Network reliability analysis by means of generalized matrix learning vector quantization

Guidelines suggest performing urine steroid profiling in patients with indeterminate adrenal tumors to make a noninvasive diagnosis of adrenocortical carcinoma (ACC). However, urine steroid profiling is not widely available. To determine the accuracy of clinically available serum 11-deoxycortisol, 17OH-progesterone, and 17OH-pregnenolone in diagnosing ACC. We conducted a prospective single-center cohort study of patients with adrenal masses evaluated between 2015 and 2023. Serum was analyzed by liquid chromatography-mass spectrometry for 17OH-pregnenolone, 17OH-progesterone, and 11-deoxycortisol. Reference standard for adrenal mass included histopathology, imaging characteristics, imaging follow up of 2 years, or clinical follow up of 5 years. Localized Generalized Matrix Learning Vector Quantization analysis was used to develop serum steroid score and assessed with area under receiver operating curve. Of 263 patients with adrenal masses, 44 (16.7%) were diagnosed with ACC, 161 (61%) with adrenocortical adenomas (ACAs), 27 (10%) with other adrenal malignancies, and 31 (12%) with other. Hounsfield unit ≥ 20 was demonstrated in all ACCs, in all but 1 other adrenal malignancy, and in 58 (31%) ACAs. All 3 steroids were higher in patients with ACCs vs non-ACCs, including when comparing ACCs with functioning ACAs, and with ACAs with Hounsfield unit ≥ 20 (P < .0001 for all). Localized Generalized Matrix Learning Vector Quantization analysis yielded a serum steroid score that discriminated between ACC and non-ACC groups with a mean threshold fixed area under receiver operating curve of 0.823. We showed that measurements of 11-deoxycortisol, 17OH-progesterone, and 17OH-pregnenolone could be valuable in diagnosing ACC. After appropriate validation, serum steroid score could be integrated in clinical practice.

Ordinal regression based on learning vector quantization

Less is known about the relationship between conduct disorder (CD), callous-unemotional (CU) traits, and positive and negative parenting in youth compared to early childhood. We combined traditional univariate analyses with a novel machine learning classifier (Angle-based Generalized Matrix Learning Vector Quantization) to classify youth (N = 756; 9-18 years) into typically developing (TD) or CD groups with or without elevated CU traits (CD/HCU, CD/LCU, respectively) using youth- and parent-reports of parenting behavior. At the group level, both CD/HCU and CD/LCU were associated with high negative and low positive parenting relative to TD. However, only positive parenting differed between the CD/HCU and CD/LCU groups. In classification analyses, performance was best when distinguishing CD/HCU from TD groups and poorest when distinguishing CD/HCU from CD/LCU groups. Positive and negative parenting were both relevant when distinguishing CD/HCU from TD, negative parenting was most relevant when distinguishing between CD/LCU and TD, and positive parenting was most relevant when distinguishing CD/HCU from CD/LCU groups. These findings suggest that while positive parenting distinguishes between CD/HCU and CD/LCU, negative parenting is associated with both CD subtypes. These results highlight the importance of considering multiple parenting behaviors in CD with varying levels of CU traits in late childhood/adolescence.

Recently, computation, communication and control are deeply and pervasively embedded into physical and engineered components of network System, to advance entire networked system performance and dependability. In such systems, the growing complexity of network structure result in need of increasing system reliability. Network reliability modeling and analysis need not only capture the complex dynamic process of network systems, but also need the facilities for applying, understanding and computing. The objective of this paper is to develop a suitable modeling and analysis framework to study the ad-hoc network reliability. Using the mutual transformations of reliability functions between the time-domain and the frequency-domain, we adopt frequency-domain analysis approach to model network reliability. The advantages of the proposed approach over the traditional time-domain approaches include capability to capture higher order moments of system characteristics, scalability to analyze reliability of complex network systems, efficiency in calculation and practicability in computer simulation, etc. Finally, numerical experiment results show the feasibility and efficacy of this approach.

Reliability analysis of networks using stochastic model

The opportunity costs of increasing reliability in irrigation systems

We investigate the physical layer security in non-regenerative untrusted relay networks, where the relay is a potential eavesdropper despite its cooperatively relaying the information. The current research and development of untrusted relay is often based on the assumption of perfect channel knowledge or the capability of variable-rate transmissions which is decided by instantaneous channel state information (CSI). In this paper, we investigate the secure transmission in a more practical scenario, where the source does not have or trust relay's feedback and uses fixed-rate transmissions. We first calculate the exact connection outage probability (COP) and secrecy outage probability (SOP) to describe the level of reliability and security of the communication system, respectively. Then, the closed-form of reliable-and-secure probability (RSP) is derived to character the trade-off between reliability and security. Finally, Monte-Carlo simulation results are presented to verify the validity of our theoretical analysis.

Recent earthquake events evidenced that damage of structural components in a lifeline network may cause prolonged disruption of lifeline services, which eventually results in significant socio‐economic losses in the affected area. Despite recent advances in network reliability analysis, the complexity of the problem and various uncertainties still make it a challenging task to evaluate the post‐hazard performance and connectivity of lifeline networks efficiently and accurately. In order to overcome such challenges and take advantage of merits of multi‐scale analysis, this paper develops a multi‐scale system reliability analysis method by integrating a network decomposition approach with the matrix‐based system reliability (MSR) method. In addition to facilitating system reliability analysis of large‐size networks, the multi‐scale approach enables optimizing the level of computational effort on subsystems; identifying the relative importance of components and subsystems at multiple scales; and providing a collaborative risk management framework. The MSR method is uniformly applied for system reliability analyses at both the lower‐scale (for link failure) and the higher‐scale (for system connectivity) to obtain the probability of general system events, various conditional probabilities, component importance measures, statistical correlation between subsystem failures and parameter sensitivities. The proposed multi‐scale analysis method is demonstrated by its application to a gas distribution network in Shelby County of Tennessee. A parametric study is performed to determine the number of segments during the lower‐scale MSR analysis of each pipeline based on the strength of the spatial correlation of seismic intensity. It is shown that the spatial correlation should be considered at both scales for accurate reliability evaluation. The proposed multi‐scale analysis approach provides an effective framework of risk assessment and decision support for lifeline networks under earthquake hazards. Copyright © 2009 John Wiley &amp; Sons, Ltd.

An efficient Boolean algebraic algorithm for the symbolic reliability and sensitivity analysis of coherent two-terminal networks with s independent components is described. The algorithm is also applicable to a fault tree model without NOT gates. The algorithm uses the concept originally proposed by A. Grnarov, L. Kleinrock, and M. Gerla (1979). After the algorithm is presented, the errors in the original technique are illustrated by two examples. The algorithm is extended t compute the reliability importance of a given component (sensitivity of system reliability to a given component's reliability). A computer program implementing the modified algorithm is used to solve and obtain measured time complexities for a large set of network and fault tree models.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In this paper, we propose an algorithm to construct the ordered binary decision diagram (OBDD) representing the cut function of a terminal-pair network. The algorithm recognizes isomorphic sub-problems and thus avoids redundant computations. The system reliability could be efficiently computed by the OBDD. Finally, we propose an approach to compute the importance measures for multiple components by traversing the OBDD only once. The correctness and the effectiveness of our approach are demonstrated by experiments on 30 benchmark networks. The experimental results on a 2-by-100 lattice network, which has 2/sup 99/ paths or 10,000 cuts, show an impressive improvement compared to the previous works using the sum of disjoint products method that have exponential complexity. The CPU time of our method, including the calculation of not only the reliability but also the importance measures, for a 100-stage lattice network is only about 0.24 seconds. Thus, this approach is very helpful for the reliability and sensitivity analysis of large networks.

: Analysis of network reliability is of major importance in computer, communication and power networks. Even the simplest models lead to computational problems which are NP-hard for general networks, although polynomial-time algorithms do exist for certain network configurations such as 'ladders' and 'wheels' and for some series-parallel structures such as the well-known 'two-terminal' series-parallel networks. In this paper, we show that a class of series-parallel networks, for which only exponentially complex algorithms were previously known, can be analyzed in polynomial time. In doing this, we introduce a new reliability-preserving graph reduction of general applicability and produce a linear-time algorithm for computing the reliability of any graph with an underlying series-parallel structure.

Reliable service provisioning in converged multimedia network environment

In this paper, we consider problems related to the network reliability problem restricted to circulant graphs (networks). Let 1/spl les/s/sub 1/<s/sub 2/<...<s/sub k//spl les/[n/2] be given integers. An undirected circulant graph, C/sub n//sup s1,s2,...,sk/, has n vertices 0, 1, 2, ..., n-1, and for each s/sub i/ (1/spl les/i/spl les/k) and j (0/spl les/j/spl les/n-1) there is an edge between j and j+s/sub i/ mod n. Let T(C/sub n//sup s1,s2,...,sk/) stand for the number of spanning trees of C/sub n//sup s1,s2,...,sk/. For this special class of graphs, a general and most recent result is obtained by Y. P. Zhang et al (Discrete Mathematics vol. 223, pp.337-350, 2000) where it is shown that T(C/sub n//sup s1,s2,...,sk/)=na/sub n//sup 2/ where a/sub n/ satisfies a linear recurrence relation of order 2/sup sk-1/. In this paper we obtain further properties of the numbers a/sub n/ by considering their combinatorial structures. Using these properties we investigate the open problem posed in the Conclusion of Y. P. Zhang et al. We describe our technique and asymptotic properties of the numbers, using examples.

The goal of a reliable network is to ensure that all transmitted messages arrive at their intended destinations uncorrupted. Most network design techniques concern themselves with channel capacity allocation and cost constraints and ignore reliability analysis. Conversely, network reliability analysis methods focus on connectivity and ignore traffic loads and channel capacities. This paper presents a design strategy to enhance the reliability of an existing network by modifying its topology. This method takes into account traffic distributions. A new reliability metric is introduced which is the probability that all transmitted messages arrive at their intended destinations under the given traffic distribution. The design strategy adds links to the network to maximize the probability that all transmitted messages arrive at their intended destinations. The network design technique uses an approximate network reliability analysis algorithm to achieve quadratic-time performance. >

ABSTRACTIn the process of reliability analysis of computer network, there is the high probability of network failure in the application process by using the current analysis method. In this paper, a reliability analysis method of computer network based on intelligent cloud computing method is proposed. The proposed method is integrated with the theoretical idea of analytic hierarchy process. From the research angle of network reliability, the reliability model of computer network is set up by using the failure criterion of computer network, and the intelligent cloud computing method is used to calculate the reliability state parameters of the network. According to the obtained state parameters of network reliability analysis, the reliability analysis matrix of computer network is established, and then to accurately complete the reliability analysis of computer network. The simulation results show that the reliability analysis method of computer network based on intelligent cloud computing is highly accurate and robust.