Archivio della ricerca della Scuola Superiore Sant'Anna
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La condizione disumana. Hannah Arendt, l'intelligenza artificiale e le frontiere digitali
No full textA bitter aftertaste? The effects of privatisation reforms on evaluations of health systems across Europe
No full textWhat is the effect of healthcare privatisation on citizens’ views of the health system? Using an original data set of legislative changes in health policy and European Social Survey data on healthcare system evaluation, we analyse the link between healthcare privatisation reforms and citizens’ evaluations of health systems across 30 European countries. Our results show that after an initial positive reaction, privatisation is associated with the worsening of attitudes towards the health system. Partisanship of the government enacting privatisation matters for evaluations, but this effect of partisanship is relatively short-termed. Most importantly, results point to the critical importance of proximity. Privatisation reforms with more direct effects on healthcare use are evaluated more negatively compared with those with indirect effects, demonstrating the importance of policy proximity to beneficiaries. Similarly, an individual’s proximity to policy, which refers to direct exposure to health policy based on health status, has a more immediate and extended effect on evaluations compared to characteristics unrelated to care use, such as ideology. Overall, the results demonstrate that citizens react to privatisation in a dynamic way and discriminate between different types of privatisation reforms, contributing to a better understanding of the individual and the temporal dimension of policy effects
Assessing the Robustness of In-Switch Neural Networks Against Adversarial DDoS Attacks
No full textIn data-plane deployment of DNNs allows for improved edge functionality and intrusion detection. We report on the increased resilience of in-switch LUT-distilled neural networks to whitebox and black-box adversarial attacks
How to Motivate Enterprises to Use New Technologies: From the Perspective of Digitalization Cost in Digital Supply Chain Finance
No full textSynergizing Artificial Intelligence and Remote Sensing for Enhanced Crop Growth Parameter Estimation and Yield Prediction in Mediterranean Agroecosystems: A Systematic Literature Review
No full textThe agricultural sector faces increasing pressure to meet global food demand due to population growth. Challenges such as climate change, resource scarcity, and environmental degradation will further increase this problem. These issues are particularly critical in the Mediterranean region, which is characterized by water-limited conditions and soils poor in organic matter and mineral nutrients. As a step toward ensuring food security, optimized resource utilization strategies and actionable plans for stakeholders are necessary. Reliable estimation of crop growth parameters and yield prediction under different climatic and agronomic scenarios have emerged as critical tools in driving these changes.
Various conventional crop growth parameter estimation and yield prediction methods have emerged as methods for optimizing resource utilization, identifying risks, and enabling effective decision-making. However, conventional methods, including empirical, statistical, and process-based models, often face limitations such as co-linearity among predictor variables, assumptions of stationarity, and the inability to capture complex biophysical and biochemical interactions at large scales. These shortcomings highlight the need for more robust and adaptable approaches. Advanced technologies, particularly Artificial Intelligence (AI) and Remote Sensing (RS) have revolutionized agriculture by uncovering hidden patterns, enabling large-scale monitoring, and improving prediction accuracy. This research evaluates the state-of-the-art in the synergized use of AI and RS for crop growth parameter estimation and yield prediction in Mediterranean agroecosystems.
A systematic literature review was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. Keywords and Boolean operators were used to search titles, abstracts, and keywords in selected databases, including Web of Science and Scopus. The review included English and French publications focusing on the Mediterranean region, encompassing Southern European, Middle Eastern, and North African countries bordering the Mediterranean Sea. Publications that were duplicated, unrelated to the study objectives, or outside the geographical focus were excluded. Out of 551 initial publications retrieved, 117 met the inclusion criteria and were selected for detailed review.
The findings reveal a rising interest in integrating AI and RS for estimating crop growth parameters and predicting yield. Multispectral RS products, such as Landsat-8 and Sentinel-2, are the most frequently utilized data sources. Additionally, Sentinel-1 microwave sensors and Unmanned Aerial Vehicle (UAV)-based imagery are increasingly employed alongside ground-based sensors. Among AI methodologies, Machine Learning (ML) algorithms like Random Forest (RF), Artificial Neural Networks (ANN), and Support Vector Machines (SVM) dominate, while Deep Learning (DL) techniques such as Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks have gained prominence since 2020. Most publications were produced between 2020 and 2024, with Italy, Spain, and France being the most studied regions.
The study underscores the transformative potential of integrating AI and RS for crop growth parameter estimation and yield prediction in Mediterranean agroecosystems. By leveraging diverse data sources, algorithms, and sensor technologies, these advancements address the limitations of traditional models, enhance scalability and accuracy, and support sustainable agriculture in resource-limited environments
BiFlu2: A Miniaturized Electro-Fluidic Circuit for All-On-Board Control of Hydraulically-Actuated Biorobotic Organs
No full textThis paper presents the design and implementation of BiFlu2, a compact electro-fluidic circuit engineered for implantable biorobotic organs with hydraulic actuators. BiFlu2 enables fluidic control with a limited number of components, i.e. two bistable 3-way valves for bidirectional fluid transfer and an additional one for fluid (de)multiplexing. The system is designed to operate with minimal power consumption, making it suitable for long-term operation upon implantation. The design incorporates an ESP32 microprocessor for wireless remote control, as well as pressure sensors to monitor real-time actuators' performance. BiFlu2 has been tested and validated through simulations and experimental setups, demonstrating efficient hydraulic control of an artificial detrusor integrated with a soft artificial bladder. The experimental results show reliable performance, low power consumption, and effective actuator control for the voiding process of an artificial bladder provided with a hydraulic detrusor. This study highlights the BiFlu2 potential for use in fluidically actuated implantable biorobotic organs, where compact size and energy efficiency are critical. Refer to the supplementary video for a summary of our findings
Medical & healthcare robotics: a roadmap for enhanced precision, safety, and efficacy
No full textMedical robotics holds transformative potential for healthcare. Robots excel in tasks requiring precision, including surgery and minimally invasive interventions, and they can enhance diagnostics through improved automated imaging techniques. Despite the application potentials, the adoption of robotics still faces obstacles, such as high costs, technological limitations, regulatory issues, and concerns about patient safety and data security. This roadmap, authored by an international team of experts, critically assesses the state of medical robotics, highlighting existing challenges and emphasizing the need for novel research contributions to improve patient care and clinical outcomes. It explores advancements in machine learning, highlighting the importance of trustworthiness and interpretability in robotics, the development of soft robotics for surgical and rehabilitation applications, and the role of image-guided robotic systems in diagnostics and therapy. Mini, micro, and nano robotics for surgical interventions, as well as rehabilitation and assistive robots, are also discussed. Furthermore, the roadmap addresses service robots in healthcare, covering navigation, logistics, and telemedicine. For each of the topics addressed, current challenges and future directions to improve patient care through medical robotics are suggested
Learned Compression of Nonlinear Time Series With Random Access
No full textTime series data plays a crucial role in many fields, including finance, healthcare, industry, and environmental monitoring. The storage and retrieval of time series data can be challenging due to their unstoppable growth. In fact, these applications often sacrifice precious historical data to make room for new data. General-purpose compressors like Xz and Zstd can mitigate this problem with their good compression ratios, but they lack efficient random access on compressed data, thus preventing real-time analyses. Ad-hoc streaming solutions, instead, typically optimise only for compression and decompression speed, while giving up compression effectiveness and random access functionality. Furthermore, all these methods lack awareness of certain special regularities of time series, whose trend over time can often be described by some linear and nonlinear functions. To address these issues, we introduce NeaTS, a randomly-accessible compression scheme that approximates the time series with a sequence of nonlinear functions of different kinds and shapes, carefully selected and placed by a partitioning algorithm to minimise the space. The approximation residuals are bounded, which allows storing them in little space and thus recovering the original data losslessly, or simply discarding them to obtain a lossy time series representation with maximum error guarantees. Our experiments show that NeaTS improves the compression ratio of the state-of-the-art lossy compressors that use linear or nonlinear functions (or both) by up to 14%. Compared to lossless compressors, NeaTS emerges as the only approach to date providing, simultaneously, compression ratios close to or better than the best existing compressors, a much faster decompression speed, and orders of magnitude more efficient random access, thus enabling the storage and real-time analysis of massive and ever-growing amounts of (historical) time series data
Tryptophan Metabolism in Neurodevelopment and Its Implications For Neurodevelopmental Disorders
No full textThe role of tryptophan metabolism has been recognized in a wide range of physiological and pathological processes but is still only partially understood. Growing evidence highlights the importance of maintaining tryptophan homeostasis throughout life, with its disruption now linked to various neuropsychiatric conditions spanning from early life to aging. While it is increasingly evident that alterations in tryptophan metabolism have significant implications for both neurodevelopmental and neurodegenerative disorders, research has predominantly focused on the latter, leaving neurodevelopmental aspects comparatively underexplored. This review provides a comprehensive overview of both preclinical and clinical studies, highlighting the intricate relationship between tryptophan metabolism and neurodevelopment. Particular focus is given to the kynurenine pathway and gut microbiota-derived indole production, two interconnected metabolic branches with profound effects on brain maturation, plasticity, and immune regulation. Finally, we examine the pathophysiological consequences of tryptophan dysregulation in neurodevelopmental disorders, including autism spectrum disorder, attention-deficit/hyperactivity disorder, and Rett syndrome. We also discuss potential therapeutic strategies targeting tryptophan metabolism in these conditions