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In this paper, we investigate empirical data that raise challenging issues with respect to focus sensitivity of the Hungarian additive particle is ‘also, too’. In Hungarian, the additive particle is attached to a constituent, and the is-phrase cannot occupy the structural focus position. This raises the issue how to capture the focus sensitivity of is. We propose a primarily pragmatic, context-based analysis of the Hungarian additive particle, where the particle associates with the pragmatic focus (Lambrecht 1994) determined on basis of the immediate question under discussion (Roberts 2012). Important evidence for this claim is that the Hungarian additive particle can take different semantic associates, corresponding to the pragmatic focus of the sentence. After discussing the Hungarian data, we will present the analysis in the framework of Role and Reference Grammar (Van Valin & LaPolla 1997; Van Valin 2005). To capture Hungarian and English data in a uniform way, important extensions of the framework will be proposed.

Artificial Intelligence in Education (AIED) may be described as the next big disruptor in higher education, however, AIED still only remains “evidence of a potential” Balslev (2020). Practical experience with AI in higher education is very limited and potential pedagogical applications of AI has so far not been given much attention. The objective of this paper is to analyse and discuss concrete applications of AI to support different learning activities in higher education using the ABC Learning Design approach Young and Perovic (2016). The purpose of the paper is to contribute to research in the practical use of AI in higher education by presenting the AI Pedagogy Planner and to start the important theoretical discussion of AI applications from a pedagogical point of view. The paper is based on empirical data from nine selected cases of AI use in higher education in Portugal, the United Kingdom and Denmark, respectively. The analysis demonstrated that there is a need for new views on the pedagogical use of AI in higher education. However, the paper goes further and outlines an AI Pedagogy Planner combining six overall learning activities with eight types of AI applications.

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.

Pavarini et al. (2021) argue for the potential benefits of using games and other technologies to collect empirical data to enhance bioethics research. They propose a methodology called “design bioe...

In this paper we describe the risks of complex applied research, especially in work domains where professional practitioners are scarce. For such research, careful preparation and piloting is needed, especially when estimating sample size is required for a full study. However, such pilot work may reduce the potential sample size for the full study. We describe how the these issues have been addressed in applied psychology contexts. We then present a case study illustrating how we determined sample size for a study investigating the impact of workplace interruptions on errors that intensive care unit nurses might make during medication preparation and administration. The pilot work was performed in a functionally related domain to nursing-bartending-and bartender participants filled cocktail orders. Pilot 1 investigated performance with 0 interruptions and applied a model from a field observation to estimate probable effect sizes and sample sizes with 1 or 4 interruptions per medication scenario. Pilot 2 collected empirical data on the effect of 1 or 4 interruptions per cocktail scenario on cocktail errors and estimated sample size for the medication study, which was subsequently successfully run. The applied community could benefit from further discussions about these issues and the means for addressing them. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

In this paper, the impact of interference due to the geomagnetic field on a static magnetic localization setup for capsule endoscopy, which is suitable for a wearable application, was investigated. For this purpose, a study was carried out in which the average abdomen size of 15 subjects was evaluated. With the determined geometry values, a setup consisting of three elliptical sensor rings was modeled. Simulations were performed, where the magnetic flux density was evaluated at the sensors by using different-sized magnets. The measured values were compared with each other and the geomagnetic flux density. The results revealed that the measured values were for all evaluated magnet sizes of the order of the geomagnetic flux density, which is problematic since the calibration of sensors is no longer valid if the orientation of the wearable sensor array is changed. However, it is suggested that a differential measurement is suitable for the proposed system and could reduce static interference caused by the geomagnetic field.

Simulations are playing an increasingly important role in paleobiology. When designing a simulation study, many decisions have to be made and common challenges will be encountered along the way. Here, we outline seven rules for executing a good simulation study. We cover topics including the choice of study question, the empirical data used as a basis for the study, statistical and methodological concerns, how to validate the study, and how to ensure it can be reproduced and extended by others. We hope that these rules and the accompanying examples will guide paleobiologists when using simulation tools to address fundamental questions about the evolution of life.

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).

The interaction between pedestrians and vehicles is an inevitable phenomenon at unsignalized midblock crosswalks. This study aims to contribute to a better understanding of the interaction between vehicle yielding and pedestrian gap acceptance (VY and PGA). A microscopic traffic flow model was established to describe the interaction and explores its effect on traffic flow. The VY and PGA behaviors were converged into the proposed model. The proposed model was accomplished in a time step simulation. The results stability and descriptive power of the proposed model were analyzed. The proposed model was also validated using empirical data. The effects of the traffic and geometric factors on the operation of the unsignalized midblock crosswalks were discussed based on numerical experiments. Accordingly, the recommendations on choosing the proper control mode of midblock crosswalks (unsignalized or signalized) were proposed.

This short commentary on the target paper by "A cluster analysis of harmony in the McGill Billboard" by Shaffer et al. starts with observing that not all harmonic progressions that are theoretically possible are equally common. Instead, some progressions are more popular than others in popular. In fact, certain harmonic progressions are closely associated with specific styles and sub-genres and it is the aim of the target paper to provide a meaningful classification system for harmonic progression. The commentary identifies several strengths of the target paper, including a nice balance between rigorous empirical work and providing a context and interpretations that are musicologically well-informed. In its critique the commentary points to the limitations of only using harmonic bigrams (i.e. the transitions between two chords) as the empirical data and the missing link to related literature on harmonic modelling in the music information retrieval community.

Kitting is a materials-feeding principle commonly used with mixed-model assembly, but literature is lacking with respect to how kit quality can be supported. The purpose of this paper is to create an understanding of the links between kit preparation design aspects and kit preparation error types, that can be useful to support kit quality. The paper draws on empirical data from a multiple case study in the automotive industry to study how typical kit errors are linked to eight kit preparation design aspects: location, work organisation, storage policy, batching policy, storage packaging, kit carrier and container, picking information system, and error communication. The findings suggest several opportunities related to kit preparation design aspects for preventing kit errors and facilitating kit error corrections. The paper extends earlier knowledge and can support kit quality of industrial kit preparation.

The objective of this paper is to explore uncertainty sources which influence SMEs performance. A framework of uncertainty sources from previous literature was tested against empirical data based o...

Today there are many definitions about quality in education, but few are specific to e-learning. Those it is broad, it is associated with different contexts, interests of some authors, incomplete and with lack of rigor As a result, research in this area is difficult when comparing studies to get conclusions, advance new researches or understand the definition in detail. In this context, we developed a qualitative research, collecting empirical data from a set of e-learning experts of 17 countries and complementing it with the bibliography. As a result, we have identified 4 key areas with 24 characteristics that should be included in a definition of quality for e-learning. They can be useful for universities, e-learning managers, quality agencies and other stakeholders who want to build a new definition, adjust existing ones or implement, improve and identify quality in e-learning.

How can we best learn the history of a protein’s evolution? Ideally, a model of sequence evolution should capture both the process that generates genetic variation and the functional constraints determining which changes are fixed. However, in practical terms the most suitable approach may simply be the one that combines the convenience of easily available input data with the ability to return useful parameter estimates. For example, we might be interested in a measure of the strength of selection (typically obtained using a codon model) or an ancestral structure (obtained using structural modeling based on inferred amino acid sequence and side chain configuration). But what if data in the relevant state-space are not readily available? We show that it is possible to obtain accurate estimates of the outputs of interest using an established method for handling missing data. Encoding observed characters in an alignment as ambiguous representations of characters in a larger state-space allows the application of models with the desired features to data that lack the resolution that is normally required. This strategy is viable because the evolutionary path taken through the observed space contains information about states that were likely visited in the “unseen” state-space. To illustrate this, we consider two examples with amino acid sequences as input. We show that \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$$\omega$$\end{document}, a parameter describing the relative strength of selection on nonsynonymous and synonymous changes, can be estimated in an unbiased manner using an adapted version of a standard 61-state codon model. Using simulated and empirical data, we find that ancestral amino acid side chain configuration can be inferred by applying a 55-state empirical model to 20-state amino acid data. Where feasible, combining inputs from both ambiguity-coded and fully resolved data improves accuracy. Adding structural information to as few as 12.5% of the sequences in an amino acid alignment results in remarkable ancestral reconstruction performance compared to a benchmark that considers the full rotamer state information. These examples show that our methods permit the recovery of evolutionary information from sequences where it has previously been inaccessible. [Ancestral reconstruction; natural selection; protein structure; state-spaces; substitution models.]

Theoretical models are typically developed through a deductive process where a researcher formulates a system of dynamic equations from hypothesized mechanisms. Recent advances in algorithmic methods can discover dynamic models inductively– directly from data. Most previous research has tested these methods by rediscovering models from synthetic data generated by the already known model. Here we apply Sparse Identification of Nonlinear Dynamics (SINDy) to discover mechanistic equations for disease dynamics from case notification data for measles, chickenpox, and rubella. The discovered models provide a good qualitative fit to the observed dynamics for all three diseases, However, the SINDy chickenpox model appears to overfit the empirical data, and recovering qualitatively correct rubella dynamics requires using power spectral density in the goodness-of-fit criterion. When SINDy uses a library of second-order functions, the discovered models tend to include mass action incidence and a seasonally varying transmission rate–a common feature of existing epidemiological models for childhood infectious diseases. We also find that the SINDy measles model is capable of out-of-sample prediction of a dynamical regime shift in measles case notification data. These results demonstrate the potential for algorithmic model discovery to enrich scientific understanding by providing a complementary approach to developing theoretical models.

Penelitian ini bertujuan untuk membahas dan mengetahui Peran Teknologi Informasi Dalam Proses Kegiatan Belajar Mengajar Disekolah Tingkat Dasar, Metode yang digunakan dalam menelitian ini adalah Penelitian kualitatif deskripsif dengan teori subtantif berdasarkan konsep-konsep yang timbul dari data empiris data diperoleh dari data sekunder dan literatur yang sesuai dengan kajian serta sesuai kondisi lapangan. Hasil dari penelitian ini adalah peran dari teknologi informasi ini dalam kegiatan belajar mengajar (KBM) diantaranya: mempermudah dalam menyampaikan pelajaran, peserta didik dapat mudah memahami pelajaran yang disampaikan oleh guru, dan pembelajaran yang efektif bagi guru untuk mencapai proses belajar yang maksimal sesuai aturan kependidikan dan lain sebagainya.

In the article, the analysis of the characteristics of client-oriented personnel is executed on the basis of theoretical and empirical data. The levels of employee orientation to the client are defi ned. The analysis of methods for assessing the client-orientation of the staff is carried out. The need to develop a set of assessment tools is justifi ed. The limitations and results of the application of staff assessment methods are also given.

Introduction. When studying fire safety laws and regulations, including foreign ones, one can draw a conclusion that protecting people during evacuation is mainly ensured by dividing the building into fire compartments, specifying geometric properties of escape routes and exits, and by equipping buildings with automated fire prevention systems. However, in order to assess the actual possibility of evacuating people, their number, physical condition, and degree of mobility must be known, as well as the number of patients who will need to be moved on stretchers or in wheel-chairs. Main (analytical) part. This paper provides schedules for occupancy rates of medical departments in different units of a health care facility, namely, in the internal medicine and inpatient surgical units. Obtained empirical values presented in this publication are part of daily operating process and are included in the workflow: each day head nurses collect data on rates of patient occupancy in medical departments. The total number of processed empirical data was 297,000 values. Conclusions. The studies that have been carried out demonstrate which geometrical properties must be ensured for refuge areas and that buildings of the inpatient surgical type are the most relevant location where they can be designed. Areas of refuge must be provided for surgery and intensive care departments where people can wait for rescue. Some categories of patients in intensive care units, as well as in the surgical department, are not subject to evacuation, because it is not possible to interrupt surgeries after reaching a certain stage.

Cognitive models have been paramount for modeling phenomena for which empirical data are unavailable, scarce, or only partially relevant. These approaches are based on methods dedicated to preparing experts and then to elicit their opinions about the variables that describe the phenomena under study. In time series forecasting exercises, elicitation processes seek to obtain accurate estimates, overcoming human heuristic biases, while being less time consuming. This paper aims to compare the performance of cognitive and mathematical time series predictors, regarding accuracy. The results are based on the comparison of predictors of the cognitive and mathematical models for several time series from the M3-Competition. From the results, one can see that cognitive models are, at least, as accurate as ARIMA models predictions.

Objectives. The aim of the work is to demonstrate the fundamental difference between fibre-reinforced foam concrete and traditionally-used wall materials in terms of tensile strength.Method. An analysis of the effectiveness of heat-insulating and structural fibre-reinforced concrete in terms of their mechanical and physical properties, including frost resistance, is carried out.Results. The characteristics of the most important operational properties of fibre-reinforced foam concrete for insulating and structural purposes are given. The possibility of expanding the range of prefabricated products in connection with the level of tensile strength of fibre-reinforced foam concrete under bending is substantiated. Empirical data concerning the practical use of original products from fibrereinforced foam concrete in the construction of residential and public buildings in Rostov-on-Don is presented.Conclusion. It is shown that, in order to increase the heat transfer resistance of building envelopes, the use of fibre-reinforced foam concrete in the rehabilitation of existing buildings can ensure their high fire safety subject to the required sanitary and hygienic conditions. Fibre-reinforced foam concrete, being a gas-filled concrete of non-autoclave hardening, allows new design solutions to be offered for heat-insulating and structural purposes in the construction complex of the Russian Federation, which practical application expands the range of factory-made products, reduces energy consumption and contributes to the diversity of the architectural appearance of buildings.

When an emergency occurs, as a factor in online emergency information dissemination, the peak represents the maximum strength and turning point. However, the role of these peaks in emergencies is still unclear. In this article, we try to disentangle this problem and investigate the effect of these peaks on the lifecycle and influence of online emergency information dissemination. We first propose an Online Emergency Information Dissemination Process (OEIDP) model drawing from the ideas of system cybernetics and system identification to present the emergency lifecycle with a decay parameter, time constant, and delay parameter. Based on this model, we then analyze the effect of peaks using empirical data from the Weibo, WeChat, and 20000 media platforms of 169 emergencies. The results reveal the positive significant impacts of the peak time, peak volume, and spikes on emergency information dissemination. The results enable us to make accurate judgements when facing peaks and remind us of the need to balance the strongest manifestation and final influence. Our work complements the gaps on the peak effect in the existing communication literature and introduces the concept of system cybernetics to solve information dissemination which provides a new direction for future studies to solve related problems using system control techniques.

IFAC-PapersOnLine, 53 (5), ISSN:2405-8963, 3rd IFAC Workshop on Cyber-Physical & Human Systems, CPHS 2020. Proceedings

Machine learning (ML) methods have become useful tools that, in conjunction with sensing devices for quality evaluation, allow for quick and effective evaluation of the quality of food commodities based on empirical data. This review presents the recent advances in machine learning methods and their use with various sensing devices to detect defects in horticultural products. There are technical hurdles in tackling major issues around defect detection in fruit and vegetables as well as various other food items, such as achieving fast, early and quantitative assessments. The role that ML methods have played towards addressing such issues are reviewed, the present limitations highlighted, and future prospects identified.

Engineer-to-Order companies design and manufacture complex products based on specific customer requirements. Their project-driven processes and non-repetitive production causes various inefficiencies, which lead to productivity losses. Conventional approaches such as Lean Manufacturing and Lean Construction are limited in mitigating these losses due to their challenging implementation in the Engineer-to-Order environment. New concepts and technologies from Industry 4.0 have the potential to mitigate these losses through digitizing processes but are little researched in the Engineer-to-Order industry. This article classifies traditional losses from Engineer-to-Order manufacturing companies and through literature review identifies several Lean as well as Industry 4.0 methods that have the potential to mitigate these losses. The results are presented in a framework which can be used to develop a Lean and Industry 4.0 assessment tool for companies supporting the implementation of these concepts to mitigate the presented loss categories. Further research should focus on validating the framework with empirical data.

This paper analyzes a relatively new investment product named as constant leverage certificate (CLC), which is designed to provide a multiple of the return of its underlying asset on a daily basis. Based on the literature on leveraged ETFs, which have a similar design, it is well-known that such a strategy does not reproduce the corresponding multiple of the underlying in the long run. But due to the typically much larger leverage factors of CLCs compared to leveraged ETFs, it is questionable whether many of the results found for leveraged ETFs can be applied to these certificates as well. Against this background, I study the drivers of the long-term deviation of the product return from the leveraged return of the underlying, test a generalized version of the theoretical long-term return model originally developed for leveraged ETFs with a simulation study, and analyze the return distribution based on the theoretical model and empirical data. In contrast to prior literature, my results indicate that the effect of compounding is much more pronounced than the noncompounding deviation also for short-term investment periods. The theoretical model, however, is relatively accurate despite much larger leverage factors.

Even though often travelling on the same roads, it has been shown that cyclists and car drivers interpret their environment differently, which can lead to misunderstandings and collisions. Based on the Minimum Required Attention (MiRA) theory and the Salience, Effort, Expectancy, Value (SEEV) model, it is investigated whether the attentional requirements put on drivers and cyclists are different in urban intersections, and how difficult it is to fulfil the requirements for the two road user groups. Additionally, glance data from 23 participants who both cycled and drove along an urban route are compared with respect to information sampling strategies and the fulfilment of attentional requirements depending on its type for three intersections. Generally, more attentional requirements existed for cyclists, and due to where they occur relative to the infrastructure, in combination with the physical aspects of cycling, they are less likely to be fulfilled. This was also corroborated by the empirical data, which showed that requirements clearly visible from the infrastructural design are fulfilled more often than those that are not. Overall, the theoretical evaluation of the infrastructure was confirmed by the empirical data, such that the proposed method can be used as a starting point for a theoretical, human centred evaluation of traffic infrastructure. (C) 2019 Elsevier Ltd. All rights reserved. Funding Agencies|Swedish Strategic Vehicle Research and Innovation Programme (FFI) [2017-05526]

Due to the interactions among adjacent roads in urban road networks, traffic congestion gradually propagates to neigboring roads, resulting in regional congestion. To develop advanced regional traffic control strategies, it is necessary to clearly understand the characteristics of regional congestion evolution. To this end, this paper proposes a data-driven approach to mine the spatiotemporal associations of regional traffic congestion. By introducing both time and space attributes, the intra-transaction spatiotemporal Apriori (IntraT-ST-Apriori) algorithm is developed to address the static features of regional traffic congestion; while the inter-transaction spatiotemporal Apriori (InterT-ST-Apriori) algorithm is developed to capture the dynamic characteristics of regional traffic congestion. Case studies are carried out for the urban road network in Tianjin, China, based on empirical data. The results indicate that the Intra-ST-Apriori algorithm can excavate the underlying associations of regional traffic congestion. Furthermore, the congestion propagation trajectories can be clearly revealed based on the InterT-ST-Apriori algorithm. It is expected that the proposed approach can support the regional traffic management and control, significantly relieving traffic congestion.

Onomatopoeias constitute a much neglected subject in linguistics. The rather scarce literature on onomatopoeias is derived from a lack of reliable empirical data on the topic. In order to bridge this gap, we have compiled a parallel corpus of literary texts featuring onomatopoeias: the Onomatopoeia Parallel Corpus (ONPACOR). The corpus consists of onomatopoeias in English, Spanish and French extracted from comics and representative corpora of each language. ONPACOR has been built on the basis of existing translations to the languages of reference. This article describes the methodology used to compile the corpus, as well as the applications that it can have.

The management of conventional irrigation system, which is still being applied in the recent time is probabilistic, especially in analyzing discharge for planning of irrigation operation and maintenance. It can be seen from the process of exerted data analysis, which is two-week or ten-days empirical data analysis. Therefore, to change the management of conventional irrigation system into modern requires flexibly and real-time based due to climate change in Indonesia. The available discharge prediction analysis is done using mathematic model analysis as the replacement of probabilistic model and the use of real time observation data by utilizing automatic weather observation technology. This new concept has been attempted in Irrigation Area of Wadaslintang and Banyumas Regency, showing that automatic weather observation worked successfully and can be used as the data within mathematic model analysis. The result indicates that telemetry instruments work well as expected. The difference between the use of mathematic method of Artificial Neural Network (ANN) with probabilistic method of P80 shows that ANN method is closer to real compared to the probabilistic P80. It is shown with the validation result measured from January to August 2015. Overal l , errors between water surcharge prediction with ANN and realisation is 77%. According to the results, it is suggested that dynamic mathematic measurement method is needed, due to dynamic condition of climate in spite of not neglecting probabilistic method as comparison.

Causality is a widely-used concept in theoretical and empirical economics. The recent financial economics literature has used the standard Granger causality to detect for the presence of contemporaneous links among financial institutions, that, in turn, determine a network structure. Subsequent studies have combined the estimated networks with traditional pricing or risk measurement models to improve their fit to empirical data. In this paper, we provide two contributions. First, we show how to use a linear factor model as a device for estimating a combination of several networks that monitor the links across variables from different viewpoints. Second, we highlight the advantages of combining quantile-based methods with the Granger causality when the focus is on risk propagation. The empirical evidence supports our contributions.

This paper discusses the possibility of expanding the ideas of the validity of medical decisions of a diagnostic nature, which are made in the framework of so-called evidence-based medicine. An approach is proposed that allows building special data in the process of intelligent analysis of accumulated empirical data, which characterize the causality of a diagnosed effect–logical conditions (characteristic functions) that take the value true in all instances of the presence of the target effect and the value false for all instances of its absence in the training sample of precedents. This problem is solved based on the expanding sequences of training samples using: (a) a formal refinement of the concept of similarity of precedent descriptions as a binary algebraic operation, and (b) a mathematical technique for generating empirical dependences in the style of the JSM method of automated support for scientific research. The features and capabilities of the developed approach are described based on the example of solving the problem of analyzing the causes and predicting the pseudoprogression of brain tumors.

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.

Inferring reciprocal effects or causality between variables is a central aim of behavioral and psychological research. To address reciprocal effects, a variety of longitudinal models that include cross-lagged relations have been proposed in different contexts and disciplines. However, the relations between these cross-lagged models have not been systematically discussed in the literature. This lack of insight makes it difficult for researchers to select an appropriate model when analyzing longitudinal data, and some researchers do not even think about alternative cross-lagged models. The present research provides a unified framework that clarifies the conceptual and mathematical similarities and differences between these models. The unified framework shows that existing longitudinal models can be effectively classified based on whether the model posits unique factors and/or dynamic residuals and what types of common factors are used to model changes. The latter is essential to understand how cross-lagged parameters are interpreted. We also present an example using empirical data to demonstrate that there is great risk of drawing different conclusions depending on the cross-lagged models used. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Large concourses have been developed in shopping malls, bus and train terminals, concert, theatre halls, and other places where people stand or/and are in constant motion. This study aims to develop a novel method to analyze people's space use and navigation, demonstrate how people explore a large space, specifically a concourse in a university, and determine relationships of space with people's movements and activities. By collecting empirical data of spatial uses in a large concourse and implementing a method of spatial analysis based on spatial grids to analyze such data, this study seeks to verify how effectively people's movements and activities can be established in a large space and which features have significant effect in developing and managing a large space. A total of 865 activities in 265 days were collected and analyzed in a large space of a concourse in a university campus. By adopting a locational-based method of geographical analysis, the proposed method effectively illustrated relationships of people's movement, activities, and satisfaction in visual and quantitative values. As a result, spatial features were found to be significant elements in improving spatial satisfaction. Space environment performance, spatial form, user capacity, equipment adequacy, and equipment conditions were major spatial features considered for improving people's satisfaction in a large space. Highlights We proposed a new multi-layer-grid-based analysis method for a large indoor built environment. People's movements, activities, and satisfaction in a large indoor space were collected, traced, and analyzed. We quantified relationships among movements, activities, spatial features and satisfaction in a large indoor space. Assumption of causal symmetry between spatial features and people's behavior was tested with empirical data.

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.

Background Computer simulations have been widely applied in population genetics and evolutionary studies. A great deal of effort has been made over the past two decades in developing simulation tools. However, there are not many simulation tools suitable for studying population admixture. Results We here developed a forward-time simulator, AdmixSim 2, an individual-based tool that can flexibly and efficiently simulate population genomics data under complex evolutionary scenarios. Unlike its previous version, AdmixSim 2 is based on the extended Wright-Fisher model, and it implements many common evolutionary parameters to involve gene flow, natural selection, recombination, and mutation, which allow users to freely design and simulate any complex scenario involving population admixture. AdmixSim 2 can be used to simulate data of dioecious or monoecious populations, autosomes, or sex chromosomes. To our best knowledge, there are no similar tools available for the purpose of simulation of complex population admixture. Using empirical or previously simulated genomic data as input, AdmixSim 2 provides phased haplotype data for the convenience of further admixture-related analyses such as local ancestry inference, association studies, and other applications. We here evaluate the performance of AdmixSim 2 based on simulated data and validated functions via comparative analysis of simulated data and empirical data of African American, Mexican, and Uyghur populations. Conclusions AdmixSim 2 is a flexible simulation tool expected to facilitate the study of complex population admixture in various situations.

Context Tailoring mechanisms allow performance dashboards to vary their appearance as a response to changing requirements (e.g., adapting to multiple users or multiple domains). Objective We analyze existing research on tailored dashboards and investigate different proposed approaches. Methodology We performed a systematic literature review. Our search processes yielded a total of 1,764 papers, out of which we screened 1,243 and ultimately used six for data collection. Results Tailored dashboards, while being introduced almost thirty years ago, did not receive much research attention. However, the area is expanding in recent years and we observed common patterns in novel tailoring mechanisms. Since none of the existing solutions have been running for extended periods of time in real-world scenarios, this lack of empirical data is a likely cause of vaguely described research designs and important practical issues being overlooked. Implications Based on our findings we propose types of tailoring mechanisms taking into account the timing and nature of recommendations. This classification is grounded in empirical data and serves as a step ahead to a more unifying way of looking at tailoring capabilities in the context of dashboards. Finally, we outline a set of recommendations for future research, as well as a series of steps to follow to make studies more attractive to practitioners.

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.

In modern coexistence theory, species coexistence can either arise via stabilizing mechanisms that increase niche differences or equalizing mechanisms that reduce fitness differences. Having a common currency for interpreting these mechanisms is essential for synthesizing knowledge across different studies and systems. Several methods for quantifying niche and fitness differences exist, but it remains unknown to what extent these methods agree on the reasons why species coexist. Here, we apply four common methods to quantify niche and fitness differences to one simulated and two empirical data sets. We ask if different methods result in different insights into what drives species coexistence. We find that different methods disagree on the effects of resource supply rates (simulated data), and of plant traits or phylogenetic distance (empirical data), on niche and fitness differences. More specifically, these methods often do not agree better than expected by chance. We argue for (1) a better understanding of what connects and sets apart different methods, and (2) the simultaneous application of multiple methods to enhance a more complete insight into why species coexist.

The primary goal of this paper was to answer the question: Do empirical data from real-world projects align with Design for Adaptability (DfA) strategies reported in the literature? The da...

In this research, we adopted the data envelopment analysis (DEA) method to evaluate the research efficiency of the colleges in University C from 2015 to 2017. We created an evaluation model to measure the research efficiency of the various colleges and analyzed the empirical data to formulate constructive improvement recommendations and calculate improvement targets for the inefficient units. We also highlighted the benchmark units to serve as a model for the other units. Findings showed that the overall research efficiency of University C has been improving year by year. Moreover, the substantiated colleges outperformed the unsubstantiated colleges in terms of research efficiency.

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.

Exploratory and confirmatory factor analyses are commonly employed to identify and explore underlying factor structures. Unfortunately, when variable selection is involved, results often fluctuate across studies making it difficult to determine the “best” and most replicable factor structure. This study proposes a new factor analysis variable selection algorithm called the Replicable Factor Analytic Solutions (RFAS) that incorporates sound statistical and psychometric practices when selecting the final factor structure, while simultaneously examining the observed variables and factor structures replicability. This article outlines the algorithm development and rationale for each decision in the algorithm’s development. An example using simulated and empirical data is also provided to display the algorithm results and delineate the utility of these results for future analysts.

Cities with restriction on the purchase of cars are still the main promotion areas of new energy passenger cars. This paper constructs a model which can quantify the cities' development potential of new energy passenger cars. The model subdivides the development potential into three first level indexes and nine second level factors. In order to evaluate the driving effects of new energy vehicle license, the paper also constructs a new energy vehicle license value model, and uses entropy method to analyze and evaluate the weight of empirical data. Model calculation results show that the influence of license on the development potential of new energy passenger cars is the largest.

While the HCI community has developed many unique shape-changing artifacts for supporting novel interactions and experiences, little research has investigated long-term organic experiences of living with these objects. In parallel, Wiberg has proposed the notion of the materiality of interaction as a new ontology for tangible computing. However, cases of designing for materiality are still sparse. The overarching goal of this doctorial study is to fill in the gap between materiality and shape change. To do so, I plan to adopt design-oriented HCI approaches to frame my design implementations and field deployments. Fabricating shape-changing artifacts may extend practices of designing for materiality. Reflecting on design processes and accumulating empirical data of these devices may enrich the understanding of materiality in interaction design and HCI fields.

The acquisition of translation equivalents is often considered a special component of bilingual children's vocabulary development, as bilinguals have to learn words that share the same meaning across their two languages. This study examined three contrasting accounts for bilingual children's acquisition of translation equivalents relative to singlets (i.e., words that are first labels for a referent): the Avoidance Account whereby translation equivalents are harder to learn, the Preference Account whereby translation equivalents are easier to learn, and the Neutral Account whereby translation equivalents and singlets are learned similarly. To adjudicate between these accounts, Study 1 explored patterns of translation equivalent learning under a novel computational model — the Bilingual Vocabulary Model — which quantifies translation equivalent knowledge as a function of the probability of learning words in each language, and includes a bias parameter that varies the difficulty of learning translation equivalents according to each account. Study 2 tested model-derived predictions against vocabulary data from 200 French–English bilingual children aged 18–33 months. Results showed a close match between the model predictions and bilingual children's patterns of translation equivalent learning. At smaller vocabulary sizes, data matched the Preference Account, while at larger vocabulary sizes they matched the Neutral Account. Our findings show that patterns of translation equivalent learning emerge predictably from the word learning process, and potentially reveal a qualitative shift in translation equivalent learning as bilingual children develop and learn more words.

Long-term mitigation scenarios developed by integrated assessment models underpin major aspects of recent IPCC reports and have been critical to identify the system transformations that are required to meet stringent climate goals. However, they have been criticized for proposing pathways that may prove challenging to implement in the real world and for failing to capture the social and institutional challenges of the transition. There is a growing interest to assess the feasibility of these scenarios, but past research has mostly focused on theoretical considerations. This paper proposes a novel and versatile multidimensional framework that allows evaluating and comparing decarbonization pathways by systematically quantifying feasibility concerns across geophysical, technological, economic, socio-cultural and institutional dimensions. This framework enables to assess the timing, disruptiveness and scale of feasibility concerns, and to identify trade-offs across different feasibility dimensions. As a first implementation of the proposed framework, we map the feasibility concerns of the IPCC 1.5 °C Special Report scenarios. We select 24 quantitative indicators and propose feasibility thresholds based on insights from an extensive analysis of the literature and empirical data. Our framework is, however, flexible and allows evaluations based on different thresholds or aggregation rules. Our analyses show that institutional constraints, which are often not accounted for in scenarios, are key drivers of feasibility concerns. Moreover, we identify a clear intertemporal trade-off, with early mitigation being more disruptive but preventing higher and persistent feasibility concerns produced by postponed mitigation action later in the century.

This paper addresses the essential part of modelling primary user (PU) activity pattern in the time domain, which involved choosing the best distribution fit to represent idle and busy time. The accurate PU activity model plays a vital role in developing high-performance cognitive radio (CR) network. This work formulates the PU activity model by using the empirical data measured from wireless local area network (WLAN) testbed. The detected idle time analysed in this work in two different scenarios, then a statistical approach performed to find the best fits. The finding shows the generalised Pareto (GP) distribution as the best fit with DKS=0.266 compared to other distribution fits

This article focuses on the application of financial intelligence based on artificial intelligence technology. The article first analyzes the artificial intelligence technology and its applicability in the financial field, and then analyzes the idea of financial intelligence application construction. This article takes a financial robot actually put into use by a company as an example, and introduces the specific applications and effects of financial intelligence applications in the financial field from four aspects: basic operation, intelligent processing, data statistics, and risk monitoring. This article provides empirical data from the application of artificial intelligence in finance, and provide reference for other enterprises to use artificial intelligence technology.