Constructing a dental implant ontology for domain specific clustering and life span analysis

Dental implant and prosthetics is a growing industry that follows the increasing aged populations that incur a higher percentage of tooth loss [1]. The dental implant sector is one of the most technical oriented fields in dentistry with many new techniques, devices, and materials being invented and put to clinical trials. Most innovations and technologies tend to be protected by intellectual property rights (IPRs) through patents. Thus, this research identifies the life spans of dental implant (DI) key technologies using patent analysis. Key patents and their frequently appearing phrases are analyzed for the construction of the DI ontology. Afterward, the life spans of DI technical clusters are defined based on the ontology schema. This research demonstrates the feasibility of using text mining and data mining techniques to extract key phrases from a set of DI patents with different patent classifications (e.g., UPC, IPC) as the basis for building a domain-specific ontology. The case study of ontological sub-clustering for dental implants demonstrates life span mapping of the technology and the ability to use clusters to represent stages of development and maturity in specific technology life cycles.     

Genetic operators for combinatorial optimization in TSP and microarray gene ordering

This paper deals with some new operators of genetic algorithms and demonstrates their effectiveness to the traveling salesman problem (TSP) and microarray gene ordering. The new operators developed are nearest fragment operator based on the concept of nearest neighbor heuristic, and a modified version of order crossover operator. While these result in faster convergence of Genetic Algorithm (GAs) in finding the optimal order of genes in microarray and cities in TSP, the nearest fragment operator can augment the search space quickly and thus obtain much better results compared to other heuristics. Appropriate number of fragments for the nearest fragment operator and appropriate substring length in terms of the number of cities/genes for the modified order crossover operator are determined systematically. Gene order provided by the proposed method is seen to be superior to other related methods based on GAs, neural networks and clustering in terms of biological scores computed using categorization of the genes. Shubhra Sankar Ray is a Visiting Research Fellow at the Center for Soft Computing Research: A National Facility, Indian Statistical Institute, Kolkata, India. He received the M.Sc. in Electronic Science and M.Tech in Radiophysics & Electronics from University of Calcutta, Kolkata, India, in 2000 and 2002, respectively. Till March 2006, he had been a Senior Research Fellow of the Council of Scientific and Industrial Research (CSIR), New Delhi, India, working at Machine Intelligence Unit, Indian Statistical Institute, India. His research interests include bioinformatics, evolutionary computation, neural networks, and data mining. Sanghamitra Bandyopadhyay is an Associate Professor at Indian Statistical Institute, Calcutta, India. She did her Bachelors in Physics and Computer Science in 1988 and 1992 respectively. Subsequently, she did her Masters in Computer Science from Indian Institute of Technology (IIT), Kharagpur in 1994 and Ph.D in Computer Science from Indian Statistical Institute, Calcutta in 1998. She has worked in Los Alamos National Laboratory, Los Alamos, USA, in 1997, as a graduate research assistant, in the University of New South Wales, Sydney, Australia, in 1999, as a post doctoral fellow, in the Department of Computer Science and Engineering, University of Texas at Arlington, USA, in 2001 as a faculty and researcher, and in the Department of Computer Science and Engineering, University of Maryland Baltimore County, USA, in 2004 as a visiting research faculty. Dr. Bandyopadhyay is the first recipient of Dr. Shanker Dayal Sharma Gold Medal and Institute Silver Medal for being adjudged the best all round post graduate performer in IIT, Kharagpur in 1994. She has received the Indian National Science Academy (INSA) and the Indian Science Congress Association (ISCA) Young Scientist Awards in 2000, as well as the Indian National Academy of Engineering (INAE) Young Engineers' Award in 2002. She has published over ninety articles in international journals, conference and workshop proceedings, edited books and journal special issues and served as the Program Co-Chair of the 1st International Conference on Pattern Recognition and Machine Intelligence, 2005, Kolkata, India, and as the Tutorial Co-Chair, World Congress on Lateral Computing, 2004, Bangalore, India. She is on the editorial board of the International Journal on Computational Intelligence. Her research interests include Evolutionary and Soft Computation, Pattern Recognition, Data Mining, Bioinformatics, Parallel & Distributed Systems and VLSI. Sankar K. Pal (www.isical.ac.in/∼sankar) is the Director and Distinguished Scientist of the Indian Statistical Institute. He has founded the Machine Intelligence Unit, and the Center for Soft Computing Research: A National Facility in the Institute in Calcutta. He received a Ph.D. in Radio Physics and Electronics from the University of Calcutta in 1979, and another Ph.D. in Electrical Engineering along with DIC from Imperial College, University of London in 1982. He worked at the University of California, Berkeley and the University of Maryland, College Park in 1986-87; the NASA Johnson Space Center, Houston, Texas in 1990-92 & 1994; and in US Naval Research Laboratory, Washington DC in 2004. Since 1997 he has been serving as a Distinguished Visitor of IEEE Computer Society (USA) for the Asia-Pacific Region, and held seve ral visiting positions in Hong Kong and Australian universities. Prof. Pal is a Fellow of the IEEE, USA, Third World Academy of Sciences, Italy, International Association for Pattern recognition, USA, and all the four National Academies for Science/Engineering in India. He is a co-author of thirteen books and about three hundred research publications in the areas of Pattern Recognition and Machine Learning, Image Processing, Data Mining and Web Intelligence, Soft Computing, Neural Nets, Genetic Algorithms, Fuzzy Sets, Rough Sets, and Bioinformatics. He has received the 1990 S.S. Bhatnagar Prize (which is the most coveted award for a scientist in India), and many prestigious awards in India and abroad including the 1999 G.D. Birla Award, 1998 Om Bhasin Award, 1993 Jawaharlal Nehru Fellowship, 2000 Khwarizmi International Award from the Islamic Republic of Iran, 2000–2001 FICCI Award, 1993 Vikram Sarabhai Research Award, 1993 NASA Tech Brief Award (USA), 1994 IEEE Trans. Neural Networks Outstanding Paper Award (USA), 1995 NASA Patent Application Award (USA), 1997 IETE-R.L. Wadhwa Gold Medal, the 2001 INSA-S.H. Zaheer Medal, and 2005-06 P.C. Mahalanobis Birth Centenary Award (Gold Medal) for Lifetime Achievement . Prof. Pal is an Associate Editor of IEEE Trans. Pattern Analysis and Machine Intelligence, IEEE Trans. Neural Networks [1994–98, 2003–06], Pattern Recognition Letters, Neurocomputing (1995–2005), Applied Intelligence, Information Sciences, Fuzzy Sets and Systems, Fundamenta Informaticae, Int. J. Computational Intelligence and Applications, and Proc. INSA-A; a Member, Executive Advisory Editorial Board, IEEE Trans. Fuzzy Systems, Int. Journal on Image and Graphics, and Int. Journal of Approximate Reasoning; and a Guest Editor of IEEE Computer.     

Equilibrium analysis through separation of user and network behavior

Internet complexity makes reasoning about traffic equilibrium difficult, partly because users react to congestion. This difficulty calls for an analytic technique that is simple, yet have enough details to capture user behavior and flexibly address a broad range of issues.This paper presents such a technique. It treats traffic equilibrium as a balance between an inflow controlled by users, and an outflow controlled by the network (link capacity, congestion avoidance, etc.). This decomposition is demonstrated with a surfing session model, and validated with a traffic trace and NS2 simulations.The technique’s accessibility and breadth are illustrated through an analysis of several issues concerning the location, stability, robustness and dynamics of traffic equilibrium.     

Behavior generation for interactive virtual humans using context‐dependent interaction meshes and automated constraint extraction

Interaction meshes are a promising approach for generating natural behaviors of virtual characters during ongoing user interactions. In this paper, we propose several extensions to the interaction mesh approach based on statistical analyses of the underlying example interactions. By applying principal component analysis and correlation analysis in addition to joint distance calculations, both the interaction mesh topology and the constraints used for mesh optimization can be generated in an automated fashion that accounts for the spatial and temporal contexts of the interaction. Copyright © 2015 John Wiley & Sons, Ltd.     

A knowledge centric methodology for dental implant technology assessment using ontology based patent analysis and clinical meta-analysis

The medical equipment industry has been one of the fastest growing sectors of the decade with predicted global sales reaching US$ 430 billion in 2017 [22]. During the period from 1995 to 2008, the patent applications in medical technology increased rapidly worldwide (World Intellectual Property Organization, 2012). Patent analysis, although useful in forecasting technology development trends, has posed a challenging analysis task since the volume and diversity of new patent applications has surpassed the ability of regular firms and research teams to process and identify relevant information. Further, medical related technologies rely on clinical trials to validate and gain regulatory approval for patient treatment even though patents, protecting the intellectual property rights of inventors, have been granted. This research focuses on developing a knowledge centric methodology and system to analyze and assess viable medical technology innovations and trends considering both patents and clinical reports. Specifically, the design innovations of dental implant connections are used as a case study. A novel and generic methodology combining ontology based patent analysis and clinical meta-analysis is developed to analyze and identify the most effective patented techniques in the dental implant field. The research establishes and verifies a computer supported analytical approach and system for the strategic prediction of medical technology development trends.     

Ab initio molecular orbital calculations on specific interactions between urokinase-type plasminogen activator and its receptor

Cancer invasions and metastases are controlled by various proteases. In particular, the binding of urokinase-type plasminogen activator (uPA) to the uPA receptor (uPAR) existing on the surface of cancer cell is considered to be a trigger for cancer invasions. In the present study, we determined the structure of uPA and uPAR complex in water and investigated the specific interactions between uPA and uPAR by ab initio molecular orbital (MO) calculations based on fragment MO method. The result indicates that the 20–26 amino acid residues of uPA are important for the binding between uPA and uPAR, and that the electrostatic interactions between the charged amino acid residues existing in both uPA and uPAR have large contribution to the binding. The influence of crystal water molecules existing between uPA and uPAR was also investigated to be significant on the specific interactions between uPA and uPAR. These results are expected to be informative for developing new medicines blocking the binding of uPA and uPAR.     

Human–automation teams and adaptable control for future air traffic management

The design and management of human–automation teams for future air traffic systems require an understanding of principles of cognitive systems engineering, allocation of function and team adaptation. The current article proposes a framework of human–automation team adaptable control that incorporates adaptable automation [Oppermann, R., Simm, H., 1994. Adaptability: user-initiated individualization. In: Oppermann, R. (Ed.), Adaptive User Support: Ergonomic Design of Manually and Automatically Adaptable Software. Lawrence Erlbaum Associates, Hillsdale, NJ, pp. 14–64] with an Extended Control Model of Joint Cognitive System functioning [Hollnagel, E., Nåbo, A., Lau, I., 21–24 July 2003. A systemic model for driver-in-control. In: Paper Presented at the Second International Driving Symposium on Human Factors in Driver Assessment, Training, and Vehicle Design, Public Policy Center, University of Iowa, Park City, UT] nested within a dynamic view of team adaptation [Burke, C.S., Stagl, K.C., Salas, E., Pierce, L., Kendall, D., 2006. Understanding team adaptation: a conceptual analysis and model. Journal of Applied Psychology 91, 1189–1207]. Modeling the temporal dynamics of the coordination of human–automation teams under conditions of Free Flight requires an appreciation of the episodic, cyclical nature of team processes from transition to action phases, along with the distinction of team processes from emergent states [Marks, M.A., Mathieu, J.E., Zaccaro, S.J., 2001. A temporally based framework and taxonomy of team processes. Academy of Management Review 26, 356–376]. The conceptual framework of human–automation team adaptable control provides a basis for future research and design.

Relevance to industry

The current article provides a conceptual framework to direct future investigations to determine the optimal design and management of Human–automation teams for Free Flight-based air traffic management systems.     

Multi-spectral and multi-perspective video arrays for driver body tracking and activity analysis

This paper presents a novel approach to recognizing driver activities using a multi-perspective (i.e., four camera views) multi-modal (i.e., thermal infrared and color) video-based system for robust and real-time tracking of important body parts. The multi-perspective characteristics of the system provides redundant trajectories of the body parts, while the multi-modal characteristics of the system provides robustness and reliability of feature detection and tracking. The combination of a deterministic activity grammar (called ‘operation-triplet’) and a Hidden Markov model-based classifier provides semantic-level analysis of human activity. The application context for this research is that of intelligent vehicles and driver assistance systems. Experimental results in real-world street driving demonstrate effectiveness of the proposed system.     

Assessment of User Affective and Belief States for Interface Adaptation: Application to an Air Force Pilot Task

We describe an Affect and Belief Adaptive Interface System (ABAIS) designed to compensate for performance biases caused by users’ affective states and active beliefs. The ABAIS architecture implements an adaptive methodology consisting of four steps: sensing/inferring user affective state and performance-relevant beliefs; identifying their potential impact on performance; selecting a compensatory strategy; and implementing this strategy in terms of specific GUI adaptations. ABAIS provides a generic adaptive framework for integrating a variety of user assessment methods (e.g. knowledge-based, self-reports, diagnostic tasks, physiological sensing), and GUI adaptation strategies (e.g. content- and format-based). The ABAIS performance bias prediction is based on empirical findings from emotion research combined with detailed knowledge of the task context. The initial ABAIS prototype was demonstrated in the context of an Air Force combat task, used a knowledge-based approach to assess the pilot’s anxiety level, and adapted to the pilot’s anxiety and belief states by modifying selected cockpit instrument displays in response to detected changes in these states.     

An analysis of the interface between evolutionary algorithm operators and problem features for water resources problems. A case study in water distribution network design

Evolutionary Algorithms (EAs) have been widely employed to solve water resources problems for nearly two decades with much success. However, recent research in hyperheuristics has raised the possibility of developing optimisers that adapt to the characteristics of the problem being solved. In order to select appropriate operators for such optimisers it is necessary to first understand the interaction between operator and problem. This paper explores the concept of EA operator behaviour in real world applications through the empirical study of performance using water distribution networks (WDN) as a case study. Artificial networks are created to embody specific WDN features which are then used to evaluate the impact of network features on operator performance. The method extracts key attributes of the problem which are encapsulated in the natural features of a WDN, such as topologies and assets, on which different EA operators can be tested. The method is demonstrated using small exemplar networks designed specifically so that they isolate individual features. A set of operators are tested on these artificial networks and their behaviour characterised. This process provides a systematic and quantitative approach to establishing detailed information about an algorithm's suitability to optimise certain types of problem. The experiment is then repeated on real-world inspired networks and the results are shown to fit with the expected results.     

Discrete Laplace–Beltrami operators for shape analysis and segmentation

Shape analysis plays a pivotal role in a large number of applications, ranging from traditional geometry processing to more recent 3D content management. In this scenario, spectral methods are extremely promising as they provide a natural library of tools for shape analysis, intrinsically defined by the shape itself. In particular, the eigenfunctions of the Laplace–Beltrami operator yield a set of real-valued functions that provide interesting insights in the structure and morphology of the shape. In this paper, we first analyze different discretizations of the Laplace–Beltrami operator (geometric Laplacians, linear and cubic FEM operators) in terms of the correctness of their eigenfunctions with respect to the continuous case. We then present the family of segmentations induced by the nodal sets of the eigenfunctions, discussing its meaningfulness for shape understanding.     

Pareto–Koopmans efficiency in two‐stage network data envelopment analysis in the presence of undesirable intermediate products and nondiscretionary factors

Many studies have recently been conducted on the evaluation of system performance with a two‐stage network structure in data envelopment analysis (DEA) literature. One of the topics of interest to researchers has been the mitigation of undesirable products or nondiscretionary factors into their corresponding possible production set (PPS) and their impact on overall efficiency calculations. Determination of decision‐making units (DMUs) with Pareto–Koopmans efficiency status is decisive in identifying benchmark units. The calculated overall efficiency status is compromised when both undesirable products and nondiscretionary factors are present. This work utilizes an axiomatic approach. A novel PPS for a two‐stage network in presence of undesirable intermediate products and nondiscretionary exogenous inputs is introduced. Based on this PPS and by focusing on the principle of mathematical dominance, new models for evaluating overall and divisional efficiencies are presented. In addition, by proposing a two‐step network DEA approach, a necessary and sufficient condition for detection of DMUs with Pareto–Koopmans efficiency status is provided. And by introducing a two‐step algorithm, a novel technique for determining overall efficiency conditions is produced. Finally, the proposed technology is applied to a practical example, and outcomes are discussed.