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Mathematical modeling describes how events, concepts, and systems of interest behave in the world using mathematical concepts. This research approach can be applied to theory construction and testing by using empirical data to evaluate whether the specific theory can explain the empirical data or whether the theory fits the data available. Although extensively used in the physical sciences and engineering, as well as some social and behavioral sciences to examine theoretical claims and form predictions of future events and behaviors, theory-oriented mathematical modeling is less common in educational technology research. This article explores the potential of using theory-oriented mathematical modeling for theory construction and testing in the field of educational technology. It presents examples of how this approach was used in social, behavioral, and educational disciplines, and provides rationale for why educational technology research can benefit from a theory-oriented model-testing approach.

The problem of quantification of emotions in the choice between alternatives is considered. The alternatives are evaluated in a dual manner. From one side, they are characterized by rational features defining the utility of each alternative. From the other side, the choice is affected by emotions labeling the alternatives as attractive or repulsive, pleasant or unpleasant. A decision maker needs to make a choice taking into account both these features, the utility of alternatives and their attractiveness. The notion of utility is based on rational grounds, while the notion of attractiveness is vague and rather is based on irrational feelings. A general method, allowing for the quantification of the choice combining rational and emotional features, is described. Despite that emotions seem to avoid precise quantification, their quantitative evaluation is possible at the aggregate level. The analysis of a series of empirical data demonstrates the efficiency of the approach, including the realistic behavioral problems that cannot be treated by the standard expected utility theory.

One of the most important ways to improve pedestrian spaces is to quantitatively analyze the preferences of pedestrians through concrete and empirical data. This requires a psychological evaluation on pedestrians and an analysis on correlations between pedestrian space elements and pedestrians’ preferences. In this study, the relationship between landscape elements in pedestrian space and the preferences of pedestrians was analyzed. The quantitative information of landscape elements was obtained from landscape sequences because pedestrians experience the landscape as a sequence, not individual scenes. The analysis was used to develop a comprehensive index of how pedestrians look at pedestrian spaces. A psychological evaluation was performed to obtain pedestrian preferences for different spaces and the influencing factors. This study clarified the landscape elements preferred by pedestrians, and the results can be used to develop basic guidelines for the planning and design of pedestrian spaces.

We measure bias and efficiency of parameter estimates in the conditional logit (CL) and independent availability logit (IAL) models. Our Monte Carlo experiments consider both no choice set formation where individuals choose from the full set of alternatives, and when choice sets are stochastically formed and individuals choose from a subset of all alternatives. We also compare the performance of the two models using empirical data on paddlefish angler preferences and catch-and-release regulations in Oklahoma. Both the CL and IAL work well when their own assumptions hold, but not under the alternative’s assumptions. The IAL approximates the attribute-based cutoff well in empirical data. While neither the IAL nor the CL is universally preferred, based on our findings, we recommend the IAL when the true consideration sets are unknown.

In recent years, rather than enclosing data within a single organization, exchanging and combining data from different domains has become an emerging practice. Many studies have discussed the economic and utility value of data and data exchange, but the characteristics of data that contribute to problem-solving through data combination have not been fully understood. In big data and interdisciplinary data combinations, large-scale data with many variables are expected to be used, and value is expected to be created by combining data as much as possible. In this study, we conducted three experiments to investigate the characteristics of data, focusing on the relationships between data combinations and variables in each dataset, using empirical data shared by the local government. The results indicate that even datasets that have a few variables are frequently used to propose solutions for problem-solving. Moreover, we found that even if the datasets in the solution do not have common variables, there are some well-established solutions to these problems. The findings of this study shed light on the mechanisms behind data combination for solving problems involving multiple datasets and variables.

Unleashing greenhouse gases especially CO2 has been proven to be detrimental in many aspects both globally and individually. Numerous techniques and models have been developed so far to achieve the best-matched outputs in terms of their coincidence to the empirical data. Among them, those which are based on artificial intelligence have gained more attention on the account of their outstanding potential to decode the relationships between inputs and outputs. The selection of the best AI-based technique for implementation of the CO2 approaches is fairly vital as it plays a crucial role to yield the most accurate results. This article has been assigned to scrutinize a relatively broad range of AI-based techniques by reviewing research works which were used to carry out CO2 capture to fully understand and compare their advantages and disadvantages and throw light on promising research areas.

Operating room (OR) scheduling is one of the most important tasks for hospital managers in terms of improving efficiency in hospitals. In this study, scheduling policies for elective and emergency procedures that minimize the total expected cost of OR operations are investigated. A simulation optimization approach is used to develop schedules and evaluate the operational impact of reserving capacity for emergency patients in an OR system. In order to mitigate the possible negative effects of unused capacity should a lower than expected number of emergency cases arrive, assigning any unused capacity to standby patients is examined. This study looks at the effect of hospital size in terms of the number of surgeries performed, mean surgery duration, and the variability in surgery duration on scheduling policy based on empirical data from three hospitals. Specifically, we investigate the best policies in terms of OR efficiency with and without reserved capacity for emergency arrivals and different probabilities for standby patient availability.

Production engineering (2020). doi:10.1007/s11740-020-00998-1, Published by WGP, Hannover

Fast and accurate threat detection is critical for animal survival. Reducing perceptual ambiguity by integrating multiple sources of sensory information can enhance perception and reduce response latency. However, studies addressing the link between behavioral correlates of multisensory integration and its underlying neural basis are rare. Fish that detect an urgent threat escape with an explosive behavior known as C-start. The C-start is driven by an identified neural circuit centered on the Mauthner cell, an identified neuron capable of triggering escapes in response to visual and auditory stimuli. Here we demonstrate that goldfish can integrate visual looms and brief auditory stimuli to increase C-start probability. This multisensory enhancement is inversely correlated to the salience of the stimuli, with weaker auditory cues producing a proportionally stronger multisensory effect. We also show that multisensory stimuli reduced C-start response latency, with most escapes locked to the presentation of the auditory cue. We make a direct link between behavioral data and its underlying neural mechanism by reproducing the behavioral data with an integrate-and-fire computational model of the Mauthner cell. This model of the Mauthner cell circuit suggests that excitatory inputs integrated at the soma are key elements in multisensory decision making during fast C-start escapes. This provides a simple but powerful mechanism to enhance threat detection and survival.

The purpose of the paper is to identify the dimensions of the strategy of resources allocation of Polish households members and test the hypothesis concerning risky shift effect in the relationship between strategy of family decision making and trade-off in family scarce resources allocation. These dimensions were identified on the basis of nationwide empirical data gathered on a representative sample of 1020 respondents nested in 410 households. SEM-Tree hybrid models are used in the analysis of the results, which combine the confirmatory structural equation models with exploratory and predictive classification and regression trees. This allows to apply structural modeling for the study of heterogeneous populations and to assess the hierarchical impact of exogenous predictors on the identification of segments with separate and unique model structural parameters. The approach combines the advantages of a model approach (at the stage of constructing hypotheses on structural relationships and specifications of measurement models) and exploration-based data (at the stage of recursive division of the sample).

In the Registered Modeling Report format, authors specify the experimental design, models, and inference mechanism before they have seen the data and reviewers evaluate the level of detail and quality of the proposed plan. While useful, the Registered Modeling Report is limited in its ability to incentivize strong tests. I propose an extension to the Registered Modeling Report format, the Really Risky Registered Modeling Report, in which reviewers are required to evaluate whether a bad fit between the model predictions and the empirical data is plausible. The two crucial additions are that authors include prior predictions in the protocol and that reviewers set a data prior in Stage 1. Only protocols containing predictions that are not almost guaranteed to be confirmed when brought in contact with empirical data are eligible for in principle acceptance. Adopting the Really Risky Registered Modeling Report will lead to strong model tests and solid evidence.

The present study aims to develop a mechanism for improving supply chain reliability using blockchain technology. The objectives of the study are (1) to evaluate 23 countries on their ease of doing business based on blockchain technology; (2) to look at the structure of blockchain technology; and (3) to create a blockchain-based mechanism for reliability improvement in supply chains. To understand how blockchain and supply chains are integrated, we have conducted a systematic review of the latest supply chain trends. The results show that supply chain actors crave improved visibility and traceability of goods moving through the supply chain. We show how blockchain technology could contribute to this. We use the Blockchain & Cryptocurrencies Regulation Index methodology to obtain empirical data. Supply chain weak points are identified, and blockchain-based means to fix them are highlighted. Findings show that blockchain is a game-changer that improves the exchange of data between network participants, making them transparent, accessible, immutable, and secure. Blockchain was also found to have a positive impact on supply chain collaboration. However, this technology also comes with certain political and legal risks that need to be overcome.

Traditional machine learning approaches usually hold the assumption that data for model training and in real applications are created following the identical and independent distribution (i.i.d.). However, several relevant research topics have demonstrated that such condition may not always describe the real scenarios. One particular case is that the patterns are equipped with diverse and changeable style information. In this paper, a novel classification framework named Style Neutralization Generative Adversarial Classifier (SN-GAC), based on an upgraded U-Net architecture, and trained adversarially with the Generative Adversarial Network (GAN) framework, is introduced to accomplish the classification in such disparate and inconsistent data information case. The generative model in SN-GAC neutralizes style information from the original style-discriminative patterns (style-source) by building the mapping function from them to their style-free counterparts (corresponding standard examples, standard-target). A well-learned generator in the SN-GAC framework is capable of producing the targeted style-neutralized data (generated-target), satisfying the i.i.d. condition. Additionally, SN-GAC is trained adversarially, where an independent discriminator is used to surveil and supervise the training progress of the above-mentioned generator by distinguishing between the real and the generated. Simultaneously, an auxiliary classifier is also embedded in the discriminator to assign the correct class label of both the real and generated data. This process proves effective to aid the generator to produce high-quality human-readable style-neutralized patterns. It will then be further fine-tuned for the sake of promoting the final classification performance. Extensive experiments have adequately demonstrated the effectiveness of the proposed SN-GAC framework: it outperforms several relevant state-of-the-art baselines on two empirical data sets in the non-i.i.d. data classification task.

Studies of voluntary vaccination decisions by rational individuals predict that the population will reach a Nash equilibrium with vaccination coverage below the societal optimum. Human decision-making involves mechanisms in addition to rational calculations of self-interest, such as imitation of successful others. Previous research had shown that imitation alone cannot achieve better results. Under realistic choices of the parameters it may lead to equilibrium vaccination coverage even below the Nash equilibrium. However, these findings rely on the widely accepted use of Fermi functions for modeling the probabilities of switching to another strategy. We consider here a more general functional form of the switching probabilities. It is consistent with functions that give best fits for empirical data in a widely cited psychological experiment and involves one additional parameter $\alpha$. This parameter can be loosely interpreted as a degree of open-mindedness. We found both by means of simulations and analytically that sufficiently high values of $\alpha$ will drive the equilibrium vaccination coverage arbitrarily close to the societal optimum., Comment: 31 pages, 11 figures

Crowd logistics is a novel shipping concept where delivery operations are carried out by using existing resources, namely vehicle capacity and drivers from the crowd, thereby offering potential for economic, social, and environmental benefits. Despite the promise of this new logistics model, little is known about its actual functioning, performance, and impact. This paper presents a pioneering study of the performance of a real crowd-shipping system in the U.S. using empirical data from 2 years of operations. We contribute to the literature by: (1) defining performance metrics and developing models that account for the specificity of crowd-shipping systems by distinguishing the essential stages from bidding to acceptance and delivery of shipments, (2) identifying the significant covariates, including shipment features, built environment, and socio-demographic factors giving rise to different delivery performance outcomes, and (3) deriving sensitivity analysis to study the performance and implications of crowd-shipping in urban and suburban areas. The analysis is formalized as two-level nested logit models with nests representing bidding and delivery outcomes. The results show that not only does the delivery outcome performance vary significantly between urban and suburban areas, but the explanatory factors also vary significantly for the two contexts. Additionally, several factors have ambiguous impacts depending on the stage. Larger shipment size (versus strict deadlines) leads to increasing (decreasing) the likelihood of bids being placed, while having the opposite effect when it comes to the delivery phase. The findings highlight the need for developing different strategies to foster and improve the performance of this novel system depending on both the urban–suburban shipping context and the stage of delivery.

Patterns of human behavior over extended periods of time are important for characterizing human exposure to hazardous chemicals. Because longitudinal behavior patterns for an individual are difficult to obtain, exposure-assessors have characterized such patterns by linking daily records from multiple individuals. In an earlier publication, we developed an alternative strategy that was based on agent-based simulation modeling. Specifically, we created a software program, Agent-Based Model of Human Activity Patterns (ABMHAP), that generates year-long longitudinal behavior patterns. In this paper, we both calibrate and evaluate ABMHAP using human behavior data from the U.S. Environmental Protection Agency’s Consolidated Human Activity Database (CHAD). We use the longitudinal data (data on individuals' activities over multiple days) in CHAD to parameterize ABMHAP, and we use single-day behavior data from CHAD to evaluate ABMHAP predictions. We evaluate ABMHAP’s ability to simulate sleeping, eating, commuting, and working (or attending school) for four populations: working adults, nonworking adults, school-age children, and preschool children. The results demonstrate that ABMHAP, when parameterized with empirical data, can capture both interindividual and intraindividual variation in behaviors in different types of individuals. We propose that simulating annual activity patterns via ABMHAP may allow exposure-assessors to characterize exposure-related behavior in ways not possible with traditional survey methods.

We investigate properties of empirical data under random uncertainty. A new approach is proposed to improve the accuracy of constructing a probability density function and estimating its error. The method uses Richardson extrapolation and Runge rules for kernel estimates with various smoothing options. It is shown that the application of the Runge rules enables evaluating the error of kernel estimates for a probability density function and the value of its second derivative.

Trucking companies deliver a large number of containers every day to container terminals at hub ports. Truck drivers for the delivery operation can experience long waiting times when they arrive at peak hours. This study proposes a scheduling method for appointments that considers the cost of trucks staying in the terminal, demurrage cost, container delivery cost, number of appointments allowed at each time window and block, and number of trucks available during each time window. Unlike previous studies, this study considers the effects of the appointments on the waiting time at the terminal when the appointment schedule is constructed. This paper introduces a mathematical formulation and a heuristic algorithm based on the Frank–Wolfe algorithm to solve the problem within a reasonable computational time. Numerical experiments are conducted to compare the proposed algorithm with the other heuristic approaches and analyze the effects of the appointments using empirical data. In addition, the impact of appointments by multiple trucking companies is examined.

Previous entry timing studies have reinforced the timing wisdom by primarily focusing on new markets. This inductive study investigates how firms make decisions about the entry speed of new product features to a well-developed market using a classification of two dimensions of entry speed, namely internal and external. The analysis uses empirical data from 565 new product entry events by followers (not first movers) in the Chinese auto industry from 2011 to 2013 to explore the impacts of the follower’s product line breadth and product pre-entry experience on its internal and external feature-entry speed. We find that both product line breadth and product pre-entry experience can accelerate the speed of internal entry while only the firm product line breadth deters the external entry. Additionally, market-specific uncertainty moderates the relationship between breadth, experience and entry speed decisions. Greater market uncertainty tends to boost the internal and external entry speed than that of lower uncertainty.

Fragility functions are an important tool in earthquake engineering, used to compute the probabilities of different damage states as a function of seismic response. They can be developed for large systems like buildings and bridges, as well as for individual structural and non-structural components, such as those used in the FEMA P-58 Seismic Performance Assessment Procedure. There are currently a number of problems associated with some P-58 non-structural mechanical component fragility functions and related loss predictions, including non-convergence when fitting the fragility functions in some cases and non-monotonic loss predictions. In this study, we recommend improvements to these fragility functions and loss predictions. Firstly, we recommend using the maximum likelihood method to fit the fragility functions to the underlying empirical data. This mitigates the non-convergence problems when fitting and makes predictions that better align with damage observed in past events. To compute predicted losses for anchored mechanical components, it is necessary to additionally consider anchorage damage, which can be predicted using fragility functions based on building code provisions. We recommend refining the current FEMA P-58 method for predicting anchored mechanical component losses, such that component and anchorage damage are calculated directly according to their corresponding fragility functions. The proposed method yields more intuitive loss predictions that vary monotonically with anchorage capacity. It also leads to better predictions of losses relative to damage observed in previous events. If implemented, the recommendations made in this paper would enhance the FEMA P-58 Seismic Performance Assessment Procedure.

Human intentions are internal processes that can be deduced by observation of their resulting actions. Hence, an observation-based model of human intention is needed. The Jordan model of human intention in traffic is presented and applied on empirical data for two different intentions: driver’s braking intention and pedestrian’s crossing intention. The analysis shows that the behavior postulated within the theoretically developed Jordan model is observable in both sets of empirical data of drivers and of pedestrians while they perform intended actions. It can be assumed, that the Jordan model is universally applicable on very distinct intentions, not only limited to traffic scenarios. The Jordan model enhances the development of cooperative control among humans and machines. On the way towards a design of cooperative behavior among humans and vehicles in traffic, the Jordan model contributes with a systematic sequence and description of human behavior that is reflecting intention. Consequently an integration of the Jordan model of human intention with the model of cooperative and shared control Flemisch et al. (Ergonomics 57(3):343–360, 2014) is proposed. This integration facilitates the cooperation between human and system on strategic, tactical, and operational level.

A new response time-based method for coding omitted item responses in computer-based testing is introduced and illustrated with empirical data. The new method is derived from the theory of missing data problems of Rubin and colleagues and embedded in an item response theory framework. Its basic idea is using item response times to statistically test for each individual item whether omitted responses are missing completely at random (MCAR) or missing due to a lack of ability and, thus, not at random (MNAR) with fixed type-1 and type-2 error levels. If the MCAR hypothesis is maintained, omitted responses are coded as not administered (NA), and as incorrect (0) otherwise. The empirical illustration draws from the responses given by N = 766 students to 70 items of a computer-based ICT skills test. The new method is compared with the two common deterministic methods of scoring omitted responses as 0 or as NA. In result, response time thresholds from 18 to 58 s were identified. With 61%, more omitted responses were recoded into 0 than into NA (39%). The differences in difficulty were larger when the new method was compared to deterministically scoring omitted responses as NA compared to scoring omitted responses as 0. The variances and reliabilities obtained under the three methods showed small differences. The paper concludes with a discussion of the practical relevance of the observed effect sizes, and with recommendations for the practical use of the new method as a method to be applied in the early stage of data processing.

This paper discusses the outstanding problem of replicability of empirical data in the context of recent work on meta-analysis, especially within the field of evidence-based medicine. Specifically, it deals with the methodological issue of how to determine the degrees of heterogeneity between different collected studies. After critically reviewing the standard measures used to quantify meta-analytical heterogeneity, we argue that they should be revised in such a way to take into account the statistical power of the individual studies. We thus propose some new measures of heterogeneity. Subsequently, we apply them to re-assess concrete case-studies from clinical research, thereby showing explicitly how the relevant values of heterogeneity diverge from those obtained with the original measures.

In this study, MoS2 gas sensor based on field effect transistor has been proposed and the adsorption of NO2 molecules on the channel surface can lead to significant changes on its electronic and transport properties. The analytical models have been developed for the NO2 gas sensors by making an initial assumption that the gate voltage is directly proportional to the gas concentration. The performance of this sensor, is predicted and investigated by support vector regression (SVR) and artificial neural network (ANN) algorithms. The MoS2 gas sensor displays current changes upon exposure to very low concentrations of NO2. The comparison between analytical model, ANN and SVR with the empirical data shows the successful model construction. However, ANN outperforms the SVR approach and gives more accurate results.

Traditionally, the visual analogue scale (VAS) has been proposed to overcome the limitations of ordinal measures from Likert-type scales. However, the function of VASs to overcome the limitations of response styles to Likert-type scales has not yet been addressed. Previous research using ranking and paired comparisons to compensate for the response styles of Likert-type scales has suffered from limitations, such as that the total score of ipsative measures is a constant that cannot be analyzed by means of many common statistical techniques. In this study we propose a new scale, called the Visual Analogue Scale for Rating, Ranking, and Paired-Comparison (VAS-RRP), which can be used to collect rating, ranking, and paired-comparison data simultaneously, while avoiding the limitations of each of these data collection methods. The characteristics, use, and analytic method of VAS-RRPs, as well as how they overcome the disadvantages of Likert-type scales, ranking, and VASs, are discussed. On the basis of analyses of simulated and empirical data, this study showed that VAS-RRPs improved reliability, response style bias, and parameter recovery. Finally, we have also designed a VAS-RRP Generator for researchers' construction and administration of their own VAS-RRPs.

Agent-based modeling (ABM) is a type of computer simulation that creates a virtual society and allows controlled experimentation. ABM has the potential to be a powerful tool for exploring criminological theory and testing the plausibility of crime prevention interventions when data are unavailable, when they would be unethical to collect, or when policy-makers need an answer quickly. This paper takes stock of the current literature to discuss the potential contributions of ABM, assess current practice, identify shortcomings that threaten the validity of findings using ABM, and to make suggestions regarding the construction and communication of future work using ABM. We systematically searched major databases to find all publications using ABM to simulate urban crime patterns and coded publications to quantify the following information: (1) characteristics of the publication, the model and the agents, (2) model purpose, (3) crime type investigated, and (4) interrogation of the model via sensitivity testing and validation. After sifting papers according to our inclusion criteria, we identified and reviewed 45 publications. Models informed by the opportunity theory framework dominated. Most publications lacked detail sufficient to enable replication. Many did not include clear a rationale for modeling choices, parameter selection or calibration. Rarely were parameters calibrated using empirical data. Model validation was limited and inconsistent across papers. ABM offers significant potential for criminological enquiry. However, at present, the lack of model detail reported in publications makes it difficult to assess where sufficient evidence exists to support—and where gaps limit—the development of models that reflect extant conditions and offender decision-making. For the field to progress, as a minimum, standardized reporting that encourages transparency will be necessary.

To solve the problem of fuzziness and uncertainty of traffic states in signalized intersection, a method was proposed for estimating traffic condition based on stacked de-noising auto-encoder model. The simulation data and empirical data were used to train the model, and the K-means clustering method was used to determine the traffic state thresholds and the data were divided into three categories based on the threshold values. Relevant features based on the reconstruction theory of de-noising auto-encoder were automatically extracted, and unsupervised greedy layer-wise pre-training and supervised fine-tuning were utilized to train the deep auto-encoder network, so that it had good robust performance on obtaining the traffic state characters with low quality in the complex environment. From the experimental results, the proposed method obtains an accuracy of 91.5% in simulation data and 88% in empirical data, which is better by 7.1% than using decision tree model.

Countless bibliometric indexes have been proposed to assess researchers’ productivities, in particular, in fields where the author sequence is regarded helpful in determining authors’ individual credits. Unfortunately, the most popular h-index ignores author ranks and leads to bias with multi-author publications; and of the many bibliometric counting methods proposed for assigning credit to authors, such as harmonic or geometric counting, none seems to have been widely adopted yet. In this work, I challenge the assumption that the total credit for a publication be equal to 1. This total-credit normalization assumption diminishes first-author credit and may impede adoption of multi-author-aware credit assignment rules. Other than on relative contributions, author credit could be based on variables such as accountability, which remains unchanged for the first (and potentially, the last) author regardless of additional coauthors. Therefore, I study the adequacy of several counting methods for first-author-normalized credit, giving full credit to the first author while also crediting coauthors. Harmonic counting has been shown to agree well with empirical data; however, unlike geometric counting, harmonic counting results in unbounded total credit for a publication with first-author-credit normalization in the limit of many authors. I therefore propose adaptable geometric counting and evaluate how it combines the advantages of harmonic and geometric counting through an additional parameter. I show that the golden ratio is a parameter for geometric counting that agrees as well as harmonic counting with empirical data for total-credit normalization; and I discuss the impact of using adaptable geometric counting with first-author-normalized credit. In particular, the latter features bounded total credits even when full credit is given to first authors. In conclusion, geometric counting with the golden ratio can be used for credit assignment without having to choose a parameter value, yet offers customization potential and can be combined with either normalization assumption.

The debate on whether the peer-review system is in crisis has been heated recently. A variety of alternative systems have been proposed to improve the system and make it sustainable. However, we lack sufficient evidence and data related to these issues. Here we used a previously developed agent-based model of the scientific publication and peer-review system calibrated with empirical data to compare the efficiency of five alternative peer-review systems with the conventional system. We modelled two systems of immediate publication, with and without online reviews (crowdsourcing), a system with only one round of reviews and revisions allowed (re-review opt-out) and two review-sharing systems in which rejected manuscripts are resubmitted along with their past reviews to any other journal (portable) or to only those of the same publisher but of lower impact factor (cascade). The review-sharing systems outperformed or matched the performance of the conventional one in all peer-review efficiency, reviewer effort and scientific dissemination metrics we used. The systems especially showed a large decrease in total time of the peer-review process and total time devoted by reviewers to complete all reports in a year. The two systems with immediate publication released more scientific information than the conventional one but provided almost no other benefit. Re-review opt-out decreased the time reviewers devoted to peer review but had lower performance on screening papers that should not be published and relative increase in intrinsic quality of papers due to peer review than the conventional system. Sensitivity analyses showed consistent findings to those from our main simulations. We recommend prioritizing a system of review-sharing to create a sustainable scientific publication and peer-review system.

The inspection conditions may significantly affect the visual detectability of impact damage in aircraft composite skins. The goal of the present study was to identify the most affecting factors related to standard field control procedures and thus establish reasonable barely visible impact damage threshold in the frameworks of Irkut MC-21 aircraft certification. The probability of surface damage detection as a function of damage size was evaluated in relation to qualification of experts, paint color, viewing distance and surface contamination. The experiments with representative panels were carried out, the empirical detectability data was analyzed by methods of mathematical statistics. Using non-parametric tests the statistical significance of mentioned in-service factors was estimated. The bootstrap method was applied to make empirical data sufficiently representative. The minimum reliably detectable sizes of dents caused by 25 mm indenter for considered inspection conditions were identified. For detailed inspection in case of the clean surface the size of 0.3 mm can be accepted as a relevant criterion, in case of contamination this value can grow up to three times. For general inspection the values of 1 mm for clean and 1.7 mm for contaminated surface can be accepted conservatively. The obtained results were shown to be consistent with the literature and were recommended for use on the stages of design and maintenance.

Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data., Design Type(s)data integration objective • source-based data transformation objective • modeling and simulation objectiveMeasurement Type(s)brain measurementTechnology Type(s)functional magnetic resonance imaging • Diffusion Weighted ImagingFactor Type(s)Sample Characteristic(s)Macaca • brain Machine-accessible metadata file describing the reported data (ISA-Tab format)

Perturbations, such as fluctuations in abundance, can ripple across species assemblages through ecological interactions. Furthermore, the way in which ecological interactions are organized into a network and the interaction strengths connecting species may be important for cascading effects. Previous work revealed that network structure determines how cascading effects spread across species assemblages. A next step is to understand how interaction strengths influence cascading effects. Here, we assume that perturbations have negative effects, and we evaluate whether interaction strength affects network robustness to cascading effects in mutualistic interactions, and examine the role of network structure in mediating perturbation cascades when interaction strength is incorporated. We combine empirical data on 18 mutualistic networks, two simulations scenarios, and network theory, to investigate how network structure affects perturbation spreading time, a proxy of network robustness to cascading effects. Simulations in which we included interaction strength presented higher mean spreading time, indicating that interaction strength increases network robustness. Richness, modularity, and nestedness had a strong, positive effect, on mean perturbation spreading time regardless of the interaction strengths. We found that network structure and the distribution of interaction strengths affected communities’ robustness to perturbation spreading. Our results contribute to the discussion on the danger that ecosystems face when species, and interactions alike, become extinct.

Knowledge communication plays a fundamental role in studies of science of science. This paper aims to examine inter-specialty communication patterns within a discipline using author citation networks. Two metrics are designed, including average knowledge flow and average shortest distance. They are used to identify the impact and diffusion characteristics of inter-specialty knowledge communication. We apply these metrics to an empirical data set of Chinese library and information science (CLIS) publications. We find that the two metrics portray different aspects of knowledge communication in CLIS and conclude that indirect paths, the size of specialties, and the communication structure among specialties may lead to the differences.

In container yard of container terminals, a storage charge is imposed to encourage customers to limit the space required for their containers. This study addresses the storage price scheduling problem for the storage of outbound containers. The price schedule consists of the free-time limit, which is the maximum duration for a container to stay in the yard without any charge, and storage charge per day for storing a container past the free-time-limit. Empirical data suggests that the efficiency of loading operations significantly depends on the space utilized by a vessel’s outbound containers. Mathematical models are developed to determine the optimal storage price schedule in such a manner that the terminal’s total profit is maximized or the total system’s cost is minimized. Both single and multi-vessel cases are considered. Properties of the optimal solution are derived from the mathematical models and numerical experiments are conducted to validate solutions.

While neuroscience has made enormous progress in understanding the brain, the implications of these empirical findings for ontological questions in philosophy including the mind–body problem remain yet unclear. In the first paper, I discussed the model of brain that as implied and supported by the empirical data. This leads me now to the question of an empirically plausible ontology of brain. Therefore, the aim in this second paper is the ontological characterization of the brain in terms of a process-based ontology that avoids what Whitehead described as “simple location” and “fallacy of misplaced concreteness”. The discussion of the model of the brain is complemented by developing a process-based ontological characterization of the brain. Specifically, as based on Whitehead, I argue that “simple location” of the brain as thing or object in time and space amounts to nothing but an abstraction rendering what Whitehead described as “fallacy of misplaced concreteness”. Instead of describing the brain as static, non-temporal and isolated thing or object, I characterize the brain ontologically by dynamic, temporal, and relational processes. This leads me to a process-based ontology of brain which may be specified in spatiotemporal terms. Since the world’s larger spatiotemporal range or scale contains, e.g., nests, the smaller one of the brain, I characterize their ontological relationship by “spatiotemporal nestedness” and “spatiotemporal directedness”. Such spatiotemporal relationship between world and brain precludes the confusion between the world as whole and the brain as part, e.g., “mereological confusion”. I conclude that process-based or better, more specifically, spatiotemporal ontology of the brain and its relationship to the world may offer novel views on the question for the ontological relationship between mind and brain, e.g., the mind–brain problem, by converting or reformulating it as “world-brain problem”.

Connectivity has become a top conservation priority in response to landscape fragmentation. Many methods have been developed to identify areas of the landscape with high potential connectivity for wildlife movement. However, each makes different assumptions that may produce different predictions, and few comparative tests against empirical movement data are available. We compared predictive performance of the most-used connectivity models, cost-distance and circuit theory models. We hypothesized that cost-distance would better predict elk migration paths, while circuit theory would better predict wolverine dispersal paths, due to alignment of the methods’ assumptions with the movement ecology of each process. We used each model to predict elk migration paths and wolverine dispersal paths in the Greater Yellowstone Ecosystem, then used telemetry data collected from actual movements to assess predictive performance. Methods for validating connectivity models against empirical data have not been standardized, thus we applied and compared four alternative methods. Our findings generally supported our hypotheses. Circuit theory models consistently predicted wolverine dispersal paths better than cost-distance, though cost-distance models predicted elk migration paths only slightly better than circuit theory. In most cases, our four validation methods supported similar conclusions, but provided complementary perspectives. We reiterate suggestions that alignment of connectivity model assumptions with focal species movement ecology is an important consideration when selecting a modeling approach for conservation practice. Additional comparative tests are needed to better understand how relative model performance may vary across species, movement processes, and landscapes, and what this means for effective connectivity conservation.

Today, widespread growth in the world is intertwined with multichannel monitoring systems based on remote sensing. Such systems allow for the collection of operational information that focuses on the environmental status of marine systems on different scales. The technique of identifying real-time abnormal phenomena in the marine environment requires the presence of algorithmic remote sensing measurements and appropriate software. The present study presents a new monitoring system for remotely sensed anomalies on the marine surface, equipped with a “spotting” model based on empirical data. The experimental verification of the efficiency of the system and the algorithms developed was based on data from the Cosmos-1500 satellite for several seas. Finally, it is stressed that the proposed system, in addition to water, can also be used to detect abnormal phenomena in both air and soil.

The study of human mobility has gained much attention in recent years. To date, various models have been developed to predict human mobility patterns for intra- and/or inter-city cases. These models incorporate the populations as proxy variables in the place of real variables which cannot be observed easily. However, inaccuracies in predicting human mobility within cities are usually encountered. One source of inaccuracies in intra-city scenarios arises from the fact that cities’ populations are influenced by people from other areas. Therefore, population cannot be regarded as a good proxy variable for movement modeling. The objectives of this article are to introduce new proxy variables for use in current models for predicting human mobility patterns within cities, and to evaluate the accuracy of the predictions. In this study, we have introduced new proxy variables, namely, venues and check-ins, extracted from location-based social networks (LBSNs). In order to evaluate the models, we have compared our results with empirical data obtained from taxi vehicles, based on trip distances and destination population distributions. The Sorensen similarity index (SSI) and R-squared measures were also used to compare the performances of models using each variable. The results show that all models with LBSN variables can capture real human movements better within Manhattan, New York City. Our analytical results indicated that the predicted trips using LBSN data are more similar to the real trips, on average, by about 20% based on the SSI. Moreover, the R-squared measures obtained from regression analyses were enhanced significantly.

The benefits of active learning are well established. Nevertheless, many instructors are unable to implement active learning in the classroom due to pressures to cover many core topics in a principles course. This paper discusses a pedagogical tool that addresses this problem: the flipped classroom. In a flipped classroom, students are asked to study classroom material prior to class, allowing more class time to be used for active learning activities such as group discussion, problem solving, and experiments. This paper reports on our experience “flipping” a large enrollment (900+ students) microeconomic principles classroom using a methodology that was enhanced over the course of several years. Students were assigned to watch multimedia pre-lectures which included embedded questions prior to attending each lecture which then focused on active learning activities. Using empirical data collected from our semester-long case study, we provide an argument for best practices aimed at improving the effectiveness of a flipped classroom model to increase student learning outcomes.

This paper discusses issues of lexical synonymy. Two German adjectives with the core meaning ›very good‹ are in focus: vorzuglich and vortrefflich. Our empirical data is taken from the Leipzig Corpora Collection. According to the »classical« view, near-synonyms differ from each other semantically. Hence the aim of synonym analysis is to arrive at definitions by finding out distinctive semantic features. We show that near-synonyms can possess individual combinatorial properties without clear semantic distinctions. Combinatorial preferences or restrictions often go back to usage based co-occurrences. The semantic class of co-occurrence partners is also an important factor. Furthermore, combinatorial preferences can go back to affinity with certain thematic domains, discursive practices, stylistic registers etc.

Despite the fact that diffusion research has existed for more than a century, a quantitative review covering this subject in a broad and general context is still lacking. This article reviews diffusion research by providing an extensive bibliometric and clustering analysis. In total, we identified thirteen clusters comprising 6,811 publications over the period of 2002---2011, and thereby describe the characteristics of diffusion research in an extensive and general way based on quantitative bibliometric methods. The analysis reveals that diffusion research is highly interdisciplinary in character, involving several disciplines from ethnology to economics, with many overlapping research trails. The concluding section indicates that diffusion research seems to be data driven and relies heavily on solely empirical studies. Consequently, influential publications rely on empirical data that support and change theories in modest ways only. In this contribution, we propose a review method that produces a fairly good overview of the research area and which can be applied to any knowledge field to replace or complement the traditional literature review.

The aim of the present study was to analyze pistol and compact rifle marksmanship in close quarter combat (CQC). There is currently a controversy about what weapon is most appropriate to use in CQC, short weapon as pistols or long weapons as rifle. Forty two participants conducted two close quarter combat simulations, one with pistol and one with compact rifle. Marksmanship and time to cover the simulation were measured. Data obtained showed no significant differences between pistol and compact rifle. In addition, it was observed a tendency to obtain higher scores with compact rifle, probably be due to the higher stability provided in the shot action. Compact rifle could be an effective election in actual theaters of operation in which asymmetrical and symmetrical combat are combined and also in police interventions due to compact rifle provide higher fire power and range and obtained the same shooting performance than pistol. To the best of our knowledge this is the first study that provides empirical data to solve the discussion about the efficiency of the use of rifle and pistol in close quarter combat in military and police interventions. These results could be used to improve the military and police interventions efficiency. Sin financiación 2.098 JCR (2017) Q2, 46/94 Health Care Sciences and Services; Q3, 13/25 Medical Informatics 0.619 SJR (2017) Q2, 27/190 Health Informatics, 12/64 Health Information Management, 83/1258 Information Systems, 1087/2878 Medicine (miscellaneous) No data IDR 2017 UEM

In the literature, there are two major proposals for resolving the syntax-semantics mismatch characterizing complex predicates. The ‘verb-raising’ approach resolves the mismatch via syntactic movement (or its analog), whereas the ‘argument-sharing’ approach does so by positing merged argument structures for complex predicates at the syntax-semantics interface. Focusing on two types of complex predicates in Japanese—syntactic compound verbs and the so-called -te form complex predicate—I discuss some novel empirical data posing challenges to both approaches in addition to the set of well-known observations from the literature illustrating the tension between the two strategies. The paper then argues for a synthesis of these two approaches within a variant of categorial grammar, taking advantage of the logical perspective on the syntax-semantics interface characteristic of certain recent variants of categorial grammar. The proposed analysis integrates the analytic insights of the two previous approaches seamlessly, and has both theoretical and empirical advantages over the two: theoretically, it clarifies the deeper connection between the theory-neutral analytic intuitions guiding the two approaches; empirically, it provides straightforward solutions for both the old and new empirical problems, enabling a previously unattained unified treatment of complex predicates that has a wider empirical coverage than its competitors.

Ants build a trail that leads to a new location when they move their colony. The trail’s traffic flows smoothly, regardless of the density on the trail. To the best of our knowledge, such a phenomenon has been reported only for ant species. The trail’s capacity is known as trail traffic flow. In this paper, we propose a probabilistic model of trail traffic flow, which overcomes some inadequacies of the kinetic model previously proposed in the literature. Our model answers a question unsolved by the previous model, namely, how many worker ants form such a density-independent trail. We focus on ants’ responses to mutual contacts that involve individuals in trail formation. We propose a model in which contact frequency predicts the number of worker ants that form a trail. We verify that our model’s estimates match the empirical data that ant experts reported in the literature. In modeling and evaluation, we discuss an intelligent ant species, the house-hunting ant Temnothorax albipennis, which is popular among the ant experts.

Meta-analyses are becoming a popular tool for supporting benefit transfers, but the availability of studies is a direct consequence of policy issues, research funding, and investigator interest. We investigate fragility versus robustness of the meta-equation by considering sample selection, removing one observation or study at a time with replacement, and removing/adding regressors. Several key variables are found to be robust, strengthening the argument for their use in policy prescriptions. The key insights are that these methods can be used to parse meta-data to identify the most appropriate set(s) of observations and regressors to support literature evaluations, benefit transfers and other practical applications using statical summaries of empirical data.

After the discovery of the “mirror” neurons in primates, some researchers tended to explain action understanding as a result of functioning of these units. The proponents of the traditional view on the nature of this cognitive and social phenomenon assume that the mirror neurons do not provide action understanding or provide it only partly. There exist empirical data that cannot be explained through the mirror neuron model of understanding others’ actions. Analyzing the mirror neuron data, I revise their function and propose an alternative role of this type of neurons. At first, goals and intentions of the executor’s action are coded outside the mirror neuron system. If the action is important for the observer and can be useful in his own motor repertoire, his/her mirror neuron system implicitly reproduces the action, retrieving the kinematics and sensory consequences the observer experienced in the past while executing the same action. Thus, the implicit reproduction facilitates the observer to execute this action either immediately or in the future. More likely, precisely this, but not action understanding, is the function of the mirror neurons.

This paper aims to improve the accuracy of standard compartment models in modeling the dynamics of an influenza pandemic. Standard compartment models, which are commonly used in influenza simulations, make unrealistic assumptions about human behavioral responses during a pandemic outbreak. Existing simulation models with public avoidance also make a rigid assumption regarding the human behavioral response to influenza. This paper incorporates realistic assumptions regarding individuals' avoidance behaviors in a standard compartment model. Both the standard and modified models are parameterized, implemented, and compared in the research context of the 2009 H1N1 influenza outbreak in Arizona. The modified model with heterogeneous coping behaviors forecasts influenza spread dynamics better than the standard model when evaluated against the empirical data, especially for the beginning of the 2009---2010 normal influenza season starting in October 2009 (i.e., the beginning of the second wave of 2009 H1N1). We end the paper with a discussion of the use of simulation models in efforts to help communities effectively prepare for and respond to influenza pandemics.

In some circumstances, people interact with a virtual keyboard by triggering a binary switch to guide a moving cursor to target characters or items. Such switch keyboards are commonly used by patients with severely restricted motor capabilities. Typing with such systems enables patients to interact with colleagues, but it is slow and error prone. We develop a methodology that can automate an important part of the design process for optimally structured switch keyboards. We show how to optimize the design of simple switch keyboard systems in a way that minimizes the average entry time while satisfying an acceptable error rate. The first step is to model the user’s ability to use a switch keyboard correctly for different cursor durations. Once the model is defined, our optimization approach assigns characters to locations on the keyboard, identifies an optimal cursor duration, and considers a variety of cursor paths. For our particular case, we show how to build a user model from empirical data and demonstrate that the resulting optimized keyboards are quite different from existing keyboard designs. Electronic supplementary material The online version of this article (doi:10.1186/s41235-016-0007-6) contains supplementary material, which is available to authorized users.

This article addresses the role of spatial interaction in social networks. We analyse empirical data describing a network of leisure contacts and show that the probability to accept a person as a contact scales in distance with similar to d (-aEuro parts per thousand 1.4). Moreover, the analysis reveals that the number of contacts an individual possesses is independent from its spatial location and the spatial distribution of opportunities. This means that individuals living in areas with a low accessibility to other persons (rural areas) exhibit at average the same number of contacts compared to individuals living in areas with high accessibility (urban areas). Low accessibility is thus compensated with a higher background probability to accept other candidates as social contacts. In addition, we propose a model for large-scale social networks involving a spatial and social interaction between individuals. Simulation studies are conducted using a synthetic population based on census data as input. The results show that the model is capable of reproducing the spatial structure, but, however, fails to reproduce other topological characteristics. Both, the analysis of empirical data and the simulation results provide a further evidence that spatial interaction is a crucial aspect of social networks. Yet, it appears that spatial proximity does only explain the spatial structure of a network but has no significant impact on its topology.

The purpose of editing is to correct erroneous entries in the dataset and assure the quality of data. It takes a lot of resources to correct all errors, so editing procedures where only a subset of errors to be corrected are sought after. Correcting only a subset of all errors will influence the final estimates, and tools evaluating the properties of the estimates like bias and variance need to be available. This paper introduces a probabilistic editing procedure where the responses are selected for editing through Poisson Mixture (PoMix) sampling and a bias adjusted GREG estimator is used for estimation. An expression for the variance of the bias adjusted GREG estimator is derived, and variance estimator is proposed. The effectiveness of the proposed editing procedure and the GREG estimator is illustrated using empirical data from Statistics Sweden.