bluuebunny/crossref_metadata_2025 huggingface dataset:
3D Bio-Printing of CS/Gel/HA/Gr Hybrid Osteochondral Scaffolds
Authors: Hu Xueyan , Man Yuan , Li Wenfang , Li Liying , Xu Jie , Parungao Roxanne , Wang Yiwei , Zheng Shuangshuang , Nie Yi , Liu Tianqing , Song Kedong
Published: September 2019

Cartilage is an important tissue contributing to the structure and function of support and protection in the human body. There are many challenges for tissue cartilage repair. However, 3D bio-printing of osteochondral scaffolds provides a promising solution. This study involved preparing bio-inks with different proportions of chitosan (Cs), Gelatin (Gel), and Hyaluronic acid (HA). The rheological properties of each bio-ink was used to identify the optimal bio-ink for printing. To improve the mechanical properties of the bio-scaffold, Graphene (GR) with a mass ratio of 0.024, 0.06, and 0.1% was doped in the bio-ink. Bio-scaffolds were prepared using 3D printing technology. The mechanical strength, water absorption rate, porosity, and degradation rate of the bio-scaffolds were compared to select the most suitable scaffold to support the proliferation and differentiation of cells. P3 Bone mesenchymal stem cells (BMSCs) were inoculated onto the bio-scaffolds to study the biocompatibility of the scaffolds. The results of SEM showed that the Cs/Gel/HA scaffolds with a GR content of 0, 0.024, 0.06, and 0.1% had a good three-dimensional porous structure and interpenetrating pores, and a porosity of more than 80%. GR was evenly distributed on the scaffold as observed by energy spectrum analyzer and polarizing microscope. With increasing GR content, the mechanical strength of the scaffold was enhanced, and pore walls became thicker and smoother. BMSCs were inoculated on the different scaffolds. The cells distributed and extended well on Cs/Gel/HA/GR scaffolds. Compared to traditional methods in tissue-engineering, this technique displays important advantages in simulating natural cartilage with the ability to finely control the mechanical and chemical properties of the scaffold to support cell distribution and proliferation for tissue repair.
Numerical Analysis of the Diaphragm Valve Throttling Characteristics
Authors: Liu Yingnan , Lu Liang , Zhu Kangwu
Published: September 2019

The throttling characteristics of the diaphragm valve are numerically studied in this paper. Firstly, the diaphragm deformation performance is analyzed by a finite element method, while the upper boundary morphology of the internal flow field under different valve openings was obtained. Then the two-dimensional simulation of the weir diaphragm valve flow field is carried out in order to explore the optimal design of flow path profile. The study shows that the throttling characteristics can be improved by flatting the ridge side wall, widening the top of the ridge and gently flatting the internal protruding of the flow path. In addition, using the local grid encryption techniques based on velocity gradient adaptive and y+ adaptive can improve the accuracy of simulation results. Finally, a cavitation two-phase flow simulation is carried out. The results show that cavitation may occur below 50% opening of diaphragm valve in ultra-pure water system, which becomes more intense with the increase of inlet pressure and even leading to flow saturation on the micro-orifice state.
On the Prospects of Multiport Devices for Photon-Number-Resolving Detection
Authors: Teo Yong Siah , Jeong Hyunseok , Řeháček Jaroslav , Hradil Zdeněk , Sánchez-Soto Luis L. , Silberhorn Christine
Published: September 2019

Ideal photon-number-resolving detectors form a class of important optical components in quantum optics and quantum information theory. In this article, we theoretically investigate the potential of multiport devices having reconstruction performances approaching that of the Fock-state measurement. By recognizing that all multiport devices are minimally complete, we first provide a general analytical framework to describe the tomographic accuracy (or quality) of these devices. Next, we show that a perfect multiport device with an infinite number of output ports functions as either the Fock-state measurement when photon losses are absent or binomial mixtures of Fock-state measurements when photon losses are present and derive their respective expressions for the tomographic transfer function. This function is the scaled asymptotic mean squared error of the reconstructed photon-number distributions uniformly averaged over all distributions in the probability simplex. We then supply more general analytical formulas for the transfer function for finite numbers of output ports in both the absence and presence of photon losses. The effects of photon losses on the photon-number resolving power of both infinite- and finite-size multiport devices are also investigated.
Multiple Applications of Ion Beams in Life Science
Authors: Tanaka Atsushi
Published: September 2019

Welcome to the Special Issue of Quantum Beam Science that features application of ion beams in biology and medicine [...]
“Speaking Out Would Be a Step Beyond Just Not Believing”—On the Performativity of Testimony When Moving Out of Islam
Authors: Vliek Maria
Published: September 2019

This article investigates the narratives of people moving out of Islam in contemporary Europe. In particular, it focusses on the potential performance of non-belief in the form of speech. By critically examining the function of testimony in conversion and deconversion narratives, this article problematises the assumed boundaries of belief, non-belief, and the function of the performance of identity. It does so by investigating contemplations over private and public performances, since the performance of speech was thought to have different effects in both spheres. Whilst public discourses on leaving Islam and speaking freely were always weighed, in private these were related to familial bonds, love, and belonging. On the other hand, considering speaking out in public was often contextualised with reference to potential secularist appropriation of their stories as ‘native testimonial’. As such, my interlocutors show that testifying of one’s religious transformation in the case of moving out of Islam was neither central nor conditional. Speech was mostly considered a ‘step beyond’ not believing.
Conflicting Paradigms of Religious and Bureaucratic Authority in a British Mosque
Authors: El-Yousfi Amin
Published: September 2019

This article analyses an on-going conflict between two groups (Bargil and Kardal) over the management of a mosque located in an area near London. Based on fourteen months of intensive fieldwork, including participant observation, informal chats and semi-structured interviews, this article offers an in-depth and original account of the transformations taking place in mosques concerning the role of imams and mosque committee members. By analysing the object of conflict, the organisational structure, the dynamic of the groups and its leaders, as well as the process of bureaucratisation of mosques as a material condition, I intend to scrutinise the role and status of the imam and mosque committee members. The primary aim of this article is to re-examine and challenge the narrative of decline in religious authority (in Western mosques) propounded by some scholars as being the result of individualisation and the rise of new religious figures outside traditional institutions. I suggest that rather than experiencing a decline in imams’ religious authority, mosques have become controlled by the bureaucratic authority of the committee members. In other words, imams’ religious authority is still exercised, yet only within the bureaucratic framework set by the committee members.
Alterations in Canadian Hydropower Production Potential Due to Continuation of Historical Trends in Climate Variables
Authors: Amir Jabbari Amirali , Nazemi Ali
Published: September 2019

The vitality, timing, and magnitude of hydropower production is driven by streamflow, which is determined by climate variables, in particular precipitation and temperature. Accordingly, changes in climate characteristics can cause alterations in hydropower production potential. This delineates a critical energy security concern, especially in places such as Canada, where recent changes in climate are substantial and hydropower production is important for both domestic use and exportation. Current Canadian assessments, however, are limited as they mainly focus on a small number of power plants across the country. In addition, they implement scenario-led top-down impact assessments that are subject to large uncertainties in climate, hydrological, and energy models. To avoid these limitations, we propose a bottom-up impact assessment based on the historical information on climatic trends and causal links between climate variables and hydropower production across political jurisdictions. Using this framework, we estimate expected monthly gain/loss in regional hydropower production potential under the continuation of historical climate trends. Our findings show that Canada’s production potential is expected to increase, although the net gain/loss is subject to significant variations across different regions. Our results suggest increasing potential in Yukon, Ontario, and Quebec but decreasing production in the North Western Territories and Nunavut, British Columbia, and Alberta.
The Impact of the Radar-Sampling Volume on Multiwavelength Spaceborne Radar Measurements Using Airborne Radar Observations
Authors: Pfitzenmaier Lukas , Battaglia Alessandro , Kollias Pavlos
Published: September 2019

Multiwavelength radar observations have demonstrated great potential in improving microphysical retrievals of cloud properties especially in ice and snow precipitation systems. Advancements in spaceborne radar technology have already fostered the launch in 2014 of the first multiwavelength radar system in space, while several future spaceborne multiwavelength radar concepts are under consideration. However, due to antenna size limitations, the sampling volume of spaceborne radars is considerably larger than those achieved by surface- and airborne-based radars. Here, the impact of these large sampling volumes in the information content of the Dual-Wavelength Ratio estimates at Ka-W, Ku-Ka is investigated. High-resolution airborne multiwavelength radar observations during the Olympic Mountain Experiment (OLYMPEx) are used to perform retrievals of ice/snow characteristic particle size, such as mass-weighted particle diameter. To mimic the different satellite sampling volumes, a moving average is applied to the airborne measurements. The radar-observed variables (reflectivity and dual-wavelength ratios) and retrieved microphysical properties at the coarser resolution are compared against those at the original resolution. Our analysis indicates that future Ka-W spaceborne radar missions should take into account the impact of the radar resolution volume on the retrieval of microphysical properties and avoid footprints larger than 2–3 km.
Field-Scale Rice Yield Estimation Using Sentinel-1A Synthetic Aperture Radar (SAR) Data in Coastal Saline Region of Jiangsu Province, China
Authors: Wang Jianjun , Dai Qixing , Shang Jiali , Jin Xiuliang , Sun Quan , Zhou Guisheng , Dai Qigen
Published: September 2019

In recent years, a large number of salterns have been converted into rice fields in the coastal region of Jiangsu Province, Eastern China. The high spatial heterogeneity of soil salinity has caused large within-field variabilities in grain yield of rice. The identification of low-yield areas within a field is an important initial step for precision farming. While optical satellite remote sensing can provide valuable information on crop growth and yield potential, the availability of cloud-free optical image data is often hampered by unfavorable weather conditions. Synthetic aperture radar (SAR) offers an alternative due to its nearly day-and-night and all-weather capability in data acquisition. Given the free data access of the Sentinels, this study aimed at developing a Sentinel-1A-based SAR index for rice yield estimation. The proposed SAR simple difference (SSD) index uses the change of the Sentinel-1A backscatter in vertical-horizontal (VH) polarization between the end of the tillering stage and the end of grain filling stage (SSDVH). A strong exponential relationship has been identified between the SSDVH and rice yield, producing accurate yield estimation with a root mean square error (RMSE) of 0.74 t ha−1 and a relative error (RE) of 7.93%.
Convective/Stratiform Precipitation Classification Using Ground-Based Doppler Radar Data Based on the K-Nearest Neighbor Algorithm
Authors: Yang Zhida , Liu Peng , Yang Yi
Published: September 2019

Stratiform and convective rain types are associated with different cloud physical processes, vertical structures, thermodynamic influences and precipitation types. Distinguishing convective and stratiform systems is beneficial to meteorology research and weather forecasting. However, there is no clear boundary between stratiform and convective precipitation. In this study, a machine learning algorithm, K-nearest neighbor (KNN), is used to classify precipitation types. Six Doppler radar (WSR-98D/SA) data sets from Jiangsu, Guangzhou and Anhui Provinces in China were used as training and classification samples, and the 2A23 product of the Tropical Precipitation Measurement Mission (TRMM) was used to obtain the training labels and evaluate the classification performance. Classifying precipitation types using KNN requires three steps. First, features are selected from the radar data by comparing the range of each variable for different precipitation types. Second, the same unclassified samples are classified with different k values to choose the best-performing k. Finally, the unclassified samples are put into the KNN algorithm with the best k to classify precipitation types, and the classification performance is evaluated. Three types of cases, squall line, embedded convective and stratiform cases, are classified by KNN. The KNN method can accurately classify the location and area of stratiform and convective systems. For stratiform classifications, KNN has a 95% probability of detection, 8% false alarm rate, and 87% cumulative success index; for convective classifications, KNN yields a 78% probability of detection, a 13% false alarm rate, and a 69% cumulative success index. These results imply that KNN can correctly classify almost all stratiform precipitation and most convective precipitation types. This result suggests that KNN has great potential in classifying precipitation types.
Derivation of Hyperspectral Profile of Extended Pseudo Invariant Calibration Sites (EPICS) for Use in Sensor Calibration
Authors: Shrestha Mahesh , Hasan Nahid , Leigh Larry , Helder Dennis
Published: September 2019

Reference of Earth-observing satellite sensor data to a common, consistent radiometric scale is an increasingly critical issue as more of these sensors are launched; such consistency can be achieved through radiometric cross-calibration of the sensors. A common cross-calibration approach uses a small set of regions of interest (ROIs) in established Pseudo-Invariant Calibration Sites (PICS) mainly located throughout North Africa. The number of available cloud-free coincident scene pairs available for these regions limits the usefulness of this approach; furthermore, the temporal stability of most regions throughout North Africa is not known, and limited hyperspectral information exists for these regions. As a result, it takes more time to construct an appropriate cross-calibration dataset. In a previous work, Shrestha et al. presented an analysis identifying 19 distinct “clusters” of spectrally similar surface cover that are widely distributed across North Africa, with the potential to provide near-daily cloud-free imaging for most sensors. This paper proposes a technique to generate a representative hyperspectral profile for these clusters. The technique was used to generate the profile for the cluster containing the largest number of aggregated pixels. The resulting profile was found to have temporal uncertainties within 5% across all the spectral regions. Overall, this technique shows great potential for generation of representative hyperspectral profiles for any North African cluster, which could allow the use of the entire North Africa Saharan region as an extended PICS (EPICS) dataset for sensor cross-calibration. This should result in the increased temporal resolution of cross-calibration datasets and should help to achieve a cross-calibration quality similar to that of individual PICS in a significantly shorter time interval. It also facilitates the development of an EPICS based absolute calibration model, which can improve the accuracy and consistency in simulating any sensor’s top of atmosphere (TOA) reflectance.
Use of Very High-Resolution Optical Data for Landslide Mapping and Susceptibility Analysis along the Karnali Highway, Nepal
Authors: Amatya Pukar , Kirschbaum Dalia , Stanley Thomas
Published: September 2019

The Karnali highway is a vital transport link and the only primary roadway that connects the remote Karnali region to the lowlands in Mid-Western Nepal. Every year there are reports of landslides blocking the road, making this area largely inaccessible. However, little effort has focused on systematically identifying landslides and landslide-prone areas along this highway. In this study, landslides were mapped with an object-based approach from very high-resolution optical satellite imagery obtained by the DigitalGlobe constellation in 2012 and PlanetScope in 2018. Landslides ranging from 10 to 30,496 m2 were detected within a 3 km buffer along the highway. Most of the landslides were located at lower elevations (between 500–1500 m) and on steep south-facing slopes. Landslides tended to cluster closer to the highway, near drainage channels and away from faults. Landslides were also most prevalent within the Kuncha Formation geologic class, and the forested and agricultural land cover classes. A susceptibility map was then created using a logistic regression methodology to highlight patterns in landslide activity. The landslide susceptibility map showed a good prediction rate with an area under the curve (AUC) of 0.90. A total of 33% of the study arealies in high/very high susceptibility zones. The map highlighted the lower elevated areas between Bangesimal and Manma towns with the Kuncha Formation geologic class as being the most hazardous. The banks of the Karnali River, its tributaries and areas near the highway were also highly susceptible to landslides. The results highlight the potential of very high-resolution optical imagery for documenting detailed spatial information on landslide occurrence, which enables susceptibility assessment in remote and data scarce regions such as the Karnali highway.
Field-Effect Transistor Biosensors for Biomedical Applications: Recent Advances and Future Prospects
Authors: Vu Cao-An , Chen Wen-Yih
Published: September 2019

During recent years, field-effect transistor biosensors (Bio-FET) for biomedical applications have experienced a robust development with evolutions in FET characteristics as well as modification of bio-receptor structures. This review initially provides contemplation on this progress by analyzing and summarizing remarkable studies on two aforementioned aspects. The former includes fabricating unprecedented nanostructures and employing novel materials for FET transducers whereas the latter primarily synthesizes compact molecules as bio-probes (antibody fragments and aptamers). Afterwards, a future perspective on research of FET-biosensors is also predicted depending on current situations as well as its great demand in clinical trials of disease diagnosis. From these points of view, FET-biosensors with infinite advantages are expected to continuously advance as one of the most promising tools for biomedical applications.
Wearable Sensors for Estimation of Parkinsonian Tremor Severity during Free Body Movements
Authors: Hssayeni Murtadha D. , Jimenez-Shahed Joohi , Burack Michelle A. , Ghoraani Behnaz
Published: September 2019

Tremor is one of the main symptoms of Parkinson’s Disease (PD) that reduces the quality of life. Tremor is measured as part of the Unified Parkinson Disease Rating Scale (UPDRS) part III. However, the assessment is based on onsite physical examinations and does not fully represent the patients’ tremor experience in their day-to-day life. Our objective in this paper was to develop algorithms that, combined with wearable sensors, can estimate total Parkinsonian tremor as the patients performed a variety of free body movements. We developed two methods: an ensemble model based on gradient tree boosting and a deep learning model based on long short-term memory (LSTM) networks. The developed methods were assessed on gyroscope sensor data from 24 PD subjects. Our analysis demonstrated that the method based on gradient tree boosting provided a high correlation (r = 0.96 using held-out testing and r = 0.93 using subject-based, leave-one-out cross-validation) between the estimated and clinically assessed tremor subscores in comparison to the LSTM-based method with a moderate correlation (r = 0.84 using held-out testing and r = 0.77 using subject-based, leave-one-out cross-validation). These results indicate that our approach holds great promise in providing a full spectrum of the patients’ tremor from continuous monitoring of the subjects’ movement in their natural environment.
Machine Learning-Based Fast Banknote Serial Number Recognition Using Knowledge Distillation and Bayesian Optimization
Authors: Choi Eunjeong , Chae Somi , Kim Jeongtae
Published: September 2019

We investigated a machine-learning-based fast banknote serial number recognition method. Unlike existing methods, the proposed method not only recognizes multi-digit serial numbers simultaneously but also detects the region of interest for the serial number automatically from the input image. Furthermore, the proposed method uses knowledge distillation to compress a cumbersome deep-learning model into a simple model to achieve faster computation. To automatically decide hyperparameters for knowledge distillation, we applied the Bayesian optimization method. In experiments using Japanese Yen, Korean Won, and Euro banknotes, the proposed method showed significant improvement in computation time while maintaining a performance comparable to a sequential region of interest (ROI) detection and classification method.
Influence of BMI on Gait Characteristics of Young Adults: 3D Evaluation Using Inertial Sensors
Authors: Rosso Valeria , Agostini Valentina , Takeda Ryo , Tadano Shigeru , Gastaldi Laura
Published: September 2019

Overweight/obesity is a physical condition that affects daily activities, including walking. The main purpose of this study was to identify if there is a relationship between body mass index (BMI) and gait characteristics in young adults. 12 normal weight (NW) and 10 overweight/obese (OW) individuals walked at a self-selected speed along a 14 m indoor path. H-Gait system, combining seven inertial sensors (fixed on pelvis and lower limbs), was used to record gait data. Walking speed, spatio-temporal parameters and joint kinematics in 3D were analyzed. Differences between NW and OW and correlations between BMI and gait parameters were evaluated. Conventional spatio-temporal parameters did not show statistical differences between the two groups or correlations with the BMI. However, significant results were pointed out for the joint kinematics. OW showed greater hip joint angles in frontal and transverse planes, with respect to NW. In the transverse plane, OW showed a greater knee opening angle and a shorter length of knee and ankle trajectories. Correlations were found between BMI and kinematic parameters in the frontal and transverse planes. Despite some phenomena such as soft tissue artifact and kinematics cross-talk, which have to be more deeply assessed, current results show a relationship between BMI and gait characteristics in young adults that should be looked at in osteoarthritis prevention.
A Novel, Portable and Fast Moisture Content Measuring Method for Grains Based on an Ultra-Wideband (UWB) Radar Module and the Mode Matching Method
Authors: Zhang Chi , Shi Zhichao , Yang Haiying , Zhou Xiaoguang , Wu Zidan , Jayas Digvir S.
Published: September 2019

To perform fast and portable grain moisture measurements under field conditions, a novel moisture sensor was designed, which consisted of a coaxial waveguide, a circular waveguide, and an isolation layer. The electromagnetic characteristics of the sensor were simulated and measured. The analytical model, which represented the relationship between the reflection coefficient of the sensor and the complex permittivity of grain, was established by using the mode matching method. The reflection coefficient of the sensor was measured by using an ultra-wideband (UWB) radar module, and the moisture content of grains was calculated from the complex permittivity by using density-independent model. To verify the performance of the proposed method, wheat, rough rice, and barley were taken as examples. The measured results in the range from 1.0% to 26.0%, wet basis, agreed well with the reference values (R2 was more than 0.99), and the maximum absolute errors for wheat, rough rice, and barley were 1.1%, 1.0%, and 1.4%, respectively. In addition, the effect of isolation layer was discussed. Both the simulation results and the experimental results showed that the isolation layer improved the stability of sensor.
Edible Gelatin Diagnosis Using Laser-Induced Breakdown Spectroscopy and Partial Least Square Assisted Support Vector Machine
Authors: Zhang Hao , Wang Shun , Li Dongxian , Zhang Yanyan , Hu Jiandong , Wang Ling
Published: September 2019

Edible gelatin has been widely used as a food additive in the food industry, and illegal adulteration with industrial gelatin will cause serious harm to human health. The present work used laser-induced breakdown spectroscopy (LIBS) coupled with the partial least square–support vector machine (PLS-SVM) method for the fast and accurate estimation of edible gelatin adulteration. Gelatin samples with 11 different adulteration ratios were prepared by mixing pure edible gelatin with industrial gelatin, and the LIBS spectra were recorded to analyze their elemental composition differences. The PLS, SVM, and PLS-SVM models were separately built for the prediction of gelatin adulteration ratios, and the hybrid PLS-SVM model yielded a better performance than only the PLS and SVM models. Besides, four different variable selection methods, including competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MC-UVE), random frog (RF), and principal component analysis (PCA), were adopted to combine with the SVM model for comparative study; the results further demonstrated that the PLS-SVM model was superior to the other SVM models. This study reveals that the hybrid PLS-SVM model, with the advantages of low computational time and high prediction accuracy, can be employed as a preferred method for the accurate estimation of edible gelatin adulteration.
Carbon-Based Electrode Materials for Microsupercapacitors in Self-Powering Sensor Networks: Present and Future Development
Authors: Smith A. , Li Qi , Vyas Agin , Haque Mohammad , Wang Kejian , Velasco Andres , Zhang Xiaoyan , Thurakkal Shameel , Quellmalz Arne , Niklaus Frank , Gylfason Kristinn , Lundgren Per , Enoksson Peter
Published: September 2019

There is an urgent need to fulfill future energy demands for micro and nanoelectronics. This work outlines a number of important design features for carbon-based microsupercapacitors, which enhance both their performance and integration potential and are critical for complimentary metal oxide semiconductor (CMOS) compatibility. Based on these design features, we present CMOS-compatible, graphene-based microsupercapacitors that can be integrated at the back end of the line of the integrated circuit fabrication. Electrode materials and their interfaces play a crucial role for the device characteristics. As such, different carbon-based materials are discussed and the importance of careful design of current collector/electrode interfaces is emphasized. Electrode adhesion is an important factor to improve device performance and uniformity. Additionally, doping of the electrodes can greatly improve the energy density of the devices. As microsupercapacitors are engineered for targeted applications, device scaling is critically important, and we present the first steps toward general scaling trends. Last, we outline a potential future integration scheme for a complete microsystem on a chip, containing sensors, logic, power generation, power management, and power storage. Such a system would be self-powering.
False Data Detection for Fog and Internet of Things Networks
Authors: Fantacci Romano , Nizzi Francesca , Pecorella Tommaso , Pierucci Laura , Roveri Manuel
Published: September 2019

The Internet of Things (IoT) context brings new security issues due to billions of smart end-devices both interconnected in wireless networks and connected to the Internet by using different technologies. In this paper, we propose an attack-detection method, named Data Intrusion Detection System (DataIDS), based on real-time data analysis. As end devices are mainly resource constrained, Fog Computing (FC) is introduced to implement the DataIDS. FC increases storage, computation capabilities, and processing capabilities, allowing it to detect promptly an attack with respect to security solutions on the Cloud. This paper also considers an attack tree to model threats and vulnerabilities of Fog/IoT scenarios with heterogeneous devices and suggests countermeasure costs. We verify the performance of the proposed DataIDS, implementing a testbed with several devices that measure different physical quantities and by using standard data-gathering protocols.
Practical Modeling of GNSS for Autonomous Vehicles in Urban Environments
Authors: Lee Woosik , Cho Hyojoo , Hyeong Seungho , Chung Woojin
Published: September 2019

Autonomous navigation technology is used in various applications, such as agricultural robots and autonomous vehicles. The key technology for autonomous navigation is ego-motion estimation, which uses various sensors. Wheel encoders and global navigation satellite systems (GNSSs) are widely used in localization for autonomous vehicles, and there are a few quantitative strategies for handling the information obtained through their sensors. In many cases, the modeling of uncertainty and sensor fusion depends on the experience of the researchers. In this study, we address the problem of quantitatively modeling uncertainty in the accumulated GNSS and in wheel encoder data accumulated in anonymous urban environments, collected using vehicles. We also address the problem of utilizing that data in ego-motion estimation. There are seven factors that determine the magnitude of the uncertainty of a GNSS sensor. Because it is impossible to measure each of these factors, in this study, the uncertainty of the GNSS sensor is expressed through three variables, and the exact uncertainty is calculated. Using the proposed method, the uncertainty of the sensor is quantitatively modeled and robust localization is performed in a real environment. The approach is validated through experiments in urban environments.
Vision-Based Autonomous Crack Detection of Concrete Structures Using a Fully Convolutional Encoder–Decoder Network
Authors: Islam M. M. Manjurul , Kim Jong-Myon
Published: September 2019

The visual inspection of massive civil infrastructure is a common trend for maintaining its reliability and structural health. However, this procedure, which uses human inspectors, requires long inspection times and relies on the subjective and empirical knowledge of the inspectors. To address these limitations, a machine vision-based autonomous crack detection method is proposed using a deep convolutional neural network (DCNN) technique. It consists of a fully convolutional neural network (FCN) with an encoder and decoder framework for semantic segmentation, which performs pixel-wise classification to accurately detect cracks. The main idea is to capture the global context of a scene and determine whether cracks are in the image while also providing a reduced and essential picture of the crack locations. The visual geometry group network (VGGNet), a variant of the DCCN, is employed as a backbone in the proposed FCN for end-to-end training. The efficacy of the proposed FCN method is tested on a publicly available benchmark dataset of concrete crack images. The experimental results indicate that the proposed method is highly effective for concrete crack classification, obtaining scores of approximately 92% for both the recall and F1 average.
ClusterMap Building and Relocalization in Urban Environments for Unmanned Vehicles
Authors: Pan Zhichen , Chen Haoyao , Li Silin , Liu Yunhui
Published: September 2019

Map building and map-based relocalization techniques are important for unmanned vehicles operating in urban environments. The existing approaches require expensive high-density laser range finders and suffer from relocalization problems in long-term applications. This study proposes a novel map format called the ClusterMap, on the basis of which an approach to achieving relocalization is developed. The ClusterMap is generated by segmenting the perceived point clouds into different point clusters and filtering out clusters belonging to dynamic objects. A location descriptor associated with each cluster is designed for differentiation. The relocalization in the global map is achieved by matching cluster descriptors between local and global maps. The solution does not require high-density point clouds and high-precision segmentation algorithms. In addition, it prevents the effects of environmental changes on illumination intensity, object appearance, and observation direction. A consistent ClusterMap without any scale problem is built by utilizing a 3D visual–LIDAR simultaneous localization and mapping solution by fusing LIDAR and visual information. Experiments on the KITTI dataset and our mobile vehicle illustrates the effectiveness of the proposed approach.
A Deep-Learning-Driven Light-Weight Phishing Detection Sensor
Authors: Wei Bo , Hamad Rebeen Ali , Yang Longzhi , He Xuan , Wang Hao , Gao Bin , Woo Wai Lok
Published: September 2019

This paper designs an accurate and low-cost phishing detection sensor by exploring deep learning techniques. Phishing is a very common social engineering technique. The attackers try to deceive online users by mimicking a uniform resource locator (URL) and a webpage. Traditionally, phishing detection is largely based on manual reports from users. Machine learning techniques have recently been introduced for phishing detection. With the recent rapid development of deep learning techniques, many deep-learning-based recognition methods have also been explored to improve classification performance. This paper proposes a light-weight deep learning algorithm to detect the malicious URLs and enable a real-time and energy-saving phishing detection sensor. Experimental tests and comparisons have been conducted to verify the efficacy of the proposed method. According to the experiments, the true detection rate has been improved. This paper has also verified that the proposed method can run in an energy-saving embedded single board computer in real-time.
A College Knowledge Program for Latino Immigrant Families: Examining Parental Academic Involvement and Adolescents’ Academic Goals
Authors: Martinez Griselda , Chavira Gabriela
Published: September 2019

The current study examined how parents’ and adolescents’ reports of parental involvement were associated with adolescents’ academic goals before and after participating in a college knowledge program. Twelve parent-adolescent dyads (Mage = 13.58) participated in the program. Thematic analysis was used to analyze these data and create themes that emerged based on patterns in parents’ and adolescents’ semi-structured interviews. Findings suggested that while parents’ reports of their involvement remained relatively the same (high involvement), half of the adolescents indicated increases in their academic goals and perceived parental involvement after participating in the program. This study highlights the role of a college knowledge program on parents’ and adolescents’ changes in perceived parental involvement and academic goals. The study findings identify an avenue to help families access additional capital that can help their children pursue their academic goals.
Is Physical Performance a Differentiating Element between More or Less Successful Football Teams?
Authors: Asian Clemente Jose Antonio , Requena Bernardo , Jukic Igor , Nayler Jack , Hernández Alfredo Santalla , Carling Christopher
Published: September 2019

This study investigated the time-motion characteristics of football teams in the Spanish first division, in relation to their final competitive level as defined by league position (Champions League, Europa League, Upper mid-table, lower mid-table and relegation). Match observations (n = 9641) were collected using a multiple-camera computerized tracking system during the 2013–2014 competitive season. The following match parameters were analyzed: total distance, relative distance (m·min−1), distance < 14 km·h−1, >14 km·h−1, between 14–21 km·h−1, >21 km·h−1, and >24 km·h−1. Total distance and distance at different velocities (>14, 21, and 24 km·h−1) in and out of ball possession were also analyzed. A repeated analysis of variance and a comparison of effect sizes were carried out to compare the performance of the teams. The analysis of the data showed differences in physical performance characteristics between competitive levels. The volume of distance covered in the variables analyzed did not relate to success in soccer. Both successful and unsuccessful teams presented the same running requirements at higher velocities. These findings provide valuable information about the physical demands of the running requirements according to their final position in the league table.
Towards Carbon-Neutral Mobility in Finland: Mobility and Life Satisfaction in Day-to-Day Life
Authors: Höysniemi Sakari , Salonen Arto O.
Published: September 2019

Finland, a prosperous Nordic country with a population of 5.5 million and significant distances between towns, though quite short distances traveled by car, is aiming to be a carbon-neutral society by 2035. Due to the level of urgency, a technological pathway with decarbonization of fuels and innovation only, is unlikely to be sufficient. Instead, a more systemic change based on a transformative pathway with demand-side management, i.e., measures based on behavioral change, is vital. In this research we were interested in learning how life satisfaction relates to the behavioral intentions of Finnish citizens, regarding a sustainable modal shift. We focused on walking, cycling, public transport and reduction in car use, e.g., a transition from fossil fuels to active mobility, from ownership to usership. Data were collected via a questionnaire in April 2017. The respondents (n = 2052) provided 2335 comments as to why they considered a specific sustainable modality as being important to them. We applied both qualitative and quantitative methods in order to establish how the mobility behavior of citizens manifests nationwide and the types of arguments that citizens put forward concerning their mobility intentions. The results indicate that there is a strong relationship between the respondents’ reduced use of private cars and their life satisfaction. There is a concern about sustainability and a willingness to change current mobility practices, as well as signs of altruism, while hedonic concerns such as health and personal finances dominate the responses. Furthermore, concerns about social injustice, such as a lack of public transport, are emerging themes, i.e., when enacting mobility transitions it is vital to focus on how to enable a meaningful life for all demographic groups using suitable mobility services.
Short-Term Forecasting of Land Use Change Using Recurrent Neural Network Models
Authors: Cao Cong , Dragićević Suzana , Li Songnian
Published: September 2019

Land use change (LUC) is a dynamic process that significantly affects the environment, and various approaches have been proposed to analyze and model LUC for sustainable land use management and decision making. Recurrent neural network (RNN) models are part of deep learning (DL) approaches, which have the capability to capture spatial and temporal features from time-series data and sequential data. The main objective of this study was to examine variants of the RNN models by applying and comparing them when forecasting LUC in short time periods. Historical land use data for the City of Surrey, British Columbia, Canada were used to implement the several variants of the RNN models. The land use (LU) data for years 1996, 2001, 2006, and 2011 were used to train the DL models to enable the short-term forecast for the year 2016. For the 2011 to 2016 period, only 4.5% of the land use in the study area had changed. The results indicate that an overall accuracy of 86.9% was achieved, while actual changes in each LU type were forecasted with a relatively lower accuracy. However, only 25% of changed raster cells correctly forecasted the land use change. This research study demonstrates that RNN models provide a suite of valuable tools for short-term LUC forecast that can inform and complement the traditional long-term planning process; however, further additional geospatial data layers and considerations of driving factors of LUC need to be incorporated for model improvements.
Effect of Landscape Microclimates on Thermal Comfort and Physiological Wellbeing
Authors: Liu Binyi , Lian Zefeng , Brown Robert D.
Published: September 2019

Global climate change and intensifying heat islands have reduced human thermal comfort and health in urban outdoor environments. However, there has been little research that has focused on how microclimates affect human thermal comfort, both psychologically and physiologically. We investigated the effect of a range of landscape microclimates on human thermal comfort and health using questionnaires and physiological measurements, including skin temperature, skin conductance, and heart rate variability, and compared the results with the effect of prevailing climate conditions in open spaces. We observed that in landscape microclimates, thermal sensation votes significantly decreased from 1.18 ± 0.66 (warm–hot) to 0.23 ± 0.61 (neutral–slightly warm), and thermal comfort increased from 1.18 ± 0.66 (uncomfortable–neutral) to 0.23 ± 0.61 (neutral–comfortable). In the landscape microclimates, skin temperature and skin conductance decreased 0.3 ± 0.8 °C and 0.6 ± 1.0 μs, respectively, while in the control, these two parameters increased by 0.5 ± 0.9 °C and 0.2 ± 0.7 μs, respectively. Further, in landscape microclimates, subject heart rate variability increased significantly. These results suggest landscape microclimates improve human thermal comfort and health, both psychologically and physiologically. These findings can provide an evidence base that will assist urban planners in designing urban environments for the health and wellbeing of residents.
Comprehensive Methodology for Sustainable Power Supply in Emerging Countries
Authors: Peñalvo-López Elisa , Pérez-Navarro Ángel , Hurtado Elías , Cárcel-Carrasco F. Javier
Published: September 2019

Electricity has become one of the main driving forces for development, especially in remote areas where the lack of energy is linked to poverty. Traditionally, in these areas power is supplied by grid extension projects, which are expensive, or stand-alone systems based on fossil fuels. An actual alternative to these solutions is community micro-grid projects based on distributed renewable energy sources. However, these solutions need to introduce a holistic approach in order to be successfully implemented in real cases. The main purpose of this research work is the definition and development of a comprehensive methodology to encourage the use of decentralized renewable power systems to provide power supply to non-electrified areas. The methodology follows a top-down approach. Its main novelty is that it interlinks a macro and micro analysis dimension, considering not only the energy context of the country where the area under study is located and its development towards a sustainable scenario; but also the potential of renewable power generation, the demand side management opportunities and the socio-economic aspects involved in the final decision on what renewable energy solution would be the most appropriate for the considered location. The implementation of this methodology provides isolated areas a tool for sustainable energy development based on an environmentally friendly and socially participatory approach. Results of implementing the methodology in a case study showed the importance of introducing a holistic approach in supplying power energy to isolated areas, stating the need for involving all the different stakeholders in the decision-making process. Despite final raking on sustainable power supply solutions may vary from one area to another, the implementation of the methodology follows the same procedure, which makes it an inestimable tool for governments, private investors and local communities.
Development of Economic Integration in the Central Yangtze River Megaregion from the Perspective of Urban Network Evolution
Authors: He Dan , Sun Zhijing , Gao Peng
Published: September 2019

Megaregions are the new engines of global and regional economic growth, and they often are considered a principal urbanization platform in China. To understand megaregional processes’ responses to China’s regional policies, this study focused on two aspects of integration development in the Central Yangtze River megaregion between 2000 and 2014: The internal collaborative networks using enterprises’ headquarters-branch locations as a proxy measurement and the role of regional transportation in the integration networks. Based on a three-step network analysis, the Central Yangtze River megaregion was increasingly similar to a polycentric urban system with Wuhan, Changsha, and Nanchang as the dominant service cities, and there were some indications of a preliminary urban network formation. However, integration development remained a government-led administrative process with administrative boundaries that significantly influenced the network structure. A panel regression analysis further suggested that transportation accessibility to the three central cities was the key determinant of network participation for the peripheral cities compared to economic performance. This work contributes to the debate on the hierarchical-administrative properties of China’s megaregions and transportation implications of the economic integration process.