The influence of context and process when implementing e-health

Peer reviewedPublisher PD

Empirical evaluation of the effects of experience on code quality and programmer productivity: an exploratory study

There is a widespread belief in both SE and other branches of science that experience helps professionals to improve their performance. However, cases have been reported where experience not only does not have a positive influence but sometimes even degrades the performance of professionals

Evaluation model for Big Data integration tools

Given the growing demand and need by enterprises for data and information to positively support the decision-making process, there is no doubt about the importance of selecting the correct and appropriate integration tool for the different types of business. For this reason, the essential objective of this study is to create a model that will serve as a basis to evaluate the different alternatives and solutions that exist in the market able to overcome the big data integration challenges. The evaluation process of data integration product begins with the definition and prioritisation of critical requirements and criteria. In this evaluation model, the characteristics evaluated are categorised into three main groups: ease of integration and implementation, quality of service and support, and costs. After identifying the essential criteria and characteristics, it is necessary to determine the weights that these criteria should have in the evaluation. Then, it needs to verify which solutions existing in the market best fit the needs of the business and can satisfy them more effectively. And lastly, compare those solutions adopting this framework. It is essential to carry out a weighted evaluation, based on well-defined criteria like ease of use, quality of technical support, data privacy and security. This process is fundamental to verify if the solution offers what the organization needs if it meets the business requirements and their integration needs.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/201

Postural and event behaviours which differed in frequency within a single day in 21 working donkeys.

<p>Ear orientation: FF/SS  =  one ear forwards, one sideways; SS/BB  =  one ear sideways, one backwards; SD/SD  =  both ears sideways and facing down.</p

Using an artificial agent as a behavior model to promote assistive technology acceptance

Despite technological advancements in assistive technologies, studies show high rates of non-use. Because of the rising numbers of people with disabilities, it is important to develop strategies to increase assistive technology acceptance. The current research investigated the use of an artificial agent (embedded into a system) as a persuasive behavior model to influence individuals’ technology acceptance beliefs. Specifically, we examined the effect of agent-delivered behavior modeling vs. two non-modeling instructional methods (agent-delivered instructional narration and no agent, text-only instruction) on individuals’ computer self-efficacy and perceived ease of use of an assistive technology. Overall, the results of the study confirmed our hypotheses, showing that the use of an artificial agent as a behavioral model leads to increased computer self-efficacy and perceived ease of use of a system. The implications for the inclusion of an artificial agent as a model in promoting technology acceptance are discussed

Introduction to Decision Support Systems

Decision support systems (DSSs) are computer programs that, by using expert knowledge, simulation models and/or databases, are of assistance in the decision-making process as they offer management recommendations and/or options. The principal aim of a DSS is to improve the quality, speed and effectiveness of decisions. Since their beginnings in the 1960s, DSSs have been established as being an effective decision-making tool in different areas including agriculture. Weed science has not been immune to their influence, and since the end of the 1980s, a batch of DSSs have been developed towards the recognition and identification of seeds and seedlings, herbicide selection and the economic assessment of management strategies. Despite being powerful tools, DSSs have certain constraints and also a given resistance to their use. I hope that this chapter will serve to give a general insight into DSSs and their use in weed science, as well as to encourage the spreading of these systems in order to establish sustainable agriculture