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  1. Book ; Online: Intelligent Optimization Modelling in Energy Forecasting

    Hong, Wei-Chiang

    2020  

    Abstract: Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of ... ...

    Abstract Accurate energy forecasting is important to facilitate the decision-making process in order to achieve higher efficiency and reliability in power system operation and security, economic energy use, contingency scheduling, the planning and maintenance of energy supply systems, and so on. In recent decades, many energy forecasting models have been continuously proposed to improve forecasting accuracy, including traditional statistical models (e.g., ARIMA, SARIMA, ARMAX, multi-variate regression, exponential smoothing models, Kalman filtering, Bayesian estimation models, et cetera) and artificial intelligence models (e.g., artificial neural networks (ANNs), knowledge-based expert systems, evolutionary computation models, support vector regression, et cetera). Recently, due to the great development of optimization modeling methods (e.g., quadratic programming method, differential empirical mode method, evolutionary algorithms, meta-heuristic algorithms, et cetera) and intelligent computing mechanisms (e.g., quantum computing, chaotic mapping, cloud mapping, seasonal mechanism, et cetera), many novel hybrid models or models combined with the above-mentioned intelligent-optimization-based models have also been proposed to achieve satisfactory forecasting accuracy levels. It is important to explore the tendency and development of intelligent-optimization-based modeling methodologies and to enrich their practical performances, particularly for marine renewable energy forecasting
    Keywords Information technology ; Electronic computers. Computer science
    Size 1 electronic resource (262 pages)
    Publisher MDPI - Multidisciplinary Digital Publishing Institute
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT020480991
    ISBN 9783039283644 ; 9783039283651 ; 3039283642 ; 3039283650
    DOI 10.3390/books978-3-03928-365-1
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  2. Book ; Online: Hybrid Advanced Techniques for Forecasting in Energy Sector

    Hong, Wei-Chiang

    2018  

    Abstract: Accurate forecasting performance in the energy sector is a primary factor in the modern restructured power market, accomplished by any novel advanced hybrid techniques. Particularly in the Big Data era, forecasting models are always based on a complex ... ...

    Abstract Accurate forecasting performance in the energy sector is a primary factor in the modern restructured power market, accomplished by any novel advanced hybrid techniques. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated by factors such as seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. To comprehensively address this issue, it is insufficient to concentrate only on simply hybridizing evolutionary algorithms with each other, or on hybridizing evolutionary algorithms with chaotic mapping, quantum computing, recurrent and seasonal mechanisms, and fuzzy inference theory in order to determine suitable parameters for an existing model. It is necessary to also consider hybridizing or combining two or more existing models (e.g., neuro-fuzzy model, BPNN-fuzzy model, seasonal support vector regression-chaotic quantum particle swarm optimization (SSVR-CQPSO), etc.). These advanced novel hybrid techniques can provide more satisfactory energy forecasting performances.This book aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards recent developments, i.e., hybridizing or combining any advanced techniques in energy forecasting, with the superior capabilities over the traditional forecasting approaches, with the ability to overcome some embedded drawbacks, and with the very superiority to achieve significant improved forecasting accuracy
    Keywords Electronic computers. Computer science ; Engineering (General). Civil engineering (General)
    Size 1 electronic resource (250 p.)
    Publisher MDPI - Multidisciplinary Digital Publishing Institute
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT020099293
    ISBN 9783038972907 ; 9783038972914 ; 3038972908 ; 3038972916
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  3. Book ; Online: Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

    Hong, Wei-Chiang

    2018  

    Abstract: More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are ... ...

    Abstract More accurate and precise energy demand forecasts are required when energy decisions are made in a competitive environment. Particularly in the Big Data era, forecasting models are always based on a complex function combination, and energy data are always complicated. Examples include seasonality, cyclicity, fluctuation, dynamic nonlinearity, and so on. These forecasting models have resulted in an over-reliance on the use of informal judgment and higher expenses when lacking the ability to determine data characteristics and patterns. The hybridization of optimization methods and superior evolutionary algorithms can provide important improvements via good parameter determinations in the optimization process, which is of great assistance to actions taken by energy decision-makers.This book aimed to attract researchers with an interest in the research areas described above. Specifically, it sought contributions to the development of any hybrid optimization methods (e.g., quadratic programming techniques, chaotic mapping, fuzzy inference theory, quantum computing, etc.) with advanced algorithms (e.g., genetic algorithms, ant colony optimization, particle swarm optimization algorithm, etc.) that have superior capabilities over the traditional optimization approaches to overcome some embedded drawbacks, and the application of these advanced hybrid approaches to significantly improve forecasting accuracy
    Keywords Electronic computers. Computer science ; Computer engineering. Computer hardware
    Size 1 electronic resource (250 p.)
    Publisher MDPI - Multidisciplinary Digital Publishing Institute
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT020099296
    ISBN 9783038972860 ; 9783038972877 ; 303897286X ; 3038972878
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  4. Book ; Online: Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting

    Hong, Wei-Chiang

    2018  

    Abstract: The development of kernel methods and hybrid evolutionary algorithms (HEAs) to support experts in energy forecasting is of great importance to improving the accuracy of the actions derived from an energy decision maker, and it is crucial that they are ... ...

    Abstract The development of kernel methods and hybrid evolutionary algorithms (HEAs) to support experts in energy forecasting is of great importance to improving the accuracy of the actions derived from an energy decision maker, and it is crucial that they are theoretically sound. In addition, more accurate or more precise energy demand forecasts are required when decisions are made in a competitive environment. Therefore, this is of special relevance in the Big Data era. These forecasts are usually based on a complex function combination. These models have resulted in over-reliance on the use of informal judgment and higher expense if lacking the ability to catch the data patterns. The novel applications of kernel methods and hybrid evolutionary algorithms can provide more satisfactory parameters in forecasting models. We aimed to attract researchers with an interest in the research areas described above. Specifically, we were interested in contributions towards the development of HEAs with kernel methods or with other novel methods (e.g., chaotic mapping mechanism, fuzzy theory, and quantum computing mechanism), which, with superior capabilities over the traditional optimization approaches, aim to overcome some embedded drawbacks and then apply these new HEAs to be hybridized with original forecasting models to significantly improve forecasting accuracy
    Keywords Electronic computers. Computer science ; Engineering (General). Civil engineering (General)
    Size 1 electronic resource (186 p.)
    Publisher MDPI - Multidisciplinary Digital Publishing Institute
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT020099299
    ISBN 9783038972921 ; 9783038972938 ; 3038972924 ; 3038972932
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  5. Article: [Risk factors of postoperative stroke in elderly patients with hip fracture].

    Xin, Hong-Wei

    Zhongguo gu shang = China journal of orthopaedics and traumatology

    2022  Volume 35, Issue 4, Page(s) 337–341

    Abstract: Objective: To study the risk factors of stroke after of elderly patients with hip fracture after operation.: Methods: From March 2012 to June 2017, 500 elderly patients with hip fracture who underwent hip replacement were selected, including 286 ... ...

    Abstract Objective: To study the risk factors of stroke after of elderly patients with hip fracture after operation.
    Methods: From March 2012 to June 2017, 500 elderly patients with hip fracture who underwent hip replacement were selected, including 286 males and 214 females, aged from 60 to 76 years old with an average of (68.49±11.85) years. They were divided into stroke group with 30 cases and control group with 470 cases according to the occurrence of acute stroke within two weeks after operation. The general data and serum contents of cytokines IL-1, IL-6, IL-10 and TNF-α were compared between the two groups. The overall survival of the two groups were followed up.
    Results: There was no significant difference in sex, age, anesthesia method, operation time, intraoperative blood loss, preoperative IL-1, IL-6, IL-10 and TNF-α contenta between stroke group and control group(
    Conclusion: Postoperative stroke in elderly patients with hip fracture affects the prognosis of the disease. The increase of inflammatory cytokines IL-1 and TNF-α after operation is an independent risk factor for stroke.
    MeSH term(s) Aged ; Cytokines ; Female ; Hip Fractures/surgery ; Humans ; Hypotension ; Interleukin-1 ; Interleukin-10 ; Interleukin-6 ; Male ; Middle Aged ; Risk Factors ; Stroke/etiology ; Tumor Necrosis Factor-alpha
    Chemical Substances Cytokines ; Interleukin-1 ; Interleukin-6 ; Tumor Necrosis Factor-alpha ; Interleukin-10 (130068-27-8)
    Language Chinese
    Publishing date 2022-04-29
    Publishing country China
    Document type Journal Article
    ISSN 1003-0034
    ISSN 1003-0034
    DOI 10.12200/j.issn.1003-0034.2022.04.007
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Introduction to the Protein Condensates Virtual Special Issue.

    Wang, Hong-Wei

    Biochemistry

    2022  Volume 61, Issue 22, Page(s) 2441–2442

    MeSH term(s) Proteins
    Chemical Substances Proteins
    Language English
    Publishing date 2022-11-15
    Publishing country United States
    Document type Editorial
    ZDB-ID 1108-3
    ISSN 1520-4995 ; 0006-2960
    ISSN (online) 1520-4995
    ISSN 0006-2960
    DOI 10.1021/acs.biochem.2c00621
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: The Protein Acetylation after Traumatic Spinal Cord Injury: Mechanisms and Therapeutic Opportunities.

    Li, Hong-Wei / Zhang, Hai-Hong

    International journal of medical sciences

    2024  Volume 21, Issue 4, Page(s) 725–731

    Abstract: Spinal cord injury (SCI) leads to deficits of various normal functions and is difficult to return to a normal state. Histone and non-histone protein acetylation after SCI is well documented and regulates spinal cord plasticity, axonal growth, and sensory ...

    Abstract Spinal cord injury (SCI) leads to deficits of various normal functions and is difficult to return to a normal state. Histone and non-histone protein acetylation after SCI is well documented and regulates spinal cord plasticity, axonal growth, and sensory axon regeneration. However, our understanding of protein acetylation after SCI is still limited. In this review, we summarize current research on the role of acetylation of histone and non-histone proteins in regulating neuron growth and axonal regeneration in SCI. Furthermore, we discuss inhibitors and activators targeting acetylation-related enzymes, such as α-tubulin acetyltransferase 1 (αTAT1), histone deacetylase 6 (HDAC6), and sirtuin 2 (SIRT2), to provide promising opportunities for recovery from SCI. In conclusion, a comprehensive understanding of protein acetylation and deacetylation in SCI may contribute to the development of SCI treatment.
    MeSH term(s) Humans ; Axons/metabolism ; Histones/metabolism ; Acetylation ; Nerve Regeneration ; Spinal Cord Injuries/drug therapy ; Spinal Cord Injuries/metabolism ; Tubulin/metabolism ; Tubulin/therapeutic use
    Chemical Substances Histones ; Tubulin
    Language English
    Publishing date 2024-02-12
    Publishing country Australia
    Document type Journal Article ; Review
    ZDB-ID 2151424-0
    ISSN 1449-1907 ; 1449-1907
    ISSN (online) 1449-1907
    ISSN 1449-1907
    DOI 10.7150/ijms.92222
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Graphene in cryo-EM specimen optimization.

    Liu, Nan / Wang, Hong-Wei

    Current opinion in structural biology

    2024  Volume 86, Page(s) 102823

    Abstract: Specimen preparation is a critical but challenging step in high-resolution cryogenic electron microscopy (cryo-EM) structural analysis of macromolecules. In the past decade, graphene has gained much recognition as the supporting substrate to optimize ... ...

    Abstract Specimen preparation is a critical but challenging step in high-resolution cryogenic electron microscopy (cryo-EM) structural analysis of macromolecules. In the past decade, graphene has gained much recognition as the supporting substrate to optimize cryo-EM specimen preparation. It improves macromolecule embedding in ice, reduces beam-induced motion, while imposing negligible background noise. Various types of graphene-coated cryo-EM grids were implemented to improve the robustness and efficiency of specimen preparation. Graphene functionalization by different means has been proved specifically useful in addressing challenges related to the air-water interface (AWI), such as preferential orientation and sample denaturation. Graphene sandwich specimen preparation sets a new direction to explore in cryo-EM analysis of biological specimens. In this review, we discuss the current challenges and future prospects of graphene application in cryo-EM analysis of macromolecules.
    Language English
    Publishing date 2024-04-29
    Publishing country England
    Document type Journal Article ; Review
    ZDB-ID 1068353-7
    ISSN 1879-033X ; 0959-440X
    ISSN (online) 1879-033X
    ISSN 0959-440X
    DOI 10.1016/j.sbi.2024.102823
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Book ; Online: Short-Term Load Forecasting by Artificial Intelligent Technologies

    Fan, Guo-Feng / Li, Ming-Wei / Hong, Wei-Chiang

    2019  

    Abstract: In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing ... ...

    Abstract In last few decades, short-term load forecasting (STLF) has been one of the most important research issues for achieving higher efficiency and reliability in power system operation, to facilitate the minimization of its operation cost by providing accurate input to day-ahead scheduling, contingency analysis, load flow analysis, planning, and maintenance of power systems. There are lots of forecasting models proposed for STLF, including traditional statistical models (such as ARIMA, SARIMA, ARMAX, multi-variate regression, Kalman filter, exponential smoothing, and so on) and artificial-intelligence-based models (such as artificial neural networks (ANNs), knowledge-based expert systems, fuzzy theory and fuzzy inference systems, evolutionary computation models, support vector regression, and so on). Recently, due to the great development of evolutionary algorithms (EA) and novel computing concepts (e.g., quantum computing concepts, chaotic mapping functions, and cloud mapping process, and so on), many advanced hybrids with those artificial-intelligence-based models are also proposed to achieve satisfactory forecasting accuracy levels. In addition, combining some superior mechanisms with an existing model could empower that model to solve problems it could not deal with before; for example, the seasonal mechanism from the ARIMA model is a good component to be combined with any forecasting models to help them to deal with seasonal problems
    Keywords Computer engineering. Computer hardware ; Electronic computers. Computer science
    Size 1 electronic resource (444 p.)
    Publisher MDPI - Multidisciplinary Digital Publishing Institute
    Document type Book ; Online
    Note English ; Open Access
    HBZ-ID HT020102026
    ISBN 9783038975823 ; 9783038975830 ; 3038975826 ; 3038975834
    Database ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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  10. Book: Allen reference atlas

    Dong, Hong-Wei

    a digital color brain atlas of the C57black/6J male mouse ; Allen brain atlas

    2008  

    Title variant Allen brain atlas ; C57BL/6J male mouse
    Institution Allen Institute for Brain Science
    Author's details Hong-Wei Dong. Allen Institute for Brain Science
    Language English
    Size IX, 366 S. : überw. Ill.
    Publisher Wiley
    Publishing place Hoboken, NJ
    Publishing country United States
    Document type Book
    Accompanying material 1 CD-ROM (12 cm)
    HBZ-ID HT015101127
    ISBN 0-470-05408-5 ; 978-0-470-05408-6
    Database Catalogue ZB MED Medicine, Health

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