bluuebunny/crossref_metadata_2025 huggingface dataset:
Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine
Authors: Stromann Oliver , Nascetti Andrea , Yousif Osama , Ban Yifang
Published: December 2019

Mapping Earth’s surface and its rapid changes with remotely sensed data is a crucial task to understand the impact of an increasingly urban world population on the environment. However, the impressive amount of available Earth observation data is only marginally exploited in common classifications. In this study, we use the computational power of Google Earth Engine and Google Cloud Platform to generate an oversized feature set in which we explore feature importance and analyze the influence of dimensionality reduction methods to object-based land cover classification with Support Vector Machines. We propose a methodology to extract the most relevant features and optimize an SVM classifier hyperparameters to achieve higher classification accuracy. The proposed approach is evaluated in two different urban study areas of Stockholm and Beijing. Despite different training set sizes in the two study sites, the averaged feature importance ranking showed similar results for the top-ranking features. In particular, Sentinel-2 NDVI, NDWI, and Sentinel-1 VV temporal means are the highest ranked features and the experiment results strongly indicated that the fusion of these features improved the separability between urban land cover and land use classes. Overall classification accuracies of 94% and 93% were achieved in Stockholm and Beijing study sites, respectively. The test demonstrated the viability of the methodology in a cloud-computing environment to incorporate dimensionality reduction as a key step in the land cover classification process, which we consider essential for the exploitation of the growing Earth observation big data. To encourage further research and development of reliable workflows, we share our datasets and publish the developed Google Earth Engine and Python scripts as free and open-source software.
A Deep Learning Trained Clear-Sky Mask Algorithm for VIIRS Radiometric Bias Assessment
Authors: Liang Xingming , Liu Quanhua , Yan Banghua , Sun Ninghai
Published: December 2019

Clear-sky mask (CSM) is a crucial influence on the calculating accuracy of the sensor radiometric biases for spectral bands of visible, infrared, and microwave regions. In this study, a fully connected deep neural network (FCDN) was proposed to generate CSM for the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-Orbiting Partnership (S-NPP) and NOAA-20 satellites. The model, well-trained by S-NPP data, was used to generate both S-NPP and NOAA-20 CSMs for the independent data, and the results were validated against the biases between the sensor observations and Community Radiative Transfer Model (CRTM) calculations (O-M). The preliminary result shows that the FCDN-CSM model works well for identifying clear-sky pixels. Both O-M mean biases and standard deviations were comparable with the Advance Clear-Sky Processor over Ocean (ACSPO) and were significantly better than a prototype cloud mask (PCM) and the case without a clear-sky check. In addition, by replacing CRTM brightness temperatures (BTs) with the atmosphere air temperature and water vapor contents as input features, the FCDN-CSM exhibits its potential to generate fast and accurate VIIRS CSM onboard follow-up Joint Polar Satellite System (JPSS) satellites for sensor calibration and validation before the physics-based CSM is available.
Hybrid Wavelet and Principal Component Analyses Approach for Extracting Dynamic Motion Characteristics from Displacement Series Derived from Multipath-Affected High-Rate GNSS Observations
Authors: Kaloop Mosbeh R. , Yigit Cemal O. , El-Mowafy Ahmed , Dindar Ahmet A. , Bezcioglu Mert , Hu Jong Wan
Published: December 2019

Nowadays, the high rate GNSS (Global Navigation Satellite Systems) positioning methods are widely used as a complementary tool to other geotechnical sensors, such as accelerometers, seismometers, and inertial measurement units (IMU), to evaluate dynamic displacement responses of engineering structures. However, the most common problem in structural health monitoring (SHM) using GNSS is the presence of surrounding structures that cause multipath errors in GNSS observations. Skyscrapers and high-rise buildings in metropolitan cities are generally close to each other, and long-span bridges have towers, main cable, and suspender cables. Therefore, multipath error in GNSS observations, which is typically added to the measurement noise, is inevitable while monitoring such flexible engineering structures. Unlike other errors like atmospheric errors, which are mostly reduced or modeled out, multipath errors are the largest remaining unmanaged error sources. The high noise levels of high-rate GNSS solutions limit their structural monitoring application for detecting load-induced semi-static and dynamic displacements. This study investigates the estimation of accurate dynamic characteristics (frequency and amplitude) of structural or seismic motions derived from multipath-affected high-rate GNSS observations. To this end, a novel hybrid model using both wavelet-based multiscale principal component analysis (MSPCA) and wavelet transform (MSPCAW) is designed to extract the amplitude and frequency of both GNSS relative- and PPP- (Precise Point Positioning) derived displacement motions. To evaluate the method, a shaking table with a GNSS receiver attached to it, collecting 10 Hz data, was set up close to a building. The table was used to generate various amplitudes and frequencies of harmonic motions. In addition, 50-Hz linear variable differential transformer (LVDT) observations were collected to verify the MSMPCAW model by comparing their results. The results showed that the MSPCAW could be efficiently used to extract the dynamic characteristics of noisy dynamic movements under seismic loads. Furthermore, the dynamic behavior of seismic motions can be extracted accurately using GNSS-PPP, and its dominant frequency equals that extracted by LVDT and relative GNSS positioning method. Its accuracy in determining the amplitude approaches 91.5% relative to the LVDT observations.
Modelling Distributions of Rove Beetles in Mountainous Areas Using Remote Sensing Data
Authors: Dittrich Andreas , Roilo Stephanie , Sonnenschein Ruth , Cerrato Cristiana , Ewald Michael , Viterbi Ramona , Cord Anna F.
Published: December 2019

Mountain ecosystems are biodiversity hotspots that are increasingly threatened by climate and land use/land cover changes. Long-term biodiversity monitoring programs provide unique insights into resulting adverse impacts on plant and animal species distribution. Species distribution models (SDMs) in combination with satellite remote sensing (SRS) data offer the opportunity to analyze shifts of species distributions in response to these changes in a spatially explicit way. Here, we predicted the presence probability of three different rove beetles in a mountainous protected area (Gran Paradiso National Park, GPNP) using environmental variables derived from Landsat and Aster Global Digital Elevation Model data and an ensemble modelling approach based on five different model algorithms (maximum entropy, random forest, generalized boosting models, generalized additive models, and generalized linear models). The objectives of the study were (1) to evaluate the potential of SRS data for predicting the presence of species dependent on local-scale environmental parameters at two different time periods, (2) to analyze shifts in species distributions between the years, and (3) to identify the most important species-specific SRS predictor variables. All ensemble models showed area under curve (AUC) of the receiver operating characteristics values above 0.7 and true skills statistics (TSS) values above 0.4, highlighting the great potential of SRS data. While only a small proportion of the total area was predicted as highly suitable for each species, our results suggest an increase of suitable habitat over time for the species Platydracus stercorarius and Ocypus ophthalmicus, and an opposite trend for Dinothenarus fossor. Vegetation cover was the most important predictor variable in the majority of the SDMs across all three study species. To better account for intra- and inter-annual variability of population dynamics as well as environmental conditions, a continuation of the monitoring program in GPNP as well as the employment of SRS with higher spatial and temporal resolution is recommended.
Generating High-Quality and High-Resolution Seamless Satellite Imagery for Large-Scale Urban Regions
Authors: Li Xinghua , Li Zhiwei , Feng Ruitao , Luo Shuang , Zhang Chi , Jiang Menghui , Shen Huanfeng
Published: December 2019

Urban geographical maps are important to urban planning, urban construction, land-use studies, disaster control and relief, touring and sightseeing, and so on. Satellite remote sensing images are the most important data source for urban geographical maps. However, for optical satellite remote sensing images with high spatial resolution, certain inevitable factors, including cloud, haze, and cloud shadow, severely degrade the image quality. Moreover, the geometrical and radiometric differences amongst multiple high-spatial-resolution images are difficult to eliminate. In this study, we propose a robust and efficient procedure for generating high-resolution and high-quality seamless satellite imagery for large-scale urban regions. This procedure consists of image registration, cloud detection, thin/thick cloud removal, pansharpening, and mosaicking processes. Methodologically, a spatially adaptive method considering the variation of atmospheric scattering, and a stepwise replacement method based on local moment matching are proposed for removing thin and thick clouds, respectively. The effectiveness is demonstrated by a successful case of generating a 0.91-m-resolution image of the main city zone in Nanning, Guangxi Zhuang Autonomous Region, China, using images obtained from the Chinese Beijing-2 and Gaofen-2 high-resolution satellites.
Fabrication of Thermal Conductivity Detector Based on MEMS for Monitoring Dissolved Gases in Power Transformer
Authors: Tan Tingliang , Sun Jianhai , Chen Tingting , Zhang Xinxiao , Zhu Xiaofeng
Published: December 2019

In this work, a high sensitivity micro-thermal conductivity detector (μTCD) with four thermal conductivity cells was proposed. Compared with conventional TCD sensors, the thermal conductivity cell in this work was designed as a streamlined structure; the thermistors were supported by a strong cantilever beam and suspended in the center of the thermal conductivity cell, which was able to greatly reduce the dead volume of the thermal conductivity cell and the heat loss of the substrate, improving the detection sensitivity. The experimental results demonstrated that the μTCD shows good stability and high sensitivity, which could rapidly detect light gases with a detection limit of 10 ppm and a quantitative repeatability of less than 1.1%.
Development and In Situ Application of Deformation Monitoring System for Concrete-Face Rockfill Dam Using Fiber Optic Gyroscope
Authors: Liao Cheng , Cai Desuo , Chen Hongxun , Luo Weili , Li Miao
Published: December 2019

Deformation monitoring is of importance to ensure the operation status of concrete-face rockfill dams (CFRD). This paper reported a novel fiber optic gyroscope (FOG) monitoring system for continuously monitoring face slab deformation of CFRD, which consisted of a permanent monitoring pipeline and a sensing vehicle. The monitoring pipeline was made of steel pipes and polyvinyl chloride polymer connectors, which was embedded in a slot of the crushing-type sidewall beneath the concrete face slab of CFRD, forming a permanent monitoring channel. The sensing vehicle was equipped with a high-precision FOG sensor. A low-pass filter was designed to eliminate the vibration noise of the angular velocities of the sensing vehicle during the monitoring process. An in situ test was carried out on the Shuibuya dam, the highest CFRD in the world. The measurements of the FOG monitoring system agreed well with traditional instrument measurements, serving as validation of the system. The FOG monitoring system has the advantages of excellent repeatability, long service life, distributed monitoring, and automatic measurement.
Nonlinear Maximization of the Sum-Frequency Component from Two Ultrasonic Signals in a Bubbly Liquid
Authors: Tejedor Sastre María Teresa , Vanhille Christian
Published: December 2019

Techniques based on ultrasound in nondestructive testing and medical imaging analyze the response of the source frequencies (linear theory) or the second-order frequencies such as higher harmonics, difference and sum frequencies (nonlinear theory). The low attenuation and high directivity of the difference-frequency component generated nonlinearly by parametric arrays are useful. Higher harmonics created directly from a single-frequency source and the sum-frequency component generated nonlinearly by parametric arrays are attractive because of their high spatial resolution and accuracy. The nonlinear response of bubbly liquids can be strong even at relatively low acoustic pressure amplitudes. Thus, these nonlinear frequencies can be generated easily in these media. Since the experimental study of such nonlinear waves in stable bubbly liquids is a very difficult task, in this work we use a numerical model developed previously to describe the nonlinear propagation of ultrasound interacting with nonlinearly oscillating bubbles in a liquid. This numerical model solves a differential system coupling a Rayleigh–Plesset equation and the wave equation. This paper performs an analysis of the generation of the sum-frequency component by nonlinear mixing of two signals of lower frequencies. It shows that the amplitude of this component can be maximized by taking into account the nonlinear resonance of the system. This effect is due to the softening of the medium when pressure amplitudes rise.
Anomaly Detection Based Latency-Aware Energy Consumption Optimization For IoT Data-Flow Services
Authors: Luo Yuansheng , Li Wenjia , Qiu Shi
Published: December 2019

The continuous data-flow application in the IoT integrates the functions of fog, edge, and cloud computing. Its typical paradigm is the E-Health system. Like other IoT applications, the energy consumption optimization of IoT devices in continuous data-flow applications is a challenging problem. Since the anomalous nodes in the network will cause the increase of energy consumption, it is necessary to make continuous data flows bypass these nodes as much as possible. At present, the existing research work related to the performance of continuous data-flow is often optimized from system architecture design and deployment. In this paper, a mathematical programming method is proposed for the first time to optimize the runtime performance of continuous data flow applications. A lightweight anomaly detection method is proposed to evaluate the reliability of nodes. Then the node reliability is input into the optimization algorithm to estimate the task latency. The latency-aware energy consumption optimization for continuous data-flow is modeled as a mixed integer nonlinear programming problem. A block coordinate descend-based max-flow algorithm is proposed to solve this problem. Based on the real-life datasets, the numerical simulation is carried out. The simulation results show that the proposed strategy has better performance than the benchmark strategy.
Intelligent Management of Chemical Warehouses with RFID Systems
Authors: Zhao Jumin , Xue Fangfang , Li Deng-ao
Published: December 2019

At present, most chemical warehouses rely on human management, which is a time-consuming and laborious process. Therefore, it is very meaningful to use radio frequency identification (RFID) systems for the intelligent management of chemicals. Detecting the remaining amount of chemicals is an important process in the management of a chemical warehouse. It helps managers find the chemicals that are going to run out and replenish them in time. However, in a traditional chemical warehouse, managers usually inspect each chemical on the shelf in turn manually, which is a waste of time and labor. Although some solutions using RFID technology have been proposed, they are expensive and difficult to deploy in a real environment. In order to solve this problem, we propose an intelligent system called the RF-Detector in this paper, which combines robotics and RFID technology. An RFID reader and an antenna are installed on the robot, which achieves automatic scanning of the chemicals. The RF-Detector can achieve two functions: One function is to detect the remaining amount of chemicals using the changes in received signal strength indication (RSSI) and read rate, and the other is to locate chemicals using the phase curve, so that managers can quickly find the chemicals with an insufficient amount remaining. In this paper we implement the RF-Detector and evaluate its performance. The experimental results show that the RF-Detector achieves about 93% detection accuracy and 92% positioning accuracy for chemicals.
Fast Response Solar-Blind Photodetector with a Quasi-Zener Tunneling Effect Based on Amorphous In-Doped Ga2O3 Thin Films
Authors: Fang Mingzhi , Zhao Weiguo , Li Feifei , Zhu Deliang , Han Shun , Xu Wangying , Liu Wenjun , Cao Peijiang , Fang Ming , Lu Youming
Published: December 2019

A high-performance solar-blind photodetector with a metal–semiconductor–metal structure was fabricated based on amorphous In-doped Ga2O3 thin films prepared at room temperature by radio frequency magnetron sputtering. The photodetector shows a high responsivity (18.06 A/W) at 235 nm with a fast rise time (4.9 μs) and a rapid decay time (230 μs). The detection range was broadened compared with an individual Ga2O3 photodetector because of In doping. In addition, the uneven In distribution at different areas in the film results in different resistances, which causes a quasi-Zener tunneling internal gain mechanism. The quasi-Zener tunneling internal gain mechanism has a positive impact on the fast response speed and high responsivity.
Fast Method of Registration for 3D RGB Point Cloud with Improved Four Initial Point Pairs Algorithm
Authors: Li Peng , Wang Ruisheng , Wang Yanxia , Gao Ge
Published: December 2019

Three-dimensional (3D) point cloud registration is an important step in three-dimensional (3D) model reconstruction or 3D mapping. Currently, there are many methods for point cloud registration, but these methods are not able to simultaneously solve the problem of both efficiency and precision. We propose a fast method of global registration, which is based on RGB (Red, Green, Blue) value by using the four initial point pairs (FIPP) algorithm. First, the number of different RGB values of points in a dataset are counted and the colors in the target dataset having too few points are discarded by using a color filter. A candidate point set in the source dataset are then generated by comparing the similarity of colors between two datasets with color tolerance, and four point pairs are searched from the two datasets by using an improved FIPP algorithm. Finally, a rigid transformation matrix of global registration is calculated with total least square (TLS) and local registration with the iterative closest point (ICP) algorithm. The proposed method (RGB-FIPP) has been validated with two types of data, and the results show that it can effectively improve the speed of 3D point cloud registration while maintaining high accuracy. The method is suitable for points with RGB values.
A Visual and Persuasive Energy Conservation System Based on BIM and IoT Technology
Authors: Wu I-Chen , Liu Chi-Chang
Published: December 2019

Comfort level in the human body is an index that is always difficult to evaluate in a general and objective manner. Therefore, building owners and managers have been known to adjust environmental physical parameters such as temperature, humidity, and air quality based on people’s subjective sensations to yield satisfactory feelings of comfort. Furthermore, electricity consumption could be reduced by minimizing unnecessary use of heating and cooling equipment based on precise knowledge of comfort levels in interior spaces. To achieve the aforementioned objectives, this study undertook the following four tasks: first, providing visualization and smart suggestion functions to assist building managers and users in analyzing and developing plans based on the demands of space usage and electrical equipment; second, using Internet of Things technology to minimize the difference between real situations and those simulated in building information modeling (BIM); third, accurately evaluating interior environment comfort levels and improving equipment operating efficiency based on quantized comfort levels; and fourth, establishing a persuasive workflow for building energy saving systems. Through developing this system, COZyBIM will help to enhance the satisfactions of comfort level in interior space and operate energy consuming equipment efficiently, to reach the target of energy saving.
MOFs-Derived Nano-CuO Modified Electrode as a Sensor for Determination of Hydrazine Hydrate in Aqueous Medium
Authors: Lu Yaqi , Wu Dan , Li Ziyin , Lin Quanjie , Ma Xiuling , Zhang Zhangjing , Xiang Shengchang
Published: December 2019

It very important to be able to efficiently detect hydrazine hydrate in an aqueous medium due to its high toxicity. Here, we have proposed a new idea: to construct a sensor for the rapid determination of hydrazine hydrate based on the nano-CuO derived by controlled pyrolysis of HKUST-1 [Cu3(BTC)2(H2O)3]. The as-prepared CuO at 400 °C possesses a uniform appearance with nano-structure via SEM images, and the nano-CuO-400 has exhibited excellent electrocatalytic activity towards hydrazine oxidation. Amperometric i-t curves shows the peak current as linearly proportional to the hydrazine concentration within 1.98–169.3 μmol L−1 and 232–2096 μmol L−1 with the detection limit of 2.55 × 10−8 mol L−1 and 7.01 × 10−8 mol L−1, respectively. Moreover, the sensor constructed in the experiment shows good selectivities, and it is feasible to determining actual water samples.
Design of an Impedance-Controlled Hot Snare Polypectomy Device
Authors: Thornton CurtisLee , Choi JungHun
Published: December 2019

This paper goes through the process of first designing a feedback system that allows for the measuring of impedance while using the hot snare polypectomy method. The electrosurgical unit used in this study was the Olympus PSD-30. After the impedance-controlled feedback system was completed, the device was tested under a range of power settings from 10 W–50 W. The test was performed ex vivo using porcine colon samples. Using the information gathered from these tests, a technique of determining the threshold of perforation and implementing a system to automatically stop the applied current from the PSD-30 was developed. The data showed that after an increase in impedance of 25% from that of the initially measured impedance, perforation ensued in the tissue samples. Using this information, the device was programmed to interrupt the PSD-30 at this threshold point. This final design was tested and proved able to automatically prevent the event of perforation from occurring, resulting in the ability to prevent serious complications.
Performance-Enhanced Non-Enzymatic Glucose Sensor Based on Graphene-Heterostructure
Authors: Sakr Mahmoud A. , Elgammal Karim , Delin Anna , Serry Mohamed
Published: December 2019

Non-enzymatic glucose sensing is a crucial field of study because of the current market demand. This study proposes a novel design of glucose sensor with enhanced selectivity and sensitivity by using graphene Schottky diodes, which is composed of graphene (G)/platinum oxide (PtO)/n-silicon (Si) heterostructure. The sensor was tested with different glucose concentrations and interfering solutions to investigate its sensitivity and selectivity. Different structures of the device were studied by adjusting the platinum oxide film thickness to investigate its catalytic activity. It was found that the film thickness plays a significant role in the efficiency of glucose oxidation and hence in overall device sensitivity. 0.8–2 μA output current was obtained in the case of 4–10 mM with a sensitivity of 0.2 μA/mM.cm2. Besides, results have shown that 0.8 μA and 15 μA were obtained by testing 4 mM glucose on two different PtO thicknesses, 30 nm and 50 nm, respectively. The sensitivity of the device was enhanced by 150% (i.e., up to 30 μA/mM.cm2) by increasing the PtO layer thickness. This was attributed to both the increase of the number of active sites for glucose oxidation as well as the increase in the graphene layer thickness, which leads to enhanced charge carriers concentration and mobility. Moreover, theoretical investigations were conducted using the density function theory (DFT) to understand the detection method and the origins of selectivity better. The working principle of the sensors puts it in a competitive position with other non-enzymatic glucose sensors. DFT calculations provided a qualitative explanation of the charge distribution across the graphene sheet within a system of a platinum substrate with D-glucose molecules above. The proposed G/PtO/n-Si heterostructure has proven to satisfy these factors, which opens the door for further developments of more reliable non-enzymatic glucometers for continuous glucose monitoring systems.
Social Representations of the Disadvantaged Childhood’s Asylum of Horta in the Press (Azores, Portugal): From the Constitutional Monarchy to the First Republic
Authors: Serpa Sandro , Ferreira Carlos Miguel , Santos Ana Isabel
Published: December 2019

The aim of this article is to understand the symbolic representations of the assistance strategies aimed at disadvantaged children, expressed in two newspapers published on the island of Faial, in the Azores, in the second half of the 19th and early 20th centuries (covering the time horizon between the end of the monarchic period and the implementation of the First Republic). The technique of documentary analysis and a subsequent qualitative thematic content analysis of childcare news collected in two local newspapers were used. The discursive records produced by the press on the assistance strategies value, on the one hand, an axiological dimension and forms of charitable intervention and, on the other hand, aggregate and reconcile the discourses and techniques inherent to charitable and philanthropic models. This mutual assimilation underlies the achievement of the same objective: The moralization and integration of disadvantaged invalid childhood and, above all, the protection of the existing social order. We conclude that, perhaps contrary to what would be expected, the charitable logic articulated in a concomitant way with the philanthropic logic survived, even with the stabilization of the republican period (result of a revolution that deposed the regime of the constitutional monarchy and implemented the republican regime in 1910 in Portugal, whose political elites mobilized an official discourse that advocated the separation between the State and Religion, assigning the State the function of social assistance for children and youth). This demonstrates a certain dissociation, as well as a relative autonomy of conceptions about child and youth care between republican political ideology and current social practices, at least in this specific context.
Community-Based Responses to Negative Health Impacts of Sexual Humanitarian Anti-Trafficking Policies and the Criminalization of Sex Work and Migration in the US
Authors: Hoefinger Heidi , Musto Jennifer , Macioti P. G. , Fehrenbacher Anne E. , Mai Nicola , Bennachie Calum , Giametta Calogero
Published: December 2019

System-involvement resulting from anti-trafficking interventions and the criminalization of sex work and migration results in negative health impacts on sex workers, migrants, and people with trafficking experiences. Due to their stigmatized status, sex workers and people with trafficking experiences often struggle to access affordable, unbiased, and supportive health care. This paper will use thematic analysis of qualitative data from in-depth interviews and ethnographic fieldwork with 50 migrant sex workers and trafficked persons, as well as 20 key informants from legal and social services, in New York and Los Angeles. It will highlight the work of trans-specific and sex worker–led initiatives that are internally addressing gaps in health care and the negative health consequences that result from sexual humanitarian anti-trafficking interventions that include policing, arrest, court-involvement, court-mandated social services, incarceration, and immigration detention. Our analysis focuses on the impact of criminalization on sex workers and their experiences with sexual humanitarian efforts intended to protect and control them. We argue that these grassroots community-based efforts are a survival-oriented reaction to the harms of criminalization and a response to vulnerabilities left unattended by mainstream sexual humanitarian approaches to protection and service provision that frame sex work itself as the problem. Peer-to-peer interventions such as these create solidarity and resiliency within marginalized communities, which act as protective buffers against institutionalized systemic violence and the resulting negative health outcomes. Our results suggest that broader public health support and funding for community-led health initiatives are needed to reduce barriers to health care resulting from stigma, criminalization, and ineffective anti-trafficking and humanitarian efforts. We conclude that the decriminalization of sex work and the reform of institutional practices in the US are urgently needed to reduce the overall negative health outcomes of system-involvement.
Is a Regional Coordination Approach to Air Pollution Management Helpful? Evidence from China
Authors: Zhang Xuehui , Tan Zhidong , Chang Bao-Guang , Chan Kam C.
Published: December 2019

In February 2017 China began to require the regional coordination of four ministries and 28 cities surrounding Beijing to manage air pollution. The Coordination attempts to unify air pollution standards and implements various new methods to monitor air pollution. Leveraging the natural experiment and using a difference-in-differences research design, we note that firms located in the treatment cities invest more in the environment than those in the control cities. In addition, we find that non-state-owned firms (non-SOEs) respond more strongly than SOEs. The findings remain qualitatively the same after accounting for selection bias in the cities included in the Coordination. Most importantly, air quality improves for treatment cities after the implementation of the Coordination. Our findings offer lessons to other emerging markets for implementing their air pollution management programs. Specifically, we sharpen our knowledge of the administrative management needed to improve coordination among government agencies and local officials in the management of air pollution and suggest that the government can play an active role in enhancing air pollution management.
Spatial Heterogeneity of Winter Wheat Yield and Its Determinants in the Yellow River Delta, China
Authors: Chu Lin , Huang Chong , Liu Qingsheng , Cai Chongfa , Liu Gaohuan
Published: December 2019

Understanding spatial differences of crop yields and quantitatively exploring the relationship between crop yields and influencing factors are of great significance in increasing regional crop yields, promoting sustainable development of regional agriculture and ensuring regional food security. This study investigates spatial heterogeneity of winter wheat yield and its determinants in the Yellow River Delta (YRD) region. The spatial pattern of winter wheat in 2015 was mapped through time series similarity analysis. Winter wheat yield was estimated by integrating phenological information into yield model, and cross-validation was performed using actual yield data. The geographical detector method was used to analyze determinants influencing winter wheat yield. This study concluded that the overall classification accuracy for winter wheat is 88.09%. The estimated yield agreed with actual yield, with R2 value of 0.74 and root mean square error (RMSE) of 1.02 t ha−1. Cumulative temperature, soil salinity and their interactions were key determinants affecting winter wheat yield. Several measures are recommended to ensure sustainable crop production in the YRD region, including improving irrigation and drainage systems to reduce soil salinity, selecting salt-tolerant winter wheat varieties, and improving agronomy techniques to extend effective cumulative temperature.
Discovering Technology Opportunity by Keyword-Based Patent Analysis: A Hybrid Approach of Morphology Analysis and USIT
Authors: Feng Lijie , Niu Yuxiang , Liu Zhenfeng , Wang Jinfeng , Zhang Ke
Published: December 2019

As innovative technology is being developed at an accelerated rate, the identification of technology opportunities is especially critical for both companies and governments. Among various approaches to search for opportunities, one of the most frequently used is to discover technology opportunity from patent data. In line with it, this paper aims to propose a hybrid approach based on morphological analysis (MA) and unified structured inventive thinking (USIT) for technology opportunity discovery (TOD) through patent analysis using text mining and Word2Vec clustering analysis to explore the intrinsic links of innovation elements. A basic morphology matrix is constructed according to patent information and then is extended using the innovation algorithms that are reorganized from USIT. Technology opportunities are analyzed at two layers to generate new technical ideas. To illustrate the research process and validate its utility, this paper selects the technology of coalbed methane (CBM) extraction as a use case. This hybrid approach contributes by suggesting a semi-autonomous and systematic procedure to perform MA for TOD. By integrating the innovation algorithms, this approach improves the procedure of value extension in MA.
Does the Energy Efficiency of Power Companies Affect Their Industry Status? A DEA Analysis of Listed Companies in Thermal Power Sector
Authors: Chai Jian , Fan Wenyue , Han Jing
Published: December 2019

Under the concept of green development, coal and oil control policies were frequently introduced, but the “cold” thinking under the “hot” environment is more conducive to the smooth promotion of the “transitional period” of China’s power-structure transformation. How to better consider production efficiency and environmental benefits is the theme that thermal power enterprises need to constantly break through. The most feasible “transitional period” path for thermal power enterprises is improving the efficiency of energy saving, reducing emissions, and simultaneously ensuring benefits and social awareness. This paper selected 17 listed companies in China’s thermal power sector, combined with their input resources, output, and other indicators, using a slacks-based measure–data envelopment analysis (SBM–DEA) model with undesirable outputs, and comprehensively analyzed the relationship among the technical efficiency improvement and the industry status, including price earnings ratio (P/E ratio) in the recent two years. The results show that the improvement of energy efficiency can indeed optimize enterprises’ operations and improve P/E ratio. Although clean-energy power generation has better environmental benefits, it is still lacking in efficiency; therefore, the balance between efficiency and environment sustainability should concern us.
The Effect of Female Employment on Corporate Sustainability in Terms of Tax Avoidance
Authors: Rhee Chang Seop , Woo Sohee , Kim Dong Hyun
Published: December 2019

Recent studies on tax avoidance report that having female executives in companies increases accounting transparency and firm value by reducing tax avoidance. These studies explain that the more risk-averse and conservative characteristics of females affect corporate decision making about tax strategies if the company has female executives. In this study, we assume that the higher the proportion of female employees in a company, the more likely the risk-averse and conservative characteristics of females will affect the firm’s decision making, thereby reducing tax avoidance activities and improving corporate sustainability. To verify this hypothesis, we empirically analyze the association between the female employee ratio and tax avoidance. From the empirical results, we find increasing the female employee ratio may reduce the level of tax avoidance. We also find that companies with a high percentage of female employees show less tax avoidance than those with a low percentage of female employees. The findings suggest that by increasing the ratio of female employees, a company can improve its sustainability in terms of tax avoidance by forming a risk-averse and conservative corporate environment.
Does CSR Moderate the Relationship between Corporate Governance and Chinese Firm’s Financial Performance? Evidence from the Shanghai Stock Exchange (SSE) Firms
Authors: Ali Rizwan , Sial Muhammad Safdar , Brugni Talles Vianna , Hwang Jinsoo , Khuong Nguyen Vinh , Khanh Thai Hong Thuy
Published: December 2019

We have performed a focalized investigation to explore how corporate social responsibility (CSR) moderates the relationship between corporate governance and firms’ financial performance. We applied a panel regression to examine this relationship from a sample of 3400 Shanghai Stock Exchange (SSE) listed firms, based on yearly observations from 2009 to 2018. Our results show that the presence of female directors on the board is associated with improved firms’ performance and that corporate social responsibility (CSR) moderates this relation, thus indicating that sharing strategic decision-making with female board members revealed a better relationship between CSR and firms’ financial performance. Our findings showed that foreign institutional investors positively influenced firms’ financial performance and that CSR moderates the relation between foreign institutional shareholders and the firm’s financial performance. Supported by corporate governance theories, such as resource dependence and stakeholder theory, our results help to better understand the nexus among corporate governance, firms’ performance and corporate social responsibility. These findings are advantageous to government departments in emerging countries in terms of encouraging marketing practitioners and participants to implement CSR practices and change the attitude associated with CSR implications. This study highlighted the problems of the foreign institutional investors’ scheme, which was the main contribution to the financial market reform of China after 2003. These findings offer significant implications to corporate affairs executives and managers, practitioners, academicians, state officials, and policy-makers, and might provide China with the opportunity to extend its market liberalization to the global markets. This research also contributes to the existing literature, which investigates how CSR moderates the relationship between corporate governance and firms’ financial performance in the Chinese market context.
A Neutrosophic AHP and TOPSIS Framework for Supply Chain Risk Assessment in Automotive Industry of Pakistan
Authors: Junaid Muhammad , Xue Ye , Syed Muzzammil Wasim , Li Ji Zu , Ziaullah Muhammad
Published: December 2019

Risk is inherent in all parts of life and brings consequences, but when it specifically emerges in supply chains, it is susceptible. Therefore, this study aims at identifying and assessing supply chain risks and developing criteria for managing these risks. Supply chain (SC) risks consist of complex, uncertain, and vague information, but risk assessment techniques in the literature have been unable to handle complexity, uncertainty, and vagueness. Therefore, this study presents a holistic approach to supply chain risk management. In this paper, neutrosophic (N) theory is merged with the analytic hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS) to deal with complexity, uncertainty, and vagueness. Then the proposed methodology is practically implemented through a case study on the automotive industry. SC resilience, SC agility, and SC robustness were selected as criteria for managing supply chain risks and analyzed using N-AHP. Furthermore, seventeen risks were identified and assessed by using N-TOPSIS. Results suggest supply chain resilience is the most important criterion for managing supply chain risks. Moreover, supplier delivery delays, supplier quality problems, supplier communication failures, and forecasting errors are the most vulnerable risks that occur in supply chains of the automotive industry in Pakistan.
Sustainability and Corporate Governance: Transparency and Excessive Directors’ Remuneration in Listed Companies during the Global Financial Crisis
Authors: Merino Elena , Manzaneque-Lizano Montserrat , Sanchez-Araque Jesus
Published: December 2019

This article empirically examines the connection between the board of directors’ characteristics and excesses in remuneration for directors from a sustainability perspective, highlighting the role of information transparency on remuneration control. Using data from 73 listed companies in the period 2007–2012 (the global financial crisis), we find that (1) board size presents a non-linear relationship with excessive total directors’ remuneration during the crisis period; (2) other board characteristics (board independence, duality and directors’ ownership) do not show a significant relationship with excessive directors´ remuneration; and (3) voluntary transparency on directors’ remuneration significantly contributes to controlling excessive total directors’ remuneration, which contributes to the long-term sustainability of the firm. The results of this study provide good reasons to take into account the effect of corporate governance characteristics and transparency on the remuneration excesses committed during the global financial crisis.
Study on the Accessibility and Recreational Development Potential of Lakeside Areas Based on Bike-Sharing Big Data Taking Wuhan City as an Example
Authors: Wu Jing , Ling Changlong , Li Xinzhuo
Published: December 2019

Accessibility is an important factor in measuring the recreational development potential of Wuhan lakeside areas where people like bike-sharing services for leisure. By using bike-sharing big data, this paper visualizes the spatiotemporal distribution characteristics and depicts the free flows of OD (Original Points and Destination Points) points of the bike-sharing activities taking place within 4 km of 21 lakes in the Wuhan Third Ring Road on an important holiday. Based on these distribution laws, statistics and spatial measurement are used to measure and compare the theoretical accessibility and actual accessibility of these lakeside areas at different grid scales in order to estimate the recreational development potential and explore the causes and possible suggestions behind the recreational potential. Results show that Ziyang Lake, Shai Lake, and South Lake have great recreational potential in improving their accessibility, whereas the Hankou lake dense area has a saturated recreational development potential due to its high accessibility characteristics. The differences in the water environment, surrounding road traffic conditions, and construction situations in these lakes influence their accessibility. Some differences are also observed between the actual and theoretical accessibility of most of these lakes, and there is a long way to go for real improvement of their recreational development potential. To better exploit the recreational development potential, improving the accessibility of these lakes remains an important issue that needs to be addressed as soon as possible.
Research on the Effect of Urbanization on China’s Carbon Emission Efficiency
Authors: Li Lianshui , Cai Yang , Liu Liang
Published: December 2019

Improvements in carbon emission efficiency are crucial to China’s economic growth; carbon emission reduction and urbanization are two of the focuses of research on carbon emission efficiency. This paper selects 2000–2015 panel data from 30 provinces in China, evaluates the carbon emission efficiency of each province using the DEA method and, based on the STIRPAT expansion form, empirically looks at the effect of urbanization on carbon emission efficiency. The results show that, during the chosen time frame, not only did the carbon emission efficiency of China’s provinces show an upward trend but the carbon emission efficiency of the Eastern, Central and Western regions differed markedly, with the highest efficiency in the Eastern region, the second highest in the Central region and the lowest in the Western region. After controlling for population density, economic development level, energy intensity and industrial structure, urbanization we determine that urbanization can indeed improve carbon emission efficiency, although there are regional differences. Urbanization is conducive to improvements in carbon emission efficiency in both the Central and Western regions but the promotion effect of the Western region is stronger. The effect in the Eastern region is not significant. Based on the conclusions above, this paper puts forward policy recommendations that promote both China’s lower carbon efficiency and future environmental protection.
Building a Framework of Evaluating Human–Environment Relationships: Considering the Differences between Subjective Evaluations and Objective Assessments
Authors: Liu Ying , Deng Wei , Peng Li
Published: December 2019

The human–environment relationship is bidirectional, meaning that human attitudes and behavior to nature are at the root of environmental change, while changes in the environment affect human attitudes and behavior. It is necessary to analyze the human–environment relationship from two aspects: (a) Whether there is a good objective basis for maintaining an environment, and (b) whether people report that they are satisfied with that environment. This study attempted to construct a framework to evaluate the human–environment relationship considering these two aspects. The framework consists of three parts: Traditional evaluation, indicator construction, and evaluation considering the relationship between subjective and objective assessment. Traditional evaluations consist of subjective evaluations and objective assessments. Indicator construction focuses on putting forward indicators that quantitively evaluate the human–environment relationship, considering the results of objective assessments and subjective evaluations. The indicators introduced in this study include MD (match degree) and OSC (objective assessment and subjective evaluation comparison) to explain the difference and the relationship between objective assessments and subjective evaluations of the environment. Then, based on the indicator value, a matrix containing four situations (Match-H, Match-L, H-L, and L-H) was constructed to explore why a human–environment relationship may not be harmonious. Since the upper Minjiang River basin is a typical area, because of its intensive human activity, as well as its fragile ecological environment, this study chose it as a case study and used it to verify the framework. Through the framework construction and application, this study found that: (1) The framework of this study provided a more comprehensive method to evaluate the human–environment relationship; (2) as the subjective evaluation was based on individual comprehensive tradeoffs, the evaluation combining the subjective and objective assessment was more accurate; (3) environmental conditions were the basis, and human activities were the key factors, for the coordination of human–environment relationships; so the matrix put forward in this study was necessary for finding the cause of human–environment incongruity.