Special Issue: Perovskite Nanostructures: From Material Design to Applications

In the past decade, perovskite materials have attracted great scientific and technological interest due to their interesting opto-electronic properties. Nanostructuring of the perovskites, due to their reduced dimensions are advantageous in offering large surface area, controlled transport and charge carrier mobility, strong absorption and photoluminescence, and confinement effects. These features, together with the unique tunability in composition, shape, and functionalities in addition to the ability to form efficient, low-cost, and light-active structures make the perovskite nanostructures efficient functional components for multiple applications, ranging from photovoltaics and batteries to lasing and light-emitting diodes. The purpose of this Special Issue is to give an overview of the latest experimental findings concerning the tunability in composition, shape, functionalities, growth conditions, and synthesis procedures of perovskite structures and to identify the critical parameters for producing materials with functional characteristics.

Defects and impurities in colloidal Ga2O3 nanocrystals: new opportunities for photonics and lighting

Defects, both native and extrinsic, critically determine functional properties of metal oxides. Gallium oxide has recently gained significant attention for its promise in microelectronics, owing to the unique combination of conductivity and high breakdown voltage, and solid-state lighting, owing to the strong photoluminescence in the visible spectral region. These properties are associated with the presence of native defects that can form both donor and acceptor states in Ga2O3. Recently, Ga2O3 nanocrystal synthesis in solution and optical glasses has been developed, allowing for a range of new applications in photonics, lighting, and photocatalysis. This review focuses on the structure and properties of Ga2O3 nanocrystals with a particular emphasis on the electronic structure and interaction of defects in reduced dimensions and their role in the observed photoluminescence properties. In addition to native defects, the effect of selected external impurities, including lanthanide and aliovalent dopants, and alloying on the emission properties of Ga2O3 nanocrystals are also discussed.

Analysis on Ensemble Methods for the Prediction of Cardiovascular Disease

Coronary Heart disease is one of the dominant sources of death and morbidity for the people worldwide. The identification of cardiac disease in the clinical review is considered one of the main problems. As the amount of data grows increasingly, interpretation and retrieval become even more complex. In addition, the Ensemble learning prediction model seems to be an important fact in this area of study. The prime aim of this paper is also to forecast CHD accurately. This paper is intended to offer a modern paradigm for prediction of cardiovascular diseases with the use of such processes such as pre-processing, detection of features, feature selection and classification. The pre-processing will initially be performed using the ordinal encoding technique, and the statistical and the features of higher order are extracted using the Fisher algorithm. Later, the minimization of record and attribute is performed, in which principle component analysis performs its extensive part in figuring out the “curse of dimensionality.” Lastly, the process of prediction is carried out by the different Ensemble models (SVM, Gaussian Naïve Bayes, Random forest, K-nearest neighbor, Logistic regression, decision tree and Multilayer perceptron that intake the features with reduced dimensions. Finally, in comparison to such success metrics the reliability of the proposal work is compared and its superiority has been confirmed. From the analysis, Naïve bayes with regards to accuracy is 98.4% better than other Ensemble algorithms.

Kinematic study of a road power generator for the port transport network

Nowadays, energy-saving applications are based on alternative energy sources that enable autonomous powering of electrical consumers. Within the port transport network, 62% of freight transport is carried by road. One solution for the alternative generation of electrical energy for lighting and power supply of small power consumers is the use of road electromechanical systems such as artificial road bumps with an electromagnetic generator that converts the energy of vehicle movement into electrical energy. This article deals with a road power generator transducer with a slider-rocker kinematic pair for converting translational motion into rotational motion. The authors conducted a kinematic study of the actuating force transducer and analysed its performance at various connecting rod lengths with the required speed and dimensional criteria. They determined the effect of the connecting rod length on the trajectory and speed of the rocker arm. Analysis has shown that a connecting rod length of 100 and 150 mm, providing a working section with a uniform increase in speed of the rocker arm, satisfies the specified requirements. For further investigation of the electromechanical power system, the researchers adopted a 100 mm long connecting rod, as this allows the unit to be realised with reduced dimensions.

Urdu Handwritten Characters Data Visualization and Recognition Using Distributed Stochastic Neighborhood Embedding and Deep Network

In this paper, we make use of the 2-dimensional data obtained through t-Stochastic Neighborhood Embedding (t-SNE) when applied on high-dimensional data of Urdu handwritten characters and numerals. The instances of the dataset used for experimental work are classified in multiple classes depending on the shape similarity. We performed three tasks in a disciplined order; namely, (i) we generated a state-of-the-art dataset of both the Urdu handwritten characters and numerals by inviting a number of native Urdu participants from different social and academic groups, since there is no publicly available dataset of such type till date, then (ii) applied classical approaches of dimensionality reduction and data visualization like Principal Component Analysis (PCA), Autoencoders (AE) in comparison with t-Stochastic Neighborhood Embedding (t-SNE), and (iii) used the reduced dimensions obtained through PCA, AE, and t-SNE for recognition of Urdu handwritten characters and numerals using a deep network like Convolution Neural Network (CNN). The accuracy achieved in recognition of Urdu characters and numerals among the approaches for the same task is found to be much better. The novelty lies in the fact that the resulting reduced dimensions are used for the first time for the recognition of Urdu handwritten text at the character level instead of using the whole multidimensional data. This results in consuming less computation time with the same accuracy when compared with processing time consumed by recognition approaches applied to other datasets for the same task using the whole data.

Magnetless Optical Circulator Based on an Iron Garnet with Reduced Magnetization Saturation

A three-port circulator for optical communication systems comprising a photonic crystal slab made of a magneto-optical material in which an magnetizing element is not required to keep its magnetic domains aligned is suggested for the first time. By maximizing the incorporation of europium to its molecular formula, the magneto-optical material can remain in the saturated magnetic state even in the absence of an external DC magnetic field. Two- and three-dimensional simulations of the device performed with full-wave electromagnetic solvers based on the finite element method demonstrate that, at the 1550 nm wavelength, the insertion loss, isolation, and reflection levels are equal to or better than −1 dB, −14 dB, and −20 dB, respectively. Since its operation does not require an electromagnet or a permanent magnet, the suggested circulator is much more compact, being able to reach footprints in the range of three orders of magnitude smaller, when compared to other circulator designs referred to in the literature and the presented results can be useful for the design of other nonreciprocal devices with reduced dimensions for optical communication systems.

Sparse HJ Biplot: A New Methodology via Elastic Net

The HJ biplot is a multivariate analysis technique that allows us to represent both individuals and variables in a space of reduced dimensions. To adapt this approach to massive datasets, it is necessary to implement new techniques that are capable of reducing the dimensionality of the data and improving interpretation. Because of this, we propose a modern approach to obtaining the HJ biplot called the elastic net HJ biplot, which applies the elastic net penalty to improve the interpretation of the results. It is a novel algorithm in the sense that it is the first attempt within the biplot family in which regularisation methods are used to obtain modified loadings to optimise the results. As a complement to the proposed method, and to give practical support to it, a package has been developed in the R language called SparseBiplots. This package fills a gap that exists in the context of the HJ biplot through penalized techniques since in addition to the elastic net, it also includes the ridge and lasso to obtain the HJ biplot. To complete the study, a practical comparison is made with the standard HJ biplot and the disjoint biplot, and some results common to these methods are analysed.