2013 Conference Abstracts: Annual Undergraduate Research Conference at the Interface of Biology and Mathematics

URC Schedule and Abstract Book for the Fifth Annual Undergraduate Research Conference at the Interface of Biology and Mathematics Date: November 16-17, 2013Plenary Speaker: Mariel Vazquez, Associate Professor of Mathematics at San Francisco State UniversityFeatured Speaker: Andrew Liebhold, Research Entomologist for the USDA Forest Servic

Frontiers in Ultra-Precision Machining

Ultra-precision machining is a multi-disciplinary research area that is an important branch of manufacturing technology. It targets achieving ultra-precision form or surface roughness accuracy, forming the backbone and support of today’s innovative technology industries in aerospace, semiconductors, optics, telecommunications, energy, etc. The increasing demand for components with ultra-precision accuracy has stimulated the development of ultra-precision machining technology in recent decades. Accordingly, this Special Issue includes reviews and regular research papers on the frontiers of ultra-precision machining and will serve as a platform for the communication of the latest development and innovations of ultra-precision machining technologies

Enhancing statistical wind speed forecasting models : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering at Massey University, Manawatū Campus, New Zealand

In recent years, wind speed forecasting models have seen significant development and growth. In particular, hybrid models have been emerging since the last decade. Hybrid models combine two or more techniques from several categories, with each model utilizing its distinct strengths. Mainly, data-driven models that include statistical and Artificial Intelligence/Machine Learning (AI/ML) models are deployed in hybrid models for shorter forecasting time horizons (< 6hrs). Literature studies show that machine learning models have gained enormous potential owing to their accuracy and robustness. On the other hand, only a handful of studies are available on the performance enhancement of statistical models, despite the fact that hybrid models are incomplete without statistical models. To address the knowledge gap, this thesis identified the shortcomings of traditional statistical models while enhancing prediction accuracy. Three statistical models are considered for analyses: Grey Model [GM(1,1)], Markov Chain, and Holt’s Double Exponential Smoothing models. Initially, the problems that limit the forecasting models' applicability are highlighted. Such issues include negative wind speed predictions, failure of predetermined accuracy levels, non-optimal estimates, and additional computational cost with limited performance. To address these concerns, improved forecasting models are proposed considering wind speed data of Palmerston North, New Zealand. Several methodologies have been developed to improve the model performance and fulfill the necessary and sufficient conditions. These approaches include adjusting dynamic moving window, self-adaptive state categorization algorithm, a similar approach to the leave-one-out method, and mixed initialization method. Keeping in view the application of the hybrid methods, novel MODWT-ARIMA-Markov and AGO-HDES models are further proposed as secondary objectives. Also, a comprehensive analysis is presented by comparing sixteen models from three categories, each for four case studies, three rolling windows, and three forecasting horizons. Overall, the improved models showed higher accuracy than their counter traditional models. Finally, the future directions are highlighted that need subsequent research to improve forecasting performance further

Modelling forest landscape dynamics in Glen Affric, northern Scotland

Consideration of forest management at the landscape scale is essential if commitments to the conservation of biodiversity are to be upheld. The ecosystem management approach, developed largely in North America, has made use of various landscape modelling tools to assist in planning for biodiversity maintenance and ecological restoration. The roles of habitat suitability models, metapopulation models, spatially explicit population models (SEPMs) and forest landscape dynamics models (FLDMs) in the planning process are discussed and a review of forest dynamics models is presented. Potential is identified for developing landscape models in the UK for both landscape restoration projects and semi-natural woodland management. Glen Affric, in northern Scotland contains a large area of native pine and birch woodland and is the subject of a long-term restoration project. A new model, GALDR (Glen Affric Landscape Dynamics Reconstruction) is introduced and is believed to be the first FLDM developed for British woodland. The theory behind the model is described in detail and preliminary results and sensitivity analyses are presented. Furthermore, GALAM (Glen Affric Lichen Abundance Model), a new SEPM for the rare epiphytic lichen Bryoria furcellata is also described. Results of simulations from the linked GALDR and GALAM models are presented which shed light on the role of landscape heterogeneity in determining the dynamics of lichen habitats and populations. It is concluded that, whilst much work will be required to develop a management-oriented decision support system from the GALDR model, the modelling process may aid researchers in the identification of knowledge gaps in ecological theory relevant to management and restoration

Applied Metaheuristic Computing

For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC

2019 GREAT Day Program

SUNY Geneseo’s Thirteenth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1013/thumbnail.jp

Optimization Methods Applied to Power Systems Ⅱ

Electrical power systems are complex networks that include a set of electrical components that allow distributing the electricity generated in the conventional and renewable power plants to distribution systems so it can be received by final consumers (businesses and homes). In practice, power system management requires solving different design, operation, and control problems. Bearing in mind that computers are used to solve these complex optimization problems, this book includes some recent contributions to this field that cover a large variety of problems. More specifically, the book includes contributions about topics such as controllers for the frequency response of microgrids, post-contingency overflow analysis, line overloads after line and generation contingences, power quality disturbances, earthing system touch voltages, security-constrained optimal power flow, voltage regulation planning, intermittent generation in power systems, location of partial discharge source in gas-insulated switchgear, electric vehicle charging stations, optimal power flow with photovoltaic generation, hydroelectric plant location selection, cold-thermal-electric integrated energy systems, high-efficiency resonant devices for microwave power generation, security-constrained unit commitment, and economic dispatch problems

Abstracts of Papers, 88th Annual Meeting of the Virginia Academy of Science

Full list of Abstracts from the 88th Annual Meeting of the Virginia Academy of Science, May 20 - 21, 2010, James Madison University Harrisonburg, V