Constraint Based Diagnosis Algorithms For Multiprocessors

Constraint-based diagnosis algorithms for multiprocessors A. Petri, P. Urban, J. Altmann, M. Dal Cin, E. Selenyi, K. Tilly, A. Pataricza In the latest years, new ideas appeared in system level diagnosis of multiprocessor systems. In contrary to the traditional diagnosis models (like PMC, BGM, etc.) which use strictly graph-oriented methods to determine the faulty components in a system, these new theories prefer AI-based algorithms, especially CSP methods. Syndrome decoding, the basic problem of self-diagnosis, can be easily transformed into constraints between the state of the tester and the tested components. Therefore, the diagnosis algorithm can be derived from a special constraint solving algorithm. The "benign" nature of the constraints (all their variables, representing the fault states of the components, have a very limited domain; the constraints are simple and similar to each other) reduces the algorithm's complexity so it can be converted to a powerful distributed diagnosis method with a minimal overhead. Experimental algorithms (using both centralized and distributed approach) were implemented for a Parsytec GC massively parallel multiprocessor system

Intravenous levosimendan-norepinephrine combination during off-pump coronary artery bypass grafting in a hemodialysis patient with severe myocardial dysfunction

This the case of a 63 year-old man with end-stage renal disease (on chronic hemodialysis), unstable angina and significantly impaired myocardial contractility with low left ventricular ejection fraction, who underwent off-pump one vessel coronary bypass surgery. Combined continuous levosimendan and norepinephrine infusion (at 0.07 μg/kg/min and 0.05 μg/kg/min respectively) started immediately after anesthesia induction and continued for 24 hours. The levosimendan/norepinephrine combination helped maintain an appropriate hemodynamic profile, thereby contributing to uneventful completion of surgery and postoperative hemodynamic stability. Although levosimendan is considered contraindicated in ESRD patients, this case report suggests that combined perioperative levosimendan/norepinephrine administration can be useful in carefully selected hemodialysis patients with impaired myocardial contractility and ongoing myocardial ischemia, who undergo off-pump myocardial revascularization surgery

Constraint Based System-Level Diagnosis of Multiprocessors

Massively parallel multiprocessors induce new requirements for system-level fault diagnosis, like handling a huge number of processing elements in an inhomogeneous system. Traditional diagnostic models (like PMC, BGM, etc.) are insufficient to fulfill all of these requirements. This paper presents a novel modelling technique, based on a special area of artificial intelligence (AI) methods: constraint satisfaction (CS). The constraint based approach is able to handle functional faults in a similar way to the Russel-Kime model. Moreover, it can use multiple-valued logic to deal with system components having multiple fault modes. The resolution of the produced models can be adjusted to fit the actual diagnostic goal. Consequently, constrint based methods are applicable to a much wider range of multiprocessor architectures than earlier models. The basic problem of system-level diagnosis, syndrome decoding, can be easily transformed into a constraint satisfaction problem (CSP). Thus, the diagnosis algorithm can be derived from the related constraint solving algorithm. Different abstraction leveles can be used for the various diagnosis resolutions, employing the same methodology. As examples, two algorithms are described in the paper; both of them is intended for the Parsytec GCel massively parallel system. The centralized method uses a more elaborate system model, and provides detailed diagnostic information, suitable for off-line evaluation. The distributed method makes fast decisions for reconfiguration control, using a simplified model. Keywords system-level self-diagnosis, massively parallel computing systems, constraint satisfaction, diagnostic models, centralized and distributed diagnostic algorithms

Levosimendan Administration in Limb Ischemia: Multicomponent Signaling Serving Kidney Protection

AIMS AND OBJECTIVES: Acute renal failure is a severe complication of lower extremity major arterial reconstructions, which could even be fatal. Levosimendan is a dual-acting positive inotropic and vasodilatory agent, which is suspected to have protective effects against cardiac ischemia. However, there is no data available on lower limb or remote organ ischemic injuries therefore the aim of the study was to investigate the effect of levosimendan on lower limb ischemia-reperfusion injury and the corollary renal dysfunction. METHODS: Male Wistar rats underwent 180 min bilateral lower limb ischemia followed by 4 or 24 hours of reperfusion. Intravenous Levosimendan was administered continuously (0.2mug/bwkg/min) throughout the whole course of ischemia and the first 3h of reperfusion. Results were compared with sham-operated and ischemia-reperfusion groups. Hemodynamic monitoring was performed by invasive arterial blood pressure measurement. Kidney and lower limb muscle microcirculation was registered by a laser Doppler flowmeter. After 4h and 24h of reperfusion, serum, urine and histological samples were collected. RESULTS: Systemic hemodynamic parameters and microcirculation of kidney and the lower limb significantly improved in the Levosimendan treated group. Muscle viability was significantly preserved 4 and 24 hours after reperfusion. At the same time, renal functional laboratory tests and kidney histology demonstrated significantly less expressive kidney injury in Levosimendan groups. TNF-alpha levels were significantly less elevated in the Levosimendan group 4 hours after reperfusion. CONCLUSION: The results claim a protective role for Levosimendan administration during major vascular surgeries to prevent renal complications

Resilient Computing Curriculum Draft -- ReSIST NoE Deliverable D16

This Deliverable presents the first version of ReSIST's Curriculum in Resilient Computing, limited to the description of the syllabi for the first year (Semesters 1 and 2) and indicates the line and title for the curriculum in the second year (semesters 3 and 4) and propose it to the general discussion for improvements. The curriculum will be updated and completed in successive versions that will take advantage of a large open discussion inside and outside ReSIS