than 2%, thus robustly contributing to the efficient automation of grinding and dressing operations. The promising results of this study foster the EMI-based sensor monitoring approach to fault diagnosis in dressing operations and its effective implementation for industrial grinding process automation.
Cable Interlayer Slip Damage Identification Based on the Derivatives of Eigenparameters
Authors: Zhong Jintu , Yan Quansheng , Mei Liu , Ye Xijun , Wu Jie
Published: December 2018

Cables are the main load-bearing structural components of long-span bridges, such as suspension bridges and cable-stayed bridges. When relative slip occurs among the wires in a cable, the local bending stiffness of the cable will significantly decrease, and the cable enters a local interlayer slip damage state. The decrease in the local bending stiffness caused by the local interlayer slip damage to the cable is symmetric or approximately symmetric for multiple elements at both the fixed end and the external load position. An eigenpair sensitivity identification method is introduced in this study to identify the interlayer slip damage to the cable. First, an eigenparameter sensitivity calculation formula is deduced. Second, the cable is discretized as a mass-spring-damping structural system considering stiffness and damping, and the magnitude of the cable interlayer slip damage is simulated based on the degree of stiffness reduction. The Tikhonov regularization method is introduced to solve the damage identification equation of the inverse problem, and artificial white noise is introduced to evaluate the robustness of the method to noise. Numerical examples of stayed cables are investigated to illustrate the efficiency and accuracy of the method proposed in this study.
An Intelligent Wireless System for Field Ecology Monitoring and Forest Fire Warning
Authors: Zheng Yili , Zhao Yandong , Liu Weiping , Liu Shengbo , Yao Ruting
Published: December 2018

Based on Internet-of-Things and multi-sensor technology, an intelligent wireless monitoring system was developed to obtain field ecological parameters and provide forest fire warning in real-time. The GPRS and China’s Beidou satellite communication were selectively used for date transmission in the field with weak cell phone signals. This monitoring system is mainly composed of several field ecological monitoring stations, a cloud server, and online system software. Atmosphere, soil, sunlight and plant parameters of different regions are obtained real-time by sensors stably and reliably. This system has functions such as field ecological data storage, dynamic query, report generation, and data analysis. As an example of typical application, the forest fire weather grade, which was supplemented with the litter layer soil humidity, was calculated to realize the early warning of the local forest fire in this system through continuous experiments at Beijing Jiufeng National Forest Park from March to May 2017 and Inner Mongolia from March to June 2018. The success ratios of data transmission through Beidou satellite were 98.57%, 99.43%, 99.59%, and 98.85%, respectively, in Beijing, and through GPRS were 99.89% and 99.90% in Inner Mongolia. Long-term real-time field ecological monitoring and forest fire warning were successfully realized. This system can be widely used for big data field acquisition and analysis in forest and agriculture regions.
Development of Limited-Angle Iterative Reconstruction Algorithms with Context Encoder-Based Sinogram Completion for Micro-CT Applications
Authors: Jin Shih-Chun , Hsieh Chia-Jui , Chen Jyh-Cheng , Tu Shih-Huan , Chen Ya-Chen , Hsiao Tzu-Chien , Liu Angela , Chou Wen-Hsiang , Chu Woei-Chyn , Kuo Chih-Wei
Published: December 2018

Limited-angle iterative reconstruction (LAIR) reduces the radiation dose required for computed tomography (CT) imaging by decreasing the range of the projection angle. We developed an image-quality-based stopping-criteria method with a flexible and innovative instrument design that, when combined with LAIR, provides the image quality of a conventional CT system. This study describes the construction of different scan acquisition protocols for micro-CT system applications. Fully-sampled Feldkamp (FDK)-reconstructed images were used as references for comparison to assess the image quality produced by these tested protocols. The insufficient portions of a sinogram were inpainted by applying a context encoder (CE), a type of generative adversarial network, to the LAIR process. The context image was passed through an encoder to identify features that were connected to the decoder using a channel-wise fully-connected layer. Our results evidence the excellent performance of this novel approach. Even when we reduce the radiation dose by 1/4, the iterative-based LAIR improved the full-width half-maximum, contrast-to-noise and signal-to-noise ratios by 20% to 40% compared to a fully-sampled FDK-based reconstruction. Our data support that this CE-based sinogram completion method enhances the efficacy and efficiency of LAIR and that would allow feasibility of limited angle reconstruction.
Fast Detection of Sclerotinia Sclerotiorum on Oilseed Rape Leaves Using Low-Altitude Remote Sensing Technology
Authors: Cao Feng , Liu Fei , Guo Han , Kong Wenwen , Zhang Chu , He Yong
Published: December 2018

Sclerotinia sclerotiorum, one of the major diseases infecting oilseed rape leaves, has seriously affected crop yield and quality. In this study, an indoor unmanned aerial vehicle (UAV) low-altitude remote sensing simulation platform was built for disease detection. Thermal, multispectral and RGB images were acquired before and after being artificially inoculated with Sclerotinia sclerotiorum on oilseed rape leaves. New image registration and fusion methods based on scale-invariant feature transform (SIFT) were presented to construct a fused database using multi-model images. The changes of temperature distribution in different sections of infected areas were analyzed by processing thermal images, the maximum temperature difference (MTD) on a single leaf reached 1.7 degrees Celsius 24 h after infection. Four machine learning models were established using thermal images and fused images respectively, including support vector machine (SVM), random forest (RF), K-nearest neighbor (KNN) and naïve Bayes (NB). The results demonstrated that the classification accuracy was improved by 11.3% after image fusion, and the SVM model obtained a classification accuracy of 90.0% on the task of classifying disease severity. The overall results indicated the UAV low-altitude remote sensing simulation platform equipped with multi-sensors could be used to early detect Sclerotinia sclerotiorum on oilseed rape leaves.
Partial Angular Sparse Representation Based DOA Estimation Using Sparse Separate Nested Acoustic Vector Sensor Array
Authors: Li Jianfeng , Li Zheng , Zhang Xiaofei
Published: December 2018

In this paper, the issue of direction of arrival (DOA) estimation is discussed, and a partial angular sparse representation (SR)-based method using a sparse separate nested acoustic vector sensor (SSN-AVS) array is developed. Traditional AVS array is improved by separating the pressure sensor array and velocity sensor array into two different sparse array geometries with nested relationship. This improved array geometry can achieve large degrees of freedom (DOF) after the extended vectorization of the cross-covariance matrix, and only partial SR of the angle is required by exploiting the cyclic phase ambiguity caused by the large inter-element spacing of the virtual array. Joint sparse recovery is developed to amend the grid offset and unitary transformation is utilized to transform the complex atoms into real-valued ones. After sparse recovery, the sparse vector can simultaneously provide high-resolution but ambiguous angle estimation and unambiguous reference angle estimation embedded in the AVS array, and they are combined to obtain unique and high-resolution DOA estimation. Compared to other state-of-the-art DOA estimation methods using the AVS array, the proposed algorithm can provide better DOA estimation performance while requiring lower complexity. Multiple simulation results verify the effectiveness of the approach.
Model-Based Fault Diagnosis of an Anti-Lock Braking System via Structural Analysis
Authors: Chen Qi , Tian Wenfeng , Chen Wuwei , Ahmed Qadeer , Wu Yanming
Published: December 2018

The anti-lock braking system (ABS) is an essential part in ensuring safe driving in vehicles. The Security of onboard safety systems is very important. In order to monitor the functions of ABS and avoid any malfunction, a model-based methodology with respect to structural analysis is employed in this paper to achieve an efficient fault detection and identification (FDI) system design. The analysis involves five essential steps of SA applied to ABS, which includes critical faults analysis, fault modelling, fault detectability analysis and fault isolability analysis, Minimal Structural Over-determined (MSO) sets selection, and MSO-based residual design. In terms of the four faults in the ABS, they are evaluated to be detectable through performing a structural representation and making the Dulmage-Mendelsohn decomposition with respect to the fault modelling, and then they are proved to be isolable based on the fault isolability matrix via SA. After that, four corresponding residuals are generated directly by a series of suggested equation combinations resulting from four MSO sets. The results generated by numerical simulations show that the proposed FDI system can detect and isolate all the injected faults, which is consistent with the theoretical analysis by SA, and also eventually validated by experimental testing on the vehicle (EcoCAR2) ABS.
A Novel Positioning System Based on Coverage Area Pruning in Wireless Sensor Networks
Authors: Huang Shih-Chang , Li Fu-Gong
Published: December 2018

Wireless sensor networks are commonly applied in environmental monitoring applications. The crucial factor in such applications is to accurately retrieve the location of a monitoring event. Although many technologies have been proposed for target positioning, the devices used in such methods require better computational abilities or special hardware that is unsuitable for sensor networks with limited ability. Therefore, a range-free positioning algorithm, named coverage area pruning positioning system (CAPPS), is proposed in this study. First, the proposed CAPPS approach determines the area that includes the target approximately by using sensor nodes that can detect the target. Next, CAPPS uses sensor nodes that cannot detect the target to prune the area to improve positioning accuracy. The radio coverage variation is evaluated in a practical scenario, and a heuristic mechanism is proposed to reduce false positioning probability. Simulation results show that the size of the positioning area computed by CAPPS is smaller than that computed using distance vector hop, angle of arrival, and received signal strength indicator by approximately 98%, 97%, and 93%, respectively. In the radio variation scenario, the probability of determining an area excluding the target can be reduced from 50%–95% to 10%–30% by applying the proposed centroid point mechanism.
An RFID-Based Sensor for Masonry Crack Monitoring
Authors: Donelli Massimo
Published: December 2018

A radio frequency identifier (RFID) tag sensor for the real time monitoring of cracks in civil engineering building is presented in this work. The RFID tag is equipped with a piezoelectric sensor able to detect small movements of crack in order to prevent collapses of buildings or civil engineering structures. The information is delivered by using the modulated scattering technique (MST) which permits to obtain high operative ranges. The sensor is passive, the power supply is provided by means of a suitable rect-antenna and a Chockcroft-Walton multiplier circuit powered by means of the impinging interrogating electromagnetic wave. A system prototype, operating in the X band at 10 GHz, has been designed fabricated and experimentally assessed in a realistic scenario obtaining promising results.
Answering the Min-Cost Quality-Aware Query on Multi-Sources in Sensor-Cloud Systems
Authors: Li Mohan , Sun Yanbin , Jiang Yu , Tian Zhihong
Published: December 2018

In sensor-based systems, the data of an object is often provided by multiple sources. Since the data quality of these sources might be different, when querying the observations, it is necessary to carefully select the sources to make sure that high quality data is accessed. A solution is to perform a quality evaluation in the cloud and select a set of high-quality, low-cost data sources (i.e., sensors or small sensor networks) that can answer queries. This paper studies the problem of min-cost quality-aware query which aims to find high quality results from multi-sources with the minimized cost. The measurement of the query results is provided, and two methods for answering min-cost quality-aware query are proposed. How to get a reasonable parameter setting is also discussed. Experiments on real-life data verify that the proposed techniques are efficient and effective.
SVM Optimization for Brain Tumor Identification Using Infrared Spectroscopic Samples
Authors: Fabelo Himar , Ortega Samuel , Casselden Elizabeth , Loh Jane , Bulstrode Harry , Zolnourian Ardalan , Grundy Paul , M. Callico Gustavo , Bulters Diederik , Sarmiento Roberto
Published: December 2018

The work presented in this paper is focused on the use of spectroscopy to identify the type of tissue of human brain samples employing support vector machine classifiers. Two different spectrometers were used to acquire infrared spectroscopic signatures in the wavenumber range between 1200–3500 cm−1. An extensive analysis was performed to find the optimal configuration for a support vector machine classifier and determine the most relevant regions of the spectra for this particular application. The results demonstrate that the developed algorithm is robust enough to classify the infrared spectroscopic data of human brain tissue at three different discrimination levels.
Electrochemical Instrumentation of an Embedded Potentiostat System (EPS) for a Programmable-System-On-a-Chip
Authors: Muñoz-Martínez Adrián Iván , González Peña Omar Israel , Colomer-Farrarons Jordi , Rodríguez-Delgado José Manuel , Ávila-Ortega Alfonso , Dieck-Assad Graciano
Published: December 2018

Under the main features required on portable devices in electrochemical instrumentation is to have a small size, low power consumption, economically affordable and precision in the measurements. This paper describes the development of a programmable Embedded Potentiostat System (EPS) capable of performing electrochemical sensing over system-on-a-chip platforms. Furthermore, the study explains a circuit design and develops some validation of the entire system. The hardware validation is performed by electrochemical experiments such as Double Step Chronoamperometry (DSC), Linear Sweep Voltammetry (LSV) and Cyclic Voltammetry (CV); moreover, a comparison of the experimental signals between a commercial potentiostat and the EPS was done by analysis of errors on the response signal. Results illustrate that the EPS is capable of handling currents in the range of absolute values of 86.44 to 3000 nA and having control voltages in the range of ±2 V. The device can support from 50 to 2000 samples per second. The EPS capabilities were compared with other compact potentiostats. The programmable EPS is an original approach which hugely reduces the hardware complexity and leads the way to create new applications for Point-of-Care or industrial developments with a reusable full electronics module.