Archivio della ricerca della Scuola Superiore Sant'Anna
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Global burden of 292 causes of death in 204 countries and territories and 660 subnational locations, 1990–2023: a systematic analysis for the Global Burden of Disease Study 2023
No full textBackground Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. Methods GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. Findings The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6–47·0) in 1990 to 63·4 years (63·1–63·7) in 2023. For males, mean age increased from 45·4 years (45·1–45·7) to 61·2 years (60·7–61·6), and for females it increased from 48·5 years (48·1–48·8) to 65·9 years (65·5–66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9–81·0) and for males 74·8 years (74·8–74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5–38·4) for females and 35·6 years (35·2–35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. Interpretation We examined global mortality patterns over the past three decades, highlighting—with enhanced estimation methods—the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. Funding Gates Foundation
A Unilateral Active Knee Exoskeleton to Assist Individuals With Hemiparesis—A Pilot Study
No full textAdaptive Drift Compensation for Soft Sensorized Finger Using Continual Learning
No full textStrain sensors are gaining popularity in soft robotics for acquiring tactile data due to their flexibility and ease of integration. Tactile sensing plays a critical role in soft grippers, enabling them to safely interact with unstructured environments and precisely detect object properties. However, a significant challenge with these systems is their high non-linearity, time-varying behavior, and long-term signal drift. In this paper, we introduce a continual learning (CL) approach to model a soft finger equipped with piezoelectric-based strain sensors for proprioception. To tackle the aforementioned challenges, we propose an adaptive CL algorithm that integrates a Long Short-Term Memory (LSTM) network with a memory buffer for rehearsal and includes a regularization term to keep the model's decision boundary close to the base signal while adapting to time-varying drift. We conduct nine different experiments, resetting the entire setup each time to demonstrate signal drift. We also benchmark our algorithm against two other methods and conduct an ablation study to assess the impact of different components on the overall performance
Il contratto della transizione energetica
No full textLa ricerca indaga l’impatto della disciplina multilivello della transizione ecologica del mercato elettrico sul governo delle relazioni tra privati, con particolare riguardo alla tutela contrattuale dei clienti finali.
Il volume intende offrire uno studio sistematico dei profili strutturali e rimediali degli strumenti contrattuali per l’approvvigionamento energetico, dagli istituti di nuova introduzione per la promozione della transizione al tradizionale contratto di fornitura, anch’esso inevitabilmente sollecitato dalle trasformazioni in atto.
In coerenza con un quadro normativo che valorizza la vocazione strategica dell’atto di consumo (anche) verso gli obiettivi generali della neutralità climatica e del contrasto alla povertà energetica, il lavoro mira a definire i contorni, i meccanismi operativi e le modalità di interazione con il diritto interno di un diritto privato regolatorio europeo dell’energia maggiormente sensibile alle complessità e alle urgenze della contemporaneità
Be Passionate, and Keep Going. On the Role of Passion for Helping Entrepreneurs for the Public Good in Solving Identity Contestation
No full textIn modern times, characterized by grand challenges, new forms of entrepreneurship are crucial. Among them, this research embraces the perspective of entrepreneurs for the public good, individuals who act to embed new societal values while solving wicked problems. However, the context they are embedded in may trigger a sensation of oppression, and entrepreneurs may experience role overload. Feeling that their role identity is threatened, their entrepreneurial identity might be contested. Based on identity work theory, the study theorizes on the peculiar moment of entrepreneurial identity contestation, arguing that passion might help entrepreneurs in solving and elaborating on their identity. According to several scholars, entrepreneurial identity is capable of predicting passion. Thus, embedding the dualistic model of passion, I theorize on its role in entrepreneurial identity contestation, seeking to broaden the identity work literature. Firstly, I focus on a peculiar moment of the entrepreneurial identity work, that is, identity contestation. Secondly, I integrate the dualistic model of passion within identity work, suggesting the positive shade of obsessive passion. Lastly, the study, embracing the proposed perspective of entrepreneurship for the public good, aims to lay the foundations for intriguing research on how grand challenges, as contextual conditions, shape entrepreneurship
Diagnosis and Prognostication With PET Imaging: The (Next) Future in Light Chain Amyloidosis
No full textAl centro della crisi della sanità italiana: prossimità e integrazione tra narrazione medica e risposta politica
No full textThe article evolves around two different perspectives, that of medical doctors and that of policymakers, on two crucial shortfalls within the public healthcare’s crisis in Italy: the underdevelopment of proximity healthcare services and the integration of health care and social care. The first part explores the medical narrative around such problems on the ground of the qualitative research data. In the second part, the policy response is discussed through an analysis of the essential features of the ongoing reforms proximity healthcare and long-term care for the elderly. One year after the conference that marked the conclusion of the Project Quale sanità per il futuro? Per una medicina più equa e sostenibile, conducted by the Center for Religious Studies of the Bruno Kessler Foundation, the article provides new lenses to understand the evolution of the Italian National Health Service today
Toward low-complexity neural networks for failure management in optical networks
No full textMachine learning (ML) continues to show its potential and efficacy in automating network management tasks, such as failure management. However, as ML deployment considerations broaden, aspects that go beyond predictive performance, such as a model’s computational complexity (CC), start to gain significance, as higher CC incurs higher costs and energy consumption. Balancing high predictive performance with reduced CC is an important aspect, and therefore, it needs more investigation, especially in the context of optical networks. In this work, we focus on the problem of reducing the CC of ML models, specifically neural networks (NNs), for the use case of failure identification in optical networks. We propose an approach that exploits the relative activity of neurons in NNs to reduce their size (and hence, their CC). Our proposed approach, referred to as iterative neural removal (INR), iteratively computes neurons’ activity and removes neurons with no activity until reaching a predefined stopping condition. We also propose another approach, referred to as guided knowledge distillation (GKD), that combines INR with knowledge distillation (KD), a known technique for compression of NNs. GKD inherently determines the size of the compressed NN without requiring any manual suboptimal selection or other time-consuming optimization strategies, as in traditional KD. To quantify the effectiveness of INR and GKD, we evaluate their performance against pruning (i.e., a well-known NN compression technique) in terms of impact on predictive performance and reduction in CC and memory footprint. For the considered scenario, experimental results on testbed data show that INR and GKD are more effective than pruning in reducing CC and memory footprint
3D Virtual Activation Volume for Automated Grasping in Teleoperated Robotic Manipulation
No full textThis research study proposes a new method to improve robotic teleoperation during grasping tasks.
In particular, the target is to reduce the operator's mental burden in grasping tasks, ensuring that the robotic grasping task is simplified for the operator.
This simplification aims to improve the task grasping execution time and, in the meantime, increase its grasping success rate.
The method is based on an adaptive 3D Virtual Activation Volume (VaV), which acts as a dynamic constraint to support the operator in the grasping task.
This volume is centered around the object to grasp, and leverages advanced 3D modeling and active control techniques by using machine learning algorithms.
In conclusion, the experimental results demonstrate that the proposed method enhances the precision of Human-Robot Interaction (HRI), resulting in a more intuitive teleoperation experience, with a 24% decrease in grasping execution time and a 26.4% increase in grasping success rate