Global Solar Irradiation Modelling and Prediction Using Machine Learning Models for Their Potential Use in Renewable Energy Applications

 Mathematics 2022, 10(24), 4746

Global solar irradiation is an important variable that can be used to determine the suitability of an area to install solar systems; nevertheless, due to the limitations of requiring measurement stations around the entire world, it can be correlated with different meteorological parameters. To confront this issue, different locations in Rias Baixas (Autonomous Community of Galicia, Spain) and combinations of parameters (month and average temperature, among others) were used to develop various machine learning models (random forest -RF-, support vector machine -SVM- and artificial neural network -ANN-). These three approaches were used to model and predict (one month ahead) monthly global solar irradiation using the data from six measurement stations. Afterwards, these models were applied to seven different measurement stations to check if the knowledge acquired could be extrapolated to other locations. In general, the ANN models offered the best results for the development and testing phases of the model, as well as for the phase of knowledge extrapolation to other locations. In this sense, the selected ANNs obtained a mean absolute percentage error (MAPE) value between 3.9 and 13.8% for the model development and an overall MAPE between 4.1 and 12.5% for the other seven locations. ANNs can be a capable tool for modelling and predicting monthly global solar irradiation in areas where data are available and for extrapolating this knowledge to nearby areas.

Host–Guest Complexes

 Int. J. Mol. Sci. 2022, 23(24), 15730

This article belongs to the Special Issue Host-Guest Complexes and corresponds with the special issue editorial. In this Special Issue, we hope to address both the structural aspects of the formation and stability of these inclusion complexes as well as the energetic aspects associated with them, together with the different instrumental techniques used to characterise them, addressing the aspects related to molecular recognition and conformational switching. Of course, we must also take into account the aspects related to the technological applications of these compounds. In fact, they show important potentialities in topics such as superconductivity phenomena, the design of sensors, and food chemistry, agricultural chemistry, or their applications in matters of the environment.

Comparison of machine learning techniques for reservoir outflow forecasting

 Nat. Hazards Earth Syst. Sci., 22, 3859–3874, 2022

Reservoirs play a key role in many human soci- eties due to their capability to manage water resources. In addition to their role in water supply and hydropower pro- duction, their ability to retain water and control the flow makes them a valuable asset for flood mitigation. This is a key function, since extreme events have increased in the last few decades as a result of climate change, and therefore, the application of mechanisms capable of mitigating flood dam- age will be key in the coming decades. Having a good esti- mation of the outflow of a reservoir can be an advantage for water management or early warning systems. When histori- cal data are available, data-driven models have been proven a useful tool for different hydrological applications. In this sense, this study analyzes the efficiency of different machine learning techniques to predict reservoir outflow, namely mul- tivariate linear regression (MLR) and three artificial neu- ral networks: multilayer perceptron (MLP), nonlinear au- toregressive exogenous (NARX) and long short-term mem- ory (LSTM). These techniques were applied to forecast the outflow of eight water reservoirs of different characteristics located in the Miño River (northwest of Spain). In general, the results obtained showed that the proposed models pro- vided a good estimation of the outflow of the reservoirs, im- proving the results obtained with classical approaches such as to consider reservoir outflow equal to that of the previous day. Among the different machine learning techniques anaAbstract. Reservoirs play a key role in many human soci- eties due to their capability to manage water resources. In addition to their role in water supply and hydropower pro- duction, their ability to retain water and control the flow makes them a valuable asset for flood mitigation. This is a key function, since extreme events have increased in the last few decades as a result of climate change, and therefore, the application of mechanisms capable of mitigating flood dam- age will be key in the coming decades. Having a good esti- mation of the outflow of a reservoir can be an advantage for water management or early warning systems. When histori- cal data are available, data-driven models have been proven a useful tool for different hydrological applications. In this sense, this study analyzes the efficiency of different machine learning techniques to predict reservoir outflow, namely mul- tivariate linear regression (MLR) and three artificial neu- ral networks: multilayer perceptron (MLP), nonlinear au- toregressive exogenous (NARX) and long short-term mem- ory (LSTM). These techniques were applied to forecast the outflow of eight water reservoirs of different characteristics located in the Miño River (northwest of Spain). In general, the results obtained showed that the proposed models pro- vided a good estimation of the outflow of the reservoirs, im- proving the results obtained with classical approaches such as to consider reservoir outflow equal to that of the previous day. Among the different machine learning techniques analyzed, the NARX approach was the option that provided the best estimations on average.

Prospecting the role of nanotechnology in extending the shelf-life of fresh produce and in developing advanced packaging

 Food Packaging and Shelf Life, 34, 100955, 2022

Fruits and vegetables contain excellent amounts of nutritional and bioactive compounds. The maintenance their shelf-life and prevention from decay, quality deterioration, and microbial spoilage of the fresh produce are the major challenges for food processing industries. Several techniques such as physical, chemical, and bio-preservation are used to extend the shelf-life of fresh produce. However, these techniques could not fully sustain because of their higher cost, and side-effects. In past few decades, nanotechnology came into existence, which provides a green, novel and cutting-edge solution to preserve fresh produce. Organic, inorganic, and combined engineered nanomaterials (nano-particles, nano-composites, nano-emulsion, nano-tracers, nano-packaging, and nano-sensors) are broadly used in shelf-life improvement of fresh produce because of their broad surface to volume ratio, higher barrier property, and better antimicrobial spectrum. This review comprehensively discusses various methods, components, and roles of nanotechnology for extending the shelf-life of fresh produce and scope of developing advanced packaging.