Mixed Framework Research Articles

MiNNIE: a Mixed Multigrid Method for Real-time Simulation of Nonlinear Near-Incompressible Elastics

We propose MiNNIE, a simple yet comprehensive framework for real-time simulation of nonlinear near-incompressible elastics. To avoid the common volumetric locking issues at high Poisson's ratios of linear finite element methods (FEM), we build MiNNIE upon a mixed FEM framework and further incorporate a pressure stabilization term to ensure excellent convergence of multigrid solvers. Our pressure stabilization strategy injects bounded influence on nodal displacement which can be eliminated using a quasiNewton method. MiNNIE has a specially tailored GPU multigrid solver including a modified skinning-space interpolation scheme, a novel vertex Vanka smoother, and an efficient dense solver using Schur complement. MiNNIE supports various elastic material models and simulates them in real-time, supporting a full range of Poisson's ratios up to 0.5 while handling large deformations, element inversions, and self-collisions at the same time.

A Yin-Yang approach: Investigating students’ beliefs about teaching and learning in music among college students in Macau, China

This exploratory study aims to investigate the beliefs of college students in Macau, China, regarding teaching and learning in the field of music education, including factors influencing students’ views and their ideal music classroom environment. Employing a Rigorous Mixed Methods Framework that included surveys, observations, interviews, and reflective essays, a sample of ( n = 120). Chinese college students and ( n = 3) college music instructors were selected for participation. In the initial phase, findings revealed a nuanced perspective among students, demonstrating a stronger preference for the Constructivist Conception over the Traditional Conception. However, during the second and third phases, the findings revealed a preference for traditional and master-apprentice styles of learning in practice. Through the analysis of all three phases of research, this study unveiled a complex and juxtaposed ideology of music learning among Chinese students. While there was a misalignment between students’ ideologies and behaviors, they consistently expressed a desire for a more collaborative, creative, supportive, and open environment in which they can freely share their musical passion. Rather than adhering to a binary approach, the findings suggest the necessity of embracing a more nuanced “Yin-Yang” perspective in Macau.

PENYUSUNAN STRATEGI UNTUK MENINGKATKAN BRAND AWARENESS PADA PT PRIORITAS INTI SEJAHTERA MENGGUNAKAN ANALISIS SWOT

Seeks to improve brand recognition among the company's B2B partners to support future market expansion. This qualitative descriptive research, conducted from February to June 2024, comprises 89 pages, excluding appendices. Utilizing the 7P Marketing Mix framework, the research assesses the company’s internal and external environments, resulting in Internal Factor Evaluation (IFE) and External Factor Evaluation (EFE) scores of 5.53403537 and 6.2229091116, respectively. The analysis positions the company in Quadrant 1, indicative of a Growth Oriented Strategy. This strategy recommends an assertive implementation of the 7P Marketing Mix components. The SO strategy harnesses strengths and opportunities, the ST strategy employs strengths to counter threats, the WO strategy exploits opportunities to address weaknesses, and the WT strategy aims to mitigate weaknesses and avert threats. The findings indicate that PT Prioritas Inti Sejahtera’s brand awareness is relatively low, attributed to its recent market entry. This is evidenced by the lower number of application downloads and active users compared to competitors. Therefore, a Growth Oriented Strategy is proposed to increase brand awareness.

Crisis geographies from above and below: Constructing globality during the COVID-19 pandemic.

In this paper, we posit that the 'global' status of the pandemic is not an essentialized feature of the crisis, but a product of social construction by political leaders. More specifically, we examine how political leaders of a superpower and a peripheral nation produce the pandemic's globality through crisis geographies from above and below. Utilizing a mixed methods framework, we analyse public speeches by Donald Trump of the United States and Rodrigo Duterte of the Philippines through a critical approach to text analytics. Quantitatively, we found that besides mentioning their own homelands, Western countries featured more prominently in Trump's speeches while Asian neighbours were more salient in Duterte's speeches during the pandemic. However, the United States and China were consistently the most central in the crisis geographies of the pandemic of both speakers. Qualitatively, we further characterized the discourses surrounding these global pronouncements as: (a) collective reflexive positioning on the world stage, (b) charting zones of hope and (c) scapegoating zones of blame. Taken together, implications of this work are discussed in terms of understanding pandemic leadership in national and international contexts, recognizing its negotiated embeddedness in global structural hierarchies and enhancing critical approaches to geopolitical psychology.

Spatiotemporal Topic Modeling Reveals Storm‐Driven Advection and Stirring Control Plankton Community Variability in an Open Ocean Eddy

AbstractPhytoplankton communities in the open ocean are high‐dimensional, sparse, and spatiotemporally heterogeneous. The advent of automated imaging systems has enabled high‐resolution observation of these communities, but the amounts of data and their statistical properties make analysis with traditional approaches challenging. Spatiotemporal topic models offer an unsupervised and interpretable approach to dimensionality reduction of sparse, high‐dimensional categorical data. Here we use topic modeling to analyze neural‐network‐classified phytoplankton imagery taken in and around a retentive eddy during the 2021 North Atlantic EXport Processes in the Ocean from Remote Sensing (EXPORTS) field campaign. We investigate the role physical‐biological interactions play in altering plankton community composition within the eddy. Analysis of a water mass mixing framework suggests that storm‐driven surface advection and stirring were major drivers of the progression of the eddy plankton community away from a diatom bloom over the course of the cruise.

Cross-sectional study of factors related to declining participation in community gathering places providing communal meals: A social marketing framework

Objectives While Community gathering places known as "kayoi-no-ba" providing communal meals play a crucial role in promoting the health of older adults, the researches supporting their operations and maintenance is limited. Social marketing has been successful in implementing strategic public health programs. This study aimed to identify factors associated with declining participation in kayoi-no-ba providing communal meals using a social marketing framework based on the 4Ps marketing mix (product, promotion, place, and price).Methods In November 2019, this cross-sectional study surveyed kayoi-no-ba providing communal meals nationwide via a mail. A total of 580 kayoi-no-ba organizers with complete data participated. Changes in the number of participants were assessed using a questionnaire in order to determine the decrease since their establishment. Survey items were organized based on the 4Ps marketing mix framework and treated as independent variables. Multivariate Poisson regression analysis was conducted with participant decrease as the dependent variable, and prevalence ratios (PR) were calculated. The opening period and the number of participants at the time of the survey were included as covariates.Results A total of 154 kayoi-no-ba experienced the decline in participants (26.6%). Multivariate analysis revealed the following. For product variables, a high frequency of monthly events was significantly associated with a lower PR for participant decrease (0.92). Among promotion variables, kayoi-no-ba with a registration system had a significantly higher PR for participant decrease (1.49). For place variables, a higher population density in the region was significantly associated with a lower PR (0.90), whereas a higher ratio of the population aged 65 years and older was significantly associated with a higher PR (1.05). The price variables did not show a significant association with the decrease in participants.Conclusions Except price, the other three marketing 4Ps were significantly associated with decreases in participants at kayoi-no-ba providing communal meals. Future efforts should focus on developing and evaluating kayoi-no-ba strategies to ensure sustained participant engagement and promote the health of older adults through communal meals.

Analyzing the Fast-Food Marketing Strategies Impacting Consumer Buying Behavior

Kentucky Fried Chicken (KFC) has long been a dominant player in the global fast-food industry, known for its distinct fried chicken offerings. This study explores the effect of KFC’s marketing strategies on consumer buying behavior, using the marketing mix framework (4P: Product, Price, Place, Promotion). Primary data were gathered from a survey of 150 consumers in Malaysia via Google Forms, while secondary data included company reports and industry publications. Regression analysis was employed to determine the relationship between the independent variables (product, price, promotion, and place) and consumer buying behavior. Results indicated that product, price, and promotion significantly impact consumer decisions, with the product being the most influential factor (beta = 0.525). Price and promotion also had positive, albeit smaller, effects, while place was statistically insignificant. The R² value of 0.755 suggests that 75.5% of consumer behavior variation can be explained by these factors. These findings underscore the importance of product differentiation and effective promotional strategies in shaping consumer preferences, while the role of physical locations may be diminishing in the fast-food sector due to the rise of delivery services

Robust day-ahead scheduling of cooperative energy communities considering multiple aggregators

Future cities must play a vital role in reducing energy consumption and decarbonizing the electricity sector, thus evolving from passive structures towards more efficient smart cities. This transition can be facilitated by energy communities. This emerging paradigm consists of collectivizing a set of residential installations equipped with onsite renewable generators and storage assets (i.e., prosumers), which can eventually share resources to pursue collective welfare. This paper focuses on cooperative communities, where prosumers share resources without seeking selfish monetary counterparts. Despite their apparent advantages, energy management and scheduling of energy communities suppose a challenge for conventional tools due to the high level of uncertainty (especially due to intermittent renewable generation and random demand), and privacy concerns among prosumers. This paper addresses these issues. Specifically, a novel management structure based on multiple aggregators is proposed. This paradigm preserves users' confidential features while allowing them to extract the full potential of their assets. To efficiently manage the variety of assets available under uncertainty, an adaptive robust day-ahead scheduling model is developed, which casts as a solvable and portable Mixed Integer Linear Programming framework, which eases its implementation in real-world cases. The new proposal concerns uncertain generation and demand using a polyhedral representation of the uncertainty set. A case study is conducted to validate the developed model, showing promising results. Moreover, different results are obtained and analysed. Finally, it is worth remarking on how the level of robustness impacts the collective bill, incrementing it by 75 % when risk-averse conditions are assumed. In addition, the role of storage assets under pessimistic conditions is remarked, pointing out that these assets rule the scheduling plan of the community instead of renewable generators.

The Electro-Excitation Form Factors for Low-Lying States of 7Li Nucleus

The transverse electron scattering form factors have been studied for low –lying excited states of 7Li nucleus. These states are specified by J? T= (0.478MeV), (4.63MeV) and (6.68MeV). The transitions to these states are taking place by both isoscalar and isovector components. These form factors have been analyzed in the framework of the multi-nucleon configuration mixing of harmonic oscillator shell model with size parameter brms=1.74fm. The universal two-body of Cohen-Kurath is used to generate the 1p-shell wave functions. The core polarization effects are included in the calculations through effective g-factors and resolved many discrepancies with experiments. A higher configuration effect outside the 1p-shell model space, such as the 2p-shell, enhances the form factors for q-values and reproduces the data. The present results are compared with other theoretical models. PACS: 25.30.Bf Elastic electron scattering - 25.30.Dh Inelastic electron scattering to specific states – 21.60.Cs Shell model – 27.20. +n 5? A ?19

Toward a computational mixed methods framework to measure online deliberative discourse

This article proposes and tests a reproducible framework for a computational method to measure social media-based deliberative discourse by analyzing commentary surrounding the Canadian convoy protests of COVID-19 vaccine mandates and restrictions. Employing a combination of analytic calculations, alongside tools such as Google Perspective and Linguistic Inquiry and Word Count (LIWC), this article assesses the quality of online deliberative discourse using established measures of deliberation including the variables rationality, interactivity, equality, and civility. We propose computational approaches to measuring these variables, and work toward validating our approach by observing correlations between an established computational measure of online deliberation-cognitive complexity. This computational approach is tested using Twitter and Reddit commentary related to the convoy protests that took place in Ottawa, Canada, during February 2022, which influenced the emergence of similar protests around the world. In addition to testing our proposed online deliberative discourse measurement framework, this case study provides insight into the deliberative characteristics of the Twitter and Reddit social media platforms.

Indian Planning Commission and the Aftermath

The article discusses the context and the objectives of setting up a Planning Commission in a mixed economy framework that India had adopted in the post-colonial period. It also deals with its working, the method of framing a Five-Year Plan, the operation of a plan in the federal democratic system and its adaptation in the paradigm shift from a command economy to a market-driven system. The article further discusses both its contributions, as well as the various frailties that have crept into its functioning, and makes a case for restructuring and reforms. The article briefly indicates the shape of things to come after dismantling the Planning Commission in 2015 and replacing it with a new institution called the National Institution for Transforming India (NITI) Aayog, with new mandates. JEL Code: O29

A mixed Ce/Eu metal-organic framework for ratiometric detection of Al3+ ion.

As a heavy metal ion, excessive aluminum ions pose a serious threat to human health and the ecological environment. Developing a simple, efficient, and fast detection method to detect the content of aluminum ions is of great significance, especially for ensuring human health and ecological safety. Herein, the mixed rare earth metal-organic framework (Ce0.74Eu0.26TPTC and Ce0.62Eu0.38TPTC) were prepared based on simple ligand 1,1':4',1″-Terphenyl-2',4,4″,5'-tetracarboxylic acid (H4TPTC). The Ce0.74Eu0.26TPTC and Ce0.62Eu0.38TPTC have dual luminescence centers, which can be used as ratio fluorescent probes to detect Al3+ ions, making the detection results more accurate and reliable. Therefore, this work can promote the further development of rare earth-based MOFs in the detection of heavy metal ions.

DeepResBat: Deep residual batch harmonization accounting for covariate distribution differences

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10,085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat.

Mixed Crystalline Covalent Heptazine Frameworks with Built‐in Heterojunction Structures towards Efficient Photocatalytic Formic Acid Dehydrogenation

AbstractCovalent heptazine frameworks (CHFs) are widely utilized in the recent years as potential photocatalysts. However, their limited conjugated structures, low crystallinity and small surface areas have restricted the practical photocatalysis performance. Along this line, we report herein the synthesis of a kind of mixed crystalline CHF (m‐CHF‐1) with built‐in heterojunction structure, which can efficiently catalyze the formic acid dehydrogenation by visible light driven photocatalysis. The m‐CHF‐1 is synthesized from 2,5,8‐triamino‐heptazine and dicyanobenzene (DCB) in the molten salts, in which DCB plays as organic molten co‐solvent to promote the rapid and ordered polymerization of 2,5,8‐triamino‐heptazine. The m‐CHF‐1 is formed by embedding phenyl‐linked heptazine (CHF−Ph) units in the poly(heptazine imide) (PHI) network similar to doping. The CHF−Ph combined with PHI form an effective type II heterojunction structure, which promote the directional transfer of charge carriers. And the integration of CHF−Ph makes m‐CHF‐1 have smaller exciton binding energy than pure PHI, the charge carriers are more easily dissociated to form free electrons, resulting in higher utilization efficiency of the carriers. The largest hydrogen evolution rate reaches a value of 42.86 mmol h−1 g−1 with a high apparent quantum yield of 24.6 % at 420 nm, which surpasses the majority of other organic photocatalysts.

Chief Sustainability Officer (CSO) power and sustainability-based compensation for climate change disclosure and carbon performance: is it different for developed and developing nations?

Purpose Companies are increasingly appointing a Chief Sustainability Officer (CSO) to anchor the need to highlight climate change at the senior management level. This study aims to examine how CSO power and sustainability-based compensation influence climate reporting and carbon performance. Design/methodology/approach Using one of the largest data sets to date, consisting of 18,834 company years through the author’s observations, spanning an 11-year period (2011–2021) in 33 countries. This paper used quantitative methods – specifically, ordinal logistic regression estimation. This paper measures the level of climate change disclosure based on the carbon disclosure leadership methodology. Carbon performance is based on the intensity of carbon emissions (Scope 1, Scope 2), which is a quantitative and relatively more objective measure. Findings The results suggest that climate change disclosure continued to increase and the carbon emissions intensity of the companies in this study gradually decreased over the sample period. This paper finds that the presence of the CSO within the top management team has a positive and significant influence on the level of information on climate change of the companies in the sample. This finding confirms the idea that the managerial capacity of CSOs motivates the disclosure of climate change. The empirical results confirm that there are differences in the role that the CSO and sustainability-based compensation play in influencing the quality of climate information disclosure in developed and developing countries. Originality/value The recourse on a mixed theoretical framework, which highlights upper echelons theory, argues the understanding of the role of CSOs in explaining the relationship between climate change disclosure–carbon performance relationship. The novelty of the study lies in the approaches adopted to describe the quality of climate change disclosure. To control for endogeneity, this paper uses a difference-in-difference analysis by adding a firm to the Morgan Stanley Capital International index as an exogenous shock.

Holo4Care: a MR framework for assisting in activities of daily living by context-aware action recognition

AbstractThe evolution of virtual and augmented reality devices in recent years has encouraged researchers to develop new systems for different fields. This paper introduces Holo4Care, a context-aware mixed reality framework designed for assisting in activities of daily living (ADL) using the HoloLens 2. By leveraging egocentric cameras embedded in these devices, which offer a close-to-wearer perspective, our framework establishes a congruent relationship, facilitating a deeper understanding of user actions and enabling effective assistance. In our approach, we extend a previously established action estimation architecture after conducting a thorough review of state-of-the-art methods. The proposed architecture utilizes YOLO for hand and object detection, enabling action estimation based on these identified elements. We have trained new models on well-known datasets for object detection, incorporating action recognition annotations. The achieved mean Average Precision (mAP) is 33.2% in the EpicKitchens dataset and 26.4% on the ADL dataset. Leveraging the capabilities of the HoloLens 2, including spatial mapping and 3D hologram display, our system seamlessly presents the output of the action recognition architecture to the user. Unlike previous systems that focus primarily on user evaluation, Holo4Care emphasizes assistance by providing a set of global actions based on the user’s field of view and hand positions that reflect their intentions. Experimental results demonstrate Holo4Care’s ability to assist users in activities of daily living and other domains.

Novel multi-degree-of-freedom force–displacement mixed control framework for low loading rate substructure hybrid testing

In substructure hybrid tests, imposing realistic multi-degree-of-freedom (MDOF) displacement and force boundary conditions on a specimen involves controlling several actuators to emulate the MDOF boundary conditions of the experimental substructure. This setup presents significant challenges for decoupling control in MDOF systems. Due to the typically high stiffness of the specimen in the axial direction, force control is preferred over displacement control, which has limited accuracy in this direction. Typically, force control in the axial direction is combined with simultaneous displacement control in the softer direction, forming an MDOF force-displacement mixed control scheme. This scheme transforms force control into displacement control in the inner loop in the axial direction to ensure safe loading. The scheme involves addressing two major challenges: the nonlinear force–displacement transformation and the nonlinear coordinate transformation from the local actuator space (LAS) to the global Cartesian coordinate (GCC) system. To address these challenges, the Modified Newton–Raphson iteration integrated with proportional integral plus feed-forward (MNR-PIFF) and incremental kinematic transformation integrated with proportional integral plus feed-forward (IKT-PIFF) methods are proposed. Based on these methods, a novel MDOF force–displacement mixed control (NMFDC) framework is developed for substructure hybrid testing. A 6-DOF cyclic test simulation using the NMFDC framework was conducted to demonstrate its superiority. Furthermore, a 6-DOF cyclic test and substructure hybrid test at low loading rates were performed to validate the NMFDC framework because of the loading equipment limit. Experimental results and numerical simulations indicate that the NMFDC framework achieves swifter response with lower error levels. For applications in fast and real-time hybrid testing, dynamic loading equipment need to be used, the PIFF controller in the NMFDC framework should be replaced by adaptive time delay compensation in future developments.

DeepResBat: deep residual batch harmonization accounting for covariate distribution differences.

Pooling MRI data from multiple datasets requires harmonization to reduce undesired inter-site variabilities, while preserving effects of biological variables (or covariates). The popular harmonization approach ComBat uses a mixed effect regression framework that explicitly accounts for covariate distribution differences across datasets. There is also significant interest in developing harmonization approaches based on deep neural networks (DNNs), such as conditional variational autoencoder (cVAE). However, current DNN approaches do not explicitly account for covariate distribution differences across datasets. Here, we provide mathematical results, suggesting that not accounting for covariates can lead to suboptimal harmonization. We propose two DNN-based covariate-aware harmonization approaches: covariate VAE (coVAE) and DeepResBat. The coVAE approach is a natural extension of cVAE by concatenating covariates and site information with site- and covariate-invariant latent representations. DeepResBat adopts a residual framework inspired by ComBat. DeepResBat first removes the effects of covariates with nonlinear regression trees, followed by eliminating site differences with cVAE. Finally, covariate effects are added back to the harmonized residuals. Using three datasets from three continents with a total of 2787 participants and 10085 anatomical T1 scans, we find that DeepResBat and coVAE outperformed ComBat, CovBat and cVAE in terms of removing dataset differences, while enhancing biological effects of interest. However, coVAE hallucinates spurious associations between anatomical MRI and covariates even when no association exists. Future studies proposing DNN-based harmonization approaches should be aware of this false positive pitfall. Overall, our results suggest that DeepResBat is an effective deep learning alternative to ComBat. Code for DeepResBat can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/harmonization/An2024_DeepResBat.

A Coding Framework and Benchmark Towards Low-Bitrate Video Understanding.

Video compression is indispensable to most video analysis systems. Despite saving the transportation bandwidth, it also deteriorates downstream video understanding tasks, especially at low-bitrate settings. To systematically investigate this problem, we first thoroughly review the previous methods, revealing that three principles, i.e., task-decoupled, label-free, and data-emerged semantic prior, are critical to a machine-friendly coding framework but are not fully satisfied so far. In this paper, we propose a traditional-neural mixed coding framework that simultaneously fulfills all these principles, by taking advantage of both traditional codecs and neural networks (NNs). On one hand, the traditional codecs can efficiently encode the pixel signal of videos but may distort the semantic information. On the other hand, highly non-linear NNs are proficient in condensing video semantics into a compact representation. The framework is optimized by ensuring that a transportation-efficient semantic representation of the video is preserved w.r.t. the coding procedure, which is spontaneously learned from unlabeled data in a self-supervised manner. The videos collaboratively decoded from two streams (codec and NN) are of rich semantics, as well as visually photo-realistic, empirically boosting several mainstream downstream video analysis task performances without any post-adaptation procedure. Furthermore, by introducing the attention mechanism and adaptive modeling scheme, the video semantic modeling ability of our approach is further enhanced. Fianlly, we build a low-bitrate video understanding benchmark with three downstream tasks on eight datasets, demonstrating the notable superiority of our approach. All codes, data, and models will be open-sourced for facilitating future research.

Best Practices and Lessons Learned From the Public Health Disability Specialists Program: Addressing the Needs of People With Disabilities During COVID-19.

The Association of State and Territorial Health Officials (ASTHO) and the National Association of County and City Health Officials (NACCHO) applied funding issued by the US Centers for Disease Control and Prevention (CDC) to implement the Public Health Disability Specialists Program, part of a project to address the needs of people with disabilities during the COVID-19 pandemic. Disability specialists (subject matter experts) were embedded within state, territorial, and city/county health departments to help ensure disability inclusion in emergency planning, mitigation, and recovery efforts. To evaluate the success of the Disability Specialists Program in improving emergency response planning, mitigation, and recovery efforts for people with disabilities within participating jurisdictions. Disability specialists worked with their assigned jurisdictions to conduct standardized baseline health department needs assessments to identify existing gaps and inform development and implementation of improvement plans. CDC, ASTHO, and NACCHO implemented a mixed methods framework to evaluate specialists' success. State, territorial, and local health departments across 28 jurisdictions between January 2021 and July 2022. Average number of categories of gaps addressed and qualitative documentation of strategies, barriers, and promising practices. Specialists identified 1010 gaps (approximately 36 per jurisdiction) across eight needs assessment categories, most related to mitigation, recovery, resilience, and sustainability efforts (n=213) and communication (n=193). Specialists addressed an average of three categories of gaps identified; common focus areas included equitable COVID-19 vaccine distribution and accessible communications. Specialists commonly mentioned barriers related to limited health agency capacity (eg, resources) and community mistrust. Promising practices to address barriers included sharing best practices through peer-to-peer networks and building and strengthening partnerships between health departments and the disability community. Embedding disability specialists within state, territorial, and local health departments improved jurisdictional ability to meet evolving public health needs for the entire community, including people with disabilities.