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The purpose of this paper is to produce and investigate a reliable and effective aircraft brake lining material.

In total, four different Cu‐based brake lining samples were produced by powder metallurgy. The produced samples were tested in an experimental setup on friction, wear and mechanical performance, then metallographic and scanning electron microscope inspections were performed. Hardness, compression strength and density of copper‐based brake linings were also tested. Moreover, a numerical simulation of the structural reaction of a sample under compression was carried out by using ANSYS.

According to tests, suitable aircraft brake lining material composition would be 75% Cu, 5% Fe, 5% Sn, 5% Graphite, 5% SiO₂, 5% MoS₂.

The paper will help designers choose the most proper ingredient combination for aircraft brake lining materials.

The paper notes that adding silicon dioxide and graphite forms a porosive structure. In addition, porosity brings a low strength and high wear rate.

Purpose: To examine the effects of Industry 5.0 on distance education.

Need for the study: As educational institutions worldwide strive to adapt to rapid technological advancements and shifting educational demands, this research provides actionable insights on integrating these technologies to not only augment learning outcomes but also to ensure inclusivity and accessibility in education. Exploring the convergence of Industry 5.0 and distance learning is imperative for shaping future educational practices and policies.

Methodology: Systematic analysis: 18 articles obtained from ScienceDirect, Scopus, Web of Science (WoS), ERIC, and Google Scholar databases.

Findings: The findings highlight the need for in-depth research into the relationship between Industry 5.0 and distance education, revealing the importance of technological integrations and educational methodologies in these fields. It has been determined that certain countries, such as Indonesia, are pioneers in this field, but a global spread and participation has not yet been realised. Frequently used keywords and methodological trends in the studies indicate that the research field needs to expand and diversify. The codes and categories obtained from the studies were categorised under the following themes: (1) Transformation in Education, (2) Technological Innovations and Applications, (3) Pandemic and Digital Transformation, and (4) Next-Generation Communication Networks.

Practical implications: Implications of this study are profound, offering educators, policymakers, and technology developers a blueprint for integrating Industry 5.0 technologies into distance learning frameworks. Demonstrating how artificial intelligence (AI), robotics, and human–machine interfaces can be leveraged to tailor educational experiences, this research highlights pathways to enhance learner engagement and achievement across educational contexts.

Machine learning algorithms are useful to effectively analyse, and therefore automatically classify online reviews. The purpose of this paper is to demonstrate a novel text-mining framework and its potential for use in the classification of unstructured hotel reviews.

Well-known data mining methods (i.e. boosted decision trees (BDT), classification and regression trees (C&RT) and random forests (RF)) in conjunction with incorporating five-fold cross-validation are used to predict the star rating of the hotel reviews. To achieve this goal, extracted features are used to create a composite variable (CV) to deploy into machine learning algorithms as the main feature (variable) during the learning process.

BDT outperformed the other alternatives in the exact accuracy rate (EAR) and multi-class accuracy rate (MCAR) by reaching the accuracy rates of 0.66 and 0.899, respectively. Moreover, phrases such as “clean”, “friendly”, “nice”, “perfect” and “love” are shown to be associated with four and five stars, whereas, phrases such as “horrible”, “never”, “terrible” and “worst” are shown to be associated with one and two-star hotels, as it would be the intuitive expectation.

To the best of the knowledge, there is no study in the existent literature, which synthesizes the knowledge obtained from individual features and uses them to create a single composite variable that is powerful enough to predict the star rates of the user-generated reviews. This study believes that the proposed method also provides policymakers with a unique window in the thoughts and opinions of individual users, which may be used to augment the current decision-making process.

The purpose of this paper is to enhance the limited fluorescence of mica titania (MT) effect pigments by coating them with peripherally substituted zinc phthalocyanines (ZnPc).

The effects of deposition medium, temperature, time, initial organic pigment/MT ratio on deposition behaviour and efficiencies were investigated separately for ZnPc, nitro (TNZnPc) and amino (TAZnPc) substituted ZnPc’s.

TNZnPc could be deposited in the form of well-defined crystals on MT with per cent 64 ± 5 efficiency in chloroform at 50°C within 5 h and the amount deposited was linearly dependent on the initial TNZnPc concentration. TNZnPc fluorescence emissions, which appear mainly at 460 and 685 nm in pure pigments, could be observed in combination with MT (MT-TNZnPc) as well. A spectral analysis on the non-overlapping region of the spectrum with two different excitations (385 and 630 nm) revealed that, respectively, up to threefold to sixfold increase is possible to attain, depending on the excitation wavelengths.

Efficiency of deposition could not be taken above per cent 11 ± 4 with TAZnPc. Although ZnPc could be deposited with per cent 57 ± 3 efficiency, the pigment was not effective in imposing its fluorescence characteristics over MT.

Combining inorganic effect pigments with organic dye molecules is an idea that has been elaborated mainly for producing different colour effects and stabilization of dye molecules against agglomeration. Here, for the first time, it is used to enhance the fluorescence of the effect pigments.