bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Heavy Precipitation Simulated by the WRF Model Using 4D-Var Data Assimilation with TRMM 3B42 and GPM IMERG over the Huaihe River Basin, China
Authors: Yi Lu , Zhang Wanchang , Wang Kai
Published: April 2018

To obtain independent, consecutive, and high-resolution precipitation data, the four-dimensional variational (4D-Var) method was applied to directly assimilate satellite precipitation products into the Weather Research and Forecasting (WRF) model. The precipitation products of the Tropical Rainfall Measuring Mission 3B42 (TRMM 3B42) and its successor, the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM IMERG) were assimilated in this study. Two heavy precipitation events that occurred over the Huaihe River basin in eastern China were studied. Before assimilation, the WRF model simulations were first performed with different forcing data to select more suitable forcing data and determine the control experiments for the subsequent assimilation experiments. Then, TRMM 3B42 and GPM IMERG were separately assimilated into the WRF. The simulated precipitation results in the outer domain (D01), with a 27-km resolution, and the inner domain (D02), with a 9-km resolution, were evaluated in detail. The assessments showed that (1) 4D-Var with TRMM 3B42 or GPM IMERG could both significantly improve WRF precipitation predictions at a time interval of approximately 12 h; (2) the WRF simulated precipitation assimilated with GPM IMERG outperformed the one with TRMM 3B42; (3) for the WRF output precipitation assimilated with GPM IMERG over D02, which has spatiotemporal resolutions of 9 km and 50 s, the correlation coefficients of the studied events in August and November were 0.74 and 0.51, respectively, at the point and daily scales, and the mean Heidke skill scores for the two studied events both reached 0.31 at the grid and hourly scales. This study can provide references for the assimilation of TRMM 3B42 or GPM IMERG into the WRF model using 4D-Var, which is especially valuable for hydrological applications of GPM IMERG during the transition period from the TRMM era into the GPM era.
The Use of Three-Dimensional Convolutional Neural Networks to Interpret LiDAR for Forest Inventory
Authors: Ayrey Elias , Hayes Daniel J.
Published: April 2018

As light detection and ranging (LiDAR) technology becomes more available, it has become common to use these datasets to generate remotely sensed forest inventories across landscapes. Traditional methods for generating these inventories employ the use of height and proportion metrics to measure LiDAR returns and relate these back to field data using predictive models. Here, we employ a three-dimensional convolutional neural network (CNN), a deep learning technique that scans the LiDAR data and automatically generates useful features for predicting forest attributes. We test the accuracy in estimating forest attributes using the three-dimensional implementations of different CNN models commonly used in the field of image recognition. Using the best performing model architecture, we compared CNN performance to models developed using traditional height metrics. The results of this comparison show that CNNs produced 12% less prediction error when estimating biomass, 6% less in estimating tree count, and 2% less when estimating the percentage of needleleaf trees. We conclude that using CNNs can be a more accurate means of interpreting LiDAR data for forest inventories compared to standard approaches.
A Novel Affine and Contrast Invariant Descriptor for Infrared and Visible Image Registration
Authors: Liu Xiangzeng , Ai Yunfeng , Zhang Juli , Wang Zhuping
Published: April 2018

Infrared and visible image registration is a very challenging task due to the large geometric changes and the significant contrast differences caused by the inconsistent capture conditions. To address this problem, this paper proposes a novel affine and contrast invariant descriptor called maximally stable phase congruency (MSPC), which integrates the affine invariant region extraction with the structural features of images organically. First, to achieve the contrast invariance and ensure the significance of features, we detect feature points using moment ranking analysis and extract structural features via merging phase congruency images in multiple orientations. Then, coarse neighborhoods centered on the feature points are obtained based on Log-Gabor filter responses over scales and orientations. Subsequently, the affine invariant regions of feature points are determined by using maximally stable extremal regions. Finally, structural descriptors are constructed from those regions and the registration can be implemented according to the correspondence of the descriptors. The proposed method has been tested on various infrared and visible pairs acquired by different platforms. Experimental results demonstrate that our method outperforms several state-of-the-art methods in terms of robustness and precision with different image data and also show its effectiveness in the application of trajectory tracking.
Automatic Discovery and Geotagging of Objects from Street View Imagery
Authors: Krylov Vladimir A. , Kenny Eamonn , Dahyot Rozenn
Published: April 2018

Many applications, such as autonomous navigation, urban planning, and asset monitoring, rely on the availability of accurate information about objects and their geolocations. In this paper, we propose the automatic detection and computation of the coordinates of recurring stationary objects of interest using street view imagery. Our processing pipeline relies on two fully convolutional neural networks: the first segments objects in the images, while the second estimates their distance from the camera. To geolocate all the detected objects coherently we propose a novel custom Markov random field model to estimate the objects’ geolocation. The novelty of the resulting pipeline is the combined use of monocular depth estimation and triangulation to enable automatic mapping of complex scenes with the simultaneous presence of multiple, visually similar objects of interest. We validate experimentally the effectiveness of our approach on two object classes: traffic lights and telegraph poles. The experiments report high object recall rates and position precision of approximately 2 m, which is approaching the precision of single-frequency GPS receivers.
Estimation of Longitudinal Force and Sideslip Angle for Intelligent Four-Wheel Independent Drive Electric Vehicles by Observer Iteration and Information Fusion
Authors: Chen Te , Chen Long , Xu Xing , Cai Yingfeng , Jiang Haobin , Sun Xiaoqiang
Published: April 2018

Exact estimation of longitudinal force and sideslip angle is important for lateral stability and path-following control of four-wheel independent driven electric vehicle. This paper presents an effective method for longitudinal force and sideslip angle estimation by observer iteration and information fusion for four-wheel independent drive electric vehicles. The electric driving wheel model is introduced into the vehicle modeling process and used for longitudinal force estimation, the longitudinal force reconstruction equation is obtained via model decoupling, the a Luenberger observer and high-order sliding mode observer are united for longitudinal force observer design, and the Kalman filter is applied to restrain the influence of noise. Via the estimated longitudinal force, an estimation strategy is then proposed based on observer iteration and information fusion, in which the Luenberger observer is applied to achieve the transcendental estimation utilizing less sensor measurements, the extended Kalman filter is used for a posteriori estimation with higher accuracy, and a fuzzy weight controller is used to enhance the adaptive ability of observer system. Simulations and experiments are carried out, and the effectiveness of proposed estimation method is verified.
Flow Channel Influence of a Collision-Based Piezoelectric Jetting Dispenser on Jet Performance
Authors: Zhou Can , Deng Guiling , Li Junhui , Duan Ji’an
Published: April 2018

To improve the jet performance of a bi-piezoelectric jet dispenser, mathematical and simulation models were established according to the operating principle. In order to improve the accuracy and reliability of the simulation calculation, a viscosity model of the fluid was fitted to a fifth-order function with shear rate based on rheological test data, and the needle displacement model was fitted to a nine-order function with time based on real-time displacement test data. The results show that jet performance is related to the diameter of the nozzle outlet and the cone angle of the nozzle, and the impacts of the flow channel structure were confirmed. The approach of numerical simulation is confirmed by the testing results of droplet volume. It will provide a reliable simulation platform for mechanical collision-based jet dispensing and a theoretical basis for micro jet valve design and improvement.
dPCR: A Technology Review
Authors: Quan Phenix-Lan , Sauzade Martin , Brouzes Eric
Published: April 2018

Digital Polymerase Chain Reaction (dPCR) is a novel method for the absolute quantification of target nucleic acids. Quantification by dPCR hinges on the fact that the random distribution of molecules in many partitions follows a Poisson distribution. Each partition acts as an individual PCR microreactor and partitions containing amplified target sequences are detected by fluorescence. The proportion of PCR-positive partitions suffices to determine the concentration of the target sequence without a need for calibration. Advances in microfluidics enabled the current revolution of digital quantification by providing efficient partitioning methods. In this review, we compare the fundamental concepts behind the quantification of nucleic acids by dPCR and quantitative real-time PCR (qPCR). We detail the underlying statistics of dPCR and explain how it defines its precision and performance metrics. We review the different microfluidic digital PCR formats, present their underlying physical principles, and analyze the technological evolution of dPCR platforms. We present the novel multiplexing strategies enabled by dPCR and examine how isothermal amplification could be an alternative to PCR in digital assays. Finally, we determine whether the theoretical advantages of dPCR over qPCR hold true by perusing studies that directly compare assays implemented with both methods.
Synchronized High-Speed Vision Sensor Network for Expansion of Field of View
Authors: Noda Akihito , Tabata Satoshi , Ishikawa Masatoshi , Yamakawa Yuji
Published: April 2018

We propose a 500-frames-per-second high-speed vision (HSV) sensor network that acquires frames at a timing that is precisely synchronized across the network. Multiple vision sensor nodes, individually comprising a camera and a PC, are connected via Ethernet for data transmission and for clock synchronization. A network of synchronized HSV sensors provides a significantly expanded field-of-view compared with that of each individual HSV sensor. In the proposed system, the shutter of each camera is controlled based on the clock of the PC locally provided inside the node, and the shutters are globally synchronized using the Precision Time Protocol (PTP) over the network. A theoretical analysis and experiment results indicate that the shutter trigger skew among the nodes is a few tens of microseconds at most, which is significantly smaller than the frame interval of 1000-fps-class high-speed cameras. Experimental results obtained with the proposed system comprising four nodes demonstrated the ability to capture the propagation of a small displacement along a large-scale structure.
Data Decomposition Techniques with Multi-Scale Permutation Entropy Calculations for Bearing Fault Diagnosis
Authors: Yasir Muhammad Naveed , Koh Bong-Hwan
Published: April 2018

This paper presents the local mean decomposition (LMD) integrated with multi-scale permutation entropy (MPE), also known as LMD-MPE, to investigate the rolling element bearing (REB) fault diagnosis from measured vibration signals. First, the LMD decomposed the vibration data or acceleration measurement into separate product functions that are composed of both amplitude and frequency modulation. MPE then calculated the statistical permutation entropy from the product functions to extract the nonlinear features to assess and classify the condition of the healthy and damaged REB system. The comparative experimental results of the conventional LMD-based multi-scale entropy and MPE were presented to verify the authenticity of the proposed technique. The study found that LMD-MPE’s integrated approach provides reliable, damage-sensitive features when analyzing the bearing condition. The results of REB experimental datasets show that the proposed approach yields more vigorous outcomes than existing methods.
How to Stop Disagreeing and Start Cooperatingin the Presence of Asymmetric Packet Loss
Authors: Morales-Ponce Oscar , Schiller Elad M. , Falcone Paolo
Published: April 2018

We consider the design of a disagreement correction protocol in multi-vehicle systems. Vehicles broadcast in real-time vital information such as position, direction, speed, acceleration, intention, etc. This information is then used to identify the risks and adapt their trajectory to maintain the highest performance without compromising the safety. To minimize the risk due to the use of inconsistent information, all cooperating vehicles must agree whether to use the exchanged information to operate in a cooperative mode or use the only local information to operate in an autonomous mode. However, since wireless communications are prone to failures, it is impossible to deterministically reach an agreement. Therefore, any protocol will exhibit necessary disagreement periods. In this paper, we investigate whether vehicles can still cooperate despite communication failures even in the scenario where communication is suddenly not available. We present a deterministic protocol that allows all participants to either operate a cooperative mode when vehicles can exchange all the information in a timely manner or operate in autonomous mode when messages are lost. We show formally that the disagreement time is bounded by the time that the communication channel requires to deliver messages and validate our protocol using NS-3 simulations. We explain how the proposed solution can be used in vehicular platooning to attain high performance and still guarantee high safety standards despite communication failures.
Evolutionary Design of Convolutional Neural Networks for Human Activity Recognition in Sensor-Rich Environments
Authors: Baldominos Alejandro , Saez Yago , Isasi Pedro
Published: April 2018

Human activity recognition is a challenging problem for context-aware systems and applications. It is gaining interest due to the ubiquity of different sensor sources, wearable smart objects, ambient sensors, etc. This task is usually approached as a supervised machine learning problem, where a label is to be predicted given some input data, such as the signals retrieved from different sensors. For tackling the human activity recognition problem in sensor network environments, in this paper we propose the use of deep learning (convolutional neural networks) to perform activity recognition using the publicly available OPPORTUNITY dataset. Instead of manually choosing a suitable topology, we will let an evolutionary algorithm design the optimal topology in order to maximize the classification F1 score. After that, we will also explore the performance of committees of the models resulting from the evolutionary process. Results analysis indicates that the proposed model was able to perform activity recognition within a heterogeneous sensor network environment, achieving very high accuracies when tested with new sensor data. Based on all conducted experiments, the proposed neuroevolutionary system has proved to be able to systematically find a classification model which is capable of outperforming previous results reported in the state-of-the-art, showing that this approach is useful and improves upon previously manually-designed architectures.
Microplate Chemiluminescent Assay for DNA Detection Using Apoperoxidase-Oligonucleotide as Capture Conjugate and HRP-Streptavidin Signaling System
Authors: Sakharov Ivan
Published: April 2018

A covalent conjugate of horseradish apoperoxidase and amino-containing oligonucleotide was synthesized for the first time. Using the obtained conjugate as a capture reagent chemiluminescent microtiter plate-based assay for detection of 35-mer fragment of hepatitis B virus (HBV) DNA (proof-of-concept analyte) was developed. To detect the target DNA, a signaling system consisted of biotinylated reporter oligonucleotide and HRP-streptavidin conjugate was used. The high sensitivity of the assay was due to the enhanced chemiluminescence reaction, where 3-(10′-phenothiazinyl)propane-1-sulfonate/N-morpholinopyridine pair was used as an enhancer. Under the optimized conditions the limit of detection and a working range of the assay were 3 pM and 6–100 pM, respectively. The assay sensitivity was 1.6 × 105 RLU/pM of target. The coefficient of variation (CV) for determination of HBV DNA within the working range was lower than 6%.
Proximity-Induced Artefacts in Magnetic Imaging with Nitrogen-Vacancy Ensembles in Diamond
Authors: Tetienne Jean-Philippe , Broadway David A. , Lillie Scott E. , Dontschuk Nikolai , Teraji Tokuyuki , Hall Liam T. , Stacey Alastair , Simpson David A. , Hollenberg Lloyd C. L.
Published: April 2018

Magnetic imaging with ensembles of nitrogen-vacancy (NV) centres in diamond is a recently developed technique that allows for quantitative vector field mapping. Here we uncover a source of artefacts in the measured magnetic field in situations where the magnetic sample is placed in close proximity (a few tens of nm) to the NV sensing layer. Using magnetic nanoparticles as a test sample, we find that the measured field deviates significantly from the calculated field, in shape, amplitude and even in sign. By modelling the full measurement process, we show that these discrepancies are caused by the limited measurement range of NV sensors combined with the finite spatial resolution of the optical readout. We numerically investigate the role of the stand-off distance to identify an artefact-free regime, and discuss an application to ultrathin materials. This work provides a guide to predict and mitigate proximity-induced artefacts that can arise in NV-based wide-field magnetic imaging, and also demonstrates that the sensitivity of these artefacts to the sample can make them a useful tool for magnetic characterisation.
On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
Authors: Shen Tingting , Wang Tao , Sun Yanzan , Wu Yating , Jin Yanliang
Published: April 2018

As nodes in wireless sensor networks are usually powered by nonrenewable batteries, energy efficient design becomes critical. This paper considers a battery-powered transmitter using on-off keying (OOK) modulation and studies its energy efficiency in terms of the battery’s energy consumption for per bit transmission (BECPB). In particular, the transmitter may use one of two distinct types of batteries with battery utilization factor (BUF) depending on discharge current. The first has an instantaneous discharge current (IDC)-based BUF, while the second has a mean discharge current (MDC)-based BUF. For each type of battery, a closed-form BECPB expression is derived under a Rayleigh channel when a prescribed symbol error rate (SER) is guaranteed. Then theoretical analysis is made to study the impact of battery characteristic parameter γ , communication distance d and bandwidth B on the BECPB. Finally, the analysis is corroborated by numerical experimental results, which reveal that: the BECPB for each type of battery increases with γ and d; the BECPB for the two batteries first decreases and then increases with B, and there exists the optimal bandwidth corresponding to the minimum BECPB; the battery with IDC-based BUF corresponds to a larger BECPB. When γ and d are large, the BECPB for each type of battery is significantly higher than that for the ideal battery whose BUF is aways 1. For instance, when γ = 0.015 , d = 90 m and B = 10 kHz, the BECPB for IDC-based and MDC-based battery is nearly 60% amd 25% higher than that of the ideal battery, respectively.
Path Diversity Improved Opportunistic Routing for Underwater Sensor Networks
Authors: Bai Weigang , Wang Haiyan , He Ke , Zhao Ruiqin
Published: April 2018

The packets carried along a pre-defined route in underwater sensor networks are very vulnerble. Node mobility or intermittent channel availability easily leads to unreachable routing. Opportunistic routing has been proven to be a promising paradigm to design routing protocols for underwater sensor networks. It takes advantage of the broadcast nature of the wireless medium to combat packet losses and selects potential paths on the fly. Finding an appropriate forwarding candidate set is a key issue in opportunistic routing. Many existing solutions ignore the impact of candidates location distribution on packet forwarding. In this paper, a path diversity improved candidate selection strategy is applied in opportunistic routing to improve packet forwarding efficiency. It not only maximizes the packet forwarding advancements but also takes the candidate’s location distribution into account. Based on this strategy, we propose two effective routing protocols: position improved candidates selection (PICS) and position random candidates selection (PRCS). PICS employs two-hop neighbor information to make routing decisions. PRCS only uses one-hop neighbor information. Simulation results show that both PICS and PRCS can significantly improve network performance when compared with the previous solutions, in terms of packet delivery ratio, average energy consumption and end-to-end delay.
A Single LiDAR-Based Feature Fusion Indoor Localization Algorithm
Authors: Wang Yun-Ting , Peng Chao-Chung , Ravankar Ankit A. , Ravankar Abhijeet
Published: April 2018

In past years, there has been significant progress in the field of indoor robot localization. To precisely recover the position, the robots usually relies on multiple on-board sensors. Nevertheless, this affects the overall system cost and increases computation. In this research work, we considered a light detection and ranging (LiDAR) device as the only sensor for detecting surroundings and propose an efficient indoor localization algorithm. To attenuate the computation effort and preserve localization robustness, a weighted parallel iterative closed point (WP-ICP) with interpolation is presented. As compared to the traditional ICP, the point cloud is first processed to extract corners and line features before applying point registration. Later, points labeled as corners are only matched with the corner candidates. Similarly, points labeled as lines are only matched with the lines candidates. Moreover, their ICP confidence levels are also fused in the algorithm, which make the pose estimation less sensitive to environment uncertainties. The proposed WP-ICP architecture reduces the probability of mismatch and thereby reduces the ICP iterations. Finally, based on given well-constructed indoor layouts, experiment comparisons are carried out under both clean and perturbed environments. It is shown that the proposed method is effective in significantly reducing computation effort and is simultaneously able to preserve localization precision.
Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning
Authors: Yao Ye , Hu Weitong , Zhang Wei , Wu Ting , Shi Yun-Qing
Published: April 2018

Computer-generated graphics (CGs) are images generated by computer software. The rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs) with the naked eye. In this paper, we propose a method based on sensor pattern noise (SPN) and deep learning to distinguish CGs from NIs. Before being fed into our convolutional neural network (CNN)-based model, these images—CGs and NIs—are clipped into image patches. Furthermore, three high-pass filters (HPFs) are used to remove low-frequency signals, which represent the image content. These filters are also used to reveal the residual signal as well as SPN introduced by the digital camera device. Different from the traditional methods of distinguishing CGs from NIs, the proposed method utilizes a five-layer CNN to classify the input image patches. Based on the classification results of the image patches, we deploy a majority vote scheme to obtain the classification results for the full-size images. The experiments have demonstrated that (1) the proposed method with three HPFs can achieve better results than that with only one HPF or no HPF and that (2) the proposed method with three HPFs achieves 100% accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.
A Refined Crop Drought Monitoring Method Based on the Chinese GF-1 Wide Field View Data
Authors: Chang Sheng , Wu Bingfang , Yan Nana , Zhu Jianjun , Wen Qi , Xu Feng
Published: April 2018

In this study, modified perpendicular drought index (MPDI) models based on the red-near infrared spectral space are established for the first time through the analysis of the spectral characteristics of GF-1 wide field view (WFV) data, with a high spatial resolution of 16 m and the highest frequency as high as once every 4 days. GF-1 data was from the Chinese-made, new-generation high-resolution GF-1 remote sensing satellites. Soil-type spatial data are introduced for simulating soil lines in different soil types for reducing errors of using same soil line. Multiple vegetation indices are employed to analyze the response to the MPDI models. Relative soil moisture content (RSMC) and precipitation data acquired at selected stations are used to optimize the drought models, and the best one is the Two-band enhanced vegetation index (EVI2)-based MPDI model. The crop area that was statistically significantly affected by drought from a local governmental department, and used for validation. High correlations and small differences in drought-affected crop area was detected between the field observation data from the local governmental department and the EVI2-based MPDI results. The percentage of bias is between −21.8% and 14.7% in five sub-areas, with an accuracy above 95% when evaluating the performance via the data for the whole study region. Generally the proposed EVI2-based MPDI for GF-1 WFV data has great potential for reliably monitoring crop drought at a relatively high frequency and spatial scale. Currently there is almost no drought model based on GF-1 data, a full exploitation of the advantages of GF-1 satellite data and further improvement of the capacity to observe ground surface objects can provide high temporal and spatial resolution data source for refined monitoring of crop droughts.
An Improved Fast Self-Calibration Method for Hybrid Inertial Navigation System under Stationary Condition
Authors: Liu Bingqi , Wei Shihui , Su Guohua , Wang Jiping , Lu Jiazhen
Published: April 2018

The navigation accuracy of the inertial navigation system (INS) can be greatly improved when the inertial measurement unit (IMU) is effectively calibrated and compensated, such as gyro drifts and accelerometer biases. To reduce the requirement for turntable precision in the classical calibration method, a continuous dynamic self-calibration method based on a three-axis rotating frame for the hybrid inertial navigation system is presented. First, by selecting a suitable IMU frame, the error models of accelerometers and gyros are established. Then, by taking the navigation errors during rolling as the observations, the overall twenty-one error parameters of hybrid inertial navigation system (HINS) are identified based on the calculation of the intermediate parameter. The actual experiment verifies that the method can identify all error parameters of HINS and this method has equivalent accuracy to the classical calibration on a high-precision turntable. In addition, this method is rapid, simple and feasible.
Displacement and Strain Measurement up to 1000 °C Using a Hollow Coaxial Cable Fabry-Perot Resonator
Authors: Zhu Chen , Chen Yizheng , Zhuang Yiyang , Huang Jie
Published: April 2018

We present a hollow coaxial cable Fabry-Perot resonator for displacement and strain measurement up to 1000 °C. By employing a novel homemade hollow coaxial cable made of stainless steel as a sensing platform, the high-temperature tolerance of the sensor is dramatically improved. A Fabry-Perot resonator is implemented on this hollow coaxial cable by introducing two highly-reflective reflectors along the cable. Based on a nested structure design, the external displacement and strain can be directly correlated to the cavity length of the resonator. By tracking the shift of the amplitude reflection spectrum of the microwave resonator, the applied displacement and strain can be determined. The displacement measurement experiment showed that the sensor could function properly up to 1000 °C. The sensor was also employed to measure the thermal strain of a steel plate during the heating process. The stability of the novel sensor was also investigated. The developed sensing platform and sensing configurations are robust, cost-effective, easy to manufacture, and can be flexibly designed for many other measurement applications in harsh high-temperature environments.
Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation
Authors: Broumandan Ali , Lachapelle Gérard
Published: April 2018

Location information is one of the most vital information required to achieve intelligent and context-aware capability for various applications such as driverless cars. However, related security and privacy threats are a major holdback. With increasing focus on using Global Navigation Satellite Systems (GNSS) for autonomous navigation and related applications, it is important to provide robust navigation solutions, yet signal spoofing for illegal or covert transportation and misleading receiver timing is increasing and now frequent. Hence, detection and mitigation of spoofing attacks has become an important topic. Several contributions on spoofing detection have been made, focusing on different layers of a GNSS receiver. This paper focuses on spoofing detection utilizing self-contained sensors, namely inertial measurement units (IMUs) and vehicle odometer outputs. A spoofing detection approach based on a consistency check between GNSS and IMU/odometer mechanization is proposed. To detect a spoofing attack, the method analyses GNSS and IMU/odometer measurements independently during a pre-selected observation window and cross checks the solutions provided by GNSS and inertial navigation solution (INS)/odometer mechanization. The performance of the proposed method is verified in real vehicular environments. Mean spoofing detection time and detection performance in terms of receiver operation characteristics (ROC) in sub-urban and dense urban environments are evaluated.
Silk Fibroin Nanoparticles for Drug Delivery: Effect of Bovine Serum Albumin and Magnetic Nanoparticles Addition on Drug Encapsulation and Release
Authors: Gianak Olga , Pavlidou Eleni , Sarafidis Charalambos , Karageorgiou Vassilis , Deliyanni Eleni
Published: April 2018

Silk fibroin nanoparticles were prepared in the present study based on phase separation between silk fibroin and polyvinyl alcohol. The drug encapsulation efficiency of the prepared nanoparticles was examined at a range of concentrations from 10 ppm to 500 ppm for pramipexole, curcumin, and propranolol hydrochloride. Silk fibroin nanoparticles encapsulated with propranolol presented the highest drug release profile. In order to improve the drug encapsulation efficiency and drug release performance, a modification of silk fibroin nanoparticles with bovine serum albumin and magnetic nanoparticles was tried. The modification was found to improve the drug encapsulation and release of the modified nanoparticles. Bovine-serum-modified nanoparticles presented the best improvement.
Transmodern War Contexts in English Literature
Authors: Aliaga-Lavrijsen Jessica
Published: April 2018

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Supported Decision-Making from Theory to Practice: Implementing the Right to Enjoy Legal Capacity
Authors: Harding Rosie , Taşcıoğlu Ezgi
Published: April 2018

The right to equal recognition before the law, protected by Article 12 of the United Nations (UN) Convention on the Rights of Persons with Disabilities (CRPD), mandates the use of supported decision-making practices to enable disabled people, particularly those with intellectual and/or psychosocial disabilities, to enjoy their legal capacity. Finding ways to translate this theoretical mandate into practice poses a number of particularly challenging socio-legal issues, which this research seeks to address. The English Mental Capacity Act 2005 (MCA) sets out a right to support with decision-making (s.1(3)), underpinned by a presumption of capacity (s.1(2)). Qualitative interviews with intellectually disabled people, their supporters, and care and support professionals were undertaken to explore how disabled people make decisions in their everyday lives, the kinds of support they need, and the strategies for supported decision-making used in practice. Analysis of these interviews suggests that a range of supported decision-making techniques have been developed in practice and are effective in supporting everyday preferences and some life choices. Paradoxically, it appears that as decisions become more complex, the support available to disabled people reduces. Specifically, much less support is available for more difficult decisions around finances, healthcare and legal matters. We argue that the reasons for this are due to a web of regulatory, social and policy issues. We conclude that implementing the right to enjoy legal capacity through supported decision-making will require a combination of regulatory reform, social change and policy amendment.
Relative Age Effect in Swedish Male and Female Tennis Players Born in 1998–2001
Authors: Gerdin Göran , Hedberg Marie , Hageskog Carl-Axel
Published: April 2018

The relative age effect (RAE) has been extensively debated and researched in both popular media and academic discourse. This study examined RAE in Swedish tennis players born in 1998–2001. The study was conducted in 2015–2016 and includes all ranked Swedish tennis players (n = 1835) registered in the Swedish Tennis Association database from the year 2014. The results show that when the birth dates of the corresponding Swedish population and all the ranked players are compared, they show a moderate RAE; however, the higher up they are in the ranking system, the greater the RAE becomes. Top 10 players display an average of 64.1% being born in the first half of the year. Some gender differences were also found, with a greater proportion of both higher and lower ranked females being born in the first half of the year. In our discussion of the findings we raise several issues that need to be addressed to provide more equal opportunities for all junior players regardless of birth date. Resolving ongoing problems associated with RAE in competitive sports such as tennis is important both in term of prolonged participation in the sport and increased performance. Suggestions made in this article include recognising RAE when designing the format of competitions/tournaments, not using official rankings until the juniors get older, addressing RAE in a “gender sensitive” way, and conducting further in-depth studies in which RAE is understood/examined as being associated with environmental factors. Although these findings show the RAE effect in Swedish tennis players, thus pointing at the need for further consideration in terms of ranking and selection procedures to ensure equal opportunities for player development, the study also concludes by reasserting an emphasis on a holistic approach to player development in which coaches focus on the developmentally appropriate needs and potential of each individual player regardless of their biological age.
Estimations of Nitrogen Concentration in Sugarcane Using Hyperspectral Imagery
Authors: Miphokasap Poonsak , Wannasiri Wasinee
Published: April 2018

This study aims to estimate the spatial variation of sugarcane Canopy Nitrogen Concentration (CNC) using spectral data, which were measured from a spaceborne hyperspectral image. Stepwise Multiple Linear Regression (SMLR) and Support Vector Regression (SVR) were applied to calibrate and validate the CNC estimation models. The raw spectral reflectance was transformed into a First-Derivative Spectrum (FDS) and absorption features to remove the spectral noise and finally used as input variables. The results indicate that the estimation models developed by non-linear SVR based Radial Basis Function (RBF) kernel yield the higher correlation coefficient with CNC compared with the models computed by SMLR. The best model shows the coefficient of determination value of 0.78 and Root Mean Square Error (RMSE) value of 0.035% nitrogen. The narrow sensitive spectral wavelengths for quantifying nitrogen content in the combined cultivar environments existed mainly in the electromagnetic spectrum of the visible-red, longer portion of red edge, shortwave infrared regions and far-near infrared. The most important conclusion from this experiment is that spectral signals from the space hyperspectral data contain the meaningful information for quantifying sugarcane CNC across larger geographic areas. The nutrient deficient areas could be corrected by applying suitable farm management.
Digitalization and Environmental Aims in Municipalities
Authors: Ringenson Tina , Höjer Mattias , Kramers Anna , Viggedal Anna
Published: April 2018

Many municipalities express a wish to use digital technologies to achieve environmental aims. However, there is still a need for a better understanding of how this should practically be done, both among municipalities and among ICT developers. We have used workshops and literature studies to formulate technological abilities of digitalization. We use two EU directives that are relevant for municipal environmental goals and combine the activities that these directives indicate with technological abilities of digitalization, in order to formulate practical implementations of digital technology to help these activities and reach the directives’ goals. We suggest that this method can be used for any municipal goal, as follows: (1) Identify the objective (in our case set by the EU-directives); (2) Identify what activities these points will require or generate; (3a) From a municipal viewpoint: Based on the results of 1 and 2, formulate and structure ideas of how digitalization can support the objectives and how those ideas can be implemented; (3b) From a provider’s viewpoint: Investigate what digital solutions supporting 1 and 2 exist, or how existing services can be tweaked to support the objectives and explore how new digital solutions supporting 1 and 2 can be developed.
Efficiency and Risk in Sustaining China’s Food Production and Security: Evidence from Micro-Level Panel Data Analysis of Japonica Rice Production
Authors: Wang Chengjun , Zhang Zhaoyong , Fei Ximin
Published: April 2018

Sustainable food production and food security are always challenging issues in China. This paper constructs a multi-element two-level constant-elasticity-of-substitution (CES) model to assess technological progress in, and its contribution to, japonica rice production in China. The results show that the speed of technological progress in the production of japonica rice on average was 0.44% per annum in 1985–2013, and technological progress has contributed significantly to the growth of japonica rice production in China. Robustness checks show that the results appear to be sensitive to which sub-sample is used. Labour and some other inputs are found to be significant but negative, especially during the middle sampling period of 1994–2006 and in eastern and western regions. This has important policy implications on the impact of rural-to-urban migration and farmers’ human development.
Tourist Satisfaction, Image, and Loyalty from an Interregional Perspective: An Analysis of Neighboring Areas with Distinct Characteristics
Authors: Gim Tae-Hyoung Tommy
Published: April 2018

A comparison is made among the three areas of the Korean Capital Region to investigate the effects of their different tourism resources on attribute satisfaction and the relationships that attribute satisfaction has with overall satisfaction, country image, and tourist loyalty. Three partial least squares structural equation models find that costs, lodging, and communication are consistently unimportant, but the most important variable differs by area. In Seoul (an international shopping center), shopping is paramount; food is most important in Incheon (where its local cuisines have been well publicized); and satisfaction with tourism administration services is highly valued in Gyeonggi (which lacks facilities for foreign tourists). At the construct level, overall happiness exerts a stronger direct effect on image than on loyalty, but if its indirect effect is considered, the total effect on loyalty surpasses that on image. This is particularly true in Gyeonggi, where tourism resources are less exploited.
Manufacturer’s Decision as Consumers’ Low-Carbon Preference Grows
Authors: Yang Lei , Chen Meng , Cai Yiji , Tsai Sang-Bing
Published: April 2018

This paper investigates five channel structures for manufacturers including three single channels and two dual channels. Consumers’ low-carbon preference is considered to explore how market demands and channel selections will change as it remains stable and grows. To compare performances of the five channel structures, we further get the critical points consisting of construction cost of a platform, revenue proportion through a third-party platform, and offline proportion of total demands. The findings show that, when the construction cost is low, a self-owned platform performs better than a retail channel and a third-party platform. If the offline proportion is high, manufacturers would adopt or add a retail channel. When the manufacturers’ revenue proportion is high, a third-party platform is more profitable. If the consumers’ low-carbon preference grows, dual channels can be chosen to satisfy the increasing online and offline demands. The critical revenue proportion and offline proportion become smaller while the construction cost becomes larger. In addition, numerical analysis is provided to show profit changes and robustness of channel structure. Our findings can provide useful insights for decision-makers to implement low-carbon sustainability.
Land Cover Based Landscape Pattern Dynamics of Anhui Province Using GlobCover and MCD12Q1 Global Land Cover Products
Authors: Zhao Jinling , Wang Jie , Jin Yu , Fan Lingling , Xu Chao , Liang Dong , Huang Linsheng
Published: April 2018

The development and free distribution of global land cover (GLC) products have greatly assisted in the evolution and analysis of relationships between land cover and landscape pattern. In this study, GlobCover and MCD12Q1 GLC datasets of 2005 and 2009 were comparatively used to analyze the variation of land cover in Anhui Province, China at both the class and landscape scale. The land cover classification schemes of both datasets were firstly reclassified to six types of forestland, grassland, wetland, cropland, artificial area, and others, and then FRAGSTATS was used to calculate the landscape indices. The results showed that from 2005 to 2009, the area density of ‘cropland’ landscape decreased, and it increased for ‘wetland’ and ‘artificial area’. The landscape fragmentation of ‘forestland’ and ‘grassland’ were larger. Moreover, over the same period, the class edge (CE) of ‘cropland’ was diminished; while the CE of ‘wetland’ was enhanced and the aggregation became larger. Conversely, the aggregation and shape complexity of ‘artificial area’ remained the same. The clumpiness index (CLUMPY) of ‘cropland’ varied from 0.8995 to 0.9050, indicating a higher aggregation and more concentrated distribution. The heterogeneity index (HT) value of MCD12Q1 and GlobCover datasets varied, respectively, from 0.9642 to 0.9053 and from 0.8867 to 0.8751, demonstrating that the landscape heterogeneity of Anhui Province was reduced from 2005 to 2009. Driving force analysis (DFA) was just performed for ‘artificial area’, ‘cropland’, and ‘wetland’ according to the 2005–2009 statistical yearbook data, because they were apt to be affected by human activities over a relatively short period of time.
Infrastructures as Catalysts: Precipitating Uneven Patterns of Development from Large-Scale Infrastructure Investments
Authors: Rammelt Crelis
Published: April 2018

While infrastructure investments in developing regions may bring about aggregate benefits, the distribution of those benefits cannot be ignored. The present paper examines such distributional effects based on two illustrations: rural roads in Ethiopia and flood control systems in Bangladesh. In both cases, the infrastructures promote particular development patterns towards market-economic transformations and integration. We liken the introduction of these infrastructures to the addition of a catalyst in a chemical reaction. Rural roads, for example, catalyse existing flows of agricultural labour, while flood control catalyses agricultural productivity. Taking the analogy a step further, the effects of a catalyst are known to vary due to the presence of so-called inhibitors and promoters. Applying this to the two cases, the paper reveals that, among other factors, the ownership (or lack thereof) of modes of transportation in Ethiopia and land resources in Bangladesh represent significant promoters (or inhibitors) that can help to explain the unequal distribution of benefits. This question is by no means new; past technical assistance programmes were already fiercely criticized for exacerbating inequalities. Today, commercial and political interests are again intensifying infrastructural investments in developing regions with profound impacts on local economies and livelihoods. Revisiting the question of distribution is, therefore, as relevant as ever.
One-Dimensional Analytical Modeling of Pressure- Retarded Osmosis in a Parallel Flow Configuration for the Desalination Industry in the State of Kuwait
Authors: Al-Anzi Bader S. , Thomas Ashly
Published: April 2018

The present study deals with the application of one-dimensional (1D) analytical expressions for a parallel flow configuration in pressure-retarded osmosis (PRO) exchangers by using actual brine and feed salinity values from the Kuwait desalination industry. The 1D expressions are inspired by the effectiveness-number of transfer unit (ε-NTU) method used in heat exchanger analysis and has been developed to “size” an osmotically-driven membrane process (ODMP) mass exchanger given the operating conditions and desired performance. The driving potentials in these mass exchangers are the salinity differences between feed and draw solution. These 1D model equations are employed to determine mass transfer units (MTU) as a function of different dimensionless groups such as mass flowrate ratio (MR), recovery ratio (RR), concentration factors (CF) and effectiveness (ε). The introduction of new dimensionless groups such as the dilution rate ratio (DRR) and dilution rate (DR) would be used to relate the actual water permeation to the brine draw stream. The results show that a maximum power of 0.28 and 2.6 kJ can be produced by the PRO system using seawater or treated wastewater effluent (TWE) as the feed solution, respectively, which might be able to reduce the power consumption of the desalination industry in Kuwait.