Mode III crack growth in linear hardening materials with strain gradient effects

The flow-theory version of couple stress strain gradient plasticity is adopted for investigating the asymptotic fields near a steadily propagating crack-tip, under Mode III loading conditions. By incorporating a material characteristic length, typically of the order of few microns for ductile metals, the adopted constitutive model accounts for the microstructure of the material and can capture the strong size effects arising at small scales. The effects of microstructure result in a substantial increase in the singularities of the skew-symmetric stress and couple stress fields, which occurs also for a small hardening coefficient. The symmetric stress field turns out to be non-singular according to the asymptotic solution for the stationary crack problem in linear elastic couple stress materials. The performed asymptotic analysis can provide useful predictions about the increase of the traction level ahead of the crack-tip due to the sole contribution of the rotation gradient, which has been found relevant and non-negligible at the micron scale.     

Sampling Errors in the Estimation of Peak Wind-Induced Internal Forces in Low-Rise Structures

Peak values in time histories of wind effects may be obtained by using the entire information inherent in the time series of the wind effect, a method entailing the estimation of the probability distribution of the peak through the application of the classical Rice procedure extended for non-Gaussian time histories. We present estimates of the sampling errors inherent in this method. These are needed for structural reliability calculations and for decisions on the requisite length of wind tunnel pressure records, especially for database-assisted design. If based on the analysis of 1-h-long records generated by Monte Carlo simulation, typical sampling errors in the estimation of peaks of time histories corresponding to windstorms of 1-h duration are about 5%. If based on 30- or 20-min records, they are about 1.5 times or twice as large, respectively. Consideration of the sampling errors in reliability calculations entails an estimated increase in the requisite safety margins with respect to wind loading of roughly 2, 3, and 5% if 1-h, 30-min, or 20-min records are used, respectively.     

Probabilistic Design space determination in pharmaceutical product development: A Bayesian/latent variable approach

To find the design space (DS) of a pharmaceutical process, quantification of the “assurance of quality” for the product under development is required. In this study, latent‐variable modeling is combined with multivariate Bayesian regression to identify a subset of input combinations (process operating conditions and raw materials properties) within which the DS of the product will lie at a probability equal to, or greater than, an assigned threshold. Partial least‐squares regression is used to obtain a linear transformation between the original multidimensional input space and a low‐dimensional latent space. The input domain is then discretized on its lower dimensional representation and a Bayesian posterior predictive approach is used to quantify the probability that the critical quality attributes of the product will meet their specifications for each discretization point. The methodology is tested on two case studies taken from the literature, one of which involving experimental data. The ability of the proposed approach to obtain a probabilistic identification of the DS, while simultaneously reducing the computational burden for the discretization of the input domain and providing a simple graphical representation of the DS, is shown. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2438–2449, 2018     

Enhanced Visualization of Detected 3D Geometric Differences

The wide availability of 3D acquisition devices makes viable their use for shape monitoring. The current techniques for the analysis of time‐varying data can efficiently detect actual significant geometric changes and rule out differences due to irrelevant variations (such as sampling, lighting and coverage). On the other hand, the effective visualization of such detected changes can be challenging when we want to show at the same time the original appearance of the 3D model. In this paper, we propose a dynamic technique for the effective visualization of detected differences between two 3D scenes. The presented approach, while retaining the original appearance, allows the user to switch between the two models in a way that enhances the geometric differences that have been detected as significant. Additionally, the same technique is able to visually hides the other negligible, yet visible, variations. The main idea is to use two distinct screen space time‐based interpolation functions for the significant 3D differences and for the small variations to hide. We have validated the proposed approach in a user study on a different class of datasets, proving the objective and subjective effectiveness of the method.     

Sources of information for innovation: the role of companies’ motivations

The paper analyses the information sources that companies use to innovate. The analysis compares two science-oriented sources of information (universities and public research institutes) with two market-oriented sources (clients and suppliers). The analysis demonstrates the importance of motivations as determinants of the sources of information used. Adding motivations to the established ‘structural’ explanations provides a new way of capturing companies’ search for information and emphasises the direction rather than the breadth of the search process. The results of this analysis also have implications both for policy-making and for companies’ managers, which are discussed in the paper’s conclusions.     

Extracting knowledge patterns with a social network analysis approach: an alternative methodology for assessing the impact of power inventors

This paper proposes a new, alternative analysis of patent data in order to extract knowledge patterns from inventors’ collaboration networks. Indeed, moving from a basic network analysis, we provide new developments to map and study co-inventorship. The goal of this research is to provide an overall understanding of the dynamics concerning knowledge flows in inventive activities. We show how the network of inventors is, on average, increasing in size: more and more inventors are contributing to technology innovations and they are more connected to each other. We also show to what extent inventors from different countries tend to cooperate with their local peers or internationally. Furthermore, an analysis of the clustering of inventors is carried out to show differences across countries in the structure of inventors’ communities, with a particular focus on the dynamics of collaboration for power inventors (i.e. star inventors).     

On weak differential uniformity of vectorial Boolean functions as a cryptographic criterion

We study the relation among some security parameters for vectorial Boolean functions which prevent attacks on the related block cipher. We focus our study on a recently-introduced security criterion, called weak differential uniformity, which prevents the existence of an undetectable trapdoor based on imprimitive group action. We present some properties of functions with low weak differential uniformity, especially for the case of power functions and 4-bit S-Boxes.     

Object-based urban structure type pattern recognition from Landsat TM with a Support Vector Machine

This study evaluates the potential of object-based image analysis in combination with supervised machine learning to identify urban structure type patterns from Landsat Thematic Mapper (TM) images. The main aim is to assess the influence of several critical choices commonly made during the training stage of a learning machine on the classification performance and to give recommendations for classifier-dependent intelligent training. Particular emphasis is given to assess the influence of size and class distribution of the training data, the approach of training data sampling (user-guided or random) and the type of training samples (squares or segments) on the classification performance of a Support Vector Machine (SVM). Different feature selection algorithms are compared and segmentation and classifier parameters are dynamically tuned for the specific image scene, classification task, and training data. The performance of the classifier is measured against a set of reference data sets from manual image interpretation and furthermore compared on the basis of landscape metrics to a very high resolution reference classification derived from light detection and ranging (lidar) measurements. The study highlights the importance of a careful design of the training stage and dynamically tuned classifier parameters, especially when dealing with noisy data and small training data sets. For the given experimental set-up, the study concludes that given optimized feature space and classifier parameters, training an SVM with segment-shaped samples that were sampled in a guided manner and are balanced between the classes provided the best classification results. If square-shaped samples are used, a random sampling provided better results than a guided selection. Equally balanced sample distributions outperformed unbalanced training sets.