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Cell type-dependent role of transforming growth factor-βsignaling on postnatal neural stem cell proliferation and migration
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作者 Kierra Ware Joshua Peter +1 位作者 Lucas McClain Yu Luo 《Neural Regeneration Research》 2026年第3期1151-1161,共11页
Adult neurogenesis continuously produces new neurons critical for cognitive plasticity in adult rodents.While it is known transforming growth factor-βsignaling is important in embryonic neurogenesis,its role in postn... Adult neurogenesis continuously produces new neurons critical for cognitive plasticity in adult rodents.While it is known transforming growth factor-βsignaling is important in embryonic neurogenesis,its role in postnatal neurogenesis remains unclear.In this study,to define the precise role of transforming growth factor-βsignaling in postnatal neurogenesis at distinct stages of the neurogenic cascade both in vitro and in vivo,we developed two novel inducible and cell type-specific mouse models to specifically silence transforming growth factor-βsignaling in neural stem cells in(mGFAPcre-ALK5fl/fl-Ai9)or immature neuroblasts in(DCXcreERT2-ALK5fl/fl-Ai9).Our data showed that exogenous transforming growth factor-βtreatment led to inhibition of the proliferation of primary neural stem cells while stimulating their migration.These effects were abolished in activin-like kinase 5(ALK5)knockout primary neural stem cells.Consistent with this,inhibition of transforming growth factor-βsignaling with SB-431542 in wild-type neural stem cells stimulated proliferation while inhibited the migration of neural stem cells.Interestingly,deletion of transforming growth factor-βreceptor in neural stem cells in vivo inhibited the migration of postnatal born neurons in mGFAPcre-ALK5fl/fl-Ai9 mice,while abolishment of transforming growth factor-βsignaling in immature neuroblasts in DCXcreERT2-ALK5fl/fl-Ai9 mice did not affect the migration of these cells in the hippocampus.In summary,our data supports a dual role of transforming growth factor-βsignaling in the proliferation and migration of neural stem cells in vitro.Moreover,our data provides novel insights on cell type-specific-dependent requirements of transforming growth factor-βsignaling on neural stem cell proliferation and migration in vivo. 展开更多
关键词 adult neurogenesis DOUBLECORTIN HIPPOCAMPUS MIGRATION neural stem cells PROLIFERATION transforming growth factor-β
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基于BSimilar优化PTransformer的光伏功率短期预测
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作者 张文广 蔡浩 +1 位作者 刘科 孙盼荣 《动力工程学报》 北大核心 2026年第1期77-84,102,共9页
为提高光伏功率短期预测的精度,提出了考虑光伏设备性能退化因素的相似日算法优化的分时段多通道独立光伏功率短期预测方法。首先,在PTransformer模型中用分时段与通道独立的方法来处理光伏输入数据,以降低空间复杂度及提高长时间数据... 为提高光伏功率短期预测的精度,提出了考虑光伏设备性能退化因素的相似日算法优化的分时段多通道独立光伏功率短期预测方法。首先,在PTransformer模型中用分时段与通道独立的方法来处理光伏输入数据,以降低空间复杂度及提高长时间数据序列的关注度。其次,运用Transformer的编码器模型,通过自身注意力机制捕捉光伏序列特征之间的依赖关系,进行光伏功率的短期预测。最后,运用夹角余弦距离计算相似度并考虑光伏设备性能退化因素确定相似日,利用其功率数据优化PTransformer模型,以改善功率数据的滞后性。结果表明:相比典型的光伏功率短期预测方法,所提方法训练速度更快,预测精准度更高,并且对复杂天气状况下的光伏功率也有较好的预测结果。 展开更多
关键词 光伏功率 短期预测 性能退化 贝叶斯分析 transformER 相似日
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融合群分解与Transformer-KAN的短期风速预测
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作者 史加荣 张思怡 《南京信息工程大学学报》 北大核心 2026年第1期60-68,共9页
针对风速固有的不稳定性,通过融合群分解(Swarm Decomposition,SWD)、Transformer和Kolmogorov-Arnold网络(KAN),提出一种SWD-Transformer-KAN预测模型.首先,利用SWD对原始风速数据进行分解,以提取关键特征.其次,针对每个被分解的子序列... 针对风速固有的不稳定性,通过融合群分解(Swarm Decomposition,SWD)、Transformer和Kolmogorov-Arnold网络(KAN),提出一种SWD-Transformer-KAN预测模型.首先,利用SWD对原始风速数据进行分解,以提取关键特征.其次,针对每个被分解的子序列,建立Transformer-KAN模型,所建模型充分利用了Transformer的时序处理能力和KAN的非线性逼近能力.最后,对所有子序列的预测结果进行叠加,得到最终的风速预测值.为了验证所提出模型的有效性,将其与其他模型进行实验对比,结果表明,SWD-Transformer-KAN模型具有最优的预测性能,其决定系数(R2)高达99.91%. 展开更多
关键词 风速预测 群分解 transformER Kolmogorov-Arnold网络
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基于Transformer模型堤坝渗漏入口精准识别方法研究
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作者 梁越 赵硕 +4 位作者 喻金桃 许彬 张斌 龚胜勇 舒云林 《岩土工程学报》 北大核心 2026年第1期187-195,共9页
渗漏是堤坝工程面临的主要安全隐患,渗漏入口精确识别与定位对降低堤坝风险至关重要。通过堤坝渗漏入口示踪剂分布及其运移特征模拟数据,训练学习Transformer模型以确定最优参数条件并分析该条件下该模型的预测效果,进一步通过室内模型... 渗漏是堤坝工程面临的主要安全隐患,渗漏入口精确识别与定位对降低堤坝风险至关重要。通过堤坝渗漏入口示踪剂分布及其运移特征模拟数据,训练学习Transformer模型以确定最优参数条件并分析该条件下该模型的预测效果,进一步通过室内模型试验验证该模型的可靠性。研究表明:①当迭代次数达600次时,模型预测的流速最大值相对误差最小,且最大流速值坐标与真实渗漏入口坐标最为接近,预测效果最佳;在此条件下,当数据采集时长为50 s时,模型预测的流速最大值相对偏差最小,预测效果最优。②在最佳迭代次数和数据采集时长条件下,模型预测精度超过95%,渗漏入口大小和渗漏流量的预测值与真实值差异极小,且流速和位置预测相对误差均较低,其中位置预测相对误差低于5%。③将电导率试验采集数据转换为示踪剂浓度并输入至该模型进行流速分布预测,可知该模型能准确定位渗漏入口位置,且流速和渗漏入口坐标的预测平均相对误差均低于10%,进而验证了该模型在渗漏入口定位中的有效性与准确性。相关研究成果可为堤坝渗漏入口精确识别奠定理论基础和提供技术支撑。 展开更多
关键词 堤坝 渗漏入口 transformer模型 精准识别 室内模型试验
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基于长短期记忆网络-Transformer模型参数优化的锂离子电池剩余使用寿命预测
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作者 高建树 郝世宇 党一诺 《汽车工程师》 2026年第1期32-39,共8页
为提高锂离子电池剩余使用寿命(RUL)预测的准确性,提出了一种基于长短期记忆(LSTM)网络-Transformer模型参数优化的RUL预测方法,采用网格搜索法选取模型的超参数,利用LSTM网络提取锂离子电池时间序列中的长短期依赖关系,使用Transforme... 为提高锂离子电池剩余使用寿命(RUL)预测的准确性,提出了一种基于长短期记忆(LSTM)网络-Transformer模型参数优化的RUL预测方法,采用网格搜索法选取模型的超参数,利用LSTM网络提取锂离子电池时间序列中的长短期依赖关系,使用Transformer的自注意力机制处理全局信息并对超参数进行优化,通过全连接层进行最终的寿命预测。基于美国国家航空航天局(NASA)数据集和先进生命周期工程中心(CALCE)数据集的试验验证结果表明,模型在更短的序列长度、更少的隐藏层数量和训练次数等条件下,在多种评价指标上均优于LSTM网络模型、Transformer模型及其他神经网络模型,具有更高的预测精度和鲁棒性。最后,通过不同电池的对比试验进一步验证了模型在不同电池数据上的泛化能力。 展开更多
关键词 锂离子电池 剩余使用寿命预测 参数优化 长短期记忆神经网络 transformER 混合模型
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基于VMD-CWT和Swin Transformer的滚动轴承故障诊断方法
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作者 曾信凌 龙江 +1 位作者 魏友 吴云飞 《机械制造与自动化》 2025年第6期18-23,34,共7页
针对滚动轴承故障信号存在噪声干扰且故障特征提取不精确的问题,提出一种基于变分模态分解(VMD)、连续小波变换(CWT)和Swin Transformer网络相结合的滚动轴承智能故障诊断方法。利用变分模态分解对信号进行降噪,通过CWT将重构后的信号... 针对滚动轴承故障信号存在噪声干扰且故障特征提取不精确的问题,提出一种基于变分模态分解(VMD)、连续小波变换(CWT)和Swin Transformer网络相结合的滚动轴承智能故障诊断方法。利用变分模态分解对信号进行降噪,通过CWT将重构后的信号转换为时频图;以二维特征图像作为输入训练Swin Transformer模型,实现滚动轴承的智能故障诊断。试验结果表明:VMD-CWT结合Swin Transformer网络的方法具有更高的故障诊断精度,实测数据中测试集准确率高达99.79%。 展开更多
关键词 滚动轴承 变分模态分解 连续小波变换 Swin transformer 故障诊断
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基于Transformer-卷积神经网络模型实现单节点腰部康复训练动作识别任务
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作者 余圣涵 成贤锴 +1 位作者 郑跃 杨颖 《中国组织工程研究》 北大核心 2026年第16期4125-4136,共12页
背景:惯性测量单元被广泛用于人体姿态感知与动态捕捉。深度学习已逐步替代传统规则与特征工程,广泛应用于动作识别任务。卷积神经网络在提取局部动态特征方面表现良好,Transformer则在建模长时序依赖方面展现出强大能力。目的:通过基于... 背景:惯性测量单元被广泛用于人体姿态感知与动态捕捉。深度学习已逐步替代传统规则与特征工程,广泛应用于动作识别任务。卷积神经网络在提取局部动态特征方面表现良好,Transformer则在建模长时序依赖方面展现出强大能力。目的:通过基于Transformer-卷积神经网络融合模型识别方法,实现在单惯性传感器条件下的腰部康复训练动作识别任务。方法:采集6名健康受试者佩戴单个惯性传感器条件下执行腰部康复动作的加速度与角速度数据,以动作类型为数据进行标注,制作腰部康复动作数据集。通过腰部康复动作数据集对Transformer-卷积神经网络融合模型进行训练,构建动作分类模型。通过留一交叉验证评估模型准确性,并与线性判别分析、支持向量机、多层感知、经典Transformer等模型进行性能对比。结果与结论:在5类动作识别任务中,Transformer-卷积神经网络模型准确率达96.67%,F1-score为0.9669。在单传感器输入的条件下,相较于传统模型,在识别精度与泛化能力方面具有明显优势。验证了基于单惯性测量单元数据的深度模型在腰部康复动作分类任务中的实用性,为轻量化、高部署性的居家腰部康复训练系统提供基础。 展开更多
关键词 慢性腰痛 康复训练 深度学习 transformER 单节点惯性传感器 动作分类
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Transforming growth factor-beta 1 enhances discharge activity of cortical neurons 被引量:1
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作者 Zhihui Ren Tian Li +5 位作者 Xueer Liu Zelin Zhang Xiaoxuan Chen Weiqiang Chen Kangsheng Li Jiangtao Sheng 《Neural Regeneration Research》 SCIE CAS 2025年第2期548-556,共9页
Transforming growth factor-beta 1(TGF-β1)has been extensively studied for its pleiotropic effects on central nervous system diseases.The neuroprotective or neurotoxic effects of TGF-β1 in specific brain areas may de... Transforming growth factor-beta 1(TGF-β1)has been extensively studied for its pleiotropic effects on central nervous system diseases.The neuroprotective or neurotoxic effects of TGF-β1 in specific brain areas may depend on the pathological process and cell types involved.Voltage-gated sodium channels(VGSCs)are essential ion channels for the generation of action potentials in neurons,and are involved in various neuroexcitation-related diseases.However,the effects of TGF-β1 on the functional properties of VGSCs and firing properties in cortical neurons remain unclear.In this study,we investigated the effects of TGF-β1 on VGSC function and firing properties in primary cortical neurons from mice.We found that TGF-β1 increased VGSC current density in a dose-and time-dependent manner,which was attributable to the upregulation of Nav1.3 expression.Increased VGSC current density and Nav1.3 expression were significantly abolished by preincubation with inhibitors of mitogen-activated protein kinase kinase(PD98059),p38 mitogen-activated protein kinase(SB203580),and Jun NH2-terminal kinase 1/2 inhibitor(SP600125).Interestingly,TGF-β1 significantly increased the firing threshold of action potentials but did not change their firing rate in cortical neurons.These findings suggest that TGF-β1 can increase Nav1.3 expression through activation of the ERK1/2-JNK-MAPK pathway,which leads to a decrease in the firing threshold of action potentials in cortical neurons under pathological conditions.Thus,this contributes to the occurrence and progression of neuroexcitatory-related diseases of the central nervous system. 展开更多
关键词 central nervous system cortical neurons ERK firing properties JNK Nav1.3 p38 transforming growth factor-beta 1 traumatic brain injury voltage-gated sodium currents
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基于Transformer-XGBoost框架的轨交车辆电池多视角数据健康诊断研究
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作者 王健 毛建 +4 位作者 唐超伟 孙小康 候晓双 王春生 廖垠钦 《电源技术》 北大核心 2026年第1期129-142,共14页
锂离子电池凭借其高能量密度和长寿命,在轨道交通与储能系统中得到了广泛应用,但随着充放电循环次数的增加,其健康状态(SOH)逐步衰退,给电池管理带来安全风险与维护挑战。传统的SOH预测方法主要依赖单一视角的增量容量分析(ICA)及常规... 锂离子电池凭借其高能量密度和长寿命,在轨道交通与储能系统中得到了广泛应用,但随着充放电循环次数的增加,其健康状态(SOH)逐步衰退,给电池管理带来安全风险与维护挑战。传统的SOH预测方法主要依赖单一视角的增量容量分析(ICA)及常规数据驱动模型,难以全面捕捉电池退化过程中电化学特性与时序动态的多尺度变化,导致预测精度和鲁棒性均受限。提出了一种基于多视角数据分析的SOH预测方法,通过融合电压视图与时间视图下的增量容量(IC)曲线信息构建多视图健康因子(HI),并设计了结合Transformer与极限梯度提升(XGBoost)的预测框架。其中,Transformer采用动态时间窗调整和双尺度注意力机制,以适应不同退化阶段下的时序特征提取。而XGBoost则通过引入物理信息约束,进一步提升了预测的稳定性与鲁棒性。在马里兰大学的PL13电池训练集中,该方法实现的均方根误差(RMSE)仅为3.13×10^(−3),决定系数R^(2)高达0.997;而在PL11电池测试集中,RMSE仅为4.57×10^(−3),R^(2)达到0.994,充分验证了该方法在多视角特征融合和动态时序建模方面的卓越性能。 展开更多
关键词 健康状态 多视角数据分析 transformER XGBoost 电池管理系统
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基于LSTM-Transformer模型的突水条件下矿井涌水量预测
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作者 李振华 姜雨菲 +1 位作者 杜锋 王文强 《河南理工大学学报(自然科学版)》 北大核心 2026年第1期77-85,共9页
目的矿井涌水量精准预测对预防矿井水害和保障矿井安全生产具有重要意义,为精准预测矿井涌水量,构建适用于华北型煤田受底板L_(1-4)灰岩含水层和奥陶系灰岩含水层水害威胁的矿井涌水量预测模型。方法以河南某典型矿井的水文监测数据为基... 目的矿井涌水量精准预测对预防矿井水害和保障矿井安全生产具有重要意义,为精准预测矿井涌水量,构建适用于华北型煤田受底板L_(1-4)灰岩含水层和奥陶系灰岩含水层水害威胁的矿井涌水量预测模型。方法以河南某典型矿井的水文监测数据为基础,提出LSTMTransformer模型。利用LSTM捕捉矿井涌水量的动态时序特征,通过Transformer的多头注意力机制分析含水层水位变化和矿井涌水量之间的复杂时序关联,构建水位动态变化驱动下的矿井涌水量精准预测框架。结果结果表明,LSTM-Transformer模型预测精度显著优于LSTM,CNN,Transformer和CNN-LSTM模型的,其均方根误差为20.91 m^(3)/h,平均绝对误差为16.08 m^(3)/h,平均绝对百分比误差为1.12%,且和单因素涌水量预测模型相比,水位-涌水量双因素预测模型预测结果更加稳定。结论LSTM-Transformer模型成功克服传统方法在捕捉复杂水文地质系统中水位-涌水量动态关联上的局限,为矿井涌水量动态预测提供可解释性强、鲁棒性好的解决方案,也为类似地质条件下矿井涌水量预测提供了新方法。 展开更多
关键词 涌水量预测 水位动态响应 LSTM-transformer耦合模型 时间序列预测 注意力机制 矿井安全生产
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A Robust Image Watermarking Based on DWT and RDWT Combined with Mobius Transformations
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作者 Atheer Alrammahi Hedieh Sajedi 《Computers, Materials & Continua》 2025年第7期887-918,共32页
Ensuring digital media security through robust image watermarking is essential to prevent unauthorized distribution,tampering,and copyright infringement.This study introduces a novel hybrid watermarking framework that... Ensuring digital media security through robust image watermarking is essential to prevent unauthorized distribution,tampering,and copyright infringement.This study introduces a novel hybrid watermarking framework that integrates Discrete Wavelet Transform(DWT),Redundant Discrete Wavelet Transform(RDWT),and Möbius Transformations(MT),with optimization of transformation parameters achieved via a Genetic Algorithm(GA).By combining frequency and spatial domain techniques,the proposed method significantly enhances both the imper-ceptibility and robustness of watermark embedding.The approach leverages DWT and RDWT for multi-resolution decomposition,enabling watermark insertion in frequency subbands that balance visibility and resistance to attacks.RDWT,in particular,offers shift-invariance,which improves performance under geometric transformations.Möbius transformations are employed for spatial manipulation,providing conformal mapping and spatial dispersion that fortify watermark resilience against rotation,scaling,and translation.The GA dynamically optimizes the Möbius parameters,selecting configurations that maximize robustness metrics such as Peak Signal-to-Noise Ratio(PSNR),Structural Similarity Index Measure(SSIM),Bit Error Rate(BER),and Normalized Cross-Correlation(NCC).Extensive experiments conducted on medical and standard benchmark images demonstrate the efficacy of the proposed RDWT-MT scheme.Results show that PSNR exceeds 68 dB,SSIM approaches 1.0,and BER remains at 0.0000,indicating excellent imperceptibility and perfect watermark recovery.Moreover,the method exhibits exceptional resilience to a wide range of image processing attacks,including Gaussian noise,JPEG compression,histogram equalization,and cropping,achieving NCC values close to or equal to 1.0.Comparative evaluations with state-of-the-art watermarking techniques highlight the superiority of the proposed method in terms of robustness,fidelity,and computational efficiency.The hybrid framework ensures secure,adaptive watermark embedding,making it highly suitable for applications in digital rights management,content authentication,and medical image protection.The integration of spatial and frequency domain features with evolutionary optimization presents a promising direction for future watermarking technologies. 展开更多
关键词 Digital watermarking Möbius transforms discrete wavelet transform redundant discrete wavelet transform genetic algorithm ROBUSTNESS geometric attacks
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基于CNN-Transformer-ARG的双护盾TBM掘进速度预测模型
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作者 刘永胜 沈军宏 +1 位作者 李达 候超 《河海大学学报(自然科学版)》 北大核心 2026年第1期112-118,176,共8页
为准确预测双护盾TBM掘进速度,提出了一种结合CNN、Transformer以及自适应残差门控(ARG)机制的智能预测模型。该模型通过双层卷积模块提取不同视角下掘进参数的局部特征,通过Transformer捕捉掘进参数的全局特征,并引入ARG机制动态加权... 为准确预测双护盾TBM掘进速度,提出了一种结合CNN、Transformer以及自适应残差门控(ARG)机制的智能预测模型。该模型通过双层卷积模块提取不同视角下掘进参数的局部特征,通过Transformer捕捉掘进参数的全局特征,并引入ARG机制动态加权所提取的局部和全局特征,基于历史掘进段监测数据预测未来掘进段的掘进速度均值、最大值和最小值。采用四川某山地轨道交通项目提取的927组掘进数据对模型进行了验证,结果表明:模型预测的均方误差、平均绝对误差、均方根误差和决定系数分别为0.07、0.21、0.26和0.86,均优于3个对比模型;模型提取的多源特征经过权重分配关注重点信息后提升了预测结果的精度,验证了ARG机制对于多源模型的有效性,可为类似结构模型多源特征数据流的处理提供参考。 展开更多
关键词 双护盾TBM 掘进速度预测 transformER 自适应残差门控
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A kinetic model for austenite grain growth during continuous casting considering massive type peritectic transformation
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作者 Peng Lan Hua-song Liu Jia-quan Zhang 《Journal of Iron and Steel Research International》 2025年第4期920-934,共15页
The continuous growth behavior of austenite grain in 20Cr peritectic steel was analyzed by experiment and theoretical modeling.The peculiar casting experiment with different cooling rates was achieved by multigradient... The continuous growth behavior of austenite grain in 20Cr peritectic steel was analyzed by experiment and theoretical modeling.The peculiar casting experiment with different cooling rates was achieved by multigradient operation scheme,and different morphologies in austenite grain were observed at the target location.The increase in austenite grain size with increasing cooling rate was firstly revealed in steels.The anomalous grain growth theoretically results from the mechanism of peritectic transformation transiting from the diffusional to massive type,and the additional energy storage stimulates the grain boundary migration.A new kinetic model to predict the growth behavior of austenite grain during continuous cooling process was developed,and the energy storage induced by massive type peritectic transformation was novelly taken into account.The parameters in the model were fitted by multiphase field modeling and experimental results.The kinetic model was finally verified by austenite grain size in laboratory test as well as the trial data at different locations in continuously cast bloom.The coarsening behavior of austenite grain during continuous casting was predicted based on the simulated temperature history.It is found that the grain coarsening occurs generally in the mold zone at high temperature for 20Cr steel and then almost levels off in the following process.The austenite finish transformation temperature Tγand primary cooling intensity show great influence on the grain coarsening.As Tγdecreases by 1℃,the austenite grain size decreases by 4μm linearly.However,the variation of Tγagainst heat flux is in a nonlinear relationship,suggesting that low cooling rate is much more harmful for austenite grain coarsening in continuous casting. 展开更多
关键词 Austenite grain growth Continuous casting Massive type transformation Kinetic model Peritectic steel
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层级特征融合Transformer的图像分类算法
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作者 段士玺 王博 《电子科技》 2026年第2期72-78,共7页
针对传统ViT(Vision Transformer)模型难以完成图像多层级分类问题,文中提出了基于ViT的图像分类模型层级特征融合视觉Transformer(Hierarchical Feature Fusion Vision Transformer,HICViT)。输入数据经过ViT提取模块生成多个不同层级... 针对传统ViT(Vision Transformer)模型难以完成图像多层级分类问题,文中提出了基于ViT的图像分类模型层级特征融合视觉Transformer(Hierarchical Feature Fusion Vision Transformer,HICViT)。输入数据经过ViT提取模块生成多个不同层级的特征图,每个特征图包含不同层次的抽象特征表示。基于层级标签将ViT提取的特征映射为多级特征,运用层级特征融合策略整合不同层级信息,有效增强模型的分类性能。在CIFRA-10、CIFRA-100和CUB-200-2011这3个数据集将所提模型与多种先进深度学习模型进行对比和分析。在CIFRA-10数据集,所提方法在第1层级、第2层级和第3层级的分类精度分别为99.70%、98.80%和97.80%。在CIFRA-100数据集,所提方法在第1层级、第2层级和第3层级的分类精度分别为95.23%、93.54%和90.12%。在CUB-200-2011数据集,所提方法在第1层级和第2层级的分类精度分别为98.09%和93.66%。结果表明,所提模型的分类准确率优于其他对比模型。 展开更多
关键词 深度学习 卷积神经网络 transformER 图像分类 层级特征 特征融合 多头注意力 Vision transformer
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A Full-Newton Step Feasible Interior-Point Algorithm for the Special Weighted Linear Complementarity Problems Based on Algebraic Equivalent Transformation
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作者 Jing GE Mingwang ZHANG Panjie TIAN 《Journal of Mathematical Research with Applications》 2025年第4期555-568,共14页
In this paper,we propose a new full-Newton step feasible interior-point algorithm for the special weighted linear complementarity problems.The proposed algorithm employs the technique of algebraic equivalent transform... In this paper,we propose a new full-Newton step feasible interior-point algorithm for the special weighted linear complementarity problems.The proposed algorithm employs the technique of algebraic equivalent transformation to derive the search direction.It is shown that the proximity measure reduces quadratically at each iteration.Moreover,the iteration bound of the algorithm is as good as the best-known polynomial complexity for these types of problems.Furthermore,numerical results are presented to show the efficiency of the proposed algorithm. 展开更多
关键词 interior-point algorithm weighted linear complementarity problem algebraic equivalent transformation search direction iteration complexity
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Sotatercept:A novel therapeutic approach for pulmonary arterial hypertension through transforming growth factor-βsignaling modulation
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作者 Jyoti Bajpai Mehul Saxena +1 位作者 Akshyaya Pradhan Surya Kant 《World Journal of Methodology》 2025年第3期63-69,共7页
Pulmonary arterial hypertension(PAH)is a progressive disease marked by degeneration of the lung’s blood vessels.As the disease progresses,the resistance to blood flow in the pulmonary arteries increases,putting a str... Pulmonary arterial hypertension(PAH)is a progressive disease marked by degeneration of the lung’s blood vessels.As the disease progresses,the resistance to blood flow in the pulmonary arteries increases,putting a strain on the right side of the heart as it pumps blood through the lungs.PAH is characterized by changes in the structure of blood vessels and excessive cell growth.Untreated PAH leads to irreversible right-sided heart failure,often despite medical intervention.Patients experience a gradual decline in function until they are unable to perform daily activities.Advances in treatment have improved the prognosis for many PAH patients.Currently approved therapies target the prostacyclin,endothelin,nitric oxide,or phosphodiesterase pathways to slow the progression of the disease.To address the unmet need for effective PAH therapies,research efforts are focused on identifying new targets and developing therapies that specifically address the underlying disease mechanisms and restore vascular wall homeostasis.Among these,sotatercept,a fusion protein that targets the transforming growth factor-βsuperfamily signaling pathway,has emerged as a promising therapeutic option.In this review,we examine the available evidence from clinical trials to assess the potential of sotatercept as a treatment for PAH. 展开更多
关键词 Pulmonary artery DRUGS Mean pulmonary artery pressure transforming growth factor-βpathway PROTEIN
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基于GASF-CWT转换和特征融合的变压器故障诊断方法
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作者 穆娜瓦尔·阿不都克热木 吐松江·卡日 +3 位作者 谢丽蓉 张淑敏 刘鹏伟 韦强宇 《现代电子技术》 北大核心 2026年第2期95-102,共8页
针对电力变压器一维油色谱特征数据输入限制深度学习模型性能,以及单一数据转换方法无法充分反映原始序列数据重要特征,从而影响故障诊断准确率等问题,提出一种基于GASF-CWT转换和特征融合的变压器故障诊断方法。首先,使用格拉姆求和角... 针对电力变压器一维油色谱特征数据输入限制深度学习模型性能,以及单一数据转换方法无法充分反映原始序列数据重要特征,从而影响故障诊断准确率等问题,提出一种基于GASF-CWT转换和特征融合的变压器故障诊断方法。首先,使用格拉姆求和角场(GASF)、连续小波变换(CWT)将一维变压器故障样本数据转换为特征图像。其次,以ResNet50网络作为基础模型,并在其特征提取层添加特征融合模块,将转换后的两种图像同时输入模型,为模型提供更全面的特征信息;最后,在模型的残差结构中添加高效通道注意力(ECA)模块,增强网络对重要特征的关注并抑制无关特征,实现高效特征提取的变压器故障诊断方法。实验结果表明,所提方法的故障诊断准确率达到94.64%,相比于性能最好的常用RF方法提升6.38%,具有较好的诊断能力,可为电力变压器安全可靠运行提供重要参考。 展开更多
关键词 电力变压器 故障诊断 格拉姆求和角场 连续小波变换 特征融合 高效通道注意力
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Calcifying nanoparticles induce apoptosis and calcification in bone marrow mesenchymal stem cells via the transforming growth factor-β/Smad pathway
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作者 Xuan-Li Su Fu-Rong Xu +9 位作者 Jian Yang San-Qiang Niu Hao-Jie Shi Yu-Fan He Zhen-Hao Li Pankaj Bagari Xiang-Wei Wu Xin-Yu Peng Hong-Wei Zhang Mei-Yan Wang 《World Journal of Stem Cells》 2025年第12期93-108,共16页
BACKGROUND Pathological calcification is a common feature of many diseases.Calcifying nanoparticles(CNPs)are considered potential inducers of this abnormal calcification,but their specific effects on bone marrow mesen... BACKGROUND Pathological calcification is a common feature of many diseases.Calcifying nanoparticles(CNPs)are considered potential inducers of this abnormal calcification,but their specific effects on bone marrow mesenchymal stem cells(BMSCs)remain unclear.BMSCs are key cells in bone formation and repair,and their aberrant apoptosis and calcification are closely related to disease progression.AIM To explore whether CNPs can induce apoptosis and calcification in BMSCs and analyzed the relationship between these processes.The differential effects of CNPs and nanoscale hydroxyapatites(nHAPs)in inducing apoptosis and calcification in BMSCs were also compared.METHODS CNPs obtained in the early stage were identified by electron microscopy and particle size analysis.BMSCs were cultured with various treatments,including different concentrations of nHAPs,CNPs[2 McFarland(MCF)turbidity,4 MCF,6 MCF],and a transforming growth factor(TGF)-βinhibitor(SB431542)for 72 hours.The isolated CNPs exhibited the expected sizes and shapes.RESULTS Exposure to CNPs and nHAPs suppressed cell proliferation and promoted apoptosis in a concentration-dependent manner,with CNPs exhibiting significantly stronger effects.Alizarin Red staining indicated an increase in calcium deposition with exposure to increasing concentrations of nHAPs and CNPs.Quantitative reverse-transcription polymerase chain reaction results indicated that medium concentrations of nHAPs and CNPs significantly enhanced the expression of pro-apoptotic and pro-calcification markers,whereas the expression of anti-apoptotic Bcl-2 was reduced compared with untreated controls.Western blotting results showed that medium concentrations of CNPs and nHAPs increased the expression of osteopontin,bone morphogenetic protein-2,TGF-β/Smad,Bax,and caspase-3 and decreased Bcl-2 expression compared with controls.CONCLUSION CNPs and nHAPs induced apoptosis and calcification in BMSCs,with CNPs being the most potent.Additionally,the TGF-βinhibitor SB431542 significantly reduced the occurrence of apoptosis and calcification.A correlation was found between apoptosis and calcification,which is likely mediated through the TGF-β/Smad signaling pathway. 展开更多
关键词 NANOPARTICLES Bone marrow mesenchymal stem cells CALCIFICATION APOPTOSIS transforming growth factor-β/Smad signaling pathway HYDROXYAPATITE
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Qufeng Jiejing formula(祛风解痉方)ameliorated the injury ofairway smooth muscle cells induced by platelet-derived growth factor-BB through the transforming growth factor-β1/Smads signalingpathway
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作者 FAN Changzheng ZHANG Qiong +5 位作者 FAN Maorong MENG Hongxu CONG Xiaodong FAN Yiling YUAN Shasha MIAO Qing 《Journal of Traditional Chinese Medicine》 2025年第4期730-738,共9页
OBJECTIVE:To explore the role and mechanism of Qufeng Jiejing(祛风解痉,QFJJ)formula in the asthma progression.METHODS:The Bagg Albino/c mice treated with Ovalbumin and AL(OH)3,and airway smooth muscle cells(ASMCs)trea... OBJECTIVE:To explore the role and mechanism of Qufeng Jiejing(祛风解痉,QFJJ)formula in the asthma progression.METHODS:The Bagg Albino/c mice treated with Ovalbumin and AL(OH)3,and airway smooth muscle cells(ASMCs)treated with platelet-derived growth factor(PDGF)-BB to establish a asthma model in vivo and in vitro.The cell morphology was observed with microscope and immunofluorescence staining.The cell viability was assessed with methyl thiazolyl tetrazolium assay.The tumor necrosis factor-αlpha(TNF-α),interleukin-1beta(IL-1β),laminin,fibronectin and collagen IV levels in the ASMCs were detected with corresponding enzyme linked immunosorbent assay kits.Transwell and wound healing assays were conducted to test the cell migration.The TGF-β1,Smad2 and Smad3 levels were measured with Western blot.RESULTS:We found that QFJJ formula treatment dramatically decreased the cell viability,TNF-α,IL-1β,laminin,fibronectin and collagen IV levels in the PDGFBB stimulated ASMCs.Additionally,the protein levels of TGF-β1,Smad2 and Smad3 in the PDGF-BB stimulated ASMCs were prominently depleted after QFJJ formula treatment.Besides,SRI treatment neutralized the role of QFJJ formula in the PDGF-BB stimulated ASMCs.CONCLUSION:QFJJ formula effectively relieved the asthma progression through ameliorate the ASMCs function,which was achieved through suppressing the TGF-β1/Smads signaling pathway. 展开更多
关键词 asthma myocytes smooth muscle transforming growth factor beta1 Smad proteins signal transduction Qufeng Jiejing formula
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基于麻雀搜索算法优化Transformer的短文本情感分析方法
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作者 胡翔 《微处理机》 2026年第1期53-58,共6页
短文本情感分析面临诸多挑战,如语义稀疏、表达简洁、缺乏上下文信息等,导致情感特征提取不完整,进而影响分类精度。为解决这些问题,提出基于麻雀搜索算法(SSA)优化Transformer的短文本情感分析方法。该方法通过构建词向量矩阵,转变短... 短文本情感分析面临诸多挑战,如语义稀疏、表达简洁、缺乏上下文信息等,导致情感特征提取不完整,进而影响分类精度。为解决这些问题,提出基于麻雀搜索算法(SSA)优化Transformer的短文本情感分析方法。该方法通过构建词向量矩阵,转变短文本的表现形式;利用Transformer模型提取情感特征,并引入SSA优化模型超参数;将所提取情感特征输入全连接层+Softmax分类器中,采用交叉熵损失的梯度下降算法衡量文本预测情感与真实情感之间的差异,完成短文本情感分析。SSA具有全局搜索能力强、收敛速度快等优点,能有效优化Transformer模型的超参数,提升模型性能。试验结果表明,所提出方法的迭代损失值较低,分类精度较高,能够较好地捕捉情感特征且对各类情感区分能力强。 展开更多
关键词 麻雀搜索算法 transformer模型 短文本情感分析 情感特征
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