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

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..

A human–AI collaboration workflow for archaeological sites detection

This paper illustrates the results obtained by using pre-trained semantic segmentation deep learning models for the detection of archaeological sites within the Mesopotamian floodplains environment. The models were fine-tuned using openly available satellite imagery and vector shapes coming from a large corpus of annotations (i.e., surveyed sites). A randomized test showed that the best model reaches a detection accuracy in the neighborhood of 80%. Integrating domain expertise was crucial to define how to build the dataset and how to evaluate the predictions, since defining if a proposed mask counts as a prediction is very subjective. Furthermore, even an inaccurate prediction can be useful when put into context and interpreted by a trained archaeologist. Coming from these considerations we close the paper with a vision for a Human–AI collaboration workflow. Starting with an annotated dataset that is refined by the human expert we obtain a model whose predictions can either be combined to create a heatmap, to be overlaid on satellite and/or aerial imagery, or alternatively can be vectorized to make further analysis in a GIS software easier and automatic. In turn, the archaeologists can analyze the predictions, organize their onsite surveys, and refine the dataset with new, corrected, annotations

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

Fingerprint Adversarial Presentation Attack in the Physical Domain

With the advent of the deep learning era, Fingerprint-based Authentication Systems (FAS) equipped with Fingerprint Presentation Attack Detection (FPAD) modules managed to avoid attacks on the sensor through artificial replicas of fingerprints. Previous works highlighted the vulnerability of FPADs to digital adversarial attacks. However, in a realistic scenario, the attackers may not have the possibility to directly feed a digitally perturbed image to the deep learning based FPAD, since the channel between the sensor and the FPAD is usually protected. In this paper we thus investigate the threat level associated with adversarial attacks against FPADs in the physical domain. By materially realising fakes from the adversarial images we were able to insert them into the system directly from the “exposed” part, the sensor. To the best of our knowledge, this represents the first proof-of-concept of a fingerprint adversarial presentation attack. We evaluated how much liveness score changed by feeding the system with the attacks using digital and printed adversarial images. To measure what portion of this increase is due to the printing itself, we also re-printed the original spoof images, without injecting any perturbation. Experiments conducted on the LivDet 2015 dataset demonstrate that the printed adversarial images achieve ∼ 100% attack success rate against an FPAD if the attacker has the ability to make multiple attacks on the sensor (10) and a fairly good result (∼ 28%) in a one-shot scenario. Despite this work must be considered as a proof-of-concept, it constitutes a promising pioneering attempt confirming that an adversarial presentation attack is feasible and dangerous

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