bluuebunny/crossref_metadata_2025 huggingface dataset:
A PAPR Reduction Technique for Fast Touch Sensors Adopting a Multiple Frequency Driving Method on Large Display Panels
Authors: Kim Piljoong , Han Sanghyun , Jung Yunho , Lee Seongjoo
Published: January 2021

The multiple frequency driving method (MFDM) capacitive touch system (CTS), which drives transmit (TX) electrodes in parallel, has been developed to improve the touch-sensitivity of large touch screens at high speed. However, when driving multiple TX electrodes at the same time, TX signals are merged through the touch panel, which results in increasing the peak-to-average power ratio (PAPR) of combined signals. Due to the high PAPR, the signal is distorted out of the power amplifier’s linear range, causing a touch malfunction. The MFDM CTS can avoid this problem by reducing the drive voltage or partially driving the TX electrodes in parallel. However, these methods cause a significant performance drop with respect to signal-to-noise ratio (SNR) in the MFDM systems. This paper proposes a stack method which reduces PAPR effectively without the performance degradation of MFDM and achieves real-time touch sensitivity in large display panels. The proposed method allocates a suitable phase for each TX electrode to reduce the peak power of combined signals. Instead of investigating all of the phases for the total number of TX electrodes, the optimal phase is estimated from the highest frequency to the lowest one and fixed one by one, which can reduce the required time to find a suitable phase considerably. As a result, it enables high-speed sensing of multi-touch on a large touch screen and effectively reduces PAPR to secure high signal-to-noise-ratio (SNR). Through experiments, it was verified that the proposed method in this paper has an SNR of 39.36 dB, achieving a gain of 19.35 and 5.98 dB compared to the existing touch system method and the algorithm used in the communication system, respectively.
WSN-SES/MB: System Entity Structure and Model Base Framework for Large-Scale Wireless Sensor Networks
Authors: Nam Su Man , Kim Hyung Jong
Published: January 2021

Large-scale wireless sensor networks are characterized by stringent energy and computation restrictions. It is exceedingly difficult to change a sensor network’s environment configurations, such as the number of sensor nodes, after deployment of the nodes. Although several simulators are able to variously construct simulation models for sensor networks before their deployment, the configurations should be modified with extra human effort as the simulators cannot freely generate diverse models. In this paper, we propose a novel framework, called a system entity structure and model base for large-scale wireless sensor networks (WSN-SES/MB), which is based on discrete event system specification formalism. Our proposed framework synthesizes the structure and models for sensor networks through our modeling construction process. The proposed framework achieves time and cost savings in constructing discrete event simulation-based models. In addition, the framework increases the diversity of simulation models by the process’s pruning algorithm. The simulation results validate that the proposed framework provides up to 8% time savings and up to 23% cost savings as compared to the manual extra effort.
Image-Based Automatic Watermeter Reading under Challenging Environments
Authors: Hong Qingqi , Ding Yiwei , Lin Jinpeng , Wang Meihong , Wei Qingyang , Wang Xianwei , Zeng Ming
Published: January 2021

With the rapid development of artificial intelligence and fifth-generation mobile network technologies, automatic instrument reading has become an increasingly important topic for intelligent sensors in smart cities. We propose a full pipeline to automatically read watermeters based on a single image, using deep learning methods to provide new technical support for an intelligent water meter reading. To handle the various challenging environments where watermeters reside, our pipeline disentangled the task into individual subtasks based on the structures of typical watermeters. These subtasks include component localization, orientation alignment, spatial layout guidance reading, and regression-based pointer reading. The devised algorithms for orientation alignment and spatial layout guidance are tailored to improve the robustness of our neural network. We also collect images of watermeters in real scenes and build a dataset for training and evaluation. Experimental results demonstrate the effectiveness of the proposed method even under challenging environments with varying lighting, occlusions, and different orientations. Thanks to the lightweight algorithms adopted in our pipeline, the system can be easily deployed and fully automated.
A Methodology for Discriminant Time Series Analysis Applied to Microclimate Monitoring of Fresco Paintings
Authors: Ramírez Sandra , Zarzo Manuel , Perles Angel , García-Diego Fernando-Juan
Published: January 2021

The famous Renaissance frescoes in Valencia’s Cathedral (Spain) have been kept under confined temperature and relative humidity (RH) conditions for about 300 years, until the removal of the baroque vault covering them in 2006. In the interest of longer-term preservation and in order to maintain these frescoes in good condition, a unique monitoring system was implemented to record both air temperature and RH. Sensors were installed at different points at the vault of the apse during the restoration process. The present study proposes a statistical methodology for analyzing a subset of RH data recorded by the sensors in 2008 and 2010. This methodology is based on fitting different functions and models to the time series, in order to classify the different sensors.The methodology proposed, computes classification variables and applies a discriminant technique to them. The classification variables correspond to estimates of model parameters of and features such as mean and maximum, among others. These features are computed using values of functions such as spectral density, sample autocorrelation (sample ACF), sample partial autocorrelation (sample PACF), and moving range (MR). The classification variables computed were structured as a matrix. Next, sparse partial least squares discriminant analysis (sPLS-DA) was applied in order to discriminate sensors according to their position in the vault. It was found that the classification of sensors derived from Seasonal ARIMA-TGARCH showed the best performance (i.e., lowest classification error rate). Based on these results, the methodology applied here could be useful for characterizing the differences in RH, measured at different positions in a historical building.
Application of Fiber Bragg Grating Sensor Technology to Leak Detection and Monitoring in Diaphragm Wall Joints: A Field Study
Authors: Zhang Yapeng , Chen Congxin , Zheng Yun , Shao Yong , Sun Chaoyi
Published: January 2021

Joints between diaphragm wall panels are weak spots in wall construction. It is essential that potential leak sites are detected prior to excavation. In this study, a novel leak detection and monitoring system is presented that is based on fiber Bragg grating (FBG) sensing technology. A field study was performed in a deep excavation supported by diaphragm walls (in Hohhot, China) to validate the feasibility and effectiveness of the proposed method. Two schemes were trialed; one using pipes made of stainless steel, and one used a pipeless method. The results of the field study are presented and discussed. They show that potential leak sites in the wall joints could be determined prior to excavation using the proposed detection method. Stainless steel is a good material to use to make the detection tube because it can protect the FBG sensors and heating belts from damage and is more sensitive to water leakage. The field study provides good evidence for the feasibility of the new detection system. It also provides valuable experience for the field application of the system and has generated useful data to use in follow-up work.
Estimation and Analysis of GNSS Differential Code Biases (DCBs) Using a Multi-Spacing Software Receiver
Authors: Wang Ye , Zhao Lin , Gao Yang
Published: January 2021

In the use of global navigation satellite systems (GNSS) to monitor ionosphere variations by estimating total electron content (TEC), differential code biases (DCBs) in GNSS measurements are a primary source of errors. Satellite DCBs are currently estimated and broadcast to users by International GNSS Service (IGS) using a network of GNSS hardware receivers which are inside structure fixed. We propose an approach for satellite DCB estimation using a multi-spacing GNSS software receiver to analyze the influence of the correlator spacing on satellite DCB estimates and estimate satellite DCBs based on different correlator spacing observations from the software receiver. This software receiver-based approach is called multi-spacing DCB (MSDCB) estimation. In the software receiver approach, GNSS observations with different correlator spacings from intermediate frequency datasets can be generated. Since each correlator spacing allows the software receiver to output observations like a local GNSS receiver station, GNSS observations from different correlator spacings constitute a network of GNSS receivers, which makes it possible to use a single software receiver to estimate satellite DCBs. By comparing the MSDCBs to the IGS DCB products, the results show that the proposed correlator spacing flexible software receiver is able to predict satellite DCBs with increased flexibility and cost-effectiveness than the current hardware receiver-based DCB estimation approach.
Efficient Resource-Aware Convolutional Neural Architecture Search for Edge Computing with Pareto-Bayesian Optimization
Authors: Yang Zhao , Zhang Shengbing , Li Ruxu , Li Chuxi , Wang Miao , Wang Danghui , Zhang Meng
Published: January 2021

With the development of deep learning technologies and edge computing, the combination of them can make artificial intelligence ubiquitous. Due to the constrained computation resources of the edge device, the research in the field of on-device deep learning not only focuses on the model accuracy but also on the model efficiency, for example, inference latency. There are many attempts to optimize the existing deep learning models for the purpose of deploying them on the edge devices that meet specific application requirements while maintaining high accuracy. Such work not only requires professional knowledge but also needs a lot of experiments, which limits the customization of neural networks for varied devices and application scenarios. In order to reduce the human intervention in designing and optimizing the neural network structure, multi-objective neural architecture search methods that can automatically search for neural networks featured with high accuracy and can satisfy certain hardware performance requirements are proposed. However, the current methods commonly set accuracy and inference latency as the performance indicator during the search process, and sample numerous network structures to obtain the required neural network. Lacking regulation to the search direction with the search objectives will generate a large number of useless networks during the search process, which influences the search efficiency to a great extent. Therefore, in this paper, an efficient resource-aware search method is proposed. Firstly, the network inference consumption profiling model for any specific device is established, and it can help us directly obtain the resource consumption of each operation in the network structure and the inference latency of the entire sampled network. Next, on the basis of the Bayesian search, a resource-aware Pareto Bayesian search is proposed. Accuracy and inference latency are set as the constraints to regulate the search direction. With a clearer search direction, the overall search efficiency will be improved. Furthermore, cell-based structure and lightweight operation are applied to optimize the search space for further enhancing the search efficiency. The experimental results demonstrate that with our method, the inference latency of the searched network structure reduced 94.71% without scarifying the accuracy. At the same time, the search efficiency increased by 18.18%.
In-Situ Estimation of Soil Water Retention Curve in Silt Loam and Loamy Sand Soils at Different Soil Depths
Authors: Zeitoun Reem , Vandergeest Mark , Vasava Hiteshkumar Bhogilal , Machado Pedro Vitor Ferrari , Jordan Sean , Parkin Gary , Wagner-Riddle Claudia , Biswas Asim
Published: January 2021

The soil water retention curve (SWRC) shows the relationship between soil water (θ) and water potential (ψ) and provides fundamental information for quantifying and modeling soil water entry, storage, flow, and groundwater recharge processes. While traditionally it is measured in a laboratory through cumbersome and time-intensive methods, soil sensors measuring in-situ θ and ψ show strong potential to estimate in-situ SWRC. The objective of this study was to estimate in-situ SWRC at different depths under two different soil types by integrating measured θ and ψ using two commercial sensors: time-domain reflectometer (TDR) and dielectric field water potential (e.g., MPS-6) principles. Parametric models were used to quantify θ—ψ relationships at various depths and were compared to laboratory-measured SWRC. The results of the study show that combining TDR and MPS-6 sensors can be used to estimate plant-available water and SWRC, with a mean difference of −0.03 to 0.23 m3m−3 between the modeled data and laboratory data, which could be caused by the sensors’ lack of site-specific calibration or possible air entrapment of field soil. However, consistent trends (with magnitude differences) indicated the potential to use these sensors in estimating in-situ and dynamic SWRC at depths and provided a way forward in overcoming resource-intensive laboratory measurements.
Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning
Authors: Chen Kuiyu , Zhang Shuning , Zhu Lingzhi , Chen Si , Zhao Huichang
Published: January 2021

Automatically recognizing the modulation of radar signals is a necessary survival technique in electronic intelligence systems. In order to avoid the complex process of the feature extracting and realize the intelligent modulation recognition of various radar signals under low signal-to-noise ratios (SNRs), this paper proposes a method based on intrapulse signatures of radar signals using adaptive singular value reconstruction (ASVR) and deep residual learning. Firstly, the time-frequency spectrums of radar signals under low SNRs are improved after ASVR denoising processing. Secondly, a series of image processing techniques, including binarizing and morphologic filtering, are applied to suppress the background noise in the time-frequency distribution images (TFDIs). Thirdly, the training process of the residual network is achieved using TFDIs, and classification under various conditions is realized using the new-trained network. Simulation results show that, for eight kinds of modulation signals, the proposed approach still achieves an overall probability of successful recognition of 94.1% when the SNR is only −8 dB. Outstanding performance proves the superiority and robustness of the proposed method.
Correction: Sahayadhas, A., et al. Detecting Driver Drowsiness Based on Sensors: A Review. Sensors 2012, 12, 16937–16953
Authors: Sahayadhas Arun , Sundaraj Kenneth , Murugappan Murugappan
Published: January 2021

The authors wish to make the following corrections to this paper [...]
Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning
Authors: Alshazly Hammam , Linse Christoph , Barth Erhardt , Martinetz Thomas
Published: January 2021

This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy using custom-sized input tailored for each deep architecture to achieve the best performance. We conducted extensive sets of experiments on two CT image datasets, namely, the SARS-CoV-2 CT-scan and the COVID19-CT. The results show superior performances for our models compared with previous studies. Our best models achieved average accuracy, precision, sensitivity, specificity, and F1-score values of 99.4%, 99.6%, 99.8%, 99.6%, and 99.4% on the SARS-CoV-2 dataset, and 92.9%, 91.3%, 93.7%, 92.2%, and 92.5% on the COVID19-CT dataset, respectively. For better interpretability of the results, we applied visualization techniques to provide visual explanations for the models’ predictions. Feature visualizations of the learned features show well-separated clusters representing CT images of COVID-19 and non-COVID-19 cases. Moreover, the visualizations indicate that our models are not only capable of identifying COVID-19 cases but also provide accurate localization of the COVID-19-associated regions, as indicated by well-trained radiologists.
PID++: A Computationally Lightweight Humanoid Motion Control Algorithm
Authors: Arciuolo Thomas F. , Faezipour Miad
Published: January 2021

Currently robotic motion control algorithms are tedious at best to implement, are lacking in automatic situational adaptability, and tend to be static in nature. Humanoid (human-like) control is little more than a dream, for all, but the fastest computers. The main idea of the work presented in this paper is to define a radically new, simple, and computationally lightweight approach to humanoid motion control. A new Proportional-Integral-Derivative (PID) controller algorithm called PID++ is proposed in this work that uses minor adjustments with basic arithmetic, based on the real-time encoder position input, to achieve a stable, precise, controlled, dynamic, adaptive control system, for linear motion control, in any direction regardless of load. With no PID coefficients initially specified, the proposed PID++ algorithm dynamically adjusts and updates the PID coefficients Kp, Ki and Kd periodically. No database of values is required to be stored as only the current and previous values of the sensed position with an accurate time base are used in the computations and overwritten in each read interval, eliminating the need of deploying much memory for storing and using vectors or matrices. Complete in its implementation, and truly dynamic and adaptive by design, engineers will be able to use this algorithm in commercial, industrial, biomedical, and space applications alike. With characteristics that are unmistakably human, motion control can be feasibly implemented on even the smallest microcontrollers (MCU) using a single command and without the need of reprogramming or reconfiguration.
User Recommendation for Data Sharing in Social Internet of Things
Authors: Bok Kyoungsoo , Kim Yeondong , Choi Dojin , Yoo Jaesoo
Published: January 2021

As various types of data are generated on the social Internet of things (SIoT), which combine the Internet of things (IoT) and social networks, the relations of IoT devices should be established for necessary data exchange. In this paper, we propose a user recommendation scheme that facilitates data sharing through an analysis of an interaction between an IoT device and a user in the SIoT. An interrelation between a user and an IoT device as well as an interrelation between users exist simultaneously in the SIoT. Hence, the interaction between users must be analyzed to identify the interest keywords, and the interaction between IoT devices and users to determine the user’s preference of IoT device. Moreover, the proposed scheme calculates the similarity between users based on the IoT device preference based on IoT device usage frequency and interest keywords, which are identified through an analysis between the user and IoT device and that between users. Subsequently, it recommends top-N users who have a high similarity as the users for data sharing. Furthermore, the performance of the proposed scheme is verified through performance evaluation based on the precision, recall, and F-measure.
FSR Systems for Detection of Air Objects Using Cosmic Radio Emissions
Authors: Kabakchiev Hristo , Behar Vera , Garvanov Ivan , Kabakchieva Dorina , Kabakchiev Avgust , Rohling Hermann
Published: January 2021

The paper analyses the possibility of Forward Scatter Radar (FSR) systems to detect airplanes using cosmic emission from pulsars and planets (pulsar, Sun, Moon). A suboptimal multichannel algorithm for joint detection and evaluation of the parameters of the forward scattering signal created by an airplane (duration and velocity) is proposed, with preliminary compensation of the powerful direct signal emitted by cosmic sources (pulsar, Sun and Moon). The expressions for calculation of the Signal-to-Noise Ratio (SNR) at the input of the detector and the compensator are obtained. The detection characteristics are also obtained, and the requirements for the suppression coefficient of the compensator are evaluated. A methodology for calculating the maximum distance for detecting an aircraft using a described algorithm is proposed. The obtained results show that due to the Forward Scatter (FS) effect, there is the theoretical possibility to detect airplanes at close ranges by FSRs, which use very weak signals from cosmic sources.
Clinical Evaluation of Respiratory Rate Measurements on COPD (Male) Patients Using Wearable Inkjet-Printed Sensor
Authors: Al-Halhouli Ala’aldeen , Al-Ghussain Loiy , Khallouf Osama , Rabadi Alexander , Alawadi Jafar , Liu Haipeng , Al Oweidat Khaled , Chen Fei , Zheng Dingchang
Published: January 2021

Introduction: Chronic Obstructive Pulmonary Disease (COPD) is a progressive disease that causes long-term breathing problems. The reliable monitoring of respiratory rate (RR) is very important for the treatment and management of COPD. Based on inkjet printing technology, we have developed a stretchable and wearable sensor that can accurately measure RR on normal subjects. Currently, there is a lack of comprehensive evaluation of stretchable sensors in the monitoring of RR on COPD patients. We aimed to investigate the measurement accuracy of our sensor on COPD patients. Methodology: Thirty-five patients (Mean ± SD of age: 55.25 ± 13.76 years) in different stages of COPD were recruited. The measurement accuracy of our inkjet-printed (IJPT) sensor was evaluated at different body postures (i.e., standing, sitting at 90°, and lying at 45°) on COPD patients. The RR recorded by the IJPT sensor was compared with that recorded by the reference e-Health sensor using paired T-test and Wilcoxon signed-rank test. Analysis of variation (ANOVA) was performed to investigate if there was any significant effect of individual difference or posture on the measurement error. Statistical significance was defined as p-value less than 0.05. Results: There was no significant difference between the RR measurements collected by the IJPT sensor and the e-Health reference sensor overall and in three postures (p > 0.05 in paired T-tests and Wilcoxon signed-rank tests). The sitting posture had the least measurement error of −0.0542 ± 1.451 bpm. There was no significant effect of posture or individual difference on the measurement error or relative measurement error (p > 0.05 in ANOVA). Conclusion: The IJPT sensor can accurately measure the RR of COPD patients at different body postures, which provides the possibility for reliable monitoring of RR on COPD patients.
Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning
Authors: Kentsch Sarah , Cabezas Mariano , Tomhave Luca , Groß Jens , Burkhard Benjamin , Lopez Caceres Maximo Larry , Waki Katsushi , Diez Yago
Published: January 2021

Invasive blueberry species endanger the sensitive environment of wetlands and protection laws call for management measures. Therefore, methods are needed to identify blueberry bushes, locate them, and characterise their distribution and properties with a minimum of disturbance. UAVs (Unmanned Aerial Vehicles) and image analysis have become important tools for classification and detection approaches. In this study, techniques, such as GIS (Geographical Information Systems) and deep learning, were combined in order to detect invasive blueberry species in wetland environments. Images that were collected by UAV were used to produce orthomosaics, which were analysed to produce maps of blueberry location, distribution, and spread in each study site, as well as bush height and area information. Deep learning networks were used with transfer learning and unfrozen weights in order to automatically detect blueberry bushes reaching True Positive Values (TPV) of 93.83% and an Overall Accuracy (OA) of 98.83%. A refinement of the result masks reached a Dice of 0.624. This study provides an efficient and effective methodology to study wetlands while using different techniques.
PHD Filter for Object Tracking in Road Traffic Applications Considering Varying Detectability
Authors: Törő Olivér , Bécsi Tamás , Gáspár Péter
Published: January 2021

This paper considers the object detection and tracking problem in a road traffic situation from a traffic participant’s perspective. The information source is an automotive radar which is attached to the ego vehicle. The scenario characteristics are varying object visibility due to occlusion and multiple detections of a vehicle during a scanning interval. The goal is to maintain and report the state of undetected though possibly present objects. The proposed algorithm is based on the multi-object Probability Hypothesis Density filter. Because the PHD filter has no memory, the estimate of the number of objects present can change abruptly due to erroneous detections. To reduce this effect, we model the occlusion of the object to calculate the state-dependent detection probability. Thus, the filter can maintain unnoticed but probably valid hypotheses for a more extended period. We use the sequential Monte Carlo method with clustering for implementing the filter. We distinguish between detected, undetected, and hidden particles within our framework, whose purpose is to track hidden but likely present objects. The performance of the algorithm is demonstrated using highway radar measurements.
Smart Data-Driven Policy on Unmanned Aircraft Systems (UAS): Analysis of Drone Users in U.S. Cities
Authors: Chen Yu-Che , Huang Chenyu
Published: January 2021

Realizing the benefits of drones while minimizing public concerns requires development and implementation of drone use policies that are grounded in an understanding of drone users and their behavior. This study aims to contribute to data-driven smart cities by filling our gap in knowledge about city drone users and their compliance behavior. The literature review has identified the main factors affecting drone policy compliance. This study collects data via a national survey of adults on drone behavior and focuses on city drone users. The results show that city drone users are younger with more dispersed educational backgrounds and income distribution than those in the general population. Moreover, civic duty, trust in government, and knowledge about regulatory requirements are motivators for drone users to comply with drone regulation.
Introducing the “15-Minute City”: Sustainability, Resilience and Place Identity in Future Post-Pandemic Cities
Authors: Moreno Carlos , Allam Zaheer , Chabaud Didier , Gall Catherine , Pratlong Florent
Published: January 2021

The socio-economic impacts on cities during the COVID-19 pandemic have been brutal, leading to increasing inequalities and record numbers of unemployment around the world. While cities endure lockdowns in order to ensure decent levels of health, the challenges linked to the unfolding of the pandemic have led to the need for a radical re-think of the city, leading to the re-emergence of a concept, initially proposed in 2016 by Carlos Moreno: the “15-Minute City”. The concept, offering a novel perspective of “chrono-urbanism”, adds to existing thematic of Smart Cities and the rhetoric of building more humane urban fabrics, outlined by Christopher Alexander, and that of building safer, more resilient, sustainable and inclusive cities, as depicted in the Sustainable Development Goal 11 of the United Nations. With the concept gaining ground in popular media and its subsequent adoption at policy level in a number of cities of varying scale and geographies, the present paper sets forth to introduce the concept, its origins, intent and future directions.
Moving toward Generalizability? A Scoping Review on Measuring the Impact of Living Labs
Authors: Bronson Kelly , Devkota Rachana , Nguyen Vivian
Published: January 2021

The living labs (LLs) approach has been applied around the globe to generate innovation within and suited to real-life problems and contexts. Despite the promise of the LL approach for addressing complex challenges like socio-ecological change, there is a gap in practitioner and academic community knowledge surrounding how to measure and evaluate both the performance of a given LL process and its wider impacts. Notably, this gap appears particularly acute in LLs designed to address environmental or agricultural sustainability. This article seeks to verify and address this knowledge gap by conducting an adopted scoping review method which uses a combination of tools for text mining alongside human text analysis. In total, 138 academic articles were screened, out of which 88 articles were read in full and 41 articles were found relevant for this study. The findings reveal limited studies putting forward generalizable approaches or frameworks for evaluating the impact of LLs and even fewer in the agricultural or sustainability sector. The dominant method for evaluation used in the literature is comparative qualitative case studies. This research uncovers a potential tension regarding LL work: the specificity of LL studies works against the development of evaluation indicators and a universal framework to guide the impact assessment of LLs across jurisdictions and studies in order to move toward generalizability.
Mapping Paddy Rice with Satellite Remote Sensing: A Review
Authors: Zhao Rongkun , Li Yuechen , Ma Mingguo
Published: January 2021

Paddy rice is a staple food of three billion people in the world. Timely and accurate estimation of the paddy rice planting area and paddy rice yield can provide valuable information for the government, planners and decision makers to formulate policies. This article reviews the existing paddy rice mapping methods presented in the literature since 2010, classifies these methods, and analyzes and summarizes the basic principles, advantages and disadvantages of these methods. According to the data sources used, the methods are divided into three categories: (I) Optical mapping methods based on remote sensing; (II) Mapping methods based on microwave remote sensing; and (III) Mapping methods based on the integration of optical and microwave remote sensing. We found that the optical remote sensing data sources are mainly MODIS, Landsat, and Sentinel-2, and the emergence of Sentinel-1 data has promoted research on radar mapping methods for paddy rice. Multisource data integration further enhances the accuracy of paddy rice mapping. The best methods are phenology algorithms, paddy rice mapping combined with machine learning, and multisource data integration. Innovative methods include the time series similarity method, threshold method combined with mathematical models, and object-oriented image classification. With the development of computer technology and the establishment of cloud computing platforms, opportunities are provided for obtaining large-scale high-resolution rice maps. Multisource data integration, paddy rice mapping under different planting systems and the connection with global changes are the focus of future development priorities.
Collective Action with Altruists: How Are Citizens Led Renewable Energy Communities Developed?
Authors: Dóci Gabriella
Published: January 2021

Transition to a sustainable future requires not only technical but also societal changes, including changes in behavioral patterns and consumer roles. Renewable energy communities embody such changes: they are mainly residential communities that break with their passive consumer role and produce energy from renewable sources in order to meet primarily local needs. Although the number of these communities has increased remarkably in the last decade in many Western countries, as has the academic attention paid to them, we still have limited knowledge on how they are formed and operate. It is unclear how they get their members to work collectively on a voluntary basis for a common goal; that is, energy production at the local level, and overcome the challenge of free-riding. This article seeks to contribute to a better understanding of the institutional and social context in which these communities operate, as well as of the way they are created and function. Therefore, the research question addressed is: What factors influence renewable energy communities’ formation and organization? In particular, the interest is in strategies for group formation, task distribution, collective action, communication, decision making, and problem-solving. This paper addresses the research question through a comparative assessment of case studies in Germany and the Netherlands. It analyzes different communities—of distinct sizes, location, and using various technologies—and assesses the commonalities between them and their general practices that led to the successful project implementation. The results show that, contrary to Olson’s expectation about voluntary collective action, renewable energy communities can realize their goals based on the work of only a few volunteers who develop the project without receiving any additional reward and who also accept free-riding. However, the larger the community’s size and the complexity of the project, the more likely it is that they need to formally organize the procedure or count on external support.
Foreign Ownership and Corporate Social Responsibility: Evidence from China
Authors: Guo Mingyuan , Zheng Chendi
Published: January 2021

This paper employs the data of corporate social responsibility rating score of A-share listed companies in China from 2009 to 2018 as a sample to verify the impacts of foreign ownership on corporate social responsibility. Furthermore, this paper explores the moderating role of legal institutional distance and economic institutional distance in the impact of foreign ownership on corporate social responsibility. The empirical results of panel data models show that: Firstly, foreign ownership has a significant positive impact on corporate social responsibility. Secondly, legal institutional distance and economic institutional distance have a positive moderating role in the impacts of foreign ownership on corporate social responsibility. The results of propensity score matching, two-stage least squares and alternative variables methods also give strong backing to the above conclusions. Finally, this paper puts forward that China’s listed companies are supposed to make full use of the supervision power of foreign ownership to promote corporate social responsibility.
Seismic Performance of Existing RC Structural Walls Retrofitted in Flexure by Wall End Plate
Authors: Kim Su-Yong , Cho Uijin , Chung Joo-Hong , Bae Baek-Il , Choi Chang-Sik
Published: January 2021

A study was conducted for the flexural retrofit of an old apartment house composed of a reinforced concrete (RC) shear wall structure. For the shear wall, a vertical retrofit was performed to both ends of the wall targeting the slender wall to improve the flexural performance. The retrofit materials were steel plate, epoxy, and non-shrink grout, and they were connected to the existing shear wall using post-installed chemical anchors. The concrete at the wall ends was broken and retrofitted as much as necessary to maintain the shear wall’s length. The points to be noted are the fracture of the welded part of the flexural retrofit and the chemical anchor’s pulling. After the real scale specimen was fabricated by simulating the existing shear wall, the retrofit effect was experimentally verified by breaking the wall ends, retrofitting the ends, and applying repeated cyclic lateral loads. A total of three specimens were fabricated and retrofitted using steel plates and steel tubes. Since this experiment evaluated the flexural performance, the experiment was performed with a cantilever setting. The flexural retrofit effect using steel was analyzed through the load–displacement relationship, stiffness degradation, and energy dissipation, and through this, the bond of the retrofit and the behavior of the wall were verified.
Green Development Efficiency and Its Influencing Factors in China’s Iron and Steel Industry
Authors: Zhang Junfeng , Liu Jianxu , Li Jing , Gao Yuyan , Zhao Chuansong
Published: January 2021

Analyzing the potential for green development and its influencing factors is an important part of the energy savings and low-carbon economic growth of China’s iron and steel industry (ISI). Many studies have concentrated on improving the ISI’s energy use and pollution control efficiencies, analyzing the influencing factors from the perspectives of regions and firms. However, no study has focused on measuring the provincial green development efficiency (GDE) in the ISI. The selected driving forces of the GDE do not consider regional or industrial characteristics. In this study, based on provincial panel data for 2006–2015 in China, the GDE of the Chinese ISI was evaluated using the super-slack-based measure (super-SBM) model. China’s 28 provinces were divided into different groups through cluster analysis. Then, a Tobit model was constructed to explore the factors influencing the GDE. The key results show the following: (1) The GDE values decline, fluctuating from 0.628 in 2006 to 0.571 in 2015, decreasing by 1.1% annually. Among the provinces, wide differences exist in the GDE values for the ISI, with the highest average GDE value being observed in Beijing and the lowest in Shanxi. (2) The provinces with high R&D expenditure inputs and high GDE values are mostly located in the eastern region, while the provinces with low R&D expenditure inputs and low GDE values are located in the central and western regions. (3) The export demand, property structure, and capital investment have significant positive effects on the ISI’s GDE in the eastern and western regions, while the energy consumption structure and industry scale have negative impacts on the improvement of the GDE in the central region. (4) Specific policy recommendations for sustainable development in the ISI mainly include further strengthening investment in R&D, expanding exports, adjusting energy consumption structures, and deepening the reform of stated-own enterprises.
Market Efficiency and Nonlinear Analysis of Soybean Futures
Authors: Yin Tao , Wang Yiming
Published: January 2021

In this paper, the multifractal detrended fluctuation analysis (MF-DFA) method is used to identify the multifractal structure of in the Chicago Board of Trade (CBOT) soybean futures and quantitatively describe the inefficiency and nonlinearity of the market. The data is the daily price of CBOT soybean futures from 3 January 2000 to 20 December 2019, with a total of 5025 trading days. The empirical results also show that the perspective based on MF-DFA can explain the market’s nonlinear, long-range correlation, predictability and other financial anomalies. At the same time, the prediction of price change direction and risk degree of the market are further studied. It is pointed out that multifractal characteristics are generated under the joint action of fat-tail distribution and long-range correlation. Investors can make use of these market characteristics to make arbitrage possible. Finally, based on the empirical results, some policy suggestions are put forward: strengthening rational investment education, strengthening supervision, reducing information asymmetry and other measures to improve market efficiency.
The Impact of Higher Education on Economic Growth in ASEAN-5 Countries
Authors: Maneejuk Paravee , Yamaka Woraphon
Published: January 2021

This study analyzed the nonlinear impacts of education, particularly higher education, on economic growth in the ASEAN-5 countries (i.e., Thailand, Indonesia, Malaysia, Singapore, and the Philippines) over the period 2000–2018. The impacts of education on economic growth are assessed through various education indicators, consisting of public expenditure on tertiary education per student, enrolment rates of primary, secondary, and tertiary levels, educated workforce, and the novel indicator of unemployment rates with advanced education. This study establishes nonlinear regression models—the time-series kink regression and the panel kink regression—to investigate the kink effects of education on the individual country’s economic growth and the ASEAN-5 region, respectively. There are three main findings. Firstly, the nonlinear effects of the government expenditure per tertiary student on economic growth are confirmed for the ASEAN-5 region. However, the impacts do not follow the law of diminishing returns. Secondly, our findings reveal that an increase in unemployment of advanced educated workers can positively or negatively impact economic growth, which requires an appropriate policy to handle the negative impacts. Lastly, secondary and higher education enrollment rates can contribute to the ASEAN-5’s economic growth (both the individual and regional levels). However, the regional analysis reveals that higher education impacts become twice as strong when the enrollment rates are greater than a certain level (a kink point). Therefore, we may conclude that secondary enrollment rates positively affect economic growth; however, higher education is the key to future growth and sustainability.
CSR Implication and Disclosure in Higher Education: Uncovered Points. Results from a Systematic Literature Review and Agenda for Future Research
Authors: Adhikariparajuli Mahalaxmi , Hassan Abeer , Siboni Benedetta
Published: January 2021

This research reviews and analyzes prior corporate social responsibility (CSR) studies centered on its implications and disclosure in higher education institutions (HEIs). Nine major databases were analyzed to review research articles from various business, management, higher education, and accounting journals for the period of 2004–2020. We follow the seven-step systematic review guidelines developed by Fink 2019 and we base our review analysis on fifty-eight journal articles. The systematic literature review results show a significant increase in the number of CSR article publications and the extent and trend of disclosure. The majority of prior research was based on questionnaires to evaluate the HEIs curriculum and focus on the CSR implication process. However, HEIs are still lagging behind in CSR implication and disclosure, and with a long way to go to obtain sustainability goals. From the study, several opportunities for future research emerged. This study can be useful for HEIs policymakers and practitioners to access the usefulness of CSR implications and disclosures in HEIs. In addition, this analysis assists scholars to explore in-depth the uncovered points related to CSR in HEIs context. This is the first systematic review of CSR implications and disclosures that comprehensively covers higher education institutions as a sector and presents a reference for academic literature from 2004 to 2020.
Creating a National Urban Research and Development Platform for Advancing Urban Experimentation
Authors: Newton Peter , Frantzeskaki Niki
Published: January 2021

Transformative changes are required for a 21st century sustainable urban development transition involving multiple interconnected domains of energy, water, transport, waste, and housing. This will necessitate a step change in performance goals and tangible solutions. Regenerative urban development has emerged as a major pathway, together with decarbonisation, climate adaptation involving new blue-green infrastructures, and transition to a new green, circular economy. These grand challenges are all unlikely to be realised with current urban planning and governance systems within a time frame that can mitigate environmental, economic, and social disruption. A new national platform for urban innovation has been envisaged and implemented in Australia that is capable of enabling engagement of multiple stakeholders across government, industry, and community as well as real time synchronous collaboration, visioning, research synthesis, experimentation, and decision-making. It targets large strategic metropolitan, mission-scale transition challenges as well as more tactical neighbourhood-scale projects. This paper introduces the iHUB: National Urban Research and Development Platform, its underlying concepts, and multiple layers of technical (IT/AV), software/analytical, data, and engagement, as envisioned and implemented in Australia’s four largest capital cities and five collaborating foundation universities.
Influences of Learning Approaches, Student Engagement, and Satisfaction with Learning on Measures of Sustainable Behavior in a Social Sciences Student Sample
Authors: Muñoz-García Antonio , Villena-Martínez Mª Dolores
Published: January 2021

In this empirical study, we address the relationship between the dimensions of learning in higher education (i.e., student engagement, approaches to learning, and satisfaction with learning) and sustainability (i.e., austerity altruistic, pro-ecological and equitable behavior). The results demonstrate that there is a positive linear relationship between engagement with learning and deep and strategic approaches, motivation and strategies, and altruistic, equitable and pro-ecological behavior. Austerity, however, only correlated with high dedication to learning. Satisfaction with learning was associated with altruistic and equitable behavior and an overall measure of sustainability, and was independently associated with austerity and pro-ecological behavior. Engagement with learning was associated with sustainable behavior, especially with altruistic and pro-ecological behavior in its three expressions, namely, vigor, absorption and dedication. In contrast, austerity was found to be only associated with engagement with learning. As a whole, and in line with Bronfenbrenner’s theory, the results of this study suggest that the aforementioned systems are interconnected and mutually influence each other.
A System Dynamics Model for Assessing the Efficacy of Lethal Control for Sustainable Management of Ochotona curzoniae on Tibetan Plateau
Authors: Qu Jiapeng , Liu Zelin , Guo Zhenggang , Li Yikang , Zhou Huakun
Published: January 2021

When population abundances exceed the economical or ecological threshold, animals are always regarded as pests, so effective and sustainable management strategies are required. As a native species widely distributed on Tibetan Plateau, plateau pika is regarded as a pest when its abundance is too high. For decades, plateau pika were controlled using toxic baits by both the local government and shepherds. However, how pika population fluctuates after lethal control is far from certain. Based on our previous studies, demographic parameters of plateau pika were acquired. A system dynamics model is developed to parameterize a population model for this species. The model incorporates two age categories (juvenile and adult) of both sexes, and uses density-dependent factors, including reproduction, mortality, and migration. The model is used originally to analyze the effect of pika management on the population dynamics and resulting abundance, in order to plan optimal pest controlling strategies. The results show that lethal control is efficient when continuously conducted once each year, or twice with 2-year intervals. For sustainable controlling pest abundance, comprehensive strategies should be considered. An appropriate population model could be used to explore the optimal strategies and provide important reference into decision making about pest management.