Data Record Research Articles

Development of a web-based tool to assess daily rating of perceived exertion in agility dogs

ObjectiveTo develop a web-based tool for daily use by agility handlers to log rating of perceived exertion (RPE) for dogs as an aid in quantifying daily exercise and training load and to improve training and conditioning strategies.ProceduresFocus group meetings with small groups of handlers were conducted via internet—based video conferencing using a semi-structured interview format. Meeting notes were coded for reflexive thematic analysis. The RPE logging tool was revised based on handler feedback. Each handler was asked to log their dog’s daily RPE data for 1 week. Data were analyzed to assess compliance and timeliness of entries. Participants completed a post-logging questionnaire to provide feedback about their experiences.ResultsEighteen agility dog handlers participated in all phases of the project. Handler and dog demographics were similar to previously reported demographics of agility participants in the United States. Reflexive thematic analysis of their comments related to the initial draft RPE logging tool yielded 3 initial themes, all of which supported a fourth and major theme: the need for specific and detailed training resources before agility handlers utilized the RPE tool. Of 18 handlers, 14 (78%) submitted logging records for the full week. Median time for data entry was 87 s (IQR = 56–117 s), and 92% of logging records were entered within 1 day of the events which were being recorded. In the follow-up questionnaire the handlers did not identify any major concerns. Based on all available data from the handlers, a final version of the RPE logging tool was produced.Conclusion and clinical relevanceAgility dog handlers are very interested in developing and validating tools to quantify training load for their dogs. The final RPE logging tool was quick and easy to use. Further validation of this logging tool is required with a larger number of handlers and comparison to physiologic data from exercising dogs.

System Design and Launch of a Hybrid Rocket with a Star-Fractal Swirl Fuel Grain Toward an Altitude of 15 km

To achieve low-cost and on-demand launches of microsatellites, the authors have been researching and developing a micro hybrid rocket since 2014. In 2018, a ballistic launch experiment was performed using the developed hybrid rocket, where it reached an altitude of about 6.2 km. The rocket engine had a 3D-printed solid fuel grain made of acrylonitrile butadiene styrene (ABS) resin in combination with a nitrous oxide oxidizer. The fuel grain port had a star-fractal swirl geometry in order to increase the surface area of the port, to promote the laminar–turbulent transition by increasing the friction resistance, and to give a swirling velocity component to the oxidizer flow. This overcame the hybrid rocket’s drawback of a low fuel regression rate; i.e., it achieved a higher fuel gas generation rate compared with a classical port geometry. In 2021, the hybrid rocket engine was scaled up, and its total impulse was increased to over 50 kNs; it reached an altitude of 15 km. In addition to the engine, other components were also improved, such as through the incorporation of lightweight structures, low-shock separation devices, a high-reliability telemetry device, and a data logger, while keeping costs low. The rocket was launched and reached an altitude of about 10.1 km, which broke the previous Japanese altitude record of 8.3 km for hybrid rockets. This presentation will report on the developed components from the viewpoint of system design and the results of the ballistic launch experiments.

Open-Source Data Logger System for Real-Time Monitoring and Fault Detection in Bench Testing

This paper presents the design and development of a proof of concept (PoC) open-source data logger system for wireless data acquisition via Wi-Fi aimed at bench testing and fault detection in combustion and electric engines. The system integrates multiple sensors, including accelerometers, microphones, thermocouples, and gas sensors, to monitor critical parameters, such as vibration, sound, temperature, and CO2 levels. These measurements are crucial for detecting anomalies in engine performance, such as ignition and combustion faults. For combustion engines, temperature sensors detect operational anomalies, including diesel engines operating beyond the normal range of 80 °C to 95 °C and gasoline engines between 90 °C and 110 °C. These readings help identify failures in cooling systems, thermostat valves, or potential coolant leaks. Acoustic sensors identify abnormal noises indicative of issues such as belt misalignment, valve knocking, timing irregularities, or loose parts. Vibration sensors detect displacement issues caused by engine mount failures, cracks in the engine block, or defects in pistons and valves. These sensors can work synergistically with acoustic sensors to enhance fault detection. Additionally, CO2 and organic compound sensors monitor fuel combustion efficiency and detect failures in the exhaust system. For electric motors, temperature sensors help identify anomalies, such as overloads, bearing problems, or excessive shaft load. Acoustic sensors diagnose coil issues, phase imbalances, bearing defects, and faults in chain or belt systems. Vibration sensors detect shaft and bearing problems, inadequate motor mounting, or overload conditions. The collected data are processed and analyzed to improve engine performance, contributing to reduced greenhouse gas (GHG) emissions and enhanced energy efficiency. This PoC system leverages open-source technology to provide a cost-effective and versatile solution for both research and practical applications. Initial laboratory tests validate its feasibility for real-time data acquisition and highlight its potential for creating datasets to support advanced diagnostic algorithms. Future work will focus on enhancing telemetry capabilities, improving Wi-Fi and cloud integration, and developing machine learning-based diagnostic methodologies for combustion and electric engines.

Constraining net long-term climate feedback from satellite-observed internal variability possible by the mid-2030s

Abstract. Observing climate feedbacks to long-term global warming, which are crucial climate regulators, is not feasible within the observational record. However, linking them to top-of-the-atmosphere flux variations in response to natural surface temperature fluctuations (internal variability feedbacks) is a viable approach. We explore the use of relating internal variability to forced climate feedbacks in models and applying the resulting relationship to observations to constrain forced climate feedbacks. Our findings reveal strong longwave and shortwave feedback relationships in models during the 14-year overlap with the Clouds and the Earth's Radiant Energy System (CERES) record. Yet, due to the weaker relationship between internal variability and forced climate longwave feedbacks, the net feedback relationship remains weak, even over longer periods beyond the CERES record. However, after about half a century, this relationship strengthens, primarily due to reinforcements of the internal variability and forced climate shortwave feedback relationship. We therefore explore merging the satellite records with reanalysis to establish an extended data record. The resulting constraint suggests a stronger negative forced climate net feedback than the model's distribution and an equilibrium climate sensitivity of about 2.59 K (1.95 to 3.12 K, 5 %–95 % confidence intervals). Nevertheless, this method does not account for certain factors like biogeophysical–chemical feedbacks, inactive on short timescales and not represented in most models, along with differences in historical warming patterns, which may lead to misrepresenting climate sensitivity. Additionally, continuous satellite observations until at least the mid-2030s are essential for using purely observed estimates of the net internal variability feedback to constrain the net forced climate feedback and, consequently, climate sensitivity.

A digital and nurse-led support intervention, first year after prostate cancer treatment: a single-arm feasibility study in a Swedish primary care setting

BackgroundThe prevalence of patients with prostate cancer is increasing, and the first year after treatment is a vulnerable period for patients as they experience symptoms and unmet needs. Although electronic patient-reported outcomes that focus on patient self-management have demonstrated benefits, evidence is sparse regarding patients with prostate cancer in primary care settings and the role of nurses as a supportive resource. The significant role of primary care in future cancer care is on the agenda. This study aims to test the feasibility of a complex intervention that includes electronic patient-reported outcomes and self-management advice in an app, combined with nurse-led support, in primary care settings during the first year after curative intended prostate cancer treatment.MethodsThe intervention lasted four weeks and was a single-arm study. Feasibility was assessed by examining the recruitment process, retention rate, adherence to the reporting of symptoms in the app, and acceptability of the intervention. Data was collected through (1) logged data from the app that included patient-reported outcomes and self-management advice, (2) field notes by the nurse, and (3) semi-structured interviews with patients. Descriptive statistics were applied to logged data. The interviews and the field notes were analysed using qualitative content analysis.ResultsThe recruitment rate was 55%, yielding 11 patients with high retention as all completed the intervention. Adherence to reporting was 100%, and all functions in the app were used. Individual variation in how patients used the app was found, which was attributed to patients’ current needs. In total, 36 health dialogues with the nurse (virtual, face-to-face, telephone) were performed; all first dialogues lasted longer, while follow-ups were shorter. Patients described that the health dialogues covered relevant subjects and that the combination of using the app and health dialogues was tailored and provided supplementary support. No adverse events occurred; however, a few technical difficulties interfered with the intervention, and the patients gave valuable suggestions for improvement. Furthermore, patients suggested that the intervention should start immediately after treatment.ConclusionAs the patients adhered to and accepted the intervention, it was considered feasible. Findings suggest intervention should start directly after treatment ends.

Design and characterization of high-performance energetic hydrogels with enhanced mechanical and explosive properties

Polymeric hydrogels, known for their excellent mechanical properties and pre-cross-linking flowability, provide a promising solution for recycling waste propellants, ensuring safety and maintaining explosive performance. This study developed a double cross-linked network energetic hydrogel that effectively combines mechanical strength with explosive capabilities. Using a Ford 4 Cup, temperature data logger, universal testing machine, and detonation performance tests, we examined the impacts of kinematic viscosity, cross-linking time, compressive strength, and explosive properties. The optimal kinematic viscosity for stabilizing hollow glass microspheres (GM) was found to be 129.7 mm2/s. Cross-linking time was negatively correlated with initiator, catalyst levels, and reaction temperature, but positively correlated with retarder content. Compressive strength increased with acrylamide (AM) content and showed an initial rise before decreasing with N,N′-methylenebisacrylamide (MBAA) content and reaction temperature. The maximum compressive strength was achieved with 5% MBAA (of AM mass fraction) at 40 °C. Detonation velocity and steel plate damage decreased with increasing AM content and initially increased then decreased with GM content. A balance of mechanical and explosive properties was achieved with 6% AM and 4% GM, resulting in a detonation velocity of 4536 m/s. This hydrogel shows significant potential for waste munitions management.

Gulf of Mexico Case Study Highlights Smart-Completion Application

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 218794, “Multifaceted Smart Completion Application: A Gulf of Mexico Case Study,” by Jeffrey P. Manning, Ben J. Carter, SPE, and Leila Bannister, Occidental Petroleum, et al. The paper has not been peer reviewed. _ Reservoir heterogeneity, stacked pay systems, reservoir compartmentalization, and high temperature and pressure are common challenges seen in Gulf of Mexico (GOM) fields. The use of conventional completion design in vertical wells with commingled production from multiple zones has led to multiple operational challenges. In the complete paper, the authors explore the use of intelligent completions in deepwater GOM as a solution. Benefits of Intelligent Completions Intelligent completions allow collection, transmission, and validation of real-time, high-frequency data. This enables analysis of completion, production, and reservoir data while selected reservoir zones are controlled and monitored remotely in real time. Intelligent completions such as multiphase flowmeters, bottomhole pressure gauges, and cased-hole logging tools enable real-time data gathering and monitoring. Additionally, downhole flow-control systems, pinpoint stimulation, intelligent injection, and multizonal isolation technology are examples of intelligent completions that enable production optimization. This study concentrates on the application of downhole intelligent choke systems in wells that use radio frequency identification technology. Combining this technology with smart sensors or Global Positioning System technology enables sensor data to be transmitted wirelessly. Operations such as opening and closing of valves, indexing and release mechanisms, and a variety of other functions can be implemented using this technology. Multiple tools can be run at the same time without compromising the tool’s internal diameter. Each tool has a unique address; therefore, tags can be coded with data that identifies an individual tool to be actuated. Intelligent completions can be broadly categorized into the following: - Downhole flow-control systems: These are passive or active control systems to control flow from each zone. They are remotely controlled by hydraulic lines from the surface facility. - Permanent downhole sensors: These are discrete or distributed systems that monitor downhole parameters. - Permanent downhole chemical injection: These systems allow for treatment at different points in the wellbore and flowlines to prevent issues caused by corrosion, hydrates, asphaltenes, paraffin, and scaling. Case Study: Practical Use of Intelligent Completions in the GOM The operated field in the northern GOM has successfully used intelligent completions in multiple vertical producing wells to help significantly reduce operating costs associated with topsides water processing, mitigate the risk of sand production, and allow for better subsurface understanding using individual zone-flow tests (this synopsis concentrates on the first two of these three aspects). Intelligent completions enabled these operations without the use of an intervention vessel.

Absolute Intercalibration of Spaceborne Microwave Radiometers

Abstract Absolute calibration of spaceborne microwave radiometer observations consists of accurate determination of antenna cold space spillover, cross-polarization contamination, and nonlinearity coefficients of the receivers. We deem the GMI sensor to be the most accurate calibrated spaceborne microwave radiometer due to its unique calibration design features and its carefully planned orbit maneuvers. We demonstrate how to transfer the GMI calibration to other spaceborne radiometers, whose operations have sufficient time overlap with GMI. Specifically, we show results for WindSat and AMSR2. The sensor intercalibration is based on brightness temperature matchups between GMI and the other instruments over both open ocean and rainforest scenes. To assess the calibration accuracy, we compare the intercalibrated brightness temperatures with radiative transfer model calculations. In addition, we provide in situ validation results for wind speed and water vapor retrievals from the intercalibrated sensors. The intercalibration methodology allows for the creation of a multidecadal climate data record from passive microwave satellite observations. Significance Statement Creating a long-term climate data record of satellite observations of ocean winds, water vapor, and other variables requires careful and accurate calibration of the various sensors that are used. In particular, it is important to achieve the best possible consistency between the measurements from all the different instruments. This is a challenging task as the configuration and accuracy of these instruments can differ widely. The purpose of our paper is to demonstrate and validate the basic methodology for performing this intercalibration. The backbone of our method is data observed by a well-calibrated sensor that measures the passive microwave emission from Earth’s surface and atmosphere. We show how to transfer its calibration standard to other sensors.

Efficiency and Precision in Printing: Study of the Control System of a Rotogravure Machine

Rotogravure printing is a well-liked and effective printing technique in the packaging sector of a printing cylinder. In making an automatic rotogravure machine, it is necessary to design an appropriate control system. In Indonesia, semi-automatic technology is still used to make rotogravure machines, and printing speeds are often slow. For automation machines, they are still imported. Apart from that, the machine's ability to adjust its precision is also limited. The purpose of this article is to examine the control system for a 6-cylinder automatic rotogravure machine, and discuss the rotogravure machine printing process, important aspects of rotogravure machine operation, differentiation in selecting motor types for torque and speed control, as well as advantages in efficiency and precision control over the environment. The method of using a literature review is qualitative-descriptive. The main findings on rotogravure machines include five main aspects: (1) ink flow management, (2) control of the drying system, (3) register control, (4) tension and speed control, and (5) fault detection, data logging, and analysis.

A Review on IOT based Power Loom Automation

Abstract: Automation is the process to reduce the man power and the idea of automation is to provide a test run in an Atmega328p microcontroller-based system to monitor and control the production operation. The main objective of this project is to control the electric power wastage and take data logs from a motor and also to check the performance of an employee in industries. This system works on Atmega328p microcontroller so it requires low cost and gives efficient performance in unnecessary electric power wastage in power loom motors. All the system is designed on basis of Atmega328p microcontroller, proximity sensor and RFID. The system includes IoT design to observe worker performance online from anywhere in the world.

Determining Petrophysical Properties Using PLTs and Conventional Logs of the Middle Shale Member (4th Pay) of the Zubair Formation in the South Rumaila Oil Field, Southern Iraq

The petrophysical evaluation of the Zubair Formation's Middle Shale member (4th Pay) in the South Rumaila oil field has been conducted to characterize the reservoir and quantify its hydrocarbon prospects. Although the 4th Pay has a substantial oil reserve, it has never been studied. A gamma ray, resistivity, density, and neutron log were used to estimate the shale volume, porosity, water saturation, and hydrocarbon saturation for four oil-bearing subunits in the middle shale member (4th Pay), producing net pays of Middle Shale Member 7 (13 m), 10 (31 m), 20 (11 m) and 25 (16 m), respectively. Shale volume calculated using the Gamma-Ray Log method, averages between 0.05 % and 0.53 %. Effective porosity ranges from (0.08) % to (0.21) %, indicating good reservoir quality; permeability ranges from 0.9 to 193.6 mD, indicating well-sorted sands. Hydrocarbon saturation varies from 0.29 to 0.89, while water saturation ranges from (0.11) to (0.72). Production Log Tool and Nuclear Magnetic Resonance logs were used to estimate permeability. Production Logging Tool log permeability values ranged between 5.2 (Middle Shale Member 10, 3425 mD) and 49.7 (Middle Shale Member 25, 3448.5 mD), while Nuclear Magnetic Resonance log permeability values ranged between 5.81 (Middle Shale Member 10, 3425 mD and 49 (Middle Shale Member 25, 3448.5 mD). According to the correspondence between the two logs, the correspondence value equals 85%. Thus, the reservoir permeability can be calculated using the radial flow equation calculated from the Production Logging Tool log and generalized to areas where there is no core. Results show that permeability (K) is highest in subunits Middle Shale Member 20 and 25, which also have high porosity, low shale content, and fine grain sorting, making them key production subunits. Subunit Middle Shale Member, although lower in permeability, is the main reservoir with the most oil reserves. Subunits Middle Shale Member 5 and 7 have lower permeability due to high shale content and heterogeneity, with low porosity. Subunit Middle Shale Member 0 has very low permeability. The high oil shale content, due to its poor porosity and heterogeneity, renders it unproductive.

Nonlinear Analysis and Solution for an Overhead Line Magnetic Energy Harvester with an Active Rectifier

Recently, there has been a significant focus on developing various energy harvesting technologies to power remote electronic sensors, data loggers, and communication devices for smart grid systems. Among these technologies, magnetic energy harvesting stands out as one straightforward method to extract substantial power from current-carrying overhead lines. Due to the relatively small size of the harvester, the high currents in the distribution system quickly saturate its magnetic core. Consequently, the magnetic harvester operates in a highly nonlinear manner. The nonlinear nature of the downstream AC to DC converters further complicates the process, making precise analytical modeling a challenging task. In this paper, a clamped type overhead line magnetic energy harvester with a controlled active rectifier generating significant DC output power is investigated. A piecewise nonlinear analytical model of the magnetic harvester is derived and reported. The modeling approach is based on the application of the Froelich equation. The chosen approximation method allowed for a complete piecewise nonlinear analytical treatise of the harvester’s behavior. The main findings of this study include a closed-form solution that accounts for both the core and rectifiers’ nonlinearities and provides an accurate quantitative prediction of the harvester’s key parameters such as the transfer window width, optimal pulse location, average DC output current, and average output power. To facilitate the study, a nonlinear model of the core was developed in simulation software, based on parameters extracted from core experimental data. Furthermore, theoretical predictions were verified through comparison with a computer simulation and experimental results of a laboratory prototype harvester. Good agreement between the theoretical, simulation, and experimental results was found.

IoT based real-time water quality monitoring system in water treatment plants (WTPs)

This study presents the development and implementation of an Internet of Things (IoT)-based real-time water quality monitoring system tailored for water treatment plants (WTPs). The system integrates advanced sensor technologies to continuously monitor key water quality parameters such as pH, dissolved oxygen (DO), total dissolved solids (TDS), and temperature. Data collected by these sensors is transmitted through a robust communication network to a centralized monitoring platform that utilizes cloud-based storage and analytics. The system's design includes a PLC-based control mechanism, allowing for flexible setup modifications and the easy addition of new monitoring parameters. The IoT-based system, powered by a low-energy 29-W configuration, offers accurate and reliable data with a minimal error margin of 0.1–0.2 across various parameters. The research highlights the system's ability to provide real-time alerts, historical data logging, and remote monitoring, all of which contribute to enhanced operational efficiency, proactive maintenance, and informed decision-making. This innovative approach to water quality management not only improves the effectiveness of WTP operations but also ensures environmental sustainability and public health safety. The study underscores the significant potential of IoT technologies in revolutionizing water quality monitoring practices.

Calibration and Validation of NOAA-21 Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper Sensor Data Record Data

The Ozone Mapping and Profiler Suites (OMPS) Nadir Mapper (NM) is a grating spectrometer within the OMPS nadir instruments onboard the SNPP, NOAA-20, and NOAA-21 satellites. It is designed to measure Earth radiance and solar irradiance spectra in wavelengths from 300 nm to 380 nm for operational retrievals of the nadir total column ozone. This study presents calibration and validation analysis results for the NOAA-21 OMPS NM SDR data to meet the JPSS scientific requirements. The NOAA-21 OMPS SDR calibration derives updates of several previous OMPS algorithms, including the dark current correction algorithm, one-time wavelength registration from ground to on-orbit, daily intra-orbit wavelength shift correction, and stray light correction. Additionally, this study derives an empirical scale factor to remove 2.2% of systematic biases in solar flux data, which were caused by pre-launch solar calibration errors of the OMPS nadir instruments. The validation of the NOAA-21 OMPS SDR data is conducted using various methods. For example, the 32-day average method and radiative transfer model are employed to estimate inter-sensor radiometric calibration differences from either the SNPP or NOAA-20 data. The quality of the NOAA-21 OMPS NM SDR data is largely consistent with that of the SNPP and NOAA-20 OMPS data, with differences generally within ±2%. This meets the scientific requirements, except for some deviations mainly in the dichroic range between 300 nm and 303 nm. The deep convective cloud target approach is used to monitor the stability of NOAA-21 OMPS reflectance above 330 nm, showing a variation of 0.5% over the observed period. Data from the NOAA-21 VIIRS M1 band are used to estimate OMPS NM data geolocation errors, revealing that along-track errors can reach up to 3 km, while cross-track errors are generally within ±1 km.

Predicting Early Dropout in a Digital Tobacco Cessation Intervention: Replication and Extension Study.

Detecting early dropout from digital interventions is crucial for developing strategies to enhance user retention and improve health-related behavioral outcomes. Bricker and colleagues proposed a single metric that accurately predicted early dropout from 4 digital tobacco cessation interventions based on log-in data in the initial week after registration. Generalization of this method to additional interventions and modalities would strengthen confidence in the approach and facilitate additional research drawing on it to increase user retention. This study had two research questions (RQ): RQ1-can the study by Bricker and colleagues be replicated using data from a large-scale observational, multimodal intervention to predict early dropout? and RQ2-can first-week engagement patterns identify users at the greatest risk for early dropout, to inform development of potential "rescue" interventions? Data from web users were drawn from EX, a freely available, multimodal digital intervention for tobacco cessation (N=70,265). First-week engagement was operationalized as any website page views or SMS text message responses within 1 week after registration. Early dropout was defined as having no subsequent engagement after that initial week through 1 year. First, a multivariate regression model was used to predict early dropout. Model predictors were dichotomous measures of engagement in each of the initial 6 days (days 2-7) following registration (day 1). Next, 6 univariate regression models were compared in terms of their discrimination ability to predict early dropout. The sole predictor of each model was a dichotomous measure of whether users had reengaged with the intervention by a particular day of the first week (calculated separately for each of 2-7 days). For RQ1, the area under the receiver operating characteristic curve (AUC) of the multivariate model in predicting dropout after 1 week was 0.72 (95% CI 0.71-0.73), which was within the range of AUC metrics found in the study by Bricker and colleagues. For RQ2, the AUCs of the univariate models increased with each successive day until day 4 (0.66, 95% CI 0.65-0.67). The sensitivity of the models decreased (range 0.79-0.59) and the specificity increased (range 0.48-0.73) with each successive day. This study provides independent validation of the use of first-week engagement to predict early dropout, demonstrating that the method generalizes across intervention modalities and engagement metrics. As digital intervention researchers continue to address the challenges of low engagement and early dropout, these results suggest that first-week engagement is a useful construct with predictive validity that is robust across interventions and definitions. Future research should explore the applicability and efficiency of this model to develop interventions to increase retention and improve health behavioral outcomes.

Global evaluation of NOAA-20 VIIRS dark target aerosol products over land and ocean

The Dark Target (DT) algorithm has been applied to the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi-NPP (SNPP) and NOAA-20 satellites to extend the aerosol data record from the Moderate Resolution Imaging Spectrometer (MODIS). The newly released VIIRS DT Version 2.0 (V2.0) dataset includes NOAA-20 aerosol products for the first time. This study provides the first evaluation of NOAA-20 VIIRS DT aerosol products, including aerosol optical depth (AOD) and Ångstrom exponent (AE), for the period 2018–2022 using Aerosol Robotic Network (AERONET) and Maritime Aerosol Network (MAN) measurements. In addition, the operational DT aerosol products from SNPP VIIRS (V2.0) and MODIS (Collection 6.1) are also used for comparison purposes. Overall, NOAA-20 AODs over land and ocean exhibit good validation metrics globally, showing comparability to MODIS products and superiority over overestimated SNPP products. Nevertheless, it is worth noting that in South Africa specifically, NOAA-20 land AODs tends to display more pronounced negative biases, despite all products being underestimated. Furthermore, all ocean AE products demonstrate high correlation coefficients (>0.83) with the ground-based data, meeting the fraction of expected accuracy (>80%). Encouragingly, NOAA-20 AE product has significantly improved the persistent issue of overestimation at low-value in MODIS product, making it a more preferable choice for usage. Error analysis reveals that the performance of all three land products decreases in sparsely vegetated areas and when the solar zenith angle is small. However, NOAA-20 AOD exhibits relatively stable performance and is less affected by variations in aerosol loading, observation geometry, and surface vegetation cover. In addition, the performance of AOD and AE over ocean is significantly influenced by scattering angle and wind speed. This research is anticipated to serve as a reference for the utilization of operational VIIRS DT aerosol products and possible algorithm optimization.

A deep learning-assisted inversion for EM logging tool with tilted-coil antennas in VTI media

A deep learning-assisted inversion for EM logging tool with tilted-coil antennas in VTI media

A Self-Administered Gamified Mobile Application for Secondary Prevention of Heart Disease in Patients Following a Cardiac Event (MyHeartMate): Process Evaluation from a Randomized Controlled Trial.

Objective: This study reports the process evaluation of a randomized controlled trial of the MyHeartMate app for patients with coronary heart disease (CHD). Materials and Methods: Data were collected on engagement in the intervention group from the app platform logs and self-reported usage and via interviews for perspectives of usefulness and acceptability. Participants' (n = 194) data logs showed 80.4% entered baseline data. Tracking (≥30 days) occurred for body mass index (23.2%), blood pressure (BP) (21.1%), and exercise (23.7%). Missions completed on ≥4 days were 47.5% for healthy eating and 30.9% for exercise; 50.5% used the word game. One quarter (26.8%) was classified as "engaged" (tracked exercise and/or BP ≥5 times and completed ≥18 missions in the first 30 days). There were no differences between participants who were "engaged" or not in baseline characteristics or 6-month outcomes, although engaged users were more likely to meet 4/5 recommended risk factor guideline levels (30.8% vs. 17.6%, P = 0.03). Results: Participants perceived tracking, prompts, and rewards as useful for recovery and risk factor-related lifestyle change, and games were enjoyed. Engagement decreased with time. Acceptability was limited when app processes were difficult to understand, not personalized enough and/or the overall game concept was disliked. Conclusions: Insight into user's perceptions and use of gamified app components is essential to address barriers to uptake and optimize potential health benefits. The target audience for a serious game app for CHD remains uncertain.

Characterizing Asymptotic Dependence between a Satellite Precipitation Product and Station Data in the Northern US Rocky Mountains via the Tail Dependence Regression Framework With a Gibbs Posterior Inference Approach

ABSTRACTThe use of satellite precipitation products (SPP) allows for precipitation information to be collected nearly globally, but questions remain regarding their ability to reproduce extreme precipitation over mountainous terrain. In this work, we assess the ability of the precipitation estimation from remotely sensed information using artificial neural networks‐climate data record (PERSIANN‐CDR) to capture daily precipitation extremes by comparing PERSIANN‐CDR with corresponding station data in the summer at remote locations in the northern US Rocky Mountains of Wyoming, Idaho, and Montana. The assessment utilizes the regular variation framework from extreme value theory and consists of two parts: (1) evaluating the extent to which PERSIANN‐CDR can capture precipitation extremes through inference on an asymptotic dependence parameter, concluding that the level of asymptotic dependence is moderate throughout the region; (2) developing a tail dependence regression modeling framework and a Gibbs posterior approach for inference to investigate the degree to which elevation and topographic heterogeneity impact the level of asymptotic dependence, finding that the inclusion of a set of meteorological covariates, when combined with the PERSIANN‐CDR output, yields an increased level of asymptotic dependence with station data.

Toward Understanding the Effects of Intelligence of a Virtual Character during an Immersive Jigsaw Puzzle Co-Solving Task

In virtual reality, creating intelligent virtual characters has been a long-lasting endeavor. However, while researchers have investigated several aspects of a virtual character's intelligence, little attention has been paid to the impact of the implemented intelligence levels assigned to a virtual character during human-virtual character collaboration. Thus, we conducted a within-group study ( \(N=24\) ) to explore how three different intelligence levels (low vs. medium vs. high) assigned to a virtual character can impact how study participants perceive that virtual character and interact with the task they are instructed to complete. Specifically, for our study, we developed a jigsaw puzzle game and instructed our participants to solve it with the help of a virtual character. During the jigsaw puzzle solving process, we collected application logs related to how the participants executed the task and observed the virtual environment. Moreover, after each condition, we asked the participants to respond using a questionnaire that examined their social presence, how they perceived the character's intelligence and compared it with their own, and how they rated the virtual character's realism. Our results indicated that the different intelligence levels assigned to the virtual characters impacted participants’ responses on several variables, including co-presence, perceived intelligence, intelligence comparison, and character interaction and behavior realism. Moreover, based on the collected logged data, we found that the intelligence levels assigned to our virtual character significantly impacted the performance of our participants. Our results could be valuable to the research community for creating more engaging experiences with intelligent virtual characters for collaborative tasks in immersive environments.