bluuebunny/crossref_metadata_2025 huggingface dataset:
A Multi-Objective Modeling Method of Multi-Satellite Imaging Task Planning for Large Regional Mapping
Authors: Chen Yaxin , Xu Miaozhong , Shen Xin , Zhang Guo , Lu Zezhong , Xu Junfei
Published: January 2020

Regional remote sensing image products are playing an important role in an increasing number of application fields. Aiming at multi-satellite imaging task planning for large-area image acquisition, this paper proposes a multi-objective modeling method. First, we analyzed the core requirements of regional mapping for multi-satellite imaging mission planning: Full coverage of the target area and low consumption of satellite resources. Second, an optimization model with two objective functions, namely the maximum target area coverage and minimum satellite resource utilization, was established. Using the selection of imaging strips and their swing angles as two types of decision variables, the regional decomposition and satellite resource allocation were integrated into the planning model. Third, two efficient algorithms, Vatti and non-dominated sorting genetic algorithm (NSGA-II), were used for objective function calculation and model solving, respectively. Finally, the experiments used Hubei, Finland, and Congo as the target areas and GF1, GF6, ZY1-02C, and ZY3 as imaging satellites to verify the modeling method proposed in this paper. The experiments showed that the proposed multi-objective modeling method could complete the coverage of regional targets with fewer satellite resources and improve the satellite application efficiency significantly.
Bringing Earth Observation to Schools with Digital Integrated Learning Environments
Authors: Hodam Henryk , Rienow Andreas , Jürgens Carsten
Published: January 2020

The digital integrated learning environments (ILEs) for earth observation described in this article are bringing the complex topic of earth observation into classrooms. They are intended to give pupils with no prior experience in remote sensing the opportunity to solve tasks with earth observation data by using the same means that professionals have at hand. These learning environments integrate remote sensing tools and background knowledge in a comprehensive e-learning environment. They are tailored for use in schools, whereby the curriculum typically does not include earth observation, teachers are generally not familiar with its concepts, and the technical infrastructure is still not quite ready for digital teaching resources. To make the learning environments applicable, the special demands and obstacles presented by a school environment have to be considered. These obstacles are used to derive the requirements for the use of satellite data in school classes and create classroom resources in terms of technology, didactics, and e-learning. The concept itself was developed ten years ago, and since, then multiple applications have been created and used in classes. Data from an online questionnaire focuses on the specific qualities of the learning modules, enabling us to assess whether the concept works, and where there is need for improvement. The results show that the learning environments are being used, and that they continue to open the minds of pupils and teachers alike to a new perspective on the earth.
Mapping Landslides on EO Data: Performance of Deep Learning Models vs. Traditional Machine Learning Models
Authors: Prakash Nikhil , Manconi Andrea , Loew Simon
Published: January 2020

Mapping landslides using automated methods is a challenging task, which is still largely done using human efforts. Today, the availability of high-resolution EO data products is increasing exponentially, and one of the targets is to exploit this data source for the rapid generation of landslide inventory. Conventional methods like pixel-based and object-based machine learning strategies have been studied extensively in the last decade. In addition, recent advances in CNN (convolutional neural network), a type of deep-learning method, has been widely successful in extracting information from images and have outperformed other conventional learning methods. In the last few years, there have been only a few attempts to adapt CNN for landslide mapping. In this study, we introduce a modified U-Net model for semantic segmentation of landslides at a regional scale from EO data using ResNet34 blocks for feature extraction. We also compare this with conventional pixel-based and object-based methods. The experiment was done in Douglas County, a study area selected in the south of Portland in Oregon, USA, and landslide inventory extracted from SLIDO (Statewide Landslide Information Database of Oregon) was considered as the ground truth. Landslide mapping is an imbalanced learning problem with very limited availability of training data. Our network was trained on a combination of focal Tversky loss and cross-entropy loss functions using augmented image tiles sampled from a selected training area. The deep-learning method was observed to have a better performance than the conventional methods with an MCC (Matthews correlation coefficient) score of 0.495 and a POD (probability of detection) rate of 0.72 .
GOM20: A Stable Geodetic Reference Frame for Subsidence, Faulting, and Sea-Level Rise Studies along the Coast of the Gulf of Mexico
Authors: Wang Guoquan , Zhou Xin , Wang Kuan , Ke Xue , Zhang Yongwei , Zhao Ruibin , Bao Yan
Published: January 2020

We have established a stable regional geodetic reference frame using long-history (13.5 years on average) observations from 55 continuously operated Global Navigation Satellite System (GNSS) stations adjacent to the Gulf of Mexico (GOM). The regional reference frame, designated as GOM20, is aligned in origin and scale with the International GNSS Reference Frame 2014 (IGS14). The primary product from this study is the seven-parameters for transforming the Earth-Centered-Earth-Fixed (ECEF) Cartesian coordinates from IGS14 to GOM20. The frame stability of GOM20 is approximately 0.3 mm/year in the horizontal directions and 0.5 mm/year in the vertical direction. The regional reference frame can be confidently used for the time window from the 1990s to 2030 without causing positional errors larger than the accuracy of 24-h static GNSS measurements. Applications of GOM20 in delineating rapid urban subsidence, coastal subsidence and faulting, and sea-level rise are demonstrated in this article. According to this study, subsidence faster than 2 cm/year is ongoing in several major cities in central Mexico, with the most rapid subsidence reaching to 27 cm/year in Mexico City; a large portion of the Texas and Louisiana coasts are subsiding at 3 to 6.5 mm/year; the average sea-level-rise rate (with respect to GOM20) along the Gulf coast is 2.6 mm/year with a 95% confidence interval of ±1 mm/year during the past five decades. GOM20 provides a consistent platform to integrate ground deformational observations from different remote sensing techniques (e.g., GPS, InSAR, LiDAR, UAV-Photogrammetry) and ground surveys (e.g., tide gauge, leveling surveying) into a unified geodetic reference frame and enables multidisciplinary and cross-disciplinary research.
GNSS/INS-Assisted Structure from Motion Strategies for UAV-Based Imagery over Mechanized Agricultural Fields
Authors: Hasheminasab Seyyed Meghdad , Zhou Tian , Habib Ayman
Published: January 2020

Acquired imagery by unmanned aerial vehicles (UAVs) has been widely used for three-dimensional (3D) reconstruction/modeling in various digital agriculture applications, such as phenotyping, crop monitoring, and yield prediction. 3D reconstruction from well-textured UAV-based images has matured and the user community has access to several commercial and opensource tools that provide accurate products at a high level of automation. However, in some applications, such as digital agriculture, due to repetitive image patterns, these approaches are not always able to produce reliable/complete products. The main limitation of these techniques is their inability to establish a sufficient number of correctly matched features among overlapping images, causing incomplete and/or inaccurate 3D reconstruction. This paper provides two structure from motion (SfM) strategies, which use trajectory information provided by an onboard survey-grade global navigation satellite system/inertial navigation system (GNSS/INS) and system calibration parameters. The main difference between the proposed strategies is that the first one—denoted as partially GNSS/INS-assisted SfM—implements the four stages of an automated triangulation procedure, namely, imaging matching, relative orientation parameters (ROPs) estimation, exterior orientation parameters (EOPs) recovery, and bundle adjustment (BA). The second strategy— denoted as fully GNSS/INS-assisted SfM—removes the EOPs estimation step while introducing a random sample consensus (RANSAC)-based strategy for removing matching outliers before the BA stage. Both strategies modify the image matching by restricting the search space for conjugate points. They also implement a linear procedure for ROPs’ refinement. Finally, they use the GNSS/INS information in modified collinearity equations for a simpler BA procedure that could be used for refining system calibration parameters. Eight datasets over six agricultural fields are used to evaluate the performance of the developed strategies. In comparison with a traditional SfM framework and Pix4D Mapper Pro, the proposed strategies are able to generate denser and more accurate 3D point clouds as well as orthophotos without any gaps.
Global Mean Sea Surface Height Estimated from Spaceborne Cyclone-GNSS Reflectometry
Authors: Qiu Hui , Jin Shuanggen
Published: January 2020

Mean sea surface height (MSSH) is an important parameter, which plays an important role in the analysis of the geoid gap and the prediction of ocean dynamics. Traditional measurement methods, such as the buoy and ship survey, have a small cover area, sparse data, and high cost. Recently, the Global Navigation Satellite System-Reflectometry (GNSS-R) and the spaceborne Cyclone Global Navigation Satellite System (CYGNSS) mission, which were launched on 15 December 2016, have provided a new opportunity to estimate MSSH with all-weather, global coverage, high spatial-temporal resolution, rich signal sources, and strong concealability. In this paper, the global MSSH was estimated by using the relationship between the waveform characteristics of the delay waveform (DM) obtained by the delay Doppler map (DDM) of CYGNSS data, which was validated by satellite altimetry. Compared with the altimetry CNES_CLS2015 product provided by AVISO, the mean absolute error was 1.33 m, the root mean square error was 2.26 m, and the correlation coefficient was 0.97. Compared with the sea surface height model DTU10, the mean absolute error was 1.20 m, the root mean square error was 2.15 m, and the correlation coefficient was 0.97. Furthermore, the sea surface height obtained from CYGNSS was consistent with Jason-2′s results by the average absolute error of 2.63 m, a root mean square error ( RMSE ) of 3.56 m and, a correlation coefficient ( R ) of 0.95.
Establishment of a New Quantitative Evaluation Model of the Targets’ Geometry Distribution for Terrestrial Laser Scanning
Authors: Yang Ronghua , Meng Xiaolin , Xiang Zejun , Li Yingmin , You Yangsheng , Zeng Huaien
Published: January 2020

The precision of target-based registration is related to the geometry distribution of targets, while the current method of setting the targets mainly depends on experience, and the impact is only evaluated qualitatively by the findings from empirical experiments and through simulations. In this paper, we propose a new quantitative evaluation model, which is comprised of the rotation dilution of precision ( r D O P , assessing the impact of targets’ geometry distribution on the rotation parameters) and the translation dilution of precision ( t D O P , assessing the impact of targets’ geometry distribution on the translation parameters). Here, the definitions and derivation of relevant formulas of the r D O P and t D O P are given, the experience conclusions are theoretically proven by the model of r D O P and t D O P , and an accurate method for determining the optimal placement location of targets and the scanner is proposed by calculating the minimum value of r D O P and t D O P . Furthermore, we can refer to the model ( r D O P and t D O P ) as a unified model of the geometric distribution evaluation model, which includes the D O P model in GPS.
Mapping Current Fields in a Bay Using a Coast-Fitting Tomographic Inversion
Authors: Chen Minmo , Zhu Ze-Nan , Zhang Chuanzheng , Zhu Xiao-Hua , Wang Min , Fan Xiaopeng , Zhao Ruixiang , Lin Ju , Kaneko Arata
Published: January 2020

Coast-fitting tomographic inversion that is based on function expansion using three types of normal modes (the Dirichlet, Neumann, and open boundary modes) is proposed to reconstruct current fields from the coastal acoustic tomography (CAT) data. The superiority of the method was validated while using CAT data that were obtained in 2015 in the Dalian Bay. The semidiurnal tidal and residual current fields were accurately reconstructed over the entire model domain surrounded by coasts and open boundaries. The proposed method was effective, particularly around the peripheral regions of the tomography domain and the near-coast regions outside the domain, where accurate results are not expected from the conventional inverse method based on function expansion by Fourier function series with no coast fittings. The error velocity for the semidiurnal tidal currents was 2.2 cm s−1, which was calculated from the root-mean-square-difference between the CAT-observed and inverted range-averaged currents that were obtained along the nine peripheral transmission paths. The error velocity for the residual currents estimated from the 12-h mean net residual transport at the bay mouth was 0.9 cm s−1. The errors were significantly smaller than the amplitude of the tidal and residual currents.
Performance Analysis of Distributed Estimation for Data Fusion Using a Statistical Approach in Smart Grid Noisy Wireless Sensor Networks
Authors: Seneviratne Chatura , Wijesekara Patikiri Arachchige Don Shehan Nilmantha , Leung Henry
Published: January 2020

Internet of Things (IoT) can significantly enhance various aspects of today’s electric power grid infrastructures for making reliable, efficient, and safe next-generation Smart Grids (SGs). However, harsh and complex power grid infrastructures and environments reduce the accuracy of the information propagating through IoT platforms. In particularly, information is corrupted due to the measurement errors, quantization errors, and transmission errors. This leads to major system failures and instabilities in power grids. Redundant information measurements and retransmissions are traditionally used to eliminate the errors in noisy communication networks. However, these techniques consume excessive resources such as energy and channel capacity and increase network latency. Therefore, we propose a novel statistical information fusion method not only for structural chain and tree-based sensor networks, but also for unstructured bidirectional graph noisy wireless sensor networks in SG environments. We evaluate the accuracy, energy savings, fusion complexity, and latency of the proposed method by comparing the said parameters with several distributed estimation algorithms using extensive simulations proposing it for several SG applications. Results prove that the overall performance of the proposed method outperforms other fusion techniques for all considered networks. Under Smart Grid communication environments, the proposed method guarantees for best performance in all fusion accuracy, complexity and energy consumption. Analytical upper bounds for the variance of the final aggregated value at the sink node for structured networks are also derived by considering all major errors.
Recognition of Negative Emotion Using Long Short-Term Memory with Bio-Signal Feature Compression
Authors: Lee JeeEun , Yoo Sun K.
Published: January 2020

Negative emotion is one reason why stress causes negative feedback. Therefore, many studies are being done to recognize negative emotions. However, emotion is difficult to classify because it is subjective and difficult to quantify. Moreover, emotion changes over time and is affected by mood. Therefore, we measured electrocardiogram (ECG), skin temperature (ST), and galvanic skin response (GSR) to detect objective indicators. We also compressed the features associated with emotion using a stacked auto-encoder (SAE). Finally, the compressed features and time information were used in training through long short-term memory (LSTM). As a result, the proposed LSTM used with the feature compression model showed the highest accuracy (99.4%) for recognizing negative emotions. The results of the suggested model were 11.3% higher than with a neural network (NN) and 5.6% higher than with SAE.
Photo Composition with Real-Time Rating
Authors: Li Yi-Feng , Yang Chuan-Kai , Chang Yi-Zhen
Published: January 2020

Taking a photo has become a part of our daily life. With the powerfulness and convenience of a smartphone, capturing what we see may have never been easier. However, taking good photos may not be always easy or intuitive for everyone. As numerous studies have shown that photo composition plays a very important role in making a good photo, in this study, we, therefore, propose to develop a photo-taking app to give a real-time suggestion through a scoring mechanism to guide a user to take a good photo. Due to the concern of real-time performance, only eight commonly used composition rules are adopted in our system, and several detailed evaluations have been conducted to prove the effectiveness of our system.
The Calibration of Displacement Sensors
Authors: Haitjema Han
Published: January 2020

Displacement measuring sensors play an essential role in all aspects of dimensional metrology. They can be used for direct displacement measurements but more often they are part of a measurement system, such as an atomic force microscope, roughness tester or a coordinate measuring machine (CMM). In order to achieve traceable measurements that can be related to the meter, these sensors must be calibrated against a reference standard that is more noise- and error-free than the sensor under test. A description of the various methods to achieve the ultimate traceability, repeatability and accuracy of such a calibration system is the main part of this paper. Various interferometric methods will be reviewed including several methods that use directly a primary standard as a reference: either an iodine-stabilized laser or a frequency comb. It is shown that various methods exist to quantify or mitigate the periodic errors that are inherent to interferometric methods. Also it is shown that knowledge of this periodicity may lead to a separation of periodic and non-periodic non-linearity errors of both the calibration instrument as the sensor under test. This review is limited to small-range sensors, typically with a range <100 μm. It is concluded that today’s technology enables sound and traceable sensor calibration up to the sub-nano and even picometer level of uncertainties
Fault Detection and Exclusion for Tightly Coupled GNSS/INS System Considering Fault in State Prediction
Authors: Wang Shizhuang , Zhan Xingqun , Zhai Yawei , Liu Baoyu
Published: January 2020

To ensure navigation integrity for safety-critical applications, this paper proposes an efficient Fault Detection and Exclusion (FDE) scheme for tightly coupled navigation system of Global Navigation Satellite Systems (GNSS) and Inertial Navigation System (INS). Special emphasis is placed on the potential faults in the Kalman Filter state prediction step (defined as “filter fault”), which could be caused by the undetected faults occurring previously or the Inertial Measurement Unit (IMU) failures. The integration model is derived first to capture the features and impacts of GNSS faults and filter fault. To accommodate various fault conditions, two independent detectors, which are respectively designated for GNSS fault and filter fault, are rigorously established based on hypothesis-test methods. Following a detection event, the newly-designed exclusion function enables (a) identifying and removing the faulty measurements and (b) eliminating the effect of filter fault through filter recovery. Moreover, we also attempt to avoid wrong exclusion events by analyzing the underlying causes and optimizing the decision strategy for GNSS fault exclusion accordingly. The FDE scheme is validated through multiple simulations, where high efficiency and effectiveness have been achieved in various fault scenarios.
3D Hermite Transform Optical Flow Estimation in Left Ventricle CT Sequences
Authors: Mira Carlos , Moya-Albor Ernesto , Escalante-Ramírez Boris , Olveres Jimena , Brieva Jorge , Vallejo Enrique
Published: January 2020

Heart diseases are the most important causes of death in the world and over the years, the study of cardiac movement has been carried out mainly in two dimensions, however, it is important to consider that the deformations due to the movement of the heart occur in a three-dimensional space. The 3 D + t analysis allows to describe most of the motions of the heart, for example, the twisting motion that takes place on every beat cycle that allows us identifying abnormalities of the heart walls. Therefore, it is necessary to develop algorithms that help specialists understand the cardiac movement. In this work, we developed a new approach to determine the cardiac movement in three dimensions using a differential optical flow approach in which we use the steered Hermite transform (SHT) which allows us to decompose cardiac volumes taking advantage of it as a model of the human vision system (HVS). Our proposal was tested in complete cardiac computed tomography (CT) volumes ( 3 D + t ), as well as its respective left ventricular segmentation. The robustness to noise was tested with good results. The evaluation of the results was carried out through errors in forwarding reconstruction, from the volume at time t to time t + 1 using the optical flow obtained (interpolation errors). The parameters were tuned extensively. In the case of the 2D algorithm, the interpolation errors and normalized interpolation errors are very close and below the values reported in ground truth flows. In the case of the 3D algorithm, the results were compared with another similar method in 3D and the interpolation errors remained below 0.1. These results of interpolation errors for complete cardiac volumes and the left ventricle are shown graphically for clarity. Finally, a series of graphs are observed where the characteristic of contraction and dilation of the left ventricle is evident through the representation of the 3D optical flow.
ATEX-HOF Methodology: Innovation Driven by Human and Organizational Factors (HOF) in Explosive Atmosphere Risk Assessment
Authors: Geng Jie , Murè Salvina , Demichela Micaela , Baldissone Gabriele
Published: January 2020

ATEX (explosive atmosphere) risk assessment is required when any equipment or system could generate a potentially explosive atmosphere. Despite the fact that many operations on plants and equipment containing dangerous substances are performed by operators, influences of human and organizational factors (HOF) are mostly neglected in the ATEX risk assessment. The integrated methodology described here is proposed to address two challenges: (1) identification of the HOF influence on the ATEX risk assessment, and (2) quantification of the HOF influence. The proposed methodology enriches the traditional ATEX risk assessment procedure, which consists of four steps: (1) area classification, (2) ignition source identification, (3) damage analysis, and (4) ATEX risk evaluation. The advantages of the ATEX-HOF methodology are demonstrated through the application to a paint mixing station in an automotive manufacturing plant. The ATEX risk assessment methodologies are mainly semi-quantitative. The ATEX-HOF methodology provides a quantitative analysis for the area classification and ignition source identification, and a semi-quantitative approach for the damage analysis. As a result, the ATEX-HOF risk evaluation becomes more accurate. An event tree-based probabilistic assessment has been introduced, considering both the technical barrier failure (Prtbf) and the human intervention in terms of human error probability (HEP). The case study allowed for demonstrating how taking HOFs into account is particularly important in companies where the safety culture is lower and consequently, the usual hypothesis of the correctness of operator intervention (in maintenance, normal operations, and emergency) could bring to non-conservative results.
Forecasting Clean Energy Consumption in China by 2025: Using Improved Grey Model GM (1, N)
Authors: Cheng Maolin , Li Jiano , Liu Yun , Liu Bin
Published: January 2020

Forecasting China’s clean energy consumption has great significance for China in making sustainably economic development strategies. Because the main factors affecting China’s clean energy consumption are economic scale and population size, and there are three variables in total, this paper tries to simulate and forecast China’s clean energy consumption using the grey model GM (1, 3). However, the conventional grey GM (1, N) model has great simulation and forecasting errors, the main reason for which is the structural inconsistency between the grey differential equation for parameter estimation and the whitening equation for forecasting. In this case, this paper improves the conventional model and provides an improved model GM (1, N). The modeling results show that the improved grey model GM (1, N) built with the method proposed improves simulation and forecasting precision greatly compared with conventional models. To compare the model with other forecasting models, this paper builds a grey GM (1, 1) model, a regression model and a difference equation model. The comparison results show that the improved grey model GM (1, N) built with the method proposed shows simulation and forecasting precision superior to that of other models as a whole. In the final section, the paper forecasts China’s clean energy consumption from 2019 to 2025 using the improved grey model GM (1, N). The forecasting results show that, by 2025, China’s clean energy consumption shall reach the equivalent of 1.504976082 billion tons of standard coal. From 2019 to 2025, clean energy consumption shall increase by 11.32% annually on average, far above the economic growth rate, indicating China’s economic growth shall have a great demand for clean energy in the future. Studies have shown that China’s clean energy consumption shall increase rapidly with economic growth and population increase in the next few years.
A Place-Based Approach to Agricultural Nonmaterial Intangible Cultural Ecosystem Service Values
Authors: Petway Joy R. , Lin Yu-Pin , Wunderlich Rainer F.
Published: January 2020

Though agricultural landscape biodiversity and ecosystem service (ES) conservation is crucial to sustainability, agricultural land is often underrepresented in ES studies, while cultural ES associated with agricultural land is often limited to aesthetic and tourism recreation value only. This study mapped 7 nonmaterial-intangible cultural ES (NICE) valuations of 34 rural farmers in western Taiwan using the Social Values for Ecosystem Services (SolVES) methodology, to show the effect of farming practices on NICE valuations. However, rather than a direct causal relationship between the environmental characteristics that underpin ES, and respondents’ ES valuations, we found that environmental data is not explanatory enough for causality within a socio-ecological production landscape where one type of land cover type (a micro mosaic of agricultural land cover) predominates. To compensate, we used a place-based approach with Google Maps data to create context-specific data to inform our assessment of NICE valuations. Based on 338 mapped points of 7 NICE valuations distributed among 6 areas within the landscape, we compared 2 groups of farmers and found that farmers’ valuations about their landscape were better understood when accounting for both the landscape’s cultural places and environmental characteristics, rather than environmental characteristics alone. Further, farmers’ experience and knowledge influenced their NICE valuations such that farm areas were found to be sources of multiple NICE benefits demonstrating that farming practices may influence ES valuation in general.
Financial Literacy: The Case of Poland
Authors: Swiecka Beata , Yeşildağ Eser , Özen Ercan , Grima Simon
Published: January 2020

Financial literacy is a path to sustainability and has an important role in ensuring the financial sustainability of individuals, families, enterprises and national economies. The level of these economic indicators such as debt, payment discipline, savings and financial management all translate into prosperity or insolvency and bankruptcy and result partially from financial literacy. The higher the level of financial literacy, especially of young people, the more favourable the level of economic indicators, which translates into the economy and sustainable development. With this study we aim to determine the level of financial literacy of high school students in Poland and to determine whether financial literacy changes according to gender. The most important element that distinguishes our study from the others is that or study was carried out with a large sample of high school students with an average age of 15–16 years. In addition, the effect of gender on financial literacy at an early age was investigated, also comparing the wider themes to the so-called narrow themes. The results of the research demonstrated a good and partially very good, level of financial knowledge of the young people in Poland. 45.3% obtained an average level score and 43.8% achieved a high-level score in financial knowledge. This result shows that they can be rational in their financial decision making. However although, it is understood that gender makes a difference on financial behaviour and use of financial instruments, gender does not make any difference on the level of financial knowledge. Moreover, the financial literacy level of males is found to be higher than females.
A Predictive System Informed by Students’ Similar Behaviour
Authors: Burgos Daniel
Published: January 2020

It is quite complex to adapt instruction to student needs in view of online education owing to the ensuing communication disconnection in such learning environments. Decision support schemes offer assistance by automatically gathering students’ data and forwarding them to the tutor in the appropriate perspective, in order to predict their behaviour and implement some action beforehand to avert or promote the final upshot. This study shows of a decision support scheme known as u-Tutor that is centred on the similarity computation between learners in the past, and how it was used in a real-case scenario. For this case study, this tool has been utilized by two real courses comprising of 392 learners alongside academic faculty, as of 2015 to 2019. The analysis offered focuses on 3 research areas: (1) perceived usefulness, (2) usability of the tool and (3) success rate of classification. From the acquired data, it can be seen that the teaching group managed to offer excellent approximations for those learners who eventually managed to pass the course, whereas u-Tutor seemed to be an early warning for learners at risk, indicating its capacity as a tutors’ supportive tool.
Environmental and Management Considerations for Adopting the Halophyte Salicornia bigelovii Torr. as a Sustainable Seawater-Irrigated Crop.
Authors: Garza-Torres Rodolfo , Troyo-Diéguez Enrique , Nieto-Garibay Alejandra , Lucero-Vega Gregorio , Magallón-Barajas Francisco Javier , García-Galindo Emilio , Fimbres-Acedo Yenitze , Murillo-Amador Bernardo
Published: January 2020

Salicornia bigelovii Torr. is a potential new crop for coastal and saline lands, because of the oil content of its seeds, its properties as fresh vegetable, forage, and other uses. As a true halophyte, it can grow with seawater irrigation. The aim of this study was to determine the phenology and water requirements of Salicornia as a new plant resource in growing areas for salt-tolerant crops in coastal and saline lands, and elucidate scenarios of sustainability about these issues. Water requirements were estimated in experimental plots on the coastal line and fulfilled with drip irrigation connected to seawater aquaculture discharge ponds, 30 m from the sea. The recorded phenological events were germination, flowering, fructification, maturation, and physiological death. Results reflect the difficulty to adopt it as a new crop because of its long-life cycle, around nine months, contrasting with the life cycle of common crops, from three to four months. Irrigation needs reached a depth of 240 cm, significantly exceeding those of conventional crops. Such limitations are highlighted, but also its potential use as a biofilter of coastal aquaculture effluents, being a productive target-biomass, feasible to be used as a dual-purpose use of water and energy required in aquaculture farms.
Endogenous Political, Institutional, Cultural, and Geographic Determinants of Intermunicipal Cooperation—Evidence from Slovakia
Authors: Melichová Katarína , Varecha Lukáš
Published: January 2020

All over Europe, but especially in post-communist countries, the institutional environment has been undergoing major changes. In Slovakia, regaining their autonomy has led local governments on the path of fragmentation, unsustainably high expenditures for the provision of public services, and an increase in transaction costs. Current policies targeting these issues are heavily focused on intermunicipal cooperation (IMC). Based on four case studies of different institutional arrangements, this paper aims to investigate which endogenous political, institutional, cultural, and geographic factors influence cooperation among Slovak municipalities. Through the application of social network analysis and regression analysis, we reached several relevant conclusions. A number of common assumptions were confirmed, namely that population size and heterogeneity play a major role, but also that the impact of political affiliation as a deciding factor of IMC is not as straightforward as previous evidence suggested. Results also underline the importance of cross-sectoral partnerships such as the EU’s LEADER initiative as a viable alternative to more traditional forms of IMC (but with some limitations).
South American Expert Roundtable: Increasing Adaptive Governance Capacity for Coping with Unintended Side Effects of Digital Transformation
Authors: Viale Pereira Gabriela , Estevez Elsa , Cardona Diego , Chesñevar Carlos , Collazzo-Yelpo Pablo , Cunha Maria Alexandra , Diniz Eduardo Henrique , Ferraresi Alex Antonio , Fischer Frida Marina , Cardinelle Oliveira Garcia Flúvio , Joia Luiz Antonio , Luciano Edimara M. , de Albuquerque João Porto , Quandt Carlos O. , Sánchez Rios Rodrigo , Sánchez Aurora , Damião da Silva Eduardo , Silva-Junior João Silvestre , Scholz Roland W.
Published: January 2020

This paper presents the main messages of a South American expert roundtable (ERT) on the unintended side effects (unseens) of digital transformation. The input of the ERT comprised 39 propositions from 20 experts representing 11 different perspectives. The two-day ERT discussed the main drivers and challenges as well as vulnerabilities or unseens and provided suggestions for: (i) the mechanisms underlying major unseens; (ii) understanding possible ways in which rebound effects of digital transformation may become the subject of overarching research in three main categories of impact: development factors, society, and individuals; and (iii) a set of potential action domains for transdisciplinary follow-up processes, including a case study in Brazil. A content analysis of the propositions and related mechanisms provided insights in the genesis of unseens by identifying 15 interrelated causal mechanisms related to critical issues/concerns. Additionally, a cluster analysis (CLA) was applied to structure the challenges and critical developments in South America. The discussion elaborated the genesis, dynamics, and impacts of (groups of) unseens such as the digital divide (that affects most countries that are not included in the development of digital business, management, production, etc. tools) or the challenge of restructuring small- and medium-sized enterprises (whose service is digitally substituted by digital devices). We identify specific issues and effects (for most South American countries) such as lack of governmental structure, challenging geographical structures (e.g., inclusion in high-performance transmission power), or the digital readiness of (wide parts) of society. One scientific contribution of the paper is related to the presented methodology that provides insights into the phenomena, the causal chains underlying “wanted/positive” and “unwanted/negative” effects, and the processes and mechanisms of societal changes caused by digitalization.
The Framework of the Principles of Sustainable Urban Ecosystems Development and Functioning
Authors: Cepeliauskaite Gabriele , Stasiskiene Zaneta
Published: January 2020

Nowadays, the increasing rate of human population in urban areas and the importance of urban functions pose a number of ecological cross-boundary challenges. The scientific research of the twentieth and twenty-first centuries revealed dramatic consequences of human intervention into natural ecosystems. The outcome of inadequate consumerism, which promotes industrial growth, and the pursuit of a better life is not only damaging to the natural ecosystems, but will eventually lead the World to dramatic climate change outcomes. Therefore, scientists are looking for ways for sustainable urban transformation without endangering the human population and the environment. According to the identified assumptions, the article investigates the principles of sustainable urban ecosystems. The authors consistently seek to explain the concept of sustainable urban ecosystems in both a broad and narrow sense, focusing on the individual constructs of this term—sustainability, city and ecosystems. The theories and approaches of urbanization and ecosystems are also analyzed in the article in order to highlight the opportunities and specific features of the application. The principles of sustainable urban ecosystems are provided in the modeled system which exposes the interfaces of identified principles and can be used for future studies.
Cultural and Natural Resources in Tourism Island: Bibliometric Mapping
Authors: del Río-Rama María , Maldonado-Erazo Claudia , Álvarez-García José , Durán-Sánchez Amador
Published: January 2020

Island ecosystems have very specific physical, economic and socio-cultural characteristics, which are shared by most of these ecosystems regardless of their geographical area. These characteristics include well defined geographical boundaries that lead to a greater degree of isolation, lack of economically exploitable resources, great external dependence for consumption, cultural and natural heritage with a high uniqueness level and a high concentration of endemic plant and animal species. All of them are responsible for the high development dependence level linked to the tourism activity of these ecosystems. Thus, island ecosystems are currently an important international tourism destination, where a great diversity of very attractive natural and cultural resources and of great heritage value are concentrated. This fact allows for the development of tourism activities of great heterogeneity among countries or regions of the world that host these island ecosystems. The aim of this research was to identify and analyze, through a bibliometric and bibliographic analysis, the scientific production indexed in the international Scopus database, which addresses the subject of use of cultural and natural resources by tourism in island ecosystems. This scientific mapping allows us to observe the evolution of scientific production in this field of study. The results show that this is a new subject (a large number of transient authors), predominantly affiliated with the United States and Spain. The most followed research lines to date are destination management followed very far by responsible environmental behavior and the impacts of sustainable development. However, the keywords with the highest co-occurrence show that the hot topics are tourism exploitation in the ecotourism field and sustainable tourism development. This research is considered the first bibliometric study carried out which is related to this thematic approach, providing a clear in-depth analysis for researchers and thereby facilitating the approach of future research work.
Sustainable Traffic Management in an Urban Area: An Integrated Framework for Real-Time Traffic Control and Route Guidance Design
Authors: de Luca Stefano , Di Pace Roberta , Memoli Silvio , Pariota Luigi
Published: January 2020

This paper focuses on the presentation of an integrated framework based on two advanced strategies, aimed at mitigating the effect of traffic congestion in terms of performance and environmental impact. In particular, the paper investigates the “operational benefits” that can be derived from the combination of traffic control (TC) and route guidance (RG) strategies. The framework is based on two modules and integrates a within-day traffic control method and a day-to-day behavioral route choice model. The former module consists of an enhanced traffic control model that can be applied to design traffic signal decision variables, suitable for real-time optimization. The latter designs the information consistently with predictive user reactions to the information itself. The proposed framework is implemented to a highly congested sub-network in the city center of Naples (Italy) and different scenarios are tested and compared. The “do nothing” scenario (current; DN) and the “modeled compliance” (MC) scenario, in which travelers’ reaction to the information (i.e., compliance) is explicitly represented. In order to evaluate the effectiveness of the proposed strategy and the modeling framework, the following analyses are carried out: (i) Network performance analysis; (ii) system convergence and stability analysis, as well as the compliance evolution over time; (iii) and emissions and fuel consumption impact analysis.
Application of Water Poverty Index (WPI) in Spatial Analysis of Water Stress in Koshi River Basin, Nepal
Authors: Koirala Saroj , Fang Yiping , Dahal Nirmal Mani , Zhang Chenjia , Pandey Bikram , Shrestha Sabita
Published: January 2020

Water and poverty interface is strongly interconnected and a robust assessment of water stress is crucial to identify needy areas and develop appropriate intervention for poverty reduction. Water Poverty Index (WPI) provides an interdisciplinary tool to assess water stress by linking physical estimates of water availability with socio-economic drivers of poverty. This study presents an application of Water Poverty Index (WPI) to estimate and compare the level of water stress in 27 districts of Koshi River Basin in Nepal. Based on data availability, relevance to the study area and review of literatures, 12 indicators were selected under five key components outlined by WPI. The study result shows medium-low degree (WPI = 54.4) of water poverty in the Koshi River Basin in Nepal. The WPI score varies widely (from 49.75 to 69.29) along the districts and it was found that districts in Tarai regions and urban areas were more water stressed compared to the districts in mid-hill and high-hill regions. Priorities for intervention must be given to the districts in Tarai regions and urban areas with a low WPI score, explicitly on the sector regarding access to water and sanitation to address water poverty in the basin.
The Importance Given to Food Naturalness Attributes by Millennial University Students
Authors: Jorge Eloi , Lopez-Valeiras Ernesto , Gonzalez-Sanchez Maria Beatriz
Published: January 2020

So-called natural food is one of the most significant current trends in the food business. Despite this trend, previous research on the measurement of naturalness has made no distinction between different groups of consumers. Therefore, the objective of this study is to explore the attributes important to millennial university students when evaluating food naturalness. The study is based on a questionnaire administered to a sample of 372 respondents. Using a partial least square (PLS) methodology, it performs a standard confirmatory factor analysis for measurement and validations. As a result, it identifies one attribute linked to how the food is grown and eight attributes associated to how it is produced and processed. These findings have several implications. Apart from testing previous scales in a millennial context, they confirm that market strategies must take different understandings of naturalness into account contingent upon the consumer group.
Examining the Impacts of Mega-Events on Urban Development Using Coupling Analysis: A Case Study of the Boao Forum for Asia
Authors: He Biao , Zhu Lianxin , Cai Xiaomei , Li Jun (Justin) , Zhu Hong
Published: January 2020

Mega-events, as a strategic approach taken by entrepreneurial governments, have the ability to transform the image of a city. This study explores mega-events and their relationship to urban development by developing a coupling coordination degree model and using the official statistics of Qionghai, China, from 2010 to 2015. The results of this study show that the dynamic of coordination between mega-events and urban development is a classical S-shaped growth curve. In the coupling system, this study also reveals that relevance index and the economy, as sub-indicators, make significant contributions to mega-events and urban development, respectively. Finally, the researchers concluded that the international large-scale events can significantly promote the urban development in host cities. This study reveals theoretical issues and practical implications for policy makers and event managers to achieve integrated and coordinated development between mega-events and urban development.
Tourist Flow Management: Social Impact Evaluation through Social Network Analysis
Authors: Acampa Giovanna , Grasso Mariolina , Marino Giorgia , Parisi Claudia Mariaserena
Published: January 2020

The present paper was prompted by the activity carried out within the scope of an EU-funded project (WARMEST). It calls to analyse the reasons for the degradation of the Patio de Los Leones, which attracts over 2 million tourists per year to Granada in Spain. We review here the most advanced studies and regulations on the assessment of the social impact of mass tourism and present a novel methodology to analyse its effects. We dug into the material available on social networks—especially feedback to posts published on major relevant sites—and got a comprehensive picture of the thoughts that were expressed there and a comprehensive assessment of the citizens’ opinion on the social impact of tourism in Granada. Thus, we obtained a new indicator called “C.1.2 index modified”, which measures the level of dissatisfaction of citizens with the tourists’ pressure; we propose to replace the existing ETIS index with C.1.2, which is mainly based on direct surveys that are often carried out with very limited resources. At the end of the research, we could point out topics that are especially important to the citizens, thus allowing us to define a strategic action plan with a bottom-up approach.