bluuebunny/crossref_metadata_2025 huggingface dataset:
Object-Based Change Detection in the Cerrado Biome Using Landsat Time Series
Authors: Bueno Inacio T. , Acerbi Júnior Fausto W. , Silveira Eduarda M. O. , Mello José M. , Carvalho Luís M. T. , Gomide Lucas R. , Withey Kieran , Scolforo José Roberto S.
Published: March 2019

Change detection methods are often incapable of accurately detecting changes within time series that are heavily influenced by seasonal variations. Techniques for de-seasoning time series or methods that apply the spatial context have been used to improve the results of change detection. However, few studies have explored Landsat’s shortwave infrared channel (SWIR 2) to discriminate between seasonal changes and land use/land cover changes (LULCC). Here, we explored the effectiveness of Operational Land Imager (OLI) spectral bands and vegetation indices for detecting deforestation in highly seasonal areas of Brazilian savannas. We adopted object-based image analysis (OBIA), applying a multidate segmentation to an OLI time series to generate input data for discrimination of deforestation from seasonal changes using the Random Forest (RF) algorithm. We found adequate separability between deforested objects and seasonal changes using SWIR 2. Using spectral indices computed from SWIR 2, the RF algorithm generated a change map with an overall accuracy of 88.3%. For deforestation, the producer’s accuracy was 88.0% and the user’s accuracy was 84.6%. The SWIR 2 channel as well as the mid-infrared burn index presented the highest importance among spectral variables computed by the RF average impurity decrease measure. Our results give support to further change detection studies regarding to suitable spectral channels and provided a useful foundation for savanna change detection using an object-based method applied to Landsat time series.
Population Mapping with Multisensor Remote Sensing Images and Point-Of-Interest Data
Authors: Yang Xuchao , Ye Tingting , Zhao Naizhuo , Chen Qian , Yue Wenze , Qi Jiaguo , Zeng Biao , Jia Peng
Published: March 2019

Fine-resolution population distribution mapping is necessary for many purposes, which cannot be met by aggregated census data due to privacy. Many approaches utilize ancillary data that are related to population density, such as nighttime light imagery and land use, to redistribute the population from census to finer-scale units. However, most of the ancillary data used in the previous studies of population modeling are environmental data, which can only provide a limited capacity to aid population redistribution. Social sensing data with geographic information, such as point-of-interest (POI), are emerging as a new type of ancillary data for urban studies. This study, as a nascent attempt, combined POI and multisensor remote sensing data into new ancillary data to aid population redistribution from census to grid cells at a resolution of 250 m in Zhejiang, China. The accuracy of the results was assessed by comparing them with WorldPop. Results showed that our approach redistributed the population with fewer errors than WorldPop, especially at the extremes of population density. The approach developed in this study—incorporating POI with multisensor remotely sensed data in redistributing the population onto finer-scale spatial units—possessed considerable potential in the era of big data, where a substantial volume of social sensing data is increasingly being collected and becoming available.
Trends in Woody and Herbaceous Vegetation in the Savannas of West Africa
Authors: Anchang Julius Y. , Prihodko Lara , Kaptué Armel T. , Ross Christopher W. , Ji Wenjie , Kumar Sanath S. , Lind Brianna , Sarr Mamadou A. , Diouf Abdoul A. , Hanan Niall P.
Published: March 2019

We assess 32 years of vegetation change in the West African Sudano-Sahelian region following the drought events of the 1970s and 1980s. Change in decadal mean rain use efficiency is used to diagnose trends in woody vegetation that is expected to respond more slowly to post-drought rainfall gains, while change in the slope of the productivity–rainfall relationship is used to infer changing herbaceous conditions between early and late periods of the time series. The linearity/non-linearity of the productivity–rainfall relationship and its impact on the interpretation of overall greening trends, and specific woody and herbaceous vegetation trends, is also examined. Our results show a mostly positive association between productivity and rainfall (69% of pixels), which can be best described as linear (32%) or saturating (37%). Choosing the ‘best’ model at a specific location using Akaike Information Criterion has no discernible effect on the interpretation of overall greening or herbaceous trends, but does influence the detection of trends in woody vegetation. We conclude that widespread recovery in woody vegetation is responsible for the post-drought greening phenomenon reported elsewhere for the Sahel and Sudanian sub-regions. Meanwhile, trends in herbaceous vegetation are less pronounced, with no consistent indication towards either herbaceous degradation or recovery.
A Remote Sensing Based Integrated Approach to Quantify the Impact of Fluvial and Pluvial Flooding in an Urban Catchment
Authors: Muthusamy Manoranjan , Rivas Casado Monica , Salmoral Gloria , Irvine Tracy , Leinster Paul
Published: March 2019

Pluvial (surface water) flooding is often the cause of significant flood damage in urban areas. However, pluvial flooding is often overlooked in catchments which are historically known for fluvial floods. In this study, we present a conceptual remote sensing based integrated approach to enhance current practice in the estimation of flood extent and damage and characterise the spatial distribution of pluvial and fluvial flooding. Cockermouth, a town which is highly prone to flooding, was selected as a study site. The flood event caused by named storm Desmond in 2015 (5-6/12/2015) was selected for this study. A high resolution digital elevation model (DEM) was produced from a composite digital surface model (DSM) and a digital terrain model (DTM) obtained from the Environment Agency. Using this DEM, a 2D flood model was developed in HEC-RAS (v5) 2D for the study site. Simulations were carried out with and without pluvial flooding. Calibrated models were then used to compare the fluvial and combined (pluvial and fluvial) flood damage areas for different land use types. The number of residential properties affected by both fluvial and combined flooding was compared using a combination of modelled results and data collected from Unmanned Aircraft Systems (UAS). As far as the authors are aware, this is the first time that remote sensing data, hydrological modelling and flood damage data at a property level have been combined to differentiate between the extent of flooding and damage caused by fluvial and pluvial flooding in the same event. Results show that the contribution of pluvial flooding should not be ignored, even in a catchment where fluvial flooding is the major cause of the flood damages. Although the additional flood depths caused by the pluvial contribution were lower than the fluvial flood depths, the affected area is still significant. Pluvial flooding increased the overall number of affected properties by 25%. In addition, it increased the flood depths in a number of properties that were identified as being affected by fluvial flooding, in some cases by more than 50%. These findings show the importance of taking pluvial flooding into consideration in flood management practices. Further, most of the data used in this study was obtained via remote sensing methods, including UAS. This demonstrates the merit of developing a remote sensing based framework to enhance current practices in the estimation of both flood extent and damage.
Refocusing Moving Ship Targets in SAR Images Based on Fast Minimum Entropy Phase Compensation
Authors: Huang Xiangli , Ji Kefeng , Leng Xiangguang , Dong Ganggang , Xing Xiangwei
Published: March 2019

Moving ship targets appear blurred and defocused in synthetic aperture radar (SAR) images due to the translation motion during the coherent processing. Motion compensation is required for refocusing moving ship targets in SAR scenes. A novel refocusing method for moving ship is developed in this paper. The method is exploiting inverse synthetic aperture radar (ISAR) technique to refocus the ship target in SAR image. Generally, most cases of refocusing are for raw echo data, not for SAR image. Taking into account the advantages of processing in SAR image, the processing data are SAR image rather than raw echo data in this paper. The ISAR processing is based on fast minimum entropy phase compensation method, an iterative approach to obtain the phase error. The proposed method has been tested using Spaceborne TerraSAR-X, Gaofeng-3 images and airborne SAR images of maritime targets.
A Through-Hole Lead Connection Method for Thin-Film Thermocouples on Turbine Blades
Authors: Deng Jinjun , Wang Weihua , Hui Liuan , Zhang Jietong , Jin Xinhang
Published: March 2019

To solve the current problems with thin-film thermocouple signals on turbine blades in ultra-high temperature environments, this study explores the use of a through-hole lead connection technology for high-temperature resistant nickel alloys. The technique includes through-hole processing, insulation layer preparation, and filling and fixing of a high-temperature resistant conductive paste. The through-hole lead connection preparation process was optimized by investigating the influence of the inner diameter of the through-hole, solder volume, and temperature treatment on the contact strength and surface roughness of the thin-film for contact resistance. Finally, the technology was combined with a thin-film thermocouple to perform multiple thermal cycling experiments on the surface of the turbine blade at a temperature of 1000 °C. The results show that the through-hole lead connection technology can achieve a stable output of the thin-film thermocouple signal on the turbine blade.
Spatiotemporal Rule of Heat Transfer on a Soil/Finned Tube Interface
Authors: Huang Yongsheng , Li Wenbin , Xu Daochun , Wu Yafeng
Published: March 2019

To efficiently harvest environmental micro-energy from shallow soil, simulated analysis, theoretical arithmetic and experimental verification are performed to explore the spatiotemporal rules of heat transfer on a soil/finned tube interface. Simulations are carried out for 36 types of different working conditions, and the empirical formulas for temperature and heat flux are obtained. The temperature and heat flux can be calculated using the formulas if the soil temperature, soil moisture content and finned tube initial temperature are known. The simulations also show that the highest heat flux can reach approximately 0.30 mW/mm2, and approximately 1507.96 mW of energy can be harvested through the finned tube. Theoretical arithmetic indicates that the heat transfer rate of the copper finned tube is 76.77% higher than that of the bare tube, the highest rate obtained in any study to date. Results also show that the finned tube should be placed where the soil moisture is greater than 30% to get more heat from the soil. A field experiment is carried out in the city of Harbin in Northeast China, where a thermoelectric power generation device has been installed and temperature data have been monitored for a certain time. The results are in good agreement with those obtained from the simulation analysis. The heat transfer processes and heat transfer steady state on the soil/finned tube interface are revealed in this work and are of great importance for the use of geothermal energy.
Ontology-Defined Middleware for Internet of Things Architectures
Authors: Caballero Víctor , Valbuena Sergi , Vernet David , Zaballos Agustín
Published: March 2019

The Internet of Things scenario is composed of an amalgamation of physical devices. Those physical devices are heterogeneous in their nature both in terms of communication protocols and in data exchange formats. The Web of Things emerged as a homogenization layer that uses well-established web technologies and semantic web technologies to exchange data. Therefore, the Web of Things enables such physical devices to the web, they become Web Things. Given such a massive number of services and processes that the Internet of Things/Web of Things enables, it has become almost mandatory to describe their properties and characteristics. Several web ontologies and description frameworks are devoted to that purpose. Ontologies such as SOSA/SSN or OWL-S describe the Web Things and their procedures to sense or actuate. For example, OWL-S complements SOSA/SSN in describing the procedures used for sensing/actuating. It is, however, not its scope to be specific enough to enable a computer program to interpret and execute the defined flow of control. In this work, it is our goal to investigate how we can model those procedures using web ontologies in a manner that allows us to directly deploy the procedure implementation. A prototype implementation of the results of our research is implemented along with an analysis of several use cases to show the generality of our proposal.
Measurement Noise Recommendation for Efficient Kalman Filtering over a Large Amount of Sensor Data
Authors: Park Sebin , Gil Myeong-Seon , Im Hyeonseung , Moon Yang-Sae
Published: March 2019

To effectively maintain and analyze a large amount of real-time sensor data, one often uses a filtering technique that reflects characteristics of original data well. This paper proposes a novel method for recommending the measurement noise for Kalman filtering, which is one of the most representative filtering techniques. Kalman filtering corrects inaccurate values of input sensor data, and its filtering performance varies depending on the input noise parameters. In particular, if the noise parameters determined based on the user’s experience are incorrect, the accuracy of Kalman filtering may be reduced significantly. Based on this observation, this paper addresses how to determine the measurement noise variance, a major input parameter of Kalman filtering, by analyzing past sensor data and how to use the estimated noise to improve the filtering accuracy. More specifically, to estimate the measurement noise variance, two analytical methods are proposed: one a transform-based method using a wavelet transform and the other a learning-based method using a denoising autoencoder. Experimental results show that the proposed methods estimated the measurement noise variance accurately and were superior to the experience-based method in the filtering accuracy.
Classification of Sonar Targets in Air: A Neural Network Approach
Authors: Kroh Patrick K. , Simon Ralph , Rupitsch Stefan J.
Published: March 2019

Ultrasonic sonar sensors are commonly used for contactless distance measurements in application areas such as automotive and mobile robotics. They can also be exploited to identify and classify sound-reflecting objects (targets), which may then be used as landmarks for navigation. In the presented work, sonar targets of different geometric shapes and sizes are classified with custom-engineered features. Artificial neural networks (ANNs) with multiple hidden layers are applied as classifiers and different features are tested as well as compared. We concentrate on features that are related to target strength estimates derived from pulse-compressed echoes. In doing so, one is able to distinguish different target geometries with a high rate of success and to perform tests with ANNs regarding their capabilities for size discrimination of targets with the same geometric shape. A comparison of achievable classifier performance with wideband and narrowband chirp excitation signals was conducted as well. The research indicates that our engineered features and excitation signals are suitable for the target classification task.
Design and Manufacturing of a Disposable, Cyclo-Olefin Copolymer, Microfluidic Device for a Biosensor †
Authors: Prada Jorge , Cordes Christina , Harms Carsten , Lang Walter
Published: March 2019

This contribution outlines the design and manufacturing of a microfluidic device implemented as a biosensor for retrieval and detection of bacteria RNA. The device is fully made of Cyclo-Olefin Copolymer (COC), which features low auto-fluorescence, biocompatibility and manufacturability by hot-embossing. The RNA retrieval was carried on after bacteria heat-lysis by an on-chip micro-heater, whose function was characterized at different working parameters. Carbon resistive temperature sensors were tested, characterized and printed on the biochip sealing film to monitor the heating process. Off-chip and on-chip processed RNA were hybridized with capture probes on the reaction chamber surface and identification was achieved by detection of fluorescence tags. The application of the mentioned techniques and materials proved to allow the development of low-cost, disposable albeit multi-functional microfluidic system, performing heating, temperature sensing and chemical reaction processes in the same device. By proving its effectiveness, this device contributes a reference to show the integration potential of fully thermoplastic devices in biosensor systems.
A Review of Point Set Registration: From Pairwise Registration to Groupwise Registration
Authors: Zhu Hao , Guo Bin , Zou Ke , Li Yongfu , Yuen Ka-Veng , Mihaylova Lyudmila , Leung Henry
Published: March 2019

This paper presents a comprehensive literature review on point set registration. The state-of-the-art modeling methods and algorithms for point set registration are discussed and summarized. Special attention is paid to methods for pairwise registration and groupwise registration. Some of the most prominent representative methods are selected to conduct qualitative and quantitative experiments. From the experiments we have conducted on 2D and 3D data, CPD-GL pairwise registration algorithm and JRMPC groupwise registration algorithm seem to outperform their rivals both in accuracy and computational complexity. Furthermore, future research directions and avenues in the area are identified.
All-Printed Human Activity Monitoring and Energy Harvesting Device for Internet of Thing Applications
Authors: Ali Shawkat , Khan Saleem , Bermak Amine
Published: March 2019

A self-powered device for human activity monitoring and energy harvesting for Internet of Things (IoT) devices is proposed. The self-powered device utilizes flexible Nano-generators (NGs), flexible diodes and off-the-shelf capacitors. During footsteps the NGs generate an AC voltage then it is converted into DC using rectifiers and the DC power is stored in a capacitor for powering the IoT devices. Polydimethylsiloxane (PDMS) and zinc stannate (ZnSnO3) composite is utilized for the NG active layer, indium tin oxide (ITO) and aluminum (Al) are used as the bottom and top electrodes, respectively. Four diodes are fabricated on the bottom electrode of the NG and connected in bridge rectifier configuration. A generated voltage of 18 Vpeak was achieved with a human footstep. The self-powered smart device also showed excellent robustness and stable energy scavenger from human footsteps. As an application we demonstrate human activity detection and energy harvesting for IoT devices.
Sensitivity Analysis and Optimal Design of a Stator Coreless Axial Flux Permanent Magnet Synchronous Generator
Authors: Wang Wenqiang , Zhou Shaoqi , Mi Hongju , Wen Yadong , Liu Hua , Zhang Guoping , Guo Jianyong
Published: March 2019

In this paper, the modified initial design procedure and economic optimization design of a stator coreless axial flux permanent magnet synchronous generator (AFPMSG) are presented to improve the design accuracy, efficiency, and economy. Static magnetic field finite-element analysis (FEA) is applied to the magnetic equivalent circuit (MEC) method to increase the accuracy of electromagnetic parameters and reduce the iteration times. The accuracy and efficiency of the initial design is improved by the combination of MEC method and static magnetic field FEA in the design procedure. For the economic optimization, the permanent magnetic (PM) material volume model, which affects the cost of the AFPMSG the most, is derived, and the influence degree of the main structure parameters, to the performance, is distinguished and sorted by sensitivity analysis. The hybrid genetic algorithm that combines the simulated annealing and father-offspring selection method is studied and adopted to search for the best optimization solution from the different influence degree and nonlinear interaction parameters. A 1 kW AFPMSG is designed and optimized via the proposed design procedure and optimization design. Finally, 3D finite-element models of the generator are simulated and compared to confirm the validity of the proposed improved design and the generator performance.
Evaluation on the Sustainability of Urban Public Pension System in China
Authors: Zhao Qing , Mi Haijie
Published: March 2019

Against the background of population aging and economic downturn, the sustainability of pension systems has aroused great concern for governments across the world. To better reflect the pressure of pension payments in the changing context, the paper aims to forecast the annual pension gap of the public pension system for urban employees in China. By the use of Cohort-component population projections and stochastic projection models, the distribution of flow-based annual pension gap in the next fifty years are estimated under basic assumptions. The results show that the pension gap continues to exist from 2017 and keeps expanding until 2070 without any policy reform. Sensitivity analyses of demographics and various combinations of policy parameters on the distribution of future pension gaps are displayed. Wider pension coverage with lower policy threshold is more likely to face larger long-term pension gap.
Improving Financial Service Innovation Strategies for Enhancing China’s Banking Industry Competitive Advantage during the Fintech Revolution: A Hybrid MCDM Model
Authors: Zhao Qun , Tsai Pei-Hsuan , Wang Jin-Long
Published: March 2019

The recent emergence and rapid growth of new financial services by financial technology (Fintech) companies have driven banking institutions towards operational innovation in order to gain sustainable competitive advantage. This study aims to conduct an in-depth investigation of the banking sector in response to the challenges brought by Fintech startups. Based on the service innovation theory, we propose a novel hybrid multiple criteria decision-making method (MCDM) to evaluate service innovation strategies for improving the sustainability of China’s banking industry during the Fintech revolution. A six-dimensional model comprising 20 sub-criteria is constructed and both the decision making trial and evaluation laboratory (DEMATEL) technique and DEMATEL-based analytic network process (DANP) are used to explore interrelationships among the indices and their related weights. Finally, the modified VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) method is employed to evaluate performance gaps in the four major types of commercial banks in China—state-owned, joint-stock, city commercial banks, and other credit cooperatives—in the field of service innovation. The improvement priorities, ranked from highest to lowest, are new business partners, new service concepts, organizational innovation, technological innovation, new customer interactions, and new revenue models. These results will provide strategies for the sustainable development of China’s banking industry and the implementation of changes in response to the impact of the Fintech revolution.
Analysis on the Choice of Livelihood Strategy for Peasant Households Renting out Farmland: Evidence from Western Poverty-Stricken Areas in China
Authors: Cai Jie , Wang Ting , Xia Xianli , Chen Yazhi , Lv Hongqiang , Li Ni
Published: March 2019

Investigating the choice of livelihood strategies has great significance for improving the living standards of peasant households who rent out farmland. This study evaluates the impact of renting-out land on households’ livelihood strategies in China’s western poverty-stricken areas. Data were obtained from cross sectional survey of 585 field survey data from peasant households who rent out land. The K-means clustering method was used to classify the livelihood strategies of the sample households. In view of sustainable livelihood framework, this paper used combination weighting model based on game theory to calculate the quo of households’ livelihood capital. The Multinomial Logistic Regression was used to explore the relationship between livelihood capitals and livelihood strategies. Results show that: livelihood strategy of households who rent out the land can be divided into “agricultural-led” livelihood strategy, “working-oriented” livelihood strategy and “part-time” livelihood strategy. Additionally, the results of Multinomial Logistic Regression show that the households with high human capital and financial capital tend to choose the “working-oriented” livelihood strategy and the households with high natural capital tend to choose the “agricultural-led” livelihood strategy. Therefore, in order to realize the sustainable livelihood of these households, different policy support should be proposed based on the heterogeneity of households in the process of land transfer.
Agrobiodiversity and Public Food Procurement Programs in Brazil: Influence of Local Stakeholders in Configuring Green Mediated Markets
Authors: L. Resque Antonio Gabriel , Coudel Emilie , Piketty Marie-Gabrielle , Cialdella Nathalie , Sá Tatiana , Piraux Marc , Assis William , Le Page Christophe
Published: March 2019

The last few years have seen the emergence of different initiatives designed to promote the biodiversification of agroecosystems as a counterpoint to the global expansion of homogenized industrial agriculture. In Brazil, two food procurement programs demonstrate the potential to promote discussions related to this agroecological transition: the National School Meal Program (Programa Nacional de Alimentação Escolar, PNAE) and the Food Procurement Program (Programa de Aquisição de Alimentos, PAA). The objectives of this paper are to analyze: (a) how these procurement programs currently integrate agrobiodiversity (crops and cropping systems) according to the local context; (b) the main challenges that key stakeholders perceive for the adoption of biodiverse systems; and (c) the extent to which the key stakeholders involved in these programs associate agrobiodiversity with the provision of ecosystem services. We carried out this research in 2017 in two contrasting municipalities in the eastern part of the Brazilian Amazon, Paragominas and Irituia. Our research shows that these programs have included up to 42 species in Irituia and 32 species in Paragominas. Perennial crop species are the most common type of culture in Irituia (up to 50%), while vegetables are the most common in Paragominas (up to 47%). Although in both municipalities stakeholders identify a large number of ecosystem services (up to 17), services mentioned in Irituia were more closely related to agrobiodiversity. Stakeholders indirectly associated with the programs have a broader view of ecosystem services. We conclude that these procurement programs can be useful tools to promote the biodiversification of local production systems, but their potential may depend on involving institutions not directly associated with their administration. Additionally, despite the observed differences in production context, providing more ecosystem services appears to be a compelling motivation for promoting changes in agroecosystems.
Service Ecosystem Design for Improving the Service Sustainability: A Case of Career Counselling Services in the Italian Higher Education Institution
Authors: Nie Zichao , Zurlo Francesco , Camussi Elisabetta , Annovazzi Chiara
Published: March 2019

The call for further research on service sustainability at all levels has been increasingly stated within the past years. However, limited research has been conducted with regard to the macro level, in which services are inevitably influenced by social, cultural, economic and technological factors. This paper develops an ecosystem approach within the service context to design a service not only from a micro level (e.g., service experience, service encounters), but also from a social level in order to help businesses and public sectors to transform the relationship among individual, collective, social, and environmental systems and increase service sustainability. Then, it applies the developed Service Ecosystem Design (SED) model in a Career Counselling Service (CCS) in the University of Milano-Bicocca (Unimib) in order to demonstrate the usefulness of this model. The empirical data were collected from service providers to illustrate the current service system maps and from first-year undergraduates to understand their perceptions and expectations on campus CCS. The results show that the Unimib CCS is a research-oriented career service and the fundamental service improvements should focus on designing the peer counselling, follow-up, pre-service, a holistic online platform, law education, technology education, optional course, hands-on experiences and student activities.
Debt Risk Evaluation of Toll Freeways in Mainland China Using the Grey Approach
Authors: Mao Xinhua , Gan Jiahua , Zhao Xilong
Published: March 2019

With a proactive loan policy to raise construction funds, a large number of toll freeways have been built in Mainland China in the past three decades. However, it brought about a long-term heavy debt burden for most provincial governments. To ensure financial sustainability of toll freeways, an accurate and appropriate debt risk evaluation has become necessary. This research aims to explore debt risk factors and calculate the overall debt risk levels of toll freeways using the grey approach. Debt risk factors were identified as belonging to five categories—debt scale, debt structure, debt management, external environment, and solvency—and three new debt risk factors were added for specific concern of toll freeways—toll revenue, free cash flow, and earnings before interest, tax, depreciation, and amortization (EBITDA) margin. Debt risk levels of toll freeways in 29 provinces in Mainland China were evaluated by the proposed method and classified into three groups–low debt risk, medium debt risk, and high debt risk according to grey possibility degree ranges. Calculation results show that six provinces have low debt risk, 10 provinces have medium debt risk, and 13 provinces have high debt risk. Additionally, some specific policies to reduce toll freeway debt risk were provided based on the evaluation findings.
Trans-Provincial Convergence of per Capita Energy Consumption in Urban China, 1990–2015
Authors: Bao Chao , Wang Hongjie
Published: March 2019

Recognizing the change in regulation of energy consumption may help China to control total energy consumption and realize sustainable development during rapid urbanization and industrialization. This paper re-examined the trans-provincial convergence of per capita energy consumption from 1990–2015 using five different kinds of methods for 30 Chinese provinces. Results show that per capita energy consumption across Chinese provinces was convergent. However, the results obtained by different methods were slightly different. First, it shows a weak beta-unconditional convergence during the entire period, as well as a significant beta-unconditional and conditional piecewise convergence from 1990–2000 and 2001–2015. Second, it shows a significant sigma-convergence indicated by a marked decrease in the standard deviation of logarithm (SDlog) and the coefficient of variation (CV). Third, the kernel density curve became narrower during 1990–2015, indicating that the per capita energy consumption of each Chinese province converged to a common equilibrium level, which was about 80% of the national average. Fourth, the intra-distributional mobility index implied a weak gamma-convergence. Fifth, the first difference of DF (Dickey-Fuller), ADF (Augmented Dickey-Fuller), and PP (Phillips-Perron) unit-root tests all suggested a stochastic convergence. On the whole, the results from this paper contribute to a more in-depth understanding of the status quo of per capita energy consumption in China, as well as a meaningful implication for differentiated energy policies and sustainable development strategies.
Slow Steaming as Part of SECA Compliance Strategies among RoRo and RoPax Shipping Companies
Authors: Raza Zeeshan , Woxenius Johan , Finnsgård Christian
Published: March 2019

Many geographically peripheral member states of the EU are critically dependent on short sea Roll-on/Roll-off (RoRo) and mixed freight–passenger (RoPax) shipping services for intra-European trade. The implementation of the Sulfur Emission Control Area (SECA) regulation was expected to raise the operating cost for RoRo and RoPax shipping, and slow steaming was proposed as an immediate solution to save the increased cost. Previous research has investigated the issue of slow steaming and SECA using a quantitative approach. However, the reaction of the RoRo and RoPax shipping firms toward slow steaming as a mitigating factor in the face of expected additional SECA compliance costs using qualitative methodology has not been explored yet. In addition, the knowledge regarding the impact of slow steaming on the competitiveness of short sea RoRo and RoPax with respect to service quality is limited. This article has addressed these issues through the analysis of multiple cases focusing on RoRo and RoPax firms operating in the North and Baltic Seas. Overall, our findings suggest that the 0.1% SECA regulation of 2015 requiring the use of higher-priced MGO has not caused slow steaming in the RoRo and RoPax segments to a large extent. The increased bunker prices are partially transferred to the customers via increased Bunker Adjustment Factor and partly borne by the shipowners. We have found that out of 11 case firms in our study only one RoRo and one RoPax firm have reduced vessel speeds to compensate for the additional SECA compliance costs. We conclude that for RoPax and RoRo segment bunker prices, rigorous competition and, most important, different service quality requirements have significantly restricted the potential implementation of slow steaming.
Parametric Assessment of Seasonal Drought Risk to Crop Production in Bangladesh
Authors: Alamgir Mahiuddin , Mohsenipour Morteza , Homsi Rajab , Wang Xiaojun , Shahid Shamsuddin , Shiru Mohammed , Alias Nor , Yuzir Ali
Published: March 2019

Droughts are more damaging when they occur during crop growing season. This research assessed the spatial distribution of drought risks to crops in Bangladesh. Catastrophe theory-based weighting method was used to estimate drought hazard, exposure, and risk by avoiding potential human bias. Ten major crops, including eight different types of rice, wheat, and potato, were selected for evaluation of drought risk. Results showed that 32.4%, 27.2%, and 16.2% of land in Bangladesh is prone to extreme Kharif (May-October), Rabi (November-April), and pre-Kharif (March-May) droughts, respectively. Among the major crops, Hybrid Boro rice cultivated in 18.2% of the area is found to be highly vulnerable to droughts, which is followed by High Yield Varity (HYV) Boro (16.9%), Transplant Aman (16.4%), HYV Aman (14.1%), and Basic Aman (12.4%) rice. Hybrid Boro rice in 12 districts, different varieties of Aman rice in 10 districts, and HYV Boro rice in 9 districts, mostly located in the north and northwest of Bangladesh, are exposed to high risk of droughts. High frequency of droughts and use of more land for agriculture have made the region highly prone to droughts. The methodology adopted in this study can be utilized for unbiased estimation of drought risk in agriculture in order to adopt necessary risk reduction measures.
Estimating China’s Trade with Its Partner Countries within the Belt and Road Initiative Using Neural Network Analysis
Authors: Dumor Koffi , Yao Li
Published: March 2019

The Belt and Road Initiative (BRI) under the auspices of the Chinese government was created as a regional integration and development model between China and her trade partners. Arguments have been raised as to whether this initiative will be beneficial to participating countries in the long run. We set to examine how to estimate this trade initiative by comparing the relative estimation powers of the traditional gravity model with the neural network analysis using detailed bilateral trade exports data from 1990 to 2017. The results show that neural networks are better than the gravity model approach in learning and clarifying international trade estimation. The neural networks with fixed country effects showed a more accurate estimation compared to a baseline model with country-year fixed effects, as in the OLS estimator and Poisson pseudo-maximum likelihood. On the other hand, the analysis indicated that more than 50% of the 6 participating East African countries in the BRI were able to attain their predicted targets. Kenya achieved an 80% (4 of 5) target. Drawing from the lessons of the BRI and the use of neural network model, it will serve as an important reference point by which other international trade interventions could be measured and compared.
A Hybrid Methodology for Validation of Optimization Solutions Effects on Manufacturing Sustainability with Time Study and Simulation Approach for SMEs
Authors: Ghafoorpoor Yazdi Poorya , Azizi Aydin , Hashemipour Majid
Published: March 2019

The properties of small- and medium-sized enterprises (SMEs) make them one of the most important categories of enterprises for the economics of challenging world. SMEs, in most countries, are still enterprises with marketing and financial challenges. In addition, most of these challenges are related to their production and product characteristics. On the other hand, SMEs should fulfil the costumer’s demands. In order to reach these goals, SMEs must reach the highest level of production quality and quantity and successfully sustain them. Consequently, various manufacturing paradigms have been offered by an Industry 4.0 concept, which offers a variety of solutions to increase the productivity and enhance the performance of SMEs. It should be noted that implementation of these manufacturing paradigms for SMEs is quite difficult and sometimes risky for several reasons. Still, amidst all these difficulties and challenges, the benefits and idealism of the Industry 4.0 paradigms prevail. From productivity to market, it is difficult to deny that SMEs are frightened by the challenges they face and fleeing from the potential of overcoming them. This paper is an extended version of the research by Ghafoorpoor Yazdi et al. (2018) and conducts a hybrid methodology to satisfy the SMEs by validating and verifying any optimization idea before implementing the Industry 4.0 concept. To reach the study goals, an intelligent Material Handling System (MHS) with agent-based control architecture has been developed. The developed MHS has been utilized for auto parts distribution. The system performance has been evaluated, and some solutions have been provided to optimize the performance of system. To evaluate the target system’s performance, an analytical time study method has been utilized. The time study has an Overall Equipment Effectiveness (OEE) standard approach to identifying the matters that need to be resolved and optimized to increase system performance. The other part of the methodology is generating a simulation model of the real system by use of ARENA® software to evaluate the system’s performance before implementing the optimization idea and modifying the real system. Furthermore, as the sustainability strategies create many synergistic effects for SMEs, after evaluating the effects of the optimization ideas on OEE percentage, the influence of the OEE changes on manufacturing sustainability has been investigated. The results show that optimizing the OEE in SMEs with sustainability approaches can create competitive advantages, rather than simply focusing on reducing unsustainability.
Feature Input Symmetry Algorithm of Multi-Modal Natural Language Library Based on BP Neural Network
Authors: Lin Hao
Published: March 2019

When using traditional knowledge retrieval algorithms to analyze whether the feature input of words in multi-modal natural language library is symmetrical, the symmetry of words cannot be analyzed, resulting in inaccurate analysis results. A feature input symmetric algorithm of multi-modal natural language library based on BP (back propagation) neural network is proposed in this paper. A Chinese abstract generation method based on multi-modal neural network is used to extract Chinese abstracts from images in multi-modal natural language library. The Word Sense Disambiguation (WSD) Model is constructed by the BP neural network. After the word or text disambiguation is performed on the Chinese abstract in the multi-modal natural language library, the feature input symmetry problem in the multi-modal natural language library is analyzed according to the sentence similarity. The experimental results show that the proposed algorithm can effectively analyze the eigenvalue symmetry problem of the multi-modal natural language library. The maximum error rate of the analysis algorithm is 7%, the growth rate of the analysis speed is up to 50%, and the average analysis time is 540.56 s. It has the advantages of small error and high efficiency.
On Generating Functions for Boole Type Polynomials and Numbers of Higher Order and Their Applications
Authors: Simsek Yilmaz , So Ji Suk
Published: March 2019

The purpose of this manuscript is to study and investigate generating functions for Boole type polynomials and numbers of higher order. With the help of these generating functions, many properties of Boole type polynomials and numbers are presented. By applications of partial derivative and functional equations for these functions, derivative formulas, recurrence relations and finite combinatorial sums involving the Apostol-Euler polynomials, the Stirling numbers and the Daehee numbers are given.
Investigation of Mixture Modelling Algorithms as a Tool for Determining the Statistical Likelihood of Serological Exposure to Filariasis Utilizing Historical Data from the Lymphatic Filariasis Surveillance Program in Vanuatu
Authors: Joseph Hayley , Sullivan Sarah , Wood Peter , Melrose Wayne , Taleo Fasihah , Graves Patricia
Published: March 2019

As the prevalence of lymphatic filariasis declines, it becomes crucial to adequately eliminate residual areas of endemicity and implement surveillance. To this end, serological assays have been developed, including the Bm14 Filariasis CELISA which recommends a specific optical density cut-off level. We used mixture modelling to assess positive cut-offs of Bm14 serology in children in Vanuatu using historical OD (Optical Density) ELISA values collected from a transmission assessment survey (2005) and a targeted child survey (2008). Mixture modelling is a statistical technique using probability distributions to identify subpopulations of positive and negative results (absolute cut-off value) and an 80% indeterminate range around the absolute cut-off (80% cut-off). Depending on programmatic choices, utilizing the lower 80% cut-off ensures the inclusion of all likely positives, however with the trade-off of lower specificity. For 2005, country-wide antibody prevalence estimates varied from 6.4% (previous cut-off) through 9.0% (absolute cut-off) to 17.3% (lower 80% cut-off). This corroborated historical evidence of hotspots in Pentecost Island in Penama province. For 2008, there were no differences in the prevalence rates using any of the thresholds. In conclusion, mixture modelling is a powerful tool that allows closer monitoring of residual transmission spots and these findings supported additional monitoring which was conducted in Penama in later years. Utilizing a statistical data-based cut-off, as opposed to a universal cut-off, may help guide program decisions that are better suited to the national program.
A Bimolecular Multicellular Complementation System for the Detection of Syncytium Formation: A New Methodology for the Identification of Nipah Virus Entry Inhibitors
Authors: García-Murria María J. , Expósito-Domínguez Neus , Duart Gerard , Mingarro Ismael , Martinez-Gil Luis
Published: March 2019

Fusion of viral and cellular membranes is a key step during the viral life cycle. Enveloped viruses trigger this process by means of specialized viral proteins expressed on their surface, the so-called viral fusion proteins. There are multiple assays to analyze the viral entry including those that focus on the cell-cell fusion induced by some viral proteins. These methods often rely on the identification of multinucleated cells (syncytium) as a result of cell membrane fusions. In this manuscript, we describe a novel methodology for the study of cell-cell fusion. Our approach, named Bimolecular Multicellular Complementation (BiMuC), provides an adjustable platform to qualitatively and quantitatively investigate the formation of a syncytium. Furthermore, we demonstrated that our procedure meets the requirements of a drug discovery approach and performed a proof of concept small molecule high-throughput screening to identify compounds that could block the entry of the emerging Nipah virus.
Kuru, the First Human Prion Disease
Authors: Liberski Paweł P. , Gajos Agata , Sikorska Beata , Lindenbaum Shirley
Published: March 2019

Kuru, the first human prion disease was transmitted to chimpanzees by D. Carleton Gajdusek (1923–2008). In this review, we summarize the history of this seminal discovery, its anthropological background, epidemiology, clinical picture, neuropathology, and molecular genetics. We provide descriptions of electron microscopy and confocal microscopy of kuru amyloid plaques retrieved from a paraffin-embedded block of an old kuru case, named Kupenota. The discovery of kuru opened new vistas of human medicine and was pivotal in the subsequent transmission of Creutzfeldt–Jakob disease, as well as the relevance that bovine spongiform encephalopathy had for transmission to humans. The transmission of kuru was one of the greatest contributions to biomedical sciences of the 20th century.
Direct Determination of Dynamic Elastic Modulus and Poisson’s Ratio of Timoshenko Rods
Authors: Leon Guadalupe , Chen Hung-Liang (Roger)
Published: March 2019

In this paper, the exact solution of the Timoshenko circular beam vibration frequency equation under free-free boundary conditions was determined with an accurate shear shape factor. The exact solution was compared with a 3-D finite element calculation using the ABAQUS program, and the difference between the exact solution and the 3-D finite element method (FEM) was within 0.15% for both the transverse and torsional modes. Furthermore, relationships between the resonance frequencies and Poisson’s ratio were proposed that can directly determine the elastic constants. The frequency ratio between the 1st bending mode and the 1st torsional mode, or the frequency ratio between the 1st bending mode and the 2nd bending mode for any rod with a length-to-diameter ratio, L/D ≥ 2 can be directly estimated. The proposed equations were used to verify the elastic constants of a steel rod with less than 0.36% error percentage. The transverse and torsional frequencies of concrete, aluminum, and steel rods were tested. Results show that using the equations proposed in this study, the Young’s modulus and Poisson’s ratio of a rod can be determined from the measured frequency ratio quickly and efficiently.