Numerical simulation research on ecological protection device for marine water intake engineering based on cfd

1941-1948The ecological protection originated from marine engineering is the key factor for the healthy and sustainable development of marine ecological system. Combined with the tidal dynamic characteristics, a new optimistic ecological protection device concept model is designed for marine water intake engineering to avoid the ecological loss, which following the principles of safe, stable, easy installation and maintenance, unaffected water intake. Based on computational fluid dynamics (CFD), the three dimensional mathematical model of ecological protection device for marine water intake engineering is constructed to simulate and evaluate the feasibility of the proposed device. The flow field and pressure distribution are simulated and analyzed before and after the device installation. The numerical results of flow velocity in the intake, intake box and intake channel show that the ecological protection device has a weak effect on the flow field and the device can provide ecological safeguard for marine intake engineering. The pressure distribution from the ecological protection device would also reflect the underlying reason of the flow velocity change

Unraveling the Rich Fragmentation Dynamics Associated with S-H Bond Fission Following Photoexcitation of H ₂S at Wavelengths ∼129.1 nm

H2S is being detected in the atmospheres of ever more interstellar bodies, and photolysis is an important mechanism by which it is processed. Here, we report H Rydberg atom time-of-flight measurements following the excitation of H2S molecules to selected rotational (JKaKc′) levels of the 1B1 Rydberg state associated with the strong absorption feature at wavelengths of λ ∼ 129.1 nm. Analysis of the total kinetic energy release spectra derived from these data reveals that all levels predissociate to yield H atoms in conjunction with both SH(A) and SH(X) partners and that the primary SH(A)/SH(X) product branching ratio increases steeply with ⟨Jb2⟩, the square of the rotational angular momentum about the b-inertial axis in the excited state. These products arise via competing homogeneous (vibronic) and heterogeneous (Coriolis-induced) predissociation pathways that involve coupling to dissociative potential energy surfaces (PES(s)) of, respectively, 1A″ and 1A′ symmetries. The present data also show H + SH(A) product formation when exciting the JKaKc′ = 000 and 111 levels, for which ⟨Jb2⟩ = 0 and Coriolis coupling to the 1A′ PES(s) is symmetry forbidden, implying the operation of another, hitherto unrecognized, route to forming H + SH(A) products following excitation of H2S at energies above ∼9 eV. These data can be expected to stimulate future ab initio molecular dynamic studies that test, refine, and define the currently inferred predissociation pathways available to photoexcited H2S molecules

A dementia classification framework using frequency and time-frequency features based on EEG signals.

Alzheimer's Disease (AD) accounts for 60-70% of all dementia cases, and clinical diagnosis at its early stage is extremely difficult. As several new drugs aiming to modify disease progression or alleviate symptoms are being developed, to assess their efficacy, novel robust biomarkers of brain function are urgently required. This study aims to explore a routine to gain such biomarkers using the quantitative analysis of Electroencephalography (QEEG). This paper proposes a supervised classification framework which uses EEG signals to classify healthy controls (HC) and AD participants. The framework consists of data augmentation, feature extraction, K-Nearest Neighbour (KNN) classification, quantitative evaluation and topographic visualisation. Considering the human brain either as a stationary or a dynamical system, both frequency-based and time-frequency-based features were tested in 40 participants. Results: a) The proposed method can achieve up to 99% classification accuracy on short (4s) eyes open EEG epochs, with the KNN algorithm that has best performance when compared to alternative machine learning approaches; b) The features extracted using the wavelet transform produced better classification performance in comparison to the features based on FFT; c) In the spatial domain, the temporal and parietal areas offer the best distinction between healthy controls and AD. The proposed framework can effectively classify HC and AD participants with high accuracy, meanwhile offering identification and localisation of significant QEEG features. These important findings and the proposed classification framework could be used for the development of a biomarker for the diagnosis and monitoring of disease progression in AD

Investigation of optimal Split ratio for high-speed permanent-magnet brushless machines

The split ratio, i.e., the ratio of rotor outer diameter to stator outer diameter, is one of the most vital design parameters for permanent-magnet (PM) machines due to its significant impact on the machine torque or power density. However, it has been optimized analytically in the existing papers with due account only for the stator copper loss, which is reasonable for low-to-medium speed PM machines. For high-speed PM machines (HSPMMs), the negligence of stator iron loss and the mechanical stress on the rotor will lead to a deviation of optimal split ratio and actual torque capability. In this paper, the optimal split ratio of HSPMM is investigated analytically with the consideration of stator iron loss as well as the mechanical stress on the rotor. The influence of air-gap length and rotor pole pairs on the optimal split ratio is elaborated. Both the analytical and finite-element analysis reveal that the optimal split ratio for HSPMM will be significantly reduced, when stator iron loss and mechanical constraints are taken into account

Morphology and Orientation Selection of Non-Metallic Inclusions in Electrified Molten Metal

The effect of electric current on morphology and orientation selection of non-metallic inclusions in molten metal has been investigated using theoretical modelling and numerical calculation. Two geometric factors, namely the circularity (fc) and alignment ratio (fe) were introduced to describe the inclusions shape and configuration. Electric current free energy was calculated and the values were used to determine the thermodynamic preference between different microstructures. Electric current promotes the development of inclusion along the current direction by either expatiating directional growth or enhancing directional agglomeration. Reconfiguration of the inclusions to reduce the system electric resistance drives the phenomena. The morphology and orientation selection follows the routine to reduce electric free energy. The numerical results are in agreement with our experimental observations

The Ljapunov-Schmidt reduction for some critical problems

This is a survey about the application of the Ljapunov-Schmidt reduction for some critical problems

Partial Wave Analysis of J/ψ→γ(K+K−π+π−)J/\psi \to \gamma (K^+K^-\pi^+\pi^-)

BES data on $J/\psi \to \gamma (K^+K^-\pi^+\pi^-)$ are presented. The $K^*\bar K^*$ contribution peaks strongly near threshold. It is fitted with a broad $0^{-+}$ resonance with mass $M = 1800 \pm 100$ MeV, width $\Gamma = 500 \pm 200$ MeV. A broad $2^{++}$ resonance peaking at 2020 MeV is also required with width $\sim 500$ MeV. There is further evidence for a $2^{-+}$ component peaking at 2.55 GeV. The non-$K^*\bar K^*$ contribution is close to phase space; it peaks at 2.6 GeV and is very different from $K^{*}\bar{K^{*}}$.Comment: 15 pages, 6 figures, 1 table, Submitted to PL

Accurate Diagnosis of Colorectal Cancer Based On Histopathology Images Using Artificial Intelligence

Background: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods: Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, \u3e 14,680 WSIs, from \u3e 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. Results: Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. Conclusions: This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition