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Determining the factors that positively and negatively affect the resilience of transport networks provides valuable information that leads to a deeper understanding of the preparedness and response of networks to external disruptions. Over the past few decades, several review papers have explored various interpretations of transport network resilience and its calculation metrics. Nevertheless, only a limited number of these papers have paid attention on the utilisation of empirical data in resilience studies. This paper, through a systematic literature review, contributes to filling this gap. To this end, from a pool of 127 relevant articles, a subset of 53 articles using real-world data was selected. The paper analyses and classifies empirical findings in transport network resilience studies. In particular, it highlights and thoroughly discusses spatial patterns of resilience and relevant influencing factors that positively or negatively affect the resilience attributes of a transport network. Although it is possible to place the empirical results within the theoretical framework proposed by the literature, two main issues on target reference levels arise from the graphical representation of transport network resilience as suggested by the theory. Based on these findings, research gaps are identified and future directions for transport researchers are proposed. [ABSTRACT FROM AUTHOR]

A variety of criteria may influence the efficacy of networks of marine protected areas (MPA) designed to enhance biodiversity conservation and provide fisheries benefits. Meta‐analyses have evaluated the influence of MPA attributes on abundance, biomass, and size structure of harvested species, reporting that MPA size, age, depth, and connectivity influence the strength of MPA responses. However, few empirical MPA evaluation studies have used consistent sampling methodology across multiple MPAs and years. Our collaborative fisheries research program systematically sampled 12 no‐take or highly protective limited‐take MPAs and paired fished reference areas across a network spanning 1100 km of coastline to evaluate the factors driving MPA efficacy across a large geographic region. We found that increased size and age consistently contributed to increased fish catch, biomass, and positive species responses inside MPAs, while accounting for factors such as latitude, primary productivity, and distance to the nearest MPA. Our study provides a model framework to collaboratively engage diverse stakeholders in fisheries research and provide high‐quality data to assess the success of conservation strategies. [ABSTRACT FROM AUTHOR]

Understanding processes and mechanisms of how species assemble in a metacommunity is crucial for illuminating the factors that contribute to the maintenance of biodiversity and developing management decisions. Ecologists have proposed a number of analytical methods for identifying the effects of various ecological processes, but there is no consensus on the best approach. Our study extends the existing framework which synthesizes multiple analytical methods and incorporates community data across space and time to understand the underlying ecological processes. We extended this framework by 1) including null‐model‐based analytical methods; 2) defining metacommunity archetypes that illustrate extreme cases of metacommunities, to observe how well they can be distinguished by different summary statistics, 3) applying the extended framework to real‐world vegetation data from a subtropical forest and interpreting the results, and 4) discussing the potential advantages, limitations, and future directions of applying this framework. We used a process‐based metacommunity simulation model to generate a simulated community dataset and applied random forest (RF) approach to estimate the strength of ecological processes in the process‐based model by considering the summary statistics calculated by the analytical methods as predictors. We also quantified the performance of the trained RF and applied it to estimate the strength of ecological processes in Fushan Forest Dynamics Plot. Our results demonstrate the framework's flexibility in incorporating different analytical methods and its generality to be applied to different community systems. We highlight its theoretical values in evaluating the performance of different statistics or indices in identifying ecological processes and its practical values in assessing the strength of ecological processes underlying real‐world metacommunities. Future improvements should focus on synthesizing statistics that capture specific signals of ecological processes and evaluating the robustness of estimation against dataset complexity and incompleteness. [ABSTRACT FROM AUTHOR]

Cognitive radio is a smart radio that can change its transmitter parameter based on interaction with the environment in which it operates. The demand for frequency spectrum is growing due to a big data issue as many Internet of Things (IoT) devices are in the network. Based on previous research, most frequency spectrum was used, but some spectrums were not used, called spectrum hole. Energy detection is one of the spectrum sensing methods that has been frequently used since it is easy to use and does not require license users to have any prior signal understanding. But this technique is incapable of detecting at low signal-to-noise ratio (SNR) levels. Therefore, the wavelet-based sensing is proposed to overcome this issue and detect spectrum holes. The main objective of this work is to evaluate the performance of wavelet-based sensing and compare it with the energy detection technique. The findings show that the percentage of detection in wavelet-based sensing is 83% higher than energy detection performance. This result indicates that the wavelet-based sensing has higher precision in detection and the interference towards primary user can be decreased. [ABSTRACT FROM AUTHOR]

Simple Summary: Wittgensteinian ethicists argue that we should not rely on a set of principles if we want to know how to treat non-human animals. Instead, we should look at how we witness and encounter animals in our lives. We admire wild animals, we feed our pets, and we cure them as patients. For Wittgensteinian animal ethicists, moral reflection should start from these ways of thinking about animals. However, our understanding of animals can change depending on context and circumstance. Not everyone thinks about animals in the same way. It is, therefore, important that Wittgensteinian animal ethicists are informed about the ways that people think about animals. We argue that this information should come from data gathered by social sciences such as sociology, psychology or anthropology. Wittgensteinian approaches to animal ethics highlight the significance of practical concepts like 'pet', 'patient', or 'companion' in shaping our understanding of how we should treat non-human animals. For Wittgensteinian animal ethicists, moral principles alone cannot ground moral judgments about our treatment of animals. Instead, moral reflection must begin with acknowledging the practical relations that tie us to animals. Morality emerges within practical contexts. Context-dependent conceptualisations form our moral outlook. In this paper, we argue that Wittgensteinians should, for methodological reasons, pay more attention to empirical data from the social sciences such as sociology, psychology or anthropology. Such data can ground Wittgensteinians' moral inquiry and thereby render their topical views more dialectically robust. [ABSTRACT FROM AUTHOR]

This paper deals with the modeling and numerical simulations of multilane vehicular traffic according to the kinetic theory of active particles methods. The main idea of this theory is to consider each driver–vehicle system as a micro-system, where the microscopic state of particles is described by position, velocity, and activity which is an appropriate variable for modeling the quality of the driver-vehicle. The interactions between micro-systems are modeled by stochastic game theory. This leads to the derivation of a mathematical model within the framework of the approach of kinetic theory. The well-posedness of the related Cauchy problem for the spatially homogeneous case is established. Numerical simulations are carried out to show the ability of the proposed model to reproduce the empirical data such as the asymptotic property in time and the emerging behavior of clusters with a particular focus on the road environment conditions. [ABSTRACT FROM AUTHOR]

In this paper the authors address the strengths of the traditional psychoanalytic model of adolescence, but suggest that it applies mainly to disturbed young people and misrepresents the majority of well-functioning adolescents. This blurs distinctions between normality and pathology, affecting diagnosis and clinical technique, promoting as well a skewed image of the phase in the general culture. To redress this difficulty, the authors propose revision and elaboration of the classical model through the addition of a perspective based on a developmental model of two systems of self-regulation that effectively describes the full range of functioning and generates a wider repertoire of techniques. Using data from 38 published cases, they counter some traditional premises, demonstrating that psychoanalysis can be the treatment of choice for disturbed adolescents, as it is highly effective, accepted by most adolescents and their parents when it includes concurrent parent work, and can be brought to proper termination with improved outcomes. [ABSTRACT FROM AUTHOR]

Urban road facilities are typically shared by heterogeneous traffic, such as cars, buses, and bicycles. This paper investigates the car and bicycle traffic at the network level by extending the 3D-MFD model to the car-bicycle network. Statistical 3D-MFDs for the car-bicycle traffic are proposed and empirically estimated for three selected networks in Shanghai. Simulation-based 3D-MFDs are used to determine the network bicycle capacity and validate statistical 3D-MFDs. Estimation results quantify negative marginal effects of car and bicycle density on network car speeds, and confirm the negative effect of bi-modal speed difference on bicycle traffic. When looking into the predicted results of 3D-MFDs, the maximum bi-modal production changes with the accumulation of bicycles, indicating car-bicycle interactions within the network. Few empirical observations are in the optimum operation regime, which indicate the potential of improving the multi-modal networks. Findings of this study can benefit network designs and traffic operations, such as adjusting separation types, implementing stricter bicycle management, and extending bicycle priority in certain areas. [ABSTRACT FROM AUTHOR]

Income disparity has become a mainstay of the international critique and public discourse on commercial surrogacy. Using existing empirical data, including our two respective field studies in India and the United States, we analyze surrogacy from a gender perspective and show how the visibility of gender disparities in a transnational context encourages assumptions at the local and national context. In doing so, we highlight the narrative of inequality, explore the complexity of surrogacy outside of a one-note narrative, and show how that narrative operates to overshadow the complex, lived experiences of those engaged in surrogacy. [ABSTRACT FROM AUTHOR]

With the public's interest in skincare increasing over the years, many studies have been conducted on the effects of LED masks and functional cosmetics on skincare issues such as enlarged pores, excess sebum secretion, etc. However, most studies were carried out within 4 weeks, which is a quite short period of time, as well as neglected factors such as age, gender, and skincare methods (mixed, single) and thus lack reliability in their findings. Hence, based on empirical data, this study analyzed the main effects of LED masks and functional cosmetics under various conditions on facial skin over a 12-week period. Results of the analysis show that the main effects of the items on facial skin under various monitored conditions are empirically significant. [ABSTRACT FROM AUTHOR]

Fare policy plays an important role in transit operations and management. To better coordinate and achieve the multidimensional goals of a proposed fare adjustment policy (e.g., increasing revenue, managing demand, and improving equity), a fundamental step is to evaluate its travel pattern impacts, which helps us consider the policy in a bigger socioeconomic context. Existing studies rarely investigate the impacts of such a policy on different users' and user groups' travel patterns and transit operators' farebox revenue using longitudinal data from sources such as smartcard data. To fill this gap, we exploit 24 weeks' smartcard data from Wuhan, China, to empirically quantify those impacts. We find that (a) the fare increase had significant but varying impacts on travel patterns across users and user groups; (b) confronting the fare increase, commuter groups identified by the topic model reduced their trip frequency more but later as compared to other groups; (c) low-accessibility, long-distance, and single-destination metro riders were less sensitive to the fare increase; (d) when there was a system-wide fare increase with a distance-based structure, trip purposes and socioeconomic statuses could better predict the impacts on the travel demand and farebox revenue than spatiality. These findings indicate that increasing average fares while offering discounted tickets for frequent and/or captive riders could maintain the existing ridership and farebox revenue and possibly increase additional ridership. [ABSTRACT FROM AUTHOR]

Natural systems contain more complexity than is accounted for in models of modern coexistence theory. Coexistence modelling often disregards variation arising from stochasticity in biological processes, heterogeneity among individuals and plasticity in trait values. However, these unaccounted‐for sources of uncertainty are likely to be ecologically important and have the potential to impact estimates of coexistence. We applied a Bayesian modelling framework to data from an annual plant community in Western Australia to propagate uncertainty in coexistence outcomes using the invasion criterion and ratio of niche to fitness differences. We found accounting for this uncertainty altered predictions of coexistence versus competitive exclusion for 3 out of 14 species pairs and yielded a probability of priority effects for an additional species pair. The propagation of uncertainty arising from sources of biological complexity improves our ability to predict coexistence more accurately in natural systems. [ABSTRACT FROM AUTHOR]

This article summarises the research studies published in the special issue on Project Management and Scheduling devoted to the 18th International Conference on Project Management and Scheduling (PMS). The special issue contains state-of-the art research in the field of (non-)robust project and machine scheduling and the contribution of each individual study to the academic literature are discussed. We notice that there is a growing interest in the research community to investigate robust scheduling approaches and optimisation problems observed in real-life business settings. This allows us to derive some interesting future research directions for the project and machine scheduling community. [ABSTRACT FROM AUTHOR]

• Method to validate the alignment of successive camera views for vehicle tracking. • Provides direct feedback from tracking output to ensure the alignment quality. • The method automatically detects and quantifies: gap, overlap and scale factors. • If problems are found the output can be used to recalibrate the alignment. • Found and fixed alignment errors in NGSIM, cleaned NGSIM data will be shared. This paper develops the partial trajectory method to align the views from successive fixed cameras that are used for video-based vehicle tracking across multiple camera views. The method is envisioned to serve as a validation tool of whatever alignment has already been performed between the cameras to ensure high fidelity with the actual vehicle movements as they cross the boundaries between cameras. The strength of the method is that it operates on the output of vehicle tracking in each camera rather than secondary features visible in the camera view that are unrelated to the traffic dynamics (e.g., fixed fiducial points). Thereby providing a direct feedback path from the tracking to ensure the quality of the alignment in the context of the traffic dynamics. The method uses vehicle trajectories within successive camera views along a freeway to deduce the presence of an overlap or a gap between those cameras and quantify how large the overlap or gap is. The partial trajectory method can also detect scale factor errors between successive cameras. If any error is detected, ideally one would redo the original camera alignment, if that is not possible, one could use the calculations from the algorithm to post hoc address the existing alignment. This research manually re-extracted the individual vehicle trajectories within each of the seven camera views from the NGSIM I-80 dataset. These trajectories are simply an input to the algorithm. The resulting method transcends the dataset and should be applicable to most methods that seek to extract vehicle trajectories across successive cameras. That said, the results reveal fundamental errors in the NGSIM dataset, including unaccounted for overlap at the boundaries between successive cameras, which leads to systematic speed and acceleration errors at the six camera interfaces. This method also found scale factor errors in the original NGSIM homographies. In response to these findings, we identified a new aerial photo of the NGSIM site and generated new homographies. To evaluate the impact of the partial trajectory method on the actual trajectory data, the manually re-extracted data were projected into the new coordinate system and smoothed. The re-extracted data shows much greater fidelity to the actual vehicle motion. The re-extracted data also tracks the vehicles over a 14% longer distance and adds 23% more vehicles compared to the original NGSIM dataset. As of publication, the re-extracted data from this paper will be released to the research community. [ABSTRACT FROM AUTHOR]

Identifying causality between variables is essential to reveal their interactions. In previous studies, causality between variables has been captured at specific scales. However, it was not taken into account the difference in causality between different scales. In other words, it remains an open question about which scale of causality contributes most to correct causality. In this paper, a multi-scale structural complexity method of bivariate time series was developed from the recent advances in measuring the complexity of visual images. This method can identify the direction and strength of causality between variables by total complexity. In addition, depending on the contribution from the local complexity of each scale to the total complexity, it determines which scaling scale(i.e., levels of resolution) is responsible for causality. Our results indicated that local complexity at the medium scale contributes the most to total complexity. More interestingly, this conclusion holds not just for synthetic data but also for empirical data. Consequently, our finding provides not only a novel perspective to reveal the causality between variables from the perspective of complexity, but also a reference to choose an appropriate scale to study the relationship between variables. • The method of BS-MSC is presented for identifying multi-scale causality. • This method can identify the strength and direction of causality between variables. • This method can obtain the local causality at each temporal scale. • This method can determine which scale is primarily responsible for the causality. [ABSTRACT FROM AUTHOR]

When forecasting dust/sand storms, it is extremely important to determine meteorological conditions favoring the wind-induced lifting of particles, especially critical wind speed above which the separation of particles from the surface occurs. The results of comparison of dust storm diagnosis skill using five methods for the determination of critical wind speed are presented. The methods are validated using the weather station observation series for 10 years (2010–2019). [ABSTRACT FROM AUTHOR]

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

The goal of this paper is to revisit the so-called aspectual se, frequently cited over the last three decades as a new function of reflexive pronouns in Spanish and other languages, which refers to facultative uses of the reflexive pronoun where it has no effect on the valency or diathesis of the verb. I will focus on four empirical problems that such accounts face when dealing with corpus data: the requirement of a delimited object for transitive verbs, the semantic implications of the aspectual function of the reflexive pronoun, the unacceptability of the reflexive pronoun with some predicates, and the fact that these accounts have ignored a number of predicates that also take this facultative reflexive pronoun. I argue that a larger sample of both contexts and verbs, obtained by exhaustively analyzing corpus data, is necessary to improve our understanding of these uses. [ABSTRACT FROM AUTHOR]

The 2016 Kumamoto earthquake (main-shock: Mw 7.0, April 16th) has induced a widespread disruption of lifeline systems across the affected area. In Uki City (Kumamoto prefecture, Japan), water supply had been intermittently cut off for several days: as a result, around 50 repair operations on various locations of the underground pipeline system have been carried out by municipal services. In order to better constrain the vulnerability of such infrastructure with respect to future earthquakes, the collected empirical data is exploited to derive repair-rate functions for ductile pipelines (ductile iron and polyvinyl chloride) of small diameter (less than 100 mm). Due to the relatively low number of data points over a limited range of seismic intensity, the derivation of purely empirical damage function appears to be subject to significant statistical biases. Therefore, a Bayesian updating framework is adopted, where prior information on the parameters of the repair-rate function is estimated from existing damage functions from the literature. Moreover, the uncertainty related to the characterization of the Peak Ground Velocity at the location of the pipelines is taken into account by: (i) the generation of shake-maps with different assumptions on ground-motion prediction equations or fault models, and (ii) the inclusion of a spatially correlated field of the intra-event shake-map error term (i.e., modelling of the inherent variability of the seismic intensity). The results show that the derived repair-rate equation is consistent with some existing functions for ductile and earthquake-resistant pipeline segments. The effect of specific land conditions (e.g., topographic/geological factors) is also investigated, with the possibility to further parametrize the repair-rate function. Finally, the developed damage functions are applied to stochastic simulations of the seismic performance of the water network, while keeping track of various sources of uncertainties and quantifying their impact on the system's loss distribution. [ABSTRACT FROM AUTHOR]

The goal of this paper is to revisit the so-called aspectual se, frequently cited over the last three decades as a new function of reflexive pronouns in Spanish and other languages, which refers to facultative uses of the reflexive pronoun where it has no effect on the valency or diathesis of the verb. I will focus on four empirical problems that such accounts face when dealing with corpus data: the requirement of a delimited object for transitive verbs, the semantic implications of the aspectual function of the reflexive pronoun, the unacceptability of the reflexive pronoun with some predicates, and the fact that these accounts have ignored a number of predicates that also take this facultative reflexive pronoun. I argue that a larger sample of both contexts and verbs, obtained by exhaustively analyzing corpus data, is necessary to improve our understanding of these uses. [ABSTRACT FROM AUTHOR]

This article attempts to answer the following question: how significant is theological normativity for church development practice? At a time when many leaders are busy developing local churches according to secular organisational theories, models and experiences, the following question arises: to what extent does the normative character of the Bible impact Christian faith, church life and ministry and how might normative theology affect development projects that are carried out by churches? The overriding issue that both current practice and the article focus on, is how theological normativity and empirical data can be integrated in the area of church development. After explaining the concepts of theological normativity and empirical data and the logical relationship between them, the article discusses how different national (Norwegian) and international researchers within practical theology attempt to resolve problems relating to the integration of normative theology with empirical data. The article's authors believe that these attempts end up with either assimilation or integration, neither of which is beneficial for church development practice. Therefore, the authors propose an alternative integration model that distinguishes between three levels / degrees of theological normativity, corresponding to three different fields and areas of church development. From this perspective, one can see how the integration of empirical data in the three levels of normativity brings both challenges and opportunities. The authors' conclusion and recommendation concerning the function and significance of the theological normativity in church development is that theological normativity bestows the church with identity and at same time has a guiding and corrective function. [ABSTRACT FROM AUTHOR]

Station batteries used as a backup source to supply station direct-current (dc) power to relays, etc., present a source of personnel exposure to dc arc flash. However, dc arc flash is less understood compared with the arc-flash phenomena that occur in alternating-current (ac) systems. Some models exist to quantify the potential hazards posed by dc electrical systems; however, there is little empirical data available. Additionally, the lower voltage and/or lower capacity station batteries are even less researched. As a result, a series of arc-flash tests were performed at the Dolan Technology Center of American Electric Power (AEP) utilizing two 125 Vdc batteries with rated capacities of 100 and 150 Ah. For these tests, working distance and gap width were used as independent variables. Upon the conclusion of testing, various relationships were analyzed to generate a predictive model for incident energy. This model serves as a compilation of the empirical data collected during testing and will contribute to future research efforts to better understand dc arc-flash incidents from station batteries. [ABSTRACT FROM AUTHOR]

In order to check whether the results of traffic experiments can reveal the empirical traffic conditions, this study discusses the similarities and differences between them. The empirical data come from the special single-lane traffic when one lane is temporarily closed during the maintenance work. The experimental data are obtained from the platoons in one experiment under open boundary condition. Three perspectives are used for checking: the statistics of all the platoons, the evolution of stable platoons and the characteristics of four important traffic flow distributions (velocity, time-headway, velocity difference and time-headway difference at continuous locations). We find in all these areas, the empirical data and the experimental data are qualitatively similar. But the quantitative differences could be measured, e.g. the oscillation amplitude of traffic parameters in the experiment is usually larger. These results can help to improve the quality of traffic experiments in the future. [ABSTRACT FROM AUTHOR]

• Using the data this paper assesses what goes unseen by the perception sensors. • This work fuses microscopic vehicle trajectory and loop detector data. • Correcting for unknown spatial offset and slowly varying time offset. • Yielding a rich data set for studying traffic dynamics. • Anticipates synchronization issues that will arise with connected vehicles. It is becoming common to use instrumented probe vehicles (IPV) equipped with perception sensors (e.g., LIDAR or radar) to monitor ambient traffic for empirical studies of traffic dynamics and driver behavior, while the rapidly advancing field of autonomous vehicles (AV) uses perception sensors to identify and mitigate hazards. Yet the perception sensors are far from perfect, so targets of interest go undetected. The "unseen" targets might lead to inaccurate theories of traffic flow or worse, autonomous vehicle crashes. The challenge with the unseen targets is that it is difficult to detect the absence. There is little study of how vehicle-mounted perception sensors perform "in the wild" because often the perception sensors themselves usually provide the best measurements available. The focus of this work is IPV data for offline study, but the general findings are also relevant for perception sensors used in AV applications. While there is a growing number of IPV data sets, to date most suffer from the lack of independent validation of the perception sensors. This paper fuses individual vehicle actuations from loop detectors and concurrent ambient vehicle trajectories collected from an IPV to provide a rare opportunity to assess the perception sensor performance in situ, using the loop detector data to "see" what is missed by the perception sensors. While the specific results are unique to the IPV, one should expect similar biases from other perception sensor data; though without an independent measure like the loop detectors in this study, it will be difficult to quantify what goes unseen in those data sets. These findings also highlight the need to develop means to actively calibrate mobile perception sensors for AV's. It is important to capture the behavior of these systems in situ, as maintained by private owners, though we envision in the case of an AV the role of the loop detectors might be replaced with localization information from other AV's. The data fusion has added benefits for the study of traffic dynamics, with the loop detectors providing continuous monitoring at the detector locations and the IPV providing spatially rich information about the ambient traffic as it travels between detector stations. This fused data set will be available upon publication. [ABSTRACT FROM AUTHOR]

Empirical data is presented on the pulse‐echo performance of poly‐vinylidenefluoride (PVDF) ultrasound transducers monitored in‐situ at temperatures in the range 50–130°C. Measurements were performed over a 5 h period beginning at the sensors' initial exposure to temperature. A reduction in pulse‐echo amplitude was observed at all temperatures in the range and was found to be proportional to temperature increase. Four different thicknesses of PVDF film were used for the trials and small differences in behaviour were observed between sensors constructed using each thickness. On average, the overall loss was found to increase linearly with temperature from a 16% drop in pk–pk pulse‐echo voltage observed at 60°C to a 95% drop in performance at 120°C after 5 h. Rates of signal loss were found to follow a logarithmic decay, particularly at temperatures above 70°C where significant reductions were observed in the initial seconds/minutes following exposure to temperature. [ABSTRACT FROM AUTHOR]

The celebrated integer relation finding algorithm PSLQ has been successfully used in many applications. PSLQ was only analyzed theoretically for exact input data, however, when the input data are irrational numbers, they must be approximate ones due to the finite precision of the computer. When the algorithm takes empirical data (inexact data with error bounded) instead of exact real numbers as its input, how do we theoretically ensure the output of the algorithm to be an exact integer relation? In this paper, we investigate the PSLQ algorithm for empirical data as its input. Firstly, we give a termination condition for this case. Secondly, we analyze a perturbation on the hyperplane matrix constructed from the input data and hence disclose a relationship between the accuracy of the input data and the output quality (an upper bound on the absolute value of the inner product of the exact data and the computed integer relation), which naturally leads to an error control strategy for PSLQ. Further, we analyze the complexity bound of the PSLQ algorithm for empirical data. Examples on transcendental numbers and algebraic numbers show the meaningfulness of our error control strategy. [ABSTRACT FROM AUTHOR]

Highlights • A 12 month mixed-method longitudinal study of energy demand in Kuwaiti villas. • Building energy models grounded with physical and social empirical data. • Space cooling accounts for 50% to 75% of total energy use in the case study villas. • Air conditioning thermostat settings are a key driver of energy use. • Insulation and double glazing contribute to more uniform internal temperatures. Abstract This paper investigates the physical and social determinants of energy use in four case study villas in Kuwait. The investigation consisted of a 12 month mixed-method longitudinal monitoring and surveying study followed by building energy modelling. Monitoring and surveying provided empirical data about household energy use, internal temperatures, building and system characteristics and occupant energy-use behaviours. This data was used to ground models to better predict energy use. Models grounded with empirical data show that space cooling accounts for 50% to 75% of total energy use in villas. Results suggest that drivers of cooling energy are varied, with 30% of cooling energy in some villas attributable to heat gains from appliances. Furthermore, while the study found no distinct reduction in measured and modelled annual energy use intensities of villas with improved levels of efficiency (insulation and double glazing), such measures did contribute to more uniform internal temperatures. The study also identified key occupant energy-consuming behaviours including a tendency not to adjust air conditioning (AC) thermostat settings during periods of summer travel and lack of regular AC maintenance. The approach and findings of the study can inform future studies in Kuwait seeking to understand energy use in a nationally representative sample. [ABSTRACT FROM AUTHOR]

In 2009, the European Centre for Disease Prevention and Control (ECDC) estimated that multidrug-resistant (MDR) bacterial infections were responsible for 25,000 extra-deaths per year. In 2015, another report estimated that 12,500 extra-deaths were attributable to MDR bacteria every year in France. Recently, the United Nations claimed that resistance to antimicrobials was a global scourge, forecasting 10 million deaths in 2050. Surprisingly, our antibiotic resistance surveillance system in Marseille, France, did not allowed us to observe similar trends. We herein compared our data on extremely drug-resistant (XDR)/pandrug-resistant (PDR) patient extra-deaths to evaluations and predictions from these reports. First, we retrospectively collect and analyze antibiotic resistance data produced by our settings between November 2009 and March 2015 to look for 30-day deaths attributable to XDR/PDR strains belonging to 11 bacterial species/genus. In parallel, we performed a PubMed literature search to look for articles published prior to July 2016 and describing human deaths due to PDR strains. Overall, 35,723 patients were infected by at least one bacterial species/genus of interest and 85 by XDR/PDR strains. Of these patients, only one death was attributable to a XDR bacterial infection in a patient with strong comorbidities and two consecutive septic shocks. Our literature review shows that only four articles described human deaths due to PDR bacteria. All together, these data allowed us to conclude that there is a large discrepancy between the real count of deaths attributable to XDR/PDR bacteria and alarmist predictions. [ABSTRACT FROM AUTHOR]

Large-scale empirical data, the sample size and the dimension are high, often exhibit various characteristics. For example, the noise term follows unknown distributions or the model is very sparse that the number of critical variables is fixed while dimensionality grows with n. The authors consider the model selection problem of lasso for this kind of data. The authors investigate both theoretical guarantees and simulations, and show that the lasso is robust for various kinds of data. [ABSTRACT FROM AUTHOR]

The goal of project control is monitoring the project progress during project execution to detect potential problems and taking corrective actions when necessary. Tolerance limits are a tool to assess whether the project progress is acceptable or not, and generate warnings signals that act as triggers for corrective action to the project manager. In this paper, three distinct types of tolerance limits that have been proposed in literature are validated on a large and diverse set of real-life projects mainly situated in the construction sector. Moreover, a novel approach to construct tolerance limits that integrate the project risk information into the monitoring process is introduced. The results of the empirical experiment have shown that integrating project-specific information into the construction of the tolerance limits results in a higher efficiency of the monitoring process. More specifically, while including cost information increases the efficiency only marginally, incorporating the available resource information substantially improves the efficiency of the monitoring process. Furthermore, when projects are not restricted by scarce resources, the efficiency can be enhanced by integrating the available project risk information. [ABSTRACT FROM AUTHOR]

We build a complex networks model of combat System-of-Systems (SoS) based on empirical data from a real war-game, this model is a combination of command & control (C2) subnetwork, sensors subnetwork, influencers subnetwork and logistical support subnetwork, each subnetwork has idiographic components and statistical characteristics. The C2 subnetwork is the core of whole combat SoS, it has a hierarchical structure with no modularity, of which robustness is strong enough to maintain normal operation after any two nodes is destroyed; the sensors subnetwork and influencers subnetwork are like sense organ and limbs of whole combat SoS, they are both flat modular networks of which degree distribution obey GEV distribution and power-law distribution respectively. The communication network is the combination of all subnetworks, it is an assortative Small-World network with core–periphery structure, the Intelligence & Communication Stations/Command Center integrated with C2 nodes in the first three level act as the hub nodes in communication network, and all the fourth-level C2 nodes, sensors, influencers and logistical support nodes have communication capability, they act as the periphery nodes in communication network, its degree distribution obeys exponential distribution in the beginning, Gaussian distribution in the middle, and power-law distribution in the end, and its path length obeys GEV distribution. The betweenness centrality distribution, closeness centrality distribution and eigenvector centrality are also been analyzed to measure the vulnerability of nodes. [ABSTRACT FROM AUTHOR]

A clear understanding of car following behavior and microscopic relationships is critical for advancing traffic flow theory. Without empirical microscopic data, plausible but incorrect hypotheses perpetuate in the vacuum. The Next Generation Simulation (NGSIM) project was undertaken to collect such data and the NGSIM data set has become the de facto standard, underlying the vast majority of empirically based advances of the past decade. But there has been a growing minority of researchers who have found unrealistic relationships in the NGSIM data. To date, the critical findings have almost exclusively come from the existing NGSIM database itself. Unfortunately, as this paper shows, the NGSIM errors are beyond anything that could be corrected strictly through cleaning or interpolation of the reported NGSIM data. This paper takes the deepest evaluation yet of the NGSIM data. This research manually re-extracts the vehicle trajectories from a portion of the original NGSIM video to explicitly quantify NGSIM errors, e.g., piecewise constant speeds punctuated by brief periods of large acceleration exhibited by the NGSIM data were not evident in the newly extracted trajectories. This point is particularly troublesome for applications that rely on acceleration, e.g., most car following models. The magnitude of errors exhibit a dependency on speed, location and vehicle length. Examples are shown where a real vehicle stopped but the NGSIM trajectory does not and then overruns the location of the real leader. Needless to say, the re-extracted trajectories showed much cleaner speed-spacing relationships than the corresponding raw NGSIM trajectories. Finally, this work tracked the original NGSIM vehicles seen in one camera and added another 236 vehicles (11%) visible before/after the period of NGSIM tracking. As of publication, the manually re-extracted data from this paper will be released to the research community. [ABSTRACT FROM AUTHOR]

The various theoretical approaches advanced over the past 50 years to explain the origins of agriculture have prompted much discussion and debate. Most recently, controversy has arisen concerning the utility of two Darwinian approaches; namely, cultural niche construction (CNC) and human behavioral ecology-derived optimal foraging theory (OFT). Recent papers advocate for the primacy of cultural niche construction, calling for optimal foraging approaches to be all but disregarded in the quest to explain how and why foragers became farmers (Smith, 2015, 2016; Zeder, 2015, 2016). In particular, it is claimed that archaeological, paleo-environmental, and paleontological evidence from the Neotropics of northern South America fail to meet predictions derived from OFT theory, while predictions said to be derived from CNC-based approaches are supported (Smith, 2015, 2016; Zeder, 2015). However, a number of misreadings of the northern South America evidence are made in those discussions, while some pertinent literature is not considered. In this paper we discuss these misreadings and provide a clear re-articulation of the original data and interpretations, finding support for OFT predictions. Our re-evaluations of OFT and CNC further suggest they can, in fact, be complimentary explanatory approaches. [ABSTRACT FROM AUTHOR]

Traffic infrastructure and its traffic flows has rapidly developed in recent decades. This development brings benefits to society, but on the other hand has many negative impacts on the environment. Among the most significant impacts of road traffic is direct mortality of free-ranging animals due to vehicle collisions. The main aim of this study was to compare the significance of traffic intensity fluctuation and ungulate behavioural patterns with the probability of ungulate-vehicle collisions (UVCs) occurrence in the Czech Republic. Our research question was whether the probability of UVC occurrence is influenced mainly by vehicle traffic related factors (traffic intensity, road types) or by ungulate locomotory activity. We used information on UVCs from 2011 to 2013. We used Spearman’s rank ρ correlation coefficients to examine relationships between UVCs and traffic intensity fluctuation, and between UVCs and the locomotory activity of red deer and wild boar during 24-h cycles in respective months. The results indicate that the traffic intensity is not always the main factor causing the UVCs. A thorough analysis of our data showed that the main peaks of UVCs occur at time when animals have the highest locomotory activity. Our study proves high negative correlation between traffic intensity fluctuation and UVCs on motorways and expressways, which means ungulates tend to avoid crossing roads at peak traffic intensity. Next our study clearly shows that locomotory activity of ungulates is a more important factor in probability of UVC incidence than traffic intensity in the case of first-, second-, third-class roads and other roads. [ABSTRACT FROM AUTHOR]

This paper presents the methodology and results from a study to extract empirical microscopic vehicular interactions from a probe vehicle instrumented with sensors to monitor the ambient vehicles as it traverses a 28 mi long freeway corridor. The contributions of this paper are two fold: first, the general method and approach to seek a cost-effective balance between automation and manual data reduction that transcends the specific application. Second, the resulting empirical data set is intended to help advance traffic flow theory in general and car following models in particular. Generally the collection of empirical microscopic vehicle interaction data is either too computationally intensive or labor intensive. Historically automatic data extraction does not provide the precision necessary to advance traffic flow theory, while the labor demands of manual data extraction have limited past efforts to small scales. Key to the present study is striking the right balance between automatic and manual processing. Recognizing that any empirical microscopic data for traffic flow theory has to be manually validated anyway, the present study uses a “pretty good” automated processing algorithm followed by detailed manual cleanup using an efficient user interface to rapidly process the data. The study spans roughly two hours of data collected on a freeway during the afternoon peak of a typical weekday that includes recurring congestion. The corresponding data are being made available to the research community to help advance traffic flow theory in general and car following models in particular. [ABSTRACT FROM AUTHOR]

A study was designed to investigate the effects of external variables, including blood type, flooring surface, footwear tread depth and blood dryness, on the appearance of blood-based footwear marks, with particular reference to simulating a specific casework scenario. Results showed that footwear marks left in human blood tended to be of greater quality than those in equine blood, highlighting a potential issue in applying data generated with equine blood to human bloodstains in casework. Footwear tread effects were also dependent on blood type, but the type of flooring surface did not affect the appearance of the mark. Under some conditions, as the blood dried, the amount of detail retained from footwear contact decreased. These results provide the beginnings of an empirical evidence base to allow a more accurate interpretation of blood-based footwear marks in forensic casework. When applied to a disputed bloodstain in a specific case, these results also demonstrate the importance of such experiments in narrowing the range of explanations possible in the interpretation of forensic evidence. [ABSTRACT FROM AUTHOR]

We use Deep Artificial Neural Networks (ANNs) to estimate GARCH parameters for empirical financial time series. The algorithm we develop, allows us to fit autocovariance of squared returns of financial data, with certain time lags, the second order statistical moment, and the fourth order standardised moment. We have compared the time taken for the ANN algorithm to predict parameters for many time windows (around 4000), to that of the time taken for the Maximum Likelihood Estimation (MLE) methods of MatLabs's inbuilt statistical and econometric toolbox. The algorithm developed predicts all GARCH parameters in around 0.1 s, compared to the 11 seconds of the MLE method. Furthermore, we use a Model Confidence Set analysis to determine how accurate our parameter prediction algorithm is, when predicting volatility. The volatility prediction of different securities obtained employing the ANN has an error of around 25%, compared to 40% for the MLE methods. • We present an ANN to predict GARCH parameters using statistical features. • The ANN is near one hundred times faster than MLE methods. • The algorithm forecasts volatility with a significant increase in accuracy over MLE. [ABSTRACT FROM AUTHOR]

The combination of ethical principles and empirical data is critical. The protocol is not derived only from ethical principles. The empiricist developing the protocol must understand the ethics. [ABSTRACT FROM AUTHOR]

A way of representing the daily changes in temperature in the lower layers of the earth's atmosphere is offered in the paper. On the basis of known dimensionless empirical data on temperature changes, the primary form of the Fourier series is constructed. To match the extreme points and the values of the final temperature in the form of the Fourier series with the newly obtained experimental data, the boundaries of links are displaced and linear deformation of curves is performed. The method can be generalized for the three- and multilink conversions of various periodic functions. [ABSTRACT FROM AUTHOR]

To evaluate progress on political biodiversity objectives, biodiversity monitoring provides information on whether intended results are being achieved. Despite scientific proof that monitoring and evaluation increase the (cost) efficiency of policy measures, cost estimates for monitoring schemes are seldom available, hampering their inclusion in policy programme budgets., Empirical data collected from 12 case studies across Europe were used in a power analysis to estimate the number of farms that would need to be sampled per major farm type to detect changes in species richness over time for four taxa (vascular plants, earthworms, spiders and bees). A sampling design was developed to allocate spatially, across Europe, the farms that should be sampled., Cost estimates are provided for nine monitoring scenarios with differing robustness for detecting temporal changes in species numbers. These cost estimates are compared with the Common Agricultural Policy ( CAP) budget (2014-2020) to determine the budget allocation required for the proposed farmland biodiversity monitoring., Results show that the bee indicator requires the highest number of farms to be sampled and the vascular plant indicator the lowest. The costs for the nine farmland biodiversity monitoring scenarios corresponded to 0·01%-0·74% of the total CAP budget and to 0·04%-2·48% of the CAP budget specifically allocated to environmental targets., Synthesis and applications. The results of the cost scenarios demonstrate that, based on the taxa and methods used in this study, a Europe-wide farmland biodiversity monitoring scheme would require a modest share of the Common Agricultural Policy budget. The monitoring scenarios are flexible and can be adapted or complemented with alternate data collection options (e.g. at national scale or voluntary efforts), data mobilization, data integration or modelling efforts. [ABSTRACT FROM AUTHOR]

Genomic selection is promising for plant breeding, particularly for perennial crops. Multivariate analysis, which considers several traits jointly, takes advantage of the genetic correlations to increase accuracy. The aim of this study was to empirically evaluate the potential of a univariate and multivariate genomic mixed model (G-BLUP) compared to the traditional univariate pedigree-based BLUP (T-BLUP) when analyzing progeny tests of oil palm, the world’s major oil crop. The dataset comprised 478 crosses between two heterotic groups, A and B, with 140 and 131 parents, respectively, genotyped with 313 simple sequence repeat markers. The traits were bunch number and average bunch weight. We found that G-BLUP with a genomic matrix based on a similarity index gave a higher likelihood than T-BLUP. In addition, multivariate G-BLUP improved the accuracy of additive effects (breeding values or general combining abilities, GCAs), in particular for the less heritable trait, and of dominance effects (specific combining abilities, SCAs). The average increase in accuracy was 22.5 % for GCAs and 18.7 % for SCAs. Using 160 markers in group A and 90 in group B was enough to reach maximum GCA prediction accuracy. The contrasted history of the parental groups likely explained the higher benefit of G-BLUP over T-BLUP for group A than for group B. Finally, G-BLUP should be used instead of T-BLUP to analyze oil palm progeny tests, with a multivariate approach for correlated traits. G-BLUP will allow breeders to consider SCAs in addition to GCAs when selecting between the progeny-tested parents. [ABSTRACT FROM AUTHOR]

Simulation has played an important role in project-management studies of the last decades, but in order for them to produce practical results, a realistic distribution model for activity durations is indispensable. The construction industry often has needed historical records of project executions, to serve as inputs to the distribution models, but a clearly outlined calibration procedure is not always readily available, nor are their results readily interpretable. This study seeks to illustrate how data from the construction industry can be used to derive realistic input distributions. Therefore, the Parkinson simulation model with a lognormal core is applied to a large empirical dataset from the literature and the results are described. From a discussion of these results, an empirical classification of project executions is presented. Three possible uses are presented for the calibration procedure and the classification in project management simulation studies. These were validated using a case study of a construction company. [ABSTRACT FROM AUTHOR]

Due to the existence of free software and pedagogical guides, the use of Data Envelopment Analysis (DEA) has been further democratized in recent years. Nowadays, it is quite usual for practitioners and decision makers with no or little knowledge in operational research to run their own efficiency analysis. Within DEA, several alternative models allow for an environmental adjustment. Four alternative models, each user-friendly and easily accessible to practitioners and decision makers, are performed using empirical data of 90 primary schools in the State of Geneva, Switzerland. Results show that the majority of alternative models deliver divergent results. From a political and a managerial standpoint, these diverging results could lead to potentially ineffective decisions. As no consensus emerges on the best model to use, practitioners and decision makers may be tempted to select the model that is right for them, in other words, the model that best reflects their own preferences. Further studies should investigate how an appropriate multi-criteria decision analysis method could help decision makers to select the right model. [ABSTRACT FROM AUTHOR]

The residential sector represents some 30% of global electricity consumption but the underlying composition and drivers are still only poorly understood. The drivers are many, varied, and complex, including local climate, household demographics, household behaviour, building stock and the type and number of appliances. There is considerable variation across households and, until recently, often a lack of good data. This study draws upon a detailed household dataset from the Australian Smart Grid Smart City project to build a household electricity consumption model. A statistical linear regression model for household energy demand was established and tested for both individual households and regional aggregations of households. The model showed only reasonable performance in forecasting the consumption of individual households – highlighting the influence of factors beyond those surveyed – but good performance for aggregated household consumption. Models such as this would seem highly useful for a range of stakeholders including individual households trying to understand the potential implications of different choices, utilities looking to better forecast the impact of different possible residential trends and policy makers seeking to assist households in improving their energy efficiency through targeted policies and programs. [ABSTRACT FROM AUTHOR]

HIV vaccine trials (HVTs) are ethically complex, and sound informed consent processes should facilitate optimal decision-making for participants. This study aimed to explore representations of critical HVT-related concepts to enhance the consent process. Four focus group discussions were conducted with participants from key constituencies at a South African HVT site. Thematic analysis was employed to identify representations of key HVT-related concepts. The findings suggest that (potential) participants may negotiate multiple, competing versions of HVT-related concepts in a somewhat unrecognized process, which may have significant implications for the consent process. Stakeholders involved in consent and engagement activities at sites should be assisted to elicit, engage, and resolve competing representations of HVT-related concepts. More empirical research is needed to explore how such stakeholders address competing representations in their interactions with potential participants. [ABSTRACT FROM PUBLISHER]

Coalitions and alliances are ubiquitous in humans and many other mammals, being part of the fabric of complex social systems. Field biologists and ethologists have accumulated a vast amount of data on coalition and alliance formation, while theoretical biologists have developed modelling approaches. With the accumulation of empirical data and sophisticated theory, we are now potentially able to answer a host of questions about how coalitions emerge and are maintained in a population over time, and how the psychology of this type of cooperation evolved. Progress can only be achieved, however, by effectively bridging the communication gap that currently exists between empiricists and theoreticians. In this paper, we aim to do so by asking three questions: (I) What are the primary questions addressed by theoreticians interested in coalition formation, and what are the main building blocks of their models? (2) Do empirical observations support the assumptions of current models, and if not, how can we improve this situation? (3) Has theoretical work led to a better understanding of coalition formation, and what are the most profitable lines of inquiry for the future? Our overarching goal is to promote the integration of theoretical and field biology by motivating empirical scientists to collect data on aspects of coalition formation that are currently poorly quantified and to encourage theoreticians to develop a comprehensive theory of coalition formation that is testable under real-world conditions. [ABSTRACT FROM AUTHOR]

Aim. In this article, we develop a method, that we call the gameplay review method, for measuring players’ engagement with digital games through players’ interactions with video recordings of their own gameplay.Method. The gameplay review method arose from a microsociological study of the gameplay of eight young adults who each played approximately 20 hours of WORLD OF WARCRAFT over a 3-month period in 2012. Using data from in-depth interviews and audiovisual recordings of one of the eight participants, this article focuses on how the method leveraged participants’ knowledge of their experiences and ties that knowledge to measures of engagement. We outline the method’s four-step process (producing Level I data, analyzing Level I data, producing Level II data, analyzing Level II data) to guide the generation and analysis of rich video data. The method involves focused discussions on selected recorded segments of participants’ gameplay and is a means of connecting game design with both empirical and interpretive data.Results. We show how, as participants progress, they learn about game design features and deepen their understanding of games. We found that participants’ developing perceptions of, and relationships to, design features affect their engagement with digital games.Conclusion. The gameplay review method is significant in its ability to measure engagement by dealing explicitly with empirical and interpretive data. [ABSTRACT FROM AUTHOR]

CEDSS (Community Energy Demand Social Simulator) is an empirical agent-based model designed and built as part of a multi-method social science project investigating the determinants of domestic energy demand. Ideally, empirical modellers, within and beyond social simulation, would prefer to work from an integrated dataset, gatheredfor the purposes of developing the model. In practice, many have to work with less than ideal data, often including processed data from multiple sources external to the project. Moreover, what data will be required may not be clear at the start of the project. This paper describes the approach to dealing with these factors taken in developing CEDSS, and presents the completed model together with an outline of the calibration and validation procedure used. The discussion section draws together the most distinctive features of empirical data collection, processing and use for and in CEDSS, and argues that the approach taken is sufficiently robust to underpin the model's purpose - to generate scenarios of domestic energy demand to 2049. [ABSTRACT FROM AUTHOR]

• Multimodal regressions are defined for mean speed with respect to accumulations or stop durations (two-fluid models). • A Macroscopic traffic states analysis comes from an unprecedent complete trips database from pNEUMA Experiment in Athens. • A two-stage trip-based macroscopic model is proposed to enhance traffic modelling at urban regional scale. Multi-modal interactions at the network-level remain unexplored due to the lack of high-resolution data for all transportation modes involved. The current work investigates the effect of multi-modal interactions at space-mean network speed for each mode using the dataset from pNEUMA experiment that was carried out in a congested city centre network of Athens, Greece. Explanatory variables considered are the accumulation and the stopped fraction of vehicles of each mode. Firstly, a multi-modal mean speed MFD is considered by assuming that the mean speed of each mode can be expressed in terms of accumulations of all modes. The quality of multi-modal MFD fits is compared to the uni-modal ones, where the mean speed of a given mode is assumed to be a function of the accumulation of that mode only. Secondly, the classical two-fluid model is extended to multi-modal networks. An analysis on the ergodicity assumption in the context of stopped fraction is also presented. This work also introduces a network-level dynamic model that uses the stopped fraction of vehicles. This so-called extended trip-based model simulates the stop-and-go pattern of the vehicles thereby reproducing the evolution of network congestion. This work is the first to explore in this direction. The results from the classical trip-based and the extended trip-based models are compared and validated with the empirical data. [ABSTRACT FROM AUTHOR]