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  • Multiscale spectral modelling for nonstationary time series within an ordered multiple-trial experiment

    Embleton, Jonathan; Knight, Marina I.; Ombao, Hernando (The Annals of Applied Statistics, Institute of Mathematical Statistics, 2022-12) [Article]
    Within the neurosciences it is natural to observe variability across time in the dynamics of an underlying brain process. Wavelets are essential in analysing brain signals because, even within a single trial, brain signals exhibit nonstationary behaviour. However, neurological signals generated within an experiment may also potentially exhibit evolution across trials (replicates), even for identical stimuli. As neurologists consider localised spectra of brain signals to be most informative, we propose the MULtiple-Trials Locally Stationary Wavelet process (MULT-LSW) that fills the gap in the literature by directly giving a stochastic wavelet representation of the time series of ordered replicates itself. MULT-LSW yields a natural desired time- and trial-localisation of the process dynamics, capturing nonstationary behaviour both within and across trials. While current techniques are restricted by the assumption of uncorrelated replicates, here we account for between-trial correlation. We rigorously develop the associated wavelet spectral estimation framework along with its asymptotic properties. By means of thorough simulation studies, we demonstrate the theoretical estimator properties hold in practice. A real data investigation into the evolutionary dynamics of the hippocampus and nucleus accumbens, during an associative learning experiment, demonstrates the applicability of our proposed methodology as well as the new insights it provides. Our model is general and facilitates wider experimental data analysis than the current literature allows.
  • A comprehensive review of Quercus semecarpifolia Sm.: An ecologically and commercially important Himalayan tree

    Rawat, Balwant; Rawat, Janhvi M.; Purohit, Sumit; Singh, Gajendra; Sharma, Pradeep Kumar; Chandra, Anup; Shabaaz Begum, J. P.; Venugopal, Divya; Jaremko, Mariusz; Qureshi, Kamal A. (Frontiers in Ecology and Evolution, Frontiers Media SA, 2022-09-29) [Article]
    Himalayan mountain forests have been a potential candidate for the investigation of perturbations due to the complex geography in which they sustain and the sensitivity of the species toward human disturbance and climate change. Among various tree species, brown oak (Quercus semecarpifolia), a very important component of the Himalayan mountains, has been identified as a keystone species due to its substantial economic and ecological benefits. Maintenance of microclimate and suitable habitats with a rich source of natural resources makes Q. semecarpifolia the most preferred forest for luxuriant growth of ground flora, shelter for fauna, and multipurpose uses by the local people. In a climax community, it plays a critical role in environmental balance both at the local and regional levels. Unfortunately, it has become one of the most overexploited tree species of the Himalayan region over the last few decades due to its high demand for dry season fodder and firewood. The wide range of seedling distribution 348–4,663 individuals ha–1 is evidence of the disturbance accompanied by poor regeneration in Q. semecarpifolia forests. Moreover, litter accumulation and grass cover adversely affect seed germination. The ecological cost of oak forest degradation is perhaps more important and damage is irreversible. Thus, continuous demand and extensive threats accompanied by poor regeneration have drawn the attention of stakeholders to conserve this species. However, propagation protocol, especially the pre-sowing treatment of the species, has not been impressive for large-scale multiplication. This review is comprehensive information on distribution, phenology, regeneration pattern, human threat, conservation approaches, and management of Q. semecarpifolia in the Himalayan region.
  • A Lagged Particle Filter for Stable Filtering of Certain High-Dimensional State-Space Models

    Ruzayqat, Hamza Mahmoud; Er-raiy, Aimad; Beskos, Alexandros; Crisan, Dan; Jasra, Ajay; Kantas, Nikolas (SIAM/ASA Journal on Uncertainty Quantification, Society for Industrial & Applied Mathematics (SIAM), 2022-09-28) [Article]
    We consider the problem of high-dimensional filtering of state-space models (SSMs) at discrete times. This problem is particularly challenging as analytical solutions are typically not available and many numerical approximation methods can have a cost that scales exponentially with the dimension of the hidden state. Inspired by lag-approximation methods for the smoothing problem [G. Kitagawa and S. Sato, Monte Carlo smoothing and self-organising state-space model, in Sequential Monte Carlo Methods in Practice, Springer, New York, 2001, pp. 178–195; J. Olsson et al., Bernoulli, 14 (2008), pp. 155–179], we introduce a lagged approximation of the smoothing distribution that is necessarily biased. For certain classes of SSMs, particularly those that forget the initial condition exponentially fast in time, the bias of our approximation is shown to be uniformly controlled in the dimension and exponentially small in time. We develop a sequential Monte Carlo (SMC) method to recursively estimate expectations with respect to our biased filtering distributions. Moreover, we prove for a class of SSMs that can contain dependencies amongst coordinates that as the dimension d→∞ the cost to achieve a stable mean square error in estimation, for classes of expectations, is of O(Nd2) per unit time, where N is the number of simulated samples in the SMC algorithm. Our methodology is implemented on several challenging high-dimensional examples including the conservative shallow-water model.
  • CPES-QSM: A Quantitative Method Towards the Secure Operation of Cyber-Physical Energy Systems

    Ospina, Juan; Venkataramanan, Venkatesh; Konstantinou, Charalambos (IEEE Internet of Things Journal, IEEE, 2022-09-28) [Article]
    Power systems are evolving into cyber-physical energy systems (CPES) mainly due to the integration of modern communication and Internet-of-Things (IoT) devices. CPES security evaluation is challenging since the physical and cyber layers are often not considered holistically. Existing literature focuses on only optimizing the operation of either the physical or cyber layer while ignoring the interactions between them. This paper proposes a metric, the Cyber-Physical Energy System Quantitative Security Metric (CPES-QSM), that quantifies the interaction between the cyber and physical layers across three domains: electrical, cyber-risk, and network topology. A method for incorporating the proposed cyber-metric into operational decisions is also proposed by formulating a cyber-constrained AC optimal power flow (C-ACOPF) that considers the status of all the CPES layers. The C-ACOPF considers the vulnerabilities of physical and cyber networks by incorporating factors such as voltage stability, contingencies, graph-theory, and IoT cyber risks, while using a multi-criteria decision-making technique. Simulation studies are conducted using standard IEEE test systems to evaluate the effectiveness of the proposed metric and the C-ACOPF formulation.
  • Optimal Gain-scheduled POD for Power Systems with Hybrid HVDC Links

    Bertozzi, Otavio; Chamorro, Harold R.; Kotb, Omar; Prieto-Araujo, Eduardo; Ahmed, Shehab (IEEE, 2022-09-28) [Conference Paper]
    The evolving High Voltage Direct Current (HVDC) technology integrated into the modern power grids can help improve operation stability and damp undesired low-frequency oscillations in the system. This paper presents a Power Oscillation Damping (POD) strategy for power systems with a hybrid (LCC-VSC) HVDC link. The work consists of a centralized supervision algorithm that monitors the dynamics of several system variables and sets the appropriate gains to the POD controller from a lookup table (LUT) generated offline via simulation-based Particle Swarm optimization analysis. The mathematical modeling for the test system with an embedded HVDC link is presented, and the optimal tuning problem is defined using performance-oriented objective functions. Details for the detection and scheduling algorithm, LUT construction, and controller structure are provided. The nonlinear simulation model is implemented in MATLAB, and the results support the effectiveness of the proposed approach.

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