bluuebunny/crossref_metadata_2025 huggingface dataset:
PhotonLabeler: An Inter-Disciplinary Platform for Visual Interpretation and Labeling of ICESat-2 Geolocated Photon Data
Authors: Malambo Lonesome , Popescu Sorin
Published: September 2020

NASA’s ICESat-2 space-borne photon-counting lidar mission is providing global elevation measurements that will provide significant benefits to a variety of ecosystem related research applications. Given the novelty of elevation and the derived data products from the ICESat-2 mission, the research community needs software tools that can facilitate photon-level analyses to support product validation and development new analysis methods. Here, we describe PhotonLabeler, a free graphic user interface (GUI) for manual labeling and visualization of ICESat-2 Geolocated Photon data (ATL03). Developed in MATLAB, the GUI facilitates the reading and display of ATL03 Hierarchical Data Format (HDF) files, the manual labeling of individual photons into target classes of choice using a number of point selections tools and enables eventual saving of labeled data in ASCII format. Other capabilities include saving and loading of labeling sessions to manage labeling tasks over time. We expect labeled data generated using the application to serve two main purposes. First, serve as reference data for validating various products from ICESat-2 mission, especially for study sites around the world that do not have existing reference datasets such as airborne lidar. Second, serve as training and validation data in the development of new algorithms for generating various ICESat-2 data products. We demonstrate the first use case through a validation case study for the land and vegetation product (ATL08), which provides canopy and terrain height estimates, over two sites. For the first site, located in northwestern Zambia, we used ICESat-2 ATL03 data acquired at night and for our second site in Texas, US, we used ATL03 data acquired during the day. ATL08 canopy and terrain height data showed good agreement (mean R2 > 0.8) with corresponding height metrics generated from manually labeled data. A comparison between PhotonLabeler and ATL08 photon labels also showed good agreement −93.3% and 95.4% overall accuracies for the Texas and Zambia site, respectively. These results, while limited in scope, show how PhotonLabeler can facilitate photon-level analyses for ICESat-2 data products beyond the ATL08 product. The PhotonLabeler application is freely available as a compiled MATLAB binary to enable free access and utilization by interested researchers.
JMLNet: Joint Multi-Label Learning Network for Weakly Supervised Semantic Segmentation in Aerial Images
Authors: Guo Rongxin , Sun Xian , Chen Kaiqiang , Zhou Xiao , Yan Zhiyuan , Diao Wenhui , Yan Menglong
Published: September 2020

Weakly supervised semantic segmentation in aerial images has attracted growing research attention due to the significant saving in annotation cost. Most of the current approaches are based on one specific pseudo label. These methods easily overfit the wrongly labeled pixels from noisy label and limit the performance and generalization of the segmentation model. To tackle these problems, we propose a novel joint multi-label learning network (JMLNet) to help the model learn common knowledge from multiple noisy labels and prevent the model from overfitting one specific label. Our combination strategy of multiple proposals is that we regard them all as ground truth and propose three new multi-label losses to use the multi-label guide segmentation model in the training process. JMLNet also contains two methods to generate high-quality proposals, which further improve the performance of the segmentation task. First we propose a detection-based GradCAM (GradCAMD) to generate segmentation proposals from object detectors. Then we use GradCAMD to adjust the GrabCut algorithm and generate segmentation proposals (GrabCutC). We report the state-of-the-art results on the semantic segmentation task of iSAID and mapping challenge dataset when training with bounding boxes annotations.
UAV Photogrammetry for Concrete Bridge Inspection Using Object-Based Image Analysis (OBIA)
Authors: Zollini Sara , Alicandro Maria , Dominici Donatella , Quaresima Raimondo , Giallonardo Marco
Published: September 2020

Monitoring infrastructures is becoming an important and challenging issue. In Italy, the heritage consists of more than 60,000 bridges, which need to be inspected and detected in order to guarantee their strength and durability function during nominal lifespan. In this paper, a non-destructive survey methodology for study concrete bridges surface deterioration and viaducts is presented. Terrestrial and unmanned aerial vehicle (UAV) photogrammetry has been used for visual inspection of a standard concrete overpass in L’Aquila (Italy). The obtained orthomosaic has been processed by means of Object-Based Image Analysis (OBIA) to identify and classify deteriorated areas and decay forms. The results show a satisfactory identification and survey of deteriorated areas. It has also been possible to quantify metric information, such as width and length of cracks and extension of weathered areas. This allows to perform easy and fast periodic inspections over time in order to evaluate the evolution of deterioration and plan urgency of preservation or maintenance measures.
A Network Sensor Fusion Approach for a Behaviour-Based Smart Energy Environment for Co-Making Spaces
Authors: Chang Teng-Wen , Huang Hsin-Yi , Hung Chung-Wen , Datta Sambit , McMinn Terrance
Published: September 2020

User behaviour and choice is a significant parameter in the consumption patterns of energy in the built environment. This paper introduces a behavior-based approach for developing smart energy applications. With the rapid development of wireless sensor networks and the Internet of Things (IoT), human-computer interfaces can be created through the mapping of user experiences. These applications can provide users with dynamic feedback on their energy consumption patterns in their built environment. The paper describes a “Sensible Energy System” (SENS) that is based on user experience design methods with sensor network technology. Through SENS, solar energy simulation is combined with device consumption data in order to achieve an IoT network to facilitate the interaction between user behaviors and electricity consumption. The interaction between users and devices through SENS can not only optimize power consumption, but also provide consumers with additional choice and dynamic decision making regarding their consumption. This article provides an (1) understanding and analysis of users’ spatial interaction, explains the (2) planning of the new smart environment design and user experiences, discusses (3) designing a suitable Wireless sensor network (WSN) agent and energy connection, describes (4) the information that has been collected, and (5) incorporates a rooftop solar potential simulation for predicting energy outputs into the sensor network model.
Reliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions
Authors: Jacobsen Malte , Dembek Till A. , Ziakos Athanasios-Panagiotis , Gholamipoor Rahil , Kobbe Guido , Kollmann Markus , Blum Christopher , Müller-Wieland Dirk , Napp Andreas , Heinemann Lutz , Deubner Nikolas , Marx Nikolaus , Isenmann Stefan , Seyfarth Melchior
Published: September 2020

Atrial fibrillation (AF) is the most common arrhythmia and has a major impact on morbidity and mortality; however, detection of asymptomatic AF is challenging. This study aims to evaluate the sensitivity and specificity of non-invasive AF detection by a medical wearable. In this observational trial, patients with AF admitted to a hospital carried the wearable and an ECG Holter (control) in parallel over a period of 24 h, while not in a physically restricted condition. The wearable with a tight-fit upper armband employs a photoplethysmography technology to determine pulse rates and inter-beat intervals. Different algorithms (including a deep neural network) were applied to five-minute periods photoplethysmography datasets for the detection of AF. A total of 2306 h of parallel recording time could be obtained in 102 patients; 1781 h (77.2%) were automatically interpretable by an algorithm. Sensitivity to detect AF was 95.2% and specificity 92.5% (area under the receiver operating characteristics curve (AUC) 0.97). Usage of deep neural network improved the sensitivity of AF detection by 0.8% (96.0%) and specificity by 6.5% (99.0%) (AUC 0.98). Detection of AF by means of a wearable is feasible in hospitalized but physically active patients. Employing a deep neural network enables reliable and continuous monitoring of AF.
Deep Learning Approach for Multimodal Biometric Recognition System Based on Fusion of Iris, Face, and Finger Vein Traits
Authors: Alay Nada , Al-Baity Heyam H.
Published: September 2020

With the increasing demand for information security and security regulations all over the world, biometric recognition technology has been widely used in our everyday life. In this regard, multimodal biometrics technology has gained interest and became popular due to its ability to overcome a number of significant limitations of unimodal biometric systems. In this paper, a new multimodal biometric human identification system is proposed, which is based on a deep learning algorithm for recognizing humans using biometric modalities of iris, face, and finger vein. The structure of the system is based on convolutional neural networks (CNNs) which extract features and classify images by softmax classifier. To develop the system, three CNN models were combined; one for iris, one for face, and one for finger vein. In order to build the CNN model, the famous pertained model VGG-16 was used, the Adam optimization method was applied and categorical cross-entropy was used as a loss function. Some techniques to avoid overfitting were applied, such as image augmentation and dropout techniques. For fusing the CNN models, different fusion approaches were employed to explore the influence of fusion approaches on recognition performance, therefore, feature and score level fusion approaches were applied. The performance of the proposed system was empirically evaluated by conducting several experiments on the SDUMLA-HMT dataset, which is a multimodal biometrics dataset. The obtained results demonstrated that using three biometric traits in biometric identification systems obtained better results than using two or one biometric traits. The results also showed that our approach comfortably outperformed other state-of-the-art methods by achieving an accuracy of 99.39%, with a feature level fusion approach and an accuracy of 100% with different methods of score level fusion.
Research on Temperature Variation during Coal and Gas Outbursts: Implications for Outburst Prediction in Coal Mines
Authors: Zhang Chaolin , Wang Enyuan , Xu Jiang , Peng Shoujian
Published: September 2020

Coal and gas outbursts are among the most severe disasters threatening the safety of coal mines around the world. They are dynamic phenomena characterized by large quantities of coal and gas ejected from working faces within a short time. Numerous researchers have conducted studies on outburst prediction, and a variety of indices have been developed to this end. However, these indices are usually empirical or based on local experience, and the accurate prediction of outbursts is not feasible due to the complicated mechanisms of outbursts. This study conducts outburst experiments using large-scale multifunctional equipment developed in the laboratory to develop a more robust outburst prediction method. In this study, the coal temperature during the outburst process was monitored using temperature sensors. The results show that the coal temperature decreased rapidly as the outburst progressed. Meanwhile, the coal temperature in locations far from the outburst mouth increased. The coal broken in the stress concentration state is the main factor causing the abnormal temperature rise. The discovery of these phenomena lays a theoretical foundation and provides an experimental basis for an effective outburst prediction method. An outburst prediction method based on monitoring temperature was proposed, and has a simpler and faster operation process and is not easily disturbed by coal mining activities. What is more, the critical values of coal temperature rises or temperature gradients can be flexibly adjusted according to the actual situations of different coal mines to predict outbursts more effectively and accurately.
Reinforcement Learning Based Fast Self-Recalibrating Decoder for Intracortical Brain–Machine Interface
Authors: Zhang Peng , Chao Lianying , Chen Yuting , Ma Xuan , Wang Weihua , He Jiping , Huang Jian , Li Qiang
Published: September 2020

Background: For the nonstationarity of neural recordings in intracortical brain–machine interfaces, daily retraining in a supervised manner is always required to maintain the performance of the decoder. This problem can be improved by using a reinforcement learning (RL) based self-recalibrating decoder. However, quickly exploring new knowledge while maintaining a good performance remains a challenge in RL-based decoders. Methods: To solve this problem, we proposed an attention-gated RL-based algorithm combining transfer learning, mini-batch, and weight updating schemes to accelerate the weight updating and avoid over-fitting. The proposed algorithm was tested on intracortical neural data recorded from two monkeys to decode their reaching positions and grasping gestures. Results: The decoding results showed that our proposed algorithm achieved an approximate 20% increase in classification accuracy compared to that obtained by the non-retrained classifier and even achieved better classification accuracy than the daily retraining classifier. Moreover, compared with a conventional RL method, our algorithm improved the accuracy by approximately 10% and the online weight updating speed by approximately 70 times. Conclusions: This paper proposed a self-recalibrating decoder which achieved a good and robust decoding performance with fast weight updating and might facilitate its application in wearable device and clinical practice.
Numerical Analysis and Recursive Compensation of Position Deviation for a Sub-Millimeter Resolution OFDR
Authors: Cheng Yueying , Luo Mingming , Liu Jianfei , Luan Nannan
Published: September 2020

We analyze the source of the position deviation and propose a demodulation recursive compensation algorithm to ensure a sub-millimeter resolution in improved optical frequency domain reflectometry. The position deviation between the geometric path and optical path changes with the temperature or strain, due to the elastic-optic and thermal-optic effects. It accumulates along the fiber and becomes large enough to affect the spectral correlation between the measured and reference spectra at the fiber end. The proposed algorithm compensates for the position deviation of each measuring point and aligns the measured spectra with its reference. The numerical and experimental results both reveal that the signal-to-noise ratio of the correlation is improved doubly and a sub-millimeter spatial resolution becomes available at a 30 m fiber end. The recursive compensation algorithm helps to restrain the correlation degeneration at the fiber end and promises an effective approach to a sub-millimeter resolution in optical frequency domain reflectometry.
Simultaneously Low Rank and Group Sparse Decomposition for Rolling Bearing Fault Diagnosis
Authors: Zheng Kai , Bai Yin , Xiong Jingfeng , Tan Feng , Yang Dewei , Zhang Yi
Published: September 2020

Singular value decomposition (SVD) methods have aroused wide concern to extract the periodic impulses for bearing fault diagnosis. The state-of-the-art SVD methods mainly focus on the low rank property of the Hankel matrix for the fault feature, which cannot achieve satisfied performance when the background noise is strong. Different to the existing low rank-based approaches, we proposed a simultaneously low rank and group sparse decomposition (SLRGSD) method for bearing fault diagnosis. The major contribution is that the simultaneously low rank and group sparse (SLRGS) property of the Hankel matrix for fault feature is first revealed to improve performance of the proposed method. Firstly, we exploit the SLRGS property of the Hankel matrix for the fault feature. On this basis, a regularization model is formulated to construct the new diagnostic framework. Furthermore, the incremental proximal algorithm is adopted to achieve a stationary solution. Finally, the effectiveness of the SLRGSD method for enhancing the fault feature are profoundly validated by the numerical analysis, the artificial bearing fault experiment and the wind turbine bearing fault experiment. Simulation and experimental results indicate that the SLRGSD method can obtain superior results of extracting the incipient fault feature in both performance and visual quality as compared with the state-of-the-art methods.
Developing Artefact Removal Algorithms to Process Data from a Microwave Imaging Device for Haemorrhagic Stroke Detection
Authors: Sohani Behnaz , Puttock James , Khalesi Banafsheh , Ghavami Navid , Ghavami Mohammad , Dudley Sandra , Tiberi Gianluigi
Published: September 2020

In this paper, we present an investigation of different artefact removal methods for ultra-wideband Microwave Imaging (MWI) to evaluate and quantify current methods in a real environment through measurements using an MWI device. The MWI device measures the scattered signals in a multi-bistatic fashion and employs an imaging procedure based on Huygens principle. A simple two-layered phantom mimicking human head tissue is realised, applying a cylindrically shaped inclusion to emulate brain haemorrhage. Detection has been successfully achieved using the superimposition of five transmitter triplet positions, after applying different artefact removal methods, with the inclusion positioned at 0°, 90°, 180°, and 270°. The different artifact removal methods have been proposed for comparison to improve the stroke detection process. To provide a valid comparison between these methods, image quantification metrics are presented. An “ideal/reference” image is used to compare the artefact removal methods. Moreover, the quantification of artefact removal procedures through measurements using MWI device is performed.
The Feasibility of Modelling the Crown Profile of Larix olgensis Using Unmanned Aerial Vehicle Laser Scanning Data
Authors: Quan Ying , Li Mingze , Zhen Zhen , Hao Yuanshuo , Wang Bin
Published: September 2020

Unmanned aerial vehicle (UAV) laser scanning, as an emerging form of near-ground light detection and ranging (LiDAR) remote sensing technology, is widely used for crown structure extraction due to its flexibility, convenience, and high point density. Herein, we evaluated the feasibility of using a low-cost UAV-LiDAR system to extract the fine-scale crown profile of Larix olgensis. Specifically, individual trees were isolated from LiDAR point clouds and then stratified from the point clouds of segmented individual tree crowns at 0.5 m intervals to obtain the width percentiles of each layer as profile points. Four equations (the parabola, Mitscherlich, power, and modified beta equations) were then applied to model the profiles of the entire and upper crown. The results showed that a region-based hierarchical cross-section analysis algorithm can successfully delineate 77.4% of the field-measured trees in high-density (>2400 trees/ha) forest stands. The crown profile generated with the 95th width percentile was adequate when compared with the predicted value of the existing field-based crown profile model (the Pearson correlation coefficient (ρ) was 0.864, root mean square error (RMSE) = 0.3354 m). The modified beta equation yielded slightly better results than the other equations for crown profile fitting and explained 85.9% of the variability in the crown radius for the entire crown and 87.8% of this variability for the upper crown. Compared with the cone and 3D convex hull volumes, the crown volumes predicted by our profile models had significantly smaller errors. The results revealed that the crown profile can be well described by using UAV-LiDAR, providing a novel way to obtain crown profile information without destructive sampling and showing the potential of the use of UAV-LiDAR in future forestry investigations and monitoring.
Resolution Improvement of Light Field Imaging via a Nematic Liquid Crystal Microlens with Added Multi-Walled Carbon Nanotubes
Authors: Li Hui , Yu Yi , Peng Jing , Wu Yuntao , Zhang Yanduo
Published: September 2020

A relatively simple method to improve the image resolution of light field based on a liquid crystal (LC) microlens doped with multi-walled carbon nanotubes (MWCNTs) was developed and evaluated. As the nanoparticles were doped in LC, its electro-optical features could enhance, leading to a short response time compared to the pure LC microlens. With the maximum use of the proposed LC microlens, a method combining aperiodicity extraction and weighted average algorithm was adopted to realize the high-resolution light field imaging. The aperiodicity extraction method was proposed, which could effectively improve resolution of view angle image. For synthesizing the full resolution image at 0 Vrms and the extracted view angle image of light field imaging at 2.0 Vrms, the final high-resolution light field imaging could be obtained in a short time by weighted average algorithm. In this way, the common problem of low resolution in light field imaging could be solved. This proposed method was in good agreement with our experimental results. And it was also in line with the development of the trend of the smart imaging sensor combining algorithm with hardware.
Fusing Visual Attention CNN and Bag of Visual Words for Cross-Corpus Speech Emotion Recognition
Authors: Seo Minji , Kim Myungho
Published: September 2020

Speech emotion recognition (SER) classifies emotions using low-level features or a spectrogram of an utterance. When SER methods are trained and tested using different datasets, they have shown performance reduction. Cross-corpus SER research identifies speech emotion using different corpora and languages. Recent cross-corpus SER research has been conducted to improve generalization. To improve the cross-corpus SER performance, we pretrained the log-mel spectrograms of the source dataset using our designed visual attention convolutional neural network (VACNN), which has a 2D CNN base model with channel- and spatial-wise visual attention modules. To train the target dataset, we extracted the feature vector using a bag of visual words (BOVW) to assist the fine-tuned model. Because visual words represent local features in the image, the BOVW helps VACNN to learn global and local features in the log-mel spectrogram by constructing a frequency histogram of visual words. The proposed method shows an overall accuracy of 83.33%, 86.92%, and 75.00% in the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), the Berlin Database of Emotional Speech (EmoDB), and Surrey Audio-Visual Expressed Emotion (SAVEE), respectively. Experimental results on RAVDESS, EmoDB, SAVEE demonstrate improvements of 7.73%, 15.12%, and 2.34% compared to existing state-of-the-art cross-corpus SER approaches.
Classification and Segmentation of Longitudinal Road Marking Using Convolutional Neural Networks for Dynamic Retroreflection Estimation
Authors: Chun Chanjun , Lee Taehee , Kwon Sungil , Ryu Seung-Ki
Published: September 2020

Road markings constitute one of the most important elements of the road. Moreover, they are managed according to specific standards, including a criterion for a luminous contrast, which can be referred to as retroreflection. Retroreflection can be used to measure the reflection properties of road markings or other road facilities. It is essential to manage retroreflection in order to improve road safety and sustainability. In this study, we propose a dynamic retroreflection estimation method for longitudinal road markings, which employs a luminance camera and convolutional neural networks (CNNs). The images that were captured by a luminance camera were input into a classification and regression CNN model in order to determine whether the longitudinal road marking was accurately acquired. A segmentation model was also developed and implemented in order to accurately present the longitudinal road marking and reference plate if a longitudinal road marking was determined to exist in the captured image. The retroreflection was dynamically measured as a driver drove along an actual road; consequently, the effectiveness of the proposed method was demonstrated.
Teenage Drivers’ Views of a Classroom and Closed-Road Post-License Advanced Driving Program, Guard Your Life
Authors: Mims Lauren , Brooks Johnell O. , Jenkins Casey , Schwambach Breno , Gubitosa Daniel
Published: September 2020

In the US, teenage drivers have an increased risk of being involved in crashes. To address this, post-license advanced driving programs have emerged. This study gains teenagers’ perspective of a post-license defensive driving program focused on a hands-on introduction to emergency braking, skid recovery and the dangers of distracted driving. The teenagers completed a survey immediately following the program and a phone interview three months later. The open-ended survey and phone interview items reflect the program’s key concepts. During the follow-up phone interview, the majority of teenagers reported using the skills experienced and half of the participants who participated in the phone interview reported using skills that they learned to avoid a crash. Almost all teenagers reported anticipating or changing their driving behaviors, specifically by reducing distractions, having a heightened awareness and changing their driving position. The survey and follow-up phone interview results suggest that the teenagers benefited from the skills introduced and, from the teenagers’ perspective, has helped them avoid crashes.
A Reflexive GOAL Framework for Achieving Student-Centered Learning in European Higher Education: From Class Learning to Community Engagement
Authors: S. Constantinou Costas
Published: September 2020

Student-centered learning (SCL) has been acknowledged and supported by research as very important for helping students develop life-long independent learning and transferable skills. Yet the implementation of SCL in European higher education has been patchy where experts in the field call for the need for a framework that could guide higher education institutions (HEIs) in designing and implementing SCL. This paper aims to fill in this identified gap by reflecting on the basic literature and social theory to propose the reflexive GOAL (Goals (vision and objectives), Organization (structures), Actions (immersion into structures and actions), and Learning a culture (instilled through reflexivity)) framework for the implementation of SCL in higher education in its broader sense to encompass elements from in-class learning to extra-curricular and community engagement.
Financial Development, Institutional Quality, and Environmental Degradation Nexus: New Evidence from Asymmetric ARDL Co-Integration Approach
Authors: Ahmed Farhan , Kousar Shazia , Pervaiz Amber , Ramos-Requena José Pedro
Published: September 2020

The aim of this study is threefold; first, the study investigates the symmetric impact of trade openness, financial development, and institutional quality on environmental degradation and environmental sustainability. Second, the study examines the asymmetric relationship between financial development, institutional quality, and environmental degradation. Third, the study examines the asymmetric relationship between financial development, institutional quality, and environmental sustainability. For this purpose, the study utilized the data of Pakistan from 1996 to 2018. The study applied Augmented Dickey–Fuller (ADF), Phillips Parron (PP) and Zivote, and Andrews unit root test to check the properties of stationarity of the data. This study applied the Auto Regressive Distributive Lags (ARDL) model to investigate symmetric relationships while the Non-Linear Auto Regressive Distributive Lag Model (NARDL) approach is utilized to investigate the asymmetric relationship among variables. ARDL bounds testing approach utilized to investigate long-run co-integration while short-run dynamics have been investigated by applying the error correction method (ECM). This study found the significant long-run symmetric and asymmetric association of institutional quality (IQ) and financial development (FD) with environmental degradation (ED) and environmental sustainability. However, IQ- has an insignificant association with environmental sustainability. Moreover, dynamic multiplier analysis indicates that positive shock to FD and IQ has a stronger impact on environmental degradation while a positive or negative shock to FD; both have a stronger impact on environmental sustainability. However, a positive or negative shock to IQ has a smaller impact on environmental sustainability. Moreover, the study also found a significant long-run symmetric association of trade openness with environmental degradation and environmental sustainability. This study suggests that the quality of institutions, financial development, and trade openness is necessary to enhance the quality of the environment.
Sustaining the Family Business through Open Innovation: The Role of Technological Acquisitions in Shareholder Value Creation
Authors: Ahn He Soung
Published: September 2020

Although technological acquisitions have attracted much attention as a prominent means of open innovation that allows firms to complement internal innovation, their shareholder value creation effects should be influenced by whether the acquiring family businesses can successfully realize technological synergies. Thus, the purpose of this paper is to investigate whether market participants perceive family businesses to create more value when undertaking technological acquisitions. Using a sample of 614 acquisitions by 71 family businesses between 2000 and 2014 in South Korea, an event study methodology is adopted. Empirical analysis yields strong support for the prediction that the stock market does not perceive family businesses to be able to create superior synergy through technological acquisitions compared to when they undertake nontechnological acquisitions. The competitive landscape also has implications for the shareholder value creation effects of technological acquisitions by family businesses. Overall, the findings of this paper provide insight into the shareholder wealth implications of technological acquisitions undertaken by family businesses.
Lattice Boltzmann Method Applied to Nuclear Reactors—A Systematic Literature Review
Authors: Bocanegra Cifuentes Johan Augusto , Borelli Davide , Cammi Antonio , Lomonaco Guglielmo , Misale Mario
Published: September 2020

Nuclear engineering requires computationally efficient methods to simulate different components and systems of plants. The Lattice Boltzmann Method (LBM), a numerical method with a mesoscopic approach to Computational Fluid Dynamic (CFD) derived from the Boltzmann equation and the Maxwell–Boltzmann distribution, can be an adequate option. The purpose of this paper is to present a review of the recent applications of the Lattice Boltzmann Method in nuclear engineering research. A systematic literature review using three databases (Web of Science, Scopus, and ScienceDirect) was done, and the items found were categorized by the main research topics into computational fluid dynamics and neutronic applications. The features of the problem addressed, the characteristics of the numerical method, and some relevant conclusions of each study are resumed and presented. A total of 45 items (25 for computational fluid dynamics applications and 20 for neutronics) was found on a wide range of nuclear engineering problems, including thermal flow, turbulence mixing of coolant, sedimentation of impurities, neutron transport, criticality problem, and other relevant issues. The LBM results in being a flexible numerical method capable of integrating multiphysics and hybrid schemes, and is efficient for the inner parallelization of the algorithm that brings a widely applicable tool in nuclear engineering problems. Interest in the LBM applications in this field has been increasing and evolving from early stages to a mature form, as this review shows.
Pathways to Scaling Agroecology in the City Region: Scaling out, Scaling up and Scaling deep through Community-Led Trade
Authors: Nicol Poppy
Published: September 2020

Scaling agroecology has the potential to support more sustainable and just food futures. This article investigates a case of community-led trade operating in the city region of London. Drawing upon interviews with stakeholders and practice-based ethnographic fieldwork, challenges and opportunities for agroecology are examined. Three dimensions of scaling agroecology are identified as pathways to sustainable and just food futures in the city region: scaling out, scaling up and scaling deep. Findings suggest scaling out agroecology requires access to secure, affordable land and infrastructure for agroecological communities of practice, alongside investment in capacity building for agroecological communities of practice via learning platforms (such as training programmes) and knowledge exchange (such as farmer-to-farmer and trader-to-trader learning). Second, scaling up agroecology requires transformations in policy, planning and legislation that value and invest in agroecological practices and divest in unsustainable and unjust food systems, supported via translocal networks for exchange of good practice. Third, scaling deep agroecology requires investment both in transformative learning opportunities and networks that support agroecological communities of practices, including those with lived experience of food injustice. Findings have implications for the question regarding scaling agroecology in the city region.
More Sustainable Bioenergy by Making Use of Regional Alternative Biomass?
Authors: Pehlken Alexandra , Wulf Kalle , Grecksch Kevin , Klenke Thomas , Tsydenova Nina
Published: September 2020

Bioenergy is a building block of the ongoing transformation toward renewables-based energy systems. Bioenergy supply chains are regionally embedded and need to be seen in a place-based context with specific characteristics and constraints. Using a German case study, the potential of regionally embedded bioenergy chains in the past and the future is analyzed and discussed in this paper. The analysis integrates socio-ecological data and applies sustainability criteria in a multi-criteria decision analysis (MCDA) using the Preference Ranking Organization Method for Enriched Evaluation (PROMETHEE) methodology. The case study is focused on an industrial biogas fermenter in northwestern Germany, which currently uses predominantly maize as a substrate for bioenergy. Objectives for future development according to the ambitions of the UN Sustainable Development Goals and the EU Renewable Energy Directive (RED II) discussion are set and include the involvement of the farmer as biogas plant operator and other regional stakeholders. Since the focus of the research is put on the contribution of alternative biomass, such as grass, for the optimization of bioenergy settings, the question concentrates on how different mixtures of alternative biomass can be embedded into a sustainable management of both the landscape and the energy system. The main findings are threefold: (i) bioenergy supply chains that involve alternative biomass and grass from grasslands provide optimization potentials compared to the current corn-based practice, (ii) with respect to more sustainable practices, grass from grassland and alternative bioenergy supply chains are ranked higher than chains with increased shares of corn silage, and, more generic, (iii) optimization potentials relate to several spheres of the social–ecological system where the bioenergy structure is embedded. To conclude, sustainable enablers are discussed to realize optimization potentials and emphasize the integration of regional stakeholders in making use of alternative biomass and in making regional bioenergy more sustainable.
Is the Time-Domain Reflectometry (TDR) Technique Suitable for Moisture Content Measurement in Low-Porosity Building Materials?
Authors: Freitas Teresa Stingl , Guimarães Ana Sofia , Roels Staf , Peixoto de Freitas Vasco , Cataldo Andrea
Published: September 2020

Measuring moisture content in building materials is essential both for professional practice and for research. However, this is a very complex task, especially when long-term minor destructive measurements are desired. The time-domain reflectometry (TDR) technique is commonly used for soil moisture measurements, but its application in construction materials is considered a relatively new method, particularly for low-porosity building materials. The major obstacles to its current use in construction materials are (1) the difficulty of ensuring good contact between the TDR probe and the material, and (2) the lack of appropriate conversion functions between the measured relative permittivity and the moisture content of building materials. This paper intends to contribute to overcoming these difficulties by explaining in detail all the required steps to monitor moisture content in real-scale limestone walls. For that, a device is presented to guarantee the correct installation of the TDR probes on the walls, and a calibration procedure through the gravimetric method is proposed to avoid the use of an unsuitable calibration function developed for soil moisture measurements. In addition, the importance of the individual probe calibration is discussed, as well as TDR advantages and disadvantages for construction materials. The results obtained so far reveal that the TDR technique is suitable to detect moisture content variations in limestone, which is a low-porosity building material.
Information Asymmetry as a Barrier in Upgrading the Position of Local Producers in the Global Value Chain—Evidence from the Apple Sector in Poland
Authors: Pietrzak Michał , Chlebicka Aleksandra , Kraciński Paweł , Malak-Rawlikowska Agata
Published: September 2020

The typical approach in the business strand of literature on inter-organisational forms of cooperation is based on the Porter’s value chain model or on the body of literature related to the supply chain. However, there is extended research on value chains based on a different theoretical tradition, rooted in world-systems theory and commodity chain concepts, which recently tend to merge under the umbrella of the Global Value Chain (GVC). We use this eclectic approach as a theoretical framework to investigate the issue of informational asymmetries considered as a barrier in upgrading the position of local producers in the GVC by enhancing quality. As an empirical illustration, we use the Polish apple sector. Poland is one of the largest apple producers in the world with a strong export orientation and linkages with the global value chain. The study provides an insight into the barriers of upgrading the position in GVC and ways to overcome them. Responsibility for the final quality offered for the end-user is strongly dispersed across many actors in the chain, while interrelations between them are plagued by the information asymmetry problem. Therefore, the upstream transmission of end-user quality expectations within the chain fails due to the lack of orchestrating incentives and causes the misbehaviour in conducting different activities in the chain. Thus, attempts to upgrade the position of Polish apple growers in the GVC should be focused on overcoming information asymmetries. Mechanisms such as branding, standardisation and certification seem to be promising ways forward.
Using Remote Sensing Data to Study the Coupling Relationship between Urbanization and Eco-Environment Change: A Case Study in the Guangdong-Hong Kong-Macao Greater Bay Area
Authors: Wu Zijing , Li Zhijian , Zeng Hui
Published: September 2020

Promoting the coordinated development of urbanization and eco-environment is essential for the development of urban agglomerations. As an emerging economic growth pole in China, the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has become a research hotspot in recent years. However, relevant studies in this area have been largely constrained by the incomparability of statistical data between the inland part of the GBA and the two Special Administrative Regions (Hong Kong and Macao) of the GBA. This study used nighttime light data and the normalized difference vegetation index (NDVI) to evaluate the level of urbanization and eco-environment in the GBA, respectively. Then we adopted the gravity center model to analyze the overall coupling situation between urbanization and eco-environment change. We also adopted a coordination index to determine the spatial differentiation characteristics of this coupling in the GBA from 2000 to 2018. The results show that (1) the spatial pattern of urbanization and eco-environment both show an approximately circular structure, and the change rates of these two variables show significant spatiotemporal differentiation; (2) on the whole, urban development and eco-environment construction became more coupled in the GBA during 2000–2018, as indicated by the continuously decreasing distance between the gravity centers of urbanization and eco-environment; and (3) as for the spatial differentiation characteristic of this coupling, the GBA was generally dominated by slightly uncoupled units, while the spatial distribution of different coupling types transformed from a circular structure to a relatively random form.
Estimation of Daily Stage–Discharge Relationship by Using Data-Driven Techniques of a Perennial River, India
Authors: Kumar Manish , Kumari Anuradha , Kushwaha Daniel Prakash , Kumar Pravendra , Malik Anurag , Ali Rawshan , Kuriqi Alban
Published: September 2020

Modeling the stage-discharge relationship in river flow is crucial in controlling floods, planning sustainable development, managing water resources and economic development, and sustaining the ecosystem. In the present study, two data-driven techniques, namely wavelet-based artificial neural networks (WANN) and a support vector machine with linear and radial basis kernel functions (SVM-LF and SVM-RF), were employed for daily discharge (Q) estimation. The hydrological data of daily stage (H) and discharge (Q) from June to October for 10 years (2004–2013) at the Govindpur station, situated in the Burhabalang river basin, Orissa, were considered for analysis. For model construction, an optimum number of inputs (lags) was extracted using the partial autocorrelation function (PACF) at a 5% level of significance. The outcomes of the WANN, SVM-LF, and SVM-RF models were appraised over the observed value of Q based on performance indicators, viz., root mean square error (RMSE), Nash–Sutcliffe efficiency (NSE), Pearson’s correlation coefficient (PCC), and Willmott index (WI), and through visual inspection (time variation, scatter plot, and Taylor diagram). Results of the evaluation showed that the SVM-RF model (RMSE = 104.426 m3/s, NSE = 0.925, PCC = 0.964, WI = 0.979) outperformed the WANN and SVM-LF models with the combination of three inputs, i.e., current stage, one-day antecedent stage, and discharge, during the testing period. In addition, the SVM-RF model was found to be more reliable and robust than the other models and having important implications for water resources management at the study site.
Globalization and Regionalization: Empirical Evidence from Itinerary Structure and Port Organization of World Cruise of Cunard
Authors: Li Xumao , Wang Chengjin , Ducruet César
Published: September 2020

Cruise tourism is an obviously global industry in different dimensions. From a geographical perspective, cruise ships are mobile and capable of being repositioned at a company’s notice, which forms the inherent basis for its global spatial layout. As a branch of the cruise industry, the world cruise is clearly globalizing in geographical space by offering long itinerary, even round-the-world trips, for everyone. Using the schedule data from 2018 to 2019, this paper analyzes the spatial characteristics of the itinerary and port organization of Cunard, a world cruise company. We find that the itinerary distribution and port organization of Cunard are both globalization and regionalization, and the latter is the core and main component of the former. Under the influence of the COVID-19 epidemic, the global mobility of cruises has ground to a halt, while local mobility offers the possibility of its resumption as soon as possible. Turning to the regional voyage with the shorter and simple itinerary is feasible for Cunard and other world cruises, which is conducive to the realization of the resuming voyage as soon as possible. Moreover, strict boarding and safety onboard are essential.
Sustainable Circular Mobility: User-Integrated Innovation and Specifics of Electric Vehicle Owners
Authors: Wurster Simone , Heß Philipp , Nauruschat Michael , Jütting Malte
Published: September 2020

The circular economy (CE) represents an environmentally and sustainability-focused economic paradigm that has gained momentum in recent years. Innovation ecosystems are the evolving interconnected sets of actors, activities, artefacts, and institutions who are vital to the innovative performances of single actors or actor groups consisting largely of firms in the products and services sector. To develop sustainable CE ecosystems, participating firms need to involve the consumers and users in their innovation processes. The automotive industry is to a large extent an industry in which incorporating customer requirements in product development is critical to success. In addition, growing expectations and growing awareness of environmental issues drive the industry to develop environmentally friendly products. However, CE solutions and, specifically, sustainable tyres have not yet been given due consideration. Likewise, the specific preferences of the end-users of sustainability-focused cars such as electric vehicles (EVs) and users of biofuels are unknown in the CE context so far. Based on the current state of research, this article addresses an important, unexplored topic of product circularity. Being the first article on consumer interests and active contributions to CE automotive products, it also extends the first articles on CE software products. A survey of 168 traditional car owners (no EV/biofuels users), 29 users of biofuels, and 40 EV affine consumers was conducted in Germany to create an empirical foundation for the specification of CE configuration software for sustainable automotive products, particularly sustainable tyres. The results show different preferences among these user groups, but also the importance of other characteristics not captured by the distinction by car ownership. In particular, the perception of climate change and the use of test reports or rating portals were variables that had significant influence on configuration preferences.