Water is the basis of life in the world. Unfortunately, resources are shrinking at an alarming rate. The lack of access to water is still the biggest problem in the modern world. The key to solving it is to find new unconventional ways to obtain water from alternative sources. Fog collectors are becoming an increasingly important way of water harvesting as there are places in the world where fog is the only source of water. Our aim is to apply electrospun fiber technology, due to its high surface area, to increase fog collection efficiency. Therefore, composites consisting of hydrophobic and hydrophilic fibers were successfully fabricated using a two-nozzle electrospinning set up. This design enables the realization of optimal meshes for harvesting water from fog. In our studies we focused on combining hydrophobic, polystyrene (PS) and hydrophilic, polyamide 6 (PA6) surface properties in the produced meshes, without any chemical modifications, based on new hierarchical composites for collecting water. This combination of hydrophobic and hydrophilic material cause water to condense on the hydrophobic microfibers and to run down on the hydrophilic nanofibers. By adjusting the fraction of PA6 nanofibers we were able to tune the mechanical properties of PS meshes and importantly increase the efficiency in collecting water. We combined a few characterization methods together with novel image processing protocols for the analysis of fiber fractions in the constructed meshes. The obtained results show a new single-step method to produce meshes with enhanced mechanical properties and water collecting abilities that can be applied in existing Fog Water Collectors. This is a new promising design for fog collectors with nano-and macro-fibers which are able to efficiently harvest water, showing a great application in comparison to commercially available standard meshes.
Multi-step forecasting is very challenging and there are a lack of studies available that consist of machine learning algorithms and methodologies for multi-step forecasting. It has also been found that lack of collaborations between these different fields is creating a barrier to further developments. In this paper, multi-step time series forecasting are performed on three nonlinear electric load datasets extracted from Open-Power-System-Data.org using two machine learning models. Multi-step forecasting performance of Auto-Regressive Integrated Moving Average (ARIMA) and Long-Short-Term-Memory (LSTM) based Recurrent Neural Networks (RNN) models are compared. Comparative analysis of forecasting performance of the two models reveals that the LSTM model has superior performance in comparison to the ARIMA model for multi-step electric load forecasting.
Human action recognition targets recognising different actions from a sequence of observations and different environmental conditions. A wide different applications is applicable to vision based action recognition research. This can include video surveillance, tracking, health care, and human–computer interaction. However, accurate and effective vision based recognition systems continue to be a big challenging area of research in the field of computer vision. This review introduces the most recent human action recognition systems and provides the advances of state-of-the-art methods. To this end, the direction of this research is sorted out from hand-crafted representation based methods including holistic and local representation methods with various sources of data, to a deep learning technology including discriminative and generative models and multi-modality based methods. Next, the most common datasets of human action recognition are presented. This review introduces several analyses, comparisons and recommendations that help to find out the direction of future research.
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