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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.

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.

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.

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.

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.

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.

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.

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

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.

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.

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]

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.

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.

The topic of airplane boarding is receiving increasing attention in practice and in the scientific literature. Shorter boarding times can reduce the time an airplane spends at the gate (the airplane turn-around time), resulting in annual cost savings of several hundred thousand dollars per airplane. Although several researchers have analyzed the boarding process purely theoretically or with simulation models, little empirical research has been performed, even though empirical research is the basis for any theoretical or simulation model. In this paper, we provide the fundamentals for this research area by presenting the results of an empirical study conducted at a large European airport. The aim of this study is to determine whether and to what extent certain factors, such as the number of passengers, the capacity of the airplane, and the amount of carry-on baggage, influence boarding times. Boarding times and additional data for short- and medium-haul flights with single-aisle airplanes have been manually collected in a field study and analyzed. The analyses yield the counter-intuitive result that a significant effect on the boarding time of a flight by the average amount of carry-on baggage per passenger cannot be demonstrated. Finally, we develop a regression model to predict boarding times based on the number of passengers and the capacity of the airplane. This straightforward model explains more than 85% of the variance in the boarding time and could therefore easily be used in the daily business of an airline to estimate the expected boarding times per flight. Furthermore, we compare our regression model to various simulation and analytical models as well as other empirical data for validation and out-of-sample testing.

In this work, we employ a minimal agent-based model to explore the mechanisms that regulate competition between memes that spread online. We investigate the case in which each piece of information has a quality, and the higher is the quality the higher are the chances of being transmitted. The model allows us to study the impact of influential nodes on the spreading behavior. We show that meme’s quality does not guarantee virility, but there is a strong correlation between the meme’s success and the influence of the agent who introduced it. When trust is incorporated into the model and the agents can decided whether or not to accept a meme, we show that both lifetime and popularity distributions have broad power-law tails indicating that only a few memes spread virally through the population reproducing perfectly the broad distributions obtained from empirical data.

In order to explore the complexity of multivariate time series, we propose a novel method: multiscale multivariate weighted fractional entropy (MMWFE). The research results show that MMWFE is able to measure the complexity of multivariate data correctly and reflect more information contained in the time series. In this paper, the reliability of the proposed method is supported by simulations on generated and empirical data. We analyze simulated pink noise and white noise to test the validity of this method, and the result is consistent with the fact that pink noise is more complex than white noise. Meanwhile, MMWFE shows a better robustness. MMWFE is then employed to bivariate stock return and volume to explore the complexity of stock markets. It successfully distinguishes Asia, Europe and Americas markets. Finally, dynamic MMWFE is applied to explore the evolution of complexity for mining more information containing in nonlinear time series.

In this paper, we analyse the recent principal volatility components analysis procedure. The procedure overcomes several difficulties in modelling and forecasting the conditional covariance matrix in large dimensions arising from the curse of dimensionality. We show that outliers have a devastating effect on the construction of the principal volatility components and on the forecast of the conditional covariance matrix and consequently in economic and financial applications based on this forecast. We propose a robust procedure and analyse its finite sample properties by means of Monte Carlo experiments and also illustrate it using empirical data. The robust procedure outperforms the classical method in simulated and empirical data.

Recently, the installed solar generating resources have been increasing rapidly. Consequently, forecasting for solar generating resources are becoming an important work to integrate utility-scale solar generating resources into power systems. As solar generating resources are variable, uncontrollable, and uncertain, accurate and reliable forecasting enables higher penetrations of solar generating resources to be deployed on the electrical power grid. Accurate forecasting of solar resources contributes to evaluation of system reserves over large geographic area and to transmission system planning. To increase the penetration of solar generating resources on the electric power grid, the accurate power forecasting of geographically distributed solar generating resources is needed. In this paper, we propose a hybrid spatio-temporal forecasting of solar generating resources based on the naive Bayesian classifier approach and spatial modelling approach. To validate our forecasting model, we use the empirical data from the practical solar farms in South Korea.

This mini-review presents extensions of the voter model that incorporate various plausible features of real decision-making processes by individuals. Although these generalizations are not calibrated by empirical data, the resulting dynamics are suggestive of realistic collective social behaviors., Comment: 13 pages, 16 figures. Version 2 contains various proofreading improvements. V3: fixed one trivial typo

This study examines the dynamic relationship between perceived privacy concerns and use behaviors of a new social networking application (SNA) over time. By integrating the theory of belief updating and an extended privacy trade-off model from a longitudinal perspective as the basis for theoretical underpinning, this study proposes a privacy trade-off model showing the role of initial use and initial self-disclosure on updating users' pre-perceptions of using a new SNA to initial perceptions of using it. The trade-off between pre-privacy concern and pre-perceived benefit determines the initial SNA usage and self-disclosure behaviors. Then, initial privacy trade-off determines the continued use behavior and continued self-disclosure. Using an empirical data collected three phases from actual mobile app users, this study validates the proposed model and hypotheses. Including the proposed dynamic model of privacy trade-off, this study provides important contributions to theory and practice in the privacy literature.

Even though the strategic management literature focuses heavily on dynamic capabilities, researchers continue to debate how these capabilities influence firm performance. To contribute to this discussion, this paper investigates the role that dynamic bundles consisting of dynamic and ordinary capabilities play in firm performance. Our insights are based on empirical data obtained from 191 top managers in a cross-industry sample and refer to two firm domains: operations and research and development (R&D). Thus, our analysis revolves around dynamic new product development and its corresponding ordinary manufacturing capabilities as well as dynamic and ordinary technological capabilities. The results indicate that the performance effects of both dynamic capabilities are mediated by the complementary ordinary capabilities. Specifically, our findings reveal a full mediation of the ordinary manufacturing capabilities and that ordinary technological capabilities have a partial mediation effect.

Context: The intensive use of models is a cornerstone of the Model-Driven Engineering (MDE) paradigm and its claimed gains in productivity. However, in order to maximize these productivity gains, it is important to adequately select the modeling formalism to be used. Unfortunately, the MDE community still lacks empirical data to support such choice. Objective: This paper aims at contributing to filling this gap by reporting an empirical study in which two types of domain model notations, graphical vs. textual, are compared regarding their efficiency and effectiveness during the creation of domain models. Method: A quasi-experiment was designed in which 127 participants were randomly classified in four groups. Then, each group was randomly assigned to a different combination of notation and application. All the participants were students enrolled in the 6th semester of the Computer Engineering degree at the University of Alicante. The statistical procedure applied was a two-factor multivariate analysis of variance (two-way MANOVA). Results: The data shows a statistically significant effect of notation type on the efficiency and effectiveness of domain modelling activities, independently from the application being modelled. Conclusions: The joint examination of our results and those of previous studies suggests that, in MDE, different tasks call for different types of notations. Therefore, MDE environments should offer both textual and graphical notations, and assist developers in selecting the most suitable one depending on the task being carried out. In particular, our data suggest that domain model creation tasks are better supported by graphical notations. To augment the validity of the conclusions of this paper, the experiment should be replicated with different subject profiles, notations, domain model sizes, tasks and application types. This study was partially funded by the Spanish Ministry of Economy, Industry and Competitiveness under the project TIN2016-78103-C2-2-R “Plataforma para la publicación y consumo de datos abiertos para una ciudad inteligente”.

Many project management and scheduling studies have modelled activity durations as a range of values to express the stochastic nature of projects in progress. A wide variety of simulation models have been proposed that all rely on pre-defined statistical probability distributions for the durations of project activities. Ideally, these distributions reflect the real stochastic nature of the activities to assure that the simulations imitate the expected reality in the best possible way. However, the distributions are often selected ad hoc, relying on a class of distributions that are often used in the statistical literature, but without having much links with the features of real projects. Recently, a calibration method has been proposed in literature and validated on a set of 24 projects that makes use of real project data to derive realistic statistical distributions. This paper builds further on the validation of this calibration method in three different ways. First, the procedure is now successfully used on a set of 125 projects (for which 83 could be used for the final analysis) from different sectors. Secondly, the procedure has been extended with a partitioning step performed by humans with experience in the particular project. Finally, some procedural extensions have been proposed to test the necessity of each step of the procedure.

In this paper, a new method to model the time-dependent corrosion wastage for main deck panel in bulk carrier is presented based on empirical data. In this modeling, recorded data of thickness measurements performed on bulk carrier of the Islamic Republic of Iran Shipping Lines from 1983 to 2016, which had the age of 6–28 years at the time of measurement were used. After monitoring and deleting suspicious data, 3814 and 3828 measured points from the deck plate and deck longitudinals elements were used, respectively. Due to non-sufficient information for all the years of ships lifetime, one of the previous models was considered as basic model. The coefficients were updated in modeling using the BAYESIAN method and applying the Markov chain Monte Carlo simulation. The parameters in this method were statistically considered and this method is useful to predict the corrosion wastage thickness of all ship structure elements per year of the ships lifetimes. Even a new data can update the modeling coefficients and provide a better and more accurate model. The resulting models with updated coefficients were used to estimate the corrosion wastage thickness for consecutive years of ship lifetime and its achievements, and important results were summarized.

In a pioneering work in Nature journal, a counter-intuitive prediction that escape rates of people under panic conditions will be enhanced if an obstacle such as a column or a barrier is placed on the “upstream” side of an exit was demonstrated through a simulation model. However, the prediction lacked empirical verification. Despite the substantial works in this topic in the past decade, there is currently a lack of knowledge on how and to what extent the obstacle near an exit can enhance the pedestrian outflow at the bottlenecks during emergency escape. Therefore, the aim of this paper is to present a critical review on the performance of an obstacle near an exit and identify future research directions. It is found that although there is a general consensus on the beneficial effect of an obstacle, there is a large uncertainty on the situations on which the positive effect of obstacle could be observed. In addition, verification of the model’s prediction with empirical data with humans is still largely unexplored. There is no clear established relationship between the exit width, obstacle distance and obstacle size/shape. Also, quantitative understanding of the nature of the clogging transition due to obstacle is a challenging task. Further, researchers have questioned the implementation of such obstacles at bottlenecks in real life scenario. A systematic approach of optimising architectural adjustments that enhances escape dynamics of pedestrians’ crowd in indoor and outdoor public spaces needs to be conducted in future.

As the energy consumption in buildings accounts for about 30% of the total energy consumption, there is a growing need for people to pay attention to energy saving in buildings . From the perspective of building life cycle, buildings consume 70–90% of energy for their operation, and therefore it is very important to reduce the energy consumed for building operation. In this regard, we developed an Artificial Neural Network (ANN) model, which predicts when the heating system should run in order to reduce energy usage on winter mornings when the energy consumption is high. Especially, existing research about heating timing uses typical variables as input data of the ANN model. But in this study, accuracy of prediction is improved by adding time variable to the ANN model. Consequently, the predictive data of the ANN model were found to be significantly similar to the empirical data of BEMS , and the prediction performance of the ANN model was approximately 13.13% of CvRMSE and 0.197% of MBE , thus satisfying ASHERAE guideline 14. Therefore, the ANN model proposed in this study would help not only in reducing the energy consumed in buildings but also in providing pleasant thermal comfort by predicting the optimal heating timing.

Best-worst scaling (BWS) has become so useful that practitioners feel pressure to include ever more items in their experiments. Researchers wanting more items and enough observations of each item by each respondent to support individual respondent-level utility models may greatly increase the burden on respondents, resulting in respondent fatigue and potentially in lower quality responses. Wirth and Wolfrath (2012) proposed two methods for creating BWS designs that allow for large numbers of items and respondent-level utility estimation, Sparse and Express BWS. This study aims to uncover the recommended approach when the goal is recovering individual respondent-level utilities and intends to do so by comparing the relative ability of Sparse and Express BWS to capture the utilities that would have resulted from a full BWS experiment, one with at least three observations of each item by each respondent. The current study repeats previous comparisons of Sparse and Express BWS using a new empirical data set. It also extends previous findings by collecting enough observations from each respondent for both a full experiment and one of the proposed methods, Express BWS and Sparse BWS. The results replicate and extend previous findings regarding the superior ability of the Sparse BWS methodology, relative to Express, to reproduce “known” utilities or utilities that result from a full BWS design.

Proton Exchange Membrane Fuel Cells regarded as promising devices for energy conversion systems. This study aims to introduce an exact modeling scheme for the proton exchange membrane fuel cell, which can imitate and model the electrical, electrochemical features of an actual Proton Exchange Membrane Fuel Cell stack. Most of the developed schemes are experimental, multi-variable, and comprise various non-linear terms which must be assessed precisely to assure a decent estimation. In this study, a novel optimization model is employed to determine the Proton Exchange Membrane fuel cell parameters accurately. The Shark Smell Optimizer simulates the hunting process for a Shark. Hence, Shark Smell Optimizer is a nature-inspired and metaheuristic optimization algorithm depends on the rules of metaheuristic proficiency, exploration and exploitation terms to evade errors in local optimums and to obtain appropriate responses. The introduced Shark Smell Optimizing method is examined on five commercial proton exchange membrane fuel cells stack concerning empirical data. Many types of research and operation examination are executed to verify the effectuality of the proposed scheme. Additionally, for more authentication, Proton Exchange Membrane Fuel Cells modeling with Shark Smell Optimizer obtained data is compared with other optimization algorithm results. A statistical analysis also carried out for proposed method, the obtained show the reliability of Shark Smell Optimizer in comparison to other utilized techniques.

Analytical predictions and black box approaches are well-known strategies for the prediction of forming processes. Each of them has its own advantages and weaknesses. Analytical predictions lack flexibility, but usually have a strong theoretical foundation. Black box approach means models based purely on empirical data, these models provides tailored solutions, but do not have any valid physical background. In this paper, we propose to merge these two concepts into a single procedure: a gray box approach. The gray box approach combines the best features from both prediction strategies: on the one hand, it provides a theoretical ground for the considered manufacturing process; on the other hand, it can be tailored for a given task. Prediction of the springback for the air bending process was taken as an example to test the concept. In the experimental investigation stage, construction steel S235JR sheets of various thicknesses have been used, which resulted in 174 bending tests. The obtained results have been used both to train and to test the gray box model. Validation tests revealed that the gray box approach is a suitable option for springback prediction, as it provides more accurate and consistent results than the analytical and black box approaches. ispartof: pages:45-50 ispartof: Procedia Manufacturing vol:27 pages:45-50 ispartof: ICAFT/SFU/AutoMetForm 2018 location:Chemnitz, Germany date:6 Nov - 7 Nov 2018 status: published

In this paper, we propose two new centralized resource allocation methods extending Lozano and Villa’s method (2004) to more general cases. The main assumption in this study is that all Decision Making Units (DMUs) are operating under the supervision of a central DMU in order to introduce the input and output targets for all units in the next production period. This paper considers two planning ideas. The first one is maximizing outputs produced with future planned resources and removing all input inefficiencies as much as possible while making all units strongly efficient. The second one is optimizing total revenue and cost functions to reach the best performance of the system. All units are assumed to be able to adjust their input consumptions and output productions in the current Production Possibility Set (PPS). Finally, we show that the two proposed methods provide the same results when input and output prices are replaced with their corresponding shadow prices. The proposed methods are illustrated with two empirical data sets.

This work is a part of the research aimed at developing an algorithm for classifying a family of partial precedence that allows us to evaluate the generalizing ability of the algorithm in the process of its synthesis from empirical data and to manage this process. One of species of tulip has been chosen as an object of research. This process is carried out via morphological features of tulip. The information about tulip is obtained from Central herbarium of the institute of Botany of Uzbek Academy of sciences.

The active inference framework, and in particular its recent formulation as a partially observable Markov decision process (POMDP), has gained increasing popularity in recent years as a useful approach for modelling neurocognitive processes. This framework is highly general and flexible in its ability to be customized to model any cognitive process, as well as simulate predicted neuronal responses based on its accompanying neural process theory. It also affords both simulation experiments for proof of principle and behavioral modelling for empirical studies. However, there are limited resources that explain how to build and run these models in practice, which limits their widespread use. Most introductions assume a technical background in programming, mathematics, and machine learning. In this paper we offer a step-by-step tutorial on how to build POMDPs, run simulations using standard MATLAB routines, and fit these models to empirical data. We assume a minimal background in programming and mathematics, thoroughly explain all equations, and provide exemplar scripts that can be customized for both theoretical and empirical studies. Our goal is to provide the reader with the requisite background knowledge and practical tools to apply active inference to their own research. We also provide optional technical sections and several appendices, which offer the interested reader additional technical details. This tutorial should provide the reader with all the tools necessary to use these models and to follow emerging advances in active inference research.

Due to the increasing need for computational power, the market has shifted towards big centralized data centers. Understanding the nature of the dynamics of these data centers from machine and job/task perspective is critical to design efficient data center management policies like optimal resource/power utilization, capacity planning and optimal (reactive and proactive) maintenance scheduling. Whereas jobs/tasks dynamics have received a lot of attention, the study of the dynamics of the underlying machines supporting the jobs/tasks execution has received much less attention, even when these dynamics would substantially affect the performance of the jobs/tasks execution. Given the limited data available from large computing installations, only a few previous studies have inspected data centers and only concerning failures and their root causes. In this paper, we study the 2011 Google data center traces from the machine dynamics perspective. First, we characterize the machine events and their underlying distributions in order to have a better understanding of the entire machine lifecycle. Second, we propose a data-driven model to enable the estimate of the expected number of available machines at any instant of time. The model is parameterized and validated using the empirical data collected by Google during a one month period.

Reliable empirical data are required to understand and model realistic pedestrian navigation behaviours at public crowd-gathering places such as public transportation hubs and shopping malls. Naturalistic walking big data could provide reliable information for investigating realistic pedestrian behaviours and to overcome critical shortcomings of the data collected through controlled laboratory experiments. In this work, we investigate pedestrian navigation in indoor open spaces using naturalistic walking big data collected through video recordings. The extracted data include 299,082 trajectories of individual pedestrians who navigated through the atrium of the Informatics Forum building of the University of Edinburgh. We compare several pedestrian vector fields by calibrating several cellular automaton (CA) models and we finally identify a generalized vector field for pedestrians who are walking in indoor open space environments under normal walking conditions. The output of this study could be useful in enhancing CA-based pedestrian simulation models by representing pedestrian navigation as well as route-choice behaviours more realistically in those models. Simulation tools based on such enhanced models can facilitate practitioners, such as public building designers, to optimize designs considering naturalistic pedestrian behaviours in open spaces.

Periodic-Frequent pattern mining is an empirical data mining technique that is used to investigate the customer purchase behavior pattern in the retail market. It provides the most extreme advantages to a retailer for product placements, product segmentation, store layout, selling strategies and different merchandising decisions to improve customer satisfaction as well as increase the sales. Traditionally, the single minimum support based frequent pattern mining approaches missed many lower supported items for high support threshold value, and on the other sites, the number of frequent patterns explode for low support threshold value. The retail market is highly seasonal, a number of items those are not periodic-frequent at all but those are periodic and frequent at a specific season, as results a huge number of seasonal periodic-frequent itemsets are remains out of reach. To resolve this problem, we propose an approach for extracting seasonally periodic-frequent patterns with user specified minimum support and maximum periodicity thresholds on multilevel retail supermarket dataset in this paper. The dataset has been created by collecting transaction slips in a Supermarket of Bangladesh. The results of our proposed approach extract many hidden seasonal patterns in transactional database for easy merchandising decisions and it shows the superiority over the traditional systems.

The Hodrick-Prescott filter is a convenient and therefore widely and routinely applied detrending method in macroeconomics working with empirical data. However, James Hamilton has recently gained attention with his vigorous advice against it and a proposal of a better alternative. Before abandoning Hodrick-Prescott and uncritically switching to the Hamilton regression filter, or before by force of habit ignoring Hamilton's contribution altogether, this paper, in a nontechnical and elementary manner, provides a little methodological reflection about the premisses behind the two approaches. In addition, it sets up a stylized oscillatory scenario in which the Hamilton filter dramatically misjudges the trend. On the other hand, it sketches a modification of the Hodrick-Prescott approach and also a search strategy that, at least under similar conditions, can help find a more appropriate degree of trend smoothing than the conventional choice of lambda = 1600 for quarterly data.

The purpose of this article is to identify, classify and contrast several types of online manufacturing platforms (OMPs), which are virtual platforms that connect and facilitate the interaction between two or more entities (e.g., firms and customers) in the manufacturing domain. We propose a taxonomy of OMPs emerging from the empirical data. To do so, we implement a multiple case study, triangulating data available on the web with interviews. We also use a novel type of experiential research: we perform some transactional activities (e.g., order the design and manufacture of a product) to better understand how the firms and their OMPs work. We select and assess the platforms of 71 firms in 21 different countries. The results suggest that there are closed online manufacturing platforms (COMPs), which optimize the industrial equipment and installations of major manufacturers and their clients, and multi-sided online manufacturing platforms (MOMPs), which connect customers with independent fabricators. This study provides insights that can help us to better understand the diversity and potential of OMP and the synergies that are created between them and manufacturing firms.

This paper proposes a macroscopic fluid modeling framework to assist with strategic decision making of a platform for operating a large-scale on-demand app-based ride-hailing system. The framework captures the spatiotemporal characteristics of a ride-hailing system, and is flexible in representing control policies that a platform is implementing. It thus enables the analysis of a large-scale ride-hailing system with observable market responses and facilitates the optimization of the control polices. As a demonstration of the proposed framework, we customize it for a ride-hailing system operated by Didi Chuxing in a large city of China, and conduct an empirical study of the system. In doing so, we calibrate the model with a high degree of granularity using empirical data, and then investigate optimal pricing strategies to achieve various managerial objectives of the platform. To our best knowledge, the proposed model is the first of its kind that offers a tractable way to support the analysis and optimization of large-scale ride-hailing systems. The empirical study also provides one of the first testbeds for examining various operations strategies for ride-hailing services.

Computer simulation for pedestrian dynamics is at the same time an application area in which research has completed its cycle and an active and lively research context in which contributions from different disciplines still produce advancements on the state of the art. The study of effects of the presence of groups in the simulated population is object of growing interest in the community. While previous results have started to investigate the phenomenon, implying conflicting tendencies for pedestrians, mechanisms for the flexible management of cohesion among group members are still investigated both in continuous and discrete modelling efforts. The present effort is aimed at extending previous modelling efforts, preserving and improving the capability to generate overall plausible aggregated dynamics, while at the same time improving the precision in the microscopic group dynamics, with particular attention to the shape of dyads. The paper presents validated results based on empirical data from a controlled experiment, also discussing qualitative results in different common situations (bends and bottlenecks).

Enabled by the big data driven user profiling and precision bidding techniques, online programmatic advertising markets have evolved from the traditional website-buying or ad-slot-buying models to a fine-grained and real-time trading model at the level of ad impressions (i.e., ad inventory). As a result, Web publishers are now facing a challenging decision of allocating the ad inventory across multiple advertising models, which has a direct and important influence on both their individual revenues, and the market-wide supply-demand balance. In this paper, we propose a novel hierarchical ad inventory allocation framework (AIAF), taking into consideration the possible scenarios of ad inventory allocation in programmatic advertising markets. AIAF explicitly captures the specific features of ad inventory allocation in each of three levels (i.e., channel level, market level and platform level), and also their influence-feedback effects. We present the general solution process for solving this model on the basis of its property analysis. An illustrative instantiation of our AIAF model is formulated to demonstrate its applications in supporting publishers’ decision-making on the ad inventory allocation. We also conduct experiments based on empirical data so as to validate the model and analysis. Our research findings indicate that 1) our AIAF model outperforms other single-level and two-level allocation strategies; 2) the fine-grained optimization is superior to that of the coarse-grained level; 3) allocation decisions should be made on the basis of the comparative marginal revenue instead of the absolute marginal revenue.

An alternatve approach of super efficiency slack-based measure data envelopment analysis has been proposed to make the projection of an efficient decision making unit strongly-Pareto efficient. The approach however did not examine the simultaneous effect of non-discretionary factors and integer requirements on efficiency measures. To obtain more accurate efficiency measures, this paper proposes a two-stage approach of super efficiency slack-based measure in non-discretionary factors and mixed integer requirements. Both new factors and requirements were first integrated into the existing approach. The optimal solution of the proposed approach was then obtained by transforming its fractional form to a linear form. Thus, the scalar measures of the proposed approach can now deal directly with discretionary mixed integer input saving-output surplus and discretionary mixed integer input excess-output shortfall. The practicability of the proposed approach was tested using empirical data of Malaysian community colleges.

The origin of intelligence has fascinated scientists for a long time. Over the past 100 years, many scholars have observed the connection between entropy and intelligence. In the present study, we investigated a potential origin of the swarm intelligence in humans. The present study shows that a competitive economy consisting of a large number of self-interested agents can be mapped to a Boltzmann-like system, where entropy and energy play roles of swarm intelligence and income, respectively. However, different from the physical entropy in the Boltzmann system, the entropy (or swarm intelligence) in the economic system is a self-referential variable, which may be a key characteristic for distinguishing between biological and physical systems. Furthermore, we employ the household income data from 66 countries and Hong Kong SAR to test the validity of the Boltzmann-like distribution. Remarkably, the empirical data are perfectly consistent with the theoretical results. This finding implies that the competitive behaviors among a colony of self-interested agents will spontaneously prompt the colony to evolve to a state of higher technological level, although each agent has no willingness to evolve.

Longitudinal speed reduction markings (LSRMs) are designed to alert drivers to an upcoming change in roadway geometry (e.g. direct connectors with smaller radii). In Beijing, LSRMs are usually installed on direct connectors of urban expressways. The objective of this paper is to examine the influence of LSRMs on vehicle operation and driver behavior, and evaluate the decelerating effectiveness of LSRMs on direct connectors with different radii. Empirical data were collected in a driving simulator, and indicators representing vehicle operation status and driving behavior were proposed. To examine the influence of LSRMs, an analysis segment was defined, which begins 500 m prior to the entering point of the connector and ends at the exiting point of the connector. Furthermore, the analysis segment was evenly divided into a series of subsections; the length of each subsection is 50 m. This definition is introduced based on the assumption that drivers would decelerate smoothly in advance of the connector. The analysis results show that drivers tend to decelerate earlier when the radii were 200 m or 300 m. When approaching the connector, drivers tend to decelerate at 500 m thru 250 m in advance of the connector with a 200 m radius; deceleration happens at 300 m–0 m in advance of the connector with a 300 m radius. On the connector, drivers controlled the throttle pedal use at 100 thru 300 m after the entering point when the radius was 200 m; deceleration occurred in two regions when the radius was 300 m: 0 m–900 m from the entering point, and the last 1,000 m of the connector. The analytical results further revealed that LSRMs would be effective at reducing speeds when the radius of the direct connector was 300 m.

Recent research has called for new theory development because variables in entrepreneurship have been shown to follow power laws. Usually, simulation is used to validate these new theories. However, validation has been insufficient because it fails to provide a quantitative comparison of distribution parameters. This neglect can cause misleading conclusions. To address this insufficiency, we contribute a four-step method: the possible simulation parameter range (PSPR). The fundamental advantage of the method is to compare distribution parameters of both empirical data and simulation results. We demonstrate the method's usefulness with an illustrative example.

CO2 emissions caused by private motorized traffic for the city of Graz, a typical European inland city with about 320 000 citizens, are investigated. The main methodology is a newly developed agent-based model that incorporates empirical data about the mobility behavior of the citizens in order to calculate the traveled routes, the resulting traffic and subsequent emissions. To assess the impact of different policies on CO2 emissions, different scenarios are simulated and their results are compared to a base line scenario. The model features a local and temporal resolution, effects like congestion and stop-and-go traffic as well as commuters to and from other regions. In addition to the evaluation of certain policies (like a focus on electric cars, telecommuting or an improvement of the road infrastructure), a method is provided, that makes it possible to compare many diverse scenarios, featuring technological changes, societal changes or changes in the road network, all within the same framework. The findings suggest that one of the most promising strategies to decrease urban CO2 emissions is to focus on the use of electric cars, especially if it is combined with offering alternatives to private car traffic and incentives for telecommuting. Banning the use of old cars only yields a significant result if a large amount of cars is affected, which would make such a policy difficult to implement. Expanding the road network has no significant positive effect and may even encourage using cars, therefore leading to even more CO2 emissions. Due to its flexible structure the presented model can be used to evaluate policies beyond what is presented in this study. It can easily be adapted to other conditions and geographical regions.

Office-building occupants' behaviors during their arrivals and departures foreseeably have a large impact on a building's energy consumption since many occupants in control of appliances will turn their devices on or off at these entry and departure events. Consequently, occupants would have various types of energy-use patterns that coincide with their entry and departure events and that repeat over time. Despite the value that knowledge of such patterns would have on better tracking energy-use behaviors, these patterns have not been well explored with empirical data in the literature. Therefore, this paper studies occupants' energy-use behaviors in office buildings to identify and investigate energy-use patterns at entry and departure events. In particular, this research evaluates (1) the delay intervals that manifest between the occupants' entry/departure events and the beginning/end of the occupants' energy-consuming behaviors, and (2) changes in electricity consumption caused by occupants at entry/departure events to identify recurring—and thereby predictable—energy-use patterns associated with individual occupants. In the pursuit of this objective, the energy-use behaviors of 12 occupants in two office buildings were tracked during a four-month period. Results from statistical analyses performed on the collected data reveal that an occupant in an office building typically follows a consistent, recurring delay-interval pattern. In addition, the results show each occupant also follows a recurring pattern of power changes at entry/departure events. By identifying recurring, occupant-specific energy-use behavior patterns, this study significantly contributes to the current body of research and can be used to support research efforts into energy-load disaggregation.