Discriminating surface-breaking from subsurface cracks in pressurized water pipes by means of a parametric modulation technique

The discrimination of surface-breaking cracks from cracks that are entirely embedded within the material is paramount to the assessment of the integrity of pressurized water pipes. This work addresses this issue investigating the use of a parametric modulation technique towards this end. Tests are conducted on 1D surface-breaking cracks that are either dry or contain water. The response of these defects is investigated as a function of crack depth and closure, as well as of amplitude and frequency of the modulation. The ensuing results suggest that fluid-filled and dry cracks can be discriminated from each other. The origin of the different response of these defects is discussed in terms of both crack's stiffness and modulation-induced dynamics.     

Using process data to generate an optimal control policy via apprenticeship and reinforcement learning

Reinforcement learning (RL) is a data-driven approach to synthesizing an optimal control policy. A barrier to wide implementation of RL-based controllers is its data-hungry nature during online training and its inability to extract useful information from human operator and historical process operation data. Here, we present a two-step framework to resolve this challenge. First, we employ apprenticeship learning via inverse RL to analyze historical process data for synchronous identification of a reward function and parameterization of the control policy. This is conducted offline. Second, the parameterization is improved online efficiently under the ongoing process via RL within only a few iterations. Significant advantages of this framework include to allow for the hot-start of RL algorithms for process optimal control, and robust abstraction of existing controllers and control knowledge from data. The framework is demonstrated on three case studies, showing its potential for chemical process control.     

A Comprehensive Understanding of Lithium–Sulfur Battery Technology

Lithium–sulfur batteries (LSBs) are regarded as a new kind of energy storage device due to their remarkable theoretical energy density. However, some issues, such as the low conductivity and the large volume variation of sulfur, as well as the formation of polysulfides during cycling, are yet to be addressed before LSBs can become an actual reality. Here, presented is a comprehensive overview illustrating the techniques capable of mitigating these undesirable problems together with the electrochemical performances associated to the different proposed solutions. In particular, the analysis is organized by separately addressing cathode, anode, separator, and electrolyte. Furthermore, to better understand the chemistry and failure mechanisms of LSBs, important characterization techniques applied to energy storage systems are reviewed. Similarly, considerations on the theoretical approaches used in the energy storage field are provided, as they can become the key tool for the design of the next generation LSBs. Afterward, the state of the art of LSBs technology is presented from a geopolitical perspective by comparing the results achieved in this field by the main world actors, namely Asia, North America, and Europe. Finally, this review is concluded with the application status of LSBs technology, and its prospects are offered.     

Identification of Potential Leukocyte Biomarkers Related to Drug Recovery of CFTR: Clinical Applications in Cystic Fibrosis

The aim of this study was the identification of specific proteomic profiles, related to a restored cystic fibrosis transmembrane conductance regulator (CFTR) activity in cystic fibrosis (CF) leukocytes before and after ex vivo treatment with the potentiator VX770. We used leukocytes, isolated from CF patients carrying residual function mutations and eligible for Ivacaftor therapy, and performed CFTR activity together with proteomic analyses through micro-LC–MS. Bioinformatic analyses of the results obtained revealed the downregulation of proteins belonging to the leukocyte transendothelial migration and regulation of actin cytoskeleton pathways when CFTR activity was rescued by VX770 treatment. In particular, we focused our attention on matrix metalloproteinase 9 (MMP9), because the high expression of this protease potentially contributes to parenchyma lung destruction and dysfunction in CF. Thus, the downregulation of MMP9 could represent one of the possible positive effects of VX770 in decreasing the disease progression, and a potential biomarker for the prediction of the efficacy of therapies targeting the defect of Cl⁻ transport in CF.     

Mimicking Cellular Signaling Pathways within Synthetic Multicompartment Vesicles with Triggered Enzyme Activity and Induced Ion Channel Recruitment

Subcellular compartmentalization of cells, a defining characteristic of eukaryotes, is fundamental for the fine tuning of internal processes and the responding to external stimuli. Reproducing and controlling such compartmentalized hierarchical organization, responsiveness, and communication is important toward understanding biological systems and applying them to smart materials. Herein, a cellular signal transduction strategy (triggered release from subcompartments) is leveraged to develop responsive, purely artificial, polymeric multicompartment assemblies. Incorporation of responsive nanoparticles—loaded with enzymatic substrate or ion channels—as subcompartments inside micrometer‐sized polymeric vesicles (polymersomes) allowed to conduct bioinspired signaling cascades. Response of these subcompartments to an externally added stimulus is achieved and studied by using confocal laser scanning microscopy (CLSM) coupled with in situ fluorescence correlation spectroscopy (FCS). Signal triggered activity of an enzymatic reaction is demonstrated in multicompartments through recombination of compartmentalized substrate and enzyme. In parallel, a two‐step signaling cascade is achieved by triggering the recruitment of ion channels from inner subcompartments to the vesicles' membrane, inducing ion permeability, mimicking endosome‐mediated insertion of internally stored channels. This design shows remarkable versatility, robustness, and controllability, demonstrating that multicompartment polymer‐based assemblies offer an ideal scaffold for the development of complex cell‐inspired responsive systems for applications in biosensing, catalysis, and medicine.     

Safety First? Users’ Perception of Wearable Sensor Networks for Aging

Mobile Networks and Applications - Population aging is considered to be one of the most significant social transformations of the twenty-first century. In this paper, we evaluate the acceptability...     

Big data analytics business value and firm performance: linking with environmental context

Previous studies, grounded on the resource based view, have already explored the relationship between the business value that Big Data Analytics (BDA) can bring to firm performance. However, the role played by the environmental characteristics in which companies operate has not been investigated in the literature. We inform the theory, in that direction, via the integration of the contingency theory to the resource based view theory of the firm. This original and integrative model examines the moderating influence of environmental features on the relationship between BDA business value and firm performance. The combination of survey data and secondary financial data on a representative sample of medium and large companies makes possible the statistical validation of our research model. The results offer evidence that BDA business value leads to higher firm performance, namely financial performance, market performance and customer satisfaction. More original is the demonstration that this relationship is stronger in munificent environments, while the dynamism of the environment does not have any moderating effect on the performance of BDA solutions. It means that managers working for firms in markets with a growing demand are in the best position to profit from BDA.     

The mineral profile affects the coagulation pattern and cheese-making efficiency of bovine milk

Natural variations in milk minerals, their relationships, and their associations with the coagulation process and cheese-making traits present an opportunity for the differentiation of milk destined for high-quality natural products, such as traditional specialties or Protected Designation of Origin (PDO) cheeses. The aim of this study was to quantify the effects of the native contents of Ca, P, Na, K, and Mg on 18 traits describing traditional milk coagulation properties (MCP), curd firming over time (CF_t) equation parameters, cheese yield (CY) measures, and nutrient recoveries in the curd (REC) using models that either included or omitted the simultaneous effects of milk fat and casein contents. The results showed that, by including milk fat and casein and the minerals in the statistical model, we were able to determine the specific effects of each mineral on coagulation and cheese-making efficiency. In general, about two-thirds of the apparent effects of the minerals on MCP and the CF_t equation parameters are actually mediated by their association with milk composition, especially casein content, whereas only one-third of the effects are direct and independent of milk composition. In the case of cheese-making traits, the effects of the minerals were mediated only negligibly by their association with milk composition. High Ca content had a positive effect on the coagulation pattern and cheese-making traits, favoring water retention in the curd in particular. Phosphorus positively affected the cheese-making traits in that it was associated with an increase in CY in terms of curd solids, and in all the nutrient recovery traits. However, a very high P content in milk was associated with lower fat recovery in the curd. The variation in the Na content in milk only mildly affected coagulation, whereas with regard to cheese-making, protein recovery was negatively associated with high concentrations of this mineral. Potassium seemed not to be actively involved in coagulation and the cheese-making process. Magnesium content tended to slow coagulation and reduce CY measures. Further studies on the relationships of minerals with casein and protein fractions could deepen our knowledge of the role of all minerals in coagulation and the cheese-making process.     

Multivariate Analysis of <Superscript>1</Superscript>H-NMR Spectra of Genetically Characterized Extra Virgin Olive Oils and Growth Soil Correlations

Mono-varietal extra virgin olive oils were micro-extracted from drupes that were selectively collected from 28 trees distributed in five different Southern Italian Apulian areas. Nuclear Magnetic Resonance (NMR) profiles of these oil samples were correlated to the genetic (young green material) and soil (samples collected within the foliage projection) data of the tree of origin. Genetic analysis, performed on the samples using SSRs (Simple Sequence Repeats) by 9 microsatellite loci, confirmed the specific cultivar assignment (among Cima di Mola, Coratina, Ogliarola, and Oliva Rossa cultivars). Chemometric methods applied to ¹H-NMR spectroscopic data were used for cultivar and geographical origin discrimination of the studied extra virgin olive oils. Linear Discriminant Analysis (LDA) afforded a high reliability degree for discriminating cultivars (almost 90% of prediction ability), and a good assigning ability for the geographical origin (Ogliarola and Coratina samples used as subsets). Soil analyses were performed for each tree. Regression analysis was applied to soil composition in order to correlate available nutrients and total metals with the content of fatty acids and minor components present in monovarietal extra virgin olive oils. In the case of oleic and linoleic fatty acids, and for some terpenes, B, Cr, Mn, Zn were found to give significant correlations. Zn and Mn were the most significant trace elements for all the correlations found (p < 0.01). The results obtained (genetic, spectroscopic and soil analyses) are discussed as a multidisciplinary approach for setting up a strategy for a cultivar and/or geographic origin certification committed database construction.     

A simple tool for two-dimensional quantification of filler dispersion: A proposal

An effective filler dispersion within a polymeric matrix is known to be a crucial requirement for obtaining the desired performance from a composite. A quantification of this parameter cannot be always easily achieved and several methodologies have been proposed so far, generally based on complex image processing algorithms. However, no standard has been established yet.

In order to contribute to the quantitative evaluation of the filler dispersion in a polymeric matrix, a novel straightforward two-dimensional alternative method based on the analysis of electron microscopy micrographs is here proposed. Several case studies were considered to test the reliability of the approach, providing as resulting output, either a qualitative polar plot representative of the analyzed image and an index to measure the filler dispersion.