Parallel Approach Research Articles

BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification

A significant issue in computer-aided diagnosis (CAD) for medical applications is brain tumor classification. Radiologists could reliably detect tumors using machine learning algorithms without extensive surgery. However, a few important challenges arise, such as (i) the selection of the most important deep learning architecture for classification (ii) an expert in the field who can assess the output of deep learning models. These difficulties motivate us to propose an efficient and accurate system based on deep learning and evolutionary optimization for the classification of four types of brain modalities (t1 tumor, t1ce tumor, t2 tumor, and flair tumor) on a large-scale MRI database. Thus, a CNN architecture is modified based on domain knowledge and connected with an evolutionary optimization algorithm to select hyperparameters. In parallel, a Stack Encoder–Decoder network is designed with ten convolutional layers. The features of both models are extracted and optimized using an improved version of Grey Wolf with updated criteria of the Jaya algorithm. The improved version speeds up the learning process and improves the accuracy. Finally, the selected features are fused using a novel parallel pooling approach that is classified using machine learning and neural networks. Two datasets, BraTS2020 and BraTS2021, have been employed for the experimental tasks and obtained an improved average accuracy of 98% and a maximum single-classifier accuracy of 99%. Comparison is also conducted with several classifiers, techniques, and neural nets; the proposed method achieved improved performance.

Parallel finite layer method for land subsidence and its homotopy parameter inversion

Land subsidence is caused by the interaction between groundwater flow and soil deformation. At present, in the field of land subsidence calculation, there is an increasing tendency to use Biot's consolidation theory to precisely simulate the coupling processes of flow and deformation. However, there are some practical problems with large-scale numerical computation, such as large computational loads, low efficiency, and difficulty in obtaining parameters. The Finite Layer Method employs orthogonality of analytical functions to achieve decoupling among various series terms. And the method can be enhanced through parallel computing to optimize the efficiency of solving complex and extensive problems due to its decoupling property. Based on this, combined with the nonlinear homotopy method, a new approach for the reversal of land subsidence is proposed. The validity of the parallel program is confirmed through comparison with established solutions, and the impact of various factors on the efficiency of parallel computing is analyzed. The results indicate that the parallel approach enhances computational efficiency, while the homotopy inversion technique exhibits broad convergence characteristics.

Estimation of Diameter at Breast Height in Tropical Forests Based on Terrestrial Laser Scanning and Shape Diameter Function

Estimating forest carbon content typically requires the precise measurement of the trees’ diameter at breast height (DBH), which is crucial for maintaining the health and sustainability of natural forests. Currently, Terrestrial Laser Scanning (TLS) systems are commonly used to acquire forest point cloud data for DBH estimation. However, traditional circular fitting methods face challenges such as a reliance on forest elevation normalization and underfitting of large trees. This study explores a novel approach, the Shape Diameter Function (SDF) algorithm model, leveraging the advantages of three-dimensional point cloud information to replace traditional circular fitting methods. This study employed a parallel approach, combining the Digital Elevation Model (DEM) with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to segment tree point clouds at breast height. Additionally, a point cloud SDF algorithm based on an octree structure was proposed to accurately estimate individual tree DBH. The research data were obtained from tropical secondary forests located in Cameroon, Peru, Indonesia, and Guyana, with forest ground point cloud data acquired via TLS. The experimental results demonstrated the superior performance of the SDF algorithm in estimating DBH. Compared with the Random Sample Consensus (RANSAC) and Hough transform methods, the Root Mean Square Error (RMSE) decreased by 28.1% and 47.8%, respectively. Particularly in estimating DBH for large trees, the SDF algorithm exhibited smaller errors, indicating a closer alignment between the estimated individual tree DBH values and those obtained from manual measurements. This study presented a more accurate DBH estimation algorithm, contributing to the exploration of improved forest carbon content estimation methods.

PaSTG: A Parallel Spatio-Temporal GCN Framework for Traffic Forecasting in Smart City

Predicting future traffic conditions from urban sensor data is crucial for smart city applications. Recent traffic forecasting methods are derived from Spatio-Temporal Graph Convolution Networks (STGCNs). Despite their remarkable achievements, these spatio-temporal models have mainly been evaluated on small-scale datasets. In light of the rapid growth of the Internet of Things and urbanization, cities are witnessing an increased deployment of sensors, resulting in the collection of extensive sensor data to provide more accurate insights into citywide traffic dynamics. Spatio-temporal graph modeling on large-scale traffic data is challenging due to the memory constraint of the computing device. For traffic forecasting, subgraph sampling from road networks onto multiple devices is feasible. Many GCN sampling methods have been proposed recently. However, combining these with STGCNs degrades performance. This is primarily due to prediction biases introduced by each sampled subgraph, which analyze traffic states from a regional perspective. Addressing these challenges, we introduce a parallel STGCN framework called PaSTG. PaSTG divides the road network into regions, each processed by an individual STGCN in a device. To mitigate regional biases, Aggregation Blocks in PaSTG merge spatial-temporal features from each STBlock. This collaboration enhances traffic forecasting. Furthermore, PaSTG implements pipeline parallelism and employs a graph partition algorithm for optimized pipeline efficiency. We evaluate PaSTG on various STGCNs using three traffic datasets on multiple GPUs. Results demonstrate that our parallel approach applies widely to diverse STGCN models, surpassing existing GCN samplers by up to 57.4% in prediction accuracy. Additionally, the parallel framework achieves speedups of up to 2.87x and 4.70x in training and inference compared to GCN samplers.

THE INFLUENCE OF FINANCIAL LITERACY, PERSONAL SALES, AND CONSUMER TRUST ON PRIVATE HEALTH INSURANCE PRODUCT PURCHASE DECISIONS

The definition of risk in insurance is the uncertainty of future events that can cause financial losses. With health insurance, these risks can be managed so as not to cause financial losses. However, in general, Indonesian people have not placed health insurance as a priority to fulfill the need for security. The purpose of this study is to verify the relationship between Financial Literacy, Personal Selling and Consumer Trust on Purchasing Decisions with two theoretical approaches Theory of Planned Behavior and Social Exchange Theory. The data source used is primary data, which is obtained directly from respondents using an online questionnaire in the form of Google Form. The data collected was 521 data. The results of the study prove that Financial Literacy, Personal Selling, and Consumer Trust have a positive effect on Purchasing Decisions. The originality of this research is that it is the first attempt to evaluate private health insurance customers in Indonesia using the Theory of Planned Behavior and Social Exchange Theory approaches in parallel.

Advancing Molecular Dynamics: Toward Standardization, Integration, and Data Accessibility in Structural Biology.

Molecular dynamics (MD) simulations have become a valuable tool in structural biology, offering insights into complex biological systems that are difficult to obtain through experimental techniques alone. The lack of available data sets and structures in most published computational work has limited other researchers' use of these models. In recent years, the emergence of online sharing platforms and MD database initiatives favor the deposition of ensembles and structures to accompany publications, favoring reuse of the data sets. However, the lack of uniform metadata collection, formats, and what data are deposited limits the impact and its use by different communities that are not necessarily experts in MD. This Perspective highlights the need for standardization and better resource sharing for processing and interpreting MD simulation results, akin to efforts in other areas of structural biology. As the field moves forward, we will see an increase in popularity and benefits of MD-based integrative approaches combining experimental data and simulations through probabilistic reasoning, but these too are limited by uniformity in experimental data availability and choices on how the data are modeled that are not trivial to decipher from papers. Other fields have addressed similar challenges comprehensively by establishing task forces with different degrees of success. The large scope and number of communities to represent the breadth of types of MD simulations complicates a parallel approach that would fit all. Thus, each group typically decides what data and which format to upload on servers like Zenodo. Uploading data with FAIR (findable, accessible, interoperable, reusable) principles in mind including optimal metadata collection will make the data more accessible and actionable by the community. Such a wealth of simulation data will foster method development and infrastructure advancements, thus propelling the field forward.

To Tell, Not to Yell: The Effect of Writer's Intents on Readers’ Perceived Helpfulness of Online Product Reviews

Perceived review helpfulness has been investigated extensively; however, the influence of the review writer's intents behind a review has not been considered. This study investigates the effect of the writer's intents to provide information, express emotions, or call for action on reviews’ helpfulness, thereby bridging the gap between the writer's perspective and the reader's perspective. The writer's intents are operationalized through speech acts, an implicit, yet systematic, approach in which the intents of the writer are captured at the linguistic level, that is, how things are said. A unique parallel approach based on both readers’ perceptions and linguistic theory is used to operationalize the speech acts. This approach allows multiple speech acts per sentence, and it can estimate the influence of each speech act separately. The results show that assertive acts positively impact review helpfulness, whereas expressive and call-for-action acts have a negative effect. This confirms previous literature showing that the major drivers of review helpfulness are often related to information delivery. In this research, the idea that consumers like reviews with accurate information is detected at the level of the writer's intent, which is new in the context of text analysis to assess online review helpfulness.

Un imprevisto tra capo e collo: la sindrome di Lemierre

A healthy 15-year-old boy was hospitalized due to the persistence of lateral cervical swelling associated with fever and worsening of headache. He underwent a neck and facial mass CT scan showing a thrombophlebitis of the internal jugular vein extended to the intracranial sinuses. The diagnosis of Lemierre syndrome (LS) was formalized with positive blood colture for Fusobacterium necrophorum. Intravenous antibiotic therapy associated with anticoagulant therapy was started with complete clinical recovery after 4 weeks of treatment. A close clinical-instrumental follow-up was set up. Lemierre syndrome is a thrombophlebitis of the internal jugular vein, that in this case was extended to the intracranial sinuses. It is a rare condition, secondary to the extension of an infectious process starting from the oropharynx. The clinical picture is characterized by high fever, persistent pharyngitis followed by sepsis, pneumo-nia or atypical laterocervical pain. Fusobacterium necrophorum, a gram-negative anaerobe bacteriu,, is the responsible pathogen in most of the cases (>90%). The antibiotic treatment is prolonged (at least 4-6 weeks); occasionally a parallel surgical approach is necessary. The start of anticoagulant thera-py for internal venous thrombosis associated with LS is still matter of debate.

Nearly optimal stabilization of unknown continuous-time nonlinear systems: A new parallel control approach

This paper develops a novel online nearly optimal control (ONOC) method for unknown continuous-time (CT) nonaffine nonlinear systems without recovering unknown systems. First, a dynamic control law is proposed for CT nonaffine nonlinear systems using parallel control. To achieve the proposed dynamic control law, an affine augmented system (AAS) is constructed according to the original system, and an augmented performance index (API) is constructed on the basis of the original performance index (OPI). Then, the stability relationship between the original system and the AAS is provided, and it is proven that, by selecting a suitable parameter in the API, optimal control of the AAS with the API is equivalent to near-optimal control of the original system with the OPI. Subsequently, based on the proposed dynamic control law, we extend integral reinforcement learning (IRL) to completely unknown CT nonaffine systems, and it is further proved that closed-loop signals are uniformly ultimately bounded (UUB) without the assumption that the input dynamics are bounded. Furthermore, the OPI can be set to an arbitrary positive-definite form, and the UUB bound for the state vector can be predetermined. Lastly, simulations are offered to exhibit the correctness of the developed ONOC method. Source code of this paper is available at: https://github.com/lujingweihh/Adaptive-dynamic-programming-algorithms/tree/main/model_free_integral_reinforcement_learning.

Multi-objective genetic algorithm calibration of colored self-compacting concrete using DEM: an integrated parallel approach.

A detailed numerical simulation of Colored Self-Compacting Concrete (CSCC) was conducted in this research. Emphasis was placed on an innovative calibration methodology tailored for ten unique CSCC mix designs. Through the incorporation of multi-objective optimization, MATLAB's Genetic Algorithm (GA) was seamlessly integrated with PFC3D, a prominent Discrete Element Modeling (DEM) software package. This integration facilitates the exchange of micro-parameter values, where MATLAB's GA optimizes these parameters, which are then input into PFC3D to simulate the behavior of CSCC mix designs. The calibration process is fully automated through a MATLAB script, complemented by a fish script in PFC, allowing for an efficient and precise calibration mechanism that automatically terminates based on predefined criteria. Central to this approach is the Uniaxial Compressive Strength (UCS) test, which forms the foundation of the calibration process. A distinguishing aspect of this study was the incorporation of pigment effects, reflecting the cohesive behavior of cementitious components, into the micro-parameters influencing the cohesion coefficient within DEM. This innovative approach ensured significant alignment between simulations and observed macro properties, as evidenced by fitness values consistently exceeding 0.94. This investigation not only expanded the understanding of CSCC dynamics but also contributed significantly to the discourse on advanced concrete simulation methodologies, underscoring the importance of multi-objective optimization in such studies.

Exploring quality of life post sudden onset hearing loss: A convergent parallel approach.

Sudden onset hearing loss (SOHL) is rare and presents differently to individuals; hence, it is complex to diagnose. The impact on the quality of life (QoL) varies for individuals and their support structure. However, the exploration of research designs is warranted. This study explored the lived experiences of adults post-SOHL diagnosis and the impact on the QoL. Facilitators of emotional and social aspects of counselling provided by audiologists post-SOHL diagnosis were established. This was a convergent parallel research study. Data were collected from the two primary participants and three secondary participants, face-to-face and telephonically. The Hearing Handicap Inventory for Adults (HHIA) screening tool and the semi-structured interviews were used for data collection. The data sets were analysed independently,viz. descriptive analysis and thematic analysis, to confirm the impact on the QoL post-SOHL diagnosis. The HHIA scores obtained were 84% and 50% for P1 and P2, respectively. Key themes that emerged from the interviews revealed that communication difficulties mostly impacted the QoL, which in turn influenced their mental and social well-being. Aural rehabilitation was perceived as ineffective support, thus the inability to reduce the impact on the QoL post-SOHL diagnosis. The integrated findings indicated the impact on the QoL post-SOHL diagnosis. Convergent parallel methods should be considered by researchers to understand rare auditory pathologies and their impact on the QoL.Contribution:Person-centred care (PCC) and family-centred care (FCC) are facilitators of counselling that audiologists can employ as QoL management strategies post-SOHL diagnosis.

An ensemble-based approach for short-term load forecasting for buildings with high proportion of renewable energy sources

The increasing integration of renewable energy sources into large buildings and structures has emphasized the importance of effective control systems to optimize resource use, grid stability, and reliability. In this context, load forecasting is critical for predicting future energy demand and managing the intermittency and variability of renewable energy resources to ensure a stable performance. This study proposes an ensemble-based short-term load forecasting (STLF) method tailored for buildings. It combines parallel and series approaches for a 24-hour forecasting horizon. The parallel approach uses similar historical days to capture the general load trend, holidays, and user behavior, while the series component employs a deep learning-based neural network to address short-term changes. The final ensemble-based forecast is the culmination of these two methods. Evaluated using real load profile data sets with 15-minute resolution, the proposed method demonstrates more than 23% and 24% improvements in MAE and RMSE, respectively, compared to other methods. These results demonstrated the superior performance of the proposed method in terms of both accuracy and robustness, making it an effective solution for short-term load forecasting in buildings heavily reliant on renewable energy.

A Study on the Thermal Behavior of Series and Parallel Connection Methods in the Process of Hydrogenation of Ship-Borne Hydrogen Storage Cylinder

As a subdivision of the hydrogen energy application field, ship-borne hydrogen fuel cell systems have certain differences from vehicle or other application scenarios in terms of their structural type, safety, environmental adaptability, and test verification. The connection method of the ship-borne hydrogen storage cylinder (SHSC) is very important for the hydrogen fuel cell ship, and the structural parameters of the SHSC are particularly important in the hydrogen refueling process. To ensure the safe and reliable operation of the hydrogen-powered ship, research on the filling of the SHSC under different connection modes was carried out during refueling. In our study, a thermal flow physical model of the SHSC was established to research the hydrogen refueling process of the series and parallel SHSCs. The influence of series and parallel modes of the SHSCs on the hydrogen refueling process was explored, and the evolution law of the internal flow field, pressure, and temperature of series and parallel SHSCs under different filling parameters was analyzed by numerical simulation. Our results confirmed the superiority of the parallel modular approach in terms of thermal safety during refueling. The results can supply a technical basis for the future development of hydrogen refueling stations and ship-board hydrogenation control algorithms.

Accelerating Multiple Sequence Alignments Using Parallel Computing

Multiple sequence alignment (MSA) stands as a critical tool for understanding the evolutionary and functional relationships among biological sequences. Obtaining an exact solution for MSA, termed exact-MSA, is a significant challenge due to the combinatorial nature of the problem. Using the dynamic programming technique to solve MSA is recognized as a highly computationally complex algorithm. To cope with the computational demands of MSA, parallel computing offers the potential for significant speedup in MSA. In this study, we investigated the utilization of parallelization to solve the exact-MSA using three proposed novel approaches. In these approaches, we used multi-threading techniques to improve the performance of the dynamic programming algorithms in solving the exact-MSA. We developed and employed three parallel approaches, named diagonal traversing, blocking, and slicing, to improve MSA performance. The proposed method accelerated the exact-MSA algorithm by around 4×. The suggested approaches could be basic approaches to be combined with many existing techniques. These proposed approaches could serve as foundational elements, offering potential integration with existing techniques for comprehensive MSA enhancement.

Relationship between Idealized Influence (II) and Performance Contracts (PC) in Road Agencies in Kenya

Purpose: This study entailed a diagnostic analysis of the relationship between idealized influence and performance contracts in road agencies in Kenya. The objective of this research was to investigate the relationship between idealized influence and performance contracts in road agencies in Kenya. Operationalization of variables included idealized influence as an independent variable and performance contracts as a dependent variable. Theories that guided the study included transformational leadership theory and public management theory.
 Materials and Methods: Descriptive research design was used for the study and pragmatism was adopted as the philosophical framework for the research. Convergent parallel mixed method approach was adopted where data was collected through questionnaires for quantative data and structured interviews for qualitative data. The target population for the research was all staff members at top management and other officers in job group 6 (six) and above for the three road agencies in Kenya. These road agencies included Kenya National Highway Authority (KeNHA), Kenya Urban Roads Authority (KURA) and Kenya Rural Roads Authority (KeRRA). Data collected was analyzed through descriptive statistics, inferential analysis and hypothesis testing. The collected data was presented through tables and figures. Findings from the above analysis indicated that the performance contracts process and its outcomes in road agencies in Kenya could be predicted by idealized influence. The null hypothesis was rejected as p value was less than p<0.05 threshold of significance level and the study concluded that idealized influence has a significant relationship with performance contracts in road agencies in Kenya.
 Findings: The study rejected the null hypothesis that there is no significant relationship between idealized influence and performance contracts in road agencies in Kenya. The foregoing was supported by both results and findings from descriptive statistics, hypothesis tested and structured interviews conducted in the three road agencies. Leaders who were interviewed in these road agencies indicated that they also practiced integrity and leadership behaviors that could be emulated by their followers.
 Implications to Theory, Practice and Policy: The study thus supported the transformational leadership theory that was adopted for the study. Further, the study has bridged and addressed gaps on motivation, training and lack of leadership as identified in the statement of problem. This study has recommended that policy makers in road agencies should include idealized influence in their leadership and also align it with their strategies as detailed in strategic plans, performance contracts and individual workplans to enhance motivation, training and capacity building among employees of the studied organizations. The study also contributed to the transformational leadership theory as the was supported by the findings that leaders in these organizations should be role models and practice integrity in the process of performance contracting in road agencies. The study further recommended that policy makers in road agencies should entrench appropriate leadership (transformational leadership) during the process of performance contracting in road agencies.

A self-adaptive parallel image stitching algorithm for unmanned aerial vehicles in edge computing environments

ABSTRACT The overlap between edge computing and unmanned aerial systems enables Unmanned Aerial Vehicles (UAV) to quickly offload image processing tasks onto edge devices, avoiding the transmission of images over long distances. To improve the speed and efficiency of UAV image stitching in an edge computing environment, this paper proposes an adaptive UAV image parallel stitching algorithm in an edge computing environment. The algorithm incorporates both route-based parallel processing and inverted binary tree-based parallel processing, dividing the image stitching task into multiple processes and allocating them to different cores based on the CPU core count, number of flight routes, and number of images, thereby enhancing computational efficiency in edge scenarios. The experimental results indicate that, when the number of flight routes is greater than or equal to the number of CPUs, the adaptive algorithm will employ the more efficient route parallelism. Conversely, when the number of flight routes is less than the number of CPUs, the efficiency of inverted binary tree parallelism is higher. In the same experimental environment and dataset, the adaptive image stitching algorithm demonstrates an efficiency improvement of approximately 2–10 times compared to other algorithms, with no significant degradation in image quality. This demonstrates that in edge environments, the utilization of multi-threaded adaptive route and inverted binary tree-based parallel approaches can effectively harness the computing resources of edge devices, significantly improving the stitching speed of UAV images and providing technical support for rapid real-time monitoring by UAV.

Investigating market-based opportunities for the provision of nutritious and safe diets to prevent childhood stunting: a UKRI-GCRF action against stunting hub protocol paper

BackgroundInadequate access to affordable, safe, desirable and convenient nutrient-dense food is one of the underlying causes of child stunting. While targeted nutrition-sensitive interventions (eg, backyard ‘nutri-gardens’) may increase dietary diversity...

Vendor capabilities development in blockchain sourcing: A parallel play approach

Vendor capabilities development has been an important research stream in the information systems (IS) sourcing literature. The existing research, which was based on the context of implementing centralized enterprise systems, focused on the adoption of deliberate learning and implicit learning, with the assumption that effective selection between these two learning approaches is sufficient for service providers to acquire vendor capabilities. However, this assumption may not hold in implementing a decentralized system based on enterprise blockchain. Because an enterprise blockchain system affords a new way of governing transactions and demands a new governance mode, it creates an uncertain and complex landscape in which the mere selection between deliberate learning and implicit learning becomes insufficient. Addressing this gap is crucial because many enterprise blockchain projects fail due to the incompetence of service providers. By conducting an in-depth case study of a successful blockchain service provider, our study reveals a new mechanism for developing vendor capabilities: parallel play. As with parallel play by preschoolers, the service provider combines a set of learning approaches featuring borrowing from peers and learning through experimentation. Our study extends the IS sourcing literature on vendor capabilities development from the context of implementing centralized enterprise systems to the context of implementing decentralized enterprise blockchain systems. Our findings also provide guidelines for blockchain service providers to follow when acquiring vendor capabilities and for clients to use when selecting service providers.

Complement receptor 3 is required for maximum in vitro trogocytic killing of the parasite Trichomonas vaginalis by human neutrophil-like cells.

Trichomonas vaginalis (Tv) is a parasite that causes trichomoniasis, a prevalent sexually-transmitted infection. Neutrophils are found at the site of infection, and can rapidly kill the parasite in vitro, using trogocytosis. However, the specific molecular players in neutrophil killing of Tv are unknown. Here, we show that complement proteins play a role in Tv killing by human neutrophil-like cells (NLCs). Using CRISPR/Cas9, we generated NLCs deficient in each of three complement receptors (CRs) known to be expressed on human neutrophils: CR1, CR3, and CR4. Using in vitro trogocytosis assays, we found that CR3, but not CR1 or CR4 is required for maximum trogocytosis of the parasite by NLCs, with NLCs lacking CR3 demonstrating ~40% reduction in trogocytosis, on average. We also observed a reduction in NLC killing of Tv in CR3 knockout, but not CR1 or CR4 knockout NLCs. On average, NLCs lacking CR3 had ~50% reduction in killing activity. We also used a parallel approach of pre-incubating NLCs with blocking antibodies against CR3, which similarly reduced NLC killing of parasites. These data support a model in which Tv is opsonized by the complement protein iC3b, and bound by neutrophil CR3 receptor, to facilitate trogocytic killing of the parasite.

Deployment of Point-of-Care Echocardiography to Improve Cardiac Diagnostic Access Among American Indians.

American Indians face significant barriers to diagnosis and management of cardiovascular disease. We sought to develop a real-world implementation model for improving access to echocardiography within the Indian Health Service, the American Indian Structural Heart Disease Partnership. The American Indian Structural Heart Disease Partnership was implemented and evaluated via a 4-step process of characterizing the system where it would be instituted, building point-of-care echocardiography capacity, deploying active case finding for structural heart disease, and evaluating the approach from the perspective of the clinician and patient. Data were collected and analyzed using a parallel convergent mixed methods approach. Twelve health care providers successfully completed training in point-of-care echocardiography. While there was perceived usefulness of echocardiography, providers found it difficult to integrate screening point-of-care echocardiography into their workday given competing demands. By the end of 12 months, 6 providers continued to actively utilize point-of-care echocardiography. Patients who participated in the study felt it was an acceptable and effective approach. They also identified access to transportation as a notable challenge to accessing echocardiograms. Over the 12-month period, a total of 639 patients were screened, of which 36 (5.6%) had a new clinically significant abnormal finding. The American Indian Structural Heart Disease Partnership model exhibited several promising strategies to improve access to screening echocardiography for American Indian populations. However, competing priorities for Indian Health Service providers' time limited the amount of integration of screening echocardiography into outpatient practice. Future endeavors should explore community-based solutions to develop a more sustainable model with greater impact on case detection, disease management, and improved outcomes.