bluuebunny/crossref_metadata_2025 huggingface dataset:
A Simple Wireless Sensor Node System for Electricity Monitoring Applications: Design, Integration, and Testing with Different Piezoelectric Energy Harvesters
Authors: Yang Zongxian , Zarabi Sid , Fernandes Egon , Rua-Taborda Maria-Isabel , Debéda Hélène , Salehian Armaghan , Nairn David , Wei Lan
Published: November 2018

Real time electricity monitoring is critical to enable intelligent and customized energy management for users in residential, educational, and commercial buildings. This paper presents the design, integration, and testing of a simple, self-contained, low-power, non-invasive system at low cost applicable for such purpose. The system is powered by piezoelectric energy harvesters (EHs) based on PZT and includes a microcontroller unit (MCU) and a central hub. Real-time information regarding the electricity consumption is measured and communicated by the system, which ultimately offers a dependable and promising solution as a wireless sensor node. The dynamic power management ensures the system to work with different types of PZT EHs at a wide range of input power. Thus, the system is robust against fluctuation of the current in the electricity grid and requires minimum adjustment if EH unit requires exchange or upgrade. Experimental results demonstrate that this unit is in a position to read and transmit 60 Hz alternating current (AC) sensor signals with a high accuracy no less than 91.4%. The system is able to achieve an operation duty cycle from <1 min up to 18 min when the current in an electric wire varies from 7.6 A to 30 A, depending on the characteristics of different EHs and intensity of current being monitored.
Silicon Field Effect Transistor as the Nonlinear Detector for Terahertz Autocorellators
Authors: Ikamas Kęstutis , Nevinskas Ignas , Krotkus Arūnas , Lisauskas Alvydas
Published: November 2018

We demonstrate that the rectifying field effect transistor, biased to the subthreshold regime, in a large signal regime exhibits a super-linear response to the incident terahertz (THz) power. This phenomenon can be exploited in a variety of experiments which exploit a nonlinear response, such as nonlinear autocorrelation measurements, for direct assessment of intrinsic response time using a pump-probe configuration or for indirect calibration of the oscillating voltage amplitude, which is delivered to the device. For these purposes, we employ a broadband bow-tie antenna coupled Si CMOS field-effect-transistor-based THz detector (TeraFET) in a nonlinear autocorrelation experiment performed with picoseconds-scale pulsed THz radiation. We have found that, in a wide range of gate bias (above the threshold voltage V th = 445 mV), the detected signal follows linearly to the emitted THz power. For gate bias below the threshold voltage (at 350 mV and below), the detected signal increases in a super-linear manner. A combination of these response regimes allows for performing nonlinear autocorrelation measurements with a single device and avoiding cryogenic cooling.
A Reliable Health Indicator for Fault Prognosis of Bearings
Authors: Duong Bach Phi , Khan Sheraz Ali , Shon Dongkoo , Im Kichang , Park Jeongho , Lim Dong-Sun , Jang Byungtae , Kim Jong-Myon
Published: November 2018

Estimation of the remaining useful life (RUL) of bearings is important to avoid abrupt shutdowns in rotary machines. An important task in RUL estimation is the construction of a suitable health indicator (HI) to infer the bearing condition. Conventional health indicators rely on features of the vibration acceleration signal and are predominantly calculated without considering its non-stationary nature. This often results in an HI with a trend that is difficult to model, as well as random fluctuations and poor correlation with bearing degradation. Therefore, this paper presents a method for constructing a bearing’s HI by considering the non-stationarity of the vibration acceleration signals. The proposed method employs the discrete wavelet packet transform (DWPT) to decompose the raw signal into different sub-bands. The HI is extracted from each sub-band signal, smoothened using locally weighted regression, and evaluated using a gradient-based method. The HIs showing the best trends among all the sub-bands are iteratively accumulated to construct an HI with the best trend over the entire life of the bearing. The proposed method is tested on two benchmark bearing datasets. The results show that the proposed method yields an HI that correlates well with bearing degradation and is relatively easy to model.
Adaptive Data Boosting Technique for Robust Personalized Speech Emotion in Emotionally-Imbalanced Small-Sample Environments
Authors: Bang Jaehun , Hur Taeho , Kim Dohyeong , Huynh-The Thien , Lee Jongwon , Han Yongkoo , Banos Oresti , Kim Jee-In , Lee Sungyoung
Published: November 2018

Personalized emotion recognition provides an individual training model for each target user in order to mitigate the accuracy problem when using general training models collected from multiple users. Existing personalized speech emotion recognition research has a cold-start problem that requires a large amount of emotionally-balanced data samples from the target user when creating the personalized training model. Such research is difficult to apply in real environments due to the difficulty of collecting numerous target user speech data with emotionally-balanced label samples. Therefore, we propose the Robust Personalized Emotion Recognition Framework with the Adaptive Data Boosting Algorithm to solve the cold-start problem. The proposed framework incrementally provides a customized training model for the target user by reinforcing the dataset by combining the acquired target user speech with speech from other users, followed by applying SMOTE (Synthetic Minority Over-sampling Technique)-based data augmentation. The proposed method proved to be adaptive across a small number of target user datasets and emotionally-imbalanced data environments through iterative experiments using the IEMOCAP (Interactive Emotional Dyadic Motion Capture) database.
Preparation of Graphite Oxide Containing Different Oxygen-Containing Functional Groups and the Study of Ammonia Gas Sensitivity
Authors: Luo Liming , Peng Tongjiang , Yuan Mingliang , Sun Hongjuan , Dai Shichan , Wang Long
Published: November 2018

A series of graphite oxide samples were prepared using the modified Hummers method. Flake graphite was used as the raw material and the reaction temperature of the aqueous solution was changed (0 °C, 30 °C, 50 °C, 60 °C, 70 °C, 80 °C, and 100 °C). X-ray diffraction, Fourier-transform infrared spectroscopy, Raman spectral analysis, X-ray photoelectron spectroscopy, and contact angle tests were performed to characterize the structure, chemical bonding, type, and content of oxygen-containing functional groups of the graphite oxide samples. The results showed that the type and content of each oxygen-containing functional group could be controlled by changing the reaction temperature with the addition of water. As the temperature of the system increased, the degree of oxidation of the graphite oxide samples first increased and then decreased. Too high a temperature (100 °C) of the system led to the formation of epoxy groups by the decomposition of some hydroxyl groups in the samples, causing the reduction of oxygen-containing functional groups between the graphite layers, poor hydrophilic properties, and low moisture content. When the system temperature was 50 °C, the interlayer spacing of the graphite oxide samples was at its highest, the graphite was completely oxidized (C/O = 1.85), and the oxygen-containing functional groups were mainly composed of hydroxyl groups (accounting for approximately 28.88% of the total oxygen-containing functional groups). The high content of hydroxyl and carboxyl groups had good hydrophilic ability and showed the highest moisture content. The sample at 50 °C had better sensitivity to ammonia because of its high hydroxyl group and carboxyl group content, with the sample showing an excellent profile when the ammonia concentration was 20–60 ppm.
A Bias Compensation Method for Distributed Moving Source Localization Using TDOA and FDOA with Sensor Location Errors
Authors: Liu Zhixin , Wang Rui , Zhao Yongjun
Published: November 2018

Current bias compensation methods for distributed localization consider the time difference of arrival (TDOA) and frequency difference of arrival (FDOA) measurements noise, but ignore the negative influence by the sensor location uncertainties on source localization accuracy. Therefore, a new bias compensation method for distributed localization is proposed to improve the localization accuracy in this paper. This paper derives the theoretical bias of maximum likelihood estimation when the sensor location errors and positioning measurements noise both exist. Using the rough estimate result by MLE to subtract the theoretical bias can obtain a more accurate source location estimation. Theoretical analysis and simulation results indicate that the theoretical bias derived in this paper matches well with the actual bias in moderate noise level so that it can prove the correctness of the theoretical derivation. Furthermore, after bias compensation, the estimate accuracy of the proposed method achieves a certain improvement compared with existing methods.
Factor Graph-Assisted Distributed Cooperative Positioning Algorithm in the GNSS System
Authors: Tang Chengkai , Zhang Lingling , Zhang Yi , Song Houbing
Published: November 2018

The development of smart cities calls for improved accuracy in navigation and positioning services; due to the effects of satellite orbit error, ionospheric error, poor quality of navigation signals and so on, it is difficult for existing navigation technology to achieve further improvements in positioning accuracy. Distributed cooperative positioning technology can further improve the accuracy of navigation and positioning with existing GNSS (Global Navigation Satellite System) systems. However, the measured range error and the positioning error of the cooperative nodes exhibit larger reductions in positioning accuracy. In response to this question, this paper proposed a factor graph-aided distributed cooperative positioning algorithm. It establishes the confidence function of factor graphs theory with the ranging error and the positioning error of the coordinated nodes and then fuses the positioning information of the coordinated nodes by the confidence function. It can avoid the influence of positioning error and ranging error and improve the positioning accuracy of cooperative nodes. In the simulation part, the proposed algorithm is compared with a mainly coordinated positioning algorithm from four aspects: the measured range error, positioning error, convergence speed, and mutation error. The simulation results show that the proposed algorithm leads to a 30–60% improvement in positioning accuracy compared with other algorithms under the same measured range error and positioning error. The convergence rate and mutation error elimination times are only 1 / 5 to 1 / 3 of the other algorithms.
Integrable Near-Infrared Photodetectors Based on Hybrid Erbium/Silicon Junctions
Authors: Gioffré Mariano , Coppola Giuseppe , Iodice Mario , Casalino Maurizio
Published: November 2018

This paper presents the design, fabrication, and characterization of Schottky erbium/silicon photodetectors working at 1.55 µm. These erbium/silicon junctions are carefully characterized using both electric and optical measurements at room temperature. A Schottky barrier ΦB of ~673 meV is extrapolated; the photodetectors show external responsivity of 0.55 mA/W at room temperature under an applied reverse bias of 8 V. In addition, the device performance is discussed in terms of normalized noise and noise-equivalent power. The proposed devices will pave the way towards the development of Er-based photodetectors and light sources to be monolithically integrated in the same silicon substrate, and both operating at 1.55 µm.
A Fluidic Biosensor Based on a Phase-Sensitive Low-Coherence Spectral-Domain Interferometer
Authors: Guo Cuixia , Yang Xiaojie , Shen Zhiyuan , Wu Jian-Ping , Zhong Suyi , He Yonghong , Guan Tian , Chen Fangyi
Published: November 2018

A phase-sensitive fluidic biosensor based on a spectral-domain low-coherence interferometer is presented in this paper. With a fiber optic probe employing the common-path interferometric configuration, subnanometric changes in thickness of the molecular layers can be detected through phase analysis of the acquired interference signal from the sensor surface. Advantages of this biosensor include its picometer-scale thickness sensitivity, 13.9-ms time response, and tolerance to the fluctuation in concentration of the target solution. The capabilities of this biosensor in monitoring specific molecular binding and recognizing specific molecular was successfully demonstrated by using the reactions between the molecules of protein A and IgG. The calculated minimum detectable concentration of IgG is 0.11 µg/mL.
Fabrication and Thermoelectric Characterization of Transition Metal Silicide-Based Composite Thermocouples
Authors: Yakaboylu Gunes A. , Pillai Rajalekshmi C. , Sabolsky Katarzyna , Sabolsky Edward M.
Published: November 2018

Metal silicide-based thermocouples were fabricated by screen printing thick films of the powder compositions onto alumina tapes followed by lamination and sintering processes. The legs of the embedded thermocouples were composed of composite compositions consisting of MoSi2, WSi2, ZrSi2, or TaSi2 with an additional 10 vol % Al2O3 to form a silicide–oxide composite. The structural and high-temperature thermoelectric properties of the composite thermocouples were examined using X-ray diffraction, scanning electron microscopy and a typical hot–cold junction measurement technique. MoSi2-Al2O3 and WSi2-Al2O3 composites exhibited higher intrinsic Seebeck coefficients (22.2–30.0 µV/K) at high-temperature gradients, which were calculated from the thermoelectric data of composite//Pt thermocouples. The composite thermocouples generated a thermoelectric voltage up to 16.0 mV at high-temperature gradients. The MoSi2-Al2O3//TaSi2-Al2O3 thermocouple displayed a better performance at high temperatures. The Seebeck coefficients of composite thermocouples were found to range between 20.9 and 73.0 µV/K at a temperature gradient of 1000 °C. There was a significant difference between the calculated and measured Seebeck coefficients of these thermocouples, which indicated the significant influence of secondary silicide phases (e.g., Mo5Si3, Ta5Si3) and possible local compositional changes on the overall thermoelectric response. The thermoelectric performance, high sensitivity, and cost efficiency of metal silicide–alumina ceramic composite thermocouples showed promise for high-temperature and harsh-environment sensing applications.
Membership Function-Weighted Non-Linear Fitting Method for Optical-Sensing Modeling and Reconstruction
Authors: Meng Shuo , Du Zhenhui , Yuan Liming , Wang Shuanke , Han Ruiyan , Wang Xiaoyu
Published: November 2018

Imprecise measurements present universally due to variability in the measurement error. We devised a very simple membership function to evaluate fuzzily the quality of optical sensing with a small dataset, where a normal distribution cannot be assumed. The proposed membership function was further used as a weighting function for non-linear curve fitting under expected mathematical model constraints, namely the membership function-weighted Levenberg–Marquardt (MFW-LM) algorithm. The robustness and effectiveness of the MFW-LM algorithm were demonstrated by an optical-sensing simulation and two practical applications. (1) In laser-absorption spectroscopy, molecular spectral line modeling was greatly improved by the method. The measurement uncertainty of temperature and pressure were reduced dramatically, by 53.3% and 43.5%, respectively, compared with the original method. (2) In imaging, a laser beam-profile reconstruction from heavy distorted observations was improved by the method. As the dynamic range of the infrared camera increased from 256 to 415, the detailed resolution of the laser-beam profiles increased by an amazing 360%, achieving high dynamic-range imaging to capture optical signal details. Therefore, the MFW-LM algorithm provides a robust and effective tool for fitting a proper physical model and precision parameters from low-quality data.
Estimating Three-Dimensional Body Orientation Based on an Improved Complementary Filter for Human Motion Tracking
Authors: Yi Chunzhi , Ma Jiantao , Guo Hao , Han Jiahong , Gao Hefu , Jiang Feng , Yang Chifu
Published: November 2018

Rigid body orientation determined by IMU (Inertial Measurement Unit) is widely applied in robotics, navigation, rehabilitation, and human-computer interaction. In this paper, aiming at dynamically fusing quaternions computed from angular rate integration and FQA algorithm, a quaternion-based complementary filter algorithm is proposed to support a computationally efficient, wearable motion-tracking system. Firstly, a gradient descent method is used to determine a function from several sample points. Secondly, this function is used to dynamically estimate the fusion coefficient based on the deviation between measured magnetic field, gravity vectors and their references in Earth-fixed frame. Thirdly, a test machine is designed to evaluate the performance of designed filter. Experimental results validate the filter design and show its potential of real-time human motion tracking.
Sensor Information Fusion by Integrated AI to Control Public Emotion in a Cyber-Physical Environment
Authors: Choi Seul-Gi , Cho Sung-Bae
Published: November 2018

The cyber-physical system (CPS) is a next-generation smart system that combines computing with physical space. It has been applied in various fields because the uncertainty of the physical world can be ideally controlled using cyber technology. In terms of environmental control, studies have been conducted to enhance the effectiveness of the service by inducing ideal emotions in the service space. This paper proposes a CPS control system for inducing emotion based on multiple sensors. The CPS can expand the constrained environmental sensors of the physical space variously by combining the virtual space with the physical space. The cyber space is constructed in a Unity 3D space that can be experienced through virtual reality devices. We collect the temperature, humidity, dust concentration, and current emotion in the physical space as an environmental control elements, and the control illumination, color temperature, video, sound and volume in the cyber space. The proposed system consists of an emotion prediction module using modular Bayesian networks and an optimal stimulus decision module for deriving the predicted emotion to the target emotion based on utility theory and reinforcement learning. To verify the system, the performance is evaluated using the data collected from real situations.
End-User Feedback on a Low-Cost Portable Air Quality Sensor System—Are We There Yet?
Authors: Robinson Johanna Amalia , Kocman David , Horvat Milena , Bartonova Alena
Published: November 2018

Low-cost sensors are a current trend in citizen science projects that focus on air quality. Until now, devices incorporating such sensors have been tested primarily for their technical capabilities and limitations, whereas their usability and acceptability amongst the public rarely goes beyond proof of concept, leaving user experience (UX) unstudied. The authors argue that UX should be taken into account to make sure that products and services are fit for purpose. Nineteen volunteers tested and evaluated a prototype device and provided feedback through semi-structured interviews and during focus group sessions. Their UX was then coded using mixed coding methods regarding device functionality and recommendations for future product development. The results indicate that UX can identify potentially problematic design aspects while giving deeper insights into user needs. For example, UX recognized that one of the most important aspects of user involvement and motivation was successful data harvesting, which frequently failed. This study recommends that future developers of low-cost portable air quality sensor systems prioritize reliable data transmission to minimize data loss. This will ensure an efficient and positive UX that supports user engagement in citizen science based research where collecting sensor-based data is the primary objective.
Determination of the Optimal Sensing Temperature in Pt/Ta2O5/MoO3 Schottky Contacted Nanobelt Straddling Heterojunction
Authors: Cheung Ka Wai , Yu Jerry , Ho Derek
Published: November 2018

Nanostructured Schottky barrier gas sensors have emerged as novel semiconductor devices with large surface areas and unique electronic characteristics. Although it is widely known that operating these gas sensors requires heating to an optimal temperature for the highest sensitivity, the fundamental mechanism that governs the temperature-dependent sensitivity has yet been well understood. In this work, we present new evidence to support that thermionic field emission (TFE) is the dominant transport mechanism for Schottky contacted nanostructured heterojunction gas sensors at their optimal sensing temperature. Through the fabrication and characterization of Pt/MoO3 Schottky contacts, and Pt/Ta2O5/MoO3 heterojunctions, we found a previously unreported connection between TFE transport and optimal gas sensing temperature. This connection enables the description of Schottky barrier gas sensing performance using transport theory, which is a major step towards systematic engineering of gas sensors with nanostructured high-k oxide layers.
Decreasing Frequency Splits of Hemispherical Resonators by Chemical Etching
Authors: Wang Yuting , Pan Yao , Qu Tianliang , Jia Yonglei , Yang Kaiyong , Luo Hui
Published: November 2018

The hemispherical resonator gyroscope (HRG) has attracted the interest of the world inertial navigation community because of its exceptional performance, ultra-high reliability and its potential to be miniaturized. These devices achieve their best performance when the differences in the frequencies of the two degenerate working modes are eliminated. Mechanical treatment, laser ablation, ion-beams etching, etc., have all been applied for the frequency tuning of resonators, however, they either require costly equipment and procedures, or alter the quality factors of the resonators significantly. In this paper, we experimentally investigated for the first time the use of a chemical etching procedure to decrease the frequency splits of hemispherical resonators. We provide a theoretical analysis of the chemical etching procedure, as well as the relation between frequency splits and mass errors. Then we demonstrate that the frequency split could be decreased to below 0.05 Hz by the proposed chemical etching procedure. Results also showed that the chemical etching method caused no damage to the quality factors. Compared with other tuning methods, the chemical etching method is convenient to implement, requiring less time and labor input. It can be regarded as an effective trimming method for obtaining medium accuracy hemispherical resonator gyroscopes.
Novel GIS Based Machine Learning Algorithms for Shallow Landslide Susceptibility Mapping
Authors: Shirzadi Ataollah , Soliamani Karim , Habibnejhad Mahmood , Kavian Ataollah , Chapi Kamran , Shahabi Himan , Chen Wei , Khosravi Khabat , Thai Pham Binh , Pradhan Biswajeet , Ahmad Anuar , Bin Ahmad Baharin , Tien Bui Dieu
Published: November 2018

The main objective of this research was to introduce a novel machine learning algorithm of alternating decision tree (ADTree) based on the multiboost (MB), bagging (BA), rotation forest (RF) and random subspace (RS) ensemble algorithms under two scenarios of different sample sizes and raster resolutions for spatial prediction of shallow landslides around Bijar City, Kurdistan Province, Iran. The evaluation of modeling process was checked by some statistical measures and area under the receiver operating characteristic curve (AUROC). Results show that, for combination of sample sizes of 60%/40% and 70%/30% with a raster resolution of 10 m, the RS model, while, for 80%/20% and 90%/10% with a raster resolution of 20 m, the MB model obtained a high goodness-of-fit and prediction accuracy. The RS-ADTree and MB-ADTree ensemble models outperformed the ADTree model in two scenarios. Overall, MB-ADTree in sample size of 80%/20% with a resolution of 20 m (area under the curve (AUC) = 0.942) and sample size of 60%/40% with a resolution of 10 m (AUC = 0.845) had the highest and lowest prediction accuracy, respectively. The findings confirm that the newly proposed models are very promising alternative tools to assist planners and decision makers in the task of managing landslide prone areas.
A Video Based Fire Smoke Detection Using Robust AdaBoost
Authors: Wu Xuehui , Lu Xiaobo , Leung Henry
Published: November 2018

This work considers using camera sensors to detect fire smoke. Static features including texture, wavelet, color, edge orientation histogram, irregularity, and dynamic features including motion direction, change of motion direction and motion speed, are extracted from fire smoke to train and test with different combinations. A robust AdaBoost (RAB) classifier is proposed to improve training and classification accuracy. Extensive experiments on well known challenging datasets and application for fire smoke detection demonstrate that the proposed fire smoke detector leads to a satisfactory performance.
Energy-Efficient Medium Access Control Protocols for Cognitive Radio Sensor Networks: A Comparative Survey
Authors: Luitel Subash , Moh Sangman
Published: November 2018

The increase of application areas in wireless sensor networks demands novel solutions in terms of energy consumption and radio frequency management. Cognitive radio sensor networks (CRSNs) are key for ensuring efficient spectrum management, by making it possible to use the unused licensed frequency spectrum together with the unlicensed frequency spectrum. Sensor nodes powered by energy-constrained batteries necessarily require energy-efficient protocols at the routing and medium access control (MAC) layers. In CRSNs, energy efficiency is more important because the sensor nodes consume additional energy for spectrum sensing and management. To the best of authors’ knowledge, there is no survey on “energy-efficient” MAC protocols for CRSNs in the literature, even though a conceptual review on MAC protocols for CRSNs was presented at a conference recently. In this paper, energy-efficient MAC protocols for CRSNs are extensively surveyed and qualitatively compared. Open issues, and research challenges in the design of MAC protocols for CRSNs, are also discussed.
Road Scene Simulation Based on Vehicle Sensors: An Intelligent Framework Using Random Walk Detection and Scene Stage Reconstruction
Authors: Li Yaochen , Cui Zhichao , Liu Yuehu , Zhu Jihua , Zhao Danchen , Yuan Jian
Published: November 2018

Road scene model construction is an important aspect of intelligent transportation system research. This paper proposes an intelligent framework that can automatically construct road scene models from image sequences. The road and foreground regions are detected at superpixel level via a new kind of random walk algorithm. The seeds for different regions are initialized by trapezoids that are propagated from adjacent frames using optical flow information. The superpixel level region detection is implemented by the random walk algorithm, which is then refined by a fast two-cycle level set method. After this, scene stages can be specified according to a graph model of traffic elements. These then form the basis of 3D road scene models. Each technical component of the framework was evaluated and the results confirmed the effectiveness of the proposed approach.
Robust Radiation Sources Localization Based on the Peak Suppressed Particle Filter for Mixed Multi-Modal Environments
Authors: Gao Wenrui , Wang Weidong , Zhu Hongbiao , Huang Guofu , Wu Dongmei , Du Zhijiang
Published: November 2018

This paper addresses a detection problem where sparse measurements are utilized to estimate the source parameters in a mixed multi-modal radiation field. As the limitation of dimensional scalability and the unimodal characteristic, most existing algorithms fail to detect the multi-point sources gathered in narrow regions, especially with no prior knowledge about intensity and source number. The proposed Peak Suppressed Particle Filter (PSPF) method utilizes a hybrid scheme of multi-layer particle filter, mean-shift clustering technique and peak suppression correction to solve the major challenges faced by current existing algorithms. Firstly, the algorithm realizes sequential estimation of multi-point sources in a cross-mixed radiation field by using particle filtering and suppressing intensity peak value, while existing algorithms could just identify single point or spatially separated point sources. Secondly, the number of radioactive sources could be determined in a non-parametric manner as the fact that invalid particle swarms would disperse automatically. In contrast, existing algorithms either require prior information or rely on expensive statistic estimation and comparison. Additionally, to improve the prediction stability and convergent performance, distance correction module and configuration maintenance machine are developed to sustain the multimodal prediction stability. Finally, simulations and physical experiments are carried out in aspects such as different noise level, non-parametric property, processing time and large-scale estimation, to validate the effectiveness and robustness of the PSPF algorithm.
Is Flood Risk Capitalized into Real Estate Market Value? A Mahalanobis-Metric Matching Approach to the Housing Market in Gyeonggi, South Korea
Authors: Jung Eunah , Yoon Heeyeun
Published: November 2018

In this study, we investigate how far away and for how long past flooding affected single-family housing values in Gyeonggi, South Korea. In order to empirically explore the geographic and temporal extent of the effects, we adopt two analytical methods: random-intercept multilevel modeling and Mahalanobis-metric matching modeling. The analytical results suggest that the geographic extent of the discount effect of a flooding disaster is within 300 m from an inundated area. Market values of housing located 0–100, 100–200, and 200–300 m from inundated areas were lower by 11.0%, 7.4%, and 6.3%, respectively, than counterparts in the control group. The effect lasted only for 12 months after the disaster and then disappeared. During the first month, 1–3 months, and 3–6 months after a flood, housing units in the disaster-influenced area (within 300 m of the inundated area) were worth, on average, 57.6%, 49.2%, and 45.9% less than control units, respectively. Also, within the following 6 months, the discount effects were reduced to 33.2%. On the other hand, the results showed no statistically significant effects on market values more than 12 months after the disaster. By providing insights into how people perceive and respond to natural hazards, this research provides practical lessons for establishing sustainable disaster management and urban resilience strategies.
Predictive Analytics Approach to Improve and Sustain College Students’ Non-Cognitive Skills and Their Educational Outcome
Authors: Yi John C. , Kang-Yi Christina D. , Burton Flavia , Chen H. David
Published: November 2018

The application of predictive analytics in higher education has increasingly gained acceptance and interest over the years. In this study, a predictive model is developed to map students’ non-cognitive skills against their class performance. Our predictive analytics model identified the non-cognitive skills that predicted new students’ class performance based on the dataset collected early in the semester. Based on the predictive analytics results, tailored teaching to improve students’ non-cognitive skills was offered in a required class designed for undergraduate business students. The improvement in the average final semester grade for students in the tailored-taught classes based on our predicted analytics approach was 9%, which was higher than that of the class grade taught without the approach. The study finding also demonstrates a long-term, sustainable positive effect to the students with the predictive analytics approach.
Risk Assessment of Ex-Post Transaction Cost in Construction Projects Using Structural Equation Modeling
Authors: Ali Zaigham , Zhu Fangwei , Hussain Shahid
Published: November 2018

The transaction cost (TC) escalation is the pervasive problem in the construction industry, which is continuously a threat to maintaining the life cycle cost of projects. Researchers have described the reality of risk for economic transactions. This study has taken the risk as a phenomenon to explore its influence on ex-post TC in construction projects. A questionnaire survey was undertaken from industry professionals to assess the risk of ex-post TC escalation in public-sector construction projects. In total, 475 surveys were conducted in Pakistan and used in the analysis. The data were analyzed using structural equation modeling (SEM) and the measurement and structural model was validated to determine the influence of risk on ex-post TC. The final SEM results show that internal and external risk, including sub hypothesized risks, positively influence TC. The weight of relative importance shows technical risk (23.82%) and environmental risk (22.88%) as significant sub-contributors from internal and external sources, respectively. This study recommends substantial investment in human capacity development to reduce the deficiencies in the ex-ante phase of the projects that help to reduce the risk of ex-post TC escalation. It also suggests the adoption of strict policies on contingency claims, and recommends nontraditional ways of monitoring to overcome the risk of ex-post TC. This study’s results provide valuable information for industry professionals and practitioners to maintain life cycle costs as a contribution to sustainable construction.
Principal Component Analysis of Price Fluctuation in the Smart Grid Electricity Market
Authors: Li Kun , Cursio Joseph D. , Sun Yunchuan
Published: November 2018

Large price fluctuations have become a significant character and impede resource allocation in the electricity market. Negative prices and peak load spike prices coexist and represent over-supply and over-demand, respectively. It is important to interpret the impact of these extreme prices on sustainable power management from the perspective of economics. In this paper, we build a principal component analysis (PCA) to assess the impact of the two opposite phenomena on the smart grid electricity system. We perform a big-data study using intra-day data from the Pennsylvania, New Jersey, and Maryland (PJM) electricity system with over 11,000 transmission lines. As the contribution, this paper (1) measures the price fluctuations from the perspective of economics, (2) captures and observes the full-length behavior of negative and spike pricing in a modern smart grid system with multi-transmission lines and high-frequency price updates, and (3) employs methods with distinctive advantages to bring more in-depth findings to interpret the smart grid system. We find that spike prices hold the principal explanatory power for electricity market fluctuation in all the transmission lines. The results are consistent with previous studies about resolutions such as electrical energy storage, transmission capacity upgrade, and demand response.
Resilient or Not: A Comparative Case Study of Ten Local Water Markets in China
Authors: Liu Yi , Li Peng , Zhang Zhiwei
Published: November 2018

Despite the global expansion of water markets, their resilience has received little scholarly attention, even though they are vulnerable to external and internal disturbances. Since the 1990s, the water market has been actively promoted by China as an important institutional coordination mechanism for efficient water use. This article examines what contextual factors, in configurations, contribute to the resilience of water markets in China. We distinguish between resilient and factitious water markets as two outcome variables and distil four conditions from market environmentalism to explain the variance in their outcomes: ownership of water entitlements, market intermediaries, water pricing, and spot/forward trade categories. Using crisp-set qualitative comparative analysis (csQCA), we analyzed seven resilient and three factitious water markets in China. Our findings show that a water market’s framework is multidimensional and complex and that no necessary conditions contribute to resilience. Two sufficient solutions display the configurational complexity of water markets’ resilience. Path 1 includes strong intermediary, uncompetitive price, and forward water trade. Path 2 includes privatization of water entitlements, spot contracts, and competitive pricing. Weak intermediary together with forward water trade determines factitious water markets. The QCA results reveal that there exist multiple paths that a resilient water market can follow and develop. Therefore, policymakers must be cautious about pushing for water market indiscriminately, especially by over-privatization and unlimited investment in water banks.
(Un)Sustainable Development(s) in International Economic Law: A Quest for Sustainability
Authors: Aseeva Anna
Published: November 2018

This article aims to contribute to the ongoing debate on post-capitalist economy by exploring the contours of a sustainability-oriented model of economic governance. To this end, the article analyzes the issues of sustainable development in the three main strands of international economic law (trade, investment, and finance) at national and transnational levels. The analysis reveals a policy interdependence between international economic law and sustainable development. The latter hence represents a specific regulatory construct that aims at compensating the losses of exhaustible resources with investments in technology and knowledge. This, however, merely justifies and legitimizes the over-exploitation of certain parts of the globe, including not only their natural resources, but also human and other capitals. To overcome these unsustainable models, the article proposes a paradigm shift away from the standard of sustainable development in international economic law, towards one of sustainability. The idea is to replace sustainable development with sustainable economy. Law can act as a trigger of such a shift through ensuring trust and cooperation between public institutions, private companies, civil society, local communities, and individual citizens.
Trends of Ecological Footprints and Policy Direction for Sustainable Development in Mongolia: A Case Study
Authors: Volodya Enkhjargal , Yeo Min Ju , Kim Yong Pyo
Published: November 2018

In Mongolia, the Ecological Footprint (EF) has increased from 6.8 million global hectares (gha) in 1961 to 14.6 million gha in 2012; whilst the Biological capacity (BC) has decreased from 50.6 million gha in 1961 to 39.0 million gha in 2012. Throughout the study period, the grazing land footprint and carbon uptake land footprint have been the two major contributors to the EF variation. If the current trend continues, the EF will surpass the BC by around 2083. To ensure sustainable development in Mongolia, policy directions are advised. For the grazing land footprint, regulating the livestock numbers and herding practice changes are suggested. For the carbon uptake footprint, an increase in the combustion efficiency of the coal fired power plants and the extensive utilization of the renewable energy are suggested. Simple estimations of future carbon dioxide (CO2) emission changes based on various scenarios have been carried out. The amount of CO2 emissions in the mitigation scenarios decreased by up to 31.4% compared with the baseline scenario in 2030 in Mongolia. This case study is the first attempt to propose sustainable development strategies based on the estimation of Mongolia’s ecological consumption pattern.
Environmental Regulation and Corporate Financing—Quasi-Natural Experiment Evidence from China
Authors: Liu Xinghe , Wang Enxian , Cai Danting
Published: November 2018

The Environmental Protection Law, which includes 70 articles and major changes in six aspects compared to the old law, is called in Chinese society the new Environment Protection Law. When the law was implemented in 2014, it was an important event in China that could be seen as a natural experiment. Based on a difference-in-differences model, this paper considers all of the listed heavily polluting enterprises between 2011 and 2016 as the experimental group and all of the other firms in the same industries listed on the Chinese stock market as the control group and examines the impact of the new Environmental Protection Law on the corporate financing of heavily polluting enterprises and its mechanisms. The results show that the strict environmental law caused Chinese listed enterprises to face higher environmental regulation costs, public pressure and environmental litigation. The financing capacity of heavily polluting enterprises has dropped significantly, especially in areas with higher regulatory intensity. Furthermore, since the new Environmental Protection Law was established, overinvestment by China’s heavily polluting enterprises has been significantly inhibited, and the decline in financing capacity exerts a mediating effect. The ultimate economic consequences of the new Environmental Protection Law are to decrease the corporate value of heavily polluting industries.
Research on Interactions between the Economy and Environment in Tourism Development: Case of Qingyang, China
Authors: Lu Chenyu , Pang Min , Yang Jiaqi , Wang Dai
Published: November 2018

Tourism has rapidly developed into one of the world’s largest industries, and represents a new growth point for stimulating a region’s economic and social development. Therefore, a clear understanding of the interactive relationship between the tourism industry, the economy and the environment, as well as the potential mechanisms involved, can play an important role in improving the coordination between these three elements and forming a sustainable development policy. By utilizing quantitative analysis methods and models, this study uses Qingyang City, Gansu Province, as a case study, and tests the relationship between the development of the tourism industry, the economy and the environment. This study makes a number of important conclusions: (1) a mutually promoting relationship does not exist between the development of the tourism industry and economic growth; instead, a unidirectional relationship is found between the two elements. Similarly, a mutually promoting relationship does not exist between the development of the tourism industry and environmental quality; rather, a unidirectional relationship is found between the two elements. (2) The “curse effect” on economic growth imposed by the development of the tourism industry is found to exist. The development of the tourism industry not only exerts a direct negative influence on economic growth, but also adversely affects it in an indirect way through its influence over some transmission factors. Additionally, the curse effect on the environment imposed by the development of the tourism industry is also found to exist. The development of the tourism industry not only imposes a direct negative influence on the environment, but also adversely affects it in an indirect way through its influence over some transmission factors. (3) During the process of regional development, the relationship between the tourism industry, the economy and the environment needs to be correctly addressed. Through effective policies, it should be possible to realize the coordination of and sustainable development between the tourism industry, the economy and the environment.
A Gender Analysis of Changing Livelihood Activities in the Rural Areas of Central Nepal
Authors: Paudel Khatiwada Shanta , Deng Wei , Paudel Bikash , Khatiwada Janak , Zhang Jifei , Wan Jiangjun
Published: November 2018

Gender mainstreaming is a key for achieving inclusive economic growth, poverty reduction and equitable development. A gender disaggregated analysis of changing livelihood activities is, therefore, crucial for devising gender-sensitive policies and programs. This paper presents a gender disaggregated analysis to assess trend and influencing factors of switching livelihood to higher returning activities at the intra-household level in three villages in rural areas of central Nepal. The result showed that both men and women have changed their livelihood activities from subsistence to cash-earning activities in the last decade. However, the livelihood activities of men have changed considerably compared to women. Men are primarily attracted to out-migration and non-farm wage-based jobs whereas women to market-oriented commercial farming and rural enterprises. Individual as well as location-specific characteristics influence behavior switching to higher returning activities irrespective of gender, while the ethnicity of a household influences only women. Building human and financial capital through education and training along with strengthening access to credit and increasing connectivity through rural road and market centers would be pivotal for encouraging rural men and women to change traditional subsistence activities to higher returning undertakings, leading to equitable livelihood improvement in rural Nepal.
Ecosystem Services and Their Relationships in the Grain-for-Green Programme—A Case Study of Duolun County in Inner Mongolia, China
Authors: Dang Dongliang , Li Xiaobing , Li Shengkun , Dou Huashun
Published: November 2018

Grassland restoration projects are currently being implemented to mitigate human disturbance to the natural environment and reduce grassland degradation. China’s Grain-for-Green Programme (GFGP), including one project implemented in Duolun County, China, in 2000, has significantly improved the overall ecological health of this region. Using a modeling approach, this study quantified changes in four ecosystem services (ESs), including Net Primary Production (NPP), soil conservation (SC), water yield (WY), and sandstorm prevention (SP), in Duolun County between 2000 and 2016. We found the total NPP, water yield, and soil conservation increased by 80.44%, 248.2%, and 12.2%, respectively, during this period, while the sandstorm prevention decreased by 55.9%. Unlike other areas of GFGP implementation, the improvement of the ecological environment in Duolun County is largely attributed to the increased of vegetation coverage (88%) instead of land use circulation (12%). We found the grassland is a factor that reduces the trade-off while this effect was related with the grassland coverage. Future policies should be based on the mechanisms of vegetation underlying the ESs change and the relationships of ESs in order to achieve sustainable provision of ESs.