Professional and community satisfaction with the Brazilian family health strategy

OBJECTIVE: To analyze the strengths and limitations of the Family Health Strategy from the perspective of health care professionals and the community. METHODS: Between June-August 2009, in the city of Vespasiano, Minas Gerais State, Southeastern Brazil, a questionnaire was used to evaluate the Family Health Strategy (ESF) with 77 healthcare professionals and 293 caregivers of children under five. Health care professional training, community access to health care, communication with patients and delivery of health education and pediatric care were the main points of interest in the evaluation. Logistic regression analysis was used to obtain odds ratios and 95% confidence intervals as well as to assess the statistical significance of the variables studied. RESULTS: The majority of health care professionals reported their program training was insufficient in quantity, content and method of delivery. Caregivers and professionals identified similar weaknesses (services not accessible to the community, lack of healthcare professionals, poor training for professionals) and strengths (community health worker-patient communications, provision of educational information, and pediatric care). Recommendations for improvement included: more doctors and specialists, more and better training, and scheduling improvements. Caregiver satisfaction with the ESF was found to be related to perceived benefits such as community health agent household visits (OR 5.8, 95%CI 2.8;12.1), good professional-patient relationships (OR 4.8, 95%CI 2.5;9.3), and family-focused health (OR 4.1, 95%CI 1.6;10.2); and perceived problems such as lack of personnel (OR 0.3, 95%CI 0.2;0.6), difficulty with access (OR 0.2, 95%CI 0.1;0.4), and poor quality of care (OR 0.3, 95%CI 0.1;0.6). Overall, 62% of caregivers reported being generally satisfied with the ESF services. CONCLUSIONS: Identifying the limitations and strengths of the Family Health Strategy from the healthcare professional and caregiver perspective may serve to advance primary community healthcare in Brazil.

Phex: a computational tool for plate heat exchanges design problems

This paper presents a computational tool (PHEx) developed in Excel VBA for solving sizing and rating design problems involving Chevron type plate heat exchangers (PHE) with 1-pass-1-pass configuration. The rating methodology procedure used in the program is outlined, and a case study is presented with the purpose to show how the program can be used to develop sensitivity analysis to several dimensional parameters of PHE and to observe their effect on transferred heat and pressure drop.

Meta-heuristics self-parameterization in a multi-agent scheduling system using case-based reasoning

This paper proposes a novel agent-based approach to Meta-Heuristics self-configuration. Meta-heuristics are algorithms with parameters which need to be set up as efficient as possible in order to unsure its performance. A learning module for self-parameterization of Meta-heuristics (MH) in a Multi-Agent System (MAS) for resolution of scheduling problems is proposed in this work. The learning module is based on Case-based Reasoning (CBR) and two different integration approaches are proposed. A computational study is made for comparing the two CBR integration perspectives. Finally, some conclusions are reached and future work outlined.

Probability and statistics: selected problems

Probability and Statistics—Selected Problems is a unique book for senior undergraduate and graduate students to fast review basic materials in Probability and Statistics. Descriptive statistics are presented first, and probability is reviewed secondly. Discrete and continuous distributions are presented. Sample and estimation with hypothesis testing are presented in the last two chapters. The solutions for proposed excises are listed for readers to references.

Estudo e análise do sequenciamento de tarefas de produção: job shop scheduling

Esta dissertação apresenta um estudo sobre os problemas de sequenciamento de tarefas de produção do tipo job shop scheduling. Os problemas de sequenciamento de tarefas de produção pretendem encontrar a melhor sequência para o processamento de uma lista de tarefas, o instante de início e término de cada tarefa e a afetação de máquinas para as tarefas. Entre estes, encontram-se os problemas com máquinas paralelas, os problemas job shop e flow shop. As medidas de desempenho mais comuns são o makespan (instante de término da execução de todas as tarefas), o tempo de fluxo total, a soma dos atrasos (tardiness), o atraso máximo, o número de tarefas que são completadas após a data limite, entre outros. Num problema do tipo job shop, as tarefas (jobs) consistem num conjunto de operações que têm de ser executadas numa máquina pré-determinada, obedecendo a um determinado sequenciamento com tempos pré-definidos. Estes ambientes permitem diferentes cenários de sequenciamento das tarefas. Normalmente, não são permitidas interrupções no processamento das tarefas (preemption) e pode ainda ser necessário considerar tempos de preparação dependentes da sequência (sequence dependent setup times) ou atribuir pesos (prioridades) diferentes em função da importância da tarefa ou do cliente. Pretende-se o estudo dos modelos matemáticos existentes para várias variantes dos problemas de sequenciamento de tarefas do tipo job shop e a comparação dos resultados das diversas medidas de desempenho da produção. Este trabalho contribui para demonstrar a importância que um bom sequenciamento da produção pode ter na sua eficiência e consequente impacto financeiro.

Scalable Intelligence for Scheduling Systems

A personalização é um aspeto chave de uma interação homem-computador efetiva. Numa era em que existe uma abundância de informação e tantas pessoas a interagir com ela, de muitas maneiras, a capacidade de se ajustar aos seus utilizadores é crucial para qualquer sistema moderno. A criação de sistemas adaptáveis é um domínio bastante complexo que necessita de métodos muito específicos para ter sucesso. No entanto, nos dias de hoje ainda não existe um modelo ou arquitetura padrão para usar nos sistemas adaptativos modernos. A principal motivação desta tese é a proposta de uma arquitetura para modelação do utilizador que seja capaz de incorporar diferentes módulos necessários para criar um sistema com inteligência escalável com técnicas de modelação. Os módulos cooperam de forma a analisar os utilizadores e caracterizar o seu comportamento, usando essa informação para fornecer uma experiência de sistema customizada que irá aumentar não só a usabilidade do sistema mas também a produtividade e conhecimento do utilizador. A arquitetura proposta é constituída por três componentes: uma unidade de informação do utilizador, uma estrutura matemática capaz de classificar os utilizadores e a técnica a usar quando se adapta o conteúdo. A unidade de informação do utilizador é responsável por conhecer os vários tipos de indivíduos que podem usar o sistema, por capturar cada detalhe de interações relevantes entre si e os seus utilizadores e também contém a base de dados que guarda essa informação. A estrutura matemática é o classificador de utilizadores, e tem como tarefa a sua análise e classificação num de três perfis: iniciado, intermédio ou avançado. Tanto as redes de Bayes como as neuronais são utilizadas, e uma explicação de como as preparar e treinar para lidar com a informação do utilizador é apresentada. Com o perfil do utilizador definido torna-se necessária uma técnica para adaptar o conteúdo do sistema. Nesta proposta, uma abordagem de iniciativa mista é apresentada tendo como base a liberdade de tanto o utilizador como o sistema controlarem a comunicação entre si. A arquitetura proposta foi desenvolvida como parte integrante do projeto ADSyS - um sistema de escalonamento dinâmico - utilizado para resolver problemas de escalonamento sujeitos a eventos dinâmicos. Possui uma complexidade elevada mesmo para utilizadores frequentes, daí a necessidade de adaptar o seu conteúdo de forma a aumentar a sua usabilidade. Com o objetivo de avaliar as contribuições deste trabalho, um estudo computacional acerca do reconhecimento dos utilizadores foi desenvolvido, tendo por base duas sessões de avaliação de usabilidade com grupos de utilizadores distintos. Foi possível concluir acerca dos benefícios na utilização de técnicas de modelação do utilizador com a arquitetura proposta.

Environmental management problems, future generations and social decisions

The decisions of many individuals and social groups, taking according to well-defined objectives, are causing serious social and environmental problems, in spite of following the dictates of economic rationality. There are many examples of serious problems for which there are not yet appropriate solutions, such as management of scarce natural resources including aquifer water or the distribution of space among incompatible uses. In order to solve these problems, the paper first characterizes the resources and goods involved from an economic perspective. Then, for each case, the paper notes that there is a serious divergence between individual and collective interests and, where possible, it designs the procedure for solving the conflict of interests. With this procedure, the real opportunities for the application of economic theory are shown, and especially the theory on collective goods and externalities. The limitations of conventional economic analysis are shown and the opportunity to correct the shortfalls is examined. Many environmental problems, such as climate change, have an impact on different generations that do not participate in present decisions. The paper shows that for these cases, the solutions suggested by economic theory are not valid. Furthermore, conventional methods of economic valuation (which usually help decision-makers) are unable to account for the existence of different generations and tend to obviate long-term impacts. The paper analyzes how economic valuation methods could account for the costs and benefits enjoyed by present and future generations. The paper studies an appropriate consideration of preferences for future consumption and the incorporation of sustainability as a requirement in social decisions, which implies not only more efficiency but also a fairer distribution between generations than the one implied by conventional economic analysis.

Qualité des statistiques de causes de décès: problèmes actuels et perspectives. [Quality of statistics on causes of death: current problems and perspectives].

Statistics of causes of death remain an important source of epidemiological data for the evaluation of various medical and health problems. The improvement of analytical techniques and, above all, the transformation of demographic and morbid structures of populations have prompted researchers in the field to give more importance to the quality of death certificates. After describing the data collection system presently used in Switzerland, the paper discusses various indirect estimations of the quality of Swiss data and reviews the corresponding international literature.

Social freezing in Switzerland and worldwide--a blessing for women today?

This is a critical review of the medical, ethical, judicial and financial aspects of the so called "social freezing", the cryopreservation of a woman's oocytes for non-medical purposes. The possibility of storing the eggs of fertile women in order to prevent age-related fertility decline is being widely promoted by fertility centres and the lay press throughout the world. Research data has shown that social freezing should ideally be performed on women around 25 years of age in order to increase their chances of a future pregnancy. In reality, it is mostly performed after the age of 35. Unfortunately, social freezing is in general not a solution for the underlying societal problems to fit in with professionally active women and having children. It only delays the existing problems. Furthermore, it creates a lot of potential new problems. A great deal more should be undertaken to offer real solutions to the underlying societal problems which are in part: pre-school education, care in the event of childhood illness, and the many weeks of school holidays, acceptance of professionally active women having children, and more job offers with a workload <100%.). Furthermore, society should be informed about the decreasing chances of pregnancy with increasing maternal (and paternal) age as well as the increasing risks of miscarriage and obstetric/neonatal complications. Detailed information for woman considering social freezing is crucial. Every doctor, proposing social freezing to his patients, should be up to date with all these details. Follow-up studies on the outcome of these children are needed.

Metaheuristics for the bus-driver scheduling problem

We present new metaheuristics for solving real crew scheduling problemsin a public transportation bus company. Since the crews of thesecompanies are drivers, we will designate the problem by the bus-driverscheduling problem. Crew scheduling problems are well known and severalmathematical programming based techniques have been proposed to solvethem, in particular using the set-covering formulation. However, inpractice, there exists the need for improvement in terms of computationalefficiency and capacity of solving large-scale instances. Moreover, thereal bus-driver scheduling problems that we consider can present variantaspects of the set covering, as for example a different objectivefunction, implying that alternative solutions methods have to bedeveloped. We propose metaheuristics based on the following approaches:GRASP (greedy randomized adaptive search procedure), tabu search andgenetic algorithms. These metaheuristics also present some innovationfeatures based on and genetic algorithms. These metaheuristics alsopresent some innovation features based on the structure of the crewscheduling problem, that guide the search efficiently and able them tofind good solutions. Some of these new features can also be applied inthe development of heuristics to other combinatorial optimizationproblems. A summary of computational results with real-data problems ispresented.

Problems of Bridge Supporting and Expansion Devices and an Experimental Comparison of the Dynamic Behavior of Rigid and Elastomeric Bearings, HR-105, 1965

The design of satisfactory supporting and expansion devices for highway bridges is a problem which has concerned bridge design engineers for many years. The problems associated with these devices have been emphasized by the large number of short span bridges required by the current expanded highway program of expressways and interstate highways. The initial objectives of this investigation were: (1) To review and make a field study of devices used for the support of bridge superstructures and for provision of floor expansion; (2) To analyze the forces or factors which influence the design and behavior of supporting devices and floor expansion systems; and (3) To ascertain the need for future research particularly on the problems of obtaining more economical and efficient supporting and expansion devices, and determining maximum allowable distance between such devices. The experimental portion was conducted to evaluate one of the possible simple and economical solutions to the problems observed in the initial portion. The investigation reported herein is divided into four major parts or phases as follows: (1) A review of literature; (2) A survey by questionnaire of design practice of a number of state highway departments and consulting firms; (3) Field observation of existing bridges; and, (4) An experimental comparison of the dynamic behavior of rigid and elastomeric bearings.

Raaka-aineiden hankinta Fescon Oy:ssä

Tämän tutkimuksen tavoitteena on selvittää raaka-aineiden hankintaa kuivatuotteita valmistavassa yrityksessä. Työ sisältää oikea-aikaiseen tilausrytmiin ja eräkoon määrittämisen liittyviä menetelmiä ja päätöksiä hankintojen tehostamiseksi. Tässä tutkimuksessa tarkastellaan myös varastojen merkitystä ja materiaalinohjauksen keinoja sekä ostotoiminnan kannalta keskeisimpiä tehokkaaseen varastonohjaukseen liittyviä tunnuslukuja. Tutkimuksen teoriaosa on syntetisoiva kirjallisuustutkimus. Työn empiirisessä osassa analysoidaan aluksi case-yrityksen ostotoiminnan ja varastojenhallinnan nykytilaa. Tämän jälkeen selvitään kunkin nimikkeen hankintamäärät ja tehdään näille luokittelu ABC-analyysiin pohjautuen. Tämän jälkeen ostotoimintaa analysoidaan toimittajien osalta. Lopuksi laaditaan optimaalinen tilausrytmi ja tilausmäärät kullekin nimikkeelle sekä esitetään kehityskohteita. Työn empiirinen osa on case- eli tapaustutkimus sekä toimintatutkimus. ABC-analyysia voidaan hyödyntää määriteltäessä miten erilaisten nimikkeiden materiaalivirtoja tulisi ostotoiminnan kannalta ohjata. Analyysi perustuu resurssien keskittämiseen niihin luokkiin, jotka ovat rahalliselta arvoltaan suurimmat eli joihin sitoutuu eniten pääomaa. Tärkeimpänä tavoitteena on kokonaiskustannusten pienentäminen.

Solution of aerospace problems using structured and unstructured strategies

Products developed at industries, institutes and research centers are expected to have high level of quality and performance, having a minimum waste, which require efficient and robust tools to numerically simulate stringent project conditions with great reliability. In this context, Computational Fluid Dynamics (CFD) plays an important role and the present work shows two numerical algorithms that are used in the CFD community to solve the Euler and Navier-Stokes equations applied to typical aerospace and aeronautical problems. Particularly, unstructured discretization of the spatial domain has gained special attention by the international community due to its ease in discretizing complex spatial domains. This work has the main objective of illustrating some advantages and disadvantages of numerical algorithms using structured and unstructured spatial discretization of the flow governing equations. Numerical methods include a finite volume formulation and the Euler and Navier-Stokes equations are applied to solve a transonic nozzle problem, a low supersonic airfoil problem and a hypersonic inlet problem. In a structured context, these problems are solved using MacCormack’s implicit algorithm with Steger and Warming’s flux vector splitting technique, while, in an unstructured context, Jameson and Mavriplis’ explicit algorithm is used. Convergence acceleration is obtained using a spatially variable time stepping procedure.

The impact of scheduling on the implementation of the new Ontario curriculum: teachers' perceptions /

This qualitative study explored secondary teachers' perceptions of scheduling in relation to pedagogy, curriculum, and observation of student learning. Its objective was to determine the best way to organize the scheduling for the delivery of Ontario's new 4-year curriculum. Six participants were chosen. Two were teaching in a semestered timetable, 1 in a traditional timetable, and 3 had experience in both schedules. Participants related a pressure cooker "lived experience" with weaker students in the semester system experiencing a particularly harsh environment. The inadequate amount of time for review in content-heavy courses, gap scheduling problems, catch-up difficulties for students missing classes, and the fast pace of semestering are identified as factors negatively impacting on these students. Government testing adds to the pressure by shifting teachers' time and attention in the classroom from deeper learning to a superficial coverage of material, from curriculum as lived to curriculum as text to be covered. Scheduling choice should be available in public education to accommodate the needs of all students. Curriculum guidelines need to be revamped to reflect the content that teachers believe is necessary for a successful course delivery. Applied level courses need to be developed for students who are not academically inferior but learn differently.

Reinforcement Learning Approaches to Power System Scheduling

One major component of power system operation is generation scheduling. The objective of the work is to develop efficient control strategies to the power scheduling problems through Reinforcement Learning approaches. The three important active power scheduling problems are Unit Commitment, Economic Dispatch and Automatic Generation Control. Numerical solution methods proposed for solution of power scheduling are insufficient in handling large and complex systems. Soft Computing methods like Simulated Annealing, Evolutionary Programming etc., are efficient in handling complex cost functions, but find limitation in handling stochastic data existing in a practical system. Also the learning steps are to be repeated for each load demand which increases the computation time.Reinforcement Learning (RL) is a method of learning through interactions with environment. The main advantage of this approach is it does not require a precise mathematical formulation. It can learn either by interacting with the environment or interacting with a simulation model. Several optimization and control problems have been solved through Reinforcement Learning approach. The application of Reinforcement Learning in the field of Power system has been a few. The objective is to introduce and extend Reinforcement Learning approaches for the active power scheduling problems in an implementable manner. The main objectives can be enumerated as:(i) Evolve Reinforcement Learning based solutions to the Unit Commitment Problem.(ii) Find suitable solution strategies through Reinforcement Learning approach for Economic Dispatch. (iii) Extend the Reinforcement Learning solution to Automatic Generation Control with a different perspective. (iv) Check the suitability of the scheduling solutions to one of the existing power systems.First part of the thesis is concerned with the Reinforcement Learning approach to Unit Commitment problem. Unit Commitment Problem is formulated as a multi stage decision process. Q learning solution is developed to obtain the optimwn commitment schedule. Method of state aggregation is used to formulate an efficient solution considering the minimwn up time I down time constraints. The performance of the algorithms are evaluated for different systems and compared with other stochastic methods like Genetic Algorithm.Second stage of the work is concerned with solving Economic Dispatch problem. A simple and straight forward decision making strategy is first proposed in the Learning Automata algorithm. Then to solve the scheduling task of systems with large number of generating units, the problem is formulated as a multi stage decision making task. The solution obtained is extended in order to incorporate the transmission losses in the system. To make the Reinforcement Learning solution more efficient and to handle continuous state space, a fimction approximation strategy is proposed. The performance of the developed algorithms are tested for several standard test cases. Proposed method is compared with other recent methods like Partition Approach Algorithm, Simulated Annealing etc.As the final step of implementing the active power control loops in power system, Automatic Generation Control is also taken into consideration.Reinforcement Learning has already been applied to solve Automatic Generation Control loop. The RL solution is extended to take up the approach of common frequency for all the interconnected areas, more similar to practical systems. Performance of the RL controller is also compared with that of the conventional integral controller.In order to prove the suitability of the proposed methods to practical systems, second plant ofNeyveli Thennal Power Station (NTPS IT) is taken for case study. The perfonnance of the Reinforcement Learning solution is found to be better than the other existing methods, which provide the promising step towards RL based control schemes for practical power industry.Reinforcement Learning is applied to solve the scheduling problems in the power industry and found to give satisfactory perfonnance. Proposed solution provides a scope for getting more profit as the economic schedule is obtained instantaneously. Since Reinforcement Learning method can take the stochastic cost data obtained time to time from a plant, it gives an implementable method. As a further step, with suitable methods to interface with on line data, economic scheduling can be achieved instantaneously in a generation control center. Also power scheduling of systems with different sources such as hydro, thermal etc. can be looked into and Reinforcement Learning solutions can be achieved.