In the last two decades, data regarding engineering design and product development has increased rapidly. Big data exploration and mining offer numerous opportunities for engineering design; however, owing to the multitude of data sources and formats coupled with the high complexity of the design process, these techniques are yet to be utilised to the best of their full potential. In this study, a comprehensive assessment of the state-of-the-art data-driven engineering design (DDED) in the last 20 years was conducted. A scientometric approach was employed wherein first, a systematic article acquisition procedure was performed, where a dataset of 3339 articles related to engineering design and big data analytics applications were extracted from Web of Science (WoS) and Scopus. Thereafter, this dataset was reduced to a dataset of 366 articles based on concise data screening. The resulting articles were used to analyse the dynamics of research in DDED throughout the last 20 years and to detect the primary research topics related to DDED, the most influential authors, and the papers with the highest impact in the DDED domain. Furthermore, the co-occurrence network of keywords/keyphrases and co-authorship networks were constructed and analysed to reveal the interconnection of the research topics and the collaboration between the most prolific authors. Finally, an insight how big data analytics is being applied through product development activities to support decision-making in engineering design was presented.

We present BiqBin, an exact solver for linearly constrained binary quadratic problems. Our approach is based on an exact penalty method to first efficiently transform the original problem into an instance of Max-Cut, and then to solve the Max-Cut problem by a branch-and-bound algorithm. All the main ingredients are carefully developed using new semidefinite programming relaxations obtained by strengthening the existing relaxations with a set of hypermetric inequalities, applying the bundle method as the bounding routine and using new strategies for exploring the branch-and-bound tree. Furthermore, an efficient C implementation of a sequential and a parallel branch-and-bound algorithm is presented. The latter is based on a load coordinator-worker scheme using MPI for multi-node parallelization and is evaluated on a high-performance computer. The new solver is benchmarked against BiqCrunch, GUROBI, and SCIP on four families of (linearly constrained) binary quadratic problems. Numerical results demonstrate that BiqBin is a highly competitive solver. The serial version outperforms the other three solvers on the majority of the benchmark instances. We also evaluate the parallel solver and show that it has good scaling properties. The general audience can use it as an on-line service available at http://www.biqbin.eu .

In this article, we contribute to the longstanding challenge of how to explain the listener’s acceptability for a particular piece of music, using harmony as one of the crucial dimensions in music, one of the least examined in this context. We propose three measures for the complexity of harmony: (i) the complexity based on usage of the basic tonal functions and parallels in the harmonic progression, (ii) the entropies of unigrams and bigrams in the sequence of chords, and (iii) the regularity of the harmonic progression. Additionally, we propose four measures for the acceptability of musical pieces (perceptual variables): difficulty, pleasantness, recognition, and repeatability. These measures have been evaluated in each musical example within our dataset, consisting of 160 carefully selected musical excerpts from different musical styles. The first and the third complexity measures and the musical style of excerpts were determined by the first author using criteria described in the article, while the entropies were computed by computer using Shannon’s formula, after the harmonic progression was determined. The four perceptual variables were obtained by a group of 21 participants, taking their mean values as the final score. A statistical analysis of this dataset shows that all the measures of complexity are consistent and are together with the musical style important features in explaining the musical acceptability. These relations were further elaborated by regression tree analysis for difficulty and pleasantness after unigram entropy was eliminated due to high correlation with bigram entropy. Results offer reasonable interpretations and also illuminate the relative importance of the predictor variables. In particular, the regularity of the harmonic progression is in both cases the most important predictor.