Valutazione Del Benessere Psico-Fisico Nell'aderenza Terapeutica Nelle Donne Con Malattia Renale Policistica Autosomica Dominante: Uno Studio Osservazionale

Evaluation Of The Psychophysical Well-Being In The Compliance Of Women With Autosomal Dominant Policystic Kidney Disease: An Observational Study BACKGROUND: Autosomal dominant polycystic kidney disease is the most common inherited renal disease and affects less than 1 every 400-1,000 people. There are many effective treatments, including blood pressure management, physical activity, low sodium diet and hydration. Therapeutic education is part of a patient's care and treatment. This approach is an essential strategy in order to face the current healthcare scenario, in which the number of people affected by chronic diseases is progressively increasing. OBJECTIVES: This article aims to analyze the effect of therapeutic education in patients with ADPKD, the level of adherence to pharmacological therapy and their compliance to dietetic and lifestyle recommendations as part of a nursing-led education. METHODS: This is a prospective, longitudinal, observational pilot study. The following measurements were used: Kidney Disease Quality of life - Short Form, Hospital Anxiety and Depression Scale, Body Uneasiness Test. At the T0 visit, a nurse selected patients and carried out a personalized educational intervention with the aims of adhering to drug therapies, monitoring blood pressure and dietary behavior (physical activity and water intake). At the T1 visit, patients performed psychological tests. At the T2 visit, the following evaluations were performed: a psychological interview together with the delivery and evaluation of the tests performed, an interview with the nurse to evaluate the adherence to the prescriptions, and a control of parameters such as physical activity, diet, water intake, drug therapy, and blood pressure. RESULTS: Therapeutic education can have a positive impact on patients' health by improving adherence to the pharmacological therapy, diet and lifestyle. CONCLUSIONS: Therapeutic education improve the patient's knowledge, treatments and correct behaviors as well as promotes an independent management of the disease. Through an educational intervention, the patient acquires the ability and the awareness to modify the wrong behaviors and to guarantee a balance between his needs and the pathology, thus improving the quality of life

The European AI Act’s Impact on Financial Markets: From Governance to Co-Regulation

The study explores the general regulatory challenges arising from the need to mediate between a horizontal approach to AI regulation and the sector-specific dimensions of financial markets, with regards to the sectoral regulations that may already regulate some AI tools in the financial sector, the structural features of AI-driven financial services, as well as the traditional objectives of financial regulation. In this vein, this paper intends to identify the gaps left open by the AI Act’s requirements for a full control over financial AI-related risks. In second stance, it aims at providing first guidelines for the implementation of the AI Act’s horizontal requirements in the financial sector. To these ends, the analysis demonstrates the relevance of the governance strategies regarding ICT risks that financial institutions – and their boards of directors – will have to develop under the DORA, as an effective venue for addressing detected regulatory gaps in the field of financial AI. Outside the scope of single financial institutions’ compliance, and shifting to a market-wide perspective, the acknowledgment of the shortcomings related to the tools of supervised co-regulation (as regulatory sandboxes) proposed by the AI Act, confirms the importance of the enactment of sound supervisory policies by financial authorities. In this respect, it is shown how the AI Act’s provisions on AI supervision, matched with the rules under the DORA, pose fruitful legal grounds for the consolidation of financial AI supervisory schemes, based on the collaboration between different financial authorities, and between financial and other technology-relevant authorities, as the European Artificial Intelligence Board

Virtual cultural tour personalization by means of an adaptive e-learning system: A case study

Visiting a real or virtual museum or an archaeological site can be a hard task, especially in case of large sites provided with many works of art or ancient ruins. For this reason most historical sites provide guided tours, to improve visitors satisfaction and interest. In this work we explore the use of an e-learning environment, called Lecomps5, to provide museums or other cultural sites with the capability of automatically planning personalized tours, according to visitors needs and interests. Lecomps5 allows a domain expert, through a suitable GUI, to build a pool of learning components concerning a given site. Then the system, by means of an embedded planner, generates a personalized tour through the works of art, on the basis of the visitor's artistic interests and needs. We propose a first application of this system to an ancient archaeological site called Lucus Feroniae, showing how an e-learning platform can be successfully used for guiding visitors as well. © 2009 Springer Berlin Heidelberg

Lecomps5: a Framework for the Automatic Building of Personalized Learning Sequences

Abstract. In the context of distance learning, Adaptive Web-based Educational System focus on personalization and adaptation, that is on “learner’s satisfac- tion”. In this paper we address the other side of the coin, that is the "teacher’s satisfaction" problem, which is quite seldom taken into account in educational systems. We present a new version of the Lecomps5 Web-based Educational System, a system capable of providing personalization and adaptation on the basis of learner’s knowledge, learning styles and learning progresses. In this new version, a framework provides the teacher with an easy and flexible tool for managing learning material, expressing different didactic strategies and se- quencing personalized courses by means of an embedded planner. Such func- tionalities are supported by the system basing on evaluations of learner’s knowledge, learning styles, and learning progresses. We report on a first con- trolled experiment, we made to evaluate the “teacher’s satisfaction”

Student and Teacher Perspectives Testing a System for Adaptive e-Learning

Personalization is becoming a mandatory requirement in Web-based Education and long distance learning in general, representing a flexible way of learning the exact amount of knowledge to reach a given learning goal. This approach saves time and money and it is particularly suited for life-long learning. The drawback is that the teacher has to produce some effort to prepare didactic material and while research in this field proposes several intelligent systems providing personalization with advanced didactic strategies, teacher’s point of view is less considered. In this chapter we extend our previous work that aimed to build an adaptive system for education called LS-Plan, taking into account both teacher’s and student’s needs. In particular we carried out a comprehensive evaluation of the system embedded into an Adaptive Educational Hypermedia called Lecomps5, in order to experiment and prove the added value of the system

The Lecomps5 framework for personalized web-based learning: A teacher's satisfaction perspective

Adaptive web-based educational systems provide learners with personalized courses, where learning material is delivered to learners taking into account their personal learning needs, learning styles and learning progresses. In this paper we show the Lecomps5 system, a didactic framework, supporting the automated production and adaptation of personalized courses, implemented in the Lecomps5 system. In particular, this framework was designed in order to address the teacher's satisfaction issue, arising in many systems that are quite demanding in terms of the teacher's work and range of activities. Lecomps5 allows the teacher, through a simple and intuitive didactic tool, to define learning material, specify its characteristics pertaining to personalization and define, to some extent, the didactic strategies to be applied. In order to support both the management of learning material and the automated construction of personalized courses, the system embeds a planner, based on Linear Temporal Logic. The selection of learning material, its sequencing, and the delivery of courses, is performed according to both learners' initial and run-time knowledge and learning styles. The teacher can focus more on her didactic tasks and preferences rather than on the available authoring tools, and spend less time to generate courses. Finally we show encouraging results from experimentation we conducted to test the system from a teacher's point of view. (C) 2010 Elsevier Ltd. All rights reserved

Definition and Analysis of a System for the Automated Comparison of Curriculum Sequencing Algorithms in Adaptive Distance Learning

LS-Lab provides automatic support to comparison/evaluation of the Learning Object Sequences produced by different Curriculum Sequencing Algorithms. Through this framework a teacher can verify the correspondence between the behaviour of different sequencing algorithms and her pedagogical preferences. In fact the teacher can compare algorithms outcomes over sample individual cases, represented by input student models. Such comparison can be accomplished through subjective observation of the sequences, and by evaluating the metrics computed and presented by the system. LS-Lab architecture allows extending the framework with both additional algorithms and metrics. According to the different algorithms needs, suitably varied data structures for the student models are managed. We show also the result of an experimental analysis, conducted to unveil LS-Lab usefulness, as perceived by teachers. Teacher's appreciation, acceptance of the system, and expected advantages, were analyzed through an experimental application involving 30 teachers, with 3 student models, and 3 different sequencing algorithms

A module for adaptive course configuration and assessment in moodle

Personalization and Adaptation are among the main challenges in the field of e-learning, where currently just few Learning Management Systems, mostly experimental ones, support such features. In this work we present an architecture that allows moodle to interact with the Lecomps system, an adaptive learning system developed earlier by our research group, that has been working in a stand-alone modality so far. In particular, the Lecomps responsibilities are circumscribed to the sole production of personalized learning objects sequences and to the management of the student model, leaving to moodle all the rest of the activities for course delivery. The Lecomps system supports the "dynamic" adaptation of learning objects sequences, basing on the student model, i.e., learner's Cognitive State and Learning Style. Basically, this work integrates two main Lecomps tasks into moodle, to be directly managed by it: Authentication and Quizzes. All in all, and so far, the advantage of the presented integration is in the real possibility to deliver and take personalized courses, residing and basically remaining into the moodle environment. © 2010 Springer-Verlag

Automated and flexible comparison of course sequencing algorithms in the LS-Lab framework

Curriculum Sequencing is one of the most interesting challenges in learning environments, such as Intelligent Tutoring Systems and e-learning. The goal is to automatically produce personalized sequences of didactic materials or activities, on the basis of each individual student's model. In this paper we present the extension of the LS-Lab framework, supporting an automated and flexible comparison of the outputs coming from a variety of Curriculum Sequencing algorithms over the same student models. The main aim of LS-Lab is to provide researchers or teachers with a ready-to-use and possibly extensible environment, supporting a reasonably low-cost experimentation of several sequencing algorithms. The system accepts a student model as input, together with the selection of the algorithms to be used and a given learning material; then the algorithms are applied, the resulting courses are shown to the user, and some metrics computed over the selected characteristics are presented, for the user's appraisal. © 2010 Springer-Verlag