Archivio istituzionale della ricerca - Università dell'Insubria
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Experimental insights on CIRLEM: Enhancing energy efficiency and flexibility in buildings
Full text linkThe growing complexity of urban energy systems, climate uncertainties, and geopolitical disruptions highlight the need for energy flexibility and smart management. Recent developments in smart buildings enable real-time adaptability and collective energy behavior through the deployment of Reinforcement Learning (RL), which optimizes energy use, integrates distributed resources, and enhances demand response. However, challenges in communication, system diversity, and user intervention must be addressed for scalable and secure multi-agent RL-based management. This study evaluates the application of CIRLEM, a previously developed and introduced Energy Management system that integrates Collective Intelligence (CI) with an online, value-based, model-free RL algorithm. The experiment is carried out in Building Energy Living Lab in France, as one of the pilots of COLLECTiEF, an European funded Horizon 2020 project, equipped with an advanced building management system for one year. The control algorithm interacts with the building management system every 15 min, optimizing setpoints based on real-time monitoring of energy use and indoor environmental conditions. The results indicate an 18% reduction in overall energy use compared to the reference baseline, with heating and cooling demands decreasing by 5% and 32%, respectively. Additionally, peak power demand is curtailed up to 15% for heating and 50% for cooling. The performance of the control algorithm is in an excellent level for more than 50% of the time in 1-month analyses through achieving load reduction and shifting. This experimental study demonstrates that CIRLEM effectively enhances energy flexibility while maintaining thermal comfort, demonstrating its potential for broader implementation, paving the way decentralized energy management solutions in smart buildings and urban energy networks
Nitrogen Position Matters: Synthetic Strategies, Functional Behavior and Dual Roles in Medicine and Materials in the Imidazopyridine Family
No full textImidazopyridines are a versatile class of nitrogen-fused heterocycles bridging medicinal chemistry and materials science. Their π-conjugated framework allows broad structural tuning, yielding diverse biological and photophysical properties. The best-known isomers, imidazo[1,2-a]pyridine and imidazo[1,5-a]pyridine, have been widely studied as pharmacophores and luminescent materials, respectively. The less explored imidazo[4,5-b]
and imidazo[4,5-c]pyridines are now emerging as alternative scaffolds with distinctive electronic and functional behavior. This review summarizes synthetic strategies, electronic features, and key applications—from kinase inhibition and antiviral activity to fluorescence imaging, down-conversion, Organic Light Emitting Diode (OLED)/Light-emitting Electrochemical Cell (LEC) and hybrid optoelectronic systems—outlining how imidazopyridines
can evolve from molecular frameworks into multifunctional platforms for bioimaging and advanced optoelectronic technologies
Fertility-sparing vs hysterectomy for uterine STUMP: A pragmatic clinical study
No full textBackground: Uterine smooth muscle tumors of uncertain malignant potential (STUMP) are rare neoplasms with unpredictable clinical behavior. Optimal management, particularly in reproductive-aged women, remains controversial, with limited data comparing the safety of fertility-sparing versus hysterectomy. Methods: This multicentre retrospective cohort study included women aged 18-85 with histologically confirmed STUMP treated at 17 Italian gynecologic oncology centers from 2010 to 2023. Patients underwent either fertility-sparing surgery (myomectomy or hysteroscopic resection) or definitive surgery (hysterectomy ± salpingo-oophorectomy). Kaplan-Meier and Cox models were used to compare recurrence-free survival (RFS) and overall survival (OS). Results: Median (range) follow-up was 51 (1-291) months. Among 401 women, 106 (26.4 %) received fertility-sparing treatment (mean [± SD] age: 35.3 ± 6.8 years) and 295 (73.6 %) underwent definitive surgery (mean [± SD] age: 47.7 ± 9.2). At total follow-up, recurrence occurred in 12.5 % of patients, predominantly within the pelvis. Median RFS was longer after definitive surgery than after fertility-sparing procedures (50.0 vs 42.5 months; HR 2.39 [95 % CI 1.36-4.19]), although this difference disappeared when benign (leiomyoma) recurrences were excluded (HR 1.74 [95 % CI 0.90-3.34]). At last available follow-up, 97.5 % of patients were alive, with no significant OS difference between treatment groups (HR 0.22 [95 % CI 0.27-1.79]). Outcomes were comparable across menopausal status and concurrent adnexal removal. Conclusion: Definitive surgery reduces recurrence risk, but long-term survival is similarly excellent after fertility-sparing surgery in appropriately selected women with STUMP. Conservative management represents a reasonable option for patients desiring fertility, provided they receive counseling regarding recurrence risk, diagnostic uncertainty, and the need for long-term surveillance
International multicentric validation of a novel T classification system for cancer of the nasal vestibule
No full textStudy aim Cancer of the nasal vestibule (CNV) is an underrecognized head and neck malignancy, lacking a distinct ICD-O-3 topography code, and a specific T classification. The goal of this study was to assess which of the currently used T classifications provides the most accurate predictive and discriminatory accuracy. Methods The four currently used classifications (UICC Sinonasal, UICC NMSC, Wang and Rome) were assessed in a retrospective multicenter cohort established within the Head & Neck and Skin Groupe Européen de Curiethérapie / European SocieTy for Radiotherapy & Oncology Working Group. Through multivariable disease-specific and recurrence-free survival analyses, it was evaluated which staging system was most valuable. Results 609 CNV cases were retrieved from 21 tertiary care centers. Only the Wang and New Rome systems provided accurate prognostic stratification as they showed diminishing survival rates and increasing hazards of disease-specific death and disease recurrence with each successive T category. Compared to Wang, the New Rome system employs more objective criteria and, since it includes four T categories, it can easily be integrated with cN stage to obtain a specific clinical staging for the CNV, which has also resulted superior compared to the current UICC/AJCC systems in this study. Conclusion The New Rome classification exhibits a superior predictive and descriptive precision compared to the Wang and both UICC/AJCC systems. The New Rome’s T category structure would allow an integration into the wider UICC/AJCC system once the nasal vestibule is acknowledged as a different subsite
Il Mediterraneo di Paul Lanzky (1852-1936): lirica e filosofia in un dimenticato discepolo di Nietzsche (con cinque poesie da Am Mittelmeer, 1890
No full textReal-World Efficacy of IL-23 Inhibitors in Psoriasis Affecting High-Impact Areas: Indirect Comparison of Tildrakizumab 200 mg, Risankizumab, and Guselkumab-IL PSO (Italian Landscape Psoriasis)
No full textIntroduction: Psoriasis involving special or high-impact areas (scalp, nails, palms/soles, genitalia) is associated with a disproportionate functional and psychological burden that is often underestimated by conventional severity scores and remains challenging to treat effectively. This study compared the real-world efficacy and safety of three interleukin (IL)-23p19 inhibitors-risankizumab, guselkumab, and tildrakizumab 200 mg-in patients with moderate-to-severe plaque psoriasis with involvement of high-impact sites. Methods: This multicenter retrospective study included 670 patients treated for at least 52 weeks across 37 Italian dermatology centers. Patients received risankizumab (n = 254), guselkumab (n = 177), or tildrakizumab 200 mg (n = 239) according to approved regimens. Effectiveness was assessed using Psoriasis Area Severity Index (PASI) and site-specific Physician's Global Assessment (PGA) scores (scalp, nails, palms/soles, genitalia) at weeks 4, 16, 36, and 52. Safety was evaluated through reported adverse events. Results: Risankizumab demonstrated the fastest and most pronounced reduction in PASI, achieving PASI90 and PASI100 responses in 89.6% and 82.1% of patients at week 52, respectively. Tildrakizumab 200 mg showed a slower onset but comparable long-term efficacy, particularly in nail and palmoplantar psoriasis. At week 52, complete nail clearance (fn-PGA = 0) was achieved in 90.0% of patients treated with risankizumab, 76.7% with tildrakizumab, and 66.7% with guselkumab. Palmoplantar and genital psoriasis showed near-complete resolution across all treatment groups by week 52. Scalp involvement improved markedly with all agents, with lower residual disease observed with risankizumab. All treatments were well tolerated, with infrequent and predominantly mild adverse events and no major safety concerns. Conclusion: In real-world clinical practice, IL-23p19 inhibitors provide high and sustained efficacy in psoriasis affecting high-impact sites. Risankizumab offers faster and deeper responses, while tildrakizumab 200 mg represents an effective long-term option, particularly in patients with higher BMI or more treatment-resistant disease. These findings support a personalized approach to biologic selection based on disease localization, patient characteristics, and therapeutic goals
Privacy Compliance Analysis in Mobile and Wearable Applications
Full text linkIn recent years, smartphones and wearable devices have been the two most dynamic ecosystems, with billions of users and millions of applications driving their growth.
Indeed, according to Datareportal, as of July 2025, there are 7.4 billion smartphones in use globally, while wearable devices have reached 600 million units according to Statista.
Furthermore, there are 8.93 million applications (aka, apps) released worldwide, with 3.553 million apps in the Google Play Store and 1.642 million in the Apple App Store, as reported by Bankmycell. On average, each user installs more than 40 apps on their device.
However, the growth of these two ecosystems is built on a trade-off in user privacy, as 65.83\% of the ecosystem’s revenue comes from advertising. This raises concerns about the serious invasion of users' privacy as app developers and hackers continuously exploit their sensitive information for revenue reasons. Although the European Union and the USA have enacted laws to protect privacy, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA), that require apps to notify users and obtain explicit consent before collecting and processing sensitive data, violations remain widespread and have become increasingly sophisticated. Specifically, these violations can take advantage of users' common smartphone usage habits and the weaknesses of smartphone operating systems (OS), especially the Android OS.
Indeed, in this thesis, we first introduce a novel attack vector that demonstrates how sharing images containing sensitive metadata can unintentionally or intentionally lead to the leakage of users' personal or confidential information. To validate our finding and assess its prevalence, we use traditional analysis. While the results confirm that this newly discovered attack vector has a significant impact, they also highlight the inherent limitations of traditional analysis. Therefore, we propose a new solution based on Large Language Models (LLM) to build an early-warning system capable of detecting potential leaks of sensitive metadata embedded in images. Our evaluation, conducted on datasets from traditional analysis, shows highly promising results.
Finally, we develop our LLM-based solution toward a more general framework by assessing privacy non-compliance in wearable apps. Specifically, we evaluate whether these apps respect users’ privacy in sharing sensitive data and its destinations across 14 sensitive categories as defined by Google.In recent years, smartphones and wearable devices have been the two most dynamic ecosystems, with billions of users and millions of applications driving their growth.
Indeed, according to Datareportal, as of July 2025, there are 7.4 billion smartphones in use globally, while wearable devices have reached 600 million units according to Statista.
Furthermore, there are 8.93 million applications (aka, apps) released worldwide, with 3.553 million apps in the Google Play Store and 1.642 million in the Apple App Store, as reported by Bankmycell. On average, each user installs more than 40 apps on their device.
However, the growth of these two ecosystems is built on a trade-off in user privacy, as 65.83\% of the ecosystem’s revenue comes from advertising. This raises concerns about the serious invasion of users' privacy as app developers and hackers continuously exploit their sensitive information for revenue reasons. Although the European Union and the USA have enacted laws to protect privacy, such as the General Data Protection Regulation (GDPR) or the California Consumer Privacy Act (CCPA), that require apps to notify users and obtain explicit consent before collecting and processing sensitive data, violations remain widespread and have become increasingly sophisticated. Specifically, these violations can take advantage of users' common smartphone usage habits and the weaknesses of smartphone operating systems (OS), especially the Android OS.
Indeed, in this thesis, we first introduce a novel attack vector that demonstrates how sharing images containing sensitive metadata can unintentionally or intentionally lead to the leakage of users' personal or confidential information. To validate our finding and assess its prevalence, we use traditional analysis. While the results confirm that this newly discovered attack vector has a significant impact, they also highlight the inherent limitations of traditional analysis. Therefore, we propose a new solution based on Large Language Models (LLM) to build an early-warning system capable of detecting potential leaks of sensitive metadata embedded in images. Our evaluation, conducted on datasets from traditional analysis, shows highly promising results.
Finally, we develop our LLM-based solution toward a more general framework by assessing privacy non-compliance in wearable apps. Specifically, we evaluate whether these apps respect users’ privacy in sharing sensitive data and its destinations across 14 sensitive categories as defined by Google
