The rotor is an essential actuator of quadrotor UAV, and is prone to failure due to high speed rotation and environmental disturbances. It is difficult to diagnose rotor faults and identify the fault localization simultaneously. In this paper, we propose a fault diagnosis and localization scheme based on the Extended State Observer (ESO) and Deep Forest (DF). This scheme can accurately complete the fault diagnosis and localization for the quadrotor UAV actuator without knowing the fault size by combining the model-based and the data-driven methods. First, we obtain the angular acceleration residual signal of the quadrotor UAV by using ESO. The residual signal is the difference between the observed state of ESO and the true fault state. Then, we design the residual feature analysis method by considering the position distribution of the quadrotor UAV actuator. This method can embed the actuator fault localization information into the fault data by simultaneously considering pitch and roll of the quadrotor UAV. Finally, we complete the fault diagnosis and localization of the quadrotor UAV actuator by processing the fault data by using DF. This scheme has the advantages of straightforward observer modeling, strong generalization ability, adaptability to small sample data, and few hyperparameters. Our simulation results indicate that the accuracy of the proposed scheme reaches more than 99% for the unknown size of the quadrotor UAV actuator fault. In recent years, quadrotor Unmanned Aerial Vehicle (UAV) has gradually become an important part of people’s lives . The quaThe fault diagnosis methods for the quadrotor UAV actuator are mainly categorized into model-based and data-driven methods . Ren et Actuator fault type diagnosis and fault localization identification are very important for quadrotor UAV actuator fault diagnosis, but they are usually carried out separately. In this paper, we propose a fault diagnosis and localization scheme for a quadrotor UAV actuator based on ESO-DF under the condition of unknown fault size. In this scheme, model-based and data-driven methods are combined to realize fault type diagnosis and fault localization identification simultaneously. The Extended State Observer (ESO) is an observer in Active Disturbance Rejection Control (ADRC) . ESO canIn order to better extract the fault features of quadrotor UAV actuators, we have made corresponding improvements to ESO . We extr1. We have improved the ESO combined with the characteristics of the quadrotor UAV actuator failure. We obtain the angular acceleration containing the fault characteristics of the quadrotor UAV actuator through ESO.2. Aiming at the problem of fault location identification of quadrotor UAV actuators, we designed an analysis method of actuator fault residual characteristics. By combining the angular acceleration information of pitch and roll, the method embeds the fault location information of the actuator into the fault data.3. Aiming at the problem that the judgment of the fault type and the fault location cannot be obtained at the same time, we designed an actuator fault diagnosis and localization scheme based on ESO-DF. By combining the model-based method and the data-driven method, the scheme fully extracts fault features and achieves high-accuracy fault diagnosis and localization results.The contributions of this paper are summarized as follows: The remainder of this paper is organized as follows. The problem definition and basic theory are introduced in The quadrotor UAV is a rigid body.The mass of the quadrotor UAV is uniformly distributed and the center of mass overlaps with the geometric center.The four actuators of the quadrotor UAV are evenly distributed on the fuselage according to the X shape, as shown in In the simulation experiment of this paper, we have the following three assumptions on the mathematical model of the quadrotor UAV:m, g represent the mass and gravitational acceleration of the quadrotor drone, respectively. The six-degree-of-freedom mathematical model  of the qIn this paper, the quadrotor UAV actuator faults mainly include speed constant deviation faults and speed gain loss faults .The speed constant deviation fault is usually caused by bias current or bias voltage in the circuit. The fault model is as follows.The speed gain loss fault is usually caused by mechanical damage to the motor or propeller. The fault model is as follows.The purpose of ESO is to solve the core problem of the observation of disturbances in ADRC. It borrows the idea of the state observer in modern control theory, that is, on the basis of observing the state variables The following is an introduction to the ESO design of a second-order system. Equation  is the Ax˙1=x2x˙2Equation .(10)x˙1y of the controlled object. u is the output value of the controller. For better tracking of the expanded third-order system, the following linear ESO is established for Equation :(11)e1=aDeep Forest (DF) is an ensemble learning method based on decision tree proposed by Professor Zhihua Zhou in 2017. DF mainly consists of two parts as shown in MGS is a method of feature expansion. The original features are cut by sliding windows to enhance the classification effect of CF. In In MGS, Forest A and Forest B are two kinds of decision trees. Decision trees can complete the classification task of input features. The original 400-dimensional input features are transformed into 3618-dimensional features after feature extraction by MGS. This process completes the enhancement of the original features, which is beneficial to the classification process of CF.CF is a module that completes the classification work inside DF, as shown in In this section, the overall framework of our proposed ESO-DF scheme is presented first in The overall framework of fault diagnosis and localization of the quadrotor UAV actuator based on ESO-DF under the condition of unknown fault size is shown in ESO can accurately track the movement status of the quadrotor UAV according to maneuver control command. ESO can estimate the approximate total disturbance based on the input and output data of the system. The performance of ESO is only related to the estimation accuracy of andwidth . In Equadwidth [1a˙1=a2+β1When Equation , it can We designed an actuator residual feature analysis method by combining the actuator position distribution of quadrotor UAV. Since the actuators of the quadrotor UAV are X-shaped and evenly distributed around the body, the four actuators will have different effects on pitch and roll when they fail separately; therefore, we embedded fault localization information into the fault data by splicing the angular acceleration of pitch and roll in the time dimension, as shown in The attitude maneuver of the quadrotor UAV is carried out according to the maneuver control command at The workflow of the proposed ESO-DF based actuator fault diagnosis and localization scheme is depicted in The entire process can be split into two parts: training and testing. The specific steps are set out below.Injecting faults into actuators while maneuvering: injecting faults into actuators while the quadrotor UAV is performing attitude maneuvers.Extracting residual fault signals: According to the control torque and the angular acceleration information measured by the sensor, the ESO outputs the angular acceleration residual sequence containing the actuator fault information, that is, the fault data.Embedding fault localization information into fault data: we apply residual feature analysis method to embed fault localization information into fault data. The fault data obtained by embedding fault location information are a time series of a certain length.Train Fault Diagnosis and Localization Model: we obtain the fault diagnosis and localization model trained by injecting fault data into DF.Obtain testing data: we follow steps 1, 2, and 3 of the training process to obtain test failure data.Actuator fault diagnosis and localization: We complete the fault diagnosis and localization of the quadrotor UAV actuator by processing the test fault data using the trained model.The evaluation metrics used in this paper include accuracy, precision, recall, and y is the sample true fault type label. y is completely equal to The accuracy is one of the important evaluation metrics to measure the fault diagnosis model. The calculation formula is as follows:For each sample under each fault type, the precision is the ratio of the number of correctly predicted labels to the number of labels predicted to be correct by the classifier. The calculation formula is as follows:For each sample under each fault type, the recall is the proportion of the number of correct predicted labels in the total number of correct labels. The calculation formula is as follows:The In this paper, the maneuver control instructions provided to the quadrotor UAV are shown in Equations –19). Th. Th19). Equation . In EquaEquation , ωb=100,During the experiment, the size of the roll angle maneuver control command The actuators of the quadrotor UAV are evenly distributed on the fuselage, as shown in It can be seen from We complete the fault diagnosis and localization of the quadrotor UAV actuator under the condition of unknown fault size by using the DF model to process the fault data. In order to verify the effectiveness of the DF algorithm, we introduce Random Forest (RF) for comparison. The reason we chose RF for comparison is that RF fault diagnosis is widely used and DF is an improved algorithm based on RF. In this paper, the parameters of DF and RF all use default parameters. We completed a final comparative experiment by comparing the two methods and whether ESO was used, as shown in The results of the training and testing accuracy of the above four schemes for the fault diagnosis and localization of the quadrotor UAV actuator are shown in Equation  are showAccording to the fault diagnosis results of various indicators above, we verify that the DF scheme is superior to the RF scheme. Because ESO can better extract the fault features of the system, the effects of fault diagnosis and localization are improved after the introduction of ESO. Through our proposed ESO-DF method, the fault type diagnosis and fault localization identification of the quadrotor UAV actuator under the condition of unknown fault size can be completed at the same time.In this paper, we completed the fault diagnosis and localization of unknown fault size of quadrotor UAV actuator by using the ESO-DF based scheme. The scheme achieves high-accuracy fault diagnosis and localization results by combining model-based method with data-driven method. In addition, we designed a residual characteristic analysis method by considering both pitch and roll. This method is straightforward in design, but it can embed fault localization information into fault data. The simulation results indicate that the fault type and fault localization of the actuator can be provided precisely. By comparing the results of other methods, this scheme has better generalization ability and accuracy. In future work, we will try to conduct experimental verification on a real quadrotor UAV.


Residential segregation has brought significant challenges to cities worldwide and has important implications for health. This study aimed to assess income segregation in the 152 largest Brazilian cities in the SALURBAL Project. We identify specific socioeconomic characteristics related to residential segregation by income using the Brazilian demographic census of 2010 and calculated the income dissimilarity index (IDI) at the census tract level for each city, subsequently comparing it with Gini and other local socioeconomic variables. We evaluated our results’ robustness using a bootstrap correction to the IDI to examine the consequences of using different income cut-offs in substantial urban and regional inequalities. We identified a two minimum wage cut-off as the most appropriate. We found little evidence of upward bias in the calculation of the IDI regardless of the cut-off used. Among the ten most segregated cities, nine are in the Northeast region, with Brazil's highest income inequality and poverty. Our results indicate that the Gini index and poverty are the main variables associated with residential segregation.The online version contains supplementary material available at 10.1007/s43545-022-00491-9. The phenomenon of the expansion of cities and the transformation of urban areas has led to the creation of various forms of spatial territories. Central and peripheral areas are objects of theoretical and empirical studies worldwide. Since then, socioeconomic configurations have emerged from dynamic city centers with multiple and often conflicting features. In this sense, residential segregation and inequality arise due to a series of actions that can be subjective and individual or material due to a superstructure of discrimination , initially proposed by Duncan and Duncan , allows Salud Urbana en Latin América (SALURBAL) or Urban Health in Latin America project is an initiative that aims to support urban policies that promote health and health equity in cities of the region  Is there a bias in the IDI to make the indicator inconsistent? (ii) What is the best income cut-off for shares of low-income households to calculate IDI? (iii) how is the IDI associated with other socioeconomic indicators in a context of high regional inequality?Our first contribution is a quantitative analysis of the IDI based on the concern expressed by Taeuber and Taeuber . They poRoyuela and Vargas  and MazzTorres  calculatHere, we found that the cut-off of 2 minimum wages is adequate to define a low-income minority group at the national level in Brazil since it represents 33.8% of the sample according to the census of 2010. It means that the percentage of the low-income group is a major factor in the analysis of residential segregation measured by income. Therefore, in the context of great regional income inequality, the spatial distribution of the low-income minority is essential to measure the heterogeneity within a city and efficiently capture the level of segregation. The heterogeneity we refer to means reaching a low-income minority group capable of adequately representing intra-urban and regional inequality patterns. Finally, using linear regression models, we find that the Gini index and poverty are the main variables associated with residential segregation, measured by the IDI.Our work contributes in multiple dimensions to the socioeconomic field of studies on segregation. First, the evaluation of the IDI by income levels in one of the unequal countries in the world is something to be taken into account since the large Brazilian cities concentrate more than 85% of the country’s population. Defining a cut-off for low-income minority groups is necessary to have a consistent indicator in quantitative and qualitative analyses, as the interpretation of the IDI can be adapted in different economic, sociological, geographic, and public health contexts, among others. Regarding the tools and means of measurement for indicators of segregation by income, race, education, or others, the application of random sampling tests developed by Tivadar  showed aWe can define income segregation as the uneven classification of households according to their income level within the urban space  project comprising urban agglomerations with more than 100,000 inhabitants in 2010. The urban agglomerations (L1ADs) comprise 422 sub-city units . They can be defined as a single administrative unit  or a combination of adjacent administrative units  that are part of an urban extension determined from satellite imagery  for 2010. In Brazil, data at the census tract level are collected every ten years. The 2020 demographic census has been postponed to 2022 because of the COVID-19 pandemic. The income segregation index is based on the number of private households under certain minimum wage thresholds. For the dissimilarity index, which compares two social groups, the index was calculated for the following pairs: households earning from 0 to ½ minimum wage vs. the total (households with or without income); households earning 0 to 1 wage vs. the total; households earning 0 to 2 wages vs. the total and, finally, households earning 0 to 3 wages vs. the total. The IBGE did not compute households that did not report income for this category IBGE .The socioeconomic variables examined with IDI are shown in Table Three models were estimated, taking the IDI as the exposure variable. In model 1, the Gini index was used as the outcome and the SEI in the second. In the third model, we included all other covariates.The allocation of individuals within a space can be purely random or reflect the influence of economic, social, and environmental determinants. In general, the systematic allocation process is influenced by the preferences or restrictions of individuals and occupied areas.Allen et al. , Mazza aThen, we can use the dissimilarity index The allocation is given by a set of probabilities Systematic segregation is present when there is Therefore, we can rewrite Maximum likelihood functions can estimate the conditional probabilities as independent allocations of multinomial distributions, and the function’s log-likelihood can be described as:The maximum likelihood estimator is given by When we assume that the size of units Therefore, with a complete population or with a random sample, The bias can arise when the area population is small or the minority group proportion is very low. Consequently, the index is affected by differences in the proportion of the minority in the population and the size of the areal unit of analysis, making it difficult to compare the indices across cities. Fosset  presentsWe followed these recommendations by working with census tract level data and explored the bias generated by different income cut-offs. In order to evaluate bias embedded in the IDI, we use randomization models with bootstrap. For that, we take as reference Efron  in whichθ. Efron  also demmentclass2pt{minimBootstrap samples are generated from the original dataset. Each bootstrap sample has The bootstrap bias estimator based on Concerns about the bias embedded in the dissimilarity index are not recent. Carrington and Troske  developeThe linear regression method is a classic technique of statistical mathematics and an essential part of modern econometrics theory  increase in income segregation. Finally, we included the entire set of variables in the last model. We observed that the Gini index and poverty rate continue to have a relevant association with residential income segregation  and , respectively. In addition to economic and social issues, we evidence that income inequality poses growing challenges given the worldwide urban changes.We also estimated the models using the IDI corrected with the bootstrap method to provide more robustness to the results. As expected, the results were maintained. Online Appendix A shows the estimated coefficients with marginal changes, but the effects and significance remained unchanged.Segregation is among the most prominent urban problems in rapid urbanization in developing economies. Haddad  states tThe relationship between the evenness indices that measure segregation and other socioeconomic indicators is widely analyzed, especially in developed countries Darroch . HoweverAround twenty indices were researched and conceptually related to some of the dimensions of residential segregation , also known as the scale effect, can arise  presented by Garcia-López and Moreno-Monroy  for 121 Additional work examining the different types and dimensions of segregation and new measurement tools are needed to understand better how income segregation affects the social construction of individuals and families and, thereby, support the permanent reduction of urban inequality and poverty.Discussing methods of measuring segregation is not a simple matter. Segregation quantifies how heterogeneous population groups are distributed in the urban space, indicating how far an income minority group is from the city's average income. If we take a small percentage of low-income minority groups or large spatial areas as a base, the dissimilarity index can be affected by deviations from evenness, and the indicator could be strongly biased.We present a comprehensive description of the segregation by income in the Brazilian cities as the largest urban agglomerations in the country. According to Massey and Denton , a highlTo show which cut-off to define low-income households best deals with urban income segregation within large cities and the pattern of regional inequality, we use statistical analyses of correlation and distribution between the calculated IDI and the respective low-income minority group. Statistics point out that the cut-off of households earning up to 2 minimum wages yields an average proportion in the low-income minority group of 33.8% of households, seeming reasonable to work with this cut-off. Bias levels calculated on IDI using bootstrap methods were irrelevant to the study since they did not change the regressions’ estimates.The fact that the Brazilian census tracts are built taking into account only technical criteria means that each sector's delimited area is spatially balanced, leading to each having approximately the same number of households. However, checking the indicator's bias is important for future research advancement and constitutes a new statistical framework to be taken into account when dealing with other types of residential segregation.As expected, inequality and poverty were strongly associated with segregation. Considering that our analysis is about Brazil's most significant metropolitan areas, it is essential to highlight that this study contributes to the perspective of urban spaces characterized as socially segregated and of high spatial concentration, involving inequality and urban poverty. The advantages of urbanization have been discussed in many international forums. However, social disparities, the lack of equity in income distribution, and the possible benefits of urbanization still seem far from the reality of Brazilian cities. Furthermore, despite recent advances, the fight against poverty is an evident challenge for public policies, given the difficulty governments face in maintaining a minimum income policy capable of breaking the processes of social reproduction of poverty.This paper has limitations in data and analysis, as the available information at the census level is from 2010. Relevant changes in income patterns have taken place in Brazil and worldwide in recent years. However, no other study has addressed such issues or used the analytical approach to investigate income segregation in Brazilian cities.Therefore, we highlight that the study of income residential segregation plays a significant role in understanding the social relationships within large cities. Future research should focus on other determinants, in addition to income. Understanding segregation patterns by gender, race, and education is essential for developing effective social and health policies focusing on the long term. Therefore, building a consolidated research agenda that can provide adequate scientific support to guide policies to reduce segregation.Supplementary file1 (DOCX 12 kb)Below is the link to the electronic supplementary material.


When compressing a soft bellow, the bellow will contract and pump out the fluid inside the bellow. Utilizing this property, we propose a novel actuation method called compressing bellow actuation (CBA), which can output fluidic power and tendon-driven force simultaneously. Based on the CBA method, a double-acting soft actuator (DASA) combining fluidic elastomer actuator (FEA) and tendon-driven metacarpophalangeal (MCP) joint is proposed for robotic finger design. The proposed DASA exhibits both compliance and adaptiveness of FEAs, and controllability and large output force of the tendon-driven methods. The fluid in the bellow can be either air or water or even integration of the two, thus constituting three different actuation modes. Mathematical modeling of the relationship between bellow compression displacement and DASA’s bending angle is developed. Furthermore, experimental characterizations of DASA’s bending angle and blocking force are conducted at different actuation modes. The double-acting method can availably promote the bending angle of an FEA by up to 155%, and the blocking force by up to 132% when the FEA is water-filled. A soft robotic hand with a forearm prototype based on the DASA fingers is fabricated for the demonstration of finger motion and gripping applications. Robotic hands and grippers are essential and fundamental components to make the robots interact with the environment. Traditional robotic hands consisted of rigid joints and links  and usuaSoft robotic hands are emerging rapidly with various working principles , i.e., fPrevious researchers have combined two or more actuation modes to design soft robotic hands or grippers for better performance ,18,19,20Metal bellows are utilized to absorb the dimensional changes as expansion joints because the changes in temperature and pressure will vary the length and radius of pipes ,23. By cThe working principle and modeling of the CBA are shown in At the initial state displayed in . The experimental platform is presented in The first experiment is designed to test the relationship between the compression displacement of the bellow For each condition, the bellow was compressed by 30 mm. For every 5 mm displacement increment, we measured the bending angle of the MCP joint The MCP joint bends almost 70 degrees when the bellow is linearly compressed by 30 mm. However, the MCP joint does not rotate when the bellow linear displacement is less than 15 mm. Because of the tendon displacement, we need to find the maximum MCP bending angle, which is far less than the bellow linear displacement that we need to completely squeeze fluid in bellow and out to obtain the largest FEA bending angle; therefore, we make the tendon slack at the initial state like For the bending angle test of the FEA part with different water volumes, the maximum bending angles are: full water in both bellow and FEA  joint of a human finger, and the FEA part functions as phalanges  and interphalangeal joints (PIP and DIP joints) . Figure The compression of the bellow is actuated by three cables which are controlled by a motor. The lengths of the five fingers’ inner chambers are different, so the bending angle of each finger is different when motors rotate at the same angle. The relationship between the flexion angle of the DASA finger and bellow compression displacement has been inferred in In this research, we propose a novel actuator design for soft robotic hands, which can output fluidic power and tendon-driven force simultaneously using a single motor, defined as the double-acting soft actuator. For the DASA concept, the MCP joint rotation is tendon-driven, and the FEA is driven by fluidic power. The DASA is achieved by the compressing bellow actuation (CBA) method. The mathematical modeling of the relationship between the bending angle of the double-acting actuator and the linear compression displacement of the bellow is built in this paper. Experimental characterizations of the DASA are conducted at different actuation modes. The MCP joint can effectively promote the flexion angle of the FEA up to 155%, and the blocking force up to 132% when the FEA and bellow are filled with water compared to gas. We fabricated a soft robotic hand with five independently controlled DASA fingers, which can lift items weighing up to 3 kg in both vertical and horizontal directions.There are some limitations in the current design that can be improved in future research. Firstly, the robotic hand prototype is too bulky to function as a prosthetic hand. The actuation system weight and size can be decreased by optimizing the mechanical structure, improving the control system, and selecting more durable materials. Secondly, the DASA finger bending motion is only achieved by controlling the motor directly without sophisticated sensors and feedback control. In the future, tactile sensors can be embedded in the soft finger to assess the contact tendency of the objects, and more sophisticated functions can be implemented.


International Fusion Materials Irradiation Facility-DEMO-Oriented NEutron Source (IFMIF-DONES) is a high-intensity neutron irradiation facility for qualification of fusion reactor materials, which is being designed as part of the European roadmap to fusion-generated electricity. Its main purpose is to study the behavior of materials properties under irradiation in a neutron flux able to simulate the same effects in terms of relevant nuclear responses as those expected in the first wall of the DEMO reactor which is envisaged to follow ITER. It is thus a key facility to support the design, licensing and safe operation of DEMO as well as of the fusion power plants that will be developed afterwards. The start of its construction is foreseen in the next few years. In this contribution, an overview of the IFMIF-DONES neutron source is presented together with a snapshot of the current engineering design status and of the relevant key results achieved within the EUROfusion Work Package Early Neutron Source (WPENS) as part of the 2014–2020 EURATOM Research and Training Programme, complementary to the EU Horizon 2020 Framework Programme (FP8). Moreover, some information on the future developments of the project are given. The latter will be the worst exposed component, potentially undergoing a damage dose rate in excess of 15 dpaNRT displacement per atom per full power year (dpa/fpy) of operation . In. In41]. The experimental results have shown that with the proposed TAA heating jackets configuration, it is possible to reach the minimum Backplate temperature required (> 200 °C) before injecting the lithium.The HFTM prototype was tested in the HELOKA helium loop Fig.  in a firThe tests in the HELOKA helium loop confirmed the excellent thermal performance predicted by the design analyses in the operation temperature range 250–550 °C, with very low temperature spreads in the specimen payload and agile transient control behaviour. Stresses and vibrations in the HFTM structure were well below a critical level.In 2020, new tests were carried out within the FP8 WPENS work programme, including three new fabricated irradiation capsules Fig. .Fig. 24HThese experiments showed that the capsules reached the specified temperature levels without problems . Some inThe HFTM capsules are densely packed with EUROFER specimens and it was originally proposed to use the sodium-potassium alloy NaK-78 to fill any void volume to improve the heat conduction and obtain uniform temperature distribution.However, investigations have shown that under neutron irradiation the potassium in the NaK-78 alloy produces argon. This argon generation has the potential to rise massively the internal capsule pressure causing its structural failure. Furthermore, argon gas bubbles can be trapped in the tiny gaps between the specimens in the stack and act as an insulator leading to an inhomogeneous temperature distribution of the specimens. Therefore, it has been decided, in IFMIF-DONES, to replace the NaK-78 with sodium which does not produce argon or other insoluble gases. However, being sodium solid at room temperature and having higher surface tension, filling and wetting processes needed to be demonstrated. Therefore, experiments have been performed at KIT  to: (1) The experiments have shown that at a temperature of 430 °C or higher a good wetting of the EUROFER specimens by sodium is reached. This temperature level is not critical for the filling process. In conclusion, a successful sodium filling of the prototype capsule was demonstrated .The optimization and standardization of the Small Sample Testing Technique (SSTT) employed for the small size specimens to be irradiated in IFMIF-DONES is a relevant point which needs to be addressed, especially in connection with the licensing process of the future fusion reactors. Thus, validation tasks on sample geometry and testing procedures have been going on at CIEMAT (Spain) as part of the FP8 WPENS work programme. Fabrication and testing of small samples have been performed to confirm their suitability and to optimize the testing procedures Fig. . AlthougDue to the strong irradiation in the TC environment, the Target Assembly is scheduled for annual replacement during the yearly long beam stop period (20 days) of the facility. Therefore, the replacement operations must be carried out inside the test cell by RH devices. The necessary RH procedures and tools were developed and tested by ENEA using the full-scale mockup of the IFMIF target Fig.  during tThe RH tests confirmed the feasibility of the planned operations and provided good estimates for the required times (the whole TAA replacement could be done in less than 3 days). Furthermore, a list of detailed improvements to make the TAA design better suited for RH was derived .Nevertheless, it should be pointed out that the TAA design for IFMIF-DONES is slightly different from the one tested, at least regarding the number of connections and how these connections are managed. Also, the layout of the lithium pipes has been changed. Although the suggested improvements in the design of the TAA are still valid, the intervention time for the maintenance of the new TAA design has to be assessed again and maybe further modifications will be required once the validation of the new TAA concept will be completed. This assessment will be part of the RH validation tasks in the FP9 work programme.Since 2015, the development of IFMIF-DONES has constantly progressed within the FP8 EUROfusion WPENS, from the starting conceptual design – based on the original IFMIF concept – to the current baseline design –54. The A further important achievement of the project was the compilation of the complete Preliminary Safety Analysis Report (PSAR) which was issued in 2019 and later updated in 2020. This document, which formed the basis for the Safety Analysis Report (SAR) recently released , represeAnother important achievement was the inclusion of IFMIF-DONES, in 2018, in the list of the most strategic European research facilities by the Strategy Forum on Research Infrastructures (ESFRI). This has opened up the possibility to apply for EU funding for the preparation of the project construction activities in the framework of the Horizon 2020 project DONES-PreP (DONES-Preparatory Phase). The main objective of the DONES-PreP project, which ended in December 2021, was to draft the Consortium Agreement for the construction phase of facility in Granada, focusing in particular on the governance, legal and financial aspects.Finally, an important step of the IFMIF-DONES activities during FP8 took place in 2019 with the organization of three external design reviews (DRw) for selected areas of the project, namely Safety and Neutronics; Logistics and Remote Handling; and Buildings and Plant Systems. The objectives of the DRw were to verify and ensure the adequacy of the analyses and of the design carried out in the concerned areas. The review panels consisted of recognized experts representing international organizations and projects similar to IFMIF-DONES . As general conclusion of the DRw meetings, the quality of the work developed was rated very high. Nevertheless, the review panels identified some management and design issues which were discussed and summarized in the DRw reports along with a number of proposed corrective actions. The actions were duly implemented in the design work during the following year.The main achievement of the H2020 (FP8) Early Neutron Source work package is a consolidated preliminary engineering design baseline for the IFMIF-DONES facility. During the course of the project, the earlier conceptual design based on the previous IFMIF configuration has evolved, some aspects have been reviewed and re-designed, some necessary validation activities have been proposed and carried out. Also, important validation results and experience were obtained from the activities performed in the past IFMIF/EVEDA project and in the framework of the Broader Approach collaboration with Japan, which is still going on, and then incorporated in the IFMIF-DONES design baseline.The present maturity level of the IFMIF-DONES design allowed to define the working programme of the Horizon Europe (FP9) Early Neutron Source work package which will be focused on completing the engineering design of the facility; on performing the remaining experimental validation and qualification activities; on making all the required transversal analyses and on preparing the technical specifications for the launch of the tenders in view of the construction of IFMIF-DONES infrastructure, components and equipment. The present status will allow a smooth transfer of the engineering design of IFMIF-DONES to the project team in charge of the construction of the facility.


In the development of anti-breast cancer drugs, the quantitative structure-activity relationship model of compounds is usually used to select potential active compounds. However, the existing methods often have problems such as low model prediction performance, lack of overall consideration of the biological activity and related properties of compounds, and difficulty in directly selection candidate drugs. Therefore, this paper constructs a complete set of compound selection framework from three aspects: feature selection, relationship mapping and multi-objective optimization problem solving. In feature selection part, a feature selection method based on unsupervised spectral clustering is proposed. The selected features have more comprehensive information expression ability. In the relationship mapping part, a variety of machine learning algorithms are used for comparative experiments. Finally, the CatBoost algorithm is selected to perform the relationship mapping between each other, and better prediction performance is achieved. In the multi-objective optimization part, based on the analysis of the conflict relationship between the objectives, the AGE-MOEA algorithm is improved and used to solve this problem. Compared with various algorithms, the improved algorithm has better search performance. In China, breast cancer ranks fourth in the national cancer incidence, second only to lung cancer, colorectal cancer and gastric cancer2. Therefore, the development of anti-breast cancer drugs is of great significance. In the field of medicinal chemistry, many scholars have studied and analyzed a large number of drugs, and found that compounds with antagonistic activity may be candidate drugs for the treatment of breast cancer3. When measuring the quality of a candidate drug, the biological activity of the compound  model to predict new compounds with better biological activity, the research cost is greatly reduced and the research efficiency is improved.Breast cancer is the most common cancer in women worldwide. From the 2020 edition of the Global Cancer Statistical Report released by the International Agency for Research on Cancer in February 20219. This is often inefficient, and the model prediction results often have large deviations. In addition, these studies often only consider the relationship between individual molecular descriptors and a single target, ignoring the interaction between a large number of molecular descriptors in actual drug preparation. In recent years, various machine learning algorithms have been widely used in the construction of QSAR models13, and have achieved good predictive performance. However, many current studies mainly focus on building a relationship model between the molecular descriptors of compounds and their biological activities and their ADMET properties to improve the predictive performance of the models. Lack of exploration of the relationship between molecular descriptor selection process, compound biological activity and AMDET properties, it is impossible to directly select drug candidates.In traditional research methods, linear weighting is often used to explore the relationship between the molecular descriptors of compounds and their biological activity and ADMET propertiesFrom multiple perspectives, a new feature selection method based on unsupervised spectral clustering is designed. The selected features have less redundancy and more comprehensive information expression ability.14 is improved and used to solve the problem. Compared with many algorithms, the improved algorithm achieves better search performance.Based on the analysis of the conflict relationship of six optimization objectives, the AGE-MOEA algorithmA complete anti-breast cancer candidate compound selection framework was proposed, which provided guidance for the selection of candidate compounds.Therefore, this paper discusses the selection of molecular descriptors, the relationship mapping model between molecular descriptors and their biological activities and five ADMET properties, and multi-objective optimization. A complete selection framework for anti-breast cancer drug candidates was constructed. Specifically, the contributions of this paper are as follows.15, CFS algorithm16. However, sample class labeling is often difficult to obtain, so in recent years, unsupervised feature selection algorithms have attracted wide attention from scholars.Feature selection is the process of selecting independent feature subsets with stronger expression information from the original feature set. Generally, drug candidate data has the characteristics of high-dimensional small samples, and there is often a lot of redundant information. Feature selection not only helps to remove redundant features and reduce the training time of the model, but also helps to improve the prediction performance of the model. Traditional feature selection methods are often based on supervised, such as Relief algorithm17 proposed an unsupervised feature selection algorithm based on entropy ranking, which uses information entropy to measure the importance of features, so as to select the optimal feature subset. Hou et al.18 proposed a multi-view feature selection method based on adaptive similarity and view weight. By learning the common similarity matrix of different views, the common structure of each view is described. The sparse 19 proposed an unsupervised multi-view feature selection method based on similarity matrix learning and matrix correction. Feature selection is embedded in the learning of data manifold structure graph20. The two promote each other, which can effectively reduce information redundancy and retain feature correlation. Xie et al.21 proposed the idea of unsupervised feature selection based on spectral clustering(FSSC). On the basis of using the adaptive spectral clustering algorithm to cluster the correlation coefficient matrix between features, a feature importance measure method combining feature discrimination and feature independence is proposed to filter the features in the cluster. The experimental results show that FSSC achieves better performance in three unsupervised feature selection methods. However, it is worth noting that FSSC only conducts experiments on small sample data sets with a sample size of about 100, and its performance on this issue is slightly bleak.Dash et al.Therefore, this paper continues the follow-up work of FSSC, and uses the correlation coefficient, cosine similarity and grey correlation degree between features to mine the hidden layer relationship between features from multiple perspectives. At the same time, after using the spectral clustering algorithm for feature clustering, the sum of the weights of the edges connected to the features in the cluster is used as the measure of the importance of the current features, and the important features are selected.QSAR relationship mapping model is one of the computer-aided tools for drug discovery and design. Through the description information of compounds, the relationship between activity, toxicity and carcinogenicity of compounds is established. In recent years, with the development of machine learning algorithms, the predictive performance of QSAR relationship mapping models has also been improved.22 established and compared 24 predictive models and found that the predictive performance of the nonlinear model was better. Gu et al.23 used graph attention network to classify and predict the properties of drug ADMET, and achieved good classification results, but did not discuss the selection of molecular descriptors for optimal compounds, and the overall prediction of their algorithm Performance is low. Xie et al.24 used a neural network model to predict and analyze the properties of drug ADMET, and the prediction accuracy was improved to a certain extent, but did not discuss the selection of compounds. Jia25 used a variety of machine learning algorithms to build a quantitative prediction model between compounds and molecular descriptors, but lacked an overall consideration of the pros and cons of compound treatments. Xu et al.26 combined particle swarm algorithm and machine learning algorithm to predict the biological activity and ADMET properties of the compound respectively, and analyzed the drug mechanism of the compound comprehensively. However, the overall prediction performance of the model is still low, and in addition, the overall consideration of the biological activity and ADMET properties of the compound is lacking, and the task of compound selection cannot be directly performed.Miler et al.27 to map the relationship between each other, which achieves higher prediction performance and lays a foundation for the optimization of subsequent compound selection problems.Therefore, this paper combines a variety of machine learning algorithms to experiment, and finally selects CatBoost algorithmIn order to select drugs, on the basis of completing the selection of compound molecular descriptors and the construction of the relationship mapping model, it is necessary to combine the six relationship mapping models to solve the optimization problem, so as to obtain the value range of important molecular descriptors corresponding to the six optimization objectives as good as possible.g(x) and h(x) are inequality constraint and equality constraint respectively; i and j are the numbers of inequality constraints and equality constraints, respectively.Generally, the multi-objective optimization problem can be defined as follows.28. When there is no conflict between the objectives, the optimal solution can be obtained by optimizing each objective function independently. However, in many problems, there are often both conflict and non-conflict relationships between goals. Therefore, it is necessary to analyze the relationship between the objectives before selecting the multi-objective optimization method. However, in the study of many drug optimization problems, this is often absent. In addition, the Pareto front of the optimization problem is often unknown. For this reason, Abel et al.28 proposed to combine the results of various optimization algorithms as the approximation of the Pareto front of the current problem after deduplication and used it to solve the subsequent problems, and achieved ideal evaluation results.In solving multi-objective optimization problems, it is an important condition to discuss the conflict between objectives29 algorithm retains the excellent individuals in the population through non-dominated sorting, and achieves good solution performance. However, there are problems such as high computational complexity and easy loss of excellent individuals. On the basis of NSGA algorithm, NSGA-2 algorithm30 introduces fast non-dominated sorting, crowding distance, elite strategy and other techniques to solve the above problems. However, when the dimension of the target to be optimized is high , the populations will become non-dominated. Therefore, the effect of NSGA-2 algorithm in solving high-dimensional multi-objective optimization problems will become very unsatisfactory. To this end, NSGA-331 changed the crowding degree distance to the reference point method in the selection process, and achieved better search performance in high-dimensional target optimization problems. Different from the NSGA-2 algorithm approach, MOEA/D32 explicitly decomposes the multi-objective optimization problem into several scalar optimization sub-problems defined by a set of weight vectors. Using a steady-state evolution model, an appropriate representation of the Pareto front can be achieved by defining a set of “prior” generated uniformly distributed weight vectors. It has faster convergence speed and lower computational complexity, and the obtained solution distribution is also more uniform. AGE-MOEA14 considers the search performance of different Pareto front-end ensemble shapes, and achieves better search performance on multiple tasks. Although the AGE-MOEA algorithm has good local search ability, it has some deficiencies in global search ability, and the diversity of solution results is also poor.On the basis of determining the conflict relationship between objectives, it is necessary to determine the corresponding optimization algorithm to solve the problem. Because of the high efficiency of genetic algorithm, it has been widely used in solving optimization problems. The NSGA28 adopted the solution set of various optimization algorithms as the approximation of the Pareto front end of this problem. Finally, this paper adds improvements to the AGE-MOEA algorithm to enhance the search performance of the algorithm. The improved algorithm is used to solve the problem in this paper, and the superiority of the improved algorithm is verified.In order to make up for the insufficiency of current research, this paper adds an analysis of the conflict relationship between optimization objectives. At the same time, considering that the Pareto front end of this paper is unknown, Abel et al.First, a multi-view processing method is proposed to explore the deep relationship between features and the obtained relationship matrix is regarded as a weighted undirected graph. Use spectral clustering algorithm to perform subgraph segmentation and complete feature clustering. The proposed novel feature importance measurement method is used for feature selection to obtain the final candidate feature subset.Then, using the selected feature subset, a variety of machine learning algorithms are used to construct the relationship model between molecular descriptors and compound bioactivity, molecular descriptors and ADMET properties. A CatBoost algorithm based on decision tree and ensemble learning is selected to construct the subsequent optimization objective function.14 is improved, and the improved algorithm is used to solve the final multi-objective optimization problem.Finally, for the above six optimization objectives, after determining the Pareto front end of this problem, the conflict relationship between the objectives is analyzed. The AGE-MOEA algorithmThis experiment aims to construct a drug selection framework for optimal molecular descriptors of anti-breast cancer-related compounds. Among them, only when the compound has excellent biological activity and its ADMET properties are at least 3 human-friendly, can it meet the candidate criteria. Therefore, this problem can be regarded as the solution of a constrained nonlinear multi-objective optimization problem. The specific solution process of this paper is as follows:Considering the large amount of redundant data in the original molecular descriptors, the features with more than 90% of the molecular descriptors with 0 value and the features with a correlation greater than 95% were eliminated. In addition, in order to facilitate the subsequent optimization processing, the hERG values in ADMET properties  Then, the node with the largest sum of edge weights in the current cluster is used as the most important feature representation in the current cluster (representing the strongest correlation between the current feature and other features in the cluster.) Then, the most important features in the current cluster are stored in the candidate feature subset. After repeating the above filtering operation for 10 times, features with a frequency greater than 10 are taken as the final features.27 was finally selected to construct the relationship mapping model between them. Among them, CatBoost is a GBDT framework with fewer parameters, support for categorical variables, and high accuracy, implemented with oblivious trees as base learners. He solved the problems of Gradient Bias and Prediction shift, which can effectively reduce the occurrence of over-fitting and improve the accuracy and generalization ability of the algorithm.In the compound selection process, the six objectives of the current compound bioactivity  and normalized using the normalization method in NSGA-331 (line 6). The value of the parameter p is calculated from the first non-dominated front . Then, in each iteration process, the differential evolution operator is used for crossover, and the polynomial mutation operator is used for mutation to generate offspring (line 4). The resulting population is then divided into non-dominated levels(or fronts) using a non-dominated sorting algorithm35 is used to replace the simulated binary crossover used in the original algorithm to generate the offspring.The differential evolution operatorTo objectively measure the performance of the relational mapping model, we use mean squared error to evaluate the model’s performance on the 36 and hypervolume (HV)37, are introduced here to evaluate the model search performance. The specific calculation method is as follows.Similarly, in order to objectively measure the performance of various multi-objective optimization algorithms, two indicators, inverted generational distance plus  Core(TM) i7-12700KF CPU and 32GB RAM. The experimental software platform is Pychame 2019, and the algorithm libraries used are mainly Sklearn and Pymoo.28. Crossover probability In this experiment, the number of clusters of spectral clustering used in feature selection is 25. A 10-fold cross-validation method was used to train and evaluate the relational mapping model. In solving the multi-objective optimization problem, the initial population size is 200, and the number of iterations is 210, which is consistent with Reference39, including three files, namely ERThe experimental data set was from the DrugBank molecular database of Alberta UniversityThe ERThe Molecula_Descriptor.xlsx file gives 729 molecular descriptors (i.e. independent variables) for 1974 compounds. The first column is also the SMILES formula for compounds(numbered in the same order as above), followed by a total of 729 columns, each representing a molecular descriptor of the compound (i.e. an independent variable). Molecular descriptors of compounds are a series of parameters used to describe the structure and properties of compounds, including physical and chemical properties , topological structure characteristics , etc.The ADMET.xlsx file provides data on the five ADMET properties of the above 1974 compounds. The first column is also the SMILES formula (the numbering order is the same as that before) representing the structure of the compound. The following five columns correspond to the ADMET properties of each compound, and the corresponding values are provided by the binary classification method. Caco-2: ’1’ represents the compound of intestinal epithelial cell permeability is good, ’0’ represents the compound of intestinal epithelial cell permeability is poor; cYP3A4: ’1’ means the compound can be metabolized by CYP3A4, ’0’ means the compound can not be metabolized by CYP3A4; hERG: ’1’ represents that the compound has cardiotoxicity, and ’0’ represents that the compound does not have cardiotoxicity; hOB: ’1’ indicates that the oral bioavailability of the compound is good, and ’0’ indicates that the oral bioavailability of the compound is poor; mN: ’1’ means the compound is genotoxic, ’0’ means the compound is not genotoxic.The feature selection algorithm proposed in this paper is used to run 10 times on the data set after data cleaning. The features with a frequency greater than 10 times are selected as the final candidate features, and 37 molecular descriptors are finally obtained. In order to more specifically reflect the specific meaning of the molecular descriptors selected, the classification of their pharmacological properties was counted here, and the results are shown in Table 40, the founder of molecular connectivity. It can simultaneously characterize the topological structure and electrical characteristics of compound molecules, and has been widely used in QSAR studies of drugs. Similar to the electrotopological state index, the extended topological chemical atom(ETA) index41 was proposed by Roy and Ghosh for the extension of the concept of topological chemical uniqueness of arrival(TAU) developed in the valence electron mobility (VEM) environment in the late 1980s. It is equally important in the study of toxicity and ecotoxicity modeling in the field of quantitative structure-activity relationships (QSARs). It can be seen from Table The electrotopological state index is a two-dimensional molecular descriptor based on the atomic level proposed by Kier and Hall21 algorithm. The results are shown in Table In order to measure the pros and cons of more specific feature selection algorithm. In this paper, the selected molecular descriptors are used to combine the SVM algorithm for entclass1pt{minimaIt can be seen from Table 27 is compared with a variety of traditional machine learning algorithms  for 10-fold cross validation and compared with the PsoBpSvm26 algorithm in the prediction of six optimization objectives. The results are shown in Table The CatBoost algorithmAs can be seen from Table 28 is adopted here. A variety of optimization algorithms  are used to run 10 times respectively. The calculated results are combined and de-duplicated as the approximate solution of the Pareto front of this problem. At the same time, in order to analyze the conflict relationship between the various objectives, this paper uses the parallel coordinate system42 used in28 to analyze the relationship between the various objectives. It should be noted that the parallel coordinate system is often used to visualize the values of each target to represent the correlation between the paired targets. The greater the slope of the line between one target and another, the greater the potential conflict between the two. The specific results are shown in Fig. Because in this problem, the Pareto front is unknown. Therefore, the treatment method of Reference43.It can be seen from Fig. To supplement the correlation analysis between objectives, we analyzed the correlation between objectives in the selected Pareto front approximation, as shown in Fig. It can be seen from Fig. Due to the different correlation between the objectives, the complexity of the Pareto surface shape is determined. Through the previous analysis, it can be seen that there are both conflict and mutual promotion relationships among the six optimization objectives. It can be seen that the Pareto surface corresponding to this problem is more complex. Considering the advantage of AGE-MOEA algorithm in exploring complex Pareto surface shape, this paper chooses AGE-MOEA algorithm as the benchmark algorithm and improves it. At the same time, in order to visually display the results of various algorithms, It can be seen from Fig. It can be seen from Table After the above solution, the optimal range of 37 important molecular descriptors selected out in the anti-breast cancer candidate drugs can be obtained, which provides a direction for the selection of compounds. The specific value range is shown in Table In the selection of the number of clusters, it is usually based on the elbow rule or the contour coefficient. However, in this experiment, it was found that the effect of subsequent experiments using the features selected by such methods was poor. The reason is that the differences between features are small. Therefore, in this experiment, we will select the number of clusters according to the prediction performance of the SVM model after 10 cross-validations. The specific experimental results are shown in Table It can be seen from Table In order to analyze the influence of the initial population size on the algorithm, the initial population size is discussed here. The specific results are shown in Table It can be seen from Table Aiming at the selection of anti-breast cancer candidate drugs, this paper proposes a complete drug selection framework from three aspects: feature selection, relationship mapping and multi-objective optimization. In the feature selection part, the relationship between features is explored from multiple perspectives of correlation coefficient, cosine similarity and gray correlation coefficient. Spectral clustering is used for feature clustering and a new feature importance measurement method is proposed to select the final intra-cluster features. In the relationship mapping part, a variety of machine learning algorithms are used for comparative experiments. Finally, the CatBoost algorithm is selected for relationship modeling, which achieves better prediction performance. In the multi-objective optimization part, based on the analysis of the conflict relationship between the objectives, the AGE-MOEA algorithm is improved. The improved algorithm is used to solve this problem, which achieves better search performance than many algorithms. We hope that the anti-breast cancer drug selection framework proposed in this paper can provide guidance for actual drug selection.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.


This study presents a novel cognitive system for assistive robots that rely on artificial intelligence (AI) representation and reasoning features/services to support decision-making processes of healthcare assistants. We proposed an original integration of AI-based features, that is, knowledge representation and reasoning and automated planning to 1) define a human-in-the-loop continuous assistance procedure that helps clinicians in evaluating and managing patients and; 2) to dynamically adapt robot behaviors to the specific needs and interaction abilities of patients. The system is deployed in a realistic assistive scenario to demonstrate its feasibility to support a clinician taking care of several patients with different conditions and needs.By 2030, the World Health Organization (WHO) foresees a worldwide workforce shortfall of healthcare professionals, with dramatic consequences for patients, economies, and communities. Research in assistive robotics has experienced an increasing attention during the last decade demonstrating its utility in the realization of intelligent robotic solutions for healthcare and social assistance, also to compensate for such workforce shortages. Nevertheless, a challenge for effective assistive robots is dealing with a high variety of situations and  The devd people . A key cd people  to realisistance  and thusceptance .personalization and adaptability features to support patients characterized by heterogeneous health-related needs. Taking inspiration from cognitive architecture research (knowledge representation and reasoning and automated planning to 1) define a human-in-the-loop process for continuous evaluation and treatment of patients and; 2) to dynamically adapt robot behaviors to the specific needs and interaction abilities of patients.This study presents a cognitive system for assistive robots that rely on ontology-based representation and reasoning capabilities to support healthcare professionals and elderly users during assessment and therapy administration. More specifically, the presented approach pursues a human-in-the-loop methodology that leverages a “robot-based” user profiling and artificial intelligence (AI) representation and reasoning features/services to support decision-making processes of healthcare assistants. The objective is, on the one hand, to support healthcare professionals during patient assessment and therapy administration and, on the other hand, to provide assistive robots with research , we propmutual assistance between the clinician and the robot through a “mixed-initiative” work flow. The role of the clinician is essential to refine and validate decisions made by the robot. In turn, the robot supports the clinician in the screening and monitoring of patients as well as the administration of a therapy. In this regard, the main contribution of the work concerns the correlation between standard screening practices used by therapists with the internal user model used by the robot. This correlation allows a robot to correctly interpret health-related data about patients provided by therapists. In particular, it enables the transfer of knowledge from clinicians to robots and is thus crucial to synthesizing effective and personalized assistive behaviors.The system is deployed on a social assistive robot and validated in a realistic scenario. We showed how an assistive robot endowed with cognitive control features is able to autonomously contextualize its behavior and effectively support both patients and clinicians in the synthesis of personalized cognitive interventions. A key point stands in the User modeling capabilities of the robot rely on an ontological reification of the International Classification of Functioning, Disability and HealthA profiling procedure is performed through a robotic platform during the administration of the Mini-Mental State Examination (MMSE) to patients with suspected cognitive decline. As shown in user-centered design and realize effective assistive technologies  in different situations. Related works usually do not integrate online reasoning mechanisms that allow assistive robots to autonomously reason about the specific needs of a user and autonomously  decide the kind of intervention that best fit such needs. Conversely, our approach pursues a highly flexible solution implementing the cognitive capabilities needed to understand health conditions of users and (autonomously) personalize assistance accordingly, under the supervision of a human expert.Nevertheless, the aforementioned works present a “rigid” and static representation as they usually do not rely on a well-structured ontological formalism to characterize proposing a set of tests suitable for the specific needs of a patient, 2) administering the chosen tests, and 3) monitoring (and reasoning over) the performance of the patient. In this way, the employment of assistive robots can alleviate clinicians in some of their activities and, thus, support them in dealing with a larger number of patients. In addition, a robot can continuously and proactively stimulate patients by administrating suitable exercises and generally motivating the participation and the adherence to the therapy.We aimed at leveraging the interaction capabilities of socially assistive robots to support clinicians in assessing and monitoring the cognitive state of patients. We envisage a multi-actor HRI approach in which an assistive robot can facilitate the interactions. In particular, we proposed a continuous assessment and monitoring procedure in which a robot supports a clinician by: 1) intervention plan to follow. The general structure is depicted in We envisage a novel cognitive intervention program where a robot constantly supports clinicians in evaluating/monitoring the cognitive state of a patient and in making decisions about the It is worth noticing that the clinician is constantly involved in the decisional process and maintains control over the decisions made by the robot, validating them. Each cycle consists of a number of human–robot interaction steps aiming at 1) profiling the health state of a person (steps 1.1 and 1.2), 2) defining an intervention plan suitable for the specific health needs of a patient (steps 2.1 and 2.2) and, 3) executing the plan by administrating exercises within a certain temporal horizon  and evaluating outcomes (steps 3.1 and 3.2).evolving cognitive state of a patient with the support of a robot. Two “feedback chains” are considered as shown in The cyclic repetition of these phases allows a clinician to continuously monitor and assess the cognitive capabilities in order to contextualize assistive behaviors and effectively support both patients and clinicians. Taking inspiration from cognitive architectures  controllers capable of (autonomously) personalize and adapt robot behaviors to the specific features of different application scenarios, for example, service robots , defined by the World Health Organization (WHO)  . User prThere are several factors that can be considered when modeling users. Different choices would support different robot behaviors and different levels/types of adaptation. Broadly speaking, the creation of a complete and effective user model is crucial to realize human–robot interactions characterized by adaptability, trust building, effective communication, and explainability . In the knowledge about users  in different situations. Such works usually do not integrate online reasoning mechanisms to allow assistive robots to autonomously reason about the specific needs of a user and autonomously  decide the kind of intervention that best fit such needs. Conversely, our approach pursues a highly flexible solution implementing the cognitive capabilities needed to understand health conditions of users and (autonomously) personalize assistance accordingly, under the supervision of a human expert.Concerning our contribution, other works have used the ICF framework to characterize cognitive and physical conditions of users. The work  introducno impairment, 2) the value 1 denotes soft impairment, 3) the value 2 denotes medium impairment, 4) the value 3 denotes serious impairment, and 5) the value 4 denotes full impairment.The ICF classification aimed at organizing and documenting information on functioning and disability. It pursues the interpretation of functioning as a dynamic interaction among health conditions of a person, environmental factors, and personal factors. Each defined concept characterizes a specific aspect concerning the physical or cognitive functioning of a person. The level of functioning of each physical/cognitive aspect is represented by the following scale: 1) the value 0 denotes functioning and disabilities while the other part deals with contextual factors. The former is further organized into the components body functions and body structures that are the ones considered in the design of the ontological model. The body is an integral part of human functioning and the bio-psychosocial model considers it in interaction with other components. Body functions are thus the physiological aspects of body systems, while structures are the anatomical support . Several ICF concepts describe the functioning of mental faculties and have been used to define user profiles. The concept OrientationFunctioning characterizes the functioning of general mental functions of known and ascertaining one’s relation to time, to place, to self, objects, and space. The concept AttentionFunctioning characterizes specific mental functions focusing on external stimulus or internal experience for the required period of time. The concept MemoryFunctioning characterizes specific mental functions of encoding, storing information, and retrieving it as needed.The ICF classification is organized into two parts. A part deals with interaction capabilities of a person and thus identify interaction preferences determining the way a robot should interact with a person while administrating exercises. The concept SeeingFunctioning models specific functions related to seeing the presence of light and sensing the form, the size, shape, and color of visual stimuli. The concept HearingFunctioning models sensory functions related to sensing the presence of sounds and discriminating the location, pitch, loudness, and quality of sounds.Other ICF concepts have been instead used to characterize the what kind of cognitive exercise a person needs) and the way such actions should be executed .Information gathered during the profiling phase and its representation based on ICF allow an assistive robot to autonomously reason about the intervention plan that “best fit” the specific needs of a person . Knowledge-reasoning processes analyze such measurements  to autonomously infer the physical or cognitive impairments characterizing the functioning state of a person. Following ICF classification, the ontological model defines a number of concepts that represent different properties of intervention plans. A robot can indeed be endowed with a number of “programs” implementing known tests suitable to evaluate/stimulate different functioning qualities, for example, the Free and Cued Selective Reminding Test for episodic long-term memory assessment or the Trailing Making Test form A for selective attention assessment.In addition to this knowledge, the ontology characterizes effects on the functioning qualities of a person. The defined semantics characterizes these “programs” according to the functioning qualities they address. For example, an interactive program implementing the Free and Cued Selective Reminding Test is classified as an intervention action whose effects can “improve”  the functioning quality MemoryFunctioning. The obtained ontological model fosters an integrated representation of knowledge about the health state of a person and intervention capabilities of a robot. Knowledge processing mechanisms then use this integrated knowledge to infer a set of actions suited to address the inferred impairments of a patient  and AttentionFunctioning as no impairment  only actions implementing the Free and Cued Selective Reminding Test (or other similar tests) are inferred as suitable to a patient.Taking inspiration from some works in manufacturing that define the concept of function , we char patient . For exaProfile encapsulates a rich set of information that can be analyzed to infer interaction capabilities of a person and define robot interaction preferences accordingly. If the analysis of a profile infers a medium impairment of the quality HearingFunctioning then, the interactions between the patient and the robot should rely mainly on visual and textual messages rather than voice and audio. In case that audio interactions cannot be avoided  it would be possible to properly set the sound level of the robot in order to help the assisted person as much as possible.A how intervention plans should be carried out by the robot in order to effectively interact with the considered patient. In this regard, we have defined four interaction parameters characterizing the execution of robot actions: 1) sound level, 2) subtitle, 3) font size, and 4) explanation. The sound level is an enumeration parameter with values \{ none, regular, high\} specifying the volume of audio communications and messages from the robot to the patient. Patients with soft or medium hearing impairment represented as HearingFunctioning would need a high sound level, while audio would be completely excluded for persons with serious impairments in order to use different interaction modalities. The subtitle is an enumeration parameter with values \{ none, yes, no\} specifying the need of supporting audio messages through text. Patients with no, soft, or medium impairment of SeeingFunctioning and medium or serious impairment of HearingFunctioning would need subtitles to better understand instructions and messages from the robot.Knowledge-reasoning mechanisms thus infer also font size is a binary parameter with values \{ regular, large\} specifying the size of the font of text messages and subtitles, if used. Patients with medium impairment of SeeingFunctioning would need large fonts in text messages in order to better read their content. Finally, explanation is a binary parameter  specifying the need of explaining an exercise to a patient before its execution. Such instructions would be particularly needed for patients with impaired MemoryFunctioning or OrientationFunctioning. Clearly, the way such explanations are carried out complies with the interaction parameters described earlier.The To demonstrate the feasibility of our cycle-based approach, we considered ASTRO, an assistive robot equipped with several sensors  and two tablets  see Fig. We deplwho you are), 2) synthesize personalized intervention plans (what you need) considering a set of 10 cognitive tests typically used to further investigate and evaluate the cognitive state of a person, and 3) select the appropriate interaction modalities of the robot (how you like it) among the ones supported by the robotic platform. An off-line analysis and discussion of the results with the clinicians are reported at the end of the section to emphasize the importance of the human-in-the-loop approach  represent user profiles with respect to ICF  that are suitable for the considered user and then decide personalized intervention plan .As shown in The user profile is generated according to the scoring obtained through the administration of the Mini-Mental State Examination (MMSE). The MMSE represents the most used screening test for cognitive status, and it is adopted worldwide by clinicians to briefly assess persons with the suspect of dementia. It encompasses 21 items which cover tests of orientation, recall, registration, naming, comprehension, calculation and attention, writing, repetition, drawing, and reading . The cogMemoryFunctioning is defined as specific mental functions of registering and sorting information and retrieving it as needed. This ICF function can be described by the MMSE items which cover the recalling, counting, and spelling tests. Based on the same similarity approach, the overall mapping shown in no impairment). If the patient partially accomplished the task, an intermediate value of the ICF score is assigned (mild impairment). If the patient did not accomplish the task, a higher value of ICF score is attributed (hard impairment).The caregiver stores the results of the assessment into the robot knowledge base through the same dedicated technical interface. The overall score of the MMSE is automatically processed by the robot at the end of the session, by parsing the annotated answers. The robot automatically correlates MMSE items with (relevant) ICF functions the robot uses to represent the cognitive state of a patient. According to this correlation, the robot then builds a user profile by mapping received MMSE scores to ICF scores. The video and audio of the administration sessions were recorded by the robot’s frontal camera. The recorded videos were off-line analyzed by the clinician to extract the quality of interaction  with the robot and additional evidence of the cognitive decline  . Table 1inferred impaired functioning qualities of a patient. The assistive capabilities of such actions depend on the features of the associated cognitive tests and thus on the functioning qualities stimulated by them. According to the inferred impairments and the recommendations generated by the developed knowledge processing mechanisms, a number of these tests are selected for administration.The subsequent considered actions consist in the selection of a number of cognitive tests to administer, regarding the Free and Cued Selective Reminding Test, the Rey’s Figure Test, the Forward Digit Test, and the Backward Digit Span Test evaluate and stimulate the functioning quality MemoryFunctioning. The Trailing Making Test form A and the Trailing Making Test form B evaluate and stimulate the functioning quality AttentionFunctioning. The Stroop Test evaluates and stimulates the functioning quality OrientationFunctioning. The Boston Naming Test 40-item, the Animals Test, and the Denomination Test evaluate and stimulate the functioning quality LanguageFunctioning.We have considered a total number of 10 cognitive tests that are typically used to further investigate and evaluate the cognitive state of a person. The personalization. ranking values of known cognitive tests  for each user profile. The value expresses the significance of a specific test/action considering the cognitive impairments inferred for a particular user profile. The higher the computed ranking value the more is the relevance of a particular action for the corresponding user. The experiments have been performed with the objective to demonstrate the capability of combining this knowledge with the ICF scores of profile 8 whose cognitive state is characterized by several impairments as can be seen from the ICF scores resulting from the outcome of MMSE in Forward Digit Test addressing MemoryFunctioning . This reasoning step relies on a number of inference rules that link ICF scores to the interaction preferences introduced in PerceptualFunction (PER) and MemoryFunctioning (MEM). MEM is linked to the interaction preference explanation as described in sound level, font size, and subtitles. As shown in measure the functioning qualities HearingFunctioning and SeeingFuncitoning and infer the related interaction preferences as described in Once a personalized set of interventions has been defined, user profiles are further evaluated to decide how such actions should be performed . Such cognitive impairments represent a crucial risk factor for the transition to mild cognitive impairment to a full-blown dementia syndrome. In addition, the subject number 8, which showed a more severe impairment related to languages, was suggested to undergo also a denomination test and the Boston Naming Test, both tests for language domain. Such suggestions were not mentioned in the other two subjects. Moreover, the memory function, tested by the Free and Cued Selective Reminding Test, was suggested to be studied in almost all the subjects except for the not impaired subject numbers 3 and 5.Interestingly, the system reports less need for further test administration for subject numbers 4 and 7, respectively, the two subjects with worst cognitive performances. This is an expected performance as the implemented knowledge processing mechanisms assign ranks to intervention actions in a “non-linear way.” In particular, lower ranks are assigned to tests addressing too compromised functions as they are supposed to be managed separately. That may represent a counter-intuitive data, but a common clinic routine. Too much compromised subjects’ condition makes the clinical picture already clear and further analysis fruitless. Therefore, clinical practice is a balance between the need to accomplish an explicit vision of the case, and the economy of time and resources. On the other hand, regarding the subjects with a non-impaired neuro-cognitive profile , the system proposed fewer tests as informative. In conclusion, such results are aligned with standard clinical practice, thus the assistive robot could represent a useful tool for assessment process refinement.knowledge representation and reasoning and decision making and problem solving, two crucial capabilities to achieve personalization and adaptation of assistive behaviors. A human-in-the-loop approach is pursued to define a process in which a clinician is involved into the decisional process and an interleave of cognitive state evaluation and test administration allows her to maintain the control over the decisions made by a robot and its resulting assistive behaviors. The approach was demonstrated to be feasible and effective in a realistic scenario with eight participants. A clinician supervised the procedure evaluating the robot’s behavior.This study presents an original cyclic procedure to support healthcare assistance with robots endowed with a novel integration of AI-based technologies supporting decision making module can suggest/schedule an appropriate personalized care plan. This finding can suggest that the proposed ontologies based on ICF score can be generalized to be applied to social robots to improve and personalize the human–robot interaction as well as to provide a care plan to the caregiver.This study presented a first concrete result of a research initiative whose long-term goal is to foster the development of intelligent assistive robots capable of supporting healthcare professionals in dealing with larger number of patients. Indeed, despite the small sample size, the results suggest how the robot’s interaction parameters can be fine-tuned to the residual abilities and the cognitive profile of the person who it is interacting with. In this sense, a better understanding of patients’ social, cognitive, and biological aspects will allow assistive robots to represent such information into their cognitive system, and use it to autonomously take more initiative to support both clinicians and patients. According to the feedback obtained during the off-line discussion with the clinicians, the The future work plan aims at addressing two main aspects of overcoming current limitations. First, from a technical perspective, it aims to investigate user assessment and profile-building functions  to better identify user needs and to extend the set of assistive services supported by the cognitive architecture to enlarge the application opportunities. Second, from the user perspective, future work should consider larger involvement of participants’ cohort so as to perform a systematic evaluation to assess its concrete effectiveness, usability, and acceptance in real contexts.