Using Markov models for availability assessment of a large scale cross harbour tunnel

AbstractG protein-coupled receptors (GPCRs) play key roles in cellular signalling. GPCRs are suggested to form dimers and higher order oligomers in response to activation. However, we do not fully understand GPCR activation at larger scales and in an in vivo context. We have characterised oligomeric configurations of the adenosine 2a receptor (A2aR) by combining large-scale molecular dynamics simulations with Markov state models. Receptor activation results in enhanced oligomerisation, more diverse oligomer populations, and a more connected oligomerisation network. The active state conformation of the A2aR shifts protein-protein association interfaces to those involving intracellular loop ICL3 and transmembrane helix TM6. Binding of PIP2 to A2aR stabilises protein-protein interactions via PIP2-mediated association interfaces. These results indicate that A2aR oligomerisation is responsive to the local membrane lipid environment. This in turn suggests a modulatory effect on A2aR whereby a given oligomerisation profile favours the dynamic formation of specific supra-molecular signalling complexes.

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Fitting hidden semi-Markov models to breakpoint rainfall data

Journal of Applied Probability ◽

10.1239/jap/1085496598 ◽

2001 ◽

Vol 38 (A) ◽

pp. 142-157 ◽

Cited By ~ 30

Author(s):

John Sansom ◽

Peter Thomson

Keyword(s):

Dwell Time ◽

Large Scale ◽

Markov Models ◽

Transition Probabilities ◽

Rainfall Data ◽

The Em Algorithm ◽

Semi Markov Process ◽

The Times ◽

Log Normal ◽

Disjoint Domains

The paper proposes a hidden semi-Markov model for breakpoint rainfall data that consist of both the times at which rain-rate changes and the steady rates between such changes. The model builds on and extends the seminal work of Ferguson (1980) on variable duration models for speech. For the rainfall data the observations are modelled as mixtures of log-normal distributions within unobserved states where the states evolve in time according to a semi-Markov process. For the latter, parametric forms need to be specified for the state transition probabilities and dwell-time distributions.Recursions for constructing the likelihood are developed and the EM algorithm used to fit the parameters of the model. The choice of dwell-time distribution is discussed with a mixture of distributions over disjoint domains providing a flexible alternative. The methods are also extended to deal with censored data. An application of the model to a large-scale bivariate dataset of breakpoint rainfall measurements at Wellington, New Zealand, is discussed.

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Understanding Future Water Challenges in a Highly Regulated Indian River Basin—Modelling the Impact of Climate Change on the Hydrology of the Upper Narmada

Water ◽

10.3390/w12061762 ◽

2020 ◽

Vol 12 (6) ◽

pp. 1762 ◽

Cited By ~ 2

Author(s):

Nathan Rickards ◽

Thomas Thomas ◽

Alexandra Kaelin ◽

Helen Houghton-Carr ◽

Sharad K. Jain ◽

...

Keyword(s):

Climate Change ◽

Water Resources ◽

River Basin ◽

Large Scale ◽

Monsoon Season ◽

Future Climate Change ◽

Impact Of Climate Change ◽

Global Circulation Models ◽

The Impact ◽

Availability Assessment

The Narmada river basin is a highly regulated catchment in central India, supporting a population of over 16 million people. In such extensively modified hydrological systems, the influence of anthropogenic alterations is often underrepresented or excluded entirely by large-scale hydrological models. The Global Water Availability Assessment (GWAVA) model is applied to the Upper Narmada, with all major dams, water abstractions and irrigation command areas included, which allows for the development of a holistic methodology for the assessment of water resources in the basin. The model is driven with 17 Global Circulation Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5) ensemble to assess the impact of climate change on water resources in the basin for the period 2031–2060. The study finds that the hydrological regime within the basin is likely to intensify over the next half-century as a result of future climate change, causing long-term increases in monsoon season flow across the Upper Narmada. Climate is expected to have little impact on dry season flows, in comparison to water demand intensification over the same period, which may lead to increased water stress in parts of the basin.

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Large-scale event detection using semi-hidden Markov models

Proceedings Ninth IEEE International Conference on Computer Vision ◽

10.1109/iccv.2003.1238661 ◽

2003 ◽

Cited By ~ 34

Author(s):

Somboon Hongeng ◽

Ramakant Nevatia

Keyword(s):

Hidden Markov Models ◽

Event Detection ◽

Large Scale ◽

Markov Models ◽

Hidden Markov

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Fitting hidden semi-Markov models to breakpoint rainfall data

Journal of Applied Probability ◽

10.1017/s0021900200112744 ◽

2001 ◽

Vol 38 (A) ◽

pp. 142-157 ◽

Cited By ~ 6

Author(s):

John Sansom ◽

Peter Thomson

Keyword(s):

Dwell Time ◽

Large Scale ◽

Markov Models ◽

Transition Probabilities ◽

Rainfall Data ◽

The Em Algorithm ◽

Semi Markov Process ◽

The Times ◽

Log Normal ◽

Disjoint Domains

The paper proposes a hidden semi-Markov model for breakpoint rainfall data that consist of both the times at which rain-rate changes and the steady rates between such changes. The model builds on and extends the seminal work of Ferguson (1980) on variable duration models for speech. For the rainfall data the observations are modelled as mixtures of log-normal distributions within unobserved states where the states evolve in time according to a semi-Markov process. For the latter, parametric forms need to be specified for the state transition probabilities and dwell-time distributions. Recursions for constructing the likelihood are developed and the EM algorithm used to fit the parameters of the model. The choice of dwell-time distribution is discussed with a mixture of distributions over disjoint domains providing a flexible alternative. The methods are also extended to deal with censored data. An application of the model to a large-scale bivariate dataset of breakpoint rainfall measurements at Wellington, New Zealand, is discussed.

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A Spatial-Contextual Indoor Trajectory Prediction Approach via Hidden Markov Models

Wireless Communications and Mobile Computing ◽

10.1155/2022/6719514 ◽

2022 ◽

Vol 2022 ◽

pp. 1-13

Author(s):

Peng Wang ◽

Jing Yang ◽

Jianpei Zhang

Keyword(s):

Large Scale ◽

Markov Models ◽

Hidden Markov ◽

Shopping Mall ◽

Prediction Problem ◽

Unified Framework ◽

Trajectory Prediction ◽

Indoor Space ◽

Prediction Approach ◽

Moving Patterns

Unlike outdoor trajectory prediction that has been studied many years, predicting the movement of a large number of users in indoor space like shopping mall has just been a hot and challenging issue due to the ubiquitous emerging of mobile devices and free Wi-Fi services in shopping centers in recent years. Aimed at solving the indoor trajectory prediction problem, in this paper, a hybrid method based on Hidden Markov approach is proposed. The proposed approach clusters Wi-Fi access points according to their similarities first; then, a frequent subtrajectory based HMM which captures the moving patterns of users has been investigated. In addition, we assume that a customer’s visiting history has certain patterns; thus, we integrate trajectory prediction with shop category prediction into a unified framework which further improves the predicting ability. Comprehensive performance evaluation using a large-scale real dataset collected between September 2012 and October 2013 from over 120,000 anonymized, opt-in consumers in a large shopping center in Sydney was conducted; the experimental results show that the proposed method outperforms the traditional HMM and perform well enough to be usable in practice.

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BrainIAK tutorials: User-friendly learning materials for advanced fMRI analysis

10.31219/osf.io/j4sbc ◽

2019 ◽

Cited By ~ 2

Author(s):

Manoj Kumar ◽

Cameron Thomas Ellis ◽

Qihong Lu ◽

Hejia Zhang ◽

Mihai Capota ◽

...

Keyword(s):

Machine Learning ◽

Functional Connectivity ◽

Open Source ◽

Programming Languages ◽

High Performance ◽

Large Scale ◽

Markov Models ◽

Matrix Analysis ◽

Fmri Analysis ◽

User Friendly

Advanced brain imaging analysis methods, including multivariate pattern analysis (MVPA), functional connectivity, and functional alignment, have become powerful tools in cognitive neuroscience over the past decade. These tools are implemented in custom code and separate packages, often requiring different software and language proficiencies. Although usable by expert researchers, novice users face a steep learning curve. These difficulties stem from the use of new programming languages (e.g., Python), learning how to apply machine-learning methods to high-dimensional fMRI data, and minimal documentation and training materials. Furthermore, most standard fMRI analysis packages (e.g., AFNI, FSL, SPM) focus on preprocessing and univariate analyses, leaving a gap in how to integrate with advanced tools. To address these needs, we developed BrainIAK (brainiak.org), an open-source Python software package that seamlessly integrates several cutting-edge, computationally efficient techniques with other Python packages (e.g., Nilearn, Scikit-learn) for file handling, visualization, and machine learning. To disseminate these powerful tools, we developed user-friendly tutorials (in Jupyter format; https://brainiak.org/tutorials/) for learning BrainIAK and advanced fMRI analysis in Python more generally. These materials cover techniques including: MVPA (pattern classification and representational similarity analysis); parallelized searchlight analysis; background connectivity; full correlation matrix analysis; inter-subject correlation; inter-subject functional connectivity; shared response modeling; event segmentation using hidden Markov models; and real-time fMRI. For long-running jobs or large memory needs we provide detailed guidance on high-performance computing clusters. These notebooks were successfully tested at multiple sites, including as problem sets for courses at Yale and Princeton universities and at various workshops and hackathons. These materials are freely shared, with the hope that they become part of a pool of open-source software and educational materials for large-scale, reproducible fMRI analysis and accelerated discovery.

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Modelling Small-Scale Storage Interventions in Semi-Arid India at the Basin Scale

Sustainability ◽

10.3390/su13116129 ◽

2021 ◽

Vol 13 (11) ◽

pp. 6129

Author(s):

Robyn Horan ◽

Pawan S. Wable ◽

Veena Srinivasan ◽

Helen E. Baron ◽

Virginie J. D. Keller ◽

...

Keyword(s):

Water Resource Management ◽

Large Scale ◽

Data Availability ◽

Small Scale ◽

Catchment Scale ◽

Check Dams ◽

Basin Scale ◽

The Impact ◽

Availability Assessment ◽

Semi Arid

There has been renewed interest in the performance, functionality, and sustainability of traditional small-scale storage interventions (check dams, farm bunds and tanks) used within semi-arid regions for the improvement of local water security and landscape preservation. The Central Groundwater Board of India is encouraging the construction of such interventions for the alleviation of water scarcity and to improve groundwater recharge. It is important for water resource management to understand the hydrological effect of these interventions at the basin scale. The quantification of small-scale interventions in hydrological modelling is often neglected, especially in large-scale modelling activities, as data availability is low and their hydrological functioning is uncertain. A version of the Global Water Availability Assessment (GWAVA) water resources model was developed to assess the impact of interventions on the water balance of the Cauvery Basin and two smaller sub-catchments. Model results demonstrate that farm bunds appear to have a negligible effect on the average annual simulated streamflow at the outlets of the two sub-catchments and the basin, whereas tanks and check dams have a more significant and time varying effect. The open water surface of the interventions contributed to an increase in evaporation losses across the catchment. The change in simulated groundwater storage with the inclusion of interventions was not as significant as catchment-scale literature and field studies suggest. The model adaption used in this study provides a step-change in the conceptualisation and quantification of the consequences of small-scale storage interventions in large- or basin-scale hydrological models.

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