External Data Sources Research Articles

Development and Evaluation of a Multisource Approach to Extend Mortality Follow-Up for Older Adults With Advanced Cancer Enrolled in Randomized Trials.

Mortality data can complement primary end points from cancer clinical trials. Yet, identifying deaths after trial completion is challenging, as timely and comprehensive vital status data are unavailable in the United States. We developed and evaluated a multisource approach to capture death data after clinical trial completion. Individuals age 70 years and older with incurable solid tumors or lymphoma and ≥1 aging-related condition were enrolled from October 2014 to March 2019 (ClinicalTrials.gov identifier: NCT02107443 and NCT02054741). Participants provided consent to link trial information to external sources. We developed a stepped approach for extended death capture using (1) active trial follow-up up to 1 year, (2) linkage to the National Death Index (NDI), and (3) obituary searches, thus generating a 5-year survival curve. In a random sample of 50 participants who died during trial follow-up, we estimated sensitivity of death data using NDI and obituary sources and computed survival times by data source. The two trials enrolled 1,169 participants; mean age was 76 years; 46% were female; and gastrointestinal cancer (30%) and lung cancer (26%) were the most common cancer types. Across data sources, maximum follow-up was >7 years; 5-year survival was 18%. In total, there were 841 deaths: 603 identified during trial follow-up; 199 from the NDI; and 39 from obituary searches. The sensitivity for death capture was 92% for the NDI and 94% for the obituary searches compared with the trial data, and computed survival times were similar across data sources. Extending clinical trial mortality follow-up through linkage with external data sources was feasible and accurate. Future cancer clinical trials should collect necessary consent and patient identifiers for vital status linkages that can enhance understanding of longer-term outcomes.

The Next Horizon of Drug Development: External Control Arms and Innovative Tools to Enrich Clinical Trial Data.

Conducting clinical trials (CTs) has become increasingly costly and complex in terms of designing and operationalizing. These challenges exist in running CTs on novel therapies, particularly in oncology and rare diseases, where CTs increasingly target narrower patient groups. In this study, we describe external control arms (ECA) and other relevant tools, such as virtualization and decentralized clinical trials (DCTs), and the ability to follow the clinical trial subjects in the real world using tokenization. ECAs are typically constructed by identifying appropriate external sources of data, then by cleaning and standardizing it to create an analysis-ready data file, and finally, by matching subjects in the external data with the subjects in the CT of interest. In addition, ECA tools also include subject-level meta-analysis and simulated subjects' data for analyses. By implementing the recent advances in digital health technologies and devices, virtualization, and DCTs, realigning of CTs from site-centric designs to virtual, decentralized, and patient-centric designs can be done, which reduces the patient burden to participate in the CTs and encourages diversity. Tokenization technology allows linking the CT data with real-world data (RWD), creating more comprehensive and longitudinal outcome measures. These tools provide robust ways to enrich the CT data for informed decision-making, reduce the burden on subjects and costs of trial operations, and augment the insights gained for the CT data.

ProxyDet: Synthesizing Proxy Novel Classes via Classwise Mixup for Open-Vocabulary Object Detection

Open-vocabulary object detection (OVOD) aims to recognize novel objects whose categories are not included in the training set. In order to classify these unseen classes during training, many OVOD frameworks leverage the zero-shot capability of largely pretrained vision and language models, such as CLIP. To further improve generalization on the unseen novel classes, several approaches proposed to additionally train with pseudo region labeling on the external data sources that contain a substantial number of novel category labels beyond the existing training data. Albeit its simplicity, these pseudo-labeling methods still exhibit limited improvement with regard to the truly unseen novel classes that were not pseudo-labeled. In this paper, we present a novel, yet simple technique that helps generalization on the overall distribution of novel classes. Inspired by our observation that numerous novel classes reside within the convex hull constructed by the base (seen) classes in the CLIP embedding space, we propose to synthesize proxy-novel classes approximating novel classes via linear mixup between a pair of base classes. By training our detector with these synthetic proxy-novel classes, we effectively explore the embedding space of novel classes. The experimental results on various OVOD benchmarks such as LVIS and COCO demonstrate superior performance on novel classes compared to the other state-of-the-art methods. Code is available at https://github.com/clovaai/ProxyDet.

Learning from user behavior: A survey-assist algorithm for longitudinal mobility data collection

GPS-based travel surveys are widely used in mobility studies to gather crucial qualitative data, like purpose, transportation mode and replaced mode. However, survey response still poses a burden to users, especially in long-term mobility studies, leading to response fatigue. We explore a survey-assist strategy to ease this burden by a novel, user-level modeling approach that leverages past responses from each user to predict responses for new trips, without relying on external data sources like GIS data.We investigate three main algorithms for predicting responses: (i) clustering trips and extrapolating responses for similar trips, (ii) using random forest classification, and (iii) clustering that uses a hybrid algorithm to determine spatial structure, which is then fed as input to a classic random forest classifier. The clustering approach can flexibly predict responses for even complex qualitative survey questions; it achieved F-scores of 65%. The random forest pipeline uses architecture that restricts it to predicting three predetermined survey questions: trip purpose, mode, and replaced mode. However, it achieved F-scores of 78%.While the survey-assist approach has been implemented by several proprietary systems, to our knowledge, this is the first exploration in the academic literature. It follows that this is also the first rigorous evaluation of multiple algorithms that can implement the approach. The evaluation uses a large scale, publicly available, longitudinal dataset consisting of ≈ 92 k trips from 235 users over a period of roughly one and a half years.With this approach, travel surveys can be pre-filled with the predicted responses for each trip, thus streamlining the survey process for users. Combined with an active learning system that requests user input on low-confidence predictions, models can be updated and improved over time to better support the long-term collection of longitudinal qualitative data.

Abstract 5182: Exploiting tumor RNA-sequencing data for prediction of immune checkpoint inhibition response

Abstract Immune checkpoint inhibitors (ICI) have become the standard of care as a first- or second-line systemic treatment for patients with various cancer types. Unfortunately, many patients do not respond to this treatment and the occurrence of immune-related adverse events varies widely. There is an urgent need for robust predictive biomarkers for ICI response to identify patients with likely clinical benefit from this costly treatment. Several biomarkers, aimed at predicting response to ICI, have been proposed, including PD-L1 expression, tumor mutation burden (TMB), infiltration of cytotoxic T-cells in the tumor, Microsatellite Instability (MSI) and expression of various immune gene signatures. However, these individual biomarkers have suboptimal performance and multiple complementary omics technologies are required to properly quantify them. Integration of these multi-omic biomarkers has proven valuable in increasing the accuracy and robustness of ICI response prediction.We have developed a computational pipeline that determines the expressed mutation burden (eTMB), MSI status, fraction of infiltrating immune cells and various immune gene expression signatures directly from the RNA-sequencing profile of the tumor. Each biomarker is quantified using a dedicated algorithm that applies machine learning (eTMB and MSI) or computational deconvolution (infiltrating immune cells) and integrates external data sources to maximize performance. Algorithm performance has been validated on large cohorts of tumor RNA-sequencing data with matching gold standard quantification of said biomarkers. As a proof of concept, we applied our pipeline to tumor RNA-sequencing data of 45 gastric cancer patients treated with pembrolizumab. Responders showed significantly higher eTMB scores and were enriched among the MSI-H patients. Fractions of CD8 T-cells and M1 Macrophages, together with interferon gamma and cytotoxic T-cell gene expression signatures, were significantly increased in responders. Notably, integrating these biomarkers into a single prediction model improved prediction of response to checkpoint inhibition therapy compared to predictions based on eTMB or MSI alone. Taken together, our approach enables the quantification of various ICI response biomarkers from a single omics layer (RNA-sequencing) and can contribute to ICI response prediction. Citation Format: Pieter Mestdagh, Pieter-Jan Van Dam, Frederick De Baene, Carolina Fierro, Elise Van Hoof, Emmanuel Rivière, Hanne Dangreau, Reindert Van Cauwenberge, Jo Vandesompele. Exploiting tumor RNA-sequencing data for prediction of immune checkpoint inhibition response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5182.

Revolutionizing the used car market: Predicting prices with XGBoost

In contemporary dynamic automobile industry, informed decision-making is crucial for success. Accurate sales predictions are essential for optimizing operations and resource allocation in the used car market. This article explores the application of machine learning, specifically the eXtreme Gradient Boosting(XGBoost) algorithm, in predicting used car prices in the UK. The dataset consists of over 110,000 data points from various brands, including features like model, year, mileage, and more. Data preprocessing, including outlier removal and feature scaling, is performed to prepare the dataset. XGBoost, a powerful machine learning algorithm, is selected for price prediction and compared with traditional regression models. The results show that eXtreme Gradient Boosting(XGBoost) outperforms other models in terms of R2 score, Mean Absolute Error, and Mean Square Error. Its accuracy and efficiency make it a valuable tool for both buyers and sellers in the used car market. However, challenges such as data quality, feature engineering complexity, and model interpretability remain. Future research directions include improving data quality, exploring advanced feature engineering techniques, and integrating external data sources. This study highlights the potential of machine learning in enhancing decision-making in the dynamic used car market.

Exploring the Future of Indoor Navigation: AR Technology

Indoor navigation within complex environments remains a recurring challenge in the age of rapid technological advancement. Traditional methods often fall short when applied to intricate indoor spaces like shopping malls, airports, and corporate campuses. This paper introduces ARNav, an innovative Augmented Reality (AR) based indoor navigation system designed to enhance indoor navigation accuracy and user experience. ARNav utilizes AR technology to overlay digital information, such as arrows, markers, and directional cues, onto the real-time camera view of users' devices. This dynamic overlay simplifies navigation while offering an immersive experience. The system achieves precise indoor positioning through sensor fusion and mapping techniques, boasts an intuitive user-centric design, and implements robust data security measures. Energy efficiency optimization ensures longer battery life, and the system seamlessly integrates with external data sources, offering real-time information on points of interest and events within indoor spaces. A user feedback mechanism allows for continuous improvement, placing user needs at the forefront of development. This paper discusses the design and implementation of ARNav, highlighting the technologies and mathematical models for accurate positioning and AR overlay. ARNav represents a promising solution for indoor wayfinding, combining AR technology with precise indoor navigation to enhance user experiences. The paper explores potential future developments, including advanced AR features, machine learning integration, and compatibility with various devices and platforms, thus contributing to the field of indoor navigation and AR technology.

Veritas: Layer-2 Scaling Solution for Decentralized Oracles on Ethereum Blockchain with Reputation and Real-Time Considerations

Blockchainsand smart contracts are pivotal in transforming interactions between systems and individuals, offering secure, immutable, and transparent trust-building mechanisms without central oversight. However, Smart Contracts face limitations due to their reliance on blockchain-contained data, a gap addressed by ’Oracles’. These bridges to external data sources introduce the ’Oracle problem’, where maintaining blockchain-like security and transparency becomes vital to prevent data integrity issues. This paper presents Veritas, a novel decentralized oracle system leveraging a layer-2 scaling solution, enhancing smart contracts’ efficiency and security on Ethereum blockchains. The proposed architecture, explored through simulation and experimental analyses, significantly reduces operational costs while maintaining robust security protocols. An innovative node selection process is also introduced to minimize the risk of malicious data entry, thereby reinforcing network security. Veritas offers a solution to the Oracle problem by aligning with blockchain principles of security and transparency, and demonstrates advancements in reducing operational costs and bolstering network integrity. While the study provides a promising direction, it also highlights potential areas for further exploration in blockchain technology and oracle system optimization.

Examining the Generalizability of Pretrained De-identification Transformer Models on Narrative Nursing Notes.

Narrative nursing notes are a valuable resource in informatics research with unique predictive signals about patient care. The open sharing of these data, however, is appropriately constrained by rigorous regulations set by the Health Insurance Portability and Accountability Act (HIPAA) for the protection of privacy. Several models have been developed and evaluated on the open-source i2b2 dataset. A focus on the generalizability of these models with respect to nursing notes remains understudied. The study aims to understand the generalizability of pretrained transformer models and investigate the variability of personal protected health information (PHI) distribution patterns between discharge summaries and nursing notes with a goal to inform the future design for model evaluation schema. Two pretrained transformer models (RoBERTa, ClinicalBERT) fine-tuned on i2b2 2014 discharge summaries were evaluated on our data inpatient nursing notes and compared with the baseline performance. Statistical testing was deployed to assess differences in PHI distribution across discharge summaries and nursing notes. RoBERTa achieved the optimal performance when tested on an external source of data, with an F1 score of 0.887 across PHI categories and 0.932 in the PHI binary task. Overall, discharge summaries contained a higher number of PHI instances and categories of PHI compared with inpatient nursing notes. The study investigated the applicability of two pretrained transformers on inpatient nursing notes and examined the distinctions between nursing notes and discharge summaries concerning the utilization of personal PHI. Discharge summaries presented a greater quantity of PHI instances and types when compared with narrative nursing notes, but narrative nursing notes exhibited more diversity in the types of PHI present, with some pertaining to patient's personal life. The insights obtained from the research help improve the design and selection of algorithms, as well as contribute to the development of suitable performance thresholds for PHI.

Data to Inform Academic Instruction for Students with Extensive Support Needs: Availability, Use, and Perceptions

ABSTRACT Students with extensive support needs (ESN), most of whom are taught in separate settings by special educators without extensive academic preparation, have difficulty making progress in the general education curriculum. Although there is evidence that data-based decision-making improves achievement for a wide range of students, there is little research on teachers’ data use for academic instruction of students with ESN. In this study based on 403 survey responses from teachers in nine states, we examined the types of data that were available, how teachers perceived and used data, and how their perceptions were related to data use. Respondents more often reported having access to teacher-developed data sources than to external data (e.g. published assessments, local assessments). Even when external data sources were available, respondents more often used teacher-developed data. Respondents most often reported using data to set goals and tailor instruction, and least often to group students. Attitudes toward data and perceptions of personal data competence were positively associated with data use. School leadership and organizational supports were associated only with decisions about grouping students. There were group differences in data use depending on the type of data they perceived as useful.

Improving leadership skills of infection prevention and control teams by psychological empowerment: study protocol for a cluster randomised controlled trial (IP-POWER)

IntroductionInfection prevention and control (IPC) teams are routinely confronted with intense emotions in their daily work, as they are involved in many change processes with front-line medical staff, for example,...

Predicting disease-gene associations through self-supervised mutual infomax graph convolution network

Illuminating associations between diseases and genes can help reveal the pathogenesis of syndromes and contribute to treatments, but a large number of associations remained unexplored. To identify novel disease-gene associations, many computational methods have been developed using disease and gene-related prior knowledge. However, these methods remain of relatively inferior performance due to the limited external data sources and the inevitable noise among the prior knowledge. In this study, we have developed a new method, Self-Supervised Mutual Infomax Graph Convolution Network (MiGCN), to predict disease-gene associations under the guidance of external disease-disease and gene-gene collaborative graphs. The noises within the collaborative graphs were eliminated by maximizing the mutual information between nodes and neighbors through a graphical mutual infomax layer. In parallel, the node interactions were strengthened by a novel informative message passing layer to improve the learning ability of graph neural network. The extensive experiments showed that our model achieved performance improvement over the state-of-art method by more than 8 % on AUC. The datasets, source codes and trained models of MiGCN are available at https://github.com/biomed-AI/MiGCN.

Predicting Probability of Success for Phase III Trials via Propensity-Score-Based External Data Borrowing

Given the rising costs and time length of confirmatory phase III trials, drug developers have become increasingly reliant on quantitative methods to support critical decisions such as whether drug should continue development after completing a phase II study. One such method that is commonly used is to estimate the probability of success (PoS) of a phase III trial. PoS is computed by averaging the traditional power function over a prior distribution for the unknown treatment effect, which is often estimated using observed phase II data. However, phase II trials are often small due to budgetary, logistical, or ethical considerations, which can increase the variability of phase II results and provide misleading PoS calculations. In this article, we develop a new PoS framework that leverages external data sources to increase the understanding of the phase II study data and hence the accuracy of PoS calculations. To mitigate the risk of bias associated with external data borrowing, we augment the control arm of the phase II study using the propensity-score-based MAP (PS-MAP) prior, which allow to objectively incorporate patient-level information. We demonstrate via simulation and an example application in non-small cell lung cancer that incorporation of external data into the traditional PoS framework can enable more robust decision-making in clinical development by engendering larger values, on average, when the treatment under study is truly effective and smaller values, on average, when the treatment under study is truly ineffective.

The sensitivities and adaptive capacity of public lands visitors

Lands and waters administered by governing entities for public use (i.e., “public lands”) are subject to changing social and ecological conditions (e.g., overcrowding, drought). Public lands managers are often tasked with addressing these changes while balancing conservation goals and public use mandates, and their decisions can significantly and inequitably impact visitor sensitivities to different types of exposures. To gain insights into visitor sensitivities and their adaptive capacity to mitigate the impacts of exposures, we draw upon a comprehensive monitoring effort conducted in collaboration with the U.S. Fish and Wildlife Service (Service) to understand visitor experiences on national wildlife refuges (refuges). We collected data from 10,556 visitors to 68 refuges during 2018–2019, then segmented respondents into unique visitor types based on their frequency of visiting “this refuge” where they were contacted, their participation across different activities at that refuge, and visits to other public lands for purposes of their primary activity, all during the 12 months prior to being contacted. We then explored differences among the resulting visitor types in their (a) purpose of visit, (b) satisfaction with opportunities during their visit, and (c) demographic characteristics. Finally, we used external data sources to explore the sensitivities and adaptive capacity of visitors’ home communities. Our approach identified eight types of visitors with distinct sensitivities and adaptive capacities. For example, the type categorized as “most sensitive” due to activity specialization and site dependency was more likely to engage in activities (e.g., fishing, hunting) that may be subsistence uses of public lands and more often lived in communities with reduced adaptive capacity. Our assessment supports public lands decision-making by helping to understand and address social inequities that may arise or be exacerbated by rapidly changing conditions.

Enhancing Person Re-Identification Performance Through In Vivo Learning.

This research investigates the potential of in vivo learning to enhance visual representation learning for image-based person re-identification (re-ID). Compared to traditional self-supervised learning (which require external data), the introduced in vivo learning utilizes supervisory labels generated from pedestrian images to improve re-ID accuracy without relying on external data sources. Three carefully designed in vivo learning tasks, leveraging statistical regularities within images, are proposed without the need for laborious manual annotations. These tasks enable feature extractors to learn more comprehensive and discriminative person representations by jointly modeling various aspects of human biological structure information, contributing to enhanced re-ID performance. Notably, the method seamlessly integrates with existing re-ID frameworks, requiring minimal modifications and no additional data beyond the existing training set. Extensive experiments on diverse datasets, including Market1501, CUHK03-NP, Celeb-reID, Celeb-reid-light, PRCC, and LTCC, demonstrate substantial enhancements in rank-1 precision compared to state-of-the-art methods.

Global Statistics and Data Sciences (GSDS), BeiGene USA, Fulton, MD

Pediatric cancer consists of a diverse group of rare diseases. Due to limited patient populations, standard randomized and controlled trials are often infeasible. As a result, single-arm trials are common in pediatric oncology and the use of external controls is often desirable or necessary to help generate actionable evidence and contextualize trial results. In this paper, we illustrate unique features in pediatric oncology clinical trials and describe their impact on the use of external controls. Various types of relevant external control data sources are described in terms of their utility and drawbacks. Statistical methodologies and design implications with external control are discussed. Two recent case studies using external controls to support pediatric oncology drug development are described in detail.

Global Statistics and Data Sciences (GSDS), BeiGene USA, Fulton, MD

Pediatric cancer consists of a diverse group of rare diseases. Due to limited patient populations, standard randomized and controlled trials are often infeasible. As a result, single-arm trials are common in pediatric oncology and the use of external controls is often desirable or necessary to help generate actionable evidence and contextualize trial results. In this paper, we illustrate unique features in pediatric oncology clinical trials and describe their impact on the use of external controls. Various types of relevant external control data sources are described in terms of their utility and drawbacks. Statistical methodologies and design implications with external control are discussed. Two recent case studies using external controls to support pediatric oncology drug development are described in detail.

Identifying a Cohort of People Who Are Transgender and Gender-Diverse Within Saskatchewan's Administrative Health Databases.

This was a retrospective cohort study. Algorithms were developed to identify a cohort of people who were trans and gender diverse (PTGD) among provincial-level administrative health databases (physician, hospital, emergency department, and pharmacy) from April 1, 2012 to September 30, 2020. Then, healthcare usage was compared between the identified cohort and the general population. There were 6466 unique individuals identified in the cohort, out of a total population of 1.2 million Saskatchewan residents (~0.5%). They had a mean age of 42.5 (SD 17.7) years. 1946 (30.1%) had a female sex marker and 4560 (69.9%) had a male sex marker, which may not indicate their lived gender. The cohort had increased healthcare usage 2 years prior to their index date, compared to the general population, which continued to rise to 1 year past their index date across physician, emergency department visits, and hospitalizations. The results for drugs were mixed. The percentage of PTGD identified in Saskatchewan was comparable to other studies. Healthcare utilization among the cohort was higher than the general population. Further research could use external data sources to validate and improve the cohort identification methods. The large majority of individuals with a male sex marker deserves further investigation.

Transdisciplinary integration of knowledge in the environment of a virtual stem center

The subject of the article is the role of ontological systems in improving the processes of structuring and analyzing scientific content, especially in the context of STEM education.
 The goal of the work is to research and analyze the application of ontological systems in the context of managing educational materials in STEM education. The use of such approaches is focused on developing effective methods for structuring and presenting educational knowledge in the STEM education system, emphasizing the importance of integrating different scientific disciplines to optimize the educational process. In accordance with the purpose, the following tasks were set: to develop a methodology for creating and implementing ontological systems in STEM education and to develop the architecture of a virtual STEM center that would provide the implementation of the proposed principles.
 The research is based on the following methods: For the development of the architecture of the virtual STEM center, we used UML diagrams. We developed UML diagrams of roles and activities that illustrate the interaction of different users and systems, as well as demonstrate work processes and interactions in multi-agent systems. Special attention is paid to activity diagrams, which reflect the processing of user requests and the interaction of the stemua.science agent with other components of the STEM center.
 The following results were obtained: A modular system architecture of the virtual STEM center was developed and described using UML diagrams, which includes roles such as the STEM center administrator, editor, author, and user, as well as the administrator of the CIT "Polyhedron". The interaction of these roles with the virtual STEM center is described in detail, revealing the mechanisms of their interaction and joint work aimed at creating, filling, and editing content in the transdisciplinary STEM center.
 The process of optimizing work processes in the modular system of the virtual STEM center is also considered. Ways to fill and use the T-STEM center in an ontological form have been identified. The interaction of software entities of the T-STEM center in an ontological form has been analyzed.
 Conclusions: Based on the conducted research, it is concluded that the use of ontological systems in the context of managing educational materials in STEM education is an effective method for structuring and presenting scientific content, promoting the integration of various scientific disciplines, and optimizing the learning process. It is determined that ontological systems are an effective method for structuring and presenting scientific content, facilitating the integration of various scientific disciplines, and optimizing the learning process. The modular architecture of the system is found to facilitate efficient interaction among different roles and automate workflow processes. Integration with a multi-agent system allows for the use of external data sources and ensures interoperability with other systems. For the further development of the system, research is needed to enhance the efficiency of role interactions and workflow automation. Additionally, research on integrating the system with other STEM education systems is necessary.

A taxonomy of earth observation data for sustainable finance

Corporate Environmental, Social and Governance (ESG) reporting has been subject to heightened attention and demand within the financial sector, with the objective of efficiently directing capital towards firms engaging in sustainable practices. Effective ESG monitoring is challenging, given the prevalence of self-disclosed internal data and managerial signalling incentives, presenting a need for comprehensive and diverse external data sources to augment existing ESG-related disclosure. Earth Observation (EO) technologies – particularly satellite data – play a crucial role in collecting spatial data on land, water, and atmosphere, making them highly useful for facilitating transition in the sector. This paper aims to outline the various ways in which EO data can be applied for the purposes of (i) future academic research in the subject area of sustainable finance and (ii) detailed ESG reporting and monitoring by practitioners. Using the ESG Key Performance Indicator (KPI) framework established by the European Commission and EFFAS, we present a framework listing all applicable KPIs against the types of satellite imagery that can be utilised in each case. Additionally, for ESG KPIs that EO data cannot directly address, we compile an ancillary list to explore potential indirect applications. To underscore the wealth of available EO data sources that can be used for sustainable finance research, we present a comprehensive catalogue of all open-access and relevant private satellite missions. Listed missions are categorised based on their spatial resolution, temporal resolution, and mission duration, facilitating research with specific requirements for these parameters.