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In recent years, with the rapid development of China's air transport industry and the change in market consumption structure, service quality has become one of the important factors affecting airline revenue. How to formulate a reasonable pricing strategy and maintain competitiveness in the fierce market competition has become an urgent problem for airlines. First, the impact factor of service quality level in the traditional pricing model is introduced and a static price competition model for multi-oligopoly airlines based on service quality is established in this paper. And then, a dynamic pricing model based on service quality of the multi-oligopoly airlines is established. The model incorporates the weight factor of service quality impact, which is used to indicate the weight of the service quality level in the process of airline dynamic pricing. The research results show that the service quality level of airlines has an indispensable influence on its development. Airlines should improve service quality as soon as possible to enhance market competitiveness and achieve sustainable development. [ABSTRACT FROM AUTHOR]

Targeted environmental and ecosystem management remain crucial in control of dengue. However, providing detailed environmental information on a large scale to effectively target dengue control efforts remains a challenge. An important piece of such information is the extent of the presence of potential dengue vector breeding sites, which consist primarily of open containers such as ceramic jars, buckets, old tires, and flowerpots. In this paper we present the design and implementation of a pipeline to detect outdoor open containers which constitute potential dengue vector breeding sites from geotagged images and to create highly detailed container density maps at unprecedented scale. We implement the approach using Google Street View images which have the advantage of broad coverage and of often being two to three years old which allows correlation analyses of container counts against historical data from manual surveys. Containers comprising eight of the most common breeding sites are detected in the images using convolutional neural network transfer learning. Over a test set of images the object recognition algorithm has an accuracy of 0.91 in terms of F-score. Container density counts are generated and displayed on a decision support dashboard. Analyses of the approach are carried out over three provinces in Thailand. The container counts obtained agree well with container counts from available manual surveys. Multi-variate linear regression relating densities of the eight container types to larval survey data shows good prediction of larval index values with an R-squared of 0.674. To delineate conditions under which the container density counts are indicative of larval counts, a number of factors affecting correlation with larval survey data are analyzed. We conclude that creation of container density maps from geotagged images is a promising approach to providing detailed risk maps at large scale. [ABSTRACT FROM AUTHOR]

This paper develops two-stage inverse data envelopment analysis models with undesirable outputs to formulate resource plans for 16 Chinese listed commercial banks whose outputs are increased and overall efficiency is kept unchanged in the short term. We use these models to meet three different output targets, namely, increasing both the desirable and undesirable outputs by the same percentage, increasing these outputs by different percentages, and increasing only the desirable outputs while keeping the undesirable outputs unchanged. We find that operation cost and interest expense are more flexible than labor in the adjustment process and that deposits have no obvious law of change. The findings of this work provide some suggestions for bank managers. [ABSTRACT FROM AUTHOR]

Autonomous Vehicles (AV) technology is emerging. Field tests on public roads have been on going in several states in the US as well as in Europe and Asia. During the US public road tests, crashes with AV involved happened, which becomes a concern to the public. Most previous studies on AV safety relied heavily on assessing drivers’ performance and behaviors in a simulation environment and developing automated driving system performance in a closed field environment. However, contributing factors and the mechanism of AV-related crashes have not been comprehensively and quantitatively investigated due to the lack of field AV crash data. By harnessing California’s Report of Traffic Collision Involving an Autonomous Vehicle Database, which includes the AV crash data from 2014 to 2018, this paper investigates by far the most current and complete AV crash database in the US using statistical modeling approaches that involve both ordinal logistic regression and CART classification tree. The quantitative analysis based on ordinal logistic regression and CART models has successfully explored the mechanism of AV-related crash, via both perspectives of crash severity and collision types. Particularly, the CART model reveals and visualize the hierarchical structure of the AV crash mechanism with knowledge of how these traffic, roadway, and environmental contributing factors can lead to crashes of various serveries and collision types. Statistical analysis results indicate that crash severity significantly increases if the AV is responsible for the crash. The highway is identified as the location where severe injuries are likely to happen. AV collision types are affected by whether the vehicle is on automated driving mode, whether the crashes involve pedestrians/cyclists, as well as the roadway environment. The method used in this research provides a proven approach to statistically analyze and understand AV safety issues. And this benefit is potential be even enhanced with an increasing sample size of AV-related crashes records in the future. The comprehensive knowledge obtained ultimately facilitates assessing and improving safety performance of automated vehicles. [ABSTRACT FROM AUTHOR]

This paper examines the link between reliance on Facebook for news, political knowledge, and political engagement in the Philippines. We tested five hypotheses using data gathered from an online survey of 978 Filipinos conducted from February 1 to March 31, 2016. Findings support the hypothesis that those who rely less on social media as a news source exhibit higher levels of perceived knowledge about politics than those who rely more on it for news. Controlling for traditional news use, following political officials or institutions on social media is associated with higher levels of political interest and engagement, those with more politically active friends on Facebook have higher levels of exposure to political content online, and there is a positive correlation between Facebook being a source of information about politics and discussing politics more often with others. However, the hypothesis that those with more friends on their network who are politically active, will have greater political knowledge and more political engagement than those who have few politically active friends on their Facebook network is not supported. [ABSTRACT FROM AUTHOR]

We use agent-based computer simulation to test the effect of environmental conditions (available biomass/carrying capacity and environmental risk) on the development of wealth inequality and patron-client herding networks in nomadic pastoral economies. Our results show that 1) wealth inequality reaches very high levels when carrying capacity is high and risk is low, and 2) patron-client contract herding networks increase in size and duration when carrying capacity is high and risk is low. We compare empirical data from the Mongol (1206–1368 CE) and Xiongnu (209 BCE– 48 CE) empires with simulation results to develop an explanatory mechanism for the apparent correlation between nomadic empire creation and positive environmental conditions. We argue that the internal dynamics of nomadic pastoral societies are sufficient to produce high degrees of inequality and hierarchical herding networks. Nomadic empires are more likely to form during key periods of increased biomass and decreased environmental risk. [ABSTRACT FROM AUTHOR]

We undertook a systematic review of the diagnostic accuracy of artificial intelligence-based software for identification of radiologic abnormalities (computer-aided detection, or CAD) compatible with pulmonary tuberculosis on chest x-rays (CXRs). We searched four databases for articles published between January 2005-February 2019. We summarized data on CAD type, study design, and diagnostic accuracy. We assessed risk of bias with QUADAS-2. We included 53 of the 4712 articles reviewed: 40 focused on CAD design methods (“Development” studies) and 13 focused on evaluation of CAD (“Clinical” studies). Meta-analyses were not performed due to methodological differences. Development studies were more likely to use CXR databases with greater potential for bias as compared to Clinical studies. Areas under the receiver operating characteristic curve (median AUC [IQR]) were significantly higher: in Development studies AUC: 0.88 [0.82–0.90]) versus Clinical studies (0.75 [0.66–0.87]; p-value 0.004); and with deep-learning (0.91 [0.88–0.99]) versus machine-learning (0.82 [0.75–0.89]; p = 0.001). We conclude that CAD programs are promising, but the majority of work thus far has been on development rather than clinical evaluation. We provide concrete suggestions on what study design elements should be improved. [ABSTRACT FROM AUTHOR]

Group A rotaviruses (RVA) are one of the major causes of acute gastroenteritis (AGE) in young children worldwide. Owing to lack of proper surveillance programs and health facilities, developing countries of Asia and Africa carry a disproportionately heavy share of the RVA disease burden. The aim of this hospital-based study was to investigate the circulation of RVA genotypes in Rawalpindi and Islamabad, Pakistan in 2015 and 2016, prior to the implementation of RVA vaccine. 639 faecal samples collected from children under 10 years of age hospitalized with AGE were tested for RVA antigen by ELISA. Among 171 ELISA positive samples, 143 were successfully screened for RT-PCR and sequencing. The prevalence of RVA was found to be 26.8% with the highest frequency (34.9%) found among children of age group 6–11 months. The most predominant circulating genotypes were G3P[8] (22.4%) followed by G12P[6] (20.3%), G2P[4] (12.6%), G1P[8] (11.9%), G9P[6] (11.9%), G3P[4] (9.1%), G1P[6] (4.2%), G9P[8] (4.2%), and G3P[6] (0.7%). A single mixed genotype G1G3P[8] was also detected. The findings of this study provide baseline data, that will help to assess if future vaccination campaigns using currently available RVA vaccine will reduce RVA disease burden and instigate evolutionary changes in the overall RVA biology. The high prevalence of RVA infections in Pakistan require to improve and strengthen the surveillance and monitoring system for RVA. This will provide useful information for health authorities in planning public health care strategies to mitigate the disease burden caused by RVA. [ABSTRACT FROM AUTHOR]

Background: Ageing is becoming a considerable public health burden in China, which produces great societal development challenges. Healthy and active longevity could ease the ageing burden on families and communities. To date, most studies of the oldest-old distribution are focused on a simple scale from spatial perspective, and the multi-scale spatio-temporal clusters trend in the oldest-old population has not yet been determined. Thus, the objective in present study is to use a new method to evaluate the spatio-temporal pattern and detect the risk clusters in the oldest-old population from three scales. Methods: Individuals aged 65 years or older and individuals aged 80 years or older on three scales in China from 2000 to 2010 were used. The exploratory spatial data analysis was performed using Moran’s I statistic, and the pattern of the oldest-old clusters among humans was examined by using the spatial scan statistical method. Then, spatial stratified heterogeneity was used to explore the factors affecting the spatial heterogeneity of the oldest-old population. Results: The oldest-old index in the southeast coastal areas is higher than that in the northwest inland areas in China. A three-ladder terrain distribution of the oldest-old index from west to east is obvious. The overall pattern of the oldest-old index evolves from a “concave” shape to an “east-west uplift, and northern collapse” shape. Space-time analysis revealed that high-risk areas were concentrated in five regions: the Yangtze River Delta, the Pearl River Delta, the Southeast Coast, Sichuan and Chongqing, and the Central Plains. The oldest-old cluster at different scales shows a similar pattern, but local differences exist. The risk at the prefecture scale and county scale is greater than at the interprovincial scale; the sublevel can identify clusters that have not been identified at the previous level, especially the bordering areas of prefectures and counties; and more risk units and greater relative risk are found in urban areas than in rural areas. Conclusions: The results emphasized that spatial scan statistics can be used to estimate the spatial clusters of the oldest-old people. The detection of these clusters might be highly useful in the surveillance of the ageing phenomenon, thus helping local public health authorities measure the population burden at all locations, identifying geographical areas that require more attention, and evaluating the impacts of intervention programs. [ABSTRACT FROM AUTHOR]

Tourist congestion at hot spots has been a major management concern for UNESCO World Heritage Sites and other iconic protected areas. A growing number of heritage sites employ technologies, such as cameras and electronic ticket-checking systems, to monitor user levels, but data collected by these monitoring technologies are often under-utilized. In this study, we illustrated how to integrate data from hot spots by camera-captured monitoring and entrance counts to manage use levels at a World Heritage Site in Southeastern China. 6,930 photos of a congestion hotspot (scenic outlook on a trail) were collected within the park at a 10-minute interval over 105 days from January to November 2017. The entrance counts were used to predict daily average and maximum use level at the hotspots. Results showed that the average use level at the congestion hotspot did not exceed the use limit mandated by the park administration agency. However, from 9:20 am to 12:00 pm, the use level at hotspots exceeded visitor preferred use level. Visitor use level was significantly higher at the hotspot during a major Chinese “Golden Week”. The daily entrance counts significantly predicted the average and maximum use level at the hotspot. Based on our findings, park managers can achieve the management goals by permitting the corresponding number of visitors passing the entrances. The gap manifested the complexities in visitor capacity management at high-use World Heritage Sites and other protected areas and calls for innovative monitoring and management strategies. [ABSTRACT FROM AUTHOR]

Recent years have seen tremendous advances in the scientific study of networks, as more and larger data sets of relationships among nodes have become available in many different fields. This has led to pathbreaking discoveries of near-universal network behavior over time, including the principle of preferential attachment and the emergence of scaling in complex networks. Missing from the set of network analysis methods to date is a measure that describes for each node how its relationship (or links) with other nodes changes from one period to the next. Conventional measures of network change for the most part show how the degrees of a node change; these are scalar comparisons. Our contribution is to use, for the first time, the cosine similarity to capture not just the change in degrees of a node but its relationship to other nodes. These are vector (or matrix)-based comparisons, rather than scalar, and we refer to them as “rewiring” coefficients. We apply this measure to three different networks over time to show the differences in the two types of measures. In general, bigger increases in our rewiring measure are associated with larger increases in network density, but this is not always the case. [ABSTRACT FROM AUTHOR]

We describe a network-based method to obtain a subset of representative variables from clinical data of subjects of the second Singapore Longitudinal Aging Study (SLAS-2), while preserving to a good extent the predictive performance of the full set with regards to a multi-faceted index of successful aging, SAGE. To examine differences in predictive performance of high-degree nodes (“hubs”) and high-centrality ones (“cores”), we implement four subsetting strategies (two degree-based, two centrality-based) and obtain four surrogate sets of variables, which we use as input features for machine learning models to predict the SAGE index of subjects. All four models have variables belonging to the physical, cardiovascular, cognitive and immunological domains among their fifteen most important predictors. A fifth domain (leisure-time activities, LTA) is also present in some form. From a comparison of the surrogate sets’ size and predictive performance, a centrality-based approach (selection of the most central variable-nodes within each cluster) yielded the smallest-sized surrogate set, while having high prediction accuracy (measured by its model’s area-under-curve, AUC) in comparison to its analogous degree-based strategy (selection of the highest-degree nodes per cluster). Inclusion of the next most-central variables yielded negligible changes in predictive performance while more than doubling the surrogate set size. The centrality-based approach thus yields a surrogate set which offers a good balance between number of variables and prediction performance, and can act as a representative subset of the SLAS-2 clinical dataset. [ABSTRACT FROM AUTHOR]

Canine circovirus (CanineCV) was detected, together with canine parvovirus (CPV), in samples from an outbreak of fatal gastroenteritis in dogs in Argentina. We obtained the full-length genome of this recently discovered virus by overlapping PCR, designated strain UBA-Baires. Sequence analysis revealed a highly conserved genome but also showed several unique mutations in amino acids from the capsid protein that have not been previously reported. Phylogenetic analysis shows that this strain is more closely related to European strains than to viruses detected in North America or Asia. Although the pathogenic role of CanineCV in dogs is still unclear, this study highlights the importance of CanineCV as a coinfecting virus in disease development. To our knowledge, this is the first report of the involvement of CanineCV in severe clinical disease in dogs in South America. Our results expand our information on the geographical extent of this virus and contribute to the understanding of its role in disease. [ABSTRACT FROM AUTHOR]

Background: Network Scale Up (NSU) is a promising tool for size estimation of sensitive issues. In this study we investigated the important methodological considerations to employ this method for estimating behaviors, such as abortion, which happens in a particular age-gender group. Methods: We recruited 1250 males and 1250 females aged 18 to 50. Abortion rate was calculated through direct question and NSU methodology. The NSU was applied on three sub-samples (male, female and aggregate). Integrating replies to 25 reference groups, we estimated the network size (C) of respondents and its age-gender structure. To calculate the part of network that is subject to abortion, we compared two approaches: proportional and data based. The Visibility Factor (VF) was calculated through 222 females who had abortion. Direct estimate was considered as gold standard. Results: Using C’s derived from proportional method, the Relative Bias (RB) in the male and female samples was 33% and 84%. Applying the data-based C’s, the RB in the gender-specific and aggregate samples was 5% and 2%. Conclusion: The proportional method overestimates the prevalence. The data-based method to calculate the C is superior. The determination of the age-sex distribution of the network and the specific VF is essential. [ABSTRACT FROM AUTHOR]

Hand, foot, and mouth disease (HFMD) is well recognized as one of the major threats to children's health globally. The increasing complexity of the etiology of HFMD still challenges disease control in China. There is little surveillance of the molecular epidemiological characteristics of the enteroviruses (EVs) that cause HFMD in Neijiang city or the Sichuan Basin area in Southwest China. In this study, demographic and epidemiological information for 14,928 probable HFMD cases was extracted and analyzed to describe the epidemic features of HFMD in Neijiang city from Jan 2010 to Dec 2017. The swab samples of select probable HFMD cases from 2012 to 2017 were tested by reverse transcription (RT) real-time PCR to identify the serotype distribution of EVs, and 110 randomly selected RT-real-time PCR positive samples were then amplified and analyzed for the VP1 or VP4 regions of EVs to further analyze the phylogenetic characteristics of the circulating strains in this area. The eight-year average annual incidence was 49.82 per 100,000 in Neijiang. The incidence rates varied between 19.51 and 70.73 per 100,000, demonstrating peaks of incidence in even-number years (2012, 2014 and 2016). The median age of the probable cases was 27 months and the interquartile range (25th to 75th percentile) of ages for the probable HFMD cases was between 14 and 42 months. The male-to-female ratio of the probable HFMD cases was 1.47:1, and scattered children were the major population classification (81.7%). Two epidemic peaks were observed: one major peak between April and July and the other lesser peak between October and December. Of 6513 probable cases tested with RT-real-time PCR, 4015 (61.6%) were positive for enterovirus with the serotype distribution as follows: EV71+, 30.1% (n = 1210); CV-A16+, 28.7% (n = 1154) and a sole pan-enterovirus+, 41.1% (n = 1651). A total of 91 cases (82.7%, 91/110) were successfully amplified and underwent phylogenetic analysis: all EV71+ cases were C4a serotype (n = 23/30); all CV-A16+ cases were B2b serotype (n = 24/30); of 42 sole pan-enterovirus+ samples, 20 were CV-A6, 14 were CV-A10 and the rest within this group were CV-A4 (n = 4), CV-A8 (n = 2), CV-A9 (n = 1) and CV-B3 (n = 1). Our findings provide important evidence that aids the improvement of strategies for vaccination against HFMD and comprehensive disease control in China. [ABSTRACT FROM AUTHOR]

Accurately predicting reference evapotranspiration (ET0) with limited climatic data is crucial for irrigation scheduling design and agricultural water management. This study evaluated eight machine learning models in four categories, i.e. neuron-based (MLP, GRNN and ANFIS), kernel-based (SVM, KNEA), tree-based (M5Tree, XGBoost) and curve-based (MARS) models, for predicting daily ET0 with maximum/maximum temperature and precipitation data during 2001–2015 from 14 stations in various climatic regions of China, i.e., arid desert of northwest China (NWC), semi-arid steppe of Inner Mongolia (IM), Qinghai-Tibetan Plateau (QTP), (semi-)humid cold-temperate northeast China (NEC), semi-humid warm-temperate north China (NC), humid subtropical central China (CC) and humid tropical south China (SC). The results showed machine learning models using only temperature data obtained satisfactory daily ET0 estimates (on average R2 = 0.829, RMSE = 0.718 mm day−1, NRMSE = 0.250 and MAE = 0.508 mm day−1). The prediction accuracy was improved by 7.6% across China when information of precipitation was further considered, particularly in (sub)tropical humid regions (by 9.7% in CC and 12.4% in SC). The kernel-based SVM, KNEA and curve-based MARS models generally outperformed the others in terms of prediction accuracy, with the best performance by KNEA in NWC and IM, by SVM in QTP, CC and SC, and very similar performance by them in NEC and NC. SVM (1.9%), MLP (2.0%), MARS (2.6%) and KNEA (6.4%) showed relatively small average increases in RMSE during testing compared with training RMSE. SVM is highly recommended for predicting daily ET0 across China in light of best accuracy and stability, while KNEA and MARS are also promising powerful models. [ABSTRACT FROM AUTHOR]

A new genus and species of non-hadrosaurid hadrosauroid, Gobihadros mongoliensis, is described from a virtually complete and undeformed skull and postcranial skeleton, as well as extensive referred material, collected from the Baynshire Formation (Cenomanian-Santonian) of the central and eastern Gobi Desert, Mongolia. Gobihadros mongoliensis is the first non-hadrosaurid hadrosauroid from the Late Cretaceous of central Asia known from a complete, articulated skull and skeleton. The material reveals the skeletal anatomy of a proximate sister taxon to Hadrosauridae in remarkable detail. Gobihadros is similar to Bactrosaurus johnsoni and Gilmoreosaurus mongoliensis, but can be distinguished from them in several autapomorphic traits, including the maximum number (three) of functional dentary teeth per tooth position, a premaxillary oral margin with a ‘double-layer morphology’, and a sigmoidal dorsal outline of the ilium with a well-developed, fan-shaped posterior process. All of these characters in Gobihadros are inferred to be convergent in Hadrosauridae. Phylogenetic analysis positions Gobihadros mongoliensis as a Bactrosaurus-grade hadrosauromorph hadrosauroid. Its relationship with Maastrichtian hadrosaurids from Asia (e.g., Saurolophus angustirostris, Kerberosaurus manakini, Wulagasaurus dongi, Kundurosaurus nagornyi) are sufficiently distant to indicate that these latter taxa owe their distribution to migration from North America across Beringia, rather than having a common Asian origin with Go. mongoliensis. [ABSTRACT FROM AUTHOR]

Bemisia tabaci is one of the most threatening pests in agriculture, causing significant losses to many important crops on a global scale. The dramatic increase and availability of sequence data for B. tabaci species complex and its bacterial endosymbionts is critical for developing emerging sustainable pest management strategies which are based on pinpointing the global diversity of this important pest and its bacterial endosymbionts. To unravel the global genetic diversity of B. tabaci species complex focusing on its associated endosymbionts, along with Israeli whitefly populations collected in this study, we combined available sequences in databases, resulting in a total of 4,253 mitochondrial cytochrome oxidase I (mtCOI) sequences from 82 countries and 1,226 16S/23S rRNA endosymbiont sequences from 32 countries that were analyzed. Using Bayesian phylogenetic analysis, we identified two new B. tabaci groups within the species complex and described the global distribution of endosymbionts within this complex. Our analyses revealed complex divergence of the different endosymbiont sequences within the species complex, with overall one Hamiltonella, two Porteria (P1 and P2), two Arsenophonus (A1 and A2), two Wolbachia (super-groups O and B), four Cardinium (C1-C4) and three Rickettsia (R1-R3) groups were identified. Our comprehensive analysis provides an updated important resource for this globally important pest and its secondary symbionts, which have been a major subject for research in last three decades. [ABSTRACT FROM AUTHOR]

This study examines how negative shocks due to, for example, natural disasters propagate through supply chains. We apply a simulation technique to actual supply chain data covering most Japanese firms. To investigate the property of the propagation in the network, we test different types of artificial negative shocks. We find that, first, network structures severely affect the speed of propagation in the short run, and the total loss in the long run. The scale-free nature of the actual supply-chain network—that is, the power-law degree distribution—leads to faster propagation. Second, more intensive damages—that is, more damages suffered by fewer firms—result in faster propagation than extensive damages of the same total size. Third, the actual supply-chain network has innate robustness that comes from substitutability of supplies. If the supply-chain network has severe substitutability, the propagation of negative shocks becomes substantially large. Fourth, direct damages in urban regions promote faster propagation than those in rural regions. Fifth, different sectoral damages show significant differences in the speed of propagation. Finally, we check the indirect damage triggered by a single firm’s loss: 9.7% of all firms contribute to significant loss, and this loss accounts for more than 10% of the damage to the entire production. The simulations conspicuously show that different direct damages, even if they have the same total magnitude of damages, can generate considerably different damages because of the structure of the supply-chain network. [ABSTRACT FROM AUTHOR]