Fish Driven Bioturbation as a Viable Aquaculture Technology for Increasing Production and Improving Ecological Sustainability of Giant Tiger Shrimp Penaeus Monodon (FABRlClUS)

A general introduction to the problems faced in the shrimp culture due to waste formation and its consequent environmental hazards and production problems of Giant tiger shrimp, Penaeus monodon is highlighted by the author in this thesis. The objective of the present work was to assess the potential of brackish water finfish to improve bottom soil conditions and thereby increase the growth and production of Penaeus monodon. The salient findings of the present study are summarized in chapter 7. This is followed by the references cited in the thesis and list ofpublications originated from the present study.

Integrating TPM and QFD for improving quality in maintenance engineering

To provide maintenance engineering community with a model named “Maintenance quality function deployment” (MQFD) for nourishing the synergy of quality function deployment (QFD) and total productive maintenance (TPM) and enhancing maintenance quality of products and equipment.The principles of QFD and TPM were studied. MQFD model was designed by coupling these two principles. The practical implementation feasibility of MQFD model was checked in an automobile service station.Both QFD and TPM are popular approaches and several benefits of implementing them have been reported worldwide. Yet the world has not nourished the synergic power of integrating them. The MQFD implementation study reported in this paper has revealed its practical validity

Semantic web mining and the representation, analysis, and evolution of web space

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: Growing numbers of researchers work on improving the results of Web Mining by exploiting semantic structures in the Web, and they use Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself. The second aim of this paper is to use these concepts to circumscribe what Web space is, what it represents and how it can be represented and analyzed. This is used to sketch the role that Semantic Web Mining and the software agents and human agents involved in it can play in the evolution of Web space.

Semantic web mining

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: an increasing number of researchers is working on improving the results of Web Mining by exploiting semantic structures in the Web, and they make use of Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself. The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks. Given the enormous size even of today’s Web, it is impossible to manually enrich all of these resources. Therefore, automated schemes for learning the relevant information are increasingly being used. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming more and more common. Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.

Improving food and nutritional security of small and marginal coconut growers through diversification of crops and enterprises

This paper presents the impact of integrating interventions like nutrition gardening, livestock rearing, product diversification and allied income generation activities in small and marginal coconut homesteads along with nutrition education in improving the food and nutritional security as well as the income of the family members. The activities were carried out through registered Community Based Organizations (CBOs) in three locations in Kerala, India during 2005-2008. Data was collected before and after the project periods through interviews using a pre-tested questionnaire containing statements indicating the adequacy, quality and diversity of food materials. Fifty respondents each were randomly selected from the three communities, thereby resulting in a total sample size of 150. The data was analysed using SPSS by adopting statistical tools like frequency, average, percentage analysis, t – test and regression. Participatory planning and implementation of diverse interventions notably intercropping and off-farm activities along with nutrition education brought out significant improvements in the food and nutritional security, in terms of frequency and quantity of consumption as well as diet diversity. At the end of the project, 96%of the members became completely food secure and 72% nutritionally secure. The overall consumption of fruits, vegetables and milk by both children and adults and egg by children recorded increase over the project period. Consumption of fish was more than the Recommended Dietary Intake (RDI) level during pre and post project periods. Project interventions like nutrition gardening could bring in surplus consumption of vegetables (35%) and fruits (10%) than RDI. In spite of the increased consumption of green leafy vegetables and milk and milk products over the project period, the levels of consumption were still below the RDI levels. CBO-wise analysis of the consumption patterns revealed the need for location-specific interventions matching to the needs and preferences of the communities.

Improving cropping systems of semi-arid south-western Madagascar under multiple ecological and socio-economic constraints

Das Mahafaly Plateau im südwestlichen Madagaskar ist gekennzeichnet durch raue klimatische Bedingungen, vor allem regelmäßige Dürren und Trockenperioden, geringe Infrastruktur, steigende Unsicherheit, hohe Analphabetenrate und regelmäßige Zerstörung der Ernte durch Heuschreckenplagen. Da 97% der Bevölkerung von der Landwirtschaft abhängen, ist eine Steigerung der Produktivität von Anbausystemen die Grundlage für eine Verbesserung der Lebensbedingungen und Ernährungssicherheit in der Mahafaly Region. Da wenig über die Produktivität von traditionellen extensiven und neu eingeführten Anbaumethoden in diesem Gebiet bekannt ist, waren die Zielsetzungen der vorliegenden Arbeit, die limitierenden Faktoren und vielversprechende alternative Anbaumethoden zu identifizieren und diese unter Feldbedingungen zu testen. Wir untersuchten die Auswirkungen von lokalem Viehmist und Holzkohle auf die Erträge von Maniok, der Hauptanbaufrucht der Region, sowie die Beiträge von weiteren Faktoren, die im Untersuchungsgebiet ertragslimitierend sind. Darüber hinaus wurde in der Küstenregion das Potenzial für bewässerten Gemüseanbau mit Mist und Holzkohle untersucht, um zu einer Diversifizierung von Einkommen und Ernährung beizutragen. Ein weiterer Schwerpunkt dieser Arbeit war die Schätzung von Taubildung und deren Beitrag in der Jahreswasserbilanz durch Testen eines neu entworfenen Taumessgerätes. Maniok wurde über drei Jahre und in drei Versuchsfeldern in zwei Dörfern auf dem Plateau angebaut, mit applizierten Zeburindermistraten von 5 und 10 t ha-1, Holzkohleraten von 0,5 und 2 t ha-1 und Maniokpflanzdichten von 4500 Pflanzen ha-1. Maniokknollenerträge auf Kontrollflächen erreichten 1 bis 1,8 t Trockenmasse (TM) ha-1. Mist führte zu einer Knollenertragssteigerung um 30 - 40% nach drei Jahren in einem kontinuierlich bewirtschafteten Feld mit geringer Bodenfruchtbarkeit, hatte aber keinen Effekt auf den anderen Versuchsfeldern. Holzkohle hatte keinen Einfluss auf Erträge über den gesamten Testzeitraum, während die Infektion mit Cassava-Mosaikvirus zu Ertragseinbußen um bis zu 30% führte. Pflanzenbestände wurden felder-und jahresübergreifend um 4-54% des vollen Bestandes reduziert, was vermutlich auf das Auftreten von Trockenperioden und geringe Vitalität von Pflanzmaterial zurückzuführen ist. Karotten (Daucus carota L. var. Nantaise) und Zwiebeln (Allium cepa L. var. Red Créole) wurden über zwei Trockenzeiten mit lokal erhältlichem Saatgut angebaut. Wir testeten die Auswirkungen von lokalem Rindermist mit einer Rate von 40 t ha-1, Holzkohle mit einer Rate von 10 t ha-1, sowie Beschattung auf die Gemüseernteerträge. Lokale Bewässerungswasser hatte einen Salzgehalt von 7,65 mS cm-1. Karotten- und Zwiebelerträge über Behandlungen und Jahre erreichten 0,24 bis 2,56 t TM ha-1 beziehungsweise 0,30 bis 4,07 DM t ha-1. Mist und Holzkohle hatten keinen Einfluss auf die Erträge beider Kulturen. Beschattung verringerte Karottenerträge um 33% im ersten Jahr, während sich die Erträge im zweiten Jahr um 65% erhöhten. Zwiebelerträge wurden unter Beschattung um 148% und 208% im ersten und zweiten Jahr erhöht. Salines Bewässerungswasser sowie Qualität des lokal verfügbaren Saatgutes reduzierten die Keimungsraten deutlich. Taubildung im Küstendorf Efoetsy betrug 58,4 mm und repräsentierte damit 19% der Niederschlagsmenge innerhalb des gesamten Beobachtungszeitraum von 18 Monaten. Dies weist darauf hin, dass Tau in der Tat einen wichtigen Beitrag zur jährlichen Wasserbilanz darstellt. Tageshöchstwerte erreichten 0,48 mm. Die getestete Tauwaage-Vorrichtung war in der Lage, die nächtliche Taubildung auf der metallischen Kondensationsplatte zuverlässig zu bestimmen. Im abschließenden Kapitel werden die limitierenden Faktoren für eine nachhaltige Intensivierung der Landwirtschaft in der Untersuchungsregion diskutiert.

Improving Multi-class Text Classification with Naive Bayes

There are numerous text documents available in electronic form. More and more are becoming available every day. Such documents represent a massive amount of information that is easily accessible. Seeking value in this huge collection requires organization; much of the work of organizing documents can be automated through text classification. The accuracy and our understanding of such systems greatly influences their usefulness. In this paper, we seek 1) to advance the understanding of commonly used text classification techniques, and 2) through that understanding, improve the tools that are available for text classification. We begin by clarifying the assumptions made in the derivation of Naive Bayes, noting basic properties and proposing ways for its extension and improvement. Next, we investigate the quality of Naive Bayes parameter estimates and their impact on classification. Our analysis leads to a theorem which gives an explanation for the improvements that can be found in multiclass classification with Naive Bayes using Error-Correcting Output Codes. We use experimental evidence on two commonly-used data sets to exhibit an application of the theorem. Finally, we show fundamental flaws in a commonly-used feature selection algorithm and develop a statistics-based framework for text feature selection. Greater understanding of Naive Bayes and the properties of text allows us to make better use of it in text classification.

On Convergence Properties of the EM Algorithm for Gaussian Mixtures

"Expectation-Maximization'' (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite Gaussian mixtures. We show that the EM step in parameter space is obtained from the gradient via a projection matrix $P$, and we provide an explicit expression for the matrix. We then analyze the convergence of EM in terms of special properties of $P$ and provide new results analyzing the effect that $P$ has on the likelihood surface. Based on these mathematical results, we present a comparative discussion of the advantages and disadvantages of EM and other algorithms for the learning of Gaussian mixture models.

On the Convergence of Stochastic Iterative Dynamic Programming Algorithms

Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms, including the TD(lambda) algorithm of Sutton (1988) and the Q-learning algorithm of Watkins (1989), can be motivated heuristically as approximations to dynamic programming (DP). In this paper we provide a rigorous proof of convergence of these DP-based learning algorithms by relating them to the powerful techniques of stochastic approximation theory via a new convergence theorem. The theorem establishes a general class of convergent algorithms to which both TD(lambda) and Q-learning belong.

Improving Multiclass Text Classification with the Support Vector Machine

We compare Naive Bayes and Support Vector Machines on the task of multiclass text classification. Using a variety of approaches to combine the underlying binary classifiers, we find that SVMs substantially outperform Naive Bayes. We present full multiclass results on two well-known text data sets, including the lowest error to date on both data sets. We develop a new indicator of binary performance to show that the SVM's lower multiclass error is a result of its improved binary performance. Furthermore, we demonstrate and explore the surprising result that one-vs-all classification performs favorably compared to other approaches even though it has no error-correcting properties.

Convergence Rates of Approximation by Translates

In this paper we consider the problem of approximating a function belonging to some funtion space Φ by a linear comination of n translates of a given function G. Ussing a lemma by Jones (1990) and Barron (1991) we show that it is possible to define function spaces and functions G for which the rate of convergence to zero of the erro is 0(1/n) in any number of dimensions. The apparent avoidance of the "curse of dimensionality" is due to the fact that these function spaces are more and more constrained as the dimension increases. Examples include spaces of the Sobolev tpe, in which the number of weak derivatives is required to be larger than the number of dimensions. We give results both for approximation in the L2 norm and in the Lc norm. The interesting feature of these results is that, thanks to the constructive nature of Jones" and Barron"s lemma, an iterative procedure is defined that can achieve this rate.

Technological convergence: a strategic perspective

The information and communication technologies (ICT) sectors are in a process of technological convergence. Determinant factors in this process are the liberalisation of the telecommunications markets and technological change. Many firms are engaged in a process of mergers and alliances to position themselves in this new framework. Technological and demand uncertainties are very important. Our objective in this paper is to study the economic determinants of the strategies of the firms. With this aim, we review some key technological and demand aspects. We shed some light on the strategic motivations of the firms by establishing a parallel with the evolution of the retailing sector

The mismatch between results on parental involvement and teacher's attitudes : is convergence ahead?

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Improving Label Space Usage for Ethernet Label Switched Paths

Ethernet is becoming the dominant aggregation technology for carrier transport networks; however, as it is a LAN technology, native bridged ethernet does not fulfill all the carrier requirements. One of the schemes proposed by the research community to make ethernet fulfill carrier requirements is ethernet VLAN-label switching (ELS). ELS allows the creation of label switched data paths using a 12-bit label encoded in the VLAN TAG control information field. Previous label switching technologies such as MPLS use more bits for encoding the label. Hence, they do not suffer from label sparsity issues as ELS might. This paper studies the sparsity issues resulting from the reduced ELS VLAN-label space and proposes the use of the label merging technique to improve label space usage. Experimental results show that label merging considerably improves label space usage

Improving Clustering Algorithms for Image Segmentation using Contour and Region Information

In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach