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Low cost temperature sensors are increasingly used by ecologists to assess climatic variation and change on ecologically relevant scales. Although cost-effective, if not deployed with proper solar radiation shielding, the observations recorded from these sensors will be biased and inaccurate. Manufactured radiation shields are effective at minimizing this bias, but are expensive compared to the cost of these sensors. Here, we provide a detailed methodology for constructing a compact version...
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School building stock retrofit forms a key part of UK’s commitment to net-zero carbon target by 2050. However, with a changing climate, the retrofit of school buildings may have unintended consequences on classroom thermal environments and cognitive performance of children in non-heating seasons. This paper aims to quantify the impact of school stock retrofit in accordance with increasingly tightening energy efficiency regulatory requirements on cognitive performance of English children,...
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Machine learning presents opportunities for tracking evidence on climate change adaptation, including text-based methods from natural language processing. In theory, such tools can analyse more data in less time, using fewer resources and with less risk of bias. However, the first generation of adaptation studies have delivered only proof of concepts. Reviewing these first studies, we argue that future efforts should focus on creating more diverse datasets, investigating concrete hypotheses,...
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The study reported here focuses on integrating climate change issues in School curriculum in The Gambia. The objectives are to determine the extent to which climate-related themes presently feature in the Upper Basic (lower secondary) schools of The Gambia; propose an integration plan for enhanced climate themes in the school curriculum; and identify suitable teaching methods for conveying climate change information to students. A curriculum audit of two subject areas in the Upper Basic...
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The study reported here focuses on integrating climate change issues in School curriculum in The Gambia. The objectives are to determine the extent to which climate-related themes presently feature in the Upper Basic (lower secondary) schools of The Gambia; propose an integration plan for enhanced climate themes in the school curriculum; and identify suitable teaching methods for conveying climate change information to students. A curriculum audit of two subject areas in the Upper Basic...
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Model Predictive Control has gained much attention due to its potential to improve building operations by reducing costs, integrating renewable energy sources, and increasing thermal comfort. This paper aims to compare the accuracy of grey-box models based on resistance–capacitance (RC) networks and Long-Short-Term Memory (LSTM) neural networks in the prediction of the buildings’ thermal response, which is a key feature for the successful implementation of predictive controllers. Indoor air...
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The objective of this research is to investigate air flow distribution inside a light weight test room which is naturally ventilated using artificial neural networks. The test room is situated in a relatively sheltered location and is ventilated through adjustable louvres. Indoor air temperature and velocity are measured at four locations and six different levels. The outside local temperature, relative humidity, wind velocity and direction are also monitored. The collected data are used to...
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Subsistence farmers in Sahelian Africa are highly vulnerable to the rainfall effects associated with climate change. Permanent or temporary out-migration can provide an individual or family the opportunity to mitigate against these effects. One major challenge to quantifying the impact of climate change on out-migration is lack of appropriate spatial and temporal data. Out-migration data must be adequately detailed to include both long- and short-term departures. The climate data must...
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Personal thermal comfort models are crucial for the future of human-in-the-loop HVAC control in energy-efficient buildings. Individual comfort models, compared to average population responses, can provide the personalization required for successful control. In this work, we frame the thermal preference prediction task as a multivariate, multi-class classification problem and use deep learning and time-series-based approach for thermal preference prediction. We combine l1 regularization with...
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Personal thermal comfort models are crucial for the future of human-in-the-loop HVAC control in energy-efficient buildings. Individual comfort models, compared to average population responses, can provide the personalization required for successful control. In this work, we frame the thermal preference prediction task as a multivariate, multi-class classification problem and use deep learning and time-series-based approach for thermal preference prediction. We combine l1 regularization with...
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Despite the trajectory towards climate catastrophe, governments are failing to take disability-inclusive climate action. We discuss how the scientific community could advance and hasten the development of disability-inclusive climate resilience, and which areas should be prioritized.
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Abstract The scientific literature on climate change adaptation has become too large to assess manually. Beyond standard scientometrics, questions about if and how the field is progressing thus remain largely unanswered. Here we provide a novel, inquisitive, computer-assisted evidence mapping methodology that combines expert interviews ( n = 26) and structural topic modelling to evaluate open-ended research questions on progress in the field. We apply this to 62 191 adaptation-relevant...
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