Data Record Research Articles

Comparative Evaluation of Neural Network Models for Optimizing ECG Signal in Non-Uniform Sampling Domain

Electrocardiographic signals (ECG) are ubiquitous, which justifies the research of their optimal storage and transmission. However, proposals for non-uniform signal sampling must take into account the priority of diagnostic data accuracy and record integrity, as well as robustness to noise and interference. In this study, two novel methods are introduced, each utilizing a distinct neural network architecture for optimizing non-uniform sampling of ECG signal. A transformer model refines each time point selection through an iterative process using gradient descent optimization, with the goal of minimizing the mean squared error between the original and resampled signals. It adaptively modifies time points, which improves the alignment between both signals. In contrast, the Temporal Convolutional Network model trains on the original signal, and gradient descent optimization is utilized to improve the selection of time points. Evaluation of both strategies’ efficacy is performed by calculating signal distances at lower and higher sampling rates. First, a collection of synthetic data points that resembled the P-QRS-T wave was used to train the model. Then, the ECG-ID database for real data analysis was used. Filtering to remove baseline wander followed by evaluation and testing were carried out in the real patient data. The results, in particular MSE = 0.0005, RMSE = 0.0216, and Pearson’s CC = 0.9904 for 120 sps in the case of the transformer patient data model, provide viable paths for maintaining the precision and dependability of ECG-based diagnostic systems at much lower sampling rate. Outcomes indicate that both techniques are effective at improving the fidelity between the original and modified ECG signals.

Proportional increment of oxygen consumption, heart rate and core body temperature in the digesting Python bivittatus.

The Burmese python has a remarkable digestive physiology with large elevations of metabolic rate and heart rate following feeding. Here, we investigated the relationship between heart rate, oxygen consumption and core body temperature during digestion in five pythons (Python bivittatus) by implantation of data loggers. The snakes were placed in respirometers at 30±0.1°C for 26 days and voluntarily ingested three meals of different size, whilst heart rate, core body temperature and oxygen consumption rate were measured continuously. Both oxygen consumption and heart rate increased severalfold during digestion, and metabolic heat production increased core body temperature by 2°C, explaining 12% of the observed tachycardia. The rise in core body temperature means that standard metabolic rate increased during digestion, and we estimate that failure to account for core body temperature leads to a 4% overestimation of the specific dynamic action (SDA) response. Our study reveals a close correlation between oxygen consumption and heart rate during digestion, further supporting the use of heart rate as a proxy for metabolism.

User Engagement With mHealth Interventions to Promote Treatment Adherence and Self-Management in People With Chronic Health Conditions: Systematic Review.

There are numerous mobile health (mHealth) interventions for treatment adherence and self-management; yet, little is known about user engagement or interaction with these technologies. This systematic review aimed to answer the following questions: (1) How is user engagement defined and measured in studies of mHealth interventions to promote adherence to prescribed medical or health regimens or self-management among people living with a health condition? (2) To what degree are patients engaging with these mHealth interventions? (3) What is the association between user engagement with mHealth interventions and adherence or self-management outcomes? (4) How often is user engagement a research end point? Scientific database (Ovid MEDLINE, Embase, Web of Science, PsycINFO, and CINAHL) search results (2016-2021) were screened for inclusion and exclusion criteria. Data were extracted in a standardized electronic form. No risk-of-bias assessment was conducted because this review aimed to characterize user engagement measurement rather than certainty in primary study results. The results were synthesized descriptively and thematically. A total of 292 studies were included for data extraction. The median number of participants per study was 77 (IQR 34-164). Most of the mHealth interventions were evaluated in nonrandomized studies (157/292, 53.8%), involved people with diabetes (51/292, 17.5%), targeted medication adherence (98/292, 33.6%), and comprised apps (220/292, 75.3%). The principal findings were as follows: (1) >60 unique terms were used to define user engagement; "use" (102/292, 34.9%) and "engagement" (94/292, 32.2%) were the most common; (2) a total of 11 distinct user engagement measurement approaches were identified; the use of objective user log-in data from an app or web portal (160/292, 54.8%) was the most common; (3) although engagement was inconsistently evaluated, most of the studies (99/195, 50.8%) reported >1 level of engagement due to the use of multiple measurement methods or analyses, decreased engagement across time (76/99, 77%), and results and conclusions suggesting that higher engagement was associated with positive adherence or self-management (60/103, 58.3%); and (4) user engagement was a research end point in only 19.2% (56/292) of the studies. The results revealed major limitations in the literature reviewed, including significant variability in how user engagement is defined, a tendency to rely on user log-in data over other measurements, and critical gaps in how user engagement is evaluated (infrequently evaluated over time or in relation to adherence or self-management outcomes and rarely considered a research end point). Recommendations are outlined in response to our findings with the goal of improving research rigor in this area. PROSPERO International Prospective Register of Systematic Reviews CRD42022289693; https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022289693.

Unraveling the hidden relationships between seismic multiattributes, well-dynamic data, and Brazilian pre-salt carbonate reservoirs productivity: A shallow versus deep machine-learning approach

During the initial phases of an E&P project, the most reliable data on the reservoir’s deliverability are acquired via drill stem tests (DST), which provide the productivity per flow unit whenever production logging tool data are available. However, DSTs are restricted to a few kilometers, whereas seismic data cover large areas. The integration of these data has been challenging, particularly due to the difference in scale between them. So, a new workflow to determine the relationship between poststack seismic attributes and reservoir productivity using classic supervised (shallow) and deep-learning regression algorithms was developed. The DST parameters were predicted over the entire seismic cube, which can be extremely valuable for the decision-making process. The data set is from the Brazilian deep-water presalt carbonate reservoirs of the Mero Field, which is a well-explored area with a plethora of test and production data. It is adjacent to an underexplored Central Libra appraisal plan area, which is covered by the same seismic survey. Thus, any relationships between seismic attributes and well productivity data observed at the Mero Field are extrapolated to the adjacent underexplored area. Ten seismic attributes and DST data from 10 wells of the Mero Field were used to train shallow and deep-learning supervised regression algorithms for the prediction of flow capacity and productivity index seismic cubes. Twenty development wells (blind tests) were used for the assessment of our predictive models. The highest percentage of correct predictions at the blind test wells (85%) was obtained with random forest regression using six attributes derived from a spectrally balanced full-stack volume, and neither amplitude-variation-with-offset nor inversion data were needed. Deep learning provided lower performance (75%) at a higher computational cost. It demonstrated a new reservoir derisking tool that can be used for project optimization in areas covered by the same seismic survey.

Dwarf shrubs may mitigate the negative effects of climate change on spiders by moderating microclimate

Climate change negatively affects arthropod biodiversity worldwide. Mitigating the resulting arthropod decline is a great challenge. Dwarf shrubs in open areas might buffer microclimatic extremities by reducing the solar radiation reaching the ground and weakening air circulation near the soil surface. Forest steppes are mosaics of forests and grasslands covering a vast area in Eurasia. This heterogeneous ecosystem offers the opportunity to study the effect of small habitat features, i.e. dwarf shrubs, in dry grasslands and compare the fauna of rosemary-leaved willow (Salix rosmarinifolia) shrubs with forest patch interiors, open grasslands and their edges. We hypothesized that the dwarf shrub microhabitat has a wetter and cooler microclimate than open grassland and a different spider community composition than other forest-steppe microhabitats. We recorded microclimatic parameters with data loggers, measured soil moisture with TDR and collected ground-dwelling spiders with pitfall traps. We detected the highest soil moisture (6.26 ± 1.21%, mean ± 95% confidence interval) and air humidity (80.19 ± 3.19%) in forests and the lowest in grasslands (4.36 ± 0.65%; 66.59 ± 2.53%, respectively). The warmest microhabitats were grasslands (23.23 ± 0.51°C), whereas the coolest microhabitats were forests (18.92 ± 0.41°C). The distinct microclimate of dwarf shrubs was cooler (21.46 ± 0.41°C) and moister (5.43 ± 0.53%) than the surrounding semi-desert like grassland. Furthermore, we found a different spider community composition and trait state composition of spiders in forests, edges, grasslands and dwarf shrub microhabitats. Forests (9.90 ± 0.95) and edges (11.44 ± 1.27) hosted a higher species richness than grasslands (7.08 ± 4.27) and dwarf shrubs (5.09 ± 1.33). We collected larger spiders on the edges than in dwarf shrub microhabitats. The dwarf shrubs hosted a different microclimate and spider community composition from the grassland. Climate change in the forest-steppe region is assumed to be driven by a combination of warming and drying. In the coming decades, drought frequency and severity are predicted to increase. Woody vegetation, even dwarf shrubs, creates a thermal and moisture heterogeneity that might aid arthropods in buffering macroclimatic warming through behavioural thermoregulation. Therefore, their presence on grasslands can benefit the conservation of specialised grassland arthropods.

Analysis of Student Activities Based on Log Files in E-Learning Using Clustering Algorithm

This study was conducted to analyse log data generated by e-learning platforms such as Moodle or similar platforms. The main objective of this research is to identify patterns and insights that can help improve the student learning experience and the efficiency of platform management. This study identifies the most effective clustering algorithms for grouping students based on their behaviour and achievements in the e-learning environment, namely K-means and K-Medoids. The methods used to determine the optimal number of clusters are the Silhouette Coefficient method and the Elbow method, utilizing both methods to determine the best clustering algorithm results. Based on the analysis using K-means and K-Medoids clustering methods on the log data of the programming algorithm course, the total number of action logs over a one-month period is 95,461, with an average of 892 action logs per participant. The distribution of action logs based on class (A, B, and C) shows variation in the number and average of action logs per class. Types of activities such as assignments, forums, video views, and course views have different average frequencies, with video views being the most frequently visited activity. Pearson correlations between activity types show strong relationships between activities, with the highest correlation between visits and course views. The optimal number of clusters based on the Elbow and K-Medoids methods is three clusters. Cluster 3 in K-Means has the best performance with the smallest DBI value (0.424) and the smallest centroid distance (21,168.534).

Acoustic telemetry captures the full annual migration of alewife between Chesapeake Bay and the Gulf of Maine

Anadromous fish are declining at a global scale, and a more holistic approach to management is needed that addresses threats across their freshwater, estuarine, and ocean habitats. In this study, we used acoustic telemetry to track adult alewife Alosa pseudoharengus in Chesapeake Bay, USA, to evaluate, for the first time, habitat use throughout the entire annual migration cycle. Fifty adult alewife were tagged in the Choptank River, Maryland, in spring 2022, and detection data were obtained via collaborative acoustic telemetry networks along the Eastern Seaboard of the USA and Canada. Water temperature data were also collected using data loggers in the Choptank River and from satellite measurements of sea surface temperature in Chesapeake Bay and the ocean. In total, 48 tags (96%) were detected at least once, 14 tagged fish (28%) were detected in the ocean migrating north to the Gulf of Maine and Bay of Fundy, and 5 tagged fish (10%) returned to the Choptank River in spring 2023. With few exceptions, tagged fish were detected at temperatures of 7-16°C across all habitats (river to ocean and back). Alewife made extensive use of tidal and non-tidal portions of the Choptank River during spring, migrated to summer habitats that are experiencing rapid warming, and passed through areas of high incidental catch on both the outgoing and return migration. This study highlights how acoustic telemetry can refine our understanding of river-specific migrations of anadromous fish and the management implications of their movements in regions with collaborative acoustic telemetry networks.

Temperature Excursions in Cold Chain Management-Assessing the Accuracy of Refrigerator Temperature Probes.

Background: Medications requiring refrigeration for stability are commonly used across hospitals. Temperature-sensitive medications may not have their temperature requirements maintained due to breaches in the cold chain, especially refrigerator failure. This is usually caused by malfunction of the refrigerator unit or by power outage. After multiple power outages at our institution involving refrigerators with temperature probes located in different areas of the refrigerator, we hypothesised that the fixed temperature probe may not accurately reflect the temperature of the medications themselves. Methods: We conducted simulations of power outages in a commonly used medication refrigerator, using additional temperature data loggers, placed on the refrigerator shelf and inside a cardboard box to replicate the temperature inside medication containers to determine if there was a difference in the time to breach cold chain conditions (>8°C) and to return to appropriate refrigerated temperatures (<8°C) when power was restored. Results: All data loggers took a longer time to breach cold chain conditions than the refrigerator probe (12.5 minutes vs 23-26 minutes) but took longer to return to acceptable temperature after power was restored (17.5 minutes vs 70.5-89 minutes). Conclusion: This exploratory research suggests that temperatures vary within a refrigerator depending on the type and location of probe and that medications within may take longer to breach cold chain conditions but also take longer to return to cold chain conditions compared with fixed refrigerator temperature probes. Further research is required to determine whether these variations occur across different sizes/brands of refrigerators and the effect on stability on refrigerated medications.

Version 1 NOAA-20/OMPS Nadir Mapper total column SO2 product: continuation of NASA long-term global data record

Abstract. For nearly 2 decades, the Ozone Monitoring Instrument (OMI) aboard the NASA Aura spacecraft (launched in 2004) and the Ozone Mapping and Profiler Suite (OMPS) aboard the NASA/NOAA Suomi National Polar-orbiting Partnership (SNPP) satellite (launched in 2011) have been providing global monitoring of SO2 column densities from both anthropogenic and volcanic activities. Here, we describe the version-1 NOAA-20 (N20)/OMPS SO2 product (https://doi.org/10.5067/OMPS/OMPS_N20_NMSO2_PCA_L2_Step1.1, Li et al., 2023), aimed at extending the long-term climate data record. To achieve this goal, we apply a principal component analysis (PCA) retrieval technique, also used for the OMI and SNPP/OMPS SO2 products, to N20/OMPS. For volcanic SO2 retrievals, the algorithm is identical between N20 and SNPP/OMPS and produces consistent retrievals for eruptions such as Kilauea in 2018 and Raikoke in 2019. For anthropogenic SO2 retrievals, the algorithm has been customized for N20/OMPS, considering its greater spatial resolution and reduced signal-to-noise ratio as compared with SNPP/OMPS. Over background areas, N20/OMPS SO2 slant column densities (SCDs) show relatively small biases, comparable retrieval noise with SNPP/OMPS (after aggregation to the same spatial resolution), and remarkable stability with essentially no drift during 2018–2023. Over major anthropogenic source areas, the two OMPS retrievals are generally well-correlated, but N20/OMPS SO2 is biased low, especially for India and the Middle East, where the differences reach ∼ 20 % on average. The reasons for these differences are not fully understood but are partly due to algorithmic differences. Better agreement (typical differences of ∼ 10 %–15 %) is found over degassing volcanoes. SO2 emissions (https://doi.org/10.5067/MEASURES/SO2/DATA406, Fioletov et al., 2022) from large point sources, inferred from N20/OMPS retrievals, agree well with those based on OMI, SNPP/OMPS, and the TROPOspheric Monitoring Instrument (TROPOMI), with correlation coefficients &gt;0.98 and overall differences &lt;10 %. The ratios between the estimated emissions and their uncertainties offer insights into the ability of different satellite instruments to detect and quantify SO2 sources. While TROPOMI has the highest ratios of all four sensors, the ratios from N20/OMPS are slightly greater than OMI and substantially greater than SNPP/OMPS. Overall, our results suggest that the version-1 N20/OMPS SO2 product will successfully continue the long-term OMI and SNPP/OMPS SO2 data records. Efforts currently underway will further enhance the consistency of retrievals between different instruments, facilitating the development of multi-decade, coherent global SO2 datasets across multiple satellites.

SLOPE: Safety LOg PEripherals implementation and software drivers for a safe RISC-V microcontroller unit

The focus of this manuscript is related to the main safety issues regarding a mixed criticality system running multiple concurrent tasks. Our concerns are related to the guarantee of Freedom of Interference between concurrent partitions, and to the respect of the Worst Case Execution Time for tasks. Moreover, we are interested in the evaluation of resources budgeting and the study of system behavior in case of occurring random hardware failures. In this paper we present a set of Safety LOg PEripherals (SLOPE): Performance Monitoring Unit (PMU), Execution Tracing Unit (ETU), Error Management Unit (EMU), Time Management Unit (TMU) and Data Log Unit (DLU); then, an implementation of SLOPE on a single core RISC-V architecture is proposed. Such peripherals are able to collect software and hardware information about execution, and eventually trigger recovery actions to mitigate a possible dangerous misbehavior. We show results of the hardware implementation and software testing of the units with a dedicated software library. For the PMU we standardized the software layer according to embedded Performance Application Programming Interface (ePAPI), and compared its functionality with a bare-metal use of the library. To test the ETU we compared the hardware simulation results with software ones, to understand if overflow may occur in internal hardware buffers during tracing. In conclusion, designed devices introduce new instruments for system investigation for RISC-V technologies and can generate an execution profile for safety related tasks.

An ultraviolet light data logger for studies of organismal ecology and physiology

Abstract Monitoring environmental conditions over time is essential for understanding how species regulate those conditions to satisfy their physiological needs. Variation in ultraviolet (UV) light exposure, for instance, is crucial for numerous taxa, influencing behaviour as well as essential physiological processes like vitamin D3 synthesis. Yet, despite these considerations, no commercially available data loggers exist to record UV exposures over time, preventing inquiry into this important but neglected fitness demand. To overcome this challenge, I designed the first UV data logger, custom built on the open‐source Arduino platform. This device logs UV exposures (in voltage or converted to UV index) and date‐time information for each UV reading, at user‐defined intervals. The integration of a low‐power timer into the assembly, and its weather‐resistant housing, enables long‐term use (at least several months). The cost of one device (including housing) is under £45/$55 USD. Calibration and performance tests reveal that this device is reliable under various weather conditions and produces accurate UV readings under both artificial lighting and sunlight. Both the device and its customised housing can be easily replicated and expanded upon (e.g. inclusion of additional sensors). Overall, this device represents a crucial first step in our ability to better characterise UV exposure patterns in habitats for physiological ecology studies. Exposure to UV light is adaptive for many types of organisms, both in the wild and in captivity (e.g. zoos and private hobbyists), and so I expect a broad array of scientific and public users alike to benefit from using this device.

Enhancing Solar Power Efficiency: Smart Metering and ANN-Based Production Forecasting

This paper presents a comprehensive and comparative study of solar energy forecasting in Morocco, utilizing four machine learning algorithms: Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), recurrent neural networks (RNNs), and artificial neural networks (ANNs). The study is conducted using a smart metering device designed for a photovoltaic system at an industrial site in Benguerir, Morocco. The smart metering device collects energy usage data from a submeter and transmits it to the cloud via an ESP-32 card, enhancing monitoring, efficiency, and energy utilization. Our methodology includes an analysis of solar resources, considering factors such as location, temperature, and irradiance levels, with PVSYST simulation software version 7.2, employed to evaluate system performance under varying conditions. Additionally, a data logger is developed to monitor solar panel energy production, securely storing data in the cloud while accurately measuring key parameters and transmitting them using reliable communication protocols. An intuitive web interface is also created for data visualization and analysis. The research demonstrates a holistic approach to smart metering devices for photovoltaic systems, contributing to sustainable energy utilization, smart grid development, and environmental conservation in Morocco. The performance analysis indicates that ANNs are the most effective predictive model for solar energy forecasting in similar scenarios, demonstrating the lowest RMSE and MAE values, along with the highest R2 value.

Novel method to determine bedrock from a single sensor based seismic reflection signal using Haar wavelet transform

The determination of bedrock depth is crucial across various earth sciences and related fields. Geophysical techniques, notably the continuous wavelet transform, are increasingly employed to map subsurface bedrock structures. This method allows the transformation of 1D time domain seismic reflection signals into a 2D image, providing time–frequency representations for feature extraction. Leveraging the advantages of wavelet theory, particularly the Haar wavelet known for its simplicity and compact support, this study utilizes continuous Haar wavelet local maxima lines to enhance bedrock analysis. Seismic reflection data is acquired using an extremely sensitive geophone as a receiver along with a high dynamic range data logger. The Common Midpoint (CMP) approach, coupled with optimized offset distances, is utilized to ensure robust data quality and signal strength. Simple sources, such as a sledgehammer, generate sound waves for data collection prior to borehole drilling, correlating acquired data with lithogeological information. This research emphasizes the significance of adjusting parameters like sampling frequency, choice of wavelet, and optimal offset distances. The methodology employed, involving a single geophone, a one-channel data recorder, and a basic sledgehammer as a sound source, offers an inexpensive, straightforward, and non-invasive approach. The study demonstrates the effectiveness of Haar wavelet local maxima lines in accurately determining bedrock depth, producing results closely aligned with both predicted and actual depths.

Impact of Texting-Induced Distraction on Driving Behavior Based on Field Operation Tests

Driver distraction has proven to be among the top contributory factors in road crashes worldwide. The involvement of drivers in secondary activities, such as phone usage, conversation with co-passengers, reaching for objects, eating, and so forth, have been shown to be significant causes of in-vehicle driver distraction. Text messaging using mobile phones while driving can be detrimental to the primary driving task as it invokes manual, visual, and cognitive distractions. Numerous studies in the past have analyzed the effect of texting on driver distraction using driving simulators. This study followed a field operation test–based approach to simulate real road conditions and evaluate the impact of texting on driving behavior. The study was conducted at the connected autonomous vehicle testbed at the Indian Institute of Technology Hyderabad, using an instrumented test vehicle. Data on gaze parameters and vehicle kinematics were recorded using eye-tracker glasses and a high-end global positioning system (GPS) data logger. Thirty-one participants drove on the pre-defined path at the testbed under baseline and texting conditions. The findings demonstrated that the texting task significantly affected fixation count, mean speed, and lateral acceleration. The texting task led to a reduction in mean fixation counts, mean speed, and mean lateral acceleration by 44.37%, 34%, and 46.72%, respectively. The age and driving experience of the participants had a significant effect on their behavior. The results indicate the critical potential of texting-based driver distraction and suggest the enforcement of stringent rules for texting while driving to create a safer road environment.

PSIII-12 Dynamics of neonatal dairy calf behaviors and their association with early gut microbiota

Abstract The behavior of dairy cattle can be both an indicator and a determinant of their productivity and health. Additionally, animal behavior is also associated with their gastrointestinal microbiota through the bi-directional pathways of the gut-brain axis, influencing nutrient utilization and gut health. Few studies have looked at the dynamics of neonatal calf behaviors and their associations with early microbiota development. Therefore, the objective of this study is to observe feeding behaviors and activity of neonatal dairy calves and investigate its associations with the diversity and composition of early-stage gut microbiota. We used Holstein-Angus crossed calves (n = 25) raised in individual outdoor hutches and bucket-fed milk replacer twice daily. The number of milk exposures until calves were successfully bucket trained was recorded, along with milk consumption times during the AM feedings on d 7, 11, and 14. We also monitored the activity of the calves using HOBO Pendant G Data Logger accelerometers secured on the right hind leg of each calf to monitor standing and lying behavior from d 3 to d 13. Fecal samples were collected from the recto-anal junction of the calves at d 7 and 14 after birth and gut microbiota were analyzed using full-length 16S rRNA gene amplicon sequencing on an Oxford Nanopore sequencer. The Generalized Linear Model (GLM) was applied to evaluate effects of gender and birth weight on behavior and gut microbiota using RStudio 4.2.1. The Linear discriminant analysis Effect Size (LEfSe) was used to determine the unique bacterial species for each behavioral group. The results revealed that there was no influence of sex or birth weight on bucket training or consumption time; however, male calves exhibited significantly increased total standing time compared with female calves. We found no significant difference in microbiota between fast-learning calves (one milk exposure; n = 3) and slow-learning calves (5 or more exposures; n = 3). We observed greater alpha diversity of slow drinking calves (top 25%; n = 6) compared with fast drinking calves (top 25%; n = 6) on d 7 (Chao1; P = 0.03). The LEfSe analysis identified Akkermansia muciniphilia to be more abundant in fast drinking calves, and Megasphaera elsdenii to be more abundant in slow drinking calves. Additionally, a trend of differing microbiota composition was observed between the most active and least active calves (n = 4 per group) on d 14 (Bray-Curtis; P = 0.1), and many Prevotella species were more abundant in the most active calves. In conclusion, this study sheds light on the gut-brain axis of neonatal dairy calves at the species-level and provides further insights into cognitive, consumptive, and active behaviors.

Evaluation of environmental geochemical signatures due to RO rejects on arid agricultural farms and tangible solutions

The impact of reverse osmosis (RO) rejects in the groundwater presents a significant challenge in arid regions. This study collected groundwater samples, product water, and reverse osmosis brine (ROB) from evaporation ponds and analyzed them for major ions and trace elements. Test boreholes were drilled near the ROB site along the flow direction, and borehole sediment samples were collected. The samples were predominantly gravelly sand, and the depth to water level fluctuated around 30 m below ground level (bgl), with minerals mainly consisting of calcite, gypsum, and Quartz. Data loggers reflected a rise in water level (<22 m bgl) corresponding to higher electrical conductivity (>16 mS/Cm) during the cropping period in many locations, confirming the impact of ROB in groundwater. The results were further supported by enriched signatures of δ18O (∼ +1.5‰) and δ2H (∼ +15‰). The saturation index of the minerals reflected that carbonate minerals (Calcite > Dolomite) were saturated in the ROB relative to the groundwater. The vertical variation of mineral assemblages in the boreholes indicated gypsum precipitation in the capillary zone along with calcite and dolomite. The assemblage varies as the groundwater moves from the disposal site. The speciation of different compounds along the groundwater path indicated higher carbonate and sulfate species (CaCO3 > CaHCO3> CaSO4 > NaSO4 > MgSO4) near the disposal site, with variations along the flow direction. Considering the significant variation in temperature in the region (5 to 50 ℃), the water sample composition was modeled using PHREEQC, suggesting that the increase in temperature led to supersaturation of Epsomite and Gypsum compositions. The ROB was theoretically mixed with groundwater and product water in different proportions, and an optimum composition (10:90) for safe disposal was derived and tested fit for reuse in agriculture.

Open-Source Hardware Design of Modular Solar DC Nanogrid

The technical feasibility of solar photovoltaic (PV) direct current (DC) nanogrids is well established, but the components of nanogrids are primarily commercially focused on alternating current (AC)-based systems. Thus, DC converter-based designs at the system level require personnel with high degree of technical knowledge, which results in high costs. To enable a democratization of the technology by reducing the costs, this study provides a novel modular plug-and-play open-source DC nanogrid. The system can be customized according to consumer requirements, enabling the supply of various voltage levels to accommodate different device voltage needs. The step-by-step design process of the converter, controller, data logger, and assembly of the complete system is provided. A time-domain simulation and stability analysis of the designed system were conducted in MATLAB/Simulink (version 2024b) as well as experimental validation. The results show that transforming the nanogrid from a distribution network to a device makes it suitable for various user-specific applications, such as remotely supplying power to campsites, emergency vehicles like ambulances, and small houses lacking grid electricity. The modular DC nanogrid includes all the features available in a DC distribution network, as well as data logging, which enhances the user experience and promotes the use of solar-powered DC grid systems.

124 Effects of Saccharomyces cerevisiae and dietary type offered to beef cattle on intake, feeding behavior, nutrient digestibility, and ruminal fermentation

Abstract The effects of live yeast and diet type offered to beef steers on intake, feeding behavior, nutrient digestibility, and ruminal fermentation profile were evaluated. Ruminally cannulated beef steers [n = 8; body weight (BW) = 550 ± 20 kg] were used in a duplicated 4 × 4 Latin square design following a 2 × 2 factorial arrangement of treatments: A) presence of live-yeast [Saccharomyces cerevisiae (CNCM I-1077), at 1 × 1010 CFUּ animal-1ּ d-1]; and B) diet type [steam-flaked corn-based grower or finisher diets (ad libitum intake)]. Live yeast was delivered with gel-capsules (0.25 g, as is) twice daily via ruminal-cannula. Four, 35 d periods were used. Feed refusal was subtracted from offered (dry matter basis) to calculate feed intake (DMI). The 24 h feeding behavior assessment was conducted on d 31 by trained personnel (time spent eating, ruminating, chewing, drinking, resting, and meal size/number). A 5-d fecal collection (twice daily) was used for the digestibility assessment. Ruminal pH was continuously measured (7 d, intra-ruminal Dascor wireless data loggers). Ruminal ammonia-N and volatile fatty acids (VFA) were analyzed at 0, 2, 4, 8, 16, and 23 h on d 35. The GLIMMIX procedure of SAS was used with animals as the experimental unit. No live yeast × diet interactions (P ≥ 0.17) were observed. Intake was not affected (P = 0.83) by live yeast, while steers offered the grower diet consumed more (P ≤ 0.01) NDF, ADF, and hemicellulose compared with the finisher diet. Live yeast increased (P ≤ 0.02) the dietary NDF, ADF, and hemicellulose digestibility of the grower diet by 13.7, 12.8, and 14.6%, and on the finisher diet by 6.7, 9, and 6.4%, respectively. Live yeast did not affect (P ≥ 0.23) major feeding behavior activities measured. Steers offered grower diet spent more time (P ≤ 0.01) on each meal, ruminating, eating, and chewing and less time (P &amp;lt; 0.01) resting. Live yeast did not affect (P ≥ 0.81) ruminal pH variables measured. Steers offered the grower diet had a greater (P &amp;lt; 0.01) ruminal pH pattern compared with the finisher diet. The ruminal ammonia-N and volatile fatty acids were not affected (P ≥ 0.16) by live yeast, while steers offered the finisher diet had a greater (P ≤ 0.01) total VFA and propionate molar proportion, while a lower (P ≤ 0.01) ammonia-N and acetate molar proportion compared with the grower diet. Regardless of the diet type, live yeast improved digestibility of fiber fractions, while dry matter intake was unaffected. Grower diets were more prone to stimulate feeding behavior activities than finisher, while live yeast seemed to not affect such variables. Expected effects of diet type on ruminal fermentation were ratified, while live yeast inclusion did not affect the ruminal fermentation variables evaluated.

479 Pain control for the castration of newborn bull calves by elastration

Abstract All methods of castration are known to cause pain regardless of the method, or age of the animal. Our objective was to explore providing pain control for bull calves castrated by elastration prior to 1 wk of age. Singleton, bull calves (n = 85) born at the Ontario Beef Research Centre were enrolled and randomly assigned to 1 of 5 treatment groups at birth: a single dose of oral meloxicam at castration (1ORAL), a dose of oral meloxicam at castration and a second oral dose 1 wk later (2ORAL), a dose of injectable meloxicam at castration (1INJECT), not castrated (SHAM), or castrated without pain control (PAINFUL). Calves were treated within 48 h of birth and followed until weaning. All calves were observed 2 h/d for 13 different pain-associated behaviors (e.g., tail flicks, kicking, licking the scrotum) on the day of castration (d 0), as well as d 1, 2, 7, 8, and 9. To record general activity, from d -1 to d 14 each calf wore an Onset HOBO Pendant G Data Logger attached to one of their hind legs. Calves were weighed weekly until they were turned-out to pasture and thereafter weighed bi-weekly until weaning. Five behaviors were statistically significant between treatment groups. PAINFUL calves had roughly three times the rate of licking (P &amp;gt; 0.05) compared with calves in 1ORAL, 2ORAL, and 1INJECT groups, and four times the rate of licking compared with SHAM calves. Additionally, PAINFUL calves had two times the rate of tail flicks (P &amp;lt; 0.05) compared with meloxicam-treated calves, and five times the rate of tail flicks compared with SHAM calves. A treatment effect (P &amp;lt; 0.05) was observed for standing time, with PAINFUL calves spending the least amount of time standing on average in a 24-h period. No statistical time differences were seen between the remaining treatment groups. No treatment or treatment*time interaction effects were observed for lying time. A tendency was seen for treatment effect on lying time (P = 0.0581), with PAINFUL calves spending the most time lying on average and having a significance difference compared with all other treatment groups (LSM &amp;gt; 0.05). No treatment effect was seen for average daily gain up to wk 8, and for final weaning weights. Weight at time of castration was significant (P &amp;lt; 0.05); however, calves were not blocked by BW when assigned to treatment. In conclusion, meloxicam-treated calves expressed decreased rates of pain-associated behaviors compared with PAINFUL calves, and BW did not differ at time of weaning. Our results provide evidence that calves castrated by elastration before 1 wk of age and received meloxicam, experience less pain.

Multiple channels, low-cost, and dual data storage data logger for building a soil temperature network

Temperature measurement is critical in many areas of research, particularly in agriculture, where it can have a significant impact on crop health and yield. Experiments such as seed germination often require numerous temperature sensors to collect extensive data. Typically, data loggers are used to store information, but market options are expensive and offer limited, non-customizable inputs (channels), creating challenges for comprehensive soil temperature monitoring. This study aims to develop a network of open-source, low-cost data loggers with multiple customizable channels for local and remote temperature data storage. The hardware includes Arduino, temperature sensors, a Real Time Clock, and a LoRa module to transmit data to a LILYGO TTGO board, which sends it to a remote MongoDB database while also storing it locally on a microSD card. In addition, a digital tool was developed to retrieve and display both current and historical readings from the MongoDB database. The total cost of this hardware is approximately US$ 72 (based on current prices) for the simplest network, which is approximately 18 % of the commercial cost. The system achieved a root mean square error (RMSE) of 1.6 °C compared to a manual sampling probe thermometer, proving it to be a reliable measurement source. The hardware developed in this study surpasses commercial options by allowing the integration of multiple sensors and emitters, creating a network of data loggers at a lower cost. In addition to the hardware, an open-source digital tool was developed to visualize historical data at no additional cost.