Structural damage detection method using frequency response functions

Structural damage detection using measured dynamic data for pattern recognition is a promising approach. These pattern recognition techniques utilize artificial neural networks and genetic algorithm to match pattern features. In this study, an artificial neural network–based damage detection method using frequency response functions is presented, which can effectively detect nonlinear damages for a given level of excitation. The main objective of this article is to present a feasible method for structural vibration–based health monitoring, which reduces the dimension of the initial frequency response function data and transforms it into new damage indices and employs artificial neural network method for detecting different levels of nonlinearity using recognized damage patterns from the proposed algorithm. Experimental data of the three-story bookshelf structure at Los Alamos National Laboratory are used to validate the proposed method. Results showed that the levels of nonlinear damages can be identified precisely by the developed artificial neural networks. Moreover, it is identified that artificial neural networks trained with summation frequency response functions give higher precise damage detection results compared to the accuracy of artificial neural networks trained with individual frequency response functions. The proposed method is therefore a promising tool for structural assessment in a real structure because it shows reliable results with experimental data for nonlinear damage detection which renders the frequency response function–based method convenient for structural health monitoring.

Cryptographic hash functions

Cryptographic hash functions are an important tool of cryptography and play a fundamental role in efficient and secure information processing. A hash function processes an arbitrary finite length input message to a fixed length output referred to as the hash value. As a security requirement, a hash value should not serve as an image for two distinct input messages and it should be difficult to find the input message from a given hash value. Secure hash functions serve data integrity, non-repudiation and authenticity of the source in conjunction with the digital signature schemes. Keyed hash functions, also called message authentication codes (MACs) serve data integrity and data origin authentication in the secret key setting. The building blocks of hash functions can be designed using block ciphers, modular arithmetic or from scratch. The design principles of the popular Merkle–Damgård construction are followed in almost all widely used standard hash functions such as MD5 and SHA-1.

On hash functions using checksums

We analyse the security of iterated hash functions that compute an input dependent checksum which is processed as part of the hash computation. We show that a large class of such schemes, including those using non-linear or even one-way checksum functions, is not secure against the second preimage attack of Kelsey and Schneier, the herding attack of Kelsey and Kohno and the multicollision attack of Joux. Our attacks also apply to a large class of cascaded hash functions. Our second preimage attacks on the cascaded hash functions improve the results of Joux presented at Crypto’04. We also apply our attacks to the MD2 and GOST hash functions. Our second preimage attacks on the MD2 and GOST hash functions improve the previous best known short-cut second preimage attacks on these hash functions by factors of at least 226 and 254, respectively. Our herding and multicollision attacks on the hash functions based on generic checksum functions (e.g., one-way) are a special case of the attacks on the cascaded iterated hash functions previously analysed by Dunkelman and Preneel and are not better than their attacks. On hash functions with easily invertible checksums, our multicollision and herding attacks (if the hash value is short as in MD2) are more efficient than those of Dunkelman and Preneel.

On the Collision and Preimage Resistance of Certain Two-Call Hash Functions

In this paper we present concrete collision and preimage attacks on a large class of compression function constructions making two calls to the underlying ideal primitives. The complexity of the collision attack is above the theoretical lower bound for constructions of this type, but below the birthday complexity; the complexity of the preimage attack, however, is equal to the theoretical lower bound. We also present undesirable properties of some of Stam’s compression functions proposed at CRYPTO ’08. We show that when one of the n-bit to n-bit components of the proposed 2n-bit to n-bit compression function is replaced by a fixed-key cipher in the Davies-Meyer mode, the complexity of finding a preimage would be 2 n/3. We also show that the complexity of finding a collision in a variant of the 3n-bits to 2n-bits scheme with its output truncated to 3n/2 bits is 2 n/2. The complexity of our preimage attack on this hash function is about 2 n . Finally, we present a collision attack on a variant of the proposed m + s-bit to s-bit scheme, truncated to s − 1 bits, with a complexity of O(1). However, none of our results compromise Stam’s security claims.

On Randomizing Hash Functions to Strengthen the Security of Digital Signatures

Halevi and Krawczyk proposed a message randomization algorithm called RMX as a front-end tool to the hash-then-sign digital signature schemes such as DSS and RSA in order to free their reliance on the collision resistance property of the hash functions. They have shown that to forge a RMX-hash-then-sign signature scheme, one has to solve a cryptanalytical task which is related to finding second preimages for the hash function. In this article, we will show how to use Dean’s method of finding expandable messages for finding a second preimage in the Merkle-Damgård hash function to existentially forge a signature scheme based on a t-bit RMX-hash function which uses the Davies-Meyer compression functions (e.g., MD4, MD5, SHA family) in 2 t/2 chosen messages plus 2 t/2 + 1 off-line operations of the compression function and similar amount of memory. This forgery attack also works on the signature schemes that use Davies-Meyer schemes and a variant of RMX published by NIST in its Draft Special Publication (SP) 800-106. We discuss some important applications of our attack.

Cryptographic Hash Functions

In the modern era of information and communication technology, cryptographic hash functions play an important role in ensuring the authenticity, integrity, and nonrepudiation goals of information security as well as efficient information processing. This entry provides an overview of the role of hash functions in information security, popular hash function designs, some important analytical results, and recent advances in this field.

Accounting for management costs in sensitivity analyses of matrix population models

Traditional sensitivity and elasticity analyses of matrix population models have been used to inform management decisions, but they ignore the economic costs of manipulating vital rates. For example, the growth rate of a population is often most sensitive to changes in adult survival rate, but this does not mean that increasing that rate is the best option for managing the population because it may be much more expensive than other options. To explore how managers should optimize their manipulation of vital rates, we incorporated the cost of changing those rates into matrix population models. We derived analytic expressions for locations in parameter space where managers should shift between management of fecundity and survival, for the balance between fecundity and survival management at those boundaries, and for the allocation of management resources to sustain that optimal balance. For simple matrices, the optimal budget allocation can often be expressed as simple functions of vital rates and the relative costs of changing them. We applied our method to management of the Helmeted Honeyeater (Lichenostomus melanops cassidix; an endangered Australian bird) and the koala (Phascolarctos cinereus) as examples. Our method showed that cost-efficient management of the Helmeted Honeyeater should focus on increasing fecundity via nest protection, whereas optimal koala management should focus on manipulating both fecundity and survival simultaneously. These findings are contrary to the cost-negligent recommendations of elasticity analysis, which would suggest focusing on managing survival in both cases. A further investigation of Helmeted Honeyeater management options, based on an individual-based model incorporating density dependence, spatial structure, and environmental stochasticity, confirmed that fecundity management was the most cost-effective strategy. Our results demonstrate that decisions that ignore economic factors will reduce management efficiency. ©2006 Society for Conservation Biology.

A new twist on mode indicator functions

Mode indicator functions (MIFs) are used in modal testing and analysis as a means of identifying modes of vibration, often as a precursor to modal parameter estimation. Various methods have been developed since the MIF was introduced four decades ago. These methods are quite useful in assisting the analyst to identify genuine modes and, in the case of the complex mode indicator function, have even been developed into modal parameter estimation techniques. Although the various MIFs are able to indicate the existence of a mode, they do not provide the analyst with any descriptive information about the mode. This paper uses the simple summation type of MIF to develop five averaged and normalised MIFs that will provide the analyst with enough information to identify whether a mode is longitudinal, vertical, lateral or torsional. The first three functions, termed directional MIFs, have been noted in the literature in one form or another; however, this paper introduces a new twist on the MIF by introducing two MIFs, termed torsional MIFs, that can be used by the analyst to identify torsional modes and, moreover, can assist in determining whether the mode is of a pure torsion or sway type (i.e., having a rigid cross-section) or a distorted twisting type. The directional and torsional MIFs are tested on a finite element model based simulation of an experimental modal test using an impact hammer. Results indicate that the directional and torsional MIFs are indeed useful in assisting the analyst to identify whether a mode is longitudinal, vertical, lateral, sway, or torsion.

Information-theoretic analysis of brain white matter fiber orientation distribution functions

We propose a new information-theoretic metric, the symmetric Kullback-Leibler divergence (sKL-divergence), to measure the difference between two water diffusivity profiles in high angular resolution diffusion imaging (HARDI). Water diffusivity profiles are modeled as probability density functions on the unit sphere, and the sKL-divergence is computed from a spherical harmonic series, which greatly reduces computational complexity. Adjustment of the orientation of diffusivity functions is essential when the image is being warped, so we propose a fast algorithm to determine the principal direction of diffusivity functions using principal component analysis (PCA). We compare sKL-divergence with other inner-product based cost functions using synthetic samples and real HARDI data, and show that the sKL-divergence is highly sensitive in detecting small differences between two diffusivity profiles and therefore shows promise for applications in the nonlinear registration and multisubject statistical analysis of HARDI data.

Combining meta- and mega-analytic approaches for multi-site diffusion imaging based genetic studies: From the enigma-DTI working group

Meta-analyses estimate a statistical effect size for a test or an analysis by combining results from multiple studies without necessarily having access to each individual study's raw data. Multi-site meta-analysis is crucial for imaging genetics, as single sites rarely have a sample size large enough to pick up effects of single genetic variants associated with brain measures. However, if raw data can be shared, combining data in a "mega-analysis" is thought to improve power and precision in estimating global effects. As part of an ENIGMA-DTI investigation, we use fractional anisotropy (FA) maps from 5 studies (total N=2, 203 subjects, aged 9-85) to estimate heritability. We combine the studies through meta-and mega-analyses as well as a mixture of the two - combining some cohorts with mega-analysis and meta-analyzing the results with those of the remaining sites. A combination of mega-and meta-approaches may boost power compared to meta-analysis alone.

Dynamic pattern of suicide in Australia, 1986-2005: A descriptive-analytic study

Objective This study explores the spatiotemporal variations of suicide across Australia from 1986 to 2005, discusses the reasons for dynamic changes, and considers future suicide research and prevention strategies. Design Suicide (1986–2005) and population data were obtained from the Australian Bureau of Statistics. A series of analyses were conducted to examine the suicide pattern by sex, method and age group over time and geography. Results Differences in suicide rates across sex, age groups and suicide methods were found across geographical areas. Male suicides were mainly completed by hanging, firearms, gases and self-poisoning. Female suicides were primarily completed by hanging and self-poisoning. Suicide rates were higher in rural areas than in urban areas (capital cities and regional centres). Suicide rates by firearms were higher in rural areas than in urban areas, while the pattern for self-poisoning showed the reverse trend. Suicide rates had relatively stable trend for the total population and those aged between 15 and 54, while suicide decreased among 55 years and over during the study period. There was a decrease in suicides by firearms during the study period especially after 1996 when a new firearm control law was implemented, while suicide by hanging continued to increase. Areas with a high proportion of indigenous population (eg, northwest of Queensland and top north of the Northern Territory) had shown a substantial increase in suicide incidence after 1995. Conclusions Suicide rates varied over time and space and across sexes, age groups and suicide methods. This study provides detailed patterns of suicide to inform suicide control and prevention strategies for specific subgroups and areas of high and increased risk.

The kallikrein-related peptidase family: Dysregulation and functions during cancer progression

Cancer is the second leading cause of death with 14 million new cases and 8.2 million cancer-related deaths worldwide in 2012. Despite the progress made in cancer therapies, neoplastic diseases are still a major therapeutic challenge notably because of intra- and inter-malignant tumour heterogeneity and adaptation/escape of malignant cells to/from treatment. New targeted therapies need to be developed to improve our medical arsenal and counter-act cancer progression. Human kallikrein-related peptidases (KLKs) are secreted serine peptidases which are aberrantly expressed in many cancers and have great potential in developing targeted therapies. The potential of KLKs as cancer biomarkers is well established since the demonstration of the association between KLK3/PSA (prostate specific antigen) levels and prostate cancer progression. In addition, a constantly increasing number of in vitro and in vivo studies demonstrate the functional involvement of KLKs in cancer-related processes. These peptidases are now considered key players in the regulation of cancer cell growth, migration, invasion, chemo-resistance, and importantly, in mediating interactions between cancer cells and other cell populations found in the tumour microenvironment to facilitate cancer progression. These functional roles of KLKs in a cancer context further highlight their potential in designing new anti-cancer approaches. In this review, we comprehensively review the biochemical features of KLKs, their functional roles in carcinogenesis, followed by the latest developments and the successful utility of KLK-based therapeutics in counteracting cancer progression.

Implications of gain functions in fisheries management

The appealing concept of optimal harvesting is often used in fisheries to obtain new management strategies. However, optimality depends on the objective function, which often varies, reflecting the interests of different groups of people. The aim of maximum sustainable yield is to extract the greatest amount of food from replenishable resources in a sustainable way. Maximum sustainable yield may not be desirable from an economic point of view. Maximum economic yield that maximizes the profit of fishing fleets (harvesting sector) but ignores socio-economic benefits such as employment and other positive externalities. It may be more appropriate to use the maximum economic yield that which is based on the value chain of the overall fishing sector, to reflect better society's interests. How to make more efficient use of a fishery for society rather than fishing operators depends critically on the gain function parameters including multiplier effects and inclusion or exclusion of certain costs. In particular, the optimal effort level based on the overall value chain moves closer to the optimal effort for the maximum sustainable yield because of the multiplier effect. These issues are illustrated using the Australian Northern Prawn Fishery.

Robust estimating functions and bias correction for longitudinal data analysis

Robust methods are useful in making reliable statistical inferences when there are small deviations from the model assumptions. The widely used method of the generalized estimating equations can be "robustified" by replacing the standardized residuals with the M-residuals. If the Pearson residuals are assumed to be unbiased from zero, parameter estimators from the robust approach are asymptotically biased when error distributions are not symmetric. We propose a distribution-free method for correcting this bias. Our extensive numerical studies show that the proposed method can reduce the bias substantially. Examples are given for illustration.

Unbiased estimating equations from working correlation models for irregularly timed repeated measures

The method of generalized estimating equation-, (GEEs) has been criticized recently for a failure to protect against misspecification of working correlation models, which in some cases leads to loss of efficiency or infeasibility of solutions. However, the feasibility and efficiency of GEE methods can be enhanced considerably by using flexible families of working correlation models. We propose two ways of constructing unbiased estimating equations from general correlation models for irregularly timed repeated measures to supplement and enhance GEE. The supplementary estimating equations are obtained by differentiation of the Cholesky decomposition of the working correlation, or as score equations for decoupled Gaussian pseudolikelihood. The estimating equations are solved with computational effort equivalent to that required for a first-order GEE. Full details and analytic expressions are developed for a generalized Markovian model that was evaluated through simulation. Large-sample ".sandwich" standard errors for working correlation parameter estimates are derived and shown to have good performance. The proposed estimating functions are further illustrated in an analysis of repeated measures of pulmonary function in children.