bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Three Deep Learning Models for Early Crop Classification Using Sentinel-1A Imagery Time Series—A Case Study in Zhanjiang, China
Authors: Zhao Hongwei , Chen Zhongxin , Jiang Hao , Jing Wenlong , Sun Liang , Feng Min
Published: November 2019

Timely and accurate estimation of the area and distribution of crops is vital for food security. Optical remote sensing has been a key technique for acquiring crop area and conditions on regional to global scales, but great challenges arise due to frequent cloudy days in southern China. This makes optical remote sensing images usually unavailable. Synthetic aperture radar (SAR) could bridge this gap since it is less affected by clouds. The recent availability of Sentinel-1A (S1A) SAR imagery with a 12-day revisit period at a high spatial resolution of about 10 m makes it possible to fully utilize phenological information to improve early crop classification. In deep learning methods, one-dimensional convolutional neural networks (1D CNNs), long short-term memory recurrent neural networks (LSTM RNNs), and gated recurrent unit RNNs (GRU RNNs) have been shown to efficiently extract temporal features for classification tasks. However, due to the complexity of training, these three deep learning methods have been less used in early crop classification. In this work, we attempted to combine them with an incremental classification method to avoid the need for training optimal architectures and hyper-parameters for data from each time series. First, we trained 1D CNNs, LSTM RNNs, and GRU RNNs based on the full images’ time series to attain three classifiers with optimal architectures and hyper-parameters. Then, starting at the first time point, we performed an incremental classification process to train each classifier using all of the previous data, and obtained a classification network with all parameter values (including the hyper-parameters) at each time point. Finally, test accuracies of each time point were assessed for each crop type to determine the optimal time series length. A case study was conducted in Suixi and Leizhou counties of Zhanjiang City, China. To verify the effectiveness of this method, we also implemented the classic random forest (RF) approach. The results were as follows: (i) 1D CNNs achieved the highest Kappa coefficient (0.942) of the four classifiers, and the highest value (0.934) in the GRU RNNs time series was attained earlier than with other classifiers; (ii) all three deep learning methods and the RF achieved F measures above 0.900 before the end of growth seasons of banana, eucalyptus, second-season paddy rice, and sugarcane; while, the 1D CNN classifier was the only one that could obtain an F-measure above 0.900 for pineapple before harvest. All results indicated the effectiveness of the solution combining the deep learning models with the incremental classification approach for early crop classification. This method is expected to provide new perspectives for early mapping of croplands in cloudy areas.
Research into a Multi-Variate Surveillance Data Fusion Processing Algorithm
Authors: Mao Yi , Yang Yi , Hu Yuxin
Published: November 2019

Targeted information sources include radar and ADS (Automatic Dependent Surveillance) for civil ATM (Air Traffic Management) systems, and the new navigation system based on satellites has the capability of global coverage. In order to solve the surveillance problem in mid-and-high altitude airspace and approaching airspace, this paper proposes a filter-based covariance matrix weighting method, measurement variance weighting method, and measurement-first weighted fusion method weighting integration algorithm to improve the efficiency of data integration calculation under fixed accuracy. Besides this, this paper focuses on the technology of the integration of a multi-radar surveillance system and automated related surveillance system in the ATM system and analyzes the constructional method of a multigeneration surveillance data integration system, as well as establishing the targeted model of sensors and the target track and designing the logical structure of multi-radar and ADS data integration.
Automatic Multi-Camera Extrinsic Parameter Calibration Based on Pedestrian Torsors †
Authors: Truong Anh Minh , Philips Wilfried , Deligiannis Nikos , Abrahamyan Lusine , Guan Junzhi
Published: November 2019

Extrinsic camera calibration is essential for any computer vision task in a camera network. Typically, researchers place a calibration object in the scene to calibrate all the cameras in a camera network. However, when installing cameras in the field, this approach can be costly and impractical, especially when recalibration is needed. This paper proposes a novel, accurate and fully automatic extrinsic calibration framework for camera networks with partially overlapping views. The proposed method considers the pedestrians in the observed scene as the calibration objects and analyzes the pedestrian tracks to obtain extrinsic parameters. Compared to the state of the art, the new method is fully automatic and robust in various environments. Our method detect human poses in the camera images and then models walking persons as vertical sticks. We apply a brute-force method to determines the correspondence between persons in multiple camera images. This information along with 3D estimated locations of the top and the bottom of the pedestrians are then used to compute the extrinsic calibration matrices. We also propose a novel method to calibrate the camera network by only using the top and centerline of the person when the bottom of the person is not available in heavily occluded scenes. We verified the robustness of the method in different camera setups and for both single and multiple walking people. The results show that the triangulation error of a few centimeters can be obtained. Typically, it requires less than one minute of observing the walking people to reach this accuracy in controlled environments. It also just takes a few minutes to collect enough data for the calibration in uncontrolled environments. Our proposed method can perform well in various situations such as multi-person, occlusions, or even at real intersections on the street.
Holiday Travel Behaviour and Correlated CO2 Emissions—Modelling Trend and Future Scenarios for Austrian Tourists
Authors: Kapeller Marie Lisa , Füllsack Manfred , Jäger Georg
Published: November 2019

The footprint of tourism through travel is contributing significantly to the accumulation of human-made CO2. Due to different options in transportation, resulting emissions depend strongly on the choices of individuals on how to travel. In Austria, land travel is the main mode of transportation, though air travel has shown a significant increase during the last decades. We present a model to estimate past and future emission trends of land and air travel for domestic (inbound) and international (outbound) travel destinations. For this, we use a combination of two software models, a social-economic individual-based model to simulate the decision processes of holiday travel and an emission calculation model to estimate single travel-based CO2 emissions. Our model is supported by data (reference year 2016) on tourism demand, holiday destinations, household wealth and emissions of different transportation modes. Our model evaluation successfully reproduced historical data of travel demand in the period 2003–2019 and explores several future trends of (a) business-as-usual, (b) green transition and (c) aviation preference increase. We calculated a current CO2 footprint of 5.8 million tonnes in 2019, which could increase to 7.3 million tonnes by 2030 if the current trend continues. A necessary decrease of transportation emissions is only possible when reducing air travel. In case of a green transition towards more land travel, total emissions could be kept constant compared to current emission levels. However, an overall reduction of holiday travel related CO2 below 3.5 million tonnes has not been observed even under the best circumstances due to projected increases in the total population and increases in wealth.
Impacts of Clean Energy Substitution for Polluting Fossil-Fuels in Terminal Energy Consumption on the Economy and Environment in China
Authors: Chen Hao , He Ling , Chen Jiachuan , Yuan Bo , Huang Teng , Cui Qi
Published: November 2019

China has initiated various dedicated policies on clean energy substitution for polluting fossil-fuels since the early 2010s to alleviate severe carbon emissions and environmental pollution and accelerate clean energy transformation. Using the autoregressive integrated moving average (ARIMA) regression, we project the potentials of substituting coal and oil with clean energy for different production sectors in China toward the year 2030. Based on the projections, a dynamic multi-sectoral computable general equilibrium model, CHINAGEM, is employed to examine: the impacts of future clean energy substitution on China’s energy production, outputs of non-energy sectors, macro-economy, and CO2 emissions. First, we found that most production sectors are projected to replace polluting fossil-fuels with clean energy in their terminal energy consumption in 2017–2030. Second, clean energy substitution enables producing green co-benefits that would enable improvements in energy production structure, reductions in national CO2 emissions, and better real GDP and employment. Third, technological progress in non-fossil-fuel electricity could further benefit China’s clean and low-carbon energy transformation, accelerating the reduction in CO2 emissions and clean energy substitution. Furthermore, the most beneficiary are energy-intensive and high carbon-emission sectors owing to the drop in coal and oil prices, while the most negatively affected are the downstream sectors of electricity. Through research, various tentative improvement policies are recommended, including financial support, renewable electricity development, clean energy utilization technology, and clean coal technologies.
Comparison between Different Prescription Methods for Aerobic Training in Young Adults
Authors: Policarpo Fernando , Valdivia-Moral Pedro , Luna-Cruz Maria do Socorro , Véliz-Burgos Alex , Mansilla-Sepulveda Juan , Estay-Sepulveda Juan Guillermo
Published: November 2019

Anaerobic threshold and respiratory compensation are used to determine the intensity of aerobic exercise. This study aims to compare heart rate values relative to the percentages of 50%, 60%, 70%, and 80%, a comparison calculated through the reserve heart rate (HRR) and reserve oxygen consumption (VO2R) equations corresponding to the oxygen consumption obtained by the direct method. The sample was composed of 11 men and 10 women: mean age, 21.4 ± 2.8 years. Volunteers performed a maximal treadmill test in which oxygen uptake was measured using the VO2000® metabolic analyzer. The mean body fat percentage was 15.68 ± 8.02% corresponding to a lean body mass of 54.8 ± 13.0 kg and a maximal oxygen uptake (VO2max) of 56.0 ± 8.4 mL/Kg·min−1. The measured intensities (50%, 60%, 70% and 80% of VO2max) presented significant differences (p < 0.05) for each reference value. Mean values calculated by the HRR equation demonstrated a tendency to underestimate the intensities, while the values calculated by the VO2R equation showed a tendency to overestimate the intensities. As the main conclusion, it is pointed out that both methods were effective for determining the intensity of aerobic training. However, they presented significant differences, and the equations should be adjusted to increase precision. Thus, the use of HRR is recommended for the determination of training intensities.
Socioeconomic Impact of Genome Editing on Agricultural Value Chains: The Case of Fungal-Resistant and Coeliac-Safe Wheat
Authors: Maaß Oliver , Consmüller Nicola , Kehlenbeck Hella
Published: November 2019

Genome editing (GE) is gaining increasing importance in plant breeding, since it provides opportunities to develop improved crops with high precision and speed. However, little is known about the socioeconomic impact of genome editing on agricultural value chains. This qualitative study analyzes how genome-edited crops could affect agriculture value chains. Based on the hypothetical case of producing and processing fungal-resistant and coeliac-safe wheat in Germany, we conducted semi-structured, in-depth interviews with associations and companies operating in the value chains of wheat. A value chain analysis and qualitative content analysis were combined to assess the costs and benefits of the crops studied along the value chains of wheat. The results show that the use of fungal-resistant and coeliac-safe wheat can provide benefits at each step of the value chains. Fungal-resistant wheat benefits actors by reducing the problems and costs resulting from fungal-diseases and mycotoxins. Coeliac-safe wheat benefits actors by producing high value-added products, which can be safely consumed by patients suffering from coeliac disease. However, the results also show that low acceptance of GE by society and food retailers poses a significant barrier for the use of genome-edited crops in agricultural value chains.
Assessing the Snow Disaster and Disaster Resistance Capability for Spring 2019 in China’s Three-River Headwaters Region
Authors: Shao Quanqin , Liu Guobo , Li Xiaodong , Huang Haibo , Fan Jiangwen , Wang Liya , Liu Jiyuan , Guo Xingjian
Published: November 2019

Frequent snowfall and low temperatures led to a considerable snow disaster in some areas of China’s Three-River Headwaters Region (TRHR) in Qinghai province in the spring of 2019, exerting a considerably negative influence on animal husbandry production in local grasslands. Based on a model of snow disaster classification and quantitative estimations of disaster-stricken animal husbandry, we propose a comprehensive disaster resistance capability index (CDRCI) using remote sensing, ground monitoring, and statistical investigations. With a comprehensive assessment of the space distribution and the magnitude of snow disasters, combined with a quantitative determination of disaster-stricken animal husbandry, the proposed CDRCI calculates how grassland animal husbandry is affected by snow disasters in different counties of the TRHR. The results indicate that approximately 2.31 million sheep and yaks were affected by moderate to severe snow disasters in the TRHR, accounting for 78.3% of the total livestock in the affected region. Of these affected livestock, approximately 1.54 million sheep and yaks were specifically affected by severe snow disasters, accounting for 52.1% of the total number of livestock. The CDRCIs for grassland animal husbandry in both Yushu and were moderate, being higher for the former than for the latter. We confirmed that the proposed CDRCI can accurately evaluate the magnitude of snow disasters in terms of how they affect grassland animal husbandry. The CDRCI is a way of relating the number of animal deaths to spatial disaster prevention and resistance. We expect that this research will provide important theoretical support for formulating snow disaster resistance policy, for example for increasing the construction of grassland animal husbandry infrastructure as well as providing greater stored forage material.
Designing Urban Green Blue Infrastructure for Mental Health and Elderly Wellbeing
Authors: Andreucci Maria Beatrice , Russo Alessio , Olszewska-Guizzo Agnieszka
Published: November 2019

The main objective of this essay is to illustrate the state-of-the-art on ‘mental health-sensitive’ open space design in the built environment. Urban Green Blue Infrastructure can contribute to urbanites’ mental health and wellbeing as well as healthy aging, while providing co-benefits balancing the negative impacts of climate change, through the provision of integrated ecosystem services. There are a number of ways that exposure to and affiliation with Nature have shown to support mental health, but we are still missing the necessary evidence of the actual benefits achieved, as well as the key performance indicators and metrics to monitor and adapt our open space to the growing urban challenges. After introducing the key concepts of degenerative mental disorders as they are growing in the urban environment, and the emerging green blue infrastructure design approach, the authors present international case studies describing how evidence-based design and Nature-based Solutions have been found to be beneficial, especially to those diagnosed with mental disorders. Subsequently, in a comparative critical analysis, the authors look closer at a number of design solutions capable, at different scales, to support healthy aging through exposure to, and affiliation with, biodiversity.
Exploring On-Site Safety Knowledge Transfer in the Construction Industry
Authors: Huang Ying-Hua , Yang Tzung-Ru
Published: November 2019

A primary cause of occupational accidents is on-site workers not having proper or even adequate safety knowledge and awareness, leading to them failing to employ safety measures, equipment, or behavior to protect themselves. The complexity of construction projects and changes in organizational personnel complicate the safety knowledge transfer process. Therefore, to reduce occupational accidents in the construction industry, this study explored the on-site safety knowledge transfer process as well as its relationship with a safe working environment; it did this to understand the associations between various constructs in the process, which could be used as a reference for management personnel to promote on-site safety education and behaviors. This would allow safety knowledge to be learned and practiced by on-site workers, changing their unsafe behaviors and creating a safe on-site work environment. This study used structural equation modeling to empirically study the relationship between various constructs during safety knowledge transfer on a construction site. The results revealed that an excellent safety knowledge transfer environment can lead to favorable safety behavior as well as safety knowledge application and inspiration of on-site workers, which would affect their safety behaviors. More satisfactory safety behaviors of on-site workers could produce a safer working environment on the construction site. Moreover, although safety application and inspiration do not directly affect the safety of a work environment, they do so indirectly through safety behaviors.
Probabilistic Health Risk Assessment of Vehicular Emissions as an Urban Health Indicator in Dhaka City
Authors: Iqbal Asif , Afroze Shirina , Rahman Md. Mizanur
Published: November 2019

Emissions modelling is an important tool for assessing the urban health status of any city, but often the assessments are affected by the uncertainty of the data used for the modelling. Therefore, a Monte Carlo simulation technique was used for a probabilistic emissions modelling of Dhaka City by simulating 20,000 scenarios for the highest and lowest values of traffic volume and speed profiles for each of the major road links. Only nitrogen oxide (NOx) emissions from on-road vehicles were considered, as vehicular sources are major contributors. Each dataset included two peak periods and an offpeak period of the day to cover the diurnal variation within each road link. Using the probability of the magnitude of emissions along with the corresponding health risk, a series of spatial urban health risk severity scenarios was generated for 2018 and 2024, suggesting that transportation and environmental planning is required for urban sustainability.
Inventory Sharing Strategy for Disposable Medical Items between Two Hospitals
Authors: Zhang Ping , Yan Hong , Pang King Wah
Published: November 2019

When urgent situations occur (e.g., inaccurate demand forecast, traffic accidents, or infectious disease outbreaks), the stock of rescue medical items in the hospital might not be enough to cater to the drastically increased demand. Comparing with placing an expensive emergent replenishment order with dealers, requesting inventory sharing from another hospital with excessive stocks could save time and cost. This paper investigates the operation of the inventory sharing mechanism between two independent hospitals with the consideration of patient behavior. We first identified the inventory decisions when hospitals are under a no-sharing scenario, and derive hospitals’ sharing policies and inventory policies under the sharing scenario. Through numerical experiments, we found that the inventory sharing option is profitable for hospitals compared to emergent replenishment. Furthermore, we investigated the effects of patient behavior, safety inventory level of the hospital, and other cost parameters on inventory decisions. Under the sharing policy, the increase of hospital j’s emergent request rate and safety inventory level increased the optimal initial inventory level of hospital i, while the increase of hospital j’s initial inventory level decreased the optimal inventory level of hospital i. This paper provides more practical suggestions for hospitals’ inventory sharing operation.
Applying Effective Sensory Marketing to Sustainable Coffee Shop Business Management
Authors: Jang Ha-Won , Lee Soo-Bum
Published: November 2019

This study focused on sensory marketing that appeals to the five senses of coffee shop customers (“sight,” “smell,” “sound,” “taste,” and “touch”) to provide a sustainable growth model for the saturated coffee shop market. The study identified the relationships among coffee shops’ sensory marketing factors, the “PAD” emotions (pleasure, arousal, and dominance), flow, and behavioral intentions. It employed an online survey of coffee shop customers aged 20 years and older. A total of 608 surveys were used for the final analysis. The results showed that there are statistically meaningful relationships between “sight” and dominance, “sound” and arousal, “taste” and dominance, “taste” and arousal, “touch” and dominance, and “touch” and arousal. In addition, it was confirmed that there are significant relationships among the PAD emotions; pleasure also has positive effects on flow and behavioral intentions. Finally, this study found significant moderating effects of hedonic and utilitarian usage motivations on the hypothesized relationships. Based on our findings, several important academic and business implications are provided, which can contribute to the sustainability of coffee shops.
Does Social Capital Increase Innovation Speed? Empirical Evidence from China
Authors: Zhang Xiaotang , Zhang Haili , Song Michael
Published: November 2019

Past studies have suggested that social capital is a sustainable competitive advantage that leads to sustainable organizational growth and performance. However, few studies have explored how innovation speed moderates the relationship between social capital and sustainable organizational performance in China where the government plays key roles in promoting sustainable development goals. This paper develops a “social capital-innovation speed-performance” framework to investigate the mechanism of social capital influencing innovation speed, which in turn affects sustainable organizational growth and performance. Based on data collected from 125 Chinese firms, hierarchical moderated regression analyses indicate that structural social capital positively affects sustainable organizational performance but has no significant impact on sustainable innovation speed; relational social capital has no significant impact on sustainable organizational performance and is negatively correlated with innovation speed; cognitive social capital positively correlates with sustainable organizational performance and affects innovation speed, and government ties affect sustainable organizational performance and positively impact innovation speed. The study findings suggest that in China, increasing government ties is the most important social capital in creating sustainable organizational growth and performance. Both cognitive social capital and government ties are conducive to accelerating innovation speed, which gives firms a sustainable competitive advantage to achieve sustainable organizational performance.
FinTech and Sustainable Development: Evidence from China Based on P2P Data
Authors: Deng Xiang , Huang Zhi , Cheng Xiang
Published: November 2019

In the current international context, the ways in which financial technology (FinTech) affects sustainable development need to be urgently identified. However, relevant studies are rare and there is no consensus on the optimal indicator system for sustainable development. Therefore, this study proposes an indicator system to evaluate sustainability and conducts in-depth analysis of the relationship between FinTech and sustainable development based on data of peer-to-peer platforms (P2P) in 31 Chinese provinces. The empirical results show the existence of a U-shaped relationship between FinTech and sustainable development, mainly determined by the pattern of extensive economic growth. Furthermore, heterogeneity analysis indicates that there are significant regional differences in its impact on sustainable development, being significant in China’s eastern and central regions and insignificant in the western region; moreover, the impact on the central region is significantly higher than that on the eastern region. Our research not only has strong practical significance but also contributes significantly to the literature on FinTech and sustainable development.
Development, Validation, and Application of School Audit Tool (SAT): An Effective Instrument for Assessing Traffic Safety and Operation Around Schools
Authors: Shaaban Khaled , Abdur-Rouf Khadija
Published: November 2019

There is a need for a reliable school audit tool with well-defined scales to convert qualitative evaluation of existing school sites into a quantitative assessment in order to help public agencies to improve schools’ safety and efficiency. In this study, a new, simple, and versatile School Audit Tool (SAT) was developed and tested. SAT was formed using a 30-item checklist categorized into four domains: school site assessment, road network assessment, parking/loading assessment, and active transport assessment. The tool was applied on a sample of 22 schools. Then, categorical and item-by-item Intraclass Correlation Coefficients (ICC) were calculated to validate the tool. The results showed acceptable overall test-retest (ICC = 0.919) and inter-rater reliability (ICC = 0.843) across all items and domains. SAT’s adaptable framework to assess and compare the safety and efficiency of schools is reliable, easy-to-use, and comprehensive. The tool is also effective in ranking schools and identifying items that may require upgrades or modifications.
Environmental Satisfaction, Residential Satisfaction, and Place Attachment: The Cases of Long-Term Residents in Rural and Urban Areas in China
Authors: Chen Ning (Chris) , Hall C. Michael , Yu Kangkang , Qian Cheng
Published: November 2019

Drawing on literature from environmental psychology and urban planning, this study evaluates the relationships between environmental satisfaction, residential satisfaction, and place attachment in the context of both rural and urban areas in China. A field survey was carried out with 490 valid questionnaires collected in rural areas and 420 from urban areas in China. Partial least squares path modeling was applied for testing the relationships between the three main constructs. The results indicate a significant mediating role of residential satisfaction between environmental satisfaction and place attachment, suggesting the importance of residential satisfaction in residents’ attachment-building to place. This study also found significant differences between rural and urban contexts with the mediating effect of residential satisfaction being absent from the rural sample.
Comparison of Methods for Pretreatment and Quantification of Bulk Asbestos Samples for Polarized Light Microscopy Analysis to Evaluate Asbestos-Containing Waste
Authors: Ham Seunghon , Hwang Sungho , Yoon Chungsik
Published: November 2019

This study aimed to compare sample pretreatment procedures for the identification and quantification of asbestos. The performance of visual estimation and point counting procedures for evaluating asbestos-containing waste was investigated, and the effect of analytical experience was studied. The efficacy of pretreatments for the identification and quantification of asbestos in various sample matrices was compared. To evaluate the effect of experience on analytical accuracy, three analysts with different analytical experiences were selected. There were significant differences in the quantitative analysis results obtained using different pretreatments. False negatives were reported when asbestos, especially amphiboles, were analyzed by a less-experienced analyst. Quantification via point counting and visual estimation resulted in differences in the asbestos content. The results of point counting were more accurate than those of visual estimation for all analysts, regardless of the asbestos type and concentration. Experience in asbestos analysis affected accuracy and precision. The findings show that pretreatment is an important factor in qualitative analysis. Appropriate pretreatments should be assigned based on the properties of the sample. For quantitative analysis, the accuracy of the results depends on the experience of the analyst. Until analysts are fully trained, all their analysis results should be checked by an experienced analyst. Point counting is an adequate quantitative method for analyzing samples with low concentrations.
Optimum Tilt Angle and Orientation of Photovoltaic Thermal System for Application in Greater Toronto Area, Canada
Authors: Hailu Getu , Fung Alan S.
Published: November 2019

We present a study conducted to obtain optimum tilt angle and orientation of a solar panel for the collection of maximum solar irradiation. The optimum tilt angle and orientation were determined using isotropic and anisotropic diffuse sky radiation models (isotropic and anisotropic models). The four isotropic models giving varying optimum tilt angles in the range of 37 to 44°. On the other hand, results of the four anisotropic models were more consistent, with optimum tilt angles ranging between 46–47°. Both types of models indicated that the collector tilt should be changed four times a year to receive more solar radiation. The results also indicate that the solar panel should be installed with orientation west or east of due south with a flatter tilt angle. A 15° change in orientation west or east of due south results in less than 1% reduction of the total solar radiation received. For a given optimum tilt angle, the effect of photovoltaic/thermal (PV/T) orientation west or east of due south on the outlet temperature was determined using a one-dimensional steady state heat transfer model. It was found that there is less than 1.5% decrease in outlet temperature for a PV/T panel oriented up to 15° east or west of due south from March to December. This result indicates that existing roofs with orientations angles up to 15° east or west of due south can be retrofitted with a PV/T system without changing the roof shape.
Contractor Selection for Sgraffito Decoration of Cultural Heritage Buildings Using the WASPAS-SVNS Method
Authors: Morkunaite Zydrune , Bausys Romualdas , Kazimieras Zavadskas Edmundas
Published: November 2019

Sgraffito is an ancient decorative technique applied in walls, produced by using layers of cement plaster tinted in contrasting colours to a moistened surface. This decoration originated in Italy and has been expanded in different European cities or villages. Nowadays, this decoration technique has almost disappeared because of decomposition, inexperience, or lack of knowledge. The preservation and restoration of the sgraffito technique is the most challenging and vital task of cultural heritage buildings’ preservation. Consequently, the conservation of sgraffito depends not only on the historical and architectural knowledge, studies, and conservation projects of the cultural heritage buildings, but also demands the experience, proper qualification, and knowledge of the contractor. This paper presents six principal criteria. Three possible variants for contractor selection for sgraffito decoration of cultural heritage buildings are proposed, and six principal criteria for their evaluation are suggested. The research employs the Analytic Hierarchy Process (AHP), SWARA (Step-Wise Weights Assessment Ratio Analysis), and WASPAS-SVNS (Weight Aggregated Sum Product Assessment-Single- Valued Neutrosophic Set) methods.
Deep Recurrent Neural Network and Data Filtering for Rumor Detection on Sina Weibo
Authors: Xu Yichun , Wang Chen , Dan Zhiping , Sun Shuifa , Dong Fangmin
Published: November 2019

Social media makes it easy for individuals to publish and consume news, but it also facilitates the spread of rumors. This paper proposes a novel deep recurrent neural model with a symmetrical network architecture for automatic rumor detection in social media such as Sina Weibo, which shows better performance than the existing methods. In the data preparing phase, we filter the posts according to the followers of the user. We then use sequential encoding for the posts and multiple embedding layers to get better feature representation, and multiple recurrent neural network layers to capture the dynamic temporal signals characteristic. The experimental results on the Sina Weibo dataset show that: 1. the sequential encoding performs better than the term frequency-inverse document frequency (TF-IDF) or the doc2vec encoding scheme; 2. the model is more accurate when trained on the posts from the users with more followers; and 3. the model achieves superior improvements over the existing works on the accuracy of detection, including the early detection.
Algebraic Properties of the Block Cipher DESL
Authors: Matheis Kenneth , Steinwandt Rainer , Suárez Corona Adriana
Published: November 2019

The Data Encryption Standard Lightweight extension (DESL) is a lightweight block cipher which is very similar to DES, but unlike DES uses only a single S-box. This work demonstrates that this block cipher satisfies comparable algebraic properties to DES—namely, the round functions of DESL generate the alternating group and both ciphers resist multiple right-hand sides attacks.
Enhanced Membrane Computing Algorithm for SAT Problems Based on the Splitting Rule
Authors: Hao Le , Liu Jun
Published: November 2019

Boolean propositional satisfiability (SAT) problem is one of the most widely studied NP-complete problems and plays an outstanding role in many domains. Membrane computing is a branch of natural computing which has been proven to solve NP problems in polynomial time with a parallel compute mode. This paper proposes a new algorithm for SAT problem which combines the traditional membrane computing algorithm of SAT problem with a classic simplification rule, the splitting rule, which can divide a clause set into two axisymmetric subsets, deal with them respectively and simultaneously, and obtain the solution of the original clause set with the symmetry of their solutions. The new algorithm is shown to be able to reduce the space complexity by distributing clauses with the splitting rule repeatedly, and also reduce both time and space complexity by executing one-literal rule and pure-literal rule as many times as possible.
Dynamic Soft Sensor Development for Time-Varying and Multirate Data Processes Based on Discount and Weighted ARMA Models
Authors: Li Longhao , Dai Yongshou
Published: November 2019

To solve the soft sensor modeling (SSMI) problem in a nonlinear chemical process with dynamic time variation and multi-rate data, this paper proposes a dynamic SSMI method based on an autoregressive moving average (ARMA) model of weighted process data with discount (DSSMI-AMWPDD) and optimization methods. For the sustained influence of auxiliary variable data on the dominant variables, the ARMA model structure is adopted. To reduce the complexity of the model, the dynamic weighting model is combined with the ARMA model. To address the weights of auxiliary variable data with different sampling frequencies, a calculation method for AMWPDD is proposed using assumptions that are suitable for most sequential chemical processes. The proposed method can obtain a discount factor value (DFV) of auxiliary variable data, realizing the dynamic fusion of chemical process data. Particle swarm optimization (PSO) is employed to optimize the soft sensor model parameters. To address the poor convergence problem of PSO, ω-dynamic PSO (ωDPSO) is used to improve the PSO convergence via the dynamic fluctuation of the inertia weight. A continuous stirred tank reactor (CSTR) simulation experiment was performed. The results show that the proposed DSSMI-AMWPDD method can effectively improve the SSM prediction accuracy for a nonlinear time-varying chemical process. The AMWPDD proposed in this paper can reflect the dynamic change of chemical process and improve the accuracy of SSM data prediction. The ω dynamic PSO method proposed in this paper has faster convergence speed and higher convergence accuracy, thus, these models correlate with the concept of symmetry.
First Detection of Microcystin-LR in the Amazon River at the Drinking Water Treatment Plant of the Municipality of Macapá, Brazil
Authors: D.C. Oliveira Elane , Castelo-Branco Raquel , Silva Luis , Silva Natalina , Azevedo Joana , Vasconcelos Vitor , Faustino Silvia , Cunha Alan
Published: November 2019

Human poisoning by microcystin has been recorded in many countries, including Brazil, where fatal cases have already occurred. The Amazon River is the main source of drinking water in municipalities such as Macapá, where there is no monitoring of cyanobacteria and cyanotoxins. This study investigated the presence of cyanobacteria and cyanotoxins in samples from a drinking water treatment plant (DWTP) that catches water from the Amazon River. The toxin analyses employed ELISA, LC/MS, and molecular screening for genes involved in the production of cyanotoxins. The sampling was carried out monthly from April 2015 to April 2016 at the intake (raw water) and exit (treated water) of the DWTP. This study reports the first detection of microcystin-LR (MC-LR) in the Amazon River, the world’s largest river, and in its treated water destined for drinking water purposes in Macapá, Brazil. The cyanobacterial density and MC-LR concentration were both low during the year. However, Limnothrix planctonica showed a density peak (± 900 cells mL−1) in the quarter of June–August 2015, when MC-LR was registered (2.1 µg L−1). Statistical analyses indicate that L. planctonica may produce the microcystin.
Proteomic Characterization of High-Density Lipoprotein Particles from Non-Diabetic Hemodialysis Patients
Authors: Florens Nans , Calzada Catherine , Delolme Frédéric , Page Adeline , Guebre Egziabher Fitsum , Juillard Laurent , Soulage Christophe O.
Published: November 2019

Chronic kidney disease is associated with an increased cardiovascular risk, and altered biological properties of high-density lipoproteins (HDL) may play a role in these events. This study aimed to describe the HDL proteome from non-diabetic hemodialysis patients and identify potential pathways affected by the dysregulated expression of HDL proteins. HDL were sampled from nine non-diabetic hemodialysis (HD) and eight control patients. Samples were analyzed using a nano-RSLC coupled with a Q-Orbitrap. Data were processed by database searching using SequestHT against a human Swissprot database and quantified with a label-free quantification approach. Proteins that were in at least five of the eight control and six of the nine HD patients were analyzed. Analysis was based on pairwise ratios and the ANOVA hypothesis test. Among 522 potential proteins, 326 proteins were identified to be in the HDL proteome from HD and control patients, among which 10 were significantly upregulated and nine downregulated in HD patients compared to the control patients (p < 0.05). Up and downregulated proteins were involved in lipid metabolism, hemostasis, wound healing, oxidative stress, and apoptosis pathways. This difference in composition could partly explain HDL dysfunction in the chronic kidney disease (CKD) population and participate in the higher cardiovascular risk observed in this population.
Schmallenberg Disease—A Newly Emerged Culicoides-Borne Viral Disease of Ruminants
Authors: Endalew Abaineh , Faburay Bonto , Wilson William , Richt Juergen
Published: November 2019

First appearing in 2011 in Northern Europe, Schmallenberg virus (SBV), an Orthobunyavirus of the Simbu serogroup, is associated with clinical disease mainly in ruminants such as cattle, sheep and goats. The clinical signs are characterized by abortion and congenital deformities in newborns. The virus is transmitted by Culicoides midges of the Obsoletus complex. SBV infection induces a solid protective immunity that persists for at least 4 or 6 years in sheep and cattle, respectively. SBV infection can be diagnosed directly by real-time RT-qPCR and virus isolation or indirectly by serological assays. Three vaccines are commercially available in Europe. This article provides a comprehensive literature review on this emerging disease regarding pathogenesis, transmission, diagnosis, control and prevention. This review also highlights that although much has been learned since SBV’s first emergence, there are still areas that require further study to devise better mitigation strategies.
Treatment of Highly Pathogenic H7N9 Virus-Infected Mice with Baloxavir Marboxil
Authors: Kiso Maki , Yamayoshi Seiya , Furusawa Yuri , Imai Masaki , Kawaoka Yoshihiro
Published: November 2019

Viral neuraminidase inhibitors show limited efficacy in mice infected with H7N9 influenza A viruses isolated from humans. Although baloxavir marboxil protected mice from lethal challenge infection with a low pathogenic avian influenza H7N9 virus isolated from a human, its efficacy in mice infected with a recent highly pathogenic version of H7N9 human isolates is unknown. Here, we examined the efficacy of baloxavir marboxil in mice infected with a highly pathogenic human H7N9 virus, A/Guangdong/17SF003/2016. Treatment of infected mice with a single 1.5 mg/kg dose of baloxavir marboxil protected mice from the highly pathogenic human H7N9 virus infection as effectively as oseltamivir treatment at 50 mg/kg twice a day for five days. Daily treatment for five days at 15 or 50 mg/kg of baloxavir marboxil showed superior therapeutic efficacy, largely preventing virus replication in respiratory organs. These results indicate that baloxavir marboxil is a valuable candidate treatment for human patients suffering from highly pathogenic H7N9 virus infection.
Efficient Expression and Processing of Ebola Virus Glycoprotein Induces Morphological Changes in BmN Cells but Cannot Rescue Deficiency of Bombyx Mori Nucleopolyhedrovirus GP64
Authors: Huang Jinshan , Liu Na , Xu Fanbo , Ayepa Ellen , Amanze Charles , Sun Luping , Shen Yaqin , Yang Miao , Yang Shuwen , Shen Xingjia , Hao Bifang
Published: November 2019

Ebola virus (EBOV) disease outbreaks have resulted in many fatalities, yet no licensed vaccines are available to prevent infection. Recombinant glycoprotein (GP) production may contribute to finding a cure for Ebola virus disease, which is the key candidate protein for vaccine preparation. To explore GP1,2 expression in BmN cells, EBOV-GP1,2 with its native signal peptide or the GP64 signal peptide was cloned and transferred into a normal or gp64 null Bombyx mori nucleopolyhedrovirus (BmNPV) bacmid via transposition. The infectivity of the recombinant bacmids was investigated after transfection, expression and localization of EBOV-GP were investigated, and cell morphological changes were analyzed by TEM. The GP64 signal peptide, but not the GP1,2 native signal peptide, caused GP1,2 localization to the cell membrane, and the differentially localized GP1,2 proteins were cleaved into GP1 and GP2 fragments in BmN cells. GP1,2 expression resulted in dramatic morphological changes in BmN cells in the early stage of infection. However, GP1,2 expression did not rescue GP64 deficiency in BmNPV infection. This study provides a better understanding of GP expression and processing in BmN cells, which may lay a foundation for EBOV-GP expression using the BmNPV baculovirus expression system.
In Vivo Characterization of Tick-Borne Encephalitis Virus in Bank Voles (Myodes glareolus)
Authors: Michelitsch Anna , Tews Birke Andrea , Klaus Christine , Bestehorn-Willmann Malena , Dobler Gerhard , Beer Martin , Wernike Kerstin
Published: November 2019

Tick-borne encephalitis is the most important tick-transmitted zoonotic virus infection in Eurasia, causing severe neurological symptoms in humans. The causative agent, the tick-borne encephalitis virus (TBEV), circulates between ticks and a variety of mammalian hosts. To study the interaction between TBEV and one of its suspected reservoir hosts, bank voles of the Western evolutionary lineage were inoculated subcutaneously with either one of eight TBEV strains or the related attenuated Langat virus, and were euthanized after 28 days. In addition, a subset of four strains was characterized in bank voles of the Carpathian linage. Six bank voles were inoculated per strain, and were housed together in groups of three with one uninfected in-contact animal each. Generally, most bank voles did not show any clinical signs over the course of infection. However, one infected bank vole died and three had to be euthanized prematurely, all of which had been inoculated with the identical TBEV strain (Battaune 17-H9, isolated in 2017 in Germany from a bank vole). All inoculated animals seroconverted, while none of the in-contact animals did. Viral RNA was detected via real-time RT-PCR in the whole blood samples of 31 out of 74 inoculated and surviving bank voles. The corresponding serum sample remained PCR-negative in nearly all cases (29/31). In addition, brain and/or spine samples tested positive in 11 cases, mostly correlating with a positive whole blood sample. Our findings suggest a good adaption of TBEV to bank voles, combining in most cases a low virulence phenotype with detectable virus replication and hinting at a reservoir host function of bank voles for TBEV.
A New Water Environmental Load and Allocation Modeling Framework at the Medium–Large Basin Scale
Authors: Liu Qiankun , Jiang Jingang , Jing Changwei , Liu Zhong , Qi Jiaguo
Published: November 2019

Waste load allocation (WLA), as a well-known total pollutant control strategy, is designed to distribute pollution responsibilities among polluters to alleviate environmental problems, but the current policy is unfair and limited to single scale or single pollution types. In this paper, a new, alternative, multi-scale, and multi-pollution WLA modeling framework was developed, with a goal of producing optimal and fair allocation quotas at multiple scales. The new WLA modeling framework integrates multi-constrained environmental Gini coefficients (EGCs) and Delphi-analytic hierarchy process (Delphi-AHP) optimization models to achieve the stated goal. The new WLA modeling framework was applied in a case study in the Xian-jiang watershed in Zhejiang Province, China, in order to test its validity and usefulness. The results, in comparison with existing practices by the local governments, suggest that the simulated pollutant load quota at the watershed scale is much fairer than the existing policies and even has some environmental economic benefits at the pollutant source scale. As the new WLA is a process-based modeling framework, it should be possible to adopt this approach in other similar geographic areas.