Data Record Research Articles

Implementasi Teknologi Blockchain Dalam Pengembangan Aplikasi Manajemen Penyaluran Bantuan Sosial Berbasis Web

The distribution of social assistance is a crucial component of the government's efforts to reducepoverty and social inequality. However, the implementation of aid programs often faces issuesrelated to transparency, data security, and the risk of misuse. Blockchain technology, with its abilityto provide a transparent and immutable record of data, offers a potential solution to address theseissues. Blockchain can increase transparency by providing a digital ledger that can be accessed byall relevant parties, without the possibility of data manipulation. The technology also allows forimmutable data logging, thus addressing data security concerns and the risk of misuse. In addition,the use of smart contracts allows for the automation of the aid distribution process in accordancewith predetermined provisions, thereby reducing errors and potential abuse. As such, blockchainoffers a potential solution to increase effectiveness and accountability in the implementation ofsocial assistance programs.

Comparison of a two-step Tempus600 hub solution single-tube vs. container-based, one-step pneumatic transport system.

Efficient and timely transportation of clinical samples is pivotal to ensure accurate diagnoses and effective patient care. During the transportation process, preservation of sample integrity is crucial to avoid pre-analytical aberrations on laboratory results. Here, we present a comparative analysis between a two-step Tempus600 hub solution single-tube and a one-step, container-based pneumatic transport system (PTS) from Airco, for the in-house transportation of blood samples. Ten blood samples from healthy volunteers were split in 10 mL collection tubes filled at full or half capacity for transportation with the two PTS (about 250 m). To compare the impact of transportation, markers of hemolysis such as lactate dehydrogenase (LDH), potassium (K+), and the hemolysis index (HI), were determined. Additionally, differences in HI in routine samples and repeated transportation was investigated. To assess and compare the mechanistic impact profiles, we recorded the acceleration profiles of the two PTS using a shock data logger. Transportation using the Tempus600 hub solution resulted in 49 and 46 % higher HI with samples filled to total or half capacity, respectively. Routine samples transported with the Tempus600 hub solution showed a higher median HI by 23 and 33 %. Additionally, shock logger analysis showed an elevated amount of shocks (6.5 fold) and shock intensities (1.8 fold). The Tempus600 hub solution caused an increased number of unreportable LDH or K+ results based on the hemolysis index. However, it was only statistically significant for LDH (p<0.01 and p<0.08)- while the comparisons for K+ were not statistically significant (p<0.28 and p<0.56).

Circadian rhythm in atrial fibrillation detected by implantable loop recorder: a post-hoc analysis of a randomized clinical trial

Abstract Introduction Previous studies suggest the existence of circadian, weekly, and seasonal rhythms in the occurrence of atrial fibrillation (AF), which may be utilized to improve the yield of screening. The heterogeneity of AF natural history also necessitates better phenotyping to overcome the challenges of identifying candidates for screening and treatment. Purpose To explore the potential circadian rhythm in AF occurrence in persons without known arrhythmia but at high risk of AF and stroke. Methods Post-hoc analysis of the LOOP study (Atrial Fibrillation Detected by Continuous Electrocardiogram Monitoring Using Implantable Loop Recorder to Prevent Stroke in High-Risk Individuals), which randomized 6004 AF naïve participants (≥70 years old with additional stroke risk factors) 1:3 to receive implantable loop recorder screening or usual care. All loop recorder data was extracted to obtain the date and time of AF episode onset, AF duration, mean and max heart rate (HR) during AF, and symptom annotations. New-onset AF episodes lasting ≥6 minutes were adjudicated, and all subsequent episodes were included in the analysis. Results A total of 41,713 AF episodes (median duration 18 (8-64) minutes) were identified among 430 participants (Table 1). The median number of episodes for each participant with AF was 21 (4-82) and the median AF burden was 0.17% (0.04%-0.83%) of time from debut to end of monitoring. The most and least frequent days of AF onset were Sunday (15.3%) and Friday (13.6%), respectively, while AF occurred slightly more often during spring (26%) than in other seasons. The median time of onset was 09:57:15 AM (05:15:30 AM – 03:33:00 PM). The most frequent time of onset was at 09:00 AM (rounded to the nearest hour), which was two times more likely than at 09:00 PM, and 22% of all episodes occurred between 08:00 and 11:00 AM (Figure 1). A total of 25,329 (61%) episodes occurred between midnight and noon (AM). Nocturnal episodes, defined as episodes with onset between 10:00 PM and 07:00 AM, occurred in 368 (86%) AF participants and constituted a median of 40% of all episodes and 37% (14%-63%) of each participant’s episodes. The median duration of nocturnal episodes was 28 (10-104) minutes compared to 14 (8-44) minutes for daytime episodes. The averaged heart rate during AF was 81 (71-96) beats per minute (BPM), while 330 (77%) participants had AF episodes with heart rates equal to or above 100 BPM, and the duration of these episodes was 50 (15-201) minutes compared to 30 (21-85) minutes for slower episodes (below 100 BPM). Conclusion A circadian pattern was observed for screening-detected AF. AF onset occurred predominantly between 08:00 and 11:00 AM, representing more than one in five episodes. Nocturnal AF occurred in most participants with AF and accounted for almost half of all episodes, while these episodes also lasted longer than daytime episodes. This may warrant future studies investigating the timing of AF screening.

Traditional and contemporary building thermal sensations prediction models in a warm-humid climate

ABSTRACT Passive ways of providing thermal comfort have been intensively researched in many nations. Despite researchers’ strenuous efforts, most studies on thermal comfort concerns are centred on continents such as Asia, America, Europe, and Africa, with a handful of studies comparing building types in the sub-Saharan area. However, none has focused on statistical models to predict the thermal sensations of occupants in traditional and modern structures during the dry season. Hence, this study investigated interior relative humidity, air temperature, and occupants’ thermal feelings in traditional and modern structures in the dry season in Okigwe, Nigeria. The data for this investigation was collected by physically measuring relative humidity and air temperature using data loggers. A longitudinal survey technique was also used to elicit answers from the residents of the selected traditional and modern structures on the thermal feeling of comfort temperature. To predict the inhabitants’ thermal sensations in the building types investigated, two statistical models were developed: ‘TSVt = 0.65 (MITt) – 13.5’ for traditional structures and ‘TSVc = 0.74 (MITc) – 16’ for modern. The comfort range and neutral temperature of ‘3.15 ≤ TSV ≤ 4.85’ 1–7 yielded 26.8°C as the neutral temperature for the occupants of traditional buildings, with the comfort ranging between 25.6°C and 28.2°C. Contemporary building occupants’ neutral temperature and comfort ranges from 25.9°C to 28.2°C. The study therefore recommended the use of the developed models by Architects, Planners, and Designers for predicting the occupants’ sensations of the building types before preliminary investigations and design sketches, as with such information, the designer possesses the necessary tools for providing buildings that are thermally comfortable for maximum comfort and minimal consumption of energy.

Effects of High Tunnel Soil Solarization on Sclerotinia sclerotiorum in the Temperate Climate of Central Kentucky.

Diseases caused by Sclerotinia spp. can affect a wide range of plants, including vegetables, with yield losses ranging from 10 to 50%. Sclerotinia diseases can be especially problematic in high tunnels where high-value vegetable crops are planted in early spring to extend the growing season, achieve earlier harvest, and bring higher profits. Fungicide applications and crop rotations are limited due to product application restrictions and constraints on time, crop resistance, and profitability. Soil solarization is a cultural management method that uses transparent polyethylene to raise soil temperatures via solar irradiation to kill pathogens, pests, and weeds. A two-year study was conducted in a Kentucky high tunnel to determine the maximum temperature potential of solarization at various soil depths at different durations during different seasons and to identify temperatures at which S. sclerotiorum sclerotia lose viability. The experiment included solarization treatments of 2, 4, and 6 weeks and a non-solarized control implemented in spring, summer, and fall. Sclerotia and temperature data loggers were buried at 5.1, 10.2, and 15.2 cm soil depths. The number of hours at which soil temperatures reached ≥ 40 °C was greatest in summer in both years, followed by fall, and then spring. The highest average daily maximum soil temperature reached was 48.9°C, which occurred during the summer 6-week solarization in Year 1. The viability of buried sclerotia was overall lower in solarized treatments compared to non-solarized treatments in both years. In general, the 2-week solarization treatment had significantly higher percent sclerotial germination than the 4-week and 6-week treatments, which were not significantly different from one another. The viability of sclerotia was progressively higher with burial depth. In both years, sclerotia germination was significantly lower in summer compared to spring and fall.

Barriers and facilitators for universal gestational diabetes Mellitus screening in a low resource setting: a cross-sectional study in Sri Lanka

Gestational Diabetes Mellitus (GDM) is associated with adverse maternal and perinatal outcomes and increased risk of developing diabetes in later life. Sri Lanka adopted universal GDM screening at two time points in pregnancy (before 12 weeks and between 24 and 28 weeks) in 2014 and its utilization has not been assessed. This study assessed the utilization of GDM screening services and associated factors among antenatal mothers in Southern Sri Lanka. A cross-sectional study was carried out in a consecutive sample of 420 postpartum mothers who delivered at three major hospitals in Sri Lanka. Data were collected on socio-demographics, pregnancy, availability and utilization of screening services and logistics using an interviewer-administered questionnaire and a data record sheet. Data was analysed using SPSS software. Chi-square test and binary logistic regression was used to assess the association between variables. The coverage of first and second screening tests were 91.4% and 94.5% and timeliness were 72.4% and 59.5%, respectively. The median period of amenorrhoea at the first and second screening were 10.0 (inter-quartile range: 2.5) weeks and 28.0 (inter-quartile range: 1.5) weeks. A higher utilization was associated with higher income (OR = 3.4, 95% CI: 1.1–10.5) and planned pregnancy (OR = 4.9, 95% CI: 2.2–10.7) for the first screening test. Primiparity (OR = 0.3, 95% CI: 0.1–0.9) and proximity of the nearest laboratory (OR = 3.5, 95% CI: 1.0–12.0) were positively associated with the second screening test. The timeliness of both screening tests was associated with planned pregnancy (OR = 2.1, 95% CI: 1.1-4.0 and OR = 2.3, 95% CI: 1.1–4.5) and being non-employed during the pregnancy (OR = 2.5, 95% CI: 1.4–4.3 and OR = 2.3, 95% CI: 1.3–4.1). A high utilization of GDM screening was observed. Timeliness should be ensured through improved availability and accessibility of screening facilities.

Improved modelling of low-pressure rotor speed in commercial turbofan engines: A comprehensive analysis of machine learning approaches

Accurate engine thrust modelling offers notable opportunities for reducing fuel consumption, mitigating emission effects, managing air traffic, and optimizing the preliminary design of aircraft. This study focuses on the development of machine learning models to predict the low-pressure rotor speed (N1) parameter, which is closely related to the thrust of turbofan engines, for commercial aircraft during the cruise phase. The analysis utilizes a Flight Data Recorder (FDR) dataset from 1086 actual flights involving twin-engine, narrow-body, short-to-medium haul commercial aircraft equipped with CFM56-7B turbofan engines. To achieve accurate N1 parameter predictions, the study employs machine learning models, including Deep Learning (DL), Random Forest (RF), Gradient Boosted Machines (GBM), and Artificial Neural Networks (ANN). These models are evaluated using statistically significant indicators, demonstrating that machine learning models yield highly accurate results. Among the models tested, the DL model provides the most precise estimates of the N1 parameter. This study not only advances the understanding of engine thrust modelling through machine learning but also provides practical insights for the aerospace industry. The findings underline the potential of machine learning techniques in delivering superior prediction accuracy, which can be integrated into real-time flight management systems, ultimately contributing to more sustainable and efficient aviation operations.

Pollutant Dispersion of Aircraft Exhaust Gas during the Landing and Takeoff Cycle with Improved Gaussian Diffusion Model

Evaluating aviation emissions and examining the dispersion properties of contaminants are crucial for understanding atmospheric pollution. To assess the pollutant emissions and dispersion of aircraft during the landing and takeoff (LTO) cycle, and address air pollution surrounding the airport resulting from flight operations, this study evaluated emissions throughout the LTO phase based on Quick Access Recorder (QAR) data in conjunction with the first-order approximation method. An improved Gaussian diffusion model for mobile point sources was employed to examine the diffusion characteristics of contaminants. Additionally, CFD calculation outcomes for various exhaust velocities and wind speeds were utilized to validate the trustworthiness of the improved Gaussian model. The discussion also encompasses the influence of diffusion time, wind direction, wind speed, temperature gradient, and particle deposition on the concentration distribution of contaminants. The findings indicated that the Gaussian diffusion model aligned with the results of the CFD calculations. The diffusion distribution of contaminants around airports varies over time and is significantly influenced by atmospheric environmental factors, including wind direction, wind speed, and atmospheric stability. Specifically, a change in wind direction from 0° to 45° caused a shift of approximately 1000 m in the contaminant’s center. An increase in wind speed from 3 m/s to 5 m/s led to a decrease in concentration by about 15%. Furthermore, a transition in atmospheric stability from category ‘a’ (very unstable) to ‘f’ (very stable) resulted in a two-order-of-magnitude increase in contaminant concentrations.

Synthesis of Silver Nanoparticles and Forced Convective Heat Transfer Coefficient Measurement of Silver-Water Nanofluids at Laminar Flow

Heat management in industries and other areas is a major issue currently worldwide. Conventional heat transfer fluids like water and ethylene glycol are not very effective. Nanofluids (NFs), composed of nanoparticles (NPs) dispersed throughout a base fluid (BF), have recently been identified as promising heat management agents for various industrial and other applications. Heat transmission is one of the many uses for AgNPs–water NFs (silver NPs (AgNPs) dispersed in water). The goal of this work was to determine the AgNPs–water NFs' forced convective heat transfer coefficient (HTC) in laminar flow. Due to this, we present the results of the HTC study of AgNPs–water NFs in laminar flow in this paper. First, using AgNO3 as a precursor, a chemical reduction approach was used for facile synthesis of AgNPs in one pot at ambient temperature. Synthesized NPs were examined using ultraviolet-visible spectroscopy (UV-vis), X-ray diffraction (XRD), and transform electron microscopy (TEM) techniques. The average particle size obtained from TEM is 25 nm. The required quantity of AgNPs was added to the water to produce 0.5 and 1.0 vol% of AgNPs–water NFs. Next, zeta potential and dynamic light scattering (DLS), in addition to time-lapse observation and captured picture comparison, were used to assess the stability of the formulated NFs. To calculate the forced convective HTC of prepared NFs in laminar flow, a vertical shell and tube heat transfer apparatus and computer-based data recorder were used. According to the results, in comparison to BF, the HTC of 0.5 and 1.0 vol% NFs was 3.3 and 5 times greater, respectively which encourages the use of AgNPs–water NFs in industry and other heat management. Jagannath University Journal of Science, Volume 11, Number 1, June 2024, pp. 13−22

Pre-extension Popularization of Improved Dual Purpose Koekoek Chickens in Tahtay Maichew District Central Zone of Tigray, Ethiopia

Central zone of Tigray has potential and poultry stock. However, the productivity in terms of egg and meat is low since the majority of household depends on local chickens and rearing of their chickens through a traditional production practices. Then, the objectives of the pre-extension popularization of Koekoek breed was i) to popularize and evaluate the production performance the Koekoek breed chickens in the small holder Farmers and ii) To analyze farmers feedback towards this improved Koekoek chickens breed at smallholder farmers' management. T/maichew District, My-sie kebelle was selected based on the project financial support of Irish Aid Operational Research (OR) purposely. A total of 17 household heads willingly participated to adopt the Koekoek breeds. Data record sheet and semi-structured questionnaires were prepared to collect the required data. Then, using simple descriptive and Likert measurement level used to achieve result. The finding of the research shows that an annual average yield was found to be 204 eggs per year/ chicken. Similarly, the respondents agreed on that the chickens have well adapted, good growing ability, relatively resistance to diseases, have good scavenging ability, and productivity as well have medium eggs size and good egg and meat tasty of Koekoek chickens. The improved chickens’ breed (Koekoek) is better to be transferred to other mandate areas of Axum Agricultural Research Center. In addition, collaboration of stakeholders needs to be strengthening so that large scale demonstration community production system is going to be functional. Strengthen the human resource and institutional innovations are important point for dissemination of the improved poultry breeds to all beneficiaries of small holder farmers.

Generating Long-term GRACE-like Total Water Storage Change Products using Conditional Generative Adversarial Networks

Abstract. Since 2002, the Gravity Recovery And Climate Experiment (GRACE) and its Follow-On (GRACE-FO, hereafter GRACE) missions have offered global observations of total water storage (TWS). However, the relatively short record of GRACE data poses a significant challenge for researchers to investigate the full range and long-term variability in TWS. In this study, we present RecGAN, a novel Conditional Generative Adversarial Network (CGAN) comprising a RecNet generator and pixel discriminator. Our approach aims to generate long-term GRACE-like TWS observations by calibrating the WaterGAP Global Hydrology Model (WGHM). The generator is trained to produce observations conforming to the distribution of GRACE data, while the discriminator is trained to assess whether each generated pixel resembles GRACE data. Our results show that RecGAN effectively enhances the consistency between GRACE observations and WGHM-derived TWS changes, achieving improved correlation coefficients, Nash-Sutcliffe Efficiency, and Normalized Root-Mean-Square Error. In addition, RecGAN is robust to different GRACE mascon data, crop sizes used during the training period, and hydrological models targeted for calibration. This study illustrates a promising application of employing CGANs to fine-tune the WGHM output to match GRACE observations. This approach enables the generation of longterm TWS change datasets, which are invaluable for evaluating long-term water storage fluctuations, allocating water resources, and forecasting future hydrological extremes.

Rubella epidemiology in Lesotho after vaccine introduction: a five-year review, 2018–2022

BackgroundThe rubella virus is a major contributor to birth defects globally and is preventable by vaccination. In 2020, the world was supposed to be free of both rubella and Congenital Rubella Syndrome (CRS) however this goal has yet to be realized with only 93 out of 194 WHO member states confirmed rubella-free in 2020.MethodsA retrospective measles and rubella case-based surveillance data record review was conducted from 2018 to 2022 to document rubella epidemiology after the introduction of rubella vaccination in Lesotho and progress toward elimination. All samples submitted for surveillance purposes and tested for rubella were considered but only filtered according to inclusion and exclusion criteria. Descriptive statistics were used to analyse the data.ResultsOf the 1041 samples that were tested for rubella between 2018 and 2022, 10 (1%) were confirmed measles positive and were excluded from further analysis. The median age of the respondents was 6.0 (IQR 4.0–8.0.) years. About 643 (62.4%) of respondents were in the age category of 5 - <13 years. Rubella prevalence was 1% (95% CI; 0.5 -1.8%). The non-measles, non-rubella rash illness rate of 2 per 100 000 population was obtained at the national level each year of the study period but by only 2 of the country’s 10 districts in 2021.ConclusionsThe study showed low rubella prevalence. Rubella infection was predominant in those aged 5 - < 13 years. Failure to meet surveillance targets at certain time points during the study period may have led to an underestimation of rubella cases. There is a need to improve the quality of measles and rubella surveillance in Lesotho. Supplementary immunization activities would also be useful in closing immunity gaps, limiting outbreaks, and advancing rubella and CRS elimination in Lesotho.

Partial migration pays off in black-faced cormorants: insights from post-breeding GPS tracking

Seasonal variation in food availability or adverse weather patterns can create challenging conditions for year-round survival. Birds experiencing such fluctuations may migrate to locations with better food availability in the non-breeding period. While some species are obligate migrants, others are facultative migrants where various post-breeding strategies exist within populations. Several cormorant and shag species are facultative migrants with a range of post-breeding movement strategies within populations. Currently, most of the information available on cormorant post-breeding strategies originates from banding studies and little is known about their movements, time budget or habitat use during the non-breeding period. In the present study, Australian endemic black-faced cormorants (Phalacrocorax fuscescens), breeding on Notch Island, Victoria, were tracked with GPS data loggers during the non-breeding period. Different post-breeding strategies were observed. Individuals were either resident (N = 36, 69%), remaining within a short distance of the colony (16.7 ± 2.2 km) or migratory (N = 16, 31%), travelling greater distances (250.7 ± 24.4 km) to establish a new central place foraging location. Data from individuals which covered the entire non-breeding period indicated that, across strategies, individual birds had one area of residency during the non-breeding period. Migratory individuals were found to have lower daily activity levels, spending less time flying and foraging, than resident individuals during the early non-breeding period, possibly indicating that they had access to greater prey resources. Regardless of the post-breeding strategy, individuals utilised foraging locations that were of similar seabed depths year-round suggesting individual specialisation in prey resources or foraging habitats.

The station system measures the water level and amount of rain that can save the data and online through Web Server

This research paper, the aim is to create a water level and rain gauge station system that can record data and online through Web Server. To be monitor the water level of the river and the amount of water in the rainy season easily by looking through the website. The design of the water level measurement system and the amount of rain is measured with a hydrostatic sensor, Tipping Bucket. A small measuring station, easy to install, uses solar energy and has a Data Logger to record data in parallel with an online database. In the research process, the principle of correlation is used to find the relationship between two variables to find the error of the sensor, namely the water level sensor (Hydrostatic) and the rain sensor (Tipping Bucket). Through research, it was found that the measuring station, the database system can work correctly and with high stability, the water level measurement sensor is highly accurate, the error value is between 0.01 - 0.36 m, the accuracy rate is 97.38%. 98.97% compared to water level measurement stations in nearby areas. For the rain sensor, from the actual count compared to the count with the program 3 times, it is seen that there is 1 error with a deviation value of 0.3 mm. From the recorded data of 2 months (July and August) it was found that the water level is still low to moderate, away from the dangerous water level of 6.84 meters and the amount of rain is still quite high with the amount of rain. The days account for 64.51% and 54.83% of July and August respectively, but the amount of rain will be moderate to moderate. The speed used to send data from the measuring station to the web server via 3G Internet will be between 4 – 6 seconds.

An Evaluation of Sentinel-3 SYN VGT Products in Comparison to the SPOT/VEGETATION and PROBA-V Archives

Sentinel-3 synergy (SYN) VEGETATION (VGT) products were designed to provide continuity to the SPOT/VEGETATION (SPOT VGT) base products archive. Since the PROBA-V mission acted as a gap filler between SPOT VGT and Sentinel-3, and in principle, a continuous series of data products from the combined data archives of SPOT VGT (1998–2014), PROBA-V (2013–2020) and Sentinel-3 SYN VGT (from 2018 onwards) are available to users, the consistency of Sentinel-3 SYN VGT with both the latest SPOT VGT (VGT-C3) and PROBA-V (PV-C2) archives is highly relevant. In past years, important changes have been implemented in the SYN VGT processing baseline. The archive of SYN VGT products is therefore intrinsically inconsistent, leading to different consistency levels with SPOT VGT and PROBA-V throughout the years. A spatio-temporal intercomparison of the combined time series of VGT-C3, PV-C2 and Sentinel-3 SYN VGT 10-day NDVI composite products with an external reference from LSA-SAF, and an intercomparison of Sentinel-3 SYN V10 products with a climatology of VGT-C3 resp. PV-C2 for three distinct periods with different levels of product quality have shown that the subsequent processing baseline updates have indeed resulted in better-quality products. It is therefore essential to reprocess the entire Sentinel-3 SYN VGT archive; a uniform data record of standard SPOT VGT, PROBA-V and Sentinel-3 SYN VGT products, spanning over 25 years, would provide valuable input for a wide range of applications.

More transparent and explainable machine learning algorithms are required to provide enhanced and sustainable dataset understanding

For detailed dataset interrogation and auditing purposes the lack of dataset explainability/transparency of the majority of available machine-learning (ML) models poses limitations. There is a tendency for ML models to focus on prediction speed and accuracy at the expense of transparently revealing dataset relationships. A case is made here to broaden that focus and for ML models to offer alternative configurations tailored to provide more explanations about how individual predictions are derived. Indeed, those striving to achieve sustainable objectives should not rely on opaque ML models and seek transparency as a fundamental objective of good modelling practice (GMP). Doing so tends to boost trust and confidence in the outputs of models relating to complex socio-environmental systems (SES), particularly those being used to potentially justify controversial social, political and ethical decisions. Currently, the transparent open box algorithms (TOB) are the only ML algorithms available that are configured specifically to routinely provide detailed data record relationships for each of their predictions. This study describes the data mining benefits of the Python-coded optimized data-matching TOB algorithms generally, and when applied to environmental datasets characterized by complex non-linear relationships involving many variables.

Validation of a data logger for measuring standing time and seat angles for community-based standing devices

Context Little is understood about community-based standing device use and the impact of standing on health outcomes (e.g. pressure injury) in those living with spinal cord injury (SCI). This project reports on the accuracy of a commercially available data logger for measuring standing time and seat angle. Methods A standing frame and a standing manual wheelchair were each instrumented with a commercially available data logger and each was tested by an non-disabled participant. Standing time in the standing frame was calculated from the data logger and compared to a user-recorded standing log over a two-month period in a laboratory environment. The standing wheelchair’s seat angle was calculated using motion capture and compared to the calculated seat angle from the data logger. Average seat interface pressures were also captured during the testing of the standing wheelchair. Results The data logger demonstrated high accuracy (99.99999%) in classifying sitting and standing in the standing frame when compared to a user-recorded standing log. The wheelchair seat angle calculated from the data logger demonstrated a high level of agreement with the motion lab calculations of seat angle (ICC = 0.96 (0.95, 0.97)). The data logger seat angle results also demonstrated strong relationships to average seat pressure and rear dispersion index, measures relevant to pressure injuries. Conclusions The data logger appears to be an appropriate tool for determining standing time and seat angle in standing devices, which may aid clinicians and researchers to better understand the use and impact of standing technologies on health outcomes.

Studying the Flow Experience in Computer-Supported Collaborative Learning

Computer-Supported Collaborative Learning (CSCL) is recognized as an effective methodology for fostering social interaction mediated by technology in ways that potentially trigger learning. The successful implementation of CSCL hinges on factors such as the scripting mechanics for activity sequencing proposed by Collaborative Learning Flow Patterns (CLFP). Yet, research in CSCL scripts has not studied if CLFPs achieves the so-called notion of “flow experience,” defined as an optimal state in which individuals are engaged and absorbed in an activity. This study proposes an approach to measure flow in the case of the Pyramid CLFP and studies the factors that influence the flow experience in the PyramidApp tool. The study tests a model that uses analysis of the Flow Short Scale and data logs. The findings show that there is a relationship between factors such as the speed of individual contributions and active participation in groups with the flow experience. Notably, the quantity of participation does not exhibit a discernible impact on the flow. The study emphasizes the interest of the modelled factors and the proposed approach for learning analytics to understand the flow experience in CLFP implementations.

Nonlinear Analysis and Closed-Form Solution for Overhead Line Magnetic Energy Harvester Behavior

Recently, much attention has been given to the development of various energy harvesting technologies to power remote electronic sensors, data loggers, and communicators that can be installed on smart grid systems. Magnetic energy harvesting is, perhaps, the most straightforward way to capture a significant amount of power from a current-carrying overhead line. Since the harvester is expected to have a small size, the high currents of the distribution system easily saturate its magnetic core. As a result, the operation of the magnetic harvester is highly nonlinear and makes precise analytical modeling difficult. The operation of an overhead line magnetic energy harvester (OLMEH) generating significant DC power output into a constant voltage load was investigated in this paper. The analysis method was based on the Froelich equation to analytically model the nonlinearity of the core’s BH characteristic. The main findings of this piecewise nonlinear analysis include a closed-form solution that accounts for both the core and rectifiers’ nonlinearities and provides an accurate prediction of OLMEH transfer window length, output current, and harvested power. Continuous and discontinuous operational modes are identified and the mode transition boundary is obtained quantitatively. The theoretical investigation was concluded by comparison with a computer simulation and also verified by the experimental results of a laboratory prototype harvester. A good agreement was found.

An Approach for Estimating the Contributions of Various Real-World Usage Conditions towards the Attained Utility Factor of Plug-in Hybrid Electric Vehicles

Plug-in hybrid electric vehicles (PHEVs) are designed to enable the electrification of a large portion of the distance vehicles travel while utilizing relatively small batteries via taking advantage of the fact that long-distance travel days tend to be infrequent for many vehicle owners. PHEVs also relieve range anxiety through seamless switching to hybrid driving—an efficient mode of fuel-powered operation—whenever the battery reaches a low state of charge. Stemming from the perception that PHEVs are a well-rounded solution to reducing greenhouse gas (GHG) emissions, various metrics exist to infer the effectiveness of GHG reduction, with utility factor (UF) being prominent among such metrics. Recently, articles in the literature have called into question whether the theoretical values of UF agree with the real-world performance of PHEVs, while also suggesting that infrequent charging was the likely cause for observed deviations. However, it is understood that other reasons could also be responsible for UF mismatch. This work proposes an approach that combines theoretical modeling of UF under progressively relaxed assumptions (including the statistical distribution of daily traveled distance, charging behavior, and attainable electric range), along with vehicle data logs, to quantitatively infer the contributions of various real-world factors towards the observed mismatch between theoretical and real-world UF. A demonstration of the proposed approach using data from three real-world vehicles shows that all contributing factors could be significant. Although the presented results (via the small sample of vehicles) are not representative of the population, the proposed approach can be scaled to larger datasets.