Archivio della ricerca della Scuola Superiore Sant'Anna
Not a member yet
26957 research outputs found
Sort by
Modern tools for sustainable agriculture: a review of intelligent crop protection technologies
No full textModern agriculture is under increasing pressure due to rising global food demand and the intensifying effects of climate change. Traditional crop protection methods often struggle to meet the current demands for higher productivity, reduced environmental impact, and long-term sustainability. This review highlights the importance of intelligent crop protection as a timely and necessary approach to address these challenges. There is a clear need for advanced Technologies and strategies to enable efficient, sustainable, and precise crop management. Intelligent crop protection integrates several modern technologies to enhance decision-making, minimize resource utilization, and protect crops more effectively. We review the application of technologies such as the Internet of Things (IoT), artificial intelligence (AI), machine learning (ML), robotics and automation, remote sensing, precision agriculture tools, and genome editing in crop protection. IoT devices, such as soil sensors, weather stations, and drones, real-time monitoring of field conditions. AI and ML assist in detecting pests and diseases early through image analysis and predictive models. Robotics enhances the accuracy and efficiency of tasks such as spraying and weeding. Genome editing techniques, such as CRISPR, are used to develop crop varieties with enhanced resistance to pests and diseases. By integrating these technologies, intelligent crop protection systems can reduce chemical use, conserve labor and resources, and promote more sustainable farming practices. This review demonstrates how adopting these innovations can help farmers enhance productivity and resilience while addressing the environmental and social challenges of modern agriculture
Biomarkers involved in the transmission of Maternal Psychological Stress through the Placenta to the Fetus
No full textThis book offers a comprehensive overview of how maternal psychological stress during pregnancy and postpartum affects both maternal health and fetal development. Drawing on the latest research, it explores how stress-related biomarkers like cortisol can cross the placenta, influencing the child’s health from birth through adulthood―and even across generations.
Written by leading experts, the 15 chapters examine the biological and psychological mechanisms involved, including the HPA axis and brain-gut axis. Topics include the impact of stress on pregnancy outcomes (e.g., pre-eclampsia, prematurity, low birth weight), neonatal health (e.g., colic, altered gut microbiota), and long-term risks such as autism, ADHD, and cardiovascular disease.
The book also addresses key stressors―such as intimate partner violence, war, and economic hardship―with a focus on pregnancy-specific stress as particularly harmful.
Essential reading for clinicians, researchers, and students in maternal and neonatal health, this volume also serves as a valuable resource for policymakers and healthcare stakeholders
Unlocking the benefits of transparent and reusable science for climate risk management
No full textPeople around the world seek climate risk information to guide their decisions. For instance, projections about future flood risk inform where households choose to live, how lenders manage credit risks, and which communities receive federal funding. Yet data limitations and fundamental validation challenges raise important concerns about the reliability of such projections. The principles of transparency and reusability help address these concerns by enabling scrutiny of assumptions and methods, development of foundational data and tools, and consistent application of evaluation standards. While there is ongoing debate about how much transparency commercial climate risk services should provide, many expect noncommercial actors to lead the way on operationalizing transparency and reusability to fulfill their knowledge-building role in the climate risk ecosystem. However, despite prominent success stories, we find a substantial gap between principles and practice: Only four percent of the most-cited peer-reviewed climate risk studies in recent years fully share their data and code although this is a widely accepted minimum standard for transparency. We highlight low-cost measures that noncommercial researchers can take now to improve transparency and reusability. We also emphasize that transformative progress requires substantial investment, cross-sector collaboration, and careful consideration of tradeoffs, data rights, and multiple perspectives on equity. We hope this perspective accelerates both immediate actions and longer-term conversations to improve the ability of science to effectively support timely, evidence-based, and sound climate risk management
A multimodal machine learning approach to forecast upper limb motor recovery after stroke using kinematic and electromyographic data – A pilot-study
No full textBackground: Forecasting post-stroke rehabilitation outcome is essential to personalize therapeutic strategies. Traditional approaches rely on clinical assessment scales, which, while essential, may benefit from complementary objective measures. In this direction, robot-assisted assessment offers the unprecedented possibility of precisely collecting patients’ physiological signals, enabling a data-driven approach to recovery assessments. Leveraging the use of multimodal features collected during assessment sessions performed before and after one month of robotic rehabilitation, we developed a machine learning approach to forecast the upper limb (UL) motor recovery in stroke survivors after rehabilitation. Methods: This study evaluated a 4-week rehabilitation program, using both standard physical therapy and the ALEx robot to promote UL motor recovery in 11 subacute stroke survivors, compared to 6 healthy individuals. Kinematic measures and surface electromyography (sEMG) were collected during a 3D reaching task involving six target points. From these tasks, 76 sEMG features, 18 kinematics features and 1 multimodal feature were extracted. To forecast the UL motor recovery post intervention, a two-step machine learning approach was devised: a machine learning regression model was developed and validated to predict the Fugl-Meyer Assessment for UL (FMA-UL) post rehabilitation, whereas an anomaly detection algorithm identified patients who exhibited limited or no motor recovery post intervention. The anomaly detection approach used a fully-connected autoencoder that identified patients with reduced recovery likelihood. The regression models, optimized via a nested Leave-One-Subject-Out approach, guided feature selection and refined hyperparameters to predict FMA-UL scores post intervention. Results: The optimized regression model achieved an RMSE in predicting the FMA-UL post intervention of 5.45. The autoencoder effectively identified patients with reduced recovery potential, showing a higher distribution of reconstruction errors for these individuals. Conclusions: The findings confirm that combining kinematic and sEMG data improves motor recovery assessment. The proposed machine learning approach holds potential for aiding clinicians and therapists in identifying patients who are more likely to recover UL motor functions before rehabilitation begins. By accurately predicting recovery outcomes, this method can help guide the development of personalized therapeutic strategies, optimizing treatment planning in advance
Innovation coherence, financial slack, and SMEs' success in competitive innovation policies: Evidence from the EU SME-instrument
No full textInnovation policies are a key instrument to support small and medium-sized companies in overcoming financial constraints and pursuing high-risk and cutting-edge innovation projects. Yet, the mechanisms through which internal capabilities and innovation resource configurations influence success in such competitive arenas remain underexplored. In this scenario, this study explores how innovation coherence – the alignment between a firm's new project and its existing patent portfolio – and available financial slack shape the likelihood of securing public innovation funding. Funding evaluations are critical policy tools, as they not only improve access to resources but also act as market signals of technological quality and implementation capacity during the competitive evaluation process. Against this backdrop, we test our framework in the context of the EU Horizon 2020 SME Instrument, one of the most competitive innovation policy scheme in Europe, using a novel measure of innovation coherence derived from text analysis of firms' patent portfolios and project proposals, and combining its effect with financial slack. Innovation coherence and financial slack are found to strongly and positively influence evaluation outcomes, offering both theoretical and practical contributions for SMEs and policymakers engaged in competitive innovation policy initiatives