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Coastal ecosystems and human communities are threatened worldwide by climate change, and shocks from social, market and political change. There is an urgent global need to promote resilient food production and livelihoods in the face of these shocks. Small-scale fisheries (SSF) in rural settings can be particularly vulnerable as they frequently lack the resources, rights and infrastructure to respond to shocks originating outside the focal systems. We examined ecological and social outcomes of environmental extremes in a SSF socio-ecological system (SES) by using long-term oceanographic (between 2010-2019) and ecological (2006-2018) data tracking change in a kelp forest ecosystem of Baja California, Mexico, and concurrent documentation of proactive and reactive actions of a fishing community organized in a cooperative. Results indicate a complex landscape of ‘winners’ and ‘losers’ among species and fisheries exposed to unprecedented environmental extremes, including marine heat waves and prolonged hypoxia, and a suite of adaptive actions by the local fishing cooperative, and others in the region, that have helped confront these rapid and drastic changes. Cooperatives have established voluntary marine reserves to promote recovery of affected populations and have invested in diversification of activities enabled by access rights, collective decision-making, and participatory science programs. Results indicate that local actions can support social and ecological resilience in the face of shocks, and that enabling locally-driven adaptation pathways is critical to resilience. This case study highlights the crucial importance of strengthening and supporting rights, governance, capacity, flexibility, learning, and agency for coastal communities to respond to change and sustain their livelihoods and ecosystems in the long run. 

This article investigates the application of machine learning-based probabilistic prediction methodologies to estimate the performance of silicon-based solar cells. The concept of confidence-bound regions is introduced and the advantages of this concept are discussed in detail. The results show that the optical and electrical performance of a photovoltaic device can be accurately estimated using Gaussian processes with accurate knowledge of the uncertainty in the prediction values. It is also shown that cell design parameters can be estimated for a desired performance metric and trained machine learning models can be deployed as a standalone application. 

The increasing number of Photovoltaic (PV) systems connected to the power grid are vulnerable to the projection of shadows from moving clouds. Global Solar Irradiance (GSI) forecasting allows smart grids to optimize the energy dispatch, preventing energy shortages caused by occlusion of the sun. This investigation compares the performances of machine learning algorithms (not requiring labelled images for training) for realtime segmentation of clouds in images acquired using a ground-based infrared sky imager. Real-time segmentation is utilized to extract cloud features using only the pixels in which clouds are detected. 

Moving clouds affect the Global Solar Irradiance (GSI) that reaches the surface of the Earth. As a consequence, the amount of resources available to meet the energy demand in a smart grid powered using Photovoltaic (PV) systems depends on the shadows projected by passing clouds. This research introduces an algorithm for tracking clouds to predict Sun occlusion. Using thermal images of clouds, the algorithm is capable of estimating multiple wind velocity fields with different altitudes, velocity magnitudes and directions. 

This paper presents the use of a Restricted Boltzmann Machine to develop an unsupervised machine learning approach to process breathing sounds to predict breathing rates and depth or length of breaths. Breath detection and monitoring has been the subject of several studies involving the health monitoring of patients on respirators. We are proposing to extend the use of non-invasive techniques to provide measures of physical exhaustion or activity. The level of activity or exhaustion could be used to prevent accidents or manage exposure to physically demanding environments such as firefighting or working underwater.