bluuebunny/crossref_metadata_2025 huggingface dataset:
Effectiveness of Virtual Reality for Children and Adolescents with Autism Spectrum Disorder: An Evidence-Based Systematic Review
Authors: Mesa-Gresa Patricia , Gil-Gómez Hermenegildo , Lozano-Quilis José-Antonio , Gil-Gómez José-Antonio
Published: August 2018

Autism Spectrum Disorder (ASD) is a neurodevelopmental disease that is specially characterized by impairments in social communication and social skills. ASD has a high prevalence in children, affecting 1 in 160 subjects. Virtual reality (VR) has emerged as an effective tool for intervention in the health field. Different recent papers have reviewed the VR-based treatments in ASD, but they have an important limitation because they only use clinical databases and do not include important technical indexes such as the Web of Science index or the Scimago Journal & Country Rank. To our knowledge, this is the first contribution that has carried out an evidence-based systematic review including both clinical and technical databases about the effectiveness of VR-based intervention in ASD. The initial search identified a total of 450 records. After the exclusion of the papers that are not studies, duplicated articles, and the screening of the abstract and full text, 31 articles met the PICO (Population, Intervention, Comparison and Outcomes) criteria and were selected for analysis. The studies examined suggest moderate evidence about the effectiveness of VR-based treatments in ASD. VR can add many advantages to the treatment of ASD symptomatology, but it is necessary to develop consistent validations in future studies to state that VR can effectively complement the traditional treatments.
A Survey of Anomaly Detection in Industrial Wireless Sensor Networks with Critical Water System Infrastructure as a Case Study
Authors: Ramotsoela Daniel , Abu-Mahfouz Adnan , Hancke Gerhard
Published: August 2018

The increased use of Industrial Wireless Sensor Networks (IWSN) in a variety of different applications, including those that involve critical infrastructure, has meant that adequately protecting these systems has become a necessity. These cyber-physical systems improve the monitoring and control features of these systems but also introduce several security challenges. Intrusion detection is a convenient second line of defence in case of the failure of normal network security protocols. Anomaly detection is a branch of intrusion detection that is resource friendly and provides broader detection generality making it ideal for IWSN applications. These schemes can be used to detect abnormal changes in the environment where IWSNs are deployed. This paper presents a literature survey of the work done in the field in recent years focusing primarily on machine learning techniques. Major research gaps regarding the practical feasibility of these schemes are also identified from surveyed work and critical water infrastructure is discussed as a use case.
Wheel Force Sensor-Based Techniques for Wear Detection and Analysis of a Special Road
Authors: Yan Huawen , Zhang Weigong , Wang Dong
Published: August 2018

Automobile proving ground is important for the research of vehicles which are used for vehicle dynamics, durability testing, braking testing, etc. However, the road in automobile proving grounds will inevitably be damaged with the extension of the service life. In most previous research, equipment similar to a laser profilometer was used to detect the quality of the road, the principle of which is to reflect the quality of the road by measuring the roughness of the pavement. This method ignores the elastic deformation of the road itself when the vehicle is traveling and it is difficult to compensate for the error. Therefore, this paper presents a new method based on a force sensor to reduce the impact of elastic deformation, such as tire deformation, pavement deformation, and wheel rim deformation. In this study, force sensors mounted on the wheels collect the three-dimensional dynamic force of the wheel. The presented method has been tested with two sets of cobblestone road loads, and the result shows that the load intensities imposed by the test vehicle on the target road are 88.3%, 91.0%, and 92.05% of the intensity of the load imposed by the test vehicle on a standard road in three respective dimensions. It is clear that the proposed method has strong potential effectiveness to be applied for wear detection and analysis of a special road.
A Resonant Pressure Microsensor Based on Double-Ended Tuning Fork and Electrostatic Excitation/Piezoresistive Detection
Authors: Shi Xiaoqing , Lu Yulan , Xie Bo , Li Yadong , Wang Junbo , Chen Deyong , Chen Jian
Published: August 2018

This paper presents a resonant pressure microsensor relying on electrostatic excitation and piezoresistive detection where two double-ended tuning forks were used as resonators, enabling differential outputs. Pressure under measurement caused the deformation of the pressure sensitive membrane, leading to stress buildup of the resonator under electrostatic excitation with a corresponding shift of the resonant frequency detected piezoresistively. The proposed microsensor was fabricated by simplified SOI-MEMS technologies and characterized by both open-loop and closed-loop circuits, producing a quality factor higher than 10,000, a sensitivity of 79.44 Hz/kPa and an accuracy rate of over 0.01% F.S. In comparison to the previously reported resonant piezoresistive sensors, the proposed device used single-crystal silicon as piezoresistors, which was featured with low DC biased voltages, simple sensing structures and fabrication steps. In addition, the two double-ended tuning forks were used as resonators, producing high quality factors and differential outputs, which further improved the sensor performances.
Analytical Model for the Duty Cycle in Solar-Based EH-WSN for Environmental Monitoring
Authors: Galmés Sebastià , Escolar Soledad
Published: August 2018

A technology drift is currently taking place from traditional battery-powered sensor networks, which exhibit limited lifetime, to the new Energy-Harvesting Wireless Sensor Networks (EH-WSN), which open the way towards self-sustained operation. However, this emergent modality also brings up new challenges, especially due to the time-varying nature and unpredictability of ambient energy sources. Most proposals for implementing EH-WSN rely on heuristic approaches to redesign the duty-cycling mechanism at the MAC layer, with the ultimate goal of optimizing network performance while preserving self-sustained and continuous operation. In contrast to the common system-wide reduced duty cycle of battery-powered sensor networks, the duty cycle in EH-WSN is much larger and adapted to the energy harvesting rate and traffic load of each node in the network. In this paper, we focus on solar-based EH-WSN devoted to environmental monitoring. In contrast to current works, we follow an analytical approach, which results into closed-form expressions for the duty cycle and initial energy storage that guarantee self-sustained operation to any node in a solar-based EH-WSN. To center the analysis, we consider TinyOS sensor nodes, though we postulate that the essential components of the obtained formulation will contribute to further develop duty cycle adaptation schemes for TinyOS and other software platforms.
Non-Destructive Classification of Diversely Stained Capsicum annuum Seed Specimens of Different Cultivars Using Near-Infrared Imaging Based Optical Intensity Detection
Authors: Manattayil Jyothsna Konkada , Ravichandran Naresh Kumar , Wijesinghe Ruchire Eranga , Shirazi Muhammad Faizan , Lee Seung-Yeol , Kim Pilun , Jung Hee-Young , Jeon Mansik , Kim Jeehyun
Published: August 2018

The non-destructive classification of plant materials using optical inspection techniques has been gaining much recent attention in the field of agriculture research. Among them, a near-infrared (NIR) imaging method called optical coherence tomography (OCT) has become a well-known agricultural inspection tool since the last decade. Here we investigated the non-destructive identification capability of OCT to classify diversely stained (with various staining agents) Capsicum annuum seed specimens of different cultivars. A swept source (SS-OCT) system with a spectral band of 1310 nm was used to image unstained control C. annuum seeds along with diversely stained Capsicum seeds, belonging to different cultivar varieties, such as C. annuum cv. PR Ppareum, C. annuum cv. PR Yeol, and C. annuum cv. Asia Jeombo. The obtained cross-sectional images were further analyzed for the changes in the intensity of back-scattered light (resulting due to dye pigment material and internal morphological variations) using a depth scan profiling technique to identify the difference among each seed category. The graphically acquired depth scan profiling results revealed that the control specimens exhibit less back-scattered light intensity in depth scan profiles when compared to the stained seed specimens. Furthermore, a significant back-scattered light intensity difference among each different cultivar group can be identified as well. Thus, the potential capability of OCT based depth scan profiling technique for non-destructive classification of diversely stained C. annum seed specimens of different cultivars can be sufficiently confirmed through the proposed scheme. Hence, when compared to conventional seed sorting techniques, OCT can offer multipurpose advantages by performing sorting of seeds in respective to the dye staining and provides internal structural images non-destructively.
Preferred Placement and Usability of a Smart Textile System vs. Inertial Measurement Units for Activity Monitoring
Authors: Mokhlespour Esfahani Mohammad Iman , Nussbaum Maury A.
Published: August 2018

Wearable sensors and systems have become increasingly popular in recent years. Two prominent wearable technologies for human activity monitoring are smart textile systems (STSs) and inertial measurement units (IMUs). Despite ongoing advances in both, the usability aspects of these devices require further investigation, especially to facilitate future use. In this study, 18 participants evaluate the preferred placement and usability of two STSs, along with a comparison to a commercial IMU system. These evaluations are completed after participants engaged in a range of activities (e.g., sitting, standing, walking, and running), during which they wear two representatives of smart textile systems: (1) a custom smart undershirt (SUS) and commercial smart socks; and (2) a commercial whole-body IMU system. We first analyze responses regarding the usability of the STS, and subsequently compared these results to those for the IMU system. Participants identify a short-sleeved shirt as their preferred activity monitor. In additional, the SUS in combination with the smart socks is rated superior to the IMU system in several aspects of usability. As reported herein, STSs show promise for future applications in human activity monitoring in terms of usability.
Grey Model Optimized by Particle Swarm Optimization for Data Analysis and Application of Multi-Sensors
Authors: Li Chenming , Gao Hongmin , Qiu Junlin , Yang Yao , Qu Xiaoyu , Wang Yongchang , Bi Zhuqing
Published: August 2018

Data on the effective operation of new pumping station is scarce, and the unit structure is complex, as the temperature changes of different parts of the unit are coupled with multiple factors. The multivariable grey system prediction model can effectively predict the multiple parameter change of a nonlinear system model by using a small amount of data, but the value of its q parameters greatly influences the prediction accuracy of the model. Therefore, the particle swarm optimization algorithm is used to optimize the q parameters and the multi-sensor temperature data of a pumping station unit is processed. Then, the change trends of the temperature data are analyzed and predicted. Comparing the results with the unoptimized multi-variable grey model and the BP neural network prediction method trained under insufficient data conditions, it is proved that the relative error of the multi-variable grey model after optimizing the q parameters is smaller.
An Augmented Reality System Using Improved-Iterative Closest Point Algorithm for On-Patient Medical Image Visualization
Authors: Wu Ming-Long , Chien Jong-Chih , Wu Chieh-Tsai , Lee Jiann-Der
Published: August 2018

In many surgery assistance systems, cumbersome equipment or complicated algorithms are often introduced to build the whole system. To build a system without cumbersome equipment or complicated algorithms, and to provide physicians the ability to observe the location of the lesion in the course of surgery, an augmented reality approach using an improved alignment method to image-guided surgery (IGS) is proposed. The system uses RGB-Depth sensor in conjunction with the Point Cloud Library (PCL) to build and establish the patient’s head surface information, and, through the use of the improved alignment algorithm proposed in this study, the preoperative medical imaging information obtained can be placed in the same world-coordinates system as the patient’s head surface information. The traditional alignment method, Iterative Closest Point (ICP), has the disadvantage that an ill-chosen starting position will result only in a locally optimal solution. The proposed improved para-alignment algorithm, named improved-ICP (I-ICP), uses a stochastic perturbation technique to escape from locally optimal solutions and reach the globally optimal solution. After the alignment, the results will be merged and displayed using Microsoft’s HoloLens Head-Mounted Display (HMD), and allows the surgeon to view the patient’s head at the same time as the patient’s medical images. In this study, experiments were performed using spatial reference points with known positions. The experimental results show that the proposed improved alignment algorithm has errors bounded within 3 mm, which is highly accurate.
Temperature Stability of a Hybrid Polarization-Maintaining Photonic Crystal Fiber Resonator and Its Application in a Resonant Fiber Optic Gyro
Authors: He Nie , Li Zhuoyan , Zhang Gongshen , Liu An’an , Zhou Ding , Chen Peng , Liu Chengxiang , Wu Xu
Published: August 2018

A fiber ring resonator (FRR) constructed using a Panda polarization-maintaining fiber does not effectively solve the problem of temperature-related polarization fluctuation, which considerably limits the detection accuracy of the resonant fiber optic gyro. The polarization-maintaining photonic crystal fiber (PM-PCF) can improve the thermal stability of the FRR. In this study, a structure that can simultaneously detect the polarization fluctuation of two FRRs is designed. We analyzed and verified the polarization phase shift errors of these two types of fibers, which are caused by the thermally induced birefringence changes. Theoretical simulation and experimental results confirm that a PM-PCF can be used to optimize the FRR, which can effectively suppress the polarization fluctuation.
Promoting Pollution-Free Routes in Smart Cities Using Air Quality Sensor Networks
Authors: Ramos Francisco , Trilles Sergio , Muñoz Andrés , Huerta Joaquín
Published: August 2018

Nowadays, citizens have a huge concern about the quality of life in their cities, especially regarding the level of pollution. Air quality level is of great importance, not only to plan our activities but also to take precautionary measures for our health. All levels of governments are concerned about it and have built their indexes to measure the air quality level in their countries, regions or cities. Taking into account the existing sensor infrastructure within smart cities, it makes possible to evaluate these indices and to know anywhere the level of pollution in real-time. In this scenario, the main objective of the current work is to foster citizens’ awareness about pollution by offering pollution-free routes. To achieve this goal, a technology-agnostic methodology is presented, which allows for creating pollution-free routes across cities depending on the level of pollution in each zone. The current work includes an extensive study of existing air quality indices, and proposes and carries forward to deployment of the defined methodology in a big city, such as Madrid (Spain).
Adaptive Computing Optimization in Software-Defined Network-Based Industrial Internet of Things with Fog Computing
Authors: Wang Juan , Li Di
Published: August 2018

In recent years, cloud computing and fog computing have appeared one after the other, as promising technologies for augmenting the computing capability of devices locally. By offloading computational tasks to fog servers or cloud servers, the time for task processing decreases greatly. Thus, to guarantee the Quality of Service (QoS) of smart manufacturing systems, fog servers are deployed at network edge to provide fog computing services. In this paper, we study the following problems in a mixed computing system: (1) which computing mode should be chosen for a task in local computing, fog computing or cloud computing? (2) In the fog computing mode, what is the execution sequence for the tasks cached in a task queue? Thus, to solve the problems above, we design a Software-Defined Network (SDN) framework in a smart factory based on an Industrial Internet of Things (IIoT) system. A method based on Computing Mode Selection (CMS) and execution sequences based on the task priority (ASTP) is proposed in this paper. First, a CMS module is designed in the SDN controller and then, after operating the CMS algorithm, each task obtains an optimal computing mode. Second, the task priorities can be calculated according to their real-time performance and calculated amount. According to the task priority, the SDN controller sends a flow table to the SDN switch to complete the task transmission. In other words, the higher the task priority is, the earlier the fog computing service is obtained. Finally, a series of experiments and simulations are performed to evaluate the performance of the proposed method. The results show that our method can achieve real-time performance and high reliability in IIoT.
Three-Dimensional Terahertz Coded-Aperture Imaging Based on Back Projection
Authors: Chen Shuo , Luo Chenggao , Wang Hongqiang , Wang Wenpeng , Peng Long , Zhuang Zhaowen
Published: August 2018

Terahertz coded-aperture imaging (TCAI) can overcome the difficulties of traditional radar in forward-looking and high-resolution imaging. Three-dimensional (3D) TCAI relies mainly on the reference-signal matrix (RSM), the large size and poor accuracy of which reduce the computational efficiency and imaging ability, respectively. According to the previous research on TCAI, traditional TCAI cannot reduce the heavy computational burden while the improved TCAI achieve reconstructing the target parts of different ranges in parallel. However, large-sized RSM still accounts for the computational complexity of traditional TCAI and the improved TCAI. Therefore, this paper proposes a more efficient imaging method named back projection (BP)-TCAI (BP-TCAI). Referring to the basic principle of BP, BP-TCAI can not only divide the scattering information in different ranges but also project the range profiles into different imaging subareas. In this way, the target parts in different subareas can be reconstructed simultaneously to synthesize the whole 3D target and thus decomposes the computational complexity thoroughly. During the pulse compression and projection processes, the signal-to-noise ratio (SNR) of BP-TCAI is also improved. This present the imaging method, model and procedures of traditional TCAI, the improved TCAI and the proposed BP-TCAI. Numerical experimental results prove BP-TCAI to be more effective and efficient than previous imaging methods of TCAI. Besides, BP-TCAI can also be seen as synthetic aperture radar (SAR) imaging with coding technology. Therefore, BP-TCAI opens a future gate combining traditional SAR and coded-aperture imaging.
An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
Authors: Wang Hui , Li Youming , Chang Tingcheng , Chang Shengming
Published: August 2018

Coverage maintenance is a bottleneck restricting the development of underwater acoustic sensor networks (UASNs). Since the energy of the nodes is limited, the coverage of UASNs may gradually decrease as the network operates. Thus, energy-saving coverage control is crucial for UASNs. To solve the above problems, this paper proposes a coverage-control strategy (referred to as ESACC) that establishes a sleep–wake scheduling mechanism based on the redundancy of deployment nodes. The strategy has two main parts: (1) Node sleep scheduling based on a memetic algorithm. To ensure network monitoring performance, only some nodes are scheduled to work, with redundant nodes in a low-power hibernation state, reducing energy consumption and prolonging the network lifetime. The goal of node scheduling is to find a minimum set of nodes that can cover the monitoring area, and a memetic algorithm can solve this problem. (2) Wake-up scheme. During network operation, sleeping nodes are woken to cover the dead nodes and maintain high coverage. This scheme not only reduces the network energy consumption but takes into account the monitoring coverage of the network. The experimental data show that ESACC performs better than current algorithms, and can improve the network life cycle while ensuring high coverage.
Self-Calibration of Angular Position Sensors by Signal Flow Networks
Authors: Gao Zhenyi , Zhou Bin , Hou Bo , Li Chao , Wei Qi , Zhang Rong
Published: August 2018

Angle position sensors (APSs) usually require initial calibration to improve their accuracy. This article introduces a novel offline self-calibration scheme in which a signal flow network is employed to reduce the amplitude errors, direct-current (DC) offsets, and phase shift without requiring extra calibration instruments. In this approach, a signal flow network is firstly constructed to overcome the parametric coupling caused by the linearization model and to ensure the independence of the parameters. The model parameters are stored in the nodes of the network, and the intermediate variables are input into the optimization pipeline to overcome the local optimization problem. A deep learning algorithm is also used to improve the accuracy and speed of convergence to a global optimal solution. The results of simulations show that the proposed method can achieve a high identification accuracy with a relative parameter identification error less than 0.001‰. The practical effects were also verified by implementing the developed technique in a capacitive APS, and the experimental results demonstrate that the sensor error after signal calibration could be reduced to only 6.98%.
A New Method for GNSS Multipath Mitigation with an Adaptive Frequency Domain Filter
Authors: Yu Siqi , Guo Fei , Zhang Xiaohong , Liu Wanke , Li Xin , Wu Renpan
Published: August 2018

Multipath is the dominant error source for most fixed Global Navigation Satellite Systems (GNSS) sites and stations. The presence of multipath, particularly the multipath effect on pseudorange measurement, seriously affects positioning accuracy. Unfortunately, multipath effect reduction is still a challenging issue in high-accuracy GNSS positioning applications due to its special properties. To minimize the impact of the multipath effect, this paper focused on pseudorange multipath mitigation. First, the frequency spectrum of the code-minus-carrier divergence (CMCD) for Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), and BeiDou Navigation Satellite System (BDS) satellites observed in different environments were analyzed, where we found that periodic fluctuations appeared in GPS and GLONASS as well as medium earth orbit (MEO) and inclined geosynchronous satellite orbit (IGSO) of BDS satellites in some situations, which manifested as peaks in the frequency domain. The results showed that the location of the frequency peaks in the frequency domain, width, and basic frequency spectrum intensity were different between different satellites and environments, causing difficulty in reducing the error impact. To eliminate such period fluctuations mainly caused by the multipath effect, a novel method based on a frequency domain filter was proposed in this paper. One of the keys of the proposed method was the use of short-time Fourier transformation (STFT) in the GNSS signal data processing to calculate the accurate local frequency spectrum when code-minus-carrier divergence (CMCD) was assumed to be time varying. Once the frequency spectrum was obtained, a new spectrum peak extraction method was used to locate the peak frequency position. By interpolation and inverse Fourier transformation, the influence of the spectrum peaks could be effectively eliminated, thus improving pseudorange precision. The experimental results showed that the periodic multipath effect could be greatly reduced by the proposed method.
Liquid Level Measurement Model Outside of Closed Containers Based on Continuous Sound Wave Amplitude
Authors: Zhang Yanjun , Zhang Bin , Zhang Liang , Li Yunchao , Gao Xiaolong , Liu Zhaojun
Published: August 2018

This research put forward an exogenous liquid level measurement method based on continuous sound wave amplitude. The distribution of round piston transducers in the sound field of a metal solid was analyzed by building 15 Multi-Gaussian Beam superposition models; the calculation method for echo sound pressure was worked out according to the reflection and refraction properties of ultrasonic wave. The continuous wave with three amplitudes was used as the driving source of ultrasonic sensor, and two single-crystal sensors with the same diameter were used as the transmitting terminal and receiving terminal of ultrasonic waves to carry out experiments for four groups of containers of different wall thickness and to compare the characteristics of echo energy of driving sources with three amplitudes above and below the liquid levels with different wall thickness. Two groups of sensors of different diameters were used to measure the liquid levels of experimental models, and the measuring errors of the two groups of sensors were analyzed and compared. The experimental result shows that the measuring error of the model is less than 5 mm, so it is applicable to the level measurement of liquids or liquid mixtures in many sectors.
Internet of Things with Lightweight Identities Implemented Using DNS DANE—Architecture Proposal
Authors: Kamola Mariusz
Published: August 2018

Domain Name Service (DNS) and its certification related resource records are appealing alternative to the standard X.509 certification framework, in provision of identities for Internet of Things (IoT) smart devices. We propose to also use DNS to store device owner identification data in device certificates. A working demonstration software has been developed as proof of this concept, which uses an external identity provider run by national authorities. As a result, smart devices are equipped with certificates that safely identify both the device and its owner. Hardware requirements make such a framework applicable to constrained devices. It stimulates mutual trust in machine-to-machine and man-to-machine communication, and creation of a friendlier environment for sale, lease, and data exchange. Further extensions of the proposed architecture are also discussed.
Development of a Novel Q-body Using an In Vivo Site-Specific Unnatural Amino Acid Incorporation System
Authors: Kurumida Yoichi , Hayashi Nobuhiro
Published: August 2018

A Q-body capable of detecting target molecules in solutions could serve as a simple molecular detection tool. The position of the fluorescent dye in a Q-body affects sensitivity and therefore must be optimized. This report describes the development of Nef Q-bodies that recognize Nef protein, one of the human immunodeficiency virus (HIV)’s gene products, in which fluorescent dye molecules were placed at various positions using an in vivo unnatural amino acid incorporation system. A maximum change in fluorescence intensity of 2-fold was observed after optimization of the dye position. During the process, some tryptophan residues of the antibody were found to quench the fluorescence. Moreover, analysis of the epitope indicated that some amino acid residues of the antigen located near the epitope affected the fluorescence intensity.
Nepal Government’s Emergency Response to the 2015 Earthquake: A Case Study
Authors: Shrestha Bahul , Pathranarakul Pairote
Published: August 2018

This paper utilizes the National Disaster Response Framework 2013 guidelines to analyze the large-scale disaster response of the Nepal government’s institutional system in the wake of the 2015 earthquake. The methodology includes in-depth interviews with key informants, focus group discussions, field observations, and document analysis. The study found that despite limitations in institutional capacity and scarcity of resources, government institutions such as the Nepal Army, the Nepal Police, the Armed Police Force, the District Administration Offices, the Ministry of Home Affairs, and major public hospitals made a significant contribution to support the victims. Nevertheless, it also revealed the current weaknesses of those institutions in terms of response effectiveness and provides recommendations for enhancing their capacity.
Agricultural Internet Entrepreneurs’ Social Network Behaviors and Entrepreneurship Financing Performance
Authors: Yan Zichun , Wang Kai , Wang Ze-Yu , Yu Jian , Tsai Sang-Bing , Li Guodong
Published: August 2018

From the perspective of social network behavior, our paper discusses the relationship between agricultural entrepreneurial social network behavior and entrepreneurial financing performance in an Internet crowdfunding setting, and investigates the moderating role of entrepreneurial team size. In this paper, the data-mining method is used to capture and collate data regarding 7585 venture projects on an Internet crowdfunding platform between 2014 and 2017. Ordinary least squares (OLS) and Probit models are applied to the empirical test. The results show that first, compared with other industries, the effect of agricultural entrepreneurs’ quality information disclosure on entrepreneurship financing performance is lower, whereas the effect of their social network interaction is higher. Second, the entrepreneurial team size has a positive moderating role on the former and a negative moderating role on the latter. Our research is of significance for agricultural enterprises to raise their financial performance in Internet crowdfunding, especially for Chinese agricultural micro-enterprises.
Biogas Potential of Coffee Processing Waste in Ethiopia
Authors: Chala Bilhate , Oechsner Hans , Latif Sajid , Müller Joachim
Published: August 2018

Primary coffee processing is performed following the dry method or wet method. The dry method generates husk as a by-product, while the wet method generates pulp, parchment, mucilage, and waste water. In this study, characterization, as well as the potential of husk, pulp, parchment, and mucilage for methane production were examined in biochemical methane potential assays performed at 37 °C. Pulp, husk, and mucilage had similar cellulose contents (32%). The lignin contents in pulp and husk were 15.5% and 17.5%, respectively. Mucilage had the lowest hemicellulose (0.8%) and lignin (5%) contents. The parchment showed substantially higher lignin (32%) and neutral detergent fiber (96%) contents. The mean specific methane yields from husk, pulp, parchment, and mucilage were 159.4 ± 1.8, 244.7 ± 6.4, 31.1 ± 2.0, and 294.5 ± 9.6 L kg−1 VS, respectively. The anaerobic performance of parchment was very low, and therefore was found not to be suitable for anaerobic fermentation. It was estimated that, in Ethiopia, anaerobic digestion of husk, pulp, and mucilage could generate as much as 68 × 106 m3 methane per year, which could be converted to 238,000 MWh of electricity and 273,000 MWh of thermal energy in combined heat and power units. Coffee processing facilities can utilize both electricity and thermal energy for their own productive purposes.
The Relationship between Unbilled Accounts Receivable and Financial Performance of Construction Contractors
Authors: Jung Minhyuk , You Shira , Chi Seokho , Yu Ilhan , Hwang Bon-Gang
Published: August 2018

Operating profit is one of the most important measures in financial statements to evaluate a organizational performance. In the construction industry, however, the profit has a possibility to be misestimated as a loss and can be included in Unbilled Accounts Receivable (UAR) and shown as a profit; this is due to the uncertainty of predicting a total construction cost and project progress on which the calculation of profit is based. UAR results from the different perceptions regarding project progress between clients and contractors and can include costs related to loss that cannot be acknowledged as a progress. Therefore, UAR can be a significant clue to understanding estimation errors of a contractor’s financial performance data. This study investigated the possibility of estimation error of contractors’ operating profit by analyzing the relationship between UAR and other relevant financial performance measures. The accounting data of 41 Korean major contractors was collected and analyzed based on the correlation analysis. The results of this study implies that the profit of construction companies has the possibility to contain estimation errors, causing a significant variance in the process of adjusting the evaluation errors at the end of projects, which can cause unexpected losses to investors. In addition, this study found that the UAR containing estimation errors could be different depending on market in which contractors operate; therefore, when dealing with contractors’ financial performance data, it is necessary to discern whether their profit data contains distortion and, in the case that errors are included, appropriate data preprocessing should be conducted for more reliable and sustainable construction investment and project management.
Fast Identification of Urban Sprawl Based on K-Means Clustering with Population Density and Local Spatial Entropy
Authors: Liu Lingbo , Peng Zhenghong , Wu Hao , Jiao Hongzan , Yu Yang , Zhao Jie
Published: August 2018

As urban sprawl is proven to jeopardize the sustainability system of cities, the identification of urban sprawl is essential for urban studies. Compared with previous related studies which tend to utilize more and more complicated variables to recognize urban sprawl while still retaining an element of uncertainty, this paper instead proposes a simplified model to identify urban sprawl patterns. This is a working theory which is based on a diagram interpretation of the classic urban spatial structure patterns of the Chicago School. The method used in our study is K-means clustering with gridded population density and local spatial entropy. The results and comparison with open population data and mobile phone data verify the assumption and furthermore indicate that the accuracy of source population data will limit the precision of output identification. This article concludes that urban sprawl is mainly dominated by population and surrounding unevenness. Moreover, the Floating Catchment Area (FCA) local spatial entropy method presented in this research brings about an integration of Shannon entropy, Tobler’s first law of geography and the Moore neighborhood, improving the spatial homogeneity and locality of Batty’s Spatial Entropy model which can only be used in a general scope.
Experimentalist Governance to Foster Cooperation in the Baltic Sea Region: A Focus on the Turku Process
Authors: Jetoo Savitri
Published: August 2018

The Baltic Sea is one of the most severely polluted water bodies on earth, with stressors resulting from anthropogenic pressures of 85 million inhabitants in nine coastal countries. All are members of the European Union (EU) with the exception of Russia. This exception poses challenges for governing the Sea, as Russia is excluded as a member country from EU Baltic Sea governing policies, such as the EU Strategy for the Baltic Sea Region (EUSBSR). This added complexity has led to the emergence of new forms of cooperation to include Russia in the governing process. One such initiative is the Turku process, an initiative by the cities of Turku (Finland), Hamburg (Germany), and St. Petersburg (Russia) to promote cooperation, especially with Russian partners. Since its emergence in 2010, there has been no study of it in the literature. This study aims to bridge this gap by analyzing the history and evolution of the Turku process under the lens of experimentalist governance. It aims to illustrate the experimentalist governance perspective through the Turku process and to present the theoretical foundations of the concept. It does the former through key informant interviews with main actors in the Turku Process and the latter with the help of the literature on experimentalist governance. This study adds to the dialogue on governance in an especially challenging time when the Ukraine crisis has negatively impacted EU–Russia relations.
Assessing Soil Metal Levels in an Industrial Environment of Northwestern China and the Phytoremediation Potential of Its Native Plants
Authors: Liu Yuan , Yang Yujing , Li Changxiao , Ni Xilu , Ma Wenchao , Wei Hong
Published: August 2018

Various industrial activities contribute heavy metals to terrestrial ecosystems. In order to evaluate the soil quality of industrial areas and to identify the potential phytoremediator from the native plant species, we collected 45 surface soil samples and 21 plant species in a typical industrial area of northwestern China. The results showed that the average values of the Cd, Cr, As, Pb, Cu, and Zn in the soils were 36.91, 1.67, 7.20, 1.38, 1.27, and 6.66 times, respectively, compared with the corresponding background values. The average single factor pollution index for heavy metals decreased in the order of Cd > As > Zn > Cr > Cu > Pb. The study area was seriously polluted by Cd and As, slightly polluted by Zn, and had relatively little contamination by Cr, Pb, and Cu. In terms of the average Nemerow synthetic pollution index in every sampling site, 97.78% of the samples were seriously polluted and 2.22% of the samples were moderately polluted, which indicated that almost all of the samples in the industrial area were seriously polluted. The results of the biomass, heavy metal concentrations, bioconcentration factors (BCF), and translocation factors (TF) for the native plants showed that Achnatherum splendens for metal Cr presented a phytostabilization potential, Artemisia scoparia and Echinochloa crusgalli for metal Cu and Halogeton arachnoideus for metal Zn presented a phytoextraction potential, and all of the studied plants were limited as phytoremediators for Cd or Pb contaminated soil.
Using Insights from Prospect Theory to Enhance Sustainable Decision Making by Agribusinesses in Argentina
Authors: Gonzalez-Ramirez Jimena , Arora Poonam , Podesta Guillermo
Published: August 2018

Farm production often involves family-owned agribusinesses where decisions are made by households or individuals, not corporate managers. As these decisions have important economic, environmental, and social implications, decision-making processes must be understood to foster sustainable agricultural production. Decision experiments, involving lotteries, targeting farmers in the Argentine Pampas were used to estimate prospect theory (PT) parameters. Results suggest that decisions under risk are better represented by prospect theory than by expected utility (EU) theory: Decision makers treat gains and losses differently and use subjective probabilities of outcomes; they are quite loss averse and are more likely to overweigh probabilities of infrequent events, such as large droughts or floods. Statistical testing revealed heterogeneity in the risk tied to land tenure (land owners vs. renters) and agribusiness roles (farmers vs. technical advisors). Perceptions of risk, probability, and outcomes played a large role in the sustainability of production. Due to a strong desire to avoid losses, decision makers have a greater short term focus: Immediate economic outcomes are more salient, and environmental and social investments are framed as costs rather than long-term gains. This research can help design policies, programs, and tools that assist agribusinesses in managing better contradictions across the triple bottom line to ensure greater sustainability.
Efficiency Analysis of Official Development Assistance Provided by Korea
Authors: Hwang Yun-Gi , Park Soohyun , Kim Daecheol
Published: August 2018

Results of the CDI and QODA evaluation developed by OECD showed that Korea’s aid presented low efficiency compared to other aid countries. However, these methods represent a qualitative assessment of the effectiveness of each country’s aid and are not applicable to the evaluation of actual aid projects and the identification of causes of the inefficiency. Therefore, it is needed to grasp the reality of Korea’s aid and to identify the cause of aid inefficiency to set up a better ODA policy. The purpose of this study is to improve the effectiveness of Korea’s Official Development Assistance (ODA) provided to developing countries. To do this, we analyzed the efficiency of ODA provided to 33 recipient countries by Korea through data envelopment analysis method. The effects of three factors, illiteracy, integrity, and GDP, on efficiency were also investigated by utilizing a Tobit regression analysis. As a result of the DEA efficiency analysis, it was found that the average efficiency was about 65.74%. By region, the average efficiency scores of Asia, Africa, Central and South America, and Middle East and Eastern Europe were about 47.8%, 78.9%, 70.4%, and 62.7%, respectively. This indicates that Asian countries are inefficient compared to countries from other regions. It was also found that GDP, integrity, and illiteracy have positive effects on efficiency. The methodology proposed in this study can be used for other studies to create an aid policy that produces efficient results.
Spatial Zoning Strategy of Urbanization Based on Urban Climate Co-Movement: A Case Study in Shanghai Mainland Area
Authors: Jiang Yunfang , Hou Luyao , Shi Tiemao , Ning Yuemin
Published: August 2018

Urbanization has brought with it large populations in cities, which has then led to changes in urban land use intensity and spatial patterns, resulting in changes in underlying surfaces and urban climate. The impacts of the early urbanization process and the rapid development of the international metropolis on the interactive development of spatial zoning, urban climate, and urbanization in the main region of Shanghai are studied. This study has important practical and methodological implications with respect to two major themes in the current urban planning area of China, specifically, the construction of new urbanization and the changes in urban climate adaptation. Through the experiences of the human activities model from ecology, factors are selected based on the effects of climate on four dimensions, namely, economy, urban construction, ecological, and environment, where the weight of each index is determined by the coefficient of the variation method and the important spatial factors influencing the climate effect are screened out. The four important influential factors are population density, road density, built-up areas, and the green coverage ratio of spatial distribution. A quantitative analysis determined that there exists a consistent relationship between urban climate factors and the four urbanization spatial factors. Based on urbanization classification that considered each factor evaluation along with integrated analyses and statistical correlation analyses of the spatial grid index using ArcGIS software, the urban space partition level is identified, and urban spatial zoning strategies based on the co-movement of urban climate system are put forward. Combined with the zoning study of land use and the urban heat island distribution pattern, the spatial zoning strategy of controlling urbanization intensity based on the urban climate system is proposed. This research will guide the integration of the urbanization spatial structure and urban climate system toward rational development in Shanghai city.
Exploring the Intersections of Emotional Solidarity and Ethic of Care: An Analysis of Their Synergistic Contributions to Sustainable Community Tourism Development
Authors: Dangi Tek B.
Published: August 2018

Interactions between tourists and residents have been interpreted and analyzed applying various theoretical lenses. One of such lens is the study of perception of emotional solidarity forged between individuals based on inter-dependent activities and another is the application of lens of ethic of care, which suggests developing synergetic relationships between individuals based on mutual respect, a caring attitude, and understanding that is marked by equality of relations. No research to date has attempted to examine how these two theoretical constructs complement one another. The study explored how integrating the intersections of two complementary constructs-Emotional Solidarity (ES) and Ethic of Care (EC) could potentially contribute to enhancing stakeholders-tourists interactions, and subsequently contribute to sustainable tourism development. The study applied a mixed qualitative research blending emergent data with theory driven data conducting semi-structured interviews with 40 tourism stakeholders in Bryan-College Station (BCS), Texas. The study findings suggest that tourism contributed to fostering cultural pride and respect; sharing the diverse history and culture of BCS with tourists filled with ethic of care; and, enhanced tourism stakeholders’ perceptions of emotional solidarity with tourists and residents alike. The study suggested the need for strong interconnections between ES and EC theories and emphasized their synergistic application in BCS tourism in particular and sustainable tourism in general.
Evaluating Water Use Efficiency in China’s Western Provinces Based on a Slacks-Based Measure (SBM)-Undesirable Window Model and a Malmquist Productivity Index
Authors: Luo Yuyan , Yin Lu , Qin Yong , Wang Zhong , Gong Yanfeng
Published: August 2018

This paper evaluated the water use efficiency in 12 Chinese western provinces from 2005 to 2015. Based on data availability and the index selection rationality and the slacks-based measure (SBM)-undesirable Window analysis model and the Malmquist productivity index, the water resource inputs and outputs were measured to analyze water use efficiency. Total investment in fixed assets for the whole society and total water, made up of total agricultural water, total industrial water, and total domestic water, were used as the input indexes, and regional GDP and waste water discharge were the output indexes, with the waste water discharge being regarded as an undesirable output. The data from different years and different provinces in the same period, and data from the same year and the same provinces in different periods were compared in order to derive the water resource efficiency and technical changes over time and space. It was found that the total water resource factor productivity in the 12 provinces grew slowly in the study period, that water resource technical progress positively affected water use efficiency, and that a lack of technical efficiency restricted water use efficiency growth. Several suggestions are given to optimize water use efficiency in the 12 provinces. The research findings and suggestions provide valuable reference for studies in related fields.
Some Symmetric Identities Involving Fubini Polynomials and Euler Numbers
Authors: Jianhong Zhao , Zhuoyu Chen
Published: August 2018

The aim of this paper is to use elementary methods and the recursive properties of a special sequence to study the computational problem of one kind symmetric sums involving Fubini polynomials and Euler numbers, and give an interesting computational formula for it. At the same time, we also give a recursive calculation method for the general case.