Research on the End Surface Dent of the Main Shaft Forging

In the process of the stretching of the shaft forgings, if the process parameters are not properly selected, the end-face dent will take place. The end-face dent affects the performance of large forgings and leads to much material wasting. Finite element method was employed to perform numerical simulation of the stretching of a main shaft with an upper flat anvil and a lower V-shaped anvil. The orthogonal test table was designed by selecting the anvil width, the Reduction ratio and the feed as influencing factors. Accordingly, simulations were carried out to solve the end-face dent values under different parameter combinations. The analysis showed that the optimal parameter combination gave an anvil width ratio of 0.75, a Reduction ratio of 0.2, and an initial feed of 300 mm. Through extremum difference analysis, it was found that among the three factors are the feed, the reduction ratio, and the anvil width ratio in the decreasing order of the influence on the end- face dent. Comprehensive analysis showed that when the anvil is relatively narrow, increasing the relative feed can reduce the end-surface dent remarkably. It is advisable that during the stretching of shaft forgings with a flat upper anvil and a V-shaped lower anvil, the combination of the anvil width ratio of 0.75, the reduction ratio of 0.2, and increasing the feed can reduce the end-face dent, thereby reducing the end cutting and saving material costs

A Homogenization Approach for Gradient-Dominated Stochastic Optimization

Gradient dominance property is a condition weaker than strong convexity, yet it sufficiently ensures global convergence for first-order methods even in non-convex optimization. This property finds application in various machine learning domains, including matrix decomposition, linear neural networks, and policy-based reinforcement learning (RL). In this paper, we study the stochastic homogeneous second-order descent method (SHSODM) for gradient-dominated optimization with $\alpha \in [1, 2]$ based on a recently proposed homogenization approach. Theoretically, we show that SHSODM achieves a sample complexity of $O(\epsilon^{-7/(2 \alpha) +1})$ for $\alpha \in [1, 3/2)$ and $\tilde{O}(\epsilon^{-2/\alpha})$ for $\alpha \in [3/2, 2]$. We further provide a SHSODM with a variance reduction technique enjoying an improved sample complexity of $O( \epsilon ^{-( 7-3\alpha ) /( 2\alpha )})$ for $\alpha \in [1,3/2)$. Our results match the state-of-the-art sample complexity bounds for stochastic gradient-dominated optimization without \emph{cubic regularization}. Since the homogenization approach only relies on solving extremal eigenvector problems instead of Newton-type systems, our methods gain the advantage of cheaper iterations and robustness in ill-conditioned problems. Numerical experiments on several RL tasks demonstrate the efficiency of SHSODM compared to other off-the-shelf methods

Hi-SAM: Marrying Segment Anything Model for Hierarchical Text Segmentation

The Segment Anything Model (SAM), a profound vision foundation model pre-trained on a large-scale dataset, breaks the boundaries of general segmentation and sparks various downstream applications. This paper introduces Hi-SAM, a unified model leveraging SAM for hierarchical text segmentation. Hi-SAM excels in text segmentation across four hierarchies, including stroke, word, text-line, and paragraph, while realizing layout analysis as well. Specifically, we first turn SAM into a high-quality text stroke segmentation (TSS) model through a parameter-efficient fine-tuning approach. We use this TSS model to iteratively generate the text stroke labels in a semi-automatical manner, unifying labels across the four text hierarchies in the HierText dataset. Subsequently, with these complete labels, we launch the end-to-end trainable Hi-SAM based on the TSS architecture with a customized hierarchical mask decoder. During inference, Hi-SAM offers both automatic mask generation (AMG) mode and promptable segmentation mode. In terms of the AMG mode, Hi-SAM segments text stroke foreground masks initially, then samples foreground points for hierarchical text mask generation and achieves layout analysis in passing. As for the promptable mode, Hi-SAM provides word, text-line, and paragraph masks with a single point click. Experimental results show the state-of-the-art performance of our TSS model: 84.86% fgIOU on Total-Text and 88.96% fgIOU on TextSeg for text stroke segmentation. Moreover, compared to the previous specialist for joint hierarchical detection and layout analysis on HierText, Hi-SAM achieves significant improvements: 4.73% PQ and 5.39% F1 on the text-line level, 5.49% PQ and 7.39% F1 on the paragraph level layout analysis, requiring 20x fewer training epochs. The code is available at https://github.com/ymy-k/Hi-SAM.Comment: GitHub repository: https://github.com/ymy-k/Hi-SA

High-Density Genetic Map Construction and Qtl Mapping of a Zigzag-Shaped Stem Trait in Tea Plant (Camellia Sinensis)

The highly unique zigzag-shaped stem phenotype in tea plants boasts significant ornamental value and is exceptionally rare. To investigate the genetic mechanism behind this trait, we developed BC1 artificial hybrid populations. Our genetic analysis revealed the zigzag-shaped trait as a qualitative trait. Utilizing whole-genome resequencing, we constructed a high-density genetic map from the BC1 population, incorporating 5,250 SNP markers across 15 linkage groups, covering 3,328.51 cM with an average marker interval distance of 0.68 cM. A quantitative trait locus (QTL) for the zigzag-shaped trait was identified on chromosome 4, within a 61.2 to 97.2 Mb range, accounting for a phenotypic variation explained (PVE) value of 13.62%. Within this QTL, six candidate genes were pinpointed. To better understand their roles, we analyzed gene expression in various tissues and individuals with erect and zigzag-shaped stems. The results implicated CsXTH (CSS0035625) and CsCIPK14 (CSS0044366) as potential key contributors to the zigzag-shaped stem formation. These discoveries lay a robust foundation for future functional genetic mapping and tea plant genetic enhancement

The association between Chinese visceral adiposity index and cardiometabolic multimorbidity among Chinese middle-aged and older adults: a national cohort study

ObjectiveThis study aimed to explore the association between the Chinese visceral adiposity index (CVAI) and cardiometabolic multimorbidity in middle-aged and older Chinese adults.MethodsThe data used in this study were obtained from a national cohort, the China Health and Retirement Longitudinal Study (CHARLS, 2011-2018 wave). The CVAI was measured using previously validated biomarker estimation formulas, which included sex, age, body mass index, waist circumference, triglycerides, and high-density lipoprotein cholesterol. The presence of two or more of these cardiometabolic diseases (diabetes, heart disease, and stroke) is considered as cardiometabolic multimorbidity. We used Cox proportional hazard regression models to examine the association between CVAI and cardiometabolic multimorbidity, adjusting for a set of covariates. Hazard ratios (HRs) and 95% confidence intervals (CIs) were used to show the strength of the associations. We also conducted a subgroup analysis between age and sex, as well as two sensitivity analyses. Receiver operator characteristic curves (ROC) were used to test the predictive capabilities and cutoff value of the CVAI for cardiometabolic multimorbidity.ResultsA total of 9028 participants were included in the final analysis, with a mean age of 59.3 years (standard deviation: 9.3) and women accounting for 53.7% of the sample population. In the fully-adjusted model, compared with participants in the Q1 of CVAI, the Q3 (HR = 2.203, 95% CI = 1.039 – 3.774) and Q4 of CVAI (HR = 3.547, 95% CI = 2.100 – 5.992) were associated with an increased risk of cardiometabolic multimorbidity. There was no evidence of an interaction between the CVAI quartiles and sex or age in association with cardiometabolic multimorbidity (P >0.05). The results of both sensitivity analyses suggested that the association between CVAI and cardiometabolic multimorbidity was robust. In addition, the area under ROC and ideal cutoff value for CVAI prediction of cardiometabolic multimorbidity were 0.685 (95% CI = 0.649-0.722) and 121.388.ConclusionThe CVAI is a valid biomarker with good predictive capability for cardiometabolic multimorbidity and can be used by primary healthcare organizations in the future for early warning, prevention, and intervention with regard to cardiometabolic multimorbidity

The effect of MWA protocols upon morphology and IVIM parameters of hepatic ablation zones—a preliminary in vivo animal study with an MRI-compatible microwave ablation device

PURPOSEWe aimed to explore the effect of microwave ablation (MWA) protocols upon morphology and instant changes in intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) parameters on MWA zones in porcine livers.METHODSAccording to the empirical protocol for MWA in tumors less than 3 cm in our hospital, the power and application duration were assigned as five groups: A, 60 W × 5 min (n = 6); B, 80 W × 3 min (n = 7); C, 80 W × 5 min (n = 10); D, 100 W × 3 min (n = 10); E, 100 W × 5 min (n = 9). Spearman correlation between MWA protocols, morphological metrics, and instant post-ablation IVIM parameters was performed.RESULTSThere was fair positive correlation between energy delivery and short axis (RSpearman = 0.426, P= .005) of the white zone. There was moderate-to-good positive correlation between wattage and short axis (RSpearman = 0.584, P < .001) of the white zone. For post-ablation IVIM parameters in the white zone, only wattage had moderate-to-good positive correlation with D value (RSpearman= 0.574, P < .001) or ADC value (RSpearman = 0.550, P < .001). No correlation between energy delivery, wattage, duration, and f value was observed (RSpearman = 0.185, P = .24; RSpearman= − 0.001, P = .99; RSpearman = 0.203, P = .20, respectively).CONCLUSIONThe increase in the short axis of the white zone is more likely to be affected by wattage than energy delivery. The instant post-ablation IVIM is feasible in monitoring the MWA zones since the f value in the white zones is not sensitive to changes in MWA protocols, which is promising in evaluating the instant effect of MWA

An Enhanced ADMM-based Interior Point Method for Linear and Conic Optimization

The ADMM-based interior point (ABIP, Lin et al. 2021) method is a hybrid algorithm that effectively combines interior point method (IPM) and first-order methods to achieve a performance boost in large-scale linear optimization. Different from traditional IPM that relies on computationally intensive Newton steps, the ABIP method applies the alternating direction method of multipliers (ADMM) to approximately solve the barrier penalized problem. However, similar to other first-order methods, this technique remains sensitive to condition number and inverse precision. In this paper, we provide an enhanced ABIP method with multiple improvements. Firstly, we develop an ABIP method to solve the general linear conic optimization and establish the associated iteration complexity. Secondly, inspired by some existing methods, we develop different implementation strategies for ABIP method, which substantially improve its performance in linear optimization. Finally, we conduct extensive numerical experiments in both synthetic and real-world datasets to demonstrate the empirical advantage of our developments. In particular, the enhanced ABIP method achieves a 5.8x reduction in the geometric mean of run time on $105$ selected LP instances from Netlib, and it exhibits advantages in certain structured problems such as SVM and PageRank. However, the enhanced ABIP method still falls behind commercial solvers in many benchmarks, especially when high accuracy is desired. We posit that it can serve as a complementary tool alongside well-established solvers

PLEKHA4 is a novel prognostic biomarker that reshapes the tumor microenvironment in lower-grade glioma

BackgroundLower-grade glioma (LGG) is a primary intracranial tumor that carry a high risk of malignant transformation and limited therapeutic options. Emerging evidence indicates that the tumor microenvironment (TME) is a superior predictor for tumor progression and therapy response. PLEKHA4 has been demonstrated to be a biomarker for LGG that correlate with immune infiltration. However, the fundamental mechanism by which PLEKHA4 contributes to LGG is still poorly understood.MethodsMultiple bioinformatic tools, including Tumor Immune Estimation Resource (TIMER), Gene Expression Profiling Interactive Analysis (GEPIA2), Shiny Methylation Analysis Resource Tool (SMART), etc., were incorporated to analyze the PLEKHA4. ESTIMATE, ssGSEA, CIBERSORT, TIDE and CellMiner algorithms were employed to determine the association of PLEKHA4 with TME, immunotherapy response and drug sensitivities. Immunohistochemistry (IHC)-based tissue microarrays and M2 macrophage infiltration assay were conducted to verify their associations.ResultsPLEKHA4 expression was found to be dramatically upregulated and strongly associated with unfavorable overall survival (OS) and disease-specific survival (DSS) in LGG patients, as well as their poor clinicopathological characteristics. Cox regression analysis identified that PLEKHA4 was an independent prognostic factor. Methylation analysis revealed that DNA methylation correlates with PLEKHA4 expression and indicates a better outcome in LGG. Moreover, PLEKHA4 was remarkably correlated with immune responses and TME remodeling, as evidenced by its positive correlation with particular immune marker subsets and the putative infiltration of immune cells. Surprisingly, the proportion of M2 macrophages in TME was strikingly higher than others, inferring that PLEKHA4 may regulate the infiltration and polarization of M2 macrophages. Evidence provided by IHC-based tissue microarrays and M2 macrophage infiltration assay further validated our findings. Moreover, PLEKHA4 expression was found to be significantly correlated with chemokines, interleukins, and their receptors, further supporting the critical role of PLEKHA4 in reshaping the TME. Additionally, we found that PLEKHA4 expression was closely associated with drug sensitivities and immunotherapy responses, indicating that PLEKHA4 expression also had potential clinical significance in guiding immunotherapy and chemotherapy in LGG.ConclusionPLEKHA4 plays a pivotal role in reshaping the TME of LGG patients, and may serve as a potential predictor for LGG prognosis and therapy

Molecular basis of ligand recognition and activation of human V2 vasopressin receptor.

Vasopressin type 2 receptor (V2R) belongs to the vasopressin (VP)/oxytocin (OT) receptor subfamily of G protein-coupled receptors (GPCRs), which comprises at least four closely related receptor subtypes: V1aR, V1bR, V2R, and OTR. These receptors are activated by arginine vasopressin (AVP) and OT, two endogenous nine-amino acid neurohypophysial hormones, which are thought to mediate a biologically conserved role in social behavior and sexual reproduction. V2R is mainly expressed in the renal collecting duct principal cells and mediates the antidiuretic action of AVP by accelerating water reabsorption, thereby playing a vital role in controlling water homeostasis. Moreover, numerous gain-of-function and loss-of-function mutations of V2R have been identified and are closely associated with human diseases, including nephrogenic syndrome of inappropriate diuresis (NSIAD) and X-linked congenital nephrogenic diabetes insipidus (NDI). Thus, V2R has attracted intense interest as a drug target. However, due to a lack of structural information, how AVP recognizes and activates V2R remains elusive, which hampers the V2R-targeted drug design. Here, we determined a 2.6 Å resolution cryo-EM structure of the full-length, G s -coupled human V2R bound to AVP (Fig. 1a; Supplementary information, Table S1). The G s protein was engineered based on mini-G s that was used in the crystal structure determination of the G s -coupled adenosine A 2A receptor (A 2A R) to stabilize the V2R–G s protein complex (Supplementary information, Data S1). The final structure of the AVP–V2R–G s complex contains all residues of AVP (residues 1–9), the Gα s Ras-like domain, Gβγ subunits, Nb35, scFv16, and the V2R residues from T31 to L339 8.57 (superscripts refer to Ballesteros–Weinstein numbering). The majority of amino acid side chains, including AVP, transmembrane domain (TMD), all flexible intracellular loops (ICLs) and extracellular loops (ECLs) except for ICL3 and G185–G188 in ECL2, were well resolved in the model, refined against the EM density map (Fig. 1a; Supplementary information, Figs. S1–3). The complex structure can provide detailed information on the binding interface between AVP and helix bundle of the receptor, as well as the receptor–G s interface

Robust memristors based on layered two-dimensional materials

Van der Waals heterostructures are formed by stacking layers of different two-dimensional materials and offer the possibility to design new materials with atomic-level precision. By combining the valuable properties of different 2D systems, such heterostructures could potentially be used to address existing challenges in the development of electronic devices, particularly those that require vertical multi-layered structures. Here we show that robust memristors with good thermal stability, which is lacking in traditional memristors, can be created from a van der Waals heterostructure composed of graphene/MoS2–xO x/graphene. The devices exhibit excellent switching performance with an endurance of up to 107 and a high operating temperature of up to 340 °C. With the help of in situ electron microscopy, we show that the thermal stability is due to the MoS2–xO x switching layer, as well as the graphene electrodes and the atomically sharp interface between the electrodes and the switching layer. We also show that the devices have a well-defined conduction channel and a switching mechanism that is based on the migration of oxygen ions. Finally, we demonstrate that the memristor devices can be fabricated on a polyimide substrate and exhibit good endurance against over 1,000 bending cycles, illustrating their potential for flexible electronic applications