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Deep learning to estimate ocean subsurface salinity structure in the Indian Ocean using satellite observations

Journal of Oceanology and Limnology 2024年第2期42卷 377-389页

作者： Jifeng QI Guimin SUN bowen xie Delei LI Baoshu YIN CAS Key Laboratory of Ocean Circulation and Waves Institute of OceanologyChinese Academy of SciencesQingdao 266071China University of Chinese Academy of Sciences Beijing 100049China School of Mathematics and Physics Qingdao University of Science and TechnologyQingdao 266061China CAS Engineering Laboratory for Marine Ranching Institute of OceanologyChinese Academy of SciencesQingdao 266071China

Accurately estimating the ocean subsurface salinity structure(OSSS)is crucial for understanding ocean dynamics and predicting climate variations.We present a convolutional neural network(CNN)model to estimate the OSSS in the Indian Ocean using satellite data and Argo observations.We evaluated the performance of the CNN model in terms of its vertical and spatial distribution,as well as seasonal variation of OSSS estimation.Results demonstrate that the CNN model accurately estimates the most significant salinity features in the Indian Ocean using sea surface data with no significant differences from Argo-derived OSSS.However,the estimation accuracy of the CNN model varies with depth,with the most challenging depth being approximately 70 m,corresponding to the halocline layer.Validations of the CNN model’s accuracy in estimating OSSS in the Indian Ocean are also conducted by comparing Argo observations and CNN model estimations along two selected sections and four selected boxes.The results show that the CNN model effectively captures the seasonal variability of salinity,demonstrating its high performance in salinity estimation using sea surface data.Our analysis reveals that sea surface salinity has the strongest correlation with OSSS in shallow layers,while sea surface height anomaly plays a more significant role in deeper layers.These preliminary results provide valuable insights into the feasibility of estimating OSSS using satellite observations and have implications for studying upper ocean dynamics using machine learning techniques.

关键词： machine learning convolutional neural network(CNN) ocean subsurface salinity structure(OSSS) Indian Ocean satellite observations

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Method for Detecting Industrial Defects in Intelligent Manufacturing Using Deep Learning

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Computers, Materials & Continua 2024年第1期78卷 1329-1343页

作者： bowen Yu Chunli xie College of Mechanical and Electrical Engineering Northeast Forestry UniversityHarbin150040China

With the advent of Industry 4.0,marked by a surge in intelligent manufacturing,advanced sensors embedded in smart factories now enable extensive data collection on equipment operation.The analysis of such data is pivotal for ensuring production safety,a critical factor in monitoring the health status of manufacturing apparatus.Conventional defect detection techniques,typically limited to specific scenarios,often require manual feature extraction,leading to inefficiencies and limited versatility in the overall process.Our research presents an intelligent defect detection methodology that leverages deep learning techniques to automate feature extraction and defect localization processes.Our proposed approach encompasses a suite of components:the high-level feature learning block(HLFLB),the multi-scale feature learning block(MSFLB),and a dynamic adaptive fusion block(DAFB),working in tandem to extract meticulously and synergistically aggregate defect-related characteristics across various scales and hierarchical levels.We have conducted validation of the proposed method using datasets derived from gearbox and bearing assessments.The empirical outcomes underscore the superior defect detection capability of our approach.It demonstrates consistently high performance across diverse datasets and possesses the accuracy required to categorize defects,taking into account their specific locations and the extent of damage,proving the method’s effectiveness and reliability in identifying defects in industrial components.

关键词： Industrial defect detection deep learning intelligent manufacturing

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Unravelling the nature of the active species as well as the Mn doping effect over gamma-Al_(2)O_(3) catalyst for eliminating AsH_(3) and PH_(3)

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Journal of Environmental Sciences 2024年第2期136卷 213-225页

作者： bowen Xu Jiaxin Qu Xueqian Wang Langlang Wang Yu Pu Ping Ning Yibing xie Yixing Ma Qiang Ma Faculty of Environmental Science and Engineering Kunming University of Science and TechnologyKunming 650500China School of Architecture and Civil Engineering Chengdu UniversityChengdu 610106China

To investigate the enhancing effect of Mn on the performance of simultaneous catalytic oxidation of AsH_(3)and PH_(3)by CuO-Al_(2)O_(3)in a reducing atmosphere under micro-oxygen conditions,Cu-Mn modifiedγ-Al_(2)O_(3)catalysts were prepared.The characteristics of the catalysts showed that Mn reduced the crystallinity of the active CuO component,increased the number of oxygen vacancies and acidic sites on the catalyst surface,enhanced the mobility of surface oxygen,and the interaction between copper and manganese promoted the redox cycling ability of the catalysts and improved their oxidation performance,which increased the conversion frequency(TOF)by 2.54×10^(-2)to 3.07×10^(-2)sec^(-1).On the other hand,the introduction of Mn reduced the production of phosphate and As_(2)O_(3)on the catalyst surface by30.96%and 44.9%,which reduced the coverage and inerting of the active sites by phosphate and As_(2)O_(3),resulting in an 8 hr(6 hr)improvement in the stability of PH_(3)(AsH_(3))removal.

关键词： AsH_(3)-PH_(3)removal Oxygen activation Mn doping Stability enhancement

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Sulfur doped iron-nitrogen-hard carbon nanosheets as efficient and robust noble metal-free catalysts for oxygen reduction reaction in PEMFC

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Journal of Energy Chemistry 2024年第2期89卷 422-433,I0010页

作者： Bin Liu Jiawang Li bowen Yan Qi Wei Xingyu Wen Huarui xie Huan He Pei Kang Shen Zhi Qun Tian Collaborative Innovation Center of Sustainable Energy Materials School of Physical Science and TechnologyGuangxi UniversityGuangxi Key Laboratory of Electrochemical Energy MaterialsState Key Laboratory of Featured Metal Materials and Life-cycle Safety for Composite StructuresNanning 530004GuangxiChina

Transition metal-nitrogen-carbon(M-N-C)as a promising substitute for the conventional noble metalbased catalyst still suffers from low activity and durability for oxygen reduction reaction(ORR)in proton exchange membrane fuel cells(PEMFCs).To tackle the issue,herein,a new type of sulfur-doped ironnitrogen-hard carbon(S-Fe-N-HC)nanosheets with high activity and durability in acid media were developed by using a newly synthesized precursor of amide-based polymer with Fe ions based on copolymerizing two monomers of 2,5-thiophene dicarboxylic acid(TDA)as S source and 1,8-diaminonaphthalene(DAN)as N source via an amination reaction.The as-synthesized S-Fe-N-HC features highly dispersed atomic Fe Nxmoieties embedded into rich thiophene-S doped hard carbon nanosheets filled with highly twisted graphite-like microcrystals,which is distinguished from the majority of M-N-C with soft or graphitic carbon structures.These unique characteristics endow S-Fe-N-HC with high ORR activity and outstanding durability in 0.5 M H_(2)SO_(4).Its initial half-wave potential is 0.80 V and the corresponding loss is only 21 m V after 30,000 cycles.Meanwhile,its practical PEMFC performance is a maximum power output of 628.0 mW cm^(-2)and a slight power density loss is 83.0 m W cm^(-2)after 200-cycle practical operation.Additionally,theoretical calculation shows that the activity of Fe Nxmoieties on ORR can be further enhanced by sulfur doping at meta-site near FeN_(4)C.These results evidently demonstrate that the dual effect of hard carbon substrate and S doping derived from the precursor platform of amid-polymers can effectively enhance the activity and durability of Fe-N-C catalysts,providing a new guidance for developing advanced M-N-C catalysts for ORR.

关键词： Transition metal-nitrogen-carbon Oxygen reduction reaction Hard carbon Amide based polymer reaction Proton exchange membrane cells

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Does burning fat make tumor immune hot? Discovery of cD47 overexpression by radiation induced fatty acid oxidation

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Genes & Diseases 2023年第1期10卷 7-9页

作者： Nian Jiang bowen xie Ming Fan Jian Jian Li Department of Radiation Oncology University of California Davis School of MedicineSacramentoCA 95817USA Department of Neurosurgery National Clinical Research Center for Geriatric DisordersXiangya HospitalCentral South UniversityChangshaHunan 410008China Institute for Immunology and School of Medicine Tsinghua UniversityBeijing 100084China NCl-Designated Comprehensive Cancer Center University of California DavisSacramentoCA 95817USA

Although extensively studied,it is unknown what is the major cellular energy driving tumor metastasis after anti-cancer radiotherapy.Metabolic reprogramming is one of the fundamental hallmarks in carcinogenesis and tumor progression featured with the increased glycolysis in solid tumors.However,accumulating evidence indicates that in addition to the rudimentary glycolytic pathway,tumor cells are capable of reactivating mitochondrial OxPHOS under genotoxic stress condition to meet the increasing cellular fuel demand for repairing and surviving anti-cancer radiation.Such dynamic metabolic rewiring may play a key role in cancer therapy resistance and metastasis.Interestingly,data from our group and others have demonstrated that cancer cells can re-activate mitochondrial oxidative respiration to boost an annexing energy to meet the increasing cellular fuel demand for tumor cells surviving genotoxic anti-cancer therapy with metastatic potential.

关键词： CD47 Immunecheckpoint Immunotherapy Metabolic rewiring Radiation therapy Tumor acquired resistance

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An Algorithm for Short-Circuit Current Interval in Distribution Networks with Inverter Type Distributed Generation Based on Affine Arithmetic

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Energy Engineering 2024年第7期121卷 1903-1920页

作者： Yan Zhang bowen Du Benren Pan GuannanWang Guoqiang xie Tong Jiang Electric Power Research Institute State Grid Jiangxi Electric Power Co.Ltd.Nanchang330096China School of Electrical and Electronic Engineering North China Electric Power UniversityBeijing102206China

During faults in a distribution network,the output power of a distributed generation(DG)may be uncertain.Moreover,the output currents of distributed power sources are also affected by the output power,resulting in uncertainties in the calculation of the short-circuit current at the time of a fault.Additionally,the impacts of such uncertainties around short-circuit currents will increase with the increase of distributed power sources.Thus,it is very important to develop a method for calculating the short-circuit current while considering the uncertainties in a distribution network.In this study,an affine arithmetic algorithm for calculating short-circuit current intervals in distribution networks with distributed power sources while considering power fluctuations is presented.The proposed algorithm includes two stages.In the first stage,normal operations are considered to establish a conservative interval affine optimization model of injection currents in distributed power sources.Constrained by the fluctuation range of distributed generation power at the moment of fault occurrence,the model can then be used to solve for the fluctuation range of injected current amplitudes in distributed power sources.The second stage is implemented after a malfunction occurs.In this stage,an affine optimization model is first established.This model is developed to characterizes the short-circuit current interval of a transmission line,and is constrained by the fluctuation range of the injected current amplitude of DG during normal operations.Finally,the range of the short-circuit current amplitudes of distribution network lines after a short-circuit fault occurs is predicted.The algorithm proposed in this article obtains an interval range containing accurate results through interval operation.Compared with traditional point value calculation methods,interval calculation methods can provide more reliable analysis and calculation results.The range of short-circuit current amplitude obtained by this algorithm is slightly larger than

关键词： Short circuit calculation inverter type distributed power supplies affine arithmetic distribution network

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严寒干热地区集热冷却联用系统试验研究

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太阳能学报 2024年第1期45卷 102-108页

作者：齐典伟饶博闻徐凤谢丽蓉张佳琦新疆大学建筑工程学院乌鲁木齐830046 同济大学机械与能源学院上海201800 新疆大学电气工程学院乌鲁木齐830046

该文结合新疆地区昼夜大温差气候特性,在乌鲁木齐市搭建一套小型太阳能集热——天空辐射冷却两用系统,通过对比PE防风膜层数和测试不同流速工况下的系统运行结果来探究两用装置的集热/冷却潜力。试验结果表明:系统在日间集热模式下的最... 详细信息

该文结合新疆地区昼夜大温差气候特性,在乌鲁木齐市搭建一套小型太阳能集热——天空辐射冷却两用系统,通过对比PE防风膜层数和测试不同流速工况下的系统运行结果来探究两用装置的集热/冷却潜力。试验结果表明:系统在日间集热模式下的最大进出口温升为6.40℃,最大瞬时集热功率为721.88 W/m^(2);夜间冷却模式下最大进出口温降为0.94℃,出口温度相对环境温降可达1.43℃,最大瞬时冷却功率为50.89 W/m^(2),这为其在实际中的工业集热应用和本地区的温湿度独立控制空调系统高温冷冻水制备提供可能。

关键词：辐射冷却太阳能集热被动冷却太阳辐射大气窗口

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脂质体凝胶负载油酸促进慢性烧伤创面的修复

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中国组织工程研究 2024年第22期28卷 3524-3531页

作者：王毛毛张庆吴博文谢岩宁夏医科大学临床医学院宁夏回族自治区银川市750004 宁夏医科大学生育力保持教育部重点实验室宁夏回族自治区银川市750004 宁夏医科大学总医院人体组织与器官库宁夏回族自治区银川市750003

背景:油酸能够调节炎症和免疫反应,具有修复皮肤创面的潜力,但油酸在病灶处滞留时间短、在空气中易自氧化而变质,需要合适的药物载体才能充分发挥疗效。目的:探究油酸脂质体凝胶修复慢性烧伤创面的疗效。方法:采用薄膜分散法制备油酸脂... 详细信息

背景:油酸能够调节炎症和免疫反应,具有修复皮肤创面的潜力,但油酸在病灶处滞留时间短、在空气中易自氧化而变质,需要合适的药物载体才能充分发挥疗效。目的:探究油酸脂质体凝胶修复慢性烧伤创面的疗效。方法:采用薄膜分散法制备油酸脂质体溶液,随后溶于泊洛沙姆凝胶基质中制备油酸脂质体凝胶。①体外实验:将不同体积油酸脂质体凝胶加入细胞培养基中制备油酸脂质体凝胶溶液(体积比分别为1∶3、1∶9、1∶27),应用阿尔玛蓝染色检测不同体积比油酸脂质体凝胶溶液对人角质形成细胞和人成纤维细胞增殖的影响,结晶紫染色观察细胞形态。②体内实验:取成年SD大鼠,采用背部全层烧伤叠加创面皮下注射表柔比星复合因素构建慢性烧伤创面模型,将造模成功的30只大鼠随机分5组,每组6只:油酸脂质体凝胶组、油酸组、脂质体凝胶组、阳性对照组、阴性对照组创面分别外敷涂抹油酸脂质体凝胶、油酸、脂质体凝胶、重组人表皮生长因子凝胶与生理盐水的纱布,隔日换药一次,总共给药16次,观察创面愈合情况。结果与结论:①体外实验:阿尔玛蓝试剂检测与结晶紫染色显示,体积比为1∶9的油酸脂质体凝胶溶液可促进人角质形成细胞和人成纤维细胞的增殖。②体内实验:油酸脂质体凝胶组创面愈合时间短于其他4组(P<0.01),用药第4,8,12,16,20天的创面愈合率高于其他4组(P<0.01)。给药结束后,苏木精-伊红染色显示5组创面均上皮化愈合,其中油酸脂质体凝胶组表皮厚度最接近于正常皮肤,并且优于其他4组;免疫组化染色显示,油酸脂质体凝胶组创面中细胞角蛋白10、肿瘤蛋白63、α-平滑肌肌动蛋白、Ⅰ型胶原、肿瘤坏死因子α、白细胞介素6、丙二醛及超氧化物歧化酶的表达最接近正常皮肤,并且优于其他4组。用药第12,32天,油酸脂质体凝胶组创面匀浆上清中肿瘤坏死因子α、白细胞介素6、丙二醛及超氧化物歧化酶的表达最接近正常皮肤,并且优于其他4组。③结果表明:油酸脂质体凝胶可促进角质形成细胞和成纤维细胞的增殖,减轻炎症反应及氧化应激损伤,促进慢性烧伤创面的愈合。

关键词：慢性烧伤创面脂质体凝胶油酸创面愈合炎症反应氧化应激细胞增殖

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中文文本纠错软件测试用例的选择生成方法

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计算机应用 2024年第1期44卷 101-112页

作者：冯程皓谢振平丁博文江南大学人工智能与计算机学院江苏无锡214000 江苏省媒体设计与软件技术重点实验室(江南大学) 江苏无锡214000

针对目前尚无有效的中文文本纠错软件测试用例生成方法的情况,为了服务于软件纠错性能的测量并为软件提供优化方向,设计了一种面向多用户的、工程化的中文文本纠错软件测试用例选择生成方法(SGMT-CCS)。定义了两种不同的可供用户选择的... 详细信息

针对目前尚无有效的中文文本纠错软件测试用例生成方法的情况,为了服务于软件纠错性能的测量并为软件提供优化方向,设计了一种面向多用户的、工程化的中文文本纠错软件测试用例选择生成方法(SGMT-CCS)。定义了两种不同的可供用户选择的用例评判标准:错误数量密度和错误种类密度。设计了三个模块:测试用例自动化生成模块、测试用例选择模块以及测试用例优先级排序模块。在SGMT-CCS中,用户可以:1)在测试用例自动化生成的过程中自定义错误最小间隔和用例集大小;2)在测试用例选择的过程中自定义错误最小间隔和期望值;3)在测试用例选择和优先级排序的过程中选择不同的用例评判标准进行自定义操作,以适应不同数据集的要求。实验结果表明,SGMT-CCS能够在较短的时间内获得有效的测试用例,选择模块实验在模拟的需求情况下都能满足用户自定义目标,优先级排序模块实验验证了相较于排序前,在不同评判标准下的不同时间段内都能有效提高测试效率。

关键词：测试用例生成中文文本纠错可选择生成回归测试自然语言处理

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基于上下游市场联动的燃煤电厂两阶段决策模型

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中国电机工程学报 2024年第8期44卷 3036-3045,I0008页

作者：廖志伟郑广昱谢汛恺王博文张文锦华南理工大学电力学院广东省广州市510640

随着大宗能源价格大幅波动及新能源上网冲击,作为现阶段不可或缺的燃煤电厂经营举步艰难。对上游燃料采购及仓储成本进行优化管控的同时,对下游电力市场竞价上网进行联动决策,可在一定程度上提高双碳背景下火力发电企业的生存空间。针... 详细信息

随着大宗能源价格大幅波动及新能源上网冲击,作为现阶段不可或缺的燃煤电厂经营举步艰难。对上游燃料采购及仓储成本进行优化管控的同时,对下游电力市场竞价上网进行联动决策,可在一定程度上提高双碳背景下火力发电企业的生存空间。针对上下游市场联动决策周期不一致以及固定发电成本二次曲线无法反映发电成本变动的问题,提出一种基于上下游市场联动的购煤与竞价燃煤电厂两阶段优化决策模型。首先提出燃煤电厂上下游市场的联动模型;其次建立购煤与竞价策略两阶段决策模型,并讨论条件风险价值对决策的影响;最后以燃煤电厂A进行算例分析和两阶段决策模型的有效性验证。算例结果表明,所提两阶段决策模型可以解决上述问题,实现利润最大化。

关键词：电煤市场日前电力市场条件风险价值滚动优化两阶段决策

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