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.

Analysis of Autopilot Behavior

NASA Technical Reports Server (NTRS)

Sherry, Lance; Polson, Peter; Feay, Mike; Palmer, Everett; Null, Cynthia H. (Technical Monitor)

1998-01-01

Aviation and cognitive science researchers have identified situations in which the pilot's expectations for behavior of autopilot avionics are not matched by the actual behavior of the avionics. These "automation surprises" have been attributed to differences between the pilot's model of the behavior of the avionics and the actual behavior encoded in the avionics software. A formal technique is described for the analysis and measurement of the behavior of the cruise pitch modes of a modern Autopilot. The analysis characterizes the behavior of the Autopilot as situation-action rules. The behavior of the cruise pitch mode logic for a contemporary modern Autopilot was found to include 177 rules, including Level Change (23), Vertical Speed (16), Altitude Capture (50), and Altitude Hold (88). These rules are determined based on the values of 62 inputs. Analysis of the rule-based model also shed light on the factors cited in the literature as contributors to "automation surprises."

RuleGO: a logical rules-based tool for description of gene groups by means of Gene Ontology

PubMed Central

Gruca, Aleksandra; Sikora, Marek; Polanski, Andrzej

2011-01-01

Genome-wide expression profiles obtained with the use of DNA microarray technology provide abundance of experimental data on biological and molecular processes. Such amount of data need to be further analyzed and interpreted in order to obtain biological conclusions on the basis of experimental results. The analysis requires a lot of experience and is usually time-consuming process. Thus, frequently various annotation databases are used to improve the whole process of analysis. Here, we present RuleGO—the web-based application that allows the user to describe gene groups on the basis of logical rules that include Gene Ontology (GO) terms in their premises. Presented application allows obtaining rules that reflect coappearance of GO-terms describing genes supported by the rules. The ontology level and number of coappearing GO-terms is adjusted in automatic manner. The user limits the space of possible solutions only. The RuleGO application is freely available at http://rulego.polsl.pl/. PMID:21715384

Documentation Supplement for Base Case v4.10_FTransport - Updates for Final Transport Rule

EPA Pesticide Factsheets

View the document that describes the changes implemented as a result for the Final Cross-State Air Pollution Rule (Transport Rule) analysis in EPA’s application of the Integrated Planning Model (IPM).

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.

Applying the Rule Space Model to Develop a Learning Progression for Thermochemistry

NASA Astrophysics Data System (ADS)

Chen, Fu; Zhang, Shanshan; Guo, Yanfang; Xin, Tao

2017-12-01

We used the Rule Space Model, a cognitive diagnostic model, to measure the learning progression for thermochemistry for senior high school students. We extracted five attributes and proposed their hierarchical relationships to model the construct of thermochemistry at four levels using a hypothesized learning progression. For this study, we developed 24 test items addressing the attributes of exothermic and endothermic reactions, chemical bonds and heat quantity change, reaction heat and enthalpy, thermochemical equations, and Hess's law. The test was administered to a sample base of 694 senior high school students taught in 3 schools across 2 cities. Results based on the Rule Space Model analysis indicated that (1) the test items developed by the Rule Space Model were of high psychometric quality for good analysis of difficulties, discriminations, reliabilities, and validities; (2) the Rule Space Model analysis classified the students into seven different attribute mastery patterns; and (3) the initial hypothesized learning progression was modified by the attribute mastery patterns and the learning paths to be more precise and detailed.

Concurrence of rule- and similarity-based mechanisms in artificial grammar learning.

PubMed

Opitz, Bertram; Hofmann, Juliane

2015-03-01

A current theoretical debate regards whether rule-based or similarity-based learning prevails during artificial grammar learning (AGL). Although the majority of findings are consistent with a similarity-based account of AGL it has been argued that these results were obtained only after limited exposure to study exemplars, and performance on subsequent grammaticality judgment tests has often been barely above chance level. In three experiments the conditions were investigated under which rule- and similarity-based learning could be applied. Participants were exposed to exemplars of an artificial grammar under different (implicit and explicit) learning instructions. The analysis of receiver operating characteristics (ROC) during a final grammaticality judgment test revealed that explicit but not implicit learning led to rule knowledge. It also demonstrated that this knowledge base is built up gradually while similarity knowledge governed the initial state of learning. Together these results indicate that rule- and similarity-based mechanisms concur during AGL. Moreover, it could be speculated that two different rule processes might operate in parallel; bottom-up learning via gradual rule extraction and top-down learning via rule testing. Crucially, the latter is facilitated by performance feedback that encourages explicit hypothesis testing. Copyright © 2015 Elsevier Inc. All rights reserved.

Rule acquisition in formal decision contexts based on formal, object-oriented and property-oriented concept lattices.

PubMed

Ren, Yue; Li, Jinhai; Aswani Kumar, Cherukuri; Liu, Wenqi

2014-01-01

Rule acquisition is one of the main purposes in the analysis of formal decision contexts. Up to now, there have been several types of rules in formal decision contexts such as decision rules, decision implications, and granular rules, which can be viewed as ∧-rules since all of them have the following form: "if conditions 1,2,…, and m hold, then decisions hold." In order to enrich the existing rule acquisition theory in formal decision contexts, this study puts forward two new types of rules which are called ∨-rules and ∨-∧ mixed rules based on formal, object-oriented, and property-oriented concept lattices. Moreover, a comparison of ∨-rules, ∨-∧ mixed rules, and ∧-rules is made from the perspectives of inclusion and inference relationships. Finally, some real examples and numerical experiments are conducted to compare the proposed rule acquisition algorithms with the existing one in terms of the running efficiency.

Rule Acquisition in Formal Decision Contexts Based on Formal, Object-Oriented and Property-Oriented Concept Lattices

PubMed Central

Ren, Yue; Aswani Kumar, Cherukuri; Liu, Wenqi

2014-01-01

Rule acquisition is one of the main purposes in the analysis of formal decision contexts. Up to now, there have been several types of rules in formal decision contexts such as decision rules, decision implications, and granular rules, which can be viewed as ∧-rules since all of them have the following form: “if conditions 1,2,…, and m hold, then decisions hold.” In order to enrich the existing rule acquisition theory in formal decision contexts, this study puts forward two new types of rules which are called ∨-rules and ∨-∧ mixed rules based on formal, object-oriented, and property-oriented concept lattices. Moreover, a comparison of ∨-rules, ∨-∧ mixed rules, and ∧-rules is made from the perspectives of inclusion and inference relationships. Finally, some real examples and numerical experiments are conducted to compare the proposed rule acquisition algorithms with the existing one in terms of the running efficiency. PMID:25165744

Automated visualization of rule-based models

PubMed Central

Tapia, Jose-Juan; Faeder, James R.

2017-01-01

Frameworks such as BioNetGen, Kappa and Simmune use “reaction rules” to specify biochemical interactions compactly, where each rule specifies a mechanism such as binding or phosphorylation and its structural requirements. Current rule-based models of signaling pathways have tens to hundreds of rules, and these numbers are expected to increase as more molecule types and pathways are added. Visual representations are critical for conveying rule-based models, but current approaches to show rules and interactions between rules scale poorly with model size. Also, inferring design motifs that emerge from biochemical interactions is an open problem, so current approaches to visualize model architecture rely on manual interpretation of the model. Here, we present three new visualization tools that constitute an automated visualization framework for rule-based models: (i) a compact rule visualization that efficiently displays each rule, (ii) the atom-rule graph that conveys regulatory interactions in the model as a bipartite network, and (iii) a tunable compression pipeline that incorporates expert knowledge and produces compact diagrams of model architecture when applied to the atom-rule graph. The compressed graphs convey network motifs and architectural features useful for understanding both small and large rule-based models, as we show by application to specific examples. Our tools also produce more readable diagrams than current approaches, as we show by comparing visualizations of 27 published models using standard graph metrics. We provide an implementation in the open source and freely available BioNetGen framework, but the underlying methods are general and can be applied to rule-based models from the Kappa and Simmune frameworks also. We expect that these tools will promote communication and analysis of rule-based models and their eventual integration into comprehensive whole-cell models. PMID:29131816

Expert system for web based collaborative CAE

NASA Astrophysics Data System (ADS)

Hou, Liang; Lin, Zusheng

2006-11-01

An expert system for web based collaborative CAE was developed based on knowledge engineering, relational database and commercial FEA (Finite element analysis) software. The architecture of the system was illustrated. In this system, the experts' experiences, theories and typical examples and other related knowledge, which will be used in the stage of pre-process in FEA, were categorized into analysis process and object knowledge. Then, the integrated knowledge model based on object-oriented method and rule based method was described. The integrated reasoning process based on CBR (case based reasoning) and rule based reasoning was presented. Finally, the analysis process of this expert system in web based CAE application was illustrated, and an analysis example of a machine tool's column was illustrated to prove the validity of the system.

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.

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.

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.

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

Checking Flight Rules with TraceContract: Application of a Scala DSL for Trace Analysis

NASA Technical Reports Server (NTRS)

Barringer, Howard; Havelund, Klaus; Morris, Robert A.

2011-01-01

Typically during the design and development of a NASA space mission, rules and constraints are identified to help reduce reasons for failure during operations. These flight rules are usually captured in a set of indexed tables, containing rule descriptions, rationales for the rules, and other information. Flight rules can be part of manual operations procedures carried out by humans. However, they can also be automated, and either implemented as on-board monitors, or as ground based monitors that are part of a ground data system. In the case of automated flight rules, one considerable expense to be addressed for any mission is the extensive process by which system engineers express flight rules in prose, software developers translate these requirements into code, and then both experts verify that the resulting application is correct. This paper explores the potential benefits of using an internal Scala DSL for general trace analysis, named TRACECONTRACT, to write executable specifications of flight rules. TRACECONTRACT can generally be applied to analysis of for example log files or for monitoring executing systems online.

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.

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.

Simple methods of exploiting the underlying structure of rule-based systems

NASA Technical Reports Server (NTRS)

Hendler, James

1986-01-01

Much recent work in the field of expert systems research has aimed at exploiting the underlying structures of the rule base for reasons of analysis. Such techniques as Petri-nets and GAGs have been proposed as representational structures that will allow complete analysis. Much has been made of proving isomorphisms between the rule bases and the mechanisms, and in examining the theoretical power of this analysis. In this paper we describe some early work in a new system which has much simpler (and thus, one hopes, more easily achieved) aims and less formality. The technique being examined is a very simple one: OPS5 programs are analyzed in a purely syntactic way and a FSA description is generated. In this paper we describe the technique and some user interface tools which exploit this structure.

A CLIPS expert system for clinical flow cytometry data analysis

NASA Technical Reports Server (NTRS)

Salzman, G. C.; Duque, R. E.; Braylan, R. C.; Stewart, C. C.

1990-01-01

An expert system is being developed using CLIPS to assist clinicians in the analysis of multivariate flow cytometry data from cancer patients. Cluster analysis is used to find subpopulations representing various cell types in multiple datasets each consisting of four to five measurements on each of 5000 cells. CLIPS facts are derived from results of the clustering. CLIPS rules are based on the expertise of Drs. Stewart, Duque, and Braylan. The rules incorporate certainty factors based on case histories.

Analysis of Rules for Islamic Inheritance Law in Indonesia Using Hybrid Rule Based Learning

NASA Astrophysics Data System (ADS)

Khosyi'ah, S.; Irfan, M.; Maylawati, D. S.; Mukhlas, O. S.

2018-01-01

Along with the development of human civilization in Indonesia, the changes and reform of Islamic inheritance law so as to conform to the conditions and culture cannot be denied. The distribution of inheritance in Indonesia can be done automatically by storing the rule of Islamic inheritance law in the expert system. In this study, we analyze the knowledge of experts in Islamic inheritance in Indonesia and represent it in the form of rules using rule-based Forward Chaining (FC) and Davis-Putman-Logemann-Loveland (DPLL) algorithms. By hybridizing FC and DPLL algorithms, the rules of Islamic inheritance law in Indonesia are clearly defined and measured. The rules were conceptually validated by some experts in Islamic laws and informatics. The results revealed that generally all rules were ready for use in an expert system.

Image segmentation using association rule features.

PubMed

Rushing, John A; Ranganath, Heggere; Hinke, Thomas H; Graves, Sara J

2002-01-01

A new type of texture feature based on association rules is described. Association rules have been used in applications such as market basket analysis to capture relationships present among items in large data sets. It is shown that association rules can be adapted to capture frequently occurring local structures in images. The frequency of occurrence of these structures can be used to characterize texture. Methods for segmentation of textured images based on association rule features are described. Simulation results using images consisting of man made and natural textures show that association rule features perform well compared to other widely used texture features. Association rule features are used to detect cumulus cloud fields in GOES satellite images and are found to achieve higher accuracy than other statistical texture features for this problem.

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.

Skills, rules and knowledge in aircraft maintenance: errors in context

NASA Technical Reports Server (NTRS)

Hobbs, Alan; Williamson, Ann

2002-01-01

Automatic or skill-based behaviour is generally considered to be less prone to error than behaviour directed by conscious control. However, researchers who have applied Rasmussen's skill-rule-knowledge human error framework to accidents and incidents have sometimes found that skill-based errors appear in significant numbers. It is proposed that this is largely a reflection of the opportunities for error which workplaces present and does not indicate that skill-based behaviour is intrinsically unreliable. In the current study, 99 errors reported by 72 aircraft mechanics were examined in the light of a task analysis based on observations of the work of 25 aircraft mechanics. The task analysis identified the opportunities for error presented at various stages of maintenance work packages and by the job as a whole. Once the frequency of each error type was normalized in terms of the opportunities for error, it became apparent that skill-based performance is more reliable than rule-based performance, which is in turn more reliable than knowledge-based performance. The results reinforce the belief that industrial safety interventions designed to reduce errors would best be directed at those aspects of jobs that involve rule- and knowledge-based performance.

Evaluating MEDEVAC Force Structure Requirements Using an Updated Army Scenario, Total Army Analysis Admission Data, Monte Carlo Simulation, and Theater Structure.

PubMed

Fulton, Lawrence; Kerr, Bernie; Inglis, James M; Brooks, Matthew; Bastian, Nathaniel D

2015-07-01

In this study, we re-evaluate air ambulance requirements (rules of allocation) and planning considerations based on an Army-approved, Theater Army Analysis scenario. A previous study using workload only estimated a requirement of 0.4 to 0.6 aircraft per admission, a significant bolus over existence-based rules. In this updated study, we estimate requirements for Phase III (major combat operations) using a simulation grounded in previously published work and Phase IV (stability operations) based on four rules of allocation: unit existence rules, workload factors, theater structure (geography), and manual input. This study improves upon previous work by including the new air ambulance mission requirements of Department of Defense 51001.1, Roles and Functions of the Services, by expanding the analysis over two phases, and by considering unit rotation requirements known as Army Force Generation based on Department of Defense policy. The recommendations of this study are intended to inform future planning factors and already provided decision support to the Army Aviation Branch in determining force structure requirements. Reprint & Copyright © 2015 Association of Military Surgeons of the U.S.

Characterising bias in regulatory risk and decision analysis: An analysis of heuristics applied in health technology appraisal, chemicals regulation, and climate change governance.

PubMed

MacGillivray, Brian H

2017-08-01

In many environmental and public health domains, heuristic methods of risk and decision analysis must be relied upon, either because problem structures are ambiguous, reliable data is lacking, or decisions are urgent. This introduces an additional source of uncertainty beyond model and measurement error - uncertainty stemming from relying on inexact inference rules. Here we identify and analyse heuristics used to prioritise risk objects, to discriminate between signal and noise, to weight evidence, to construct models, to extrapolate beyond datasets, and to make policy. Some of these heuristics are based on causal generalisations, yet can misfire when these relationships are presumed rather than tested (e.g. surrogates in clinical trials). Others are conventions designed to confer stability to decision analysis, yet which may introduce serious error when applied ritualistically (e.g. significance testing). Some heuristics can be traced back to formal justifications, but only subject to strong assumptions that are often violated in practical applications. Heuristic decision rules (e.g. feasibility rules) in principle act as surrogates for utility maximisation or distributional concerns, yet in practice may neglect costs and benefits, be based on arbitrary thresholds, and be prone to gaming. We highlight the problem of rule-entrenchment, where analytical choices that are in principle contestable are arbitrarily fixed in practice, masking uncertainty and potentially introducing bias. Strategies for making risk and decision analysis more rigorous include: formalising the assumptions and scope conditions under which heuristics should be applied; testing rather than presuming their underlying empirical or theoretical justifications; using sensitivity analysis, simulations, multiple bias analysis, and deductive systems of inference (e.g. directed acyclic graphs) to characterise rule uncertainty and refine heuristics; adopting "recovery schemes" to correct for known biases; and basing decision rules on clearly articulated values and evidence, rather than convention. Copyright © 2017. Published by Elsevier Ltd.

Dynamic association rules for gene expression data analysis.

PubMed

Chen, Shu-Chuan; Tsai, Tsung-Hsien; Chung, Cheng-Han; Li, Wen-Hsiung

2015-10-14

The purpose of gene expression analysis is to look for the association between regulation of gene expression levels and phenotypic variations. This association based on gene expression profile has been used to determine whether the induction/repression of genes correspond to phenotypic variations including cell regulations, clinical diagnoses and drug development. Statistical analyses on microarray data have been developed to resolve gene selection issue. However, these methods do not inform us of causality between genes and phenotypes. In this paper, we propose the dynamic association rule algorithm (DAR algorithm) which helps ones to efficiently select a subset of significant genes for subsequent analysis. The DAR algorithm is based on association rules from market basket analysis in marketing. We first propose a statistical way, based on constructing a one-sided confidence interval and hypothesis testing, to determine if an association rule is meaningful. Based on the proposed statistical method, we then developed the DAR algorithm for gene expression data analysis. The method was applied to analyze four microarray datasets and one Next Generation Sequencing (NGS) dataset: the Mice Apo A1 dataset, the whole genome expression dataset of mouse embryonic stem cells, expression profiling of the bone marrow of Leukemia patients, Microarray Quality Control (MAQC) data set and the RNA-seq dataset of a mouse genomic imprinting study. A comparison of the proposed method with the t-test on the expression profiling of the bone marrow of Leukemia patients was conducted. We developed a statistical way, based on the concept of confidence interval, to determine the minimum support and minimum confidence for mining association relationships among items. With the minimum support and minimum confidence, one can find significant rules in one single step. The DAR algorithm was then developed for gene expression data analysis. Four gene expression datasets showed that the proposed DAR algorithm not only was able to identify a set of differentially expressed genes that largely agreed with that of other methods, but also provided an efficient and accurate way to find influential genes of a disease. In the paper, the well-established association rule mining technique from marketing has been successfully modified to determine the minimum support and minimum confidence based on the concept of confidence interval and hypothesis testing. It can be applied to gene expression data to mine significant association rules between gene regulation and phenotype. The proposed DAR algorithm provides an efficient way to find influential genes that underlie the phenotypic variance.

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.

Model-based approach for design verification and co-optimization of catastrophic and parametric-related defects due to systematic manufacturing variations

NASA Astrophysics Data System (ADS)

Perry, Dan; Nakamoto, Mark; Verghese, Nishath; Hurat, Philippe; Rouse, Rich

2007-03-01

Model-based hotspot detection and silicon-aware parametric analysis help designers optimize their chips for yield, area and performance without the high cost of applying foundries' recommended design rules. This set of DFM/ recommended rules is primarily litho-driven, but cannot guarantee a manufacturable design without imposing overly restrictive design requirements. This rule-based methodology of making design decisions based on idealized polygons that no longer represent what is on silicon needs to be replaced. Using model-based simulation of the lithography, OPC, RET and etch effects, followed by electrical evaluation of the resulting shapes, leads to a more realistic and accurate analysis. This analysis can be used to evaluate intelligent design trade-offs and identify potential failures due to systematic manufacturing defects during the design phase. The successful DFM design methodology consists of three parts: 1. Achieve a more aggressive layout through limited usage of litho-related recommended design rules. A 10% to 15% area reduction is achieved by using more aggressive design rules. DFM/recommended design rules are used only if there is no impact on cell size. 2. Identify and fix hotspots using a model-based layout printability checker. Model-based litho and etch simulation are done at the cell level to identify hotspots. Violations of recommended rules may cause additional hotspots, which are then fixed. The resulting design is ready for step 3. 3. Improve timing accuracy with a process-aware parametric analysis tool for transistors and interconnect. Contours of diffusion, poly and metal layers are used for parametric analysis. In this paper, we show the results of this physical and electrical DFM methodology at Qualcomm. We describe how Qualcomm was able to develop more aggressive cell designs that yielded a 10% to 15% area reduction using this methodology. Model-based shape simulation was employed during library development to validate architecture choices and to optimize cell layout. At the physical verification stage, the shape simulator was run at full-chip level to identify and fix residual hotspots on interconnect layers, on poly or metal 1 due to interaction between adjacent cells, or on metal 1 due to interaction between routing (via and via cover) and cell geometry. To determine an appropriate electrical DFM solution, Qualcomm developed an experiment to examine various electrical effects. After reporting the silicon results of this experiment, which showed sizeable delay variations due to lithography-related systematic effects, we also explain how contours of diffusion, poly and metal can be used for silicon-aware parametric analysis of transistors and interconnect at the cell-, block- and chip-level.

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.

Comparative analysis of expert and machine-learning methods for classification of body cavity effusions in companion animals.

PubMed

Hotz, Christine S; Templeton, Steven J; Christopher, Mary M

2005-03-01

A rule-based expert system using CLIPS programming language was created to classify body cavity effusions as transudates, modified transudates, exudates, chylous, and hemorrhagic effusions. The diagnostic accuracy of the rule-based system was compared with that produced by 2 machine-learning methods: Rosetta, a rough sets algorithm and RIPPER, a rule-induction method. Results of 508 body cavity fluid analyses (canine, feline, equine) obtained from the University of California-Davis Veterinary Medical Teaching Hospital computerized patient database were used to test CLIPS and to test and train RIPPER and Rosetta. The CLIPS system, using 17 rules, achieved an accuracy of 93.5% compared with pathologist consensus diagnoses. Rosetta accurately classified 91% of effusions by using 5,479 rules. RIPPER achieved the greatest accuracy (95.5%) using only 10 rules. When the original rules of the CLIPS application were replaced with those of RIPPER, the accuracy rates were identical. These results suggest that both rule-based expert systems and machine-learning methods hold promise for the preliminary classification of body fluids in the clinical laboratory.

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.

Developing an International Register of Clinical Prediction Rules for Use in Primary Care: A Descriptive Analysis

PubMed Central

Keogh, Claire; Wallace, Emma; O’Brien, Kirsty K.; Galvin, Rose; Smith, Susan M.; Lewis, Cliona; Cummins, Anthony; Cousins, Grainne; Dimitrov, Borislav D.; Fahey, Tom

2014-01-01

PURPOSE We describe the methodology used to create a register of clinical prediction rules relevant to primary care. We also summarize the rules included in the register according to various characteristics. METHODS To identify relevant articles, we searched the MEDLINE database (PubMed) for the years 1980 to 2009 and supplemented the results with searches of secondary sources (books on clinical prediction rules) and personal resources (eg, experts in the field). The rules described in relevant articles were classified according to their clinical domain, the stage of development, and the clinical setting in which they were studied. RESULTS Our search identified clinical prediction rules reported between 1965 and 2009. The largest share of rules (37.2%) were retrieved from PubMed. The number of published rules increased substantially over the study decades. We included 745 articles in the register; many contained more than 1 clinical prediction rule study (eg, both a derivation study and a validation study), resulting in 989 individual studies. In all, 434 unique rules had gone through derivation; however, only 54.8% had been validated and merely 2.8% had undergone analysis of their impact on either the process or outcome of clinical care. The rules most commonly pertained to cardiovascular disease, respiratory, and musculoskeletal conditions. They had most often been studied in the primary care or emergency department settings. CONCLUSIONS Many clinical prediction rules have been derived, but only about half have been validated and few have been assessed for clinical impact. This lack of thorough evaluation for many rules makes it difficult to retrieve and identify those that are ready for use at the point of patient care. We plan to develop an international web-based register of clinical prediction rules and computer-based clinical decision support systems. PMID:25024245

Efficiency in Rule- vs. Plan-Based Movements Is Modulated by Action-Mode.

PubMed

Scheib, Jean P P; Stoll, Sarah; Thürmer, J Lukas; Randerath, Jennifer

2018-01-01

The rule/plan motor cognition (RPMC) paradigm elicits visually indistinguishable motor outputs, resulting from either plan- or rule-based action-selection, using a combination of essentially interchangeable stimuli. Previous implementations of the RPMC paradigm have used pantomimed movements to compare plan- vs. rule-based action-selection. In the present work we attempt to determine the generalizability of previous RPMC findings to real object interaction by use of a grasp-to-rotate task. In the plan task, participants had to use prospective planning to achieve a comfortable post-handle rotation hand posture. The rule task used implementation intentions (if-then rules) leading to the same comfortable end-state. In Experiment A, we compare RPMC performance of 16 healthy participants in pantomime and real object conditions of the experiment, within-subjects. Higher processing efficiency of rule- vs. plan-based action-selection was supported by diffusion model analysis. Results show a significant response-time increase in the pantomime condition compared to the real object condition and a greater response-time advantage of rule-based vs. plan-based actions in the pantomime compared to the real object condition. In Experiment B, 24 healthy participants performed the real object RPMC task in a task switching vs. a blocked condition. Results indicate that plan-based action-selection leads to longer response-times and less efficient information processing than rule-based action-selection in line with previous RPMC findings derived from the pantomime action-mode. Particularly in the task switching mode, responses were faster in the rule compared to the plan task suggesting a modulating influence of cognitive load. Overall, results suggest an advantage of rule-based action-selection over plan-based action-selection; whereby differential mechanisms appear to be involved depending on the action-mode. We propose that cognitive load is a factor that modulates the advantageous effect of implementation intentions in motor cognition on different levels as illustrated by the varying speed advantages and the variation in diffusion parameters per action-mode or condition, respectively.

QoE collaborative evaluation method based on fuzzy clustering heuristic algorithm.

PubMed

Bao, Ying; Lei, Weimin; Zhang, Wei; Zhan, Yuzhuo

2016-01-01

At present, to realize or improve the quality of experience (QoE) is a major goal for network media transmission service, and QoE evaluation is the basis for adjusting the transmission control mechanism. Therefore, a kind of QoE collaborative evaluation method based on fuzzy clustering heuristic algorithm is proposed in this paper, which is concentrated on service score calculation at the server side. The server side collects network transmission quality of service (QoS) parameter, node location data, and user expectation value from client feedback information. Then it manages the historical data in database through the "big data" process mode, and predicts user score according to heuristic rules. On this basis, it completes fuzzy clustering analysis, and generates service QoE score and management message, which will be finally fed back to clients. Besides, this paper mainly discussed service evaluation generative rules, heuristic evaluation rules and fuzzy clustering analysis methods, and presents service-based QoE evaluation processes. The simulation experiments have verified the effectiveness of QoE collaborative evaluation method based on fuzzy clustering heuristic rules.

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…

State Identification of Hoisting Motors Based on Association Rules for Quayside Container Crane

NASA Astrophysics Data System (ADS)

Li, Q. Z.; Gang, T.; Pan, H. Y.; Xiong, H.

2017-07-01

Quay container crane hoisting motor is a complex system, and the characteristics of long-term evolution and change of running status of there is a rule, and use it. Through association rules analysis, this paper introduced the similarity in association rules, and quay container crane hoisting motor status identification. Finally validated by an example, some rules change amplitude is small, regular monitoring, not easy to find, but it is precisely because of these small changes led to mechanical failure. Therefore, using the association rules change in monitoring the motor status has the very strong practical significance.

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...

Investigation of work zone crash casualty patterns using association rules.

PubMed

Weng, Jinxian; Zhu, Jia-Zheng; Yan, Xuedong; Liu, Zhiyuan

2016-07-01

Investigation of the casualty crash characteristics and contributory factors is one of the high-priority issues in traffic safety analysis. In this paper, we propose a method based on association rules to analyze the characteristics and contributory factors of work zone crash casualties. A case study is conducted using the Michigan M-94/I-94/I-94BL/I-94BR work zone crash data from 2004 to 2008. The obtained association rules are divided into two parts including rules with high-lift, and rules with high-support for the further analysis. The results show that almost all the high-lift rules contain either environmental or occupant characteristics. The majority of association rules are centered on specific characteristics, such as drinking driving, the highway with more than 4 lanes, speed-limit over 40mph and not use of traffic control devices. It should be pointed out that some stronger associated rules were found in the high-support part. With the network visualization, the association rule method can provide more understandable results for investigating the patterns of work zone crash casualties. Copyright © 2016 Elsevier Ltd. All rights reserved.

Using an improved association rules mining optimization algorithm in web-based mobile-learning system

NASA Astrophysics Data System (ADS)

Huang, Yin; Chen, Jianhua; Xiong, Shaojun

2009-07-01

Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

An Intelligent Decision Support System for Workforce Forecast

DTIC Science & Technology

2011-01-01

ARIMA ) model to forecast the demand for construction skills in Hong Kong. This model was based...Decision Trees ARIMA Rule Based Forecasting Segmentation Forecasting Regression Analysis Simulation Modeling Input-Output Models LP and NLP Markovian...data • When results are needed as a set of easily interpretable rules 4.1.4 ARIMA Auto-regressive, integrated, moving-average ( ARIMA ) models

The Acquisition of Plural Marking in English and German Revisited: Schemata Versus Rules.

ERIC Educational Resources Information Center

Kopcke, Klaus-Michael

1998-01-01

Investigates whether inflectional morphology is rule-based or whether the assumption of pattern association is more adequate to account for the facts, arguing for the latter based on analysis of acquisitional data. Review of earlier literature on the subject examines experiments with German- and English-speaking children and supports the schema…

75 FR 56976 - Fisheries Off West Coast States; Coastal Pelagic Species Fisheries; Annual Specifications

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-17

... proposed action. The profitability of these vessels as a result of this proposed rule is based on the...-vessel price per mt to conduct a profitability analysis because cost data for the harvesting operations... drop in profitability based on this rule as the 2010/2011 available harvest (11,000 mt) is twice the...

A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network

NASA Astrophysics Data System (ADS)

Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.

2018-02-01

Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.

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.

Influence evaluation of loading conditions during pressurized thermal shock transients based on thermal-hydraulics and structural analyses

NASA Astrophysics Data System (ADS)

Katsuyama, Jinya; Uno, Shumpei; Watanabe, Tadashi; Li, Yinsheng

2018-03-01

The thermal hydraulic (TH) behavior of coolant water is a key factor in the structural integrity assessments on reactor pressure vessels (RPVs) of pressurized water reactors (PWRs) under pressurized thermal shock (PTS) events, because the TH behavior may affect the loading conditions in the assessment. From the viewpoint of TH behavior, configuration of plant equipment and their dimensions, and operator action time considerably influence various parameters, such as the temperature and flow rate of coolant water and inner pressure. In this study, to investigate the influence of the operator action time on TH behavior during a PTS event, we developed an analysis model for a typical Japanese PWR plant, including the RPV and the main components of both primary and secondary systems, and performed TH analyses by using a system analysis code called RELAP5. We applied two different operator action times based on the Japanese and the United States (US) rules: Operators may act after 10 min (Japanese rules) and 30 min (the US rules) after the occurrence of PTS events. Based on the results of TH analysis with different operator action times, we also performed structural analyses for evaluating thermal-stress distributions in the RPV during PTS events as loading conditions in the structural integrity assessment. From the analysis results, it was clarified that differences in operator action times significantly affect TH behavior and loading conditions, as the Japanese rule may lead to lower stresses than that under the US rule because an earlier operator action caused lower pressure in the RPV.

Robust Strategy for Rocket Engine Health Monitoring

NASA Technical Reports Server (NTRS)

Santi, L. Michael

2001-01-01

Monitoring the health of rocket engine systems is essentially a two-phase process. The acquisition phase involves sensing physical conditions at selected locations, converting physical inputs to electrical signals, conditioning the signals as appropriate to establish scale or filter interference, and recording results in a form that is easy to interpret. The inference phase involves analysis of results from the acquisition phase, comparison of analysis results to established health measures, and assessment of health indications. A variety of analytical tools may be employed in the inference phase of health monitoring. These tools can be separated into three broad categories: statistical, rule based, and model based. Statistical methods can provide excellent comparative measures of engine operating health. They require well-characterized data from an ensemble of "typical" engines, or "golden" data from a specific test assumed to define the operating norm in order to establish reliable comparative measures. Statistical methods are generally suitable for real-time health monitoring because they do not deal with the physical complexities of engine operation. The utility of statistical methods in rocket engine health monitoring is hindered by practical limits on the quantity and quality of available data. This is due to the difficulty and high cost of data acquisition, the limited number of available test engines, and the problem of simulating flight conditions in ground test facilities. In addition, statistical methods incur a penalty for disregarding flow complexity and are therefore limited in their ability to define performance shift causality. Rule based methods infer the health state of the engine system based on comparison of individual measurements or combinations of measurements with defined health norms or rules. This does not mean that rule based methods are necessarily simple. Although binary yes-no health assessment can sometimes be established by relatively simple rules, the causality assignment needed for refined health monitoring often requires an exceptionally complex rule base involving complicated logical maps. Structuring the rule system to be clear and unambiguous can be difficult, and the expert input required to maintain a large logic network and associated rule base can be prohibitive.

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

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

Efficiency in Rule- vs. Plan-Based Movements Is Modulated by Action-Mode

PubMed Central

Scheib, Jean P. P.; Stoll, Sarah; Thürmer, J. Lukas; Randerath, Jennifer

2018-01-01

The rule/plan motor cognition (RPMC) paradigm elicits visually indistinguishable motor outputs, resulting from either plan- or rule-based action-selection, using a combination of essentially interchangeable stimuli. Previous implementations of the RPMC paradigm have used pantomimed movements to compare plan- vs. rule-based action-selection. In the present work we attempt to determine the generalizability of previous RPMC findings to real object interaction by use of a grasp-to-rotate task. In the plan task, participants had to use prospective planning to achieve a comfortable post-handle rotation hand posture. The rule task used implementation intentions (if-then rules) leading to the same comfortable end-state. In Experiment A, we compare RPMC performance of 16 healthy participants in pantomime and real object conditions of the experiment, within-subjects. Higher processing efficiency of rule- vs. plan-based action-selection was supported by diffusion model analysis. Results show a significant response-time increase in the pantomime condition compared to the real object condition and a greater response-time advantage of rule-based vs. plan-based actions in the pantomime compared to the real object condition. In Experiment B, 24 healthy participants performed the real object RPMC task in a task switching vs. a blocked condition. Results indicate that plan-based action-selection leads to longer response-times and less efficient information processing than rule-based action-selection in line with previous RPMC findings derived from the pantomime action-mode. Particularly in the task switching mode, responses were faster in the rule compared to the plan task suggesting a modulating influence of cognitive load. Overall, results suggest an advantage of rule-based action-selection over plan-based action-selection; whereby differential mechanisms appear to be involved depending on the action-mode. We propose that cognitive load is a factor that modulates the advantageous effect of implementation intentions in motor cognition on different levels as illustrated by the varying speed advantages and the variation in diffusion parameters per action-mode or condition, respectively. PMID:29593612

Association Rule Based Feature Extraction for Character Recognition

NASA Astrophysics Data System (ADS)

Dua, Sumeet; Singh, Harpreet

Association rules that represent isomorphisms among data have gained importance in exploratory data analysis because they can find inherent, implicit, and interesting relationships among data. They are also commonly used in data mining to extract the conditions among attribute values that occur together frequently in a dataset [1]. These rules have wide range of applications, namely in the financial and retail sectors of marketing, sales, and medicine.

Differences in Characteristics of Aviation Accidents during 1993-2012 Based on Flight Purpose

NASA Technical Reports Server (NTRS)

Evans, Joni K.

2016-01-01

Usually aviation accidents are categorized and analyzed within flight conduct rules (Part 121, Part 135, Part 91) because differences in accident rates within flight rules have been demonstrated. Even within a particular flight rule the flights have different purposes. For many, Part 121 flights are synonymous with scheduled passenger transport, and indeed this is the largest group of Part 121 accidents. But there are also non-scheduled (charter) passenger transport and cargo flights. The primary purpose of the analysis reported here is to examine the differences in aviation accidents based on the purpose of the flight. Some of the factors examined are the accident severity, aircraft characteristics and accident occurrence categories. Twenty consecutive years of data were available and utilized to complete this analysis.

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.

76 FR 76815 - Business Opportunity Rule

Federal Register 2010, 2011, 2012, 2013, 2014

2011-12-08

...The Commission is adopting final amendments to its Trade Regulation Rule entitled ``Disclosure Requirements and Prohibitions Concerning Business Opportunities'' (``Business Opportunity Rule'' or ``Rule''). Among other things, the Business Opportunity Rule has been amended to broaden its scope to cover business opportunity sellers not covered by the interim Business Opportunity Rule, such as sellers of work-at-home opportunities, and to streamline and simplify the disclosures that sellers must provide to prospective purchasers. The final Rule is based upon the comments received in response to an Advance Notice of Proposed Rulemaking (``ANPR''), an Initial Notice of Proposed Rulemaking (``INPR''), a Revised Notice of Proposed Rulemaking (``RNPR''), a public workshop, a Staff Report, and other information discussed herein. This document also contains the text of the final Rule and the Rule's Statement of Basis and Purpose (``SBP''), including a Regulatory Analysis.

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).

Mechanical Response Analysis of Long-life Asphalt Pavement Structure of Yunluo High-speed on the Semi-rigid Base

NASA Astrophysics Data System (ADS)

Liu, Feng; Wu, Chuanhai; Xu, Xinquan; Li, Hao; Wang, Zhixiang

2018-01-01

In order to grasp the rule of the strain change of the semi-rigid asphalt pavement structure under the FWD load and provide a reliable theoretical and practical basis for the design of the pavement structure, based on the test section of Guangdong Yunluo expressway, taking FWD as the loading tool, by using the finite element analysis software ANSYS, the internal variation rules of each pavement structural layer were obtained. Based on the results of the theoretical analysis, the measured strain sensor was set up in the corresponding layer of the pavement structure, and the strain test plan was determined. Based on the analysis of the strain data obtained from several structural layers and field monitoring, the rationality of the type pavement structure and the strain test scheme were verified, so as to provide useful help for the design and the maintenance of the pavement structure.

Structure-activity relationships and prediction of the phototoxicity and phototoxic potential of new drugs.

PubMed

Barratt, Martin D

2004-11-01

Relationships between the structure and properties of chemicals can be programmed into knowledge-based systems such as DEREK for Windows (DEREK is an acronym for "Deductive Estimation of Risk from Existing Knowledge"). The DEREK for Windows computer system contains a subset of over 60 rules describing chemical substructures (toxophores) responsible for skin sensitisation. As part of the European Phototox Project, the rule base was supplemented by a number of rules for the prospective identification of photoallergens, either by extension of the scope of existing rules or by the generation of new rules where a sound mechanistic rationale for the biological activity could be established. The scope of the rules for photoallergenicity was then further refined by assessment against a list of chemicals identified as photosensitisers by the Centro de Farmacovigilancia de la Comunidad Valenciana, Valencia, Spain. This paper contains an analysis of the mechanistic bases of activity for eight important groups of photoallergens and phototoxins, together with rules for the prospective identification of the photobiological activity of new or untested chemicals belonging to those classes. The mechanism of action of one additional chemical, nitrofurantoin, is well established; however, it was deemed inappropriate to write a rule on the basis of a single chemical structure.

[Research on the Clinical Alarm Management Mechanism Based on Closed-loop Control Theory].

PubMed

Lin, Zhongkuan; Zheng, Kun; Shen, Yunming; Wu, Yunyun

2018-05-30

This paper proposes a clinical alarm management system based on the theory of the closed loop control. The alarm management mechanism can be divided into the expected standard, improving execution rule, rule execution, medical devices with alarm functions, results analysis strategy and the output link. And, we make relevant application and discussion. Results showed that the mechanism can be operable and effective.

Item hierarchy-based analysis of the Rivermead Mobility Index resulted in improved interpretation and enabled faster scoring in patients undergoing rehabilitation after stroke.

PubMed

Roorda, Leo D; Green, John R; Houwink, Annemieke; Bagley, Pam J; Smith, Jane; Molenaar, Ivo W; Geurts, Alexander C

2012-06-01

To enable improved interpretation of the total score and faster scoring of the Rivermead Mobility Index (RMI) by studying item ordering or hierarchy and formulating start-and-stop rules in patients after stroke. Cohort study. Rehabilitation center in the Netherlands; stroke rehabilitation units and the community in the United Kingdom. Item hierarchy of the RMI was studied in an initial group of patients (n=620; mean age ± SD, 69.2±12.5y; 297 [48%] men; 304 [49%] left hemisphere lesion, and 269 [43%] right hemisphere lesion), and the adequacy of the item hierarchy-based start-and-stop rules was checked in a second group of patients (n=237; mean age ± SD, 60.0±11.3y; 139 [59%] men; 103 [44%] left hemisphere lesion, and 93 [39%] right hemisphere lesion) undergoing rehabilitation after stroke. Not applicable. Mokken scale analysis was used to investigate the fit of the double monotonicity model, indicating hierarchical item ordering. The percentages of patients with a difference between the RMI total score and the scores based on the start-and-stop rules were calculated to check the adequacy of these rules. The RMI had good fit of the double monotonicity model (coefficient H(T)=.87). The interpretation of the total score improved. Item hierarchy-based start-and-stop rules were formulated. The percentages of patients with a difference between the RMI total score and the score based on the recommended start-and-stop rules were 3% and 5%, respectively. Ten of the original 15 items had to be scored after applying the start-and-stop rules. Item hierarchy was established, enabling improved interpretation and faster scoring of the RMI. Copyright © 2012 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

CARSVM: a class association rule-based classification framework and its application to gene expression data.

PubMed

Kianmehr, Keivan; Alhajj, Reda

2008-09-01

In this study, we aim at building a classification framework, namely the CARSVM model, which integrates association rule mining and support vector machine (SVM). The goal is to benefit from advantages of both, the discriminative knowledge represented by class association rules and the classification power of the SVM algorithm, to construct an efficient and accurate classifier model that improves the interpretability problem of SVM as a traditional machine learning technique and overcomes the efficiency issues of associative classification algorithms. In our proposed framework: instead of using the original training set, a set of rule-based feature vectors, which are generated based on the discriminative ability of class association rules over the training samples, are presented to the learning component of the SVM algorithm. We show that rule-based feature vectors present a high-qualified source of discrimination knowledge that can impact substantially the prediction power of SVM and associative classification techniques. They provide users with more conveniences in terms of understandability and interpretability as well. We have used four datasets from UCI ML repository to evaluate the performance of the developed system in comparison with five well-known existing classification methods. Because of the importance and popularity of gene expression analysis as real world application of the classification model, we present an extension of CARSVM combined with feature selection to be applied to gene expression data. Then, we describe how this combination will provide biologists with an efficient and understandable classifier model. The reported test results and their biological interpretation demonstrate the applicability, efficiency and effectiveness of the proposed model. From the results, it can be concluded that a considerable increase in classification accuracy can be obtained when the rule-based feature vectors are integrated in the learning process of the SVM algorithm. In the context of applicability, according to the results obtained from gene expression analysis, we can conclude that the CARSVM system can be utilized in a variety of real world applications with some adjustments.

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

Evidence flow graph methods for validation and verification of expert systems

NASA Technical Reports Server (NTRS)

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

1989-01-01

The results of an investigation into the use of evidence flow graph techniques for performing validation and verification of expert systems are given. A translator to convert horn-clause rule bases into evidence flow graphs, a simulation program, and methods of analysis were developed. 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 affect the output from a set of rules.

META II Complex Systems Design and Analysis (CODA)

DTIC Science & Technology

2011-08-01

37  3.8.7  Variables, Parameters and Constraints ............................................................. 37  3.8.8  Objective...18  Figure 7: Inputs, States, Outputs and Parameters of System Requirements Specifications ......... 19...Design Rule Based on Device Parameter ....................................................... 57  Figure 35: AEE Device Design Rules (excerpt

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.

78 FR 24693 - Draft Qualitative Risk Assessment of Risk of Activity/Food Combinations for Activities (Outside...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-26

... Analysis and Risk-Based Preventive Controls for Human Food'' (the proposed preventive controls rule) and... Farm.'' The purpose of the draft RA is to provide a science-based risk analysis of those activity/food... Food, Drug, and Cosmetic Act for hazard analysis and risk-based preventive controls (the proposed...

On a Formal Tool for Reasoning About Flight Software Cost Analysis

NASA Technical Reports Server (NTRS)

Spagnuolo, John N., Jr.; Stukes, Sherry A.

2013-01-01

A report focuses on the development of flight software (FSW) cost estimates for 16 Discovery-class missions at JPL. The techniques and procedures developed enabled streamlining of the FSW analysis process, and provided instantaneous confirmation that the data and processes used for these estimates were consistent across all missions. The research provides direction as to how to build a prototype rule-based system for FSW cost estimation that would provide (1) FSW cost estimates, (2) explanation of how the estimates were arrived at, (3) mapping of costs, (4) mathematical trend charts with explanations of why the trends are what they are, (5) tables with ancillary FSW data of interest to analysts, (6) a facility for expert modification/enhancement of the rules, and (7) a basis for conceptually convenient expansion into more complex, useful, and general rule-based systems.

Layout optimization with assist features placement by model based rule tables for 2x node random contact

NASA Astrophysics Data System (ADS)

Jun, Jinhyuck; Park, Minwoo; Park, Chanha; Yang, Hyunjo; Yim, Donggyu; Do, Munhoe; Lee, Dongchan; Kim, Taehoon; Choi, Junghoe; Luk-Pat, Gerard; Miloslavsky, Alex

2015-03-01

As the industry pushes to ever more complex illumination schemes to increase resolution for next generation memory and logic circuits, sub-resolution assist feature (SRAF) placement requirements become increasingly severe. Therefore device manufacturers are evaluating improvements in SRAF placement algorithms which do not sacrifice main feature (MF) patterning capability. There are known-well several methods to generate SRAF such as Rule based Assist Features (RBAF), Model Based Assist Features (MBAF) and Hybrid Assisted Features combining features of the different algorithms using both RBAF and MBAF. Rule Based Assist Features (RBAF) continue to be deployed, even with the availability of Model Based Assist Features (MBAF) and Inverse Lithography Technology (ILT). Certainly for the 3x nm node, and even at the 2x nm nodes and lower, RBAF is used because it demands less run time and provides better consistency. Since RBAF is needed now and in the future, what is also needed is a faster method to create the AF rule tables. The current method typically involves making masks and printing wafers that contain several experiments, varying the main feature configurations, AF configurations, dose conditions, and defocus conditions - this is a time consuming and expensive process. In addition, as the technology node shrinks, wafer process changes and source shape redesigns occur more frequently, escalating the cost of rule table creation. Furthermore, as the demand on process margin escalates, there is a greater need for multiple rule tables: each tailored to a specific set of main-feature configurations. Model Assisted Rule Tables(MART) creates a set of test patterns, and evaluates the simulated CD at nominal conditions, defocused conditions and off-dose conditions. It also uses lithographic simulation to evaluate the likelihood of AF printing. It then analyzes the simulation data to automatically create AF rule tables. It means that analysis results display the cost of different AF configurations as the space grows between a pair of main features. In summary, model based rule tables method is able to make it much easier to create rule tables, leading to faster rule-table creation and a lower barrier to the creation of more rule tables.

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%.

An Account of Old English Stress.

ERIC Educational Resources Information Center

McCully, C. B.; Hogg, R. M.

1990-01-01

An analysis of stress patterns in Old English, from the perspective of a framework based on lexicalist metrical phonology, indicates that there was a central Old English stress rule that operated from left-to-right, in contrast to to the central rule for present day English. (46 references) (Author/CB)

A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record.

PubMed

Wright, Adam; Pang, Justine; Feblowitz, Joshua C; Maloney, Francine L; Wilcox, Allison R; Ramelson, Harley Z; Schneider, Louise I; Bates, David W

2011-01-01

Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete. To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100,000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100,000 records to assess its accuracy. Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100,000 randomly selected patients showed high sensitivity (range: 62.8-100.0%) and positive predictive value (range: 79.8-99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone. We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts.

Rule-based modeling: a computational approach for studying biomolecular site dynamics in cell signaling systems

PubMed Central

Chylek, Lily A.; Harris, Leonard A.; Tung, Chang-Shung; Faeder, James R.; Lopez, Carlos F.

2013-01-01

Rule-based modeling was developed to address the limitations of traditional approaches for modeling chemical kinetics in cell signaling systems. These systems consist of multiple interacting biomolecules (e.g., proteins), which themselves consist of multiple parts (e.g., domains, linear motifs, and sites of phosphorylation). Consequently, biomolecules that mediate information processing generally have the potential to interact in multiple ways, with the number of possible complexes and post-translational modification states tending to grow exponentially with the number of binary interactions considered. As a result, only large reaction networks capture all possible consequences of the molecular interactions that occur in a cell signaling system, which is problematic because traditional modeling approaches for chemical kinetics (e.g., ordinary differential equations) require explicit network specification. This problem is circumvented through representation of interactions in terms of local rules. With this approach, network specification is implicit and model specification is concise. Concise representation results in a coarse graining of chemical kinetics, which is introduced because all reactions implied by a rule inherit the rate law associated with that rule. Coarse graining can be appropriate if interactions are modular, and the coarseness of a model can be adjusted as needed. Rules can be specified using specialized model-specification languages, and recently developed tools designed for specification of rule-based models allow one to leverage powerful software engineering capabilities. A rule-based model comprises a set of rules, which can be processed by general-purpose simulation and analysis tools to achieve different objectives (e.g., to perform either a deterministic or stochastic simulation). PMID:24123887

Fuzzy Rule Suram for Wood Drying

NASA Astrophysics Data System (ADS)

Situmorang, Zakarias

2017-12-01

Implemented of fuzzy rule must used a look-up table as defuzzification analysis. Look-up table is the actuator plant to doing the value of fuzzification. Rule suram based of fuzzy logic with variables of weather is temperature ambient and humidity ambient, it implemented for wood drying process. The membership function of variable of state represented in error value and change error with typical map of triangle and map of trapezium. Result of analysis to reach 4 fuzzy rule in 81 conditions to control the output system can be constructed in a number of way of weather and conditions of air. It used to minimum of the consumption of electric energy by heater. One cycle of schedule drying is a serial of condition of chamber to process as use as a wood species.

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.

Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery.

PubMed

Belgiu, Mariana; Dr Guţ, Lucian; Strobl, Josef

2014-01-01

The increasing availability of high resolution imagery has triggered the need for automated image analysis techniques, with reduced human intervention and reproducible analysis procedures. The knowledge gained in the past might be of use to achieving this goal, if systematically organized into libraries which would guide the image analysis procedure. In this study we aimed at evaluating the variability of digital classifications carried out by three experts who were all assigned the same interpretation task. Besides the three classifications performed by independent operators, we developed an additional rule-based classification that relied on the image classifications best practices found in the literature, and used it as a surrogate for libraries of object characteristics. The results showed statistically significant differences among all operators who classified the same reference imagery. The classifications carried out by the experts achieved satisfactory results when transferred to another area for extracting the same classes of interest, without modification of the developed rules.

Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery

PubMed Central

Belgiu, Mariana; Drǎguţ, Lucian; Strobl, Josef

2014-01-01

The increasing availability of high resolution imagery has triggered the need for automated image analysis techniques, with reduced human intervention and reproducible analysis procedures. The knowledge gained in the past might be of use to achieving this goal, if systematically organized into libraries which would guide the image analysis procedure. In this study we aimed at evaluating the variability of digital classifications carried out by three experts who were all assigned the same interpretation task. Besides the three classifications performed by independent operators, we developed an additional rule-based classification that relied on the image classifications best practices found in the literature, and used it as a surrogate for libraries of object characteristics. The results showed statistically significant differences among all operators who classified the same reference imagery. The classifications carried out by the experts achieved satisfactory results when transferred to another area for extracting the same classes of interest, without modification of the developed rules. PMID:24623959

Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery

NASA Astrophysics Data System (ADS)

Belgiu, Mariana; ǎguţ, Lucian, , Dr; Strobl, Josef

2014-01-01

The increasing availability of high resolution imagery has triggered the need for automated image analysis techniques, with reduced human intervention and reproducible analysis procedures. The knowledge gained in the past might be of use to achieving this goal, if systematically organized into libraries which would guide the image analysis procedure. In this study we aimed at evaluating the variability of digital classifications carried out by three experts who were all assigned the same interpretation task. Besides the three classifications performed by independent operators, we developed an additional rule-based classification that relied on the image classifications best practices found in the literature, and used it as a surrogate for libraries of object characteristics. The results showed statistically significant differences among all operators who classified the same reference imagery. The classifications carried out by the experts achieved satisfactory results when transferred to another area for extracting the same classes of interest, without modification of the developed rules.

Strength Analysis on Ship Ladder Using Finite Element Method

NASA Astrophysics Data System (ADS)

Budianto; Wahyudi, M. T.; Dinata, U.; Ruddianto; Eko P., M. M.

2018-01-01

In designing the ship’s structure, it should refer to the rules in accordance with applicable classification standards. In this case, designing Ladder (Staircase) on a Ferry Ship which is set up, it must be reviewed based on the loads during ship operations, either during sailing or at port operations. The classification rules in ship design refer to the calculation of the structure components described in Classification calculation method and can be analysed using the Finite Element Method. Classification Regulations used in the design of Ferry Ships used BKI (Bureau of Classification Indonesia). So the rules for the provision of material composition in the mechanical properties of the material should refer to the classification of the used vessel. The analysis in this structure used program structure packages based on Finite Element Method. By using structural analysis on Ladder (Ladder), it obtained strength and simulation structure that can withstand load 140 kg both in static condition, dynamic, and impact. Therefore, the result of the analysis included values of safety factors in the ship is to keep the structure safe but the strength of the structure is not excessive.

A novel methodology for building robust design rules by using design based metrology (DBM)

NASA Astrophysics Data System (ADS)

Lee, Myeongdong; Choi, Seiryung; Choi, Jinwoo; Kim, Jeahyun; Sung, Hyunju; Yeo, Hyunyoung; Shim, Myoungseob; Jin, Gyoyoung; Chung, Eunseung; Roh, Yonghan

2013-03-01

This paper addresses a methodology for building robust design rules by using design based metrology (DBM). Conventional method for building design rules has been using a simulation tool and a simple pattern spider mask. At the early stage of the device, the estimation of simulation tool is poor. And the evaluation of the simple pattern spider mask is rather subjective because it depends on the experiential judgment of an engineer. In this work, we designed a huge number of pattern situations including various 1D and 2D design structures. In order to overcome the difficulties of inspecting many types of patterns, we introduced Design Based Metrology (DBM) of Nano Geometry Research, Inc. And those mass patterns could be inspected at a fast speed with DBM. We also carried out quantitative analysis on PWQ silicon data to estimate process variability. Our methodology demonstrates high speed and accuracy for building design rules. All of test patterns were inspected within a few hours. Mass silicon data were handled with not personal decision but statistical processing. From the results, robust design rules are successfully verified and extracted. Finally we found out that our methodology is appropriate for building robust design rules.

Equations for Scoring Rules When Data Are Missing

NASA Technical Reports Server (NTRS)

James, Mark

2006-01-01

A document presents equations for scoring rules in a diagnostic and/or prognostic artificial-intelligence software system of the rule-based inference-engine type. The equations define a set of metrics that characterize the evaluation of a rule when data required for the antecedence clause(s) of the rule are missing. The metrics include a primary measure denoted the rule completeness metric (RCM) plus a number of subsidiary measures that contribute to the RCM. The RCM is derived from an analysis of a rule with respect to its truth and a measure of the completeness of its input data. The derivation is such that the truth value of an antecedent is independent of the measure of its completeness. The RCM can be used to compare the degree of completeness of two or more rules with respect to a given set of data. Hence, the RCM can be used as a guide to choosing among rules during the rule-selection phase of operation of the artificial-intelligence system..

How accurate are interpretations of curriculum-based measurement progress monitoring data? Visual analysis versus decision rules.

PubMed

Van Norman, Ethan R; Christ, Theodore J

2016-10-01

Curriculum based measurement of oral reading (CBM-R) is used to monitor the effects of academic interventions for individual students. Decisions to continue, modify, or terminate these interventions are made by interpreting time series CBM-R data. Such interpretation is founded upon visual analysis or the application of decision rules. The purpose of this study was to compare the accuracy of visual analysis and decision rules. Visual analysts interpreted 108 CBM-R progress monitoring graphs one of three ways: (a) without graphic aids, (b) with a goal line, or (c) with a goal line and a trend line. Graphs differed along three dimensions, including trend magnitude, variability of observations, and duration of data collection. Automated trend line and data point decision rules were also applied to each graph. Inferential analyses permitted the estimation of the probability of a correct decision (i.e., the student is improving - continue the intervention, or the student is not improving - discontinue the intervention) for each evaluation method as a function of trend magnitude, variability of observations, and duration of data collection. All evaluation methods performed better when students made adequate progress. Visual analysis and decision rules performed similarly when observations were less variable. Results suggest that educators should collect data for more than six weeks, take steps to control measurement error, and visually analyze graphs when data are variable. Implications for practice and research are discussed. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

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.

Systematic Analysis of the Decision Rules of Traditional Chinese Medicine

PubMed Central

Bin-Rong, Ma; Xi-Yuan, Jiang; Su-Ming, Liso; Huai-ning, Zhu; Xiu-ru, Lin

1981-01-01

Chinese traditional medicine has evolved over many centuries, and has accumulated a body of observed relationships between symptoms, signs and prognoses, and the efficacy of alternative treatments and prescriptions. With the assistance of a computer-based clinical data base for recording the diagnostic and therapeutic practice of skilled practitioners of Chinese traditional medicine, a systematic program is being conducted to identify and define the clinical decision-making rules that underlie current practice.

Impact of Operating Rules on Planning Capacity Expansion of Urban Water Supply Systems

NASA Astrophysics Data System (ADS)

de Neufville, R.; Galelli, S.; Tian, X.

2017-12-01

This study addresses the impact of operating rules on capacity planning of urban water supply systems. The continuous growth of metropolitan areas represents a major challenge for water utilities, which often rely on industrial water supply (e.g., desalination, reclaimed water) to complement natural resources (e.g., reservoirs). These additional sources increase the reliability of supply, equipping operators with additional means to hedge against droughts. How do their rules for using industrial water supply impact the performance of water supply system? How might it affect long-term plans for capacity expansion? Possibly significantly, as demonstrated by the analysis of the operations and planning of a water supply system inspired by Singapore. Our analysis explores the system dynamics under multiple inflow and management scenarios to understand the extent to which alternative operating rules for the use of industrial water supply affect system performance. Results first show that these operating rules can have significant impact on the variability in system performance (e.g., reliability, energy use) comparable to that of hydro-climatological conditions. Further analyses of several capacity expansion exercises—based on our original hydrological and management scenarios—show that operating rules significantly affect the timing and magnitude of critical decisions, such as the construction of new desalination plants. These results have two implications: Capacity expansion analysis should consider the effect of a priori uncertainty about operating rules; and operators should consider how their flexibility in operating rules can affect their perceived need for capacity.

26 CFR 1.482-8 - Examples of the best method rule.

Code of Federal Regulations, 2014 CFR

2014-04-01

... illustrate the comparative analysis required to apply this rule. As with all of the examples in these... retail market in the United States. The study concludes that this segment of the U.S. market, which is not exploited by USSub, may generate substantial profits. Based on this study, FP enters into a...

Logic Analysis of Painting Modeling Rules and Avoiding Narrative Viewing

ERIC Educational Resources Information Center

Zhu, Feng; Shao, Jie

2009-01-01

Painting modeling rules are constructed based on objective representing with material substances as the main body and the construction methods and orders are mostly limited to narrative viewing and expression, which, obviously, is not the best method. Logistic thinking in virtue of modeling art could gender a more "painting-like"…

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.

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.

A method and knowledge base for automated inference of patient problems from structured data in an electronic medical record

PubMed Central

Pang, Justine; Feblowitz, Joshua C; Maloney, Francine L; Wilcox, Allison R; Ramelson, Harley Z; Schneider, Louise I; Bates, David W

2011-01-01

Background Accurate knowledge of a patient's medical problems is critical for clinical decision making, quality measurement, research, billing and clinical decision support. Common structured sources of problem information include the patient problem list and billing data; however, these sources are often inaccurate or incomplete. Objective To develop and validate methods of automatically inferring patient problems from clinical and billing data, and to provide a knowledge base for inferring problems. Study design and methods We identified 17 target conditions and designed and validated a set of rules for identifying patient problems based on medications, laboratory results, billing codes, and vital signs. A panel of physicians provided input on a preliminary set of rules. Based on this input, we tested candidate rules on a sample of 100 000 patient records to assess their performance compared to gold standard manual chart review. The physician panel selected a final rule for each condition, which was validated on an independent sample of 100 000 records to assess its accuracy. Results Seventeen rules were developed for inferring patient problems. Analysis using a validation set of 100 000 randomly selected patients showed high sensitivity (range: 62.8–100.0%) and positive predictive value (range: 79.8–99.6%) for most rules. Overall, the inference rules performed better than using either the problem list or billing data alone. Conclusion We developed and validated a set of rules for inferring patient problems. These rules have a variety of applications, including clinical decision support, care improvement, augmentation of the problem list, and identification of patients for research cohorts. PMID:21613643

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.

Updated systematic review and meta-analysis of the performance of risk prediction rules in children and young people with febrile neutropenia.

PubMed

Phillips, Robert S; Lehrnbecher, Thomas; Alexander, Sarah; Sung, Lillian

2012-01-01

Febrile neutropenia is a common and potentially life-threatening complication of treatment for childhood cancer, which has increasingly been subject to targeted treatment based on clinical risk stratification. Our previous meta-analysis demonstrated 16 rules had been described and 2 of them subject to validation in more than one study. We aimed to advance our knowledge of evidence on the discriminatory ability and predictive accuracy of such risk stratification clinical decision rules (CDR) for children and young people with cancer by updating our systematic review. The review was conducted in accordance with Centre for Reviews and Dissemination methods, searching multiple electronic databases, using two independent reviewers, formal critical appraisal with QUADAS and meta-analysis with random effects models where appropriate. It was registered with PROSPERO: CRD42011001685. We found 9 new publications describing a further 7 new CDR, and validations of 7 rules. Six CDR have now been subject to testing across more than two data sets. Most validations demonstrated the rule to be less efficient than when initially proposed; geographical differences appeared to be one explanation for this. The use of clinical decision rules will require local validation before widespread use. Considerable uncertainty remains over the most effective rule to use in each population, and an ongoing individual-patient-data meta-analysis should develop and test a more reliable CDR to improve stratification and optimise therapy. Despite current challenges, we believe it will be possible to define an internationally effective CDR to harmonise the treatment of children with febrile neutropenia.

Updated Systematic Review and Meta-Analysis of the Performance of Risk Prediction Rules in Children and Young People with Febrile Neutropenia

PubMed Central

Phillips, Robert S.; Lehrnbecher, Thomas; Alexander, Sarah; Sung, Lillian

2012-01-01

Introduction Febrile neutropenia is a common and potentially life-threatening complication of treatment for childhood cancer, which has increasingly been subject to targeted treatment based on clinical risk stratification. Our previous meta-analysis demonstrated 16 rules had been described and 2 of them subject to validation in more than one study. We aimed to advance our knowledge of evidence on the discriminatory ability and predictive accuracy of such risk stratification clinical decision rules (CDR) for children and young people with cancer by updating our systematic review. Methods The review was conducted in accordance with Centre for Reviews and Dissemination methods, searching multiple electronic databases, using two independent reviewers, formal critical appraisal with QUADAS and meta-analysis with random effects models where appropriate. It was registered with PROSPERO: CRD42011001685. Results We found 9 new publications describing a further 7 new CDR, and validations of 7 rules. Six CDR have now been subject to testing across more than two data sets. Most validations demonstrated the rule to be less efficient than when initially proposed; geographical differences appeared to be one explanation for this. Conclusion The use of clinical decision rules will require local validation before widespread use. Considerable uncertainty remains over the most effective rule to use in each population, and an ongoing individual-patient-data meta-analysis should develop and test a more reliable CDR to improve stratification and optimise therapy. Despite current challenges, we believe it will be possible to define an internationally effective CDR to harmonise the treatment of children with febrile neutropenia. PMID:22693615

The study on dynamic cadastral coding rules based on kinship relationship

NASA Astrophysics Data System (ADS)

Xu, Huan; Liu, Nan; Liu, Renyi; Lu, Jingfeng

2007-06-01

Cadastral coding rules are an important supplement to the existing national and local standard specifications for building cadastral database. After analyzing the course of cadastral change, especially the parcel change with the method of object-oriented analysis, a set of dynamic cadastral coding rules based on kinship relationship corresponding to the cadastral change is put forward and a coding format composed of street code, block code, father parcel code, child parcel code and grandchild parcel code is worked out within the county administrative area. The coding rule has been applied to the development of an urban cadastral information system called "ReGIS", which is not only able to figure out the cadastral code automatically according to both the type of parcel change and the coding rules, but also capable of checking out whether the code is spatiotemporally unique before the parcel is stored in the database. The system has been used in several cities of Zhejiang Province and got a favorable response. This verifies the feasibility and effectiveness of the coding rules to some extent.

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.

Clinical decision rules, spinal pain classification and prediction of treatment outcome: A discussion of recent reports in the rehabilitation literature

PubMed Central

2012-01-01

Clinical decision rules are an increasingly common presence in the biomedical literature and represent one strategy of enhancing clinical-decision making with the goal of improving the efficiency and effectiveness of healthcare delivery. In the context of rehabilitation research, clinical decision rules have been predominantly aimed at classifying patients by predicting their treatment response to specific therapies. Traditionally, recommendations for developing clinical decision rules propose a multistep process (derivation, validation, impact analysis) using defined methodology. Research efforts aimed at developing a “diagnosis-based clinical decision rule” have departed from this convention. Recent publications in this line of research have used the modified terminology “diagnosis-based clinical decision guide.” Modifications to terminology and methodology surrounding clinical decision rules can make it more difficult for clinicians to recognize the level of evidence associated with a decision rule and understand how this evidence should be implemented to inform patient care. We provide a brief overview of clinical decision rule development in the context of the rehabilitation literature and two specific papers recently published in Chiropractic and Manual Therapies. PMID:22726639

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.

77 FR 66149 - Significant New Use Rules on Certain Chemical Substances

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-02

... ecological structural activity relationship (EcoSAR) analysis of test data on analogous esters, EPA predicts... milligram/cubic meter (mg/m\\3\\) as an 8-hour time-weighted average. In addition, based on EcoSAR analysis of... the PMN substance via the inhalation route. In addition, based on EcoSAR analysis of test data on...

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

Dynamic Magnification Factor in a Box-Shape Steel Girder

NASA Astrophysics Data System (ADS)

Rahbar-Ranji, A.

2014-01-01

The dynamic effect of moving loads on structures is treated as a dynamic magnification factor when resonant is not imminent. Studies have shown that the calculated magnification factors from field measurements could be higher than the values specified in design codes. It is the main aim of present paper to investigate the applicability and accuracy of a rule-based expression for calculation of dynamic magnification factor for lifting appliances used in marine industry. A steel box shape girder of a crane is considered and transient dynamic analysis using computer code ANSYS is implemented. Dynamic magnification factor is calculated for different loading conditions and compared with rule-based equation. The effects of lifting speeds, acceleration, damping ratio and position of cargo are examined. It is found that rule-based expression underestimate dynamic magnification factor.

Rapid Transfer of Abstract Rules to Novel Contexts in Human Lateral Prefrontal Cortex

PubMed Central

Cole, Michael W.; Etzel, Joset A.; Zacks, Jeffrey M.; Schneider, Walter; Braver, Todd S.

2011-01-01

Flexible, adaptive behavior is thought to rely on abstract rule representations within lateral prefrontal cortex (LPFC), yet it remains unclear how these representations provide such flexibility. We recently demonstrated that humans can learn complex novel tasks in seconds. Here we hypothesized that this impressive mental flexibility may be possible due to rapid transfer of practiced rule representations within LPFC to novel task contexts. We tested this hypothesis using functional MRI and multivariate pattern analysis, classifying LPFC activity patterns across 64 tasks. Classifiers trained to identify abstract rules based on practiced task activity patterns successfully generalized to novel tasks. This suggests humans can transfer practiced rule representations within LPFC to rapidly learn new tasks, facilitating cognitive performance in novel circumstances. PMID:22125519

Explosive site percolation with a product rule.

PubMed

Choi, Woosik; Yook, Soon-Hyung; Kim, Yup

2011-08-01

We study the site percolation under Achlioptas process with a product rule in a two-dimensional square lattice. From the measurement of the cluster size distribution P(s), we find that P(s) has a very robust power-law regime followed by a stable hump near the transition threshold. Based on the careful analysis on the PP(s) distribution, we show that the transition should be discontinuous. The existence of the hysteresis loop in order parameter also verifies that the transition is discontinuous in two dimensions. Moreover, we also show that the transition nature from the product rule is not the same as that from a sum rule in two dimensions.

Ontology based decision system for breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Trabelsi Ben Ameur, Soumaya; Cloppet, Florence; Wendling, Laurent; Sellami, Dorra

2018-04-01

In this paper, we focus on analysis and diagnosis of breast masses inspired by expert concepts and rules. Accordingly, a Bag of Words is built based on the ontology of breast cancer diagnosis, accurately described in the Breast Imaging Reporting and Data System. To fill the gap between low level knowledge and expert concepts, a semantic annotation is developed using a machine learning tool. Then, breast masses are classified into benign or malignant according to expert rules implicitly modeled with a set of classifiers (KNN, ANN, SVM and Decision Tree). This semantic context of analysis offers a frame where we can include external factors and other meta-knowledge such as patient risk factors as well as exploiting more than one modality. Based on MRI and DECEDM modalities, our developed system leads a recognition rate of 99.7% with Decision Tree where an improvement of 24.7 % is obtained owing to semantic analysis.

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

Retrosynthetic Analysis-Guided Breaking Tile Symmetry for the Assembly of Complex DNA Nanostructures.

PubMed

Wang, Pengfei; Wu, Siyu; Tian, Cheng; Yu, Guimei; Jiang, Wen; Wang, Guansong; Mao, Chengde

2016-10-11

Current tile-based DNA self-assembly produces simple repetitive or highly symmetric structures. In the case of 2D lattices, the unit cell often contains only one basic tile because the tiles often are symmetric (in terms of either the backbone or the sequence). In this work, we have applied retrosynthetic analysis to determine the minimal asymmetric units for complex DNA nanostructures. Such analysis guides us to break the intrinsic structural symmetries of the tiles to achieve high structural complexities. This strategy has led to the construction of several DNA nanostructures that are not accessible from conventional symmetric tile designs. Along with previous studies, herein we have established a set of four fundamental rules regarding tile-based assembly. Such rules could serve as guidelines for the design of DNA nanostructures.

Multi-hazard Assessment and Scenario Toolbox (MhAST): A Framework for Analyzing Compounding Effects of Multiple Hazards

NASA Astrophysics Data System (ADS)

Sadegh, M.; Moftakhari, H.; AghaKouchak, A.

2017-12-01

Many natural hazards are driven by multiple forcing variables, and concurrence/consecutive extreme events significantly increases risk of infrastructure/system failure. It is a common practice to use univariate analysis based upon a perceived ruling driver to estimate design quantiles and/or return periods of extreme events. A multivariate analysis, however, permits modeling simultaneous occurrence of multiple forcing variables. In this presentation, we introduce the Multi-hazard Assessment and Scenario Toolbox (MhAST) that comprehensively analyzes marginal and joint probability distributions of natural hazards. MhAST also offers a wide range of scenarios of return period and design levels and their likelihoods. Contribution of this study is four-fold: 1. comprehensive analysis of marginal and joint probability of multiple drivers through 17 continuous distributions and 26 copulas, 2. multiple scenario analysis of concurrent extremes based upon the most likely joint occurrence, one ruling variable, and weighted random sampling of joint occurrences with similar exceedance probabilities, 3. weighted average scenario analysis based on a expected event, and 4. uncertainty analysis of the most likely joint occurrence scenario using a Bayesian framework.

Synthesis of Polysyllabic Sequences of Thai Tones Using a Generative Model of Fundamental Frequency Contours

NASA Astrophysics Data System (ADS)

Seresangtakul, Pusadee; Takara, Tomio

In this paper, the distinctive tones of Thai in running speech are studied. We present rules to synthesize F0 contours of Thai tones in running speech by using the generative model of F0 contours. Along with our method, the pitch contours of Thai polysyllabic words, both disyllabic and trisyllabic words, were analyzed. The coarticulation effect of Thai tones in running speech were found. Based on the analysis of the polysyllabic words using this model, rules are derived and applied to synthesize Thai polysyllabic tone sequences. We performed listening tests to evaluate intelligibility of the rules for Thai tones generation. The average intelligibility scores became 98.8%, and 96.6% for disyllabic and trisyllabic words, respectively. From these result, the rule of the tones' generation was shown to be effective. Furthermore, we constructed the connecting rules to synthesize suprasegmental F0 contours using the trisyllable training rules' parameters. The parameters of the first, the third, and the second syllables were selected and assigned to the initial, the ending, and the remaining syllables in a sentence, respectively. Even such a simple rule, the synthesized phrases/senetences were completely identified in listening tests. The MOSs (Mean Opinion Score) was 3.50 while the original and analysis/synthesis samples were 4.82 and 3.59, respectively.

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.

Use of an explicit rule decreases procrastination in university students.

PubMed

Johnson, Paul E; Perrin, Christopher J; Salo, Allen; Deschaine, Elyssa; Johnson, Beth

2016-06-01

The procrastination behavior of students from a small rural university was decreased by presenting them with a rule indicating that a sooner final due date for a writing assignment would be contingent on procrastination during earlier phases of the paper. A counterbalanced AB BA design was used to measure the effects of the rule-based treatment across 2 introductory psychology classes (N = 33). Overall, participants engaged in less procrastination, missed fewer deadlines, and produced higher quality writing in the treatment condition. © 2016 Society for the Experimental Analysis of Behavior.

On the costs and benefits of emotional labor: a meta-analysis of three decades of research.

PubMed

Hülsheger, Ute R; Schewe, Anna F

2011-07-01

This article provides a quantitative review of the link of emotional labor (emotion-rule dissonance, surface acting, and deep acting) with well-being and performance outcomes. The meta-analysis is based on 494 individual correlations drawn from a final sample of 95 independent studies. Results revealed substantial relationships of emotion-rule dissonance and surface acting with indicators of impaired well-being (ρs between .39 and .48) and job attitudes (ρs between -.24 and -.40) and a small negative relationship with performance outcomes (ρs between -.20 and -.05). Overall, deep acting displayed weak relationships with indicators of impaired well-being and job attitudes but positive relationships with emotional performance and customer satisfaction (ρs .18 and .37). A meta-analytic regression analysis provides information on the unique contribution of emotion-rule dissonance, surface acting, and deep acting in statistically predicting well-being and performance outcomes. Furthermore, a mediation analysis confirms theoretical models of emotional labor which suggest that surface acting partially mediates the relationship of emotion-rule dissonance with well-being. Implications for future research as well as pragmatic ramifications for organizational practices are discussed in conclusion.

Scale characters analysis for gully structure in the watersheds of loess landforms based on digital elevation models

NASA Astrophysics Data System (ADS)

Zhu, Hongchun; Zhao, Yipeng; Liu, Haiying

2018-04-01

Scale is the basic attribute for expressing and describing spatial entity and phenomena. It offers theoretical significance in the study of gully structure information, variable characteristics of watershed morphology, and development evolution at different scales. This research selected five different areas in China's Loess Plateau as the experimental region and used DEM data at different scales as the experimental data. First, the change rule of the characteristic parameters of the data at different scales was analyzed. The watershed structure information did not change along with a change in the data scale. This condition was proven by selecting indices of gully bifurcation ratio and fractal dimension as characteristic parameters of watershed structure information. Then, the change rule of the characteristic parameters of gully structure with different analysis scales was analyzed by setting the scale sequence of analysis at the extraction gully. The gully structure of the watershed changed with variations in the analysis scale, and the change rule was obvious when the gully level changed. Finally, the change rule of the characteristic parameters of the gully structure at different areas was analyzed. The gully fractal dimension showed a significant numerical difference in different areas, whereas the variation of the gully branch ratio was small. The change rule indicated that the development degree of the gully obviously varied in different regions, but the morphological structure was basically similar.

Scale characters analysis for gully structure in the watersheds of loess landforms based on digital elevation models

NASA Astrophysics Data System (ADS)

Zhu, Hongchun; Zhao, Yipeng; Liu, Haiying

2018-06-01

Scale is the basic attribute for expressing and describing spatial entity and phenomena. It offers theoretical significance in the study of gully structure information, variable characteristics of watershed morphology, and development evolution at different scales. This research selected five different areas in China's Loess Plateau as the experimental region and used DEM data at different scales as the experimental data. First, the change rule of the characteristic parameters of the data at different scales was analyzed. The watershed structure information did not change along with a change in the data scale. This condition was proven by selecting indices of gully bifurcation ratio and fractal dimension as characteristic parameters of watershed structure information. Then, the change rule of the characteristic parameters of gully structure with different analysis scales was analyzed by setting the scale sequence of analysis at the extraction gully. The gully structure of the watershed changed with variations in the analysis scale, and the change rule was obvious when the gully level changed. Finally, the change rule of the characteristic parameters of the gully structure at different areas was analyzed. The gully fractal dimension showed a significant numerical difference in different areas, whereas the variation of the gully branch ratio was small. The change rule indicated that the development degree of the gully obviously varied in different regions, but the morphological structure was basically similar.

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

Community detection in complex networks by using membrane algorithm

NASA Astrophysics Data System (ADS)

Liu, Chuang; Fan, Linan; Liu, Zhou; Dai, Xiang; Xu, Jiamei; Chang, Baoren

Community detection in complex networks is a key problem of network analysis. In this paper, a new membrane algorithm is proposed to solve the community detection in complex networks. The proposed algorithm is based on membrane systems, which consists of objects, reaction rules, and a membrane structure. Each object represents a candidate partition of a complex network, and the quality of objects is evaluated according to network modularity. The reaction rules include evolutionary rules and communication rules. Evolutionary rules are responsible for improving the quality of objects, which employ the differential evolutionary algorithm to evolve objects. Communication rules implement the information exchanged among membranes. Finally, the proposed algorithm is evaluated on synthetic, real-world networks with real partitions known and the large-scaled networks with real partitions unknown. The experimental results indicate the superior performance of the proposed algorithm in comparison with other experimental algorithms.

Hospitals push back against reimbursement cuts due to Two-Midnight rule.

PubMed

2016-04-01

The American Hospital Association (AHA) and other hospitals are suing CMS, challenging the 0.2% cut in Medicare reimbursement that CMS instituted to compensate for the financial effect of the Two-Midnight rule. CMS' actuaries reported that inpatient claims are likely to increase under the rule, resulting in $220 million additional reimbursement for hospitals. Hospitals disagree and a study commissioned by the AHA concluded that the CMS study was based on data not available to the public and that data from the Medicare Provider and Analysis Review (MedPAR) would lead to a different conclusion. The AHA suit asks CMS to rescind the cut, restore the base rate for Medicare payments to its previous level, and reimburse hospitals retroactively for the reductions.

Multisite Evaluation of a Data Quality Tool for Patient-Level Clinical Data Sets

PubMed Central

Huser, Vojtech; DeFalco, Frank J.; Schuemie, Martijn; Ryan, Patrick B.; Shang, Ning; Velez, Mark; Park, Rae Woong; Boyce, Richard D.; Duke, Jon; Khare, Ritu; Utidjian, Levon; Bailey, Charles

2016-01-01

Introduction: Data quality and fitness for analysis are crucial if outputs of analyses of electronic health record data or administrative claims data should be trusted by the public and the research community. Methods: We describe a data quality analysis tool (called Achilles Heel) developed by the Observational Health Data Sciences and Informatics Collaborative (OHDSI) and compare outputs from this tool as it was applied to 24 large healthcare datasets across seven different organizations. Results: We highlight 12 data quality rules that identified issues in at least 10 of the 24 datasets and provide a full set of 71 rules identified in at least one dataset. Achilles Heel is a freely available software that provides a useful starter set of data quality rules with the ability to add additional rules. We also present results of a structured email-based interview of all participating sites that collected qualitative comments about the value of Achilles Heel for data quality evaluation. Discussion: Our analysis represents the first comparison of outputs from a data quality tool that implements a fixed (but extensible) set of data quality rules. Thanks to a common data model, we were able to compare quickly multiple datasets originating from several countries in America, Europe and Asia. PMID:28154833

77 FR 52977 - Regulatory Capital Rules: Advanced Approaches Risk-Based Capital Rule; Market Risk Capital Rule

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-30

... Corporation 12 CFR Parts 324, 325 Regulatory Capital Rules: Advanced Approaches Risk-Based Capital Rule... 325 RIN 3064-AD97 Regulatory Capital Rules: Advanced Approaches Risk-Based Capital Rule; Market Risk... the agencies' current capital rules. In this NPR (Advanced Approaches and Market Risk NPR) the...

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.

Association Rule Analysis for Tour Route Recommendation and Application to Wctsnop

NASA Astrophysics Data System (ADS)

Fang, H.; Chen, C.; Lin, J.; Liu, X.; Fang, D.

2017-09-01

The increasing E-tourism systems provide intelligent tour recommendation for tourists. In this sense, recommender system can make personalized suggestions and provide satisfied information associated with their tour cycle. Data mining is a proper tool that extracting potential information from large database for making strategic decisions. In the study, association rule analysis based on FP-growth algorithm is applied to find the association relationship among scenic spots in different cities as tour route recommendation. In order to figure out valuable rules, Kulczynski interestingness measure is adopted and imbalance ratio is computed. The proposed scheme was evaluated on Wangluzhe cultural tourism service network operation platform (WCTSNOP), where it could verify that it is able to quick recommend tour route and to rapidly enhance the recommendation quality.

Numerical analysis of strain localization for transversely isotropic model with non-coaxial flow rule

NASA Astrophysics Data System (ADS)

Wei, Ding; Cong-cong, Yu; Chen-hui, Wu; Zheng-yi, Shu

2018-03-01

To analyse the strain localization behavior of geomaterials, the forward Euler schemes and the tangent modulus matrix are formulated based on the transversely isotropic yield criterion with non-coaxial flow rule developed by Lade, the program code is implemented based on the user subroutine (UMAT) of ABAQUS. The influence of the material principal direction on the strain localization and the bearing capacity of the structure are investigated and analyzed. Numerical results show the validity and performance of the proposed model in simulating the strain localization behavior of geostructures.

78 FR 64425 - Current Good Manufacturing Practice and Hazard Analysis and Risk-Based Preventive Controls for...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-29

... DEPARTMENT OF HEALTH AND HUMAN SERVICES Food and Drug Administration 21 CFR Parts 16, 225, 500, 507, and 579 [Docket No. FDA-2011-N-0922] Current Good Manufacturing Practice and Hazard Analysis and Risk- Based Preventive Controls for Food for Animals; Public Meeting on Proposed Rule AGENCY: Food and...

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.

IOTA simple rules in differentiating between benign and malignant ovarian tumors.

PubMed

Tantipalakorn, Charuwan; Wanapirak, Chanane; Khunamornpong, Surapan; Sukpan, Kornkanok; Tongsong, Theera

2014-01-01

To evaluate the diagnostic performance of IOTA simple rules in differentiating between benign and malignant ovarian tumors. A study of diagnostic performance was conducted on women scheduled for elective surgery due to ovarian masses between March 2007 and March 2012. All patients underwent ultrasound examination for IOTA simple rules within 24 hours of surgery. All examinations were performed by the authors, who had no any clinical information of the patients, to differentiate between benign and malignant adnexal masses using IOTA simple rules. Gold standard diagnosis was based on pathological or operative findings. A total of 398 adnexal masses, in 376 women, were available for analysis. Of them, the IOTA simple rules could be applied in 319 (80.1%) including 212 (66.5%) benign tumors and 107 (33.6%) malignant tumors. The simple rules yielded inconclusive results in 79 (19.9%) masses. In the 319 masses for which the IOTA simple rules could be applied, sensitivity was 82.9% and specificity 95.3%. The IOTA simple rules have high diagnostic performance in differentiating between benign and malignant adnexal masses. Nevertheless, inconclusive results are relatively common.

True Dopers or Negligent Athletes? An Analysis of Anti-Doping Rule Violations Reported to the World Anti-Doping Agency 2010-2012.

PubMed

de Hon, Olivier; van Bottenburg, Maarten

2017-12-06

The sanction that an athlete receives when an anti-doping rule violation has been committed depends on the specific circumstances of the case. Anti-doping tribunals decide on the final sanction, following the rules of the World Anti-Doping Code. To assess the athletes' degree of fault based on the length of sanctions imposed on them to feed policy-related discussions. Analysing data from the results management database of the World Anti-Doping Agency for anonymous information of anti-doping rule violations in eight selected sports covering the years 2010-2012. Four out of ten athletes who committed an anti-doping rule violation received a suspension that was lower than the standard. This is an indication that tribunals in many instances are not convinced that the athletes concerned were completely at fault, that mitigating circumstances were applicable, or that full responsibility of the suspected violation should not be held against them. Anabolic agents, peptide hormones, and hormone modulators lead to higher sanctions, as do combinations of several anti-doping rule violations. This first analysis of information from the World Anti-Doping Agency's results management database indicates that a large proportion of the athletes who commit anti-doping rule violations may have done this unintentionally. Anti-doping professionals should strive to improve this situation in various ways.

Construction of a Clinical Decision Support System for Undergoing Surgery Based on Domain Ontology and Rules Reasoning

PubMed Central

Bau, Cho-Tsan; Huang, Chung-Yi

2014-01-01

Abstract Objective: To construct a clinical decision support system (CDSS) for undergoing surgery based on domain ontology and rules reasoning in the setting of hospitalized diabetic patients. Materials and Methods: The ontology was created with a modified ontology development method, including specification and conceptualization, formalization, implementation, and evaluation and maintenance. The Protégé–Web Ontology Language editor was used to implement the ontology. Embedded clinical knowledge was elicited to complement the domain ontology with formal concept analysis. The decision rules were translated into JENA format, which JENA can use to infer recommendations based on patient clinical situations. Results: The ontology includes 31 classes and 13 properties, plus 38 JENA rules that were built to generate recommendations. The evaluation studies confirmed the correctness of the ontology, acceptance of recommendations, satisfaction with the system, and usefulness of the ontology for glycemic management of diabetic patients undergoing surgery, especially for domain experts. Conclusions: The contribution of this research is to set up an evidence-based hybrid ontology and an evaluation method for CDSS. The system can help clinicians to achieve inpatient glycemic control in diabetic patients undergoing surgery while avoiding hypoglycemia. PMID:24730353

Construction of a clinical decision support system for undergoing surgery based on domain ontology and rules reasoning.

PubMed

Bau, Cho-Tsan; Chen, Rung-Ching; Huang, Chung-Yi

2014-05-01

To construct a clinical decision support system (CDSS) for undergoing surgery based on domain ontology and rules reasoning in the setting of hospitalized diabetic patients. The ontology was created with a modified ontology development method, including specification and conceptualization, formalization, implementation, and evaluation and maintenance. The Protégé-Web Ontology Language editor was used to implement the ontology. Embedded clinical knowledge was elicited to complement the domain ontology with formal concept analysis. The decision rules were translated into JENA format, which JENA can use to infer recommendations based on patient clinical situations. The ontology includes 31 classes and 13 properties, plus 38 JENA rules that were built to generate recommendations. The evaluation studies confirmed the correctness of the ontology, acceptance of recommendations, satisfaction with the system, and usefulness of the ontology for glycemic management of diabetic patients undergoing surgery, especially for domain experts. The contribution of this research is to set up an evidence-based hybrid ontology and an evaluation method for CDSS. The system can help clinicians to achieve inpatient glycemic control in diabetic patients undergoing surgery while avoiding hypoglycemia.

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

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 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.

Intelligent bandwith compression

NASA Astrophysics Data System (ADS)

Tseng, D. Y.; Bullock, B. L.; Olin, K. E.; Kandt, R. K.; Olsen, J. D.

1980-02-01

The feasibility of a 1000:1 bandwidth compression ratio for image transmission has been demonstrated using image-analysis algorithms and a rule-based controller. Such a high compression ratio was achieved by first analyzing scene content using auto-cueing and feature-extraction algorithms, and then transmitting only the pertinent information consistent with mission requirements. A rule-based controller directs the flow of analysis and performs priority allocations on the extracted scene content. The reconstructed bandwidth-compressed image consists of an edge map of the scene background, with primary and secondary target windows embedded in the edge map. The bandwidth-compressed images are updated at a basic rate of 1 frame per second, with the high-priority target window updated at 7.5 frames per second. The scene-analysis algorithms used in this system together with the adaptive priority controller are described. Results of simulated 1000:1 band width-compressed images are presented. A video tape simulation of the Intelligent Bandwidth Compression system has been produced using a sequence of video input from the data base.

Rebuilding the NAVSEA Early Stage Ship Design Environment

DTIC Science & Technology

2010-04-01

rules -of- thumb to base these crucial decisions upon. With High Performance Computing (HPC) as an enabler, the vision is to explore all downstream...the results of the analysis back into LEAPS. Another software development worthy of discussion here is Intelligent Ship Arrangements ( ISA ), which...constraints and rules set by the users ahead of time. When used in a systematic and stochastic way, and when integrated using LEAPS, having this

Scalable Adaptive Architectures for Maritime Operations Center Command and Control

DTIC Science & Technology

2011-05-06

the project to investigate the possibility of using earlier work on the validation and verification of rule bases in addressing the dynamically ...support the organization. To address the dynamically changing rules of engagement of a maritime force as it crosses different geographical areas, GMU... dynamic analysis, makes use of an Occurrence Graph that corresponds to the dynamics (or execution) of the Petri Net, to capture properties

On Decision-Making Among Multiple Rule-Bases in Fuzzy Control Systems

NASA Technical Reports Server (NTRS)

Tunstel, Edward; Jamshidi, Mo

1997-01-01

Intelligent control of complex multi-variable systems can be a challenge for single fuzzy rule-based controllers. This class of problems cam often be managed with less difficulty by distributing intelligent decision-making amongst a collection of rule-bases. Such an approach requires that a mechanism be chosen to ensure goal-oriented interaction between the multiple rule-bases. In this paper, a hierarchical rule-based approach is described. Decision-making mechanisms based on generalized concepts from single-rule-based fuzzy control are described. Finally, the effects of different aggregation operators on multi-rule-base decision-making are examined in a navigation control problem for mobile robots.

Development of an expert system for analysis of Shuttle atmospheric revitalization and pressure control subsystem anomalies

NASA Technical Reports Server (NTRS)

Lafuse, Sharon A.

1991-01-01

The paper describes the Shuttle Leak Management Expert System (SLMES), a preprototype expert system developed to enable the ECLSS subsystem manager to analyze subsystem anomalies and to formulate flight procedures based on flight data. The SLMES combines the rule-based expert system technology with the traditional FORTRAN-based software into an integrated system. SLMES analyzes the data using rules, and, when it detects a problem that requires simulation, it sets up the input for the FORTRAN-based simulation program ARPCS2AT2, which predicts the cabin total pressure and composition as a function of time. The program simulates the pressure control system, the crew oxygen masks, the airlock repress/depress valves, and the leakage. When the simulation has completed, other SLMES rules are triggered to examine the results of simulation contrary to flight data and to suggest methods for correcting the problem. Results are then presented in form of graphs and tables.

A decision-support system for the analysis of clinical practice patterns.

PubMed

Balas, E A; Li, Z R; Mitchell, J A; Spencer, D C; Brent, E; Ewigman, B G

1994-01-01

Several studies documented substantial variation in medical practice patterns, but physicians often do not have adequate information on the cumulative clinical and financial effects of their decisions. The purpose of developing an expert system for the analysis of clinical practice patterns was to assist providers in analyzing and improving the process and outcome of patient care. The developed QFES (Quality Feedback Expert System) helps users in the definition and evaluation of measurable quality improvement objectives. Based on objectives and actual clinical data, several measures can be calculated (utilization of procedures, annualized cost effect of using a particular procedure, and expected utilization based on peer-comparison and case-mix adjustment). The quality management rules help to detect important discrepancies among members of the selected provider group and compare performance with objectives. The system incorporates a variety of data and knowledge bases: (i) clinical data on actual practice patterns, (ii) frames of quality parameters derived from clinical practice guidelines, and (iii) rules of quality management for data analysis. An analysis of practice patterns of 12 family physicians in the management of urinary tract infections illustrates the use of the system.

A Scala DSL for RETE-Based Runtime Verification

NASA Technical Reports Server (NTRS)

Havelund, Klaus

2013-01-01

Runtime verification (RV) consists in part of checking execution traces against formalized specifications. Several systems have emerged, most of which support specification notations based on state machines, regular expressions, temporal logic, or grammars. The field of Artificial Intelligence (AI) has for an even longer period of time studied rule-based production systems, which at a closer look appear to be relevant for RV, although seemingly focused on slightly different application domains, such as for example business processes and expert systems. The core algorithm in many of these systems is the Rete algorithm. We have implemented a Rete-based runtime verification system, named LogFire (originally intended for offline log analysis but also applicable to online analysis), as an internal DSL in the Scala programming language, using Scala's support for defining DSLs. This combination appears attractive from a practical point of view. Our contribution is in part conceptual in arguing that such rule-based frameworks originating from AI may be suited for RV.

The family as the foundation of political rule in western philosophy: a comparative analysis of Aristotle's politics and Hegel's philosophy of right.

PubMed

Jarvis, Douglas E

2011-01-01

For both Aristotle and Hegel, the family is the foundation in which the universalized rule of law is validated according to the political structure of the 'Polis' or 'State' itself. This composite whole or structure of society (Ancient Polis/Modern State) is the political end of humanity for both philosophers, which in turn finds its primordial beginning in the family. For Aristotle, it is in the kingly rule of the household that the property-based distinction of citizenship is set for the rule of his ideal Polis. For Hegel, it is in the love affirmed through caring affection within the nuclear family that the dialectical framework for the freedom of civil society, and the rational unity of a congregational 'spirit' in the State, finds its foundation. For both thinkers, the family sets the base for a political theory that defines citizenship in a manner that transcends the particularities of kin bonds.

Simple Rules, Not So Simple: The Use of International Ovarian Tumor Analysis (IOTA) Terminology and Simple Rules in Inexperienced Hands in a Prospective Multicenter Cohort Study.

PubMed

Meys, Evelyne; Rutten, Iris; Kruitwagen, Roy; Slangen, Brigitte; Lambrechts, Sandrina; Mertens, Helen; Nolting, Ernst; Boskamp, Dieuwke; Van Gorp, Toon

2017-12-01

 To analyze how well untrained examiners - without experience in the use of International Ovarian Tumor Analysis (IOTA) terminology or simple ultrasound-based rules (simple rules) - are able to apply IOTA terminology and simple rules and to assess the level of agreement between non-experts and an expert.  This prospective multicenter cohort study enrolled women with ovarian masses. Ultrasound was performed by non-expert examiners and an expert. Ultrasound features were recorded using IOTA nomenclature, and used for classifying the mass by simple rules. Interobserver agreement was evaluated with Fleiss' kappa and percentage agreement between observers.  50 consecutive women were included. We observed 46 discrepancies in the description of ovarian masses when non-experts utilized IOTA terminology. Tumor type was misclassified often (n = 22), resulting in poor interobserver agreement between the non-experts and the expert (kappa = 0.39, 95 %-CI 0.244 - 0.529, percentage of agreement = 52.0 %). Misinterpretation of simple rules by non-experts was observed 57 times, resulting in an erroneous diagnosis in 15 patients (30 %). The agreement for classifying the mass as benign, malignant or inconclusive by simple rules was only moderate between the non-experts and the expert (kappa = 0.50, 95 %-CI 0.300 - 0.704, percentage of agreement = 70.0 %). The level of agreement for all 10 simple rules features varied greatly (kappa index range: -0.08 - 0.74, percentage of agreement 66 - 94 %).  Although simple rules are useful to distinguish benign from malignant adnexal masses, they are not that simple for untrained examiners. Training with both IOTA terminology and simple rules is necessary before simple rules can be introduced into guidelines and daily clinical practice. © Georg Thieme Verlag KG Stuttgart · New York.

Computationally assisted screening and design of cell-interactive peptides by a cell-based assay using peptide arrays and a fuzzy neural network algorithm.

PubMed

Kaga, Chiaki; Okochi, Mina; Tomita, Yasuyuki; Kato, Ryuji; Honda, Hiroyuki

2008-03-01

We developed a method of effective peptide screening that combines experiments and computational analysis. The method is based on the concept that screening efficiency can be enhanced from even limited data by use of a model derived from computational analysis that serves as a guide to screening and combining the model with subsequent repeated experiments. Here we focus on cell-adhesion peptides as a model application of this peptide-screening strategy. Cell-adhesion peptides were screened by use of a cell-based assay of a peptide array. Starting with the screening data obtained from a limited, random 5-mer library (643 sequences), a rule regarding structural characteristics of cell-adhesion peptides was extracted by fuzzy neural network (FNN) analysis. According to this rule, peptides with unfavored residues in certain positions that led to inefficient binding were eliminated from the random sequences. In the restricted, second random library (273 sequences), the yield of cell-adhesion peptides having an adhesion rate more than 1.5-fold to that of the basal array support was significantly high (31%) compared with the unrestricted random library (20%). In the restricted third library (50 sequences), the yield of cell-adhesion peptides increased to 84%. We conclude that a repeated cycle of experiments screening limited numbers of peptides can be assisted by the rule-extracting feature of FNN.

A 3/D finite element approach for metal matrix composites based on micromechanical models

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

Svobodnik, A.J.; Boehm, H.J.; Rammerstorfer, F.G.

Based on analytical considerations by Dvorak and Bahel-El-Din, a 3/D finite element material law has been developed for the elastic-plastic analysis of unidirectional fiber-reinforced metal matrix composites. The material law described in this paper has been implemented in the finite element code ABAQUS via the user subroutine UMAT. A constitutive law is described under the assumption that the fibers are linear-elastic and the matrix is of a von Mises-type with a Prager-Ziegler kinematic hardening rule. The uniaxial effective stress-strain relationship of the matrix in the plastic range is approximated by a Ramberg-Osgood law, a linear hardening rule or a nonhardeningmore » rule. Initial yield surface of the matrix material and for the fiber reinforced composite are compared to show the effect of reinforcement. Implementation of this material law in a finite element program is shown. Furthermore, the efficiency of substepping schemes and stress corrections for the numerical integration of the elastic-plastic stress-strain relations for anisotropic materials are investigated. The results of uniaxial monotonic tests of a boron/aluminum composite are compared to some finite element analyses based on micromechanical considerations. Furthermore a complete 3/D analysis of a tensile test specimen made of a silicon-carbide/aluminum MMC and the analysis of an MMC inlet inserted in a homogenous material are shown. 12 refs.« less

Intelligent bandwidth compression

NASA Astrophysics Data System (ADS)

Tseng, D. Y.; Bullock, B. L.; Olin, K. E.; Kandt, R. K.; Olsen, J. D.

1980-02-01

The feasibility of a 1000:1 bandwidth compression ratio for image transmission has been demonstrated using image-analysis algorithms and a rule-based controller. Such a high compression ratio was achieved by first analyzing scene content using auto-cueing and feature-extraction algorithms, and then transmitting only the pertinent information consistent with mission requirements. A rule-based controller directs the flow of analysis and performs priority allocations on the extracted scene content. The reconstructed bandwidth-compressed image consists of an edge map of the scene background, with primary and secondary target windows embedded in the edge map. The bandwidth-compressed images are updated at a basic rate of 1 frame per second, with the high-priority target window updated at 7.5 frames per second. The scene-analysis algorithms used in this system together with the adaptive priority controller are described. Results of simulated 1000:1 bandwidth-compressed images are presented.

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.

Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques

PubMed Central

Sanmiquel, Lluís; Bascompta, Marc; Rossell, Josep M.; Anticoi, Hernán Francisco; Guash, Eduard

2018-01-01

An analysis of occupational accidents in the mining sector was conducted using the data from the Spanish Ministry of Employment and Social Safety between 2005 and 2015, and data-mining techniques were applied. Data was processed with the software Weka. Two scenarios were chosen from the accidents database: surface and underground mining. The most important variables involved in occupational accidents and their association rules were determined. These rules are composed of several predictor variables that cause accidents, defining its characteristics and context. This study exposes the 20 most important association rules in the sector—either surface or underground mining—based on the statistical confidence levels of each rule as obtained by Weka. The outcomes display the most typical immediate causes, along with the percentage of accidents with a basis in each association rule. The most important immediate cause is body movement with physical effort or overexertion, and the type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident are different between the two scenarios. Data-mining techniques were chosen as a useful tool to find out the root cause of the accidents. PMID:29518921

Analysis of Occupational Accidents in Underground and Surface Mining in Spain Using Data-Mining Techniques.

PubMed

Sanmiquel, Lluís; Bascompta, Marc; Rossell, Josep M; Anticoi, Hernán Francisco; Guash, Eduard

2018-03-07

An analysis of occupational accidents in the mining sector was conducted using the data from the Spanish Ministry of Employment and Social Safety between 2005 and 2015, and data-mining techniques were applied. Data was processed with the software Weka. Two scenarios were chosen from the accidents database: surface and underground mining. The most important variables involved in occupational accidents and their association rules were determined. These rules are composed of several predictor variables that cause accidents, defining its characteristics and context. This study exposes the 20 most important association rules in the sector-either surface or underground mining-based on the statistical confidence levels of each rule as obtained by Weka. The outcomes display the most typical immediate causes, along with the percentage of accidents with a basis in each association rule. The most important immediate cause is body movement with physical effort or overexertion, and the type of accident is physical effort or overexertion. On the other hand, the second most important immediate cause and type of accident are different between the two scenarios. Data-mining techniques were chosen as a useful tool to find out the root cause of the accidents.

Proposed Clinical Decision Rules to Diagnose Acute Rhinosinusitis Among Adults in Primary Care.

PubMed

Ebell, Mark H; Hansen, Jens Georg

2017-07-01

To reduce inappropriate antibiotic prescribing, we sought to develop a clinical decision rule for the diagnosis of acute rhinosinusitis and acute bacterial rhinosinusitis. Multivariate analysis and classification and regression tree (CART) analysis were used to develop clinical decision rules for the diagnosis of acute rhinosinusitis, defined using 3 different reference standards (purulent antral puncture fluid or abnormal finding on a computed tomographic (CT) scan; for acute bacterial rhinosinusitis, we used a positive bacterial culture of antral fluid). Signs, symptoms, C-reactive protein (CRP), and reference standard tests were prospectively recorded in 175 Danish patients aged 18 to 65 years seeking care for suspected acute rhinosinusitis. For each reference standard, we developed 2 clinical decision rules: a point score based on a logistic regression model and an algorithm based on a CART model. We identified low-, moderate-, and high-risk groups for acute rhinosinusitis or acute bacterial rhinosinusitis for each clinical decision rule. The point scores each had between 5 and 6 predictors, and an area under the receiver operating characteristic curve (AUROCC) between 0.721 and 0.767. For positive bacterial culture as the reference standard, low-, moderate-, and high-risk groups had a 16%, 49%, and 73% likelihood of acute bacterial rhinosinusitis, respectively. CART models had an AUROCC ranging from 0.783 to 0.827. For positive bacterial culture as the reference standard, low-, moderate-, and high-risk groups had a likelihood of acute bacterial rhinosinusitis of 6%, 31%, and 59% respectively. We have developed a series of clinical decision rules integrating signs, symptoms, and CRP to diagnose acute rhinosinusitis and acute bacterial rhinosinusitis with good accuracy. They now require prospective validation and an assessment of their effect on clinical and process outcomes. © 2017 Annals of Family Medicine, Inc.

Use of Six Sigma Worksheets for assessment of internal and external failure costs associated with candidate quality control rules for an ADVIA 120 hematology analyzer.

PubMed

Cian, Francesco; Villiers, Elisabeth; Archer, Joy; Pitorri, Francesca; Freeman, Kathleen

2014-06-01

Quality control (QC) validation is an essential tool in total quality management of a veterinary clinical pathology laboratory. Cost-analysis can be a valuable technique to help identify an appropriate QC procedure for the laboratory, although this has never been reported in veterinary medicine. The aim of this study was to determine the applicability of the Six Sigma Quality Cost Worksheets in the evaluation of possible candidate QC rules identified by QC validation. Three months of internal QC records were analyzed. EZ Rules 3 software was used to evaluate candidate QC procedures, and the costs associated with the application of different QC rules were calculated using the Six Sigma Quality Cost Worksheets. The costs associated with the current and the candidate QC rules were compared, and the amount of cost savings was calculated. There was a significant saving when the candidate 1-2.5s, n = 3 rule was applied instead of the currently utilized 1-2s, n = 3 rule. The savings were 75% per year (£ 8232.5) based on re-evaluating all of the patient samples in addition to the controls, and 72% per year (£ 822.4) based on re-analyzing only the control materials. The savings were also shown to change accordingly with the number of samples analyzed and with the number of daily QC procedures performed. These calculations demonstrated the importance of the selection of an appropriate QC procedure, and the usefulness of the Six Sigma Costs Worksheet in determining the most cost-effective rule(s) when several candidate rules are identified by QC validation. © 2014 American Society for Veterinary Clinical Pathology and European Society for Veterinary Clinical Pathology.

Dialog detection in narrative video by shot and face analysis

NASA Astrophysics Data System (ADS)

Kroon, B.; Nesvadba, J.; Hanjalic, A.

2007-01-01

The proliferation of captured personal and broadcast content in personal consumer archives necessitates comfortable access to stored audiovisual content. Intuitive retrieval and navigation solutions require however a semantic level that cannot be reached by generic multimedia content analysis alone. A fusion with film grammar rules can help to boost the reliability significantly. The current paper describes the fusion of low-level content analysis cues including face parameters and inter-shot similarities to segment commercial content into film grammar rule-based entities and subsequently classify those sequences into so-called shot reverse shots, i.e. dialog sequences. Moreover shot reverse shot specific mid-level cues are analyzed augmenting the shot reverse shot information with dialog specific descriptions.

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.

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.

Automatic Learning of Fine Operating Rules for Online Power System Security Control.

PubMed

Sun, Hongbin; Zhao, Feng; Wang, Hao; Wang, Kang; Jiang, Weiyong; Guo, Qinglai; Zhang, Boming; Wehenkel, Louis

2016-08-01

Fine operating rules for security control and an automatic system for their online discovery were developed to adapt to the development of smart grids. The automatic system uses the real-time system state to determine critical flowgates, and then a continuation power flow-based security analysis is used to compute the initial transfer capability of critical flowgates. Next, the system applies the Monte Carlo simulations to expected short-term operating condition changes, feature selection, and a linear least squares fitting of the fine operating rules. The proposed system was validated both on an academic test system and on a provincial power system in China. The results indicated that the derived rules provide accuracy and good interpretability and are suitable for real-time power system security control. The use of high-performance computing systems enables these fine operating rules to be refreshed online every 15 min.

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.

Intelligent Gearbox Diagnosis Methods Based on SVM, Wavelet Lifting and RBR

PubMed Central

Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

2010-01-01

Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis. PMID:22399894

Intelligent gearbox diagnosis methods based on SVM, wavelet lifting and RBR.

PubMed

Gao, Lixin; Ren, Zhiqiang; Tang, Wenliang; Wang, Huaqing; Chen, Peng

2010-01-01

Given the problems in intelligent gearbox diagnosis methods, it is difficult to obtain the desired information and a large enough sample size to study; therefore, we propose the application of various methods for gearbox fault diagnosis, including wavelet lifting, a support vector machine (SVM) and rule-based reasoning (RBR). In a complex field environment, it is less likely for machines to have the same fault; moreover, the fault features can also vary. Therefore, a SVM could be used for the initial diagnosis. First, gearbox vibration signals were processed with wavelet packet decomposition, and the signal energy coefficients of each frequency band were extracted and used as input feature vectors in SVM for normal and faulty pattern recognition. Second, precision analysis using wavelet lifting could successfully filter out the noisy signals while maintaining the impulse characteristics of the fault; thus effectively extracting the fault frequency of the machine. Lastly, the knowledge base was built based on the field rules summarized by experts to identify the detailed fault type. Results have shown that SVM is a powerful tool to accomplish gearbox fault pattern recognition when the sample size is small, whereas the wavelet lifting scheme can effectively extract fault features, and rule-based reasoning can be used to identify the detailed fault type. Therefore, a method that combines SVM, wavelet lifting and rule-based reasoning ensures effective gearbox fault diagnosis.

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.

ECONOMIC ANALYSIS FOR THE GROUND WATER RULE ...

EPA Pesticide Factsheets

The Ground Water Rule Economic Analysis provides a description of the need for the rule, consideration of regulatory alternatives, baseline analysis including national ground water system profile and an estimate of pathogen and indicator occurrence (Chapter 4), a risk assessment and benefits analysis (Chapter 5), and a cost analysis ( Chapter 6). Chapters 4, 5 and 6, selected appendices and sections of other chapters will be peer reviewed. The objective of the Economic Analysis Document is to support the final Ground Water Rule.

Rule-Based Expert Systems in the Command Estimate: An Operational Perspective

DTIC Science & Technology

1990-06-01

control measures. 5. Prepare COA statement(s) and sketch(es). The key inputs for developing courses of action are the DFD process of IPB, data stores...mission, or a change of information provides new direction to this process for that particular operation." Formal scientific analysis of the command...30 5. Delivery of outside news . This feature contributes to the commanders insatiable need for current information. Artificial intelligence ana rule

An operation support expert system based on on-line dynamics simulation and fuzzy reasoning for startup schedule optimization in fossil power plants

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

Matsumoto, H.; Eki, Y.; Kaji, A.

1993-12-01

An expert system which can support operators of fossil power plants in creating the optimum startup schedule and executing it accurately is described. The optimum turbine speed-up and load-up pattern is obtained through an iterative manner which is based on fuzzy resonating using quantitative calculations as plant dynamics models and qualitative knowledge as schedule optimization rules with fuzziness. The rules represent relationships between stress margins and modification rates of the schedule parameters. Simulations analysis proves that the system provides quick and accurate plant startups.

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.

ARNetMiT R Package: association rules based gene co-expression networks of miRNA targets.

PubMed

Özgür Cingiz, M; Biricik, G; Diri, B

2017-03-31

miRNAs are key regulators that bind to target genes to suppress their gene expression level. The relations between miRNA-target genes enable users to derive co-expressed genes that may be involved in similar biological processes and functions in cells. We hypothesize that target genes of miRNAs are co-expressed, when they are regulated by multiple miRNAs. With the usage of these co-expressed genes, we can theoretically construct co-expression networks (GCNs) related to 152 diseases. In this study, we introduce ARNetMiT that utilize a hash based association rule algorithm in a novel way to infer the GCNs on miRNA-target genes data. We also present R package of ARNetMiT, which infers and visualizes GCNs of diseases that are selected by users. Our approach assumes miRNAs as transactions and target genes as their items. Support and confidence values are used to prune association rules on miRNA-target genes data to construct support based GCNs (sGCNs) along with support and confidence based GCNs (scGCNs). We use overlap analysis and the topological features for the performance analysis of GCNs. We also infer GCNs with popular GNI algorithms for comparison with the GCNs of ARNetMiT. Overlap analysis results show that ARNetMiT outperforms the compared GNI algorithms. We see that using high confidence values in scGCNs increase the ratio of the overlapped gene-gene interactions between the compared methods. According to the evaluation of the topological features of ARNetMiT based GCNs, the degrees of nodes have power-law distribution. The hub genes discovered by ARNetMiT based GCNs are consistent with the literature.

Evaluation of IOTA Simple Ultrasound Rules to Distinguish Benign and Malignant Ovarian Tumours.

PubMed

Garg, Sugandha; Kaur, Amarjit; Mohi, Jaswinder Kaur; Sibia, Preet Kanwal; Kaur, Navkiran

2017-08-01

IOTA stands for International Ovarian Tumour Analysis group. Ovarian cancer is one of the common cancers in women and is diagnosed at later stage in majority. The limiting factor for early diagnosis is lack of standardized terms and procedures in gynaecological sonography. Introduction of IOTA rules has provided some consistency in defining morphological features of ovarian masses through a standardized examination technique. To evaluate the efficacy of IOTA simple ultrasound rules in distinguishing benign and malignant ovarian tumours and establishing their use as a tool in early diagnosis of ovarian malignancy. A hospital based case control prospective study was conducted. Patients with suspected ovarian pathology were evaluated using IOTA ultrasound rules and designated as benign or malignant. Findings were correlated with histopathological findings. Collected data was statistically analysed using chi-square test and kappa statistical method. Out of initial 55 patients, 50 patients were included in the final analysis who underwent surgery. IOTA simple rules were applicable in 45 out of these 50 patients (90%). The sensitivity for the detection of malignancy in cases where IOTA simple rules were applicable was 91.66% and the specificity was 84.84%. Accuracy was 86.66%. Classifying inconclusive cases as malignant, the sensitivity and specificity was 93% and 80% respectively. High level of agreement was found between USG and histopathological diagnosis with Kappa value as 0.323. IOTA simple ultrasound rules were highly sensitive and specific in predicting ovarian malignancy preoperatively yet being reproducible, easy to train and use.

Knowledge From Pictures (KFP)

NASA Technical Reports Server (NTRS)

Truszkowski, Walt; Paterra, Frank; Bailin, Sidney

1993-01-01

The old maxim goes: 'A picture is worth a thousand words'. The objective of the research reported in this paper is to demonstrate this idea as it relates to the knowledge acquisition process and the automated development of an expert system's rule base. A prototype tool, the Knowledge From Pictures (KFP) tool, has been developed which configures an expert system's rule base by an automated analysis of and reasoning about a 'picture', i.e., a graphical representation of some target system to be supported by the diagnostic capabilities of the expert system under development. This rule base, when refined, could then be used by the expert system for target system monitoring and fault analysis in an operational setting. Most people, when faced with the problem of understanding the behavior of a complicated system, resort to the use of some picture or graphical representation of the system as an aid in thinking about it. This depiction provides a means of helping the individual to visualize the bahavior and dynamics of the system under study. An analysis of the picture augmented with the individual's background information, allows the problem solver to codify knowledge about the system. This knowledge can, in turn, be used to develop computer programs to automatically monitor the system's performance. The approach taken is this research was to mimic this knowledge acquisition paradigm. A prototype tool was developed which provides the user: (1) a mechanism for graphically representing sample system-configurations appropriate for the domain, and (2) a linguistic device for annotating the graphical representation with the behaviors and mutual influences of the components depicted in the graphic. The KFP tool, reasoning from the graphical depiction along with user-supplied annotations of component behaviors and inter-component influences, generates a rule base that could be used in automating the fault detection, isolation, and repair of the system.

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.

Uncertainty modelling and analysis of volume calculations based on a regular grid digital elevation model (DEM)

NASA Astrophysics Data System (ADS)

Li, Chang; Wang, Qing; Shi, Wenzhong; Zhao, Sisi

2018-05-01

The accuracy of earthwork calculations that compute terrain volume is critical to digital terrain analysis (DTA). The uncertainties in volume calculations (VCs) based on a DEM are primarily related to three factors: 1) model error (ME), which is caused by an adopted algorithm for a VC model, 2) discrete error (DE), which is usually caused by DEM resolution and terrain complexity, and 3) propagation error (PE), which is caused by the variables' error. Based on these factors, the uncertainty modelling and analysis of VCs based on a regular grid DEM are investigated in this paper. Especially, how to quantify the uncertainty of VCs is proposed by a confidence interval based on truncation error (TE). In the experiments, the trapezoidal double rule (TDR) and Simpson's double rule (SDR) were used to calculate volume, where the TE is the major ME, and six simulated regular grid DEMs with different terrain complexity and resolution (i.e. DE) were generated by a Gauss synthetic surface to easily obtain the theoretical true value and eliminate the interference of data errors. For PE, Monte-Carlo simulation techniques and spatial autocorrelation were used to represent DEM uncertainty. This study can enrich uncertainty modelling and analysis-related theories of geographic information science.

Java implementation of Class Association Rule algorithms

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

Tamura, Makio

2007-08-30

Java implementation of three Class Association Rule mining algorithms, NETCAR, CARapriori, and clustering based rule mining. NETCAR algorithm is a novel algorithm developed by Makio Tamura. The algorithm is discussed in a paper: UCRL-JRNL-232466-DRAFT, and would be published in a peer review scientific journal. The software is used to extract combinations of genes relevant with a phenotype from a phylogenetic profile and a phenotype profile. The phylogenetic profiles is represented by a binary matrix and a phenotype profile is represented by a binary vector. The present application of this software will be in genome analysis, however, it could be appliedmore » more generally.« less

Automated detection of pain from facial expressions: a rule-based approach using AAM

NASA Astrophysics Data System (ADS)

Chen, Zhanli; Ansari, Rashid; Wilkie, Diana J.

2012-02-01

In this paper, we examine the problem of using video analysis to assess pain, an important problem especially for critically ill, non-communicative patients, and people with dementia. We propose and evaluate an automated method to detect the presence of pain manifested in patient videos using a unique and large collection of cancer patient videos captured in patient homes. The method is based on detecting pain-related facial action units defined in the Facial Action Coding System (FACS) that is widely used for objective assessment in pain analysis. In our research, a person-specific Active Appearance Model (AAM) based on Project-Out Inverse Compositional Method is trained for each patient individually for the modeling purpose. A flexible representation of the shape model is used in a rule-based method that is better suited than the more commonly used classifier-based methods for application to the cancer patient videos in which pain-related facial actions occur infrequently and more subtly. The rule-based method relies on the feature points that provide facial action cues and is extracted from the shape vertices of AAM, which have a natural correspondence to face muscular movement. In this paper, we investigate the detection of a commonly used set of pain-related action units in both the upper and lower face. Our detection results show good agreement with the results obtained by three trained FACS coders who independently reviewed and scored the action units in the cancer patient videos.

77 FR 47572 - Retrospective Analysis of Existing Rules

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-09

... INTERNATIONAL TRADE COMMISSION 19 CFR Chapter II Retrospective Analysis of Existing Rules AGENCY... Order 13579 of July 11, 2011, the Commission recently adopted its Plan for Retrospective Analysis of... submitted in connection with the Commission's Preliminary Plan for Retrospective Analysis of Existing Rules...

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.

Intelligent Diagnostic Assistant for Complicated Skin Diseases through C5's Algorithm.

PubMed

Jeddi, Fatemeh Rangraz; Arabfard, Masoud; Kermany, Zahra Arab

2017-09-01

Intelligent Diagnostic Assistant can be used for complicated diagnosis of skin diseases, which are among the most common causes of disability. The aim of this study was to design and implement a computerized intelligent diagnostic assistant for complicated skin diseases through C5's Algorithm. An applied-developmental study was done in 2015. Knowledge base was developed based on interviews with dermatologists through questionnaires and checklists. Knowledge representation was obtained from the train data in the database using Excel Microsoft Office. Clementine Software and C5's Algorithms were applied to draw the decision tree. Analysis of test accuracy was performed based on rules extracted using inference chains. The rules extracted from the decision tree were entered into the CLIPS programming environment and the intelligent diagnostic assistant was designed then. The rules were defined using forward chaining inference technique and were entered into Clips programming environment as RULE. The accuracy and error rates obtained in the training phase from the decision tree were 99.56% and 0.44%, respectively. The accuracy of the decision tree was 98% and the error was 2% in the test phase. Intelligent diagnostic assistant can be used as a reliable system with high accuracy, sensitivity, specificity, and agreement.

Gene Model Annotations for Drosophila melanogaster: The Rule-Benders

PubMed Central

Crosby, Madeline A.; Gramates, L. Sian; dos Santos, Gilberto; Matthews, Beverley B.; St. Pierre, Susan E.; Zhou, Pinglei; Schroeder, Andrew J.; Falls, Kathleen; Emmert, David B.; Russo, Susan M.; Gelbart, William M.

2015-01-01

In the context of the FlyBase annotated gene models in Drosophila melanogaster, we describe the many exceptional cases we have curated from the literature or identified in the course of FlyBase analysis. These range from atypical but common examples such as dicistronic and polycistronic transcripts, noncanonical splices, trans-spliced transcripts, noncanonical translation starts, and stop-codon readthroughs, to single exceptional cases such as ribosomal frameshifting and HAC1-type intron processing. In FlyBase, exceptional genes and transcripts are flagged with Sequence Ontology terms and/or standardized comments. Because some of the rule-benders create problems for handlers of high-throughput data, we discuss plans for flagging these cases in bulk data downloads. PMID:26109356

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.

Static and Completion Analysis for Planning Knowledge Base Development and Verification

NASA Technical Reports Server (NTRS)

Chien, Steve A.

1996-01-01

A key obstacle hampering fielding of AI planning applications is the considerable expense of developing, verifying, updating, and maintaining the planning knowledge base (KB). Planning systems must be able to compare favorably in terms of software lifecycle costs to other means of automation such as scripts or rule-based expert systems.

[The method and application to construct experience recommendation platform of acupuncture ancient books based on data mining technology].

PubMed

Chen, Chuyun; Hong, Jiaming; Zhou, Weilin; Lin, Guohua; Wang, Zhengfei; Zhang, Qufei; Lu, Cuina; Lu, Lihong

2017-07-12

To construct a knowledge platform of acupuncture ancient books based on data mining technology, and to provide retrieval service for users. The Oracle 10 g database was applied and JAVA was selected as development language; based on the standard library and ancient books database established by manual entry, a variety of data mining technologies, including word segmentation, speech tagging, dependency analysis, rule extraction, similarity calculation, ambiguity analysis, supervised classification technology were applied to achieve text automatic extraction of ancient books; in the last, through association mining and decision analysis, the comprehensive and intelligent analysis of disease and symptom, meridians, acupoints, rules of acupuncture and moxibustion in acupuncture ancient books were realized, and retrieval service was provided for users through structure of browser/server (B/S). The platform realized full-text retrieval, word frequency analysis and association analysis; when diseases or acupoints were searched, the frequencies of meridian, acupoints (diseases) and techniques were presented from high to low, meanwhile the support degree and confidence coefficient between disease and acupoints (special acupoint), acupoints and acupoints in prescription, disease or acupoints and technique were presented. The experience platform of acupuncture ancient books based on data mining technology could be used as a reference for selection of disease, meridian and acupoint in clinical treatment and education of acupuncture and moxibustion.

Using pattern enumeration to accelerate process development and ramp yield

NASA Astrophysics Data System (ADS)

Zhuang, Linda; Pang, Jenny; Xu, Jessy; Tsai, Mengfeng; Wang, Amy; Zhang, Yifan; Sweis, Jason; Lai, Ya-Chieh; Ding, Hua

2016-03-01

During a new technology node process setup phase, foundries do not initially have enough product chip designs to conduct exhaustive process development. Different operational teams use manually designed simple test keys to set up their process flows and recipes. When the very first version of the design rule manual (DRM) is ready, foundries enter the process development phase where new experiment design data is manually created based on these design rules. However, these IP/test keys contain very uniform or simple design structures. This kind of design normally does not contain critical design structures or process unfriendly design patterns that pass design rule checks but are found to be less manufacturable. It is desired to have a method to generate exhaustive test patterns allowed by design rules at development stage to verify the gap of design rule and process. This paper presents a novel method of how to generate test key patterns which contain known problematic patterns as well as any constructs which designers could possibly draw based on current design rules. The enumerated test key patterns will contain the most critical design structures which are allowed by any particular design rule. A layout profiling method is used to do design chip analysis in order to find potential weak points on new incoming products so fab can take preemptive action to avoid yield loss. It can be achieved by comparing different products and leveraging the knowledge learned from previous manufactured chips to find possible yield detractors.

Systematic methods for knowledge acquisition and expert system development

NASA Technical Reports Server (NTRS)

Belkin, Brenda L.; Stengel, Robert F.

1991-01-01

Nine cooperating rule-based systems, collectively called AUTOCREW which were designed to automate functions and decisions associated with a combat aircraft's subsystems, are discussed. The organization of tasks within each system is described; performance metrics were developed to evaluate the workload of each rule base and to assess the cooperation between the rule bases. Simulation and comparative workload results for two mission scenarios are given. The scenarios are inbound surface-to-air-missile attack on the aircraft and pilot incapacitation. The methodology used to develop the AUTOCREW knowledge bases is summarized. Issues involved in designing the navigation sensor selection expert in AUTOCREW's NAVIGATOR knowledge base are discussed in detail. The performance of seven navigation systems aiding a medium-accuracy INS was investigated using Kalman filter covariance analyses. A navigation sensor management (NSM) expert system was formulated from covariance simulation data using the analysis of variance (ANOVA) method and the ID3 algorithm. ANOVA results show that statistically different position accuracies are obtained when different navaids are used, the number of navaids aiding the INS is varied, the aircraft's trajectory is varied, and the performance history is varied. The ID3 algorithm determines the NSM expert's classification rules in the form of decision trees. The performance of these decision trees was assessed on two arbitrary trajectories, and the results demonstrate that the NSM expert adapts to new situations and provides reasonable estimates of the expected hybrid performance.

Factor Retention in Exploratory Factor Analysis: A Comparison of Alternative Methods.

ERIC Educational Resources Information Center

Mumford, Karen R.; Ferron, John M.; Hines, Constance V.; Hogarty, Kristine Y.; Kromrey, Jeffery D.

This study compared the effectiveness of 10 methods of determining the number of factors to retain in exploratory common factor analysis. The 10 methods included the Kaiser rule and a modified Kaiser criterion, 3 variations of parallel analysis, 4 regression-based variations of the scree procedure, and the minimum average partial procedure. The…

77 FR 65645 - Retrospective Analysis of Existing Rules: Notice of Staff Memorandum

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-30

...] Retrospective Analysis of Existing Rules: Notice of Staff Memorandum SUMMARY: Take notice that the Commission... Analysis of Existing Rules prepared in response to Executive Order 13579, which requested independent regulatory agencies issue plans for periodic retrospective analysis of their existing regulations. [[Page...

Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding.

PubMed

Gardner, Brian; Grüning, André

2016-01-01

Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule's error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism.

An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography

PubMed Central

Hu, Hai; Guo, Shengxin; Liu, Ran

2017-01-01

Artifacts removal and rhythms extraction from electroencephalography (EEG) signals are important for portable and wearable EEG recording devices. Incorporating a novel grouping rule, we proposed an adaptive singular spectrum analysis (SSA) method for artifacts removal and rhythms extraction. Based on the EEG signal amplitude, the grouping rule determines adaptively the first one or two SSA reconstructed components as artifacts and removes them. The remaining reconstructed components are then grouped based on their peak frequencies in the Fourier transform to extract the desired rhythms. The grouping rule thus enables SSA to be adaptive to EEG signals containing different levels of artifacts and rhythms. The simulated EEG data based on the Markov Process Amplitude (MPA) EEG model and the experimental EEG data in the eyes-open and eyes-closed states were used to verify the adaptive SSA method. Results showed a better performance in artifacts removal and rhythms extraction, compared with the wavelet decomposition (WDec) and another two recently reported SSA methods. Features of the extracted alpha rhythms using adaptive SSA were calculated to distinguish between the eyes-open and eyes-closed states. Results showed a higher accuracy (95.8%) than those of the WDec method (79.2%) and the infinite impulse response (IIR) filtering method (83.3%). PMID:28674650

Knowledge-based low-level image analysis for computer vision systems

NASA Technical Reports Server (NTRS)

Dhawan, Atam P.; Baxi, Himanshu; Ranganath, M. V.

1988-01-01

Two algorithms for entry-level image analysis and preliminary segmentation are proposed which are flexible enough to incorporate local properties of the image. The first algorithm involves pyramid-based multiresolution processing and a strategy to define and use interlevel and intralevel link strengths. The second algorithm, which is designed for selected window processing, extracts regions adaptively using local histograms. The preliminary segmentation and a set of features are employed as the input to an efficient rule-based low-level analysis system, resulting in suboptimal meaningful segmentation.

The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process

PubMed Central

Dahamna, Badisse; Guillemin-Lanne, Sylvie; Darmoni, Stefan J; Faviez, Carole; Huot, Charles; Katsahian, Sandrine; Leroux, Vincent; Pereira, Suzanne; Richard, Christophe; Schück, Stéphane; Souvignet, Julien; Lillo-Le Louët, Agnès; Texier, Nathalie

2017-01-01

Background Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Classical Pharmacovigilance process is limited by underreporting which justifies the current interest in new knowledge sources such as social media. The Adverse Drug Reactions from Patient Reports in Social Media (ADR-PRISM) project aims to extract ADRs reported by patients in these media. We identified 5 major challenges to overcome to operationalize the analysis of patient posts: (1) variable quality of information on social media, (2) guarantee of data privacy, (3) response to pharmacovigilance expert expectations, (4) identification of relevant information within Web pages, and (5) robust and evolutive architecture. Objective This article aims to describe the current state of advancement of the ADR-PRISM project by focusing on the solutions we have chosen to address these 5 major challenges. Methods In this article, we propose methods and describe the advancement of this project on several aspects: (1) a quality driven approach for selecting relevant social media for the extraction of knowledge on potential ADRs, (2) an assessment of ethical issues and French regulation for the analysis of data on social media, (3) an analysis of pharmacovigilance expert requirements when reviewing patient posts on the Internet, (4) an extraction method based on natural language processing, pattern based matching, and selection of relevant medical concepts in reference terminologies, and (5) specifications of a component-based architecture for the monitoring system. Results Considering the 5 major challenges, we (1) selected a set of 21 validated criteria for selecting social media to support the extraction of potential ADRs, (2) proposed solutions to guarantee data privacy of patients posting on Internet, (3) took into account pharmacovigilance expert requirements with use case diagrams and scenarios, (4) built domain-specific knowledge resources embeding a lexicon, morphological rules, context rules, semantic rules, syntactic rules, and post-analysis processing, and (5) proposed a component-based architecture that allows storage of big data and accessibility to third-party applications through Web services. Conclusions We demonstrated the feasibility of implementing a component-based architecture that allows collection of patient posts on the Internet, near real-time processing of those posts including annotation, and storage in big data structures. In the next steps, we will evaluate the posts identified by the system in social media to clarify the interest and relevance of such approach to improve conventional pharmacovigilance processes based on spontaneous reporting. PMID:28935617

Spotting Incorrect Rules in Signed-Number Arithmetic by the Individual Consistency Index.

DTIC Science & Technology

1981-08-01

meaning of dimensionality of achievement data. It also shows the importance of construct validity, even in criterion referenced testing of the cognitive ... aspect of performance, and that the traditional means of item analysis that are based on taking the variances of binary scores and content analysis

Space station systems analysis study. Part 3: Documentation. Volume 5: Cost and schedule data

NASA Technical Reports Server (NTRS)

1977-01-01

Cost estimates for the space station systems analysis were recorded. Space construction base costs and characteristics were cited as well as mission hardware costs and characteristics. Also delineated were cost ground rules, the program schedule, and a detail cost estimate and funding distribution.

Role of medial cortical, hippocampal and striatal interactions during cognitive set-shifting.

PubMed

Graham, Steven; Phua, Elaine; Soon, Chun Siong; Oh, Tomasina; Au, Chris; Shuter, Borys; Wang, Shih-Chang; Yeh, Ing Berne

2009-05-01

To date, few studies have examined the functional connectivity of brain regions involved in complex executive function tasks, such as cognitive set-shifting. In this study, eighteen healthy volunteers performed a cognitive set-shifting task modified from the Wisconsin card sort test while undergoing functional magnetic resonance imaging. These modifications allowed better disambiguation between cognitive processes and revealed several novel findings: 1) peak activation in the caudate nuclei in the first instance of negative feedback signaling a shift in rule, 2) lowest caudate activation once the rule had been identified, 3) peak hippocampal activation once the identity of the rule had been established, and 4) decreased hippocampal activation during the generation of new rule candidates. This pattern of activation across cognitive set-shifting events suggests that the caudate nuclei play a role in response generation when the identity of the new rule is unknown. In contrast, the reciprocal pattern of hippocampal activation suggests that the hippocampi help consolidate knowledge about the correct stimulus-stimulus associations, associations that become inappropriate once the rule has changed. Functional connectivity analysis using Granger Causality Mapping revealed that caudate and hippocampal regions interacted indirectly via a circuit involving the medial orbitofrontal and posterior cingulate regions, which are known to bias attention towards stimuli based on expectations built up from task-related feedback. Taken together, the evidence suggests that these medial regions may mediate striato-hippocampal interactions and hence affect goal-directed attentional transitions from a response strategy based on stimulus-reward heuristics (caudate-dependent) to one based on stimulus-stimulus associations (hippocampus-dependent).

77 FR 8114 - Plan for Retrospective Analysis of Existing Rules

Federal Register 2010, 2011, 2012, 2013, 2014

2012-02-14

... Existing Rules without significant changes from the version that was preliminarily proposed. Accordingly... modification or elimination of existing regulations. Once an appropriate rule change has been identified, the... INTERNATIONAL TRADE COMMISSION 19 CFR Chapter II Plan for Retrospective Analysis of Existing Rules...

HERB: A production system for programming with hierarchical expert rule bases: User's manual, HERB Version 1. 0

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

Hummel, K.E.

1987-12-01

Expert systems are artificial intelligence programs that solve problems requiring large amounts of heuristic knowledge, based on years of experience and tradition. Production systems are domain-independent tools that support the development of rule-based expert systems. This document describes a general purpose production system known as HERB. This system was developed to support the programming of expert systems using hierarchically structured rule bases. HERB encourages the partitioning of rules into multiple rule bases and supports the use of multiple conflict resolution strategies. Multiple rule bases can also be placed on a system stack and simultaneously searched during each interpreter cycle. Bothmore » backward and forward chaining rules are supported by HERB. The condition portion of each rule can contain both patterns, which are matched with facts in a data base, and LISP expressions, which are explicitly evaluated in the LISP environment. Properties of objects can also be stored in the HERB data base and referenced within the scope of each rule. This document serves both as an introduction to the principles of LISP-based production systems and as a user's manual for the HERB system. 6 refs., 17 figs.« less

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.

An investigation of care-based vs. rule-based morality in frontotemporal dementia, Alzheimer's disease, and healthy controls.

PubMed

Carr, Andrew R; Paholpak, Pongsatorn; Daianu, Madelaine; Fong, Sylvia S; Mather, Michelle; Jimenez, Elvira E; Thompson, Paul; Mendez, Mario F

2015-11-01

Behavioral changes in dementia, especially behavioral variant frontotemporal dementia (bvFTD), may result in alterations in moral reasoning. Investigators have not clarified whether these alterations reflect differential impairment of care-based vs. rule-based moral behavior. This study investigated 18 bvFTD patients, 22 early onset Alzheimer's disease (eAD) patients, and 20 healthy age-matched controls on care-based and rule-based items from the Moral Behavioral Inventory and the Social Norms Questionnaire, neuropsychological measures, and magnetic resonance imaging (MRI) regions of interest. There were significant group differences with the bvFTD patients rating care-based morality transgressions less severely than the eAD group and rule-based moral behavioral transgressions more severely than controls. Across groups, higher care-based morality ratings correlated with phonemic fluency on neuropsychological tests, whereas higher rule-based morality ratings correlated with increased difficulty set-shifting and learning new rules to tasks. On neuroimaging, severe care-based reasoning correlated with cortical volume in right anterior temporal lobe, and rule-based reasoning correlated with decreased cortical volume in the right orbitofrontal cortex. Together, these findings suggest that frontotemporal disease decreases care-based morality and facilitates rule-based morality possibly from disturbed contextual abstraction and set-shifting. Future research can examine whether frontal lobe disorders and bvFTD result in a shift from empathic morality to the strong adherence to conventional rules. Published by Elsevier Ltd.

An Investigation of Care-Based vs. Rule-Based Morality in Frontotemporal Dementia, Alzheimer’s Disease, and Healthy Controls

PubMed Central

Carr, Andrew R.; Paholpak, Pongsatorn; Daianu, Madelaine; Fong, Sylvia S.; Mather, Michelle; Jimenez, Elvira E.; Thompson, Paul; Mendez, Mario F.

2015-01-01

Behavioral changes in dementia, especially behavioral variant frontotemporal dementia (bvFTD), may result in alterations in moral reasoning. Investigators have not clarified whether these alterations reflect differential impairment of care-based vs. rule-based moral behavior. This study investigated 18 bvFTD patients, 22 early onset Alzheimer’s disease (eAD) patients, and 20 healthy age-matched controls on care-based and rule-based items from the Moral Behavioral Inventory and the Social Norms Questionnaire, neuropsychological measures, and magnetic resonance imaging (MRI) regions of interest. There were significant group differences with the bvFTD patients rating care-based morality transgressions less severely than the eAD group and rule-based moral behavioral transgressions more severely than controls. Across groups, higher care-based morality ratings correlated with phonemic fluency on neuropsychological tests, whereas higher rule-based morality ratings correlated with increased difficulty set-shifting and learning new rules to tasks. On neuroimaging, severe care-based reasoning correlated with cortical volume in right anterior temporal lobe, and rule-based reasoning correlated with decreased cortical volume in the right orbitofrontal cortex. Together, these findings suggest that frontotemporal disease decreases care-based morality and facilitates rule-based morality possibly from disturbed contextual abstraction and set-shifting. Future research can examine whether frontal lobe disorders and bvFTD result in a shift from empathic morality to the strong adherence to conventional rules. PMID:26432341

A Swarm Optimization approach for clinical knowledge mining.

PubMed

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

2015-10-01

Rule-based classification is a typical data mining task that is being used in several medical diagnosis and decision support systems. The rules stored in the rule base have an impact on classification efficiency. Rule sets that are extracted with data mining tools and techniques are optimized using heuristic or meta-heuristic approaches in order to improve the quality of the rule base. In this work, a meta-heuristic approach called Wind-driven Swarm Optimization (WSO) is used. The uniqueness of this work lies in the biological inspiration that underlies the algorithm. WSO uses Jval, a new metric, to evaluate the efficiency of a rule-based classifier. Rules are extracted from decision trees. WSO is used to obtain different permutations and combinations of rules whereby the optimal ruleset that satisfies the requirement of the developer is used for predicting the test data. The performance of various extensions of decision trees, namely, RIPPER, PART, FURIA and Decision Tables are analyzed. The efficiency of WSO is also compared with the traditional Particle Swarm Optimization. Experiments were carried out with six benchmark medical datasets. The traditional C4.5 algorithm yields 62.89% accuracy with 43 rules for liver disorders dataset where as WSO yields 64.60% with 19 rules. For Heart disease dataset, C4.5 is 68.64% accurate with 98 rules where as WSO is 77.8% accurate with 34 rules. The normalized standard deviation for accuracy of PSO and WSO are 0.5921 and 0.5846 respectively. WSO provides accurate and concise rulesets. PSO yields results similar to that of WSO but the novelty of WSO lies in its biological motivation and it is customization for rule base optimization. The trade-off between the prediction accuracy and the size of the rule base is optimized during the design and development of rule-based clinical decision support system. The efficiency of a decision support system relies on the content of the rule base and classification accuracy. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

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.

Orthogonal search-based rule extraction for modelling the decision to transfuse.

PubMed

Etchells, T A; Harrison, M J

2006-04-01

Data from an audit relating to transfusion decisions during intermediate or major surgery were analysed to determine the strengths of certain factors in the decision making process. The analysis, using orthogonal search-based rule extraction (OSRE) from a trained neural network, demonstrated that the risk of tissue hypoxia (ROTH) assessed using a 100-mm visual analogue scale, the haemoglobin value (Hb) and the presence or absence of on-going haemorrhage (OGH) were able to reproduce the transfusion decisions with a joint specificity of 0.96 and sensitivity of 0.93 and a positive predictive value of 0.9. The rules indicating transfusion were: 1. ROTH > 32 mm and Hb < 94 g x l(-1); 2. ROTH > 13 mm and Hb < 87 g x l(-1); 3. ROTH > 38 mm, Hb < 102 g x l(-1) and OGH; 4. Hb < 78 g x l(-1).

Compartmental and Spatial Rule-Based Modeling with Virtual Cell.

PubMed

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

2017-10-03

In rule-based modeling, molecular interactions are systematically specified in the form of reaction rules that serve as generators of reactions. This provides a way to account for all the potential molecular complexes and interactions among multivalent or multistate molecules. Recently, we introduced rule-based modeling into the Virtual Cell (VCell) modeling framework, permitting graphical specification of rules and merger of networks generated automatically (using the BioNetGen modeling engine) with hand-specified reaction networks. VCell provides a number of ordinary differential equation and stochastic numerical solvers for single-compartment simulations of the kinetic systems derived from these networks, and agent-based network-free simulation of the rules. In this work, compartmental and spatial modeling of rule-based models has been implemented within VCell. To enable rule-based deterministic and stochastic spatial simulations and network-free agent-based compartmental simulations, the BioNetGen and NFSim engines were each modified to support compartments. In the new rule-based formalism, every reactant and product pattern and every reaction rule are assigned locations. We also introduce the rule-based concept of molecular anchors. This assures that any species that has a molecule anchored to a predefined compartment will remain in this compartment. Importantly, in addition to formulation of compartmental models, this now permits VCell users to seamlessly connect reaction networks derived from rules to explicit geometries to automatically generate a system of reaction-diffusion equations. These may then be simulated using either the VCell partial differential equations deterministic solvers or the Smoldyn stochastic simulator. Copyright © 2017 Biophysical Society. Published by Elsevier Inc. All rights reserved.

QUEST/Ada (query utility environment for software testing of Ada: The development of a program analysis environment for Ada, task 1, phase 3

NASA Technical Reports Server (NTRS)

Brown, David B.

1991-01-01

The results of research and development efforts of the first six months of Task 1, Phase 3 of the project are presented. The goals of Phase 3 are: (1) to further refine the rule base and complete the comparative rule base evaluation; (2) to implement and evaluate a concurrency testing prototype; (3) to convert the complete (unit-level and concurrency) testing prototype to a workstation environment; and (4) to provide a prototype development document to facilitate the transfer of research technology to a working environment. These goals were partially met and the results are summarized.

75 FR 13 - Alternate Fracture Toughness Requirements for Protection Against Pressurized Thermal Shock Events

Federal Register 2010, 2011, 2012, 2013, 2014

2010-01-04

...The Nuclear Regulatory Commission (NRC) is amending its regulations to provide alternate fracture toughness requirements for protection against pressurized thermal shock (PTS) events for pressurized water reactor (PWR) pressure vessels. This final rule provides alternate PTS requirements based on updated analysis methods. This action is desirable because the existing requirements are based on unnecessarily conservative probabilistic fracture mechanics analyses. This action reduces regulatory burden for those PWR licensees who expect to exceed the existing requirements before the expiration of their licenses, while maintaining adequate safety, and may choose to comply with the final rule as an alternative to complying with the existing 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.

Revised Interim Final Consolidated Enforcement Response and Penalty Policy for the Pre-Renovation Education Rule; Renovation, Repair and Painting Rule; and Lead-Based Paint Activities Rule

EPA Pesticide Factsheets

This is the revised version of the Interim Final Consolidated Enforcement Response and Penalty Policy for the Pre-Renovation Education Rule; Renovation, Repair and Painting Rule; and Lead-Based Paint Activities Rule.

Application of a Novel Diagnostic Rule in the Differential Diagnosis between Acute Gouty Arthritis and Septic Arthritis.

PubMed

Lee, Kwang-Hoon; Choi, Sang-Tae; Lee, Soo-Kyung; Lee, Joo-Hyun; Yoon, Bo-Young

2015-06-01

Septic arthritis and gout are major diseases that should be suspected in patients with acute monoarthritis. These two diseases are clinically similar and often indistinguishable without the help of synovial fluid analysis. Recently, a novel diagnostic rule for gout without synovial fluid analysis was developed and showed relevant performances. This study aimed to determine whether this diagnostic rule could perform well in distinguishing gout from septic arthritis. The diagnostic rule comprises 7 clinical and laboratory variables, each of which is given a specified score. The probability of gout is classified into 3 groups according to the sum of the scores: high (≥ 8), intermediate (> 4 to < 8) and low probability (≤ 4). In this retrospective study, we applied this diagnostic rule to 136 patients who presented as acute monoarthritis and were subsequently diagnosed as acute gout (n = 82) and septic arthritis (n = 54) based on synovial fluid analysis. The mean sum of scores of acute gout patients was significantly higher than that of those with septic arthritis (8.6 ± 0.2 vs. 3.6 ± 0.32, P < 0.001). Patients with acute gout had significantly more 'high', and less 'low' probabilities compared to those with septic arthritis (Eta[η]: 0.776). The prevalence of acute gouty arthritis, as confirmed by the presence of monosodium crystal, was 95.5% (61/64), 57.5% (19/33), and 5.1% (2/39) in high, intermediate and low probability group, respectively. The recently introduced diagnostic rule properly discriminates acute gout from septic arthritis. It may help physicians diagnose gout in cases difficult to be differentiated from septic arthritis.

Building v/s Exploring Models: Comparing Learning of Evolutionary Processes through Agent-based Modeling

NASA Astrophysics Data System (ADS)

Wagh, Aditi

Two strands of work motivate the three studies in this dissertation. Evolutionary change can be viewed as a computational complex system in which a small set of rules operating at the individual level result in different population level outcomes under different conditions. Extensive research has documented students' difficulties with learning about evolutionary change (Rosengren et al., 2012), particularly in terms of levels slippage (Wilensky & Resnick, 1999). Second, though building and using computational models is becoming increasingly common in K-12 science education, we know little about how these two modalities compare. This dissertation adopts agent-based modeling as a representational system to compare these modalities in the conceptual context of micro-evolutionary processes. Drawing on interviews, Study 1 examines middle-school students' productive ways of reasoning about micro-evolutionary processes to find that the specific framing of traits plays a key role in whether slippage explanations are cued. Study 2, which was conducted in 2 schools with about 150 students, forms the crux of the dissertation. It compares learning processes and outcomes when students build their own models or explore a pre-built model. Analysis of Camtasia videos of student pairs reveals that builders' and explorers' ways of accessing rules, and sense-making of observed trends are of a different character. Builders notice rules through available blocks-based primitives, often bypassing their enactment while explorers attend to rules primarily through the enactment. Moreover, builders' sense-making of observed trends is more rule-driven while explorers' is more enactment-driven. Pre and posttests reveal that builders manifest a greater facility with accessing rules, providing explanations manifesting targeted assembly. Explorers use rules to construct explanations manifesting non-targeted assembly. Interviews reveal varying degrees of shifts away from slippage in both modalities, with students who built models not incorporating slippage explanations in responses. Study 3 compares these modalities with a control using traditional activities. Pre and posttests reveal that the two modalities manifested greater facility with accessing and assembling rules than the control. The dissertation offers implications for the design of learning environments for evolutionary change, design of the two modalities based on their strengths and weaknesses, and teacher training for the same.

Evaluation of IOTA Simple Ultrasound Rules to Distinguish Benign and Malignant Ovarian Tumours

PubMed Central

Kaur, Amarjit; Mohi, Jaswinder Kaur; Sibia, Preet Kanwal; Kaur, Navkiran

2017-01-01

Introduction IOTA stands for International Ovarian Tumour Analysis group. Ovarian cancer is one of the common cancers in women and is diagnosed at later stage in majority. The limiting factor for early diagnosis is lack of standardized terms and procedures in gynaecological sonography. Introduction of IOTA rules has provided some consistency in defining morphological features of ovarian masses through a standardized examination technique. Aim To evaluate the efficacy of IOTA simple ultrasound rules in distinguishing benign and malignant ovarian tumours and establishing their use as a tool in early diagnosis of ovarian malignancy. Materials and Methods A hospital based case control prospective study was conducted. Patients with suspected ovarian pathology were evaluated using IOTA ultrasound rules and designated as benign or malignant. Findings were correlated with histopathological findings. Collected data was statistically analysed using chi-square test and kappa statistical method. Results Out of initial 55 patients, 50 patients were included in the final analysis who underwent surgery. IOTA simple rules were applicable in 45 out of these 50 patients (90%). The sensitivity for the detection of malignancy in cases where IOTA simple rules were applicable was 91.66% and the specificity was 84.84%. Accuracy was 86.66%. Classifying inconclusive cases as malignant, the sensitivity and specificity was 93% and 80% respectively. High level of agreement was found between USG and histopathological diagnosis with Kappa value as 0.323. Conclusion IOTA simple ultrasound rules were highly sensitive and specific in predicting ovarian malignancy preoperatively yet being reproducible, easy to train and use. PMID:28969237

Evaluation of CERT Secure Coding Rules through Integration with Source Code Analysis Tools

DTIC Science & Technology

2008-06-01

Fortify SCA 3 2.2 Compass / ROSE 6 3 Project Analysis 9 3.1 Measuring and Analysis 9 4 Results 13 4.1 Fortify Results 13 4.1.1 CERT C++ Secure...Fortify C Rules 23 Appendix B Fortify C++ Rules 35 Appendix C C Rules Implemented in Compass Rose 43 Appendix D ROSE C++ Rules 51 References...to Daniel Quinlan at Lawrence Livermore National Laboratory for all his help getting us up and running with Compass /ROSE. Thanks to our SEI editor

Report on FY15 alloy 617 code rules development

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

Sham, Sam; Jetter, Robert I; Hollinger, Greg

2015-09-01

Due to its strength at very high temperatures, up to 950°C (1742°F), Alloy 617 is the reference construction material for structural components that operate at or near the outlet temperature of the very high temperature gas-cooled reactors. However, the current rules in the ASME Section III, Division 5 Subsection HB, Subpart B for the evaluation of strain limits and creep-fatigue damage using simplified methods based on elastic analysis have been deemed inappropriate for Alloy 617 at temperatures above 650°C (1200°F) (Corum and Brass, Proceedings of ASME 1991 Pressure Vessels and Piping Conference, PVP-Vol. 215, p.147, ASME, NY, 1991). The rationalemore » for this exclusion is that at higher temperatures it is not feasible to decouple plasticity and creep, which is the basis for the current simplified rules. This temperature, 650°C (1200°F), is well below the temperature range of interest for this material for the high temperature gas-cooled reactors and the very high temperature gas-cooled reactors. The only current alternative is, thus, a full inelastic analysis requiring sophisticated material models that have not yet been formulated and verified. To address these issues, proposed code rules have been developed which are based on the use of elastic-perfectly plastic (EPP) analysis methods applicable to very high temperatures. The proposed rules for strain limits and creep-fatigue evaluation were initially documented in the technical literature (Carter, Jetter and Sham, Proceedings of ASME 2012 Pressure Vessels and Piping Conference, papers PVP 2012 28082 and PVP 2012 28083, ASME, NY, 2012), and have been recently revised to incorporate comments and simplify their application. Background documents have been developed for these two code cases to support the ASME Code committee approval process. These background documents for the EPP strain limits and creep-fatigue code cases are documented in this report.« less

Ubiquitous Computing Services Discovery and Execution Using a Novel Intelligent Web Services Algorithm

PubMed Central

Choi, Okkyung; Han, SangYong

2007-01-01

Ubiquitous Computing makes it possible to determine in real time the location and situations of service requesters in a web service environment as it enables access to computers at any time and in any place. Though research on various aspects of ubiquitous commerce is progressing at enterprises and research centers, both domestically and overseas, analysis of a customer's personal preferences based on semantic web and rule based services using semantics is not currently being conducted. This paper proposes a Ubiquitous Computing Services System that enables a rule based search as well as semantics based search to support the fact that the electronic space and the physical space can be combined into one and the real time search for web services and the construction of efficient web services thus become possible.

HYPO: A Precedent-Based Legal Reasoner

DTIC Science & Technology

1987-11-01

identify legal issues in the analysis of law school exam- ination fact patterns involving the contracts law of offer and acceptance. The program...primarily used i’-then rules and an ATN to represent its legal knowledge of contract law . It used heuristics for distinguishing "hard" and "easy" legal...similar to Gardner’s. Also, the use of rules in Meldmdn (to define the elements of a claim) and Gardner (to define ingredients of contract law ) are similar

Supervised Learning in Spiking Neural Networks for Precise Temporal Encoding

PubMed Central

Gardner, Brian; Grüning, André

2016-01-01

Precise spike timing as a means to encode information in neural networks is biologically supported, and is advantageous over frequency-based codes by processing input features on a much shorter time-scale. For these reasons, much recent attention has been focused on the development of supervised learning rules for spiking neural networks that utilise a temporal coding scheme. However, despite significant progress in this area, there still lack rules that have a theoretical basis, and yet can be considered biologically relevant. Here we examine the general conditions under which synaptic plasticity most effectively takes place to support the supervised learning of a precise temporal code. As part of our analysis we examine two spike-based learning methods: one of which relies on an instantaneous error signal to modify synaptic weights in a network (INST rule), and the other one relying on a filtered error signal for smoother synaptic weight modifications (FILT rule). We test the accuracy of the solutions provided by each rule with respect to their temporal encoding precision, and then measure the maximum number of input patterns they can learn to memorise using the precise timings of individual spikes as an indication of their storage capacity. Our results demonstrate the high performance of the FILT rule in most cases, underpinned by the rule’s error-filtering mechanism, which is predicted to provide smooth convergence towards a desired solution during learning. We also find the FILT rule to be most efficient at performing input pattern memorisations, and most noticeably when patterns are identified using spikes with sub-millisecond temporal precision. In comparison with existing work, we determine the performance of the FILT rule to be consistent with that of the highly efficient E-learning Chronotron rule, but with the distinct advantage that our FILT rule is also implementable as an online method for increased biological realism. PMID:27532262

Classification Based on Pruning and Double Covered Rule Sets for the Internet of Things Applications

PubMed Central

Zhou, Zhongmei; Wang, Weiping

2014-01-01

The Internet of things (IOT) is a hot issue in recent years. It accumulates large amounts of data by IOT users, which is a great challenge to mining useful knowledge from IOT. Classification is an effective strategy which can predict the need of users in IOT. However, many traditional rule-based classifiers cannot guarantee that all instances can be covered by at least two classification rules. Thus, these algorithms cannot achieve high accuracy in some datasets. In this paper, we propose a new rule-based classification, CDCR-P (Classification based on the Pruning and Double Covered Rule sets). CDCR-P can induce two different rule sets A and B. Every instance in training set can be covered by at least one rule not only in rule set A, but also in rule set B. In order to improve the quality of rule set B, we take measure to prune the length of rules in rule set B. Our experimental results indicate that, CDCR-P not only is feasible, but also it can achieve high accuracy. PMID:24511304

Classification based on pruning and double covered rule sets for the internet of things applications.

PubMed

Li, Shasha; Zhou, Zhongmei; Wang, Weiping

2014-01-01

The Internet of things (IOT) is a hot issue in recent years. It accumulates large amounts of data by IOT users, which is a great challenge to mining useful knowledge from IOT. Classification is an effective strategy which can predict the need of users in IOT. However, many traditional rule-based classifiers cannot guarantee that all instances can be covered by at least two classification rules. Thus, these algorithms cannot achieve high accuracy in some datasets. In this paper, we propose a new rule-based classification, CDCR-P (Classification based on the Pruning and Double Covered Rule sets). CDCR-P can induce two different rule sets A and B. Every instance in training set can be covered by at least one rule not only in rule set A, but also in rule set B. In order to improve the quality of rule set B, we take measure to prune the length of rules in rule set B. Our experimental results indicate that, CDCR-P not only is feasible, but also it can achieve high accuracy.

Inferring the Limit Behavior of Some Elementary Cellular Automata

NASA Astrophysics Data System (ADS)

Ruivo, Eurico L. P.; de Oliveira, Pedro P. B.

Cellular automata locally define dynamical systems, discrete in space, time and in the state variables, capable of displaying arbitrarily complex global emergent behavior. One core question in the study of cellular automata refers to their limit behavior, that is, to the global dynamical features in an infinite time evolution. Previous works have shown that for finite time evolutions, the dynamics of one-dimensional cellular automata can be described by regular languages and, therefore, by finite automata. Such studies have shown the existence of growth patterns in the evolution of such finite automata for some elementary cellular automata rules and also inferred the limit behavior of such rules based upon the growth patterns; however, the results on the limit behavior were obtained manually, by direct inspection of the structures that arise during the time evolution. Here we present the formalization of an automatic method to compute such structures. Based on this, the rules of the elementary cellular automata space were classified according to the existence of a growth pattern in their finite automata. Also, we present a method to infer the limit graph of some elementary cellular automata rules, derived from the analysis of the regular expressions that describe their behavior in finite time. Finally, we analyze some attractors of two rules for which we could not compute the whole limit set.

Power System Transient Stability Based on Data Mining Theory

NASA Astrophysics Data System (ADS)

Cui, Zhen; Shi, Jia; Wu, Runsheng; Lu, Dan; Cui, Mingde

2018-01-01

In order to study the stability of power system, a power system transient stability based on data mining theory is designed. By introducing association rules analysis in data mining theory, an association classification method for transient stability assessment is presented. A mathematical model of transient stability assessment based on data mining technology is established. Meanwhile, combining rule reasoning with classification prediction, the method of association classification is proposed to perform transient stability assessment. The transient stability index is used to identify the samples that cannot be correctly classified in association classification. Then, according to the critical stability of each sample, the time domain simulation method is used to determine the state, so as to ensure the accuracy of the final results. The results show that this stability assessment system can improve the speed of operation under the premise that the analysis result is completely correct, and the improved algorithm can find out the inherent relation between the change of power system operation mode and the change of transient stability degree.

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.

Structural analysis of eyespots: dynamics of morphogenic signals that govern elemental positions in butterfly wings

PubMed Central

2012-01-01

Background To explain eyespot colour-pattern determination in butterfly wings, the induction model has been discussed based on colour-pattern analyses of various butterfly eyespots. However, a detailed structural analysis of eyespots that can serve as a foundation for future studies is still lacking. In this study, fundamental structural rules related to butterfly eyespots are proposed, and the induction model is elaborated in terms of the possible dynamics of morphogenic signals involved in the development of eyespots and parafocal elements (PFEs) based on colour-pattern analysis of the nymphalid butterfly Junonia almana. Results In a well-developed eyespot, the inner black core ring is much wider than the outer black ring; this is termed the inside-wide rule. It appears that signals are wider near the focus of the eyespot and become narrower as they expand. Although fundamental signal dynamics are likely to be based on a reaction-diffusion mechanism, they were described well mathematically as a type of simple uniformly decelerated motion in which signals associated with the outer and inner black rings of eyespots and PFEs are released at different time points, durations, intervals, and initial velocities into a two-dimensional field of fundamentally uniform or graded resistance; this produces eyespots and PFEs that are diverse in size and structure. The inside-wide rule, eyespot distortion, structural differences between small and large eyespots, and structural changes in eyespots and PFEs in response to physiological treatments were explained well using mathematical simulations. Natural colour patterns and previous experimental findings that are not easily explained by the conventional gradient model were also explained reasonably well by the formal mathematical simulations performed in this study. Conclusions In a mode free from speculative molecular interactions, the present study clarifies fundamental structural rules related to butterfly eyespots, delineates a theoretical basis for the induction model, and proposes a mathematically simple mode of long-range signalling that may reflect developmental mechanisms associated with butterfly eyespots. PMID:22409965

Structural analysis of eyespots: dynamics of morphogenic signals that govern elemental positions in butterfly wings.

PubMed

Otaki, Joji M

2012-03-13

To explain eyespot colour-pattern determination in butterfly wings, the induction model has been discussed based on colour-pattern analyses of various butterfly eyespots. However, a detailed structural analysis of eyespots that can serve as a foundation for future studies is still lacking. In this study, fundamental structural rules related to butterfly eyespots are proposed, and the induction model is elaborated in terms of the possible dynamics of morphogenic signals involved in the development of eyespots and parafocal elements (PFEs) based on colour-pattern analysis of the nymphalid butterfly Junonia almana. In a well-developed eyespot, the inner black core ring is much wider than the outer black ring; this is termed the inside-wide rule. It appears that signals are wider near the focus of the eyespot and become narrower as they expand. Although fundamental signal dynamics are likely to be based on a reaction-diffusion mechanism, they were described well mathematically as a type of simple uniformly decelerated motion in which signals associated with the outer and inner black rings of eyespots and PFEs are released at different time points, durations, intervals, and initial velocities into a two-dimensional field of fundamentally uniform or graded resistance; this produces eyespots and PFEs that are diverse in size and structure. The inside-wide rule, eyespot distortion, structural differences between small and large eyespots, and structural changes in eyespots and PFEs in response to physiological treatments were explained well using mathematical simulations. Natural colour patterns and previous experimental findings that are not easily explained by the conventional gradient model were also explained reasonably well by the formal mathematical simulations performed in this study. In a mode free from speculative molecular interactions, the present study clarifies fundamental structural rules related to butterfly eyespots, delineates a theoretical basis for the induction model, and proposes a mathematically simple mode of long-range signalling that may reflect developmental mechanisms associated with butterfly eyespots.

Situation-Assessment And Decision-Aid Production-Rule Analysis System For Nuclear Plant Monitoring And Emergency Preparedness

NASA Astrophysics Data System (ADS)

Gvillo, D.; Ragheb, M.; Parker, M.; Swartz, S.

1987-05-01

A Production-Rule Analysis System is developed for Nuclear Plant Monitoring. The signals generated by the Zion-1 Plant are considered. A Situation-Assessment and Decision-Aid capability is provided for monitoring the integrity of the Plant Radiation, the Reactor Coolant, the Fuel Clad, and the Containment Systems. A total of 41 signals are currently fed as facts to an Inference Engine functioning in the backward-chaining mode and built along the same structure as the E-Mycin system. The Goal-Tree constituting the Knowledge Base was generated using a representation in the form of Fault Trees deduced from plant procedures information. The system is constructed in support of the Data Analysis and Emergency Preparedness tasks at the Illinois Radiological Emergency Assessment Center (REAC).

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.

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.

An evaluation and implementation of rule-based Home Energy Management System using the Rete algorithm.

PubMed

Kawakami, Tomoya; Fujita, Naotaka; Yoshihisa, Tomoki; Tsukamoto, Masahiko

2014-01-01

In recent years, sensors become popular and Home Energy Management System (HEMS) takes an important role in saving energy without decrease in QoL (Quality of Life). Currently, many rule-based HEMSs have been proposed and almost all of them assume "IF-THEN" rules. The Rete algorithm is a typical pattern matching algorithm for IF-THEN rules. Currently, we have proposed a rule-based Home Energy Management System (HEMS) using the Rete algorithm. In the proposed system, rules for managing energy are processed by smart taps in network, and the loads for processing rules and collecting data are distributed to smart taps. In addition, the number of processes and collecting data are reduced by processing rules based on the Rete algorithm. In this paper, we evaluated the proposed system by simulation. In the simulation environment, rules are processed by a smart tap that relates to the action part of each rule. In addition, we implemented the proposed system as HEMS using smart taps.

N Reasons Why Production-Rules are Insufficient Models for Expert System Knowledge Representation Schemes

DTIC Science & Technology

1991-02-01

3 2.2 Hybrid Rule/Fact Schemas .............................................................. 3 3 THE LIMITATIONS OF RULE BASED KNOWLEDGE...or hybrid rule/fact schemas. 2 UNCLASSIFIED .WA UNCLASSIFIED ERL-0520-RR 2.1 Propositional Logic The simplest form of production-rules are based upon...requirements which may lead to poor system performance. 2.2 Hybrid Rule/Fact Schemas Hybrid rule/fact relationships (also known as Predicate Calculus ) have

[Research on the combustion mechanism of asphalt and the composition of harmful gas based on infrared spectral analysis].

PubMed

Wu, Ke; Zhu, Kai; Huang, Zhi-yi; Wang, Jin-chang; Yang, Qin-min; Liang, Pei

2012-08-01

By using the Rosemount gas analyzer and the test platform of fixed bed built by carbon furnace, the harmful gaseous compositions and the release rules of asphalt and mortar under high temperature rate were analyzed quantitatively based on infrared spectral analysis technology. The results indicated that the combustion process of the asphalt and mortar can be approximately divided into two stages stage of primary volatile combustion, and stage of secondary volatile release combined with fixed carbon combustion in isothermal condition with high heating rate. The major gaseous products are CO2, CO, NO, NO2 and SO2. the volatile content is one of the key factors affecting the release rules of gaseous combustion products in asphalt, and reducing the volatile content in asphalt materials can effectively reduce the generation of gaseous combustion products, especially CO.

Extending LMS to Support IRT-Based Assessment Test Calibration

NASA Astrophysics Data System (ADS)

Fotaris, Panagiotis; Mastoras, Theodoros; Mavridis, Ioannis; Manitsaris, Athanasios

Developing unambiguous and challenging assessment material for measuring educational attainment is a time-consuming, labor-intensive process. As a result Computer Aided Assessment (CAA) tools are becoming widely adopted in academic environments in an effort to improve the assessment quality and deliver reliable results of examinee performance. This paper introduces a methodological and architectural framework which embeds a CAA tool in a Learning Management System (LMS) so as to assist test developers in refining items to constitute assessment tests. An Item Response Theory (IRT) based analysis is applied to a dynamic assessment profile provided by the LMS. Test developers define a set of validity rules for the statistical indices given by the IRT analysis. By applying those rules, the LMS can detect items with various discrepancies which are then flagged for review of their content. Repeatedly executing the aforementioned procedure can improve the overall efficiency of the testing process.

Regulatory Impact Analysis for the Final Cross-State Air Pollution Rule

EPA Pesticide Factsheets

This Regulatory Impact Analysis (RIA) presents the health and welfare benefits, costs, and other impacts of the Transport Rule, also called the Cross-State Air Pollution Rule, focusing primarily on 2014.

TARGET's role in knowledge acquisition, engineering, validation, and documentation

NASA Technical Reports Server (NTRS)

Levi, Keith R.

1994-01-01

We investigate the use of the TARGET task analysis tool for use in the development of rule-based expert systems. We found TARGET to be very helpful in the knowledge acquisition process. It enabled us to perform knowledge acquisition with one knowledge engineer rather than two. In addition, it improved communication between the domain expert and knowledge engineer. We also found it to be useful for both the rule development and refinement phases of the knowledge engineering process. Using the network in these phases required us to develop guidelines that enabled us to easily translate the network into production rules. A significant requirement for TARGET remaining useful throughout the knowledge engineering process was the need to carefully maintain consistency between the network and the rule representations. Maintaining consistency not only benefited the knowledge engineering process, but also has significant payoffs in the areas of validation of the expert system and documentation of the knowledge in the system.

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.

Evaluation of Potential Continuation Rules for Mepolizumab Treatment of Severe Eosinophilic Asthma.

PubMed

Gunsoy, Necdet B; Cockle, Sarah M; Yancey, Steven W; Keene, Oliver N; Bradford, Eric S; Albers, Frank C; Pavord, Ian D

Mepolizumab significantly reduces exacerbations in patients with severe eosinophilic asthma. The early identification of patients likely to receive long-term benefit from treatment could ensure effective resource allocation. To assess potential continuation rules for mepolizumab in addition to initiation criteria defined as 2 or more exacerbations in the previous year and blood eosinophil counts of 150 cells/μL or more at initiation or 300 cells/μL or more in the previous year. This post hoc analysis included data from 2 randomized, double-blind, placebo-controlled studies (NCT01000506 and NCT01691521) of mepolizumab in patients with severe eosinophilic asthma (N = 1,192). Rules based on blood eosinophils, physician-rated response to treatment, FEV 1 , Asthma Control Questionnaire (ACQ-5) score, and exacerbation reduction were assessed at week 16. To assess these rules, 2 key metrics accounting for the effects observed in the placebo arm were developed. Patients not meeting continuation rules based on physician-rated response, FEV 1 , and the ACQ-5 score still derived long-term benefit from mepolizumab. Nearly all patients failing to reduce blood eosinophils had counts of 150 cells/μL or less at baseline. For exacerbations, assessment after 16 weeks was potentially premature for predicting future exacerbations. There was no evidence of a reliable physician-rated response, ACQ-5 score, or lung function-based continuation rule. The added value of changes in blood eosinophils at week 16 over baseline was marginal. Initiation criteria for mepolizumab treatment provide the best method for assessing patient benefit from mepolizumab treatment, and treatment continuation should be reviewed on the basis of a predefined reduction in long-term exacerbation frequency and/or oral corticosteroid dose. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Automatic Fault Recognition of Photovoltaic Modules Based on Statistical Analysis of Uav Thermography

NASA Astrophysics Data System (ADS)

Kim, D.; Youn, J.; Kim, C.

2017-08-01

As a malfunctioning PV (Photovoltaic) cell has a higher temperature than adjacent normal cells, we can detect it easily with a thermal infrared sensor. However, it will be a time-consuming way to inspect large-scale PV power plants by a hand-held thermal infrared sensor. This paper presents an algorithm for automatically detecting defective PV panels using images captured with a thermal imaging camera from an UAV (unmanned aerial vehicle). The proposed algorithm uses statistical analysis of thermal intensity (surface temperature) characteristics of each PV module to verify the mean intensity and standard deviation of each panel as parameters for fault diagnosis. One of the characteristics of thermal infrared imaging is that the larger the distance between sensor and target, the lower the measured temperature of the object. Consequently, a global detection rule using the mean intensity of all panels in the fault detection algorithm is not applicable. Therefore, a local detection rule based on the mean intensity and standard deviation range was developed to detect defective PV modules from individual array automatically. The performance of the proposed algorithm was tested on three sample images; this verified a detection accuracy of defective panels of 97 % or higher. In addition, as the proposed algorithm can adjust the range of threshold values for judging malfunction at the array level, the local detection rule is considered better suited for highly sensitive fault detection compared to a global detection rule.

Challenges for Rule Systems on the Web

NASA Astrophysics Data System (ADS)

Hu, Yuh-Jong; Yeh, Ching-Long; Laun, Wolfgang

The RuleML Challenge started in 2007 with the objective of inspiring the issues of implementation for management, integration, interoperation and interchange of rules in an open distributed environment, such as the Web. Rules are usually classified as three types: deductive rules, normative rules, and reactive rules. The reactive rules are further classified as ECA rules and production rules. The study of combination rule and ontology is traced back to an earlier active rule system for relational and object-oriented (OO) databases. Recently, this issue has become one of the most important research problems in the Semantic Web. Once we consider a computer executable policy as a declarative set of rules and ontologies that guides the behavior of entities within a system, we have a flexible way to implement real world policies without rewriting the computer code, as we did before. Fortunately, we have de facto rule markup languages, such as RuleML or RIF to achieve the portability and interchange of rules for different rule systems. Otherwise, executing real-life rule-based applications on the Web is almost impossible. Several commercial or open source rule engines are available for the rule-based applications. However, we still need a standard rule language and benchmark for not only to compare the rule systems but also to measure the progress in the field. Finally, a number of real-life rule-based use cases will be investigated to demonstrate the applicability of current rule systems on the Web.

Empirical Development of an MMPI Subscale for the Assessment of Combat-Related Posttraumatic Stress Disorder.

ERIC Educational Resources Information Center

Keane, Terence M.; And Others

1984-01-01

Developed empirically based criteria for use of the Minnesota Multiphasic Personality Inventory (MMPI) to aid in the assessment and diagnosis of Posttraumatic Stress Disorder (PTSD) in patients (N=200). Analysis based on an empircally derived decision rule correctly classified 74 percent of the patients in each group. (LLL)

A Comparative Analysis of the Snort and Suricata Intrusion-Detection Systems

DTIC Science & Technology

2011-09-01

Category: Test Rules Test #6: Simple LFI Attack 43 Snort True Positive: Snort generated an alert based on the ‘/etc/ passwd ’ string passed...through an HTTP command. Suricata True Positive: Suricata generated an alert based on the ‘/etc/ passwd ’ string passed through an HTTP command

78 FR 65407 - Self-Regulatory Organizations; NYSE Arca, Inc.; Notice of Filing of Proposed Rule Change To List...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-31

... selling investments for the Portfolio. Investments are selected based on fundamental and quantitative... Equity Portfolio. Investments are selected based on fundamental and quantitative analysis. MFS uses... trust and is registered with the Commission as an open-end management investment company.\\5\\ SSgA Funds...

Using global sensitivity analysis to evaluate the uncertainties of future shoreline changes under the Bruun rule assumption

NASA Astrophysics Data System (ADS)

Le Cozannet, Gonéri; Oliveros, Carlos; Castelle, Bruno; Garcin, Manuel; Idier, Déborah; Pedreros, Rodrigo; Rohmer, Jeremy

2016-04-01

Future sandy shoreline changes are often assed by summing the contributions of longshore and cross-shore effects. In such approaches, a contribution of sea-level rise can be incorporated by adding a supplementary term based on the Bruun rule. Here, our objective is to identify where and when the use of the Bruun rule can be (in)validated, in the case of wave-exposed beaches with gentle slopes. We first provide shoreline change scenarios that account for all uncertain hydrosedimentary processes affecting the idealized low- and high-energy coasts described by Stive (2004)[Stive, M. J. F. 2004, How important is global warming for coastal erosion? an editorial comment, Climatic Change, vol. 64, n 12, doi:10.1023/B:CLIM.0000024785.91858. ISSN 0165-0009]. Then, we generate shoreline change scenarios based on probabilistic sea-level rise projections based on IPCC. For scenario RCP 6.0 and 8.5 and in the absence of coastal defenses, the model predicts an observable shift toward generalized beach erosion by the middle of the 21st century. On the contrary, the model predictions are unlikely to differ from the current situation in case of scenario RCP 2.6. To get insight into the relative importance of each source of uncertainties, we quantify each contributions to the variance of the model outcome using a global sensitivity analysis. This analysis shows that by the end of the 21st century, a large part of shoreline change uncertainties are due to the climate change scenario if all anthropogenic greenhousegas emission scenarios are considered equiprobable. To conclude, the analysis shows that under the assumptions above, (in)validating the Bruun rule should be straightforward during the second half of the 21st century and for the RCP 8.5 scenario. Conversely, for RCP 2.6, the noise in shoreline change evolution should continue dominating the signal due to the Bruun effect. This last conclusion can be interpreted as an important potential benefit of climate change mitigation.

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.

Analysis on composition rules of Chinese patent drugs treating pain-related diseases based on data mining method.

PubMed

Tang, Shi-Huan; Shen, Dan; Yang, Hong-Jun

2017-08-24

To analyze the composition rules of oral prescriptions in the treatment of headache, stomachache and dysmenorrhea recorded in National Standard for Chinese Patent Drugs (NSCPD) enacted by Ministry of Public Health of China and then make comparison between them to better understand pain treatment in different regions of human body. Constructed NSCPD database had been constructed in 2014. Prescriptions treating the three pain-related diseases were searched and screened from the database. Then data mining method such as association rules analysis and complex system entropy method integrated in the data mining software Traditional Chinese Medicine Inheritance Support System (TCMISS) were applied to process the data. Top 25 drugs with high frequency in the treatment of each disease were selected, and 51, 33 and 22 core combinations treating headache, stomachache and dysmenorrhea respectively were mined out as well. The composition rules of the oral prescriptions for treating headache, stomachache and dysmenorrhea recorded in NSCPD has been summarized. Although there were similarities between them, formula varied according to different locations of pain. It can serve as an evidence and reference for clinical treatment and new drug development.

Parsing the roles of the frontal lobes and basal ganglia in task control using multivoxel pattern analysis

PubMed Central

Kehagia, Angie A.; Ye, Rong; Joyce, Dan W.; Doyle, Orla M.; Rowe, James B.; Robbins, Trevor W.

2017-01-01

Cognitive control has traditionally been associated with the prefrontal cortex, based on observations of deficits in patients with frontal lesions. However, evidence from patients with Parkinson’s disease (PD) indicates that subcortical regions also contribute to control under certain conditions. We scanned 17 healthy volunteers while they performed a task switching paradigm that previously dissociated performance deficits arising from frontal lesions in comparison with PD, as a function of the abstraction of the rules that are switched. From a multivoxel pattern analysis by Gaussian Process Classification (GPC), we then estimated the forward (generative) model to infer regional patterns of activity that predict Switch / Repeat behaviour between rule conditions. At 1000 permutations, Switch / Repeat classification accuracy for concrete rules was significant in the basal ganglia, but at chance in the frontal lobe. The inverse pattern was obtained for abstract rules, whereby the conditions were successfully discriminated in the frontal lobe but not in the basal ganglia. This double dissociation highlights the difference between cortical and subcortical contributions to cognitive control and demonstrates the utility of multivariate approaches in investigations of functions that rely on distributed and overlapping neural substrates. PMID:28387585

Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems

USGS Publications Warehouse

Clark, Roger N.; Swayze, Gregg A.; Livo, K. Eric; Kokaly, Raymond F.; Sutley, Steve J.; Dalton, J. Brad; McDougal, Robert R.; Gent, Carol A.

2003-01-01

Imaging spectroscopy is a tool that can be used to spectrally identify and spatially map materials based on their specific chemical bonds. Spectroscopic analysis requires significantly more sophistication than has been employed in conventional broadband remote sensing analysis. We describe a new system that is effective at material identification and mapping: a set of algorithms within an expert system decision‐making framework that we call Tetracorder. The expertise in the system has been derived from scientific knowledge of spectral identification. The expert system rules are implemented in a decision tree where multiple algorithms are applied to spectral analysis, additional expert rules and algorithms can be applied based on initial results, and more decisions are made until spectral analysis is complete. Because certain spectral features are indicative of specific chemical bonds in materials, the system can accurately identify and map those materials. In this paper we describe the framework of the decision making process used for spectral identification, describe specific spectral feature analysis algorithms, and give examples of what analyses and types of maps are possible with imaging spectroscopy data. We also present the expert system rules that describe which diagnostic spectral features are used in the decision making process for a set of spectra of minerals and other common materials. We demonstrate the applications of Tetracorder to identify and map surface minerals, to detect sources of acid rock drainage, and to map vegetation species, ice, melting snow, water, and water pollution, all with one set of expert system rules. Mineral mapping can aid in geologic mapping and fault detection and can provide a better understanding of weathering, mineralization, hydrothermal alteration, and other geologic processes. Environmental site assessment, such as mapping source areas of acid mine drainage, has resulted in the acceleration of site cleanup, saving millions of dollars and years in cleanup time. Imaging spectroscopy data and Tetracorder analysis can be used to study both terrestrial and planetary science problems. Imaging spectroscopy can be used to probe planetary systems, including their atmospheres, oceans, and land surfaces.

Multispectral processing based on groups of resolution elements

NASA Technical Reports Server (NTRS)

Richardson, W.; Gleason, J. M.

1975-01-01

Several nine-point rules are defined and compared with previously studied rules. One of the rules performed well in boundary areas, but with reduced efficiency in field interiors; another combined best performance on field interiors with good sensitivity to boundary detail. The basic threshold gradient and some modifications were investigated as a means of boundary point detection. The hypothesis testing methods of closed-boundary formation were also tested and evaluated. An analysis of the boundary detection problem was initiated, employing statistical signal detection and parameter estimation techniques to analyze various formulations of the problem. These formulations permit the atmospheric and sensor system effects on the data to be thoroughly analyzed. Various boundary features and necessary assumptions can also be investigated in this manner.

Enhancements to the Design Manager's Aide for Intelligent Decomposition (DeMAID)

NASA Technical Reports Server (NTRS)

Rogers, James L.; Barthelemy, Jean-Francois M.

1992-01-01

This paper discusses the addition of two new enhancements to the program Design Manager's Aide for Intelligent Decomposition (DeMAID). DeMAID is a knowledge-based tool used to aid a design manager in understanding the interactions among the tasks of a complex design problem. This is done by ordering the tasks to minimize feedback, determining the participating subsystems, and displaying them in an easily understood format. The two new enhancements include (1) rules for ordering a complex assembly process and (2) rules for determining which analysis tasks must be re-executed to compute the output of one task based on a change in input to that or another task.

Enhancements to the Design Manager's Aide for Intelligent Decomposition (DeMaid)

NASA Technical Reports Server (NTRS)

Rogers, James L.; Barthelemy, Jean-Francois M.

1992-01-01

This paper discusses the addition of two new enhancements to the program Design Manager's Aide for Intelligent Decomposition (DeMAID). DeMAID is a knowledge-based tool used to aid a design manager in understanding the interactions among the tasks of a complex design problem. This is done by ordering the tasks to minimize feedback, determining the participating subsystems, and displaying them in an easily understood format. The two new enhancements include (1) rules for ordering a complex assembly process and (2) rules for determining which analysis tasks must be re-executed to compute the output of one task based on a change in input to that or another task.

Beta Hebbian Learning as a New Method for Exploratory Projection Pursuit.

PubMed

Quintián, Héctor; Corchado, Emilio

2017-09-01

In this research, a novel family of learning rules called Beta Hebbian Learning (BHL) is thoroughly investigated to extract information from high-dimensional datasets by projecting the data onto low-dimensional (typically two dimensional) subspaces, improving the existing exploratory methods by providing a clear representation of data's internal structure. BHL applies a family of learning rules derived from the Probability Density Function (PDF) of the residual based on the beta distribution. This family of rules may be called Hebbian in that all use a simple multiplication of the output of the neural network with some function of the residuals after feedback. The derived learning rules can be linked to an adaptive form of Exploratory Projection Pursuit and with artificial distributions, the networks perform as the theory suggests they should: the use of different learning rules derived from different PDFs allows the identification of "interesting" dimensions (as far from the Gaussian distribution as possible) in high-dimensional datasets. This novel algorithm, BHL, has been tested over seven artificial datasets to study the behavior of BHL parameters, and was later applied successfully over four real datasets, comparing its results, in terms of performance, with other well-known Exploratory and projection models such as Maximum Likelihood Hebbian Learning (MLHL), Locally-Linear Embedding (LLE), Curvilinear Component Analysis (CCA), Isomap and Neural Principal Component Analysis (Neural PCA).

The effect of multiple primary rules on population-based cancer survival

PubMed Central

Weir, Hannah K.; Johnson, Christopher J.; Thompson, Trevor D.

2015-01-01

Purpose Different rules for registering multiple primary (MP) cancers are used by cancer registries throughout the world, making international data comparisons difficult. This study evaluates the effect of Surveillance, Epidemiology, and End Results (SEER) and International Association of Cancer Registries (IACR) MP rules on population-based cancer survival estimates. Methods Data from five US states and six metropolitan area cancer registries participating in the SEER Program were used to estimate age-standardized relative survival (RS%) for first cancers-only and all first cancers matching the selection criteria according to SEER and IACR MP rules for all cancer sites combined and for the top 25 cancer site groups among men and women. Results During 1995–2008, the percentage of MP cancers (all sites, both sexes) increased 25.4 % by using SEER rules (from 14.6 to 18.4 %) and 20.1 % by using IACR rules (from 13.2 to 15.8 %). More MP cancers were registered among females than among males, and SEER rules registered more MP cancers than IACR rules (15.8 vs. 14.4 % among males; 17.2 vs. 14.5 % among females). The top 3 cancer sites with the largest differences were melanoma (5.8 %), urinary bladder (3.5 %), and kidney and renal pelvis (2.9 %) among males, and breast (5.9 %), melanoma (3.9 %), and urinary bladder (3.4 %) among females. Five-year survival estimates (all sites combined) restricted to first primary cancers-only were higher than estimates by using first site-specific primaries (SEER or IACR rules), and for 11 of 21 sites among males and 11 of 23 sites among females. SEER estimates are comparable to IACR estimates for all site-specific cancers and marginally higher for all sites combined among females (RS 62.28 vs. 61.96 %). Conclusion Survival after diagnosis has improved for many leading cancers. However, cancer patients remain at risk of subsequent cancers. Survival estimates based on first cancers-only exclude a large and increasing number of MP cancers. To produce clinically and epidemiologically relevant and less biased cancer survival estimates, data on all cancers should be included in the analysis. The multiple primary rules (SEER or IACR) used to identify primary cancers do not affect survival estimates if all first cancers matching the selection criteria are used to produce site-specific survival estimates. PMID:23558444

Performance-based contracting in home-care work in The Netherlands: professionalism under pressure?

PubMed

Oomkens, Rosanne; Hoogenboom, Marcel; Knijn, Trudie

2016-07-01

Our aim was to improve the understanding of the relationships between performance-based contracting, management supportiveness and professionalism in home care. Using path analysis, this article explores the relationships between home-care workers' perceptions of management support, implementation of performance-based contracting (i.e. use of strict time registration rules and cost-efficiency measures) and autonomy and intrinsic job satisfaction. We hypothesised that: use of strict time registration rules and cost-efficiency measures relates to lower levels of autonomy and intrinsic job satisfaction (H1); there is an indirect relationship between use of strict time registration rules and use of cost-efficiency measures and intrinsic job satisfaction via autonomy (H2); higher levels of management support relate to the use of looser time registration rules and less use of cost-efficiency measures (H3); and higher levels of management support relate to higher levels of autonomy and intrinsic job satisfaction (H4). We used data from a cross-sectional survey conducted in 2010 of a sample of Dutch home-care workers (N = 156, response rate = 34%). Overall, our study suggests that the consequences of performance-based contracting for professionalism are ambiguous. More specifically, using strict time registration rules is related to lower levels of autonomy, whereas using cost-efficiency measures does not seem to affect autonomy (H1). Performance-based contracting has no consequences for the level of fulfilment home-care workers find in their job, as neither of the two contracting dimensions measured was directly or indirectly related to intrinsic job satisfaction (H1, H2). The role of managers must be taken into account when studying performance-based contracting, because perceived higher management support is related to managers' less frequent use of both strict time registration rules and of cost-efficiency measures (H3). The insight we gained into the importance of supportive managers for both autonomy and job satisfaction (H4) can help home-care organisations improve the attractiveness of home-care work. © 2015 John Wiley & Sons Ltd.

Applications of rule-induction in the derivation of quantitative structure-activity relationships.

PubMed

A-Razzak, M; Glen, R C

1992-08-01

Recently, methods have been developed in the field of Artificial Intelligence (AI), specifically in the expert systems area using rule-induction, designed to extract rules from data. We have applied these methods to the analysis of molecular series with the objective of generating rules which are predictive and reliable. The input to rule-induction consists of a number of examples with known outcomes (a training set) and the output is a tree-structured series of rules. Unlike most other analysis methods, the results of the analysis are in the form of simple statements which can be easily interpreted. These are readily applied to new data giving both a classification and a probability of correctness. Rule-induction has been applied to in-house generated and published QSAR datasets and the methodology, application and results of these analyses are discussed. The results imply that in some cases it would be advantageous to use rule-induction as a complementary technique in addition to conventional statistical and pattern-recognition methods.

Applications of rule-induction in the derivation of quantitative structure-activity relationships

NASA Astrophysics Data System (ADS)

A-Razzak, Mohammed; Glen, Robert C.

1992-08-01

Recently, methods have been developed in the field of Artificial Intelligence (AI), specifically in the expert systems area using rule-induction, designed to extract rules from data. We have applied these methods to the analysis of molecular series with the objective of generating rules which are predictive and reliable. The input to rule-induction consists of a number of examples with known outcomes (a training set) and the output is a tree-structured series of rules. Unlike most other analysis methods, the results of the analysis are in the form of simple statements which can be easily interpreted. These are readily applied to new data giving both a classification and a probability of correctness. Rule-induction has been applied to in-house generated and published QSAR datasets and the methodology, application and results of these analyses are discussed. The results imply that in some cases it would be advantageous to use rule-induction as a complementary technique in addition to conventional statistical and pattern-recognition methods.

AVNM: A Voting based Novel Mathematical Rule for Image Classification.

PubMed

Vidyarthi, Ankit; Mittal, Namita

2016-12-01

In machine learning, the accuracy of the system depends upon classification result. Classification accuracy plays an imperative role in various domains. Non-parametric classifier like K-Nearest Neighbor (KNN) is the most widely used classifier for pattern analysis. Besides its easiness, simplicity and effectiveness characteristics, the main problem associated with KNN classifier is the selection of a number of nearest neighbors i.e. "k" for computation. At present, it is hard to find the optimal value of "k" using any statistical algorithm, which gives perfect accuracy in terms of low misclassification error rate. Motivated by the prescribed problem, a new sample space reduction weighted voting mathematical rule (AVNM) is proposed for classification in machine learning. The proposed AVNM rule is also non-parametric in nature like KNN. AVNM uses the weighted voting mechanism with sample space reduction to learn and examine the predicted class label for unidentified sample. AVNM is free from any initial selection of predefined variable and neighbor selection as found in KNN algorithm. The proposed classifier also reduces the effect of outliers. To verify the performance of the proposed AVNM classifier, experiments are made on 10 standard datasets taken from UCI database and one manually created dataset. The experimental result shows that the proposed AVNM rule outperforms the KNN classifier and its variants. Experimentation results based on confusion matrix accuracy parameter proves higher accuracy value with AVNM rule. The proposed AVNM rule is based on sample space reduction mechanism for identification of an optimal number of nearest neighbor selections. AVNM results in better classification accuracy and minimum error rate as compared with the state-of-art algorithm, KNN, and its variants. The proposed rule automates the selection of nearest neighbor selection and improves classification rate for UCI dataset and manually created dataset. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Patterns Exploration on Patterns of Empirical Herbal Formula of Chinese Medicine by Association Rules

PubMed Central

Huang, Li; Yuan, Jiamin; Yang, Zhimin; Xu, Fuping; Huang, Chunhua

2015-01-01

Background. In this study, we use association rules to explore the latent rules and patterns of prescribing and adjusting the ingredients of herbal decoctions based on empirical herbal formula of Chinese Medicine (CM). Materials and Methods. The consideration and development of CM prescriptions based on the knowledge of CM doctors are analyzed. The study contained three stages. The first stage is to identify the chief symptoms to a specific empirical herbal formula, which can serve as the key indication for herb addition and cancellation. The second stage is to conduct a case study on the empirical CM herbal formula for insomnia. Doctors will add extra ingredients or cancel some of them by CM syndrome diagnosis. The last stage of the study is to divide the observed cases into the effective group and ineffective group based on the assessed clinical effect by doctors. The patterns during the diagnosis and treatment are selected by the applied algorithm and the relations between clinical symptoms or indications and herb choosing principles will be selected by the association rules algorithm. Results. Totally 40 patients were observed in this study: 28 patients were considered effective after treatment and the remaining 12 were ineffective. 206 patterns related to clinical indications of Chinese Medicine were checked and screened with each observed case. In the analysis of the effective group, we used the algorithm of association rules to select combinations between 28 herbal adjustment strategies of the empirical herbal formula and the 190 patterns of individual clinical manifestations. During this stage, 11 common patterns were eliminated and 5 major symptoms for insomnia remained. 12 association rules were identified which included 5 herbal adjustment strategies. Conclusion. The association rules method is an effective algorithm to explore the latent relations between clinical indications and herbal adjustment strategies for the study on empirical herbal formulas. PMID:26495415

An on-line expert system for diagnosing environmentally induced spacecraft anomalies using CLIPS

NASA Technical Reports Server (NTRS)

Lauriente, Michael; Rolincik, Mark; Koons, Harry C; Gorney, David

1993-01-01

A new rule-based, expert system for diagnosing spacecraft anomalies is under development. The knowledge base consists of over two-hundred rules and provide links to historical and environmental databases. Environmental causes considered are bulk charging, single event upsets (SEU), surface charging, and total radiation dose. The system's driver translates forward chaining rules into a backward chaining sequence, prompting the user for information pertinent to the causes considered. The use of heuristics frees the user from searching through large amounts of irrelevant information (varying degrees of confidence in an answer) or 'unknown' to any question. The expert system not only provides scientists with needed risk analysis and confidence estimates not available in standard numerical models or databases, but it is also an effective learning tool. In addition, the architecture of the expert system allows easy additions to the knowledge base and the database. For example, new frames concerning orbital debris and ionospheric scintillation are being considered. The system currently runs on a MicroVAX and uses the C Language Integrated Production System (CLIPS).

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.

The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process.

PubMed

Bousquet, Cedric; Dahamna, Badisse; Guillemin-Lanne, Sylvie; Darmoni, Stefan J; Faviez, Carole; Huot, Charles; Katsahian, Sandrine; Leroux, Vincent; Pereira, Suzanne; Richard, Christophe; Schück, Stéphane; Souvignet, Julien; Lillo-Le Louët, Agnès; Texier, Nathalie

2017-09-21

Adverse drug reactions (ADRs) are an important cause of morbidity and mortality. Classical Pharmacovigilance process is limited by underreporting which justifies the current interest in new knowledge sources such as social media. The Adverse Drug Reactions from Patient Reports in Social Media (ADR-PRISM) project aims to extract ADRs reported by patients in these media. We identified 5 major challenges to overcome to operationalize the analysis of patient posts: (1) variable quality of information on social media, (2) guarantee of data privacy, (3) response to pharmacovigilance expert expectations, (4) identification of relevant information within Web pages, and (5) robust and evolutive architecture. This article aims to describe the current state of advancement of the ADR-PRISM project by focusing on the solutions we have chosen to address these 5 major challenges. In this article, we propose methods and describe the advancement of this project on several aspects: (1) a quality driven approach for selecting relevant social media for the extraction of knowledge on potential ADRs, (2) an assessment of ethical issues and French regulation for the analysis of data on social media, (3) an analysis of pharmacovigilance expert requirements when reviewing patient posts on the Internet, (4) an extraction method based on natural language processing, pattern based matching, and selection of relevant medical concepts in reference terminologies, and (5) specifications of a component-based architecture for the monitoring system. Considering the 5 major challenges, we (1) selected a set of 21 validated criteria for selecting social media to support the extraction of potential ADRs, (2) proposed solutions to guarantee data privacy of patients posting on Internet, (3) took into account pharmacovigilance expert requirements with use case diagrams and scenarios, (4) built domain-specific knowledge resources embeding a lexicon, morphological rules, context rules, semantic rules, syntactic rules, and post-analysis processing, and (5) proposed a component-based architecture that allows storage of big data and accessibility to third-party applications through Web services. We demonstrated the feasibility of implementing a component-based architecture that allows collection of patient posts on the Internet, near real-time processing of those posts including annotation, and storage in big data structures. In the next steps, we will evaluate the posts identified by the system in social media to clarify the interest and relevance of such approach to improve conventional pharmacovigilance processes based on spontaneous reporting. ©Cedric Bousquet, Badisse Dahamna, Sylvie Guillemin-Lanne, Stefan J Darmoni, Carole Faviez, Charles Huot, Sandrine Katsahian, Vincent Leroux, Suzanne Pereira, Christophe Richard, Stéphane Schück, Julien Souvignet, Agnès Lillo-Le Louët, Nathalie Texier. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 21.09.2017.

Using High Speed Smartphone Cameras and Video Analysis Techniques to Teach Mechanical Wave Physics

ERIC Educational Resources Information Center

Bonato, Jacopo; Gratton, Luigi M.; Onorato, Pasquale; Oss, Stefano

2017-01-01

We propose the use of smartphone-based slow-motion video analysis techniques as a valuable tool for investigating physics concepts ruling mechanical wave propagation. The simple experimental activities presented here, suitable for both high school and undergraduate students, allows one to measure, in a simple yet rigorous way, the speed of pulses…

Identifying Engineering Students' English Sentence Reading Comprehension Errors: Applying a Data Mining Technique

ERIC Educational Resources Information Center

Tsai, Yea-Ru; Ouyang, Chen-Sen; Chang, Yukon

2016-01-01

The purpose of this study is to propose a diagnostic approach to identify engineering students' English reading comprehension errors. Student data were collected during the process of reading texts of English for science and technology on a web-based cumulative sentence analysis system. For the analysis, the association-rule, data mining technique…

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.

Big data mining analysis method based on cloud computing

NASA Astrophysics Data System (ADS)

Cai, Qing Qiu; Cui, Hong Gang; Tang, Hao

2017-08-01

Information explosion era, large data super-large, discrete and non-(semi) structured features have gone far beyond the traditional data management can carry the scope of the way. With the arrival of the cloud computing era, cloud computing provides a new technical way to analyze the massive data mining, which can effectively solve the problem that the traditional data mining method cannot adapt to massive data mining. This paper introduces the meaning and characteristics of cloud computing, analyzes the advantages of using cloud computing technology to realize data mining, designs the mining algorithm of association rules based on MapReduce parallel processing architecture, and carries out the experimental verification. The algorithm of parallel association rule mining based on cloud computing platform can greatly improve the execution speed of data mining.

Implementing a Commercial Rule Base as a Medication Order Safety Net

PubMed Central

Reichley, Richard M.; Seaton, Terry L.; Resetar, Ervina; Micek, Scott T.; Scott, Karen L.; Fraser, Victoria J.; Dunagan, W. Claiborne; Bailey, Thomas C.

2005-01-01

A commercial rule base (Cerner Multum) was used to identify medication orders exceeding recommended dosage limits at five hospitals within BJC HealthCare, an integrated health care system. During initial testing, clinical pharmacists determined that there was an excessive number of nuisance and clinically insignificant alerts, with an overall alert rate of 9.2%. A method for customizing the commercial rule base was implemented to increase rule specificity for problematic rules. The system was subsequently deployed at two facilities and achieved alert rates of less than 1%. Pharmacists screened these alerts and contacted ordering physicians in 21% of cases. Physicians made therapeutic changes in response to 38% of alerts presented to them. By applying simple techniques to customize rules, commercial rule bases can be used to rapidly deploy a safety net to screen drug orders for excessive dosages, while preserving the rule architecture for later implementations of more finely tuned clinical decision support. PMID:15802481

Rule groupings in expert systems using nearest neighbour decision rules, and convex hulls

NASA Technical Reports Server (NTRS)

Anastasiadis, Stergios

1991-01-01

Expert System shells are lacking in many areas of software engineering. Large rule based systems are not semantically comprehensible, difficult to debug, and impossible to modify or validate. Partitioning a set of rules found in CLIPS (C Language Integrated Production System) into groups of rules which reflect the underlying semantic subdomains of the problem, will address adequately the concerns stated above. Techniques are introduced to structure a CLIPS rule base into groups of rules that inherently have common semantic information. The concepts involved are imported from the field of A.I., Pattern Recognition, and Statistical Inference. Techniques focus on the areas of feature selection, classification, and a criteria of how 'good' the classification technique is, based on Bayesian Decision Theory. A variety of distance metrics are discussed for measuring the 'closeness' of CLIPS rules and various Nearest Neighbor classification algorithms are described based on the above metric.

Automated revision of CLIPS rule-bases

NASA Technical Reports Server (NTRS)

Murphy, Patrick M.; Pazzani, Michael J.

1994-01-01

This paper describes CLIPS-R, a theory revision system for the revision of CLIPS rule-bases. CLIPS-R may be used for a variety of knowledge-base revision tasks, such as refining a prototype system, adapting an existing system to slightly different operating conditions, or improving an operational system that makes occasional errors. We present a description of how CLIPS-R revises rule-bases, and an evaluation of the system on three rule-bases.

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

A mobile asset sharing policy for hospitals with real time locating systems.

PubMed

Demircan-Yıldız, Ece Arzu; Fescioglu-Unver, Nilgun

2016-01-01

Each year, hospitals lose a considerable amount of time and money due to misplaced mobile assets. In addition the assets which remain in departments that frequently use them depreciate early, while other assets of the same type in different departments are rarely used. A real time locating system can prevent these losses when used with appropriate asset sharing policies. This research quantifies the amount of time a medium size hospital saves by using real time locating system and proposes an asset selection rule to eliminate the asset usage imbalance problem. The asset selection rule proposed is based on multi objective optimization techniques. The effectiveness of this rule on asset to patient time and asset utilization rate variance performance measures were tested using discrete event simulation method. Results show that the proposed asset selection rule improved the usage balance significantly. Sensitivity analysis showed that the proposed rule is robust to changes in demand rates and user preferences. Real time locating systems enable saving considerable amount of time in hospitals, and they can still be improved by integrating decision support mechanisms. Combining tracking technology and asset selection rules helps improve healthcare services.

mmpdb: An Open-Source Matched Molecular Pair Platform for Large Multiproperty Data Sets.

PubMed

Dalke, Andrew; Hert, Jérôme; Kramer, Christian

2018-05-29

Matched molecular pair analysis (MMPA) enables the automated and systematic compilation of medicinal chemistry rules from compound/property data sets. Here we present mmpdb, an open-source matched molecular pair (MMP) platform to create, compile, store, retrieve, and use MMP rules. mmpdb is suitable for the large data sets typically found in pharmaceutical and agrochemical companies and provides new algorithms for fragment canonicalization and stereochemistry handling. The platform is written in Python and based on the RDKit toolkit. It is freely available from https://github.com/rdkit/mmpdb .

Empirical Analysis and Refinement of Expert System Knowledge Bases

DTIC Science & Technology

1990-03-31

the number of hidden units and the error rates is listed in Figure 6. 3.3. Cancer Data A data qet for eva!ukting th.- Frognosis of breast cancer ...Alternative Rule Induction Methods A data set for evaluating the prognosis of breast cancer recurrence was analyzed by Michalski’s AQI5 rule induction program...AQ15 7 2 32% PVM 2 1 23% Figure 6-3: Comparative Summa-y for AQI5 and PVM on Breast Cancer Data 6.2.2. Alternative Decision Tree Induction Methods

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.

Rules implementing Sections 201 and 210 of the Public Utility Regulatory Policies Act of 1978: a regulatory history

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

Danziger, R.N.; Caples, P.W.; Huning, J.R.

1980-09-15

An analysis is made of the rules implementing Sections 201 and 210 of the Public Utility Regulatory Policies Act of 1978 (PURPA). The act provides that utilities must purchase power from qualifying producers of electricity at nondiscriminatory rates, and it exempts private generators from virtually all state and Federal utility regulations. Most of the analysis presented is taken from the perspective of photovoltaics (PV) and solar thermal electric point-focusing distributed receivers (pfdr). It is felt, however, that the analysis is applicable both to cogeneration and other emerging technologies. Chapters presented are: The FERC Response to Oral Comments on the Proposedmore » Rules Implementing Sections 201 and 210 of PURPA; Additional Changes Made or Not Made That Were Addressed in Other Than Oral Testimony; View on the Proposed Rules Implementing Sections 201 and 210 of PURPA; Response to Comments on the Proposed 201 and 210 Rules; and Summary Analysis of the Environmental Assessment of the Rules. Pertinent reference material is provided in the Appendices, including the text of the rules. (MCW)« less

Multiple-rule bias in the comparison of classification rules

PubMed Central

Yousefi, Mohammadmahdi R.; Hua, Jianping; Dougherty, Edward R.

2011-01-01

Motivation: There is growing discussion in the bioinformatics community concerning overoptimism of reported results. Two approaches contributing to overoptimism in classification are (i) the reporting of results on datasets for which a proposed classification rule performs well and (ii) the comparison of multiple classification rules on a single dataset that purports to show the advantage of a certain rule. Results: This article provides a careful probabilistic analysis of the second issue and the ‘multiple-rule bias’, resulting from choosing a classification rule having minimum estimated error on the dataset. It quantifies this bias corresponding to estimating the expected true error of the classification rule possessing minimum estimated error and it characterizes the bias from estimating the true comparative advantage of the chosen classification rule relative to the others by the estimated comparative advantage on the dataset. The analysis is applied to both synthetic and real data using a number of classification rules and error estimators. Availability: We have implemented in C code the synthetic data distribution model, classification rules, feature selection routines and error estimation methods. The code for multiple-rule analysis is implemented in MATLAB. The source code is available at http://gsp.tamu.edu/Publications/supplementary/yousefi11a/. Supplementary simulation results are also included. Contact: edward@ece.tamu.edu Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:21546390

Unifying model of carpal mechanics based on computationally derived isometric constraints and rules-based motion - the stable central column theory.

PubMed

Sandow, M J; Fisher, T J; Howard, C Q; Papas, S

2014-05-01

This study was part of a larger project to develop a (kinetic) theory of carpal motion based on computationally derived isometric constraints. Three-dimensional models were created from computed tomography scans of the wrists of ten normal subjects and carpal spatial relationships at physiological motion extremes were assessed. Specific points on the surface of the various carpal bones and the radius that remained isometric through range of movement were identified. Analysis of the isometric constraints and intercarpal motion suggests that the carpus functions as a stable central column (lunate-capitate-hamate-trapezoid-trapezium) with a supporting lateral column (scaphoid), which behaves as a 'two gear four bar linkage'. The triquetrum functions as an ulnar translation restraint, as well as controlling lunate flexion. The 'trapezoid'-shaped trapezoid places the trapezium anterior to the transverse plane of the radius and ulna, and thus rotates the principal axis of the central column to correspond to that used in the 'dart thrower's motion'. This study presents a forward kinematic analysis of the carpus that provides the basis for the development of a unifying kinetic theory of wrist motion based on isometric constraints and rules-based motion.

77 FR 73498 - Self-Regulatory Organizations; Chicago Board Options Exchange, Incorporated; Notice of Filing and...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-10

... Effectiveness of a Proposed Rule Change To Amend Its Rule Related to Multi-Class Broad- Based Index Option... Rule Change The Exchange proposes to amend its rule related to multi-class broad-based index option... is to (i) clarify that the term ``Multi-Class Broad-Based Index Option Spread Order (Multi-Class...

Household Implementation of Smoke-Free Rules in Homes and Cars: A Focus on Adolescent Smoking Behavior and Secondhand Smoke Exposure.

PubMed

Parks, Michael J; Kingsbury, John H; Boyle, Raymond G; Evered, Sharrilyn

2018-01-01

This study addresses the dearth of population-based research on how comprehensive household smoke-free rules (ie, in the home and car) relate to tobacco use and secondhand smoke (SHS) exposure among adolescents. Analysis of 2014 Minnesota Youth Tobacco Survey. Representative sample of Minnesota youth. A total of 1287 youth who lived with a smoker. Measures included household smoke-free rules (no rules, partial rules-home or car, but not both-and comprehensive rules), lifetime and 30-day cigarette use, 30-day cigarette and other product use, and SHS exposure in past 7 days in home and car. Weighted multivariate logistic, zero-inflated Poisson, and zero-inflated negative binomial regressions were used. Compared to comprehensive rules, partial and no smoke-free rules were significantly and positively related to lifetime cigarette use (respectively, adjusted odds ratio [AOR] = 1.80, 95% confidence interval [CI] = 1.24-2.61; AOR = 2.87, 95% CI = 1.93-4.25), and a similar significant pattern was found for 30-day cigarette use (respectively, AOR = 2.20, 95% CI = 1.21-4.02; AOR = 2.45, 95% CI = 1.34-4.50). No smoke-free rules significantly predicted using cigarettes and other tobacco products compared to comprehensive rules. In both descriptive and regression analyses, we found SHS exposure rates in both the home and car were significantly lower among youth whose household implemented comprehensive smoke-free rules. Comprehensive smoke-free rules protect youth from the harms of caregiver tobacco use. Relative to both partial and no smoke-free rules, comprehensive smoke-free rules have a marked impact on tobacco use and SHS exposure among youth who live with a smoker. Health promotion efforts should promote comprehensive smoke-free rules among all households and particularly households with children and adolescents.

Whip Rule Breaches in a Major Australian Racing Jurisdiction: Welfare and Regulatory Implications

PubMed Central

Hood, Jennifer; McDonald, Carolyn; Wilson, Bethany; McManus, Phil; McGreevy, Paul

2017-01-01

Simple Summary An evidence-based analysis of whip rule breaches in horse racing is needed to address community expectations that racehorses are treated humanely. The study provides the first peer-reviewed characterisation of whip rule breaches and their regulatory outcomes in horseracing, and considers the relationship between rules affecting racing integrity and the welfare of racehorses in a major Australian racing jurisdiction. Abstract Whip use in horseracing is increasingly being questioned on ethical, animal welfare, social sustainability, and legal grounds. Despite this, there is weak evidence for whip use and its regulation by Stewards in Australia. To help address this, we characterised whip rule breaches recorded by Stewards using Stewards Reports and Race Diaries from 2013 and 2016 in New South Wales (NSW) and the Australian Capital Territory (ACT). There were more recorded breaches at Metropolitan (M) than Country (C) or Provincial (P) locations, and by riders of horses that finished first, second, or third than by riders of horses that finished in other positions. The most commonly recorded breaches were forehand whip use on more than five occasions before the 100-metre (m) mark (44%), and whip use that raises the jockey’s arm above shoulder height (24%). It is recommended that racing compliance data be analysed annually to inform the evidence-base for policy, education, and regulatory change, and ensure the welfare of racehorses and racing integrity. PMID:28275207

Parallel inferencing method and apparatus for rule-based expert systems

NASA Technical Reports Server (NTRS)

Schwuttke, Ursula M. (Inventor); Moldovan, Dan (Inventor); Kuo, Steve (Inventor)

1993-01-01

The invention analyzes areas of conditions with an expert knowledge base of rules using plural separate nodes which fire respective rules of said knowledge base, each of said rules upon being fired altering certain of said conditions predicated upon the existence of other said conditions. The invention operates by constructing a P representation of all pairs of said rules which are input dependent or output dependent; constructing a C representation of all pairs of said rules which are communication dependent or input dependent; determining which of the rules are ready to fire by matching the predicate conditions of each rule with the conditions of said set; enabling said node means to simultaneously fire those of the rules ready to fire which are defined by said P representation as being free of input and output dependencies; and communicating from each node enabled by said enabling step the alteration of conditions by the corresponding rule to other nodes whose rules are defined by said C matrix means as being input or communication dependent upon the rule of said enabled node.

Using Rule-Based Computer Programming to Unify Communication Rules Research.

ERIC Educational Resources Information Center

Sanford, David L.; Roach, J. W.

This paper proposes the use of a rule-based computer programming language as a standard for the expression of rules, arguing that the adoption of a standard would enable researchers to communicate about rules in a consistent and significant way. Focusing on the formal equivalence of artificial intelligence (AI) programming to different types of…

Reducing the Conflict Factors Strategies in Question Answering System

NASA Astrophysics Data System (ADS)

Suwarningsih, W.; Purwarianti, A.; Supriana, I.

2017-03-01

A rule-based system is prone to conflict as new knowledge every time will emerge and indirectly must sign in to the knowledge base that is used by the system. A conflict occurred between the rules in the knowledge base can lead to the errors of reasoning or reasoning circulation. Therefore, when added, the new rules will lead to conflict with other rules, and the only rules that really can be added to the knowledge base. From these conditions, this paper aims to propose a conflict resolution strategy for a medical debriefing system by analyzing scenarios based upon the runtime to improve the efficiency and reliability of systems.

Gender-Based Behavioral Analysis for End-User Development and the "RULES" Attributes

ERIC Educational Resources Information Center

Tzafilkou, Katerina; Protogeros, Nicolaos; Karagiannidis, Charalampos; Koumpis, Adamantios

2017-01-01

This paper addresses the role of gender in End-User Development (EUD) environments and examines whether there are gender differences in performance and in correlations between performance and a set of behavioral attributes. Based on a review of the most prominent EUD-related behavioral Human Computer Interaction (HCI) theories, and the influence…

Experiments in Knowledge Refinement for a Large Rule-Based System

DTIC Science & Technology

1993-08-01

empirical analysis to refine expert system knowledge bases. Aritificial Intelligence , 22:23-48, 1984. *! ...The Addison- Weslev series in artificial intelligence . Addison-Weslev. Reading, Massachusetts. 1981. Cooke, 1991: ttoger M. Cooke. Experts in...ment for classification systems. Artificial Intelligence , 35:197-226, 1988. 14 Overall, we believe that it will be possible to build a heuristic system

Models of Quantitative Estimations: Rule-Based and Exemplar-Based Processes Compared

ERIC Educational Resources Information Center

von Helversen, Bettina; Rieskamp, Jorg

2009-01-01

The cognitive processes underlying quantitative estimations vary. Past research has identified task-contingent changes between rule-based and exemplar-based processes (P. Juslin, L. Karlsson, & H. Olsson, 2008). B. von Helversen and J. Rieskamp (2008), however, proposed a simple rule-based model--the mapping model--that outperformed the…

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.

Sample path analysis of contribution and reward in cooperative groups.

PubMed

Toyoizumi, Hiroshi

2009-02-07

Explaining cooperative behavior is one of the major challenges in both biology and human society. The individual reward in cooperative group depends on how we share the rewards in the group. Thus, the group size dynamics in a cooperative group and reward-allocation rule seem essential to evaluate the emergence of cooperative groups. We apply a sample path-based analysis called an extension of Little's formula to general cooperative group. We show that the expected reward is insensitive to the specific reward-allocation rule and probabilistic structure of group dynamics, and the simple productivity condition guarantees the expected reward to be larger than the average contribution. As an example, we take social queues to see the insensitivity result in detail.

Data characteristic analysis of air conditioning load based on fast Fourier transform

NASA Astrophysics Data System (ADS)

Li, Min; Zhang, Yanchi; Xie, Da

2018-04-01

With the development of economy and the improvement of people's living standards, air conditioning equipment is more and more popular. The influence of air conditioning load for power grid is becoming more and more serious. In this context it is necessary to study the characteristics of air conditioning load. This paper analyzes the data of air conditioning power consumption in an office building. The data is used for Fast Fourier Transform by data analysis software. Then a series of maps are drawn for the transformed data. The characteristics of each map were analyzed separately. The hidden rules of these data are mined from the angle of frequency domain. And these rules are hard to find in the time domain.

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.

SIRE: A Simple Interactive Rule Editor for NICBES

NASA Technical Reports Server (NTRS)

Bykat, Alex

1988-01-01

To support evolution of domain expertise, and its representation in an expert system knowledge base, a user-friendly rule base editor is mandatory. The Nickel Cadmium Battery Expert System (NICBES), a prototype of an expert system for the Hubble Space Telescope power storage management system, does not provide such an editor. In the following, a description of a Simple Interactive Rule Base Editor (SIRE) for NICBES is described. The SIRE provides a consistent internal representation of the NICBES knowledge base. It supports knowledge presentation and provides a user-friendly and code language independent medium for rule addition and modification. The SIRE is integrated with NICBES via an interface module. This module provides translation of the internal representation to Prolog-type rules (Horn clauses), latter rule assertion, and a simple mechanism for rule selection for its Prolog inference engine.

QUEST/Ada (Query Utility Environment for Software Testing of Ada): The development of a prgram analysis environment for Ada, task 1, phase 2

NASA Technical Reports Server (NTRS)

Brown, David B.

1990-01-01

The results of research and development efforts are described for Task one, Phase two of a general project entitled The Development of a Program Analysis Environment for Ada. The scope of this task includes the design and development of a prototype system for testing Ada software modules at the unit level. The system is called Query Utility Environment for Software Testing of Ada (QUEST/Ada). The prototype for condition coverage provides a platform that implements expert system interaction with program testing. The expert system can modify data in the instrument source code in order to achieve coverage goals. Given this initial prototype, it is possible to evaluate the rule base in order to develop improved rules for test case generation. The goals of Phase two are the following: (1) to continue to develop and improve the current user interface to support the other goals of this research effort (i.e., those related to improved testing efficiency and increased code reliable); (2) to develop and empirically evaluate a succession of alternative rule bases for the test case generator such that the expert system achieves coverage in a more efficient manner; and (3) to extend the concepts of the current test environment to address the issues of Ada concurrency.

Knowledge-driven institutional change: an empirical study on combating desertification in northern China from 1949 to 2004.

PubMed

Yang, Lihua; Wu, Jianguo

2012-11-15

Understanding institutional changes is crucial for environmental management. Here we investigated how institutional changes influenced the process and result of desertification control in northern China between 1949 and 2004. Our analysis was based on a case study of 21 field sites and a meta-analysis of additional 29 sites reported in the literature. Our results show that imposed knowledge-driven institutional change was often perceived as a more progressive, scientific, and rational type of institutional change by entrepreneurs, scholars, experts, and technicians, while voluntary, knowledge-driven institutional change based on indigenous knowledge and experiences of local populations was discouraged. Our findings also demonstrate that eight working rules of imposed knowledge-driven institutional change can be applied to control desertification effectively. These rules address the issues of perception of potential gains, entrepreneurs' appeals and support, coordination of multiple goals, collaboration among multiple organizations, interest distribution and conflict resolution, incremental institutional change, external intervention, and coordination among the myriad institutions involved. Imposed knowledge-driven institutional change tended to be more successful when these rules were thoroughly implemented. These findings provide an outline for implementing future institutional changes and policy making to combat desertification and other types of ecological and environmental management. Copyright © 2012 Elsevier Ltd. All rights reserved.

Raft cultivation area extraction from high resolution remote sensing imagery by fusing multi-scale region-line primitive association features

NASA Astrophysics Data System (ADS)

Wang, Min; Cui, Qi; Wang, Jie; Ming, Dongping; Lv, Guonian

2017-01-01

In this paper, we first propose several novel concepts for object-based image analysis, which include line-based shape regularity, line density, and scale-based best feature value (SBV), based on the region-line primitive association framework (RLPAF). We then propose a raft cultivation area (RCA) extraction method for high spatial resolution (HSR) remote sensing imagery based on multi-scale feature fusion and spatial rule induction. The proposed method includes the following steps: (1) Multi-scale region primitives (segments) are obtained by image segmentation method HBC-SEG, and line primitives (straight lines) are obtained by phase-based line detection method. (2) Association relationships between regions and lines are built based on RLPAF, and then multi-scale RLPAF features are extracted and SBVs are selected. (3) Several spatial rules are designed to extract RCAs within sea waters after land and water separation. Experiments show that the proposed method can successfully extract different-shaped RCAs from HR images with good performance.

A Local Learning Rule for Independent Component Analysis

PubMed Central

Isomura, Takuya; Toyoizumi, Taro

2016-01-01

Humans can separately recognize independent sources when they sense their superposition. This decomposition is mathematically formulated as independent component analysis (ICA). While a few biologically plausible learning rules, so-called local learning rules, have been proposed to achieve ICA, their performance varies depending on the parameters characterizing the mixed signals. Here, we propose a new learning rule that is both easy to implement and reliable. Both mathematical and numerical analyses confirm that the proposed rule outperforms other local learning rules over a wide range of parameters. Notably, unlike other rules, the proposed rule can separate independent sources without any preprocessing, even if the number of sources is unknown. The successful performance of the proposed rule is then demonstrated using natural images and movies. We discuss the implications of this finding for our understanding of neuronal information processing and its promising applications to neuromorphic engineering. PMID:27323661

DTFP-Growth: Dynamic Threshold-Based FP-Growth Rule Mining Algorithm Through Integrating Gene Expression, Methylation, and Protein-Protein Interaction Profiles.

PubMed

Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan; Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan; Mallik, Saurav; Bhadra, Tapas; Mukherji, Ayan

2018-04-01

Association rule mining is an important technique for identifying interesting relationships between gene pairs in a biological data set. Earlier methods basically work for a single biological data set, and, in maximum cases, a single minimum support cutoff can be applied globally, i.e., across all genesets/itemsets. To overcome this limitation, in this paper, we propose dynamic threshold-based FP-growth rule mining algorithm that integrates gene expression, methylation and protein-protein interaction profiles based on weighted shortest distance to find the novel associations among different pairs of genes in multi-view data sets. For this purpose, we introduce three new thresholds, namely, Distance-based Variable/Dynamic Supports (DVS), Distance-based Variable Confidences (DVC), and Distance-based Variable Lifts (DVL) for each rule by integrating co-expression, co-methylation, and protein-protein interactions existed in the multi-omics data set. We develop the proposed algorithm utilizing these three novel multiple threshold measures. In the proposed algorithm, the values of , , and are computed for each rule separately, and subsequently it is verified whether the support, confidence, and lift of each evolved rule are greater than or equal to the corresponding individual , , and values, respectively, or not. If all these three conditions for a rule are found to be true, the rule is treated as a resultant rule. One of the major advantages of the proposed method compared with other related state-of-the-art methods is that it considers both the quantitative and interactive significance among all pairwise genes belonging to each rule. Moreover, the proposed method generates fewer rules, takes less running time, and provides greater biological significance for the resultant top-ranking rules compared to previous methods.

Toward an Application Guide for Safety Integrity Level Allocation in Railway Systems.

PubMed

Ouedraogo, Kiswendsida Abel; Beugin, Julie; El-Koursi, El-Miloudi; Clarhaut, Joffrey; Renaux, Dominique; Lisiecki, Frederic

2018-02-02

The work in the article presents the development of an application guide based on feedback and comments stemming from various railway actors on their practices of SIL allocation to railway safety-related functions. The initial generic methodology for SIL allocation has been updated to be applied to railway rolling stock safety-related functions in order to solve the SIL concept application issues. Various actors dealing with railway SIL allocation problems are the intended target of the methodology; its principles will be summarized in this article with a focus on modifications and precisions made in order to establish a practical guide for railway safety authorities. The methodology is based on the flowchart formalism used in CSM (common safety method) European regulation. It starts with the use of quantitative safety requirements, particularly tolerable hazard rates (THR). THR apportioning rules are applied. On the one hand, the rules are related to classical logical combinations of safety-related functions preventing hazard occurrence. On the other hand, to take into account technical conditions (last safety weak link, functional dependencies, technological complexity, etc.), specific rules implicitly used in existing practices are defined for readjusting some THR values. SIL allocation process based on apportioned and validated THR values is finally illustrated through the example of "emergency brake" subsystems. Some specific SIL allocation rules are also defined and illustrated. © 2018 Society for Risk Analysis.

The Interactive Effects of the Availability of Objectives and/or Rules on Computer-Based Learning: A Replication.

ERIC Educational Resources Information Center

Merrill, Paul F.; And Others

To replicate and extend the results of a previous study, this project investigated the effects of behavioral objectives and/or rules on computer-based learning task performance. The 133 subjects were randomly assigned to an example-only, objective-example, rule example, or objective-rule example group. The availability of rules and/or objectives…

Medication regularity of pulmonary fibrosis treatment by contemporary traditional Chinese medicine experts based on data mining.

PubMed

Zhang, Suxian; Wu, Hao; Liu, Jie; Gu, Huihui; Li, Xiujuan; Zhang, Tiansong

2018-03-01

Treatment of pulmonary fibrosis by traditional Chinese medicine (TCM) has accumulated important experience. Our interest is in exploring the medication regularity of contemporary Chinese medical specialists treating pulmonary fibrosis. Through literature search, medical records from TCM experts who treat pulmonary fibrosis, which were published in Chinese and English medical journals, were selected for this study. As the object of study, a database was established after analysing the records. After data cleaning, the rules of medicine in the treatment of pulmonary fibrosis in medical records of TCM were explored by using data mining technologies such as frequency analysis, association rule analysis, and link analysis. A total of 124 medical records from 60 doctors were selected in this study; 263 types of medicinals were used a total of 5,455 times; the herbs that were used more than 30 times can be grouped into 53 species and were used a total of 3,681 times. Using main medicinals cluster analysis, medicinals were divided into qi-tonifying, yin-tonifying, blood-activating, phlegm-resolving, cough-suppressing, panting-calming, and ten other major medicinal categories. According to the set conditions, a total of 62 drug compatibility rules have been obtained, involving mainly qi-tonifying, yin-tonifying, blood-activating, phlegm-resolving, qi-descending, and panting-calming medicinals, as well as other medicinals used in combination. The results of data mining are consistent with clinical practice and it is feasible to explore the medical rules applicable to the treatment of pulmonary fibrosis in medical records of TCM by data mining.

WellnessRules: A Web 3.0 Case Study in RuleML-Based Prolog-N3 Profile Interoperation

NASA Astrophysics Data System (ADS)

Boley, Harold; Osmun, Taylor Michael; Craig, Benjamin Larry

An interoperation study, WellnessRules, is described, where rules about wellness opportunities are created by participants in rule languages such as Prolog and N3, and translated within a wellness community using RuleML/XML. The wellness rules are centered around participants, as profiles, encoding knowledge about their activities conditional on the season, the time-of-day, the weather, etc. This distributed knowledge base extends FOAF profiles with a vocabulary and rules about wellness group networking. The communication between participants is organized through Rule Responder, permitting wellness-profile translation and distributed querying across engines. WellnessRules interoperates between rules and queries in the relational (Datalog) paradigm of the pure-Prolog subset of POSL and in the frame (F-logic) paradigm of N3. An evaluation of Rule Responder instantiated for WellnessRules revealed acceptable Web response times.

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.

Tool Efficiency Analysis model research in SEMI industry

NASA Astrophysics Data System (ADS)

Lei, Ma; Nana, Zhang; Zhongqiu, Zhang

2018-06-01

One of the key goals in SEMI industry is to improve equipment through put and ensure equipment production efficiency maximization. This paper is based on SEMI standards in semiconductor equipment control, defines the transaction rules between different tool states, and presents a TEA system model which is to analysis tool performance automatically based on finite state machine. The system was applied to fab tools and verified its effectiveness successfully, and obtained the parameter values used to measure the equipment performance, also including the advices of improvement.

78 FR 62417 - Regulatory Capital Rules: Regulatory Capital, Implementation of Basel III, Capital Adequacy...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-10-22

..., Standardized Approach for Risk-Weighted Assets, Market Discipline and Disclosure Requirements, Advanced Approaches Risk-Based Capital Rule, and Market Risk Capital Rule AGENCY: Federal Deposit Insurance... Assets, Market Discipline and Disclosure Requirements, Advanced Approaches Risk-Based Capital Rule, and...

Architecture For The Optimization Of A Machining Process In Real Time Through Rule-Based Expert System

NASA Astrophysics Data System (ADS)

Serrano, Rafael; González, Luis Carlos; Martín, Francisco Jesús

2009-11-01

Under the project SENSOR-IA which has had financial funding from the Order of Incentives to the Regional Technology Centers of the Counsil of Innovation, Science and Enterprise of Andalusia, an architecture for the optimization of a machining process in real time through rule-based expert system has been developed. The architecture consists of an acquisition system and sensor data processing engine (SATD) from an expert system (SE) rule-based which communicates with the SATD. The SE has been designed as an inference engine with an algorithm for effective action, using a modus ponens rule model of goal-oriented rules.The pilot test demonstrated that it is possible to govern in real time the machining process based on rules contained in a SE. The tests have been done with approximated rules. Future work includes an exhaustive collection of data with different tool materials and geometries in a database to extract more precise rules.

Creative Strategic Intelligence Analysis and Decision Making Within the Elements of National Power. Proteus Futures Workshop, held Carlise, PA on 14-16 Aug 2007

DTIC Science & Technology

2007-08-01

Professor Ayers suggests the facts have been present since 1979 and the initiation of Khomeini’s objectives: Islamic Rule over Iran, expanding Islamic Rule ...Dajjal (Anti-Christ), establish Islam as the global religion, and rule the world for seven to nine years. True believers are to seek out martyrdom for...without the need for new rules and constructs. He uses the same rules and analysis that he applies to physical combat and pleads that we should stop

Rules for success in environmental negotiations

USGS Publications Warehouse

Taylor, J.G.; Burkardt, N.; Lamb, B. L.

1995-01-01

Scientists at the Mid-Continent Ecological Science Center of the National Biological Service conducted a series of case studies of Federal Energy Regulatory Commission license consultations. The goal of these studies was to test hypotheses about factors that contribute to success in interagency negotiations. Based on their analysis of six case studies, the researchers constructed a list of ten "rules for success." Examples include: Analyze the incentives of each party to negotiate, paying special attention to parties who gain by not negotiating; Clarify the technical issues so that all agree and they coincide with resource management objectives; and Make sure the final agreement is feasible from both a physical and a policy perspective so that it can actually be implemented. These rules can be used to plan for negotiations and to diagnose ongoing negotiations.

Pre-Publication Copy of the Regulatory Impact Analysis for the Proposed RMP Reconsideration Rule

EPA Pesticide Factsheets

This is a pre-publication version of the Regulatory Impact Analysis for the 2018 Proposed RMP Reconsideration Rule. The official version of the document will be available in the docket once the proposed rule is published in the Federal Register.

Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis group.

PubMed

Timmerman, Dirk; Van Calster, Ben; Testa, Antonia; Savelli, Luca; Fischerova, Daniela; Froyman, Wouter; Wynants, Laure; Van Holsbeke, Caroline; Epstein, Elisabeth; Franchi, Dorella; Kaijser, Jeroen; Czekierdowski, Artur; Guerriero, Stefano; Fruscio, Robert; Leone, Francesco P G; Rossi, Alberto; Landolfo, Chiara; Vergote, Ignace; Bourne, Tom; Valentin, Lil

2016-04-01

Accurate methods to preoperatively characterize adnexal tumors are pivotal for optimal patient management. A recent metaanalysis concluded that the International Ovarian Tumor Analysis algorithms such as the Simple Rules are the best approaches to preoperatively classify adnexal masses as benign or malignant. We sought to develop and validate a model to predict the risk of malignancy in adnexal masses using the ultrasound features in the Simple Rules. This was an international cross-sectional cohort study involving 22 oncology centers, referral centers for ultrasonography, and general hospitals. We included consecutive patients with an adnexal tumor who underwent a standardized transvaginal ultrasound examination and were selected for surgery. Data on 5020 patients were recorded in 3 phases from 2002 through 2012. The 5 Simple Rules features indicative of a benign tumor (B-features) and the 5 features indicative of malignancy (M-features) are based on the presence of ascites, tumor morphology, and degree of vascularity at ultrasonography. Gold standard was the histopathologic diagnosis of the adnexal mass (pathologist blinded to ultrasound findings). Logistic regression analysis was used to estimate the risk of malignancy based on the 10 ultrasound features and type of center. The diagnostic performance was evaluated by area under the receiver operating characteristic curve, sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), positive predictive value (PPV), negative predictive value (NPV), and calibration curves. Data on 4848 patients were analyzed. The malignancy rate was 43% (1402/3263) in oncology centers and 17% (263/1585) in other centers. The area under the receiver operating characteristic curve on validation data was very similar in oncology centers (0.917; 95% confidence interval, 0.901-0.931) and other centers (0.916; 95% confidence interval, 0.873-0.945). Risk estimates showed good calibration. In all, 23% of patients in the validation data set had a very low estimated risk (<1%) and 48% had a high estimated risk (≥30%). For the 1% risk cutoff, sensitivity was 99.7%, specificity 33.7%, LR+ 1.5, LR- 0.010, PPV 44.8%, and NPV 98.9%. For the 30% risk cutoff, sensitivity was 89.0%, specificity 84.7%, LR+ 5.8, LR- 0.13, PPV 75.4%, and NPV 93.9%. Quantification of the risk of malignancy based on the Simple Rules has good diagnostic performance both in oncology centers and other centers. A simple classification based on these risk estimates may form the basis of a clinical management system. Patients with a high risk may benefit from surgery by a gynecological oncologist, while patients with a lower risk may be managed locally. Copyright © 2016 Elsevier Inc. All rights reserved.

Speciation in Western Scrub-Jays, Haldane’s rule, and genetic clines in secondary contact

PubMed Central

2014-01-01

Background Haldane’s Rule, the tendency for the heterogametic sex to show reduced fertility in hybrid crosses, can obscure the signal of gene flow in mtDNA between species where females are heterogametic. Therefore, it is important when studying speciation and species limits in female-heterogametic species like birds to assess the signature of gene flow in the nuclear genome as well. We studied introgression of microsatellites and mtDNA across a secondary contact zone between coastal and interior lineages of Western Scrub-Jays (Aphelocoma californica) to test for a signature of Haldane’s Rule: a narrower cline of introgression in mtDNA compared to nuclear markers. Results Our initial phylogeographic analysis revealed that there is only one major area of contact between coastal and interior lineages and identified five genetic clusters with strong spatial structuring: Pacific Slope, Interior US, Edwards Plateau (Texas), Northern Mexico, and Southern Mexico. Consistent with predictions from Haldane’s Rule, mtDNA showed a narrower cline than nuclear markers across a transect through the hybrid zone. This result is not being driven by female-biased dispersal because neutral diffusion analysis, which included estimates of sex-specific dispersal rates, also showed less diffusion of mtDNA. Lineage-specific plumage traits were associated with nuclear genetic profiles for individuals in the hybrid zone, indicating that these differences are under genetic control. Conclusions This study adds to a growing list of studies that support predictions of Haldane’s Rule using cline analysis of multiple loci of differing inheritance modes, although alternate hypotheses like selection on different mtDNA types cannot be ruled out. That Haldane’s Rule appears to be operating in this system suggests a measure of reproductive isolation between the Pacific Slope and interior lineages. Based on a variety of evidence from the phenotype, ecology, and genetics, we recommend elevating three lineages to species level: A. californica (Pacific Slope); A. woodhouseii (Interior US plus Edwards Plateau plus Northern Mexico); A. sumichrasti (Southern Mexico). The distinctive Edwards Plateau population in Texas, which was monophyletic in mtDNA except for one individual, should be studied in greater detail given habitat threat. PMID:24938753

RANWAR: rank-based weighted association rule mining from gene expression and methylation data.

PubMed

Mallik, Saurav; Mukhopadhyay, Anirban; Maulik, Ujjwal

2015-01-01

Ranking of association rules is currently an interesting topic in data mining and bioinformatics. The huge number of evolved rules of items (or, genes) by association rule mining (ARM) algorithms makes confusion to the decision maker. In this article, we propose a weighted rule-mining technique (say, RANWAR or rank-based weighted association rule-mining) to rank the rules using two novel rule-interestingness measures, viz., rank-based weighted condensed support (wcs) and weighted condensed confidence (wcc) measures to bypass the problem. These measures are basically depended on the rank of items (genes). Using the rank, we assign weight to each item. RANWAR generates much less number of frequent itemsets than the state-of-the-art association rule mining algorithms. Thus, it saves time of execution of the algorithm. We run RANWAR on gene expression and methylation datasets. The genes of the top rules are biologically validated by Gene Ontologies (GOs) and KEGG pathway analyses. Many top ranked rules extracted from RANWAR that hold poor ranks in traditional Apriori, are highly biologically significant to the related diseases. Finally, the top rules evolved from RANWAR, that are not in Apriori, are reported.

Regulatory impact analysis: benefits and costs of proposed surface water treatment rule and total coliform rule

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

Not Available

1987-09-01

This report contains an analysis of the costs and benefits of controlling microbial contaminants in drinking water through the promulgation of two regulations: (1) the Surface Water Treatment Rule (SWTR); and (2) the Total Coliform Rule. This regulatory impact analysis (RIA) was prepared in accordance with Executive Order 12291, which requires that the costs and benefits of all major rules be examined and compared. The major topical areas covered in the RIA are as follows: problem definition; market imperfections, the need for federal regulation, and consideration of regulatory alternatives; assessment of total costs; assessment of benefits; regulatory flexibility act andmore » Paperwork Reduction Act analyses; and a summary of costs and benefits.« less

Urinary metabolic profiling of asymptomatic acute intermittent porphyria using a rule-mining-based algorithm.

PubMed

Luck, Margaux; Schmitt, Caroline; Talbi, Neila; Gouya, Laurent; Caradeuc, Cédric; Puy, Hervé; Bertho, Gildas; Pallet, Nicolas

2018-01-01

Metabolomic profiling combines Nuclear Magnetic Resonance spectroscopy with supervised statistical analysis that might allow to better understanding the mechanisms of a disease. In this study, the urinary metabolic profiling of individuals with porphyrias was performed to predict different types of disease, and to propose new pathophysiological hypotheses. Urine 1 H-NMR spectra of 73 patients with asymptomatic acute intermittent porphyria (aAIP) and familial or sporadic porphyria cutanea tarda (f/sPCT) were compared using a supervised rule-mining algorithm. NMR spectrum buckets bins, corresponding to rules, were extracted and a logistic regression was trained. Our rule-mining algorithm generated results were consistent with those obtained using partial least square discriminant analysis (PLS-DA) and the predictive performance of the model was significant. Buckets that were identified by the algorithm corresponded to metabolites involved in glycolysis and energy-conversion pathways, notably acetate, citrate, and pyruvate, which were found in higher concentrations in the urines of aAIP compared with PCT patients. Metabolic profiling did not discriminate sPCT from fPCT patients. These results suggest that metabolic reprogramming occurs in aAIP individuals, even in the absence of overt symptoms, and supports the relationship that occur between heme synthesis and mitochondrial energetic metabolism.

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.

Final June Revisions Rule Significant Contribution Assessment TSD

EPA Pesticide Factsheets

This Technical Support Document (TSD) presents quantitative assessments of the relationship between the final February revisions to the Transport Rule, the final June revisions rule, and the original analysis conducted for the final Transport Rule.

When More Is Less: Feedback Effects in Perceptual Category Learning

ERIC Educational Resources Information Center

Maddox, W. Todd; Love, Bradley C.; Glass, Brian D.; Filoteo, J. Vincent

2008-01-01

Rule-based and information-integration category learning were compared under minimal and full feedback conditions. Rule-based category structures are those for which the optimal rule is verbalizable. Information-integration category structures are those for which the optimal rule is not verbalizable. With minimal feedback subjects are told whether…

Movement rules for individual-based models of stream fish

Treesearch

Steven F. Railsback; Roland H. Lamberson; Bret C. Harvey; Walter E. Duffy

1999-01-01

Abstract - Spatially explicit individual-based models (IBMs) use movement rules to determine when an animal departs its current location and to determine its movement destination; these rules are therefore critical to accurate simulations. Movement rules typically define some measure of how an individual's expected fitness varies among locations, under 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.

Sonographic Diagnosis of Tubal Cancer with IOTA Simple Rules Plus Pattern Recognition

PubMed Central

Tongsong, Theera; Wanapirak, Chanane; Tantipalakorn, Charuwan; Tinnangwattana, Dangcheewan

2017-01-01

Objective: To evaluate diagnostic performance of IOTA simple rules plus pattern recognition in predicting tubal cancer. Methods: Secondary analysis was performed on prospective database of our IOTA project. The patients recruited in the project were those who were scheduled for pelvic surgery due to adnexal masses. The patients underwent ultrasound examinations within 24 hours before surgery. On ultrasound examination, the masses were evaluated using the well-established IOTA simple rules plus pattern recognition (sausage-shaped appearance, incomplete septum, visible ipsilateral ovaries) to predict tubal cancer. The gold standard diagnosis was based on histological findings or operative findings. Results: A total of 482 patients, including 15 cases of tubal cancer, were evaluated by ultrasound preoperatively. The IOTA simple rules plus pattern recognition gave a sensitivity of 86.7% (13 in 15) and specificity of 97.4%. Sausage-shaped appearance was identified in nearly all cases (14 in 15). Incomplete septa and normal ovaries could be identified in 33.3% and 40%, respectively. Conclusion: IOTA simple rules plus pattern recognition is relatively effective in predicting tubal cancer. Thus, we propose the simple scheme in diagnosis of tubal cancer as follows. First of all, the adnexal masses are evaluated with IOTA simple rules. If the B-rules could be applied, tubal cancer is reliably excluded. If the M-rules could be applied or the result is inconclusive, careful delineation of the mass with pattern recognition should be performed. PMID:29172273

Analyses of interactions among pair-rule genes and the gap gene Krüppel in Bombyx segmentation.

PubMed

Nakao, Hajime

2015-09-01

In the short-germ insect Tribolium, a pair-rule gene circuit consisting of the Tribolium homologs of even-skipped, runt, and odd-skipped (Tc-eve, Tc-run and Tc-odd, respectively) has been implicated in segment formation. To examine the application of the model to other taxa, I studied the expression and function of pair-rule genes in Bombyx mori, together with a Bombyx homolog of Krüppel (Bm-Kr), a known gap gene. Knockdown embryos of Bombyx homologs of eve, run and odd (Bm-eve, Bm-run and Bm-odd) exhibited asegmental phenotypes similar to those of Tribolium knockdowns. However, pair-rule gene interactions were similar to those of both Tribolium and Drosophila, which, different from Tribolium, shows a hierarchical segmentation mode. Additionally, the Bm-odd expression pattern shares characteristics with those of Drosophila pair-rule genes that receive upstream regulatory input. On the other hand, Bm-Kr knockdowns exhibited a large posterior segment deletion as observed in short-germ insects. However, a detailed analysis of these embryos indicated that Bm-Kr modulates expression of pair-rule genes like in Drosophila, although the mechanisms appear to be different. This suggested hierarchical interactions between Bm-Kr and pair-rule genes. Based on these results, I concluded that the pair-rule gene circuit model that describes Tribolium development is not applicable to Bombyx. Copyright © 2015 Elsevier Inc. All rights reserved.

Sonographic Diagnosis of Tubal Cancer with IOTA Simple Rules Plus Pattern Recognition

PubMed

Tongsong, Theera; Wanapirak, Chanane; Tantipalakorn, Charuwan; Tinnangwattana, Dangcheewan

2017-11-26

Objective: To evaluate diagnostic performance of IOTA simple rules plus pattern recognition in predicting tubal cancer. Methods: Secondary analysis was performed on prospective database of our IOTA project. The patients recruited in the project were those who were scheduled for pelvic surgery due to adnexal masses. The patients underwent ultrasound examinations within 24 hours before surgery. On ultrasound examination, the masses were evaluated using the well-established IOTA simple rules plus pattern recognition (sausage-shaped appearance, incomplete septum, visible ipsilateral ovaries) to predict tubal cancer. The gold standard diagnosis was based on histological findings or operative findings. Results: A total of 482 patients, including 15 cases of tubal cancer, were evaluated by ultrasound preoperatively. The IOTA simple rules plus pattern recognition gave a sensitivity of 86.7% (13 in 15) and specificity of 97.4%. Sausage-shaped appearance was identified in nearly all cases (14 in 15). Incomplete septa and normal ovaries could be identified in 33.3% and 40%, respectively. Conclusion: IOTA simple rules plus pattern recognition is relatively effective in predicting tubal cancer. Thus, we propose the simple scheme in diagnosis of tubal cancer as follows. First of all, the adnexal masses are evaluated with IOTA simple rules. If the B-rules could be applied, tubal cancer is reliably excluded. If the M-rules could be applied or the result is inconclusive, careful delineation of the mass with pattern recognition should be performed. Creative Commons Attribution License

Analysis and Synthesis of Adaptive Neural Elements and Assembles

DTIC Science & Technology

1992-02-17

effects of neuromodulators on electrically activity. Based on the simulations it appears that there are potentially novel mechanisms with which modulatory...and Byrne, J.H. A learning rule based on empirically-derived activity-dependent neuromodulation supports operant conditioning in a small network...dependent neuromodulation can support operant conditioning in a small oscillatory network". 2. Society for Neuroscience Short Course on Neural

Analysis of habitat-selection rules using an individual-based model

Treesearch

Steven F. Railsback; Bret C. Harvey

2002-01-01

Abstract - Despite their promise for simulating natural complexity,individual-based models (IBMs) are rarely used for ecological research or resource management. Few IBMs have been shown to reproduce realistic patterns of behavior by individual organisms.To test our IBM of stream salmonids and draw conclusions about foraging theory,we analyzed the IBM ’s ability to...

Mining Student Data Captured from a Web-Based Tutoring Tool: Initial Exploration and Results

ERIC Educational Resources Information Center

Merceron, Agathe; Yacef, Kalina

2004-01-01

In this article we describe the initial investigations that we have conducted on student data collected from a web-based tutoring tool. We have used some data mining techniques such as association rule and symbolic data analysis, as well as traditional SQL queries to gain further insight on the students' learning and deduce information to improve…

Systematic methods for knowledge acquisition and expert system development

NASA Technical Reports Server (NTRS)

Belkin, Brenda L.; Stengel, Robert F.

1991-01-01

Nine cooperating rule-based systems, collectively called AUTOCREW, were designed to automate functions and decisions associated with a combat aircraft's subsystem. The organization of tasks within each system is described; performance metrics were developed to evaluate the workload of each rule base, and to assess the cooperation between the rule-bases. Each AUTOCREW subsystem is composed of several expert systems that perform specific tasks. AUTOCREW's NAVIGATOR was analyzed in detail to understand the difficulties involved in designing the system and to identify tools and methodologies that ease development. The NAVIGATOR determines optimal navigation strategies from a set of available sensors. A Navigation Sensor Management (NSM) expert system was systematically designed from Kalman filter covariance data; four ground-based, a satellite-based, and two on-board INS-aiding sensors were modeled and simulated to aid an INS. The NSM Expert was developed using the Analysis of Variance (ANOVA) and the ID3 algorithm. Navigation strategy selection is based on an RSS position error decision metric, which is computed from the covariance data. Results show that the NSM Expert predicts position error correctly between 45 and 100 percent of the time for a specified navaid configuration and aircraft trajectory. The NSM Expert adapts to new situations, and provides reasonable estimates of hybrid performance. The systematic nature of the ANOVA/ID3 method makes it broadly applicable to expert system design when experimental or simulation data is available.

Common-Sense Rule Inference

NASA Astrophysics Data System (ADS)

Lombardi, Ilaria; Console, Luca

In the paper we show how rule-based inference can be made more flexible by exploiting semantic information associated with the concepts involved in the rules. We introduce flexible forms of common sense reasoning in which whenever no rule applies to a given situation, the inference engine can fire rules that apply to more general or to similar situations. This can be obtained by defining new forms of match between rules and the facts in the working memory and new forms of conflict resolution. We claim that in this way we can overcome some of the brittleness problems that are common in rule-based systems.

Rule-Based Event Processing and Reaction Rules

NASA Astrophysics Data System (ADS)

Paschke, Adrian; Kozlenkov, Alexander

Reaction rules and event processing technologies play a key role in making business and IT / Internet infrastructures more agile and active. While event processing is concerned with detecting events from large event clouds or streams in almost real-time, reaction rules are concerned with the invocation of actions in response to events and actionable situations. They state the conditions under which actions must be taken. In the last decades various reaction rule and event processing approaches have been developed, which for the most part have been advanced separately. In this paper we survey reaction rule approaches and rule-based event processing systems and languages.

Vortex Advisory System Safety Analysis : Volume 1. Analytical Model

DOT National Transportation Integrated Search

1978-09-01

The Vortex Advisory System (VAS) is based on wind criterion--when the wind near the runway end is outside of the criterion, all interarrival Instrument Flight Rules (IFR) aircraft separations can be set at 3 nautical miles. Five years of wind data ha...

Computer-Based Linguistic Analysis.

ERIC Educational Resources Information Center

Wright, James R.

Noam Chomsky's transformational-generative grammar model may effectively be translated into an equivalent computer model. Phrase-structure rules and transformations are tested as to their validity and ordering by the computer via the process of random lexical substitution. Errors appearing in the grammar are detected and rectified, and formal…

[Traditional Chinese medicine inheritance system analysis of professor Ding Yuanqing in treating tic disorder medication based on experience].

PubMed

Sun, Lu-yan; Li, Qing-peng; Zhao, Li-li; Ding, Yuan-qing

2015-08-01

In recent years, the incidence of tic disorders has increased, and it is not uncommon for the patients to treat the disease. The pathogenesis and pathogenesis of Western medicine are not yet clear, the clinical commonly used western medicine has many adverse reactions, traditional Chinese medicine (TCM) research is increasingly valued. Based on the software of TCM inheritance assistant system, this paper discusses Ding Yuanqing's experience in treating tic disorder with Professor. Collect yuan Qing Ding professor in treating tic disorder of medical records by association rules Apriori algorithm, complex system entropy clustering without supervision and data mining method, carries on the analysis to the selected 800 prescriptions, to determine the frequency of use of prescription drugs, the association rules between the drug and digging out the 12 core combination and the first six new prescription, medication transferred to the liver and extinguish wind, cooling blood and relieving convulsion, Qingxin soothe the nerves, with the card cut, flexible application, strict compatibility.

Fuzzy logic application for modeling man-in-the-loop space shuttle proximity operations. M.S. Thesis - MIT

NASA Technical Reports Server (NTRS)

Brown, Robert B.

1994-01-01

A software pilot model for Space Shuttle proximity operations is developed, utilizing fuzzy logic. The model is designed to emulate a human pilot during the terminal phase of a Space Shuttle approach to the Space Station. The model uses the same sensory information available to a human pilot and is based upon existing piloting rules and techniques determined from analysis of human pilot performance. Such a model is needed to generate numerous rendezvous simulations to various Space Station assembly stages for analysis of current NASA procedures and plume impingement loads on the Space Station. The advantages of a fuzzy logic pilot model are demonstrated by comparing its performance with NASA's man-in-the-loop simulations and with a similar model based upon traditional Boolean logic. The fuzzy model is shown to respond well from a number of initial conditions, with results typical of an average human. In addition, the ability to model different individual piloting techniques and new piloting rules is demonstrated.

Adjusting Wavelet-based Multiresolution Analysis Boundary Conditions for Robust Long-term Streamflow Forecasting Model

NASA Astrophysics Data System (ADS)

Maslova, I.; Ticlavilca, A. M.; McKee, M.

2012-12-01

There has been an increased interest in wavelet-based streamflow forecasting models in recent years. Often overlooked in this approach are the circularity assumptions of the wavelet transform. We propose a novel technique for minimizing the wavelet decomposition boundary condition effect to produce long-term, up to 12 months ahead, forecasts of streamflow. A simulation study is performed to evaluate the effects of different wavelet boundary rules using synthetic and real streamflow data. A hybrid wavelet-multivariate relevance vector machine model is developed for forecasting the streamflow in real-time for Yellowstone River, Uinta Basin, Utah, USA. The inputs of the model utilize only the past monthly streamflow records. They are decomposed into components formulated in terms of wavelet multiresolution analysis. It is shown that the model model accuracy can be increased by using the wavelet boundary rule introduced in this study. This long-term streamflow modeling and forecasting methodology would enable better decision-making and managing water availability risk.

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

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

A traditional programming technique for controlling the display of optional flight information in a civil transport cockpit is compared to a rule-based technique for the same function. This application required complex decision logic and a frequently modified rule base. The techniques are evaluated for execution efficiency and implementation ease; the criterion used to calculate the execution efficiency is the total number of steps required to isolate hypotheses that were true and the criteria used to evaluate the implementability are ease of modification and verification and explanation capability. It is observed that the traditional program is more efficient than the rule-based program; however, the rule-based programming technique is more applicable for improving programmer productivity.

Refractive, corneal and ocular residual astigmatism: distribution in a German population and age-dependency - the Gutenberg health study.

PubMed

Schuster, Alexander Karl-Georg; Pfeiffer, Norbert; Schulz, Andreas; Hoehn, René; Ponto, Katharina A; Wild, Philipp S; Blettner, Maria; Beutel, Manfred E; Lackner, Karl J; Münzel, Thomas; Mirshahi, Alireza

2017-12-01

Worldwide, the most frequent cause of visual impairment is uncorrected refractive error. This analysis focused on the distribution and associations of refractive, corneal and ocular residual astigmatism. As part of the Gutenberg Health study, a population-based cross-sectional study was conducted in the general population of Germany. A comprehensive ophthalmological examination including refraction, tonometry, and Scheimpflug imaging of the anterior cornea (Pachycam) was performed. In addition to the magnitude and type (with-the-rule, against-the-rule, oblique) of the refractive or corneal astigmatism, we calculated the vector components (J 0 , J 45 ) of both astigmatisms and calculated the ocular residual astigmatism. We performed multiple quantile regression analysis to evaluate the factors associated with refractive, corneal and ocular residual astigmatisms. A total of 13,558 subjects (49% female) with a mean age of 54.0 years (range 35-74 years) were included in this study. The prevalence of refractive astigmatism (>1.0D) was 13.0% in right eyes and 12.0% in left eyes, and 85% of these subjects wore spectacles. The distribution of refractive astigmatism showed a two-peak distribution with high astigmatism for with-the-rule and against-the-rule astigmatism. The associated factors were corneal curvature, age and sex for the different astigmatisms (p < 0.001). We analyzed the prevalence of different astigmatisms within a European population. We confirmed a shift with aging from with-the-rule to against-the-rule astigmatism to refractive and corneal astigmatism. Astigmatism has a large impact on visual perception; more than 85% of people with astigmatism over one diopter wore glasses for distance vision.

Pooled individual patient data from five countries were used to derive a clinical prediction rule for coronary artery disease in primary care.

PubMed

Aerts, Marc; Minalu, Girma; Bösner, Stefan; Buntinx, Frank; Burnand, Bernard; Haasenritter, Jörg; Herzig, Lilli; Knottnerus, J André; Nilsson, Staffan; Renier, Walter; Sox, Carol; Sox, Harold; Donner-Banzhoff, Norbert

2017-01-01

To construct a clinical prediction rule for coronary artery disease (CAD) presenting with chest pain in primary care. Meta-Analysis using 3,099 patients from five studies. To identify candidate predictors, we used random forest trees, multiple imputation of missing values, and logistic regression within individual studies. To generate a prediction rule on the pooled data, we applied a regression model that took account of the differing standard data sets collected by the five studies. The most parsimonious rule included six equally weighted predictors: age ≥55 (males) or ≥65 (females) (+1); attending physician suspected a serious diagnosis (+1); history of CAD (+1); pain brought on by exertion (+1); pain feels like "pressure" (+1); pain reproducible by palpation (-1). CAD was considered absent if the prediction score is <2. The area under the ROC curve was 0.84. We applied this rule to a study setting with a CAD prevalence of 13.2% using a prediction score cutoff of <2 (i.e., -1, 0, or +1). When the score was <2, the probability of CAD was 2.1% (95% CI: 1.1-3.9%); when the score was ≥ 2, it was 43.0% (95% CI: 35.8-50.4%). Clinical prediction rules are a key strategy for individualizing care. Large data sets based on electronic health records from diverse sites create opportunities for improving their internal and external validity. Our patient-level meta-analysis from five primary care sites should improve external validity. Our strategy for addressing site-to-site systematic variation in missing data should improve internal validity. Using principles derived from decision theory, we also discuss the problem of setting the cutoff prediction score for taking action. Copyright © 2016 Elsevier Inc. All rights reserved.

On the fusion of tuning parameters of fuzzy rules and neural network

NASA Astrophysics Data System (ADS)

Mamuda, Mamman; Sathasivam, Saratha

2017-08-01

Learning fuzzy rule-based system with neural network can lead to a precise valuable empathy of several problems. Fuzzy logic offers a simple way to reach at a definite conclusion based upon its vague, ambiguous, imprecise, noisy or missing input information. Conventional learning algorithm for tuning parameters of fuzzy rules using training input-output data usually end in a weak firing state, this certainly powers the fuzzy rule and makes it insecure for a multiple-input fuzzy system. In this paper, we introduce a new learning algorithm for tuning the parameters of the fuzzy rules alongside with radial basis function neural network (RBFNN) in training input-output data based on the gradient descent method. By the new learning algorithm, the problem of weak firing using the conventional method was addressed. We illustrated the efficiency of our new learning algorithm by means of numerical examples. MATLAB R2014(a) software was used in simulating our result The result shows that the new learning method has the best advantage of training the fuzzy rules without tempering with the fuzzy rule table which allowed a membership function of the rule to be used more than one time in the fuzzy rule base.

Beyond Molecular Codes: Simple Rules to Wire Complex Brains

PubMed Central

Hassan, Bassem A.; Hiesinger, P. Robin

2015-01-01

Summary Molecular codes, like postal zip codes, are generally considered a robust way to ensure the specificity of neuronal target selection. However, a code capable of unambiguously generating complex neural circuits is difficult to conceive. Here, we re-examine the notion of molecular codes in the light of developmental algorithms. We explore how molecules and mechanisms that have been considered part of a code may alternatively implement simple pattern formation rules sufficient to ensure wiring specificity in neural circuits. This analysis delineates a pattern-based framework for circuit construction that may contribute to our understanding of brain wiring. PMID:26451480

GraDit: graph-based data repair algorithm for multiple data edits rule violations

NASA Astrophysics Data System (ADS)

Ode Zuhayeni Madjida, Wa; Gusti Bagus Baskara Nugraha, I.

2018-03-01

Constraint-based data cleaning captures data violation to a set of rule called data quality rules. The rules consist of integrity constraint and data edits. Structurally, they are similar, where the rule contain left hand side and right hand side. Previous research proposed a data repair algorithm for integrity constraint violation. The algorithm uses undirected hypergraph as rule violation representation. Nevertheless, this algorithm can not be applied for data edits because of different rule characteristics. This study proposed GraDit, a repair algorithm for data edits rule. First, we use bipartite-directed hypergraph as model representation of overall defined rules. These representation is used for getting interaction between violation rules and clean rules. On the other hand, we proposed undirected graph as violation representation. Our experimental study showed that algorithm with undirected graph as violation representation model gave better data quality than algorithm with undirected hypergraph as representation model.

Target-Based Maintenance of Privacy Preserving Association Rules

ERIC Educational Resources Information Center

Ahluwalia, Madhu V.

2011-01-01

In the context of association rule mining, the state-of-the-art in privacy preserving data mining provides solutions for categorical and Boolean association rules but not for quantitative association rules. This research fills this gap by describing a method based on discrete wavelet transform (DWT) to protect input data privacy while preserving…

Combination Rules for Morse-Based van der Waals Force Fields.

PubMed

Yang, Li; Sun, Lei; Deng, Wei-Qiao

2018-02-15

In traditional force fields (FFs), van der Waals interactions have been usually described by the Lennard-Jones potentials. Conventional combination rules for the parameters of van der Waals (VDW) cross-termed interactions were developed for the Lennard-Jones based FFs. Here, we report that the Morse potentials were a better function to describe VDW interactions calculated by highly precise quantum mechanics methods. A new set of combination rules was developed for Morse-based FFs, in which VDW interactions were described by Morse potentials. The new set of combination rules has been verified by comparing the second virial coefficients of 11 noble gas mixtures. For all of the mixed binaries considered in this work, the combination rules work very well and are superior to all three other existing sets of combination rules reported in the literature. We further used the Morse-based FF by using the combination rules to simulate the adsorption isotherms of CH 4 at 298 K in four covalent-organic frameworks (COFs). The overall agreement is great, which supports the further applications of this new set of combination rules in more realistic simulation systems.

Multi-user investigation organizer

NASA Technical Reports Server (NTRS)

Panontin, Tina L. (Inventor); Williams, James F. (Inventor); Carvalho, Robert E. (Inventor); Sturken, Ian (Inventor); Wolfe, Shawn R. (Inventor); Gawdiak, Yuri O. (Inventor); Keller, Richard M. (Inventor)

2009-01-01

A system that allows a team of geographically dispersed users to collaboratively analyze a mishap event. The system includes a reconfigurable ontology, including instances that are related to and characterize the mishap, a semantic network that receives, indexes and stores, for retrieval, viewing and editing, the instances and links between the instances, a network browser interface for retrieving and viewing screens that present the instances and links to other instances and that allow editing thereof, and a rule-based inference engine, including a collection of rules associated with establishment of links between the instances. A possible conclusion arising from analysis of the mishap event may be characterized as one or more of: not a credible conclusion; an unlikely conclusion; a credible conclusion; conclusion needs analysis; conclusion needs supporting data; conclusion proposed to be closed; and an un-reviewed conclusion.

A Short Note on Rules and Higher Order Rules.

ERIC Educational Resources Information Center

Scandura, Joseph M.

This brief paper argues that structural analysis--an extended form of cognitive task analysis--demonstrates that both domain dependent and domain independent knowledge can be derived from specific content domains. It is noted that the major difference between the two is that lower order rules (specific knowledge) are derived directly from specific…

Short-term effects of air pollution on lower respiratory diseases and forecasting by the group method of data handling

NASA Astrophysics Data System (ADS)

Zhu, Wenjin; Wang, Jianzhou; Zhang, Wenyu; Sun, Donghuai

2012-05-01

Risk of lower respiratory diseases was significantly correlated with levels of monthly average concentration of SO2; NO2 and association rules have high lifts. In view of Lanzhou's special geographical location, taking into account the impact of different seasons, especially for the winter, the relations between air pollutants and the respiratory disease deserve further study. In this study the monthly average concentration of SO2, NO2, PM10 and the monthly number of people who in hospital because of lower respiratory disease from January 2001 to December 2005 are grouped equidistant and considered as the terms of transactions. Then based on the relational algebraic theory we employed the optimization relation association rule to mine the association rules of the transactions. Based on the association rules revealing the effects of air pollutants on the lower respiratory disease, we forecast the number of person who suffered from lower respiratory disease by the group method of data handling (GMDH) to reveal the risk and give a consultation to the hospital in Xigu District, the most seriously polluted district in Lanzhou. The data and analysis indicate that individuals may be susceptible to the short-term effects of pollution and thus suffer from lower respiratory diseases and this effect presents seasonal.

Clinical Decision Support for a Multicenter Trial of Pediatric Head Trauma

PubMed Central

Swietlik, Marguerite; Deakyne, Sara; Hoffman, Jeffrey M.; Grundmeier, Robert W.; Paterno, Marilyn D.; Rocha, Beatriz H.; Schaeffer, Molly H; Pabbathi, Deepika; Alessandrini, Evaline; Ballard, Dustin; Goldberg, Howard S.; Kuppermann, Nathan; Dayan, Peter S.

2016-01-01

Summary Introduction For children who present to emergency departments (EDs) due to blunt head trauma, ED clinicians must decide who requires computed tomography (CT) scanning to evaluate for traumatic brain injury (TBI). The Pediatric Emergency Care Applied Research Network (PECARN) derived and validated two age-based prediction rules to identify children at very low risk of clinically-important traumatic brain injuries (ciTBIs) who do not typically require CT scans. In this case report, we describe the strategy used to implement the PECARN TBI prediction rules via electronic health record (EHR) clinical decision support (CDS) as the intervention in a multicenter clinical trial. Methods Thirteen EDs participated in this trial. The 10 sites receiving the CDS intervention used the Epic® EHR. All sites implementing EHR-based CDS built the rules by using the vendor’s CDS engine. Based on a sociotechnical analysis, we designed the CDS so that recommendations could be displayed immediately after any provider entered prediction rule data. One central site developed and tested the intervention package to be exported to other sites. The intervention package included a clinical trial alert, an electronic data collection form, the CDS rules and the format for recommendations. Results The original PECARN head trauma prediction rules were derived from physician documentation while this pragmatic trial led each site to customize their workflows and allow multiple different providers to complete the head trauma assessments. These differences in workflows led to varying completion rates across sites as well as differences in the types of providers completing the electronic data form. Site variation in internal change management processes made it challenging to maintain the same rigor across all sites. This led to downstream effects when data reports were developed. Conclusions The process of a centralized build and export of a CDS system in one commercial EHR system successfully supported a multicenter clinical trial. PMID:27437059

KOSMOS: a universal morph server for nucleic acids, proteins and their complexes.

PubMed

Seo, Sangjae; Kim, Moon Ki

2012-07-01

KOSMOS is the first online morph server to be able to address the structural dynamics of DNA/RNA, proteins and even their complexes, such as ribosomes. The key functions of KOSMOS are the harmonic and anharmonic analyses of macromolecules. In the harmonic analysis, normal mode analysis (NMA) based on an elastic network model (ENM) is performed, yielding vibrational modes and B-factor calculations, which provide insight into the potential biological functions of macromolecules based on their structural features. Anharmonic analysis involving elastic network interpolation (ENI) is used to generate plausible transition pathways between two given conformations by optimizing a topology-oriented cost function that guarantees a smooth transition without steric clashes. The quality of the computed pathways is evaluated based on their various facets, including topology, energy cost and compatibility with the NMA results. There are also two unique features of KOSMOS that distinguish it from other morph servers: (i) the versatility in the coarse-graining methods and (ii) the various connection rules in the ENM. The models enable us to analyze macromolecular dynamics with the maximum degrees of freedom by combining a variety of ENMs from full-atom to coarse-grained, backbone and hybrid models with one connection rule, such as distance-cutoff, number-cutoff or chemical-cutoff. KOSMOS is available at http://bioengineering.skku.ac.kr/kosmos.

A simple signaling rule for variable life-adjusted display derived from an equivalent risk-adjusted CUSUM chart.

PubMed

Wittenberg, Philipp; Gan, Fah Fatt; Knoth, Sven

2018-04-17

The variable life-adjusted display (VLAD) is the first risk-adjusted graphical procedure proposed in the literature for monitoring the performance of a surgeon. It displays the cumulative sum of expected minus observed deaths. It has since become highly popular because the statistic plotted is easy to understand. But it is also easy to misinterpret a surgeon's performance by utilizing the VLAD, potentially leading to grave consequences. The problem of misinterpretation is essentially caused by the variance of the VLAD's statistic that increases with sample size. In order for the VLAD to be truly useful, a simple signaling rule is desperately needed. Various forms of signaling rules have been developed, but they are usually quite complicated. Without signaling rules, making inferences using the VLAD alone is difficult if not misleading. In this paper, we establish an equivalence between a VLAD with V-mask and a risk-adjusted cumulative sum (RA-CUSUM) chart based on the difference between the estimated probability of death and surgical outcome. Average run length analysis based on simulation shows that this particular RA-CUSUM chart has similar performance as compared to the established RA-CUSUM chart based on the log-likelihood ratio statistic obtained by testing the odds ratio of death. We provide a simple design procedure for determining the V-mask parameters based on a resampling approach. Resampling from a real data set ensures that these parameters can be estimated appropriately. Finally, we illustrate the monitoring of a real surgeon's performance using VLAD with V-mask. Copyright © 2018 John Wiley & Sons, Ltd.

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 fully-nonlocal energy-based formulation and high-performance realization of the quasicontinuum method

NASA Astrophysics Data System (ADS)

Amelang, Jeff

The quasicontinuum (QC) method was introduced to coarse-grain crystalline atomic ensembles in order to bridge the scales from individual atoms to the micro- and mesoscales. Though many QC formulations have been proposed with varying characteristics and capabilities, a crucial cornerstone of all QC techniques is the concept of summation rules, which attempt to efficiently approximate the total Hamiltonian of a crystalline atomic ensemble by a weighted sum over a small subset of atoms. In this work we propose a novel, fully-nonlocal, energy-based formulation of the QC method with support for legacy and new summation rules through a general energy-sampling scheme. Our formulation does not conceptually differentiate between atomistic and coarse-grained regions and thus allows for seamless bridging without domain-coupling interfaces. Within this structure, we introduce a new class of summation rules which leverage the affine kinematics of this QC formulation to most accurately integrate thermodynamic quantities of interest. By comparing this new class of summation rules to commonly-employed rules through analysis of energy and spurious force errors, we find that the new rules produce no residual or spurious force artifacts in the large-element limit under arbitrary affine deformation, while allowing us to seamlessly bridge to full atomistics. We verify that the new summation rules exhibit significantly smaller force artifacts and energy approximation errors than all comparable previous summation rules through a comprehensive suite of examples with spatially non-uniform QC discretizations in two and three dimensions. Due to the unique structure of these summation rules, we also use the new formulation to study scenarios with large regions of free surface, a class of problems previously out of reach of the QC method. Lastly, we present the key components of a high-performance, distributed-memory realization of the new method, including a novel algorithm for supporting unparalleled levels of deformation. Overall, this new formulation and implementation allows us to efficiently perform simulations containing an unprecedented number of degrees of freedom with low approximation error.

Association Rule-based Predictive Model for Machine Failure in Industrial Internet of Things

NASA Astrophysics Data System (ADS)

Kwon, Jung-Hyok; Lee, Sol-Bee; Park, Jaehoon; Kim, Eui-Jik

2017-09-01

This paper proposes an association rule-based predictive model for machine failure in industrial Internet of things (IIoT), which can accurately predict the machine failure in real manufacturing environment by investigating the relationship between the cause and type of machine failure. To develop the predictive model, we consider three major steps: 1) binarization, 2) rule creation, 3) visualization. The binarization step translates item values in a dataset into one or zero, then the rule creation step creates association rules as IF-THEN structures using the Lattice model and Apriori algorithm. Finally, the created rules are visualized in various ways for users’ understanding. An experimental implementation was conducted using R Studio version 3.3.2. The results show that the proposed predictive model realistically predicts machine failure based on association rules.

The neural basis for novel semantic categorization.

PubMed

Koenig, Phyllis; Smith, Edward E; Glosser, Guila; DeVita, Chris; Moore, Peachie; McMillan, Corey; Gee, Jim; Grossman, Murray

2005-01-15

We monitored regional cerebral activity with BOLD fMRI during acquisition of a novel semantic category and subsequent categorization of test stimuli by a rule-based strategy or a similarity-based strategy. We observed different patterns of activation in direct comparisons of rule- and similarity-based categorization. During rule-based category acquisition, subjects recruited anterior cingulate, thalamic, and parietal regions to support selective attention to perceptual features, and left inferior frontal cortex to helps maintain rules in working memory. Subsequent rule-based categorization revealed anterior cingulate and parietal activation while judging stimuli whose conformity with the rules was readily apparent, and left inferior frontal recruitment during judgments of stimuli whose conformity was less apparent. By comparison, similarity-based category acquisition showed recruitment of anterior prefrontal and posterior cingulate regions, presumably to support successful retrieval of previously encountered exemplars from long-term memory, and bilateral temporal-parietal activation for perceptual feature integration. Subsequent similarity-based categorization revealed temporal-parietal, posterior cingulate, and anterior prefrontal activation. These findings suggest that large-scale networks support relatively distinct categorization processes during the acquisition and judgment of semantic category knowledge.

Rule-based simulation models

NASA Technical Reports Server (NTRS)

Nieten, Joseph L.; Seraphine, Kathleen M.

1991-01-01

Procedural modeling systems, rule based modeling systems, and a method for converting a procedural model to a rule based model are described. Simulation models are used to represent real time engineering systems. A real time system can be represented by a set of equations or functions connected so that they perform in the same manner as the actual system. Most modeling system languages are based on FORTRAN or some other procedural language. Therefore, they must be enhanced with a reaction capability. Rule based systems are reactive by definition. Once the engineering system has been decomposed into a set of calculations using only basic algebraic unary operations, a knowledge network of calculations and functions can be constructed. The knowledge network required by a rule based system can be generated by a knowledge acquisition tool or a source level compiler. The compiler would take an existing model source file, a syntax template, and a symbol table and generate the knowledge network. Thus, existing procedural models can be translated and executed by a rule based system. Neural models can be provide the high capacity data manipulation required by the most complex real time models.

Qualitative Discovery in Medical Databases

NASA Technical Reports Server (NTRS)

Maluf, David A.

2000-01-01

Implication rules have been used in uncertainty reasoning systems to confirm and draw hypotheses or conclusions. However a major bottleneck in developing such systems lies in the elicitation of these rules. This paper empirically examines the performance of evidential inferencing with implication networks generated using a rule induction tool called KAT. KAT utilizes an algorithm for the statistical analysis of empirical case data, and hence reduces the knowledge engineering efforts and biases in subjective implication certainty assignment. The paper describes several experiments in which real-world diagnostic problems were investigated; namely, medical diagnostics. In particular, it attempts to show that: (1) with a limited number of case samples, KAT is capable of inducing implication networks useful for making evidential inferences based on partial observations, and (2) observation driven by a network entropy optimization mechanism is effective in reducing the uncertainty of predicted events.

Interactive Vulnerability Analysis Enhancement Results

DTIC Science & Technology

2012-12-01

from JavaEE web based applications to other non-web based Java programs. Technology developed in this effort should be generally applicable to other...Generating a rule is a 2 click process that requires no input from the user. • Task 3: Added support for non- Java EE applications Aspect’s...investigated a variety of Java -based technologies and how IAST can support them. We were successful in adding support for Scala, a popular new language, and

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...

Dynamic Approaches to Language Processing

ERIC Educational Resources Information Center

Srinivasan, Narayanan

2007-01-01

Symbolic rule-based approaches have been a preferred way to study language and cognition. Dissatisfaction with rule-based approaches in the 1980s lead to alternative approaches to study language, the most notable being the dynamic approaches to language processing. Dynamic approaches provide a significant alternative by not being rule-based and…

Investigation of model-based physical design restrictions (Invited Paper)

NASA Astrophysics Data System (ADS)

Lucas, Kevin; Baron, Stanislas; Belledent, Jerome; Boone, Robert; Borjon, Amandine; Couderc, Christophe; Patterson, Kyle; Riviere-Cazaux, Lionel; Rody, Yves; Sundermann, Frank; Toublan, Olivier; Trouiller, Yorick; Urbani, Jean-Christophe; Wimmer, Karl

2005-05-01

As lithography and other patterning processes become more complex and more non-linear with each generation, the task of physical design rules necessarily increases in complexity also. The goal of the physical design rules is to define the boundary between the physical layout structures which will yield well from those which will not. This is essentially a rule-based pre-silicon guarantee of layout correctness. However the rapid increase in design rule requirement complexity has created logistical problems for both the design and process functions. Therefore, similar to the semiconductor industry's transition from rule-based to model-based optical proximity correction (OPC) due to increased patterning complexity, opportunities for improving physical design restrictions by implementing model-based physical design methods are evident. In this paper we analyze the possible need and applications for model-based physical design restrictions (MBPDR). We first analyze the traditional design rule evolution, development and usage methodologies for semiconductor manufacturers. Next we discuss examples of specific design rule challenges requiring new solution methods in the patterning regime of low K1 lithography and highly complex RET. We then evaluate possible working strategies for MBPDR in the process development and product design flows, including examples of recent model-based pre-silicon verification techniques. Finally we summarize with a proposed flow and key considerations for MBPDR implementation.

Fire flame detection based on GICA and target tracking

NASA Astrophysics Data System (ADS)

Rong, Jianzhong; Zhou, Dechuang; Yao, Wei; Gao, Wei; Chen, Juan; Wang, Jian

2013-04-01

To improve the video fire detection rate, a robust fire detection algorithm based on the color, motion and pattern characteristics of fire targets was proposed, which proved a satisfactory fire detection rate for different fire scenes. In this fire detection algorithm: (a) a rule-based generic color model was developed based on analysis on a large quantity of flame pixels; (b) from the traditional GICA (Geometrical Independent Component Analysis) model, a Cumulative Geometrical Independent Component Analysis (C-GICA) model was developed for motion detection without static background and (c) a BP neural network fire recognition model based on multi-features of the fire pattern was developed. Fire detection tests on benchmark fire video clips of different scenes have shown the robustness, accuracy and fast-response of the algorithm.

The research of selection model based on LOD in multi-scale display of electronic map

NASA Astrophysics Data System (ADS)

Zhang, Jinming; You, Xiong; Liu, Yingzhen

2008-10-01

This paper proposes a selection model based on LOD to aid the display of electronic map. The ratio of display scale to map scale is regarded as a LOD operator. The categorization rule, classification rule, elementary rule and spatial geometry character rule of LOD operator setting are also concluded.

Research on key technology of the verification system of steel rule based on vision measurement

NASA Astrophysics Data System (ADS)

Jia, Siyuan; Wang, Zhong; Liu, Changjie; Fu, Luhua; Li, Yiming; Lu, Ruijun

2018-01-01

The steel rule plays an important role in quantity transmission. However, the traditional verification method of steel rule based on manual operation and reading brings about low precision and low efficiency. A machine vison based verification system of steel rule is designed referring to JJG1-1999-Verificaiton Regulation of Steel Rule [1]. What differentiates this system is that it uses a new calibration method of pixel equivalent and decontaminates the surface of steel rule. Experiments show that these two methods fully meet the requirements of the verification system. Measuring results strongly prove that these methods not only meet the precision of verification regulation, but also improve the reliability and efficiency of the verification system.

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.

Connecting clinical and actuarial prediction with rule-based methods.

PubMed

Fokkema, Marjolein; Smits, Niels; Kelderman, Henk; Penninx, Brenda W J H

2015-06-01

Meta-analyses comparing the accuracy of clinical versus actuarial prediction have shown actuarial methods to outperform clinical methods, on average. However, actuarial methods are still not widely used in clinical practice, and there has been a call for the development of actuarial prediction methods for clinical practice. We argue that rule-based methods may be more useful than the linear main effect models usually employed in prediction studies, from a data and decision analytic as well as a practical perspective. In addition, decision rules derived with rule-based methods can be represented as fast and frugal trees, which, unlike main effects models, can be used in a sequential fashion, reducing the number of cues that have to be evaluated before making a prediction. We illustrate the usability of rule-based methods by applying RuleFit, an algorithm for deriving decision rules for classification and regression problems, to a dataset on prediction of the course of depressive and anxiety disorders from Penninx et al. (2011). The RuleFit algorithm provided a model consisting of 2 simple decision rules, requiring evaluation of only 2 to 4 cues. Predictive accuracy of the 2-rule model was very similar to that of a logistic regression model incorporating 20 predictor variables, originally applied to the dataset. In addition, the 2-rule model required, on average, evaluation of only 3 cues. Therefore, the RuleFit algorithm appears to be a promising method for creating decision tools that are less time consuming and easier to apply in psychological practice, and with accuracy comparable to traditional actuarial methods. (c) 2015 APA, all rights reserved).

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

Forest fire autonomous decision system based on fuzzy logic

NASA Astrophysics Data System (ADS)

Lei, Z.; Lu, Jianhua

2010-11-01

The proposed system integrates GPS / pseudolite / IMU and thermal camera in order to autonomously process the graphs by identification, extraction, tracking of forest fire or hot spots. The airborne detection platform, the graph-based algorithms and the signal processing frame are analyzed detailed; especially the rules of the decision function are expressed in terms of fuzzy logic, which is an appropriate method to express imprecise knowledge. The membership function and weights of the rules are fixed through a supervised learning process. The perception system in this paper is based on a network of sensorial stations and central stations. The sensorial stations collect data including infrared and visual images and meteorological information. The central stations exchange data to perform distributed analysis. The experiment results show that working procedure of detection system is reasonable and can accurately output the detection alarm and the computation of infrared oscillations.

Extraction of decision rules via imprecise probabilities

NASA Astrophysics Data System (ADS)

Abellán, Joaquín; López, Griselda; Garach, Laura; Castellano, Javier G.

2017-05-01

Data analysis techniques can be applied to discover important relations among features. This is the main objective of the Information Root Node Variation (IRNV) technique, a new method to extract knowledge from data via decision trees. The decision trees used by the original method were built using classic split criteria. The performance of new split criteria based on imprecise probabilities and uncertainty measures, called credal split criteria, differs significantly from the performance obtained using the classic criteria. This paper extends the IRNV method using two credal split criteria: one based on a mathematical parametric model, and other one based on a non-parametric model. The performance of the method is analyzed using a case study of traffic accident data to identify patterns related to the severity of an accident. We found that a larger number of rules is generated, significantly supplementing the information obtained using the classic split criteria.

Evaluation of a rule base for decision making in general practice.

PubMed Central

Essex, B; Healy, M

1994-01-01

BACKGROUND. Decision making in general practice relies heavily on judgmental expertise. It should be possible to codify this expertise into rules and principles. AIM. A study was undertaken to evaluate the effectiveness, of rules from a rule base designed to improve students' and trainees' management decisions relating to patients seen in general practice. METHOD. The rule base was developed after studying decisions about and management of thousands of patients seen in one general practice over an eight year period. Vignettes were presented to 93 fourth year medical students and 179 general practitioner trainees. They recorded their perception and management of each case before and after being presented with a selection of relevant rules. Participants also commented on their level of agreement with each of the rules provided with the vignettes. A panel of five independent assessors then rated as good, acceptable or poor, the participants' perception and management of each case before and after seeing the rules. RESULTS. Exposure to a few selected rules of thumb improved the problem perception and management decisions of both undergraduates and trainees. The degree of improvement was not related to previous experience or to the stated level of agreement with the proposed rules. The assessors identified difficulties students and trainees experienced in changing their perceptions and management decisions when the rules suggested options they had not considered. CONCLUSION. The rules developed to improve decision making skills in general practice are effective when used with vignettes. The next phase is to transform the rule base into an expert system to train students and doctors to acquire decision making skills. It could also be used to provide decision support when confronted with difficult management decisions in general practice. PMID:8204334

Determination of the ground state of an Au-supported FePc film based on the interpretation of Fe K - and L -edge x-ray magnetic circular dichroism measurements

NASA Astrophysics Data System (ADS)

Natoli, Calogero R.; Krüger, Peter; Bartolomé, Juan; Bartolomé, Fernando

2018-04-01

We determine the magnetic ground state of the FePc molecule on Au-supported thin films based on the observed values of orbital anisotropy and spectroscopic x-ray magnetic circular dichroism (XMCD) measurements at the Fe K and L edges. Starting from ab initio molecular orbital multiplet calculations for the isolated molecule, we diagonalize the spin-orbit interaction in the subspace spanned by the three lowest spin triplet states of 3A2 g and 3Eg symmetry in the presence of a saturating magnetic field at a polar angle θ with respect to the normal to the plane of the film, plus an external perturbation representing the effect of the molecules in the stack on the FePc molecule under consideration. We find that the orbital moment of the ground state strongly depends on the magnetic field direction in agreement with the sum rule analysis of the L23-edge XMCD data. We calculate integrals over the XMCD spectra at the Fe K and L23 edges as used in the sum rules and explicitly show that they agree with the expectation values of the orbital moment and effective spin moment of the ground state. On the basis of this analysis, we can rule out alternative candidates proposed in the literature.

Pattern Analysis in Social Networks with Dynamic Connections

NASA Astrophysics Data System (ADS)

Wu, Yu; Zhang, Yu

In this paper, we explore how decentralized local interactions of autonomous agents in a network relate to collective behaviors. Most existing work in this area models social network in which agent relations are fixed; instead, we focus on dynamic social networks where agents can rationally adjust their neighborhoods based on their individual interests. We propose a new connection evaluation rule called the Highest Weighted Reward (HWR) rule, with which agents dynamically choose their neighbors in order to maximize their own utilities based on the rewards from previous interactions. Our experiments show that in the 2-action pure coordination game, our system will stabilize to a clustering state where all relationships in the network are rewarded with the optimal payoff. Our experiments also reveal additional interesting patterns in the network.

Toward rules relating zinc finger protein sequences and DNA binding site preferences.

PubMed

Desjarlais, J R; Berg, J M

1992-08-15

Zinc finger proteins of the Cys2-His2 type consist of tandem arrays of domains, where each domain appears to contact three adjacent base pairs of DNA through three key residues. We have designed and prepared a series of variants of the central zinc finger within the DNA binding domain of Sp1 by using information from an analysis of a large data base of zinc finger protein sequences. Through systematic variations at two of the three contact positions (underlined), relatively specific recognition of sequences of the form 5'-GGGGN(G or T)GGG-3' has been achieved. These results provide the basis for rules that may develop into a code that will allow the design of zinc finger proteins with preselected DNA site specificity.

Patients' feelings about ward nursing regimes and involvement in rule construction.

PubMed

Alexander, J

2006-10-01

This study compared two acute psychiatric ward nursing regimes, focusing on ward rules as a means of investigating the relationship between the flexibility/inflexibility of the regimes and patient outcomes. Previous studies identified an association between ward rules and patient aggression. A link between absconding and nurses' attitudes towards rule enforcement has also been explored. However, an in-depth exploration of ward rules from the perspective of nurses and patients had not been undertaken previously. The study aimed to discover the content of rules within acute psychiatric wards; to explore patients' responses to the rules; to evaluate the impact of rules and rule enforcement on nurse-patient relationships and on ward events; and to investigate the relationship between ward rules, ward atmosphere and ward design. The relevance of sociological theory emerged from the data analysis. During this process, the results were moved up to another conceptual level to represent the meaning of lived experience at the level of theory. For example, nurses' descriptions of their feelings in relation to rule enforcement were merged as role ambivalence. This concept was supported by examples from the transcripts. Other possible explanations for the data and the connections between them were checked by returning to each text unit in the cluster and ensuring that it fitted with the emergent theory. The design centred on a comparative interview study of 30 patients and 30 nurses within two acute psychiatric wards in different hospitals. Non-participant observations provided a context for the interview data. Measures of the Ward Atmosphere Scale, the Hospital-Hostel Practices Profile, ward incidents and levels of as required (PRN) medication were obtained. The analysis of the quantitative data was assisted by spss, and the qualitative analysis by QSR *NUDIST. Thematic and interpretative phenomenological methods were used in the analysis of the qualitative data. A series of 11 interrelated concepts emerged from an analysis of the data, and a synthesis of the main themes. This paper focuses on the results and recommendations that emerged from the quantitative and qualitative patient data. A further paper will focus on nurses' perceptions of the same topics.

A fuzzy classifier system for process control

NASA Technical Reports Server (NTRS)

Karr, C. L.; Phillips, J. C.

1994-01-01

A fuzzy classifier system that discovers rules for controlling a mathematical model of a pH titration system was developed by researchers at the U.S. Bureau of Mines (USBM). Fuzzy classifier systems successfully combine the strengths of learning classifier systems and fuzzy logic controllers. Learning classifier systems resemble familiar production rule-based systems, but they represent their IF-THEN rules by strings of characters rather than in the traditional linguistic terms. Fuzzy logic is a tool that allows for the incorporation of abstract concepts into rule based-systems, thereby allowing the rules to resemble the familiar 'rules-of-thumb' commonly used by humans when solving difficult process control and reasoning problems. Like learning classifier systems, fuzzy classifier systems employ a genetic algorithm to explore and sample new rules for manipulating the problem environment. Like fuzzy logic controllers, fuzzy classifier systems encapsulate knowledge in the form of production rules. The results presented in this paper demonstrate the ability of fuzzy classifier systems to generate a fuzzy logic-based process control system.

A neural network architecture for implementation of expert systems for real time monitoring

NASA Technical Reports Server (NTRS)

Ramamoorthy, P. A.

1991-01-01

Since neural networks have the advantages of massive parallelism and simple architecture, they are good tools for implementing real time expert systems. In a rule based expert system, the antecedents of rules are in the conjunctive or disjunctive form. We constructed a multilayer feedforward type network in which neurons represent AND or OR operations of rules. Further, we developed a translator which can automatically map a given rule base into the network. Also, we proposed a new and powerful yet flexible architecture that combines the advantages of both fuzzy expert systems and neural networks. This architecture uses the fuzzy logic concepts to separate input data domains into several smaller and overlapped regions. Rule-based expert systems for time critical applications using neural networks, the automated implementation of rule-based expert systems with neural nets, and fuzzy expert systems vs. neural nets are covered.

When more is less: Feedback effects in perceptual category learning ☆

PubMed Central

Maddox, W. Todd; Love, Bradley C.; Glass, Brian D.; Filoteo, J. Vincent

2008-01-01

Rule-based and information-integration category learning were compared under minimal and full feedback conditions. Rule-based category structures are those for which the optimal rule is verbalizable. Information-integration category structures are those for which the optimal rule is not verbalizable. With minimal feedback subjects are told whether their response was correct or incorrect, but are not informed of the correct category assignment. With full feedback subjects are informed of the correctness of their response and are also informed of the correct category assignment. An examination of the distinct neural circuits that subserve rule-based and information-integration category learning leads to the counterintuitive prediction that full feedback should facilitate rule-based learning but should also hinder information-integration learning. This prediction was supported in the experiment reported below. The implications of these results for theories of learning are discussed. PMID:18455155

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.

Stabilization and analytical tuning rule of double-loop control scheme for unstable dead-time process

NASA Astrophysics Data System (ADS)

Ugon, B.; Nandong, J.; Zang, Z.

2017-06-01

The presence of unstable dead-time systems in process plants often leads to a daunting challenge in the design of standard PID controllers, which are not only intended to provide close-loop stability but also to give good performance-robustness overall. In this paper, we conduct stability analysis on a double-loop control scheme based on the Routh-Hurwitz stability criteria. We propose to use this unstable double-loop control scheme which employs two P/PID controllers to control first-order or second-order unstable dead-time processes typically found in process industries. Based on the Routh-Hurwitz stability necessary and sufficient criteria, we establish several stability regions which enclose within them the P/PID parameter values that guarantee close-loop stability of the double-loop control scheme. A systematic tuning rule is developed for the purpose of obtaining the optimal P/PID parameter values within the established regions. The effectiveness of the proposed tuning rule is demonstrated using several numerical examples and the result are compared with some well-established tuning methods reported in the literature.

Quantitative knowledge acquisition for expert systems

NASA Technical Reports Server (NTRS)

Belkin, Brenda L.; Stengel, Robert F.

1991-01-01

A common problem in the design of expert systems is the definition of rules from data obtained in system operation or simulation. While it is relatively easy to collect data and to log the comments of human operators engaged in experiments, generalizing such information to a set of rules has not previously been a direct task. A statistical method is presented for generating rule bases from numerical data, motivated by an example based on aircraft navigation with multiple sensors. The specific objective is to design an expert system that selects a satisfactory suite of measurements from a dissimilar, redundant set, given an arbitrary navigation geometry and possible sensor failures. The systematic development is described of a Navigation Sensor Management (NSM) Expert System from Kalman Filter convariance data. The method invokes two statistical techniques: Analysis of Variance (ANOVA) and the ID3 Algorithm. The ANOVA technique indicates whether variations of problem parameters give statistically different covariance results, and the ID3 algorithms identifies the relationships between the problem parameters using probabilistic knowledge extracted from a simulation example set. Both are detailed.

On the eddy-resolving capability of high-order discontinuous Galerkin approaches to implicit LES / under-resolved DNS of Euler turbulence

NASA Astrophysics Data System (ADS)

Moura, R. C.; Mengaldo, G.; Peiró, J.; Sherwin, S. J.

2017-02-01

We present estimates of spectral resolution power for under-resolved turbulent Euler flows obtained with high-order discontinuous Galerkin (DG) methods. The '1% rule' based on linear dispersion-diffusion analysis introduced by Moura et al. (2015) [10] is here adapted for 3D energy spectra and validated through the inviscid Taylor-Green vortex problem. The 1% rule estimates the wavenumber beyond which numerical diffusion induces an artificial dissipation range on measured energy spectra. As the original rule relies on standard upwinding, different Riemann solvers are tested. Very good agreement is found for solvers which treat the different physical waves in a consistent manner. Relatively good agreement is still found for simpler solvers. The latter however displayed spurious features attributed to the inconsistent treatment of different physical waves. It is argued that, in the limit of vanishing viscosity, such features might have a significant impact on robustness and solution quality. The estimates proposed are regarded as useful guidelines for no-model DG-based simulations of free turbulence at very high Reynolds numbers.

Improving the Interpretability of Classification Rules Discovered by an Ant Colony Algorithm: Extended Results.

PubMed

Otero, Fernando E B; Freitas, Alex A

2016-01-01

Most ant colony optimization (ACO) algorithms for inducing classification rules use a ACO-based procedure to create a rule in a one-at-a-time fashion. An improved search strategy has been proposed in the cAnt-Miner[Formula: see text] algorithm, where an ACO-based procedure is used to create a complete list of rules (ordered rules), i.e., the ACO search is guided by the quality of a list of rules instead of an individual rule. In this paper we propose an extension of the cAnt-Miner[Formula: see text] algorithm to discover a set of rules (unordered rules). The main motivations for this work are to improve the interpretation of individual rules by discovering a set of rules and to evaluate the impact on the predictive accuracy of the algorithm. We also propose a new measure to evaluate the interpretability of the discovered rules to mitigate the fact that the commonly used model size measure ignores how the rules are used to make a class prediction. Comparisons with state-of-the-art rule induction algorithms, support vector machines, and the cAnt-Miner[Formula: see text] producing ordered rules are also presented.

Maintenance Audit through Value Analysis Technique: A Case Study

NASA Astrophysics Data System (ADS)

Carnero, M. C.; Delgado, S.

2008-11-01

The increase in competitiveness, technological changes and the increase in the requirements of quality and service have forced a change in the design and application of maintenance, as well as the way in which it is considered within the managerial strategy. There are numerous maintenance activities that must be developed in a service company. As a result the maintenance functions as a whole have to be outsourced. Nevertheless, delegating this subject to specialized personnel does not exempt the company from responsibilities, but rather leads to the need for control of each maintenance activity. In order to achieve this control and to evaluate the efficiency and effectiveness of the company it is essential to carry out an audit that diagnoses the problems that could develop. In this paper a maintenance audit applied to a service company is developed. The methodology applied is based on the expert systems. The expert system by means of rules uses the weighting technique SMART and value analysis to obtain the weighting between the decision functions and between the alternatives. The expert system applies numerous rules and relations between different variables associated with the specific maintenance functions, to obtain the maintenance state by sections and the general maintenance state of the enterprise. The contributions of this paper are related to the development of a maintenance audit in a service enterprise, in which maintenance is not generally considered a strategic subject and to the integration of decision-making tools such as the weighting technique SMART with value analysis techniques, typical in the design of new products, in the area of the rule-based expert systems.

An XML-Based Manipulation and Query Language for Rule-Based Information

NASA Astrophysics Data System (ADS)

Mansour, Essam; Höpfner, Hagen

Rules are utilized to assist in the monitoring process that is required in activities, such as disease management and customer relationship management. These rules are specified according to the application best practices. Most of research efforts emphasize on the specification and execution of these rules. Few research efforts focus on managing these rules as one object that has a management life-cycle. This paper presents our manipulation and query language that is developed to facilitate the maintenance of this object during its life-cycle and to query the information contained in this object. This language is based on an XML-based model. Furthermore, we evaluate the model and language using a prototype system applied to a clinical case study.

Integrated layout based Monte-Carlo simulation for design arc optimization

NASA Astrophysics Data System (ADS)

Shao, Dongbing; Clevenger, Larry; Zhuang, Lei; Liebmann, Lars; Wong, Robert; Culp, James

2016-03-01

Design rules are created considering a wafer fail mechanism with the relevant design levels under various design cases, and the values are set to cover the worst scenario. Because of the simplification and generalization, design rule hinders, rather than helps, dense device scaling. As an example, SRAM designs always need extensive ground rule waivers. Furthermore, dense design also often involves "design arc", a collection of design rules, the sum of which equals critical pitch defined by technology. In design arc, a single rule change can lead to chain reaction of other rule violations. In this talk we present a methodology using Layout Based Monte-Carlo Simulation (LBMCS) with integrated multiple ground rule checks. We apply this methodology on SRAM word line contact, and the result is a layout that has balanced wafer fail risks based on Process Assumptions (PAs). This work was performed at the IBM Microelectronics Div, Semiconductor Research and Development Center, Hopewell Junction, NY 12533

Analysis of North Atlantic tropical cyclone intensify change using data mining

NASA Astrophysics Data System (ADS)

Tang, Jiang

Tropical cyclones (TC), especially when their intensity reaches hurricane scale, can become a costly natural hazard. Accurate prediction of tropical cyclone intensity is very difficult because of inadequate observations on TC structures, poor understanding of physical processes, coarse model resolution and inaccurate initial conditions, etc. This study aims to tackle two factors that account for the underperformance of current TC intensity forecasts: (1) inadequate observations of TC structures, and (2) deficient understanding of the underlying physical processes governing TC intensification. To tackle the problem of inadequate observations of TC structures, efforts have been made to extract vertical and horizontal structural parameters of latent heat release from Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data products. A case study of Hurricane Isabel (2003) was conducted first to explore the feasibility of using the 3D TC structure information in predicting TC intensification. Afterwards, several structural parameters were extracted from 53 TRMM PR 2A25 observations on 25 North Atlantic TCs during the period of 1998 to 2003. A new generation of multi-correlation data mining algorithm (Apriori and its variations) was applied to find roles of the latent heat release structure in TC intensification. The results showed that the buildup of TC energy is indicated by the height of the convective tower, and the relative low latent heat release at the core area and around the outer band. Adverse conditions which prevent TC intensification include the following: (1) TC entering a higher latitude area where the underlying sea is relative cold, (2) TC moving too fast to absorb the thermal energy from the underlying sea, or (3) strong energy loss at the outer band. When adverse conditions and amicable conditions reached equilibrium status, tropical cyclone intensity would remain stable. The dataset from Statistical Hurricane Intensity Prediction Scheme (SHIPS) covering the period of 1982-2003 and the Apriori-based association rule mining algorithm were used to study the associations of underlying geophysical characteristics with the intensity change of tropical cyclones. The data have been stratified into 6 TC categories from tropical depression to category 4 hurricanes based on their strength. The result showed that the persistence of intensity change in the past and the strength of vertical shear in the environment are the most prevalent factors for all of the 6 TC categories. Hyper-edge searching had found 3 sets of parameters which showed strong intramural binds. Most of the parameters used in SHIPS model have a consistent "I-W" relation over different TC categories, indicating a consistent function of those parameters in TC development. However, the "I-W" relations of the relative momentum flux and the meridional motion change from tropical storm stage to hurricane stage, indicating a change in the role of those two parameters in TC development. Because rapid intensification (RI) is a major source of errors when predicting hurricane intensity, the association rule mining algorithm was performed on RI versus non-RI tropical cyclone cases using the same SHIPS dataset. The results had been compared with those from the traditional statistical analysis conducted by Kaplan and DeMaria (2003). The rapid intensification rule with 5 RI conditions proposed by the traditional statistical analysis was found by the association rule mining in this study as well. However, further analysis showed that the 5 RI conditions can be replaced by another association rule using fewer conditions but with a higher RI probability (RIP). This means that the rule with all 5 constraints found by Kaplan and DeMaria is not optimal, and the association rule mining technique can find a rule with fewer constraints yet fits more RI cases. The further analysis with the highest RIPs over different numbers of conditions has demonstrated that the interactions among multiple factors are responsible for the RI process of TCs. However, the influence of factors saturates at certain numbers. This study has shown successful data mining examples in studying tropical cyclone intensification using association rules. The higher RI probability with fewer conditions found by association rule technique is significant. This work demonstrated that data mining techniques can be used as an efficient exploration method to generate hypotheses, and that statistical analysis should be performed to confirm the hypotheses, as is generally expected for data mining applications.

77 FR 61330 - Policies Regarding Mobile Spectrum Holdings

Federal Register 2010, 2011, 2012, 2013, 2014

2012-10-09

... the transaction and is based on the size of the post-transaction Herfindahl-Hirschman Index (HHI) \\26... further case-by-case market analysis those markets in which, post-transaction, the HHI would be greater... De Facto Transfer Leasing Arrangements and Petition for Declaratory Ruling that the Transaction is...

Some Surprising Errors in Numerical Differentiation

ERIC Educational Resources Information Center

Gordon, Sheldon P.

2012-01-01

Data analysis methods, both numerical and visual, are used to discover a variety of surprising patterns in the errors associated with successive approximations to the derivatives of sinusoidal and exponential functions based on the Newton difference-quotient. L'Hopital's rule and Taylor polynomial approximations are then used to explain why these…

Chemical Safety Information, Site Security and Fuels Regulatory Relief Act: Public Distribution of Off-Site Consequence Analysis Information Fact Sheet

EPA Pesticide Factsheets

Based on assessments of increased risk of terrorist/criminal activity, EPA and DOJ have issued a rule that allows public access to OCA information in ways that are designed to minimize likelihood of chemical accidents and public harm.

Function-Altering Effects of Rule Phrasing in the Modulation of Instructional Control

ERIC Educational Resources Information Center

Henley, Amy J.; Hirst, Jason M.; DiGennaro Reed, Florence D.; Becirevic, Amel; Reed, Derek D.

2017-01-01

This study evaluated the effects of four instructional variants on instruction following under changing reinforcement schedules using an operant task based on Hackenberg and Joker's "Journal of the Experimental Analysis of Behavior," 62, 367-383 (1994) experimental preparation. Sixteen college-aged adults served as participants and were…

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…

77 FR 75085 - Proposed Significant New Use Rule on Ethoxylated, Propoxylated Diamine Diaryl Substituted...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-12-19

... applications. Based on ecological structure activity relationship (EcoSAR) analysis of test data on analogous... significant regulatory action under Executive Order 12866. I. National Technology Transfer and Advancement Act Since this action does not involve any technical standards, section 12(d) of the National Technology...

Final Revisions Rule Significant Contribution Assessment TSD

EPA Pesticide Factsheets

This Technical Support Document (TSD) presents quantitative assessments of the relationship between final revisions to the Transport Rule and the original analysis conducted for the final Transport Rule.

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.

Rule-Based and Information-Integration Category Learning in Normal Aging

ERIC Educational Resources Information Center

Maddox, W. Todd; Pacheco, Jennifer; Reeves, Maia; Zhu, Bo; Schnyer, David M.

2010-01-01

The basal ganglia and prefrontal cortex play critical roles in category learning. Both regions evidence age-related structural and functional declines. The current study examined rule-based and information-integration category learning in a group of older and younger adults. Rule-based learning is thought to involve explicit, frontally mediated…

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.

Statistical Learning of Origin-Specific Statically Optimal Individualized Treatment Rules

PubMed Central

van der Laan, Mark J.; Petersen, Maya L.

2008-01-01

Consider a longitudinal observational or controlled study in which one collects chronological data over time on a random sample of subjects. The time-dependent process one observes on each subject contains time-dependent covariates, time-dependent treatment actions, and an outcome process or single final outcome of interest. A statically optimal individualized treatment rule (as introduced in van der Laan et. al. (2005), Petersen et. al. (2007)) is a treatment rule which at any point in time conditions on a user-supplied subset of the past, computes the future static treatment regimen that maximizes a (conditional) mean future outcome of interest, and applies the first treatment action of the latter regimen. In particular, Petersen et. al. (2007) clarified that, in order to be statically optimal, an individualized treatment rule should not depend on the observed treatment mechanism. Petersen et. al. (2007) further developed estimators of statically optimal individualized treatment rules based on a past capturing all confounding of past treatment history on outcome. In practice, however, one typically wishes to find individualized treatment rules responding to a user-supplied subset of the complete observed history, which may not be sufficient to capture all confounding. The current article provides an important advance on Petersen et. al. (2007) by developing locally efficient double robust estimators of statically optimal individualized treatment rules responding to such a user-supplied subset of the past. However, failure to capture all confounding comes at a price; the static optimality of the resulting rules becomes origin-specific. We explain origin-specific static optimality, and discuss the practical importance of the proposed methodology. We further present the results of a data analysis in which we estimate a statically optimal rule for switching antiretroviral therapy among patients infected with resistant HIV virus. PMID:19122792

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

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.

Verification and Validation of KBS with Neural Network Components

NASA Technical Reports Server (NTRS)

Wen, Wu; Callahan, John

1996-01-01

Artificial Neural Network (ANN) play an important role in developing robust Knowledge Based Systems (KBS). The ANN based components used in these systems learn to give appropriate predictions through training with correct input-output data patterns. Unlike traditional KBS that depends on a rule database and a production engine, the ANN based system mimics the decisions of an expert without specifically formulating the if-than type of rules. In fact, the ANNs demonstrate their superiority when such if-then type of rules are hard to generate by human expert. Verification of traditional knowledge based system is based on the proof of consistency and completeness of the rule knowledge base and correctness of the production engine.These techniques, however, can not be directly applied to ANN based components.In this position paper, we propose a verification and validation procedure for KBS with ANN based components. The essence of the procedure is to obtain an accurate system specification through incremental modification of the specifications using an ANN rule extraction algorithm.

The rules of the Rue Morgue

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

Vanzi, M.

1995-12-31

This paper, it is evident, is mostly a joke, based on the fascinating (but not original) consideration of any failure analysis as a detective story. The Poe`s tale is a perfect instrument (but surely not the only possible one) for playing the game. If any practical application of ``The Rules of the Rue Morgue`` may be expected, it is on the possibility of defining what leaves us unsatisfied when a Failure Analyst`s result sounds out of tune. The reported Violations to the Dupin Postulate summarize the objections that the author would like to repeat for his own analyses, and formore » those cases in which he is required to review the work of other. On the constructive side, the proposed Rules, it has been repeatedly said, are common sense indications, and are surely not exhaustive, on a practical ground. Skill, patience, luck and memory are also required, but, unfortunately, not always and not together available. It should be of the greatest aid for the Failure Analyst community, in any case, that each public report could point out how it obeyed to a widely accepted set of failure analysis rules. Maybe -- why not? -- the Rules of the Rue Morgue. As a last consideration, for concluding the joke, the author invites his readers to open the original Poe`s tale at the very beginning of the story, when Monsieur Dupin is introduced. Thinking of the Failure Analyst as a member of the excellent family of the Scientists, many of us will sigh and smile.« less

Rule modification in junior sport: Does it create differences in player movement?

PubMed

Gastin, Paul B; Allan, Matthew D; Bellesini, Kylie; Spittle, Michael

2017-10-01

To determine the effects of rule modification on player movement during matchplay in junior Australian football (AF). Quasi-experimental study design. Time-motion analysis was used to record variables pertaining to player movement including total distance covered, high-speed running (HSR) distance (>14.4km/h) and HSR efforts. GPS data obtained from 145 players (7-12 years) were analysed across four junior AF leagues and three age group combinations (U8/U9, U9/U10 and U11/U12). The four leagues were collapsed into two separate conditions (compliant and non-compliant) based on their adherence to a modified junior sport policy. To control for the influence of age and physical maturity, a secondary analysis was performed on an adequately matched U8 subset of data (n=48). Significant differences (p<0.05) were found between compliant and non-compliant leagues for age and all player movement variables, with participants in the compliant leagues achieving less player movement. Significant differences were also evident between conditions in the U8 subset in total and relative distance and HSR efforts, with moderate to very large differences (29-60%) observed for all player movement variables. Rule modifications limits the extent and intensity of player movement in junior AF compared to standard playing conditions. The unintended effect of reduced physical activity with rule modifications should be compensated for with additional activities wherever possible. League administrators and policy makers should consider the objectives of rule modifications and weigh up both positive and negative outcomes. Copyright © 2017. Published by Elsevier Ltd.

Rocket Design for the Future

NASA Technical Reports Server (NTRS)

Follett, William W.; Rajagopal, Raj

2001-01-01

The focus of the AA MDO team is to reduce product development cost through the capture and automation of best design and analysis practices and through increasing the availability of low-cost, high-fidelity analysis. Implementation of robust designs reduces costs associated with the Test-Fall-Fix cycle. RD is currently focusing on several technologies to improve the design process, including optimization and robust design, expert and rule-based systems, and collaborative technologies.

Centrifugal compressor surge detecting method based on wavelet analysis of unsteady pressure fluctuations in typical stages

NASA Astrophysics Data System (ADS)

Izmaylov, R.; Lebedev, A.

2015-08-01

Centrifugal compressors are complex energy equipment. Automotive control and protection system should meet the requirements: of operation reliability and durability. In turbocompressors there are at least two dangerous areas: surge and rotating stall. Antisurge protecting systems usually use parametric or feature methods. As a rule industrial system are parametric. The main disadvantages of anti-surge parametric systems are difficulties in mass flow measurements in natural gas pipeline compressor. The principal idea of feature method is based on the experimental fact: as a rule just before the onset of surge rotating or precursor stall established in compressor. In this case the problem consists in detecting of unsteady pressure or velocity fluctuations characteristic signals. Wavelet analysis is the best method for detecting onset of rotating stall in spite of high level of spurious signals (rotating wakes, turbulence, etc.). This method is compatible with state of the art DSP systems of industrial control. Examples of wavelet analysis application for detecting onset of rotating stall in typical stages centrifugal compressor are presented. Experimental investigations include unsteady pressure measurement and sophisticated data acquisition system. Wavelet transforms used biorthogonal wavelets in Mathlab systems.

Automated rule-base creation via CLIPS-Induce

NASA Technical Reports Server (NTRS)

Murphy, Patrick M.

1994-01-01

Many CLIPS rule-bases contain one or more rule groups that perform classification. In this paper we describe CLIPS-Induce, an automated system for the creation of a CLIPS classification rule-base from a set of test cases. CLIPS-Induce consists of two components, a decision tree induction component and a CLIPS production extraction component. ID3, a popular decision tree induction algorithm, is used to induce a decision tree from the test cases. CLIPS production extraction is accomplished through a top-down traversal of the decision tree. Nodes of the tree are used to construct query rules, and branches of the tree are used to construct classification rules. The learned CLIPS productions may easily be incorporated into a large CLIPS system that perform tasks such as accessing a database or displaying information.

Learning temporal rules to forecast instability in continuously monitored patients.

PubMed

Guillame-Bert, Mathieu; 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. © The Author 2016. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A two-stage stochastic rule-based model to determine pre-assembly buffer content

NASA Astrophysics Data System (ADS)

Gunay, Elif Elcin; Kula, Ufuk

2018-01-01

This study considers instant decision-making needs of the automobile manufactures for resequencing vehicles before final assembly (FA). We propose a rule-based two-stage stochastic model to determine the number of spare vehicles that should be kept in the pre-assembly buffer to restore the altered sequence due to paint defects and upstream department constraints. First stage of the model decides the spare vehicle quantities, where the second stage model recovers the scrambled sequence respect to pre-defined rules. The problem is solved by sample average approximation (SAA) algorithm. We conduct a numerical study to compare the solutions of heuristic model with optimal ones and provide following insights: (i) as the mismatch between paint entrance and scheduled sequence decreases, the rule-based heuristic model recovers the scrambled sequence as good as the optimal resequencing model, (ii) the rule-based model is more sensitive to the mismatch between the paint entrance and scheduled sequences for recovering the scrambled sequence, (iii) as the defect rate increases, the difference in recovery effectiveness between rule-based heuristic and optimal solutions increases, (iv) as buffer capacity increases, the recovery effectiveness of the optimization model outperforms heuristic model, (v) as expected the rule-based model holds more inventory than the optimization model.

[Analysis on traditional Chinese medicine prescriptions treating cancer-related anorexia syndrome based on grey system theory combined with multivariate analysis method and discovery of new prescriptions].

PubMed

Chen, Song-Lin; Chen, Cong; Zhu, Hui; Li, Jing; Pang, Yan

2016-01-01

Cancer-related anorexia syndrome (CACS) is one of the main causes for death at present as well as a syndrome seriously harming patients' quality of life, treatment effect and survival time. In current clinical researches, there are fewer reports about empirical traditional Chinese medicine(TCM) prescriptions and patent prescriptions treating CACS, and prescription rules are rarely analyzed in a systematic manner. As the hidden rules are not excavated, it is hard to have an innovative discovery and knowledge of clinical medication. In this paper, the grey screening method combined with the multivariate statistical method was used to build the ″CACS prescriptions database″. Based on the database, totally 359 prescriptions were selected, the frequency of herbs in prescription was determined, and commonly combined drugs were evolved into 4 new prescriptions for different syndromes. Prescriptions of TCM in treatment of CACS gave priority to benefiting qi for strengthening spleen, also laid emphasis on replenishing kidney essence, dispersing stagnated liver-qi and dispersing lung-qi. Moreover, interdependence and mutual promotion of yin and yang should be taken into account to reflect TCM's holism and theory for treatment based on syndrome differentiation. The grey screening method, as a valuable traditional Chinese medicine research-supporting method, can be used to subjectively and objectively analyze prescription rules; and the new prescriptions can provide reference for the clinical use of TCM for treating CACS and the drug development. Copyright© by the Chinese Pharmaceutical Association.

Rule-based navigation control design for autonomous flight

NASA Astrophysics Data System (ADS)

Contreras, Hugo; Bassi, Danilo

2008-04-01

This article depicts a navigation control system design that is based on a set of rules in order to follow a desired trajectory. The full control of the aircraft considered here comprises: a low level stability control loop, based on classic PID controller and the higher level navigation whose main job is to exercise lateral control (course) and altitude control, trying to follow a desired trajectory. The rules and PID gains were adjusted systematically according to the result of flight simulation. In spite of its simplicity, the rule-based navigation control proved to be robust, even with big perturbation, like crossing winds.

Color vision: "OH-site" rule for seeing red and green.

PubMed

Sekharan, Sivakumar; Katayama, Kota; Kandori, Hideki; Morokuma, Keiji

2012-06-27

Eyes gather information, and color forms an extremely important component of the information, more so in the case of animals to forage and navigate within their immediate environment. By using the ONIOM (QM/MM) (ONIOM = our own N-layer integrated molecular orbital plus molecular mechanics) method, we report a comprehensive theoretical analysis of the structure and molecular mechanism of spectral tuning of monkey red- and green-sensitive visual pigments. We show that interaction of retinal with three hydroxyl-bearing amino acids near the β-ionone ring part of the retinal in opsin, A164S, F261Y, and A269T, increases the electron delocalization, decreases the bond length alternation, and leads to variation in the wavelength of maximal absorbance of the retinal in the red- and green-sensitive visual pigments. On the basis of the analysis, we propose the "OH-site" rule for seeing red and green. This rule is also shown to account for the spectral shifts obtained from hydroxyl-bearing amino acids near the Schiff base in different visual pigments: at site 292 (A292S, A292Y, and A292T) in bovine and at site 111 (Y111) in squid opsins. Therefore, the OH-site rule is shown to be site-specific and not pigment-specific and thus can be used for tracking spectral shifts in any visual pigment.

Excusing exclusion: Accounting for rule-breaking and sanctions in a Swedish methadone clinic.

PubMed

Petersson, Frida J M

2013-11-01

Methadone maintenance treatment has been subjected to much debate and controversy in Sweden during the last decades. Thresholds for getting access are high and control policies strict within the programmes. This article analyses how professionals working in a Swedish methadone clinic discuss and decide on appropriate responses to clients' rule-breaking behaviour. The research data consist of field notes from observations of three interprofessional team meetings where different clients' illicit drug use is discussed. A micro-sociological approach and accounts analysis was applied to the data. During their decision-oriented talk at the meetings, the professionals account for: (1) sanctions, (2) nonsanction, (3) mildness. In accounting for (2) and (3), they also account for clients' rule-breaking behaviour. Analysis shows how these ways of accounting are concerned with locating blame and responsibility for the act in question, as well as with constructing excuses and justifications for the clients and for the professionals themselves. In general, these results demonstrate that maintenance treatment in everyday professional decision-making, far from being a neutral evidence-based practice, involves a substantial amount of professional discretion and moral judgements. Sanctions are chosen according to the way in which a deviance from the rules is explained and, in doing so, a certain behaviour is deemed to be serious, dangerous and unacceptable - or excusable. Copyright © 2013 Elsevier B.V. All rights reserved.

Determination of the rockfall source in an urban settlement area by using a rule-based fuzzy evaluation

NASA Astrophysics Data System (ADS)

Aksoy, H.; Ercanoglu, M.

2006-10-01

The evaluation of the rockfall initiation mechanism and the simulation of the runout behavior is an important issue in the prevention and remedial measures for potential rockfall hazards in highway protection, in forest preservation, and especially in urban settlement areas. In most of the studies in the literature, the extent of the rockfall hazard was determined by various techniques basing on the selection of a rockfall source, generally defined as zones of rock bodies having slope angles higher than a certain value, proposed by general practice. In the present study, it was aimed to carry out a rule-based fuzzy analysis on the discontinuity data of andesites in the city of Ankara, Turkey, in order to bring a different and rather systematic approach to determine the source areas for rockfall hazard in an urban settlement, based on the discontinuity and natural slope features. First, to obtain rock source areas (RSAs), data obtained from the field studies were combined with a rule-based fuzzy evaluation, incorporating the altitude difference, the number of discontinuities, the number of wedges and the number of potential slides as the parameters of the fuzzy sets. After processing the outputs of the rule-based fuzzy system and producing the linguistic definitions, it could be possible to obtain potential RSAs. According to the RSA maps, 1.7% of the study area was found to have "high RSA", and 5.8% of the study area was assigned as "medium RSA". Then, potential rockfall hazard map was prepared. At the final stage, based upon the high and medium RSAs, 3.6% of the study area showed "high rockfall potential", while areal distribution of "medium rockfall potential" was found as 7.9%. Both RSA and potential rockfall hazard map were in accordance with the observations performed in the field.

76 FR 1145 - Alabama Power Company; Notice of Application for Amendment of License and Soliciting Comments...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-01-07

... drought-based temporary variance of the Martin Project rule curve and minimum flow releases at the Yates... requesting a drought- based temporary variance to the Martin Project rule curve. The rule curve variance...

Predicting Mycobacterium tuberculosis Complex Clades Using Knowledge-Based Bayesian Networks

PubMed Central

Bennett, Kristin P.

2014-01-01

We develop a novel approach for incorporating expert rules into Bayesian networks for classification of Mycobacterium tuberculosis complex (MTBC) clades. The proposed knowledge-based Bayesian network (KBBN) treats sets of expert rules as prior distributions on the classes. Unlike prior knowledge-based support vector machine approaches which require rules expressed as polyhedral sets, KBBN directly incorporates the rules without any modification. KBBN uses data to refine rule-based classifiers when the rule set is incomplete or ambiguous. We develop a predictive KBBN model for 69 MTBC clades found in the SITVIT international collection. We validate the approach using two testbeds that model knowledge of the MTBC obtained from two different experts and large DNA fingerprint databases to predict MTBC genetic clades and sublineages. These models represent strains of MTBC using high-throughput biomarkers called spacer oligonucleotide types (spoligotypes), since these are routinely gathered from MTBC isolates of tuberculosis (TB) patients. Results show that incorporating rules into problems can drastically increase classification accuracy if data alone are insufficient. The SITVIT KBBN is publicly available for use on the World Wide Web. PMID:24864238

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.

Knowledge-based control of an adaptive interface

NASA Technical Reports Server (NTRS)

Lachman, Roy

1989-01-01

The analysis, development strategy, and preliminary design for an intelligent, adaptive interface is reported. The design philosophy couples knowledge-based system technology with standard human factors approaches to interface development for computer workstations. An expert system has been designed to drive the interface for application software. The intelligent interface will be linked to application packages, one at a time, that are planned for multiple-application workstations aboard Space Station Freedom. Current requirements call for most Space Station activities to be conducted at the workstation consoles. One set of activities will consist of standard data management services (DMS). DMS software includes text processing, spreadsheets, data base management, etc. Text processing was selected for the first intelligent interface prototype because text-processing software can be developed initially as fully functional but limited with a small set of commands. The program's complexity then can be increased incrementally. The intelligent interface includes the operator's behavior and three types of instructions to the underlying application software are included in the rule base. A conventional expert-system inference engine searches the data base for antecedents to rules and sends the consequents of fired rules as commands to the underlying software. Plans for putting the expert system on top of a second application, a database management system, will be carried out following behavioral research on the first application. The intelligent interface design is suitable for use with ground-based workstations now common in government, industrial, and educational organizations.

European Marketing Authorizations Granted Based on a Single Pivotal Clinical Trial: The Rule or the Exception?

PubMed

Morant, Anne Vinther; Vestergaard, Henrik Tang

2018-07-01

A minimum of two positive, adequate, and well-controlled clinical trials has historically been the gold standard for providing substantial evidence to support regulatory approval of a new medicine. Nevertheless, the present analysis of European Marketing Authorizations granted between 2012 and 2016 showed that 45% of new active substances were approved based on a single pivotal clinical trial. For therapeutic areas such as oncology and cardiovascular diseases, approvals based on a single pivotal trial are the rule rather than the exception, whereas new medicines within the nervous system area were generally supported by two or more pivotal trials. While overall similar trends have been observed in the US, the recent US Food and Drug Administration approvals of nervous system medicines based on a single pivotal trial suggest that a case-by-case scientific evaluation of the totality of evidence is increasingly applied to facilitate faster access of new medicines to patients suffering from serious diseases. © 2017 American Society for Clinical Pharmacology and Therapeutics.

Evaluation of EMERGE, a Medical Decision Making Aid for Analysis of Chest Pain

PubMed Central

Hudson, Donna L.; Cohen, Moses E.; Deedwania, Prakash C.; Watson, Patricia E.

1983-01-01

EMERGE, a rule-based medical decision making aid for analysis of chest pain in the emergency room, was evaluated using retrospective patient data. The analysis consisted of two phases. In the initial phase, patient cases were run in order to make minor modifications and adjustments in the criteria used for determination of admission. In the second phase, patient cases were analyzed to determine the effectiveness of the EMERGE system in arriving at the proper conclusion.

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.

[Response to US review rules on patent subject matter of traditional Chinese medicine compositions].

PubMed

Liu, Pan; Cao, Ya-di; Gong, Rui-Juan; Liu, Wei

2018-02-01

The United States Patent and Trademark Office(USPTO) issued Interim Guidance on Patent Subject Matter Eligibility on December 16， 2014， bringing certain effects to the review rules on patent application of Chinese medicine compositions. Based on the Interim Guidance， cases analysis was used in this paper to analyze the patent subject matter issues of traditional Chinese medicine compositions in the United States. The researches have shown that the application documents should be properly written in the United States when the patent for Chinese medicine compositions is applied， which can improve the probability of authorization. Copyright© by the Chinese Pharmaceutical Association.

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.

A network model of behavioural performance in a rule learning task.

PubMed

Hasselmo, Michael E; Stern, Chantal E

2018-04-19

Humans demonstrate differences in performance on cognitive rule learning tasks which could involve differences in properties of neural circuits. An example model is presented to show how gating of the spread of neural activity could underlie rule learning and the generalization of rules to previously unseen stimuli. This model uses the activity of gating units to regulate the pattern of connectivity between neurons responding to sensory input and subsequent gating units or output units. This model allows analysis of network parameters that could contribute to differences in cognitive rule learning. These network parameters include differences in the parameters of synaptic modification and presynaptic inhibition of synaptic transmission that could be regulated by neuromodulatory influences on neural circuits. Neuromodulatory receptors play an important role in cognitive function, as demonstrated by the fact that drugs that block cholinergic muscarinic receptors can cause cognitive impairments. In discussions of the links between neuromodulatory systems and biologically based traits, the issue of mechanisms through which these linkages are realized is often missing. This model demonstrates potential roles of neural circuit parameters regulated by acetylcholine in learning context-dependent rules, and demonstrates the potential contribution of variation in neural circuit properties and neuromodulatory function to individual differences in cognitive function.This article is part of the theme issue 'Diverse perspectives on diversity: multi-disciplinary approaches to taxonomies of individual differences'. © 2018 The Author(s).

[The development of a computer model in the quantitative assessment of thallium-201 myocardial scintigraphy].

PubMed

Raineri, M; Traina, M; Rotolo, A; Candela, B; Lombardo, R M; Raineri, A A

1993-05-01

Thallium-201 scintigraphy is a widely used noninvasive procedure for the detection and prognostic assessment of patients with suspected or proven coronary artery disease. Thallium uptake can be evaluated by a visual analysis or by a quantitative interpretation. Quantitative scintigraphy enhances disease detection in individual coronary arteries, provides a more precise estimate of the amount of ischemic myocardium, distinguishing scar from hypoperfused tissue. Due to the great deal of data, analysis, interpretation and comparison of thallium uptake can be very complex. We designed a computer-based system for the interpretation of quantitative thallium-201 scintigraphy data uptake. We used a database (DataEase 4.2-DataEase Italia). Our software has the following functions: data storage; calculation; conversion of numerical data into different definitions classifying myocardial perfusion; uptake data comparison; automatic conclusion; comparison of different scintigrams for the same patient. Our software is made up by 4 sections: numeric analysis, descriptive analysis, automatic conclusion, clinical remarks. We introduced in the computer system appropriate information, "logical paths", that use the "IF ... THEN" rules. The software executes these rules in order to analyze the myocardial regions in the 3 phases of scintigraphic analysis (stress, redistribution, re-injection), in the 3 projections (LAO 45 degrees, LAT,ANT), considering our uptake cutoff, obtaining, finally, the automatic conclusions. For these reasons, our computer-based system could be considered a real "expert system".

Redundancy checking algorithms based on parallel novel extension rule

NASA Astrophysics Data System (ADS)

Liu, Lei; Yang, Yang; Li, Guangli; Wang, Qi; Lü, Shuai

2017-05-01

Redundancy checking (RC) is a key knowledge reduction technology. Extension rule (ER) is a new reasoning method, first presented in 2003 and well received by experts at home and abroad. Novel extension rule (NER) is an improved ER-based reasoning method, presented in 2009. In this paper, we first analyse the characteristics of the extension rule, and then present a simple algorithm for redundancy checking based on extension rule (RCER). In addition, we introduce MIMF, a type of heuristic strategy. Using the aforementioned rule and strategy, we design and implement RCHER algorithm, which relies on MIMF. Next we design and implement an RCNER (redundancy checking based on NER) algorithm based on NER. Parallel computing greatly accelerates the NER algorithm, which has weak dependence among tasks when executed. Considering this, we present PNER (parallel NER) and apply it to redundancy checking and necessity checking. Furthermore, we design and implement the RCPNER (redundancy checking based on PNER) and NCPPNER (necessary clause partition based on PNER) algorithms as well. The experimental results show that MIMF significantly influences the acceleration of algorithm RCER in formulae on a large scale and high redundancy. Comparing PNER with NER and RCPNER with RCNER, the average speedup can reach up to the number of task decompositions when executed. Comparing NCPNER with the RCNER-based algorithm on separating redundant formulae, speedup increases steadily as the scale of the formulae is incrementing. Finally, we describe the challenges that the extension rule will be faced with and suggest possible solutions.

Towards application of rule learning to the meta-analysis of clinical data: an example of the metabolic syndrome.

PubMed

Wojtusiak, Janusz; Michalski, Ryszard S; Simanivanh, Thipkesone; Baranova, Ancha V

2009-12-01

Systematic reviews and meta-analysis of published clinical datasets are important part of medical research. By combining results of multiple studies, meta-analysis is able to increase confidence in its conclusions, validate particular study results, and sometimes lead to new findings. Extensive theory has been built on how to aggregate results from multiple studies and arrive to the statistically valid conclusions. Surprisingly, very little has been done to adopt advanced machine learning methods to support meta-analysis. In this paper we describe a novel machine learning methodology that is capable of inducing accurate and easy to understand attributional rules from aggregated data. Thus, the methodology can be used to support traditional meta-analysis in systematic reviews. Most machine learning applications give primary attention to predictive accuracy of the learned knowledge, and lesser attention to its understandability. Here we employed attributional rules, the special form of rules that are relatively easy to interpret for medical experts who are not necessarily trained in statistics and meta-analysis. The methodology has been implemented and initially tested on a set of publicly available clinical data describing patients with metabolic syndrome (MS). The objective of this application was to determine rules describing combinations of clinical parameters used for metabolic syndrome diagnosis, and to develop rules for predicting whether particular patients are likely to develop secondary complications of MS. The aggregated clinical data was retrieved from 20 separate hospital cohorts that included 12 groups of patients with present liver disease symptoms and 8 control groups of healthy subjects. The total of 152 attributes were used, most of which were measured, however, in different studies. Twenty most common attributes were selected for the rule learning process. By applying the developed rule learning methodology we arrived at several different possible rulesets that can be used to predict three considered complications of MS, namely nonalcoholic fatty liver disease (NAFLD), simple steatosis (SS), and nonalcoholic steatohepatitis (NASH).

Rule-Based Design of Plant Expression Vectors Using GenoCAD.

PubMed

Coll, Anna; Wilson, Mandy L; Gruden, Kristina; Peccoud, Jean

2015-01-01

Plant synthetic biology requires software tools to assist on the design of complex multi-genic expression plasmids. Here a vector design strategy to express genes in plants is formalized and implemented as a grammar in GenoCAD, a Computer-Aided Design software for synthetic biology. It includes a library of plant biological parts organized in structural categories and a set of rules describing how to assemble these parts into large constructs. Rules developed here are organized and divided into three main subsections according to the aim of the final construct: protein localization studies, promoter analysis and protein-protein interaction experiments. The GenoCAD plant grammar guides the user through the design while allowing users to customize vectors according to their needs. Therefore the plant grammar implemented in GenoCAD will help plant biologists take advantage of methods from synthetic biology to design expression vectors supporting their research projects.

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.

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.

Rule based artificial intelligence expert system for determination of upper extremity impairment rating.

PubMed

Lim, I; Walkup, R K; Vannier, M W

1993-04-01

Quantitative evaluation of upper extremity impairment, a percentage rating most often determined using a rule based procedure, has been implemented on a personal computer using an artificial intelligence, rule-based expert system (AI system). In this study, the rules given in Chapter 3 of the AMA Guides to the Evaluation of Permanent Impairment (Third Edition) were used to develop such an AI system for the Apple Macintosh. The program applies the rules from the Guides in a consistent and systematic fashion. It is faster and less error-prone than the manual method, and the results have a higher degree of precision, since intermediate values are not truncated.

Simulation-Based Rule Generation Considering Readability

PubMed Central

Yahagi, H.; Shimizu, S.; Ogata, T.; Hara, T.; Ota, J.

2015-01-01

Rule generation method is proposed for an aircraft control problem in an airport. Designing appropriate rules for motion coordination of taxiing aircraft in the airport is important, which is conducted by ground control. However, previous studies did not consider readability of rules, which is important because it should be operated and maintained by humans. Therefore, in this study, using the indicator of readability, we propose a method of rule generation based on parallel algorithm discovery and orchestration (PADO). By applying our proposed method to the aircraft control problem, the proposed algorithm can generate more readable and more robust rules and is found to be superior to previous methods. PMID:27347501

Comprehensive Child Welfare Information System. Final rule.

PubMed

2016-06-02

This final rule replaces the Statewide and Tribal Automated Child Welfare Information Systems (S/TACWIS) rule with the Comprehensive Child Welfare Information System (CCWIS) rule. The rule also makes conforming amendments in rules in related requirements. This rule will assist title IV-E agencies in developing information management systems that leverage new innovations and technology in order to better serve children and families. More specifically, this final rule supports the use of cost-effective, innovative technologies to automate the collection of high-quality case management data and to promote its analysis, distribution, and use by workers, supervisors, administrators, researchers, and policy makers.

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity.

PubMed

Ahlberg, Ernst; Amberg, Alexander; Beilke, Lisa D; Bower, David; Cross, Kevin P; Custer, Laura; Ford, Kevin A; Van Gompel, Jacky; Harvey, James; Honma, Masamitsu; Jolly, Robert; Joossens, Elisabeth; Kemper, Raymond A; Kenyon, Michelle; Kruhlak, Naomi; Kuhnke, Lara; Leavitt, Penny; Naven, Russell; Neilan, Claire; Quigley, Donald P; Shuey, Dana; Spirkl, Hans-Peter; Stavitskaya, Lidiya; Teasdale, Andrew; White, Angela; Wichard, Joerg; Zwickl, Craig; Myatt, Glenn J

2016-06-01

Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guideline. Knowledge from proprietary corporate mutagenicity databases could be used to increase the predictive performance for selected chemical classes as well as expand the applicability domain of these (Q)SAR models. This paper outlines a mechanism for sharing knowledge without the release of proprietary data. Primary aromatic amine mutagenicity was selected as a case study because this chemical class is often encountered in pharmaceutical impurity analysis and mutagenicity of aromatic amines is currently difficult to predict. As part of this analysis, a series of aromatic amine substructures were defined and the number of mutagenic and non-mutagenic examples for each chemical substructure calculated across a series of public and proprietary mutagenicity databases. This information was pooled across all sources to identify structural classes that activate or deactivate aromatic amine mutagenicity. This structure activity knowledge, in combination with newly released primary aromatic amine data, was incorporated into Leadscope's expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated. Copyright © 2016 Elsevier Inc. All rights reserved.

[Study on spectrum analysis of X-ray based on rotational mass effect in special relativity].

PubMed

Yu, Zhi-Qiang; Xie, Quan; Xiao, Qing-Quan

2010-04-01

Based on special relativity, the formation mechanism of characteristic X-ray has been studied, and the influence of rotational mass effect on X-ray spectrum has been given. A calculation formula of the X-ray wavelength based upon special relativity was derived. Error analysis was carried out systematically for the calculation values of characteristic wavelength, and the rules of relative error were obtained. It is shown that the values of the calculation are very close to the experimental values, and the effect of rotational mass effect on the characteristic wavelength becomes more evident as the atomic number increases. The result of the study has some reference meaning for the spectrum analysis of characteristic X-ray in application.

Pushing the rules: effects and aftereffects of deliberate rule violations.

PubMed

Wirth, Robert; Pfister, Roland; Foerster, Anna; Huestegge, Lynn; Kunde, Wilfried

2016-09-01

Most of our daily life is organized around rules and social norms. But what makes rules so special? And what if one were to break a rule intentionally? Can we simply free us from the present set of rules or do we automatically adhere to them? How do rule violations influence subsequent behavior? To investigate the effects and aftereffects of violating simple S-R rule, we conducted three experiments that investigated continuous finger-tracking responses on an iPad. Our experiments show that rule violations are distinct from rule-based actions in both response times and movement trajectories, they take longer to initiate and execute, and their movement trajectory is heavily contorted. Data not only show differences between the two types of response (rule-based vs. violation), but also yielded a characteristic pattern of aftereffects in case of rule violations: rule violations do not trigger adaptation effects that render further rule violations less difficult, but every rule violation poses repeated effort on the agent. The study represents a first step towards understanding the signature and underlying mechanisms of deliberate rule violations, they cannot be acted out by themselves, but require the activation of the original rule first. Consequently, they are best understood as reformulations of existing rules that are not accessible on their own, but need to be constantly derived from the original rule, with an add-on that might entail an active tendency to steer away from mental representations that reflect (socially) unwanted behavior.

Validation and subsequent development of the DEREK skin sensitization rulebase by analysis of the BgVV list of contact allergens.

PubMed

Barratt, M D; Langowski, J J

1999-01-01

The DEREK knowledge-based computer system contains a subset of approximately 50 rules describing chemical substructures (toxophores) responsible for skin sensitization. This rulebase, based originally on Unilever historical in-house guinea pig maximization test data, has been subject to extensive validation and is undergoing refinement as the next stage of its development. As part of an ongoing program of validation and testing, the predictive ability of the sensitization rule set has been assessed by processing the structures of the 84 chemical substances in the list of contact allergens issued by the BgVV (German Federal Institute for Health Protection of Consumers). This list of chemicals is important because the biological data for each of the chemicals have been carefully scrutinized and peer reviewed, a key consideration in an area of toxicology in which much unreliable and potentially misleading data have been published. The existing DEREK rulebase for skin sensitization identified toxophores for skin sensitization in the structures of 71 out of the 84 chemicals (85%). The exercise highlighted areas of chemistry where further development of the rulebase was required, either by extension of the scope of existing rules or by generation of new rules where a sound mechanistic rationale for the biological activity could be established. Chemicals likely to be acting as photoallergens were identified, and new rules for photoallergenicity have subsequently been written. At the end of the exercise, the refined rulebase was able to identify toxophores for skin sensitization for 82 of the 84 chemicals in the BgVV list.

Coreference analysis in clinical notes: a multi-pass sieve with alternate anaphora resolution modules.

PubMed

Jonnalagadda, Siddhartha Reddy; Li, Dingcheng; Sohn, Sunghwan; Wu, Stephen Tze-Inn; Wagholikar, Kavishwar; Torii, Manabu; Liu, Hongfang

2012-01-01

This paper describes the coreference resolution system submitted by Mayo Clinic for the 2011 i2b2/VA/Cincinnati shared task Track 1C. The goal of the task was to construct a system that links the markables corresponding to the same entity. The task organizers provided progress notes and discharge summaries that were annotated with the markables of treatment, problem, test, person, and pronoun. We used a multi-pass sieve algorithm that applies deterministic rules in the order of preciseness and simultaneously gathers information about the entities in the documents. Our system, MedCoref, also uses a state-of-the-art machine learning framework as an alternative to the final, rule-based pronoun resolution sieve. The best system that uses a multi-pass sieve has an overall score of 0.836 (average of B(3), MUC, Blanc, and CEAF F score) for the training set and 0.843 for the test set. A supervised machine learning system that typically uses a single function to find coreferents cannot accommodate irregularities encountered in data especially given the insufficient number of examples. On the other hand, a completely deterministic system could lead to a decrease in recall (sensitivity) when the rules are not exhaustive. The sieve-based framework allows one to combine reliable machine learning components with rules designed by experts. Using relatively simple rules, part-of-speech information, and semantic type properties, an effective coreference resolution system could be designed. The source code of the system described is available at https://sourceforge.net/projects/ohnlp/files/MedCoref.

Direct observation limits on antimatter gravitation

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

Fischler, Mark; Lykken, Joe; Roberts, Tom

2008-06-01

The proposed Antihydrogen Gravity experiment at Fermilab (P981) will directly measure the gravitational attraction g between antihydrogen and the Earth, with an accuracy of 1% or better. The following key question has been asked by the PAC: Is a possible 1% difference between g and g already ruled out by other evidence? This memo presents the key points of existing evidence, to answer whether such a difference is ruled out (a) on the basis of direct observational evidence; and/or (b) on the basis of indirect evidence, combined with reasoning based on strongly held theoretical assumptions. The bottom line is thatmore » there are no direct observations or measurements of gravitational asymmetry which address the antimatter sector. There is evidence which by indirect reasoning can be taken to rule out such a difference, but the analysis needed to draw that conclusion rests on models and assumptions which are in question for other reasons and are thus worth testing. There is no compelling evidence or theoretical reason to rule out such a difference at the 1% level.« less

Misalignment Effect Function Measurement for Oblique Rotation Axes: Counterintuitive Predictions and Theoretical Extensions

NASA Technical Reports Server (NTRS)

Ellis, Stephen R.; Adelstein, Bernard D.; Yeom, Kiwon

2013-01-01

The Misalignment Effect Function (MEF) describes the decrement in manual performance associated with a rotation between operators' visual display frame of reference and that of their manual control. It now has been empirically determined for rotation axes oblique to canonical body axes and is compared with the MEF previously measured for rotations about canonical axes. A targeting rule, called the Secant Rule, based on these earlier measurements is derived from a hypothetical process and shown to describe some of the data from three previous experiments. It explains the motion trajectories determined for rotations less than 65deg in purely kinematic terms without the need to appeal to a mental rotation process. Further analysis of this rule in three dimensions applied to oblique rotation axes leads to a somewhat surprising expectation that the difficulty posed by rotational misalignment should get harder as the required movement is shorter. This prediction is confirmed. Geometry underlying this rule also suggests analytic extensions for predicting more generally the difficulty of making movements in arbitrary directions subject to arbitrary misalignments.

Medicare and Medicaid programs; fire safety requirements for certain health care facilities; amendment. Final rule.

PubMed

2006-09-22

This final rule adopts the substance of the April 15, 2004 tentative interim amendment (TIA) 00-1 (101), Alcohol Based Hand Rub Solutions, an amendment to the 2000 edition of the Life Safety Code, published by the National Fire Protection Association (NFPA). This amendment allows certain health care facilities to place alcohol-based hand rub dispensers in egress corridors under specified conditions. This final rule also requires that nursing facilities at least install battery-operated single station smoke alarms in resident rooms and common areas if they are not fully sprinklered or they do not have system-based smoke detectors in those areas. Finally, this final rule confirms as final the provisions of the March 25, 2005 interim final rule with changes and responds to public comments on that rule.

Research of Litchi Diseases Diagnosis Expertsystem Based on Rbr and Cbr

NASA Astrophysics Data System (ADS)

Xu, Bing; Liu, Liqun

To conquer the bottleneck problems existing in the traditional rule-based reasoning diseases diagnosis system, such as low reasoning efficiency and lack of flexibility, etc.. It researched the integrated case-based reasoning (CBR) and rule-based reasoning (RBR) technology, and put forward a litchi diseases diagnosis expert system (LDDES) with integrated reasoning method. The method use data mining and knowledge obtaining technology to establish knowledge base and case library. It adopt rules to instruct the retrieval and matching for CBR, and use association rule and decision trees algorithm to calculate case similarity.The experiment shows that the method can increase the system's flexibility and reasoning ability, and improve the accuracy of litchi diseases diagnosis.

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.

Knowledge-based approach to video content classification

NASA Astrophysics Data System (ADS)

Chen, Yu; Wong, Edward K.

2001-01-01

A framework for video content classification using a knowledge-based approach is herein proposed. This approach is motivated by the fact that videos are rich in semantic contents, which can best be interpreted and analyzed by human experts. We demonstrate the concept by implementing a prototype video classification system using the rule-based programming language CLIPS 6.05. Knowledge for video classification is encoded as a set of rules in the rule base. The left-hand-sides of rules contain high level and low level features, while the right-hand-sides of rules contain intermediate results or conclusions. Our current implementation includes features computed from motion, color, and text extracted from video frames. Our current rule set allows us to classify input video into one of five classes: news, weather, reporting, commercial, basketball and football. We use MYCIN's inexact reasoning method for combining evidences, and to handle the uncertainties in the features and in the classification results. We obtained good results in a preliminary experiment, and it demonstrated the validity of the proposed approach.

Knowledge-based approach to video content classification

NASA Astrophysics Data System (ADS)

Chen, Yu; Wong, Edward K.

2000-12-01

A framework for video content classification using a knowledge-based approach is herein proposed. This approach is motivated by the fact that videos are rich in semantic contents, which can best be interpreted and analyzed by human experts. We demonstrate the concept by implementing a prototype video classification system using the rule-based programming language CLIPS 6.05. Knowledge for video classification is encoded as a set of rules in the rule base. The left-hand-sides of rules contain high level and low level features, while the right-hand-sides of rules contain intermediate results or conclusions. Our current implementation includes features computed from motion, color, and text extracted from video frames. Our current rule set allows us to classify input video into one of five classes: news, weather, reporting, commercial, basketball and football. We use MYCIN's inexact reasoning method for combining evidences, and to handle the uncertainties in the features and in the classification results. We obtained good results in a preliminary experiment, and it demonstrated the validity of the proposed approach.

Diagnostic validity of physical examination tests for common knee disorders: An overview of systematic reviews and meta-analysis.

PubMed

Décary, Simon; Ouellet, Philippe; Vendittoli, Pascal-André; Roy, Jean-Sébastien; Desmeules, François

2017-01-01

More evidence on diagnostic validity of physical examination tests for knee disorders is needed to lower frequently used and costly imaging tests. To conduct a systematic review of systematic reviews (SR) and meta-analyses (MA) evaluating the diagnostic validity of physical examination tests for knee disorders. A structured literature search was conducted in five databases until January 2016. Methodological quality was assessed using the AMSTAR. Seventeen reviews were included with mean AMSTAR score of 5.5 ± 2.3. Based on six SR, only the Lachman test for ACL injuries is diagnostically valid when individually performed (Likelihood ratio (LR+):10.2, LR-:0.2). Based on two SR, the Ottawa Knee Rule is a valid screening tool for knee fractures (LR-:0.05). Based on one SR, the EULAR criteria had a post-test probability of 99% for the diagnosis of knee osteoarthritis. Based on two SR, a complete physical examination performed by a trained health provider was found to be diagnostically valid for ACL, PCL and meniscal injuries as well as for cartilage lesions. When individually performed, common physical tests are rarely able to rule in or rule out a specific knee disorder, except the Lachman for ACL injuries. There is low-quality evidence concerning the validity of combining history elements and physical tests. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

Fusion of GFP and phase contrast images with complex shearlet transform and Haar wavelet-based energy rule.

PubMed

Qiu, Chenhui; Wang, Yuanyuan; Guo, Yanen; Xia, Shunren

2018-03-14

Image fusion techniques can integrate the information from different imaging modalities to get a composite image which is more suitable for human visual perception and further image processing tasks. Fusing green fluorescent protein (GFP) and phase contrast images is very important for subcellular localization, functional analysis of protein and genome expression. The fusion method of GFP and phase contrast images based on complex shearlet transform (CST) is proposed in this paper. Firstly the GFP image is converted to IHS model and its intensity component is obtained. Secondly the CST is performed on the intensity component and the phase contrast image to acquire the low-frequency subbands and the high-frequency subbands. Then the high-frequency subbands are merged by the absolute-maximum rule while the low-frequency subbands are merged by the proposed Haar wavelet-based energy (HWE) rule. Finally the fused image is obtained by performing the inverse CST on the merged subbands and conducting IHS-to-RGB conversion. The proposed fusion method is tested on a number of GFP and phase contrast images and compared with several popular image fusion methods. The experimental results demonstrate that the proposed fusion method can provide better fusion results in terms of subjective quality and objective evaluation. © 2018 Wiley Periodicals, Inc.

Opinion evolution based on cellular automata rules in small world networks

NASA Astrophysics Data System (ADS)

Shi, Xiao-Ming; Shi, Lun; Zhang, Jie-Fang

2010-03-01

In this paper, we apply cellular automata rules, which can be given by a truth table, to human memory. We design each memory as a tracking survey mode that keeps the most recent three opinions. Each cellular automata rule, as a personal mechanism, gives the final ruling in one time period based on the data stored in one's memory. The key focus of the paper is to research the evolution of people's attitudes to the same question. Based on a great deal of empirical observations from computer simulations, all the rules can be classified into 20 groups. We highlight the fact that the phenomenon shown by some rules belonging to the same group will be altered within several steps by other rules in different groups. It is truly amazing that, compared with the last hundreds of presidential voting in America, the eras of important events in America's history coincide with the simulation results obtained by our model.

Contributions of Lateral and Orbital Frontal Regions to Abstract Rule Acquisition and Reversal in Monkeys

PubMed Central

La Camera, Giancarlo; Bouret, Sebastien; Richmond, Barry J.

2018-01-01

The ability to learn and follow abstract rules relies on intact prefrontal regions including the lateral prefrontal cortex (LPFC) and the orbitofrontal cortex (OFC). Here, we investigate the specific roles of these brain regions in learning rules that depend critically on the formation of abstract concepts as opposed to simpler input-output associations. To this aim, we tested monkeys with bilateral removals of either LPFC or OFC on a rapidly learned task requiring the formation of the abstract concept of same vs. different. While monkeys with OFC removals were significantly slower than controls at both acquiring and reversing the concept-based rule, monkeys with LPFC removals were not impaired in acquiring the task, but were significantly slower at rule reversal. Neither group was impaired in the acquisition or reversal of a delayed visual cue-outcome association task without a concept-based rule. These results suggest that OFC is essential for the implementation of a concept-based rule, whereas LPFC seems essential for its modification once established. PMID:29615854

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.

A Rule Based Approach to ISS Interior Volume Control and Layout

NASA Technical Reports Server (NTRS)

Peacock, Brian; Maida, Jim; Fitts, David; Dory, Jonathan

2001-01-01

Traditional human factors design involves the development of human factors requirements based on a desire to accommodate a certain percentage of the intended user population. As the product is developed human factors evaluation involves comparison between the resulting design and the specifications. Sometimes performance metrics are involved that allow leniency in the design requirements given that the human performance result is satisfactory. Clearly such approaches may work but they give rise to uncertainty and negotiation. An alternative approach is to adopt human factors design rules that articulate a range of each design continuum over which there are varying outcome expectations and interactions with other variables, including time. These rules are based on a consensus of human factors specialists, designers, managers and customers. The International Space Station faces exactly this challenge in interior volume control, which is based on anthropometric, performance and subjective preference criteria. This paper describes the traditional approach and then proposes a rule-based alternative. The proposed rules involve spatial, temporal and importance dimensions. If successful this rule-based concept could be applied to many traditional human factors design variables and could lead to a more effective and efficient contribution of human factors input to the design process.

Medicaid program; state plan home and community-based services, 5-year period for waivers, provider payment reassignment, and home and community-based setting requirements for Community First Choice and home and community-based services (HCBS) waivers. Final rule.

PubMed

2014-01-16

This final rule amends the Medicaid regulations to define and describe state plan section 1915(i) home and community-based services (HCBS) under the Social Security Act (the Act) amended by the Affordable Care Act. This rule offers states new flexibilities in providing necessary and appropriate services to elderly and disabled populations. This rule describes Medicaid coverage of the optional state plan benefit to furnish home and community based-services and draw federal matching funds. This rule also provides for a 5-year duration for certain demonstration projects or waivers at the discretion of the Secretary, when they provide medical assistance for individuals dually eligible for Medicaid and Medicare benefits, includes payment reassignment provisions because state Medicaid programs often operate as the primary or only payer for the class of practitioners that includes HCBS providers, and amends Medicaid regulations to provide home and community-based setting requirements related to the Affordable Care Act for Community First Choice State plan option. This final rule also makes several important changes to the regulations implementing Medicaid 1915(c) HCBS waivers.

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.

High energy dispersion relations for the high temperature Bi2Sr2CaCu2O8 superconductor from laser-based angle-resolved photoemission spectroscopy.

PubMed

Zhang, Wentao; Liu, Guodong; Meng, Jianqiao; Zhao, Lin; Liu, Haiyun; Dong, Xiaoli; Lu, Wei; Wen, J S; Xu, Z J; Gu, G D; Sasagawa, T; Wang, Guiling; Zhu, Yong; Zhang, Hongbo; Zhou, Yong; Wang, Xiaoyang; Zhao, Zhongxian; Chen, Chuangtian; Xu, Zuyan; Zhou, X J

2008-07-04

Laser-based angle-resolved photoemission spectroscopy measurements have been carried out on the high energy electron dynamics in Bi2Sr2CaCu2O8 high temperature superconductor. Our superhigh resolution data, momentum-dependent measurements, and complete analysis provide important information to judge the nature of the high energy dispersion and kink. Our results rule out the possibility that the high energy dispersion from the momentum distribution curve (MDC) may represent the true bare band as believed in previous studies. We also rule out the possibility that the high energy kink represents electron coupling with some high energy modes as proposed before. Through detailed MDC and energy distribution curve analyses, we propose that the high energy MDC dispersion may not represent intrinsic band structure.

Adaptive fractional order sliding mode control for Boost converter in the Battery/Supercapacitor HESS.

PubMed

Wang, Jianlin; Xu, Dan; Zhou, Huan; Zhou, Tao

2018-01-01

In this paper, an adaptive fractional order sliding mode control (AFSMC) scheme is designed for the current tracking control of the Boost-type converter in a Battery/Supercapacitor hybrid energy storage system (HESS). In order to stabilize the current, the adaptation rules based on state-observer and Lyapunov function are being designed. A fractional order sliding surface function is defined based on the tracking current error and adaptive rules. Furthermore, through fractional order analysis, the stability of the fractional order control system is proven, and the value of the fractional order (λ) is being investigated. In addition, the effectiveness of the proposed AFSMC strategy is being verified by numerical simulations. The advantages of good transient response and robustness to uncertainty are being indicated by this design, when compared with a conventional integer order sliding mode control system.

Category Learning Strategies in Younger and Older Adults: Rule Abstraction and Memorization

PubMed Central

Wahlheim, Christopher N.; McDaniel, Mark A.; Little, Jeri L.

2016-01-01

Despite the fundamental role of category learning in cognition, few studies have examined how this ability differs between younger and older adults. The present experiment examined possible age differences in category learning strategies and their effects on learning. Participants were trained on a category determined by a disjunctive rule applied to relational features. The utilization of rule- and exemplar-based strategies was indexed by self-reports and transfer performance. Based on self-reported strategies, both age groups had comparable frequencies of rule- and exemplar-based learners, but older adults had a higher frequency of intermediate learners (i.e., learners not identifying with a reliance on either rule- or exemplar-based strategies). Training performance was higher for younger than older adults regardless of the strategy utilized, showing that older adults were impaired in their ability to learn the correct rule or to remember exemplar-label associations. Transfer performance converged with strategy reports in showing higher fidelity category representations for younger adults. Younger adults with high working memory capacity were more likely to use an exemplar-based strategy, and older adults with high working memory capacity showed better training performance. Age groups did not differ in their self-reported memory beliefs, and these beliefs did not predict training strategies or performance. Overall, the present results contradict earlier findings that older adults prefer rule- to exemplar-based learning strategies, presumably to compensate for memory deficits. PMID:26950225

The Effect of the "Zero Tolerance for Head Contact" Rule Change on the Risk of Concussions in Youth Ice Hockey Players.

PubMed

Krolikowski, Maciej P; Black, Amanda M; Palacios-Derflingher, Luz; Blake, Tracy A; Schneider, Kathryn J; Emery, Carolyn A

2017-02-01

Ice hockey is a popular winter sport in Canada. Concussions account for the greatest proportion of all injuries in youth ice hockey. In 2011, a policy change enforcing "zero tolerance for head contact" was implemented in all leagues in Canada. To determine if the risk of game-related concussions and more severe concussions (ie, resulting in >10 days of time loss) and the mechanisms of a concussion differed for Pee Wee class (ages 11-12 years) and Bantam class (ages 13-14 years) players after the 2011 "zero tolerance for head contact" policy change compared with players in similar divisions before the policy change. Cohort study; Level of evidence, 3. The retrospective cohort included Pee Wee (most elite 70%, 2007-2008; n = 891) and Bantam (most elite 30%, 2008-2009; n = 378) players before the rule change and Pee Wee (2011-2012; n = 588) and Bantam (2011-2012; n = 242) players in the same levels of play after the policy change. Suspected concussions were identified by a team designate and referred to a sport medicine physician for diagnosis. Incidence rate ratios (IRRs) were estimated based on multiple Poisson regression analysis, controlling for clustering by team and other important covariates and offset by game-exposure hours. Incidence rates based on the mechanisms of a concussion were estimated based on univariate Poisson regression analysis. The risk of game-related concussions increased after the head contact rule in Pee Wee (IRR, 1.85; 95% CI, 1.20-2.86) and Bantam (IRR, 2.48; 95% CI, 1.17-5.24) players. The risk of more severe concussions increased after the head contact rule in Pee Wee (IRR, 4.12; 95% CI, 2.00-8.50) and Bantam (IRR, 7.91; 95% CI, 3.13-19.94) players. The rates of concussions due to body checking and direct head contact increased after the rule change. The "zero tolerance for head contact" policy change did not reduce the risk of game-related concussions in Pee Wee or Bantam class ice hockey players. Increased concussion awareness and education after the policy change may have contributed to the increased risk of concussions found after the policy change.

Cover estimations using object-based image analysis rule sets developed across multiple scales in pinyon-juniper woodlands

USDA-ARS?s Scientific Manuscript database

Numerous studies have been conducted that evaluate the utility of remote sensing for monitoring and assessing vegetation and ground cover to support land management decisions and complement ground-measurements. However, few land cover comparisons have been made using high-resolution imagery and obj...

A Simple Algorithm for Obtaining Nearly Optimal Quadrature Rules for NURBS-based Isogeometric Analysis

DTIC Science & Technology

2012-01-05

Università degli Studi di Pavia bIstituto di Matematica Applicata e Tecnologie Informatiche “E. Magenes” del CNR, Pavia cDAEIMI, Università degli Studi di...Cassino d Institute for Computational Engineering and Sciences, University of Texas at Austin eDipartimento di Matematica , Università degli Studi di

76 FR 61566 - Significant New Use Rules on Certain Chemical Substances

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-05

... foam control agents. Based on EcoSAR analysis of test data on analogous epoxides, EPA predicts toxicity... control; and an unscheduled DNA synthesis in mammalian cells in culture (OPPTS Test Guideline 870.5550) in...) under section 5(a)(2) of the Toxic Substances Control Act (TSCA) for 36 chemical substances which were...

Design as a Fusion Problem

DTIC Science & Technology

2008-07-01

consider a proof as a composition relative to some system of music or as a painting. From the Bayesian perspective, any sufciently complex problem has...these types algorithms based on maximum entropy analysis. An example is the Bar-Shalom- Campo Fusion Rule: Xf (kjk) = X2(kjk) + (P22 P21)U1[X1(kjk

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…

Symbolic Model-Based SAR Feature Analysis and Change Detection

DTIC Science & Technology

1992-02-01

normalization fac- tor described above in the Dempster rule of combination. Another problem is that in certain cases D-S overweights prior probabilities compared...Beaufort Sea data set and the Peru data set. The Phoenix results are described in section 6.2.2 including a partial trace of the opera- tion of the

[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.

Robust linear discriminant analysis with distance based estimators

NASA Astrophysics Data System (ADS)

Lim, Yai-Fung; Yahaya, Sharipah Soaad Syed; Ali, Hazlina

2017-11-01

Linear discriminant analysis (LDA) is one of the supervised classification techniques concerning relationship between a categorical variable and a set of continuous variables. The main objective of LDA is to create a function to distinguish between populations and allocating future observations to previously defined populations. Under the assumptions of normality and homoscedasticity, the LDA yields optimal linear discriminant rule (LDR) between two or more groups. However, the optimality of LDA highly relies on the sample mean and pooled sample covariance matrix which are known to be sensitive to outliers. To alleviate these conflicts, a new robust LDA using distance based estimators known as minimum variance vector (MVV) has been proposed in this study. The MVV estimators were used to substitute the classical sample mean and classical sample covariance to form a robust linear discriminant rule (RLDR). Simulation and real data study were conducted to examine on the performance of the proposed RLDR measured in terms of misclassification error rates. The computational result showed that the proposed RLDR is better than the classical LDR and was comparable with the existing robust LDR.

An efficient incremental learning mechanism for tracking concept drift in spam filtering

PubMed Central

Sheu, Jyh-Jian; Chu, Ko-Tsung; Li, Nien-Feng; Lee, Cheng-Chi

2017-01-01

This research manages in-depth analysis on the knowledge about spams and expects to propose an efficient spam filtering method with the ability of adapting to the dynamic environment. We focus on the analysis of email’s header and apply decision tree data mining technique to look for the association rules about spams. Then, we propose an efficient systematic filtering method based on these association rules. Our systematic method has the following major advantages: (1) Checking only the header sections of emails, which is different from those spam filtering methods at present that have to analyze fully the email’s content. Meanwhile, the email filtering accuracy is expected to be enhanced. (2) Regarding the solution to the problem of concept drift, we propose a window-based technique to estimate for the condition of concept drift for each unknown email, which will help our filtering method in recognizing the occurrence of spam. (3) We propose an incremental learning mechanism for our filtering method to strengthen the ability of adapting to the dynamic environment. PMID:28182691

Baxter v. Montana, libertarianism, and end-of-life: the ripe time for a paradigm shift.

PubMed

Ruble, James H

2010-09-01

Baxter v. Montana (2009 WL 5155363 [Mont. 2009]) is a recent decision from the Montana Supreme Court that provides new legal insight into the societal issue of aid in dying. This case involves interests of persons with terminal illness, medical practitioners, law enforcement, legislative and judicial bodies, as well as the citizens of Montana. A summary judgment ruling at the Montana district court level was based almost entirely on a constitutional fundamental rights analysis. In contrast, the Montana Supreme Court affirming decision was based almost entirely on a statutory rights analysis. Both rulings from the Montana courts support the position that licensed prescribers in Montana who provide aid in dying assistance to terminally ill patients have some immunity from criminal prosecution. Each side in the case argued what they believed to be the intents and purposes of the people of Montana. Baxter v. Montana illustrates different methods to determine the will of the people concerning aid in dying and public policy. This case very subtly suggests a paradigm shift may be occurring in aid in dying policy.

Advanced Weather Awareness and Reporting Enhancements

NASA Technical Reports Server (NTRS)

Busquets, Anthony M. (Technical Monitor); Ruokangas, Corinne Clinton; Kelly, Wallace E., III

2005-01-01

AWARE (Aviation Weather Awareness and Reporting Enhancements) was a NASA Cooperative Research and Development program conducted jointly by Rockwell Scientific, Rockwell Collins, and NASA. The effort culminated in an enhanced weather briefing and reporting tool prototype designed to integrate graphical and text-based aviation weather data to provide clear situational awareness in the context of a specific pilot, flight and equipment profile. The initial implementation of AWARE was as a web-based preflight planning tool, specifically for general aviation pilots, who do not have access to support such as the dispatchers available for commercial airlines. Initial usability tests showed that for VFR (Visual Flight Rules) pilots, AWARE provided faster and more effective weather evaluation. In a subsequent formal usability test for IFR (Instrument Flight Rules) pilots, all users finished the AWARE tests faster than the parallel DUAT tests, and all subjects graded AWARE higher for effectiveness, efficiency, and usability. The decision analysis basis of AWARE differentiates it from other aviation safety programs, providing analysis of context-sensitive data in a personalized graphical format to aid pilots/dispatchers in their complex flight requirements.

KOSMOS: a universal morph server for nucleic acids, proteins and their complexes

PubMed Central

Seo, Sangjae; Kim, Moon Ki

2012-01-01

KOSMOS is the first online morph server to be able to address the structural dynamics of DNA/RNA, proteins and even their complexes, such as ribosomes. The key functions of KOSMOS are the harmonic and anharmonic analyses of macromolecules. In the harmonic analysis, normal mode analysis (NMA) based on an elastic network model (ENM) is performed, yielding vibrational modes and B-factor calculations, which provide insight into the potential biological functions of macromolecules based on their structural features. Anharmonic analysis involving elastic network interpolation (ENI) is used to generate plausible transition pathways between two given conformations by optimizing a topology-oriented cost function that guarantees a smooth transition without steric clashes. The quality of the computed pathways is evaluated based on their various facets, including topology, energy cost and compatibility with the NMA results. There are also two unique features of KOSMOS that distinguish it from other morph servers: (i) the versatility in the coarse-graining methods and (ii) the various connection rules in the ENM. The models enable us to analyze macromolecular dynamics with the maximum degrees of freedom by combining a variety of ENMs from full-atom to coarse-grained, backbone and hybrid models with one connection rule, such as distance-cutoff, number-cutoff or chemical-cutoff. KOSMOS is available at http://bioengineering.skku.ac.kr/kosmos. PMID:22669912

Economic Analysis of the National Pollutant Discharge Elimination System (NPDES) Electronic Reporting Final Rule

EPA Pesticide Factsheets

This Economic Analysis (EA) quantifies the costs and savings of the proposed NPDES Electronic Reporting Rule, while acknowledging many of the qualitative benefits that will result from its implementation.

Accurate crop classification using hierarchical genetic fuzzy rule-based systems

NASA Astrophysics Data System (ADS)

Topaloglou, Charalampos A.; Mylonas, Stelios K.; Stavrakoudis, Dimitris G.; Mastorocostas, Paris A.; Theocharis, John B.

2014-10-01

This paper investigates the effectiveness of an advanced classification system for accurate crop classification using very high resolution (VHR) satellite imagery. Specifically, a recently proposed genetic fuzzy rule-based classification system (GFRBCS) is employed, namely, the Hierarchical Rule-based Linguistic Classifier (HiRLiC). HiRLiC's model comprises a small set of simple IF-THEN fuzzy rules, easily interpretable by humans. One of its most important attributes is that its learning algorithm requires minimum user interaction, since the most important learning parameters affecting the classification accuracy are determined by the learning algorithm automatically. HiRLiC is applied in a challenging crop classification task, using a SPOT5 satellite image over an intensively cultivated area in a lake-wetland ecosystem in northern Greece. A rich set of higher-order spectral and textural features is derived from the initial bands of the (pan-sharpened) image, resulting in an input space comprising 119 features. The experimental analysis proves that HiRLiC compares favorably to other interpretable classifiers of the literature, both in terms of structural complexity and classification accuracy. Its testing accuracy was very close to that obtained by complex state-of-the-art classification systems, such as the support vector machines (SVM) and random forest (RF) classifiers. Nevertheless, visual inspection of the derived classification maps shows that HiRLiC is characterized by higher generalization properties, providing more homogeneous classifications that the competitors. Moreover, the runtime requirements for producing the thematic map was orders of magnitude lower than the respective for the competitors.

Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

NASA Technical Reports Server (NTRS)

Stclair, D. C.; Sabharwal, C. L.; Bond, W. E.; Hacke, Keith

1988-01-01

Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

Developing a modular architecture for creation of rule-based clinical diagnostic criteria.

PubMed

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

2016-01-01

With recent advances in computerized patient records system, there is an urgent need for producing computable and standards-based clinical diagnostic criteria. Notably, constructing rule-based clinical diagnosis criteria has become one of the goals in the International Classification of Diseases (ICD)-11 revision. However, few studies have been done in building a unified architecture to support the need for diagnostic criteria computerization. In this study, we present a modular architecture for enabling the creation of rule-based clinical diagnostic criteria leveraging Semantic Web technologies. The architecture consists of two modules: an authoring module that utilizes a standards-based information model and a translation module that leverages Semantic Web Rule Language (SWRL). In a prototype implementation, we created a diagnostic criteria upper ontology (DCUO) that integrates ICD-11 content model with the Quality Data Model (QDM). Using the DCUO, we developed a transformation tool that converts QDM-based diagnostic criteria into Semantic Web Rule Language (SWRL) representation. We evaluated the domain coverage of the upper ontology model using randomly selected diagnostic criteria from broad domains (n = 20). We also tested the transformation algorithms using 6 QDM templates for ontology population and 15 QDM-based criteria data for rule generation. As the results, the first draft of DCUO contains 14 root classes, 21 subclasses, 6 object properties and 1 data property. Investigation Findings, and Signs and Symptoms are the two most commonly used element types. All 6 HQMF templates are successfully parsed and populated into their corresponding domain specific ontologies and 14 rules (93.3 %) passed the rule validation. Our efforts in developing and prototyping a modular architecture provide useful insight into how to build a scalable solution to support diagnostic criteria representation and computerization.

Economic Observation in 3Q E-business Fight - According to Analysis of Resource Allocation and Contract

NASA Astrophysics Data System (ADS)

Kuang, Ruihu; Chen, Zeming; Kuang, Juchi

Based on relation of resource allocation and marginal benefit of e-commence provider, both of origin and essence of the 3Q e-business fight were analyzed; and then contents of the contract between e-business company and users were elaborated. Moreover, liability for Qihoo's breach of the contract in 3Q e-business fight was discussed. Analysis of the contract indicated that blame of infringing on privacy of users from public, media or even a law professor for Tengxun Company is not exactly justicial. Some controversial rules which are not fit for usual practices in the QQ contract such as narrow definition of privacy were found out, whose reason lies in no relevant e-business standards or rules in our country. In the end, this passage points out that actions of government who intervened in market operations of Tengxun Company and QQ Company are inappropriate and unnecessary. Thus, responsibility for facing up to incomplete market rules of e-commence lies with government and government should strengthen market supervision by legislation so as to guide healthy development of e-business market, which is a key lesson we learn from the 3Q e-business fight.

The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator.

PubMed

Roh, S D; Kim, S W; Cho, W S

2001-10-01

The numerical modelling and process simulation for the fault diagnosis of rotary kiln incinerator were accomplished. In the numerical modelling, two models applied to the modelling within the kiln are the combustion chamber model including the mass and energy balance equations for two combustion chambers and 3D thermal model. The combustion chamber model predicts temperature within the kiln, flue gas composition, flux and heat of combustion. Using the combustion chamber model and 3D thermal model, the production-rules for the process simulation can be obtained through interrelation analysis between control and operation variables. The process simulation of the kiln is operated with the production-rules for automatic operation. The process simulation aims to provide fundamental solutions to the problems in incineration process by introducing an online expert control system to provide an integrity in process control and management. Knowledge-based expert control systems use symbolic logic and heuristic rules to find solutions for various types of problems. It was implemented to be a hybrid intelligent expert control system by mutually connecting with the process control systems which has the capability of process diagnosis, analysis and control.

Static Analysis Alert Audits: Lexicon and Rules

DTIC Science & Technology

2016-11-04

collaborators • Includes a standard set of well-defined determinations for static analysis alerts • Includes a set of auditing rules to help auditors make...consistent decisions in commonly-encountered situations Different auditors should make the same determination for a given alert! Improve the quality and...scenarios • Establish assumptions auditors can make • Overall: help make audit determinations more consistent We developed 12 rules • Drew on our own

17 CFR 240.13e-102 - Schedule 13E-4F. Tender offer statement pursuant to section 13(e) (1) of the Securities Exchange...

Code of Federal Regulations, 2014 CFR

2014-04-01

... the Office of EDGAR and Information Analysis at (202) 551-3610. (2) If filing the Schedule in paper... Data Gathering, Analysis, and Retrieval (EDGAR) system in accordance with the EDGAR rules set forth in... exemption from Rule 10b-13, the predecessor to Rule 14e-5.] Part I—Information Required To Be Sent to...

17 CFR 240.13e-102 - Schedule 13E-4F. Tender offer statement pursuant to section 13(e) (1) of the Securities Exchange...

Code of Federal Regulations, 2012 CFR

2012-04-01

... the Office of EDGAR and Information Analysis at (202) 551-3610. (2) If filing the Schedule in paper... Data Gathering, Analysis, and Retrieval (EDGAR) system in accordance with the EDGAR rules set forth in... exemption from Rule 10b-13, the predecessor to Rule 14e-5.] Part I—Information Required To Be Sent to...

17 CFR 240.13e-102 - Schedule 13E-4F. Tender offer statement pursuant to section 13(e) (1) of the Securities Exchange...

Code of Federal Regulations, 2011 CFR

2011-04-01

... the Office of EDGAR and Information Analysis at (202) 551-3610. (2) If filing the Schedule in paper... Data Gathering, Analysis, and Retrieval (EDGAR) system in accordance with the EDGAR rules set forth in... exemption from Rule 10b-13, the predecessor to Rule 14e-5.] Part I—Information Required To Be Sent to...

17 CFR 240.13e-102 - Schedule 13E-4F. Tender offer statement pursuant to section 13(e) (1) of the Securities Exchange...

Code of Federal Regulations, 2013 CFR

2013-04-01

... the Office of EDGAR and Information Analysis at (202) 551-3610. (2) If filing the Schedule in paper... Data Gathering, Analysis, and Retrieval (EDGAR) system in accordance with the EDGAR rules set forth in... exemption from Rule 10b-13, the predecessor to Rule 14e-5.] Part I—Information Required To Be Sent to...

Applications of artificial intelligence V; Proceedings of the Meeting, Orlando, FL, May 18-20, 1987

NASA Technical Reports Server (NTRS)

Gilmore, John F. (Editor)

1987-01-01

The papers contained in this volume focus on current trends in applications of artificial intelligence. Topics discussed include expert systems, image understanding, artificial intelligence tools, knowledge-based systems, heuristic systems, manufacturing applications, and image analysis. Papers are presented on expert system issues in automated, autonomous space vehicle rendezvous; traditional versus rule-based programming techniques; applications to the control of optional flight information; methodology for evaluating knowledge-based systems; and real-time advisory system for airborne early warning.

A Web-Based Multidrug-Resistant Organisms Surveillance and Outbreak Detection System with Rule-Based Classification and Clustering

PubMed Central

Tseng, Yi-Ju; Wu, Jung-Hsuan; Ping, Xiao-Ou; Lin, Hui-Chi; Chen, Ying-Yu; Shang, Rung-Ji; Chen, Ming-Yuan; Lai, Feipei

2012-01-01

Background The emergence and spread of multidrug-resistant organisms (MDROs) are causing a global crisis. Combating antimicrobial resistance requires prevention of transmission of resistant organisms and improved use of antimicrobials. Objectives To develop a Web-based information system for automatic integration, analysis, and interpretation of the antimicrobial susceptibility of all clinical isolates that incorporates rule-based classification and cluster analysis of MDROs and implements control chart analysis to facilitate outbreak detection. Methods Electronic microbiological data from a 2200-bed teaching hospital in Taiwan were classified according to predefined criteria of MDROs. The numbers of organisms, patients, and incident patients in each MDRO pattern were presented graphically to describe spatial and time information in a Web-based user interface. Hierarchical clustering with 7 upper control limits (UCL) was used to detect suspicious outbreaks. The system’s performance in outbreak detection was evaluated based on vancomycin-resistant enterococcal outbreaks determined by a hospital-wide prospective active surveillance database compiled by infection control personnel. Results The optimal UCL for MDRO outbreak detection was the upper 90% confidence interval (CI) using germ criterion with clustering (area under ROC curve (AUC) 0.93, 95% CI 0.91 to 0.95), upper 85% CI using patient criterion (AUC 0.87, 95% CI 0.80 to 0.93), and one standard deviation using incident patient criterion (AUC 0.84, 95% CI 0.75 to 0.92). The performance indicators of each UCL were statistically significantly higher with clustering than those without clustering in germ criterion (P < .001), patient criterion (P = .04), and incident patient criterion (P < .001). Conclusion This system automatically identifies MDROs and accurately detects suspicious outbreaks of MDROs based on the antimicrobial susceptibility of all clinical isolates. PMID:23195868

IM and Q-D Rules: An Analysis by French Club MURAT

DTIC Science & Technology

1996-08-01

and Q-D Rules: An Analysis by French Club Murat 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) 5d. PROJECT NUMBER...IM(1) AND Q-D RULES: AN ANALYSIS BY FRENCH CLUB MURAT by Jean ISLER (2) - Jean G. GOLIGER (3) - Daniel BOCHAND (4) Georges QUEROL (5) - Louis PICARD...6) and Joël FERRON (7) CLUB MURAT - BP 129 78 148 VELIZY CEDEX - FRANCE Tel : (33) - (1) 39.46.15.50 Fax : (33) - (1) 39.46.15.38 ABSTRACT The

Extracting decision rules from police accident reports through decision trees.

PubMed

de Oña, Juan; López, Griselda; Abellán, Joaquín

2013-01-01

Given the current number of road accidents, the aim of many road safety analysts is to identify the main factors that contribute to crash severity. To pinpoint those factors, this paper shows an application that applies some of the methods most commonly used to build decision trees (DTs), which have not been applied to the road safety field before. An analysis of accidents on rural highways in the province of Granada (Spain) between 2003 and 2009 (both inclusive) showed that the methods used to build DTs serve our purpose and may even be complementary. Applying these methods has enabled potentially useful decision rules to be extracted that could be used by road safety analysts. For instance, some of the rules may indicate that women, contrary to men, increase their risk of severity under bad lighting conditions. The rules could be used in road safety campaigns to mitigate specific problems. This would enable managers to implement priority actions based on a classification of accidents by types (depending on their severity). However, the primary importance of this proposal is that other databases not used here (i.e. other infrastructure, roads and countries) could be used to identify unconventional problems in a manner easy for road safety managers to understand, as decision rules. Copyright © 2012 Elsevier Ltd. All rights reserved.

Gene Ontology synonym generation rules lead to increased performance in biomedical concept recognition.

PubMed

Funk, Christopher S; Cohen, K Bretonnel; Hunter, Lawrence E; Verspoor, Karin M

2016-09-09

Gene Ontology (GO) terms represent the standard for annotation and representation of molecular functions, biological processes and cellular compartments, but a large gap exists between the way concepts are represented in the ontology and how they are expressed in natural language text. The construction of highly specific GO terms is formulaic, consisting of parts and pieces from more simple terms. We present two different types of manually generated rules to help capture the variation of how GO terms can appear in natural language text. The first set of rules takes into account the compositional nature of GO and recursively decomposes the terms into their smallest constituent parts. The second set of rules generates derivational variations of these smaller terms and compositionally combines all generated variants to form the original term. By applying both types of rules, new synonyms are generated for two-thirds of all GO terms and an increase in F-measure performance for recognition of GO on the CRAFT corpus from 0.498 to 0.636 is observed. Additionally, we evaluated the combination of both types of rules over one million full text documents from Elsevier; manual validation and error analysis show we are able to recognize GO concepts with reasonable accuracy (88 %) based on random sampling of annotations. In this work we present a set of simple synonym generation rules that utilize the highly compositional and formulaic nature of the Gene Ontology concepts. We illustrate how the generated synonyms aid in improving recognition of GO concepts on two different biomedical corpora. We discuss other applications of our rules for GO ontology quality assurance, explore the issue of overgeneration, and provide examples of how similar methodologies could be applied to other biomedical terminologies. Additionally, we provide all generated synonyms for use by the text-mining community.

Comparative analysis of targeted metabolomics: dominance-based rough set approach versus orthogonal partial least square-discriminant analysis.

PubMed

Blasco, H; Błaszczyński, J; Billaut, J C; Nadal-Desbarats, L; Pradat, P F; Devos, D; Moreau, C; Andres, C R; Emond, P; Corcia, P; Słowiński, R

2015-02-01

Metabolomics is an emerging field that includes ascertaining a metabolic profile from a combination of small molecules, and which has health applications. Metabolomic methods are currently applied to discover diagnostic biomarkers and to identify pathophysiological pathways involved in pathology. However, metabolomic data are complex and are usually analyzed by statistical methods. Although the methods have been widely described, most have not been either standardized or validated. Data analysis is the foundation of a robust methodology, so new mathematical methods need to be developed to assess and complement current methods. We therefore applied, for the first time, the dominance-based rough set approach (DRSA) to metabolomics data; we also assessed the complementarity of this method with standard statistical methods. Some attributes were transformed in a way allowing us to discover global and local monotonic relationships between condition and decision attributes. We used previously published metabolomics data (18 variables) for amyotrophic lateral sclerosis (ALS) and non-ALS patients. Principal Component Analysis (PCA) and Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA) allowed satisfactory discrimination (72.7%) between ALS and non-ALS patients. Some discriminant metabolites were identified: acetate, acetone, pyruvate and glutamine. The concentrations of acetate and pyruvate were also identified by univariate analysis as significantly different between ALS and non-ALS patients. DRSA correctly classified 68.7% of the cases and established rules involving some of the metabolites highlighted by OPLS-DA (acetate and acetone). Some rules identified potential biomarkers not revealed by OPLS-DA (beta-hydroxybutyrate). We also found a large number of common discriminating metabolites after Bayesian confirmation measures, particularly acetate, pyruvate, acetone and ascorbate, consistent with the pathophysiological pathways involved in ALS. DRSA provides a complementary method for improving the predictive performance of the multivariate data analysis usually used in metabolomics. This method could help in the identification of metabolites involved in disease pathogenesis. Interestingly, these different strategies mostly identified the same metabolites as being discriminant. The selection of strong decision rules with high value of Bayesian confirmation provides useful information about relevant condition-decision relationships not otherwise revealed in metabolomics data. Copyright © 2014 Elsevier Inc. All rights reserved.

A self-learning rule base for command following in dynamical systems

NASA Technical Reports Server (NTRS)

Tsai, Wei K.; Lee, Hon-Mun; Parlos, Alexander

1992-01-01

In this paper, a self-learning Rule Base for command following in dynamical systems is presented. The learning is accomplished though reinforcement learning using an associative memory called SAM. The main advantage of SAM is that it is a function approximator with explicit storage of training samples. A learning algorithm patterned after the dynamic programming is proposed. Two artificially created, unstable dynamical systems are used for testing, and the Rule Base was used to generate a feedback control to improve the command following ability of the otherwise uncontrolled systems. The numerical results are very encouraging. The controlled systems exhibit a more stable behavior and a better capability to follow reference commands. The rules resulting from the reinforcement learning are explicitly stored and they can be modified or augmented by human experts. Due to overlapping storage scheme of SAM, the stored rules are similar to fuzzy rules.

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.

Design of impact-resistant boron/aluminum large fan blade

NASA Technical Reports Server (NTRS)

Salemme, C. T.; Yokel, S. A.

1978-01-01

The technical program was comprised of two technical tasks. Task 1 encompassed the preliminary boron/aluminum fan blade design effort. Two preliminary designs were evolved. An initial design consisted of 32 blades per stage and was based on material properties extracted from manufactured blades. A final design of 36 blades per stage was based on rule-of-mixture material properties. In Task 2, the selected preliminary blade design was refined via more sophisticated analytical tools. Detailed finite element stress analysis and aero performance analysis were carried out to determine blade material frequencies and directional stresses.

Adiabiatic invariants of the Kepler problem: an elementary treatment

NASA Astrophysics Data System (ADS)

Borghi, Riccardo

2013-09-01

An elementary introduction to the adiabatic invariants of the Kepler problem is proposed. Unlike the other didactical expositions already present in the literature, which are based on the Hamilton-Jacobi theory of mechanics, our derivation is suitable to be grasped even by first-year undergraduates. A central role in the present analysis is played by an elementary proof of the virial theorem for the Kepler problem which is based on the chain rule for derivatives. As a byproduct of our analysis, an interpretation of Keplerian orbit eccentricities in terms of the time average of the position vector direction is also provided.

Research and development for Onboard Navigation (ONAV) ground based expert/trainer system: ONAV entry expert system code

NASA Technical Reports Server (NTRS)

Bochsler, Daniel C.

1988-01-01

A complete listing is given of the expert system rules for the Entry phase of the Onboard Navigation (ONAV) Ground Based Expert Trainer System for aircraft/space shuttle navigation. These source listings appear in the same format as utilized and required by the C Language Integrated Production System (CLIPS) expert system shell which is the basis for the ONAV entry system. A schematic overview is given of how the rules are organized. These groups result from a partitioning of the rules according to the overall function which a given set of rules performs. This partitioning was established and maintained according to that established in the knowledge specification document. In addition, four other groups of rules are specified. The four groups (control flow, operator inputs, output management, and data tables) perform functions that affect all the other functional rule groups. As the name implies, control flow ensures that the rule groups are executed in the order required for proper operation; operator input rules control the introduction into the CLIPS fact base of various kinds of data required by the expert system; output management rules control the updating of the ONAV expert system user display screen during execution of the system; and data tables are static information utilized by many different rule sets gathered in one convenient place.

A new hybrid case-based reasoning approach for medical diagnosis systems.

PubMed

Sharaf-El-Deen, Dina A; Moawad, Ibrahim F; Khalifa, M E

2014-02-01

Case-Based Reasoning (CBR) has been applied in many different medical applications. Due to the complexities and the diversities of this domain, most medical CBR systems become hybrid. Besides, the case adaptation process in CBR is often a challenging issue as it is traditionally carried out manually by domain experts. In this paper, a new hybrid case-based reasoning approach for medical diagnosis systems is proposed to improve the accuracy of the retrieval-only CBR systems. The approach integrates case-based reasoning and rule-based reasoning, and also applies the adaptation process automatically by exploiting adaptation rules. Both adaptation rules and reasoning rules are generated from the case-base. After solving a new case, the case-base is expanded, and both adaptation and reasoning rules are updated. To evaluate the proposed approach, a prototype was implemented and experimented to diagnose breast cancer and thyroid diseases. The final results show that the proposed approach increases the diagnosing accuracy of the retrieval-only CBR systems, and provides a reliable accuracy comparing to the current breast cancer and thyroid diagnosis systems.

Age-Related Brain Activation Changes during Rule Repetition in Word-Matching.

PubMed

Methqal, Ikram; Pinsard, Basile; Amiri, Mahnoush; Wilson, Maximiliano A; Monchi, Oury; Provost, Jean-Sebastien; Joanette, Yves

2017-01-01

Objective: The purpose of this study was to explore the age-related brain activation changes during a word-matching semantic-category-based task, which required either repeating or changing a semantic rule to be applied. In order to do so, a word-semantic rule-based task was adapted from the Wisconsin Sorting Card Test, involving the repeated feedback-driven selection of given pairs of words based on semantic category-based criteria. Method: Forty healthy adults (20 younger and 20 older) performed a word-matching task while undergoing a fMRI scan in which they were required to pair a target word with another word from a group of three words. The required pairing is based on three word-pair semantic rules which correspond to different levels of semantic control demands: functional relatedness, moderately typical-relatedness (which were considered as low control demands), and atypical-relatedness (high control demands). The sorting period consisted of a continuous execution of the same sorting rule and an inferred trial-by-trial feedback was given. Results: Behavioral performance revealed increases in response times and decreases of correct responses according to the level of semantic control demands (functional vs. typical vs. atypical) for both age groups (younger and older) reflecting graded differences in the repetition of the application of a given semantic rule. Neuroimaging findings of significant brain activation showed two main results: (1) Greater task-related activation changes for the repetition of the application of atypical rules relative to typical and functional rules, and (2) Changes (older > younger) in the inferior prefrontal regions for functional rules and more extensive and bilateral activations for typical and atypical rules. Regarding the inter-semantic rules comparison, only task-related activation differences were observed for functional > typical (e.g., inferior parietal and temporal regions bilaterally) and atypical > typical (e.g., prefrontal, inferior parietal, posterior temporal, and subcortical regions). Conclusion: These results suggest that healthy cognitive aging relies on the adaptive changes of inferior prefrontal resources involved in the repetitive execution of semantic rules, thus reflecting graded differences in support of task demands.

Development of probabilistic operating rules for Hluhluwe Dam, South Africa

NASA Astrophysics Data System (ADS)

Ndiritu, J.; Odiyo, J.; Makungo, R.; Mwaka, B.; Mthethwa, N.; Ntuli, C.; Andanje, A.

2017-08-01

Hluhluwe Dam, with a 30 million m3 reservoir that supplies water for irrigation and Hluhluwe municipality in Kwa-Zulu Natal Province, South Africa, was consistently experiencing low storage levels over several non-drought years since 2001. The dam was operated by rules of thumb and there were no records of water releases for irrigation - the main user of the dam. This paper describes an assessment of the historic behaviour of the reservoir since its completion in 1964 and the development of operating rules that accounted for: i) the multiple and different levels of reliability at which municipal and irrigation demands need to be supplied, and ii) inter-annual and inter-decadal variability of climate and inflows into the dam. The assessment of the behaviour of the reservoir was done by simulation assuming trigonometric rule curves that were optimized to maximize both yield and storage state using the SCE-UA method. The resulting reservoir behaviour matched the observed historic trajectory reasonably well and indicated that the dam has mainly been operated at a demand of 10 million m3/year until 2000 when the demand suddenly rose to 25 million m3/year. Operating rules were developed from a statistical analysis of the base yields from 500 simulations of the reservoir each using 5 year-long stochastically generated sequences of inflows, rainfall and evaporation. After the implementation of the operating rules in 2009, the storage state of the dam improved and matched those of other reservoirs in the region that had established operating rules.

The cost-effectiveness of diagnostic management strategies for adults with minor head injury.

PubMed

Holmes, M W; Goodacre, S; Stevenson, M D; Pandor, A; Pickering, A

2012-09-01

To estimate the cost-effectiveness of diagnostic management strategies for adults with minor head injury. A mathematical model was constructed to evaluate the incremental costs and effectiveness (Quality Adjusted Life years Gained, QALYs) of ten diagnostic management strategies for adults with minor head injuries. Secondary analyses were undertaken to determine the cost-effectiveness of hospital admission compared to discharge home and to explore the cost-effectiveness of strategies when no responsible adult was available to observe the patient after discharge. The apparent optimal strategy was based on the high and medium risk Canadian CT Head Rule (CCHRhm), although the costs and outcomes associated with each strategy were broadly similar. Hospital admission for patients with non-neurosurgical injury on CT dominated discharge home, whilst hospital admission for clinically normal patients with a normal CT was not cost-effective compared to discharge home with or without a responsible adult at £39 and £2.5 million per QALY, respectively. A selective CT strategy with discharge home if the CT scan was normal remained optimal compared to not investigating or CT scanning all patients when there was no responsible adult available to observe them after discharge. Our economic analysis confirms that the recent extension of access to CT scanning for minor head injury is appropriate. Liberal use of CT scanning based on a high sensitivity decision rule is not only effective but also cost-saving. The cost of CT scanning is very small compared to the estimated cost of caring for patients with brain injury worsened by delayed treatment. It is recommended therefore that all hospitals receiving patients with minor head injury should have unrestricted access to CT scanning for use in conjunction with evidence based guidelines. Provisionally the CCHRhm decision rule appears to be the best strategy although there is considerable uncertainty around the optimal decision rule. However, the CCHRhm rule appears to be the most widely validated and it therefore seems appropriate to conclude that the CCHRhm rule has the best evidence to support its use. Copyright © 2011 Elsevier Ltd. All rights reserved.

Design of integration-ready metasurface-based infrared absorbers

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

Ogando, Karim, E-mail: karim@cab.cnea.gov.ar; Pastoriza, Hernán

2015-07-28

We introduce an integration ready design of metamaterial infrared absorber, highly compatible with many kinds of fabrication processes. We present the results of an exhaustive experimental characterization, including an analysis of the effects of single meta-atom geometrical parameters and collective arrangement. We confront the results with the theoretical interpretations proposed in the literature. Based on the results, we develop a set of practical design rules for metamaterial absorbers in the infrared region.

Is heart transplantation after circulatory death compatible with the dead donor rule?

PubMed

Nair-Collins, Michael; Miller, Franklin G

2016-05-01

Dalle Ave et al (2016) provide a valuable overview of several protocols for heart transplantation after circulatory death. However, their analysis of the compatibility of heart donation after circulatory death (DCD) with the dead donor rule (DDR) is flawed. Their permanence-based criteria for death, which depart substantially from established law and bioethics, are ad hoc and unfounded. Furthermore, their analysis is self-defeating, because it undercuts the central motivation for DDR as both a legal and a moral constraint, rendering the DDR vacuous and trivial. Rather than devise new and ad hoc criteria for death for the purpose of rendering DCD nominally consistent with DDR, we contend that the best approach is to explicitly abandon DDR. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Cell Phones: Rule-Setting, Rule-Breaking, and Relationships in Classrooms

ERIC Educational Resources Information Center

Charles, Anita S.

2012-01-01

Based on a small qualitative study, this article focuses on understanding the rules for cell phones and other social networking media in schools, an aspect of broader research that led to important understandings of teacher-student negotiations. It considers the rules that schools and teachers make, the rampant breaking of these rules, the…