"Trying" to be Entrepreneurial

If we are to understand how entrepreneurial intentions evolve, we must embrace theories reflecting the inherent dynamics of human decision making. While the dominant model of entrepreneurial intentions remains invaluable, capturing the dynamics is necessary to advance our understanding of how intent becomes action. To this end, we offer Bagozzi’s Theory of Trying (TT) as a theory-driven model that assumes a dynamic pathway to intent. Rather than focusing on intentions toward a static target behavior, TT focuses on intentions toward a dynamic goal. To support this perspective, we offer striking new evidence that the emergent intentions process is indeed dynamic.intentions, theory of trying, tipping points, reciprocal causation, entrepreneurship, entrepreneurial cognition

Young people's social agency and community action orientation: a partial least squares-path modeling approach using a self anchoring scale

In this paper we introduce the validation of a novel and specific indicator to measure social agency, namely Community Action Orientation (CAO). The study defines the CAO exploratory model as a second order Partial Least Squares-Path Model (PLSPM). The study identifies the latent variables of orientation toward the community selecting 14 items considered into three sets, suggesting how people invest in their local context on both a personal and collective level. Data have been collected through a self anchoring Cantril’s scale administered to 862 young people living in the administrative district of Naples. The exploratory analysis outcomes show a structure composed of three latent variables (dimensions), correlated with each other. We assumed that CAO is the endogenous variable in order to explain the agency of young individuals in their local context. To explain the interdependence among these latent variables, a theoretical model was hypothesized and constructed. Evidence of the use of the Cantril scale in conjunction with the PLS-PM corroborates the consistency of the approach

Sparse Modeling for Image and Vision Processing

In recent years, a large amount of multi-disciplinary research has been conducted on sparse models and their applications. In statistics and machine learning, the sparsity principle is used to perform model selection---that is, automatically selecting a simple model among a large collection of them. In signal processing, sparse coding consists of representing data with linear combinations of a few dictionary elements. Subsequently, the corresponding tools have been widely adopted by several scientific communities such as neuroscience, bioinformatics, or computer vision. The goal of this monograph is to offer a self-contained view of sparse modeling for visual recognition and image processing. More specifically, we focus on applications where the dictionary is learned and adapted to data, yielding a compact representation that has been successful in various contexts.Comment: 205 pages, to appear in Foundations and Trends in Computer Graphics and Visio

The relationship between noise and annoyance around Orly

The extent to which annoyance estimated by an isopsophic index is a good forecaster for annoyance perceived near airport approaches was investigated. An index of sensed annoyance is constructed, and the relationship between the annoyance index and the isopsophic index is studied

Metabolomics : a tool for studying plant biology

In recent years new technologies have allowed gene expression, protein and metabolite profiles in different tissues and developmental stages to be monitored. This is an emerging field in plant science and is applied to diverse plant systems in order to elucidate the regulation of growth and development. The goal in plant metabolomics is to analyze, identify and quantify all low molecular weight molecules of plant organisms. The plant metabolites are extracted and analyzed using various sensitive analytical techniques, usually mass spectrometry (MS) in combination with chromatography. In order to compare the metabolome of different plants in a high through-put manner, a number of biological, analytical and data processing steps have to be performed. In the work underlying this thesis we developed a fast and robust method for routine analysis of plant metabolite patterns using Gas Chromatography-Mass Spectrometry (GC/MS). The method was performed according to Design of Experiment (DOE) to investigate factors affecting the extraction and derivatization of the metabolites from leaves of the plant Arabidopsis thaliana. The outcome of metabolic analysis by GC/MS is a complex mixture of approximately 400 overlapping peaks. Resolving (deconvoluting) overlapping peaks is time-consuming, difficult to automate and additional processing is needed in order to compare samples. To avoid deconvolution being a major bottleneck in high through-put analyses we developed a new semi-automated strategy using hierarchical methods for processing GC/MS data that can be applied to all samples simultaneously. The two methods include base-line correction of the non-processed MS-data files, alignment, time-window determinations, Alternating Regression and multivariate analysis in order to detect metabolites that differ in relative concentrations between samples. The developed methodology was applied to study the effects of the plant hormone GA on the metabolome, with specific emphasis on auxin levels in Arabidopsis thaliana mutants defective in GA biosynthesis and signalling. A large series of plant samples was analysed and the resulting data were processed in less than one week with minimal labour; similar to the time required for the GC/MS analyses of the samples