This article presents an algorithmic framework for real-time condition monitoring and state forecasting using multivariate data demonstrated on thermal imagery data of a ship's engine. The proposed method aims to improve the accuracy, efficiency, and robustness of condition monitoring and state predictions by identifying the most informative sampling locations of high-dimensional datasets and extracting the underlying dynamics of the system. The method is based on a combination of Proper Orthogonal Decomposition (POD), Optimal Sampling Location (OSL), and Dynamic Mode Decomposition (DMD), allowing the identification of key features in the system's behavior and predicting future states. Based on thermal imagery data, it is shown how thermal areas of interest can be classified via POD. By extracting the POD modes of the data, dimensions can be drastically reduced and via OSL, optimal sampling locations are found. In addition, nonlinear kernel-based Support Vector Regression (SVR) is used to build models between the optimal locations, enabling the imputation of erroneous data to improve the overall robustness. To build predictive data-driven models, DMD is applied on the subspace obtained by OSL, which leads to an intensive lower demand of computational resources, making the proposed method real-time applicable. Furthermore, an unsupervised approach for anomaly detection is proposed using OSL. The anomaly detection framework is coupled with the state prediction framework, which extends the capabilities to real-time anomaly predictions. In summary, this study proposes a robust predictive condition monitoring framework for real-time risk assessment.

This year we celebrate the 150th anniversary of the law of mass action. This law is often assumed to have been "there" forever, but it has its own history, background, and a definite starting point. The law has had an impact on chemistry, biochemistry, biomathematics, and systems biology that is difficult to overestimate. It is easily recognized that it is the direct basis for computational enzyme kinetics, ecological systems models, and models for the spread of diseases. The article reviews the explicit and implicit role of the law of mass action in systems biology and reveals how the original, more general formulation of the law emerged one hundred years later ab initio as a very general, canonical representation of biological processes.

BACKGROUND: Multivariate approaches are important due to their versatility and applications in many fields as it provides decisive advantages over univariate analysis in many ways. Genome wide association studies are rapidly emerging, but approaches in hand pay less attention to multivariate relation between genotype and phenotype. We introduce a methodology based on a BLAST approach for extracting information from genomic sequences and Soft- Thresholding Partial Least Squares (ST-PLS) for mapping genotype-phenotype relations. RESULTS: Applying this methodology to an extensive data set for the model yeast Saccharomyces cerevisiae, we found that the relationship between genotype-phenotype involves surprisingly few genes in the sense that an overwhelmingly large fraction of the phenotypic variation can be explained by variation in less than 1% of the full gene reference set containing 5791 genes. These phenotype influencing genes were evolving 20% faster than non-influential genes and were unevenly distributed over cellular functions, with strong enrichments in functions such as cellular respiration and transposition. These genes were also enriched with known paralogs, stop codon variations and copy number variations, suggesting that such molecular adjustments have had a disproportionate influence on Saccharomyces yeasts recent adaptation to environmental changes in its ecological niche. CONCLUSIONS: BLAST and PLS based multivariate approach derived results that adhere to the known yeast phylogeny and gene ontology and thus verify that the methodology extracts a set of fast evolving genes that capture the phylogeny of the yeast strains. The approach is worth pursuing, and future investigations should be made to improve the computations of genotype signals as well as variable selection procedure within the PLS framework.