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In epidemiological surveys, data missing not at random (MNAR) due to survey nonresponse may potentially lead to a bias in the risk factor estimates. We propose an approach based on Bayesian data augmentation and survival modelling to reduce the nonresponse bias. The approach requires additional information based on follow-up data. We present a case study of smoking prevalence using FINRISK data collected between 1972 and 2007 with a follow-up to the end of 2012 and compare it to other commonly applied missing at random (MAR) imputation approaches. A simulation experiment is carried out to study the validity of the approaches. Our approach appears to reduce the nonresponse bias substantially, where as MAR imputation was not successful in bias reduction., 19 pages, 2 figures

Drug Target Commons (DTC) is a web platform (database with user interface) for community-driven bioactivity data integration and standardization for comprehensive mapping, reuse and analysis of compound-target interaction profiles. End users can search, upload, edit, annotate and export expert-curated bioactivity data for further analysis, using an application programmable interface, database dump or tab-delimited text download options. To guide chemical biology and drug-repurposing applications, DTC version 2.0 includes updated clinical development information for the compounds and target gene-disease associations, as well as cancer-type indications for mutant protein targets, which are critical for precision oncology developments.

Device-to-device (D2D) communications has recently gathered significant research interest due to its efficient utilization of already depleting wireless spectrum. In this article, we considered a scenario where D2D users communicate in the presence of cellular users in an overlay network setup. In order to analyze the revenue of service providers in monetary terms, the paper provides exact expressions of operator profit for both D2D and cellular users. More specifically, we take into account different network parameters including user density, transmit power and channel variations to understand their impact on the total revenue of the operator. Finally, we derive the balancing value of frequency partitioning factor and provide relevant discussion on the analytical expression. Our findings show that D2D communications outperform the conventional cellular communications in terms of revenue generation capability. Our results have been verified by performing extensive simulations.

Summary Knowledge of the full target space of bioactive substances, approved and investigational drugs as well as chemical probes, provides important insights into therapeutic potential and possible adverse effects. The existing compound-target bioactivity data resources are often incomparable due to non-standardized and heterogeneous assay types and variability in endpoint measurements. To extract higher value from the existing and future compound target-profiling data, we implemented an open-data web platform, named Drug Target Commons (DTC), which features tools for crowd-sourced compound-target bioactivity data annotation, standardization, curation, and intra-resource integration. We demonstrate the unique value of DTC with several examples related to both drug discovery and drug repurposing applications and invite researchers to join this community effort to increase the reuse and extension of compound bioactivity data., Graphical Abstract, Highlights • DTC is a crowd-sourcing-based web platform to annotate drug-target bioactivity data • The open environment improves data harmonization for drug repurposing applications • DTC offers a comprehensive, reproducible, and sustainable bioactivity knowledge base, Tang et al. launches a novel crowd-sourcing effort to standardize the collection, management, curation, and annotation of the notoriously heterogeneous compound-target bioactivity measurements. The web-based community platform aims to provide the most comprehensive, reproducible, and sustainable bioactivity knowledge base for the end users.

The sensory-chemical profiles of Finnish honeys (labeled as buckwheat, cloudberry-bog, lingonberry, sweet clover, willowherb and multifloral honeys) were investigated using a multi-analytical approach. The sensory test (untrained panel, n = 62) was based on scaling and check-all-that-apply (CATA) methods accompanied with questions on preference and usage of honey. The results were correlated with corresponding profiles of odor-active compounds, determined using gas chromatography coupled with mass spectrometry/olfactometry (GC–MS/O). Botanical origins and chemical compositions including sugars were evaluated using NMR spectroscopy. A total of 73 odor-active compounds were listed based on GC–O. Sweet and mild honeys with familiar sensory properties were preferred by the panelists (PCA, R2X(1) = 0.7) while buckwheat and cloudberry-bog honeys with strong odor, flavor and color were regarded as unfamiliar and unpleasant. The data will give the honey industry novel information on honey properties in relation to the botanical origin, and consumer preference.

In this paper, we propose a task scheduling strategy for an Internet of Things (IoT) node powered by renewable energy (RE). The node is assumed to have a rechargeable battery and an RE harvester. Moreover, the node is requested to perform M tasks over a planning period of N ≥ M time slots. Each task is assigned a priority rating and a reward. With these considerations we develop a mathematical framework to optimize the utility of the node, defined as the sum of rewards over the specified planning horizon. Using the proposed framework, we derive a genie-aided strategy, which serves as a performance benchmark for online algorithms. We then propose an online task scheduling strategy, which uses existing forecasting methods to estimate future RE production. We finally evaluate the performance of the proposed strategy and its robustness to forecasting errors through extensive simulations. The impact of system parameters such as battery size and RE harvesting capacity are also examined numerically.

Do-calculus is concerned with estimating the interventional distribution of an action from the observed joint probability distribution of the variables in a given causal structure. All identifiable causal effects can be derived using the rules of do-calculus, but the rules themselves do not give any direct indication whether the effect in question is identifiable or not. Shpitser and Pearl constructed an algorithm for identifying joint interventional distributions in causal models, which contain unobserved variables and induce directed acyclic graphs. This algorithm can be seen as a repeated application of the rules of do-calculus and known properties of probabilities, and it ultimately either derives an expression for the causal distribution, or fails to identify the effect, in which case the effect is non-identifiable. In this paper, the R package causaleffect is presented, which provides an implementation of this algorithm. Functionality of causaleffect is also demonstrated through examples., Comment: This is the version published in the Journal of Statistical Software

Feedback loops are typical motifs appearing in gene regulatory networks. In some well-studied model organisms, including Escherichia coli, autoregulated genes, i.e., genes that activate or repress themselves through their protein products, are the only feedback interactions. For these types of interactions, the Michaelis-Menten (MM) formulation is a suitable and widely used approach, which always leads to stable steady-state solutions representative of homeostatic regulation. However, in many other biological phenomena, such as cell differentiation, cancer progression, and catastrophes in ecosystems, one might expect to observe bistable switchlike dynamics in the case of strong positive autoregulation. To capture this complex behavior we use the generalized family of MM kinetic models. We give a full analysis regarding the stability of autoregulated genes. We show that the autoregulation mechanism has the capability to exhibit diverse cellular dynamics including hysteresis, a typical characteristic of bistable systems, as well as irreversible transitions between bistable states. We also introduce a statistical framework to estimate the kinetics parameters and probability of different stability regimes given observational data. Empirical data for the autoregulated gene SCO3217 in the SOS system in Streptomyces coelicolor are analyzed. The coupling of a statistical framework and the mathematical model can give further insight into understanding the evolutionary mechanisms toward different cell fates in various systems.

Aims: Health examination surveys (HES) provide important information about population health and health-related factors, but declining participation rates threaten the representativeness of collected data. It is hard to conduct national HESs at examination clinics near to every sampled individual. Thus, it is interesting to look into the possible association between the distance from home to the examination clinic and non-participation, and whether there is a certain distance after which the participation activity decreases considerably. Methods: Data from two national HESs conducted in Finland in 2011 and 2012 were used and a logistic regression model was fitted to investigate how distance was related to non-participation. Results: We found out that non-participation modestly increased with distance to the examination clinic. An additional analysis indicated that the option of having an examination at home may decrease the effect of distance to participation. Conclusions: Long distances from home to the examination clinic are one reason for low participation activity. Possible bias caused by these differences in participation could be decreased by providing the option of a home examination.

Inferring interactions between co-occurring species is key to identify processes governing community assembly. Incorporating interspecific interactions in predictive models is common in ecology, yet most methods do not adequately account for indirect interactions (where an interaction between two species is masked by their shared interactions with a third) and assume interactions do not vary along environmental gradients. Markov random fields (MRF) overcome these limitations by estimating interspecific interactions, while controlling for indirect interactions, from multispecies occurrence data. We illustrate the utility of MRFs for ecologists interested in interspecific interactions, and demonstrate how covariates can be included (a set of models known as Conditional Random Fields, CRF) to infer how interactions vary along environmental gradients. We apply CRFs to two data sets of presence-absence data. The first illustrates how blood parasite (Haemoproteus, Plasmodium, and nematode microfilaria spp.) co-infection probabilities covary with relative abundance of their avian hosts. The second shows that co-occurrences between mosquito larvae and predatory insects vary along water temperature gradients. Other applications are discussed, including the potential to identify replacement or shifting impacts of highly connected species along climate or land-use gradients. We provide tools for building CRFs and plotting/interpreting results as an R package.

Business travel has increased substantially during the past few decades. Business travel costs are one of the main controllable costs in international corporations, and thus companies are imposing stricter policies on corporate travel to create savings and efficiency. For travel management, the current literature suggests two alternative management strategies based on either a control-oriented or a commitment-oriented approach. In this paper we present an in-depth case study that investigates the impact that each type of strategy has on corporate travel policy compliance. Specifically, we investigate how the strategies are executed in a triadic travel supply chain setting, consisting of a corporate travel buyer, a business travel agency and a technology provider. Our findings show that both the control and commitment-based strategies are used in all stages of the travel process. The seminal finding is that the competitiveness and high quality of services provided internally by the buyer in collaboration with the triad members – rather than strict control and monitoring – is essential to travel policy compliance. This finding shows that corporate travel management shares similar perspectives to leisure travel in that service quality is key to securing business. Furthermore, a proactive approach to control via a well-established and reasonable travel policy is needed.

Multivariate methods often rely on a sample covariance matrix. The conventional estimators of a covariance matrix require complete data vectors on all subjects-an assumption that can frequently not be met. For example, in many fields of life sciences that are utilizing modern measuring technology, such as mass spectrometry, left-censored values caused by denoising the data are a commonplace phenomena. Left-censored values are low-level concentrations that are considered too imprecise to be reported as a single number but known to exist somewhere between zero and the laboratory's lower limit of detection. Maximum likelihood-based covariance matrix estimators that allow the presence of the left-censored values without substituting them with a constant or ignoring them completely are considered. The presented estimators efficiently use all the information available and thus, based on simulation studies, produce the least biased estimates compared to often used competing estimators. As the genuine maximum likelihood estimate can be solved fast only in low dimensions, it is suggested to estimate the covariance matrix element-wise and then adjust the resulting covariance matrix to achieve positive semi-definiteness. It is shown that the new approach succeeds in decreasing the computation times substantially and still produces accurate estimates. Finally, as an example, a left-censored data set of toxic chemicals is explored. ML based covariance matrix estimator for left-censored data is introduced.Computation times are decreased considerably with parallelized pairwise estimation.The proposed estimators produce unbiased estimates with reasonable variation.

Mobile applications are increasingly exploiting cloud computing to overcome the resource limitations of mobile devices. To this end, the most computationally expensive tasks are offloaded to the cloud and the mobile application simply interacts with a remote service through a network connection. One way to establish such a connection is given by remote display access, in which a mobile device just operates as a thin client by relaying the input events to a server and updating the screen based on the content received. In this article, we specifically address remote display access as a means for mobile cloud computing, with focus on its power consumption at mobile devices. Different from most of the existing literature, we take an experimental approach based on real user sessions employing different remote access protocols and types of applications, including gaming. Through several experiments, we characterize the impact of the different protocols and their features on power consumption and network utilization. We conclude our analysis with considerations on usability and user experience.

In the traveling salesman problem with pickup, delivery, and ride-time constraints (TSPPD-RT), a vehicle located at a depot is required to service a number of requests where the requests are known before the route is formed. Each request consists of (i) a pickup location (origin), (ii) a delivery location (destination), and (iii) a maximum allowable travel time from the origin to the destination (maximum ride-time). The problem is to design a tour for the vehicle that (i) starts and ends at the depot, (ii) services all requests, (iii) ensures that each request's ride-time does not exceed its maximum ride-time, and (iv) minimizes the total travel time required by the vehicle to service all requests (objective function). A capacity constraint that may be present is that the weight or volume of the undelivered requests on the vehicle must always be no greater than the vehicle's capacity. In this article, we concurrently analyze the TSPPD-RT with capacity constraints and without capacity constraints. We describe two mathematical formulations of the problem. These formulations are used to derive new lower bounds on the solution to the problem. Then, we provide two exact methods for finding the optimal route that minimizes the total travel cost. Our extensive computational analysis on both versions of the TSPPD-RT shows that the proposed algorithms are capable of solving to optimality instances involving up to 50 requests. © 2015 Wiley Periodicals, Inc. NETWORKS, 2015

Bloom filter (BF) based forwarding was proposed recently in several protocol alternatives to IP multicast. Some of these protocols avoid the state in intermediate routers and leave the burden of scalability management to the multicast source and end-hosts. Still, the existing BF-based protocols have scalability limitations and require explicit network management as well as non-trivial functionality from the network components. In this work we address the scalability limitations of the BF-based forwarding protocols by partitioning end-hosts into clusters. We propose several algorithms to do the partitioning so as to decrease the overall traffic in the network. We evaluate our algorithms in a real Internet topology, demonstrating the ability of the proposed design to save up to 70% of traffic volume in the large-scale topology for big groups of subscribers, and up to 30% for small groups.

Project portfolios for the annual maintenance of infrastructure assets may contain dozens of projects which are selected out of hundreds of candidate projects. In the selection of these projects, it is necessary to account for multiple evaluation criteria, project interdependencies, and uncertainties about project performance as well as financial and other relevant constraints. In this paper, we report how Robust Portfolio Modeling (RPM) has been used repeatedly at the Finnish Transport Agency (FTA) for bridge maintenance programming. At FTA, project selection decisions are guided by the RPM's Core Index values which are derived from portfolio-level computations and reflect incomplete information about the relative importance of evaluation criteria. To-date, this application has been rerun with fresh data for six consecutive years. By drawing on experiences from this application, we discuss preconditions for the successful use of RPM or other methods of Portfolio Decision Analysis in comparable settings. We also develop an approximative algorithm for computing non-dominated portfolios in large project selection problems. We report a repeated application of RPM to bridge maintenance project selection.We identify general features that contributed to the success of this application.We develop an algorithm for solving non-dominated portfolios in large RPM problems.

We consider the discretization and solution of eigenvalue problems of elliptic operators with random coefficients. For solving the resulting systems of equations we present a new and efficient spectral inverse iteration based on the stochastic Galerkin approach with respect to a polynomial chaos basis. The curse of dimensionality inherent in normalization over parameter spaces is avoided by a solution of a non-linear system of equations defining the Galerkin coefficients. For reference we also present an algorithm for adaptive stochastic collocation. Functionality of the algorithms is demonstrated by applying them on four examples of a given model problem. Convergence of the Galerkin-based method is analyzed and the results are tested against the collocated reference solutions and theoretical predictions.

We consider two complementary aspects of mathematical skills, i.e. procedural fluency and conceptual understanding, from a point of view that is related to modern e-learning environments and computer-based assessment. Pedagogical background of teaching mathematics is discussed, and it is proposed that the traditional book medium has determined much of its historical development, including the classical style of presenting mathematical knowledge. Information technology is likely to be an emerging game changer in learning and teaching of mathematics, and we argue that the potential of e-learning platforms extends beyond simple drill exercises to complex problems (having several`right¨solutions) that are expected to improve conceptual understanding. We discuss these ideas referring two experiences and observations from a STACK-based automatic assessment system at Aalto University since 2006. The article is concluded by visions for the future development involving, e.g. learning analytics based on students' answer data.

Outlier detection covers the wide range of methods aiming at identifying observations that are considered unusual. Novelty detection, on the other hand, seeks observations among newly generated test data that are exceptional compared with previously observed training data. In many applications, the general existence of novelty is of more interest than identifying the individual novel observations. For instance, in high-throughput cancer treatment screening experiments, it is meaningful to test whether any new treatment effects are seen compared with existing compounds. Here, we present hypothesis tests for such global level novelty. The problem is approached through a set of very general assumptions, making it innovative in relation to the current literature. We introduce test statistics capable of detecting novelty. They operate on local neighborhoods and their null distribution is obtained by the permutation principle. We show that they are valid and able to find different types of novelty, e.g. location and scale alternatives. The performance of the methods is assessed with simulations and with applications to real data sets.

In this study, the temporal trends in the rates of multiple maternities in Denmark were analyzed and an association between the rates of multiple maternities and the socioeconomic variations in Denmark was obtained. The findings are compared with the results obtained in studies of Swedish rates. Obtained temporal differences in the twinning rates (TWRs) were similar to the results presented by Fellman and Eriksson (2014). Temporal variation in rates of multiple maternities in Sweden (1751–2000). JP Journal of Biostatistics, 11, 2, 143–156.

The dial-a-ride problem (DARP) involves the dispatching of a fleet of vehicles to transport customers requesting service and is one of the most challenging tasks of combinatorial optimization. We study the DARP as a constraint satisfaction problem, where the goal is to find a feasible solution with respect to the time, capacity, and precedence constraints, or to prove infeasibility. The main contribution of our work is a new robust method for this problem formulation. The algorithm is based on a dynamic subroutine that finds for any set of customers a maximum cluster, that is, a maximal set of customers that can be served by a single vehicle. The performance of the algorithm is analyzed and evaluated by means of computational experiments, justifying the efficiency of the solution method.

Business-to-business (B2B) and business-to-consumer (B2C) markets differ in many ways as documented in the contemporary marketing literature. However, many behavioral characteristics of human beings – particularly those related to judgment and decision-making – are present across diverse contexts. From this insight, we derive a proposition: many behavioral price concepts developed in the past B2C behavioral price research may be applicable in B2B context as well. The objective of this paper is to examine this proposition through analyzing the existing evidence on five important behavioral price concepts: reference price, price thresholds, acceptable price range, price as an indicator of quality, and the price–perceived value model. At a more general level, the objective of this paper is to demonstrate the importance of recognizing how buyers' responses to prices and price information differ from the traditional assumptions about such behaviors in B2B marketing literature. The results provide strong evidence for the applicability of the reference price concept in B2B markets. The price–perceived value model is widely applied in B2B pricing, although in narrow form. Use of price as an indicator of quality also receives some support. For price thresholds and acceptable price range little research activity exists in B2B domain. Overall, while there has been some behavioral price research specifically in a B2B context, nevertheless it is comparatively sparse, and for some concepts virtually non-existent. We end the paper with a call that more behavioral price research is needed as such research has potential to help business marketing managers make more effective pricing decisions.

We hypothesize that brain activity can be used to control future information retrieval systems. To this end, we conducted a feasibility study on predicting the relevance of visual objects from brain activity. We analyze both magnetoencephalographic (MEG) and gaze signals from nine subjects who were viewing image collages, a subset of which was relevant to a predetermined task. We report three findings: i) the relevance of an image a subject looks at can be decoded from MEG signals with performance significantly better than chance, ii) fusion of gaze-based and MEG-based classifiers significantly improves the prediction performance compared to using either signal alone, and iii) non-linear classification of the MEG signals using Gaussian process classifiers outperforms linear classification. These findings break new ground for building brain-activity-based interactive image retrieval systems, as well as for systems utilizing feedback both from brain activity and eye movements.

The response of a vibrating beam to a force depends on many physical parameters including those determined by material properties. Damage caused by fatigue or cracks results in local reductions in stiffness parameters and may drastically alter the response of the beam. Data obtained from the vibrating beam are often subject to uncertainties and/or errors typically modeled using probability densities. The goal of this paper is to estimate and quantify the uncertainty in damage modeled as a local reduction in stiffness using uncertain data. We present various frameworks and methods for solving this parameter determination problem. We also describe a mathematical analysis to determine and compute useful output data for each method. We apply the various methods in a specified sequence that allows us to interface the various inputs and outputs of these methods in order to enhance the inferences drawn from the numerical results obtained from each method. Numerical results are presented using both simulated and experimentally obtained data from physically damaged beams.

We present two types of approach to the analysis of recurrent events for discretely measured data, and show how these methods can complement each other when analysing co-residential partnership histories. Sequence analysis is a descriptive tool that gives an overall picture of the data and helps to find typical and atypical patterns in histories. Event history analysis is used to make conclusions about the effects of covariates on the timing and duration of the partnerships. As a substantive question, we studied how family background and childhood socio-emotional characteristics were related to later partnership formation and stability in a Finnish cohort born in 1959. We found that high self-control of emotions at age 8 was related to a lower risk of partnership dissolution and for women a lower probability of repartnering. Child-centred parenting practices during childhood were related to a lower risk of dissolution for women. Socially active boys were faster at forming partnerships as men. peerReviewed

We analysed geographic variation in the number of males per 100 females at birth also called the secondary sex ratio (SR), the crude birth rate (CBR), the total fertility rate (TFR) and the tw inning rate (TWR). Earlier studies have noted geographic variations in the TWR and racial differences in the SR. Statistical analyses have shown that comparisons between SRs demand large data sets because random fluctuations in moderate data are marked. Co nsequently, reliable results presuppose national birth data. In this study, we analysed historical demographic data and investigated the geographic variations between the counties in Sweden for the SR among the live born (1749 - 1869), the CBR in 1751 - 1870, the TFR in 1860 and the TWR in 1751 - 1860. We built spatial models and as regressors we used geographic co - ordinates for the residences of the counties in Sweden. The influence of the CBR and TFR on the SR and TWR was examined. For all variables, we obtaine d spatial variations, albeit of different patterns and power. Hence, no common spatial pattern for the demographic variables SR, TFR, CBR and TWR was detected, but a better fit was noted for TFR, CBR and TWR than for SR.

We propose and test a model for studying customer engagement within VCEs.Hedonic, social, and cognitive benefits positively influence customer engagement.Personal integrative benefits do not influence customer engagement.System-specific VCE characteristics influence the perceived benefits of a VCE.The results are robust across three datasets of actual VCE users. Customer engagement has been labeled as a prerequisite for the success of virtual customer environments. A key challenge for organizations serving their customers via these environments is how to stimulate customer engagement. This study is among the first to shed light on this issue by examining customer engagement and its drivers. Using the theory of uses and gratification as theoretical lens, we develop a model that relates characteristics of virtual customer environments, perceived benefits of using these environments and customer engagement intentions. The model is validated using partially least squares structural equation modeling on three samples of real users of different virtual customer environments in the Dutch telecom industry. The results provide clear support for the validity of the hypothesized relationships and show high robustness of the findings across the three datasets. An important finding of this study is that cognitive, social integrative and hedonic benefits appear to be significant in their influence on customer engagement intentions. Overall, the findings add to the underexplored field of customer engagement study and hold implications for research into and the management of virtual customer environments.