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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]

Abstract 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.

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]

Management and organizational history has devoted relatively little attention to what constitutes 'data', hindering potentially productive discussions between diverse research programs. This article analyzes variations in how research programs observe and create empirical data. To do so, we introduce the difference between form and medium as implicated in all observations. Observation is a tight coupling (form), which is made of, and made possible by, loosely coupled elements (media). In this perspective, empirical data are media through which historians observe the 'past' in order to transform it into 'history'. To explore the value of this meta-perspective on data, the article analyzes, how empirical data as media of loosely coupled elements are created differently in the works of Kr. Erslev, Michel Foucault, Hayden White, and Niklas Luhmann. The analysis shows that the construction of empirical data as specific kinds of media constitutes radically different potentialities for the way in which 'history' can be formed. Our paper contributes to the 'historical turn', by opening a space for comparison and discussion between different research programs, thereby potentially leading to increased integration between the fields of history and management and organization studies [ABSTRACT FROM AUTHOR]

We provide a philosophical reconstruction and analysis of the debate on the scientific status of cosmic inflation that has played out in recent years. In a series of critical papers, Ijjas, Steinhardt, and Loeb have questioned the scientificality of current views on cosmic inflation. Proponents of cosmic inflation, such as Guth and Linde, have in turn defended the scientific credentials of their approach. We argue that, while this defense, narrowly construed, is successful against Ijjas, Steinhardt, and Loeb, the latters' reasoning does point to a significant epistemic issue that arises with respect to inflationary theory. We claim that a broadening of the concept of theory assessment to include meta-empirical considerations is needed to address that issue in an adequate way. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) powered Electric Vehicle Charging Stations (PVCS) have received extensive attention recently due to the complementary relationship of PV energy and electric vehicles. This paper proposes a methodology aimed at assisting a Charging Point Operator (CPO) in determining the size of the main components of such PVCS. The modular structure of the method gives flexibility for possible use on a new sizing problem by modifying key parameters such as the EV charging demand (i.e., arrival/departure times and energy needed to fill the battery), the EV charging strategy or the business model, independently from each other. It is of particular interest for a CPO that sizes many PVCS operated in the same environment (for example, a car park at a workplace). In that case, the CPO first has to apply the method on a representative charging station. Next, he can re-use parts of the obtained results to drastically speed up (from weeks to hours) the sizing of the other charging stations. The proposed method has been applied to the EVCS of an industrial research complex in southern France. The input dataset used to apply the method consists of more than 32,000 charging transactions spanning over 6 years with 350 EV users and 80 charging points. Three charging strategies with different levels of complexity are investigated, including Mean Power, Plug and Charge, and Solar Smart Charging. The considered business model is based on the maximization of the self-production rate. The numerical findings reveal that employing a straightforward charging strategy, such as Mean Power, leads to a substantial reduction of nearly half in the required size of the PV plant compared to the basic Plug and Charge mode. In addition, our analysis demonstrates that Solar Smart Charging has the potential to decrease the PV plant size by nearly three times. [ABSTRACT FROM AUTHOR]

Voice assistants are gradually becoming an increasingly common feature of our everyday life. However, the naturalness of communication provided by them usually leaves much to be desired. It may be caused by the fact that many chat-bots are trained on artificially created linguistic data sets and on fictional dialogues modeled by linguists on the basis of common phrasebooks or communication textbooks. As a result, the necessary pragmatic result can be achieved, but the feeling of unnatural communication of a voice assistant remains, which often reveals itself by the use of archaic phrases or remarks that are not quite suitable for the situation. It seems that the situation can to be improved by referring to real speech data namely, to a representative volume of sound recordings of real speech communication. The paper discusses some approaches to the analysis of speech data from the sound corpus ""One Day of Speech"", which is the most representative resource of Russian everyday spoken communication. The pragmatic structure of professional and everyday conversations is considered, as well as the linguistic content of standard modules, such as Greeting and Farewell. As a practical recommendation, we can suggest increasing the variability of answers not due to the lexical diversity of phrases, but due to a more diverse intonation implementation for the most typical replicas of spoken Russian.

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]

Photovoltaic (PV) powered Electric Vehicle Charging Stations (PVCS) have received extensive attention recently due to the complementary relationship of PV energy and electric vehicles. This paper proposes a methodology aimed at assisting a Charging Point Operator (CPO) in determining the size of the main components of such PVCS. The modular structure of the method gives flexibility for possible use on a new sizing problem by modifying key parameters such as the EV charging demand (i.e., arrival/departure times and energy needed to fill the battery), the EV charging strategy or the business model, independently from each other. It is of particular interest for a CPO that sizes many PVCS operated in the same environment (for example, a car park at a workplace). In that case, the CPO first has to apply the method on a representative charging station. Next, he can re-use parts of the obtained results to drastically speed up (from weeks to hours) the sizing of the other charging stations. The proposed method has been applied to the EVCS of an industrial research complex in southern France. The input dataset used to apply the method consists of more than 32,000 charging transactions spanning over 6 years with 350 EV users and 80 charging points. Three charging strategies with different levels of complexity are investigated, including Mean Power, Plug and Charge, and Solar Smart Charging. The considered business model is based on the maximization of the self-production rate. The numerical findings reveal that employing a straightforward charging strategy, such as Mean Power, leads to a substantial reduction of nearly half in the required size of the PV plant compared to the basic Plug and Charge mode. In addition, our analysis demonstrates that Solar Smart Charging has the potential to decrease the PV plant size by nearly three times.

The mechanism of slope stability prediction is formulated based on its material, geometrical and environmental situation, and slope stability prediction has been accepted as a tool for analyzing and predicting future structure stability based on geotechnical properties and failure mechanisms. However, the study of slope instability is complex and usually difficult to explain by mathematical methods. The number of slope cases limits the accuracy of slope stability prediction, and the variability of soil or rock parameters of slopes poses new challenges for prediction using conventional algorithms. To improve the accuracy of slope stability state prediction, this paper proposes an efficient slope stability state prediction method with a highly robust convolutional neural network named the multiscale, multichannel, one-dimensional convolutional neural network (MSC-1DCNN) and substantial empirical data collected worldwide. The collected dataset is amplified. Additionally, the probability of failure is calculated considering the variability of soil or rock parameters. Compared with some state-of-the-art prediction methods, the MSC-1DCNN presents high prediction accuracy. The proposed method is applied to a slope, and the results indicate that this paper provides a reliable slope stability state prediction method for homogeneous slopes worldwide. [ABSTRACT FROM AUTHOR]

The article examines the impact of the Russian war in Ukraine on children, explores the challenges they face, and the need for targeted support. Drawing on sociological research, the author highlights the disruption of social interactions, the environment, and poor education caused by the war. She also analyzes age, regional, and migration differences in socio-psychological consequences and needs. Based on empirical data and a study conducted by the Sociological Group “Rating” among mothers, the article underscores specific manifestations of the war’s impact on Ukrainian children. The study emphasizes the importance of an individualized approach to supporting children’s resilience in times of war, taking into account their unique traumatic experiences and emphasizing the importance of addressing their specific needs to foster their development. [ABSTRACT FROM AUTHOR]

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This paper intends to examine the connections between Kazakhstan's environmental indicators (CO2 emissions and solid waste), urbanization, economic development, and energy consumption. Time-series data for the years 1992 to 2020 were used in the paper; the data were derived from World Development Indicators found in the World Bank database. The methodology of this paper implements the Vector Error Correction Model based on the Granger causality Test. The empirical findings show that there is evidence that, in the near term, both urbanization and energy consumption contribute to CO2 emissions in Kazakhstan and in addition, the results demonstrate that urbanization contribute to energy consumption. Therefore, policies are required to reduce the effects of urbanization by boosting public instruments to preserve environmental quality and use more energy in sustainable manner. In addition, regulations for energy conservation are required across all industries, but particularly in the transportation and energy sector. These policies must also promote and maximize the use of alternative energy sources.

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]

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.

The consumer use phase of textiles and fashion contributes one-third to the carbon and water footprint, which is the highest for the life cycle assessment technique. However, it is only estimated by questionnaire or estimated data. We carried out a 180-day direct data tracking for three volunteer families with different demographic characteristics. The results show that consumer behaviors significantly influence the carbon and water scarcity footprint. Moreover, the uncertainty between the test data and the energy label estimated data is revealed. For family 1, the energy label-based electricity consumption carbon footprint is 0.57 kgCO2e, while the empirical data result is 2.79 kgCO2e. We proposed an improved carbon footprint evaluation method with the correction parameters, which can be estimated using the sampling data from the customers. The results show that applying actual data collection and building a database is necessary to reduce the astonishing errors, making error analysis possible and meaningful. [ABSTRACT FROM AUTHOR]

The financing of long-term care and the planning of care capacity are of increasing interest due to demographic changes and the ageing population in many countries. Since many care-intensive conditions begin to manifest at higher ages, a better understanding and assessment of the expected costs, required infrastructure, and number of qualified personnel are essential. To evaluate the overall burden of institutional care, we derive a model based on the duration of stay in dependence and the intensity of help provided to elderly individuals. This article aims to model both aspects using novel longitudinal data from nursing homes in the canton of Geneva in Switzerland. Our data contain comprehensive health and care information, including medical diagnoses, levels of dependence, and physical and psychological impairments on 21,758 individuals. We build an accelerated failure time model to study the influence of selected factors on the duration of care and a beta regression model to describe the intensity of care. We show that apart from age and gender, the duration of stay before death is mainly affected by the underlying diseases and the number of different diagnoses. Simultaneously, care intensity is driven by the individual level of dependence and specific limitations. Using both evaluations, we approximate the overall care severity for individual profiles. Our study sheds light on the relevant medical, physical, and psychological health indicators that need to be accounted for, not only by care providers but also by policy-makers and insurers. [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 paper applies the variational methods to learn the parameters and the probability of evidence of directed graphic models (also known as Bayesian networks (BNs)) when data contains missing values. One class of variational methods, the Bethe/Kikuchi approximate algorithm, is combined with Expectation-Maximization (EM) to learn BN model parameters from data using an upper limit on cluster size. Our proposed method is novel since the Bethe/Kikuchi algorithms are typically only used to approximate marginal distributions. We review popular Bethe/Kikuchi approximate algorithms, including Belief Propagation (BP), Iterative Join Graph Propagation (IJGP), and Fundamental Cycle Base (FCB) algorithms for discrete BNs, and conduct experiments to test their learning performance using a wide range of discrete BNs in practice. Experiments show that by using IJGP or FCB algorithms, with EM, we can obtain more accurate results than the conventional approximate algorithms, such as Evidence Pre-propagation Importance Sampling and Variational Inference. When exact methods are intractable the IJGP/FCB with EM can be an effective alternative solution.

Here, we analyse the behaviour of the higher order standardised moments of financial time series when we truncate a large data set into smaller and smaller subsets, referred to below as time windows. We look at the effect of the economic environment on the behaviour of higher order moments in these time windows. We observe two different scaling relations of higher order moments when the data sub sets' length decreases; one for longer time windows and another for the shorter time windows. These scaling relations drastically change when the time window encompasses a financial crisis. We also observe a qualitative change of higher order standardised moments compared to the gaussian values in response to a shrinking time window. Moreover, we model the observed scaling laws by analysing the hierarchy of rare events on higher order moments. We extend the analysis of the scaling relations to incorporate the effects these scaling relations have upon risk. We decompose the return series within these time windows and carry out a Value-at-Risk calculation. In doing so, we observe the manifestation of the scaling relations through the change in the Value-at-Risk level.

The partition function Ƶ ${\boldsymbol{Ƶ}}$ is a normalization constant for normalizing all the distributions in probabilistic inference. Ƶ ${\boldsymbol{Ƶ}}$ is closely related to the log probability of evidence (log p(e) $\mathrm{log}\unicode{x0200A}p(e)$) for Bayesian networks (BNs), which plays an important role in many applications, such as parameter learning, classification, and clustering. However, evaluating Ƶ ${\boldsymbol{Ƶ}}$ and log p(e) $\mathrm{log}\unicode{x0200A}p(e)$ for BNs containing both discrete and continuous variables (known as hybrid Bayesian networks [HBNs]) is generally difficult for analytical and simulation‐based solutions. This paper describes the work addressing the problem of estimating the partition function for HBNs when exact methods become inefficient or numerically unstable. We use dynamic discretization to provide a discretized version of the model and then perform region‐based free energy optimization to obtain the log p(e) $\mathrm{log}\unicode{x0200A}p(e)$ for the HBN efficiently, which is called the DDJT‐region partition function (RPR) algorithm. It is novel since the region‐based methods have previously only been applied to discrete BNs. We show that the log p(e) $\mathrm{log}\unicode{x0200A}p(e)$ obtained by DDJT‐RPR is exact for discretized models and with space complexity equal to that in marginal inference, thus generally applicable to a wide range of applications. To demonstrate these properties, we combine the DDJT‐RPR algorithm with an improved expectation‐maximization algorithm to learn Gaussian mixture models (GMMs), for applications requiring data only and with prior knowledge, which performed more robustly than conventional GMM learning algorithms. [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]

The appropriate theoretical explanation and the empirical prediction of adopting ethical behaviors by individuals are among the challenges in the economic literature. The most important theory in predicting the empirical behavior of individuals, i.e. the theory of expected utility, lacks unanimity towards dealing with ethical behaviors and explaining the mechanism of the emergence of such behaviors in everyday life. However, previous studies also had less achievement in predicting the variability of the ethical behaviors of individuals in the proposed models. In this study, a new behavioral model is presented to complete the previous models by highlighting the role of economic variables and incorporating the individuals’ self-interests in ethical decisions. In order to measure the predictive power of the proposed model in explaining the ethical behavior of time volunteering, data from nearly 25,000 people of twenty-five countries around the world is evaluated. The results of statistical analysis indicate the significance of the effects of economic variables on time volunteering while getting the more desirable explanation of the variability of ethical behaviors compared to the previous models. In addition, the findings show that economic variables have more explanatory power than other variables in sustainable ethical behavior model while the cultural and personality variables have more explanatory power than other variables in unsustainable ethical behavior model. These results, with emphasis on the economic aspects of adopting ethical behaviors, can provide a new horizon for further understanding and the new context for the emergence of such behaviors.