Medication management ability in older patients: Time for a reappraisal

Background. Adhering to drug regimens is a complex and multidimensional task. Elderly patients usually take an average of seven drugs but most fail to adhere to the prescribed regimen. Several performance-based instruments have been developed to assess a patient\u2019s capacity to manage drugs but with inconsistent results. Aims. The aim of the study was to assess the prevalence of impaired medical management capacity in a sample of the oldest old hospitalized elderly patients and the main clinical factors associated with potential unintentional non-adherence. Methods. Forty-six consecutive patients were enrolled in the geriatric transitional care unit of Ospedale Policlinico San Martino, Genoa, Italy. All patients received an abbreviated comprehensive geriatric assessment and a hand grip assessment for sarcopenia. Patients\u2019 medication management ability was assessed by administering the DRUGS tool 48-74 hours before hospital discharge. Results. The results showed a negative correlation between age and total medication management score. A positive correlation was detected between functional status, cognitive status, and medication management score. Hand grip strength < 9 kg correlated with a significant worsening of medical management capacity. In contrast, multiple morbidities and the mean number of drugs were not associated with the medical management score. Conclusions. This preliminary study indicated that drug management capacity mainly relies on frailty markers, such as functional status, sarcopenia, and cognitive performance. Further studies are warranted to identify a subset of medical parameters that can accurately predict impaired medical management ability early, particularly for highly vulnerable elderly patients

Use of the Natural Circulation Flow Map for Natural Circulation Systems Evaluation

The aim of this paper is to collect and resume the work done to build and develop, at the University of Pisa, an engineering tool related to the natural circulation. After a brief description of the different loop flow regimes in single phase and two phase, the derivation of a suitable tool to judge the NC performance in a generic system is presented. Finally, an extensive comparison among the NC performance of various nuclear power plants having different design is done to show a practical application of the NC flow map

Understanding peace through the world news

Peace is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peace through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country’s profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peace.Peace is a principal dimension of well-being and is the way out of inequity and violence. Thus, its measurement has drawn the attention of researchers, policymakers, and peacekeepers. During the last years, novel digital data streams have drastically changed the research in this field. The current study exploits information extracted from a new digital database called Global Data on Events, Location, and Tone (GDELT) to capture peace through the Global Peace Index (GPI). Applying predictive machine learning models, we demonstrate that news media attention from GDELT can be used as a proxy for measuring GPI at a monthly level. Additionally, we use explainable AI techniques to obtain the most important variables that drive the predictions. This analysis highlights each country’s profile and provides explanations for the predictions, and particularly for the errors and the events that drive these errors. We believe that digital data exploited by researchers, policymakers, and peacekeepers, with data science tools as powerful as machine learning, could contribute to maximizing the societal benefits and minimizing the risks to peace

Opening the black box: a primer for anti-discrimination

The pervasive adoption of Artificial Intelligence (AI) models in the modern information society, requires counterbalancing the growing decision power demanded to AI models with risk assessment methodologies. In this paper, we consider the risk of discriminatory decisions and review approaches for discovering discrimination and for designing fair AI models. We highlight the tight relations between discrimination discovery and explainable AI, with the latter being a more general approach for understanding the behavior of black boxes

GLocalX - From Local to Global Explanations of Black Box AI Models

Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLOCALX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLOCALX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLOCALX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLOCALX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications.Artificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLOCALX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLOCALX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLOCALX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLOCALX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications

Intracavity intensity noise suppression in the inverse Compton scattering source BriXSinO exploiting carrier-envelope offset manipulation

We report on a technique that exploits the control of the carrier -envelope offset to suppress the frequency-to-intensity noise conversion in the locking of a mode-locking laser against a high-finesse optical enhancement resonator. A proper combination of the laser carrier-envelope offset and the resonator finesse allows the improvement of the signal-to-noise ratio of the optical intensity trapped into the optical resonator. In this paper, we show the application of this technique in the laser system of the inverse Compton scattering source BriXSinO, currently under development in Milan, Italy, demonstrating the possibility of achieving an intracavity intensity noise reduction of a factor of 20

MOBILITY ATLAS BOOKLET: AN URBAN DASHBOARD DESIGN AND IMPLEMENTATION

Abstract. The new data sources give the possibility to answer analytically the questions that arise from mobility manager. The process of transforming raw data into knowledge is very complex, and it is necessary to provide metaphors of visualizations that are understandable to decision makers. Here, we propose an analytical platform that extracts information on the mobility of individuals from mobile phone by applying Data Mining methodologies. The main results highlighted here are both technical and methodological. First, communicating information through visual analytics techniques facilitates understanding of information to those who have no specific technical or domain knowledge. Secondly, the API system guarantees the ability to export aggregates according to the granularity required, enabling other actors to produce new services based on the extracted models. For the future, we expect to extend the platform by inserting other layers. For example, a layer for measuring the sustainability index of a territory, such as the ability of public transport to attract private mobility or the index that measures how many private vehicle trips can be converted into electrical mobility.</p

Origin and destination attachment: study of cultural integration on Twitter

The cultural integration of immigrants conditions their overall socio-economic integration as well as natives’ attitudes towards globalisation in general and immigration in particular. At the same time, excessive integration—or assimilation—can be detrimental in that it implies forfeiting one’s ties to the origin country and eventually translates into a loss of diversity (from the viewpoint of host countries) and of global connections (from the viewpoint of both host and home countries). Cultural integration can be described using two dimensions: the preservation of links to the origin country and culture, which we call origin attachment, and the creation of new links together with the adoption of cultural traits from the new residence country, which we call destination attachment. In this paper we introduce a means to quantify these two aspects based on Twitter data. We build origin and destination attachment indices and analyse their possible determinants (e.g., language proximity, distance between countries), also in relation to Hofstede’s cultural dimension scores. The results stress the importance of language: a common language between origin and destination countries favours origin attachment, as does low proficiency in the host language. Common geographical borders seem to favour both origin and destination attachment. Regarding cultural dimensions, larger differences among origin and destination countries in terms of Individualism, Masculinity and Uncertainty appear to favour destination attachment and lower origin attachment