bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of Dietary Supplement Label Database in Italy: Focus of FoodEx2 Coding
Authors: Durazzo Alessandra , Camilli Emanuela , D’Addezio Laura , Piccinelli Raffaela , Mantur-Vierendeel Angelika , Marletta Luisa , Finglas Paul , Turrini Aida , Sette Stefania
Published: December 2019

The sector of food supplements is certainly varied and growing: an ever wider offer of new products is launched on the market every year. This is reflected in new reorganization of drug companies and new marketing strategies, in the adoption of new production technologies with resulting changes in dietary supplements regulation. In this context, information on composition reported in labels of selected dietary supplements was collected and updated for the development of a Dietary Supplement Label Database according to products’ availability on the Italian market and also including items consumed in the last Italian Dietary Survey. For each item, a code was assigned following the food classification and description system FoodEx2, revision 2. A total of 558 products have been entered into the database at present, trying to give a uniform image and representation of the major classes of food supplements, and 82 descriptors have been compiled. Various suggestions on how the number of FoodEx2 system descriptors could be expanded were noted during the compilation of the database and the coding procedure, which are presented in this article. Limits encountered in compiling the database are represented by the changes in the formulation of products on the market and therefore by the need for a constant database update. The database here presented can be a useful tool in clinical trials, dietary plans, and pharmacological programs.
The Inhibitory Potential of Selected Essential Oils on Fusarium spp. Growth and Mycotoxins Biosynthesis in Maize Seeds
Authors: Perczak Adam , Gwiazdowska Daniela , Gwiazdowski Romuald , Juś Krzysztof , Marchwińska Katarzyna , Waśkiewicz Agnieszka
Published: December 2019

Owing to their rich chemical composition, essential oils (EOs) have many interesting properties, including antimicrobial activities. The presence of Fusarium and their secondary metabolites, mycotoxins, in cereal crops is a serious problem in agriculture, which consequently affects food quality. The aim of the present study was to investigate the effects of selected EOs on the growth of Fusarium graminearum and F. culmorum and the biosynthesis of mycotoxins in maize seeds. Chromatographic analysis of ergosterol as a fungal growth indicator showed a significant inhibition of Fusarium growth (83.24–99.99%) compared to the control samples, which as a consequence resulted in a reduction in mycotoxin concentrations. The addition of cinnamon, palmarosa, orange, and spearmint EOs was shown to be the most effective in reducing zearalenone concentration (99.10–99.92%). Deoxynivalenol analysis confirmed a very high reduction of this compound at the application all tested EOs (90.69–100%). The obtained results indicated that EOs have a great potential to inhibit growth of Fusarium fungi as well as reduce the concentration of mycotoxins in maize seed.
Fungicide Treatments to Control Seed-borne Fungi of Sunflower Seeds
Authors: Addrah Mandela Elorm , Zhang Yuanyuan , Zhang Jian , Liu Lin , Zhou Hongyou , Chen Weidong , Zhao Jun
Published: December 2019

Seed-borne fungi in 69 sunflower cultivars were evaluated which comprised 52 confectionery and 17 oilseed types. Seed coats were placed on both NP-10 (Nonylphenol Ethoxylate based surfacant −10) and potato dextrose agar (PDA) media to culture fungi. The rate of contamination among the different varieties was calculated by counting seed coats with fungal colonies. The rate of contamination in the confectionary group (88%) was significantly (p ≤ 0.05) higher than in the oilseed group (71%). Of the 52 confectionery varieties, the dominant fungi recovered were Verticillium dahliae along with Alternaria spp., Fusarium spp., and Rhizopus spp., whereas the oilseed type varieties were contaminated with only V. dahliae. Molecular identification of fungal species via BLAST (Basic Alignment Search Tool) was performed on fungal sequences obtained from PCR (Polymerase Chain Reaction) analysis. The results included five Alternaria spp. that included Alternaria tenuissima, Alternaria alternata, Alternaria helianthiinficiens, Alternaria longipes, and Alternaria tamaricis, three Fusarium spp. such as Fusarium oxysporum, Fusarium incarnatum, and Fusarium proliferatum, and V. dahliae and Cladosporium cladosporioides. These were identified from pure fungal cultures recovered from seed coats. To efficiently control seed-borne fungi, four broad spectrum fungicides (carbendazim, triadimefon, caprio F-500, and flusilazole) were screened against V. dahliae isolate Gn3, which was isolated from a diseased LD 5009 sunflower plant. Flusilazole was selected based on its low half-maximal effective concentration value (EC50), 78.7 µg/mL. Seeds of diseased LD 5009 plants obtained from two different locations treated with formulated flusilazole fungicide at optimum parameters showed a significant (p ≤ 0.05) increase in seed germination and a decrease in contamination rate from 98% to less than 10%. The results affirmed that confectionery cultivars are much more susceptible to fungal contamination than oilseeds, and also that seed pretreatment is a suitable way to prevent the spread of soil- and seed-borne fungi in sunflower production.
High-Throughput Sequencing Reveals Bell Pepper Endornavirus Infection in Pepper (Capsicum annum) in Slovakia and Enables Its Further Molecular Characterization
Authors: Tomašechová Jana , Hančinský Richard , Predajňa Lukáš , Kraic Ján , Mihálik Daniel , Šoltys Katarína , Vávrová Silvia , Böhmer Miroslav , Sabanadzovic Sead , Glasa Miroslav
Published: December 2019

Ribosomal RNA-depleted total RNAs from a sweet pepper plant (Capsicum annuum, labelled as N65) grown in western Slovakia and showing severe virus-like symptoms (chlorosis, mottling and deformation of leaf lamina) were subjected to high-throughput sequencing (HTS) on an Illumina MiSeq platform. The de novo assembly of ca. 5.5 million reads, followed by mapping to the reference sequences, revealed the coinfection of pepper by several viruses; i.e., cucumber mosaic virus (CMV), watermelon mosaic virus (WMV), pepper cryptic virus 2 (PCV2) and bell pepper endornavirus (BPEV). A complete polyprotein-coding genomic sequence (14.6 kb) of BPEV isolate N65 was determined. A comparison of BPEV-N65 sequences with BPEV genomes available in GenBank showed 86.1% to 98.6% identity at the nucleotide level. The close phylogenetic relationship with isolates from India and China resulted in their distinct grouping compared to the other BPEV isolates. Further analysis has revealed the presence of BPEV in sweet or chili peppers obtained from various sources and locations in Slovakia (plants grown in gardens, greenhouse or retail shop). Additionally, the partial sequencing of two genomic portions from 15 BPEV isolates revealed that the Slovak isolates segregated into two molecular clusters, indicating a genetically distinct population (mean inter-group nucleotide divergence reaching 12.7% and 14.5%, respectively, based on the genomic region targeted). Due to the mix infections of BPEV-positive peppers by potato virus Y (PVY) and/or CMV, the potential role of individual viruses in the observed symptomatology could not be determined. This is the first evidence and characterization of BPEV from the central European region.
Dominant Consumer Attitudes in the Sharing Economy—A Representative Study in Hungary
Authors: Buda Gabriella , Pethes Barbara , Lehota József
Published: December 2019

As a result of the digital revolution, new business models are emerging, and one of the most dynamic is the sharing economy. In many cases, the strategic communication of sharing economy firms is linked to current socio-economic trends, such as digital innovation, consumers’ empowerment, experience gaining (instead of stock), environmental awareness, and community building. In our research (a nationwide representative sample of 3520), we aimed to determine how open the Hungarian population is toward sharing economy services. Furthermore, we explored the relationship between openness and consumers’ socio-demographic factors, attitudes related to the current consumer trends and Internet usage habits. As a result, we found that 38.4% of the Hungarian population is open toward sharing economy services. From a socio-demographic point of view, wealthy, metropolitan, family-oriented, educated, and younger people are more open toward sharing activities. In terms of consumer attitudes, people who take risks, like having a social life, are environmentally and health conscious, spend their leisure time actively, enjoy quality things, and have a positive attitude toward digitalization are more open to using the sharing economy services. As a final result of the regression modeling, we found that the examined consumer attitudes and Internet usage habits determine openness, but socio-demographic factors largely lose their significant effect, except for generation and wealth, in the case of the integrated model. Our results show that a well-defined and relatively large segment is open to the sharing economy, and sharing economy companies could target them directly to achieve a more sustainable environment.
Real-Time GNSS-Derived PWV for Typhoon Characterizations: A Case Study for Super Typhoon Mangkhut in Hong Kong
Authors: He Qimin , Zhang Kefei , Wu Suqin , Zhao Qingzhi , Wang Xiaoming , Shen Zhen , Li Longjiang , Wan Moufeng , Liu Xiaoyang
Published: December 2019

Typhoons can be serious natural disasters for the sustainability and development of society. The development of a typhoon usually involves a pre-existing weather disturbance, warm tropical oceans, and a large amount of moisture. This implies that a large variation in the atmospheric water vapor over the path of a typhoon can be used to study the characteristics of the typhoon. This is the reason that the variation in precipitable water vapor (PWV) is often used to capture the signature of a typhoon in meteorology. This study investigates the usability of real-time PWV retrieved from global navigation satellite systems (GNSS) for typhoons’ characterizations, and especially, the following aspects were investigated: (1) The correlation between PWV and atmospheric parameters including pressure, temperature, precipitation, and wind speed; (2) water vapor transportation during a typhoon period; and (3) the correlation between the movement of a typhoon and the transportation of water vapor. The case study selected for this research was Super Typhoon Mangkhut that occurred in mid-September 2018 in Hong Kong. The PWV time series were obtained from a conversion of GNSS-derived zenith total delays (ZTDs) using observations at 10 stations selected from the Hong Kong GNSS continuously operating reference stations (CORS) network, which are also located along the path of the typhoon. The Bernese GNSS Software (ver. 5.2) was used to obtain the ZTDs; and the root mean square (RMS) of the differences between the GNSS-ZTDs and International GNSS Service post-processed ZTDs time series was less than 8 mm. The RMS of the differences between the GNSS-PWVs (i.e., the ZTDs converted PWVs) and radiosonde-derived PWVs (RS-PWVs) time series was less than 2 mm. The changes in PWV reflect the variation in wind speed during the typhoon period to a certain degree, and their correlation coefficient was 0.76, meaning a significant positive correlation. In addition, a new approach was proposed to estimate the direction and speed of a typhoon’s movement using the time difference of PWV arrival at different sites. The direction and speed estimated agreed well with the ones published by the China Meteorological Administration. These results suggest that GNSS-derived PWV has a great potential for the monitoring and even prediction of typhoon events, especially for near real-time warnings.
Flash-Flood Susceptibility Assessment Using Multi-Criteria Decision Making and Machine Learning Supported by Remote Sensing and GIS Techniques
Authors: Costache Romulus , Pham Quoc Bao , Sharifi Ehsan , Linh Nguyen Thi Thuy , Abba S.I. , Vojtek Matej , Vojteková Jana , Nhi Pham Thi Thao , Khoi Dao Nguyen
Published: December 2019

Concerning the significant increase in the negative effects of flash-floods worldwide, the main goal of this research is to evaluate the power of the Analytical Hierarchy Process (AHP), fi (kNN), K-Star (KS) algorithms and their ensembles in flash-flood susceptibility mapping. To train the two stand-alone models and their ensembles, for the first stage, the areas affected in the past by torrential phenomena are identified using remote sensing techniques. Approximately 70% of these areas are used as a training data set along with 10 flash-flood predictors. It should be remarked that the remote sensing techniques play a crucial role in obtaining eight out of 10 flash-flood conditioning factors. The predictive capability of predictors is evaluated through the Information Gain Ratio (IGR) method. As expected, the slope angle results in the factor with the highest predictive capability. The application of the AHP model implies the construction of ten pair-wise comparison matrices for calculating the normalized weights of each flash-flood predictor. The computed weights are used as input data in kNN–AHP and KS–AHP ensemble models for calculating the Flash-Flood Potential Index (FFPI). The FFPI also is determined through kNN and KS stand-alone models. The performance of the models is evaluated using statistical metrics (i.e., sensitivity, specificity and accuracy) while the validation of the results is done by constructing the Receiver Operating Characteristics (ROC) Curve and Area Under Curve (AUC) values and by calculating the density of torrential pixels within FFPI classes. Overall, the best performance is obtained by the kNN–AHP ensemble model.
Improving Route Selections in ZigBee Wireless Sensor Networks
Authors: Meka Srikar , Fonseca Benedito
Published: December 2019

The ZigBee wireless communication specifications forecast the use of multihop routes between nodes and define that nodes select their routes based on their costs. The specifications define how to compute a route cost from the probability of successfully transmitting on each of the routes’ links; and it is recommended that such probabilities be obtained by counting received link status messages or averaging link quality indicators from received packets. In this paper, we study the performance of these two recommended procedures, show that they can lead to degraded route selections, and propose a procedure that can improve route selections without modifications to the ZigBee protocol or frame formats. Our procedure estimates the probability of successful transmission on each link, based on information from the medium access layer during unicast packet transmissions, and includes a modification into how ZigBee nodes treat routing messages internally in order to reduce variations in the link cost estimates. Focusing on a home environment with one or two hops, our simulation results show that, in several scenarios, our procedure performs better than either of the two procedures recommended in the ZigBee specifications.
Time Series Multiple Channel Convolutional Neural Network with Attention-Based Long Short-Term Memory for Predicting Bearing Remaining Useful Life
Authors: Jiang Jehn-Ruey , Lee Juei-En , Zeng Yi-Ming
Published: December 2019

This paper proposes two deep learning methods for remaining useful life (RUL) prediction of bearings. The methods have the advantageous end-to-end property that they take raw data as input and generate the predicted RUL directly. They are TSMC-CNN, which stands for the time series multiple channel convolutional neural network, and TSMC-CNN-ALSTM, which stands for the TSMC-CNN integrated with the attention-based long short-term memory (ALSTM) network. The proposed methods divide a time series into multiple channels and take advantage of the convolutional neural network (CNN), the long short-term memory (LSTM) network, and the attention-based mechanism for boosting performance. The CNN performs well for extracting features from data with multiple channels; dividing a time series into multiple channels helps the CNN extract relationship among far-apart data points. The LSTM network is excellent for processing temporal data; the attention-based mechanism allows the LSTM network to focus on different features at different time steps for better prediction accuracy. PRONOSTIA bearing operation datasets are applied to the proposed methods for the purpose of performance evaluation and comparison. The comparison results show that the proposed methods outperform the others in terms of the mean absolute error (MAE) and the root mean squared error (RMSE) of RUL prediction.
Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method
Authors: Sharon Ho , Elamvazuthi Irraivan , Lu Cheng-Kai , Parasuraman S. , Natarajan Elango
Published: December 2019

Rheumatoid arthritis (RA) is an autoimmune illness that impacts the musculoskeletal system by causing chronic, inflammatory, and systemic effects. The disease often becomes progressive and reduces physical function, causes suffering, fatigue, and articular damage. Over a long period of time, RA causes harm to the bone and cartilage of the joints, weakens the joints’ muscles and tendons, eventually causing joint destruction. Sensors such as accelerometer, wearable sensors, and thermal infrared camera sensor are widely used to gather data for RA. In this paper, the classification of medical disorders based on RA and orthopaedics datasets using Ensemble methods are discussed. The RA dataset was gathered from the analysis of white blood cell classification using features extracted from the image of lymphocytes acquired from a digital microscope with an electronic image sensor. The orthopaedic dataset is a benchmark dataset for this study, as it posed a similar classification problem with several numerical features. Three ensemble algorithms such as bagging, Adaboost, and random subspace were used in the study. These ensemble classifiers use k-NN (K-nearest neighbours) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is accessed using holdout and 10-fold cross-validation evaluation methods. The assessment was based on set of performance measures such as precision, recall, F-measure, and receiver operating characteristic (ROC) curve. The performance was also measured based on the comparison of the overall classification accuracy rate between different ensembles classifiers and the base learners. Overall, it was found that for Dataset 1, random subspace classifier with k-NN shows the best results in terms of overall accuracy rate of 97.50% and for Dataset 2, bagging-RF shows the highest overall accuracy rate of 94.84% over different ensemble classifiers. The findings indicate that the efficiency of the base classifiers with ensemble classifier have substantially improved.
A Flexible Wireless Dielectric Sensor for Noninvasive Fluid Monitoring
Authors: Zhu Heng-Tian , Chen Ye , Xiong Yi-Feng , Xu Fei , Lu Yan-Qing
Published: December 2019

A flexible wireless dielectric sensor is presented here for noninvasively monitoring the permittivity and conductivity of fluids, based on resistor–inductor–capacitor (RLC) resonant circuit and capacitively coupled contactless conductivity detection (C4D) technique. The RLC sensor consists of one single-turn inductor and one interdigital capacitor. The resonant frequency of the device is sensitive to the surrounding environment, thanks to the electric field leaked out between the interdigital capacitor electrodes. Through the high-frequency structure simulator (HFSS) simulation, and experiments on ethanol/water solutions and NaCl solutions, it was confirmed that a fluid’s permittivity and conductivity could be detected by the return loss curve (S11). With great repeatability and stability, the proposed sensor has potential for broad applications, especially in wearable low-cost smart devices.
A Deep Adversarial Approach Based on Multi-Sensor Fusion for Semi-Supervised Remaining Useful Life Prognostics
Authors: Verstraete David , Droguett Enrique , Modarres Mohammad
Published: December 2019

Multi-sensor systems are proliferating in the asset management industry. Industry 4.0, combined with the Internet of Things (IoT), has ushered in the requirements of prognostics and health management systems to predict the system’s reliability and assess maintenance decisions. State of the art systems now generate big machinery data and require multi-sensor fusion for integrated remaining useful life prognostic capabilities. When dealing with these data sets, traditional prediction methods are not equipped to handle the multiple sensor signals in unison. To address this challenge, this paper proposes a new, deep, adversarial approach to any remaining useful life prediction in which a novel, non-Markovian, variational, inference-based model, incorporating an adversarial methodology, is derived. To evaluate the proposed approach, two public multi-sensor data sets are used for the remaining useful life prediction applications: (1) CMAPSS turbofan engine dataset, and (2) FEMTO Pronostia rolling element bearing data set. The proposed approach obtains favorable results when against similar deep learning models.
Multiple Facets of Migration Research: Key Questions, Topics, and Avenues yet to Be Explored
Authors: Visvizi Anna , Lytras Miltiadis D. , Pachocka Marta
Published: December 2019

Migration and its diverse forms, including economic migration, irregular migration, forced migration, as well as the plethora of factors that drive people’s decisions to leave their habitat and seek fortune in new places, occupy a dominant position in contemporary research and political debate. Academic literature today brims with contributions elaborating on the complexities and implications of migration, thus not only opening several avenues of research, but also delivering in-depth insights into the phenomenon of migration. This notwithstanding, certain topics require greater attention of the research community if ways of navigating the complex challenges and opportunities specific to migration are to be identified. This overview offers some leads in this respect. Against this backdrop, this introduction also highlights that much more work needs to be done to trigger the momentum for the inclusion of information and communication technology (ICT) in migration research. A case is made that in times of the 4th industrial revolution the nexus between ICT and migration needs to be taken seriously.
The Approach of SMEs to Using the Customer Databases and CRM: Empirical Study in the Slovak Republic
Authors: Ližbetinová Lenka , Štarchoň Peter , Weberová Dagmar , Nedeliaková Eva , Juříková Martina
Published: December 2019

Sustainability of (small and medium-sized enterprises) SMEs in a globalized economy depends on several factors. According to available studies, effective Customer Relationship Management (CRM) is one of the factors affecting the competitiveness of SMEs in a positive way. This paper is focused on creating customer databases and on using the CRM strategy in SMEs in Slovakia. The aim of the paper is to cluster SMEs in Slovakia based on using customer databases and CRM and to identify differences in clusters according to the selected attributes. The outputs of the research focused on marketing communication and on creating the CRM strategy in Slovak enterprises are presented in the paper. Quantitative nonexperimental research using the questionnaires was conducted at the end of 2017 and throughout the year 2018. The research results are based on the responses of 1009 Slovak SMEs. Basic descriptive statistics, K-means cluster analysis, Analysis of variance (ANOVA), and Tukey HSD test were used to process the data. The resulting clusters show three typical attitudes of Slovak SMEs to creating the database, using the CRM and its effectiveness. Moreover, attitude to the application of ethical principles and holistic, integrated, and socially responsible marketing are presented in the clusters. Following the results, the fact that SMEs can be divided into the three following clusters with equal size can be stated: 1st cluster (in 92.2% and consisting of micro- and small-sized enterprises), customer databases are created for the purpose of marketing communication; 2nd cluster, consisting of the highest percentage of medium-sized enterprises and enterprises with foreign equity participation using the customer databases and CRM; and 3rd cluster characterized with smaller-sized enterprises with domestic property not focused on marketing communication and using the customer databases. The research results provide the basis for further research and support for small and medium-sized enterprises.
Multi-Stakeholder Initiatives as Social Innovation for Governance and Practice: A Review of Responsible Mining Initiatives
Authors: Sauer Philipp C. , Hiete Michael
Published: December 2019

Mining mineral resources has been found to be an ambiguous way of achieving sustainable development. It can spark the economic development of poor regions, but at the same time it is associated with severe sustainability issues, particularly in areas with governance deficits. Recent developments have spurred a vibrant debate on how to achieve the development opportunities while minimizing the sustainability impacts. As a result, voluntary multi-stakeholder initiatives (MSI) originated with differing foci, aims, and organizational designs. Such MSIs for responsible mining can be interpreted as a social innovation from two perspectives: (1) stakeholder groups cooperate to complement, concretize, initiate, and prepare, but also compete with other forms of governance and possibly replace them; (2) the MSIs support implementing responsible mining in practice. A structured review of 20 MSIs’ documents along an analytical framework covering governance, change in practice, and diffusion shows that the two roles of MSIs can hardly be separated, as the change in practice is enabled via a change in governance. Moreover, the MSIs are found to be a valuable complement to traditional governance of responsible mining, as they transcend national borders, allow for the inclusion of informal miners into professionalization, and offer support to companies to enhance their sustainability performance. Nevertheless, the MSIs are voluntary and relatively young. This limits their power and requires further research for which six avenues are identified in this paper.
Implementing a Sustainable Model for Anti-Money Laundering in the United Nations Development Goals
Authors: Dobrowolski Zbysław , Sułkowski Łukasz
Published: December 2019

The United Nations Sustainable Development Goals (SDGs), adopted in 2015, are the most relevant global agreements on 17 of the most important issues that are crucial to all countries and their societies. The achievement of all SDGs requires a reduction in the scale of money laundering destabilizing domestic economies. This original research study has shown that the supreme audit institutions’ (SAI) capacity to audit and evaluate anti-money laundering system is limited for a number of reasons including the lack of global SAI standards and guidelines for auditing anti-money laundering systems. The aim of this research was to create a generally accepted approach to auditing anti-money laundering outcomes. To address the research goal, interviews were conducted with 15 representatives of 11 SAIs. In addition, members of the International Organization of Supreme Audit Institutions (INTOSAI) were interviewed and SAI audits of anti-money laundering entities were analysed. A literature review was also carried out. Based on this research study, an original approach both to managing anti-money laundering and auditing such activities was created. This research provides the basis for strengthening risk management that supports an efficient realization of SDGs. The proposed Sustainable Model for Anti-Money Laundering will enhance SAI’s audit capacity and significantly improve parliamentary watchdogs’ investigative functions.
A Study of the Socioeconomic Forces Driving Air Pollution Based on a DPSIR Model in Henan Province, China
Authors: Chuai Xiaoming , Fan Chao , Wang Mingshi , Wang Jiajia , Han Yanjun
Published: December 2019

The 3D spatiotemporal distribution (spatial and annual-month-daily temporal) features of the air quality index (AQI), air pollutants (PM2.5, PM10, SO2, NO2, CO, and O3-8h), and air pollution risks (R) from 2003 to 2018 were investigated to understand the severity of air pollution in Henan province. The multiyear ascending trends for AQI and R values, with a peak in 2015, were observed in 2003 to 2018 since the annual population-weighted average concentrations of PM2.5, PM10, NO2, and O3-8h were always higher than the CAAQs II from 2013 to 2017 in Henan province. Changes in the monthly moving average AQI values in Henan province conformed to a U-shaped pattern, with the highest values in the winter (from December to February) and the lowest in the summer (from June to August). Triple peaks for AQI values of 8:00‒10:00 a.m., 6:00‒8:00 p.m., and 2:00‒4:00 p.m. in the representative municipalities corresponded with the morning and evening traffic tendencies and photochemical process. A spatial analysis indicated that there were decreasing trends for air pollution from northwest to southeast in Henan province. Data on 48 concrete parameters were collected from 2003 to 2017 to construct a driving force‒pressure‒state‒influence‒response (DPSIR) model for assessing the socioeconomic forces driving air pollution in this province. It was the too-rapid growth of the driving force index (DFI), induced by urban development and population growth (UDPG), economic growth and change of industrial structure (EGCIS), and energy consumption growth and structure change (ECGSC), that led to a direct increase in the atmospheric pollution burden, i.e., total emissions from air pollution and industrial emissions, which are linearly correlated to values of UDPG and ECGSC, respectively (p < 0.05). Furthermore, the prediction models for AQI and R values in Henan province, with the growth rates being 4.251 DFI−1 and 0.0816 DFI−1, respectively, were simulated by multiple linear regression analysis. Therefore, the integrated risks of air pollution in Henan province were originally driven by DFI.
The Effect of Social Presence and Chatbot Errors on Trust
Authors: Toader Diana-Cezara , Boca Grațiela , Toader Rita , Măcelaru Mara , Toader Cezar , Ighian Diana , Rădulescu Adrian T.
Published: December 2019

This article explores the potential of Artificial Intelligence (AI) chatbots for creating positive change by supporting customers in the digital realm. Our study, which focuses on the customer and his/her declarative psychological responses to an interaction with a virtual assistant, will fill a gap in the digital marketing research, where little attention has been paid to the impact of Error and Gender, as well as the extent to which Social Presence and Perceived Competence mediate the relationships between Anthropomorphic design cues and Trust. We provide consistent evidence of the significant negative effect of erroneous conversational interfaces on several constructs considered in our conceptual model, such as: perceived competence, trust, as well as positive consumer responses. We also provide support to previous research findings and confirm that people employ a biased thinking across gender and this categorization also influences their acceptance of chatbots taking social roles. The results of an empirical study demonstrated that highly anthropomorphized female chatbots that engage in social behaviors are significantly shaping positive consumer responses, even in the error condition. Moreover, female virtual assistants are much more commonly forgiven when committing errors compared to male chatbots.
Validation of the Topp-Leone-Lomax Model via a Modified Nikulin-Rao-Robson Goodness-of-Fit Test with Different Methods of Estimation
Authors: Yadav Abhimanyu Singh , Goual Hafida , Alotaibi Refah Mohammed , H Rezk , Ali M. Masoom , Yousof Haitham M.
Published: December 2019

In this paper, we introduce a new univariate version of the Lomax model as well as a simple type copula-based construction via Morgenstern family and via Clayton copula for introducing a new bivariate and a multivariate type extension of the new model. The new density has a strong physical interpretation and can be a symmetric function and unimodal with a heavy tail with positive skewness. The new failure rate function can be “upside-down”, “decreasing” with many different shapes and “decreasing-constant”. Some mathematical and statistical properties of the new model are derived. The model parameters are estimated using different estimation methods. For comparing the estimation methods, Markov Chain Monte Carlo (MCMC) simulations are performed. The applicability of the new model is illustrated via four real data applications, these data sets are symmetric and right skewed. We constructed a modified Chi-Square goodness-of-fit test based on Nikulin-Rao-Robson test in the case of complete and censored sample for the new model. Different simulation studies are performed along applications on real data for validation propose.
A Method to Optimize the Electron Spectrum for Simulating Thermo-Mechanical Response to X-ray Radiation
Authors: Ran Xianwen , Wang Bo , Zhang Kun , Tang Wenhui
Published: December 2019

The X-ray pulse originating from high altitude nuclear detonation (HAND) is mainly soft X-ray and its intensity is high enough to gasify the penetrated material and then lead to the severe thermo-mechanical deformation of unpenetrated material from the gasified blow-off effect. This effect cannot be directly reproduced in a lab for the lack of the X-ray source like HAND. At present, the low-energy relativistic electron beams resulting from an electron accelerator are usually used to approximately reproduce this effect, but the difference in the energy-deposited profile in materials between the electron and X-ray cannot be eliminated. In this paper, the symmetric linear least squares method was used to optimize the electron spectrum, and the general Monte Carlo N-Particle Transport Code calculations showed the optimized spectrum can produce the same energy-deposited profile in aluminum, copper, and tantalum with the soft X-rays like 1 keV or 3 keV spectrums. This indicates that it is possible to simulate the severe thermo-mechanical deformation resulting from HAND using the optimized electron spectrums.
Mouse Cytomegalovirus Differentially Exploits Cell Surface Glycosaminoglycans in a Cell Type-Dependent and MCK-2-Independent Manner
Authors: Pontejo Sergio M , Murphy Philip M
Published: December 2019

Many viruses initiate interaction with target cells by binding to cell surface glycosaminoglycans (GAGs). Heparan sulfate (HS) appears to be particularly important in fibroblasts, epithelial cells and endothelial cells, where it represents the dominant GAG. How GAGs influence viral infectivity in HS-poor target cells such as macrophages has not been clearly defined. Here, we show that mouse cytomegalovirus (MCMV) targets HS in susceptible fibroblasts and cultured salivary gland acinar cells (SGACs), but not in macrophage cell lines and primary bone marrow-derived macrophages, where chondroitin sulfate was the dominant virus-binding GAG. MCK-2, an MCMV-encoded GAG-binding chemokine that promotes infection of macrophages as part of a gH/gL/MCK-2 entry complex, was dispensable for MCMV attachment to the cell surface and for direct infection of SGACs. Thus, MCMV tropism for target cells is markedly influenced by differential GAG expression, suggesting that the specificity of anti-GAG peptides now under development as HCMV therapeutics may need to be broadened for effective application as anti-viral agents.
Evidence of Long-Distance Aerial Convection of Variola Virus and Implications for Disease Control
Authors: MacIntyre Chandini Raina , Das Arpita , Chen Xin , Silva Charitha De , Doolan Con
Published: December 2019

Two distinct phenomena of airborne transmission of variola virus (smallpox) were described in the pre-eradication era—direct respiratory transmission, and a unique phenomenon of transmission over greater distances, referred to as “aerial convection”. We conducted an analysis of data obtained from a systematic review following the PRISMA criteria, on the long-distance transmission of smallpox. Of 8179 studies screened, 22 studies of 17 outbreaks were identified—12 had conclusive evidence of aerial convection and five had partially conclusive evidence. Aerial convection was first documented in 1881 in England, when smallpox incidence had waned substantially following mass vaccination, making unusual transmissions noticeable. National policy at the time stipulated spatial separation of smallpox hospitals from other buildings and communities. The evidence supports the transmission of smallpox through aerial convection at distances ranging from 0.5 to 1 mile, and one instance of 15 km related to bioweapons testing. Other explanations are also possible, such as missed chains of transmission, fomites or secondary aerosolization from contaminated material such as bedding. The window of observation of aerial convection was within the 100 years prior to eradication. Aerial convection appears unique to the variola virus and is not considered in current hospital infection control protocols. Understanding potential aerial convection of variola should be an important consideration in planning for smallpox treatment facilities and protecting potential contacts and surrounding communities.
An Internet-Based Survey of Influenza Vaccination Coverage in Healthcare Workers in China, 2018/2019 Season
Authors: Liu Haitao , Tan Yayun , Zhang Muli , Peng Zhibin , Zheng Jiandong , Qin Ying , Guo Zhiqiang , Yao Junhua , Pang Fen , Ma Teng , Duan Wenjing , Li Zhongjie , Feng Luzhao , Hao Mo
Published: December 2019

Influenza vaccination coverage was low among healthcare workers (HCWs) in China. In October 2018, the National Health Commission of China began to require all hospitals to provide free influenza vaccination for HCWs to increase vaccine uptake, and no study on vaccine coverage among HCWs at the national level after the announcement of new policy. This evaluation aims to investigate self-reported influenza vaccination coverage among HCWs and factors that may affect vaccine receipt during the 2018/2019 influenza season. We delivered an opt-in internet panel survey among registered HCWs of DXY forum (the biggest online forum for HCWs in China). The survey was self-administered using a standard questionnaire to collect information on demographics, occupational characteristics, policy implementation, influenza vaccination and influence factors. We conducted multivariate logistic regression analysis to assess factors associated with receipt of influenza vaccine. The response rate of this online survey was 3.6%. The seasonal influenza vaccine coverage reported among HCWs surveyed during the 2018/2019 season was 11.6% (472/4078). Only 19.0% (774/4078) of HCWs surveyed reported free policy in their workplace. Combing free policy and workplace requirement proved to be effective to improve influenza vaccination coverage in HCWs (PR = 6.90, 95% CI: 6.03–7.65). The influenza vaccination coverage among surveyed HCWs in China was low during the 2018/2019 season. To increase future vaccination uptake, we recommend a multi-faceted strategy that include free policy, workplace requirement and promotion, on-site vaccination, and monitoring.
Assessing the Retention Capacity of an Experimental Green Roof Prototype
Authors: Silva Mariana da , K. Najjar Mohammad , W. A. Hammad Ahmed , Haddad Assed , Vazquez Elaine
Published: December 2019

Cities with high urbanization produce impervious areas. Drainage network overload results in recurring flooding. Much of the damage could be prevented through proper urban planning and good drainage practices. While Low Impact Development techniques have been known for several years, it is essential to encourage the association of this type of technology with conventional micro-drainage structures to increase rainfall runoff at the source where it is generated. Thus, the present work aims to analyze the efficiency of the use of the green roof technique in reducing the peak of the flow and the retention capacity when subjected to heavy rains at the building scale, and also explores its effects in tropical climatic zones with measurements during the summer and fall. The method used was experimental analysis of the Green Roof prototype with bromeliad’s at CESA-UFRJ, whose main results are the hydrogram of each rain event and the runoff coefficient for rainfall in the range of 100 mm/h and 150 mm/h.
Assessment of Effective Dose from Radioactive Isotopes Contained in Mineral Waters Received by Patients During Hydrotherapy Treatments
Authors: Karpińska Maria , Kapała Jacek , Raciborska Agnieszka , Mnich Stanisław
Published: December 2019

Hydrotherapy is a natural treatment and health protection method. Treatments using natural mineral water are gaining popularity as an alternative to pharmacotherapy or as additional support to pharmacotherapy in many types of diseases. The aim of the study was to determine the effective dose obtained as a result of baths and inhalations using popular mineral water samples. A total of 18 commercially available water samples used in hydrotherapy were tested for their radioactive isotope content. The following isotopes were found: 40K, 208Tl, 212Bi, 212Pb, 214Bi, 214Pb, 226Ra, 228Ac, 234Th. Effective doses received by patients during inhalation and bathing using the tested mineral water samples were determined. The collected sample activity was measured using gamma spectrometry. The effective doses received by patients from a series of inhalation treatments ranged from 170.4 to 22.9 µSv. Infants receive the highest effective dose as a result of inhalation of mineral water. The doses received by patients as a result of bathing in the studied mineral water samples were in the range of from 0.04 to 1.1 µSv and were comparable with doses from ordinary baths in tap water (0.06 µSv). The determined doses are very low; thus, they are unlikely to cause noticeable biological effects.
Isotopic Signatures as an Indicator of Long-Term Water-Use Efficiency of Haloxylon Plantations on the Dried Aral Sea Bed
Authors: Khamzina Asia , An Jiae , Chang Hanna , Son Yowhan
Published: December 2019

The desiccation of the Aral Sea due to water withdrawal from contributing rivers has resulted in an unprecedented change in the region’s climate, from maritime to hot dry desert. Afforestation has been implemented on the desiccated seafloor—the Aralkum Desert—for stabilizing the exposed substrate. However, studies on the long-term status of the afforested sites are limited. Here, we examined C and N isotopic signatures in Haloxylon aphyllum plantations, as indicators of time-integrated plant response to the prevalent water and salinity constraints, in northern Aralkum, Kazakhstan. Foliar 13C composition analysis in a chronosequence of H. aphyllum plantation sites (aged 1–27 years) on the sandy substrate revealed a significant trend towards higher water-use efficiency in older plantations, possibly in response to declining water availability. A lack of correlation between plant 13C signature and soil electrical conductivity suggests no history of salt stress despite the saline environment. Furthermore, 15N enrichment in plant tissue in the water-limited Aralkum ecosystem indicates the relative openness of N cycling. There was an increase in species richness and self-propagation at the plot scale, indicating successful afforestation effort. Coupled with other approaches, isotope discrimination might elucidate mechanisms underlying stress tolerance in H. aphyllum, which could support the afforestation efforts.
Using OCO-2 Satellite Data for Investigating the Variability of Atmospheric CO2 Concentration in Relationship with Precipitation, Relative Humidity, and Vegetation over Oman
Authors: Golkar Foroogh , Al-Wardy Malik , Saffari Seyedeh Fatemeh , Al-Aufi Kathiya , Al-Rawas Ghazi
Published: December 2019

Recognition of the carbon dioxide (CO2) concentration variations over time is critical for tracing the future changes in climate both globally and regionally. In this study, a time series analysis of atmospheric CO2 concentration and its relationship with precipitation, relative humidity (RH), and vegetation is investigated over Oman. The daily XCO2 data from OCO-2 satellite was obtained from September 2014 to March 2019. The daily RH and precipitation data were also collected from the ground weather stations, and the Normalized Difference Vegetation Index was obtained from MODIS. Oman was studied in four distinct regions where the main emphasis was on the Monsoon Region in the far south. The CO2 concentration time series indicated a significant upward trend over different regions for the study period, with annual cycles being the same for all regions except the Monsoon Region. This is indicative of RH, precipitation, and consequently vegetation cover impact on atmospheric CO2 concentration, resulting in an overall lower annual growth in the Monsoon Region. Simple and multiple correlation analyses of CO2 concentration with mentioned parameters were performed in zero to three-month lags over Oman. They showed high correlations mainly during the rainfall period in the Monsoon Region.
Vehicle Electrification Impacts on Energy Consumption for Different Connected-Autonomous Vehicle Scenario Runs
Authors: Islam Ehsan Sabri , Moawad Ayman , Kim Namdoo , Rousseau Aymeric
Published: December 2019

Transportation system simulation is a widely accepted approach to evaluate the impact of transport policy deployment. In developing a transportation system deployment model, the energy impact of the model is extremely valuable for sustainability and validation. It is expected that different penetration levels of Connected-Autonomous Vehicles (CAVs) will impact travel behavior due to changes in potential factors such as congestion, miles traveled, etc. Along with such impact analyses, it is also important to further quantify the regional energy impact of CAV deployment under different factors of interest. The objective of this paper is to study the energy consumption of electrified vehicles in the future for different penetration levels of CAVs deployment in the City of Chicago. The paper will further provide a statistical analysis of the results to evaluate the impact of the different penetration levels on the different electrified powertrains used in the study.
Un enigma sobre el enigma. Safo de Lesbos y el acertijo de la carta
Authors: Gardella Hueso Mariana
Published: December 2019

Los enigmas tradicionales poseen una importante función filosófica, ya que muestran los límites del conocimiento sobre el mundo y del conocimiento de sí. Desde una perspectiva filosófica, analizaré en este trabajo el enigma de la carta que aparece en Safo, la comedia de Antífanes (Ateneo, Banquete de los sabios X.450e-451b = fr. 194 KA). Para ello presentaré, en primer lugar, algunas observaciones preliminares sobre la tradición de los enigmas en Grecia antigua, deteniéndome principalmente en las definiciones de “enigma” ofrecidas por Aristóteles y Clearco, en las estrategias utilizadas para ocultar la solución y en la conexión entre los acertijos y el humor. En segundo lugar, examinaré algunos fragmentos de comedias en los que aparecen mujeres intelectuales conocidas o mujeres anónimas pronunciando enigmas. Estas fuentes proporcionarán un marco para analizar, en tercer lugar, el enigma que aparece en la comedia de Antífanes. Aunque se ha indicado que este acertijo muestra la importancia de la escritura epistolar y la práctica de la lectura silenciosa, me gustaría sugerir que también podría referir a los enigmas en sí mismos y al modo en que estos se transmiten y resuelven.