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A novel bio-based polyurethane foam for eco-friendly and sustainable refurbishment of heritage buildings
No full textEnergy renovation in historic buildings requires balancing architectural, historical, and esthetic preservation with eco-friendly and sustainable refurbishment. This study develops a novel bio-based polyurethane (bio-PUR) foam and evaluates its thermal and mechanical properties through laboratory tests, real-scale applications, and numerical simulations. Laboratory tests show that bio-PUR has higher mechanical strength than conventional PUR and comparable thermal performance, with a thermal conductivity of 0.036 W/mK, confirmed using nitrogen thermal control. A real-scale test, conducted in a laboratory at the University of Sannio in wintertime, validated the material’s performance, showing only a 4% deviation from theoretical thermal transmittance values and consistent heat flux data. Numerical simulations applied bio-PUR foam in two historical buildings in Milan and Naples, comparing it to traditional insulation materials. Primary energy demand for heating is reduced in both climates, with a slight higher efficacy for traditional PUR, because of its lower thermal conductivity. Similar trends were observed in summer season. Indoor air temperature analysis revealed improved thermal stability with bio-PUR in winter, while potential overheating in summer can occur under free-running conditions. Overall, bio-based PUR foam provides competitive thermal properties and environmental benefits, resulting in a promising solution for the green, resilient, and sustainable renovation of heritage buildings
Hydrological Model Calibration in Data-Scarce Mediterranean Catchments: A Comparative Assessment of Three Strategies
No full textHydrological calibration in data-scarce catchments is challenged by non-stationary regimes, fragmented data, and systematic measurement errors. Conventional calibration approaches often assume continuous records and rely on standard performance metrics, which can bias calibration toward high flows and exacerbate parameter equifinality—ultimately reducing robustness under data limitations. This study provides a systematic comparison of three calibration strategies—Kling–Gupta Efficiency (KGE), a non-parametric variant (RNP), and Flow Duration Curve (FDC)-based calibration—together with their time-consistent counterparts (SKGE, SRNP, and SRMSE). All schemes are implemented for the lumped HBV-type TUW model across nine catchments in southern Italy and evaluated using independent metrics targeting overall hydrograph agreement, high-flow behavior, and FDC quantile matching (Q5–Q95). The results reveal that the time-consistent KGE-based strategy excels during in calibration (NSE = 0.56, RMSE = 4.65 m3/s) but shows notable declines in validation (NSE = 0.40, RMSE = 3.91 m3/s), indicating sensitivity to non-stationarity. The RNP-based approach demonstrates enhanced validation robustness (NSE = 0.51, RMSE = 3.60 m3/s) and low-flow accuracy, with NSElnQ = 0.30 and low-flow accuracy, leveraging its non-parametric structure. The SRNP variant further enhances performance in validation (NSE = 0.52, RMSE = 3.42 m3/s), along with superior low-flow performance (NSElnQ = 0.48). The FDC-based strategy effectively reproduces flow distributions during calibration (NSE = 0.41, minimal PBIAS = −0.03%) but exhibits limited temporal transferability (validation NSE = 0.25, RMSE = 4.50 m3/s). Time-consistent variants reduce parameter dispersion by approximately 2–8% (relative to full-period calibration) and improve validation metrics by 5–15% across all catchments. Overall, time-consistent calibration provides a practical pathway to increase robustness under non-stationary, data-scarce Mediterranean conditions, highlighting a systematic trade-off between calibration accuracy and validation reliability
Tutor della tesi di Dottorato di ricerca di interesse nazionale in Studi religiosi, Università degli Studi di Modena e Reggio Emilia, XXXIX ciclo, “Appartenenza religiosa, gestione dei dati personali e soggettività giuridico-politica”, dott. Ignazio Barbetta.
No full textUrinary miR-191-5p levels are significantly reduced after radical prostatectomy in patients with prostate cancer
No full textBackground:
MicroRNAs (miRNA) detection in urine samples may be a scarce invasive approach for diagnosis and monitoring prostate cancer (PCa). The role of miR-191-5p in prostate oncogenesis, as well as its function as a potential diagnostic and prognostic biomarker has been described in many cancers. However, its role as a diagnostic non-invasive indicator in PCa is still under investigated.
Methods:
We performed an extracellular vesicle (EV)-based miRNA-sequencing of urine samples (n = 12/group) collected from PCa patients before (T0) and three months after (T1) radical prostatectomy, and healthy individuals. An independent cohort of PCa paired urine samples (n = 25/group) and controls (n = 22) was used to validate our sequencing data by RT-qPCR. Furthermore, we conducted comprehensive in silico analyses on urine and tissue samples extracted from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, respectively. Receiver operator curves analysis was employed to assess the diagnostic value of miR-191-5p. To explore the role(s) of miR-191-5p in prostate carcinogenesis, we predicted miR-191-5p potential targets using four miRNA/target-gene pair databases (i.e., miRDB, miRTarBase, TarBase, and TargetScan). Finally, we corroborated the potential correlation between miR-191-5p and its targets by in silico investigation using TCGA dataset.
Results:
Differential expression analysis revealed a significant upregulation of miR-191-5p in PCa patients compared to healthy individuals. Remarkably, urinary miR-191-5p expression levels significantly decreased after surgery in both our discovery and validation cohorts of urine samples by miRNA-seq and RT-qPCR, respectively. Bioinformatics analyses further confirmed high levels of miR-191-5p in urine and tissue samples from PCa patients compared to controls, particularly in patients with high Gleason score. Moreover, miR-191-5p showed a strong diagnostic value in urine and tissue samples. Finally, target prediction analysis identified the genes Satb1 and Ctdsp2 as potential targets of miR-191-5p. This was supported by a significant inverse correlation between the expression of miR-191-5p and these genes in TCGA PCa tissues. Moreover, both Satb1 and Ctdsp2 were downregulated in PCa tissues, and especially in high Gleason score tumors compared to normal and low Gleason score samples.
Conclusions:
MiR-191-5p is highly expressed in urine and tissue samples from patients with prostate cancer. Our findings, although preliminary, support miR-191-5p as a promising minimally invasive biomarker for diagnosis of PCa patients
Fra traduzione e codifica di una lingua: la Parabola del Figliol Prodigo nelle varietà walser italiane Fonti e documentazione
No full textThe textual production in the Italian Walser varieties is generally limited for
number of sources and chronological range. For this reason, of particular interest are the
translations of the Parable of the Prodigal Son collected in the first half of the nineteenth
century during some of the first linguistic investigation which directly involved the
Alemannic communities of the Italian Alps. The translations of the Parable represent
a useful source for the reconstruction of the evolution of the Walser settlements in the
Alps, as well as for a contrastive and diachronic analysis of the linguistic heritage of the
communities
Costituzione, Comuni, diritto all’abitare
No full textLa relazione prende spunto dalla sentenza n. 186 del 2025 della Corte costituzionale, soffermandosi in modo particolare sui poteri dei Comuni e il diritto all'abitare
La ricezione di Schiller fra teoria della conoscenza, filosofia della cultura e metafisica in Ernst Cassirer
No full textProcess mining-driven modeling and simulation to enhance fault diagnosis in cyber–physical systems
No full textCyber–Physical Systems (CPSs) tightly interconnect digital and physical operations within production environments, enabling real-time monitoring, control, optimization, and autonomous decision-making that directly enhance manufacturing processes and productivity. The inherent complexity of these systems can lead to faults that require robust and interpretable diagnoses to maintain system dependability and operational efficiency. However, manual modeling of faulty behaviors requires extensive domain expertise and cannot leverage the low-level sensor data of the CPS. Furthermore, although powerful, deep learning-based techniques produce black-box diagnostics that lack interpretability, limiting their practical adoption. To address these challenges, we set forth a method that performs unsupervised characterization of system states and state transitions from low-level sensor data, uses several process mining techniques to model faults through interpretable stochastic Petri nets, simulates such Petri nets for a comprehensive understanding of system behavior under faulty conditions, and performs Petri net-based fault diagnosis. The method begins with detecting collective anomalies involving multiple samples in low-level sensor data. These anomalies are then transformed into structured event logs, enabling the data-driven discovery of interpretable Petri nets through process mining. By enhancing these Petri nets with timing distributions, the approach supports the simulation of faulty behaviors. Finally, faults can be diagnosed online by checking collective anomalies with the Petri nets and the corresponding simulations. The method is applied to the Robotic Arm Dataset (RoAD), a benchmark collected from a robotic arm deployed in a scale-replica smart manufacturing assembly line. The application to RoAD demonstrates the method's effectiveness in modeling, simulating, and classifying faulty behaviors in CPSs. The modeling results demonstrate that our method achieves a satisfactory interpretability-simulation accuracy trade-off with up to 0.676 arc-degree simplicity, 0.395 R2, and 0.088 RMSE. In addition, the fault identification results show that the method achieves an F1 score of up to 98.925%, while maintaining a low conformance checking time of 0.020 s, which competes with other deep learning-based methods
Urban Flood Vulnerability, Economic Loss, Functionality Loss, and Restoration Modeling in Built Environment: Probabilistic Model Formulation and Application in a Mixed Formal and Informal Setting
No full textFlooding is well recognized as one of the most destructive natural disasters, often leading to heavy losses of lives, property, and economy. Flood impacts are often presented in terms of flood vulnerability/fragility functions considering univariate intensity measures. However, flooding duration also plays an instrumental role in physical damage, functionality loss, and economic losses. This prompts the development of multivariate flood vulnerability and loss models; however, such models are not available in the existing literature to the best of our knowledge. To address this gap, we develop multivariate and univariate flood vulnerability models, economic loss models, and restoration models for Reinforced Concrete (RC), masonry, steel, and semi-permanent buildings. Similar models are also developed for agricultural land using actual damage/loss data. Bivariate models considering inundation depth and inundation duration are developed using response surface method, whereas several univariate vulnerability and loss models are also presented. The models are interpreted together with the outcomes of two-dimensional flood hazard analysis to outline and exemplify probabilistic flooding scenarios and likely consequences. The sum of observations highlights that although physical damage can be limited in building structures, considerable functionality and economic losses will be inevitable due to urban flooding