Search

Abstract Power electronic converters are widely used in various fields of electrical equipment. Due to their fast dynamics and non-linear nature, controlling them requires dealing with various complexities. Therefore, having a well-designed, high-speed, and robust controller is critical to ensure the effective operation of these devices. In a DC-DC converter, steady-state performance with minimum error and fast dynamic response relies on controller design. This paper presents the design of a multi-stage PID controller with an N-filter combined with a one plus proportional derivative (1+PD) controller. This controller illustrates fast tracking reference voltage; additionally, it shows incredible results when the DC-DC converter operates in different modes. The parameters of the proposed controller are effectively determined using the golden eagle optimization (GEO) algorithm. Furthermore, a comprehensive comparison between the proposed controller, proportional–integral–derivative (PID), and fractional order PID (FOPID) controllers, as well as different metaheuristic optimization methods in various conditions, has been conducted to demonstrate the effectiveness of the proposed controller. The behavior of the closed-loop system under different conditions has been thoroughly investigated. The superior time and frequency domain characteristics of the closed-loop system with the PIDn(1+PD) controller highlight its superiority over other controllers. The demonstrated enhancements in settling time, voltage regulation accuracy, and transient response emphasize the potential applicability of the proposed control strategy in real-world power electronics systems, particularly in scenarios requiring high efficiency, stability, and dynamic performance.

Abstract This paper presents a pioneering approach to bolstering network security and privacy by implementing chaotic optical communication with a hybrid optical feedback system (HOFS). The current baseline methods in network security are often less feasible for hybrid feedback systems, including limited robustness, compromised security, and synchronization challenges. Therefore, this paper proposes a hybrid approach to address these shortcomings by integrating the HOFS into chaotic optical communication systems (HOFS-COCS) to overcome the baseline challenges. This paper aims to improve network security while significantly maintaining efficient communication channels. Moreover, We designed two algorithms, one for chaotic maps generation and another for text encryption and decryption, to improve security in the hybrid feedback system. Our findings demonstrate through rigorous experimentation and analysis that the proposed (HOFS-COCS) method significantly improves network security by enabling reliable chaos generation, synchronization, and secure message transmission in chaotic optical communication systems. This research represents a significant advancement towards enhanced secrecy and synchronization in chaotic optical communication systems, promising a paradigm shift in network security protocols.

Abstract This research article meticulously examines advanced power electronic converters crucial for optimizing electrolyzer perfor- mance in hydrogen production systems. It conducts a thorough review of mature electrolyzer types, detailing their specifications, electric models, manufacturers, and scalability. To meet the high current and stable DC voltage demands of industrial electrolyzers, the study delves into a broad spectrum of AC-DC and DC-DC converter topologies. It explores cutting-edge solutions like 12-pulse and 20-pulse rectifiers, advanced higher multi-pulse rectifiers utilizing conventional and auto-connected transformer units, Multi-Step Auto-connected Transformers (MSAT), polygon autotransformers, and auxiliary filters and circuits such as pulse multiplication circuits and active power filters. These innovations significantly reduce harmonic distortions and enhance power quality, addressing challenges inherent in conventional 6-pulse diode bridge rectifiers. The research also focuses on the efficiency and power factor correction capabilities of Active Front End (AFE) converters and the 3L-DNPC rectifier. Additionally, it investigates various DC-DC converters, including the Continuous Input Current Non-Isolated Bidirec- tional Interleaved Buck-Boost DC-DC Converter, the Interleaved Buck Converter (IBC) with extended duty cycles, the 3-level buck-boost converter with a coupled inductor, Quadratic converters designed for fault tolerance, the three-level interleaved buck converter, among others. Converter designs like the galvanically isolated half-bridge converter with controlled reverse-blocking switches for achieving zero voltage switching (ZVS), the Push–Pull isolated DC-DC converter prioritizing current stability, and the Isolated Full-Bridge Boost Converter (IFBBC) adept at handling fluctuating power outputs from renewable sources are also explored. Moreover, the study scrutinizes a range of converter configurations such as the Two-stage ZVT boost converter with LCL-Type SRC, multiphase interleaved DC-DC stage, and three-port isolated DC/DC converter for efficient integration with multiple energy sources concurrently. Discussing feature trends in power-to-hydrogen systems, the research reviews multi-cell modular rectifiers and multi-stage rectifiers. Overall, the study underscores the critical role of advanced converter topologies in enhancing efficiency, reliability, and power quality in electrolyzer systems, thereby contributing significantly to the progression of sustainable energy technologies.

Abstract MXenes, a novel class of two-dimensional (2D) materials known for their excellent electronic conductivity and hydrophilicity, have emerged as promising candidates for lithium-ion battery anodes. This study presents a simple wet-chemical method for depositing interconnected SnO2 nanoparticles (NPs) onto MXene sheets. The SnO2 NPs act as both a high-capacity energy source and a spacer to prevent MXene sheets from restacking. The highly conductive MXene facilitates rapid electron and lithium-ion transport and mitigates the volume changes of SnO₂ during the lithiation/delithiation process by confining the SnO₂ NPs between the MXene layers. This composite anode, SnO2@MXene, leverages the high capacity of SnO2 and the structural and mechanical stability MXene provides. The SnO2@MXene anode exhibits superior electrochemical performance, with a high specific capacity of 678 mAh g− 1 at a current rate of 2.0 A g− 1 over 500 cycles, outperforming pristine MXenes and SnO2 nanoparticles.

Abstract Bearing degradation is the primary cause of electrical machine failures, making reliable condition monitoring essential to prevent breakdowns. This paper presents a novel hybrid model for the detection of multiple faults in bearings, combining Long Short-Term Memory (LSTM) networks with random forest (RF) classifiers, further enhanced by the Grey Wolf Optimization (GWO) algorithm. The proposed approach is structured in three stages: first, time and frequency domain features are manually extracted from vibration signals; second, these features are processed by a dual-layer LSTM network, which is specifically designed to capture complex temporal relationships within the data; finally, the GWO algorithm is employed to optimize feature selection from the LSTM outputs, feeding the most relevant features into the RF classifier for fault classification. The model was rigorously evaluated using a dataset comprising six distinct bearing health conditions: healthy, outer race fault, ball fault, inner race fault, compounded fault, and generalized degradation. The hybrid LSTM-RF-GWO model achieved a remarkable classification accuracy of 98.97%, significantly outperforming standalone models such as LSTM (93.56%) and RF (98.44%). Furthermore, the inclusion of GWO led to an additional accuracy improvement of 0.39% compared to the hybrid LSTM-RF model without optimization. Other performance metrics, including precision, kappa coefficient, false negative rate (FNR), and false positive rate (FPR), were also improved, with precision reaching 99.28% and the kappa coefficient achieving 99.13%. The FNR and FPR were reduced to 0.0071 and 0.0015, respectively, underscoring the model’s effectiveness in minimizing misclassifications. The experimental results demonstrate that the proposed hybrid LSTM-RF-GWO framework not only enhances fault detection accuracy but also provides a robust solution for distinguishing between closely related fault conditions, making it a valuable tool for predictive maintenance in industrial applications.

Abstract We developed and evaluated an electronic health record (EHR)-based model for suicide risk specific to an American Indian patient population. Using EHR data for all patients over 18 with a visit between 1/1/2017 and 10/2/2021, we developed a model for the risk of a suicide attempt or death in the 90 days following a visit. Features included demographics, medications, diagnoses, and scores from relevant screening tools. We compared the predictive performance of logistic regression and random forest models against existing suicide screening, which was augmented to include the history of previous attempts or ideation. During the study, 16,835 patients had 331,588 visits, with 490 attempts and 37 deaths by suicide. The logistic regression and random forest models (area under the ROC (AUROC) 0.83 [0.80–0.86]; both models) performed better than enhanced screening (AUROC 0.64 [0.61–0.67]). These results suggest that an EHR-based suicide risk model can add value to existing practices at Indian Health Service clinics.

Abstract DC grid fault protection techniques have previously faced challenges such as fixed thresholds, insensitivity to high-resistance faults, and dependency on specific threshold settings. These limitations can lead to elevated fault currents in the grid, particularly affecting multi-modular converters (MMCs) vulnerability to large fault current transients. This paper proposes a novel approach that combines the disjoint-based Bootstrap Aggregating (Bagging) technique and Bayesian optimization (BO) for fault detection in DC grids. Disjoint partitions reduce variance and enhance Ensemble Artificial Neural Network (EANN) performance, while BO optimizes EANN architecture. The proposed approach uses multiple transient periods instead of a fixed time to train the model. Transient periods are segmented into multiple 1 ms intervals, and each interval trains a separate neural network. In this way, a robust local relay is created that does not require high-speed communication systems. Additionally, a discrete wavelet transform (DWT) is applied to select detailed coefficients of the transient fault current, measured at the DC line’s sending terminal for fault protection. EANN is trained in comprehensive offline data that considers noise impact. Simulation results demonstrate the scheme’s ability to detect faults as high as 400 Ω accurately. This makes it a robust, reliable, and effective solution for fault detection on high-voltage direct current (HVDC) transmission lines. Lastly, this research provides the first-ever scientometric analysis of HVDC transmission line fault protection using neural network algorithms, highlighting major research themes and trends. The scientometric analysis was based on a dataset of 136 available research articles from the Scopus database from the last ten years. Therefore, this research provides valuable insights into the use of ANN for HVDC transmission line fault protection.

Purpose: The coronavirus disease 2019 (COVID-19) pandemic led to overall interrupted medical care, resulting in disease progression and morbidity. The study aimed to evaluate clinicopathological presentations and severity of eyelid malignancy during the COVID era, comparing the results with patients presented during pre-COVID era and evaluating the reasons for delayed presentation in patients with advanced eyelid malignancy in the COVID-era. Methods: This was a comparative, ambispective observational study. Data regarding demographic, clinical, histopathology, and treatment modalities of confirmed cases of eyelid malignancy admitted during the period from March 2020 to September 2022 (defined as COVID era group) were collected and compared with retrospectively collected data from March 2017 to March 2020 (defined as pre-COVID era group). COVID era patients were further categorized as localized and advanced disease. Patients with advanced eyelid malignancy during COVID era were further subjected to a questionnaire to evaluate the reasons for delayed presentation. Results: In total, 115 patients of eyelid malignancy were studied [COVID era group 40 (35%) and pre-COVID era group 75 (65%)]. A statistically significant increase in the duration of disease (P = 0.0001) and advanced tumor T-stage (P = 0.03) was noticed in the COVID era group. Demographic, histopathology, and ocular morbidity details were similar in both groups. The common reasons that led to delay in presentation and treatment in advanced T-stage patients during the COVID era were fear of acquiring COVID, lockdown, and lack of medical facilities. Conclusion: During the COVID-19 pandemic, both hospital- and patient-related factors contributed to delay in presentation, leading to advanced-stage disease indicating need of triaging of eyelid malignancy in the event of such pandemics in future.

Introduction: Many countries from South-East Asia reported an epidemic of sino-orbital mucormycosis (SOM), otherwise a rare disease, during the coronavirus disease 2019 pandemic. SOM, a potentially fatal disease, is typically treated with orbital exenteration and systemic antifungals after metabolic stabilization. There is no clear evidence of survival benefit of exenteration in the literature, and thus, there have been attempts at globe conserving treatments like orbital infusion after limited debridement and intraorbital injections with Amphotericin B (IOAB). Methods: We conducted a prospective comparative interventional study at a tertiary eye care hospital to evaluate treatment outcomes with the use of adjunctive IOAB in cases of SOM with mild to moderate orbital disease. Results: Thirty-six patients of SOM with mild to moderate orbital disease were recruited in the study. In the intervention group, 23/26 (885%) eyes had stable orbital disease at the end of treatment (4–6 weeks). No deterioration in visual acuity was noted as a result of treatment. In 8/26 (30.77%) patients, inflammation was noted as a side effect of IOAB requiring temporary discontinuation of injections. The mean follow-up for cases was 14.2 months (range 12–15 months). 1/23 (4.35%) patients had relapse of orbital disease at 3 months. Twenty-one patients are alive on last follow-up. Of the patients who refused treatment (controls), 2/9 (22.22%) patients relapsed. One of these patients with relapse underwent exenteration, while the other was managed with IOAB. At a follow-up of 14 months (range 12–15 months), eight patients are alive. On evaluating the ocular parameters in salvaged eyes, improvement in extraocular movements was noted in 75–80% cases. The degree of proptosis and resistance to retropulsion did not change significantly. Conclusion: In the current study, an improvement in the globe salvage rates was noted in cases of SOM with mild to moderate orbital disease treated with adjunctive IOAB as compared to controls at a mean follow-up of 14 months, although it did not achieve statistical significance. The study supports the inclusion of IOAB in routine management of mild to moderate orbital disease.

This was a prospective, interventional, comparative study conducted on patients with congenital simple severe ptosis. A novel method of silicone rod fixation to the tarsus (tarsal tunnel technique, group 1) was done compared with a conventional technique of silicone rod fixation (suture fixation technique, group 2) in frontalis suspension surgery. A total of 30 patients were randomized into two groups of 15 patients each. Postoperatively, MRD1, vertical palpebral aperture, and eyelid fold height were comparable in both groups till the last follow-up with stability in eyelid position. Patient satisfaction scores showed similar results with good-fair satisfaction grading in 13 patients in group 1 and 11 patients in group 2 at 6 months follow-up. No significant complications occurred in either group. Tarsal tunnel fixation of silicone rods is a novel method in sling surgery with optimal cosmetic and function outcomes. Further long-term studies are needed to validate the results of the technique.

Abstract Due to the limited hydrophobic properties of porcelain insulators, applying anti-pollution flashover coatings is crucial to enhance their functionality. This research outlines a classification system for assessing contamination levels on 22 kV porcelain insulators, both with and without coatings. It synthesizes six classification criteria derived through both numerical simulations and experimental studies to effectively gauge contamination severity. The study examined insulators treated with Room Temperature Vulcanizing (RTV) silicone under three different conditions: uncoated, partially coated, and fully coated. Additionally, the research assessed the effects of humidity on these polluted insulators to understand environmental impacts on their performance. The criteria, which are the flashover voltage (x1), fifth to third harmonics of leakage current (x2), maximum electric field (x3), total harmonic index (x4), insulation resistance (x5) and dielectric loss (x6), were proposed for evaluating the insulator’s string condition. The finite element method (FEM) was used to simulate an electric field. Then, based on the proposed criteria, the performances of the Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Multi-layer Perceptron (MLP) have been trained and compared to classify polluted insulator conditions with and without coating. The established criteria facilitate precise monitoring of the condition of high-voltage insulators, ensuring quick and effective responses that support the stability of the electrical power system.

Abstract Load Frequency Control (LFC) is essential for maintaining the stability of Islanded Microgrids (IMGs) that rely extensively on Renewable Energy Sources (RES). This paper introduces a groundbreaking 1PD-PI (one + Proportional + Derivative-Proportional + Integral) controller, marking its inaugural use in improving LFC performance within IMGs. The creation of this advanced controller stems from the amalgamation of 1PD and PI control strategies. Furthermore, the paper presents the Mountaineering Team Based Optimization (MTBO) algorithm, a novel meta-heuristic technique introduced for the first time to effectively tackle LFC challenges. This algorithm, inspired by principles of intellectual and environmental evolution and coordinated human behavior, is utilized to optimize the controller gains. The effectiveness of the proposed methodology is rigorously evaluated within a simulated IMG environment using MATLAB/SIMULINK. This simulated IMG incorporates diverse power generation sources, including Diesel Engine Generators (DEGs), Microturbines (MTs), Fuel Cells (FCs), Energy Storage Systems (ESSs), and RES units like Wind Turbine Generators (WTGs) and Photovoltaics (PVs). This paper employs the Integral Time Multiplied by the Squared Error (ITSE) and Integral of Time Multiplied By Absolute Error (ITAE) indicators as the primary performance metrics, conventionally used to mitigate frequency deviations. To achieve optimal controller parameter tuning, a weighted composite objective function is formulated. This function incorporates multiple components: modified objective functions related to both ITSE and ITAE, along with a term addressing overshoot and settling time. Each component is assigned an appropriate weighting factor to prioritize specific performance aspects. By employing distinct objective functions for different aspects of control performance, the derivation of optimized controller gains is facilitated. The efficacy and contribution of the proposed methodology are rigorously demonstrated within the context of RES-based IMGs, featuring a comparative analysis with well-known optimization algorithms, including Particle Swarm Optimization (PSO) and the Whale Optimization Algorithm (WOA). These algorithms are used to optimize the 1PD-PI controller, resulting in three control schemes: 1PD-PI/MTBO, 1PD-PI/WOA, and 1PD-PI/PSO. The effectiveness of these control schemes is evaluated under various loading conditions, incorporating parametric uncertainties and nonlinear factors of physical constraints. Three case studies, presented in eight scenarios (I-VIII), are utilized to comprehensively assess the efficiency, robustness, and sensitivity of the proposed approach. This analysis extends beyond the time domain, considering the stability evaluation of the proposed control scheme. Simulation results unequivocally establish the superior performance of the MTBO algorithm-optimized 1PD-PI controller compared to its counterparts. This superiority is evident in terms of minimized settling time, reduced peak undershoot and overshoot, and enhanced error-integrating performance characteristics within the system responses. Improvements are observed in both the high range and within the 80–90% range for criteria such as overshoot, undershoot, and the numerical values of the objective functions. This paper underscores the practicality and effectiveness of the 1PD-PI/MTBO control scheme, offering valuable insights into the management of frequency disturbances in RES-based IMGs.

The transport sector is experiencing a notable transition towards sustainability, propelled by technological progress, innovative materials, and a dedication to environmental preservation. This study explicitly examines the incorporation of electric vehicles (EVs) into the power grid, with a particular emphasis on passenger automobiles. Our analysis emphasises the vital importance of updated transport infrastructure in decreasing greenhouse gas emissions and aiding carbon reduction efforts in electricity networks. The analysis uncovers that adopting electric vehicles offers significant advantages, including enhanced grid efficiency and decreased emissions. However, it also brings issues concerning the design and operation of power systems at both the transmission and distribution levels. Key players are crucial in tackling these difficulties to improve electric vehicle integration into the grid. The study determines the most effective ways for distributing and providing electric vehicle charging infrastructure, and investigates the efforts made to establish common standards in order to solve current challenges. This research contributes to the advancement of sustainable mobility and energy systems by conducting a thorough examination of the impact of electric vehicles on power systems and offering appropriate integration solutions.

Context: Viral hepatitis is a serious health problem globally and in endemic countries like India. Viral hepatitis may present as mono-infection or co-infection caused by hepatitis A virus (HAV), hepatitis B virus, hepatitis C virus, hepatitis D virus, and hepatitis E virus (HEV). Co-infection with two or more viruses may lead to serious complications and increased mortality. To find out seroprevalence and co-infection of HAV and HEV infections in various age groups. Materials and Methods: This study was done retrospectively on 475 samples for HAV immunoglobulin M (IgM) and HEV IgM antibodies from the patients with a history of viral hepatitis between January 2015 and December 2015. HAV IgM antibody testing was done by chemiluminescent microparticle immunoassay, and HEV IgM antibodies were done by enzyme-linked immunosorbent assay. Results: Out of 475 samples, 181 samples were positive for HAV and/or HEV infection with an overall prevalence of 38.1%. Seroprevalence of HAV infection was found 9.4%, of HEV infection 23.3%, and of HEV-HAV co-infection 5.2%. Conclusion: HAV and HEV infections are a major cause of viral hepatitis in India. Although both the infections are self-limiting, co-infection in rare cases may lead to acute hepatic failure and worsen the prognosis. HAV infections are more prevalent globally but HEV now found more prevalent in sporadic cases because of available vaccination for HAV, timely diagnosis and improved living standards.

The article presents the replicable model of Curriculum and Community Based Academic Learning with Research, Action and Service (CCBALwRAS) through a framework of '2 Cs', the 'curriculum' and the 'community'. It puts forth a support strategy for participatory community-based interventions by formally inducting higher education institutions (HEIs) to achieve long-term sustainable, workable solutions for local communities. The study is based on a multi-stakeholder partnership, facilitated through partnerships between the HEI, committed faculty-student participation and civil society, executed between 2015 and 2020. The article draws from the execution and actual field experience of the partnership, along with qualitative data from the evaluation and assessment of the programme delivered. Given that the model developed is based on the experiences of a private fee-dependent HEI in a metro city, the study does not take into consideration infrastructural and resource constraints of other HEIs. The article focuses on the role of HEIs as fulfilling dual responsibilities of knowledge creation and dissemination while addressing socioeconomic, developmental and environmental needs within their immediate communities. The resultant pedagogical shift and the model proposed is an effective solution for equity, capacity building and civic-mindedness, which will nurture socially aware and critically conscious global citizens.

Educators worldwide are seeking innovative teaching strategies for engaging remote learners. This study explores the impact of an online role-play on students' perceptions regarding Interprofessional education (IPE) and the usage of online role-play as a pedagogic approach. An online role-play for introducing fundamentals of IPE was organised as an activity during the monthly discussion forum of Foundation for Advancement of International Medical Education and Research (FAIMER) fellowship at one of the regional centres in India. After the role-play, the six role-players provided a written semi-structured reflective feedback while 22 observers responded to an open-ended questionnaire. The obtained data were qualitatively analysed using an inductive approach. The role-player's reflections highlighted pre-requisites, strengths and challenges of an online role-play, whereas the observers reported an appreciation of the value of online role-play in learning interprofessional competencies. This study highlights online role-play as an innovative approach to remotely introduce the tenets of IPE.

Stories constitute a significant part of the Indian preschool curriculum due to their various benefits in preschool child development. However, the teachers' practice of stories and their perception regarding the influence of stories on preschool child development are vital determining factors in the benefit preschoolers receive from stories. The global concern for providing quality education for preschool children necessitates the exploration of teachers' practices and perceptions; however, such studies in the Indian context, where education is shaped by its multicultural and linguistic diversities, are limited. The present study provides an original contribution to exploring the Indian preschool teachers' practice of stories and perception of the influence of stories on preschool child development using a developed questionnaire, Teachers' Practice and Perception of Stories (TPPS). Ninety-six preschool teachers from English medium schools in Mangalore, India, participated in the study. The findings indicated that irrespective of teachers' demographic variables, they played an active role in telling stories to children and positively believed that stories facilitate speech and language, cognition, literacy, and socio-pragmatic development in preschoolers. The findings broadly apply stories for preschool child development in clinical and school settings.

Some school districts have centralized school operations as a means to ensure universal access to schools of choice. Yet, centralization can infringe on charter school autonomy. We explore how district and charter school leaders in three contexts perceive and negotiate the trade-offs between centralized versus school-based autonomy. We find that decision-making and governance of student transportation are inherently place-specific, with conditions of history, geography, infrastructure, and state governance dominating leaders' preferences. Therefore, despite the recognized benefits, school leaders maintain vastly different perspectives about the feasibility and appeal of centralized transportation within the charter sector.

BACKGROUND: Practical and feasible methods for matching implementation strategies to diagnosed barriers of evidence-based interventions in real-world contexts are lacking. This evaluation compared actual implementation strategies applied with those recommended by an expert opinion-based tool to improve guideline-concordant cirrhosis care in a Veterans Health Administration national learning collaborative effort. METHODS: This convergent parallel mixed-methods study aimed to (1) identify pre-implementation Consolidated Framework for Implementation Research (CFIR) barriers to cirrhosis care through focus groups with frontline providers, (2) generate 20 recommended strategies using focus group identified barriers entered into the CFIR-Expert Recommendations for Implementing Change (ERIC) Implementation Strategy Matching Tool, (3) survey providers over two consecutive years on the actual use of 73 ERIC strategies and determine strategy effectiveness, (4) compare actual versus recommended strategy use, and (5) compare actual versus expected barriers by reverse applying the CFIR-ERIC Matching Tool. RESULTS: Eighteen semi-structured focus groups were conducted with 197 providers representing 95 VA sites to identify barriers to quality improvement, including cirrhosis care complexity, clarity of national goals, and local leadership support. The CFIR-ERIC Matching Tool recommended strategies such as assessing for readiness and needs, promoting adaptability, building local groups, preparing champions, and working with opinion leaders and early adopters. Subsequent strategy surveys found that sites used the top 20 recommended strategies no more frequently than other strategies. However, 14 (70%) of the top recommended strategies were significantly positively associated with cirrhosis care compared to 48% of actual strategies. Reverse CFIR-ERIC matching found that the strategies most used in the first year corresponded to the following barriers: opinion leaders, access to knowledge and information, and resources. The strategies most frequently employed in the second year addressed barriers such as champions, cosmopolitanism, readiness for implementation, relative priority, and patient needs and resources. Strategies used in both years were those that addressed adaptability, trialability, and compatibility. CONCLUSIONS: This study is among the first to empirically evaluate the relationship between CFIR-ERIC Matching Tool recommended strategies and actual strategy selection and effectiveness in the real world. We found closer connections between recommended strategies and strategy effectiveness compared to strategy frequency, suggesting validity of barrier identification, and application of the expert-informed tool.