bluuebunny/crossref_metadata_2025 huggingface dataset:
Red Wine, Resveratrol and Atrial Fibrillation
Authors: Stephan Laura , Almeida Eduardo , Markoski Melissa , Garavaglia Juliano , Marcadenti Aline
Published: October 2017

Atrial fibrillation (AF) is a common cardiac arrhythmia that is associated with increased risk for cardiovascular disease and overall mortality. Excessive alcohol intake is a well-known risk factor for AF, but this correlation is less clear with light and moderate drinking. Besides, low doses of red wine may acutely prolong repolarization and slow cardiac conduction. Resveratrol, a bioactive polyphenol found in grapes and red wine, has been linked to antiarrhythmic properties and may act as an inhibitor of both intracellular calcium release and pathological signaling cascades in AF, eliminating calcium overload and preserving the cardiomyocyte contractile function. However, there are still no clinical trials at all that prove that resveratrol supplementation leads to improved outcomes. Besides, no observational study supports a beneficial effect of light or moderate alcohol intake and a lower risk of AF. The purpose of this review is to briefly describe possible beneficial effects of red wine and resveratrol in AF, and also present studies conducted in humans regarding chronic red wine consumption, resveratrol, and AF.
The Impact of Iodine Status on the Recall Rate of the Screening Program for Congenital Hypothyroidism: Findings from Two National Studies in Iran
Authors: Mehran Ladan , Yarahmadi Shahin , Khalili Davood , Nazeri Pantea , Delshad Hossein , Abdollahi Zahra , Azhang Nasrin , Azizi Fereidoun
Published: October 2017

Back ground: Iodine deficiency is one of the important factors in increasing the recall rate in congenital hypothyroidism (CH) screening programs. The present study assessed whether the iodine status of the general population may predict the recall rate or vice versa. Methods: In the current national study, among 1,382,229 live births delivered between March 2010 and March 2011, 1,288,237 neonates were screened for detecting CH by TSH (thyroid stimulating hormone) measurement via heel prick sampling. Simultaneously, a total of 11,280 school-aged children, aged 7–8 years, were selected using random multi-cluster sampling for measurement of urinary iodine. Results: A negative correlation was found between median urinary iodine (MUI) and the recall rate (r = −0.33, p = 0.03). No correlation was found between MUIC (median urinary iodine concentration) and the incidence rate of CH. Linear regression analysis showed a 0.1% increase in the recall rate for a one unit decrease in MUIC (β = −0.11, 95% CI: −0.2, −0.1, p = 0.03). MUIC, at a cut-off point of 144.7 µg/L, was predictive for a recall rate < 3% (p = 0.05). Conclusion: Frequencies of TSH ≥ 5 mU/L may be a more sensitive indicator for iodine status during pregnancy rather than in the general population. As higher recall rates reflect inadequate iodine nutrition, sufficient iodine supplementation is needed to reduce the recall rate in such communities.
Kiwifruit Non-Sugar Components Reduce Glycaemic Response to Co-Ingested Cereal in Humans
Authors: Mishra Suman , Edwards Haley , Hedderley Duncan , Podd John , Monro John
Published: October 2017

Kiwifruit (KF) effects on the human glycaemic response to co-ingested wheat cereal were determined. Participants (n = 20) consumed four meals in random order, all being made to 40 g of the same available carbohydrate, by adding kiwifruit sugars (KF sug; glucose, fructose, sucrose 2:2:1) to meals not containing KF. The meals were flaked wheat biscuit (WB)+KFsug, WB+KF, WB+guar gum+KFsug, WB+guar gum+KF, that was ingested after fasting overnight. Blood glucose was monitored 3 h and hunger measured at 180 min post-meal using a visual analogue scale. KF and guar reduced postprandial blood glucose response amplitude, and prevented subsequent hypoglycaemia that occurred with WB+KFsug. The area between the blood glucose response curve and baseline from 0 to 180 min was not significantly different between meals, 0–120 min areas were significantly reduced by KF and/or guar. Area from 120 to 180 min was positive for KF, guar, and KF+guar, while the area for the WB meal was negative. Hunger at 180 min was significantly reduced by KF and/or guar when compared with WB. We conclude that KF components other than available carbohydrate may improve the glycaemic response profile to co-ingested cereal food.
Effect of Cellulose Nanocrystals and Bacterial Cellulose on Disintegrability in Composting Conditions of Plasticized PHB Nanocomposites
Authors: Seoane Irene , Manfredi Liliana , Cyras Viviana , Torre Luigi , Fortunati Elena , Puglia Debora
Published: October 2017

Poly(hydroxybutyrate) (PHB)-based films, reinforced with bacterial cellulose (BC) or cellulose nanocrystals (CNC) and plasticized using a molecular (tributyrin) or a polymeric plasticizer (poly(adipate diethylene)), were produced by solvent casting. Their morphological, thermal, wettability, and chemical properties were investigated. Furthermore, the effect of adding both plasticizers (20 wt % respect to the PHB content) and biobased selected nanofillers added at different contents (2 and 4 wt %) on disintegrability in composting conditions was studied. Results of contact angle measurements and calorimetric analysis validated the observed behavior during composting experiments, indicating how CNC aggregation, due to the hydrophilic nature of the filler, slows down the degradation rate but accelerates it in case of increasing content. In contrast, nanocomposites with BC presented an evolution in composting similar to neat PHB, possibly due to the lower hydrophilic character of this material. The addition of the two plasticizers contributed to a better dispersion of the nanoparticles by increasing the interaction between the cellulosic reinforcements and the matrix, whereas the increased crystallinity of the incubated samples in a second stage in composting provoked a reduction in the disintegration rate.
A Facile and Effective Method to Fabricate Superhydrophobic/Superoeophilic Surface for the Separation of Both Water/Oil Mixtures and Water-in-Oil Emulsions
Authors: Li Feiran , Wang Ziran , Pan Yunlu , Zhao Xuezeng
Published: October 2017

Superhydrophobic/superoleophilic surfaces (water contact angle greater than 150° with low hysteresis, with an oil contact angle smaller than 5°) have a wide-range of applications in oil/water separation. However, most of the essential methods to fabricate this kind of surface are complex, inflexible, and costly. Moreover, most methods focus on separating immiscible oil and water mixtures but lack the ability to demulsify surfactant-stabilized emulsions, which is widely present in industry and daily life. In this study, a facile and effective method was developed to fabricate superhydrophobic/superoleophilic surfaces that can be easily applied on almost all kinds of solid substrates. The treated porous substrates (e.g., steel mesh; cotton) can separate oil/water mixtures or absorb oil from a mixture. Furthermore, the compressed treated cotton is capable of demulsifying stabilized water-in-oil emulsions with high efficiency. The simple, low-cost, and material-unrestricted method provides an efficient way to separate oil/water mixtures of various kinds and has great potential in energy conservation and environmental protection.
Automated Detection of Branch Shaking Locations for Robotic Cherry Harvesting Using Machine Vision
Authors: Amatya Suraj , Karkee Manoj , Zhang Qin , Whiting Matthew D.
Published: October 2017

Automation in cherry harvesting is essential to reduce the demand for seasonal labor for cherry picking and reduce the cost of production. The mechanical shaking of tree branches is one of the widely studied and used techniques for harvesting small tree fruit crops like cherries. To automate the branch shaking operation, different methods of detecting branches and cherries in full foliage canopies of the cherry tree have been developed previously. The next step in this process is the localization of shaking positions in the detected tree branches for mechanical shaking. In this study, a method of locating shaking positions for automated cherry harvesting was developed based on branch and cherry pixel locations determined using RGB images and 3D camera images. First, branch and cherry regions were located in 2D RGB images. Depth information provided by a 3D camera was then mapped on to the RGB images using a standard stereo calibration method. The overall root mean square error in estimating the distance to desired shaking points was 0.064 m. Cherry trees trained in two different canopy architectures, Y-trellis and vertical trellis systems, were used in this study. Harvesting testing was carried out by shaking tree branches at the locations selected by the algorithm. For the Y-trellis system, the maximum fruit removal efficiency of 92.9% was achieved using up to five shaking events per branch. However, maximum fruit removal efficiency for the vertical trellis system was 86.6% with up to four shakings per branch. However, it was found that only three shakings per branch would achieve a fruit removal percentage of 92.3% and 86.4% in Y and vertical trellis systems respectively.
Linear Multi-Task Learning for Predicting Soil Properties Using Field Spectroscopy
Authors: Qi Haijun , Paz-Kagan Tarin , Karnieli Arnon , Li Shaowen
Published: October 2017

Field spectroscopy has been suggested to be an efficient method for predicting soil properties using quantitative mathematical models in a rapid and non-destructive manner. Traditional multivariate regression algorithms usually regard the modeling of each soil property as a single task, which means only one response variable is considered as the output during modeling. Therefore, these algorithms are less suitable for the prediction of several key soil properties with low concentrations or unobvious spectral absorption signals. In the current study, we investigated the performance of a linear multi-task learning (LMTL) algorithm based on a regularized dirty model for modeling and predicting several key soil properties using field spectroscopy (350–2500 nm) as an integrated approach. We tested seven key soil properties including available nitrogen (N), phosphorus (P) and potassium (K), pH, water content (WC), organic matter (OM), and electrical conductivity (EC) in drylands. The model performances of LMTL models were compared with the commonly used single-task algorithm of the partial least squares regression (PLS-R). Our results show that the LMTL models outperformed the PLS-R models with the advantage of shared features; the ratio of performance to deviation (RPD) values in the validation set improved by 10.24%, 4.93%, 25.77%, 11.76%, 6.74%, 53.13%, and 3.15% for N, P, K, pH, WC, OM, and EC, respectively. The best prediction was obtained for OM with RPD = 2.29, indicating high accuracy (RPD > 2). The prediction results of N, P, WC, and pH were categorized as of moderate accuracy (1.4 < RPD < 2), while K and EC were categorized as of poor accuracy (RPD < 1.4). However, the explanatory power of the LMTL models was moderate due to fewer features being selected by the regularization algorithm of the LMTL approach, which should be further studied in the soil spectral analysis. Our results highlight the use of LMTL in field spectroscopy analysis that can improve the generalization performance of regression models for predicting soil properties.
Assessment of Errors Caused by Forest Vegetation Structure in Airborne LiDAR-Derived DTMs
Authors: Simpson Jake , Smith Thomas , Wooster Martin
Published: October 2017

Airborne Light Detection and Ranging (LiDAR) is a survey tool with many applications in forestry and forest research. It can capture the 3D structure of vegetation and topography quickly and accurately over thousands of hectares of forest. However, very few studies have assessed how accurately LiDAR can measure surface topography under forest canopies, which may be important, for example, in relation to analysis of pre- and post-burn surface height maps used to quantify the combustion of organic soils. Here, we use ground survey equipment to assess digital terrain model (DTM) accuracy in a deciduous broadleaf forest, during both leaf-on and leaf-off conditions. Using the leaf-on LiDAR dataset we quantitatively assess vertical vegetation structure, and use this as a categorical explanatory variable for DTM accuracy. In the presence of leaf-on vegetation, DTM accuracy is severely reduced, with low-stature undergrowth vegetation (such as ferns) causing the greatest errors (RMSE > 1 m). Errors are lower under leaf-off conditions (RMSE = 0.22 m), but still of a magnitude similar to that reported for mean depths of burn in fires involving organic soils. We highlight the need for adequate ground control schemes to accompany any forest-based airborne LiDAR survey which require highly accurate DTMs.
A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering—Part 2: Application to XCO2 Retrievals from OCO-2
Authors: Reuter Maximilian , Buchwitz Michael , Schneising Oliver , Noël Stefan , Bovensmann Heinrich , Burrows John
Published: October 2017

Satellite retrievals of the atmospheric dry-air column-average mole fraction of CO 2 (XCO 2 ) based on hyperspectral measurements in appropriate near (NIR) and short wave infrared (SWIR) O 2 and CO 2 absorption bands can help to answer important questions about the carbon cycle but the precision and accuracy requirements for XCO 2 data products are demanding. Multiple scattering of light at aerosols and clouds can be a significant error source for XCO 2 retrievals. Therefore, so called full physics retrieval algorithms were developed aiming to minimize scattering related errors by explicitly fitting scattering related properties such as cloud water/ice content, aerosol optical thickness, cloud height, etc. However, the computational costs for multiple scattering radiative transfer (RT) calculations can be immense. Processing all data of the Orbiting Carbon Observatory-2 (OCO-2) can require up to thousands of CPU cores and the next generation of CO 2 monitoring satellites will produce at least an order of magnitude more data. For this reason, the Fast atmOspheric traCe gAs retrievaL FOCAL has been developed reducing the computational costs by orders of magnitude by approximating multiple scattering effects with an analytic solution of the RT problem of an isotropic scattering layer. Here we confront FOCAL for the first time with measured OCO-2 data and protocol the steps undertaken to transform the input data (most importantly, the OCO-2 radiances) into a validated XCO 2 data product. This includes preprocessing, adaptation of the noise model, zero level offset correction, post-filtering, bias correction, comparison with the CAMS (Copernicus Atmosphere Monitoring Service) greenhouse gas flux inversion model, comparison with NASA’s operational OCO-2 XCO 2 product, and validation with ground based Total Carbon Column Observing Network (TCCON) data. The systematic temporal and regional differences between FOCAL and the CAMS model have a standard deviation of 1.0 ppm. The standard deviation of the single sounding mismatches amounts to 1.1 ppm which agrees reasonably well with FOCAL’s average reported uncertainty of 1.2 ppm. The large scale XCO 2 patterns of FOCAL and NASA’s operational OCO-2 product are similar and the most prominent difference is that FOCAL has about three times less soundings due to the inherently poor throughput (11%) of the MODIS (moderate-resolution imaging spectroradiometer) based cloud screening used by FOCAL’s preprocessor. The standard deviation of the difference between both products is 1.1 ppm. The validation of one year (2015) of FOCAL XCO 2 data with co-located ground based TCCON observations results in a standard deviations of the site biases of 0.67 ppm (0.78 ppm without bias correction) and an average scatter relative to TCCON of 1.34 ppm (1.60 ppm without bias correction).
Airborne LiDAR Data Filtering Based on Geodesic Transformations of Mathematical Morphology
Authors: Li Yong , Yong Bin , Van Oosterom Peter , Lemmens Mathias , Wu Huayi , Ren Liliang , Zheng Mingxue , Zhou Jiajun
Published: October 2017

The capability of acquiring accurate and dense three-dimensional geospatial information that covers large survey areas rapidly enables airborne light detection and ranging (LiDAR) has become a powerful technology in numerous fields of geospatial applications and analysis. LiDAR data filtering is the first and essential step for digital elevation model generation, land cover classification, and object reconstruction. The morphological filtering approaches have the advantages of simple concepts and easy implementation, which are able to filter non-ground points effectively. However, the filtering quality of morphological approaches is sensitive to the structuring elements that are the key factors for the filtering success of mathematical operations. Aiming to deal with the dependence on the selection of structuring elements, this paper proposes a novel filter of LiDAR point clouds based on geodesic transformations of mathematical morphology. In comparison to traditional morphological transformations, the geodesic transformations only use the elementary structuring element and converge after a finite number of iterations. Therefore, this algorithm makes it unnecessary to select different window sizes or determine the maximum window size, which can enhance the robustness and automation for unknown environments. Experimental results indicate that the new filtering method has promising and competitive performance for diverse landscapes, which can effectively preserve terrain details and filter non-ground points in various complicated environments.
Exploring Subpixel Learning Algorithms for Estimating Global Land Cover Fractions from Satellite Data Using High Performance Computing
Authors: Kumar Uttam , Ganguly Sangram , Nemani Ramakrishna R. , Raja Kumar S , Milesi Cristina , Sinha Ruchita , Michaelis Andrew , Votava Petr , Hashimoto Hirofumi , Li Shuang , Wang Weile , Kalia Subodh , Gayaka Shreekant
Published: October 2017

Land cover (LC) refers to the physical and biological cover present over the Earth’s surface in terms of the natural environment such as vegetation, water, bare soil, etc. Most LC features occur at finer spatial scales compared to the resolution of primary remote sensing satellites. Therefore, observed data are a mixture of spectral signatures of two or more LC features resulting in mixed pixels. One solution to the mixed pixel problem is the use of subpixel learning algorithms to disintegrate the pixel spectrum into its constituent spectra. Despite the popularity and existing research conducted on the topic, the most appropriate approach is still under debate. As an attempt to address this question, we compared the performance of several subpixel learning algorithms based on least squares, sparse regression, signal–subspace and geometrical methods. Analysis of the results obtained through computer-simulated and Landsat data indicated that fully constrained least squares (FCLS) outperformed the other techniques. Further, FCLS was used to unmix global Web-Enabled Landsat Data to obtain abundances of substrate (S), vegetation (V) and dark object (D) classes. Due to the sheer nature of data and computational needs, we leveraged the NASA Earth Exchange (NEX) high-performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into four classes, namely forest, farmland, water and urban areas (in conjunction with nighttime lights data) over California, USA using a random forest classifier. Validation of these LC maps with the National Land Cover Database 2011 products and North American Forest Dynamics static forest map shows a 6% improvement in unmixing-based classification relative to per-pixel classification. As such, abundance maps continue to offer a useful alternative to high-spatial-resolution classified maps for forest inventory analysis, multi-class mapping, multi-temporal trend analysis, etc.
Performance Analysis of Ship Wake Detection on Sentinel-1 SAR Images
Authors: Graziano Maria , Grasso Marco , D’Errico Marco
Published: October 2017

A novel technique for ship wake detection has been recently proposed and applied on X-band Synthetic Aperture Radar images provided by COSMO/SkyMed and TerraSAR-X. The approach shows that the vast majority of wake features are correctly detected and validated in critical situations. In this paper, the algorithm was applied to 28 wakes imaged by Sentinel-1 mission with different polarizations and incidence angles with the aim of testing the method’s robustness with reference to radar frequency and resolution. The detection process is properly modified. The results show that the features were correctly classified in 78.5% of cases, whereas false confirmations occur mainly on Kelvin cusps. Finally, the results were compared with the algorithm performance on X-band images, showing that no significant difference arises. In fact, the total false confirmations rate was 15.8% on X-band images and 18.5% on C-band images. Moreover, since the main criticality concerns again the false confirmation of Kelvin cusps, the same empirical criterion suggested for the X-band SAR images yielded a negligible 1.5% of false detection rate.
A Novel De-Noising Method for Improving the Performance of Full-Waveform LiDAR Using Differential Optical Path
Authors: Cheng Yang , Cao Jie , Hao Qun , Xiao Yuqing , Zhang Fanghua , Xia Wenze , Zhang Kaiyu , Yu Haoyong
Published: October 2017

A novel de-noising method for improving the performance of full-waveform light detection and ranging (LiDAR) based on differential optical path is proposed, and the mathematical models of this method are developed and verified. Backscattered full-waveform signal (BFWS) is detected by two avalanche photodiodes placed before and after the focus of the focusing lens. On the basis of the proposed method, some simulations are carried out and conclusions are achieved. (1) Background noise can be suppressed effectively and peak points of the BFWS are transformed into negative-going zero-crossing points as stop timing moments. (2) The relative increment percentage of the signal-to-noise ratio based on the proposed method first dramatically increases with the increase of the distance, and then the improvement gets smaller by increasing the distance. (3) The differential Gaussian fitting with the Levenberg-Marquardt algorithm is applied, and the results show that it can decompose the BFWS with high accuracy. (4) The differential distance should not be larger than c/2 × τrmin, and two variable gain amplifiers can eliminate the inconsistency of two differential beams. The results are beneficial for designing a better performance full-waveform LiDAR.
Spatiotemporal Pixelization to Increase the Recognition Score of Characters for Retinal Prostheses
Authors: Kim Hyun Seok , Park Kwang Suk
Published: October 2017

Most of the retinal prostheses use a head-fixed camera and a video processing unit. Some studies proposed various image processing methods to improve visual perception for patients. However, previous studies only focused on using spatial information. The present study proposes a spatiotemporal pixelization method mimicking fixational eye movements to generate stimulation images for artificial retina arrays by combining spatial and temporal information. Input images were sampled with a resolution that was four times higher than the number of pixel arrays. We subsampled this image and generated four different phosphene images. We then evaluated the recognition scores of characters by sequentially presenting phosphene images with varying pixel array sizes (6 × 6, 8 × 8 and 10 × 10) and stimulus frame rates (10 Hz, 15 Hz, 20 Hz, 30 Hz, and 60 Hz). The proposed method showed the highest recognition score at a stimulus frame rate of approximately 20 Hz. The method also significantly improved the recognition score for complex characters. This method provides a new way to increase practical resolution over restricted spatial resolution by merging the higher resolution image into high-frame time slots.
An Electronic System for the Contactless Reading of ECG Signals
Authors: Parente Francesca , Santonico Marco , Zompanti Alessandro , Benassai Mario , Ferri Giuseppe , D’Amico Arnaldo , Pennazza Giorgio
Published: October 2017

The aim of this work is the development of a contactless capacitive sensory system for the detection of (Electrocardiographic) ECG-like signals. The acquisition approach is based on a capacitive coupling with the patient body performed by electrodes integrated in a front-end circuit. The proposed system is able to detect changes in the electric charge related to the heart activity. Due to the target signal weakness and to the presence of other undesired signals, suitable amplification stages and analogue filters are required. Simulated results allowed us to evaluate the effectiveness of the approach, whereas experimental measurements, recorded without contact to the skin, have validated the practical effectiveness of the proposed architecture. The system operates with a supply voltage of ±9 V with an overall power consumption of about 10 mW. The analogue output of the electronic interface is connected to an ATmega328 microcontroller implementing the A/D conversion and the data acquisition. The collected data can be displayed on any multimedia support for real-time tracking applications.
Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary)
Authors: Clements Andrea L. , Griswold William G. , RS Abhijit , Johnston Jill E. , Herting Megan M. , Thorson Jacob , Collier-Oxandale Ashley , Hannigan Michael
Published: October 2017

In May 2017, a two-day workshop was held in Los Angeles (California, U.S.A.) to gather practitioners who work with low-cost sensors used to make air quality measurements. The community of practice included individuals from academia, industry, non-profit groups, community-based organizations, and regulatory agencies. The group gathered to share knowledge developed from a variety of pilot projects in hopes of advancing the collective knowledge about how best to use low-cost air quality sensors. Panel discussion topics included: (1) best practices for deployment and calibration of low-cost sensor systems, (2) data standardization efforts and database design, (3) advances in sensor calibration, data management, and data analysis and visualization, and (4) lessons learned from research/community partnerships to encourage purposeful use of sensors and create change/action. Panel discussions summarized knowledge advances and project successes while also highlighting the questions, unresolved issues, and technological limitations that still remain within the low-cost air quality sensor arena.
Electrical and Self-Sensing Properties of Ultra-High-Performance Fiber-Reinforced Concrete with Carbon Nanotubes
Authors: You Ilhwan , Yoo Doo-Yeol , Kim Soonho , Kim Min-Jae , Zi Goangseup
Published: October 2017

This study examined the electrical and self-sensing capacities of ultra-high-performance fiber-reinforced concrete (UHPFRC) with and without carbon nanotubes (CNTs). For this, the effects of steel fiber content, orientation, and pore water content on the electrical and piezoresistive properties of UHPFRC without CNTs were first evaluated. Then, the effect of CNT content on the self-sensing capacities of UHPFRC under compression and flexure was investigated. Test results indicated that higher steel fiber content, better fiber orientation, and higher amount of pore water led to higher electrical conductivity of UHPFRC. The effects of fiber orientation and drying condition on the electrical conductivity became minor as sufficiently high amount of steel fibers, 3% by volume, was added. Including only steel fibers did not impart UHPFRC with piezoresistive properties. Addition of CNTs substantially improved the electrical conductivity of UHPFRC. Under compression, UHPFRC with a CNT content of 0.3% or greater had a self-sensing ability that was activated by the formation of cracks, and better sensing capacity was achieved by including greater amount of CNTs. Furthermore, the pre-peak flexural behavior of UHPFRC was precisely simulated with a fractional change in resistivity when 0.3% CNTs were incorporated. The pre-cracking self-sensing capacity of UHPFRC with CNTs was more effective under tensile stress state than under compressive stress state.
Motion-Blur-Free High-Speed Video Shooting Using a Resonant Mirror
Authors: Inoue Michiaki , Gu Qingyi , Jiang Mingjun , Takaki Takeshi , Ishii Idaku , Tajima Kenji
Published: October 2017

This study proposes a novel concept of actuator-driven frame-by-frame intermittent tracking for motion-blur-free video shooting of fast-moving objects. The camera frame and shutter timings are controlled for motion blur reduction in synchronization with a free-vibration-type actuator vibrating with a large amplitude at hundreds of hertz so that motion blur can be significantly reduced in free-viewpoint high-frame-rate video shooting for fast-moving objects by deriving the maximum performance of the actuator. We develop a prototype of a motion-blur-free video shooting system by implementing our frame-by-frame intermittent tracking algorithm on a high-speed video camera system with a resonant mirror vibrating at 750 Hz. It can capture 1024 × 1024 images of fast-moving objects at 750 fps with an exposure time of 0.33 ms without motion blur. Several experimental results for fast-moving objects verify that our proposed method can reduce image degradation from motion blur without decreasing the camera exposure time.
Model-Based Localization and Tracking Using Bluetooth Low-Energy Beacons
Authors: Daniş F. , Cemgil Ali
Published: October 2017

We introduce a high precision localization and tracking method that makes use of cheap Bluetooth low-energy (BLE) beacons only. We track the position of a moving sensor by integrating highly unreliable and noisy BLE observations streaming from multiple locations. A novel aspect of our approach is the development of an observation model, specifically tailored for received signal strength indicator (RSSI) fingerprints: a combination based on the optimal transport model of Wasserstein distance. The tracking results of the entire system are compared with alternative baseline estimation methods, such as nearest neighboring fingerprints and an artificial neural network. Our results show that highly accurate estimation from noisy Bluetooth data is practically feasible with an observation model based on Wasserstein distance interpolation combined with the sequential Monte Carlo (SMC) method for tracking.
Development of a Shipboard Remote Control and Telemetry Experimental System for Large-Scale Model’s Motions and Loads Measurement in Realistic Sea Waves
Authors: Jiao Jialong , Ren Huilong , Adenya Christiaan Adika , Chen Chaohe
Published: October 2017

Wave-induced motion and load responses are important criteria for ship performance evaluation. Physical experiments have long been an indispensable tool in the predictions of ship’s navigation state, speed, motions, accelerations, sectional loads and wave impact pressure. Currently, majority of the experiments are conducted in laboratory tank environment, where the wave environments are different from the realistic sea waves. In this paper, a laboratory tank testing system for ship motions and loads measurement is reviewed and reported first. Then, a novel large-scale model measurement technique is developed based on the laboratory testing foundations to obtain accurate motion and load responses of ships in realistic sea conditions. For this purpose, a suite of advanced remote control and telemetry experimental system was developed in-house to allow for the implementation of large-scale model seakeeping measurement at sea. The experimental system includes a series of technique sensors, e.g., the Global Position System/Inertial Navigation System (GPS/INS) module, course top, optical fiber sensors, strain gauges, pressure sensors and accelerometers. The developed measurement system was tested by field experiments in coastal seas, which indicates that the proposed large-scale model testing scheme is capable and feasible. Meaningful data including ocean environment parameters, ship navigation state, motions and loads were obtained through the sea trial campaign.
Village Building Identification Based on Ensemble Convolutional Neural Networks
Authors: Guo Zhiling , Chen Qi , Wu Guangming , Xu Yongwei , Shibasaki Ryosuke , Shao Xiaowei
Published: October 2017

In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86.
Artificial Neural Network to Predict Vine Water Status Spatial Variability Using Multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV)
Authors: Poblete Tomas , Ortega-Farías Samuel , Moreno Miguel , Bardeen Matthew
Published: October 2017

Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential (Ψstem). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500–800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the Ψstem spatial variability of a drip-irrigated Carménère vineyard in Talca, Maule Region, Chile. The coefficient of determination (R2) obtained between ANN outputs and ground-truth measurements of Ψstem were between 0.56–0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate Ψstem with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of −9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26–0.27 MPa, 0.32–0.34 MPa and −24.2–25.6%, respectively.
Ratiometric Decoding of Pheromones for a Biomimetic Infochemical Communication System
Authors: Wei Guangfen , Thomas Sanju , Cole Marina , Rácz Zoltán , Gardner Julian
Published: October 2017

Biosynthetic infochemical communication is an emerging scientific field employing molecular compounds for information transmission, labelling, and biochemical interfacing; having potential application in diverse areas ranging from pest management to group coordination of swarming robots. Our communication system comprises a chemoemitter module that encodes information by producing volatile pheromone components and a chemoreceiver module that decodes the transmitted ratiometric information via polymer-coated piezoelectric Surface Acoustic Wave Resonator (SAWR) sensors. The inspiration for such a system is based on the pheromone-based communication between insects. Ten features are extracted from the SAWR sensor response and analysed using multi-variate classification techniques, i.e., Linear Discriminant Analysis (LDA), Probabilistic Neural Network (PNN), and Multilayer Perception Neural Network (MLPNN) methods, and an optimal feature subset is identified. A combination of steady state and transient features of the sensor signals showed superior performances with LDA and MLPNN. Although MLPNN gave excellent results reaching 100% recognition rate at 400 s, over all time stations PNN gave the best performance based on an expanded data-set with adjacent neighbours. In this case, 100% of the pheromone mixtures were successfully identified just 200 s after they were first injected into the wind tunnel. We believe that this approach can be used for future chemical communication employing simple mixtures of airborne molecules.
Risk for Researchers Studying Social Deviance or Criminal Behavior
Authors: Brougham Patricia L. , Uttley Clarissa M.
Published: October 2017

Abstract: Researchers often encounter dangerous situations while conducting social research. The concept of risk to researchers refers to the possible harm that may occur to researchers while in the field or after leaving a research project. This study explores issues experienced by social scientists engaged in research on social deviance or criminal behavior. The goal of this research was to discover the types of risk experienced by social scientists and any mediating factors affecting the experience of risk. An online survey was conducted to gather data on issues experienced by social scientists. This study found that researchers experienced a variety of risks within the categories of physical/health, emotional, legal, and personal/professional. Each of the survey options for risk were reported by at least one respondent; however, the greatest number of risks reported were of an emotional or personal/professional nature. There were no mediating factors found to be significant in relation to the experience of risk. This was a surprising finding especially for the variable of gender as it is suggested that gender plays a role in the experience of difficulties.
Transnationalism among Second-Generation Muslim Americans: Being and Belonging in Their Transnational Social Field
Authors: Byng Michelle
Published: October 2017

An increase in transnationalism, the ability of individuals and families to travel and maintain relationships across national borders, has led to questions about its impact on identity especially for the children of migrants. When combined with concerns about global and national security such as those that are associated with Muslims and Islam, then questions about the strength national identity are particularly pertinent. This analysis uses the theories of transnational social fields and intersectionality to examine the transnational experiences of second-generation Muslim Americans. It relies on qualitative interview data. The data show the intersection of their national, religious, and gender identities. It demonstrates that they experience transnational being in their parents’ country of origin and belonging in the United States. Nationality, religion, and gender influence what they experience in each location. The analysis demonstrates the stability and centrality of American national identity in what second-generation Muslims experience in both locations. Moreover, their belonging in the United States rests squarely on their perceptions of themselves as Americans and their construction of their Muslim identity as an American religious identity.
Analysis of Economical and Environmental Costs for the Selection of Municipal Solid Waste Treatment and Disposal Scenarios through Multicriteria Analysis (ELECTRE Method)
Authors: De Medina-Salas Lorena , Castillo-González Eduardo , Giraldi-Díaz Mario , Guzmán-González Víctor
Published: October 2017

Decision-making for the selection of treatment alternatives and landfilling of municipal solid waste (MSW) is based on the experience and judgment of those management responsible, without considering multicriteria analysis. Therefore, the purpose of this research was to determine the treatment scenario and landfilling of MSW with the lowest environmental and economic costs in a medium-sized city. The methodology included the definition and data processing of the project (population, generation, and composition of MSW), for 16 years. In the design of scenarios, recycling, composting, incineration with energy recovery, and landfilling treatments were proposed; later, the combinations of treatments for each type of residue were generated. The results showed 36 scenarios, then the ELECTRE method was applied to the five with the lowest economical and environmental costs. Finally from the latter, one dominant scenario was determined: organic waste in composting; plastic, paper, and glass in recycling; and ‘others’ in landfilling. It is concluded that the final decision on the scenario is adapted to the particular conditions of the locality.
The Role of Access to Finance in Explaining Cross-National Variation in Entrepreneurial Activity: A Panel Data Approach
Authors: Anton Sorin , Bostan Ionel
Published: October 2017

The aim of the paper is to investigate to what extent access to finance explains differences in entrepreneurial activity across European Union (EU) member countries. We use a dataset containing information across countries and time to investigate the determinants of entrepreneurial activity in twenty-five EU members over the period between 2007 and 2013. Our sample comprises both periods of difficult access to finance and periods of excessive liquidities. Employing a panel data model with fixed effects, we found a positive relationship between access to finance and entrepreneurial activities. Furthermore, we showed that this positive relationship is more important for the individuals who believe they have the required skills and knowledge to start a business. The results proved to be robust when we employed different measures of entrepreneurial activity and several proxies for access to finance. Our findings provide empirical evidence for the need of policy initiatives at national and EU level to facilitate the creation of sustainable new ventures.
Balancing Rural and Urban Development: Applying Coordinated Urban–Rural Development (CURD) Strategy to Achieve Sustainable Urbanisation in China
Authors: Li Ling-Hin
Published: October 2017

Land in rural China has been under a separate and closed management system for decades even after the urban land reform that started in the late 1980s. The blurred property rights over rural land have been hindering the rural welfare as surplus rural land in sub-urban areas cannot be circulated into more economic use without first being requisitioned by the state. This traditional conversion process creates a lot of problems, among them are the compensation standard as well as displacement of rural residents to the city, where they cannot find adequate welfare protection. The prolonged disparity in economic outcomes for rural and urban residents in China in the process of urbanisation has made the authority realise that land-based local finance is no longer an option. Coordinated Urban and Rural Development (CURD) ideology arises to set a level playing field by giving the rural residents comparable welfare status as their urban counterparts’ one. The CURD ideology is basically linked to the strategic development of the three main issues in the rural area of China, or in the Chinese terminology: San Nong. These three issues are rural villages, rural enterprises and rural farmers (nong cun, nong ye, nong min). CURD ideology is to preserve the livelihood of rural villages, facilitate and promote rural enterprises and increase the living standard of rural farmers. Most importantly, however, CURD policy package bestows rural residents with property rights over their farmland so that they could sub-co1ntract the user-rights to other urban commercial entities for higher benefits. While CURD policies are applied in a lot of different regions in China including Chongqing in the West, Qingdao in the North, Zhongshan in the South and Wuhan in the middle, we focus our examination in Chengdu as the Chengdu model has been widely documented and highly regarded as the most successful model in implementing the CURD strategies. From our case study, we find that CURD policies reduce the pressure for rural residents to migrate to the city for better job opportunities, which in turn reduce the need to expand the development scale, especially housing needs, of the urban configuration. Consequently, CURD ideology helps contribute to a more sustainable urbanisation process in China that accommodates and balances the needs and interests of both the city and rural residents.
Safety-Culture Exploration in Taiwan’s Metal Industries: Identifying the Workers’ Background Influence on Safety Climate
Authors: Lin Shu-Chiang , Mufidah Ilma , Persada Satria
Published: October 2017

The present study aims to assess the safety-climate level in Taiwan’s metal industries, as well as to identify the influence of workers’ backgrounds on the safety climate. An earlier report showed that a poor safety culture was related to the cause of accidents in Taiwan’s traditional manufacturing industries. This study surveyed a total of 839 workers who voluntarily participated and completed the safety-culture questionnaires. These workers were from a Taiwanese metal company and its five satellite companies. Three safety-climate factors, namely safety perception, safety communication and safety-management systems, were assessed. Confirmatory factor analysis (CFA) was conducted by developing structural equation modeling to ensure the questionnaire’s validity. The influence of workers’ backgrounds on the safety climate was identified by using one-way ANOVA. The reliability result of the questionnaire was above the acceptable level. The overall safety-climate score was 4.22 out of a five-point scale for safety perception, 4.23 for safety-management systems and 3.97 for safety communication. The scores indicate a good level of safety climate, with room for improvement in safety communication. Additionally, the influence of workers’ backgrounds on the safety climate was confirmed. Based on the validity test, it was also found that the questionnaire could be improved by reconstructing its questions in its development process in order to increase the safety-climate model’s reliability and validity, as well as its model fit.
Do Peer Firms Affect Firm Corporate Social Responsibility?
Authors: Yang Shenggang , Ye Heng , Zhu Qi
Published: October 2017

Peer-firm strategies are a critical factor for corporate finance, and corporate social responsibility (CSR) is the main trend for evaluating the behavior of firms. On the basis of the connection between peer strategy and CSR, this paper explores the CSR strategies employed by a sample of Chinese firms during the 2008–2015 period. Our two main empirical findings are as follows. First, the CSR strategies of firms have a positive effect on their CSR behavior. Second, when there is the CSR gap between firms and peer firms, firms will feel the pressure from stakeholders and the public and improve the level of CSR performance. Our paper enriches empirical research on the CSR behavior of Chinese firms.