780 research outputs found
Using Exploratory Data Analytics to Identify Deficiencies in mCherry Red Fluorescent Protein and Suggest Improvements
Fitxes dels barri
Glass transition and cooperative rearranging regions in amorphous thermoplastic nanocomposites
The aim of this work was to study the effect of nanofillers on the structural relaxation phenomena occurring in amorphous
poly(ethylene-terephthalate)/poly(cyclohexane-dimethanol terephthalate) copolymer (PET/PCHDMT) nanocomposites in
correspondence with the glass transition temperature. PET/PCHDMT nanocomposites were prepared by melt mixing with
an organicmodified montmorillonite at different processing temperatures. Differential scanning calorimetry analysis revealed
that addition of the organic modifier alone causes a decrease of the glass transition temperature and an increase of the specific
heat discontinuity. Nanocomposites showed a higher glass transition temperature and a lower specific heat discontinuity
compared with samples obtained by adding organic modifier to PET/PCHDMT. Both effects were more relevant for samples
processed at lower temperatures. Therefore, the glass transition temperature was studied by introducing the concept of
fictive temperature and relaxation time. It was found that nanocomposites have a higher apparent activation energy and
an increased size of cooperatively rearranging regions compared with neat PET/PCHDMT. Both effects are more relevant
for nanocomposites processed at lower temperatures. All the discussed effects are explained by considering the enhanced
confinement of PET/PCHDMT macromolecules, due to the presence of intercalated lamellae of organofiller. The efficiency of
intercalation is increased by decreased processing temperature, which involves an increase of the nano-confinement area of
the polymer
ACTIVE FLOW CONTROL OF AN OVER-EXPANDED NOZZLE BY SHOCK VECTOR CONTROL
Thrust vectoring obtained by the nozzle flow manipulation technique known as Shock Vector Control (SVC) is investigated numerically. In the shock vector control method, a shock structure is generated in the main flow by using transversal continuous
blowing. The pressure distribution on the nozzle walls becomes asymmetric, thus giving rise to a lateral force. The open-loop response of the nozzle and the thrust vectoring effectiveness/controllability are investigated by using a CFD tool based on the compressible URANS equations. Nozzle performances and thrust vector angles have been computed for different nozzle pressure ratios in the range of over-expanded conditions. The latter represent the worst case, where the shock structure generated by the control is amplified by the re-compression requirements imposed by the external ambient pressure
Using Artificial Intelligence for the Diagnosis of Prostate Cancer: The Paper of Yuichiro Oishi et al. Is a Step Forward on the Way of Precision Medicine
Yuichiro Oishi et al. presented an interesting study reporting the ability of Artifi- cial Intelligence (AI) to diagnose and locate prostate cancer from multiparametric MRI (mpMRI) [1]. The authors evaluated the diagnostic performance of their AI with a ROC analysis; interestingly the area under the ROC curve was 0.985, while the sensitivity and the specificity were 0.875 and 0.961 (p < 0.01), respectively. Figure 1 of the paper shows that the regions of the prostate labeled by AI as prostate cancer correspond strictly to the cancer areas identified at pathological examination of the gland. These good results justified the strong conclusions of the paper: “diagnostic partition using the superpixel method and SVM-computed likelihood maps enables automated diagnosis of prostate cancer location and shape in mpMRI” [1]. Many aspects of this paper deserve to be emphasized. During the last two decades, numerous attempts to use radiomics for the diagnosis of cancer have been made [2]. So far, the dimensions of the dataset have always been a major limiting factor for the AI training and consequently for its diagnostic performance. The AI-based computer-aided diagnosis used in this study interestingly reached a good result with only a small number of patients, apparently overcoming the need for a large dataset. The authors achieved this result by sampling all the peripheral zone pixels for training the Support Vector Machine. Using this strategy, the dataset which resulted was very large despite the small number of patients included in the study. Because of the previous consideration, the strategy proposed by Yuichiro Oishi et al. will probably be crucial in the development of future diagnostic tools
All you need is ratings: A clustering approach to synthetic rating datasets generation
The public availability of collections containing user preferences is of vital importance for performing offline evaluations in the field of recommender systems. However, the number of rating datasets is limited because of the costs required for their creation and the fear of violating the privacy of the users by sharing them. For this reason, numerous research attempts investigated the creation of synthetic collections of ratings using generative approaches. Nevertheless, these datasets are usually not reliable enough for conducting an evaluation campaign. In this paper, we propose a method for creating synthetic datasets with a configurable number of users that mimic the characteristics of already existing ones. We empirically validated the proposed approach by exploiting the synthetic datasets for evaluating different recommenders and by comparing the results with the ones obtained using real datasets
Managing patients taking novel oral anticoagulants (NOAs) in dentistry: A discussion paper on clinical implications
Background
The aim of this paper is to contribute to the discussion on how to approach patients taking new orally administered anticoagulants (NOAs) dabigatran etexilate (a direct thrombin inhibitor), rivaroxaban and apixaban (factor Xa inhibitors), before, during and after dental treatment in light of the more recent knowledges.
Discussion
In dentistry and oral surgery, the major concerns in treatment of patients taking direct thrombin inhibitors and factor Xa inhibitors is the risk of haemorrhage and the absence of a specific reversal agent. The degree of renal function, the complexity of the surgical procedure and the patient\u2019s risk of bleeding due to other concomitant causes, are the most important factors to consider during surgical dental treatment of patients taking NOAs. For patients requiring simple dental extraction or minor oral surgery procedures, interruption of NOA is not generally necessary, while an higher control of bleeding and discontinuation of the drug (at least 24 h) should be requested before invasive surgical procedures, depending on renal functionality.
Summary
The clinician has to consider that the number of patients taking NOAs is rapidly increasing. Since available data are not sufficient to establish an evidence-based dental management, the dentist must use caution and attention when treating patients taking dabigatran, rivaroxaban and apixaban
robust die compensation in sheet metal design through the integration of dual response surface and shape function optimization
In sheet metal forming, springback represents a major drawback increasing die set-up problems, especially for ultra-high strength steels. Finite Element Analysis is a well-established method to simulate the process during design, and multicriteria optimizations, for example, via surrogate models, are investigated in order to develop integrated design. Since to take into account also springback compensation die design may involve a large number of geometric variables, this paper presents a robust design formulation, based on the adoption of the shape function optimization, to describe springback in terms of weights directly associated to global shape variations of the die shape. Doing so, multicriteria optimization, which involves also die compensation, can be set up in a more intuitive approach, as requested in the preliminary steps of die design. After the introduction of the industrial problem, the mathematical formulation of the shape function optimization is presented together with its novel extension to Robust Design, which is based on the Dual Response Surface. Through a test case derived from the head part of a B-pillar, stamped from a Dual Phase sheet 1.5 mm thick, this novel extension investigates the effect of 6% variation from nominal values of initial yield stress and thickness. Results demonstrate the feasibility of the procedure, underlying that an optimal compensation may not be optimal in terms of process robustness
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