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In this study, we address the question whether (and to what extent, respectively) altmetrics are related to the scientific quality of papers (as measured by peer assessments). Only a few studies have previously investigated the relationship between altmetrics and assessments by peers. In the first step, we analyse the underlying dimensions of measurement for traditional metrics (citation counts) and altmetrics–by using principal component analysis (PCA) and factor analysis (FA). In the second step, we test the relationship between the dimensions and quality of papers (as measured by the post-publication peer-review system of F1000Prime assessments)–using regression analysis. The results of the PCA and FA show that altmetrics operate along different dimensions, whereas Mendeley counts are related to citation counts, and tweets form a separate dimension. The results of the regression analysis indicate that citation-based metrics and readership counts are significantly more related to quality, than tweets. This result on the one hand questions the use of Twitter counts for research evaluation purposes and on the other hand indicates potential use of Mendeley reader counts. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, scientific social networks (SSNs) have become the fastest and most convenient way for researchers to communicate with each other. Many published papers are shared via SSNs every day, resulting in the problem of information overload. How to appropriately recommend personalized and highly valuable papers for researchers is becoming more urgent. However, when recommending papers in SSNs, only a small amount of positive instances are available, leaving a vast amount of unlabelled data, in which negative instances and potential unseen positive instances are mixed together, which naturally belongs to One-Class Collaborative Filtering (OCCF) problem. Therefore, considering the extreme data imbalance and data sparsity of this OCCF problem, a hybrid approach of Social and Content aware One-class Recommendation of Papers in SSNs, termed SCORP, is proposed in this study. Unlike previous approaches recommended to address the OCCF problem, social information, which has been proved playing a significant role in performing recommendations in many domains, is applied in both the profiling of content-based filtering and the collaborative filtering to achieve superior recommendations. To verify the effectiveness of the proposed SCORP approach, a real-life dataset from CiteULike was employed. The experimental results demonstrate that the proposed approach is superior to all of the compared approaches, thus providing a more effective method for recommending papers in SSNs. [ABSTRACT FROM AUTHOR]

Scientists often perceive a trade-off between quantity and quality in scientific publishing: finite amounts of time and effort can be spent to produce few high-quality papers or subdivided to produce many papers of lower quality. Despite this perception, previous studies have indicated the opposite relationship, in which productivity (publishing more papers) is associated with increased paper quality (usually measured by citation accumulation). We examine this question in a novel way, comparing members of the National Academy of Sciences with themselves across years, and using a much larger dataset than previously analyzed. We find that a member’s most highly cited paper in a given year has more citations in more productive years than in in less productive years. Their lowest cited paper each year, on the other hand, has fewer citations in more productive years. To disentangle the effect of the underlying distributions of citations and productivities, we repeat the analysis for hypothetical publication records generated by scrambling each author’s citation counts among their publications. Surprisingly, these artificial histories re-create the above trends almost exactly. Put another way, the observed positive relationship between quantity and quality can be interpreted as a consequence of randomly drawing citation counts for each publication: more productive years yield higher-cited papers because they have more chances to draw a large value. This suggests that citation counts, and the rewards that have come to be associated with them, may be more stochastic than previously appreciated. [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]

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]

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]

As researchers use computational methods to study complex social behaviors at scale, the validity of this computational social science depends on the integrity of the data. On July 2, 2015, Jason Baumgartner published a dataset advertised to include “every publicly available Reddit comment” which was quickly shared on Bittorrent and the Internet Archive. This data quickly became the basis of many academic papers on topics including machine learning, social behavior, politics, breaking news, and hate speech. We have discovered substantial gaps and limitations in this dataset which may contribute to bias in the findings of that research. In this paper, we document the dataset, substantial missing observations in the dataset, and the risks to research validity from those gaps. In summary, we identify strong risks to research that considers user histories or network analysis, moderate risks to research that compares counts of participation, and lesser risk to machine learning research that avoids making representative claims about behavior and participation on Reddit. [ABSTRACT FROM AUTHOR]

Aiming to solve the safety problems of high-speed railway operation and management, one new method is urgently needed to construct on the basis of the rough set theory and the uncertainty measurement theory. The method should carefully consider every factor of high-speed railway operation that realizes the measurement indexes of its safety operation. After analyzing the factors that influence high-speed railway safety operation in detail, a rough measurement model is finally constructed to describe the operation process. Based on the above considerations, this paper redistricts the safety influence factors of high-speed railway operation as 16 measurement indexes which include staff index, vehicle index, equipment index and environment. And the paper also provides another reasonable and effective theoretical method to solve the safety problems of multiple attribute measurement in high-speed railway operation. As while as analyzing the operation data of 10 pivotal railway lines in China, this paper respectively uses the rough set-based measurement model and value function model (one model for calculating the safety value) for calculating the operation safety value. The calculation result shows that the curve of safety value with the proposed method has smaller error and greater stability than the value function method’s, which verifies the feasibility and effectiveness. [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]

China’s fast economic growth contributes to the rapid development of its urbanization process, and also renders a series of industrial accidents, which often cause loss of life, damage to property and environment, thus requiring the associated risk analysis and safety control measures to be implemented in advance. However, incompleteness of historical failure data before the occurrence of accidents makes it difficult to use traditional risk analysis approaches such as probabilistic risk analysis in many cases. This paper aims to develop a new methodology capable of assessing regional industrial safety (RIS) in an uncertain environment. A hierarchical structure for modelling the risks influencing RIS is first constructed. The hybrid of evidential reasoning (ER) and Analytical Hierarchy Process (AHP) is then used to assess the risks in a complementary way, in which AHP is hired to evaluate the weight of each risk factor and ER is employed to synthesise the safety evaluations of the investigated region(s) against the risk factors from the bottom to the top level in the hierarchy. The successful application of the hybrid approach in a real case analysis of RIS in several major districts of Beijing (capital of China) demonstrates its feasibility as well as provides risk analysts and safety engineers with useful insights on effective solutions to comprehensive risk assessment of RIS in metropolitan cities. The contribution of this paper is made by the findings on the comparison of risk levels of RIS at different regions against various risk factors so that best practices from the good performer(s) can be used to improve the safety of the others. [ABSTRACT FROM AUTHOR]

Several network features and information retrieval methods have been proposed to elucidate the structure of citation networks and to detect important nodes. However, it is difficult to retrieve information related to trends in an academic field and to detect cutting-edge areas from the citation network. In this paper, we propose a novel framework that detects the trend as the growth direction of a citation network using network representation learning(NRL). We presume that the linear growth of citation network in latent space obtained by NRL is the result of the iterative edge additional process of a citation network. On APS datasets and papers of some domains of the Web of Science, we confirm the existence of trends by observing that an academic field grows in a specific direction linearly in latent space. Next, we calculate each node’s degree of trend-following as an indicator called the intrinsic publication year (IPY). As a result, there is a correlation between the indicator and the number of future citations. Furthermore, a word frequently used in the abstracts of cutting-edge papers (high-IPY paper) is likely to be used often in future publications. These results confirm the validity of the detected trend for predicting citation network growth. [ABSTRACT FROM AUTHOR]

Background: When attempting to statistically distinguish between a null and an alternative hypothesis, many researchers in the life and social sciences turn to binned statistical analysis methods, or methods that are simply based on the moments of a distribution (such as the mean, and variance). These methods have the advantage of simplicity of implementation, and simplicity of explanation. However, when null and alternative hypotheses manifest themselves in subtle differences in patterns in the data, binned analysis methods may be insensitive to these differences, and researchers may erroneously fail to reject the null hypothesis when in fact more sensitive statistical analysis methods might produce a different result when the null hypothesis is actually false. Here, with a focus on two recent conflicting studies of contagion in mass killings as instructive examples, we discuss how the use of unbinned likelihood methods makes optimal use of the information in the data; a fact that has been long known in statistical theory, but perhaps is not as widely appreciated amongst general researchers in the life and social sciences. Methods: In 2015, Towers et al published a paper that quantified the long-suspected contagion effect in mass killings. However, in 2017, Lankford & Tomek subsequently published a paper, based upon the same data, that claimed to contradict the results of the earlier study. The former used unbinned likelihood methods, and the latter used binned methods, and comparison of distribution moments. Using these analyses, we also discuss how visualization of the data can aid in determination of the most appropriate statistical analysis methods to distinguish between a null and alternate hypothesis. We also discuss the importance of assessment of the robustness of analysis results to methodological assumptions made (for example, arbitrary choices of number of bins and bin widths when using binned methods); an issue that is widely overlooked in the literature, but is critical to analysis reproducibility and robustness. Conclusions: When an analysis cannot distinguish between a null and alternate hypothesis, care must be taken to ensure that the analysis methodology itself maximizes the use of information in the data that can distinguish between the two hypotheses. The use of binned methods by Lankford & Tomek (2017), that examined how many mass killings fell within a 14 day window from a previous mass killing, substantially reduced the sensitivity of their analysis to contagion effects. The unbinned likelihood methods used by Towers et al (2015) did not suffer from this problem. While a binned analysis might be favorable for simplicity and clarity of presentation, unbinned likelihood methods are preferable when effects might be somewhat subtle. [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]

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]

Pruning is applied in order to combat over-fitting problem where the tree is pruned back with the goal of identifying decision tree with the lowest error rate on previously unobserved instances, breaking ties in favour of smaller trees with high accuracy. In this paper, pruning with Bayes minimum risk is introduced for estimating the risk-rate. This method proceeds in a bottom-up fashion converting a parent node of a subtree to a leaf node if the estimated risk-rate of the parent node for that subtree is less than the risk-rates of its leaf. This paper proposes a post-pruning method that considers various evaluation standards such as attribute selection, accuracy, tree complexity, and time taken to prune the tree, precision/recall scores, TP/FN rates and area under ROC. The experimental results show that the proposed method produces better classification accuracy and its complexity is not much different than the complexities of reduced-error pruning and minimum-error pruning approaches. The experiments also demonstrate that the proposed method shows satisfactory performance in terms of precision score, recall score, TP rate, FP rate and area under ROC. [ABSTRACT FROM AUTHOR]

A considerable body of research has documented the negative effects of job burnout on employees and their organizations, emphasizing the importance of the identification of early signs of the phenomenon for the purposes of prevention and intervention. However, such timely identification is difficult due to the time and cost of assessing the burnout levels of all employees in an organization using established scales. In this paper, we propose an innovative way to identify employees at risk of job burnout by analyzing their e-mail communication patterns. Building on the Job Demands–Resources model, we theorize about the relationship between e-mail communication patterns and levels of employee exhaustion and disengagement (two dimensions of burnout). We analyzed 52,190 e-mails exchanged between 57 employees of a medium sized R&D company over a five-month period. We then related these employees’ communication patterns to their levels of burnout, collected using an established scale (the OLBI–Oldenburg Burnout Inventory). Our results provide support for the overall proposition of the paper, that e-mail communications can be used to identify individuals at risk of job burnout. Our models explain up to 34% of the variance of burnout and up to 37% and 19% respectively of the variance of exhaustion and disengagement. They also successfully distinguish between employees with a higher risk of burnout and those with lower levels of risk (F1 score of 84% with recall of 100% and 73% precision). We discuss the implications of our results and present suggestions for future research. [ABSTRACT FROM AUTHOR]

In this paper, we propose a hybrid method to forecast the stock prices called High-order-fuzzy-fluctuation-Trends-based Back Propagation(HTBP)Neural Network model. First, we compare each value of the historical training data with the previous day's value to obtain a fluctuation trend time series (FTTS). On this basis, the FTTS blur into fuzzy time series (FFTS) based on the fluctuation of the increasing, equality, decreasing amplitude and direction. Since the relationship between FFTS and future wave trends is nonlinear, the HTBP neural network algorithm is used to find the mapping rules in the form of self-learning. Finally, the results of the algorithm output are used to predict future fluctuations. The proposed model provides some innovative features:(1)It combines fuzzy set theory and neural network algorithm to avoid overfitting problems existed in traditional models. (2)BP neural network algorithm can intelligently explore the internal rules of the actual existence of sequential data, without the need to analyze the influence factors of specific rules and the path of action. (3)The hybrid modal can reasonably remove noises from the internal rules by proper fuzzy treatment. This paper takes the TAIEX data set of Taiwan stock exchange as an example, and compares and analyzes the prediction performance of the model. The experimental results show that this method can predict the stock market in a very simple way. At the same time, we use this method to predict the Shanghai stock exchange composite index, and further verify the effectiveness and universality of the method. [ABSTRACT FROM AUTHOR]

Traffic surveillance systems are interesting to many researchers to improve the traffic control and reduce the risk caused by accidents. In this area, many published works are only concerned about vehicle detection in normal conditions. The camera may vibrate due to wind or bridge movement. Detection and tracking of vehicles is a very difficult task when we have bad weather conditions in winter (snowy, rainy, windy, etc.), dusty weather in arid and semi-arid regions, at night, etc. Also, it is very important to consider speed of vehicles in the complicated weather condition. In this paper, we improved our method to track and count vehicles in dusty weather with vibrating camera. For this purpose, we used a background subtraction based strategy mixed with an extra processing to segment vehicles. In this paper, the extra processing included the analysis of the headlight size, location, and area. In our work, tracking was done between consecutive frames via a generalized particle filter to detect the vehicle and pair the headlights using the connected component analysis. So, vehicle counting was performed based on the pairing result, with Centroid of each blob we calculated distance between two frames by simple formula and hence dividing it by the time between two frames obtained from the video. Our proposed method was tested on several video surveillance records in different conditions such as dusty or foggy weather, vibrating camera, and in roads with medium-level traffic volumes. The results showed that the new proposed method performed better than our previously published method and other methods, including the Kalman filter or Gaussian model, in different traffic conditions. [ABSTRACT FROM AUTHOR]

Although racism has been posited as driver of racial/ethnic inequities in healthcare, the relationship between racism and health service use and experience has yet to be systematically reviewed or meta-analysed. This paper presents a systematic review and meta-analysis of quantitative empirical studies that report associations between self-reported racism and various measures of healthcare service utilisation. Data were reviewed and extracted from 83 papers reporting 70 studies. Studies included 250,850 participants and were conducted predominately in the U.S. The meta-analysis included 59 papers reporting 52 studies, which were analysed using random effects models and mean weighted effect sizes. Racism was associated with more negative patient experiences of health services (HSU-E) (OR = 0.351 (95% CI [0.236,0.521], k = 19), including lower levels of healthcare-related trust, satisfaction, and communication. Racism was not associated with health service use (HSU-U) as an outcome group, and was not associated with most individual HSU-U outcomes, including having had examinations, health service visits and admissions to health professionals and services. Racism was associated with health service use outcomes such as delaying/not getting healthcare, and lack of adherence to treatment uptake, although these effects may be influenced by a small sample of studies, and publication bias, respectively. Limitations to the literature reviewed in terms of study designs, sampling methods and measurements are discussed along with suggested future directions in the field. [ABSTRACT FROM AUTHOR]

Publication metadata help deliver rich analyses of scholarly communication. However, research concepts and ideas are more effectively expressed through unstructured fields such as full texts. Thus, the goals of this paper are to employ a full-text enabled method to extract terms relevant to disciplinary vocabularies, and through them, to understand the relationships between disciplines. This paper uses an efficient, domain-independent term extraction method to extract disciplinary vocabularies from a large multidisciplinary corpus of PLoS ONE publications. It finds a power-law pattern in the frequency distributions of terms present in each discipline, indicating a semantic richness potentially sufficient for further study and advanced analysis. The salient relationships amongst these vocabularies become apparent in application of a principal component analysis. For example, Mathematics and Computer and Information Sciences were found to have similar vocabulary use patterns along with Engineering and Physics; while Chemistry and the Social Sciences were found to exhibit contrasting vocabulary use patterns along with the Earth Sciences and Chemistry. These results have implications to studies of scholarly communication as scholars attempt to identify the epistemological cultures of disciplines, and as a full text-based methodology could lead to machine learning applications in the automated classification of scholarly work according to disciplinary vocabularies. [ABSTRACT FROM AUTHOR]

This study shows how liking politicians’ public Facebook posts can be used as an accurate measure for predicting present-day voter intention in a multiparty system. We highlight that a few, but selective digital traces produce prediction accuracies that are on par or even greater than most current approaches based upon bigger and broader datasets. Combining the online and offline, we connect a subsample of surveyed respondents to their public Facebook activity and apply machine learning classifiers to explore the link between their political liking behaviour and actual voting intention. Through this work, we show that even a single selective Facebook like can reveal as much about political voter intention as hundreds of heterogeneous likes. Further, by including the entire political like history of the respondents, our model reaches prediction accuracies above previous multiparty studies (60–70%). The main contribution of this paper is to show how public like-activity on Facebook allows political profiling of individual users in a multiparty system with accuracies above previous studies. Beside increased accuracies, the paper shows how such parsimonious measures allows us to generalize our findings to the entire population of a country and even across national borders, to other political multiparty systems. The approach in this study relies on data that are publicly available, and the simple setup we propose can with some limitations, be generalized to millions of users in other multiparty systems. [ABSTRACT FROM AUTHOR]

The language that scientists use to frame biological invasions may reveal inherent bias—including how data are interpreted. A frequent critique of invasion biology is the use of value-laden language that may indicate context bias. Here we use a systematic study of language and interpretation in papers drawn from invasion biology to evaluate whether there is a link between the framing of papers and the interpretation of results. We also examine any trends in context bias in biological invasion research. We examined 651 peer-reviewed invasive species competition studies and implemented a rigorous systematic review to examine bias in the presentation and interpretation of native and invasive competition in invasion biology. We predicted that bias in the presentation of invasive species is increasing, as suggested by several authors, and that bias against invasive species would result in misinterpreting their competitive dominance in correlational observational studies compared to causative experimental studies. We indeed found evidence of bias in the presentation and interpretation of invasive species research; authors often introduced research with invasive species in a negative context and study results were interpreted against invasive species more in correlational studies. However, we also found a distinct decrease in those biases since the mid-2000s. Given that there have been several waves of criticism from scientists both inside and outside invasion biology, our evidence suggests that the subdiscipline has somewhat self-corrected apparent biases. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of different components involved in the evaluation process. [ABSTRACT FROM AUTHOR]

With more and more political candidates using social media for campaigning, researchers are looking at measuring the effectiveness of this medium. Most research, however, concentrates on the bare count of likes (or twitter mentions) in an attempt to correlate social media presence and winning. In this paper, we propose a novel method, Interaction Strength Plot (IntS) to measure the passive interactions between a candidate’s posts on Facebook and the users (liking the posts). Using this method on original Malaysian General Election (MGE13) and Australian Federal Elections (AFE13) Facebook Pages (FP) campaign data, we label an FP as performing well if both the posting frequency and the likes gathered are above average. Our method shows that over 60% of the MGE13 candidates and 85% of the AFE13 candidates studied in this paper had under-performing FP. Some of these FP owners would have been identified as popular based on bare count. Thus our performance chart is a vital step forward in measuring the effectiveness of online campaigning. [ABSTRACT FROM AUTHOR]

The estimation of the correct number of dimensions is a long-standing problem in psychometrics. Several methods have been proposed, such as parallel analysis (PA), Kaiser-Guttman’s eigenvalue-greater-than-one rule, multiple average partial procedure (MAP), the maximum-likelihood approaches that use fit indexes as BIC and EBIC and the less used and studied approach called very simple structure (VSS). In the present paper a new approach to estimate the number of dimensions will be introduced and compared via simulation to the traditional techniques pointed above. The approach proposed in the current paper is called exploratory graph analysis (EGA), since it is based on the graphical lasso with the regularization parameter specified using EBIC. The number of dimensions is verified using the walktrap, a random walk algorithm used to identify communities in networks. In total, 32,000 data sets were simulated to fit known factor structures, with the data sets varying across different criteria: number of factors (2 and 4), number of items (5 and 10), sample size (100, 500, 1000 and 5000) and correlation between factors (orthogonal, .20, .50 and .70), resulting in 64 different conditions. For each condition, 500 data sets were simulated using lavaan. The result shows that the EGA performs comparable to parallel analysis, EBIC, eBIC and to Kaiser-Guttman rule in a number of situations, especially when the number of factors was two. However, EGA was the only technique able to correctly estimate the number of dimensions in the four-factor structure when the correlation between factors were .7, showing an accuracy of 100% for a sample size of 5,000 observations. Finally, the EGA was used to estimate the number of factors in a real dataset, in order to compare its performance with the other six techniques tested in the simulation study. [ABSTRACT FROM AUTHOR]

Objective: This paper reflected on the prevalence of hypertension and diabetes in Bangladesh, which is spreading rapidly in low-income countries. The rationale of constructing more health centers for addressing NCDs was assessed in this paper by determining the relationship between prevalence of NCDs, particularly hypertension and diabetes, and distance to health facilities. Methods: From BDHS (Bangladesh Health and Demographic Survey) 2011 data set, 7544 samples were analyzed to demonstrate association between Non-communicable diseases (NCD) and distance from respondents’ home to health facilities like hospitals, community clinics, pharmacies or doctors’ chambers, and community facilities like market, post office or cinema hall. Bivariate analysis was conducted between accessibility to health facilities and prevalence of the diseases. The causal relationship between the spatial effects and the prevalence of the diseases were analyzed by applying Generalized Linear Mixed Model (GLMM) was fitted. Results: Fitting linear mixed effect models, we found that hypertension and diabetes react differently with various spatial effects. Distance from home to hospital had significant effect (P < 0.001) on hypertension showing people living further from the facilities or town centers seemed to be less hypertensive, whereas diabetes showed no such affiliation. Conclusion: Higher prevalence of diabetes (40.9%) over hypertension (26.5%) in people aging 35 or higher, have appeared to have caused the difference, which concluded that each non-communicable disease should be dealt to its own merit for policy making instead considering as a group of diseases. [ABSTRACT FROM AUTHOR]

This study uses several bibliometric indices to explore the temporal course of publication trends regarding the bilingual advantage in executive control over a ten-year window. These indices include the number of published papers, numbers of citations, and the journal impact factor. According to the information available in their abstracts, studies were classified into one of four categories: supporting, ambiguous towards, not mentioning, or challenging the bilingual advantage. Results show that the number of papers challenging the bilingual advantage increased notably in 2014 and 2015. Both the average impact factor and the accumulated citations as of June 2016 were equivalent between categories. However, of the studies published in 2014, those that challenge the bilingual advantage accumulated more citations in June 2016 than those supporting it. Our findings offer evidence-based bibliometric information about the current state of the literature and suggest a change in publication trends regarding the literature on the bilingual advantage. [ABSTRACT FROM AUTHOR]

In this paper, complex network theory is used to make time-series analysis of key indicators of governance structure and financing data. We analyze scientific listed companies’ governance data from 2010 to 2014 and divide them into groups in accordance with the similarity they share. Then we select sample companies to analyze their financing data and explore the influence of governance structure on financing decision and the financing preference they display. This paper reviews relevant laws and regulations of financing from the perspective of law and economics, then proposes reasonable suggestions to consummate the law for the purpose of regulating listed companies’ financing. The research provides a reference for making qualitative analysis on companies’ financing. [ABSTRACT FROM AUTHOR]

TB is rated as one of the world’s deadliest diseases and South Africa ranks 9th out of the 22 countries with hardest hit of TB. Although many pieces of research have been carried out on this subject, this paper steps further by inculcating past knowledge into the model, using Bayesian approach with informative prior. Bayesian statistics approach is getting popular in data analyses. But, most applications of Bayesian inference technique are limited to situations of non-informative prior, where there is no solid external information about the distribution of the parameter of interest. The main aim of this study is to profile people living with TB in South Africa. In this paper, identical regression models are fitted for classical and Bayesian approach both with non-informative and informative prior, using South Africa General Household Survey (GHS) data for the year 2014. For the Bayesian model with informative prior, South Africa General Household Survey dataset for the year 2011 to 2013 are used to set up priors for the model 2014. [ABSTRACT FROM AUTHOR]

The popularity of online information generally experiences a rising and falling evolution. This paper considers the “burst”, “peak”, and “fade” key events together as a representative summary of popularity evolution. We propose a novel prediction task—predicting when popularity undergoes these key events. It is of great importance to know when these three key events occur, because doing so helps recommendation systems, online marketing, and containment of rumors. However, it is very challenging to solve this new prediction task due to two issues. First, popularity evolution has high variation and can follow various patterns, so how can we identify “burst”, “peak”, and “fade” in different patterns of popularity evolution? Second, these events usually occur in a very short time, so how can we accurately yet promptly predict them? In this paper we address these two issues. To handle the first one, we use a simple moving average to smooth variation, and then a universal method is presented for different patterns to identify the key events in popularity evolution. To deal with the second one, we extract different types of features that may have an impact on the key events, and then a correlation analysis is conducted in the feature selection step to remove irrelevant and redundant features. The remaining features are used to train a machine learning model. The feature selection step improves prediction accuracy, and in order to emphasize prediction promptness, we design a new evaluation metric which considers both accuracy and promptness to evaluate our prediction task. Experimental and comparative results show the superiority of our prediction solution. [ABSTRACT FROM AUTHOR]

The Global Financial Crisis of 2007-2008 wiped out US$37 trillions across global financial markets, this value is equivalent to the combined GDPs of the United States and the European Union in 2014. The defining moment of this crisis was the failure of Lehman Brothers, which precipitated the October 2008 crash and the Asian Correction (March 2009). Had the Federal Reserve seen these crashes coming, they might have bailed out Lehman Brothers, and prevented the crashes altogether. In this paper, we show that some of these market crashes (like the Asian Correction) can be predicted, if we assume that a large number of adaptive traders employing competing trading strategies. As the number of adherents for some strategies grow, others decline in the constantly changing strategy space. When a strategy group grows into a giant component, trader actions become increasingly correlated and this is reflected in the stock price. The fragmentation of this giant component will leads to a market crash. In this paper, we also derived the mean-field market crash forecast equation based on a model of fusions and fissions in the trading strategy space. By fitting the continuous returns of 20 stocks traded in Singapore Exchange to the market crash forecast equation, we obtain crash predictions ranging from end October 2008 to mid-February 2009, with early warning four to six months prior to the crashes. [ABSTRACT FROM AUTHOR]

Citation metrics are increasingly used to appraise published research. One challenge is whether and how to normalize these metrics to account for differences across scientific fields, age (year of publication), type of document, database coverage, and other factors. We discuss the pros and cons for normalizations using different approaches. Additional challenges emerge when citation metrics need to be combined across multiple papers to appraise the corpus of scientists, institutions, journals, or countries, as well as when trying to attribute credit in multiauthored papers. Different citation metrics may offer complementary insights, but one should carefully consider the assumptions that underlie their calculation. [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 proposes a method to predict fluctuations in the prices of cryptocurrencies, which are increasingly used for online transactions worldwide. Little research has been conducted on predicting fluctuations in the price and number of transactions of a variety of cryptocurrencies. Moreover, the few methods proposed to predict fluctuation in currency prices are inefficient because they fail to take into account the differences in attributes between real currencies and cryptocurrencies. This paper analyzes user comments in online cryptocurrency communities to predict fluctuations in the prices of cryptocurrencies and the number of transactions. By focusing on three cryptocurrencies, each with a large market size and user base, this paper attempts to predict such fluctuations by using a simple and efficient method. [ABSTRACT FROM AUTHOR]

Tie strength and tags have been separately suggested as possible attributes for photo access controls in Social Network Services. However, an evaluation is missing about the benefits/drawbacks of adding one or both of these attributes to the ones already used in access controls for Social Network Services (groups and individuals). In this paper, we describe an experiment with 48 participants using access controls that include tie strength and tags (separately and simultaneously) together with attributes for groups and individuals. We analyze the results using several quantitative and qualitative metrics. We find that users consider these two new attributes useful in defining their sharing policies, and they prefer to employ access controls that consider tags and tie strength jointly. Specifically, users believe that tie strength improves policy understandability and that tags help them define sharing policies faster. However, we also observe that when users employ these two attributes they tend to make more mistakes in terms of the resulting sharing policy. We hypothesize that this could be caused by the lack of experience using tie strength and tags in access controls. [ABSTRACT FROM AUTHOR]

The paper investigates and compares the relationship between inflation and inflation uncertainty under inflation targeting and, alternatively, a conventional fixed exchange rate system, for a group of emerging countries. To do so we estimate GARCH in mean models and we find that there is a bi-directional relationship between inflation and inflation uncertainty under the two monetary regimes. It is also found that the fixed exchange rate regime has no impact on average inflation and inflation inertia, while inflation targeting has been successful at lowering both average inflation and inflation persistence. [ABSTRACT FROM AUTHOR]

Adult illiteracy is a major problem worldwide especially in developing countries. Adult Basic Education (ABE) programs working in this context are not very effective due to lack of motivation for the people who are not literate. The reason is inadequate learning content and content delivery methods. This situation calls for developing novel learning content and a learner-directed content delivery approach. This paper presents an exploratory study investigating the use of the Environmental Print Material (EPM) as learning content for the non-literate population of Pakistan. The EPM content is presented to the adult non-literate population in two ethnographic studies. The most frequently recognized content is selected and utilized as learning content in a Computer Assisted Learning (CAL) application. An empirical study is conducted upon two groups with 107 participants to compare the EPM-based learning content with Traditional Learning Content (TLC). As many as 54 participants participated in the experimental group (presented with EPM-based learning content), whereas 53 participants took part in the control group (presented with TLC content). The results reveal that the experimental group performed significantly better compared to the control group in recognition, pronunciation, and recall of the presented content. The meta-analysis of the results shows a large effect size of (1.05) with confidence interval in the range (0.798–1.315). The results claim that the EPM has potential to be considered as learning content in the ABE programs. [ABSTRACT FROM AUTHOR]

Statistics and Epidemiology are crucial both in clinical decision-making and clinical research. Teaching these disciplines in a Bachelor’s Degree in Medicine is a significant challenge. In this paper, we aim to describe two participatory teaching methods used in a yearlong second-year course that includes both Epidemiology and Statistics, and to analyze how these two methodologies affect the students’ perception of the course and their abilities related to these subjects. Both methodologies consist in carrying out a specific practical activity. The first practical activity is carried out using a website and aims to help students understand concepts and interpret information; the second involves analyzing a database using a statistical package and, subsequently, producing a scientific report. In addition, we prepared a questionnaire to find out the students’ perception of these issues. The nine questionnaire items were assessed using a rating scale and adapted to characteristics of the course, which covers Epidemiology and Statistics in an integrated manner. Then we assessed the differences in perception before and after the activities were carried out. The results show that the students’ perception improved significantly in the following items: “importance of Statistics and Epidemiology in Medicine”; “usefulness in clinical practice”; “understanding concepts”; “ability to perform a statistical analysis”; and “ability to sort data”. The difference was not significant in the remaining four items. In conclusion, the students’ perception of their ability in Statistics and Epidemiology significantly improved after completing the practical activities, and their perception of importance and usefulness of these subjects also improved. [ABSTRACT FROM AUTHOR]

An important task of human visual cognition is to make inferences about properties of objects. One such property is an object's causal history: what happened to the object in its past (e.g., “this paper has been folded”). There is relatively little research on whether and how we make such inferences. We took photographs of objects from six different materials (‘wax’, ‘aluminum foil’, ‘gold foil’, ‘chicken wire’, ‘putty’, ‘cardboard’) transformed by one of four shape-altering transformations (‘folded’, ‘bent’, ‘crumpled’, ‘twisted’). By varying execution of transformation and viewpoint, we obtained 30 images of each material/transformation combination (720 images). We asked different groups of participants to: (1) name transformations and materials, (2) rate images with respect to the extent they belonged to each transformation or material class, and (3) classify images into the four transformation classes. Our results show that participants can infer transformations from object shape–with accuracy being modulated by object material. This inference of causal history from observed object shape shows that we can distinguish between intrinsic (material) and extrinsic (transformation) properties of the object. The separation of observed shape features by their causal origin (‘shape scission’) presumably involves both perceptual and cognitive abilities. [ABSTRACT FROM AUTHOR]

Railway seat inventory control aims to maximize ticket sale profits by determining a selling policy on the reservation horizon. This paper introduces a dynamic bid price approach in railway seat inventory control problem. Multi-dimensional demand is taken into consideration in modeling the problem, in which passenger transfer is our main focus. A new approximate approach is designed to this problem. Numerical examples are presented to evaluate the efficiency of this approach. Simulation experiments are conducted to verify the impact of transfer under different scenarios. [ABSTRACT FROM AUTHOR]

This paper considers the DCSK system under jamming environment. We consider both fast and slow switching pulse jamming (PJ). We derive approximations of the system bit error rates (BERs) in analytic expressions containing only well-known special functions, from which we show that for both jamming cases increasing the spreading factor can enhance the BER. In addition, we reveal that under the fast switching PJ, the BER does not depend on the jamming duty cycle ρ when ρ ≤ 0.5, however when ρ > 0.5, increasing its value degrades the BER. Moreover, we find out that under the slow switching PJ, increasing ρ may either enhance or degrade the BER. [ABSTRACT FROM AUTHOR]

Supplier selection is one of the critical processes in supplier chain management which is associated with the flow of goods and services from the supplier of raw material to the final consumer. The purpose of this paper is to present a novel approach and improves the supplier selection in a multi-item/multi-supplier environment, and provide the importance and the reliability of the criteria by handling vagueness and imperfection of information in decision making process. First, principal component analysis (PCA) method is used to reduce the number of supplier selection criteria in pharmaceutical companies. Next, using the most important criteria resulted from the PCA method, the importance and the reliability of the selected criteria are assessed by a group of decision-maker (DM). Then, the importance value of each supplier with respect to each product is obtained via the Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) based on the concept of Z-numbers called Z-TOPSIS. Finally, these values are used as inputs in a mixed integer linear programming (MILP) to determine the suppliers and the amount of the products provided from the related suppliers. To validate the proposed methodology, an application is performed in a pharmaceutical company. The results show that the proposed method could provide promising results in decision making process more appropriately. [ABSTRACT FROM AUTHOR]

Predicting and analyzing behaviors of investors is of great value to financial institutions. This paper uses survey data from about 9,000 individual investors across China to explore the predictability of decision behaviors by studying demographic characteristics that are relatively easy to obtain. After applying Pearson’s chi-squared test, Spearman rank correlation test, and several data mining methods, we verified that demographic characteristics are closely linked to decision behaviors, and it would be an economical and feasible solution for financial organizations to build initial behavioral prediction models especially when investors’ behavioral data are insufficient. [ABSTRACT FROM AUTHOR]

The relationship between oil price and investor sentiment is crucial to economic activity. Disentangling the shocks in crude oil price by structural VAR model, this paper analyzes the interaction between oil price shocks and investor sentiment by linear and nonlinear causality approach, TVP-VAR mode and NARDL model. The results reveal that changes of oil-specific demand shock not only linearly but also nonlinearly cause changes of investor sentiment while there is no significant link between other oil shocks (oil supply shock and aggregate demand shock) and investor sentiment. In addition, the study discovers that the oil-specific demand shock generally positively affects investor sentiment over time, and it has positive and asymmetric effects on investor sentiment in the short-run. In other words, it is the negative oil-specific demand shock rather than the positive component that has the significant impact on investor sentiment for short-run. This study could enrich current theories on the interaction between oil price and investor sentiment and serve as a supplement to current literature. [ABSTRACT FROM AUTHOR]

Objectives: The paper presents data on the two problems of misconduct and marginality in management, business and economics (MBE) journals and their practices to combat these problems. Design: Data was collected in three phases. First, all publicly retracted papers in MBE journals were identified through keywords searches in 7 major databases (n = 1329 journals). Second, a focused survey was distributed to editors involved in such retractions (n = 64; response rate = 28%). Finally, a survey was administered to all active journals in the seven databases to collect data on editors’ perceptions and practices related to the two problems (n = 937, response rate = 31.8%). Frequency analyses, cross tabulations, and qualitative analyses of open answers were used to examine the data. Results: 184 retracted papers in MBE journals were identified in 2005–2015 (no retraction was found before 2005). From 2005–2007 to 2012–2015, the number of retractions increased by a factor ten with an all-time high in 2015. The survey to journals with reported retractions illustrates how already a few cases of suspected misconduct put a strain on the editorial workload. The survey to all active journals revealed that 42% of the respondents had started to use software to screen all submitted papers, and that a majority recognized the problem of marginality, as indicated by salami-style submissions. According to some editors, reviewers easily spot such submissions whereas others argued that authors may submit thinly sliced papers in parallel to several journals, which means that this practice is only discovered post-publication. The survey question on ways to support creative contributions stimulated a rich response of ideas regarding editorial vision, engaged boards and developmental approaches. The study uses data from three specialized fields, but its findings may be highly relevant to many journals in the social sciences. [ABSTRACT FROM AUTHOR]

Purpose: The primary aim of this paper is to conduct a thorough and systematic review of the empirical and practitioner research on executive, leadership and business coaching to assess the current empirical evidence for the effectiveness of coaching and the mechanisms underlying it. Background: Organisations are increasingly using business coaching as an intervention to improve the productivity and performance of their senior personnel. A consequence of this increased application is the demand for empirical data to understand the process by which it operates and its demonstrable efficacy in achieving pre-set goals. Method: This paper is a systematic review of the academic and practitioner literature pertaining to the effectiveness of business and executive coaching as a developmental intervention for organisations. It focuses on published articles, conference papers and theses that cover business, leadership or executive coaching within organisations over the last 10 years. Conclusions: The main findings show that coaching is an effective tool that benefits organisations and a number of underlying facets contribute to this effectiveness. However, there is deficiency and scope for further investigation in key aspects of the academic research and we identify several areas that need further research and practitioner attention. ​ [ABSTRACT FROM AUTHOR]

There appears to be little doubt as to the existence of an intentional technological resolve to produce convergent tools during the Middle Palaeolithic. However, the use of these pieces as pointed tools is still subject to debate: i.e., handheld tool vs. hafted tool. Present-day technological analysis has begun to apply new methodologies in order to quantify shape variability and to decipher the role of the morphology of these pieces in relation to function; for instance, geometric morphometric analyses have recently been applied with successful results. This paper presents a study of this type of analysis on 37 convergent tools from level Ga of Payre site (France), dated to MIS 8–7. These pieces are non-standardized knapping products produced by discoidal and orthogonal core technologies. Moreover, macro-wear studies attest to various activities on diverse materials with no evidence of hafting or projectile use. The aim of this paper is to test the geometric morphometric approach on non-standardized artefacts applying the Elliptical Fourier analysis (EFA) to 3D contours and to assess the potential relationship between size and shape, technology and function. This study is innovative in that it is the first time that this method, considered to be a valuable complement for describing technological and functional attributes, is applied to 3D contours of lithic products. Our results show that this methodology ensures a very good degree of accuracy in describing shape variations of the sharp edges of technologically non-standardized convergent tools. EFA on 3D contours indicates variations in deviations of the outline along the third dimension (i.e., dorso-ventrally) and yields quantitative and insightful information on the actual shape variations of tools. Several statistically significant relationships are found between shape variation and use-wear attributes, though the results emphasize the large variability of the shape of the convergent tools, which, in general, does not show a strong direct association with technological features and function. This is in good agreement with the technological context of this chronological period, characterized by a wide diversity of non-standardized tools adapted to multipurpose functions for varied subsistence activities. [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]

Debates over the pros and cons of a “publish or perish” philosophy have inflamed academia for at least half a century. Growing concerns, in particular, are expressed for policies that reward “quantity” at the expense of “quality,” because these might prompt scientists to unduly multiply their publications by fractioning (“salami slicing”), duplicating, rushing, simplifying, or even fabricating their results. To assess the reasonableness of these concerns, we analyzed publication patterns of over 40,000 researchers that, between the years 1900 and 2013, have published two or more papers within 15 years, in any of the disciplines covered by the Web of Science. The total number of papers published by researchers during their early career period (first fifteen years) has increased in recent decades, but so has their average number of co-authors. If we take the latter factor into account, by measuring productivity fractionally or by only counting papers published as first author, we observe no increase in productivity throughout the century. Even after the 1980s, adjusted productivity has not increased for most disciplines and countries. These results are robust to methodological choices and are actually conservative with respect to the hypothesis that publication rates are growing. Therefore, the widespread belief that pressures to publish are causing the scientific literature to be flooded with salami-sliced, trivial, incomplete, duplicated, plagiarized and false results is likely to be incorrect or at least exaggerated. [ABSTRACT FROM AUTHOR]

We revisit the results of the recent Reproducibility Project: Psychology by the Open Science Collaboration. We compute Bayes factors—a quantity that can be used to express comparative evidence for an hypothesis but also for the null hypothesis—for a large subset (N = 72) of the original papers and their corresponding replication attempts. In our computation, we take into account the likely scenario that publication bias had distorted the originally published results. Overall, 75% of studies gave qualitatively similar results in terms of the amount of evidence provided. However, the evidence was often weak (i.e., Bayes factor < 10). The majority of the studies (64%) did not provide strong evidence for either the null or the alternative hypothesis in either the original or the replication, and no replication attempts provided strong evidence in favor of the null. In all cases where the original paper provided strong evidence but the replication did not (15%), the sample size in the replication was smaller than the original. Where the replication provided strong evidence but the original did not (10%), the replication sample size was larger. We conclude that the apparent failure of the Reproducibility Project to replicate many target effects can be adequately explained by overestimation of effect sizes (or overestimation of evidence against the null hypothesis) due to small sample sizes and publication bias in the psychological literature. We further conclude that traditional sample sizes are insufficient and that a more widespread adoption of Bayesian methods is desirable. [ABSTRACT FROM AUTHOR]