Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models

This paper presents some forecasting techniques for energy demand and price prediction, one day ahead. These techniques combine wavelet transform (WT) with fixed and adaptive machine learning/time series models (multi-layer perceptron (MLP), radial basis functions, linear regression, or GARCH). To create an adaptive model, we use an extended Kalman filter or particle filter to update the parameters continuously on the test set. The adaptive GARCH model is a new contribution, broadening the applicability of GARCH methods. We empirically compared two approaches of combining the WT with prediction models: multicomponent forecasts and direct forecasts. These techniques are applied to large sets of real data (both stationary and non-stationary) from the UK energy markets, so as to provide comparative results that are statistically stronger than those previously reported. The results showed that the forecasting accuracy is significantly improved by using the WT and adaptive models. The best models on the electricity demand/gas price forecast are the adaptive MLP/GARCH with the multicomponent forecast; their MSEs are 0.02314 and 0.15384 respectively.

Using secondary knowledge to support decision tree classification of retrospective clinical data

Retrospective clinical data presents many challenges for data mining and machine learning. The transcription of patient records from paper charts and subsequent manipulation of data often results in high volumes of noise as well as a loss of other important information. In addition, such datasets often fail to represent expert medical knowledge and reasoning in any explicit manner. In this research we describe applying data mining methods to retrospective clinical data to build a prediction model for asthma exacerbation severity for pediatric patients in the emergency department. Difficulties in building such a model forced us to investigate alternative strategies for analyzing and processing retrospective data. This paper describes this process together with an approach to mining retrospective clinical data by incorporating formalized external expert knowledge (secondary knowledge sources) into the classification task. This knowledge is used to partition the data into a number of coherent sets, where each set is explicitly described in terms of the secondary knowledge source. Instances from each set are then classified in a manner appropriate for the characteristics of the particular set. We present our methodology and outline a set of experiential results that demonstrate some advantages and some limitations of our approach. © 2008 Springer-Verlag Berlin Heidelberg.

Ranking efficient decision-making units in data envelopment analysis using fuzzy concept

This paper introduces a new mathematical method for improving the discrimination power of data envelopment analysis and to completely rank the efficient decision-making units (DMUs). Fuzzy concept is utilised. For this purpose, first all DMUs are evaluated with the CCR model. Thereafter, the resulted weights for each output are considered as fuzzy sets and are then converted to fuzzy numbers. The introduced model is a multi-objective linear model, endpoints of which are the highest and lowest of the weighted values. An added advantage of the model is its ability to handle the infeasibility situation sometimes faced by previously introduced models.

Managerial job satisfaction:a longitudinal study

The study examines the job satisfaction of supervisors and managers in four organisations over time. It also considers the importance which they attached to different facets of their job. The major objectives were: To examine the constituent dimensions of job satisfaction at intervals over one year. To examine reasons for change inthe level of job satisfaction at intervals over one year. To provide information on job satisfaction for those concerned with job satisfaction policies. The sample consisted of one hundred and eight people. Each was interviewed on at least three occasions over the course of a year. Interviews took place at predetermined time intervals. The study shows that job satisfaction is dynamic over a relatively short period of time. The ratings which supervisors and managers gave to aspects of their job did not, however, all change by equal amounts or in the same direction. Changes in job satisfaction were associated with events experienced but it was the meaning of those events to correspondents which appeared to be particularly important. People tended to adopt a localised frame of reference when considering their work situation. Certain job variables, such as variety, were consistently and positively correlated with job satisfaction. With some other variables, the relationship varied across time. Frequently, age and job level moderated the association between independent variables and job satisfaction. Links were found between the quality of life and job satisfaction. There was a consistent positive association between job satisfaction and life satisfaction. However, the job was rarely considered to be the main factor contributing to a person's quality of life. The research highlights the difficulties and desirability of introducing standardised job satisfaction policies in the light of individual differences. In addition, it demonstrates that merely correlating variables with job satisfaction at one point in time may conceal complex relationships and meanings. A new measure of job satisfaction - whereby facets are assessed and rated relative to each other was also developed as part of this study.

The autonomous navigation of an electric vehicle

A navigation and positioning system for an electric automatic guided vehicle has been designed and implemented on an industrial pallet truck. The system includes an optical sensor mounted on the vehicle, capable of recognizing special markers at a distance of 0.3m. Software implemented in a z-80 microprocessor controls the sensor, performs all data processing and contains the decision making processes necessary for the vehicle to navigate its way to its task location. A second microprocessor is used to control the vehicle's drive motors under instruction from the navigation unit, to accurately position the vehicle at its destination. The sensor reliably recognises markers at vehicle speeds up to 1ms- 1, and the system has been integrated into a multiprocessor controlled wire-guidance system and applied to a prototype vehicle.

Public sector investment appraisal:the place of flexibility in the decision making process

This thesis reviews the main methodological developments in public sector investment appraisal and finds growing evidence that appraisal techniques are not fulfilling their earlier promise. It is suggested that an important reason for this failure lies in the inability of these techniques to handle uncertainty except in a highly circumscribed fashion. It is argued that a more fruitful approach is to strive for flexibility. Investment projects should be formulated with a view to making them responsive to a wide range of possible future events, rather than embodying a solution which is optimal for one configuration of circumstances only. The distinction drawn in economics between the short and the long run is used to examine the nature of flexibility. The concept of long run flexibility is applied to the pre-investment range of choice open to the decisionmaker. It is demonstrated that flexibility is reduced at a very early stage of decisionmaking by the conventional system of appraisal which evaluates only a small number of options. The pre-appraisal filtering process is considered further in relation to decisionmaking models. It is argued that for public sector projects the narrowing down of options is best understood in relation to an amended mixed scanning model which places importance on the process by which the 'national interest ' is determined. Short run flexibility deals with operational characteristics, the degree to which particular projects may respond to changing demands when the basic investment is already in place. The tension between flexibility and cost is noted. A short case study on the choice of electricity generating plant is presented. The thesis concludes with a brief examination of the approaches used by successive British governments to public sector investment, particularly in relation to the nationalised industries

Knowledge engineering for mental-health risk assessment and decision support

Mental-health risk assessment practice in the UK is mainly paper-based, with little standardisation in the tools that are used across the Services. The tools that are available tend to rely on minimal sets of items and unsophisticated scoring methods to identify at-risk individuals. This means the reasoning by which an outcome has been determined remains uncertain. Consequently, there is little provision for: including the patient as an active party in the assessment process, identifying underlying causes of risk, and eecting shared decision-making. This thesis develops a tool-chain for the formulation and deployment of a computerised clinical decision support system for mental-health risk assessment. The resultant tool, GRiST, will be based on consensual domain expert knowledge that will be validated as part of the research, and will incorporate a proven psychological model of classication for risk computation. GRiST will have an ambitious remit of being a platform that can be used over the Internet, by both the clinician and the layperson, in multiple settings, and in the assessment of patients with varying demographics. Flexibility will therefore be a guiding principle in the development of the platform, to the extent that GRiST will present an assessment environment that is tailored to the circumstances in which it nds itself. XML and XSLT will be the key technologies that help deliver this exibility.

Strategic positioning of manufacturing operations within global supply chains

In global environment, a company has to make many decisions that impact upon its position in global supply chain networks such as outsourcing, offshoring, joint venture, vertical/horizontal integration, etc. All these decisions impact on the company’s strategic position, and hence on competitive space and performance. Therefore, it is important for a company to carefully manage strategic positioning by making careful decisions about the adoption of alternative manufacturing and supply chain activities. Unfortunately, there is no complete process studied in strategic positioning of manufacturing operations within global supply chain. Therefore, the work presented in this paper has investigated leading research and industrial practices to create a formal and rational decision process. An analysis of previous literature, industrial practices, and the resulting decision process are all presented in this paper.

The right time and place:strategic positioning for competitive manufacture

Increasingly in the UK, companies that have traditionally considered themselves as manufacturers are being advised to now see themselves as service providers and to reconsider whether to have any production capability. A key challenge is to translate this strategy into a selection of product and service-centred activities within the company's supply chain networks. Strategic positioning is concerned with the choice of business activities a company carries out itself, compared to those provided by suppliers, partners, distributors and even customers. In practice, strategic positioning is directly impacted by such decisions as outsourcing, off-shoring, partnering, technology innovation, acquisition and exploitation. If companies can better understand their strategic positioning, they can make more informed decisions about the adoption of alternative manufacturing and supply chain activities. Similarly, they are more likely to reject those that, like off-shoring, are currently en vogue but are highly likely to erode competitive edge and business success. Our research has developed a new concept we call 'competitive space' as a means of appreciating the strategic positioning of companies, along with a structured decision process for managing competitive space. Our ideas about competitive space, along with the decision process itself, have been developed and tested on a range of manufacturers. As more and more manufacturers are encouraged to move towards system integration and a serviceable business model, the challenge is to identify the appropriate strategic position for their organisations, or in other words, to identify their optimum competitive space for manufacture.

Strategic positioning of manufacturing operations within global supply chains

This paper describes research that has sought to create a structured and integrated methodology that guides manufacturers through the decision of strategic positioning within global supply chains. The position of a company is concerned with deciding a boundary and configuration of internal and external business activities to the company and is directly related to initiatives such as outsourcing, make or buy, and offshoring. This paper provides an in-depth description of this concept, describes work carried out to form a methodology for strategic positioning within the global supply chain, and presents the details of the methodology. This research has made a significant contribution to the knowledge on how manufacturing companies can form a strategic positioning within global supply chains.

Quantifying short-term dynamics of Parkinson's disease using self-reported symptom data from an internet social network

Background: Parkinson’s disease (PD) is an incurable neurological disease with approximately 0.3% prevalence. The hallmark symptom is gradual movement deterioration. Current scientific consensus about disease progression holds that symptoms will worsen smoothly over time unless treated. Accurate information about symptom dynamics is of critical importance to patients, caregivers, and the scientific community for the design of new treatments, clinical decision making, and individual disease management. Long-term studies characterize the typical time course of the disease as an early linear progression gradually reaching a plateau in later stages. However, symptom dynamics over durations of days to weeks remains unquantified. Currently, there is a scarcity of objective clinical information about symptom dynamics at intervals shorter than 3 months stretching over several years, but Internet-based patient self-report platforms may change this. Objective: To assess the clinical value of online self-reported PD symptom data recorded by users of the health-focused Internet social research platform PatientsLikeMe (PLM), in which patients quantify their symptoms on a regular basis on a subset of the Unified Parkinson’s Disease Ratings Scale (UPDRS). By analyzing this data, we aim for a scientific window on the nature of symptom dynamics for assessment intervals shorter than 3 months over durations of several years. Methods: Online self-reported data was validated against the gold standard Parkinson’s Disease Data and Organizing Center (PD-DOC) database, containing clinical symptom data at intervals greater than 3 months. The data were compared visually using quantile-quantile plots, and numerically using the Kolmogorov-Smirnov test. By using a simple piecewise linear trend estimation algorithm, the PLM data was smoothed to separate random fluctuations from continuous symptom dynamics. Subtracting the trends from the original data revealed random fluctuations in symptom severity. The average magnitude of fluctuations versus time since diagnosis was modeled by using a gamma generalized linear model. Results: Distributions of ages at diagnosis and UPDRS in the PLM and PD-DOC databases were broadly consistent. The PLM patients were systematically younger than the PD-DOC patients and showed increased symptom severity in the PD off state. The average fluctuation in symptoms (UPDRS Parts I and II) was 2.6 points at the time of diagnosis, rising to 5.9 points 16 years after diagnosis. This fluctuation exceeds the estimated minimal and moderate clinically important differences, respectively. Not all patients conformed to the current clinical picture of gradual, smooth changes: many patients had regimes where symptom severity varied in an unpredictable manner, or underwent large rapid changes in an otherwise more stable progression. Conclusions: This information about short-term PD symptom dynamics contributes new scientific understanding about the disease progression, currently very costly to obtain without self-administered Internet-based reporting. This understanding should have implications for the optimization of clinical trials into new treatments and for the choice of treatment decision timescales.

Nonlinear mixing and mode correlations in a short Raman fiber laser

In the present paper we experimentally demonstrate a generation in a short Raman fiber laser having 10 000 different longitudinal modes only. We design the laser using 12 meters of commercially available fiber. Contrary to the recently demonstrated single longitudinal mode DFB Raman laser and short DBR Raman laser, in the laser under study the number of modes is high enough for efficient nonlinear interactions. Experimentally measured time dynamics reveals the presence of mode correlations in the radiation: the measured extreme events lasts for more than 10 round-trips.

Understanding dynamics of strategic decision-making in venture creation:a process study of effectuation and causation

This study draws upon effectuation and causation as examples of planning-based and flexible decision-making logics, and investigates dynamics in the use of both logics. The study applies a longitudinal process research approach to investigate strategic decision-making in new venture creation over time. Combining qualitative and quantitative methods, we analyze 385 decision events across nine technology-based ventures. Our observations suggest a hybrid perspective on strategic decision-making, demonstrating how effectuation and causation logics are combined, and how entrepreneurs’ emphasis on these logics shifts and re-shifts over time. We induce a dynamic model which extends the literature on strategic decision-making in venture creation.

Methods of Adaptive Extraction and Analysis of Knowledge for Knowledge-base Construction and Fast Decision Making

An approach for knowledge extraction from the information arriving to the knowledge base input and also new knowledge distribution over knowledge subsets already present in the knowledge base is developed. It is also necessary to realize the knowledge transform into parameters (data) of the model for the following decision-making on the given subset. It is assumed to realize the decision-making with the fuzzy sets’ apparatus.

Fuzzy Membership Functions in a Fuzzy Decision Making Problem

Authors analyses questions of the subjective uncertainty and inexactness situations in the moment of using expert information and another questions which are connected with expert information uncertainty by fuzzy sets with rough membership functions in this article. You can find information about integral problems of individual expert marks and about connection among total marks “degree of inexactness” with sensibility of measurement scale. A lot of different situation which are connected with distribution of the function accessory significance and orientation of the concrete take to task decision making are analyses here.