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The magnesium-rich intermetallic ternary compound NdNiMg15 in space group P-4/nmm (no. 26) space group in trigonal phase is studied theoretically using hybrid functional theory (HP-B3PW91) in the density functional theory (DFT) framework. The calculation results are in good agreement with experimental data. The metallic nature of the compound is estimated from the electronic properties. The magnetic as well as metallic character of the compound is due to the Nd 4f electrons. The title compound exhibits good conductivity as revealed by the thermoelectric analysis. The magnetic phase of the material indicates its stability within the G-type antiferromagnetic phase (G-AFM), governed by direct Nd–Nd exchange interactions with a magnetic moment of 3.57µB/Nd per unit cell. The AFM phase of the studied compound is also confirmed by post-DFT measurements at TN = 8.15 K as well as θ = −2.5 K. The antiferromagnetic nature of this compound could enable its use in magnetic probes to read data stored in antiferromagnetic moments while leaving them unaffected by disturbing magnetic fields.

Sol-gel procedure was taken into consideration to produce ferrite series with chemical formula MnYbyFe2-yO4 with y = 0.00 to 0.10 (step = 0.025). Structural, electrical and magnetic aspects of ytterbium (Yb) switched Mn-ferrites were the main focus. Every sample exhibited cubic phase spinel assembly. Exchange of ytterbium with iron in the system produced enlargement in coercivity while abridgment in remanence and saturation magnetization. Involvement of Yb ion in the ferrite assembly led to reduction of both real and imaginary constituents of permittivity. The ytterbium introduction in the lattice enhanced the value of impedance. The examination of hysteresis loop was done under the range of -2K to +2KOe. Temperature dependent resistivity, activation energy and drift mobility were carried out by two probe method. The characterization outcomes recommended that the compounds are appropriate for microwave absorption usages.

Manufacturing has been rejuvenated by automation and digitalization. This has brought forth the new industrial era also called Industry 4.0. During the last few years, we have collaborated with companies from various industries that have all been going through this transformation. Through these collaborations, we have collected numerous examples of (sometimes troublesome) experiences with Big Data applications of production analytics. These experiences reflect the current state of production data and the challenges it poses. Our goal in this paper is to share those experiences and lessons learned in dealing with practical issues from data acquisition to data management and finally to data analytics.

In the current era of Industry 4.0, sensor data used in connection with machine learning algorithms can help manufacturing industries to reduce costs and to predict failures in advance. This paper addresses a binary classification problem found in manufacturing engineering, which focuses on how to ensure product quality delivery and at the same time to reduce production costs. The aim behind this problem is to predict the number of faulty products, which in this case is extremely low. As a result of this characteristic, the problem is reduced to an imbalanced binary classification problem. The authors contribute to imbalanced classification research in three important ways. First, the industrial application coming from the electronic manufacturing industry is presented in detail, along with its data and modelling challenges. Second, a modified cost-sensitive classification strategy based on a combination of Voronoi diagrams and genetic algorithm is applied to tackle this problem and is compared to several base classifiers. The results obtained are promising for this specific application. Third, in order to evaluate the flexibility of the strategy, and to demonstrate its wide range of applicability, 25 real-world data sets are selected from the KEEL repository with different imbalance ratios and number of features. The strategy, in this case implemented without a predefined cost, is compared with the same base classifiers as those used for the industrial problem.

A facile chemical precipitation method for slab and flower like WO3 microstructure having three-dimensional (3D) architecture is reported. The as-prepared WO3 powder specimens were characterized by X-ray diffraction (XRD) combined with Rietveld refinements, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy and UV–vis spectroscopy. SEM and XRD results revealed that the hierarchical WO3 microstructures with uniform flower-like morphology are congregated by number of interleaving nanosheets which have thickness of 150–180 nm and are crystallized in a single monoclinic WO3 phase. Thermo-gravimetric analysis shows a major weight loss of 8% due to evaporation of adsorbed water. The impedance spectroscopy of compact WO3 pellets showed that all samples were semiconducting with a minute difference in electrical properties. The photo-catalytic activity of the as-prepared WO3 samples was evaluated by the degradation of Rhodamine B (RhB) under ultra-violet light irradiation. The results showed that 3D hierarchical architectures exhibits high photo-catalytic efficiency compared with the slab like structure due to their porous hierarchical structures. The optical band gap energy of slab like structure is found to be lowered compared to flower-like WO3 structures. These tunable optical and transport features allow the development of new materials for potential applications as photo-catalysts, transparent conducting electrodes, electrochromic and sensor devices.

Classification is a major constituent of the data mining tool kit. Well-known methods for classification are either built on the principle of logic or on statistical reasoning. For imbalanced and noisy cases, classification may however fail to deliver on basic data mining goals, i.e., identifying statistical dependencies in data. In this article, we propose a novel strategy for data mining based on partitioning of the feature space through Voronoi tessellation and Genetic Algorithm, where the latter is applied to solve a combinatorial optimization problem. We apply the suggested methodology to a range of classification problems of varying imbalance and noise and compare the performance of the suggested method with well-known classification methods such as (SVM, KNN, and ANN). The results obtained indicate the proposed methodology to be well suited for data mining tasks in case of highly imbalanced classes and significant noise.

Today, in modern factories, each step in manufacturing produces a bulk of valuable as well as highly precise information. This provides a great opportunity for understanding the hidden statistical dependencies in the process. Systematic analysis and utilization of advanced analytical methods can lead towards more informed decisions. In this article we discuss some of the challenges related to big data analysis in manufacturing and relevant solutions to some of these challenges.

Summary Carrier frequency offset (CFO) results in multiaccess interference and intersymbol interference and degrades the bit error rate performance. Mitigating the effects of CFO always burdens the receiver with respect to architecture and power. This disadvantage increases with increase in number of users. In the present work, bit error rate analysis of multiuser group orthogonal–orthogonal frequency division multiple access scheme for variable rate and single rate over pedestrian channel in the presence of CFO is presented. Through analysis, it is proved that by incorporating carrier interferometry codes in the scheme, CFO effects can be removed without burdening receiver. It is also shown by analysis that effect of CFO is rate dependent. Finally, simulation results are provided to confirm the analytical results.