Knee Design: Implications for Creation .vs. Evolution

This paper traces important features In human knee design that allows the unique function of plantigrade bipedalism (walking two legged on the soles of the feet). Concepts of biomechanlcal importance related to human gait and the problem of knee flexion contracture are discussed. Alleged hominid ancestors would have had to overcome a flexed knee stance to become efficient bipeds. Knees discovered In the fossil record, however, are fully functional. Joint replacement research has carefully followed a reproduction of the original design for a most unique Joint - the human knee

WinePeer - A Pre-Launch Strategic Analysis

WinePeer is a mobile application that enables wine consumers to rate wines in 60 seconds for the purposes of developing an evolving taste profile with the potential to be leveraged in many different ways. This work determines the viability of WinePeer as a business venture through providing a comprehensive analysis of the external environment including the wine industry supply chain, regulatory influences and global wine industry trends. Drawing on the work of Kim and Mauborgne, this analysis draws on Blue Ocean Strategy to address wine consumers and competitors utilizing a values-based assessment involving the creation of value curves to highlight areas competitors are under or over delivering with respect to consumer expectations. The WinePeer business model attempts to carve out a market niche by eliminating, reducing, increasing and/or creating values and through developing a value curve that focuses resources from underappreciated values to those desired by consumers. In this manner, it provides a differentiated offering that distinguishes WinePeer from potential competitors .The viability of WinePeer’s business modelled was confirmed through the identification of four revenue streams and through addressing all issues related to funding the venture’s operations

Prediction of internal temperatures during hot summer conditions with time series forecasting models

A novel application using adaptive autoregressive time series forecasting with exogenous inputs (i.e. ARX) has been developed in order to provide reliable short-term forecasts of the internal temperatures in dwellings during hot summer conditions (i.e. heatwaves). The study shows that with proper selection of the predictors, based on the Akaike Information Criterion (AIC), the forecasts provide acceptable accuracy for periods up to 72 hours. The hourly results for the analysed dwellings showed a Mean Absolute Error (MAE) below 0.63°C and 0.49°C for the two case study dwellings across the 3-day forecasting period, during the 2015 heatwave. These findings point to the potential for using time series forecasting as part of an overheating warning system in buildings, especially those housing vulnerable occupants

Key Considerations: COVID-19 in the Context of Conflict and Displacement - Myanmar

This brief focuses on COVID-19 in Myanmar and how the interplay between conflict, displacement and inter-communal tensions may influence disease control. All health emergencies have social and political challenges, but sensitive consideration and effective management of these is especially important where there is past or ongoing conflict, and where trust in authorities imposing disease control may be low. Myanmar faces COVID-19 alongside serious humanitarian and health system vulnerabilities. The country has a range of conflicts and non-state actors who must be factored into a public health response. This brief highlights key considerations for COVID-19 against this complex governance backdrop. It can be read in conjunction with the SSHAP briefing on COVID-19 in South East Asia which outlines emerging evidence on COVID-19 control measures in the region, with a particular focus on marginal populations including transnational migrants, stateless populations, those working in the informal economy and the urban poor.DFIDWellcome Trus

Forecasting indoor temperatures during heatwaves using time series models

Early prediction of impending high temperatures in buildings could play a vital role in reducing heat-related morbidity and mortality. A recursive, AutoRegressive time series model using eXogenous inputs (ARX) and a rolling forecasting origin has been developed to provide reliable short-term forecasts of the internal temperatures in dwellings during hot summer conditions, especially heatwaves. The model was tested using monitored data from three case study dwellings recorded during the 2015 heatwave. The predictor variables were selected by minimising the Akaike Information Criterion (AIC), in order to automatically identify a near-optimal model. The model proved capable of performing multi-step-ahead predictions during extreme heat events with an acceptable accuracy for periods up to 72 h, with hourly results achieving a Mean Absolute Error (MAE) below 0.7 °C for every forecast. Comparison between ARX and AutoRegressive Moving Average models with eXogenous inputs (ARMAX) models showed that the ARX models provided consistently more reliable multi-step-ahead predictions. Prediction intervals, at the 95% probability level, were adopted to define a credible interval for the forecast temperatures at different prediction horizons. The results point to the potential for using time series forecasting as part of an overheating early-warning system in buildings housing vulnerable occupants or contents

Can semi-parametric additive models outperform linear models, when forecasting indoor temperatures in free-running buildings?

A novel application combining semi-parametric Generalized Additive Models (GAMs) with logistic GAMs was developed to forecast indoor temperatures and window opening states during prolonged heatwaves. GAM models were compared to AutoRegressive models with eXogenous inputs (ARX) and validated against monitored data from two case study dwellings, located near to Loughborough in the UK, during the 2013 heatwave. Input variables were selected using backward stepwise regressions based on minimisation of the Akaike Information Criterion (AIC) and Mean Absolute Error (MAE), for the ARX and GAM models respectively. Comparison of the models showed that whilst GAMs are capable of improving the forecasting accuracy, the improvements are significant only up to 3-6 hours ahead. During heatwaves and over longer forecasting horizons, GAMs were found to be less reliable and accurate than ARX models. The marginal improvement in forecasting accuracy at shorter horizons did not justify the additional computational time and risk of instability associated with more complex GAMs, at longer forecasting horizons. Whilst, logistic GAMs were shown to adequately predict the window opening state, incorporating knowledge of the window state did not significantly improve the accuracy of the indoor temperature predictions

Impacting Agriculture and Natural Resource Policy: County Commissioners’ Decision-Making Behaviors and Communication Preferences

Elected officials at the local, state, and national levels play key roles in shaping the agriculture and natural resources (ANR) sectors through the development and implementation of ANR policies and regulations. As such, it has become necessary for members of the ANR community to understand the policy formation process and how to communicate effectively with elected officials about ANR policies and issues. However, little research has been conducted at the local level to examine how local elected officials (LEOs) interact with information specific to ANR policies to make decisions. This study was designed to assess the communication and information-seeking preferences and behaviors of LEOs that impact their decisions about ANR issues and policies. Of the sources of communication considered by LEOs when making ANR policy decisions, respondents in this study identified communication from farmers and ranchers as having the highest impact on their decision-making. This finding supports the use of farmers and ranchers as opinion leaders in impacting ANR policies. LEOs in this study also reported they would seek factual information from multiple sources to understand the positive or negative impact of the ANR policy before voting on the ANR issue

A new empirical model incorporating spatial interpolation of meteorological data for the prediction of overheating risks in UK dwellings

Heat-related morbidity and mortality is anticipated to increase as climatic change induced overheating become increasingly common. The development of building-specific predictive models has the potential to alert occupants and emergency services to the severity of impending risks. This research aims to evaluate the implementation of a newly developed time series model for overheating prediction. Since risk forecasting is contingent upon the accuracy of the model at different future time steps, the sensitivity of model outputs to the uncertainty in the data inputs needs to be understood. Internal and external climatic variables were monitored in an unoccupied domestic dwelling in order to evaluate the empirical model’s predictive accuracy. The uncertainty related to the proximity of external weather stations was evaluated using data taken from four nearby weather stations and further bespoke data sets derived by interpolation. The results confirmed the overall accuracy of the newly developed time series predictive model, whilst highlighting the benefits of climatic data interpolation in reducing predictive uncertainties. The empirically derived modelling approach showed a low variance to the actual temperature evolution over a seven-day predictive period, pointing to its validity as a robust model for the prediction of future overheating risks

Community Citizen Inquiry: The Case of nQuire

Community citizen science inquiry is a combination of two ideas: first, citizen science inquiry (mass participation citizen science and learning to be a scientist through scientific inquiry, (Herodotou et al, 2017), and a concern to involve participation by the members of a community for community purposes. We describe the work we have done on this topic developing from our initial experiments in supporting inquiry learning with school children using technology and describing how this work has developed through conducting a number of studies involving students in a university technical college, and distance education students, then widened into investigation of the ideas with members of the public conducting citizen inquiries. These experiences have been supported by the development of the web platform nQuire (http://www.nquire.org.uk) which is now the basis of our work.Our approach to citizen inquiry is driven by a concern for the nature of the participation from which members of the public can benefit and goes beyond involving people solely as volunteer data collectors. We report here on our experiences of the different types of inquiries or missions which have run on the platform since 2018, and the views of some participants on their involvement. We also discuss the issues in developing our vision of community citizen science inquiry and a potential centre for democratising research

A high-resolution indoor heat-health warning system for dwellings

Climate change projections indicate that the world's most populated regions will experience more frequent, intense and longer-lasting heatwave periods over the coming decades. Such events are likely to result in widespread overheating in the built environment, with a consequential increase in heat-related morbidity and mortality. In order to warn the population of such risks, Heat-Health Warning Systems (HHWSs) are being progressively adopted world-wide. Current HHWSs are, however, based solely on weather observations and forecasts and are unable to identify precisely where, when, or to what extent individual buildings (and their occupants) will be affected. In contrast, AutoRegressive models with eXogenous inputs (ARX) have been demonstrated to reliably forecast indoor temperatures in individual rooms using minimal data. Thus, the large-scale deployment of forecasting models could theoretically enable the development of a high-resolution indoor HHWS (iHHWS). In this study, ARX models were tested over the long-lasting UK heatwave of 2018 using hourly monitored dry-bulb temperature data from 25 rooms (12 living rooms and 13 bedrooms) in 12 dwellings, located within the London Urban Heat Island (UHI). The study investigates different approaches to improving the reliability of room-based heat exposure predictions at longer forecasting horizons. The effectiveness of the iHHWS system was assessed by evaluating the accuracy of predictions (using fixed and adaptive temperature thresholds) at different lead times (1, 3, 6, 12, 24, 48 and 72 h ahead). Compared to forecasted indoor temperatures, a Cumulative Heat Index (CHI) metric was shown to increase the reliability of heat-health warnings up to 24 h ahead