In recent years, statistical data revealed a strong dynamism in terms of Research and Development (R&D) growth, of development of innovation structures and emergence of programs, and a growth in terms of industrial integration. However, the political and financial changes from 2007 at the global level revealed as well how fragile are these
outcomes in terms of sustainable development strategy, and how far dependent are from the external markets and political structures. Based on the available information and data, we will present the changes observed in recent years, and understand how the industrial integration can be achieved with clear linkages to the national innovation system and the importance of R&D policy to structure the articulation between innovation and the economic market. The challenges and potential problems can be anticipated if the social implications are also present on the policy options, and if clear technology assessment procedures are taken. When this does not occur, the probability of endogenization of innovation and R&D will become problematic. For these reasons it can be difficult to talk
about a “success story” when all these elements are not considered as a whole and are only seen through very specific angles and with very specialized approaches.

Summary The emergence of bacterial resistance to different antibiotics in clinical use, together with the knowledge on the mechanisms by which bacteria resist the action of aminoglycosides, have contributed to the renewed interest in these molecules as potential antimicrobials. Here, we give an overview on natural and semisynthetic aminoglycosides and their structural features and modes of action, focusing on the structural insight underlying resistance mechanisms. Developments on carbohydrate chemistry and microarray technology are highlighted as powerful approaches toward generation of new aminoglycosides and for screening their interactions with RNAs and proteins. The link between antibiotic uptake and the human gut microbiome is also addressed, focusing on gut microbiome function and composition, antibiotic-induced alterations in host health, and antibiotic resistance. In addition, strategies to modulate human microbiome responses to antibiotics are discussed as novel approaches for aminoglycoside usage and for the effectiveness of antibiotic therapy.

Energy storage systems (ESS) are seen as one of the main pillars for a renewable-based energy system. Selecting the most suitable and sustainable ESS for a given project is a problem that involves multiple stakeholders with quite often diverging objectives that cannot all be fulfilled by a single technology. Several studies are available that tackle this problem applying multi-criteria decision analysis (MCDA). However, these use very different Multi-Attribute Decision Making (MADM) approaches, criteria and goals for decision support, why their results are difficult to compare or to reproduce. This work presents a review of existing MCDA-literature using MADM as a tool for sustainability evaluation of grid-tied ESS. Available studies are summarized, the goals, used MADM methods, and quantification of criteria are analyzed and discussed to provide tentative recommendations. The reviewed studies cover multiple technologies ranging from electrochemical, mechanical or electric ESS. Considered criteria are mainly structured around technology, economy, society, and environment, comprising a high number of individual sub-criteria. The aggregation of these criteria is mainly realized through the Analytic Hierarchy Process (AHP) in combination with a wide set of other methods. The quantification of various criteria is often based on different literature sources wherein context-free data for cost, and environmental impacts are used, leading in some cases to inconsistent comparisons in the assessments. Only in a few cases, assessments are linked to specific application requirements, which are decisive factors for the design of an ESS. A minority of the reviewed works include a representative set of decision-makers in their approaches, wherein the number or type of participants is often not communicated transparently. Therefore, most of the studies are considered to have a limited orientation towards practical decision making, but they provide valuable information regarding MADM method development.

This volume focuses on the collection of waste and waste streams as an integral aspect of sustainable waste management. The authors take economic models and behavioral studies into account to go beyond just descriptions of waste collections technologies and collection route design. Models and tools for sustainable waste collection are described in detail, and the authors provide a comprehensive, integrated methodology to design waste collection systems that reduce environmental impacts, are economically viable, and achieve buy-in and participation from target populations.
Part I of the book provides fundamentals and context on waste hierarchy, including waste prevention, reduction and reuse, waste collection itself, and steps such as preparation for recycling, recycling, treatment, and landfilling. Background in environmental, social, and economic concerns surrounding waste collection is also provided here. Part II addresses tools for design, operation, and maintenance of waste collection systems. Part III focuses on how the tools presented in Part II can be used to support sustainability assessments and decisions that consider the entire life cycle of waste and the role of waste collection programs in waste prevention, reduction, reuse, recycling, treatment, and disposal. Part IV addresses the challenges of developing sustainable waste management systems and addresses the role of waste collection in sustainable waste management in the future.

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https://docentes.fct.unl.pt/lblm/files/sensorsactbchem-2019-285-445.pdf

The normality of the log-return of stock prices is often assumed by the market players in order to use some useful results, as for instance, the Black-Scholes formula for pricing European options. However, several studies regarding different indexes have shown that the normality assumption of the returns usually fails.
In this paper we analyse the normality assumption for intra-day and inter-day log-returns, comparing opening prices and/or closing prices for a large number of companies quoted in the Nasdaq Composite index. We use the Pearson's Chi-Square, Kolmogorov-Smirnov, Anderson-Darling, Shapiro-Wilks and Jarque-Bera goodness-of-fit tests to study the normality assumption.
We find that the failure rate in the normality assumption for the log-return of stock prices is not the same for intra-day and inter-day prices, is somewhat test dependent and strongly dependent on some extreme price observations.
To the best of our knowledge, this is the first study on the normality assumption for the log-return of stock prices dealing simultaneously with a large number of companies and normality tests, and at the same time considering various scenarios of intra-day, inter-day prices and data trimming.