68994 research outputs found
Sort by
The Role of Paternal Mental Health During the Perinatal Period: From Preconception to Postpartum
No full textTraditionally, research on the developmental origins of health and disease has predominantly focused on maternal pregnancy exposures, leaving paternal health largely underexplored. The role of paternal factors in offspring health is an emerging field that addresses direct genetic and epigenetic influences and indirect environmental impacts. Paternal health may influence epigenetic changes in sperm production and/or quality, mainly induced by environmental exposures such as diet and smoking, which can determine an intergenerational transmission. Moreover, paternal mental health issues such as anxiety or depression can negatively impact emotional and/or behavioral outcomes in offspring. More specifically, another cutting-edge topic regards how the pre-conception use of some psychotropic drugs by the future father could negatively impact gestational, fetal, and/or neonatal outcomes. Despite the critical role of paternal factors, there is still a significant gap in research focusing on paternal variables in the perinatal period, being commonly conceived as a maternal-centric medical practice that often neglects paternal role in the occurrence and/or maintenance of maternal mental health condition and/or their role in fetal and/or neonatal outcomes. This chapter will explore these dynamics in depth, examining the impact of paternal health behaviors on offspring across different domains from genetic and epigenetic factors to mental health and behavioral outcomes. The final goal includes addressing knowledge gaps among clinicians as well as providing a more comprehensive understanding of paternal influences during the perineal period. Finally, the authors will propose a set of recommendations regarding the management of preconceptional care that equally engage both parents in the reproductive health process
Classification of Physical Fatigue on Heart Rate by Wearable Devices
No full textWearable-based evaluation of physical fatigue is carried out by means of multiple sensors. Generally, monitoring of heart rate (HR), which can be derived from either photoplethysmogram (PPG) or electrocardiogram, is included. This work presents an approach for classification of physical fatigue only on HR signals. Electromyogram (EMG) signals are instead employed for labeling, as the analysis of their power spectrum is considered a reference method for evaluating fatigue. In detail, the paper has the dual purpose of i) avoiding a multimodal analysis that would increase power consumption, and ii) defining a methodology easily applicable to the majority of commercial wearable devices, usually equipped with a PPG sensor. The experimental analysis is carried out for a set of HR and EMG signals acquired on subjects monitored by wireless devices. Specifically, physical fatigue felt by arms is detected during isometric muscle contractions. Then, the classification is implemented by comparing several machine learning algorithms. For all algorithms, the proposed approach for classification of physical fatigue reveals good performance in terms of Accuracy values, which are always higher than 85%. The boosting algorithm provides the highest value of Accuracy = 90.64% and the highest value of F1 = 89.78%. The obtained results, comparable to the literature values directly based on EMG feature extraction, prove the efficacy of the proposed approach, even more the performed classification is not simply intra-subject, but also inter-subject
Rigenerare l’isola madre: un insieme di opere pubbliche o un processo di rivitalizzazione di un ecosistema urbano per una pluralità di vite?
No full textFree and Immobilized Cells of Torulaspora delbrueckii and Lachancea thermotolerans in Sparkling Wine: Innovative Application in Secondary Bottle Fermentation
No full textSparkling wine production involves secondary alcoholic fermentation, during which carbon dioxide is trapped, creating effervescence and enhancing sensory complexity. This study evaluated the impact of Torulaspora delbrueckii and Lachancea thermotolerans yeast species using free and immobilized cells in secondary fermentation of sparkling wine, in comparison with Saccharomyces cerevisiae. Immobilized S. cerevisiae enabled faster refermentation compared to free cells, while immobilization resulted in a slower process in non-Saccharomyces strains. Biomass monitoring showed stable viable cells for immobilized S. cerevisiae during fermentation, while non-Saccharomyces strains showed a consistent reduction. Volatile profiles were positively influenced by immobilization using S. cerevisiae strains, which produced a constant increase in key aroma compounds, such as geraniol and ethyl acetate, throughout fermentation. Non-Saccharomyces strains contributed to enhanced fruity and floral aromas with variations in volatiles during refermentation. Sparkling wines fermented with immobilized L. thermotolerans were noted for ripe fruit aromas, while T. delbrueckii increased floral notes. S. cerevisiae fermentations showed higher acidity and balanced structure. These findings highlight the influence of yeast species and the yeast immobilization procedures in secondary fermentation, modulating fermentation dynamics and aroma development, and offer a promising strategy to tailor sparkling wine quality and sensory complexity
Rethinking Medical Knowledge in the Post‐Pandemic Era: A Meta‐Narrative Review of Evidence‐Based Medicine's Critiques and Future Directions
No full textEvidence-Based Medicine (EBM) has been celebrated as a paradigm shift in clinical practice, yet it has also provoked sustained critique across medicine and the social sciences. This article offers a meta-narrative review of critical perspectives, synthesizing debates from medicine, philosophy, sociology, and health policy. We propose a four-dimensional framework—epistemological, methodological, political, and practical—to map these critiques and explore their interconnections. The COVID-19 pandemic acted as a stress test for EBM, amplifying pre-existing tensions and raising questions about its theoretical foundations, methodological hierarchies, institutional authority, and practical applicability. Rather than rejecting EBM outright, critics increasingly call for its renewal, advocating for epistemological pluralism, methodological innovation, and greater sensitivity to context, power, and uncertainty. This sociological analysis contributes to rethinking medical knowledge production in a post-pandemic era
Process regulation control using Echo State Networks: an ESN-based deep neural network approach for PID control
Get PDFIn automation systems, Proportional-Integral-Derivative (PID) controllers are extensively used for their simplicity, reliability, and ability to maintain desired process levels by continuously adjusting control inputs to minimize the error between the set-point and the actual process variable. Despite the advantages, they have to face challenges such as handling non-linearities, sensitivity to parameter tuning, limited adaptability to changing process dynamics, and susceptibility to disturbances. For these reasons, various advanced control strategies and adaptive algorithms based on deep neural network are being developed and implemented. The aim of the proposed study is to introduce an innovative method for the regulation of processes, using an ESN-based deep neural network model. As preliminary results a case study on a heating system is considered. The model was trained using data generated under the control of a PID controller and tested on a using a TCLab module. The results indicate that the trained model effectively predicts the outputs of the heating system and the corresponding temperature values in alignment with the target temperatures. This study would be a starting point for a future deep methodological exploration of the mathematical aspects of ESN models to ensure the long-term stability of the system and its limitations
ANTI-SUIT INJUNCTIONS, ECHR AND THE PUBLIC POLICY DEFENCE
No full textThis article examines a specific injunctive remedy: the anti suit injunction. This is a discretionary judicial order directed at a private party, intended either to prohibit the initiation of proceedings in another forum or to compel the party to cease any proceedings already commenced in that forum under the threat of financial or personal sanctions. After outli ning the key judicial developments that have established the incompatibility of anti-suit injunctions with the European legal order, the analysis shifts to the impact of Brexit and the conflict with Russia on the issuance of such injunctions by courts. Within this framework, the article primarily focuses on the possibility of identifying a new legal basis for restricting the circula tion of anti-suit injunctions under the general clause of international public polic
Il decesso da COVID-19 e l’assicurazione privata contro gli infortuni: riflessioni a margine della sentenza della Corte di Cassazione n. 3016/2025
No full textIl contributo analizza la sentenza n. 3016/2025 della Corte di Cassazione, che si è espressa sulla (im)possibilità di qualificare l’infezione da SARS-CoV-2, contratta da un medico nell’esercizio dell’attività professionale, come “infortunio” ai sensi di una polizza assicurativa privata. Prendendo le mosse dalla struttura del contratto di assicurazione contro gli infortuni e dal significato dei requisiti di causa fortuita, violenta ed esterna, lo scritto esamina i limiti sistemici di un’interpretazione estensiva della nozione di infortunio, anche alla luce delle interferenze con i criteri tipici dell’assicurazione sociale. Viene sottolineata l’importanza di salvaguardare l’equilibrio del contratto assicurativo privato, pur nella consapevolezza delle esigenze di protezione collettiva emerse durante la pandemia. In conclusione, si riflette sulla possibilità di sviluppare modelli di cooperazione tra pubblico e privato nella gestione dei rischi sistemici, nel rispetto della struttura e della funzione del contratto assicurativo.This paper analyses Italian Supreme Court ruling no. 3016/2025, which addressed whether a SARS-CoV2 infection contracted by a physician in the course of professional activity could be classified as an “accident” under a private insurance policy. Starting from the structure of accident insurance contracts and the role of the traditional tripartite requirements – fortuitous, violent, and external causation – the paper explores the systemic limits of an expansive interpretation of “accident”, particularly in light of possible overlaps with social insurance categories. The study emphasizes the importance of preserving the contractual balance inherent in private insurance agreements, even in the face of collective protection needs arising from the pandemic. Finally, it considers the viability of public-private partnerships in addressing systemic risks, without undermining the structure and purpose of insurance contracts
Segmentation of Motion Artifacts in Wearable PPG Signals Using Lightweight Neural Networks
Get PDFThe widespread adoption of wearable health monitoring devices has highlighted the need for robust algorithms to detect and mitigate motion artifacts in photoplethysmographic (PPG) signals. This article introduces a novel approach to motion artifact segmentation, supported by the creation of a new dataset. PPG signals from publicly available repositories were curated and enhanced with precise segmentation masks, enabling rigorous model training and evaluation. Three deep learning architectures—UNet, LSTM-UNet, and Atrous-UNet—were assessed using a comprehensive set of metrics, including dice score, intersection over union (IoU), and average Hausdorff distance (AHD), to highlight the model performance beyond standard evaluations. This multimetric approach underscored the importance of addressing diverse aspects such as boundary precision and overall robustness. Atrous-UNet emerged as the most effective model, offering a balance of high performance and computational efficiency suitable for real-time deployment. The models were validated on real-world data from open-source wearable devices, EmotiBit and Bangle.js 2 (accuracy above 80% for both), demonstrating generalizability across varying hardware and environmental conditions. Comparisons with expert and nonexpert human annotators revealed that the models significantly outperformed the two groups in reliability and detection consistency. The models were optimized using quantization techniques to enable deployment on resource-constrained devices. Although this introduced some performance losses, the models retained robust artifact detection capabilities with an accuracy of over 78%
