High‐entropy materials, especially high‐entropy alloys and oxides, have gained significant interest over the years due to their unique structural characteristics and correlated possibilities for tailoring of functional properties. The developments in the area of high‐entropy oxides are highlighted here, with emphasis placed on their fundamental understanding, including entropy‐dominated phase‐stabilization effects and prospective applications, e.g., in the field of electrochemical energy storage. Critical comments on the different classes of high‐entropy oxides are made and the underlying principles for the observed properties are summarized. The diversity of materials design, provided by the entropy‐mediated phase‐stabilization concept, allows engineering of new oxide candidates for practical applications, warranting further studies in this emerging field of materials science.

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In article 1806236, Horst Hahn, Leonardo Velasco, Ben Breitung, and co‐workers highlight developments in the area of high‐entropy oxides, which have been the subject of significant interest recently. Entropy‐dominated phase‐stabilization effects and prospective applications in the field of electrochemical energy storage are discussed.

Air quality forecasting system has acquired high importance in atmospheric pollution due to its negative impacts on the environment and human health. The artificial neural network is one of the most common soft computing methods that can be pragmatic for carving such complex problem. In this paper, we used a multilayer perceptron neural network to forecast the daily averaged concentration of the respirable suspended particulates with aerodynamic diameter of not more than 10 μm (PM10) in Algiers, Algeria. The data for training and testing the network are based on the data sampled from 2002 to 2006 collected by SAMASAFIA network center at El Hamma station. The meteorological data, air temperature, relative humidity, and wind speed, are used as inputs network parameters in the formation of model. The training patterns used correspond to 41 days data. The performance of the developed models was evaluated on the basis index of agreement and other statistical parameters. It was seen that the overall performance of model with 15 neurons is better than the ones with 5 and 10 neurons. The results of multilayer network with as few as one hidden layer and 15 neurons were quite reasonable than the ones with 5 and 10 neurons. Finally, an error around 9% has been reached.

Owing to electric-field screening, the modification of magnetic properties in ferromagnetic metals by applying small voltages is restricted to a few atomic layers at the surface of metals. Bulk metallic systems usually do not exhibit any magneto-electric effect. Here, we report that the magnetic properties of micron-scale ferromagnetic metals can be modulated substantially through electrochemically-controlled insertion and extraction of hydrogen atoms in metal structure. By applying voltages of only ~ 1 V, we show that the coercivity of micrometer-sized SmCo5, as a bulk model material, can be reversibly adjusted by ~ 1 T, two orders of magnitudes larger than previously reported. Moreover, voltage-assisted magnetization reversal is demonstrated at room temperature. Our study opens up a way to control the magnetic properties in ferromagnetic metals beyond the electric-field screening length, paving its way towards practical use in magneto-electric actuation and voltage-assisted magnetic storage.

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