On The Use Of Polyurethane Matrix Carbon Fiber Composites For Strengthening Concrete Structures

Fiber-reinforced polymer (FRP) composite materials have effectively been used in numerous reinforced concrete civil infrastructure strengthening projects. Although a significant body of knowledge has been established for epoxy matrix carbon FRPs and epoxy adhesives, there is still a need to investigate other matrices and adhesive types. One such matrix/adhesive type yet to be heavily researched for infrastructure application is polyurethane. This thesis investigates use of polyurethane matrix carbon fiber composites for strengthening reinforced concrete civil infrastructure. Investigations on mirco- and macro-mechanical composite performance, strengthened member flexural performance, and bond durability under environmental conditioning will be presented. Results indicate that polyurethane carbon composites could potentially be a viable option for strengthening concrete structures

Fingerprint recognition with embedded presentation attacks detection: are we ready?

The diffusion of fingerprint verification systems for security applications makes it urgent to investigate the embedding of software-based presentation attack detection algorithms (PAD) into such systems. Companies and institutions need to know whether such integration would make the system more “secure” and whether the technology available is ready, and, if so, at what operational working conditions. Despite significant improvements, especially by adopting deep learning approaches to fingerprint PAD, current research did not state much about their effectiveness when embedded in fingerprint verification systems. We believe that the lack of works is explained by the lack of instruments to investigate the problem, that is, modeling the cause-effect relationships when two non-zero error-free systems work together. Accordingly, this paper explores the fusion of PAD into verification systems by proposing a novel investigation instrument: a performance simulator based on the probabilistic modeling of the relationships among the Receiver Operating Characteristics (ROC) of the two individual systems when PAD and verification stages are implemented sequentially. As a matter of fact, this is the most straightforward, flexible, and widespread approach. We carry out simulations on the PAD algorithms’ ROCs submitted to the most recent editions of LivDet (2017-2019), the state-of-the-art NIST Bozorth3, and the top-level Veryfinger 12 matchers. Reported experiments explore significant scenarios to get the conditions under which fingerprint matching with embedded PAD can improve, rather than degrade, the overall personal verification performance

Does living in previously exposed malaria or warm areas is associated with a lower risk of severe COVID-19 infection in Italy?

Incidence of Covid-19 positivity (21/2/2020-28/3/2020) in provinces of 4 Italian regions whose territory was described as previously exposed to Malaria was compared with those of other provinces of the same regions. The climate of such provinces was compared with the climate of the other provinces in some regions. Previously malarial areas show a lower risk than other provinces of the same regions: Mantua (Lombardy) RR=0.94 (CI95%0.89-0.99); Venice-Rovigo (Veneto) RR=0.61 (CI95%0.58-0.65); Ferrara-Ravenna (Emilia-Romagna) RR=0.37 (CI95%0.35-0.41); CagliariOristano-SouthSardinia (Sardinia) RR=0.25 (0.17-0.31). The maximum temperature in March 2020 in those provinces was higher in mean 1.5° for other provinces. The lower frequency of COVID-19 in the provinces previously exposed to Malaria of four Italian regions does not reveal a causal link. The phenomenon has emerged independently in all the regions investigated. People born between the 1920s and 1950s were those most exposed to malaria years ago and today are the most exposed to the severest forms of COVID-19. A warmer climate seems to be associated with a lower risk of COVID, in line with the evidence highlighted in equatorial states where a lower lethality of the virus has emerged, however this regardless of the presence of Malaria. This may suggest that climate and not Malaria is the real risk factor, though further studies need to determine the role of the association climate / COVID

Optical properties of bulk high-entropy diborides for solar energy applications

So far, the studies regarding the innovative High-Entropy Borides (HEBs), which belong to the more general class of Ultra-high temperature ceramics (UHTCs), have been entirely confined to their fabrication or characterization from the microstructural, mechanical and oxidation resistance viewpoints. In this work, the optical properties of two members of HEBs, i.e. (Hf0.2Zr0.2Ta0.2Mo0.2Ti0.2)B2 and (Hf0.2Nb0.2Ta0.2Mo0.2Ti0.2)B2, are evaluated for the first time to assess their possible utilization in the thermal solar energy field. The bulk samples (96.5 % and 97.4 % dense, respectively) are obtained as single-phase products by Spark Plasma Sintering (1950 °C/20 min/20 MPa) starting from powders previously synthesized by Self-propagating High-temperature Synthesis (SHS). The optical characterization, whose results are discussed by comparing HEBs to the individual borides, shows that they are characterized by intrinsic spectral selectivity and low thermal emittance, resulting therefore interesting for high-temperature solar absorbers applications

Characterization of single-nucleotide polymorphisms in 20 genes affecting milk quality in cattle, sheep, goat and buffalo

AbstractMilk products are important dietary sources of nutrients, providing energy, high quality proteins, and a variety of vitamins and minerals. Recent researches have focused on altering fat and protein contents of milk, in order to improve its nutrient content to more suitably reflect current dietary recommendations and trends. We characterized single nucleotide polymorphisms (SNPs) in 20 candidate genes expected to have an influence on fat composition of milk in four ruminant species (cattle, sheep, goat and buffalo). Genes belonged to different families, including transporters, fatty acid biosynthesis, receptors and enzymes for saturation/desaturation. For each gene, PCR primers were designed using bovine sequence to amplify 3 gene fragments, that covered coding and non coding regions. For each gene, we found polymorphisms in at least one species, but none that was present in homologous fragments of all four species. As expected, different SNPs were found across species, but for a very few genes. We..

Partial purification and MALDI-TOF MS analysis of UN1, a tumor antigen membrane glycoprotein.

UN1 is a membrane glycoprotein that is expressed in immature human thymocytes, a subpopulation of peripheral T lymphocytes, the HPB acute lymphoblastic leukemia (ALL) T-cell line and fetal thymus. We previously reported the isolation of a monoclonal antibody (UN1 mAb) recognizing the UN1 protein that was classified as "unclustered" at the 5th and 6th International Workshop and Conference on Human Leukocyte Differentiation Antigens. UN1 was highly expressed in breast cancer tissues and was undetected in non-proliferative lesions and in normal breast tissues, indicating a role for UN1 in the development of a tumorigenic phenotype of breast cancer cells. In this study, we report a partial purification of the UN1 protein from HPB-ALL T cells by anion-exchange chromatography followed by immunoprecipitation with the UN1 mAb and MALDI-TOF MS analysis. This analysis should assist in identifying the amino acid sequence of UN

Machine learning approach using MLP and SVM algorithms for the fault prediction of a centrifugal pump in the oil and gas industry

The demand for cost-effective, reliable and safe machinery operation requires accurate fault detection and classification to achieve an efficient maintenance strategy and increase performance. Furthermore, in strategic sectors such as the oil and gas industry, fault prediction plays a key role to extend component lifetime and reduce unplanned equipment thus preventing costly breakdowns and plant shutdowns. This paper presents the preliminary development of a simple and easy to implement machine learning (ML) model for early fault prediction of a centrifugal pump in the oil and gas industry. The data analysis is based on real-life historical data from process and equipment sensors mounted on the selected machinery. The raw sensor data, mainly from temperature, pressure and vibrations probes, are denoised, pre-processed and successively coded to train the model. To validate the learning capabilities of the ML model, two different algorithms-the Support Vector Machine (SVM) and the Multilayer Perceptron (MLP)-are implemented in KNIME platform. Based on these algorithms, potential faults are successfully recognized and classified ensuring good prediction accuracy. Indeed, results from this preliminary work show that the model allows us to properly detect the trends of system deviations from normal operation behavior and generate fault prediction alerts as a maintenance decision support system for operatives, aiming at avoiding possible incoming failures

The effect of prime-site occupancy on the hepatitis C virus NS3 protease structure.

We recently reported a new class of inhibitors of the chymotrypsin-like serine protease NS3 of the hepatitis C virus. These inhibitors exploit the binding potential of the S′ site of the protease, which is not generally used by the natural substrates. The effect of prime-site occupancy was analyzed by circular dichroism spectroscopy and limited proteolysis-mass spectrometry. Generally, nonprime inhibitors cause a structural change in NS3. Binding in the S′ site produces additional conformational changes with different binding modes, even in the case of the NS3/4A cofactor complex. Notably, inhibitor binding either in the S or S′ site also has profound effects on the stabilization of the protease. In addition, the stabilization propagates to regions not in direct contact with the inhibitor. In particular, the N-terminal region, which according to structural studies is endowed with low structural stability and is not stabilized by nonprime inhibitors, was now fully protected from proteolytic degradation. From the perspective of drug design, P-P′ inhibitors take advantage of binding pockets, which are not exploited by the natural HCV substrates; hence, they are an entry point for a novel class of NS3/4A inhibitors. Here we show that binding of each inhibitor is associated with a specific structural rearrangement. The development of a range of inhibitors belonging to different classes and an understanding of their interactions with the protease are required to address the issue of the most likely outcome of viral protease inhibitor therapy, that is, viral resistanc

Anomaly Detection for Diagnosing Failures in a Centrifugal Compressor Train

Predicting machine failures is of the utmost importance in industrial systems as it can turn expensive crashes and repair costs into affordable maintenance costs. To this end, this paper presents preliminary work for detecting failures in a centrifugal compressor train based on sensorial data. We show the detection capabilities of a two-step process consisting of: (1) a preprocessing step to reduce the dimensionality of the input data using Principal Component Analysis, and (2) an anomaly detection step using the Mahalanobis distance to detect anomalous observations on the sensors' data. The experiments using real-world data demonstrate the feasibility of our approach and the ability of the method to detect the failures eight days in advance

Dedicated versus mainstreaming approaches in local climate plans in Europe

Cities are gaining prominence committing to respond to the threat of climate change, e.g., by developing local climate plans or strategies. However, little is known regarding the approaches and processes of plan development and implementation, or the success and effectiveness of proposed measures. Mainstreaming is regarded as one approach associated with (implementation) success, but the extent of integration of local climate policies and plans in ongoing sectoral and/or development planning is unclear. This paper analyses 885 cities across the 28 European countries to create a first reference baseline on the degree of climate mainstreaming in local climate plans. This will help to compare the benefits of mainstreaming versus dedicated climate plans, looking at policy effectiveness and ultimately delivery of much needed climate change efforts at the city level. All core cities of the European Urban Audit sample were analyzed, and their local climate plans classified as dedicated or mainstreamed in other local policy initiatives. It was found that the degree of mainstreaming is low for mitigation (9% of reviewed cities; 12% of the identified plans) and somewhat higher for adaptation (10% of cities; 29% of plans). In particular horizontal mainstreaming is a major effort for local authorities; an effort that does not necessarily pay off in terms of success of action implementation. This study concludes that climate change issues in local municipalities are best tackled by either, developing a dedicated local climate plan in parallel to a mainstreamed plan or by subsequently developing first the dedicated and later a mainstreaming plan (joint or subsequent “dual track approach”). Cities that currently provide dedicated local climate plans (66% of cities for mitigation; 26% of cities for adaptation) may follow-up with a mainstreaming approach. This promises effective implementation of tangible climate actions as well as subsequent diffusion of climate issues into other local sector policies. The development of only broad sustainability or resilience strategies is seen as critical.We thank the many council representatives that supported the datacollection. Special thanks to Birgit Georgi who helped in setting up this large net work of researchers across the EU-28. We also thank the EU COST Action TU 0902 (ledbyRichardDawson) that established the core research network and the positive engagement and interaction of th emembers of this group. OH is Fellow of the Tyndall Centre for Climate Change Research and was funded by the UK EPSRC LC Transforms: Low Carbon Transitions of Fleet Operations in Metropolitan Sites Project (grant number EP/N010612/1). EKL was supported by the Ministry of Education, Youth and Sports, Czechia, within the National Sustainability Program I (NPU I) (grant number LO1415). DG ac-knowledges support by the Ministry of Education, University and Research (MIUR), Italy ("Departments of Excellence" grant L. 232/2016). HO was supported by the Ministry of Education and Research, Estonia (grantnumberIUT34-17). MO acknowledges funding from the Ministry of Economy and Competitiveness (MINECO), Spain (grant number IJCI-2016-28835). SS acknowledges that CENSE's research is partially funded by the Science Foundation, Portugal (grant number UID/AMB/04085/2019). The paper reflects only the views of the authors. The European Union, the European Environment Agency or other supporting bodies are not liable for any use that may be made of the information that is provided in this manuscript