Perceptions and imaginaries about the fourth industrial revolution between geographies of opportunity and discontent: Some reflections on the Italian case

The pervasiveness of advanced technologies and their disruptive impact on society have spurred the debate on the emergence of a new industrial revolution and on its positive and negative effects, both at an individual and spatial level. This paper aims to contribute to this debate, focusing attention on the perception of changes related to the Fourth Industrial Revolution and exploring new methods of analysis of the manifestations of both techno-enthusiasm and opposition to it. Starting from the extensive literature in this field, the work adopts two research perspectives: the study of imaginaries and narratives developed around the Fourth Industrial Revolution, which convey different messages from social groups and places; the geographies of opportunity and discontent, which address the resentment expressed by some localities towards advanced technological models and growing inequalities. In this work the Fourth Industrial Revolution is not interpreted through data about the technological variables or interviews to protagonists of the phenomenon; rather, emphasis is on the points of view of non-institutional subjects and, in particular, the opinions expressed by people on the Web. For this reason, the sentiment analysis has been adopted to identify both positive and negative polarities and the relevance of specific feelings through the selection of key words related to the notion of the Fourth Industrial Revolution. The empirical analysis based on this methodology focuses on the Italian case in a specific period (first and second phase of the pandemic, from January 2020 to September 2021) and, at a local level, on the comparison between four medium-sized cities (Pisa, Lecce, Taranto and Terni). This paper also tries to extend recent contributions through the provision of new perspectives for the definition of policies designed with the involvement of the population and places regarding both the processes of technological change and the definition of new socio-spatial models

Uncertainty quantification in energy management procedures

Complex energy systems are made up of a number of components interacting together via different energy vectors. The assessment of their performance under dynamic working conditions, where user demand and energy prices vary over time, requires a simulation tool. Regardless of the accuracy of this procedure, the uncertainty in data, obtained both by measurements or by forecasting, is usually non-negligible and requires the study of the sensitivity of results versus input data. In this work, polynomial chaos expansion technique is used to evaluate the variation of cogeneration plant performance with respect to the uncertainty of energy prices and user requests. The procedure allows to obtain this information with a much lower computational cost than that of usual Monte-Carlo approaches. Furthermore, all the tools used in this paper, which were developed in Python, are published as free and open source software

The Role of MicroRNAs in Influencing Body Growth and Development

Body growth and development are regulated among others by genetic and epigenetic factors. MicroRNAs (miRNAs) are epigenetic regulators of gene expression that act at the post-transcriptional level, thereby exerting a strong influence on regulatory gene networks. Increasing studies suggest the importance of miRNAs in the regulation of the growth plate and growth hormone (GH)-insulin-like growth factor (IGF) axis during the life course in a broad spectrum of animal species, contributing to longitudinal growth. This review summarizes the role of miRNAs in regulating growth in different in vitro and in vivo models acting on GH, GH receptor (GHR), IGFs, and IGF1R genes besides current knowledge in humans, and highlights that this regulatory system is of importance for growth

Design Considerations for Efficient and Effective Microarray Studies

This paper describes the theoretical and practical issues in experimental design for gene expression microarrays. Specifically, this paper (1) discusses the basic principles of design (randomization, replication, and blocking) as they pertain to microarrays, and (2) provides some general guidelines for statisticians designing microarray studies

The Iterative Signature Algorithm for the analysis of large scale gene expression data

We present a new approach for the analysis of genome-wide expression data. Our method is designed to overcome the limitations of traditional techniques, when applied to large-scale data. Rather than alloting each gene to a single cluster, we assign both genes and conditions to context-dependent and potentially overlapping transcription modules. We provide a rigorous definition of a transcription module as the object to be retrieved from the expression data. An efficient algorithm, that searches for the modules encoded in the data by iteratively refining sets of genes and conditions until they match this definition, is established. Each iteration involves a linear map, induced by the normalized expression matrix, followed by the application of a threshold function. We argue that our method is in fact a generalization of Singular Value Decomposition, which corresponds to the special case where no threshold is applied. We show analytically that for noisy expression data our approach leads to better classification due to the implementation of the threshold. This result is confirmed by numerical analyses based on in-silico expression data. We discuss briefly results obtained by applying our algorithm to expression data from the yeast S. cerevisiae.Comment: Latex, 36 pages, 8 figure