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Exploring a business incubator process: Evidence from different European cities in the IN-HABIT project.
No full textIncubators are useful tools to foster the launch of innovative start-ups. This paper investigates the process of an incubation model that supports entrepreneurs at an initial stage of their work through the development of a business plan from scratch with innovative tools. Specifically, we focus on the impact of the application of this methodology to a European project – IN-HABIT - involving 4 different cities in Europe. Based on a qualitative approach using semi-structured interviews (multiple-choice questionnaire), we critically analysed the whole process and compared the findings to understand strengths and weaknesses across different phases of the program and city contexts. A questionnaire for each city from a representative of the organisation applying the incubation tool has been collected. Results highlight how the context in which the incubation program is carried out and the specific topic of interest can influence the whole process. Although this research mainly explored the methodology's application in different contexts, further study will be needed to better assess the long-term outcomes for participants. From a practical perspective, the study suggests that successful implementation and replication of incubation programs in diverse urban settings require: local adaptation of the methodology to context-specific needs; engagement of local stakeholders to ensure outreach and participation; and integration of incubation activities within broader urban innovation ecosystems. These insights can inform practitioners, policymakers, and project designers seeking to apply or replicate inclusive incubation models in other territorial or thematic contexts
A machine learning and Particle Swarm Optimization approach for desiccant wheel modeling and performance prediction
No full textAccurate modeling of desiccant wheels (DWs) is critical for the design and optimization of energy-efficient dehumidification systems. This study presents a novel approach for predicting DW performance by coupling machine learning (ML) models with Particle Swarm Optimization (PSO) for hyperparameter tuning. To validate the effectiveness of this metaheuristic approach, the performance of the PSO-optimized models was rigorously benchmarked against counterparts tuned using conventional Bayesian Optimization (BO). Four distinct ML models, including Artificial Neural Network (ANN), k-Nearest Neighbors (KNN), Random Forest (RF), and Support Vector Regressor (SVR), were developed to predict the process air outlet temperature (Tp,out) and humidity ratio (ωp,out). The models were trained and validated on a comprehensive dataset, uniquely expanded to include experimental data from low-humidity and low-temperature deep dehumidification conditions. The results demonstrate that the PSO-optimized Artificial Neural Network (PSO-ANN) model provides superior predictive accuracy. For the process outlet temperature, the PSO-ANN model achieved a Coefficient of Determination (R2) of 0.9985 and a Root Mean Square Error (RMSE) of 0.3204 °C. For the outlet humidity ratio, it achieved an R2 of 0.9984 and a RMSE of 0.1497 g/kg. Furthermore, a SHAP (SHapley Additive exPlanations) analysis confirmed that the model’s predictions are physically consistent and interpretable. The developed high-fidelity model serves as a robust and reliable tool for the advanced analysis and design of desiccant air conditioning systems across a wide range of operational scenarios
Discovering Palaeontological Cultural Heritage Using Micro Computed Tomography: The G.A.M.P.S. Case Study
No full textFossils reveal information lost over time, offering insights into the life and death of single individuals as well as of the entire populations and of the paleoenvironment. Their management as cultural heritage must consider the needs of preservation, research, and dissemination. Today, palaeontological research can be improved through digital technologies and sophisticated imaging methods like Computed Tomography (CT). However, the use of such techniques is still underexploited in the context of palaeontological museum, while the availability of virtual collections would reduce the necessity of manipulation of the real objects, boosting research and preservation strategies. Additionally, high quality images and 3D reconstruction would enhance the quality of museum visit, allowing visitors to examine the fossils with increased details and a deeper scientific meaning. Simultaneously, virtual tours of museums including 3D reconstructions of fossils would be possible thanks to digital technology. This paper presents the case study of the G.A.M.P.S. permanent exhibition (Scandicci, Italy), where advanced micro-CT scan techniques were applied to some examples of the Tuscany Pliocene collection, such as shark teeth, shells, crabs. Impressive 3D reconstructions were obtained, including the initial reconstruction of the Chlamydoselachus lawleyi shark. All the reconstructions are now part of both real and virtual exhibition
Validazione di dati GNSS-meteo con radiosondaggi: prestazioni di una nuova rete su mare per il monitoraggio atmosferico
No full textNegli ultimi anni, l’utilizzo dei segnali provenienti dalle costellazioni di Global Navigation Satellite Systems (GNSS) si è affermato come una tecnica affidabile per la stima continua del contenuto di vapore acqueo atmosferico, attraverso la determinazione del ritardo zenitale troposferico (Zenith Tropospheric Delay, ZTD). Nell’ambito di alcuni progetti INTERREG (PROTERINA-3 Évolution, PROTERINA4Future) è stata realizzata una nuova infrastruttura GNSS-meteo per il monitoraggio integrato delle condizioni atmosferiche su mare tramite sistemi installati a bordo di una flotta di navi di linea operativa sull’alto Tirreno. Tale infrastruttura combina reti di ricevitori GNSS permanenti con stazioni meteorologiche di superficie, consentendo la generazione in tempo quasi reale di prodotti come ZTD, Integrated Water Vapor (IWV) e parametri termoigrometrici superficiali, con elevata risoluzione temporale e spaziale.
Al fine di validare le prestazioni del sistema e quantificare l’accuratezza dei prodotti derivati, è stata organizzata una campagna di radiosondaggi sperimentali a basso costo presso le stazioni della rete, con il rilascio di palloni meteorologici dotati di sensori per la misura diretta dei profili verticali di temperatura, pressione e umidità relativa. Le osservazioni dei radiosondaggi, considerate lo standard di riferimento per la calibrazione e la validazione dei modelli atmosferici, sono state confrontate con le stime simultanee fornite dalla rete GNSS e dai sensori a terra.
Il presente lavoro descrive da un lato le prestazioni del sistema GNSS-meteo di osservazione, il primo operativo per un lungo periodo (oltre 4 anni), dall’altro il sistema di radiosondaggio a basso costo sviluppato appositamente per la validazione. Sono stati analizzati i dati di confronto per i lanci effettuati durante differenti stagioni e con diverse condizioni atmosferiche. I risultati mostrano una buona correlazione tra i valori di IWV derivati da GNSS e quelli stimati dai radiosondaggi. Il lavoro presenta infine anche un confronto tra queste osservazioni ed i dati ottenuti da modelli di rianalisi (MERRA-2, ERA5). Questa validazione dimostra l’affidabilità di entrambi i sistemi di misura - GNSS-meteo e radiosondaggi sperimentali - ponendoli come utili strumenti da utilizzare in maniera complementare o in alternativa a sistemi di misura convenzionali
Effect of SGLT2 Inhibitors + DPP-4 Inhibitors on Urine Microbiota in Type 2 Diabetes
No full textAims: A reduced compliance, due to urogenital minor infections, frequently compromises the clinical efficacy of SGLT2 inhibitors in subjects with type 2 diabetes (T2D). The combined use of SGLT2 inhibitors and dipeptidyl-peptidase four inhibitors seems to reduce the incidence of such side effects. We evaluated how these drugs, alone or in combination, might influence resident urinary microbiota. Materials and Methods: An open label, randomised clinical study was conducted on 30 T2D individuals for 12 weeks to compare the impact of Empagliflozin and Empagliflozin/Linagliptin on clinical parameters and urinary microbiota. Fifteen healthy individuals served as baseline controls. The composition of urinary bacterial populations was evaluated by Real-Time quantitative PCR and 16S rRNA gene sequencing. Results: BMI was reduced by both treatments, while fasting glucose and HbA1c significantly improved only with the combination. At baseline, T2D showed a higher total bacterial load and abundance of Bacillota than controls. The prevalence and proportion of bacterial species profoundly differed between the groups, revealing a urinary dysbiosis in T2D. A different effect of Empagliflozin alone or combined with Linagliptin on microbial populations was observed: Empagliflozin increased the total bacterial load of Bacillota and Aerococcus, while the combination therapy restored a microbial community similar to that of controls, further reducing the prevalence of potential urinary pathogens. Conclusions: In T2D subjects, the combination of Empagliflozin and Ligandliptin might help in restoring a normal composition of the urinary microbiota, likely improving compliance and persistence in therapy with SGLT2 inhibitors
Assessing the Suitability of Digestate and Compost as Organic Fertilizers: A Comparison of Different Biological Stability Indices for Sustainable Development in Agriculture
No full textTowards continuous water stress classification in tomato plants via fuzzy Hoeffding trees and in-vivo biosensors
No full textWater stress and drought have a critical impact on plant growth and health, influencing and compromising agricultural productivity. Tools that can predict water stress in crops through quantifiable indicators provide valuable information and facilitate timely interventions aimed at maintaining and/or restoring optimal growth conditions before visible and difficult-to-recover symptoms appear. This study introduces an explainable Plant Health Monitoring System (PHMS), based on the continuous monitoring of water stress parameters in tomato plants using a novel in-vivo biosensor called "Bioristor". Our system integrates an explainable incremental classifier by design, specifically experimenting with the traditional Hoeffding decision tree and its fuzzy variant. By analyzing data from the Bioristor, the system evaluates plant health and classifies it into two distinct categories. Additionally, it employs an incremental learning approach, allowing the model to adapt and update during the monitoring period to maintain high classification performance. This continuous monitoring ensures the early detection of water stress, enabling prompt corrective actions. We present results based on a real-world dataset, leveraging four features derived from ionic currents within the plant sap, as measured by the Bioristor. The system performance was evaluated in terms of classification accuracy and model complexity, yielding promising outcomes. Moreover, the extracted decision rules offer valuable insights for implementing effective countermeasures to sustain plant health for extended periods
Exploiting (min,+)/(max,+) Isomorphism to Speed up Convolutions
No full text(min,+) and (max,+) algebra lie at the core of theories for the analysis of worst-case performance bounds. Two such theories are in
fact Deterministic Network Calculus and Real-time Calculus, used in real-time networks and systems, respectively. In both algebras,
computing expressions can be computationally expensive. In particular, the convolution operation, which is quite frequent, can be
very time-consuming – sometimes taking hours or not completing at all. In fact, its operands are represented as pseudo-periodic
sequences of segments and points – henceforth elements for short – which may have different periods. As already observed in the
literature, a convolution requires that every couple of elements belonging to different operands be elaborated, up to the least common
multiple (lcm) of their periods. In this paper, leveraging the isomorphism between (min,+) and (max,+) algebras, we prove formally
that there is, in general, a much smaller bound than said lcm, which allows one to greatly reduce the number of elementary operations
required for a convolution. Accordingly, we devise a new algorithm for (min,+) and (max,+) convolution, called super-isospeed, which
avoids unnecessary computations. The super-isospeed algorithm is considerably faster than the ones known so far, and it reduces the
computation times by orders of magnitude. Unlike other works that address the same problem, our method is both exact (i.e., does not
introduce any approximation), and is not limited to operands of particular shapes (e.g., concave/convex, sub-/superadditive, etc.)
Integrated Inverse Design of Antenna Superstrate for Radiation and Scattering Problems
No full textA multi-functional integrated inverse-design method (MFI-IDM) of antenna superstrate is proposed to achieve the desired radiation pattern (DRP) and low radar cross section (RCS) of antenna by employing Born iterative-type methods. When the internal and external incident sources of the antenna are known, the proposed method first designs the corresponding far-field total and scattering fields of the superstrate based on physical definitions derived from the DRP and RCS requirements. Then, based on the designated domain of the antenna superstrate, its electromagnetic parameters can be reconstructed with the help of the inverse scattering algorithms. As a result, the antenna with inverse-designed superstrate can be modeled and finally meets the expected requirements of both high radiation performance and low scattering characteristics. Two schemes of multi-functional integrated design, involving single and multiple superstrates, are introduced. Additionally, a stepwise approximation strategy and a progressive design with multi-superstrate are proposed to avoid the divergence problem of inverse scattering algorithm. Based on two-dimensional numerical calculations, the inverse-designed dielectric-type superstrates are further extended to the applications of three-dimensional (3D) antennas. By dielectric equivalence, the designed dielectric-type superstrate was fabricated by using the 3D printing technology. The effectiveness of the proposed method is well validated by the full-wave simulations and measurements. When compared with other similar antennas, the final antenna with inverse-designed superstrate shows good electrical performance, achieving a measured 81° flat-topped beamwidth and 12.5 dB bistatic RCS reduction. Different from the traditional separated forward methods and single-objective inverse-design method, the proposed method efficiently completes the multi-functional inverse-design within a framework, thereby overcoming the limitations of traditional design method for multi-functional antennas
La popular music statunitense
No full textDefiniamo e circoscriviamo innanzitutto il campo, la terminologia e la
metodologia generale di questo capitolo: cercheremo di cogliere alcune
delle tendenze della popular music – termine di difficile traduzione che fa
riferimento a un insieme di generi che non appartengono né alla musica
“colta” né a quella della tradizione popolare (Fabbri, 2024) e che comprendono un’ampia gamma di stili che vanno dal rock’n’roll al beat, dal rock
all’r&b, dal soul all’hip-hop, dal funk al punk, dall’heavy metal al grunge e oltre – della tradizione afroamericana e angloamericana emerse negli
Stati Uniti negli ultimi cinquant’anni. Senza pretese di esaustività (impensabile in un saggio di questa lunghezza), cercheremo di evidenziare come
la popular music si collochi alla convergenza di fenomeni musicali, sociali
e tecnologici, la cui comprensione, per quanto minimale, non può escludere nessuna di queste tre componenti. In particolare, ci soffermeremo su
tre macroaree: 1. il cosiddetto mainstream, cioè quegli artisti – principal-
mente, ma non esclusivamente, bianchi – che hanno dominato l’evolversi
dei gusti del pubblico “medio”, riportando un importante successo commerciale; 2. il punk, laboratorio di resistenza culturale e fenomeno emblematico delle sottoculture musicali bianche degli ultimi tre decenni del xx
secolo, caratterizzato dal rifiuto della virtuosità tecnica e da una dimensione antagonista e prefigurativa1; 3. la musica nera, colta soprattutto nel suo rapporto con le rivendicazioni politiche della comunità afroamericana e
quindi intesa come veicolo capace di accogliere e amplificare le voci della
protesta negli anni successivi alla stagione del Civil Rights Movement