Data Record Research Articles

Evaluation of pediatric patients with palpitations via cardiac event recorder, Holter monitoring and transesophageal electrophysiologic study in detecting arrhythmia

Objective: Palpitations are a common reason for referral to pediatric cardiologists, the diagnostic workup involves ambulatory, non-invasive recording devices and invasive procedures. We aimed to evaluate arrhythmic symptoms of pediatric patients with a cardiac event recorder, Holter monitoring and transesophageal electrophysiologic study (TEEPS) results. Materials and Methods: Retrospective evaluation of pediatric patients who fitted an event recorder at tertiary University Hospital between January 2002 and August 2012. The data obtained from the same patients’ as cardiac event recorder, Holter monitoring and TEEPS results were studied for comparison. Results: During the study period, 40 patients who had all data of cardiac event recorder, Holter monitoring and TEEPS were included. The median age of the patients included in the study was 12 [interquartile range, 7-15] years. Using the Holter monitoring, supraventricular extrasystoles (SVEs) were detected in six (15%) patients and ventricular extrasystoles (VESs) in three (7.5%). According to the event recorder data of the 40 patients, there was sinus tachycardia in 20 (50%), supraventricular tachycardia (SVT) in three (7.5%), and SVEs in two (5%) patients. The event recorder data of the remaining 15 patients were assessed as normal. The analysis of the TEEPS results revealed atrioventricular nodal reentrant tachycardia in four (10%), atrioventricular reentry tachycardia in three (7.5%), and normal results in the remaining 33 (82.5%) patients. Conclusions: This study demonstrated that a cardiac event recorder may also be considered as an important diagnostic tool in the diagnosis and ruling out of SVT in pediatric patients for whom the cause of arrhythmia cannot be identified with the Holter monitor, in patients who do not accept the use of TEEPS method following the use of Holter monitor, and in patients in whom SVT cannot be stimulated by TEEPS yet complaints persist.

UTILIZATION OF CAR PARKING ULTRASONIC SENSORS AS HYBRID TECHNOLOGY FOR MONITORING PEAT WATER LEVEL BASED ON INTERNET OF THINGS (IoT) AND DATA LOGGER WITH FIREBASE WEB

Peatland water level monitoring systems have been developed to facilitate water level readings. Currently, car parking sensors are rarely used for this purpose. The tool used in this research is a hybrid technology of iot-based car parking sensors and data loggers. Water level (TMA) data is analyzed using a data logger and Firebase web. The data logger records data which is then integrated on the Firebase web to facilitate remote monitoring. Data (TMA) is sent every hour, so in one day the tool will send data 24 times. The status that can be seen from the Firebase web dashboard includes water level (TMA), water condition indicators (safe, caution, danger), last data update status, tool placement map, and a collection of data history. This research uses a pre-experiment method this tool requires a current of 200 mAh, a 10 watt peak solar panel, and a 12.5 ampere hour battery to turn on constantly. The utilization of ultrasonic sensor with esp 32 microcontroller is effective in recording Real-Time water level data. Iot plays an important role because it allows monitoring (TMA) from anywhere with internet availability. The maximum water level reading data occurred on November 31, 2023 at 40 cm from the peat soil surface and is comparable to the rainfall data on November 29, 2023 at 2.2 mm / day (Light Rain). The minimum water level reading occurred on December 16, 2023 at -5 cm (flooding) from the peat soil surface and was proportional to the rainfall data on December 16, 2023 of 53 mm/day (Heavy Rain). So it can be concluded that the minimum and maximum water level distance data fall into the safe category for peatland water levels that are between 0-40 cm (PP No. 57 of 2016).

Pengaruh Penambahan Butanol Pada Bahan Bakar Solar Terhadap Karakteristik Pembakaran Single Droplet

Dependence on fossil fuels is one of the most serious threats to the global environment. In addition, petroleum reserves are also decreasing. Meanwhile, the level of oil consumption is always increasing to meet the demand for fuel. Economical and sustainable alternative energy solutions are essential to meet the growing global energy demand without compromising environmental sustainability. One type of alternative fuel that shows very promising potential is biofuel, especially biodiesel. Biodiesel has potential as a more environmentally friendly alternative to diesel fuel, but its low combustion rate and higher viscosity compared to conventional diesel fuel present technical challenges that need to be overcome. The different chemical properties of butanol from biodiesel allow it to have a positive impact on combustion characteristics, such as viscosity, flash point, heating value, and combustion rate. The combustion characteristics that will be studied in this research using single droplet combustion testing include droplet visualization to measure the dimensions of the flame, temperature during the combustion process recorded by a thermocouple, data logger, and video recording during the combustion process to obtain information regarding the ignition delay value. The test steps were repeated five times for each fuel variation and then averaged. In this study, the samples tested included pure diesel, diesel blend with 10% butanol, diesel blend with 20% butanol, diesel blend with 30% butanol, diesel blend with 40% butanol and diesel blend with 50% butanol. From this study, the visualization of the flame is obtained, where the combustion of pure diesel oil droplets has a higher and wider flame, while the flame of the butanol diesel mixture produces a flame whose maximum height is lower than pure diesel oil. The ignition delay time of pure diesel oil is greater than the ignition delay time of the diesel butanol blend. In addition, the maximum combustion temperature produced by each blend is different

Design and Development of Fractional Order Convolutional Neural Network Based Fractional Order Nonlinear Reactor Power Simulator for CANDU-PHWR

A highly complex nonlinear Reactor Regulating System (RRS) of Canadian Deuterium Uranium Pressurized Heavy Water Reactor (CANDU-PHWR) based Nuclear Power Plant (NPP) simulated in the present research. The internal design of RRS is secured and vendor controlled which is embedded in AC-132 Programmable Logic Controller (PLC). Therefore, the problem of the identification of the RRS controller model is addressed. A data-driven Fractional Order Nonlinear MIMO Hammerstein Model (FO-NC-MIMO-HM) of NPP is identified using an Adaptive Immune Algorithm (AIA) based on a Global Search Strategy (GSS) and Auxiliary Model Recursive Least Square Method (AMRLSM). Parameters of FO-MIMO-HM are identified using Innovative Real-Time Plant Operational Data (IRTPOD). The original PLC-based controller is replaced with a new Fractional Order Convolutional Neural Network (FO-CNN) based Fractional Order Nonlinear Controller (FO-NC). Therefore, a visual Simulator is developed for detailed modeling, control, simulation, and analysis of the proposed design scheme for RRS in Visual Basic (VB) Software. The performance of the proposed design scheme is tested and validated for different modes of RRS against benchmark data obtained from Plant Data Recorder (PDR) and found in close agreement well within the design bounds.

Estimating the saturated hydraulic conductivity of the vadose zone with an automatic infiltration data logger for the BEST algorithm

Estimating the saturated hydraulic conductivity of the vadose zone with an automatic infiltration data logger for the BEST algorithm

Wpływ zastosowania uzdatniaczów gleby na temperaturę i wilgotność środowiska glebowego

In cities, environmental and social impacts are increased every year due to high temperatures due to the heat island of the city. An effective struggle against the heat island of the city is the green infrastructure, where woody plants such as trees and shrubs play an irreplaceable role. Wood in the city is affected by a number of stress factors, especially high temperatures and lack of precipitation. In order for them to perform all their important functions and help reduce the negative impacts of the city's heat island, all requirements for their successful development and growth must be fulfilled. Soil conditions are an important factor affecting the condition of trees. Unsuitable soil conditions which include lack of water, compacted soil, result in insufficient development of the root system which directly affects the quality of the above-ground part of the plants. A possible treatment to improve the soil environment in the root zone of trees is the use of soil conditioners applied in the form of soil injection. The paper deals with the effect of soil conditioners (Hydrogel®, mycorrhizal mix Endomyk PROF + Trichoderma, and their combination) in the form of injection on the temperature and humidity conditions of the soil environment in a young plants of trees (species) Acer campestre L. in the city of Znojmo (South Moravia region). Plants without application of any preparation served as a Control variant. Before the actual application of conditioners MINILOG data loggers with temperature sensors and VIRRIB moisture sensors were placed in the soil which recorded changes in soil temperature and volumetric soil moisture in six-hour intervals every day in the period from February 2021 till December 2022. These changes were recorded in two profiles 0.1 - 0.4 and 0.4 - 0.7 meters. From the recorded results, it can be said that the application of soil conditioners had a significant effect on the temperature and humidity conditions of the soil environment. The variant with the soil conditioner Hydrogel® and the variant with the soil conditioner Hydrogel® in combination with the mycorrhizal mix in most cases show higher values of soil moisture and temperature compared to the Control variant. This trend is most evident in the soil profile of 0.4 - 0.7 m in the growing season.

Enhanced Energy Efficiency through Path Planning for Off-Road Missions of Unmanned Tracked Electric Vehicle

The primary objective of this research is to address the existing gap about the use of a path-planning algorithm that will reduce energy consumption in off-road applications of tracked electric vehicles. The study focuses on examining various off-road terrains and their impact on energy consumption to validate the effectiveness of the proposed solution. To achieve this, a tracked electric vehicle energy model that incorporates vehicle dynamics is developed and verified using real vehicle driving data logs. This model serves as the foundation for devising a strategy that can effectively enhance the energy efficiency of off-road tracked electric vehicles in real-world scenarios. The analysis involves a thorough examination of different off-road terrains to identify strategies that can adapt to diverse landscapes. The path planning strategy employed in this study is a modified version of the A*, called the Energy-Efficient Path Planning (EEPP) algorithm, specifically tailored for the dynamic energy consumption model of off-road tracked electric vehicles. The energy consumption of the produced paths is then compared using the validated energy consumption model of the tracked electric vehicle. It is important to note that the identification of an energy-efficient path heavily relies on the characteristics of the vehicle and the dynamic energy consumption model that has been developed. Furthermore, the algorithm takes into account real-world and practical considerations associated with off-road applications during its development and evaluation process. The results of the comprehensive analysis comparing the EEPP algorithm with the A* algorithm demonstrate that our proposed approach achieves energy savings of up to 6.93% and extends the vehicle’s operational range by 7.45%.

Effort in manual operation of reproductive health cryostorage facilities

Research questionWhat areas of manual IVF cryostorage operations are common to the safe operation of IVF cryostorage facilities and require effort from embryologists? DesignObservational time and motion data were collected by two observers equipped with the digital cameras over 2 weeks at four well-characterized US IVF centres (sites α, β, γ and δ) from 12 participants performing cryostorage tasks. To understand the work processes of the different sites and assist in the data analysis, informal interviews were conducted with the study participants and laboratory directors. Data were analysed to identify work processes that might be eliminated or diminished by automation and software improvements. ResultsOn average, it took 3.4 data record queries per retrieval from cryostorage to identify a cane, while the canister was lifted an average of 1.5 times per retrieval, with a mean 11.8 ± 9.2 s per lift. Of the total time spent working with cryostorage equipment, 47.25% was of a fatiguing nature. Sites α, β and γ utilized one person to fill the liquid nitrogen storage Dewars, while site δ had two technicians working in tandem to move and fill the Dewars, with different frequencies and determination factors for refills and efficiencies. ConclusionsThis time and motion study demonstrated significant time investment, task redundancy and fatiguing working conditions among embryologists using manual cryostorage processes. There was a disparity of processes and space capacity across different laboratories. Some of these issues may be addressed by the integration of automation and technology solutions.

A near-global multiyear climate data record of the fine-mode and coarse-mode components of atmospheric pure dust

Abstract. A new four-dimensional, multiyear, and near-global climate data record of the fine-mode (submicrometer in terms of diameter) and coarse-mode (supermicrometer in terms of diameter) components of atmospheric pure dust is presented. The separation of the two modes of dust in detected atmospheric dust layers is based on a combination of (1) the total pure-dust product provided by the well-established European Space Agency (ESA) “LIdar climatology of Vertical Aerosol Structure” (LIVAS) database and (2) the coarse-mode component of pure dust provided by the first step of the two-step POlarization LIdar PHOtometer Networking (POLIPHON) technique, developed in the framework of the European Aerosol Research Lidar Network (EARLINET). Accordingly, the fine-mode component of pure dust is extracted as the residual between the LIVAS total pure dust and the coarse-mode component of pure dust. Intermediate steps involve the implementation of regionally dependent lidar-derived lidar ratio values and AErosol RObotic NETwork (AERONET)-based climatological extinction-to-volume conversion factors, facilitating conversion of dust backscatter into extinction and subsequently extinction into mass concentration. The decoupling scheme is applied to observations from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) at 532 nm. The final products consist of the fine mode and coarse mode of atmospheric pure dust, quality-assured profiles of backscatter coefficient at 532 nm, extinction coefficient at 532 nm, and mass concentration for each of the two components. The datasets are established primarily with the original L2 horizontal (5 km) and vertical (60 m) resolution of the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) along the CALIPSO orbit path and secondly in averaged profiles of seasonal–temporal resolution, 1° × 1° spatial resolution, and the original vertical resolution of CALIPSO, focusing on the latitudinal band extending between 70° S and 70° N and covering more than 15 years of Earth observations (June 2006–December 2021). The quality of the CALIPSO-based fine-mode and coarse-mode dust products is assessed through the use of AERONET fine-mode and coarse-mode aerosol optical thickness (AOT) interpolated to 532 nm and the AERosol properties – Dust (AER-D) campaign airborne in situ particle size distributions (PSDs) as reference datasets during atmospheric conditions characterized by dust presence. The near-global fine-mode and coarse-mode pure-dust climate data record is considered unique with respect to a wide range of potential applications, including climatological, time series, and trend analysis over extensive geographical domains and temporal periods, validation of atmospheric dust models and reanalysis datasets, assimilation activities, and investigation of the role of airborne dust in radiation and air quality.

Development and Evaluation of an Arduino-Based Data Logging System Integrated with Microsoft Excel for Monitoring On-Grid Photovoltaic Systems

This paper presents the development and evaluation of an Arduino-based data logging system integrated with Microsoft Excel for monitoring on-grid photovoltaic (PV) systems. The system combines open-source hardware and software to enable real-time data acquisition, logging, and analysis of key performance metrics such as solar irradiance, temperature, voltage, and current levels. Leveraging the versatility of Arduino microcontrollers and the accessibility ofMicrosoft Excel, the proposed system offers a cost-effective and user-friendly solution for PV system monitoring. The integration of the MS Data Streamer add-in for Excel facilitates seamless data logging and visualization, empowering PV system owners, researchers, and practitioners with actionable insights for optimizing system performance and contributing to a sustainable energy future. Experimental validation of the system demonstrates its effectiveness in accurately measuring and logging sensor data, highlighting its potential for widespread adoption in renewable energy monitoring applications.

Research on techniques for detecting brute-force attacks on corporate email

Conventional brute-force attacks can now be detected and identified based on statistical analysis of logs and traffic data. However, they fail to detect low-frequency and distributed brute-force attack behaviors. To address different attack methods, new detection techniques have emerged. This study compares various machine learning algorithms and selects two methods, namely the clustering algorithm k-means and bdscan, as well as the decision tree algorithm for data learning. In one approach, normal user login data is integrated with enterprise email log data. The data is first statistically analyzed and filtered, followed by quantifying data characteristics using information entropy. Subsequently, machine learning algorithms are employed for classification, and the results are visualized for display. In another approach, labeled raw data is used to train a model using the decision tree algorithm. By comparing the two analysis results, a more accurate model can be obtained. These analytical methods can help enterprises strengthen email security and defend against low-frequency and distributed brute-force attacks.

Pre-Launch Polarization Assessment of JPSS-3 and -4 VIIRS VNIR Bands and Comparison with Previous Builds

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument, deployed on multiple satellites including the Suomi National Polar-orbiting Partnership (S-NPP), National Oceanic and Atmospheric Administration 20 (NOAA-20), NOAA-21, Joint Polar Satellite System (JPSS-3), and JPSS-4 spacecraft, with launches in 2011, 2017, 2022, 2032, and 2027, respectively, has polarization sensitivity that affects the at-aperture radiometric Sensor Data Record (SDR) calibration in the Visible Near InfraRed (VNIR) spectral region. These SDRs are key inputs into the VIIRS atmospheric, land, and water Environmental Data Records (EDRs) that are integral to weather and climate applications. If the polarization sensitivity of the VIIRS instrument is left uncorrected, EDR quality will degrade, causing diminished quality of weather and climate data. Pre-launch characterization of the instrument’s polarization sensitivity was performed to mitigate this on-orbit calibration effect and improve the quality of the EDRs. Specialized ground test equipment, built specifically for the VIIRS instrument, enabled high-fidelity characterization of the instrument’s polarization performance. This paper will discuss the polarization sensitivity characterization test approach, methodology, and results for the JPSS-3 and -4 builds. This includes a description of the ground test equipment, instrument requirements, and how the testing was executed and analyzed. A comparison of the polarization sensitivity results of the on-orbit S-NPP, NOAA-20, and -21 instruments with the JPSS-3 and -4 VIIRS instruments will be discussed as well.

The Influence of Land Cover on Spatio-Temporal Variation of Air Temperature in Grogol District, Central Java – Indonesia

As an urban fringe of Surakarta City-Indonesia, Grogol District is experienced by increasing temperature. The purpose of this study is: 1) to determine the spatial and temporal variations of air temperature in the Grogol District and 2) to determine the difference in air temperature between built-up land cover and non-built-up land cover, particularly rice fields. Air temperature data was obtained by temperature data logger from four sample points on October 27th to December 13th, 2021. Air temperature and land cover data were analyzed by descriptive and inferential statistics as well as spatial interpolation on air temperature data. The result indicates that higher air temperatures are spread over the northern part which is dominated by settlements. Lower air temperatures are found in the southern part which is dominated by rice fields. The temporal variation of air temperature at the four sample points shows almost the same pattern. However, the air temperature was higher in the built-up land cover than in the rice fields. The statistical difference test also showed that there were differences between the two land covers. In conclusion, Grogol District is affected by the urban heat island phenomenon from Surakarta City. This is particularly related to land cover change and increasing population. Therefore, to prevent the increasing temperature, the protection of the open greenspace area should be implemented.

Divergent determinants on interannual variability of terrestrial water cycle across the globe

Intensifying variability in precipitation under a changing climate is projected to amplify fluctuation in terrestrial hydrological cycle, leading to more severe water-related disasters. The connections between interannual variability of hydrological components and factors influencing these connections have not been clearly defined yet. Based on terrestrial water budget from Climate Data Record, we identify dominant factors influencing partitioning interannual variability of precipitation (P) into that of evapotranspiration (E), runoff (Q), and water storage deviation (ΔS) across the globe by employing geographical detector model (GDM). Sensitivities of the variability partitioning to dominant factors are quantified for different hydroclimate regions by linear regression model and law of total differential. Results show that dominant factors influencing precipitation variability partitioning (VP) are different across distinct hydroclimate conditions. Comparing the statistical index (q value) of the GDM, it can be seen that surface air temperature (Ta), snow water equivalent (SWE) and water storage capacity (Smax) are dominant factors of VP in humid, semi-arid and arid regions, respectively. Changes in P variability largely can transfer into Q variability in humid region. The P variability partitioned into Q variability is dramatically reduced in semi-arid region with SWE decreasing, while P variability partitioned into ΔS variability increases with Smax increasing in arid region. Joint effects of Ta and coefficient of variation of precipitation (Pcv) are found to be the most important interaction in determining VP across the globe. Furthermore, warmer temperatures in humid region cause >90 % of the change in precipitation variability to be transferred to Q variability change. In semi-arid region with snowfall, decreased SWE has strong effect on changes in ΔS (30–40 %) and Q (20–40 %) variability. Our findings imply a changing VP and more severe impacts of hydrological extremes under future climate, where intensive changes in Ta, SWE and land cover are projected.

A hybrid ensemble learning merging approach for enhancing the super drought computation over Lake Victoria Basin

This study introduces a novel Hybrid Ensemble Machine-Learning (HEML) algorithm to merge long-term satellite-based reanalysis precipitation products (SRPPs), enabling the estimation of super drought events in the Lake Victoria Basin (LVB) during the period of 1984 to 2019. This study considers three widely used Machine learning (ML) models, including RF (Random Forest), GBM (Gradient Boosting Machine), and KNN (k-nearest Neighbors), for the emerging HEML approach. The three SRPPs, including CHIRPS (Climate Hazards Group Infra-Red Precipitation with Station), ERA5-Land, and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network-Climate Data Record), were used to merge for developing new precipitation estimates from HEML model. Additionally, classification and regression models were employed as base learners in developing this algorithm. The newly developed HEML datasets were compared with other ML and SRPP products for super-drought monitoring. The Standardized precipitation evapotranspiration index (SPEI) was used to estimate super drought characteristics, including Drought frequency (DF), Drought Duration (DD), and Drought Intensity (DI) from machine learning and SRPPs products in LVB and compared with RG observation. The results revealed that the HEML algorithm shows excellent performance (CC = 0.93) compared to the single ML merging method and SRPPs against observation. Furthermore, the HEML merging product adeptly captures the spatiotemporal patterns of super drought characteristics during both training (1984–2009) and testing (2010–2019) periods. This research offers crucial insights for near-real-time drought monitoring, water resource management, and informed policy decisions.

MICAnet: A Deep Convolutional Neural Network for mineral identification on Martian surface

Mineral identification plays a vital role in understanding the diversity and past habitability of the Martian surface. Mineral mapping by the traditional manual method is time-consuming and the unavailability of ground truth data limited the research on building supervised learning models. To address this issue an augmentation process is already proposed in the literature that generates training data replicating the spectra in the MICA (Minerals Identified in CRISM Analysis) spectral library while preserving absorption signatures and introducing variability. This study introduces MICAnet, a specialized Deep Convolutional Neural Network (DCNN) architecture for mineral identification using the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) hyperspectral data. MICAnet is inspired by the Inception-v3 and InceptionResNet-v1 architectures, but it is tailored with 1-dimensional convolutions for processing the spectra at the pixel level of a hyperspectral image. To the best of the authors’ knowledge, this is the first DCNN architecture solely dedicated to mineral identification on the Martian surface. The model is evaluated by its matching with a TRDR (Targeted Reduced Data Record) dataset obtained using a hierarchical Bayesian model. The results demonstrate an impressive f-score of at least .77 among different mineral groups in the MICA library, which is on par with or better than the unsupervised models previously applied to this objective.

Effects of Nanocoating on the Performance of Photovoltaic Solar Panels in Al Seeb, Oman

Solar photovoltaic (PV) panels are projected to become the largest contributor of clean electricity generation worldwide. Maintenance and cleaning strategies are crucial for optimizing solar PV operations, ensuring a satisfactory economic return of investment. Nanocoating may have potential for optimizing PV operations; however, there is insufficient scientific evidence that supports this idea. Therefore, this study aims to investigate the effectiveness of nanocoating on the performance of solar photovoltaic (PV) panels installed in Al Seeb, Oman. A further study was also carried out to observe the influence of coating layers on the performance of PV panels. One SiO2 nanocoated solar panel, another regularly cleaned PV panel, and a reference uncleaned panel were used to carry out the study. The site of the study was treeless and sandy, with a hot and dry climate. A data logger was connected to the solar PV panel and glass panel to record the resulting voltage, current, temperature, and solar radiation. It was observed that nanocoated PV panels outperformed both regular PV panels and uncleaned PV panels. Nanocoated PV panels demonstrated an average efficiency of 21.6%, showing a 31.7% improvement over uncleaned panels and a 9.6% improvement over regularly cleaned panels. Although nanocoating displayed high efficiency, regular cleaning also contributes positively. Furthermore, even though nanocoated PV panels outperformed the other two panels, it is important to note that the performance difference between the regular cleaned PV panels and the nanocoated PV panels was small. This indicates that regular cleaning strategies and nanocoating can further contribute to maintaining a more efficient solar PV system. Coating in many layers was also observed to influence the performance of PV panels insignificantly, mainly the fourth layer coating appeared to have formed sufficient mass to retain heat.

Chlorophyll a in lakes and streams of the United States (2005–2022)

The concentration of chlorophyll a in phytoplankton and periphyton represents the amount of algal biomass. We compiled an 18-year record (2005–2022) of pigment data from water bodies across the United States (US) to support efforts to develop process-based, machine learning, and remote sensing models for prediction of harmful algal blooms (HABs). To our knowledge, this dataset of nearly 84,000 sites and over 1,374,000 pigment measurements is the largest compilation of harmonized discrete, laboratory-extracted chlorophyll data for the US. These data were compiled from the Water Quality Portal (WQP) and previously unpublished U.S. Geological Survey’s National Water Quality Laboratory (NWQL) data. Data were harmonized for reporting units, pigment type, duplicate values, collection depth, site name, negative values, and some extreme values. Across the country, data show great variation by state in sampling frequency, distribution, and methods. Uses for such data include the calibration of models, calibration of field sensors, examination of relationship to nutrients and other drivers, evaluation of temporal trends, and other applications addressing local to national scale concerns.

Alcohol Interventions on Online Grocery Shopping Platforms

Introduction & BackgroundThere is opportunity to engage light to moderate drinkers in alcohol reduction interventions as a preventative measure. In the space of online grocery shopping there is an added challenge in intervention development in the form of deceptive patterns, which influence consumer behaviour in unhealthy ways including automating behaviour and encouraging overconsumption. Objectives & ApproachThe objectives of this study are to: 1) identify deceptive patterns in the online grocery shopping context, 2) develop interventions which support healthier decision making in this context, 3) apply those interventions to appropriate product categories. The method utilised in the first objective is heuristic analysis which was conducted across eleven major online grocery shopping platforms. The interventions were then developed using the Rapid Iterative Testing and Evaluation (RITE) method, which involved interviewing participants and iterating upon the inventions after every interview. Each interview was analysed using content analysis. When incorporating the interventions into the online grocery shopping environment, interviews were conducted to gain insight into drinking and purchasing habits of consumers. These final interviews were then analysed with inductive thematic analysis. Relevance to Digital FootprintsDigital Footprints underpin the entire intervention development space. The background of the project is built upon human shopping and interaction behaviour online when encountering deceptive patterns. These deceptive patterns have been established using mobile gaming micro-transaction data, online grocery shopping log-in and rewards data, among other data sources. Digital Footprints data can further support the findings from the thematic analysis by further showing cultural and social trends around drinking (e.g., increased purchasing of seasonal beers and ciders in the summer and during sporting tournaments). The purpose of the drinking identified through those social and cultural trends gauge the appropriateness of proposed alcohol interventions. Beyond this, digital footprints data around engagement with health and wellness promoting applications (e.g., active users and app downloads) provides greater insight into the types of health messaging that garner attention and can be used to further inform how to approach those currently outside the health-engaged group. Digital footprints serve to attach larger societal trends to the smaller-scaled interviews and thematic analysis conducted as part of the study. ResultsInitial findings have shown opportunities for nudging light to moderate drinkers who primarily consume beer, wine, or cider. Spirits have been identified as difficult to substitute due to a lack of substitution options in the low alcohol spirit category that are widely available on the consumer market via online grocery retailers. Conclusions & ImplicationsWithout significant change, costs to the National Health Service (NHS) in alcohol related harm are set to rise by over 1 billion GBP. Alcohol consumption is a public health risk and there are opportunities to utilise digital footprint informed interventions to engage the public in preventative interventions.

Effects of Measles Vaccine and Its Associated Risk Factors Among Children Presenting in a Tertiary Care Hospital, Rawalpindi/Islamabad

Objectives: To identify risk factors associated with the development of measles among children under 12 years of age in a tertiary care hospital in Rawalpindi/Islamabad and to determine the effectiveness of measles vaccination in the study population. Methods: A retrospective case control study was done from January 2022 to January 2023 at the Department of Pediatrics, Holy Family Hospital (HFH), Rawalpindi & PAEC General Hospital, Islamabad. Total 600 children were included in the study. The hospital data record had children under the age of 12 with measles. Case controls were enrolled without measles at the same hospital, matched for age and sex. Study outcomes were analyzed by multivariant regression test adjusted with age and sex. Results: The average age of the children was 32.24±14.58 months. Mothers with low education were mostly have measles [OR: 4.1, 95% CI: 1.9-6.5, for primary schooling and OR: 1.1, 95% CI: 0.7-2.0, for matriculation]. Children with measles were mostly not breastfed during the first 2 years of life [OR: 0.8, 95% CI: 0.5-1.4]. Cases were also more likely to have never been vaccinated [OR: 8.2, 95% CI: 5.5-15.0] and never vaccinated other children at home [OR: 6.5, 95% CI: 4.5-10.0]. Conclusion: The study concluded that breastfeeding and proper weaning practices are necessary to eradicate measles.