The Role of Firm Size in Training Provision Decisions: evidence from Spain

The level of training provided by small firms to their employees is below that provided by their larger counterparts. The provision of firm-related training is believed to be associated to certain characteristics of the firm. In this paper we argue that small firms provide fewer training opportunities as they are less likely to be associated with these characteristics than large firms. The suitability of estimating training decisions as a double-decision process is examined here: first, a firm has to decide whether to provide training or not and, second, having decided to do so, the amount of training to provide. The differences in training provision between small and large firms are decomposed in order to analyse the individual contribution of these characteristics to explaining the gap. The results show that small firms face greater obstacles in accessing training and that the main reasons for that are related to their technological activity and the geographical scope of the market in which they operate.

Defining housing market areas using commuting and migration algorithms. Catalonia (Spain) as an applied case study

In the literature on housing market areas, different approaches can be found to defining them, for example, using travel-to-work areas and, more recently, making use of migration data. Here we propose a simple exercise to shed light on which approach performs better. Using regional data from Catalonia, Spain, we have computed housing market areas with both commuting data and migration data. In order to decide which procedure shows superior performance, we have looked at uniformity of prices within areas. The main finding is that commuting algorithms present more homogeneous areas in terms of housing prices.

Music-supported training is more efficient than functional motor training for recovery of fine motor skills in stroke patients

MOTOR IMPAIRMENTS ARE COMMON AFTER STROKE but efficacious therapies for these dysfunctions are scarce. Extending an earlier study on the effects of music-supported training (MST), behavioral indices of motor function were obtained before and after a series of training sessions to assess whether this new treatment leads to improved motor functions. Furthermore, music-supported training was contrasted to functional motor training according to the principles of constraint-induced therapy (CIT). In addition to conventional physiotherapy, 32 stroke patients with moderately impaired motor function and no previous musical experience received 15 sessions of MST over a period of three weeks, using a manualized, step-bystep approach. A control group consisting of 15 patients received 15 sessions of CIT in addition to conventional physiotherapy. A third group of 30 patients received exclusively conventional physiotherapy and served as a control group for the other three groups. Fine as well as gross motor skills were trained by using either a MIDI-piano or electronic drum pads programmed to emit piano tones. Motor functions were assessed by an extensive test battery. MST yielded significant improvement in fine as well as gross motor skills with respect to speed, precision, and smoothness of movements. These improvements were greater than after CIT or conventional physiotherapy. In conclusion, with equal treatment intensity, MST leads to more pronounced improvements of motor functions after stroke than CIT.

The process of basic training, applied training, maintaining the performance of an observer

In the field of observational methodology the observer is obviously a central figure, and close attention should be paid to the process through which he or she acquires, applies, and maintains the skills required. Basic training in how to apply the operational definitions of categories and the rules for coding, coupled with the opportunity to use the observation instrument in real-life situations, can have a positive effect in terms of the degree of agreement achieved when one evaluates intra- and inter-observer reliability. Several authors, including Arias, Argudo, & Alonso (2009) and Medina and Delgado (1999), have put forward proposals for the process of basic and applied training in this context. Reid y De Master (1982) focuses on the observer's performance and how to maintain the acquired skills, it being argued that periodic checks are needed after initial training because an observer may, over time, become less reliable due to the inherent complexity of category systems. The purpose of this subsequent training is to maintain acceptable levels of observer reliability. Various strategies can be used to this end, including providing feedback about those categories associated with a good reliability index, or offering re-training in how to apply those that yield lower indices. The aim of this study is to develop a performance-based index that is capable of assessing an observer's ability to produce reliable observations in conjunction with other observers.

Un análisis optimista de la educación matemática en la formación de maestros de educación infantil = An optimistic analysis of mathematics education in training pre-school teachers

This article offers a panorama of mathematics training for future teachers at pre-school level in Spain. With this goal in mind, this article is structured infour sections: where we come from, where we are, where we’re going and where we want to go. It offers, in short, a brief analysis that shows the efforts made to ensure there is sufficient academic and scientific rigour in teachers’ studies at pre-school in general and students’ mathematics education in particular. Together with a description of the progress made in recent years, it also raises some questions for all those involved in training future teachers for this educational stage

MCMC Analysis Of Classical Time Series Algorithms

Identification of order of an Autoregressive Moving Average Model (ARMA) by the usual graphical method is subjective. Hence, there is a need of developing a technique to identify the order without employing the graphical investigation of series autocorrelations. To avoid subjectivity, this thesis focuses on determining the order of the Autoregressive Moving Average Model using Reversible Jump Markov Chain Monte Carlo (RJMCMC). The RJMCMC selects the model from a set of the models suggested by better fitting, standard deviation errors and the frequency of accepted data. Together with deep analysis of the classical Box-Jenkins modeling methodology the integration with MCMC algorithms has been focused through parameter estimation and model fitting of ARMA models. This helps to verify how well the MCMC algorithms can treat the ARMA models, by comparing the results with graphical method. It has been seen that the MCMC produced better results than the classical time series approach.

Evaluating End-User Training Effectiveness in ERP System Implementation

Today’s business world demands more and more internal and external integration and transparency among companies at all fields. Integrated ERP (enterprise resource planning) systems offer a possibility to improve business practices and procedures by providing a unified view on the business including all functions and departments. Due to the obvious benefits, the popularity of integrated ERP systems keeps growing. The implementation of ERP systems has however proven risky. The implementation projects tend to be long, extensive, and costly – and often they end up in a failure. Due to the significant task and role changes ERP implementation brings to almost everybody in the company, training has been identified as one of the most critical success factors of an ERP implementation. To ensure that the training is conducted in the most effective and successful manner, the training outcomes should be evaluated. So far, training evaluation has however gained only limited attention at most companies investing in different training programs. Uponor corporation has initiated a large ERP implementation and process harmonization program in 2004. Thousands of end-users have been trained during this project so far, and the work still continues until the project is completed in 2010. In this thesis, the evaluation of end-user training in Uponor’s ERP program is brought further from the current state of performing the basic participant satisfaction survey in the end of each class. The results show that in order to reach reliable training effectiveness evaluation results, not only the reaction towards training but also transfer of skills and attitudes and the final results of the training program should be evaluated.

Development of Calculation Procedures for Wastewater Treatment Technologies for a Training System

Efficient designs and operations of water and wastewater treatment systems are largely based on mathematical calculations. This even applies to training in the treatment systems. Therefore, it is necessary that calculation procedures are developed and computerised a priori for such applications to ensure effectiveness. This work was aimed at developing calculation procedures for gas stripping, depth filtration, ion exchange, chemical precipitation, and ozonation wastewater treatment technologies to include them in ED-WAVE, a portable computer based tool used in design, operations and training in wastewater treatment. The work involved a comprehensive online and offline study of research work and literature, and application of practical case studies to generate ED-WAVE compatible representations of the treatment technologies which were then uploaded into the tool.

Eye Fundus Image Analysis for Automatic Detection of Diabetic Retinopathy

Diabetes is a rapidly increasing worldwide problem which is characterised by defective metabolism of glucose that causes long-term dysfunction and failure of various organs. The most common complication of diabetes is diabetic retinopathy (DR), which is one of the primary causes of blindness and visual impairment in adults. The rapid increase of diabetes pushes the limits of the current DR screening capabilities for which the digital imaging of the eye fundus (retinal imaging), and automatic or semi-automatic image analysis algorithms provide a potential solution. In this work, the use of colour in the detection of diabetic retinopathy is statistically studied using a supervised algorithm based on one-class classification and Gaussian mixture model estimation. The presented algorithm distinguishes a certain diabetic lesion type from all other possible objects in eye fundus images by only estimating the probability density function of that certain lesion type. For the training and ground truth estimation, the algorithm combines manual annotations of several experts for which the best practices were experimentally selected. By assessing the algorithm’s performance while conducting experiments with the colour space selection, both illuminance and colour correction, and background class information, the use of colour in the detection of diabetic retinopathy was quantitatively evaluated. Another contribution of this work is the benchmarking framework for eye fundus image analysis algorithms needed for the development of the automatic DR detection algorithms. The benchmarking framework provides guidelines on how to construct a benchmarking database that comprises true patient images, ground truth, and an evaluation protocol. The evaluation is based on the standard receiver operating characteristics analysis and it follows the medical practice in the decision making providing protocols for image- and pixel-based evaluations. During the work, two public medical image databases with ground truth were published: DIARETDB0 and DIARETDB1. The framework, DR databases and the final algorithm, are made public in the web to set the baseline results for automatic detection of diabetic retinopathy. Although deviating from the general context of the thesis, a simple and effective optic disc localisation method is presented. The optic disc localisation is discussed, since normal eye fundus structures are fundamental in the characterisation of DR.

Training system for conceptual design and evaluation for wastewater treatment

Rising population, rapid urbanisation and growing industrialisation have severely stressed water quality and its availability in Malawi. In addition, financial and institutional problems and the expanding agro industry have aggravated this problem. The situation is worsened by depleting water resources and pollution from untreated sewage and industrial effluent. The increasing scarcity of clean water calls for the need for appropriate management of available water resources. There is also demand for a training system for conceptual design and evaluation for wastewater treatment in order to build the capacity for technical service providers and environmental practitioners in the country. It is predicted that Malawi will face a water stress situation by 2025. In the city of Blantyre, this situation is aggravated by the serious pollution threat from the grossly inadequate sewage treatment capacity. This capacity is only 23.5% of the wastewater being generated presently. In addition, limited or non-existent industrial effluent treatment has contributed to the severe water quality degradation. This situation poses a threat to the ecologically fragile and sensitive receiving water courses within the city. This water is used for domestic purposes further downstream. This manuscript outlines the legal and policy framework for wastewater treatment in Malawi. The manuscript also evaluates the existing wastewater treatment systems in Blantyre. This evaluation aims at determining if the effluent levels at the municipal plants conform to existing standards and guidelines and other associated policy and regulatory frameworks. The raw material at all the three municipal plants is sewage. The typical wastewater parameters are Biochemical Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), and Total Suspended Solids (TSS). The treatment target is BOD5, COD, and TSS reduction. Typical wastewater parameters at the wastewater treatment plant at MDW&S textile and garments factory are BOD5 and COD. The treatment target is to reduce BOD5 and COD. The manuscript further evaluates a design approach of the three municipal wastewater treatment plants in the city and the wastewater treatment plant at Mapeto David Whitehead & Sons (MDW&S) textile and garments factory. This evaluation utilises case-based design and case-based reasoning principles in the ED-WAVE tool to determine if there is potential for the tool in Blantyre. The manuscript finally evaluates the technology selection process for appropriate wastewater treatment systems for the city of Blantyre. The criteria for selection of appropriate wastewater treatment systems are discussed. Decision support tools and the decision tree making process for technology selection are also discussed. Based on the treatment targets and design criteria at the eight cases evaluated in this manuscript in reference to similar cases in the ED-WAVE tool, this work confirms the practical use of case-based design and case-based reasoning principles in the ED-WAVE tool in the design and evaluation of wastewater treatment 6 systems in sub-Sahara Africa, using Blantyre, Malawi, as the case study area. After encountering a new situation, already collected decision scenarios (cases) are invoked and modified in order to arrive at a particular design alternative. What is necessary, however, is to appropriately modify the case arrived at through the Case Study Manager in order to come up with a design appropriate to the local situation taking into account technical, socio-economic and environmental aspects. This work provides a training system for conceptual design and evaluation for wastewater treatment.

The family school : the impact of a group training programme on overactive hard-to-manage preschool children and their parents

Föräldraskap upplevs som en utmanande uppgift i dag och det påstås att föräldrar oftare än förr skulle var i behov av råd och stöd beträffande barnuppfostran. Denna uppgift kan ytterligare försvåras om det i familjen finns ett hyperaktivt okoncentrerat barn att uppfostra. Detta arbete undersökte effekterna av ett kortvarigt gruppbaserat interventionsprogram benämnt Familjeskolan POP (Preschool Overactivity Programme). Familjeskolan är avsedd för familjer med barn i lekåldern, som visar beteendesvårigheter såsom ADHD (Attention Deficit Hyperactivity Disorder), ODD (Oppositional Deficit Disorder) eller CD (Conduct Disorder). Målet för Familjeskolan är att öka föräldrarnas kunskaper och självförtroende då de har ett krävande svårhanterligt barn att uppfostra. Familjeskolan strävar också till att reducera barns icke-önskvärda beteenden genom att öka deras sociala färdigheter och koncentrationsförmåga. Familjeskolan verkställdes i Helsingfors vid ADHD- centrets lokaliteter. 45 mödrar och deras barn från huvudstadsregionen deltog i denna undersökning. Av dessa deltog 33 i Familjeskola-programmet medan de 12 övriga bildade den s.k. kontrollgruppen. Undersökningsresultaten tyder på förbättringar beträffande både moderns och faderns föräldrakunskaper efter Familjeskola-interventionen. Det är att lägga märke till att enbart mödrar deltog i interventionsprogrammet. Efter programmet klarade mödrar enligt egen utsaga vardagen bättre. Speciellt hade de blivit bättre på att hantera barnens beteendesvårigheter och hyperaktivt okoncentrerat beteende. Resultaten påvisade också att programmet var effektivast för de mödrar som före Familjeskolan upplevde sig besitta ringa föräldrakunskaper. Mödrarna rapporterade en signifikant minskning i barnens totala beteendesvårigheter. Efter interventionen ansåg mödrarna att deras barn var mindre olydiga, hyperaktiva samt att deras beteendesvårigheter var lindrigare. Enligt dagvårdspersonalen hade barnens totala beteendesvårigheter och problem med koncentration och hyperaktivitet också minskat. Motsvarande förbättringar uppnåddes inte i kontrollgruppen. Resultaten från uppföljningsintervjun, visade också att barnens beteendeförändringar var bestående både hemma och i daghemmet. Både föräldrar och dagvårdspersonalen rapporterade en signifikant minskning i barnens totala svårigheter jämfört med innan familjerna påbörjade interventionen. Föräldrarna rapporterade en marginell minskning i barnens ADHD-liknande beteende, beteendesvårigheter och i svårigheter med kamrater, dagvårdspersonalen däremot rapporterade en signifikant minskning i barnens beteendesvårigheter, hyperaktivt/okoncentrerat beteende samt i svårigheter med kamrater mellan innan familjerna påbörjade interventionen och uppföljningen ett år efter. Resultaten av denna undersökning stödjer hypotesen att kortvariga gruppbaserade interventionsprogram kan åstadkomma permanenta förbättringar i föräldrakunskaper och barns beteende. Detta gäller främst hyperaktivitet, koncentrationssvårigheter och trotsighet.

Adherence and perceptions regarding simulation training in undergraduate health Sciences

BACKGROUND: Simulation techniques are spreading rapidly in medicine. Suc h resources are increasingly concentrated in Simulation Laboratories. The MSRP-USP is structuring such a laboratory and is interested in the prevalence of individual initiatives that could be centralized there. The MSRP-USP currently has five full-curriculum courses in the health sciences: Medicine, Speech Therapy, Physical Therapy, Nutrition, and Occupational Therapy, all consisting of core disciplines. GOAL: To determine the prevalence of simulation techniques in the regular courses at MSRP-USP. METHODS: Coordinators of disciplines in the various courses were interviewed using a specifically designed semi-structured questionnaire, and all the collected data were stored in a dedicated database. The disciplines were grouped according to whether they used (GI) or did not use (GII) simulation resources. RESULTS AND DISCUSSION: 256 disciplines were analyzed, of which only 18.3% used simulation techniques, varying according to course: Medicine (24.7.3%), Occupational Therapy (23.0%), Nutrition (15.9%), Physical Therapy (9.8%), and Speech Therapy (9.1%). Computer simulation programs predominated (42.5%) in all five courses. The resources were provided mainly by MSRP-USP (56.3%), with additional funding coming from other sources based on individual initiatives. The same pattern was observed for maintenance. There was great interest in centralizing the resources in the new Simulation Laboratory in order to facilitate maintenance, but there was concern about training and access to the material. CONCLUSIONS: 1) The MSRP-USP simulation resources show low complexity and are mainly limited to computer programs; 2) Use of simulation varies according to course, and is most prevalent in Medicine; 3) Resources are scattered across several locations, and their acquisition and maintenance depend on individual initiatives rather than central coordination or curricular guidelines

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.