Kenya : a closer look at culture and early childhood education

Includes bibliographical references.This project is a study of the culture of South Eastern Kenya and its effects on the education system in those regions. Cultural issues such as religion, gender perceptions, and community are covered. The research for this project was compiled from magazines, journals, and books written by professionals who have experienced life in Kenya. Informational also came from personal contact with a native of Kenya. In this paper it can be seen that early childhood education has changed greatly and gained higher importance recently. It shows that Kenyan's are working very hard, in cooperation with several United States groups, to improve educational opportunities for all children, both urban and rural. The research illustrated the strong connection between culture and education. In Kenya, they are nearly inseparable.B.S.Ed. (Bachelor of Science in Education

Annular variability and eddy-zonal flow interactions in a simplified atmospheric GCM. Part 1: Characterization of high and low frequency behaviour

Experiments have been performed using a simplified, Newtonian forced, global circulation model to investigate how variability of the tropospheric jet can be characterized by examining the combined fluctuations of the two leading modes of annular variability. Eddy forcing of this variability is analyzed in the phase space of the leading modes using the vertically integrated momentum budget. The nature of the annular variability and eddy forcing depends on the time scale. At low frequencies the zonal flow and baroclinic eddies are in quasi equilibrium and anomalies propagate poleward. The eddies are shown primarily to reinforce the anomalous state and are closely balanced by the linear damping, leaving slow evolution as a residual. At high frequencies the flow is strongly evolving and anomalies are initiated on the poleward side of the tropospheric jet and propagate equatorward. The eddies are shown to drive this evolution strongly: eddy location and amplitude reflect the past baroclinicity, while eddy feedback on the zonal flow may be interpreted in terms of wave breaking associated with baroclinic life cycles in lateral shear

Personality characteristics associated with susceptibility to false memories

Accepted ManuscriptThis study examined whether certain personality characteristics are associated with susceptibility to false memories. Participants first answered questions from the Myers-Briggs Type Indicator in order to measure various personality characteristics. They then watched a video excerpt, the simulated eyewitness event. They were next encouraged to lie about the videotaped event during an interview. A week later, some participants recognized confabulated events as being from the video. Two personality characteristics in particular—the introversion-extroversion and thinking—feeling dimensions—were associated with susceptibility to false memories.Frost, P., Sparrow, S. & Barry, J. (2006). Personality Characteristics Associated with Susceptibility to False Memories. The American Journal of Psychology, 119(2), 193-204. http://www.jstor.org/stable/2044533

Dynamic and Thermodynamic Control of the Response of Winter Climate and Extreme Weather to Projected Arctic Sea‐Ice Loss

A novel sub‐sampling method has been used to isolate the dynamic effects of the response of the North Atlantic Oscillation (NAO) and the Siberian High (SH) from the total response to projected Arctic sea‐ice loss under 2°C global warming above preindustrial levels in very large initial‐condition ensemble climate simulations. Thermodynamic effects of Arctic warming are more prominent in Europe while dynamic effects are more prominent in Asia/East Asia. This explains less‐severe cold extremes in Europe but more‐severe cold extremes in Asia/East Asia. For Northern Eurasia, dynamic effects overwhelm the effect of increased moisture from a warming Arctic, leading to an overall decrease in precipitation. We show that the response scales linearly with the dynamic response. However, caution is needed when interpreting inter‐model differences in the response because of internal variability, which can largely explain the inter‐model spread in the NAO and SH response in the Polar Amplification Model Intercomparison Project

Assessing long-term future climate change impacts on extreme low wind events for offshore wind turbines in the UK exclusive economic zone

The impacts of climate change must be considered while planning offshore wind turbines (OWT), as it will result in more frequent and severe weather extremes. To ensure the dependability and affordability of wind energy, it is necessary to address extreme low wind speed events (LWE). This study aims to assess the reliability of wind power in the future by analyzing the rise of low wind durations and intensities in two future periods, 2021–2040 and 2061–2080, compared to the historical period of 1981–2000. The research compares the results for four main regions in the UK EEZ: East, South, West, and North. We examine different cut-in thresholds of 3 m/s, 4 m/s, 5 m/s, and 6 m/s in the UK exclusive economic zone (EEZ). The seasonal variations in LWE durations <4 m/s demonstrate that summer and autumn have an increase in most of the LWE durations occurrence in the 2061–2080 period in all regions compared to the historical period. Using five days running mean wind speed, the return time for 6 m/s cut-in wind speed shows that OWT will be vulnerable to frequent extreme LWE in most areas, with most sites experiencing a return period of up to 20 years. According to the return year region median and the Risk Ratio (RR) calculations, it is suggested that the South region exhibits a diminished risk of experiencing more frequent instances of wind speeds surpassing the cut-in threshold, specifically when utilizing cut-in thresholds of 5 m/s and 6 m/s, during the period spanning 2021–2040, as compared to the historical period. Furthermore, when employing 6-, 7-, and 8-day running means, the analysis reveals that the return period for wind speeds of 4 m/s in the Western region remains consistently recommended throughout the 2021–2040 period. In contrast, utilizing a 6-day time window for assessing the return period of 4 m/s wind speeds indicates a notable escalation in risk across all regions during the 2061–2080 period

Skilful probabilistic medium‐range precipitation and temperature forecasts over Vietnam for the development of a future dengue early warning system

Dengue fever is a source of substantial health burden in Vietnam. Given the well‐established influence of temperature and precipitation on vector biology and disease transmission, predictions of meteorological variables, such as those issued by ECMWF as a world‐leading provider of global ensemble forecasts, are likely to be valuable model inputs to a future dengue early warning system. In the absence of established verification at municipal and regional scales, this study assesses the skill of rainy season (May–October) ensemble precipitation and 2‐m temperature retrospective forecasts over North and South Vietnam initialized for dates during the period 2001–2020, evaluated against the ERA5 reanalysis for the same period. Forecasts are found to be significantly skilful compared with both climatology and persistence for lead times up to 10 days, including for cumulative precipitation values considered against independent rain gauge data. Rank histograms demonstrate that ensembles generally avoid excessive bias and consistently positive CRPSS values indicate substantial skill for temperature and cumulative precipitation forecasts for all spatial scales considered, despite differences in rainy season characteristics between North and South Vietnam. This forecast reliability demonstrates that meteorological input data based on ECMWF ensemble forecasts would add appreciably more value to the development of a future dengue early warning system compared to reference forecasts like climatology or persistence. These results raise hope for further exploration of predictive skill for relevant meteorological variables, particularly focused on their downscaling to produce district‐level epidemiological forecasts for urban areas where dengue is most prevalent

A comparative climate-resilient energy design: Wildfire Resilient Load Forecasting Model using multi-factor deep learning methods

Power grid damage and blackouts are increasing with climate change. Load forecasting methods that integrate climate resilience are therefore essential to facilitate timely and accurate network reconfiguration during periods of extreme stress. Our paper proposes a generalised Wildfire Resilient Load Forecasting Model (WRLFM) to predict electricity load based on operational data of a Distribution Network (DN) in Australia during wildfire seasons in 2015–2020. We demonstrate that load forecasting during wildfire seasons is more challenging than during non-wildfire seasons, motivating an imperative need to improve forecast performance during wildfire seasons. To develop the robust WRLFM, comprehensive comparative analyses were conducted to determine proper Machine Learning (ML) forecast structures and methods for incorporating multiple factors. Bi-directional Gated Recurrent Unit (Bi-GRU) and Vision Transformer (ViT) were selected as they performed the best among all 13 recently trending ML methods. Multi-factors were incorporated to contribute to forecast performance, including input sequence structures, calendar information, flexible correlation-based temperature conditions, and categorical Fire Weather Index (FWI). High-resolution categorical FWI was used to build a forecasting model with climate resilience for the first time, significantly enhancing the average stability of forecast performances by 42%. A sensitivity analysis compared data set patterns and model performances during wildfire and non-wildfire seasons. The improvement rate of load forecasting performance during wildfire seasons was more than two times greater than in non-wildfire seasons. This indicates the significance and effectiveness of applying the WRLFM to improve forecast accuracy under extreme weather risks. Overall, the WRLFM reduces the Mean Absolute Percentage Error (MAPE) of the forecast by 14.37% and 20.86% for Bi-GRU and ViT-based models, respectively, achieving an average forecast MAPE of around 3%

A generalised multi-factor deep learning electricity load forecasting model for wildfire-prone areas

This paper proposes a generalised and robust multi-factor Gated Recurrent Unit (GRU) based Deep Learning (DL) model to forecast electricity load in distribution networks during wildfire seasons. The flexible modelling methods consider data input structure, calendar effects and correlation-based leading temperature conditions. Compared to the regular use of instantaneous temperature, the Mean Absolute Percentage Error (MAPE) is decreased by 30.73% by using the proposed input feature selection and leading temperature relationships. Our model is generalised and applied to eight real distribution networks in Victoria, Australia, during the wildfire seasons of 2015-2020. We demonstrate that the GRU-based model consistently outperforms another DL model, Long Short-Term Memory (LSTM), at every step, giving average improvements in Mean Squared Error (MSE) and MAPE of 10.06% and 12.86%, respectively. The sensitivity to large-scale climate variability in training data sets, e.g. El Ni\~no or La Ni\~na years, is considered to understand the possible consequences for load forecasting performance stability, showing minimal impact. Other factors such as regional poverty rate and large-scale off-peak electricity use are potential factors to further improve forecast performance. The proposed method achieves an average forecast MAPE of around 3%, giving a potential annual energy saving of AU\$80.46 million for the state of Victoria