68994 research outputs found
Sort by
Implementation of Ventilated Permeable Roof Solutions in Urban Microclimate Models: A Preliminary Study
No full textAmong urban heat mitigation strategies related to the building’s envelope, the performance of cool and green roofs has been assessed since decades. However, their effectiveness is guaranteed only if their functionality level is maintained over time through periodic maintenance or cleaning interventions. Moreover, they are not always applicable, due to architectural constraints. Ventilated and permeable tiled roofs (VPRs), easily applicable and extremely durable, also own environmental benefits related to the reduced incoming heat flows and related cooling energy use. However, their eventual impact on urban overheating has never been investigated, especially since their hygrothermal exchanges are not explicitly modeled into existing climate assessment tools. This work aims to implement a VPR in the urban climate model urban weather generator (UWG) and numerically evaluate its functioning compared to other roofing solutions. The implemented VPR model has been partly validated against experimental measurements on real-scale roofs. Simulation results show a good performance of the VPR in terms of surface temperature, internal gains, and heat waste released from the HVAC to the outdoor environmen
Development and integration of Machine Learning models for optimising production processes in Software-as-a-Service solutions
No full textThe thesis is set in the context of Industry 4.0 and the Horizon Europe AIDEAS project, addressing the use of Machine Learning techniques for the optimisation of production processes within Software-as-a-Service solutions for operational decision support. The overall objective is to investigate the potential of Artificial Intelligence methodologies in production scheduling, with a particular focus on their applicability in real and complex industrial contexts. The first part consists of a systematic review of the literature, supported by bibliometric analyses on Scopus and Web of Science, in order to identify the main AI techniques used in scheduling problems, in particular Particle Swarm Optimisation, Neural Networks and Reinforcement Learning, and the benefits reported in the industrial field. However, the analysis highlights a limited availability of documented case studies based on real industrial applications, confirming the relevance of the empirical contribution of this research. The experimental part of the thesis includes two industrial case studies developed in collaboration with European manufacturing companies. The first case concerns Multiscan, a Spanish company operating in the assembly of fruit and vegetable sorting machines. The scheduling problem is modelled as a Hybrid Flow-Shop with integrated Open-Shop block, including constraints related to both machines and operator assignment. The scheduling problem is formulated as a Markov Decision Process and solved using Deep Reinforcement Learning, allowing for integrated management of the sequence of operations and human resources. The results show that the learned policy outperforms traditional approaches, such as the FIFO rule and a metaheuristic based on Ant Colony Optimisation, in terms of average delay, operational stability and inference times, allowing the production plan to be recalculated in less than ten seconds. The second case study has been carried out at PAMA S.p.A., an Italian manufacturer of boring machines, and focuses on optimising the machining process for cast iron columns. In this context, two artificial neural networks are designed and trained to predict material removal parameters, with the aim of reducing rework while maintaining very tight dimensional tolerances. The dataset is constructed from actual process measurements and undergoes a thorough pre-processing and correlation analysis phase. The Multi-Layer Perceptron models achieve errors in the order of microns and, in 85% of validation cases, suggest lower removal than the traditional method, indicating a potential improvement in term. Finally, the thesis proposes a conceptual model of a "Provider Planner" based on Deep Reinforcement Learning within a Manufacturing as a Service architecture, capable of dynamically managing orders from multiple customers and adapting almost in real time to changes in demand and the availability of production resources
Oral Administration of Crocus sativus Tepals Extract Restores High-Fat Diet-Induced Gut Dysbiosis and Modulates Intestinal Inflammation and Hepatic Lipid Metabolism
No full textMetabolic diseases have increased worldwide in recent decades, mainly due to a sedentary lifestyle and an unhealthy diet, with diet identified as an important regulator of gut microbiota composition. The use of natural products, such as Crocus sativus tepals extract (CTE) could be a promising approach to alleviate metabolic disorders. The aim was to investigate the potential ameliorative mechanisms of CTE in metabolic disorders induced by a high-fat diet in an animal model, focusing on the composition of the gut microbiota and its relationship with the gut-liver axis. We analyzed liver-related biochemical and morphological parameters in mice fed a 60% fat diet for 14 weeks and orally treated with CTE during the last 5 weeks of the diet. In addition, jejunal and liver histology, intestinal barrier integrity, inflammation and oxidative stress, liver inflammation and lipid metabolism were investigated. The results showed that oral administration of CTE restored the composition of the gut microbiota and specifically promoted short-chain fatty acids-producing and anti-inflammatory bacterial genera. It also improved intestinal barrier integrity and reduced inflammation in the jejunum and liver, along with a suppression of Fas and CerS6 expression in the liver and a reduction in circulating free fatty acids and β-hydroxybutyrate levels. Our results indicate a possible link between the gut microbiota and the metabolic benefits of treatment with CTE, suggesting its therapeutic potential for the prevention or treatment of metabolic disorders
Does Prior Exposure Affect Retention? A Real-World, Multicentre Assessment of IL-17 Inhibitor Cycling in Psoriatic Arthritis
No full textIntroduction: Interleukin-17 inhibitors (IL-17i) represent a key therapeutic option for psoriatic arthritis (PsA), but real-world evidence regarding the effectiveness of cycling strategies within this class is lacking. This study evaluated the real-world retention of IL-17i in PsA, focusing on whether prior IL-17i exposure affects subsequent IL-17i persistence. Methods: This multicentre, retrospective, observational study included consecutive patients with PsA treated with an IL-17i across 24 Italian rheumatology centres. The primary outcome was drug retention, analysed using Kaplan–Meier methods, with differences between IL-17i-naïve and IL-17i-experienced patients assessed with the log-rank test. Secondary outcomes included baseline clinical characteristics and predictors of discontinuation. Results: A total of 868 patients were included (59.3% female, 40.7% male; median age 56 [48–63] years; 89.3% IL-17i-naïve). The overall median IL-17i retention rate was 90.7% [95% CI 88.7–92.8] at 6 months, 77.5% [95% CI 74.6– 80.6] at 12 months, 60.9% [95% CI 57.3–64.8] at 24 months, and 52.1% [95% CI 48.1–56.4] at 36 months. Among IL-17i-naïve patients, retention rates were 90.5%, 77.6%, 61.7%, and 53.9% at 6, 12, 24, and 36 months, respectively. Among IL-17i-experienced patients, the corresponding retention rates were 92.2%, 77.0%, 54.0%, and 33.9%. In multivariable Cox regression, male sex and prior IL-17 inhibitor exposure were associated with a lower risk of discontinuation, whereas axial involvement, a higher number of previous biologic/targeted synthetic diseasemodifying anti-rheumatic drugs (b/tsDMARDs), and later calendar year of IL-17i initiation predicted poorer retention. Conclusions: IL-17i showed high long-term retention in real-world PsA, with no significant difference between naïve and previously exposed patients. These findings support the sustained effectiveness of IL-17i therapy and suggest that cycling within the class may remain a reasonable option for selected cases
A taste of North Macedonia: Seasonal variation in the microbiota, physico-chemical traits, and morpho-textural profile of a traditional brined raw goat's milk cheese
No full textThis study provides a comprehensive characterization of a traditional Macedonian brined raw goat's milk cheese, focusing on how seasonal production (spring vs. autumn) shapes its physicochemical traits, morpho-textural properties, and microbial ecology. Cheese samples produced in autumn exhibited stronger acidification, higher titratable acidity, lower water activity, and higher NaCl content than spring cheeses, reflecting variability associated with artisanal, non-standardized processing. Texture profile analysis showed that cohesiveness and springiness were significantly affected by season, whereas hardness and adhesiveness remained comparable across batches. A combined culture-dependent and 16S rRNA gene–based metataxonomic approach revealed seasonally distinct microbiota. Viable microbial populations composed of mesophilic aerobes (up to 6.51 log cfu g−1 at 60 days of ripening), presumptive mesophilic lactobacilli and lactococci (up to 6.51 and 7.18 log cfu g−1 at 60 days of ripening, respectively), presumptive coagulase-negative and coagulase-positive staphylococci (up to 6.97 and 1.76 log cfu g−1 at 60 days of ripening, respectively), and Enterobacteriaceae (up to 1.25 log cfu g−1 at 60 days of ripening) were detected. Spring cheeses were characterized by higher relative abundances of Carnobacteriaceae, Enterococcus, Serratia, and Tetragenococcus halophilus, whereas autumn cheeses were dominated by Companilactobacillus and Lactococcus, alongside various Enterobacteriaceae. Beta-diversity analysis confirmed significant clustering of cheese microbiota by season. In total, 134 lactic acid bacteria isolates were obtained from the dairy environment, milk, brine, and cheese. These included Lactococcus lactis, Levilactobacillus brevis, multiple Enterococcus species, Pediococcus pentosaceus, Lacticaseibacillus paracasei, Marinilactibacillus psychrotolerans, and Companilactobacillus alimentarius. Many isolates showed strong proteolytic activity, several produced exopolysaccharides, and a subset exhibited lipolytic capacity, underscoring their technological potential. Screening for the histidine decarboxylase gene hdcA revealed that only the C. alimentarius isolate was positive, excluding this strain from consideration as an adjunct culture, whereas all other isolates were hdcA-negative and therefore suitable candidates from a histamine-safety perspective. Overall, this integrated analysis highlights the rich microbial diversity and seasonal variability of this artisanal cheese and supports the selection of safe autochthonous lactic acid bacteria for future product valorization
Stability and Bifurcation in a Delayed Predator–Prey Model with Environmental Stress and Dynamic Carrying Capacity
No full textWe develop a delayed predator–prey model that integrates nonlinear environmental stress and a dynamic carrying capacity into the prey’s growth function. The model introduces a discrete time delay in the predator’s response, capturing ecological lags such as gestation or behavioral adaptation. Unlike previous studies, our framework couples environmental degradation and biological delay, two destabilizing forces often treated in isolation, to examine their combined impact on ecosystem dynamics. We derive analytical conditions for the local and global stability of equilibrium states and identify critical Hopf bifurcation thresholds as functions of the delay and environmental stress level. The model reveals how interactions between these parameters govern transitions between extinction, stable coexistence, and sustained oscillations. Notably, we extend the classical Wangersky–Cunningham framework by incorporating feedback-regulated carrying capacity. Numerical simulations validate the theoretical predictions and map resilience boundaries that highlight tipping points in ecological regimes. The results have practical implications for biodiversity conservation and adaptive ecosystem management, especially under anthropogenic stress and delayed biological feedbacks
In vitro & In vivo Metabolism Studies of New Psychoactive Substances and Histopathological Patterns of Drug-Related Deaths: Comparative Forensic Analysis
No full textThis study investigates the metabolic pathways of four synthetic cathinones (3-CMC, 4-CMC, 4-BMC, NEPD) and N,N-Dimethyltryptamine (DMT) derivatives (DMT-Boc, DMT-isopropylcarbamate, DMT-pivaloylamide, DMT-THP), focusing on identifying biomarkers for forensic detection. Using human hepatocytes, liver microsomes, zebrafish embryos and, authentic human samples key metabolites were identified through liquid chromatography-high-resolution mass spectrometry. For synthetic cathinones, major metabolic reactions included N-deethylation, ketoreduction, and glucuronidation, with novel metabolic pathways observed in 3-CMC, 4-CMC and, NEPD. For DMT derivatives, N-demethylation, N-oxidation and, hydroxylation were the predominant metabolic reactions. Specific metabolites were proposed as markers of consumption for both synthetic cathinones and DMT derivatives. Histopathological analysis of autopsy cases showed significant tissue alterations in the heart, liver, lungs, and kidneys due to drug intake, including edema, fibrosis, and ischemic damage. These findings highlight the importance of combining toxicological and histopathological approaches in the study of NPSs and provide essential insights for forensic toxicologists, aiding in the detection of these substances in suspected abuse cases and highlighting the importance of comprehensive metabolite profiling in toxicological investigations
Anomaly Detection in Real-time Continuous Fruit-based Monitoring of Olive via Extensimeter
No full textIn this study, we analyze the real-time measurements collected by extensimeter (fruit gauge) in olive orchards to identify their anomalies. The field data are collected by two different kinds of extensimeters (strain gauges and variable linear resistance transducer) with hourly temporal resolution and a time span of 3.5 months in 2019, 3 months in 2020, and 2.5 months in 2021. To recognize the outliers in the sensor records, conventional statistical approaches including sliding window techniques such as Moving Mean Absolute Deviation, Median Absolute Deviation as well as one innovative method that integrates the sliding window technique with Moving T-square (SWT-T-square) are implemented. The performance of the mentioned approaches is evaluated using well-known statistical indices such as the Confusion Matrix, accuracy, sensitivity, and specificity criterion. To visually compare the models’ performance, the results of the methods are represented using plots that represent the number of outliers against window size. The results prove that the SWT-T-square integrated model outperforms others in recognition of the outliers. It is useful for acquiring more robust data and identification of sensor non-functionality or low accuracy during continuous monitoring
Circulating biomarkers for risk stratification and prediction of mortality and adverse outcomes in age-related diseases
No full textL’invecchiamento biologico rappresenta un processo complesso e multidimensionale, guidato dall’interazione di meccanismi molecolari e cellulari che, nel tempo, favoriscono l’insorgenza di patologie età-correlate e aumentano la vulnerabilità individuale agli esiti avversi. Poiché l’età cronologica non riflette l'eterogeneità delle traiettorie di invecchiamento, emerge la necessità di strumenti capaci di intercettare la dimensione biologica del rischio. In questo contesto, il presente lavoro ha esplorato il valore prognostico di biomarcatori circolanti rappresentativi di differenti assi dell’invecchiamento, con l’obiettivo di integrare segnali provenienti da stress metabolico, infiammazione cronica, danno tissutale e neurodegenerazione. I risultati dimostrano che indicatori di disfunzione metabolica, di attivazione infiammatoria sistemica e di stress cellulare risultano associati in modo indipendente a mortalità ed esiti avversi, mentre l’integrazione di più biomarcatori in modelli compositi migliora significativamente la stratificazione del rischio rispetto agli approcci clinici tradizionali. Parallelamente, alterazioni di microRNA circolanti coinvolti nei processi neurodegenerativi si configurano come potenziali strumenti per l’identificazione precoce del danno cognitivo. Complessivamente, questi dati supportano un modello integrato di valutazione dell’invecchiamento biologico. In tale prospettiva, pannelli multimarcatori consentono di coglierne la natura sistemica e interconnessa, offrendo basi per strategie prognostiche e assistenziali più personalizzate.Biological aging is a complex and multidimensional process resulting from interconnected molecular and cellular mechanisms. Over time, these alterations increase susceptibility to age-related diseases and adverse outcomes. Since chronological age underestimates the heterogeneity of aging trajectories, tools capable of capturing the biological dimension of risk are needed
This work investigated the prognostic value of circulating biomarkers representing distinct biological axes of aging, including metabolic stress, chronic inflammation, tissue damage, and neurodegeneration. Markers of metabolic dysfunction, systemic inflammatory activation, and cellular stress were independently associated with mortality and adverse outcomes. The integration of multiple biomarkers into composite models significantly improved risk stratification compared with conventional clinical approaches. In parallel, alterations in circulating microRNAs involved in neurodegenerative pathways emerged as potential tools for the early identification of cognitive decline.
Overall, these findings support an integrated framework for the assessment of biological aging. Multimarker panels capture its systemic and interconnected nature and provide a foundation for more personalized prognostic and care strategies
Misurare il greenwashing: una review sistematica della letteratura metodologica
No full textIl presente lavoro propone una revisione sistematica della letteratura a livello metodologico (Systematic Methodological Literature Review – SMLR) per comprendere come il GW sia stato operazionalizzato nella ricerca empirica e discute come la nostra comprensione del fenomeno sia influenzata dalle scelte metodologiche adottate dai ricercatori empiric