Effect of cracks on the service life of RC structures exposed to chlorides

To move towards a more sustainable concrete, the enhancement of its durability is strongly encouraged and, dealing in particular with reinforced concrete (RC), this mainly means to prevent the damage due to environmental actions, e.g. due to chloride-induced corrosion. Therefore, there is the need of models aimed at designing durable structures. Usually the service life design models consider concrete in uncracked condition. In real structures, however, several phenomena can generate cracks on concrete surface, leading to an acceleration of the corrosion of steel rebar. A number of studies have been recently carried out in order to evaluate the influence of cracks on reinforced concrete durability in chloride-contaminated environment, however the knowledge of the effect of cracks on the initiation and propagation periods is still lacking. Furthermore, few studies have considered additional protection strategies, such as the use of stainless steel rebar. In this work, experimental results are presented concerning the influence of cracks on the service life of reinforced concrete structures in order to evaluate if cracks lead to an earlier corrosion initiation induced by chlorides. Prismatic specimens, reinforced with carbon steel and 304L stainless steel bars, were longitudinally cracked and exposed to ponding with 3.5% NaCl solution. The monitoring of corrosion behaviour showed that when cracks reached the steel surface corrosion initiated immediately

Effects of Energy Efficiency Measures in the Beef Cold Chain: A Life Cycle-based Study

Abstract Circular economy and industrial symbiosis represent a production and consumption model involving sharing, lending, reusing, and recycling existing materials and products in the most efficient way to increase sustainability and reduce or eliminate waste. Beef production has a high impact on the environment in different impact categories, especially those activities related to livestock breeding and feeding. In this study, a life cycle assessment and a life cycle cost evaluation are carried out investigating potential energy efficiency measures to promote industrial symbiosis scenarios referring to a proposed baseline scenario. Three main potential measures are evaluated: energy recovery from waste via anaerobic digestion, integration of renewable sources at warehouses, including solar PV panels, and the replacement of auxiliary equipment at the retailer. It was found that energy reconversion of food waste through anaerobic digestion and cogeneration provides the most valuable benefits to the supply chain. From the economic perspective, using a conventional life cycle cost assessment, the energy production from the use of wastes for anaerobic digestion proved to be the best potential option

In Celiac Disease, a Subset of Autoantibodies against Transglutaminase Binds Toll-Like Receptor 4 and Induces Activation of Monocytes

BACKGROUND: Celiac disease is a small intestine inflammatory disorder with multiple organ involvement, sustained by an inappropriate immune response to dietary gluten. Anti-transglutaminase antibodies are a typical serological marker in patients with active disease, and may disappear during a gluten-free diet treatment. Involvement of infectious agents and innate immunity has been suggested but never proven. Molecular mimicry is one of the mechanisms that links infection and autoimmunity. METHODS AND FINDINGS: In our attempt to clarify the pathogenesis of celiac disease, we screened a random peptide library with pooled sera of patients affected by active disease after a pre-screening with the sera of the same patients on a gluten-free diet. We identified a peptide recognized by serum immunoglobulins of patients with active disease, but not by those of patients on a gluten-free diet. This peptide shares homology with the rotavirus major neutralizing protein VP-7 and with the self-antigens tissue transglutaminase, human heat shock protein 60, desmoglein 1, and Toll-like receptor 4. We show that antibodies against the peptide affinity-purified from the sera of patients with active disease recognize the viral product and self-antigens in ELISA and Western blot. These antibodies were able to induce increased epithelial cell permeability evaluated by transepithelial flux of [(3)H] mannitol in the T84 human intestinal epithelial cell line. Finally, the purified antibodies induced monocyte activation upon binding Toll-like receptor 4, evaluated both by surface expression of activation markers and by production of pro-inflammatory cytokines. CONCLUSIONS: Our findings show that in active celiac disease, a subset of anti-transglutaminase IgA antibodies recognize the viral protein VP-7, suggesting a possible involvement of rotavirus infection in the pathogenesis of the disease, through a mechanism of molecular mimicry. Moreover, such antibodies recognize self-antigens and are functionally active, able to increase intestinal permeability and induce monocyte activation. We therefore provide evidence for the involvement of innate immunity in the pathogenesis of celiac disease through a previously unknown mechanism of engagement of Toll-like receptor 4

In vitro Biphasic Effect of Honey Bee Venom on Basophils from Screened Healthy Blood Donors

Apis mellifera L. bee venom is the most studied hymenoptera allergen, but many aspects of its action on human basophils remain unclear. Allergologists seek evidence of the effectiveness of bee venom immunotherapy as this approach is the chosen treatment for systemic allergic reactions. The effect of bee venom on human basophils in vitro has not been studied in detail for many reasons, including the paucity of basophils in peripheral blood, inter-individual basophil response variability, and the reliability and predictability of basophil activation tests. We conducted a brief preliminary survey of the effect of Apis bee venom on healthy asymptomatic (non-allergic) subjects. A dose of an aqueous commercial extract of Apis bee venom as high as 10 µg/mL activated resting basophils (CD63=+80-90%, CD203c=+30%), while it inhibited the expression of CD63 (-50%) following basophil stimulation by the soluble agonists formyl-Met-Leu-Phe or anti-IgE. The activation of resting basophils appeared to be dose-related. Only when basophils were activated with an IgE-mediated agonist, did bee venom extract exhibit a possible priming mechanism at the lowest doses used only via CD63, while it was ineffective via CD203c. Autocrine interleukin-3 may play a role in the observed biphasic behavior

An Exploratory Study of Field Failures

Field failures, that is, failures caused by faults that escape the testing phase leading to failures in the field, are unavoidable. Improving verification and validation activities before deployment can identify and timely remove many but not all faults, and users may still experience a number of annoying problems while using their software systems. This paper investigates the nature of field failures, to understand to what extent further improving in-house verification and validation activities can reduce the number of failures in the field, and frames the need of new approaches that operate in the field. We report the results of the analysis of the bug reports of five applications belonging to three different ecosystems, propose a taxonomy of field failures, and discuss the reasons why failures belonging to the identified classes cannot be detected at design time but shall be addressed at runtime. We observe that many faults (70%) are intrinsically hard to detect at design-time

Deep defects in InGaN LEDs: modeling the impact on the electrical characteristics

Deep defects have a fundamental role in determining the electro-optical characteristics and in the efficiency of InGaN light-emitting diodes (LEDs). However, modeling their effect on the electrical characteristics of the LED is not straightforward. In this paper we analyze the impact of the defects on the electrical characteristics of LEDs: we analyze three single-quantum-well (SQW) InGaN/GaN LED wafers, which differ in the density of defects. Through steady-state photocapacitance (SSPC) and light-capacitance-voltage measurements, the energy levels of these deep defects and their concentrations have been estimated. By means of a simulation campaign, we show that these defects have a fundamental impact on the current voltage characteristic of LEDs, especially in the sub turn-on region. The model adopted takes into consideration trap assisted tunneling as the main mechanism responsible for current leakage in forward bias. For the first time, we use in simulations the defect parameters (concentration, energy) extracted from SSPC. In this way, we can reproduce with great accuracy the current-voltage characteristics of InGaN LEDs in a wide current range (from pA to mA). In addition, based on SSPC measurements, we demonstrate that the defect density in the active region scales with the QW thickness. This supports the hypothesis that defects are incorporated in In-containing layers, consistently with recent publications

Defects in III-N LEDs: experimental identification and impact on electro-optical characteristics

III-N light-emitting-diodes (LEDs) are subject of intense investigations, thanks to their high efficiency and great reliability. The quality of the semiconductor material has a significant impact on the electro-optical performance of LEDs: for this reason, a detailed characterization of defect properties and the modeling of the impact of defects on device performance are of fundamental importance. This presentation addresses this issue, by discussing a set of recent case studies on the topic; specifically, we focus on the experimental characterization of defects, and on the modeling of their impact on the electro-optical characteristics of the devices

What Else Do the Deep Learning Techniques Tell Us about Voltage Dips Validity? Regional-Level Assessments with the New QuEEN System Based on Real Network Configurations

The paper presents the performance evaluation of the DELFI (Deep Learning for False voltage dip Identification) classifier for evaluating voltage dip validity, now available in the QuEEN monitoring system. In addition to the usual event characteristics, QuEEN now automatically classifies events in terms of validity based on criteria that make use of either a signal processing technique (current criterion) or an artificial intelligence algorithm (new criterion called DELFI). Some preliminary results obtained from the new criterion had suggested its full integration into the monitoring system. This paper deals with the comparison of the effectiveness of the DELFI criterion compared to the current one in evaluating the events validity, starting from a large set of events. To prove the enhancement achieved with the DELFI classifier, an in-depth analysis has been carried out by cross-comparing the results both with the neutral system configuration and with the events characteristics (duration/residual voltage). The results clearly show a better match of DELFI classifications with network and events characteristics. Moreover, the DELFI classifier has allowed us to highlight specific situations concerning power quality at regional level, resolving the uncertainties due to the current validity criterion. In details, three groups of regions can be highlighted with respect to the frequency of the occurrence of false events

Advanced Machine Learning Functionalities in the Medium Voltage Distributed Monitoring System QuEEN: A Macro-Regional Voltage Dips Severity Analysis

This paper presents the integration of advanced machine learning techniques in the medium voltage distributed monitoring system QuEEN. This system is aimed to monitor voltage dips in the Italian distribution network mainly for survey and research purposes. For each recorded event it is able to automatically evaluate its residual voltage and duration from the corresponding voltage rms values and provide its “validity” (invalidating any false events caused by voltage transformers saturation) and its “origin”(upstream or downstream from the measurement point) by proper procedures and algorithms (current techniques). On the other hand, in the last years new solutions have been proposed by RSE to improve the assessment of the validity and origin of the event: the DELFI classifier (DEep Learning for False voltage dips Identification) and the FExWaveS + SVM classifier (Features Extraction from Waveform Segmentation + Support Vector Machine classifier). These advanced functionalities have been recently integrated in the monitoring system thanks to the automated software tool called QuEEN PyService. In this work, intensive use of these advanced techniques has been carried out for the first time on a significant number of monitored sites (150) starting from the data recorded from 2018 to 2021. Besides, the comparison between the results of the innovative technique (validity and origin of severe voltage dips) with respect to the current ones has been performed at the macro-regional level too. The new techniques are shown to have a not negligible impact on the severe voltage dips number and confirm a non-homogenous condition among the Italian macro-regional areas