'The application of Bayesian Optimization and Classifier Systems in Nurse Scheduling'

Abstract. Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person's assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.

Sense-making in theory and practice: a metatheoretical foundation and application for health information seeking

This thesis attempts to provide deeper historical and theoretical grounding for sense-making, thereby illustrating its applicability to practical information seeking research. In Chapter One I trace the philosophical origins of Brenda Dervin’s theory known as “sense making,” reaching beyond current scholarship that locates the origins of sense-making in twentieth-century Phenomenology and Communication theory and find its rich ontological, epistemological, and etymological heritage that dates back to the Pre-Socratics. After exploring sense-making’s Greek roots, I examine sense-making’s philosophical undercurrents found in Hegel’s Phenomenology of Spirit (1807), where he also returns to the simplicity of the Greeks for his concept of sense. With Chapter Two I explore sense-making methodology and find, in light of the Greek and Hegelian dialectic, a dialogical bridge connecting sense-making’s theory with pragmatic uses. This bridge between Dervin’s situation and use occupies a distinct position in sense-making theory. Moreover, building upon Brenda Dervin’s model of sense-making, I use her metaphors of gap and bridge analogy to discuss the dialectic and dialogic components of sense making. The purpose of Chapter Three is pragmatic – to gain insight into the online information-seeking needs, experiences, and motivation of first-degree relatives (FDRs) of breast cancer survivors through the lens of sense-making. This research analyses four questions: 1) information-seeking behavior among FDRs of cancer survivors compared to survivors and to undiagnosed, non-related online cancer information seekers in the general population, 2) types of and places where information is sought, 3) barriers or gaps and satisfaction rates FDRs face in their cancer information quest, and 4) types and degrees of cancer information and resources FDRs want and use in their information search for themselves and other family members. An online survey instrument designed to investigate these questions was developed and pilot tested. Via an email communication, the Susan Love Breast Cancer Research Foundation distributed 322,000 invitations to its membership to complete the survey, and from March 24th to April 5th 10,692 women agreed to take the survey with 8,804 volunteers actually completing survey responses. Of the 8,804 surveys, 95% of FDRs have searched for cancer information online, and 84% of FDRs use the Internet as a sense-making tool for additional information they have received from doctors or nurses. FDRs report needing much more information than either survivors or family/friends in ten out of fifteen categories related to breast and ovarian cancer. When searching for cancer information online, FDRs also rank highest in several of sense-making’s emotional levels: uncertainty, confusion, frustration, doubt, and disappointment than do either survivors or friends and family. The sense-making process has existed in theory and praxis since the early Greeks. In applying sense–making’s theory to a contemporary problem, the survey reveals unaddressed situations and gaps of FDRs’ information search process. FDRs are a highly motivated group of online information seekers whose needs are largely unaddressed as a result of gaps in available online information targeted to address their specific needs. Since FDRs represent a quarter of the population, further research addressing their specific online information needs and experiences is necessary.

Information and the evolution of codon bias

The informational properties of biological systems are the subject of much debate and research. I present a general argument in favor of the existence and central importance of information in organisms, followed by a case study of the genetic code (specifically, codon bias) and the translation system from the perspective of information. The codon biases of 831 Bacteria and Archeae are analyzed and modeled as points in a 64-dimensional statistical space. The major results are that (1) codon bias evolution does not follow canonical patterns, and (2) the use of coding space in organsims is a subset of the total possible coding space. These findings imply that codon bias is a unique adaptive mechanism that owes its existence to organisms' use of information in representing genes, and that there is a particularly biological character to the resulting biased coding and information use.

Influence of information about trip time variability, personal benefits, and environmental harm from cars versus public transportation on the choice of transportation mode

The influence of information about trip time variability, personal benefits, or environmental harm from cars or public transportation on commuting mode choice (car or subway) is examined in an experimental study. In addition to these experimentally manipulated variables, the influence of prior attitudes towards the subway was verified. The sample is made up of habitual users of the car to travel to work (N = 220, age M = 37.4, SD = 8.1, 63.2% women). The results show that providing information about the advantages of public transportation, as well as prior attitudes towards the subway, decrease the preference, choice, and perceived control of car use. Of the experimentally manipulated variables, information about the variability of trip time had the greatest influence. These results highlight the importance of taking into account these variables to implement institutional campaigns to reduce car use as transportation mode.

'The application of Bayesian Optimization and Classifier Systems in Nurse Scheduling'

Abstract. Two ideas taken from Bayesian optimization and classifier systems are presented for personnel scheduling based on choosing a suitable scheduling rule from a set for each person's assignment. Unlike our previous work of using genetic algorithms whose learning is implicit, the learning in both approaches is explicit, i.e. we are able to identify building blocks directly. To achieve this target, the Bayesian optimization algorithm builds a Bayesian network of the joint probability distribution of the rules used to construct solutions, while the adapted classifier system assigns each rule a strength value that is constantly updated according to its usefulness in the current situation. Computational results from 52 real data instances of nurse scheduling demonstrate the success of both approaches. It is also suggested that the learning mechanism in the proposed approaches might be suitable for other scheduling problems.

Organizational, systems and human issues in production planning, scheduling and control

With global markets and global competition, pressures are placed on manufacturing organizations to compress order fulfillment times, meet delivery commitments consistently and also maintain efficiency in operations to address cost issues. This chapter argues for a process perspective on planning, scheduling and control that integrates organizational planning structures, information systems as well as human decision makers. The chapter begins with a reconsideration of the gap between theory and practice, in particular for classical scheduling theory and hierarchical production planning and control. A number of the key studies of industrial practice are then described and their implications noted. A recent model of scheduling practice derived from a detailed study of real businesses is described. Socio-technical concepts are then introduced and their implications for the design and management of planning, scheduling and control systems are discussed. The implications of adopting a process perspective are noted along with insights from knowledge management. An overview is presented of a methodology for the (re-)design of planning, scheduling and control systems that integrates organizational, system and human perspectives. The most important messages from the chapter are then summarized.

An Information Theoretic Approach to Automatic Query Expansion

International audience

Wildfire Assessment Using FARSITE Fire Modeling: A Case Study in the Chihuahua Desert of Mexico

The Chihuahua desert is one of the most biologically diverse ecosystems in the world, but suffers serious degradation because of changes in fire regimes resulting in large catastrophic fires. My study was conducted in the Sierra La Mojonera (SLM) natural protected area in Mexico. The purpose of this study was to implement the use of FARSITE fire modeling as a fire management tool to develop an integrated fire management plan at SLM. Firebreaks proved to detain 100% of wildfire outbreaks. The rosetophilous scrub experienced the fastest rate of fire spread and lowland creosote bush scrub experienced the slowest rate of fire spread. March experienced the fastest rate of fire spread, while September experienced the slowest rate of fire spread. The results of my study provide a tool for wildfire management through the use geospatial technologies and, in particular, FARSITE fire modeling in SLM and Mexico.

Model selection and the vectorial misspecification-resistant information criterion in multivariate time series

The thesis deals with the problem of Model Selection (MS) motivated by information and prediction theory, focusing on parametric time series (TS) models. The main contribution of the thesis is the extension to the multivariate case of the Misspecification-Resistant Information Criterion (MRIC), a criterion introduced recently that solves Akaike’s original research problem posed 50 years ago, which led to the definition of the AIC. The importance of MS is witnessed by the huge amount of literature devoted to it and published in scientific journals of many different disciplines. Despite such a widespread treatment, the contributions that adopt a mathematically rigorous approach are not so numerous and one of the aims of this project is to review and assess them. Chapter 2 discusses methodological aspects of MS from information theory. Information criteria (IC) for the i.i.d. setting are surveyed along with their asymptotic properties; and the cases of small samples, misspecification, further estimators. Chapter 3 surveys criteria for TS. IC and prediction criteria are considered for: univariate models (AR, ARMA) in the time and frequency domain, parametric multivariate (VARMA, VAR); nonparametric nonlinear (NAR); and high-dimensional models. The MRIC answers Akaike’s original question on efficient criteria, for possibly-misspecified (PM) univariate TS models in multi-step prediction with high-dimensional data and nonlinear models. Chapter 4 extends the MRIC to PM multivariate TS models for multi-step prediction introducing the Vectorial MRIC (VMRIC). We show that the VMRIC is asymptotically efficient by proving the decomposition of the MSPE matrix and the consistency of its Method-of-Moments Estimator (MoME), for Least Squares multi-step prediction with univariate regressor. Chapter 5 extends the VMRIC to the general multiple regressor case, by showing that the MSPE matrix decomposition holds, obtaining consistency for its MoME, and proving its efficiency. The chapter concludes with a digression on the conditions for PM VARX models.

[evaluation Of Data On Live Birth Certificates From The Information System On Live Births (sinasc) In Campinas, São Paulo, 2009].

To assess the completeness and reliability of the Information System on Live Births (Sinasc) data. A cross-sectional analysis of the reliability and completeness of Sinasc's data was performed using a sample of Live Birth Certificate (LBC) from 2009, related to births from Campinas, Southeast Brazil. For data analysis, hospitals were grouped according to category of service (Unified National Health System, private or both), 600 LBCs were randomly selected and the data were collected in LBC-copies through mothers and newborns' hospital records and by telephone interviews. The completeness of LBCs was evaluated, calculating the percentage of blank fields, and the LBCs agreement comparing the originals with the copies was evaluated by Kappa and intraclass correlation coefficients. The percentage of completeness of LBCs ranged from 99.8%-100%. For the most items, the agreement was excellent. However, the agreement was acceptable for marital status, maternal education and newborn infants' race/color, low for prenatal visits and presence of birth defects, and very low for the number of deceased children. The results showed that the municipality Sinasc is reliable for most of the studied variables. Investments in training of the professionals are suggested in an attempt to improve system capacity to support planning and implementation of health activities for the benefit of maternal and child population.

Confiabilidade da informação sobre hospitalizações por causas externas de um hospital público em São José dos Campos, São Paulo, Brasil

OBJETIVO: Conhecer a qualidade dos dados de internação por causas externas em São José dos Campos, São Paulo. MÉTODO: Foram estudadas as internações pelo Sistema Único de Saúde por lesões decorrentes de causas externas no primeiro semestre de 2003, no Hospital Municipal, referência para o atendimento ao trauma no Município, por meio da comparação dos dados registrados no Sistema de Informações Hospitalares com os prontuários de 990 internações. A concordância das variáveis relativas à vítima, à internação e ao agravo foi avaliada pela taxa bruta de concordância e pelo coeficiente Kappa. As lesões e as causas externas foram codificadas segundo a 10ª revisão da Classificação Internacional de Doenças, respectivamente, capítulos XIX e XX. RESULTADOS: A taxa de concordância bruta foi de boa qualidade para as variáveis relativas à vítima e à internação, variando de 89,0% a 99,2%. As lesões tiveram concordância ótima, exceto os traumatismos do pescoço (k=0,73), traumatismos múltiplos (k=0,67) e fraturas do tórax (k=0,49). As causas externas tiveram concordância ótima para acidentes de transporte (k=0,90) e quedas (k=0,83). A confiabilidade foi menor para agressões (k=0,50), causas indeterminadas (k=0,37), e complicações da assistência médica (k=0,03). Houve concordância ótima nos acidentes de transporte em pedestres, ciclistas e motociclistas. CONCLUSÃO: A maioria das variáveis de estudo teve boa qualidade no nível de agregação analisado. Algumas variáveis relativas à vítima e alguns tipos de causas externas necessitam de aperfeiçoamento da qualidade dos dados. O perfil da morbidade hospitalar encontrado confirmou os acidentes de transporte como importante causa externa de internação hospitalar no Município.

Genetic Variants of Diabetes Risk and Incident Cardiovascular Events in Chronic Coronary Artery Disease

Objective: To determine whether information from genetic risk variants for diabetes is associated with cardiovascular events incidence. Methods: From the about 30 known genes associated with diabetes, we genotyped single-nucleotide polymorphisms at the 10 loci most associated with type-2 diabetes in 425 subjects from the MASS-II Study, a randomized study in patients with multi-vessel coronary artery disease. The combined genetic information was evaluated by number of risk alleles for diabetes. Performance of genetic models relative to major cardiovascular events incidence was analyzed through Kaplan-Meier curve comparison and Cox Hazard Models and the discriminatory ability of models was assessed for cardiovascular events by calculating the area under the ROC curve. Results: Genetic information was able to predict 5-year incidence of major cardiovascular events and overall-mortality in non-diabetic individuals, even after adjustment for potential confounders including fasting glycemia. Non-diabetic individuals with high genetic risk had a similar incidence of events then diabetic individuals (cumulative hazard of 33.0 versus 35.1% of diabetic subjects). The addition of combined genetic information to clinical predictors significantly improved the AUC for cardiovascular events incidence (AUC = 0.641 versus 0.610). Conclusions: Combined information of genetic variants for diabetes risk is associated to major cardiovascular events incidence, including overall mortality, in non-diabetic individuals with coronary artery disease.

Continental passive microwave-based rainfall estimation algorithm: Application to the Amazon Basin

This paper presents a new statistical algorithm to estimate rainfall over the Amazon Basin region using the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI). The algorithm relies on empirical relationships derived for different raining-type systems between coincident measurements of surface rainfall rate and 85-GHz polarization-corrected brightness temperature as observed by the precipitation radar (PR) and TMI on board the TRMM satellite. The scheme includes rain/no-rain area delineation (screening) and system-type classification routines for rain retrieval. The algorithm is validated against independent measurements of the TRMM-PR and S-band dual-polarization Doppler radar (S-Pol) surface rainfall data for two different periods. Moreover, the performance of this rainfall estimation technique is evaluated against well-known methods, namely, the TRMM-2A12 [ the Goddard profiling algorithm (GPROF)], the Goddard scattering algorithm (GSCAT), and the National Environmental Satellite, Data, and Information Service (NESDIS) algorithms. The proposed algorithm shows a normalized bias of approximately 23% for both PR and S-Pol ground truth datasets and a mean error of 0.244 mm h(-1) ( PR) and -0.157 mm h(-1)(S-Pol). For rain volume estimates using PR as reference, a correlation coefficient of 0.939 and a normalized bias of 0.039 were found. With respect to rainfall distributions and rain area comparisons, the results showed that the formulation proposed is efficient and compatible with the physics and dynamics of the observed systems over the area of interest. The performance of the other algorithms showed that GSCAT presented low normalized bias for rain areas and rain volume [0.346 ( PR) and 0.361 (S-Pol)], and GPROF showed rainfall distribution similar to that of the PR and S-Pol but with a bimodal distribution. Last, the five algorithms were evaluated during the TRMM-Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA) 1999 field campaign to verify the precipitation characteristics observed during the easterly and westerly Amazon wind flow regimes. The proposed algorithm presented a cumulative rainfall distribution similar to the observations during the easterly regime, but it underestimated for the westerly period for rainfall rates above 5 mm h(-1). NESDIS(1) overestimated for both wind regimes but presented the best westerly representation. NESDIS(2), GSCAT, and GPROF underestimated in both regimes, but GPROF was closer to the observations during the easterly flow.

New perspectives on the processing and release of public information

This article discusses issues related to the organization and reception of information in the context of services and public information systems driven by technology. It stems from the assumption that in a ""technologized"" society, the distance between users and information is almost always of cognitive and socio-cultural nature, a product of our effort to design communication. In this context, we favor the approach of the information sign, seeking to answer how a documentary message turns into information, i.e. a structure recognized as socially useful. Observing the structural, cognitive and communicative aspects of the documentary message, based on Documentary Linguistics, Terminology, as well as on Textual Linguistics, the policy of knowledge management and innovation of the Government of the State of Sao Paulo is analyzed, which authorizes the use of Web 2.0, also questioning to what extent this initiative represents innovation in the environment of libraries.

Human gait recognition using extraction and fusion of global motion features

This paper proposes a novel computer vision approach that processes video sequences of people walking and then recognises those people by their gait. Human motion carries different information that can be analysed in various ways. The skeleton carries motion information about human joints, and the silhouette carries information about boundary motion of the human body. Moreover, binary and gray-level images contain different information about human movements. This work proposes to recover these different kinds of information to interpret the global motion of the human body based on four different segmented image models, using a fusion model to improve classification. Our proposed method considers the set of the segmented frames of each individual as a distinct class and each frame as an object of this class. The methodology applies background extraction using the Gaussian Mixture Model (GMM), a scale reduction based on the Wavelet Transform (WT) and feature extraction by Principal Component Analysis (PCA). We propose four new schemas for motion information capture: the Silhouette-Gray-Wavelet model (SGW) captures motion based on grey level variations; the Silhouette-Binary-Wavelet model (SBW) captures motion based on binary information; the Silhouette-Edge-Binary model (SEW) captures motion based on edge information and the Silhouette Skeleton Wavelet model (SSW) captures motion based on skeleton movement. The classification rates obtained separately from these four different models are then merged using a new proposed fusion technique. The results suggest excellent performance in terms of recognising people by their gait.