Accuracy Of Data Collection Research Articles

Forward-Looking Roadmaps for Long-Term Continuous Water Quality Monitoring: Bottlenecks, Innovations, and Prospects in a Critical Review.

Long-term continuous monitoring (LTCM) of water quality can bring far-reaching influences on water ecosystems by providing spatiotemporal data sets of diverse parameters and enabling operation of water and wastewater treatment processes in an energy-saving and cost-effective manner. However, current water monitoring technologies are deficient for long-term accuracy in data collection and processing capability. Inadequate LTCM data impedes water quality assessment and hinders the stakeholders and decision makers from foreseeing emerging problems and executing efficient control methodologies. To tackle this challenge, this review provides a forward-looking roadmap highlighting vital innovations toward LTCM, and elaborates on the impacts of LTCM through a three-hierarchy perspective: data, parameters, and systems. First, we demonstrate the critical needs and challenges of LTCM in natural resource water, drinking water, and wastewater systems, and differentiate LTCM from existing short-term and discrete monitoring techniques. We then elucidate three steps to achieve LTCM in water systems, consisting of data acquisition (water sensors), data processing (machine learning algorithms), and data application (with modeling and process control as two examples). Finally, we explore future opportunities of LTCM in four key domains, water, energy, sensing, and data, and underscore strategies to transfer scientific discoveries to general end-users.

Coverage Blind Area Repair Based on Perceived Multimedia Data Driven in Mobile Wireless Sensor Networks

Wireless sensor networks deploy a large number of wireless sensor nodes in the monitoring area to complete the target monitoring task. The coverage effect is an important index to evaluate wireless sensor networks. The effect of network coverage is realized by the wireless sensor nodes deployed in the monitoring area, because the reasonable deployment of sensor nodes can affect the accuracy, reliability, real time, and integrity of monitoring target data collection. The main purpose of network coverage is to use the least number of wireless sensor nodes to complete the monitoring task of targets in the monitoring area and to avoid the emergence of coverage blind areas. Random node deployment, electronic equipment failure, software error, fire spread, flood, and other phenomena may lead to large-scale failure of wireless sensor nodes. For the failure of some nodes, the phenomenon of coverage blind area appears in the network. In order to repair the coverage blind area in the network, a coverage blind area repair algorithm for wireless sensor networks is proposed in this article. The coverage blind area attracts the nodes, makes the nodes move, and repairs the coverage blind area in the network.

Utilizing Geospatial Data for Assessing Energy Security: Mapping Small Solar Home Systems Using Unmanned Aerial Vehicles and Deep Learning

Solar home systems (SHS), a cost-effective solution for rural communities far from the grid in developing countries, are small solar panels and associated equipment that provides power to a single household. A crucial resource for targeting further investment of public and private resources, as well as tracking the progress of universal electrification goals, is shared access to high-quality data on individual SHS installations including information such as location and power capacity. Though recent studies utilizing satellite imagery and machine learning to detect solar panels have emerged, they struggle to accurately locate many SHS due to limited image resolution (some small solar panels only occupy several pixels in satellite imagery). In this work, we explore the viability and cost-performance tradeoff of using automatic SHS detection on unmanned aerial vehicle (UAV) imagery as an alternative to satellite imagery. More specifically, we explore three questions: (i) what is the detection performance of SHS using drone imagery; (ii) how expensive is the drone data collection, compared to satellite imagery; and (iii) how well does drone-based SHS detection perform in real-world scenarios? To examine these questions, we collect and publicly-release a dataset of high-resolution drone imagery encompassing SHS imaged under a variety of real-world conditions and use this dataset and a dataset of imagery from Rwanda to evaluate the capabilities of deep learning models to recognize SHS, including those that are too small to be reliably recognized in satellite imagery. The results suggest that UAV imagery may be a viable alternative to identify very small SHS from perspectives of both detection accuracy and financial costs of data collection. UAV-based data collection may be a practical option for supporting electricity access planning strategies for achieving sustainable development goals and for monitoring the progress towards those goals.

Occurrence Patterns of Traumatic Brain Injury Within the Emergency Department and Internal Screening Process Efficacy During the COVID-19 Pandemic: Retrospective Analysis.

BackgroundTraumatic brain injury (TBI) is one of the leading causes of death in pediatric patients. Continued recruitment of pediatric TBI participants into a biobank amidst the COVID-19 pandemic not only necessitates adaptive changes to traditional recruitment methods but also requires an evaluation of emergency department (ED) utilization by TBI-presenting patients.ObjectiveThe primary objective of this exploratory retrospective study was to evaluate pediatric TBI-related ED utilization during the pandemic. The secondary objective was to appraise the efficacy of the research team’s internal screening processes.MethodsPotential participants (ie, individuals who met all inclusion criteria and would be approached by a consenter) were screened from an ED’s electronic health record system. Data regarding their visit were recorded in a Health Insurance Portability and Accountability Act–compliant manner, which were cleaned through Google Sheets. Cleaned data were then coded as either a screening variable or a hospital utilization variable to examine the effects of the pandemic on internal operations and hospital utilization patterns. The variables were compared between select months during the pandemic in 2020 to analogous months in 2019 in the R programming language via the two-sample Student t test and the Mann-Whitney-Wilcoxon rank-sum test.ResultsThe sample (N=2321) consisted of 1245 entries from 2019 and 1076 entries from 2020. A significantly greater proportion of potential participants (P<.001) were identified in 2020 (222/633, 35.1%) than in 2019 (195/908, 21.4%). A significantly greater proportion of potential participants (P<.001) had a visit reason indicative of a TBI in 2020 (181/222, 81.5%) than in 2019 (103/195, 52.8%). A significantly greater proportion of these injuries (P=.02) occurred inside (39/181, 21.5%) in 2020 than in 2019 (11/103, 10.7%). No significant difference was found across the mechanism of injury categories reported for potential participants between 2019 and 2020. Potential participants were significantly older (P=.006) in 2019 (mean 8.93 years) than in 2020 (mean 7.31 years). Screeners spent significantly longer (P=.03) to identify potential participants in March 2020 (55 minutes) than in March 2019 (32 minutes), but spent significantly less time (P=.01) to do so in July 2020 (22 minutes) than in July 2019 (42 minutes). Screening coverage was significantly lower (P<.001) in March 2020 (241.8 hours) than in March 2019 (346.5 hours). Screening coverage was significantly greater (P<.001) in April 2020 (611.5 hours) and July 2020 (513.5 hours) than in April 2019 (470.5 hours) and July 2019 (404.3 hours), respectively.ConclusionsThere was a significant increase in the rate of incoming TBI cases to the ED during the COVID-19 pandemic, warranting continued enrollment with added safety measures. Additionally, refinement of internal processes improved the accuracy of data collection. As demonstrated in this study, researchers can leverage ongoing data collection to facilitate process improvements and evaluate the impact of unexpected global events on their research.

Interrelationship between Three Dimensions of Sustainable Performance Measurement among Malaysian Manufacturing Companies

Corporate financial responsibility is the sole driving force of business in the business sector, as the primary goals of a firm are to maximize profit and grow shareholder value. However, in the previous decade, larger corporate obligations have been recognized, including environmental, local community, working conditions, and ethical standards. With the rise of "sustainability" reasons, unprecedented climate change is being measured into corporate strategy in the direction of sustainable thinking. The triple bottom line (TBL) is a sustainability assessment that combines environmental and social factors in addition to profitability and the rate of return on investment. The purpose of this research is to look into the interrelationships between the three dimensions of sustainable performance measurement (SPM) in Malaysian manufacturing companies. A questionnaire is issued to 600 manufacturing companies in the Federation of Malaysian Manufacturers (FMM) as part of a quantitative data collection approach. All the proposed hypotheses are supported, thus showing the interrelationship between three dimensions of SPM existed. Consequently, Malaysian companies are lagging behind in terms of SPM’s adoption compared to developed countries. Because of that, more government efforts to facilitate the adoption of SPM practice among companies are needed. The sole focus on the manufacturing industry, accuracy of data collection and time constraint are the limitations of this study. Therefore, it is suggested that further research should explore more the interrelationship between the three dimensions of SPM.&#x0D;

Abstract P4-12-02: Improving patient-reported outcome data capture for clinical research: ePRO in ISPY 2, a phase 2 breast cancer study

Abstract Introduction: Advances in technology and internet capability have provided an opportunity for efficient collection of Patient Reported Outcomes (PRO) during medical treatment. Here we describe the development and implementation of a system for monitoring patient reported adverse events (AEs) and quality of life (QoL) using electronic PRO (ePRO) instruments for patients enrolled on the Investigation of Serial studies to Predict Your Therapeutic Response with Imaging And moLecular analysis (I-SPY 2 TRIAL), a phase II adaptive platform clinical trial for locally advanced breast cancer. Methods: We designed an ePRO system to increase the accuracy of patient-reported QoL and AE data collection with the intent to act on symptoms in real time. Using the OpenClinica electronic data capture system, we developed rules-based logic to build automated ePRO surveys, customized to the I-SPY 2 treatment schedule. Weekly surveys contained a maximum of 126 validated, branching logic questions from the Patient Reported Outcomes Measurement Information System (PROMIS®) Health Measures and the National Cancer Institute’s Patient Reported Outcomes - Common Terminology Criteria for Adverse Events (PRO-CTCAE™) instruments. We piloted ePROs at the University of California, San Francisco (UCSF) to evaluate compatibility with a variety of I-SPY 2 patient scenarios (e.g., dose delays). We then staggered rollout of the ePRO system to 22 I-SPY 2 sites to ensure technological feasibility. In order to improve accuracy of data collection, we utilized real-time tracking and developed a Clinical Research Coordinator (CRC) training manual, which integrated workflow diagrams with technical solutions. CRCs were trained using remote video sessions. Results: The UCSF ePRO pilot began in September of 2020. Over 9-months, we accrued 43 I-SPY 2 patients (average age of 43.8 years), whose interactions with the ePRO system informed design improvements. Of the patients who received a baseline ePRO survey, the completion rate was 75.9% (average age of 44.2 years). This represents an increase from the 15-20% baseline completion rate for the 360 UCSF I-SPY 2 patients who received paper-based PRO surveys between May 2012 - January 2019. As of June 2021, the ePRO system was operational at all 22 I-SPY 2 sites. The UCSF pilot revealed that engagement with patients at critical timepoints improved survey completion. CRCs facilitated patient participation by sending instructional emails and communicating with patients weekly. We tracked data completeness using a Patient Tracking report, which displayed each patient’s survey completion history. This real-time tool enabled CRCs to identify patients who had not completed ePRO surveys prior to their visit, so they could be provided a tablet computer to complete the survey in the clinic. After introducing tablets into the workflow at UCSF, patient completion of the baseline survey increased from 75.9% to 80%. Conclusion: The transition from paper to electronic QOL and AE data collection improves the ability of patients to complete PRO surveys, but the process must also be optimized and integrated into clinical workflow and trial conduct. In the future, we will present additional results highlighting the feasibility of multilingual ePRO integration into I-SPY 2. ePRO also provides a new opportunity for data analysis, as well as the potential to reduce high grade toxicity through early intervention. It will allow us to assess QoL and AE data by drug regimen, site, provider, and study treatment. The creation of clinician-facing reports also enables access to patient responses in real-time. By implementing ePRO within I-SPY 2, we not only increase efficiency and accuracy of patient-reported data collection, but also improve quality of care and patient safety. Citation Format: Anna Northrop, Anika Christofferson, Michelle Melisko, Laura Sit, Ebunoluwa Olunuga, Ananya Mittal, Adi Goldman, Thelma Brown, Diane Heditsian, Bev Parker, Susie Brain, Carol Simmons, Alessandra Taboada, Kathryn J Ruddy, Tina Hieken, Mara Piltin, Kiri Cook, Carolina Salvador, Candace Mainor, Anosheh Afghahi, Sarah Tevis, Anne Blaes, Irene Kang, Susan Melin, Laura Esserman, Adam Asare, Dawn L Hershman, Amrita Basu. Improving patient-reported outcome data capture for clinical research: ePRO in ISPY 2, a phase 2 breast cancer study [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P4-12-02.

Standardization of seizure response times and data collection in an epilepsy monitoring unit

ObjectiveWe sought to improve seizure response times in the epilepsy monitoring unit, improve the accuracy and reliability of seizure response time data collection, and develop a standardized and automated approach for seizure response data collection in the EMU. MethodsWe used Quality Improvement (QI) methodology to understand the EMU workflow involved in responding to seizures (a process map); to create a theory of change that stated the desired aim, potential drivers/barriers and interventions (i.e., key driver diagram) and perform iterative interventions to address some of the drivers plan-do-study-act (PDSA) cycles. We performed three PDSA cycles with a focus on improving the seizure alert system in our EMU. Adjustments were made to the methodology as it became clear that this was a systems issue, and our project would need to focus on improving the system rather than iteratively improving a functioning (stable) system. ResultsOver a 6-month period, 252 seizure response times were recorded and analyzed. We performed 3 interventions. The first was initiating twice monthly meetings with nursing and EEG techs to discuss the project and provide feedback on response times. The second was the implementation of a new Hill-Rom seizure alert system to reduce alert times and automate data tracking. The third was implementing a new alert deactivation system to reduce variability in the data. Following these 3 interventions, variation, and data collection methods were improved while also maintaining improvements in seizure response times. SignificanceWe identified and implemented an alert system in our EMU which led to more efficient and accurate data collection while maintaining improved response times that resulted from the first intervention. This lays the groundwork for future QI initiatives and has created a framework for standardizing seizure response time recording and data collection that can be replicated at other centers with similar infrastructure, personnel and workflows.

Implementation of Career Information Services in Choose Department in Higher Collection in MAN 2 Cirebon

Education at the high school level aims to prepare students who will continue their education to a higher level, as well as prepare students who will immediately work when they have completed high school education. Experience in the field shows that there are still many MAN 2 Cirebon students who are still confused about choosing a major or study program to enter in higher education, especially for class XII students. Students make career plans based only on their will and desire without considering their abilities, there are even some students who leave career choices to their peers or other people.&#x0D; The purpose of this study was to determine the implementation and provision of career information services in choosing majors in higher education at MAN 2 Cirebon and the factors that became obstacles in these services.&#x0D; This study uses a descriptive qualitative research method with a phenomenological approach. This study relies on the accuracy of data collection to obtain valid research results. Data collection techniques in this study used observation, interviews, and documentation of the implementation of career information services in choosing majors in higher education at MAN 2 Cirebon. The validity of the data uses data triangulation.&#x0D; Based on the research that has been done, it is found that the implementation of career information services in choosing a major in higher education at MAN 2 Cirebon is very useful and has an effect on students' interest in continuing to college. This can be seen from the data of students who are accepted into universities from year to year which has increased quite a bit. The implementation of career information services in choosing majors in higher education at MAN 2 Cirebon makes researchers pay special attention so that researchers conduct research by taking that title.

Implementation of Career Information Services in Choose Department in Higher Collection in MAN 2 Cirebon

Education at the high school level aims to prepare students who will continue their education to a higher level, as well as prepare students who will immediately work when they have completed high school education. Experience in the field shows that there are still many MAN 2 Cirebon students who are still confused about choosing a major or study program to enter in higher education, especially for class XII students. Students make career plans based only on their will and desire without considering their abilities, there are even some students who leave career choices to their peers or other people.&#x0D; The purpose of this study was to determine the implementation and provision of career information services in choosing majors in higher education at MAN 2 Cirebon and the factors that became obstacles in these services.&#x0D; This study uses a descriptive qualitative research method with a phenomenological approach. This study relies on the accuracy of data collection to obtain valid research results. Data collection techniques in this study used observation, interviews, and documentation of the implementation of career information services in choosing majors in higher education at MAN 2 Cirebon. The validity of the data uses data triangulation.&#x0D; Based on the research that has been done, it is found that the implementation of career information services in choosing a major in higher education at MAN 2 Cirebon is very useful and has an effect on students' interest in continuing to college. This can be seen from the data of students who are accepted into universities from year to year which has increased quite a bit. The implementation of career information services in choosing majors in higher education at MAN 2 Cirebon makes researchers pay special attention so that researchers conduct research by taking that title.

A rendezvous point‐based data gathering in underwater wireless sensor networks for monitoring applications

SummaryIn underwater wireless sensor networks (UWSNs), most of the mobile sink data gathering scheme assumes that sensor nodes are static. Also, very limited set of works aim to eliminate the data redundancy during data gathering. Moreover, the data collection latency, error rate and load have to be considered in addition to the buffer space and energy level of the nodes. Here, a mobile sink data gathering technique is designed where rendezvous points (RPs) and sojourn times are selected. The RPs, where the mobile sinks stop to gather the data, are selected based on the node density, current traffic load and energy level of nodes. The proposed scheme, that is, adaptive distributed mobile sink data gathering (ADMSDG), eliminates data redundancy even by considering mobility of all the nodes including sink node. Here, multihop data gathering is also considered to address the energy hole problem near the sink based on similarity index. To examine the result, proposed scheme and other state‐of‐the‐art schemes are implemented in NS2.30 having the same environment. Obtained results show that when proposed scheme is experimented with respect to number of nodes, the proposed ADMSDG technique achieves 18% high data collection accuracy with 5% reduced data collection delay and 5% less energy consumption. Similarly, when proposed scheme is experimented with respect to transmission range, the proposed ADMSDG technique achieves 18% high data collection accuracy with 12% reduced data collection delay and 8% less energy consumption.

Design and Implementation of Data Analysis System of Social Network

To solve the problem that data collection efficiency of social network analysis technology based on single-mode network structure is low and cannot meet the problem of data information processing of large-scale social network, the social network data analysis system based on the network node centrality theory is proposed. The naive Bayesian classification algorithm is used to improve the traditional data mining mode. The traditional social network data analysis technology is compared with the social network data analysis system proposed in this research from four aspects: multi-platform network data processing response time, data acquisition and processing accuracy, social network data feature condition introduction rate, and data feature condition introduction accuracy. The accuracy of data collection and processing in the traditional social network data analysis system is 89.26%, the response time of multi-platform network data processing is 7.32s, the introduction rate of data feature conditions is 82.65%, and the accuracy rate of data feature conditions is 78.88%. The data processing accuracy of the data analysis system proposed in this research based on the network node centrality theory is 98.99%, the response time of multi-platform network data processing is 1.35s, the data feature condition introduction rate is 97.91%, and the data feature condition introduction accuracy rate is 97.39%. The social network data analysis system proposed in this research is significantly better than the traditional social network data analysis system in all aspects, and its data processing performance is very good especially in multi-platform social network. The results show that the social network data analysis system based on network centrality theory proposed in this research is feasible and can meet the daily application and research requirements of social network data collection, storage, analysis and visual display, which has the advantages of fast data processing speed, high accuracy and wide range of data storage.

Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery.

Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.

The feasibility and validity of using a real time location system (RTLS) to measure bedside contact time.

The association between the nurse-to-patient ratio and patient outcomes has been extensively investigated. Real time location systems have the potential capability of measuring the actual amount of bedside contact patients receive. This study aimed to determine the feasibility and accuracy of real time location systems as a measure of the amount of contact time that nurses spent in the patients' bed space. An exploratory, observational, feasibility study was designed to compare the accuracy of data collection between manual observation performed by a researcher and real time location systems data capture capability. Four nurses participated in the study, which took place in 2019 on two hospital wards. They were observed by a researcher while carrying out their work activities for a total of 230 minutes. The amount of time the nurses spent in the patients' bed space was recorded in 10-minute blocks of time and the real time location systems data were extracted for the same nurse at the time of observation. Data were then analysed for the level of agreement between the observed and the real time location systems measured data, descriptively and graphically using a kernel density and a scatter plot. The difference (in minutes) between researcher observed and real time location systems measured data for the 23, 10-minute observation blocks ranged from zero (complete agreement) to 5 minutes. The mean difference between the researcher observed and real time location systems time in the patients' bed space was one minute (10% of the time). On average, real time location systems measured time in the bed space was longer than the researcher observed time. There were good levels of agreement between researcher observation and real time location systems data of the time nurses spend at the bedside. This study confirms that it is feasible to use real time location systems as an accurate measure of the amount of time nurses spend at the patients' bedside.

Accuracy and acceptability of eHealth data collection for an early intensive behavioral intervention program

AbstractEarly intensive behavioral intervention (EIBI) is a treatment designed to increase adaptive behavior and decrease maladaptive behaviors for children with autism spectrum disorder (ASD). EIBI service providers typically collect data using pen‐and‐paper. Participants were four service providers employed at a large community‐based EIBI program. Differences in accuracy between collecting discrete‐trial‐teaching (DTT) data and challenging behavior data using pen‐and‐paper and an eHealth electronic data collection (EDC) application were assessed. The social validity of both methods of data collection was also examined. Pen‐and‐paper and EDC were equally accurate, but participants preferred using pen‐and‐paper. Our accuracy findings agreed with previous comparisons of EDC and pen‐and‐paper. Both methods of data collection are viable for an EIBI program; however, social validity considerations will determine the ease of EIBI programs transitioning to using an eHealth tool for data collection.

The Research Progress of Electrical Impedance Tomography for Lung Monitoring.

Medical imaging can intuitively show people the internal structure, morphological information, and organ functions of the organism, which is one of the most important inspection methods in clinical medical diagnosis. Currently used medical imaging methods can only be applied to some diagnostic occasions after qualitative lesions have been generated, and the general imaging technology is usually accompanied by radiation and other conditions. However, electrical impedance tomography has the advantages of being noninvasive and non-radiative. EIT (Electrical Impedance Tomography) is also widely used in the early diagnosis and treatment of some diseases because of these advantages. At present, EIT is relatively mature and more and more image reconstruction algorithms are used to improve imaging resolution. Hardware technology is also developing rapidly, and the accuracy of data collection and processing is continuously improving. In terms of clinical application, EIT has also been used for pathological treatment of lungs, the brain, and the bladder. In the future, EIT has a good application prospect in the medical field, which can meet the needs of real-time, long-term monitoring and early diagnosis. Aiming at the application of EIT in the treatment of lung pathology, this article reviews the research progress of EIT, image reconstruction algorithms, hardware system design, and clinical applications used in the treatment of lung diseases. Through the research and introduction of several core components of EIT technology, it clarifies the characteristics of EIT system complexity and its solutions, provides research ideas for subsequent research, and once again verifies the broad development prospects of EIT technology in the future.

Posture Data Automatic Extraction of Ornamental Turtle Based on Computer Vision Technology

The posture data of ornamental turtle is a quantitative index to evaluate the quality of the turtle. From the young turtle to the adult turtle, the posture data is constantly changing and the quality can also change accordingly. An integrated observation platform is designed and implemented. The automatic extraction of five posture data, i.e., the length of carapace, the width of carapace, the height of carapace, the length of plastron, and the width of plastron, is realized by using OpenCV based computer vision technology. The results of the experiment and the data of artificial measurement were compared and analyzed. The average fitting degree of the two values reached 99.2%. This method can extract the posture data of ornamental turtle quickly and accurately, and then track its growth and development. It will greatly reduce the workload of data collection in the process of ornamental turtle breeding, and effectively improve the accuracy of data collection. At the same time, it will reduce the risk of damage, and have important production and application value.

Residential Water Meters as Edge Computing Nodes: Disaggregating End Uses and Creating Actionable Information at the Edge.

We present a new, open source, computationally capable datalogger for collecting and analyzing high temporal resolution residential water use data. Using this device, execution of water end use disaggregation algorithms or other data analytics can be performed directly on existing, analog residential water meters without disrupting their operation, effectively transforming existing water meters into smart, edge computing devices. Computation of water use summaries and classified water end use events directly on the meter minimizes data transmission requirements, reduces requirements for centralized data storage and processing, and reduces latency between data collection and generation of decision-relevant information. The datalogger couples an Arduino microcontroller board for data acquisition with a Raspberry Pi computer that serves as a computational resource. The computational node was developed and calibrated at the Utah Water Research Laboratory (UWRL) and was deployed for testing on the water meter for a single-family residential home in Providence City, UT, USA. Results from field deployments are presented to demonstrate the data collection accuracy, computational functionality, power requirements, communication capabilities, and applicability of the system. The computational node’s hardware design and software are open source, available for potential reuse, and can be adapted to specific research needs.

REDCap on FHIR: Clinical Data Interoperability Services

BackgroundDespite widespread use of electronic data capture (EDC) systems for research and electronic health records (EHR), most transfer of data between EHR and EDC systems is manual and error prone. Increased adoption of Health Level Seven Fast Healthcare Interoperability Resource (FHIR) application programming interfaces (APIs) in recent years by EHR systems has increased the availability of patient data for external applications such as REDCap. ObjectiveDescribe the development of the REDCap Clinical Data Interoperability Services (CDIS) module that provides seamless data exchange between the REDCap research EDC and any EHR system with a FHIR API. CDIS enables end users to independently set up their data collection projects, map EHR data to fields, and adjudicate data transfer without project-by-project involvement from Health Information Technology staff. MethodsWe identified two use cases for EHR data transfer into REDCap. Clinical Data Pull (CDP) automatically pulls EHR data into user-defined REDCap fields and replaces the workflow of having to transcribe or copy and paste data from the EHR. Clinical Data Mart (CDM) collects all specified data for a patient over a given time period and replaces the process of importing EHR data for registries from research databases. With an iterative process, we designed our access control, authentication, variable selection, and mapping interfaces in such a way that end users could easily set up and use CDIS. ResultsSince its release, the REDCap CDIS has been used to pull over 19.5 million data points for 82 projects at Vanderbilt University Medical Center. Software and documentation are available through the REDCap Consortium. ConclusionsThe new REDCap Clinical Data and Interoperability Services (CDIS) module leverages the FHIR standard to enable real-time and direct data extraction from the EHR. Researchers can self-service the mapping and adjudication of EHR data into REDCap. The uptake of CDIS at VUMC and other REDCap consortium sites is improving the accuracy and efficiency of EHR data collection by reducing the need for manual transcription and flat file uploads.

A Qualitative Research of Perception Investigations in Caregivers Under the COVID-19 Pandemic

We enthusiastically read the article entitled “The experiences of health-care providers during the COVID-19 crisis in China: a qualitative study” by Liu and colleagues [1]. Although this study highlights the support needed to protect the physical and mental wellness of caregivers in the COVID-19 pandemic, the potential limitations that existed in its qualitative research methods may undermine the accuracy of data collection indicated by our previous work [2,3]. First, the information gleaned from the participants could have been more productive if the authors used focus group as it leverages the power of the group dynamics to encourage back and forth conversation leading to fully exploring and expanding participant’s true feelings, thoughts, and experiences toward the COVID-19 compared to the in-depth interviews [4]. Even though some researchers claim that in-depth interviews (e.g., phone interview) are better to investigate a sensitive subject like the COVID-19 crisis that participants may feel uncomfortable to share their thoughts in front of a group of people, the recent study confirms that 1) sensitive and intimate disclosures are more likely to occur in the focus group; 2) some certain sensitive themes only happen in the focus group; and 3) neither sensitive themes emerged exclusively nor frequently in indepth interviews compared to focus group [5]. Second, ensuring the homogeneity of interest is way more critical than the general heterogeneity like the demographics of the recruited participants [6,7]. Specifically, the desirable combination of interest consensus and common ground with diverse experience is important to generate rich information. However, the perceptions of the COVID-19 pandemic may be different between physicians and nurses based on the duty of these two different professional roles. This is because nurses are more likely to interact with COVID-19 patients directly and more frequently than physicians. Therefore, according to the primary research interest that investigates the experiences of fighting the COVID-19 pandemic, the participants should have been limited to nurses as the frontline caregivers to maintain the homogeneity of interest. Third, considering that all participants were recruited from one local hospital, convenient sampling appears better than snowball sampling as the authors can recruit participants based on their availability and accessibility [8]. This is because scheduling participants at a time when they have more energy may yield richer information data than reaching out to them through phone interviews. These suggested changes in data collection and participant enrollment should be implemented in future qualitative research for the investigation of global pandemic crisis to reach more convincing conclusions.

Skeletal Muscle of Molecular Motor and Its Application in Active Rehabilitation of Human Lower Limbs

Nanotechnology is an innovative science and technology that has the most opportunity to be applied in the market, and its intrinsic value is immeasurable. Some developed countries have invested a lot in research activities. The purpose of this paper is to explore the biomechanical principle of the skeletal muscle of the molecular motor and its application in the active rehabilitation of the lower limbs. In this paper, based on the force generation mechanism and biomechanical model of skeletal muscle, and guided by the mechanical principle equation of skeletal muscle contraction of the collective operation mechanism of a molecular motor, and combined with the experiment of active rehabilitation design of human lower limbs, it is demonstrated in the results that the angle error after yaw angle fitting can be controlled within −1° − 1°, which has a strong effect, and in the rehabilitation effect in fruit, the swing phase accounts for about 40% and the support phase accounts for about 60%. It can be concluded that the accuracy of rehabilitation data collection and the effectiveness of gait parameters detection in the process of human rehabilitation, as well as the actual recognition effect of lower limb movement pattern recognition, is good and efficient. Molecular materials play an important role in biology or medicine. With the development of technology, they contribute to the recovery of skeletal muscle.