To carry out performance evaluation of an asynchronous system, the system is modeled as Time Petri Net (TPN) and an iteration of Petri net simulations produces its performance index. The TPN model needs to satisfy required properties such as deadlock freeness. We proposed a symbolic representation of TPN for SAT-based bounded model checking. In the proposed encoding scheme, firing of transitions and elapsing of place delays are expressed as boolean formulas discretely. Our representation can work with relaxed ∃-step semantics which enables to perform each step by two or more transitions. We applied the encoding to example TPN models and checked the deadlock freeness using SAT solver. The results of experiments demonstrated the effectiveness of the proposed representation.

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SUMMARYDuring a software development phase where a product is progressively elaborated, it is difficult to guarantee that the refined product retains its original behaviors. In this paper, we propose a method to detect refinement errors in UML sequence diagrams using LTSA (Labeled Transition System Analyzer). The method integrates multiple sequence diagrams using hMSC (high-level Message Sequence Charts) into a sequence diagram. Then, the method translates the diagram into FSP representation, which is the input language of LTSA. The method also supports some combined fragment operators in the UML 2.0 specification. We applied the method to some examples of refined sequence diagrams and checked the correctness of refinement. As a result, we confirmed the method can detect refinement errors in practical time.

We examined the effects of resilience on life stress by measuring subjective and physiological responses. Subjects were 32 college students who reported no remarkable subjective burden at the start of the study (initial period: T1), but some level of stressful burden 3 months later (second period: T2). Resilience levels were evaluated using the Bidimensional Resilience Scale (BRS), which measures innate and acquired resilience. The subjective stress level was assessed with the Stress Response Scale-18 (SRS-18). Saliva was also collected to measure the salivary secretory immunoglobulin A (sIgA) level. BRS was performed at T1, and SRS and saliva collection were administered at both T1 and T2. The subjects were divided into high-and low-resilience groups according to their median scores at T1. Additionally, the high-resilience group was classified as either high-innate resilience (HIR) or high-acquired resilience (HAR), and the low-resilience group as low-innate resilience (LIR) or low-acquired resilience (LAR), respectively, to reveal information in more detail. The depression-anxiety score for the SRS-18 in the low-resilience group was significantly higher than that in the high-resilience group at T2. The sIgA level of the high-resilience group was significantly higher than that of low-resilience group at T2. There were significant negative correlations between innate resilience and depression-anxiety, and total SRS-18 score; and negative correlations among acquired resilience and depression-anxiety, helplessness, total SRS-18 score, and sIgA level, respectively. These results suggested that the stress response in low-innate and acquired resilience differs depending on the length of time elapsed with stress, because low-innate and acquired resilience causes higher susceptibility to "depression-anxiety" as a psychological stress response factor. On the other hand, higher acquired resilience improved immune function in the presence of subjective burden.