Search Results (168)

Background: Angle’s dental class asymmetries not associated with crossbite are malocclusions that are often underestimated in pediatric patients. However, they may be associated with alterations in the development of the stomatognathic system. Objective: The objective of this study was to evaluate the prevalence of Angle’s class asymmetries without crossbite in primary-school-aged children and to investigate possible associations with perinatal, clinical, and functional variables. Materials and Methods: This cross-sectional observational study analyzed a sample of 391 children aged 6 to 11 years, attending a primary school in the metropolitan area of Milan, Italy. Data were systematically collected through both clinical examination and patient history, with the aim of identifying significant correlations with the occurrence of dental asymmetries in the absence of crossbite. Results. The results revealed a higher prevalence of occlusal asymmetries associated with factors such as oral breathing, low tongue posture, type of delivery, formula feeding, and systemic diseases during the first three years of life. Advanced carious lesions and inclination of the occlusal plane were significantly associated with asymmetry. Conclusions: The study highlights the importance of early diagnosis and a multidisciplinary approach to prevent malocclusions and complex craniofacial dysfunctions later in life. Full article

Water scarcity is a relevant issue whose impact can be mitigated through sustainable solutions. Humidification–dehumidification (HDH) cycles powered by photovoltaic thermal (PVT) modules enable pure water production in remote areas. In this study, models have been developed and validated for the main components of the system, the humidifier and the dehumidifier. A unique HDH-PVT prototype was built and experimentally tested at the SolarTech Lab of Politecnico di Milano in Milan, Italy. The experimental system is a Closed Air Closed Water—Water Heated (CACW-WH) that mimics a Closed Air Open Water—Water Heated (CAOW-WH) cycle through brine cooling, pure water mixing, and recirculation, avoiding a continuous waste of water. Tests were performed varying the mass flow ratio (MR) between 0.346 and 2.03 during summer and autumn in 2023 and 2024. The experimental results enabled the verification of the developed models. The optimal system performance was obtained for an MR close to 1 and a maximum cycle temperature of $44 °\mathrm{C}$, enabling a 0.51 gain output ratio (GOR) and 0.72% recovery ratio (RR). The electrical and thermal energy generation of the PVT modules satisfied the whole consumption of the system enabling pure water production exploiting only the solar resource available. The PVT-HDH system proved the viability of the proposed solution for a sustainable self-sufficient desalination system in remote areas, thus successfully addressing water scarcity issues exploiting a renewable energy source. Full article

Hepatocellular carcinoma (HCC) is a leading cause of cancer death, with liver transplantation (LT) offering a curative option for early-stage patients who cannot undergo resection. Although LT provides good long-term outcomes within standard criteria, recurrence occurs in approximately 8–20% of recipients and often leads to poor survival. Traditionally, LT eligibility relied on strict criteria like the Milan criteria, which are effective in selecting patients with low recurrence but may exclude patients who could benefit from transplantation. In response, new expanded criteria and models using tumor biology have been developed for better risk stratification, allowing more personalized selection and management. Despite these advances, recurrence remains a major clinical challenge, with no consensus on optimal imaging timing or frequency post-LT. Treatment depends on the recurrence’s extent and location, including surgical resection and locoregional therapies. Systemic treatments are promising, especially for unresectable or extrahepatic recurrence, though most evidence comes from small retrospective studies, limiting the development of standardized protocols. Future research should focus on addressing these gaps and guiding evidence-based post-transplant care. This is a narrative review summarizing recent advances in HCC recurrence. Full article

The rapid rise of e-commerce is intensifying pressure on last-mile delivery networks, making the strategic placement of parcel lockers an urgent urban challenge. In this work, we adapt multilayer two-mode Social Network Analysis to the parcel-locker siting problem, modeling city-scale systems as bipartite networks linking spatially resolved demand zones to locker locations using only open-source demographic and geographic data. We introduce two new Social Network Analysis metrics, Dual centrality and Coverage centrality, designed to identify both structurally critical and highly accessible lockers within the network. Applying our framework to Milan, Rome, and Naples, we find that conventional coverage-based strategies successfully maximize immediate service reach, but tend to prioritize redundant hubs. In contrast, Dual centrality reveals a distinct set of lockers whose presence is essential for maintaining overall connectivity and resilience, often acting as hidden bridges between user communities. Comparative analysis with state-of-the-art multi-criteria optimization baselines confirms that our network-centric metrics deliver complementary, and in some cases better, guidance for robust locker placement. Our results show that a network-analytic lens yields actionable guidance for resilient last-mile locker siting. The method is reproducible from open data (potential-access weights) and plug-in compatible with observed assignments. Importantly, the path-based results (Coverage centrality) are adjacency-driven and thus largely insensitive to volumetric weights. Full article

Particulate matter, particularly PM_2.5, poses a significant threat to public health due to its ability to disperse widely and its detrimental impact on the respiratory and circulatory systems upon inhalation. Consequently, it is imperative to maintain regular monitoring and assessment of particulate matter levels to anticipate air pollution events and promptly mitigate their adverse effects. However, predicting air quality is inherently complex, given the multitude of variables that influence it. Deep learning models, renowned for their ability to capture nonlinear relationships, offer a promising approach to address this challenge, with hybrid architectures demonstrating enhanced performance. This study aims to develop and evaluate a hybrid model integrating Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for forecasting PM_2.5 levels in India, Milan, and Frankfurt. A comparative analysis with established deep learning and machine learning techniques substantiates the superior predictive capabilities of the proposed CNN-RNN model. The findings underscore its potential as an effective tool for air quality prediction, with implications for informed decision-making and proactive intervention strategies to safeguard public health. Full article

Background: Museums are increasingly being recognized not only as cultural institutions but also as potential resources for enhancing psychological well-being. Prior research has shown that museum visits can reduce stress and anxiety, yet there is a pressing need for evidence-based interventions supported by neurophysiological data. While neuroscientific studies suggest a combined role of emotional and cognitive mechanisms in aesthetic experiences, less is known about the neural predictors of individual responsiveness to such interventions. Methods: This study was conducted in two Milan-based museums and included an initial profiling phase (sociodemographic information, trait anxiety, perceived stress, museum experience), followed by pre- and post-visit assessments of state anxiety and mood. Electrocortical activity was recorded via a portable brain–computer interface (BCI), focusing on the theta/beta ratio (TBR) as a marker of cortical–subcortical integration. Results: Museum visits were associated with significant improvements in mood (M = 1.17; p < 0.001) and reductions in state anxiety (M = −6.36; p < 0.001) in both arts and science museums. The baseline TBR predicted the magnitude of state anxiety change, alongside individual differences in trait anxiety and perceived stress. Conclusions: These findings support the idea that aesthetic experiences in museums engage both emotional and cognitive systems, and that resting state neurophysiological markers can help forecast individual responsiveness to well-being interventions. Such insights not only contribute to existing knowledge about the cognitive and emotional processes during aesthetic fruition, but could also guide future applications of personalized interventions in museum settings, further integrating cultural participation with mental health promotion. Full article

Background/Objectives: Inflammation-based markers have emerged as potential prognostic tools in hepatocellular carcinoma (HCC), but comparative data with classical prognostic factors in untreated HCC are limited. This study aimed to evaluate and compare the prognostic performance of inflammatory and conventional markers using Harrell’s concordance index (C-index). Methods: This retrospective study included 250 patients with untreated HCC. Prognostic variables included age, BCLC stage, Child–Pugh classification, Milan criteria, MELD score, AFP, albumin, Charlson comorbidity index, and the inflammation-based markers neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), Systemic Inflammation Response Index (SIRI), and Systemic Immune-inflammation Index (SIII). Survival was analyzed using Cox regression. Predictive performance was assessed using the C-index, Akaike Information Criterion (AIC), and likelihood ratio tests. Results: Among the classical markers, BCLC showed the highest predictive performance (C-index: 0.717), while NLR ranked highest among the inflammatory markers (C-index: 0.640), above the MELD score and Milan criteria. In multivariate analysis, NLR ≥ 2.3 remained an independent predictor of overall survival (HR: 1.787; 95% CI: 1.264–2.527; p < 0.001), along with BCLC stage, albumin, Charlson index, and Milan criteria. Including NLR in the model modestly improved the C-index (from 0.781 to 0.794) but significantly improved model fit (Δ–2LL = 10.75; p = 0.001; lower AIC). Conclusions: NLR is an accessible, cost-effective, and independent prognostic marker for overall survival in untreated HCC. It shows discriminative power comparable to or greater than most conventional predictors and may complement classical stratification tools for HCC. Full article

This paper aims to explore the potentialities and systemic relationships between the ‘regenerative’ process and ‘circular economy’ concept within the conservation and reuse of a built cultural heritage framework through contextualizing the concept of ‘process programming’ of the Preventive and Planned Conservation methodology. As a case study, it depicts a decommissioned industrial agricultural silo in Corbetta—a small historic city with its hinterland located in the protected Southern Milan Regional Agricultural Park. The context includes the industrial agricultural lands of the 20th century, together with historical water infrastructure, farmhouses, and the typical flora of the Lombardy region, all evidences of Corbetta’s rural archaeological values and the sophisticated material culture of its past collective production/economy system—the locus in which the silo was once one of the main productive symbols of Corbetta’s agricultural identity. Within such a complex and challenging context, this paper argues in favor of the constructive role of such a methodology in upholding circular economy principles within the process of conservation and reuse of the silo, highlighting its broader application of the ‘coevolution’ concept from a multidisciplinary long-term perspective. Full article

The growing demand for energy-efficient heating, ventilation, and air conditioning (HVAC) systems has increased interest in multi-source heat pumps as a sustainable solution. While extensive research has been conducted on heat pump performance prediction, there is still a lack of practical tools for early-stage system evaluation. This study addresses that gap by developing regression-based models to estimate the performance of various heat pump configurations, including air-source, ground-source, and dual-source systems. A simplified performance estimation model was created, capable of delivering results with accuracy levels comparable to TRNSYS simulation outputs, making it a valuable and accessible tool for system evaluation. The analysis was conducted across nine climatic zones in Italy, considering key environmental factors such as air temperature, ground temperature, and solar irradiance. Among the tested configurations, hybrid systems like Solar-Assisted Ground-Source Heat Pumps (SAGSHP) achieved the highest performance, with SCOP values up to 4.68 in Palermo and SEER values up to 5.33 in Milan. Regression analysis confirmed strong predictive accuracy (R² = 0.80–0.95) and statistical significance (p < 0.05), emphasizing the models’ reliability across different configurations and climatic conditions. By offering easy-to-use regression formulas, this study enables engineers and policymakers to estimate heat pump performance without relying on complex simulations. Full article

The increasing frequency of extreme weather events, combined with the historic degradation of urban water systems, has prompted cities worldwide to reconsider the role of water in urban planning. This study examines the restoration and integration of historical watercourses into contemporary urban environments through blue and green infrastructure (BGI). Focusing on three case study cities—Poznań (Poland), Milan (Italy), and Beijing (China)—this research explores both spatial and regulatory conditions for reintroducing surface water into cityscapes. Utilizing historical maps, contemporary land use data, and spatial planning documents, this study applies a GIS-based multi-criteria decision analysis (GIS-MCDA) to assess restoration potential. The selected case studies, including the redesign of Park Rataje in Poznań, canal daylighting projects in Milan, and the multifunctional design of Beijing’s Olympic Forest Park, illustrate diverse approaches to ecological revitalization. The findings emphasize that restoring or recreating urban water systems can enhance urban resilience, ecological connectivity, and the quality of public space. Full article

This study presents a surrogate modeling framework designed for the rapid yet reliable assessment of sonic boom impacts. The methodology is demonstrated through two case studies: a transatlantic flight from Milan to New York, highlighting the sonic boom impact along the route; and a representative supersonic overflight of Italy, quantifying the population exposure to varying noise levels. Aerodynamic numerical simulations were carried out using an open-source code to capture near-field pressure signatures at three critical mission points. These signatures were used to compute the Whitham F-functions, which were then propagated through a homogeneous atmosphere to the ground using the Whitham equal area rule. The resulting N-waves enabled the computation of aircraft shape factors, which were employed in a regression model to predict the sonic boom characteristics across the full mission profile. Finally, the integration of noise metrics and geographical information system software provided the evaluation of environmental impact and population noise exposure. Full article

The study presents the first application of the Connected Linear Network (CLN) package implemented in MODFLOW-USG to an existing Ground-Source Heat Pump (GSHP) system. The numerical element was specifically adapted by the authors in a previous study to simulate vertical Borehole Heat Exchangers (BHEs) and is here applied for the first time to evaluate the heat transfer in Milano subsurface induced by a GSHP system. The evaluation of interference between geothermal systems and wells is an important topic, especially in densely populated areas, which has scarcely been explored in the literature. Specifically, the aim is to evaluate the thermal perturbation and the possible interference between BHE systems and the drinkable water wells of the Armi pumping station managed by MM S.p.A. The simulation results show moderate groundwater thermal perturbation: approximately 3 °C at 100 m downgradient of the borefield and, furthermore, a limited impact (maximum 1 °C) in just two wells of the Armi pumping station. After 3 years of GSHP system operation, the thermal perturbation can extend for kilometers, but with limited variation in groundwater temperature (lower than 1 °C). Although the predicted groundwater temperature variation is not critical, the real-time monitoring of temperatures coupled with numerical modeling is essential to prevent thermal interference and optimize GSHP system performance. Full article

Hepatocellular carcinoma (HCC) is one of the leading causes of liver transplant worldwide. While liver transplantation offers a survival advantage for early-stage HCC patients, post-transplant recurrence remains a significant concern, affecting up to 15% of recipients. We sought to conduct a comprehensive review related to HCC recurrence after liver transplant. Tumor-related factors such as poor differentiation, vascular invasion, and elevated tumor biomarkers like alpha-fetoprotein are key predictors of recurrence. Donor-related factors, including graft type and surgical procedures, can also influence outcomes, though their effects are less conclusive. Advancements in patient selection criteria and scoring systems, such as the Milan Criteria and RETREAT score, have improved risk stratification by incorporating tumor size, biomarkers, and response to pre-transplant treatment. Despite these measures, recurrent HCC after transplantation poses treatment challenges. Curative approaches such as resection are feasible for localized or oligometastatic recurrence and offer the best outcomes when applicable. Locoregional treatments, including ablation and transarterial chemoembolization, provide options for unresectable cases but have limited long-term efficacy. Systemic therapies, including targeted agents like sorafenib, regorafenib, and lenvatinib, have shown modest benefits in managing advanced recurrent HCC. Emerging immunotherapy approaches hold promise but face unique challenges due to the required immunosuppression in transplant recipients. Multidisciplinary evaluation remains essential for tailoring treatment plans. Future efforts should focus on refining predictive tools and exploring novel therapies to improve survival outcomes for patients with recurrent HCC after liver transplantation. Full article

Background/Objectives: Mucoepidermoid carcinoma (MEC) is the most common malignant tumor of the minor salivary glands, often affecting the hard palate. Preoperative diagnosis and surgical planning are challenging due to anatomical complexity and limitations in sampling, generally obtained by fine-needle aspiration (FNA). This study retrospectively evaluated the diagnostic and therapeutic performance of a high-definition ultrasound (HDUS)-guided fine-needle aspiration cytology/biopsy (FNAC/FNAB) protocol in diagnosing intraoral MEC, based on the Milan System for Reporting Salivary Gland Cytopathology (MSRSGC), with the relative clinical outcomes. Methods: A cohort of 64 patients with histologically confirmed MEC of the minor salivary glands, treated between 2000 and 2022, was retrospectively analyzed. All patients underwent HDUS-guided FNAC/FNAB, imaging (CT, MRI, and panoramic X-ray), and subsequent surgical treatment. The cytological specimens were classified using the MSRSGC. Surgical margins, histopathological findings, lymph node status, and follow-up outcomes were recorded. Results: Of 64 MECs, 42 cases were finally diagnosed as low-grade (LG)/intermediate grade (IG) and 22 as high-grade (HG) carcinomas, using a two-tier histological classification system. HDUS accurately delineated the lesion size, infiltration depth, and bone proximity, with excellent correlation with surgical specimens (difference ≤ 0.6 mm). MSRSGC classification distributed the cases across all categories, with 28 classified as malignant (category VI). Repeat FNAC improved the diagnostic yield in non-diagnostic and atypical cases. FNAB confirmed the cytological findings in all cases, with immunohistochemistry investigation with Ki-67 supporting tumor grading. Surgical margins were clear in all resections. Lymph node metastases were identified in all patients who underwent neck dissection (n = 18), all with HG-MEC. No recurrences occurred among the LG/IG-MEC patients during a median 2-year follow-up. Conclusions: The combined use of HDUS and FNAC/FNAB, interpreted through the MSRSGC framework, offers a highly accurate, minimally invasive approach for preoperative diagnosis and surgical planning in intraoral MEC. HDUS-guided cytology significantly improves diagnostic reliability, particularly in LG/IG and cystic variants, facilitating tailored surgical management. Also, the clinical outcomes may support the possibility of using a simplified grading classification for two histopathological types. Full article

Ensuring the trustworthiness of data used in real-time analytics remains a critical challenge in smart city monitoring and decision-making. This is because the traditional data validation methods are insufficient for handling the dynamic and heterogeneous nature of Internet of Things (IoT) data streams. This paper describes a semantic IoT streaming data validation approach to provide a semantic IoT data model and process IoT streaming data with the semantic stream processing systems to check the quality requirements of IoT streams. The proposed approach enhances the understanding of smart city data while supporting real-time, data-driven decision-making and monitoring processes. A publicly available sensor dataset collected from a busy road in Milan city is constructed, annotated and semantically processed by the proposed approach and its architecture. The architecture, built on a robust semantic-based system, incorporates a reasoning technique based on forward rules, which is integrated within the semantic stream query processing system. It employs serialized Resource Description Framework (RDF) data formats to enhance stream expressiveness and enables the real-time validation of missing and inconsistent data streams within continuous sliding-window operations. The effectiveness of the approach is validated by deploying multiple RDF stream instances to the architecture before evaluating its accuracy and performance (in terms of reasoning time). The approach underscores the capability of semantic technology in sustaining the validation of IoT streaming data by accurately identifying up to 99% of inconsistent and incomplete streams in each streaming window. Also, it can maintain the performance of the semantic reasoning process in near real time. The approach provides an enhancement to data quality and credibility, capable of providing near-real-time decision support mechanisms for critical smart city applications, and facilitates accurate situational awareness across both the application and operational levels of the smart city. Full article