bluuebunny/crossref_metadata_2025 huggingface dataset:
Crop Monitoring Using Satellite/UAV Data Fusion and Machine Learning
Authors: Maimaitijiang Maitiniyazi , Sagan Vasit , Sidike Paheding , Daloye Ahmad M. , Erkbol Hasanjan , Fritschi Felix B.
Published: April 2020

Non-destructive crop monitoring over large areas with high efficiency is of great significance in precision agriculture and plant phenotyping, as well as decision making with regards to grain policy and food security. The goal of this research was to assess the potential of combining canopy spectral information with canopy structure features for crop monitoring using satellite/unmanned aerial vehicle (UAV) data fusion and machine learning. Worldview-2/3 satellite data were tasked synchronized with high-resolution RGB image collection using an inexpensive unmanned aerial vehicle (UAV) at a heterogeneous soybean (Glycine max (L.) Merr.) field. Canopy spectral information (i.e., vegetation indices) was extracted from Worldview-2/3 data, and canopy structure information (i.e., canopy height and canopy cover) was derived from UAV RGB imagery. Canopy spectral and structure information and their combination were used to predict soybean leaf area index (LAI), aboveground biomass (AGB), and leaf nitrogen concentration (N) using partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and extreme learning regression (ELR) with a newly proposed activation function. The results revealed that: (1) UAV imagery-derived high-resolution and detailed canopy structure features, canopy height, and canopy coverage were significant indicators for crop growth monitoring, (2) integration of satellite imagery-based rich canopy spectral information with UAV-derived canopy structural features using machine learning improved soybean AGB, LAI, and leaf N estimation on using satellite or UAV data alone, (3) adding canopy structure information to spectral features reduced background soil effect and asymptotic saturation issue to some extent and led to better model performance, (4) the ELR model with the newly proposed activated function slightly outperformed PLSR, RFR, and SVR in the prediction of AGB and LAI, while RFR provided the best result for N estimation. This study introduced opportunities and limitations of satellite/UAV data fusion using machine learning in the context of crop monitoring.
Mapping the Distribution of Shallow Groundwater Occurrences Using Remote Sensing-Based Statistical Modeling over Southwest Saudi Arabia
Authors: Alshehri Fahad , Sultan Mohamed , Karki Sita , Alwagdani Essam , Alsefry Saleh , Alharbi Hassan , Sahour Hossein , Sturchio Neil
Published: April 2020

Identifying shallow (near-surface) groundwater in arid and hyper-arid areas has significant societal benefits, yet it is a costly operation when traditional methods (geophysics and drilling) are applied over large domains. In this study, we developed and successfully applied methodologies that rely heavily on readily available temporal, visible, and near-infrared radar and thermal remote sensing data sets and field data, as well as statistical approaches to map the distribution of shallow (1–5 m deep) groundwater occurrences in Al Qunfudah Province, Saudi Arabia, and to identify the factors controlling their development. A four-fold approach was adopted: (1) constructing a digital database to host relevant geologic, hydrogeologic, topographic, land use, climatic, and remote sensing data sets, (2) identifying the distribution of areas characterized by shallow groundwater levels, (3) developing conceptual and statistical models to map the distribution of shallow groundwater occurrences, and (4) constructing an artificial neural network (ANN) and multivariate regression (MR) models to map the distribution of shallow groundwater, test the models over areas of known depth to groundwater (area of Al Qunfudah city and surroundings: 294 km2), and apply the better of the two models to map the shallow groundwater occurrences across the entire Al Qunfudah Province (area: 4680 km2). Findings include: (1) high performance for the ANN (92%) and MR (88%) models in predicting the distribution of shallow groundwater using temporal-derived remote sensing products (e.g., normalized difference vegetation index (NDVI), radar backscatter coefficient, precipitation, and brightness temperature) and field data (depth to water table), (2) areas witnessing shallow groundwater levels show high NDVI (mean and standard deviation (STD)), radar backscatter coefficient values (mean and STD), and low brightness temperature (mean and STD) compared to their surroundings, (3) correlations of temporal groundwater levels and satellite-based precipitation suggest that the observed (2017–2019) rise in groundwater levels is related to an increase in precipitation in these years compared to the previous three years (2014–2016), and (4) the adopted methodologies are reliable, cost-effective, and could potentially be applied to identify shallow groundwater along the Red Sea Hills and in similar settings worldwide.
Roof Plane Segmentation from Airborne LiDAR Data Using Hierarchical Clustering and Boundary Relabeling
Authors: Li Li , Yao Jian , Tu Jingmin , Liu Xinyi , Li Yinxuan , Guo Lianbo
Published: April 2020

The roof plane segmentation is one of the key issues for constructing accurate three-dimensional building models from airborne light detection and ranging (LiDAR) data. Region growing is one of the most widely used methods to detect roof planes. It first selects one point or region as a seed, and then iteratively expands to neighboring points. However, region growing has two problems. The first problem is that it is hard to select the robust seed points. The other problem is that it is difficult to detect the accurate boundaries between two roof planes. In this paper, to solve these two problems, we propose a novel approach to segment the roof planes from airborne LiDAR point clouds using hierarchical clustering and boundary relabeling. For the first problem, we first extract the initial set of robust planar patches via an octree-based method, and then apply the hierarchical clustering method to iteratively merge the adjacent planar patches belonging to the same plane until the merging cost exceeds a predefined threshold. These merged planar patches are regarded as the robust seed patches for the next region growing. The coarse roof planes are generated by adding the non-planar points into the seed patches in sequence using region growing. However, the boundaries of coarse roof planes may be inaccurate. To solve this problem, namely, the second problem, we refine the boundaries between adjacent coarse planes by relabeling the boundary points. At last, we can effectively extract high-quality roof planes with smooth and accurate boundaries from airborne LiDAR data. We conducted our experiments on two datasets captured from Vaihingen and Wuhan using Leica ALS50 and Trimble Harrier 68i, respectively. The experimental results show that our proposed approach outperforms several representative approaches in both visual quality and quantitative metrics.
Automated Filtering of Multibeam Water-Column Data to Detect Relative Abundance of Giant Kelp (Macrocystis pyrifera)
Authors: Schimel Alexandre C. G. , Brown Craig J. , Ierodiaconou Daniel
Published: April 2020

Modern multibeam echosounders can record backscatter data returned from the water above the seafloor. These water-column data can potentially be used to detect and map aquatic vegetation such as kelp, and thus contribute to improving marine habitat mapping. However, the strong sidelobe interference noise that typically contaminates water-column data is a major obstacle to the detection of targets lying close to the seabed, such as aquatic vegetation. This article presents an algorithm to filter the noise and artefacts due to interference from the sidelobes of the receive array by normalizing the slant-range signal in each ping. To evaluate the potential of the filtered data for the detection of aquatic vegetation, we acquired a comprehensive water-column dataset over a controlled experimental site. The experimental site was a transplanted patch of giant kelp (Macrocystis pyrifera) forest of known biomass and spatial configuration, obtained by harvesting several individuals from a nearby forest, measuring and weighing them, and arranging them manually on an area of seafloor previously bare. The water-column dataset was acquired with a Kongsberg EM 2040 C multibeam echosounder at several frequencies (200, 300, and 400 kHz) and pulse lengths (25, 50, and 100 μs). The data acquisition process was repeated after removing half of the plants, to simulate a thinner forest. The giant kelp plants produced evident echoes in the water-column data at all settings. The slant-range signal normalization filter greatly improved the visual quality of the data, but the filtered data may under-represent the true amount of acoustic energy in the water column. Nonetheless, the overall acoustic backscatter measured after filtering was significantly lower, by 2 to 4 dB on average, for data acquired over the thinned forest compared to the original experiment. We discuss the implications of these results for the potential use of multibeam echosounder water-column data in marine habitat mapping.
Evolution of Backscattering Coefficients of Drifting Multi-Year Sea Ice during End of Melting and Onset of Freeze-up in the Western Beaufort Sea
Authors: Kim Seung Hee , Kim Hyun-Cheol , Hyun Chang-Uk , Lee Sungjae , Ha Jung-Seok , Kim Joo-Hong , Kwon Young-Joo , Park Jeong-Won , Han Hyangsun , Jeong Seong-Yeob , Kim Duk-jin
Published: April 2020

Backscattering coefficients of Sentinel-1 synthetic aperture radar (SAR) data of drifting multi-year sea ice in the western Beaufort Sea during the transition period between the end of melting and onset of freeze-up are analyzed, in terms of the incidence angle dependence and temporal variation. The mobile sea ice surface is tracked down in a 1 km by 1 km region centered at a GPS tracker, which was installed during a field campaign in August 2019. A total of 24 Sentinel-1 images spanning 17 days are used and the incidence angle dependence in HH- and HV-polarization are −0.24 dB/deg and −0.10 dB/deg, respectively. Hummocks and recently frozen melt ponds seem to cause the mixture behavior of surface and volume scattering. The normalized backscattering coefficients in HH polarization gradually increased in time at a rate of 0.15 dB/day, whereas the HV-polarization was relatively flat. The air temperature from the ERA5 hourly reanalysis data has a strong negative relation with the increasing trend of the normalized backscattering coefficients in HH-polarization. The result of this study is expected to complement other previous studies which focused on winter or summer seasons in other regions of the Arctic Ocean.
Performance Analysis of Open Source Time Series InSAR Methods for Deformation Monitoring over a Broader Mining Region
Authors: Karamvasis Kleanthis , Karathanassi Vassilia
Published: April 2020

Time Series Interferometric Synthetic Aperture Radar (TSInSAR) methods have been widely and successfully applied for spatiotemporal ground deformation monitoring. The main groups of methodological approaches are often referred to as Persistent Scatterer (PS), Small Baseline (SB), and hybrid approaches that incorporate PS and SB concepts. While TSInSAR techniques have long been able to provide accurate deformation rates for various applications, their corresponding performance in complex environments such as mining areas has to be investigated. This study focuses on comparing the performance of three open source TSInSAR toolboxes (Stamps, Giant, Mintpy) over an extended region that includes an active opencast coal mine. We present the deformation results of each TSInSAR method on a Sentinel-1 dataset of 125 acquisitions spanning around 2.5 years over the Ptolemaida-Florina coal mine site that is characterized by several environmental and surface deformation conditions. First, a cross-comparison analysis is presented over different land cover classes. The study shows that all TSInSAR methods are capable for generating similar ground deformation results when the area has stable ground scattering conditions and the dataset sufficient temporal sampling. The most controversial results between TSInSAR approaches were found in land cover classes that include medium to high vegetation. An external comparative analysis between the different results from TSInSAR methods and leveling measurements is also performed. Stamps approach presented the best agreement with the in-situ deformation rates. The Giant approach yielded the best cumulative deformation results due to our a priori knowledge of temporal behavior of deformation in the vicinity of the leveling locations. Finally, we discuss the main pros and cons of each TSInSAR approach and we highlight the importance of comparison analysis that can provide insights and can lead to better interpretation of the results.
Longitudinal, Lateral, Vertical, and Temporal Thermal Heterogeneity in a Large Impounded River: Implications for Cold-Water Refuges
Authors: Mejia Francine H. , Torgersen Christian E. , Berntsen Eric K. , Maroney Joseph R. , Connor Jason M. , Fullerton Aimee H. , Ebersole Joseph L. , Lorang Mark S.
Published: April 2020

Dam operations can affect mixing of the water column, thereby influencing thermal heterogeneity spatially and temporally. This occurs by restricting or eliminating connectivity in longitudinal, lateral, vertical, and temporal dimensions. We examined thermal heterogeneity across space and time and identified potential cold-water refuges for salmonids in a large impounded river in inland northwestern USA. To describe these patterns, we used thermal infrared (TIR) imagery, in situ thermographs, and high-resolution, 3-D hydraulic mapping. We explained the median water temperature and probability of occurrence of cool-water areas using generalized additive models (GAMs) at reach and subcatchment scales, and we evaluated potential cold-water refuge occurrence in relation to these patterns. We demonstrated that (1) lateral contributions from tributaries dominated thermal heterogeneity, (2) thermal variability at confluences was approximately an order of magnitude greater than of the main stem, (3) potential cold-water refuges were mostly found at confluences, and (4) the probability of occurrence of cool areas and median water temperature were associated with channel geomorphology and distance from dam. These findings highlight the importance of using multiple approaches to describe thermal heterogeneity in large, impounded rivers and the need to incorporate these types of rivers in the understanding of thermal riverscapes because of their limited representation in the literature.
Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data
Authors: Tziolas Nikolaos , Tsakiridis Nikolaos , Ben-Dor Eyal , Theocharis John , Zalidis George
Published: April 2020

Earth observation (EO) has an immense potential as being an enabling tool for mapping spatial characteristics of the topsoil layer. Recently, deep learning based algorithms and cloud computing infrastructure have become available with a great potential to revolutionize the processing of EO data. This paper aims to present a novel EO-based soil monitoring approach leveraging open-access Copernicus Sentinel data and Google Earth Engine platform. Building on key results from existing data mining approaches to extract bare soil reflectance values the current study delivers valuable insights on the synergistic use of open access optical and radar images. The proposed framework is driven by the need to eliminate the influence of ambient factors and evaluate the efficiency of a convolutional neural network (CNN) to effectively combine the complimentary information contained in the pool of both optical and radar spectral information and those form auxiliary geographical coordinates mainly for soil. We developed and calibrated our multi-input CNN model based on soil samples (calibration = 80% and validation 20%) of the LUCAS database and then applied this approach to predict soil clay content. A promising prediction performance (R2 = 0.60, ratio of performance to the interquartile range (RPIQ) = 2.02, n = 6136) was achieved by the inclusion of both types (synthetic aperture radar (SAR) and laboratory visible near infrared–short wave infrared (VNIR-SWIR) multispectral) of observations using the CNN model, demonstrating an improvement of more than 5.5% in RMSE using the multi-year median optical composite and current state-of-the-art non linear machine learning methods such as random forest (RF; R2 = 0.55, RPIQ = 1.91, n = 6136) and artificial neural network (ANN; R2 = 0.44, RPIQ = 1.71, n = 6136). Moreover, we examined post-hoc techniques to interpret the CNN model and thus acquire an understanding of the relationships between spectral information and the soil target identified by the model. Looking to the future, the proposed approach can be adopted on the forthcoming hyperspectral orbital sensors to expand the current capabilities of the EO component by estimating more soil attributes with higher predictive performance.
State Observability through Prior Knowledge: Analysis of the Height Map Prior for Track Cycling
Authors: Koller Tom L. , Frese Udo
Published: April 2020

Inertial navigation systems suffer from unbounded errors in the position and orientation estimates. This drift can be corrected by applying prior knowledge, instead of using exteroceptive sensors. We want to show that the use of prior knowledge can yield full observability of the position and orientation. A previous study showed that track cyclers can be tracked drift-free with an IMU as the only sensor and the knowledge that the bike drives on the track. In this paper, we analyze the observability of the pose in the experiment we conducted. Furthermore, we improve the pose estimation of the previous study. The observability is analyzed by testing the weak observability criterion with a Jacobian rank test. The improved estimator is presented and evaluated on a dataset with three 60-round trials (10 km each). The average RMS is 1.08 m and the estimate is drift-free. The observability analysis reveals that the system can gain complete observability in the curves and observability of the orientation on the straight parts of the race track.
Innovative Multi-Target Estimating with Clutter-Suppression Technique for Pulsed Radar Systems
Authors: Nieh Jo-Yen , Cheng Yuan-Pin
Published: April 2020

Linear frequency modulation (LFM) waveforms have high Doppler-shift endurance because of the relative wide modulation bandwidth to the Doppler variation. The Doppler shift of the moving objects, nevertheless, constantly introduces obscure detection range offsets despite the exceptional Doppler tolerance in detection energy loss from LFM. An up-down-chirped LFM waveform is an efficient scheme to resolve the true target location and velocity by averaging the detection offset of two detection pairs from each single chirp LFM in opposite slopes. However, in multiple velocity-vary-target scenarios, without an efficient grouping scheme to find the detection pair of each moving target, the ambiguous detection results confine the applicability of precise target estimation by using these Doppler-tolerated waveforms. A succinct, three-multi-Doppler-shift-compensation (MDSC) scheme is applied to resolve the range and velocity of two moving objects by sorting the correct LFM detection pair of each target, even though the unresolvable scenarios of two close-by targets imply a fatal disability of detecting objects under a cluttered background. An innovative clutter-suppressed multi-Doppler-shift compensation (CS-MDSC) scheme is introduced in this research to compensate for the critical insufficient of resolving two overlapping objects with different velocities by solely MDSC. The CS-MDSC has been shown to successfully overcome this ambiguous scenario by integrating Doppler-selective moving target indication (MTI) filters to mitigate the distorting of near-zero-Doppler objects.
Analysis of Tillage Depth and Gear Selection for Mechanical Load and Fuel Efficiency of an Agricultural Tractor Using an Agricultural Field Measuring System
Authors: Kim Yeon-Soo , Kim Wan-Soo , Baek Seung-Yun , Baek Seung-Min , Kim Young-Joo , Lee Sang-Dae , Kim Yong-Joo
Published: April 2020

This study was conducted to analyze the effects of tillage depth and gear selection on the mechanical load and fuel efficiency of an agricultural tractor during plow tillage. In order to analyze these effects, we developed an agricultural field measuring system consisting of a load measurement part (wheel torque meter, proximity sensor, and real-time kinematic (RTK) global positioning system (GPS)) and a tillage depth measurement part (linear potentiometer and inclinometer). Field tests were carried out using moldboard plows with a maximum tillage depth of 20 cm and three gear selections (M2H, M3L, and M3H) in a rice stubble paddy field for plow tillage. The average travel speed and slip ratio had the lowest M2H and the highest M3L. M3H had the highest theoretical speed, but the travel speed was 0.13 km/h lower than M3L due to the reduction in the axle rotational speed at deep tillage depth. Regarding engine load, the higher the gear, the greater the torque and the lower the axle rotation speed. The front axle load was not significantly affected by the tillage depth as compared to other mechanical parts, except for the M3H gear. The rear axle load generated about twice the torque of the front wheel and overall, it tended to show a higher average rear axle torque at higher gear selection. The rear axle load and fuel rate were found to be most affected by the combination of the tillage depth and gear selection combination. Overall, field test results show that the M3H had the highest fuel efficiency and a high working speed while overcoming high loads at the same tillage depth. In conclusion, M3H is the most suitable gear stage for plow cultivation, and the higher the gear stage and the deeper the tillage depth during plowing, the higher the fuel efficiency. The results of this study will be useful for analyzing mechanical load and fuel efficiency during farm operations. In a future study, we will conduct load analysis studies in other farming operations that consider various soil mechanics factors as well as tillage depths and gear selections.
A Survey of Context-Aware Access Control Mechanisms for Cloud and Fog Networks: Taxonomy and Open Research Issues
Authors: Kayes A. S. M. , Kalaria Rudri , Sarker Iqbal H. , Islam Md. Saiful , Watters Paul A. , Ng Alex , Hammoudeh Mohammad , Badsha Shahriar , Kumara Indika
Published: April 2020

Over the last few decades, the proliferation of the Internet of Things (IoT) has produced an overwhelming flow of data and services, which has shifted the access control paradigm from a fixed desktop environment to dynamic cloud environments. Fog computing is associated with a new access control paradigm to reduce the overhead costs by moving the execution of application logic from the centre of the cloud data sources to the periphery of the IoT-oriented sensor networks. Indeed, accessing information and data resources from a variety of IoT sources has been plagued with inherent problems such as data heterogeneity, privacy, security and computational overheads. This paper presents an extensive survey of security, privacy and access control research, while highlighting several specific concerns in a wide range of contextual conditions (e.g., spatial, temporal and environmental contexts) which are gaining a lot of momentum in the area of industrial sensor and cloud networks. We present different taxonomies, such as contextual conditions and authorization models, based on the key issues in this area and discuss the existing context-sensitive access control approaches to tackle the aforementioned issues. With the aim of reducing administrative and computational overheads in the IoT sensor networks, we propose a new generation of Fog-Based Context-Aware Access Control (FB-CAAC) framework, combining the benefits of the cloud, IoT and context-aware computing; and ensuring proper access control and security at the edge of the end-devices. Our goal is not only to control context-sensitive access to data resources in the cloud, but also to move the execution of an application logic from the cloud-level to an intermediary-level where necessary, through adding computational nodes at the edge of the IoT sensor network. A discussion of some open research issues pertaining to context-sensitive access control to data resources is provided, including several real-world case studies. We conclude the paper with an in-depth analysis of the research challenges that have not been adequately addressed in the literature and highlight directions for future work that has not been well aligned with currently available research.
SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things
Authors: Haseeb Khalid , Almogren Ahmad , Ud Din Ikram , Islam Naveed , Altameem Ayman
Published: April 2020

Nowadays, the integration of Wireless Sensor Networks (WSN) and the Internet of Things (IoT) provides a great concern for the research community for enabling advanced services. An IoT network may comprise a large number of heterogeneous smart devices for gathering and forwarding huge data. Such diverse networks raise several research questions, such as processing, storage, and management of massive data. Furthermore, IoT devices have restricted constraints and expose to a variety of malicious network attacks. This paper presents a Secure Sensor Cloud Architecture (SASC) for IoT applications to improve network scalability with efficient data processing and security. The proposed architecture comprises two main phases. Firstly, network nodes are grouped using unsupervised machine learning and exploit weighted-based centroid vectors for the development of intelligent systems. Secondly, the proposed architecture makes the use of sensor-cloud infrastructure for boundless storage and consistent service delivery. Furthermore, the sensor-cloud infrastructure is protected against malicious nodes by using a mathematically unbreakable one-time pad (OTP) encryption scheme to provide data security. To evaluate the performance of the proposed architecture, different simulation experiments are conducted using Network Simulator (NS3). It has been observed through experimental results that the proposed architecture outperforms other state-of-the-art approaches in terms of network lifetime, packet drop ratio, energy consumption, and transmission overhead.
EUROPA: A Case Study for Teaching Sensors, Data Acquisition and Robotics via a ROS-Based Educational Robot
Authors: Karalekas Georgios , Vologiannidis Stavros , Kalomiros John
Published: April 2020

Robots have become a popular educational tool in secondary education, introducing scientific, technological, engineering and mathematical concepts to students all around the globe. In this paper EUROPA, an extensible, open software and open hardware robotic platform is presented focusing on teaching physics, sensors, data acquisition and robotics. EUROPA’s software infrastructure is based οn Robot Operating System (ROS). It includes easy to use interfaces for robot control and interaction with users and thus can easily be incorporated in Science, Technology, Engineering and Mathematics (STEM) and robotics classes. EUROPA was designed taking into account current trends in educational robotics. An overview of widespread robotic platforms is presented, documenting several critical parameters of interest such as their architecture, sensors, actuators and controllers, their approximate cost, etc. Finally, an introductory STEM curriculum developed for EUROPA and applied in a class of high school students is presented.
A Novel Analog Front End with Voltage-Dependent Input Impedance and Bandpass Amplification for Capacitive Biopotential Measurements
Authors: Nakamura Hajime , Sakajiri Yuichiro , Ishigami Hiroshi , Ueno Akinori
Published: April 2020

This paper proposes a novel analogue front end (AFE) that has three features: voltage-dependent input impedance, bandpass amplification, and stray capacitance reduction. With a view to applying the AFE to capacitive biopotential measurements (CBMs), the three features were investigated separately in a schematic and mathematical manner. Capacitive electrocardiogram (cECG) or capacitive electromyogram (cEMG) measurements using the AFE were performed in low-humidity conditions (below 35% relative humidity) for a total of seven human subjects. Performance evaluation of the AFE revealed the following: (1) the proposed AFE in cECG measurement with 1.70-mm thick clothing reduced the baseline recovery time and root mean square voltage of respiratory interference in subjects with healthy-weight body mass index (BMI), and increased R-wave amplitude for overweight-BMI subjects; and (2) the proposed AFE in cEMG measurement of biceps brachii muscle yielded stable electromyographic waveforms without the marked DC component for all subjects and a significant (p < 0.01) increase in the signal-to-noise ratio. These results indicate that the proposed AFE can provide a feasible balance between sensitivity and stability in CBMs, and it could be a versatile replacement for the conventional voltage follower used in CBMs.
Personal Guides: Heterogeneous Robots Sharing Personal Tours in Multi-Floor Environments
Authors: Rodriguez Igor , Zabala Unai , Marín-Reyes Pedro , Jauregi Ekaitz , Lorenzo-Navarro Javier , Lazkano Elena , Castrillón-Santana Modesto
Published: April 2020

GidaBot is an application designed to setup and run a heterogeneous team of robots to act as tour guides in multi-floor buildings. Although the tours can go through several floors, the robots can only service a single floor, and thus, a guiding task may require collaboration among several robots. The designed system makes use of a robust inter-robot communication strategy to share goals and paths during the guiding tasks. Such tours work as personal services carried out by one or more robots. In this paper, a face re-identification/verification module based on state-of-the-art techniques is developed, evaluated offline, and integrated into GidaBot’s real daily activities, to avoid new visitors interfering with those attended. It is a complex problem because, as users are casual visitors, no long-term information is stored, and consequently, faces are unknown in the training step. Initially, re-identification and verification are evaluated offline considering different face detectors and computing distances in a face embedding representation. To fulfil the goal online, several face detectors are fused in parallel to avoid face alignment bias produced by face detectors under certain circumstances, and the decision is made based on a minimum distance criterion. This fused approach outperforms any individual method and highly improves the real system’s reliability, as the tests carried out using real robots at the Faculty of Informatics in San Sebastian show.
ICT-Based Health Care Services for Individuals with Spinal Cord Injuries: A Feasibility Study
Authors: Jang Wan-ho , Lee Seung-bok , Kim Dong-wan , Lee Yun-hwan , Uhm Yun-jeong , Yang Seung-wan , Kim Jeong-hyun , Kim Jong-bae
Published: April 2020

In the Republic of Korea, 90.5% of those living with spinal cord injury (SCI) are faced with medical complications that require chronic care. Some of the more common ones include urinary tract infections, pressure sores, and pain symptomatology. These and other morbidities have been recognized to deteriorate the individual’s health, eventually restricting their community participation. Telerehabilitation, using information and communication technology, has propelled a modern-day movement in providing comprehensive medical services to patients who have difficulty in mobilizing themselves to medical care facilities. This study aims to verify the effectiveness of health care and management in the SCI population by providing ICT-based health care services. We visited eight individuals living with chronic SCI in the community, and provided ICT-based health management services. After using respiratory and urinary care devices with the provision of home visit occupational therapy, data acquisition was achieved and subsequently entered into a smart device. The entered information was readily accessible to the necessary clinicians and researchers. The clients were notified if there were any concerning results from the acquired data. Subsequently, they were advised to follow up with their providers for any immediate medical care requirements. Digital hand-bike ergometers and specialized seating system cushions are currently in development. The ICT-based health care management service for individuals with SCI resulted in a favorable expected level of outcome. Based on the results of this study, we have proposed and are now in preparation for a randomized clinical trial.
Integrating SIF and Clearness Index to Improve Maize GPP Estimation Using Continuous Tower-Based Observations
Authors: Chen Jidai , Liu Xinjie , Du Shanshan , Ma Yan , Liu Liangyun
Published: April 2020

Solar-induced chlorophyll fluorescence (SIF) has been proven to be well correlated with vegetation photosynthesis. Although multiple studies have found that SIF demonstrates a strong correlation with gross primary production (GPP), SIF-based GPP estimation at different temporal scales has not been well explored. In this study, we aimed to investigate the quality of GPP estimates produced using the far-red SIF retrieved at 760 nm (SIF760) based on continuous tower-based observations of a maize field made during 2017 and 2018, and to explore the responses of GPP and SIF to different meteorological conditions, such as the amount of photosynthetically active radiation (PAR), the clearness index (CI, representing the weather condition), the air temperature (AT), and the vapor pressure deficit (VPD). Firstly, our results showed that the SIF760 tracked GPP well at both diurnal and seasonal scales, and that SIF760 was more linearly correlated to PAR than GPP was. Therefore, the SIF760–GPP relationship was clearly a hyperbolic relationship. For instantaneous observations made within a period of half an hour, the R2 value was 0.66 in 2017 and 2018. Based on daily mean observations, the R2 value was 0.82 and 0.76 in 2017 and 2018, respectively. Secondly, it was found that the SIF760–GPP relationship varied with the environmental conditions, with the CI being the dominant factor. At both diurnal and seasonal scales, the ratio of GPP to SIF760 decreased noticeably as the CI increased. Finally, the SIF760-based GPP models with and without the inclusion of CI were trained using 70% of daily observations from 2017 and 2018 and the models were validated using the remaining 30% of the dataset. For both linear and non-linear models, the inclusion of the CI greatly improved the SIF760-based GPP estimates based on daily mean observations: the value of R2 increased from 0.71 to 0.82 for the linear model and from 0.82 to 0.87 for the non-linear model. The validation results confirmed that the SIF760-based GPP estimation was improved greatly by including the CI, giving a higher R2 and a lower RMSE. These values improved from R2 = 0.66 and RMSE = 7.02 mw/m2/nm/sr to R2 = 0.76 and RMSE = 6.36 mw/m2/nm/sr for the linear model, and from R2 = 0.71 and RMSE = 4.76 mw/m2/nm/sr to R2 = 0.78 and RMSE = 3.50 mw/m2/nm/sr for the non-linear model. Therefore, our results demonstrated that SIF760 is a reliable proxy for GPP and that SIF760-based GPP estimation can be greatly improved by integrating the CI with SIF760. These findings will be useful in the remote sensing of vegetation GPP using satellite, airborne, and tower-based SIF data because the CI is usually an easily accessible meteorological variable.
Seasonality and Efficiency of the Hotel Industry in the Balearic Islands: Implications for Economic and Environmental Sustainability
Authors: Sáez-Fernández Francisco Javier , Jiménez-Hernández Ignacio , Ostos-Rey María del Sol
Published: April 2020

Tourism seasonality generates negative environmental and economic impacts. This paper analyzes the effects of seasonality on the efficiency of the hotel industry in the Balearic Islands (Spain). To that end, a sample of hotel establishments is divided into two groups (those that close down during the off-season and those that do not). Data envelopment analysis (DEA) is applied to assess the radial efficiency of each of the selected hotels; then, directional distance functions (DDFs) are used to measure the degree of efficiency with which these hotels use each of the inputs that form part of their production process. To the best of our knowledge, this is the first time that the said technique has been applied to the hospitality industry to examine the effects of seasonality. The results of this study suggest that those establishments that do not close down their operations are markedly more efficient than the ones that do. Moreover, they are more efficient in the use of each input. Therefore, a reduction in the levels of tourism seasonality would improve the economic sustainability of the hotels and reduce the environmental pressure at peak times. Finally, in line with the theoretical hypotheses formulated, the results regarding the specific efficiency levels for each input show that the greater the degree of flexibility with which these inputs are used, the higher the efficiency.
Agricultural Heritage Systems and Landscape Perception among Tourists. The Case of Lamole, Chianti (Italy)
Authors: Santoro Antonio , Venturi Martina , Agnoletti Mauro
Published: April 2020

The Globally Important Agricultural Heritage Systems (GIAHS) program, promoted by the Food and Agriculture Organization (FAO), recognizes the multifunctional role of agricultural heritage systems. Traditional terraced landscapes represent important touristic destinations, and Chianti is one of the most well-known areas of Italy for rural tourism. The high-quality landscape of Lamole, consisting of forests and terraced agricultural areas, is included in the Italian National Register of Historical Rural Landscapes thanks to local farmers who recently restored the traditional landscape, considering it important both for tourism and product quality. The main aim of this research was to investigate, using anonymous questionnaires, whether tourists are aware of the characteristics of the Lamole landscape in comparison with other parts of Chianti. Results show that tourists clearly express their preference for the traditional landscape, which is comprised of a mosaic of agricultural patches with dry-stone terraces and forests, and that the level of landscape diversification is similar to 180 years ago. As tourism is a major resource, public institutions should support farmers in preserving the traditional landscape, investing in paths and information, as requested by tourists. The methodology has proved to be useful for addressing local planning, and to help farmers to achieve sustainable development in well-known touristic rural areas.
Sustainable Sesame (Sesamum indicum L.) Production through Improved Technology: An Overview of Production, Challenges, and Opportunities in Myanmar
Authors: Myint Daisy , Gilani Syed A. , Kawase Makoto , Watanabe Kazuo N.
Published: April 2020

This paper aims to review the research achievements concerning sustainable sesame (Sesamum indicum L.) production and outlook on the production constraints and future perspectives for Myanmar sesame. Sesame is an economically and nutritionally important crop, and it is prized for oil. The global sesame market demand is rising with increasing health awareness. Meanwhile, there is high competition in the market among producing countries for an international trade. Smallholder farmers in developing countries cultivate sesame as a cash crop on marginal soils. The edible oilseed sectors currently face several challenges, including ones affecting sesame crops. For sustainable production of sesame, an integrated approach is needed to overcome these challenges and the critical limiting factors should be identified. In recent years, sesame genomic resources, including molecular markers, genetic maps, genome sequences, and online functional databases, are available for sesame genetic improvement programs. Since ancient times, sesame has been cultivated in Myanmar, but productivity is still lower than that of other sesame producing countries. Myanmar sesame production is limited by many factors, including production technology, research and development, etc. With integration of these genomic resources, crop production and protection techniques, postharvest practices, crop improvement programs, and capacity building will play a crucial role for improving sesame production in Myanmar.
The Trade Network Structure of the “One Belt and One Road” and Its Environmental Effects
Authors: Chen Shaowei , Qiang Qiang (Patrick)
Published: April 2020

Using bilateral trade data along the “One Belt and One Road” (OBOR) from 2005–2016, this paper analyzes the structure and characteristics of the OBOR trade network. We find that the centrality, clustering coefficient, eigenvector centrality and average node strength are generally increasing, and China holds the core position in the network. Furthermore, we use three-stage least squares (3SLS) for analysis of the relationship between CO2 emissions and trade network characteristics and find that the status of country nodes in the trade network has a significant impact on CO2 emissions, with differences between high- and low-income countries. Our results provide additional insights regarding the impacts of the characteristics of the OBOR trade network on CO2 emissions.
Teaching towards Design-Based Learning in Manufacturing Technology Course: Sino–Australia Joint Undergraduate Program
Authors: Jiang Zhigang , Chandrasekaran Siva , Zhao Gang , Liu Jing , Wang Yanan
Published: April 2020

The internationalized higher education in Engineering has made the sustainable future of Chinese regional universities prosper in the recent decade. The teaching practices of engineering courses pose many challenges in Sino–foreign joint undergraduate programs. The design-based learning (DBL) approach addresses students learning challenges in the joint undergraduate program facilitated by the Wuhan University of Science and Technology (WUST) in central China along with Deakin University (DU), Australia. Following the seven general principles of DBL, a project of process planning was performed for teaching and learning in the Manufacturing Technology course. An implicit meta-cognitive competence was developed through performing the engineering project tutoring, diverse learning tasks and normative assessment criteria. The DBL pedagogy succeeds in bridging the diverse knowledge systems in the specialized courses of Manufacturing Technology between Chinese and Australian programs in Mechanical Engineering. Many achievements and awards won by the students demonstrate a satisfactory result in the case study on the teaching practice towards DBL. The pedagogy towards DBL truly improves the teaching quality of the courses in joint programs and further strengthens the internationalized engineering education for the sustainable development of regional universities in China.
Corruption, Economic Development and Haze Pollution: Evidence from 139 Global Countries
Authors: Liu Yajie , Dong Feng
Published: April 2020

Long-term exposure to haze pollution will not only affect citizens’ health and shorten their life expectancy, but also cause unpredictable economic losses. In addition, it has become the focus of worldwide concern whether and how institutional quality affects haze pollution. In this study, we explored the impacts of political corruption on haze pollution in 139 global countries. We employed a geographical detector model to identify the driving factors of spatial differentiation in global haze pollution. In addition, corruption degree and per capita gross domestic production (GDP) were used as threshold variables to analyze whether there is a nonlinear relationship between corruption and haze pollution. The main results are as follows. (1) The corruption perception index (CPI) was negatively correlated with haze pollution and had a strong and stable explanatory power for the heterogeneity of haze pollution. Besides, the degree of corruption had a significant triple threshold effect on haze pollution. When the CPI crossed the double threshold value, strengthening institutional quality could inhibit haze pollution. (2) Per capita GDP significantly determined how institutional quality exerted an effect on haze pollution, which was also a key factor affecting spatial heterogeneity of PM2.5 concentration. In high-income countries, choosing a more honest ruling party could substantially reduce haze pollution, while in low-income countries, an incompetent government could increase the degree of haze pollution. (3) The “Matthew effect” was manifested in our study. It indicated that the higher was the level of economic development, the lower was the severity of haze pollution. Based on these results, we state that policy makers cannot simply alleviate haze pollution through anti-corruption construction. For low-income countries, ensuring economic growth is the prerequisite for the substantial alleviation of haze pollution. On the contrary, high-income countries should pay more attention to the integrity of government institutions and strengthen the awareness of anti-corruption.
Evaluation of Biogas Potential from Livestock Manures and Multicriteria Site Selection for Centralized Anaerobic Digester Systems: The Case of Jalisco, México
Authors: Díaz-Vázquez Diego , Alvarado-Cummings Susan Caroline , Meza-Rodríguez Demetrio , Senés-Guerrero Carolina , de Anda José , Gradilla-Hernández Misael Sebastián
Published: April 2020

The state of Jalisco is the largest livestock producer in Mexico, leading in the production of swine, eggs, and milk. This immense production generates enormous amounts of waste as a byproduct of the process itself. The poor management of livestock-derived waste can lead to multiple environmental problems like nutrient accumulation in soil, water eutrophication, and air pollution. The aim of this work is to establish a replicable geographic information system (GIS)-based methodology for selecting priority sites in which to implement anaerobic digestion units. These units will use multiple parameters that evaluate environmental risks and viability factors for the units themselves. A weighted overlay analysis was used to identify critical regions and, based on the results, clusters of individual livestock production units (LPUs) across the state were selected. Nitrogen and phosphorus recovery, as well as the energetic potential of the selected clusters, were calculated. Four clusters located mainly in the Los Altos region of Jalisco were selected as critical and analyzed. The results indicate that Jalisco has the potential to generate 5.5% of its total electricity demand if the entirety of its livestock waste is treated and utilized in centralized anaerobic digestion units. Additionally, 49.2 and 31.2 Gg of nitrogen and phosphorus respectively could be valorized, and there would be an estimated total reduction of 3012.6 Gg of carbon dioxide equivalent (CO2eq).