LSSVM Model for Penetration Depth Detection in Underwater Arc Welding Process

Abstract. For underwater arc welding, it is much more complexity and difficulty to detect penetration depth than land arc welding. Based on least squares support vector machines (LSSVM), welding current, arc voltage, travel speed, contact-tube-to-work distance, and weld pool width are extracted as input units. Penetration depth is predicted in underwater flux-cored arc welding (FCAW). For improvement prediction performance, the LSSVM parameters are adaptively optimized. The experimental results show that this model can achieve higher identification precision and is more suitable to detect the depth of underwater FCAW penetration than back propagation neural networks (BPNN)

Multimodal ultrasound imaging: a method to improve the accuracy of sentinel lymph node diagnosis in breast cancer

AimThis study assessed the utility of multimodal ultrasound in enhancing the accuracy of breast cancer sentinel lymph node (SLN) assessment and compared it with single-modality ultrasound.MethodsPreoperative examinations, including two-dimensional ultrasound (2D US), intradermal contrast-enhanced ultrasound (CEUS), intravenous CEUS, shear-wave elastography (SWE), and surface localization, were conducted on 86 SLNs from breast cancer patients. The diagnostic performance of single and multimodal approaches for detecting metastatic SLNs was compared to postoperative pathological results.ResultsAmong the 86 SLNs, 29 were pathologically diagnosed as metastatic, and 57 as non-metastatic. Single-modality ultrasounds had AUC values of 0.826 (intradermal CEUS), 0.705 (intravenous CEUS), 0.678 (2D US), and 0.677 (SWE), respectively. Intradermal CEUS significantly outperformed the other methods (p<0.05), while the remaining three methods had no statistically significant differences (p>0.05). Multimodal ultrasound, combining intradermal CEUS, intravenous CEUS, 2D US, and SWE, achieved an AUC of 0.893, with 86.21% sensitivity and 84.21% specificity. The DeLong test confirmed that multimodal ultrasound was significantly better than the four single-modal ultrasound methods (p<0.05). Decision curve analysis and clinical impact curves demonstrated the superior performance of multimodal ultrasound in identifying high-risk SLN patients.ConclusionMultimodal ultrasound improves breast cancer SLN identification and diagnostic accuracy

A global monthly field of seawater pH over 3 decades: a machine learning approach

The continuous uptake of anthropogenic CO2 by the ocean leads to ocean acidification, which is an ongoing threat to the marine ecosystem. The ocean acidification rate was globally documented in the surface ocean but limited below the surface. Here, we present a monthly four-dimensional 1°×1° gridded product of global seawater pH, derived from a machine learning algorithm trained on pH observations at total scale and in-situ temperature from the Global Ocean Data Analysis Project (GLODAP). The constructed pH product covers the years 1992–2020 and depths from the surface to 2 km on 41 levels. Three types of machine learning algorithms were used in the pH product construction, including self-organizing map neural networks for region dividing, a stepwise algorithm for predictor selection, and feed-forward neural networks (FFNN) for non-linear relationship regression. The performance of the machine learning algorithm was validated using real observations by a cross validation method, where four repeating iterations were carried out with 25 % varied observations for each evaluation and 75 % for training. The constructed pH product is evaluated through comparisons to time series observations and the GLODAP pH climatology. The overall root mean square error between the FFNN constructed pH and the GLODAP measurements is 0.028, ranging from 0.044 in the surface to 0.013 at 2000 m. The pH product is distributed through the data repository of the Marine Science Data Center of the Chinese Academy of Sciences at http://dx.doi.org/10.12157/IOCAS.20230720.001 (Zhong et al., 2023)

Neutrino Physics with JUNO

The Jiangmen Underground Neutrino Observatory (JUNO), a 20 kton multi-purposeunderground liquid scintillator detector, was proposed with the determinationof the neutrino mass hierarchy as a primary physics goal. It is also capable ofobserving neutrinos from terrestrial and extra-terrestrial sources, includingsupernova burst neutrinos, diffuse supernova neutrino background, geoneutrinos,atmospheric neutrinos, solar neutrinos, as well as exotic searches such asnucleon decays, dark matter, sterile neutrinos, etc. We present the physicsmotivations and the anticipated performance of the JUNO detector for variousproposed measurements. By detecting reactor antineutrinos from two power plantsat 53-km distance, JUNO will determine the neutrino mass hierarchy at a 3-4sigma significance with six years of running. The measurement of antineutrinospectrum will also lead to the precise determination of three out of the sixoscillation parameters to an accuracy of better than 1\%. Neutrino burst from atypical core-collapse supernova at 10 kpc would lead to ~5000inverse-beta-decay events and ~2000 all-flavor neutrino-proton elasticscattering events in JUNO. Detection of DSNB would provide valuable informationon the cosmic star-formation rate and the average core-collapsed neutrinoenergy spectrum. Geo-neutrinos can be detected in JUNO with a rate of ~400events per year, significantly improving the statistics of existing geoneutrinosamples. The JUNO detector is sensitive to several exotic searches, e.g. protondecay via the $p\to K^++\bar\nu$ decay channel. The JUNO detector will providea unique facility to address many outstanding crucial questions in particle andastrophysics. It holds the great potential for further advancing our quest tounderstanding the fundamental properties of neutrinos, one of the buildingblocks of our Universe

Research on the Stability of the Spacer Fluid Interface in Dual-Layer Pipe Dual-Gradient Drilling

Dual-layer pipe dual-gradient drilling technology is an emerging technology for solving the problem of the narrow safety density window in deepwater drilling. The unstable spacer fluid interface in this technology directly affects the dual-gradient pressure system in the annulus, causing changes in the drilling mud performance and affecting the control of bottom hole pressure and rock removal with drilling mud. Therefore, the key to the stable operation of dual-layer pipe dual-gradient drilling technology is to maintain the stability of the spacer fluid interface. Based on this, a seawater-spacer fluid-drilling mud annular flow model was established in this study, with a bottom hole pressure control step of 0.2 MPa, and the spacer fluid height after a single control was used as the evaluation index to study the influence of annular flow velocity, the spacer fluid properties, and the drill string rotation speed on the stability of the spacer fluid interface. The results show that in the determined conditions of the seawater and drilling mud system, the annular fluid flow rate and the physical parameters of the spacer fluid are the main factors affecting the stability of the spacer fluid interface. When the annular fluid flow rate increased within the range of 0.04~0.2 m/s, the liquidity index of the spacer fluid increased between 0.5 and 0.9, the consistency coefficient increased in the range of 0.6 to 1.4 Pa⋅sn, and the stability of the spacer fluid interface decreased. However, the stability of the spacer fluid interface increased with the increase in its density in the range of 1100~1500 kg/m3. The results obtained in this study can provide a reference for selecting the operating parameters to ensure the stable operation of dual-gradient pressure systems

NUMERICAL SIMULATION ANALYSIS AND THE DRAG REDUCTION PERFORMANCE INVESTIGATION ON CIRCULAR CYLINDER WITH DIMPLES AT SUBCRITICAL REYNOLDS NUMBER

In order to study the drag reduction performance of non-smooth circular cylinder,the SST k-ω turbulence model was been used to simulate the flow around a circular cylinder and studied the drag reduction performance of a circular cylinder at the subcritical Reynolds number Re= 40 000. The sensitivity analysis of dimples ’ structure parameters,which include the depth,the inner shape and the distribution of dimples is carried out to study the influence of various parameters on the drag reduction of the flow around a circular cylinder. The results show that the circular cylinder with dimples has a good effect on drag reduction. Drag coefficient and lift coefficient decrease with the increasement of dimple depth first and then decrease increase.Moreover,the circular cylinder with dimples has the best drag reduction effect when h = 0. 015 D. The average drag coefficient of the circular cylinder with cylindrical dimples is 0. 923 while the average drag coefficient of the circular cylinder with spherical dimples is 0. 94. The average drag coefficient of the circular cylinder with dimples by diamond distribution is 0. 923 while the average drag coefficient of the circular cylinder with dimples by rectangle is 0. 973

Carbon Sinks and Variations of pCO(2) in the Southern Ocean From 1998 to 2018 Based on a Deep Learning Approach

The Southern Ocean comprises 25% of the global ocean surface area, accounts for nearly half of the total carbon sink of the global oceans, and is a place that significantly reduces the impacts of anthropogenic CO2 emissions. Due to the sparsity of observational data, the changes in Southern Ocean carbon sinks over time remain uncertain. In this study, we integrated correlation analysis and a feedforward neural network to improve the accuracy of carbon flux estimations in the Southern Ocean. Based on observation data from 1998-2018, we reconstructed the Southern Ocean's pCO(2) grid data during this period. The root-mean-square error obtained by fitting the observation data was 8.86 mu atm, indicating that the results were better than those of the two primary statistically based models in the Surface Ocean pCO(2) mapping intercomparison. The results also showed that the Southern Ocean's capacity to act as a carbon sink has gradually increased since 2000; it reduced during 2010-2013 but increased significantly after that. The Southern Ocean's seasonality is characterized by minimum carbon uptake in winter due to increased upwelling; this is followed by a rapid increase toward maximum uptake in summer, which is mainly biologically driven. There is an apparent double-ring structure in the Southern Ocean, as noted in other studies. This study confirms that the inner ring (50-70 degrees S) is a carbon source area gradually transforming into a carbon sink, while the outer ring (35-50 degrees S) continues to serve as a carbon sink

CASI-Net: A Novel and Effect Steel Surface Defect Classification Method Based on Coordinate Attention and Self-Interaction Mechanism

The surface defects of a hot-rolled strip will adversely affect the appearance and quality of industrial products. Therefore, the timely identification of hot-rolled strip surface defects is of great significance. In order to improve the efficiency and accuracy of surface defect detection, a lightweight network based on coordinate attention and self-interaction (CASI-Net), which integrates channel domain, spatial information, and a self-interaction module, is proposed to automatically identify six kinds of hot-rolled steel strip surface defects. In this paper, we use coordinate attention to embed location information into channel attention, which enables the CASI-Net to locate the region of defects more accurately, thus contributing to better recognition and classification. In addition, features are converted into aggregation features from the horizontal and vertical direction attention. Furthermore, a self-interaction module is proposed to interactively fuse the extracted feature information to improve the classification accuracy. The experimental results show that CASI-Net can achieve accurate defect classification with reduced parameters and computation

Cell Metabolomics Reveals the Potential Mechanism of Aloe Emodin and Emodin Inhibiting Breast Cancer Metastasis

Metastasis is one of the main obstacles for the treatment and prognosis of breast cancer. In this study, the effects and possible mechanisms of aloe emodin (AE) and emodin (EMD) for inhibiting breast cancer metastasis were investigated via cell metabolomics. First, a co-culture model of MCF-7 and HUVEC cells was established and compared with a traditional single culture of MCF-7 cells. The results showed that HUVEC cells could promote the development of cancer cells to a malignant phenotype. Moreover, AE and EMD could inhibit adhesion, invasion, and angiogenesis and induce anoikis of MCF-7 cells in co-culture model. Then, the potential mechanisms behind AE and EMD inhibition of MCF-7 cell metastasis were explored using a metabolomics method based on UPLC-Q-TOF/MS multivariate statistical analysis. Consequently, 27 and 13 biomarkers were identified in AE and EMD groups, respectively, including polyamine metabolism, methionine cycle, TCA cycle, glutathione metabolism, purine metabolism, and aspartate synthesis. The typical metabolites were quantitatively analyzed, and the results showed that the inhibitory effect of AE was significantly better than EMD. All results confirmed that AE and EMD could inhibit metastasis of breast cancer cells through different pathways. Our study provides an overall view of the underlying mechanisms of AE and EMD against breast cancer metastasis