Integrative Approach Research Articles

Premenstrual Syndrome: An Overview of the Review Analysis, and Potential Role of Herbal and Dietary Supplements

: A premenstrual syndrome is a group of regular physiological, mental, emotional, and behavioral symptoms that appear periodically in the luteal phase of the menstrual cycle, and they vanish quickly with the beginning of menstruation or a few days afterward. Thus, severe premenstrual syndrome (PMS) affects 5–8% of women; the majority of these individuals also match the factors for premenstrual dysphoric disorder (PMDD). Premenstrual syndrome typically includes physical problems like headache, tiredness, indigestion, insomnia, nausea, and breast tenderness as well as symptoms associated with emotions (mood swings, anxiousness, and aggressiveness). Although the true mechanism of PMS is unknown, a number of theories point to increased sensitivity to typical hormonal changes and imbalances in neurotransmitters. A multidisciplinary team using an integrative holistic approach in the direction of an individualized strategy gradually manages PMS. The first phase entails educating women about PMS, self-screening, herbs like St John’s Wort, chaste berry, saffron; ginkgo biloba; chamomile, evening primrose, turmeric and so on, various nutrition supplements as Vitamin B6; calcium; magnesium and lifestyle adjustments like dietary modifications; exercise; yoga; sleep hygiene. The second step involves implementing various relaxation therapies, cognitive behavioral therapy (CBT), stress reduction techniques, and acupuncture. The available treatment as various drugs like SSRIs; serotonergic antidepressants; anxiolytics and hormonal therapy that are used currently to treat PMS, exhibits unwanted side effects. Therefore, this issue and other similar gynecological conditions have been discussed and managed with herbal medicines and other natural solutions in this comprehensive piece of work.

Аналіз застосування технологій ПЛІС в складі IoT

The subject of study in this article and work is the modern technologies of programmable logic devices (PLD) classified as FPGA, and the peculiarities of its application in Internet-of-Things domain at different architectural layers of the implementation, as well as the elements of decision making during choosing of appropriate solutions based on FPGA in the context of implementation of IoT tasks with the requirements of intensive computations. The goal is to analyze the possibilities and peculiarities of the application of technology of programmable logic devices as part of the modern architecture of the Internet of Things with the improvement of decision making process during the choosing of appropriate technical solutions for the use of FPGA with taking into account the types of tasks. Tasks: to analyze the current state of demands regarding the use of FPGA technology in IoT projects; to analyze the challenges and limitations for the application of FPGA technology at different layers during the prototyping of IoT infrastructure; to propose ways of the use of FPGA technology in the IoT infrastructure with the use of high-speed computations; to analyze the advantages and disadvantages of the proposed approaches of integration of FPGA technology; to provide recommendations and determine the further direction of the research of the use of FPGA technology as a part of the IoT infrastructure considering of AI/ML tasks; to provide a practical example of the use of FPGA technology during the creation of a high-performance IoT system. According to the tasks, the following results were obtained. The analysis of the current state of the use of FPGA technology in IoT projects is performed, that showed the significant potential of these technologies for the implementation of high-speed computations in Internet of Things systems. A classification of the ways of applying FPGA in the tasks of real-time data processing and implementation of neural networks is proposed. The set of limitations of the possibility of FPGA technology applying at different layers of IoT infrastructure is defined. It includes high equipment costs, the need for specific design skills, limitations in energy consumption, and the complexity of the integration with existing systems. The practical example of the use of the IoT system with the implementation of high-intensive computations is provided. Conclusions. The main contribution and scientific novelty of the obtained results is that the conducted analysis allows to make a decision about the possibility of applying programmable logic in the construction of IoT projects and home automation systems. Based on the proposed classification of types of IoT tasks, there is a possibility of making a decision on the application of FPGA technology as a separate integrated circuit in the system or using a complete instance of FPGA as a Service in the cloud environment.

Ontology-based GraphQL server generation for data access and data integration

In a GraphQL Web API, a so-called GraphQL schema defines the types of data objects that can be queried, and so-called resolver functions are responsible for fetching the relevant data from underlying data sources. Thus, we can expect to use GraphQL not only for data access but also for data integration, if the GraphQL schema reflects the semantics of data from multiple data sources, and the resolver functions can obtain data from these data sources and structure the data according to the schema. However, there does not exist a semantics-aware approach to employ GraphQL for data integration. Furthermore, there are no formal methods for defining a GraphQL API based on an ontology. In this work, we introduce a framework for using GraphQL in which a global domain ontology informs the generation of a GraphQL server that answers requests by querying heterogeneous data sources. The core of this framework consists of an algorithm to generate a GraphQL schema based on an ontology and a generic resolver function based on semantic mappings. We provide a prototype, OBG-gen, of this framework, and we evaluate our approach over a real-world data integration scenario in the materials design domain and two synthetic benchmark scenarios (Linköping GraphQL Benchmark and GTFS-Madrid-Bench). The experimental results of our evaluation indicate that: (i) our approach is feasible to generate GraphQL servers for data access and integration over heterogeneous data sources, thus avoiding a manual construction of GraphQL servers, and (ii) our data access and integration approach is general and applicable to different domains where data is shared or queried via different ways.

Bridge Builders: Bridging Resettlement Gaps Through a Relationship-Based Volunteer Program

The Bridge Builders (BB) program is an initiative implemented by Refugee Community Partnership (RCP), an organization that centers refugees. This program matches volunteers with refugee families in reciprocal relationships, facilitating integration and social support. Sixteen volunteers participated in semi-structured interviews that elicited their views on the BB program. Volunteers valued the relationship-based model and its interrogation of power structures. They highlighted challenges in accountability and navigating boundaries with their matched families. Volunteers expressed desire for increased opportunities to connect with other volunteers and the broader RCP community. Findings show strengths and challenges of relationship-based approaches in refugee resettlement and integration.

Evaluating Real World Health System Resource Utilization and Costs for a Risk-Based Breast Cancer Screening Approach in the Canadian PERSPECTIVE Integration and Implementation Project.

A prospective cohort study was undertaken within the PERSPECTIVE I&I project to evaluate healthcare resource utilization and costs associated with breast cancer risk assessment and screening and overall costs stratified by risk level, in Ontario, Canada. From July 2019 to December 2022, 1997 females aged 50 to 70 years consented to risk assessment and received their breast cancer risk level and personalized screening action plan in Ontario. The mean costs for risk-stratified screening-related activities included risk assessment, screening and diagnostic costs. The GETCOST macro from the Institute of Clinical Evaluative Sciences (ICES) assessed the mean overall healthcare system costs. For the 1997 participants, 83.3%, 14.4% and 2.3% were estimated to be average, higher than average, and high risk, respectively (median age (IQR): 60 [56-64] years). Stratification into the three risk levels was determined using the validated multifactorial CanRisk prediction tool that includes family history information, a polygenic risk score (PRS), breast density and established lifestyle/hormonal risk factors. The mean number of genetic counseling visits, mammograms and MRIs per individual increased with risk level. High-risk participants incurred the highest overall mean risk-stratified screening-related costs in 2022 CAD (±SD) at CAD 905 (±269) followed by CAD 580 (±192) and CAD 521 (±163) for higher-than-average and average-risk participants, respectively. Among the breast screening-related costs, the greatest cost burden across all risk groups was the risk assessment cost, followed by total diagnostic and screening costs. The mean overall healthcare cost per participant (±SD) was the highest for the average risk participants with CAD 6311 (±19,641), followed by higher than average risk with CAD 5391 (±8325) and high risk with CAD 5169 (±7676). Although high-risk participants incurred the highest risk-stratified screening-related costs, their costs for overall healthcare utilization costs were similar to other risk levels. Our study underscored the importance of integrating risk stratification as part of the screening pathway to support breast cancer detection at an earlier and more treatable stage, thereby reducing costs and the overall burden on the healthcare system.

Identification of Hamiltonian systems using neural networks and first integrals approaches

This research introduces a class of non-parametric identifiers based on differential neural networks represented by Hamiltonian dynamics. The structure of the identifier corresponds to the form of a canonical Hamiltonian system that uses the evolution of generalized coordinates and momentums. The learning laws of the identifier come from applying the first integrals approach, which justifies the design of an exact identifier considering the time invariance of the Hamiltonian, with a finite number of activation functions in the identifier structure. The first integrals approach derives several learning laws for the proposed class of identifiers. The learning laws design uses the estimated derivative of the generalized momentum assessed via a super-twisting differentiator with multiple inputs and outputs. All proposed laws require the solution of differential continuous-time Riccati equations and nonlinear differential equations for the learning laws, which depend on the identification error and state constraints. The developed identifier was evaluated compared to an identifier that did not consider the Hamiltonian constraint using first integrals. This comparison included a numerical evaluation of the identifier considering its application to a classical Hamiltonian system associated with the Kepler dynamics representing satellite orbital evolution. This evaluation confirmed that the identification results were improved with the proposed learning laws regarding the class of Hamiltonian structures, and the quality indicators based on the mean square error were several times lower.

Comments on integrability in the symmetric orbifold

We present a map between the excitation of the symmetric-product orbifold CFT of T4, and of the worldsheet-integrability description of AdS3 × S3 × T4 of Lloyd, Ohlsson Sax, Sfondrini, and Stefański at k = 1. We discuss the map in the absence of RR fluxes, when the theory is free, and at small RR flux, h ≪ 1, where the symmetric-orbifold CFT is deformed by a marginal operator from the twist-two sector. We discuss the recent results of Gaberdiel, Gopakumar, and Nairz, who computed from the perturbed symmetric-product orbifold the central extension to the symmetry algebra of the theory and its coproduct. We show that it coincides with the h ≪ 1 expansion of the lightcone symmetry algebra known from worldsheet integrability, and that hence the S matrix found by Gaberdiel, Gopakumar, and Nairz maps to the one bootstrapped by the worldsheet integrability approach.

INVESTIGATING UNIVERSITY LEVEL STUDENTS’ PREFERENCES BETWEEN TRADITIONAL AND BLENDED LEARNING APPROACHES

This research assessed the students’ attitudes toward blended learning at universities, specifically accenting delivery mode preferences, exam format preferences, and trust in the digital instructional resources. The study used a quantitative research method with descriptive survey design. A purposive sample of 450 students from public and private universities was used. The study utilized the validated instruments. It was also notable that students had highly positive inclination toward face-to-face interaction, and hands-on practice, which suggested a hybrid approach of face-to-face teaching and online instruction was preferred over the fully online style of teaching. The analysis revealed that university type and academic year influence attitudes. Concerning the exam types, students were more comfortable with traditional paper and pencil tests; but male students were more accepting of technology-based tests. The level of trust in digital instructional materials was high, although it varied by year of study. Thus, teachers should adopt a gradual approach in integration of blended learning into their classroom. There is need to support learning from diverse backgrounds. The study can help inform educational policy, course development & faculty professional growth in institutions.

Study Profile of the Tsuruoka Metabolomics Cohort Study (TMCS).

The Tsuruoka Metabolomics Cohort Study (TMCS) is an ongoing population-based cohort study being conducted in the rural area of Yamagata Prefecture, Japan. This study aimed to enhance the precision prevention of multi-factorial, complex diseases, including non-communicable and aging-associated diseases, by improving risk stratification and prediction measures. At baseline, 11,002 participants aged 35-74 years were recruited in Tsuruoka City, Yamagata Prefecture, Japan, between 2012 and 2015, with an ongoing follow-up survey. Participants underwent various measurements, examinations, tests, and questionnaires on their health, lifestyle, and social factors. This study uses an integrative approach with deep molecular profiling to identify potential biomarkers linked to phenotypes that underpin disease pathophysiology and provide better mechanistic insights into social health determinants. The TMCS incorporates multi-omics data, including genetic and metabolomic analyses of 10,933 participants, and comprehensive data collection ranging from physical, psychological, behavioral, and social to biological data. The metabolome is used as a phenotypic probe because it is sensitive to changes in physiological and external conditions. The TMCS focuses on collecting outcomes for cardiovascular disease, cancer incidence and mortality, disability and functional decline due to aging and disease sequelae, and the variation in health status within the body represented by omics analysis that lies between exposure and disease. It contains several sub-studies on aging, heated tobacco products, and women's health. This study is notable for its robust design, high participation rate (89%), and long-term repeated surveys. Moreover, it contributes to precision prevention in Japan and East Asia as a well-established multi-omics platform.

LSTM Network-Based Adaptation Approach for Dynamic Integration in Intelligent End-Edge-Cloud Systems

LSTM Network-Based Adaptation Approach for Dynamic Integration in Intelligent End-Edge-Cloud Systems

Exploring the Potential Mechanisms of Action of Gentiana Veitchiorum Hemsl. Extract in the Treatment of Cholestasis using UPLC-MS/MS, Systematic Network Pharmacology, and Molecular Docking.

Gentiana veitchiorum Hemsl. (GV) has a long history in Tibetan medicine for treating hepatobiliary disease cholestasis. However, the mechanisms mediating its efficacy in treating cholestasis have yet to be determined. To elucidate the mechanisms of action of GV in the treatment of cholestasis, an integrated approach combining ultra performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) analysis with network pharmacology was established. A comprehensive analysis of the chemical composition of GV was achieved by UPLC-MS/MS. Subsequently, a network pharmacology method that integrated target prediction, a protein-protein interaction (PPI) network, gene set enrichment analysis, and a component- target-pathway network was established, and finally, molecular docking and experiments in vitro were conducted to verify the predicted results. Twenty compounds that were extracted from GV were identified by UPLC-MS/MS analysis. Core proteins such as AKT1, TNF, and IL6 were obtained through screening in the Network pharmacology PPI network. The Kyoto Encyclopedia of the Genome (KEGG) pathway predicted that GV could treat cholestasis by acting on signaling pathways such as TNF/IL-17 / PI3K-Akt. Network pharmacology suggested that GV might exert a therapeutic effect on cholestasis by regulating the expression levels of inflammatory mediators, and the results were further confirmed by the subsequent construction of an LPS-induced RAW 264.7 cell model. In this study, UPLC-MS/MS analysis, network pharmacology, and experiment validation were used to explore potential mechanisms of action of GV in the treatment of cholestasis.

Investigation on itraconazole solubility in aqueous solutions based on models, solvent effect, thermodynamic analysis and quantum chemical calculations

The mole-fraction solubility of itraconazole in four aqueous blends of ethanol/isopropanol/DMSO/methanol within the temperature range of 283.15 to 323.15 K was experimentally obtained using the isothermal shake-flask method. Under the identical temperature and ethanol/isopropanol/DMSO/methanol composition, itraconazole solubility in DMSO+water is much higher than that in ethanol/isopropanol/methanol + water. At the same temperature, the solubility increases monotonically with organic solvent concentration. X-ray power diffraction analysis demonstrated that over the course of the investigations, there was no crystal transition or solvate formation. The modified van’t Hoff-Jouyban-Acree and Jouyban-Acree models adequately related the solubility to solvent composition and temperature, with relative average deviations (RADs) not exceeding 7.65 %. Furthermore, the extended Hildebrand solubility approach was utilized to quantitatively characterize the solubility behavior at 298.15 K for the mixtures of ethanol/isopropanol/DMSO/methanol plus water. In both instances, the RADs were maintained below 4.12 %. The solubility parameter and dipolarity-polarizability of solutions have a major impact on the solubility fluctuation, as indicated by the analysis of the linear solvation energy relationship. The preferential solvation of itraconazole at 298.15 K was examined using the efficient approach of inverse Kirkwood-Buff integrals. The preferred solvation parameters showed positive values in blends within rich and moderate ethanol/isopropanol/DMSO/methanol composition regions. This suggests that the organic solvents preferentially solvated itraconazole. When itraconazole dissolved in the blends, thermodynamic analysis of the entropy-enthalpy compensation and dissolution parameters revealed both an endothermic and an enthalpy-driven mechanism. Furthermore, the microscopic electrostatic characteristics of basicity and acidity were effectively demonstrated by means of the electrostatic potential of molecular surface. The −C=O and −N=groups of the itraconazole molecule, which link the five-membered ring, are the primary targets of the electrophilic attack. An independent gradient model based on Hirshfeld partition analysis was used to demonstrate the weak interactions between itraconazole and solvents.

Photonics for Neuromorphic Computing: Fundamentals, Devices, and Opportunities.

In the dynamic landscape of Artificial Intelligence (AI), two notable phenomena are becoming predominant: the exponential growth of large AI model sizes and the explosion of massive amount of data. Meanwhile, scientific research such as quantum computing and protein synthesis increasingly demand higher computing capacities. As the Moore's Law approaches its terminus, there is an urgent need for alternative computing paradigms that satisfy this growing computing demand and break through the barrier of the von Neumann model. Neuromorphic computing, inspired by the mechanism and functionality of human brains, uses physical artificial neurons to do computations and is drawing widespread attention. This review studies the expansion of optoelectronic devices on photonic integration platforms that has led to significant growth in photonic computing, where photonic integrated circuits (PICs) have enabled ultrafast artificial neural networks (ANN) with sub-nanosecond latencies, low heat dissipation, and high parallelism. In particular, various technologies and devices employed in neuromorphic photonic AI accelerators, spanning from traditional optics to PCSEL lasers are examined. Lastly, it is recognized that existing neuromorphic technologies encounter obstacles in meeting the peta-level computing speed and energy efficiency threshold, and potential approaches in new devices, fabrication, materials, and integration to drive innovation are also explored. As the current challenges and barriers in cost, scalability, footprint, and computing capacity are resolved one-by-one, photonic neuromorphic systems are bound to co-exist with, if not replace, conventional electronic computers and transform the landscape of AI and scientific computing in the foreseeablefuture.

Sosialisasi Peran Artificial Intelligence Terhadap Proses Pembelajaran Mahasiswa Di Universitas Pelita Bangsa

The rapid advancement of information technology greatly impacts education, especially in learning. Artificial Intelligence (AI) is pivotal in various sectors, including education. This study explores AI's role in student learning at Universitas Pelita Bangsa. AI adaptation addresses challenges like educator scarcity, offering tailored learning experiences. It fosters active engagement and immediate feedback. However, overreliance on AI may hinder creativity and change traditional teaching. A study was conducted at Universitas Pelita Bangsa, engaging 50 students through a Zoom seminar. Topics included AI introduction, educational applications, and pros and cons. Findings show high satisfaction (75% satisfied, 20% highly satisfied). Enhanced learning quality was observed. In conclusion, AI integration holds potential for better learning outcomes. Continuous evaluation and adaptability are vital. This study highlights the importance of student-centric approaches in AI integration.

Quantum Path Integral Approach for Vehicle Routing Optimization With Limited Qubit

Quantum Path Integral Approach for Vehicle Routing Optimization With Limited Qubit

Predicting an Optimal Virtual Data Model for Uniform Access to Large Heterogeneous Data

ABSTRACT The growth of generated data in the industry requires new efficient big data integration approaches for uniform data access by end-users to perform better business operations. Data virtualization systems, including Ontology-Based Data Access (ODBA) query data on-the-fly against the original data sources without any prior data materialization. Existing approaches by design use a fixed model e.g., TABULAR as the only Virtual Data Model — a uniform schema built on-the-fly to load, transform, and join relevant data. While other data models, such as GRAPH or DOCUMENT, are more flexible and, thus, can be more suitable for some common types of queries, such as join or nested queries. Those queries are hard to predict because they depend on many criteria, such as query plan, data model, data size, and operations. To address the problem of selecting the optimal virtual data model for queries on large datasets, we present a new approach that (1) builds on the principal of OBDA to query and join large heterogeneous data in a distributed manner and (2) calls a deep learning method to predict the optimal virtual data model using features extracted from SPARQL queries. OPTIMA — implementation of our approach currently leverages state-of-the-art Big Data technologies, Apache-Spark and Graphx, and implements two virtual data models, GRAPH and TABULAR, and supports out-of-the-box five data sources models: property graph, document-based, e.g., wide-columnar, relational, and tabular, stored in Neo4j, MongoDB, Cassandra, MySQL, and CSV respectively. Extensive experiments show that our approach is returning the optimal virtual model with an accuracy of 0.831, thus, a reduction in query execution time of over 40% for the tabular model selection and over 30% for the graph model selection.

Triangular Greenness Index Analysis for Monitoring Fungal Disease in Pine Trees: A UAV-based Approach

The Triangular Greenness Index (TGI) is a vegetation index derived from high-resolution aerial images acquired using unmanned aerial vehicles (UAVs). It serves as a valuable tool for quantifying vegetation health and dynamics in the visible spectrum. The TGI combines key components, including red reflectance and green reflectance, extracted from UAV-based imagery. The red component represents chlorophyll absorption and photosynthetic activity, while the green component reflects vegetation density and canopy structure. By integrating these components, the TGI offers a comprehensive measure of photosynthetically active vegetation, utilizing UAVs as a data collection platform. This study highlight the importance of the TGI derived from UAV-based imagery in monitoring vegetation changes, assessing ecosystem responses, and tracking variations in land cover and biodiversity. Furthermore, the application of TGI analysis using UAV-based aerial imagery shows promise in accurately identifying and monitoring vegetation affected by fungal diseases. This integrated approach enables the detection of diseased trees based on distinct changes in greenness observed in their foliage. Because fungal diseases dry the plant and cause the green areas to disappear. The integration of UAV technology enhances the accuracy and efficiency of TGI calculation, contributing to effective management and conservation strategies in the context of fungal disease detection in vegetation. In this study, TGI was produced using UAV-based orthophoto and healthy and sick trees were determined. According to the accuracy analysis, producer accuracy for detecting green plants was 99.7% and user accuracy was 98.5%. Fungal disease could be detected with 98.5% producer accuracy and 96.5% user accuracy. The overall accuracy of the study was calculated as 98.6%.

Risk management in the beverage production industry using FMEA and fuzzy cognitive map in an uncertain environment

Proper and systematic management of food industry failures can improve the quality of products and save a lot on the costs of organizations and people’s health. One of the conventional methods for risk assessment is the Failure Modes and Effects Analysis (FMEA) which is often performed in a phase or stage. Compared to the combined methods, this method is less accurate due to similar priorities of failure in the evaluation and the lack of consideration of the interaction between risks. The current research has applied an integrated approach based on two techniques, FMEA and Fuzzy Cognitive Map (FCM), in a multi-stage manner to increase assessment accuracy and ranking of failures. By considering the risks of an industry in an uncertain environment and the causal relationships between failures, this approach can evaluate the industry’s risks better than conventional methods. In the research method, the initial prioritization of failures by the FMEA method is used as the input of the multi-stage FCM. The cause-and-effect relationship between the failures is determined by experts and the functional records of the processes, and the FCM is prepared. Since no research evaluates the risks of the malting industry step by step and considers the causal relationships between the risks, the present study has improved risk evaluation in the malting industry by using a multi-stage FCM. The ranking results with the proposed hybrid approach and its comparison with the conventional methods showed that the rating became more accurate, and the multiple priorities were improved. Managers of the malt beverage industry can make effective investment decisions to reduce or better control the risks of this industry by using the results of applying the proposed approach.

Institutional constraints to social dialogue in work integration of persons with disabilities: Slovakia and Norway compared

PurposeStudies on the work integration of persons with disabilities (PwD) and the role of social dialogue therein are scarce. The study examines how the different systems of workers’ representation and industrial relations in Slovakia and Norway facilitate PwD work integration. Taking a social ecosystem perspective, we acknowledge the role of various stakeholders and their interactions in supporting PwD work integration. The paper’s conceptual contribution lies in including social dialogue actors in this ecosystem.Design/methodology/approachEvidence was collected via desk research, 35 semi-structured in-depth interviews with 51 respondents and stakeholder workshops in 2019–2020.FindingsThe findings from Norway confirm the expected coordination of unions and employers in PwD work integration. Evidence from Slovakia shows that in decentralised industrial relations systems, institutional constraints beyond the workplace determine employers’ and worker representatives’ approaches in PwD integration. Most policy-level outcomes are contested, as integration occurs predominantly via sheltered workplaces without interest representation.Social implicationsThis paper identifies the primary sources of variation in the work integration of PwD. It also highlights opportunities for social partners across both situations to exercise agency and engagement to improve PwD work integration.Originality/valueBy integrating two streams of literature – social policy and welfare state and industrial relations – this paper examines PwD work integration from a social ecosystem perspective. Empirically, it offers novel qualitative comparative evidence on trade unions’ and employers’ roles in Slovakia and Norway.

Adverse childhood experiences and yoga as "a practice of liberation".

The long-term negative health consequences of adverse childhood experiences (ACEs) pose a significant public health concern. Effective and engaging interventions that simultaneously address physical, mental, and socio-emotional health and mitigate the negative sequelae related to ACEs are needed. Promising evidence points to the impact of yoga and mindfulness on physiological and emotional health outcomes. Yet, little is known about those who have experienced multiple ACEs and their perspectives on the influence of yoga and mindfulness on their lives. The current study examines the phenomenological experiences of adults with high ACEs who engage in yoga practice. Twelve adults participated in in-depth, semistructured interviews. These included nine cisgender women, two cisgender men, and one gender nonbinary individual; 66.8% White, 16.6% multiracial, 8.3% Black/African American, and 8.3% Asian American/Pacific Islander. Participants were aged 20-63 years and endorsed four or more ACEs. They practiced yoga at least once a week for at least 6 months. Themes that emerged included: healing from trauma-related symptoms, integration of the whole self in mind-body practice, corrective experiences through yoga and mindfulness, and healing beyond talk therapy. Individuals with ACEs may experience yoga as a potentially valuable method for promoting healing through an integrative approach. (PsycInfo Database Record (c) 2024 APA, all rights reserved).