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The purpose of this paper is to investigate the effect of doping element on the microwave absorption performance of hexagonal nano boron nitride (h-nBN)-reinforced basalt fabric (BF)/epoxy composites. A new type of hybrid composite that will be produced by the use of boron nitride as an additive that leads to increased radar absorption capability will be developed and a new material that can be used in aeronautical radar applications.

This study is focused on the microwave absorption properties of h-nBN doped basalt fabric-reinforced epoxy composites. Basalt fabric (BF)/epoxy composites (pure composites) and the BF/h-nBN (1 Wt.% h-nBN doped composites) hybrid composites were fabricated by vacuum infusion method. Phase identification of the composites were performed using X-ray diffraction (XRD), the 2θ scan range was from 10 to 60 with the scanning speed of 3°/min and surface morphologies of the composites were investigated using scanning electron microscopy (SEM). Microwave properties of samples were investigated through transmission/reflection measurements in Agilent brand 2-Port PNA-L Network Analyzer in the frequency range of 3–18 GHz. The prepared sample is positioned between two horn antennas with and without metal plate.

Experimental results show that h-nBN doped composite was synthesized successfully and the produced hexagonal nano boron nitride-added fiber laminated composite material has good absorption behavior when they are used with metallic sheets and good for isolation applications at many points in the 3–18 GHz band.

This paper will contribute to the literature on the use of basalt fabric, which are new types of fibers, and hexagonal nano boron nitride and the effects of boron nitride on radar absorption properties of composite material. It presents detail characterization of each composite by using XRD and scanning electron microscopy.

Estimates the output‐oriented and input‐specific technical efficiency in two samples of Greek, durum wheat farms – organic and conventional ones – using Kalirajan and Obwona’s stochastic varying coefficient regression model. Findings indicate that the organic wheat farms examined are relatively more efficient. Reasons may include lower profit margins and restrictions on inputs permitted, which may force organic farmers to be more cautious with input use. However, technical efficiency scores are still relatively low for both types of wheat farming. Therefore, considerable scope for cost reducing and farm income improvement may exist in both farming modes. This realization could prove crucial for the long‐run viability and the future course of organic wheat farming.

Semantic retrieval and discovery of datasets published as open data remains a challenging task. The datasets inherently originate in the globally distributed web jungle, lacking the luxury of centralized database administration, database schemes, shared attributes, vocabulary, structure and semantics. The existing dataset catalogs provide basic search functionality relying on keyword search in brief, incomplete or misleading textual metadata attached to the datasets. The search results are thus often insufficient. However, there exist many ways of improving the dataset discovery by employing content-based retrieval, machine learning tools, third-party (external) knowledge bases, countless feature extraction methods and description models and so forth.

In this paper, the authors propose a modular framework for rapid experimentation with methods for similarity-based dataset discovery. The framework consists of an extensible catalog of components prepared to form custom pipelines for dataset representation and discovery.

The study proposes several proof-of-concept pipelines including experimental evaluation, which showcase the usage of the framework.

To the best of authors’ knowledge, there is no similar formal framework for experimentation with various similarity methods in the context of dataset discovery. The framework has the ambition to establish a platform for reproducible and comparable research in the area of dataset discovery. The prototype implementation of the framework is available on GitHub.