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Brain-responsive neurostimulation (RNS) is firmly ensconced among treatment options for drug-resistant focal epilepsy, but over a quarter of patients treated with the RNS® System do not experience meaningful seizure reduction. Initial titration of RNS therapy is typically similar for all patients, raising the possibility that treatment response might be enhanced by consideration of patient-specific variables. Indeed, small, single-centre studies have yielded preliminary evidence that RNS System effectiveness depends on the brain state during which stimulation is applied. The generalizability of these findings remains unclear, however, and it is unknown whether state-dependent effects of responsive neurostimulation are also stratified by location of the seizure onset zone where stimulation is delivered. We aimed to determine whether state-dependent effects of the RNS System are evident in the large, diverse, multi-centre cohort of RNS System clinical trial participants and to test whether these effects differ between mesiotemporal and neocortical epilepsies. Eighty-one of 256 patients treated with the RNS System across 31 centres during clinical trials met the criteria for inclusion in this retrospective study. Risk states were defined in relation to phases of daily and multi-day cycles of interictal epileptiform activity that are thought to determine seizure likelihood. We found that the probabilities of risk state transitions depended on the stimulation parameter being changed, the starting seizure risk state and the stimulated brain region. Changes in two commonly adjusted stimulation parameters, charge density and stimulation frequency, produced opposite effects on risk state transitions depending on seizure localization. Greater variance in acute risk state transitions was explained by state-dependent responsive neurostimulation for bipolar stimulation in neocortical epilepsies and for monopolar stimulation in mesiotemporal epilepsies. Variability in the effectiveness of RNS System therapy across individuals may relate, at least partly, to the fact that current treatment paradigms do not account fully for fluctuations in brain states or locations of simulation sites. State-dependence of electrical brain stimulation may inform the development of next-generation closed-loop devices that can detect changes in brain state and deliver adaptive, localization-specific patterns of stimulation to maximize therapeutic effects. [ABSTRACT FROM AUTHOR]

Cargo e-bikes are expected to convey heavy loads in all aspects of daily life. Also, these vehicles are expected to maintain a consistent speed to meet mobility needs while optimizing the battery design. In this paper, a control model is developed to improve rotational speed motor control via the battery model predictive controller (MPC) thermal model designed based on experimental field test data. Experimental field tests are performed to provide the relation between battery surface and ambient temperatures in different road types and weight conditions. For this purpose, in different slope ranges, the pedal load/activity and voltage-current data are logged to use as experimental input in an MPCintegrated 1D model. To obtain the desired thermal conditions in the Li-Ion battery, the MPC battery thermal model is defined based on the thermal lumped model approach. In the next step, the generated MPC model is used as a function for longitudinal speed control in the MPC motor torque control model subjected to uncertain road disturbances. Then, the outputs of the control models are compared using the MPC parameters oc weight factors and prediction horizon. Thus, the speed control model for cargo e-bikes is presented with increased robustness using the MPC battery thermal lumped model approach considering energy and Li-Ion battery life-cycle efficiency methods regardless of driving performance needs. [ABSTRACT FROM AUTHOR]

Power converters and their interfacing networks are often treated as modular state-space blocks for small-signal stability studies in microgrids; they are interconnected by matching the input and output states of the network and converters. Virtual resistors have been widely used in existing models to generate a voltage for state-space models of the network that require voltage inputs. This paper accurately quantifies the adverse impacts of adding the virtual resistance and proposes an alternative method for network modelling that eliminates the requirement of the virtual resistor when interfacing converters with microgrids. The proposed nonlinear method allows initialization, time-domain simulations of the nonlinear model, and linearization and eigenvalue generation. A numerically linearized small-signal model is used to generate eigenvalues and is compared with the eigenvalues generated using the existing modelling method with virtual resistances. Deficiencies of the existing method and improvements offered by the proposed modelling method are clearly quantified. Electromagnetic transient (EMT) simulations using detailed switching models are used for validation of the proposed modelling method.

Aedes aegypti (L.) is an invasive mosquito responsible for vectoring diseases such as dengue, Zika and Chikungunya. Dengue affects a large proportion of the global population, with the World Health Organization estimating that half the global population is at risk, with 390 million infections occurring each year. Control of mosquito vector populations over large geographical scales can be improved and made economically viable by computer models with potential to aid decision support.We introduce a method for estimating mosquito abundance (population size) over time in biocontrol programmes that involve the release of sterilised insects. We employ Bayesian state‐space modelling to provide insight into population trajectories using data from an application of incompatible insect technique (IIT) biological control, in Far North Queensland, Australia. The general approach could be adapted to other insect species.We demonstrate how the modelling approach can estimate trajectories of abundance over time as an unmarked–release–recapture analysis. Additionally, it provides a means for quantifying population suppression in IIT programmes (a statistic that can be challenging to estimate in practice) using counterfactuals.Modelling results show that estimated population trajectories exhibit similar temporal patterns to raw trapping rate data collected in the field, for example, the presence of peaks (and troughs) associated with the timing of rainfall events. Additional confidence in our model was demonstrated through a cross‐validation study where we left out each of the six landscapes from our dataset, fit the model using the remaining five regions and assessed its predictive skill. Modelled counterfactuals allowed us to estimate that population suppression in treated landscapes was 95%–99%.Synthesis and applications. Our model can provide valuable insights that can shape decision support systems in sterile insect technique and incompatible insect technique programmes operating over large geographical scales. The model helps determine how many sterile/incompatible insects should be released over time and how population control is progressing (via use of counterfactual scenarios). These outcomes are achieved because the model provides estimates of wild‐type populations over time, even when there has been no differentiation between sterile/incompatible and wild‐type insects caught in traps. [ABSTRACT FROM AUTHOR]

In this paper, the explicit state-space model for a multi-inverter system including grid-following inverter-based generators (IBGs) and grid-forming IBGs is developed by the two-level component connection method (CCM), which modularized inverter control blocks at the primary level and IBGs at the secondary level. Based on the comprehensive state-space model representing full order of system dynamics, eigenvalues of the overall system are thoroughly analyzed, identifying potential adverse impacts of not only grid-following inverters, but also grid forming inverters on the system smallsignal stability, with the underlying principle of oscillations also understood. Numerical and simulation results validate effectiveness of the proposed methodology on IEEE benchmarking 39-bus system. [ABSTRACT FROM AUTHOR]

The efficiency of a nation's progress is determined by a variety of factors; however, transportation plays a critical role in boosting progress because it facilitates trade and communication between countries. The majority of transportation is powered by fossil fuels such as gasoline or diesel, which will be depleted in less than 50 years. Another option is to operate transportation systems after replacing conventional vehicles with electric vehicles (EV). Powering these vehicles with green electricity contributes zero carbon emissions from production to the final product. Together with the controller, an efficient charger ensures that the entire system is reliable and stable. The current work focuses on charging an off-board EV from greener energy sources (both a fuel cell and PV array forming a micro-grid) based on their availability via an efficient converter controlled by an adaptive multi-objective controller. A novel multi-output-based adaptive neuro fuzzy inference system (ANFIS) controller for charging the off-board EV at a constant current and voltage for both line and load regulations is proposed, in the current work. A comparison study of grid partitioning and subtractive clustering was conducted in order to select an optimized algorithm for generating FIS. Novelty is achieved by ensuring closed-loop stability is the main aim of the work. The entire work was created with the MATLAB/Simulink software. [ABSTRACT FROM AUTHOR]

Forecasting intermittent time series represents a challenging task whose importance increases together with the number of series sporadically observed. However, given the difficulties in modelling the presence of zeros, few methods are available. This article introduces a novel state-space approach defined as Intermittent Local Level (ILL). Our approach allows integrating the intermittent nature of time series and forecasting efficiently. Indeed, the proposed state-space model assumes a Bernoulli dynamics that allows switching between zeros and positive values. Moreover, we derive the unobserved dynamics of the time series and provide a simple method for estimating and forecasting. In addition, our approach allows deriving prediction intervals for intermittent observations. Finally, we compare our method's performance with those of standard intermittent models as well as other benchmarks, using the daily number of new cases of COVID-19 observed in nearly 3000 American counties. Predicting the number of newly infected people is important, not only for hospitals but also for policy makers in general. Empirical results show that the suggested approach clearly outperforms the Croston model and its variants when forecasting the number of new Coronavirus cases over a two-week period. In addition, it compares well with non-intermittent benchmarks both in point forecast and prediction intervals. [ABSTRACT FROM AUTHOR]

The current situation of COVID-19 measures makes it difficult to accurately assess the prevalence of SARS-CoV-2 due to a decrease in reporting rates, leading to missed initial transmission events and subsequent outbreaks. There is growing recognition that wastewater virus data assist in estimating potential infections, including asymptomatic and unreported infections. Understanding the COVID-19 situation hidden behind the reported cases is critical for decision-making when choosing appropriate social intervention measures. However, current models implicitly assume homogeneity in human behaviour, such as virus shedding patterns within the population, making it challenging to predict the emergence of new variants due to variant-specific transmission or shedding parameters. This can result in predictions with considerable uncertainty. In this study, we established a state-space model based on wastewater viral load to predict both reported cases and potential infection numbers. Our model using wastewater virus data showed high goodness-of-fit to COVID-19 case numbers despite the dataset including waves of two distinct variants. Furthermore, the model successfully provided estimates of potential infection, reflecting the superspreading nature of SARS-CoV-2 transmission. This study supports the notion that wastewater surveillance and state-space modelling have the potential to effectively predict both reported cases and potential infections.

The ecological impact of environmental changes at high latitudes (e.g., increasing temperature, and decreased sea ice cover) on low-trophic species, such as bowhead whales, are poorly understood. Key to understanding the vulnerability of zooplanktivorous predators to climatic shifts in prey is knowing whether they can make behavioural or distributional adjustments to maintain sufficient prey acquisition rates. However, little is known about how foraging behaviour and associated environmental conditions fluctuate over space and time. We collected long-term movement (average satellite transmission days were 397 (± 204 SD) in 2012 and 484 (± 245 SD) in 2013) and dive behaviour data for 25 bowhead whales (Balaena mysticetus) equipped with time-depth telemetry tags, and used hierarchical switching-state-space models to quantify their movements and behaviours (resident and transit). We examined trends in inferred two-dimensional foraging behaviours based on dive shape of Eastern Canada-West Greenland bowhead whales in relation to season and sea ice, as well as animal sex and age via size. We found no differences with regards to whale sex and size, but we did find evidence that subsurface foraging occurs year-round, with peak foraging occurring in fall (7.3 hrs d-1 ± 5.70 SD; October) and reduced feeding during spring (2.7 hrs d-1 ± 2.55 SD; May). Although sea ice cover is lowest during summer foraging, whales selected areas with 65% (± 36.1 SD) sea ice cover. During winter, bowheads occurred in areas with 90% (± 15.5 SD) ice cover, providing some open water for breathing. The depth of probable foraging varied across seasons with animals conducting epipelagic foraging dives (< 200 m) during spring and summer, and deeper mesopelagic dives (> 400 m) during fall and winter that approached the sea bottom, following the seasonal vertical migration of lipid-rich zooplankton. Our findings suggest that, compared to related species (e.g., right whales), bowheads forage at relatively low rates and over a large geographic area throughout the year. This suggests that bowhead whales have the potential to adjust their behaviours (e.g., increased time allocated to feeding) and shift their distributions (e.g., occupy higher latitude foraging grounds) to adapt to climate-change induced environmental conditions. However, the extent to which energetic consumption may vary seasonally is yet to be determined.

This paper describes the fuzzy logic controller (FLC) for a positive output LUO converter (POLC), which uses a voltage controller to achieve the output voltage regulation. The fourth-order POLC DC–DC converter produces positive output voltage utilizing the voltage lift technique. Due to the converter’s switching functions and its inherently time-varying nature, it exhibits a highly nonlinear dynamic response. To enhance the dynamic performance of the converter, controllers are designed using small signal model and frequency response analysis. The closed loop is used to evaluate converter performance in the presence of variations in reference voltage, source voltage and load disturbances. The power loss analysis of the converter is performed. The comparative results show that FLC gives good dynamic response for wide range of variations than the proportional–integral (PI) controller. The prototype model is built using dsPIC (30F4011) microcontroller, and the experimental results are presented to validate the simulation results. [ABSTRACT FROM AUTHOR]

Resource managers frequently are tasked with mitigating or reversing adverse effects of invasive species through management policies and actions. In Lake Superior, of the Laurentian Great Lakes, invasive sea lamprey populations are suppressed to protect valuable fish stocks. However, the relationship between choice of long‐term control strategy and the future chance of achieving the suppression target is unclear.Using a 60+ year time series of suppression effort and monitoring data from 50 assessment sites located on Lake Superior tributaries, we developed a Bayesian state‐space model to forecast the probability of suppressing lamprey below the suppression target.With annual application of lampricide (i.e. lamprey‐specific pesticide) at historical mean levels, we forecasted a 15% chance of achieving the Lake Superior sea lamprey suppression target in 2040.Increasing lampricide effort and/or supplementing lampricide control with age‐1 recruitment reduction increased suppression chance. Annual application of the maximum historical lampricide effort resulted in a 50% predicted chance of achieving the target, annual application of the mean historic lampricide effort plus a 40% reduction in recruitment resulted in a 54% chance, and the maximum amount of effort considered (maximum historic lampricide and 60% reduction in recruitment) resulted in a 94% chance.Policy implications. We developed a simulation model from a robust, long‐term monitoring dataset that improves understanding of why long‐term sea lamprey suppression objectives have been difficult to achieve in Lake Superior. Furthermore, the model provides a means to gauge efficacy of sea lamprey control policy and action scenarios based on forecasted chance of achieving the suppression target. Creating processes for iteratively refining our forecasting model with stakeholder and technical‐expert input and integration with a decision analysis framework could strengthen the link between ecological knowledge obtained from long‐term monitoring and invasive sea lamprey management. [ABSTRACT FROM AUTHOR]

Load and supply parameters may be uncertain in microgrids (MGs) due for instance to the intermittent nature of renewable energy sources among others. Guaranteeing reliable and stable MGs despite parameter uncertainties is crucial for their correct operation. Their stability and dynamical features are directly related to the controllers' parameters and power-sharing coefficients. Hence, to maintain power good quality within the desirable range of system parameters and to have a satisfactory response to sudden load changes, careful selection of the controllers and power-sharing coefficients are necessary. In this paper, a simple design approach for the optimal design of controllers' parameters is presented in an islanded MG. To that aim, an optimization problem is formulated based on a small-signal state-space model and solved by three different optimization techniques including particle swarm optimization (PSO), genetic algorithm (GA), and a proposed approach based on the combination of both PSO and GA. The optimized coefficients are selected to guarantee desirable static and dynamic responses in a wide range of operations regardless of the number of inverters, system configuration, output impedance differences, and load types. Through the proposed design and tuning method, the performance of the MG is improved as compared to those obtained using state-of-art techniques. This fact is demonstrated by using numerical simulations performed on a detailed model implemented in PSIM© software. [ABSTRACT FROM AUTHOR]

The emergence of the COVID-19 pandemic in 2020 led to the implementation of legal restrictions on individual activities, significantly impacting traffic and air pollution levels in urban areas. This study employs a state-space intervention method to investigate the effects of three major COVID-19 lockdowns in March 2020, November 2020, and January 2021 on London's air quality. Data were collected from 20 monitoring stations across London (central, ultra-low emission zone, and greater London), with daily measurements of NO x , PM 10 , and PM 2.5 for four years (January 2019-December 2022). Furthermore, the developed model was adjusted for seasonal effects, ambient temperature, and relative humidity. This study found significant reductions in the NO x levels during the first lockdown: 49% in central London, 33% in the ultra-low emission zone (ULEZ), and 37% in greater London. Although reductions in NO x were also observed during the second and third lockdowns, they were less than the first lockdown. In contrast, PM 10 and PM 2.5 increased by 12% and 1%, respectively, during the first lockdown, possibly due to higher residential energy consumption. However, during the second lockdown, PM 10 and PM 2.5 levels decreased by 11% and 13%, respectively, and remained unchanged during the third lockdown. These findings highlight the complex dynamics of urban air quality and underscore the need for targeted interventions to address specific pollution sources, particularly those related to road transport. The study provides valuable insights into the effectiveness of lockdown measures and informs future air quality management strategies.

Objective: In the literature, non-linear mathematical growth models are often used to estimate the number of coronavirus disease-2019 (COVID-19) cases. Specific algorithms such as mathematical optimization technique need to be employed for parameter estimation. In this work, a novel method to estimate COVID-19 daily cases and reproduction number is proposed for COVID-19. Material and Methods: In this study, the daily number of COVID- 19 cases between January 01 and November 16, 2020 has been estimated online via AR(1) (autoregressive time-series model of order 1) and the adaptive Kalman filter (AKF). After calculating the estimate for daily cases, the reproduction number estimate was obtained. Results: It is quite a simple method to model the daily case number by time series with the time-varying parameter AR(1) stochastic process and estimated the time-varying parameter with online AKF. The method is online. Only the data points on the last day are sufficient. Conclusion: The COVID-19 data have been modeled in state space, and the AKF has been employed to estimate the number of daily cases. The estimation results were obtained for the number of daily cases using the AR(1) model. Since the estimation using the AR(1) stochastic process does not require any other modeling assumption, it is a simple approach to model the daily case number time series with the time-varying parameter AR(1) stochastic process and estimated the time-varying parameter with online AKF. We suggest that the simplest method for the reproduction number estimation will be obtained by modeling the daily case via an AR(1) model. [ABSTRACT FROM AUTHOR]

As an annual ice-associated species, bowhead whales (Balaena mysticetus) are known to move northward in mid-to-late March and southward in early winter while following the annual cycle of sea ice decay and formation. We sought to determine when and where different demographic groups of Eastern Canada-West Greenland bowhead whales foraged throughout their range and what seasonal patterns occurred in their migratory and residency behaviour over a 16-year time period (2001–2016). Fifty-nine bowhead whales were equipped with satellite telemetry tags and hierarchical switching-state-space models (HSSSM) were used to infer probable foraging and travelling behaviour. Overall, 18,294 locations were predicted with the HSSSM and 70% of the locations (n = 12,784) were associated with probable foraging behaviour and 15% (n = 2709) included movements consistent with travelling behaviour. Both males and females were found to reside in Hudson Strait during winter. Females showed a slight preference for more northern regions (e.g. Gulf of Boothia) for feeding during summer compared with males who appeared to spend more time in more southern foraging grounds (e.g. Cumberland Sound). Females in Gulf of Boothia were significantly larger than females in Cumberland Sound but males were of comparable sizes in both regions. Lancaster Sound had the lowest occupancy, representing less than 0.8% of all HSSSM locations (n = 154) suggesting that this area may not be preferred by subadult male or female bowhead whales. Understanding whale movement behaviour will assist in anticipating patterns in distribution shifts associated with warming. [ABSTRACT FROM AUTHOR]

Abstract Analysis of high‐resolution data offers greater opportunity to understand the nature of data variability, behaviours, trends and to detect small changes. Climate studies often require complete time series data which, in the presence of missing data, means imputation must be undertaken. Research on the imputation of high‐resolution temporal climate time series data is still at an early phase. In this study, multiple approaches to the imputation of missing values were evaluated, including a structural time series model with Kalman smoothing, an autoregressive integrated moving average (ARIMA) model with Kalman smoothing and multiple linear regression. The methods were applied to complete subsets of data from 12 month time series of hourly temperature, humidity and wind speed data from four locations along the coast of Western Australia. Assuming that observations were missing at random, artificial gaps of missing observations were studied using a five‐fold cross‐validation methodology with the proportion of missing data set to 10%. The techniques were compared using the pooled mean absolute error, root mean square error and symmetric mean absolute percentage error. The multiple linear regression model was generally the best model based on the pooled performance indicators, followed by the ARIMA with Kalman smoothing. However, the low error values obtained from each of the approaches suggested that the models competed closely and imputed highly plausible values. To some extent, the performance of the models varied among locations. It can be concluded that the modelling approaches studied have demonstrated suitability in imputing missing data in hourly temperature, humidity and wind speed data and are therefore recommended for application in other fields where high‐resolution data with missing values are common.

Recent research is revealing how cognitive processes are supported by a complex interplay between the brain and the rest of the body, which can be investigated by the analysis of physiological features such as breathing rhythms, heart rate, and skin conductance. Heart rate dynamics are of particular interest as they provide a way to track the sympathetic and parasympathetic outflow from the autonomic nervous system, which is known to play a key role in modulating attention, memory, decision-making, and emotional processing. However, extracting useful information from heartbeats about the autonomic outflow is still challenging due to the noisy estimates that result from standard signal-processing methods. To advance this state of affairs, we propose a novel approach in how to conceptualise and model heart rate: instead of being a mere summary of the observed inter-beat intervals, we introduce a modelling framework that views heart rate as a hidden stochastic process that drives the observed heartbeats. Moreover, by leveraging the rich literature of state-space modelling and Bayesian inference, our proposed framework delivers a description of heart rate dynamics that is not a point estimate but a posterior distribution of a generative model. We illustrate the capabilities of our method by showing that it recapitulates linear properties of conventional heart rate estimators, while exhibiting a better discriminative power for metrics of dynamical complexity compared across different physiological states., Competing Interests: Declaration of competing interest None Declared, (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Analysis of high‐resolution data offers greater opportunity to understand the nature of data variability, behaviours, trends and to detect small changes. Climate studies often require complete time series data which, in the presence of missing data, means imputation must be undertaken. Research on the imputation of high‐resolution temporal climate time series data is still at an early phase. In this study, multiple approaches to the imputation of missing values were evaluated, including a structural time series model with Kalman smoothing, an autoregressive integrated moving average (ARIMA) model with Kalman smoothing and multiple linear regression. The methods were applied to complete subsets of data from 12 month time series of hourly temperature, humidity and wind speed data from four locations along the coast of Western Australia. Assuming that observations were missing at random, artificial gaps of missing observations were studied using a five‐fold cross‐validation methodology with the proportion of missing data set to 10%. The techniques were compared using the pooled mean absolute error, root mean square error and symmetric mean absolute percentage error. The multiple linear regression model was generally the best model based on the pooled performance indicators, followed by the ARIMA with Kalman smoothing. However, the low error values obtained from each of the approaches suggested that the models competed closely and imputed highly plausible values. To some extent, the performance of the models varied among locations. It can be concluded that the modelling approaches studied have demonstrated suitability in imputing missing data in hourly temperature, humidity and wind speed data and are therefore recommended for application in other fields where high‐resolution data with missing values are common. [ABSTRACT FROM AUTHOR]

Abstract Roof windows are widely used in northern European countries, contributing positively by giving daylight, passive solar heat and view to the outside. In order to improve their thermal property, triple glazing unit together with external shutter are more and more common on the market. Additionally, the junction part between window and roof is also important since it greatly influences the linear thermal transmittance (LTT) along edges of the window and the daylight level of the room. This research presents a parametric analysis for roof windows with triple glazing unit and external shutter from perspectives of energy, daylight and thermal comfort. The investigation can be described in two parts: • Analysis of thermal and comfort performance for triple glazing unit with an external shutter. • Analysis of combined performance of daylight level and LTT for roof windows. Performances of energy and thermal comfort of triple glazing unit with external shutter can be influenced by different properties, including the width of the cavity between shutter and external pane, air penetration rate through the cavity between shutter and external pane, the tilt angle of the window. The study conducts analysis on the energy and comfort performances of the window by calculating U-value of the entire window and internal surface temperature of the glazing. The calculations are performed by a model developed via state-space modelling using Simulink/MATLAB. The results reveal that the external shutter improves both the thermal and comfort performances of the window. The ways of installing windows on a roof and cutting on the internal wall along window edges also have great influences on the combined performance of daylight level and LTT along the edge between window and roof. Therefore, daylight and LTT are also evaluated with different parameters, including the thickness of roof insulation, installation level of windows on the roof, cutting of lining and extra insulation around the perimeter of windows. The analysis is conducted using DIVA/Rhino and Flixo. The calculations show that the lower installation level and extra insulation around the window frame can decrease linear thermal transmittance of the entire window by more than 60%. [ABSTRACT FROM AUTHOR]

Pine wilt disease (PWD) invaded southern Japan in the early 1900s and has gradually expanded its range to northern Honshu (Japanese mainland). The disease is caused by a pathogenic North American nematode, which is transmitted by native pine sawyer beetles. Recently, the disease has invaded other portions of East Asia and Europe where extensive mortality of host pines is anticipated to resemble historical patterns seen in Japan.There is a critical need to identify the main drivers of PWD invasion spread so as to predict the future spread and evaluate containment strategies in newly invaded world regions. But the coupling of pathogen and vector population dynamics introduces considerable complexity that is important for understanding this and other plant disease invasions.In this study, we analysed historical (1980–2011) records of PWD infection and vector abundance, which were spatially extensive but recorded at coarse categorical levels (none, low and high) across 403 municipalities in northern Honshu. We employed a multistate occupancy model that accounted both for demographic stochasticity and observation errors in categorical data.Analysis revealed that sparse sawyer populations had lower probabilities of transition to high abundance than did more abundant populations even when regional abundance stayed the same, suggesting the existence of positive density dependence, that is an Allee effect, in sawyer dynamics. Climatic conditions (average accumulated degree days) substantially limited invasion spread in northern regions, but this climatic influence on sawyer dynamics was generally weaker than the Allee effect.Our results suggest that tactics (eg sanitation logging of infected pines) which strengthen Allee effects in sawyer dynamics may be effective strategies for slowing the spread of PWD. This study presents a flexible multistate occupancy model that accounted both for demographic stochasticity and observation errors in categorical data. It has been applied to the rough but extensive survey records of the pine wilt disease, which is a world hazardous pest disease of pine forests. [ABSTRACT FROM AUTHOR]

This paper's objective is to apply a novel identification technique to obtain a dynamic state-space wake inflow model accounting for the effects of a stationary/moving ground beneath a hovering rotor. The identification is based on a dataset of computational simulations (training set) provided by an in-house free-wake mid-fidelity solver. The evaluation of the wake contribution is optimised through a generalised mirror-image model to reduce the computational cost without losing accuracy. Stationary and moving ground effects are analysed and discussed in the present work, considering both parallel and inclined ground with respect to the rotor disk and heaving and pitching ground motion. The proposed state-space wake inflow model relates the inflow coefficients to a set of inputs, including the aerodynamic hub loads (akin to the well-known Pitt-Peters approach) and the kinematic degrees of freedom of the ground. For a number of rotor/ground configurations, the identified model is successfully validated against the simulations directly provided by the nonlinear, free-wake aerodynamic solver. [ABSTRACT FROM AUTHOR]

Storage of season-dependent agro-materials is a key process in providing food, feed and biomass throughout the whole year. We review the state of the art in physical modelling, simulation and control of ventilated bulk storage facilities, and in particular the storage of potatoes, from a state-space perspective. The basic physical relations that describe the dynamic behaviour of air and potatoes in a storage facility use laws of conservation of mass, energy and momentum and corresponding constitutive laws. However, a very complex physically based storage model is obtained if the macro climate in the air channels and the micro climate around the potato and all the interactions between air and potato are taken into consideration. Therefore, lumped, 1-D, 2-D and 3-D macro, and also some micro climate storage models were developed. These models basically focus on heat and moisture generation and transportation. The latest developments give more and more insight into the spatial distribution of the macro climate, using CFD simulation techniques. Traditionally, the indoor climate instead of the potato quality is controlled, to maintain a good product quality. However, quality control of the product can be realised if there is knowledge between the spatial distribution of temperature and moisture and the product quality. With the developments in CFD simulation, control algorithms and the state space framework, modelling and control of product quality is within reach. [ABSTRACT FROM AUTHOR]

While invasive fish management is heavily focussed on containment measures when introductions occur, examples from invasive species management in terrestrial systems suggest that there may also be considerable conservation benefits in implementing localized control programmes. We conducted a field-based experiment to assess the effectiveness of removing a globally significant invasive fish, eastern gambusia Gambusia holbrooki, from natural wetland habitats of south-eastern Australia. With recent work suggesting the impacts of eastern gambusia may be minimal for species with generalist life-history strategies, we hypothesized that the removal of eastern gambusia will reduce localized population growth of the invasive species, but will have little influence on the population growth of more generalist sympatric wetland fish species. We used a predictive modelling approach to investigate changes in eastern gambusia populations following removal activities, and how sympatric fish species responded to such changes. Although eastern gambusia rapidly populated habitats, we demonstrated that control actions substantially reduced the rate of population increase over the four-month study period. This suggests that control may be an effective localized strategy to suppress eastern gambusia densities. There was however, no evidence of any response to the removal actions by any of the three sympatric fish species investigated - carp gudgeon ( Hypseleotris spp.), Australian smelt ( Retropinna semoni) and the invasive common carp ( Cyprinus carpio). These results support previous work which suggests that the flexible life-history strategies and behavioural traits of all three species allow co-existence with eastern gambusia. The study highlights the importance of understanding the potential outcomes of control options which is particularly pertinent for established aquatic invasive species where information on control effectiveness, population dynamics and/or ecosystem response is currently lacking. [ABSTRACT FROM AUTHOR]

This study focuses on the potential improvement of environmental variables modelling by using linear state-space models, as an improvement of the linear regression model, and by incorporating a constructed hydro-meteorological covariate. The Kalman filter predictors allow to obtain accurate predictions of calibration factors for both seasonal and hydro-meteorological components. This methodology can be used to analyze the water quality behaviour by minimizing the effect of the hydrological conditions. This idea is illustrated based on a rather extended data set relative to the River Ave basin (Portugal) that consists mainly of monthly measurements of dissolved oxygen concentration in a network of water quality monitoring sites. The hydro-meteorological factor is constructed for each monitoring site based on monthly precipitation estimates obtained by means of a rain gauge network associated with stochastic interpolation (kriging). A linear state-space model is fitted for each homogeneous group (obtained by clustering techniques) of water monitoring sites. The adjustment of linear state-space models is performed by using distribution-free estimators developed in a separate section. [ABSTRACT FROM AUTHOR]

Abstract: Delamination during drilling of composite materials is critical problem. Thrust force acting on workpiece being drilled plays a key role in delaminating the composite, therefore its modelling is of great importance. This work presents with example, step-by-step procedure to capture complex drilling dynamics via a mathematical model. A glass fibre reinforced plastic composite material is drilled at constant feed rate. The corresponding thrust force time response is recorded. Third order transfer function between thrust force and feed rate representing the system is identified using system identification toolbox of Matlab®. This transfer functions is then converted into corresponding state-space model. Good match is observed between thrust force response from state-space model and experiments. [Copyright &y& Elsevier]

Robert, M., Faraj, A., McAllister, M. K., and Rivot, E. 2010. Bayesian state-space modelling of the De Lury depletion model: strengths and limitations of the method, and application to the Moroccan octopus fishery. – ICES Journal of Marine Science, 67: 1272–1290.The strengths and limitations of a Bayesian state-space modelling framework are investigated for a De Lury depletion model that accommodates two recruitment pulses per year. The framework was applied to the Moroccan fishery for common octopus (Octopus vulgaris) between 1982 and 2002. To allow identifiability, natural mortality (M) and the recruitment rhythm were fixed, and the variance of both process and observation errors were assumed to be equal. A simulation–estimation (SE) approach was derived to test the performance of the method. If the data showed responses to harvest, the estimates of the most important figures, i.e. the initial abundance and the second recruitment pulse, were accurate, with relatively small bias. Results confirm that greater depletion yields smaller bias and uncertainty and that inferences are sensitive to the mis-specification of M. The 21 depletion series in the Moroccan dataset were jointly treated in a hierarchical model including random walk to capture the systematic fluctuations in estimates of catchability and initial abundance. The model provides estimates of the annual recruitment and monthly octopus population size. The recruitment estimates could be used to investigate the link between recruitment variability and the coastal North African upwelling regime to improve understanding of the dynamics and management of octopus stocks. [ABSTRACT FROM PUBLISHER]

Continuous-time modelling remains a somewhat ‘idealized’ representation tool. Even though conceptualizing a dynamic process as a continuous process has clear appeal from a theoretical standpoint, practical tools that allow researchers to effectively map an idealized continuous model onto a set of discrete-time observed data are still lacking observed data. Irregularly spaced longitudinal data frequently arise in empirical settings because of the prevalence of longitudinal studies with partially randomized measurement intervals and other related designs. We present a practical approach that capitalizes on a nonparametric spline interpolation approach to impute the gaps in irregularly spaced panel data. Simulated and empirical examples are provided to demonstrate the applicability of the proposed approach to studies of group-based dynamics using panel data. [ABSTRACT FROM AUTHOR]

Abstract: An adaptive nonlinear state space-based modelling system has been designed to predict daily mean concentrations of PM10 for Bordeaux metropolitan area. The nonlinear model structure is based on empirical relationships between the measured PM10 and other primary pollutants and meteorological variables. An Extended Kalman filter algorithm is used to estimate 1-day ahead prediction of the extended state, containing model parameters and daily mean PM10. A key characteristic of such a system is that its behaviour can be adapted to the short-term changes of air pollution and consequently the model can handle the time-evolving nature of the phenomena and does not need frequent adjustments. The method is applied to data from a monitoring site in Bordeaux (south France). Experimental results show that the model accurately predicts daily mean PM10. The application of the Extended Kalman filter explains about 70% of the variance with an absolute mean error less than 4.5μg/m3. The approximate index of agreement value for the period covered is 0.90. [Copyright &y& Elsevier]

Model predictive control (MPC) algorithms with state-space process modelling, both linear and nonlinear, and state estimation methods for this algorithms are the subject of this paper. The considerations are under realistic assumption that processes are under influence of external disturbances and their models are not precise. This leads in most cases to errors in state estimation and, further, may lead to errors in feedback control. Attention has been paid to this problem in recent years. Two approaches are now available, an earlier one with additional disturbance state modelling and process-and-disturbance state estimation and a more recently proposed approach with different way of disturbance modelling and estimation of the process state only. The main aim of this paper is to provide a comprehensive comparison of the two mentioned approaches, including also discussion of available state estimation algorithms. To the best knowledge of the authors, there is still a lack of clear understanding of the differences between the mentioned approaches, in particular from practical point of view. After short presentation of the two methods and analysis of their theoretical aspects, a comprehensive comparative analysis is provided on a nonlinear example of the polymerization reactor. [ABSTRACT FROM AUTHOR]