Search

Decision models such as the drift-diffusion model (DDM) are widely used and broadly accepted for their ability to account remarkably well for binary choices between perceptual hypotheses about a stimulus or for preferential decisions between valued options (such as snacks or consumer products), and for their response time (RT) distributions, as a function of the stimulus or the option values. The DDM is built on an evidence accumulation to bound concept, where, in the value domain, a decision maker repeatedly samples the mental representations of the values of the options on offer until satisfied that there is enough evidence in favor of one option over the other. As the value signals that drive the DDM evidence are derived from value estimates that are not known to the decision maker with certainty, repeated sequential samples are necessary to average out noise. The classic DDM typically does not allow for different options to have different levels of variability in their value representations. However, recent value-based decision studies have shown that decision makers often report levels of certainty regarding value estimates that vary across choice options. There is therefore a need to extend the DDM to include an option-specific value certainty component. We present several such DDM extensions and validate them against empirical data from four previous studies. The data supports best a DDM version in which the drift of the accumulation is based on a sort of signal-to-noise ratio of value for each option (rather than a mere accumulation of samples from the corresponding value distributions). This DDM variant accounts for the positive impact of value certainty on choice consistency and for the negative impact of value certainty on RT.

Traffic calming techniques are extensively used as safety tools to reduce the speed of motorised vehicles along any type of paved road. The objective of this study is to evaluate the efficacy of isolated speed humps on urban roads in India and to suggest an optimised geometry of speed humps for the desired level of speed reduction. For this, five different urban road sections were identified in Delhi and video graphic data were collected at each study section. The median speed of vehicles at each grid is estimated and speed profiles were developed for each category of vehicle approaching the speed hump. The speed behaviour of the vehicles approaching a circular or parabolic shape of isolated speed hump are examined and compared. This study developed speed profile models using regression techniques for an individual vehicle and also for the combined data. The observed speed of the vehicle was considered as a response variable and the physical properties of the speed humps were considered as explanatory variables. Further, the combined linear equation was considered for determining the optimum height and entry radius of the speed hump for the desired speed by using the simplex method.

Uncertainty affects the overall performance of railway systems. It can be captured tracking the successive departure and arrival events of trains at stations in the scheduled railways. Markov chains has shown to be an effective means to model variabilities in the departure and arrival delays of trains. The empirical data of actual operations are used to develop the Markov matrices. Transitions between delay states comprise the internal dynamics of running and dwelling processes in probabilistic terms, presenting particular patterns of deterioration, recovery and state keeping in the prevailing timetable setting. All these features conform to the practice of railway operations as well as train dispatchers and drivers. Hence, delay-based Markov chains becomes a simple and effective tool to predict delays in the following events of trains and distribution of delays over a longer period of time using simple performance measures. We have developed the homogeneous and non-homogeneous Markov models and tested their performances using the actual data collected in the Netherlands railway network. The analysis and evaluation show that the railway corridor considered in this study performs well and the corresponding Markov chain models can be used effectively to predict train delays and to evaluate timetable robustness. The outcomes of the homogeneous and non-homogeneous model are evaluated with respect to the observed data resulting that the non-homogeneous model performs better than the homogeneous model.

The coordinated motion of large flocks of starlings is fascinating for both laymen and scientists. During their aerial displays, the darkness of flocks often changes, for instance dark bands propagate through the flock (so-called agitation waves) and small or large parts of the flock darken. The causes of dark bands in agitation waves have recently been shown to depend on changes in orientation of birds relative to the observer rather than changes in density of the flock, but what causes other changes in darkness need to be studied still and this is the aim of the present investigation. Because we cannot empirically relate changes in darkness in flocks to quantities, such as position and orientation of the flock and of its members relative to the observer, we study this in a computational model. We use StarDisplay, a model of collective motion of starlings, because its flocks resemble empirical data in many properties, such as their three-dimensional shape, their manner of turning, the correlation of heading of its group-members, and its internal structure regarding density and stability of neighbors. We show that the change in darkness in the flocks perceived by an observer on the ground mostly depends on the observer’s distance to the flock and on the degree of exposure of the wing surface of flock members to the observer, and that darkness appears to decrease when birds roll during sharp turns. Remarkably, the darkness of the flock perceived by the observer was neither affected by the orientation of the flock relative to the observer nor by the density of the flock. Further studies are needed to investigate changes in darkness for flocks under predation.

The use of Hierarchical Bayes (HB) estimation techniques for choice-based conjoint (CBC) data offers the opportunity to directly use HB draws for preference simulations. This paper analyzes the use of HB draws for shares of choice predictions. Five different choice rules are compared: the first choice rule applied to HB draws, the logit choice rule applied to HB draws, the randomized first choice rule, the traditional first choice rule and the traditional logit choice rule. Each two different holdout choice scenarios are constructed containing one time two extremely similar and the other time very unique alternatives to assess how well the choice rules tolerate the IIA property in predicting choice shares. We present a Monte Carlo study to systematically explore shares of choice predictions based on the different choice rules and further verify whether our findings hold in empirical settings. The key finding of our Monte Carlo study is that using HB draws either combined with the first choice rule or the logit choice rule substantially improves choice share predictions when compared to the other choice rules, regardless of the type of holdout choice scenario. While the logit choice rule applied to HB draws performs a touch better for simulated data, the first choice rule applied to HB draws provides the best choice share predictions for each of the five empirical data sets. Using HB draws does not only provide the best predictive validity but, more importantly, it is theoretically correct when applying a Bayesian estimation approach to CBC data.

Contribution: This article discusses the use of manufacturing simulation games to study collaborative problem-solving skills in engineering students. The simulation represents the mass production paradigm in which large quantities of identical products are produced. Empirical data is collected from the simulation to evaluate the skills engineering students used in solving the problem and their group effectiveness. Background: The use of simulation games to teach problem solving in design and manufacturing is an effective approach to convey concepts to students. Simulation games engage students in experiential and collaborative learning with fun elements. Research Questions: How does hands-on simulation engage students in collaborative problem solving? How does participation in collaborative problem solving affect group effectiveness? Methodology: This work presents a study of 37 university-level engineering students in the United States. Participants worked in groups completing the simulation game and responded to surveys on their various skills used. Findings: Participants utilized analytical, metacognitive, and thinking skills in their engagement, reported that the simulation games enhanced their understanding of manufacturing concepts and active collaboration improved problem-solving effectiveness.

Proper scoring rules are used to assess the out-of-sample accuracy of probabilistic forecasts, with different scoring rules rewarding distinct aspects of forecast performance. Herein, we re-investigate the practice of using proper scoring rules to produce probabilistic forecasts that are ‘optimal’ according to a given score and assess when their out-of-sample accuracy is superior to alternative forecasts, according to that score. Particular attention is paid to relative predictive performance under misspecification of the predictive model. Using numerical illustrations, we document several novel findings within this paradigm that highlight the important interplay between the true data generating process, the assumed predictive model and the scoring rule. Notably, we show that only when a predictive model is sufficiently compatible with the true process to allow a particular score criterion to reward what it is designed to reward, will this approach to forecasting reap benefits. Subject to this compatibility, however, the superiority of the optimal forecast will be greater, the greater is the degree of misspecification. We explore these issues under a range of different scenarios and using both artificially simulated and empirical data.

The present research primarily investigates the applicability of different roadway capacity estimation methods under mixed-traffic conditions, operating with weak-lane discipline. The study also explores the feasibility and advantages of stochastic capacity estimation methods for mixed traffic scenarios on urban roads in India. Three methods, namely Traffic-stream models (calibrated empirically), Product limit method (PLM), and Sustained Flow Index (SFI), are used to estimate the capacity for five different categories of road sections. The Van-Aerde model is found to be the best representative for estimating deterministic roadway capacity. The PLM method is observed to provide the freedom of selecting desirable operational quality. And the SFI method provides the maximum sustained flow for the roadway infrastructure. As a practical outcome, a novel Level of Service monitoring framework is developed by augmenting the traditional volume-to-capacity ratio with the breakdown probabilities.

Objectives Universities have turned to SARS-CoV-2 models to examine campus reopening strategies. While these studies have explored a variety of modeling techniques, none have used empirical data. Methods In this study, we use an empirical proximity network of college freshmen obtained using smartphone Bluetooth to simulate the spread of the virus. We investigate the role of immunization, testing, isolation, mask wearing, and social distancing in the presence of implementation challenges and imperfect compliance. Results We show that frequent testing could drastically reduce the spread of the virus if levels of immunity are low, but its effects are limited if immunity is more ubiquitous. Furthermore, moderate levels of mask wearing and social distancing could lead to additional reductions in cumulative incidence, but their benefit decreases rapidly as immunity and testing frequency increase. However, if immunity from vaccination is imperfect or declines over time, scenarios not studied here, frequent testing and other interventions may play more central roles. Conclusions Our findings suggest that although regular testing and isolation are powerful tools, they have limited benefit if immunity is high or other interventions are widely adopted. If universities can attain even moderate levels of vaccination, masking, and social distancing, they may be able to relax the frequency of testing to once every four weeks.

Structural change is important driver of productivity. Existing decompositions of structural change rely heavily on the share weights of firms. Ideally, one would need a complete census of all firms to calculate the share weights. However, empirical data are often less than ideal. Another limitation of the structural change decompositions is that existing firms that switch from one industry to another are mixed with startups and bankrupt firms. To address both these limitations, we develop an alternative productivity decomposition of structural change that avoids the use of share weights, which can be useful whenever the share weights are problematic or unavailable. Our decomposition also takes the product switch explicitly into account and applies to both the level and percentage change of productivity. Application to Finnish agriculture illustrates the proposed approach.

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.

Despite increasing interest in the use of digital tools in boardgames for both commercial and research purposes, little research has to date explored how and why these tools are used. We interviewed 18 professionals working in the boardgame industry to explore the combination of digital tools and tabletop play, which affords new experiences and opportunities for both players and designers. We generated five key themes from the interview data. Participants engaged with ontological questions about the fundamental nature of games; they showed strong opinions about the use of digital tools; they discussed the impacts of digital tools for game design as well as in their design practice; they raised concerns about the costs to develop and maintain such tools; and they considered how they affect the in-game player experience. From these themes, we generate five design principles for digital tools for boardgame play: traceability, completeness, integration, privacy, and materiality. Grounded in empirical data, these design principles guide game designers and researchers seeking to explore this novel design space. Our research focuses attention on the role of digital components in play and on the need for thoughtful implementation that considers the entire lifecycle of the game, from development through publication and, ultimately, archival access.

The article considers the assessment of students' independent work in a distance learningon the basis of digital educational traces. Independent work in a mixed training can be performed on the online platform LMS, and the use of educational digital data allows you to study the specifics of student interaction with the course materials and the ability to solve the problem. For the assessment of time and the quality of independent work of students is determined by a set of criteria and indicators, selected quantitative metrics and offers a methodology that can be used to assess the progress of each student. The method includes algorithms for assessing the success of independent work on the basis of empirical data educational analytics. The developed algorithms allow to interpret the information on performance of independent work, to evaluate its success and to correct a trajectory of training of the student.

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.

Mopeds (electric bicycles and light motorcycles) are commonly used as a personal transportation mode in China. However, the understanding of characteristics of left-turning mopeds at signal-controlled intersections has been relatively limited. To bridge this gap, firstly, this paper proposed a video conversion method of moped movement data acquisition. Then, a method of data accuracy verification was introduced by comparing the results between the field experiment and the video conversion method. Secondly, the ideal traffic space for left-turn mopeds from different entrances was defined to analyse the characteristics of the left-turning mopeds at intersections. Further, three indicators, namely, transverse distance, the proportion of left-turning mopeds with crossing behaviour, and the average number of avoidance behaviour, were proposed and used to analyse the asymmetrical characteristics behaviour, crossing behaviour, and avoidance behaviour. Finally, based on empirical data collected from five signal-controlled intersections, the proposed methods and behaviours were analysed. This paper provides both a valid method of obtaining the position data of mopeds and a reliable basis for improving the safety of left-turning moped riders at urban signal-controlled intersections.

Purpose The purpose of this paper is to examine the applicability of the complexity-based temporary innovation system (TIS) framework to social innovation and examine the extent to which “nature-based solution” (NBS) projects may be understood through a TIS lens. It is proposed that TIS provides a framework to facilitate multi-actor engagement in social innovation responses to the complexity of wicked problems? The goal is to explore if TIS provides a useful framework for understanding the evolution of social innovation projects and enabling more consciously designed and facilitated social innovation with the potential for large-scale, long-term impact. Design/methodology/approach The research uses a case study methodology in which 10 NBS projects in 3 European cities are examined and compared to the expected features of a TIS as proposed by anonymised for the review process (2018; 2019) Findings Of the 10 NBS projects, only 3 were “TIS-like”, each of which was targeting wicked problems in the city/community. As only one of the remaining 7 projects was aimed at a wicked problem, the authors concluded that the TIS framework may be best suited to those social innovations that address one or more wicked problems and that NBS projects may not display this feature. Research limitations/implications The authors conclude with a reflection on theoretical insights arising from applying the TIS framework to NBS in particular, and social innovation generally – and proposes the next steps in developing the TIS framework in relation to social innovation. Originality/value This paper applies a new complexity framework to empirical data that have not been examined previously. This analysis contributes to the development of a new framework for designing and analysing complex social innovation initiatives and challenges existing theories presenting NBSs as addressing complex “wicked” problems.

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.

Business model experimentation has been identified as a key driver for business competitiveness but is underexplored in the sustainability and circular economy spheres. What is business model experimentation for the circular economy? This study follows a two-step approach: a literature analysis followed by a qualitative practitioner study. Based on these, circular business model experimentation is defined as an iterative approach to develop and test circular value propositions in a real-life context with customers and stakeholders, starting with a shared goal. It involves rapid learning based on empirical data to provide evidence on the viability of circular value propositions. Iterations involve increased complexity of experiments. There is a learning focus on initiating wider transitions, such as transforming consumer behaviours for the circular economy. We visualise the emerging research landscape, including research streams from business, transitions, engineering, and design. Practically, we illuminate how practitioners view the concept and current experimentation tools and approaches.

Action-stopping in humans involves bursts of beta oscillations in prefrontal-basal ganglia regions. To determine the functional role of these beta bursts we took advantage of the Race Model framework describing action-stopping. We incorporated beta bursts in three race model variants, each implementing a different functional contribution of beta to action-stopping. In these variants, we hypothesized that a transient increase in beta could 1) modulate decision thresholds, 2) change stop accumulation rates, or 3) promote the interaction between the Stop and the Go process. We then tested the model predictions using EEG recordings in humans performing a Stop-signal task. We found that the model variant in which beta increased decision thresholds for a brief period of time best explained the empirical data. The model parameters fitted to the empirical data indicated that beta bursts involve a stronger decision threshold modulation for the Go process than for the Stop process. This suggests that prefrontal beta influences stopping by temporarily holding the response from execution. Our study further suggests that human action-stopping could be multi-staged with the beta acting as a pause, increasing the response threshold for the Stop process to modulate behavior successfully. Our novel approach of introducing transient oscillations into the race model and testing against human neurophysiological data allowed us to discover potential mechanisms of prefrontal beta, possibly generalizing its role in situations requiring executive control over actions.

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.

Представлена робота присвячена вивченню проблеми визначення затримок транспортних засобів при виїзді з прилеглих територій на міські автомагістралі. На даний час вже існує велика кількість різних підходів щодо визначення транспортних затримок на нерегульованих перехрестях, але вони не в повній мірі враховують особливості руху автомобілів на виїздах з прилеглих територій. На основі результатів натурних спостережень за рухом транспортних засобів при виїзді з прилеглих територій в статті проведено оцінювання результатів визначення затримок за запропонованим підходом, в який закладено принципи системи масового обслуговування, що дозволяє враховувати пріоритетність руху автомобілів на конкуруючих напрямках. Встановлено, що відхилення фактичних транспортних затримок і затримок отриманих за запропонованим підходом для обох маневрів, поворот праворуч (злиття) і ліворуч (пересічення) не перевищує 10 %, що свідчить про можливість його використання на таких об’єктах як виїзди з прилеглих територій. На основі отриманих емпіричних даних встановлено, що розподіл фактичних затримок транспортних засобів при виїзді з прилеглих територій для всіх досліджуваних випадків відповідає логнормальному закону з достатньо високою імовірністю. Для зменшення відхилень модельних і фактичних значень транспортних затримок при виїзді з прилеглих територій необхідно в моделі врахувати стохастичні характеристики параметру процесу здійснення відповідних маневрів. Таким параметром є граничний часовий інтервал між автомобілями в потоці на головному напрямку, за якого будь-який автомобіль, що очікує на здійснення маневру при виїзді з прилеглих територій, може виконати поворот праворуч або ліворуч, який можна визначити за рахунок проведення статистичної обробки результатів натурних спостережень. Ключові слова: транспортний потік, затримка, граничний інтервал, виїзд з прилеглої території, натурні спостереження.

While hierarchical linear modeling is often used in social science research, the assumption of normally distributed residuals at the individual and cluster levels can be violated in empirical data. Previous studies have focused on the effects of nonnormality at either lower or higher level(s) separately. However, the violation of the normality assumption simultaneously across all levels could bias parameter estimates in unforeseen ways. This article aims to raise awareness of the drawbacks associated with compounded nonnormality residuals across levels when the number of clusters range from small to large. The effects of the breach of the normality assumption at both individual and cluster levels were explored. A simulation study was conducted to evaluate the relative bias and the root mean square of the model parameter estimates by manipulating the normality of the data. The results indicate that nonnormal residuals have a larger impact on the random effects than fixed effects, especially when the number of clusters and cluster size are small. In addition, for a simple random-effects structure, the use of restricted maximum likelihood estimation is recommended to improve parameter estimates when compounded residuals across levels show moderate nonnormality, with a combination of small number of clusters and a large cluster size.

Consecutive interpreting (CI) plays a crucial role in interpreting practice and education. To gain a better understanding of the skills and competencies needed on the path to consecutive expertise,...

The technological advancement realized in the discovery and embrace of both IoT and IIoT is totally indispensable. Many systems and subsystems both robust and miniaturized have made their existence into the technical arena due to IoT. It goes without saying that IoT has brought into light very diverse benefits that cut across universal applications.However, the pre-requisite of a network channel existence for an IoT operation to be successful is the only pitfall that this essentially unique system possesses. There is a significant amount of danger associated with transmission networks. They have very substantial susceptibility to both online and offline threats by malicious cyber intentions.This paper focuses on the analyses of the threats posed to these IoT networks through Artificial Neural Networks. Specifically, a model is trained through recurrent and convolutional neural network to do intensive analysis on the threat intensity, type and threat source for data logging purposes. The Intruder detection system (IDS) explored in this paper registers a success rate of 99% based on the empirical data posed to the model.

Artificial intelligence technology is developing rapidly in recent years. The purpose of this paper is to measure the distance to an object using this. In order to measure the distance, two separate pictures from same angles of the object will be taken. It extracts sizes for the same object in two pictures. In order to do this in real time, object detection technology of Artificial Intelligence on mobile phone was used. In this paper, a method for measuring the distance from two pictures is presented. The proposed method was implemented as a prototype on iOS. In order to measure the performance of distance measurement, experiments were conducted in various environments. In the experiments, the empirical data yielded some discrepancies with the actual measurement. This was a result of errors occurring in the object detection process where the actual size of the object was calculated. Despite these discrepancies, this method of object detection may be widely used in instances where accurate measurements are not necessarily required such as guidance systems for the visually impaired.

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.

Choosing an appropriate statistical model to analyze reciprocal relations between individuals’ attitudes, beliefs, or behaviors over time can be challenging. Often, decisions for or against specific models are rather implicit and it remains unclear whether the statistical approach fits the theory of interest. For longitudinal models, this is problematic since within- and between-person processes can be confounded leading to wrong conclusions. Taking the perspective of the reinforcing spirals model (RSM) focusing on media effects and selection, we compare six statistical models that were recently used to analyze the RSM and show their ability to separate within- and between-person components. Using empirical data capturing respondents’ development during adolescence, we show that results vary across statistical models. Further, Monte Carlo simulations indicate that some approaches might lead to wrong conclusions if specific communication dynamics are present. In sum, we recommend using approaches that explicitly model and clearly separate within- and between-person effects.

COVID-19 is a serious epidemic that has an unmistakable impact on all aspects of our lives, including the educational process. Most of the world has adopted Virtual Classes (VCs) to sustain teaching and learning. While prior research about such e-learning technologies has been focusing on the initial adoption, this research investigates the factors influencing the students’ desire and intention to continue using VCs, especially after the crisis subsides. This study extends the literature by developing a model that integrates pre-and post-adoption constructs and incorporates technological characteristics, namely, task technology fit, convenience, and compatibility into the Expectation Confirmation Model (ECM) to study the post-adoption Continuance Intention (CI) of VCs. The model is empirically validated using the partial least squares–structural equation modelling method and proved to have a reasonable description power (R2=62%) in terms of students’ CI. The survey empirical data is also supported by interviews with some students. The results support all the hypothesized relationships and confirm that the integration of technical characteristics in the ECM provides an appropriate framework to explain students’ intention to continue using VCs, which forms a good base for practitioners to consider a wide range of technological features for preparing applications. Yet, the model still requires to be extended with other stakeholders, including teachers, and other constructs like personal, psychological, social, and environmental factors. © 2021 The Authors.

An approach to the study of wave processes in the human cardiovascular system is presented in order to identify the constituent components in the complex signal of the pulse pressure wave—direct and reflected waves. This makes it possible to calculate the pulse wave propagation time and velocity, which are indicators that play an established role in the diagnosis and prognosis of cardiovascular diseases. Testing of the approach is based on a combination of methods for measuring the pulse wave with subsequent mathematical processing of empirical data and methods for direct numerical modeling of hemodynamic processes in the arterial tree.

Predicting students’ course grades has important university-related applications, such as providing predictive assistance to students who may fail to graduate. However, it is a quite challenging task. On the one hand, each student has very limited historical course grades. On the other hand, a student may exhibit very different performance on different types of courses. As a result, most existing grade prediction methods don’t address such challenges and cannot achieve good results. Through empirical data analysis, we find that a group of students achieve similar grades over a set of similar courses. Based on the observations, we first construct student-course graphs, student-student graphs and course-course graphs to capture student-course dependency, student similarity and course similarity, respectively. Then, we propose a model named DGTEAM to specifically deal with these three dynamic graphs to obtain the representations of students and courses. The obtained representations of each course and student are applied to predict the grades. We conduct experiments on a real-world dataset and the result verifies the superiority of our model.

Cruise ports seek to secure cruise calls, with most of them competing to accommodate the most profitable activities of all: home-porting. Meanwhile, an uninterrupted growth of cruise activities is based on the evolution of renewed business strategies that alter home-porting selection criteria. The present study examines the criteria structuring cruise lines decisions of which port to use for home-porting with the use of a database constructed with input from cruise lines, ports and cruise terminal operators, and stakeholders. Methodologically, it does so via a survey and the analysis (descriptive statistics, one-way ANOVA, and Kruskal-Wallis non-parametric test) of the 109 replies received. The collected empirical data reveals (a) the hierarchy of the home-porting selection criteria; (b) the variance of the perspectives of cruise lines, cruise ports, and other stakeholders; and (c) the role of the experience of cruise professionals in shaping their assessment of the significance of each criterion - while the comparison of the findings with those of past researches identifies the evolution and potential alterations of this hierarchy of home-porting selection criteria.

This paper presents specific approaches and techniques aimed at developing the skill of compiling bibliography lists for research and project works in schoolchildren. The applied methodology involved the observation method, analysis and synthesis of the obtained empirical data and the modelling method. The author of the paper introduces a variety of techniques: games; monitoring of finished project assignments, their assessment and review; elaboration of instructions, recommendations; gathering of negative language material; editing, etc. It is concluded that the development of this kind of activity requires gradual and systemic training of students from early adolescence.

Introduction.Interdisciplinary research is a most relevant issue in science and education. The integration of intellectual resources with research and production infrastructure is acknowledged as the main goal of interdisciplinary research in the international practice.The main (analytical) part.The authors propose the following methodological approach to the study, based on the distribution of interdisciplinary methods into groups by the scale of the research subject (material). In this case, the studies have the following levels: microlevel, supramolecular level, material research, design research. The paper presents research methods used at each of these levels. The co-authors propose to optimize the study of performance characteristics of building materials and fire retardants through the use of a compatibility chart with regard for the study levels and the analysis of methods of experimental research at each level.Using a compatibility chart.The methodology of the study. A research into the fire retardant efficiency of esters of phosphoric acid, used to modify wood, was selected as a practical example for compatibility diagrams. The project encompasses a number of methods applicable to compatibility charts: the method of elemental analysis, the Gibbs energy assessment method, the sample surface assessment method, the electron microscopy method, methods of assessing fire-hazardous characteristics of wood, the water sorption method, strength and biosecurity assessment methods.Conclusions.The co-authors first proposed an algorithm for generalizing the empirical data on mechanochemical characteristics of materials using interdisciplinary methods in the form of a compatibility chart. This methodology optimizes research into any composite materials though it preserves targeted research methods and eliminates impractical and concomitant experimental studies, thus, reducing labour costs and environmental impacts.

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.

Structural PET foam has a plethora of industry applications, such as inlays for wind turbine blades. However relevant material properties, especially mechanical characteristics, vary significantly between foam types. With new foam compositions being continuously developed by leading vendors, there naturally is limited empirical data in literature on optimal milling parameters for these novel foams. To investigate the milling behavior, raw machine tool data was collected for a test series. Using these data, several distinct process features were calculated. The features were then used to evaluate the performance of several machine learning algorithms in regard to their predictive accuracy.

Optimizing the positive end-expiratory pressure remains challenging for any clinician treating a patient with acute respiratory distress syndrome. This paper presents an approach to automate a PEEP titration maneuver and identify the best PEEP according to maximal compliance. The respiratory system was modeled by a single-compartment model, and parameters were estimated using multiple linear regression. A classifier identified the best PEEP using the scaled relative change in compliance between PEEP levels based on empirical data from previous manual PEEP titrations. An experimental system allows the in vivo testing of the automated PEEP titration, including additional safety measures. The complete system was tested in a single animal experiment and correctly identified the best PEEP. The introduced system is a step closer towards an automated, standardized PEEP optimization and closed-loop control of mechanical ventilation.

Understanding the molecular mechanisms involved in gene regulation is one of the basic issues of systems biology. Conventional approaches to modeling gene regulation often fail to distinguish between the model properties that adequately reflect the characteristics of a biological system and those associated with the nature of the model. A logical approach was developed for modeling the regulation of gene expression, which provides maximal further use of the available empirical data and minimal use of a priory hypotheses. Rules of gene regulation were formulated in the form of linguistic conditions, that is, in a language similar to human languages. Fuzzy linguistic modeling (FLM) was used as a method that more accurately reflects the actual data. This study is the first to employ FLM in constructing models of the activities of cis-regulatory modules (CRMs) and to apply the method to the problems related to expression of genes involved in biochemical patterning of embryos. Essential details were thus clarified for the mechanisms by which primary morphological gradients are read by a combination of two or more cis-regulatory modules of the same gene.

The problems of artificial intelligence, control and decision-making with incomplete or unreliable information include a wide class of problems of abductive explanation of the observed, including cause–effect problems. The study is devoted to the substantiation of the method of logical formation of hypotheses explaining the observed. Means of knowledge representation and derivation of hypotheses are proposed. A language possessing the property of substitutability is introduced. The properties of language and the calculi introduced in it provide hypothesizing by combining deduction and abduction. In contrast to the well-known logical methods of abduction, the proposed techniques make it possible to derive hypotheses (minorants) that are necessary and sufficient for a formal explanation of the observed. Based on minorants in combination with the basic theory of the subject area, reliable causes of the observed or relevant circumstances leading to these causes are formed. In this case, in situations with the availability of empirical data, these causes and circumstances can also be formed in plausible versions. Examples from technology and medicine are considered.

The article deals with the problem of using the classification and regression trees (CRT) method to identify socio-economic features of agricultural households at risk of financial exclusion. Financial exclusion applies to people/households who do not use or use financial services and products to a small extent, both of their own choice and due to barriers in accessing financial products and services. The research used empirical data on agricultural households in Central Pomerania (Poland). Based on the use of the decision tree method, it was shown that nearly half of the surveyed farms do not use financial products and services or use them to a small extent, therefore they are financially excluded. Higher acreage indicates a lower risk of financial exclusion. The age of the household head influences the risk of financial exclusion. The fact of saving confirms the desire to multiply capital, hence in the group of farms where this occurs, the risk of financial exclusion is lower than in the remaining ones.

This paper applies the variational methods to learn the parameters and the probability of evidence of directed graphic models (also known as Bayesian networks (BNs)) when data contains missing values. One class of variational methods, the Bethe/Kikuchi approximate algorithm, is combined with Expectation-Maximization (EM) to learn BN model parameters from data using an upper limit on cluster size. Our proposed method is novel since the Bethe/Kikuchi algorithms are typically only used to approximate marginal distributions. We review popular Bethe/Kikuchi approximate algorithms, including Belief Propagation (BP), Iterative Join Graph Propagation (IJGP), and Fundamental Cycle Base (FCB) algorithms for discrete BNs, and conduct experiments to test their learning performance using a wide range of discrete BNs in practice. Experiments show that by using IJGP or FCB algorithms, with EM, we can obtain more accurate results than the conventional approximate algorithms, such as Evidence Pre-propagation Importance Sampling and Variational Inference. When exact methods are intractable the IJGP/FCB with EM can be an effective alternative solution.

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

PurposeThe purpose of this paper is to present a new human dynamics model to explain the process of verified users' (VUs) posting on Sina micro-blog.Design/methodology/approachA common human dynamics research method with three steps is applied. Firstly, a large-scale behavioral dataset is collected involving 495 VUs and five topics on Sina micro-blog. Second, five important indicators that reveal the characteristics of posting behavior are analyzed. Then, a quantitative model is constructed to describe the process of posting behavior, and its validity is verified by simulations.FindingsThree important characteristics of VUs' micro-blog posting behavior are observed: fat-tailed distribution, fluctuation and periodicity. These characteristics do not fit the assumption of interest-driven models proposed by previous literature. An optimized task-driven model is introduced to describe this complex phenomenon mathematically. The model is verified on empirical data, confirming that task-driven models can be optimized to explore information behavior on social media.Originality/valueBeing different from previous studies that mainly describe common users' posting behavior on social media by applying interest-driven models, this paper customizes an optimized task-driven model for VUs, who mainly treat social media as a platform for work and play a crucial role in information creation on social media.

Why can’t we keep as many items as we want in working memory? It has long been debated whether this resource limitation is a bug (a downside of our fallible biological system) or instead a feature (an optimal response to a computational problem). We propose that the resource limitation is a consequence of a useful feature. Specifically, we propose that flexible cognition requires time-based binding, and time-based binding necessarily limits the number of (bound) memoranda that can be stored simultaneously. Time-based binding is most naturally instantiated via neural oscillations, for which there exists ample experimental evidence. We report simulations that illustrate this theory and that relate it to empirical data. We also compare the theory to several other (feature and bug) resource theories.

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.

Animals seem to rely on antipredator behavior to avoid vehicle collisions. There is an extensive body of antipredator behavior theory that have been used to predict the distance/time animals should escape from predators. These models have also been used to guide empirical research on escape behavior from vehicles. However, little is known as to whether antipredator behavior models are appropriate to apply to an approaching high-speed vehicle. We addressed this gap by (a) providing an overview of the main hypothesis and predictions of different antipredator behavior models via a literature review, (b) exploring whether these models can generatequantitativepredictions on escape distance when parameterized with empirical data from the literature, and (c) evaluating their sensitivity to vehicle approach speed via a simulation approach where we assessed model performance based on changes in effect size with variations in the slope of the flight initiation distance (FID) vs. approach speed relationship. We used literature on birds for goals (b) and (c). We considered the following eight models: the economic escape model, Blumstein’s economic escape model, the optimal escape model, the perceptual limit hypothesis, the visual cue model, the flush early and avoid the rush (FEAR) hypothesis, the looming stimulus hypothesis, and the Bayesian model of escape behavior. We were able to generatequantitativepredictions about escape distances with the last five models. However, we were only able to assess sensitivity to vehicle approach speed for the last three models. The FEAR hypothesis is most sensitive to high-speed vehicles when the species follows the spatial (FID remains constant as speed increases) and the temporal margin of safety (FID increases with an increase in speed) rules of escape. The looming stimulus effect hypothesis reached small to intermediate levels of sensitivity to high-speed vehicles when a species follows the delayed margin of safety (FID decreases with an increase in speed). The Bayesian optimal escape model reached intermediate levels of sensitivity to approach speed across all escape rules (spatial, temporal, delayed margins of safety) but only for larger (> 1 kg) species, but was not sensitive to speed for smaller species. Overall, no single antipredator behavior model could characterize all different types of escape responses relative to vehicle approach speed but some models showed some levels of sensitivity for certain rules of escape. We derive some applied applications of our finding by suggesting the estimation of critical vehicle approach speeds for managing populations that are especially susceptible to road mortality. Overall, we recommend that new escape behavior models specifically tailored to high- speeds vehicles should be developed to better predict quantitatively the responses of animals to an increase in the frequency of cars, airplanes, drones, etc. they will be facing in the next decade.

A continuous flow model of infantry behaviour in military conflict is proposed, combining a crowd flow model based on conservation of individuals, empirical data on walking speed under varying crow...

International audience; One-handed Back-of-Device (BoD) interaction proved to be desired and sometimes unavoidable with a mobile touchscreen device, for both preferred and non-preferred hands. Although users' two hands are asymmetric, the impact of this asymmetry on the performance of mobile interaction has been little studied so far. Research on one-handed BoD interaction mostly focused on the preferred hand, even though users cannot avoid in real life to handle their phone with their non-preferred hand. To better design one-handed BoD interaction tailored for each hand, the identification and measure of the impact of their asymmetry are critical. In this paper, we study the impact on the performance of the asymmetry between the preferred and the non-preferred hands when interacting with one hand in the back of a mobile touch surface. Empirical data indicates that users' preferred hand performs better than the non-preferred hand in target acquisition tasks, for both time (+10%) and accuracy (+20%). In contrast, for steering tasks, we found little difference in performance between users' preferred and non-preferred hands. These results are useful for the HCI community to design mobile interaction techniques tailored for each hand only when it is necessary. We present implications for research and design directly based on the findings of the study, in particular, to reduce the impact of the asymmetry between hands and improve the performance of both hands for target acquisition.

Tracheal tissue engineering has become an active area of interest among clinical and scientific communities; however, methods to evaluate success of in vivo tissue-engineered solutions remain primarily qualitative. These evaluation methods have generally relied on the use of photographs to qualitatively demonstrate tracheal patency, endoscopy to image healing over time, and histology to determine the quality of the regenerated extracellular matrix. Although those generally qualitative methods are valuable, they alone may be insufficient. Therefore, to quantitatively assess tracheal regeneration, we recommend the inclusion of microcomputed tomography (μCT) to quantify tracheal patency as a standard outcome analysis. To establish a standard of practice for quantitative μCT assessment for tracheal tissue engineering, we recommend selecting a constant length to quantify airway volume. Dividing airway volumes by a constant length provides an average cross-sectional area for comparing groups. We caution against selecting a length that is unjustifiably large, which may result in artificially inflating the average cross-sectional area and thereby diminishing the ability to detect actual differences between a test group and a healthy control. Therefore, we recommend selecting a length for μCT assessment that corresponds to the length of the defect region. We further recommend quantifying the minimum cross-sectional area, which does not depend on the length, but has functional implications for breathing. We present empirical data to elucidate the rationale for these recommendations. These empirical data may at first glance appear as expected and unsurprising. However, these standard methods for performing μCT and presentation of results do not yet exist in the literature, and are necessary to improve reporting within the field. Quantitative analyses will better enable comparisons between future publications within the tracheal tissue engineering community and empower a more rigorous assessment of results. IMPACT STATEMENT: The current study argues for the standardization of microcomputed tomography (μCT) as a quantitative method for evaluating tracheal tissue-engineered solutions in vivo or ex vivo. The field of tracheal tissue engineering has generally relied on the use of qualitative methods for determining tracheal patency. A standardized quantitative evaluation method currently does not exist. The standardization of μCT for evaluation of in vivo studies would enable a more robust characterization and allow comparisons between groups within the field. The impact of standardized methods within the tracheal tissue engineering field as presented in the current study would greatly improve the quality of published work.

Route choice is often modelled as a two-step procedure in which travellers choose their routes from small sets of promising candidates. Many methods developed to identify such choice sets rely on assumptions about the mechanisms behind the route choice and require corresponding data sets. Furthermore, existing approaches often involve considerable complexity or perform many repeated shortest path queries. This makes it difficult to apply these methods in comprehensive models with numerous origin-destination pairs. In this paper, we address these issues by developing an algorithm that efficiently identifies locally optimal routes. Such paths arise from travellers acting rationally on local scales, whereas unknown factors may affect the routes on larger scales. Though methods identifying locally optimal routes are available already, these algorithms rely on approximations and return only few, heuristically chosen paths for specific origin-destination pairs. This conflicts with the demands of route choice models, where an exhaustive search for many origins and destinations would be necessary. We therefore extend existing algorithms to return (almost) all admissible paths between a large number of origin-destination pairs. We test our algorithm on a road network modelling the Canadian province British Columbia and analyze the distribution of locally optimal paths in the province., Comment: Keywords: alternative paths; choice set; local optimality; road network; route choice