In this paper, we performed an analysis of the 500 most relevant scientific articles published since 2018, concerning machine learning methods in the field of climate and numerical weather prediction using the Google Scholar search engine. The most common topics of interest in the abstracts were identified, and some of them examined in detail: in numerical weather prediction research—photovoltaic and wind energy, atmospheric physics and processes; in climate research—parametrizations, extreme events, and climate change. With the created database, it was also possible to extract the most commonly examined meteorological fields (wind, precipitation, temperature, pressure, and radiation), methods (Deep Learning, Random Forest, Artificial Neural Networks, Support Vector Machine, and XGBoost), and countries (China, USA, Australia, India, and Germany) in these topics. Performing critical reviews of the literature, authors are trying to predict the future research direction of these fields, with the main conclusion being that machine learning methods will be a key feature in future weather forecasting.

/pdf/machine-learning-in-weather-prediction-and-climate-analyses-142sdet1.pdf

Machine Learning in Weather Prediction and Climate Analyses—Applications and Perspectives

In the last few years, electron microscopy has experienced a new methodological paradigm aimed to fix the bottlenecks and overcome the challenges of its analytical workflow. Machine learning and artificial intelligence are answering this call providing powerful resources towards automation, exploration, and development. In this review, we evaluate the state-of-the-art of machine learning applied to electron microscopy (and obliquely, to materials and nano-sciences). We start from the traditional imaging techniques to reach the newest higher-dimensionality ones, also covering the recent advances in spectroscopy and tomography. Additionally, the present review provides a practical guide for microscopists, and in general for material scientists, but not necessarily advanced machine learning practitioners, to straightforwardly apply the offered set of tools to their own research. To conclude, we explore the state-of-the-art of other disciplines with a broader experience in applying artificial intelligence methods to their research (e.g., high-energy physics, astronomy, Earth sciences, and even robotics, videogames, or marketing and finances), in order to narrow down the incoming future of electron microscopy, its challenges and outlook.

Machine learning in electron microscopy for advanced nanocharacterization: current developments, available tools and future outlook.

Improving Thermal Energy Storage (TES) of buildings using Phase Change Material (PCM) is widely used to develop energy efficient building envelope. In this study, optimum location of PCM, thermal insulation, and air were investigated in a concrete block to improve indoor thermal comfort of the building. A comparative study of six Thermal Efficient Concrete Block (TECB-1, TECB-2, TECB-3, TECB-4, TECB-5, and TECB-6), having three slots each, embedded with PCM, PU Foam, and air in varying order was conducted to identify the optimum location of PCM for improving indoor thermal performance. Additionally, TES characteristics and thermal stability of PCM were also evaluated. TECB-4 and TECB-5 obtained a time lag of 127.5 min and 125 min respectively in comparison with TECB-0. Maximum average percentage reduction of 12.5% in indoor peak temperature and lowest average thermal amplitude of 13.4 °C was shown by TECB-5. The temperature profiling of indoor surfaces validates the improved thermal behaviour of TECB-4 and TECB-5. This study signifies that optimum location of using PCM, PU Foam, and air in the building concrete block is PCM on outer side, PU Foam on inner side, and air in middle for improving indoor thermal performance of buildings in tropical climates. 

Location optimization of phase change material for thermal energy storage in concrete block for development of energy efficient buildings

Extreme climates have become frequent nowadays, causing increased heat stress in human daily life. Personal thermal management (PTM), a technology that controls the human body's microenvironment, has become a promising strategy to address heat stress. While effective in ordinary environments, traditional high-performance fibers, such as ultrafine, porous, highly thermally conductive, and phase change materials, fall short when dealing with harsh conditions or large temperature fluctuations. Aerogels, a third-generation superinsulation material, have garnered extensive attention among researchers for their thermal management applications in building energy conservation, transportation, and aerospace, attributed to their extremely low densities and thermal conductivity. While aerogels have historically faced challenges related to weak mechanical strength and limited secondary processing capacity, recent advancements have witnessed notable progress in the development of wearable aerogels for PTM. This progress underscores their potential applications within extremely harsh environments, serving as self-powered smart devices and sensors. This Review offers a timely overview of wearable aerogels and their PTM applications with a particular focus on their wearability and suitability. Finally, the discussion classifies five types of PTM applications based on aerogel function: thermal insulation, heating, cooling, adaptive regulation (involving thermal insulation, heating, and cooling), and utilization of aerogels as wearable smart devices. 

Wearable Aerogels for Personal Thermal Management and Smart Devices

Abstract Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency and intensity of extremes and other associated events are continuously increasing due to climate change and global warming. The accurate prediction, characterization, and attribution of atmospheric extreme events is, therefore, a key research field in which many groups are currently working by applying different methodologies and computational tools. Machine learning and deep learning methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric extreme events. This paper reviews machine learning and deep learning approaches applied to the analysis, characterization, prediction, and attribution of the most important atmospheric extremes. A summary of the most used machine learning and deep learning techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. The critical literature review has been extended to extreme events related to rainfall and floods, heatwaves and extreme temperatures, droughts, severe weather events and fog, and low-visibility episodes. A case study focused on the analysis of extreme atmospheric temperature prediction with ML and DL techniques is also presented in the paper. Conclusions, perspectives, and outlooks on the field are finally drawn. 

Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review

Energy performance of a rural residential building with PCM-silica aerogel sunspace in severe cold regions

Climate change is one of the prominent factors that causes an increased severity of extreme precipitation which, in turn, has a huge impact on drainage systems by means of flooding. Intensity–duration–frequency (IDF) curves play an essential role in designing robust drainage systems against extreme precipitation. It is important to incorporate the potential threat from climate change into the computation of IDF curves. Most existing works that have achieved this goal were based on Generalized Extreme Value (GEV) analysis combined with various circulation model simulations. Inspired by recent works that used machine learning algorithms for spatial downscaling, this paper proposes an alternative method to perform projections of precipitation intensity over short durations using machine learning. The method is based on temporal downscaling, a downscaling procedure performed over the time scale instead of the spatial scale. The method is trained and validated using data from around two thousand stations in the US. Future projection of IDF curves is calculated and discussed.

Machine Learning for Projecting Extreme Precipitation Intensity for Short Durations in a Changing Climate

Extreme precipitation is one of the most important climate hazards that pose a significant threat to human property and life. Understanding extreme precipitation events helps to manage thei...

Extreme Precipitation Analysis and Prediction for a Changing Climate

Background Traditional Chinese Exercise (TCE) has expanded out of China into the world and is frequently used in the prevention and treatment of many diseases. Although many studies have explored the ability of TCE as an intervention for neurodegenerative diseases, there are limited articles summarizing the research. The aim of this study was to investigate current research trends around TCE for neurodegenerative diseases and predict future directions for such research. Materials and Methods Data was collected from the Web of Science Core Collection (WoSCC). All articles and reviews on TCE in relation to neurodegenerative diseases were retrieved. The data analysis was performed using the CiteSpace (5.8R3) software, and the results were displayed in network maps. Results The search identified 220 publications between 1999 and 2021. The United States was the most productive country (n = 89), followed by China (n = 68). The United States had the greatest centrality, indicating its tremendous global influence and close collaborations with other countries. Fuzhong Li from the Oregon Research Institute, United States, was not only the most prolific author (n = 5), but also the most co-cited author (n = 120). The Shanghai University of Sport contributed to the most publications (n = 12). PLOS ONE was the most published journal, while Movement Disorders was the most cited journal. Tai Chi, Parkinson’s disease, quality of life, balance, and older adult were the most high-frequency keywords, while Alzheimer’s disease had the highest centrality. Conclusion The number of publications on TCE related to neurodegenerative diseases has shown major growth in the past decade. However, there is a need for research institutions to strengthen cooperation between countries and institutions. Tai Chi, Parkinson’s disease (PD), Alzheimer’s disease, older adults and falls reduction have been the recent research focus. It is anticipated that in the future, PD will continue to be a central focus with the effects of Baduanjin, Wuqinxi, and Yijinjing requiring further research.

/pdf/traditional-chinese-exercise-for-neurodegenerative-diseases-3g21qdpt.pdf

Traditional Chinese Exercise for Neurodegenerative Diseases: A Bibliometric and Visualized Analysis With Future Directions

Extreme precipitation has posed a huge risk to society and the environment. It is crucial to be able to accurately analyze extreme precipitation in order to reduce its potential risk. This ...

Validating and Enhancing Extreme Precipitation Projections by Downscaled Global Climate Model Results and Copula Methods

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