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Passive Micromixers

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ISBN: 9783038970071 9783038970088 Year: Pages: VIII, 166 Language: Englisch
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Chemical Technology --- Technology (General)
Added to DOAB on : 2018-08-20 17:31:11
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Micro-total analysis systems and lab-on-a-chip platforms are widely used for sample preparation and analysis, drug delivery, and biological and chemical syntheses. A micromixer is an important component in these applications. Rapid and efficient mixing is a challenging task in the design and development of micromixers. The flow in micromixers is laminar, and, thus, the mixing is primarily dominated by diffusion. Recently, diverse techniques have been developed to promote mixing by enlarging the interfacial area between the fluids or by increasing the residential time of fluids in the micromixer. Based on their mixing mechanism, micromixers are classified into two types: active and passive. Passive micromixers are easy to fabricate and generally use geometry modification to cause chaotic advection or lamination to promote the mixing of the fluid samples, unlike active micromixers, which use moving parts or some external agitation/energy for the mixing. Many researchers have studied various geometries to design efficient passive micromixers. Recently, numerical optimization techniques based on computational fluid dynamic analysis have been proven to be efficient tools in the design of micromixers. The current Special Issue covers new mechanisms, design, numerical and/or experimental mixing analysis, and design optimization of various passive micromixers.

Hybrid Advanced Optimization Methods with Evolutionary Computation Techniques in Energy Forecasting

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ISBN: 9783038972860 9783038972877 Year: Pages: 250 DOI: 10.3390/books978-3-03897-287-7 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science
Added to DOAB on : 2018-10-19 11:45:03
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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.

Kernel Methods and Hybrid Evolutionary Algorithms in Energy Forecasting

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ISBN: 9783038972921 9783038972938 Year: Pages: 186 DOI: 10.3390/books978-3-03897-293-8 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science --- General and Civil Engineering
Added to DOAB on : 2018-10-22 10:01:53
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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.

Development and Application of Nonlinear Dissipative Device in Structural Vibration Control

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ISBN: 9783038970378 9783038970385 Year: Pages: X, 232 Language: Englisch
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: General and Civil Engineering
Added to DOAB on : 2018-08-21 15:25:22
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This book entitled Development and Application of Nonlinear Dissipative Device in Structural Vibration Control contains contributions that focus on the development and application of innovative nonlinear dissipative systems that mitigate the potentially catastrophic effects of extreme loading by incorporating new materials or effective mechanical control technologies. Moreover, new nonlinear analytical methods for distinctive vibrating structures under different excitations are also highlighted in this book. It is notable that nonlinear dampers prove superior in energy dissipation compared to linear dampers, such as the wide frequency band of vibration attenuation and high robustness. In light of this, nonlinear dampers have been utilized in many different cases. For example, pounding-tuned mass dampers are employed to alleviate the excessive vibration of power transmission towers, and self-powered magnetorheological dampers are used to suppress the undesirable vibration of long stay cables. The content of this book covers a wide variety of topics that can be mainly divided into three categories, namely, new nonlinear dissipative devices, new simulation tools for vibrating structures undergoing nonlinear stage, and new design/optimum methods for dissipative devices and isolation systems. The contributions presented in this book can provide valuable and constructive reference material for the further study of nonlinear dampers and structures.

Biological Activity of Natural Secondary Metabolite Products

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ISBN: 9783038971740 9783038971757 Year: Pages: 466 DOI: 10.3390/books978-3-03897-175-7 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Social and Public Welfare --- Biology --- Anthropology
Added to DOAB on : 2018-09-20 12:02:36
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Natural secondary metabolite products, which are isolated from plants, animals, microorganisms, etc., are classified as polyketides, isoprenoids, aromatics (phenylpropanoids), alkaloids, etc. Their chemical diversity and variety of biological activities have attracted the attention of chemists, biochemists, biologists, etc. The Special Issue on "Biological Activity of Natural Secondary Metabolite Products" is intended to offer biological active natural products as candidates and/or leads for pharmaceuticals, dietary supplements, functional foods, cosmetics, food additives, etc. The research fields of this Special Issue include natural products chemistry, phytochemistry, pharmacognosy, food chemistry, bioorganic synthetic chemistry, chemical biology, molecular biology, molecular pharmacology, and other related research fields of bioactive natural secondary metabolite products. Original research and review articles on all topics in these research fields are invited. I am looking forward to receiving many submissions from outstanding experts in these research fields.

First-Principles Approaches to Metals, Alloys, and Metallic Compounds

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ISBN: 9783038973584 9783038973591 Year: Pages: 180 DOI: 10.3390/books978-3-03897-359-1 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Mining and Metallurgy --- Chemistry (General)
Added to DOAB on : 2018-11-26 11:24:24
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Current fundamental electronic-structure theory allows for the accurate prediction and characterization of elemental metals adopting any allotropic structure, intermetallic compounds, and other metal-rich phases. From an engineering perspective, there is a need for structural materials that are suitable for mechanical and civil engineering as well as energy production and conversion. While different microstructural features influence the macroscopic behaviour, quantum-mechanical simulation may enormously accelerate and guide the entire development process since atomistic modelling allows for the generation of structural models and the calculation of enthalpies and other free energies as a function of pressure and temperature. Among other things, this volume covers high-manganese steels, some of which have come to light within Collaborative Research Centre 761 (“Steel ab initio”). In particular, it deals with short-range ordering from experiment and theory, also highlighting carbide-like precipitates, and it bridges the gap between atomistic and continuum levels, in particular for hydrogen embrittlement. Molecular dynamics simulates crack propagation, and first-principles theory helps in growing better intermetallic thin films and predicts structural and elastic properties. Eventually, multiscale modelling of hydrogen transport is provided, and the chemical reasons for H-trapping κ-carbides are highlighted. First-principles theory has acquired a powerful role in the fundamental and applied research of metals, alloys, and metallic compounds.

Roles of NF-κB in Cancer and Their Therapeutic Approaches

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ISBN: 9783038971177 9783038971184 Year: Pages: X, 330 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Oncology --- Biology
Added to DOAB on : 2018-08-15 10:52:09
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Although mortality rates have declined in recent years, the majority of cancers are still difficult to treat and the medical need for better cancer treatment is evident. The current anticancer armamentarium includes many active agents that are applied across tumor types. However, most of these broadly-active anticancer drugs have a small therapeutic index and barely discriminate between malignant and normal cells. In recent years the focus has shifted to the development of rationally designed, molecularly-targeted therapy for the treatment of a specific cancer, therefore offering the promise of greater specificity coupled with reduced systemic toxicity. NF-kB transcription factor family as emerged as such a promising target for cancer therapy. This Special Issue will explore the routes from NF-kB basic research, cancer research and oncogenomics into the development of NF-kB-based cancer therapeutics and biomarkers.We invite research and review papers in any area of the NF-kB field that are related, but not limited to, fundamental understanding of NF-kB signaling pathways, gene expression profiling, epigenetic regulation, diagnostic, prognostic and pharmacogenomic biomarkers, molecular targets driving the progression of human cancers, cancer drug development on these targets, clinical trial with new agents, and validation in animal models.We hope that this Special Issue reflects the exciting era that we are living in with respect to the field of NF-kB and its applications in cancer research.

Hybrid Advanced Techniques for Forecasting in Energy Sector

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ISBN: 9783038972907 9783038972914 Year: Pages: 250 DOI: 10.3390/books978-3-03897-291-4 Language: English
Publisher: MDPI - Multidisciplinary Digital Publishing Institute
Subject: Computer Science --- General and Civil Engineering
Added to DOAB on : 2018-10-19 10:39:42
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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.

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MDPI - Multidisciplinary Digital Publishing Institute (8)


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CC by-nc-nd (8)


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english (6)

englisch (2)


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2018 (8)