bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimating the Aboveground Biomass for Planted Forests Based on Stand Age and Environmental Variables
Authors: Peng Dailiang , Zhang Helin , Liu Liangyun , Huang Wenjiang , Huete Alfredo R. , Zhang Xiaoyang , Wang Fumin , Yu Le , Xie Qiaoyun , Wang Cheng , Luo Shezhou , Li Cunjun , Zhang Bing
Published: September 2019

Measuring forest aboveground biomass (AGB) at local to regional scales is critical to understanding their role in regional and global carbon cycles. The Three-North Shelterbelt Forest Program (TNSFP) is the largest ecological restoration project in the world, and has been ongoing for over 40 years. In this study, we developed models to estimate the planted forest aboveground biomass (PF_AGB) for Yulin, a typical area in the project. Surface reflectances in the study area from 1978 to 2013 were obtained from Landsat series images, and integrated forest z-scores were constructed to measure afforestation and the stand age of planted forest. Normalized difference vegetation index (NDVI) was combined with stand age to develop an initial model to estimate PF_AGB. We then developed additional models that added environment variables to our initial model, including climatic factors (average temperature, total precipitation, and total sunshine duration) and a topography factor (slope). The model which combined the total precipitation and slope greatly improved the accuracy of PF_AGB estimation compared to the initial model, indicating that the environmental variables related to water distribution indirectly affected the growth of the planted forest and the resulting AGB. Afforestation in the study area occurred mainly in the early 1980s and early 21st century, and the PF_AGB in 2003 was 2.3 times than that of 1998, since the fourth term TNSFP started in 2000. The PF_AGB in 2013 was about 3.33 times of that in 2003 because many young trees matured. The leave-one-out cross-validation (LOOCV) approach showed that our estimated PF_AGB had a significant correlation with field-measured data (correlation coefficient (r) = 0.89, p < 0.001, root mean square error (RMSE) = 6.79 t/ha). Our studies provided a method to estimate long time series PF_AGB using satellite repetitive measures, particularly for arid or semi-arid areas.
Landslide Deformation Monitoring by Adaptive Distributed Scatterer Interferometric Synthetic Aperture Radar
Authors: Jia Hongguo , Zhang Hao , Liu Luyao , Liu Guoxiang
Published: September 2019

Landslide is the second most frequent geological disaster after earthquake, which causes a large number of casualties and economic losses every year. China frequently experiences devastating landslides in mountainous areas. Interferometric Synthetic Aperture Radar (InSAR) technology has great potential for detecting potentially unstable landslides across wide areas and can monitor surface displacement of a single landslide. However traditional time series InSAR technology such as persistent scatterer interferometry (PSI) and small-baseline subset (SBAS) cannot identify enough points in mountainous areas because of dense vegetation and steep terrain. In order to improve the accuracy of landslide hazard detection and the reliability of landslide deformation monitoring in areas lacking high coherence stability point targets, this study proposes an adaptive distributed scatterer interferometric synthetic aperture radar (ADS-InSAR) method based on the spatiotemporal coherence of the distributed scatterer (DS), which automatically adjusts its detection threshold to improve the spatial distribution density and reliability of DS detection in the landslide area. After time series network modeling and deformation calculation of the ADS target, the displacement deformation of the landslide area can be accurately extracted. Shuibuya Town in Enshi Prefecture, Hubei Province, China, was used as a case study, along with 18 Sentinal-1A images acquired from March 2016 to April 2017. The ADS-InSAR method was used to obtain regional deformation data. The deformation time series was combined with hydrometeorological and related data to analyze landslide deformation. The results show that the ADS-InSAR method can effectively improve the density of DS distribution, successfully detect existing ancient landslide groups and determine multiple potential landslide areas, enabling early warning for landslide hazards. This study verifies the reliability and accuracy of ADS-InSAR for landslide disaster prevention and mitigation.
Multiple-Object-Tracking Algorithm Based on Dense Trajectory Voting in Aerial Videos
Authors: Yang Tao , Li Dongdong , Bai Yi , Zhang Fangbing , Li Sen , Wang Miao , Zhang Zhuoyue , Li Jing
Published: September 2019

In recent years, UAV technology has developed rapidly. Due to the mobility, low cost, and variable monitoring altitude of UAVs, multiple-object detection and tracking in aerial videos has become a research hotspot in the field of computer vision. However, due to camera motion, small target size, target adhesion, and unpredictable target motion, it is still difficult to detect and track targets of interest in aerial videos, especially in the case of a low frame rate where the target position changes too much. In this paper, we propose a multiple-object-tracking algorithm based on dense-trajectory voting in aerial videos. The method models the multiple-target-tracking problem as a voting problem of the dense-optical-flow trajectory to the target ID, which can be applied to aerial-surveillance scenes and is robust to low-frame-rate videos. More specifically, we first built an aerial video dataset for vehicle targets, including a training dataset and a diverse test dataset. Based on this, we trained the neural network model by using a deep-learning method to detect vehicles in aerial videos. Thereafter, we calculated the dense optical flow in adjacent frames, and generated effective dense-optical-flow trajectories in each detection bounding box at the current time. When target IDs of optical-flow trajectories are known, the voting results of the optical-flow trajectories in each detection bounding box are counted. Finally, similarity between detection objects in adjacent frames was measured based on the voting results, and tracking results were obtained by data association. In order to evaluate the performance of this algorithm, we conducted experiments on self-built test datasets. A large number of experimental results showed that the proposed algorithm could obtain good target-tracking results in various complex scenarios, and performance was still robust at a low frame rate by changing the video frame rate. In addition, we carried out qualitative and quantitative comparison experiments between the algorithm and three state-of-the-art tracking algorithms, which further proved that this algorithm could not only obtain good tracking results in aerial videos with a normal frame rate, but also had excellent performance under low-frame-rate conditions.
TCANet for Domain Adaptation of Hyperspectral Images
Authors: S. Garea Alberto S. , Heras Dora B. , Argüello Francisco
Published: September 2019

The use of Convolutional Neural Networks (CNNs) to solve Domain Adaptation (DA) image classification problems in the context of remote sensing has proven to provide good results but at high computational cost. To avoid this problem, a deep learning network for DA in remote sensing hyperspectral images called TCANet is proposed. As a standard CNN, TCANet consists of several stages built based on convolutional filters that operate on patches of the hyperspectral image. Unlike the former, the coefficients of the filter are obtained through Transfer Component Analysis (TCA). This approach has two advantages: firstly, TCANet does not require training based on backpropagation, since TCA is itself a learning method that obtains the filter coefficients directly from the input data. Second, DA is performed on the fly since TCA, in addition to performing dimensional reduction, obtains components that minimize the difference in distributions of data in the different domains corresponding to the source and target images. To build an operating scheme, TCANet includes an initial stage that exploits the spatial information by providing patches around each sample as input data to the network. An output stage performing feature extraction that introduces sufficient invariance and robustness in the final features is also included. Since TCA is sensitive to normalization, to reduce the difference between source and target domains, a previous unsupervised domain shift minimization algorithm consisting of applying conditional correlation alignment (CCA) is conditionally applied. The results of a classification scheme based on CCA and TCANet show that the DA technique proposed outperforms other more complex DA techniques.
Classification of Micro-Damage in Piezoelectric Ceramics Using Machine Learning of Ultrasound Signals
Authors: Tripathi Gaurav , Anowarul Habib , Agarwal Krishna , Prasad Dilip
Published: September 2019

Ultrasound based structural health monitoring of piezoelectric material is challenging if a damage changes at a microscale over time. Classifying geometrically similar damages with a difference in diameter as small as 100 μ m is difficult using conventional sensing and signal analysis approaches. Here, we use an unconventional ultrasound sensing approach that collects information of the entire bulk of the material and investigate the applicability of machine learning approaches for classifying such similar defects. Our results show that appropriate feature design combined with simple k-nearest neighbor classifier can provide up to 98% classification accuracy even though conventional features for time-series data and a variety of classifiers cannot achieve close to 70% accuracy. The newly proposed hybrid feature, which combines frequency domain information in the form of power spectral density and time domain information in the form of sign of slope change, is a suitable feature for achieving the best classification accuracy on this challenging problem.
A Computationally Efficient Labeled Multi-Bernoulli Smoother for Multi-Target Tracking
Authors: Liu Rang , Fan Hongqi , Li Tiancheng , Xiao Huaitie
Published: September 2019

A forward–backward labeled multi-Bernoulli (LMB) smoother is proposed for multi-target tracking. The proposed smoother consists of two components corresponding to forward LMB filtering and backward LMB smoothing, respectively. The former is the standard LMB filter and the latter is proved to be closed under LMB prior. It is also shown that the proposed LMB smoother can improve both the cardinality estimation and the state estimation, and the major computational complexity is linear with the number of targets. Implementation based on the Sequential Monte Carlo method in a representative scenario has demonstrated the effectiveness and computational efficiency of the proposed smoother in comparison to existing approaches.
Deep Learning-Based LOS and NLOS Identification in Wireless Body Area Networks
Authors: Cwalina Krzysztof K. , Rajchowski Piotr , Blaszkiewicz Olga , Olejniczak Alicja , Sadowski Jaroslaw
Published: September 2019

In this article, the usage of deep learning (DL) in ultra-wideband (UWB) Wireless Body Area Networks (WBANs) is presented. The developed approach, using channel impulse response, allows higher efficiency in identifying the direct visibility conditions between nodes in off-body communication with comparison to the methods described in the literature. The effectiveness of the proposed deep feedforward neural network was checked on the basis of the measurement data for dynamic scenarios in an indoor environment. The obtained results clearly prove the validity of the proposed DL approach in the UWB WBANs and high (over 98.6% for most cases) efficiency for LOS and NLOS conditions classification.
Underground Wireless Data Transmission Using 433-MHz LoRa for Agriculture
Authors: Hardie Marcus , Hoyle Donald
Published: September 2019

Wireless underground sensor networks (WUSNs) have potential for providing real-time data for agriculture and other industries without exposing sensors and communication infrastructure to damage. However, soil is a difficult environment for radio communication due to its dielectric properties and variable moisture content. Low-power, wide-area network (LPWAN) technologies have been used to develop aboveground sensor networks for many industries, but have not yet been successfully developed for underground applications. In this study, we developed a 433-MHz LoRa-based testbed for evaluating both underground-to-underground (UG2UG) and underground-to-aboveground (UG2AG) wireless communication technologies in four in situ soils. The maximum transmission distance for UG2UG operation was 4–20 m depending on soil type, whilst UG2AG operation was able to communicate up to 100–200 m, depending on the operating variables and soil properties. Signal quality and the maximum transmission distance were influenced by transmitter (TX) burial depth, TX power, data rate, receiver (RX) antenna type, and to a lesser extent, soil parameters. Results suggest that with improvements to power management, the development of 433-MHz LoRa-based UG2AG WUSNs for agricultural applications is readily achievable, whilst UG2UG applications appear unlikely without substantial improvement in transmission distance.
Interactive OCT-Based Tooth Scan and Reconstruction
Authors: Lai Yu-Chi , Lin Jin-Yang , Yao Chih-Yuan , Lyu Dong-Yuan , Lee Shyh-Yuan , Chen Kuo-Wei , Chen I-Yu
Published: September 2019

Digital dental reconstruction can be a more efficient and effective mechanism for artificial crown construction and period inspection. However, optical methods cannot reconstruct those portions under gums, and X-ray-based methods have high radiation to limit their applied frequency. Optical coherence tomography (OCT) can harmlessly penetrate gums using low-coherence infrared rays, and thus, this work designs an OCT-based framework for dental reconstruction using optical rectification, fast Fourier transform, volumetric boundary detection, and Poisson surface reconstruction to overcome noisy imaging. Additionally, in order to operate in a patient’s mouth, the caliber of the injector is small along with its short penetration depth and effective operation range, and thus, reconstruction requires multiple scans from various directions along with proper alignment. However, flat regions, such as the mesial side of front teeth, may not have enough features for alignment. As a result, we design a scanning order for different types of teeth starting from an area of abundant features for easier alignment while using gyros to track scanned postures for better initial orientations. It is important to provide immediate feedback for each scan, and thus, we accelerate the entire signal processing, boundary detection, and point-cloud alignment using Graphics Processing Units (GPUs) while streamlining the data transfer and GPU computations. Finally, our framework can successfully reconstruct three isolated teeth and a side of one living tooth with comparable precisions against the state-of-art method. Moreover, a user study also verifies the effectiveness of our interactive feedback for efficient and fast clinic scanning.
An Unsupervised Framework for Online Spatiotemporal Detection of Activities of Daily Living by Hierarchical Activity Models
Authors: Negin Farhood , Brémond François
Published: September 2019

Automatic detection and analysis of human activities captured by various sensors (e.g., sequences of images captured by RGB camera) play an essential role in various research fields in order to understand the semantic content of a captured scene. The main focus of the earlier studies has been widely on supervised classification problem, where a label is assigned to a given short clip. Nevertheless, in real-world scenarios, such as in Activities of Daily Living (ADL), the challenge is to automatically browse long-term (days and weeks) stream of videos to identify segments with semantics corresponding to the model activities and their temporal boundaries. This paper proposes an unsupervised solution to address this problem by generating hierarchical models that combine global trajectory information with local dynamics of the human body. Global information helps in modeling the spatiotemporal evolution of long-term activities, hence, their spatial and temporal localization. Moreover, the local dynamic information incorporates complex local motion patterns of daily activities into the models. Our proposed method is evaluated using realistic datasets captured from observation rooms in hospitals and nursing homes. The experimental data on a variety of monitoring scenarios in hospital settings reveals how this framework can be exploited to provide timely diagnose and medical interventions for cognitive disorders, such as Alzheimer’s disease. The obtained results show that our framework is a promising attempt capable of generating activity models without any supervision.
The Effect of Selected Operational Factors on the Vibroactivity of Upper Gearbox Housings Made of Composite Materials
Authors: Figlus Tomasz , Kozioł Mateusz , Kuczyński Łukasz
Published: September 2019

This paper presents the results of laboratory tests in which evaluation was performed regarding the effect of selected operating factors on the vibroactivity of upper gearbox housings made of three different fiber reinforced polymer composite materials with diverse layouts (cross and random) and types of reinforcing fibers: glass fiber and carbon fiber. The results of tests for composite housings were compared with those for a steel housing. The tests showed that composite housings had a weight lower by more than 60% compared to the steel housing. A multisensor measuring system consisting of vibration acceleration transducers, a directional microphone and a data acquisition card with software was used for the study. Tests of the vibroactivity of upper gear housings were carried out at different loads and rotational speeds of toothed gears. The study showed that composite housings are less sensitive to changes in the rotational speed that steel housings. The tests showed that at a higher rotational speed of the gear transmission, housings made of composite materials had a comparable or lower level of vibration. Tests and analyses of the vibroactivity of housings performed at different loads of the gear allow the conclusion that composite housings, despite a considerably lower weight than steel housings, are less sensitive to changes in the load of the gearing.
Lower Limb Locomotion Activity Recognition of Healthy Individuals Using Semi-Markov Model and Single Wearable Inertial Sensor
Authors: Li Haoyu , Derrode Stéphane , Pieczynski Wojciech
Published: September 2019

Lower limb locomotion activity is of great interest in the field of human activity recognition. In this work, a triplet semi-Markov model-based method is proposed to recognize the locomotion activities of healthy individuals when lower limbs move periodically. In the proposed algorithm, the gait phases (or leg phases) are introduced into the hidden states, and Gaussian mixture density is introduced to represent the complex conditioned observation density. The introduced sojourn state forms the semi-Markov structure, which naturally replicates the real transition of activity and gait during motion. Then, batch mode and on-line Expectation-Maximization (EM) algorithms are proposed, respectively, for model training and adaptive on-line recognition. The algorithm is tested on two datasets collected from wearable inertial sensors. The batch mode recognition accuracy reaches up to 95.16%, whereas the adaptive on-line recognition gradually obtains high accuracy after the time required for model updating. Experimental results show an improvement in performance compared to the other competitive algorithms.
A Two-Level WiFi Fingerprint-Based Indoor Localization Method for Dangerous Area Monitoring
Authors: Li Fei , Liu Min , Zhang Yue , Shen Weiming
Published: September 2019

Localization technologies play an important role in disaster management and emergence response. In areas where the environment does not change much after an accident or in the case of dangerous areas monitoring, indoor fingerprint-based localization can be used. In such scenarios, a positioning system needs to have both a high accuracy and a rapid response. However, these two requirements are usually conflicting since a fingerprint-based indoor localization system with high accuracy usually has complex algorithms and needs to process a large amount of data, and therefore has a slow response. This problem becomes even worse when both the size of monitoring area and the number of reference nodes increase. To address this challenging problem, this paper proposes a two-level positioning algorithm in order to improve both the accuracy and the response time. In the off-line stage, a fingerprint database is divided into several sub databases by using an affinity propagation clustering (APC) algorithm based on Shepard similarity. The online stage has two steps: (1) a coarse positioning algorithm is adopted to find the most similar sub database by matching the cluster center with the fingerprint of the node tested, which will narrow the search space and consequently save time; (2) in the sub database area, a support vector regression (SVR) algorithm with its parameters being optimized by particle swarm optimization (PSO) is used for fine positioning, thus improving the online positioning accuracy. Both experiment results and actual implementations proved that the proposed two-level localization method is more suitable than other methods in term of algorithm complexity, storage requirements and localization accuracy in dangerous area monitoring.
Feature Point Matching Based on Distinct Wavelength Phase Congruency and Log-Gabor Filters in Infrared and Visible Images
Authors: Liu Xiaomin , Li Jun-Bao , Pan Jeng-Shyang
Published: September 2019

Infrared and visible image matching methods have been rising in popularity with the emergence of more kinds of sensors, which provide more applications in visual navigation, precision guidance, image fusion, and medical image analysis. In such applications, image matching is utilized for location, fusion, image analysis, and so on. In this paper, an infrared and visible image matching approach, based on distinct wavelength phase congruency (DWPC) and log-Gabor filters, is proposed. Furthermore, this method is modified for non-linear image matching with different physical wavelengths. Phase congruency (PC) theory is utilized to obtain PC images with intrinsic and affluent image features for images containing complex intensity changes or noise. Then, the maximum and minimum moments of the PC images are computed to obtain the corners in the matched images. In order to obtain the descriptors, log-Gabor filters are utilized and overlapping subregions are extracted in a neighborhood of certain pixels. In order to improve the accuracy of the algorithm, the moments of PCs in the original image and a Gaussian smoothed image are combined to detect the corners. Meanwhile, it is improper that the two matched images have the same PC wavelengths, due to the images having different physical wavelengths. Thus, in the experiment, the wavelength of the PC is changed for different physical wavelengths. For realistic application, BiDimRegression method is proposed to compute the similarity between two points set in infrared and visible images. The proposed approach is evaluated on four data sets with 237 pairs of visible and infrared images, and its performance is compared with state-of-the-art approaches: the edge-oriented histogram descriptor (EHD), phase congruency edge-oriented histogram descriptor (PCEHD), and log-Gabor histogram descriptor (LGHD) algorithms. The experimental results indicate that the accuracy rate of the proposed approach is 50% higher than the traditional approaches in infrared and visible images.
Soft and Deformable Sensors Based on Liquid Metals
Authors: Kim Taeyeong , Kim Dong-min , Lee Bong Jae , Lee Jungchul
Published: September 2019

Liquid metals are one of the most interesting and promising materials due to their electrical, fluidic, and thermophysical properties. With the aid of their exceptional deformable natures, liquid metals are now considered to be electrically conductive materials for sensors and actuators, major constituent transducers in soft robotics, that can experience and withstand significant levels of mechanical deformation. For the upcoming era of wearable electronics and soft robotics, we would like to offer an up-to-date overview of liquid metal-based soft (thus significantly deformable) sensors mainly but not limited to researchers in relevant fields. This paper will thoroughly highlight and critically review recent literature on design, fabrication, characterization, and application of liquid metal devices and suggest scientific and engineering routes towards liquid metal sensing devices of tomorrow.
Aerial Grasping with a Lightweight Manipulator Based on Multi-Objective Optimization and Visual Compensation
Authors: Chen Haoyao , Quan Fengyu , Fang Linxu , Zhang Shiwu
Published: September 2019

Autonomous grasping with an aerial manipulator in the applications of aerial transportation and manipulation is still a challenging problem because of the complex kinematics/dynamics and motion constraints of the coupled rotors-manipulator system. The paper develops a novel aerial manipulation system with a lightweight manipulator, an X8 coaxial octocopter and onboard visual tracking system. To implement autonomous grasping control, we develop a novel and efficient approach that includes trajectory planning, visual trajectory tracking and kinematic compensation. Trajectory planning for aerial grasping control is formulated as a multi-objective optimization problem, while motion constraints and collision avoidance are considered in the optimization. A genetic method is applied to obtain the optimal solution. A kinematic compensation-based visual trajectory tracking is introduced to address the coupled affection between the manipulator and octocopter, with the advantage of discarding the complex dynamic parameter calibration. Finally, several experiments are performed to verify the effectiveness of the proposed approach.
A Comprehensive Survey of Recent Routing Protocols for Underwater Acoustic Sensor Networks
Authors: Islam Tariq , Lee Yong Kyu
Published: September 2019

Underwater Sensor Networks (UWSN) have attracted huge attention due to their significance in oceanic observation and exploration. They offer a vast number of applications, many of which require routing the sensed data to a centralized location. This makes routing an important part of the design of such applications. In this paper, we present a comprehensive survey of recently proposed routing protocols for UWSNs. We evaluate the proposed schemes through an extensive set of parameters that define the core characteristics of a routing protocol. Moreover, we present a summary of the methods used by each scheme to familiarize readers with the basic operations of the schemes. We also present our view of the strengths and weakness of each scheme. For ease of description, the addressed routing protocols are divided into two categories: localization-based, and localization-free routing schemes. Each of the two categories is further divided into the protocols that consider node mobility, and those that do not. Lastly, we present our view on open research topics.
Developments in the HCV Screening Technologies Based on the Detection of Antigens and Antibodies
Authors: Warkad Shrikant Dashrath , Song Keum-Soo , Pal Dilipkumar , Nimse Satish Balasaheb
Published: September 2019

Hepatitis C virus (HCV) accounts for 15%–20% of cases of acute infection, and chronic HCV infection is developed in about 50%–80% of HCV patients. Unfortunately, due to the lack of proper medical care, difficulty in screening for HCV infection, and lack of awareness resulted in chronic HCV infection in 71 million people on a global scale, and about 399,000 deaths in 2016. It is crucial to recognize that the effective use of antiviral medicines can cure more than 95% of HCV infected people. The Global Health Sector Strategy (GHSS) aim is to reduce the new HCV infections and the HCV associated mortality by 90% and 65%, respectively. Therefore, the methods that are simple, yet powerful enough to detect HCV infections with high sensitivity, specificity, and a shorter window period are crucial to restrain the global burden of HCV healthcare. This article focuses on the technologies used for the detection of HCV in clinical specimens.
On-Chip Non-Dispersive Infrared CO2 Sensor Based on an Integrating Cylinder †
Authors: Jia Xiaoning , Roels Joris , Baets Roel , Roelkens Gunther
Published: September 2019

In this paper, we propose a novel, miniaturized non-dispersive infrared (NDIR) CO2 sensor implemented on a silicon chip. The sensor has a simple structure, consisting of a hollow metallic cylindrical cavity along with access waveguides. A detailed analysis of the proposed sensor is presented. Simulation with 3D ray tracing shows that an integrating cylinder with 4 mm diameter gives an equivalent optical path length of 3.5 cm. The sensor is fabricated using Deep Reactive Ion Etching (DRIE) and wafer bonding. The fabricated sensor was evaluated by performing a CO2 concentration measurement, showing a limit of detection of ∼100 ppm. The response time of the sensor is only ∼2.8 s, due to its small footprint. The use of DRIE-based waveguide structures enables mass fabrication, as well as the potential co-integration of flip-chip integrated midIR light-emitting diodes (LEDs) and photodetectors, resulting in a compact, low-power, and low-cost NDIR CO2 sensor.
Determination of Vehicle Trajectory through Optimization of Vehicle Bounding Boxes using a Convolutional Neural Network
Authors: Seong Seonkyeong , Song Jeongheon , Yoon Donghyeon , Kim Jiyoung , Choi Jaewan
Published: September 2019

In this manuscript, a new method for the determination of vehicle trajectories using an optimal bounding box for the vehicle is developed. The vehicle trajectory is extracted using images acquired from a camera installed at an intersection based on a convolutional neural network (CNN). First, real-time vehicle object detection is performed using the YOLOv2 model, which is one of the most representative object detection algorithms based on CNN. To overcome the inaccuracy of the vehicle location extracted by YOLOv2, the trajectory was calibrated using a vehicle tracking algorithm such as a Kalman filter and intersection-over-union (IOU) tracker. In particular, we attempted to correct the vehicle trajectory by extracting the center position based on the geometric characteristics of a moving vehicle according to the bounding box. The quantitative and qualitative evaluations indicate that the proposed algorithm can detect the trajectories of moving vehicles better than the conventional algorithm. Although the center points of the bounding boxes obtained using the existing conventional algorithm are often outside of the vehicle due to the geometric displacement of the camera, the proposed technique can minimize positional errors and extract the optimal bounding box to determine the vehicle location.
Big Data and Human Resources Management: The Rise of Talent Analytics
Authors: Nocker Manuela , Sena Vania
Published: September 2019

The purpose of this paper is to discuss the opportunities talent analytics offers HR practitioners. As the availability of methodologies for the analysis of large volumes of data has substantially improved over the last ten years, talent analytics has started to be used by organizations to manage their workforce. This paper discusses the benefits and costs associated with the use of talent analytics within an organization as well as to highlight the differences between talent analytics and other sub-fields of business analytics. It will discuss a number of case studies on how talent analytics can improve organizational decision-making. From the case studies, we will identify key channels through which the adoption of talent analytics can improve the performance of the HR function and eventually of the whole organization. While discussing the opportunities that talent analytics offer organizations, this paper highlights the costs (in terms of data governance and ethics) that the widespread use of talent analytics can generate. Finally, it highlights the importance of trust in supporting the successful implementation of talent analytics projects.
Hamstring-to-Quadriceps Ratio in Female Athletes with a Previous Hamstring Injury, Anterior Cruciate Ligament Reconstruction, and Controls
Authors: Kellis Eleftherios , Galanis Nikiforos , Kofotolis Nikolaos
Published: September 2019

Muscle strength imbalances around the knee are often observed in athletes after anterior cruciate ligament (ACL) surgery and hamstring muscle injury. This study examined three hamstrings-to-quadriceps (H:Q) strength ratio types (conventional, functional, and mixed) in thirteen female athletes with a history of hamstring injury, fourteen basketball players following ACL reconstruction and 34 controls. The conventional (concentric H:Q) peak torque ratio was evaluated at 120°·s−1 and 240°·s−1. The functional (eccentric hamstring to concentric quadriceps) torque ratio was evaluated at 120°·s−1. Finally, the mixed (eccentric hamstrings at 30°·s−1 to concentric quadriceps at 240°·s−1) torque ratio was calculated. Both ACL and the hamstring-injured groups showed a lower quadriceps and hamstrings strength compared with controls (p < 0.05). However, non-significant group differences in the H:Q ratio were found (p > 0.05). Isokinetic assessment of muscle strength may be useful for setting appropriate targets of training programs for athletes with a history of ACL surgery or hamstring strain. However, isokinetic evaluation of the H:Q ratio is not injury—specific and it does not vary between different methods of calculating the H:Q ratio.
Assessment of Wheat Straw Cover and Yield Performance in a Rice-Wheat Cropping System by Using Landsat Satellite Data
Authors: Memon Muhammad Sohail , Jun Zhou , Sun Chuanliang , Jiang Chunxia , Xu Weiyue , Hu Qiong , Yang Hangxu , Ji Changying
Published: September 2019

Proper straw cover information is one of the most important inputs for agroecosystem and environmental modeling, but the availability of accurate information remains limited. However, several remote-sensing (RS)-based studies have provided a residue cover estimation and provided spatial distribution mapping of paddy rice areas in a constant field condition. Despite this, the performance of rice crops with straw applications has received little attention. Furthermore, there are no methods currently available to quantify the wheat straw cover (WSC) percentage and its effect on rice crops in the rice-wheat cropping region on a large scale and a continuous basis. The novel approach proposed in this study demonstrates that the Landsat satellite data and seven RS-based indices, e.g., (i) normalized difference vegetation index (NDVI), (ii) Normalized difference senescent vegetation index (NDSVI), (iii) Normalized difference index 5 (NDI5), (iv) Normalized difference index 7 (NDI7), (v) Simple tillage index (STI), (vi) Normalized difference tillage index (NDTI), and (vii) Shortwave red normalized difference index (SRNDI), can be used to estimate the WSC percentage and determine the performance of rice crops over the study area in Changshu county, China. The regression model shows that the NDTI index performed better in differentiating the WSC at sampling points with a coefficient of determination (R2 = 0.80) and root mean squared difference (RMSD = 8.46%) compared to that of other indices, whereas the overall accuracy for mapping WSC was observed to be 84.61% and the kappa coefficient was κ = 0.76. Moreover, the rice yield model was established by correlating between the peak NDVI values and rice grain yield collected from ground census data, with R2 = 0.85. The finding also revealed that the highest estimated yield (8439.67 kg/ha) was recorded with 68% WCS in the study region. This study confirmed that the NDVI and NDTI algorithms are very effective and robust indicators. Also, it can be strongly concluded that multispectral Landsat satellite imagery is capable of measuring the WSC percentage and successively determines the impact of different WSC percentages on rice crop yield within fields or across large regions through remote sensing (RS) and geographical information system (GIS) techniques for the long-term planning of agriculture sustainability in rice-wheat cropping systems.
Influence of the Mechanism of the Community of Shared Future on the Innovation Performance of Knowledge Workers: The Intermediary Role of Extra-Role Behavior
Authors: Gu Jianping , Wu Hanxiao , Li Yan , Hermawan Evan David
Published: September 2019

This paper investigates the relationship between the community of shared future, extra-role behavior, and innovation performance. This paper conducts an empirical study based on 334 valid sample data items of 53 technology-based small to medium enterprises (SMEs) in China. The data were subjected to structural equation analysis using SPSS 20.0 software. The results show that the community of shared future positively affects the extra-role behavior of employees and their innovation performance and has a positive impact on knowledge sharing behavior, voice behavior, and helping behavior. Knowledge sharing behavior, voice behavior, and helping behavior all play a mediating role between the community of shared future and employees’ innovation performance.
The Impact of Corporate Social Responsibility on Long-Term Relationships in the Business-to-Business Market
Authors: Lee Hangeun , Lee Seong Ho
Published: September 2019

The purpose of this study is to investigate the effect of partner firms’ corporate social responsibility (CSR) activities on long-term relationships in business-to-business (B2B) industries. We developed a research model to capture the correlations between a partner firm’s CSR reputation (i.e., business practice, CSR reputation, and philanthropic CSR reputation), trust, information sharing, risk–reward sharing, and long-term relationships. Drawing on the stakeholder and social exchange theories, we hypothesized that a partner firm’s CSR reputation would affect long-term relationships through trust, information sharing, and risk–reward sharing. Through empirical analysis, we found that business practice CSR is positively related to long-term relationships through trust and B2B partnership. We also present some theoretical and managerial implications of this study.
Sustainable Product-Service Systems and Circular Economies
Authors: Hernandez Ricardo J
Published: September 2019

Sustainable product-service systems (PSSs) have proven to be a very good alternative for developing new business models and transforming traditional offers into sustainable ones. Environmental, social and economic benefits support the idea of developing this type of system. However, there are identified challenges that have stopped the expansion of the concept into the market. The framework that explains the principles and building blocks of a circular economy according to the Ellen MacArthur Foundation can be an interesting lens to analyse the challenges companies faced when they wanted to implement a sustainable PSS. This framework is particularly useful to understand the internal and external forces companies are dealing with in a transformation from traditional business models to ones like sustainable PSSs that need special conditions of operation. Design for sustainability as an area of study will shift in the coming years to focus its attention on the requirements of circular economics as the paradigm of production and consumption. Sustainable products, services and systems will be developed according to the conditions established by those circular conditions.
Experimental Investigation on Shear Strength Parameters of Lime Stabilized Loess
Authors: Jia Liang , Guo Jian , Jiang Yanbin , Fu Yong , Zhou Zhidong , Lim Sin Mei , Zhao Xiushao
Published: September 2019

Loess is a typical collapsible soil, which is widely distributed in the upper and middle areas around the Yellow River of China. The stabilization of loess with lime provides a significant improvement in the physical and the mechanical characteristics of the loess and is therefore widely used in the pavement base and subgrade. Therefore, a systematic investigation of Mohr-Coulomb failure envelope of lime stabilized loess needs to be conducted. In this pursuit, the present research envisages the investigation of the effects of the lime content, porosity and curing time on the strength parameters (friction angle (φ) and cohesion (c)), using a series of triaxial tests performed on lime stabilized loess specimens. The experimental results revealed that the friction angle (φ) was independent of the lime content, the porosity and the curing time of the specimen for a given lime stabilized loess, while the factors mentioned above had a significant effect on the cohesion (c) of the lime stabilized loess. For a relatively short curing time (7 days), the change in the lime content did not present an obvious effect on the cohesion (c) of the stabilized loess. However, when the curing time (28, 90 and 180 days) was longer, the increase of the lime content significantly enhanced the cohesion of the stabilized loess. When the lime content was constant, the cohesion (c) of the stabilized loess increased linearly with the decrease in the void ratio. A power function equation was proposed to assess the comprehensive influences of the factors like the lime content, porosity and curing time on cohesion (c). Finally, the Mohr-Coulomb failure envelopes were drawn based on the triaxial test for 47 specimens with various curing time and confining pressure, and the shear strength parameters obtained by the proposed equation were also compared with the experimental results.
A Framework for Measuring Urban Sustainability in an Emerging Region: The City of Duhok as a Case Study
Authors: Hassan Azad , Kotval-K Zeenat
Published: September 2019

The City of Duhok in Iraq, as one of the Kurdistan Region’s (KR) main cities, is concerned about sustainability but lacks the measures to guide urban policies. This study bridges this gap and offers an example of the use of urban sustainability indicators in an emerging region that experiences rapid urbanization and growth. The substantial objective of this study was to develop a functional framework of indicators to assess and measure urban sustainability for the city after KR’s declaration of autonomy in 1991 until 2010. That is, we limited our investigation to examining previous research, which decisively contains the approach to “measuring urban sustainability”. The study followed a three-step approach to examine urban sustainability as an integration of a few other relevant studies. The study concluded with two facts: First, the lack of progress on urban sustainability in the first decades resulted from the destabilized era that left the city administratively fragmented. Second, the political and economic watershed led to steady progress towards urban sustainability post-2005. The study highlights nine urban sustainability indicators, from a total of 39 indicators, that played an important role in navigating the general trend of urban sustainability in the city and how they can be used to promote future sustainable practices.
How Productive Services Affect Apple Production Technical Efficiency: Promote or Inhibit?
Authors: Zhang Congying , Chang Qian , Huo Xuexi
Published: September 2019

Agricultural productive services provide a new entry point to solve the “labor dilemma” and contributes to the sustainable development of the apple industry. In this study, we establish a random frontier model with the Translog production function to analyze the influence of productive services on the technical efficiency of apple production based on a microscopic survey data of 661 apple farmers. The results indicate that the purchasing proportions of productive services are obviously different among the different links of apple production, while those among different regions are not obvious. Overall, productive services have a positive effect on improving the technical efficiency of apple production, but productive services in different links have a different effect; specifically, productive services in the bagging link have a positive effect on the technical efficiency of apple production, productive services in the pest controlling link have a negative effect, and productive services in other links have no significant effect. We suggest that policymakers should promote the orderly development of agricultural productive services, focus on improving the popularity of productive services in bagging links, and improve the quality of productive services in the pest control link.
Team Social Responsibility Embedded in Correlates of Universalism Values, Sport Involvement, and Team Identification for Sustainable Management in Sporting Settings
Authors: Gau Li-Shiue , Huang Jui-Chuan , Chen Meng-I , Naylor Michael
Published: September 2019

This study investigated the fans’ knowledge of team social responsibility (TSR) as well as the motivation and intentions associated with TSR. Other correlates included in the conceptual model are the fans’ value orientation of universalism, sport involvement, and team identification. A questionnaire was administered at three Chinese Professional Baseball League (CPBL) games in Taiwan (n = 520). It was found that perceptions of team social responsibility were linked to TSR-induced motivation to watch baseball. Universalism values, spectator sport involvement with CPBL, and team identification were found to be positively related to TSR knowledge, TSR-linked motivation, and TSR-linked intentions. The results provide useful insights that professional baseball teams can use to pursue a goal of sustainability and to ensure a uniquely engaged fan base.
Sustainable Human Resource Management: How to Create a Knowledge Sharing Behavior through Organizational Justice, Organizational Support, Satisfaction and Commitment
Authors: Cugueró-Escofet Natàlia , Ficapal-Cusí Pilar , Torrent-Sellens Joan
Published: September 2019

Knowledge sharing (KS) behavior is one of the main drivers to generate social sustainability. It predicts high organizational performance and innovation capabilities, and creates enjoyment and happiness in helping others. Even if incentives to enhance KS behaviors exist, employees would still be reluctant to share knowledge. For this reason, we test a comprehensive model of sustainable human resource management with the inclusion of KS to explain how to enhance collaborative practices in terms of voluntary knowledge sharing. In a comprehensive model, we incorporate organizational justice, employee perceived organizational support, job satisfaction and affective organizational commitment, and how they relate in order to generate knowledge sharing behavior. Using a sample of 1350 employees working for multinational firms operating in Spain, the present research obtains two main results. First, organizational justice, employee perceived organizational support and affective organizational commitment are positively related with KS. Second, employee perceived organizational support, job satisfaction and affective organizational commitment play a mediating role between organizational justice and KS, which reinforces the positive relationship between both constructs. Consequently, employees would be more willing to cooperate and share in fair organizational contexts, especially when they are satisfied and affectively committed, and when their contributions are valued and recognized. Finally, we discuss human resource management’s (HRM) practical interventions and recommendations for future research on sustainable organizations.