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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.

This paper looks into the emerging trends of makerspaces and outsourced 3D printing services, and examines the natural progression between them. Also, by presenting survey empirical data, it investigates the reasons why individual users and enterprises have started to choose such services and the benefits acquired from this choice, that lead to the promotion of creativity, innovation, and competition. Fundamental working principles both for makerspaces and outsourced 3D printing services are being analyzed. Through the conduction of a survey among two different makerspaces’ users, authors attempt to further clarify the factors that motivate users to choose to participate in makerspaces or use the services offered by an outsourced 3D printing provider. 3D printing technologies have been recognized as being the new industrial revolution. However, because of the nature of the additive manufacturing process, a high level of expertise is required in order to accomplish an acceptable result. The recent proliferation of makerspaces as well as outsourced 3D printing services offers alternative solutions towards overcoming the aforementioned challenges. Makerspaces are currently growing rapidly, aiming to help local communities to get accustomed with emerging manufacturing technologies such as 3D printing, especially in modern STEM educational practices. On the other hand, outsourcing has emerged as one of the top business practices of our time due to its numerous benefits for the companies involved. The paper makes a comparison between makerspaces and outsourced services and presents relevant original survey data from users.

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.

Eradication has been achieved for many vertebrate pest control programs, primarily on small, isolated islands, but has never been considered a practical goal for invasive sea lampreys in the Laurentian Great Lakes. Our objective was to examine evidence relevant to the feasibility of setting eradication as a management goal for Great Lakes sea lampreys. Bomford and O'Brien (1995) listed six conditions for successful eradication of a vertebrate pest; here we examine evidence that these conditions are likely to be met for Great Lakes sea lampreys, with a focus on the first condition: that removal of the pest through control can exceed their rate of replenishment. We analyzed two data sets – one empirical and one synthetic – to estimate stock-recruitment relationships and calculate the exploitation rate necessary for extinction. The empirical data set included the effect of existing lampricide control and suggested an exploitation rate of 59%, in addition to lampricide control, would be sufficient for eventual eradication. The synthetic data set, derived from a simulation of stream-level recruitment dynamics in the absence of lampricide control, suggested that an overall exploitation rate of 90% would be sufficient. We suggest that both of these targets could be achieved. Meeting the other conditions will depend on the scale of the eradication effort, and on development of an exploitation strategy, such as genetic biocontrol, that can target sea lampreys in presently invulnerable habitats. Overall, we concluded that eradication of sea lampreys from the Great Lakes should not be dismissed as a prospective goal.

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.

Building community resilience to climate-induced disaster shocks requires an innovative, adaptive, and forward-looking approach. However, empirical data on such initiatives is lacking. Adopting an Adaptive Governance Framework, we gathered evidence that collaborative multi-loop social learning by multilevel institutions (local, regional, and national) can significantly enhance community resilience to climate induced disaster shocks and reduce gaps between institutional disaster governance responsibilities and capacities. Following a Case Study approach, we investigated the disaster resilience of two coastal communities in Bangladesh. Our primary data collection techniques were Key Informant Interviews and document reviews. The results of our investigation revealed three key prerequisites for building community resilience to nature-triggered disasters like cyclones or floods: i) the presence of multiple nested institutional structures at the local level; ii) multi-loop social learning at multiple institutional levels; and iii) documentation of lessons learned from each disaster and the application of these lessons to disaster governance at all institutional levels. We documented that bridging organizations play a decisive role in documenting and scaling-up lessons learned from episodic extreme weather events. Therefore, more emphasis needs to be placed on the importance of bridging organizations in scaling up lessons from episodic disaster events into national-level policy and practice.

BACKGROUND: By the beginning of December 2020, some vaccines against COVID-19 already presented efficacy and security, which qualify them to be used in mass vaccination campaigns. Thus, setting up strategies of vaccination became crucial to control the COVID-19 pandemic. METHODS: We use daily COVID-19 reports from Chicago and New York City (NYC) from 01-Mar2020 to 28-Nov-2020 to estimate the parameters of an SEIR-like epidemiological model that accounts for different severity levels. To achieve data adherent predictions, we let the model parameters to be time-dependent. The model is used to forecast different vaccination scenarios, where the campaign starts at different dates, from 01-Oct-2020 to 01-Apr-2021. To generate realistic scenarios, disease control strategies are implemented whenever the number of predicted daily hospitalizations reaches a preset threshold. RESULTS: The model reproduces the empirical data with remarkable accuracy. Delaying the vaccination severely affects the mortality, hospitalization, and recovery projections. In Chicago, the disease spread was under control, reducing the mortality increment as the start of the vaccination was postponed. In NYC, the number of cases was increasing, thus, the estimated model predicted a much larger impact, despite the implementation of contention measures. The earlier the vaccination campaign begins, the larger is its potential impact in reducing the COVID-19 cases, as well as in the hospitalizations and deaths. Moreover, the rate at which cases, hospitalizations and deaths increase with the delay in the vaccination beginning strongly depends on the shape of the incidence of infection in each city.

Improving product nutrition has become an increasingly important area of innovation in the food and beverage industry. However, the contribution made by a product’s nutritional quality to the firm’s bottom line has been little studied. To fill this gap, we first developed an analytical model to study the relationship between a product’s nutritional quality and the firm’s profit, and then investigated the role of package innovation and advertising in this relationship. The results show that a product’s nutritional quality has a concave relationship with the firm’s profit, and that the relationship is influenced positively by both package innovation and advertising. Finally, we empirically examined product nutrition across a wide range of product categories and corresponding firms’ profits over multiple years to test if the results derived from our analytical models hold for empirical data from real businesses. Furthermore, we empirically investigated how a new product’s nutritional quality impacts the firm’s stock return after the product’s introduction.

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.

Seasonally-flooded varzea forests of Western Amazonia are one of the most productive and biodiverse wetland forests in the world. However, data on their soil CO2 emissions, soil organic matter decomposition rates, and soil C stocks are scarce. This is a concern because hydrological changes are predicted to lead to increases in the height, extent, and duration of seasonal floods, which are likely to have a significant effect on soil C stocks and fluxes. However, with no empirical data, the impact of altered flood regimes on varzea soil C cycles remains uncertain. This study quantified the effects of maximum annual flood height and soil moisture on soil CO2 efflux rate (Rs) and soil organic matter decomposition rate (k) in the varzea forests of Pacaya Samiria National Reserve, Peru. The study was conducted between May and August 2017. The results showed that Rs (10.6–182.7 mg C m−2 h–1) and k (0.016–0.078) varied between and within sites, and were considerably lower than the values reported for other tropical forests. In addition, Rs was negatively affected by flood height (P

Background Competing priorities make using a transparent and evidence-based approach important when deciding to recommend new vaccines. We conducted a literature review to document the processes and frameworks for national decision-making on new vaccine introductions and explored which key features have evolved since 2010. Methods We searched literature published on policymaking related to vaccine introduction from March 2010 to August 2020 in six databases. We screened articles for eligibility with the following exclusion criteria: non-human or hypothetical vaccines, the sole focus on economic evaluation or decision to adopt rather than policy decision-making. We employed nine broad categories of criteria from the 2012 review for categorization and abstracted data on the country, income level, vaccine, and other relevant criteria. Results Of the 3808 unique references screened, 116 met eligibility criteria and were classified as: a) framework of vaccine adoption decision-making (27), b) studies that analyse empirical data on or examples of vaccine adoption decision-making (45), c) theoretical and empirical articles that provide insights into the vaccine policymaking process (44 + 17 already included in the previous categories). Commonly reported criteria for decision-making were the burden of disease; vaccine efficacy/effectiveness, safety; impact on health and non-health outcomes; economic evaluation and cost-effectiveness of alternative interventions. Programmatic and acceptability aspects were not as often considered. Most (50; 82%) of the 61 articles describing the process of vaccine introduction policymaking highlighted the role of country, regional, or global evidence-informed recommendations and a robust national governance as enabling factors for vaccine adoption. Conclusions The literature on vaccine adoption decision-making has expanded since 2010. We found that policymakers and expert advisory committee members (e.g., National Immunization Technical Advisory Group [NITAG]) increasingly value the interventions based on economic evaluations. The results of this review could guide discussions on evidence-informed immunization decision-making among country, sub-regional, and regional stakeholders.

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.

There have been vociferous calls for 'tree-centered' vegetation models to refine predictions of forest carbon (C) cycling. Unfortunately, our global survey at flux-tower sites indicates insufficient empirical data support for this much-needed model development. We urge for a new generation of studies across large environmental gradients that strategically pair long-term ecosystem monitoring with manipulative experiments on mature trees. For this, we outline a versatile experimental framework to build cross-scale data archives of C uptake and allocation to structural, non-structural, and respiratory sinks. Community-wide efforts and discussions are needed to implement this framework, especially in hitherto underrepresented tropical forests. Global coordination and realistic priorities for data collection will thereby be key to achieve and maintain adequate empirical support for tree-centered vegetation modeling.

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.

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.

Traditional drilling is still not appropriate for working with Inconel625 and Inconel718 alloys, since such metals become hard to remove substances. The aim of this article is really to evaluate the two separate metals from empirical data of the micro electrochemical machining (μ-ECM) process on overcut. The overcut was most much a concern when alloys were extracted during machining. Unconventional scientific data is derived from studies unless the authors are using the same instruments for testing the same process variables. Those process variables are chosen for evaluation, such as the volts, the chloride concentration as well as the feed rate of stainless-steel electrodes. Apart from the material removal rate and surface roughness, the overcut is one of the important response variables of μ-ECM. The best possible outcome of an overcut value of 0.021 mm has achieved by Inconel718 alloy. The effects of the overcut on Inconel alloys are compared.

Background Condition-specific quality of life (QoL) instruments are more representative of the priorities of people with lived experience. As such, the development of the first and only bipolar disorder (BD) specific measurement instrument, the Quality of Life in Bipolar Disorder (QoL.BD) questionnaire, marked an important step forward for the literature. The present systematic review aims to characterise applications of the QoL.BD in the BD literature and review empirical findings obtained from studies using this measure. Methods A systematic search identified 37 peer-reviewed publications which reported original empirical data using the QoL.BD in a BD population. No restrictions were placed on language/study type. Results Adaptations to the QoL.BD displayed appropriate psychometric properties. Although clinical trials were typically underpowered, promising effect sizes for a number of treatment modalities were reported. QoL.BD scores were moderately correlated with depressive symptoms; a number of candidate predictors were identified. Limitations Due to resource limitations, the present review used one database (Google Scholar), and a single author reviewed articles for eligibility. On balance the risks of missing relevant studies were deemed minimal. Conclusion A sizeable, international body of evidence now exists regarding the measurement, presentation, and treatment of condition-specific aspects of QoL in BD. Key avenues for future research include large scale, randomized control clinical trials using the QoL.BD as a primary outcome, and granular exploration of potential correlates of QoL.BD domain scores. Finally, longer follow-up periods are required to inform understanding of the dynamic relationship between clinical variables and condition-specific aspects of QoL in BD.

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

Standard models for capital requirements restrict the correlation between different risk factors to the linear measure and do not consider undertaking-specific parameters. We propose a comprehensive framework for risk aggregation in non-life insurance using copulas and two levels of aggregation: base-level aggregation (different assets, different lines of insurance) and top-level aggregation (assets and liabilities). Using empirical data from Korean and German insurance companies, we compare our internal risk model with three regulatory standard models (Korean RBC, Solvency II, Swiss Solvency Test). We show that the standard models significantly overestimate the potential risk size for the insurers considered in this study by 61.2% and 57.8% on average for the Korean and the German cases respectively, where almost half of the overestimation level results from the uniform risk profile imposed by regulations and the other half comes from the linear correlation assumption. The differences between standard models and internal models might distort competition when both approaches are used in one market.

this article is based on Pakistan's empirical data to analyze the profitability of major and minor crops for 1981-2018. The purpose of the paper was to analyze the pattern of average income per hectare of minor and major crops in Pakistan. The null hypothesis claimed that the average income per hectare of minor crops is equal to or less than the average income per hectare of major crops, in contrast to the alternative hypothesis that the average income per hectare of minor crops is higher than the average income per hectare of major crops. The result obtain on the basis of the analysis was 1.10, i.e. more than one, and came to the conclusion that the average income per hectare of minor crops is higher than the average income per hectare of major crops. The profitability of minor crops also dominated the crop sector. But some fluctuations were observed in 1980, 1981, 1983, 1990, 1992 and from 2004 to 2010, when the profitability of minor crops was lower than that of the major crops. In general, the profitability of small crops demonstrates a steady upward trend since 2011 and contributes to an increase in farmers' income.

The current work demonstrates an empirical investigation on the two-phase flow frictional pressure drops attributes of the R600a/3GS oil within an inner diameter horizontal tube, sized 8 mm. Furthermore, experiments were carried out at the nominal oil concentrations between 0% and 4% at the saturation temperature of 20 °C, having the heat flux amounting to 10.0 kW·m−2, together with the mass flux that ranged between 150 and 300 kg·m−2·s−1. Moreover, the impact of the nominal oil concentration, together with the mass flux and quality on the frictional pressure drops were not only investigated but also debated. As the findings indicate, the existence of oil augments the two-phase frictional pressure drop approximately between 0 and 60% with the present test conditions. Meanwhile, the comparison of the experimental data of the two-phase frictional pressure drops was carried out with some of the relationships present in literatures. The result showed the fact that the experimental data reasonably agree with the correlation of Schlager, with 95% of the empirical points being in an error bandwidth amounting to ±25%. Besides that, a new correlation for predicting the frictional pressure drop of R600a/oil mixture was modified on the basis of the local attributes of the oil. In accordance with the forecasted values from the modified correlation, there is an acceptable agreement with the empirical data. Moreover, the deviations are located within –20%–+15%. Accordingly, it could be put to use for predicting the frictional pressure drops of R600a/oil mixture in evaporator while designing a refrigeration system.

Compared with their male counterparts, female entrepreneurs receive a disproportionately small amount of funding. One frequently cited reason for this disparity is the lack of female representation in the entrepreneurial funding decision-making process. Today, crowdfunding offers an innovative avenue for both male and female entrepreneurs and democratizes the funding decision-making process. Individual consumers (i.e., consumer-investors or backers) can now unite to influence funding outcomes. Yet, questions remain as to the motivations and behaviors of these backers supporting entrepreneurial projects through crowdfunding. This study integrates social capital theory along with the theory of choice homophily to examine the motivating factors of both male and female backers. It tests the conceptual model by analyzing empirical data collected from more than 2,000 Spanish crowdfunding backers. The findings confirm that compared with male backers, female backers support internal and external social ties to a greater extent when deciding on what projects to support.

As the occurrence of traumatic events has increased in mainstream media the study of Trauma-Related Sleep Disturbance (TRSD) has gained more traction within the field of sleep health. TRSD is prevalent in youth and can have deleterious outcomes that impact function, yet empirical data that examine this topic are limited. This review seeks to highlight the extant literature focusing on TRSD and to identify critical areas of future research.

SUMMARY Super-spreading events in an outbreak can change the nature of an epidemic. Therefore, it is useful for public health teams to determine if an ongoing outbreak has any contribution from such events, which may be amenable to interventions. We estimated the basic reproductive number (R0) and the dispersion factor (k) from empirical data on clusters of epidemiologically-linked COVID-19 cases in Hong Kong, Japan and Singapore. This allowed us to infer the presence or absence of super-spreading events during the early phase of these outbreaks. The relatively large values of k implied that large cluster sizes, compatible with super-spreading, were unlikely.

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.

Parking pricing is considered a powerful tool for improving parking management and relieving traffic congestion in metropolitan areas. This study uses field-collected panel data over four time periods before and after the implementation of a new parking pricing policy in Nanning to investigate the time-varying effect of pricing on on-street parking characteristics, including parking duration and parking turnover. Results show that parking duration decreases as parking price increases, with the relationship presenting a growing elasticity. The elasticity of parking turnover fluctuates due perhaps to the influence of vehicle ownership. Kolmogorov–Smirnov (K-S) tests show that on-street parking characteristics differ significantly before and after the implementation of a new parking pricing structure. Strategies that can improve parking services are also investigated based on the empirical data and modeling results.

In the Industry 4.0 Era, big data and automation will require precise knowledge that allows one to control, monitor and predict a process. Electroforming, which is the fabrication of free-standing components using electrodeposition, is rapidly gaining acceptance as a sustainable additive manufacturing technology. However, current knowledge of electroforming is based on empirical data, and academic engagement in this area has been limited. This article throws light on some of the complex issues surrounding the electrochemical and chemical behaviour during electroforming, which are yet unresolved. The differences between cathodic reactions in sulfamates and sulfates, ambiguities related to the role of boric acid and paucity of data on anode reactions are highlighted.

Background Military personnel and Veterans are at increased risk for suicide. Theoretical and conceptual arguments have suggested that elevated levels of acquired capability (AC) could be an explanatory factor accounting for this increased risk. However, empirical research utilizing the Acquired Capability for Suicide Scale (ACSS) in military populations has yielded mixed findings. Methods To better ascertain what factors are associated with AC, and whether methodological limitations may be contributing to mixed findings, a systematic review was conducted. Results A total of 31 articles utilized the ACSS to examine factors associated with AC, including combat history, in United States (U.S.) military personnel and Veterans. Nearly all studies (96.8%) were rated high risk of bias. Use of the ACSS varied, with seven different iterations utilized. Nearly all studies examined correlations between the ACSS and sample characteristics, mental health and clinical factors, Interpersonal Theory of Suicide constructs, and/or suicide-specific variables. Results of higher-level analyses, dominated by cross-sectional designs, often contradicted correlational findings, with inconsistent findings across studies. Limitations Included studies were non-representative of all U.S. military and Veteran populations and may only generalize to these populations. Conclusions Due to the high risk of bias, inconsistent use of the ACSS, lack of sample heterogeneity, and variability in factors examined, interpretation of current ACSS empirical data is cautioned. Suggestions for future research, contextualized by these limitations, are discussed.

Building volume loss can be directly correlated with earthquake fatalities and is therefore considered to be a better damage descriptor for estimating risk to occupants than traditional damage states. Empirical volume loss studies are limited and the study presented herein represents what is likely the first attempt to develop a relationship between commercial unreinforced brick masonry (URBM) buildings attributes, ground motion, and fatalities based on empirical data. Data from two New Zealand earthquakes was utilized to develop a model that predicts the probability of a URBM buildings being in a volume loss damage states (VDS) and the associated probability of an occupant fatality. To demonstrate application of the model, two New Zealand earthquake scenarios are presented and discussed. The model is intended to be applied at a broad scale, to capture an average response over a large number of New Zealand URBM buildings, with applicability to other countries having stocks of comparable URBM buildings.

This paper identifies three common risk factors in the returns on cryptocurrencies, which are related to cryptocurrency market return, market capitalization (size) and momentum of cryptocurrencies. Investigating a collection of 78 cryptocurrencies, we find that there are anomalous returns that decrease with size and increase with return momentum, and the momentum effect is more significant in small cryptocurrencies. Moreover, Fama-Macbeth regressions show the size and momentum combine to capture the cross-sectional variation in average cryptocurrency returns. In the tests of the three-factor model, we find most cryptocurrencies and their portfolios have significant exposures to the proposed three factors with insignificant intercepts, demonstrating that the three factors explain average cryptocurrency returns very well.

This essay deconstructs the ultra-dark side of social media and explores the variety of ‘bad’ behaviour online by looking at a wide spectrum of exploitative practices. Through the use of primary data from an online platform, we posit the question ‘What’s the worst thing you’ve done online’? We collect, code and synthesise the fully anonymised discussions and develop a classification model for bad online behaviour. We combine the categories that emerge from our empirical data with those proposed by Baccarella, Wagner, Kietzmann, and McCarthy (2018) and develop a new combined (meta-) classification model that captures both the dark side of social networking and the ultra-dark. A framework is proposed for conceptualising the spectrum of exploitative practices and the essay concludes by providing a series of management considerations.

We propose that exposure to moral identity symbolization (i.e., outwardly projected displays of one’s morality) leads observers to increase their helping behavior because they perceive the symbolizer as more scrutinizing of their moral characters, especially when observers expect or have an ongoing relationship with the symbolizer. We further propose that the effect of moral identity symbolization on observer behavior is diminished when symbolization involves behaviors that threaten the autonomy of observers (i.e., moral proselytizing). Empirical data from four studies, consisting of field surveys and experiments, supports our hypotheses. Taken together, this research suggests that moral identity symbolization in the workplace leads to helping behavior in observers as a function of heightened perceptions of moral scrutiny, but that such outward display of morality is only related to helping behavior when the symbolizers avoid proselytizing and when there is an ongoing relationship between the observers and the symbolizers.

The transfer of a leader's passion to employees is crucial to successful innovation, but it becomes especially challenging when the organization becomes large and complex. This study examines both the antecedents and consequences of corporate leaders’ passion transfer in the context of multinational corporations (MNCs). We consider two types of passion among leaders: “self-enhancing” passion, which enhances a leader's own self-identity, and “self-transcending” passion, which helps members share an organizational identity. Our empirical data, collected at the level of the parent firm and subsidiaries, show that the successful transfer of a leader's passion from headquarters to local staff is significantly and positively associated with innovation. Passion transfer is positively affected by both types of leaders’ passion and mutual communication, and negatively affected by incentive-based organizational barriers. We also find that self-enhancing passion affects innovation both directly and indirectly through passion transfer, whereas self-transcending passion affects innovation only through passion transfer. This study was supported by the Fundamental Research Funds for the Central Universities, China (Grant Numbers: NJ2019026; NS2018050); and the Human Resource Development Research Base of Jiangsu Province, China (grant number: 2017ZSJD002).

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.

It has recently been established that volatile organic compounds (VOCs) successfully transfer between clothing even with a short contact of 10 s, highlighting the potential to use VOCs in forensic reconstruction scenarios, such as sexual assault cases. The mid and low volatility compounds transferred in greater amounts than high volatility compounds. This study presents empirical data addressing the persistence of transferred VOCs on clothing for the first time. A series of experiments were carried out to determine the persistence of VOCs on clothing for time periods of up 4 weeks, on natural and synthetic fibres, and at three different environmental temperatures. The data indicate that the highest VOC amounts are generally obtained for shorter persistence times of up to 1 d. Whilst high volatility compounds were not recovered in sufficient amounts to allow quantification, the four other transferred VOCs were successfully quantified for persistence times of up to 4 weeks. The persistence for mid-volatility compounds follows decay curve trends in line with those previously obtained for fibres, glass and pollen. When comparing the persistence of VOCs on a natural and a synthetic fibre, for a persistence time of 1 h, the transferred VOCs were retained on a natural fibre in higher amounts than on a synthetic fibre. However, for longer persistence times the concentration of VOCs was similar between the two fabrics. Lastly, lower environmental temperatures resulted in higher recoveries for most VOCs, especially for short persistence times. These findings demonstrate that optimal recovery of VOCs from clothing occurs when the fabric is kept at cooler temperatures and analysed soon after the fragrance transfer occurred, although VOC recovery was possible at higher temperatures and after longer persistence times. Given the transfer and persistence characteristics of VOCs from fragrance, there is potential for fragrance to be used as a form of trace in forensic reconstruction approaches.

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.

How to leverage massive online data to understand consumer preferences over products and services has accumulated significant attention in business. In this paper, we analyze consumer preferences by modeling it as a decision-making problem of ranking alternatives with consumers' online ratings. We propose a data-driven fuzzy preference analysis (D-FPA) method to obtain the priorities of alternatives. We show that the D-FPA is tractable and with high computation efficiency. In addition, we propose a natural indicator to measure the reliability of the derived ranking results and suggest thresholds of this indicator for better control of the method. A real-world application about online film rating is provided to illustrate the D-FPA, demonstrating that the derived ranking results converge rapidly and remain stable with the observed empirical data. Finally, we show how to build up an effective recommendation system with empirical data from MovieLens.

The mismatch between photovoltaic generation and residential load leads to relative modest rates of self-consumption of solar electricity unless expensive storage solutions are locally available. One alternative to batteries is the aggregation of demand of different prosumers, as the collective load diagram might be better adapted to the solar resource. This hypothesis is tested for empirical data from 18 dwellings and 3 small businesses in the city of Lisbon, Portugal. Results show that a relatively low number of dwellings and a local small shop with a PV system without any storage will reach 90% self-consumption rates at a much lower cost than an individually owned PV system with 1 kWh/kWp storage system. Keywords: Photovoltaics, Self-consumption, Demand aggregation, Solar community

In this study a numerical approach to investigate the dust release from bulk solid during handling is presented. The modelling framework relies on the Discrete Element Method (DEM) to describe the bulk particle motion coupled with Computational Fluid Dynamics (CFD) to model the gas and the dust phase as part of a multiphase framework. Build up on already successfully applied models for dust emissions, dust release functions are integrated into the coupled DEM/CFD. This enables to simulate the dust release as a result of the impact forces/moments during particle contact and due to particle-fluid interaction. The DEM/CFD model is successfully calibrated and benchmarked to empirical data with mostly good results for variations of the two cases of an overflown heap of bulk material and free fall and impact of the latter. By using the calibrated model, a parameter study in a rotating drum is performed as an illustrative application.

This study utilised a previously developed support vector machine (SVM) (trained using empirical data from 56 dairy farms) for predicting and analysing annual dairy farm electricity consumption to help improve the sustainability of the projected expansion of milk production in Ireland. Firstly, the capability of the SVM to predict annual electricity consumption was investigated at both a farm and catchment-level (combined consumption). Electricity consumption data were attained from 16 pasture-based, Irish dairy farms between June 2016 and May 2017 in conjunction with farm data related to herd size, milk production, infrastructural equipment and managerial tendencies, required to generate predictions using the SVM. The SVM predicted annual electricity consumption of dairy farms to within 10.4% (relative prediction error). Concurrently, catchment-level electricity consumption was predicted with an error value less than 5.0%. Secondly, an investigation was carried out to assess the impact of increasing herd size and milk production on dairy farm related electricity consumption at a catchment-level across ten hypothetical infrastructural scenarios. The dairy expansion analysis showed electricity economies of scale across all ten infrastructural scenarios. The greatest reduction in electricity consumption per litre was observed when all farms employed ground water for pre-cooling milk with two additional parlour units, reducing by 4% in 2018, relative to a base scenario (no change to infrastructural equipment). The results presented in this article demonstrate the potential effectiveness of the SVM as a macro-level simulation forecast tool for dairy farm electricity consumption that may be used to quantify the impact of milk production on electricity resources, or to offer decision support to dairy farmers.

Summary Induced energy-saving efficiency improvements strongly influence energy use and climate change. This mechanism has previously been studied by bottom-up methods in models, but the effect is debatable because of lack of empirical data needed to calibrate model parameters. We provide a top-down calibration of the relation between historical rates of various efficiency changes and energy’s share of costs. To do this, we develop a modification of Solow’s model of economic productivity. We find that a 1% rise in energy cost share increases energy-use efficiency by about 1.2% in the following 20 years, a higher gain compared to previous bottom-up estimates. When we incorporate this relationship into an integrated assessment model, we find that carbon prices save up to 30% more energy by 2120, relative to model configurations without the inducing mechanism. A carbon tax induces energy-saving efficiency improvements and could therefore be a more effective mitigation tool than previously recognized.

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.

Introduction We conducted a systematic review of the literature on cigar research on youth to identify potential future research agenda to generate evidence to inform cigar regulations to prevent cigar use among youth. Methods We searched articles on Medline, EMBASE, and PsycINFO in April 2017 to identify articles relevant to cigars and adolescents. Two independent coders examined 48 articles to determine eligibility: (1) published between 2000-April 2017; (2) published in English; (3) conducted in the United States; (4) published in a peer-review journal; (5) examined cigars, cigarillos, or little cigars; (6) included youth (12–18 years old); and (7) included empirical data. Three independent coders reviewed the included articles (n = 48) to identify whether the studies addressed FDA's Research Priorities. Results The studies addressed FDA's Research Priorities of “behavior” (n = 48), “communications” (n = 4), “marketing influences” (n = 1), and “impact analysis” (n = 1). Studies on “behavior” underscored the need for improvements in measurement, such as using brand names and distinguishing cigar products. The review revealed the need for restrictions on cigar flavors, development of media campaigns and interventions, increasing the cost (via taxation), and evaluating the impact of cigar policies. Conclusions The studies mostly focused on surveillance of behaviors and use patterns, which revealed cigar specific issues to address in policies to decrease cigar use among youth. The lack of studies addressing other FDA's research priorities highlighted the critical need for future studies that inform prevention of youth cigar use.