Quantitative Assessment Approach Research Articles

Non-Invasive Assessment of Coronary Microvascular Dysfunction Using Vascular Deformation-Based Flow Estimation.

Non-invasive computation of the index of microcirculatory resistance from coronary computed tomography angiography (CTA), referred to as IMR[Formula: see text], is a promising approach for quantitative assessment of coronary microvascular dysfunction (CMD). However, the computation of IMR[Formula: see text] remains an important unresolved problem due to its high requirement for the accuracy of coronary blood flow. Existing CTA-based methods for estimating coronary blood flow rely on physiological assumption models to indirectly identify, which leads to inadequate personalization of total and vessel-specific flow. To overcome this challenge, we propose a vascular deformation-based flow estimation (VDFE) model to directly estimate coronary blood flow for reliable IMR[Formula: see text] computation. Specifically, we extract the vascular deformation of each vascular segment from multi-phase CTA. The concept of inverse problem solving is applied to implicitly derive coronary blood flow based on the physical constraint relationship between blood flow and vascular deformation. The vascular deformation constraints imposed on each segment within the vascular structure ensure sufficient individualization of coronary blood flow. Experimental studies on 106 vessels collected from 89 subjects demonstrate the validity of our VDFE, achieving an IMR[Formula: see text] accuracy of 82.08 %. The coronary blood flow estimated by VDFE has better reliability than the other four existing methods. Our proposed VDFE is an effective approach to non-invasively compute IMR[Formula: see text] with excellent diagnostic performance. The VDFE has the potential to serve as a safe, effective, and cost-effective clinical tool for guiding CMD clinical treatment and assessing prognosis.

Comparison of text information from information sources based on the cosine similarity algorithm

This article presents research aimed at developing an optimal concept of analysis and comparison of information sources based on large volumes of textual information using natural language processing (NLP) methods. The object of the study was Telegram news channels, which are used as sources of text data. Texts were pre-processed, including cleaning, tokenization, and lemmatization, to form a global dictionary consisting of unique words from all information sources. For each source, a vector representation of the texts was built, the dimension of which corresponds to the number of unique words in the global dictionary. The frequency of use of each word in the channel's texts was displayed in the corresponding positions of the vector. By applying the cosine similarity algorithm to pairs of vectors, a square matrix was obtained that demonstrates the degree of similarity between different sources. The results of the study show the effectiveness of the proposed approach for quantitative assessment of the similarity of textual data from different sources. The need for further optimization of the algorithm was identified, in particular by parameterization to achieve a balance between accuracy and computational cost, as well as the separation of words with excessive weight, such as specific terms or channel names. The proposed method can be applied to the analysis of information flows, the identification of relationships between sources and the study of the socio-cultural influence of media content in the conditions of the modern information environment.

Therapeutic siRNA Loaded to RISC as Single and Double Strands Requires an Appropriate Quantitative Assay for RISC PK Assessment.

In recent years, therapeutic siRNA projects are booming in the biotech and pharmaceutical industries. As these drugs act by silencing the target gene expression, a critical step is the binding of antisense strands of siRNA to RNA-induced silencing complex (RISC) and then degrading their target mRNA. However, data that we recently obtained suggest that double-stranded siRNA can also load to RISC. This brings a new understanding of the mechanism of RISC loading which may have a potential impact on how quantification of RISC loaded siRNA should be performed. By combining RNA immune precipitation and probe-based hybridization LC-fluorescence approach, we have developed a novel assay that can accurately quantify the RISC-bound antisense strand, irrespective of which form (double-stranded or single-stranded) is loaded on RISC. In addition, this novel assay can discriminate between the 5'-phosphorylated antisense (5'p-AS) and the nonphosphorylated forms, therefore specifically quantifying the RISC bound 5'p-AS. In comparison, stem-loop qPCR assay does not provide discrimination and accurate quantification when the oligonucleotide analyte exists as a mixture of double and single-stranded forms. Taking together, RISC loading assay with probe-hybridization LC-fluorescence technique would be a more accurate and specific quantitative approach for RISC-associated pharmacokinetic assessment.

Critical analysis of non-price actions driving installation of biogas technology to upgrade existing buildings in Ghana: a demand theory approach

The gradual degradation of faeces and other organic materials through the process of fermentation results in the manufacture of biogas. Generally, as the cost of construction increases, the demand for biogas should fall, which in this case is the opposite. Thus, this study seeks to identify drivers of the current surge in the installation of the biogas system in Ghana for the decisions that could be implemented to reinforce gains. This study adopted a quantitative survey-based assessment approach to identify the drivers for policy formulation. The results show that reductions in greenhouse gas (GHG) emissions, training of trainees and the community, cost savings, public education, bank loans, available waste stock, etc. are the key drivers accelerating the continuous growth of the industry in Ghana. Furthermore, a high degree of agreement was observed from the participants regarding the non-price drivers of biogas. The results present government agencies including the Ministry of Energy, the Energy Commission and industries with an action plan to restructure the concept of renewable energy sources in Ghana. Overall, this study provides a comprehensive outlook of biogas generation system, which could be explicitly used in capacity development to create a sustainable progress of biogas development in Ghana.

Quantitative assessment of static voltage stability for power system with high-penetration wind power based on energy function

As the scale of power systems expands, the penetration of renewable energy sources and power electronic devices are accelerating. With the growing complexity of power system, there is an urgent need for in-depth research on the impact of reactive power on static voltage stability. This paper proposes a quantitative assessment approach of static voltage stability for the power system with high-penetration wind power based on the energy function. A quantitative assessment framework for voltage stability indices based on power flow analysis has been established. The classic static voltage stability index L based on reactive power has been comprehensively analyzed. Additionally, combining the energy function and power flow analysis, a new quantitative index for voltage stability based on bus reactive power injection has been proposed. Finally, the effectiveness of this index has been validated by case studies. With the increasing penetration of wind power, the trend of the change of the assessment index of voltage margin is analyzed.

A Synthesis on Impact Assessment Models from the Perspective of Evolution of the EU Common Agricultural Policy

Purpose: This paper aims to provide a comprehensive analysis of the current quantitative impact assessment methodologies, examining their strengths and weaknesses in terms of data requirements, as well as their consideration of social, economic, and environmental factors. Furthermore, it aims to elucidate the necessity for new-generation agricultural impact assessment models to incorporate advancements in information technology, communication tools, and big data analytics. Design/Methodology/Approach: In this review, the term "agricultural modelling platforms" is used to denote different equilibrium models and these models are divided into two categories according on their methodological approaches. The first category adopts a "systems approach," which includes general and partial equilibrium type models, as well as sector models. The "agent-based approach" is used by the second category. This review, thus, is primarily concerned with contrasting farm-representative models with farm-based models. Findings: The study emphasizes the importance of methodology and criteria in modelling exercises, considering factors like analysis level, environmental impact, and relationship between agriculture and the environment. It suggests that selecting the right modelling scale and tool requires asking the right research questions in advance. Originality/Value: In this study, general and partial equilibrium models, which allow impact analysis of policies implemented in the agricultural sector, which is increasingly faced with ecological and social problems, and Agent Based Models (ABM), which allow the problems in question to be included in the models, are compared and provided guidance to policy makers. It is aimed to reveal their advantages/disadvantages against each other. The originality of this study is that this comparison is made by taking into account the evolution of the European Common Agricultural Policy (CAP) since its establishment.

An Online Native Mass Spectrometry Approach for Fast, Sensitive, and Quantitative Assessment of Adeno-Associated Virus Capsid Content Ratios.

Adeno-associated viruses (AAVs) have emerged as a leading platform for in vivo therapeutic gene delivery and offer tremendous potential in the treatment and prevention of human disease. The fast-paced development of this growing class of therapeutics, coupled with their intrinsic structural complexity, places a high demand on analytical methods capable of efficiently monitoring product quality to ensure safety and efficacy, as well as to support manufacturing and process optimization. Importantly, the presence and relative abundance of both empty and partially filled AAV capsid subpopulations are of principal concern, as these represent the most common product-related impurities in AAV manufacturing and have a direct impact on therapeutic potential. For this reason, the capsid content, or ratio of empty and partial capsids to those packaged with the full-length therapeutic genome, has been identified by regulatory agencies as a critical quality attribute (CQA) that must be carefully controlled to meet clinical specifications. Established analytical methods for the quantitation of capsid content ratios often suffer from long turnaround times, low throughput, and high sample demands that are not well-suited to the narrow timelines and limited sample availability typical of process development. In this study, we present an integrated online native mass spectrometry platform that aims to minimize sample handling and maximize throughput and robustness for rapid and sensitive quantitation of AAV capsid content ratios. The primary advantages of this platform for AAV analysis include the ability to perform online buffer exchange under low flow conditions to maintain sample stability with minimal sample dilution, as well as the ability to achieve online charge reduction via dopant-modified desolvation gas. By exploiting the latter, enhanced spectral resolution of signals arising from empty, partial, and full AAV capsids was accomplished in the m/z domain to facilitate improved spectral interpretation and quantitation that correlated well with the industry standard analytical ultracentrifugation (AUC) method for capsid content ratio determination. The utility of this approach was further demonstrated in several applications, including the rapid and universal screening of different AAV serotypes, evaluation of capsid content for in-process samples, and the monitoring of capsid stability when subjected to thermal stress conditions.

Optimizing the preparation of multi-colored dye-tracer proppants: A potential approach for quantitative localization and volume assessment of proppant flowback in multistage fractured horizontal wells

Proppant flowback is a common phenomenon after hydraulic fracturing of oil and gas wells. Especially in the large-scale sanding mode of horizontal wells, sand production after fracturing is usually serious. Proppant flowback can not only clog wellbore and wear out equipment, add additional hauling and disposal costs, but also reduce conductivity of the propped fractures, severely affecting oil and gas production. However, there is still a lack of effective means for locating sand production in horizontal wells. Traditional tracer proppants mainly use radioactive elements to monitor the shape of fractures, but they cannot tell the location and amount of sand production. In this work, dye tracer proppant of eight different colors was prepared by dyeing quartz sand proppant. The results show that all the eight kinds of dyes can dye the quartz sand proppant effectively and evenly. The optimal ratio of proppant mass to MB solution volume was determined to be 1:0.015, and the ratio of proppant mass to fixing agent volume was 1:0.0125. The dyed tracer proppant showed good dyeing stability at pH values of 6–13 and surfactant concentrations of 0–200 mg/L. The salinity and fracturing fluid did not affect its stability, and temperature had a negative effect on the stain stability. However, even in the most severe case of decolorization in the experiment, that is, after the dyed proppant was soaked for 24 h at a high temperature of 120 °C, the color of the dried dyed proppant was still clearly distinguished from that of ordinary quartz sand. In theory, using these dyed proppants can potentially tell quantitively the location and volume of sand production during horizontal large-scale fracturing flowback. Since this method has not been used in hydraulic fracturing, it only provides a new way to monitor the sand production of horizontal wells after fracturing. It can provide valuable data for adjusting subsequent fracturing process and has important implications for improving efficiency of fracturing operation of unconventional oil and gas reservoirs.

Topical Diclofenac Reduces Joint Synovitis in Hand Osteoarthritis: A Pilot Investigation Using Fluorescent Optical Imaging.

Synovitis, the inflammation of joint synovia, is a prominent feature of osteoarthritis (OA) manifested by enhanced synovial vascularity, endothelial leakage, and perivascular oedema. In this pilot study, we assessed the effect of topical diclofenac in hand OA (HOA) using the established semi-quantitative methods Magnetic Resonance Imaging (MRI) and Ultrasonography (US), and compared them with Fluorescent Optical Imaging (FOI), an emerging imaging modality. Ten patients with symptomatic and diagnosed HOA used topical diclofenac for 14 days, with FOI, MRI, US, and subjective pain assessed at Baseline and after 7 (Day 8), and 14 (Day 15) days of treatment. Changes in synovitis were assessed for all 10 joints of the hand (via sum scores), and separately for the two joints most affected by synovitis. A new, fully quantitative approach for objective synovitis assessment based on the FOI images was also developed and applied. The semi-quantitative analysis of the sum scores showed a small decrease in synovitis throughout the treatment duration across the different imaging modalities. The effect of the treatment was more prominent on the two most affected joints, with a synovitis reduction vs Baseline of 21.1% and 34.2% on Day 8 and Day 15, respectively, in the FOI. The quantitative FOI pixel analysis further strengthened the evidence for this effect, with observed reduction of 17.8% and 42.4% for Days 8 and 15, respectively. A similar trend was observed for subjective pain perception, with a reduction of 7.2 and 13.3 mm on Days 8 and 15. This pilot study evidenced the effect of topical diclofenac on reducing synovitis in hand OA in semi- and fully quantitative analyses, with the effect being stronger in the most affected joints. Further, supporting studies are needed to probe the accuracy of the quantitative pixel analysis of FOI images.

Innovative fiber optic–based approach of HER2 expression quantitative assessment using inPROBE technology.

Innovative fiber optic–based approach of HER2 expression quantitative assessment using inPROBE technology.

Environmental impact assessment and remediation decision-making of a contaminated megasite: Combining LCA and IO-LCA

Environmental impact assessment is crucial for the green and sustainable development of contaminated sites. Although life cycle assessment (LCA) is a widely used quantitative approach for environmental impact assessment, its application in the context of megasite remediation is relatively limited and does not consider impacts on the entire industry chain. In this study, the LCA method and actual engineering data were used to carry out a detailed environmental impact assessment and decision-making on two remediation alternatives for a contaminated megasite in China. Subsequently, employing the input-output LCA (IO-LCA) method with Chinese local data and cost breakdown, a further environmental impact assessment of the selected alternative was performed. The findings revealed that Alt 1, which combined multiple remediation technologies, achieved a superior overall environmental impact score of 6.94 MPt compared to Alt 2, primarily based on landfill (score of 7.89 MPt). Despite the considerable uncertainty in the LCA results, Alt 1 had a 98.1% probability of outperforming Alt 2. In terms of the entire economic chain, the selected Alt 1 was estimated to emit 1.06 × 1010 kg CO2-eq, 6.06 × 106 kg of PM, 4.8 × 106 kg of NOx, 3.49 × 106 kg of SOx, 5.06 × 105 kg of COD, and 3.29 × 104 kg of NH3-N. The IO-LCA results indicated that LCA may substantially underestimated the environmental footprint of contaminated megasites remediation. This study provided valuable insight into the environmental assessment of contaminated megasites remediation and the selection and optimization of alternatives from a green and sustainable perspective.

Balancing privacy and performance in federated learning: A systematic literature review on methods and metrics

Federated learning (FL) as a novel paradigm in Artificial Intelligence (AI), ensures enhanced privacy by eliminating data centralization and brings learning directly to the edge of the user's device. Nevertheless, new privacy issues have been raised particularly during training and the exchange of parameters between servers and clients. While several privacy-preserving FL solutions have been developed to mitigate potential breaches in FL architectures, their integration poses its own set of challenges. Incorporating these privacy-preserving mechanisms into FL at the edge computing level can increase both communication and computational overheads, which may, in turn, compromise data utility and learning performance metrics. This paper provides a systematic literature review on essential methods and metrics to support the most appropriate trade-offs between FL privacy and other performance-related application requirements such as accuracy, loss, convergence time, utility, communication, and computation overhead. We aim to provide an extensive overview of recent privacy-preserving mechanisms in FL used across various applications, placing a particular focus on quantitative privacy assessment approaches in FL and the necessity of achieving a balance between privacy and the other requirements of real-world FL applications. This review collects, classifies, and discusses relevant papers in a structured manner, emphasizing challenges, open issues, and promising research directions.

The relationship between disaster resilience and household food security in a disaster-prone area in Kumamoto prefecture, Japan

This study explores the relationship between residents’ disaster resilience and potential household food security in the context of natural disasters. Disaster resilience capacity consists of absorptive capacity, adaptive capacity, and transformative capacity, while household food security is composed of food availability, accessibility, and utilization. Based on data from 539 questionnaires administered to residents in Kumamoto Prefecture, Japan, this study examines households’ disaster resilience capacity and food security conditions. The entropy method is adopted as a quantitative assessment approach to integrate the data, and a Tobit model is constructed to detect the correlation between households’ disaster resilience capacity and food security. We draw five main findings from the results. (1) Over half of the respondents do not have good food security; moreover, food accessibility is the poorest dimension, as reflected by low scores for water purification capacity and facility preparedness. (2) Most of the respondents do not have high disaster resilience capacity; their transformative capacity is the lowest, followed by absorptive and adaptive capacity. (3) There is a significant positive correlation between disaster resilience and household food security. (4) Disaster damage experience restrains residents’ food utilization. (5) The elderly and senior population may be exposed to food-borne diseases because of their low food utilization. This study provides insights into the influence of disaster resilience activities on household food security before a disaster hits. The study informs the debate on the association between disaster resilience and household food security so as to aid future disaster risk reduction management.

A risk-based quantitative approach for priority assessment of ageing bridges accounting for deterioration

The significant role of bridges in transportation networks requires continuous improvement of bridge management strategies, which has been even more exacerbated by the recent bridge collapses worldwide. This study proposes a framework for the prioritization and preliminary risk assessment of existing bridges subjected to seismic and traffic hazards. The proposed method adopts quantitative expressions to define risk in a simplified manner and, consequently, the priority associated with a certain bridge within a bridge stock. The three primary components of risk are considered: vulnerability, exposure, and hazard, which are specified according to the characteristics of the bridge, site, and road network. The bridge state of conservation is accounted for in the vulnerability component through average and critical defect condition indicators. The proposed method is implemented in a case-study of thirty real bridges in northern Italy. The impact of defects on vulnerability and priority ranking of bridges, the assumptions for defects condition and each subcomponent’s contribution to risk classification are discussed. The prioritization results are presented in absolute and normalized formats, with the latter providing a clearer view of the bridges’ relative priority within the stock. Finally, the proposed method’s findings are compared with the current Italian qualitative guidelines for bridge management.

Clinical application value of Ultrafast Pulse Wave Velocity in early cardiovascular injury of Immunoglobulin A nephropathy.

To investigate the application and the related influencing factors of ultrafast pulse wave velocity (ufPWV) in theevaluation of carotid artery elasticity in patients with Immunoglobulin A nephropathy (IgAN). A totalof 156 IgAN patients and 50 healthy individuals were selected as the control group. The ufPWV technique was employed tomeasure carotid arterial elasticity parameters, including carotid intima-media thickness (cIMT), pulse wave velocity at thebeginning of systole (BS-PWV) and pulse wave velocity at the end of systole (ES-PWV). Across the three groups(control group, IgAN patients with normal renal function, and IgAN patients with renal dysfunction) there was an increasingtrend observed in cIMT, BS-PWV, and ES-PWV. Additionally, ES-PWV exhibited higher sensitivity than BS-PWV. Correlationanalysis revealed that BS-PWV and ES-PWV were positively correlated with age, body mass index (BMI), systolic bloodpressure (SBP), high-sensitivity C-reactive protein (hs-CRP), creatinine (Cr), blood urea nitrogen (BUN), and uric acid (UA)and negatively correlated with estimated glomerular filtration rate (eGFR). The ufPWV technique allows for therapid and direct measurement of arterial elasticity parameters in the neck and represents a novel approach for the early diagnosisand quantitative assessment of arterial stiffness risk in IgAN patients.

Mapping the knowledge structure of artificial neural network research in the stock market: a bibliometric analysis and future research pathways

PurposeIn past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.Design/methodology/approachTo provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.FindingsThe results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.Originality/valueTo the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.

The influence of gross profit margin, net profit margin, company size, and sales growth on profit growth (in manufacturing companies registered on bei in 2019-2022)

The aim of the research is to analyze the influence of gross profit margin, net profit margin, company size and sales growth on profit growth (in manufacturing companies listed on the IDX in 2019-2022). The research was produced using secondary data and a purposive sampling method, namely the researcher carried out the best assessment to fulfill what had been determined, using a quantitative assessment approach, the total sample was 11 companies. The data was analyzed using multiple linear analysis techniques via the SPSS 23 application. The research results stated that GPM, NPM, company size and sales growth simultaneously and significantly influenced profit growth (in manufacturing companies listed on the BEI in 2019-2022). Gross Profit Margin, Net Profit Margin, Sales Growth have a partial and significant influence on Profit Growth (in manufacturing companies listed on the IDX in 2019-2022). Company size has no influence and is not partially significant on Profit Growth (in manufacturing companies listed on the IDX for the 2019-2022 period).

A survey of the most prevalent sustainability initiatives at universities

Purpose The purpose of this study is to identify the most prevalent initiatives undertaken by leading universities in sustainability and offer a roadmap for other institutions seeking to undertake similar actions and contribute to more effective implementation of sustainability practices. Design/methodology/approach By using a quantitative assessment approach, the study sheds light on successful initiatives implemented by universities worldwide, spanning six categories: transportation, waste management, curriculum, food and dining, water and energy. Each category is clearly related to one or more of the 17 sustainable development goals. A cluster analysis was also applied to identify regional trends in preferred initiatives. Findings The study underlines the importance of integrating sustainability principles into the curricula of higher education institutions (HEIs) as well as educating staff members on energy and water management. The most common and impactful initiatives in the studied six categories have been identified. Many of the initiatives mentioned in the study do not just result in reducing ecological footprint but also provide economic savings as well. Differences among regions and countries were observed in the implementation of initiatives. Cultural and habitual factors should not be disregarded during the selection process of initiatives. Originality/value The findings of this study may help universities to take their first steps toward implementing initiatives that can effectively promote sustainable development. Results will aid other HEIs in planning for next steps while outlining the more common initiatives.

A Quantitative Gait Assessment Approach Using a Wearable Device and Its Validation for Different Neurological Disorder Conditions

Improving independent mobility in people with various gait abnormalities is a major goal of rehabilitation therapy. While quantitative gait assessment is crucial to provide meaningful feedback on each treatment, many gait wearables have been validated with the standard method. However, none of the studies focused on validation of gait characterization in different classes of the pathological population compared with the control population. Hence, a novel wearable device called Gait Watch, worn on both the lower limbs, to estimate spatiotemporal and kinematic parameters of heterogeneous gait-impaired groups has been evaluated and its outcomes have been statistically analyzed. This article presents a statistical validation of gait dynamics of the control group and patients diagnosed with cerebral vascular accident, traumatic brain injury, peripheral nervous system, psychiatric issues, and seizures. Using the preliminary analysis results, a set of descriptive variables that allow for disease differentiation was selected to provide appropriate treatment to patients with various ailments and enhance their gait quality. The extraction of clinically significant gait parameters of interest using proposed Gait Watch would ascertain various neurological conditions as well as accurately quantify the extent of the difference in various gait parameters in subjects with different gait conditions when compared with the control group.

Actigraphy methodology in the Kids Mod PAH trial: Physical activity as a functional endpoint in pediatric clinical trials.

Pulmonary vasodilator treatment can improve hemodynamics, right ventricular function, symptoms, and survival in pediatric pulmonary hypertension (PH). However, clinical trial data are lacking due to many constraints. One major limitation is the lack of relevant trial endpoints reflective of hemodynamics or functional status in patients in whom standard exercise testing is impractical, unreliable, or not reproducible. The Kids Mod PAH trial (Mono- vs. Duo Therapy for Pediatric Pulmonary Arterial Hypertension) is an ongoing multicenter, Phase III, randomized, open-label, pragmatic trial to compare the safety and efficacy of first-line combination therapy (sildenafil and bosentan) to first-line monotherapy (sildenafil alone) in 100 pediatric patients with PH across North America. Investigators will measure participants' physical activity with a research-grade, wrist-worn actigraphy device at multiple time points as an exploratory secondary outcome. Vector magnitude counts per minute and activity intensity will be compared between the treatment arms. By directly and noninvasively measuring physical activity in the ambulatory setting, we aim to identify a novel, simple, inexpensive, and highly reproducible approach for quantitative assessment of exercise tolerance in pediatric PH. These data will increase the field's understanding of the effect of pulmonary vasodilator treatment on daily activity - a quantitative measure of functional status and wellbeing in pediatric PH and a potential primary outcome for future clinical trials in children with cardiopulmonary disorders.