Data Record Research Articles

Pediatric nurses' fatigue descriptions in occupational injury reports: A descriptive qualitative study1.

Despite the high risks associated with occupational fatigue in healthcare, few organizations require nurses to screen and report fatigue symptoms. As a result, little is known about if and how nurses would report fatigue while on the job. To determine if hospital-based pediatric nurses reported fatigue as part of an active injury reporting method. This secondary analysis of qualitative data used a descriptive design with content analysis. Data from the parent study were collected at a U.S. pediatric hospital where nurses verbally reported on-shift injuries or near misses and pre- and post-shift health status via a digital voice recorder. Researchers used content analysis to independently code data for nurses' references to fatigue. Codes were then analyzed for patterns and themes. Approximately 30% (n = 104) of participants reported fatigue-related content. Emergent themes were Work Stressors, Individual Risk Factors, Fatigue Descriptors, Adverse Outcomes from Fatigue, Fatigue Buffers, Descriptors for Buffered Fatigue, and Favorable Outcomes from Buffered Fatigue. Fatigue descriptions align with prior literature, demonstrating the accuracy of the voice recorder data collection method. In addition, nurses expressed uncertainty about the appropriateness of reporting fatigue symptoms. Findings from this study support pediatric nurses will report fatigue, when provided an opportunity. Additional efforts are needed to better understand effective ways to improve fatigue reporting among nurses, including harnessing current technology for real-time reporting and how to change the culture around fatigue reporting.

Low-level turbulence risk assessment and visualization using temporal rate of change of headwind of an aircraft

In this study, we focused on the temporal rate of change of headwind, which is one of the recorded parameters in the aircraft’s in-flight quick access recorder data. We selected the Laplace distribution and utilized the scaling parameter to construct a low-level turbulence risk assessment model. Using this model, we calculated the risk of low-level turbulence occurrence at five airports in Japan based on the month, time of day, and wind speed. We visualized how the geographical conditions at each airport influenced risk in relation to airport wind speeds. We developed a low-level turbulence visualization site linked to weather conditions using these results to enable pilots to easily verify low-level turbulence risk and incorporate this information into their flight routines. These findings are anticipated to significantly enhance aircraft safety.

Monitoring of thermal conditions and snow dynamics at periglacial block accumulations in a low mountain range in central Germany

AbstractThe Rhoen Mountains, a relict periglacial landscape in central Germany, feature a wide range of openwork block accumulations. Although located in a temperate climate, those have characteristics comparable with cold regions of higher altitude or latitude such as arctic‐alpine species, longer lasting snow patches or the discussed existence of summer or even year‐round ice lenses in one of the largest of these landforms in the Central German Uplands. This study aims for a characterization of the microclimatic conditions of two neighbouring block accumulations. Therefore, temperatures were registered by data loggers along profiles, and snow dynamics were monitored using time‐lapse cameras and terrestrial laser scans. These observations are finally compared with geophysical measurements to address the question of potential isolated low‐altitude permafrost occurrences. Mean ground surface temperatures show an inverse thermal gradient along the Schafstein block accumulation. Furrows were identified as the cold spots in winter, whereas snow melt holes are signs of a chimney effect. In summer, cold air flows out at ventilation holes along the front causing temperatures of up to 25°C below air temperatures, although no clear signs of permafrost were detected. Temperature correlations reveal periods indicative of a recurring internal summer air circulation. Coarse blocky substrate also favours ground cooling of the smaller Mathesberg block accumulation compared with its surroundings. Winter temperatures are influenced by a persistent snowbank forming due to drifting and blowing snow at the leeward edge of a plateau as little amounts of snow are sufficient to be redistributed by westerlies. The prolonged melt of the snowbank might have had or still has a local hydrological and geomorphological impact. Uncertainties remain regarding the behaviour of the microclimate of block accumulations in a warming climate. Being ‘cold spots’ of high ecological value further investigations are suggested.

Evaluation of a Temperature/Humidity Data Logger for the Usage in Cattle Barns.

Climate change is a worldwide problem that is manifested in livestock farming with a decrease in animal health and welfare and economic losses due to heat stress. Therefore, a precise and continuous recording of the barn climate is essential to be able to implement actions at a certain threshold. The aim of this study was to evaluate a logger for temperature and humidity (Kestrel Drop D2) marketed for on-farm use in comparison to various other temperature/humidity data loggers under field conditions. Four different sensors were used and placed in different settings in cattle barns to correlate temperature and humidity measurements. Data were recorded for over a year in total. The data were very highly correlated. Furthermore, the area under the curve for the evaluated logger in comparison to the other ones was 0.99 to 1.0, using a temperature-humidity index cut-off of 72, often set to define heat stress. In conclusion, the evaluated logger performed equally well as the other used devices. For on-farm use, it is suitable.

Summer Energy Use and Comfort Analysis in Rural Chinese Dwellings: A Case Study of Low-Income Older Populations in Shandong

This paper aims to investigate the indoor environmental conditions and energy use behaviours of older individuals in rural cold climates of China, with a specific focus on cooling practices during the summer months in the Shandong region. This study employs a mixed-method approach, combining quantitative indoor environmental monitoring with qualitative interviews and observations, to explore the relationship between environmental factors, household living conditions, and energy use patterns across five types of elderly households: three generations living together, older people living with grandchildren, older people living with children, older couples living together, and older people living alone. Data collection was conducted over five weeks during the summer of 2023 using HOBO UX100-003 data loggers, while external weather conditions were monitored by the China Meteorological Administration. Face-to-face interviews were conducted to gain deeper insights into daily cooling behaviours and energy use. The results reveal that cooling practices and indoor environmental conditions vary significantly among the different household types. Multigenerational households showed more complex energy use dynamics, with younger family members frequently operating high-energy appliances like air conditioners, while older individuals tended to rely on natural ventilation and electric fans to reduce energy costs. In contrast, older couples and solitary older individuals demonstrated more conservative cooling behaviours, often enduring higher indoor temperatures due to limited financial resources and a desire to minimize energy expenditures. Despite the high energy use intensity in some households, many homes failed to achieve comfortable indoor environments, particularly in dwellings with minimal insulation and older building materials. This study concludes that economic status, household structure, and building characteristics play crucial roles in shaping cooling behaviours and indoor comfort during the summer.

Think Beyond the Room: Measuring Relative Humidity in the Home Cage and Its Impact on Reproduction in Laboratory Mice, Mus musculus.

Relative humidity (RH) is measured in vivaria with a broad range to accommodate seasonal fluctuations. It is assumed that measurements in the room (macroenvironment) reflect those in the cage (microenvironment). However, there is limited data comparing RH in the macroenvironment to the microenvironment and how the mice may be affected by variations in RH that fall within husbandry recommendations. This study aimed to compare RH in the macroenvironment to that of the microenvironment in various group sizes of laboratory mice; and examine how variation in microenvironmental RH impacts pup survival. Temperature and RH were measured using a temperature/humidity data logger attached to a solid top cage lid. The lid was rotated across N = 48 breeding trios and N = 33 same sex cages on a C57BL/6J background. Further, once a week, a single breeding trio was selected (N = 23) to compare RH readings to weekly rates of pup loss in a larger breeding colony. Across all cages, RH was higher in the microenvironment than the macroenvironment. RH was universally higher in the summer than in the winter, and increased with group size. For breeding cages, as microenvironmental RH increased, the proportion of pups lost each week decreased in a linear relationship. No threshold of decreased mortality could be identified. These data highlight RH as a potential extrinsic factor. While these patterns are correlational, they warrant further research focused on the causative role of RH on mouse welfare.

Ensemble automated approaches for producing high‐quality herbarium digital records

AbstractPremiseOne of the slowest steps in digitizing natural history collections is converting labels associated with specimens into a digital data record usable for collections management and research. Here, we address how herbarium specimen labels can be converted into digital data records via extraction into standardized Darwin Core fields.MethodsWe first showcase the development of a rule‐based approach and compare outcomes with a large language model–based approach, in particular ChatGPT4. We next quantified omission and commission error rates across target fields for a set of labels transcribed using optical character recognition (OCR) for both approaches. For example, we find that ChatGPT4 often creates field names that are not Darwin Core compliant while rule‐based approaches often have high commission error rates.ResultsOur results suggest that these approaches each have different strengths and limitations. We therefore developed an ensemble approach that leverages the strengths of each individual method and documented that ensembling strongly reduced overall information extraction errors.DiscussionThis work shows that an ensemble approach has particular value for creating high‐quality digital data records, even for complicated label content. While human validation is still needed to ensure the best possible quality, automated approaches can speed digitization of herbarium specimen labels and are likely to be broadly usable for all natural history collection types.

Preliminary Mapping of the Spatial Variability in the Microclimate in Tropical Greenhouses: A Pepper Crop Perspective

The objectives of this experiment were to (1) discern the spatial variability in climatic parameters within a greenhouse throughout different phenological stages of pepper cultivation and (2) develop an empirical model aimed at establishing predictive equations for temperature, relative humidity, vapor pressure deficit, and crop evapotranspiration (ETc) within the greenhouse considering the climatic parameters recorded on the outside. The experiment was conducted in the coastal area of Ecuador within a bamboo-constructed greenhouse facility. Pepper plants were cultivated in plastic bags using a specific cultivation medium common in Ecuador and a fertigation system. Climatic parameters were monitored within the greenhouse using data loggers, and the external conditions were recorded using an external meteorological station throughout the duration of the pepper cultivation. Statistical analyses revealed correlations between internal climatic parameters and plant growth stages, as well as external climatic conditions. The spatial distribution analysis of climatic parameters within the greenhouse revealed that the lowest values for temperature (27 °C) and vapor pressure deficit (VPD) (1.25 kPa) and the highest values for relative humidity (RH) (68%) were observed on the northwest corner of the greenhouse. This observed pattern was linked to the prevailing wind direction (south–east (SE)) outside the greenhouse. Stepwise regression analyses identified significant outdoor climate variables (RH, temperature, VPD, and instantaneous wind speed (WS) Inst) in the climatic conditions recorded within the greenhouse.

Cyberattack event logs classification using deep learning with semantic feature analysis

Event logs play a crucial role in cybersecurity by detecting potentially malicious network activities and preventing data loss or theft. Previous work did not place a high value on log messages and their impact on security breach prediction and intrusion detection. This research paper introduces a novel approach for log message analysis applied to a dataset of event logs collected from various web sources. Event log messages were analyzed and categorized based on event and attack types with an explainable AI emphasizing the value of its key data. The study aims to enhance intrusion detection and minimize performance degradation by identifying suspicious events. In this regard, a new semantic vectorization framework is proposed, leveraging deep learning architectures to develop semantic discriminating log features, offering a cogent explanation and classification of event log messages. The use of BERT deep embeddings as a baseline for the prediction model allows for visualizing and interpreting the formulation of log message semantic features. Several empirical scenarios are set and conducted extensively to evaluate the performance of the event log classifier, considering the attack type, event type, and zero-shot logs. The experimental results demonstrate that the proposed event log classifier outperforms state-of-the-art machine learning models, achieving a recall of 99.27% and a precision of 99.29%. This highlights the model’s ability to accurately identify events of a particular type by detecting as many suspicious events as feasible while minimizing the misclassification rate.

Analysis of Earthquake Vulnerability of The Demak Coastal Area based on the HVSR (Horizontal To Vertical Spectral Ratio) Method

<p>Most of the Demak Coastal area, especially on the north coast, has a subsurface structure which is quite thick sediment. The impact of an earthquake will cause damage to subsurface structures and buildings above it, so a method is needed that can determine earthquake vulnerability in an area to take mitigation steps. This research aims to analyze vulnerability to earthquakes based on natural frequency (fo), amplification (Ao) and seismic vulnerability index (SVI). The research was carried out by measuring the microseismic signal response at 89 locations using a 3-component seismograph and data logger. The research results showed that the dominant frequency varied from 0.26 – 5.26 Hz, the amplification factor varied from 0.51 – 3.56 and the seismic susceptibility index varied from 0.14-14.77 micro cm<sup>2</sup>/s. The study area was classified into low SVI (SVI < 5), medium SVI (5 < SVI < 10), and high SVI (SVI > 10). The potential earthquake hazard described by SVI with a value range of 10 < SVI (high classification) is found at observation station 60 (Bedono Hamlet) and observation station 70 (Karangwaru Hamlet Cemetery)</p>

CTNet: Improving the non-stationary predictive ability of remaining useful life of aero-engine under multiple time-varying operating conditions

Remaining useful life (RUL) estimation has been widely studied in prognostics and health management. Multiple time-varying operating conditions explicitly influence the measured performance parameters of the aero-engine, which results in non-stationarity and impedes the predictive ability of deep learning models. Previous data-driven studies primarily ignore the causality between operating condition descriptors and measured performance parameters, which is inconsistent with practical aero-engine engineering applications. To address this issue, we propose a causality Transformer network (CTNet), which improves the prediction stability of non-stationary series under multiple operating conditions. First, a novel graph structure is used to capture the connectivity relationship between operating condition descriptors and measured sensor nodes. Then the causality attention is proposed to inject additional causality information into the self-attention module without affecting the similarity computations of query and key values within the window. The causality attention can generate distinguishable attention for operating conditions, which properly release the predictive potential for non-stationary series. Extensive experiments are performed on public datasets (CMAPSS, N-CMAPSS), showing that CTNet outperforms existing state-of-the-art methods. We have also conducted experiments on the real-world quick access recorder (QAR) data, which validates the performance of the proposed model in perceiving time-varying operating conditions.

A hybrid inversion algorithm to obtain the resistivity of the uninvaded zone based on the array induction log

This study investigates the impact of the drilling mud invasion on the borehole-measured resistivity. The primary objective is to retrieve the true resistivity of the formation, which helps in identifying different fluids in the reservoir. To achieve this goal, We proposed a hybrid inversion approach integrating the Levenberg-Marquardt and Markov Chain Monte Carlo algorithms with a five-parameter formation resistivity model. Synthetic and real-world data are utilized to assess the method's robustness and reliability. The simulated result indicated that the method is reliable when the data noise level is less than 5%.The method applied to real-world data revealed that the resistivity profile on the water zone showed a slight increase in the inverted resistivity from measured resistivity. Meanwhile, in the oil zone, the calculated resistivity revealed a high deviation from the measured resistivity, indicating the effects of mud invasion. The introduced methods are only applicable when the invasions of mud occur within the range of the logging tool's depth of investigation. Moreover, the method may give no reliable result when the invasion exceeds the tool's investigation depth. It indicates its limitation.

Limited Preservation of Strike‐Slip Surface Displacement in the Geomorphic Record

AbstractOffset geomorphic markers are commonly used to interpret slip history of strike‐slip faults and have played an important role in forming earthquake recurrence models. These data sets are typically analyzed using cumulative probability methods to interpret average amounts of slip in past earthquakes. However, interpretation of the geomorphic record to infer surface slip history is complicated by slip variability, measurement uncertainty, and modification of offset features in the landscape. To investigate how well geomorphic data record surface slip, we use offset measurements from recent strike‐slip surface ruptures (n = 39), faults with geomorphic evidence of multiple strike‐slip earthquakes (n = 29), and synthetic slip distributions with added noise (n10,000) to examine the constraints of the geomorphic record and the underlying assumptions of the cumulative offset probability distribution analysis method. We find that the geomorphic record is unlikely to resolve more than two paleo‐slip distributions, except in specific cases with low slip variability, high slip‐per‐event, and semiarid climate. In cases where site‐specific conditions allow for interpretation of more than two earthquakes, lateral extrapolation along a fault is not straightforward because on‐fault displacement and distributed deformation may be spatially variable in each earthquake. We also find that average slip in modern earthquakes is adequately recovered by probability methods, but the reported prevalence of strike‐slip faults with characteristic slip history is not supported by geomorphic data. We also propose updated methods to interpret slip history and construct uncertainty bounds for paleo‐slip distributions.

Exploring Storm Intensities and the Implications on Green Stormwater Infrastructure Design

ABSTRACTThe peak intensity that occurs during a storm event can drive the performance of a green stormwater infrastructure (GSI), which may or may not align with the expected performance of the GSI. Both the peak intensity volume and where it occurs within an event are found to influence the GSI response. The design criteria set the expectation of how well a GSI will manage stormwater within a watershed. The Villanova University bioinfiltration rain garden (BRG) has been monitored since 2003, providing a long hydrological data record that is used to study local and observed rainfall patterns in comparison to design criteria to understand the impact of storm intensity on GSI performance. Intensities for all the storms recorded at the site were assessed at different timesteps and compared to the intensities typically used by the design storm approach in meeting regulatory criteria. There were 1482 storm events analysed and for all timesteps, the values commonly used for meeting design regulations were seen to be well above what was observed at the BRG, with 98% of the storms occurring below these values. Out of the 1482 storms, only 46 storms (3%) had effective durations longer than 10 h and no storm observed had an effective duration longer than 22 h, yet their peak intensities were still below the peak intensity associated with design regulations. This finding highlights the difference in the duration these sites are designed to manage (typically 24 h), in comparison to the ones experienced by the systems. The peak intensity analysis done at the different timesteps shows that for the storms recorded at the BRG, the intensities vary with changing time intervals and events. Of all the assessed events, only two events recorded larger intensities than the regionally specified NOAA C design storm, demonstrating the skewness of the approach. There was no trend in peak rain intensities over the 20‐year rainfall record. This study concludes that due to their dynamic performance, vegetated GSI have a natural resilience to a variety of precipitation patterns and climate changes that may be compromised when designing to a static value set through design storms.

ГІБРИДНИЙ МЕТОД АНАЛІЗУ ТА ІДЕНТИФІКАЦІЇ ШКІДЛИВОГО DOH-ТРАФІКУ

This study addresses the challenges associated with detecting DNS over HTTPS (DoH) traffic, a relatively new protocol that has not been extensively researched. The detection methods discussed include TLS inspection, application logging, and open-source tools such as Zeek and RITA. TLS inspection, which involves decrypting and analyzing traffic, is the most intrusive and requires full control over the network and client configurations. Application logging, such as that available in Mozilla Firefox, necessitates administrative control over client systems, which may be impractical. Zeek analyzes network logs to identify domains accessed without regular DNS queries, while JA3 fingerprints and RITA focus on detecting malicious DoH traffic by analyzing TLS handshake parameters and beacon-like activities, respectively. Additionally, maintaining up-to-date blacklists of IP addresses and SNI values can help identify DoH traffic but faces scalability and evasion challenges. The study highlights that no current solution is entirely feasible, with many requiring excessive administrative overhead or failing to scale effectively. A hybrid approach using machine learning models and traffic analysis, as illustrated by the CIRA-CIC-DoHBrw-2020 dataset, is proposed for more effective detection of malicious DoH traffic. This approach involves the architecture of a two-stage DoH traffic identification system is presented, consisting of three subsystems: traffic, training and evaluation, and identification. They operate sequentially, with the system's function being traffic identification, training, testing, and information processing within the DoH protocol. The next step is process of cross-validation, which involves training a machine learning model K times, with each iteration using a different fold as the validation set, while the remaining folds serve as the training set. The aim of this work: Development and implementation a DoH traffic identification system, which, unlike existing solutions, is based on a hybrid approach to identifying malicious traffic using open tools for detecting encrypted DNS traffic and specialized machine learning models.

Determination of Average Water Demand and Diurnal Consumption Pattern for a Multifamily Apartment Complex in Peshawar

Abstract: Accurately estimating and understanding domestic water demand and consumption patterns is essential for the efficient planning and management of drinking water supply systems. Traditional methods for estimating water demand are labor-intensive and often rely on assumptions. To overcome these limitations, this study employed a novel approach using highprecision data loggers to monitor water levels continuously in rooftop tanks at 5-minute intervals within a residential apartment complex in Peshawar, Pakistan. The findings indicate that per capita water consumption varies between 97 and 209 liters per capita per day (lpcd), with an average consumption of 173 lpcd. This methodology offers enhanced accuracy in quantifying water usage, providing a robust alternative to conventional estimation techniques.

Long‐term monitoring of water table and saltwater intrusion dynamics through time‐lapse transient electromagnetic

AbstractUnderstanding hydrogeological processes often requires continuous measurements, which in many cases are not cost‐efficient. Time‐lapse geophysical studies are invaluable for investigating dynamic subsurface processes. They offer several advantages over traditional borehole‐based monitoring methods, such as being cost‐efficient and non‐intrusive. However, to be effective as an early warning tool, these studies must provide real‐time information on the processes. This is often not feasible with traditional geophysical methods, which typically do not offer real‐time monitoring. In this study, we present a windowed inversion approach for time‐lapse transient electromagnetic (TEM) monitoring. The advantages of this approach include: (i) immediate and continuous results as each time‐step dataset is acquired; (ii) no limitations on the size of the monitoring period or dataset; and (iii) the use of model information from previous inversion results for robust and continuous outcomes. The results from a synthetic study are followed by two field case studies, demonstrating the advantages of the windowed inversion compared to the inversion of the full dataset. We monitored the water table dynamics of a shallow unconfined aquifer over 8 months and the saltwater intrusion in a confined aquifer over 10 months using a monitoring TEM (mTEM) system while collecting nearly continuous daily measurements. With the windowed approach, the water table dynamics was recovered with an accuracy of 10 cm, and the changes in salinity were tracked with results comparable to those of an EC data logger.

Assessment of Bleaching Stress Vulnerability of Lakshadweep Islands using Google Earth Engine (GEE)

Coral reefs, among the Earth’s most diverse and valuable ecosystems, face unprecedented challenges due to climate change. Coral bleaching is a phenomenon wherein corals lose their symbiotic zooxanthellae owing to various stressors, leading to a whitening effect of the coral tissues. In recent decades, climate change has intensified coral bleaching events. Multiple stressors, including elevated Sea Surface Temperature (SST), extreme irradiance levels, and various biotic and abiotic factors trigger bleaching events. Coral bleaching is primarily driven by thermal stress caused by elevated SSTs. Climate change has worsened bleaching’s frequency and intensity. Global bleaching events are often linked to planetary ocean-atmospheric circulation processes such as El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD). This study focused on assessing the vulnerability of coral reefs in the Lakshadweep region of India from 2016 to 2023 using National Oceanic and Atmospheric Administration’s Climate Data Record Optimum Interpolation Sea Surface Temperature (NOAA CDR OISST) daily data. GOOGLE EARTH ENGINE (GEE) is a cloud computing platform which is used to collect and generate the base data for this study. The vulnerability assessment utilized two bleaching indices: SST anomaly and Degree Heating Week (DHW). Analysis of DHW data reveals that 2020 experienced the highest SST anomaly residence time due to IOD event of 2019, as compared to 2016 and 2023 which are known El Niño years. All Lakshadweep islands exhibited vulnerability, although in varying degrees across different areas. Based on the magnitude, intensity, and frequency of bleaching stress, the islands are categorized into different categories of vulnerability. This study identifies Baliyapaniyam, Cheriyam-Kalpeni and Suhelipar reefs as very highly vulnerable reefs in Lakshadweep. This study highlights the urgent need for monitoring and management measures to mitigate the impacts of climate change on coral reef ecosystems using spatial vulnerability patterns.

Near-real-time atmospheric and oceanic science products of Himawari-8 and Himawari-9 geostationary satellites over the South China Sea

Abstract. The initial release of near-real-time (NRT) atmospheric and oceanic science products from Japanese Himawari-8 and Himawari-9 (H8/9) geostationary (GEO) satellites over the South China Sea (SCS) was unveiled in 2024. The primary objective behind crafting these NRT H8/9 satellite products is to facilitate weather and marine environment monitoring, enhance maritime security, and aid ocean navigation, among other purposes. As part of this investigation, a novel NRT data processing system was devised to generate a variety of regional H8/9 GEO satellite science products within a resolution of 10 min and a gridded resolution of 0.05° × 0.05° from 3 November 2022 to the present. This algorithm system was built upon the preceding Fengyun (FY) geostationary satellite algorithm test bed (FYGAT), which was the prototype of the FY-4 GEO meteorological satellite science product operational processing system. These regional H8/9 GEO satellite science products encompass a range of crucial data such as cloud mask, fraction, height, phase, optical, and microphysical properties; layered precipitable water; and sea surface temperature. We subjected these products to rigorous evaluations against high-quality analogous satellite products and reanalysis data spanning 1 year in 2023. The validations underscore a strong consistency between the H8/9 GEO satellite atmospheric and oceanic science products over the SCS and the referenced products. Nevertheless, slight discrepancies in these satellite science products were identified, primarily stemming from variations in sensor/dataset characteristics, retrieval algorithms, and geometric conditions. These outcomes demonstrate the suitability of the first edition of NRT atmospheric and oceanic science products of H8/9 satellites over the SCS in supporting the intended quantitative applications. This NRT GEO satellite data record is publicly accessible through the File Transfer Protocol (FTP) provided by the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) in China. Free access to the dataset is possible via https://doi.org/10.6084/m9.figshare.25015853 (Liu et al., 2024).

Intelligence data acquisition based on embedded system in Chinese cuisine cooker (CCICR V1.0)

The Chinese cooking process, intricate and diverse, encompasses a wide range of dishes, generating substantial data that often poses significant challenges for automation. To address these challenges, this paper introduces a versatile, highly reliable, user-friendly, and intelligent data recorder specifically designed for Chinese cuisine-the Chinese Cuisine Intelligent Cooker Recorder-V1.0 (CCICR-V1.0). This device is intended to record data generated during the cooking process and provide essential support for intelligent cooking robot systems. The core of CCICR-V1.0 is a STM32 microcontroller, equipped with sensors that monitor temperature, pressure, and attitude. These sensors facilitate the intelligent collection of data related to dishes, ingredients, and the movements of cookware throughout the cooking process. To ensure efficient data acquisition and storage, CCICR-V1.0 employs a multi-task data buffering storage method that effectively allocates CPU resources. NAND Flash is used as the storage medium, guaranteeing secure storage, management, and preservation of high-frequency, multi-channel data. Experimental results demonstrate the effectiveness of CCICR-V1.0 in achieving multi-channel, high-frequency data acquisition and storage in complex environments. This leads to an increase in automation efficiency of 89.9%. The weighing module demonstrates a maximum relative error of only 0.288%, while the attitude sensor experiment shows an attitude information error of just 0.22°C. Furthermore, the non-contact infrared temperature measurement module exhibits impressive performance with a maximum absolute error of 0.258∘C\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^\\circ C$$\\end{document} and a maximum relative error of 0.88%. Notably, this measurement module has achieved a cost reduction of approximately 91.7% and an accuracy improvement of around 40%. Additionally, the response time of CCICR-V1.0 is less than 3 ms. With its ability to effectively capture data related to Chinese cooking, CCICR-V1.0 holds commercial value for widespread adoption and application in the field of intelligent cooking.