C(NN)FD -- a deep learning framework for turbomachinery CFD analysis

Deep Learning methods have seen a wide range of successful applications across different industries. Up until now, applications to physical simulations such as CFD (Computational Fluid Dynamics), have been limited to simple test-cases of minor industrial relevance. This paper demonstrates the development of a novel deep learning framework for real-time predictions of the impact of manufacturing and build variations on the overall performance of axial compressors in gas turbines, with a focus on tip clearance variations. The associated scatter in efficiency can significantly increase the $CO_2$ emissions, thus being of great industrial and environmental relevance. The proposed \textit{C(NN)FD} architecture achieves in real-time accuracy comparable to the CFD benchmark. Predicting the flow field and using it to calculate the corresponding overall performance renders the methodology generalisable, while filtering only relevant parts of the CFD solution makes the methodology scalable to industrial applications

Degradation and toxicity reduction of phenol by ultrasound waves

The effects of parameters such as pH, kinetic constants and initial phenol concentration on the sonochemical degradation of phenol and toxicity assay were studied. The experimental results showed that lower pH and lower concentration of phenol favor the phenol degradation. But the rates of phenol degradation under sonication have always been quite low. It is found that the rate of ultrasonic degradation of phenol for initial concentration of 1 mg/L is 0.018 min-1 but later it reduces with increasing of phenol initial concentration substantially and the experimental data fitted well with pseudo first-order reaction rate equation. Bioassay tests showed that phenol was toxic to Daphnia magna and so resulted in quite low LC50 values. Comparison of toxicity units (TU) between phenol and effluent toxicity showed that the TU value for effluent was 1.21 times lower than that obtained for phenol solely. Thus, the toxicity of metabolites formed during the degradation of phenol is lower than the toxicity of phenol itself. KEY WORDS: Phenol, Ultrasound, Sonochemistry, Toxicity assay  Bull. Chem. Soc. Ethiop. 2007, 21(1), 33-38

Generalized Grassmannian Coherent States For Pseudo-Hermitian nn Level Systems

The purpose of this paper is to generalize fermionic coherent states for two-level systems described by pseudo-Hermitian Hamiltonian \cite{Trifonov}, to n-level systems. Central to this task is the expression of the coherent states in terms of generalized Grassmann variables. These kind of Grassmann coherent states satisfy bi-overcompleteness condition instead of over-completeness one, as it is reasonably expected because of the biorthonormality of the system. Choosing an appropriate Grassmann weight function resolution of identity is examined. Moreover Grassmannian coherent and squeezed states of deformed group $SU_{q}(2)$ for three level pseudo-Hermitian system are presented.Comment: 17 page

Application of Deep Neural Network to Predict the High-Cycle Fatigue Life of AISI 1045 Steel Coated by Industrial Coatings

In this study, deep learning approach was utilized for fatigue behavior prediction, analysis, and optimization of the coated AISI 1045 mild carbon steel with galvanization, hardened chromium, and nickel materials with different thicknesses of 13 and 19 mu m were used for coatings and afterward fatigue behavior of related specimens were achieved via rotating bending fatigue test. Experimental results revealed fatigue life improvement up to 60% after applying galvanization coat on untreated material. Obtained experimental data were used for developing a Deep Neural Network (DNN) modelling and accuracy of more than 99%.was achieved. Predicted results have a fine agreement with experiments. In addition, parametric analysis was carried out for optimization which indicated that coating thickness of 10-15 mu m had the highest effects on fatigue life improvement

Heterodyne laser tracking at high Doppler rates

A design is described for a transmitter/receiver system that may be used in a spaceborne laser heterodyne tracking system to produce a high-precision interferometer. We present a two-color laser scheme that enables accurate phase measurement even in the presence of a large Doppler offset between the incoming and outgoing signals. The beat note between the two lasers provides a built-in frequency reference, while the delay line produced by the travel time of the tracking signal provides a stable self-comparison that measures drift in the frequency reference so that it may be corrected for. The resulting noise in the link is only the residual laser phase jitter and the shot noise in the phase measurement

Enhanced superconducting proximity effect in clean ferromagnetic domain structures

We investigate the superconducting proximity effect in a clean magnetic structure consisting of two ferromagnetic layered domains with antiparallel magnetizations in contact with a superconductor. Within the quasiclassical Green's function approach we find that the penetration of the superconducting correlations into the magnetic domains can be enhanced as compared to the corresponding single domain structure. This enhancement depends on an effective exchange field which is determined by the thicknesses and the exchange fields of the two domains. The pair amplitude function oscillates spatially inside each domain with a period inversely proportional to the local exchange field. While the oscillations have a decreasing amplitude with distance inside the domain which is attached to the superconductor, they are enhancing in the other domain and can reach the corresponding normal metal value for a zero effective exchange field. We also find that the corresponding oscillations in the Fermi level proximity density of states as a function of the second domain's thickness has an growing amplitude over a range which depends on the effective exchange field. Our findings can be explained as the result of cancellation of the exchange fields induced phases gained by an electron inside the two domains with antiparallel magnetizations.Comment: 7 pages, 4 figure

A comparative analysis of classifiers in cancer prediction using multiple data mining techniques

In recent years, application of data mining methods in health industry has received increased attention from both health professionals and scholars. This paper presents a data mining framework for detecting breast cancer based on real data from one of Iran hospitals by applying association rules and the most commonly used classifiers. The former were adopted for reducing the size of datasets, while the latter were chosen for cancer prediction. A k-fold cross validation procedure was included for evaluating the performance of the proposed classifiers. Among the six classifiers used in this paper, support vector machine achieved the best results, with an accuracy of 93%. It is worth mentioning that the approach proposed can be applied for detecting other diseases as well

Phenotypic and genotypic characteristics of tetracycline resistant Acinetobacter baumannii isolates from nosocomial infections at Tehran hospitals

Objective(s): To date, the most important genes responsible for tetracycline resistance among Acinetobacter baumannii isolates have been identified as tet A and tet B. This study was carried out to determine the rate of resistance to tetracycline and related antibiotics, and mechanisms of resistance. Materials and Methods: During the years 2010 and 2011, a total of 100 A. baumannii isolates were recovered from patients in different hospitals of Tehran, Iran. Antimicrobial susceptibility to tetracycline, minocycline, doxicycline and tigecycline was evaluated by E-test. Polymerase chain reaction (PCR) of the tet A and tet B genes was performed using specific primers, after which the isolates were subjected to Repetitive Extragenic Palindromic-PCR (PCR) to identify the major genotypes. Results: Of all isolates, 89 were resistant to tetracycline (MIC50 = 32 mu g/ml, MIC90 = 512 mu g/ml). Minocycline with the resistant rate of 35 (MIC50 = 16 mu g/ml, MIC90 = 32 mu g/ml) and doxicycline with the resistant rate of 25 (MIC50 = 16 mu g/ml, MIC90= 32 mu g/ml) have a good activity against A. baumannii isolates. All isolates were sensitive to tigecycline. Frequencies of tet B and tet A genes and coexistence of tet A and tet B among the isolates resistant to tetracycline, were 87.6, 2.2 and 1.1, respectively. Distribution of REP-types among A. baumannii isolates was types A (40), B (30), C (10), D (5) and E (5). Conclusion: It seems that tet A and tet B genes play an important role in the induction of resistance towards tetracyclines used in this study. It is suggested that further studies focus on other antimicrobial drugs and combinations in order to achieve a successful therapy against multi drug resistance (MDR) A. baumannii strains in Iran

Structural Biology of Peanut Allergens

Peanuts are a cause of one of the most common food allergies. Allergy to peanuts not only affects a significant fraction of the population, but it is relatively often associated with strong reactions in sensitized individuals. Peanut and tree nut allergies, which start in childhood are often persistent and continue through life, as opposed to other food allergies that resolve with age. Cherefore, peanut allergens are one of the most intensively studied food allergens. In this review we focus on the structural studies of peanut allergens. Despite the fact that these allergens are attracting a lot of interest and several of them have had their structures experimentally determined, still some molecular properties of peanut allergens are not well understood. Peanut allergens like other allergens belong to just a few protein families. Allergens from the cupin superfamily (Ara h 1 and Ara h 3), 2S albumins (Arah 2 and Ara h 6), Ara h 8 (pathogenesis related class-10 protein) and Ara h 5 (profilin) are relatively well characterized in terms of their 3D structures. However some peanut allergens like Ara h 7 (2S albumin), Ara h 9 (nonspecific lipid-transfer protein), and especially oleosins (Ara h 10 and Ara h 11) and defensins (Ara h 12 and Ara h 13), still are waiting for such characterization

Enhanced Approaches for Identifying Amadori Products: Application to Peanut Allergens

The dry roasting of peanuts is suggested to influence allergic sensitization as a result of the formation of advanced glycation end products (AGEs) on peanut proteins. Identifying AGEs is technically challenging. The AGEs of a peanut allergen were probed with nano-scale liquid chromatography−electrospray ionization−mass spectrometry (nanoLC−ESI−MS) and tandem mass spectrometry (MS/MS) analyses. Amadori product ions matched to expected peptides and yielded fragments that included a loss of three waters and HCHO. As a result of the paucity of b and y ions in the MS/MS spectrum, standard search algorithms do not perform well. Reactions with isotopically labeled sugars confirmed that the peptides contained Amadori products. An algorithm was developed on the basis of information content (Shannon entropy) and the loss of water and HCHO. Results with test data show that the algorithm finds the correct spectra with high precision, reducing the time needed to manually inspect data. Computational and technical improvements allowed for better identification of the chemical differences between modified and unmodified proteins