Irregular migration in senegal faith, dreams and human smuggling through the desert and sea

The paper provides a bottom up view on human smuggling facilitation and the rationale behind extremely high-risk and complex irregular migration journeys from the Kolda region of Senegal across the Sahara and through the Atlantic sea. Interviews with aspiring, returned and deported migrants as well as smugglers and their associates provide insights into the organization of migration facilitation and how this is sustained in the current policy context. The research highlights the role of religious beliefs in preparing for and interpreting the experiences of harrowing journeys with a high risk of harm and death. The study also sheds light on hitherto under-recognised gendered aspects of the infrastructure of migration facilitation in Kolda: while migration is male dominated, women play a critical role in enabling migration by mobilising religious and financial support. The paper also discusses the differences in the social constructions of male and female migration and the differences in their social relations with smugglers and other actors involved in facilitating irregular migration. In conclusion the authors suggest that there is a need to revisit migration policies that are based on dissuading migrants through risk-awareness campaigns and heightened controls towards policies that address global structural inequalities that drive migration; develop a more accurate understanding of personal and family aspirations for change; create more opportunities for legal migration; initiate discourses on culturally sensitive topics such as female migration and failed migration, and the role of Islamic spiritual leaders (Marabouts)

Aspects of Objective Priors and Computations for Bayesian Modelling

Bayesian statistics is flourishing nowadays not only because it provides ways to combine prior knowledge with statistical models but also because many algorithms have become available to sample from the resulting posterior distributions. However, how to specify a good objective prior can be very difficult. This is largely because ignorance does not have a unique definition. For sampling from posterior distributions, Markov Chain Monte Carlo (MCMC) methods are main tools. However, as statistical models become more and more sophisticated, there is a need for more efficient MCMC methods than the traditional ones. For objective prior specifications, we present a new principle to express ignorance through the global distance structure. This principle allows us to assign the prior weight to points in parameter space according to their correspondences to the statistical models displayed in the structure of the global distance. This method is applied to simple problems such as location family, scale family and location-scale family. It is also applied to the one-way random effect model which attracts considerable interest from many researchers. The method considered here allows us to avoid the dependency of the priors on the experimental design, which has been seriously disputed, and enables the resulting prior to reflect how the models change with respect to the population and not the collected samples. Of MCMC methods for sampling from posterior distributions, the Hamiltonian Monte Carlo (HMC) method is one that has the potential to avoid random-walk behaviour. It does so by exploiting ideas from Hamiltonian dynamics. Its performance, however, depends on the choice of step-size which is required by this method when numerically solving the Hamiltonian equations. We propose an algorithm, which we call HMC with stochastic step-size, to automatically tune the step-size by exploiting the local curvature information. We also present a meta-algorithm which includes HMC, HMC with stochastic step-size and the ordinary Metropolis-Hastings algorithm as a special case. Finally, we come to a sophisticated hierarchical model developed for analysing the exco-toxicology data. We present ways to obtain more informative posterior samples by embedding the marginalized approach and advanced samplers into the entire Gibbs structure of the modified MCMCglmm algorithm provided by Craig (2013). The combination of the marginalized approach and HMC with stochastic step-size is found to be the best choice among a range of methods for the challenging problem of sampling the hyper-parameters in the model

椎骨脳底動脈の長さの測定 : 3次元的アプローチ

広島大学(Hiroshima University)博士(医学)Doctor of Philosophy in Medical Sciencedoctora

Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey

Growing progress in sensor technology has constantly expanded the number and range of low-cost, small, and portable sensors on the market, increasing the number and type of physical phenomena that can be measured with wirelessly connected sensors. Large-scale deployments of wireless sensor networks (WSN) involving hundreds or thousands of devices and limited budgets often constrain the choice of sensing hardware, which generally has reduced accuracy, precision, and reliability. Therefore, it is challenging to achieve good data quality and maintain error-free measurements during the whole system lifetime. Self-calibration or recalibration in ad hoc sensor networks to preserve data quality is essential, yet challenging, for several reasons, such as the existence of random noise and the absence of suitable general models. Calibration performed in the field, without accurate and controlled instrumentation, is said to be in an uncontrolled environment. This paper provides current and fundamental self-calibration approaches and models for wireless sensor networks in uncontrolled environments

Model Predictive Energy-Maximising Tracking Control for a Wavestar-Prototype Wave Energy Converter

To date, one of the main challenges in the wave energy field is to achieve energy-maximizing control in order to reduce the levelized cost of energy (LCOE). This paper presents a model predictive velocity tracking control method based on a hierarchical structure for a Wavestar-like deivce in the WEC-SIM benchmark. The first part of the system structure aims to estimate the wave excitation moment (WEM) by using a Kalman filter. Then, an extended Kalman filter (EKF) is chosen to obtain the amplitude and angular frequency of the WEM in order to compute the reference velocity. Following this, a low-level model predictive control (MPC) method is designed to ensure the wave energy converter (WEC) tracks the optimal reference velocity for maximum energy extraction from irregular waves. Two Gaussian Process (GP) models are considered to predict the future wave excitation moment and future reference velocity, which are needed in MPC design. The proposed strategy can give a new vision for energy-maximizing tracking control based on MPC