Recently, advancements in computational machinery have facilitated the integration of artificial intelligence (AI) to almost every field and industry. This fast-paced development in AI and sensing technologies have stirred an evolution in the realm of robotics. Concurrently, augmented reality (AR) applications are providing solutions to a myriad of robotics applications, such as demystifying robot motion intent and supporting intuitive control and feedback. In this paper, research papers combining the potentials of AI and AR in robotics over the last decade are presented and systematically reviewed. Four sources for data collection were utilized: Google Scholar, Scopus database, the International Conference on Robotics and Automation 2020 proceedings, and the references and citations of all identified papers. A total of 29 papers were analyzed from two perspectives: a theme-based perspective showcasing the relation between AR and AI, and an application-based analysis highlighting how the robotics application was affected. These two sections are further categorized based on the type of robotics platform and the type of robotics application, respectively. We analyze the work done and highlight some of the prevailing limitations hindering the field. Results also explain how AR and AI can be combined to solve the model-mismatch paradigm by creating a closed feedback loop between the user and the robot. This forms a solid base for increasing the efficiency of the robotic application and enhancing the user’s situational awareness, safety, and acceptance of AI robots. Our findings affirm the promising future for robust integration of AR and AI in numerous robotic applications. Artificial intelligence (AI) is the science of empowering machines with human-like intelligence . It is aSpecifically, in the field of robotics, AI is optimizing a robot’s autonomy in planning tasks and interacting with the world. The AI robot offers a greater advantage over the conventional robot that can only apply pre-defined reflex actions . AI roboThe field of robotics has also benefited from the rising technology of augmented reality (AR). AR expands a user’s physical world by augmenting his/her view with digital information . AR devi1) What is the current state of the field on research incorporating both AR and AI in Robotics?2) What are the various elements and disciplines of AR and AI used and how are they intertwined?3) What are some of the current applications that have benefited from the inclusion of AR and AI? And how were these applications affected?What has preceded affirms that the benefits of combining AI and AR in robotics are manifold, and special attention should be given to such efforts. There are several review papers highlighting the integration of augmented reality to robotics from different perspectives such as human-robot interaction , industrResults, and research question 3 is answered in Discussion.To the best of our knowledge, this is the first literature review combining AR and AI in robotics where papers are systematically collected, reviewed, and analyzed. A categorical analysis is presented, where papers are classified based on which technology supports the other, i.e., AR supporting AI or vice versa, all under the hood of robotics. We also classify papers into their perspective robotic applications (for example grasping) and explain how this application was improved. Research questions 1 and 2 are answered in The remainder of the paper is organized according to the following sections: Methods, which specifies the survey methodology adopted as well as inclusion and exclusion criteria, Results, which presents descriptive statistics and analysis on the total number of selected papers in this review (29 papers), Discussion, which presents an analysis on each paper from different perspectives, and finally Concluding Remarks, which highlights key findings and proposes future research.This paper follows a systematic approach in collecting literature. We adopt the systematic approach set forth in Introduction. This section outlines the used keywords (step 3) and the used databases (step 4).Steps 1 and 2 were explicitly identified in the Regarding keywords, this review targets papers that combine augmented reality with artificial intelligence in robotics. The first source used was Google Scholar denoted by G. Initially, we excluded the words surgery and education  to narrow down the total number of output papers. Concurrently, there are several papers reviewing AI Robots in surgical applications  and AI ith of June and ended in July 21st 2020. G5 search key was explored between August 11th and August 20th, 2020 and finally, ICRA 2020 proceedings were explored between August 20th and August 31st 2020.The time range of this review includes papers spanning the years between 2010 and 2020. Note that the process of paper collection for search keys G1, G2, G3, G4, S1, and S2 started on the 30The selection process was as follows: First duplicates, patents, and non-English papers were excluded. Then, some papers were directly excluded by scanning their titles, while others were further evaluated by looking into their abstract and keywords and downloading those that are relevant. Downloaded papers are then scanned through quickly going over their headers, sub-headers, figures, and conclusions. Starting from a total of 1,200, 329, and 1,483 papers from Google Scholar, Scopus database, and ICRA proceedings respectively, the total number of selected papers were funneled down to 13, 8, and 3 papers, respectively. After that, we looked into the references and citations of these 24 papers and selected a total of five papers. The inclusion and exclusion criteria were as follows:• Papers with a non-English content• Duplicate papers• Patents• The application should directly involve a robot• Artificial Intelligence is involved in the Robotics Application. Although the words artificial intelligence and machine learning are used interchangeably in this paper, most of the cited work is more accurately a machine learning application. Artificial intelligence remains the broader concept of machines acting with intelligence and thinking as humans, with machine learning being the subset of algorithms mainly concerned with developing models based on data in order to identify patterns and make decisions.• An Augmented Reality technology is utilized in the paper.The process flow is also illustrated in A total of 29 papers were selected and individually examined through checking their abstracts, conclusions, and analyzing their main content. This section presents how the collected literature was classified into categories and presents some descriptive statistics visualized through figures and tables.There are two parallel categorizations in this paper: a theme-based categorization and an application-based categorization. Initially, all papers were grouped into two clusters based on a theme-based grouping of how AR and AI serve each other in a certain robotics application. The two distinguished clusters were as follows: AR supports AI, and AI supports AR. Each of these clusters is further explained below along with the total number of papers per group.This cluster groups papers in which a certain augmented reality visualization facilitates the integration of artificial intelligence in robotics. An example is an augmented reality application which provides visual feedback that aids in AI robot performance testing.Papers in which the output of AI algorithms and neural networks support an accurate display of augmented reality markers and visualizations.Another remarkable pattern was noted among the 29 papers in terms of the specific robotics application that this AR-AI alliance is serving. In consequence, a parallel categorization of the 29 reviewed articles is realized, and three clusters were distinguished as follows:A robot learns to achieve a certain task, and the task is visualized to the human using AR. This category combines papers on learning from demonstration (LFD) and learning to augment human performance.A robot intelligently plans a certain path, task, or grasp, and the user can visualize robot information and feedback through AR.A robot depends on AI vision algorithms to localize itself or uses object detection and recognition to perceive the environment. AR serves here in identifying the robot’s intent.For the sake of analyzing historical and graphical aspects of the reviewed topic, Additional quantitative data are detailed in Another set of distinctive features were extracted through analyzing three attributes, mainly the type of robot platform used, the type of AR technology employed, and the nature of the AI method performed, for each of the three robotics applications. The results are depicted in Regarding the type of AR hardware/technology used, it was noted that the HMD was the most commonly used for all robotics applications covered, followed by the tablet or the desktop-based monitor. Spatial AR, or projection-based AR, was the least commonly used given its rigidness in terms of mobility and setup. As for the used AI, there was a variety of methods used, including regression, support vector machine (SVM), and Q-learning. However, neural networks, including YOLO and SSD deep neural networks, were the more commonly used across the three robotics applications. Neural networks were utilized in 42, 25, and 80% of the reviewed papers in the learning, planning, and perception categories, respectively.Augmented reality technology has created a new paradigm for human-robot interaction. Through enabling a human-friendly visualization of how a robot is perceiving its environment, an improved human-in-the-loop model can be achieved . The useResults, a theme-based analysis capturing the relation between AR and AI in a robotic context  and an application-based analysis focusing on the perspective of how the robotic application itself was improved. We have also compiled a qualitative table  position trajectory. A Kinect camera captures the user’s motion, and an AR HMD visualizes the desired trajectory versus the demonstrated trajectory, which allows the operator to estimate the error  and correct accordingly. This approach was tested by a single operator and showed a 20% decrease in the tracking errors of demonstrations compared to manual tracking.The authors in AR solutions were investigated in A HoloLens HMD was also used in A semi-automatic object labeling method was developed in The authors in A spatial programming by demonstration (PBD) called GhostAR was developed in The authors in An AR assistive-grasping system was implemented in The authors in A teleoperation system was developed in The authors in Overall, the advantages of augmented reality in facilitating the integration of AI to robotics applications are manifold. AR technologies can provide a user-friendly and intuitive medium to visualize the learning process and provide the live learned state of the robot. They also provide a medium for the robot to share its present and future intent, such as the robot perceived knowledge and the robot’s planned actions based on its AI algorithms. Although the AR HMDs - such as those provided by Microsoft HoloLens and Oculus Rift - are the most commonly used for an intuitive HRI, they still have their limitations such as their narrow field of view (FOV) and impractical weight. Other AR interfaces used included mobile phones, tablets, and desktop displays. The latter is more practical in simulations, otherwise, the user will need to split attention between the actual robot and the augmented display. Tablets and mobile phones are generally more intuitive but impractical in situations where the user has to use both hands. Spatial AR, also known as projection-based AR, is less used due to its mobility restrictions.In this cluster, AI contributes to an accurate and more reliable augmented reality application, or interface, such as applying deep learning for detecting obstacles in the robot’s path. Papers are also grouped depending on the type of robotic platform used.The authors in In The authors in Alternatively, The authors in An AR teleoperation interface was implemented in The authors in . used a A teleoperation surveillance system was proposed in As implied in literature, artificial intelligence techniques are a great means for a robust visualization and an improved user experience. Traditional techniques to augment information on objects or targets are mainly using fiducial AR markers, which are impractical in cases of new environments such as in urban search and rescue (USAR) scenarios. On one hand deep learning can improve robot perception of its environment to detect objects and properly augment related information on each. On the other hand, it can be used to localize the robot itself and reveal information during its live performance. A key consideration for these systems is the processing requirements versus the current capabilities of the hardware.This section focuses on the areas in which AR and AI were applied. In other words, we explain here how the challenges of a certain robotics application - such as learning from demonstration and robot localization - were addressed through leveraging resources from augmented reality and artificial intelligence. We divide this into three main headings: Learning (12 papers), Planning (8 papers), and Perception (9 papers). In general terms, a robot is said to learn from its environment or from the human if it can develop novel skills from past experience and adapt according to the situation at hand. According to the collected literature, we divide the scope of learning here to two basic paradigms: Learning from demonstration and Learning to augment human performance.Robot learning from demonstration (LFD) is described as the ability of a robot to learn a policy – identified as a mapping between the robot world state and the needed actions – through utilizing the dataset of user demonstrated behavior . This daIn In In A tablet was used in Machine Learning opens a great avenue for improving the quality and efficiency of tasks performed by humans, such as maintenance and troubleshooting, multitasking work, or even teleoperation. AI would be used for understanding data and providing suggestions that would augment (improve) human performance of the task at hand. In the following analysis, we analyze content within this perspective, focusing on how the application was improved.Multitasking is improved in In An “Autocomplete” framework was proposed in In this section, two learning paradigms were discussed, robot learning from demonstration (LFD) and robot learning to augment human performance. The presented literature affirms that AR and AI will be extensively integrated in these two robotics applications in the near future. In the former, AR serves as a user-friendly training interphase and has a great potential for swarm mobile robotics, as multiple users can more easily train a multi-robot system. In the context of manipulators and robotic arms, visualizing demonstrations in real time allows the user to understand trajectories, correct for errors, and introduce new constraints to the system. In the latter, there is a potentially growing avenue to employ AI in robotic applications that understand user instructions (of the task at hand) and employ AR to visualize what the robot understands and interactively ask for feedback from the user. This has a great potential in complex applications where multiple factors concurrently affect the process, such as in the cases of teleoperating unmanned aerial vehicles (UAVs) or controlling mobile robots in dynamic environments like in the case of USAR.This cluster groups papers in which AI is integrated to improve task planning, path planning, and grasping.In Optimal path planning through reinforcement learning was done in A deep AR grasp planning system was proposed in The authors in Through using grasp quality measurements in Based on the literature presented, we foresee several opportunities for the utilization of AR and AI in future planning and manipulation tasks. This can result in a paradigm shift in collaborative human-in-the-loop frameworks, where AI can add the needed system complexities and AR can bridge the gap for the user to understand these complexities. For example, the challenges of assistive robotic manipulators  to peoplThis cluster groups papers in which AI is integrated for robot and environment perception through object detection or localization.In In In To reduce the time spent on restricting the workspace of mobile co-robots, authors in The DBSCAN algorithm was used in linear binary model which classifies the tracked points of the RGB color into either blue or green, based on a logistic regression learning of the blue and green color features applied during calibration.In order to localize the robot and properly augment the information on each robot in a multi-robot system, authors in NDDS plugin tool. Both networks, upon receiving a live video stream from the HoloLens, predicted accurate 3D pose of the robot, with the SSPE being 66% faster. Estimating the pose based on depth sensor data was investigated in The authors in In this section, almost all the literature is an integration of AI to improve the AR experience, whether in innovating robust calibration methods or improving the tracking and object detection capabilities of AR systems. This provides an insight of what is done and what can be done to achieve a smooth integration of augmented reality applications. These methods are still limited in terms of robustness to ambient conditions like lighting, and the problem of increased computational time is still impractical for some applications. However, this can be mitigated in the future as hardware power is constantly improving and cloud computing is becoming ubiquitous.As robots become ubiquitous, there are ethical considerations ranging from liability to privacy. The notion of a robot’s ability to do ethical decision making was first framed in This paper provided a systematic review of literature on robotics which have employed artificial intelligence (AI) algorithms and augmented reality (AR) technology. A total of 29 papers were selected and analyzed within two perspectives: A theme-based analysis featuring the relation between AR and AI, and an application-based analysis focusing on how this relation has affected the robotics application. In each group, the 29 papers were further clustered based on the type of robotics platform and the type of robotics application, respectively. The major insights that can be drawn from this review are summarized below.Augmented reality is a promising tool to facilitate the integration of AI to numerous robotics application. To counter the effect of increased complexity in understanding AI systems, AR offers an intuitive way of visualizing the robot internal state and its live training process. This is done through augmenting live information to the user via an HMD, a desktop-based GUI, a mobile phone, or a spatial projection system. This proved to improve several applications, such as learning by demonstration tasks, grasping, and planning. Learning from demonstration for robot manipulators is a field that has greatly benefited from the integration of AR and AI for an intuitive and user-friendly method of teaching, as done in To improve the AR experience, accurate and reliable calibration and object localization methods are needed. As can be seen from the literature, artificial intelligence is a viable element supporting this notion for robotics applications. AR markers are widely used but are limited in dynamic environments and in cases of occlusions. Deep neural networks for object detection and robot localization seem the most promising for unstructured robotic environments , althougThe potentials of integrating these two elements in robotics applications are manifold and provide a means of deciphering the traditional human-robot mismatch model. Specifically, in the context of human-robot collaboration, AI can be used to understand the real user intent filtered from the perceived tasks the robot traditionally performs as in the work of This study confirms the many benefits of integrating AR and AI in robotics and reveals that the field is fertile and expects a striking surge in scholarly work. This result aligns with the current trends of incorporating more AI in the field of robotics . After tDespite the well-developed resources, some limitations need to be addressed for powerful implementation of AR and AI in robotics. For example, AR devices are still hardware-limited, and some do not support advanced graphical processing, which challenges the implementation of computationally intensive AI algorithms on AR devices in real-time. Current methods rely on external remote servers for heavy computations, which might be impractical in some cases. Furthermore, vision-based approaches to track objects using AR markers are prone to errors and performance drops largely when occlusions happen or under challenging lighting conditions. Further improvements in AR hardware are needed to improve processing, battery life, and weight; all are elements needed for AR use for an extended period of time.Future work can apply new out-of-the-box AItbox1 techniques to improve the AR experience with tracking methods robust in dynamic situations. Additional work is needed in AI to better understand human preferences in “how,” “when,” and “what” AR visual displays are shown to the user while debugging or performing a collaborative task with a robot. This can be framed when a robot can fully understand the “user intent” and show the user only relevant information through an intuitive AR interface. Similarly, AR holds potentials for integrating AI in complex robotics applications, such as grasping tasks in highly cluttered environments, detecting targets and localizing robots in dynamic environments and urban search and rescue, and teleoperating UAVs applying intelligent navigation and path planning. The future will have AI and AR in robotics ubiquitous and robust, just like networking, a given in a robotic system.Methods, there is a possible incomplete documentation of research papers that do not contain a specified keyword, rather contain another synonym or an implied meaning in text.The major limitation of this systematic review is the potential underrepresentation of some papers combining AR, AI, and robotics. Given the choice of search terms identified in


Home rehabilitation is a growing rehabilitation service in many countries, but scientific knowledge of its components and outcomes is still limited. The aim of this study was to investigate; 1) which changes in functioning and self-rated health could be identified in relation to a home rehabilitation program in a population of community-dwelling citizens, and 2) how socio-demographic factors, health conditions and home rehabilitation interventions were associated to change in functioning and self-rated health after the home rehabilitation program.The sample consisted of participants in a municipal home rehabilitation project in Sweden and consisted of 165 community-dwelling citizens. General Linear Models (ANOVA repeated measures) was used for identifying changes in rehabilitation outcomes. Logistic regressions analysis was used to investigate associations between rehabilitation outcomes and potential factors associated to outcome.Overall improvements in functioning and self-rated health were found after the home rehabilitation program. Higher frequencies of training sessions with occupational therapists, length of home rehabilitation, and orthopaedic conditions of upper extremities and spine as the main health condition, were associated with rehabilitation outcomes.The result indicates that the duration of home rehabilitation interventions and intensity of occupational therapy, as well as the main medical condition may have an impact on the outcomes of home rehabilitation and needs to be considered when planning such programs. However, more research is needed to guide practice and policymaking. The increased proportion of older adults in the population is expected to have an impact on healthcare systems worldwide , 2. ThisTo guide home rehabilitation programs, research is needed on how different factors such as gender, types and length of intervention, and health conditions affect the outcome . TherefoThe present study analyses already collected data derived from a home rehabilitation improvement project in a Swedish municipality with approximately 140,000 citizens. The project was initiated by the municipality to develop their rehabilitation program and was conducted without a control group. The project involved team-based rehabilitation executed by occupational therapists, physiotherapists and rehabilitation assistants in the person’s home and neighbourhood. An overview of the home rehabilitation interventions is presented in Fig. Community-dwelling adults that were assessed to need rehabilitation and lived in a selected geographical area  were eligible for the municipal home rehabilitation program. Between 2015 and 2019, a total 212 adults participated in the program from different ages and with a range of health conditions. The participants gave informed consent to participate in the municipality project and each received up to 12 weeks of home rehabilitation.For the purpose of this present study, access to de-identified data was granted by the municipality in line with Swedish laws and regulations . The SweDuring the rehabilitation program, data were collected by the occupational therapists and physiotherapists involved. For this study, the municipality provided the collected and de-identified data from three points of time; baseline (T1), at the end of home rehabilitation intervention T2) and at 2-month follow-up (T3). A total of 165 participants with data from T1 and T2 were available for analysis for this study  examines the person’s functioning in daily activities  and was feeding, indoor mobility, toilet management, transfer, dressing/undressing, grooming, cooking, bath/shower, housework, and outdoor mobility. The variables continence and communication were considered less relevant as outcomes of home rehabilitation interventions and were not included in our ADL-index. The score for each included item was dichotomized as 0 = independent (score 3–2) vs. 1 = dependent (score 1–0). The dichotomized scores were summarized, thus giving an ADL index value from 0 to 10. In addition, the ADL index differences between T1 and T3 was calculated and dichotomized to create a variable where 0 = no change/deterioration and 1 = improvement over time. Cronbach’s alpha analysis demonstrated good inter-item reliability of the ADL-index used in this study (0.85).For this study, an ADL index was created including the items The General Motor Function assessment scale (GMF) includes 11 mobility functions and 10 upper limb functions  and threturning over in bed to climbing stairs and transfer outdoors. Each item was rated 0 = independent (score 0) or 1 = dependent (score 1–2) . The second index, GMF dependence upper limb, included the 10 variables for arm movements and grip functions. Items were rated as 0 = independent (score 0) or 1 = dependent (score 1–2) . Even here, the difference between T1-T3 was calculated and dichotomized to create a variable in terms of 0 = no change/deterioration vs. 1 = improvement over time.For this study, two GMF dependence indexes were created. The first index, GMF dependence mobility, included the 11 mobility functions ranging from EQ VAS, which is included in the European Quality of Life Five Dimension Five Level Scale (EQ-5D-5L) [EQ VAS in particular, participants assess their health status on the present day using a vertical visual analogue scale ranging from 0 “the worst imaginable health” to 100 “the best imaginable health” [Self-rated health was measured through Q-5D-5L) . The occ health” .EQ VAS as a dependent continuous variable for self-rated health. The EQ-5D-5L has demonstrated satisfactory reliability, validity and responsiveness in older adults [In this study we use the r adults .Data on participant characteristics were their age (years), gender , living situation , main reasons for home rehabilitation, number of training sessions by rehabilitation staff and length of home rehabilitation in weeks. The main reason for home rehabilitation was defined by the health condition described in professional assessments, diagnoses and/or the person’s own experienced problems leading to the need for rehabilitation.conditions in the circulatory and respiratory systems , mobility limitations including fall risk , multimorbidity and/or frailty , neurological conditions excl. stroke , orthopaedic conditions upper extremities and spine , orthopaedic conditions lower extremities and pelvis  and stroke (acute and post).For this study, data were categorized using information on current diagnosis, medical condition, and affected body part . If specific information about current diagnosis or medical condition was missing, the person’s medical history was used as a guide to indicate an appropriate category. Seven categories were identified; multimorbidity and/or frailty were chosen as the reference category.General Linear Models (ANOVA repeated measures) were first performed to analyse changes over time in functioning  and in self-rated health (EQ VAS) between baseline and at the end of home rehabilitation intervention (T1-T2) and between baseline and 2-month follow-up (T1-T3). Logistic regression analyses were performed to investigate associations between independent variables and the outcomes of functioning and self-rated health in terms of no change/deterioration vs. improvement. Firstly, bivariate analyses were performed to investigate associations for each variable in relation to the outcomes. In the next step, multivariate analyses were performed for all independent variables in the model. Within the variable main reason for rehabilitation, P-values < 0.05 were considered statistically significant. Statistical analyses were performed with IBM SPSS Statistic version 26.The number of training sessions that each participant received from an occupational therapist and physiotherapist per week during home rehabilitation were analysed through logistic regression models. Visits by rehabilitation assistants were few and consequently these were not included in the regression models. Potential interaction between intensity and duration was tested by adding an interaction term (number of training session per number of weeks) in each logistic regression model. Orthopaedic conditions (lower extremities and pelvis) were the most frequent reasons for needing home rehabilitation. The participant received an average of 2.8 training sessions per week and the average duration of the training was 8.5 weeks. The rehabilitation outcome scores demonstrated that the participants had a moderate to mild disability at baseline.A description of baseline characteristics, home rehabilitation interventions and baseline rehabilitation outcomes are presented in Table EQ VAS showed significant improvement between both T1 and T2  and between T1 and T3 .The result from the GLM repeated measures is presented in Table orthopaedic condition in the upper extremities and spine was associated with increased functioning in ADL after rehabilitation. The relationship was slightly attenuated in the multivariate model that included gender, age, living situation, number of training sessions and length in weeks and was no longer statistically significant (p = 0.052). The result also points to an association between orthopaedic conditions (lower extremities and pelvis)  and an increase in ADL after intervention, although not statistically significant at 5% level  and upper limb function was found. Both relations remained statistically significant in the multivariate model.Table The results of the bivariate and multivariate logistic regression analyses with self-rated health as the outcome are presented in Table In this study we investigated changes in functioning and self-rated health in relation to a home rehabilitation program. We also investigated how socio-demographic factors, health conditions and intensity and duration of home rehabilitation interventions were associated to change in functioning and self-rated health after home rehabilitation. We found improvements in functioning and self-rated health after a home rehabilitation program and at 2-month follow-up. This is in line with several systematic reviews of home rehabilitation programs –35. We fWe also found an association between longer duration of training in weeks and improved upper limb functions. Although home rehabilitation programs are usually described as intensive interventions during a fixed duration , 15, theIn our study we found that intensive occupational therapy was a factor associated with improved mobility functions, and intensive physiotherapy also demonstrated a tendency in the same direction. Home rehabilitation teams usually vary in composition, with professionals from health- and social care . Researcorthopaedic conditions in upper extremities and spine were significantly more likely to improve in ADL than the other participants, indicating some benefits for that client group in receiving home rehabilitation. An explanation for their improvement could be that these conditions often are associated with fewer residual conditions compared to e.g., stroke or multimorbidity.Previous studies have shown that gender , 32, ageWhile home rehabilitation programs generally should not be limited in terms of participants’ age or condition , some prIn this study, we analysed de-identified data from a real-life setting with a sample varying in age, gender, living situations and health conditions. This contributes to the strength and relevance of our study when results are implemented in practice. Our results can also be externally generalized to similar contexts.The study sample was rather small yielding low precision in the estimated effect measures with wide confidence intervals. Furthermore, no control group was included. Together, this prevents us from drawing definitive conclusions, even though our results point to several implications for research and practice.A strength of our study is that the data were collected with accepted and validated instruments. However, for older people with minor functional limitations, the GMF shows ceiling effects  which maOverall improvements in functioning and self-rated health were found after a home rehabilitation program in a population of community-dwelling citizens. The result indicates that duration, intensity of occupational therapy and the main condition for rehabilitation may have an impact on the rehabilitation outcomes. Increased frequency of training sessions and longer duration of the program with professional rehabilitation staff, is vital to consider when implementing home rehabilitation programs. More research on home rehabilitation programs in different contexts, using a large sample and control groups is needed to further guide clinical practice and policymaking.


Furthermore, cystic mice show significantly higher β3‐AR levels than healthy controls, suggesting a correlation between receptor expression and disease development. Finally, β3‐AR is expressed in human renal tissue and localizes to cyst‐lining epithelial cells in patients. Thus, β3‐AR is a potentially interesting target for the development of new treatments for ADPKD.Autosomal dominant polycystic kidney disease (ADPKD) disrupts renal parenchyma through progressive expansion of fluid‐filled cysts. The only approved pharmacotherapy for ADKPD involves the blockade of the vasopressin type 2 receptor (V2R). V2R is a GPCR expressed by a subset of renal tubular cells and whose activation stimulates cyclic AMP (cAMP) accumulation, which is a major driver of cyst growth. The β3‐adrenergic receptor (β3‐AR) is a GPCR expressed in most segments of the murine nephron, where it modulates cAMP production. Since sympathetic nerve activity, which leads to activation of the β3‐AR, is elevated in patients affected by ADPKD, we hypothesize that β3‐AR might constitute a novel therapeutic target. We find that administration of the selective β3‐AR antagonist SR59230A to an ADPKD mouse model (Pkd1 The β3‐AR is a GPCR expressed in most segments of the murine nephron, where it modulates cAMP production. We investigate β3‐AR potential as therapeutic target in ADPKD and show that targeted β3‐AR blockade leads to a reduction in kidney/body weight through a decrease in total cAMP renal levels in a mouse model of ADPKD. Furthermore we report evidence of β3‐AR presence in biopsies from healthy and ADPKD patients. We demonstrate that the β3‐AR, which like the V2R regulates cAMP signaling in the kidney, may be a promising new target for ADPKD therapy. The β3‐AR‐selective blocker SR59230A reduces cAMP levels as well as kidney size, preserves renal parenchyma integrity and produces a trend toward functional recovery in a mouse model of ADPKD. We also show that β3‐AR is expressed in human kidneys and that its expression is increased in samples from ADPKD patients1ADPKD affects 1:400–1:1000 individuals worldwide and is the fourth leading cause of kidney failure. Its progression is characterized by formation and growth of fluid‐filled cysts arising from various nephron segments that disrupt the surrounding renal parenchyma over the course of many years, ultimately producing end stage renal disease (ESRD) in ~50% of patients . Most importantly, β3‐AR blockade via the selective antagonist SR59230A decreases cAMP levels and partially ameliorates the cystic phenotype in these mice, producing a reduction in kidney size and a trend towards preservation of renal function. Finally, we find that β3‐AR is also expressed in kidney samples from healthy and ADPKD patients, thereby demonstrating its potential relevance in the context of ADPKD therapeutic development.Recently, β3‐adrenergic receptor (β3‐AR) was shown to be expressed in several segments of the murine nephron, including thin and thick ascending limbs, distal convoluted tubule, and cortical collecting ducts (CCDs), whereas it was not detected in the proximal tubule and medullary collecting ducts. Selective activation of β3‐AR increases cAMP levels in isolated mouse renal tubules and activates key proteins involved in transepithelial water and solute movement  and 0.1% acetic acid in acetonitrile (mobile phase B) run at 0.4 ml/min as per the following gradient: 5% B for 0.3 min, 5%–90% B over 0.7 min, 95% B for 1 min, 95%–5% B over 0.1 min, and 5% B for 1.40 min. Total run time was 3.5 min. The mass spectrometer was operated in multiple reaction monitoring mode under positive ionization, with the declustering potential and collision energy settings of 131 and 35 V for SR59230A and 40 and 35 V for loperamide, respectively. The MRM transitions for SR59230A and loperamide (IS) were, respectively, m/z 325.77 → m/z 131.1 and m/z 478.14 → m/z 267.1.Plasma and kidney homogenates (25 µl) were processed by protein precipitation with 150 µl of acetonitrile containing 15 ng/ml of loperamide as the internal standard (IS). Calibration curve samples of SR59230A (0.001–10 µM) were prepared by spiking naïve mouse plasma and kidney homogenates with known concentrations of SR59230A and processed similarly along with study samples. Samples were centrifuged at 4800 2.3Upon collection kidneys were immediately snap‐frozen in liquid nitrogen, ground to a fine powder in pre‐chilled ceramic mortars and then homogenized in HCl 0.1 M. Homogenates were pelleted at 600 g for 10 min at 4°C to remove debris and supernatants were processed without acetylation using an enzyme immunoassay kit according to manufacturer instructions (Enzo Life Sciences). Results are expressed in pmol/mg protein.2.42O2 for 7 min. Sections were incubated for 10′ with serum‐free protein block (Dako) and then incubated overnight at +4°C with anti‐β3‐AR  followed by 1 h incubation with a biotinylated mouse secondary antibody (1:1000), which was detected by the Dako Real EnVision, Peroxidase/DAB kit (Dako). Sections were counterstained with Mayer's hematoxylin (blue). The primary antibody was omitted in negative controls. Digital images were acquired with an Olympus C‐7070 Wide Zoom camera (Olympus Imaging Corp.) and Leica/Leitz DMRB (Leica Mikroskopie & Systeme GmbH). Aperio ImageScope Software (Aperio Technologies) was used to measure the staining intensity and the percentage of positive cells using the positive pixel count v9 algorithm. All patients gave their informed consent to use remaining portions of kidney specimens for clinical studies, after diagnosis. The study was carried out according to the principles of the Declaration of Helsinki.Normal tissue was obtained via open kidney biopsy from two cadaveric organ donors and via needle biopsy from two living kidney donors. Tissue from normal portions of nephrectomized kidneys was also obtained from three patients who underwent nephrectomy for the presence of renal cell carcinoma. Pathological renal tissue was also obtained from six ADPKD patients who underwent nephrectomy for the excision of renal cell carcinoma. Formalin‐fixed paraffin‐embedded (FFPE) renal samples were obtained from the archive of the Institute of Pathology, University of Bari, Italy. Thin sections (2 µm) of FFPE kidney specimens were deparaffinized and hydrated through xylene and graded alcohol series. After antigen retrieval in EDTA (pH 8), the endogenous peroxidase activity was quenched by incubation in a solution of 3% H2.5Mouse kidney sections were co‐incubated with the appropriate primary antibodies, β3‐AR , calbindin , p‐NKCC2 , and AQP2 , followed by 1 h RT incubation with AlexaFluor‐conjugated secondary antibodies (Life Technologies) and Hoechst nuclear staining . Confocal images were obtained with a LSM780 (Carl Zeiss). Images are the product of eightfold averaging and contrast and brightness values were selected to ensure that all pixels were within the liner range.2.6Snap‐frozen mouse kidneys were processed and the homogenates quantified as previously reported   analysis on plasma and renal tissue samples from wt, non‐induced male mice treated with SR59230A (4 mg/kg) and euthanized after 4 h. As expected, both plasma and tissue samples contained detectable levels of SR59230A, with kidney levels (Avg 0.3 µM ± 0.2) about 20‐fold higher than those in plasma (Avg 0.015 μM ± 0.009)  Figure . To test) Figure . Thus, i) Figure . In addi) Figure , cystic ) Figure . Thus, i) Figure . In addi) Figure , cystic ) Figure .fl/fl; Pax8rtTA; TetO‐Cre miceChronic SR59230A treatment (4 mg/kg) ameliorates the cystic phenotype in Pdk1. To investigate the effect of SR59230A treatment on cyst progression we administered SR59230A for 4 weeks. Six weeks after the inactivation of Pkd1 gene, coincident with the onset of cyst development  in human normal (Figure l Figure ,d kidneyl Figure . The spel Figure . To the 4ADPKD is characterized by numerous biochemical alterations that disrupt cellular homeostasis. While this complexity provides investigators with a wealth of options to pursue in searching for potential treatments, many affected pathways are involved in processes critical for the entire organism. The approval of Tolvaptan, together with ongoing trials on the somatostatin analogue, octreotide, provide a strong rationale for pursuing additional therapies that target GPCRs and aspects of cAMP signaling (Tellman et al., Hyperactivation of the sympathetic system has been reported in patients affected by ADPKD (Klein et al., Since sympathetic nerve activity is elevated in patients with chronic kidney disease including ADPKD (Klein et al., fl/fl; Pax8rtTA; TetO‐Cre cystic male mice daily for 4 weeks. Our results show that chronic SR59230A treatment led to a significant reduction in kidney/body weight and to a pronounced trend toward the rescue of renal function. Interestingly, β3‐AR protein quantified in total kidney lysates from healthy and cystic SR‐treated animals was markedly increased in the latter group. This result points to a possible correlation between β3‐AR expression and disease development. In fact, increased levels of receptor expression are consistent with our hypothesis that over‐stimulation of β3‐AR could contribute to disease progression in ADPKD. Moreover, elevated β3‐AR receptor levels have already been reported in the context of other pathological conditions (Calvani et al., Thus, a targeted approach that selectively inhibits β3‐AR might decrease cAMP production, thus improving the cystic phenotype. Among the few available β3‐AR‐selective antagonists we chose SR59230A, a molecule that has been previously reported to block β3‐AR in vivo (Bruno et al., Beta 3‐AR is widely expressed in various human tissues (Schena & Caplan, The authors declare no conflict of interest.G.S., M.J.C., and M.C. designed the study; G.S., S.C., and L.O. carried out experiments; G.S. analyzed the data, designed the figures, and drafted and revised the paper; M.J.C., M.C., and F.P.S. revised the paper. All authors approved the final version of the manuscript.


Among community-dwelling older adults who require long-term care and use outpatient rehabilitation services, we aimed to examine the characteristics of patients who discontinued using outpatient rehabilitation services to prevent exposure to COVID-19 and the effects of this discontinuation on patient frailty.Participants were 119 older adults  requiring long-term care who used outpatient rehabilitation services. Our outpatient rehabilitation service involved day care for older adults undergoing rehabilitation including pick-up and drop-off services under the long-term care insurance system. They were divided into two groups: participants who discontinued using outpatient rehabilitation services and participants who continued their use. To find the factors associated with this discontinuation, binomial logistic regression analysis was performed, in which the following independent variables were used: gender, age, height, weight, long-term care level, grip strength, and normal walking speed. Frailty status was investigated in April 2020 and again in September 2020 through interviews and telephone surveys utilizing the Frailty Screening Index.Women and normal walking speed were independent factors associated with the discontinuation of outpatient rehabilitation due to COVID-19. According to the results of the Frailty Screening Index, similar tendencies were exhibited in both April and September. The discontinued group indicated that they were getting less exercise and having stronger feelings of fatigue than the continuing group.The findings are in accordance with other studies indicating that women are more likely to employ prevention measures against COVID-19, as seen among the community-dwelling older adults requiring long-term care who used outpatient rehabilitation services. People with higher levels of physical function were also likely to refrain from using outpatient rehabilitation services. Results further suggested that the discontinued group had more frailty-related factors  than the continuing group. Since December 2019, infections caused by the novel coronavirus, COVID-19, have been spreading throughout the world . The WorThe possibility has been suggested that young people would experience decreased physical activity due to COVID-19, such as a decrease in the number of their steps . Among oMany community-dwelling older adults, who need long-term support or care, commute from their homes to use facilities covered by long-term care insurance. During the COVID-19 epidemic, patients who discontinued their use of facilities covered by long-term care insurance were forced to make large changes in their way of life. Because they differ in this way from healthy older adults, shedding light on the unique effects of the pandemic on those who need long-term care or who commute from their homes to use facilities covered by long-term care insurance is an important issue in Japan due to its rapidly aging society.The purpose of this research was to study community-dwelling older adults needing long-term support who utilize outpatient rehabilitation services and to shed light on the characteristics of patients who discontinued their use of outpatient rehabilitation services because of COVID-19 prevention efforts. It also aimed to examine differences regarding frailty level between discontinuation and continuation of outpatient rehabilitation services.As of April 1, 2020, the outpatient rehabilitation service, located in Tochigi prefecture, had 143 of its patients registered in this prospective observational study. Our outpatient rehabilitation service involved day care for older adults undergoing rehabilitation including pick-up and drop-off services under the long-term care insurance system. The rehabilitation program includes muscle strengthening exercises (by machine training), exercise therapy (using an ergometer), activities of daily living/movement guidance, among others. To determine eligible participants, the following individuals were excluded: 12 users whose walking speed and grip strength could not be measured, six users for whom data were unavailable, four users who were absent long-term for a reason other than COVID-19, one user who took 141 s for 5-m walking test and one user over the age of 100. The participant group, therefore, included 119 patients needing long-term support and care  and who used outpatient rehabilitation services at least once a week . All parThe participants were divided into two groups: one included those who discontinued use of outpatient rehabilitation services because of COVID-19 prevention (“discontinued group”); the other included those who continued use (“continuing group”). The definition of discontinued was a 2-week or longer absence from outpatient rehabilitation during the period of the nationwide state of emergency declaration in Japan  for COVID-19 prevention. The basic attributes , grip strength, and normal walking speed of all participants were referenced from information in their patient charts dated March and April 2020.Grip strength was measured using a digital dynamometer . Measurement was performed with the participants seated in a relaxed position (not resting on the back of the chair), holding the dynamometer to the side of the body in an extended elbow position. Measurements were taken twice on both the left and right sides, with the maximum values of the right and left sides having been used as the representative values.To measure walking speed, the participants were asked to walk at their normal walking speed along an 11 m straight course with marks on the floor indicating 3 m and 8 m. The time taken to walk between the 3 m and 8 m marks was measured. The selection of the conditions for the assessment of normal walking speed and the distribution of acceleration and deceleration paths were guided by prior established guidelines .Frailty status was assessed twice, in April 2020 and in September 2020, through questionnaires and telephone surveys utilizing the Frailty Screening Index. Those who had terminated the use of outpatient rehabilitation were interviewed by telephone. Based on prior studies, the Frailty Screening Index asks the following questions: for weight loss, “Have you lost 2 kg or more in the past 6 months?” (Yes = 1 point); for walking speed decline, “Do you think you walk slower than before?” (Yes = 1 point); for exercise, “Do you go for a walk for your health at least once a week?” (No = 1 point); for memory, “Can you recall what happened 5 minutes ago?” (No = 1 point); and for feeling fatigued, “In the last 2 weeks, have you felt tired without a reason?” (Yes = 1 point). A score of three or more was defined as frailty, a score of one or two was defined as pre-frailty, and a score of zero was defined as robust . Before t-test for age, height, weight, grip strength, and normal walking speed, Fisher’s exact test for gender, and the Wilcoxon rank sum test for long-term care level. A seven-stage ordinal scale was used for long-term care level . A statistically significant difference was also found in the answers to questions about exercise and feeling fatigued in the survey conducted in September . Despite identifying two significant questions, we observed no significant differences between participants of the two groups in terms of them being generally frailty, pre-frailty, or robust (as previously defined).Finally, From the results, “female” and “fast walking speed” were extracted as characteristics of patients who discontinued use of outpatient rehabilitation services because of COVID-19 prevention. According to a prior study, females implemented infection countermeasures and prevention against COVID-19 to a greater extent than males; even the oldest amongst the women implemented the countermeasures . That stNext, from the Frailty Screening Index analysis results, the group that had discontinued outpatient rehabilitation in April had fewer opportunities for exercise and had stronger feelings of fatigue compared to the continuing group. We believe that the primary cause of the reduction in exercise opportunities was that the participants had discontinued using outpatient rehabilitation services. Additionally, as a factor in feeling fatigued, it has been reported that for people aged 65 or older, exercising with others is linked to preventing depression . In otheThe results of this study have shed light on the characteristics of COVID-19 prevention among older adults who need long-term support or care and utilize outpatient rehabilitation services and the effect of prevention measures on their frailty. A prior study has reported that a comparison of before and after the spread of COVID-19 indicated that weekly physical activity duration was reduced by approximately 30%, regardless of the presence of frailty . From thA limitation of this study was that it was a single center study; thus, the number of participants was small and gender ratio bias may have occurred. Going forward, it would be desirable to explore the long-term impact of discontinuing the use of outpatient rehabilitation services by exploring a larger facility and increasing the number of participants. Furthermore, because this study required participants to undergo telephone-based investigations, we chose to use the Frailty Screening Index, which is a measurement tool for assessing frailty that can be easily administered. Another reason for choosing this index was the report of the Clinical Guide for Frailty of Japan, which described the tool as valid .In conclusion, we found that, among the community-dwelling elderly people requiring long-term care who were using outpatient rehabilitation services, women and people with higher levels of physical function were refraining from physical activities. Furthermore, our findings suggested that frailty status in the discontinued group was lower than that in the continuing group.10.7717/peerj.11160/supp-1Supplemental Information 1Click here for additional data file.


Lower activator concentration (10% vs. 20%) and curing temperature (70 vs. 100 °C) caused an increase in GPBM compressive strength; the highest value was measured as 93.3 MPa. The highest RA was measured for GPBMs, provided alkali activator ratio (Na2SiO3/NaOH) was >2 and its concentration was 20%. The mathematical model developed in this study proved FFA quantity, and thus GPBM mechanical properties, as key factors influencing RA. In the light of these results, the lightweight GPBMs can be excellent materials for the construction sector dedicated to immobilization, storage, and disposal for radionuclides or barriers against radiation; however, multiple steps of their production require careful optimization.Waste ashes and radiation are hazardous environmental and health factors; thus, a lot of attention is paid to their reduction. We present eco-geopolymer building materials (GPBMs) based on the class F fly ashes (FFAs) from thermal power plants (TPPs) and their implementation as a barrier against radioactive radiation. Different methods of production, ratios of FFA to alkali activator, and temperatures of curing were tested. Small spherical particles and higher content of SiO The production of ordinary Portland cement (OPC) consumes both energy and natural resources. As stated in the International Energy Agency’s Greenhouse Gas R&D Programme, cement production releases approximately 50% of total COGeopolymers have been studied extensively as an alternative building material that is environmentally friendly and serves as part of sustainable development ,3,4. Com2SiO3) or potassium silicate (K2SiO3) [Davidovits introduced the term “geopolymers” to assign a new class of aluminosilicate materials with a three-dimensional structure, and he developed alkali activated fly ash (FA)-based geopolymers . FA is a(K2SiO3) . The final properties of a geopolymer depend on many variables concerning raw materials, i.e., (1) mineralogical and chemical composition, (2) structural properties, and (3) physical properties. Similarly, many factors affect the properties of the geopolymer during the production process; (4) ratio of raw materials, (5) duration of mixing and temperature of the mixture, (6) rheology modifiers, accelerators, or retarders influencing the setting time, and (7) curing time. As the design and the appropriate selection of ingredients in laboratory conditions are costly and time-consuming, to achieve the mechanical properties supporting the usage of FA in the building industry, research using data-driven methods, artificial intelligence, machine learning methods, and precise mathematical modeling should be developed . −3) is used for wall and roof barriers [® matrix). Approximately 29.6 m3 (38.15 t) of waste with total activity around 4.94 × 1012 Bq were immobilized between 2003 and 2010 [Much research has been performed to assess geopolymer materials as a matrix for immobilization, storage, and disposal of radionuclides or as barriers against radiation. Different types of harmful radiation, i.e., neutron, X-ray, and gamma-ray can penetrate the conventional cement walls and roofs and easily escape from accelerators, hospitals, and nuclear power plants. If required, heavy concrete . The study intended to reduce the environmental problem, contribute to a circular economy and zero waste principles by means of waste FFA reuse as a raw material, and reduce carbon footprint and global warming by using cementless geopolymer technology and GPBMs with a high capacity of RA . Therefore, following the definition of “eco-materials” in the Eco-products Directory 2010, geopolymers developed in this study refer to “materials  that possess excellent characteristics with good performance, which can be manufactured, used, and recycled or disposed of, while having only a low impact on the environment as well as being kind to humans”. With the above in mind, we described developed materials as eco-geopolymer.In most countries, the vast majority of FA is stored in authorized warehouse sites. The limited possibilities of the usage of class F fly ash (FFA) necessitate efforts to solve the environmental and economic problems with waste. In terms of the number of power plants, Turkey ranks near the top 15 in the world . Accordi−1, soak time 180 min, and relative pressure (p/p0) in the range from 0.021 to 0.994 for 44 measuring points. The results were analyzed using the AsiQwin software .Morphology of FFAs supplied from Catalagzi and Isken TPPs was determined with a Zeiss/EVO 40 scanning electron microscope . Sieve analysis  of the FFAs was performed according to the Turkish Standards—European Norm (TS EN) 933-10 . The che2SiO3 . Alkali activator NaOH was used at a ratio of 10%, 15%, 25% alkaline activator to FFA (by weight); or NaOH/Na2SiO3 solutions  were used at a ratio of 10% and 20% alkaline activator to FFA (by weight)  . The sam2SiO3 solution required short mixing to obtain a homogeneous paste. For “normal” mixing, FFA, NaOH, and Na2SiO3 were mixed for 1 min [To produce GPBMs based on FFA (without the RILEM Cembureau standard sand), “separate” and “normal” mixing methods were tested. For “separate” mixing, FFA was mixed with NaOH for 10 min to enable the leaching of ions. Although, the paste was relatively viscous, subsequently added Naor 1 min . To prodor 1 min . The pasad is the air-dried mass, and Vg is the gross volume of the samples.GPBM density was calculated according to the following equation (Equation (1))sa is the saturated mass and md is the dried masss is the sample mass (according to the Archimedes principle).The water absorption of the samples was analyzed according to TS EN 772-3 . Water aThe porosity was a measure of “empty” spaces in a material and was expressed as a volume of voids relatively to the total volume (apparent porosity).The flexural strength test was performed according to the ASTM C348-14  and the RA measurements were performed using a Geiger–Müller counter and Cs-137, a source of gamma rays of high energy. The scheme of the experimental setup is given in In the first step, gamma rays emitted from the radioactive source were measured in the absence of GPBMs a. In then) was defined as an individual sample at given conditions. The repeatability of the result of measurement was below 9%.In accordance with TS EN 12390-3 , all exp2 and Al2O3, the most important determinants of the geopolymerization process, were relatively high in both the Catalagzi TPP FFA and the Isken TPP FFA. Acid oxides SiO2 and Al2O3, occurring mainly in the glassy phase, show high reactivity in an alkaline environment [2 content in the raw materials, and a compressive strength increases along with the Si/Al ratio [2/Al2O3 was 2.07 and 2.90. Therefore, geopolymerization could be favored in forming a poly(silate-siloxo) (-Si-O-Al-O-Si-O-) structure in which the Si/Al ratio is 2 and a poly(silate-disiloxo) (-Si-O-Al-O-Si-O-Si-O) structure in which the Si/Al ratio is 3, rather than poly(silate) (-Si-O-Al-O-) in which the Si/Al ratio is 1 [2 + Al2O3 + Fe2O3 components higher than 70%, while for class C FAs this ratio is higher than 50 (by weight) (ASTM C618) [FFAs are considered as important raw materials for geopolymer production; however, their reactivity depends on chemical and physical properties. To prove the potential of the Catalagzi TPP FFA and the Isken TPP FFA for utilization in the geopolymerization process, the chemical composition was analyzed . The amoironment ,42. Due Al ratio ,21, hightio is 1 ,44. AccoTM C618) . The cheLOI (%), an indicator of residual carbon content, is a critical parameter when evaluating the effectiveness of the geopolymerization process. LOI values were determined as 1.52% and 1.78% for the Catalagzi TPP FFA and Isken TPP FFA, respectively . AlthougDifferent chemical composition was related to a variance in particle morphology of the Catalagzi TPP FFA and the Isken TPP FFA a,b. Gene2 g−1 were measured for Isken TPP FA , and curing temperature were the key factors modifying geopolymer porosity  and extrinsic parameters   and lower curing temperature (70 °C) caused an increase in compressive strength of GPBMs, in comparison to a higher temperature (100 °C). The opposite effect was found for the higher amount of activator ; a decrease in the curing temperature (70 °C) caused a decrease in compressive strength of GPBMs.Generally, the raw material reactivity was controlled by numerous intrinsic  , which cprocess) . It is wprocess) . Flexuraprocess) . The comprocess)  were sel2SiO3/NaOH at a ratio of 2.5 and concentration of 20% (2SiO3 to NaOH) and its concentration was 20%  was 20% , GPBMs pre water ,58. ThusBearing in mind the importance of factors described above, such as chemical composition of the raw materials, ratio of ingredients, and curing temperature, it is necessary to have a tool that enables the selection of individual parameters and results in maximum RA values for geopolymer barriers against radiation. Mathematical modeling and appropriate algorithms can provide this without the need for costly experimentation.ix, iy) and q(ix) . The ith point error εi which gives the difference between iy and q(ix)  can be described by the following equation:Deviations during experiments are inevitable, but extreme deviations of individual measurement points should be eliminated. The least-squares method overcomes the deviation in experimental data. This technique results in a fitted curve. In order to find an optimally fitted curve, the data points and fitted curve can be denoted by th particle, the curve, and undefined coefficients can be calculated [If the equalities above are arranged for an lculated  with theThe basis of equations in this study is related to the working principle of the least-squares fit method considering linear combinations of functions of the variables derived from experimental data. The importance of the mathematical model is to facilitate the prediction of RA % without the need for experiments. Furthermore, due to the optimization of the mathematical model, a mixture design with a maximum RA % was obtained.Numerical algorithms for optimization of nonlinear constraint can be divided into two methods, namely the gradient-based method and direct search method. Whereas the gradient-based optimization method depends on the first and second derivatives, the direct search method does not use the derivatives. The convergence of the direct search method is slower than the convergence of the gradient-based method, but the former method is more robust and has higher tolerance to noise in the objective function and constraints. In this study, two different numerical direct search methods were used, namely the simulated annealing (SA) and the differential evolution (DE) algorithms .The SA algorithm is a random optimization method and simple stochastic function minimizer. The method is based on the terminology and genesis of the annealing process, i.e., a feature related to the temperature variation to be embedded in the operational characteristics of the algorithm. Using optimization terminology, annealing allows the structure to escape from a local minimum, explore, and settle on a minimum. Due to the annealing procedure, the optimum point avoids the local point and allows the global optimum of a given function to be approximated. The objective function in the DE method is defined under equality or inequality constraints. In the first step, the initial population determined with the target vector and donor vector is created by a mutation. To define the test vector for the new generation, the target vector and donor vectors are compared with the purpose of choosing the lower one. Therefore, if the test vector is lower than the target vector, the optimization algorithm is finished, else the procedure returns to the mutation step to produce a new generation.Considering the results of the experiments , optimumfor GPBMs produced from the Catalagzi TPP FFA at 70 °Cfor GPBMs produced from the Isken TPP FFA at 100 °C2SiO3 (g), FFAs (g), and RILEM Cembureau sand (g), respectively. By calculating the optimum of mixture ingredients, the equations presented above were maximized to find the maximum of the RA. The SA and DE algorithms were applied separately considering upper and lower bounds expressed as follows:In the above equations, a, b, c, and d are quantities of NaOH (g), NaThe constraints of optimization in Equations (16)–(21) were obtained considering the maximum value of raw materials presented in After the optimization process for GPBMs produced from the Catalagzi and Isken TPP FFAs at 70 °C (CTPP-70 and ITPP-70) and 100 °C (CTPP-100 and ITPP-100), the unknown coefficient of the predicted polynomial functions and optimum value of the RA % were calculated as presented in 2SiO3 and FFA. The red circle points in 2SiO3  instead of the cement, which accounts for a net reduction in energy use and greenhouse gas during the production; (ii) use of waste FFAs instead of the natural non-renewable sources; (iii) reuse of FFAs to reduce the storage of toxic waste; (iv) and increased life of building structures due to the improved material durability. With the above in mind and proving better engineering properties, we describe the developed GPBM material as an eco-geopolymer.The suitable features of GPBMs produced from the Catalagzi and Isken TPP FFAs consist of (i) use of raw materials (FFAs) and chemical activators 2SiO3/NaOH ratio of 1–2.5, and 24 h of curing at 70 or 100 °C all represent the conditions for GPBM production that result in final materials with an average compressive strength of 40–44 and 58–63 MPa for the GPBM produced from the Catalagzi TPP and Isken TPP FFAs, respectively.FFAs with total aggregate content of 70–80%, 12 M NaOH, the Na(2)Higher reactivity of the Isken TPP FFA, and thus better mechanical and physical properties of the geopolymer, resulted from finer particles and greater surface area of raw material. The highest compressive strength was measured as 93.3 MPa for the GPBM produced with 10% NaOH and cured at 100 °C.(3)−3, porosity of 19.9%, water absorption of 12.4%, and compressive strength of 57.3 MPa; thus, eco-friendly GPBMs are lightweight construction materials with good mechanical properties.The best GPBM (produced from the Isken TPP FFA) had the highest RA of 12.5%, density of 1.70 g cm(4)According to the mathematical model developed in this study, the effect of FFA/alkali activator type and quantity on RA is an important issue. Optimization is required to obtain maximum RA values. Mathematical modeling and appropriate algorithms can provide this without costly experimentation.The detailed conclusions include:However, the performance of GPBMs under radiation must be deeply understood. Radiation can lead to a change in the microstructure of a material and related chemical, physical, and mechanical parameters; thus, the dependences require further studies. Predicting GPBM properties and methodology with mathematical models seems irreplaceable, particularly for production of sophisticated materials for special purposes. Attempts have been made to produce materials such as radiation shielding concrete (RSC)  and ambi


Background: Motor recovery after stroke is based on neuronal plasticity and the structural reorganization of the brain. Questions are debated about the proper moment to start rehabilitation in the acute period of stroke, the significance of rehabilitation interventions during the so-called “plastic window”, and the advantages of modern and traditional programs. The aims of this study were to evaluate the role of different rehabilitation strategies and their combinations for motor recovery and the impact on functional disability by way of neurological and functional outcomes 3 months after ischemic stroke. Methods: We used three rehabilitation approaches: early rehabilitation from the first day of stroke (Phase I), traditional exercise programs (Phase II), and an author’s new method of biofeedback rehabilitation using motion sensors and augmented reality (AR) rehabilitation (Phase III). Clinical and functional outcomes were measured on the 90th day after stroke. We developed algorithms for quantifying the quality of movements during the execution of tasks in the motor domains of the AR rehabilitation program. Results: Phase I of rehabilitation led to an improvement in functional independence, and the recovery of motor functions of the extremities with an absence of mortality and clinical deterioration. AR rehabilitation led to significant improvement both with respect to clinical and functional scores on scales and to variables reflecting the quality of movements. Patients who were actively treated during Phases II and III achieved the same final level of motor recovery and functional outcomes as that of participants who had only received AR rehabilitation during Phase III. Patients who underwent outpatient observation after Phase I showed a deficit of spontaneous motor recovery on the 90th day after stroke. Conclusions: Early rehabilitation was successful but was not enough; rehabilitation programs should be carried out throughout the entire “sensitive period” of poststroke plasticity. The newly developed AR biofeedback motion training is effective and safe as a separate rehabilitation method in the early recovery period of moderately severe, hemiparalytic, and ischemic stroke. These two rehabilitation approaches must be applied together or after each other, not instead of each other, as shown in clinical practice. Stroke continues to be a topical problem. In most countries, stroke is the leading cause of death and one of the main reasons for adult-onset disability . In 2013Brain recovery from stroke is based on neuronal plasticity that is generally defined as changes or rewiring in the neural network. Most stroke deficits see the highest rate of recovery during the first 3 to 6 months after stroke because, during this period of time, the brain is most susceptible to restoration . There aSeveral studies have shown the potential of early rehabilitation to improve motor recovery after stroke ,12. HoweSeveral rehabilitation approaches are used to increase the restoration of motor deficit and the improvement in quality of life ,4,5. TheDespite the recent developments in neurorehabilitation techniques focusing on motor recovery after stroke, no universally accepted model of poststroke rehabilitation exists, and research on optimal therapeutic interventions and the optimal time period to apply them remains pertinent ,18. The The study was conducted by the Department of Neurology and Neurosurgery in collaboration with the Department of Medical and Biological Cybernetics of the Siberian State Medical University (SSMU), Tomsk Regional Vascular Center (Tomsk RSC), Siberian Federal Scientific Clinical Center of Federal Medicobiological Agency, Russia from November 2018 to December 2019. The study was conducted in accordance with Resolution 5961/2018. The study protocol was reviewed and approved by the Medical Research Ethics Committee of SSMU, Tomsk, Russia . All participants provided informed consent prior to participation.Fifty stroke patients aged 18 years or more were recruited from the Tomsk RSC within the first 24 h after the onset of symptoms. Ischemic stroke in the middle cerebral artery basin was diagnosed considering their clinical history, the presence of focal neurological signs and/or symptoms, and by applying magnetic resonance imaging (MRI) or computer tomography (CT) scans. Additional inclusion criteria were Glasgow Coma Scale (GCS) of no less than 15 points, and Mini-Mental State Examination (MMSE) of no less than 20 points ,20,21. TThe neurorehabilitation of stroke patients in early recovery period (from the 1st to the 90th day of stroke) was carried out in 3 phases according to the current legislation of the Russian Federation.Phase I—early rehabilitation within the first 2 weeks of acute stroke was mandatory for all patients from the 1st day of admission to Tomsk RSC according to Order of the Ministry of Health of the Russian Federation no. 928n, “On approval of the procedure of care for patients with acute disorders of cerebral circulation”, dated 15 November 2012 . The timPhase II—inpatient rehabilitation within 28 days of stroke in Siberian Federal Scientific Clinical Center of Federal Medicobiological Agency in accordance with Order of the Department of Health of the Tomsk region no. 941, “On medical rehabilitation in specialized medical organizations at the inpatient stage”, dated 15 March 2018 . The appObservation period: within 90 days of stroke.Point 1—first day of stroke;Point 2—after Phase I of rehabilitation ;Point 3—after Phase II of rehabilitation ;Point 4—after Phase III of rehabilitation .Observation points were selected according to phases of rehabilitation :Point 1Phase III—outpatient rehabilitation within 12 days of stroke in SSMU in accordance with Order of the Department of Health of the Tomsk region no. 188, “On medical rehabilitation in outpatient stage”, dated 16 October 2018. Neurological patients who did not need round-the-clock monitoring and the individual care of junior medical staff were sent for outpatient rehabilitation . Motor rThe study population was clinically and demographically homogeneous on admission to the RSC at Point 1. Baseline characteristics are specified in Group A: 21 patients who received restorative treatment during consecutive rehabilitation Phases I–III within 90 days of stroke.Group B: 14 patients who received treatment during Phases I and III. Group B patients refused inpatient rehabilitation on Phase II and, during this time period (range 14–60 days), were on outpatient observation.Group C: 15 patients who only received treatment during Phase I. Group C patients refused further rehabilitation and were followed up on outpatient observation and examined on the 90th day of stroke.The absence of treatment in Phases II and III of rehabilitation in Group C and in Phase II of rehabilitation in Group B was associated with problems of routing and optimizing Phases II and III of the rehabilitation process for cerebral stroke, and problems in the family. Most of these patients lived in geographically remote areas of the Tomsk region.In our research, we used the augmented reality (AR) system. The effectiveness of neurorehabilitation largely depends on the motivation for correct exercise performance. In the case of AR, the accuracy and speed of task execution directly affects the patient’s assessment of their own capabilities; in this case, the achievement of a certain result motivates the patient to further perform the tasks. An important aspect is also comfortable orientation in the surrounding space without additional stress factors caused by the isolation of the visual analyzer, as happens in VR conditions. When compiling the tasks, we focused on restoring upper limb function: 3 out of 4 tasks were related to hand-control skills. We also applied the method for assessing the spectral power of the trajectory of hand movement described by Balasubramanian .To visualize the virtual environment, we used Epson Moverio BT-300 augmented reality glasses with preinstalled mobile module software NeuroRAR. For biofeedback, we used Markerless motion-capture systems Leap Motion and Microsoft Kinect. The involved kinesiotherapist could control AR rehabilitation using desktop module software NeuroRAR.(a)Accuracy domain a. The pa(b)Statics domain b. The paSeveral parameters reflecting the execution of this task can be applied to measure the quality of the made movements. Augmentation of the “variability of movements” parameter can be interpreted as making a smoother movement during the task, and by decreasing the severity of intentional tremor during movements. This variable increases when the forefinger is moved closer to the main trajectory without making extra movements such as tremors and sudden movements to the side. Increasing the “number of completed tasks” during one motor training session and enhancing the “maximal duration of movement in one approach” is gradual improvement in the control of reciprocal muscles reflected by an increase in the duration and speed of the task.(c)Capture domain c. The pa(d)Balance domain d. The paThis software implements four different motor domains for neurorehabilitation:Increasing the “height of raising the paretic leg” was evaluated relative to the set level. The level of leg raising was assessed using a height of 0.25 m for the approaching virtual barrier on a flat horizontal surface. The “variance of the displacement for the central point” variable shows displacements of the point corresponding to the geometric center of the body. This variable shows how much a person sways in the process of a calm walk between stepping over obstacles. Indirectly, this parameter can characterize the state of human balance when performing routine movement.The AR rehabilitation program includes 10 daily motor training sessions (except weekends) with a frequency of 1 motor training session per day according to AR protocol. The duration of one motor training session was 60 min. The time to complete one motor domain was 15 min. The kinesiotherapist assisted the patients and taught them how to execute the AR tasks in the neurological clinic.Sociodemographic and clinical data were extracted from electronic medical records and/or by interviewing the patients. The evaluation of neurological impairment was based on the National Institutes of Health Stroke Scale (NIHSS) ,29. The p > 0.05). Quantitative data without normal distribution were analyzed with nonparametric tests. Differences in the parameters between groups were assessed with the nonparametric Mann–Whitney U test; for related samples, we used the Wilcoxon rank-sum test. The chi-squared (χ2) test was used to assess differences in the size of distribution of qualitative data. The Bonferroni test was used as a lead for multiple post hoc comparisons. Statistical analysis was conducted on the 95% confidence level. A p value less than 0.05 was considered statistically significant. Data were analyzed with the IBM SPSS Statistics for Windows . To calculate the sample size, we also used specialized software complex IBM SPSS Sample power. In our study, there were three groups. The key parameters were connected with clinical assessment , and we used these variables to calculate the sample size. For 80% power, the minimal sample size was 13. Results are expressed as median (Me) and interquartile ranges (IQR); for categorical variables, results are expressed as percentages. The Kolmogorov–Smirnov test was used to analyze the normal distribution of the variables . Detailed comparative analysis of the FMA showed that motor impairment of the upper and lower extremities (FMA-UE and FMA-LE) regressed stepwise after each rehabilitation program, with statistical differences between observation points . There was no statistical difference between FMA-Balance-2 and FMA-Balance-3 scores (p2–3 = 0.06).Patients in Group A showed a linear improvement in scorings according to the assessment scales as a result of using consecutive rehabilitation programs (Phases I–III). Significant difference was found between phases of rehabilitation by NIHSS, mRS, and total FMA . Apart from NIHSS ratings, they did not significantly change between Points 2 and 3 in patients of Group B, who did not receive a traditional rehabilitation program (Phase II) and underwent outpatient observation during this time (p2–3). Scores on the NIHSS scale were significantly lower at Point 3 (p2–3 = 0.010). Group B showed no change in motor impairment by total FMA scale during the period of outpatient observation (p2–3). The situation was similar with regard to ADLs measured by mRS (p2–3). Evaluation of lower limb motor function by FMA-LE in Group B patients demonstrated improvement after Phase I and a significant difference between Points 2 and 4 . However, no significant differences were observed between FMA-LE-2 and FMA-LE-3 (period of outpatient observation), and between FMA-LE-3 and FMA-LE-4 (AR rehabilitation). There was an absence of significant recovery as assessed by FMA-LE due to the stimulation of the motor cortex by the AR biofeedback motion training (p3–4). Comparisons of the scores  after Phases I and III of rehabilitation in Group B demonstrated equally significant regression of motor impairment (pp1–2). The rating at 90 days poststroke showed that their clinical condition did not significantly change during outpatient observation, in which they had not received active treatment (p2–4).The clinical condition of patients in Group C was assessed after Phase I of rehabilitation and on Day 90 poststroke. Significant clinical and functional improvements were shown after early rehabilitation in Tomsk RSC in Group C patients . However, Group A patients had significantly lower total FMA-2 scores than those of Group B patients after Phase I of rehabilitation (pA–B = 0.01). Detailed comparative analysis of the FMA subscores showed that the lower scoring of patients of Group A was due to selective bad recovery of the upper extremities after early rehabilitation in comparison to Group B. However, patients in Groups A and B showed no significant difference between the total and subscale FMA-3 scores, and no significant differences were observed with respect to NIHSS-3 and mRS-3. This was in spite of Group A patients receiving active treatment/a traditional rehabilitation program (Phase II), and Group B patients being on outpatient observation during this time period (pA–B). Comparisons of the scores after AR rehabilitation  demonstrated an equally significant regression in motor impairment both in the paretic arm and in the paretic leg in Group A and B patients (pA–B). Therefore, despite the fact that Group A patients had lower FMA-UE-2 scores, at the end of the study they achieved the same level of upper limb motor recovery, assessed by FMA-UE-2, as that in Group B.Differences between the three groups of patients are shown in pA–C; pB–C). Patients in Group C, who were admitted to outpatient observation directly after Phase I of rehabilitation, had worse clinical parameters and ADLs in comparison to patients in Groups A and B at the end of the "plastic window" period. However, some special features of recovery were evident. Motor function of the lower limbs in patients in Group C recovered as well as in patients in Group A by the 90th day poststroke, and there were no significant differences in FMA-LE-4 (pA–C). The balance function was also not statistically different with regard to the FMA-Balance-4 score between the groups . Functional outcomes after three months, as assessed by mRS-4, and for neurological deficits and outcomes of motor recovery, as assessed by NIHSS-4 and FMA-4, showed significantly poorer results in Group C patients . Directly after stroke, the MAS-1 score was equal to 0 in patients of Groups A– C. At discharge from Tomsk RSC after two weeks of early rehabilitation, spasticity was registered by MAS-2 as +1 in two patients of Group A (10%) and three patients of Group C (20%). However, muscle tone in Group A did not differ during phases of rehabilitation MAS-2 = MAS-3 = MAS-4 = +1. In the same time period, patients of Group C showed an increase in spasticity on the 90th day poststroke (MAS-4 = 3). Group B patients did not have spasticity. Comparison results of the quality of movements in Group A and B patients are shown in We observed a significant increase in the “variability of movements” parameter of the statics domain in both patient groups . The accWe observed that, within the balance domain, patients significantly increased the height of raising their leg on the affected side during the fifth and tenth sessions of AR rehabilitation in comparison to during the first session . By the In this study, we assessed the clinical and functional effects of different rehabilitation programs applied to different combinations during the “plastic window” period in stroke patients. All patients included in our study received motor rehabilitation with early activation during Phase I of the program . Today, early stroke rehabilitation is recommended in many clinical practice guidelines, including the Order of the Ministry of Health of the Russian Federation no. 928n ,37, but The results of our study demonstrated that early rehabilitation, starting within the first 24 h of stroke, was successful. The primary outcome was improvements in functional independence as measured by mRS, and recovery of motor functions of the upper and lower extremities measured by FMA. Patients in each of the three groups showed significant changes in the evaluation scales between Points 1 and 2. It is difficult to estimate which part of motor recovery was due to spontaneous biological recovery based on structural neuroplasticity, and how much was dependent on the rehabilitation treatment based on synaptic plasticity . Upper lp2–4). However, they showed the worst clinical and functional outcomes by NIHSS, mRS, total FMA, and FMA-UE at the 90th day after stroke onset compared to those of Groups A and B. This indicates the negative impact of the absence of physical activity and rehabilitation interventions for the recovery of movement patterns in the upper extremities and ADL. At the same time, Group C patients showed no spontaneous recovery in the motor function of paralytic legs according to FMA-LE-4, and there were no significant differences between patients of Groups C and A. We also found no significant differences in FMA-Balance-4 between Group C and Groups A and B . With regard to the balance function, we could not exclude the role of compensatory adaptive mechanisms. In addition, there is the opinion that the rates of motor recovery for upper and lower extremities are different [The sensitive period of poststroke plasticity, when maximal recovery from neurological lesions is possible, is defined as the first 1–3 months after stroke ,45. Thisifferent . PareticpA–B). So, Phase II of motor rehabilitation with classical recovery approaches showed significant motor improvement in patients of Group A who showed no significant difference from participants of Group B by the clinical and functional scales at Point 3.In our study, we were able to assess the effects of classical stroke rehabilitation treatment for four weeks (Phase II) and the effects of applying a developed AR rehabilitation program (Phase III), and to compare the results of motor recovery in patients in Group A who had received Phase II and III treatment with patients in Group B after the AR rehabilitation program who underwent outpatient observation during Phase II. Clinical and functional outcome measures after a 90-day poststroke period showed no significant difference in Groups A and B. Members of Group A achieved the same level of motor recovery and functional outcomes after finishing the traditional rehabilitation program (Phase II) as that of Group B participants who underwent outpatient observation in this time period (Point 3). In Group A patients, motor control of the paretic arm, as assessed by FMA-UE-2, was significantly worse after Phase I in comparison to that of Group B, and significantly improved after Phase II, without significant differences in FMA-UE-3 between groups (p2–3). This can also be considered as an element of spontaneous-recovery manifestation. However, the NIHSS scale does not only characterize motor impairment, and the number of points could be decreased by restoring other impaired functions .Patients in Group B who had not received active treatment during Phase II of the study did not show any improvement in motor impairment by FMA and mRS scores at Point 3. This showed the deficiency of spontaneous motor recovery during the outpatient observation period. The average NIHSS score was statistically lower  in comparison to patients who had not. Patients of Group C who had not received active treatment performed significantly worse after three months and showed hardly any improvement after the first phase of the program. This may indicate the effectiveness of motor rehabilitation during Phases II and III of the program.The beneficial effects of the AR rehabilitation program are reflected by significant improvement during Phase III according to clinical and functional performance, and quality of movement measures in patients of Groups A and B. Interpreting the results, it becomes clear that muscle strength in the affected arm of patients in Groups A and B increased between the first and tenth AR biofeedback motion training sessions. Generally speaking, the movement accuracy and muscle force of the affected arm were augmented in Group A and B patients , which works with five dedicated VR training applications for the upper limbs and is synchronized with a high-resolution electroencephalogram (HR-EEG) system . As a reThis study was carried out in patients with moderate and mild stroke; thus, stroke patients with severe disabilities, or people with severe cognitive or communication problems were excluded. Lack of randomization after the first stage of rehabilitation is also a disadvantage of this study. However, study results support the evidence for the existence of a special sensitive period for neuronal plasticity during the three months from stroke onset, showing the positive effect of AR rehabilitation in a relatively large sample. First, we note the importance of early rehabilitation, which was successful in patients with moderate ischemic stroke in our study. Second, our study showed that Phase I is not enough for the spontaneous recovery of motor impairment, and rehabilitation programs should be carried out throughout the entire sensitive period of poststroke plasticity. Summarizing, traditional exercise programs and AR rehabilitation have proven themselves in clinical practice. The provided data are convincing in that the newly developed AR biofeedback motion training is effective and safe as a separate rehabilitation method in the early recovery period of moderately severe, hemiparalytic, and ischemic stroke. These two rehabilitation approaches must be applied together or after each other, not instead of each other, as shown in clinical practice.