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CRUDE OIL PRICE FORECASTING WITH TEI@I METHODOLOGY

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Journal of Systems Science & Complexity 2005年第2期18卷 145-166页

作者： WANGShouyang YULean k.k.lai InstituteofSystemsScience AcademyofMathematicsandSystemsScienceChineseAcademyofSciencesBeijing100080China DepartmentofManagementSciences CityUniversityofHongKongTatCheeAvenueKowloonHongKong CollegeofBusinessAdministration HunanUniversityChangsha410082China

The difficulty in crude oil price forecasting, due to inherent complexity, has attracted much attention of academic researchers and business practitioners. Various methods have been tried to solve the problem of forecasting crude oil prices. However, all of the existing models of prediction can not meet practical needs. Very recently, Wang and Yu proposed a new methodology for handling complex systems-TEI@I methodology by means of a systematic integration of text mining, econometrics and intelligent *** the framework of TEI@I methodology, econometrical models are used to model the linear components of crude oil price time series (i.e., main trends) while nonlinear components of crude oil price time series (i.e., error terms) are modelled by using artificial neural network (ANN) models. In addition, the impact of irregular and infrequent future events on crude oil price is explored using web-based text mining (WTM) and rule-based expert systems (RES) techniques. Thus, a fully novel nonlinear integrated forecasting approach with error correction and judgmental adjustment is formulated to improve prediction performance within the framework of the TEI@I methodology. The proposed methodology and the novel forecasting approach are illustrated via an example.

关键词： TEI@I methodology oil price forecasting text mining econometrics intelligence integration

INTERVAL TIME SERIES ANALYSIS WITH AN APPLICATION TO THE STERLING-DOLLAR EXCHANGE RATE

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Journal of Systems Science & Complexity 2008年第4期21卷 558-573页

作者： Ai HAN Yongmiao HONG k. k. lai Shouyang WANG Institute of Systems Science Academy of Mathematics and Systems Sciences Chinese Academy of Sciences Beijing 100190 China. Department of Economics Cornell University New York USA. Faculty of Business City University of Hong Kong Tat Chee Avenue Kowloon Hong Kong China. Institute of Systems Science Academy of Mathematics and Systems Sciences Chinese Academy of Sciences Bcijing 100190 China.

Traditional econometrics has long employed ＂points＂ to measure time series data. In real life situations, however, it suffers the loss of volatility information, since many variables are bounded by intervals in a given period. To address this issue, this paper provides a new methodology for interval time series analysis. The concept of ＂interval stochastic process＂ is formally defined as a counterpart of ＂stochastic process＂ in point-based econometrics. The authors introduce the concepts of interval stationarity, interval statistics （including interval mean, interval variance, etc.） and propose an interval linear model to investigate the dynamic relationships between interval processes. A new interval-based optimization approach for estimation is proposed, and corresponding evaluation criteria are derived. To demonstrate that the new interval method provides valid results, an empirical example on the sterling-dollar exchange rate is presented.

关键词： Evaluation criteria interval-based estimation interval linear model interval statistics interval stationarity interval stochastic process.

AN EMPIRICAL ANALYSIS OF SAMPLING INTERVAL FOR EXCHANGE RATE FORECASTING WITH NEURAL NETWORkS

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Journal of Systems Science & Complexity 2003年第2期16卷 165-176页

作者： k.k.lai Y.Nakamori WANGShouyang DepartmentofManagementSciences CityUniversityofHongKongTatCheeAvenueKowloonHongKong SchoolofknowledgeScience JapanAdvancedInstituteofScienceandTechnology1-1AsahidaiTatsunokuchiIshikawa923-1292Japan InstituteofSystemsScience AcademyofMathematicsandSystemsSciencesChineseAcademyofSciencesBeijing100080China

Artificial neural networks (ANNs) have been widely used as a promising alternative approach for forecast task because of their several distinguishing features. In this paper, we investigate the effect of different sampling intervals on predictive performance of ANNs in forecasting exchange rate time series. It is shown that selection of an appropriate sampling interval would permit the neural network to model adequately the financial time series. Too short or too long a sampling interval does not provide good forecasting accuracy. In addition, we discuss the effect of forecasting horizons and input nodes on the prediction performance of neural networks.

关键词： Neural networks sampling interval exchange rate forecasting.

TIME SERIES FORECASTING WITH MULTIPLE CANDIDATE MODELS:SELECTING OR COMBINING?

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Journal of Systems Science & Complexity 2005年第1期18卷 1-18页

作者： YULean WANGShouyang k.k.lai Y.Nakamori InstituteofSystemsScience AcademyofMathematicsandSystemsScienceChineseAcademyofSciencesBeijing100080China//SchoolofManagementGraduateSchoolofChineseAcademyofSciencesBeijing100049China InstituteofSystemsScience AcademyofMathematicsandSystemsScienceChineseAcademyofSciencesBeijing100080China//InstituteofPolicyandPlanningSciencesUniversityofTsukubaTsukubaIbaraki305-8573Japan DepartmentofManagementSciences CityUniversityofHongKongTatCheeAvenueKowloonHongKong SchoolofknowledgeScience JapanAdvancedInstituteofScienceandTechnology1-1AsahidaiIshikawa923-1292Japan

Various mathematical models have been commonly used in time series analysis and forecasting. In these processes, academic researchers and business practitioners often come up against two important problems. One is whether to select an appropriate modeling approach for prediction purposes or to combine these different individual approaches into a single forecast for the different/dissimilar modeling approaches. Another is whether to select the best candidate model for forecasting or to mix the various candidate models with different parameters into a new forecast for the same/similar modeling approaches. In this study, we propose a set of computational procedures to solve the above two issues via two judgmental criteria. Meanwhile, in view of the problems presented in the literature, a novel modeling technique is also proposed to overcome the drawbacks of existing combined forecasting methods. To verify the efficiency and reliability of the proposed procedure and modeling technique, the simulations and real data examples are conducted in this *** results obtained reveal that the proposed procedure and modeling technique can be used as a feasible solution for time series forecasting with multiple candidate models.

关键词： time series forecasting model selection stability robustness combiningforecasts

Pathogenesis, diagnosis, and treatment of epilepsy: electromagnetic stimulation-mediated neuromodulation therapy and new technologies

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Neural Regeneration Research 2025年第4期20卷 917-935页

作者： Dian Jiao lai Xu Zhen Gu Hua Yan Dingding Shen Xiaosong Gu Academy of Medical Engineering and Translational Medicine Tianjin UniversityTianjinChina key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province College of Pharmaceutical SciencesZhejiang UniversityHangzhouZhejiang ProvinceChina Affiliated Hospital of Nantong University NantongJiangsu ProvinceChina

Epilepsy is a severe,relapsing,and multifactorial neurological *** regarding the accurate diagnosis,prognosis,and in-depth pathogenesis are crucial for the precise and effective treatment of *** pathogenesis of epilepsy is complex and involves alterations in variables such as gene expression,protein expression,ion channel activity,energy metabolites,and gut microbiota *** results are lacking for conventional treatments for *** resection of lesions,drug therapy,and non-drug interventions are mainly used in clinical practice to treat pain associated with ***-pharmacological treatments,such as a ketogenic diet,gene therapy for nerve regeneration,and neural regulation,are currently areas of research *** review provides a comprehensive overview of the pathogenesis,diagnostic methods,and treatments of *** also elaborates on the theoretical basis,treatment modes,and effects of invasive nerve stimulation in neurotherapy,including percutaneous vagus nerve stimulation,deep brain electrical stimulation,repetitive nerve electrical stimulation,in addition to non-invasive transcranial magnetic stimulation and transcranial direct current *** studies have shown that electromagnetic stimulation-mediated neuromodulation therapy can markedly improve neurological function and reduce the frequency of epileptic ***,many new technologies for the diagnosis and treatment of epilepsy are being ***,current research is mainly focused on analyzing patients’clinical manifestations and exploring relevant diagnostic and treatment methods to study the pathogenesis at a molecular level,which has led to a lack of consensus regarding the mechanisms related to the disease.

关键词： diagnosis drug treatment electroencephalography epilepsy monitoring epilepsy nerve regeneration neurostimulation non-drug interventions pathogenesis prediction

MINIMAX PROGRAMMING UNDER GENERALIZED （p, r）-INVEXITY

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Journal of Systems Science & Complexity 2007年第4期20卷 501-508页

作者： S. k. MISHRA Shouyang WANG k. k. lai Department of Mathematics Statistics and Computer Science College of Basic Sciences and Humanities G. B. Pant University of Agriculture and Technology Pantnagar-263 145 India Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100080 China Department of Management Sciences City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong China

Minimax programming problems involving generalized （p, r）-invex functions are consid- ered. Parametric sufficient optimality conditions and duality results are established under the aforesaid assumptions on the obje... 详细信息

关键词： Duality minimax programming optimality conditions (p r)-invex functions.

ROLE OF α-PSEUDO-UNIVEX FUNCTIONS IN VECTOR VARIATIONAL-LIkE INEQUALITY PROBLEMS

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Journal of Systems Science & Complexity 2007年第3期20卷 344-349页

作者： S. k. MISHRA Shouyang WANG k. k. lai Department of Mathematics Statistics and Computer Science College of Basic Sciences and Humanities G. B. Pant University of Agriculture and Technology Pantnagar-263 145 India. Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100080 China. Department of Management Sciences City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong China.

In this paper, we introduce a new class of generalized convex function, namely, a-pseudounivex function, by combining the concepts of pseudo-univex and α-invex functions. Further, we establish some relationships between vector variational-like inequality problems and vector optimization problems under the assumptions of α-pseudo-univex functions. Results obtained in this paper present a refinement and improvement of previously known results.

关键词： α-pseudo-univex functions vector optimization vector variational-like inequality problems.

Meningeal lymphatic vessel crosstalk with central nervous system immune cells in aging and neurodegenerative diseases

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Neural Regeneration Research 2025年第3期20卷 763-778页

作者： Minghuang Gao Xinyue Wang Shijie Su Weicheng Feng Yaona lai kongli Huang Dandan Cao Qi Wang Science and Technology Innovation Center Guangzhou University of Chinese MedicineGuangzhouGuangdong ProvinceChina Institute of Clinical Pharmacology Guangzhou University of Chinese MedicineGuangzhouGuangdong ProvinceChina The Sixth Affiliated Hospital Sun Yat-sen UniversityGuangzhouGuangdong ProvinceChina

Meningeal lymphatic vessels form a relationship between the nervous system and periphery, which is relevant in both health and disease. Meningeal lymphatic vessels not only play a key role in the drainage of brain metabolites but also contribute to antigen delivery and immune cell activation. The advent of novel genomic technologies has enabled rapid progress in the characterization of myeloid and lymphoid cells and their interactions with meningeal lymphatic vessels within the central nervous system. In this review, we provide an overview of the multifaceted roles of meningeal lymphatic vessels within the context of the central nervous system immune network, highlighting recent discoveries on the immunological niche provided by meningeal lymphatic vessels. Furthermore, we delve into the mechanisms of crosstalk between meningeal lymphatic vessels and immune cells in the central nervous system under both homeostatic conditions and neurodegenerative diseases, discussing how these interactions shape the pathological outcomes. Regulation of meningeal lymphatic vessel function and structure can influence lymphatic drainage, cerebrospinal fluid-borne immune modulators, and immune cell populations in aging and neurodegenerative disorders, thereby playing a key role in shaping meningeal and brain parenchyma immunity.

关键词： central nervous system meningeal lymphatic vessels immunity myeloid cells lymphatic cells neurodegenerative disease

Structural Modal Parameter Recognition and Related Damage Identification Methods under Environmental Excitations: A Review

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Structural Durability & Health Monitoring 2025年第1期19卷 25-54页

作者： Chao Zhang Shang-Xi lai Hua-Ping Wang School of Civil Engineering and Mechanics Lanzhou UniversityLanzhou730000China key Laboratory of Special Functional Materials and Structural Design Ministry of EducationLanzhou UniversityLanzhou730000China

Modal parameters can accurately characterize the structural dynamic properties and assess the physical state of the ***,it is particularly signiﬁcant to identify the structural modal parameters according to the monitoring data information in the structural health monitoring(SHM)system,so as to provide a scientiﬁc basis for structural damage identiﬁcation and dynamic model modiﬁ*** view of this,this paper reviews methods for identifying structural modal parameters under environmental excitation and brieﬂy describes how to identify structural damages based on the derived modal *** paper primarily introduces data-driven modal parameter recognition methods(e.g.,time-domain,frequency-domain,and time-frequency-domain methods,etc.),brieﬂy describes damage identiﬁcation methods based on the variations of modal parameters(e.g.,natural frequency,modal shapes,and curvature modal shapes,etc.)and modal validation methods(e.g.,Stability Diagram and Modal Assurance Criterion,etc.).The current status of the application of artiﬁcial intelligence(AI)methods in the direction of modal parameter recognition and damage identiﬁcation is further *** on the pre-vious analysis,the main development trends of structural modal parameter recognition and damage identiﬁcation methods are given to provide scientiﬁc references for the optimized design and functional upgrading of SHM systems.

关键词： Structural health monitoring data information modal parameters damage identiﬁcation AI method