Chemical characterization of tin-lead glazed ceramics from Aragon (Spain) by neutron activation analysis

Majolica pottery was the most characteristic tableware produced in Spain during the Medieval and Renaissance periods. A study of the three main production centers in the historical region of Aragon during Middle Ages and Renaissance was conducted on a set of 71 samples. The samples were analyzed by instrumental neutron activation analysis (INAA), and the resulting data were interpreted using an array of multivariate statistical procedures. Our results show a clear discrimination among different production centers allowing a reliable provenance attribution of ceramic sherds from the Aragonese workshops.

Sales lead management in a global enterprise

Many companies today struggle with problems they face around sales lead management. They are suffering from inconsistent quality of leads, they miss clear sales opportunities and even cannot handle well their internal marketing lists. Meanwhile customers are better and better equipped with means to easily initiate contact via internet, via call centers etc. Investing in lead generation activities that are built on a bad process is not a good idea. Better than asking how to get more leads, companies should ask how to get better quality leads and invest in improving lead management. This study looks sales lead management as a multi step process where a company generates leads in controlled environment, qualifies them and hands over to the sales cycle. As a final step, organization needs to analyze the incomes and successes of different lead sources. Most often in sales lead management a process improvement requires setting up additional controls to enable proper tracking of all leads. A sales lead management process model for the case company is built based on the findings. Implementing the new model involves changes and improvements in some key areas of current process. Starting from the very beginning, these include redefining a bit the lead definition and revising the criteria set for qualified lead. There are some improvements to be done in the system side to enable the proposed model. Lastly a setting for responsible roles is presented.

Adjusting for the environment in DEA: a comparison of alternative models based on empirical data

Due to the existence of free software and pedagogical guides, the use of Data Envelopment Analysis (DEA) has been further democratized in recent years. Nowadays, it is quite usual for practitioners and decision makers with no or little knowledge in operational research to run their own efficiency analysis. Within DEA, several alternative models allow for an environmental adjustment. Four alternative models, each user-friendly and easily accessible to practitioners and decision makers, are performed using empirical data of 90 primary schools in the State of Geneva, Switzerland. Results show that the majority of alternative models deliver divergent results. From a political and a managerial standpoint, these diverging results could lead to potentially ineffective decisions. As no consensus emerges on the best model to use, practitioners and decision makers may be tempted to select the model that is right for them, in other words, the model that best reflects their own preferences. Further studies should investigate how an appropriate multi-criteria decision analysis method could help decision makers to select the right model.

Privatization and PPPS in transportation infrastructure: Network effects of increasing user fees

PPPS: Problem: Public-private-partnerships in transport infrastructure characteristically increase user-fees. Purpose: We aim to identify the network effects of the use of PPPs and increased user tolls in road infrastructure. Methods: We study the increases in user tolls on motorways due to the use of PPPs in the US. Results and conclusions: Among other things, the monetization of motorways is associated with an increase in toll levels that has consequences for their users, and also for the rest of the sections of the network.

Does evolution lead to maximizing behavior?

A long-standing question in biology and economics is whether individual organisms evolve to behave as if they were striving to maximize some goal function. We here formalize this "as if" question in a patch-structured population in which individuals obtain material payoffs from (perhaps very complex multimove) social interactions. These material payoffs determine personal fitness and, ultimately, invasion fitness. We ask whether individuals in uninvadable population states will appear to be maximizing conventional goal functions (with population-structure coefficients exogenous to the individual's behavior), when what is really being maximized is invasion fitness at the genetic level. We reach two broad conclusions. First, no simple and general individual-centered goal function emerges from the analysis. This stems from the fact that invasion fitness is a gene-centered multigenerational measure of evolutionary success. Second, when selection is weak, all multigenerational effects of selection can be summarized in a neutral type-distribution quantifying identity-by-descent between individuals within patches. Individuals then behave as if they were striving to maximize a weighted sum of material payoffs (own and others). At an uninvadable state it is as if individuals would freely choose their actions and play a Nash equilibrium of a game with a goal function that combines self-interest (own material payoff), group interest (group material payoff if everyone does the same), and local rivalry (material payoff differences).

A web-based tool to aid the identification of chemicals potentially posing a health risk through percutaneous exposure

Occupational hygiene practitioners typically assess the risk posed by occupational exposure by comparing exposure measurements to regulatory occupational exposure limits (OELs). In most jurisdictions, OELs are only available for exposure by the inhalation pathway. Skin notations are used to indicate substances for which dermal exposure may lead to health effects. However, these notations are either present or absent and provide no indication of acceptable levels of exposure. Furthermore, the methodology and framework for assigning skin notation differ widely across jurisdictions resulting in inconsistencies in the substances that carry notations. The UPERCUT tool was developed in response to these limitations. It helps occupational health stakeholders to assess the hazard associated with dermal exposure to chemicals. UPERCUT integrates dermal quantitative structure-activity relationships (QSARs) and toxicological data to provide users with a skin hazard index called the dermal hazard ratio (DHR) for the substance and scenario of interest. The DHR is the ratio between the estimated 'received' dose and the 'acceptable' dose. The 'received' dose is estimated using physico-chemical data and information on the exposure scenario provided by the user (body parts exposure and exposure duration), and the 'acceptable' dose is estimated using inhalation OELs and toxicological data. The uncertainty surrounding the DHR is estimated with Monte Carlo simulation. Additional information on the selected substances includes intrinsic skin permeation potential of the substance and the existence of skin notations. UPERCUT is the only available tool that estimates the absorbed dose and compares this to an acceptable dose. In the absence of dermal OELs it provides a systematic and simple approach for screening dermal exposure scenarios for 1686 substances.

User-Centered Design Gymkhana

The User-centered design (UCD) Gymkhana is a tool for human-computer interaction practitioners to demonstrate through a game the key user-centered design methods and how they interrelate in the design process.The target audiences are other organizational departments unfamiliar with UCD but whose work is related to the definition, cretaion, and update of a product service.

User Centered Desing in E-Learning Environments : from Usability to Learner Experience

The advent of the Internet had a great impact on distance education and rapidly e-learning has become a killer application. Education institutions worldwide are taking advantage of the available technology in order to facilitate education to a growing audience. Everyday, more and more people use e-learning systems, environments and contents for both training and learning. E-learning promotes educationamong people that due to different reasons could not have access to education: people who could nottravel, people with very little free time, or withdisabilities, etc. As e-learning systems grow and more people are accessing them, it is necessary to consider when designing virtual environments the diverse needs and characteristics that different users have. This allows building systems that people can use easily, efficiently and effectively, where the learning process leads to a good user experience and becomes a good learning experience.

Capturing user behavior in e-learning environments

Peer-reviewed

A Three-Level Approach for Analyzing User Behavior in Ongoing Relationships

Peer-reviewed

Chemical characterization of tin-lead glazed pottery from the Iberian Peninsula and the Canary Islands: initial steps toward a better understanding of Spanish Colonial pottery in Americas

Majolica pottery was the most characteristic tableware produced in Europe during the Medieval and Renaissance periods. Because of the prestige and importance attributed to this ware, Spanish majolica was imported in vast quantities into the Americas during the Spanish Colonial period. A study of Spanish majolica was conducted on a set of 186 samples from the 10 primary majolica production centres on the Iberian Peninsula and 22 sherds from two early colonial archaeological sites on the Canary Islands. The samples were analysed by neutron activation analysis (NAA), and the resulting data were interpreted using an array of multivariate statistical approaches. Our results show a clear discrimination between different production centres, allowing a reliable provenance attribution of the sherds from the Canary Islands.

Personal Liberty in Psychiatric Care - Towards Service User Involvement

This study explores personal liberty in psychiatric care from a service user involvement perspective. The data were collected in four phases during the period 2000-2006 in psychiatric settings in Finland. Firstly, patient satisfaction and factors associated with user involvement were studied (n = 313). Secondly, patients’ experiences of deprivation of their liberty were explored (n = 51). Thirdly, an overview on patients’ options for lodging complaints was conducted, and all complaints (n = 4645) lodged in Finland from 2000 to 2004 were examined. Fourthly, the effects of different patient education methods on inpatients’ experiences of deprivation of liberty were tested (n = 311). It emerged that patients were quite satisfied, but reported dissatisfaction in restrictions, compulsory care and information dissemination. Patients experienced restrictions on leaving the ward and on communication, confiscation of property and coercive measures as deprivation of liberty. Patients’ experienced these interventions to be negative. In Finland, the patient complaint process is complicated and not easily accessible. In general, patient complaints increased considerably in Finland during the study period. In psychiatric care the number of complaints was quite stable and complaints led more seldom to consequences. An Internet-based patient education system was equivalent with traditional education and treatment as usual in supporting personal liberty during hospital care. This dissertation provides new information about the realization of patients' rights in psychiatric care. In order to improve patients' involvement, systematic methods to increase personal liberty during care need to be developed, the procedures for patients lodging complaints should be simplified, and patients' access to information needs to be ensured using multiple 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.

Solid phase extraction of iron and lead in environmental matrices on amberlite xad-1180/pv

A solid phase extraction procedure using Amberlite XAD-1180/Pyrocatechol violet (PV) chelating resin for the determination of iron and lead ions in various environmental samples was established. The procedure is based on the sorption of lead(II) and iron(III) ions onto the resin at pH 9, followed by elution with 1 mol/L HNO3 and determination by flame atomic absorption spectrometry. The influence of alkaline, earth alkaline and some transition metals, as interferents, are discussed. The recoveries for the spiked analytes were greater than 95%. The detection limits for lead and iron by FAAS were 0.37 µg/L and 0.20 µg/L, respectively. Validation of the method described here was performed by using three certified reference materials (SRM 1515 Apple Leaves, SRM 2711 Montana Soil and NRCC-SLRS-4 Riverine Water). The procedure was successfully applied to natural waters and human hair.