Search For Solutions Research Articles

The prevalence of body dysmorphic disorder and the acceptance of cosmetic surgery in a nonclinical sample of Hungarian adults

Background: Body dysmorphic disorder (BDD) is a common, often undiagnosed, serious condition. The relationship between body dysmorphic disorder and cosmetic surgery is rather complex, as many patients with body dysmorphic disorder search for cosmetic solutions for the imagined defect in appearance. Aims: To assess the prevalence of body dysmorphic disorder and its relationship to the acceptance and use of cosmetic services and body-related variables among Hungarian adults. Methods: Body weight and height, body satisfaction, health state, aesthetic intervention-related experiences, and plans were assessed by a self-report online questionnaire that included the Body Dysmorphic Disorder Questionnaire and the Acceptance of Cosmetic Surgery Scale. Six hundred three subjects (94% women, n = 567, 6% men, n = 36, age M = 40.16 years, SD = 13.32 years) joined the research on a social media platform. Results: The prevalence of BDD is 8.1% (n = 49), 8.1% (n = 46) in women, and 8.3% (n = 3) in men. The BDD-positive group had higher BMI (U = 9641.5, p = 0.028), more plastic surgeries (χ2(1) = 19.682 , p = 0.012), and more acceptance of cosmetic surgery (U = 6664, p < 0.001). The risk of BDD is significantly higher in those who have a lower education (OR = 0.424, p = 0.031), accept cosmetic surgery (OR = 1.031, p = 0.025), plan plastic surgery (OR = 0.351, p = 0.027) and are more dissatisfied with their body (OR = 0.397, p < 0.001). Consideration of cosmetic surgery (U = 7433, p = 0.006) and planning for future plastic surgeries χ2(1) = 7.943, p = 0.019) are more frequent in females. Sixty-six women (11.6%) have already had some intervention, 26.4% plan, and 10.4% probably plan cosmetic surgery, while these data are 11.1%, 8.3% and 5.6% in males. These symptoms occur more frequently among women according to Body Dysmorphic Disorder Questionnaire: avoidance behavior (33.9% vs. 16.7%, χ2(1) = 4.539, p = 0.033), significant mental suffering caused by the perceived flaw(s) (29.8% vs. 13.9%, χ2(1) = 4.178, p = 0.041). The Acceptance of Cosmetic Surgery Scale total score has a significant positive relationship with BDD caseness (β = 0.100, p = 0.011) and the number of blepharoplasties (β = 0.111, p = 0.005) and significant negative relationship with the planning of cosmetic surgery (β = –0.491, p < 0.001). Conclusions: The prevalence of body dysmorphic disorder is high in this non-clinical, unselected Hungarian sample. The risk for body dysmorphic disorder is higher in those interested in cosmetic services; screening for this body image disorder is necessary.

Differential evolutionary particle swarm optimization with orthogonal learning for wind integrated optimal power flow

This study develops a novel variant of particle swarm optimization (PSO), which improves its balance of exploration and exploitation by modifying neighborhood topology, self-adaptive parameter strategies and deep search, namely differential evolutionary evolution PSO with orthogonal learning (OL), i.e., DEEPSO-OL in short. Evolutionary computing can explore the solution space efficiently because of its self-evolving attribute as iteration continues. The OL enhances its exploitation by focusing on deeper search for promising solutions. It utilizes the concept of orthogonal experimental design (OED) which predicts the best combination of control variables without exhaustive evaluation of all possible combinations. In addition, to avoid premature convergence in a local optimum, a stochastic star topology for particles is proposed. Such topology ensures just enough communication among the best performing particles, while encouraging them to explore other spaces. The efficacy of the algorithm is evaluated through real-world scenarios such as optimal power flow (OPF) and wind integrated OPF, which are hard to solve with classical mathematical methods. The proposed algorithm is run on a modified IEEE 30-bus test system and compared to the state-of-the-art evolutionary computing algorithms for a variety of cost objective functions with high levels of non-linearity and non-convexity. The DEEPSO-OL demonstrates its performance to generate more accurate feasible solutions and construct promising and efficient search method for real-world complex optimization problems.

Tourism Development through the Sense of UNESCO World Heritage: The Case of Hegra, Saudi Arabia

This study explores the perception of a “sense of place” among tourists visiting Hegra, an archaeological site in northern Saudi Arabia, through reviews on TripAdvisor. The 267 reviews on TripAdvisor between 2020 and 2023 were analyzed using the VADER sentiment polarity analysis tool and object modeling using the NMF machine learning algorithm. The results highlight positive factors linked to the history and uniqueness of the place while showing some critical issues related to isolation, cost, privatization, and competitiveness. The originality of the research lies in the type of case study chosen, an archaeological site of a country that has recently opened its doors to tourism, and in the pragmatic nature of the investigation, oriented towards the search for possible solutions to be adopted in terms of heritage management based on the feedback received for the development of the tourist destination.

Innovations and challenges in adsorption-based wastewater remediation: A comprehensive review

Water contamination is an escalating emergency confronting communities worldwide. While traditional adsorbents have laid the groundwork for effective water purification, their selectivity, capacity, and sustainability limitations have driven the search for more advanced solutions. Despite many technological advancements, economic, environmental, and regulatory hurdles challenge the practical application of advanced adsorption techniques in large-scale water treatment. Integrating nanotechnology, advanced material fabrication techniques, and data-driven design enabled by artificial intelligence (AI) and machine learning (ML) have led to a new generation of optimized, high-performance adsorbents. These advanced materials leverage properties like high surface area, tailored pore structures, and functionalized surfaces to capture diverse water contaminants efficiently. With a focus on sustainability and effectiveness, this review highlights the transformative potential of these advanced materials in setting new benchmarks for water purification technologies. This article delivers an in-depth exploration of the current landscape and future directions of adsorbent technology for water remediation, advocating for a multidisciplinary approach to overcome existing barriers in large-scale water treatment applications.

Quality Evaluation of New Types of Layered Composites for Flooring Materials.

The need, or even the obligation, to take care of the natural environment compels a search for new technological solutions, or for known solutions to be adapted to new applications. The maxim is 'don't harm, but improve the world for future generations'. In the wood industry in particular, given that it is based on a natural raw material, we must look for ecological solutions. Trees grow, but the demand for wood exceeds the volume of tree growth. In industrial manufacturing, one of the ways to make full use of wood is through chipless processing, which occurs during rotary cutting (peeling). In addition, wood is a natural material, each fragment of which has a range of properties. In addition, wood defects in quality manipulation generate a lot of waste. The aim of this study was to analyse the quality effect of the tested layered composites for flooring materials on production application. The practical purpose was to exchange actual sawing-based production for chipless production. The composite base layers were made of pine wood (Pinus L.) veneers with differing quality classes. The samples were subjected to three-point bending tests to calculate the moduli of elasticity and stiffness, which are the most important parameters. Because both analysed parameters describe product quality, the analyses were based on the creation of Shewhart control charts for each parameter. In theory, these control charts are tools for analysing whether the production process is stable and yields predictable results. To have full control over the process, five elements have to be applied: central line (target), two types of control lines (upper and lower) and two types of specification lines (upper and lower). New types of layered composites for flooring may be applied to production once verified using Shewhart control charts. It turns out that it is possible to produce the base layer of the flooring materials using the rotary cutting (peeling) method without having to analyse the quality of the raw material. This is a way to significantly increase the efficiency of production in every element of manufacturing.

Blood cardioplegia reduces intraoperative ventricular fibrillation and transfusion requirements compared to crystalloid cardioplegia in canine mitral valve repair.

Cardioplegic solutions are indispensable for open-heart surgeries, including mitral valve repair (MVR), a potentially curative treatment for myxomatous mitral valve disease in dogs. However, procedural methodologies are not fully established, and complications are yet to be comprehensively understood. Cardioplegic solutions contain various substances to protect the myocardium under temporal cardiac arrest. Nevertheless, ventricular fibrillation (VF) occurs as a common complication after releasing the crossclamp. Based on these backgrounds, the search for optimal cardioplegic solutions in dogs undergoing MVR is an urgent issue. This study aims to evaluate the occurrence of VF in dogs treated with blood cardioplegia (BCP) versus crystalloid cardioplegia (CCP) during MVR. A total of 251 client-owned dogs who underwent MVR from November 2015 to November 2017 were included. We retrospectively assessed the relationship between VF and type of cardioplegia (CCP or BCP) based on surgical records, including VF incidence, transfusion use, crossclamp time, and echocardiographic measurements. Logistic regression analysis showed that the CCP group was associated with the occurrence of VF (OR, 2.378; CI, 1.133-4.992; P = .022). In addition, the CCP group was associated with transfusion use (OR, 2.586; CI, 1.232-5.428, P = .022). There was no difference between the groups for the pre- and postoperative echocardiographic measurements. The BCP group had a lower incidence of VF and less transfusion use than the CCP group. This finding indicates that BCP may be a superior cardioplegic technique for MVR in dogs.

Smart forestry – a forestry 4.0 approach to intelligent and fully integrated timber harvesting

ABSTRACT Forestry in general and the wood supply chain in particular are facing a wide range of challenges. Digitalization seems to be able to provide a remedy, but the forest-based sector’s high degree of heterogeneity and the large number of actors hampers the search for solutions – in Germany and beyond. In the presented Smart Forestry approach, we apply concepts from Forestry 4.0 to achieve intelligent and fully integrated timber harvesting. It is based on digital twins that are networked and orchestrated in an Internet of Things. First, we reengineer existing forestry processes, identify the relevant objects and messages, and specify templates for digital twins. The necessary Internet of Things technologies are selected and enhanced for use in forestry. Finally, we build first prototypes of the target system that are the basis for upcoming reference experiments in practice – from the forest owner via production in the forest and logistics to the timber buyer.

MAISOTSENKO CYCLE FOR PROCESS WATER DISTILLATION AND DESALINATION

Search for new solutions for water distillation and desalination is one of the most important challenges facing scientists around the world. One of the promising methods of distillation and desalination of water is the use of the Maysotsenka cycle – a thermodynamic process, which use a psychrometric renewed energy available from the latent heat of water, which is evaporated into the airBy using the M-cycle, both energy savings and environmental benefits can be achieved. In this work, the technologies and technical solutions for distillation and water desalination systems according to the Maysovaluation cycle described in the literature are analized. Provided with diagrams of processes, installation and psychrometric diagrams. According to literature data, energy-saving distillation/desalination of water from stagnation of the M-cycle becomes 0.2…0.4 kW∙year/kg of water, which can compete with other desalination methods. The M-cycle has been shown to have the potential for a wide range of practical installations, while being characterized by a simpler installation design

Integrated design optimization method for pavement structure and materials based on further development of finite element and particle swarm optimization algorithm

The maintenance design for existing asphalt pavements, characterized by their typical structure and material composition, falls short of meeting the diverse requirements of pavements. There is an urgent need for a more rational design approach that can achieve accurate pavement design while also reducing construction costs. This paper focuses on selected conventional road surface structures and conducts an analysis of how pavement structure and materials impact fatigue life and construction expenses. It employs Python to perform three-dimensional finite element road modeling, analysis, and data extraction at specific locations, using normalized fatigue equation to calculate pavement fatigue life. The optimization variables selected are the modulus E and thickness h, with pavement maintenance cost serving as the optimization target function. The particle swarm optimization algorithm is employed for optimizing these variables. A comparative analysis is carried out for five improved optimization algorithms, optimizing combinations of inertia weight, learning factors, particle speed, number of particles, and iteration times. The results reveal that the enhanced optimization algorithms significantly improve iteration trends, featuring velocity limitations and natural selection mechanisms. Between 0 and 20 iterations, all algorithms search for the optimal solution over a wider interval with greater fluctuations. Between 20 and 30 iterations, each algorithm gradually converges towards its solution, stabilizing in the later stages. Based on optimization speed and actual engineering cost benchmarks, the NDPSO algorithm is selected, resulting in a 52.8% reduction in actual engineering costs. Adjustments to the particle count and iteration number of the NDPSO algorithm are made based on computational cost and optimization accuracy, thus choosing 20 particles and 50 iterations as the recommended parameter combination. By combining automated finite element modeling, analysis, and particle swarm optimization, this paper offers a novel and effective automated design reference for extending the lifespan of asphalt pavement structures.

A Simple Differential Evolution with Random Mutation and Crossover Constants for Constrained Optimization

The article proposes a simple version of the differential evolution algorithm (abbreviated as sDE) in which the mutation factor and crossover constant are chosen randomly in the range (0,1) during the search for the optimal solution. The sDE is the same as the original version of the differential evolution algorithm, except the user does not have to choose the best values of mutation constant and crossover constant for each optimization problem. Therefore, the optimization process is now very simple as it remains only one parameter (i.e. the population size) in the algorithm, besides the stopping criterion (e.g. number of iterations). It also consumes less computation time than the original differential evolution as it is not necessary to tune the mutation and crossover constants. In this study, the proposed technique is applied to three constrained optimizations, three engineering design problems, and six planar and spatial trusses under frequency constraints. Despite the very simple characteristics of the proposed technique, sDE gives promising results in comparison with other results in the literature.

Possibilities of using virtual reality technology in skills development

With changing threats and rapid technological progress, society is facing significant changes and challenges. Ensuring that people have the skills and capabilities to meet these challenges is key. One ‘tool’ for this could be the use of virtual reality (VR). The VR research community is becoming increasingly active in the search for solutions. In this article, we have tried to summarize the concepts related to VR education and training, followed by the challenges of using VR technology and the solutions already implemented through examples from the US, France and China. We then concluded the article by outlining possible future concepts that will need to be developed to implement VR training, with a particular focus on the issues of military training

Interaction of the national police with law enforcement units in the course of ensuring public security and order during mass events in Ukraine

The article examines the issue of interaction between law enforcement units in the course of ensuring public safety and order during mass events in Ukraine. It has been investigated that in the conditions of martial law, the powers of the National Police bodies have been significantly expanded, starting from the security and defense sector to direct tasks related to ensuring public safety and order in Ukraine.
 But, despite the fact that the issue of interaction has attracted the attention of scientists, in most cases it is considered in a wide range and certain features in the conditions of martial law remain not fully explored. Issues of interaction are extremely urgent and require coordination at the legislative level, as they do not have a generalized procedure for determining the necessity and coordination of actions.
 The security component in society is the main principle of observing the rights and freedoms of citizens, preventing rampant aggression, terrorism and mass crimes. The search for new joint compromise solutions is effective in combating these phenomena not only on the part of the National Police, but also other law enforcement agencies. Despite the fact that scientists have drawn attention to the issue of interaction, but in most cases these issues are considered in a wide range and certain features in the conditions of martial law remain incompletely researched.
 It was established that the concept of interaction should be considered as a complex phenomenon, in our case, related relationships between people in the field of law enforcement. The issue of interaction between the National Police and other law enforcement agencies requires a special approach during martial law. To ensure the protection of public safety and order during mass events, not only employees of the National Police, but also other units are involved. Coherence, interchangeability, clear coordination between all units is the basis of clear interaction between all structural units in the course of ensuring public order and public safety during mass events.
 It was determined that the interaction of the National Police with other law enforcement agencies and units in the course of ensuring public safety and order in Ukraine is an administrative set of measures aimed at increasing the efficiency and coordination of the work of the National Police with other law enforcement agencies and units in the course of ensuring public safety and order during the mass events in Ukraine.

Primary Health Care: the Relational Aspects of Social Interaction between Family Doctors and People with Disabilities

Strategic documents of the United Nations and the European Union express the belief that primary health care is a fundamental part of health care services and it must be accessible to all people and funded according to the means. Many research studies prove that enhancement of the primary health care system helps to effectively eliminate causes and risk factors for poor health and to prevent likely impairment of health in the future. However, the data of recent investigations demonstrate that the quality of health care services in Lithuania is much poorer than the average of the EU, and the institution of family doctors providing services of this type suffers from problems related to the efficiency of functioning. Therefore, it is worth investigating possible causes for this problem, to identify them and search for solutions. The paper presents the research that is aimed at examining one part of the aspects of the problem, specifically the relational aspects of social interaction between family doctors and people with disabilities from the point of view of people with disabilities and their relatives. 555 people with disabilities (PWD) and 540 respondents who had relatives with disabilities and took care of them (PWDcare) participated in the survey carried out in 2019–2020 and reported about their experience of social interaction with family doctors. The research indicated many positive tendencies, of which the most prominent was the satisfaction of the relationship, trust and understandment with family doctors. But at the same time the research revealed some more problematic areas, such as the lack of family doctors’ compassion and warmth to many patients. These results are important for improving the primary health care service and quality as well for strengthening the family doctors’ education.

A framework for design optimization across multiple concepts

In engineering design, there often exist multiple conceptual solutions to a given problem. Concept design and selection is the first phase of the design process that is estimated to affect up to 70% of the life cycle cost of a product. Currently, optimization methods are rarely used in this phase, since standard optimization methods inherently assume a fixed (given) concept; and undertaking a full-fledged optimization for each possible concept is untenable. In this paper, we aim to address this gap by developing a framework that searches for optimum solutions efficiently across multiple concepts, where each concept may be defined using a different number, or type, of variables (continuous, binary, discrete, categorical etc.). The proposed approach makes progressive data-driven decisions regarding which concept(s) and corresponding solution(s) should be evaluated over the course of search, so as to minimize the computational budget spent on less promising concepts, as well as ensuring that the search does not prematurely converge to a non-optimal concept. This is achieved through the use of a tree-structured Parzen estimator (TPE) based sampler in addition to Gaussian process (GP), and random forest (RF) regressors. Aside from extending the use of GP and RF to search across multiple concepts, this study highlights the previously unexplored benefits of TPE for design optimization. The performance of the approach is demonstrated using diverse case studies, including design of a cantilever beam, coronary stents, and lattice structures using a limited computational budget. We believe this contribution fills an important gap and capitalizes on the developments in the machine learning domain to support designers involved in concept-based design.

FGeo-TP: A Language Model-Enhanced Solver for Euclidean Geometry Problems

The application of contemporary artificial intelligence techniques to address geometric problems and automated deductive proofs has always been a grand challenge to the interdisciplinary field of mathematics and artificial intelligence. This is the fourth article in a series of our works, in our previous work, we established a geometric formalized system known as FormalGeo. Moreover, we annotated approximately 7000 geometric problems, forming the FormalGeo7k dataset. Despite the fact that FGPS (Formal Geometry Problem Solver) can achieve interpretable algebraic equation solving and human-like deductive reasoning, it often experiences timeouts due to the complexity of the search strategy. In this paper, we introduced FGeo-TP (theorem predictor), which utilizes the language model to predict the theorem sequences for solving geometry problems. The encoder and decoder components in the transformer architecture naturally establish a mapping between the sequences and embedding vectors, exhibiting inherent symmetry. We compare the effectiveness of various transformer architectures, such as BART or T5, in theorem prediction, and implement pruning in the search process of FGPS, thereby improving its performance when solving geometry problems. Our results demonstrate a significant increase in the problem-solving rate of the language model-enhanced FGeo-TP on the FormalGeo7k dataset, rising from 39.7% to 80.86%. Furthermore, FGeo-TP exhibits notable reductions in solution times and search steps across problems of varying difficulty levels.

Learning path planning methods based on learning path variability and ant colony optimization

With the advancement of education information, the scale of online education has been expanding, which brings challenges to students' learning path planning, i.e., course and learning method planning. To address the limitations of learning path planning such as insufficient personalization, the study proposes a learning path planning method based on learning path variability and ant colony optimization. First, dynamic time regularization is used to obtain learning path variability, and the K-means algorithm is used to classify students' learning types. Subsequently, an ant colony optimization algorithm is used to generate learning paths. Finally, the effectiveness of the method is tested. The results show that the loss value of the ant colony optimization algorithm converges to a minimum value of 0.1, which has the best stability of the loss function curve and the fastest convergence speed compared to other algorithms. Under the same experimental environment, the accuracy of the algorithm is as high as 0.9, which is conducive to the search for the optimal solution. The path planning method designed by the research can effectively grasp the learning characteristics and habits of students, and the accurate classification degree can reach 96.6%. With this learning path planning method, the average video learning time of students reaches a maximum of 80 min, while the average completion rate of students' course objectives is stable at 90%, which is about 20% higher than that of the GA-based learning path planning method. The method can significantly improve academic performance and educational outcomes. The method thus grasps the type of student learning, stimulates students' interest in learning, improves the effect of online learning, helps to promote education informatization and provides a boost to the deep reform of education.

Risks of mental health of foreign medical residents who study in Ukraine

IntroductionThe high level of psycho-emotional stress today significantly increases the moral burden on the mental health of medical workers. One of the most vulnerable groups of medical specialists in Ukraine are foreign resident doctors. Since in Ukraine this contingent of doctors is faced with extraordinary problems of high psycho-emotional stress, such as COVID-19 around the world and the russian war against Ukraine. Raising the issue of resident doctors, the main issue becomes not only their professional identity, but also the formation of the necessary stress resistance in different conditions of professional activity.ObjectivesTo study the level of tolerance to stress and uncertainty among foreign resident doctors in unusual conditions of performing professional duties.MethodsThe examination included the use of clinical-psychological, psychodiagnostic and psychometric research methods.ResultsAs of the beginning of 2020, 395 foreigners were studying. During the first phase of the pandemic, 118 foreigners left Ukraine. By the beginning of 2022 (before the full-scale war), 302 medical residents were trained. As of the beginning of 2023, 167 doctors are studying, of which only 61 people are on the territory of Ukraine.The primary analysis of the clinical-psychological study showed that the most common complaints among foreign resident doctors are: increased levels of feelings of tension (in 75,4%), decreased motivation for activity (73,2%), anxious (72,7%) and depressive symptoms (69,3%), frequent headaches (68.6%), constant feelings of irritation (65,4%), manifestations of aggression in relation to colleagues (63,9%) and patients(61,4%), a feeling of fear for the future (60,1%), conflicts in the family (59,5%).The study of factors that influence the increase in the level of stress among foreign resident doctors were sorted according to the principle of ordinary and extraordinary. Ordinary stress factors include: the nature of the specialty, the conditions of professional activity, a foreign country, relationships in the team. Extraordinary factors include new working conditions (professional challenges of COVID-19, war on the territory of Ukraine), increased risks of responsibility for the patient’s life (search for a treatment solution against the background of COVID-19), nature of assistance (providing assistance due to combat injuries).ConclusionsAt the end of the study, a comprehensive program will be created for the early detection of signs of adaptation disorder, which will be aimed at reducing emotional distress, tension in the learning process, support in the first years of training for medical residents.Disclosure of InterestNone Declared

Combination of photodynamic therapy with antibiotic therapy - future or misalliance?

SignificanceThe growing antibiotic resistance of microorganisms is an increasingly important challenge for modern medicine. In the next few decades, the so-called post antibiotic era is likely to begin. For this reason, the search for new solutions using PACT seems particularly promising. ApproachFour new chlorin compounds were tested and photochemically characterized for singlet oxygen generation efficiency and photostability. They have been evaluated for bactericidal properties against MRSA, E. coli (ESBL+) and C. albicans. Their synergism with antibiotics from the group of cephalosporins and carbapenems was evaluated. ResultsA reduction of approximately 3 logs or more was achieved for each of the bacterial strains. A decrease in MIC values was observed for all bacteria, the greatest for ceftriaxone. ConclusionsCombining traditional antibiotic therapy with PACT may be a promising route in the case of antibiotic-resistant infections, but requires further in-depth research.

Detecting Hidden and Irrelevant Objectives in Interactive Multiobjective Optimization

Evolutionary multi-objective optimization algorithms (EMOAs) typically assume that all objectives that are relevant to the decision-maker (DM) are optimized by the EMOA. In some scenarios, however, there are irrelevant objectives that are optimized by the EMOA but ignored by the DM, as well as, hidden objectives that the DM considers when judging the utility of solutions but are not optimized. This discrepancy between the EMOA and the DM’s preferences may impede the search for the most-preferred solution and waste resources evaluating irrelevant objectives. Research on objective reduction has focused so far on the structure of the problem and correlations between objectives and neglected the role of the DM. We formally define here the concepts of irrelevant and hidden objectives and propose methods for detecting them, based on uni-variate feature selection and recursive feature elimination, that use the preferences already elicited when a DM interacts with a ranking-based interactive EMOA (iEMOA). We incorporate the detection methods into an iEMOA capable of dynamically switching the objectives being optimized. Our experiments show that this approach can efficiently identify which objectives are relevant to the DM and reduce the number of objectives being optimized, while keeping and often improving the utility, according to the DM, of the best solution found.

Impact of Assimilation of the Tropical Cyclone Strong Winds Observed by Synthetic Aperture Radar on Analyses and Forecasts

Abstract A few high-wind observations have been obtained from satellites over the ocean around tropical cyclones (TCs), but the impact of data assimilation of such observations over the sea on forecasting has not been clear. The spaceborne synthetic aperture radar (SAR) provides high-resolution and wide-area ocean surface wind speed data around the center of a TC. In this study, the impact of data assimilation of the ocean surface wind speed of SAR (OWSAR) on regional model forecasts was investigated. The assimilated data were estimated from SAR on board Sentinel-1 and RADARSAT-2. The bias of OWSAR depends on wind speed, the observation error variance depends on wind speed and incidence angle, and the spatial observation error correlation depends on the incidence angle. The observed OWSAR is screened using the variational quality control method with the Huber norm. In the case of Typhoon Hagibis (2019), OWSAR assimilation modified the TC low-level inflow, which also modified the TC upper-level outflow. The propagation of this OWSAR assimilation effect from the surface to the upper troposphere was given by a four-dimensional variational method that searches for the optimal solution within strong constraints on the time evolution of the forecast model. Statistical validation confirmed that errors in the TC intensity forecast decreased over lead times of 15 h, but this was not statistically significant. The validation using wind profiler observations showed that OWSAR assimilation significantly improved the accuracy of wind speed predictions from the middle to the upper level of the troposphere. Significance Statement The purpose of this study was to demonstrate the impact of the assimilation of ocean surface wind speed by synthetic aperture radar (SAR) on regional model predictions. In the case of tropical cyclones, ocean surface wind speed assimilation modified inflows in the lower layer and outflows in the upper layer. The results indicate that the SAR assimilation improves the accuracy of wind speed forecasts in the middle to upper troposphere.