Search

The paper presents a novel algorithm for computing worst case execution time (WCET) or maximum termination time of real-time systems using the timed automata (TA) model checking technology. The algorithm can work on any arbitrary diagonal-free TA and can handle more cases than previously existing algorithms for WCET computation, as it can handle cycles in TA and decide whether they lead to an infinite WCET. We show soundness of the proposed algorithm and study its complexity. To our knowledge, this is the first model checking algorithm that addresses comprehensively the WCET problem of systems with cyclic behaviour. In [7] Behrmann et al. provide an algorithm for computing the minimum cost/time of reaching a goal state in priced timed automata (PTA). The algorithm has been implemented in the well-known model checking tool UPPAAL to compute the minimum time for termination of an automaton. However, we show that in certain circumstances, when infinite cycles exist, the algorithm implemented in UPPAAL may not terminate, and we provide examples which UPPAAL fails to verify. [ABSTRACT FROM AUTHOR]

Poly (ADP-ribose) polymerase inhibitors (PARPis) have demonstrated clinical activity in patients with BRCA1 and/or BRCA2 mutated breast, ovarian, prostate, and pancreatic cancers. Notably, BRCA mutations are associated with defects in the homologous recombination repair (HRR) pathway. This homologous recombination deficiency (HRD) phenotype can also be observed as genomic instability in tumour cells. Accordingly, PARPi sensitivity has been observed in various tumours with HRD, independent of BRCA mutations. Currently, four PARPis are approved by regulatory agencies for the treatment of cancer across multiple tumour types. Most indications are specific to tumours with a confirmed BRCA mutation, mutations in other HRR-related genes, HRD evidenced by genomic instability, or evidence of platinum sensitivity. Regulatory agencies have also approved companion and complementary diagnostics to facilitate patient selection for each PARPi indication. This review aims to summarise the biological basis, clinical validation, and clinical relevance of the available diagnostic methods and assays to assess HRD. • Several biomarker tests are available to determine patient selection for PARPis. • These tests identify tumours with HRD and help determine sensitivity to PARPis. • We reviewed biomarker tests to assess HRD and their relevance for PARPi treatment. [ABSTRACT FROM AUTHOR]

Modeling wastewater processes supports tasks such as process prediction, soft sensing, data analysis and computer assisted design of wastewater systems. Wastewater treatment processes are large, complex processes, with multiple controlling mechanisms, a high degree of disturbance variability and non-linear (generally stable) behavior with multiple internal recycle loops. Semi-mechanistic biochemical models currently dominate research and application, with data-driven deep learning models emerging as an alternative and supplementary approach. But these modeling approaches have grown in separate communities of research and practice, and so there is limited appreciation of the strengths, weaknesses, contrasts and similarities between the methods. This review addresses that gap by providing a detailed guide to deep learning methods and their application to wastewater process modeling. The review is aimed at wastewater modeling experts who are familiar with established mechanistic modeling approach, and are curious about the opportunities and challenges afforded by deep learning methods. We conclude with a discussion and needs analysis on the value of different ways of modeling wastewater processes and open research problems. [Display omitted] • Process focused mechanistic models are widely used but are application specific. • Deep learning (DL) has applications in prediction, control, sensing, and data analytics. • DL is data-hungry and the lack of high-quality data sets is a key issue for training. • Improve training by transfer learning, and unfitted mechanistic models. [ABSTRACT FROM AUTHOR]