The equivalent representation of the breadth-one <Emphasis Type="Italic">D</Emphasis>-invariant polynomial subspace and its discretization

This paper demonstrates the equivalence of two classes of D-invariant polynomial subspaces, i.e., these two classes of subspaces are different representations of the breadth-one D-invariant subspace. Moreover, the authors solve the discrete approximation problem in ideal interpolation for the breadth-one D-invariant subspace. Namely, the authors find the points, such that the limiting space of the evaluation functionals at these points is the functional space induced by the given D-invariant subspace, as the evaluation points all coalesce at one point.     

Objective Bayesian analysis of degradation model with respect to a Wiener process

This paper proposes an objective Bayesian method to study the degradation model with respect to a Wiener process. The Jeffreys prior and reference prior for the parameters are derived, and the propriety of the posteriors under these priors is validated. Two sampling algorithms are introduced to compute the posteriors. A simulation study is conducted to investigate the performance of the objective Bayesian procedure. Finally, the authors apply the approach to a degradation data.     

Intersections of Critical Systems Thinking and Community Based Participatory Research: A Learning Organization Example with the Autistic Community

Critical systems thinking (CST) and community based participatory research (CBPR) are distinct approaches to inquiry which share a primary commitment to holism and human emancipation, as well as common grounding in critical theory and emancipatory and pragmatic philosophy. This paper explores their intersections and complements on a historical, philosophical, and theoretical level, and then proposes a hybrid approach achieved by applying CBPR’s principles and considerations for operationalizing emancipatory practice to traditional systems thinking frameworks and practices. This hybrid approach is illustrated in practice with examples drawn from of the implementation of the learning organization model in an action research setting with the Autistic community. Our experience of being able to actively attend to, and continuously equalize, power relations within an organizational framework that otherwise has great potential for reinforcing power inequity suggests CBPR’s principles and considerations for operationalizing emancipatory practice could be useful in CST settings, and CST’s vocabulary, methods, and clarity around systems thinking concepts could be valuable to CBPR practitioners.     

Unbiased condition of the dispersion effects estimator in unreplicated two-level factorial experiments

This article presents a general form of the estimator for identifying dispersion effects from unreplicated two-level factorial experiments, and shows that the widely used estimators such as the BH, MH, and AMH estimators are all special cases of the proposed one, designated as the G estimator. The unbiased condition of the G estimator is proved, and a lower bound of variance of the G estimator is provided. A simulation based on a realistic design illustrates the variation of the variance and MSE (mean square error) of the G estimator on different coefficients. This estimator may be more flexible and has better performance than other methods such as the BH and MH estimators by appropriately selecting the coefficients.     

Comparing two clustering-based techniques to track mobile nodes in industrial wireless sensor networks

Tracking mobile nodes in dynamic and noisy conditions of industrial environments has provided a paradigm for many issues inherent in the area of distributed control systems in general and wireless sensor networks in particular. Due to the dynamic nature of the industrial environments, a practical tracking system is required that is adaptable to the changes in the environment. More specifically, given the limited resources of wireless nodes and the challenges created by harsh industrial environments there is a need for a technique that can modify the configuration of the system on the fly as new wireless nodes are added to the network and obsolete ones are removed. To address these issues, two cluster-based tracking systems, one static and the other dynamic, are proposed to organize the overall network field into a set of tracking zones, each composed of a sink node and a set of corresponding anchor nodes. To manage the wireless nodes activities and inter and intra cluster communications, an agent-based technique is employed. To compare the architectures, we report on a set of experiments performed in JADE (Java Agent Development Environment). In these experiments, we compare two agent-based approaches (dynamic and static) for managing clusters of wireless sensor nodes in a distributed tracking system. The experimental results corroborate the efficiency of the static clusters versus the robustness and effectiveness of the dynamic clusters.     

A quantum multi-agent based neural network model for failure prediction

An effective prognostic program is crucial to the predictive maintenance of complex equipment since it can improve productivity, prolong equipment life, and enhance system safety. This paper proposes a novel technique for accurate failure prognosis based on back propagation neural network and quantum multi-agent algorithm. Inspired by the extensive research of quantum computing theory and multi-agent systems, the technique employs a quantum multi-agent strategy, with the main characteristics of quantum agent representation and several operations including fitness evaluation, cooperation, crossover and mutation, for parameters optimization of neural network to avoid the deficiencies such as slow convergence and liability of getting stuck to local minima. To validate the feasibility of the proposed approach, several numerical approximation experiments were firstly designed, after which real vibrational data of bearings from the Laboratory of Cincinnati University were analyzed and used to assess the health condition for a given future point. The results were rather encouraging and indicated that the presented forecasting method has the potential to be utilized as an estimation tool for failure prediction in industrial machinery.     

A hybrid model of knowledge management for new service development

Although many theories regarding the implementation of knowledge management (KM) in organizations have been proposed and studied, most applications tend to stand alone without incorporating the business processes. Different categories of knowledge provide different benefits and how to integrate various categories of KM into a hybrid approach as an effective KM manner remains strategically important, and yet is still understudied. Therefore, in this paper a hybrid model that integrates principal KM applications for new service development (NSD) and the measurement of the resulting financial benefits have been developed. The proposed KM model incorporates newsgroups, knowledge forums, knowledge asset management and knowledge application processes as a hybrid means for sharing organizational knowledge along two axes, explicit vs. implicit and individual vs. collective. One of the largest management consulting companies in Taiwan, China, whose process model of NSD was standing alone with KM applications, was selected for the case study. A set of hybrid KM processes was developed to implement the proposed KM model, and it illustrates an application with greater financial benefits for integrating hybrid KM practices into the business process. Based on knowledge value added (KVA) validation, the proposed KM model provides a new operating system for sharing NSD knowledge within an organization. Through the case study by measuring the achieved financial results, the proposed KM model is found to provide an exclusive hybrid platform with an empirical process model to address innovative approaches and practical values of KM within an organization.     

Efficiency decomposition with shared inputs and outputs in two-stage DEA

Data envelopment analysis (DEA) is an effective non-parametric method for measuring the relative efficiencies of decision making units (DMUs) with multiple inputs and outputs. In many real situations, the internal structure of DMUs is a two-stage network process with shared inputs used in both stages and common outputs produced by the both stages. For example, hospitals have a two-stage network structure. Stage 1 consumes resources such as information technology system, plant, equipment and admin personnel to generate outputs such as medical records, laundry and housekeeping. Stage 2 consumes the same set of resources used by stage 1 (named shared inputs) and the outputs generated by stage 1 (named intermediate measures) to provide patient services. Besides, some of outputs, for instance, patient satisfaction degrees, are generated by the two individual stages together (named shared outputs). Since some of shared inputs and outputs are hard split up and allocated to each individual stage, it needs to develop two-stage DEA methods for evaluating the performance of two-stage network processes in such problems. This paper extends the centralized model to measure the DEA efficiency of the two-stage process with non splittable shared inputs and outputs. A weighted additive approach is used to combine the two individual stages. Moreover, additive efficiency decomposition models are developed to simultaneously evaluate the maximal and the minimal achievable efficiencies for the individual stages. Finally, an example of 17 city branches of China Construction Bank in Anhui Province is employed to illustrate the proposed approach.     

Piecewise Regression Mixture for Simultaneous Functional Data Clustering and Optimal Segmentation

This paper introduces a novel mixture model-based approach to the simultaneous clustering and optimal segmentation of functional data, which are curves presenting regime changes. The proposed model consists of a finite mixture of piecewise polynomial regression models. Each piecewise polynomial regression model is associated with a cluster, and within each cluster, each piecewise polynomial component is associated with a regime (i.e., a segment). We derive two approaches to learning the model parameters: the first is an estimation approach which maximizes the observed-data likelihood via a dedicated expectation-maximization (EM) algorithm, then yielding a fuzzy partition of the curves into K clusters obtained at convergence by maximizing the posterior cluster probabilities. The second is a classification approach and optimizes a specific classification likelihood criterion through a dedicated classification expectation-maximization (CEM) algorithm. The optimal curve segmentation is performed by using dynamic programming. In the classification approach, both the curve clustering and the optimal segmentation are performed simultaneously as the CEM learning proceeds. We show that the classification approach is a probabilistic version generalizing the deterministic K-means-like algorithm proposed in Hébrail, Hugueney, Lechevallier, and Rossi (2010). The proposed approach is evaluated using simulated curves and real-world curves. Comparisons with alternatives including regression mixture models and the K-means-like algorithm for piecewise regression demonstrate the effectiveness of the proposed approach.     

Estimation of Generalized DINA Model with Order Restrictions

Cognitive diagnostic models provide valuable information on whether a student has mastered each of the attributes a test intends to evaluate. Despite its generality, the generalized DINA model allows for the possibility of lower correct rates for students who master more attributes than those who know less. This paper considers the use of order-constrained parameter space of the G-DINA model to avoid such a counter-intuitive phenomenon and proposes two algorithms, the upward and downward methods, for parameter estimation. Through simulation studies, we compare the accuracy in parameter estimation and in classification of attribute patterns obtained from the proposed two algorithms and the current approach when the restricted parameter space is true. Our results show that the upward method performs the best among the three, and therefore it is recommended for estimation, regardless of the distribution of respondents’ attribute patterns, types of test items, and the sample size of the data.