Application of artifical intelligence principles to the analysis of "crazy" speech.

PubMed

Garfield, D A; Rapp, C

1994-04-01

Artificial intelligence computer simulation methods can be used to investigate psychotic or "crazy" speech. Here, symbolic reasoning algorithms establish semantic networks that schematize speech. These semantic networks consist of two main structures: case frames and object taxonomies. Node-based reasoning rules apply to object taxonomies and pathway-based reasoning rules apply to case frames. Normal listeners may recognize speech as "crazy talk" based on violations of node- and pathway-based reasoning rules. In this article, three separate segments of schizophrenic speech illustrate violations of these rules. This artificial intelligence approach is compared and contrasted with other neurolinguistic approaches and is discussed as a conceptual link between neurobiological and psychodynamic understandings of psychopathology.

RuleML-Based Learning Object Interoperability on the Semantic Web

ERIC Educational Resources Information Center

Biletskiy, Yevgen; Boley, Harold; Ranganathan, Girish R.

2008-01-01

Purpose: The present paper aims to describe an approach for building the Semantic Web rules for interoperation between heterogeneous learning objects, namely course outlines from different universities, and one of the rule uses: identifying (in)compatibilities between course descriptions. Design/methodology/approach: As proof of concept, a rule…

ML-Space: Hybrid Spatial Gillespie and Particle Simulation of Multi-Level Rule-Based Models in Cell Biology.

PubMed

Bittig, Arne T; Uhrmacher, Adelinde M

2017-01-01

Spatio-temporal dynamics of cellular processes can be simulated at different levels of detail, from (deterministic) partial differential equations via the spatial Stochastic Simulation algorithm to tracking Brownian trajectories of individual particles. We present a spatial simulation approach for multi-level rule-based models, which includes dynamically hierarchically nested cellular compartments and entities. Our approach ML-Space combines discrete compartmental dynamics, stochastic spatial approaches in discrete space, and particles moving in continuous space. The rule-based specification language of ML-Space supports concise and compact descriptions of models and to adapt the spatial resolution of models easily.

78 FR 76973 - Regulatory Capital Rules: Regulatory Capital, Implementation of Basel III, Capital Adequacy...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-20

... Discipline and Disclosure Requirements, Advanced Approaches Risk-Based Capital Rule, and Market Risk Capital..., 2013, a document adopting a final rule that revises its risk-based and leverage capital requirements... risk-based and leverage capital requirements for banking organizations. An allowance for additional...

Exploration of SWRL Rule Bases through Visualization, Paraphrasing, and Categorization of Rules

NASA Astrophysics Data System (ADS)

Hassanpour, Saeed; O'Connor, Martin J.; Das, Amar K.

Rule bases are increasingly being used as repositories of knowledge content on the Semantic Web. As the size and complexity of these rule bases increases, developers and end users need methods of rule abstraction to facilitate rule management. In this paper, we describe a rule abstraction method for Semantic Web Rule Language (SWRL) rules that is based on lexical analysis and a set of heuristics. Our method results in a tree data structure that we exploit in creating techniques to visualize, paraphrase, and categorize SWRL rules. We evaluate our approach by applying it to several biomedical ontologies that contain SWRL rules, and show how the results reveal rule patterns within the rule base. We have implemented our method as a plug-in tool for Protégé-OWL, the most widely used ontology modeling software for the Semantic Web. Our tool can allow users to rapidly explore content and patterns in SWRL rule bases, enabling their acquisition and management.

Automatic de-identification of French clinical records: comparison of rule-based and machine-learning approaches.

PubMed

Grouin, Cyril; Zweigenbaum, Pierre

2013-01-01

In this paper, we present a comparison of two approaches to automatically de-identify medical records written in French: a rule-based system and a machine-learning based system using a conditional random fields (CRF) formalism. Both systems have been designed to process nine identifiers in a corpus of medical records in cardiology. We performed two evaluations: first, on 62 documents in cardiology, and on 10 documents in foetopathology - produced by optical character recognition (OCR) - to evaluate the robustness of our systems. We achieved a 0.843 (rule-based) and 0.883 (machine-learning) exact match overall F-measure in cardiology. While the rule-based system allowed us to achieve good results on nominative (first and last names) and numerical data (dates, phone numbers, and zip codes), the machine-learning approach performed best on more complex categories (postal addresses, hospital names, medical devices, and towns). On the foetopathology corpus, although our systems have not been designed for this corpus and despite OCR character recognition errors, we obtained promising results: a 0.681 (rule-based) and 0.638 (machine-learning) exact-match overall F-measure. This demonstrates that existing tools can be applied to process new documents of lower quality.

A fuzzy hill-climbing algorithm for the development of a compact associative classifier

NASA Astrophysics Data System (ADS)

Mitra, Soumyaroop; Lam, Sarah S.

2012-02-01

Classification, a data mining technique, has widespread applications including medical diagnosis, targeted marketing, and others. Knowledge discovery from databases in the form of association rules is one of the important data mining tasks. An integrated approach, classification based on association rules, has drawn the attention of the data mining community over the last decade. While attention has been mainly focused on increasing classifier accuracies, not much efforts have been devoted towards building interpretable and less complex models. This paper discusses the development of a compact associative classification model using a hill-climbing approach and fuzzy sets. The proposed methodology builds the rule-base by selecting rules which contribute towards increasing training accuracy, thus balancing classification accuracy with the number of classification association rules. The results indicated that the proposed associative classification model can achieve competitive accuracies on benchmark datasets with continuous attributes and lend better interpretability, when compared with other rule-based systems.

Automated implementation of rule-based expert systems with neural networks for time-critical applications

NASA Technical Reports Server (NTRS)

Ramamoorthy, P. A.; Huang, Song; Govind, Girish

1991-01-01

In fault diagnosis, control and real-time monitoring, both timing and accuracy are critical for operators or machines to reach proper solutions or appropriate actions. Expert systems are becoming more popular in the manufacturing community for dealing with such problems. In recent years, neural networks have revived and their applications have spread to many areas of science and engineering. A method of using neural networks to implement rule-based expert systems for time-critical applications is discussed here. This method can convert a given rule-based system into a neural network with fixed weights and thresholds. The rules governing the translation are presented along with some examples. We also present the results of automated machine implementation of such networks from the given rule-base. This significantly simplifies the translation process to neural network expert systems from conventional rule-based systems. Results comparing the performance of the proposed approach based on neural networks vs. the classical approach are given. The possibility of very large scale integration (VLSI) realization of such neural network expert systems is also discussed.

Implementation of artificial intelligence rules in a data base management system

NASA Technical Reports Server (NTRS)

Feyock, S.

1986-01-01

The intelligent front end prototype was transformed into a RIM-integrated system. A RIM-based expert system was written which demonstrated the developed capability. The use of rules to produce extensibility of the intelligent front end, including the concept of demons and rule manipulation rules were investigated. Innovative approaches such as syntax programming were to be considered.

ASICs Approach for the Implementation of a Symmetric Triangular Fuzzy Coprocessor and Its Application to Adaptive Filtering

NASA Technical Reports Server (NTRS)

Starks, Scott; Abdel-Hafeez, Saleh; Usevitch, Bryan

1997-01-01

This paper discusses the implementation of a fuzzy logic system using an ASICs design approach. The approach is based upon combining the inherent advantages of symmetric triangular membership functions and fuzzy singleton sets to obtain a novel structure for fuzzy logic system application development. The resulting structure utilizes a fuzzy static RAM to store the rule-base and the end-points of the triangular membership functions. This provides advantages over other approaches in which all sampled values of membership functions for all universes must be stored. The fuzzy coprocessor structure implements the fuzzification and defuzzification processes through a two-stage parallel pipeline architecture which is capable of executing complex fuzzy computations in less than 0.55us with an accuracy of more than 95%, thus making it suitable for a wide range of applications. Using the approach presented in this paper, a fuzzy logic rule-base can be directly downloaded via a host processor to an onchip rule-base memory with a size of 64 words. The fuzzy coprocessor's design supports up to 49 rules for seven fuzzy membership functions associated with each of the chip's two input variables. This feature allows designers to create fuzzy logic systems without the need for additional on-board memory. Finally, the paper reports on simulation studies that were conducted for several adaptive filter applications using the least mean squared adaptive algorithm for adjusting the knowledge rule-base.

Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets.

PubMed

Park, Inho; Lee, Kwang H; Lee, Doheon

2010-06-15

Gene set analysis has become an important tool for the functional interpretation of high-throughput gene expression datasets. Moreover, pattern analyses based on inferred gene set activities of individual samples have shown the ability to identify more robust disease signatures than individual gene-based pattern analyses. Although a number of approaches have been proposed for gene set-based pattern analysis, the combinatorial influence of deregulated gene sets on disease phenotype classification has not been studied sufficiently. We propose a new approach for inferring combinatorial Boolean rules of gene sets for a better understanding of cancer transcriptome and cancer classification. To reduce the search space of the possible Boolean rules, we identify small groups of gene sets that synergistically contribute to the classification of samples into their corresponding phenotypic groups (such as normal and cancer). We then measure the significance of the candidate Boolean rules derived from each group of gene sets; the level of significance is based on the class entropy of the samples selected in accordance with the rules. By applying the present approach to publicly available prostate cancer datasets, we identified 72 significant Boolean rules. Finally, we discuss several identified Boolean rules, such as the rule of glutathione metabolism (down) and prostaglandin synthesis regulation (down), which are consistent with known prostate cancer biology. Scripts written in Python and R are available at http://biosoft.kaist.ac.kr/~ihpark/. The refined gene sets and the full list of the identified Boolean rules are provided in the Supplementary Material. Supplementary data are available at Bioinformatics online.

Oxytocin conditions trait-based rule adherence

PubMed Central

De Dreu, Carsten K.W.

2017-01-01

Abstract Rules, whether in the form of norms, taboos or laws, regulate and coordinate human life. Some rules, however, are arbitrary and adhering to them can be personally costly. Rigidly sticking to such rules can be considered maladaptive. Here, we test whether, at the neurobiological level, (mal)adaptive rule adherence is reduced by oxytocin—a hypothalamic neuropeptide that biases the biobehavioural approach-avoidance system. Participants (N = 139) self-administered oxytocin or placebo intranasally, and reported their need for structure and approach-avoidance sensitivity. Next, participants made binary decisions and were given an arbitrary rule that demanded to forgo financial benefits. Under oxytocin, participants violated the rule more often, especially when they had high need for structure and high approach sensitivity. Possibly, oxytocin dampens the need for a highly structured environment and enables individuals to flexibly trade-off internal desires against external restrictions. Implications for the treatment of clinical disorders marked by maladaptive rule adherence are discussed. PMID:27664999

Bayesian design of decision rules for failure detection

NASA Technical Reports Server (NTRS)

Chow, E. Y.; Willsky, A. S.

1984-01-01

The formulation of the decision making process of a failure detection algorithm as a Bayes sequential decision problem provides a simple conceptualization of the decision rule design problem. As the optimal Bayes rule is not computable, a methodology that is based on the Bayesian approach and aimed at a reduced computational requirement is developed for designing suboptimal rules. A numerical algorithm is constructed to facilitate the design and performance evaluation of these suboptimal rules. The result of applying this design methodology to an example shows that this approach is potentially a useful one.

Automatic rule generation for high-level vision

NASA Technical Reports Server (NTRS)

Rhee, Frank Chung-Hoon; Krishnapuram, Raghu

1992-01-01

A new fuzzy set based technique that was developed for decision making is discussed. It is a method to generate fuzzy decision rules automatically for image analysis. This paper proposes a method to generate rule-based approaches to solve problems such as autonomous navigation and image understanding automatically from training data. The proposed method is also capable of filtering out irrelevant features and criteria from the rules.

Modeling for (physical) biologists: an introduction to the rule-based approach

PubMed Central

Chylek, Lily A; Harris, Leonard A; Faeder, James R; Hlavacek, William S

2015-01-01

Models that capture the chemical kinetics of cellular regulatory networks can be specified in terms of rules for biomolecular interactions. A rule defines a generalized reaction, meaning a reaction that permits multiple reactants, each capable of participating in a characteristic transformation and each possessing certain, specified properties, which may be local, such as the state of a particular site or domain of a protein. In other words, a rule defines a transformation and the properties that reactants must possess to participate in the transformation. A rule also provides a rate law. A rule-based approach to modeling enables consideration of mechanistic details at the level of functional sites of biomolecules and provides a facile and visual means for constructing computational models, which can be analyzed to study how system-level behaviors emerge from component interactions. PMID:26178138

One Giant Leap for Categorizers: One Small Step for Categorization Theory

PubMed Central

Smith, J. David; Ell, Shawn W.

2015-01-01

We explore humans’ rule-based category learning using analytic approaches that highlight their psychological transitions during learning. These approaches confirm that humans show qualitatively sudden psychological transitions during rule learning. These transitions contribute to the theoretical literature contrasting single vs. multiple category-learning systems, because they seem to reveal a distinctive learning process of explicit rule discovery. A complete psychology of categorization must describe this learning process, too. Yet extensive formal-modeling analyses confirm that a wide range of current (gradient-descent) models cannot reproduce these transitions, including influential rule-based models (e.g., COVIS) and exemplar models (e.g., ALCOVE). It is an important theoretical conclusion that existing models cannot explain humans’ rule-based category learning. The problem these models have is the incremental algorithm by which learning is simulated. Humans descend no gradient in rule-based tasks. Very different formal-modeling systems will be required to explain humans’ psychology in these tasks. An important next step will be to build a new generation of models that can do so. PMID:26332587

Rule groupings: An approach towards verification of expert systems

NASA Technical Reports Server (NTRS)

Mehrotra, Mala

1991-01-01

Knowledge-based expert systems are playing an increasingly important role in NASA space and aircraft systems. However, many of NASA's software applications are life- or mission-critical and knowledge-based systems do not lend themselves to the traditional verification and validation techniques for highly reliable software. Rule-based systems lack the control abstractions found in procedural languages. Hence, it is difficult to verify or maintain such systems. Our goal is to automatically structure a rule-based system into a set of rule-groups having a well-defined interface to other rule-groups. Once a rule base is decomposed into such 'firewalled' units, studying the interactions between rules would become more tractable. Verification-aid tools can then be developed to test the behavior of each such rule-group. Furthermore, the interactions between rule-groups can be studied in a manner similar to integration testing. Such efforts will go a long way towards increasing our confidence in the expert-system software. Our research efforts address the feasibility of automating the identification of rule groups, in order to decompose the rule base into a number of meaningful units.

A clinical decision support system for diagnosis of Allergic Rhinitis based on intradermal skin tests.

PubMed

Jabez Christopher, J; Khanna Nehemiah, H; Kannan, A

2015-10-01

Allergic Rhinitis is a universal common disease, especially in populated cities and urban areas. Diagnosis and treatment of Allergic Rhinitis will improve the quality of life of allergic patients. Though skin tests remain the gold standard test for diagnosis of allergic disorders, clinical experts are required for accurate interpretation of test outcomes. This work presents a clinical decision support system (CDSS) to assist junior clinicians in the diagnosis of Allergic Rhinitis. Intradermal Skin tests were performed on patients who had plausible allergic symptoms. Based on patient׳s history, 40 clinically relevant allergens were tested. 872 patients who had allergic symptoms were considered for this study. The rule based classification approach and the clinical test results were used to develop and validate the CDSS. Clinical relevance of the CDSS was compared with the Score for Allergic Rhinitis (SFAR). Tests were conducted for junior clinicians to assess their diagnostic capability in the absence of an expert. The class based Association rule generation approach provides a concise set of rules that is further validated by clinical experts. The interpretations of the experts are considered as the gold standard. The CDSS diagnoses the presence or absence of rhinitis with an accuracy of 88.31%. The allergy specialist and the junior clinicians prefer the rule based approach for its comprehendible knowledge model. The Clinical Decision Support Systems with rule based classification approach assists junior doctors and clinicians in the diagnosis of Allergic Rhinitis to make reliable decisions based on the reports of intradermal skin tests. Copyright © 2015 Elsevier Ltd. All rights reserved.

Rule groupings: A software engineering approach towards verification of expert systems

NASA Technical Reports Server (NTRS)

Mehrotra, Mala

1991-01-01

Currently, most expert system shells do not address software engineering issues for developing or maintaining expert systems. As a result, large expert systems tend to be incomprehensible, difficult to debug or modify and almost impossible to verify or validate. Partitioning rule based systems into rule groups which reflect the underlying subdomains of the problem should enhance the comprehensibility, maintainability, and reliability of expert system software. Attempts were made to semiautomatically structure a CLIPS rule base into groups of related rules that carry the same type of information. Different distance metrics that capture relevant information from the rules for grouping are discussed. Two clustering algorithms that partition the rule base into groups of related rules are given. Two independent evaluation criteria are developed to measure the effectiveness of the grouping strategies. Results of the experiment with three sample rule bases are presented.

Object-Driven and Temporal Action Rules Mining

ERIC Educational Resources Information Center

Hajja, Ayman

2013-01-01

In this thesis, I present my complete research work in the field of action rules, more precisely object-driven and temporal action rules. The drive behind the introduction of object-driven and temporally based action rules is to bring forth an adapted approach to extract action rules from a subclass of systems that have a specific nature, in which…

Extracting Cross-Ontology Weighted Association Rules from Gene Ontology Annotations.

PubMed

Agapito, Giuseppe; Milano, Marianna; Guzzi, Pietro Hiram; Cannataro, Mario

2016-01-01

Gene Ontology (GO) is a structured repository of concepts (GO Terms) that are associated to one or more gene products through a process referred to as annotation. The analysis of annotated data is an important opportunity for bioinformatics. There are different approaches of analysis, among those, the use of association rules (AR) which provides useful knowledge, discovering biologically relevant associations between terms of GO, not previously known. In a previous work, we introduced GO-WAR (Gene Ontology-based Weighted Association Rules), a methodology for extracting weighted association rules from ontology-based annotated datasets. We here adapt the GO-WAR algorithm to mine cross-ontology association rules, i.e., rules that involve GO terms present in the three sub-ontologies of GO. We conduct a deep performance evaluation of GO-WAR by mining publicly available GO annotated datasets, showing how GO-WAR outperforms current state of the art approaches.

On implementing clinical decision support: achieving scalability and maintainability by combining business rules and ontologies.

PubMed

Kashyap, Vipul; Morales, Alfredo; Hongsermeier, Tonya

2006-01-01

We present an approach and architecture for implementing scalable and maintainable clinical decision support at the Partners HealthCare System. The architecture integrates a business rules engine that executes declarative if-then rules stored in a rule-base referencing objects and methods in a business object model. The rules engine executes object methods by invoking services implemented on the clinical data repository. Specialized inferences that support classification of data and instances into classes are identified and an approach to implement these inferences using an OWL based ontology engine is presented. Alternative representations of these specialized inferences as if-then rules or OWL axioms are explored and their impact on the scalability and maintenance of the system is presented. Architectural alternatives for integration of clinical decision support functionality with the invoking application and the underlying clinical data repository; and their associated trade-offs are discussed and presented.

Exact Hybrid Particle/Population Simulation of Rule-Based Models of Biochemical Systems

PubMed Central

Stover, Lori J.; Nair, Niketh S.; Faeder, James R.

2014-01-01

Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This “network-free” approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of “partial network expansion” into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility. PMID:24699269

Exact hybrid particle/population simulation of rule-based models of biochemical systems.

PubMed

Hogg, Justin S; Harris, Leonard A; Stover, Lori J; Nair, Niketh S; Faeder, James R

2014-04-01

Detailed modeling and simulation of biochemical systems is complicated by the problem of combinatorial complexity, an explosion in the number of species and reactions due to myriad protein-protein interactions and post-translational modifications. Rule-based modeling overcomes this problem by representing molecules as structured objects and encoding their interactions as pattern-based rules. This greatly simplifies the process of model specification, avoiding the tedious and error prone task of manually enumerating all species and reactions that can potentially exist in a system. From a simulation perspective, rule-based models can be expanded algorithmically into fully-enumerated reaction networks and simulated using a variety of network-based simulation methods, such as ordinary differential equations or Gillespie's algorithm, provided that the network is not exceedingly large. Alternatively, rule-based models can be simulated directly using particle-based kinetic Monte Carlo methods. This "network-free" approach produces exact stochastic trajectories with a computational cost that is independent of network size. However, memory and run time costs increase with the number of particles, limiting the size of system that can be feasibly simulated. Here, we present a hybrid particle/population simulation method that combines the best attributes of both the network-based and network-free approaches. The method takes as input a rule-based model and a user-specified subset of species to treat as population variables rather than as particles. The model is then transformed by a process of "partial network expansion" into a dynamically equivalent form that can be simulated using a population-adapted network-free simulator. The transformation method has been implemented within the open-source rule-based modeling platform BioNetGen, and resulting hybrid models can be simulated using the particle-based simulator NFsim. Performance tests show that significant memory savings can be achieved using the new approach and a monetary cost analysis provides a practical measure of its utility.

C code generation from Petri-net-based logic controller specification

NASA Astrophysics Data System (ADS)

Grobelny, Michał; Grobelna, Iwona; Karatkevich, Andrei

2017-08-01

The article focuses on programming of logic controllers. It is important that a programming code of a logic controller is executed flawlessly according to the primary specification. In the presented approach we generate C code for an AVR microcontroller from a rule-based logical model of a control process derived from a control interpreted Petri net. The same logical model is also used for formal verification of the specification by means of the model checking technique. The proposed rule-based logical model and formal rules of transformation ensure that the obtained implementation is consistent with the already verified specification. The approach is validated by practical experiments.

Traditional versus rule-based programming techniques: Application to the control of optional flight information

NASA Technical Reports Server (NTRS)

Ricks, Wendell R.; Abbott, Kathy H.

1987-01-01

To the software design community, the concern over the costs associated with a program's execution time and implementation is great. It is always desirable, and sometimes imperative, that the proper programming technique is chosen which minimizes all costs for a given application or type of application. A study is described that compared cost-related factors associated with traditional programming techniques to rule-based programming techniques for a specific application. The results of this study favored the traditional approach regarding execution efficiency, but favored the rule-based approach regarding programmer productivity (implementation ease). Although this study examined a specific application, the results should be widely applicable.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pin, F.G.

Sensor-based operation of autonomous robots in unstructured and/or outdoor environments has revealed to be an extremely challenging problem, mainly because of the difficulties encountered when attempting to represent the many uncertainties which are always present in the real world. These uncertainties are primarily due to sensor imprecisions and unpredictability of the environment, i.e., lack of full knowledge of the environment characteristics and dynamics. An approach. which we have named the {open_quotes}Fuzzy Behaviorist Approach{close_quotes} (FBA) is proposed in an attempt to remedy some of these difficulties. This approach is based on the representation of the system`s uncertainties using Fuzzy Set Theory-basedmore » approximations and on the representation of the reasoning and control schemes as sets of elemental behaviors. Using the FBA, a formalism for rule base development and an automated generator of fuzzy rules have been developed. This automated system can automatically construct the set of membership functions corresponding to fuzzy behaviors. Once these have been expressed in qualitative terms by the user. The system also checks for completeness of the rule base and for non-redundancy of the rules (which has traditionally been a major hurdle in rule base development). Two major conceptual features, the suppression and inhibition mechanisms which allow to express a dominance between behaviors are discussed in detail. Some experimental results obtained with the automated fuzzy, rule generator applied to the domain of sensor-based navigation in aprion unknown environments. using one of our autonomous test-bed robots as well as a real car in outdoor environments, are then reviewed and discussed to illustrate the feasibility of large-scale automatic fuzzy rule generation using the {open_quotes}Fuzzy Behaviorist{close_quotes} concepts.« less

Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic.

PubMed

Dehghani Soufi, Mahsa; Samad-Soltani, Taha; Shams Vahdati, Samad; Rezaei-Hachesu, Peyman

2018-06-01

Fast and accurate patient triage for the response process is a critical first step in emergency situations. This process is often performed using a paper-based mode, which intensifies workload and difficulty, wastes time, and is at risk of human errors. This study aims to design and evaluate a decision support system (DSS) to determine the triage level. A combination of the Rule-Based Reasoning (RBR) and Fuzzy Logic Classifier (FLC) approaches were used to predict the triage level of patients according to the triage specialist's opinions and Emergency Severity Index (ESI) guidelines. RBR was applied for modeling the first to fourth decision points of the ESI algorithm. The data relating to vital signs were used as input variables and modeled using fuzzy logic. Narrative knowledge was converted to If-Then rules using XML. The extracted rules were then used to create the rule-based engine and predict the triage levels. Fourteen RBR and 27 fuzzy rules were extracted and used in the rule-based engine. The performance of the system was evaluated using three methods with real triage data. The accuracy of the clinical decision support systems (CDSSs; in the test data) was 99.44%. The evaluation of the error rate revealed that, when using the traditional method, 13.4% of the patients were miss-triaged, which is statically significant. The completeness of the documentation also improved from 76.72% to 98.5%. Designed system was effective in determining the triage level of patients and it proved helpful for nurses as they made decisions, generated nursing diagnoses based on triage guidelines. The hybrid approach can reduce triage misdiagnosis in a highly accurate manner and improve the triage outcomes. Copyright © 2018 Elsevier B.V. All rights reserved.

Aqui y Alla (Here and There) Information-Based Learning Corridors between Tennessee and Puerto Rico: The Five Golden Rules in Intercultural Education

ERIC Educational Resources Information Center

Mehra, Bharat; Allard, Suzie; Qayyum, M. Asim; Barclay-McLaughlin, Gina

2008-01-01

This article proposes five information-based Golden Rules in intercultural education that represent a holistic approach to creating learning corridors across geographically dispersed academic communities. The Golden Rules are generated through qualitative analysis, grounded theory application, reflective practice, and critical research to…

A Hybrid Approach Using Case-Based Reasoning and Rule-Based Reasoning to Support Cancer Diagnosis: A Pilot Study.

PubMed

Saraiva, Renata M; Bezerra, João; Perkusich, Mirko; Almeida, Hyggo; Siebra, Clauirton

2015-01-01

Recently there has been an increasing interest in applying information technology to support the diagnosis of diseases such as cancer. In this paper, we present a hybrid approach using case-based reasoning (CBR) and rule-based reasoning (RBR) to support cancer diagnosis. We used symptoms, signs, and personal information from patients as inputs to our model. To form specialized diagnoses, we used rules to define the input factors' importance according to the patient's characteristics. The model's output presents the probability of the patient having a type of cancer. To carry out this research, we had the approval of the ethics committee at Napoleão Laureano Hospital, in João Pessoa, Brazil. To define our model's cases, we collected real patient data at Napoleão Laureano Hospital. To define our model's rules and weights, we researched specialized literature and interviewed health professional. To validate our model, we used K-fold cross validation with the data collected at Napoleão Laureano Hospital. The results showed that our approach is an effective CBR system to diagnose cancer.

Oxytocin conditions trait-based rule adherence.

PubMed

Gross, Jörg; De Dreu, Carsten K W

2017-03-01

Rules, whether in the form of norms, taboos or laws, regulate and coordinate human life. Some rules, however, are arbitrary and adhering to them can be personally costly. Rigidly sticking to such rules can be considered maladaptive. Here, we test whether, at the neurobiological level, (mal)adaptive rule adherence is reduced by oxytocin-a hypothalamic neuropeptide that biases the biobehavioural approach-avoidance system. Participants (N = 139) self-administered oxytocin or placebo intranasally, and reported their need for structure and approach-avoidance sensitivity. Next, participants made binary decisions and were given an arbitrary rule that demanded to forgo financial benefits. Under oxytocin, participants violated the rule more often, especially when they had high need for structure and high approach sensitivity. Possibly, oxytocin dampens the need for a highly structured environment and enables individuals to flexibly trade-off internal desires against external restrictions. Implications for the treatment of clinical disorders marked by maladaptive rule adherence are discussed. © The Author (2016). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

In Search of Golden Rules: Comment on Hypothesis-Testing Approaches to Setting Cutoff Values for Fit Indexes and Dangers in Overgeneralizing Hu and Bentler's (1999) Findings

ERIC Educational Resources Information Center

Marsh, Herbert W.; Hau, Kit-Tai; Wen, Zhonglin

2004-01-01

Goodness-of-fit (GOF) indexes provide "rules of thumb"?recommended cutoff values for assessing fit in structural equation modeling. Hu and Bentler (1999) proposed a more rigorous approach to evaluating decision rules based on GOF indexes and, on this basis, proposed new and more stringent cutoff values for many indexes. This article discusses…

Using an empirical and rule-based modeling approach to map cause of disturbance in U.S

Treesearch

Todd A. Schroeder; Gretchen G. Moisen; Karen Schleeweis; Chris Toney; Warren B. Cohen; Zhiqiang Yang; Elizabeth A. Freeman

2015-01-01

Recently completing over a decade of research, the NASA/NACP funded North American Forest Dynamics (NAFD) project has led to several important advancements in the way U.S. forest disturbance dynamics are mapped at regional and continental scales. One major contribution has been the development of an empirical and rule-based modeling approach which addresses two of the...

Extending rule-based methods to model molecular geometry and 3D model resolution.

PubMed

Hoard, Brittany; Jacobson, Bruna; Manavi, Kasra; Tapia, Lydia

2016-08-01

Computational modeling is an important tool for the study of complex biochemical processes associated with cell signaling networks. However, it is challenging to simulate processes that involve hundreds of large molecules due to the high computational cost of such simulations. Rule-based modeling is a method that can be used to simulate these processes with reasonably low computational cost, but traditional rule-based modeling approaches do not include details of molecular geometry. The incorporation of geometry into biochemical models can more accurately capture details of these processes, and may lead to insights into how geometry affects the products that form. Furthermore, geometric rule-based modeling can be used to complement other computational methods that explicitly represent molecular geometry in order to quantify binding site accessibility and steric effects. We propose a novel implementation of rule-based modeling that encodes details of molecular geometry into the rules and binding rates. We demonstrate how rules are constructed according to the molecular curvature. We then perform a study of antigen-antibody aggregation using our proposed method. We simulate the binding of antibody complexes to binding regions of the shrimp allergen Pen a 1 using a previously developed 3D rigid-body Monte Carlo simulation, and we analyze the aggregate sizes. Then, using our novel approach, we optimize a rule-based model according to the geometry of the Pen a 1 molecule and the data from the Monte Carlo simulation. We use the distances between the binding regions of Pen a 1 to optimize the rules and binding rates. We perform this procedure for multiple conformations of Pen a 1 and analyze the impact of conformation and resolution on the optimal rule-based model. We find that the optimized rule-based models provide information about the average steric hindrance between binding regions and the probability that antibodies will bind to these regions. These optimized models quantify the variation in aggregate size that results from differences in molecular geometry and from model resolution.

Annotation of rule-based models with formal semantics to enable creation, analysis, reuse and visualization.

PubMed

Misirli, Goksel; Cavaliere, Matteo; Waites, William; Pocock, Matthew; Madsen, Curtis; Gilfellon, Owen; Honorato-Zimmer, Ricardo; Zuliani, Paolo; Danos, Vincent; Wipat, Anil

2016-03-15

Biological systems are complex and challenging to model and therefore model reuse is highly desirable. To promote model reuse, models should include both information about the specifics of simulations and the underlying biology in the form of metadata. The availability of computationally tractable metadata is especially important for the effective automated interpretation and processing of models. Metadata are typically represented as machine-readable annotations which enhance programmatic access to information about models. Rule-based languages have emerged as a modelling framework to represent the complexity of biological systems. Annotation approaches have been widely used for reaction-based formalisms such as SBML. However, rule-based languages still lack a rich annotation framework to add semantic information, such as machine-readable descriptions, to the components of a model. We present an annotation framework and guidelines for annotating rule-based models, encoded in the commonly used Kappa and BioNetGen languages. We adapt widely adopted annotation approaches to rule-based models. We initially propose a syntax to store machine-readable annotations and describe a mapping between rule-based modelling entities, such as agents and rules, and their annotations. We then describe an ontology to both annotate these models and capture the information contained therein, and demonstrate annotating these models using examples. Finally, we present a proof of concept tool for extracting annotations from a model that can be queried and analyzed in a uniform way. The uniform representation of the annotations can be used to facilitate the creation, analysis, reuse and visualization of rule-based models. Although examples are given, using specific implementations the proposed techniques can be applied to rule-based models in general. The annotation ontology for rule-based models can be found at http://purl.org/rbm/rbmo The krdf tool and associated executable examples are available at http://purl.org/rbm/rbmo/krdf anil.wipat@newcastle.ac.uk or vdanos@inf.ed.ac.uk. © The Author 2015. Published by Oxford University Press.

Automatic rule generation for high-level vision

NASA Technical Reports Server (NTRS)

Rhee, Frank Chung-Hoon; Krishnapuram, Raghu

1992-01-01

Many high-level vision systems use rule-based approaches to solving problems such as autonomous navigation and image understanding. The rules are usually elaborated by experts. However, this procedure may be rather tedious. In this paper, we propose a method to generate such rules automatically from training data. The proposed method is also capable of filtering out irrelevant features and criteria from the rules.

Detection of pseudosinusoidal epileptic seizure segments in the neonatal EEG by cascading a rule-based algorithm with a neural network.

PubMed

Karayiannis, Nicolaos B; Mukherjee, Amit; Glover, John R; Ktonas, Periklis Y; Frost, James D; Hrachovy, Richard A; Mizrahi, Eli M

2006-04-01

This paper presents an approach to detect epileptic seizure segments in the neonatal electroencephalogram (EEG) by characterizing the spectral features of the EEG waveform using a rule-based algorithm cascaded with a neural network. A rule-based algorithm screens out short segments of pseudosinusoidal EEG patterns as epileptic based on features in the power spectrum. The output of the rule-based algorithm is used to train and compare the performance of conventional feedforward neural networks and quantum neural networks. The results indicate that the trained neural networks, cascaded with the rule-based algorithm, improved the performance of the rule-based algorithm acting by itself. The evaluation of the proposed cascaded scheme for the detection of pseudosinusoidal seizure segments reveals its potential as a building block of the automated seizure detection system under development.

A hybrid agent-based approach for modeling microbiological systems.

PubMed

Guo, Zaiyi; Sloot, Peter M A; Tay, Joc Cing

2008-11-21

Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 10(3) cells and 1.2x10(6) molecules. The model produces cell migration patterns that are comparable to laboratory observations.

Assessing Financial Education Methods: Principles vs. Rules-of-Thumb Approaches

ERIC Educational Resources Information Center

Skimmyhorn, William L.; Davies, Evan R.; Mun, David; Mitchell, Brian

2016-01-01

Despite thousands of programs and tremendous public and private interest in improving financial decision-making, little is known about how best to teach financial education. Using an experimental approach, the authors estimated the effects of two different education methodologies (principles-based and rules-of-thumb) on the knowledge,…

The Balance-Scale Task Revisited: A Comparison of Statistical Models for Rule-Based and Information-Integration Theories of Proportional Reasoning

PubMed Central

Hofman, Abe D.; Visser, Ingmar; Jansen, Brenda R. J.; van der Maas, Han L. J.

2015-01-01

We propose and test three statistical models for the analysis of children’s responses to the balance scale task, a seminal task to study proportional reasoning. We use a latent class modelling approach to formulate a rule-based latent class model (RB LCM) following from a rule-based perspective on proportional reasoning and a new statistical model, the Weighted Sum Model, following from an information-integration approach. Moreover, a hybrid LCM using item covariates is proposed, combining aspects of both a rule-based and information-integration perspective. These models are applied to two different datasets, a standard paper-and-pencil test dataset (N = 779), and a dataset collected within an online learning environment that included direct feedback, time-pressure, and a reward system (N = 808). For the paper-and-pencil dataset the RB LCM resulted in the best fit, whereas for the online dataset the hybrid LCM provided the best fit. The standard paper-and-pencil dataset yielded more evidence for distinct solution rules than the online data set in which quantitative item characteristics are more prominent in determining responses. These results shed new light on the discussion on sequential rule-based and information-integration perspectives of cognitive development. PMID:26505905

A rule-based expert system for chemical prioritization using effects-based chemical categories

EPA Science Inventory

A rule-based expert system (ES) was developed to predict chemical binding to the estrogen receptor (ER) patterned on the research approaches championed by Gilman Veith to whom this article and journal issue are dedicated. The ERES was built to be mechanistically-transparent and m...

Intuitive and deliberate judgments are based on common principles.

PubMed

Kruglanski, Arie W; Gigerenzer, Gerd

2011-01-01

A popular distinction in cognitive and social psychology has been between intuitive and deliberate judgments. This juxtaposition has aligned in dual-process theories of reasoning associative, unconscious, effortless, heuristic, and suboptimal processes (assumed to foster intuitive judgments) versus rule-based, conscious, effortful, analytic, and rational processes (assumed to characterize deliberate judgments). In contrast, we provide convergent arguments and evidence for a unified theoretical approach to both intuitive and deliberative judgments. Both are rule-based, and in fact, the very same rules can underlie both intuitive and deliberate judgments. The important open question is that of rule selection, and we propose a 2-step process in which the task itself and the individual's memory constrain the set of applicable rules, whereas the individual's processing potential and the (perceived) ecological rationality of the rule for the task guide the final selection from that set. Deliberate judgments are not generally more accurate than intuitive judgments; in both cases, accuracy depends on the match between rule and environment: the rules' ecological rationality. Heuristics that are less effortful and in which parts of the information are ignored can be more accurate than cognitive strategies that have more information and computation. The proposed framework adumbrates a unified approach that specifies the critical dimensions on which judgmental situations may vary and the environmental conditions under which rules can be expected to be successful.

Application of a swarm-based approach for phase unwrapping

NASA Astrophysics Data System (ADS)

da S. Maciel, Lucas; Albertazzi G., Armando, Jr.

2014-07-01

An algorithm for phase unwrapping based on swarm intelligence is proposed. The novel approach is based on the emergent behavior of swarms. This behavior is the result of the interactions between independent agents following a simple set of rules and is regarded as fast, flexible and robust. The rules here were designed with two purposes. Firstly, the collective behavior must result in a reliable map of the unwrapped phase. The unwrapping reliability was evaluated by each agent during run-time, based on the quality of the neighboring pixels. In addition, the rule set must result in a behavior that focuses on wrapped regions. Stigmergy and communication rules were implemented in order to enable each agent to seek less worked areas of the image. The agents were modeled as Finite-State Machines. Based on the availability of unwrappable pixels, each agent assumed a different state in order to better adapt itself to the surroundings. The implemented rule set was able to fulfill the requirements on reliability and focused unwrapping. The unwrapped phase map was comparable to those from established methods as the agents were able to reliably evaluate each pixel quality. Also, the unwrapping behavior, being observed in real time, was able to focus on workable areas as the agents communicated in order to find less traveled regions. The results were very positive for such a new approach to the phase unwrapping problem. Finally, the authors see great potential for future developments concerning the flexibility, robustness and processing times of the swarm-based algorithm.

Model-Based Anomaly Detection for a Transparent Optical Transmission System

NASA Astrophysics Data System (ADS)

Bengtsson, Thomas; Salamon, Todd; Ho, Tin Kam; White, Christopher A.

In this chapter, we present an approach for anomaly detection at the physical layer of networks where detailed knowledge about the devices and their operations is available. The approach combines physics-based process models with observational data models to characterize the uncertainties and derive the alarm decision rules. We formulate and apply three different methods based on this approach for a well-defined problem in optical network monitoring that features many typical challenges for this methodology. Specifically, we address the problem of monitoring optically transparent transmission systems that use dynamically controlled Raman amplification systems. We use models of amplifier physics together with statistical estimation to derive alarm decision rules and use these rules to automatically discriminate between measurement errors, anomalous losses, and pump failures. Our approach has led to an efficient tool for systematically detecting anomalies in the system behavior of a deployed network, where pro-active measures to address such anomalies are key to preventing unnecessary disturbances to the system's continuous operation.

Predicting the relatiave vulnerability of near-coastal species to climate change using a rule-based ecoinformatics approach

EPA Science Inventory

Background/Questions/Methods Near-coastal species are threatened by multiple climate change drivers, including temperature increases, ocean acidification, and sea level rise. To identify vulnerable habitats, geographic regions, and species, we developed a sequential, rule-based...

Knowledge base rule partitioning design for CLIPS

NASA Technical Reports Server (NTRS)

Mainardi, Joseph D.; Szatkowski, G. P.

1990-01-01

This describes a knowledge base (KB) partitioning approach to solve the problem of real-time performance using the CLIPS AI shell when containing large numbers of rules and facts. This work is funded under the joint USAF/NASA Advanced Launch System (ALS) Program as applied research in expert systems to perform vehicle checkout for real-time controller and diagnostic monitoring tasks. The Expert System advanced development project (ADP-2302) main objective is to provide robust systems responding to new data frames of 0.1 to 1.0 second intervals. The intelligent system control must be performed within the specified real-time window, in order to meet the demands of the given application. Partitioning the KB reduces the complexity of the inferencing Rete net at any given time. This reduced complexity improves performance but without undo impacts during load and unload cycles. The second objective is to produce highly reliable intelligent systems. This requires simple and automated approaches to the KB verification & validation task. Partitioning the KB reduces rule interaction complexity overall. Reduced interaction simplifies the V&V testing necessary by focusing attention only on individual areas of interest. Many systems require a robustness that involves a large number of rules, most of which are mutually exclusive under different phases or conditions. The ideal solution is to control the knowledge base by loading rules that directly apply for that condition, while stripping out all rules and facts that are not used during that cycle. The practical approach is to cluster rules and facts into associated 'blocks'. A simple approach has been designed to control the addition and deletion of 'blocks' of rules and facts, while allowing real-time operations to run freely. Timing tests for real-time performance for specific machines under R/T operating systems have not been completed but are planned as part of the analysis process to validate the design.

Simulation-based model checking approach to cell fate specification during Caenorhabditis elegans vulval development by hybrid functional Petri net with extension.

PubMed

Li, Chen; Nagasaki, Masao; Ueno, Kazuko; Miyano, Satoru

2009-04-27

Model checking approaches were applied to biological pathway validations around 2003. Recently, Fisher et al. have proved the importance of model checking approach by inferring new regulation of signaling crosstalk in C. elegans and confirming the regulation with biological experiments. They took a discrete and state-based approach to explore all possible states of the system underlying vulval precursor cell (VPC) fate specification for desired properties. However, since both discrete and continuous features appear to be an indispensable part of biological processes, it is more appropriate to use quantitative models to capture the dynamics of biological systems. Our key motivation of this paper is to establish a quantitative methodology to model and analyze in silico models incorporating the use of model checking approach. A novel method of modeling and simulating biological systems with the use of model checking approach is proposed based on hybrid functional Petri net with extension (HFPNe) as the framework dealing with both discrete and continuous events. Firstly, we construct a quantitative VPC fate model with 1761 components by using HFPNe. Secondly, we employ two major biological fate determination rules - Rule I and Rule II - to VPC fate model. We then conduct 10,000 simulations for each of 48 sets of different genotypes, investigate variations of cell fate patterns under each genotype, and validate the two rules by comparing three simulation targets consisting of fate patterns obtained from in silico and in vivo experiments. In particular, an evaluation was successfully done by using our VPC fate model to investigate one target derived from biological experiments involving hybrid lineage observations. However, the understandings of hybrid lineages are hard to make on a discrete model because the hybrid lineage occurs when the system comes close to certain thresholds as discussed by Sternberg and Horvitz in 1986. Our simulation results suggest that: Rule I that cannot be applied with qualitative based model checking, is more reasonable than Rule II owing to the high coverage of predicted fate patterns (except for the genotype of lin-15ko; lin-12ko double mutants). More insights are also suggested. The quantitative simulation-based model checking approach is a useful means to provide us valuable biological insights and better understandings of biological systems and observation data that may be hard to capture with the qualitative one.

A New Approach for Resolving Conflicts in Actionable Behavioral Rules

PubMed Central

Zhu, Dan; Zeng, Daniel

2014-01-01

Knowledge is considered actionable if users can take direct actions based on such knowledge to their advantage. Among the most important and distinctive actionable knowledge are actionable behavioral rules that can directly and explicitly suggest specific actions to take to influence (restrain or encourage) the behavior in the users' best interest. However, in mining such rules, it often occurs that different rules may suggest the same actions with different expected utilities, which we call conflicting rules. To resolve the conflicts, a previous valid method was proposed. However, inconsistency of the measure for rule evaluating may hinder its performance. To overcome this problem, we develop a new method that utilizes rule ranking procedure as the basis for selecting the rule with the highest utility prediction accuracy. More specifically, we propose an integrative measure, which combines the measures of the support and antecedent length, to evaluate the utility prediction accuracies of conflicting rules. We also introduce a tunable weight parameter to allow the flexibility of integration. We conduct several experiments to test our proposed approach and evaluate the sensitivity of the weight parameter. Empirical results indicate that our approach outperforms those from previous research. PMID:25162054

A rule-based approach to model checking of UML state machines

NASA Astrophysics Data System (ADS)

Grobelna, Iwona; Grobelny, Michał; Stefanowicz, Łukasz

2016-12-01

In the paper a new approach to formal verification of control process specification expressed by means of UML state machines in version 2.x is proposed. In contrast to other approaches from the literature, we use the abstract and universal rule-based logical model suitable both for model checking (using the nuXmv model checker), but also for logical synthesis in form of rapid prototyping. Hence, a prototype implementation in hardware description language VHDL can be obtained that fully reflects the primary, already formally verified specification in form of UML state machines. Presented approach allows to increase the assurance that implemented system meets the user-defined requirements.

Extracting business vocabularies from business process models: SBVR and BPMN standards-based approach

NASA Astrophysics Data System (ADS)

Skersys, Tomas; Butleris, Rimantas; Kapocius, Kestutis

2013-10-01

Approaches for the analysis and specification of business vocabularies and rules are very relevant topics in both Business Process Management and Information Systems Development disciplines. However, in common practice of Information Systems Development, the Business modeling activities still are of mostly empiric nature. In this paper, basic aspects of the approach for business vocabularies' semi-automated extraction from business process models are presented. The approach is based on novel business modeling-level OMG standards "Business Process Model and Notation" (BPMN) and "Semantics for Business Vocabularies and Business Rules" (SBVR), thus contributing to OMG's vision about Model-Driven Architecture (MDA) and to model-driven development in general.

Identification of rheumatoid arthritis and osteoarthritis patients by transcriptome-based rule set generation

PubMed Central

2014-01-01

Introduction Discrimination of rheumatoid arthritis (RA) patients from patients with other inflammatory or degenerative joint diseases or healthy individuals purely on the basis of genes differentially expressed in high-throughput data has proven very difficult. Thus, the present study sought to achieve such discrimination by employing a novel unbiased approach using rule-based classifiers. Methods Three multi-center genome-wide transcriptomic data sets (Affymetrix HG-U133 A/B) from a total of 79 individuals, including 20 healthy controls (control group - CG), as well as 26 osteoarthritis (OA) and 33 RA patients, were used to infer rule-based classifiers to discriminate the disease groups. The rules were ranked with respect to Kiendl’s statistical relevance index, and the resulting rule set was optimized by pruning. The rule sets were inferred separately from data of one of three centers and applied to the two remaining centers for validation. All rules from the optimized rule sets of all centers were used to analyze their biological relevance applying the software Pathway Studio. Results The optimized rule sets for the three centers contained a total of 29, 20, and 8 rules (including 10, 8, and 4 rules for ‘RA’), respectively. The mean sensitivity for the prediction of RA based on six center-to-center tests was 96% (range 90% to 100%), that for OA 86% (range 40% to 100%). The mean specificity for RA prediction was 94% (range 80% to 100%), that for OA 96% (range 83.3% to 100%). The average overall accuracy of the three different rule-based classifiers was 91% (range 80% to 100%). Unbiased analyses by Pathway Studio of the gene sets obtained by discrimination of RA from OA and CG with rule-based classifiers resulted in the identification of the pathogenetically and/or therapeutically relevant interferon-gamma and GM-CSF pathways. Conclusion First-time application of rule-based classifiers for the discrimination of RA resulted in high performance, with means for all assessment parameters close to or higher than 90%. In addition, this unbiased, new approach resulted in the identification not only of pathways known to be critical to RA, but also of novel molecules such as serine/threonine kinase 10. PMID:24690414

A Bankruptcy Problem Approach to Load-shedding in Multiagent-based Microgrid Operation

PubMed Central

Kim, Hak-Man; Kinoshita, Tetsuo; Lim, Yujin; Kim, Tai-Hoon

2010-01-01

A microgrid is composed of distributed power generation systems (DGs), distributed energy storage devices (DSs), and loads. To maintain a specific frequency in the islanded mode as an important requirement, the control of DGs’ output and charge action of DSs are used in supply surplus conditions and load-shedding and discharge action of DSs are used in supply shortage conditions. Recently, multiagent systems for autonomous microgrid operation have been studied. Especially, load-shedding, which is intentional reduction of electricity use, is a critical problem in islanded microgrid operation based on the multiagent system. Therefore, effective schemes for load-shedding are required. Meanwhile, the bankruptcy problem deals with dividing short resources among multiple agents. In order to solve the bankruptcy problem, division rules, such as the constrained equal awards rule (CEA), the constrained equal losses rule (CEL), and the random arrival rule (RA), have been used. In this paper, we approach load-shedding as a bankruptcy problem. We compare load-shedding results by above-mentioned rules in islanded microgrid operation based on wireless sensor network (WSN) as the communication link for an agent’s interactions. PMID:22163386

A bankruptcy problem approach to load-shedding in multiagent-based microgrid operation.

PubMed

Kim, Hak-Man; Kinoshita, Tetsuo; Lim, Yujin; Kim, Tai-Hoon

2010-01-01

A microgrid is composed of distributed power generation systems (DGs), distributed energy storage devices (DSs), and loads. To maintain a specific frequency in the islanded mode as an important requirement, the control of DGs' output and charge action of DSs are used in supply surplus conditions and load-shedding and discharge action of DSs are used in supply shortage conditions. Recently, multiagent systems for autonomous microgrid operation have been studied. Especially, load-shedding, which is intentional reduction of electricity use, is a critical problem in islanded microgrid operation based on the multiagent system. Therefore, effective schemes for load-shedding are required. Meanwhile, the bankruptcy problem deals with dividing short resources among multiple agents. In order to solve the bankruptcy problem, division rules, such as the constrained equal awards rule (CEA), the constrained equal losses rule (CEL), and the random arrival rule (RA), have been used. In this paper, we approach load-shedding as a bankruptcy problem. We compare load-shedding results by above-mentioned rules in islanded microgrid operation based on wireless sensor network (WSN) as the communication link for an agent's interactions.

A Rule-Based System Implementing a Method for Translating FOL Formulas into NL Sentences

NASA Astrophysics Data System (ADS)

Mpagouli, Aikaterini; Hatzilygeroudis, Ioannis

In this paper, we mainly present the implementation of a system that translates first order logic (FOL) formulas into natural language (NL) sentences. The motivation comes from an intelligent tutoring system teaching logic as a knowledge representation language, where it is used as a means for feedback to the students-users. FOL to NL conversion is achieved by using a rule-based approach, where we exploit the pattern matching capabilities of rules. So, the system consists of rule-based modules corresponding to the phases of our translation methodology. Facts are used in a lexicon providing lexical and grammatical information that helps in producing the NL sentences. The whole system is implemented in Jess, a java-implemented rule-based programming tool. Experimental results confirm the success of our choices.

Portfolio theory and the alternative decision rule of cost-effectiveness analysis: theoretical and practical considerations.

PubMed

Sendi, Pedram; Al, Maiwenn J; Gafni, Amiram; Birch, Stephen

2004-05-01

Bridges and Terris (Soc. Sci. Med. (2004)) critique our paper on the alternative decision rule of economic evaluation in the presence of uncertainty and constrained resources within the context of a portfolio of health care programs (Sendi et al. Soc. Sci. Med. 57 (2003) 2207). They argue that by not adopting a formal portfolio theory approach we overlook the optimal solution. We show that these arguments stem from a fundamental misunderstanding of the alternative decision rule of economic evaluation. In particular, the portfolio theory approach advocated by Bridges and Terris is based on the same theoretical assumptions that the alternative decision rule set out to relax. Moreover, Bridges and Terris acknowledge that the proposed portfolio theory approach may not identify the optimal solution to resource allocation problems. Hence, it provides neither theoretical nor practical improvements to the proposed alternative decision rule.

Rule Extracting based on MCG with its Application in Helicopter Power Train Fault Diagnosis

NASA Astrophysics Data System (ADS)

Wang, M.; Hu, N. Q.; Qin, G. J.

2011-07-01

In order to extract decision rules for fault diagnosis from incomplete historical test records for knowledge-based damage assessment of helicopter power train structure. A method that can directly extract the optimal generalized decision rules from incomplete information based on GrC was proposed. Based on semantic analysis of unknown attribute value, the granule was extended to handle incomplete information. Maximum characteristic granule (MCG) was defined based on characteristic relation, and MCG was used to construct the resolution function matrix. The optimal general decision rule was introduced, with the basic equivalent forms of propositional logic, the rules were extracted and reduction from incomplete information table. Combined with a fault diagnosis example of power train, the application approach of the method was present, and the validity of this method in knowledge acquisition was proved.

A semantic-based method for extracting concept definitions from scientific publications: evaluation in the autism phenotype domain.

PubMed

Hassanpour, Saeed; O'Connor, Martin J; Das, Amar K

2013-08-12

A variety of informatics approaches have been developed that use information retrieval, NLP and text-mining techniques to identify biomedical concepts and relations within scientific publications or their sentences. These approaches have not typically addressed the challenge of extracting more complex knowledge such as biomedical definitions. In our efforts to facilitate knowledge acquisition of rule-based definitions of autism phenotypes, we have developed a novel semantic-based text-mining approach that can automatically identify such definitions within text. Using an existing knowledge base of 156 autism phenotype definitions and an annotated corpus of 26 source articles containing such definitions, we evaluated and compared the average rank of correctly identified rule definition or corresponding rule template using both our semantic-based approach and a standard term-based approach. We examined three separate scenarios: (1) the snippet of text contained a definition already in the knowledge base; (2) the snippet contained an alternative definition for a concept in the knowledge base; and (3) the snippet contained a definition not in the knowledge base. Our semantic-based approach had a higher average rank than the term-based approach for each of the three scenarios (scenario 1: 3.8 vs. 5.0; scenario 2: 2.8 vs. 4.9; and scenario 3: 4.5 vs. 6.2), with each comparison significant at the p-value of 0.05 using the Wilcoxon signed-rank test. Our work shows that leveraging existing domain knowledge in the information extraction of biomedical definitions significantly improves the correct identification of such knowledge within sentences. Our method can thus help researchers rapidly acquire knowledge about biomedical definitions that are specified and evolving within an ever-growing corpus of scientific publications.

Automatic Syllabification in English: A Comparison of Different Algorithms

ERIC Educational Resources Information Center

Marchand, Yannick; Adsett, Connie R.; Damper, Robert I.

2009-01-01

Automatic syllabification of words is challenging, not least because the syllable is not easy to define precisely. Consequently, no accepted standard algorithm for automatic syllabification exists. There are two broad approaches: rule-based and data-driven. The rule-based method effectively embodies some theoretical position regarding the…

Cognitive changes in conjunctive rule-based category learning: An ERP approach.

PubMed

Rabi, Rahel; Joanisse, Marc F; Zhu, Tianshu; Minda, John Paul

2018-06-25

When learning rule-based categories, sufficient cognitive resources are needed to test hypotheses, maintain the currently active rule in working memory, update rules after feedback, and to select a new rule if necessary. Prior research has demonstrated that conjunctive rules are more complex than unidimensional rules and place greater demands on executive functions like working memory. In our study, event-related potentials (ERPs) were recorded while participants performed a conjunctive rule-based category learning task with trial-by-trial feedback. In line with prior research, correct categorization responses resulted in a larger stimulus-locked late positive complex compared to incorrect responses, possibly indexing the updating of rule information in memory. Incorrect trials elicited a pronounced feedback-locked P300 elicited which suggested a disconnect between perception, and the rule-based strategy. We also examined the differential processing of stimuli that were able to be correctly classified by the suboptimal single-dimensional rule ("easy" stimuli) versus those that could only be correctly classified by the optimal, conjunctive rule ("difficult" stimuli). Among strong learners, a larger, late positive slow wave emerged for difficult compared with easy stimuli, suggesting differential processing of category items even though strong learners performed well on the conjunctive category set. Overall, the findings suggest that ERP combined with computational modelling can be used to better understand the cognitive processes involved in rule-based category learning.

Time series regression-based pairs trading in the Korean equities market

NASA Astrophysics Data System (ADS)

Kim, Saejoon; Heo, Jun

2017-07-01

Pairs trading is an instance of statistical arbitrage that relies on heavy quantitative data analysis to profit by capitalising low-risk trading opportunities provided by anomalies of related assets. A key element in pairs trading is the rule by which open and close trading triggers are defined. This paper investigates the use of time series regression to define the rule which has previously been identified with fixed threshold-based approaches. Empirical results indicate that our approach may yield significantly increased excess returns compared to ones obtained by previous approaches on large capitalisation stocks in the Korean equities market.

Model-assisted template extraction SRAF application to contact holes patterns in high-end flash memory device fabrication

NASA Astrophysics Data System (ADS)

Seoud, Ahmed; Kim, Juhwan; Ma, Yuansheng; Jayaram, Srividya; Hong, Le; Chae, Gyu-Yeol; Lee, Jeong-Woo; Park, Dae-Jin; Yune, Hyoung-Soon; Oh, Se-Young; Park, Chan-Ha

2018-03-01

Sub-resolution assist feature (SRAF) insertion techniques have been effectively used for a long time now to increase process latitude in the lithography patterning process. Rule-based SRAF and model-based SRAF are complementary solutions, and each has its own benefits, depending on the objectives of applications and the criticality of the impact on manufacturing yield, efficiency, and productivity. Rule-based SRAF provides superior geometric output consistency and faster runtime performance, but the associated recipe development time can be of concern. Model-based SRAF provides better coverage for more complicated pattern structures in terms of shapes and sizes, with considerably less time required for recipe development, although consistency and performance may be impacted. In this paper, we introduce a new model-assisted template extraction (MATE) SRAF solution, which employs decision tree learning in a model-based solution to provide the benefits of both rule-based and model-based SRAF insertion approaches. The MATE solution is designed to automate the creation of rules/templates for SRAF insertion, and is based on the SRAF placement predicted by model-based solutions. The MATE SRAF recipe provides optimum lithographic quality in relation to various manufacturing aspects in a very short time, compared to traditional methods of rule optimization. Experiments were done using memory device pattern layouts to compare the MATE solution to existing model-based SRAF and pixelated SRAF approaches, based on lithographic process window quality, runtime performance, and geometric output consistency.

Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence.

PubMed

Naso, David; Turchiano, Biagio

2005-04-01

In many manufacturing environments, automated guided vehicles are used to move the processed materials between various pickup and delivery points. The assignment of vehicles to unit loads is a complex problem that is often solved in real-time with simple dispatching rules. This paper proposes an automated guided vehicles dispatching approach based on computational intelligence. We adopt a fuzzy multicriteria decision strategy to simultaneously take into account multiple aspects in every dispatching decision. Since the typical short-term view of dispatching rules is one of the main limitations of such real-time assignment heuristics, we also incorporate in the multicriteria algorithm a specific heuristic rule that takes into account the empty-vehicle travel on a longer time-horizon. Moreover, we also adopt a genetic algorithm to tune the weights associated to each decision criteria in the global decision algorithm. The proposed approach is validated by means of a comparison with other dispatching rules, and with other recently proposed multicriteria dispatching strategies also based on computational Intelligence. The analysis of the results obtained by the proposed dispatching approach in both nominal and perturbed operating conditions (congestions, faults) confirms its effectiveness.

Timescale analysis of rule-based biochemical reaction networks

PubMed Central

Klinke, David J.; Finley, Stacey D.

2012-01-01

The flow of information within a cell is governed by a series of protein-protein interactions that can be described as a reaction network. Mathematical models of biochemical reaction networks can be constructed by repetitively applying specific rules that define how reactants interact and what new species are formed upon reaction. To aid in understanding the underlying biochemistry, timescale analysis is one method developed to prune the size of the reaction network. In this work, we extend the methods associated with timescale analysis to reaction rules instead of the species contained within the network. To illustrate this approach, we applied timescale analysis to a simple receptor-ligand binding model and a rule-based model of Interleukin-12 (IL-12) signaling in näive CD4+ T cells. The IL-12 signaling pathway includes multiple protein-protein interactions that collectively transmit information; however, the level of mechanistic detail sufficient to capture the observed dynamics has not been justified based upon the available data. The analysis correctly predicted that reactions associated with JAK2 and TYK2 binding to their corresponding receptor exist at a pseudo-equilibrium. In contrast, reactions associated with ligand binding and receptor turnover regulate cellular response to IL-12. An empirical Bayesian approach was used to estimate the uncertainty in the timescales. This approach complements existing rank- and flux-based methods that can be used to interrogate complex reaction networks. Ultimately, timescale analysis of rule-based models is a computational tool that can be used to reveal the biochemical steps that regulate signaling dynamics. PMID:21954150

Alternative Theoretical Bases for the Study of Human Communication: The Rules Perspective.

ERIC Educational Resources Information Center

Cushman, Donald P.

Three potentially useful perspectives for the scientific development of human communication theory are the law model, the systems approach, and the rules paradigm. It is the purpose of this paper to indicate the utility of the rules perspective. For the purposes of this analysis, human communication is viewed as the successful transfer of symbolic…

Choice Rules and Accumulator Networks

PubMed Central

2015-01-01

This article presents a preference accumulation model that can be used to implement a number of different multi-attribute heuristic choice rules, including the lexicographic rule, the majority of confirming dimensions (tallying) rule and the equal weights rule. The proposed model differs from existing accumulators in terms of attribute representation: Leakage and competition, typically applied only to preference accumulation, are also assumed to be involved in processing attribute values. This allows the model to perform a range of sophisticated attribute-wise comparisons, including comparisons that compute relative rank. The ability of a preference accumulation model composed of leaky competitive networks to mimic symbolic models of heuristic choice suggests that these 2 approaches are not incompatible, and that a unitary cognitive model of preferential choice, based on insights from both these approaches, may be feasible. PMID:28670592

Building a common pipeline for rule-based document classification.

PubMed

Patterson, Olga V; Ginter, Thomas; DuVall, Scott L

2013-01-01

Instance-based classification of clinical text is a widely used natural language processing task employed as a step for patient classification, document retrieval, or information extraction. Rule-based approaches rely on concept identification and context analysis in order to determine the appropriate class. We propose a five-step process that enables even small research teams to develop simple but powerful rule-based NLP systems by taking advantage of a common UIMA AS based pipeline for classification. Our proposed methodology coupled with the general-purpose solution provides researchers with access to the data locked in clinical text in cases of limited human resources and compact timelines.

Flu Diagnosis System Using Jaccard Index and Rough Set Approaches

NASA Astrophysics Data System (ADS)

Efendi, Riswan; Azah Samsudin, Noor; Mat Deris, Mustafa; Guan Ting, Yip

2018-04-01

Jaccard index and rough set approaches have been frequently implemented in decision support systems with various domain applications. Both approaches are appropriate to be considered for categorical data analysis. This paper presents the applications of sets operations for flu diagnosis systems based on two different approaches, such as, Jaccard index and rough set. These two different approaches are established using set operations concept, namely intersection and subset. The step-by-step procedure is demonstrated from each approach in diagnosing flu system. The similarity and dissimilarity indexes between conditional symptoms and decision are measured using Jaccard approach. Additionally, the rough set is used to build decision support rules. Moreover, the decision support rules are established using redundant data analysis and elimination of unclassified elements. A number data sets is considered to attempt the step-by-step procedure from each approach. The result has shown that rough set can be used to support Jaccard approaches in establishing decision support rules. Additionally, Jaccard index is better approach for investigating the worst condition of patients. While, the definitely and possibly patients with or without flu can be determined using rough set approach. The rules may improve the performance of medical diagnosis systems. Therefore, inexperienced doctors and patients are easier in preliminary flu diagnosis.

Semantics by analogy for illustrative volume visualization☆

PubMed Central

Gerl, Moritz; Rautek, Peter; Isenberg, Tobias; Gröller, Eduard

2012-01-01

We present an interactive graphical approach for the explicit specification of semantics for volume visualization. This explicit and graphical specification of semantics for volumetric features allows us to visually assign meaning to both input and output parameters of the visualization mapping. This is in contrast to the implicit way of specifying semantics using transfer functions. In particular, we demonstrate how to realize a dynamic specification of semantics which allows to flexibly explore a wide range of mappings. Our approach is based on three concepts. First, we use semantic shader augmentation to automatically add rule-based rendering functionality to static visualization mappings in a shader program, while preserving the visual abstraction that the initial shader encodes. With this technique we extend recent developments that define a mapping between data attributes and visual attributes with rules, which are evaluated using fuzzy logic. Second, we let users define the semantics by analogy through brushing on renderings of the data attributes of interest. Third, the rules are specified graphically in an interface that provides visual clues for potential modifications. Together, the presented methods offer a high degree of freedom in the specification and exploration of rule-based mappings and avoid the limitations of a linguistic rule formulation. PMID:23576827

CT Image Sequence Analysis for Object Recognition - A Rule-Based 3-D Computer Vision System

Treesearch

Dongping Zhu; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman

1991-01-01

Research is now underway to create a vision system for hardwood log inspection using a knowledge-based approach. In this paper, we present a rule-based, 3-D vision system for locating and identifying wood defects using topological, geometric, and statistical attributes. A number of different features can be derived from the 3-D input scenes. These features and evidence...

Evaluating an ensemble classification approach for crop diversity verification in Danish greening subsidy control

NASA Astrophysics Data System (ADS)

Chellasamy, Menaka; Ferré, Ty Paul Andrew; Greve, Mogens Humlekrog

2016-07-01

Beginning in 2015, Danish farmers are obliged to meet specific crop diversification rules based on total land area and number of crops cultivated to be eligible for new greening subsidies. Hence, there is a need for the Danish government to extend their subsidy control system to verify farmers' declarations to warrant greening payments under the new crop diversification rules. Remote Sensing (RS) technology has been used since 1992 to control farmers' subsidies in Denmark. However, a proper RS-based approach is yet to be finalised to validate new crop diversity requirements designed for assessing compliance under the recent subsidy scheme (2014-2020); This study uses an ensemble classification approach (proposed by the authors in previous studies) for validating the crop diversity requirements of the new rules. The approach uses a neural network ensemble classification system with bi-temporal (spring and early summer) WorldView-2 imagery (WV2) and includes the following steps: (1) automatic computation of pixel-based prediction probabilities using multiple neural networks; (2) quantification of the classification uncertainty using Endorsement Theory (ET); (3) discrimination of crop pixels and validation of the crop diversification rules at farm level; and (4) identification of farmers who are violating the requirements for greening subsidies. The prediction probabilities are computed by a neural network ensemble supplied with training samples selected automatically using farmers declared parcels (field vectors containing crop information and the field boundary of each crop). Crop discrimination is performed by considering a set of conclusions derived from individual neural networks based on ET. Verification of the diversification rules is performed by incorporating pixel-based classification uncertainty or confidence intervals with the class labels at the farmer level. The proposed approach was tested with WV2 imagery acquired in 2011 for a study area in Vennebjerg, Denmark, containing 132 farmers, 1258 fields, and 18 crops. The classification results obtained show an overall accuracy of 90.2%. The RS-based results suggest that 36 farmers did not follow the crop diversification rules that would qualify for the greening subsidies. When compared to the farmers' reported crop mixes, irrespective of the rule, the RS results indicate that false crop declarations were made by 8 farmers, covering 15 fields. If the farmers' reports had been submitted for the new greening subsidies, 3 farmers would have made a false claim; while remaining 5 farmers obey the rules of required crop proportion even though they have submitted the false crop code due to their small holding size. The RS results would have supported 96 farmers for greening subsidy claims, with no instances of suggesting a greening subsidy for a holding that the farmer did not report as meeting the required conditions. These results suggest that the proposed RS based method shows great promise for validating the new greening subsidies in Denmark.

RuleMonkey: software for stochastic simulation of rule-based models

PubMed Central

2010-01-01

Background The system-level dynamics of many molecular interactions, particularly protein-protein interactions, can be conveniently represented using reaction rules, which can be specified using model-specification languages, such as the BioNetGen language (BNGL). A set of rules implicitly defines a (bio)chemical reaction network. The reaction network implied by a set of rules is often very large, and as a result, generation of the network implied by rules tends to be computationally expensive. Moreover, the cost of many commonly used methods for simulating network dynamics is a function of network size. Together these factors have limited application of the rule-based modeling approach. Recently, several methods for simulating rule-based models have been developed that avoid the expensive step of network generation. The cost of these "network-free" simulation methods is independent of the number of reactions implied by rules. Software implementing such methods is now needed for the simulation and analysis of rule-based models of biochemical systems. Results Here, we present a software tool called RuleMonkey, which implements a network-free method for simulation of rule-based models that is similar to Gillespie's method. The method is suitable for rule-based models that can be encoded in BNGL, including models with rules that have global application conditions, such as rules for intramolecular association reactions. In addition, the method is rejection free, unlike other network-free methods that introduce null events, i.e., steps in the simulation procedure that do not change the state of the reaction system being simulated. We verify that RuleMonkey produces correct simulation results, and we compare its performance against DYNSTOC, another BNGL-compliant tool for network-free simulation of rule-based models. We also compare RuleMonkey against problem-specific codes implementing network-free simulation methods. Conclusions RuleMonkey enables the simulation of rule-based models for which the underlying reaction networks are large. It is typically faster than DYNSTOC for benchmark problems that we have examined. RuleMonkey is freely available as a stand-alone application http://public.tgen.org/rulemonkey. It is also available as a simulation engine within GetBonNie, a web-based environment for building, analyzing and sharing rule-based models. PMID:20673321

Automated Induction Of Rule-Based Neural Networks

NASA Technical Reports Server (NTRS)

Smyth, Padhraic J.; Goodman, Rodney M.

1994-01-01

Prototype expert systems implemented in software and are functionally equivalent to neural networks set up automatically and placed into operation within minutes following information-theoretic approach to automated acquisition of knowledge from large example data bases. Approach based largely on use of ITRULE computer program.

Simulation of operating rules and discretional decisions using a fuzzy rule-based system integrated into a water resources management model

NASA Astrophysics Data System (ADS)

Macian-Sorribes, Hector; Pulido-Velazquez, Manuel

2013-04-01

Water resources systems are operated, mostly, using a set of pre-defined rules not regarding, usually, to an optimal allocation in terms of water use or economic benefits, but to historical and institutional reasons. These operating policies are reproduced, commonly, as hedging rules, pack rules or zone-based operations, and simulation models can be used to test their performance under a wide range of hydrological and/or socio-economic hypothesis. Despite the high degree of acceptation and testing that these models have achieved, the actual operation of water resources systems hardly follows all the time the pre-defined rules with the consequent uncertainty on the system performance. Real-world reservoir operation is very complex, affected by input uncertainty (imprecision in forecast inflow, seepage and evaporation losses, etc.), filtered by the reservoir operator's experience and natural risk-aversion, while considering the different physical and legal/institutional constraints in order to meet the different demands and system requirements. The aim of this work is to expose a fuzzy logic approach to derive and assess the historical operation of a system. This framework uses a fuzzy rule-based system to reproduce pre-defined rules and also to match as close as possible the actual decisions made by managers. After built up, the fuzzy rule-based system can be integrated in a water resources management model, making possible to assess the system performance at the basin scale. The case study of the Mijares basin (eastern Spain) is used to illustrate the method. A reservoir operating curve regulates the two main reservoir releases (operated in a conjunctive way) with the purpose of guaranteeing a high realiability of supply to the traditional irrigation districts with higher priority (more senior demands that funded the reservoir construction). A fuzzy rule-based system has been created to reproduce the operating curve's performance, defining the system state (total water stored in the reservoirs) and the month of the year as inputs; and the demand deliveries as outputs. The developed simulation management model integrates the fuzzy-ruled system of the operation of the two main reservoirs of the basin with the corresponding mass balance equations, the physical or boundary conditions and the water allocation rules among the competing demands. Historical information on inflow time series is used as inputs to the model simulation, being trained and validated using historical information on reservoir storage level and flow in several streams of the Mijares river. This methodology provides a more flexible and close to real policies approach. The model is easy to develop and to understand due to its rule-based structure, which mimics the human way of thinking. This can improve cooperation and negotiation between managers, decision-makers and stakeholders. The approach can be also applied to analyze the historical operation of the reservoir (what we have called a reservoir operation "audit").

The VTAC Committee: developing guidance for an alternative regulatory pathway to the anadromous salmonid protection rules

Treesearch

Michael Liquori; Peter Cafferata; Kevin Boston; Richard Gienger; David Hope

2012-01-01

In recent decades, riparian protection standards have been guided by generalized prescriptive rules. With the passage of the Anadromous Salmonid Protection rules in 2009, the Board of Forestry and Fire Protection (Board) established a regulatory pathway that provides an alternative approach for riparian protection based on site-specific criteria (14 CCR § 916....

Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks

PubMed Central

Walpole, J.; Chappell, J.C.; Cluceru, J.G.; Mac Gabhann, F.; Bautch, V.L.; Peirce, S. M.

2015-01-01

Many biological processes are controlled by both deterministic and stochastic influences. However, efforts to model these systems often rely on either purely stochastic or purely rule-based methods. To better understand the balance between stochasticity and determinism in biological processes a computational approach that incorporates both influences may afford additional insight into underlying biological mechanisms that give rise to emergent system properties. We apply a combined approach to the simulation and study of angiogenesis, the growth of new blood vessels from existing networks. This complex multicellular process begins with selection of an initiating endothelial cell, or tip cell, which sprouts from the parent vessels in response to stimulation by exogenous cues. We have constructed an agent-based model of sprouting angiogenesis to evaluate endothelial cell sprout initiation frequency and location, and we have experimentally validated it using high-resolution time-lapse confocal microscopy. ABM simulations were then compared to a Monte Carlo model, revealing that purely stochastic simulations could not generate sprout locations as accurately as the rule-informed agent-based model. These findings support the use of rule-based approaches for modeling the complex mechanisms underlying sprouting angiogenesis over purely stochastic methods. PMID:26158406

Agent-based model of angiogenesis simulates capillary sprout initiation in multicellular networks.

PubMed

Walpole, J; Chappell, J C; Cluceru, J G; Mac Gabhann, F; Bautch, V L; Peirce, S M

2015-09-01

Many biological processes are controlled by both deterministic and stochastic influences. However, efforts to model these systems often rely on either purely stochastic or purely rule-based methods. To better understand the balance between stochasticity and determinism in biological processes a computational approach that incorporates both influences may afford additional insight into underlying biological mechanisms that give rise to emergent system properties. We apply a combined approach to the simulation and study of angiogenesis, the growth of new blood vessels from existing networks. This complex multicellular process begins with selection of an initiating endothelial cell, or tip cell, which sprouts from the parent vessels in response to stimulation by exogenous cues. We have constructed an agent-based model of sprouting angiogenesis to evaluate endothelial cell sprout initiation frequency and location, and we have experimentally validated it using high-resolution time-lapse confocal microscopy. ABM simulations were then compared to a Monte Carlo model, revealing that purely stochastic simulations could not generate sprout locations as accurately as the rule-informed agent-based model. These findings support the use of rule-based approaches for modeling the complex mechanisms underlying sprouting angiogenesis over purely stochastic methods.

Organizational Knowledge Transfer Using Ontologies and a Rule-Based System

NASA Astrophysics Data System (ADS)

Okabe, Masao; Yoshioka, Akiko; Kobayashi, Keido; Yamaguchi, Takahira

In recent automated and integrated manufacturing, so-called intelligence skill is becoming more and more important and its efficient transfer to next-generation engineers is one of the urgent issues. In this paper, we propose a new approach without costly OJT (on-the-job training), that is, combinational usage of a domain ontology, a rule ontology and a rule-based system. Intelligence skill can be decomposed into pieces of simple engineering rules. A rule ontology consists of these engineering rules as primitives and the semantic relations among them. A domain ontology consists of technical terms in the engineering rules and the semantic relations among them. A rule ontology helps novices get the total picture of the intelligence skill and a domain ontology helps them understand the exact meanings of the engineering rules. A rule-based system helps domain experts externalize their tacit intelligence skill to ontologies and also helps novices internalize them. As a case study, we applied our proposal to some actual job at a remote control and maintenance office of hydroelectric power stations in Tokyo Electric Power Co., Inc. We also did an evaluation experiment for this case study and the result supports our proposal.

A hierarchical fuzzy rule-based approach to aphasia diagnosis.

PubMed

Akbarzadeh-T, Mohammad-R; Moshtagh-Khorasani, Majid

2007-10-01

Aphasia diagnosis is a particularly challenging medical diagnostic task due to the linguistic uncertainty and vagueness, inconsistencies in the definition of aphasic syndromes, large number of measurements with imprecision, natural diversity and subjectivity in test objects as well as in opinions of experts who diagnose the disease. To efficiently address this diagnostic process, a hierarchical fuzzy rule-based structure is proposed here that considers the effect of different features of aphasia by statistical analysis in its construction. This approach can be efficient for diagnosis of aphasia and possibly other medical diagnostic applications due to its fuzzy and hierarchical reasoning construction. Initially, the symptoms of the disease which each consists of different features are analyzed statistically. The measured statistical parameters from the training set are then used to define membership functions and the fuzzy rules. The resulting two-layered fuzzy rule-based system is then compared with a back propagating feed-forward neural network for diagnosis of four Aphasia types: Anomic, Broca, Global and Wernicke. In order to reduce the number of required inputs, the technique is applied and compared on both comprehensive and spontaneous speech tests. Statistical t-test analysis confirms that the proposed approach uses fewer Aphasia features while also presenting a significant improvement in terms of accuracy.

Development of a New Departure Aversion Standard for Light Aircraft

NASA Technical Reports Server (NTRS)

Borer, Nicholas K.

2017-01-01

The Federal Aviation Administration (FAA) and European Aviation Safety Agency (EASA) have recently established new light aircraft certification rules that introduce significant changes to the current regulations. The changes include moving from prescriptive design requirements to performance-based standards, transferring many of the acceptable means of compliance out of the rules and into consensus standards. In addition, the FAA/EASA rules change the performance requirements associated with some of the more salient safety issues regarding light aircraft. One significant change is the elimination of spin recovery demonstration. The new rules now call for enhanced stall warning and aircraft handling characteristics that demonstrate resistance to inadvertent departure from controlled flight. The means of compliance with these changes in a safe, cost-effective manner is a challenging problem. This paper discusses existing approaches to reducing the likelihood of departure from controlled flight and introduces a new approach, dubbed Departure Aversion, which allows applicants to tailor the amount of departure resistance, stall warning, and enhanced safety equipment to meet the new proposed rules. The Departure Aversion approach gives applicants the freedom to select the most cost-effective portfolio for their design, while meeting the safety intent of the new rules, by ensuring that any combination of the selected approaches will be at a higher equivalent level of safety than today's status quo.

Practical implementation of the double linear damage rule and damage curve approach for treating cumulative fatigue damage

NASA Technical Reports Server (NTRS)

Manson, S. S.; Halford, G. R.

1981-01-01

Simple procedures are given for treating cumulative fatigue damage under complex loading history using either the damage curve concept or the double linear damage rule. A single equation is given for use with the damage curve approach; each loading event providing a fraction of damage until failure is presumed to occur when the damage sum becomes unity. For the double linear damage rule, analytical expressions are given for determining the two phases of life. The procedure comprises two steps, each similar to the conventional application of the commonly used linear damage rule. Once the sum of cycle ratios based on Phase I lives reaches unity, Phase I is presumed complete, and further loadings are summed as cycle ratios based on Phase II lives. When the Phase II sum attains unity, failure is presumed to occur. It is noted that no physical properties or material constants other than those normally used in a conventional linear damage rule analysis are required for application of either of the two cumulative damage methods described. Illustrations and comparisons are discussed for both methods.

Rule Systems for Runtime Verification: A Short Tutorial

NASA Astrophysics Data System (ADS)

Barringer, Howard; Havelund, Klaus; Rydeheard, David; Groce, Alex

In this tutorial, we introduce two rule-based systems for on and off-line trace analysis, RuleR and LogScope. RuleR is a conditional rule-based system, which has a simple and easily implemented algorithm for effective runtime verification, and into which one can compile a wide range of temporal logics and other specification formalisms used for runtime verification. Specifications can be parameterized with data, or even with specifications, allowing for temporal logic combinators to be defined. We outline a number of simple syntactic extensions of core RuleR that can lead to further conciseness of specification but still enabling easy and efficient implementation. RuleR is implemented in Java and we will demonstrate its ease of use in monitoring Java programs. LogScope is a derivation of RuleR adding a simple very user-friendly temporal logic. It was developed in Python, specifically for supporting testing of spacecraft flight software for NASA’s next 2011 Mars mission MSL (Mars Science Laboratory). The system has been applied by test engineers to analysis of log files generated by running the flight software. Detailed logging is already part of the system design approach, and hence there is no added instrumentation overhead caused by this approach. While post-mortem log analysis prevents the autonomous reaction to problems possible with traditional runtime verification, it provides a powerful tool for test automation. A new system is being developed that integrates features from both RuleR and LogScope.

A rough set-based association rule approach implemented on a brand trust evaluation model

NASA Astrophysics Data System (ADS)

Liao, Shu-Hsien; Chen, Yin-Ju

2017-09-01

In commerce, businesses use branding to differentiate their product and service offerings from those of their competitors. The brand incorporates a set of product or service features that are associated with that particular brand name and identifies the product/service segmentation in the market. This study proposes a new data mining approach, a rough set-based association rule induction, implemented on a brand trust evaluation model. In addition, it presents as one way to deal with data uncertainty to analyse ratio scale data, while creating predictive if-then rules that generalise data values to the retail region. As such, this study uses the analysis of algorithms to find alcoholic beverages brand trust recall. Finally, discussions and conclusion are presented for further managerial implications.

A Logical Framework for Service Migration Based Survivability

DTIC Science & Technology

2016-06-24

platforms; Service Migration Strategy Fuzzy Inference System Knowledge Base Fuzzy rules representing domain expert knowledge about implications of...service migration strategy. Our approach uses expert knowledge as linguistic reasoning rules and takes service programs damage assessment, service...programs complexity, and available network capability as input. The fuzzy inference system includes four components as shown in Figure 5: (1) a knowledge

Leveraging Modeling Approaches: Reaction Networks and Rules

PubMed Central

Blinov, Michael L.; Moraru, Ion I.

2012-01-01

We have witnessed an explosive growth in research involving mathematical models and computer simulations of intracellular molecular interactions, ranging from metabolic pathways to signaling and gene regulatory networks. Many software tools have been developed to aid in the study of such biological systems, some of which have a wealth of features for model building and visualization, and powerful capabilities for simulation and data analysis. Novel high resolution and/or high throughput experimental techniques have led to an abundance of qualitative and quantitative data related to the spatio-temporal distribution of molecules and complexes, their interactions kinetics, and functional modifications. Based on this information, computational biology researchers are attempting to build larger and more detailed models. However, this has proved to be a major challenge. Traditionally, modeling tools require the explicit specification of all molecular species and interactions in a model, which can quickly become a major limitation in the case of complex networks – the number of ways biomolecules can combine to form multimolecular complexes can be combinatorially large. Recently, a new breed of software tools has been created to address the problems faced when building models marked by combinatorial complexity. These have a different approach for model specification, using reaction rules and species patterns. Here we compare the traditional modeling approach with the new rule-based methods. We make a case for combining the capabilities of conventional simulation software with the unique features and flexibility of a rule-based approach in a single software platform for building models of molecular interaction networks. PMID:22161349

Leveraging modeling approaches: reaction networks and rules.

PubMed

Blinov, Michael L; Moraru, Ion I

2012-01-01

We have witnessed an explosive growth in research involving mathematical models and computer simulations of intracellular molecular interactions, ranging from metabolic pathways to signaling and gene regulatory networks. Many software tools have been developed to aid in the study of such biological systems, some of which have a wealth of features for model building and visualization, and powerful capabilities for simulation and data analysis. Novel high-resolution and/or high-throughput experimental techniques have led to an abundance of qualitative and quantitative data related to the spatiotemporal distribution of molecules and complexes, their interactions kinetics, and functional modifications. Based on this information, computational biology researchers are attempting to build larger and more detailed models. However, this has proved to be a major challenge. Traditionally, modeling tools require the explicit specification of all molecular species and interactions in a model, which can quickly become a major limitation in the case of complex networks - the number of ways biomolecules can combine to form multimolecular complexes can be combinatorially large. Recently, a new breed of software tools has been created to address the problems faced when building models marked by combinatorial complexity. These have a different approach for model specification, using reaction rules and species patterns. Here we compare the traditional modeling approach with the new rule-based methods. We make a case for combining the capabilities of conventional simulation software with the unique features and flexibility of a rule-based approach in a single software platform for building models of molecular interaction networks.

78 FR 62017 - Regulatory Capital Rules: Regulatory Capital, Implementation of Basel III, Capital Adequacy...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-11

...The Office of the Comptroller of the Currency (OCC) and Board of Governors of the Federal Reserve System (Board), are adopting a final rule that revises their risk-based and leverage capital requirements for banking organizations. The final rule consolidates three separate notices of proposed rulemaking that the OCC, Board, and FDIC published in the Federal Register on August 30, 2012, with selected changes. The final rule implements a revised definition of regulatory capital, a new common equity tier 1 minimum capital requirement, a higher minimum tier 1 capital requirement, and, for banking organizations subject to the advanced approaches risk-based capital rules, a supplementary leverage ratio that incorporates a broader set of exposures in the denominator. The final rule incorporates these new requirements into the agencies' prompt corrective action (PCA) framework. In addition, the final rule establishes limits on a banking organization's capital distributions and certain discretionary bonus payments if the banking organization does not hold a specified amount of common equity tier 1 capital in addition to the amount necessary to meet its minimum risk-based capital requirements. Further, the final rule amends the methodologies for determining risk-weighted assets for all banking organizations, and introduces disclosure requirements that would apply to top-tier banking organizations domiciled in the United States with $50 billion or more in total assets. The final rule also adopts changes to the agencies' regulatory capital requirements that meet the requirements of section 171 and section 939A of the Dodd-Frank Wall Street Reform and Consumer Protection Act. The final rule also codifies the agencies' regulatory capital rules, which have previously resided in various appendices to their respective regulations, into a harmonized integrated regulatory framework. In addition, the OCC is amending the market risk capital rule (market risk rule) to apply to Federal savings associations, and the Board is amending the advanced approaches and market risk rules to apply to top-tier savings and loan holding companies domiciled in the United States, except for certain savings and loan holding companies that are substantially engaged in insurance underwriting or commercial activities, as described in this preamble.

Progress in mental workload measurement

NASA Technical Reports Server (NTRS)

Moray, Neville; Turksen, Burhan; Aidie, Paul; Drascic, David; Eisen, Paul

1986-01-01

Two new techniques are described, one using subjective, the other physiological data for the measurement of workload in complex tasks. The subjective approach uses fuzzy measurement to analyze and predict the difficulty of combinations of skill based and rule based behavior from the difficulty of skill based behavior and rule based behavior measured separately. The physiological technique offers an on-line real-time filter for measuring the Mulder signal at 0.1 Hz in the heart rate variability spectrum.

Evolving optimised decision rules for intrusion detection using particle swarm paradigm

NASA Astrophysics Data System (ADS)

Sivatha Sindhu, Siva S.; Geetha, S.; Kannan, A.

2012-12-01

The aim of this article is to construct a practical intrusion detection system (IDS) that properly analyses the statistics of network traffic pattern and classify them as normal or anomalous class. The objective of this article is to prove that the choice of effective network traffic features and a proficient machine-learning paradigm enhances the detection accuracy of IDS. In this article, a rule-based approach with a family of six decision tree classifiers, namely Decision Stump, C4.5, Naive Baye's Tree, Random Forest, Random Tree and Representative Tree model to perform the detection of anomalous network pattern is introduced. In particular, the proposed swarm optimisation-based approach selects instances that compose training set and optimised decision tree operate over this trained set producing classification rules with improved coverage, classification capability and generalisation ability. Experiment with the Knowledge Discovery and Data mining (KDD) data set which have information on traffic pattern, during normal and intrusive behaviour shows that the proposed algorithm produces optimised decision rules and outperforms other machine-learning algorithm.

A personalized health-monitoring system for elderly by combining rules and case-based reasoning.

PubMed

Ahmed, Mobyen Uddin

2015-01-01

Health-monitoring system for elderly in home environment is a promising solution to provide efficient medical services that increasingly interest by the researchers within this area. It is often more challenging when the system is self-served and functioning as personalized provision. This paper proposed a personalized self-served health-monitoring system for elderly in home environment by combining general rules with a case-based reasoning approach. Here, the system generates feedback, recommendation and alarm in a personalized manner based on elderly's medical information and health parameters such as blood pressure, blood glucose, weight, activity, pulse, etc. A set of general rules has used to classify individual health parameters. The case-based reasoning approach is used to combine all different health parameters, which generates an overall classification of health condition. According to the evaluation result considering 323 cases and k=2 i.e., top 2 most similar retrieved cases, the sensitivity, specificity and overall accuracy are achieved as 90%, 97% and 96% respectively. The preliminary result of the system is acceptable since the feedback; recommendation and alarm messages are personalized and differ from the general messages. Thus, this approach could be possibly adapted for other situations in personalized elderly monitoring.

Multistrategy learning: A case study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Domingos, P.

1996-12-31

Two of the most popular approaches to induction are instance-based learning (IBL) and rule generation. Their strengths and weaknesses are largely complementary. IBL methods are able to identify small details in the instance space, but have trouble with attributes that are relevant in some parts of the space but not others. Conversely, rule induction methods may overlook small exception regions, but are able to select different attributes in different parts of the instance space. The two methods have been unified in the RISE algorithm. RISE views instances as maximally specific rules, forms more general rules by gradually clustering instances ofmore » the same class, and classifies a test example by letting the nearest rule win. This approach potentially combines the advantages of rule induction and IBL, and has indeed been observed to be more accurate than each on a large number of bench-mark datasets. However, it is important to determine if this performance is indeed due to the hypothesized advantages, and to define the situations in which RISE`s bias will and will not be preferable to those of the individual approaches. This abstract reports experiments to this end in artificial domains.« less

A multilayer perceptron solution to the match phase problem in rule-based artificial intelligence systems

NASA Technical Reports Server (NTRS)

Sartori, Michael A.; Passino, Kevin M.; Antsaklis, Panos J.

1992-01-01

In rule-based AI planning, expert, and learning systems, it is often the case that the left-hand-sides of the rules must be repeatedly compared to the contents of some 'working memory'. The traditional approach to solve such a 'match phase problem' for production systems is to use the Rete Match Algorithm. Here, a new technique using a multilayer perceptron, a particular artificial neural network model, is presented to solve the match phase problem for rule-based AI systems. A syntax for premise formulas (i.e., the left-hand-sides of the rules) is defined, and working memory is specified. From this, it is shown how to construct a multilayer perceptron that finds all of the rules which can be executed for the current situation in working memory. The complexity of the constructed multilayer perceptron is derived in terms of the maximum number of nodes and the required number of layers. A method for reducing the number of layers to at most three is also presented.

Evolutionary Data Mining Approach to Creating Digital Logic

DTIC Science & Technology

2010-01-01

To deal with this problem a genetic program (GP) based data mining ( DM ) procedure has been invented (Smith 2005). A genetic program is an algorithm...that can operate on the variables. When a GP was used as a DM function in the past to automatically create fuzzy decision trees, the Report...rules represents an approach to the determining the effect of linguistic imprecision, i.e., the inability of experts to provide crisp rules. The

Knowledge acquisition for case-based reasoning systems

NASA Technical Reports Server (NTRS)

Riesbeck, Christopher K.

1988-01-01

Case-based reasoning (CBR) is a simple idea: solve new problems by adapting old solutions to similar problems. The CBR approach offers several potential advantages over rule-based reasoning: rules are not combined blindly in a search for solutions, solutions can be explained in terms of concrete examples, and performance can improve automatically as new problems are solved and added to the case library. Moving CBR for the university research environment to the real world requires smooth interfaces for getting knowledge from experts. Described are the basic elements of an interface for acquiring three basic bodies of knowledge that any case-based reasoner requires: the case library of problems and their solutions, the analysis rules that flesh out input problem specifications so that relevant cases can be retrieved, and the adaptation rules that adjust old solutions to fit new problems.

Effect of Temporal Relationships in Associative Rule Mining for Web Log Data

PubMed Central

Mohd Khairudin, Nazli; Mustapha, Aida

2014-01-01

The advent of web-based applications and services has created such diverse and voluminous web log data stored in web servers, proxy servers, client machines, or organizational databases. This paper attempts to investigate the effect of temporal attribute in relational rule mining for web log data. We incorporated the characteristics of time in the rule mining process and analysed the effect of various temporal parameters. The rules generated from temporal relational rule mining are then compared against the rules generated from the classical rule mining approach such as the Apriori and FP-Growth algorithms. The results showed that by incorporating the temporal attribute via time, the number of rules generated is subsequently smaller but is comparable in terms of quality. PMID:24587757

Integrating reasoning and clinical archetypes using OWL ontologies and SWRL rules.

PubMed

Lezcano, Leonardo; Sicilia, Miguel-Angel; Rodríguez-Solano, Carlos

2011-04-01

Semantic interoperability is essential to facilitate the computerized support for alerts, workflow management and evidence-based healthcare across heterogeneous electronic health record (EHR) systems. Clinical archetypes, which are formal definitions of specific clinical concepts defined as specializations of a generic reference (information) model, provide a mechanism to express data structures in a shared and interoperable way. However, currently available archetype languages do not provide direct support for mapping to formal ontologies and then exploiting reasoning on clinical knowledge, which are key ingredients of full semantic interoperability, as stated in the SemanticHEALTH report [1]. This paper reports on an approach to translate definitions expressed in the openEHR Archetype Definition Language (ADL) to a formal representation expressed using the Ontology Web Language (OWL). The formal representations are then integrated with rules expressed with Semantic Web Rule Language (SWRL) expressions, providing an approach to apply the SWRL rules to concrete instances of clinical data. Sharing the knowledge expressed in the form of rules is consistent with the philosophy of open sharing, encouraged by archetypes. Our approach also allows the reuse of formal knowledge, expressed through ontologies, and extends reuse to propositions of declarative knowledge, such as those encoded in clinical guidelines. This paper describes the ADL-to-OWL translation approach, describes the techniques to map archetypes to formal ontologies, and demonstrates how rules can be applied to the resulting representation. We provide examples taken from a patient safety alerting system to illustrate our approach. Copyright © 2010 Elsevier Inc. All rights reserved.

Advancing reservoir operation description in physically based hydrological models

NASA Astrophysics Data System (ADS)

Anghileri, Daniela; Giudici, Federico; Castelletti, Andrea; Burlando, Paolo

2016-04-01

Last decades have seen significant advances in our capacity of characterizing and reproducing hydrological processes within physically based models. Yet, when the human component is considered (e.g. reservoirs, water distribution systems), the associated decisions are generally modeled with very simplistic rules, which might underperform in reproducing the actual operators' behaviour on a daily or sub-daily basis. For example, reservoir operations are usually described by a target-level rule curve, which represents the level that the reservoir should track during normal operating conditions. The associated release decision is determined by the current state of the reservoir relative to the rule curve. This modeling approach can reasonably reproduce the seasonal water volume shift due to reservoir operation. Still, it cannot capture more complex decision making processes in response, e.g., to the fluctuations of energy prices and demands, the temporal unavailability of power plants or varying amount of snow accumulated in the basin. In this work, we link a physically explicit hydrological model with detailed hydropower behavioural models describing the decision making process by the dam operator. In particular, we consider two categories of behavioural models: explicit or rule-based behavioural models, where reservoir operating rules are empirically inferred from observational data, and implicit or optimization based behavioural models, where, following a normative economic approach, the decision maker is represented as a rational agent maximising a utility function. We compare these two alternate modelling approaches on the real-world water system of Lake Como catchment in the Italian Alps. The water system is characterized by the presence of 18 artificial hydropower reservoirs generating almost 13% of the Italian hydropower production. Results show to which extent the hydrological regime in the catchment is affected by different behavioural models and reservoir operating strategies.

Ontology Based Quality Evaluation for Spatial Data

NASA Astrophysics Data System (ADS)

Yılmaz, C.; Cömert, Ç.

2015-08-01

Many institutions will be providing data to the National Spatial Data Infrastructure (NSDI). Current technical background of the NSDI is based on syntactic web services. It is expected that this will be replaced by semantic web services. The quality of the data provided is important in terms of the decision-making process and the accuracy of transactions. Therefore, the data quality needs to be tested. This topic has been neglected in Turkey. Data quality control for NSDI may be done by private or public "data accreditation" institutions. A methodology is required for data quality evaluation. There are studies for data quality including ISO standards, academic studies and software to evaluate spatial data quality. ISO 19157 standard defines the data quality elements. Proprietary software such as, 1Spatial's 1Validate and ESRI's Data Reviewer offers quality evaluation based on their own classification of rules. Commonly, rule based approaches are used for geospatial data quality check. In this study, we look for the technical components to devise and implement a rule based approach with ontologies using free and open source software in semantic web context. Semantic web uses ontologies to deliver well-defined web resources and make them accessible to end-users and processes. We have created an ontology conforming to the geospatial data and defined some sample rules to show how to test data with respect to data quality elements including; attribute, topo-semantic and geometrical consistency using free and open source software. To test data against rules, sample GeoSPARQL queries are created, associated with specifications.

Rule-based topology system for spatial databases to validate complex geographic datasets

NASA Astrophysics Data System (ADS)

Martinez-Llario, J.; Coll, E.; Núñez-Andrés, M.; Femenia-Ribera, C.

2017-06-01

A rule-based topology software system providing a highly flexible and fast procedure to enforce integrity in spatial relationships among datasets is presented. This improved topology rule system is built over the spatial extension Jaspa. Both projects are open source, freely available software developed by the corresponding author of this paper. Currently, there is no spatial DBMS that implements a rule-based topology engine (considering that the topology rules are designed and performed in the spatial backend). If the topology rules are applied in the frontend (as in many GIS desktop programs), ArcGIS is the most advanced solution. The system presented in this paper has several major advantages over the ArcGIS approach: it can be extended with new topology rules, it has a much wider set of rules, and it can mix feature attributes with topology rules as filters. In addition, the topology rule system can work with various DBMSs, including PostgreSQL, H2 or Oracle, and the logic is performed in the spatial backend. The proposed topology system allows users to check the complex spatial relationships among features (from one or several spatial layers) that require some complex cartographic datasets, such as the data specifications proposed by INSPIRE in Europe and the Land Administration Domain Model (LADM) for Cadastral data.

Using Landscape-Based Decision Rules to Prioritize Locations of Fuel Treatments in the Boreal Mixedwood of Western Canada

Treesearch

Marc-André Parisien; Dave R. Junor; Victor G. Kafka

2006-01-01

This study used a rule-based approach to prioritize locations of fuel treatments in the boreal mixedwood forest of western Canada. The burn probability (BP) in and around Prince Albert National Park in Saskatchewan was mapped using the Burn-P3 (Probability, Prediction, and Planning) model. Fuel treatment locations were determined according to three scenarios and five...

Heat exchanger expert system logic

NASA Technical Reports Server (NTRS)

Cormier, R.

1988-01-01

The reduction is described of the operation and fault diagnostics of a Deep Space Network heat exchanger to a rule base by the application of propositional calculus to a set of logic statements. The value of this approach lies in the ease of converting the logic and subsequently implementing it on a computer as an expert system. The rule base was written in Process Intelligent Control software.

Rule-based mapping of fire-adapted vegetation and fire regimes for the Monongahela National Forest

Treesearch

Melissa A. Thomas-Van Gundy; Gregory J. Nowacki; Thomas M. Schuler

2007-01-01

A rule-based approach was employed in GIS to map fire-adapted vegetation and fire regimes within the proclamation boundary of the Monongahela National Forest. Spatial analyses and maps were generated using ArcMap 9.1. The resulting fireadaptation scores were then categorized into standard fire regime groups. Fire regime group V (200+ yrs) was the most common, assigned...

Competency-Based Curriculum Development: A Pragmatic Approach

ERIC Educational Resources Information Center

Broski, David; And Others

1977-01-01

Examines the concept of competency-based education, describes an experience-based model for its development, and discusses some empirically derived rules-of-thumb for its application in allied health. (HD)

78 FR 48932 - Proposed Agency Information Collection Activities; Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-12

... subject to the advanced approaches risk-based capital rules (advanced approaches banking organizations... approaches banking organizations that are savings and loan holding companies and that are subject to the... advanced approaches banking organization is required to file quarterly regulatory capital data. The...

Interpretable Decision Sets: A Joint Framework for Description and Prediction

PubMed Central

Lakkaraju, Himabindu; Bach, Stephen H.; Jure, Leskovec

2016-01-01

One of the most important obstacles to deploying predictive models is the fact that humans do not understand and trust them. Knowing which variables are important in a model’s prediction and how they are combined can be very powerful in helping people understand and trust automatic decision making systems. Here we propose interpretable decision sets, a framework for building predictive models that are highly accurate, yet also highly interpretable. Decision sets are sets of independent if-then rules. Because each rule can be applied independently, decision sets are simple, concise, and easily interpretable. We formalize decision set learning through an objective function that simultaneously optimizes accuracy and interpretability of the rules. In particular, our approach learns short, accurate, and non-overlapping rules that cover the whole feature space and pay attention to small but important classes. Moreover, we prove that our objective is a non-monotone submodular function, which we efficiently optimize to find a near-optimal set of rules. Experiments show that interpretable decision sets are as accurate at classification as state-of-the-art machine learning techniques. They are also three times smaller on average than rule-based models learned by other methods. Finally, results of a user study show that people are able to answer multiple-choice questions about the decision boundaries of interpretable decision sets and write descriptions of classes based on them faster and more accurately than with other rule-based models that were designed for interpretability. Overall, our framework provides a new approach to interpretable machine learning that balances accuracy, interpretability, and computational efficiency. PMID:27853627

The Place of Grammar in the Language Arts Curriculum.

ERIC Educational Resources Information Center

Einarsson, Robert

The history of grammar instruction includes two approaches: the handbook approach, which is practiced today, and the textbook approach. The handbook approach focuses on rules for correct writing and is an error-based view, while the textbook approach would treat grammar holistically and interpretively and would systematically explain new concepts…

Amino Acid Distribution Rules Predict Protein Fold: Protein Grammar for Beta-Strand Sandwich-Like Structures

PubMed Central

Kister, Alexander

2015-01-01

We present an alternative approach to protein 3D folding prediction based on determination of rules that specify distribution of “favorable” residues, that are mainly responsible for a given fold formation, and “unfavorable” residues, that are incompatible with that fold, in polypeptide sequences. The process of determining favorable and unfavorable residues is iterative. The starting assumptions are based on the general principles of protein structure formation as well as structural features peculiar to a protein fold under investigation. The initial assumptions are tested one-by-one for a set of all known proteins with a given structure. The assumption is accepted as a “rule of amino acid distribution” for the protein fold if it holds true for all, or near all, structures. If the assumption is not accepted as a rule, it can be modified to better fit the data and then tested again in the next step of the iterative search algorithm, or rejected. We determined the set of amino acid distribution rules for a large group of beta sandwich-like proteins characterized by a specific arrangement of strands in two beta sheets. It was shown that this set of rules is highly sensitive (~90%) and very specific (~99%) for identifying sequences of proteins with specified beta sandwich fold structure. The advantage of the proposed approach is that it does not require that query proteins have a high degree of homology to proteins with known structure. So long as the query protein satisfies residue distribution rules, it can be confidently assigned to its respective protein fold. Another advantage of our approach is that it allows for a better understanding of which residues play an essential role in protein fold formation. It may, therefore, facilitate rational protein engineering design. PMID:25625198

Phases, phase equilibria, and phase rules in low-dimensional systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frolov, T., E-mail: timfrol@berkeley.edu; Mishin, Y., E-mail: ymishin@gmu.edu

2015-07-28

We present a unified approach to thermodynamic description of one, two, and three dimensional phases and phase transformations among them. The approach is based on a rigorous definition of a phase applicable to thermodynamic systems of any dimensionality. Within this approach, the same thermodynamic formalism can be applied for the description of phase transformations in bulk systems, interfaces, and line defects separating interface phases. For both lines and interfaces, we rigorously derive an adsorption equation, the phase coexistence equations, and other thermodynamic relations expressed in terms of generalized line and interface excess quantities. As a generalization of the Gibbs phasemore » rule for bulk phases, we derive phase rules for lines and interfaces and predict the maximum number of phases than may coexist in systems of the respective dimensionality.« less

Explanation-based learning in infancy.

PubMed

Baillargeon, Renée; DeJong, Gerald F

2017-10-01

In explanation-based learning (EBL), domain knowledge is leveraged in order to learn general rules from few examples. An explanation is constructed for initial exemplars and is then generalized into a candidate rule that uses only the relevant features specified in the explanation; if the rule proves accurate for a few additional exemplars, it is adopted. EBL is thus highly efficient because it combines both analytic and empirical evidence. EBL has been proposed as one of the mechanisms that help infants acquire and revise their physical rules. To evaluate this proposal, 11- and 12-month-olds (n = 260) were taught to replace their current support rule (that an object is stable when half or more of its bottom surface is supported) with a more sophisticated rule (that an object is stable when half or more of the entire object is supported). Infants saw teaching events in which asymmetrical objects were placed on a base, followed by static test displays involving a novel asymmetrical object and a novel base. When the teaching events were designed to facilitate EBL, infants learned the new rule with as few as two (12-month-olds) or three (11-month-olds) exemplars. When the teaching events were designed to impede EBL, however, infants failed to learn the rule. Together, these results demonstrate that even infants, with their limited knowledge about the world, benefit from the knowledge-based approach of EBL.

Bridging the gap between formal and experience-based knowledge for context-aware laparoscopy.

PubMed

Katić, Darko; Schuck, Jürgen; Wekerle, Anna-Laura; Kenngott, Hannes; Müller-Stich, Beat Peter; Dillmann, Rüdiger; Speidel, Stefanie

2016-06-01

Computer assistance is increasingly common in surgery. However, the amount of information is bound to overload processing abilities of surgeons. We propose methods to recognize the current phase of a surgery for context-aware information filtering. The purpose is to select the most suitable subset of information for surgical situations which require special assistance. We combine formal knowledge, represented by an ontology, and experience-based knowledge, represented by training samples, to recognize phases. For this purpose, we have developed two different methods. Firstly, we use formal knowledge about possible phase transitions to create a composition of random forests. Secondly, we propose a method based on cultural optimization to infer formal rules from experience to recognize phases. The proposed methods are compared with a purely formal knowledge-based approach using rules and a purely experience-based one using regular random forests. The comparative evaluation on laparoscopic pancreas resections and adrenalectomies employs a consistent set of quality criteria on clean and noisy input. The rule-based approaches proved best with noisefree data. The random forest-based ones were more robust in the presence of noise. Formal and experience-based knowledge can be successfully combined for robust phase recognition.

Learning accurate and interpretable models based on regularized random forests regression

PubMed Central

2014-01-01

Background Many biology related research works combine data from multiple sources in an effort to understand the underlying problems. It is important to find and interpret the most important information from these sources. Thus it will be beneficial to have an effective algorithm that can simultaneously extract decision rules and select critical features for good interpretation while preserving the prediction performance. Methods In this study, we focus on regression problems for biological data where target outcomes are continuous. In general, models constructed from linear regression approaches are relatively easy to interpret. However, many practical biological applications are nonlinear in essence where we can hardly find a direct linear relationship between input and output. Nonlinear regression techniques can reveal nonlinear relationship of data, but are generally hard for human to interpret. We propose a rule based regression algorithm that uses 1-norm regularized random forests. The proposed approach simultaneously extracts a small number of rules from generated random forests and eliminates unimportant features. Results We tested the approach on some biological data sets. The proposed approach is able to construct a significantly smaller set of regression rules using a subset of attributes while achieving prediction performance comparable to that of random forests regression. Conclusion It demonstrates high potential in aiding prediction and interpretation of nonlinear relationships of the subject being studied. PMID:25350120

On Inference Rules of Logic-Based Information Retrieval Systems.

ERIC Educational Resources Information Center

Chen, Patrick Shicheng

1994-01-01

Discussion of relevance and the needs of the users in information retrieval focuses on a deductive object-oriented approach and suggests eight inference rules for the deduction. Highlights include characteristics of a deductive object-oriented system, database and data modeling language, implementation, and user interface. (Contains 24…

A Rules-Based Service for Suggesting Visualizations to Analyze Earth Science Phenomena.

NASA Astrophysics Data System (ADS)

Prabhu, A.; Zednik, S.; Fox, P. A.; Ramachandran, R.; Maskey, M.; Shie, C. L.; Shen, S.

2016-12-01

Current Earth Science Information Systems lack support for new or interdisciplinary researchers, who may be unfamiliar with the domain vocabulary or the breadth of relevant data available. We need to evolve the current information systems, to reduce the time required for data preparation, processing and analysis. This can be done by effectively salvaging the "dark" resources in Earth Science. We assert that Earth science metadata assets are dark resources, information resources that organizations collect, process, and store for regular business or operational activities but fail to utilize for other purposes. In order to effectively use these dark resources, especially for data processing and visualization, we need a combination of domain, data product and processing knowledge, i.e. a knowledge base from which specific data operations can be performed. In this presentation, we describe a semantic, rules based approach to provide i.e. a service to visualize Earth Science phenomena, based on the data variables extracted using the "dark" metadata resources. We use Jena rules to make assertions about compatibility between a phenomena and various visualizations based on multiple factors. We created separate orthogonal rulesets to map each of these factors to the various phenomena. Some of the factors we have considered include measurements, spatial resolution and time intervals. This approach enables easy additions and deletions based on newly obtained domain knowledge or phenomena related information and thus improving the accuracy of the rules service overall.

Solutions to time variant problems of real-time expert systems

NASA Technical Reports Server (NTRS)

Yeh, Show-Way; Wu, Chuan-Lin; Hung, Chaw-Kwei

1988-01-01

Real-time expert systems for monitoring and control are driven by input data which changes with time. One of the subtle problems of this field is the propagation of time variant problems from rule to rule. This propagation problem is even complicated under a multiprogramming environment where the expert system may issue test commands to the system to get data and to access time consuming devices to retrieve data for concurrent reasoning. Two approaches are used to handle the flood of input data. Snapshots can be taken to freeze the system from time to time. The expert system treats the system as a stationary one and traces changes by comparing consecutive snapshots. In the other approach, when an input is available, the rules associated with it are evaluated. For both approaches, if the premise condition of a fired rule is changed to being false, the downstream rules should be deactivated. If the status change is due to disappearance of a transient problem, actions taken by the fired downstream rules which are no longer true may need to be undone. If a downstream rule is being evaluated, it should not be fired. Three mechanisms for solving this problem are discussed: tracing, backward checking, and censor setting. In the forward tracing mechanism, when the premise conditions of a fired rule become false, the premise conditions of downstream rules which have been fired or are being evaluated due to the firing of that rule are reevaluated. A tree with its root at the rule being deactivated is traversed. In the backward checking mechanism, when a rule is being fired, the expert system checks back on the premise conditions of the upstream rules that result in evaluation of the rule to see whether it should be fired. The root of the tree being traversed is the rule being fired. In the censor setting mechanism, when a rule is to be evaluated, a censor is constructed based on the premise conditions of the upstream rules and the censor is evaluated just before the rule is fired. Unlike the backward checking mechanism, this one does not search the upstream rules. This paper explores the details of implementation of the three mechanisms.

Integrating the ECG power-line interference removal methods with rule-based system.

PubMed

Kumaravel, N; Senthil, A; Sridhar, K S; Nithiyanandam, N

1995-01-01

The power-line frequency interference in electrocardiographic signals is eliminated to enhance the signal characteristics for diagnosis. The power-line frequency normally varies +/- 1.5 Hz from its standard value of 50 Hz. In the present work, the performances of the linear FIR filter, Wave digital filter (WDF) and adaptive filter for the power-line frequency variations from 48.5 to 51.5 Hz in steps of 0.5 Hz are studied. The advantage of the LMS adaptive filter in the removal of power-line frequency interference even if the frequency of interference varies by +/- 1.5 Hz from its normal value of 50 Hz over other fixed frequency filters is very well justified. A novel method of integrating rule-based system approach with linear FIR filter and also with Wave digital filter are proposed. The performances of Rule-based FIR filter and Rule-based Wave digital filter are compared with the LMS adaptive filter.

Confidence of compliance: a Bayesian approach for percentile standards.

PubMed

McBride, G B; Ellis, J C

2001-04-01

Rules for assessing compliance with percentile standards commonly limit the number of exceedances permitted in a batch of samples taken over a defined assessment period. Such rules are commonly developed using classical statistical methods. Results from alternative Bayesian methods are presented (using beta-distributed prior information and a binomial likelihood), resulting in "confidence of compliance" graphs. These allow simple reading of the consumer's risk and the supplier's risks for any proposed rule. The influence of the prior assumptions required by the Bayesian technique on the confidence results is demonstrated, using two reference priors (uniform and Jeffreys') and also using optimistic and pessimistic user-defined priors. All four give less pessimistic results than does the classical technique, because interpreting classical results as "confidence of compliance" actually invokes a Bayesian approach with an extreme prior distribution. Jeffreys' prior is shown to be the most generally appropriate choice of prior distribution. Cost savings can be expected using rules based on this approach.

A Novel Biclustering Approach to Association Rule Mining for Predicting HIV-1–Human Protein Interactions

PubMed Central

Mukhopadhyay, Anirban; Maulik, Ujjwal; Bandyopadhyay, Sanghamitra

2012-01-01

Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions. In recent days, computational tools are being utilized for predicting viral-host interactions. Recently a database containing records of experimentally validated interactions between a set of HIV-1 proteins and a set of human proteins has been published. The problem of predicting new interactions based on this database is usually posed as a classification problem. However, posing the problem as a classification one suffers from the lack of biologically validated negative interactions. Therefore it will be beneficial to use the existing database for predicting new viral-host interactions without the need of negative samples. Motivated by this, in this article, the HIV-1–human protein interaction database has been analyzed using association rule mining. The main objective is to identify a set of association rules both among the HIV-1 proteins and among the human proteins, and use these rules for predicting new interactions. In this regard, a novel association rule mining technique based on biclustering has been proposed for discovering frequent closed itemsets followed by the association rules from the adjacency matrix of the HIV-1–human interaction network. Novel HIV-1–human interactions have been predicted based on the discovered association rules and tested for biological significance. For validation of the predicted new interactions, gene ontology-based and pathway-based studies have been performed. These studies show that the human proteins which are predicted to interact with a particular viral protein share many common biological activities. Moreover, literature survey has been used for validation purpose to identify some predicted interactions that are already validated experimentally but not present in the database. Comparison with other prediction methods is also discussed. PMID:22539940

Techniques and implementation of the embedded rule-based expert system using Ada

NASA Technical Reports Server (NTRS)

Liberman, Eugene M.; Jones, Robert E.

1991-01-01

Ada is becoming an increasingly popular programming language for large Government-funded software projects. Ada with its portability, transportability, and maintainability lends itself well to today's complex programming environment. In addition, expert systems have also assured a growing role in providing human-like reasoning capability and expertise for computer systems. The integration of expert system technology with Ada programming language, specifically a rule-based expert system using an ART-Ada (Automated Reasoning Tool for Ada) system shell is discussed. The NASA Lewis Research Center was chosen as a beta test site for ART-Ada. The test was conducted by implementing the existing Autonomous Power EXpert System (APEX), a Lisp-base power expert system, in ART-Ada. Three components, the rule-based expert system, a graphics user interface, and communications software make up SMART-Ada (Systems fault Management with ART-Ada). The main objective, to conduct a beta test on the ART-Ada rule-based expert system shell, was achieved. The system is operational. New Ada tools will assist in future successful projects. ART-Ada is one such tool and is a viable alternative to the straight Ada code when an application requires a rule-based or knowledge-based approach.

Spurious symptom reduction in fault monitoring

NASA Technical Reports Server (NTRS)

Shontz, William D.; Records, Roger M.; Choi, Jai J.

1993-01-01

Previous work accomplished on NASA's Faultfinder concept suggested that the concept was jeopardized by spurious symptoms generated in the monitoring phase. The purpose of the present research was to investigate methods of reducing the generation of spurious symptoms during in-flight engine monitoring. Two approaches for reducing spurious symptoms were investigated. A knowledge base of rules was constructed to filter known spurious symptoms and a neural net was developed to improve the expectation values used in the monitoring process. Both approaches were effective in reducing spurious symptoms individually. However, the best results were obtained using a hybrid system combining the neural net capability to improve expectation values with the rule-based logic filter.

A hybrid learning method for constructing compact rule-based fuzzy models.

PubMed

Zhao, Wanqing; Niu, Qun; Li, Kang; Irwin, George W

2013-12-01

The Takagi–Sugeno–Kang-type rule-based fuzzy model has found many applications in different fields; a major challenge is, however, to build a compact model with optimized model parameters which leads to satisfactory model performance. To produce a compact model, most existing approaches mainly focus on selecting an appropriate number of fuzzy rules. In contrast, this paper considers not only the selection of fuzzy rules but also the structure of each rule premise and consequent, leading to the development of a novel compact rule-based fuzzy model. Here, each fuzzy rule is associated with two sets of input attributes, in which the first is used for constructing the rule premise and the other is employed in the rule consequent. A new hybrid learning method combining the modified harmony search method with a fast recursive algorithm is hereby proposed to determine the structure and the parameters for the rule premises and consequents. This is a hard mixed-integer nonlinear optimization problem, and the proposed hybrid method solves the problem by employing an embedded framework, leading to a significantly reduced number of model parameters and a small number of fuzzy rules with each being as simple as possible. Results from three examples are presented to demonstrate the compactness (in terms of the number of model parameters and the number of rules) and the performance of the fuzzy models obtained by the proposed hybrid learning method, in comparison with other techniques from the literature.

Fifty years of computer analysis in chest imaging: rule-based, machine learning, deep learning.

PubMed

van Ginneken, Bram

2017-03-01

Half a century ago, the term "computer-aided diagnosis" (CAD) was introduced in the scientific literature. Pulmonary imaging, with chest radiography and computed tomography, has always been one of the focus areas in this field. In this study, I describe how machine learning became the dominant technology for tackling CAD in the lungs, generally producing better results than do classical rule-based approaches, and how the field is now rapidly changing: in the last few years, we have seen how even better results can be obtained with deep learning. The key differences among rule-based processing, machine learning, and deep learning are summarized and illustrated for various applications of CAD in the chest.

TOMML: A Rule Language for Structured Data

NASA Astrophysics Data System (ADS)

Cirstea, Horatiu; Moreau, Pierre-Etienne; Reilles, Antoine

We present the TOM language that extends JAVA with the purpose of providing high level constructs inspired by the rewriting community. TOM bridges thus the gap between a general purpose language and high level specifications based on rewriting. This approach was motivated by the promotion of rule based techniques and their integration in large scale applications. Powerful matching capabilities along with a rich strategy language are among TOM's strong features that make it easy to use and competitive with respect to other rule based languages. TOM is thus a natural choice for querying and transforming structured data and in particular XML documents [1]. We present here its main XML oriented features and illustrate its use on several examples.

Sum rules for the uniform-background model of an atomic-sharp metal corner

NASA Astrophysics Data System (ADS)

Streitenberger, P.

1994-04-01

Analytical results are derived for the electrostatic potential of an atomic-sharp 90° metal corner in the uniform-background model. The electrostatic potential at a free jellium edge and the jellium corner, respectively, is determined exactly in terms of the energy per electron of the uniform electron gas integrated over the background density. The surface energy, the edge formation energy and the derivative of the corner formation energy with respect to the background density are given as integrals over the electrostatic potential. The present approach represents a novel approach to such sum rules, inclusive of the Budd-Vannimenus sum rules for a free jellium surface, based on general properties of linear response functions.

An approach to knowledge engineering to support knowledge-based simulation of payload ground processing at the Kennedy Space Center

NASA Technical Reports Server (NTRS)

Mcmanus, Shawn; Mcdaniel, Michael

1989-01-01

Planning for processing payloads was always difficult and time-consuming. With the advent of Space Station Freedom and its capability to support a myriad of complex payloads, the planning to support this ground processing maze involves thousands of man-hours of often tedious data manipulation. To provide the capability to analyze various processing schedules, an object oriented knowledge-based simulation environment called the Advanced Generic Accomodations Planning Environment (AGAPE) is being developed. Having nearly completed the baseline system, the emphasis in this paper is directed toward rule definition and its relation to model development and simulation. The focus is specifically on the methodologies implemented during knowledge acquisition, analysis, and representation within the AGAPE rule structure. A model is provided to illustrate the concepts presented. The approach demonstrates a framework for AGAPE rule development to assist expert system development.

Ethical Virtues in Scientific Research

PubMed Central

Resnik, David B.

2012-01-01

Most approaches to promoting integrity in research are principle-based in that they portray ethical conduct as consisting of adherence to ethical rules, duties, or responsibilities. Bruce MacFarlane has recently criticized the principle-based approach to promoting integrity in research and offered a virtue-based alternative. MacFarlane argues that principle-based approaches do not provide adequate guidance for ethical decision-making and are not very useful in moral education. In this article, I examine and critique MacFarlane’s defense of the virtue-based approach. I argue that virtue-based and principle-based approaches to ethics are complementary and that they both can help promote research integrity. PMID:23074991

Ethical virtues in scientific research.

PubMed

Resnik, David B

2012-01-01

Most approaches to promoting integrity in research are principle-based in that they portray ethical conduct as consisting of adherence to ethical rules, duties, or responsibilities. Bruce MacFarlane has recently criticized the principle-based approach to promoting integrity in research and offered a virtue-based alternative. MacFarlane argues that principle-based approaches do not provide adequate guidance for ethical decision-making and are not very useful in moral education. In this article, I examine and critique MacFarlane's defense of the virtue-based approach. I argue that virtue-based and principle-based approaches to ethics are complementary and that they both can help promote research integrity.

ACUTE STUDIES OF INHALED CHLORINE IN F344 RATS SUGGEST ALTERNATIVE TO HABER'S RULE FOR RISK EXTRAPOLATIONS

EPA Science Inventory

Chlorine (CI2), a high-production volume air pollutant, is an irritant of interest to homeland security. Risk assessment approaches to establish egress or re-entry levels typically use an assumption based on Haber's Rule and apply a concentration times duration ("C x t") adjustme...

Searle on Language.

ERIC Educational Resources Information Center

Love, Nigel

1999-01-01

Discusses J. R. Searle's theories about language, based on three works dating from 1969-95. Looks at the distinction made between constitutive rules and regulative rules of language use, his approach to analyzing speech acts, the view of language as a means of stating facts, and the role that our conception of science plays in Searle's theorizing.…

Reservoir adaptive operating rules based on both of historical streamflow and future projections

NASA Astrophysics Data System (ADS)

Zhang, Wei; Liu, Pan; Wang, Hao; Chen, Jie; Lei, Xiaohui; Feng, Maoyuan

2017-10-01

Climate change is affecting hydrological variables and consequently is impacting water resources management. Historical strategies are no longer applicable under climate change. Therefore, adaptive management, especially adaptive operating rules for reservoirs, has been developed to mitigate the possible adverse effects of climate change. However, to date, adaptive operating rules are generally based on future projections involving uncertainties under climate change, yet ignoring historical information. To address this, we propose an approach for deriving adaptive operating rules considering both historical information and future projections, namely historical and future operating rules (HAFOR). A robustness index was developed by comparing benefits from HAFOR with benefits from conventional operating rules (COR). For both historical and future streamflow series, maximizations of both average benefits and the robustness index were employed as objectives, and four trade-offs were implemented to solve the multi-objective problem. Based on the integrated objective, the simulation-based optimization method was used to optimize the parameters of HAFOR. Using the Dongwushi Reservoir in China as a case study, HAFOR was demonstrated to be an effective and robust method for developing adaptive operating rules under the uncertain changing environment. Compared with historical or projected future operating rules (HOR or FPOR), HAFOR can reduce the uncertainty and increase the robustness for future projections, especially regarding results of reservoir releases and volumes. HAFOR, therefore, facilitates adaptive management in the context that climate change is difficult to predict accurately.

Belief Function Based Decision Fusion for Decentralized Target Classification in Wireless Sensor Networks

PubMed Central

Zhang, Wenyu; Zhang, Zhenjiang

2015-01-01

Decision fusion in sensor networks enables sensors to improve classification accuracy while reducing the energy consumption and bandwidth demand for data transmission. In this paper, we focus on the decentralized multi-class classification fusion problem in wireless sensor networks (WSNs) and a new simple but effective decision fusion rule based on belief function theory is proposed. Unlike existing belief function based decision fusion schemes, the proposed approach is compatible with any type of classifier because the basic belief assignments (BBAs) of each sensor are constructed on the basis of the classifier’s training output confusion matrix and real-time observations. We also derive explicit global BBA in the fusion center under Dempster’s combinational rule, making the decision making operation in the fusion center greatly simplified. Also, sending the whole BBA structure to the fusion center is avoided. Experimental results demonstrate that the proposed fusion rule has better performance in fusion accuracy compared with the naïve Bayes rule and weighted majority voting rule. PMID:26295399

Using fuzzy rule-based knowledge model for optimum plating conditions search

NASA Astrophysics Data System (ADS)

Solovjev, D. S.; Solovjeva, I. A.; Litovka, Yu V.; Arzamastsev, A. A.; Glazkov, V. P.; L’vov, A. A.

2018-03-01

The paper discusses existing approaches to plating process modeling in order to decrease the distribution thickness of plating surface cover. However, these approaches do not take into account the experience, knowledge, and intuition of the decision-makers when searching the optimal conditions of electroplating technological process. The original approach to optimal conditions search for applying the electroplating coatings, which uses the rule-based model of knowledge and allows one to reduce the uneven product thickness distribution, is proposed. The block diagrams of a conventional control system of a galvanic process as well as the system based on the production model of knowledge are considered. It is shown that the fuzzy production model of knowledge in the control system makes it possible to obtain galvanic coatings of a given thickness unevenness with a high degree of adequacy to the experimental data. The described experimental results confirm the theoretical conclusions.

Multi-objective design of fuzzy logic controller in supply chain

NASA Astrophysics Data System (ADS)

Ghane, Mahdi; Tarokh, Mohammad Jafar

2012-08-01

Unlike commonly used methods, in this paper, we have introduced a new approach for designing fuzzy controllers. In this approach, we have simultaneously optimized both objective functions of a supply chain over a two-dimensional space. Then, we have obtained a spectrum of optimized points, each of which represents a set of optimal parameters which can be chosen by the manager according to the importance of objective functions. Our used supply chain model is a member of inventory and order-based production control system family, a generalization of the periodic review which is termed `Order-Up-To policy.' An auto rule maker, based on non-dominated sorting genetic algorithm-II, has been applied to the experimental initial fuzzy rules. According to performance measurement, our results indicate the efficiency of the proposed approach.

Template-based procedures for neural network interpretation.

PubMed

Alexander, J A.; Mozer, M C.

1999-04-01

Although neural networks often achieve impressive learning and generalization performance, their internal workings are typically all but impossible to decipher. This characteristic of the networks, their opacity, is one of the disadvantages of connectionism compared to more traditional, rule-oriented approaches to artificial intelligence. Without a thorough understanding of the network behavior, confidence in a system's results is lowered, and the transfer of learned knowledge to other processing systems - including humans - is precluded. Methods that address the opacity problem by casting network weights in symbolic terms are commonly referred to as rule extraction techniques. This work describes a principled approach to symbolic rule extraction from standard multilayer feedforward networks based on the notion of weight templates, parameterized regions of weight space corresponding to specific symbolic expressions. With an appropriate choice of representation, we show how template parameters may be efficiently identified and instantiated to yield the optimal match to the actual weights of a unit. Depending on the requirements of the application domain, the approach can accommodate n-ary disjunctions and conjunctions with O(k) complexity, simple n-of-m expressions with O(k(2)) complexity, or more general classes of recursive n-of-m expressions with O(k(L+2)) complexity, where k is the number of inputs to an unit and L the recursion level of the expression class. Compared to other approaches in the literature, our method of rule extraction offers benefits in simplicity, computational performance, and overall flexibility. Simulation results on a variety of problems demonstrate the application of our procedures as well as the strengths and the weaknesses of our general approach.

A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules.

PubMed

Nguyen, Su; Mei, Yi; Xue, Bing; Zhang, Mengjie

2018-06-04

Designing effective dispatching rules for production systems is a difficult and timeconsuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This paper develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

Using GO-WAR for mining cross-ontology weighted association rules.

PubMed

Agapito, Giuseppe; Cannataro, Mario; Guzzi, Pietro Hiram; Milano, Marianna

2015-07-01

The Gene Ontology (GO) is a structured repository of concepts (GO terms) that are associated to one or more gene products. The process of association is referred to as annotation. The relevance and the specificity of both GO terms and annotations are evaluated by a measure defined as information content (IC). The analysis of annotated data is thus an important challenge for bioinformatics. There exist different approaches of analysis. From those, the use of association rules (AR) may provide useful knowledge, and it has been used in some applications, e.g. improving the quality of annotations. Nevertheless classical association rules algorithms do not take into account the source of annotation nor the importance yielding to the generation of candidate rules with low IC. This paper presents GO-WAR (Gene Ontology-based Weighted Association Rules) a methodology for extracting weighted association rules. GO-WAR can extract association rules with a high level of IC without loss of support and confidence from a dataset of annotated data. A case study on using of GO-WAR on publicly available GO annotation datasets is used to demonstrate that our method outperforms current state of the art approaches. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Fuzzy architecture assessment for critical infrastructure resilience

DOE Office of Scientific and Technical Information (OSTI.GOV)

Muller, George

2012-12-01

This paper presents an approach for the selection of alternative architectures in a connected infrastructure system to increase resilience of the overall infrastructure system. The paper begins with a description of resilience and critical infrastructure, then summarizes existing approaches to resilience, and presents a fuzzy-rule based method of selecting among alternative infrastructure architectures. This methodology includes considerations which are most important when deciding on an approach to resilience. The paper concludes with a proposed approach which builds on existing resilience architecting methods by integrating key system aspects using fuzzy memberships and fuzzy rule sets. This novel approach aids the systemsmore » architect in considering resilience for the evaluation of architectures for adoption into the final system architecture.« less

Algorithm Optimally Orders Forward-Chaining Inference Rules

NASA Technical Reports Server (NTRS)

James, Mark

2008-01-01

People typically develop knowledge bases in a somewhat ad hoc manner by incrementally adding rules with no specific organization. This often results in a very inefficient execution of those rules since they are so often order sensitive. This is relevant to tasks like Deep Space Network in that it allows the knowledge base to be incrementally developed and have it automatically ordered for efficiency. Although data flow analysis was first developed for use in compilers for producing optimal code sequences, its usefulness is now recognized in many software systems including knowledge-based systems. However, this approach for exhaustively computing data-flow information cannot directly be applied to inference systems because of the ubiquitous execution of the rules. An algorithm is presented that efficiently performs a complete producer/consumer analysis for each antecedent and consequence clause in a knowledge base to optimally order the rules to minimize inference cycles. An algorithm was developed that optimally orders a knowledge base composed of forwarding chaining inference rules such that independent inference cycle executions are minimized, thus, resulting in significantly faster execution. This algorithm was integrated into the JPL tool Spacecraft Health Inference Engine (SHINE) for verification and it resulted in a significant reduction in inference cycles for what was previously considered an ordered knowledge base. For a knowledge base that is completely unordered, then the improvement is much greater.

Symbolic rule-based classification of lung cancer stages from free-text pathology reports.

PubMed

Nguyen, Anthony N; Lawley, Michael J; Hansen, David P; Bowman, Rayleen V; Clarke, Belinda E; Duhig, Edwina E; Colquist, Shoni

2010-01-01

To classify automatically lung tumor-node-metastases (TNM) cancer stages from free-text pathology reports using symbolic rule-based classification. By exploiting report substructure and the symbolic manipulation of systematized nomenclature of medicine-clinical terms (SNOMED CT) concepts in reports, statements in free text can be evaluated for relevance against factors relating to the staging guidelines. Post-coordinated SNOMED CT expressions based on templates were defined and populated by concepts in reports, and tested for subsumption by staging factors. The subsumption results were used to build logic according to the staging guidelines to calculate the TNM stage. The accuracy measure and confusion matrices were used to evaluate the TNM stages classified by the symbolic rule-based system. The system was evaluated against a database of multidisciplinary team staging decisions and a machine learning-based text classification system using support vector machines. Overall accuracy on a corpus of pathology reports for 718 lung cancer patients against a database of pathological TNM staging decisions were 72%, 78%, and 94% for T, N, and M staging, respectively. The system's performance was also comparable to support vector machine classification approaches. A system to classify lung TNM stages from free-text pathology reports was developed, and it was verified that the symbolic rule-based approach using SNOMED CT can be used for the extraction of key lung cancer characteristics from free-text reports. Future work will investigate the applicability of using the proposed methodology for extracting other cancer characteristics and types.

Evaluating the Effectiveness of Auditing Rules for Electronic Health Record Systems

PubMed Central

Hedda, Monica; Malin, Bradley A.; Yan, Chao; Fabbri, Daniel

2017-01-01

Healthcare organizations (HCOs) often deploy rule-based auditing systems to detect insider threats to sensitive patient health information in electronic health record (EHR) systems. These rule-based systems define behavior deemed to be high-risk a priori (e.g., family member, co-worker access). While such rules seem logical, there has been little scientific investigation into the effectiveness of these auditing rules in identifying inappropriate behavior. Thus, in this paper, we introduce an approach to evaluate the effectiveness of individual high-risk rules and rank them according to their potential risk. We investigate the rate of high-risk access patterns and minimum rate of high-risk accesses that can be explained with appropriate clinical reasons in a large EHR system. An analysis of 8M accesses from one-week of data shows that specific high-risk flags occur more frequently than theoretically expected and the rate at which accesses can be explained away with five simple reasons is 16 - 43%. PMID:29854153

Evaluating the Effectiveness of Auditing Rules for Electronic Health Record Systems.

PubMed

Hedda, Monica; Malin, Bradley A; Yan, Chao; Fabbri, Daniel

2017-01-01

Healthcare organizations (HCOs) often deploy rule-based auditing systems to detect insider threats to sensitive patient health information in electronic health record (EHR) systems. These rule-based systems define behavior deemed to be high-risk a priori (e.g., family member, co-worker access). While such rules seem logical, there has been little scientific investigation into the effectiveness of these auditing rules in identifying inappropriate behavior. Thus, in this paper, we introduce an approach to evaluate the effectiveness of individual high-risk rules and rank them according to their potential risk. We investigate the rate of high-risk access patterns and minimum rate of high-risk accesses that can be explained with appropriate clinical reasons in a large EHR system. An analysis of 8M accesses from one-week of data shows that specific high-risk flags occur more frequently than theoretically expected and the rate at which accesses can be explained away with five simple reasons is 16 - 43%.

Evidence flow graph methods for validation and verification of expert systems

NASA Technical Reports Server (NTRS)

Becker, Lee A.; Green, Peter G.; Bhatnagar, Jayant

1988-01-01

This final report describes the results of an investigation into the use of evidence flow graph techniques for performing validation and verification of expert systems. This was approached by developing a translator to convert horn-clause rule bases into evidence flow graphs, a simulation program, and methods of analysis. These tools were then applied to a simple rule base which contained errors. It was found that the method was capable of identifying a variety of problems, for example that the order of presentation of input data or small changes in critical parameters could effect the output from a set of rules.

An analytical fuzzy-based approach to ?-gain optimal control of input-affine nonlinear systems using Newton-type algorithm

NASA Astrophysics Data System (ADS)

Milic, Vladimir; Kasac, Josip; Novakovic, Branko

2015-10-01

This paper is concerned with ?-gain optimisation of input-affine nonlinear systems controlled by analytic fuzzy logic system. Unlike the conventional fuzzy-based strategies, the non-conventional analytic fuzzy control method does not require an explicit fuzzy rule base. As the first contribution of this paper, we prove, by using the Stone-Weierstrass theorem, that the proposed fuzzy system without rule base is universal approximator. The second contribution of this paper is an algorithm for solving a finite-horizon minimax problem for ?-gain optimisation. The proposed algorithm consists of recursive chain rule for first- and second-order derivatives, Newton's method, multi-step Adams method and automatic differentiation. Finally, the results of this paper are evaluated on a second-order nonlinear system.

Optical Generation of Fuzzy-Based Rules

NASA Astrophysics Data System (ADS)

Gur, Eran; Mendlovic, David; Zalevsky, Zeev

2002-08-01

In the last third of the 20th century, fuzzy logic has risen from a mathematical concept to an applicable approach in soft computing. Today, fuzzy logic is used in control systems for various applications, such as washing machines, train-brake systems, automobile automatic gear, and so forth. The approach of optical implementation of fuzzy inferencing was given by the authors in previous papers, giving an extra emphasis to applications with two dominant inputs. In this paper the authors introduce a real-time optical rule generator for the dual-input fuzzy-inference engine. The paper briefly goes over the dual-input optical implementation of fuzzy-logic inferencing. Then, the concept of constructing a set of rules from given data is discussed. Next, the authors show ways to implement this procedure optically. The discussion is accompanied by an example that illustrates the transformation from raw data into fuzzy set rules.

Comparison of rule induction, decision trees and formal concept analysis approaches for classification

NASA Astrophysics Data System (ADS)

Kotelnikov, E. V.; Milov, V. R.

2018-05-01

Rule-based learning algorithms have higher transparency and easiness to interpret in comparison with neural networks and deep learning algorithms. These properties make it possible to effectively use such algorithms to solve descriptive tasks of data mining. The choice of an algorithm depends also on its ability to solve predictive tasks. The article compares the quality of the solution of the problems with binary and multiclass classification based on the experiments with six datasets from the UCI Machine Learning Repository. The authors investigate three algorithms: Ripper (rule induction), C4.5 (decision trees), In-Close (formal concept analysis). The results of the experiments show that In-Close demonstrates the best quality of classification in comparison with Ripper and C4.5, however the latter two generate more compact rule sets.

Extraction of spatial-temporal rules from mesoscale eddies in the South China Sea Based on rough set theory

NASA Astrophysics Data System (ADS)

Du, Y.; Fan, X.; He, Z.; Su, F.; Zhou, C.; Mao, H.; Wang, D.

2011-06-01

In this paper, a rough set theory is introduced to represent spatial-temporal relationships and extract the corresponding rules from typical mesoscale-eddy states in the South China Sea (SCS). Three decision attributes are adopted in this study, which make the approach flexible in retrieving spatial-temporal rules with different features. Spatial-temporal rules of typical states in the SCS are extracted as three decision attributes, which then are confirmed by the previous works. The results demonstrate that this approach is effective in extracting spatial-temporal rules from typical mesoscale-eddy states, and therefore provides a powerful approach to forecasts in the future. Spatial-temporal rules in the SCS indicate that warm eddies following the rules are generally in the southeastern and central SCS around 2000 m isobaths in winter. Their intensity and vorticity are weaker than those of cold eddies. They usually move a shorter distance. By contrast, cold eddies are in 2000 m-deeper regions of the southwestern and northeastern SCS in spring and fall. Their intensity and vorticity are strong. Usually they move a long distance. In winter, a few rules are followed by cold eddies in the northern tip of the basin and southwest of Taiwan Island rather than warm eddies, indicating cold eddies may be well-regulated in the region. Several warm-eddy rules are achieved west of Luzon Island, indicating warm eddies may be well-regulated in the region as well. Otherwise, warm and cold eddies are distributed not only in the jet flow off southern Vietnam induced by intraseasonal wind stress in summer-fall, but also in the northern shallow water, which should be a focus of future study.

Detailed statistical assessment of the characteristics of the ESMO Magnitude of Clinical Benefit Scale (ESMO-MCBS) threshold rules.

PubMed

Dafni, Urania; Karlis, Dimitris; Pedeli, Xanthi; Bogaerts, Jan; Pentheroudakis, George; Tabernero, Josep; Zielinski, Christoph C; Piccart, Martine J; de Vries, Elisabeth G E; Latino, Nicola Jane; Douillard, Jean-Yves; Cherny, Nathan I

2017-01-01

The European Society for Medical Oncology (ESMO) has developed the ESMO Magnitude of Clinical Benefit Scale (ESMO-MCBS), a tool to assess the magnitude of clinical benefit from new cancer therapies. Grading is guided by a dual rule comparing the relative benefit (RB) and the absolute benefit (AB) achieved by the therapy to prespecified threshold values. The ESMO-MCBS v1.0 dual rule evaluates the RB of an experimental treatment based on the lower limit of the 95%CI (LL95%CI) for the hazard ratio (HR) along with an AB threshold. This dual rule addresses two goals: inclusiveness: not unfairly penalising experimental treatments from trials designed with adequate power targeting clinically meaningful relative benefit; and discernment: penalising trials designed to detect a small inconsequential benefit. Based on 50 000 simulations of plausible trial scenarios, the sensitivity and specificity of the LL95%CI rule and the ESMO-MCBS dual rule, the robustness of their characteristics for reasonable power and range of targeted and true HRs, are examined. The per cent acceptance of maximal preliminary grade is compared with other dual rules based on point estimate (PE) thresholds for RB. For particularly small or particularly large studies, the observed benefit needs to be relatively big for the ESMO-MCBS dual rule to be satisfied and the maximal grade awarded. Compared with approaches that evaluate RB using the PE thresholds, simulations demonstrate that the MCBS approach better exhibits the desired behaviour achieving the goals of both inclusiveness and discernment. RB assessment using the LL95%CI for HR rather than a PE threshold has two advantages: it diminishes the probability of excluding big benefit positive studies from achieving due credit and, when combined with the AB assessment, it increases the probability of downgrading a trial with a statistically significant but clinically insignificant observed benefit.

Detailed statistical assessment of the characteristics of the ESMO Magnitude of Clinical Benefit Scale (ESMO-MCBS) threshold rules

PubMed Central

Dafni, Urania; Karlis, Dimitris; Pedeli, Xanthi; Bogaerts, Jan; Pentheroudakis, George; Tabernero, Josep; Zielinski, Christoph C; Piccart, Martine J; de Vries, Elisabeth G E; Latino, Nicola Jane; Douillard, Jean-Yves; Cherny, Nathan I

2017-01-01

Background The European Society for Medical Oncology (ESMO) has developed the ESMO Magnitude of Clinical Benefit Scale (ESMO-MCBS), a tool to assess the magnitude of clinical benefit from new cancer therapies. Grading is guided by a dual rule comparing the relative benefit (RB) and the absolute benefit (AB) achieved by the therapy to prespecified threshold values. The ESMO-MCBS v1.0 dual rule evaluates the RB of an experimental treatment based on the lower limit of the 95%CI (LL95%CI) for the hazard ratio (HR) along with an AB threshold. This dual rule addresses two goals: inclusiveness: not unfairly penalising experimental treatments from trials designed with adequate power targeting clinically meaningful relative benefit; and discernment: penalising trials designed to detect a small inconsequential benefit. Methods Based on 50 000 simulations of plausible trial scenarios, the sensitivity and specificity of the LL95%CI rule and the ESMO-MCBS dual rule, the robustness of their characteristics for reasonable power and range of targeted and true HRs, are examined. The per cent acceptance of maximal preliminary grade is compared with other dual rules based on point estimate (PE) thresholds for RB. Results For particularly small or particularly large studies, the observed benefit needs to be relatively big for the ESMO-MCBS dual rule to be satisfied and the maximal grade awarded. Compared with approaches that evaluate RB using the PE thresholds, simulations demonstrate that the MCBS approach better exhibits the desired behaviour achieving the goals of both inclusiveness and discernment. Conclusions RB assessment using the LL95%CI for HR rather than a PE threshold has two advantages: it diminishes the probability of excluding big benefit positive studies from achieving due credit and, when combined with the AB assessment, it increases the probability of downgrading a trial with a statistically significant but clinically insignificant observed benefit. PMID:29067214

Numerical integration of discontinuous functions: moment fitting and smart octree

NASA Astrophysics Data System (ADS)

Hubrich, Simeon; Di Stolfo, Paolo; Kudela, László; Kollmannsberger, Stefan; Rank, Ernst; Schröder, Andreas; Düster, Alexander

2017-11-01

A fast and simple grid generation can be achieved by non-standard discretization methods where the mesh does not conform to the boundary or the internal interfaces of the problem. However, this simplification leads to discontinuous integrands for intersected elements and, therefore, standard quadrature rules do not perform well anymore. Consequently, special methods are required for the numerical integration. To this end, we present two approaches to obtain quadrature rules for arbitrary domains. The first approach is based on an extension of the moment fitting method combined with an optimization strategy for the position and weights of the quadrature points. In the second approach, we apply the smart octree, which generates curved sub-cells for the integration mesh. To demonstrate the performance of the proposed methods, we consider several numerical examples, showing that the methods lead to efficient quadrature rules, resulting in less integration points and in high accuracy.

ALC: automated reduction of rule-based models

PubMed Central

Koschorreck, Markus; Gilles, Ernst Dieter

2008-01-01

Background Combinatorial complexity is a challenging problem for the modeling of cellular signal transduction since the association of a few proteins can give rise to an enormous amount of feasible protein complexes. The layer-based approach is an approximative, but accurate method for the mathematical modeling of signaling systems with inherent combinatorial complexity. The number of variables in the simulation equations is highly reduced and the resulting dynamic models show a pronounced modularity. Layer-based modeling allows for the modeling of systems not accessible previously. Results ALC (Automated Layer Construction) is a computer program that highly simplifies the building of reduced modular models, according to the layer-based approach. The model is defined using a simple but powerful rule-based syntax that supports the concepts of modularity and macrostates. ALC performs consistency checks on the model definition and provides the model output in different formats (C MEX, MATLAB, Mathematica and SBML) as ready-to-run simulation files. ALC also provides additional documentation files that simplify the publication or presentation of the models. The tool can be used offline or via a form on the ALC website. Conclusion ALC allows for a simple rule-based generation of layer-based reduced models. The model files are given in different formats as ready-to-run simulation files. PMID:18973705

Search for Minimal and Semi-Minimal Rule Sets in Incremental Learning of Context-Free and Definite Clause Grammars

NASA Astrophysics Data System (ADS)

Imada, Keita; Nakamura, Katsuhiko

This paper describes recent improvements to Synapse system for incremental learning of general context-free grammars (CFGs) and definite clause grammars (DCGs) from positive and negative sample strings. An important feature of our approach is incremental learning, which is realized by a rule generation mechanism called “bridging” based on bottom-up parsing for positive samples and the search for rule sets. The sizes of rule sets and the computation time depend on the search strategies. In addition to the global search for synthesizing minimal rule sets and serial search, another method for synthesizing semi-optimum rule sets, we incorporate beam search to the system for synthesizing semi-minimal rule sets. The paper shows several experimental results on learning CFGs and DCGs, and we analyze the sizes of rule sets and the computation time.

Evaluation of Machine Learning and Rules-Based Approaches for Predicting Antimicrobial Resistance Profiles in Gram-negative Bacilli from Whole Genome Sequence Data.

PubMed

Pesesky, Mitchell W; Hussain, Tahir; Wallace, Meghan; Patel, Sanket; Andleeb, Saadia; Burnham, Carey-Ann D; Dantas, Gautam

2016-01-01

The time-to-result for culture-based microorganism recovery and phenotypic antimicrobial susceptibility testing necessitates initial use of empiric (frequently broad-spectrum) antimicrobial therapy. If the empiric therapy is not optimal, this can lead to adverse patient outcomes and contribute to increasing antibiotic resistance in pathogens. New, more rapid technologies are emerging to meet this need. Many of these are based on identifying resistance genes, rather than directly assaying resistance phenotypes, and thus require interpretation to translate the genotype into treatment recommendations. These interpretations, like other parts of clinical diagnostic workflows, are likely to be increasingly automated in the future. We set out to evaluate the two major approaches that could be amenable to automation pipelines: rules-based methods and machine learning methods. The rules-based algorithm makes predictions based upon current, curated knowledge of Enterobacteriaceae resistance genes. The machine-learning algorithm predicts resistance and susceptibility based on a model built from a training set of variably resistant isolates. As our test set, we used whole genome sequence data from 78 clinical Enterobacteriaceae isolates, previously identified to represent a variety of phenotypes, from fully-susceptible to pan-resistant strains for the antibiotics tested. We tested three antibiotic resistance determinant databases for their utility in identifying the complete resistome for each isolate. The predictions of the rules-based and machine learning algorithms for these isolates were compared to results of phenotype-based diagnostics. The rules based and machine-learning predictions achieved agreement with standard-of-care phenotypic diagnostics of 89.0 and 90.3%, respectively, across twelve antibiotic agents from six major antibiotic classes. Several sources of disagreement between the algorithms were identified. Novel variants of known resistance factors and incomplete genome assembly confounded the rules-based algorithm, resulting in predictions based on gene family, rather than on knowledge of the specific variant found. Low-frequency resistance caused errors in the machine-learning algorithm because those genes were not seen or seen infrequently in the test set. We also identified an example of variability in the phenotype-based results that led to disagreement with both genotype-based methods. Genotype-based antimicrobial susceptibility testing shows great promise as a diagnostic tool, and we outline specific research goals to further refine this methodology.

Implementation of a spike-based perceptron learning rule using TiO2-x memristors.

PubMed

Mostafa, Hesham; Khiat, Ali; Serb, Alexander; Mayr, Christian G; Indiveri, Giacomo; Prodromakis, Themis

2015-01-01

Synaptic plasticity plays a crucial role in allowing neural networks to learn and adapt to various input environments. Neuromorphic systems need to implement plastic synapses to obtain basic "cognitive" capabilities such as learning. One promising and scalable approach for implementing neuromorphic synapses is to use nano-scale memristors as synaptic elements. In this paper we propose a hybrid CMOS-memristor system comprising CMOS neurons interconnected through TiO2-x memristors, and spike-based learning circuits that modulate the conductance of the memristive synapse elements according to a spike-based Perceptron plasticity rule. We highlight a number of advantages for using this spike-based plasticity rule as compared to other forms of spike timing dependent plasticity (STDP) rules. We provide experimental proof-of-concept results with two silicon neurons connected through a memristive synapse that show how the CMOS plasticity circuits can induce stable changes in memristor conductances, giving rise to increased synaptic strength after a potentiation episode and to decreased strength after a depression episode.

An application of object-oriented knowledge representation to engineering expert systems

NASA Technical Reports Server (NTRS)

Logie, D. S.; Kamil, H.; Umaretiya, J. R.

1990-01-01

The paper describes an object-oriented knowledge representation and its application to engineering expert systems. The object-oriented approach promotes efficient handling of the problem data by allowing knowledge to be encapsulated in objects and organized by defining relationships between the objects. An Object Representation Language (ORL) was implemented as a tool for building and manipulating the object base. Rule-based knowledge representation is then used to simulate engineering design reasoning. Using a common object base, very large expert systems can be developed, comprised of small, individually processed, rule sets. The integration of these two schemes makes it easier to develop practical engineering expert systems. The general approach to applying this technology to the domain of the finite element analysis, design, and optimization of aerospace structures is discussed.

Natural-Language Parser for PBEM

NASA Technical Reports Server (NTRS)

James, Mark

2010-01-01

A computer program called "Hunter" accepts, as input, a colloquial-English description of a set of policy-based-management rules, and parses that description into a form useable by policy-based enterprise management (PBEM) software. PBEM is a rules-based approach suitable for automating some management tasks. PBEM simplifies the management of a given enterprise through establishment of policies addressing situations that are likely to occur. Hunter was developed to have a unique capability to extract the intended meaning instead of focusing on parsing the exact ways in which individual words are used.

Rule-Based vs. Behavior-Based Self-Deployment for Mobile Wireless Sensor Networks

PubMed Central

Urdiales, Cristina; Aguilera, Francisco; González-Parada, Eva; Cano-García, Jose; Sandoval, Francisco

2016-01-01

In mobile wireless sensor networks (MWSN), nodes are allowed to move autonomously for deployment. This process is meant: (i) to achieve good coverage; and (ii) to distribute the communication load as homogeneously as possible. Rather than optimizing deployment, reactive algorithms are based on a set of rules or behaviors, so nodes can determine when to move. This paper presents an experimental evaluation of both reactive deployment approaches: rule-based and behavior-based ones. Specifically, we compare a backbone dispersion algorithm with a social potential fields algorithm. Most tests are done under simulation for a large number of nodes in environments with and without obstacles. Results are validated using a small robot network in the real world. Our results show that behavior-based deployment tends to provide better coverage and communication balance, especially for a large number of nodes in areas with obstacles. PMID:27399709

Forward-Chaining Versus A Graph Approach As The Inference Engine In Expert Systems

NASA Astrophysics Data System (ADS)

Neapolitan, Richard E.

1986-03-01

Rule-based expert systems are those in which a certain number of IF-THEN rules are assumed to be true. Based on the verity of some assertions, the rules deduce as many new conclusions as possible. A standard technique used to make these deductions is forward-chaining. In forward-chaining, the program or 'inference engine' cycles through the rules. At each rule, the premises for the rule are checked against the current true assertions. If all the premises are found, the conclusion is added to the list of true assertions. At that point it is necessary to start over at the first rule, since the new conclusion may be a premise in a rule already checked. Therefore, each time a new conclusion is deduced it is necessary to start the rule checking procedure over. This process continues until no new conclusions are added and the end of the list of rules is reached. The above process, although quite costly in terms of CPU cycles due to the necessity of repeatedly starting the process over, is necessary if the rules contain 'pattern variables'. An example of such a rule is, 'IF X IS A BACTERIA, THEN X CAN BE TREATED WITH ANTIBIOTICS'. Since the rule can lead to conclusions for many values of X, it is necessary to check each premise in the rule against every true assertion producing an association list to be used in the checking of the next premise. However, if the rule does not contain variable data, as is the case in many current expert systems, then a rule can lead to only one conclusion. In this case, the rules can be stored in a graph, and the true assertions in an assertion list. The assertion list is traversed only once; at each assertion a premise is triggered in all the rules which have that assertion as a premise. When all premises for a rule trigger, the rule's conclusion is added to the END of the list of assertions. It must be added at the end so that it will eventually be used to make further deductions. In the current paper, the two methods are described in detail, the relative advantages of each is discussed, and a benchmark comparing the CPU cycles consumed by each is included. It is also shown that, in the case of reasoning under uncertainty, it is possible to properly combine the certainties derived from rules arguing for the same conclusion when the graph approach is used.

A Model-Based Approach to Predicting Graduate-Level Performance Using Indicators of Undergraduate-Level Performance

ERIC Educational Resources Information Center

Zimmermann, Judith; Brodersen, Kay H.; Heinimann, Hans R.; Buhmann, Joachim M.

2015-01-01

The graduate admissions process is crucial for controlling the quality of higher education, yet, rules-of-thumb and domain-specific experiences often dominate evidence-based approaches. The goal of the present study is to dissect the predictive power of undergraduate performance indicators and their aggregates. We analyze 81 variables in 171…

Evaluation of a rule-based method for epidemiological document classification towards the automation of systematic reviews.

PubMed

Karystianis, George; Thayer, Kristina; Wolfe, Mary; Tsafnat, Guy

2017-06-01

Most data extraction efforts in epidemiology are focused on obtaining targeted information from clinical trials. In contrast, limited research has been conducted on the identification of information from observational studies, a major source for human evidence in many fields, including environmental health. The recognition of key epidemiological information (e.g., exposures) through text mining techniques can assist in the automation of systematic reviews and other evidence summaries. We designed and applied a knowledge-driven, rule-based approach to identify targeted information (study design, participant population, exposure, outcome, confounding factors, and the country where the study was conducted) from abstracts of epidemiological studies included in several systematic reviews of environmental health exposures. The rules were based on common syntactical patterns observed in text and are thus not specific to any systematic review. To validate the general applicability of our approach, we compared the data extracted using our approach versus hand curation for 35 epidemiological study abstracts manually selected for inclusion in two systematic reviews. The returned F-score, precision, and recall ranged from 70% to 98%, 81% to 100%, and 54% to 97%, respectively. The highest precision was observed for exposure, outcome and population (100%) while recall was best for exposure and study design with 97% and 89%, respectively. The lowest recall was observed for the population (54%), which also had the lowest F-score (70%). The generated performance of our text-mining approach demonstrated encouraging results for the identification of targeted information from observational epidemiological study abstracts related to environmental exposures. We have demonstrated that rules based on generic syntactic patterns in one corpus can be applied to other observational study design by simple interchanging the dictionaries aiming to identify certain characteristics (i.e., outcomes, exposures). At the document level, the recognised information can assist in the selection and categorization of studies included in a systematic review. Copyright © 2017 Elsevier Inc. All rights reserved.

Rule-based modeling and simulations of the inner kinetochore structure.

PubMed

Tschernyschkow, Sergej; Herda, Sabine; Gruenert, Gerd; Döring, Volker; Görlich, Dennis; Hofmeister, Antje; Hoischen, Christian; Dittrich, Peter; Diekmann, Stephan; Ibrahim, Bashar

2013-09-01

Combinatorial complexity is a central problem when modeling biochemical reaction networks, since the association of a few components can give rise to a large variation of protein complexes. Available classical modeling approaches are often insufficient for the analysis of very large and complex networks in detail. Recently, we developed a new rule-based modeling approach that facilitates the analysis of spatial and combinatorially complex problems. Here, we explore for the first time how this approach can be applied to a specific biological system, the human kinetochore, which is a multi-protein complex involving over 100 proteins. Applying our freely available SRSim software to a large data set on kinetochore proteins in human cells, we construct a spatial rule-based simulation model of the human inner kinetochore. The model generates an estimation of the probability distribution of the inner kinetochore 3D architecture and we show how to analyze this distribution using information theory. In our model, the formation of a bridge between CenpA and an H3 containing nucleosome only occurs efficiently for higher protein concentration realized during S-phase but may be not in G1. Above a certain nucleosome distance the protein bridge barely formed pointing towards the importance of chromatin structure for kinetochore complex formation. We define a metric for the distance between structures that allow us to identify structural clusters. Using this modeling technique, we explore different hypothetical chromatin layouts. Applying a rule-based network analysis to the spatial kinetochore complex geometry allowed us to integrate experimental data on kinetochore proteins, suggesting a 3D model of the human inner kinetochore architecture that is governed by a combinatorial algebraic reaction network. This reaction network can serve as bridge between multiple scales of modeling. Our approach can be applied to other systems beyond kinetochores. Copyright © 2013 Elsevier Ltd. All rights reserved.

Analyzing Large Gene Expression and Methylation Data Profiles Using StatBicRM: Statistical Biclustering-Based Rule Mining

PubMed Central

Maulik, Ujjwal; Mallik, Saurav; Mukhopadhyay, Anirban; Bandyopadhyay, Sanghamitra

2015-01-01

Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data-matrix. Finally, we have also included the integrated analysis of gene expression and methylation for determining epigenetic effect (viz., effect of methylation) on gene expression level. PMID:25830807

Analyzing large gene expression and methylation data profiles using StatBicRM: statistical biclustering-based rule mining.

PubMed

Maulik, Ujjwal; Mallik, Saurav; Mukhopadhyay, Anirban; Bandyopadhyay, Sanghamitra

2015-01-01

Microarray and beadchip are two most efficient techniques for measuring gene expression and methylation data in bioinformatics. Biclustering deals with the simultaneous clustering of genes and samples. In this article, we propose a computational rule mining framework, StatBicRM (i.e., statistical biclustering-based rule mining) to identify special type of rules and potential biomarkers using integrated approaches of statistical and binary inclusion-maximal biclustering techniques from the biological datasets. At first, a novel statistical strategy has been utilized to eliminate the insignificant/low-significant/redundant genes in such way that significance level must satisfy the data distribution property (viz., either normal distribution or non-normal distribution). The data is then discretized and post-discretized, consecutively. Thereafter, the biclustering technique is applied to identify maximal frequent closed homogeneous itemsets. Corresponding special type of rules are then extracted from the selected itemsets. Our proposed rule mining method performs better than the other rule mining algorithms as it generates maximal frequent closed homogeneous itemsets instead of frequent itemsets. Thus, it saves elapsed time, and can work on big dataset. Pathway and Gene Ontology analyses are conducted on the genes of the evolved rules using David database. Frequency analysis of the genes appearing in the evolved rules is performed to determine potential biomarkers. Furthermore, we also classify the data to know how much the evolved rules are able to describe accurately the remaining test (unknown) data. Subsequently, we also compare the average classification accuracy, and other related factors with other rule-based classifiers. Statistical significance tests are also performed for verifying the statistical relevance of the comparative results. Here, each of the other rule mining methods or rule-based classifiers is also starting with the same post-discretized data-matrix. Finally, we have also included the integrated analysis of gene expression and methylation for determining epigenetic effect (viz., effect of methylation) on gene expression level.

Using the Chain Rule as the Key Link in Deriving the General Rules for Differentiation

ERIC Educational Resources Information Center

Sprows, David

2011-01-01

The standard approach to the general rules for differentiation is to first derive the power, product, and quotient rules and then derive the chain rule. In this short article we give an approach to these rules which uses the chain rule as the main tool in deriving the power, product, and quotient rules in a manner which is more student-friendly…

Improving drivers' knowledge of road rules using digital games.

PubMed

Li, Qing; Tay, Richard

2014-04-01

Although a proficient knowledge of the road rules is important to safe driving, many drivers do not retain the knowledge acquired after they have obtained their licenses. Hence, more innovative and appealing methods are needed to improve drivers' knowledge of the road rules. This study examines the effect of game based learning on drivers' knowledge acquisition and retention. We find that playing an entertaining game that is designed to impart knowledge of the road rules not only improves players' knowledge but also helps them retain such knowledge. Hence, learning by gaming appears to be a promising learning approach for driver education. Copyright © 2013 Elsevier Ltd. All rights reserved.

Single-particle spectral density of the unitary Fermi gas: Novel approach based on the operator product expansion, sum rules and the maximum entropy method

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gubler, Philipp, E-mail: pgubler@riken.jp; RIKEN Nishina Center, Wako, Saitama 351-0198; Yamamoto, Naoki

2015-05-15

Making use of the operator product expansion, we derive a general class of sum rules for the imaginary part of the single-particle self-energy of the unitary Fermi gas. The sum rules are analyzed numerically with the help of the maximum entropy method, which allows us to extract the single-particle spectral density as a function of both energy and momentum. These spectral densities contain basic information on the properties of the unitary Fermi gas, such as the dispersion relation and the superfluid pairing gap, for which we obtain reasonable agreement with the available results based on quantum Monte-Carlo simulations.

A novel chaotic based image encryption using a hybrid model of deoxyribonucleic acid and cellular automata

NASA Astrophysics Data System (ADS)

Enayatifar, Rasul; Sadaei, Hossein Javedani; Abdullah, Abdul Hanan; Lee, Malrey; Isnin, Ismail Fauzi

2015-08-01

Currently, there are many studies have conducted on developing security of the digital image in order to protect such data while they are sending on the internet. This work aims to propose a new approach based on a hybrid model of the Tinkerbell chaotic map, deoxyribonucleic acid (DNA) and cellular automata (CA). DNA rules, DNA sequence XOR operator and CA rules are used simultaneously to encrypt the plain-image pixels. To determine rule number in DNA sequence and also CA, a 2-dimension Tinkerbell chaotic map is employed. Experimental results and computer simulations, both confirm that the proposed scheme not only demonstrates outstanding encryption, but also resists various typical attacks.

Rule-based modeling with Virtual Cell

PubMed Central

Schaff, James C.; Vasilescu, Dan; Moraru, Ion I.; Loew, Leslie M.; Blinov, Michael L.

2016-01-01

Summary: Rule-based modeling is invaluable when the number of possible species and reactions in a model become too large to allow convenient manual specification. The popular rule-based software tools BioNetGen and NFSim provide powerful modeling and simulation capabilities at the cost of learning a complex scripting language which is used to specify these models. Here, we introduce a modeling tool that combines new graphical rule-based model specification with existing simulation engines in a seamless way within the familiar Virtual Cell (VCell) modeling environment. A mathematical model can be built integrating explicit reaction networks with reaction rules. In addition to offering a large choice of ODE and stochastic solvers, a model can be simulated using a network free approach through the NFSim simulation engine. Availability and implementation: Available as VCell (versions 6.0 and later) at the Virtual Cell web site (http://vcell.org/). The application installs and runs on all major platforms and does not require registration for use on the user’s computer. Tutorials are available at the Virtual Cell website and Help is provided within the software. Source code is available at Sourceforge. Contact: vcell_support@uchc.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27497444

An architecture for rule based system explanation

NASA Technical Reports Server (NTRS)

Fennel, T. R.; Johannes, James D.

1990-01-01

A system architecture is presented which incorporate both graphics and text into explanations provided by rule based expert systems. This architecture facilitates explanation of the knowledge base content, the control strategies employed by the system, and the conclusions made by the system. The suggested approach combines hypermedia and inference engine capabilities. Advantages include: closer integration of user interface, explanation system, and knowledge base; the ability to embed links to deeper knowledge underlying the compiled knowledge used in the knowledge base; and allowing for more direct control of explanation depth and duration by the user. User models are suggested to control the type, amount, and order of information presented.

Approach to design neural cryptography: a generalized architecture and a heuristic rule.

PubMed

Mu, Nankun; Liao, Xiaofeng; Huang, Tingwen

2013-06-01

Neural cryptography, a type of public key exchange protocol, is widely considered as an effective method for sharing a common secret key between two neural networks on public channels. How to design neural cryptography remains a great challenge. In this paper, in order to provide an approach to solve this challenge, a generalized network architecture and a significant heuristic rule are designed. The proposed generic framework is named as tree state classification machine (TSCM), which extends and unifies the existing structures, i.e., tree parity machine (TPM) and tree committee machine (TCM). Furthermore, we carefully study and find that the heuristic rule can improve the security of TSCM-based neural cryptography. Therefore, TSCM and the heuristic rule can guide us to designing a great deal of effective neural cryptography candidates, in which it is possible to achieve the more secure instances. Significantly, in the light of TSCM and the heuristic rule, we further expound that our designed neural cryptography outperforms TPM (the most secure model at present) on security. Finally, a series of numerical simulation experiments are provided to verify validity and applicability of our results.

Practical implementation of the double linear damage rule and damage curve approach for treating cumulative fatigue damage

NASA Technical Reports Server (NTRS)

Manson, S. S.; Halford, G. R.

1980-01-01

Simple procedures are presented for treating cumulative fatigue damage under complex loading history using either the damage curve concept or the double linear damage rule. A single equation is provided for use with the damage curve approach; each loading event providing a fraction of damage until failure is presumed to occur when the damage sum becomes unity. For the double linear damage rule, analytical expressions are provided for determining the two phases of life. The procedure involves two steps, each similar to the conventional application of the commonly used linear damage rule. When the sum of cycle ratios based on phase 1 lives reaches unity, phase 1 is presumed complete, and further loadings are summed as cycle ratios on phase 2 lives. When the phase 2 sum reaches unity, failure is presumed to occur. No other physical properties or material constants than those normally used in a conventional linear damage rule analysis are required for application of either of the two cumulative damage methods described. Illustrations and comparisons of both methods are discussed.

A Simplified Micromechanical Modeling Approach to Predict the Tensile Flow Curve Behavior of Dual-Phase Steels

NASA Astrophysics Data System (ADS)

Nanda, Tarun; Kumar, B. Ravi; Singh, Vishal

2017-11-01

Micromechanical modeling is used to predict material's tensile flow curve behavior based on microstructural characteristics. This research develops a simplified micromechanical modeling approach for predicting flow curve behavior of dual-phase steels. The existing literature reports on two broad approaches for determining tensile flow curve of these steels. The modeling approach developed in this work attempts to overcome specific limitations of the existing two approaches. This approach combines dislocation-based strain-hardening method with rule of mixtures. In the first step of modeling, `dislocation-based strain-hardening method' was employed to predict tensile behavior of individual phases of ferrite and martensite. In the second step, the individual flow curves were combined using `rule of mixtures,' to obtain the composite dual-phase flow behavior. To check accuracy of proposed model, four distinct dual-phase microstructures comprising of different ferrite grain size, martensite fraction, and carbon content in martensite were processed by annealing experiments. The true stress-strain curves for various microstructures were predicted with the newly developed micromechanical model. The results of micromechanical model matched closely with those of actual tensile tests. Thus, this micromechanical modeling approach can be used to predict and optimize the tensile flow behavior of dual-phase steels.

A novel way of integrating rule-based knowledge into a web ontology language framework.

PubMed

Gamberger, Dragan; Krstaçić, Goran; Jović, Alan

2013-01-01

Web ontology language (OWL), used in combination with the Protégé visual interface, is a modern standard for development and maintenance of ontologies and a powerful tool for knowledge presentation. In this work, we describe a novel possibility to use OWL also for the conceptualization of knowledge presented by a set of rules. In this approach, rules are represented as a hierarchy of actionable classes with necessary and sufficient conditions defined by the description logic formalism. The advantages are that: the set of the rules is not an unordered set anymore, the concepts defined in descriptive ontologies can be used directly in the bodies of rules, and Protégé presents an intuitive tool for editing the set of rules. Standard ontology reasoning processes are not applicable in this framework, but experiments conducted on the rule sets have demonstrated that the reasoning problems can be successfully solved.

A generalization of Hamilton's rule--love others how much?

PubMed

Alger, Ingela; Weibull, Jörgen W

2012-04-21

According to Hamilton's (1964a, b) rule, a costly action will be undertaken if its fitness cost to the actor falls short of the discounted benefit to the recipient, where the discount factor is Wright's index of relatedness between the two. We propose a generalization of this rule, and show that if evolution operates at the level of behavior rules, rather than directly at the level of actions, evolution will select behavior rules that induce a degree of cooperation that may differ from that predicted by Hamilton's rule as applied to actions. In social dilemmas there will be less (more) cooperation than under Hamilton's rule if the actions are strategic substitutes (complements). Our approach is based on natural selection, defined in terms of personal (direct) fitness, and applies to a wide range of pairwise interactions. Copyright © 2011 Elsevier Ltd. All rights reserved.

On the effects of adaptive reservoir operating rules in hydrological physically-based models

NASA Astrophysics Data System (ADS)

Giudici, Federico; Anghileri, Daniela; Castelletti, Andrea; Burlando, Paolo

2017-04-01

Recent years have seen a significant increase of the human influence on the natural systems both at the global and local scale. Accurately modeling the human component and its interaction with the natural environment is key to characterize the real system dynamics and anticipate future potential changes to the hydrological regimes. Modern distributed, physically-based hydrological models are able to describe hydrological processes with high level of detail and high spatiotemporal resolution. Yet, they lack in sophistication for the behavior component and human decisions are usually described by very simplistic rules, which might underperform in reproducing the catchment dynamics. In the case of water reservoir operators, these simplistic rules usually consist of target-level rule curves, which represent the average historical level trajectory. Whilst these rules can reasonably reproduce the average seasonal water volume shifts due to the reservoirs' operation, they cannot properly represent peculiar conditions, which influence the actual reservoirs' operation, e.g., variations in energy price or water demand, dry or wet meteorological conditions. Moreover, target-level rule curves are not suitable to explore the water system response to climate and socio economic changing contexts, because they assume a business-as-usual operation. In this work, we quantitatively assess how the inclusion of adaptive reservoirs' operating rules into physically-based hydrological models contribute to the proper representation of the hydrological regime at the catchment scale. In particular, we contrast target-level rule curves and detailed optimization-based behavioral models. We, first, perform the comparison on past observational records, showing that target-level rule curves underperform in representing the hydrological regime over multiple time scales (e.g., weekly, seasonal, inter-annual). Then, we compare how future hydrological changes are affected by the two modeling approaches by considering different future scenarios comprising climate change projections of precipitation and temperature and projections of electricity prices. We perform this comparative assessment on the real-world water system of Lake Como catchment in the Italian Alps, which is characterized by the massive presence of artificial hydropower reservoirs heavily altering the natural hydrological regime. The results show how different behavioral model approaches affect the system representation in terms of hydropower performance, reservoirs dynamics and hydrological regime under different future scenarios.

Rule-based spatial modeling with diffusing, geometrically constrained molecules.

PubMed

Gruenert, Gerd; Ibrahim, Bashar; Lenser, Thorsten; Lohel, Maiko; Hinze, Thomas; Dittrich, Peter

2010-06-07

We suggest a new type of modeling approach for the coarse grained, particle-based spatial simulation of combinatorially complex chemical reaction systems. In our approach molecules possess a location in the reactor as well as an orientation and geometry, while the reactions are carried out according to a list of implicitly specified reaction rules. Because the reaction rules can contain patterns for molecules, a combinatorially complex or even infinitely sized reaction network can be defined. For our implementation (based on LAMMPS), we have chosen an already existing formalism (BioNetGen) for the implicit specification of the reaction network. This compatibility allows to import existing models easily, i.e., only additional geometry data files have to be provided. Our simulations show that the obtained dynamics can be fundamentally different from those simulations that use classical reaction-diffusion approaches like Partial Differential Equations or Gillespie-type spatial stochastic simulation. We show, for example, that the combination of combinatorial complexity and geometric effects leads to the emergence of complex self-assemblies and transportation phenomena happening faster than diffusion (using a model of molecular walkers on microtubules). When the mentioned classical simulation approaches are applied, these aspects of modeled systems cannot be observed without very special treatment. Further more, we show that the geometric information can even change the organizational structure of the reaction system. That is, a set of chemical species that can in principle form a stationary state in a Differential Equation formalism, is potentially unstable when geometry is considered, and vice versa. We conclude that our approach provides a new general framework filling a gap in between approaches with no or rigid spatial representation like Partial Differential Equations and specialized coarse-grained spatial simulation systems like those for DNA or virus capsid self-assembly.

Rule-based spatial modeling with diffusing, geometrically constrained molecules

PubMed Central

2010-01-01

Background We suggest a new type of modeling approach for the coarse grained, particle-based spatial simulation of combinatorially complex chemical reaction systems. In our approach molecules possess a location in the reactor as well as an orientation and geometry, while the reactions are carried out according to a list of implicitly specified reaction rules. Because the reaction rules can contain patterns for molecules, a combinatorially complex or even infinitely sized reaction network can be defined. For our implementation (based on LAMMPS), we have chosen an already existing formalism (BioNetGen) for the implicit specification of the reaction network. This compatibility allows to import existing models easily, i.e., only additional geometry data files have to be provided. Results Our simulations show that the obtained dynamics can be fundamentally different from those simulations that use classical reaction-diffusion approaches like Partial Differential Equations or Gillespie-type spatial stochastic simulation. We show, for example, that the combination of combinatorial complexity and geometric effects leads to the emergence of complex self-assemblies and transportation phenomena happening faster than diffusion (using a model of molecular walkers on microtubules). When the mentioned classical simulation approaches are applied, these aspects of modeled systems cannot be observed without very special treatment. Further more, we show that the geometric information can even change the organizational structure of the reaction system. That is, a set of chemical species that can in principle form a stationary state in a Differential Equation formalism, is potentially unstable when geometry is considered, and vice versa. Conclusions We conclude that our approach provides a new general framework filling a gap in between approaches with no or rigid spatial representation like Partial Differential Equations and specialized coarse-grained spatial simulation systems like those for DNA or virus capsid self-assembly. PMID:20529264

Knowledge acquisition and representation using fuzzy evidential reasoning and dynamic adaptive fuzzy Petri nets.

PubMed

Liu, Hu-Chen; Liu, Long; Lin, Qing-Lian; Liu, Nan

2013-06-01

The two most important issues of expert systems are the acquisition of domain experts' professional knowledge and the representation and reasoning of the knowledge rules that have been identified. First, during expert knowledge acquisition processes, the domain expert panel often demonstrates different experience and knowledge from one another and produces different types of knowledge information such as complete and incomplete, precise and imprecise, and known and unknown because of its cross-functional and multidisciplinary nature. Second, as a promising tool for knowledge representation and reasoning, fuzzy Petri nets (FPNs) still suffer a couple of deficiencies. The parameters in current FPN models could not accurately represent the increasingly complex knowledge-based systems, and the rules in most existing knowledge inference frameworks could not be dynamically adjustable according to propositions' variation as human cognition and thinking. In this paper, we present a knowledge acquisition and representation approach using the fuzzy evidential reasoning approach and dynamic adaptive FPNs to solve the problems mentioned above. As is illustrated by the numerical example, the proposed approach can well capture experts' diversity experience, enhance the knowledge representation power, and reason the rule-based knowledge more intelligently.

Characterizing emergent properties of immunological systems with multi-cellular rule-based computational modeling.

PubMed

Chavali, Arvind K; Gianchandani, Erwin P; Tung, Kenneth S; Lawrence, Michael B; Peirce, Shayn M; Papin, Jason A

2008-12-01

The immune system is comprised of numerous components that interact with one another to give rise to phenotypic behaviors that are sometimes unexpected. Agent-based modeling (ABM) and cellular automata (CA) belong to a class of discrete mathematical approaches in which autonomous entities detect local information and act over time according to logical rules. The power of this approach lies in the emergence of behavior that arises from interactions between agents, which would otherwise be impossible to know a priori. Recent work exploring the immune system with ABM and CA has revealed novel insights into immunological processes. Here, we summarize these applications to immunology and, particularly, how ABM can help formulate hypotheses that might drive further experimental investigations of disease mechanisms.

Automated geospatial Web Services composition based on geodata quality requirements

NASA Astrophysics Data System (ADS)

Cruz, Sérgio A. B.; Monteiro, Antonio M. V.; Santos, Rafael

2012-10-01

Service-Oriented Architecture and Web Services technologies improve the performance of activities involved in geospatial analysis with a distributed computing architecture. However, the design of the geospatial analysis process on this platform, by combining component Web Services, presents some open issues. The automated construction of these compositions represents an important research topic. Some approaches to solving this problem are based on AI planning methods coupled with semantic service descriptions. This work presents a new approach using AI planning methods to improve the robustness of the produced geospatial Web Services composition. For this purpose, we use semantic descriptions of geospatial data quality requirements in a rule-based form. These rules allow the semantic annotation of geospatial data and, coupled with the conditional planning method, this approach represents more precisely the situations of nonconformities with geodata quality that may occur during the execution of the Web Service composition. The service compositions produced by this method are more robust, thus improving process reliability when working with a composition of chained geospatial Web Services.

An Embedded Rule-Based Diagnostic Expert System in Ada

NASA Technical Reports Server (NTRS)

Jones, Robert E.; Liberman, Eugene M.

1992-01-01

Ada is becoming an increasingly popular programming language for large Government-funded software projects. Ada with it portability, transportability, and maintainability lends itself well to today's complex programming environment. In addition, expert systems have also assumed a growing role in providing human-like reasoning capability expertise for computer systems. The integration is discussed of expert system technology with Ada programming language, especially a rule-based expert system using an ART-Ada (Automated Reasoning Tool for Ada) system shell. NASA Lewis was chosen as a beta test site for ART-Ada. The test was conducted by implementing the existing Autonomous Power EXpert System (APEX), a Lisp-based power expert system, in ART-Ada. Three components, the rule-based expert systems, a graphics user interface, and communications software make up SMART-Ada (Systems fault Management with ART-Ada). The rules were written in the ART-Ada development environment and converted to Ada source code. The graphics interface was developed with the Transportable Application Environment (TAE) Plus, which generates Ada source code to control graphics images. SMART-Ada communicates with a remote host to obtain either simulated or real data. The Ada source code generated with ART-Ada, TAE Plus, and communications code was incorporated into an Ada expert system that reads the data from a power distribution test bed, applies the rule to determine a fault, if one exists, and graphically displays it on the screen. The main objective, to conduct a beta test on the ART-Ada rule-based expert system shell, was achieved. The system is operational. New Ada tools will assist in future successful projects. ART-Ada is one such tool and is a viable alternative to the straight Ada code when an application requires a rule-based or knowledge-based approach.

Examining change detection approaches for tropical mangrove monitoring

USGS Publications Warehouse

Myint, Soe W.; Franklin, Janet; Buenemann, Michaela; Kim, Won; Giri, Chandra

2014-01-01

This study evaluated the effectiveness of different band combinations and classifiers (unsupervised, supervised, object-oriented nearest neighbor, and object-oriented decision rule) for quantifying mangrove forest change using multitemporal Landsat data. A discriminant analysis using spectra of different vegetation types determined that bands 2 (0.52 to 0.6 μm), 5 (1.55 to 1.75 μm), and 7 (2.08 to 2.35 μm) were the most effective bands for differentiating mangrove forests from surrounding land cover types. A ranking of thirty-six change maps, produced by comparing the classification accuracy of twelve change detection approaches, was used. The object-based Nearest Neighbor classifier produced the highest mean overall accuracy (84 percent) regardless of band combinations. The automated decision rule-based approach (mean overall accuracy of 88 percent) as well as a composite of bands 2, 5, and 7 used with the unsupervised classifier and the same composite or all band difference with the object-oriented Nearest Neighbor classifier were the most effective approaches.

Towards a Semantically-Enabled Control Strategy for Building Simulations: Integration of Semantic Technologies and Model Predictive Control

DOE Office of Scientific and Technical Information (OSTI.GOV)

Delgoshaei, Parastoo; Austin, Mark A.; Pertzborn, Amanda J.

State-of-the-art building simulation control methods incorporate physical constraints into their mathematical models, but omit implicit constraints associated with policies of operation and dependency relationships among rules representing those constraints. To overcome these shortcomings, there is a recent trend in enabling the control strategies with inference-based rule checking capabilities. One solution is to exploit semantic web technologies in building simulation control. Such approaches provide the tools for semantic modeling of domains, and the ability to deduce new information based on the models through use of Description Logic (DL). In a step toward enabling this capability, this paper presents a cross-disciplinary data-drivenmore » control strategy for building energy management simulation that integrates semantic modeling and formal rule checking mechanisms into a Model Predictive Control (MPC) formulation. The results show that MPC provides superior levels of performance when initial conditions and inputs are derived from inference-based rules.« less

Restoring Consistency In Subjective Information For Groundwater Driven Health Risk Assessment

NASA Astrophysics Data System (ADS)

Ozbek, M. M.; Pinder, G. F.

2004-12-01

In an earlier work (Ozbek and Pinder, 2003), we constructed a fuzzy rule-based knowledge base that uses subjective expert opinion to calculate risk-based design constraints (i.e., dose and pattern of exposure) to sustain the groundwater-driven individual health risk at a desired level. Ideally, our system must be capable to produce for any individual a meaningful risk result or for any given risk a meaningful design constraint, in the sense that the result is neither the empty set nor the whole domain of the variable of interest. Otherwise we consider our system as inconsistent. We present a method based on fuzzy similarity relations to restore consistency in our implicative fuzzy rule based system used for the risk-based groundwater remediation design problem. Both a global and a local approach are considered. Even though straightforward and computationally less demanding, the global approach can affect pieces of knowledge negatively by inducing unwarranted imprecision into the knowledge base. On the other hand, the local approach, given a family of parameterized similarity relations, determines a parameter for each inference such that consistent results are computed which may not be feasible in real time applications of our knowledge base. Several scenarios are considered for comparing the two approaches that suggest that for specific applications one or several approaches ranging from a completely global to a completely local one will be more suitable than others while calculating the design constraints.

Rules out of Roles: Differences in Play Language and Their Developmental Significance

ERIC Educational Resources Information Center

Kim, Yongho; Kellogg, David

2007-01-01

Using a discourse analytic approach from the work of Hoey (1991) and a dual processing model from Wray (2000), this paper compares the language produced by the same classes of children when they are engaged in role-play and when they are playing rule-based games. We find that role-play tends to be richer in "frozen" pair parts, where the responses…

Numerical calculation of the Fresnel transform.

PubMed

Kelly, Damien P

2014-04-01

In this paper, we address the problem of calculating Fresnel diffraction integrals using a finite number of uniformly spaced samples. General and simple sampling rules of thumb are derived that allow the user to calculate the distribution for any propagation distance. It is shown how these rules can be extended to fast-Fourier-transform-based algorithms to increase calculation efficiency. A comparison with other theoretical approaches is made.

Rosetta Phase II: Measuring and Interpreting Cultural Differences in Cognition

DTIC Science & Technology

2008-07-31

approaches are used to capture culture. First, anthropology and psychiatry adopt research methods that focus on specific groups or individuals...Classical anthropology provides information about behaviors, customs, social roles, and social rules based on extended and intense observation of single...This training goes beyond rules and procedures so that military personnel can see events through the eyes of adversaries or host nationals. They must

Lightweight Tactical Client: A Capability-Based Approach to Command Post Computing

DTIC Science & Technology

2015-12-01

bundles these capabilities together is proposed: a lightweight tactical client. In order to avoid miscommunication in the future, it is... solutions and almost definitely rules out most terminal-based thin clients. UNCLASSIFIED Approved for public release

Rule-based exposure assessment versus case-by-case expert assessment using the same information in a community-based study.

PubMed

Peters, Susan; Glass, Deborah C; Milne, Elizabeth; Fritschi, Lin

2014-03-01

Retrospective exposure assessment in community-based studies is largely reliant on questionnaire information. Expert assessment is often used to assess lifetime occupational exposures, but these assessments generally lack transparency and are very time-consuming. We explored the agreement between a rule-based assessment approach and case-by-case expert assessment of occupational exposures in a community-based study. We used data from a case-control study of childhood acute lymphoblastic leukaemia in which parental occupational exposures were originally assigned by expert assessment. Key questions were identified from the completed parent questionnaires and, on the basis of these, rules were written to assign exposure levels to diesel exhaust, pesticides and solvents. We estimated exposure prevalence separately for fathers and mothers, and used κ statistics to assess the agreement between the two exposure assessment methods. Exposures were assigned to 5829 jobs among 1079 men and 6189 jobs among 1234 women. For both sexes, agreement was good for the two assessment methods of exposure to diesel exhaust at a job level (κ=0.70 for men and κ=0.71 for women) and at a person level (κ=0.74 and κ=0.75). The agreement was good to excellent for pesticide exposure among men (κ=0.74 for jobs and κ=0.84 at a person level) and women (κ=0.68 and κ=0.71 at a job and person level, respectively). Moderate to good agreement was observed for assessment of solvent exposure, which was better for women than men. The rule-based assessment approach appeared to be an efficient alternative for assigning occupational exposures in a community-based study for a selection of occupational exposures.

Measuring 'virtue' in medicine.

PubMed

Kotzee, Ben; Ignatowicz, Agnieszka

2016-06-01

Virtue-approaches to medical ethics are becoming ever more influential. Virtue theorists advocate redefining right or good action in medicine in terms of the character of the doctor performing the action (rather than adherence to rules or principles). In medical education, too, calls are growing to reconceive medical education as a form of character formation (rather than instruction in rules or principles). Empirical studies of doctors' ethics from a virtue-perspective, however, are few and far between. In this respect, theoretical and empirical study of medical ethics are out of alignment. In this paper, we survey the empirical study of medical ethics and find that most studies of doctors' ethics are rules- or principles-based and not virtue-based. We outline the challenges that exist for studying medical ethics empirically from a virtue-based perspective and canvas the runners and riders in the effort to find virtue-based assessments of medical ethics.

Conceptual model of knowledge base system

NASA Astrophysics Data System (ADS)

Naykhanova, L. V.; Naykhanova, I. V.

2018-05-01

In the article, the conceptual model of the knowledge based system by the type of the production system is provided. The production system is intended for automation of problems, which solution is rigidly conditioned by the legislation. A core component of the system is a knowledge base. The knowledge base consists of a facts set, a rules set, the cognitive map and ontology. The cognitive map is developed for implementation of a control strategy, ontology - the explanation mechanism. Knowledge representation about recognition of a situation in the form of rules allows describing knowledge of the pension legislation. This approach provides the flexibility, originality and scalability of the system. In the case of changing legislation, it is necessary to change the rules set. This means that the change of the legislation would not be a big problem. The main advantage of the system is that there is an opportunity to be adapted easily to changes of the legislation.

Analysis of mesenchymal stem cell differentiation in vitro using classification association rule mining.

PubMed

Wang, Weiqi; Wang, Yanbo Justin; Bañares-Alcántara, René; Coenen, Frans; Cui, Zhanfeng

2009-12-01

In this paper, data mining is used to analyze the data on the differentiation of mammalian Mesenchymal Stem Cells (MSCs), aiming at discovering known and hidden rules governing MSC differentiation, following the establishment of a web-based public database containing experimental data on the MSC proliferation and differentiation. To this effect, a web-based public interactive database comprising the key parameters which influence the fate and destiny of mammalian MSCs has been constructed and analyzed using Classification Association Rule Mining (CARM) as a data-mining technique. The results show that the proposed approach is technically feasible and performs well with respect to the accuracy of (classification) prediction. Key rules mined from the constructed MSC database are consistent with experimental observations, indicating the validity of the method developed and the first step in the application of data mining to the study of MSCs.

Combining human and machine intelligence to derive agents' behavioral rules for groundwater irrigation

NASA Astrophysics Data System (ADS)

Hu, Yao; Quinn, Christopher J.; Cai, Ximing; Garfinkle, Noah W.

2017-11-01

For agent-based modeling, the major challenges in deriving agents' behavioral rules arise from agents' bounded rationality and data scarcity. This study proposes a "gray box" approach to address the challenge by incorporating expert domain knowledge (i.e., human intelligence) with machine learning techniques (i.e., machine intelligence). Specifically, we propose using directed information graph (DIG), boosted regression trees (BRT), and domain knowledge to infer causal factors and identify behavioral rules from data. A case study is conducted to investigate farmers' pumping behavior in the Midwest, U.S.A. Results show that four factors identified by the DIG algorithm- corn price, underlying groundwater level, monthly mean temperature and precipitation- have main causal influences on agents' decisions on monthly groundwater irrigation depth. The agent-based model is then developed based on the behavioral rules represented by three DIGs and modeled by BRTs, and coupled with a physically-based groundwater model to investigate the impacts of agents' pumping behavior on the underlying groundwater system in the context of coupled human and environmental systems.

A guided search genetic algorithm using mined rules for optimal affective product design

NASA Astrophysics Data System (ADS)

Fung, Chris K. Y.; Kwong, C. K.; Chan, Kit Yan; Jiang, H.

2014-08-01

Affective design is an important aspect of new product development, especially for consumer products, to achieve a competitive edge in the marketplace. It can help companies to develop new products that can better satisfy the emotional needs of customers. However, product designers usually encounter difficulties in determining the optimal settings of the design attributes for affective design. In this article, a novel guided search genetic algorithm (GA) approach is proposed to determine the optimal design attribute settings for affective design. The optimization model formulated based on the proposed approach applied constraints and guided search operators, which were formulated based on mined rules, to guide the GA search and to achieve desirable solutions. A case study on the affective design of mobile phones was conducted to illustrate the proposed approach and validate its effectiveness. Validation tests were conducted, and the results show that the guided search GA approach outperforms the GA approach without the guided search strategy in terms of GA convergence and computational time. In addition, the guided search optimization model is capable of improving GA to generate good solutions for affective design.

Reconstruction of DNA sequences using genetic algorithms and cellular automata: towards mutation prediction?

PubMed

Mizas, Ch; Sirakoulis, G Ch; Mardiris, V; Karafyllidis, I; Glykos, N; Sandaltzopoulos, R

2008-04-01

Change of DNA sequence that fuels evolution is, to a certain extent, a deterministic process because mutagenesis does not occur in an absolutely random manner. So far, it has not been possible to decipher the rules that govern DNA sequence evolution due to the extreme complexity of the entire process. In our attempt to approach this issue we focus solely on the mechanisms of mutagenesis and deliberately disregard the role of natural selection. Hence, in this analysis, evolution refers to the accumulation of genetic alterations that originate from mutations and are transmitted through generations without being subjected to natural selection. We have developed a software tool that allows modelling of a DNA sequence as a one-dimensional cellular automaton (CA) with four states per cell which correspond to the four DNA bases, i.e. A, C, T and G. The four states are represented by numbers of the quaternary number system. Moreover, we have developed genetic algorithms (GAs) in order to determine the rules of CA evolution that simulate the DNA evolution process. Linear evolution rules were considered and square matrices were used to represent them. If DNA sequences of different evolution steps are available, our approach allows the determination of the underlying evolution rule(s). Conversely, once the evolution rules are deciphered, our tool may reconstruct the DNA sequence in any previous evolution step for which the exact sequence information was unknown. The developed tool may be used to test various parameters that could influence evolution. We describe a paradigm relying on the assumption that mutagenesis is governed by a near-neighbour-dependent mechanism. Based on the satisfactory performance of our system in the deliberately simplified example, we propose that our approach could offer a starting point for future attempts to understand the mechanisms that govern evolution. The developed software is open-source and has a user-friendly graphical input interface.

A novel logic-based approach for quantitative toxicology prediction.

PubMed

Amini, Ata; Muggleton, Stephen H; Lodhi, Huma; Sternberg, Michael J E

2007-01-01

There is a pressing need for accurate in silico methods to predict the toxicity of molecules that are being introduced into the environment or are being developed into new pharmaceuticals. Predictive toxicology is in the realm of structure activity relationships (SAR), and many approaches have been used to derive such SAR. Previous work has shown that inductive logic programming (ILP) is a powerful approach that circumvents several major difficulties, such as molecular superposition, faced by some other SAR methods. The ILP approach reasons with chemical substructures within a relational framework and yields chemically understandable rules. Here, we report a general new approach, support vector inductive logic programming (SVILP), which extends the essentially qualitative ILP-based SAR to quantitative modeling. First, ILP is used to learn rules, the predictions of which are then used within a novel kernel to derive a support-vector generalization model. For a highly heterogeneous dataset of 576 molecules with known fathead minnow fish toxicity, the cross-validated correlation coefficients (R2CV) from a chemical descriptor method (CHEM) and SVILP are 0.52 and 0.66, respectively. The ILP, CHEM, and SVILP approaches correctly predict 55, 58, and 73%, respectively, of toxic molecules. In a set of 165 unseen molecules, the R2 values from the commercial software TOPKAT and SVILP are 0.26 and 0.57, respectively. In all calculations, SVILP showed significant improvements in comparison with the other methods. The SVILP approach has a major advantage in that it uses ILP automatically and consistently to derive rules, mostly novel, describing fragments that are toxicity alerts. The SVILP is a general machine-learning approach and has the potential of tackling many problems relevant to chemoinformatics including in silico drug design.

Table Extraction from Web Pages Using Conditional Random Fields to Extract Toponym Related Data

NASA Astrophysics Data System (ADS)

Luthfi Hanifah, Hayyu'; Akbar, Saiful

2017-01-01

Table is one of the ways to visualize information on web pages. The abundant number of web pages that compose the World Wide Web has been the motivation of information extraction and information retrieval research, including the research for table extraction. Besides, there is a need for a system which is designed to specifically handle location-related information. Based on this background, this research is conducted to provide a way to extract location-related data from web tables so that it can be used in the development of Geographic Information Retrieval (GIR) system. The location-related data will be identified by the toponym (location name). In this research, a rule-based approach with gazetteer is used to recognize toponym from web table. Meanwhile, to extract data from a table, a combination of rule-based approach and statistical-based approach is used. On the statistical-based approach, Conditional Random Fields (CRF) model is used to understand the schema of the table. The result of table extraction is presented on JSON format. If a web table contains toponym, a field will be added on the JSON document to store the toponym values. This field can be used to index the table data in accordance to the toponym, which then can be used in the development of GIR system.

Process Materialization Using Templates and Rules to Design Flexible Process Models

NASA Astrophysics Data System (ADS)

Kumar, Akhil; Yao, Wen

The main idea in this paper is to show how flexible processes can be designed by combining generic process templates and business rules. We instantiate a process by applying rules to specific case data, and running a materialization algorithm. The customized process instance is then executed in an existing workflow engine. We present an architecture and also give an algorithm for process materialization. The rules are written in a logic-based language like Prolog. Our focus is on capturing deeper process knowledge and achieving a holistic approach to robust process design that encompasses control flow, resources and data, as well as makes it easier to accommodate changes to business policy.

Prestraining and Its Influence on Subsequent Fatigue Life

NASA Technical Reports Server (NTRS)

Halford, Gary R.; Mcgaw, Michael A.; Kalluri, Sreeramesh

1995-01-01

An experimental program was conducted to study the damaging effects of tensile and compressive prestrains on the fatigue life of nickel-base, Inconel 718 superalloy at room temperature. To establish baseline fatigue behavior, virgin specimens with a solid uniform gage section were fatigued to failure under fully-reversed strain-control. Additional specimens were prestrained to 2 percent, 5 percent, and 10 percent (engineering strains) in the tensile direction and to 2 percent (engineering strain) in the compressive direction under stroke-control, and were subsequently fatigued to failure under fully-reversed strain-control. Experimental results are compared with estimates of remaining fatigue lives (after prestraining) using three life prediction approaches: (1) the Linear Damage Rule; (2) the Linear Strain and Life Fraction Rule; and (3) the nonlinear Damage Curve Approach. The Smith-Watson-Topper parameter was used to estimate fatigue lives in the presence of mean stresses. Among the cumulative damage rules investigated, best remaining fatigue life predictions were obtained with the nonlinear Damage Curve Approach.

Experiments on neural network architectures for fuzzy logic

NASA Technical Reports Server (NTRS)

Keller, James M.

1991-01-01

The use of fuzzy logic to model and manage uncertainty in a rule-based system places high computational demands on an inference engine. In an earlier paper, the authors introduced a trainable neural network structure for fuzzy logic. These networks can learn and extrapolate complex relationships between possibility distributions for the antecedents and consequents in the rules. Here, the power of these networks is further explored. The insensitivity of the output to noisy input distributions (which are likely if the clauses are generated from real data) is demonstrated as well as the ability of the networks to internalize multiple conjunctive clause and disjunctive clause rules. Since different rules with the same variables can be encoded in a single network, this approach to fuzzy logic inference provides a natural mechanism for rule conflict resolution.

eFSM--a novel online neural-fuzzy semantic memory model.

PubMed

Tung, Whye Loon; Quek, Chai

2010-01-01

Fuzzy rule-based systems (FRBSs) have been successfully applied to many areas. However, traditional fuzzy systems are often manually crafted, and their rule bases that represent the acquired knowledge are static and cannot be trained to improve the modeling performance. This subsequently leads to intensive research on the autonomous construction and tuning of a fuzzy system directly from the observed training data to address the knowledge acquisition bottleneck, resulting in well-established hybrids such as neural-fuzzy systems (NFSs) and genetic fuzzy systems (GFSs). However, the complex and dynamic nature of real-world problems demands that fuzzy rule-based systems and models be able to adapt their parameters and ultimately evolve their rule bases to address the nonstationary (time-varying) characteristics of their operating environments. Recently, considerable research efforts have been directed to the study of evolving Tagaki-Sugeno (T-S)-type NFSs based on the concept of incremental learning. In contrast, there are very few incremental learning Mamdani-type NFSs reported in the literature. Hence, this paper presents the evolving neural-fuzzy semantic memory (eFSM) model, a neural-fuzzy Mamdani architecture with a data-driven progressively adaptive structure (i.e., rule base) based on incremental learning. Issues related to the incremental learning of the eFSM rule base are carefully investigated, and a novel parameter learning approach is proposed for the tuning of the fuzzy set parameters in eFSM. The proposed eFSM model elicits highly interpretable semantic knowledge in the form of Mamdani-type if-then fuzzy rules from low-level numeric training data. These Mamdani fuzzy rules define the computing structure of eFSM and are incrementally learned with the arrival of each training data sample. New rules are constructed from the emergence of novel training data and obsolete fuzzy rules that no longer describe the recently observed data trends are pruned. This enables eFSM to maintain a current and compact set of Mamdani-type if-then fuzzy rules that collectively generalizes and describes the salient associative mappings between the inputs and outputs of the underlying process being modeled. The learning and modeling performances of the proposed eFSM are evaluated using several benchmark applications and the results are encouraging.

Recreational System Optimization to Reduce Conflict on Public Lands

NASA Astrophysics Data System (ADS)

Shilling, Fraser; Boggs, Jennifer; Reed, Sarah

2012-09-01

In response to federal administrative rule, the Tahoe National Forest (TNF), California, USA engaged in trail-route prioritization for motorized recreation (e.g., off-highway-vehicles) and other recreation types. The prioritization was intended to identify routes that were suitable and ill-suited for maintenance in a transportation system. A recreational user survey was conducted online ( n = 813) for user preferences for trail system characteristics, recreational use patterns, and demographics. Motorized trail users and non-motorized users displayed very clear and contrasting preferences for the same system. As has been found by previous investigators, non-motorized users expressed antagonism to motorized use on the same recreational travel system, whereas motorized users either supported multiple-use routes or dismissed non-motorized recreationists' concerns. To help the TNF plan for reduced conflict, a geographic information system (GIS) based modeling approach was used to identify recreational opportunities and potential environmental impacts of all travel routes. This GIS-based approach was based on an expert-derived rule set. The rules addressed particular environmental and recreation concerns in the TNF. Route segments were identified that could be incorporated into minimal-impact networks to support various types of recreation. The combination of potential impacts and user-benefits supported an optimization approach for an appropriate recreational travel network to minimize environmental impacts and user-conflicts in a multi-purpose system.

Recreational system optimization to reduce conflict on public lands.

PubMed

Shilling, Fraser; Boggs, Jennifer; Reed, Sarah

2012-09-01

In response to federal administrative rule, the Tahoe National Forest (TNF), California, USA engaged in trail-route prioritization for motorized recreation (e.g., off-highway-vehicles) and other recreation types. The prioritization was intended to identify routes that were suitable and ill-suited for maintenance in a transportation system. A recreational user survey was conducted online (n = 813) for user preferences for trail system characteristics, recreational use patterns, and demographics. Motorized trail users and non-motorized users displayed very clear and contrasting preferences for the same system. As has been found by previous investigators, non-motorized users expressed antagonism to motorized use on the same recreational travel system, whereas motorized users either supported multiple-use routes or dismissed non-motorized recreationists' concerns. To help the TNF plan for reduced conflict, a geographic information system (GIS) based modeling approach was used to identify recreational opportunities and potential environmental impacts of all travel routes. This GIS-based approach was based on an expert-derived rule set. The rules addressed particular environmental and recreation concerns in the TNF. Route segments were identified that could be incorporated into minimal-impact networks to support various types of recreation. The combination of potential impacts and user-benefits supported an optimization approach for an appropriate recreational travel network to minimize environmental impacts and user-conflicts in a multi-purpose system.

Integrated approach using data mining-based decision tree and object-based image analysis for high-resolution urban mapping of WorldView-2 satellite sensor data

NASA Astrophysics Data System (ADS)

Hamedianfar, Alireza; Shafri, Helmi Zulhaidi Mohd

2016-04-01

This paper integrates decision tree-based data mining (DM) and object-based image analysis (OBIA) to provide a transferable model for the detailed characterization of urban land-cover classes using WorldView-2 (WV-2) satellite images. Many articles have been published on OBIA in recent years based on DM for different applications. However, less attention has been paid to the generation of a transferable model for characterizing detailed urban land cover features. Three subsets of WV-2 images were used in this paper to generate transferable OBIA rule-sets. Many features were explored by using a DM algorithm, which created the classification rules as a decision tree (DT) structure from the first study area. The developed DT algorithm was applied to object-based classifications in the first study area. After this process, we validated the capability and transferability of the classification rules into second and third subsets. Detailed ground truth samples were collected to assess the classification results. The first, second, and third study areas achieved 88%, 85%, and 85% overall accuracies, respectively. Results from the investigation indicate that DM was an efficient method to provide the optimal and transferable classification rules for OBIA, which accelerates the rule-sets creation stage in the OBIA classification domain.

Improved model reduction and tuning of fractional-order PI(λ)D(μ) controllers for analytical rule extraction with genetic programming.

PubMed

Das, Saptarshi; Pan, Indranil; Das, Shantanu; Gupta, Amitava

2012-03-01

Genetic algorithm (GA) has been used in this study for a new approach of suboptimal model reduction in the Nyquist plane and optimal time domain tuning of proportional-integral-derivative (PID) and fractional-order (FO) PI(λ)D(μ) controllers. Simulation studies show that the new Nyquist-based model reduction technique outperforms the conventional H(2)-norm-based reduced parameter modeling technique. With the tuned controller parameters and reduced-order model parameter dataset, optimum tuning rules have been developed with a test-bench of higher-order processes via genetic programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by a weighted sum of error index and controller effort. From the reported Pareto optimal front of the GP-based optimal rule extraction technique, a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP-based tuning rules has been compared with the original GA-based control performance for the PID and PI(λ)D(μ) controllers, handling four different classes of representative higher-order processes. These rules are very useful for process control engineers, as they inherit the power of the GA-based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three-dimensional plots of the required variation in PID/fractional-order PID (FOPID) controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP-based tuning rules has also been shown with credible simulation examples. Copyright © 2011 ISA. Published by Elsevier Ltd. All rights reserved.

Developing a Scientific Virtue-Based Approach to Science Ethics Training.

PubMed

Pennock, Robert T; O'Rourke, Michael

2017-02-01

Responsible conduct of research training typically includes only a subset of the issues that ought to be included in science ethics and sometimes makes ethics appear to be a set of externally imposed rules rather than something intrinsic to scientific practice. A new approach to science ethics training based upon Pennock's notion of the scientific virtues may help avoid such problems. This paper motivates and describes three implementations-theory-centered, exemplar-centered, and concept-centered-that we have developed in courses and workshops to introduce students to this scientific virtue-based approach.

Ontology-Based Method for Fault Diagnosis of Loaders.

PubMed

Xu, Feixiang; Liu, Xinhui; Chen, Wei; Zhou, Chen; Cao, Bingwei

2018-02-28

This paper proposes an ontology-based fault diagnosis method which overcomes the difficulty of understanding complex fault diagnosis knowledge of loaders and offers a universal approach for fault diagnosis of all loaders. This method contains the following components: (1) An ontology-based fault diagnosis model is proposed to achieve the integrating, sharing and reusing of fault diagnosis knowledge for loaders; (2) combined with ontology, CBR (case-based reasoning) is introduced to realize effective and accurate fault diagnoses following four steps (feature selection, case-retrieval, case-matching and case-updating); and (3) in order to cover the shortages of the CBR method due to the lack of concerned cases, ontology based RBR (rule-based reasoning) is put forward through building SWRL (Semantic Web Rule Language) rules. An application program is also developed to implement the above methods to assist in finding the fault causes, fault locations and maintenance measures of loaders. In addition, the program is validated through analyzing a case study.

Does GEM-encoding clinical practice guidelines improve the quality of knowledge bases? A study with the rule-based formalism.

PubMed

Georg, Georg; Séroussi, Brigitte; Bouaud, Jacques

2003-01-01

The aim of this work was to determine whether the GEM-encoding step could improve the representation of clinical practice guidelines as formalized knowledge bases. We used the 1999 Canadian recommendations for the management of hypertension, chosen as the knowledge source in the ASTI project. We first clarified semantic ambiguities of therapeutic sequences recommended in the guideline by proposing an interpretative framework of therapeutic strategies. Then, after a formalization step to standardize the terms used to characterize clinical situations, we created the GEM-encoded instance of the guideline. We developed a module for the automatic derivation of a rule base, BR-GEM, from the instance. BR-GEM was then compared to the rule base, BR-ASTI, embedded within the critic mode of ASTI, and manually built by two physicians from the same Canadian guideline. As compared to BR-ASTI, BR-GEM is more specific and covers more clinical situations. When evaluated on 10 patient cases, the GEM-based approach led to promising results.

Ontology-Based Method for Fault Diagnosis of Loaders

PubMed Central

Liu, Xinhui; Chen, Wei; Zhou, Chen; Cao, Bingwei

2018-01-01

This paper proposes an ontology-based fault diagnosis method which overcomes the difficulty of understanding complex fault diagnosis knowledge of loaders and offers a universal approach for fault diagnosis of all loaders. This method contains the following components: (1) An ontology-based fault diagnosis model is proposed to achieve the integrating, sharing and reusing of fault diagnosis knowledge for loaders; (2) combined with ontology, CBR (case-based reasoning) is introduced to realize effective and accurate fault diagnoses following four steps (feature selection, case-retrieval, case-matching and case-updating); and (3) in order to cover the shortages of the CBR method due to the lack of concerned cases, ontology based RBR (rule-based reasoning) is put forward through building SWRL (Semantic Web Rule Language) rules. An application program is also developed to implement the above methods to assist in finding the fault causes, fault locations and maintenance measures of loaders. In addition, the program is validated through analyzing a case study. PMID:29495646

Pilot interaction with automated airborne decision making systems

NASA Technical Reports Server (NTRS)

Rouse, W. B.; Hammer, J. M.; Mitchell, C. M.; Morris, N. M.; Lewis, C. M.; Yoon, W. C.

1985-01-01

Progress was made in the three following areas. In the rule-based modeling area, two papers related to identification and significane testing of rule-based models were presented. In the area of operator aiding, research focused on aiding operators in novel failure situations; a discrete control modeling approach to aiding PLANT operators was developed; and a set of guidelines were developed for implementing automation. In the area of flight simulator hardware and software, the hardware will be completed within two months and initial simulation software will then be integrated and tested.

Time and truth in plans

NASA Technical Reports Server (NTRS)

Hazelton, Lyman R., Jr.

1990-01-01

Some of the logical components of a rule based planning and scheduling system are described. The researcher points out a deficiency in the conventional truth maintenance approach to this class of problems and suggests a new mechanism which overcomes the problem. This extension of the idea of justification truth maintenance may seem at first to be a small philosophical step. However, it embodies a process of basic human reasoning which is so common and automatic as to escape conscious detection without careful introspection. It is vital to any successful implementation of a rule based planning reasoner.

Automated Database Mediation Using Ontological Metadata Mappings

PubMed Central

Marenco, Luis; Wang, Rixin; Nadkarni, Prakash

2009-01-01

Objective To devise an automated approach for integrating federated database information using database ontologies constructed from their extended metadata. Background One challenge of database federation is that the granularity of representation of equivalent data varies across systems. Dealing effectively with this problem is analogous to dealing with precoordinated vs. postcoordinated concepts in biomedical ontologies. Model Description The authors describe an approach based on ontological metadata mapping rules defined with elements of a global vocabulary, which allows a query specified at one granularity level to fetch data, where possible, from databases within the federation that use different granularities. This is implemented in OntoMediator, a newly developed production component of our previously described Query Integrator System. OntoMediator's operation is illustrated with a query that accesses three geographically separate, interoperating databases. An example based on SNOMED also illustrates the applicability of high-level rules to support the enforcement of constraints that can prevent inappropriate curator or power-user actions. Summary A rule-based framework simplifies the design and maintenance of systems where categories of data must be mapped to each other, for the purpose of either cross-database query or for curation of the contents of compositional controlled vocabularies. PMID:19567801

ChemStable: a web server for rule-embedded naïve Bayesian learning approach to predict compound stability.

PubMed

Liu, Zhihong; Zheng, Minghao; Yan, Xin; Gu, Qiong; Gasteiger, Johann; Tijhuis, Johan; Maas, Peter; Li, Jiabo; Xu, Jun

2014-09-01

Predicting compound chemical stability is important because unstable compounds can lead to either false positive or to false negative conclusions in bioassays. Experimental data (COMDECOM) measured from DMSO/H2O solutions stored at 50 °C for 105 days were used to predicted stability by applying rule-embedded naïve Bayesian learning, based upon atom center fragment (ACF) features. To build the naïve Bayesian classifier, we derived ACF features from 9,746 compounds in the COMDECOM dataset. By recursively applying naïve Bayesian learning from the data set, each ACF is assigned with an expected stable probability (p(s)) and an unstable probability (p(uns)). 13,340 ACFs, together with their p(s) and p(uns) data, were stored in a knowledge base for use by the Bayesian classifier. For a given compound, its ACFs were derived from its structure connection table with the same protocol used to drive ACFs from the training data. Then, the Bayesian classifier assigned p(s) and p(uns) values to the compound ACFs by a structural pattern recognition algorithm, which was implemented in-house. Compound instability is calculated, with Bayes' theorem, based upon the p(s) and p(uns) values of the compound ACFs. We were able to achieve performance with an AUC value of 84% and a tenfold cross validation accuracy of 76.5%. To reduce false negatives, a rule-based approach has been embedded in the classifier. The rule-based module allows the program to improve its predictivity by expanding its compound instability knowledge base, thus further reducing the possibility of false negatives. To our knowledge, this is the first in silico prediction service for the prediction of the stabilities of organic compounds.

Preparation of name and address data for record linkage using hidden Markov models

PubMed Central

Churches, Tim; Christen, Peter; Lim, Kim; Zhu, Justin Xi

2002-01-01

Background Record linkage refers to the process of joining records that relate to the same entity or event in one or more data collections. In the absence of a shared, unique key, record linkage involves the comparison of ensembles of partially-identifying, non-unique data items between pairs of records. Data items with variable formats, such as names and addresses, need to be transformed and normalised in order to validly carry out these comparisons. Traditionally, deterministic rule-based data processing systems have been used to carry out this pre-processing, which is commonly referred to as "standardisation". This paper describes an alternative approach to standardisation, using a combination of lexicon-based tokenisation and probabilistic hidden Markov models (HMMs). Methods HMMs were trained to standardise typical Australian name and address data drawn from a range of health data collections. The accuracy of the results was compared to that produced by rule-based systems. Results Training of HMMs was found to be quick and did not require any specialised skills. For addresses, HMMs produced equal or better standardisation accuracy than a widely-used rule-based system. However, acccuracy was worse when used with simpler name data. Possible reasons for this poorer performance are discussed. Conclusion Lexicon-based tokenisation and HMMs provide a viable and effort-effective alternative to rule-based systems for pre-processing more complex variably formatted data such as addresses. Further work is required to improve the performance of this approach with simpler data such as names. Software which implements the methods described in this paper is freely available under an open source license for other researchers to use and improve. PMID:12482326

Toward a theory of distributed word expert natural language parsing

NASA Technical Reports Server (NTRS)

Rieger, C.; Small, S.

1981-01-01

An approach to natural language meaning-based parsing in which the unit of linguistic knowledge is the word rather than the rewrite rule is described. In the word expert parser, knowledge about language is distributed across a population of procedural experts, each representing a word of the language, and each an expert at diagnosing that word's intended usage in context. The parser is structured around a coroutine control environment in which the generator-like word experts ask questions and exchange information in coming to collective agreement on sentence meaning. The word expert theory is advanced as a better cognitive model of human language expertise than the traditional rule-based approach. The technical discussion is organized around examples taken from the prototype LISP system which implements parts of the theory.

A MOOC on Approaches to Machine Translation

ERIC Educational Resources Information Center

Costa-jussà, Mart R.; Formiga, Lluís; Torrillas, Oriol; Petit, Jordi; Fonollosa, José A. R.

2015-01-01

This paper describes the design, development, and analysis of a MOOC entitled "Approaches to Machine Translation: Rule-based, statistical and hybrid", and provides lessons learned and conclusions to be taken into account in the future. The course was developed within the Canvas platform, used by recognized European universities. It…

TOPICS IN THEORY OF GENERALIZED PARTON DISTRIBUTIONS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Radyushkin, Anatoly V.

Several topics in the theory of generalized parton distributions (GPDs) are reviewed. First, we give a brief overview of the basics of the theory of generalized parton distributions and their relationship with simpler phenomenological functions, viz. form factors, parton densities and distribution amplitudes. Then, we discuss recent developments in building models for GPDs that are based on the formalism of double distributions (DDs). A special attention is given to a careful analysis of the singularity structure of DDs. The DD formalism is applied to construction of a model GPDs with a singular Regge behavior. Within the developed DD-based approach, wemore » discuss the structure of GPD sum rules. It is shown that separation of DDs into the so-called ``plus'' part and the $D$-term part may be treated as a renormalization procedure for the GPD sum rules. This approach is compared with an alternative prescription based on analytic regularization.« less

Monitoring with Data Automata

NASA Technical Reports Server (NTRS)

Havelund, Klaus

2014-01-01

We present a form of automaton, referred to as data automata, suited for monitoring sequences of data-carrying events, for example emitted by an executing software system. This form of automata allows states to be parameterized with data, forming named records, which are stored in an efficiently indexed data structure, a form of database. This very explicit approach differs from other automaton-based monitoring approaches. Data automata are also characterized by allowing transition conditions to refer to other parameterized states, and by allowing transitions sequences. The presented automaton concept is inspired by rule-based systems, especially the Rete algorithm, which is one of the well-established algorithms for executing rule-based systems. We present an optimized external DSL for data automata, as well as a comparable unoptimized internal DSL (API) in the Scala programming language, in order to compare the two solutions. An evaluation compares these two solutions to several other monitoring systems.

A knowledge-based, concept-oriented view generation system for clinical data.

PubMed

Zeng, Q; Cimino, J J

2001-04-01

Information overload is a well-known problem for clinicians who must review large amounts of data in patient records. Concept-oriented views, which organize patient data around clinical concepts such as diagnostic strategies and therapeutic goals, may offer a solution to the problem of information overload. However, although concept-oriented views are desirable, they are difficult to create and maintain. We have developed a general-purpose, knowledge-based approach to the generation of concept-oriented views and have developed a system to test our approach. The system creates concept-oriented views through automated identification of relevant patient data. The knowledge in the system is represented by both a semantic network and rules. The key relevant data identification function is accomplished by a rule-based traversal of the semantic network. This paper focuses on the design and implementation of the system; an evaluation of the system is reported separately.

A programmable rules engine to provide clinical decision support using HTML forms.

PubMed

Heusinkveld, J; Geissbuhler, A; Sheshelidze, D; Miller, R

1999-01-01

The authors have developed a simple method for specifying rules to be applied to information on HTML forms. This approach allows clinical experts, who lack the programming expertise needed to write CGI scripts, to construct and maintain domain-specific knowledge and ordering capabilities within WizOrder, the order-entry and decision support system used at Vanderbilt Hospital. The clinical knowledge base maintainers use HTML editors to create forms and spreadsheet programs for rule entry. A test environment has been developed which uses Netscape to display forms; the production environment displays forms using an embedded browser.

An adaptive signal-processing approach to online adaptive tutoring.

PubMed

Bergeron, Bryan; Cline, Andrew

2011-01-01

Conventional intelligent or adaptive tutoring online systems rely on domain-specific models of learner behavior based on rules, deep domain knowledge, and other resource-intensive methods. We have developed and studied a domain-independent methodology of adaptive tutoring based on domain-independent signal-processing approaches that obviate the need for the construction of explicit expert and student models. A key advantage of our method over conventional approaches is a lower barrier to entry for educators who want to develop adaptive online learning materials.

Low-Dimensional Nanostructures and a Semiclassical Approach for Teaching Feynman's Sum-over-Paths Quantum Theory

ERIC Educational Resources Information Center

Onorato, P.

2011-01-01

An introduction to quantum mechanics based on the sum-over-paths (SOP) method originated by Richard P. Feynman and developed by E. F. Taylor and coworkers is presented. The Einstein-Brillouin-Keller (EBK) semiclassical quantization rules are obtained following the SOP approach for bounded systems, and a general approach to the calculation of…

Bayesian networks improve causal environmental assessments for evidence-based policy

EPA Science Inventory

Rule-based weight of evidence approaches to ecological risk assessment may not account for uncertainties and generally lack probabilistic integration of lines of evidence. Bayesian networks allow causal inferences to be made from evidence by including causal knowledge about the p...

Multi-scale Modeling of the Impact Response of a Strain Rate Sensitive High-Manganese Austenitic Steel

NASA Astrophysics Data System (ADS)

Önal, Orkun; Ozmenci, Cemre; Canadinc, Demircan

2014-09-01

A multi-scale modeling approach was applied to predict the impact response of a strain rate sensitive high-manganese austenitic steel. The roles of texture, geometry and strain rate sensitivity were successfully taken into account all at once by coupling crystal plasticity and finite element (FE) analysis. Specifically, crystal plasticity was utilized to obtain the multi-axial flow rule at different strain rates based on the experimental deformation response under uniaxial tensile loading. The equivalent stress - equivalent strain response was then incorporated into the FE model for the sake of a more representative hardening rule under impact loading. The current results demonstrate that reliable predictions can be obtained by proper coupling of crystal plasticity and FE analysis even if the experimental flow rule of the material is acquired under uniaxial loading and at moderate strain rates that are significantly slower than those attained during impact loading. Furthermore, the current findings also demonstrate the need for an experiment-based multi-scale modeling approach for the sake of reliable predictions of the impact response.

Lung Cancer Assistant: a hybrid clinical decision support application for lung cancer care.

PubMed

Sesen, M Berkan; Peake, Michael D; Banares-Alcantara, Rene; Tse, Donald; Kadir, Timor; Stanley, Roz; Gleeson, Fergus; Brady, Michael

2014-09-06

Multidisciplinary team (MDT) meetings are becoming the model of care for cancer patients worldwide. While MDTs have improved the quality of cancer care, the meetings impose substantial time pressure on the members, who generally attend several such MDTs. We describe Lung Cancer Assistant (LCA), a clinical decision support (CDS) prototype designed to assist the experts in the treatment selection decisions in the lung cancer MDTs. A novel feature of LCA is its ability to provide rule-based and probabilistic decision support within a single platform. The guideline-based CDS is based on clinical guideline rules, while the probabilistic CDS is based on a Bayesian network trained on the English Lung Cancer Audit Database (LUCADA). We assess rule-based and probabilistic recommendations based on their concordances with the treatments recorded in LUCADA. Our results reveal that the guideline rule-based recommendations perform well in simulating the recorded treatments with exact and partial concordance rates of 0.57 and 0.79, respectively. On the other hand, the exact and partial concordance rates achieved with probabilistic results are relatively poorer with 0.27 and 0.76. However, probabilistic decision support fulfils a complementary role in providing accurate survival estimations. Compared to recorded treatments, both CDS approaches promote higher resection rates and multimodality treatments.

Culture and problem-solving: Congruency between the cultural mindset of individualism versus collectivism and problem type.

PubMed

Arieli, Sharon; Sagiv, Lilach

2018-06-01

This research investigates how the cultural mindset influences problem-solving. Drawing on the notion that cultural mindset influences the cognitive process individuals bring to bear at the moment of judgment, we propose that the congruency between the cultural mindset (individualistic vs. collectivistic) and problem type (rule-based vs. context-based) affects success in problem-solving. In 7 studies we incorporated the traditional approach to studying the impact of culture (i.e., comparing cultural groups) with contemporary approaches viewing cultural differences in a more dynamic and malleable manner. We first show that members of an individualistic group (Jewish Americans) perform better on rule-based problems, whereas members of collectivistic groups (ultra-Orthodox Jews and Arabs from Israel) perform better on context-based problems (Study 1). We then study Arabs in Israel using language (Arabic vs. Hebrew) to prime their collectivistic versus individualistic mindsets (Study 2). As hypothesized, among biculturals (those who internalize both cultures) Arabic facilitated solving context-based problems, whereas Hebrew facilitated solving rule-based problems. We follow up with 5 experiments priming the cultural mindset of individualism versus collectivism, employing various manifestations of the cultural dimension: focusing on the individual versus the collective (Studies 3, 6, and 7); experiencing independence versus interdependence (Study 4); and directing attention to objects versus the context (Studies 5a-b). Finally, we took a meta-analytic approach, showing that the effects found in Studies 3-6 are robust across priming tasks, problems, and samples. Taken together, the differences between cultural groups (Studies 1-2) were recreated when the individualistic/collectivistic cultural mindset was primed. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

An approach to complex acid-base problems

PubMed Central

Herd, Anthony M.

2005-01-01

OBJECTIVE To review rules and formulas for solving even the most complex acid-base problems. SOURCES OF INFORMATION MEDLINE was searched from January 1966 to December 2003. The search was limited to English-language review articles involving human subjects. Nine relevant review papers were found and provide the background. As this information is well established and widely accepted, it is not judged for strength of evidence, as is standard practice. MAIN MESSAGE An understanding of the body’s responses to acidemia or alkalemia can be gained through a set of four rules and two formulas that can be used to interpret almost any acid-base problems. Physicians should, however, remember the “golden rule” of acid-base interpretation: always look at a patient’s clinical condition. CONCLUSION Physicians practising in acute care settings commonly encounter acid-base disturbances. While some of these are relatively simple and easy to interpret, some are more complex. Even complex cases can be resolved using the four rules and two formulas. PMID:15751566

Production and perception rules underlying visual patterns: effects of symmetry and hierarchy.

PubMed

Westphal-Fitch, Gesche; Huber, Ludwig; Gómez, Juan Carlos; Fitch, W Tecumseh

2012-07-19

Formal language theory has been extended to two-dimensional patterns, but little is known about two-dimensional pattern perception. We first examined spontaneous two-dimensional visual pattern production by humans, gathered using a novel touch screen approach. Both spontaneous creative production and subsequent aesthetic ratings show that humans prefer ordered, symmetrical patterns over random patterns. We then further explored pattern-parsing abilities in different human groups, and compared them with pigeons. We generated visual plane patterns based on rules varying in complexity. All human groups tested, including children and individuals diagnosed with autism spectrum disorder (ASD), were able to detect violations of all production rules tested. Our ASD participants detected pattern violations with the same speed and accuracy as matched controls. Children's ability to detect violations of a relatively complex rotational rule correlated with age, whereas their ability to detect violations of a simple translational rule did not. By contrast, even with extensive training, pigeons were unable to detect orientation-based structural violations, suggesting that, unlike humans, they did not learn the underlying structural rules. Visual two-dimensional patterns offer a promising new formally-grounded way to investigate pattern production and perception in general, widely applicable across species and age groups.

Production and perception rules underlying visual patterns: effects of symmetry and hierarchy

PubMed Central

Westphal-Fitch, Gesche; Huber, Ludwig; Gómez, Juan Carlos; Fitch, W. Tecumseh

2012-01-01

Formal language theory has been extended to two-dimensional patterns, but little is known about two-dimensional pattern perception. We first examined spontaneous two-dimensional visual pattern production by humans, gathered using a novel touch screen approach. Both spontaneous creative production and subsequent aesthetic ratings show that humans prefer ordered, symmetrical patterns over random patterns. We then further explored pattern-parsing abilities in different human groups, and compared them with pigeons. We generated visual plane patterns based on rules varying in complexity. All human groups tested, including children and individuals diagnosed with autism spectrum disorder (ASD), were able to detect violations of all production rules tested. Our ASD participants detected pattern violations with the same speed and accuracy as matched controls. Children's ability to detect violations of a relatively complex rotational rule correlated with age, whereas their ability to detect violations of a simple translational rule did not. By contrast, even with extensive training, pigeons were unable to detect orientation-based structural violations, suggesting that, unlike humans, they did not learn the underlying structural rules. Visual two-dimensional patterns offer a promising new formally-grounded way to investigate pattern production and perception in general, widely applicable across species and age groups. PMID:22688636

Fast Reduction Method in Dominance-Based Information Systems

NASA Astrophysics Data System (ADS)

Li, Yan; Zhou, Qinghua; Wen, Yongchuan

2018-01-01

In real world applications, there are often some data with continuous values or preference-ordered values. Rough sets based on dominance relations can effectively deal with these kinds of data. Attribute reduction can be done in the framework of dominance-relation based approach to better extract decision rules. However, the computational cost of the dominance classes greatly affects the efficiency of attribute reduction and rule extraction. This paper presents an efficient method of computing dominance classes, and further compares it with traditional method with increasing attributes and samples. Experiments on UCI data sets show that the proposed algorithm obviously improves the efficiency of the traditional method, especially for large-scale data.

The load shedding advisor: An example of a crisis-response expert system

NASA Technical Reports Server (NTRS)

Bollinger, Terry B.; Lightner, Eric; Laverty, John; Ambrose, Edward

1987-01-01

A Prolog-based prototype expert system is described that was implemented by the Network Operations Branch of the NASA Goddard Space Flight Center. The purpose of the prototype was to test whether a small, inexpensive computer system could be used to host a load shedding advisor, a system which would monitor major physical environment parameters in a computer facility, then recommend appropriate operator reponses whenever a serious condition was detected. The resulting prototype performed significantly to efficiency gains achieved by replacing a purely rule-based design methodology with a hybrid approach that combined procedural, entity-relationship, and rule-based methods.

Knowledge discovery with classification rules in a cardiovascular dataset.

PubMed

Podgorelec, Vili; Kokol, Peter; Stiglic, Milojka Molan; Hericko, Marjan; Rozman, Ivan

2005-12-01

In this paper we study an evolutionary machine learning approach to data mining and knowledge discovery based on the induction of classification rules. A method for automatic rules induction called AREX using evolutionary induction of decision trees and automatic programming is introduced. The proposed algorithm is applied to a cardiovascular dataset consisting of different groups of attributes which should possibly reveal the presence of some specific cardiovascular problems in young patients. A case study is presented that shows the use of AREX for the classification of patients and for discovering possible new medical knowledge from the dataset. The defined knowledge discovery loop comprises a medical expert's assessment of induced rules to drive the evolution of rule sets towards more appropriate solutions. The final result is the discovery of a possible new medical knowledge in the field of pediatric cardiology.

A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks.

PubMed

Zhang, Haitao; Wu, Chenxue; Chen, Zewei; Liu, Zhao; Zhu, Yunhong

2017-01-01

Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets. Then we proposed a novel on-line spatial-temporal k-anonymity method that can resist such inference attacks. Our anti-attack technique generates new anonymity datasets with awareness of privacy-sensitive sequence rules. The new datasets extend the original sequence database of anonymity datasets to hide the privacy-sensitive rules progressively. The process includes two phases: off-line analysis and on-line application. In the off-line phase, sequence rules are mined from an original sequence database of anonymity datasets, and privacy-sensitive sequence rules are developed by correlating privacy-sensitive spatial regions with spatial grid cells among the sequence rules. In the on-line phase, new anonymity datasets are generated upon LBS requests by adopting specific generalization and avoidance principles to hide the privacy-sensitive sequence rules progressively from the extended sequence anonymity datasets database. We conducted extensive experiments to test the performance of the proposed method, and to explore the influence of the parameter K value. The results demonstrated that our proposed approach is faster and more effective for hiding privacy-sensitive sequence rules in terms of hiding sensitive rules ratios to eliminate inference attacks. Our method also had fewer side effects in terms of generating new sensitive rules ratios than the traditional spatial-temporal k-anonymity method, and had basically the same side effects in terms of non-sensitive rules variation ratios with the traditional spatial-temporal k-anonymity method. Furthermore, we also found the performance variation tendency from the parameter K value, which can help achieve the goal of hiding the maximum number of original sensitive rules while generating a minimum of new sensitive rules and affecting a minimum number of non-sensitive rules.

A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacks

PubMed Central

Wu, Chenxue; Liu, Zhao; Zhu, Yunhong

2017-01-01

Analyzing large-scale spatial-temporal k-anonymity datasets recorded in location-based service (LBS) application servers can benefit some LBS applications. However, such analyses can allow adversaries to make inference attacks that cannot be handled by spatial-temporal k-anonymity methods or other methods for protecting sensitive knowledge. In response to this challenge, first we defined a destination location prediction attack model based on privacy-sensitive sequence rules mined from large scale anonymity datasets. Then we proposed a novel on-line spatial-temporal k-anonymity method that can resist such inference attacks. Our anti-attack technique generates new anonymity datasets with awareness of privacy-sensitive sequence rules. The new datasets extend the original sequence database of anonymity datasets to hide the privacy-sensitive rules progressively. The process includes two phases: off-line analysis and on-line application. In the off-line phase, sequence rules are mined from an original sequence database of anonymity datasets, and privacy-sensitive sequence rules are developed by correlating privacy-sensitive spatial regions with spatial grid cells among the sequence rules. In the on-line phase, new anonymity datasets are generated upon LBS requests by adopting specific generalization and avoidance principles to hide the privacy-sensitive sequence rules progressively from the extended sequence anonymity datasets database. We conducted extensive experiments to test the performance of the proposed method, and to explore the influence of the parameter K value. The results demonstrated that our proposed approach is faster and more effective for hiding privacy-sensitive sequence rules in terms of hiding sensitive rules ratios to eliminate inference attacks. Our method also had fewer side effects in terms of generating new sensitive rules ratios than the traditional spatial-temporal k-anonymity method, and had basically the same side effects in terms of non-sensitive rules variation ratios with the traditional spatial-temporal k-anonymity method. Furthermore, we also found the performance variation tendency from the parameter K value, which can help achieve the goal of hiding the maximum number of original sensitive rules while generating a minimum of new sensitive rules and affecting a minimum number of non-sensitive rules. PMID:28767687

A comprehensive revisit of the ρ meson with improved Monte-Carlo based QCD sum rules

NASA Astrophysics Data System (ADS)

Wang, Qi-Nan; Zhang, Zhu-Feng; Steele, T. G.; Jin, Hong-Ying; Huang, Zhuo-Ran

2017-07-01

We improve the Monte-Carlo based QCD sum rules by introducing the rigorous Hölder-inequality-determined sum rule window and a Breit-Wigner type parametrization for the phenomenological spectral function. In this improved sum rule analysis methodology, the sum rule analysis window can be determined without any assumptions on OPE convergence or the QCD continuum. Therefore, an unbiased prediction can be obtained for the phenomenological parameters (the hadronic mass and width etc.). We test the new approach in the ρ meson channel with re-examination and inclusion of α s corrections to dimension-4 condensates in the OPE. We obtain results highly consistent with experimental values. We also discuss the possible extension of this method to some other channels. Supported by NSFC (11175153, 11205093, 11347020), Open Foundation of the Most Important Subjects of Zhejiang Province, and K. C. Wong Magna Fund in Ningbo University, TGS is Supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), Z. F. Zhang and Z. R. Huang are Grateful to the University of Saskatchewan for its Warm Hospitality

Enumeration of Ring–Chain Tautomers Based on SMIRKS Rules

PubMed Central

2015-01-01

A compound exhibits (prototropic) tautomerism if it can be represented by two or more structures that are related by a formal intramolecular movement of a hydrogen atom from one heavy atom position to another. When the movement of the proton is accompanied by the opening or closing of a ring it is called ring–chain tautomerism. This type of tautomerism is well observed in carbohydrates, but it also occurs in other molecules such as warfarin. In this work, we present an approach that allows for the generation of all ring–chain tautomers of a given chemical structure. Based on Baldwin’s Rules estimating the likelihood of ring closure reactions to occur, we have defined a set of transform rules covering the majority of ring–chain tautomerism cases. The rules automatically detect substructures in a given compound that can undergo a ring–chain tautomeric transformation. Each transformation is encoded in SMIRKS line notation. All work was implemented in the chemoinformatics toolkit CACTVS. We report on the application of our ring–chain tautomerism rules to a large database of commercially available screening samples in order to identify ring–chain tautomers. PMID:25158156

Using Social Media Data to Identify Potential Candidates for Drug Repurposing: A Feasibility Study.

PubMed

Rastegar-Mojarad, Majid; Liu, Hongfang; Nambisan, Priya

2016-06-16

Drug repurposing (defined as discovering new indications for existing drugs) could play a significant role in drug development, especially considering the declining success rates of developing novel drugs. Typically, new indications for existing medications are identified by accident. However, new technologies and a large number of available resources enable the development of systematic approaches to identify and validate drug-repurposing candidates. Patients today report their experiences with medications on social media and reveal side effects as well as beneficial effects of those medications. Our aim was to assess the feasibility of using patient reviews from social media to identify potential candidates for drug repurposing. We retrieved patient reviews of 180 medications from an online forum, WebMD. Using dictionary-based and machine learning approaches, we identified disease names in the reviews. Several publicly available resources were used to exclude comments containing known indications and adverse drug effects. After manually reviewing some of the remaining comments, we implemented a rule-based system to identify beneficial effects. The dictionary-based system and machine learning system identified 2178 and 6171 disease names respectively in 64,616 patient comments. We provided a list of 10 common patterns that patients used to report any beneficial effects or uses of medication. After manually reviewing the comments tagged by our rule-based system, we identified five potential drug repurposing candidates. To our knowledge, this is the first study to consider using social media data to identify drug-repurposing candidates. We found that even a rule-based system, with a limited number of rules, could identify beneficial effect mentions in patient comments. Our preliminary study shows that social media has the potential to be used in drug repurposing.

Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models

PubMed Central

2017-01-01

We describe a fully data driven model that learns to perform a retrosynthetic reaction prediction task, which is treated as a sequence-to-sequence mapping problem. The end-to-end trained model has an encoder–decoder architecture that consists of two recurrent neural networks, which has previously shown great success in solving other sequence-to-sequence prediction tasks such as machine translation. The model is trained on 50,000 experimental reaction examples from the United States patent literature, which span 10 broad reaction types that are commonly used by medicinal chemists. We find that our model performs comparably with a rule-based expert system baseline model, and also overcomes certain limitations associated with rule-based expert systems and with any machine learning approach that contains a rule-based expert system component. Our model provides an important first step toward solving the challenging problem of computational retrosynthetic analysis. PMID:29104927

Effective domain-dependent reuse in medical knowledge bases.

PubMed

Dojat, M; Pachet, F

1995-12-01

Knowledge reuse is now a critical issue for most developers of medical knowledge-based systems. As a rule, reuse is addressed from an ambitious, knowledge-engineering perspective that focuses on reusable general purpose knowledge modules, concepts, and methods. However, such a general goal fails to take into account the specific aspects of medical practice. From the point of view of the knowledge engineer, whose goal is to capture the specific features and intricacies of a given domain, this approach addresses the wrong level of generality. In this paper, we adopt a more pragmatic viewpoint, introducing the less ambitious goal of "domain-dependent limited reuse" and suggesting effective means of achieving it in practice. In a knowledge representation framework combining objects and production rules, we propose three mechanisms emerging from the combination of object-oriented programming and rule-based programming. We show these mechanisms contribute to achieve limited reuse and to introduce useful limited variations in medical expertise.

A networked voting rule for democratic representation

NASA Astrophysics Data System (ADS)

Hernández, Alexis R.; Gracia-Lázaro, Carlos; Brigatti, Edgardo; Moreno, Yamir

2018-03-01

We introduce a general framework for exploring the problem of selecting a committee of representatives with the aim of studying a networked voting rule based on a decentralized large-scale platform, which can assure a strong accountability of the elected. The results of our simulations suggest that this algorithm-based approach is able to obtain a high representativeness for relatively small committees, performing even better than a classical voting rule based on a closed list of candidates. We show that a general relation between committee size and representatives exists in the form of an inverse square root law and that the normalized committee size approximately scales with the inverse of the community size, allowing the scalability to very large populations. These findings are not strongly influenced by the different networks used to describe the individuals' interactions, except for the presence of few individuals with very high connectivity which can have a marginal negative effect in the committee selection process.

A Pilot Study on Modeling of Diagnostic Criteria Using OWL and SWRL.

PubMed

Hong, Na; Jiang, Guoqian; Pathak, Jyotishiman; Chute, Christopher G

2015-01-01

The objective of this study is to describe our efforts in a pilot study on modeling diagnostic criteria using a Semantic Web-based approach. We reused the basic framework of the ICD-11 content model and refined it into an operational model in the Web Ontology Language (OWL). The refinement is based on a bottom-up analysis method, in which we analyzed data elements (including value sets) in a collection (n=20) of randomly selected diagnostic criteria. We also performed a case study to formalize rule logic in the diagnostic criteria of metabolic syndrome using the Semantic Web Rule Language (SWRL). The results demonstrated that it is feasible to use OWL and SWRL to formalize the diagnostic criteria knowledge, and to execute the rules through reasoning.

An approach to rule-out an acute cardiovascular event or death in emergency department patients using outcome-based cutoffs for high-sensitivity cardiac troponin assays and glucose.

PubMed

Shortt, Colleen; Phan, Kim; Hill, Stephen A; Worster, Andrew; Kavsak, Peter A

2015-03-01

The application of "undetectable" high-sensitivity cardiac troponin (hs-cTn) concentrations to "rule-out" myocardial infarction is appealing, but there are analytical concerns and a lack of consensus on what concentration should be used to define the lower reportable limit; i.e., limit of detection (LoD) or limit of blank. An alternative approach is to utilize a measurable hs-cTn concentration that identifies patients at low-risk for a future cardiovascular event combined with another prognostic test, such as glucose. We assessed both of these approaches in different emergency department (ED) cohorts to rule-out an event. We used cohort 1 (all-comer ED population, n=4773; derivation cohort) to determine the most appropriate approach at presentation (i.e., Dual Panel test: hs-cTn/glucose vs. LoD vs. LoD/glucose) for an early rule-out of hospital death using the Abbott ARCHITECT hs-cTnI assay. We used cohort 2 (n=144) and cohort 3 (n=127), both early chest pain onset ED populations as the verification datasets (outcome: composite cardiovascular event at 72h) with three hs-cTn assays assessed (Abbott Laboratories, Beckman Coulter, Roche Diagnostics). In cohort 1, the sensitivity was >99% for all three approaches; however the specificity (11%; 95% CI: 10-12%) was significantly higher for the Dual Panel as compared to the LoD approach (specificity=5%; 95% CI: 4-6%). Verification of the Dual Panel in cohort 2 and cohort 3 revealed 100% sensitivity and negative predictive values for all three hs-cTn assays. The combination of a "healthy" hs-cTn concentration with glucose might effectively rule-out patients for an acute cardiovascular event at ED presentation. Copyright © 2014 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

Failure detection system design methodology. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Chow, E. Y.

1980-01-01

The design of a failure detection and identification system consists of designing a robust residual generation process and a high performance decision making process. The design of these two processes are examined separately. Residual generation is based on analytical redundancy. Redundancy relations that are insensitive to modelling errors and noise effects are important for designing robust residual generation processes. The characterization of the concept of analytical redundancy in terms of a generalized parity space provides a framework in which a systematic approach to the determination of robust redundancy relations are developed. The Bayesian approach is adopted for the design of high performance decision processes. The FDI decision problem is formulated as a Bayes sequential decision problem. Since the optimal decision rule is incomputable, a methodology for designing suboptimal rules is proposed. A numerical algorithm is developed to facilitate the design and performance evaluation of suboptimal rules.

Evaluation of techniques for increasing recall in a dictionary approach to gene and protein name identification.

PubMed

Schuemie, Martijn J; Mons, Barend; Weeber, Marc; Kors, Jan A

2007-06-01

Gene and protein name identification in text requires a dictionary approach to relate synonyms to the same gene or protein, and to link names to external databases. However, existing dictionaries are incomplete. We investigate two complementary methods for automatic generation of a comprehensive dictionary: combination of information from existing gene and protein databases and rule-based generation of spelling variations. Both methods have been reported in literature before, but have hitherto not been combined and evaluated systematically. We combined gene and protein names from several existing databases of four different organisms. The combined dictionaries showed a substantial increase in recall on three different test sets, as compared to any single database. Application of 23 spelling variation rules to the combined dictionaries further increased recall. However, many rules appeared to have no effect and some appear to have a detrimental effect on precision.

Computer vision for general purpose visual inspection: a fuzzy logic approach

NASA Astrophysics Data System (ADS)

Chen, Y. H.

In automatic visual industrial inspection, computer vision systems have been widely used. Such systems are often application specific, and therefore require domain knowledge in order to have a successful implementation. Since visual inspection can be viewed as a decision making process, it is argued that the integration of fuzzy logic analysis and computer vision systems provides a practical approach to general purpose visual inspection applications. This paper describes the development of an integrated fuzzy-rule-based automatic visual inspection system. Domain knowledge about a particular application is represented as a set of fuzzy rules. From the status of predefined fuzzy variables, the set of fuzzy rules are defuzzified to give the inspection results. A practical application where IC marks (often in the forms of English characters and a company logo) inspection is demonstrated, which shows a more consistent result as compared to a conventional thresholding method.

Cheminformatics and Data Mining Approaches for Exploring the Alternatives Testing Landscsape: Case Studies in ToxCast and Skin Sensitization (BOSC CSS)

EPA Science Inventory

Cheminformatics approaches and structure-based rules are being used to evaluate and explore the ToxCast chemical landscape and associated high-throughput screening (HTS) data. We have shown that the library provides comprehensive coverage of the knowledge domains and target inven...

A Progressive Approach to Discrete Trial Teaching: Some Current Guidelines

ERIC Educational Resources Information Center

Leaf, Justin B.; Cihon, Joseph H.; Leaf, Ronald; McEachin, John; Taubman, Mitchell

2016-01-01

Discrete trial teaching (DTT) is one of the cornerstones of applied behavior analysis (ABA) based interventions. Conventionally, DTT is commonly implemented within a prescribed, fixed manner in which the therapist is governed by a strict set of rules. In contrast to conventional DTT, a progressive approach to DTT allows the therapist to remain…

[Quantitative measures for assessing the functional state of the human body during diagnostic procedure].

PubMed

Artemenko, M V

2008-01-01

Two approaches to calculation of the qualitative measures for assessing the functional state level of human body are considered. These approaches are based on image and fuzzy set recognition theories and are used to construct diagnostic decision rules. The first approach uses the data on deviation of detected parameters from those for healthy persons; the second approach analyzes the degree of deviation of detected parameters from the approximants characterizing the correlation differences between the parameters. A method for synthesis of decision rules and the results of blood count-based research for a number of diseases (hemophilia, thrombocytopathy, hypertension, arrhythmia, hepatic cirrhosis, trichophytia) are considered. An effect of a change in the functional link between the cholesterol content in blood and the relative rate of variation of AST and ALT enzymes in blood from direct proportional (healthy state) to inverse proportional (hepatic cirrhosis) is discussed. It is shown that analysis of correlation changes in detected parameters of the human body state during diagnostic process is more effective for application in decision support systems than the state space analysis.

A hybrid intelligence approach to artifact recognition in digital publishing

NASA Astrophysics Data System (ADS)

Vega-Riveros, J. Fernando; Santos Villalobos, Hector J.

2006-02-01

The system presented integrates rule-based and case-based reasoning for artifact recognition in Digital Publishing. In Variable Data Printing (VDP) human proofing could result prohibitive since a job could contain millions of different instances that may contain two types of artifacts: 1) evident defects, like a text overflow or overlapping 2) style-dependent artifacts, subtle defects that show as inconsistencies with regard to the original job design. We designed a Knowledge-Based Artifact Recognition tool for document segmentation, layout understanding, artifact detection, and document design quality assessment. Document evaluation is constrained by reference to one instance of the VDP job proofed by a human expert against the remaining instances. Fundamental rules of document design are used in the rule-based component for document segmentation and layout understanding. Ambiguities in the design principles not covered by the rule-based system are analyzed by case-based reasoning, using the Nearest Neighbor Algorithm, where features from previous jobs are used to detect artifacts and inconsistencies within the document layout. We used a subset of XSL-FO and assembled a set of 44 document samples. The system detected all the job layout changes, while obtaining an overall average accuracy of 84.56%, with the highest accuracy of 92.82%, for overlapping and the lowest, 66.7%, for the lack-of-white-space.

Comparing Explicit Exemplar-Based and Rule-Based Corrective Feedback: Introducing Analogy-Based Corrective Feedback

ERIC Educational Resources Information Center

Thomas, Kavita E.

2018-01-01

This study introduces an approach to providing corrective feedback to L2 learners termed analogy-based corrective feedback that is motivated by analogical learning theories and syntactic alignment in dialogue. Learners are presented with a structurally similar synonymous version of their output where the erroneous form is corrected, and they must…

Improving the human readability of Arden Syntax medical logic modules using a concept-oriented terminology and object-oriented programming expressions.

PubMed

Choi, Jeeyae; Bakken, Suzanne; Lussier, Yves A; Mendonça, Eneida A

2006-01-01

Medical logic modules are a procedural representation for sharing task-specific knowledge for decision support systems. Based on the premise that clinicians may perceive object-oriented expressions as easier to read than procedural rules in Arden Syntax-based medical logic modules, we developed a method for improving the readability of medical logic modules. Two approaches were applied: exploiting the concept-oriented features of the Medical Entities Dictionary and building an executable Java program to replace Arden Syntax procedural expressions. The usability evaluation showed that 66% of participants successfully mapped all Arden Syntax rules to Java methods. These findings suggest that these approaches can play an essential role in the creation of human readable medical logic modules and can potentially increase the number of clinical experts who are able to participate in the creation of medical logic modules. Although our approaches are broadly applicable, we specifically discuss the relevance to concept-oriented nursing terminologies and automated processing of task-specific nursing knowledge.

A films based approach to intensity imbalance correction for 65nm node c:PSM

NASA Astrophysics Data System (ADS)

Cottle, Rand; Sixt, Pierre; Lassiter, Matt; Cangemi, Marc; Martin, Patrick; Progler, Chris

2005-11-01

Intensity imbalance between the 0 and π phase features of c:PSM cause gate CD control and edge placement problems. Strategies such as undercut, selective biasing, and combinations of undercut and bias are currently used in production to mitigate these problems. However, there are drawbacks to these strategies such as space CD delta through pitch, gate CD control through defocus, design rule restrictions, and reticle manufacturability. This paper investigates the application of an innovative films-based approach to intensity balancing known as the Transparent Etch Stop Layer (TESL). TESL, in addition to providing a host of reticle quality and manufacturability benefits, also can be tuned to significantly reduce imbalance. Rigorous 3D vector simulations and experimental data compare through pitch and defocus performance of TESL and conventional c:PSM for 65nm design rules.

Automated extraction of decision rules for leptin dynamics--a rough sets approach.

PubMed

Brtka, Vladimir; Stokić, Edith; Srdić, Biljana

2008-08-01

A significant area in the field of medical informatics is concerned with the learning of medical models from low-level data. The goals of inducing models from data are twofold: analysis of the structure of the models so as to gain new insight into the unknown phenomena, and development of classifiers or outcome predictors for unseen cases. In this paper, we will employ approach based on the relation of indiscernibility and rough sets theory to study certain questions concerning the design of model based on if-then rules, from low-level data including 36 parameters, one of them leptin. To generate easy to read, interpret, and inspect model, we have used ROSETTA software system. The main goal of this work is to get new insight into phenomena of leptin levels while interplaying with other risk factors in obesity.

Side-locked headaches: an algorithm-based approach.

PubMed

Prakash, Sanjay; Rathore, Chaturbhuj

2016-12-01

The differential diagnosis of strictly unilateral hemicranial pain includes a large number of primary and secondary headaches and cranial neuropathies. It may arise from both intracranial and extracranial structures such as cranium, neck, vessels, eyes, ears, nose, sinuses, teeth, mouth, and the other facial or cervical structure. Available data suggest that about two-third patients with side-locked headache visiting neurology or headache clinics have primary headaches. Other one-third will have either secondary headaches or neuralgias. Many of these hemicranial pain syndromes have overlapping presentations. Primary headache disorders may spread to involve the face and / or neck. Even various intracranial and extracranial pathologies may have similar overlapping presentations. Patients may present to a variety of clinicians, including headache experts, dentists, otolaryngologists, ophthalmologist, psychiatrists, and physiotherapists. Unfortunately, there is not uniform approach for such patients and diagnostic ambiguity is frequently encountered in clinical practice.Herein, we review the differential diagnoses of side-locked headaches and provide an algorithm based approach for patients presenting with side-locked headaches. Side-locked headache is itself a red flag. So, the first priority should be to rule out secondary headaches. A comprehensive history and thorough examinations will help one to formulate an algorithm to rule out or confirm secondary side-locked headaches. The diagnoses of most secondary side-locked headaches are largely investigations dependent. Therefore, each suspected secondary headache should be subjected for appropriate investigations or referral. The diagnostic approach of primary side-locked headache starts once one rule out all the possible secondary headaches. We have discussed an algorithmic approach for both secondary and primary side-locked headaches.

Renormalisation group corrections to neutrino mixing sum rules

NASA Astrophysics Data System (ADS)

Gehrlein, J.; Petcov, S. T.; Spinrath, M.; Titov, A. V.

2016-11-01

Neutrino mixing sum rules are common to a large class of models based on the (discrete) symmetry approach to lepton flavour. In this approach the neutrino mixing matrix U is assumed to have an underlying approximate symmetry form Ũν, which is dictated by, or associated with, the employed (discrete) symmetry. In such a setup the cosine of the Dirac CP-violating phase δ can be related to the three neutrino mixing angles in terms of a sum rule which depends on the symmetry form of Ũν. We consider five extensively discussed possible symmetry forms of Ũν: i) bimaximal (BM) and ii) tri-bimaximal (TBM) forms, the forms corresponding to iii) golden ratio type A (GRA) mixing, iv) golden ratio type B (GRB) mixing, and v) hexagonal (HG) mixing. For each of these forms we investigate the renormalisation group corrections to the sum rule predictions for δ in the cases of neutrino Majorana mass term generated by the Weinberg (dimension 5) operator added to i) the Standard Model, and ii) the minimal SUSY extension of the Standard Model.

Automated process control for plasma etching

NASA Astrophysics Data System (ADS)

McGeown, Margaret; Arshak, Khalil I.; Murphy, Eamonn

1992-06-01

This paper discusses the development and implementation of a rule-based system which assists in providing automated process control for plasma etching. The heart of the system is to establish a correspondence between a particular data pattern -- sensor or data signals -- and one or more modes of failure, i.e., a data-driven monitoring approach. The objective of this rule based system, PLETCHSY, is to create a program combining statistical process control (SPC) and fault diagnosis to help control a manufacturing process which varies over time. This can be achieved by building a process control system (PCS) with the following characteristics. A facility to monitor the performance of the process by obtaining and analyzing the data relating to the appropriate process variables. Process sensor/status signals are input into an SPC module. If trends are present, the SPC module outputs the last seven control points, a pattern which is represented by either regression or scoring. The pattern is passed to the rule-based module. When the rule-based system recognizes a pattern, it starts the diagnostic process using the pattern. If the process is considered to be going out of control, advice is provided about actions which should be taken to bring the process back into control.

Spatial Rule-Based Modeling: A Method and Its Application to the Human Mitotic Kinetochore

PubMed Central

Ibrahim, Bashar; Henze, Richard; Gruenert, Gerd; Egbert, Matthew; Huwald, Jan; Dittrich, Peter

2013-01-01

A common problem in the analysis of biological systems is the combinatorial explosion that emerges from the complexity of multi-protein assemblies. Conventional formalisms, like differential equations, Boolean networks and Bayesian networks, are unsuitable for dealing with the combinatorial explosion, because they are designed for a restricted state space with fixed dimensionality. To overcome this problem, the rule-based modeling language, BioNetGen, and the spatial extension, SRSim, have been developed. Here, we describe how to apply rule-based modeling to integrate experimental data from different sources into a single spatial simulation model and how to analyze the output of that model. The starting point for this approach can be a combination of molecular interaction data, reaction network data, proximities, binding and diffusion kinetics and molecular geometries at different levels of detail. We describe the technique and then use it to construct a model of the human mitotic inner and outer kinetochore, including the spindle assembly checkpoint signaling pathway. This allows us to demonstrate the utility of the procedure, show how a novel perspective for understanding such complex systems becomes accessible and elaborate on challenges that arise in the formulation, simulation and analysis of spatial rule-based models. PMID:24709796

A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music.

PubMed

Giraldo, Sergio I; Ramirez, Rafael

2016-01-01

Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores) of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1) quantitatively evaluate the accuracy of the induced models, (2) analyse the relative importance of the considered musical features, (3) discuss some of the learnt expressive performance rules in the context of previous work, and (4) assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules' performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules.

A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music

PubMed Central

Giraldo, Sergio I.; Ramirez, Rafael

2016-01-01

Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores) of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1) quantitatively evaluate the accuracy of the induced models, (2) analyse the relative importance of the considered musical features, (3) discuss some of the learnt expressive performance rules in the context of previous work, and (4) assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules' performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules. PMID:28066290

78 FR 21555 - Signal Booster Rules

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-11

... needed, such as when the device approaches the base station with which it is communicating; (5) be..., the signals received from base, fixed, mobile, or portable stations, with no change in frequency or... oscillations in uplink and downlink bands (such as may result from insufficient isolation between the antennas...

Rule-guided human classification of Volunteered Geographic Information

NASA Astrophysics Data System (ADS)

Ali, Ahmed Loai; Falomir, Zoe; Schmid, Falko; Freksa, Christian

2017-05-01

During the last decade, web technologies and location sensing devices have evolved generating a form of crowdsourcing known as Volunteered Geographic Information (VGI). VGI acted as a platform of spatial data collection, in particular, when a group of public participants are involved in collaborative mapping activities: they work together to collect, share, and use information about geographic features. VGI exploits participants' local knowledge to produce rich data sources. However, the resulting data inherits problematic data classification. In VGI projects, the challenges of data classification are due to the following: (i) data is likely prone to subjective classification, (ii) remote contributions and flexible contribution mechanisms in most projects, and (iii) the uncertainty of spatial data and non-strict definitions of geographic features. These factors lead to various forms of problematic classification: inconsistent, incomplete, and imprecise data classification. This research addresses classification appropriateness. Whether the classification of an entity is appropriate or inappropriate is related to quantitative and/or qualitative observations. Small differences between observations may be not recognizable particularly for non-expert participants. Hence, in this paper, the problem is tackled by developing a rule-guided classification approach. This approach exploits data mining techniques of Association Classification (AC) to extract descriptive (qualitative) rules of specific geographic features. The rules are extracted based on the investigation of qualitative topological relations between target features and their context. Afterwards, the extracted rules are used to develop a recommendation system able to guide participants to the most appropriate classification. The approach proposes two scenarios to guide participants towards enhancing the quality of data classification. An empirical study is conducted to investigate the classification of grass-related features like forest, garden, park, and meadow. The findings of this study indicate the feasibility of the proposed approach.

Design of fuzzy systems using neurofuzzy networks.

PubMed

Figueiredo, M; Gomide, F

1999-01-01

This paper introduces a systematic approach for fuzzy system design based on a class of neural fuzzy networks built upon a general neuron model. The network structure is such that it encodes the knowledge learned in the form of if-then fuzzy rules and processes data following fuzzy reasoning principles. The technique provides a mechanism to obtain rules covering the whole input/output space as well as the membership functions (including their shapes) for each input variable. Such characteristics are of utmost importance in fuzzy systems design and application. In addition, after learning, it is very simple to extract fuzzy rules in the linguistic form. The network has universal approximation capability, a property very useful in, e.g., modeling and control applications. Here we focus on function approximation problems as a vehicle to illustrate its usefulness and to evaluate its performance. Comparisons with alternative approaches are also included. Both, nonnoisy and noisy data have been studied and considered in the computational experiments. The neural fuzzy network developed here and, consequently, the underlying approach, has shown to provide good results from the accuracy, complexity, and system design points of view.

Patient Protection and Affordable Care Act; establishment of the Multi-State Plan Program for the Affordable Insurance Exchanges. Final rule.

PubMed

2014-02-24

The U.S. Office of Personnel Management (OPM) is issuing a final rule implementing modifications to the Multi-State Plan (MSP) Program based on the experience of the Program to date. OPM established the MSP Program pursuant to the Affordable Care Act. This rule clarifies the approach used to enforce the applicable standards of the Affordable Care Act with respect to health insurance issuers that contract with OPM to offer MSP options; amends MSP standards related to coverage area, benefits, and certain contracting provisions under section 1334 of the Affordable Care Act; and makes non-substantive technical changes.

A programmable rules engine to provide clinical decision support using HTML forms.

PubMed Central

Heusinkveld, J.; Geissbuhler, A.; Sheshelidze, D.; Miller, R.

1999-01-01

The authors have developed a simple method for specifying rules to be applied to information on HTML forms. This approach allows clinical experts, who lack the programming expertise needed to write CGI scripts, to construct and maintain domain-specific knowledge and ordering capabilities within WizOrder, the order-entry and decision support system used at Vanderbilt Hospital. The clinical knowledge base maintainers use HTML editors to create forms and spreadsheet programs for rule entry. A test environment has been developed which uses Netscape to display forms; the production environment displays forms using an embedded browser. Images Figure 1 PMID:10566470

QCD sum rules study of meson-baryon sigma terms

DOE Office of Scientific and Technical Information (OSTI.GOV)

Erkol, Gueray; Oka, Makoto; Turan, Guersevil

2008-11-01

The pion-baryon sigma terms and the strange-quark condensates of the octet and the decuplet baryons are calculated by employing the method of QCD sum rules. We evaluate the vacuum-to-vacuum transition matrix elements of two baryon interpolating fields in an external isoscalar-scalar field and use a Monte Carlo-based approach to systematically analyze the sum rules and the uncertainties in the results. We extract the ratios of the sigma terms, which have rather high accuracy and minimal dependence on QCD parameters. We discuss the sources of uncertainties and comment on possible strangeness content of the nucleon and the Delta.

Comparison of futility monitoring guidelines using completed phase III oncology trials.

PubMed

Zhang, Qiang; Freidlin, Boris; Korn, Edward L; Halabi, Susan; Mandrekar, Sumithra; Dignam, James J

2017-02-01

Futility (inefficacy) interim monitoring is an important component in the conduct of phase III clinical trials, especially in life-threatening diseases. Desirable futility monitoring guidelines allow timely stopping if the new therapy is harmful or if it is unlikely to demonstrate to be sufficiently effective if the trial were to continue to its final analysis. There are a number of analytical approaches that are used to construct futility monitoring boundaries. The most common approaches are based on conditional power, sequential testing of the alternative hypothesis, or sequential confidence intervals. The resulting futility boundaries vary considerably with respect to the level of evidence required for recommending stopping the study. We evaluate the performance of commonly used methods using event histories from completed phase III clinical trials of the Radiation Therapy Oncology Group, Cancer and Leukemia Group B, and North Central Cancer Treatment Group. We considered published superiority phase III trials with survival endpoints initiated after 1990. There are 52 studies available for this analysis from different disease sites. Total sample size and maximum number of events (statistical information) for each study were calculated using protocol-specified effect size, type I and type II error rates. In addition to the common futility approaches, we considered a recently proposed linear inefficacy boundary approach with an early harm look followed by several lack-of-efficacy analyses. For each futility approach, interim test statistics were generated for three schedules with different analysis frequency, and early stopping was recommended if the interim result crossed a futility stopping boundary. For trials not demonstrating superiority, the impact of each rule is summarized as savings on sample size, study duration, and information time scales. For negative studies, our results show that the futility approaches based on testing the alternative hypothesis and repeated confidence interval rules yielded less savings (compared to the other two rules). These boundaries are too conservative, especially during the first half of the study (<50% of information). The conditional power rules are too aggressive during the second half of the study (>50% of information) and may stop a trial even when there is a clinically meaningful treatment effect. The linear inefficacy boundary with three or more interim analyses provided the best results. For positive studies, we demonstrated that none of the futility rules would have stopped the trials. The linear inefficacy boundary futility approach is attractive from statistical, clinical, and logistical standpoints in clinical trials evaluating new anti-cancer agents.

Representing and computing regular languages on massively parallel networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miller, M.I.; O'Sullivan, J.A.; Boysam, B.

1991-01-01

This paper proposes a general method for incorporating rule-based constraints corresponding to regular languages into stochastic inference problems, thereby allowing for a unified representation of stochastic and syntactic pattern constraints. The authors' approach first established the formal connection of rules to Chomsky grammars, and generalizes the original work of Shannon on the encoding of rule-based channel sequences to Markov chains of maximum entropy. This maximum entropy probabilistic view leads to Gibb's representations with potentials which have their number of minima growing at precisely the exponential rate that the language of deterministically constrained sequences grow. These representations are coupled to stochasticmore » diffusion algorithms, which sample the language-constrained sequences by visiting the energy minima according to the underlying Gibbs' probability law. The coupling to stochastic search methods yields the all-important practical result that fully parallel stochastic cellular automata may be derived to generate samples from the rule-based constraint sets. The production rules and neighborhood state structure of the language of sequences directly determines the necessary connection structures of the required parallel computing surface. Representations of this type have been mapped to the DAP-510 massively-parallel processor consisting of 1024 mesh-connected bit-serial processing elements for performing automated segmentation of electron-micrograph images.« less

Complex-energy approach to sum rules within nuclear density functional theory

DOE PAGES

Hinohara, Nobuo; Kortelainen, Markus; Nazarewicz, Witold; ...

2015-04-27

The linear response of the nucleus to an external field contains unique information about the effective interaction, correlations governing the behavior of the many-body system, and properties of its excited states. To characterize the response, it is useful to use its energy-weighted moments, or sum rules. By comparing computed sum rules with experimental values, the information content of the response can be utilized in the optimization process of the nuclear Hamiltonian or nuclear energy density functional (EDF). But the additional information comes at a price: compared to the ground state, computation of excited states is more demanding. To establish anmore » efficient framework to compute energy-weighted sum rules of the response that is adaptable to the optimization of the nuclear EDF and large-scale surveys of collective strength, we have developed a new technique within the complex-energy finite-amplitude method (FAM) based on the quasiparticle random- phase approximation. The proposed sum-rule technique based on the complex-energy FAM is a tool of choice when optimizing effective interactions or energy functionals. The method is very efficient and well-adaptable to parallel computing. As a result, the FAM formulation is especially useful when standard theorems based on commutation relations involving the nuclear Hamiltonian and external field cannot be used.« less

Controlling false-negative errors in microarray differential expression analysis: a PRIM approach.

PubMed

Cole, Steve W; Galic, Zoran; Zack, Jerome A

2003-09-22

Theoretical considerations suggest that current microarray screening algorithms may fail to detect many true differences in gene expression (Type II analytic errors). We assessed 'false negative' error rates in differential expression analyses by conventional linear statistical models (e.g. t-test), microarray-adapted variants (e.g. SAM, Cyber-T), and a novel strategy based on hold-out cross-validation. The latter approach employs the machine-learning algorithm Patient Rule Induction Method (PRIM) to infer minimum thresholds for reliable change in gene expression from Boolean conjunctions of fold-induction and raw fluorescence measurements. Monte Carlo analyses based on four empirical data sets show that conventional statistical models and their microarray-adapted variants overlook more than 50% of genes showing significant up-regulation. Conjoint PRIM prediction rules recover approximately twice as many differentially expressed transcripts while maintaining strong control over false-positive (Type I) errors. As a result, experimental replication rates increase and total analytic error rates decline. RT-PCR studies confirm that gene inductions detected by PRIM but overlooked by other methods represent true changes in mRNA levels. PRIM-based conjoint inference rules thus represent an improved strategy for high-sensitivity screening of DNA microarrays. Freestanding JAVA application at http://microarray.crump.ucla.edu/focus

Does GEM-Encoding Clinical Practice Guidelines Improve the Quality of Knowledge Bases? A Study with the Rule-Based Formalism

PubMed Central

Georg, Gersende; Séroussi, Brigitte; Bouaud, Jacques

2003-01-01

The aim of this work was to determine whether the GEM-encoding step could improve the representation of clinical practice guidelines as formalized knowledge bases. We used the 1999 Canadian recommendations for the management of hypertension, chosen as the knowledge source in the ASTI project. We first clarified semantic ambiguities of therapeutic sequences recommended in the guideline by proposing an interpretative framework of therapeutic strategies. Then, after a formalization step to standardize the terms used to characterize clinical situations, we created the GEM-encoded instance of the guideline. We developed a module for the automatic derivation of a rule base, BR-GEM, from the instance. BR-GEM was then compared to the rule base, BR-ASTI, embedded within the critic mode of ASTI, and manually built by two physicians from the same Canadian guideline. As compared to BR-ASTI, BR-GEM is more specific and covers more clinical situations. When evaluated on 10 patient cases, the GEM-based approach led to promising results. PMID:14728173

Automatic inference of indexing rules for MEDLINE

PubMed Central

Névéol, Aurélie; Shooshan, Sonya E; Claveau, Vincent

2008-01-01

Background: Indexing is a crucial step in any information retrieval system. In MEDLINE, a widely used database of the biomedical literature, the indexing process involves the selection of Medical Subject Headings in order to describe the subject matter of articles. The need for automatic tools to assist MEDLINE indexers in this task is growing with the increasing number of publications being added to MEDLINE. Methods: In this paper, we describe the use and the customization of Inductive Logic Programming (ILP) to infer indexing rules that may be used to produce automatic indexing recommendations for MEDLINE indexers. Results: Our results show that this original ILP-based approach outperforms manual rules when they exist. In addition, the use of ILP rules also improves the overall performance of the Medical Text Indexer (MTI), a system producing automatic indexing recommendations for MEDLINE. Conclusion: We expect the sets of ILP rules obtained in this experiment to be integrated into MTI. PMID:19025687

Automatic inference of indexing rules for MEDLINE.

PubMed

Névéol, Aurélie; Shooshan, Sonya E; Claveau, Vincent

2008-11-19

Indexing is a crucial step in any information retrieval system. In MEDLINE, a widely used database of the biomedical literature, the indexing process involves the selection of Medical Subject Headings in order to describe the subject matter of articles. The need for automatic tools to assist MEDLINE indexers in this task is growing with the increasing number of publications being added to MEDLINE. In this paper, we describe the use and the customization of Inductive Logic Programming (ILP) to infer indexing rules that may be used to produce automatic indexing recommendations for MEDLINE indexers. Our results show that this original ILP-based approach outperforms manual rules when they exist. In addition, the use of ILP rules also improves the overall performance of the Medical Text Indexer (MTI), a system producing automatic indexing recommendations for MEDLINE. We expect the sets of ILP rules obtained in this experiment to be integrated into MTI.

A Role Calculus for ORM

NASA Astrophysics Data System (ADS)

Curland, Matthew; Halpin, Terry; Stirewalt, Kurt

A conceptual schema of an information system specifies the fact structures of interest as well as related business rules that are either constraints or derivation rules. Constraints restrict the possible or permitted states or state transitions, while derivation rules enable some facts to be derived from others. Graphical languages are commonly used to specify conceptual schemas, but often need to be supplemented by more expressive textual languages to capture additional business rules, as well as conceptual queries that enable conceptual models to be queried directly. This paper describes research to provide a role calculus to underpin textual languages for Object-Role Modeling (ORM), to enable business rules and queries to be formulated in a language intelligible to business users. The role-based nature of this calculus, which exploits the attribute-free nature of ORM, appears to offer significant advantages over other proposed approaches, especially in the area of semantic stability.

Hybrid modeling of nitrate fate in large catchments using fuzzy-rules

NASA Astrophysics Data System (ADS)

van der Heijden, Sven; Haberlandt, Uwe

2010-05-01

Especially for nutrient balance simulations, physically based ecohydrological modeling needs an abundance of measured data and model parameters, which for large catchments all too often are not available in sufficient spatial or temporal resolution or are simply unknown. For efficient large-scale studies it is thus beneficial to have methods at one's disposal which are parsimonious concerning the number of model parameters and the necessary input data. One such method is fuzzy-rule based modeling, which compared to other machine-learning techniques has the advantages to produce models (the fuzzy-rules) which are physically interpretable to a certain extent, and to allow the explicit introduction of expert knowledge through pre-defined rules. The study focuses on the application of fuzzy-rule based modeling for nitrate simulation in large catchments, in particular concerning decision support. Fuzzy-rule based modeling enables the generation of simple, efficient, easily understandable models with nevertheless satisfactory accuracy for problems of decision support. The chosen approach encompasses a hybrid metamodeling, which includes the generation of fuzzy-rules with data originating from physically based models as well as a coupling with a physically based water balance model. For the generation of the needed training data and also as coupled water balance model the ecohydrological model SWAT is employed. The conceptual model divides the nitrate pathway into three parts. The first fuzzy-module calculates nitrate leaching with the percolating water from soil surface to groundwater, the second module simulates groundwater passage, and the final module replaces the in-stream processes. The aim of this modularization is to create flexibility for using each of the modules on its own, for changing or completely replacing it. For fuzzy-rule based modeling this can explicitly mean that the re-training of one of the modules with newly available data will be possible without problem, while the module assembly does not have to be modified. Apart from the concept of hybrid metamodeling first results are presented for the fuzzy-module for nitrate passage through the unsaturated zone.

A multiple kernel support vector machine scheme for feature selection and rule extraction from gene expression data of cancer tissue.

PubMed

Chen, Zhenyu; Li, Jianping; Wei, Liwei

2007-10-01

Recently, gene expression profiling using microarray techniques has been shown as a promising tool to improve the diagnosis and treatment of cancer. Gene expression data contain high level of noise and the overwhelming number of genes relative to the number of available samples. It brings out a great challenge for machine learning and statistic techniques. Support vector machine (SVM) has been successfully used to classify gene expression data of cancer tissue. In the medical field, it is crucial to deliver the user a transparent decision process. How to explain the computed solutions and present the extracted knowledge becomes a main obstacle for SVM. A multiple kernel support vector machine (MK-SVM) scheme, consisting of feature selection, rule extraction and prediction modeling is proposed to improve the explanation capacity of SVM. In this scheme, we show that the feature selection problem can be translated into an ordinary multiple parameters learning problem. And a shrinkage approach: 1-norm based linear programming is proposed to obtain the sparse parameters and the corresponding selected features. We propose a novel rule extraction approach using the information provided by the separating hyperplane and support vectors to improve the generalization capacity and comprehensibility of rules and reduce the computational complexity. Two public gene expression datasets: leukemia dataset and colon tumor dataset are used to demonstrate the performance of this approach. Using the small number of selected genes, MK-SVM achieves encouraging classification accuracy: more than 90% for both two datasets. Moreover, very simple rules with linguist labels are extracted. The rule sets have high diagnostic power because of their good classification performance.

Reliability based design of the primary structure of oil tankers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Casella, G.; Dogliani, M.; Guedes Soares, C.

1996-12-31

The present paper describes the reliability analysis carried out for two oil tanker-ships having comparable dimensions but different design. The scope of the analysis was to derive indications on the value of the reliability index obtained for existing, typical and well designed oil tankers, as well as to apply the tentative rule checking formulation developed within the CEC-funded SHIPREL Project. The checking formula was adopted to redesign the midships section of one of the considered ships, upgrading her in order to meet the target failure probability considered in the rule development process. The resulting structure, in view of an upgradingmore » of the steel grade in the central part of the deck, lead to a convenient reliability level. The results of the analysis clearly showed that a large scatter exists presently in the design safety levels of ships, even when the Classification Societies` unified requirements are satisfied. A reliability based approach for the calibration of the rules for the global strength of ships is therefore proposed, in order to assist designers and Classification Societies in the process of producing ships which are more optimized, with respect to ensured safety levels. Based on the work reported in the paper, the feasibility and usefulness of a reliability based approach in the development of ship longitudinal strength requirements has been demonstrated.« less

Monitoring Obstructive Sleep Apnea by means of a real-time mobile system based on the automatic extraction of sets of rules through Differential Evolution.

PubMed

Sannino, Giovanna; De Falco, Ivanoe; De Pietro, Giuseppe

2014-06-01

Real-time Obstructive Sleep Apnea (OSA) episode detection and monitoring are important for society in terms of an improvement in the health of the general population and of a reduction in mortality and healthcare costs. Currently, to diagnose OSA patients undergo PolySomnoGraphy (PSG), a complicated and invasive test to be performed in a specialized center involving many sensors and wires. Accordingly, each patient is required to stay in the same position throughout the duration of one night, thus restricting their movements. This paper proposes an easy, cheap, and portable approach for the monitoring of patients with OSA, which collects single-channel ElectroCardioGram (ECG) data only. It is easy to perform from the patient's point of view because only one wearable sensor is required, so the patient is not restricted to keeping the same position all night long, and the detection and monitoring can be carried out in any place through the use of a mobile device. Our approach is based on the automatic extraction, from a database containing information about the monitored patient, of explicit knowledge in the form of a set of IF…THEN rules containing typical parameters derived from Heart Rate Variability (HRV) analysis. The extraction is carried out off-line by means of a Differential Evolution algorithm. This set of rules can then be exploited in the real-time mobile monitoring system developed at our Laboratory: the ECG data is gathered by a wearable sensor and sent to a mobile device, where it is processed in real time. Subsequently, HRV-related parameters are computed from this data, and, if their values activate some of the rules describing the occurrence of OSA, an alarm is automatically produced. This approach has been tested on a well-known literature database of OSA patients. The numerical results show its effectiveness in terms of accuracy, sensitivity, and specificity, and the achieved sets of rules evidence the user-friendliness of the approach. Furthermore, the method is compared against other well known classifiers, and its discrimination ability is shown to be higher. Copyright © 2014 Elsevier Inc. All rights reserved.

Automatic information extraction from unstructured mammography reports using distributed semantics.

PubMed

Gupta, Anupama; Banerjee, Imon; Rubin, Daniel L

2018-02-01

To date, the methods developed for automated extraction of information from radiology reports are mainly rule-based or dictionary-based, and, therefore, require substantial manual effort to build these systems. Recent efforts to develop automated systems for entity detection have been undertaken, but little work has been done to automatically extract relations and their associated named entities in narrative radiology reports that have comparable accuracy to rule-based methods. Our goal is to extract relations in a unsupervised way from radiology reports without specifying prior domain knowledge. We propose a hybrid approach for information extraction that combines dependency-based parse tree with distributed semantics for generating structured information frames about particular findings/abnormalities from the free-text mammography reports. The proposed IE system obtains a F 1 -score of 0.94 in terms of completeness of the content in the information frames, which outperforms a state-of-the-art rule-based system in this domain by a significant margin. The proposed system can be leveraged in a variety of applications, such as decision support and information retrieval, and may also easily scale to other radiology domains, since there is no need to tune the system with hand-crafted information extraction rules. Copyright © 2018 Elsevier Inc. All rights reserved.

Optimization of RET flow using test layout

NASA Astrophysics Data System (ADS)

Zhang, Yunqiang; Sethi, Satyendra; Lucas, Kevin

2008-11-01

At advanced technology nodes with extremely low k1 lithography, it is very hard to achieve image fidelity requirements and process window for some layout configurations. Quite often these layouts are within simple design rule constraints for a given technology node. It is important to have these layouts included during early RET flow development. Most of RET developments are based on shrunk layout from the previous technology node, which is possibly not good enough. A better methodology in creating test layout is required for optical proximity correction (OPC) recipe and assists feature development. In this paper we demonstrate the application of programmable test layouts in RET development. Layout pattern libraries are developed and embedded in a layout tool (ICWB). Assessment gauges are generated together with patterns for quick correction accuracy assessment. Several groups of test pattern libraries have been developed based on learning from product patterns and a layout DOE approach. The interaction between layout patterns and OPC recipe has been studied. Correction of a contact layer is quite challenge because of poor convergence and low process window. We developed test pattern library with many different contact configurations. Different OPC schemes are studied on these test layouts. The worst process window patterns are pinpointed for a given illumination condition. Assist features (AF) are frequently placed according to pre-determined rules to improve lithography process window. These rules are usually derived from lithographic models and experiments. Direct validation of AF rules is required at development phase. We use the test layout approach to determine rules in order to eliminate AF printability problem.

Object-oriented feature extraction approach for mapping supraglacial debris in Schirmacher Oasis using very high-resolution satellite data

NASA Astrophysics Data System (ADS)

Jawak, Shridhar D.; Jadhav, Ajay; Luis, Alvarinho J.

2016-05-01

Supraglacial debris was mapped in the Schirmacher Oasis, east Antarctica, by using WorldView-2 (WV-2) high resolution optical remote sensing data consisting of 8-band calibrated Gram Schmidt (GS)-sharpened and atmospherically corrected WV-2 imagery. This study is a preliminary attempt to develop an object-oriented rule set to extract supraglacial debris for Antarctic region using 8-spectral band imagery. Supraglacial debris was manually digitized from the satellite imagery to generate the ground reference data. Several trials were performed using few existing traditional pixel-based classification techniques and color-texture based object-oriented classification methods to extract supraglacial debris over a small domain of the study area. Multi-level segmentation and attributes such as scale, shape, size, compactness along with spectral information from the data were used for developing the rule set. The quantitative analysis of error was carried out against the manually digitized reference data to test the practicability of our approach over the traditional pixel-based methods. Our results indicate that OBIA-based approach (overall accuracy: 93%) for extracting supraglacial debris performed better than all the traditional pixel-based methods (overall accuracy: 80-85%). The present attempt provides a comprehensive improved method for semiautomatic feature extraction in supraglacial environment and a new direction in the cryospheric research.

A simplified approach to the pooled analysis of calibration of clinical prediction rules for systematic reviews of validation studies

PubMed Central

Dimitrov, Borislav D; Motterlini, Nicola; Fahey, Tom

2015-01-01

Objective Estimating calibration performance of clinical prediction rules (CPRs) in systematic reviews of validation studies is not possible when predicted values are neither published nor accessible or sufficient or no individual participant or patient data are available. Our aims were to describe a simplified approach for outcomes prediction and calibration assessment and evaluate its functionality and validity. Study design and methods: Methodological study of systematic reviews of validation studies of CPRs: a) ABCD2 rule for prediction of 7 day stroke; and b) CRB-65 rule for prediction of 30 day mortality. Predicted outcomes in a sample validation study were computed by CPR distribution patterns (“derivation model”). As confirmation, a logistic regression model (with derivation study coefficients) was applied to CPR-based dummy variables in the validation study. Meta-analysis of validation studies provided pooled estimates of “predicted:observed” risk ratios (RRs), 95% confidence intervals (CIs), and indexes of heterogeneity (I2) on forest plots (fixed and random effects models), with and without adjustment of intercepts. The above approach was also applied to the CRB-65 rule. Results Our simplified method, applied to ABCD2 rule in three risk strata (low, 0–3; intermediate, 4–5; high, 6–7 points), indicated that predictions are identical to those computed by univariate, CPR-based logistic regression model. Discrimination was good (c-statistics =0.61–0.82), however, calibration in some studies was low. In such cases with miscalibration, the under-prediction (RRs =0.73–0.91, 95% CIs 0.41–1.48) could be further corrected by intercept adjustment to account for incidence differences. An improvement of both heterogeneities and P-values (Hosmer-Lemeshow goodness-of-fit test) was observed. Better calibration and improved pooled RRs (0.90–1.06), with narrower 95% CIs (0.57–1.41) were achieved. Conclusion Our results have an immediate clinical implication in situations when predicted outcomes in CPR validation studies are lacking or deficient by describing how such predictions can be obtained by everyone using the derivation study alone, without any need for highly specialized knowledge or sophisticated statistics. PMID:25931829

EXSPRT: An Expert Systems Approach to Computer-Based Adaptive Testing.

ERIC Educational Resources Information Center

Frick, Theodore W.; And Others

Expert systems can be used to aid decision making. A computerized adaptive test (CAT) is one kind of expert system, although it is not commonly recognized as such. A new approach, termed EXSPRT, was devised that combines expert systems reasoning and sequential probability ratio test stopping rules. EXSPRT-R uses random selection of test items,…

Towards Principles-Based Approaches to Governance of Health-related Research using Personal Data

PubMed Central

Laurie, Graeme; Sethi, Nayha

2013-01-01

Technological advances in the quality, availability and linkage potential of health data for research make the need to develop robust and effective information governance mechanisms more pressing than ever before; they also lead us to question the utility of governance devices used hitherto such as consent and anonymisation. This article assesses and advocates a principles-based approach, contrasting this with traditional rule-based approaches, and proposes a model of principled proportionate governance. It is suggested that the approach not only serves as the basis for good governance in contemporary data linkage but also that it provides a platform to assess legal reforms such as the draft Data Protection Regulation. PMID:24416087

Towards Principles-Based Approaches to Governance of Health-related Research using Personal Data.

PubMed

Laurie, Graeme; Sethi, Nayha

2013-01-01

Technological advances in the quality, availability and linkage potential of health data for research make the need to develop robust and effective information governance mechanisms more pressing than ever before; they also lead us to question the utility of governance devices used hitherto such as consent and anonymisation. This article assesses and advocates a principles-based approach, contrasting this with traditional rule-based approaches, and proposes a model of principled proportionate governance . It is suggested that the approach not only serves as the basis for good governance in contemporary data linkage but also that it provides a platform to assess legal reforms such as the draft Data Protection Regulation.

Multiple approaches to fine-grained indexing of the biomedical literature.

PubMed

Neveol, Aurelie; Shooshan, Sonya E; Humphrey, Susanne M; Rindflesh, Thomas C; Aronson, Alan R

2007-01-01

The number of articles in the MEDLINE database is expected to increase tremendously in the coming years. To ensure that all these documents are indexed with continuing high quality, it is necessary to develop tools and methods that help the indexers in their daily task. We present three methods addressing a novel aspect of automatic indexing of the biomedical literature, namely producing MeSH main heading/subheading pair recommendations. The methods, (dictionary-based, post- processing rules and Natural Language Processing rules) are described and evaluated on a genetics-related corpus. The best overall performance is obtained for the subheading genetics (70% precision and 17% recall with post-processing rules, 48% precision and 37% recall with the dictionary-based method). Future work will address extending this work to all MeSH subheadings and a more thorough study of method combination.

Medicare Program; Merit-Based Incentive Payment System (MIPS) and Alternative Payment Model (APM) Incentive Under the Physician Fee Schedule, and Criteria for Physician-Focused Payment Models. Final rule with comment period.

PubMed

2016-11-04

The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) repeals the Medicare sustainable growth rate (SGR) methodology for updates to the physician fee schedule (PFS) and replaces it with a new approach to payment called the Quality Payment Program that rewards the delivery of high-quality patient care through two avenues: Advanced Alternative Payment Models (Advanced APMs) and the Merit-based Incentive Payment System (MIPS) for eligible clinicians or groups under the PFS. This final rule with comment period establishes incentives for participation in certain alternative payment models (APMs) and includes the criteria for use by the Physician-Focused Payment Model Technical Advisory Committee (PTAC) in making comments and recommendations on physician-focused payment models (PFPMs). Alternative Payment Models are payment approaches, developed in partnership with the clinician community, that provide added incentives to deliver high-quality and cost-efficient care. APMs can apply to a specific clinical condition, a care episode, or a population. This final rule with comment period also establishes the MIPS, a new program for certain Medicare-enrolled practitioners. MIPS will consolidate components of three existing programs, the Physician Quality Reporting System (PQRS), the Physician Value-based Payment Modifier (VM), and the Medicare Electronic Health Record (EHR) Incentive Program for Eligible Professionals (EPs), and will continue the focus on quality, cost, and use of certified EHR technology (CEHRT) in a cohesive program that avoids redundancies. In this final rule with comment period we have rebranded key terminology based on feedback from stakeholders, with the goal of selecting terms that will be more easily identified and understood by our stakeholders.

An Interval Type-2 Neural Fuzzy System for Online System Identification and Feature Elimination.

PubMed

Lin, Chin-Teng; Pal, Nikhil R; Wu, Shang-Lin; Liu, Yu-Ting; Lin, Yang-Yin

2015-07-01

We propose an integrated mechanism for discarding derogatory features and extraction of fuzzy rules based on an interval type-2 neural fuzzy system (NFS)-in fact, it is a more general scheme that can discard bad features, irrelevant antecedent clauses, and even irrelevant rules. High-dimensional input variable and a large number of rules not only enhance the computational complexity of NFSs but also reduce their interpretability. Therefore, a mechanism for simultaneous extraction of fuzzy rules and reducing the impact of (or eliminating) the inferior features is necessary. The proposed approach, namely an interval type-2 Neural Fuzzy System for online System Identification and Feature Elimination (IT2NFS-SIFE), uses type-2 fuzzy sets to model uncertainties associated with information and data in designing the knowledge base. The consequent part of the IT2NFS-SIFE is of Takagi-Sugeno-Kang type with interval weights. The IT2NFS-SIFE possesses a self-evolving property that can automatically generate fuzzy rules. The poor features can be discarded through the concept of a membership modulator. The antecedent and modulator weights are learned using a gradient descent algorithm. The consequent part weights are tuned via the rule-ordered Kalman filter algorithm to enhance learning effectiveness. Simulation results show that IT2NFS-SIFE not only simplifies the system architecture by eliminating derogatory/irrelevant antecedent clauses, rules, and features but also maintains excellent performance.

Daily life activity routine discovery in hemiparetic rehabilitation patients using topic models.

PubMed

Seiter, J; Derungs, A; Schuster-Amft, C; Amft, O; Tröster, G

2015-01-01

Monitoring natural behavior and activity routines of hemiparetic rehabilitation patients across the day can provide valuable progress information for therapists and patients and contribute to an optimized rehabilitation process. In particular, continuous patient monitoring could add type, frequency and duration of daily life activity routines and hence complement standard clinical scores that are assessed for particular tasks only. Machine learning methods have been applied to infer activity routines from sensor data. However, supervised methods require activity annotations to build recognition models and thus require extensive patient supervision. Discovery methods, including topic models could provide patient routine information and deal with variability in activity and movement performance across patients. Topic models have been used to discover characteristic activity routine patterns of healthy individuals using activity primitives recognized from supervised sensor data. Yet, the applicability of topic models for hemiparetic rehabilitation patients and techniques to derive activity primitives without supervision needs to be addressed. We investigate, 1) whether a topic model-based activity routine discovery framework can infer activity routines of rehabilitation patients from wearable motion sensor data. 2) We compare the performance of our topic model-based activity routine discovery using rule-based and clustering-based activity vocabulary. We analyze the activity routine discovery in a dataset recorded with 11 hemiparetic rehabilitation patients during up to ten full recording days per individual in an ambulatory daycare rehabilitation center using wearable motion sensors attached to both wrists and the non-affected thigh. We introduce and compare rule-based and clustering-based activity vocabulary to process statistical and frequency acceleration features to activity words. Activity words were used for activity routine pattern discovery using topic models based on Latent Dirichlet Allocation. Discovered activity routine patterns were then mapped to six categorized activity routines. Using the rule-based approach, activity routines could be discovered with an average accuracy of 76% across all patients. The rule-based approach outperformed clustering by 10% and showed less confusions for predicted activity routines. Topic models are suitable to discover daily life activity routines in hemiparetic rehabilitation patients without trained classifiers and activity annotations. Activity routines show characteristic patterns regarding activity primitives including body and extremity postures and movement. A patient-independent rule set can be derived. Including expert knowledge supports successful activity routine discovery over completely data-driven clustering.

Building a Relationship between Robot Characteristics and Teleoperation User Interfaces.

PubMed

Mortimer, Michael; Horan, Ben; Seyedmahmoudian, Mehdi

2017-03-14

The Robot Operating System (ROS) provides roboticists with a standardized and distributed framework for real-time communication between robotic systems using a microkernel environment. This paper looks at how ROS metadata, Unified Robot Description Format (URDF), Semantic Robot Description Format (SRDF), and its message description language, can be used to identify key robot characteristics to inform User Interface (UI) design for the teleoperation of heterogeneous robot teams. Logical relationships between UI components and robot characteristics are defined by a set of relationship rules created using relevant and available information including developer expertise and ROS metadata. This provides a significant opportunity to move towards a rule-driven approach for generating the designs of teleoperation UIs; in particular the reduction of the number of different UI configurations required to teleoperate each individual robot within a heterogeneous robot team. This approach is based on using an underlying rule set identifying robots that can be teleoperated using the same UI configuration due to having the same or similar robot characteristics. Aside from reducing the number of different UI configurations an operator needs to be familiar with, this approach also supports consistency in UI configurations when a teleoperator is periodically switching between different robots. To achieve this aim, a Matlab toolbox is developed providing users with the ability to define rules specifying the relationship between robot characteristics and UI components. Once rules are defined, selections that best describe the characteristics of the robot type within a particular heterogeneous robot team can be made. A main advantage of this approach is that rather than specifying discrete robots comprising the team, the user can specify characteristics of the team more generally allowing the system to deal with slight variations that may occur in the future. In fact, by using the defined relationship rules and characteristic selections, the toolbox can automatically identify a reduced set of UI configurations required to control possible robot team configurations, as opposed to the traditional ad-hoc approach to teleoperation UI design. In the results section, three test cases are presented to demonstrate how the selection of different robot characteristics builds a number of robot characteristic combinations, and how the relationship rules are used to determine a reduced set of required UI configurations needed to control each individual robot in the robot team.

Building a Relationship between Robot Characteristics and Teleoperation User Interfaces

PubMed Central

Mortimer, Michael; Horan, Ben; Seyedmahmoudian, Mehdi

2017-01-01

The Robot Operating System (ROS) provides roboticists with a standardized and distributed framework for real-time communication between robotic systems using a microkernel environment. This paper looks at how ROS metadata, Unified Robot Description Format (URDF), Semantic Robot Description Format (SRDF), and its message description language, can be used to identify key robot characteristics to inform User Interface (UI) design for the teleoperation of heterogeneous robot teams. Logical relationships between UI components and robot characteristics are defined by a set of relationship rules created using relevant and available information including developer expertise and ROS metadata. This provides a significant opportunity to move towards a rule-driven approach for generating the designs of teleoperation UIs; in particular the reduction of the number of different UI configurations required to teleoperate each individual robot within a heterogeneous robot team. This approach is based on using an underlying rule set identifying robots that can be teleoperated using the same UI configuration due to having the same or similar robot characteristics. Aside from reducing the number of different UI configurations an operator needs to be familiar with, this approach also supports consistency in UI configurations when a teleoperator is periodically switching between different robots. To achieve this aim, a Matlab toolbox is developed providing users with the ability to define rules specifying the relationship between robot characteristics and UI components. Once rules are defined, selections that best describe the characteristics of the robot type within a particular heterogeneous robot team can be made. A main advantage of this approach is that rather than specifying discrete robots comprising the team, the user can specify characteristics of the team more generally allowing the system to deal with slight variations that may occur in the future. In fact, by using the defined relationship rules and characteristic selections, the toolbox can automatically identify a reduced set of UI configurations required to control possible robot team configurations, as opposed to the traditional ad-hoc approach to teleoperation UI design. In the results section, three test cases are presented to demonstrate how the selection of different robot characteristics builds a number of robot characteristic combinations, and how the relationship rules are used to determine a reduced set of required UI configurations needed to control each individual robot in the robot team. PMID:28335431

Decision Tree based Prediction and Rule Induction for Groundwater Trichloroethene (TCE) Pollution Vulnerability

NASA Astrophysics Data System (ADS)

Park, J.; Yoo, K.

2013-12-01

For groundwater resource conservation, it is important to accurately assess groundwater pollution sensitivity or vulnerability. In this work, we attempted to use data mining approach to assess groundwater pollution vulnerability in a TCE (trichloroethylene) contaminated Korean industrial site. The conventional DRASTIC method failed to describe TCE sensitivity data with a poor correlation with hydrogeological properties. Among the different data mining methods such as Artificial Neural Network (ANN), Multiple Logistic Regression (MLR), Case Base Reasoning (CBR), and Decision Tree (DT), the accuracy and consistency of Decision Tree (DT) was the best. According to the following tree analyses with the optimal DT model, the failure of the conventional DRASTIC method in fitting with TCE sensitivity data may be due to the use of inaccurate weight values of hydrogeological parameters for the study site. These findings provide a proof of concept that DT based data mining approach can be used in predicting and rule induction of groundwater TCE sensitivity without pre-existing information on weights of hydrogeological properties.

Unsupervised Biomedical Named Entity Recognition: Experiments with Clinical and Biological Texts

PubMed Central

Zhang, Shaodian; Elhadad, Nóemie

2013-01-01

Named entity recognition is a crucial component of biomedical natural language processing, enabling information extraction and ultimately reasoning over and knowledge discovery from text. Much progress has been made in the design of rule-based and supervised tools, but they are often genre and task dependent. As such, adapting them to different genres of text or identifying new types of entities requires major effort in re-annotation or rule development. In this paper, we propose an unsupervised approach to extracting named entities from biomedical text. We describe a stepwise solution to tackle the challenges of entity boundary detection and entity type classification without relying on any handcrafted rules, heuristics, or annotated data. A noun phrase chunker followed by a filter based on inverse document frequency extracts candidate entities from free text. Classification of candidate entities into categories of interest is carried out by leveraging principles from distributional semantics. Experiments show that our system, especially the entity classification step, yields competitive results on two popular biomedical datasets of clinical notes and biological literature, and outperforms a baseline dictionary match approach. Detailed error analysis provides a road map for future work. PMID:23954592

A Framework of Simple Event Detection in Surveillance Video

NASA Astrophysics Data System (ADS)

Xu, Weiguang; Zhang, Yafei; Lu, Jianjiang; Tian, Yulong; Wang, Jiabao

Video surveillance is playing more and more important role in people's social life. Real-time alerting of threaten events and searching interesting content in stored large scale video footage needs human operator to pay full attention on monitor for long time. The labor intensive mode has limit the effectiveness and efficiency of the system. A framework of simple event detection is presented advance the automation of video surveillance. An improved inner key point matching approach is used to compensate motion of background in real-time; frame difference are used to detect foreground; HOG based classifiers are used to classify foreground object into people and car; mean-shift is used to tracking the recognized objects. Events are detected based on predefined rules. The maturity of the algorithms guarantee the robustness of the framework, and the improved approach and the easily checked rules enable the framework to work in real-time. Future works to be done are also discussed.

Predicting missing values in a home care database using an adaptive uncertainty rule method.

PubMed

Konias, S; Gogou, G; Bamidis, P D; Vlahavas, I; Maglaveras, N

2005-01-01

Contemporary literature illustrates an abundance of adaptive algorithms for mining association rules. However, most literature is unable to deal with the peculiarities, such as missing values and dynamic data creation, that are frequently encountered in fields like medicine. This paper proposes an uncertainty rule method that uses an adaptive threshold for filling missing values in newly added records. A new approach for mining uncertainty rules and filling missing values is proposed, which is in turn particularly suitable for dynamic databases, like the ones used in home care systems. In this study, a new data mining method named FiMV (Filling Missing Values) is illustrated based on the mined uncertainty rules. Uncertainty rules have quite a similar structure to association rules and are extracted by an algorithm proposed in previous work, namely AURG (Adaptive Uncertainty Rule Generation). The main target was to implement an appropriate method for recovering missing values in a dynamic database, where new records are continuously added, without needing to specify any kind of thresholds beforehand. The method was applied to a home care monitoring system database. Randomly, multiple missing values for each record's attributes (rate 5-20% by 5% increments) were introduced in the initial dataset. FiMV demonstrated 100% completion rates with over 90% success in each case, while usual approaches, where all records with missing values are ignored or thresholds are required, experienced significantly reduced completion and success rates. It is concluded that the proposed method is appropriate for the data-cleaning step of the Knowledge Discovery process in databases. The latter, containing much significance for the output efficiency of any data mining technique, can improve the quality of the mined information.

Limits to Bureaucratic Growth: The Density Dependence of Organizational Rule Births.

ERIC Educational Resources Information Center

Schulz, Martin

1998-01-01

Uses a population-ecology approach to examine whether bureaucratic rules breed more rules. Tests hypotheses about rule births with time-series data on rule production in a large United States research university. Results show that the rate of rule production declines with the number of rules in a rule population over time. Rules inhibit…

Value of Seasonal Fuzzy-based Inflow Prediction in the Jucar River Basin

NASA Astrophysics Data System (ADS)

Pulido-Velazquez, M.; Macian-Sorribes, H.

2016-12-01

The development and application of climate services in Integrated Water Resources Management (IWRM) is said to add important benefits in terms of water use efficiency due to an increase ability to foresee future water availability. A method to evaluate the economic impact of these services is presented, based on the use of hydroeconomic modelling techniques (hydroeconomic simulation) to compare the net benefits from water use in the system with and without the inflow forecasting. The Jucar River Basin (Spain) has been used as case study. Operating rules currently applied in the basin were assessed using fuzzy rule-based (FRB) systems via a co-development process involving the system operators. These operating rules use as input variable the hydrological inflows in several sub-basins, which need to be foreseen by the system operators. The inflow forecasting mechanism to preview water availability in the irrigation season (May-September) relied on fuzzy regression in which future inflows were foreseen based on past inflows and rainfall in the basin. This approach was compared with the current use of the two past year inflows for projecting the future inflow. For each irrigation season, the previewed inflows were determined using both methods and their impact on the system operation assessed through a hydroeconomic DSS. Results show that the implementation of the fuzzy inflow forecasting system offers higher economic returns. Another advantage of the fuzzy approach regards to the uncertainty treatment using fuzzy numbers, which allow us to estimate the uncertainty range of the expected benefits. Consequently, we can use the fuzzy approach to estimate the uncertainty associated with both the prediction and the associated benefits.

Stochastic simulation of multiscale complex systems with PISKaS: A rule-based approach.

PubMed

Perez-Acle, Tomas; Fuenzalida, Ignacio; Martin, Alberto J M; Santibañez, Rodrigo; Avaria, Rodrigo; Bernardin, Alejandro; Bustos, Alvaro M; Garrido, Daniel; Dushoff, Jonathan; Liu, James H

2018-03-29

Computational simulation is a widely employed methodology to study the dynamic behavior of complex systems. Although common approaches are based either on ordinary differential equations or stochastic differential equations, these techniques make several assumptions which, when it comes to biological processes, could often lead to unrealistic models. Among others, model approaches based on differential equations entangle kinetics and causality, failing when complexity increases, separating knowledge from models, and assuming that the average behavior of the population encompasses any individual deviation. To overcome these limitations, simulations based on the Stochastic Simulation Algorithm (SSA) appear as a suitable approach to model complex biological systems. In this work, we review three different models executed in PISKaS: a rule-based framework to produce multiscale stochastic simulations of complex systems. These models span multiple time and spatial scales ranging from gene regulation up to Game Theory. In the first example, we describe a model of the core regulatory network of gene expression in Escherichia coli highlighting the continuous model improvement capacities of PISKaS. The second example describes a hypothetical outbreak of the Ebola virus occurring in a compartmentalized environment resembling cities and highways. Finally, in the last example, we illustrate a stochastic model for the prisoner's dilemma; a common approach from social sciences describing complex interactions involving trust within human populations. As whole, these models demonstrate the capabilities of PISKaS providing fertile scenarios where to explore the dynamics of complex systems. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Evolutionary Local Search of Fuzzy Rules through a novel Neuro-Fuzzy encoding method.

PubMed

Carrascal, A; Manrique, D; Ríos, J; Rossi, C

2003-01-01

This paper proposes a new approach for constructing fuzzy knowledge bases using evolutionary methods. We have designed a genetic algorithm that automatically builds neuro-fuzzy architectures based on a new indirect encoding method. The neuro-fuzzy architecture represents the fuzzy knowledge base that solves a given problem; the search for this architecture takes advantage of a local search procedure that improves the chromosomes at each generation. Experiments conducted both on artificially generated and real world problems confirm the effectiveness of the proposed approach.

Compliance monitoring in business processes: Functionalities, application, and tool-support.

PubMed

Ly, Linh Thao; Maggi, Fabrizio Maria; Montali, Marco; Rinderle-Ma, Stefanie; van der Aalst, Wil M P

2015-12-01

In recent years, monitoring the compliance of business processes with relevant regulations, constraints, and rules during runtime has evolved as major concern in literature and practice. Monitoring not only refers to continuously observing possible compliance violations, but also includes the ability to provide fine-grained feedback and to predict possible compliance violations in the future. The body of literature on business process compliance is large and approaches specifically addressing process monitoring are hard to identify. Moreover, proper means for the systematic comparison of these approaches are missing. Hence, it is unclear which approaches are suitable for particular scenarios. The goal of this paper is to define a framework for Compliance Monitoring Functionalities (CMF) that enables the systematic comparison of existing and new approaches for monitoring compliance rules over business processes during runtime. To define the scope of the framework, at first, related areas are identified and discussed. The CMFs are harvested based on a systematic literature review and five selected case studies. The appropriateness of the selection of CMFs is demonstrated in two ways: (a) a systematic comparison with pattern-based compliance approaches and (b) a classification of existing compliance monitoring approaches using the CMFs. Moreover, the application of the CMFs is showcased using three existing tools that are applied to two realistic data sets. Overall, the CMF framework provides powerful means to position existing and future compliance monitoring approaches.

Compliance monitoring in business processes: Functionalities, application, and tool-support

PubMed Central

Ly, Linh Thao; Maggi, Fabrizio Maria; Montali, Marco; Rinderle-Ma, Stefanie; van der Aalst, Wil M.P.

2015-01-01

In recent years, monitoring the compliance of business processes with relevant regulations, constraints, and rules during runtime has evolved as major concern in literature and practice. Monitoring not only refers to continuously observing possible compliance violations, but also includes the ability to provide fine-grained feedback and to predict possible compliance violations in the future. The body of literature on business process compliance is large and approaches specifically addressing process monitoring are hard to identify. Moreover, proper means for the systematic comparison of these approaches are missing. Hence, it is unclear which approaches are suitable for particular scenarios. The goal of this paper is to define a framework for Compliance Monitoring Functionalities (CMF) that enables the systematic comparison of existing and new approaches for monitoring compliance rules over business processes during runtime. To define the scope of the framework, at first, related areas are identified and discussed. The CMFs are harvested based on a systematic literature review and five selected case studies. The appropriateness of the selection of CMFs is demonstrated in two ways: (a) a systematic comparison with pattern-based compliance approaches and (b) a classification of existing compliance monitoring approaches using the CMFs. Moreover, the application of the CMFs is showcased using three existing tools that are applied to two realistic data sets. Overall, the CMF framework provides powerful means to position existing and future compliance monitoring approaches. PMID:26635430

NASIS data base management system: IBM 360 TSS implementation. Volume 1: Installation standards

NASA Technical Reports Server (NTRS)

1973-01-01

The installation standards for the (NASIS) data base management system are presented. The standard approach to preparing systems documentation and the program design and coding rules and conventions are outlines. Included are instructions for preparing all major specifications and suggestions for improving the quality and efficency of the programming task.

A New Automatic Method of Urban Areas Mapping in East Asia from LANDSAT Data

NASA Astrophysics Data System (ADS)

XU, R.; Jia, G.

2012-12-01

Cities, as places where human activities are concentrated, account for a small percent of global land cover but are frequently cited as the chief causes of, and solutions to, climate, biogeochemistry, and hydrology processes at local, regional, and global scales. Accompanying with uncontrolled economic growth, urban sprawl has been attributed to the accelerating integration of East Asia into the world economy and involved dramatic changes in its urban form and land use. To understand the impact of urban extent on biogeophysical processes, reliable mapping of built-up areas is particularly essential in eastern cities as a result of their characteristics of smaller patches, more fragile, and a lower fraction of the urban landscape which does not have natural than in the West. Segmentation of urban land from other land-cover types using remote sensing imagery can be done by standard classification processes as well as a logic rule calculation based on spectral indices and their derivations. Efforts to establish such a logic rule with no threshold for automatically mapping are highly worthwhile. Existing automatic methods are reviewed, and then a proposed approach is introduced including the calculation of the new index and the improved logic rule. Following this, existing automatic methods as well as the proposed approach are compared in a common context. Afterwards, the proposed approach is tested separately in cities of large, medium, and small scale in East Asia selected from different LANDSAT images. The results are promising as the approach can efficiently segment urban areas, even in the presence of more complex eastern cities. Key words: Urban extraction; Automatic Method; Logic Rule; LANDSAT images; East AisaThe Proposed Approach of Extraction of Urban Built-up Areas in Guangzhou, China

A networked voting rule for democratic representation

PubMed Central

Brigatti, Edgardo; Moreno, Yamir

2018-01-01

We introduce a general framework for exploring the problem of selecting a committee of representatives with the aim of studying a networked voting rule based on a decentralized large-scale platform, which can assure a strong accountability of the elected. The results of our simulations suggest that this algorithm-based approach is able to obtain a high representativeness for relatively small committees, performing even better than a classical voting rule based on a closed list of candidates. We show that a general relation between committee size and representatives exists in the form of an inverse square root law and that the normalized committee size approximately scales with the inverse of the community size, allowing the scalability to very large populations. These findings are not strongly influenced by the different networks used to describe the individuals’ interactions, except for the presence of few individuals with very high connectivity which can have a marginal negative effect in the committee selection process. PMID:29657817

A rule-based system for real-time analysis of control systems

NASA Astrophysics Data System (ADS)

Larson, Richard R.; Millard, D. Edward

1992-10-01

An approach to automate the real-time analysis of flight critical health monitoring and system status is being developed and evaluated at the NASA Dryden Flight Research Facility. A software package was developed in-house and installed as part of the extended aircraft interrogation and display system. This design features a knowledge-base structure in the form of rules to formulate interpretation and decision logic of real-time data. This technique has been applied for ground verification and validation testing and flight testing monitoring where quick, real-time, safety-of-flight decisions can be very critical. In many cases post processing and manual analysis of flight system data are not required. The processing is described of real-time data for analysis along with the output format which features a message stack display. The development, construction, and testing of the rule-driven knowledge base, along with an application using the X-31A flight test program, are presented.

A rule-based system for real-time analysis of control systems

NASA Technical Reports Server (NTRS)

Larson, Richard R.; Millard, D. Edward

1992-01-01

An approach to automate the real-time analysis of flight critical health monitoring and system status is being developed and evaluated at the NASA Dryden Flight Research Facility. A software package was developed in-house and installed as part of the extended aircraft interrogation and display system. This design features a knowledge-base structure in the form of rules to formulate interpretation and decision logic of real-time data. This technique has been applied for ground verification and validation testing and flight testing monitoring where quick, real-time, safety-of-flight decisions can be very critical. In many cases post processing and manual analysis of flight system data are not required. The processing is described of real-time data for analysis along with the output format which features a message stack display. The development, construction, and testing of the rule-driven knowledge base, along with an application using the X-31A flight test program, are presented.

Integration of object-oriented knowledge representation with the CLIPS rule based system

NASA Technical Reports Server (NTRS)

Logie, David S.; Kamil, Hasan

1990-01-01

The paper describes a portion of the work aimed at developing an integrated, knowledge based environment for the development of engineering-oriented applications. An Object Representation Language (ORL) was implemented in C++ which is used to build and modify an object-oriented knowledge base. The ORL was designed in such a way so as to be easily integrated with other representation schemes that could effectively reason with the object base. Specifically, the integration of the ORL with the rule based system C Language Production Systems (CLIPS), developed at the NASA Johnson Space Center, will be discussed. The object-oriented knowledge representation provides a natural means of representing problem data as a collection of related objects. Objects are comprised of descriptive properties and interrelationships. The object-oriented model promotes efficient handling of the problem data by allowing knowledge to be encapsulated in objects. Data is inherited through an object network via the relationship links. Together, the two schemes complement each other in that the object-oriented approach efficiently handles problem data while the rule based knowledge is used to simulate the reasoning process. Alone, the object based knowledge is little more than an object-oriented data storage scheme; however, the CLIPS inference engine adds the mechanism to directly and automatically reason with that knowledge. In this hybrid scheme, the expert system dynamically queries for data and can modify the object base with complete access to all the functionality of the ORL from rules.

Alternative Theoretical Bases for the Study of Human Communication: The Systems Perspective.

ERIC Educational Resources Information Center

Monge, Peter R.

Three potentially useful perspectives for the scientific development of human communication theory are the law model, the systems approach, and the rules paradigm. It is the purpose of this paper to indicate the utility of the systems approach. The first section of this paper provides a brief account of the systems view of the world. Outlined in…

A New Approach to Automated Labeling of Internal Features of Hardwood Logs Using CT Images

Treesearch

Daniel L. Schmoldt; Pei Li; A. Lynn Abbott

1996-01-01

The feasibility of automatically identifying internal features of hardwood logs using CT imagery has been established previously. Features of primary interest are bark, knots, voids, decay, and clear wood. Our previous approach: filtered original CT images, applied histogram segmentation, grew volumes to extract 3-d regions, and applied a rule base, with Dempster-...

Transformation of Graphical ECA Policies into Executable PonderTalk Code

NASA Astrophysics Data System (ADS)

Romeikat, Raphael; Sinsel, Markus; Bauer, Bernhard

Rules are becoming more and more important in business modeling and systems engineering and are recognized as a high-level programming paradigma. For the effective development of rules it is desired to start at a high level, e.g. with graphical rules, and to refine them into code of a particular rule language for implementation purposes later. An model-driven approach is presented in this paper to transform graphical rules into executable code in a fully automated way. The focus is on event-condition-action policies as a special rule type. These are modeled graphically and translated into the PonderTalk language. The approach may be extended to integrate other rule types and languages as well.

Identifying Suicide Ideation and Suicidal Attempts in a Psychiatric Clinical Research Database using Natural Language Processing.

PubMed

Fernandes, Andrea C; Dutta, Rina; Velupillai, Sumithra; Sanyal, Jyoti; Stewart, Robert; Chandran, David

2018-05-09

Research into suicide prevention has been hampered by methodological limitations such as low sample size and recall bias. Recently, Natural Language Processing (NLP) strategies have been used with Electronic Health Records to increase information extraction from free text notes as well as structured fields concerning suicidality and this allows access to much larger cohorts than previously possible. This paper presents two novel NLP approaches - a rule-based approach to classify the presence of suicide ideation and a hybrid machine learning and rule-based approach to identify suicide attempts in a psychiatric clinical database. Good performance of the two classifiers in the evaluation study suggest they can be used to accurately detect mentions of suicide ideation and attempt within free-text documents in this psychiatric database. The novelty of the two approaches lies in the malleability of each classifier if a need to refine performance, or meet alternate classification requirements arises. The algorithms can also be adapted to fit infrastructures of other clinical datasets given sufficient clinical recording practice knowledge, without dependency on medical codes or additional data extraction of known risk factors to predict suicidal behaviour.

77 FR 52887 - Regulatory Capital Rules: Standardized Approach for Risk-Weighted Assets; Market Discipline and...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-30

... securitization framework designed to address the credit risk of exposures that involve the tranching of the... creditworthiness standards and risk-based capital requirements have been designed to be consistent with safety and...

Logistic-based patient grouping for multi-disciplinary treatment.

PubMed

Maruşter, Laura; Weijters, Ton; de Vries, Geerhard; van den Bosch, Antal; Daelemans, Walter

2002-01-01

Present-day healthcare witnesses a growing demand for coordination of patient care. Coordination is needed especially in those cases in which hospitals have structured healthcare into specialty-oriented units, while a substantial portion of patient care is not limited to single units. From a logistic point of view, this multi-disciplinary patient care creates a tension between controlling the hospital's units, and the need for a control of the patient flow between units. A possible solution is the creation of new units in which different specialties work together for specific groups of patients. A first step in this solution is to identify the salient patient groups in need of multi-disciplinary care. Grouping techniques seem to offer a solution. However, most grouping approaches in medicine are driven by a search for pathophysiological homogeneity. In this paper, we present an alternative logistic-driven grouping approach. The starting point of our approach is a database with medical cases for 3,603 patients with peripheral arterial vascular (PAV) diseases. For these medical cases, six basic logistic variables (such as the number of visits to different specialist) are selected. Using these logistic variables, clustering techniques are used to group the medical cases in logistically homogeneous groups. In our approach, the quality of the resulting grouping is not measured by statistical significance, but by (i) the usefulness of the grouping for the creation of new multi-disciplinary units; (ii) how well patients can be selected for treatment in the new units. Given a priori knowledge of a patient (e.g. age, diagnosis), machine learning techniques are employed to induce rules that can be used for the selection of the patients eligible for treatment in the new units. In the paper, we describe the results of the above-proposed methodology for patients with PAV diseases. Two groupings and the accompanied classification rule sets are presented. One grouping is based on all the logistic variables, and another grouping is based on two latent factors found by applying factor analysis. On the basis of the experimental results, we can conclude that it is possible to search for medical logistic homogenous groups (i) that can be characterized by rules based on the aggregated logistic variables; (ii) for which we can formulate rules to predict to which cluster new patients belong.

Multi-viewpoint clustering analysis

NASA Technical Reports Server (NTRS)

Mehrotra, Mala; Wild, Chris

1993-01-01

In this paper, we address the feasibility of partitioning rule-based systems into a number of meaningful units to enhance the comprehensibility, maintainability and reliability of expert systems software. Preliminary results have shown that no single structuring principle or abstraction hierarchy is sufficient to understand complex knowledge bases. We therefore propose the Multi View Point - Clustering Analysis (MVP-CA) methodology to provide multiple views of the same expert system. We present the results of using this approach to partition a deployed knowledge-based system that navigates the Space Shuttle's entry. We also discuss the impact of this approach on verification and validation of knowledge-based systems.

A Distributed Problem-Solving Approach to Rule Induction: Learning in Distributed Artificial Intelligence Systems

DTIC Science & Technology

1990-11-01

Intelligence Systems," in Distributed Artifcial Intelligence , vol. II, L. Gasser and M. Huhns (eds), Pitman, London, 1989, pp. 413-430. Shaw, M. Harrow, B...IDTIC FILE COPY A Distributed Problem-Solving Approach to Rule Induction: Learning in Distributed Artificial Intelligence Systems N Michael I. Shaw...SUBTITLE 5. FUNDING NUMBERS A Distributed Problem-Solving Approach to Rule Induction: Learning in Distributed Artificial Intelligence Systems 6

Faults Discovery By Using Mined Data

NASA Technical Reports Server (NTRS)

Lee, Charles

2005-01-01

Fault discovery in the complex systems consist of model based reasoning, fault tree analysis, rule based inference methods, and other approaches. Model based reasoning builds models for the systems either by mathematic formulations or by experiment model. Fault Tree Analysis shows the possible causes of a system malfunction by enumerating the suspect components and their respective failure modes that may have induced the problem. The rule based inference build the model based on the expert knowledge. Those models and methods have one thing in common; they have presumed some prior-conditions. Complex systems often use fault trees to analyze the faults. Fault diagnosis, when error occurs, is performed by engineers and analysts performing extensive examination of all data gathered during the mission. International Space Station (ISS) control center operates on the data feedback from the system and decisions are made based on threshold values by using fault trees. Since those decision-making tasks are safety critical and must be done promptly, the engineers who manually analyze the data are facing time challenge. To automate this process, this paper present an approach that uses decision trees to discover fault from data in real-time and capture the contents of fault trees as the initial state of the trees.

Cellular automata rule characterization and classification using texture descriptors

NASA Astrophysics Data System (ADS)

Machicao, Jeaneth; Ribas, Lucas C.; Scabini, Leonardo F. S.; Bruno, Odermir M.

2018-05-01

The cellular automata (CA) spatio-temporal patterns have attracted the attention from many researchers since it can provide emergent behavior resulting from the dynamics of each individual cell. In this manuscript, we propose an approach of texture image analysis to characterize and classify CA rules. The proposed method converts the CA spatio-temporal patterns into a gray-scale image. The gray-scale is obtained by creating a binary number based on the 8-connected neighborhood of each dot of the CA spatio-temporal pattern. We demonstrate that this technique enhances the CA rule characterization and allow to use different texture image analysis algorithms. Thus, various texture descriptors were evaluated in a supervised training approach aiming to characterize the CA's global evolution. Our results show the efficiency of the proposed method for the classification of the elementary CA (ECAs), reaching a maximum of 99.57% of accuracy rate according to the Li-Packard scheme (6 classes) and 94.36% for the classification of the 88 rules scheme. Moreover, within the image analysis context, we found a better performance of the method by means of a transformation of the binary states to a gray-scale.

Automatic programming via iterated local search for dynamic job shop scheduling.

PubMed

Nguyen, Su; Zhang, Mengjie; Johnston, Mark; Tan, Kay Chen

2015-01-01

Dispatching rules have been commonly used in practice for making sequencing and scheduling decisions. Due to specific characteristics of each manufacturing system, there is no universal dispatching rule that can dominate in all situations. Therefore, it is important to design specialized dispatching rules to enhance the scheduling performance for each manufacturing environment. Evolutionary computation approaches such as tree-based genetic programming (TGP) and gene expression programming (GEP) have been proposed to facilitate the design task through automatic design of dispatching rules. However, these methods are still limited by their high computational cost and low exploitation ability. To overcome this problem, we develop a new approach to automatic programming via iterated local search (APRILS) for dynamic job shop scheduling. The key idea of APRILS is to perform multiple local searches started with programs modified from the best obtained programs so far. The experiments show that APRILS outperforms TGP and GEP in most simulation scenarios in terms of effectiveness and efficiency. The analysis also shows that programs generated by APRILS are more compact than those obtained by genetic programming. An investigation of the behavior of APRILS suggests that the good performance of APRILS comes from the balance between exploration and exploitation in its search mechanism.

Institutional analysis of health system governance.

PubMed

Abimbola, Seye; Negin, Joel; Martiniuk, Alexandra L; Jan, Stephen

2017-11-01

It is important that researchers who study health system governance have a set of collective understandings of the meanings of governance, which can then inform the methods used in research. We present an institutional framing and definition of health system governance; that is, governance refers to making, changing, monitoring and enforcing the rules that govern the demand and supply of health services. This pervasive, relational view of governance is to be preferred to approaches that focus primarily on structures of governments and health care organizations, because health system governance involves communities and service users, and because governments in many low- and middle-income countries tend to under-govern. Therefore, the study of health system governance requires institutional analysis; an approach that focuses not only on structures, but also on the rules (both formal and informal) governing demand and supply relations. Using this 'structure-relations' lens, and based on our field experience, we discuss how this focus could be applied to the three approaches to framing and studying health system governance that we identified in the literature. In order of decreasing focus on structures ('hardware') and increasing focus on relations ('software'), they are: (1) the government-centred approach, which focuses on the role of governments, above or to the exclusion of non-government health system actors; (2) the building-block approach, which focuses on the internal workings of health care organizations, and treats governance as one of the several building blocks of organizations; and (3) the institutional approach, which focuses on how the rules governing social and economic interactions are made, changed, monitored and enforced. Notably, either or both qualitative and quantitative methods may be used by researchers in efforts to incorporate the analysis of how rules determine relations among health system actors into these three approaches to health system governance. © The Author 2017. Published by Oxford University Press in association with The London School of Hygiene and Tropical Medicine. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Improved prediction of heat of mixing and segregation in metallic alloys using tunable mixing rule for embedded atom method

NASA Astrophysics Data System (ADS)

Divi, Srikanth; Agrahari, Gargi; Ranjan Kadulkar, Sanket; Kumar, Sanjeet; Chatterjee, Abhijit

2017-12-01

Capturing segregation behavior in metal alloy nanoparticles accurately using computer simulations is contingent upon the availability of high-fidelity interatomic potentials. The embedded atom method (EAM) potential is a widely trusted interatomic potential form used with pure metals and their alloys. When limited experimental data is available, the A-B EAM cross-interaction potential for metal alloys AxB 1-x are often constructed from pure metal A and B potentials by employing a pre-defined ‘mixing rule’ without any adjustable parameters. While this approach is convenient, we show that for AuPt, NiPt, AgAu, AgPd, AuNi, NiPd, PtPd and AuPd such mixing rules may not even yield the correct alloy properties, e.g., heats of mixing, that are closely related to the segregation behavior. A general theoretical formulation based on scaling invariance arguments is introduced that addresses this issue by tuning the mixing rule to better describe alloy properties. Starting with an existing pure metal EAM potential that is used extensively in literature, we find that the mixing rule fitted to heats of mixing for metal solutions usually provides good estimates of segregation energies, lattice parameters and cohesive energy, as well as equilibrium distribution of metals within a nanoparticle using Monte Carlo simulations. While the tunable mixing rule generally performs better than non-adjustable mixing rules, the use of the tunable mixing rule may still require some caution. For e.g., in Pt-Ni system we find that the segregation behavior can deviate from the experimentally observed one at Ni-rich compositions. Despite this the overall results suggest that the same approach may be useful for developing improved cross-potentials with other existing pure metal EAM potentials as well. As a further test of our approach, mixing rule estimated from binary data is used to calculate heat of mixing in AuPdPt, AuNiPd, AuPtNi, AgAuPd and NiPtPd. Excellent agreement with experiments is observed for AuPdPt.

Tuning rules for robust FOPID controllers based on multi-objective optimization with FOPDT models.

PubMed

Sánchez, Helem Sabina; Padula, Fabrizio; Visioli, Antonio; Vilanova, Ramon

2017-01-01

In this paper a set of optimally balanced tuning rules for fractional-order proportional-integral-derivative controllers is proposed. The control problem of minimizing at once the integrated absolute error for both the set-point and the load disturbance responses is addressed. The control problem is stated as a multi-objective optimization problem where a first-order-plus-dead-time process model subject to a robustness, maximum sensitivity based, constraint has been considered. A set of Pareto optimal solutions is obtained for different normalized dead times and then the optimal balance between the competing objectives is obtained by choosing the Nash solution among the Pareto-optimal ones. A curve fitting procedure has then been applied in order to generate suitable tuning rules. Several simulation results show the effectiveness of the proposed approach. Copyright © 2016. Published by Elsevier Ltd.

Rule-based optimization and multicriteria decision support for packaging a truck chassis

NASA Astrophysics Data System (ADS)

Berger, Martin; Lindroth, Peter; Welke, Richard

2017-06-01

Trucks are highly individualized products where exchangeable parts are flexibly combined to suit different customer requirements, this leading to a great complexity in product development. Therefore, an optimization approach based on constraint programming is proposed for automatically packaging parts of a truck chassis by following packaging rules expressed as constraints. A multicriteria decision support system is developed where a database of truck layouts is computed, among which interactive navigation then can be performed. The work has been performed in cooperation with Volvo Group Trucks Technology (GTT), from which specific rules have been used. Several scenarios are described where the methods developed can be successfully applied and lead to less time-consuming manual work, fewer mistakes, and greater flexibility in configuring trucks. A numerical evaluation is also presented showing the efficiency and practical relevance of the methods, which are implemented in a software tool.

Processing in (linear) systems with stochastic input

NASA Astrophysics Data System (ADS)

Nutu, Catalin Silviu; Axinte, Tiberiu

2016-12-01

The paper is providing a different approach to real-world systems, such as micro and macro systems of our real life, where the man has little or no influence on the system, either not knowing the rules of the respective system or not knowing the input of the system, being thus mainly only spectator of the system's output. In such a system, the input of the system and the laws ruling the system could be only "guessed", based on intuition or previous knowledge of the analyzer of the respective system. But, as we will see in the paper, it exists also another, more theoretical and hence scientific way to approach the matter of the real-world systems, and this approach is mostly based on the theory related to Schrödinger's equation and the wave function associated with it and quantum mechanics as well. The main results of the paper are regarding the utilization of the Schrödinger's equation and related theory but also of the Quantum mechanics, in modeling real-life and real-world systems.

Internal quality control: best practice.

PubMed

Kinns, Helen; Pitkin, Sarah; Housley, David; Freedman, Danielle B

2013-12-01

There is a wide variation in laboratory practice with regard to implementation and review of internal quality control (IQC). A poor approach can lead to a spectrum of scenarios from validation of incorrect patient results to over investigation of falsely rejected analytical runs. This article will provide a practical approach for the routine clinical biochemistry laboratory to introduce an efficient quality control system that will optimise error detection and reduce the rate of false rejection. Each stage of the IQC system is considered, from selection of IQC material to selection of IQC rules, and finally the appropriate action to follow when a rejection signal has been obtained. The main objective of IQC is to ensure day-to-day consistency of an analytical process and thus help to determine whether patient results are reliable enough to be released. The required quality and assay performance varies between analytes as does the definition of a clinically significant error. Unfortunately many laboratories currently decide what is clinically significant at the troubleshooting stage. Assay-specific IQC systems will reduce the number of inappropriate sample-run rejections compared with the blanket use of one IQC rule. In practice, only three or four different IQC rules are required for the whole of the routine biochemistry repertoire as assays are assigned into groups based on performance. The tools to categorise performance and assign IQC rules based on that performance are presented. Although significant investment of time and education is required prior to implementation, laboratories have shown that such systems achieve considerable reductions in cost and labour.

ConsPred: a rule-based (re-)annotation framework for prokaryotic genomes.

PubMed

Weinmaier, Thomas; Platzer, Alexander; Frank, Jeroen; Hellinger, Hans-Jörg; Tischler, Patrick; Rattei, Thomas

2016-11-01

The rapidly growing number of available prokaryotic genome sequences requires fully automated and high-quality software solutions for their initial and re-annotation. Here we present ConsPred, a prokaryotic genome annotation framework that performs intrinsic gene predictions, homology searches, predictions of non-coding genes as well as CRISPR repeats and integrates all evidence into a consensus annotation. ConsPred achieves comprehensive, high-quality annotations based on rules and priorities, similar to decision-making in manual curation and avoids conflicting predictions. Parameters controlling the annotation process are configurable by the user. ConsPred has been used in the institutions of the authors for longer than 5 years and can easily be extended and adapted to specific needs. The ConsPred algorithm for producing a consensus from the varying scores of multiple gene prediction programs approaches manual curation in accuracy. Its rule-based approach for choosing final predictions avoids overriding previous manual curations. ConsPred is implemented in Java, Perl and Shell and is freely available under the Creative Commons license as a stand-alone in-house pipeline or as an Amazon Machine Image for cloud computing, see https://sourceforge.net/projects/conspred/. thomas.rattei@univie.ac.atSupplementary information: Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Moral empiricism and the bias for act-based rules.

PubMed

Ayars, Alisabeth; Nichols, Shaun

2017-10-01

Previous studies on rule learning show a bias in favor of act-based rules, which prohibit intentionally producing an outcome but not merely allowing the outcome. Nichols, Kumar, Lopez, Ayars, and Chan (2016) found that exposure to a single sample violation in which an agent intentionally causes the outcome was sufficient for participants to infer that the rule was act-based. One explanation is that people have an innate bias to think rules are act-based. We suggest an alternative empiricist account: since most rules that people learn are act-based, people form an overhypothesis (Goodman, 1955) that rules are typically act-based. We report three studies that indicate that people can use information about violations to form overhypotheses about rules. In study 1, participants learned either three "consequence-based" rules that prohibited allowing an outcome or three "act-based" rules that prohibiting producing the outcome; in a subsequent learning task, we found that participants who had learned three consequence-based rules were more likely to think that the new rule prohibited allowing an outcome. In study 2, we presented participants with either 1 consequence-based rule or 3 consequence-based rules, and we found that those exposed to 3 such rules were more likely to think that a new rule was also consequence based. Thus, in both studies, it seems that learning 3 consequence-based rules generates an overhypothesis to expect new rules to be consequence-based. In a final study, we used a more subtle manipulation. We exposed participants to examples act-based or accident-based (strict liability) laws and then had them learn a novel rule. We found that participants who were exposed to the accident-based laws were more likely to think a new rule was accident-based. The fact that participants' bias for act-based rules can be shaped by evidence from other rules supports the idea that the bias for act-based rules might be acquired as an overhypothesis from the preponderance of act-based rules. Copyright © 2017 Elsevier B.V. All rights reserved.

Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning

PubMed Central

Lin, Hsuan-Ta; Lee, Po-Ming; Hsiao, Tzu-Chien

2015-01-01

Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students' learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction. PMID:26065018

Online Pedagogical Tutorial Tactics Optimization Using Genetic-Based Reinforcement Learning.

PubMed

Lin, Hsuan-Ta; Lee, Po-Ming; Hsiao, Tzu-Chien

2015-01-01

Tutorial tactics are policies for an Intelligent Tutoring System (ITS) to decide the next action when there are multiple actions available. Recent research has demonstrated that when the learning contents were controlled so as to be the same, different tutorial tactics would make difference in students' learning gains. However, the Reinforcement Learning (RL) techniques that were used in previous studies to induce tutorial tactics are insufficient when encountering large problems and hence were used in offline manners. Therefore, we introduced a Genetic-Based Reinforcement Learning (GBML) approach to induce tutorial tactics in an online-learning manner without basing on any preexisting dataset. The introduced method can learn a set of rules from the environment in a manner similar to RL. It includes a genetic-based optimizer for rule discovery task by generating new rules from the old ones. This increases the scalability of a RL learner for larger problems. The results support our hypothesis about the capability of the GBML method to induce tutorial tactics. This suggests that the GBML method should be favorable in developing real-world ITS applications in the domain of tutorial tactics induction.

Fusion of classifiers for REIS-based detection of suspicious breast lesions

NASA Astrophysics Data System (ADS)

Lederman, Dror; Wang, Xingwei; Zheng, Bin; Sumkin, Jules H.; Tublin, Mitchell; Gur, David

2011-03-01

After developing a multi-probe resonance-frequency electrical impedance spectroscopy (REIS) system aimed at detecting women with breast abnormalities that may indicate a developing breast cancer, we have been conducting a prospective clinical study to explore the feasibility of applying this REIS system to classify younger women (< 50 years old) into two groups of "higher-than-average risk" and "average risk" of having or developing breast cancer. The system comprises one central probe placed in contact with the nipple, and six additional probes uniformly distributed along an outside circle to be placed in contact with six points on the outer breast skin surface. In this preliminary study, we selected an initial set of 174 examinations on participants that have completed REIS examinations and have clinical status verification. Among these, 66 examinations were recommended for biopsy due to findings of a highly suspicious breast lesion ("positives"), and 108 were determined as negative during imaging based procedures ("negatives"). A set of REIS-based features, extracted using a mirror-matched approach, was computed and fed into five machine learning classifiers. A genetic algorithm was used to select an optimal subset of features for each of the five classifiers. Three fusion rules, namely sum rule, weighted sum rule and weighted median rule, were used to combine the results of the classifiers. Performance evaluation was performed using a leave-one-case-out cross-validation method. The results indicated that REIS may provide a new technology to identify younger women with higher than average risk of having or developing breast cancer. Furthermore, it was shown that fusion rule, such as a weighted median fusion rule and a weighted sum fusion rule may improve performance as compared with the highest performing single classifier.

Pharmacogenomic knowledge representation, reasoning and genome-based clinical decision support based on OWL 2 DL ontologies.

PubMed

Samwald, Matthias; Miñarro Giménez, Jose Antonio; Boyce, Richard D; Freimuth, Robert R; Adlassnig, Klaus-Peter; Dumontier, Michel

2015-02-22

Every year, hundreds of thousands of patients experience treatment failure or adverse drug reactions (ADRs), many of which could be prevented by pharmacogenomic testing. However, the primary knowledge needed for clinical pharmacogenomics is currently dispersed over disparate data structures and captured in unstructured or semi-structured formalizations. This is a source of potential ambiguity and complexity, making it difficult to create reliable information technology systems for enabling clinical pharmacogenomics. We developed Web Ontology Language (OWL) ontologies and automated reasoning methodologies to meet the following goals: 1) provide a simple and concise formalism for representing pharmacogenomic knowledge, 2) finde errors and insufficient definitions in pharmacogenomic knowledge bases, 3) automatically assign alleles and phenotypes to patients, 4) match patients to clinically appropriate pharmacogenomic guidelines and clinical decision support messages and 5) facilitate the detection of inconsistencies and overlaps between pharmacogenomic treatment guidelines from different sources. We evaluated different reasoning systems and test our approach with a large collection of publicly available genetic profiles. Our methodology proved to be a novel and useful choice for representing, analyzing and using pharmacogenomic data. The Genomic Clinical Decision Support (Genomic CDS) ontology represents 336 SNPs with 707 variants; 665 haplotypes related to 43 genes; 22 rules related to drug-response phenotypes; and 308 clinical decision support rules. OWL reasoning identified CDS rules with overlapping target populations but differing treatment recommendations. Only a modest number of clinical decision support rules were triggered for a collection of 943 public genetic profiles. We found significant performance differences across available OWL reasoners. The ontology-based framework we developed can be used to represent, organize and reason over the growing wealth of pharmacogenomic knowledge, as well as to identify errors, inconsistencies and insufficient definitions in source data sets or individual patient data. Our study highlights both advantages and potential practical issues with such an ontology-based approach.

Automatic evidence quality prediction to support evidence-based decision making.

PubMed

Sarker, Abeed; Mollá, Diego; Paris, Cécile

2015-06-01

Evidence-based medicine practice requires practitioners to obtain the best available medical evidence, and appraise the quality of the evidence when making clinical decisions. Primarily due to the plethora of electronically available data from the medical literature, the manual appraisal of the quality of evidence is a time-consuming process. We present a fully automatic approach for predicting the quality of medical evidence in order to aid practitioners at point-of-care. Our approach extracts relevant information from medical article abstracts and utilises data from a specialised corpus to apply supervised machine learning for the prediction of the quality grades. Following an in-depth analysis of the usefulness of features (e.g., publication types of articles), they are extracted from the text via rule-based approaches and from the meta-data associated with the articles, and then applied in the supervised classification model. We propose the use of a highly scalable and portable approach using a sequence of high precision classifiers, and introduce a simple evaluation metric called average error distance (AED) that simplifies the comparison of systems. We also perform elaborate human evaluations to compare the performance of our system against human judgments. We test and evaluate our approaches on a publicly available, specialised, annotated corpus containing 1132 evidence-based recommendations. Our rule-based approach performs exceptionally well at the automatic extraction of publication types of articles, with F-scores of up to 0.99 for high-quality publication types. For evidence quality classification, our approach obtains an accuracy of 63.84% and an AED of 0.271. The human evaluations show that the performance of our system, in terms of AED and accuracy, is comparable to the performance of humans on the same data. The experiments suggest that our structured text classification framework achieves evaluation results comparable to those of human performance. Our overall classification approach and evaluation technique are also highly portable and can be used for various evidence grading scales. Copyright © 2015 Elsevier B.V. All rights reserved.

Understanding How Domestic Violence Shelter Rules May Influence Survivor Empowerment.

PubMed

Gregory, Katie; Nnawulezi, Nkiru; Sullivan, Cris M

2017-10-01

Domestic violence shelters have historically gone beyond providing emergency residential space for survivors by assisting in obtaining future housing, employment, health care, child care, or legal services. Domestic violence shelters are expected to operate within an empowerment philosophy, with an understanding that survivors are self-determining, can identify their needs, and know what it takes to meet those needs. Recent research has indicated that, as many shelters have become more rigid in creating rules that survivors must follow to access and retain free temporary housing, the result has been survivors' feelings of disempowerment, the complete opposite of what was originally intended. This study builds on the small amount of research conducted regarding survivors' experiences of shelter rules by specifically examining how rules were perceived to affect empowerment. Seventy-three survivors from two domestic violence shelters were asked about their experiences around specific shelter rules relating to curfew, parenting, chores, time limits, food, alcohol, drugs, and medications. A transcendental phenomenological approach was used to analyze the qualitative data, seeking explanations of how survivors made meaning of the rules and how those rules influenced their empowerment. Among those survivors who found the rules problematic, three major themes emerged: (a) rules acted as barriers to carrying out their normal, day-to-day activities; (b) the shelter staff's flexibility with rules was based on contingencies; and (c) rules negatively affected their psychological well-being, and required them to engage in protective behaviors. Recommendations are made for the reexamination and restructuring of rules within domestic violence shelters.

Analysis of correlation between pediatric asthma exacerbation and exposure to pollutant mixtures with association rule mining.

PubMed

Toti, Giulia; Vilalta, Ricardo; Lindner, Peggy; Lefer, Barry; Macias, Charles; Price, Daniel

2016-11-01

Traditional studies on effects of outdoor pollution on asthma have been criticized for questionable statistical validity and inefficacy in exploring the effects of multiple air pollutants, alone and in combination. Association rule mining (ARM), a method easily interpretable and suitable for the analysis of the effects of multiple exposures, could be of use, but the traditional interest metrics of support and confidence need to be substituted with metrics that focus on risk variations caused by different exposures. We present an ARM-based methodology that produces rules associated with relevant odds ratios and limits the number of final rules even at very low support levels (0.5%), thanks to post-pruning criteria that limit rule redundancy and control for statistical significance. The methodology has been applied to a case-crossover study to explore the effects of multiple air pollutants on risk of asthma in pediatric subjects. We identified 27 rules with interesting odds ratio among more than 10,000 having the required support. The only rule including only one chemical is exposure to ozone on the previous day of the reported asthma attack (OR=1.14). 26 combinatory rules highlight the limitations of air quality policies based on single pollutant thresholds and suggest that exposure to mixtures of chemicals is more harmful, with odds ratio as high as 1.54 (associated with the combination day0 SO 2 , day0 NO, day0 NO 2 , day1 PM). The proposed method can be used to analyze risk variations caused by single and multiple exposures. The method is reliable and requires fewer assumptions on the data than parametric approaches. Rules including more than one pollutant highlight interactions that deserve further investigation, while helping to limit the search field. Copyright © 2016 Elsevier B.V. All rights reserved.

76 FR 43879 - Business Affiliate Marketing and Disposal of Consumer Information Rules

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-22

... transaction may evidence such a relationship. II. Rule Amendments A. Affiliate Marketing Rules Section 624 of... marketing rules. The Commission has chosen a slightly different approach than the Agencies in terms of its... affiliate marketing rules and disposal rules will harmonize privacy protections for individual customers...

Metadata behind the Interoperability of Wireless Sensor Networks

PubMed Central

Ballari, Daniela; Wachowicz, Monica; Callejo, Miguel Angel Manso

2009-01-01

Wireless Sensor Networks (WSNs) produce changes of status that are frequent, dynamic and unpredictable, and cannot be represented using a linear cause-effect approach. Consequently, a new approach is needed to handle these changes in order to support dynamic interoperability. Our approach is to introduce the notion of context as an explicit representation of changes of a WSN status inferred from metadata elements, which in turn, leads towards a decision-making process about how to maintain dynamic interoperability. This paper describes the developed context model to represent and reason over different WSN status based on four types of contexts, which have been identified as sensing, node, network and organisational contexts. The reasoning has been addressed by developing contextualising and bridges rules. As a result, we were able to demonstrate how contextualising rules have been used to reason on changes of WSN status as a first step towards maintaining dynamic interoperability. PMID:22412330

Metadata behind the Interoperability of Wireless Sensor Networks.

PubMed

Ballari, Daniela; Wachowicz, Monica; Callejo, Miguel Angel Manso

2009-01-01

Wireless Sensor Networks (WSNs) produce changes of status that are frequent, dynamic and unpredictable, and cannot be represented using a linear cause-effect approach. Consequently, a new approach is needed to handle these changes in order to support dynamic interoperability. Our approach is to introduce the notion of context as an explicit representation of changes of a WSN status inferred from metadata elements, which in turn, leads towards a decision-making process about how to maintain dynamic interoperability. This paper describes the developed context model to represent and reason over different WSN status based on four types of contexts, which have been identified as sensing, node, network and organisational contexts. The reasoning has been addressed by developing contextualising and bridges rules. As a result, we were able to demonstrate how contextualising rules have been used to reason on changes of WSN status as a first step towards maintaining dynamic interoperability.

Novel approach for calculating the charge carrier mobility and Hall factor for semiconductor materials

NASA Astrophysics Data System (ADS)

Colibaba, G. V.

2018-06-01

The additive Matthiessen's rule is the simplest and most widely used rule for the rapid experimental characterization and modeling of the charge carrier mobility. However, the error when using this rule can be higher than 40% and the contribution of the assumed additional scattering channels due to the difference between the experimental data and results calculated based on this rule can be misestimated by several times. In this study, a universal semi-additive equation is proposed for the total mobility and Hall factor, which is applicable to any quantity of scattering mechanisms, where it considers the energy dependence of the relaxation time and the error is 10-20 times lower compared with Matthiessen's rule. Calculations with accuracy of 99% are demonstrated for materials with polar-optical phonon, acoustic phonon via the piezoelectric potential, ionized, and neutral impurity scattering. The proposed method is extended to the deformation potential, dislocation, localized defect, alloy potential, and dipole scattering, for nondegenerate and partially degenerate materials.

78 FR 55339 - Regulatory Capital Rules: Regulatory Capital, Implementation of Basel III, Capital Adequacy...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-10

...The Federal Deposit Insurance Corporation (FDIC) is adopting an interim final rule that revises its risk-based and leverage capital requirements for FDIC-supervised institutions. This interim final rule is substantially identical to a joint final rule issued by the Office of the Comptroller of the Currency (OCC) and the Board of Governors of the Federal Reserve System (Federal Reserve) (together, with the FDIC, the agencies). The interim final rule consolidates three separate notices of proposed rulemaking that the agencies jointly published in the Federal Register on August 30, 2012, with selected changes. The interim final rule implements a revised definition of regulatory capital, a new common equity tier 1 minimum capital requirement, a higher minimum tier 1 capital requirement, and, for FDIC-supervised institutions subject to the advanced approaches risk-based capital rules, a supplementary leverage ratio that incorporates a broader set of exposures in the denominator. The interim final rule incorporates these new requirements into the FDIC's prompt corrective action (PCA) framework. In addition, the interim final rule establishes limits on FDIC-supervised institutions' capital distributions and certain discretionary bonus payments if the FDIC-supervised institution does not hold a specified amount of common equity tier 1 capital in addition to the amount necessary to meet its minimum risk-based capital requirements. The interim final rule amends the methodologies for determining risk-weighted assets for all FDIC-supervised institutions. The interim final rule also adopts changes to the FDIC's regulatory capital requirements that meet the requirements of section 171 and section 939A of the Dodd-Frank Wall Street Reform and Consumer Protection Act. The interim final rule also codifies the FDIC's regulatory capital rules, which have previously resided in various appendices to their respective regulations, into a harmonized integrated regulatory framework. In addition, the FDIC is amending the market risk capital rule (market risk rule) to apply to state savings associations. The FDIC is issuing these revisions to its capital regulations as an interim final rule. The FDIC invites comments on the interaction of this rule with other proposed leverage ratio requirements applicable to large, systemically important banking organizations. This interim final rule otherwise contains regulatory text that is identical to the common rule text adopted as a final rule by the Federal Reserve and the OCC. This interim final rule enables the FDIC to proceed on a unified, expedited basis with the other federal banking agencies pending consideration of other issues. Specifically, the FDIC intends to evaluate this interim final rule in the context of the proposed well- capitalized and buffer levels of the supplementary leverage ratio applicable to large, systemically important banking organizations, as described in a separate Notice of Proposed Rulemaking (NPR) published in the Federal Register August 20, 2013. The FDIC is seeking commenters' views on the interaction of this interim final rule with the proposed rule regarding the supplementary leverage ratio for large, systemically important banking organizations.

Incorporation of negative rules and evolution of a fuzzy controller for yeast fermentation process.

PubMed

Birle, Stephan; Hussein, Mohamed Ahmed; Becker, Thomas

2016-08-01

The control of bioprocesses can be very challenging due to the fact that these kinds of processes are highly affected by various sources of uncertainty like the intrinsic behavior of the used microorganisms. Due to the reason that these kinds of process uncertainties are not directly measureable in most cases, the overall control is either done manually because of the experience of the operator or intelligent expert systems are applied, e.g., on the basis of fuzzy logic theory. In the latter case, however, the control concept is mainly represented by using merely positive rules, e.g., "If A then do B". As this is not straightforward with respect to the semantics of the human decision-making process that also includes negative experience in form of constraints or prohibitions, the incorporation of negative rules for process control based on fuzzy logic is emphasized. In this work, an approach of fuzzy logic control of the yeast propagation process based on a combination of positive and negative rules is presented. The process is guided along a reference trajectory for yeast cell concentration by alternating the process temperature. The incorporation of negative rules leads to a much more stable and accurate control of the process as the root mean squared error of reference trajectory and system response could be reduced by an average of 62.8 % compared to the controller using only positive rules.

An approach to combining heuristic and qualitative reasoning in an expert system

NASA Technical Reports Server (NTRS)

Jiang, Wei-Si; Han, Chia Yung; Tsai, Lian Cheng; Wee, William G.

1988-01-01

An approach to combining the heuristic reasoning from shallow knowledge and the qualitative reasoning from deep knowledge is described. The shallow knowledge is represented in production rules and under the direct control of the inference engine. The deep knowledge is represented in frames, which may be put in a relational DataBase Management System. This approach takes advantage of both reasoning schemes and results in improved efficiency as well as expanded problem solving ability.

Distributional benefit analysis of a national air quality rule.

PubMed

Post, Ellen S; Belova, Anna; Huang, Jin

2011-06-01

Under Executive Order 12898, the U.S. Environmental Protection Agency (EPA) must perform environmental justice (EJ) reviews of its rules and regulations. EJ analyses address the hypothesis that environmental disamenities are experienced disproportionately by poor and/or minority subgroups. Such analyses typically use communities as the unit of analysis. While community-based approaches make sense when considering where polluting sources locate, they are less appropriate for national air quality rules affecting many sources and pollutants that can travel thousands of miles. We compare exposures and health risks of EJ-identified individuals rather than communities to analyze EPA's Heavy Duty Diesel (HDD) rule as an example national air quality rule. Air pollutant exposures are estimated within grid cells by air quality models; all individuals in the same grid cell are assigned the same exposure. Using an inequality index, we find that inequality within racial/ethnic subgroups far outweighs inequality between them. We find, moreover, that the HDD rule leaves between-subgroup inequality essentially unchanged. Changes in health risks depend also on subgroups' baseline incidence rates, which differ across subgroups. Thus, health risk reductions may not follow the same pattern as reductions in exposure. These results are likely representative of other national air quality rules as well.

Harmonic wavelet packet transform for on-line system health diagnosis

NASA Astrophysics Data System (ADS)

Yan, Ruqiang; Gao, Robert X.

2004-07-01

This paper presents a new approach to on-line health diagnosis of mechanical systems, based on the wavelet packet transform. Specifically, signals acquired from vibration sensors are decomposed into sub-bands by means of the discrete harmonic wavelet packet transform (DHWPT). Based on the Fisher linear discriminant criterion, features in the selected sub-bands are then used as inputs to three classifiers (Nearest Neighbor rule-based and two Neural Network-based), for system health condition assessment. Experimental results have confirmed that, comparing to the conventional approach where statistical parameters from raw signals are used, the presented approach enabled higher signal-to-noise ratio for more effective and intelligent use of the sensory information, thus leading to more accurate system health diagnosis.

Phrase Frequency, Proficiency and Grammaticality Interact in Non-Native Processing: Implications for Theories of SLA

ERIC Educational Resources Information Center

Shantz, Kailen

2017-01-01

This study reports on a self-paced reading experiment in which native and non-native speakers of English read sentences designed to evaluate the predictions of usage-based and rule-based approaches to second language acquisition (SLA). Critical stimuli were four-word sequences embedded into sentences in which phrase frequency and grammaticality…

A Wittgensteinian Approach to Discerning the Meaning of Works of Art in the Practice of Critical and Contextual Studies in Secondary Art Education

ERIC Educational Resources Information Center

Cunliffe, Leslie

2006-01-01

This article advocates an approach for teaching critical and contextual studies in secondary art education based on Wittgenstein's philosophy of language in relationship to meaning as use, custom, rule following, and physiognomy. The references to meaning form four metaphorical points on a compass for directing the will for making judgments about…

Automated Assume-Guarantee Reasoning by Abstraction Refinement

NASA Technical Reports Server (NTRS)

Pasareanu, Corina S.; Giannakopoulous, Dimitra; Glannakopoulou, Dimitra

2008-01-01

Current automated approaches for compositional model checking in the assume-guarantee style are based on learning of assumptions as deterministic automata. We propose an alternative approach based on abstraction refinement. Our new method computes the assumptions for the assume-guarantee rules as conservative and not necessarily deterministic abstractions of some of the components, and refines those abstractions using counter-examples obtained from model checking them together with the other components. Our approach also exploits the alphabets of the interfaces between components and performs iterative refinement of those alphabets as well as of the abstractions. We show experimentally that our preliminary implementation of the proposed alternative achieves similar or better performance than a previous learning-based implementation.

Visualizing and understanding l'hopital's rule

NASA Astrophysics Data System (ADS)

Gordon, Sheldon P.

2017-11-01

This article uses dynamic software in Excel to demonstrate several ways in which graphical and numerical approaches can be introduced both to enhance student understanding of l'Hopital's Rule and to explain why the Rule actually works to give the 'right' answers. One of the approaches used is to visualize what is happening by examining the limits with both l'Hopital's Rule and the associated Taylor approximation to the function. The dynamic software allows students to experiment with the ideas.

Multi-criteria objective based climate change impact assessment for multi-purpose multi-reservoir systems

NASA Astrophysics Data System (ADS)

Müller, Ruben; Schütze, Niels

2014-05-01

Water resources systems with reservoirs are expected to be sensitive to climate change. Assessment studies that analyze the impact of climate change on the performance of reservoirs can be divided in two groups: (1) Studies that simulate the operation under projected inflows with the current set of operational rules. Due to non adapted operational rules the future performance of these reservoirs can be underestimated and the impact overestimated. (2) Studies that optimize the operational rules for best adaption of the system to the projected conditions before the assessment of the impact. The latter allows for estimating more realistically future performance and adaption strategies based on new operation rules are available if required. Multi-purpose reservoirs serve various, often conflicting functions. If all functions cannot be served simultaneously at a maximum level, an effective compromise between multiple objectives of the reservoir operation has to be provided. Yet under climate change the historically preferenced compromise may no longer be the most suitable compromise in the future. Therefore a multi-objective based climate change impact assessment approach for multi-purpose multi-reservoir systems is proposed in the study. Projected inflows are provided in a first step using a physically based rainfall-runoff model. In a second step, a time series model is applied to generate long-term inflow time series. Finally, the long-term inflow series are used as driving variables for a simulation-based multi-objective optimization of the reservoir system in order to derive optimal operation rules. As a result, the adapted Pareto-optimal set of diverse best compromise solutions can be presented to the decision maker in order to assist him in assessing climate change adaption measures with respect to the future performance of the multi-purpose reservoir system. The approach is tested on a multi-purpose multi-reservoir system in a mountainous catchment in Germany. A climate change assessment is performed for climate change scenarios based on the SRES emission scenarios A1B, B1 and A2 for a set of statistically downscaled meteorological data. The future performance of the multi-purpose multi-reservoir system is quantified and possible intensifications of trade-offs between management goals or reservoir utilizations are shown.

EXPOSURES AND INTERNAL DOSES OF ...

EPA Pesticide Factsheets

The National Center for Environmental Assessment (NCEA) has released a final report that presents and applies a method to estimate distributions of internal concentrations of trihalomethanes (THMs) in humans resulting from a residential drinking water exposure. The report presents simulations of oral, dermal and inhalation exposures and demonstrates the feasibility of linking the US EPA’s information Collection Rule database with other databases on external exposure factors and physiologically based pharmacokinetic modeling to refine population-based estimates of exposure. Review Draft - by 2010, develop scientifically sound data and approaches to assess and manage risks to human health posed by exposure to specific regulated waterborne pathogens and chemicals, including those addressed by the Arsenic, M/DBP and Six-Year Review Rules.

49 CFR 535.9 - Enforcement approach.

Code of Federal Regulations, 2012 CFR

2012-10-01

... balance is based upon the engines or vehicles performance above or below the applicable regulatory... consumption data for vehicles or engines covered under this rule, noncompliance will be assumed until... NHTSA Enforcement determines that a manufacturer's averaging set of vehicles or engines fails to comply...

Rational identification of aggregation hotspots based on secondary structure and amino acid hydrophobicity.

PubMed

Matsui, Daisuke; Nakano, Shogo; Dadashipour, Mohammad; Asano, Yasuhisa

2017-08-25

Insolubility of proteins expressed in the Escherichia coli expression system hinders the progress of both basic and applied research. Insoluble proteins contain residues that decrease their solubility (aggregation hotspots). Mutating these hotspots to optimal amino acids is expected to improve protein solubility. To date, however, the identification of these hotspots has proven difficult. In this study, using a combination of approaches involving directed evolution and primary sequence analysis, we found two rules to help inductively identify hotspots: the α-helix rule, which focuses on the hydrophobicity of amino acids in the α-helix structure, and the hydropathy contradiction rule, which focuses on the difference in hydrophobicity relative to the corresponding amino acid in the consensus protein. By properly applying these two rules, we succeeded in improving the probability that expressed proteins would be soluble. Our methods should facilitate research on various insoluble proteins that were previously difficult to study due to their low solubility.

Human systems immunology: hypothesis-based modeling and unbiased data-driven approaches.

PubMed

Arazi, Arnon; Pendergraft, William F; Ribeiro, Ruy M; Perelson, Alan S; Hacohen, Nir

2013-10-31

Systems immunology is an emerging paradigm that aims at a more systematic and quantitative understanding of the immune system. Two major approaches have been utilized to date in this field: unbiased data-driven modeling to comprehensively identify molecular and cellular components of a system and their interactions; and hypothesis-based quantitative modeling to understand the operating principles of a system by extracting a minimal set of variables and rules underlying them. In this review, we describe applications of the two approaches to the study of viral infections and autoimmune diseases in humans, and discuss possible ways by which these two approaches can synergize when applied to human immunology. Copyright © 2012 Elsevier Ltd. All rights reserved.

Novice Rules for Projectile Motion.

ERIC Educational Resources Information Center

Maloney, David P.

1988-01-01

Investigates several aspects of undergraduate students' rules for projectile motion including general patterns; rules for questions about time, distance, solids and liquids; and changes in rules when asked to ignore air resistance. Reports approach differences by sex and high school physics experience, and that novice rules are situation…

Kinetic Monte Carlo Method for Rule-based Modeling of Biochemical Networks

PubMed Central

Yang, Jin; Monine, Michael I.; Faeder, James R.; Hlavacek, William S.

2009-01-01

We present a kinetic Monte Carlo method for simulating chemical transformations specified by reaction rules, which can be viewed as generators of chemical reactions, or equivalently, definitions of reaction classes. A rule identifies the molecular components involved in a transformation, how these components change, conditions that affect whether a transformation occurs, and a rate law. The computational cost of the method, unlike conventional simulation approaches, is independent of the number of possible reactions, which need not be specified in advance or explicitly generated in a simulation. To demonstrate the method, we apply it to study the kinetics of multivalent ligand-receptor interactions. We expect the method will be useful for studying cellular signaling systems and other physical systems involving aggregation phenomena. PMID:18851068

Electron-ion coupling in semiconductors beyond Fermi's Golden Rule [On the electron-ion coupling in semiconductors beyond Fermi's Golden Rule

DOE Office of Scientific and Technical Information (OSTI.GOV)

Medvedev, Nikita; Li, Zheng; Tkachenko, Victor

2017-01-31

In the present study, a theoretical study of electron-phonon (electron-ion) coupling rates in semiconductors driven out of equilibrium is performed. Transient change of optical coefficients reflects the band gap shrinkage in covalently bonded materials, and thus, the heating of atomic lattice. Utilizing this dependence, we test various models of electron-ion coupling. The simulation technique is based on tight-binding molecular dynamics. Our simulations with the dedicated hybrid approach (XTANT) indicate that the widely used Fermi's golden rule can break down describing material excitation on femtosecond time scales. In contrast, dynamical coupling proposed in this work yields a reasonably good agreement ofmore » simulation results with available experimental data.« less

Visualizing and Understanding l'Hopital's Rule

ERIC Educational Resources Information Center

Gordon, Sheldon P.

2017-01-01

This article uses dynamic software in Excel to demonstrate several ways in which graphical and numerical approaches can be introduced both to enhance student understanding of l'Hopital's Rule and to explain why the Rule actually works to give the "right" answers. One of the approaches used is to visualize what is happening by examining…

Aristotle and Autism: Reconsidering a Radical Shift to Virtue Ethics in Engineering.

PubMed

Furey, Heidi

2017-04-01

Virtue-based approaches to engineering ethics have recently received considerable attention within the field of engineering education. Proponents of virtue ethics in engineering argue that the approach is practically and pedagogically superior to traditional approaches to engineering ethics, including the study of professional codes of ethics and normative theories of behavior. This paper argues that a virtue-based approach, as interpreted in the current literature, is neither practically or pedagogically effective for a significant subpopulation within engineering: engineers with high functioning autism spectrum disorder (ASD). Because the main argument for adopting a character-based approach is that it could be more successfully applied to engineering than traditional rule-based or algorithmic ethical approaches, this oversight is problematic for the proponents of the virtue-based view. Furthermore, without addressing these concerns, the wide adoption of a virtue-based approach to engineering ethics has the potential to isolate individuals with ASD and to devalue their contributions to moral practice. In the end, this paper gestures towards a way of incorporating important insights from virtue ethics in engineering that would be more inclusive of those with ASD.

Rule based design of conceptual models for formative evaluation

NASA Technical Reports Server (NTRS)

Moore, Loretta A.; Chang, Kai; Hale, Joseph P.; Bester, Terri; Rix, Thomas; Wang, Yaowen

1994-01-01

A Human-Computer Interface (HCI) Prototyping Environment with embedded evaluation capability has been investigated. This environment will be valuable in developing and refining HCI standards and evaluating program/project interface development, especially Space Station Freedom on-board displays for payload operations. This environment, which allows for rapid prototyping and evaluation of graphical interfaces, includes the following four components: (1) a HCI development tool; (2) a low fidelity simulator development tool; (3) a dynamic, interactive interface between the HCI and the simulator; and (4) an embedded evaluator that evaluates the adequacy of a HCI based on a user's performance. The embedded evaluation tool collects data while the user is interacting with the system and evaluates the adequacy of an interface based on a user's performance. This paper describes the design of conceptual models for the embedded evaluation system using a rule-based approach.

Rule based design of conceptual models for formative evaluation

NASA Technical Reports Server (NTRS)

Moore, Loretta A.; Chang, Kai; Hale, Joseph P.; Bester, Terri; Rix, Thomas; Wang, Yaowen

1994-01-01

A Human-Computer Interface (HCI) Prototyping Environment with embedded evaluation capability has been investigated. This environment will be valuable in developing and refining HCI standards and evaluating program/project interface development, especially Space Station Freedom on-board displays for payload operations. This environment, which allows for rapid prototyping and evaluation of graphical interfaces, includes the following four components: (1) a HCI development tool, (2) a low fidelity simulator development tool, (3) a dynamic, interactive interface between the HCI and the simulator, and (4) an embedded evaluator that evaluates the adequacy of a HCI based on a user's performance. The embedded evaluation tool collects data while the user is interacting with the system and evaluates the adequacy of an interface based on a user's performance. This paper describes the design of conceptual models for the embedded evaluation system using a rule-based approach.

Elements of decisional dynamics: An agent-based approach applied to artificial financial market

NASA Astrophysics Data System (ADS)

Lucas, Iris; Cotsaftis, Michel; Bertelle, Cyrille

2018-02-01

This paper introduces an original mathematical description for describing agents' decision-making process in the case of problems affected by both individual and collective behaviors in systems characterized by nonlinear, path dependent, and self-organizing interactions. An application to artificial financial markets is proposed by designing a multi-agent system based on the proposed formalization. In this application, agents' decision-making process is based on fuzzy logic rules and the price dynamics is purely deterministic according to the basic matching rules of a central order book. Finally, while putting most parameters under evolutionary control, the computational agent-based system is able to replicate several stylized facts of financial time series (distributions of stock returns showing a heavy tail with positive excess kurtosis, absence of autocorrelations in stock returns, and volatility clustering phenomenon).

Elements of decisional dynamics: An agent-based approach applied to artificial financial market.

PubMed

Lucas, Iris; Cotsaftis, Michel; Bertelle, Cyrille

2018-02-01

This paper introduces an original mathematical description for describing agents' decision-making process in the case of problems affected by both individual and collective behaviors in systems characterized by nonlinear, path dependent, and self-organizing interactions. An application to artificial financial markets is proposed by designing a multi-agent system based on the proposed formalization. In this application, agents' decision-making process is based on fuzzy logic rules and the price dynamics is purely deterministic according to the basic matching rules of a central order book. Finally, while putting most parameters under evolutionary control, the computational agent-based system is able to replicate several stylized facts of financial time series (distributions of stock returns showing a heavy tail with positive excess kurtosis, absence of autocorrelations in stock returns, and volatility clustering phenomenon).

Classification of hadith into positive suggestion, negative suggestion, and information

NASA Astrophysics Data System (ADS)

Faraby, Said Al; Riviera Rachmawati Jasin, Eliza; Kusumaningrum, Andina; Adiwijaya

2018-03-01

As one of the Muslim life guidelines, based on the meaning of its sentence(s), a hadith can be viewed as a suggestion for doing something, or a suggestion for not doing something, or just information without any suggestion. In this paper, we tried to classify the Bahasa translation of hadith into the three categories using machine learning approach. We tried stemming and stopword removal in preprocessing, and TF-IDF of unigram, bigram, and trigram as the extracted features. As the classifier, we compared between SVM and Neural Network. Since the categories are new, so in order to compare the results of the previous pipelines, we created a baseline classifier using simple rule-based string matching technique. The rule-based algorithm conditions on the occurrence of words such as “janganlah, sholatlah, and so on” to determine the category. The baseline method achieved F1-Score of 0.69, while the best F1-Score from the machine learning approach was 0.88, and it was produced by SVM model with the linear kernel.

A novel approach for connecting temporal-ontologies with blood flow simulations.

PubMed

Weichert, F; Mertens, C; Walczak, L; Kern-Isberner, G; Wagner, M

2013-06-01

In this paper an approach for developing a temporal domain ontology for biomedical simulations is introduced. The ideas are presented in the context of simulations of blood flow in aneurysms using the Lattice Boltzmann Method. The advantages in using ontologies are manyfold: On the one hand, ontologies having been proven to be able to provide medical special knowledge e.g., key parameters for simulations. On the other hand, based on a set of rules and the usage of a reasoner, a system for checking the plausibility as well as tracking the outcome of medical simulations can be constructed. Likewise, results of simulations including data derived from them can be stored and communicated in a way that can be understood by computers. Later on, this set of results can be analyzed. At the same time, the ontologies provide a way to exchange knowledge between researchers. Lastly, this approach can be seen as a black-box abstraction of the internals of the simulation for the biomedical researcher as well. This approach is able to provide the complete parameter sets for simulations, part of the corresponding results and part of their analysis as well as e.g., geometry and boundary conditions. These inputs can be transferred to different simulation methods for comparison. Variations on the provided parameters can be automatically used to drive these simulations. Using a rule base, unphysical inputs or outputs of the simulation can be detected and communicated to the physician in a suitable and familiar way. An example for an instantiation of the blood flow simulation ontology and exemplary rules for plausibility checking are given. Copyright © 2013 Elsevier Inc. All rights reserved.

Processing Diabetes Mellitus Composite Events in MAGPIE.

PubMed

Brugués, Albert; Bromuri, Stefano; Barry, Michael; Del Toro, Óscar Jiménez; Mazurkiewicz, Maciej R; Kardas, Przemyslaw; Pegueroles, Josep; Schumacher, Michael

2016-02-01

The focus of this research is in the definition of programmable expert Personal Health Systems (PHS) to monitor patients affected by chronic diseases using agent oriented programming and mobile computing to represent the interactions happening amongst the components of the system. The paper also discusses issues of knowledge representation within the medical domain when dealing with temporal patterns concerning the physiological values of the patient. In the presented agent based PHS the doctors can personalize for each patient monitoring rules that can be defined in a graphical way. Furthermore, to achieve better scalability, the computations for monitoring the patients are distributed among their devices rather than being performed in a centralized server. The system is evaluated using data of 21 diabetic patients to detect temporal patterns according to a set of monitoring rules defined. The system's scalability is evaluated by comparing it with a centralized approach. The evaluation concerning the detection of temporal patterns highlights the system's ability to monitor chronic patients affected by diabetes. Regarding the scalability, the results show the fact that an approach exploiting the use of mobile computing is more scalable than a centralized approach. Therefore, more likely to satisfy the needs of next generation PHSs. PHSs are becoming an adopted technology to deal with the surge of patients affected by chronic illnesses. This paper discusses architectural choices to make an agent based PHS more scalable by using a distributed mobile computing approach. It also discusses how to model the medical knowledge in the PHS in such a way that it is modifiable at run time. The evaluation highlights the necessity of distributing the reasoning to the mobile part of the system and that modifiable rules are able to deal with the change in lifestyle of the patients affected by chronic illnesses.

Context-Based Tourism Information Filtering with a Semantic Rule Engine

PubMed Central

Lamsfus, Carlos; Martin, David; Alzua-Sorzabal, Aurkene; López-de-Ipiña, Diego; Torres-Manzanera, Emilio

2012-01-01

This paper presents the CONCERT framework, a push/filter information consumption paradigm, based on a rule-based semantic contextual information system for tourism. CONCERT suggests a specific insight of the notion of context from a human mobility perspective. It focuses on the particular characteristics and requirements of travellers and addresses the drawbacks found in other approaches. Additionally, CONCERT suggests the use of digital broadcasting as push communication technology, whereby tourism information is disseminated to mobile devices. This information is then automatically filtered by a network of ontologies and offered to tourists on the screen. The results obtained in the experiments carried out show evidence that the information disseminated through digital broadcasting can be manipulated by the network of ontologies, providing contextualized information that produces user satisfaction. PMID:22778584

Context-based tourism information filtering with a semantic rule engine.

PubMed

Lamsfus, Carlos; Martin, David; Alzua-Sorzabal, Aurkene; López-de-Ipiña, Diego; Torres-Manzanera, Emilio

2012-01-01

This paper presents the CONCERT framework, a push/filter information consumption paradigm, based on a rule-based semantic contextual information system for tourism. CONCERT suggests a specific insight of the notion of context from a human mobility perspective. It focuses on the particular characteristics and requirements of travellers and addresses the drawbacks found in other approaches. Additionally, CONCERT suggests the use of digital broadcasting as push communication technology, whereby tourism information is disseminated to mobile devices. This information is then automatically filtered by a network of ontologies and offered to tourists on the screen. The results obtained in the experiments carried out show evidence that the information disseminated through digital broadcasting can be manipulated by the network of ontologies, providing contextualized information that produces user satisfaction.

A genetic algorithms approach for altering the membership functions in fuzzy logic controllers

NASA Technical Reports Server (NTRS)

Shehadeh, Hana; Lea, Robert N.

1992-01-01

Through previous work, a fuzzy control system was developed to perform translational and rotational control of a space vehicle. This problem was then re-examined to determine the effectiveness of genetic algorithms on fine tuning the controller. This paper explains the problems associated with the design of this fuzzy controller and offers a technique for tuning fuzzy logic controllers. A fuzzy logic controller is a rule-based system that uses fuzzy linguistic variables to model human rule-of-thumb approaches to control actions within a given system. This 'fuzzy expert system' features rules that direct the decision process and membership functions that convert the linguistic variables into the precise numeric values used for system control. Defining the fuzzy membership functions is the most time consuming aspect of the controller design. One single change in the membership functions could significantly alter the performance of the controller. This membership function definition can be accomplished by using a trial and error technique to alter the membership functions creating a highly tuned controller. This approach can be time consuming and requires a great deal of knowledge from human experts. In order to shorten development time, an iterative procedure for altering the membership functions to create a tuned set that used a minimal amount of fuel for velocity vector approach and station-keep maneuvers was developed. Genetic algorithms, search techniques used for optimization, were utilized to solve this problem.

Clinical Assistant Diagnosis for Electronic Medical Record Based on Convolutional Neural Network.

PubMed

Yang, Zhongliang; Huang, Yongfeng; Jiang, Yiran; Sun, Yuxi; Zhang, Yu-Jin; Luo, Pengcheng

2018-04-20

Automatically extracting useful information from electronic medical records along with conducting disease diagnoses is a promising task for both clinical decision support(CDS) and neural language processing(NLP). Most of the existing systems are based on artificially constructed knowledge bases, and then auxiliary diagnosis is done by rule matching. In this study, we present a clinical intelligent decision approach based on Convolutional Neural Networks(CNN), which can automatically extract high-level semantic information of electronic medical records and then perform automatic diagnosis without artificial construction of rules or knowledge bases. We use collected 18,590 copies of the real-world clinical electronic medical records to train and test the proposed model. Experimental results show that the proposed model can achieve 98.67% accuracy and 96.02% recall, which strongly supports that using convolutional neural network to automatically learn high-level semantic features of electronic medical records and then conduct assist diagnosis is feasible and effective.

Educating for Safety.

ERIC Educational Resources Information Center

Rothe, J. Peter

1991-01-01

To enhance the chance for success in educating young drivers, there should be a balance between the content, structure, and goals of traffic safety programs and the normative rules governing young people's lives. Presents recommendations for safety education based on the notion of complementarity and using a multiperspective approach. (AF)

Modified Dempster-Shafer approach using an expected utility interval decision rule

NASA Astrophysics Data System (ADS)

Cheaito, Ali; Lecours, Michael; Bosse, Eloi

1999-03-01

The combination operation of the conventional Dempster- Shafer algorithm has a tendency to increase exponentially the number of propositions involved in bodies of evidence by creating new ones. The aim of this paper is to explore a 'modified Dempster-Shafer' approach of fusing identity declarations emanating form different sources which include a number of radars, IFF and ESM systems in order to limit the explosion of the number of propositions. We use a non-ad hoc decision rule based on the expected utility interval to select the most probable object in a comprehensive Platform Data Base containing all the possible identity values that a potential target may take. We study the effect of the redistribution of the confidence levels of the eliminated propositions which otherwise overload the real-time data fusion system; these eliminated confidence levels can in particular be assigned to ignorance, or uniformly added to the remaining propositions and to ignorance. A scenario has been selected to demonstrate the performance of our modified Dempster-Shafer method of evidential reasoning.

Using fuzzy logic to integrate neural networks and knowledge-based systems

NASA Technical Reports Server (NTRS)

Yen, John

1991-01-01

Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.

A knowledge-based approach to improving optimization techniques in system planning

NASA Technical Reports Server (NTRS)

Momoh, J. A.; Zhang, Z. Z.

1990-01-01

A knowledge-based (KB) approach to improve mathematical programming techniques used in the system planning environment is presented. The KB system assists in selecting appropriate optimization algorithms, objective functions, constraints and parameters. The scheme is implemented by integrating symbolic computation of rules derived from operator and planner's experience and is used for generalized optimization packages. The KB optimization software package is capable of improving the overall planning process which includes correction of given violations. The method was demonstrated on a large scale power system discussed in the paper.

PID tuning rules for SOPDT systems: review and some new results.

PubMed

Panda, Rames C; Yu, Cheng-Ching; Huang, Hsiao-Ping

2004-04-01

PID controllers are widely used in industries and so many tuning rules have been proposed over the past 50 years that users are often lost in the jungle of tuning formulas. Moreover, unlike PI control, different control laws and structures of implementation further complicate the use of the PID controller. In this work, five different tuning rules are taken for study to control second-order plus dead time systems with wide ranges of damping coefficients and dead time to time constant ratios (D/tau). Four of them are based on IMC design with different types of approximations on dead time and the other on desired closed-loop specifications (i.e., specified forward transfer function). The method of handling dead time in the IMC type of design is important especially for systems with large D/tau ratios. A systematic approach was followed to evaluate the performance of controllers. The regions of applicability of suitable tuning rules are highlighted and recommendations are also given. It turns out that IMC designed with the Maclaurin series expansion type PID is a better choice for both set point and load changes for systems with D/tau greater than 1. For systems with D/tau less than 1, the desired closed-loop specification approach is favored.

NASIS data base management system - IBM 360/370 OS MVT implementation. 1: Installation standards

NASA Technical Reports Server (NTRS)

1973-01-01

The installation standards for the NASA Aerospace Safety Information System (NASIS) data base management system are presented. The standard approach to preparing systems documentation and the program design and coding rules and conventions are outlined. Included are instructions for preparing all major specifications and suggestions for improving the quality and efficiency of the programming task.

Complex dynamics and empirical evidence (Invited Paper)

NASA Astrophysics Data System (ADS)

Delli Gatti, Domenico; Gaffeo, Edoardo; Giulioni, Gianfranco; Gallegati, Mauro; Kirman, Alan; Palestrini, Antonio; Russo, Alberto

2005-05-01

Standard macroeconomics, based on a reductionist approach centered on the representative agent, is badly equipped to explain the empirical evidence where heterogeneity and industrial dynamics are the rule. In this paper we show that a simple agent-based model of heterogeneous financially fragile agents is able to replicate a large number of scaling type stylized facts with a remarkable degree of statistical precision.

Classifying the Indication for Colonoscopy Procedures: A Comparison of NLP Approaches in a Diverse National Healthcare System.

PubMed

Patterson, Olga V; Forbush, Tyler B; Saini, Sameer D; Moser, Stephanie E; DuVall, Scott L

2015-01-01

In order to measure the level of utilization of colonoscopy procedures, identifying the primary indication for the procedure is required. Colonoscopies may be utilized not only for screening, but also for diagnostic or therapeutic purposes. To determine whether a colonoscopy was performed for screening, we created a natural language processing system to identify colonoscopy reports in the electronic medical record system and extract indications for the procedure. A rule-based model and three machine-learning models were created using 2,000 manually annotated clinical notes of patients cared for in the Department of Veterans Affairs. Performance of the models was measured and compared. Analysis of the models on a test set of 1,000 documents indicates that the rule-based system performance stays fairly constant as evaluated on training and testing sets. However, the machine learning model without feature selection showed significant decrease in performance. Therefore, rule-based classification system appears to be more robust than a machine-learning system in cases when no feature selection is performed.

Overcoming rule-based rigidity and connectionist limitations through massively-parallel case-based reasoning

NASA Technical Reports Server (NTRS)

Barnden, John; Srinivas, Kankanahalli

1990-01-01

Symbol manipulation as used in traditional Artificial Intelligence has been criticized by neural net researchers for being excessively inflexible and sequential. On the other hand, the application of neural net techniques to the types of high-level cognitive processing studied in traditional artificial intelligence presents major problems as well. A promising way out of this impasse is to build neural net models that accomplish massively parallel case-based reasoning. Case-based reasoning, which has received much attention recently, is essentially the same as analogy-based reasoning, and avoids many of the problems leveled at traditional artificial intelligence. Further problems are avoided by doing many strands of case-based reasoning in parallel, and by implementing the whole system as a neural net. In addition, such a system provides an approach to some aspects of the problems of noise, uncertainty and novelty in reasoning systems. The current neural net system (Conposit), which performs standard rule-based reasoning, is being modified into a massively parallel case-based reasoning version.

A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

NASA Astrophysics Data System (ADS)

Zhang, Ce; Pan, Xin; Li, Huapeng; Gardiner, Andy; Sargent, Isabel; Hare, Jonathon; Atkinson, Peter M.

2018-06-01

The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.

40 CFR 1502.22 - Incomplete or unavailable information.

Code of Federal Regulations, 2013 CFR

2013-07-01

... approaches or research methods generally accepted in the scientific community. For the purposes of this... credible scientific evidence which is relevant to evaluating the reasonably foreseeable significant adverse... scientific evidence, is not based on pure conjecture, and is within the rule of reason. (c) The amended...

40 CFR 1502.22 - Incomplete or unavailable information.

Code of Federal Regulations, 2014 CFR

2014-07-01

... approaches or research methods generally accepted in the scientific community. For the purposes of this... credible scientific evidence which is relevant to evaluating the reasonably foreseeable significant adverse... scientific evidence, is not based on pure conjecture, and is within the rule of reason. (c) The amended...

40 CFR 1502.22 - Incomplete or unavailable information.

Code of Federal Regulations, 2012 CFR

2012-07-01

... approaches or research methods generally accepted in the scientific community. For the purposes of this... credible scientific evidence which is relevant to evaluating the reasonably foreseeable significant adverse... scientific evidence, is not based on pure conjecture, and is within the rule of reason. (c) The amended...

On Some Assumptions of the Null Hypothesis Statistical Testing

ERIC Educational Resources Information Center

Patriota, Alexandre Galvão

2017-01-01

Bayesian and classical statistical approaches are based on different types of logical principles. In order to avoid mistaken inferences and misguided interpretations, the practitioner must respect the inference rules embedded into each statistical method. Ignoring these principles leads to the paradoxical conclusions that the hypothesis…

40 CFR 1502.22 - Incomplete or unavailable information.

Code of Federal Regulations, 2010 CFR

2010-07-01

... approaches or research methods generally accepted in the scientific community. For the purposes of this... credible scientific evidence which is relevant to evaluating the reasonably foreseeable significant adverse... scientific evidence, is not based on pure conjecture, and is within the rule of reason. (c) The amended...

40 CFR 1502.22 - Incomplete or unavailable information.

Code of Federal Regulations, 2011 CFR

2011-07-01

... approaches or research methods generally accepted in the scientific community. For the purposes of this... credible scientific evidence which is relevant to evaluating the reasonably foreseeable significant adverse... scientific evidence, is not based on pure conjecture, and is within the rule of reason. (c) The amended...

Knowledge-Based Motion Control of AN Intelligent Mobile Autonomous System

NASA Astrophysics Data System (ADS)

Isik, Can

An Intelligent Mobile Autonomous System (IMAS), which is equipped with vision and low level sensors to cope with unknown obstacles, is modeled as a hierarchy of path planning and motion control. This dissertation concentrates on the lower level of this hierarchy (Pilot) with a knowledge-based controller. The basis of a theory of knowledge-based controllers is established, using the example of the Pilot level motion control of IMAS. In this context, the knowledge-based controller with a linguistic world concept is shown to be adequate for the minimum time control of an autonomous mobile robot motion. The Pilot level motion control of IMAS is approached in the framework of production systems. The three major components of the knowledge-based control that are included here are the hierarchies of the database, the rule base and the rule evaluator. The database, which is the representation of the state of the world, is organized as a semantic network, using a concept of minimal admissible vocabulary. The hierarchy of rule base is derived from the analytical formulation of minimum-time control of IMAS motion. The procedure introduced for rule derivation, which is called analytical model verbalization, utilizes the concept of causalities to describe the system behavior. A realistic analytical system model is developed and the minimum-time motion control in an obstacle strewn environment is decomposed to a hierarchy of motion planning and control. The conditions for the validity of the hierarchical problem decomposition are established, and the consistency of operation is maintained by detecting the long term conflicting decisions of the levels of the hierarchy. The imprecision in the world description is modeled using the theory of fuzzy sets. The method developed for the choice of the rule that prescribes the minimum-time motion control among the redundant set of applicable rules is explained and the usage of fuzzy set operators is justified. Also included in the dissertation are the description of the computer simulation of Pilot within the hierarchy of IMAS control and the simulated experiments that demonstrate the theoretical work.

A Machine Learning Approach to Student Modeling.

DTIC Science & Technology

1984-05-01

machine learning , and describe ACN, a student modeling system that incorporates this approach. This system begins with a set of overly general rules, which it uses to search a problem space until it arrives at the same answer as the student. The ACM computer program then uses the solution path it has discovered to determine positive and negative instances of its initial rules, and employs a discrimination learning mechanism to place additional conditions on these rules. The revised rules will reproduce the solution path without search, and constitute a cognitive model of

A forecast-based STDP rule suitable for neuromorphic implementation.

PubMed

Davies, S; Galluppi, F; Rast, A D; Furber, S B

2012-08-01

Artificial neural networks increasingly involve spiking dynamics to permit greater computational efficiency. This becomes especially attractive for on-chip implementation using dedicated neuromorphic hardware. However, both spiking neural networks and neuromorphic hardware have historically found difficulties in implementing efficient, effective learning rules. The best-known spiking neural network learning paradigm is Spike Timing Dependent Plasticity (STDP) which adjusts the strength of a connection in response to the time difference between the pre- and post-synaptic spikes. Approaches that relate learning features to the membrane potential of the post-synaptic neuron have emerged as possible alternatives to the more common STDP rule, with various implementations and approximations. Here we use a new type of neuromorphic hardware, SpiNNaker, which represents the flexible "neuromimetic" architecture, to demonstrate a new approach to this problem. Based on the standard STDP algorithm with modifications and approximations, a new rule, called STDP TTS (Time-To-Spike) relates the membrane potential with the Long Term Potentiation (LTP) part of the basic STDP rule. Meanwhile, we use the standard STDP rule for the Long Term Depression (LTD) part of the algorithm. We show that on the basis of the membrane potential it is possible to make a statistical prediction of the time needed by the neuron to reach the threshold, and therefore the LTP part of the STDP algorithm can be triggered when the neuron receives a spike. In our system these approximations allow efficient memory access, reducing the overall computational time and the memory bandwidth required. The improvements here presented are significant for real-time applications such as the ones for which the SpiNNaker system has been designed. We present simulation results that show the efficacy of this algorithm using one or more input patterns repeated over the whole time of the simulation. On-chip results show that the STDP TTS algorithm allows the neural network to adapt and detect the incoming pattern with improvements both in the reliability of, and the time required for, consistent output. Through the approximations we suggest in this paper, we introduce a learning rule that is easy to implement both in event-driven simulators and in dedicated hardware, reducing computational complexity relative to the standard STDP rule. Such a rule offers a promising solution, complementary to standard STDP evaluation algorithms, for real-time learning using spiking neural networks in time-critical applications. Copyright © 2012 Elsevier Ltd. All rights reserved.

Against the empirical viability of the Deutsch-Wallace-Everett approach to quantum mechanics

NASA Astrophysics Data System (ADS)

Dawid, Richard; Thébault, Karim P. Y.

2014-08-01

The subjective Everettian approach to quantum mechanics presented by Deutsch and Wallace fails to constitute an empirically viable theory of quantum phenomena. The decision theoretic implementation of the Born rule realized in this approach provides no basis for rejecting Everettian quantum mechanics in the face of empirical data that contradicts the Born rule. The approach of Greaves and Myrvold, which provides a subjective implementation of the Born rule as well but derives it from empirical data rather than decision theoretic arguments, avoids the problem faced by Deutsch and Wallace and is empirically viable. However, there is good reason to cast doubts on its scientific value.

Learning temporal rules to forecast instability in continuously monitored patients

PubMed Central

Dubrawski, Artur; Wang, Donghan; Hravnak, Marilyn; Clermont, Gilles; Pinsky, Michael R

2017-01-01

Inductive machine learning, and in particular extraction of association rules from data, has been successfully used in multiple application domains, such as market basket analysis, disease prognosis, fraud detection, and protein sequencing. The appeal of rule extraction techniques stems from their ability to handle intricate problems yet produce models based on rules that can be comprehended by humans, and are therefore more transparent. Human comprehension is a factor that may improve adoption and use of data-driven decision support systems clinically via face validity. In this work, we explore whether we can reliably and informatively forecast cardiorespiratory instability (CRI) in step-down unit (SDU) patients utilizing data from continuous monitoring of physiologic vital sign (VS) measurements. We use a temporal association rule extraction technique in conjunction with a rule fusion protocol to learn how to forecast CRI in continuously monitored patients. We detail our approach and present and discuss encouraging empirical results obtained using continuous multivariate VS data from the bedside monitors of 297 SDU patients spanning 29 346 hours (3.35 patient-years) of observation. We present example rules that have been learned from data to illustrate potential benefits of comprehensibility of the extracted models, and we analyze the empirical utility of each VS as a potential leading indicator of an impending CRI event. PMID:27274020

Primer on clinical acid-base problem solving.

PubMed

Whittier, William L; Rutecki, Gregory W

2004-03-01

Acid-base problem solving has been an integral part of medical practice in recent generations. Diseases discovered in the last 30-plus years, for example, Bartter syndrome and Gitelman syndrome, D-lactic acidosis, and bulimia nervosa, can be diagnosed according to characteristic acid-base findings. Accuracy in acid-base problem solving is a direct result of a reproducible, systematic approach to arterial pH, partial pressure of carbon dioxide, bicarbonate concentration, and electrolytes. The 'Rules of Five' is one tool that enables clinicians to determine the cause of simple and complex disorders, even triple acid-base disturbances, with consistency. In addition, other electrolyte abnormalities that accompany acid-base disorders, such as hypokalemia, can be incorporated into algorithms that complement the Rules and contribute to efficient problem solving in a wide variety of diseases. Recently urine electrolytes have also assisted clinicians in further characterizing select disturbances. Acid-base patterns, in many ways, can serve as a 'common diagnostic pathway' shared by all subspecialties in medicine. From infectious disease (eg, lactic acidemia with highly active antiviral therapy therapy) through endocrinology (eg, Conn's syndrome, high urine chloride alkalemia) to the interface between primary care and psychiatry (eg, bulimia nervosa with multiple potential acid-base disturbances), acid-base problem solving is the key to unlocking otherwise unrelated diagnoses. Inasmuch as the Rules are clinical tools, they are applied throughout this monograph to diverse pathologic conditions typical in contemporary practice.

A Wireless Sensor Network-Based Approach with Decision Support for Monitoring Lake Water Quality.

PubMed

Huang, Xiaoci; Yi, Jianjun; Chen, Shaoli; Zhu, Xiaomin

2015-11-19

Online monitoring and water quality analysis of lakes are urgently needed. A feasible and effective approach is to use a Wireless Sensor Network (WSN). Lake water environments, like other real world environments, present many changing and unpredictable situations. To ensure flexibility in such an environment, the WSN node has to be prepared to deal with varying situations. This paper presents a WSN self-configuration approach for lake water quality monitoring. The approach is based on the integration of a semantic framework, where a reasoner can make decisions on the configuration of WSN services. We present a WSN ontology and the relevant water quality monitoring context information, which considers its suitability in a pervasive computing environment. We also propose a rule-based reasoning engine that is used to conduct decision support through reasoning techniques and context-awareness. To evaluate the approach, we conduct usability experiments and performance benchmarks.

Learning to predict chemical reactions.

PubMed

Kayala, Matthew A; Azencott, Chloé-Agathe; Chen, Jonathan H; Baldi, Pierre

2011-09-26

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles, respectively, are not high throughput, are not generalizable or scalable, and lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry data set consisting of 1630 full multistep reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top-ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of nonproductive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system is generalizable, making reasonable predictions over reactants and conditions which the rule-based expert does not handle. A web interface to the machine learning based mechanistic reaction predictor is accessible through our chemoinformatics portal ( http://cdb.ics.uci.edu) under the Toolkits section.

Learning to Predict Chemical Reactions

PubMed Central

Kayala, Matthew A.; Azencott, Chloé-Agathe; Chen, Jonathan H.

2011-01-01

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles respectively are not high-throughput, are not generalizable or scalable, or lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry dataset consisting of 1630 full multi-step reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval, problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of non-productive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system is generalizable, making reasonable predictions over reactants and conditions which the rule-based expert does not handle. A web interface to the machine learning based mechanistic reaction predictor is accessible through our chemoinformatics portal (http://cdb.ics.uci.edu) under the Toolkits section. PMID:21819139

Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks

NASA Astrophysics Data System (ADS)

Miller, B. A.; Koszinski, S.; Wehrhan, M.; Sommer, M.

2015-03-01

The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which is to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches to mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m-2), covering an area of 122 km2, with accompanying maps of estimated error. For the direct modelling approach, the estimated error map was based on the internal error estimations from the model rules. For the indirect approach, the estimated error map was produced by spatially combining the error estimates of component models via standard error propagation equations. We compared these two strategies for mapping SOC stocks on the basis of the qualities of the resulting maps as well as the magnitude and distribution of the estimated error. The direct approach produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R2 values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.

Comparison of spatial association approaches for landscape mapping of soil organic carbon stocks

NASA Astrophysics Data System (ADS)

Miller, B. A.; Koszinski, S.; Wehrhan, M.; Sommer, M.

2014-11-01

The distribution of soil organic carbon (SOC) can be variable at small analysis scales, but consideration of its role in regional and global issues demands the mapping of large extents. There are many different strategies for mapping SOC, among which are to model the variables needed to calculate the SOC stock indirectly or to model the SOC stock directly. The purpose of this research is to compare direct and indirect approaches to mapping SOC stocks from rule-based, multiple linear regression models applied at the landscape scale via spatial association. The final products for both strategies are high-resolution maps of SOC stocks (kg m-2), covering an area of 122 km2, with accompanying maps of estimated error. For the direct modelling approach, the estimated error map was based on the internal error estimations from the model rules. For the indirect approach, the estimated error map was produced by spatially combining the error estimates of component models via standard error propagation equations. We compared these two strategies for mapping SOC stocks on the basis of the qualities of the resulting maps as well as the magnitude and distribution of the estimated error. The direct approach produced a map with less spatial variation than the map produced by the indirect approach. The increased spatial variation represented by the indirect approach improved R2 values for the topsoil and subsoil stocks. Although the indirect approach had a lower mean estimated error for the topsoil stock, the mean estimated error for the total SOC stock (topsoil + subsoil) was lower for the direct approach. For these reasons, we recommend the direct approach to modelling SOC stocks be considered a more conservative estimate of the SOC stocks' spatial distribution.

Dangerous "spin": the probability myth of evidence-based prescribing - a Merleau-Pontyian approach.

PubMed

Morstyn, Ron

2011-08-01

The aim of this study was to examine logical positivist statistical probability statements used to support and justify "evidence-based" prescribing rules in psychiatry when viewed from the major philosophical theories of probability, and to propose "phenomenological probability" based on Maurice Merleau-Ponty's philosophy of "phenomenological positivism" as a better clinical and ethical basis for psychiatric prescribing. The logical positivist statistical probability statements which are currently used to support "evidence-based" prescribing rules in psychiatry have little clinical or ethical justification when subjected to critical analysis from any of the major theories of probability and represent dangerous "spin" because they necessarily exclude the individual , intersubjective and ambiguous meaning of mental illness. A concept of "phenomenological probability" founded on Merleau-Ponty's philosophy of "phenomenological positivism" overcomes the clinically destructive "objectivist" and "subjectivist" consequences of logical positivist statistical probability and allows psychopharmacological treatments to be appropriately integrated into psychiatric treatment.

Application of neural based estimation algorithm for gait phases of above knee prosthesis.

PubMed

Tileylioğlu, E; Yilmaz, A

2015-01-01

In this study, two gait phase estimation methods which utilize a rule based quantization and an artificial neural network model respectively are developed and applied for the microcontroller based semi-active knee prosthesis in order to respond user demands and adapt environmental conditions. In this context, an experimental environment in which gait data collected synchronously from both inertial and image based measurement systems has been set up. The inertial measurement system that incorporates MEM accelerometers and gyroscopes is used to perform direct motion measurement through the microcontroller, while the image based measurement system is employed for producing the verification data and assessing the success of the prosthesis. Embedded algorithms dynamically normalize the input data prior to gait phase estimation. The real time analyses of two methods revealed that embedded ANN based approach performs slightly better in comparison with the rule based algorithm and has advantage of being easily-scalable, thus able to accommodate additional input parameters considering the microcontroller constraints.

Soil quality assessment using weighted fuzzy association rules

USGS Publications Warehouse

Xue, Yue-Ju; Liu, Shu-Guang; Hu, Yue-Ming; Yang, Jing-Feng

2010-01-01

Fuzzy association rules (FARs) can be powerful in assessing regional soil quality, a critical step prior to land planning and utilization; however, traditional FARs mined from soil quality database, ignoring the importance variability of the rules, can be redundant and far from optimal. In this study, we developed a method applying different weights to traditional FARs to improve accuracy of soil quality assessment. After the FARs for soil quality assessment were mined, redundant rules were eliminated according to whether the rules were significant or not in reducing the complexity of the soil quality assessment models and in improving the comprehensibility of FARs. The global weights, each representing the importance of a FAR in soil quality assessment, were then introduced and refined using a gradient descent optimization method. This method was applied to the assessment of soil resources conditions in Guangdong Province, China. The new approach had an accuracy of 87%, when 15 rules were mined, as compared with 76% from the traditional approach. The accuracy increased to 96% when 32 rules were mined, in contrast to 88% from the traditional approach. These results demonstrated an improved comprehensibility of FARs and a high accuracy of the proposed method.

Topic categorisation of statements in suicide notes with integrated rules and machine learning.

PubMed

Kovačević, Aleksandar; Dehghan, Azad; Keane, John A; Nenadic, Goran

2012-01-01

We describe and evaluate an automated approach used as part of the i2b2 2011 challenge to identify and categorise statements in suicide notes into one of 15 topics, including Love, Guilt, Thankfulness, Hopelessness and Instructions. The approach combines a set of lexico-syntactic rules with a set of models derived by machine learning from a training dataset. The machine learning models rely on named entities, lexical, lexico-semantic and presentation features, as well as the rules that are applicable to a given statement. On a testing set of 300 suicide notes, the approach showed the overall best micro F-measure of up to 53.36%. The best precision achieved was 67.17% when only rules are used, whereas best recall of 50.57% was with integrated rules and machine learning. While some topics (eg, Sorrow, Anger, Blame) prove challenging, the performance for relatively frequent (eg, Love) and well-scoped categories (eg, Thankfulness) was comparatively higher (precision between 68% and 79%), suggesting that automated text mining approaches can be effective in topic categorisation of suicide notes.

Adaptive decision rules for the acquisition of nature reserves.

PubMed

Turner, Will R; Wilcove, David S

2006-04-01

Although reserve-design algorithms have shown promise for increasing the efficiency of conservation planning, recent work casts doubt on the usefulness of some of these approaches in practice. Using three data sets that vary widely in size and complexity, we compared various decision rules for acquiring reserve networks over multiyear periods. We explored three factors that are often important in real-world conservation efforts: uncertain availability of sites for acquisition, degradation of sites, and overall budget constraints. We evaluated the relative strengths and weaknesses of existing optimal and heuristic decision rules and developed a new set of adaptive decision rules that combine the strengths of existing optimal and heuristic approaches. All three of the new adaptive rules performed better than the existing rules we tested under virtually all scenarios of site availability, site degradation, and budget constraints. Moreover, the adaptive rules required no additional data beyond what was readily available and were relatively easy to compute.

Accounting of GHG emissions and removals from forest management: a long road from Kyoto to Paris.

PubMed

Krug, Joachim H A

2018-01-03

Forests have always played an important role in agreeing on accounting rules during the past two decades of international climate policy development. Starting from activity-based gross-net accounting of selected forestry activities to mandatory accounting against a baseline-rules have changed quite rapidly and with significant consequences for accounted credits and debits. Such changes have direct consequences on incentives for climate-investments in forestry. There have also been strong arguments not to include forests into the accounting system by considering large uncertainties, procedural challenges and a fear of unearned credits corrupting the overall accounting system, among others. This paper reflects the development of respective accounting approaches and reviews the progress made on core challenges and resulting incentives. The historic development of forest management accounting rules is analysed in the light of the Paris Agreement. Pros and cons of different approaches are discussed with specific focus on the challenge to maintain integrity of the accounting approach and on resulting incentives for additional human induced investments to increase growth for future substitution and increased C storage by forest management. The review is solely based on scientific publications and official IPCC and UNFCC documents. Some rather political statements of non-scientific stakeholders are considered to reflect criticism. Such sources are indicated accordingly. Remaining and emerging requirements for an accounting system for post 2030 are highlighted. The Paris Agreement is interpreted as a "game changer" for the role of forests in climate change mitigation. Many countries rely on forests in their NDCs to achieve their self-set targets. In fact, the agreement "to achieve a balance between anthropogenic emissions by sources and removals by sinks of greenhouse gases in the second half of this century" puts pressure on the entire land sector to contribute to overall GHG emission reductions. This also concerns forests as a resource for the bio-based economy and wood products, and for increasing carbon reservoirs. By discussing the existing elements of forest accounting rules and conditions for establishing an accounting system post 2030, it is concluded that core requirements like factoring out direct human-induced from indirect human-induced and natural impacts on managed lands, a facilitation of incentives for management changes and providing safeguards for the integrity of the accounting system are not sufficiently secured by currently discussed accounting rules. A responsibility to fulfil these basic requirements is transferred to Nationally Determined Contributions. Increased incentives for additional human induced investments are not stipulated by the accounting approach but rather by the political decision to make use of the substitution effect and potential net removals from LULUCF to contribute to self-set targets.

Neurosemantics, neurons and system theory.

PubMed

Breidbach, Olaf

2007-08-01

Following the concept of internal representations, signal processing in a neuronal system has to be evaluated exclusively based on internal system characteristics. Thus, this approach omits the external observer as a control function for sensory integration. Instead, the configuration of the system and its computational performance are the effects of endogenous factors. Such self-referential operation is due to a strictly local computation in a network and, thereby, computations follow a set of rules that constitute the emergent behaviour of the system. These rules can be shown to correspond to a "logic" that is intrinsic to the system, an idea which provides the basis for neurosemantics.

How to get the most out of your orthopaedic fellowship: thinking about practice-based learning.

PubMed

Templeman, David

2012-09-01

Practice-based learning and improvement is an important skill set to develop during an orthopaedic trauma fellowship and is 1 of the 6 core competencies stated by the ACGME. The review of clinic cases is best done using a few simple models to develop a structured approach for studying cases. Three common sense and easy-to-use strategies to improve clinical practice are as follows: performing each case three times, studying the 4 quadrants of patient outcomes, and the application of the Pareto 80/20 rule. These principles help to develop a structured approach for analyzing and thinking about practice-based experiences.

Retinal hemorrhage detection by rule-based and machine learning approach.

PubMed

Di Xiao; Shuang Yu; Vignarajan, Janardhan; Dong An; Mei-Ling Tay-Kearney; Kanagasingam, Yogi

2017-07-01

Robust detection of hemorrhages (HMs) in color fundus image is important in an automatic diabetic retinopathy grading system. Detection of the hemorrhages that are close to or connected with retinal blood vessels was found to be challenge. However, most methods didn't put research on it, even some of them mentioned this issue. In this paper, we proposed a novel hemorrhage detection method based on rule-based and machine learning methods. We focused on the improvement of detection of the hemorrhages that are close to or connected with retinal blood vessels, besides detecting the independent hemorrhage regions. A preliminary test for detecting HM presence was conducted on the images from two databases. We achieved sensitivity and specificity of 93.3% and 88% as well as 91.9% and 85.6% on the two datasets.

CrossTalk: The Journal of Defense Software Engineering. Volume 27, Number 1, January/February 2014

DTIC Science & Technology

2014-02-01

deficit in trustworthiness and will permit analysis on how this deficit needs to be overcome. This analysis will help identify adaptations that are...approaches to trustworthy analysis split into two categories: product-based and process-based. Product-based techniques [9] identify factors that...Criticalities may also be assigned to decompositions and contributions. 5. Evaluation and analysis : in this task the propagation rules of the NFR

Predicting Debris-Slide Locations in Northwestern California

Treesearch

Mark E. Reid; Stephen D. Ellen; Dianne L. Brien; Juan de la Fuente; James N. Falls; Billie G. Hicks; Eric C. Johnson

2007-01-01

We tested four topographic models for predicting locations of debris-slide sources: 1) slope; 2) proximity to stream; 3) SHALSTAB with "standard" parameters; and 4) debris-slide-prone landforms, which delineates areas similar to "inner gorge" and "headwall swale" using experience-based rules. These approaches were compared in three diverse...

Personalised Information Services Using a Hybrid Recommendation Method Based on Usage Frequency

ERIC Educational Resources Information Center

Kim, Yong; Chung, Min Gyo

2008-01-01

Purpose: This paper seeks to describe a personal recommendation service (PRS) involving an innovative hybrid recommendation method suitable for deployment in a large-scale multimedia user environment. Design/methodology/approach: The proposed hybrid method partitions content and user into segments and executes association rule mining,…

Solving Complex Problems: A Convergent Approach to Cognitive Load Measurement

ERIC Educational Resources Information Center

Zheng, Robert; Cook, Anne

2012-01-01

The study challenged the current practices in cognitive load measurement involving complex problem solving by manipulating the presence of pictures in multiple rule-based problem-solving situations and examining the cognitive load resulting from both off-line and online measures associated with complex problem solving. Forty-eight participants…

77 FR 75766 - Federal Acquisition Regulation; Nondisplacement of Qualified Workers Under Service Contracts

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-21

... offeror's ingenuity in proposing a technical approach or solution based on limitations of the existing... solution. Another respondent expressed concern that the rule would limit offerors' ability to craft innovative solutions to Government requirements. Response: Under the SCA, the successor contractor must pay...

Phonological Concept Learning.

PubMed

Moreton, Elliott; Pater, Joe; Pertsova, Katya

2017-01-01

Linguistic and non-linguistic pattern learning have been studied separately, but we argue for a comparative approach. Analogous inductive problems arise in phonological and visual pattern learning. Evidence from three experiments shows that human learners can solve them in analogous ways, and that human performance in both cases can be captured by the same models. We test GMECCS (Gradual Maximum Entropy with a Conjunctive Constraint Schema), an implementation of the Configural Cue Model (Gluck & Bower, ) in a Maximum Entropy phonotactic-learning framework (Goldwater & Johnson, ; Hayes & Wilson, ) with a single free parameter, against the alternative hypothesis that learners seek featurally simple algebraic rules ("rule-seeking"). We study the full typology of patterns introduced by Shepard, Hovland, and Jenkins () ("SHJ"), instantiated as both phonotactic patterns and visual analogs, using unsupervised training. Unlike SHJ, Experiments 1 and 2 found that both phonotactic and visual patterns that depended on fewer features could be more difficult than those that depended on more features, as predicted by GMECCS but not by rule-seeking. GMECCS also correctly predicted performance differences between stimulus subclasses within each pattern. A third experiment tried supervised training (which can facilitate rule-seeking in visual learning) to elicit simple rule-seeking phonotactic learning, but cue-based behavior persisted. We conclude that similar cue-based cognitive processes are available for phonological and visual concept learning, and hence that studying either kind of learning can lead to significant insights about the other. Copyright © 2015 Cognitive Science Society, Inc.

Combining rules, background knowledge and change patterns to maintain semantic annotations.

PubMed

Cardoso, Silvio Domingos; Chantal, Reynaud-Delaître; Da Silveira, Marcos; Pruski, Cédric

2017-01-01

Knowledge Organization Systems (KOS) play a key role in enriching biomedical information in order to make it machine-understandable and shareable. This is done by annotating medical documents, or more specifically, associating concept labels from KOS with pieces of digital information, e.g., images or texts. However, the dynamic nature of KOS may impact the annotations, thus creating a mismatch between the evolved concept and the associated information. To solve this problem, methods to maintain the quality of the annotations are required. In this paper, we define a framework based on rules, background knowledge and change patterns to drive the annotation adaption process. We evaluate experimentally the proposed approach in realistic cases-studies and demonstrate the overall performance of our approach in different KOS considering the precision, recall, F1-score and AUC value of the system.

Combining rules, background knowledge and change patterns to maintain semantic annotations

PubMed Central

Cardoso, Silvio Domingos; Chantal, Reynaud-Delaître; Da Silveira, Marcos; Pruski, Cédric

2017-01-01

Knowledge Organization Systems (KOS) play a key role in enriching biomedical information in order to make it machine-understandable and shareable. This is done by annotating medical documents, or more specifically, associating concept labels from KOS with pieces of digital information, e.g., images or texts. However, the dynamic nature of KOS may impact the annotations, thus creating a mismatch between the evolved concept and the associated information. To solve this problem, methods to maintain the quality of the annotations are required. In this paper, we define a framework based on rules, background knowledge and change patterns to drive the annotation adaption process. We evaluate experimentally the proposed approach in realistic cases-studies and demonstrate the overall performance of our approach in different KOS considering the precision, recall, F1-score and AUC value of the system. PMID:29854115

Association algorithm to mine the rules that govern enzyme definition and to classify protein sequences.

PubMed

Chiu, Shih-Hau; Chen, Chien-Chi; Yuan, Gwo-Fang; Lin, Thy-Hou

2006-06-15

The number of sequences compiled in many genome projects is growing exponentially, but most of them have not been characterized experimentally. An automatic annotation scheme must be in an urgent need to reduce the gap between the amount of new sequences produced and reliable functional annotation. This work proposes rules for automatically classifying the fungus genes. The approach involves elucidating the enzyme classifying rule that is hidden in UniProt protein knowledgebase and then applying it for classification. The association algorithm, Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity. There were five datasets collected from the Swiss-Prot for establishing the annotation rules. These were treated as the training sets. The TrEMBL entries were treated as the testing set. A correct enzyme classification rate of 70% was obtained for the prokaryote datasets and a similar rate of about 80% was obtained for the eukaryote datasets. The fungus training dataset which lacks an enzyme class description was also used to evaluate the fungus candidate rules. A total of 88 out of 5085 test entries were matched with the fungus rule set. These were otherwise poorly annotated using their functional descriptions. The feasibility of using the method presented here to classify enzyme classes based on the enzyme domain rules is evident. The rules may be also employed by the protein annotators in manual annotation or implemented in an automatic annotation flowchart.

Using Nonprinciple Rays to Form Images in Geometrical Optics

NASA Astrophysics Data System (ADS)

Marx, Jeff; Mian, Shabbir

2015-11-01

Constructing ray diagrams to locate the image of an object formed by thin lenses and mirrors is a staple of many introductory physics courses at the high school and college levels, and has been the subject of some pedagogy-related articles. Our review of textbooks distributed in the United States suggests that the singular approach involves drawing principle rays to locate an object's image. We were pleasantly surprised to read an article in this journal by Suppapittayaporn et al. in which they use an alternative method to construct rays for thin lenses based on a "tilted principle axis" (TPA). In particular, we were struck by the generality of the approach (a single rule for tracing rays as compared to the typical two or three rules), and how it could help students more easily tackle challenging situations, such as multi-lens systems and occluded lenses, where image construction using principle rays may be impractical. In this paper, we provide simple "proofs" for this alternative approach for the case of thin lenses and single refracting surfaces.

Bayesian probability estimates are not necessary to make choices satisfying Bayes' rule in elementary situations.

PubMed

Domurat, Artur; Kowalczuk, Olga; Idzikowska, Katarzyna; Borzymowska, Zuzanna; Nowak-Przygodzka, Marta

2015-01-01

This paper has two aims. First, we investigate how often people make choices conforming to Bayes' rule when natural sampling is applied. Second, we show that using Bayes' rule is not necessary to make choices satisfying Bayes' rule. Simpler methods, even fallacious heuristics, might prescribe correct choices reasonably often under specific circumstances. We considered elementary situations with binary sets of hypotheses and data. We adopted an ecological approach and prepared two-stage computer tasks resembling natural sampling. Probabilistic relations were inferred from a set of pictures, followed by a choice which was made to maximize the chance of a preferred outcome. Use of Bayes' rule was deduced indirectly from choices. Study 1 used a stratified sample of N = 60 participants equally distributed with regard to gender and type of education (humanities vs. pure sciences). Choices satisfying Bayes' rule were dominant. To investigate ways of making choices more directly, we replicated Study 1, adding a task with a verbal report. In Study 2 (N = 76) choices conforming to Bayes' rule dominated again. However, the verbal reports revealed use of a new, non-inverse rule, which always renders correct choices, but is easier than Bayes' rule to apply. It does not require inversion of conditions [transforming P(H) and P(D|H) into P(H|D)] when computing chances. Study 3 examined the efficiency of three fallacious heuristics (pre-Bayesian, representativeness, and evidence-only) in producing choices concordant with Bayes' rule. Computer-simulated scenarios revealed that the heuristics produced correct choices reasonably often under specific base rates and likelihood ratios. Summing up we conclude that natural sampling results in most choices conforming to Bayes' rule. However, people tend to replace Bayes' rule with simpler methods, and even use of fallacious heuristics may be satisfactorily efficient.

Bayesian probability estimates are not necessary to make choices satisfying Bayes’ rule in elementary situations

PubMed Central

Domurat, Artur; Kowalczuk, Olga; Idzikowska, Katarzyna; Borzymowska, Zuzanna; Nowak-Przygodzka, Marta

2015-01-01

This paper has two aims. First, we investigate how often people make choices conforming to Bayes’ rule when natural sampling is applied. Second, we show that using Bayes’ rule is not necessary to make choices satisfying Bayes’ rule. Simpler methods, even fallacious heuristics, might prescribe correct choices reasonably often under specific circumstances. We considered elementary situations with binary sets of hypotheses and data. We adopted an ecological approach and prepared two-stage computer tasks resembling natural sampling. Probabilistic relations were inferred from a set of pictures, followed by a choice which was made to maximize the chance of a preferred outcome. Use of Bayes’ rule was deduced indirectly from choices. Study 1 used a stratified sample of N = 60 participants equally distributed with regard to gender and type of education (humanities vs. pure sciences). Choices satisfying Bayes’ rule were dominant. To investigate ways of making choices more directly, we replicated Study 1, adding a task with a verbal report. In Study 2 (N = 76) choices conforming to Bayes’ rule dominated again. However, the verbal reports revealed use of a new, non-inverse rule, which always renders correct choices, but is easier than Bayes’ rule to apply. It does not require inversion of conditions [transforming P(H) and P(D|H) into P(H|D)] when computing chances. Study 3 examined the efficiency of three fallacious heuristics (pre-Bayesian, representativeness, and evidence-only) in producing choices concordant with Bayes’ rule. Computer-simulated scenarios revealed that the heuristics produced correct choices reasonably often under specific base rates and likelihood ratios. Summing up we conclude that natural sampling results in most choices conforming to Bayes’ rule. However, people tend to replace Bayes’ rule with simpler methods, and even use of fallacious heuristics may be satisfactorily efficient. PMID:26347676

Definition of sampling units begets conclusions in ecology: the case of habitats for plant communities.

PubMed

Mörsdorf, Martin A; Ravolainen, Virve T; Støvern, Leif Einar; Yoccoz, Nigel G; Jónsdóttir, Ingibjörg Svala; Bråthen, Kari Anne

2015-01-01

In ecology, expert knowledge on habitat characteristics is often used to define sampling units such as study sites. Ecologists are especially prone to such approaches when prior sampling frames are not accessible. Here we ask to what extent can different approaches to the definition of sampling units influence the conclusions that are drawn from an ecological study? We do this by comparing a formal versus a subjective definition of sampling units within a study design which is based on well-articulated objectives and proper methodology. Both approaches are applied to tundra plant communities in mesic and snowbed habitats. For the formal approach, sampling units were first defined for each habitat in concave terrain of suitable slope using GIS. In the field, these units were only accepted as the targeted habitats if additional criteria for vegetation cover were fulfilled. For the subjective approach, sampling units were defined visually in the field, based on typical plant communities of mesic and snowbed habitats. For each approach, we collected information about plant community characteristics within a total of 11 mesic and seven snowbed units distributed between two herding districts of contrasting reindeer density. Results from the two approaches differed significantly in several plant community characteristics in both mesic and snowbed habitats. Furthermore, differences between the two approaches were not consistent because their magnitude and direction differed both between the two habitats and the two reindeer herding districts. Consequently, we could draw different conclusions on how plant diversity and relative abundance of functional groups are differentiated between the two habitats depending on the approach used. We therefore challenge ecologists to formalize the expert knowledge applied to define sampling units through a set of well-articulated rules, rather than applying it subjectively. We see this as instrumental for progress in ecology as only rules based on expert knowledge are transparent and lead to results reproducible by other ecologists.

Building distributed rule-based systems using the AI Bus

NASA Technical Reports Server (NTRS)

Schultz, Roger D.; Stobie, Iain C.

1990-01-01

The AI Bus software architecture was designed to support the construction of large-scale, production-quality applications in areas of high technology flux, running heterogeneous distributed environments, utilizing a mix of knowledge-based and conventional components. These goals led to its current development as a layered, object-oriented library for cooperative systems. This paper describes the concepts and design of the AI Bus and its implementation status as a library of reusable and customizable objects, structured by layers from operating system interfaces up to high-level knowledge-based agents. Each agent is a semi-autonomous process with specialized expertise, and consists of a number of knowledge sources (a knowledge base and inference engine). Inter-agent communication mechanisms are based on blackboards and Actors-style acquaintances. As a conservative first implementation, we used C++ on top of Unix, and wrapped an embedded Clips with methods for the knowledge source class. This involved designing standard protocols for communication and functions which use these protocols in rules. Embedding several CLIPS objects within a single process was an unexpected problem because of global variables, whose solution involved constructing and recompiling a C++ version of CLIPS. We are currently working on a more radical approach to incorporating CLIPS, by separating out its pattern matcher, rule and fact representations and other components as true object oriented modules.

Patients who make terrible therapeutic choices.

PubMed

Curzer, Howard J

2014-01-01

The traditional approaches to dental ethics include appeals to principles, duties (deontology), and consequences (utilitarianism). These approaches are often inadequate when faced with the case of a patient who refuses reasonable treatment and does not share the same ethical framework the dentist is using. An approach based on virtue ethics may be helpful in this and other cases. Virtue ethics is a tradition going back to Plato and Aristotle. It depends on forming a holistic character supporting general appropriate behavior. By correctly diagnosing the real issues at stake in a patient's inappropriate oral health choices and working to build effective habits, dentists can sometimes respond to ethical challenges that remain intractable given rule-based methods.

An endorsement-based approach to student modeling for planner-controlled intelligent tutoring systems

NASA Technical Reports Server (NTRS)

Murray, William R.

1990-01-01

An approach is described to student modeling for intelligent tutoring systems based on an explicit representation of the tutor's beliefs about the student and the arguments for and against those beliefs (called endorsements). A lexicographic comparison of arguments, sorted according to evidence reliability, provides a principled means of determining those beliefs that are considered true, false, or uncertain. Each of these beliefs is ultimately justified by underlying assessment data. The endorsement-based approach to student modeling is particularly appropriate for tutors controlled by instructional planners. These tutors place greater demands on a student model than opportunistic tutors. Numerical calculi approaches are less well-suited because it is difficult to correctly assign numbers for evidence reliability and rule plausibility. It may also be difficult to interpret final results and provide suitable combining functions. When numeric measures of uncertainty are used, arbitrary numeric thresholds are often required for planning decisions. Such an approach is inappropriate when robust context-sensitive planning decisions must be made. A TMS-based implementation of the endorsement-based approach to student modeling is presented, this approach is compared to alternatives, and a project history is provided describing the evolution of this approach.

Learning of Rule Ensembles for Multiple Attribute Ranking Problems

NASA Astrophysics Data System (ADS)

Dembczyński, Krzysztof; Kotłowski, Wojciech; Słowiński, Roman; Szeląg, Marcin

In this paper, we consider the multiple attribute ranking problem from a Machine Learning perspective. We propose two approaches to statistical learning of an ensemble of decision rules from decision examples provided by the Decision Maker in terms of pairwise comparisons of some objects. The first approach consists in learning a preference function defining a binary preference relation for a pair of objects. The result of application of this function on all pairs of objects to be ranked is then exploited using the Net Flow Score procedure, giving a linear ranking of objects. The second approach consists in learning a utility function for single objects. The utility function also gives a linear ranking of objects. In both approaches, the learning is based on the boosting technique. The presented approaches to Preference Learning share good properties of the decision rule preference model and have good performance in the massive-data learning problems. As Preference Learning and Multiple Attribute Decision Aiding share many concepts and methodological issues, in the introduction, we review some aspects bridging these two fields. To illustrate the two approaches proposed in this paper, we solve with them a toy example concerning the ranking of a set of cars evaluated by multiple attributes. Then, we perform a large data experiment on real data sets. The first data set concerns credit rating. Since recent research in the field of Preference Learning is motivated by the increasing role of modeling preferences in recommender systems and information retrieval, we chose two other massive data sets from this area - one comes from movie recommender system MovieLens, and the other concerns ranking of text documents from 20 Newsgroups data set.

Inside the black box: starting to uncover the underlying decision rules used in one-by-one expert assessment of occupational exposure in case-control studies

PubMed Central

Wheeler, David C.; Burstyn, Igor; Vermeulen, Roel; Yu, Kai; Shortreed, Susan M.; Pronk, Anjoeka; Stewart, Patricia A.; Colt, Joanne S.; Baris, Dalsu; Karagas, Margaret R.; Schwenn, Molly; Johnson, Alison; Silverman, Debra T.; Friesen, Melissa C.

2014-01-01

Objectives Evaluating occupational exposures in population-based case-control studies often requires exposure assessors to review each study participants' reported occupational information job-by-job to derive exposure estimates. Although such assessments likely have underlying decision rules, they usually lack transparency, are time-consuming and have uncertain reliability and validity. We aimed to identify the underlying rules to enable documentation, review, and future use of these expert-based exposure decisions. Methods Classification and regression trees (CART, predictions from a single tree) and random forests (predictions from many trees) were used to identify the underlying rules from the questionnaire responses and an expert's exposure assignments for occupational diesel exhaust exposure for several metrics: binary exposure probability and ordinal exposure probability, intensity, and frequency. Data were split into training (n=10,488 jobs), testing (n=2,247), and validation (n=2,248) data sets. Results The CART and random forest models' predictions agreed with 92–94% of the expert's binary probability assignments. For ordinal probability, intensity, and frequency metrics, the two models extracted decision rules more successfully for unexposed and highly exposed jobs (86–90% and 57–85%, respectively) than for low or medium exposed jobs (7–71%). Conclusions CART and random forest models extracted decision rules and accurately predicted an expert's exposure decisions for the majority of jobs and identified questionnaire response patterns that would require further expert review if the rules were applied to other jobs in the same or different study. This approach makes the exposure assessment process in case-control studies more transparent and creates a mechanism to efficiently replicate exposure decisions in future studies. PMID:23155187

Policy-Based Management Natural Language Parser

NASA Technical Reports Server (NTRS)

James, Mark

2009-01-01

The Policy-Based Management Natural Language Parser (PBEM) is a rules-based approach to enterprise management that can be used to automate certain management tasks. This parser simplifies the management of a given endeavor by establishing policies to deal with situations that are likely to occur. Policies are operating rules that can be referred to as a means of maintaining order, security, consistency, or other ways of successfully furthering a goal or mission. PBEM provides a way of managing configuration of network elements, applications, and processes via a set of high-level rules or business policies rather than managing individual elements, thus switching the control to a higher level. This software allows unique management rules (or commands) to be specified and applied to a cross-section of the Global Information Grid (GIG). This software embodies a parser that is capable of recognizing and understanding conversational English. Because all possible dialect variants cannot be anticipated, a unique capability was developed that parses passed on conversation intent rather than the exact way the words are used. This software can increase productivity by enabling a user to converse with the system in conversational English to define network policies. PBEM can be used in both manned and unmanned science-gathering programs. Because policy statements can be domain-independent, this software can be applied equally to a wide variety of applications.

A novel association rule mining approach using TID intermediate itemset.

PubMed

Aqra, Iyad; Herawan, Tutut; Abdul Ghani, Norjihan; Akhunzada, Adnan; Ali, Akhtar; Bin Razali, Ramdan; Ilahi, Manzoor; Raymond Choo, Kim-Kwang

2018-01-01

Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output knowledge certainly necessitates the extant state-of-the-art algorithms to rescan the entire database. Subsequently, the process incurs heavy computation cost and is not feasible for real-time applications. The paper addresses efficiently the problem of threshold dynamic updation for a given purpose. The paper contributes by presenting a novel ARM approach that creates an intermediate itemset and applies a threshold to extract categorical frequent itemsets with diverse threshold values. Thus, improving the overall efficiency as we no longer needs to scan the whole database. After the entire itemset is built, we are able to obtain real support without the need of rebuilding the itemset (e.g. Itemset list is intersected to obtain the actual support). Moreover, the algorithm supports to extract many frequent itemsets according to a pre-determined minimum support with an independent purpose. Additionally, the experimental results of our proposed approach demonstrate the capability to be deployed in any mining system in a fully parallel mode; consequently, increasing the efficiency of the real-time association rules discovery process. The proposed approach outperforms the extant state-of-the-art and shows promising results that reduce computation cost, increase accuracy, and produce all possible itemsets.

A novel association rule mining approach using TID intermediate itemset

PubMed Central

Ali, Akhtar; Bin Razali, Ramdan; Ilahi, Manzoor; Raymond Choo, Kim-Kwang

2018-01-01

Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output knowledge certainly necessitates the extant state-of-the-art algorithms to rescan the entire database. Subsequently, the process incurs heavy computation cost and is not feasible for real-time applications. The paper addresses efficiently the problem of threshold dynamic updation for a given purpose. The paper contributes by presenting a novel ARM approach that creates an intermediate itemset and applies a threshold to extract categorical frequent itemsets with diverse threshold values. Thus, improving the overall efficiency as we no longer needs to scan the whole database. After the entire itemset is built, we are able to obtain real support without the need of rebuilding the itemset (e.g. Itemset list is intersected to obtain the actual support). Moreover, the algorithm supports to extract many frequent itemsets according to a pre-determined minimum support with an independent purpose. Additionally, the experimental results of our proposed approach demonstrate the capability to be deployed in any mining system in a fully parallel mode; consequently, increasing the efficiency of the real-time association rules discovery process. The proposed approach outperforms the extant state-of-the-art and shows promising results that reduce computation cost, increase accuracy, and produce all possible itemsets. PMID:29351287

On structuring the rules of a fuzzy controller

NASA Technical Reports Server (NTRS)

Zhou, Jun; Raju, G. V. S.

1993-01-01

Since the pioneering work of Zadeh and Mamdani and Assilian, fuzzy logic control has emerged as one of the most active and fruitful research areas. The applications of fuzzy logic control can be found in many fields such as control of stream generators, automatic train operation systems, elevator control, nuclear reactor control, automobile transmission control, etc. In this paper, two new structures of hierarchical fuzzy rule-based controller are proposed to reduce the number of rules in a complete rule set of a controller. In one approach, the overall system is split into sub-systems which are treated independently in parallel. A coordinator is then used to take into account the interactions. This is done via an iterating information exchange between the lower level and the coordinator level. From the point of view of information used, this structure is very similar to central structure in that the coordinator can have at least in principle, all the information that the local controllers have.

Method to integrate clinical guidelines into the electronic health record (EHR) by applying the archetypes approach.

PubMed

Garcia, Diego; Moro, Claudia Maria Cabral; Cicogna, Paulo Eduardo; Carvalho, Deborah Ribeiro

2013-01-01

Clinical guidelines are documents that assist healthcare professionals, facilitating and standardizing diagnosis, management, and treatment in specific areas. Computerized guidelines as decision support systems (DSS) attempt to increase the performance of tasks and facilitate the use of guidelines. Most DSS are not integrated into the electronic health record (EHR), ordering some degree of rework especially related to data collection. This study's objective was to present a method for integrating clinical guidelines into the EHR. The study developed first a way to identify data and rules contained in the guidelines, and then incorporate rules into an archetype-based EHR. The proposed method tested was anemia treatment in the Chronic Kidney Disease Guideline. The phases of the method are: data and rules identification; archetypes elaboration; rules definition and inclusion in inference engine; and DSS-EHR integration and validation. The main feature of the proposed method is that it is generic and can be applied toany type of guideline.

Effective Design of Multifunctional Peptides by Combining Compatible Functions

PubMed Central

Diener, Christian; Garza Ramos Martínez, Georgina; Moreno Blas, Daniel; Castillo González, David A.; Corzo, Gerardo; Castro-Obregon, Susana; Del Rio, Gabriel

2016-01-01

Multifunctionality is a common trait of many natural proteins and peptides, yet the rules to generate such multifunctionality remain unclear. We propose that the rules defining some protein/peptide functions are compatible. To explore this hypothesis, we trained a computational method to predict cell-penetrating peptides at the sequence level and learned that antimicrobial peptides and DNA-binding proteins are compatible with the rules of our predictor. Based on this finding, we expected that designing peptides for CPP activity may render AMP and DNA-binding activities. To test this prediction, we designed peptides that embedded two independent functional domains (nuclear localization and yeast pheromone activity), linked by optimizing their composition to fit the rules characterizing cell-penetrating peptides. These peptides presented effective cell penetration, DNA-binding, pheromone and antimicrobial activities, thus confirming the effectiveness of our computational approach to design multifunctional peptides with potential therapeutic uses. Our computational implementation is available at http://bis.ifc.unam.mx/en/software/dcf. PMID:27096600

Surrogate-Assisted Genetic Programming With Simplified Models for Automated Design of Dispatching Rules.

PubMed

Nguyen, Su; Zhang, Mengjie; Tan, Kay Chen

2017-09-01

Automated design of dispatching rules for production systems has been an interesting research topic over the last several years. Machine learning, especially genetic programming (GP), has been a powerful approach to dealing with this design problem. However, intensive computational requirements, accuracy and interpretability are still its limitations. This paper aims at developing a new surrogate assisted GP to help improving the quality of the evolved rules without significant computational costs. The experiments have verified the effectiveness and efficiency of the proposed algorithms as compared to those in the literature. Furthermore, new simplification and visualisation approaches have also been developed to improve the interpretability of the evolved rules. These approaches have shown great potentials and proved to be a critical part of the automated design system.

Agent-Based Modeling in Public Health: Current Applications and Future Directions.

PubMed

Tracy, Melissa; Cerdá, Magdalena; Keyes, Katherine M

2018-04-01

Agent-based modeling is a computational approach in which agents with a specified set of characteristics interact with each other and with their environment according to predefined rules. We review key areas in public health where agent-based modeling has been adopted, including both communicable and noncommunicable disease, health behaviors, and social epidemiology. We also describe the main strengths and limitations of this approach for questions with public health relevance. Finally, we describe both methodologic and substantive future directions that we believe will enhance the value of agent-based modeling for public health. In particular, advances in model validation, comparisons with other causal modeling procedures, and the expansion of the models to consider comorbidity and joint influences more systematically will improve the utility of this approach to inform public health research, practice, and policy.

Responsive Legal Approach to Law of Human Trafficking in Indonesia

ERIC Educational Resources Information Center

Farhana

2018-01-01

Formation and legal changes influenced by the social and political dynamics. Law understood as the rules are rigid and too much emphasis on the legal aspects of the legal system or emphasize aspects of the legitimacy of the rules themselves, without associated with social problems. A Responsive legal approach is an approach the legal establishment…

Palmprint authentication using multiple classifiers

NASA Astrophysics Data System (ADS)

Kumar, Ajay; Zhang, David

2004-08-01

This paper investigates the performance improvement for palmprint authentication using multiple classifiers. The proposed methods on personal authentication using palmprints can be divided into three categories; appearance- , line -, and texture-based. A combination of these approaches can be used to achieve higher performance. We propose to simultaneously extract palmprint features from PCA, Line detectors and Gabor-filters and combine their corresponding matching scores. This paper also investigates the comparative performance of simple combination rules and the hybrid fusion strategy to achieve performance improvement. Our experimental results on the database of 100 users demonstrate the usefulness of such approach over those based on individual classifiers.

Automatic construction of a recurrent neural network based classifier for vehicle passage detection

NASA Astrophysics Data System (ADS)

Burnaev, Evgeny; Koptelov, Ivan; Novikov, German; Khanipov, Timur

2017-03-01

Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

Machine-Learning Approach for Design of Nanomagnetic-Based Antennas

NASA Astrophysics Data System (ADS)

Gianfagna, Carmine; Yu, Huan; Swaminathan, Madhavan; Pulugurtha, Raj; Tummala, Rao; Antonini, Giulio

2017-08-01

We propose a machine-learning approach for design of planar inverted-F antennas with a magneto-dielectric nanocomposite substrate. It is shown that machine-learning techniques can be efficiently used to characterize nanomagnetic-based antennas by accurately mapping the particle radius and volume fraction of the nanomagnetic material to antenna parameters such as gain, bandwidth, radiation efficiency, and resonant frequency. A modified mixing rule model is also presented. In addition, the inverse problem is addressed through machine learning as well, where given the antenna parameters, the corresponding design space of possible material parameters is identified.

The Role of Science in Behavioral Disorders.

ERIC Educational Resources Information Center

Kauffman, James M.

1999-01-01

A scientific, rule-governed approach to solving problems suggests the following assumptions: we need different rules for different purposes; rules are grounded in values; the origins and applications of rules are often misunderstood; personal experience and idea popularity are unreliable; and all truths are tentative. Each assumption is related to…

Grid occupancy estimation for environment perception based on belief functions and PCR6

NASA Astrophysics Data System (ADS)

Moras, Julien; Dezert, Jean; Pannetier, Benjamin

2015-05-01

In this contribution, we propose to improve the grid map occupancy estimation method developed so far based on belief function modeling and the classical Dempster's rule of combination. Grid map offers a useful representation of the perceived world for mobile robotics navigation. It will play a major role for the security (obstacle avoidance) of next generations of terrestrial vehicles, as well as for future autonomous navigation systems. In a grid map, the occupancy of each cell representing a small piece of the surrounding area of the robot must be estimated at first from sensors measurements (typically LIDAR, or camera), and then it must also be classified into different classes in order to get a complete and precise perception of the dynamic environment where the robot moves. So far, the estimation and the grid map updating have been done using fusion techniques based on the probabilistic framework, or on the classical belief function framework thanks to an inverse model of the sensors. Mainly because the latter offers an interesting management of uncertainties when the quality of available information is low, and when the sources of information appear as conflicting. To improve the performances of the grid map estimation, we propose in this paper to replace Dempster's rule of combination by the PCR6 rule (Proportional Conflict Redistribution rule #6) proposed in DSmT (Dezert-Smarandache) Theory. As an illustrating scenario, we consider a platform moving in dynamic area and we compare our new realistic simulation results (based on a LIDAR sensor) with those obtained by the probabilistic and the classical belief-based approaches.

Railway Online Booking System Design and Implementation

NASA Astrophysics Data System (ADS)

Zongjiang, Wang

In this paper, we define rule usefulness and introduce one approach to evaluate the rule usefulness in rough sets. And we raise one method to get most useful rules. This method is easy and effective in applications of prisoners' reform. Comparing with the method to get most interesting rules, ours is direct and objective. Rule interestingness must consider the predefined knowledge on what kind of information is interesting. Our method greatly reduces the rule numbers generated and provides a measure of rule usefulness at the same time.

Knowledge-rich temporal relation identification and classification in clinical notes

PubMed Central

D’Souza, Jennifer; Ng, Vincent

2014-01-01

Motivation: We examine the task of temporal relation classification for the clinical domain. Our approach to this task departs from existing ones in that it is (i) ‘knowledge-rich’, employing sophisticated knowledge derived from discourse relations as well as both domain-independent and domain-dependent semantic relations, and (ii) ‘hybrid’, combining the strengths of rule-based and learning-based approaches. Evaluation results on the i2b2 Clinical Temporal Relations Challenge corpus show that our approach yields a 17–24% and 8–14% relative reduction in error over a state-of-the-art learning-based baseline system when gold-standard and automatically identified temporal relations are used, respectively. Database URL: http://www.hlt.utdallas.edu/~jld082000/temporal-relations/ PMID:25414383

A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the U.S. central plains

USGS Publications Warehouse

Tadesse, Tsegaye; Brown, Jesslyn F.; Hayes, M.J.

2005-01-01

Droughts are normal climate episodes, yet they are among the most expensive natural disasters in the world. Knowledge about the timing, severity, and pattern of droughts on the landscape can be incorporated into effective planning and decision-making. In this study, we present a data mining approach to modeling vegetation stress due to drought and mapping its spatial extent during the growing season. Rule-based regression tree models were generated that identify relationships between satellite-derived vegetation conditions, climatic drought indices, and biophysical data, including land-cover type, available soil water capacity, percent of irrigated farm land, and ecological type. The data mining method builds numerical rule-based models that find relationships among the input variables. Because the models can be applied iteratively with input data from previous time periods, the method enables to provide predictions of vegetation conditions farther into the growing season based on earlier conditions. Visualizing the model outputs as mapped information (called VegPredict) provides a means to evaluate the model. We present prototype maps for the 2002 drought year for Nebraska and South Dakota and discuss potential uses for these maps.

Agent-based modeling of the immune system: NetLogo, a promising framework.

PubMed

Chiacchio, Ferdinando; Pennisi, Marzio; Russo, Giulia; Motta, Santo; Pappalardo, Francesco

2014-01-01

Several components that interact with each other to evolve a complex, and, in some cases, unexpected behavior, represents one of the main and fascinating features of the mammalian immune system. Agent-based modeling and cellular automata belong to a class of discrete mathematical approaches in which entities (agents) sense local information and undertake actions over time according to predefined rules. The strength of this approach is characterized by the appearance of a global behavior that emerges from interactions among agents. This behavior is unpredictable, as it does not follow linear rules. There are a lot of works that investigates the immune system with agent-based modeling and cellular automata. They have shown the ability to see clearly and intuitively into the nature of immunological processes. NetLogo is a multiagent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disciplines and education levels. In this paper, we summarize NetLogo applications to immunology and, particularly, how this framework can help in the development and formulation of hypotheses that might drive further experimental investigations of disease mechanisms.

Co-evolutionary data mining for fuzzy rules: automatic fitness function creation phase space, and experiments

NASA Astrophysics Data System (ADS)

Smith, James F., III; Blank, Joseph A.

2003-03-01

An approach is being explored that involves embedding a fuzzy logic based resource manager in an electronic game environment. Game agents can function under their own autonomous logic or human control. This approach automates the data mining problem. The game automatically creates a cleansed database reflecting the domain expert's knowledge, it calls a data mining function, a genetic algorithm, for data mining of the data base as required and allows easy evaluation of the information extracted. The co-evolutionary fitness functions, chromosomes and stopping criteria for ending the game are discussed. Genetic algorithm and genetic program based data mining procedures are discussed that automatically discover new fuzzy rules and strategies. The strategy tree concept and its relationship to co-evolutionary data mining are examined as well as the associated phase space representation of fuzzy concepts. The overlap of fuzzy concepts in phase space reduces the effective strategies available to adversaries. Co-evolutionary data mining alters the geometric properties of the overlap region known as the admissible region of phase space significantly enhancing the performance of the resource manager. Procedures for validation of the information data mined are discussed and significant experimental results provided.

Negation handling in sentiment classification using rule-based adapted from Indonesian language syntactic for Indonesian text in Twitter

NASA Astrophysics Data System (ADS)

Amalia, Rizkiana; Arif Bijaksana, Moch; Darmantoro, Dhinta

2018-03-01

The presence of the word negation is able to change the polarity of the text if it is not handled properly it will affect the performance of the sentiment classification. Negation words in Indonesian are ‘tidak’, ‘bukan’, ‘belum’ and ‘jangan’. Also, there is a conjunction word that able to reverse the actual values, as the word ‘tetapi’, or ‘tapi’. Unigram has shortcomings in dealing with the existence of negation because it treats negation word and the negated words as separate words. A general approach for negation handling in English text gives the tag ‘NEG_’ for following words after negation until the first punctuation. But this may gives the tag to un-negated, and this approach does not handle negation and conjunction in one sentences. The rule-based method to determine what words negated by adapting the rules of Indonesian language syntactic of negation to determine the scope of negation was proposed in this study. With adapting syntactic rules and tagging “NEG_” using SVM classifier with RBF kernel has better performance results than the other experiments. Considering the average F1-score value, the performance of this proposed method can be improved against baseline equal to 1.79% (baseline without negation handling) and 5% (baseline with existing negation handling) for a dataset that all tweets contain negation words. And also for the second dataset that has the various number of negation words in document tweet. It can be improved against baseline at 2.69% (without negation handling) and 3.17% (with existing negation handling).

A Generic Approach for Pen-Based User Interface Development

NASA Astrophysics Data System (ADS)

Macé, Sébastien; Anquetil, Éric

Pen-based interaction is an intuitive way to realize hand drawn structured documents, but few applications take advantage of it. Indeed, the interpretation of the user hand drawn strokes in the context of document is a complex problem. In this paper, we propose a new generic approach to develop such systems based on three independent components. The first one is a set of graphical and editing functions adapted to pen interaction. The second one is a rule-based formalism that models structured document composition and the corresponding interpretation process. The last one is a hand drawn stroke analyzer that is able to interpret strokes progressively, directly while the user is drawing. We highlight in particular the human-computer interaction induced from this progressive interpretation process. Thanks to this generic approach, three pen-based system prototypes have already been developed, for musical score editing, for graph editing, and for UML class diagram editing

Generalized DSS shell for developing simulation and optimization hydro-economic models of complex water resources systems

NASA Astrophysics Data System (ADS)

Pulido-Velazquez, Manuel; Lopez-Nicolas, Antonio; Harou, Julien J.; Andreu, Joaquin

2013-04-01

Hydrologic-economic models allow integrated analysis of water supply, demand and infrastructure management at the river basin scale. These models simultaneously analyze engineering, hydrology and economic aspects of water resources management. Two new tools have been designed to develop models within this approach: a simulation tool (SIM_GAMS), for models in which water is allocated each month based on supply priorities to competing uses and system operating rules, and an optimization tool (OPT_GAMS), in which water resources are allocated optimally following economic criteria. The characterization of the water resource network system requires a connectivity matrix representing the topology of the elements, generated using HydroPlatform. HydroPlatform, an open-source software platform for network (node-link) models, allows to store, display and export all information needed to characterize the system. Two generic non-linear models have been programmed in GAMS to use the inputs from HydroPlatform in simulation and optimization models. The simulation model allocates water resources on a monthly basis, according to different targets (demands, storage, environmental flows, hydropower production, etc.), priorities and other system operating rules (such as reservoir operating rules). The optimization model's objective function is designed so that the system meets operational targets (ranked according to priorities) each month while following system operating rules. This function is analogous to the one used in the simulation module of the DSS AQUATOOL. Each element of the system has its own contribution to the objective function through unit cost coefficients that preserve the relative priority rank and the system operating rules. The model incorporates groundwater and stream-aquifer interaction (allowing conjunctive use simulation) with a wide range of modeling options, from lumped and analytical approaches to parameter-distributed models (eigenvalue approach). Such functionality is not typically included in other water DSS. Based on the resulting water resources allocation, the model calculates operating and water scarcity costs caused by supply deficits based on economic demand functions for each demand node. The optimization model allocates the available resource over time based on economic criteria (net benefits from demand curves and cost functions), minimizing the total water scarcity and operating cost of water use. This approach provides solutions that optimize the economic efficiency (as total net benefit) in water resources management over the optimization period. Both models must be used together in water resource planning and management. The optimization model provides an initial insight on economically efficient solutions, from which different operating rules can be further developed and tested using the simulation model. The hydro-economic simulation model allows assessing economic impacts of alternative policies or operating criteria, avoiding the perfect foresight issues associated with the optimization. The tools have been applied to the Jucar river basin (Spain) in order to assess the economic results corresponding to the current modus operandi of the system and compare them with the solution from the optimization that maximizes economic efficiency. Acknowledgments: The study has been partially supported by the European Community 7th Framework Project (GENESIS project, n. 226536) and the Plan Nacional I+D+I 2008-2011 of the Spanish Ministry of Science and Innovation (CGL2009-13238-C02-01 and CGL2009-13238-C02-02).

Evaluation of Decision Rules in a Tiered Assessment of Inhalation Exposure to Nanomaterials.

PubMed

Brouwer, Derk; Boessen, Ruud; van Duuren-Stuurman, Birgit; Bard, Delphine; Moehlmann, Carsten; Bekker, Cindy; Fransman, Wouter; Klein Entink, Rinke

2016-10-01

Tiered or stepwise approaches to assess occupational exposure to nano-objects, and their agglomerates and aggregates have been proposed, which require decision rules (DRs) to move to a next tier, or terminate the assessment. In a desk study the performance of a number of DRs based on the evaluation of results from direct reading instruments was investigated by both statistical simulations and the application of the DRs to real workplace data sets. A statistical model that accounts for autocorrelation patterns in time-series, i.e. autoregressive integrated moving average (ARIMA), was used as 'gold' standard. The simulations showed that none of the proposed DRs covered the entire range of simulated scenarios with respect to the ARIMA model parameters, however, a combined DR showed a slightly better agreement. Application of the DRs to real workplace datasets (n = 117) revealed sensitivity up to 0.72, whereas the lowest observed specificity was 0.95. The selection of the most appropriate DR is very much dependent on the consequences of the decision, i.e. ruling in or ruling out of scenarios for further evaluation. Since a basic assessment may also comprise of other type of measurements and information, an evaluation logic was proposed which embeds the DRs, but furthermore supports decision making in view of a tiered-approach exposure assessment. © The Author 2016. Published by Oxford University Press on behalf of the British Occupational Hygiene Society.

Potential Implications of Approaches to Climate Change on the Clean Water Rule Definition of "Waters of the United States".

PubMed

Faust, Derek R; Moore, Matthew T; Emison, Gerald Andrews; Rush, Scott A

2016-05-01

The 1972 Clean Water Act was passed to protect chemical, physical, and biological integrity of United States' waters. The U.S. Environmental Protection Agency and U.S. Army Corps of Engineers codified a new "waters of the United States" rule on June 29, 2015, because several Supreme Court case decisions caused confusion with the existing rule. Climate change could affect this rule through connectivity between groundwater and surface waters; floodplain waters and the 100-year floodplain; changes in jurisdictional status; and sea level rise on coastal ecosystems. Four approaches are discussed for handling these implications: (1) "Wait and see"; (2) changes to the rule; (3) use guidance documents; (4) Congress statutorily defining "waters of the United States." The approach chosen should be legally defensible and achieved in a timely fashion to provide protection to "waters of the United States" in proactive consideration of scientifically documented effects of climate change on aquatic ecosystems.

TEMPTING system: a hybrid method of rule and machine learning for temporal relation extraction in patient discharge summaries.

PubMed

Chang, Yung-Chun; Dai, Hong-Jie; Wu, Johnny Chi-Yang; Chen, Jian-Ming; Tsai, Richard Tzong-Han; Hsu, Wen-Lian

2013-12-01

Patient discharge summaries provide detailed medical information about individuals who have been hospitalized. To make a precise and legitimate assessment of the abundant data, a proper time layout of the sequence of relevant events should be compiled and used to drive a patient-specific timeline, which could further assist medical personnel in making clinical decisions. The process of identifying the chronological order of entities is called temporal relation extraction. In this paper, we propose a hybrid method to identify appropriate temporal links between a pair of entities. The method combines two approaches: one is rule-based and the other is based on the maximum entropy model. We develop an integration algorithm to fuse the results of the two approaches. All rules and the integration algorithm are formally stated so that one can easily reproduce the system and results. To optimize the system's configuration, we used the 2012 i2b2 challenge TLINK track dataset and applied threefold cross validation to the training set. Then, we evaluated its performance on the training and test datasets. The experiment results show that the proposed TEMPTING (TEMPoral relaTion extractING) system (ranked seventh) achieved an F-score of 0.563, which was at least 30% better than that of the baseline system, which randomly selects TLINK candidates from all pairs and assigns the TLINK types. The TEMPTING system using the hybrid method also outperformed the stage-based TEMPTING system. Its F-scores were 3.51% and 0.97% better than those of the stage-based system on the training set and test set, respectively. Copyright © 2013 Elsevier Inc. All rights reserved.

To What Extent Does Children's Spelling Improve as a Result of Learning Words with the Look, Say, Cover, Write, Check, Fix Strategy Compared with Phonological Spelling Strategies?

ERIC Educational Resources Information Center

Dymock, Susan; Nicholson, Tom

2017-01-01

The ubiquitous weekly spelling test assumes that words are best learned by memorisation and testing but is this the best way? This study compared two well-known approaches to spelling instruction, the rule based and visual memory approaches. A group of 55 seven-year-olds in two Year 3 classrooms was taught spelling in small groups for three…

A Distributed Fuzzy Associative Classifier for Big Data.

PubMed

Segatori, Armando; Bechini, Alessio; Ducange, Pietro; Marcelloni, Francesco

2017-09-19

Fuzzy associative classification has not been widely analyzed in the literature, although associative classifiers (ACs) have proved to be very effective in different real domain applications. The main reason is that learning fuzzy ACs is a very heavy task, especially when dealing with large datasets. To overcome this drawback, in this paper, we propose an efficient distributed fuzzy associative classification approach based on the MapReduce paradigm. The approach exploits a novel distributed discretizer based on fuzzy entropy for efficiently generating fuzzy partitions of the attributes. Then, a set of candidate fuzzy association rules is generated by employing a distributed fuzzy extension of the well-known FP-Growth algorithm. Finally, this set is pruned by using three purposely adapted types of pruning. We implemented our approach on the popular Hadoop framework. Hadoop allows distributing storage and processing of very large data sets on computer clusters built from commodity hardware. We have performed an extensive experimentation and a detailed analysis of the results using six very large datasets with up to 11,000,000 instances. We have also experimented different types of reasoning methods. Focusing on accuracy, model complexity, computation time, and scalability, we compare the results achieved by our approach with those obtained by two distributed nonfuzzy ACs recently proposed in the literature. We highlight that, although the accuracies result to be comparable, the complexity, evaluated in terms of number of rules, of the classifiers generated by the fuzzy distributed approach is lower than the one of the nonfuzzy classifiers.

77 FR 35625 - Statement of General Policy on the Sequencing of the Compliance Dates for Final Rules Applicable...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-14

... a ``big bang'' approach where all of the rules to be adopted under Title VII go into effect... `big bang' approach to implementation would be too disruptive to the marketplace--particularly given...

Using SAR Interferograms and Coherence Images for Object-Based Delineation of Unstable Slopes

NASA Astrophysics Data System (ADS)

Friedl, Barbara; Holbling, Daniel

2015-05-01

This study uses synthetic aperture radar (SAR) interferometric products for the semi-automated identification and delineation of unstable slopes and active landslides. Single-pair interferograms and coherence images are therefore segmented and classified in an object-based image analysis (OBIA) framework. The rule-based classification approach has been applied to landslide-prone areas located in Taiwan and Southern Germany. The semi-automatically obtained results were validated against landslide polygons derived from manual interpretation.

Ssk or Esw? -- the Bloor-Lynch Debate Revisited

NASA Astrophysics Data System (ADS)

Cheng, Kai-Yuan

2014-03-01

Philosophical discussions of rule-following in the later Wittgenstein (1953, 1967) are an important source of inspiration for the development of views on the social nature of scientific knowledge. Two major opposing views in this inquiry -- Bloor's sociology of scientific knowledge (SSK) (1983, 1991, 1992, 1997) and Lynch's (1992, 1993) ethnomethodological studies of work (ESW) -- represent two positions derived from two different readings of Wittgenstein's later writings on rule-following. The aim of this paper is two-fold. One is to re-examine the noted Bloor-Lynch debate by considering Kusch's (2004) recent discussion of this debate. Another is to show that a new semantic framework of rule-following ascriptions based on a cognitive approach to the study of generics can be provided such that SSK and ESW are compatible in it (Leslie, 2009; Cheng, 2011).

Formal analysis of imprecise system requirements with Event-B.

PubMed

Le, Hong Anh; Nakajima, Shin; Truong, Ninh Thuan

2016-01-01

Formal analysis of functional properties of system requirements needs precise descriptions. However, the stakeholders sometimes describe the system with ambiguous, vague or fuzzy terms, hence formal frameworks for modeling and verifying such requirements are desirable. The Fuzzy If-Then rules have been used for imprecise requirements representation, but verifying their functional properties still needs new methods. In this paper, we propose a refinement-based modeling approach for specification and verification of such requirements. First, we introduce a representation of imprecise requirements in the set theory. Then we make use of Event-B refinement providing a set of translation rules from Fuzzy If-Then rules to Event-B notations. After that, we show how to verify both safety and eventuality properties with RODIN/Event-B. Finally, we illustrate the proposed method on the example of Crane Controller.

A knowledge-based patient assessment system: conceptual and technical design.

PubMed Central

Reilly, C. A.; Zielstorff, R. D.; Fox, R. L.; O'Connell, E. M.; Carroll, D. L.; Conley, K. A.; Fitzgerald, P.; Eng, T. K.; Martin, A.; Zidik, C. M.; Segal, M.

2000-01-01

This paper describes the design of an inpatient patient assessment application that captures nursing assessment data using a wireless laptop computer. The primary aim of this system is to capture structured information for facilitating decision support and quality monitoring. The system also aims to improve efficiency of recording patient assessments, reduce costs, and improve discharge planning and early identification of patient learning needs. Object-oriented methods were used to elicit functional requirements and to model the proposed system. A tools-based development approach is being used to facilitate rapid development and easy modification of assessment items and rules for decision support. Criteria for evaluation include perceived utility by clinician users, validity of decision support rules, time spent recording assessments, and perceived utility of aggregate reports for quality monitoring. PMID:11079970

A dual-process model of reactions to perceived stigma.

PubMed

Pryor, John B; Reeder, Glenn D; Yeadon, Christopher; Hesson-McLnnis, Matthew

2004-10-01

The authors propose a theoretical model of individual psychological reactions to perceived stigma. This model suggests that 2 psychological systems may be involved in reactions to stigma across a variety of social contexts. One system is primarily reflexive, or associative, whereas the other is rule based, or reflective. This model assumes a temporal pattern of reactions to the stigmatized, such that initial reactions are governed by the reflexive system, whereas subsequent reactions or "adjustments" are governed by the rule-based system. Support for this model was found in 2 studies. Both studies examined participants' moment-by-moment approach-avoidance reactions to the stigmatized. The 1st involved participants' reactions to persons with HIV/AIDS, and the 2nd, participants' reactions to 15 different stigmatizing conditions. (c) 2004 APA, all rights reserved

A knowledge-based patient assessment system: conceptual and technical design.

PubMed

Reilly, C A; Zielstorff, R D; Fox, R L; O'Connell, E M; Carroll, D L; Conley, K A; Fitzgerald, P; Eng, T K; Martin, A; Zidik, C M; Segal, M

2000-01-01

This paper describes the design of an inpatient patient assessment application that captures nursing assessment data using a wireless laptop computer. The primary aim of this system is to capture structured information for facilitating decision support and quality monitoring. The system also aims to improve efficiency of recording patient assessments, reduce costs, and improve discharge planning and early identification of patient learning needs. Object-oriented methods were used to elicit functional requirements and to model the proposed system. A tools-based development approach is being used to facilitate rapid development and easy modification of assessment items and rules for decision support. Criteria for evaluation include perceived utility by clinician users, validity of decision support rules, time spent recording assessments, and perceived utility of aggregate reports for quality monitoring.

Reconciled Rat and Human Metabolic Networks for Comparative Toxicogenomics and Biomarker Predictions

DTIC Science & Technology

2017-02-08

compared with the original human GPR rules (Supplementary Fig. 3). The consensus-based approach for filtering orthology annotations was designed to...ARTICLE Received 29 Jan 2016 | Accepted 13 Dec 2016 | Published 8 Feb 2017 Reconciled rat and human metabolic networks for comparative toxicogenomics...predictions in response to 76 drugs. We validate comparative predictions for xanthine derivatives with new experimental data and literature- based evidence

Finite element analysis of container ship's cargo hold using ANSYS and POSEIDON software

NASA Astrophysics Data System (ADS)

Tanny, Tania Tamiz; Akter, Naznin; Amin, Osman Md.

2017-12-01

Nowadays ship structural analysis has become an integral part of the preliminary ship design providing further support for the development and detail design of ship structures. Structural analyses of container ship's cargo holds are carried out for the balancing of their safety and capacity, as those ships are exposed to the high risk of structural damage during voyage. Two different design methodologies have been considered for the structural analysis of a container ship's cargo hold. One is rule-based methodology and the other is a more conventional software based analyses. The rule based analysis is done by DNV-GL's software POSEIDON and the conventional package based analysis is done by ANSYS structural module. Both methods have been applied to analyze some of the mechanical properties of the model such as total deformation, stress-strain distribution, Von Mises stress, Fatigue etc., following different design bases and approaches, to indicate some guidance's for further improvements in ship structural design.

Knowledge-based processing for aircraft flight control

NASA Technical Reports Server (NTRS)

Painter, John H.

1991-01-01

The purpose is to develop algorithms and architectures for embedding artificial intelligence in aircraft guidance and control systems. With the approach adopted, AI-computing is used to create an outer guidance loop for driving the usual aircraft autopilot. That is, a symbolic processor monitors the operation and performance of the aircraft. Then, based on rules and other stored knowledge, commands are automatically formulated for driving the autopilot so as to accomplish desired flight operations. The focus is on developing a software system which can respond to linguistic instructions, input in a standard format, so as to formulate a sequence of simple commands to the autopilot. The instructions might be a fairly complex flight clearance, input either manually or by data-link. Emphasis is on a software system which responds much like a pilot would, employing not only precise computations, but, also, knowledge which is less precise, but more like common-sense. The approach is based on prior work to develop a generic 'shell' architecture for an AI-processor, which may be tailored to many applications by describing the application in appropriate processor data bases (libraries). Such descriptions include numerical models of the aircraft and flight control system, as well as symbolic (linguistic) descriptions of flight operations, rules, and tactics.

Towards computerizing intensive care sedation guidelines: design of a rule-based architecture for automated execution of clinical guidelines

PubMed Central

2010-01-01

Background Computerized ICUs rely on software services to convey the medical condition of their patients as well as assisting the staff in taking treatment decisions. Such services are useful for following clinical guidelines quickly and accurately. However, the development of services is often time-consuming and error-prone. Consequently, many care-related activities are still conducted based on manually constructed guidelines. These are often ambiguous, which leads to unnecessary variations in treatments and costs. The goal of this paper is to present a semi-automatic verification and translation framework capable of turning manually constructed diagrams into ready-to-use programs. This framework combines the strengths of the manual and service-oriented approaches while decreasing their disadvantages. The aim is to close the gap in communication between the IT and the medical domain. This leads to a less time-consuming and error-prone development phase and a shorter clinical evaluation phase. Methods A framework is proposed that semi-automatically translates a clinical guideline, expressed as an XML-based flow chart, into a Drools Rule Flow by employing semantic technologies such as ontologies and SWRL. An overview of the architecture is given and all the technology choices are thoroughly motivated. Finally, it is shown how this framework can be integrated into a service-oriented architecture (SOA). Results The applicability of the Drools Rule language to express clinical guidelines is evaluated by translating an example guideline, namely the sedation protocol used for the anaesthetization of patients, to a Drools Rule Flow and executing and deploying this Rule-based application as a part of a SOA. The results show that the performance of Drools is comparable to other technologies such as Web Services and increases with the number of decision nodes present in the Rule Flow. Most delays are introduced by loading the Rule Flows. Conclusions The framework is an effective solution for computerizing clinical guidelines as it allows for quick development, evaluation and human-readable visualization of the Rules and has a good performance. By monitoring the parameters of the patient to automatically detect exceptional situations and problems and by notifying the medical staff of tasks that need to be performed, the computerized sedation guideline improves the execution of the guideline. PMID:20082700

Towards computerizing intensive care sedation guidelines: design of a rule-based architecture for automated execution of clinical guidelines.

PubMed

Ongenae, Femke; De Backere, Femke; Steurbaut, Kristof; Colpaert, Kirsten; Kerckhove, Wannes; Decruyenaere, Johan; De Turck, Filip

2010-01-18

Computerized ICUs rely on software services to convey the medical condition of their patients as well as assisting the staff in taking treatment decisions. Such services are useful for following clinical guidelines quickly and accurately. However, the development of services is often time-consuming and error-prone. Consequently, many care-related activities are still conducted based on manually constructed guidelines. These are often ambiguous, which leads to unnecessary variations in treatments and costs.The goal of this paper is to present a semi-automatic verification and translation framework capable of turning manually constructed diagrams into ready-to-use programs. This framework combines the strengths of the manual and service-oriented approaches while decreasing their disadvantages. The aim is to close the gap in communication between the IT and the medical domain. This leads to a less time-consuming and error-prone development phase and a shorter clinical evaluation phase. A framework is proposed that semi-automatically translates a clinical guideline, expressed as an XML-based flow chart, into a Drools Rule Flow by employing semantic technologies such as ontologies and SWRL. An overview of the architecture is given and all the technology choices are thoroughly motivated. Finally, it is shown how this framework can be integrated into a service-oriented architecture (SOA). The applicability of the Drools Rule language to express clinical guidelines is evaluated by translating an example guideline, namely the sedation protocol used for the anaesthetization of patients, to a Drools Rule Flow and executing and deploying this Rule-based application as a part of a SOA. The results show that the performance of Drools is comparable to other technologies such as Web Services and increases with the number of decision nodes present in the Rule Flow. Most delays are introduced by loading the Rule Flows. The framework is an effective solution for computerizing clinical guidelines as it allows for quick development, evaluation and human-readable visualization of the Rules and has a good performance. By monitoring the parameters of the patient to automatically detect exceptional situations and problems and by notifying the medical staff of tasks that need to be performed, the computerized sedation guideline improves the execution of the guideline.

Training the rivers and exploring the coasts. Knowledge evolution in the Netherlands in two engineering fields between 1800 and 1940

NASA Astrophysics Data System (ADS)

Toussaint, Bert

In this paper, the author wants to explore the knowledge development in two crucial fields, river management and coast management in the 19th century and first decades of the 20th century. Were there similar characteristics in this development? Which types of knowledge can be distinguished? Who were the principal actors in these processes? Did the knowledge evolution have a Dutch stamp or a rather international flavour? To structure the analysis, the author uses the concept of technology regime, a set of technical rules which shapes the know-how of engineers, their design rules and research processes. The analysis shows that the knowledge development of river management and coastal management followed different evolution paths between 1800 and 1940. In the field of river management, a substantial amount of mathematical and physical theories had been gradually developed since the end of the 17th century. After 1850, the regularization approach met gradually a widespread support. Empirical data, design rules, theoretical knowledge and engineering pivoted around the regularization approach, and a technology regime around this approach emerged. The regularization regime further developed in the 20th century, and handbooks were increasingly shaped by mathematical and physical reasoning and formulas. On the other hand, coastal management was until the 1880s a rather marginal activity. Coastal engineering was an extremely complex and multidimensional field of knowledge which no engineer was able to grasp. The foundation of a Dutch weather institute was a first important step towards a more theoretical approach. The Zuiderzee works (starting in 1925) gave probably the most important stimuli to scientific coastal research. It was also a main factor in setting up scientific institutes by Rijkswaterstaat. So from the 1920s, Rijkswaterstaat became a major producer of scientific knowledge, not only in tidal modelling but also in coastal research. Due to a multidisciplinary knowledge network, coastal research transformed from a marginal to a first-rank scientific field, and this transformation enabled Rijkswaterstaat to set a much higher level of ambition in coastal management. The 1953 flood and the Deltaworks marked a new era. New design rules for sea dykes and river levees, based on a revolutionary statistical risk approach were determined, and design rules for the Deltaworks estuary closures were developed, being enabled by the development of hydraulic research.

Sum rules and the role of pressure on the excitation spectrum of a confined hydrogen atom by a spherical cavity

NASA Astrophysics Data System (ADS)

Cabrera-Trujillo, R.

2017-08-01

Sum rule relations over the excitation spectrum of a quantum system contain information about both the energy spectrum and eigenfunctions of the system in a compact form, particularly regarding closure relations. In this work, the effects of pressure induced by a spherical cavity on an atomic hydrogen impurity on the dipole oscillator strength (DOS) sum rule, S k , and its logarithmic version, L k , are studied by means of a numerical approach based on a finite-difference solution to the Schrödinger equation. Pressure effects are accounted for by means of a spherical cavity of radius R 0 immersed in a medium characterized by a penetrable potential height V 0. The DOS sum rules S k and L k are investigated as a function of these cavity parameters and thus directly related to the impurity static pressure and surrounding material. One finds that the sum rules are fulfilled within the numerical precision for low pressure conditions. However, when the barrier height is large or infinite (a non-penetrable cavity), the sum rule, for positive k, differs from its closure relation. One finds that this occurs for a cavity radius {R}0< 6 au, corresponding to a pressure such that the first p-state that contributes to the sum rule has positive energy and it is due to the fact that the spherical confinement cavity potential dominates over the Coulombic interaction for the hydrogenic impurity. Thus, as pressure increases, the excitation spectrum approaches that of a particle confined by a spherical cavity while the ground state is slightly affected by the cavity and more closely resembles a hydrogenic atom. Therefore, the sum rule over the excitation spectrum tends to a particle confined by a spherical cavity, while the closure relation gives that of a confined hydrogen atom in the ground state. For negative k, low excitations are the most important and this behavior is not presented. As the {S}-2 sum rule is the static dipole polarizability, the results are compared to available data in the literature, showing excellent agreement. This behavior in the sum rule and oscillator strength in electron-impurity excitations affects optical transitions of importance in semiconductor nanostructures.

Designing P-Optimal Item Pools in Computerized Adaptive Tests with Polytomous Items

ERIC Educational Resources Information Center

Zhou, Xuechun

2012-01-01

Current CAT applications consist of predominantly dichotomous items, and CATs with polytomously scored items are limited. To ascertain the best approach to polytomous CAT, a significant amount of research has been conducted on item selection, ability estimation, and impact of termination rules based on polytomous IRT models. Few studies…

Neural Networks for the Beginner.

ERIC Educational Resources Information Center

Snyder, Robin M.

Motivated by the brain, neural networks are a right-brained approach to artificial intelligence that is used to recognize patterns based on previous training. In practice, one would not program an expert system to recognize a pattern and one would not train a neural network to make decisions from rules; but one could combine the best features of…

Interest Groups, the Courts, and Educational Equality: A Policy Regimes Approach to "Vergara v. California"

ERIC Educational Resources Information Center

Superfine, Benjamin Michael; Thompson, Alea R.

2016-01-01

In "Vergara v. California" (2014), a trial-level court ruled that California laws governing teacher tenure and dismissal were unconstitutional. This study analyzes "Vergara" in light of the shifting use of the courts to promote equal educational opportunities and the changing power bases of educational interest groups,…

Design a Fuzzy Rule-based Expert System to Aid Earlier Diagnosis of Gastric Cancer.

PubMed

Safdari, Reza; Arpanahi, Hadi Kazemi; Langarizadeh, Mostafa; Ghazisaiedi, Marjan; Dargahi, Hossein; Zendehdel, Kazem

2018-01-01

Screening and health check-up programs are most important sanitary priorities, that should be undertaken to control dangerous diseases such as gastric cancer that affected by different factors. More than 50% of gastric cancer diagnoses are made during the advanced stage. Currently, there is no systematic approach for early diagnosis of gastric cancer. to develop a fuzzy expert system that can identify gastric cancer risk levels in individuals. This system was implemented in MATLAB software, Mamdani inference technique applied to simulate reasoning of experts in the field, a total of 67 fuzzy rules extracted as a rule-base based on medical expert's opinion. 50 case scenarios were used to evaluate the system, the information of case reports is given to the system to find risk level of each case report then obtained results were compared with expert's diagnosis. Results revealed that sensitivity was 92.1% and the specificity was 83.1%. The results show that is possible to develop a system that can identify High risk individuals for gastric cancer. The system can lead to earlier diagnosis, this may facilitate early treatment and reduce gastric cancer mortality rate.

Emergence of a coherent and cohesive swarm based on mutual anticipation

PubMed Central

Murakami, Hisashi; Niizato, Takayuki; Gunji, Yukio-Pegio

2017-01-01

Collective behavior emerging out of self-organization is one of the most striking properties of an animal group. Typically, it is hypothesized that each individual in an animal group tends to align its direction of motion with those of its neighbors. Most previous models for collective behavior assume an explicit alignment rule, by which an agent matches its velocity with that of neighbors in a certain neighborhood, to reproduce a collective order pattern by simple interactions. Recent empirical studies, however, suggest that there is no evidence for explicit matching of velocity, and that collective polarization arises from interactions other than those that follow the explicit alignment rule. We here propose a new lattice-based computational model that does not incorporate the explicit alignment rule but is based instead on mutual anticipation and asynchronous updating. Moreover, we show that this model can realize densely collective motion with high polarity. Furthermore, we focus on the behavior of a pair of individuals, and find that the turning response is drastically changed depending on the distance between two individuals rather than the relative heading, and is consistent with the empirical observations. Therefore, the present results suggest that our approach provides an alternative model for collective behavior. PMID:28406173

Fuzzy logic techniques for rendezvous and docking of two geostationary satellites

NASA Technical Reports Server (NTRS)

Ortega, Guillermo

1995-01-01

Large assemblings in space require the ability to manage rendezvous and docking operations. In future these techniques will be required for the gradual build up of big telecommunication platforms in the geostationary orbit. The paper discusses the use of fuzzy logic to model and implement a control system for the docking/berthing of two satellites in geostationary orbit. The system mounted in a chaser vehicle determines the actual state of both satellites and generates torques to execute maneuvers to establish the structural latching. The paper describes the proximity operations to collocate the two satellites in the same orbital window, the fuzzy guidance and navigation of the chaser approaching the target and the final Fuzzy berthing. The fuzzy logic system represents a knowledge based controller that realizes the close loop operations autonomously replacing the conventional control algorithms. The goal is to produce smooth control actions in the proximity of the target and during the docking to avoid disturbance torques in the final assembly orbit. The knowledge of the fuzzy controller consists of a data base of rules and the definitions of the fuzzy sets. The knowledge of an experienced spacecraft controller is captured into a set of rules forming the Rules Data Base.

Deductive Glue Code Synthesis for Embedded Software Systems Based on Code Patterns

NASA Technical Reports Server (NTRS)

Liu, Jian; Fu, Jicheng; Zhang, Yansheng; Bastani, Farokh; Yen, I-Ling; Tai, Ann; Chau, Savio N.

2006-01-01

Automated code synthesis is a constructive process that can be used to generate programs from specifications. It can, thus, greatly reduce the software development cost and time. The use of formal code synthesis approach for software generation further increases the dependability of the system. Though code synthesis has many potential benefits, the synthesis techniques are still limited. Meanwhile, components are widely used in embedded system development. Applying code synthesis to component based software development (CBSD) process can greatly enhance the capability of code synthesis while reducing the component composition efforts. In this paper, we discuss the issues and techniques for applying deductive code synthesis techniques to CBSD. For deductive synthesis in CBSD, a rule base is the key for inferring appropriate component composition. We use the code patterns to guide the development of rules. Code patterns have been proposed to capture the typical usages of the components. Several general composition operations have been identified to facilitate systematic composition. We present the technique for rule development and automated generation of new patterns from existing code patterns. A case study of using this method in building a real-time control system is also presented.

Object-based land-cover classification for metropolitan Phoenix, Arizona, using aerial photography

NASA Astrophysics Data System (ADS)

Li, Xiaoxiao; Myint, Soe W.; Zhang, Yujia; Galletti, Chritopher; Zhang, Xiaoxiang; Turner, Billie L.

2014-12-01

Detailed land-cover mapping is essential for a range of research issues addressed by the sustainability and land system sciences and planning. This study uses an object-based approach to create a 1 m land-cover classification map of the expansive Phoenix metropolitan area through the use of high spatial resolution aerial photography from National Agricultural Imagery Program. It employs an expert knowledge decision rule set and incorporates the cadastral GIS vector layer as auxiliary data. The classification rule was established on a hierarchical image object network, and the properties of parcels in the vector layer were used to establish land cover types. Image segmentations were initially utilized to separate the aerial photos into parcel sized objects, and were further used for detailed land type identification within the parcels. Characteristics of image objects from contextual and geometrical aspects were used in the decision rule set to reduce the spectral limitation of the four-band aerial photography. Classification results include 12 land-cover classes and subclasses that may be assessed from the sub-parcel to the landscape scales, facilitating examination of scale dynamics. The proposed object-based classification method provides robust results, uses minimal and readily available ancillary data, and reduces computational time.

Towards a framework for agent-based image analysis of remote-sensing data

PubMed Central

Hofmann, Peter; Lettmayer, Paul; Blaschke, Thomas; Belgiu, Mariana; Wegenkittl, Stefan; Graf, Roland; Lampoltshammer, Thomas Josef; Andrejchenko, Vera

2015-01-01

Object-based image analysis (OBIA) as a paradigm for analysing remotely sensed image data has in many cases led to spatially and thematically improved classification results in comparison to pixel-based approaches. Nevertheless, robust and transferable object-based solutions for automated image analysis capable of analysing sets of images or even large image archives without any human interaction are still rare. A major reason for this lack of robustness and transferability is the high complexity of image contents: Especially in very high resolution (VHR) remote-sensing data with varying imaging conditions or sensor characteristics, the variability of the objects’ properties in these varying images is hardly predictable. The work described in this article builds on so-called rule sets. While earlier work has demonstrated that OBIA rule sets bear a high potential of transferability, they need to be adapted manually, or classification results need to be adjusted manually in a post-processing step. In order to automate these adaptation and adjustment procedures, we investigate the coupling, extension and integration of OBIA with the agent-based paradigm, which is exhaustively investigated in software engineering. The aims of such integration are (a) autonomously adapting rule sets and (b) image objects that can adopt and adjust themselves according to different imaging conditions and sensor characteristics. This article focuses on self-adapting image objects and therefore introduces a framework for agent-based image analysis (ABIA). PMID:27721916

Towards a framework for agent-based image analysis of remote-sensing data.

PubMed

Hofmann, Peter; Lettmayer, Paul; Blaschke, Thomas; Belgiu, Mariana; Wegenkittl, Stefan; Graf, Roland; Lampoltshammer, Thomas Josef; Andrejchenko, Vera

2015-04-03

Object-based image analysis (OBIA) as a paradigm for analysing remotely sensed image data has in many cases led to spatially and thematically improved classification results in comparison to pixel-based approaches. Nevertheless, robust and transferable object-based solutions for automated image analysis capable of analysing sets of images or even large image archives without any human interaction are still rare. A major reason for this lack of robustness and transferability is the high complexity of image contents: Especially in very high resolution (VHR) remote-sensing data with varying imaging conditions or sensor characteristics, the variability of the objects' properties in these varying images is hardly predictable. The work described in this article builds on so-called rule sets. While earlier work has demonstrated that OBIA rule sets bear a high potential of transferability, they need to be adapted manually, or classification results need to be adjusted manually in a post-processing step. In order to automate these adaptation and adjustment procedures, we investigate the coupling, extension and integration of OBIA with the agent-based paradigm, which is exhaustively investigated in software engineering. The aims of such integration are (a) autonomously adapting rule sets and (b) image objects that can adopt and adjust themselves according to different imaging conditions and sensor characteristics. This article focuses on self-adapting image objects and therefore introduces a framework for agent-based image analysis (ABIA).

Enhancing apoptosis in TRAIL-resistant cancer cells using fundamental response rules

PubMed Central

Piras, Vincent; Hayashi, Kentaro; Tomita, Masaru; Selvarajoo, Kumar

2011-01-01

The tumor necrosis factor related apoptosis-inducing ligand (TRAIL) induces apoptosis in malignant cells, while leaving other cells mostly unharmed. However, several carcinomas remain resistant to TRAIL. To investigate the resistance mechanisms in TRAIL-stimulated human fibrosarcoma (HT1080) cells, we developed a computational model to analyze the temporal activation profiles of cell survival (IκB, JNK, p38) and apoptotic (caspase-8 and -3) molecules in wildtype and several (FADD, RIP1, TRAF2 and caspase-8) knock-down conditions. Based on perturbation-response approach utilizing the law of information (signaling flux) conservation, we derived response rules for population-level average cell response. From this approach, i) a FADD-independent pathway to activate p38 and JNK, ii) a crosstalk between RIP1 and p38, and iii) a crosstalk between p62 and JNK are predicted. Notably, subsequent simulations suggest that targeting a novel molecule at p62/sequestosome-1 junction will optimize apoptosis through signaling flux redistribution. This study offers a valuable prospective to sensitive TRAIL-based therapy. PMID:22355661

Bayesian Estimation of Combined Accuracy for Tests with Verification Bias

PubMed Central

Broemeling, Lyle D.

2011-01-01

This presentation will emphasize the estimation of the combined accuracy of two or more tests when verification bias is present. Verification bias occurs when some of the subjects are not subject to the gold standard. The approach is Bayesian where the estimation of test accuracy is based on the posterior distribution of the relevant parameter. Accuracy of two combined binary tests is estimated employing either “believe the positive” or “believe the negative” rule, then the true and false positive fractions for each rule are computed for two tests. In order to perform the analysis, the missing at random assumption is imposed, and an interesting example is provided by estimating the combined accuracy of CT and MRI to diagnose lung cancer. The Bayesian approach is extended to two ordinal tests when verification bias is present, and the accuracy of the combined tests is based on the ROC area of the risk function. An example involving mammography with two readers with extreme verification bias illustrates the estimation of the combined test accuracy for ordinal tests. PMID:26859487

Detecting Slums from Quick Bird Data in Pune Using AN Object Oriented Approach

NASA Astrophysics Data System (ADS)

Shekhar, S.

2012-07-01

We have been witnessing a gradual and steady transformation from a pre dominantly rural society to an urban society in India and by 2030, it will have more people living in urban than rural areas. Slums formed an integral part of Indian urbanisation as most of the Indian cities lack in basic needs of an acceptable life. Many efforts are being taken to improve their conditions. To carry out slum renewal programs and monitor its implementation, slum settlements should be recorded to obtain an adequate spatial data base. This can be only achieved through the analysis of remote sensing data with very high spatial resolution. Regarding the occurrences of settlement areas in the remote sensing data pixel-based approach on a high resolution image is unable to represent the heterogeneity of complex urban environments. Hence there is a need for sophisticated method and data for slum analysis. An attempt has been made to detect and discriminate the slums of Pune city by describing typical characteristics of these settlements, by using eCognition software from quick bird data on the basis of object oriented approach. Based on multi resolution segmentation, initial objects were created and further depend on texture, geometry and contextual characteristics of the image objects, they were classified into slums and non-slums. The developed rule base allowed the description of knowledge about phenomena clearly and easily using fuzzy membership functions and the described knowledge stored in the classification rule base led to the best classification with more than 80% accuracy.

SPARQL Query Re-writing Using Partonomy Based Transformation Rules

NASA Astrophysics Data System (ADS)

Jain, Prateek; Yeh, Peter Z.; Verma, Kunal; Henson, Cory A.; Sheth, Amit P.

Often the information present in a spatial knowledge base is represented at a different level of granularity and abstraction than the query constraints. For querying ontology's containing spatial information, the precise relationships between spatial entities has to be specified in the basic graph pattern of SPARQL query which can result in long and complex queries. We present a novel approach to help users intuitively write SPARQL queries to query spatial data, rather than relying on knowledge of the ontology structure. Our framework re-writes queries, using transformation rules to exploit part-whole relations between geographical entities to address the mismatches between query constraints and knowledge base. Our experiments were performed on completely third party datasets and queries. Evaluations were performed on Geonames dataset using questions from National Geographic Bee serialized into SPARQL and British Administrative Geography Ontology using questions from a popular trivia website. These experiments demonstrate high precision in retrieval of results and ease in writing queries.

Design rules for successful governmental payments for ecosystem services: Taking agri-environmental measures in Germany as an example.

PubMed

Meyer, Claas; Reutter, Michaela; Matzdorf, Bettina; Sattler, Claudia; Schomers, Sarah

2015-07-01

In recent years, increasing attention has been paid to financial environmental policy instruments that have played important roles in solving agri-environmental problems throughout the world, particularly in the European Union and the United States. The ample and increasing literature on Payments for Ecosystem Services (PES) and agri-environmental measures (AEMs), generally understood as governmental PES, shows that certain single design rules may have an impact on the success of a particular measure. Based on this research, we focused on the interplay of several design rules and conducted a comparative analysis of AEMs' institutional arrangements by examining 49 German cases. We analyzed the effects of the design rules and certain rule combinations on the success of AEMs. Compliance and noncompliance with the hypothesized design rules and the success of the AEMs were surveyed by questioning the responsible agricultural administration and the AEMs' mid-term evaluators. The different rules were evaluated in regard to their necessity and sufficiency for success using Qualitative Comparative Analysis (QCA). Our results show that combinations of certain design rules such as environmental goal targeting and area targeting conditioned the success of the AEMs. Hence, we generalize design principles for AEMs and discuss implications for the general advancement of ecosystem services and the PES approach in agri-environmental policies. Moreover, we highlight the relevance of the results for governmental PES program research and design worldwide. Copyright © 2015 Elsevier Ltd. All rights reserved.

Rules in School. Strategies for Teachers Series.

ERIC Educational Resources Information Center

Brady, Kathryn; Forton, Mary Beth; Porter, Deborah; Wood, Chip

This book offers an approach for helping K-8 students become invested in creating and living by classroom rules. It provides techniques for: helping students articulate their hopes and dreams for school; involving students in generating classroom rules that grow out of their hopes and dreams; modeling, practicing, and role playing the rules; using…

Flip-angle based ratiometric approach for pulsed CEST-MRI pH imaging

NASA Astrophysics Data System (ADS)

Arena, Francesca; Irrera, Pietro; Consolino, Lorena; Colombo Serra, Sonia; Zaiss, Moritz; Longo, Dario Livio

2018-02-01

Several molecules have been exploited for developing MRI pH sensors based on the chemical exchange saturation transfer (CEST) technique. A ratiometric approach, based on the saturation of two exchanging pools at the same saturation power, or by varying the saturation power levels on the same pool, is usually needed to rule out the concentration term from the pH measurement. However, all these methods have been demonstrated by using a continuous wave saturation scheme that limits its translation to clinical scanners. This study shows a new ratiometric CEST-MRI pH-mapping approach based on a pulsed CEST saturation scheme for a radiographic contrast agent (iodixanol) possessing a single chemical exchange site. This approach is based on the ratio of the CEST contrast effects at two different flip angles combinations (180°/360° and 180°/720°), keeping constant the mean irradiation RF power (Bavg power). The proposed ratiometric approach index is concentration independent and it showed good pH sensitivity and accuracy in the physiological range between 6.0 and 7.4.

Retrospective checking of compliance with practice guidelines for acute stroke care: a novel experiment using openEHR’s Guideline Definition Language

PubMed Central

2014-01-01

Background Providing scalable clinical decision support (CDS) across institutions that use different electronic health record (EHR) systems has been a challenge for medical informatics researchers. The lack of commonly shared EHR models and terminology bindings has been recognised as a major barrier to sharing CDS content among different organisations. The openEHR Guideline Definition Language (GDL) expresses CDS content based on openEHR archetypes and can support any clinical terminologies or natural languages. Our aim was to explore in an experimental setting the practicability of GDL and its underlying archetype formalism. A further aim was to report on the artefacts produced by this new technological approach in this particular experiment. We modelled and automatically executed compliance checking rules from clinical practice guidelines for acute stroke care. Methods We extracted rules from the European clinical practice guidelines as well as from treatment contraindications for acute stroke care and represented them using GDL. Then we executed the rules retrospectively on 49 mock patient cases to check the cases’ compliance with the guidelines, and manually validated the execution results. We used openEHR archetypes, GDL rules, the openEHR reference information model, reference terminologies and the Data Archetype Definition Language. We utilised the open-sourced GDL Editor for authoring GDL rules, the international archetype repository for reusing archetypes, the open-sourced Ocean Archetype Editor for authoring or modifying archetypes and the CDS Workbench for executing GDL rules on patient data. Results We successfully represented clinical rules about 14 out of 19 contraindications for thrombolysis and other aspects of acute stroke care with 80 GDL rules. These rules are based on 14 reused international archetypes (one of which was modified), 2 newly created archetypes and 51 terminology bindings (to three terminologies). Our manual compliance checks for 49 mock patients were a complete match versus the automated compliance results. Conclusions Shareable guideline knowledge for use in automated retrospective checking of guideline compliance may be achievable using GDL. Whether the same GDL rules can be used for at-the-point-of-care CDS remains unknown. PMID:24886468

Effects of plasmon pole models on the G0W0 electronic structure of various oxides

NASA Astrophysics Data System (ADS)

Miglio, A.; Waroquiers, D.; Antonius, G.; Giantomassi, M.; Stankovski, M.; Côté, M.; Gonze, X.; Rignanese, G.-M.

2012-09-01

The electronic properties of three different oxides (ZnO, SnO2 and SiO2) are investigated within many-body perturbation theory in the G 0 W 0 approximation. The frequency dependence of the dielectric function is either approximated using two different well-established plasmon-pole models (one of which enforces the fulfillment of the f-sum rule) or treated explicitly by means of the contour-deformation approach. Comparing these results, it is found that the plasmon-pole model enforcing the f-sum rule gives less accurate results for all three oxides. The calculated electronic properties are also compared with the available experimental data and previous ab initio results, focusing on the d state binding energies. The G 0 W 0 approach leads to significantly improved band gaps with respect to calculations based on the density functional theory in the local density approximation.

A novel single neuron perceptron with universal approximation and XOR computation properties.

PubMed

Lotfi, Ehsan; Akbarzadeh-T, M-R

2014-01-01

We propose a biologically motivated brain-inspired single neuron perceptron (SNP) with universal approximation and XOR computation properties. This computational model extends the input pattern and is based on the excitatory and inhibitory learning rules inspired from neural connections in the human brain's nervous system. The resulting architecture of SNP can be trained by supervised excitatory and inhibitory online learning rules. The main features of proposed single layer perceptron are universal approximation property and low computational complexity. The method is tested on 6 UCI (University of California, Irvine) pattern recognition and classification datasets. Various comparisons with multilayer perceptron (MLP) with gradient decent backpropagation (GDBP) learning algorithm indicate the superiority of the approach in terms of higher accuracy, lower time, and spatial complexity, as well as faster training. Hence, we believe the proposed approach can be generally applicable to various problems such as in pattern recognition and classification.

Integrated modelling of stormwater treatment systems uptake.

PubMed

Castonguay, A C; Iftekhar, M S; Urich, C; Bach, P M; Deletic, A

2018-05-24

Nature-based solutions provide a variety of benefits in growing cities, ranging from stormwater treatment to amenity provision such as aesthetics. However, the decision-making process involved in the installation of such green infrastructure is not straightforward, as much uncertainty around the location, size, costs and benefits impedes systematic decision-making. We developed a model to simulate decision rules used by local municipalities to install nature-based stormwater treatment systems, namely constructed wetlands, ponds/basins and raingardens. The model was used to test twenty-four scenarios of policy-making, by combining four asset selection, two location selection and three budget constraint decision rules. Based on the case study of a local municipality in Metropolitan Melbourne, Australia, the modelled uptake of stormwater treatment systems was compared with attributes of real-world systems for the simulation period. Results show that the actual budgeted funding is not reliable to predict systems' uptake and that policy-makers are more likely to plan expenditures based on installation costs. The model was able to replicate the cumulative treatment capacity and the location of systems. As such, it offers a novel approach to investigate the impact of using different decision rules to provide environmental services considering biophysical and economic factors. Copyright © 2018 Elsevier Ltd. All rights reserved.

Association algorithm to mine the rules that govern enzyme definition and to classify protein sequences

PubMed Central

Chiu, Shih-Hau; Chen, Chien-Chi; Yuan, Gwo-Fang; Lin, Thy-Hou

2006-01-01

Background The number of sequences compiled in many genome projects is growing exponentially, but most of them have not been characterized experimentally. An automatic annotation scheme must be in an urgent need to reduce the gap between the amount of new sequences produced and reliable functional annotation. This work proposes rules for automatically classifying the fungus genes. The approach involves elucidating the enzyme classifying rule that is hidden in UniProt protein knowledgebase and then applying it for classification. The association algorithm, Apriori, is utilized to mine the relationship between the enzyme class and significant InterPro entries. The candidate rules are evaluated for their classificatory capacity. Results There were five datasets collected from the Swiss-Prot for establishing the annotation rules. These were treated as the training sets. The TrEMBL entries were treated as the testing set. A correct enzyme classification rate of 70% was obtained for the prokaryote datasets and a similar rate of about 80% was obtained for the eukaryote datasets. The fungus training dataset which lacks an enzyme class description was also used to evaluate the fungus candidate rules. A total of 88 out of 5085 test entries were matched with the fungus rule set. These were otherwise poorly annotated using their functional descriptions. Conclusion The feasibility of using the method presented here to classify enzyme classes based on the enzyme domain rules is evident. The rules may be also employed by the protein annotators in manual annotation or implemented in an automatic annotation flowchart. PMID:16776838