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With the increase of the camera resolution, the number of pixels contained in froth image is increased, which brings many challenges to image segmentation. Froth size and distribution are the important index in froth flotation. The segmentation of froth images is always a problem in building flotation model. In segmenting froth images, Otsu method is usually used to get a binary image for classification of froth images, this method can get a satisfactory segmentation result. However, each gray level is required to calculate each of the between-class variance, it takes a longer time in froth images with a large number of pixels. To solve this problem, an improved method is proposed in this paper. Most froth images have the pixel distribution characteristic that the gray histogram curve is a sawtooth shape. The proposed method uses polynomial to fit the curve of gray histogram and takes the characteristic of gray histogram's valley into consideration in Otsu method. Two performance comparison methods are introduced and used. Experimental comparison between Otsu method and the proposed method shows that the proposed method has a satisfactory image segmentation with a low computing time. [ABSTRACT FROM AUTHOR]

Soil is one of the most complex systems on Earth, functioning at the interface between the lithosphere, biosphere, hydrosphere, and atmosphere and generating a multitude of functions. Moreover, soil constitutes the belowground environment from which plants capture water and nutrients. Despite their great importance, soil properties are often not sufficiently considered in other disciplines, especially in spatial studies of plant distributions. Most soil properties are available as point data and, to be used in spatial analyses, need to be generalised over entire regions (i.e. digital soil mapping). Three categories of statistical approaches can be used for such purpose: geostatistical approaches (GSA), predictive-statistical approaches (PSA), and hybrid approaches (HA) that combine the two previous ones. How then to choose the best approach in a given soil study context? Does it depend on the soil properties to be spatialized, the study area’s characteristics, and/or the availability of soil data? The main aims of this study was to review the use of these three approaches to derive maps of soil properties in relation to the soil parameters, the study area characteristics, and the number of soil samples. We evidenced that the approaches that tend to show the best performance for spatializing soil properties were not necessarily the ones most used in practice. Although PSA was the most widely used, it tended to be outperformed by HA in many cases, but the latter was far less used. However, as the study settings were not always properly described and not all situations were represented in the set of papers analysed, more comparative studies would be needed across a wider range of regions, soil properties, and spatial scales to provide robust conclusions on the best spatialization methods in a specific context. [ABSTRACT FROM AUTHOR]

Analysis of functional magnetic resonance imaging (fMRI) data has revealed that brain regions can be grouped into functional brain networks (fBNs) or communities. A community in fMRI analysis signifies a group of brain regions coupled functionally with one another. In neuroimaging, functional connectivity (FC) measure can be utilized to quantify such functionally connected regions for disease diagnosis and hence, signifies the need of devising novel FC estimation methods. In this paper, we propose a novel method of learning FC by constraining its rank and the sum of non-zero coefficients. The underlying idea is that fBNs are sparse and can be embedded in a relatively lower dimension space. In addition, we propose to extract overlapping networks. In many instances, communities are characterized as combinations of disjoint brain regions, although recent studies indicate that brain regions may participate in more than one community. In this paper, large-scale overlapping fBNs are identified on resting state fMRI data by employing non-negative matrix factorization. Our findings support the existence of overlapping brain networks. [ABSTRACT FROM AUTHOR]

In this paper, we study the limit of compactness which is a graph index originally introduced for measuring structural characteristics of hypermedia. Applying compactness to large scale small-world graphs (Mehler, 2008) observed its limit behaviour to be equal 1. The striking question concerning this finding was whether this limit behaviour resulted from the specifics of small-world graphs or was simply an artefact. In this paper, we determine the necessary and sufficient conditions for any sequence of connected graphs resulting in a limit value of CB = 1 which can be generalized with some consideration for the case of disconnected graph classes (Theorem 3). This result can be applied to many well-known classes of connected graphs. Here, we illustrate it by considering four examples. In fact, our proof-theoretical approach allows for quickly obtaining the limit value of compactness for many graph classes sparing computational costs. [ABSTRACT FROM AUTHOR]

This paper presents vehicle-to-vehicle telescreen (VVT) and a multicast scheme to disseminate digital signage multimedia services to vehicular ad hoc networks (VANETs). Multimedia dissemination in VANETs is challenging because of the high packet losses (PLs), delays and longer disconnection times, which degrade the network quality of service (QoS) and user quality of experience (QoE). To reduce the PLs and delays, most existing multimedia multicast schemes in VANETs primarily select routes based on longer route expiration times (RET) or lowest path delays. The RET-based schemes suffer less from PLs when there are fewer active multicastings in the network. When the number of active multicastings increases, delay-based schemes suffer less from PLs comparatively. This tradeoff implies to design an adaptive mechanism by mutually complementing the RET-based and delay-based schemes to reduce PLs and delays. In this paper, we propose a multicriteria adaptive opportunistic treecast routing protocol (MAOTRP), which adapts the route selection mechanism according to active multicastings for efficient multimedia dissemination in VVT. The MAOTRP adjusts the weights of route selection parameters, including RET and delays, by considering their contribution in improving packet delivery ratio. MAOTRP extends a tree-based multicast protocol to provide robustness through alternate routes for link failures to reduce PLs. Through several experimental evaluations, we show that the proposed dissemination scheme improves QoS and QoE, and reduces the average disconnection time. [ABSTRACT FROM AUTHOR]

The author talks about several rules that a person can follow to develop good reading habits in graduate school and beyond. Topics discussed include the importance of developing the habit of reading on a daily basis; the need to develop comprehension skills; and the need to study the history of one's discipline. Also mentioned are the importance of creating a list of relevant journals, the need to read books, and the benefits of using a reference manager.

Wind and temperature data are important parameters in aircraft performance studies. The lack of accurate measurements of these parameters forces researchers to rely on numerical weather prediction models, which are often filtered for a larger area with decreased local accuracy. Aircraft, however, also transmit information related to weather conditions, in response to interrogation by air traffic controller surveillance radars. Although not intended for this purpose, aircraft surveillance data contains information that can be used for weather models. This paper presents a method that can be used to reconstruct a weather field from surveillance data that can be received with a simple 1090 MHz receiver. Throughout the paper, we answer two main research questions: how to accurately infer wind and temperature from aircraft surveillance data, and how to reconstruct a real-time weather grid efficiently. We consider aircraft as moving sensors that measure wind and temperature conditions indirectly at different locations and flight levels. To address the first question, aircraft barometric altitude, ground velocity, and airspeed are decoded from down-linked surveillance data. Then, temperature and wind observations are computed based on aeronautical speed conversion equations. To address the second question, we propose a novel Meteo-Particle (MP) model for constructing the wind and temperature fields. Short-term local prediction is also possible by employing a predictor layer. Using an unseen observation test dataset, we are able to validate that the mean absolute errors of inferred wind and temperature using MP model are 67% and 26% less than using the interpolated model based on GFS reanalysis data. [ABSTRACT FROM AUTHOR]

This paper describes and presents a stable and reliable set of stereo-photogrammetric normative data for global and spino-pelvic sagittal alignment, as a proven reference system for evaluating/measuring a fully unconstrained natural upright neutral standing attitude in a young healthy adult population. The methodological features described in this article will enable future studies to replicate and/or directly compare a wide range of different postural tests and/or sagittal alignment assessment procedures including the study of sagittal spine shape variations occurring during gait performance. To date, the quantitative evaluation of adult spinal deformity (ASD) has been mainly confined to the X-ray imaging approach and, more recently, to 3D X-ray reconstruction. Within the existing evaluation framework an opportunity exists for an additional approach: a quantitative evaluation procedure which is easy, accurate, relatively speedy and non-ionising, in order to monitor and track the progress of patients in the areas of both surgical and non-surgical treatment. The resources and methodology described in this paper have been proven to meet all these criteria. They have enabled full 3D posture (including 3D spine shape and sagittal alignment of the skeleton) to be consistently and successfully measured in adult volunteers. All the measurement/evaluation procedures and outcomes carried out were based entirely on the new non-ionising 3D opto-electronic stereo-photogrammetric approach described in this article. The protocol for this methodology was based on a standard set of 27 pre-selected anatomical “landmarks” on the human body, providing standard reference points for observation and measurement. A total of 124 healthy subjects were successfully assessed and, for each subject, 27 individual markers were applied to the corresponding locations on his/her body. Statistical tests to investigate gender differences were also carried out. Descriptive statistics are provided for all 15 of the spino-pelvic parameters under consideration. Results indicated significant differences between genders in five sets of parameters: Kyphosis tilt, Head tilt, Pelvic tilt, Spino-pelvic angle and T1-pelvic angle. The data also demonstrate a high degree of congruity with results obtained using the X-ray method, as evidenced by the existing literature in the field. In summary, the current study presents a new stereo-photogrammetric opto-electronic technology which can be used successfully for ASD evaluation and introduces a comprehensive set of normative data analogous to those proposed in X-ray analysis for sagittal spino-pelvic and total body alignment. [ABSTRACT FROM AUTHOR]

Analyzing and predicting the trend of price fluctuation has been receiving more and more attention, as price risk has become the focus of risk control research in heating oil futures market. A novel time series prediction model combined with the complex network method is put forward in the paper. First of all, this paper counts the cumulative time interval of different nodes in the network, and fits its growth trend with the Fourier model. Then a novel price fluctuation prediction model is established based on the effective information such as some topology properties extracted from the network. The results show that the Fourier model can predict the emergence time of new nodes in the next stage, and the established price fluctuation prediction model can infer the names of nodes in the prediction interval, so as to determine the forward-looking behavior of price evolution. Besides, liken to the NAR neural network, the prediction results obtained by the proposed method also show superiority, which has important theoretical value and academic significance for early warning and prediction of price behavior in the heating oil futures market. [ABSTRACT FROM AUTHOR]

Many species of Drosophila form conspecific pupa aggregations across the breeding sites. These aggregations could result from species-specific larval odor recognition. To test this hypothesis we used larval odors of D. melanogaster and D. pavani, two species that coexist in the nature. When stimulated by those odors, wild type and vestigial (vg) third-instar larvae of D. melanogaster pupated on conspecific larval odors, but individuals deficient in the expression of the odor co-receptor Orco randomly pupated across the substrate, indicating that in this species, olfaction plays a role in pupation site selection. Larvae are unable to learn but can smell, the Syn97CS and rut strains of D. melanogaster, did not respond to conspecific odors or D. pavani larval cues, and they randomly pupated across the substrate, suggesting that larval odor-based learning could influence the pupation site selection. Thus, Orco, Syn97CS and rut loci participated in the pupation site selection. When stimulated by conspecific and D. melanogaster larval cues, D. pavani larvae also pupated on conspecific odors. The larvae of D. gaucha, a sibling species of D. pavani, did not respond to D. melanogaster larval cues, pupating randomly across the substrate. In nature, D. gaucha is isolated from D. melanogaster. Interspecific hybrids, which result from crossing pavani female with gaucha males clumped their pupae similarly to D. pavani, but the behavior of gaucha female x pavani male hybrids was similar to D. gaucha parent. The two sibling species show substantial evolutionary divergence in organization and functioning of larval nervous system. D. melanogaster and D. pavani larvae extracted information about odor identities and the spatial location of congener and alien larvae to select pupation sites. We hypothesize that larval recognition contributes to the cohabitation of species with similar ecologies, thus aiding the organization and persistence of Drosophila species guilds in the wild. [ABSTRACT FROM AUTHOR]

Research collaborations are encouraged because a synergistic effect yielding good results often appears. However, creating and organizing a strong research group is a difficult task. One of the greatest concerns of an individual researcher is locating potential collaborators whose expertise complement his best. In this paper, we propose a method that makes link predictions in co-authorship networks, where topological features between authors such as Adamic/Adar, Common Neighbors, Jaccard's Coefficient, Preferential Attachment, Katzβ, and PropFlow may be good indicators of their future collaborations. Firstly, these topological features were systematically extracted from the network. Then, supervised models were used to learn the best weights associated with different topological features in deciding co-author relationships. Finally, we tested our models on the co-authorship networks in the research field of Coronary Artery Disease and obtained encouraging accuracy (the precision, recall, F1 score and AUC were, respectively, 0.696, 0.677, 0.671 and 0.742 for Logistic Regression, and respectively, 0.697, 0.678, 0.671 and 0.743 for SVM). This suggests that our models could be used to build and manage strong research groups. [ABSTRACT FROM AUTHOR]

Traffic congestion is an important problem both on an individual and on a societal level and much research has been done to explain and prevent their emergence. There are currently many systems which provide a reasonably good picture of actual road traffic by employing either fixed measurement points on highways or so called “floating car data” i.e. by using velocity and location data from roaming, networked, GPS enabled members of traffic. Some of these systems also offer forecasting of road conditions based on such historical data. To my knowledge there is as yet no system which offers advance notice on road conditions based on a signal which is guaranteed to occur in advance of these conditions and this is the novelty of this paper. Google Search intensity for the German word (i.e. traffic jam) peaks 2 hours ahead of the number of traffic jam reports as reported by the ADAC, a well known German automobile club and the largest of its kind in Europe. This is true both in the morning (7 am to 9 am) and in the evening (4 pm to 6 pm). The main result of this paper is then that after controlling for time-of-day and day-of-week effects we can still explain a significant additional portion of the variation of the number of traffic jam reports with Google Trends and we can thus explain well over 80% of the variation of road conditions using Google search activity. A one percent increase in Google stau searches implies a .4 percent increase of traffic jams. Our paper is a proof of concept that aggregate, timely delivered behavioural data can help fine tune modern societies and prompts for more research with better, more disaggregated data in order to also achieve practical solutions. [ABSTRACT FROM AUTHOR]

This paper introduces an optimal sizing algorithm for a hybrid renewable energy system using smart grid load management application based on the available generation. This algorithm aims to maximize the system energy production and meet the load demand with minimum cost and highest reliability. This system is formed by photovoltaic array, wind turbines, storage batteries, and diesel generator as a backup source of energy. Demand profile shaping as one of the smart grid applications is introduced in this paper using load shifting-based load priority. Particle swarm optimization is used in this algorithm to determine the optimum size of the system components. The results obtained from this algorithm are compared with those from the iterative optimization technique to assess the adequacy of the proposed algorithm. The study in this paper is performed in some of the remote areas in Saudi Arabia and can be expanded to any similar regions around the world. Numerous valuable results are extracted from this study that could help researchers and decision makers. [ABSTRACT FROM AUTHOR]

Setting: Research capacity is weakest in low and middle-income countries (LMICs) where operational research is highly relevant and needed. Structured Operational Research and Training Initiative (SORT IT) courses have been developed to train participants to conduct and publish operational research and influence policy and practice. Twenty courses were completed in Asia, Africa, Europe and the South Pacific between 2009 and 2014. Objectives: In the 20 completed SORT IT courses, to assess where the research was conducted, who was trained, who became facilitators in subsequent courses and course outcomes. Design: A cohort study of completed SORT IT courses Results: There were 236 participants (41% female) including 64 nationalities who conducted research in 59 countries, mostly from Asia and Africa (mean course duration = 9.7 months). Most participants (68%) were from government health programs and non-governmental agencies. A total of 213(90%) participants completed all milestones successfully with 41(19%) becoming subsequent course facilitators, 88% of whom were from LMICs. Of 228 manuscripts submitted to scientific journals, 197(86%) were either published or in press; in 86%, the principal investigator (first author) was a LMIC national. Papers were published in 23 scientific journals (impact factor 0.5–4.4) and covered 21 disease categories (median publication time = 5.7 months). Published papers (186) had 94,794 cumulative article views/downloads. Article views/downloads for immediate open access articles were double those from closed access journals. Conclusion: The SORT IT model has been effective in training personnel to produce relevant operational research in LMICs. It merits continued commitment and support for further scale-up and development. [ABSTRACT FROM AUTHOR]

Introduction: Translational research is central to international health policy, research and funding initiatives. Despite increasing use of the term, the translation of basic science discoveries into clinical practice is not straightforward. This systematic search and narrative synthesis aimed to examine factors enabling or hindering translational research from the perspective of basic and clinician scientists, a key stakeholder group in translational research, and to draw policy-relevant implications for organisations seeking to optimise translational research opportunities. Methods and Results: We searched SCOPUS and Web of Science from inception until April 2015 for papers reporting scientists’ views of the factors they perceive as enabling or hindering the conduct of translational research. We screened 8,295 papers from electronic database searches and 20 papers from hand searches and citation tracking, identifying 26 studies of qualitative, quantitative or mixed method designs. We used a narrative synthesis approach and identified the following themes: 1) differing concepts of translational research 2) research processes as a barrier to translational research; 3) perceived cultural divide between research and clinical care; 4) interdisciplinary collaboration as enabling translation research, but dependent on the quality of prior and current social relationships; 5) translational research as entrepreneurial science. Across all five themes, factors enabling or hindering translational research were largely shaped by wider social, organisational, and structural factors. Conclusion: To optimise translational research, policy could consider refining translational research models to better reflect scientists’ experiences, fostering greater collaboration and buy in from all types of scientists. Organisations could foster cultural change, ensuring that organisational practices and systems keep pace with the change in knowledge production brought about by the translational research agenda. [ABSTRACT FROM AUTHOR]

In the Bayesian Reinforcement Learning (BRL) setting, agents try to maximise the collected rewards while interacting with their environment while using some prior knowledge that is accessed beforehand. Many BRL algorithms have already been proposed, but the benchmarks used to compare them are only relevant for specific cases. The paper addresses this problem, and provides a new BRL comparison methodology along with the corresponding open source library. In this methodology, a comparison criterion that measures the performance of algorithms on large sets of Markov Decision Processes (MDPs) drawn from some probability distributions is defined. In order to enable the comparison of non-anytime algorithms, our methodology also includes a detailed analysis of the computation time requirement of each algorithm. Our library is released with all source code and documentation: it includes three test problems, each of which has two different prior distributions, and seven state-of-the-art RL algorithms. Finally, our library is illustrated by comparing all the available algorithms and the results are discussed. [ABSTRACT FROM AUTHOR]

Background: Distant metastasis or local recurrence after primary tumour resection remain a major clinical problem. The anaesthetic technique used during oncologic surgery is suggested to influence the metastatic process. While awaiting the results of ongoing randomised controlled trials (RCTs), we have analyzed the evidence regarding the influence of anaesthetic drugs on experimental tumour metastasis in animal studies. Methods: PubMed and Embase were searched until April 21st, 2015. Studies were included in the systematic review when they 1) assessed the effect of an anaesthetic drug used in clinical practice on the number or incidence of metastasis in animal models with experimental cancer, 2) included an appropriate control group, and 3) presented unique data. Results: 20 studies met the inclusion criteria (published between 1958–2010). Data on number of metastases could be retrieved from 17 studies. These studies described 41 independent comparisons, 33 of which could be included in the meta-analysis (MA). The incidence of metastases was studied in 3 unique papers. From these 3 papers, data on 7 independent comparisons could be extracted and included in the MA. Locally administered local anaesthetics appear to decrease the number of metastases (SMD -6.15 [-8.42; -3.88]), whereas general anaesthetics (RD: 0.136 [0.045, 0.226]), and more specifically volatile anaesthetics (SMD 0.54 [0.24; 0.84]), appear to increase the number and risk of metastases in animal models for cancer. Conclusions: Anaesthetics influence the number and incidence of metastases in experimental cancer models. Although more high quality experimental research is necessary, based on the currently available evidence from animal studies, there is no indication to suggest that locally administered local anaesthetics are harmful during surgery in cancer patients. Volatile anaesthetics, however, might increase metastasis in animal models and clinical trials investigating this possibly harmful effect should receive priority. The results of our systematic review in animal studies are broadly consistent with clinical reports that anaesthetic technique does seem to affect the tumour metastasis process. [ABSTRACT FROM AUTHOR]

13C metabolic flux analysis (13C-MFA) has been widely used to measure in vivo enzyme reaction rates (i.e., metabolic flux) in microorganisms. Mining the relationship between environmental and genetic factors and metabolic fluxes hidden in existing fluxomic data will lead to predictive models that can significantly accelerate flux quantification. In this paper, we present a web-based platform MFlux () that predicts the bacterial central metabolism via machine learning, leveraging data from approximately 100 13C-MFA papers on heterotrophic bacterial metabolisms. Three machine learning methods, namely Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Decision Tree, were employed to study the sophisticated relationship between influential factors and metabolic fluxes. We performed a grid search of the best parameter set for each algorithm and verified their performance through 10-fold cross validations. SVM yields the highest accuracy among all three algorithms. Further, we employed quadratic programming to adjust flux profiles to satisfy stoichiometric constraints. Multiple case studies have shown that MFlux can reasonably predict fluxomes as a function of bacterial species, substrate types, growth rate, oxygen conditions, and cultivation methods. Due to the interest of studying model organism under particular carbon sources, bias of fluxome in the dataset may limit the applicability of machine learning models. This problem can be resolved after more papers on 13C-MFA are published for non-model species. [ABSTRACT FROM AUTHOR]

The body of science evaluating the potential impacts of unconventional natural gas development (UNGD) has grown significantly in recent years, although many data gaps remain. Still, a broad empirical understanding of the impacts is beginning to emerge amidst a swell of research. The present categorical assessment provides an overview of the peer-reviewed scientific literature from 2009–2015 as it relates to the potential impacts of UNGD on public health, water quality, and air quality. We have categorized all available original research during this time period in an attempt to understand the weight and direction of the scientific literature. Our results indicate that at least 685 papers have been published in peer-reviewed scientific journals that are relevant to assessing the impacts of UNGD. 84% of public health studies contain findings that indicate public health hazards, elevated risks, or adverse health outcomes; 69% of water quality studies contain findings that indicate potential, positive association, or actual incidence of water contamination; and 87% of air quality studies contain findings that indicate elevated air pollutant emissions and/or atmospheric concentrations. This paper demonstrates that the weight of the findings in the scientific literature indicates hazards and elevated risks to human health as well as possible adverse health outcomes associated with UNGD. There are limitations to this type of assessment and it is only intended to provide a snapshot of the scientific knowledge based on the available literature. However, this work can be used to identify themes that lie in or across studies, to prioritize future research, and to provide an empirical foundation for policy decisions. [ABSTRACT FROM AUTHOR]

Ensembles are a well established machine learning paradigm, leading to accurate and robust models, predominantly applied to predictive modeling tasks. Ensemble models comprise a finite set of diverse predictive models whose combined output is expected to yield an improved predictive performance as compared to an individual model. In this paper, we propose a new method for learning ensembles of process-based models of dynamic systems. The process-based modeling paradigm employs domain-specific knowledge to automatically learn models of dynamic systems from time-series observational data. Previous work has shown that ensembles based on sampling observational data (i.e., bagging and boosting), significantly improve predictive performance of process-based models. However, this improvement comes at the cost of a substantial increase of the computational time needed for learning. To address this problem, the paper proposes a method that aims at efficiently learning ensembles of process-based models, while maintaining their accurate long-term predictive performance. This is achieved by constructing ensembles with sampling domain-specific knowledge instead of sampling data. We apply the proposed method to and evaluate its performance on a set of problems of automated predictive modeling in three lake ecosystems using a library of process-based knowledge for modeling population dynamics. The experimental results identify the optimal design decisions regarding the learning algorithm. The results also show that the proposed ensembles yield significantly more accurate predictions of population dynamics as compared to individual process-based models. Finally, while their predictive performance is comparable to the one of ensembles obtained with the state-of-the-art methods of bagging and boosting, they are substantially more efficient. [ABSTRACT FROM AUTHOR]

Background: Nigeria has one of the highest burdens of pneumococcal disease in the world, but accurate surveillance is lacking. Molecular detection of infectious pathogens in dried blood spots (DBS) is an ideal method for surveillance of infections in resource-limited settings because of its low cost, minimal blood volumes involved, and ease of storage at ambient temperature. Our study aim was to evaluate a Streptococcus pneumoniae real-time polymerase chain reaction (rt-PCR) assay on DBS from febrile Nigerian children on Whatman 903 and FTA filter papers, compared to the gold standard of culture. Methods: Between September 2011 to May 2015, blood was collected from children 5 years of age or under who presented to six hospital study sites throughout northern and central Nigeria with febrile illness, and inoculated into blood culture bottles or spotted onto Whatman 903 or FTA filter paper. Culture and rt-PCR were performed on all samples. Results: A total of 537 DBS specimens from 535 children were included in the study, of which 15 were culture-positive for S. pneumoniae. The rt-PCR assay detected S. pneumoniae in 12 DBS specimens (2.2%). One positive rt-PCR result was identified in a culture-negative specimen from a high-risk subject, and two positive rt-PCR results were negative on repeat testing. Six culture-confirmed cases of S. pneumoniae bacteremia were missed. Compared to culture, the overall sensitivities of Whatman 903 and FTA DBS for detection of S. pneumoniae were 57.1% (95% CI 18.4–90.1%) and 62.5% (95% CI 24.5–91.5%), respectively. Nonspecific amplification was noted in an additional 22 DBS (4.1%). Among these, six were positive for a non-S. pneumoniae pathogen on culture. Conclusions: Rt-PCR was able to detect S. pneumoniae from clinical DBS specimens, including from a culture-negative specimen. Our findings show promise of this approach as a surveillance diagnostic, but also raise important cautionary questions. Several DBS specimens were detected as S. pneumoniae by rt-PCR despite growth of a non-S. pneumoniae pathogen on culture. A precise definition of what constitutes a positive result is required to avoid falsely over-identifying specimens. [ABSTRACT FROM AUTHOR]

For high-dimensional and massive data sets, traditional centralized gene expression programming (GEP) or improved algorithms lead to increased run-time and decreased prediction accuracy. To solve this problem, this paper proposes a new improved algorithm called distributed function mining for gene expression programming based on fast reduction (DFMGEP-FR). In DFMGEP-FR, fast attribution reduction in binary search algorithms (FAR-BSA) is proposed to quickly find the optimal attribution set, and the function consistency replacement algorithm is given to solve integration of the local function model. Thorough comparative experiments for DFMGEP-FR, centralized GEP and the parallel gene expression programming algorithm based on simulated annealing (parallel GEPSA) are included in this paper. For the waveform, mushroom, connect-4 and musk datasets, the comparative results show that the average time-consumption of DFMGEP-FR drops by 89.09%%, 88.85%, 85.79% and 93.06%, respectively, in contrast to centralized GEP and by 12.5%, 8.42%, 9.62% and 13.75%, respectively, compared with parallel GEPSA. Six well-studied UCI test data sets demonstrate the efficiency and capability of our proposed DFMGEP-FR algorithm for distributed function mining. [ABSTRACT FROM AUTHOR]

The loss of talented women from senior academic positions has partly resulted from a lower number of published papers and the accompanying reduced visibility of female compared to male scientists. The reasons for these gender-differences in authorship is unclear. One potential reason is a bias in the editorial and review process of scientific journals. We investigated whether patterns of authorship and editorial outcome were biased according to gender and geographic location in the Journal of Evolutionary Biology. Such potential bias may contribute to inequality in the field. We found patterns of gender differences in authorship, but this was unrelated to the editorial decision of whether to publish the manuscript. Female first-authors (the lead role) were six times less likely to be named as the corresponding author than male first-authors, and female first-authors were more likely to be displaced as corresponding authors by female co-authors than were male first-authors. We found an under-representation of female first- and last-authors compared to baseline populations of members of the European Society for Evolutionary Biology (which publishes the Journal of Evolutionary Biology) and of Evolutionary Biology faculty at the world top-10 universities for the Life Sciences. Also, manuscripts from Asia were five times more likely to be rejected on the final decision, independent of gender. Overall our results suggest that the peer review processes we investigated at the Journal of Evolutionary Biology are predominately gender-neutral, but not neutral to geographic location. Editorial gender-bias is thus unlikely to be a contributing factor to differences in authorship in this journal. [ABSTRACT FROM AUTHOR]

With rapid economic growth since the 21st century, cities in China have experienced considerable economic and social reconstruction. Driven by rapid industrialization, urban spatial structures are undergoing evolution and change. Therefore, this paper analyzes the processes and mechanisms associated with the evolution of the commercial spatial structure in Guangzhou after the financial crisis in 2008 based on both theoretical and empirical analyses. We use a Dirichlet multinomial regression (DMR) model to extract latent semantic information and determine urban functional areas from global positioning system (GPS) and point-of-interest (POI) data collected in Guangzhou in 2009 and 2013. In addition, we use movement patterns and POI data to identify the evolution of Guangzhou's commercial zones from 2009 to 2013. The results show that the urban commercial structure in Guangzhou gradually changed from a single-center model to a multi-center model with dispersed clusters and that the distribution of the entire spatial structure changed. Meanwhile, Guangzhou’s commercial structure not only varied over time but also exhibited specific geographical features. This paper demonstrates that the proposed method can clearly identify the boundary of the commercial area in Guangzhou and provides a valid spatial-temporal model of change in the city. Moreover, this study not only expounds the future development trends of the urban spatial structure in Guangzhou from a microcosmic perspective but also provides a scientific basis for clarifying the spatial locations and development advantages of urban functions within the city. [ABSTRACT FROM AUTHOR]

Since 1997 a considerable effort has been spent on the study of the swap (switch) Markov chains on graphic degree sequences. All of these results assume some kind of regularity in the corresponding degree sequences. Recently, Greenhill and Sfragara published a breakthrough paper about irregular normal and directed degree sequences for which rapid mixing of the swap Markov chain is proved. In this paper we present two groups of results. An example from the first group is the following theorem: let be a directed degree sequence on n vertices. Denote by Δ the maximum value among all in- and out-degrees and denote by the number of edges in the realization. Assume furthermore that . Then the swap Markov chain on the realizations of is rapidly mixing. This result is a slight improvement on one of the results of Greenhill and Sfragara. An example from the second group is the following: let d be a bipartite degree sequence on the vertex set U ⊎ V, and let 0 < c1 ≤ c2 < |U| and 0 < d1 ≤ d2 < |V| be integers, where c1 ≤ d(v) ≤ c2: ∀v ∈ V and d1 ≤ d(u) ≤ d2: ∀u ∈ U. Furthermore assume that (c2 − c1 − 1)(d2 − d1 − 1) < max{c1(|V| − d2), d1(|U| − c2)}. Then the swap Markov chain on the realizations of d is rapidly mixing. A straightforward application of this latter result shows that when a random bipartite or directed graph is generated under the Erdős—Rényi G(n, p) model with mild assumptions on n and p then the degree sequence of the generated graph has, with high probability, a rapidly mixing swap Markov chain on its realizations. [ABSTRACT FROM AUTHOR]

Background: Over the past two decades, the focus of mental health care has shifted from institutionalisation to community-based programs and short hospital stays. This change means that there is an increased role for caregivers, mostly family members, in managing persons with mental illness. Although there is evidence to support the benefits of deinstitutionalisation of mental health care, there are also indications of substantial burden experienced by caregivers; the evidence of which is limited in sub-Saharan Africa. However, knowledge of the nature and extent of this burden can inform the planning of mental health services that will not only benefit patients, but also caregivers and households. Objective: To systematically review the available evidence on the economic burden of severe mental illness on primary family caregivers in sub-Saharan Africa. Methods: A comprehensive search was conducted in Pubmed, CINAHL, Econlit and Web of Science with no date limitations up to September 2016 using keywords such as "burden", "cost of illness" and "economic burden" to identify relevant published literature. Articles were appraised using a standardised data extraction tool covering themes such as physical, psychological and socioeconomic burden. Results: Seven papers were included in the review. Caregivers were mostly family members with a mean age of 46.34, female and unemployed. Five out of seven studies (71%) estimated the full economic burden of severe mental illness on caregivers. The remainder of studies just described the caregiver burden. All seven papers reported moderate to severe caregiver burden characterised by financial constraint, productivity loss and lost employment. The caregiver’s level of income and employment status, severity of patient's condition and duration of mental illness were reported to negatively affect the economic burden experienced by caregivers. Conclusion: There is paucity of studies reporting the burden of severe mental illness on caregivers in sub-Saharan Africa. Further research is needed to present the nature and extent of this burden to inform service planning and policymaking. [ABSTRACT FROM AUTHOR]

The ensemble method random forests has become a popular classification tool in bioinformatics and related fields. The out-of-bag error is an error estimation technique often used to evaluate the accuracy of a random forest and to select appropriate values for tuning parameters, such as the number of candidate predictors that are randomly drawn for a split, referred to as mtry. However, for binary classification problems with metric predictors it has been shown that the out-of-bag error can overestimate the true prediction error depending on the choices of random forests parameters. Based on simulated and real data this paper aims to identify settings for which this overestimation is likely. It is, moreover, questionable whether the out-of-bag error can be used in classification tasks for selecting tuning parameters like mtry, because the overestimation is seen to depend on the parameter mtry. The simulation-based and real-data based studies with metric predictor variables performed in this paper show that the overestimation is largest in balanced settings and in settings with few observations, a large number of predictor variables, small correlations between predictors and weak effects. There was hardly any impact of the overestimation on tuning parameter selection. However, although the prediction performance of random forests was not substantially affected when using the out-of-bag error for tuning parameter selection in the present studies, one cannot be sure that this applies to all future data. For settings with metric predictor variables it is therefore strongly recommended to use stratified subsampling with sampling fractions that are proportional to the class sizes for both tuning parameter selection and error estimation in random forests. This yielded less biased estimates of the true prediction error. In unbalanced settings, in which there is a strong interest in predicting observations from the smaller classes well, sampling the same number of observations from each class is a promising alternative. [ABSTRACT FROM AUTHOR]

This paper investigates the consequences of ignoring the clustered data structure on allometric models. Clustered data, in the form of multiple trees sampled from multiple forest stands is commonly used to develop biomass allometric models. Of 102 reviewed papers published between 2012 and 2016 that reported biomass allometric models, 84 (82%) have used a clustered sampling design. However, in as many as 80% of these, the clustered data structure was ignored, potentially violating the independence assumption in ordinary least squares methods. The consequences of ignoring clustered data structure were empirically validated using two clustered biomass datasets (of 110 and 220 trees, with the cluster size of 5 and 10 trees respectively). We showed that when Intraclass Correlation Coefficient (ICC) was higher than zero, ignoring the clustered data structure returned underestimated standard errors, affecting further the confidence interval and t-test results. The underestimation level depended on ICC (which shows the variance proportion that was caused by the forest stand) and on cluster size (the number of trees sampled from one forest stand). We also showed that using first-order autocorrelation tests, such as the traditional Durbin-Watson statistic, to detect the autocorrelation due to clustered structure could be misleading as the test may show lack of autocorrelation even though ICC is different from zero. In conclusion, when ICC is higher than zero, ignoring the clustered data structure yields over-confident biomass predictions (due to underestimated confidence interval) and/or incorrect research conclusions (due to overestimated evidence against null hypothesis in t-test). Therefore, using a modelling approach that accounts for the hierarchical structure of the data is highly recommended when any form of clustering can be identified, even if the autocorrelation is not significant. [ABSTRACT FROM AUTHOR]

Outlier removal is a fundamental data processing task to ensure the quality of scanned point cloud data (PCD), which is becoming increasing important in industrial applications and reverse engineering. Acquired scanned PCD is usually noisy, sparse and temporarily incoherent. Thus the processing of scanned data is typically an ill-posed problem. In the paper, we present a simple and effective method based on two geometrical characteristics constraints to trim the noisy points. One of the geometrical characteristics is the local density information and another is the deviation from the local fitting plane. The local density based method provides a preprocessing step, which could remove those sparse outlier and isolated outlier. The non-isolated outlier removal in this paper depends on a local projection method, which placing those points onto objects. There is no doubt that the deviation of any point from the local fitting plane should be a criterion to reduce the noisy points. The experimental results demonstrate the ability to remove the noisy point from various man-made objects consisting of complex outlier. [ABSTRACT FROM AUTHOR]

Ceramics materials based on yttria stabilised tetragonal zirconia nanopowders (3Y-TZP) are widely used in the production fuel cells, oxygen sensors, refractories, etc. and intensively studied due to their outstanding mechanical and electrical properties. To obtain ceramics with specific properties different additives can be used. This allow for control over the features of a nanopowder’ structure and its consolidation during the sintering process. It is important examine in detail the initial sintering stage of tetragonal zirconia based nanopowders modified by third element additives. The present paper shows the impact of SiO2 and SnO2 additives and the influence of different methods of dopant addition (co-precipitation and mechanical mixing) on the kinetics of the initial sintering stage of tetragonal zirconia nanopowders. It demonstrates that sintering mechanism is changed by the addition of a small amount SiO2 and SnO2 by volume to the grain boundary diffusion in 3Y-TZP nanopowders obtained by the co-precipitation method. This change led to increased activation energy. This paper explores the reasons for the acceleration in the sintering process of nanopowders 3Y-TZP with SiO2 and SnO2 additives obtained by the mixing method. It shows that the sintering mechanism is the same as that of the initial 3Y-TZP powder obtained by the co-precipitation technique. The volume diffusion mechanism was the predominant mechanism at the initial sintering stage and was accompanied by a decrease in the activation energy of the sintering process. [ABSTRACT FROM AUTHOR]

To become integrated into an ant society, myrmecophilous parasites must overcome both the defenses and the communication system of their hosts. Some aleocharine staphylinid beetles employ chemical and tactile strategies to invade colonies, where they later consume ant brood and participate in parasitic trophallaxis with host ants. By producing compounds that both appease their hosts and stimulate adoption, the beetles are able to live in and deposit their own eggs in the well defended ant nest. In the current paper, previous findings on the myrmecophilous behavior and morphological features of the staphylinid beetle Lomechusoides (formerly Lomechusa) strumosus are reviewed and re-evaluated. Hitherto unpublished results concerning the beetles’ ability to participate in the social food flow of their host ants are reported. Furthermore, we present an analysis and documentation of the behavioral interactions between beetles and host ants during the adoption process, and we report new histological and scanning electron microscopic analyses of the exocrine glands and morphological adaptations that underlie the myrmecophilous behavior of L. strumosus. The main features of L. strumosus are compared with those of the staphilinid myrmecophile Lomechusa (formerly Atemeles) pubicollis. The paper concludes with a description of the life trajectory of L. strumosus and presents a brief history and discussion of the hypotheses concerning the evolution of myrmecophily in L. strumosus and other highly adapted myrmecophilous parasites. [ABSTRACT FROM AUTHOR]

In this paper we demonstrate a potentiostat built with a single commercially available integrated circuit (IC) that does not require any external electronic components to perform electrochemical experiments. This is done using the capabilities of the Programmable System on a Chip (PSoC®) by Cypress Semiconductor, which integrates all of the necessary electrical components. This is in contrast to other recent papers that have developed potentiostats but require technical skills or specialized equipment to produce. This eliminates the process of having to make a printed circuit board and soldering on electronic components. To control the device, a graphical user interface (GUI) was developed in the python programming language. Python is open source, with a style that makes it easy to read and write programs, making it an ideal choice for open source projects. As the developed device is open source and based on a PSoC, modification to implement other electrochemical techniques is straightforward and only requires modest programming skills, but no expensive equipment or difficult techniques. The potentiostat developed here adds to the growing amount of open source laboratory equipment. To demonstrate the PSoC potentiostat in a wide range of applications, we performed cyclic voltammetry (to measure vitamin C concentration in orange juice), amperometry (to measure glucose with a glucose strip), and stripping voltammetry experiments (to measure lead in water). The device was able to perform all experiments and could accurately measure Vitamin C, glucose, and lead. [ABSTRACT FROM AUTHOR]

With the convergence of various broadcasting systems, the amount of content available in mobile terminals including IPTV has significantly increased. In this paper, we propose a system that enables users to schedule programs considering both section video clips and full video clips based on the user detection method with similar preference. And, since the system constituting the contents can be classified according to the program, the proposed method can store a program desired by the user, and thus create and schedule a kind of individual channel. Experimental results show that the proposed method has a higher prediction accuracy; this is accomplished by comparing existing channel recommendation methods with the program recommendation methods proposed in this paper. [ABSTRACT FROM AUTHOR]

Analyzing geological drilling hole images acquired by Axial View Panoramic Borehole Televiewer (APBT) is a key step to explore the geological structure in a geological exploration. Conventionally, the borehole images are examined by technicians, which is inefficient and subjective. In this paper, three dominant types of borehole-wall images on coal-rock mass structure, namely, border images, fracture images and intact rock mass images are mainly studied. The traditional image classification methods based on unified feature extraction algorithm and single classifier is not effect for the borehole images. Therefore, this paper proposes a novel two-stage classification approach to improve the classification performance of borehole images. In the first-stage classification, the border images are identified from three kinds of images based on texture features and gray-scale histograms features. For the remaining two types of images, in the second-stage classification, Gabor filter is first applied to segment the region of interest (ROI) (such as microfracture, absciss layer and horizontal cracks, etc.) and the central interference region. Then, using the same feature vector after eliminating the central interference region, fracture images are separated from intact rock mass images. We test our two-stage classification system with real borehole images. The results of experimental show that the two-stage classification method can effectively classify three major borehole-wall images with the correction rate of 95.55% in the first stage and 95% in the second stage. [ABSTRACT FROM AUTHOR]

Systematic reviews and/or meta-analyses generally provide the best evidence for medical research. Authors are recommended to use flow diagrams to present the review process, allowing for better understanding among readers. However, no studies as of yet have assessed the quality of flow diagrams in systematic review/meta-analyses. Our study aims to evaluate the quality of systematic review/meta-analyses over a period of ten years, by assessing the quality of the flow diagrams, and the correlation to the methodological quality. Two hundred articles of “systematic review” and/or “meta-analysis” from January 2004 to August 2015 were randomly retrieved in Pubmed to be assessed for the flow diagram and methodological qualities. The flow diagrams were evaluated using a 16-grade scale corresponding to the four stages of PRISMA flow diagram. It composes four parts: Identification, Screening, Eligibility and Inclusion. Of the 200 articles screened, 154 articles were included and were assessed with AMSTAR checklist. Among them, 78 articles (50.6%) had the flow diagram. Over ten years, the proportion of papers with flow diagram available had been increasing significantly with regression coefficient beta = 5.649 (p = 0.002). However, the improvement in quality of the flow diagram increased slightly but not significantly (regression coefficient beta = 0.177, p = 0.133). Our analysis showed high variation in the proportion of articles that reported flow diagram components. The lowest proportions were 1% for reporting methods of duplicates removal in screening phase, followed by 6% for manual search in identification phase, 22% for number of studies for each specific/subgroup analysis, 27% for number of articles retrieved from each database, and 31% for number of studies included in qualitative analysis. The flow diagram quality was correlated with the methodological quality with the Pearson’s coefficient r = 0.32 (p = 0.0039). Therefore, this review suggests that the reporting quality of flow diagram is less satisfactory, hence not maximizing the potential benefit of the flow diagrams. A guideline with standardized flow diagram is recommended to improve the quality of systematic reviews, and to enable better reader comprehension of the review process. [ABSTRACT FROM AUTHOR]

Context: In response to political and social factors over the last sixty years mental health systems internationally have endeavoured to transfer the delivery of care from hospitals into community settings. As a result, there has been increased emphasis on the need for better quality care planning and care coordination between hospital services, community services and patients and their informal carers. The aim of this systematic review of international research is to explore which interventions have proved more or less effective in promoting personalized, recovery oriented care planning and coordination for community mental health service users. Methods: A systematic meta-narrative review of research from 1990 to the present was undertaken. From an initial return of 3940 papers a total of 50 research articles fulfilled the inclusion criteria, including research from the UK, Australia and the USA. Findings: Three research traditions are identified consisting of (a) research that evaluates the effects of government policies on the organization, management and delivery of services; (b) evaluations of attempts to improve organizational and service delivery efficiency; (c) service-users and carers experiences of community mental health care coordination and planning and their involvement in research. The review found no seminal papers in terms of high citation rates, or papers that were consistently cited over time. The traditions of research in this topic area have formed reactively in response to frequent and often unpredictable policy changes, rather than proactively as a result of intrinsic academic or intellectual activity. This may explain the absence of seminal literature within the subject field. As a result, the research tradition within this specific area of mental health service delivery has a relatively short history, with no one dominant researcher or researchers, tradition or seminal studies amongst or across the three traditions identified. Conclusions: The research findings reviewed suggests a gap has existed internationally over several decades between policy aspirations and service level interventions aimed at improving personalised care planning and coordination and the realities of everyday practices and experiences of service users and carers. Substantial barriers to involvement are created through poor information exchange and insufficient opportunities for care negotiation. [ABSTRACT FROM AUTHOR]

Background: Clubfeet are commonly treated using the Ponseti method. This method involves weekly manipulation and casting which gradually corrects the position of the foot. However, the reasons for following a weekly interval are not clear. Question / Purpose: The aim is to investigate the influence of the cast change interval on treatment outcomes in the Ponseti method. Methods: We performed a systematic review of comparative studies in which the cast change interval was varied. Scientific databases were searched for relevant publications, screened for eligibility and assessed for a risk of bias. A 'best evidence' synthesis tool was used to synthesize the results of the included studies and draw conclusions from relevant clinical outcomes. Results: Nine papers matched the inclusion criteria, which provided data of 587 subjects who had a total of 870 clubfeet. There is strong evidence for a positive relation between cast change interval and treatment duration. However, there is no evidence for any relation between the cast change interval and the required number of casts, tenotomy rate, required surgery or failure rate. Conclusions: Accelerated versions are as effective and safe as the traditional Ponseti method. However, more research is needed to assess the long-term results and to identify an optimal cast change interval. [ABSTRACT FROM AUTHOR]

Background: Due to the disease’s progressive nature, advance care planning (ACP) is recommended for people with early stage dementia. General practitioners (GPs) should initiate ACP because of their longstanding relationships with their patients and their early involvement with the disease, however ACP is seldom applied. Aim: To determine the barriers and facilitators faced by GPs related to ACP with people with dementia. Data sources: We systematically searched the relevant databases for papers published between January 1995 and December 2016, using the terms: primary healthcare, GP, dementia, and ACP. We conducted a systematic integrative review following Whittemore and Knafl’s method. Papers containing empirical data about GP barriers and/or facilitators regarding ACP for people with dementia were included. We evaluated quality using the Mixed-Method-Appraisal-Tool and analyzed data using qualitative content analysis. Results: Ten qualitative, five quantitative, and one mixed-method paper revealed four themes: timely initiation of ACP, stakeholder engagement, important aspects of ACP the conversation, and prerequisites for ACP. Important barriers were: uncertainty about the timing of ACP, how to plan for an uncertain future, lack of knowledge about dementia, difficulties assessing people with dementia’s decisional capacities, and changing preferences. Facilitators for ACP were: an early start when cognitive decline is still mild, inclusion of all stakeholders, and discussing social and medical issues aimed at maintaining normal life. Conclusion: Discussing future care is difficult due to uncertainties about the future and the decisional capacities of people with dementia. Based on the facilitators, we recommend that GPs use a timely and goal-oriented approach and involve all stakeholders. ACP discussions should focus on the ability of people with dementia to maintain normal daily function as well as on their quality of life, instead of end-of-life-discussions only. GPs need training to acquire knowledge and skills to timely initiate collaborative ACP discussions. [ABSTRACT FROM AUTHOR]

The integrity of image content is essential, although most watermarking algorithms can achieve image authentication but not automatically repair damaged areas or restore the original image. In this paper, a self-recovery reversible image watermarking algorithm is proposed to recover the tampered areas effectively. First of all, the original image is divided into homogeneous blocks and non-homogeneous blocks through multi-scale decomposition, and the feature information of each block is calculated as the recovery watermark. Then, the original image is divided into 4×4 non-overlapping blocks classified into smooth blocks and texture blocks according to image textures. Finally, the recovery watermark generated by homogeneous blocks and error-correcting codes is embedded into the corresponding smooth block by mapping; watermark information generated by non-homogeneous blocks and error-correcting codes is embedded into the corresponding non-embedded smooth block and the texture block via mapping. The correlation attack is detected by invariant moments when the watermarked image is attacked. To determine whether a sub-block has been tampered with, its feature is calculated and the recovery watermark is extracted from the corresponding block. If the image has been tampered with, it can be recovered. The experimental results show that the proposed algorithm can effectively recover the tampered areas with high accuracy and high quality. The algorithm is characterized by sound visual quality and excellent image restoration. [ABSTRACT FROM AUTHOR]

In this paper we introduce a novel algorithm, called Exogenous Fitness, to calculate the Fitness of subnational entities and we apply it to the states of Brazil. In the last decade, several indices were introduced to measure the competitiveness of countries by looking at the complexity of their export basket. Tacchella et al (2012) developed a non-monetary metric called Fitness. In this paper, after an overview about Brazil as a whole and the comparison with the other BRIC countries, we introduce a new methodology based on the Fitness algorithm, called Exogenous Fitness. Combining the results with the Gross Domestic Product per capita (GDPp), we look at the dynamics of the Brazilian states in the Fitness-Income plane. Two regimes are distinguishable: one with high predictability and the other with low predictability, showing a deep analogy with the heterogeneous dynamics of the World countries. Furthermore, we compare the ranking of the Brazilian states according to the Exogenous Fitness with the ranking obtained through two other techniques, namely Endogenous Fitness and Economic Complexity Index. [ABSTRACT FROM AUTHOR]

Although counts of tweets citing academic papers are used as an informal indicator of interest, little is known about who tweets academic papers and who uses Twitter to find scholarly information. Without knowing this, it is difficult to draw useful conclusions from a publication being frequently tweeted. This study surveyed 1,912 users that have tweeted journal articles to ask about their scholarly-related Twitter uses. Almost half of the respondents (45%) did not work in academia, despite the sample probably being biased towards academics. Twitter was used most by people with a social science or humanities background. People tend to leverage social ties on Twitter to find information rather than searching for relevant tweets. Twitter is used in academia to acquire and share real-time information and to develop connections with others. Motivations for using Twitter vary by discipline, occupation, and employment sector, but not much by gender. These factors also influence the sharing of different types of academic information. This study provides evidence that Twitter plays a significant role in the discovery of scholarly information and cross-disciplinary knowledge spreading. Most importantly, the large numbers of non-academic users support the claims of those using tweet counts as evidence for the non-academic impacts of scholarly research. [ABSTRACT FROM AUTHOR]

Recommender systems are vulnerable to shilling attacks. Forged user-generated content data, such as user ratings and reviews, are used by attackers to manipulate recommendation rankings. Shilling attack detection in recommender systems is of great significance to maintain the fairness and sustainability of recommender systems. The current studies have problems in terms of the poor universality of algorithms, difficulty in selection of user profile attributes, and lack of an optimization mechanism. In this paper, a shilling behaviour detection structure based on abnormal group user findings and rating time series analysis is proposed. This paper adds to the current understanding in the field by studying the credibility evaluation model in-depth based on the rating prediction model to derive proximity-based predictions. A method for detecting suspicious ratings based on suspicious time windows and target item analysis is proposed. Suspicious rating time segments are determined by constructing a time series, and data streams of the rating items are examined and suspicious rating segments are checked. To analyse features of shilling attacks by a group user’s credibility, an abnormal group user discovery method based on time series and time window is proposed. Standard testing datasets are used to verify the effect of the proposed method. [ABSTRACT FROM AUTHOR]

Research on temporal characteristics of human dynamics has attracted much attentions for its contribution to various areas such as communication, medical treatment, finance, etc. Existing studies show that the time intervals between two consecutive events present different non-Poisson characteristics, such as power-law, Pareto, bimodal distribution of power-law, exponential distribution, piecewise power-law, et al. With the occurrences of new services, new types of distributions may arise. In this paper, we study the distributions of the time intervals between two consecutive visits to QQ and WeChat service, the top two popular instant messaging services in China, and present a new finding that when the value of statistical unit T is set to 0.001s, the inter-event time distribution follows a piecewise distribution of exponential and power-law, indicating the heterogeneous character of IM services users’ online behavior in different time scales. We infer that the heterogeneous character is related to the communication mechanism of IM and the habits of users. Then we develop a combination model of exponential model and interest model to characterize the heterogeneity. Furthermore, we find that the exponent of the inter-event time distribution of the same service is different in two cities, which is correlated with the popularity of the services. Our research is useful for the application of information diffusion, prediction of economic development of cities, and so on. [ABSTRACT FROM AUTHOR]

Combining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from advertising datasets. Next, fuzzy restricted Boltzmann machine (FRBM) is used to construct the fuzzy deep belief network (FDBN) with the unsupervised method layer by layer. Finally, fuzzy logistic regression (FLR) is utilized for modeling the CTR. The experimental results show that the proposed FDNN model outperforms several baseline models in terms of both data representation capability and robustness in advertising click log datasets with noise. [ABSTRACT FROM AUTHOR]

Many important applications continuously generate data, such as financial transaction administration, satellite monitoring, network flow monitoring, and web information processing. The data mining results are always evolving with the newly generated data. Obviously, for the clustering task, it is better to incrementally update the new clustering results based on the old data rather than to recluster all of the data from scratch. The incremental clustering approach is an essential way to solve the problem of clustering with growing Big Data. This paper proposes a boundary-profile-based incremental clustering (BPIC) method to find arbitrarily shaped clusters with dynamically growing datasets. This method represents the existing clustering results with a collection of boundary profiles and discards the inner points of clusters rather than keep all data. It greatly saves both time and space storage costs. To identify the boundary profile, this paper presents a boundary-vector-based boundary point detection (BV-BPD) algorithm that summarizes the structure of the existing clusters. The BPIC method processes each new point in an online fashion and updates the clustering results in a batch mode. When a new point arrives, the BPIC method either immediately labels it or temporarily puts it into a bucket according to the relationship between the new data and the boundary profiles. A bucket is employed to distinguish the noise from the potential seeds of new clusters and alleviate the effects of data order. When the bucket is full, the BPIC method will cluster the data within it and update the clustering results. Thus, the BPIC method is insensitive to noise and the order of new data, which is critical for the robustness of the incremental clustering process. In the experiments, the performance of the boundary point detection algorithm BV-BPD is compared with the state-of-the-art method. The results show that the BV-BPD is better than the state-of-the-art method. Additionally, the performance of BPIC and other two incremental clustering methods are investigated in terms of clustering quality, time and space efficiency. The experimental results indicate that the BPIC method is able to get a qualified clustering result on a large dataset with higher time and space efficiency. [ABSTRACT FROM AUTHOR]

Miniaturization of computer hardware and the demand for network capable devices has resulted in the emergence of a new class of technology called wearable computing. Wearable devices have many purposes like lifestyle support, health monitoring, fitness monitoring, entertainment, industrial uses, and gaming. Wearable devices are hurriedly being marketed in an attempt to capture an emerging market. Owing to this, some devices do not adequately address the need for security. To enable virtualization and connectivity wearable devices sense and transmit data, therefore it is essential that the device, its data and the user are protected. In this paper the use of novel Integrated Circuit Metric (ICMetric) technology for the provision of security in wearable devices has been suggested. ICMetric technology uses the features of a device to generate an identification which is then used for the provision of cryptographic services. This paper explores how a device ICMetric can be generated by using the accelerometer and gyroscope sensor. Since wearable devices often operate in a group setting the work also focuses on generating a group identification which is then used to deliver services like authentication, confidentiality, secure admission and symmetric key generation. Experiment and simulation results prove that the scheme offers high levels of security without compromising on resource demands. [ABSTRACT FROM AUTHOR]

Background: Based on single cross-sectional data, prior research finds evidence of “transportation assimilation” among U.S. immigrants: the length of stay in the U.S. is negatively correlated with public transit use. This paper revisits this question by using repeated cross-sectional data, and examines the trend of transportation assimilation over time. Methods and results: Using 1980, 1990, 2000 1% census and 2010 (1%) American Community Survey, I examine the relationship between the length of stay in the U.S. and public transit ridership among immigrants. I first run regressions separately in four data sets: I regress public transit ridership on the length of stay, controlling for other individual and geographic variables. I then compare the magnitudes of the relationship in four regressions. To study how the rate of transportation assimilation changes over time, I pool the data set and regress public transit ridership on the length of stay and its interactions with year dummies to compare the coefficients across surveys. Results confirm the conclusion of transportation assimilation: as the length of stay in the U.S. increases, an immigrant’s public transit use decreases. However, the repeated cross-section analysis suggests the assimilation rate has been decreasing in the past few decades. Conclusions: This paper finds evidence of transportation assimilation: immigrants become less likely to ride public transit as the length of stay in the U.S. increases. The assimilation rate, however, has been decreasing over time. This paper finds that the rate of public transit ridership among new immigrants upon arrival, the geographic distribution of immigrants, and the changing demographics of the U.S. immigrants play roles in affecting the trend of transportation assimilation. [ABSTRACT FROM AUTHOR]

In political campaigns, perceived candidate credibility influences the persuasiveness of messages. In campaigns aiming to influence people’s beliefs, micro-targeted campaigns (MTCs) that target specific voters using their psychological profile have become increasingly prevalent. It remains open how effective MTCs are, notably in comparison to population-targeted campaign strategies. Using an agent-based model, the paper applies recent insights from cognitive models of persuasion, extending them to the societal level in a novel framework for exploring political campaigning. The paper provides an initial treatment of the complex dynamics of population level political campaigning in a psychologically informed manner. Model simulations show that MTCs can take advantage of the psychology of the electorate by targeting voters favourable disposed towards the candidate. Relative to broad campaigning, MTCs allow for efficient and adaptive management of complex campaigns. Findings show that disliked MTC candidates can beat liked population-targeting candidates, pointing to societal questions concerning campaign regulations. [ABSTRACT FROM AUTHOR]

Competitive pressure to maximize the current bibliometric measures of productivity is jeopardizing the integrity of the scientific literature. Efforts are underway to address the ‘reproducibility crisis’ by encouraging the use of more rigorous, confirmatory methods. However, as long as productivity continues to be defined by the number of discoveries scientists publish, the impact factor of the journals they publish in and the number of times their papers are cited, they will be reluctant to accept high quality methods and consistently conduct and publish confirmatory/replication studies. This exploratory study examined a sample of rigorous Phase II-IV clinical trials, including unpublished studies, to determine if more appropriate metrics and incentives can be developed. The results suggest that rigorous procedures will help reduce false positives, but to the extent that higher quality methods are accepted as the standard of practice, the current bibliometric incentives will discourage innovative studies and encourage scientists to shift their research to less informative studies of subjects that are already being more actively investigated. However, the results also suggest that it is possible to develop a more appropriate system of rewards. In contrast to the current bibliometric incentives, evaluations of the quality of the methods and reproducibility of the results, innovation and diversity of thought, and amount of information produced may serve as measures and incentives that maintain the integrity of the scientific literature and maximize scientific progress. [ABSTRACT FROM AUTHOR]

Machine Learning (ML) methods have been proposed in the academic literature as alternatives to statistical ones for time series forecasting. Yet, scant evidence is available about their relative performance in terms of accuracy and computational requirements. The purpose of this paper is to evaluate such performance across multiple forecasting horizons using a large subset of 1045 monthly time series used in the M3 Competition. After comparing the post-sample accuracy of popular ML methods with that of eight traditional statistical ones, we found that the former are dominated across both accuracy measures used and for all forecasting horizons examined. Moreover, we observed that their computational requirements are considerably greater than those of statistical methods. The paper discusses the results, explains why the accuracy of ML models is below that of statistical ones and proposes some possible ways forward. The empirical results found in our research stress the need for objective and unbiased ways to test the performance of forecasting methods that can be achieved through sizable and open competitions allowing meaningful comparisons and definite conclusions. [ABSTRACT FROM AUTHOR]