Empirical study of correlated survival times for recurrent events with proportional hazards margins and the effect of correlation and censoring.

Background: In longitudinal studies where subjects experience recurrent incidents over a period of time, such as respiratory infections, fever or diarrhea, statistical methods are required to take into account the within-subject correlation. Methods: For repeated events data with censored failure, the independent increment (AG), marginal (WLW) and conditional (PWP) models are three multiple failure models that generalize Cox"s proportional hazard model. In this paper, we revise the efficiency, accuracy and robustness of all three models under simulated scenarios with varying degrees of within-subject correlation, censoring levels, maximum number of possible recurrences and sample size. We also study the methods performance on a real dataset from a cohort study with bronchial obstruction. Results: We find substantial differences between methods and there is not an optimal method. AG and PWP seem to be preferable to WLW for low correlation levels but the situation reverts for high correlations. Conclusions: All methods are stable in front of censoring, worsen with increasing recurrence levels and share a bias problem which, among other consequences, makes asymptotic normal confidence intervals not fully reliable, although they are well developed theoretically.

Differences in seasonal price patterns among second home rentals and hotels: Empirical evidence and practical implications

Hotels and second home rentals are two of the most important tourist accommodation options in Spain. In terms of seasonality, almost all previous studies have analysed tourism demand from the point of view either of total arrivals or the number of tourists lodged in a single accommodation type (hotels, rural accommodation, etc). However, there are no studies focusing on price seasonality orcomparing seasonality among different accommodation types. By using seasonality indicators and a price index constructed by means of hedonic methods, this paper aims to shed some light on seasonal pricing patterns among second home rentals and hotels. The paper relies on a 2004 database of 144 hotels and 1,002 apartments on the Costa Brava (northeast Spain). The results show that prices for second home rentals display a smoother seasonal pattern than hotels due to reduced price differences between shoulder (May and October) and peak periods (August)

Nutritional behaviour and beliefs of ski-mountaineers: a semi-quantitative and qualitative study.

BACKGROUND: Endurance athletes are advised to optimize nutrition prior to races. Little is known about actual athletes' beliefs, knowledge and nutritional behaviour. We monitored nutritional behaviour of amateur ski-mountaineering athletes during 4 days prior to a major competition to compare it with official recommendations and with the athletes' beliefs. METHODS: Participants to the two routes of the 'Patrouille des Glaciers' were recruited (A, 26 km, ascent 1881 m, descent 2341 m, max altitude 3160 m; Z, 53 km, ascent 3994 m, descent 4090 m, max altitude 3650 m). Dietary intake diaries of 40 athletes (21 A, 19 Z) were analysed for energy, carbohydrate, fat, protein and liquid; ten were interviewed about their pre-race nutritional beliefs and behaviour. RESULTS: Despite belief that pre-race carbohydrate, energy and fluid intake should be increased, energy consumption was 2416 ± 696 (mean ± SD) kcal · day(-1), 83 ± 17 % of recommended intake, carbohydrate intake was only 46 ± 13 % of minimal recommended (10 g · kg(-1) · day(-1)) and fluid intake only 2.7 ± 1.0 l · day(-1). CONCLUSIONS: Our sample of endurance athletes did not comply with pre-race nutritional recommendations despite elementary knowledge and belief to be compliant. In these athletes a clear and reflective nutritional strategy was lacking. This suggests a potential for improving knowledge and compliance with recommendations. Alternatively, some recommendations may be unrealistic.

Challenges to patient-centred care in a general internal medicine context: findings from an exploratory study

Introduction. If we are to promote more patient-centred approaches in care delivery, we have to better characterize the situations in which being patient-centred is difficult to achieve. Data from professionals in health and social care are important because they are the people charged with operationalizing patient-centred care (PCC) in their daily practice. However, empirical accounts from frontline care providers are still lacking, and it is important to gather experiences not only from doctors but also from the other care providers. Indeed, experiences from different professions can help inform our understanding of patient care, which is expected to be both patient-centred and collaborative. Methods. This study was based on the following research question: What factors make the provision of PCC difficult to achieve? Sample and setting. A purposeful sampling technique was used, allowing for a series of choices about the participants and their professional affiliation. Because patient-centredness is the focus, 3 professions appeared to be of special interest: general internists, nurses and social workers. The study was undertaken in the General Internal Medicine Division of a teaching hospital located in a North American context. Data Collection. To answer the research question, a methodological approach based on a theory called phenomenology was chosen. Accordingly, semi-structured interviews were used since they generate understanding of the meanings different individuals have of their lived world. Interviews with 8 physicians, 10 nurses and 10 social workers were eventually conducted. Data analysis. An inductive thematic analysis was employed to make sense of the interview data. Results. The thematic analysis allowed identifying various types of challenges to PCC. Although most of the challenges were perceived by all three groups of professionals, they were perceived to a different degree across the professions, which likely reflected the scope of practice of each profession. The challenges and their distribution across the professions are illustrated in Table 1. Examples of challenges are provided in Table 2. Discussion. There is a tension between what is supposed to be done - what stands in the philosophy of patient -centredness - and what is currently done - the real life with all the challenges to PCC. According to some participants' accounts, PCC clearly risks becoming a mere illusion for health care professionals on which too great pressures are imposed.

Estudo semi-empírico de ácidos hidroxâmicos: ácido formoidroxâmico e derivados do aleloquímico dimboa

Open chain hydroxamic acid (Hx) can exist as Z and E diastereomers of two tautomers, hydroxamic acid and hydroximic acid. The conformational stability of the formohydroxamic acid isomers evaluated by PM3 compared better to ab initio results from the literature than AM1 results. Structural data of the cyclic Hx 2,4-dihydroxy-7-metoxy-2H-1,4-benzoxazin-3(4 H)-one (DIMBOA) obtained by both semiempirical methods compared well to ab initio results. pKa data from the literature for derivatives of the aldolic isomer of DIMBOA were compared to the stability of the anions resulting from the loss of protons of their phenol and hydroxamic acid groups, determined as the difference in heat of formation between anionic and neutral forms, calculated by AM1 and PM3 methods. Good correlations between theoretical and experimental data were obtained for both semiempirical methods.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.

Analyzing customer profitability: an empirical study

The purpose of this study was to define the customer profitability of the case company as well as to specify the factors that explain customer profitability. The study was made with a quantitative research method. The research hypotheses were formulated mainly on the grounds of previous research, and were tested with statistical research methods. The research results showed that customer profitability is not equally distributed among the customers of the case company, and the majority of its customers is profitable. The interpreters for absolute customer profitability were sales volume and the customer’s location region. The interpreters for relative customer profitability were the customer’s location region and the product segment into which a customer can be classified on the basis of the products that were sold to this customer.

Inter organizational learning within and by innovation networks

The thesis deals with the phenomenon of learning between organizations in innovation networks that develop new products, services or processes. Inter organizational learning is studied especially at the level of the network. The role of the network can be seen as twofold: either the network is a context for inter organizational learning, if the learner is something else than the network (organization, group, individual), or the network itself is the learner. Innovations are regarded as a primary source of competitiveness and renewal in organizations. Networking has become increasingly common particularly because of the possibility to extend the resource base of the organization through partnerships and to concentrate on core competencies. Especially in innovation activities, networks provide the possibility to answer the complex needs of the customers faster and to share the costs and risks of the development work. Networked innovation activities are often organized in practice as distributed virtual teams, either within one organization or as cross organizational co operation. The role of technology is considered in the research mainly as an enabling tool for collaboration and learning. Learning has been recognized as one important collaborative process in networks or as a motivation for networking. It is even more important in the innovation context as an enabler of renewal, since the essence of the innovation process is creating new knowledge, processes, products and services. The thesis aims at providing enhanced understanding about the inter organizational learning phenomenon in and by innovation networks, especially concentrating on the network level. The perspectives used in the research are the theoretical viewpoints and concepts, challenges, and solutions for learning. The methods used in the study are literature reviews and empirical research carried out with semi structured interviews analyzed with qualitative content analysis. The empirical research concentrates on two different areas, firstly on the theoretical approaches to learning that are relevant to innovation networks, secondly on learning in virtual innovation teams. As a result, the research identifies insights and implications for learning in innovation networks from several viewpoints on organizational learning. Using multiple perspectives allows drawing a many sided picture of the learning phenomenon that is valuable because of the versatility and complexity of situations and challenges of learning in the context of innovation and networks. The research results also show some of the challenges of learning and possible solutions for supporting especially network level learning.

Determining risk potential through accidents and near-accidents: an empirical study in a steel factory

Fatal and permanently disabling accidents form only one per I cent of all occupational accidents but in many branches of industry they account for more than half the accident costs. Furthermore the human suffering of the victim and his family is greater in severe accidents than in slight ones. For both human and economic reasons the severe accident risks should be identified befor injuries occur. It is for this purpose that different safety analysis methods have been developed . This study shows two new possible approaches to the problem.. The first is the hypothesis that it is possible to estimate the potential severity of accidents independent of the actual severity. The second is the hypothesis that when workers are also asked to report near accidents, they are particularly prone to report potentially severe near accidents on the basis of their own subjective risk assessment. A field study was carried out in a steel factory. The results supported both the hypotheses. The reliability and the validity of post incident estimates of an accident's potential severity were reasonable. About 10 % of accidents were estimated to be potentially critical; they could have led to death or very severe permanent disability. Reported near accidents were significantly more severe, about 60 $ of them were estimated to be critical. Furthermore the validity of workers subjective risk assessment, manifested in the near accident reports, proved to be reasonable. The studied new methods require further development and testing. They could be used both in routine usage in work places and in research for identifying and setting the priorities of accident risks.

The use of mixed methods research in the behavioral sciences field

Mixed methods research involves the combined use of quantitative and qualitative methods in the same research study, and it is becoming increasingly important in several scientific areas. The aim of this paper is to review and compare through a mixed methods multiple-case study the application of this methodology in three reputable behavioural science journals: the Journal of Organizational Behavior, Addictive Behaviors and Psicothema. A quantitative analysis was carried out to review all the papers published in these journals during the period 2003-2008 and classify them into two blocks: theoretical and empirical, with the latter being further subdivided into three subtypes (quantitative, qualitative and mixed). A qualitative analysis determined the main characteristics of the mixed methods studies identified, in order to describe in more detail the ways in which the two methods are combined based on their purpose, priority, implementation and research design. From the journals selected, a total of 1.958 articles were analysed, the majority of which corresponded to empirical studies, with only a small number referring to research that used mixed methods. Nonetheless, mixed methods research does appear in all the behavioural science journals studied within the period selected, showing a range of designs, where the sequential equal weight mixed methods research design seems to stand out.

Eliminació d'artefactes en EGG mitjançant l'ús de la Multivariate Empirical Mode Decomposition

La tècnica de l’electroencefalograma (EEG) és una de les tècniques més utilitzades per estudiar el cervell. En aquesta tècnica s’enregistren els senyals elèctrics que es produeixen en el còrtex humà a través d’elèctrodes col•locats al cap. Aquesta tècnica, però, presenta algunes limitacions a l’hora de realitzar els enregistraments, la principal limitació es coneix com a artefactes, que són senyals indesitjats que es mesclen amb els senyals EEG. L’objectiu d’aquest treball de final de màster és presentar tres nous mètodes de neteja d’artefactes que poden ser aplicats en EEG. Aquests estan basats en l’aplicació de la Multivariate Empirical Mode Decomposition, que és una nova tècnica utilitzada per al processament de senyal. Els mètodes de neteja proposats s’apliquen a dades EEG simulades que contenen artefactes (pestanyeigs), i un cop s’han aplicat els procediments de neteja es comparen amb dades EEG que no tenen pestanyeigs, per comprovar quina millora presenten. Posteriorment, dos dels tres mètodes de neteja proposats s’apliquen sobre dades EEG reals. Les conclusions que s’han extret del treball són que dos dels nous procediments de neteja proposats es poden utilitzar per realitzar el preprocessament de dades reals per eliminar pestanyeigs.

Strategic choices and performance – an empirical analysis of largest European and North American energy companies

Energy industry has gone through major changes globally in past two decades. Liberalization of energy markets has led companies to integrate both vertically and horizontally. Growing concern on sustainable development and aims to decrease greenhouse gases in future will increase the portion of renewable energy in total energy production. Purpose of this study was to analyze using statistical methods, what impacts different strategic choices has on biggest European and North American energy companies’ performance. Results show that vertical integration, horizontal integration and use of renewable energy in production had the most impact on profitability. Increase in level of vertical integration decreased companies’ profitability, while increase in horizontal integration improved companies’ profitability. Companies that used renewable energy in production were less profitable than companies not using renewable energy.

Estudo da mobilização de metais e elementos traços em ambientes aquáticos do semi-árido brasileiro aplicando análises de componentes principais

The spatial and temporal retention of metals has been studied in water and sediments of the Gavião River, Anagé and Tremedal Reservoirs, located in the semi-arid region, Bahia - Brazil, in order to identify trends in the fluxes of metals from the sediments to the water column. The determination of metals was made by ICP OES and ET AAS. The application of statistical methods showed that this aquatic system presents suitable conditions to move Cd2+ and Pb2+ from the water column to the sediment.

Retrieval of kinetic rates in reactions with semi batch liquid phase using ill-posed inverse problem theory

A neural network procedure to solve inverse chemical kinetic problems is discussed in this work. Rate constants are calculated from the product concentration of an irreversible consecutive reaction: the hydrogenation of Citral molecule, a process with industrial interest. Simulated and experimental data are considered. Errors in the simulated data, up to 7% in the concentrations, were assumed to investigate the robustness of the inverse procedure. Also, the proposed method is compared with two common methods in nonlinear analysis; the Simplex and Levenberg-Marquardt approaches. In all situations investigated, the neural network approach was numerically stable and robust with respect to deviations in the initial conditions or experimental noises.

Thermal and structural study of guava (Psidium guajava L) powders obtained by two dehydration methods

Two food products (powders) were obtained by hot-air drying or lyophilisation methods on the whole guava fruits. The powders were characterised by sensory and thermal analyses (TGA-DSC), infrared spectroscopy (IR), X-ray diffraction (XRD) and scanning electron microscopy (SEM). Thermal, morphological and structural characterisations showed a similar behaviour for the two solids. TGA-DSC and IR showed the presence of pectin as the main constituent of solids. A semi-crystalline profile was evidenced by XRD, and lamellar/spherical morphologies were observed by SEM. Sensory analyses revealed an aroma highly related to guava. These value-added food products are an alternative to process guava and avoid loss during postharvest handling.