Learning from accidents: Analysis of multi-attribute events and implications to improve design and reduce human errors

High-technology accidents are likely to occur under a complex interaction of multiple active failures and latent conditions, and recent major accidents investigations are increasingly highlighting the role of human error or human-related factors as significant contributors. Latent conditions might have long incubation periods, which implies that a number of design failures may be embedded in systems until human errors trigger an accident sequence. Consequently, there is a need to scrutinise the relationship between enduring design deficiencies and human erroneous actions as a conceivable way to minimise accidents. This study will tackle this complex problem by applying an artificial neural network approach to a proprietary multi-attribute accident dataset, in order to disclose multidimensional relationships between human errors and design failures. Clustering and data mining results are interpreted to offer further insight into the latent conditions embedded in design. Implications to support the development of design failure prevention schemes are then discussed

SMT-based Model Checking for Recursive Programs

We present an SMT-based symbolic model checking algorithm for safety verification of recursive programs. The algorithm is modular and analyzes procedures individually. Unlike other SMT-based approaches, it maintains both "over-" and "under-approximations" of procedure summaries. Under-approximations are used to analyze procedure calls without inlining. Over-approximations are used to block infeasible counterexamples and detect convergence to a proof. We show that for programs and properties over a decidable theory, the algorithm is guaranteed to find a counterexample, if one exists. However, efficiency depends on an oracle for quantifier elimination (QE). For Boolean Programs, the algorithm is a polynomial decision procedure, matching the worst-case bounds of the best BDD-based algorithms. For Linear Arithmetic (integers and rationals), we give an efficient instantiation of the algorithm by applying QE "lazily". We use existing interpolation techniques to over-approximate QE and introduce "Model Based Projection" to under-approximate QE. Empirical evaluation on SV-COMP benchmarks shows that our algorithm improves significantly on the state-of-the-art.Comment: originally published as part of the proceedings of CAV 2014; fixed typos, better wording at some place

Decision Procedure for Entailment of Symbolic Heaps with Arrays

This paper gives a decision procedure for the validity of en- tailment of symbolic heaps in separation logic with Presburger arithmetic and arrays. The correctness of the decision procedure is proved under the condition that sizes of arrays in the succedent are not existentially bound. This condition is independent of the condition proposed by the CADE-2017 paper by Brotherston et al, namely, one of them does not imply the other. For improving efficiency of the decision procedure, some techniques are also presented. The main idea of the decision procedure is a novel translation of an entailment of symbolic heaps into a formula in Presburger arithmetic, and to combine it with an external SMT solver. This paper also gives experimental results by an implementation, which shows that the decision procedure works efficiently enough to use

Structural properties of crumpled cream layers

The cream layer is a complex heterogeneous material of biological origin which forms spontaneously at the air-milk interface. Here, it is studied the crumpling of a single cream layer packing under its own weight at room temperature in three-dimensional space. The structure obtained in these circumstances has low volume fraction and anomalous fractal dimensions. Direct means and noninvasive NMR imaging technique are used to investigate the internal and external structure of these systems.Comment: 9 pages, 4 figures, accepted in J. Phys. D: Appl. Phy