bluuebunny/crossref_metadata_2025 huggingface dataset:
Gender and Superstition in Modern Chinese Literature
Authors: Gvili Gal
Published: October 2019

This article offers a new perspective on the study of the discourse on superstition (mixin) in modern China. Drawing upon recent work on the import of the concept “superstition” to the colonial world during the 19th century, the article intervenes in the current study of the circulation of discursive constructs in area studies. This intervention is done in two ways: first, I identify how in the modern era missionaries and Western empires collaborated in linking anti-superstition thought to discourses on women’s liberation. Couched in promises of civilizational progress to cultures who free their women from backward superstitions, this historical connection between empire, gender and modern knowledge urges us to reorient our understanding of superstition merely as the ultimate other of “religion” or “science.” Second, in order to explore the nuances of the connection between gender and superstition, I turn to an archive that is currently understudied in the research on superstition in China. I propose that we mine modern Chinese literature by using literary methods. I demonstrate this proposal by reading China’s first feminist manifesto, The Women’s Bell by Jin Tianhe and the short story Medicine by Lu Xun.
Volatility Timing in CPF Investment Funds in Singapore: Do They Outperform Non-CPF Funds?
Authors: Shen Xiaoyi , Tsui Albert K. , Zhang Zhaoyong
Published: October 2019

The purpose of this study is to examine the volatility-timing performance of Singapore-based funds under the Central Provident Fund (CPF) Investment Scheme and non-CPF linked funds by taking into account the currency risk effect on internationally managed funds. In particular, we empirically assess whether the funds under the CPF Investment Scheme outperform non-CPF funds by examining the volatility-timing performance associated with these funds. The volatility-timing ability of CPF funds will provide the CPF board with a new method for risk classification. We employ the GARCH models and modified factor models to capture the response of funds to market abnormal conditional volatility including the weekday effect. The SMB and HML factors for non-US based funds are constructed from stock market data to exclude the contribution of the size effect and the BE/ME effect. The results show that volatility timing is one of the factors contributing to the excess return of funds. However, funds’ volatility-timing seems to be country-specific. Most of the Japanese equity funds and global equity funds under the CPF Investment Scheme are found to have the ability of volatility timing. This finding contrasts with the existing studies on Asian, ex-Japan funds and Greater China funds. Moreover, there is no evidence that funds under the CPF Investment Scheme show a better group performance of volatility timing.
Post-Disaster Building Database Updating Using Automated Deep Learning: An Integration of Pre-Disaster OpenStreetMap and Multi-Temporal Satellite Data
Authors: Ghaffarian Saman , Kerle Norman , Pasolli Edoardo , Jokar Arsanjani Jamal
Published: October 2019

First responders and recovery planners need accurate and quickly derived information about the status of buildings as well as newly built ones to both help victims and to make decisions for reconstruction processes after a disaster. Deep learning and, in particular, convolutional neural network (CNN)-based approaches have recently become state-of-the-art methods to extract information from remote sensing images, in particular for image-based structural damage assessment. However, they are predominantly based on manually extracted training samples. In the present study, we use pre-disaster OpenStreetMap building data to automatically generate training samples to train the proposed deep learning approach after the co-registration of the map and the satellite images. The proposed deep learning framework is based on the U-net design with residual connections, which has been shown to be an effective method to increase the efficiency of CNN-based models. The ResUnet is followed by a Conditional Random Field (CRF) implementation to further refine the results. Experimental analysis was carried out on selected very high resolution (VHR) satellite images representing various scenarios after the 2013 Super Typhoon Haiyan in both the damage and the recovery phases in Tacloban, the Philippines. The results show the robustness of the proposed ResUnet-CRF framework in updating the building map after a disaster for both damage and recovery situations by producing an overall F1-score of 84.2%.
Spatial Pattern of Soil Erosion Drivers and the Contribution Rate of Human Activities on the Loess Plateau from 2000 to 2015: A Boundary Line from Northeast to Southwest
Authors: Guo Xingjian , Shao Quanqin
Published: October 2019

The Loess Plateau is one of the most fragile areas in the world, where the problem of soil erosion is particularly prominent. The spatial and temporal variation characteristics and mechanisms of soil erosion in this region have always been hot topics for researchers. In this study, Revised Universal Soil Loss Equation (RUSLE) is used to estimate the soil erosion modulus of the Loess Plateau from 2000 to 2015, the dynamic characteristics of its temporal and spatial variations and driving mechanisms are determined, and meteorological data are combined with remote sensing data to quantitatively calculate the contribution rate of human activities. The results show that from 2000 to 2015, the soil erosion modulus of the Loess Plateau had a downward trend as a whole, with a rate of −0.6408 t/ha/a, but the downward trend gradually slowed down. Precipitation mainly resulted in changes in the soil erosion modulus in the northwestern part of the Loess Plateau, where a significant positive correlation was seen. Meanwhile, the Vegetation Fractional Coverage (VFC) mainly affected the southeastern part, where a significant negative correlation was measured. The human-activity contribution rate was −1.0774 on the Loess Plateau, which means human activities effectively reduced the soil erosion modulus while climate change promoted soil erosion combined with the result of the analysis of variance (ANOVA). “Hilly and gully regions” and “Gully region of Loess Plateau” as the main implementation areas of ecological projects, human activities had contribution rate of 0.5513 and 0.7805 toward the declining of soil erosion, respectively. Interestingly, the spatial differentiation characteristic of the soil erosion driving mechanisms and human contribution rates on the Loess Plateau showed the same boundary line from northeast to southwest, which was well explained by the 400-mm isohyetal line and Hu’s Line. This boundary can guide the geographical layout of the ecological management projects and urban development spaces on the Loess Plateau.
Hyperspectral Unmixing with Gaussian Mixture Model and Spatial Group Sparsity
Authors: Jin Qiwen , Ma Yong , Pan Erting , Fan Fan , Huang Jun , Li Hao , Sui Chenhong , Mei Xiaoguang
Published: October 2019

In recent years, endmember variability has received much attention in the field of hyperspectral unmixing. To solve the problem caused by the inaccuracy of the endmember signature, the endmembers are usually modeled to assume followed by a statistical distribution. However, those distribution-based methods only use the spectral information alone and do not fully exploit the possible local spatial correlation. When the pixels lie on the inhomogeneous region, the abundances of the neighboring pixels will not share the same prior constraints. Thus, in this paper, to achieve better abundance estimation performance, a method based on the Gaussian mixture model (GMM) and spatial group sparsity constraint is proposed. To fully exploit the group structure, we take the superpixel segmentation (SS) as preprocessing to generate the spatial groups. Then, we use GMM to model the endmember distribution, incorporating the spatial group sparsity as a mixed-norm regularization into the objective function. Finally, under the Bayesian framework, the conditional density function leads to a standard maximum a posteriori (MAP) problem, which can be solved using generalized expectation-maximization (GEM). Experiments on simulated and real hyperspectral data demonstrate that the proposed algorithm has higher unmixing precision compared with other state-of-the-art methods.
Sea Ice Extent Detection in the Bohai Sea Using Sentinel-3 OLCI Data
Authors: Su Hua , Ji Bowen , Wang Yunpeng
Published: October 2019

Sea ice distribution is an important indicator of ice conditions and regional climate change in the Bohai Sea (China). In this study, we monitored the spatiotemporal distribution of the Bohai Sea ice in the winter of 2017–2018 by developing sea ice information indexes using 300 m resolution Sentinel-3 Ocean and Land Color Instrument (OLCI) images. We assessed and validated the index performance using Sentinel-2 MultiSpectral Instrument (MSI) images with higher spatial resolution. The results indicate that the proposed Normalized Difference Sea Ice Information Index (NDSIIIOLCI), which is based on OLCI Bands 20 and 21, can be used to rapidly and effectively detect sea ice but is somewhat affected by the turbidity of the seawater in the southern Bohai Sea. The novel Enhanced Normalized Difference Sea Ice Information Index (ENDSIIIOLCI), which builds on NDSIIIOLCI by also considering OLCI Bands 12 and 16, can monitor sea ice more accurately and effectively than NDSIIIOLCI and suffers less from interference from turbidity. The spatiotemporal evolution of the Bohai Sea ice in the winter of 2017–2018 was successfully monitored by ENDSIIIOLCI. The results show that this sea ice information index based on OLCI data can effectively extract sea ice extent for sediment-laden water and is well suited for monitoring the evolution of Bohai Sea ice in winter.
Crop Growth Condition Assessment at County Scale Based on Heat-Aligned Growth Stages
Authors: Qian Yonglan , Yang Zhengwei , Di Liping , Rahman Md. Shahinoor , Tan Zhenyu , Xue Lei , Gao Feng , Yu Eugene Genong , Zhang Xiaoyang
Published: October 2019

Remotely sensed data have been used in crop condition monitoring for decades. Traditionally, crop growth conditions were assessed by comparing Normalized Difference Vegetation Index (NDVI) of the current year and past years at a pixel scale on the same calendar day. The assumption of this comparison is that the different years’ crops were at the same growing stage on the same day. However, this assumption is often violated in reality. This paper proposes to combine remotely sensed data and meteorological data to assess corn growth conditions at the same growth stages at county level. The proposed approach uses the active accumulated temperature (AAT) computed from Daymet, a daily weather data product, to align different years of NDVI time series at the same growth stages estimated from AATs. The study area covers Carroll County, Iowa. The best index slope extraction (BISE) method and Savitzky–Golay filter are used to filter noise and to reconstruct 11 years of corn growing season NDVI time series from 250 m MODIS daily surface reflectance data product (MOD09GQ). The corn growth stages are identified every year with precise Julian dates from AAT time series. The corn growth conditions are assessed based on the aligned growth stages. The validation of the assessed crop conditions is performed based on National Agricultural Statistics Service (NASS) reports. The study indicates that the crop condition assessment results based on aligned growth stages are consistent with the NASS reported results and they are more reliable than the results based on the same calendar days. The proposed method provides not only crop growth condition information but also crop phenology information. Potentially, it can help improve crop yield prediction since it can effectively measure crop growth changes with NDVI and AAT data.
Guided Next Best View for 3D Reconstruction of Large Complex Structures
Authors: Almadhoun Randa , Abduldayem Abdullah , Taha Tarek , Seneviratne Lakmal , Zweiri Yahya
Published: October 2019

In this paper, a Next Best View (NBV) approach with a profiling stage and a novel utility function for 3D reconstruction using an Unmanned Aerial Vehicle (UAV) is proposed. The proposed approach performs an initial scan in order to build a rough model of the structure that is later used to improve coverage completeness and reduce flight time. Then, a more thorough NBV process is initiated, utilizing the rough model in order to create a dense 3D reconstruction of the structure of interest. The proposed approach exploits the reflectional symmetry feature if it exists in the initial scan of the structure. The proposed NBV approach is implemented with a novel utility function, which consists of four main components: information theory, model density, traveled distance, and predictive measures based on symmetries in the structure. This system outperforms classic information gain approaches with a higher density, entropy reduction and coverage completeness. Simulated and real experiments were conducted and the results show the effectiveness and applicability of the proposed approach.
Measuring Marine Plastic Debris from Space: Initial Assessment of Observation Requirements
Authors: Martínez-Vicente Víctor , Clark James R. , Corradi Paolo , Aliani Stefano , Arias Manuel , Bochow Mathias , Bonnery Guillaume , Cole Matthew , Cózar Andrés , Donnelly Rory , Echevarría Fidel , Galgani François , Garaba Shungudzemwoyo P. , Goddijn-Murphy Lonneke , Lebreton Laurent , Leslie Heather A. , Lindeque Penelope K. , Maximenko Nikolai , Martin-Lauzer François-Régis , Moller Delwyn , Murphy Peter , Palombi Lorenzo , Raimondi Valentina , Reisser Julia , Romero Laia , Simis Stefan G.H. , Sterckx Sindy , Thompson Richard C. , Topouzelis Konstantinos N. , van Sebille Erik van , Veiga Joana Mira , Vethaak A. Dick
Published: October 2019

Sustained observations are required to determine the marine plastic debris mass balance and to support effective policy for planning remedial action. However, observations currently remain scarce at the global scale. A satellite remote sensing system could make a substantial contribution to tackling this problem. Here, we make initial steps towards the potential design of such a remote sensing system by: (1) identifying the properties of marine plastic debris amenable to remote sensing methods and (2) highlighting the oceanic processes relevant to scientific questions about marine plastic debris. Remote sensing approaches are reviewed and matched to the optical properties of marine plastic debris and the relevant spatio-temporal scales of observation to identify challenges and opportunities in the field. Finally, steps needed to develop marine plastic debris detection by remote sensing platforms are proposed in terms of fundamental science as well as linkages to ongoing planning for satellite systems with similar observation requirements.
Aptamer-Based Nanoporous Anodic Alumina Interferometric Biosensor for Real-Time Thrombin Detection
Authors: Pol Laura , Acosta Laura Karen , Ferré-Borrull Josep , Marsal Lluis F.
Published: October 2019

Aptamer biosensors are one of the most powerful techniques in biosensing. Achieving the best platform to use in aptamer biosensors typically includes crucial chemical modifications that enable aptamer immobilization on the surface in the most efficient manner. These chemical modifications must be well defined. In this work we propose nanoporous anodic alumina (NAA) chemically modified with streptavidin as a platform for aptamer immobilization. The immobilization of biotinylated thrombin binding aptamer (TBA) was monitored in real time by means of reflective interferometric spectroscopy (RIfS). The study has permitted to characterize in real time the path to immobilize TBA on the inner pore walls of NAA. Furthermore, this study provides an accurate label-free method to detect thrombin in real-time with high affinity and specificity.
An Innovative Technique for Identification of Missing Persons in Natural Disaster Based on Drone-Femtocell Systems
Authors: Avanzato Roberta , Beritelli Francesco
Published: October 2019

The recent development of the IoT (Internet of Things), which has enabled new types of sensors that can be easily interconnected to the Internet, will also have a significant impact in the near future on the management of natural disasters (mainly earthquakes and floods) with the aim of improving effectiveness in research, identification, and recovery of missing persons, and therefore increasing the possibility of saving lives. In this paper, more specifically, an innovative technique is proposed for the search and identification of missing persons in natural disaster scenarios by employing a drone-femtocell system and devising an algorithm capable of locating any mobile terminal in a given monitoring area. In particular, through a series of power measurements based on the reference signal received power (RSRP), the algorithm allows for the classification of the terminal inside or outside the monitoring area and subsequently identify the position with an accuracy of about 1 m, even in the presence of obstacles that act in such a way as to make the propagation of the radio signal non-isotropic.
CdTe Quantum Dots Modified with Cysteamine: A New Efficient Nanosensor for the Determination of Folic Acid
Authors: Ramírez-Herrera Doris E. , Reyes-Cruzaley Ana Patricia , Dominguez Giselle , Paraguay-Delgado Francisco , Tirado-Guízar Antonio , Pina-Luis Georgina
Published: October 2019

In this paper, we report the synthesis, characterization, and application of a new fluorescent nanosensor based on water-soluble CdTe quantum dots (QDs) coated with cysteamine (CA) for the determination of folic acid (FA). CdTe/CA QDs were characterized by high-resolution transmission electron microscopy, the zeta potential, and Fourier-transform infrared (FT-IR), UV-visible, and fluorescence spectroscopy. CdTe QDs coated with mercaptopropionic acid (MPA) and glutathione (GSH) were prepared for comparison purposes. The effect of FA on the photoluminescence intensity of the three thiol-capped QDs at pH 8 was studied. Only CdTe/CA QDs showed a notable fluorescence quenching in the presence of FA. Then, a nanosensor based on the fluorescence quenching of the CdTe QDs at pH 8 was explored. Under optimum conditions, the calibration curve showed a linear fluorescence quenching response in a concentration range of FA from 0.16 to 16.4 μM (R2 = 0.9944), with a detection limit of 0.048 μM. A probable mechanism of fluorescence quenching was proposed. The nanosensor showed good selectivity over other possible interferences. This method has been applied for FA quantification in orange beverage samples with excellent results (recoveries from 98.3 to 103.9%). The good selectivity, sensitivity, low cost, and rapidity make CdTe /CA QDs a suitable nanosensor for FA determination.
Smartphone-Based 3D Indoor Pedestrian Positioning through Multi-Modal Data Fusion
Authors: Zhao Hongyu , Cheng Wanli , Yang Ning , Qiu Sen , Wang Zhelong , Wang Jianjun
Published: October 2019

Combining research areas of biomechanics and pedestrian dead reckoning (PDR) provides a very promising way for pedestrian positioning in environments where Global Positioning System (GPS) signals are degraded or unavailable. In recent years, the PDR systems based on a smartphone’s built-in inertial sensors have attracted much attention in such environments. However, smartphone-based PDR systems are facing various challenges, especially the heading drift, which leads to the phenomenon of estimated walking path passing through walls. In this paper, the 2D PDR system is implemented by using a pocket-worn smartphone, and then enhanced by introducing a map-matching algorithm that employs a particle filter to prevent the wall-crossing problem. In addition, to extend the PDR system for 3D applications, the smartphone’s built-in barometer is used to measure the pressure variation associated to the pedestrian’s vertical displacement. Experimental results show that the map-matching algorithm based on a particle filter can effectively solve the wall-crossing problem and improve the accuracy of indoor PDR. By fusing the barometer readings, the vertical displacement can be calculated to derive the floor transition information. Despite the inherent sensor noises and complex pedestrian movements, smartphone-based 3D pedestrian positioning systems have considerable potential for indoor location-based services (LBS).
Parylene-Coated Polytetrafluoroethylene-Membrane-Based Portable Urea Sensor for Real-Time Monitoring of Urea in Peritoneal Dialysate
Authors: Park Min , Kim JeeYoung , Kim Kyounghee , Pyun Jae-Chul , Sung Gun Yong
Published: October 2019

A portable urea sensor for use in fast flow conditions was fabricated using porous polytetrafluoroethylene (PTFE) membranes coated with amine-functionalized parylene, parylene-A, by vapor deposition. The urea-hydrolyzing enzyme urease was immobilized on the parylene-A-coated PTFE membranes using glutaraldehyde. The urease-immobilized membranes were assembled in a polydimethylsiloxane (PDMS) fluidic chamber, and a screen-printed carbon three-electrode system was used for electrochemical measurements. The success of urease immobilization was confirmed using scanning electron microscopy, and fourier-transform infrared spectroscopy. The optimum concentration of urease for immobilization on the parylene-A-coated PTFE membranes was determined to be 48 mg/mL, and the optimum number of membranes in the PDMS chamber was found to be eight. Using these optimized conditions, we fabricated the urea biosensor and monitored urea samples under various flow rates ranging from 0.5 to 10 mL/min in the flow condition using chronoamperometry. To test the applicability of the sensor for physiological samples, we used it for monitoring urea concentration in the waste peritoneal dialysate of a patient with chronic renal failure, at a flow rate of 0.5 mL/min. This developed urea biosensor is considered applicable for (portable) applications, such as artificial kidney systems and portable dialysis systems.
An Exploration of Machine Learning Methods for Robust Boredom Classification Using EEG and GSR Data
Authors: Seo Jungryul , Laine Teemu H. , Sohn Kyung-Ah
Published: October 2019

In recent years, affective computing has been actively researched to provide a higher level of emotion-awareness. Numerous studies have been conducted to detect the user’s emotions from physiological data. Among a myriad of target emotions, boredom, in particular, has been suggested to cause not only medical issues but also challenges in various facets of daily life. However, to the best of our knowledge, no previous studies have used electroencephalography (EEG) and galvanic skin response (GSR) together for boredom classification, although these data have potential features for emotion classification. To investigate the combined effect of these features on boredom classification, we collected EEG and GSR data from 28 participants using off-the-shelf sensors. During data acquisition, we used a set of stimuli comprising a video clip designed to elicit boredom and two other video clips of entertaining content. The collected samples were labeled based on the participants’ questionnaire-based testimonies on experienced boredom levels. Using the collected data, we initially trained 30 models with 19 machine learning algorithms and selected the top three candidate classifiers. After tuning the hyperparameters, we validated the final models through 1000 iterations of 10-fold cross validation to increase the robustness of the test results. Our results indicated that a Multilayer Perceptron model performed the best with a mean accuracy of 79.98% (AUC: 0.781). It also revealed the correlation between boredom and the combined features of EEG and GSR. These results can be useful for building accurate affective computing systems and understanding the physiological properties of boredom.
Application of a Fluorescent Probe for the Online Measurement of PM-Bound Reactive Oxygen Species in Chamber and Ambient Studies
Authors: Brown Reece , Stevanovic Svetlana , Brown Zachary , Cai Mingfu , Zhou Shengzhen , Song Wei , Wang Xinming , Miljevic Branka , Zhao Jun , Bottle Steven , Ristovski Zoran
Published: October 2019

This manuscript details the application of a profluorescent nitroxide (PFN) for the online quantification of radical concentrations on particulate matter (PM) using an improved Particle Into Nitroxide Quencher (PINQ). A miniature flow-through fluorimeter developed specifically for use with the 9,10-bis(phenylethynyl)anthracene-nitroxide (BPEAnit) probe was integrated into the PINQ, along with automated gas phase corrections through periodic high efficiency particle arrestor (HEPA) filtering. The resulting instrument is capable of unattended sampling and was operated with a minimum time resolution of 2.5 min. Details of the fluorimeter design and examples of data processing are provided, and results from a chamber study of side-stream cigarette smoke and ambient monitoring campaign in Guangzhou, China are presented. Primary cigarette smoke was shown to have both short-lived (t1/2 = 27 min) and long-lived (t1/2 = indefinite) PM-bound reactive oxygen species (ROS) components which had previously only been observed in secondary organic aerosol (SOA).
eHomeSeniors Dataset: An Infrared Thermal Sensor Dataset for Automatic Fall Detection Research
Authors: Riquelme Fabián , Espinoza Cristina , Rodenas Tomás , Minonzio Jean-Gabriel , Taramasco Carla
Published: October 2019

Automatic fall detection is a very active research area, which has grown explosively since the 2010s, especially focused on elderly care. Rapid detection of falls favors early awareness from the injured person, reducing a series of negative consequences in the health of the elderly. Currently, there are several fall detection systems (FDSs), mostly based on predictive and machine-learning approaches. These algorithms are based on different data sources, such as wearable devices, ambient-based sensors, or vision/camera-based approaches. While wearable devices like inertial measurement units (IMUs) and smartphones entail a dependence on their use, most image-based devices like Kinect sensors generate video recordings, which may affect the privacy of the user. Regardless of the device used, most of these FDSs have been tested only in controlled laboratory environments, and there are still no mass commercial FDS. The latter is partly due to the impossibility of counting, for ethical reasons, with datasets generated by falls of real older adults. All public datasets generated in laboratory are performed by young people, without considering the differences in acceleration and falling features of older adults. Given the above, this article presents the eHomeSeniors dataset, a new public dataset which is innovative in at least three aspects: first, it collects data from two different privacy-friendly infrared thermal sensors; second, it is constructed by two types of volunteers: normal young people (as usual) and performing artists, with the latter group assisted by a physiotherapist to emulate the real fall conditions of older adults; and third, the types of falls selected are the result of a thorough literature review.
Coherent Pulse-Compression Lidar Based on 90-Degree Optical Hybrid
Authors: Yang Jing , Zhao Bin , Liu Bo
Published: October 2019

A coherent pulse-compression lidar system based on a 90-degree optical hybrid is demonstrated in this paper. In amplitude modulation (AM) mode, the returned RF chirp signal will be influenced by a random phase difference between local oscillator and echo light, causing fluctuations in the ranging results, and as a result the detection probability is small. By using the 90-degree optical hybrid, two orthogonal complementary signals are obtained to stabilize the result so as to increase the detection probability. We performed an experiment to measure the distance of a white printed wall which is about 65 m away from the system. The detection probability increased from 65% to 99.88%, and the precision is improved from 0.42 m to 0.27 m.
Sensors for Expert Grip Force Profiling: Towards Benchmarking Manual Control of a Robotic Device for Surgical Tool Movements
Authors: de Mathelin Michel , Nageotte Florent , Zanne Philippe , Dresp-Langley Birgitta
Published: October 2019

STRAS (Single access Transluminal Robotic Assistant for Surgeons) is a new robotic system based on the Anubis® platform of Karl Storz for application to intra-luminal surgical procedures. Pre-clinical testing of STRAS has recently permitted to demonstrate major advantages of the system in comparison with classic procedures. Benchmark methods permitting to establish objective criteria for ‘expertise’ need to be worked out now to effectively train surgeons on this new system in the near future. STRAS consists of three cable-driven sub-systems, one endoscope serving as guide, and two flexible instruments. The flexible instruments have three degrees of freedom and can be teleoperated by a single user via two specially designed master interfaces. In this study, small force sensors sewn into a wearable glove to ergonomically fit the master handles of the robotic system were employed for monitoring the forces applied by an expert and a trainee (complete novice) during all the steps of surgical task execution in a simulator task (4-step-pick-and-drop). Analysis of grip-force profiles is performed sensor by sensor to bring to the fore specific differences in handgrip force profiles in specific sensor locations on anatomically relevant parts of the fingers and hand controlling the master/slave system.
An Alignment Method for Strapdown Inertial Navigation Systems Assisted by Doppler Radar on a Vehicle-Borne Moving Base
Authors: Yang Bo , Xi Jianxiang , Yang Jian , Xue Liang
Published: October 2019

In this study, we investigated a novel method for high-accuracy autonomous alignment of a strapdown inertial navigation system assisted by Doppler radar on a vehicle-borne moving base, which effectively avoids the measurement errors caused by wheel-slip or vehicle-sliding. Using the gyroscopes in a strapdown inertial navigation system and Doppler radar, we calculated the dead reckoning, analyzed the error sources of the dead reckoning system, and established an error model. Then the errors of the strapdown inertial navigation system and dead reckoning system were treated as the states. Besides velocity information, attitude information was cleverly introduced into the alignment measurement to improve alignment accuracy and reduce alignment time. Therefore, the first measurement was the difference between the output attitude and velocity of the strapdown inertial navigation system and the corresponding signals from the dead reckoning system. In order to further improve the alignment accuracy, more measurement information was introduced by using the vehicle motion constraint, that is, the velocity output projection of strapdown inertial navigation system along the transverse and vertical direction of the vehicle body was also used as the second measurement. Then the corresponding state and measurement equations were established, and the Kalman filter algorithm was used for assisted alignment filtering. The simulation results showed that, with a moving base, the misalignment angle estimation accuracy was better than 0.5’ in the east direction, 0.4’ in the north direction, and 3.2’ in the vertical direction.
Indoor Intruder Tracking Using Visible Light Communications
Authors: Alsalami Farah , Ahmad Zahir , Zvanovec Stanislav , Haigh Paul , Haas Olivier , Rajbhandari Sujan
Published: October 2019

This paper proposes a comprehensive study of indoor intruder tracking using visible light communication (VLC). A realistic indoor VLC channel was developed, taking into consideration reflections, shadowing, and ambient noise. The intruder was considered smart and aiming to escape tracking. This was modelled by adding noise and disturbance to the intruder’s trajectory. We propose to extend the application of minimax filtering from state estimation in the radio frequency (RF) domain to intruder tracking using VLC. The performance of the proposed method was examined and compared with Kalman filter for both VLC and RF. The simulation results showed that the minimax filter provided marginally better tracking and was more robust to the adversary behavior of the intruder than Kalman filter, with less than 0.5 cm estimation error. In addition, minimax was significantly better than Kalman filter for RF tracking applications.
Behavior-Based Control for an Aerial Robotic Swarm in Surveillance Missions
Authors: Garcia-Aunon Pablo , del Cerro Jaime , Barrientos Antonio
Published: October 2019

Aerial robotic swarms have shown benefits for performing search and surveillance missions in open spaces in the past. Among other properties, these systems are robust, scalable and adaptable to different scenarios. In this work, we propose a behavior-based algorithm to carry out a surveillance task in a rectangular area with a flexible number of quadcopters, flying at different speeds. Once the efficiency of the algorithm is quantitatively analyzed, the robustness of the system is demonstrated with 3 different tests: loss of broadcast messages, positioning errors, and failure of half of the agents during the mission. Experiments are carried out in an indoor arena with micro quadcopters to support simulation results. Finally, a case study is proposed to show a realistic implementation in the test bed.
Analysis of Success Factors of Reward-Based Crowdfunding Campaigns Using Multi-Theory Approach in ASEAN-5 Countries
Authors: Dikaputra Rinaldo , Sulung Liyu Adhi Kasari , Kot Sebastian
Published: October 2019

This study aims to determine the multi-sided factors of crowdfunding campaigns on the fundraising success of crowdfunding projects conducted in ASEAN-5 (five big countries in The Association of Southeast Asian Nations) countries between 2014 and January 2019 through a multi-theory approach. Using secondary and cross-section data from Kickstarter, this research uses the probit regression method to analyze the data and test the hypotheses. First, in line with the goal setting theory, small-sized projects are more likely to be funded. Second, the large teams are preferable by the potential backers, in line with the resource-based view of firms. Further, the self-determination theory shows that rewards are one of the big extrinsic motivations for the backers in ASEAN. Moreover, our paper also shows the remarkable result with the funding target which has negative influence to probability success of crowdfunding. This is because our project samples are used from the ASEAN countries whose owner or investor is more concerned to be risk averse than a risk taker. These results provide insight and bring a contribution to the crowdfunding literatures in ASEAN.
Post-Exercise Recovery Following 30-Day Supplementation of Trans-Resveratrol and Polyphenol-Enriched Extracts
Authors: Jo Edward , Bartosh Rachel , Auslander Alexandra T. , Directo Dean , Osmond Adam , Wong Michael WH
Published: October 2019

Background: The purpose of this study was to investigate the effects of 30-day consumption of trans-resveratrol and polyphenol-enriched extracts on indices of exercise-induced muscle damage (EIMD) and performance following eccentric-loaded resistance exercise (ECRE). Methods: Following 30 days of resveratrol-polyphenol (RES) (n = 10) or placebo control (CTL) (n = 12) supplementation, subjects performed a bout of ECRE to induce EIMD. EIMD biomarkers, perceived soreness, pain threshold and tolerance, range of motion, and performance were measured before and 24 and 48 h after ECRE. Results: CTL subjects demonstrated increased soreness at 24 (p = 0.02) and 48 h (p = 0.03) post-ECRE, while RES subjects reported increased soreness at 24 h post-ECRE (p = 0.0003). CTL subjects exhibited decreased pain threshold in the vastus lateralis at 24 h post-ECRE (p = 0.03). CTL subjects also displayed decreased pain tolerance in the vastus intermedius at 24 h post-ECRE (p = 0.03) and the vastus lateralis at 24 (p = 0.003) and 48 h (p = 0.003). RES participants showed no change in pain threshold or tolerance from baseline. CTL subjects showed a decrease in mean (p = 0.04) and peak power (p = 0.04) at 24 h post-ECRE, while RES participants demonstrated no changes from baseline. No between-group differences were observed for the changes in serum creatine kinase. Serum C-reactive protein increased similarly in both groups at 24 h post-ECRE (p < 0.002), remaining elevated in CTL subjects while RES participants demonstrated a decline from 24 to 48 h (p = 0.04). Serum interleukin 6 increased at 24 h post-ECRE in both groups (p < 0.003) followed by a decrease from 24 to 48 h, returning to baseline levels only for RES subjects. Conclusion: Trans-resveratrol and polyphenol-enriched extract supplementation may support the attenuation of soreness and inflammation and improve performance recovery following ECRE without modulation of indirect biomarkers of EIMD.
Study on the Impact of the Export of China’s Final Use Products on Domestic SO2 Emissions
Authors: Zang Zhenjie , Zhang Hua , Liu Huifang , Wang Jun , Goetswang Kealeboga Fredah
Published: October 2019

Since China’s accession to the World Trade Organization (WTO), its export volume has achieved rapid growth. Meanwhile, the manufacturing of export products has also resulted in a large amount of SO2 emissions in China. To explore the relationship between the export of China’s final use products (ECFuP) and SO2 emissions, this paper first used the Multi-Regional Input–output (MRIO) model to study the SO2 emissions caused by ECFuP during 2003–2011. Then, this paper uses Structural Decomposition Analysis (SDA) to decompose the factors affecting SO2 emission into technical effect, structural effect and scale effect. The results showed that (1) the amounts of China’s SO2 emissions caused by the ECFuP have increased (2003–2007), declined (2007–2009), and increased again (2009–2011). (2) Scale effect is the main factor that causes the increase of SO2 emissions in China; technical effect mainly resulted in a decrease of emissions, whereas structural effect has less impact. Specifically, from 2003 to 2011, scale effect increased domestic SO2 emissions by 2.2 million tons; technical effect and structural effect reduced by 2.4 million tons and 0.5 million tons of emissions, respectively. (3) For different regions, there is a positive correlation between the consumption of the ECFuP and China’s SO2 emissions. Among them, NAFTA (accounting for 33.77%) leads to the largest SO2 emissions, and OTHER EU (5.79%) is the least. (4) From the industrial aspect, some industries with relatively small ECFuP have caused high SO2 emissions. The specific performance is as follows, among the 17 industries, Electricity, Gas and Water Supply (EGW) only occupied 0.6% of the total ECFuP, but it has the largest SO2 emissions (55%); in contrast, while Electrical and Optical Equipment (EOE) occupied 42% of the total ECFuP, its SO2 emissions only accounted for 0.2% of the total. In 2003–2011, the export trade volumes of all the industries increased, but the growth rates of less polluted industries are higher than that of heavy polluted industries. Based on the above findings, the paper also proposed some policy recommendations.
The Evolution of Industrial Agglomerations and Specialization in the Yangtze River Delta from 1990–2018: An Analysis Based on Firm-Level Big Data
Authors: Hu Shuju , Song Wei , Li Chenggu , Zhang Charlie H.
Published: October 2019

Although industrial agglomeration and specialization have been studied for more than 100 years, it is still a controversial field. In the era of big data, it is of great significance to study industrial agglomeration and regional specialization by using firm-level data. Based on 3,053,024 pieces of firm-level big data, the spatial evolution and spatial patterns of industrial agglomeration and specialization of 9 major industries in the Yangtze River Delta, China were revealed. Results show that: (1) the degree of industrial agglomeration is highly related to industrial attributes; industries which are directly related to production tend to be geographically concentrated, while industries that serve for production tend to be spatially dispersed; (2) the evolution characteristics and trajectories of industrial agglomeration vary by industries: wholesale and retail trade and real estate are becoming more spatially dispersed; information industries, leasing and commercial services, scientific research and polytechnic services, as well as finance are experiencing continuous spatial agglomeration; construction and manufacturing show a tendency of transfer from spatial agglomeration to spatial dispersion; (3) since 1990, most industries in the Yangtze River Delta have formed distinct spatial patterns of industrial specialization. Most core cities have experienced obvious deindustrialization processes; and high-end industries are clustering to the three biggest core cities of Shanghai, Nanjing, and Hangzhou.
Product Newness, Low Competition, Recent Technology, and Export Orientation as Predictors for Entrepreneurial Growth Aspirations
Authors: Carreón-Gutiérrez José Pedro , Saiz-Álvarez José Manuel
Published: October 2019

This study examines the contribution of how product newness, low competition, recent technology, and export orientation affect entrepreneurial growth aspirations moderated by financial capital. Based on a Global Entrepreneurship Monitor (GEM) sample of 512 Mexican new entrepreneurs, we use a hierarchical regression model to study the independent and interaction effects between these variables, and we apply a Chow breakpoint test and a CUSUMSQ (cumulative sum of squares of recursive residuals) test to analyze structural change and robustness. Our results suggest that achieving higher educational levels, acquiring recent technology, and product newness slightly increase the entrepreneurial growth ambition of the firm, and that financial capital positively moderates the impact of product newness and recent technology on growth aspirations. Besides this, we show that the interaction effect of financial capital with low competition and export activity on their growth aspirations is not crucial, and business angles tend to finance, primarily when the firm exports new products and services are facing a reduced number of competitors.
The Sustainable Cultivation of Mexican Nontoxic Jatropha Curcas to Produce Biodiesel and Food in Marginal Rural Lands
Authors: Pérez Guadalupe , Islas Jorge , Guevara Mirna , Suárez Raúl
Published: October 2019

The objective of this study is to identify Mexican nontoxic ecotypes of Jatropha curcas with potential for a sustainable agriculture practice to produce biodiesel and food products through a methodology and criteria of sustainability. In a rural region of Morelos state in central México, nine Mexican ecotypes of Jatropha curcas were evaluated in an experimental plantation with minimal water resources and fertilization. The experimental trial was assessed in terms of (1) toxicity, (2) growth and survival of the plants, (3) yield of seed, (4) residual biomass production, (5) oil characteristics for biodiesel production, (6) nutrimental properties of the seeds, and (7) changes in the chemical properties of the soil. Finally, two outstanding nontoxic ecotypes were identified as the most suitable for the establishment of Jatropha curcas crops to produce biodiesel and, at the same time, for food products using the de-oiled endosperm cake, for energy production from the residual biomass and for improvement of soil properties in marginal lands of rural regions of Morelos state.
Augmenting Museum Communication Services to Create Young Audiences
Authors: Nechita Florin , Rezeanu Catalina-Ionela
Published: October 2019

The aim of this paper is to demonstrate how museums use Augmented Reality (AR) to enhance communication services with their audiences and attract new ones. Still, there is no definite answer to how young audiences perceive the educational effects of experiencing this augmented space of communication as an immersive medium. This study is based on a survey of 400 students after they visited an AR technology-enhanced exhibition held by a local history museum. Two stimulus–response marketing scale metrics, widely used to assess TV commercials, were adapted for AR experiences and validated. The mediation analysis revealed an intervening emotional mechanism, in which the multisensory AR experience has educational effects through entertainment and empathy. An improved stimulus–response empirical model is proposed, in which AR technologies, as environmental multisensory stimuli, produce cognitive responses through emotional immersion. The findings have significance in improving how museums encode their message using AR technologies as a secondary communication medium with young audiences. This study could help museum professionals and application developers to find AR implementation solutions as service tools to enhancing user experience by using a widely tested scale for evaluating TV commercials applied to measure AR experiences.
The Changing Role of CO2 in the Transition to a Circular Economy: Review of Carbon Sequestration Projects
Authors: Tcvetkov Pavel , Cherepovitsyn Alexey , Fedoseev Sergey
Published: October 2019

Despite the diversity of studies on global warming and climate change mitigation technologies, research on the changing role of CO2 in the industrial processes, which is connected with the introduction of circular economy principles, is still out of scope. The purpose of this review is to answer the following question: Is technogenic CO2 still an industrial waste or has it become a valuable resource? For this purpose, statistical information from the National Energy Technology Library and the Global CCS Institute databases were reviewed. All sequestration projects (199) were divided into three groups: carbon capture and storage (65); carbon capture, utilization, and storage (100); and carbon capture and utilization (34). It was found that: (1) total annual CO2 consumption of such projects was 50.1 Mtpa in 2018, with a possible increase to 326.7 Mtpa in the coming decade; (2) total amount of CO2 sequestered in such projects could be 2209 Mt in 2028; (3) the risk of such projects being cancelled or postponed is around 31.8%; (4) CO2 is a valuable and sought-after resource for various industries. It was concluded that further development of carbon capture and utilization technologies will invariably lead to a change in attitudes towards CO2, as well as the appearance of new CO2-based markets and industries.
Development of Dog Immune System: From in Uterus to Elderly
Authors: Pereira Maria , Valério-Bolas Ana , Saraiva-Marques Cátia , Alexandre-Pires Graça , Pereira da Fonseca Isabel , Santos-Gomes Gabriela
Published: October 2019

Immune system recognize and fight back foreign microorganisms and inner modifications that lead to deficient cell and tissue functions. During a dog’s life, the immune system needs to adapt to different physiological conditions, assuring surveillance and protection in a careful and controlled way. Pregnancy alters normal homeostasis, requiring a balance between immunity and tolerance. The embryos and fetus should be protected from infections, while the female dog must tolerate the growing of semi-allografts in her uterus. After birth, newborn puppies are at great risk of developing infectious diseases, because their immune system is in development and immune memory is absent. Passive transfer of immunity through colostrum is fundamental for puppy survival in the first weeks of life, but hampers the development of an active immune response to vaccination. At the end of life, dogs experience a decline in the structure and functional competence of the immune system, compromising the immune responses to novel antigenic challenges, such as infections and vaccines. Therefore, the current article reviews the general processes related to the development of the dog´s immune system, providing an overview of immune activity throughout the dog’s life and its implications in canine health, and highlighting priority research goals.