scholarly journals Lion (Panthera leo) populations are declining rapidly across Africa, except in intensively managed areas

2015 ◽  
Vol 112 (48) ◽  
pp. 14894-14899 ◽  
Author(s):  
Hans Bauer ◽  
Guillaume Chapron ◽  
Kristin Nowell ◽  
Philipp Henschel ◽  
Paul Funston ◽  
...  
Keyword(s):  
East Africa ◽  
Southern Africa ◽  
Time Series Data ◽  
State Space Model ◽  
Central Africa ◽  
Iucn Red List ◽  
Series Data ◽  
Panthera Leo ◽  
Natural Ecosystems ◽  
Intensively Managed

We compiled all credible repeated lion surveys and present time series data for 47 lion (Panthera leo) populations. We used a Bayesian state space model to estimate growth rate-λ for each population and summed these into three regional sets to provide conservation-relevant estimates of trends since 1990. We found a striking geographical pattern: African lion populations are declining everywhere, except in four southern countries (Botswana, Namibia, South Africa, and Zimbabwe). Population models indicate a 67% chance that lions in West and Central Africa decline by one-half, while estimating a 37% chance that lions in East Africa also decline by one-half over two decades. We recommend separate regional assessments of the lion in the World Conservation Union (IUCN) Red List of Threatened Species: already recognized as critically endangered in West Africa, our analysis supports listing as regionally endangered in Central and East Africa and least concern in southern Africa. Almost all lion populations that historically exceeded ∼500 individuals are declining, but lion conservation is successful in southern Africa, in part because of the proliferation of reintroduced lions in small, fenced, intensively managed, and funded reserves. If management budgets for wild lands cannot keep pace with mounting levels of threat, the species may rely increasingly on these southern African areas and may no longer be a flagship species of the once vast natural ecosystems across the rest of the continent.

Introduction

2018 ◽  
Author(s):  
Marina Sharpe
Keyword(s):  
West Africa ◽  
Great Lakes ◽  
East Africa ◽  
North Africa ◽  
Southern Africa ◽  
Theoretical Approach ◽  
Central Africa ◽  
Horn Of Africa ◽  
Refugee Protection

This introductory chapter begins by presenting the book’s structure in section A. Section B then delineates the book’s contours, outlining four aspects of refugee protection in Africa that are not addressed. Section C provides context, with a contemporary overview of the state of refugee protection in Africa. It also looks at the major aspects of the refugee situations in each of Africa’s principal geographic sub-regions: East Africa (including the Horn of Africa), Central Africa and the Great Lakes, West Africa, Southern Africa, and North Africa. Section D then concludes with an outline of the theoretical approach to regime relationships employed throughout the book.

scholarly journals LSTM-Guided Coaching Assistant for Table Tennis Practice

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4112 ◽  
Author(s):  
Se-Min Lim ◽  
Hyeong-Cheol Oh ◽  
Jaein Kim ◽  
Juwon Lee ◽  
Jooyoung Park
Keyword(s):  
Time Series ◽  
State Space ◽  
Time Series Data ◽  
State Space Model ◽  
Skill Assessment ◽  
Series Data ◽  
High Dimensional ◽  
Table Tennis ◽  
Space Model ◽  
Low Dimensional

Recently, wearable devices have become a prominent health care application domain by incorporating a growing number of sensors and adopting smart machine learning technologies. One closely related topic is the strategy of combining the wearable device technology with skill assessment, which can be used in wearable device apps for coaching and/or personal training. Particularly pertinent to skill assessment based on high-dimensional time series data from wearable sensors is classifying whether a player is an expert or a beginner, which skills the player is exercising, and extracting some low-dimensional representations useful for coaching. In this paper, we present a deep learning-based coaching assistant method, which can provide useful information in supporting table tennis practice. Our method uses a combination of LSTM (Long short-term memory) with a deep state space model and probabilistic inference. More precisely, we use the expressive power of LSTM when handling high-dimensional time series data, and state space model and probabilistic inference to extract low-dimensional latent representations useful for coaching. Experimental results show that our method can yield promising results for characterizing high-dimensional time series patterns and for providing useful information when working with wearable IMU (Inertial measurement unit) sensors for table tennis coaching.

scholarly journals METODE STATE SPACE DALAM MERAMALKAN JUMLAH PENUMPANG KERETA API DI PULAU JAWA

2020 ◽  
Vol 9 (1) ◽  
pp. 8
Author(s):  
FITRI ANANDA DITA SARASWITA ◽  
I WAYAN SUMARJAYA ◽  
LUH PUTU IDA HARINI
Keyword(s):  
State Space ◽  
Time Series Data ◽  
Local Level ◽  
State Space Model ◽  
The State ◽  
Series Data ◽  
State Space Method ◽  
Dynamic Interactions ◽  
Level Model ◽  
Space Method

State space is an approach to model and predict together several time series data that are interconnected, and these variables have dynamic interactions. The purpose of this research is to model the number of train passengers in Java and find out the forecasting results using the state space method. The algorithm used to solve the state space model is the Kalman filter. In this research, a suitable final model is local level model with seasonal and produces MAPE value of 2%, this shows that the state space method is very accurately.

scholarly journals Tracking Poisson Parameter for Non-Stationary Discontinuous Time Series with Taylor’s Abnormal Fluctuation Scaling

Stats ◽  
2019 ◽  
Vol 2 (1) ◽  
pp. 55-69 ◽  
Author(s):  
Gen Sakoda ◽  
Hideki Takayasu ◽  
Misako Takayasu
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
State Space Model ◽  
Estimation Method ◽  
Stationary Time Series ◽  
Series Data ◽  
Large Fluctuations ◽  
Taylor’S Law ◽  
Fluctuation Scaling ◽  
Poisson Parameter

We propose a parameter estimation method for non-stationary Poisson time series with the abnormal fluctuation scaling, known as Taylor’s law. By introducing the effect of Taylor’s fluctuation scaling into the State Space Model with the Particle Filter, the underlying Poisson parameter’s time evolution is estimated correctly from given non-stationary time series data with abnormally large fluctuations. We also developed a discontinuity detection method which enables tracking the Poisson parameter even for time series including sudden discontinuous jumps. As an example of application of this new general method, we analyzed Point-of-Sales data in convenience stores to estimate change of probability of purchase of commodities under fluctuating number of potential customers. The effectiveness of our method for Poisson time series with non-stationarity, large discontinuities and Taylor’s fluctuation scaling is verified by artificial and actual time series.

scholarly journals Evaluating the Performance of Multiple Imputation Methods for Handling Missing Values in Time Series Data: A Study Focused on East Africa, Soil-Carbonate-Stable Isotope Data

Stats ◽  
2019 ◽  
Vol 2 (4) ◽  
pp. 457-467 ◽  
Author(s):  
Hossein Hassani ◽  
Mahdi Kalantari ◽  
Zara Ghodsi
Keyword(s):  
Time Series ◽  
East Africa ◽  
Quantitative Research ◽  
Missing Values ◽  
Time Series Data ◽  
Extreme Values ◽  
Singular Spectrum Analysis ◽  
Series Data ◽  
Data Set ◽  
Imputation Methods

In all fields of quantitative research, analysing data with missing values is an excruciating challenge. It should be no surprise that given the fragmentary nature of fossil records, the presence of missing values in geographical databases is unavoidable. As in such studies ignoring missing values may result in biased estimations or invalid conclusions, adopting a reliable imputation method should be regarded as an essential consideration. In this study, the performance of singular spectrum analysis (SSA) based on L 1 norm was evaluated on the compiled δ 13 C data from East Africa soil carbonates, which is a world targeted historical geology data set. Results were compared with ten traditionally well-known imputation methods showing L 1 -SSA performs well in keeping the variability of the time series and providing estimations which are less affected by extreme values, suggesting the method introduced here deserves further consideration in practice.

scholarly journals Estimating Distributions of Parameters in Nonlinear State Space Models with Replica Exchange Particle Marginal Metropolis–Hastings Method

Entropy ◽  
2022 ◽  
Vol 24 (1) ◽  
pp. 115
Author(s):  
Hiroaki Inoue ◽  
Koji Hukushima ◽  
Toshiaki Omori
Keyword(s):  
State Space ◽  
Latent Variables ◽  
Time Series Data ◽  
State Space Model ◽  
The State ◽  
Series Data ◽  
Replica Exchange ◽  
Space Model ◽  
Marginal Posterior Distribution ◽  
Exchange Particle

Extracting latent nonlinear dynamics from observed time-series data is important for understanding a dynamic system against the background of the observed data. A state space model is a probabilistic graphical model for time-series data, which describes the probabilistic dependence between latent variables at subsequent times and between latent variables and observations. Since, in many situations, the values of the parameters in the state space model are unknown, estimating the parameters from observations is an important task. The particle marginal Metropolis–Hastings (PMMH) method is a method for estimating the marginal posterior distribution of parameters obtained by marginalization over the distribution of latent variables in the state space model. Although, in principle, we can estimate the marginal posterior distribution of parameters by iterating this method infinitely, the estimated result depends on the initial values for a finite number of times in practice. In this paper, we propose a replica exchange particle marginal Metropolis–Hastings (REPMMH) method as a method to improve this problem by combining the PMMH method with the replica exchange method. By using the proposed method, we simultaneously realize a global search at a high temperature and a local fine search at a low temperature. We evaluate the proposed method using simulated data obtained from the Izhikevich neuron model and Lévy-driven stochastic volatility model, and we show that the proposed REPMMH method improves the problem of the initial value dependence in the PMMH method, and realizes efficient sampling of parameters in the state space models compared with existing methods.

scholarly journals Quantifying non-communicable diseases’ burden in Egypt using State-Space model

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0245642
Author(s):  
Somaya El-Saadani ◽  
Mohamed Saleh ◽  
Sarah A. Ibrahim
Keyword(s):  
Time Series ◽  
Health Services ◽  
State Space ◽  
Bayesian Methods ◽  
Time Series Data ◽  
State Space Model ◽  
Series Data ◽  
Non Communicable Diseases ◽  
Space Model ◽  
Health Burden

The study aimed to model and quantify the health burden induced by four non-communicable diseases (NCDs) in Egypt, the first to be conducted in the context of a less developing county. The study used the State-Space model and adopted two Bayesian methods: Particle Filter and Particle Independent Metropolis-Hastings to model and estimate the NCDs’ health burden trajectories. We drew on time-series data of the International Health Metric Evaluation, the Central Agency for Public Mobilization and Statistics (CAPMAS) Annual Bulletin of Health Services Statistics, the World Bank, and WHO data. Both Bayesian methods showed that the burden trajectories are on the rise. Most of the findings agreed with our assumptions and are in line with the literature. Previous year burden strongly predicts the burden of the current year. High prevalence of the risk factors, disease prevalence, and the disease’s severity level all increase illness burden. Years of life lost due to death has high loadings in most of the diseases. Contrary to the study assumption, results found a negative relationship between disease burden and health services utilization which can be attributed to the lack of full health insurance coverage and the pattern of health care seeking behavior in Egypt. Our study highlights that Particle Independent Metropolis-Hastings is sufficient in estimating the parameters of the study model, in the case of time-constant parameters. The study recommends using state Space models with Bayesian estimation approaches with time-series data in public health and epidemiology research.

Sign in / Sign up

Export Citation Format

Share Document