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This paper describes a study on the dynamic modeling and the motion simulation of an unmanned ocean platform to overcome the limitations of existing unmanned ocean platforms for ocean exploration. The proposed unmanned ocean vehicle combines an unmanned surface vehicle and unmanned underwater vehicle with an underwater cable. This platform is connected by underwater cable, and the forces generated in each platform can influence each other’s dynamic motion. Therefore, before developing and operating an unmanned ocean platform, it is necessary to derive a dynamic equation and analyze dynamic behavior using it. In this paper, Newton’s second law and lumped-mass method are used to derive the equations of motion of unmanned surface vehicle, unmanned underwater vehicle, and underwater cable. As the underwater cable among the components of the unmanned ocean platform is expected to affect the motion of unmanned surface vehicle and unmanned underwater vehicle, the similarity of modeling is described by comparing with the cable modeling results and the experimental data. Finally, we constructed a dynamic simulator using Matlab and Simulink, and analyzed the dynamic behavior of the unmanned ocean platform through open-loop simulation.

The main purpose of this paper was to develop a complete dynamic model of a power generation system based on two serially connected solid oxide fuel cell stacks. The uniqueness of this study lies in a different number of fuel cells in the stacks. The model consists of the electrochemical model, mass and energy balance equations implemented in MATLAB Simulink environment. Particular attention has been paid to the analysis of the transient response of the reformers, fuel cells and the burner. The dynamic behavior of the system during transient conditions was investigated by load step changing. The model evaluates electrical and thermal responses of the system at variable drawn current. It was found that a decrease of 40% in the 1st stage and 2nd solid oxide fuel cell (SOFC) stacks drawn current caused both stacks temperature to drop by 2%. An increase of the cell voltage for the 1st and 2nd SOFC stacks led to very fast steam reformer response combined with a slight decrease in reformer temperature, while a considerable burner temperature increase of 70 K can be observed. Predictions of the model provide the basic insight into the operation of the power generation-based SOFC system during various transients and support its further design modifications.

This contribution presents an assessment of the potential vulnerabilities to climate variability and change (CV & C) of the critical transportation infrastructure of Caribbean Small Island Developing States (SIDS). It focuses on potential operational disruptions and coastal inundation forced by CV & C on four coastal international airports and four seaports in Jamaica and Saint Lucia which are critical facilitators of international connectivity and socioeconomic development. Impact assessments have been carried out under climatic conditions forced by a 1.5 °C specific warming level (SWL) above pre-industrial levels, as well as for different emission scenarios and time periods in the twenty-first century. Disruptions and increasing costs due to, e.g., more frequent exceedance of high temperature thresholds that could impede transport operations are predicted, even under the 1.5 °C SWL, advocated by the Alliance of Small Island States (AOSIS) and reflected as an aspirational goal in the Paris Climate Agreement. Dynamic modeling of the coastal inundation under different return periods of projected extreme sea levels (ESLs) indicates that the examined airports and seaports will face increasing coastal inundation during the century. Inundation is projected for the airport runways of some of the examined international airports and most of the seaports, even from the 100-year extreme sea level under 1.5 °C SWL. In the absence of effective technical adaptation measures, both operational disruptions and coastal inundation are projected to increasingly affect all examined assets over the course of the century.

In temperate countries, influenza outbreaks are well correlated to seasonal changes in temperature and absolute humidity. However, tropical countries have much weaker annual climate cycles, and outbreaks show less seasonality and are more difficult to explain with environmental correlations. Here, we use convergent cross mapping, a robust test for causality that does not require correlation, to test alternative hypotheses about the global environmental drivers of influenza outbreaks from country-level epidemic time series. By moving beyond correlation, we show that despite the apparent differences in outbreak patterns between temperate and tropical countries, absolute humidity and, to a lesser extent, temperature drive influenza outbreaks globally. We also find a hypothesized U-shaped relationship between absolute humidity and influenza that is predicted by theory and experiment, but hitherto has not been documented at the population level. The balance between positive and negative effects of absolute humidity appears to be mediated by temperature, and the analysis reveals a key threshold around 75 °F. The results indicate a unified explanation for environmental drivers of influenza that applies globally.

Defects in the bearings greatly affect vibrations and performances of rotating transmission systems. Moreover, most previous works estimated the defect shape as a regular shape. However, the actual defect shape is not actually regular. To obtain more accurate vibration characteristics of a defective double row cylindrical roller bearing, an irregular-shaped defect modeling method and a dynamic model of double row cylindrical roller bearing with irregular-shaped defects are proposed in this paper. The dynamic model includes all components and their interactions. A test verification is proposed to validate the established model. The effects of the bearing load, rotating speed, and different independent shape defect sizes on the double row cylindrical roller bearing vibrations are investigated. The comparisons of vibrations between the irregular defect shape and simplified defect shape are studied. The results show that the simplified defect shape model will cause the vibrations to be overestimated. The established dynamic model with the actual defect is more reasonable than the simplified defect model. Moreover, this paper can provide a comprehensive analytical method for double row cylindrical roller bearing vibrations.

The Default Mode Network (DMN) is a brain system that mediates internal modes of cognitive activity, showing higher neural activation when one is at rest. Nowadays, there is a lot of interest in assessing functional interactions between its key regions, but in the majority of studies only association of Blood-oxygen-level dependent (BOLD) activation patterns is measured, so it is impossible to identify causal influences. There are some studies of causal interactions (i.e., effective connectivity), however often with inconsistent results. The aim of the current work is to find a stable pattern of connectivity between four DMN key regions: the medial prefrontal cortex (mPFC), the posterior cingulate cortex (PCC), left and right intraparietal cortex (LIPC and RIPC). For this purpose functional magnetic resonance imaging (fMRI) data from 30 healthy subjects (1000 time points from each one) was acquired and spectral dynamic causal modeling (DCM) on a resting-state fMRI data was performed. The endogenous brain fluctuations were explicitly modeled by Discrete Cosine Set at the low frequency band of 0.0078-0.1 Hz. The best model at the group level is the one where connections from both bilateral IPC to mPFC and PCC are significant and symmetrical in strength (p < 0.05). Connections between mPFC and PCC are bidirectional, significant in the group and weaker than connections originating from bilateral IPC. In general, all connections from LIPC/RIPC to other DMN regions are much stronger. One can assume that these regions have a driving role within the DMN. Our results replicate some data from earlier works on effective connectivity within the DMN as well as provide new insights on internal DMN relationships and brain's functioning at resting state.

Species distribution models (SDM) are widely applied to understand changing species geographical distribution and abundance patterns. However, existing SDM tools are inherently static and inadequate for modelling species distributions that are driven by dynamic environmental conditions. dynamicSDM provides novel tools that explicitly consider the temporal dimension at key SDM stages, including functions for: (a) Cleaning and filtering species occurrence records by spatial and temporal qualities; (b) Generating pseudo‐absence records through space and time; (c) Extracting spatiotemporally buffered explanatory variables; (d) Fitting SDMs whilst accounting for temporal biases and autocorrelation and (e) Projecting intra‐ and inter‐ annual geographical distributions and abundances at high spatiotemporal resolution. Package functions have been designed to be: flexible for targeting specific study species; compatible with other SDM tools; and, by utilising Google Earth Engine and Google Drive, to have low computing power and storage needs. We illustrate dynamicSDM functions with an example of a nomadic bird in southern Africa, the red‐billed quelea Quelea quelea. As dynamicSDM functions are flexible and easily applied, we suggest that these tools could be readily applied to other taxa and systems globally.

Significance The conventional parametric approach to modeling relies on hypothesized equations to approximate mechanistic processes. Although there are known limitations in using an assumed set of equations, parametric models remain widely used to test for interactions, make predictions, and guide management decisions. Here, we show that these objectives are better addressed using an alternative equation-free approach, empirical dynamic modeling (EDM). Applied to Fraser River sockeye salmon, EDM models ( i ) recover the mechanistic relationship between the environment and population biology that fisheries models dismiss as insignificant, ( ii ) produce significantly better forecasts compared with contemporary fisheries models, and ( iii ) explicitly link control parameters (spawning abundance) and ecosystem objectives (future recruitment), producing models that are suitable for current management frameworks. It is well known that current equilibrium-based models fall short as predictive descriptions of natural ecosystems, and particularly of fisheries systems that exhibit nonlinear dynamics. For example, model parameters assumed to be fixed constants may actually vary in time, models may fit well to existing data but lack out-of-sample predictive skill, and key driving variables may be misidentified due to transient (mirage) correlations that are common in nonlinear systems. With these frailties, it is somewhat surprising that static equilibrium models continue to be widely used. Here, we examine empirical dynamic modeling (EDM) as an alternative to imposed model equations and that accommodates both nonequilibrium dynamics and nonlinearity. Using time series from nine stocks of sockeye salmon ( Oncorhynchus nerka ) from the Fraser River system in British Columbia, Canada, we perform, for the the first time to our knowledge, real-data comparison of contemporary fisheries models with equivalent EDM formulations that explicitly use spawning stock and environmental variables to forecast recruitment. We find that EDM models produce more accurate and precise forecasts, and unlike extensions of the classic Ricker spawner–recruit equation, they show significant improvements when environmental factors are included. Our analysis demonstrates the strategic utility of EDM for incorporating environmental influences into fisheries forecasts and, more generally, for providing insight into how environmental factors can operate in forecast models, thus paving the way for equation-free mechanistic forecasting to be applied in management contexts.

Abstract To study social sequence learning, earlier functional magnetic resonance imaging (fMRI) studies investigated the neural correlates of a novel Belief Serial Reaction Time task in which participants learned sequences of beliefs held by protagonists. The results demonstrated the involvement of the mentalizing network in the posterior cerebellum and cerebral areas (e.g. temporoparietal junction, precuneus and temporal pole) during implicit and explicit social sequence learning. However, little is known about the neural functional interaction between these areas during this task. Dynamic causal modeling analyses for both implicit and explicit belief sequence learning revealed that the posterior cerebellar Crus I & II were effectively connected to cerebral mentalizing areas, especially the bilateral temporoparietal junction, via closed loops (i.e. bidirectional functional connections that initiate and terminate at the same cerebellar and cerebral areas). There were more closed loops during implicit than explicit learning, which may indicate that the posterior cerebellum may be more involved in implicitly learning sequential social information. Our analysis supports the general view that the posterior cerebellum receives incoming signals from critical mentalizing areas in the cerebrum to identify sequences of social actions and then sends signals back to the same cortical mentalizing areas to better prepare for others’ social actions and one’s responses to it.

The expected life and reliability of components is a critical aspect for railway applications where the expected life and maintenance intervals of rolling stock are quite demanding issues both in terms of equivalent mileage and duration. For these reasons, when the mileage of the mission is within 100 km, adopted accumulators are based on lithium titanate chemistry, which, despite a relatively low density, ensures a very long operational life both in terms of cycle and time aging. In this work, the authors introduce a benchmark test case, an Italian line between Reggio Calabria and Catanzaro, in which the required autonomy, more than 170 km, involves the usage of high-energy batteries such as LiNMC or LiFePO4 derived from corresponding automotive applications. In this work, the authors propose a simulation model based on IEC 62864-1:2016 to investigate how the combined effect of cycle and time aging should influence in different ways the design of the system and how relatively small interventions such as the partial electrification of a small intermediate section of the line should improve the overall stability and reliability of the performed engineering analysis.