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Due to the complexity of large production systems, maintenance events are diverse, simultaneous and dynamic. Appropriate maintenance management of complex large production systems can guarantee high availability and save maintenance costs. However, current maintenance decision-making methods mainly focus on the maintenance events of single-components and series connection multi-components; little research pays attention to the combination maintenance of different maintenance events. Therefore, this paper proposes a multi-event combination maintenance model based on event correlation. First, the maintenance downtime and cost of three types of maintenance events under different maintenance beginning times and degrees are analysed. Then, shared maintenance downtime and cost models are established by maintenance event correlations. In addition, a multi-event combination maintenance model is constructed to achieve the goal of the highest availability and the lowest cost rate in both the decision-making cycle and the remaining life. Moreover, a particle swarm optimization algorithm based on interval segmentation for model solving is designed. Finally, a numerical example is presented to illustrate the model. [ABSTRACT FROM AUTHOR]

The objective of this study is to establish a framework for analyzing infrastructure dynamics affecting the long-term steady state, and hence resilience in civil infrastructure systems. To this end, a multi-agent simulation model was created to capture important phenomena affecting the dynamics of coupled human-infrastructure systems and model the long-term performance regimes of infrastructure. The proposed framework captures the following three factors that shape the dynamics of coupled human-infrastructure systems: (i) engineered physical infrastructure; (ii) human actors; and (iii) chronic and acute stressors. A complex system approach was adopted to examine the long-term resilience of infrastructure based on the understanding of performance regimes, as well as tipping points at which shifts in the performance regime of infrastructure occur under the impact of external stressors and/or change in internal dynamics. The application of the proposed framework is demonstrated in a case of urban water distribution infrastructure using the data from a numerical case study network. The developed multi-agent simulation model was then used in examining the system resilience over a 100-year horizon under stressors such as population change and funding constraints. The results identified the effects of internal dynamics and external stressors on the resilience landscape of infrastructure systems. Furthermore, the results also showed the capability of the framework in capturing and simulating the underlying mechanisms affecting human-infrastructure dynamics, as well as long-term regime shifts and tipping point behaviors. Therefore, the integrated framework proposed in this paper enables building complex system-based theories for a more advanced understanding of civil infrastructure resilience. [ABSTRACT FROM AUTHOR]

When making decisions under uncertainty, individuals may form subjective expectations about probabilities of events relevant for their choice. Accurate measurement of subjective expectations is critical for high-quality data needed to analyze individual behavior. This paper reports the development and validity of a new method of eliciting point subjective expectations in developing countries. We developed a touchscreen-based application that combines an animated slider along with dynamic images that change relative sizes based on the probability indicated by the respondent. We compare our method to the more traditional approach of using beans as visual aids. First, we find that respondents have a sound understanding of basic concepts of probability. Second, we test for equality of the distributions elicited with the different methods and find them highly comparable. Third, we provide evidence that respondents report a more favorable opinion about the slider method and more willingness to complete long surveys using the slider rather than beans. Our findings suggest that the slider could be a viable elicitation method for empirical researchers who aim to collect data on subjective expectations in developing countries. [ABSTRACT FROM AUTHOR]

The integration of oilfield multidisciplinary ontology is increasingly important for the growth of the Semantic Web. However, current methods encounter performance bottlenecks either in storing data and searching for information when processing large amounts of data. To overcome these challenges, we propose a domain-ontology process based on the Neo4j graph database. In this paper, we focus on data storage and information retrieval of oilfield ontology. We have designed mapping rules from ontology files to regulate the Neo4j database, which can greatly reduce the required storage space. A two-tier index architecture, including object and triad indexing, is used to keep loading times low and match with different patterns for accurate retrieval. Therefore, we propose a retrieval method based on this architecture. Based on our evaluation, the retrieval method can save 13.04% of the storage space and improve retrieval efficiency by more than 30 times compared with the methods of relational databases. [ABSTRACT FROM AUTHOR]

Correctly recognizing emotions is an essential skill to manage interpersonal relationships in everyday life. Facial expression represents the most powerful mean to convey important information on emotional and cognitive states during interactions with others. In this paper, we analyze physiological responses triggered by an emotion recognition test, which requires the processing of facial cues. In particular, we evaluate the modulation of several Heart Rate Variability indices, collected during the Reading the Mind in the Eyes Test, accounting for test difficulty (derived from a Rasch analysis), test performances, demographic and psychological characteristics of the participants. The main idea is that emotion recognition is associated with the Autonomic Nervous System and, as a consequence, with the Heart Rate Variability. The principal goal of our study was to explore the complexity of the collected measures and their possible interactions by applying a class of flexible models, i.e., the latent class mixed models. Actually, this modelling strategy allows for the identification of clusters of subjects characterized by similar longitudinal trajectories. Both univariate and multivariate latent class mixed models were used. In fact, while the interpretation of the Heart Rate Variability indices is very difficult when considered individually, a joint evaluation provides a better description of the Autonomic Nervous System state. [ABSTRACT FROM AUTHOR]

This paper introduces a new method for acquiring and interpreting data on cognitive (or perceptual) networks. The proposed method involves the collection of multiple reports on randomly chosen pairs of individuals, and statistical means for aggregating these reports into data of conventional sociometric form. We refer to the method as “perceptual tomography” to emphasize that it aggregates multiple 3rd-party data on the perceived presence or absence of individual properties and pairwise relationships. Key features of the method include its low respondent burden, flexible interpretation, as well as its ability to find “robust intransitive” ties in the form of perceived non-edges. This latter feature, in turn, allows for the application of conventional balance clustering routines to perceptual tomography data. In what follows, we will describe both the method and an example of the implementation of the method from a recent community study among Alaska Natives. Interview data from 170 community residents is used to ascribe 4446 perceived relationships (2146 perceived edges, 2300 perceived non-edges) among 393 community members, and to assert the perceived presence (or absence) of 16 community-oriented helping behaviors to each individual in the community. Using balance theory-based partitioning of the perceptual network, we show that people in the community perceive distinct helping roles as structural associations among community members. The fact that role classes can be detected in network renderings of “tomographic” perceptual information lends support to the suggestion that this method is capable of producing meaningful new kinds of data about perceptual networks. [ABSTRACT FROM AUTHOR]

Land reclamation has become a significant way for the improvement of ecological environment in mining areas. When selecting the optimal land reclamation scheme, LNNs (linguistic neutrosophic numbers) are suitable to describe the complex fuzzy evaluation information through linguistic truth, indeterminacy and falsity membership degrees. Furthermore, the Hamacher aggregation operators are good tools to handle multi-criteria decision making problems. Accordingly, the aim of this paper is to extend Hamacher aggregation operators with LNNs and then build a decision making framework for evaluating land reclamation schemes in mining areas. First, new operational laws of LNNs based on Hamacher t-norm and t-conorm are defined. Then, several linguistic neutrosophic Hamacher aggregation operators, including the linguistic neutrosophic Hamacher weighted mean aggregation operators and linguistic neutrosophic Hamacher hybrid weighted mean aggregation operators are developed. Meanwhile, their desirable properties are proved. Thereafter, a method for decision making with linguistic neutrosophic information based on these operators is proposed to deal with complex decision problems. At last, the validity of this method is confirmed by an illustrative example of evaluating the land reclamation schemes in mining areas. In addition, the impact of the parameter in extended Hamacher aggregation operators is discussed. The merits of the proposed method are also highlighted by comparing with other decision making methods. The results show that the proposed linguistic neutrosophic Hamacher aggregation operators have great flexibility and advantages, and can provide powerful ways for the evaluation of land reclamation schemes. [ABSTRACT FROM AUTHOR]

Distribution centers (DCs) are an important part of the modern logistics system. The selection of a location for a DC is significant for saving costs and reducing externalities caused by distribution. In this paper, we propose a new hybrid method based on the analytic hierarchy process (AHP) and 2-tuple hybrid ordered weighted averaging (THOWA) to select the location of a DC in a megacity. First, we propose a new set of evaluation criteria integrating economic, political, social and ecological information based on the characteristics of Chinese megacities. Second, subjective criteria weights are calculated by AHP combining the evaluation of logistics experts. Third, experts from academia, enterprise and government assess the performance of alternatives. In addition, the overall evaluation values are aggregated by an improved THOWA operator to rank the alternatives. Finally, we conduct a sensitivity analysis to investigate the influence of criteria weights on the decision-making process. The proposed method is novel and addresses the uncertainty under limited quantitative information, which has the advantages of avoiding information loss and distortion problems in the integrating process and operating linguistic evaluation information effectively. The proposed method can be practically applied by municipal planning departments in deciding on the location of new DCs. A numerical application of the proposed method is provided. [ABSTRACT FROM AUTHOR]

Little is known about the interplay between affective and cognitive processes of decision making within the bounded rationality perspective, in particular for the debate on adaptive decision making and strategy selection. This gap in the knowledge is particularly important as affect and deliberation may direct preferences in opposite directions. How do decision makers solve such dissonance? In this paper, we address this question by exploring the use of integral affect as a choice heuristic in comparison with and in conjunction to “take the best,” and weighted addition of attributes (WADD). We operationalize theories of reliance on affect in choice through a "Take the emotionally best" algorithm. Its predictive power is experimentally tested against other models, including mixed-sequential cognitive/affective procedures. We find that individual decisions are better predicted by a sequential combination of "Take the emotionally best" and "Take the best" with a slight dominance of the former. Conditions of cognitive/affective ambivalence, low discrimination ability and high complexity provide the cognitive architecture where such blended choice strategies predict decisions more precisely. This implies that reliance on integral affect may precede the use of cognitive cues following an ecological rationality perspective rather than supporting a kind of competition between affect and cognition as implied in current literature. [ABSTRACT FROM AUTHOR]

EFs are a set of processes that supports many cognitive domains as goal setting, monitoring, planning, and cognitive-behavioural flexible control. Currently, many standardized paper-and-pencil tests or scales are used to assess EFs. These tests are easy to administer, score, and interpret but present some limitations in terms of generalizability of behaviours in real life. More recently, Information and Communication Technology has provided a higher ecological validity in the EFs assessment. In order to increase the ecological validity, we have developed a serious game (SG), named EXPANSE, which aim was to compare the participants’ game performance (latency times, and correct answers) with the results obtained in the traditional tasks and scales. 354 healthy subjects participated to the study and the findings showed significant correlations among standard tasks and the serious game. The exploratory nature of the present study, on one hand, highlighted that SG could be an additional behavioral tool to assess EFs and, on the other, we need further investigations, including clinical populations, for better defining the game sensitivity toward EF components. Finally, the results show that serious games are a promising technology for the evaluation of real cognitive behavior along with traditional evaluation. [ABSTRACT FROM AUTHOR]

The ability to innovatively use or even manufacture different tools depending on a current situation can be silhouetted against examples of stereotyped, inborn tool use/manufacture and is thus often associated to advanced cognitive processing. In this study we confronted non-specialized, yet innovative tool making birds, Goffin’s cockatoos (Cacatua goffiniana), with an apparatus featuring an out-of-reach food reward that could be placed at different distances from a tool opening. Alternatively, the food stayed at a constant distance but the tool opening in the front of the apparatus had different diameters. We used a novel material for tool manufacture (cardboard) that demanded an incrementally increased manufacturing effort from the actor, depending on the length of the tool required. We found that our subjects used two strategies to succeed in this tasks: either by making carboard-stripe tools using the full length of the material sheets originally offered or by adjusting the lengths of their tools to different goal distances. Subjects also discarded cardboard stripes that were too short to reach the goal prior to use and discarded longer pieces when the goal was further away than when it was close. Nevertheless, likely due to morphological constraints, the birds failed to adjust the widths of their tools depending on the diameter of the tool opening. [ABSTRACT FROM AUTHOR]

We present a non-parametric extension of the conditional logit model, using Gaussian process priors. The conditional logit model is used in quantitative social science for inferring interaction effects between personal features and choice characteristics from observations of individual multinomial decisions, such as where to live, which car to buy or which school to choose. The classic, parametric model presupposes a latent utility function that is a linear combination of choice characteristics and their interactions with personal features. This imposes strong and unrealistic constraints on the form of individuals’ preferences. Extensions using non-linear basis functions derived from the original features can ameliorate this problem but at the cost of high model complexity and increased reliance on the user in model specification. In this paper we develop a non-parametric conditional logit model based on Gaussian process logit models. We demonstrate its application on housing choice data from over 50,000 moving households from the Stockholm area over a two year period to reveal complex homophilic patterns in income, ethnicity and parental status. [ABSTRACT FROM AUTHOR]

Background: Violence against women by an intimate partner (DV) is a serious public health and human rights issue. Attitudes justifying DV strongly predict its perpetration and victimisation. This paper presents gendered ecological analyses of the societal acceptance of DV in 49 low- and middle-income countries (LMICs) across geographical regions. Methods and findings: We utilised data from 49 Demographic and Health Surveys conducted between 2005 and 2017, United Nations Statistics and topic-specific meta-databases. DV acceptance was measured as the justification of ‘wife-beating’ in at least one of five circumstances, and by the summative scale. Stepwise multiple linear regression examined country-level social, economic and political empowerment predictors of societal acceptance of DV amongst women, men, and the aggregate gender difference. Women were more likely than men to justify DV in Sub-Saharan Africa and South (east) Asia with societal acceptance of DV being more widespread in these regions compared with Latin America, the Caribbean, Central/West Asia and Europe. Political conflict and limited economic rights for women were associated with higher levels of DV acceptance amongst women and men. Men in more democratic countries were less likely to justify DV. Amongst women, higher national female literacy rates predicted lower levels of justification. There were higher levels of DV acceptance amongst women and a wider aggregate gender difference in countries with a larger representation of women in national parliament. Conclusion: Justification of DV is widespread amongst women and men in LMICs with acceptance rates varying across countries and regions. Gender differences in the impact of contextual factors on DV acceptance supports a gendered approach to national-level interventions. Our findings highlight the need for tailored interventions targeting DV acceptance in conflict-impacted societies. The emphasis of inter(national) policies on the ‘empowerment’ domains of widely-used gender (in)equality indices need to be coupled with strategies tackling discriminatory gender norms. [ABSTRACT FROM AUTHOR]

Understanding the sense of discourse relations between segments of text is essential to truly comprehend any natural language text. Several automated approaches have been suggested, but all rely on external resources, linguistic feature engineering, and their processing pipelines are built from substantially different models. In this paper, we introduce a novel system for sense classification of shallow discourse relations (FR system) based on focused recurrent neural networks (RNNs). In contrast to existing systems, FR system consists of a single end-to-end trainable model for handling all types and senses of discourse relations, requires no feature engineering or external resources, is language-independent, and can be applied at the word and even character levels. At its core, we present our novel generalization of the focused RNNs layer, the first multi-dimensional RNN-attention mechanism for constructing text/argument embeddings. The filtering/gating RNN enables downstream RNNs to focus on different aspects of the input sequence and project it into several embedding subspaces. These argument embeddings are then used to perform sense classification. FR system has been evaluated using the official datasets and methodology of CoNLL 2016 Shared Task. It does not fall a lot behind state-of-the-art performance on English, the most researched and supported language, but it outperforms existing best systems by 2.5% overall results on the Chinese blind dataset. [ABSTRACT FROM AUTHOR]

The integration of ubiquitous technologies in the field of education has considerably enhanced our way of learning. Such technologies enable students to get a gradual feedback about their performance and to provide adapted learning materials. It is particularly important in the domain of foreign language learning which requires intense daily practice. One of the main inputs of adaptive learning systems is the user’s understanding of a reading material. The reader’s understanding can be divided into two parts: the objective understanding and the subjective understanding. The objective understanding can be measured by comprehension questions about the content of the text. The subjective understanding is the reader’s perception of his own understanding. The subjective understanding plays an important role in the reader’s motivation, self-esteem and confidence. However, its automatic estimation remains a challenging task. This paper is one of the first to propose a method to estimate the subjective understanding. We show that using the eye gaze to predict the subjective understanding improves the estimation by 13% as compared to using comprehension questions. [ABSTRACT FROM AUTHOR]

Smartphones and other mobile devices have fundamentally changed patterns of Internet use in everyday life by making online access constantly available. The present paper offers a theoretical explication and empirical assessment of the concept of online vigilance, referring to users’ permanent cognitive orientation towards online content and communication as well as their disposition to exploit these options constantly. Based on four studies, a validated and reliable self-report measure of online vigilance was developed. In combination, the results suggest that the Online Vigilance Scale (OVS) shows a stable factor structure in various contexts and user populations and provides future work in communication, psychology, and other social sciences with a new measure of the individual cognitive orientation towards ubiquitous online communication. [ABSTRACT FROM AUTHOR]

With the deepening of the research on supply chain management, scholars have increasingly begun to investigate the impact of fairness on a supply chain, and many conclusions suggest that a simple wholesale price can coordinate a supply chain under specific conditions. However, the corresponding analysis and other optimization mechanisms that affect the situation in which the channel cannot be coordinated are either omitted or given little attention. In this paper, we constructed a dyadic supply chain with a single manufacturer and a single retailer; the manufacturer acts as a selfish leader, and the retailer acts as a follower with fairness concerns and sales efforts. For this setting, we derived the equilibrium strategy solution for a wholesale price contract and cost sharing of effort (CS-E) contract offered by the manufacturer, and the results indicated that both contracts achieved channel coordination with different requirements. Further, the profit of the manufacturer and the sales effort of the retailer under CS-E contracts were never less than those for the wholesale price contract, and there was an interval during which the retailer's profit and utility and supply chain efficiency were better than those under the wholesale price contract. In addition, we described situations in which a CS-E contract is unnecessary. These results should be a useful reference for managerial decisions and organizations. [ABSTRACT FROM AUTHOR]

Background: Regular expression matching (REM) is widely employed as the major tool for deep packet inspection (DPI) applications. For automatic processing, the regular expression patterns need to be converted to a deterministic finite automata (DFA). However, with the ever-increasing scale and complexity of pattern sets, state explosion problem has brought a great challenge to the DFA based regular expression matching. Rule grouping is a direct method to solve the state explosion problem. The original rule set is divided into multiple disjoint groups, and each group is compiled to a separate DFA, thus to significantly restrain the severe state explosion problem when compiling all the rules to a single DFA. Objective: For practical implementation, the total number of DFA states should be as few as possible, thus the data structures of these DFAs can be deployed on fast on-chip memories for rapid access. In addition, to support fast pattern update in some applications, the time cost for grouping should be as small as possible. In this study, we aimed to propose an efficient grouping method, which generates as few states as possible with as little time overhead as possible. Methods: When compiling multiple patterns into a single DFA, the number of DFA states is usually greater than the total number of states when compiling each pattern to a separate DFA. This is mainly caused by the semantic overlaps among different rules. By quantifying the interaction values for each pair of rules, the rule grouping problem can be reduced to the maximum k-cut graph partitioning problem. Then, we propose a heuristic algorithm called the one-step greedy (OSG) algorithm to solve this NP-hard problem. What’s more, a subroutine named the heuristic initialization (HI) algorithm is devised to further optimize the grouping algorithms. Results: We employed three practical rule sets for the experimental evaluation. Results show that the OSG algorithm outperforms the state-of-the-art grouping solutions regarding both the total number of DFA states and time cost for grouping. The HI subroutine also demonstrates its significant optimization effect on the grouping algorithms. Conclusions: The DFA state explosion problem has became the most challenging issue in the regular expression matching applications. Rule grouping is a practical direction by dividing the original rule sets into multiple disjoint groups. In this paper, we investigate the current grouping solutions, and propose a compact and efficient grouping algorithm. Experiments conducted on practical rule sets demonstrate the superiority of our proposal. [ABSTRACT FROM AUTHOR]

Protected areas (PAs) are a prominent approach to maintaining and enhancing biodiversity and ecosystem services. A critical question for safeguarding these resources is how PA governance processes and management structures influence their effectiveness. We conduct an impact evaluation of 12 PAs in three Central American countries to assess how processes in management restrictions, management capacity, and decentralization affect the annual change in the satellite-derived Normalized Difference Vegetation Index (NDVI). NDVI varies with greenness that relates to plant production, biomass, and important ecosystem functions related to biodiversity and ecosystem services such as water quality and carbon storage. Any loss of vegetation cover in the form of deforestation or degradation would show up as a decrease in NDVI values over time and gains in vegetation cover and regeneration as an increase in NDVI values. Management restriction categories are based on international classifications of strict versus multiple-use PAs, and capacity and decentralization categories are based on key informant interviews of PA managers. We use matching to create a counterfactual of non-protected observations and a matching estimator and regression to estimate treatment effects of each sub-sample. On average, strict and multiple-use PAs have a significant and positive effect on NDVI compared to non-protected land uses. Both high and low decentralized PAs also positively affect NDVI. High capacity PAs have a positive and significant effect on NDVI, while low capacity PAs have a negative effect on NDVI. Our findings advance knowledge on how governance and management influence PA effectiveness and suggest that capacity may be more important than governance type or management restrictions in maintaining and enhancing NDVI. This paper also provides a guide for future studies to incorporate measures of PA governance and management into impact evaluations. [ABSTRACT FROM AUTHOR]

Background: Self-harm (SH) accounts for over 5% of the workload of emergency ambulance services, and therefore Paramedics are often the first health professional in contact with people who SH. The authors of this paper have reported elsewhere the significant gaps in our understandings which exist surrounding this early care interaction, and some of the challenges paramedics and opportunities in paramedic care for people who SH. This study aimed to explore paramedics’ perceptions of caring for those who SH using Evolved Grounded Theory Methodology. Methods: This study took place between 2014–2016 in one UK ambulance service covering a population of three million people. Semi structured interviews were conducted, purposively sampling paramedics until saturation was reached. Interviews were recorded, transcribed verbatim, and coded through open, axial, and selective levels of coding, identifying the Basic Social Process (BSP) and developing a Grounded Theory. A second researcher (SH) independently reviewed early results, which were also member-checked with participants. Results: Eleven paramedics were interviewed. The following six categories emerged: Context; Judgements and values; Isolation and system failure; Managing risk; Competence at the boundary of mental and physical health needs; Professional, legal and ethical tensions. The BSP Decision making in a context of risk was identified. The final Grounded Theory that emerged was one of ‘Wicked Complexity of paramedic care for people who SH, which includes usual factors such as tiredness and frequent callers, heightened factors including lack of support and pathways, and factors specific to SH such assessing mental health and suicide risk. Conclusions: This study builds on a very small body of literature to have explored paramedic care for people who SH and has found that this care interaction provides uniquely complex challenges. The multiple influences within the categories defined in this study need considering conjointly when making improvements to care. [ABSTRACT FROM AUTHOR]

Adverse weather related to reduced visibility caused by fog and rain can seriously affect the mobility and safety of drivers. It is meaningful to develop effective intelligent transportation system (ITS) strategies to mitigate the negative effects of these different types of adverse weather related to reduced visibility by investigating the effect of rain and fog on traffic parameters. A number of previous researches focused on analyzing the effect of adverse weather related to reduced visibility by using simulated traffic and weather data. There are few researchers that addressed the impact of adverse weather instances using real-time data. Moreover, this paper conducts comprehensive investigation to clearly compare the changes of driving behavior and traffic parameters in adverse weather including fog and rain using real-time traffic and weather data collected by advanced vehicle-based traffic sensors and weather sensors. After some preliminary analysis, the analysis of variance method (ANOVA) was applied to further compare the significance of effects of these two kinds of adverse weather on traffic parameters. The conditional regression models were employed finally to explore the relationship between these two types of adverse weather and traffic parameters. The results would be beneficial to develop effective intelligent traffic control countermeasures under these different types of adverse weather conditions related to reduced visibility. [ABSTRACT FROM AUTHOR]

Wireless Mesh Networks (WMNs) integrate the advantages of WLANs and mobile Ad Hoc networks, which have become the key techniques of next-generation wireless networks in the context of emergency recovery. Wireless Mesh Networks (WMNs) are multi-hop wireless networks with instant deployment, self-healing, self-organization and self-configuration features. These capabilities make WMNs a promising technology for incident and emergency communication. An incident area network (IAN) needs a reliable and lively routing path during disaster recovery and emergency response operations when infrastructure-based communications and power resources have been destroyed and no routes are available. Power aware routing plays a significant role in WMNs, in order to provide continuous efficient emergency services. The existing power aware routing algorithms used in wireless networks cannot fully fit the characteristics of WMNs, to be used for emergency recovery. This paper proposes a power aware routing algorithm (PARA) for WMNs, which selects optimal paths to send packets, mainly based on the power level of next node along the path. This algorithm was implemented and tested in a proven simulator. The analytic results show that the proposed power node-type aware routing algorithm metric can clearly improve the network performance by reducing the network overheads and maintaining a high delivery ratio with low latency. [ABSTRACT FROM AUTHOR]

People are motivated to hold favorable attitudes about the systems on which they depend, so they justify (i.e., rationalize, defend and bolster) forms of social and economic inequality, even if the inequality is disadvantageous to them. This paper examines how this system-justifying motivation is reflected in behaviors involving interpersonal trust. In a series of three experiments using the trust game I manipulate income inequality by providing participants with higher (advantaged position) or lower (disadvantaged position) initial endowments and measure their trust toward individuals on the same or on different positions. Results show that higher income individuals trust other higher income individuals more than lower income individuals (ingroup favoritism), while lower income individuals trust higher income individuals more than lower income individuals (outgroup favoritism). It is also shown that the strength of these biases is dependent on the level of endorsement of system justifying ideology and the legitimacy of the system. More trust toward those in advantaged positions within a social system, expressed both by equally advantaged as well as by disadvantaged others, not only secures the advantaged in their positions but also reinforces the underlying inequality. [ABSTRACT FROM AUTHOR]

Feature stability, time and tempo of change, and the role of genealogy versus areality in creating linguistic diversity are important issues in current computational research on linguistic typology. This paper presents a database initiative, DiACL Typology, which aims to provide a resource for addressing these questions with specific of the extended Indo-European language area of Eurasia, the region with the best documented linguistic history. The database is pre-prepared for statistical and phylogenetic analyses and contains both linguistic typological data from languages spanning over four millennia, and linguistic metadata concerning geographic location, time period, and reliability of sources. The typological data has been organized according to a hierarchical model of increasing granularity in order to create datasets that are complete and representative. [ABSTRACT FROM AUTHOR]

In this paper the possibility of classification of X-ray images of the cervical vertebrae is studied. The images should be classified into one of the following classes—the images of healthy vertebrae and the images of vertebrae with syndesmophytes. The vertebra contours, described unambiguously by using the generalized shape language, are the basis of the analysis. As a result, the contour is represented as a chain of sinquads that determine switches. The found switches are the characteristic points of the analyzed contour. In these points additional features of the contour are determined. On the basis of these features two aforementioned classes of images are defined as fuzzy sets. Such an approach allows us to create a hierarchical algorithm of classification based on the syntactic and fuzzy description of the contour. [ABSTRACT FROM AUTHOR]

To predict what products customers will buy in next transaction is an important task. Existing work in next-basket prediction can be summarized into two paradigms. One is the item-centric paradigm, where sequential patterns are mined from customers’ transactional data and leveraged for prediction. However, these approaches usually suffer from the data sparseness problem. The other is the user-centric paradigm, where collaborative filtering techniques have been applied on customers’ historical data. However, these methods ignore the sequential behaviors of customers which is often crucial for next-basket prediction. In this paper, we introduce a hybrid method, namely the Co-Factorization model over Sequential and Historical purchase data (CFSH for short) for next-basket recommendation. Compared with existing methods, our approach conveys the following merits: 1) By mining global sequential patterns, we can avoid the sparseness problem in traditional item-centric methods; 2) By factorizing product-product and customer-product matrices simultaneously, we can fully exploit both sequential and historical behaviors to learn customer and product representations better; 3) By using a hybrid recommendation method, we can achieve better performance in next-basket prediction. Experimental results on three real-world purchase datasets demonstrated the effectiveness of our approach as compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

This paper evaluates a novel high variability phonetic training paradigm that involves presenting spoken words in adverse conditions. The effectiveness, generalizability, and longevity of this high variability phonetic training in adverse conditions was evaluated using English phoneme contrasts in three experiments with Malaysian multilinguals. Adverse conditions were created by presenting spoken words against background multi-talker babble. In Experiment 1, the adverse condition level was set at a fixed level throughout the training and in Experiment 2 the adverse condition level was determined for each participant before training using an adaptive staircase procedure. To explore the effectiveness and sustainability of the training, phonemic discrimination ability was assessed before and immediately after training (Experiments 1 and 2) and 6 months after training (Experiment 3). Generalization of training was evaluated within and across phonemic contrasts using trained and untrained stimuli. Results revealed significant perceptual improvements after just three 20-minute training sessions and these improvements were maintained after 6 months. The training benefits also generalized from trained to untrained stimuli. Crucially, perceptual improvements were significantly larger when the adverse conditions were adapted before each training session than when it was set at a fixed level. As the training improvements observed here are markedly larger than those reported in the literature, this indicates that the individualized phonetic training regime in adaptive adverse conditions (HVPT-AAC) is highly effective at improving speech perception. [ABSTRACT FROM AUTHOR]

This paper describes an approach for improving the accuracy of memory-based collaborative filtering, based on the technique for order of preference by similarity to ideal solution (TOPSIS) method. Recommender systems are used to filter the huge amount of data available online based on user-defined preferences. Collaborative filtering (CF) is a commonly used recommendation approach that generates recommendations based on correlations among user preferences. Although several enhancements have increased the accuracy of memory-based CF through the development of improved similarity measures for finding successful neighbors, there has been less investigation into prediction score methods, in which rating/preference scores are assigned to items that have not yet been selected by a user. A TOPSIS solution for evaluating multiple alternatives based on more than one criterion is proposed as an alternative to prediction score methods for evaluating and ranking items based on the results from similar users. The recommendation accuracy of the proposed TOPSIS technique is evaluated by applying it to various common CF baseline methods, which are then used to analyze the MovieLens 100K and 1M benchmark datasets. The results show that CF based on the TOPSIS method is more accurate than baseline CF methods across a number of common evaluation metrics. [ABSTRACT FROM AUTHOR]

The South African Middle Stone Age (MSA) has in recent years become increasingly important for our understanding of the emergence of ‘modern human behaviours’. Several key innovations appeared in this context for the first time, significantly pre-dating their re-invention in the European Upper Palaeolithic. One of these innovations was heat treatment of stone to improve its quality for the production of stone tools. Heat treatment may even be the oldest well-documented technique used to intentionally alter the properties of materials in general. It is commonly thought of as requiring the skilled use of fire, a high degree of planning depth and complex cognitive abilities. However, to work on these fundamental concepts we need to analyse the techniques and procedures used to heat-treat and we need to understand what they imply. In this paper, we present a direct and expedient comparison between the technical complexities of four alternative heat treatment procedures by coding the behaviours required for their set-up in so-called cognigrams, a relatively new method for understanding complexity based on the problem-solution distance. Our results show that although the techniques significantly differ in complexity, the techniques used in the MSA fall within the range of complexities known from other MSA techniques. Heat treatment in above-ground fires, as it was practised during this period in South Africa, was even one of the most complex techniques at the time of its invention. Early heat treatment can therefore be considered an important behavioural proxy that may shed light on the behaviour and socioeconomic structure of past groups. The implications of this are highlighted by the ongoing debate about ‘modernity’, ‘behavioural flexibility’ and ‘complex cognition’ of early anatomically modern humans in Africa. [ABSTRACT FROM AUTHOR]

The Colless index is one of the most popular and natural balance indices for bifurcating phylogenetic trees, but it makes no sense for multifurcating trees. In this paper we propose a family of Colless-like balance indices that generalize the Colless index to multifurcating phylogenetic trees. Each is determined by the choice of a dissimilarity D and a weight function . A balance index is sound when the most balanced phylogenetic trees according to it are exactly the fully symmetric ones. Unfortunately, not every Colless-like balance index is sound in this sense. We prove then that taking f(n) = ln(n + e) or f(n) = en as weight functions, the resulting index is sound for every dissimilarity D. Next, for each one of these two functions f and for three popular dissimilarities D (the variance, the standard deviation, and the mean deviation from the median), we find the most unbalanced phylogenetic trees according to with any given number n of leaves. The results show that the growth pace of the function f influences the notion of “balance” measured by the indices it defines. Finally, we introduce our R package “CollessLike,” which, among other functionalities, allows the computation of Colless-like indices of trees and their comparison to their distribution under Chen-Ford-Winkel’s α-γ-model for multifurcating phylogenetic trees. As an application, we show that the trees in TreeBASE do not seem to follow either the uniform model for multifurcating trees or the α-γ-model, for any values of α and γ. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new model of memristive multidirectional associative memory neural networks, which concludes the time-varying delays in leakage terms via sampled-data control. We use the input delay method to turn the sampling system into a continuous time-delaying system. Then we analyze the exponential stability and asymptotic stability of the equilibrium points for this model. By constructing a suitable Lyapunov function, using the Lyapunov stability theorem and some inequality techniques, some sufficient criteria for ensuring the stability of equilibrium points are obtained. Finally, numerical examples are given to demonstrate the effectiveness of our results. [ABSTRACT FROM AUTHOR]

Research utilizing repeated-measures such as daily assessments with self-report and/or objective measures [e.g., physical activity (PA) monitors] are important in understanding health behaviors and informing practice and policy. However, studies that utilize daily assessment often encounter issues with attrition and non-compliance. The current research yielded high levels of retention and adherence with both self-report and objective daily measures. The purpose of this paper is to highlight and discuss strategies utilized in maximizing retention, minimizing missing data, and some lessons learned from the research experience. Fifty community participants took part in a 4-week study utilizing both daily self-report questionnaires and daily use of PA monitors (Fitbit One™). This study focused on typical daily PA and was not an intervention study (e.g., participants were not randomized nor asked to change their PA behavior). Participants completed the study in two waves (wave 1 n = 10, wave 2 n = 40). The research team utilized several retention strategies including automating the data collection process, a prorated incentive structure, having a dedicated and responsive study staff, and utilizing the 2-wave process to optimize data collection during the 2nd wave. The study had 100% retention and generally positive anonymous feedback post-study. Overall, participants completed the vast majority of daily surveys (97%) and wore their Fitbits (for at least part of the day) on almost all days (99.57%) of the study, although there were individual differences. The strategies discussed and lessons learned may be useful to other researchers using daily measurements for whom adherence and retention are important issues. Future research employing these strategies in different populations, with different measurements, and for longer durations is warranted to determine generalizability. [ABSTRACT FROM AUTHOR]

In palynology, the visual classification of pollen grains from different species is a hard task which is usually tackled by human operators using microscopes. Its complete automatization would save a high quantity of resources and provide valuable improvements especially for allergy-related information systems, but also for other application fields as paleoclimate reconstruction, quality control of honey based products, collection of evidences in criminal investigations or fabric dating and tracking. This paper presents three state-of-the-art deep learning classification methods applied to the recently published POLEN23E image dataset. The three methods make use of convolutional neural networks: the first one is strictly based on the idea of transfer learning, the second one is based on feature extraction and the third one represents a hybrid approach, combining transfer learning and feature extraction. The results from the three methods are indeed very good, reaching over 97% correct classification rates in images not previously seen by the models, where other authors reported around 70. [ABSTRACT FROM AUTHOR]

Pupil diameter and microsaccades are captured by an eye tracker and compared for their suitability as indicators of cognitive load (as beset by task difficulty). Specifically, two metrics are tested in response to task difficulty: (1) the change in pupil diameter with respect to inter- or intra-trial baseline, and (2) the rate and magnitude of microsaccades. Participants performed easy and difficult mental arithmetic tasks while fixating a central target. Inter-trial change in pupil diameter and microsaccade magnitude appear to adequately discriminate task difficulty, and hence cognitive load, if the implied causality can be assumed. This paper’s contribution corroborates previous work concerning microsaccade magnitude and extends this work by directly comparing microsaccade metrics to pupillometric measures. To our knowledge this is the first study to compare the reliability and sensitivity of task-evoked pupillary and microsaccadic measures of cognitive load. [ABSTRACT FROM AUTHOR]

Responding to modern day environmental challenges for societal well-being and prosperity necessitates the integration of science into policy and practice. This has spurred the development of novel institutional structures among research organisations aimed at enhancing the impact of environmental science on policy and practice. However, such initiatives are seldom evaluated and even in cases where evaluations are undertaken, the results are rarely made publicly available. As such there is very little empirically grounded guidance available to inform other organisations in this regard. To help address this, the aim of this study is to evaluate the Baltic Eye Project at Stockholm University–a unique team consisting of researchers from different fields, science communicators, journalists and policy analysts–working collectively to support evidence-informed decision-making relating to the sustainable management of the Baltic Sea environment. Specifically, through qualitative interviews, we (1) identify the impacts achieved by the Baltic Eye Project; (2) understand the challenges and barriers experienced throughout the Baltic Eye Project; and (3) highlight the key features that are needed within research organisations to enhance the impact of science on policy and practice. Results show that despite only operating for three years, the Baltic Eye Project has achieved demonstrable impacts on a range of levels: impacts on policy and practice, impacts to individuals working within the organisation and impacts to the broader University. We also identify a range of barriers that have limited impacts to date, such as a lack of clear goals at the establishment of the Baltic Eye Project and existing metrics of academic impact (e.g. number of publications). Finally, based on the experiences of employees at the Baltic Eye Project, we identify the key organisational, individual, financial, material, practical, political, and social features of university-based boundary organisations that have impact on policy and practice. In doing so this paper provides empirically-derived guidance to help other research organisations increase their capacity to achieve tangible impacts on environmental policy and practice. [ABSTRACT FROM AUTHOR]

Scientific writing, particularly quantitative writing, is difficult to master. To help undergraduate students write more clearly about data, we sought to deconstruct writing into discrete, specific elements. We focused on statements typically used to describe data found in the results sections of research articles (quantitative comparative statements, QC). In this paper, we define the essential components of a QC statement and the rules that govern those components. Clearly defined rules allowed us to quantify writing quality of QC statements (4C scoring). Using 4C scoring, we measured student writing gains in a post-test at the end of the term compared to a pre-test (37% improvement). In addition to overall score, 4C scoring provided insight into common writing mistakes by measuring presence/absence of each essential component. Student writing quality in lab reports improved when they practiced writing isolated QC statements. Although we observed a significant increase in writing quality in lab reports describing a simple experiment, we noted a decrease in writing quality when the complexity of the experimental system increased. Our data suggest a negative correlation of writing quality with complexity. We discuss how our data aligns with existing cognitive theories of writing and how science instructors might improve the scientific writing of their students. [ABSTRACT FROM AUTHOR]

Problem Solving (PS) skills allow students to handle problems within an educational context. PS is a core competence of Computer Science education and affects programming success. In this vein, this paper aims to investigate PS ability performance in primary school pupils of a computer course, implemented according to the Neo-Piagetian theory of cognitive development. The study included 945 Slovenian pupils, ranging from fourth to sixth grade. The effects of gender, age and consecutive years of attending the course were examined on pupils’ PS ability at the pre-operational and concrete operational stages. Pupils completed a survey questionnaire with four types of tasks (a series of statements, if-statements, loops and variables) at both stages. The analysis revealed three findings: the performance of PS ability in all tasks was, at the pre-operational stage, associated positively with performance at the concrete operational stage; there were no gender differences in PS performance at both stages, and both the grade and consecutive year of taking the computer course had an effect on PS ability performance at both stages. Those in the lowest grade and those taking the course for the first year reported lower performances than their older counterparts. These findings may help curriculum designers across the world develop efficient approaches to teaching computer courses. [ABSTRACT FROM AUTHOR]

This paper proposes a global approach for the multi-view registration of unordered range scans. Our method starts with the pair-wise registration, where multi-scale descriptor is selected for feature point and the propagation of feature correspondence is accordingly accelerated. Subsequently, we design an effective rule to judge the reliability of these pair-wise registration results. According to the judgment of reliability, we propose a model fusion method, which can utilize reliable results of pair-wise registration to augment the model shape. Finally, multi-view registration can be achieved by operating the pair-wise registration, reliability judgment, and model fusion alternately. The proposed approach can be applied to scene reconstruction and robot mapping. Experimental results conducted on public datasets show that the proposed approach can automatically achieve multi-view registration of unordered range scans. Compared with other related approaches, the proposed approach has superior performances in accuracy and effectiveness. [ABSTRACT FROM AUTHOR]

The last decade has seen the emergence of new views about the mechanisms underlying specificity (or, conversely, generalization) of visual skill learning. Here, we trained participants at orientation discrimination paradigm at a peripheral position to induce position and orientation specificity and to test its underlying mechanisms. Specifically, we aimed to test whether the within-quadrant spatial gradient of generalization is determined by cortical magnification, which would show that retinotopic plasticity contributes to learning and specificity. Additionally, we aimed to test whether late parts of the learning relate differently to specificity compared to early parts. This is relevant in the context of double training papers, which suggest that rule-based mechanisms of specificity in fast, early learning also would apply to late, slower learning. Our data showed partial but significant position and orientation specificity within quadrants. Interestingly, specificity was greatest for those participants who had continued to show threshold decreases during the last five sessions of training (late, asymptotic learning). Performance gains during early learning were less related to specificity. A trend for skill to spread over larger distances towards periphery than towards central vision suggested contributions to transfer of early visual areas showing cortical magnification of central vision. Control experiments however did not support this hypothesis. In summary, our study demonstrates significant specificity after extensive perceptual learning, and indicates that asymptotic learning recruits specific mechanisms that promote specificity, and that may not be recruited yet in early parts of the learning. The contributions of different mechanisms to early and late learning suggests that following these different learning periods, generalization relies on different principles and is subjected to different limits. [ABSTRACT FROM AUTHOR]

The reliable and objective assessment of intelligence and personality has been a topic of increasing interest of contemporary neuroscience and psychology. It is known that intelligence can be measured by estimating the mental speed or velocity of information processing. This is usually measured as a reaction time during elementary cognitive task processing, while personality is often assessed by means of questionnaires. On the other hand, human personality affects the way a subject accomplishes elementary cognitive tasks and, therefore, some personality features can define intelligence. It is expected that these features, as well as mental abilities in performing cognitive tasks are associated with the brain’s electrical neural activity. Although several studies reported correlation between event-related potentials, mental ability and intelligence, there is a lack of information about time-frequency and spatio-temporal structures of neural activity which characterize this relation. In the present work, we analyzed human electroencephalograms (EEG) recorded during the performance of elementary cognitive tasks using the Schulte test, which is a paper-pencil based instrument for assessing elementary cognitive ability or mental speed. According to particular features found of the EEG structure, we divided the subjects into three groups. For subjects in each group, we applied the Sixteen Personality Factor Questionnaire (16PF) to assess the their personality traits. We demonstrated that each group exhibited a different score on the personality scale, such as warmth, reasoning, emotional stability and dominance. Summing up, we found a link between EEG features, mental abilities and personality traits. The obtained results can be of great interest for testing human personality to create automatized intelligent programs which combine simple tests and EEG measurements for real estimation of human personality traits and mental abilities. [ABSTRACT FROM AUTHOR]

The vehicular ad hoc network (VANET) has been identified as one of the most promising technologies for managing future intelligent transportation systems. This paper proposes a distributed transmission power adjustment algorithm for communication congestion control and awareness enhancement to address communication congestion problems that can arise in VANETs. The objective of the proposed algorithm is to provide maximum awareness of surrounding vehicles’ status while maintaining a communications channel load below the allowed threshold. The proposed algorithm accomplishes this by adjusting the transmission range of each vehicle in the network progressively and gradually, while monitoring the communications channel load of each vehicle. By changing the transmission range of a vehicle little by little according to the communications channel load of its neighboring vehicles, the algorithm finds the optimal transmission range that provides maximum awareness without resulting in communications congestion. In addition, the proposed algorithm appropriately controls the channel load in a fair manner without sacrificing awareness of specific vehicles in the congested situation. This allows nearby vehicles to obtain more peripheral information to help them stay away from potential hazards and maintain safety. The proposed algorithm was implemented in a simulation environment, and its performance was validated in various traffic scenarios. The simulation results show that the proposed algorithm can deal with communication congestion by controlling the transmission power fairly to a target threshold in various traffic situations. [ABSTRACT FROM AUTHOR]

Purpose: The CONSORT extension for patient reported outcomes (PROs) aims to improve reporting, but guidance on the optimal integration with clinical data is lacking. This study examines in detail the reporting of PROs and clinical data from randomized controlled trials (RCTs) in gastro-intestinal cancer to inform design and reporting of combined PRO and clinical data from trials to improve the ‘take home’ message for clinicians to use in practice. Materials and Methods: The case study was undertaken in gastro-intestinal cancer trials. Well-conducted RCTs reporting PROs with validated instruments were identified and categorized into those combining PRO and clinical data in a single paper, or those separating data into linked primary and supplemental papers. Qualitative methods were developed to examine reporting of the critical interpretation of the trial results (trial exegesis) in the papers in relation of the PRO and clinical outcomes and applied to each publication category. Results were used to inform recommendations for practice. Results: From 1917 screened abstracts, 49 high quality RCTs were identified reported in 36 combined and 15 linked primary and supplemental papers. In-depth analysis of manuscript text identified three categories for understanding trial exegesis: where authors reported a “detailed”, “general”, or absent PRO rationale and integrated interpretation of clinical and PRO results. A total of 11 (30%) and 6 (16%) combined papers reported “detailed” PRO rationale and integrated interpretation of results although only 2 (14%) and 1 (7%) primary papers achieved the same standard respectively. Supplemental papers provide better information with 11 (73%) and 3 (20%) achieving “detailed” rationale and integrated interpretation of results. Supplemental papers, however, were published a median of 20 months after the primary RCT data in lower impact factor journals (median 16.8 versus 5.2). Conclusion: It is recommended that single papers, with detailed PRO rationale and integrated PRO and clinical data are published to optimize trial exegesis. Further work to examine whether this improves the use of PRO data to inform practice is needed. [ABSTRACT FROM AUTHOR]

This paper presents an interface system based on the Dual Active Bridge (DAB) converter for Photovoltaic (PV) arrays. Two control strategies are proposed for the DAB converter to harvest the maximum power from the PV array. The first strategy is based on a simple PI controller to regulate the terminal PV voltage through the phase shift angle of the DAB converter. The Perturb and Observe (P&O) Maximum Power Point Tracking (MPPT) technique is utilized to set the reference of the PV terminal voltage. The second strategy presented in this paper employs the Artificial Neural Network (ANN) to directly set the phase shift angle of the DAB converter that results in harvesting maximum power. This feed-forward strategy overcomes the stability issues of the feedback strategy. The proposed PV interface systems are modeled and simulated using MATLAB/SIMULINK and the EMTDC/PSCAD software packages. The simulation results reveal accurate and fast response of the proposed systems. The dynamic performance of the proposed feed-forward strategy outdoes that of the feedback strategy in terms of accuracy and response time. Moreover, an experimental prototype is built to test and validate the proposed PV interface system. [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]

Traditional methods for monitoring influenza are haphazard and lack fine-grained details regarding the spatial and temporal dynamics of outbreaks. Twitter gives researchers and public health officials an opportunity to examine the spread of influenza in real-time and at multiple geographical scales. In this paper, we introduce an improved framework for monitoring influenza outbreaks using the social media platform Twitter. Relying upon techniques from geographic information science (GIS) and data mining, Twitter messages were collected, filtered, and analyzed for the thirty most populated cities in the United States during the 2013–2014 flu season. The results of this procedure are compared with national, regional, and local flu outbreak reports, revealing a statistically significant correlation between the two data sources. The main contribution of this paper is to introduce a comprehensive data mining process that enhances previous attempts to accurately identify tweets related to influenza. Additionally, geographical information systems allow us to target, filter, and normalize Twitter messages. [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]

Background: In order to adapt to societal changes, healthcare systems need to switch from a disease orientation to a patient-centered approach. Virtual patient networks are a promising tool to favor this switch and much can be learned from the open and user innovation literature where the involvement of online user communities in the innovation process is well-documented. Objectives: The objectives of this study were 1) to describe the use of online communities as a tool to capture and harness innovative ideas of end users or consumers; and 2) to point to the potential value and challenges of these virtual platforms to function as a tool to inform and promote patient-centered care in the context of chronic health conditions. Methods: A scoping review was conducted. A total of seven databases were searched for scientific articles published in English between 1995 and 2014. The search strategy was refined through an iterative process. Results: A total of 144 studies were included in the review. Studies were coded inductively according to their research focus to identify groupings of papers. The first set of studies focused on the interplay of factors related to user roles, motivations, and behaviors that shape the innovation process within online communities. Studies of the second set examined the role of firms in online user innovation initiatives, identifying different organizational strategies and challenges. The third set of studies focused on the idea selection process and measures of success with respect to online user innovation initiatives. Finally, the findings from the review are presented in the light of the particularities and challenges discussed in current healthcare research. Conclusion: The present paper highlights the potential of virtual patient communities to inform and promote patient-centered care, describes the key challenges involved in this process, and makes recommendations on how to address them. [ABSTRACT FROM AUTHOR]

Sand production and blockage are common during the drilling and production of horizontal oil and gas wells as a result of formation breakdown. The use of high-pressure rotating jets and annular helical flow is an effective way to enhance horizontal wellbore cleanout. In this paper, we propose the idea of using supercritical CO2 (SC-CO2) as washing fluid in water-sensitive formation. SC-CO2 is manifested to be effective in preventing formation damage and enhancing production rate as drilling fluid, which justifies tis potential in wellbore cleanout. In order to investigate the effectiveness of SC-CO2 helical flow cleanout, we perform the numerical study on the annular flow field, which significantly affects sand cleanout efficiency, of SC-CO2 jets in horizontal wellbore. Based on the field data, the geometry model and mathematical models were built. Then a numerical simulation of the annular helical flow field by SC-CO2 jets was accomplished. The influences of several key parameters were investigated, and SC-CO2 jets were compared to conventional water jets. The results show that flow rate, ambient temperature, jet temperature, and nozzle assemblies play the most important roles on wellbore flow field. Once the difference between ambient temperatures and jet temperatures is kept constant, the wellbore velocity distributions will not change. With increasing lateral nozzle size or decreasing rear/forward nozzle size, suspending ability of SC-CO2 flow improves obviously. A back-propagation artificial neural network (BP-ANN) was successfully employed to match the operation parameters and SC-CO2 flow velocities. A comprehensive model was achieved to optimize the operation parameters according to two strategies: cost-saving strategy and local optimal strategy. This paper can help to understand the distinct characteristics of SC-CO2 flow. And it is the first time that the BP-ANN is introduced to analyze the flow field during wellbore cleanout in horizontal wells. [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]

Abstract: Wealthy schools appoint better qualified teachers, less wealthy schools under qualified teachers. Added to this mix is a powerful teacher’s union whose policies attempt to entrench the job security of teachers in the less wealthy schools irrespective of whether they can teach their subjects or not. Can one isolate these effects from that of other socio-demographic factors that may also be affecting the performance of students when they enrol for a degree at the University of KwaZulu-Natal (UKZN)? An outcome variable that subtracts the number of courses that have been failed from the number of courses that have been passed, dividing this by the total number of years that they have spent studying for a particular degree will be used as a response variable for this paper. Objectives: The system of secondary education in South Africa is highly polarized. On the one hand, we have a group of mainly Black African students, forming about 80% of the total student population, that come from a vastly under-resourced rural or township based community. On the other hand, we have a group of predominantly White and Indian students who are able to attend a far better resourced set of private schools. Added to this mix, we have 240,000 of South Africa’s total number of 390,000 primary and secondary school teachers who belong to a powerful teacher’s union which enjoys a strong political alliance with the ruling party in South Africa. With most of their union members teaching in the less wealthy schools in South Africa, `school background’ now includes a politically motivated component that focuses on teacher self–interest rather than the education of the child. What sort of effect does school background have on the performance of students when they enter an institution of higher learning? More importantly, can one isolate the effect of school background from that of other possibly confounding factors such as gender, financial aid and the receipt of some form of residence based accommodation that will also impact on their performance while at university? Method: A total of 6,183 students enrolling for a degree at the University of KwaZulu-Natal (UKZN) over the period 2008 to 2012 were used a dataset for this study. Permission to use this dataset was given by the Teaching and Learning Office at UKZN. The database that was used for this study was obtained from the Division of Management Information (DMI) office at UKZN. The percentage based marks that students have managed to record for Mathematics, English, Biology and Accounting in their school leaving exams together with some other important but observable socio-economic factors were included in a regression model to determine how students will perform at UKZN. Socio-economic variables relating to gender, race and whether they have receivd some form of financial aid or residence based accommodation while studying at university were also included as predictor variables in our regression based model structure. Results and Conclusions: An interaction effect associated with being a Black African student who has been privileged enough to attend a quintile five school was found to be significant. A main effect associated with being able to attend a more privileged quintile 5 school however was found to be nonsignificant even after an adjustment has been made for gender, race, the receipt of some form of financial aid and residence based accommodation. Given that UKZN already has a number of bridging programs in place that target students who have come from a less privileged background, for university based policymakers, this result may help to justify the targeted selection of Black African students from the less privileged schools that is taking place. Because some of the disparity in matric performance that we are observing may also be associated with teacher competency and the protective influence of a powerful teacher’s union, this paper may also help to highlight some of the economic costs related with having under-prepared students. “A mind is a terrible thing to waste”–United Negro College Fund. [ABSTRACT FROM AUTHOR]