Research underway at the Nottingham Innovative Manufacturing Research Centre of the University of Nottingham (Nottingham, England) is entitled: Modeling and Control of Part-Fixture Behaviour in Precision Grinding of Complex Parts. It’s common to find engineering practitioners relying solely on their experience to develop fixturing solutions for an array of machining, inspection, assembly, and manufacturing operations. As workpiece geometries and manufacturing processes become more complicated, it is becoming increasingly more difficult and timeconsuming to develop effective fixturing solutions to meet every single technical and commercial requirement. During the development of new fixturing solutions, it is still a common practice to develop and test a prototype of a new fixture design in an actual manufacturing environment to evaluate the effectiveness of the proposed design. This leads to higher costs and longer lead-times, especially when ineffective fixture designs have to be iteratively improved, prototyped, and retested. The principal objective of this project is to develop a fixture-workpiece simulation technology that will allow manufacturers and fixture designers to quickly and easily evaluate the effectiveness of a fixture design and improve it without costly prototypes and tests. More specifically, the objectives of this research project include:

A systematic technique is proposed for assisting in the design and implementation of policy and addressing the need to minimize or resolve disputes that may arise in the enforcement of regulations. The Graph Model for Conflict Resolution is a methodology that facilitates the modeling and analysis of interactive multiple participant-multiple objective decision problems. In the problems considered here, decision makers and policy planners engaged in capacity building typically have different viewpoints over appropriate ways of developing options and enforcing policy choices. Incompatible understandings of resource potentials and limits, and disparities in utilization of these resources, exasperate stakeholders and make the capacity building process counterproductive and even conducive to conflict. A systematic conflict resolution technique is invaluable to policy makers and practitioners in defusing confrontations and reaching out for consensus among participants. In support of current approaches to policy planning and regulation, the Graph Model provides accurate predictions and strategic insights into short- and long-term opportunities in multiple participant-multiple objective decision situations. A conflict among the government of Canada, the Mi’kmaq First Nation, and commercial fishermen over the sharing of a natural resource in New Brunswick, Canada, is used to illustrate the advantages of this technique in practical problems.
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(KEY TERMS: Graph Model for Conflict Resolution; conflict analysis; decision support system; First Nations; fisheries; planning; sustainability; emotion.)

INTRODUCTION

To build community and public capacity, and to identify and meet development challenges, governments and policy makers must better understand the interests and needs of those most affected by their policies. Unfortunately, public round tables may degenerate into heated dialogues, which often escalate into a serious conflict. For instance, controversies may arise over the enforcement of rules affecting socioeconomic development and management of natural resources, especially when these rules are defied by citizens with special interests, are compromised by judicial rulings, or transgress international agreements. To achieve, effectively, stakeholders’ aspirations, policy makers and politicians need special skills and competencies to conceptualize a conflict, reconcile positions before exacerbation, and predict strategic resolutions before engagement. Thus, there is a tremendous need for decision support and conflict analysis techniques to help government officers, practitioners, and policy makers to analyze and resolve complex strategic conflicts.

In research investigating the role of modeling in resolving water resource conflicts, Lund and Palmer (1997) call for the application of formal modeling techniques for conflict resolution into the political and planning stages of the policy making processes. Likewise, Carraro et al. (2005) recommend the use of formal modeling approaches to gain valuable insights into strategies that are sustainable and acceptable to a wide range of interested groups. Jain and Singh (2003) and Nandalal and Simonovic (2003) summarize recent systems approaches to water management and conflict resolution, and cite numerous examples of controversies related to water issues where formal conflict models and procedures have been used. Formal decision models in multiple participant-multiple objective decision methodologies have been applied to environmental conflicts (see for example, Harboe, 1992; Hipel, 1992; and Cai et al., 2004), although there are many opportunities for more challenging applications in water resources. To motivate practitioners and researchers to use these formal models, numerous decision support systems have been developed for applications related to water conflicts and environmental policy planning (see for example, Thiessen and Loucks, 1992; Loucks, 1995; and Hipel et al., 2007).

Strategic conflict is omnipresent in society, in general, and in water resources management, in particular (Gleick, 1993; Wolf, 2002). It can be understood as an interactive decision problem among stakeholders who have inconsistent preferences over the outcome of a conflict, and different capacities to affect that outcome. For example, disputes over environmental pollution or shared utilization of resources are decision problems involving several stakeholders. A conflict analysis technique can furnish an effective and precise language of communication to express a specific dispute and to assist in the search for short term advice and a sustainable resolution.

Here, the Graph Model for Conflict Resolution (GMCR) (Fang et al., 1993) and its implementation in the decision support system, GMCR II (Hipel et al., 1997; Kilgour et al., 2001; Fang et al., 2003a,b), is used to study systematically the Burnt Church Conflict, a strategic conflict over lobster fishing in New Brunswick, Canada. The dispute erupted in late 1999, and involved the Burnt Church First Nation, which claimed historical and treaty rights to determine its own fishery plan, officers of the government of Canada, who were attempting to enforce regulations, and nonnative fishermen, who relied on the commercial fishery for their livelihood. This conflict turned violent on several occasions between 1999 and 2002. To the authors’ knowledge, this research project constitutes one of the first times a formal conflict resolution methodology has been used to analyze a dispute involving aboriginal groups and natural resources. It is a postmortem assessment of events that, although they took place in a small community, had far-reaching implications. In fact, they reverberated across Canada, especially for First Nations people, who are in protracted disputes with governments over natural resources management and allocation. The authors wish to show that a systematic study of a strategic dispute deepens understanding, puts the situation into better perspective, yields insights into the best short term and long term resolutions, and provides valuable strategic advice on policy making and regulation. In addition, a formal post-event evaluation provides lessons about past policy decisions: what went wrong, how to do better in the future, and what opportunities were missed.

Dr. Brown (right) graduated from the University of Cincinnati, Aeronautical Engineering Program with a B.S. degree in 1961. After graduation, as part of a university research contract, he worked at Wright Piittorson Air Force Base in the ARL Hypersonic: Wind Tunnel Facility where he was involved with both analytical and experimental hypersonic: research. After he received his M.S. degree in 1963, Dave took a temporary leave of absence from the University for two years and worked on the Research Staff at General Electric in Cincinnati, studying hypersonic: shockwave boundary layer interactions in hypersonic scramjet inlets as part of another Air Force Project. During his stay at GE, Dave took a self study class in advanced thermo-dynamics from the department head of the Mechanical Engineering Department and when ho returned to the University of Cincinnati, he joined the University of Cincinnati Structural Dynamics Research Laboratory (UC-SDRI,) in the Mechanical Engineering Department. This was the start of his long association with the UC-SDRL. His early work in UC-SDRL was studying cutting mechanics of the grinding process which evolved into “Grinding Dynamics” which became the main title of his Ph.D. dissertation work. During bis study of grinding dynamics, Dave became very involved in the early practical development of Fourier analysis as applied to digital signal processing, acoustics, controls, self-excited and forced vibrations. This work set the stage for subsequent developments in experimental structural dynamics, the area that is often associated with UC-SDRL. During this early period from 1966-1970. Dr. Brown worked on the Research Staff and taught undergraduate and graduate courses in thermodynamics, acoustics and vibrations. In 1970, Dr. Brown became the Director of the UC-SDRl, a position he held until he retired in the fall of 2004. During his tenure, Dave influenced and advised hundreds of students, gave many seminars, consulted with a large number of companies, was published extensively in the above mentioned areas and was invited to give numerous keynote presentations at conferences internationally. Dr. Brown is still teaching an occasional course and he continues to direct research in the areas of acoustics, controls and vibration. His students are his proudest legacy.
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Dr. Allemang (left) is a member of the faculty of the Mechanical, Industrial and Nuclear Engineering Department. University of Cincinnati, where he currently also serves as Director of the Structural Dynamics Research Laboratory (UC-SDRL). Dr. Allemang has been actively involved in the area of experimental modal analysis for over thirty five years, pioneering the use of multiple input, multiple output estimation of frequency response functions, developing the concept of cyclic averaging, formulating the modal assurance criterion (MAC) and the enhanced frequency response function and reformulating modal parameter estimation algorithms into the unified matrix (coefficient) polynomial approach (UMPA). During this period, Dr. Allemang authored or coauthored over 140 torhnical articles, including chapters for 2 different handbooks and numerous refereed articles. Dr. Allemang has participated in over 50 invited seminars or lectures in the United States as well as in Taiwan, Japan, Korea (NSF), India (NSF), Bulgium, Germany and France, including being asked to give the keynote address at both the Lenven International Seminar on Modal Analysis (ISMA, 1990) and the International Modal Analysis Conference (IMAC. 1993). During this period. Dr. Allemang has served as principal investigator or coprincipal investigator in over $2,500,000 of research with government (NASA and USAF) and commercial agencies (Boeing. General Motors, Ford. HP/Agilent, MTS, Brüel & Kjær, etc.). Dr. Allemang has worked as a consultant to a number of companies in many different structural dynamics applications since 1973. He continues to serve on the Advisory Board for the International Modal Analysis Conference (Chairman, 1086-1995), is serving on the Editorial Board of Sound and Vibration Magazine and has served as the Associate Technical Editor for Mechanical Systems and Signal Processing (MSSP) and Editor for the International Journal of Analytical and Experimental Modal Analysis (IJAEMA).

Dr. Allemang is currently involved in several areas of research which includes the experimental identification of nonlinear structural systems, the development of flexihle MATLAB® based software for modal analysis and data acquisition research, the evaluation of impedance-based modeling methods and the correlation and correction of experimental and analytical dynamic models. He also served as President for the Society of Experimental Mechanics (SEM). 2003-2004. and on the Executive Board of SEM from 1998-2006. Dr. Allemang is very active in teaching in the areas of experimental methods, vibrations and automotive design and serves as Faculty Advisor to a number of student groups at UC including the Formula SAE Team (Bearcat MotorSports), Engineering Tribunal, Tau Beta Pi and Pi Tau Sigma.

For the twelfth consecutive year, nearly 190 nations convened in November 2006, this time in Nairobi, to address the critical issue of climate change. Unfortunately, the atmosphere at these two-week annual conclaves most resembles a medieval trade fair: a hearty reunion of thousands of well-tailored diplomats (some countries send as many as 100 representatives), plus additional thousands of nongovernmental “observers,” some manning colorful information booths, others intent on picturesque mayhem to attract squadrons of riot police. Hundreds of media representatives also join the party in search of a provocative sound bite or an attention-grabbing image.These UN mega-conferences have by now developed a predictable pattern. Considerable time is occupied by tedious problems of coordinating positions and tactics, both inside the huge national delegations and within blocs of countries such as the European Union and other regional or “like-minded” coalitions. There are the usual dire warnings-fully justifiable-of impending global catastrophe. There are trivial protocol debates and ritualistic ministerial speeches exhorting complicated and unrealistic actions. There are cultural diversions such as boat rides on the Rhine or dance performances in Marrakech. As the end nears, all-night negotiating sessions contribute to a sense of destiny.
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But despite the customary self-congratulatory finale, the results at Nairobi, as at preceding meetings, were embarrassingly meager. This process has been going on every year since the 1995 Berlin Conference of Parties to the United Nations (UN) Framework Convention on Climate Change. The sheer size of the treaty, with its integral accompanying explanatory texts, definitions, and regulations, has meanwhile grown from under 40 pages to several hundred pages of numbing complexity. Much of the negotiations have centered on how the industrialized countries can dilute (for example, by emissions trading or by arbitrary estimates of emissions absorbed by ecological land use) the unrealistic emission targets that they accepted in the midnight hours nine years ago in Kyoto and how much financial resources should flow from the “rich” to the “poor” countries, which have no targets at all.

The process is inevitably slow because of the large number of parties involved. But is it necessary to have everyone at the table? In actuality, only 25 nations, about half of them in the “developing” category, are responsible for about 85% of the world’s greenhouse gas emissions. None of the other 160-plus countries accounts for even 1%! Many of the world’s largest emitters are, in fact, “newly industrializing” nations that shun even a hint of voluntary restraints by claiming affinity with the poorest developing countries. Yet India’s emissions are greater than those of Japan and Germany, Brazil emits more than the UK, and China’s emissions exceed everyone but the United States. Moreover, greenhouse gas emissions from these soi-disant “poor” nations are growing far more rapidly than those of the “rich.”

The climate meetings, obsessively focused on short-term targets and timetables applying only to industrialized nations, have become trapped in a process that is unmanageable, inefficient, and impervious to serious negotiation of complex issues that have profound environmental, economic, and social implications extending over many decades into the future. The Kyoto Protocol, lamely defended by its proponents as “the only game in town,” now best serves the interests of politicians whose rhetoric is stronger than their actions and of those commercial interests and governments that want no meaningful actions at all-notably, Saudi Arabia, Kuwait, and other Near East oil producers, and the U.S. administration, which is not unhappy with the treaty’s lack of progress.

Lessons from the ozone history

It is worth recalling that the 1987 Montreal Protocol on Substances That Deplete the Ozone Layer, later characterized by the heads of the UN Environment Program and the World Meteorological Organization as “one of the great international achievements of the century,” was negotiated by only about 30 nations in nine months, with delegations seldom exceeding six persons and with minimal attention from outside observers and media. I doubt whether the ozone treaty could have been achieved under the currently fashionable global format.

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Aging Systems Sustainment and Enabling Technologies (ASSET)

A public-private partnership is working to improve traffic microsimulation technology.

Traffic analysts today are faced with evaluating diverse and complex solutions to address congestion in transportation systems. Instead of “simply” deciding how many lanes to design for a new freeway or how long the turn bays should be at a traffic signal, practitioners now are analyzing advanced traffic signal and ramp metering systems, for example, and complex weaving and geometric configurations, intelligent transportation system strategies, multimodal corridor management plans, and congestion pricing strategies.

The Federal Highway Administration (FHWA) is a leader in developing traffic microsimulation models, dating back to the development of NETwork SIMulation (NETSIM) in the 1970s, FREeway SIMulation (FRESIM) in the 1980s, and the merging of NETSIM and FRESIM into a single CORridor SIMulation (CORSIM) model, all of which was integrated into the Traffic Software Integrated System (TSIS) package in the 1990s. In the early 1990s, TSIS/CORSIM was the only viable traffic microsimulation model available to practitioners. By the late 1990s, however, a number of commercial vendors began offering their own versions of traffic microsimulation packages to meet the growing demand. Today, the popularity of microsimulation packages continues to increase, and there is now a viable market for commercial traffic simulation vendors.

In the early 2000s, FHWA reevaluated its future role in the traffic simulation market. A survey of traffic practitioners and existing traffic simulation packages revealed that while most of the software packages, although robust and providing a range of analysis options, still have some intrinsic limitations that can affect the performance and accuracy of the model results. These limitations in the functionality of current microsimulation systems have generated questions in the transportation community. For example, simulation users view many microsimulation software packages as “black boxes” in that users are not sure how model outputs are calculated and, as a result, are not confident in the accuracy and validity of the model results.

As a result of the market assessment, FHWA decided to take a different role in the traffic simulation market. Rather than compete with the commercial simulation vendors by continuing to develop TSIS/CORSIM, FHWA would act in a “market facilitator” role by focusing public resources on fostering an environment of public-private coordination through research products that will benefit the entire traffic simulation community: practitioners, vendors, and researchers.

Enter the NGSIM Program

With the goal of improving the quality and use of traffic microsimulation tools to facilitate transportation decisionmaking, FHWA’s Traffic Analysis Tools Program began the Next Generation Simulation (NGSIM) program in 2002. NGSIM is a unique public-private partnership between FHWA, transportation consulting companies, university researchers, and foreign and domestic commercial microsimulation software developers.

The objective of the program is to develop a core of driver behavior algorithms that represent the fundamental logic in traffic microsimulation models, with supporting documentation and validation datasets. NGSIM products will be well documented, openly distributed, and free to the transportation community through the NGSIM Web site (www.ngsim.fhwa.dot.gov).

“The NGSIM program represents a model public-private partnership that has yielded demonstrable benefits for both sectors,” says Nagui Rouphail, chairman of the NGSIM stakeholder traffic modelers group and director of the Institute for Transportation Research and Education at North Carolina State University. He adds, “Here the [U.S.] Government acts as the catalyst for developing sound science and the data to back it up, while the private sector commits to participate in the development process as well as incorporating the research findings into its commercial software. This process ensures wider dissemination of the research results and even wider acceptance of the underlying science.”

The process by which individual triple helical collagen molecules assemble into mesoscopic structures (1-51-micron length fibrils with a regular axial periodic “D-banding” pattern that is independent of fibril diameter-remains an intriguing conundrum. The current, “accepted” model of tendon collagen (6) considers the characteristic “67 nm repeat” as being formed from a quarter staggered, side-by-side alignment of five triple helices(7,8), which was initially proposed by the early work of Hodges and Petruska (9). Essentially two-dimensional, this interpretation has several deficiencies that earlier theoretical work tried to address (10-12). One model suggested a layered, spiral arrangement of collagen molecules (13), though experimental data to support such a contention was lacking. The existing models for the supramolecular structure of collagen fibrils presented in a review by Jäger and Frutzl (14), fail to explain how D-banding is preserved independent of fibril diameter; how collagen fibrils “grow” with pointed ends (15,16); why the surface of collagen fibrils is not flat but corrugated, with indentations at the D-band; and how fibrils (consisting of a few molecules and up to 10 nm in diameter) form into fibrils of greater diameter (from 50 to several hundred nm) (16,17) and thence into macroscale objects (e.g., tendons). Collagen structure thus follows a well-established principle in biology-that tissue form reflects functional requirements (18). Thus, Wolff’s Law for bone expostulates that mechanical usage drives skeletal structure; likewise, variable ratios of fast and slow myofibrils are found in skeletal muscle undergoing differing amounts of work. Similarly, the spiral or twisted rope features observed by atomic force microscopy (AFM) are the nanoscale equivalent of microscopic crimps formed by relaxation of the subcomponents (plies) that make up collagen fibrils of tendon and contribute to some of their mechanical features (19). Although early transmission electron microscopy and freeze-fracture studies demonstrated the possible spiralization of the collagen structure, as presented in the pioneering work of Ruggeri (18,19), the interdependence between this behavior of the fibril and the consistent periodicity along its length remained unclear.

In this study, we provide AFM data that supports this twisted structure-in particular, we stress the attributes of the fibril morphology that make its structure intriguingly similar to a classical “rope”. We then take these observations at face value by applying a mechanical rope model, taking advantage of recent progress in the mechanical modeling of rope structure in such areas as textile yams and DNA supercoiling (20-23). Although previous studies discussed the possible generation of a rcpeatable periodicity along a fibril, none considered the variation of the fibril diameter (20,21). We show that the rope hypothesis is consistent with experimental observations by taking reasonable values for the model parameters. In particular, the produced D-banding is found to be independent of the fibril diameter. In addition we observe that the model is not sensitive to the choice of parameters. The predicted rope angle is continued experimentally and provides an independent test of the model.

MATERIALS AND METHODS

Sample preparation

A suspension of native bovine digital tendon collagen fibrils was (Ethicon, Somerville, NJ) was dialyzed at 1 mg/ml against 10 mM acetic acid before use and stored at 4°C. This preparation has been used extensively in platelet function studies, for example (22). For topological assessment by atomic force microscopy, a sample was prepared by deposition of a 20 µl droplet of the Mock solution (1 mg/ml) onto APTES-treated glass slides. A typical incubation time of

Atomic force microscopy

Commercial atomic force microscopes (Dimension 3000, Veeco, Santa Barbara, CA; JPK Nanowizard, Berlin, Germany) were used in contact mode (NPS tips, Veeco) to record both topologic (height) and error signal (deflection) images.

EXPERIMENTAL RESULTS

Topological diversity of collagen fibrils

We studied the topology of tendon collagen fibrils directly by AFM as shown in Fig. 1. Low-resolution images (Fig. 1, b and c) provide an overview at the micron scale of the nalure and diversity of the fibrils obtained from digital tendon when compared with fibrils from tail tendon (Fig. 1 d). Fibrils from tail tendon, a relatively mechanically unloaded tissue, are more uniform in structure and generally straight over the length scale examined (23). In contrast, fibrils from the load-bearing digital tendon are heterogeneous and can be classified into two populations depending on whether or not a repeatable irregularity (spiral or twisted features) is observed along the length of the fibril (observed in 32% of the fibrils studied, n = 296). These are the nanoscale homologs of “crimps” mat are characterized as wavy structures in light microscopic histology (24,25). The mechanics at low-strain range (

Abstract–Abundance indices derived from fishery-independent surveys typically exhibit much higher interannual variability than is consistent with the within-survey variance or the life history of a species. This extra variability is essentially observation noise (i.e. measurement error); it probably reflects environmentally driven factors that affect catchability over time. Unfortunately, high observation noise reduces the ability to detect important changes in the underlying population abundance. In our study, a noise-reduction technique for uncorrelated observation noise that is based on autoregressive integrated moving average (ARIMA) time series modeling is investigated. The approach is applied to 18 time series of finfish abundance, which were derived from trawl survey data from the U.S. northeast continental shelf. Although the a priori assumption of a random-walk-plus-uncorrelated-noise model generally yielded a smoothed result that is pleasing to the eye, we recommend that the most appropriate ARIMA model be identified for the observed time series if the smoothed time series will be used for further analysis of the population dynamics of a species.

Designed for multiple energy commodities, web-based EnergyBuyerSM helps companies manage risk exposure by continuously monitoring, analyzing, and forecasting market conditions. It translates information into hedging guidance: BUY if commodity is currently under-priced or WAIT if commodity is currently over-priced. Program offers accounting and reporting features, charting for technical analysis, customizable hedging parameters, and daily weather updates. WAYNE, Pa., Jan. 10 /- Planalytics, Inc. announced today the release of Planalytics(R) EnergyBuyer(SM), a hedging and risk analysis platform for multiple energy commodities. The EnergyBuyer helps companies manage risk exposure and reduce energy costs by continuously monitoring, analyzing and forecasting market conditions and suggesting when hedging actions should be taken because prices are low or when hedging actions should be avoided because prices are too high.

The EnergyBuyer expands on the proven Planalytics GasBuyer(SM) tool which, since 2000, has successfully guided commercial and industrial end-users, gas distributors and power generators through an increasingly volatile and fast- moving natural gas market. Built on this foundation, the EnergyBuyer brings in New York Mercantile Exchange (Nymex) pricing for natural gas, heating oil, crude oil, propane, coal, RBOB (gasoline) and PJM electric and then applies Planalytics’ patented modeling and forecasting technologies to present users with hedging suggestions for specific commodities and months. The EnergyBuyer captures and analyzes the fundamental, technical, trading and weather dynamics that combine to impact the price of each energy commodity and, using Planalytics’ analytical and forecasting technologies, translates this information into straightforward hedging guidance: BUY (commodity is currently under-priced) or WAIT (commodity is currently over-priced).

The EnergyBuyer also offers accounting and reporting features, charting for technical analysis, customizable hedging parameters and daily weather updates. In addition to the web-based tool, Planalytics provides specialized support services to all EnergyBuyer clients from personal consultation on hedging strategies to daily phone access with an energy meteorologist. Planalytics does not transact nor have any other interests in either the physical or financial energy markets, enabling the EnergyBuyer to offer independent and informed analysis that companies can use to optimize hedging and risk management decisions.

Statistical Time Series Analysis Toolbox v2.1 is a collection of O-Matrix functions for analyzing time-dependent observations. Functions include nonparametric, nonlinear time series analysis; singular spectrum analysis on univariate time series; and proper orthogonal decomposition analysis. STSA handles descriptive and graphical analysis, model identification, fitting and forecasting, residual diagnostic checking, spectral analysis, filtering and smoothing, and optimization.

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BRECKENRIDGE, Colorado, November 27, 2006 - Harmonic Software has released version 2.1 of STSA, The Statistical Time Series Analysis Toolbox for O-Matrix. This release includes a significant number of additions and enhancements.

STSA v2.1 includes new functions for:

o Nonparametric, nonlinear time series analysis

o Singular Spectrum Analysis (SSA) on univariate time series

o Proper Orthogonal Decomposition (POD) analysis

o Additional functions for general statistical analysis and visualization

More details on what’s new with STSA v2.1 can be found on the O-Matrix home page at: http://www.omatrix.com/stsaV21.html

The STSA toolbox is a collection of O-Matrix functions for analyzing time-dependent observations (time series). This toolbox provides an easy to use, efficient solution for a broad range of analysis requirements including:
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descriptive and graphical analysis, model identification, fitting and forecasting, residual diagnostic checking, spectral analysis, filtering and smoothing, and optimization. The functions of the toolbox combine breadth of applications, ease of use and the speed and computing power of O-Matrix to provide an integrated working environment for analyzing time series data.

The functions can be easily incorporated into other functions by the user thus limiting the time needed for writing programs or prototyping new routines.

The STSA toolbox has functions that cover most standard time series analysis plus a number of related functions not directly found in the main O-Matrix distribution or related products. The distribution includes numerous examples using real-world and simulated data that is provided and can be used as a starting point for using the Times Series Analysis toolbox.

The Time Series Analysis toolbox enables the development of analysis and turnkey solutions in a broad range of industries including:

o Financial and economic forecasting

o Sales forecasting and inventory control

o Modeling and forecasting of physical time series in areas such as hydrology, astronomy, oceanography, biology, and earth sciences

o Analysis of flight test and dynamic system data

o Medical data analysis

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