High-frequency Data Collection Research Articles

Continuous Flow with Reagent Injection on an Inlaid Microfluidic Platform Applied to Nitrite Determination.

A continuous flow with reagent injection method on a novel inlaid microfluidic platform for nitrite determination has been successfully developed. The significance of the high-frequency monitoring of nutrient fluctuations in marine environments is crucial for understanding our impacts on the ecosystem. Many in-situ systems face limitations in high-frequency data collection and have restricted deployment times due to high reagent consumption. The proposed microfluidic device employs automatic colorimetric absorbance spectrophotometry, using the Griess assay for nitrite determination, with minimal reagent usage. The sensor incorporates 10 solenoid valves, four syringes, two LEDs, four photodiodes, and an inlaid microfluidic technique to facilitate optical measurements of fluid volumes. In this flow system, Taylor-Aris dispersion was simulated for different injection volumes at a constant flow rate, and the results have been experimentally confirmed using red food dye injection into a carrier stream. A series of tests were conducted to determine a suitable injection frequency for the reagent. Following the initial system characterization, seven different standard concentrations ranging from 0.125 to 10 µM nitrite were run through the microfluidic device to acquire a calibration curve. Three different calibrations were performed to optimize plug length, with reagent injection volumes of 4, 20, and 50 µL. A straightforward signal processing method was implemented to mitigate the Schlieren effect caused by differences in refractive indexes between the reagent and standards. The results demonstrate that a sampling frequency of at least 10 samples per hour is achievable using this system. The obtained attenuation coefficients exhibited good agreement with the literature, while the reagent consumption was significantly reduced. The limit of detection for a 20 µL injection volume was determined to be 94 nM from the sample intake, and the limit of quantification was 312 nM. Going forward, the demonstrated system will be packaged in a submersible enclosure to facilitate in-situ colorimetric measurements in marine environments.

PRECISION-TBI: a study protocol for a vanguard prospective cohort study to enhance understanding and management of moderate to severe traumatic brain injury in Australia

IntroductionTraumatic brain injury (TBI) is a heterogeneous condition in terms of pathophysiology and clinical course. Outcomes from moderate to severe TBI (msTBI) remain poor despite concerted research efforts. The heterogeneity...

Understanding and mitigating global change with aquatic sensors: current challenges and future prospects

Human activities are causing global change around the world including habitat destruction, invasive species in non-native ecosystems, overexploitation, pollution, and global climate change. While traditional monitoring has long been used to quantify and aid mitigation of global change, in-situ autonomous sensors are being increasingly used for environmental monitoring. Sensors and sensor platforms that can be deployed in developed and remote areas and allow high-frequency data collection, which is critical for parameters that exhibit important short-term dynamics on the scale of days, hours, or minutes. In this article, we discuss the benefits of in-situ autonomous sensors in aquatic ecosystems as well as the many challenges that we have experienced over many years of working with these technologies. These challenges include decisions on sensor locations, sensor types, analytical specification, sensor calibration, sensor drift, the role of environmental conditions, sensor fouling, service intervals, cost of ownership, and data QA/QC. These challenges result in important tradeoffs when making decisions regarding which sensors to deploy, particularly when a network of sensors is desired to cover a large area. We also review recent advances in designing and building chemical-sensor platforms that are allowing researchers to develop the next-generation of autonomous sensors and the power of integrating multiple sensors into a network that provides increased insight into the dynamics of water quality over space and time. In the coming years, there will be an exponential growth in data related to aquatic sensing, which will be an essential part of global efforts to monitor and mitigate global change and its adverse impacts on society.

DEEP LEARNING OBJECT DETECTION APPLICATION TO SURFING WAVE QUALITY

Quantitative monitoring is imperative to the sustainable management of coastal resources. Surfing resources have been both created and degenerated or destroyed by activities in the coastal zone. Effective surfing wave quality monitoring requires identification and tracking of the breaking part of the wave and the unbroken wave crest. Remote Camera Systems (RCS) have proven their utility in being able to monitor the coastal zone and provide almost continuous, high frequency data collection. RCSs lend themselves very well to the monitoring of surf breaks which are highly dynamic. The images captured from an RCS monitoring a surf break on the west coast of Aotearoa New Zealand are used to train a Convolutional Neural Network (CNN) to detect the break points (BP), associated crest orientation and relative Still Water Level (SWL) of each instance of breaking waves in each image. Model settings and image annotations were modified over a suite of training cases to improve model efficacy, which was evaluated each epoch of training with mean Average-Precision (mAP; max 1). A mAP of 0.794 was achieved for the BP and Crest Point (CP) CNN, and 8.634 for the SWL. The model was used to detect ~1.6 M objects across ~1 million images, with a mean confidence value of all BP-CP detections of 0.63 and more than 70percent of detections being greater than 0.5. This model enables the first automation of meaningful surfing wave quality monitoring.

Conducting high-frequency data collection in low-resource settings: Lessons from a financial diary study among women engaged in sex work in Uganda

ABSTRACT Poverty and economic insecurity are driving forces in entering sex work among women in low resource areas. This increases their risk for HIV by influencing the decision-making process for high-risk behaviors. Few studies have examined the financial behaviors and capacities of women engaged in sex work (WESW). This paper describes the methodology used in a financial diary study aimed at characterizing women’s spending patterns within a larger prevention intervention trial among WESW in Uganda. From June 2019 to March 2020, a subsample of 150 women randomized to the combination HIV prevention and economic empowerment treatment was asked to complete financial diaries to monitor daily expenditures in real time. Two hundred and forty financial diaries were distributed to study participants during the financial literacy sessions at eight sites. A total of 26,919 expense entries were recorded over 6 months. Sex-work-related expenses comprised approximately 20.01% of the total. The process of obtaining quality and consistent data was challenging due to the transient and stigmatized nature of sex work coupled with women’s varying levels of education. Frequent check-ins, using peer support, code word or visuals, and a shorter time frame would allow for a more accurate collection of high-frequency data. Moreover, the ability of women to complete the financial diaries despite numerous challenges speaks to their potential value as a data collection tool, and also as an organizing tool for finances.

Streambank Erosion Phenomena and Understanding: Current Research and Future Directions

Highlights High rates of streambank erosion are detrimental to the stability and function of streams. This collection brings together six studies that represent key advances in streambank erosion research. Current research directions on streambank erosion, erodibility characterization, and sediment loading are presented. Future research directions and challenges related to high-frequency data collection and modeling are discussed. Abstract. Streams are in dynamic equilibrium with their environments, and as that environment is altered by human development and changing climate, streambank erosion is a common, but little understood, result. This article highlights the contributions of the special collection “Streambank Erosion, Sediment Dynamics, and Restoration (SER),” which assembled six studies that represent key advances in streambank erosion research, highlight current research in the field, and identify directions for future research. The studies in this special collection were grouped into three central themes: (1) streambank erosion monitoring, (2) streambank erodibility characterization, and (3) streambank erosion loading. In this article, key findings within each of these central themes are summarized, emphasizing the significant contributions of each study. Likewise, perspectives on future research directions are discussed, outlining important challenges that remain to be addressed. Overall, the studies in this special collection are unified in their overarching goal of improving quantitative and predictive understanding of streambank erosion phenomena. Keywords: Keywords., Erodibility Parameters, Jet Erosion Test, Monitoring, Sediment, Soil Erosion, Stabilization Practices, Streambank, Stream Restoration.

Intelligent Diagnosis of Bogie Traction Seat Based on PCA-OVO and SVM Optimized by Modified Arithmetic Optimization Algorithm

The bogie traction seat is the main part of urban rail vehicles and its fault status will affect the safe and smooth operation of the vehicles. For the low accuracy of the traditional detection methods, an intelligent fault diagnosis model of the traction seat based on principal component analysis with one versus one (PCA-OVO) and support vector machine (SVM) optimized by modified arithmetic optimization algorithm is proposed. Firstly, for the difficulty of high-frequency data collection under real working conditions, the simulation platform of the bogie of an urban rail vehicle is built, and the vibration signals of the traction seat are collected and processed in different domains, and then the feature extraction and fusion method based on PCA-OVO is used to obtain the sensitive feature set of the traction seat. Finally, the SVM intelligence recognition model is constructed based on the sensitive feature set data, and its parameters are optimally combined and selected by the modified arithmetic optimization algorithm after introducing the cosine factor. The experiments prove the effectiveness of the model. Experimental results show that the model is effective and provides a new model for fault diagnosis of traction seat of urban rail vehicles.

Comparing Time-Lapse PhenoCams with Satellite Observations across the Boreal Forest of Quebec, Canada

Intercomparison of satellite-derived vegetation phenology is scarce in remote locations because of the limited coverage area and low temporal resolution of field observations. By their reliable near-ground observations and high-frequency data collection, PhenoCams can be a robust tool for intercomparison of land surface phenology derived from satellites. This study aims to investigate the transition dates of black spruce (Picea mariana (Mill.) B.S.P.) phenology by comparing fortnightly the MODIS normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) extracted using the Google Earth Engine (GEE) platform with the daily PhenoCam-based green chromatic coordinate (GCC) index. Data were collected from 2016 to 2019 by PhenoCams installed in six mature stands along a latitudinal gradient of the boreal forests of Quebec, Canada. All time series were fitted by double-logistic functions, and the estimated parameters were compared between NDVI, EVI, and GCC. The onset of GCC occurred in the second week of May, whereas the ending of GCC occurred in the last week of September. We demonstrated that GCC was more correlated with EVI (R2 from 0.66 to 0.85) than NDVI (R2 from 0.52 to 0.68). In addition, the onset and ending of phenology were shown to differ by 3.5 and 5.4 days between EVI and GCC, respectively. Larger differences were detected between NDVI and GCC, 17.05 and 26.89 days for the onset and ending, respectively. EVI showed better estimations of the phenological dates than NDVI. This better performance is explained by the higher spectral sensitivity of EVI for multiple canopy leaf layers due to the presence of an additional blue band and an optimized soil factor value. Our study demonstrates that the phenological observations derived from PhenoCam are comparable with the EVI index. We conclude that EVI is more suitable than NDVI to assess phenology in evergreen species of the northern boreal region, where PhenoCam data are not available. The EVI index could be used as a reliable proxy of GCC for monitoring evergreen species phenology in areas with reduced access, or where repeated data collection from remote areas are logistically difficult due to the extreme weather.

How Can High-Frequency Sensors Capture Collaboration? A Review of the Empirical Links between Multimodal Metrics and Collaborative Constructs

This paper reviews 74 empirical publications that used high-frequency data collection tools to capture facets of small collaborative groups—i.e., papers that conduct Multimodal Collaboration Analytics (MMCA) research. We selected papers published from 2010 to 2020 and extracted their key contributions. For the scope of this paper, we focus on: (1) the sensor-based metrics computed from multimodal data sources (e.g., speech, gaze, face, body, physiological, log data); (2) outcome measures, or operationalizations of collaborative constructs (e.g., group performance, conditions for effective collaboration); (3) the connections found by researchers between sensor-based metrics and outcomes; and (4) how theory was used to inform these connections. An added contribution is an interactive online visualization where researchers can explore collaborative sensor-based metrics, collaborative constructs, and how the two are connected. Based on our review, we highlight gaps in the literature and discuss opportunities for the field of MMCA, concluding with future work for this project.

Privacy-preserving Data Aggregation against Malicious Data Mining Attack for IoT-enabled Smart Grid

Internet of Things (IoT)-enabled smart grids can achieve more reliable and high-frequency data collection and transmission compared with existing grids. However, this frequent data processing may consume a lot of bandwidth, and even put the user’s privacy at risk. Although many privacy-preserving data aggregation schemes have been proposed to solve the problem, they still suffer from some security weaknesses or performance deficiency, such as lack of satisfactory data confidentiality and resistance to malicious data mining attack. To address these issues, we propose a novel privacy-preserving data aggregation scheme (called PDAM) for IoT-enabled smart grids, which can support efficient data source authentication and integrity checking, secure dynamic user join and exit. Unlike existing schemes, the PDAM is resilient to the malicious data mining attack launched by internal or external attackers and can achieve perfect data confidentiality against not only a malicious aggregator but also a curious control center for an authorized user. The detailed security and performance analysis show that our proposed PDAM can satisfy several well-known security properties and desirable efficiency for a smart grid system. Moreover, the comparative studies and experiments demonstrate that the PDAM is superior to other recently proposed works in terms of both security and performance.

Recruiting a Probability-Based Online Panel via Postal Mail: Experimental Evidence

Once recruited, probability-based online panels have proven to enable high-quality and high-frequency data collection. In ever faster-paced societies and, recently, in times of pandemic lockdowns, such online survey infrastructures are invaluable to social research. In absence of email sampling frames, one way of recruiting such a panel is via postal mail. However, few studies have examined how to best approach and then transition sample members from the initial postal mail contact to the online panel registration. To fill this gap, we implemented a large-scale experiment in the recruitment of the 2018 sample of the German Internet Panel (GIP) varying panel recruitment designs in four experimental conditions: online-only, concurrent mode, online-first, and paper-first. Our results show that the online-only design delivers higher online panel registration rates than the other recruitment designs. In addition, all experimental conditions led to similarly representative samples on key socio-demographic characteristics.

Salinization Increase due to Climate Change Will Have Substantial Negative Effects on Inland Waters: A Call for Multifaceted Research at the Local and Global Scale

Salinization Increase due to Climate Change Will Have Substantial Negative Effects on Inland Waters: A Call for Multifaceted Research at the Local and Global Scale

Development of a Real-Time, Mobile Nitrate Monitoring Station for High-Frequency Data Collection

A mobile monitoring station was developed to measure nitrate and physicochemical water quality parameters remotely, in real-time, and at very high frequencies (thirty minutes). Several calibration experiments were performed to validate the outputs of a real-time nutrient sensor, which can be affected by optical interferences such as turbidity, pH, temperature and salinity. Whilst most of these proved to play a minor role, a data-driven compensation model was developed to account for turbidity interferences. The reliability of real-time optical sensors has been questioned previously; however, this study has shown that following compensation, the readings can be more accurate than traditional laboratory-based equipment. In addition, significant benefits are offered by monitoring waterways at high frequencies, due to rapid changes in analyte concentrations over short time periods. This, combined with the versatility of the mobile station, provides opportunities for several beneficial monitoring applications, such as of fertiliser runoff in agricultural areas in rural regions, aquaculture runoff, and waterways in environmentally sensitive areas such as the Great Barrier Reef.

Stock assessment and management of cephalopods: advances and challenges for short-lived fishery resources

Abstract Cephalopods have become an important global food source, but their sustainable management is challenged by unique life history characteristics associated with short lifespans and semelparous reproduction, high natural mortality rates, rapid and often nonasymptotic growth, and complex population structures. Weak stock-recruitment relationships together with the time-consuming work required for age validation and high-volume annual age determinations make traditional age-based modelling impractical. We propose that the best method for cephalopod assessment involves innovative depletion models, fitted with in-season data on catch numbers and fishing effort, to produce realistic estimates of stock biomass. A “fast lane” assessment approach is suggested that includes high-frequency data collection for separate, in-season stock assessments of each cohort to ensure sustainable exploitation of these short-lived resources. However, most cephalopod fisheries are data-poor and/or lack the infrastructure and resources needed to apply depletion methods; therefore, we also present alternative assessment methods that have been recently applied worldwide. We also offer suggestions for further research on the remaining challenges of cephalopod stock assessment and management.

Self‐Powered Sensors Enabled by Wide‐Bandgap Perovskite Indoor Photovoltaic Cells

AbstractA new approach to ubiquitous sensing for indoor applications is presented, using low‐cost indoor perovskite photovoltaic cells as external power sources for backscatter sensors. Wide‐bandgap perovskite photovoltaic cells for indoor light energy harvesting are presented with the 1.63 and 1.84 eV devices that demonstrate efficiencies of 21% and 18.5%, respectively, under indoor compact fluorescent lighting, with a champion open‐circuit voltage of 0.95 V in a 1.84 eV cell under a light intensity of 0.16 mW cm−2. Subsequently, a wireless temperature sensor self‐powered by a perovskite indoor light‐harvesting module is demonstrated. Three perovskite photovoltaic cells are connected in series to create a module that produces 14.5 µW output power under 0.16 mW cm−2 of compact fluorescent illumination with an efficiency of 13.2%. This module is used as an external power source for a battery‐assisted radio‐frequency identification temperature sensor and demonstrates a read range by of 5.1 m while maintaining very high frequency measurements every 1.24 s. The combined indoor perovskite photovoltaic modules and backscatter radio‐frequency sensors are further discussed as a route to ubiquitous sensing in buildings given their potential to be manufactured in an integrated manner at very low cost, their lack of a need for battery replacement, and the high frequency data collection possible.

Social dynamics of short term variability in key measures of household and community wellbeing in Bangladesh

High-frequency social data collection may facilitate improved recall, more inclusive reporting, and improved capture of intra-period variability. Although there are examples of small studies collecting particular variables at high frequency in the social science literature, to date there have been no significant efforts to collect a wide range of variables with high frequency. We have implemented the first such effort with a smartphone-based data collection approach, systematically varying the frequency of survey task and recall period, allowing the analysis of the relative merit of high-frequency data collection for different key variables in household surveys. This study of 480 farmers from northwestern Bangladesh over approximately one year of continuous data on key measures of household and community wellbeing could be particularly useful for the design and evaluation of development interventions and policies. While the data discussed here provide a snapshot of what is possible, we also highlight their strength for providing opportunities for interdisciplinary research in the household agricultural production, practices, seasonal hunger, etc., in a low-income agrarian society.

IFAD Impact Assessment – Participatory Small Irrigation Development Programme I (Pasidp I): Results From a High Frequency Data Collection – Ethiopia

This report presents the results of an ex-post impact assessment of the Participatory Small-scale Irrigation Development Programme (PASIDP), a project financed by IFAD and implemented in Ethiopia between 2008 and 2015. This agricultural project aimed at improving food security and increasing income of beneficiaries by providing access to small-scale irrigation infrastructure systems in four regions of Ethiopia. The objective of the impact assessment was to investigate both the sustainability of the impacts and the resilience capacity of beneficiaries in a context characterized by adverse weather conditions. An innovative data collection was put in place to study the impact on areas where a protracted drought was taking place. In particular, using panel data that allowed one to follow household over time, the analysis tested whether the irrigation schemes were able to provide a protective and sustained effect towards reducing vulnerability and enhancing smallholders households resilience capacity to cope with the longer term variability of the climatic shocks.

A Cost-Benefit Analysis of Automated Physiological Data Acquisition Systems Using Data-Driven Modeling.

Precision medicine and the continuous analysis of "Big data" promises to improve patient outcomes dramatically in the near future. Very recently, healthcare facilities have started to explore automatic collection of patient-specific physiological data with the aim of reducing nursing workload and decreasing manual data entry errors. In addition to those purposes, continuous physiological data can be used for the early detection and prevention of common, and possibly fatal, diseases. For instance, poor patient outcomes from sepsis, a leading cause of mortality in healthcare facilities and a major driver of hospital costs in the USA, can be mitigated when detected early using screening tools that monitor the changing dynamics of physiological data. However, the potential cost of collecting continuous physiological data remains a barrier to the widespread adoption of automated high-frequency data collection systems. In this paper, we perform cost-benefit analysis (CBA) of machine learning applied to various types of acquisition systems (with different collection intervals) to determine if the benefits of such systems will outweigh their implementation costs. Although such systems can be used in the detection of various complications, in order to showcase the immediate benefits, we focus on the early detection of sepsis, one of the major challenges of hospital systems. We present a general approach to conduct such analysis for a wide range of hospitals and highlight its applicability using a case study for a small hospital with 150 beds and 3000 annual patients where the acquisition system would collect data at 1-min intervals. Lastly, we discuss how the analysis may help guide incentives/policies with regard to adopting automated data acquisition systems.

Desorption characterization of methane and carbon dioxide in coal and its influence on outburst prediction

Coal and gas outburst is a dynamic phenomenon with violent eruptions of coal and gas from the working coal seam. It has been proved that the rapid desorption within a short period is necessary for the occurrence of an outburst. Due to limitation of the present test condition, gas desorption characterization for the first 60 s has not been researched sufficiently. In the present study, an experimental apparatus with the ability of high-frequency data collection was developed. Initial desorption characterization of methane and carbon dioxide in coal was experimentally studied. Both the initial desorption characterization of methane and carbon dioxide were experimentally studied with different equilibrium pressures. The desorbed gas pressure was measured at desorption time phase of 0–10 and 45–60 s, besides the initial amount of desorbed gas and initial diffusion velocity of coal gas were calculated to assess their risk of outburst. The results show that the gas pressure for both methane and carbon dioxide increases sharply in the initial time and then levels off, and the total amount of desorbed gas increases with the increasing desorption time. Although the amount of desorption methane is slightly larger than that of carbon dioxide at the beginning, the total amount of desorbed carbon dioxide is significantly larger than that of methane at the desorption process. Therefore, it can be concluded that the coal and carbon dioxide outburst is more dangerous than the coal and methane outburst based on the obtained experimental results.

The Technology Crisis in Neuropsychology.

Neuropsychology has fallen reliant on outdated and labor intensive methods of data collection that are slow, highly inefficient, and expensive, and provide relatively data-poor estimates of human behavior despite rapid technological advance in most other fields of medicine. Here we present a brief historical overview of current testing practices in an effort to frame the current crisis, followed by an overview of different settings in which technology can and should be integrated. Potential benefits of laboratory based assessments, remote assessments, as well as passive and high-frequency data collection tools rooted in technology are discussed, along with several relevant examples and how these technologies might be deployed. Broader issues of data security and privacy are discussed, as well as additional considerations to be addressed within each setting. Some of the historical barriers to adoption of technology are also presented, along with a brief discussion of the remaining uncertainties. While by no means intended as a comprehensive review or prescriptive roadmap, our goal is to show that there are a tremendous number of advantages to technologically driven data collection methods, and that technology should be embraced by the field. Our predictions are that the comprehensive assessments of the future will likely entail a combination of lab-based assessments, remote assessments, and passive data capture, and leading the development of these efforts will cement the role of neuropsychology at the forefront of cognitive and behavioral science.