bluuebunny/crossref_metadata_2025 huggingface dataset:
Tactile Robotic Skin with Pressure Direction Detection
Authors: Klimaszewski Jan , Janczak Daniel , Piorun Paweł
Published: October 2019

Tactile sensing is the current challenge in robotics and object manipulation by machines. The robot’s agile interaction with the environment requires pressure sensors to detect not only location and value, but also touch direction. The paper presents a new, two-layer construction of artificial robotic skin, which allows measuring the location, value, and direction of pressure from external force. The main advantages of the proposed solution are its low cost of implementation based on two FSR (Force Sensitive Resistor) matrices and real-time operation thanks to direction detection using fast matching algorithms. The main contribution is the idea of detecting the pressure direction by determining the shift between the pressure maps of the skin’s upper and lower layers. The pressure map of each layer is treated as an image and registered using a phase correlation (POC–Phase Only Correlation) method. The use of the developed device can be very wide. For example, in the field of cooperative robots, it can lead to the improvement of human machine interfaces and increased security of human–machine cooperation. The proposed construction can help meet the increasing requirements for robots in cooperation with humans, but also enable agile manipulation of objects from their surroundings.
Radiometric Assessment of a UAV-Based Push-Broom Hyperspectral Camera
Authors: Barreto M. , Johansen Kasper , Angel Yoseline , McCabe Matthew
Published: October 2019

The use of unmanned aerial vehicles (UAVs) for Earth and environmental sensing has increased significantly in recent years. This is particularly true for multi- and hyperspectral sensing, with a variety of both push-broom and snap-shot systems becoming available. However, information on their radiometric performance and stability over time is often lacking. The authors propose the use of a general protocol for sensor evaluation to characterize the data retrieval and radiometric performance of push-broom hyperspectral cameras, and illustrate the workflow with the Nano-Hyperspec (Headwall Photonics, Boston USA) sensor. The objectives of this analysis were to: (1) assess dark current and white reference consistency, both temporally and spatially; (2) evaluate spectral fidelity; and (3) determine the relationship between sensor-recorded radiance and spectroradiometer-derived reflectance. Both the laboratory-based dark current and white reference evaluations showed an insignificant increase over time (<2%) across spatial pixels and spectral bands for >99.5% of pixel–waveband combinations. Using a mercury/argon (Hg/Ar) lamp, the hyperspectral wavelength bands exhibited a slight shift of 1-3 nm against 29 Hg/Ar wavelength emission lines. The relationship between the Nano-Hyperspec radiance values and spectroradiometer-derived reflectance was found to be highly linear for all spectral bands. The developed protocol for assessing UAV-based radiometric performance of hyperspectral push-broom sensors showed that the Nano-Hyperspec data were both time-stable and spectrally sound.
Measuring Spatial and Temporal PM2.5 Variations in Sacramento, California, Communities Using a Network of Low-Cost Sensors
Authors: Mukherjee Anondo , Brown Steven G. , McCarthy Michael C. , Pavlovic Nathan R. , Stanton Levi G. , Snyder Janice Lam , D’Andrea Stephen , Hafner Hilary R.
Published: October 2019

Low-cost sensors can provide insight on the spatio-temporal variability of air pollution, provided that sufficient efforts are made to ensure data quality. Here, 19 AirBeam particulate matter (PM) sensors were deployed from December 2016 to January 2017 to determine the spatial variability of PM2.5 in Sacramento, California. Prior to, and after, the study, the 19 sensors were deployed and collocated at a regulatory air monitoring site. The sensors demonstrated a high degree of precision during all collocated measurement periods (Pearson R2 = 0.98 − 0.99 across all sensors), with little drift. A sensor-specific correction factor was developed such that each sensor reported a comparable value. Sensors had a moderate degree of correlation with regulatory monitors during the study (R2 = 0.60 − 0.68 at two sites). In a multi-linear regression model, the deviation between sensor and reference measurements of PM2.5 had the highest correlation with dew point and relative humidity. Sensor measurements were used to estimate the PM2.5 spatial variability, finding an average pairwise coefficient of divergence of 0.22 and a range of 0.14 to 0.33, indicating mostly homogeneous distributions. No significant difference in the average sensor PM concentrations between environmental justice (EJ) and non-EJ communities (p value = 0.24) was observed.
Detection of Voltage Anomalies in Spacecraft Storage Batteries Based on a Deep Belief Network
Authors: Li Xunjia , Zhang Tao , Liu Yajie
Published: October 2019

For a spacecraft, its power system is vital to its normal operation and capacity to complete flight missions. The storage battery is an essential component of a power system. As a spacecraft spends more time in orbit and its storage battery undergoes charge/discharge cycles, the performance of its storage battery will gradually decline, resulting in abnormal multivariate correlations between the various parameters of the storage battery system. When these anomalies reach a certain level, battery failure will occur. Therefore, the detection of spacecraft storage battery anomalies in a timely and accurate fashion is of great importance to the in-orbit operation, maintenance and management of a spacecraft. Thus, in this study, based on storage battery-related telemetry parameter data (including charge/discharge currents, voltages, temperatures and times) downloaded from an in-orbit satellite, a voltage anomaly detection algorithm for spacecraft storage batteries based on a deep belief network (DBN) is proposed. By establishing a neural network (NN) model depicting the correlations between each of the variables of temperature, current, pressure and charge/discharge times and voltage, this algorithm supports the detection of anomalies in the state-of-health of a storage battery in a timely fashion. The proposed algorithm is subsequently applied to the storage battery of the aforementioned in-orbit satellite. The results show the following. The anomalies detected using the proposed algorithm are more reliable, effective and visual than those obtained using the conventional multivariate anomaly detection algorithms. Compared to the classic backpropagation NN-based algorithm, the DBN-based algorithm is notably advantageous in terms of the model training time and convergence.
Research on Data Fusion Scheme for Wireless Sensor Networks with Combined Improved LEACH and Compressed Sensing
Authors: Song Yu , Liu Zhigui , He Xiaoli , Jiang Hong
Published: October 2019

There are a lot of redundant data in wireless sensor networks (WSNs). If these redundant data are processed and transmitted, the node energy consumption will be too fast and will affect the overall lifetime of the network. Data fusion technology compresses the sampled data to eliminate redundancy, which can effectively reduce the amount of data sent by the node and prolong the lifetime of the network. Due to the dynamic nature of WSNs, traditional data fusion techniques still have many problems. Compressed sensing (CS) theory has introduced new ideas to solve these problems for WSNs. Therefore, in this study we analyze the data fusion scheme and propose an algorithm that combines improved clustered (ICL) algorithm low energy adaptive clustering hierarchy (LEACH) and CS (ICL-LEACH-CS). First, we consider the factors of residual energy, distance, and compression ratio and use the improved clustered LEACH algorithm (ICL-LEACH) to elect the cluster head (CH) nodes. Second, the CH uses a Gaussian random observation matrix to perform linear compressed projection (LCP) on the cluster common (CM) node signal and compresses the N-dimensional signal into M-dimensional information. Then, the CH node compresses the data by using a CS algorithm to obtain a measured value and sends the measured value to the sink node. Finally, the sink node reconstructs the signal using a convex optimization method and uses a least squares algorithm to fuse the signal. The signal reconstruction optimization problem is modeled as an equivalent ℓ 1 -norm problem. The simulation results show that, compared with other data fusion algorithms, the ICL-LEACH-CS algorithm effectively reduces the node’s transmission while balancing the load between the nodes.
A MIMO Radar-Based DOA Estimation Structure Using Compressive Measurements
Authors: Chen Tao , Yang Jian , Guo Muran
Published: October 2019

In this paper, we propose a novel direction-of-arrival (DOA) estimation structure based on multiple-input multiple-output (MIMO) radar with colocated antennas, referred to as compressive measurement-based MIMO (CM-MIMO) radar, where the compressive sensing (CS) is employed to reduce the number of channels. Therefore, the system complexity and the computational burden are effectively reduced. It is noted that CS is used after the matched filters and that a measurement matrix with less rows than columns is multiplied with the received signals. As a result, the configurations of the transmit and receive antenna arrays are not affected by the CS and can be determined according to the practical requirements. To study the estimation performance, the Cramér–Rao bound (CRB) with respect to the DOAs of the proposed CM-MIMO radar is analyzed in this paper. The derived CRB expression is also suitable for the conventional MIMO radar by setting the measurement matrix as an identity matrix. Moreover, the CRB expression can work in the under-determined case, since the sum-difference coarray structure is considered. However, the random measurement matrix leads to high information loss, thus compromising the estimation performance. To overcome this problem, we consider that the a prior probability distribution of the DOAs associated with the targets can be obtained in many scenarios and an optimization approach for the measurement matrix is proposed in this paper, where the maximum mutual information criterion is adopted. The superiority of the proposed structure is validated by numerical simulations.
Design and Implementation of a Trust Information Management Platform for Social Internet of Things Environments
Authors: Um Tai-Won , Lee Eunhee , Lee Gyu Myoung , Yoon Yongik
Published: October 2019

As the vast amount of data in social Internet of Things (IoT) environments considering interactions between IoT and people is accumulated and processed through cloud and big data technologies, the services that utilize them are applied in various fields. The trust between IoT devices and their data is recognized as the core of IoT ecosystem creation and growth. Connection with suspicious IoT devices may pose a risk to services and system operation. Therefore, it is essential to analyze and manage trust information for devices, services, and people, as well as to provide the trust information to the other devices or users that need it. This paper presents a trust information management framework which contains a generic IoT reference model with trust capabilities to achieve the goal of converged trust information management. Additionally, a trust information management platform (TIMP) consisting of trust agents, trust information brokers, and trust information management systems has been proposed, which aims to provide trustworthy and safe interactions among people, virtual objects, and physical things. Implementing and deploying a TIMP enables a trustworthy ecosystem to be built while activating social IoT businesses by reducing transaction costs, as well as by eliminating the uncertainties in the use of social IoT services and data transactions.
Occlusion-Free Road Segmentation Leveraging Semantics for Autonomous Vehicles
Authors: Wang Kewei , Yan Fuwu , Zou Bin , Tang Luqi , Yuan Quan , Lv Chen
Published: October 2019

The deep convolutional neural network has led the trend of vision-based road detection, however, obtaining a full road area despite the occlusion from monocular vision remains challenging due to the dynamic scenes in autonomous driving. Inferring the occluded road area requires a comprehensive understanding of the geometry and the semantics of the visible scene. To this end, we create a small but effective dataset based on the KITTI dataset named KITTI-OFRS (KITTI-occlusion-free road segmentation) dataset and propose a lightweight and efficient, fully convolutional neural network called OFRSNet (occlusion-free road segmentation network) that learns to predict occluded portions of the road in the semantic domain by looking around foreground objects and visible road layout. In particular, the global context module is used to build up the down-sampling and joint context up-sampling block in our network, which promotes the performance of the network. Moreover, a spatially-weighted cross-entropy loss is designed to significantly increases the accuracy of this task. Extensive experiments on different datasets verify the effectiveness of the proposed approach, and comparisons with current excellent methods show that the proposed method outperforms the baseline models by obtaining a better trade-off between accuracy and runtime, which makes our approach is able to be applied to autonomous vehicles in real-time.
Machine Learning Techniques Applied to Multiband Spectrum Sensing in Cognitive Radios
Authors: Molina-Tenorio Yanqueleth , Prieto-Guerrero Alfonso , Aguilar-Gonzalez Rafael , Ruiz-Boqué Silvia
Published: October 2019

In this work, three specific machine learning techniques (neural networks, expectation maximization and k-means) are applied to a multiband spectrum sensing technique for cognitive radios. All of them have been used as a classifier using the approximation coefficients from a Multiresolution Analysis in order to detect presence of one or multiple primary users in a wideband spectrum. Methods were tested on simulated and real signals showing a good performance. The results presented of these three methods are effective options for detecting primary user transmission on the multiband spectrum. These methodologies work for 99% of cases under simulated signals of SNR higher than 0 dB and are feasible in the case of real signals.
An Improved Method for Spot Position Detection of a Laser Tracking and Positioning System Based on a Four-Quadrant Detector
Authors: Zhang Wugang , Guo Wei , Zhang Chuanwei , Zhao Shuanfeng
Published: October 2019

For the laser tracking and positioning system of a moving target using a four-quadrant detector, the accuracy of laser spot position detection has a serious impact on the tracking performance of the system. For moving target tracking, the traditional spot position detection method of a four-quadrant detector cannot give better consideration to both detection accuracy and operation speed. In view of this, an improved method based on piecewise low-order polynomial least squares fitting and a Kalman filter is proposed. Firstly, the tracking and positioning mathematical model of the system is created, and the experimental device is established. Then, the shortcomings of traditional methods are analyzed, and the improved method and the real-time tracking and positioning algorithm of the system are studied. Finally, through the experiment, the system operation effects are compared and analyzed before and after the improvement. The experimental results of system dynamic tracking show that, the least squares fitting of the experimental data using a 5-segment and quadratic polynomial can achieve better results. By using the improved method, the maximum tracking distance of a moving object is increased from 12 m to more than 30 m. At a distance of 7.5 m, the maximum tracking speed can reach 2.11 m/s, and the root mean square error (RMSE) of the position is less than 4.59 mm. At 15.5 m, the maximum tracking speed is 2.04 m/s and the RMSE is less than 5.42 mm. Additionally, at 23.5 m, it is 1.13 m/s and 5.71 mm.
Analysis of BDS Fractional Cycle Biases and PPP Ambiguity Resolution
Authors: Jiang Weiping , Zhao Wen , Chen Hua , Liu Xuexi , An Xiangdong , Chen Qusen
Published: October 2019

It is difficult to enable traditional precise point positioning (PPP) with ambiguity resolution (AR) due to fractional cycle biases (FCBs), which limit the accuracy and reliability of positioning results. The BeiDou Navigation Satellite System (BDS) has been providing continuous positioning, navigation, and timing (PNT) services in the global region since the end of 2018. The BDS constellation includes geostationary earth orbit (GEO), inclined geostationary orbit (IGSO), and medium earth orbit (MEO) satellites. However, its hybrid constellation structure and the satellite-side multipath effect have hindered the BDS PPP AR for two main reasons: (1) some receivers have half-cycle biases between GEO and non-GEO satellites, which result in the inconsistency of hardware delays for each satellite type; (2) the correction model for elevation-dependent satellite-side multipath effect is only applicable to IGSO and MEO, while in the case of GEO the effect cannot be effectively weakened or eliminated. To rectify these problems, a suitable strategy is proposed for estimating BDS FCBs, whereby the GEO FCBs and non-GEO FCBs are estimated independently. Results show that the FCBs estimated by the new strategy for GEO and non-GEO are more stable compared to the traditional strategy. The GEO wide-lane (WL) FCBs fluctuate less than 0.3 cycle in one month, except for C05, while the variation of non-GEO WL FCBs is about 0.1 cycle. In addition, compared to the traditional strategy, the fractions of GEO WL ambiguities after the removal of FCBs estimated by the new strategy can be improved noticeably from 53.5% to 78.5%, and from 71.8% to 92.3% for <0.15 cycle and <0.25 cycle respectively, which could be comparable with non-GEO. Simultaneously, the improvement of GEO narrow-lane (NL) ambiguities is from 28.9% to 40.2%, and from 40.4% to 53.3% for <0.10 cycle and <0.15 cycle respectively, are less noticeable. This is mainly due to the low precision IGS products for GEO. After PPP AR, the mean convergence time is shorted from 56.0 min to 43.6 min, and from 71.6 min to 62.7 min for static PPP and kinematic PPP, respectively.
Special Issue “Advanced Signal Processing in Intelligent Systems for Health Monitoring”
Authors: Abbod Maysam , Shieh Jiann-Shing
Published: October 2019

Recently, significant developments have been achieved in the field of artificial intelligence, in particular the introduction of deep learning technology that has improved the learning and prediction accuracy to unpresented levels, especially when dealing with big data and high-resolution images. Significant developments have occurred in the area of medical signal processing, measurement techniques, and health monitoring, such as vital biological signs for biomedical systems and noise and vibration of mechanical systems, which are carried out by instruments that generate large data sets. These big data sets, ultimately driven by high population growth, would require Artificial Intelligence techniques to analyse and model. In this Special Issue, papers are presented on the latest signal processing and deep learning techniques used for health monitoring of biomedical and mechanical systems.
Chaotic Signatures Exhibited by Plasmonic Effects in Au Nanoparticles with Cells
Authors: Martines-Arano Hilario , García-Pérez Blanca Estela , Vidales-Hurtado Mónica Araceli , Trejo-Valdez Martín , Hernández-Gómez Luis Héctor , Torres-Torres Carlos
Published: October 2019

The evolution of the optical absorptive effects exhibited by plasmonic nanoparticles was systematically analyzed by electronic signals modulated by a Rössler attractor system. A sol-gel approach was employed for the preparation of the studied Au nanoparticles embedded in a TiO2 thin solid film. The inclusion of the nanoparticles in an inhomogeneous biological sample integrated by human cells deposited in an ITO glass substrate was evaluated with a high level of sensitivity using an opto-electronic chaotic circuit. The optical response of the nanoparticles was determined using nanosecond laser pulses in order to guarantee the sensing performance of the system. It was shown that high-intensity irradiances at a wavelength of 532 nm could promote a change in the absorption band of the localized surface plasmon resonance associated with an increase in the nanoparticle density of the film. Moreover, it was revealed that interferometrically-controlled energy transfer mechanisms can be useful for thermo-plasmonic functions and sharp selective optical damage induced by the vectorial nature of light. Immediate applications of two-wave mixing techniques, together with chaotic effects, can be contemplated in the development of nanostructured sensors and laser-induced controlled explosions, with potential applications for biomedical photo-thermal processes.
A Novel Method for Classification of Running Fatigue Using Change-Point Segmentation
Authors: Khan Taha , Lundgren Lina E. , Järpe Eric , Olsson M. Charlotte , Viberg Pelle
Published: October 2019

Blood lactate accumulation is a crucial fatigue indicator during sports training. Previous studies have predicted cycling fatigue using surface-electromyography (sEMG) to non-invasively estimate lactate concentration in blood. This study used sEMG to predict muscle fatigue while running and proposes a novel method for the automatic classification of running fatigue based on sEMG. Data were acquired from 12 runners during an incremental treadmill running-test using sEMG sensors placed on the vastus-lateralis, vastus-medialis, biceps-femoris, semitendinosus, and gastrocnemius muscles of the right and left legs. Blood lactate samples of each runner were collected every two minutes during the test. A change-point segmentation algorithm labeled each sample with a class of fatigue level as (1) aerobic, (2) anaerobic, or (3) recovery. Three separate random forest models were trained to classify fatigue using 36 frequency, 51 time-domain, and 36 time-event sEMG features. The models were optimized using a forward sequential feature elimination algorithm. Results showed that the random forest trained using distributive power frequency of the sEMG signal of the vastus-lateralis muscle alone could classify fatigue with high accuracy. Importantly for this feature, group-mean ranks were significantly different (p < 0.01) between fatigue classes. Findings support using this model for monitoring fatigue levels during running.
High-Temperature Sensitivity in Stimulated Brillouin Scattering of 1060 nm Single-Mode Fibers
Authors: Song Sanggwon , Jung Aeri , Oh Kyunghwan
Published: October 2019

With the rapid advancement of Yb-doped fiber lasers (YDFL) whose output wavelength is near 1060 nm, passive fibers to carry the high optical power at the spectral range are also gaining significant importance. Stimulated Brillouin scattering (SBS) in the passive fibers connecting components in the lasers, especially, can set a fundamental limit in the power handling of YDFL systems. We experimentally analyzed SBS characteristics of passive single mode fibers (SMF) at a wavelength of 1060 nm. For two types of SMFs (Corning HI1060 and HI1060Flex), the Brillouin frequency (νΒ), its linewidth (ΔνΒ), and their variations with respect to the input laser power and the surrounding temperature were experimentally measured, along with the SBS threshold power (Pth). The optical heterodyne detection method was used to identify temperature-dependent SBS characteristics of fibers, and we found SMFs at λ = 1060 nm showed a temperature sensitivity in SBS frequency shift more than 40% higher than in conventional SMFs operating in C-band. Detailed procedures to measure the SBS properties are explained, and a new potential of 1060 nm SMF as a distributed temperature sensor is also discussed.
An Identity Authentication Method Combining Liveness Detection and Face Recognition
Authors: Liu Shuhua , Song Yu , Zhang Mengyu , Zhao Jianwei , Yang Shihao , Hou Kun
Published: October 2019

In this study, an advanced Kinect sensor was adopted to acquire infrared radiation (IR) images for liveness detection. The proposed liveness detection method based on infrared radiation (IR) images can deal with face spoofs. Face pictures were acquired by a Kinect camera and converted into IR images. Feature extraction and classification were carried out by a deep neural network to distinguish between real individuals and face spoofs. IR images collected by the Kinect camera have depth information. Therefore, the IR pixels from live images have an evident hierarchical structure, while those from photos or videos have no evident hierarchical feature. Accordingly, two types of IR images were learned through the deep network to realize the identification of whether images were from live individuals. In comparison with other liveness detection cross-databases, our recognition accuracy was 99.8% and better than other algorithms. FaceNet is a face recognition model, and it is robust to occlusion, blur, illumination, and steering. We combined the liveness detection and FaceNet model for identity authentication. For improving the application of the authentication approach, we proposed two improved ways to run the FaceNet model. Experimental results showed that the combination of the proposed liveness detection and improved face recognition had a good recognition effect and can be used for identity authentication.
Particle Imaging Velocimetry Gyroscope
Authors: Youssef Ahmed A. , El-Sheimy Naser
Published: October 2019

Inertial measurement units (IMUs) are typically classified as per the performance of the gyroscopes within each system. Consequently, it is critical for a system to have a low bias instability to have better performance. Nonetheless, there is no IMU available commercially that does not actually suffer from bias-instability, even for the navigation grade IMUs. This paper introduces the proposition of a novel fluid-based gyroscope, which is referred to hereafter as a particle imaging velocimetry gyroscope (PIVG). The main advantages of the PIVG include being nearly drift-free, a high signal-to-noise ratio (SNR) in comparison to commercially available high-end gyroscopes, and its low cost.
A Multi-Column CNN Model for Emotion Recognition from EEG Signals
Authors: Yang Heekyung , Han Jongdae , Min Kyungha
Published: October 2019

We present a multi-column CNN-based model for emotion recognition from EEG signals. Recently, a deep neural network is widely employed for extracting features and recognizing emotions from various biosignals including EEG signals. A decision from a single CNN-based emotion recognizing module shows improved accuracy than the conventional handcrafted feature-based modules. To further improve the accuracy of the CNN-based modules, we devise a multi-column structured model, whose decision is produced by a weighted sum of the decisions from individual recognizing modules. We apply the model to EEG signals from DEAP dataset for comparison and demonstrate the improved accuracy of our model.
Passing through Open/Closed Doors: A Solution for 3D Scanning Robots
Authors: Prieto Samuel A. , Adán Antonio , Vázquez Andrés S. , Quintana Blanca
Published: October 2019

In this article, a traversing door methodology for building scanning mobile platforms is proposed. The problem of passing through open/closed doors entails several actions that can be implemented by processing 3D information provided by dense 3D laser scanners. Our robotized platform, denominated as MoPAD (Mobile Platform for Autonomous Digitization), has been designed to collect dense 3D data and generate basic architectural models of the interiors of buildings. Moreover, the system identifies the doors of the room, recognises their respective states (open, closed or semi-closed) and completes the aforementioned 3D model, which is later integrated into the robot global planning system. This document is mainly focused on describing how the robot navigates towards the exit door and passes to a contiguous room. The steps of approaching, door-handle recognition/positioning and handle–robot arm interaction (in the case of a closed door) are shown in detail. This approach has been tested using our MoPAD platform on the floors of buildings composed of several rooms in the case of open doors. For closed doors, the solution has been formulated, modeled and successfully tested in the Gazebo robot simulation tool by using a 4DOF robot arm on board MoPAD. The excellent results yielded in both cases lead us to believe that our solution could be implemented/adapted to other platforms and robot arms.
Decision-Making Processes Underlying Pedestrian Behaviors at Signalized Crossing: Part 1. The First to Step off the Kerb
Authors: Pelé Marie , Deneubourg Jean-Louis , Sueur Cédric
Published: October 2019

Pedestrians are ideal subjects for the study of decision-making, due to the inter-individual variation in risk taking. Many studies have attempted to understand which environmental factors influence the number of times pedestrians broke the rules at road-crossings, very few focused on the decision-making process of pedestrians according to the different conditions of these variables, that is to say their perception and interpretation of the information they receive. We used survival analyses and modeling to highlight the decision-making process of pedestrians crossing the road at signalized crossings in France and in Japan. For the first pedestrians to step off the kerb, we showed that the probability to cross the road follows three different processes: one at the red signal, one just before the pedestrian signal turns green, and one after the signal has turned green. Globally, the decision of the first pedestrian to cross, whether he or she does so at the green or at the red signal, is influenced by their country of residence. We identify the use of cognitive processes such as risk sensitivity and temporal discounting, and propose new concepts based on the results of this study to decrease the incidence of rule-breaking by pedestrians.
Social Farming: An Inclusive Environment Conducive to Participant Personal Growth
Authors: Moruzzo Roberta , Di Iacovo Francesco , Funghi Alessandra , Scarpellini Paola , Diaz Salomon Espinosa , Riccioli Francesco
Published: October 2019

Social farming can ameliorate the everyday life of people engaged in farming activities, including perceived changes in mood or behavior. It can also be therapeutic, as it can address a range of public health and service provision issues. This paper presents the findings of an Italian project that explored the impact of social farming on the well-being of the participants and their ability to perform certain tasks linked to agricultural activities. In addition, this paper tries to evaluate how the organization of the network system around the participants helps them to improve their relational capabilities. Participant observations were made in class rooms and farms where the participants carried out their agricultural activities. Such observations focused on the way in which participants and other subjects (i.e., tutors and training staff) inside the network system interacted. A number of in-depth interviews were carried out with tutors and trainers in order to understand if the participants would play a relevant role in social farming activities and what that role would be.
A Typological Characterization of Organic Livestock Farms in the Natural Park Sierra de Grazalema Based on Technical and Economic Variables
Authors: Díaz-Gaona Cipriano , Sánchez-Rodríguez Manuel , Rucabado-Palomar Thais , Rodríguez-Estévez Vicente
Published: October 2019

This paper describes the typological characterization of the Natural Park Sierra de Grazalema (NPSG) livestock farms using its communal pastures (N = 23, 100% of population) in order to study their sustainability from 160 technical, economic and social variables (from direct on-farm data collection). A principal components analysis (PCA) produced four principal components related to size, livestock species, main productions and intensification level, explaining 73.6% of the variance. The subsequent cluster analysis classified the farms into four groups: C1 (medium size farms without sheep), C2 (large size and very extensive farms), C3 (farms with multipurpose sheep) and C4 (farms with dairy goat and without cattle). Forty-eight-point-seven percent of the surface was registered as organic but none of the farms’ commercialized products were organic. C2 and C3 (both having three ruminant species) are those farms that have more economic differences, the former generating the lowest profit, and the latter generating the highest; however, there is a risk to grasslands conservation from the current tendency that leads dairy farms to rapid intensification. Nevertheless, the very extensive farms are the most interesting for NPSG conservation and the administration should help to maintain the profitability of this sustainable traditional activity, which is necessary to conserve communal pastures.
Integrating Remote Sensing and Hydrologic Modeling to Assess the Impact of Land-Use Changes on the Increase of Flood Risk: A Case Study of the Riyadh–Dammam Train Track, Saudi Arabia
Authors: Abdelkarim Ashraf , Gaber Ahmed , Alkadi Ibtesam , Alogayell Haya
Published: October 2019

The current study aimed at measuring the impact of the change in land-use morphology on the increase of flood risk through its application to the case of the Riyadh–Dammam train track in Saudi Arabia. The track was exposed to drift on 18 February 2017, over a length of 10 km, in the district of Dhahran in the capital of Dammam. Flooding caused the train to drift off its track and resulted in damage to lives, property, and infrastructure. This resulted from human interventions in the preplanning land uses and changing the morphology of the land by encroaching on the valleys, which resulted in the loss of the environmental and ecological balance in the study area. In order to achieve these goals, land-use changes in the study area were monitored by analyzing successive images from the GEO-I-1 satellite with a resolution of 60 cm for the years 2011 and 2017, before and after the train drift, using the maximum likelihood classification process provided in ERDAS IMAGINE 2016. GIS was used in the processing of 1 m digital elevation models to extract the morphological changes of the wadies between 2011 and 2017. A hydrological model (HEC–HMS) was used in calculating the (flood) hydrograph curve of the wadies basins and estimating the calculation of flood water quantities and its flow rates based on the Soil Conservation Services (SCS) Unit Hydrograph Method. Rain depth was analyzed and estimated for different return periods. The HEC–RAS hydraulic modeling program was employed in developing a 2D model to calculate the velocity, depth, and spread of the flood in order to apply the risk matrix method.
Agricultural Diversity and Sustainability: General Features and Bangladeshi Illustrations
Authors: Tisdell Clement , Alauddin Mohammad , Sarker Md. Abdur Rashid , Kabir Md Anwarul
Published: October 2019

Many scientists have expressed concern that declining agricultural diversity threatens agricultural sustainability. We draw on the available literature to outline and examine mechanisms that reduce agricultural diversity and identify the at-risk attributes of agricultural sustainability. Using a three-pillar concept embodying ecological, social and economic dimensions, this article provides a comprehensive general assessment of the sustainability of agricultural systems. It pays particular attention to consequences for agricultural diversity and sustainability of the increasing dependence of agriculture on the market system and new agricultural technologies. As an illustrative example, it examines changes in the diversity and sustainability of Bangladeshi agriculture by applying a novel index of the diversity of cropping land use, an output decomposition method, and statistical techniques. Crop diversity in Bangladesh is very low and dominated by the cultivation of rice, which now depends very heavily on a limited number of high yielding varieties (HYVs). Higher rice yields in Bangladesh and seasonal changes in rice cultivation have resulted in land sparing, which make room for greater crop diversity. Nevertheless, Bangladesh’s food dependence on its rice output is very high and is critically dependent on groundwater irrigation. We recommend that Bangladesh consider increasing the diversity of its crops as a food security measure and as a hedge against a decline in its agricultural sustainability.
Identification of Schedule-Performance Indicators and Delay-Recovery Strategies for Low-Cost Housing Projects
Authors: Safapour Elnaz , Kermanshachi Sharareh , Alfasi Bahaa , Akhavian Reza
Published: October 2019

Many construction projects suffer from schedule delays that ultimately lead to considerable cost overruns and defeat the purpose of low-cost housing (LCH), which is to support low-income earners. It is, therefore, vital that the schedule delays and cost overruns be minimized. The objectives of this research were to investigate, identify, and classify the schedule-delay indicators (SDIs), prioritize them based on their level of impact, and formulate constructive strategies to improve the schedule performance. To achieve the objectives set forth, 68 interviews were conducted with professionals who are active in LCH projects, and a structured survey was developed and distributed to other experts involved in LCH projects to validate the result of the interviews and collect additional data. Survey responses were collected from 101 individuals and were analyzed. The significant SDIs were identified and classified by the research team and were ranked and prioritized, using the Relative Importance Index (RII) method. The results demonstrated that the identified SDIs could be classified into the following eight main categories: legal, design and technology, project characteristic, project management, material resource, human resource, location, and finance. The outcomes of this study will help project managers and stakeholders identify the causes of schedule delays early in the project and implement effective strategies for improving project performance in low-cost housing projects.
Performance Analyses of a Renewable Energy Powered System for Trigeneration
Authors: Bamisile Olusola , Huang Qi , Anane Paul O. K. , Dagbasi Mustafa
Published: October 2019

In this research, a novel trigeneration powered by a renewable energy (RE) source is developed and analyzed. The trigeneration system is designed to produce electricity, hot water, and cooling using two steam cycles, a gas cycle, hot water chamber, and an absorption cycle. The RE source considered in the scope of this study is biogas generated from chicken manure and maize silage. The energy and exergy analysis of the trigeneration system is performed with the aim to achieve higher efficiencies. The efficiencies are presented based on power generation, cogeneration (electricity and cooling) and trigeneration. The overall trigeneration energy and exergy efficiency for the system developed is 64% and 34.51%. The exergy destruction within the system is greatest in the combustion chamber.
Simple Soil Tests for On-Site Evaluation of Soil Health in Orchards
Authors: Thomsen Esther O. , Reeve Jennifer R. , Culumber Catherine M. , Alston Diane G. , Newhall Robert , Cardon Grant
Published: October 2019

Standard commercial soil tests typically quantify nitrogen, phosphorus, potassium, pH, and salinity. These factors alone are not sufficient to predict the long-term effects of management on soil health. The goal of this study was to assess the effectiveness and use of simple physical, biological, and chemical soil health indicator tests that can be completed on-site. Analyses were conducted on soil samples collected from three experimental peach orchards located on the Utah State Horticultural Research Farm in Kaysville, Utah. All simple tests were correlated to comparable lab analyses using Pearson’s correlation. The highest positive correlations were found between Solvita® respiration, and microbial biomass (R = 0.88), followed by our modified slake test and microbial biomass (R = 0.83). Both Berlese funnel and pit count methods of estimating soil macro-organism diversity were fairly predictive of soil health. Overall, simple commercially available chemical tests were weak indicators of soil nutrient concentrations compared to laboratory tests. Modified slake tests, Solvita® respiration and soil organism biodiversity counts may be efficient and cost-effective tools for monitoring soil health on-site.
Institutional Investors and Corporate Performance: Insights from China
Authors: Lin Shu-Ling , Lu Jun
Published: October 2019

This study uses the annual data of Chinese A-share listed companies held by institutional investors during the period of 2005–2016 for empirical analysis. First, this study uses the panel regression model to explore the relationship between institutional ownership and stock return volatility. Then, the CAPM one-factor model and the Fama–French three-factor model are used to analyze the relationship between institutional ownership and idiosyncratic risks. Finally, we estimate the relationship between institutional ownership and corporate governance. Furthermore, we compare the empirical results before, during, and after the crisis. This study uses the Hausman test and the endogenous test to validate the results. The empirical results show that the management behavior of independent institutional investors is more obvious post-crisis. However, gray institutional investors have no impact on idiosyncratic risks. In the regression of the CAPM one-factor model, domestic institutional investors have effectively reduced the idiosyncratic risks before the financial crisis. Foreign institutions’ monitoring performance before, during, and after the crisis is not obvious. All institutional ownership has a significant positive impact on the top 10 shareholders, but independent and domestic institutional ownership has a significant negative impact on senior shareholders. Institutional ownership has little impact on the movement of the first shareholder and CEO.
Resolving Conflict between Parties and Consequences for Foreign Direct Investment: The Repsol-YPF Case in Argentina
Authors: Fernández González Raquel , Arce Fariña María Elena , Garza Gil María Dolores
Published: October 2019

In 2012, the Argentine government expropriated 51% of the shares of Yacimientos Petrolíferos Fiscales S.A. (YPF) from the Spanish company Repsol S.A. The YPF was nationalized without prior compensation, violating Argentina’s own laws and, consequently, the institutional framework in force in the country. As a consequence, the country’s reputation deteriorated and, although there were several contacts with multinational enterprises to become YPF’s new partner, the investment climate was affected, making it really difficult to attract Foreign Direct Investment (FDI). In order to attract these investments after the expropriation, the Argentine government understands that it is necessary to settle the legal proceedings with Repsol. In order to avoid an imperfect judicial procedure of long duration and with high transaction costs, both parties reached a settlement agreement. This paper presents an institutional economic analysis of expropriation, contextualizing it within the Argentine institutional framework and studying the trajectory of the nationalization of YPF. In this way, it seeks to contextualize institutionally the Argentine government’s decision and the impact it has had on both the FDI and the credibility of the country’s institutional framework. It also analyzes how the resolution of the conflict occurs through an agreement between the parties that avoids the judicial process, given its high transaction costs.
Erratum: Furtak, K., et al. Analysis of Soil Properties, Bacterial Community Composition, and Metabolic Diversity in Fluvisols of a Floodplain Area. Sustainability 2019, 11, 3929
Authors: Furtak Karolina , Grządziel Jarosław , Gałązka Anna , Niedźwiecki Jacek
Published: October 2019

The authors would like to make the following correction about the published paper [...]
How to Improve Water Resources Allocation Efficiency: A Two-Stage Performance-Based Allocation Mechanism
Authors: Zhang Hui , Ren Qiyu , Li Jiaying
Published: October 2019

The fact that the water resource governor has to allocate limited water resources to two competing agricultural water users under the administrative system (AS) leads to a principal-agent issue. Hence, this paper constructs a two-stage performance-based allocation mechanism to motivate two competing water users (referred to as the agents) to act in accordance with the interests of the governor (referred to as the principal). This mechanism is about the interaction between the governor and two water users. The governor aims to improve water resources allocation efficiency and balance economic and environmental development, while each agricultural user focuses on the overall amount of water resources they have to operate and would like to ignore environment protection issues. Besides, the total water resources invested into production or environment is two water users’ private information, which is unknown to the governor. In the first stage, the governor allocates water resources between two users according to their previous performances, including production and environmental aspects. Results indicate that the equilibrium state of this mechanism could encourage two water users to focus on production and environment performances simultaneously and to help the governor transfer the pollution cost to two water users under the external of pollution cost, which motivates them to compete for available water resources. This competition between two users will directly affect users’ behaviors. These results could improve water resources allocation efficiency significantly and realize the sustainability of water resources in the agricultural field under the current AS. This perspective could also provide a new insight for the management of agricultural water resources allocation and offer relative decision support to relative governors.
Application of Export Coefficient Model and QUAL2K for Water Environmental Management in a Rural Watershed
Authors: Xin Zhuohang , Ye Lei , Zhang Chi
Published: October 2019

Water quality deterioration caused by excessive nutrient discharge from various point and non-point sources are a global challenge. Understanding the pollution sources and their respective contribution is the prerequisite for environmental planning, management and restoration. In this study, the influence of complex pollution sources on the water quality of the Dengsha River watershed in Dalian, China, was investigated. The export coefficient method was coupled with the QUAL2K water quality model to estimate the loads of ammonia nitrogen (NH4-N) and total phosphorus (TP) from different sources, and to explore their respective contributions. Results indicated that animal feedlot and crop production were major sources for NH4-N load, and crop production, soil erosion and animal feedlot are the largest three sources of TP load with an annual total contribution of 98.4%. The pollutant load exhibited an intra-annual variation mainly due to the seasonality of rainfall and anthropogenic agricultural activities. The overall waste assimilation capacity (WAC) is overloaded and suggestions for water pollution control and treatment regarding each pollution source were proposed. This study addressed a new application of QUAL2K model coupled with the export coefficient model for watershed managers towards a sustainable water environmental management, and can therefore be a reference example for other small and medium-sized rural watersheds.