Satellite technology is emerging as a promising tool for monitoring cities and cultural heritage, enabling efficient and non-invasive analysis of large areas over extended periods. In this study, we explore the potential of this method for large-scale applications, focusing on the historic centre of Pisa and its ancient city walls. Specifically, COSMO-SkyMed satellite data are used to monitor the horizontal and vertical displacements of urban walls and differential settlements in various areas of the city. The detected movements are correlated with soil morphology data and on-site evidence of cracking and deformation patterns in the structures. This approach proves to be particularly valuable for extensive linear assets such as ancient city walls, where traditional on-site monitoring can pose logistical challenges, thus aiding in the safeguarding of cultural heritage in an era of increasing environmental challenges.

The European Ground Motion Service (EGMS) delivers high-resolution information on ground motion over the European continent, by adopting Advanced Differential Interferometric Synthetic Aperture Radar (A-DInSAR) providing extensive datasets that include time series displacement data and associated velocity. However, the large volume of data can pose challenges for their analysis, particularly in studies covering extensive areas. To address this issue, a semi-automated method has been developed to identify and rank clusters of active deformation zones. This method consists of three key components: (1) the selection of landslide candidates, focused on identifying Areas of Interest (AOIs) based on the distribution of Persistent Scatterers derived from A-DInSAR analysis; (2) ranking the intensity and exposure levels associated with each landslide candidate for hazard and risk evaluations; and (3) in situ validation to evaluate the effectiveness of the methodology. The regional ranking of landslides facilitates the identification of AOIs where monitoring efforts should be prioritized or further investigations planned, particularly when these areas pose risks to infrastructure and urbanized regions. This approach supports the development of an operational service tailored to regional landslide risk mitigation, enabling the refinement of landslide catalogues by improving the delineation of landslide perimeters and assessing their current activity status. The AOIs distribution pointed out the impact on the study of the Lazio region, shedding light on the consistency of PAI Hazard maps, the landslide catalogue completeness, and the needs in frequent updates of such products. Additionally, the annual updates of EGMS products will allow regional authorities to continuously enhance and refine their landslide risk assessments.

Monitoring the performance of buildings and monuments with high cultural and artistic importance is critical for preserving the built heritage. Installing measurement devices is generally prohibited on such structures, and their behavior is assessed by analytical methods. This paper presents a novel methodology for monitoring structural performance through advanced Finite Element Analysis (FEA) and Machine Learning (ML) using the data acquired by InSAR. A cracked wall of Vittoriano building located in Rome, Italy is considered for applying this approach. The paper is organized in three main parts: (i) an advanced 3D numerical model of the wall was developed in ABAQUS. The Concrete Damage Plasticity (CDP) model was utilized for defining the masonry material based on the macro-modeling approach. A nonlinear dynamic analysis was performed to determine the wall's response to the ground settlement reported by InSAR. The model was verified by comparing the stress concentration with the real cracks observed on the wall. (ii) In the second part, an ML-based technique was implemented to predict ground movement in both short- and long-term periods. (iii) Finally, the process of developing a digital twin for monitoring wall's performance using FEA, ML and InSAR data is clearly described. Overall, the results exhibited a high accuracy of both FE model and ML-based technique for evaluating buildings' behavior over time.

Determining frequency of buildings has a crucial impact on proper analysis and design of structures. Conventional methods (i.e., in situ and numerical analysis) are generally costly and time-consuming. This study aims at developing Machine Learning (ML)-based methods for obtaining frequency of structures with the focus on masonry towers (i.e., bell towers). To this end, a database including the results of either in situ or numerical analysis on over 90 masonry towers available in literature is collected. Additionally, frequencies of 18 towers located in Venice, Italy are measured by site survey, and added to the database. Parameters with the highest influence on tower's frequency, namely height, plan dimensions and modulus of elasticity are defined as input variables for predicting natural frequency of a tower by ML-based techniques including Decision Tree (DT), Random Forest (RF), XGBoost and K-Nearest Neighbors (KNN). The models' performance is analyzed by comparing the correlation between the predicted and real values. Moreover, the models' accuracy is assessed through common performance metrics and Taylor diagram, and the most accurate model is introduced accordingly. Results highlighted the high capability of ML-based approaches for predicting towers' frequency, and the XGBoost model exhibited the highest accuracy. In the second part of the paper, the values predicted by the most accurate model are compared to those calculated by the equations proposed by design Codes (i.e., NTC008, NCSE, ASCE) and literature. Lastly, for deeper investigating the performance of masonry towers a sensitivity analysis is performed by the proposed XGBoost model, and an equation is suggested for calculating their natural frequency based on their height and plan width.

Statistical analyses of time series of A-DInSAR post-seismic data (April 6th 2009 L’Aquila earthquake), acquired in the time range 2010-2021 from the Cosmo-SkyMed (by ASI) and Sentinel-1 (by ESA) missions, have been carried out. These have allowed investigating the relationships between ground deformations and geological, hydrogeological, and geomorphological features of the study area, located in L’Aquila (Italy) historical centre (LAHC). The analysis of these data is still ongoing and offers promising research perspectives in the field of geomechanical/geotechnical subsoil characterization, based on satellite ground deformation data, also useful in seismic hazard characterization and mitigation. L’Aquila downtown is placed in the L'Aquila-Scoppito intermontane basin (Central Italy) which is a half-graben bordered by SW-dipping normal mostly active faults, filled with approximately maximum 600 meters of Plio-Quaternary continental slope, colluvial and alluvial deposits which overlie unconformably the carbonate bedrock. To assess the relationship between the geological-geomorphological and hydrogeological study area features and ground deformation in L’Aquila downtown, a correlation analysis has been carried out, between subsidence velocity and the following driving factors: water table depth, ground slope, shear wave velocity of outcropping lithologies and Red Soil thickness. Furthermore, cluster analysis and various filtering and time series treatment have been applied to these time series with the aim of analysing seasonal and deseasonalized trends. The correlation between subsidence velocity and the above-mentioned driving factors is statistically significant. It is presumable that the subsidence process is mainly controlled by the kind and thickness of lithologies involved. The above illustrated correlation analysis provides a first result, which may be improved by a multivariate approach. Let us consider a simple vertical strain model, made up of overlying layers (e.g., Red Soil, L’Aquila Breccia, etc.) in the consolidation phase. The integrated analysis of A-DInSAR and well data may allow determining the subsurface structure for the studied urban area, with particular attention to Red Soils, which, due to their high compressibility, have been recognized as lithology responsible for site-specific seismic amplifications. This may provide a promising powerful method of geotechnical characterization at urban scale and at a relatively low cost. The main achieved results can be summarized as follows: The A-DInSAR post-seismic data, recorded in the time range 2010-2021, revealed a post-seismic subsidence phenomenon, still ongoing. The correlation analysis allows us to conclude that subsidence velocities are mainly controlled by the properties and thicknesses of shallower rock layers. The subsidence velocity is positively correlated with the damage level of buildings. The cross-correlation analysis highlighted a significant correlation between seasonal fluctuations in subsidence rate and rainfall variations. The seasonally adjusted A-DInSAR time series highlighted an anomaly in the subsidence trend, observable during the Amatrice-Norcia seismic sequence of 2016. The study of this anomaly deserves attention and will be the subject of future research. Ground deformations detected by means of A-DInSAR technology may provide a promising inversion criterion, enabling us to perform a geotechnical characterization of shallow rock layers, over large areas, at relatively low costs.

Historical and recent earthquakes have struck the city of L'Aquila, located in the axial zone of the Central Apennines, Italy, causing severe damage to the historic centre. Recent studies have shown that higher intensity of damage to buildings in L’Aquila downtown, both in the case of the 2009 earthquake (Mw: 6.29) and the 1703 earthquake (Mw: 6.7), is associated with higher post-seismic subsidence rates, detected through A-DInSAR maps, in the time range 2010–2021. This finding suggests the existence of geological factors that drive deformation (and building damage). The availability of long time series of InSAR data acquired from the Cosmo-SkyMed and Sentinel-1 missions, has enabled us to analyze the relationships between ground deformations and geological, hydrogeological, and geomorphological factors that may affect them. The correlation analysis between predisposing factors and SAR deformation has highlighted an increase in subsidence strongly conditioned by the outcropping lithology, showing how compositional variations within lithology can significantly influence ground deformations. Furthermore, hydrogeology has been recognized as playing an important role in determining deformation processes: the water table depth locally influences long-term subsidence, while seasonal fluctuations in the water table may be responsible for secondary areal variations in ground deformations.

L'Aquila downtown (Central Italy) is situated in a highly seismic region, making it susceptible to numerous historical and recent earthquakes. Among these, the earthquake of Mw 6.3 on 6 April 2009, and the one of Mw 6.7 on 2 February 1703, caused severe damage or complete destruction of the majority of buildings in the historical center. An integrated statistical analysis of A-DInSAR and seismic related building damage data is illustrated. By comparing the seismic damage maps from the 2009 and 1703 earthquakes with the A-DInSAR map produced with Cosmo-SkyMed descending orbit images (acquired between 2010 and 2021), a correlation between post-seismic deformations (in terms of average velocity) and building damage intensity has been identified. Furthermore, ground and building velocities have been separately examined, in order to evaluate the impact of building features and reconstruction efforts on ground deformations. The geostatistical analysis revealed a widespread subsidence motion (until -2 mm/year) across the whole study area. Notably, neighboring points did not exhibit consistent deformation velocities, indicating a lack of spatial correlation. Additionally, Cluster Analysis has allowed recognition of recurring subsidence/uplift trends, which, in terms of shape of curve displacement vs. time, appears independent on building damage intensity or reconstruction interventions. Our results pave the way for a novel utilization of long-term series of satellite SAR data in high-risk seismic zones, serving as a valuable tool to map the most susceptible areas and mitigate seismic risk.

Time series analysis of Interferometric Synthetic Aperture Radar (InSAR) data is a crucial step for monitoring the displacement of the Earth's surface. The Persistent Scatterer InSAR (PS-InSAR) is a multi-temporal InSAR method that provides the displacement time series that can be used for studying ground deformation. From a hazard assessment perspective, the rapid detection of deformation patterns is crucial for identifying the areas that will be affected by damage due to landslides. Understanding the relationship between triggering factors, such as rainfall and the occurrence of mass movements from the interpretation of SAR time series is still a major challenge. Herein, we first review some of the traditional methods, such as Pearson correlation analysis for investigating whether there is any possible linear dependency between rainfall and ground deformation measurements. Then, we describe the time series analysis tools in the least-squares wavelet software that can be used for processing non-stationary time series which may not be evenly sampled. We demonstrate how these tools can be utilized to understand more about the relationships between displacement and rainfall time series which have different sampling rates without any need for filtering and/or aggregation.

Rapid slope instabilities (i.e., rockfalls) involving highway networks in mountainous areas pose a threat to facilities, settlements and life, thus representing a challenge for asset management plans. To identify different morphological expressions of degradation processes that lead to rock mass destabilization, we combined satellite and uncrewed aircraft system (UAS)-based products over two study sites along the State Highway 133 sector near Paonia Reservoir, Colorado (USA). Along with a PS-InSAR analysis covering the 2017–2021 interval, a high-resolution dataset composed of optical, thermal and multi-spectral imagery was systematically acquired during two UAS surveys in September 2021 and June 2022. After a pre-processing step including georeferencing and orthorectification, the final products were processed through object-based multispectral classification and change detection analysis for highlighting moisture or lithological variations and for identifying areas more susceptible to deterioration and detachments at the small and micro-scale. The PS-InSAR analysis, on the other hand, provided multi-temporal information at the catchment scale and assisted in understanding the large-scale morpho-evolution of the displacements. This synergic combination offered a multiscale perspective of the superimposed imprints of denudation and mass-wasting processes occurring on the study site, leading to the detection of evidence and/or early precursors of rock collapses, and effectively supporting asset management maintenance practices.

In the context of population concentration in large cities, assessing the risks posed by geological hazards to enhance urban resilience is becoming increasingly important. This study introduces a robust and replicable procedure for assessing ground instability hazards and associated physical risks. Specifically, our comprehensive model integrates spatial hazard assessments, multi-satellite InSAR data, and physical features of the built environment to rank and prioritize assets facing multiple risks, with a focus on ground instabilities. The model generates risk scores based on hazard probability, potential damage, and displacement rates, aiding decision-makers in identifying high-risk buildings and implementing appropriate mitigation measures to reduce economic losses. The procedure was tested in Rome, Italy, where the analysis revealed that 60% of the examined buildings (90 x 103) are at risk of ground instability. Specifically, 33%, 22%, and 5% exhibit the highest multi-risk score for sinkholes, landslides, and subsidence, respectively. Landslide risk prevails among residential structures, while retail and office buildings face a higher risk of subsidence and sinkholes. Notably, our study identified a positive correlation between mitigation expenses and the multi-risk scores of nearby buildings, highlighting the practical implications of our findings for urban planning and risk management strategies.

Remote sensing is undoubtedly one of the most practical approaches used in many fields such as structural health monitoring. The most notable deal of this method, however, is the data which are either missing or not given by satellites. In this study, an attempt has been made to propose machine learning (ML)-based models for reconstructing missing InSAR data (i.e., buildings’ displacement) to monitor their performance more properly. To this end, buildings located in the historical center of Rieti, Rome (Italy), are considered as case studies. Displacement of the points situated on different buildings’ roof, given by remote sensing, is utilized for training the relationship between inputs and outputs to the models. The input variables were points’ coordinates, height, and soil condition, while the cumulative displacement was the target output. Tree-based techniques namely decision tree, random forest and XGBoost were implemented for developing prediction models. The accuracy of the models was assessed through common performance metrics and Taylor diagram, and the most accurate model was introduced accordingly. The results demonstrated high capability of the tree-based methods for estimating the displacement of buildings. The proposed prediction models could be used for either predicting or reconstructing missing displacement of buildings at any specific point which ease structural performance monitoring remarkably.

This paper discusses a novel methodology for evaluating buildings performance through advanced Finite Element Analysis (FEA) based on the data given by Interferometric Synthetic Aperture Radar (InSAR).A cracked wall of Vittoriano building, Rome, Italy is chosen as the case study.A detailed 3D numerical model of the wall was developed in ABAQUS.Concrete Damage Plasticity (CDP) model was utilized for defining the masonry material based on the macro-modeling approach.The ground deformation acquired by InSAR is applied to the wall's base.The crack propagation and stress concentration of the numerical model was in line with the real cracks observed on the wall.The results highlighted the high reliability of the InSAR data which could be used in structural behavior assessment.

Landslide inventory maps represent a preliminary step toward landslide susceptibility, hazard, and risk assessment. The increasing enhancement of A-DInSAR (Advanced Differential Synthetic Aperture Radar Interferometry) techniques facilitates the detection of Earth’s surface displacements over large or remote areas. Moreover, applying post-processing tools to the measurements retrieved by PS-InSAR analyses (i.e., one of the most common multitemporal A-DinSAR techniques) permits the representation of gravity-driven processes evolution in both spatial and temporal terms. Nevertheless, geometric distortions linked to the orbit and acquisition parameters of the SAR sensors, along with insufficient site coverage and spatial density of the PS-InSAR analyses, may lead to a lack of information, especially in mountainous areas. To address this problem, we processed the data using different InSAR tool packages and exploited the combination of orbital geometries for different satellites at the regional and local scales. These analyses were applied over an area encompassing four regions in the Central Apennines (Italy), within the framework of a broader national project which aims at mapping and updating landslide-prone slopes interacting with urban centers. For each processed dataset, we compared the spatial coverage and the accuracy of the displacements, providing statistical correlation tests to establish the relationship between the different InSAR tool packages. Therefore, we were able to verify the possible underestimation of the velocity and coherency measurements, and then select the best dataset (or the best combination) for further analyses. Based on the comparison between the dataset and through a semi-automatic approach, we then selected several areas that exceeded specific velocity thresholds and were densely covered by PS. In these areas, classified with a high priority level, detailed analyses were performed through a set of post-processing plugins designed for the software QGIS. Spatial and temporal deformation trends of the PSI results, along with subtle surface patterns within the landslide area, were highlighted by the post-processing analyses. Thus, we derived a detailed geomorphological characterization for the high priority phenomena interacting with cities and infrastructures. While at the regional scale findings from our work help the validation and integration of multi-satellite datasets, at the local scale the proposed workflow can also support the prioritization of site-specific monitoring and intervention planning.

A-DInSAR (Advanced Differential Synthetic Aperture Radar Interferometry) is widely acknowledged as one of the most powerful remote sensing tools for measuring Earth’s surface displacements over large areas, and in particular landslides. The Persistent Scatterer Interferometry (PS-InSAR or PSI) is a common A-DInSAR multitemporal technique, which allows retrieving displacement measurements with sub-centimetric precision. Characterization and interpretation of landslides can greatly benefit from the application of A-DInSAR post-processing tools, especially when extremely slow-moving phenomena are not detectable by classical geomorphological investigations, or when complex displacement patterns need to be highlighted. Detailed representations of the spatial and temporal evolution of the processes provide useful constraints during the planning stages of reconstructions and for land use purposes. The present study is part of a broader national project, focused on updating and monitoring landslide-prone slopes interacting with urban centres in the Central Apennines (Italy), by using both geomorphological and A-DInSAR analysis. Therefore, although field surveys permitted the systematic updating of the available landslide inventories, in most cases, clear indications of displacement were outlined only by the SAR interferometry results. In this regard, the preliminary results of the ongoing research focus on specific post-processing analyses of interferometric data performed in the study area. A specific PS-toolbox software, developed by NHAZCA S.r.l. as a set of post-processing plugins for the open-source software QGIS, was specifically designed to enhance spatial and temporal deformation trends of the PSI results, as well as for visualizing the differences between multi-satellite datasets. Moreover, the PS-toolbox allowed depicting subtle surface patterns within the landslide area, shedding light on kinematics and style of activity of slope instabilities. In complex morphological conditions, as the Apennines mountainous regions, the geometric distortions and the site coverage percentage can lead to a lack of information. Therefore, we compared the coverage of PSs and the accuracy of the surface velocity maps produced using different InSAR tool packages on both Sentinel-1 and COSMO-SkyMed scenes. Thus, the comparison of the resulting datasets allowed their validation in terms of measured displacements and reliability for further processing.

Rome is characterized by millennia of urbanization. Long lasting geomorphological investigations have allowed the geomorphological description of the city centre and the valorisation of its geomorphological heritage. In this paper the spatial change of the hydrographic network in historical times is illustrated, with some examples showing how deep has been, and still it is, the link between the historical-cultural development and the natural geomorphological and hydrological characteristics of the Roman territory. In particular, the most relevant human interventions on the drainage network, in the southern area of the city centre, have been investigated. Before the land-use modifications of Roman-age, this area was drained by the most important left tributary of the Tiber River within the city walls, the Nodicus River, more recently known as Aqua Mariana. This stream has undergone many anthropogenic modifications and diversions during the centuries, and its original path is known only downstream of the San Giovanni Basilica. According to geomorphological, archaeological and geological evidences, it is possible to hypothesize that the dimension of the pre-urbanization drainage basin, as known and reconstructed in the available literature, should have been until now underestimated.

Objective.The association between obesity and periodontitis has been extensively investigated in adults but not in young people.The aim of this study was to examine the association between overweight-obesity and periodontal disease in pediatric subjects. Methods. Controlled cross-sectional study involving 100 school children of both gender (50 M and 50 F) between 7 and 12 years of age (mean age 9,19±1,57).Two groups were formed based on Body Mass Index value: test group with BMI ≥ 25 Kg/m 2 and control group with BMI ≤ 24 Kg/m 2 .Diet intake and oral hygiene habits were recorded by a specific questionnaire and the periodontal clinical parameters were evaluated.Results.The periodontal examination in the control group revealed a full-mouth plaque score (FMPS) value equal to 21.86% against 50.08% in the group of patients overweight/obese; the fullmouth bleeding score (FMBS) in the control group amounted to 12.7% against 26.24% of test group.No patient in either group included in the study presented a probing pocket depth (PPD) ≥3, so a significant difference regarding this value was not found.Regarding the frequency and quantity of food consumption, the number of obese patients who did not follow a balanced diet largely exceeded the number of normal-weight patients (70 versus 20%).Conclusions.These results focus the attention on the negative impact of obesity on gingival health in young subjects, probably due to a combination of metabolic and inflammatory profiles and the result of a careless attitude towards prevention diseases of the oral cavity.

Background: Different sources of cultured cells combined with different scaffolds (allogenic, xenogeneic, alloplastic or composite materials) have been tested extensively in vitro and in preclinical animal studies, but there have been only a few clinical trials involving humans. Aim: This study reviewed all of the English language literature published between January 1990 and December 2015 to assess the histological performance of different mesenchymal cell-scaffold constructs used for bone regeneration in human oral reconstructive procedures. Methods: An electronic search of the MEDLINE and Cochrane Central Register of Controlled Trials databases complemented by manual searching was conducted to identify studies involving histological evaluation of mesenchymal cell-scaffold constructs in human oral surgical procedures. The methodological quality of randomized controlled clinical trials and controlled clinical trials was assessed using the Cochrane Collaboration tool for assessing the risk of bias. Heterogeneity was assessed using Review Manager software. Considering the heterogeneity, the data collected were reported by descriptive methods and a meta-analysis was applied only to the articles that reported the same outcome measures. The articles were classified and described based on the material scaffolds used. Results: The search identified 1030 titles and 287 abstracts. Full-text analysis was performed for 32 articles, revealing 14 studies that fulfilled the inclusion criteria. Three randomized controlled clinical trials were identified as potentially eligible for inclusion in a meta-analysis. The studies were grouped according to the scaffold materials used: bone allograft (three studies), polyglycolic-polylactic scaffold (four studies), collagen sponge (two studies), and bovine bone matrix (five studies). The stem cells used in these studies had been sourced from the iliac crest, periosteum, dental pulp and intraoral sites. Conclusions: The very small amount of available data makes it impossible to draw any firm conclusions regarding the increase in bone formation in human oral reconstructive procedures when using graft materials engineered with autogenous stem cells.

The results of a long-lasting geomorphological survey carried out in Rome are summarized. A method aimed at integrating survey data, historical maps, aerial photographs and archaeological and geomorphological literature produced a geomorphological map of the present-day historical centre. The geomorphology of Rome is related to the paleogeographical conditions prior to the founding of the City; they allow us to recognize the stages of landscape evolution of the ancient Caput Mundi (Capital of the World). The study area has been affected by continuous man-made changes to the drainage network and to the topographic surface over the last 3000 years. It has forced the authors to develop innovative solutions to undertake effective analysis of the urban environment and the legend of the geomorphological map in this peculiar context. The resulting map is useful for urban planning and archaeological research.

Odontomas represent the most common type of odontogenic benign jaws tumors among patients younger than 20 years of age. These tumors are composed of enamel, dentine, cementum, and pulp tissue. According to the World Health Organization classification, two distinct types of odontomas are acknowledged: complex and compound odontoma. In complex odontomas, all dental tissues are formed, but appeared without an organized structure. In compound odontomas, all dental tissues are arranged in numerous tooth-like structures known as denticles. Compound odontomas are often associated with impacted adjacent permanent teeth and their surgical removal represents the best therapeutic option. A case of a 20-year-old male patient with a compound odontoma-associated of impacted maxillary canine is presented. A minimally invasive surgical technique is adopted to remove the least amount of bone tissue as far as possible.

Introduction The positioning of implants in the jaw bones with contextual graftless lateral approach sinus lifting is finding an increasingly broad consensus in the literature. Since the 1970s, various clinical research projects have been conducted on applications of biological and synthetic biomaterials in bone regenerative surgery, both in sinus lift procedures and in cystic cavity filling after cystectomy or in bone defects in regenerative periodontal surgery. Currently, we are finding that there is an increasing trend of clinicians aiming to adopt graftless techniques, with satisfactory results in terms of implant survival in the long term. In our study, through a case report, we describe a variant of graftless sinus augmentation technique with contextual implant placement, emphasizing the role of the blood clot, combined with collagen sponges, as a natural scaffold and the osteogenic potential of the subantral membrane in guided bone regeneration, with reduced morbidity of the patient. Case presentation To describe the surgical technique, the clinical case of a 38-year-old Caucasian woman with a lateral posterior edentulism was selected. The rehabilitation was solved by a graftless sinus augmentation technique with a contextual implant placement. For each implant, a resonance frequency analysis evaluation was reported as implant stability quotient values. The performance of the implant stability quotient values followed a gradual increase from time zero to the sixth month, as the clot was differentiated into osteoid tissue and then into bone tissue, due to the scaffold effect conferred by the equine collagen sponge. The stabilization phase took place between the fourth and the sixth month, according to the implant stability quotient values. Conclusions Our graftless sinus augmentation technique seems to be very predictable thanks to the osteoconductive principles on which it is based, and in association with the proper management of peri-implant soft tissue, so as to increase the amount of keratinized tissue, which could represent the new gold standard for this type of rehabilitation in the future.