Design certification with information-gap uncertainty

This paper presents a concept for formulating structural design-codes which is based on information-gap models of uncertainty rather than on probabilistic concepts. Info-gap models quantify uncertainty as the size of the gap between what is known and what could be known. Info-gap models of uncertainty are particularly useful when data on the uncertainties are quite limited. In the proposed procedure a design is certified if the robustness to failure from uncertain fluctuations exceeds a code-specified threshold. Design is thus based on immunity to uncertainty rather than on probability of survival. The key conclusion is that design-certification can exploit data about uncertainties without introducing probabilistic models. This is important when information is scarce, since verification of probabilistic models can then be difficult. Second, when partial probabilistic information is available, it can be incorporated in a hybrid info-gap/probabilistic analysis. Third, the proposed design certification incorporates recognition of the dual nature of uncertainty: that it may be pernicious but may also entail propitious possibilities. Examples are presented which illustrate the design procedure. The incorporation of partial probabilistic information is also demonstrated. The antagonism between the pernicious and propitious potentials of ambient uncertainty is illustrated.     

Redundancy optimization of static series-parallel reliabilitymodels under uncertainty

This paper extends the classical model of Ushakov on redundancy optimization of series-parallel static coherent reliability systems with uncertainty in system parameters. Their objective function represents the total capacity of a series-parallel static system, while the decision parameters are the nominal capacity and the availability of the elements. They obtain explicit expressions (both analytic and via efficient simulation) for the constraint of the program, viz, for the Cdf of the system total capacity and then show that the extended program is convex mixed-integer. Depending on whether the objective function and the associated constraints are analytically available or not, they suggest using deterministic and stochastic (simulation) optimization approaches, respectively. The last case is associated with likelihood ratios (change of probability measure). A genetic algorithm for finding the optimal redundancy is developed and supporting numerical results are presented     

Consecutive sliding window systems

This paper proposes a new model that generalizes the linear multi-state sliding window system to the case of m consecutive overlapping windows. In this model the system consists of n linearly ordered multi-state elements. Each element can have different states: from complete failure up to perfect functioning. A performance rate is associated with each state. The system fails if in each of at least m consecutive overlapping groups of r consecutive elements (windows) the sum of the performance rates of elements belonging to the group is lower than a minimum allowable level. An algorithm for system reliability evaluation is suggested which is based on an extended universal moment generating function. Examples of evaluating system reliability and elements' reliability importance indices are presented.     

Info-gap robustness for the correlation of tests and simulations of a non-linear transient

An alternative to the theory of probability is applied to the problem of assessing the robustness, to uncertainty in model parameters, of the correlation between measurements and computer simulations. The analysis is based on the theory of information-gap uncertainty, which models the clustering of uncertain events in families of nested sets instead of assuming a probability structure. The system investigated is the propagation of a transient impact through a layer of hyper-elastic material. The two sources of non-linearity are (1) the softening of the constitutive law representing the hyper-elastic material and (2) the contact dynamics at the interface between metallic and crushable materials. The robustness of the correlation between test and simulation, to sources of parameter variability, is first studied to identify the parameters of the model that significantly influence the agreement between measurements and predictions. Model updating under non-probabilistic uncertainty is then illustrated, based on two complementary immunity functions: the robustness to uncertainty and the opportunity from uncertainty. Finally an info-gap model is embedded within a probability density function to represent uncertainty in the knowledge of the model's parameters and their correlation structure. Although computationally expensive, it is demonstrated that info-gap reasoning can greatly enhance our understanding of a moderately complex system when the theory of probability cannot be applied due to insufficient information.     

Determination of spatial distribution in two-phase flow using scattered gamma radiation

A procedure has been developed and tested for measurement of the phase distribution along a chord of the flow channel in steady state two-phase flow. The method involves measurement of the zeroth and first moments of the energy spectra of Compton scattered radiation. This information allows determination of the mean position and thickness of each condensed phase region along the interrogated chord in the flow channel. Preparation of empirical calibration curves employing perspex disks, rods or tubes is explained. An iterative procedure for correcting the calibration curves for self-absorption is developed. The procedure has enabled determination of the position and thickness to within a few percent in simulated steady state tests.     

Safety implications of stochastic analysis of nuclear reactor transients

A ‘neutron-temperature random process’ is formulated by means of a probability generating function technique. The model accounts for the effect of the negative temperature feedback by treating the temperature as a continuous random variable, thereby making possible the computation of the expected values of the temperature, neutron and cumulative fission densities as well as their standard deviations. The special relationship of the stochastic behavior of the nuclear reactor to safety considerations is discussed. Typical numerical results are presented related to startup accidents and Anticipated Transient Without Scram (ATWS) in thermal reactors.     

Surgical approach to benign cecal ulcer

Benign cecal ulcer is a rare lesion, usually diagnosed during operation for suspected acute appendicitis or peritonitis of unknown origin. In the past, right hemicolectomy was recommended as the treatment of choice because of the difficulty in differentiating malignant lesions from benign cecal ulcers. However, in recent reports a more conservative approach has been suggested, consisting of selective colectomy followed by frozen section biopsy. This approach is aimed at preventing unnecessary excision of the colon and conserving the ileocecal valve. We present a 47-year-old woman operated for right lower quadrant peritonitis, believed to be due to acute appendicitis. On exploration, a biopsy-proven benign cecal ulcer was found and resected.     

An active protein kinase A (PKA) is involved in meiotic arrest of rat growing oocytes

Reinitiation of meiosis in meiotically competent, fully grown mammalian oocytes is governed by a fall in intraoocyte cAMP concentrations and the subsequent inactivation of protein kinase A (PKA). A similar reduction in intraoocyte cAMP concentrations in growing, meiotically incompetent rat oocytes not leading to resumption of meiosis, questions the involvement of PKA in the regulation of meiosis at this early stage of oocyte development. We examined the possibility of whether PKA activity maintains growing oocytes in meiotic arrest and further explored the mode of activation of PKA under conditions of relatively low cAMP concentrations. Our experiment demonstrated that inactivation of PKA stimulates growing rat oocytes to resume meiosis, and elevates the activity of their maturation-promoting factor (MPF). We also found that the expressions of type I and type II regulatory subunits (RI and RII) of PKA are higher in growing and fully grown oocytes, respectively. In addition, we revealed that the common 1:1 ratio between the regulatory (R) and catalytic (C) subunits of PKA is apparently not abrogated and, in accordance PKA activity in growing oocyte-cell extract is fully dependent on cAMP. Finally, we identified in growing oocytes, the A kinase anchoring protein (AKAP) 140, which was previously depicted in fully grown oocytes. We conclude that an active PKA prevents growing oocytes from resuming meiosis. Our findings further suggest that relatively high abundance of the PKAI isoform and/or its subcellular compartmentalization, through interaction with AKAP140, could possibly account for the high basal PKA activity at relatively low intraoocyte cAMP concentrations.     

Robustness of model-based fault diagnosis: Decisions with information-gap models of uncertainty

Fault diagnosis is analysed here as a decision between alternative hypotheses, based on uncertain evidence. W e consider severe lack of information, and perceive the uncertainty as an information gap between what is known, and what needs to be known for a perfect decision. This uncertainty is quantified with info-gap models of uncertainty, which require less information than probabilistic models. Previous work with convex set-models is extended to linear info-gap models which are not necessarily convex, as well as to more general info-gap models with arbitrary expansion properties. We define a decision algorithm based on info-gap models and prove three theorems, one establishing the connection with the earlier work on convex models, the other two showing that the algorithm is maximally robust for linear info-gap models as well as for general infogap models of uncertainty. An illustrative example is presented which shows how these results can be used for optimizing the design of a model-based fault diagnosis algorithm.     

Upper bounds on the rate of LDPC codes as a function of minimum distance

New upper bounds on the rate of low-density parity-check (LDPC) codes as a function of the minimum distance of the code are derived. The bounds apply to regular LDPC codes, and sometimes also to right-regular LDPC codes. Their derivation is based on combinatorial arguments and linear programming. The new bounds improve upon the previous bounds due to Burshtein et al. It is proved that at least for high rates, regular LDPC codes with full-rank parity-check matrices have worse relative minimum distance than the one guaranteed by the Gilbert-Varshamov bound.