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Frontiers of business Research in China 2014年第4期8卷 411-434页

作者： Allen S. Lee school of business Virginia Commonwealth University Richmond VA 23284-4000 USA

The heavy emphasis on statistical rigor that business schools in American universities place on research is questionable in general and, for business schools in China, is not necessarily a good strategy by which either to produce useful research or to distinguish themselves from business schools in the United States. The reasons for this pertain to, first, what statistical analysis is good for in science; second, what statistical analysis is not good for in science; third, what statistical analysis is and is not good for in business research; fourth, how a large sample size is not generalizable but a single case is; fifth, how action research, design research, and Herbert Simon＇s ＂sciences of the artificial＂ all demonstrate rigor without statistics; and finally, the historical context that explains the turn to statistics in American business schools but is irrelevant to China. The recommendation is for business schools in China not necessarily to reject statistical research, but rather, to also pursue other, equally scientific forms of research, including those recognized by Herbert Simon.

关键词： philosophy of science,methodology sciences of the artificial,research business researchphilosophy of technology statisticaldesign science design research action

Research Hotspot and Frontier Analysis of business school International Accreditation

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经济管理学刊（中英文版） 2020年第2期9卷 108-115页

作者： Pengbin Gao Yan Xiang Yiyuan Zhang Rong Wang school of Economics and Management Harbin Institute of Technology at WeihaiShandong Weihai 264209China College of Management and Economics Tianjin UniversityTianjin 300072China

International accreditation is the only way for the internationalization of colleges and *** article takes 380 articles in the field of business schools international accreditation in the Web of Science database as the research object,and uses the method of scientometrics and VOS viewer knowledge map analysis software to perform co-occurrence analysis on keywords,and finally reveals the research hotspot and development trend of business school international accreditation.

关键词： business school International Accreditation Scientometrics Research Hotspot

A General Association Test for High-Dimensional Random Vectors

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Acta Mathematica Sinica,English Series 2025年

作者： Yongshuai Chen Peng Lin Baoxue Zhang school of Statistics Capital University of Economics and Business

Testing the association of two high-dimensional random vectors is of fundamental importance in the statistical theory and applications. In this paper, we propose a new test statistic based on the Frobenius norm and subtracting bias technique, which is generally applicable to high-dimensional data without restricting the distributional assumptions. The limiting null distribution of the proposed test is shown to be a random variable combining a finite chi-squared-type mixture with a normal approximation. Our proposed test method can also be a normal approximation or a finite chi-squared-type mixtures under additional regularity conditions. To make the test statistic applicable, we introduce a wild bootstrap method and demonstrate its validity. The finite-sample performance of the proposed test via Monte Carlo simulations reveals that it performs better at controlling the empirical size than some existing tests, even when the normal approximation is invalid. Real data analysis is devoted to illustrating the proposed test.

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Discovery of a new family member of arsenic–vanadium polyoxometalates with high catalytic performance for one-pot coupling of CO2, epoxides and amines

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Tungsten 2025年第1期7卷 80-87页

作者： Fang-Qian Wang Yin Zhang Wei-Wei Wang Mei-Qian Fu Hong-Bing Mo Wei-Dong Yu Chao Liu Jun Yan College of Chemistry and Chemical Engineering Central South University Junior Education Department Changsha Normal University school of Science Hunan University of Technology and Business

Arsenic-vanadium polyoxometalates（POMs） represent a family of tremendous potential catalysts owing to their peculiar physical and chemical *** in CO2conversion,using POMs to catalyze the conversion of CO2and epoxide into chemical intermediates is the research ***,A new family number of arsenic-vanadium POMs,[N（CH3）4]4[As8V12O38（H2O）]·2H2O({As8V12O38}),was reported and characterized by single-crystal X-ray diffraction,fourier transform infrared spectroscopy,X-ray photoelectron spectroscopy,*** from other allotropicity,{As8V12O38}shows a different structure pattern with an I-43d *** obtained {As8V12O38} was employed as a catalyst for the *** initial catalytic reaction confirmed that {As8V12O38} can catalyze the formation of epoxy carbonate by using 1.01 kPa CO2and epoxide at 70℃.Furtherly,when added amine in the catalytic system,oxazolidinone could be obtained by high yields,which provides an efficient way to convert CO2into profitable ***,a possible catalytic reaction mechanism was proposed by using different characterization analyses.{As8V12O38} first catalyzes the epoxide and CO2to form an epoxy carbonate and then catalyzes epoxy carbonate and benzylamine into oxazolidinone.

关键词： Arsenic CO2 conversion Oxazolidinone Polyoxometalates Vanadium

Stochastic Augmented-Based Dual-Teaching for Semi-Supervised Medical Image Segmentation

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Computers, Materials & Continua 2025年第1期82卷 543-560页

作者： Hengyang Liu Yang Yuan Pengcheng Ren Chengyun Song Fen Luo school of Computer Science and Engineering Chongqing University of TechnologyChongqing400054China College of Artificial Intelligence Chongqing Technology and Business UniversityChongqing400067China

Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution ***,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than *** design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these *** be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled *** introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in *** the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local *** this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation *** two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation *** only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.

关键词： Semi-supervised medical image segmentation contrastive learning stochastic augmented

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拍卖理论和实践的跨学科协同与融合

中国科学:数学 2025年

作者：陈礴 Warwick business school University of Warwick

拍卖理论与实践是一个典型的交叉学科研究领域,融合了数学、经济学、运筹学和计算科学的理论框架与方法论.本文系统探讨这些学科在拍卖机制设计、竞拍者行为分析与市场动态优化等方面的协同作用.数学建模提供了严谨的理论基础,经济学原... 详细信息

拍卖理论与实践是一个典型的交叉学科研究领域,融合了数学、经济学、运筹学和计算科学的理论框架与方法论.本文系统探讨这些学科在拍卖机制设计、竞拍者行为分析与市场动态优化等方面的协同作用.数学建模提供了严谨的理论基础,经济学原理揭示了市场设计的内在逻辑,运筹学优化了复杂的拍卖流程,而计算技术则推动了理论向实践的高效转化.本文总结了跨学科协同对拍卖理论与实践发展的重要贡献,并展望了推动领域创新的新兴研究方向.

关键词：拍卖竞拍跨学科数学经济学运筹学计算科学

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不完整需求信息下报童供应链鲁棒契约

中国科学:数学 2025年

作者：江厚元 Serguei Netessine Judge business School University of Cambridge The Wharton school University of Pennsylvania

众所周知,当需求信息完整并且对零售商与供应商是对称时,批发价格契约无法协调报童供应链,而回购契约和收入分享契约则可以.通过鲁棒优化框架,本文研究如何制定批发价格契约和回购契约,并探讨这些常用供应链契约在需求信息有限和不完整... 详细信息

众所周知,当需求信息完整并且对零售商与供应商是对称时,批发价格契约无法协调报童供应链,而回购契约和收入分享契约则可以.通过鲁棒优化框架,本文研究如何制定批发价格契约和回购契约,并探讨这些常用供应链契约在需求信息有限和不完整时是否能协调供应链.本文采用极小极大遗憾值方法来构建零售商、供应商和系统问题的数学模型.本文得到如下结果.其一,除了退化解外,当需求变异和利润率都较大时,鲁棒批发价格契约和鲁棒回购契约都无法协调供应链.其二,供应商在鲁棒批发价格契约下的最大绝对遗憾值比在鲁棒回购契约下的最大绝对遗憾值小.其三,鲁棒批发价格契约的最大相对遗憾值通常小于鲁棒回购契约的最大相对遗憾值,并为鲁棒批发价格契约和鲁棒回购契约的最大相对遗憾值提供了渐近紧界.这些管理洞察表明,鲁棒批发价格契约不仅更易管理,而且相对于鲁棒回购契约还具有其他优势,这为批发价格契约在实践中比回购契约更受欢迎提供一些理论支持.本文还证明,无论需求信息是否完整,无论是鲁棒批发价格契约还是鲁棒回购契约,零售商在订购量上都比社会规划者更为保守.

关键词：报童供应链鲁棒契约批发价格契约回购契约供应链协调绝对遗憾值相对遗憾值

Channel pruning on frequency response

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Science China(Information Sciences) 2025年第1期68卷 159-170页

作者： Hang LIN Yifan PENG Lin BIE Chenggang YAN Xibin ZHAO Yue GAO school of Software Tsinghua University school of Automation Hangzhou Dianzi University

Network pruning has a significant role in reducing network parameters and accelerating the inference time of the network. Some existing methods prune the network based on the frequency of the data, and finally obtain a sub-network with high accuracy. However, according to our experimental analysis, different frequencies of information in the data contribute differently to the accuracy of the model, and using this information directly for pruning without making a selection will lead to incorrect results. We believe that pruning should retain the convolutional kernels in the network that process important information, while those kernels that process unimportant information should be removed. In this paper, we first investigate the meaning of each frequency band information in the spectrum and their contribution to the prediction accuracy of the network,and according to these results, we propose a new pruning method based on frequency response(PFR). Our PFR finds and removes the convolutional kernels in the network that specialize in processing unimportant information, resulting in a compact neural network model. PFR obtains significant experimental results on different datasets, for example, a 56.0% raduction of float points operations(FLOPs) on Res Net-50 and only 0.37% of Top-1 accuracy degradation on the Image Net dataset.

关键词： deep learning model compression filter pruning channel pruning frequency response

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Turbulent Agglomeration of Microparticles in a Cylinder Wake Flow Using LES-DEM:Focusing on the Effect of the Reynolds Number

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Journal of Thermal Science 2025年第1期34卷 34-49页

作者： WANG Shuang MU Lin LI Xue XIE Jun DONG Ming school of Energy and Power Engineering Dalian University of Technology school of Optical Information and Energy Engineering Wuhan Institute of Technology school of Energy and Environment Shenyang Aerospace University

Turbulent agglomeration is viewed as a promising technology for enhancing fine particle removal *** better understand particle transport,agglomeration behaviors,and fluid-particle interactions,we numerically explored these phenomena under cylindrical vortex wake influence using a coupled large eddy simulation and discrete element method（LES-DEM） *** validity of the LES approach was verified by comparison with available direct numerical simulation（DNS） *** adopted the Johnson-Kendall-Roberts（JKR） contact model for particle-particle *** particle dispersion and agglomeration characteristics of particles with different diameters（dp=2-20 μm） in the laminar and transition of shear layer（TrSL） flow regimes were *** particles were concentrated at the vortex centers,while larger particles accumulated around the *** agglomeration efficiency exhibited an M-shaped profile spanwise（y-direction）.With increasing Reynolds number,the agglomeration efficiency and turbulence intensity *** particle agglomeration efficiency peaks at a certain Reynolds ***,at higher Reynolds numbers,reducing the residence time of particles in the flow field decreases the agglomeration efficiency.

关键词： discrete element method large eddy simulation microparticle particle agglomeration

5DGWO-GAN:A Novel Five-Dimensional Gray Wolf Optimizer for Generative Adversarial Network-Enabled Intrusion Detection in IoT Systems

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Computers, Materials & Continua 2025年第1期82卷 881-911页

作者： Sarvenaz Sadat Khatami Mehrdad Shoeibi Anita Ershadi Oskouei Diego Martín Maral Keramat Dashliboroun Department of Data Science Engineering University of HoustonHoustonTX 77204USA The WPI business school Worcester Polytechnic InstituteWorcesterMA 01609USA school of Systems and Enterprises Stevens Institute of TechnologyHobokenNJ 07030USA Department of Computer Science Escuela de Ingeniería Informática de SegoviaUniversidad de ValladolidSegovia40005Spain Ernest G.Welch school of Art&Design Georgia State UniversityAtlantaGA 30303USA

The Internet of Things(IoT)is integral to modern infrastructure,enabling connectivity among a wide range of devices from home automation to industrial control *** the exponential increase in data generated by these interconnected devices,robust anomaly detection mechanisms are *** detection in this dynamic environment necessitates methods that can accurately distinguish between normal and anomalous behavior by learning intricate *** paper presents a novel approach utilizing generative adversarial networks(GANs)for anomaly detection in IoT ***,optimizing GANs involves tuning hyper-parameters such as learning rate,batch size,and optimization algorithms,which can be challenging due to the non-convex nature of GAN loss *** address this,we propose a five-dimensional Gray wolf optimizer(5DGWO)to optimize GAN *** 5DGWO introduces two new types of wolves:gamma(γ)for improved exploitation and convergence,and theta(θ)for enhanced exploration and escaping local *** proposed system framework comprises four key stages:1)preprocessing,2)generative model training,3)autoencoder(AE)training,and 4)predictive model *** generative models are utilized to assist the AE training,and the final predictive models(including convolutional neural network(CNN),deep belief network(DBN),recurrent neural network(RNN),random forest(RF),and extreme gradient boosting(XGBoost))are trained using the generated data and AE-encoded *** evaluated the system on three benchmark datasets:NSL-KDD,UNSW-NB15,and *** conducted on diverse IoT datasets show that our method outperforms existing anomaly detection strategies and significantly reduces false *** 5DGWO-GAN-CNNAE exhibits superior performance in various metrics,including accuracy,recall,precision,root mean square error(RMSE),and convergence *** proposed 5DGWO-GAN-CNNAE achieved the lowest RMSE values across the NSL-KDD,UNSW-NB15,and

关键词： Internet of things intrusion detection generative adversarial networks five-dimensional binary gray wolf optimizer deep learning