Low Usage Research Articles

Coordinating electric vehicle charging with multiagent deep Q-networks for smart grid load balancing

Integrating EVs (Electric Vehicles) with the electrical system presents essential load distribution difficulties because EV recharging structures are unpredictable and variable. The article presents an innovative technique employing multiple-agent deeper Q-Networking (MADQN) to coordinate electric automobiles and improve the electricity system balance of load. The suggested MADQN simulation rapidly optimizes battery charge plans by utilizing the capabilities of multiple agent networks as well as deeper reinforced learning. The framework adjusts to current network situations utilizing cooperative decision-making between substances, considering variables like a need for power, accessibility to green energy sources, and protection of the arrangement. Beneficial load distribution is made possible when reducing expenses and ecological damage because of the system's capacity to gather data from and modify intricate, changing circumstances. The findings from the modelling indicate how well the suggested MADQN method works to enhance network efficiency, lower peak usage, and use more sustainable power resources. These factors help build a more robust, adaptable, intelligent grid environment.

Pattern matching algorithms in blockchain for network fees reduction

Blockchain received a vast amount of attention in recent years and is still growing. The second generation of blockchain, such as Ethereum, allows execution of almost any program in EVM, making it a global protocol for distributed applications. The code deployment and each operation performed in EVM cost the network fee called gas, whose price varies and can be significant. That is why code optimization and well-chosen algorithms are crucial in programming on the blockchain. This paper evaluates the gas usage of several exact pattern matching algorithms on the EVM. We also propose an efficient implementation of the algorithms in the Solidity/YUL language. We evaluate the gas fees of all the algorithms for different parameters (such as pattern length, alphabet size, and text size). We show a significant gas fee and execution time reduction with up to 22-fold lower gas usage and 55-fold speed-up compared to StringUtils (a popular Solidity string library).

Green synthesis and characterization of gold nanoparticles from discarded computer PCBs on reduction of environmental pollution

The economic value of gold content in electronic waste, particularly waste printed circuit boards (PCBs), has generated interest in recovering gold from this material. Unfortunately, due to the severe environmental hazards associated with such waste, there is a pressing need for efficient and selective methods of gold nanoparticle recovery. A novel technique has been developed to synthesize gold nanoparticles that employ chlorate leaching and Punica granatum extract to minimize the environmental impact of electronic waste. Multiple analytical methods, including UV-Vis, XRD, EDS, SEM, and TEM analysis, were utilized to identify and characterize the extracted gold nanoparticles. The UV-Vis spectrum showed GNP-absorption peaks at a narrow range of wavelengths, primarily at 520 nm, confirming the formation of gold nanoparticles from the e-waste. The XRD analysis identified the material as metallic gold with characteristic peaks of gold crystals (111), (200), (220), and (311), corresponding to the FCC crystal structure. The SEM and TEM analysis revealed that most of the Au nanoparticles were less than 40 nm in size, with some larger particles resulting from the agglomeration of smaller ones. The EDS spectrum analysis indicated that the gold atoms emitted strong signals. At the same time, other elements such as carbon, oxygen, and metals such as Sn, Zn, Pd, Cu, and Cd showed weaker signals. The recovered gold nanoparticles were found to have a bandgap of 2.2 eV, indicating that they are a direct bandgap semiconductor suitable for use in optoelectronic applications. The environmental impact of the PCBs and the chemicals used in the process were also analyzed to address the necessity of using discarded electronic waste to reduce environmental pollution. The proposed method showed high efficiency for extracting gold nanoparticles from e-waste, with simple process steps, low chemical usage, and low cost, as demonstrated by the results.

Spending points during crises: Adaptive behavior on E-loyalty programs

This study investigates the impact of macro-level dynamics, such as the COVID-19 pandemic, on customer point-usage behavior in e-commerce, focusing on how global crises alter consumer spending and loyalty program engagement. Using big data from SPK, an F&B holding company, and employing binary logit and panel regression models, this study highlights significant shifts in post-pandemic customer behavior. The findings reveal that both customers with low and high usage levels decreased their spending, transaction frequency, and point accumulation. However, the former showed an increased propensity to use points for purchases, with heightened redemption rates and average points used per transaction, whereas the latter exhibited a decline in these areas. These results underscore the importance of understanding customer-specific responses to economic crises for designing effective loyalty program strategies in e-commerce, and offer valuable insights for the development of promotional strategies and theories in this domain.

BYO: A Unified Framework for Benchmarking Large-Scale Graph Containers

A fundamental building block in any graph algorithm is a graph container - - a data structure used to represent the graph. Ideally, a graph container enables efficient access to the underlying graph, has low space usage, and supports updating the graph efficiently. In this paper, we conduct an extensive empirical evaluation of graph containers designed to support running algorithms on large graphs. To our knowledge, this is the first apples-to-apples comparison of graph containers rather than overall systems, which include confounding factors such as differences in algorithm implementations and infrastructure. We measure the running time of 10 highly-optimized algorithms across over 20 different containers and 10 graphs. Somewhat surprisingly, we find that the average algorithm running time does not differ much across containers, especially those that support dynamic updates. Specifically, a simple container based on an off-the-shelf B-tree is only 1.22× slower on average than a highly optimized static one. Moreover, we observe that simplifying a graph-container Application Programming Interface (API) to only a few simple functions incurs a mere 1.16× slowdown compared to a complete API. Finally, we also measure batch-insert throughput in dynamic-graph containers for a full picture of their performance. To perform the benchmarks, we introduce BYO, a unified framework that standardizes evaluations of graph-algorithm performance across different graph containers. BYO extends the Graph Based Benchmark Suite (Dhulipala et al. 18), a state-of-the-art graph algorithm benchmark, to easily plug into different dynamic graph containers and enable fair comparisons between them on a large suite of graph algorithms. While several graph algorithm benchmarks have been developed to date, to the best of our knowledge, BYO is the first system designed to benchmark graph containers.

Unraveling the root causes of faults in mobile communications: A comparative analysis of different model explainability techniques

The escalating demand and complexity of monitoring services handled by Network Operations Centers (NOCs) have led Mobile Network Operators (MNOs) to prioritize automated solutions for network fault detection and diagnosis. Consequently, various Machine Learning (ML)-based Root Cause Analysis (RCA) systems have been developed, however their lack of explainability poses a challenge due to the predominantly black-box nature of ML models. This paper addresses this issue by presenting a supervised clustering methodology capable of integrating both glass-box and black-box models, the latter complemented by post-hoc explainability techniques. While black-box models excel in predictive capabilities, necessitating post-hoc techniques for explainability, glass-box models prioritize transparent decision-making, fostering a clearer understanding of the model’s behavior. This work delineates a methodology for performing RCA of faults in the User Downlink (DL) Average Throughput Key Performance Indicator (KPI), simultaneously comparing the performance of black-box models (Light Gradient-Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost)) with glass-box models (Logistic Regression (LR) and Explainable Boosting Machine (EBM)). Results revealed that the LightGBM black-box algorithm coupled with the SHapley Additive exPlanations (SHAP) method demonstrated superior performance in fault detection and diagnosis, without compromising the overall explainability. Consequently, it was possible to identify faults related to radio conditions, low network usage in specific user groups, low network capacity, and mobility issues. The paper concludes with practical mitigation strategies for each identified fault cluster.

Impacto de la telemonitorización como sistema para una adecuada titulación y adaptación de la CPAP domiciliaria en la apnea obstructiva del sueño (proyecto T-CPAP)

IntroductionContinuous Positive Airway Pressure (CPAP) constitutes the most effective treatment for Obstructive Sleep Apnea (OSA). Automatic titration systems (ATS) are predominantly used to achieve adaptation to the equipment. Home CPAP devices allow telemonitoring (TM) of the same parameters as those provided by ATS but with access to continuous usage data. Under this premise, we conducted a study on the potential validity of TM for home CPAP devices as a titration system, its direct impact on proper adaptation (AD) to the equipment, and secondarily on the healthcare resources employed to achieve it. Material and methodsAn observational study involving 318 patients with OSA who were titrated using TM to achieve AD to CPAP. Patients with OSA were consecutively recruited and evaluated at 1, 3, and 6 months after initiating treatment. Results were compared with a historical group of 307 patients with OSA who achieved AD to CPAP using ATS. Additionally, we assessed the impact on required healthcare resources. ResultsPatients with OSA who initiated CPAP treatment with TM over the first six months showed a similar AD rate compared to the historical group titrated using ATS, with lower resource usage in the TM group. ConclusionData provided by TM of home CPAP devices allow for titration and achieving similar AD as with ATS in non-complex patients.

The Risk of Technology Dominance in Using Digital Decision Aids in Assurance Engagements—Evidence from a Survey among Danish Auditors

SYNOPSIS Accounting firms continue to develop digital decision aids (DDAs) that reinforce consistent use of firms’ audit methodology and provide assurance for meeting regulator expectations. Firms actively promote use and reliance on DDAs, but relying on such systems risks over-reliance, impacting decision quality and heightening technology dominance. Surveying 725 Danish auditors, we sought to determine the level of reliance practicing auditors have on DDAs and understand how auditors experience these potentially deleterious effects in the field. Results show auditors rely more on DDAs when auditors are inexperienced, task complexity is high, there is familiarity with the DDA, and/or there is cognitive congruence. Junior auditors rely significantly more on DDAs than senior auditors, whereas auditors indicating low usage rely significantly less on DDAs. Overall, senior auditors and auditors not heavily using DDAs have deskilling concerns and are less positive on junior auditors’ as well as the profession’s skill development when using DDAs. JEL Classifications: M15; M41; M42.

CHALLENGES IN ONLINE LEARNING ENVIRONMENTS: FROM THE PERSPECTIVE OF HEARING-IMPAIRED UNIVERSITY STUDENTS AND THEIR LECTURERS

The global COVID-19 pandemic has led to significant challenges in Turkey, as in many other countries, in the transition from face-to-face to online education at all levels of education. This shift has brought forth numerous obstacles, particularly for d/Deaf and hard-of-hearing (d/Dhh) university students. Hence, this research aims to identify the challenges encountered during online learning amidst the pandemic at the School for the Handicapped (SfH), the only institution in Turkey providing vocational higher education to hearing-impaired students. Conducted as a qualitative case study at the SfH, the research involved 20 hearing-impaired students and 19 faculty members as participants. Data obtained from the Mergen Anadolu Learning Management System platform, WhatsApp Platform and other platforms, was used for online education. Interviews, open-ended questionnaires, and the researcher’s journal were compiled and analyzed inductively. Findings from the study highlighted a multitude of challenges encountered in the online learning process, affecting both faculty members and students at the SfH. The challenges encountered by participants in online learning at the SfH were found to be related to difficulty in understanding lessons, lack of subtitles and sign language translation, difficulty accessing computers and software, Internet problems, low camera usage, low motivation, and lack of interaction. Keywords: covid -19 pandemic, case study, hearing impaired, online learning

Evaluation of Interactions between Malaria Vector Control Practices and Demographic Parameters in Selected Communities in Abia State, South-East, Nigeria

Malaria, transmitted by Anopheles mosquitoes has become a debilitating and burdensome disease in Sub-Saharan Africa and conventional practices such as Long-Lasting Insecticide Treated Nets (LLITNs), and Indoor Residual Spraying (IRS) and unconventional practices are usually employed as malaria vector control measures. Hence, the objective of this paper was to evaluate the interactions that exist between these control practices and demographic parameters in selected communities across Ikwuano, Osisioma and Ohafia Local Government Areas (LGAs) of Abia State, South-East, Nigeria using multi-stage sampling technique with five hundred and ten structured questionnaires. Data obtained reveals that the predominant control practices were IRS (325; 63.7%), although it was used in combination with other control methods. This was followed by screening of windows with net (221; 43.3%), whereas only a few respondents used LLITNs. Respondent’s LGA, level of education, type of dwelling structure and household size had significant relationship with most of the malaria vector control practices/perceptions (P < 0.05). However, gender had no significant relationship with any of the malaria vector control practices/perceptions (P > 0.05). Effectiveness (306; 60.0%) was the major reason behind choice of method practiced. Respondents also chose methods that were cheap (175; 34.3%) and prevented contact with mosquitoes (154; 30.2%), but attested to not being aware of any recent Government/Non-Governmental (NGO) mosquito control intervention (386; 75.7%). Considering the low ownership and usage of LLITNs in Abia, efforts should be made towards more distribution. Control programs are advised to consider LGA, level of education, household size, and type of dwelling structure during the design and deployment of interventions.

Use of Virtual Reality and 3D Models in Contemporary Practice of Cardiology.

To provide an overview of the impact of virtual and augmented reality in contemporary cardiovascular medical practice. The utilization of virtual and augmented reality has emerged as an innovative technique in various cardiovascular subspecialties, including interventional adult, pediatric, and adult congenital as well as structural heart disease and heart failure. In particular, electrophysiology has proven valuable for both diagnostic and therapeutic procedures. The incorporation of 3D reconstruction modeling has significantly enhanced our understanding of patient anatomy and morphology, thereby improving diagnostic accuracy and patient outcomes. The interactive modeling of cardiac structure and function within the virtual realm plays a pivotal role in comprehending complex congenital, structural, and coronary pathology. This, in turn, contributes to safer interventions and surgical procedures. Noteworthy applications include septal defect device closure, transcatheter valvular interventions, and left atrial occlusion device implantation. The implementation of virtual reality has been shown to yield cost savings in healthcare, reduce procedure time, minimize radiation exposure, lower intravenous contrast usage, and decrease the extent of anesthesia required. These benefits collectively result in a more efficient and effective approach to patient care.

Barriers and Facilitators for Usage of Self-Compacting Concrete—An Interview Study

Workers in the construction industry must endure different weather conditions, long working hours, and engage in repetitive and strenuous jobs with unrealistic deadlines. Sick leaves, caused by accidents and by work-related diseases, are common in the construction industry. Hand–arm vibration from hand-held power tools is a cause of significant ill health (disorders of the blood vessels, nerves, and joints). Self-compacting concrete (SCC) is a fluid concrete and does not need to be vibrated. Despite the health advantages of SCC, its market share in Sweden is lower than in comparable countries. The aim of this article is to describe views, opinions, and knowledge concerning the work environment and health in concrete casting and to identify barriers and facilitators of SCC usage. Semi-structured interviews were conducted with 24 interviewees from the construction industry in Sweden. The answers were analysed from a human–technology–organisation (HTO) perspective in order to identify barriers and facilitators for a broader usage of SCC. The results indicate that knowledge about SCC is low within the Swedish construction industry, including educational institutions; when SCC is chosen, it is chosen exclusively due to its technical characteristics, and not because it eliminates vibrations. Barriers to a broader usage of SCC comprise an incomplete knowledge base, clients who never choose it, recipes that are said to be too demanding, and workplace traditions. Facilitators comprise large companies investing in knowledge development about SCC and engaged persons promoting it. This study used an HTO-based model (BTOH) to identify barriers and facilitators for a broader usage of SCC, thus contributing to a deeper understanding of reasons for the low usage of SCC and ways of increasing it.

Factors affecting the intention to wear helmets for e-bike riders: the case of Chinese college students

As the popularity of electric bicycles (e-bikes) continues to surge, the number of accidents involving them has commensurately increased. A significant factor contributing to the high fatality rate in these accidents is the low usage of helmets among e-bike riders. Helmets have been proven to reduce the severity of injuries, yet their usage remains unexpectedly low. This issue is particularly pronounced among college students, the primary buyer group for e-bikes. Regrettably, there is a lack of research exploring their intentions to wear helmets. Understanding determinants of their intentions to wear helmets is crucial in promoting safe e-bike travel. Therefore, the present study aims to develop an integrated theoretical model that combines the Theory of Planned Behavior (TPB) and the Health Belief Model (HBM) to examine the factors influencing e-bike riders’ helmet-wearing intentions among college students. Additionally, two variables—descriptive norms and law enforcement—are incorporated. The results indicate that the integrated model accounts for 76% of the variance in helmet-wearing intention, surpassing single-theory models. Specifically, the TPB accounts for 65%, while the HBM explains 53%. Notably, law enforcement emerges as the most influential factor, highlighting the crucial role of enforcing regulations and promoting awareness. Other significant factors include subjective and descriptive norms, attitudes, perceived benefits, perceived susceptibility, perceived barriers, and perceived severity. These findings provide valuable insights for policy development and targeted interventions aimed at improving helmet wear rates among e-bike riders, especially among the college student population.

Scalability and Performance Analysis of LoRaWAN Networks for IoT Implementation: Comprehensive Review and Research Agenda

The surge in IoT devices has created the need for a scalable and high-performance network infrastructure capable of handling massive data flows and diverse application requirements. Among the various wireless technologies available, with long-range communication capabilities, low power consumption, and cost-effectiveness, the Low-Power Wide-Area Network (LPWAN) has emerged as a highly popular choice for numerous applications. This literature review aims to provide a comprehensive overview of recent developments in scalability and performance analysis of LoRaWAN networks for IoT implementation by reviewing existing research and identifying key challenges and opportunities for the research agenda. In the research on LoRaWAN performance in IoT, five characteristics have been proven and become factors for using LoRaWAN in IoT: low energy power usage, wide coverage, low cost, stable data transmission speed, and system reliability. However, not many have discussed its security. Based on the literature review, LoRaWAN does have some characteristics that make it suitable for large-scale IoT applications. This review will contribute to advancing knowledge in this field, enabling the development of scalable and high-performance IoT systems to meet the demands of the growing IoT ecosystem

Asynchronous Privacy-Preservation Federated Learning Method for Mobile Edge Network in Industrial Internet of Things Ecosystem

The typical industrial Internet of Things (IIoT) network system relies on a real-time data upload for timely processing. However, the incidence of device heterogeneity, high network latency, or a malicious central server during transmission has a propensity for privacy leakage or loss of model accuracy. Federated learning comes in handy, as the edge server requires less time and enables local data processing to reduce the delay to the data upload. It allows neighboring edge nodes to share data while maintaining data privacy and confidentiality. However, this can be challenged by a network disruption making edge nodes or sensors go offline or experience an alteration in the learning process, thereby exposing the already transmitted model to a malicious server that eavesdrops on the channel, intercepts the model in transit, and gleans the information, evading the privacy of the model within the network. To mitigate this effect, this paper proposes asynchronous privacy-preservation federated learning for mobile edge networks in the IIoT ecosystem (APPFL-MEN) that incorporates the iteration model design update strategy (IMDUS) scheme, enabling the edge server to share more real-time model updates with online nodes and less data sharing with offline nodes, without exposing the privacy of the data to a malicious node or a hack. In addition, it adopts a double-weight modification strategy during communication between the edge node and the edge server or gateway for an enhanced model training process. Furthermore, it allows a convergence boosting process, resulting in a less error-prone, secured global model. The performance evaluation with numerical results shows good accuracy, efficiency, and lower bandwidth usage by APPFL-MEN while preserving model privacy compared to state-of-the-art methods.

Characterization of Pyrolysis Oil Extracted from High Lignocellulosic Groundnut Shell Biomass

<div>Fossil fuel reserves are swiftly depleting when consumer demand for these fuels continues to rise. In order to meet the demand and diminish the pollution derived through conventional fuels, it is crucial to employ cleaner fuels made from substitutes such as waste biomass. Also, converting waste biomass to fuel can lower usage of landfills. There are many biomass resources that are suitable for fuel production, out of which groundnut is also a potential feedstock. Groundnut shell biomass was chosen for this study, as it is a waste leftover during shelling of groundnuts for various commercial applications. The procured groundnut shells were converted to oil using pyrolysis process and was distilled. Both the pyrolysis oil and the distilled oil were analyzed using Fourier transform infrared instrument wherein the presence of functional groups such as alcohols, amines, and carboxylic acids were identified. Further analysis of the distilled oil using gas chromatography and mass spectrometry indicates that major peaks correspond to phenolic groups. Acetic groups such as vaccenic acid and dicarboxylic acid were also identified in the distilled oil. Physiochemical property tests of distilled oil reveal that the overall qualities were inferior when compared to diesel. Further studies on using groundnut shell distilled oil as a fuel for combustion engine can be done using blend strategy.</div>

Exploring Types of Photonic Neural Networks for Imaging and Computing-A Review.

Photonic neural networks (PNNs), utilizing light-based technologies, show immense potential in artificial intelligence (AI) and computing. Compared to traditional electronic neural networks, they offer faster processing speeds, lower energy usage, and improved parallelism. Leveraging light's properties for information processing could revolutionize diverse applications, including complex calculations and advanced machine learning (ML). Furthermore, these networks could address scalability and efficiency challenges in large-scale AI systems, potentially reshaping the future of computing and AI research. In this comprehensive review, we provide current, cutting-edge insights into diverse types of PNNs crafted for both imaging and computing purposes. Additionally, we delve into the intricate challenges they encounter during implementation, while also illuminating the promising perspectives they introduce to the field.

A novel user-generated content-driven and Kano model focused framework to explore the impact mechanism of continuance intention to use mobile APPs

User-generated content (UGC), which generates vast amounts of content in real-time through social networks, offers a significant opportunity for mining new knowledge. The survival of information technology products such as mobile APPs (mAPPs) depends on continuance. The exploration of the impact mechanism underlying continuance intention is crucial given the low continued usage of many mAPPs. Mining real-world UGC provides an efficient approach for user experience and product evaluation compared to traditional interviews or surveys. This study proposes a novel UGC-driven, Kano model focused, pipelined framework to automatically identify impact factors and determine the impact mechanism underlying continuance intention. The above method framework involves unsupervised clustering text analysis to identify user needs and product functions from UGC, followed by the construction of a statistical model to explore the relationship between these factors and user satisfaction and dissatisfaction (Kano model). Additionally, a model to distinguish the attributes of factors that significantly affect satisfaction or dissatisfaction is proposed. Finally, user satisfaction and dissatisfaction are used as mediators to build models of continuance and discontinuance intention. Empirical validation of the proposed method is conducted with a case study of mobile health apps, involving the mining of 86,423 user reviews and structural equation modeling based on 1025 user responses. The results indicate that the UGC-driven method effectively explores the impact mechanism of continuance and discontinuance intention.

Development of a Plasmonic Light Management Architecture Integrated within an Interface Passivation Scheme for Ultrathin Solar Cells

In response to climate and resource challenges, the transition to a renewable and decentralized energy system is imperative. Ultrathin Cu(In,Ga)Se2(CIGS)‐based solar cells are compatible with such transition due to their low material usage and improved production throughput. Despite the benchmark efficiency of CIGS technology, ultrathin configurations face efficiency drops arising from increased rear interface recombination and incomplete light absorption. Dielectric passivation schemes address rear interface recombination, but achieving simultaneous electrical and optical gains is crucial for thinning down the absorber. Plasmonic nanoparticles emerge as a solution, enhancing light interaction through resonant scattering. In the proposed architecture, the nanoparticles are encapsulated within a dielectric rear passivation layer, combining effective passivation and light trapping. A controlled deposition and encapsulation of individualized nanoparticles is achieved by an optimized process flow using microfluidic devices and nanoimprint lithography. With the developed plasmonic and passivated architecture, a 3.7 mA cm−2short‐circuit current density and a 23 mV open‐circuit voltage improvements are obtained, leading to an almost 2% increase in light‐to‐power conversion efficiency compared to a reference device. This work showcases the developed architecture potential to tackle the electrical and optical downfalls arising from the absorber thickness reduction, contributing to the dissemination of ultrathin technology.

Utilization and Patient-Reported Outcomes of Direct-to-Consumer Telemedicine During the First 6 Weeks of the COVID-19 Pandemic in the Largest Pediatric Ambulatory Network in New York State.

Objective: We aimed to (1) describe telemedicine utilization and usability during the first 6 weeks of the pandemic and (2) determine if usability varied by individual- or visit-level characteristics. Methods: We conducted a retrospective cohort study of ambulatory pediatric telemedicine visits occurring between March 10, 2020, and April 18, 2020, across a large academic health system. We performed manual chart review to assess individual- and visit-level characteristics and invited caregivers to respond to an adapted Telehealth Usability Questionnaire (TUQ). We used multiple logistic regression to determine predictors of high usability. Results: There were 3,197 ambulatory pediatric telemedicine visits, representing 2,967 unique patients. Patients were racially/ethnically diverse (42.5% non-Hispanic White) and primarily English-speaking (89.2%). Surveys were completed by 441 (17%) of those invited. Every item of the TUQ had agreement or strong agreement from the majority of respondents. Compared with non-Hispanic White, non-Hispanic Asian identity was associated with lower usability in three domains and overall, and non-Hispanic Black identity was associated with higher satisfaction and future use. As compared with caregivers of infants younger than 1 year, caregivers of older patients reported lower usability in the three domains. Conclusions: Telemedicine was successfully implemented across 18 ambulatory pediatric specialties in the largest health system in New York State at the onset of COVID-19, and caregivers found it usable and acceptable. Usability scores did not vary by visit-level characteristics but did vary by race/ethnicity and age. Further research is necessary to identify modifiable drivers of the patient experience, particularly in non-Hispanic Asian communities and older adolescents.