Gruppensupervision mit Betreuern — Eine empirische Fallstudie

Group supervision with carers — an empirical case studyGroup supervision with carers serves in expanding the attention range in the everyday working routine recapitulating action and endurance. In the reported endurance processes relevant aspects are made visible for care work by means of systematic observation. Through this, clarity in command and purpose emerges. As a consequence, problems may be localised more easily, hypotheses are raised and plans put into action. This sets off processes of change among all those supervised. Everyone picks out the creativity impulses significant for himself from the case historics for his own work as new intervention possibilitics. Therefore, over-focussing and paralysis are avoided and/or already existing ones corrected. Group supervision thus enables full use of spontaneity and creativity potential for everyone. It prevents burn-out just as much as indicating development of professional identity for the carers.     

Editorial

Ohne Zusammenfassung

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Editorial

     

The densest <Emphasis Type="Italic">k</Emphasis>-subgraph problem on clique graphs

The Densest k-Subgraph (DkS) problem asks for a k-vertex subgraph of a given graph with the maximum number of edges. The problem is strongly NP-hard, as a generalization of the well known Clique problem and we also know that it does not admit a Polynomial Time Approximation Scheme (PTAS). In this paper we focus on special cases of the problem, with respect to the class of the input graph. Especially, towards the elucidation of the open questions concerning the complexity of the problem for interval graphs as well as its approximability for chordal graphs, we consider graphs having special clique graphs. We present a PTAS for stars of cliques and a dynamic programming algorithm for trees of cliques. M.L. is co-financed within Op. Education by the ESF (European Social Fund) and National Resources. V.Z. is partially supported by the Special Research Grants Account of the University of Athens under Grant 70/4/5821.     

Semi-online scheduling with “end of sequence” information

We study a variant of classical scheduling, which is called scheduling with “end of sequence” information. It is known in advance that the last job has the longest processing time. Moreover, the last job is marked, and thus it is known for every new job whether it is the final job of the sequence. We explore this model on two uniformly related machines, that is, two machines with possibly different speeds. Two objectives are considered, maximizing the minimum completion time and minimizing the maximum completion time (makespan). Let s be the speed ratio between the two machines, we consider the competitive ratios which are possible to achieve for the two problems as functions of s. We present algorithms for different values of s and lower bounds on the competitive ratio. The proposed algorithms are best possible for a wide range of values of s. For the overall competitive ratio, we show tight bounds of ϕ + 1 ≈ 2.618 for the first problem, and upper and lower bounds of 1.5 and 1.46557 for the second problem. The authors would like to dedicate this paper to the memory of our colleague and friend Yong He who passed away in August 2005 after struggling with illness. D. Ye: Research was supported in part by NSFC (10601048).     

Conflict Management of Cooperative Organizations in Contemporary Iran: A Review Analysis

Emergence of conflicts-of-interests and of disputes in organizations, especially within and among work team systems, is inevitable. Cooperatives are self-governing organizations with democratic characters aimed at meeting the members’ needs and promoting social equity. Yet, internal organizational conflicts arise as conflicts-of-interests develop. Such conflicts can cause serious damages to the parties involved as well as harm the reputation of cooperatives as social and economic entities that tend to pursue social justice and fair distribution of income in society. This paper addresses conflict management in contemporary cooperative organizations. It is based on a preliminary research performed on factors causing conflicts in various types of cooperatives in Iran by analyzing 253 complaints made in 224 housing, consumer, transportation, producer procurement, and credit cooperatives within the central province of Tehran. It also offers some suggestions to improve conflict management within cooperative organizations.

Can institutional investors fix the corporate governance problem? Some Danish evidence

It has been advocated within corporate governance that institutional investors may discipline management in listed firms and thereby alleviate the free rider problem associated with dispersed ownership. This article tests this hypothesis using a sample of Danish listed firms during 1998–2001 determining, whether ownership by institutional investors impacts performance, measured by Tobin’s q. Using three stage least squares, it is shown that aggregate ownership by institutional investors does not influence firm performance. However, when decomposing the results, it is found that joint ownership by the largest two Danish institutional investors, has a significant negative impact firm performance. Ownership by banks and to a lesser extent insurance companies significantly influences firm performance positively. The results somehow challenge the conventional wisdom, arguing that the black box view of institutional investors should be abandon. Therefore it is suggested that a more careful analysis should be devoted to each institutional investors own legal environment.

Clustering and combinatorial optimization in recursive supervised learning

The use of combinations of weak learners to learn a dataset has been shown to be better than the use of a single strong learner. In fact, the idea is so successful that boosting, an algorithm combining several weak learners for supervised learning, has been considered to be the best off the shelf classifier. However, some problems still exist, including determining the optimal number of weak learners and the over fitting of data. In an earlier work, we developed the RPHP algorithm which solves both these problems by using a combination of global search, weak learning and pattern distribution. In this chapter, we revise the global search component by replacing it with a cluster based combinatorial optimization. Patterns are clustered according to the output space of the problem, i.e., natural clusters are formed based on patterns belonging to each class. A combinatorial optimization problem is therefore created, which is solved using evolutionary algorithms. The evolutionary algorithms identify the “easy” and the “difficult” clusters in the system. The removal of the easy patterns then gives way to the focused learning of the more complicated patterns. The problem therefore becomes recursively simpler. Over fitting is overcome by using a set of validation patterns along with a pattern distributor. An algorithm is also proposed to use the pattern distributor to determine the optimal number of recursions and hence the optimal number of weak learners for the problem. Empirical studies show generally good performance when compared to other state of the art methods.     

Discovering classes in microarray data using island counts

We present a new biclustering algorithm to simultaneously discover tissue classes and identify a set of genes that well-characterize these classes from DNA microarray data sets. We employ a combinatorial optimization approach where the object is to simultaneously identify an interesting set of genes and a partition of the array samples that optimizes a certain score based on a novel color island statistic. While this optimization problem is NP-complete in general, we are effectively able to solve problems of interest to optimality using a branch-and-bound algorithm. We have tested the algorithm on a 30 sample Cutaneous T-cell Lymphoma data set; it was able to almost perfectly discriminate short-term survivors from long-term survivors and normal controls. Another useful feature of our method is that can easily handle missing expression data.     

Optimal lot sizing under continuous price decrease

An important characteristic of high-tech industries is decreasing component prices over time. In the personal computer industry, some component prices decline at a rate of 1% per week. This paper develops an inventory model for products experiencing continuous decrease in unit price. We develop an accurate closed-form approximate solution to the model. Our results indicate that declining prices lead to substantial decrease in the optimal cycle time and much frequent ordering. This explains the heavy emphasis on just-in-time inventory management practiced by successful companies in high-tech industries. While previous models attributed the success of just-in-time policies to reduced holding cost and improved quality, under declining prices a substantial source of savings becomes lower costs of raw materials which is significant part of cost in these industries. We illustrate the results of the model with a numerical example and perform sensitivity analysis.