An alternative formulation for the fuzzy assignment problem

The existing assignment problems for assigning n jobs to n individuals are limited to the considerations of cost or profit measured as crisp. However, in many real applications, costs are not deterministic numbers. This paper develops a procedure based on Data Envelopment Analysis method to solve the assignment problems with fuzzy costs or fuzzy profits for each possible assignment. It aims to obtain the points with maximum membership values for the fuzzy parameters while maximizing the profit or minimizing the assignment cost. In this method, a discrete approach is presented to rank the fuzzy numbers first. Then, corresponding to each fuzzy number, we introduce a crisp number using the efficiency concept. A numerical example is used to illustrate the usefulness of this new method. © 2012 Operational Research Society Ltd. All rights reserved.

Flexible measures in production process:a DEA-based approach

Data envelopment analysis (DEA) has been proven as an excellent data-oriented efficiency analysis method for comparing decision making units (DMUs) with multiple inputs and multiple outputs. In conventional DEA, it is assumed that the status of each measure is clearly known as either input or output. However, in some situations, a performance measure can play input role for some DMUs and output role for others. Cook and Zhu [Eur. J. Oper. Res. 180 (2007) 692–699] referred to these variables as flexible measures. The paper proposes an alternative model in which each flexible measure is treated as either input or output variable to maximize the technical efficiency of the DMU under evaluation. The main focus of this paper is on the impact that the flexible measures has on the definition of the PPS and the assessment of technical efficiency. An example in UK higher education intuitions shows applicability of the proposed approach.

Grading of corneal transparency

Aim: To examine the academic literature on the grading of corneal transparency and to assess the potential use of objective image analysis. Method: Reference databases of academic literature were searched and relevant manuscripts reviewed. Annunziato, Efron (Millennium Edition) and Vistakon-Synoptik corneal oedema grading scale images were analysed objectively for relative intensity, edges detected, variation in intensity and maximum intensity. In addition, corneal oedema was induced in one subject using a low oxygen transmissibility (Dk/t) hydrogel contact lens worn for 3 hours under a light eye patch. Recovery from oedema was monitored over time using ultrasound pachymetry, high and low contrast visual acuity measures, bulbar hyperaemia grading and transparency image analysis of the test and control eyes. Results: Several methods for assessing corneal transparency are described in the academic literature, but none have gained widespread in clinical practice. The change in objective image analysis with printed scale grade was best described by quadratic parametric or sigmoid 3-parameter functions. ‘Pupil image scales’ (Annunziato and Vistakon-Synoptik) were best correlated to average intensity; however, the corneal section scale (Efron) was strongly correlated to variations in intensity. As expected, patching an eye wearing a low Dk/t hydrogel contact lens caused a significant (F=119.2, P<0.001) 14.3% increase in corneal thickness, which gradually recovered under open eye conditions. Corneal section image analysis was the most affected parameter and intensity variation across the slit width, in isolation, was the strongest correlate, accounting for 85.8% of the variance with time following patching, and 88.7% of the variance with corneal thickness. Conclusion: Corneal oedema is best determined objectively by the intensity variation across the width of a corneal section. This can be easily measured using a slit-lamp camera connected to a computer. Oedema due to soft contact lens wear is not easily determined over the pupil area by sclerotic scatter illumination techniques.

Zr-containing hybrid organic-inorganic mesoporous materials:hydrophobic acid catalysts for biodiesel production

Zirconium-containing periodic mesoporous organosilicas (Zr-PMOs) with varying framework organic content have been synthesized through a direct synthesis method. These materials display the excellent textural properties of the analogous inorganic solid acid Zr-SBA-15 material. However, the substitution of silica by organosilicon species provides a strong hydrophobic character. This substitution leads to meaningful differences in the environment surrounding the zirconium metal sites, leading the modification of the catalytic properties of these materials. Although lower metal incorporation is accomplished in the final materials, leading to a lower population of metal sites, hydrophobisation leads to an impressive beneficial effect on the intrinsic catalytic activity of the zirconium sites in biodiesel production by esterification/transesterification of free fatty acid -containing feedstock. Moreover, the catalytic activity of the highly hybridised materials is hardly affected in presence of large amounts of water, confirming their very good water-tolerance. This makes Zr-PMO materials interesting catalysts for biodiesel production from highly acidic water-containing feedstock. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Knowledge Learning Technology for Intelligent Tutoring Systems

In this work we suggest the technology of creation of intelligent tutoring systems which are oriented to teach knowledge. It is supposed the acquisition of expert’s knowledge by using of the Formal Concept Analysis method, then construction the test questions which are used for verification of the pupil's knowledge with the expert’s knowledge. Then the further tutoring strategy is generated by the results of this verification.

Anisotropic viscoelastic properties of undamaged asphalt mixtures

A test protocol and a data analysis method are developed in this paper on the basis of linear viscoelastic theory to characterize the anisotropic viscoelastic properties of undamaged asphalt mixtures. The test protocol includes three nondestructive tests: (1) uniaxial compressive creep test, (2) indirect tensile creep test, and (3) the uniaxial tensile creep test. All three tests are conducted on asphalt mixture specimens at three temperatures (10, 20, and 30°C) to determine the tensile and compressive properties at each temperature and then to construct the master curve of each property. The determined properties include magnitude and phase angle of the compressive complex modulus in the vertical direction, magnitude and phase angle of the tensile complex modulus, and the magnitude and phase angle of the compressive complex modulus in the horizontal plane. The test results indicate that all tested asphalt mixtures have significantly different tensile properties from compressive properties. The peak value of the master curve of the tensile complex modulus phase angle is within a range from 65 to 85°, whereas the peak value of the compressive moduli phase angle in both directions ranges from 35 to 55°. In addition, the undamaged asphalt mixtures exhibit distinctively anisotropic properties in compression. The magnitude of the compressive modulus in the vertical direction is approximately 1.2 to ̃2 times of the magnitude of the compressive modulus in the horizontal plane. Dynamic modulus tests are performed to verify the results of the proposed test protocol. The test results from the proposed test protocol match well with those from the dynamic tests. © 2012 American Society of Civil Engineers.

Using overlay test to evaluate fracture properties of field-aged asphalt concrete

Field material testing provides firsthand information on pavement conditions which are most helpful in evaluating performance and identifying preventive maintenance or overlay strategies. High variability of field asphalt concrete due to construction raises the demand for accuracy of the test. Accordingly, the objective of this study is to propose a reliable and repeatable methodology to evaluate the fracture properties of field-aged asphalt concrete using the overlay test (OT). The OT is selected because of its efficiency and feasibility for asphalt field cores with diverse dimensions. The fracture properties refer to the Paris’ law parameters based on the pseudo J-integral (A and n) because of the sound physical significance of the pseudo J-integral with respect to characterizing the cracking process. In order to determine A and n, a two-step OT protocol is designed to characterize the undamaged and damaged behaviors of asphalt field cores. To ensure the accuracy of determined undamaged and fracture properties, a new analysis method is then developed for data processing, which combines the finite element simulations and mechanical analysis of viscoelastic force equilibrium and evolution of pseudo displacement work in the OT specimen. Finally, theoretical equations are derived to calculate A and n directly from the OT test data. The accuracy of the determined fracture properties is verified. The proposed methodology is applied to a total of 27 asphalt field cores obtained from a field project in Texas, including the control Hot Mix Asphalt (HMA) and two types of warm mix asphalt (WMA). The results demonstrate a high linear correlation between n and −log A for all the tested field cores. Investigations of the effect of field aging on the fracture properties confirm that n is a good indicator to quantify the cracking resistance of asphalt concrete. It is also indicated that summer climatic condition clearly accelerates the rate of aging. The impact of the WMA technologies on fracture properties of asphalt concrete is visualized by comparing the n-values. It shows that the Evotherm WMA technology slightly improves the cracking resistance, while the foaming WMA technology provides the comparable fracture properties with the HMA. After 15 months aging in the field, the cracking resistance does not exhibit significant difference between HMA and WMAs, which is confirmed by the observations of field distresses.

A methodology for formally modeling and analyzing software architecture of mobile agent systems

A methodology for formally modeling and analyzing software architecture of mobile agent systems provides a solid basis to develop high quality mobile agent systems, and the methodology is helpful to study other distributed and concurrent systems as well. However, it is a challenge to provide the methodology because of the agent mobility in mobile agent systems.^ The methodology was defined from two essential parts of software architecture: a formalism to define the architectural models and an analysis method to formally verify system properties. The formalism is two-layer Predicate/Transition (PrT) nets extended with dynamic channels, and the analysis method is a hierarchical approach to verify models on different levels. The two-layer modeling formalism smoothly transforms physical models of mobile agent systems into their architectural models. Dynamic channels facilitate the synchronous communication between nets, and they naturally capture the dynamic architecture configuration and agent mobility of mobile agent systems. Component properties are verified based on transformed individual components, system properties are checked in a simplified system model, and interaction properties are analyzed on models composing from involved nets. Based on the formalism and the analysis method, this researcher formally modeled and analyzed a software architecture of mobile agent systems, and designed an architectural model of a medical information processing system based on mobile agents. The model checking tool SPIN was used to verify system properties such as reachability, concurrency and safety of the medical information processing system. ^ From successful modeling and analyzing the software architecture of mobile agent systems, the conclusion is that PrT nets extended with channels are a powerful tool to model mobile agent systems, and the hierarchical analysis method provides a rigorous foundation for the modeling tool. The hierarchical analysis method not only reduces the complexity of the analysis, but also expands the application scope of model checking techniques. The results of formally modeling and analyzing the software architecture of the medical information processing system show that model checking is an effective and an efficient way to verify software architecture. Moreover, this system shows a high level of flexibility, efficiency and low cost of mobile agent technologies. ^

Commitment language as a predictor of alcohol and marijuana use outcomes among adolescents in school -based group treatment

Despite widespread recognition of the problem of adolescent alcohol and other drug (AOD) abuse, research on its most common treatment modality, group work, is lacking. This research gap is alarming given that outcomes range from positive to potentially iatrogenic. This study sought to identify change mechanisms and/or treatment factors that are observable within group treatment sessions and that may predict AOD use outcomes. This NIH (F31 DA 020233-01A1) study evaluated 108, 10-19 year olds and the 19 school-based treatment groups to which they were previously assigned (R01 AA10246; PI: Wagner). Associations between motivational interviewing (MI) based change talk variables, group leader MI skills, and alcohol and marijuana use outcomes up to 12-months following treatment were evaluated. Treatment session audio recordings and transcripts (1R21AA015679-01; PI: Macgowan) were coded using a new discourse analysis coding scheme for measuring group member change talk (Amrhein, 2003). Therapist MI skills were similarly measured using the Motivational Interviewing Treatment Integrity instrument. Group member responses to commitment predicted group marijuana use at the 1-month follow up. Also, group leader empathy was significantly associated with group commitment for marijuana use at the middle and ending stages of treatment. Both of the above process measures were applied in a group setting for the first time. Building upon MI and social learning theory principles, group commitment and group member responses to commitment are new observable, in-session, process constructs that may predict positive and negative adolescent group treatment outcomes. These constructs, as well as the discourse analysis method and instruments used to measure them, raise many possibilities for future group work process research and practice.

Methods for modeling and analyzing concurrent software

Concurrent software executes multiple threads or processes to achieve high performance. However, concurrency results in a huge number of different system behaviors that are difficult to test and verify. The aim of this dissertation is to develop new methods and tools for modeling and analyzing concurrent software systems at design and code levels. This dissertation consists of several related results. First, a formal model of Mondex, an electronic purse system, is built using Petri nets from user requirements, which is formally verified using model checking. Second, Petri nets models are automatically mined from the event traces generated from scientific workflows. Third, partial order models are automatically extracted from some instrumented concurrent program execution, and potential atomicity violation bugs are automatically verified based on the partial order models using model checking. Our formal specification and verification of Mondex have contributed to the world wide effort in developing a verified software repository. Our method to mine Petri net models automatically from provenance offers a new approach to build scientific workflows. Our dynamic prediction tool, named McPatom, can predict several known bugs in real world systems including one that evades several other existing tools. McPatom is efficient and scalable as it takes advantage of the nature of atomicity violations and considers only a pair of threads and accesses to a single shared variable at one time. However, predictive tools need to consider the tradeoffs between precision and coverage. Based on McPatom, this dissertation presents two methods for improving the coverage and precision of atomicity violation predictions: 1) a post-prediction analysis method to increase coverage while ensuring precision; 2) a follow-up replaying method to further increase coverage. Both methods are implemented in a completely automatic tool.

A generalized adaptive mathematical morphological filter for LIDAR data

Airborne Light Detection and Ranging (LIDAR) technology has become the primary method to derive high-resolution Digital Terrain Models (DTMs), which are essential for studying Earth's surface processes, such as flooding and landslides. The critical step in generating a DTM is to separate ground and non-ground measurements in a voluminous point LIDAR dataset, using a filter, because the DTM is created by interpolating ground points. As one of widely used filtering methods, the progressive morphological (PM) filter has the advantages of classifying the LIDAR data at the point level, a linear computational complexity, and preserving the geometric shapes of terrain features. The filter works well in an urban setting with a gentle slope and a mixture of vegetation and buildings. However, the PM filter often removes ground measurements incorrectly at the topographic high area, along with large sizes of non-ground objects, because it uses a constant threshold slope, resulting in "cut-off" errors. A novel cluster analysis method was developed in this study and incorporated into the PM filter to prevent the removal of the ground measurements at topographic highs. Furthermore, to obtain the optimal filtering results for an area with undulating terrain, a trend analysis method was developed to adaptively estimate the slope-related thresholds of the PM filter based on changes of topographic slopes and the characteristics of non-terrain objects. The comparison of the PM and generalized adaptive PM (GAPM) filters for selected study areas indicates that the GAPM filter preserves the most "cut-off" points removed incorrectly by the PM filter. The application of the GAPM filter to seven ISPRS benchmark datasets shows that the GAPM filter reduces the filtering error by 20% on average, compared with the method used by the popular commercial software TerraScan. The combination of the cluster method, adaptive trend analysis, and the PM filter allows users without much experience in processing LIDAR data to effectively and efficiently identify ground measurements for the complex terrains in a large LIDAR data set. The GAPM filter is highly automatic and requires little human input. Therefore, it can significantly reduce the effort of manually processing voluminous LIDAR measurements.

Vivências do pai/ acompanhante no processo da hospitalização do filho

The presence of the man with the hospitalized child is still insignificant and the relationships established in hospitals culminate in several situations that can to influence his experience. The study aimed to analyze the experiences of the parent / caregiver during the hospitalization of their child. In intention to develop the research, it was conducted an exploratory and descriptive qualitative research approach, developed with 11 fathers who accompanied sick childen at the Paediatric Hospital in metropolitan area of Natal, Rio Grande do Norte, Brazil. As inclusion criteria men should be aged 18 years; have favorable emotional conditions to answer the questions are be accompanying his child aged between one to five years old in clinical or surgical. Data collection occurred in March and April 2014, using an interview script. This step prior to the approval of the Health Department of state of Rio Grande do Norte, approved by Universidade Federal do Rio Grande do Norte Committee on Ethics in Research by Certificate of Presentation and Ethics Consideration No. 22821513.1.0000.5537. The data treatment occurred following the content analysis method in thematic modality proposed by Bardin. According to statements the following categories emerged: "The presence of the father in the hospitalization of a child" and "Responsibilities and parental attitudes the hospitalization of a child”, which were analyzed and discussed based on the literature on the family in the hospitalization of the child and considerations about the care of the child. It was founded that the respondents the experienced institutionalization son were inserted in a context of active participation of tasks and sharing responsibilities. Thus it was considered in the study the need of enforcing rights of the father as a family entity in practice of them child care instead of social and gender issues that are still strongly rooted in contemporary society. Given this, it is necessary that the nursing staff consider the various situations faced by man during infant hospitalization with the first fruits of this approach to the care of the son process minimizing the sequelae stemming from being away from the family nucleus

Industrial fault detection and diagnosis using Bayesian belief network

Rapid development in industry have contributed to more complex systems that are prone to failure. In applications where the presence of faults may lead to premature failure, fault detection and diagnostics tools are often implemented. The goal of this research is to improve the diagnostic ability of existing FDD methods. Kernel Principal Component Analysis has good fault detection capability, however it can only detect the fault and identify few variables that have contribution on occurrence of fault and thus not precise in diagnosing. Hence, KPCA was used to detect abnormal events and the most contributed variables were taken out for more analysis in diagnosis phase. The diagnosis phase was done in both qualitative and quantitative manner. In qualitative mode, a networked-base causality analysis method was developed to show the causal effect between the most contributing variables in occurrence of the fault. In order to have more quantitative diagnosis, a Bayesian network was constructed to analyze the problem in probabilistic perspective.

A evasão no Curso Técnico Subsequente em Secretaria Escolar no campus São Sebastião do Instituto Federal de Brasília : uma questão de género?

Dissertação apresentada para obtenção do grau de mestre no âmbito do Mestrado em Educação Social e Intervenção Comunitária da Escola Superior de Educação do Instituto Politécnico de Santarém

Radiotherapy Treatment Assessment using DCE-MRI

Abstract

The goal of modern radiotherapy is to precisely deliver a prescribed radiation dose to delineated target volumes that contain a significant amount of tumor cells while sparing the surrounding healthy tissues/organs. Precise delineation of treatment and avoidance volumes is the key for the precision radiation therapy. In recent years, considerable clinical and research efforts have been devoted to integrate MRI into radiotherapy workflow motivated by the superior soft tissue contrast and functional imaging possibility. Dynamic contrast-enhanced MRI (DCE-MRI) is a noninvasive technique that measures properties of tissue microvasculature. Its sensitivity to radiation-induced vascular pharmacokinetic (PK) changes has been preliminary demonstrated. In spite of its great potential, two major challenges have limited DCE-MRI’s clinical application in radiotherapy assessment: the technical limitations of accurate DCE-MRI imaging implementation and the need of novel DCE-MRI data analysis methods for richer functional heterogeneity information.

This study aims at improving current DCE-MRI techniques and developing new DCE-MRI analysis methods for particular radiotherapy assessment. Thus, the study is naturally divided into two parts. The first part focuses on DCE-MRI temporal resolution as one of the key DCE-MRI technical factors, and some improvements regarding DCE-MRI temporal resolution are proposed; the second part explores the potential value of image heterogeneity analysis and multiple PK model combination for therapeutic response assessment, and several novel DCE-MRI data analysis methods are developed.

I. Improvement of DCE-MRI temporal resolution. First, the feasibility of improving DCE-MRI temporal resolution via image undersampling was studied. Specifically, a novel MR image iterative reconstruction algorithm was studied for DCE-MRI reconstruction. This algorithm was built on the recently developed compress sensing (CS) theory. By utilizing a limited k-space acquisition with shorter imaging time, images can be reconstructed in an iterative fashion under the regularization of a newly proposed total generalized variation (TGV) penalty term. In the retrospective study of brain radiosurgery patient DCE-MRI scans under IRB-approval, the clinically obtained image data was selected as reference data, and the simulated accelerated k-space acquisition was generated via undersampling the reference image full k-space with designed sampling grids. Two undersampling strategies were proposed: 1) a radial multi-ray grid with a special angular distribution was adopted to sample each slice of the full k-space; 2) a Cartesian random sampling grid series with spatiotemporal constraints from adjacent frames was adopted to sample the dynamic k-space series at a slice location. Two sets of PK parameters’ maps were generated from the undersampled data and from the fully-sampled data, respectively. Multiple quantitative measurements and statistical studies were performed to evaluate the accuracy of PK maps generated from the undersampled data in reference to the PK maps generated from the fully-sampled data. Results showed that at a simulated acceleration factor of four, PK maps could be faithfully calculated from the DCE images that were reconstructed using undersampled data, and no statistically significant differences were found between the regional PK mean values from undersampled and fully-sampled data sets. DCE-MRI acceleration using the investigated image reconstruction method has been suggested as feasible and promising.

Second, for high temporal resolution DCE-MRI, a new PK model fitting method was developed to solve PK parameters for better calculation accuracy and efficiency. This method is based on a derivative-based deformation of the commonly used Tofts PK model, which is presented as an integrative expression. This method also includes an advanced Kolmogorov-Zurbenko (KZ) filter to remove the potential noise effect in data and solve the PK parameter as a linear problem in matrix format. In the computer simulation study, PK parameters representing typical intracranial values were selected as references to simulated DCE-MRI data for different temporal resolution and different data noise level. Results showed that at both high temporal resolutions (<1s) and clinically feasible temporal resolution (~5s), this new method was able to calculate PK parameters more accurate than the current calculation methods at clinically relevant noise levels; at high temporal resolutions, the calculation efficiency of this new method was superior to current methods in an order of 102. In a retrospective of clinical brain DCE-MRI scans, the PK maps derived from the proposed method were comparable with the results from current methods. Based on these results, it can be concluded that this new method can be used for accurate and efficient PK model fitting for high temporal resolution DCE-MRI.

II. Development of DCE-MRI analysis methods for therapeutic response assessment. This part aims at methodology developments in two approaches. The first one is to develop model-free analysis method for DCE-MRI functional heterogeneity evaluation. This approach is inspired by the rationale that radiotherapy-induced functional change could be heterogeneous across the treatment area. The first effort was spent on a translational investigation of classic fractal dimension theory for DCE-MRI therapeutic response assessment. In a small-animal anti-angiogenesis drug therapy experiment, the randomly assigned treatment/control groups received multiple fraction treatments with one pre-treatment and multiple post-treatment high spatiotemporal DCE-MRI scans. In the post-treatment scan two weeks after the start, the investigated Rényi dimensions of the classic PK rate constant map demonstrated significant differences between the treatment and the control groups; when Rényi dimensions were adopted for treatment/control group classification, the achieved accuracy was higher than the accuracy from using conventional PK parameter statistics. Following this pilot work, two novel texture analysis methods were proposed. First, a new technique called Gray Level Local Power Matrix (GLLPM) was developed. It intends to solve the lack of temporal information and poor calculation efficiency of the commonly used Gray Level Co-Occurrence Matrix (GLCOM) techniques. In the same small animal experiment, the dynamic curves of Haralick texture features derived from the GLLPM had an overall better performance than the corresponding curves derived from current GLCOM techniques in treatment/control separation and classification. The second developed method is dynamic Fractal Signature Dissimilarity (FSD) analysis. Inspired by the classic fractal dimension theory, this method measures the dynamics of tumor heterogeneity during the contrast agent uptake in a quantitative fashion on DCE images. In the small animal experiment mentioned before, the selected parameters from dynamic FSD analysis showed significant differences between treatment/control groups as early as after 1 treatment fraction; in contrast, metrics from conventional PK analysis showed significant differences only after 3 treatment fractions. When using dynamic FSD parameters, the treatment/control group classification after 1st treatment fraction was improved than using conventional PK statistics. These results suggest the promising application of this novel method for capturing early therapeutic response.

The second approach of developing novel DCE-MRI methods is to combine PK information from multiple PK models. Currently, the classic Tofts model or its alternative version has been widely adopted for DCE-MRI analysis as a gold-standard approach for therapeutic response assessment. Previously, a shutter-speed (SS) model was proposed to incorporate transcytolemmal water exchange effect into contrast agent concentration quantification. In spite of richer biological assumption, its application in therapeutic response assessment is limited. It might be intriguing to combine the information from the SS model and from the classic Tofts model to explore potential new biological information for treatment assessment. The feasibility of this idea was investigated in the same small animal experiment. The SS model was compared against the Tofts model for therapeutic response assessment using PK parameter regional mean value comparison. Based on the modeled transcytolemmal water exchange rate, a biological subvolume was proposed and was automatically identified using histogram analysis. Within the biological subvolume, the PK rate constant derived from the SS model were proved to be superior to the one from Tofts model in treatment/control separation and classification. Furthermore, novel biomarkers were designed to integrate PK rate constants from these two models. When being evaluated in the biological subvolume, this biomarker was able to reflect significant treatment/control difference in both post-treatment evaluation. These results confirm the potential value of SS model as well as its combination with Tofts model for therapeutic response assessment.

In summary, this study addressed two problems of DCE-MRI application in radiotherapy assessment. In the first part, a method of accelerating DCE-MRI acquisition for better temporal resolution was investigated, and a novel PK model fitting algorithm was proposed for high temporal resolution DCE-MRI. In the second part, two model-free texture analysis methods and a multiple-model analysis method were developed for DCE-MRI therapeutic response assessment. The presented works could benefit the future DCE-MRI routine clinical application in radiotherapy assessment.