Utility Of Data Research Articles

The mediating effect of data literacy competence in the relationship between data governance and data-driven culture

PurposeThis research investigates the critical role of data governance (DG) in shaping a data-driven culture (DDC) within organizations, recognizing the transformative potential of data utilization for efficiency, opportunities, and productivity. The study delves into the influence of DG on DDC, emphasizing the mediating effect of data literacy (DL).Design/methodology/approachThe study empirically assesses 125 experienced managers in Indonesian public service sector organizations using a quantitative approach. Structural Equation Modeling (SEM) analysis was chosen to examine the impact of DG on DDC and the mediating effects of DL on this relationship.FindingsThe findings highlight that both DG and DL serve as antecedents to DDC, with DL identified as a crucial mediator, explaining a significant portion of the effects between DG and DDC.Research limitations/implicationsBeyond unveiling these relationships, the study discusses practical implications for organizational leaders and managers, emphasizing the need for effective policies and strategies in data-driven decision-making.Originality/valueThis research fills an important research gap by introducing an original model and providing empirical evidence on the dynamic interplay between DG, DL, and DDC, contributing to the evolving landscape of data-driven organizational cultures.

Inspection Robot Navigation Based on Improved TD3 Algorithm

The swift advancements in robotics have rendered navigation an essential task for mobile robots. While map-based navigation methods depend on global environmental maps for decision-making, their efficacy in unfamiliar or dynamic settings falls short. Current deep reinforcement learning navigation strategies can navigate successfully without pre-existing map data, yet they grapple with issues like inefficient training, slow convergence, and infrequent rewards. To tackle these challenges, this study introduces an improved two-delay depth deterministic policy gradient algorithm (LP-TD3) for local planning navigation. Initially, the integration of the long–short-term memory (LSTM) module with the Prioritized Experience Re-play (PER) mechanism into the existing TD3 framework was performed to optimize training and improve the efficiency of experience data utilization. Furthermore, the incorporation of an Intrinsic Curiosity Module (ICM) merges intrinsic with extrinsic rewards to tackle sparse reward problems and enhance exploratory behavior. Experimental evaluations using ROS and Gazebo simulators demonstrate that the proposed method outperforms the original on various performance metrics.

Augmenting input-output system under the curse of dimensionality: AHP-RAS integration and algorithm simulation

ABSTRACT In the estimation of input-output tables, one frequently encountered challenge is the aggregation of sectors in initial tables, which complicates the estimation of more detailed tables due to missing data—a phenomenon referred to as the “curse of dimensionality” when augmenting from lower to higher dimensions. We propose an AHP-RAS method to address the substantial data deficiencies encountered in the expansion of input-output tables. The method applies the Analytical Hierarchy Process (AHP) to transform the subjective relative importance of sectors into input coefficients using Pairwise Comparison Matrices (PCM) and eigenvector methods. Subsequently, it incorporates the RAS approach to supplement objective data, thereby integrating subjective judgement and objective information to tackle the curse of dimensionality. Unlike traditional AHP methods, this study employs binary integer programming to decompose large-scale matrices into subsets, making the AHP technique applicable to input-output tables with a broader range of sectors. We employ Monte Carlo simulation techniques to evaluate the robustness of the proposed methodology, specifically assessing the impacts of subjective judgement, objective data utilization, and the variation in sector numbers. Overall, the method overcomes the curse of dimensionality, yielding coefficients within an acceptable error margin, thereby demonstrating a significant advancement in the field of input-output analysis.

Evaluating a Novel Approach to Detect the Vertical Structure of Insect Damage in Trees Using Multispectral and Three-Dimensional Data from Drone Imagery in the Northern Rocky Mountains, USA

Remote sensing is a well-established tool for detecting forest disturbances. The increased availability of uncrewed aerial systems (drones) and advances in computer algorithms have prompted numerous studies of forest insects using drones. To date, most studies have used height information from three-dimensional (3D) point clouds to segment individual trees and two-dimensional multispectral images to identify tree damage. Here, we describe a novel approach to classifying the multispectral reflectances assigned to the 3D point cloud into damaged and healthy classes, retaining the height information for the assessment of the vertical distribution of damage within a tree. Drone images were acquired in a 27-ha study area in the Northern Rocky Mountains that experienced recent damage from insects and then processed to produce a point cloud. Using the multispectral data assigned to the points on the point cloud (based on depth maps from individual multispectral images), a random forest (RF) classification model was developed, which had an overall accuracy (OA) of 98.6%, and when applied across the study area, it classified 77.0% of the points with probabilities greater than 75.0%. Based on the classified points and segmented trees, we developed and evaluated algorithms to separate healthy from damaged trees. For damaged trees, we identified the damage severity of each tree based on the percentages of red and gray points and identified top-kill based on the length of continuous damage from the treetop. Healthy and damaged trees were separated with a high accuracy (OA: 93.5%). The remaining damaged trees were separated into different damage severities with moderate accuracy (OA: 70.1%), consistent with the accuracies reported in similar studies. A subsequent algorithm identified top-kill on damaged trees with a high accuracy (OA: 91.8%). The damage severity algorithm classified most trees in the study area as healthy (78.3%), and most of the damaged trees in the study area exhibited some amount of top-kill (78.9%). Aggregating tree-level damage metrics to 30 m grid cells revealed several hot spots of damage and severe top-kill across the study area, illustrating the potential of this methodology to integrate with data products from space-based remote sensing platforms such as Landsat. Our results demonstrate the utility of drone-collected data for monitoring the vertical structure of tree damage from forest insects and diseases.

Data science techniques to gain novel insights into quality of care: a scoping review of long-term care for older adults

Background: The increase in powerful computers and technological devices as well as new forms of data analysis such as machine learning have resulted in the widespread availability of data science in healthcare. However, its role in organizations providing long-term care (LTC) for older people LTC for older adults has yet to be systematically synthesized. This analysis provides a state-of-the-art overview of 1) data science techniques that are used with data accumulated in LTC and for what specific purposes and, 2) the results of these techniques in researching the study objectives at hand. Methods: A scoping review based on guidelines of the Joanna Briggs Institute. PubMed and Cumulative Index to Nursing and Allied Health Literature (CINAHL) were searched using keywords related to data science techniques and LTC. The screening and selection process was carried out by two authors and was not limited by any research design or publication date. A narrative synthesis was conducted based on the two aims. Results: The search strategy yielded 1,488 studies: 27 studies were included of which the majority were conducted in the US and in a nursing home setting. Text-mining/natural language processing (NLP) and support vector machines (SVMs) were the most deployed methods; accuracy was the most used metric. These techniques were primarily utilized for researching specific adverse outcomes including the identification of risk factors for falls and the prediction of frailty. All studies concluded that these techniques are valuable for their specific purposes. Discussion: This review reveals the limited use of data science techniques on data accumulated in or by LTC facilities. The low number of included articles in this review indicate the need for strategies aimed at the effective utilization of data with data science techniques and evidence of their practical benefits. There is a need for a wider adoption of these techniques in order to exploit data to their full potential and, consequently, improve the quality of care in LTC by making data-informed decisions.

Exploring Data Collection Priorities of Community Partners in Early Psychosis Care.

Learning health care networks can significantly improve the effectiveness, consistency, and cost-effectiveness of care delivery. As part of a data harmonization process, incorporation of the perspectives of community partners to maximize the relevance and utility of the data is critical. A mixed-methods focus group study was conducted with early psychosis program providers, leadership, service users, and family members to explore their priorities regarding data collection in early psychosis care. Focus group transcripts were analyzed through thematic analysis. Twenty-two focus groups comprising 178 participants were conducted across 10 early psychosis programs. Participants considered functioning, quality of life, recovery, and symptoms of psychosis as key outcomes to assess, although variation by participants' roles was also evident. Participants emphasized the clinical utility of assessing a broad range of predictors of care outcomes, favored a broad conceptualization of the constructs assessed, and indicated a preference for client-reported measures. Participants also emphasized the importance of surveys adopting a recovery-oriented, strengths-based approach. Large-scale aggregation of health care data collected as part of routine care offers opportunities for research and may have a positive impact on care delivery and quality improvement activities. However, these benefits are contingent on the data being both relevant and accessible to those who deliver and receive such care. This study highlights an approach that may inform the development of core assessment batteries used, optimizing the utility of such data for all community partners.

Developing a hybrid model with multiview learning for acoustic classification of Atlantic herring schools

AbstractAdvances in active acoustic technology have outpaced the ability to process and analyze the data in a timely manner. Currently, scientists rely on manual scrutiny or limited automation to translate acoustic backscatter to biologically meaningful metrics useful for fisheries and ecosystem management. The National Oceanic and Atmospheric Administration Northeast Fisheries Science Center has monitored the Atlantic herring population in the Gulf of Maine and Georges Bank since 1999 due to the stocks' important economic and ecological role for the commercial lobster industry. Manual scrutinization to identify Atlantic herring schools from the water column sonar data is time‐consuming and impractical for large‐scale studies. To automate this process, a hybrid model with multiview learning was proposed for automatic Atlantic herring school detection, which consists of two steps: (1) region‐of‐interest (ROI) detection and (2) ROI classification. The ROI detection step was designed to detect school‐like objects, and the ROI classification step was designed to distinguish Atlantic herring schools from other objects. The co‐training algorithm was employed for multiview learning as well as semi‐supervised learning. Within this framework, single‐view vs. multiview learning and supervised vs. semi‐supervised learning were evaluated and compared. Our results showed that multiview learning can improve the performance of the hybrid model in Atlantic herring school detection, and the utilization of unlabeled data is also helpful when the training set is small. The best‐performed model achieved an F1‐score of 0.804. This new framework provides an efficient and effective tool for automatic Atlantic herring school detection.

Enhancement of Genetic Algorithm by J.Zhang Applied to Tour Planning

Following widespread lockdowns, there has been a notable increase in people's desire to travel, leading to longer and more frequent trips. This trend has created a demand for customized itineraries and tour planning. Unfortunately, manual tour planning can be challenging to optimize, time-consuming, and increasingly complex as the number of locations increases. To automate and improve tour planning, optimization methods can be used, as they leverage algorithms to find efficient routes. The Genetic Algorithm (GA), an algorithm that mimics the course of natural evolution, is adept at navigating complex search spaces and finding optimal solutions, making it suitable for solving tour planning challenges. Building upon the work of J. Zhang (2021), this study aims to improve the performance of GA by enhancing the diversity of the population, removing redundant nodes, and reducing the execution time. Two simulators were created, one for each algorithm, to test their performance. The researchers conducted tests on both the existing and enhanced algorithms. This involved the utilization of several test data that contains coordinates of several cities in the Philippines. Based on the results, the enhanced algorithm showed better results compared to the existing algorithm. In conclusion, the enhanced algorithm performed better than the exiting algorithm

The Effect Of Business Size, Business Age, And Education Level on the Use of Accounting Information (among MSMEs in the food sector, Cisewu District, Garut Regency

Assessing the impact of factors such as age is the aim of this study, size, and level of education have from the utilisation of accountancy data in micros, small, and medium enterprises (MSMEs) operating in the food processing industry in Cisewu County, which is categorised as Ghartout District. In particular, the study will concentrate on the utilisation of accounting data in micro, small, and medium-sized enterprises (MSMEs). The data collection process for thats study is carried out using a methodical approach. For the purpose of analysing the data this is was collected and determining the importance on the findings, quantitative data analysis methods have been utilised. These tools include statistical tests. All micro, little, and medium companies (MSMEs) that are especially active in the food processing sector in Cisewu District, which is located in Ghartout Regency, are included in the scope of the study. A census of the complete population, which comprised of 62 different business units, was carried out by the research organisation in order to guarantee that the study would be accurate and comprehensive. In addition, the t-test and the F-test were utilised in order word determine the statistical to significance of the components that were linked with the utilisation of accounting information. Specifically, the results showed that the use of accountancy information by Micro, SME and Medium sized Enterprises (MSMEs) in the food industry in Cisewu District, Garut Regency was significantly and positively influenced by firm size, firm age and firm education level. This was the case in Cisewu District.

GA-FCFNN: A new forecasting method combining feature selection methods and feedforward neural networks using genetic algorithms

In the modern landscape, the fusion of forecasting and computational intelligence empowers organizations to extract invaluable insights from vast datasets, facilitating informed decision-making, swift adaptation to market dynamics, and the enhancement of competitiveness, ultimately fostering innovation. Notably, forecasting has recently garnered significant attention, particularly within the realm of big data. This modern terrain presents a dual challenge: effectively distilling essential insights from complex data and optimizing data utilization. To address this challenge, we introduce a pioneering forecasting model (FM) engineered to excel in both information extraction and data utilization. Our approach commences with a formula designed to compute feature similarity, leveraging trend change data. These similarity metrics, applied to features and their relationships with labels, inform our feature selection process. This method integrates spatial and temporal correlations among features, fostering robust interconnections within the selected subset. Subsequently, we employ a feedforward neural network (FNN) to optimize feature subsets based on test error. While the FM trained with the optimal feature subset exhibits enhanced performance, it still shows some deviation from actual values in initial forecasting results (FORs). To mitigate these discrepancies, we implement a data utilization strategy. We refine the initial FORs by considering the contribution of each feature subset to them, thereby reducing disparities between forecasts and actual values. Model parameters are fine-tuned using a genetic algorithm (GA). Finally, we conduct an experimental analysis, comparing our FM to seven similar models using publicly available datasets. Our experimental results consistently demonstrate that our FM achieves high forecasting performance (FOP).

A Data-Driven Design Approach of an Energy Data Exchange Platform

The current digitalization of energy systems, particularly in urban contexts, lags behind the requirements set by urban energy system modelling, energy management, the development of digital twins, and other data-driven services. Additionally, existing energy data lacks standardized formats and coherence, hindering its full utilization. To address this challenge, this paper introduces a use case-driven design approach tailored to reflect the requirements for data integration and privacy set by city stakeholders. The objective is to design a data exchange platform (DEP) capable of serving a diverse range of use cases. This platform will facilitate the integrated utilization of energy data in energy management systems, digital twin applications, and other activities aimed at extracting valuable insights. These efforts are specifically designed to support decision-making processes of public stakeholders, private investors, and service providers. Consequently, the platform holds potential to attract additional investments in both the clean energy transition and the digitalization of energy infrastructure.

HARNESSING DATA INSIGHTS FOR CRISIS MANAGEMENT IN U.S. PUBLIC HEALTH: LESSONS LEARNED AND FUTURE DIRECTIONS

The effective management of crises in U.S. public health relies heavily on harnessing insights from data. This paper examines the lessons learned from past crises and outlines future directions for leveraging data to enhance crisis management efforts. Drawing on case studies of significant events such as the COVID-19 pandemic and natural disasters, we analyze the role of data in informing decision-making, identifying challenges, and deriving best practices. Despite the advancements in data infrastructure, including real-time monitoring systems and predictive analytics, there remain significant challenges in data collection, integration, and analysis. The paper highlights the importance of improving data governance, investing in technology, and fostering interdisciplinary collaboration to address these challenges. Additionally, considerations such as equity and privacy are crucial in the development and implementation of data-driven strategies. Looking ahead, the paper provides recommendations for enhancing crisis management practices, including the adoption of standardized data protocols, the development of early warning systems, and the promotion of data-driven decision-making processes. By prioritizing data-driven approaches and embracing continuous learning, stakeholders can better prepare for and respond to future public health crises. This paper serves as a roadmap for policymakers, public health officials, and researchers to optimize data utilization in crisis management, ultimately safeguarding the well-being of communities across the United States.
 Keywords:  Data Insights, Crisis Management, in U.S. Public Health, Lessons Learned, Future Directions.

Disablism, racism and the spectre of eugenics in digital welfare

This article explores the historical ties between the digital welfare state and eugenics, highlighting how the use of data infrastructures for classification and governance in the digital era has roots in eugenic data practices and ideas. Through an analysis of three domains of automated decision-making – child welfare, immigration and disability benefits – the article demonstrates how these automated systems perpetuate hierarchical divisions originally shaped by ableist eugenic race science. It underscores the importance of critically engaging with this historical context of data utilisation, emphasising its entanglement with eugenic perspectives on racial, physical and mental superiority, individual and social worth, and the categorisation of data subjects as deserving or undeserving. By engaging with this history, the article provides a deeper understanding of the contemporary digital welfare state, particularly in terms of its discriminatory divisions based on race and disability, which are deeply intertwined.

Integrated Hydrological and Hydrochemical Analysis of Arsenic and Iron Behavior in Waters of a Decommissioned Tin Mine in Ehrenfriedersdorf, Germany

Arsenic contamination poses significant challenges to environmental and public health, with mining activities contributing to its wider distribution. This study investigates the fate of arsenic and iron in mine waters at a decommissioned tin mine, now a visitor mine, located in Ehrenfriedersdorf, Germany, situated in the Free State of Saxony. Despite the general shortage of data, which is common for old mining sites, we explored the complex interplay of climatic conditions, hydrological processes, and arsenic and iron behavior in the mine waters through a comprehensive approach encompassing general site characterization, mine water monitoring, and analysis of local weather data. Over a period of three years, we conducted 14 sampling campaigns, collecting a total of 95 water samples, each consisting of three filtration subsamples, resulting in the analysis of 285 water samples. These samples were collected both aboveground and underground. Aboveground samples included mine outflows, a tailing outflow, and an adjacent creek, while underground sampling points were scattered throughout the mine initially and later focused on the identified “main” mine water system. The chemical data from the analyses were correlated with local climatic water balances to reveal distinctive patterns in arsenic and iron concentrations at various locations within the mine system. Our findings shed light on the hydrological behavior of the mine, helping to elucidate the impact of precipitation and potential evapotranspiration on arsenic and iron concentrations in a tailing outflow, in the flooded mine body, and at the portal of the main dewatering gallery. Our findings emphasize the importance of sustained monitoring and the utilization of local weather data to comprehend metalloid and metal contamination risks in similar mining environments.

Implementation on Large-Scale Mobile Fitness App Usage Analysis for Smart Health

Understanding the determinants behind the exercise habits of urban residents, encompassing aerobic activities and training regimens, is of paramount importance for informed public policy formulation and urban planning. The utilization of mobile fitness app data, which characterizes exercise behaviors concerning time and location, offers a unique avenue for unveiling the key factors influencing physical activity. In this study, we conducted a comprehensive analysis of mobile fitness app data, collecting information from over 14,000 cellular towers and studying the exercise patterns of 4,000 users. Our rigorous investigation reveals that temporal elements, such as the day of the week and the time of day, wield significant influence over human exercise preferences. For instance, data shows that on weekdays, there's a 20% increase in exercise app usage during the evening hours (6 PM - 9 PM), while weekends see a 15% spike in early morning activities (6 AM - 8 AM). Geographical location also emerges as a prominent influencer. Our analysis indicates that individuals residing closer to parks or fitness centers exhibit a 25% higher likelihood of engaging in outdoor workouts. Importantly, personal income levels have surfaced as a fundamental factor significantly shaping exercise habits. Data suggests that individuals with higher incomes allocate, on average, 10% more of their time to structured training regimens. This research contributes essential insights for urban policy development and city planning, aimed at fostering a healthier and more physically active urban populace. Key Words: Tracking nutritional intake, Analyzing meals, Visualizing nutrition data, Daily nutrition overview, Calculating BMI, Image recognition for nutrition, Menu nutritional analysis, Tracking steps, Monitoring physical activity, Providing workout plans, Home workout routines, Gym workout routines, Planning a balanced diet, Diet plans based on BMI, Fitness and wellness application.

Incorporating uncertainty within dynamic interoceptive learning

IntroductionInteroception, the perception of the internal state of the body, has been shown to be closely linked to emotions and mental health. Of particular interest are interoceptive learning processes that capture associations between environmental cues and body signals as a basis for making homeostatically relevant predictions about the future. One method of measuring respiratory interoceptive learning that has shown promising results is the Breathing Learning Task (BLT). While the original BLT required binary predictions regarding the presence or absence of an upcoming inspiratory resistance, here we extended this paradigm to capture continuous measures of prediction (un)certainty.MethodsSixteen healthy participants completed the continuous version of the BLT, where they were asked to predict the likelihood of breathing resistances on a continuous scale from 0.0 to 10.0. In order to explain participants' responses, a Rescorla-Wagner model of associative learning was combined with suitable observation models for continuous or binary predictions, respectively. For validation, we compared both models against corresponding null models and examined the correlation between observed and modeled predictions. The model was additionally extended to test whether learning rates differed according to stimuli valence. Finally, summary measures of prediction certainty as well as model estimates for learning rates were considered against interoceptive and mental health questionnaire measures.ResultsOur results demonstrated that the continuous model fits closely captured participant behavior using empirical data, and the binarised predictions showed excellent replicability compared to previously collected data. However, the model extension indicated that there were no significant differences between learning rates for negative (i.e. breathing resistance) and positive (i.e. no breathing resistance) stimuli. Finally, significant correlations were found between fatigue severity and both prediction certainty and learning rate, as well as between anxiety sensitivity and prediction certainty.DiscussionThese results demonstrate the utility of gathering enriched continuous prediction data in interoceptive learning tasks, and suggest that the updated BLT is a promising paradigm for future investigations into interoceptive learning and potential links to mental health.

Utilizing domain knowledge: Robust machine learning for building energy performance prediction with small, inconsistent datasets

Machine learning (ML) applications often require large datasets, a requirement that can pose a major challenge in fields where data is sparse or inconsistent. To address this issue, we propose a novel approach that combines prior knowledge with data-driven methods to significantly reduce data dependency. This study represents a disentangled system compositionality knowledge by the method of Component-Based Machine Learning (CBML) in the context of energy-efficient building engineering. In this way, CBML incorporates semantic domain knowledge within the structure of a data-driven model. To understand the advantage of CBML, we conducted a case experiment to assess the effectiveness of this knowledge-encoded ML approach in scenarios with sparse data input (1 % - 0.0125 % sampling rate) and several typical ML methods. Our findings reveal three key advantages of this approach over traditional ML methods: 1) It significantly improves the robustness of ML models when dealing with extremely small and inconsistent datasets; 2) It allows for efficient utilization of data from diverse record collections; 3) It can handle incomplete data while maintaining high interpretability and reducing training time. These features offer a promising solution to the challenges associated with deploying data-intensive methods and contribute to more efficient real-world data usage. Additionally, we outline four essential prerequisites to ensure the successful integration of prior knowledge and ML generalization in target scenarios and open-sourced the code and dataset for community reproduction.

Accelerating Drug Development Using Spatial Multi-omics.

Spatial biology approaches enabled by innovations in imaging biomarker platforms and artificial intelligence-enabled data integration and analysis provide an assessment of patient and disease heterogeneity at ever-increasing resolution. The utility of spatial biology data in accelerating drug programs, however, requires balancing exploratory discovery investigations against scalable and clinically applicable spatial biomarker analysis.

Large-scale assessment of physical activity in a population using high-resolution hip-worn accelerometry: the German National Cohort (NAKO)

Large population-based cohort studies utilizing device-based measures of physical activity are crucial to close important research gaps regarding the potential protective effects of physical activity on chronic diseases. The present study details the quality control processes and the derivation of physical activity metrics from 100 Hz accelerometer data collected in the German National Cohort (NAKO). During the 2014 to 2019 baseline assessment, a subsample of NAKO participants wore a triaxial ActiGraph accelerometer on their right hip for seven consecutive days. Auto-calibration, signal feature calculations including Euclidean Norm Minus One (ENMO) and Mean Amplitude Deviation (MAD), identification of non-wear time, and imputation, were conducted using the R package GGIR version 2.10-3. A total of 73,334 participants contributed data for accelerometry analysis, of whom 63,236 provided valid data. The average ENMO was 11.7 ± 3.7 mg (milli gravitational acceleration) and the average MAD was 19.9 ± 6.1 mg. Notably, acceleration summary metrics were higher in men than women and diminished with increasing age. Work generated in the present study will facilitate harmonized analysis, reproducibility, and utilization of NAKO accelerometry data. The NAKO accelerometry dataset represents a valuable asset for physical activity research and will be accessible through a specified application process.

Relationship between preinduction electroencephalogram patterns and propofol sensitivity in adult patients.

To determine the precise induction dose, an objective assessment of individual propofol sensitivity is necessary. This study aimed to investigate whether preinduction electroencephalogram (EEG) data are useful in determining the optimal propofol dose for the induction of general anesthesia in healthy adult patients. Seventy healthy adult patients underwent total intravenous anesthesia (TIVA), and the effect-site target concentration of propofol was observed to measure each individual's propofol requirements for loss of responsiveness. We analyzed preinduction EEG data to assess its relationship with propofol requirements and conducted multiple regression analyses considering various patient-related factors. Patients with higher relative delta power (ρ = 0.47, p < 0.01) and higher absolute delta power (ρ = 0.34, p = 0.01) required a greater amount of propofol for anesthesia induction. In contrast, patients with higher relative beta power (ρ = -0.33, p < 0.01) required less propofol to achieve unresponsiveness. Multiple regression analysis revealed an independent association between relative delta power and propofol requirements. Preinduction EEG, particularly relative delta power, is associated with propofol requirements during the induction of general anesthesia. The utilization of preinduction EEG data may improve the precision of induction dose selection for individuals.