bluuebunny/crossref_metadata_2025 huggingface dataset:
Learned Representation of Satellite Image Series for Data Compression
Authors: Liao Liang , Xiao Jing , Li Yating , Wang Mi , Hu Ruimin
Published: February 2020

Real-time transmission of satellite video data is one of the fundamentals in the applications of video satellite. Making use of the historical information to eliminate the long-term background redundancy (LBR) is considered to be a crucial way to bridge the gap between the compressed data rate and the bandwidth between the satellite and the Earth. The main challenge lies in how to deal with the variant image pixel values caused by the change of shooting conditions while keeping the structure of the same landscape unchanged. In this paper, we propose a representation learning based method to model the complex evolution of the landscape appearance under different conditions by making use of the historical image series. Under this representation model, the image is disentangled into the content part and the style part. The former represents the consistent landscape structure, while the latter represents the conditional parameters of the environment. To utilize the knowledge learned from the historical image series, we generate synthetic reference frames for the compression of video frames through image translation by the representation model. The synthetic reference frames can highly boost the compression efficiency by changing the original intra-frame prediction to inter-frame prediction for the intra-coded picture (I frame). Experimental results show that the proposed representation learning-based compression method can save an average of 44.22% bits over HEVC, which is significantly higher than that using references generated under the same conditions. Bitrate savings reached 18.07% when applied to satellite video data with arbitrarily collected reference images.
Deep Learning in Archaeological Remote Sensing: Automated Qanat Detection in the Kurdistan Region of Iraq
Authors: Soroush Mehrnoush , Mehrtash Alireza , Khazraee Emad , Ur Jason A.
Published: February 2020

In this paper, we report the results of our work on automated detection of qanat shafts on the Cold War-era CORONA Satellite Imagery. The increasing quantity of air and space-borne imagery available to archaeologists and the advances in computational science have created an emerging interest in automated archaeological detection. Traditional pattern recognition methods proved to have limited applicability for archaeological prospection, for a variety of reasons, including a high rate of false positives. Since 2012, however, a breakthrough has been made in the field of image recognition through deep learning. We have tested the application of deep convolutional neural networks (CNNs) for automated remote sensing detection of archaeological features. Our case study is the qanat systems of the Erbil Plain in the Kurdistan Region of Iraq. The signature of the underground qanat systems on the remote sensing data are the semi-circular openings of their vertical shafts. We choose to focus on qanat shafts because they are promising targets for pattern recognition and because the richness and the extent of the qanat landscapes cannot be properly captured across vast territories without automated techniques. Our project is the first effort to use automated techniques on historic satellite imagery that takes advantage of neither the spectral imagery resolution nor very high (sub-meter) spatial resolution.
Noise Reduction of Welding Crack AE Signal Based on EMD and Wavelet Packet
Authors: He Kuanfang , Xia Zixiong , Si Yin , Lu Qinghua , Peng Yanfeng
Published: February 2020

The acoustic emission (AE) signal collected by a sensor in the welding process has an overlapping frequency band and weak characteristics under a complex noise background. It is difficult for the wavelet noise reduction method, with single basis function, to effectively match the different characteristic information of the welding crack AE signal. Taking into account the adaptive decomposition characteristics of Empirical Mode Decomposition (EMD), a novel wavelet packet noise reduction method for welding AE signal was proposed. The welding AE signal was adaptively decomposed into several Intrinsic Mode Functions (IMFs) by the EMD. The effective IMFs were selected by the frequency distribution characteristics of the welding crack AE signal. A wavelet packet, with a specific basis function, was subsequently performed on the effective IMFs, which were reconstructed to be the welding crack AE signal. The simulated and experimental results indicated that the proposed method can effectively achieve noise reduction of the welding crack AE signal, which provided a mean for structure crack detection in the welding process.
Geometric Deep Lean Learning: Deep Learning in Industry 4.0 Cyber–Physical Complex Networks
Authors: Villalba-Díez Javier , Molina Martin , Ordieres-Meré Joaquín , Sun Shengjing , Schmidt Daniel , Wellbrock Wanja
Published: February 2020

In the near future, value streams associated with Industry 4.0 will be formed by interconnected cyber–physical elements forming complex networks that generate huge amounts of data in real time. The success or failure of industry leaders interested in the continuous improvement of lean management systems in this context is determined by their ability to recognize behavioral patterns in these big data structured within non-Euclidean domains, such as these dynamic sociotechnical complex networks. We assume that artificial intelligence in general and deep learning in particular may be able to help find useful patterns of behavior in 4.0 industrial environments in the lean management of cyber–physical systems. However, although these technologies have meant a paradigm shift in the resolution of complex problems in the past, the traditional methods of deep learning, focused on image or video analysis, both with regular structures, are not able to help in this specific field. This is why this work focuses on proposing geometric deep lean learning, a mathematical methodology that describes deep-lean-learning operations such as convolution and pooling on cyber–physical Industry 4.0 graphs. Geometric deep lean learning is expected to positively support sustainable organizational growth because customers and suppliers ought to be able to reach new levels of transparency and traceability on the quality and efficiency of processes that generate new business for both, hence generating new products, services, and cooperation opportunities in a cyber–physical environment.
Lightweight Data-Security Ontology for IoT
Authors: Gonzalez-Gil Pedro , Martinez Juan Antonio , Skarmeta Antonio F.
Published: February 2020

Although current estimates depict steady growth in Internet of Things (IoT), many works portray an as yet immature technology in terms of security. Attacks using low performance devices, the application of new technologies and data analysis to infer private data, lack of development in some aspects of security offer a wide field for improvement. The advent of Semantic Technologies for IoT offers a new set of possibilities and challenges, like data markets, aggregators, processors and search engines, which rise the need for security. New regulations, such as GDPR, also call for novel approaches on data-security, covering personal data. In this work, we present DS4IoT, a data-security ontology for IoT, which covers the representation of data-security concepts with the novel approach of doing so from the perspective of data and introducing some new concepts such as regulations, certifications and provenance, to classical concepts such as access control methods and authentication mechanisms. In the process we followed ontological methodologies, as well as semantic web best practices, resulting in an ontology to serve as a common vocabulary for data annotation that not only distinguishes itself from previous works by its bottom-up approach, but covers new, current and interesting concepts of data-security, favouring implicit over explicit knowledge representation. Finally, this work is validated by proof of concept, by mapping the DS4IoT ontology to the NGSI-LD data model, in the frame of the IoTCrawler EU project.
Incentivizing for Truth Discovery in Edge-assisted Large-scale Mobile Crowdsensing
Authors: Xu Jia , Yang Shangshu , Lu Weifeng , Xu Lijie , Yang Dejun
Published: February 2020

The recent development of human-carried mobile devices has promoted the great development of mobile crowdsensing systems. Most existing mobile crowdsensing systems depend on the crowdsensing service of the deep cloud. With the increasing scale and complexity, there is a tendency to enhance mobile crowdsensing with the edge computing paradigm to reduce latency and computational complexity, and improve the expandability and security. In this paper, we propose an integrated solution to stimulate the strategic users to contribute more for truth discovery in the edge-assisted mobile crowdsensing. We design an incentive mechanism consisting of truth discovery stage and budget feasible reverse auction stage. In truth discovery stage, we estimate the truth for each task in both deep cloud and edge cloud. In budget feasible reverse auction stage, we design a greedy algorithm to select the winners to maximize the quality function under the budget constraint. Through extensive simulations, we demonstrate that the proposed mechanism is computationally efficient, individually rational, truthful, budget feasible and constant approximate. Moreover, the proposed mechanism shows great superiority in terms of estimation precision and expandability.
Comparison of Smoothing Filters in Analysis of EEG Data for the Medical Diagnostics Purposes
Authors: Kawala-Sterniuk Aleksandra , Podpora Michal , Pelc Mariusz , Blaszczyszyn Monika , Gorzelanczyk Edward Jacek , Martinek Radek , Ozana Stepan
Published: February 2020

This paper covers a brief review of both the advantages and disadvantages of the implementation of various smoothing filters in the analysis of electroencephalography (EEG) data for the purpose of potential medical diagnostics. The EEG data are very prone to the occurrence of various internal and external artifacts and signal distortions. In this paper, three types of smoothing filters were compared: smooth filter, median filter and Savitzky–Golay filter. The authors of this paper compared those filters and proved their usefulness, as they made the analyzed data more legible for diagnostic purposes. The obtained results were promising, however, the studies on finding perfect filtering methods are still in progress.
Surface Electromyography-Controlled Automobile Steering Assistance
Authors: Nacpil Edric John Cruz , Nakano Kimihiko
Published: February 2020

Disabilities of the upper limb, such as hemiplegia or upper limb amputation, can limit automobile drivers to steering with one healthy arm. For the benefit of these drivers, recent studies have developed prototype interfaces that realized surface electromyography (sEMG)-controlled steering assistance with path-following accuracy that has been validated with driving simulations. In contrast, the current study expands the application of sEMG-controlled steering assistance by validating the Myo armband, a mass-produced sEMG-based interface, with respect to the path-following accuracy of a commercially available automobile. It was hypothesized that one-handed remote steering with the Myo armband would be comparable or superior to the conventional operation of the automobile steering wheel. Although results of low-speed field testing indicate that the Myo armband had lower path-following accuracy than the steering wheel during a 90° turn and wide U-turn at twice the minimum turning radius, the Myo armband had superior path-following accuracy for a narrow U-turn at the minimum turning radius and a 45° turn. Given its overall comparability to the steering wheel, the Myo armband could be feasibly applied in future automobile studies.
Research on Features of Pipeline Crack Signal Based on Weak Magnetic Method
Authors: Liu Bin , Liu Ziqi , Luo Ning , He Luyao , Ren Jian , Zhang He
Published: February 2020

Quantitative online detection of microcracks in long-distance oil and gas pipelines is an international problem, and the effective detection method is still lacking. In this paper, a mathematical model of non-uniform distribution of crack magnetic charges is established based on the stress distribution laws of pipeline cracks under internal pressure. The weak magnetic signal characteristics of pipeline cracks with different sizes are analyzed. The internal pressure increasing factor of weak magnetic signals are extracted to analyze the corresponding relationship between crack size and weak magnetic signals. The experimental study of the X70 pipeline is carried out. The results show that the axial component of the weak magnetic signal at the crack has a maximum value near the tip, and a minimum value appears in the middle of the crack. The internal pressure increasing factor is introduced to quantify the weak magnetic signal, the crack is in a safe state (not expanding) when the internal pressure increasing factor is positive, the weak magnetic signal has a linear relationship with the crack size. However, the crack is in a dangerous state when the internal pressure increasing factor is negative, and the pipeline crack will expand as the internal pressure increases.
Detection and Mitigation of DoS and DDoS Attacks in IoT-Based Stateful SDN: An Experimental Approach
Authors: Galeano-Brajones Jesús , Carmona-Murillo Javier , Valenzuela-Valdés Juan F. , Luna-Valero Francisco
Published: February 2020

The expected advent of the Internet of Things (IoT) has triggered a large demand of embedded devices, which envisions the autonomous interaction of sensors and actuators while offering all sort of smart services. However, these IoT devices are limited in computation, storage, and network capacity, which makes them easy to hack and compromise. To achieve secure development of IoT, it is necessary to engineer scalable security solutions optimized for the IoT ecosystem. To this end, Software Defined Networking (SDN) is a promising paradigm that serves as a pillar in the fifth generation of mobile systems (5G) that could help to detect and mitigate Denial of Service (DoS) and Distributed DoS (DDoS) threats. In this work, we propose to experimentally evaluate an entropy-based solution to detect and mitigate DoS and DDoS attacks in IoT scenarios using a stateful SDN data plane. The obtained results demonstrate for the first time the effectiveness of this technique targeting real IoT data traffic.
Advanced UAV–WSN System for Intelligent Monitoring in Precision Agriculture
Authors: Popescu Dan , Stoican Florin , Stamatescu Grigore , Ichim Loretta , Dragana Cristian
Published: February 2020

The growing need for food worldwide requires the development of a high-performance, high-productivity, and sustainable agriculture, which implies the introduction of new technologies into monitoring activities related to control and decision-making. In this regard, this paper presents a hierarchical structure based on the collaboration between unmanned aerial vehicles (UAVs) and federated wireless sensor networks (WSNs) for crop monitoring in precision agriculture. The integration of UAVs with intelligent, ground WSNs, and IoT proved to be a robust and efficient solution for data collection, control, analysis, and decisions in such specialized applications. Key advantages lay in online data collection and relaying to a central monitoring point, while effectively managing network load and latency through optimized UAV trajectories and in situ data processing. Two important aspects of the collaboration were considered: designing the UAV trajectories for efficient data collection and implementing effective data processing algorithms (consensus and symbolic aggregate approximation) at the network level for the transmission of the relevant data. The experiments were carried out at a Romanian research institute where different crops and methods are developed. The results demonstrate that the collaborative UAV–WSN–IoT approach increases the performances in both precision agriculture and ecological agriculture.
eWoT: A Semantic Interoperability Approach for Heterogeneous IoT Ecosystems Based on the Web of Things
Authors: Cimmino Andrea , Poveda-Villalón María , García-Castro  Raúl
Published: February 2020

With the constant growth of Internet of Things (IoT) ecosystems, allowing them to interact transparently has become a major issue for both the research and the software development communities. In this paper we propose a novel approach that builds semantically interoperable ecosystems of IoT devices. The approach provides a SPARQL query-based mechanism to transparently discover and access IoT devices that publish heterogeneous data. The approach was evaluated in order to prove that it provides complete and correct answers without affecting the response time and that it scales linearly in large ecosystems.
5G support for Industrial IoT Applications— Challenges, Solutions, and Research gaps
Authors: Varga Pal , Peto Jozsef , Franko Attila , Balla David , Haja David , Janky Ferenc , Soos Gabor , Ficzere Daniel , Maliosz Markosz , Toka Laszlo
Published: February 2020

Industrial IoT has special communication requirements, including high reliability, low latency, flexibility, and security. These are instinctively provided by the 5G mobile technology, making it a successful candidate for supporting Industrial IoT (IIoT) scenarios. The aim of this paper is to identify current research challenges and solutions in relation to 5G-enabled Industrial IoT, based on the initial requirements and promises of both domains. The methodology of the paper follows the steps of surveying state-of-the art, comparing results to identify further challenges, and drawing conclusions as lessons learned for each research domain. These areas include IIoT applications and their requirements; mobile edge cloud; back-end performance tuning; network function virtualization; and security, blockchains for IIoT, Artificial Intelligence support for 5G, and private campus networks. Beside surveying the current challenges and solutions, the paper aims to provide meaningful comparisons for each of these areas (in relation to 5G-enabled IIoT) to draw conclusions on current research gaps.
Gated Bow-Tie Diode for Microwave to Sub-Terahertz Detection
Authors: Ašmontas Steponas , Anbinderis Maksimas , Čerškus Aurimas , Gradauskas Jonas , Sužiedėlis Algirdas , Šilėnas Aldis , Širmulis Edmundas , Umansky Vladimir
Published: February 2020

We propose a new design microwave radiation sensor based on a selectively doped semiconductor structure of asymmetrical shape (so-called bow-tie diode). The novelty of the design comes down to the gating of the active layer of the diode above different regions of the two-dimensional electron channel. The gate influences the sensing properties of the bow-tie diode depending on the nature of voltage detected across the ungated one as well as on the location of the gate in regard to the diode contacts. When the gate is located by the wide contact, the voltage sensitivity increases ten times as compared to the case of the ungated diode, and the detected voltage holds the same polarity of the thermoelectric electromotive force of hot electrons in an asymmetrically shaped n-n+ junction. Another remarkable effect of the gate placed by the wide contact is weak dependence of the detected voltage on frequency which makes such a microwave diode to be a proper candidate for the detection of electromagnetic radiation in the microwave and sub-terahertz frequency range. When the gate is situated beside the narrow contact, the two orders of sensitivity magnitude increase are valid in the microwaves but the voltage sensitivity is strongly frequency-dependent for higher frequencies.
In-Vivo and Ex-Vivo Measurements of Blood Glucose Using Whispering Gallery Modes
Authors: Liu Louis W. Y. , Kandwal Abhishek , Kogut A. , Eremenko Z. E. , Kogut E. , Islam M. T. , Dolia R. , Nosatiuk S. , Nguyen Son T.
Published: February 2020

The permittivity of blood glucose is not a strong function of its concentration in microwave or millimeter-wave frequencies. Measuring glucose concentrations remains a challenge, particularly in the presence of interference caused by the ambient leaky waves. In this paper, however, we demonstrate that a near-linear correlation between the glucose concentration and the blood permittivity was noticeably observed at a whispering gallery mode resonance. Method: the proposed sensor was a vacuum suction aspirator partially wounded with a turn of the Goubau line. This arrangement enabled a fixed cylindrical volume of a skin tissue bump or glucose/water solution to be formed and used as a whispering gallery resonator for in-vivo and ex-vivo measurements. Results: in the in-vivo study, a near-linear correlation between the glucose levels and the S21 parameters was noticeably observed at the fundamental whispering gallery resonance (i.e., at 2.18 GHz). In the ex-vivo study, a similar correlation was observed between the concentration of a glucose/water solution and the S21 parameters 56.6 GHz. Conclusion: the results of both investigations were consistent not only with the invasive measurements using the Accu-checkTM, but also with the conclusion drawn by some other research groups who have successfully measured blood glucose concentrations at millimeter-wave frequencies.
Fully Differential Touch Screen Controller with Wide Input Dynamic Range for Thin Displays
Authors: Lee Chang-Ju , Park Jong Kang , Seo Han-Eol , Huh Junho , Chun Jung-Hoon
Published: February 2020

As today’s smartphone displays become thinner, the coupling capacitance between the display electrodes and touch screen panel (TSP) electrodes is increasing significantly. The increased capacitance easily introduces time-varying display signals into the TSP, deteriorating the touch performance. In this research, we demonstrate that the maximum peak display noise in the time domain is approximately 30% of the maximum voltage difference of the display grayscale through analysis of the structure and operation of displays. Then, to mitigate display noise, we propose a circuit solution that uses a fully differential charge amplifier with an input dynamic range wider than the maximum peak of the display noise. A test chip was fabricated using a 0.35 μm CMOS process and achieved a signal-to-noise ratio of 41 dB for a 6-mm-diameter metal pillar touch when display pulses with 5-V swing were driven at 100 kHz.
How Laboratory Experiments Can Be Exploited for Monitoring Stress in the Wild: A Bridge Between Laboratory and Daily Life
Authors: Can Yekta Said , Gokay Dilara , Kılıç Dilruba Reyyan , Ekiz Deniz , Chalabianloo Niaz , Ersoy Cem
Published: February 2020

Chronic stress leads to poor well-being, and it has effects on life quality and health. Society may have significant benefits from an automatic daily life stress detection system using unobtrusive wearable devices using physiological signals. However, the performance of these systems is not sufficiently accurate when they are used in unrestricted daily life compared to the systems tested in controlled real-life and laboratory conditions. To test our stress level detection system that preprocesses noisy physiological signals, extracts features, and applies machine learning classification techniques, we used a laboratory experiment and ecological momentary assessment based data collection with smartwatches in daily life. We investigated the effect of different labeling techniques and different training and test environments. In the laboratory environments, we had more controlled situations, and we could validate the perceived stress from self-reports. When machine learning models were trained in the laboratory instead of training them with the data coming from daily life, the accuracy of the system when tested in daily life improved significantly. The subjectivity effect coming from the self-reports in daily life could be eliminated. Our system obtained higher stress level detection accuracy results compared to most of the previous daily life studies.
Detecting DDoS Attacks in Software-Defined Networks Through Feature Selection Methods and Machine Learning Models
Authors: Polat Huseyin , Polat Onur , Cetin Aydin
Published: February 2020

Software Defined Networking (SDN) offers several advantages such as manageability, scaling, and improved performance. However, SDN involves specific security problems, especially if its controller is defenseless against Distributed Denial of Service (DDoS) attacks. The process and communication capacity of the controller is overloaded when DDoS attacks occur against the SDN controller. Consequently, as a result of the unnecessary flow produced by the controller for the attack packets, the capacity of the switch flow table becomes full, leading the network performance to decline to a critical threshold. In this study, DDoS attacks in SDN were detected using machine learning-based models. First, specific features were obtained from SDN for the dataset in normal conditions and under DDoS attack traffic. Then, a new dataset was created using feature selection methods on the existing dataset. Feature selection methods were preferred to simplify the models, facilitate their interpretation, and provide a shorter training time. Both datasets, created with and without feature selection methods, were trained and tested with Support Vector Machine (SVM), Naive Bayes (NB), Artificial Neural Network (ANN), and K-Nearest Neighbors (KNN) classification models. The test results showed that the use of the wrapper feature selection with a KNN classifier achieved the highest accuracy rate (98.3%) in DDoS attack detection. The results suggest that machine learning and feature selection algorithms can achieve better results in the detection of DDoS attacks in SDN with promising reductions in processing loads and times.
Environmental Effects of Electromobility in a Sustainable Urban Public Transport
Authors: Pietrzak Krystian , Pietrzak Oliwia
Published: February 2020

This article has taken up an issue concerning the influence of the implementation of electromobility assumptions on reduction of air pollution in cities in terms of sustainable urban transport systems. The essential nature of the subject is confirmed by the following figures: transport is responsible for almost 25% of greenhouse gas emissions in Europe and is the main cause of air pollution in cities, almost 90% of city dwellers in the EU are exposed to levels of air pollutants deemed harmful by the WHO, and approximately 95% of vehicles on European roads still use fossil fuels. Therefore, the implemented transport policy, both in the international and domestic aspect of particular countries, is facing the need to significantly reduce the negative influence of transport on people and the environment. Electromobility has become one of the concepts that makes it possible to achieve this goal. Its main objective is to reduce emissions of harmful substances into the environment by deploying electric vehicles (EVs). Research conducted by the authors showed that public transport, apart from the obvious effect of decreased number of private vehicles and decreased congestion, can contribute to reduced air pollutant emissions and become a significant driver for the implementation of electromobility in cities. However, the achievement of this goal depends on taking appropriate actions not only in the transport branch but also in other sectors of economy. The following research methods were applied in the article: literature analysis, documentary method, case study, and mathematical methods. The research area was the city of Szczecin, Poland.
Bootstrap ARDL Test on the Relationship among Trade, FDI, and CO2 Emissions: Based on the Experience of BRICS Countries
Authors: He Fumei , Chang Ke-Chiun , Li Min , Li Xueping , Li Fangjhy
Published: February 2020

We used the Bootstrap Autoregressive Distributed Lagged Model (ARDL) method to test the relationship among BRICS (Brazil, Russia, India, China, and South Africa) countries’ trade, foreign direct investment (FDI), and CO2 emissions. We found that Brazil’s CO2 emissions and FDI have a cointegration relationship with the trade on the lag of one-period. Russia and India and CO2 emissions and trade have a cointegration relationship with FDI on the lag of one-period. In the long-term, Brazil’s FDI has a long-term causal relationship with the trade on the lag of one-period. The trade between Russia and India has a long-term causal relationship with FDI on the lag of one-period. Among other BRICS variables, Russian trade and FDI on the lag of one-period of CO2 emissions and FDI and CO2 emissions are on the lag of one-period on trade, which McNown et al. mentioned is the degeneration case #1 in their paper; while China’s trade and FDI on the lag of one-period of CO2 emissions is the country of degeneration case #2. When we examined short-term causality, we found that CO2 emissions showed a causal relationship with trade, while FDI and CO2 emissions were less pronounced. Trade has a positive causal relationship with FDI. These variables are different in different situations and in different countries. These results should be related to BRICS countries’ FDI, international trade development, and their different CO2 emission policies.
Governance of Social Innovation in Forestry
Authors: Ludvig Alice , Rogelja Todora , Asamer-Handler Marelli , Weiss Gerhard , Wilding Maria , Zivojinovic Ivana
Published: February 2020

Over the last decade, the term social innovation has received increased attention as a potential solution to address complex global social problems and to add collective values to society. The forest sector has great potential for fostering employment, community development and reducing increased emigration from rural to urban areas. This potential is not always realised, especially in economically weak and remote rural areas. Hence, this paper tackles the question of how social innovations in forestry are able to succeed under adverse circumstances and contribute to respond to some of the before mentioned challenges? To answer this question, we first identified four very different cases across Europe that are compatible with the criteria of social innovation. Proceeding from this starting point, comparative research was undertaken with the aim to identify the cases´ specific features and key criteria for success. In the cases considered, it is evident that the sheer determination and voluntary investment of time and effort by key individuals, who were convinced of the value of the idea for the community, provided indispensable impetus to all four social innovations. In drawing its conclusions, this paper highlights the principal areas that need consideration and that hence have implications when developing supportive policies. Furthermore, we provide some insight into future research topics that would allow us to better understand the positive impacts of social innovation in the forest-based sector.
Designing a Blockchain Model for the Paris Agreement’s Carbon Market Mechanism
Authors: Franke Laura , Schletz Marco , Salomo Søren
Published: February 2020

This paper examines the benefits and constraints of applying blockchain technology for the Paris Agreement carbon market mechanism and develops a list of technical requirements and soft factors as selection criteria to test the feasibility of two different blockchain platforms. The carbon market mechanism, as outlined in Article 6.2 of the Paris Agreement, can accelerate climate action by enabling cooperation between national Parties. However, in the past, carbon markets were limited by several constraints. Our research investigates these constraints and translates them into selection criteria to design a blockchain platform to overcome these past limitations. The developed selection criteria and assumptions developed in this paper provide an orientation for blockchain assessments. Using the selection criteria, we examine the feasibility of two distinct blockchains, Ethereum and Hyperledger Fabric, for the specific use case of Article 6.2. These two blockchain systems represent contrary forms of design and governance; Ethereum constitutes a public and permissionless blockchain governance system, while Hyperledger Fabric represents a private and permissioned governance system. Our results show that both blockchain systems can address present carbon market constraints by enhancing market transparency, increasing process automation, and preventing double counting. The final selection and blockchain system implementation will first be possible, when the Article 6 negotiations are concluded, and governance preferences of national Parties are established. Our paper informs about the viability of different blockchain systems, offers insights into governance options, and provides a valuable framework for a concrete blockchain selection in the future.
Labour Inclusion of People with Disabilities: What Role Do the Social and Solidarity Economy Entities Play?
Authors: Calderón-Milán María-José , Calderón-Milán Beatriz , Barba-Sánchez Virginia
Published: February 2020

Economic theory presupposes that the Entities of the Social and Solidarity Economy (SSE) should exhibit a greater sensitivity in the labour insertion of groups in danger of social exclusion than should the Capitalist Companies (CC). Therefore, it is expected that the SSE will employ a greater number of people with socio-labour characteristics among its workers against whom the ordinary labour market discriminates negatively. In this context, the objective of this research is focused on the analysis of socio-labour characteristics, salary differences and the degree of inequality in the distribution of salary income of the group of workers with disabilities in the SSE compared to CCs in Spain during the Great Recession (2007–2013) and the beginning of the current economic recovery (2013–2016). Using the data from the Continuous Sample of Working Histories (MCVL, in Spanish), our results show a greater sensitivity from the SSE compared to the CC in labour inclusion of workers with disabilities as well as for most socio-labour characteristics against which the ordinary labour market discriminates negatively. A second conclusion of the results is that the SSE provides a more equitable distribution of salary income for workers with disabilities than the CC, although the wages are comparatively lower.
Development and Investigation of a New Low-Cement-Consumption Concrete—Preplaced Aggregate Concrete
Authors: Lv Jing , Zhou Tianhua , Li Kunlun
Published: February 2020

Reducing consumption of cement in concrete will achieve huge benefits in decline of carbon emission, conservation of natural resources and reduction of the cost of concrete. In this paper, the low-cement-consumption concrete, preplaced aggregate concrete (PAC), is prepared and 12 types of mixtures including four water–binder ratios (W/B) and three sand–binder ratios (S/B) are designed to detect the effect of W/B and S/B on the mechanical properties and failure mechanism of PAC. Experimental and analytic results indicate that the cubic compressive strength of PAC, splitting tensile strength of PAC and elastic modulus of PAC decrease with increase in W/B and S/B. At a similar compressive strength, more than 20% increment of elastic modulus of PAC is achieved when compared with normal concrete (NC); the descent stage of stress–strain curves of PAC are steeper than that of NC and the peak strains of PAC is lower than that of NC. Guo’s model with suitable values of parameters a and b can be used to describe the stress–strain relationship of PAC. Replacing NC by PAC in concrete structures will save 15–20% cement and achieve great environmental and economic benefits.
Insights for Sustainability of Smartphone Business: Understanding Customer Switching Behavior in Smartphone Services
Authors: Oh Chang-Gyu , Park Jongpil
Published: February 2020

Competition in smartphone market has been fierce, and customers’ switching has become a primary concern for the success of smartphone businesses. Nonetheless, research on customer switching behavior regarding smartphones is still under investigation. The purpose of this study is to examine how social factors affect users’ smartphone switching intention and how customer rational and emotional factors moderate the customers’ switching behavioral intention. In an attempt to understand the switching behavior of smartphone users, this study develops and tests a model drawn from social factors (e.g., media and peer influence), customer rational factors (e.g., switching experience and confidence in decision making), and customer emotional factors (e.g., anticipated regret and alternative attractiveness). The model is tested with data collected from 302 smartphone users. The results of structural equation modeling analysis demonstrate that, when customers make switching decision, both media influence and peer influence affect behavioral switching intention. Further, this study also revealed that customer rational and emotional factors moderate the effects of media influence and peer influence toward customers’ switching intention. Overall, this study enhances the omnidirectional understanding of the switching behavior of smartphone users and can help smartphone companies in fierce competition to lead to sustainable growth.
Digital Twin Reference Model Development to Prevent Operators’ Risk in Process Plants
Authors: Bevilacqua Maurizio , Bottani Eleonora , Ciarapica Filippo Emanuele , Costantino Francesco , Di Donato Luciano , Ferraro Alessandra , Mazzuto Giovanni , Monteriù Andrea , Nardini Giorgia , Ortenzi Marco , Paroncini Massimo , Pirozzi Marco , Prist Mario , Quatrini Elena , Tronci Massimo , Vignali Giuseppe
Published: February 2020

In the literature, many applications of Digital Twin methodologies in the manufacturing, construction and oil and gas sectors have been proposed, but there is still no reference model specifically developed for risk control and prevention. In this context, this work develops a Digital Twin reference model in order to define conceptual guidelines to support the implementation of Digital Twin for risk prediction and prevention. The reference model proposed in this paper is made up of four main layers (Process industry physical space, Communication system, Digital Twin and User space), while the implementation steps of the reference model have been divided into five phases (Development of the risk assessment plan, Development of the communication and control system, Development of Digital Twin tools, Tools integration in a Digital Twin perspective and models and Platform validation). During the design and implementation phases of a Digital Twin, different criticalities must be taken into consideration concerning the need for deterministic transactions, a large number of pervasive devices, and standardization issues. Practical implications of the proposed reference model regard the possibility to detect, identify and develop corrective actions that can affect the safety of operators, the reduction of maintenance and operating costs, and more general improvements of the company business by intervening both in strictly technological and organizational terms.
Tourists and Local Stakeholders’ Perception of Ecosystem Services Provided by Summer Farms in the Eastern Italian Alps
Authors: Pachoud Carine , Da Re Riccardo , Ramanzin Maurizio , Bovolenta Stefano , Gianelle Damiano , Sturaro Enrico
Published: February 2020

In the Alps, summer farms are temporary units, where cattle are moved during summer to graze on Alpine pastures, which provide multiple ecosystem services (ESs), many of which do not have a market value. This study aimed at understanding and comparing the perceptions of summer farms and of the associated ESs by local stakeholders and tourists in a study area of the province of Trento in the eastern Italian Alps. Thirty-five online questionnaires and two focus groups were realized with local stakeholders involved in the dairy value-chain. Semi-structured interviews were conducted with 405 tourists in two representative summer farms. The perceptions of summer farms differed between local stakeholders, who mainly focused on provisioning ESs, and tourists, who mainly focused on cultural and regulating ESs. Both categories of actors rated positively eight different ESs associated with summer farms, but demonstrated a lack of knowledge of specific regulating ESs. This study showed that discussion among the different actors is required to increase mutual knowledge and to grasp the diversity of links between summer farms and ESs, in order to support public policies and private initiatives for promoting summer farm products and the sustainable development of mountain regions.
A Novel Fixed-Time-Convergent Sliding Mode Technology Using Improved Quantum Particle Swarm Optimization for Renewable Energy Inverters
Authors: Chang En-Chih
Published: February 2020

This paper describes a robust feedback technique involving novel fixed-time-convergent sliding mode technology (NFTCSMT) using improved quantum particle swarm optimization (QPSO) to obtain high-performance renewable energy inverters. Customary SMT encounters long time convergence towards the origin and the influence of the dithering. The NFTCSMT can rapidly impel system-following movement to approach the sliding manifold and effectively accelerate the convergence speed to equilibrium states. However, the NFTCSMT cannot easily select the global optimum of the controller parameters subject to large parameter changes and nonlinear interventions, leading to the dither phenomenon/steady-state error still being caused. The dither inflicts decreased control accuracy, high voltage harmonics, major harm in relation to switching components, and great thermal losses in power electronic converters. The improved QPSO including the unique property of a random compression/expansion factor is used to find optimal parameters of the NFTCSMT in practical applications, for the reason that it importantly mitigates the dither and amends convergent speed as well as guaranteeing global convergence. The presented alliance amid NFTCSMT and improved QPSO achieves faster response time and singularityless, and also yields high-accuracy tracking and dither attenuation. The robust stability using Lyapunov theorem of the suggested system has provided precise mathematical derivations. Simulations show that the suggested controller offers less than 0.1% voltage THD (total harmonic distortion) which exceeds IEEE standard 519 under heavily distorted rectifier loads, and less than 10% voltage dip which surpasses IEEE standard 1159 during step load transients. Experimental tests of an algorithmically controlled laboratory prototype (1 kW, 110 Vrms/60 Hz) of a renewable energy inverter (REI) based on digital signal processing manifest less than 0.05% voltage THD in the face of great inductor-capacitor alterations, and less than 10% voltage dip in the face of transient load scenarios.
Agricultural Practice in Poland Before and After Mandatory IPM Implementation by the European Union
Authors: Sawinska Zuzanna , Świtek Stanisław , Głowicka-Wołoszyn Romana , Kowalczewski Przemysław Łukasz
Published: February 2020

Integrated pest management (IPM), a worldwide agricultural strategy, contains methods to control or manage agricultural pests and diseases in a more efficient way, and consequently, to obtain better quality raw materials for food production. The engagement and practice of farmers play a key role in the success of this strategy. Since January 1, 2014, Poland and other European Union countries have been obligated to apply the principles of IPM. This paper shows the results of surveys conducted in 280 randomly selected farms the year before and the year following mandatory IPM implementation. The aim of this study was to gather information about farmers’ knowledge of IPM and the most commonly used plant protection methods. Our results show that law regulations do not significantly change agricultural practice. Among the non-chemical methods farmers most often comply with are: implementing the agrochemical calendar, sowing healthy material, destroying volunteer plants, rotating crop, applying balanced fertilizer, plowing stubble and preventing excess nitrogen. Integrated plant protection is not possible without proper knowledge of diseases. This factor needs improvement in Poland. The average Polish farmer lacks the knowledge about basic cereal diseases such as powdery mildew or brown rust, though larger farm operators tend to be more knowledgeable. The results of this survey demonstrate the necessity to provide informative farmer training campaigns to promote on-farm application of IPM and to improve the knowledge of disease issues.
Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability
Authors: Choi Eunjeong , Cho Soohwan , Kim Dong Keun
Published: February 2020

The purpose of this study is to design a novel custom power demand forecasting algorithm based on the LSTM Deep-Learning method regarding the recent power demand patterns. We performed tests to verify the error rates of the forecasting module, and to confirm the sudden change of power patterns in the actual power demand monitoring system. We collected the power usage data in every five-minute resolution in a day from some groups of the residential, public offices, hospitals, and industrial factories buildings in one year. In order to grasp the external factors and to predict the power demand of each facility, a comparative experiment was conducted in three ways; short-term, long-term, seasonal forecasting exp[eriments. The seasonal patterns of power demand usages were analyzed regarding the residential building. The overall error rates of power demand forecasting using the proposed LSTM module were reduced in terms of each facility. The predicted power demand data shows a certain pattern according to each facility. Especially, the forecasting difference of the residential seasonal forecasting pattern in summer and winter was very different from other seasons. It is possible to reduce unnecessary demand management costs by the designed accurate forecasting method.
Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method
Authors: Yu Wei , Ye Xiaofei , Chen Jun , Yan Xingchen , Wang Tao
Published: February 2020

The information level of the urban public transport system is constantly improving, which promotes the use of smart cards by passengers. The OD (origination–destination) travel time of passengers reflects the temporal and spatial distribution of passenger flow. It is helpful to improve the flow efficiency of passengers and the sustainable development of the city. It is an urgent problem to select appropriate indexes to evaluate OD travel time and analyze the correlation of these indexes. More than one million OD records are generated by the AFC (Auto Fare Collection) system of Nanjing metro every day. A complex network method is proposed to evaluate and analyze OD travel time. Five working days swiping data of Nanjing metro are selected. Firstly, inappropriate data are filtered through data preprocessing. Then, the OD travel time indexes can be divided into three categories: time index, complex network index, and composite index. Time index includes use time probability, passenger flow between stations, average time between stations, and time variance between stations. The complex network index is based on two models: Space P and ride time, including the minimum number of rides, and the shortest ride time. Composite indicators include inter site flow efficiency and network flow efficiency. Based on the complex network model, this research quantitatively analyzes the Pearson correlation of the indexes of OD travel time. This research can be applied to other public transport modes in combination with big data of public smart cards. This will improve the flow efficiency of passengers and optimize the layout of the subway network and urban space.