Logic programming and artificial neural networks in breast cancer detection

About 90% of breast cancers do not cause or are capable of producing death if detected at an early stage and treated properly. Indeed, it is still not known a specific cause for the illness. It may be not only a beginning, but also a set of associations that will determine the onset of the disease. Undeniably, there are some factors that seem to be associated with the boosted risk of the malady. Pondering the present study, different breast cancer risk assessment models where considered. It is our intention to develop a hybrid decision support system under a formal framework based on Logic Programming for knowledge representation and reasoning, complemented with an approach to computing centered on Artificial Neural Networks, to evaluate the risk of developing breast cancer and the respective Degree-of-Confidence that one has on such a happening.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine

Background: To enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory. Results: To address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data. Conclusions: We generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

Knowledge sharing in intrafirm networks in organization

The objective of this study was research the shared knowledge and the means of sharing with the help of social network analysis. The purpose of this study was to give descriptive information to case-organization about its situational network status in different units. The premise of the study is the success of organizational competences and networks, especially when it comes to the sharing of knowledge. The research was accomplished in a TEKES –projects, Developing Network-Based Services – The Role of Competences and Networks COMNET –projects case-organization. Lappeenranta School of Business and the case-organization started the project in co-operation. The baseline for the study was organizational competencies and organizational networks as success factors, especially from the knowledge sharing’s point of view. The research was based on triangulation, which included pre-interviews, network analyses accomplished by Webropol –e-mail survey and qualitative interviews. The results indicated that regular unit meetings were experienced to be the most important method of knowledge sharing along with e-mailing, intranet and weekly bulletins. The co-operation between units was also experienced to be important when evaluating knowledge sharing and communication. The intrafirm network was experienced tight. Dispersed units and partly unclear means of information sharing were the biggest obstacles for information communication. Knowledge sharing, communication with others and trainings were seen important in the case-organization.

Exploring knowledge worker motivation in autonomous and interdependent problem solving tasks on virtual platforms

The objective of this study is to understand why virtual knowledge workers conduct autonomous tasks and interdependent problem solving tasks on virtual platforms. The study is qualitative case study including three case organizations that tap the knowledge of expert networks, and utilize virtual platforms in the work processes. Research data includes 15 interviews, that is, five experts from each case company. According to the findings there are some specific characteristics in motivation to work on tasks on online platforms. Autonomy, self-improvement, meaningful tasks, knowledge sharing, time management, variety of contacts, and variety of tasks, and projects motivate virtual knowledge workers. Factors that may enhance individuals’ engagement to work on tasks are trust, security of continuous task flow and income, feedback, meaningful tasks and tasks that contribute to self-improvement, flexibility and effectiveness in time management, and virtual tools that support social interaction. The results also indicate that there are some differences in individuals’ motivation based on the tasks’ nature. That is, knowledge sharing and variety of contacts motivated experts who worked on interdependent problem solving tasks. Then again, autonomy and variety of tasks motivated experts who worked on autonomous tasks.

Chess Endgames and Neural Networks

The existence of endgame databases challenges us to extract higher-grade information and knowledge from their basic data content. Chess players, for example, would like simple and usable endgame theories if such holy grail exists: endgame experts would like to provide such insights and be inspired by computers to do so. Here, we investigate the use of artificial neural networks (NNs) to mine these databases and we report on a first use of NNs on KPK. The results encourage us to suggest further work on chess applications of neural networks and other data-mining techniques.

The engagement of territorial knowledge communities with European spatial planning and the territorial cohesion debate: a Baltic perspective

Recent, dramatic spatial development trends have contributed to the consolidation of a unique territorial governance landscape in the Baltic States. The paper examines the transformation of this evolving institutional landscape for planning practice and knowledge, which has been marked by the disintegration of Soviet institutions and networks, the transition to a market-based economy and the process of accession to the EU. It explores the evolution of territorial knowledge channels in the Baltic States, and the extent and nature of the engagement of actors' communities with the main knowledge arenas and resources of European spatial planning (ESP). The paper concludes that recent shifts in the evolution of these channels suggest the engagement of ESP has concentrated among epistemic communities at State and trans-national levels of territorial governance. The limited policy coordination across a broader spectrum of diverse actors is compounded by institutionally weak and fragmented professional communities of practice, fragmented government structures and marginalized advocacy coalitions.

Creating capabilities in local environments and production networks

Includes bibliography

Unveiling the relationship between complex networks metrics and word senses

The automatic disambiguation of word senses (i.e., the identification of which of the meanings is used in a given context for a word that has multiple meanings) is essential for such applications as machine translation and information retrieval, and represents a key step for developing the so-called Semantic Web. Humans disambiguate words in a straightforward fashion, but this does not apply to computers. In this paper we address the problem of Word Sense Disambiguation (WSD) by treating texts as complex networks, and show that word senses can be distinguished upon characterizing the local structure around ambiguous words. Our goal was not to obtain the best possible disambiguation system, but we nevertheless found that in half of the cases our approach outperforms traditional shallow methods. We show that the hierarchical connectivity and clustering of words are usually the most relevant features for WSD. The results reported here shed light on the relationship between semantic and structural parameters of complex networks. They also indicate that when combined with traditional techniques the complex network approach may be useful to enhance the discrimination of senses in large texts. Copyright (C) EPLA, 2012

On producing and sharing knowledge across boundaries: experiences from the interfaces of an international development research network

The number of large research networks and programmes engaging in knowledge production for development has grown over the past years. One of these programmes devoted to generating knowledge about and for development is National Centre of Competence in Research (NCCR) North–South, a cross-disciplinary, international development research network funded by the Swiss Agency for Development and Cooperation and the Swiss National Science Foundation. Producing relevant knowledge for development is a core goal of the programme and an important motivation for many of the participating researchers. Over the years, the researchers have made use of various spaces for exchange and instruments for co-production of knowledge by academic and non-academic development actors. In this article we explore the characteristics of co-producing and sharing knowledge in interfaces between development research, policy and NCCR North–South practice. We draw on empirical material of the NCCR North–South programme and its specific programme element of the Partnership Actions. Our goal is to make use of the concept of the interface to reflect critically about the pursued strategies and instruments applied in producing and sharing knowledge for development across boundaries.

Education and Ideologies of the Knowledge Economy: Europe and the Politics of Emulation

The Austrian philosopher Ludwig Wittgenstein famously proposed a style of philosophy that was directed against certain pictures [bild] that tacitly direct our language and forms of life. His aim was to show the fly the way out of the fly bottle and to fight against the bewitchment of our intelligence by means of language: “A picture held us captive. And we could not get outside it, for it lay in our language and language seemed to repeat it to us inexorably” (Wittgenstein 1953, 115). In this context Wittgenstein is talking of philosophical pictures, deep metaphors that have structured our language but he does also use the term picture in other contexts (see Owen 2003, 83). I want to appeal to Wittgenstein in my use of the term ideology to refer to the way in which powerful underlying metaphors in neoclassical economics have a strong rhetorical and constitutive force at the level of public policy. Indeed, I am specifically speaking of the notion of ‘the performative’ in Wittgenstein and Austin. The notion of the knowledge economy has a prehistory in Hayek (1937; 1945) who founded the economics of knowledge in the 1930s, in Machlup (1962; 1970), who mapped the emerging employment shift to the US service economy in the early 1960s, and to sociologists Bell (1973) and Touraine (1974) who began to tease out the consequences of these changes for social structure in the post-industrial society in the early 1970s. The term has been taken up since by economists, sociologists, futurists and policy experts recently to explain the transition to the so-called ‘new economy’. It is not just a matter of noting these discursive strands in the genealogy of the ‘knowledge economy’ and related or cognate terms. We can also make a number of observations on the basis of this brief analysis. First, there has been a succession of terms like ‘postindustrial economy’, ‘information economy’, ‘knowledge economy’, ‘learning economy’, each with a set of related concepts emphasising its social, political, management or educational aspects. Often these literatures are not cross-threading and tend to focus on only one aspect of phenomena leading to classic dichotomies such as that between economy and society, knowledge and information. Second, these terms and their family concepts are discursive, historical and ideological products in the sense that they create their own meanings and often lead to constitutive effects at the level of policy. Third, while there is some empirical evidence to support claims concerning these terms, at the level of public policy these claims are empirically underdetermined and contain an integrating, visionary or futures component, which necessarily remains untested and is, perhaps, in principle untestable.

Information-Centric Networking in Mobile and Opportunistic Networks

Information Centric Networking (ICN) as an emerging paradigm for the Future Internet has initially been rather focusing on bandwidth savings in wired networks, but there might also be some significant potential to support communication in mobile wireless networks as well as opportunistic network scenarios, where end systems have spontaneous but time-limited contact to exchange data. This chapter addresses the reasoning why ICN has an important role in mobile and opportunistic networks by identifying several challenges in mobile and opportunistic Information-Centric Networks and discussing appropriate solutions for them. In particular, it discusses the issues of receiver and source mobility. Source mobility needs special attention. Solutions based on routing protocol extensions, indirection, and separation of name resolution and data transfer are discussed. Moreover, the chapter presents solutions for problems in opportunistic Information-Centric Networks. Among those are mechanisms for efficient content discovery in neighbour nodes, resume mechanisms to recover from intermittent connectivity disruptions, a novel agent delegation mechanisms to offload content discovery and delivery to mobile agent nodes, and the exploitation of overhearing to populate routing tables of mobile nodes. Some preliminary performance evaluation results of these developed mechanisms are provided.

Developmental self-construction and -configuration of functional neocortical neuronal networks.

The prenatal development of neural circuits must provide sufficient configuration to support at least a set of core postnatal behaviors. Although knowledge of various genetic and cellular aspects of development is accumulating rapidly, there is less systematic understanding of how these various processes play together in order to construct such functional networks. Here we make some steps toward such understanding by demonstrating through detailed simulations how a competitive co-operative ('winner-take-all', WTA) network architecture can arise by development from a single precursor cell. This precursor is granted a simplified gene regulatory network that directs cell mitosis, differentiation, migration, neurite outgrowth and synaptogenesis. Once initial axonal connection patterns are established, their synaptic weights undergo homeostatic unsupervised learning that is shaped by wave-like input patterns. We demonstrate how this autonomous genetically directed developmental sequence can give rise to self-calibrated WTA networks, and compare our simulation results with biological data.

Innovation networks in China, Japan, and Korea : evidence from Japanese patent data

The growing importance of innovation in economic growth has encouraged the development of innovation capabilities in East Asia, within which China, Japan, and Korea are most important in terms of technological capabilities. Using Japanese patent data, we examine how knowledge networks have developed among these countries. We find that Japan's technological specialization saw little change, but those of Korea and China changed rapidly since 1970s. By the year 2009, technology specialization has become similar across three countries in the sense that the common field of prominent technology is "electronic circuits and communication technologies". Patent citations suggest that technology flows were largest in the electronic technology, pointing to the deepening of innovation networks in these countries.

Innovation networks in China, Japan, and Korea : further evidence from U.S. patent data

The growing importance of innovation in economic growth has encouraged the development of innovation capabilities in East Asia, within which China, Japan, and Korea are most important in terms of technological capabilities. Using U.S. patent data, we examine how knowledge networks have developed among these countries. We find that Japan's technological specialization saw gradual changes, but those of Korea and China changed rapidly since 1970s. By the year 2009, technology specialization has become similar across three countries in the sense that the common fields of prominent technology are electronics and semiconductors. Patent citations suggest that technology flows were largest in the electronics technology, pointing to the deepening of innovation networks in these countries. Together with our prior work, the Japanese and U.S. data produce similar conclusions about innovation networks.

Social issues in sustainable supply chain networks: state of the art and further research directions

The study of supply networks sustainability is a field with a long path behind. Nonetheless, most studies to date are focused on the environmental sub dimension of sustainability, while the social perspective in supply chain networks research still shows a potential for pioneering contri butions. Moreover, from the development standpoint we have observed a paradigm shift advancing from a narrow concept of development, centered on purely economic dimensions, towards more refined issues such as inclusive business, shared value or poverty footprint, all of which are highly related to supply chain activities. In this paper we present a review of the current state of the art on social sustainability of supply chains and we identify the main existing trends in this field. After conducting this study, we can state that a new sphere of knowledge is emerging at the interface between sustainable supply chain networks and development research. The academic community is called to play an important dovetailing role in this scenario by advancing both conceptual and methodological contributions.