Poverty Trap and Educational Shock: Evidence from Missionary Fields

Low growth equilibria with low investment in human capital generally tend to<br/>persist till an external shock affects the economy. In this paper we use data on<br/>Christian missions to proxy a long-lasting educational shock in Africa. We estimate<br/>the effect of this shock on the quality of children which we proxy using the rate of<br/>underweight children. Consistent with the economic theory we find that the quality<br/>of children significantly rises with the exposure to this shock and this indirect effect<br/>accounts to almost 4 percent in terms of GDP for districts with the maximal exposure<br/>

A dynamic sampling methodology for plasma etch processes using Gaussian process regression

<p>Plasma etch is a key process in modern semiconductor manufacturing facilities as it offers process simplification and yet greater dimensional tolerances compared to wet chemical etch technology. The main challenge of operating plasma etchers is to maintain a consistent etch rate spatially and temporally for a given wafer and for successive wafers processed in the same etch tool. Etch rate measurements require expensive metrology steps and therefore in general only limited sampling is performed. Furthermore, the results of measurements are not accessible in real-time, limiting the options for run-to-run control. This paper investigates a Virtual Metrology (VM) enabled Dynamic Sampling (DS) methodology as an alternative paradigm for balancing the need to reduce costly metrology with the need to measure more frequently and in a timely fashion to enable wafer-to-wafer control. Using a Gaussian Process Regression (GPR) VM model for etch rate estimation of a plasma etch process, the proposed dynamic sampling methodology is demonstrated and evaluated for a number of different predictive dynamic sampling rules. �� 2013 IEEE.</p>

Efficient Marginal Likelihood Computation for Gaussian Process Regression

In a Bayesian learning setting, the posterior distribution of a predictive model arises from a trade-off between its prior distribution and the conditional likelihood of observed data. Such distribution functions usually rely on additional hyperparameters which need to be tuned in order to achieve optimum predictive performance; this operation can be efficiently performed in an Empirical Bayes fashion by maximizing the posterior marginal likelihood of the observed data. Since the score function of this optimization problem is in general characterized by the presence of local optima, it is necessary to resort to global optimization strategies, which require a large number of function evaluations. Given that the evaluation is usually computationally intensive and badly scaled with respect to the dataset size, the maximum number of observations that can be treated simultaneously is quite limited. In this paper, we consider the case of hyperparameter tuning in Gaussian process regression. A straightforward implementation of the posterior log-likelihood for this model requires O(N^3) operations for every iteration of the optimization procedure, where N is the number of examples in the input dataset. We derive a novel set of identities that allow, after an initial overhead of O(N^3), the evaluation of the score function, as well as the Jacobian and Hessian matrices, in O(N) operations. We prove how the proposed identities, that follow from the eigendecomposition of the kernel matrix, yield a reduction of several orders of magnitude in the computation time for the hyperparameter optimization problem. Notably, the proposed solution provides computational advantages even with respect to state of the art approximations that rely on sparse kernel matrices.

A study to evaluate the introduction of simulation as a teaching strategy for the mental health and learning disability fields of nursing, in an undergraduate nursing curriculum within one higher education institution

High Fidelity Simulation or Human Patient Simulation is an educational strategy embedded within nursing curricula throughout many healthcare educational institutions. This paper reports on an evaluative study that investigated the views of a group of Year 2 undergraduate nursing students from the mental health and the learning disability fields of nursing (n = 75) in relation to simulation as a teaching pedagogy. The study took place in the simulation suite within a School of Nursing and Midwifery in the UK. Two patient scenarios were used for the session and participants completed a 22-item questionnaire consisting of three biographical information questions and a 19-item Likert scale. Descriptive statistics were employed to illustrate the data and non-parametric testing (Mann-Whitney U test) was employed to test a number of hypotheses. Overall students were positive about the introduction of patient scenarios using the human patient simulator into the undergraduate nursing curriculum. This study used a small, convenience sample in one institution and therefore the results obtained cannot be generalised to nursing education before further research can be conducted with larger samples and a mixed-method research approach. However these results provide encouraging evidence to support the use of simulation within the mental health and the learning disability fields of nursing, and the development and implementation of further simulations to complement the students��� practicum.<br/>

Simulated peer assessment initiative aimed at increasing the competency of nursing students within fields of nursing

Newly qualified nurses have been educated and assessed as being proficient carrying out certain procedures ,one such insertion of nasogastric feeding tube. Link between theory and practice will be explored. Highlighting the value of low fidelity simulation and peer assessment to enhance skills and competencies.

Econometric Methods and their Applications in Finance, Macro and Related Fields

The volume aims at providing an outlet for some of the best papers presented at the 15th Annual Conference of the African Econometric Society, which is one of the ���chapters��� of the International Econometric Society. Many of these papers represent the state of the art in financial econometrics and applied econometric modeling, and some also provide useful simulations that shed light on the models' ability to generate meaningful scenarios for forecasting and policy analysis.

Conventional in vivo irradiation procedures are insufficient to accurately determine tumor responses to non-uniform radiation fields

<p>Purpose: To determine differences in overall tumor responses measured by volumetric assessment and bioluminescence imaging (BLI) following exposure to uniform and non-uniform radiation fields in an ectopic prostate tumor model.</p><p>Materials and methods: Bioluminescent human prostate tumor xenografts were established by subcutaneous implantation into male mice. Tumors were irradiated with uniform or non-uniform field configurations using conventional in vivo irradiation procedures performed using a 225 kVp generator with custom lead shielding. Tumor responses were measured using Vernier calipers and by BLI using an in vivo imaging system. Survival was defined as the time to quadroupling of pre-treatment tumor volume.��</p><p>Results: The correlation between BLI and tumor volume measurements was found to be different for un-irradiated (R = 0.61), uniformly irradiated (R = 0.34) and partially irradiated (R = 0.30) tumors. Uniformly irradiated tumors resulted in an average tumor growth delay of 60 days with median survival of 75 days, compared to partially irradiated tumors which showed an average growth delay of 24 days and median survival of 38 days.��</p><p>Conclusions: Correlation between BLI and tumor volume measurements is lower for partially irradiated tumors than those exposed to uniform dose distributions. The response of partially irradiated tumors suggests non-uniformity in response beyond physical dose distribution within the target volume. Dosimetric uncertainty associated with conventional in vivo irradiation procedures prohibits their ability to accurately determine tumor response to non-uniform radiation fields and stresses the need for image guided small animal radiation research platforms.</p>

Topology of megagauss magnetic fields and of heat-carrying electrons produced in a high-power laser-solid interaction

<p>The intricate spatial and energy distribution of magnetic fields, self-generated during high power laser irradiation (at I��2���1013-1014W.cm-2.��m2) of a solid target, and of the heat-carrying electron currents, is studied in inertial confinement fusion (ICF) relevant conditions. This is done by comparing proton radiography measurements of the fields to an improved magnetohydrodynamic description that fully takes into account the nonlocality of the heat transport. We show that, in these conditions, magnetic fields are rapidly advected radially along the target surface and compressed over long time scales into the dense parts of the target. As a consequence, the electrons are weakly magnetized in most parts of the plasma flow, and we observe a reemergence of nonlocality which is a crucial effect for a correct description of the energetics of ICF experiments.</p>

Gaussian pulse mixing by stacked semiconductor layers

<p>The nonlinear scattering of two Gaussian pulses with different central frequencies incident at slant angles on the periodic stack of binary semiconductor layers has been modelled in the self-consistent problem formulation taking into account the dynamics of charges. The effects of the pump pulse length and central frequencies, and the stack physical and geometrical parameters on the properties of the emitted combinatorial frequency waveforms are analysed and discussed.</p>

Sprinkler irrigation of rice fields reduces grain arsenic but enhances cadmium

<p>Previous studies have demonstrated that rice cultivated under flooded conditions has higher concentrations of arsenic (As) but lower cadmium (Cd) compared to rice grown in unsaturated soils. To validate such effects over long terms under Mediterranean conditions a field experiment, conducted over 7 successive years was established in SW Spain. The impact of water management on rice production and grain arsenic (As) and cadmium (Cd) was measured, and As speciation was determined to inform toxicity evaluation. Sprinkler irrigation was compared to traditional flooding.</p><p>Both irrigation techniques resulted in similar grain yields (similar to 3000 kg grain ha(-1)). Successive sprinkler irrigation over 7 years decreased grain total As to one-sixth its initial concentration in the flooded system (0.55 to 0.09 mg As kg(-1)), while one cycle of sprinkler irrigation also reduced grain total As by one-third (0.20 mg kg(-1)). Grain inorganic As concentration increased up to 2 folds under flooded conditions compared to sprinkler irrigated fields while organic As was also lower in sprinkler system treatments, but to a lesser extent. This suggests that methylation is favored under water logging. However, sprinkler irrigation increased Cd transfer to grain by a factor of 10, reaching 0.05 mg Cd kg(-1) in 7 years. Sprinlder systems in paddy fields seem particularly suited for Mediterranean climates and are able to mitigate against excessive As accumulation, but our evidence shows that an increased Cd load in rice grain may result.</p>

A compact broadband ion beam focusing device based on laser-driven megagauss thermoelectric magnetic fields

<p>Ultra-intense lasers can nowadays routinely accelerate kiloampere ion beams. These unique sources of particle beams could impact many societal (e.g., proton-therapy or fuel recycling) and fundamental (e.g., neutron probing) domains. However, this requires overcoming the beam angular divergence at the source. This has been attempted, either with large-scale conventional setups or with compact plasma techniques that however have the restriction of short (&lt;1 mm) focusing distances or a chromatic behavior. Here, we show that exploiting laser-triggered, long-lasting (&gt;50 ps), thermoelectric multi-megagauss surface magnetic (B)-fields, compact capturing, and focusing of a diverging laser-driven multi-MeV ion beam can be achieved over a wide range of ion energies in the limit of a 5�� acceptance angle.</p>

Hybrid probabilistic wind power forecasting using temporally local Gaussian process

The demand for sustainable development has resulted in a rapid growth in wind power worldwide. Despite various approaches have been proposed to improve the accuracy and to overcome the uncertainties associated with traditional methods, the stochastic and variable nature of wind still remains the most challenging issue in accurately forecasting wind power. This paper presents a hybrid deterministic-probabilistic method where a temporally local ���moving window��� technique is used in Gaussian Process to examine estimated forecasting errors. This temporally local Gaussian Process employs less measurement data while faster and better predicts wind power at two wind farms, one in the USA and the other in Ireland. Statistical analysis on the results shows that the method can substantially reduce the forecasting error while more likely generate Gaussian-distributed residuals, particularly for short-term forecast horizons due to its capability to handle the time-varying characteristics of wind power.

Special condition wind power forecasting based on Gaussian process and similar historical data

Due to the variability of wind power, it is imperative to accurately and timely forecast the wind generation to enhance the flexibility and reliability of the operation and control of real-time power. Special events such as ramps, spikes are hard to predict with traditional methods using solely recently measured data. In this paper, a new Gaussian Process model with hybrid training data taken from both the local time and historic dataset is proposed and applied to make short-term predictions from 10 minutes to one hour ahead. A key idea is that the similar pattern data in history are properly selected and embedded in Gaussian Process model to make predictions. The results of the proposed algorithms are compared to those of standard Gaussian Process model and the persistence model. It is shown that the proposed method not only reduces magnitude error but also phase error.