bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient Location and Extraction of the Iceberg Calved Areas of the Antarctic Ice Shelves
Authors: Qi Mengzhen , Liu Yan , Lin Yijing , Hui Fengming , Li Teng , Cheng Xiao
Published: August 2020

Continuous, rapid, and precise monitoring of calving events contributes to an in-depth understanding of calving mechanisms, which have the potential to cause significant mass loss from the Antarctic ice sheet. The difficulties in the precise monitoring of iceberg calving lie with the coexistence of ice shelf advances and calving. The manual location of iceberg calving is time-consuming and painstaking, while achieving precise extraction has mostly relied on the surface textural characteristics of the ice shelves and the quality of the images. Here, we propose a new and efficient method of separating the expansion and calving processes of ice shelves. We visualized the extension process by simulating a new coastline, based on the ice velocity, and detected the calved area using the simulated coastline and single-temporal post-calving images. We extensively tested the validity of this method by extracting four annual calving datasets (from August 2015 to August 2019) from the Sentinel-1 synthetic aperture radar mosaic of the Antarctic coastline. A total of 2032 annual Antarctic calving events were detected, with areas ranging from 0.05 km2 to 6141.0 km2, occurring on almost every Antarctic ice shelf. The extraction accuracy of the calved area depends on the positioning accuracy of the simulated coastline and the spatial resolution of the images. The positioning error of the simulated coastline is less than one pixel, and the determined minimum valid extraction area is 0.05 km2, when based on 75 m resolution images. Our method effectively avoids repetition and omission errors during the calved area extraction process. Furthermore, its efficiency is not affected by the surface textural characteristics of the calving fronts and the various changes in the frontal edge velocity, which makes it fully applicable to the rapid and accurate extraction of different calving types.
Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture
Authors: Lu Bing , Dao Phuong , Liu Jiangui , He Yuhong , Shang Jiali
Published: August 2020

Remote sensing is a useful tool for monitoring spatio-temporal variations of crop morphological and physiological status and supporting practices in precision farming. In comparison with multispectral imaging, hyperspectral imaging is a more advanced technique that is capable of acquiring a detailed spectral response of target features. Due to limited accessibility outside of the scientific community, hyperspectral images have not been widely used in precision agriculture. In recent years, different mini-sized and low-cost airborne hyperspectral sensors (e.g., Headwall Micro-Hyperspec, Cubert UHD 185-Firefly) have been developed, and advanced spaceborne hyperspectral sensors have also been or will be launched (e.g., PRISMA, DESIS, EnMAP, HyspIRI). Hyperspectral imaging is becoming more widely available to agricultural applications. Meanwhile, the acquisition, processing, and analysis of hyperspectral imagery still remain a challenging research topic (e.g., large data volume, high data dimensionality, and complex information analysis). It is hence beneficial to conduct a thorough and in-depth review of the hyperspectral imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing hyperspectral information, and recent advances of hyperspectral imaging in agricultural applications. Publications over the past 30 years in hyperspectral imaging technology and applications in agriculture were thus reviewed. The imaging platforms and sensors, together with analytic methods used in the literature, were discussed. Performances of hyperspectral imaging for different applications (e.g., crop biophysical and biochemical properties’ mapping, soil characteristics, and crop classification) were also evaluated. This review is intended to assist agricultural researchers and practitioners to better understand the strengths and limitations of hyperspectral imaging to agricultural applications and promote the adoption of this valuable technology. Recommendations for future hyperspectral imaging research for precision agriculture are also presented.
Utilization of Multi-Temporal Microwave Remote Sensing Data within a Geostatistical Regionalization Approach for the Derivation of Soil Texture
Authors: Marzahn Philip , Meyer Swen
Published: August 2020

Land Surface Models (LSM) have become indispensable tools to quantify water and nutrient fluxes in support of land management strategies or the prediction of climate change impacts. However, the utilization of LSM requires soil and vegetation parameters, which are seldom available in high spatial distribution or in an appropriate temporal frequency. As shown in recent studies, the quality of these model input parameters, especially the spatial heterogeneity and temporal variability of soil parameters, has a strong effect on LSM simulations. This paper assesses the potential of microwave remote sensing data for retrieving soil physical properties such as soil texture. Microwave remote sensing is able to penetrate in an imaged media (soil, vegetation), thus being capable of retrieving information beneath such a surface. In this study, airborne remote sensing data acquired at 1.3 GHz and in different polarization is utilized in conjunction with geostatistics to retrieve information about soil texture. The developed approach is validated with in-situ data from different field campaigns carried out over the TERENO test-site “North-Eastern German Lowland Observatorium”. With the proposed approach a high accuracy of the retrieved soil texture with a mean RMSE of 2.42 (Mass-%) could be achieved outperforming classical deterministic and geostatistical approaches.
Primary Evaluation of the GCOM-C Aerosol Products at 380 nm Using Ground-Based Sky Radiometer Observations
Authors: Hoque Hossain Mohammed Syedul , Irie Hitoshi , Damiani Alessandro , Momoi Masahiro
Published: August 2020

The Global Change Observation Mission-Climate (GCOM-C) is currently the only satellite sensor providing aerosol optical thickness (AOT) in the ultraviolet (UV) region during the morning overpass time. The observations in the UV region are important to detect the presence of absorbing aerosols in the atmosphere. The recently available GCOM-C dataset of AOT at 380 nm for January to September 2019 were evaluated using ground-based SKYNET sky radiometer measurements at Chiba, Japan (35.62° N, 140.10° E) and Phimai, central Thailand (15.18° N, 102.56° E), representing urban and rural sites, respectively. AOT retrieved from sky radiometer observations in Chiba and Phimai was compared with coincident AERONET and multi-axis differential optical absorption spectroscopy (MAX-DOAS) AOT values, respectively. Under clear sky conditions, the datasets showed good agreement. The sky radiometer and GCOM-C AOT values showed a positive correlation (R) of ~0.73 for both sites, and agreement between the datasets was mostly within ±0.2 (the number of coincident points at both sites was less than 50 for the coincidence criterion of ≤30 km). At Chiba, greater differences in the AOT values were primarily related to cloud screening in the datasets. The mean bias error (MBE) (GCOM-C – sky radiometer) for the Chiba site was −0.02 for a coincidence criterion of ≤10 km. For a similar coincidence criterion, the MBE values were higher for observations at the Phimai site. This difference was potentially related to the strong influence of biomass burning during the dry season (Jan–Apr). The diurnal variations in AOT, inferred from the combination of GCOM-C and ozone monitoring instrument (OMI) observations, showed good agreement with the sky radiometer data, despite the differences in the absolute AOT values. Over Phimai, the AOT diurnal variations from the satellite and sky radiometer observations were different, likely due to the large differences in the AOT values during the dry season.
Susceptibility Analysis of the Mt. Umyeon Landslide Area Using a Physical Slope Model and Probabilistic Method
Authors: Lee Sunmin , Jang Jungyoon , Kim Yunjee , Cho Namwook , Lee Moung-Jin
Published: August 2020

Every year, many countries carry out landslide susceptibility analyses to establish and manage countermeasures and reduce the damage caused by landslides. Because increases in the areas of landslides lead to new landslides, there is a growing need for landslide prediction to reduce such damage. Among the various methods for landslide susceptibility analysis, statistical methods require information about the landslide occurrence point. Meanwhile, analysis based on physical slope models can estimate stability by considering the slope characteristics, which can be applied based on information about the locations of landslides. Therefore, in this study, a probabilistic method based on a physical slope model was developed to analyze landslide susceptibility. To this end, an infinite slope model was used as the physical slope model, and Monte Carlo simulation was applied based on landslide inventory including landslide locations, elevation, slope gradient, specific catchment area (SCA), soil thickness, unit weight, cohesion, friction angle, hydraulic conductivity, and rainfall intensity; deterministic analysis was also performed for the comparison. The Mt. Umyeon area, a representative case for urban landslides in South Korea where large scale human damage occurred in 2011, was selected for a case study. The landslide prediction rate and receiver operating characteristic (ROC) curve were used to estimate the prediction accuracy so that we could compare our approach to the deterministic analysis. The landslide prediction rate of the deterministic analysis was 81.55%; in the case of the Monte Carlo simulation, when the failure probabilities were set to 1%, 5%, and 10%, the landslide prediction rates were 95.15%, 91.26%, and 90.29%, respectively, which were higher than the rate of the deterministic analysis. Finally, according to the area under the curve of the ROC curve, the prediction accuracy of the probabilistic model was 73.32%, likely due to the variability and uncertainty in the input variables.
Estimates of Conservation Tillage Practices Using Landsat Archive
Authors: Beeson Peter C. , Daughtry Craig S.T. , Wallander Steven A.
Published: August 2020

The USDA Environmental Quality Incentives Program (EQIP) provides financial assistance to encourage producers to adopt conservation practices. Historically, one of the most common practices is conservation tillage, primarily the use of no-till planting. The objectives of this research were to determine crop residue using remote sensing, an indicator of tillage intensity, without using training data and examine its performance at the field level. The Landsat Thematic Mapper Series platforms can provide global temporal and spatial coverage beginning in the mid-1980s. In this study, we used the Normalized Difference Tillage Index (NDTI), which has proved to be robust and accurate in studies built upon training datasets. We completed 10 years of residue maps for the 150,000 km2 study area in South Dakota, North Dakota, and Minnesota and validated the results against field-level survey data. The overall accuracy was between 64% and 78% with additional improvement when survey points with suspect geolocation and satellite tillage estimates with fewer than four dates of Landsat images were excluded. This study demonstrates that, with Landsat Archive available at no cost, researchers can implement retrospective, untrained estimates of conservation tillage with sufficient accuracy for some applications.
Experimental Evaluation of 6BLEMesh: IPv6-Based BLE Mesh Networks
Authors: Darroudi Seyed Mahdi , Gomez Carles
Published: August 2020

Bluetooth Low Energy (BLE) has become a major wireless technology for the Internet of Things (IoT). Recent efforts of academia, industry and standards development organizations have focused on creating BLE mesh network solutions. 6BLEMesh is a specification being developed by the IETF that defines an IPv6-oriented approach for BLE mesh networking. In this paper, we perform an experimental evaluation of 6BLEMesh, based on a real implementation. We evaluate latency, round trip time (RTT) and energy consumption. For the latter, we model the device current consumption, we determine the energy efficiency of communication, and we obtain the theoretical device lifetime (for battery-operated devices), for three different hardware platforms. Under the assumptions in our study (including a simple 235 mAh battery, and periodic data transmission), the maximum, asymptotic, device lifetime is 573 days, whereas battery-operated router devices can also achieve 3-digit lifetimes (in days) in many scenarios. Our results also illustrate the impact on performance of BLE-level and application-level parameter settings, adaptation layer mechanisms such as IPv6 header compression, and device hardware characteristics.
A Priori-Based Subarray Selection Algorithm for DOA Estimation
Authors: Zeng Linghao , Zhang Guanghua , Han Chongzhao
Published: August 2020

A finer direction-of-arrival (DOA) estimation result needs a large and dense array; it may, however, encounter the mutual coupling effect, which degrades the performance of DOA estimation. There is a new approach to mitigating this effect by using a nonuniform array to achieve DOA estimation. In this paper, we consider a priori DOA estimation, which is easily obtained from tracking results. The a priori DOA requires us to pay close attention to the high possibility of where the DOA will appear; then, a weight according to the prior probability distribution of DOA is added to each direction, which leads the sensing matrix of DOA estimation to be near low-rank. Thus, according to the low-rank matrix approximation theory, an optimal low-rank approximate matrix is obtained and an algorithm is proposed to select the elements of the original array according to right singular vectors of the approximate matrix. After that, the impacts of different weights are analyzed, and a mixed weight is presented which has flexibility for common use. Finally, a number of numerical simulations are carried out, and the results verify the effectiveness of the proposed methods.
An Online Classification Method for Fault Diagnosis of Railway Turnouts
Authors: Ou Dongxiu , Ji Yuqing , Zhang Lei , Liu Hu
Published: August 2020

Railway turnout system is a key infrastructure to railway safety and efficiency. However, it is prone to failure in the field. Therefore, many railway departments have adopted a monitoring system to monitor the operation status of turnouts. With monitoring data collected, many researchers have proposed different fault-diagnosis methods. However, many of the existing methods cannot realize real-time updating or deal with new fault types. This paper—based on imbalanced data—proposes a Bayes-based online turnout fault-diagnosis method, which realizes incremental learning and scalable fault recognition. First, the basic conceptions of the turnout system are introduced. Next, the feature extraction and processing of the imbalanced monitoring data are introduced. Then, an online diagnosis method based on Bayesian incremental learning and scalable fault recognition is proposed, followed by the experiment with filed data from Guangzhou Railway. The results show that the scalable fault-recognition method can reach an accuracy of 99.11%, and the training time of the Bayesian incremental learning model reduces 29.97% without decreasing the accuracy, which demonstrates the high accuracy, adaptability and efficiency of the proposed model, of great significance for labor-saving, timely maintenance and further, safety and efficiency of railway transportation.
NTRU-Like Random Congruential Public-Key Cryptosystem for Wireless Sensor Networks
Authors: Ibrahim Anas , Chefranov Alexander , Hamad Nagham , Daraghmi Yousef-Awwad , Al-Khasawneh Ahmad , Rodrigues Joel J. P. C.
Published: August 2020

Wireless sensor networks (WSNs) are the core of the Internet of Things and require cryptographic protection. Cryptographic methods for WSN should be fast and consume low power as these networks rely on battery-powered devices and microcontrollers. NTRU, the fastest and secure public key cryptosystem, uses high degree, N, polynomials and is susceptible to the lattice basis reduction attack (LBRA). Congruential public key cryptosystem (CPKC), proposed by the NTRU authors, works on integers modulo q and is easily attackable by LBRA since it uses small numbers for the sake of the correct decryption. Herein, RCPKC, a random congruential public key cryptosystem working on degree N=0 polynomials modulo q, is proposed, such that the norm of a two-dimensional vector formed by its private key is greater than q. RCPKC works as NTRU, and it is a secure version of insecure CPKC. RCPKC specifies a range from which the random numbers shall be selected, and it provides correct decryption for valid users and incorrect decryption for an attacker using LBRA by Gaussian lattice reduction. RCPKC asymmetric encryption padding (RAEP), similar to its NTRU analog, NAEP, is IND-CCA2 secure. Due to the use of big numbers instead of high degree polynomials, RCPKC is about 27 times faster in encryption and decryption than NTRU. Furthermore, RCPKC is more than three times faster than the most effective known NTRU variant, BQTRU. Compared to NTRU, RCPKC reduces energy consumption at least thirty times, which allows increasing the life-time of unattended WSNs more than thirty times.
Applying a 6 DoF Robotic Arm and Digital Twin to Automate Fan-Blade Reconditioning for Aerospace Maintenance, Repair, and Overhaul
Authors: Oyekan John , Farnsworth Michael , Hutabarat Windo , Miller David , Tiwari Ashutosh
Published: August 2020

The UK is home to several major air commercial and transport hubs. As a result, there is a high demand for Maintenance, Repair, and Overhaul (MRO) services to ensure that fleets of aircraft are in airworthy conditions. MRO services currently involve heavy manual labor. This creates bottlenecks, low repeatability, and low productivity. Presented in this paper is an investigation to create an automation cell for the fan-blade reconditioning component of MRO. The design and prototype of the automation cell is presented. Furthermore, a digital twin of the grinding process is developed and used as a tool to explore the required grinding force parameters needed to effectively remove surface material. An integration of a 6-DoF industrial robot with an end-effector grinder and a computer vision system was undertaken. The computer vision system was used for the digitization of the fan-blade surface as well as tracking and guidance of material removal. Our findings reveal that our proposed system can perform material removal, track the state of the fan blade during the reconditioning process and do so within a closed-loop automated robotic work cell.
Efficient Caoshu Character Recognition Scheme and Service Using CNN-Based Recognition Model Optimization
Authors: Hong Boseon , Kim Bongjae
Published: August 2020

Deep learning-based artificial intelligence models are widely used in various computing fields. Especially, Convolutional Neural Network (CNN) models perform very well for image recognition and classification. In this paper, we propose an optimized CNN-based recognition model to recognize Caoshu characters. In the proposed scheme, an image pre-processing and data augmentation techniques for our Caoshu dataset were applied to optimize and enhance the CNN-based Caoshu character recognition model’s recognition performance. In the performance evaluation, Caoshu character recognition performance was compared and analyzed according to the proposed performance optimization. Based on the model validation results, the recognition accuracy was up to about 98.0% in the case of TOP-1. Based on the testing results of the optimized model, the accuracy, precision, recall, and F1 score are 88.12%, 81.84%, 84.20%, and 83.0%, respectively. Finally, we have designed and implemented a Caoshu recognition service as an Android application based on the optimized CNN based Cahosu recognition model. We have verified that the Caoshu recognition service could be performed in real-time.
DNA/RNA Electrochemical Biosensing Devices a Future Replacement of PCR Methods for a Fast Epidemic Containment
Authors: Santhanam Manikandan , Algov Itay , Alfonta Lital
Published: August 2020

Pandemics require a fast and immediate response to contain potential infectious carriers. In the recent 2020 Covid-19 worldwide pandemic, authorities all around the world have failed to identify potential carriers and contain it on time. Hence, a rapid and very sensitive testing method is required. Current diagnostic tools, reverse transcription PCR (RT-PCR) and real-time PCR (qPCR), have its pitfalls for quick pandemic containment such as the requirement for specialized professionals and instrumentation. Versatile electrochemical DNA/RNA sensors are a promising technological alternative for PCR based diagnosis. In an electrochemical DNA sensor, a nucleic acid hybridization event is converted into a quantifiable electrochemical signal. A critical challenge of electrochemical DNA sensors is sensitive detection of a low copy number of DNA/RNA in samples such as is the case for early onset of a disease. Signal amplification approaches are an important tool to overcome this sensitivity issue. In this review, the authors discuss the most recent signal amplification strategies employed in the electrochemical DNA/RNA diagnosis of pathogens.
Cache-Based Privacy Preserving Solution for Location and Content Protection in Location-Based Services
Authors: Cui Yuanbo , Gao Fei , Li Wenmin , Shi Yijie , Zhang Hua , Wen Qiaoyan , Panaousis Emmanouil
Published: August 2020

Location-Based Services (LBSs) are playing an increasingly important role in people’s daily activities nowadays. While enjoying the convenience provided by LBSs, users may lose privacy since they report their personal information to the untrusted LBS server. Although many approaches have been proposed to preserve users’ privacy, most of them just focus on the user’s location privacy, but do not consider the query privacy. Moreover, many existing approaches rely heavily on a trusted third-party (TTP) server, which may suffer from a single point of failure. To solve the problems above, in this paper we propose a Cache-Based Privacy-Preserving (CBPP) solution for users in LBSs. Different from the previous approaches, the proposed CBPP solution protects location privacy and query privacy simultaneously, while avoiding the problem of TTP server by having users collaborating with each other in a mobile peer-to-peer (P2P) environment. In the CBPP solution, each user keeps a buffer in his mobile device (e.g., smartphone) to record service data and acts as a micro TTP server. When a user needs LBSs, he sends a query to his neighbors first to seek for an answer. The user only contacts the LBS server when he cannot obtain the required service data from his neighbors. In this way, the user reduces the number of queries sent to the LBS server. We argue that the fewer queries are submitted to the LBS server, the less the user’s privacy is exposed. To users who have to send live queries to the LBS server, we employ the l-diversity, a powerful privacy protection definition that can guarantee the user’s privacy against attackers using background knowledge, to further protect their privacy. Evaluation results show that the proposed CBPP solution can effectively protect users’ location and query privacy with a lower communication cost and better quality of service.
The CMOS Highly Linear Current Amplifier with Current Controlled Gain for Sensor Measurement Applications
Authors: Prokop Roman , Sotner Roman , Kledrowetz Vilem
Published: August 2020

This paper introduces a new current-controlled current-amplifier suitable for precise measurement applications. This amplifier was developed with strong emphasis on linearity leading to low total harmonic distortion (THD) of the output signal, and on linearity of the gain control. The presented circuit is characterized by low input and high output impedances. Current consumption is significantly smaller than with conventional quadratic current multipliers and is comparable in order to the maximum processed input current, which is ±200 µA. This circuit is supposed to be used in many sensor applications, as well as a precise current multiplier for general analog current signal processing. The presented amplifier (current multiplier) was designed by an uncommon topology based on linear sub-blocks using MOS transistors working in their linear region. The described circuit was designed and fabricated in a C035 I3T25 0.35-µm ON Semiconductor process because of the demand of the intended application for higher supply voltage. Nevertheless, the topology is suitable also for modern smaller CMOS technologies and lower supply voltages. The performance of the circuit was verified by laboratory measurement with parameters comparable to the Cadence simulation results and presented here.
Integrity Monitoring of Multimodal Perception System for Vehicle Localization
Authors: Balakrishnan Arjun , Florez Sergio Rodriguez , Reynaud Roger
Published: August 2020

Autonomous driving systems tightly rely on the quality of the data from sensors for tasks such as localization and navigation. In this work, we present an integrity monitoring framework that can assess the quality of multimodal data from exteroceptive sensors. The proposed multisource coherence-based integrity assessment framework is capable of handling highway as well as complex semi-urban and urban scenarios. To achieve such generalization and scalability, we employ a semantic-grid data representation, which can efficiently represent the surroundings of the vehicle. The proposed method is used to evaluate the integrity of sources in several scenarios, and the integrity markers generated are used for identifying and quantifying unreliable data. A particular focus is given to real-world complex scenarios obtained from publicly available datasets where integrity localization requirements are of high importance. Those scenarios are examined to evaluate the performance of the framework and to provide proof-of-concept. We also establish the importance of the proposed integrity assessment framework in context-based localization applications for autonomous vehicles. The proposed method applies the integrity assessment concepts in the field of aviation to ground vehicles and provides the Protection Level markers (Horizontal, Lateral, Longitudinal) for perception systems used for vehicle localization.
IC-Health Project: Development of MOOCs to Promote Digital Health Literacy: First Results and Future Challenges
Authors: Perestelo-Perez Lilisbeth , Torres-Castaño Alezandra , González-González Carina , Alvarez-Perez Yolanda , Toledo-Chavarri Ana , Wagner Ana , Perello Michelle , Van Der Broucke Stephan , Díaz-Meneses Gonzalo , Piccini Barbara , Rivero-Santana Amado , Serrano-Aguilar Pedro ,   on behalf of the IC Project Consortium
Published: August 2020

Digital health literacy (DHL) is the ability to search, understand and evaluate information from digital media and apply that knowledge to solve health problems. However, currently many citizens have not developed these skills, and this compromises not only the self-management of their health, but the possibility that health services are socially sustainable. The objective of this article was to present the objectives, activities and results of the IC-Health project whose objective was to develop a series of massive open online courses (MOOCs) to improve the DHL skills of European citizens. An exploratory report on DHL’s current evidence was developed. Furthermore, a survey, focus groups and group interviews were conducted to determine DHL levels and the needs of population cohorts (children; adolescents; pregnant and lactating women; the elderly; and people affected by type 1 and type 2 diabetes). A participation strategy with end users was designed through a community of practice for the creation of MOOCs with the seven European countries that participated in the consortium. Thirty-five MOOCs were developed in eight different languages and a descriptive and exploratory assessment of MOOCs was conducted with new participants. This first evaluation indicated that MOOCs can be an effective educational resource for DHL and a facilitator of shared decision-making processes. The process of co-creation of MOOCs, the components, the challenges and the opportunities identified in this European project could be useful for other developers of MOOCs who want to co-create interventions with beneficiaries in similar settings. Further longer-term actions are still needed to improve citizens’ DHL.
Developing a Cooperative Model Converging Both Convention and Medical Tourism Stakeholders: Based on Deutsch’s Cooperation Theory
Authors: Ahn Deoksoo , Heo Jun , Kim Chulwon
Published: August 2020

This study aimed to develop the cooperative model converging stakeholders in both the convention and medical tourism sectors. It was to derive factors representing collaboration toward common goals and values recognized by both sectors from the stakeholders’ perspectives through qualitative research methods. The study applied Deutsch’s cooperation theory, which states that cooperation is directed toward group goals and selected due to rational decision-making. For a systematic and reliable analysis of the collected data, Spradley’s 12-stage technique was modified into seven stages, such as the domain, taxonomic, and component analyses, to fit this study’s context. The convention-medical tourism cooperation model was aptly named “CON-MED” cooperation model.
Influence of Smartphone Use on Emotional, Cognitive and Educational Dimensions in University Students
Authors: Morales Rodríguez Francisco Manuel , Lozano José Miguel Giménez , Linares Mingorance Pablo , Pérez-Mármol José Manuel
Published: August 2020

The use of mobile devices is one of the increasingly interactive methodologies widely promoted within the European Higher Education Area. It is, therefore, necessary to determine the potential effects of their excessive use on psychological and educational variables. The aim of the present study was to assess smartphone addiction and its relationship with emotional, cognitive, and educational dimensions in university students. Participants comprised 144 university students aged between 19 and 27 years old and studying psychology and education at the University of Granada. Various tests were administered to assess variables grouped into the following dimensions: smartphone addiction (TDM), general intelligence (Wonderlic), emotional intelligence (TMMS-24), motivation (Mape-3), creativity (CREA test), and attitudes toward competencies. An ex post facto design was employed. Direct associations were observed between addiction symptoms caused by smartphone use (withdrawal, tolerance, excessive use, and problems caused by the same) and the variables of extrinsic motivation (fear and avoidance of the task) and intrinsic motivation (motivation toward the task). The results also indicated direct relationships between the problems caused by excessive smartphone use and anxiety and extrinsic motivation toward learning. An inverse relationship was observed between smartphone addiction and the emotional intelligence dimension of clarity of feelings. The anxiety provoked by excessive smartphone use was related to the tolerance generated by such use and to cultural and artistic competencies. The data obtained thus shed light on the effect of smartphone use on emotional, cognitive, and educational dimensions in university settings.
Gasification of RDF and Its Components with Tire Pyrolysis Char as Tar-Cracking Catalyst
Authors: Šuhaj Patrik , Husár Jakub , Haydary Juma
Published: August 2020

The composition of gas produced by the gasification of refuse-derived fuel (RDF) can be affected by the content of individual components of RDF and their mutual interactions. In this work, plastics, paper, wood, textile and RDF were gasified in a two-stage gasification system and the obtained tar yields and product gas quality were compared. The two-stage reactor consisted of an air-blown gasifier and a catalytic reactor filled with carbonized tire pyrolysis char as the tar-cracking catalyst. Tire pyrolysis char is a promising alternative to expensive catalysts. The impact of temperature and catalyst amount on the tar yield and gas composition was investigated. Theoretical oxygen demand for all material classes was calculated and its effect on gas composition and tar yield is discussed. The results indicate that the gasification of plastics produces the highest amount of tar and hydrocarbon gases, while the CO2 content of the product gas remains the lowest compared to all other materials. On the other hand, the paper fraction produced hydrogen-rich gas with low tar content. The gasification of RDF at 700 °C provided the lowest tar yield compared to all other materials, indicating positive synergic effects of lignocellulosic biomass and plastics in tar reduction. The significance of these interactions was suppressed at the highest temperature of 900 °C, as the thermal cracking of tar became dominant. For CO2 content, a negative synergic effect (higher CO2 concentration) was observed.
Geometric Optimization of Atriums with Natural Lighting Potential for Detached High-Rise Buildings
Authors: Susa-Páez Alejandra , Piderit-Moreno María Beatriz
Published: August 2020

Detached high-rise office buildings with more than 15 floors in high density areas have floor plans with large surfaces that prevent natural lighting from entering their central areas. Therefore, artificial lighting is used to substitute the lighting comfort needs of their occupants for a large proportion of operational hours, resulting in high energy expenses for the building. The goal of this study is to evaluate the lighting potential of a central atrium with added clerestories and/or side lighting every four levels in a parametric 15-floor theoretical model and two floor surface areas of 900 m2 and 2500 m2, compared to a 40% glazed surface on façades without solar control devices. A total of 108 geometric variations of the atrium and adjoining spaces were analyzed using a climate-based daylight dynamic simulation method (CBDM), using DIVA-for-Rhino as the integrated evaluation tool in Rhino’s Grasshopper software, where the parametric model was built. The geometric optimization results show the design variables that allowed a significant illuminance of between 60 and 70%, using the Useful Daylighting Illuminance (UDI) indicator in a range of 100 to 2000 lux, demonstrating that the incorporation of atrium spaces as a controlled natural lighting strategy in these buildings is an environmental and sustainable perspective for architectonic design.
Research on New Energy Automobile Manufacturing Service Derivatization Based on TRIZ
Authors: Wan Hui , Rieckmann Jens Mathis , Zhang Qianqian , Ping Qiao
Published: August 2020

The development of the new energy automobile industry is crucial to the industrial structure upgrading of the manufacturing industry in developing countries. The more efficient service derivation of new energy vehicles needs to be considered from the perspective of manufacturing innovation. This article discusses the feasibility of applying the theory of Teoriya Resheniya Izobretatelskikh Zadatch (TRIZ) to the service derivative of new energy vehicles and forms a preliminary idea according to the characteristics of the service derivative of the manufacturing industry. By using the analysis tool and solution of TRIZ, this paper investigates the practical problem in developing the new energy vehicle market to verify a solution. The research shows that TRIZ can effectively generate new energy automobile manufacturing service derivative schemes and explore the service derivative path.
Covid-19 and the Search for the Common Good: The Case of Parmon Spa (Italy)
Authors: Centorrino Giovanna
Published: August 2020

The Covid-19 pandemic marks an extraordinary global crisis unseen in this last century, with its rapid spread worldwide and associated mortality burden, which is leading to profound economic consequences. In such an unprecedented scenario, most firms were not ready to deal with the resulting significant large-scale perturbations. Challenges for firms in the sector of the production of essential medical devices were among the most urgent. This study aims to investigate the behavior of a medium-size Italian enterprise that during this crisis, by converting part of its production line to the production of masks, undertook a path characterized by an ethics mindset, showing how its potentialities can also be used for the aim of common good. The case study is also presented to demonstrate that leadership ethical values and constant connection to the entrepreneurial ecosystem, maintaining a positive culture therein, have contributed toward a common good choice.
Understanding Barriers in Indian Ocean Tuna Commission Allocation Negotiations on Fishing Opportunities
Authors: Sinan Hussain , Bailey Megan
Published: August 2020

Tuna Regional Fisheries Management Organizations (RFMOs) have been given an arduous mandate under the legal framework of the United Nations Fish Stocks Agreement. Member states with different interests and objectives are required to cooperate and collaborate on the conservation and management of tuna and tuna-like species, which includes the allocation of fishing opportunities. It is well understood that the main RFMO allocation disagreements are the inability to agree on a total allowable catch, the lack of willingness to accept new members, disagreement on who should bear the conservation burden, and non-compliance with national allocations owning to perceived inequities. Addressing these elements is crucial for any organization if it is to sustain its credibility stability and legitimacy. This paper identifies additional barriers facing an equitable allocation process at the Indian Ocean Tuna Commission (IOTC). These challenges are multi-faceted and include institutional, political, and scientific barriers in the ongoing allocation negotiations, and further inhibit effective negotiation and resolution adoption as a whole. After almost 10 years of negotiations, the process has progressed little, and without agreement on these barriers it will be a challenge to adopt a stable systematic allocation process.
DISARM Early Warning System for Wildfires in the Eastern Mediterranean
Authors: Kotroni Vassiliki , Cartalis Constantinos , Michaelides Silas , Stoyanova Julia , Tymvios Fillipos , Bezes Antonis , Christoudias Theodoros , Dafis Stavros , Giannakopoulos Christos , Giannaros Theodore M. , Georgiev Christo , Karagiannidis Athanasios , Karali Anna , Koletsis Ioannis , Lagouvardos Konstantinos , Lemesios Ioannis , Mavrakou Thaleia , Papagiannaki Katerina , Polydoros Anastasios , Proestos Yiannis
Published: August 2020

This paper discusses the main achievements of DISARM (Drought and fIre ObServatory and eArly waRning system) project, which developed an early warning system for wildfires in the Eastern Mediterranean. The four pillars of this system include (i) forecasting wildfire danger, (ii) detecting wildfires with remote sensing techniques, (iii) forecasting wildfire spread with a coupled weather-fire modeling system, and (iv) assessing the wildfire risk in the frame of climate change. Special emphasis is given to the innovative and replicable parts of the system. It is shown that for the effective use of fire weather forecasting in different geographical areas and in order to account for the local climate conditions, a proper adjustment of the wildfire danger classification is necessary. Additionally, the consideration of vegetation dryness may provide better estimates of wildfire danger. Our study also highlights some deficiencies of both EUMETSAT (Exploitation of Meteorological Satellites) and LSA-SAF (Satellite Application Facility on Land Surface Analysis) algorithms in their skill to detect wildfires over islands and near the coastline. To tackle this issue, a relevant modification is proposed. Furthermore, it is shown that IRIS, the coupled atmosphere-fire modeling system developed in the frame of DISARM, has proven to be a valuable supporting tool in fire suppression actions. Finally, assessment of the wildfire danger in the future climate provides the necessary context for the development of regional adaptation strategies to climate change.
Visualized Co-Simulation of Adaptive Human Behavior and Dynamic Building Performance: An Agent-Based Model (ABM) and Artificial Intelligence (AI) Approach for Smart Architectural Design
Authors: Yi Hwang
Published: August 2020

Human (occupant) behavior has been a topic of active research in the study of architecture and energy. To integrate the work of architectural design with techniques of building performance simulation in the presence of responsive human behavior, this study proposes a computational framework that can visualize and evaluate space occupancy, energy use, and generative envelope design given a space outline. A design simulation platform based on the visual programming language (VPL) of Rhino Grasshopper (GH) and Python is presented so that users (architects) can monitor real-time occupant response to space morphology, environmental building operation, and the formal optimization of three-dimensional (3D) building space. For dynamic co-simulation, the Building Controls Virtual Test Bed, Energy Plus, and Radiance were interfaced, and the agent-based model (ABM) approach and Gaussian process (GP) were applied to represent agents’ self-learning adaptation, feedback, and impact on room temperature and illuminance. Hypothetical behavior scenarios of virtual agents with experimental building geometry were produced to validate the framework and its effectiveness in supporting dynamic simulation. The study’s findings show that building energy and temperature largely depend on ABMs and geometry configuration, which demonstrates the importance of coupled simulation in design decision-making.
A Comparative Automated Text Analysis of Airbnb Reviews in Hong Kong and Singapore Using Latent Dirichlet Allocation
Authors: Kiatkawsin Kiattipoom , Sutherland Ian , Kim Jin-Young
Published: August 2020

Airbnb has emerged as a platform where unique accommodation options can be found. Due to the uniqueness of each accommodation unit and host combination, each listing offers a one-of-a-kind experience. As consumers increasingly rely on text reviews of other customers, managers are also increasingly gaining insight from customer reviews. Thus, this present study aimed to extract those insights from reviews using latent Dirichlet allocation, an unsupervised type of topic modeling that extracts latent discussion topics from text data. Findings of Hong Kong’s 185,695 and Singapore’s 93,571 Airbnb reviews, two long-term rival destinations, were compared. Hong Kong produced 12 total topics that can be categorized into four distinct groups whereas Singapore’s optimal number of topics was only five. Topics produced from both destinations covered the same range of attributes, but Hong Kong’s 12 topics provide a greater degree of precision to formulate managerial recommendations. While many topics are similar to established hotel attributes, topics related to the host and listing management are unique to the Airbnb experience. The findings also revealed keywords used when evaluating the experience that provide more insight beyond typical numeric ratings.
Monetary Valuation and Internalization of Externalities in German Agriculture Using the Example of Nitrate Pollution: A Case-Study
Authors: Folkens Lukas , Wiedemer Volker , Schneider Petra
Published: August 2020

Plants are dependent on nitrogen for their growth. However, if more plant nutrients are deposited than the plant can absorb, the excess nitrogen seeps into the soil where it accumulates as nitrate. About 74% of the drinking water produced in Germany comes from groundwater. The legal limit of 50 milligrams of nitrate per liter of fresh water is exceeded frequently in Germany, especially in agricultural areas. High levels of nitrate in drinking water can quickly lead to health issues, under certain conditions. The nitrate problem is omnipresent in Germany. However, studies which determine the externalized costs of nitrogen eutrophication are mostly missing. The present study closes this gap by combining the results of a transdisciplinary investigation from hydrological analyses and environmental-economic calculations. Water samples were taken from a deep well in Hauneck, which is a municipality in Hesse (Germany). Afterwards, an isotope analysis was carried out to determine the groundwater residence time and possible sources of nitrate. Thus, in addition to the sources of pollution, concrete social costs were determined, using a damage cost approach as well as an avoidance cost approach. For Hauneck, it was found that about 54% of the drinking water price is directly linked to the externalization of costs. These are borne via the principle of the common burden. In addition, the isotope investigations have shown that the removal of excess nitrate will continue for decades, which will lead to long term external costs. The paper reveals how the internalization of these costs can contribute to the conservation of water resources.
Shapovalov Wave-Like Spacetimes
Authors: Osetrin Konstantin , Osetrin Evgeny
Published: August 2020

A complete classification of space-time models is presented, which admit the privileged coordinate systems, where the Hamilton–Jacobi equation for a test particle is integrated by the method of complete separation of variables with separation of the isotropic (wave) variable, on which the metric depends (wave-like Shapovalov spaces). For all types of Shapovalov spaces, exact solutions of the Einstein equations with a cosmological constant in vacuum are found. Complete integrals are presented for the eikonal equation and the Hamilton–Jacobi equation of motion of test particles.
Symmetry, Transactions, and the Mechanism of Wave Function Collapse
Authors: Cramer John Gleason , Mead Carver Andress
Published: August 2020

The Transactional Interpretation of quantum mechanics exploits the intrinsic time-symmetry of wave mechanics to interpret the ψ and ψ* wave functions present in all wave mechanics calculations as representing retarded and advanced waves moving in opposite time directions that form a quantum “handshake” or transaction. This handshake is a 4D standing-wave that builds up across space-time to transfer the conserved quantities of energy, momentum, and angular momentum in an interaction. Here, we derive a two-atom quantum formalism describing a transaction. We show that the bi-directional electromagnetic coupling between atoms can be factored into a matched pair of vector potential Green’s functions: one retarded and one advanced, and that this combination uniquely enforces the conservation of energy in a transaction. Thus factored, the single-electron wave functions of electromagnetically-coupled atoms can be analyzed using Schrödinger’s original wave mechanics. The technique generalizes to any number of electromagnetically coupled single-electron states—no higher-dimensional space is needed. Using this technique, we show a worked example of the transfer of energy from a hydrogen atom in an excited state to a nearby hydrogen atom in its ground state. It is seen that the initial exchange creates a dynamically unstable situation that avalanches to the completed transaction, demonstrating that wave function collapse, considered mysterious in the literature, can be implemented with solutions of Schrödinger’s original wave mechanics, coupled by this unique combination of retarded/advanced vector potentials, without the introduction of any additional mechanism or formalism. We also analyze a simplified version of the photon-splitting and Freedman–Clauser three-electron experiments and show that their results can be predicted by this formalism.