Modeling alternative zoning strategies in forest management

To satisfy public demands for environmental values, forest companies are facing the prospect of a reduction in wood supply and increases in costs. Some Canadian provincial governments have proposed intensifying silviculture in special zones dedicated to timber production as the means for pushing out the forest possibility frontiers. In this paper, we compare the traditional two‐zone land allocation framework which includes ecological reserves and integrated forest management zones with the triad — a three‐zone scheme which adds a zone dedicated to intensive timber production. We compare the solutions of the mixed‐integer linear programs formulated under both land‐allocation frameworks. We explore through sensitivity analysis the conditions under which the triad regime can offset the impact on timber production from increased environmental demands. We show that under the realistic conditions characteristic to Coastal British Columbia, higher environmental demands may be satisfied under the triad regime without increasing the financial burdens on the industry or reducing its wood supply. This occurs, however, only if regulatory constraints in timber production zone are flexible.     

Charakterisierung und Störstoffanalyse von rückgewonnenen Stoffströmen aus Wärmedämmverbundsystemen

Due to the growing energy requirements, the proportion of external thermal insulation composite systems (ETICS) installed has increased significantly during the past 50 years. Due to the longevity of these systems, ETICS waste is currently accumulating. Owing to the complex construction as well as a multitude of differently installed materials and substances of the past generations, there are many uncertainties and problems with the clean and low-pollutant processing of such systems. In the frame of this work, the ETICS were processed and sorted before being screened for ingredients or pollutants as well as for the purity of the recovered material fractions.     

Quality management in service ecosystems

Service-oriented architectures and Web services mature and have become more widely accepted and used by industry. This growing adoption increased the demands for new ways of using Web service technology. Users start re-combining and mediating other providers’ services in ways that have not been anticipated by their original provider. Within organisations and cross-organisational communities, discoverable services are organised in repositories providing convenient access to adaptable end-to-end business processes. This idea is captured in the term Service Ecosystem. This paper addresses the question of how quality management can be performed in such service ecosystems. Service quality management is a key challenge when services are composed of a dynamic set of heterogeneous sub-services from different service providers. This paper contributes to this important area by developing a reference model of quality management in service ecosystems. We illustrate the application of the reference model in an exploratory case study. With this case study, we show how the reference model helps to derive requirements for the implementation and support of quality management in an exemplary service ecosystem in public administration.     

Requirements Engineering f��r Product Service Systems

In recent years, manufacturing companies and service providers have moved towards offering customer-specific problem solutions. These integrated bundles usually consist of hardware, software, and service components and are called product service systems (PSS) or hybrid products. Since the success of the resulting solution depends on the understanding of all requirements, requirements engineering (RE) has become a key factor. The article analyzes the state of the art of RE for PSS based on an extensive literature review in the domains of product-, software-, and service engineering. For this, criteria are derived from the characteristics of PSS and from the task area of RE in the life cycle of PSS. Based on these criteria we analyze the most established RE approaches for their suitability for PSS. An important finding is that integrated/interdisciplinary approaches for RE are missing. Moreover, the maturity of RE approaches in the three domains varies significantly. All analyzed approaches heavily rely on concepts and solution characteristics of their own domain so that a transfer to other domains is hardly possible. This literature review lays the foundation for successful RE for PSS and especially for future research aiming at combining and integrating RE approaches and models of product-, software-, and service engineering. Such requirement models could connect concepts of single domains and enable an integrated and seamless RE for PSS.     

Electronic Meeting Support for Councils

City councils hold meetings several times a week. There is a need for computer support at certain meetings. This paper examines the potential for group support systems for use in city council meetings and shows in what ways they can be helpful in pre-meeting and post-meeting activities. The study is based on 17 computer-supported city council meetings, carried out in Stuttgart, Kornwestheim and other cities as part of the Cuparla Project between 1996 and 1998. Three of these meetings are described in the paper as brief case studies. Following this, all 17 meeting sequences are evaluated and analysed. Guidelines have been developed from the results of the study for the introduction of group support systems in city council meetings.     

Effect of Multiscale PCA De-noising in ECG Beat Classification for Diagnosis of Cardiovascular Diseases

Current trends in clinical applications demand automation in electrocardiogram (ECG) signal processing and heart beat classification. This paper examines the design of an effective recognition method to diagnose heart diseases. The proposed method consists of three main modules: de-noising module, feature extraction module, and classifier module. In the de-noising module, multiscale principal component analysis (MSPCA) is used for noise reduction of the ECG signals. In the feature extraction module, autoregressive (AR) modeling is used for extracting features. In the classifier module, different classifiers are examined such as simple logistic, k-nearest neighbor, multilayer perceptron, radial basis function networks, and support vector machines. Different experiments are carried out using the MIT-BIH arrhythmia database to classify different ECG heart beats and the performance of the proposed method is evaluated in terms of several standard metrics. The experimental results show that the proposed method is able to reduce noise from the noisy ECG signals more accurately in comparison to previous methods. The numerical results indicated that the proposed algorithm achieved 99.93 % of the classification accuracy using MSPCA de-noising and AR modeling.     

Using the Default Option Bias to Influence Decision Making While Driving

Gaining a better understanding of human–computer interaction in multiple-goal environments, such as driving, is critical as people increasingly use information technology to accomplish multiple tasks simultaneously. Extensive research shows that decision biases can be utilized as effective cues to guide user interaction in single-goal environments. This article is a first step toward understanding the effect of decision biases in multiple-goal environments. This study analyzed data from a field experiment during which a comparison was made between drivers’ decisions on parking lots in a single-goal environment and drivers’ decisions in a multiple-goal environment when being exposed to the default option bias. The article shows that the default option bias is effective in multiple-goal environments. The results have important implications for the design of human–computer interaction in multiple-goal environments.     

An Improved Neural Network Application for Short-Term Load Forecasting in Power Systems

In this paper an improved neural network application for short-term load forecasting purposes is presented. To speed up the learning process on one side, and not to jeopardize the stability performance of the learning process on the other side, the adaptive approach to the learning-rate parameter has been employed. Also, instead of learning overall load characteristics, the preprocessing of input data has been designed with the idea to learn only load demand behavior that is important for a certain period. The proposed neural network has shown good performance, even in the case of the incomplete data temperature set and at high irregularities in weekly load data.     

Effect of lattice anharmonicity in the structural phase transformation of Laves phase HfV2 alloy: A first-principles investigation

First-principles theory was developed to study the structural phase transformations in the Laves phase HfV₂ alloy. We explored the energy landscape and established the role of lattice anharmonicity underlying the structural phase transitions. Our approach is based on a phenomenological Landau theory for the structural phase transition and a mean-field approximation for the free energy. First-principles calculations were utilized to obtain the distortion energy as a function of relevant deformations, and to deduce parameters for constructing the free energy. Our result for the phase transition temperature of HfV₂ is in good agreement with experiment. We find that the high-temperature cubic C15 phase is stabilized by the effect of lattice anharmonicity. The theory also predicts an anomalous increase in shear modulus with increasing temperature for systems where the anharmonicity is pronounced.