Dynamic Data Analysis Research Articles

Managing Adverse Events in Clinical Trials with R and Shiny

Adverse events (AEs) are critical occurrences that can significantly impact patient safety and the overall success of clinical trials. Effective management of AEs is essential for ensuring regulatory compliance and safeguarding participants' well-being. This paper explores the integration of R and Shiny as innovative tools for the real-time monitoring, visualization, and analysis of AEs in clinical trials. By leveraging R’s robust statistical capabilities and Shiny’s interactive interface, researchers can enhance data management processes, facilitate informed decision- making, and foster collaboration among stakeholders. A case study is presented to illustrate the practical application of R Shiny in tracking AEs, demonstrating its effectiveness in improving trial outcomes and ensuring patient safety. The findings emphasize the need for adopting dynamic data analysis tools to optimize adverse event management in the evolving landscape of clinical research. KEYWORDS Adverse Events, Clinical Trials, R, Shiny, Data Management, Visualization, Real-Time Monitoring, Patient Safety.

Data Analysis of Dynamics in Protein Solutions Using Quasi-Elastic Neutron Scattering─Important Insights from Polarized Neutrons.

Protein dynamics play a vital role in biology. Quasi elastic neutron scattering (QENS) is an ideal method to access these dynamics. To isolate protein dynamics, it is important to separate the signal of the buffer and the protein. Normally data analysis is performed based on the assumption that the scattering spectrum is incoherent. To observe the full range of protein dynamics, it is necessary to perform the experiments in solution. This solution is usually a fully deuterated buffer, while the protein remains protonated. It is generally assumed that subtracting the buffer contribution removes all coherent signal from the measured spectrum, and the rest can be considered as purely incoherent. Up until recently, there was no way to experimentally verify this assumption. Polarized QENS experiments allow for the coherent and incoherent contributions to be separated. By comparing the results from the polarized QENS experiment and the standard analysis method from unpolarized QENS, we are thus able to check this assumption experimentally. We show that the pure incoherent spectrum obtained from polarization analysis does not match the results for unpolarized QENS. We discuss the implications of this for data analysis and possible solutions to the problem, as well as mitigation techniques for standard QENS.

Difference in hematocrit and plasma albumin levels as an early biomarker of severity and prognosis in patients with severe fever and thrombocytopenia syndrome.

Severe fever with thrombocytopenia syndrome (SFTS) is a widespread infectious disease with high mortality. Hence, identifying valuable biomarkers for detecting the early changes in SFTS is crucial. In this study, we investigated the relationship between the difference in hematocrit (HCT) and serum albumin (ALB) levels (HCT-ALB) and the prognosis of patients with SFTS virus infection. After excluding the patients who did not meet the SFTS diagnostic criteria, those with SFTS from the First Affiliated Hospital of Wannan Medical College were divided into a fatal and Nonfatal group based on their disease prognosis. A dynamic analysis of the daily laboratory data was conducted for 14 days following SFTS onset. A receiver operating characteristic (ROC) curve was used to evaluate the predictive value of HCT-ALB. Another sample of patients with SFTS admitted to the First Affiliated Hospital of Nanjing Medical University was utilized to verify the study conclusions. A total of 158 patients with SFTS were included. Among them, 126 patients were categorized in the Nonfatal group and 32 in the fatal group, leading to a mortality rate of 20.25% (32/158). Univariate analysis of the laboratory test findings and ROC curve analysis showed that alanine aminotransferase (ALT), aspartate aminotransferase (AST), HCT-ALB, and lactate dehydrogenase (LDH) had a relatively better ability to discriminate the disease condition of the patients with SFTS. Moreover, HCT-ALB served as a predictor of SFTS prognosis. Additionally, an area under the ROC curve (AUC) of 0.777 and a critical HCT-ALB value of 4.75 on day 7 were associated with a sensitivity of 83.3% and a specificity of 73.9%. On day 8 (AUC = 0.882), the critical value of HCT-ALB was 9.25, while the sensitivity was 100% and specificity was 76.5%. Further verification based on the data of 91 patients with SFTS admitted to the First Affiliated Hospital of Nanjing Medical University demonstrated a mortality rate of 51% (24/47) among those with HCT-ALB values >4.75 on day 7 of the disease course, highlighting the potential of the HCT-ALB value of >4.75 for predicting SFTS prognosis. High HCT-ALB values are closely related to the mortality of patients with SFTS. HCT-ALB is a sensitive and independent predictor of early disease in patients with SFTS.

Pupillary dilation response to the auditory food words in adolescents with obesity without binge eating disorder

Childhood obesity is a growing global public health problem. Studies suggest that environmental cues contribute to developing and maintaining obesity. We aimed to evaluate pupillary changes to auditory food words vs. nonfood words and to conduct a dynamic temporal analysis of pupil size changes in adolescents with obesity without binge eating disorder by comparing healthy-weight adolescents. In this study, a total of 63 adolescents aged 12–18 years (n = 32, obesity group (OG); n = 31, control group (CG)) were included. In an auditory paradigm, participants were presented with a series of high and low-calorie food and nonfood words. A binocular remote eye-tracking device was used to measure pupil diameter. Generalized additive mixed models (GAMMs) were used for dynamic temporal analysis of pupillometry data. The results of GAMM analysis indicated that CG had larger pupil dilation than the OG while listening to auditory food words. CG had larger pupil dilation in food words than in nonfood words. However, the OG had a similar pupillary response in food and nonfood words. Pupil dilation response to higher-calorie foods was extended over the later stages of the time period (after 2000 ms) in the OG. In summary, our findings indicated that individuals with obesity had lower pupil dilation to auditory food words compared to normal-weight peers. Adolescents with obesity had prolonged pupillary dilation in higher calories of food words. The individual psychological factors affecting the dynamic changes of pupil responses to food cues in adolescents with obesity should be examined in further studies.

A feasibility study on predicting cow calving time over 40 h in advance using heart rate and financial technical indicators

In dairy farming, the uncertainty of cow calving date often imposes waiting costs for days on farmers. Improving the accuracy of calving date prediction would mitigate these costs, specifically before a few days of the event. We monitored and analyzed the heart rate patterns of eight pregnant cows in the days leading up to calving using a dedicated monitoring device. We decomposed the heart rate data into three distinct components: trend, daily cycle, and the remainder, and discovered that the heart rate trend exhibited a sharp decline more than 40 h before the calving event via the trend turning point. To detect the turning point, we applied common financial technical indicators traditionally used to identify turning points of asset prices in trading markets for the extracted heart rate trend. This study remains a feasibility study because of the limited observations, but it indicates that these indicators can effectively capture the trend’s turning point in real time, offering a promising approach for enhanced calving prediction. In addition to discussing the practical implications for cow management, we also contemplate the broader utility of these technical indicators in the context of various dynamic scientific data analyses.

Synthesis and Evaluation of 18F-Labeled Phenylpiperazine-like Dopamine D3 Receptor Radioligands for Positron Emission Tomography Imaging.

The dopamine D3 receptor (D3R) is important in the pathophysiology of various neuropsychiatric disorders, such as depression, bipolar disorder, schizophrenia, drug addiction, and Parkinson's disease. Positron emission tomography (PET) with innovative radioligands provides an opportunity to assess D3R in vivo and to elucidate D3R-related disease mechanisms. Herein, we present the synthesis of eight 18F-labeled phenylpiperazine-like D3R-selective radioligands possessing good radiochemical purity (>97%), in vitro stability (>95%), and befitting lipophilicity. Based on in vitro binding assays and static microPET studies, the phenylpiperazine-like radioligands [18F]FBPC01 and [18F]FBPC03 were chosen as lead radioligands targeting D3R. Molecular docking further elucidated their binding mechanism. Radiolabeling conditions were optimized and then applied to an automated radiolabeling process, affording products with high specific activity (>112 GBq/μmol). Dynamic rat PET study demonstrated the specific binding of [18F]FBPC01 and [18F]FBPC03 to D3R in the brain ventricles and the pituitary gland. Validated by dynamic PET data analysis, biodistribution study, and metabolism analysis, [18F]FBPC03 exhibited the highest PET signal-to-noise ratio, good D3R-specific binding in the brain ventricles and pituitary gland of rats with few off-target binding, negligible defluorination, and stable brain metabolism, which indicated that [18F]FBPC03 was a promising D3R radioligand.

Free Energy Evaluation of Cavity Formation in Metastable Liquid Based on Stochastic Thermodynamics.

Nucleation is a fundamental and general process at the initial stage of first-order phase transition. Although various models based on the classical nucleation theory (CNT) have been proposed to explain the energetics and kinetics of nucleation, detailed understanding at nanoscale is still required. Here, in view of the homogeneous bubble nucleation, we focus on cavity formation, in which evaluation of the size dependence of free energy change is the key issue. We propose the application of a formula in stochastic thermodynamics, the Jarzynski equality, for data analysis of molecular dynamics (MD) simulation to evaluate the free energy of cavity formation. As a test case, we performed a series of MD simulations with a Lennard-Jones (LJ) fluid system. By applying an external spherical force field to equilibrated LJ liquid, we evaluated the free energy change during cavity growth as the Jarzynski's ensemble average of required works. A fairly smooth free energy curve was obtained as a function of bubble radius in metastable liquid of mildly negative pressure conditions.

Steepness-fragility insights from the temperature derivative analysis of dielectric data of a highly nonsymmetric liquid crystal dimer.

A comprehensive dynamic analysis of the dielectric relaxation-time data across a broad temperature range for both isotropic and nematic phases has been conducted on the CBO3O.Py liquid crystal dimer, the shorter chain-length compound within the highly nonsymmetric pyrene-based series of liquid crystal dimers (CBOnO.Py, with n ranging from 3 to 11). It was known from another previous study that in the nematic phase, three different relaxation processes contribute to the complex dielectric permittivity depending on the orientation of the alignment axis with respect to the probing electric field direction. The temperature-derivative analysis of the relaxation-time data using different analytic functions reveals that the critical-like description, through the dynamic scaling model, best portrays the relaxation-time data in the nematic phase as the system approaches the glass transition. A single glass transition temperature is obtained which is consistent with thermal stimulated depolarization currents experimental determinations published elsewhere. From temperature-dependent steepness index m(T), the activation-critical model is also considered as a more general analytic function from which the dynamic scaling model is a terminal approximation. Additionally, the critical-like parametrization provides insight into obtaining a universal description of the temperature-dependent steepness index m(T), for all liquid crystal compounds belonging to symmetry-selected glass formers, such as rodlike liquid crystal monomers.

Calcite e-twins as a tectonic indicator, paleo stress pattern and structural evolution of the Zagros hinterland, SE Iran

Through the examination of calcite twins, this research outlines the tectonic development and paleo stress patterns of the Paleozoic Routshon complex situated in the southeastern segment of the Sanandaj–Sirjan zone, a hinterland region of the Zagros orogeny in southeastern Iran. The study of orogenic phase indicates that the deformation event affecting the southern sector of the Sanandaj-Sirjan zone aligns with the Cimmerian orogenic phase of the Late Triassic period. A variety of structural features at both map and outcrop scales highlight the importance of slip partitioning in the structural evolution of this region, driven by inclined transpression. Observations suggest that the deformation related to contractional components includes steeply to moderately plunging folds, dip-slip domain deformation primarily involving thrusts, and ongoing deformation by strike-slip component motion, which results in thrust-related ductile shear zones. The analysis of calcite c-axis fabrics from mylonite samples obtained from these shear zones indicates a low-temperature monoclinic pattern of non-coaxial deformation. This deformation type underscores the impact of the strike-slip component in the development of progressive simple shear within thrust-related shear zones in this segment of the Sanandaj-Sirjan zone. Dynamic analysis of c-axis fabric data reveals a NE-SW orientation for the principal compressive axes (σ1) in this area. This direction, corroborated by additional data such as fault surface, GPS, and earthquake focal mechanism data, confirms that the orientation of the compressive axes (σ1) has remained consistent from the Late Triassic to the present.

Revealing static and dynamic biomarkers from postprandial metabolomics data through coupled matrix and tensor factorizations.

Longitudinal metabolomics data from a meal challenge test contains both fasting and dynamic signals, that may be related to metabolic health and diseases. Recent work has explored the multiway structure of time-resolved metabolomics data by arranging it as a three-way array with modes: subjects, metabolites, and time. The analysis of such dynamic data (where the fasting data is subtracted from postprandial states) reveals dynamic markers of various phenotypes, and differences between fasting and dynamic states. However, there is still limited success in terms of extracting static and dynamic biomarkers for the same subject stratifications. Through joint analysis of fasting and dynamic metabolomics data, our goal is to capture static and dynamic biomarkers of a phenotype for the same subject stratifications providing a complete picture, that will be more effective for precision health. We jointly analyze fasting and dynamic metabolomics data collected during a meal challenge test from the COPSAC cohort using coupled matrix and tensor factorizations (CMTF), where the dynamic data (subjects by metabolites by time) is coupled with the fasting data (subjects by metabolites) in the subjects mode. The proposed data fusion approach extracts shared subject stratifications in terms of BMI (body mass index) from fasting and dynamic signals as well as the static and dynamic metabolic biomarker patterns corresponding to those stratifications. Specifically, we observe a subject stratification showing the positive association with all fasting VLDLs and higher BMI. For the same subject stratification, a subset of dynamic VLDLs (mainly the smaller sizes) correlates negatively with higher BMI. Higher correlations of the subject quantifications with the phenotype of interest are observed using such a data fusion approach compared to individual analyses of the fasting and postprandial state. The CMTF-based approach provides a complete picture of static and dynamic biomarkers for the same subject stratifications-when markers are present in both fasting and dynamic states.

Laplace-Logistic Unit Distribution with Application in Dynamic and Regression Analysis

This manuscript presents a new two-parameter unit stochastic distribution, obtained by transforming the Laplace distribution, using a generalized logistic map, into a unit interval. The distribution thus obtained is named the Laplace-logistic unit (abbreviated LLU) distribution, and its basic stochastic properties are examined in detail. Also, the procedure for estimating parameters based on quantiles is provided, along with the asymptotic properties of the obtained estimates and the appropriate numerical simulation study. Finally, the application of the LLU distribution in dynamic and regression analysis of real-world data with accentuated “peaks” and “fat” tails is also discussed.

Implementation and research of gesture recognition in HCI field

Since the first Neural Network computer was made by Marvin Minsky and his schoolmates in 1951, the development of artificial intelligence (AI)has undergone various and huge changes. It generally evolves into a field that has infinite possibilities and a major branch of computer science that can not be ignored. With the trend of computerization and the rapid evolution of the Internet of Things (IoT), gesture recognition emerged. Various prototypes are blossoming in the laboratories, and some of them have become certain products that have practical applications in later days. At the same time, more and more deep learning technology has been applied to gesture recognition systems that greatly improve the quality of the service. The purpose of this review is to summarize and analyze the existing algorithms of Dynamic Gesture Recognition systems, which have several different methods that are based on multiple signal extractors. This study focuses on the 3D Convolutional Neural Network(CNN), which plays an important role in the Dynamic Gesture Recognition optimization algorithm and data analysis algorithm. In this paper, we reviewed past papers in the gesture recognition field, which include the sEMG method, microwave method, and vision recognition method. During the process of data collecting and summarizing the past papers, we mainly focused on the accuracy differences between recognition methods and the efficiency differences in data processing algorithms. (CNN-based, LSTM based, etc.) Then, we analyzed the main difficulties and challenges of these methods, which are briefly listed in the Introduction part. Data processing algorithms are also being studied, and a horizontal comparison between CNN-based, LSTM-based, and transformer-based algorithms is also being made. Besides this, for those problems that already have a reliable solution, we also summarized the possible solutions and listed them out. We found that the gesture recognition system has already been systematically studied and is partly used in some fields. However, the algorithm and modeling methods can still be optimized and it also needs further study to be more widely used.

Dynamic network analysis of electrophysiological task data

Abstract An important approach for studying the human brain is to use functional neuroimaging combined with a task. In electrophysiological data, this often involves a time-frequency analysis, in which recorded brain activity is time-frequency transformed and epoched around task events of interest, followed by trial-averaging of the power. While this simple approach can reveal fast oscillatory dynamics, the brain regions are analysed one at a time. This causes difficulties for interpretation and a debilitating number of multiple comparisons. In addition, it is now recognised that the brain responds to tasks through the coordinated activity of networks of brain areas. As such, techniques that take a whole-brain network perspective are needed. Here, we show how the oscillatory task responses from conventional time-frequency approaches can be represented more parsimoniously at the network level using two state-of-the-art methods: the HMM (Hidden Markov Model) and DyNeMo (Dynamic Network Modes). Both methods reveal frequency-resolved networks of oscillatory activity with millisecond resolution. Comparing DyNeMo, HMM, and traditional oscillatory response analysis, we show DyNeMo can identify task activations/deactivations that the other approaches fail to detect. DyNeMo offers a powerful new method for analysing task data from the perspective of dynamic brain networks.

Assessing dynamic co-movement of news based uncertainty indices and distance-to -default of global FinTech firms

Contemporary literature does not offer evidence on the causal relationship of default risk and investor sentiments for FinTech sector. We bridge this gap by assessing the dynamic co-movement between Distance-to-Default of leading global FinTech firms and three uncertainty indices namely Twitter Market Uncertainty Index-ENG, Twitter Economic Uncertainty Index-ENG, and US daily news-based Economic Policy. The sample period is from 07 April 2016–30 June 2021. The estimations are performed using Wavelet based analytical framework. The results reveal high dynamic co-movement of Twitter based uncertainty indices with Distance-to-Default of FinTech firms. However, there is very little co-movement between economic policy uncertainty and FinTech firms’ stability. The novelty of this work lies in the utilization of National University of Singapore’s Distance-to-Default measure, which is available in daily frequency and capable of performing dynamic data analysis. Our results offer maiden evidence of linkage of default risk and news based uncertainty indices.

Machine Learning Classification of Local Environments in Molecular Crystals.

Identifying local structural motifs and packing patterns of molecular solids is a challenging task for both simulation and experiment. We demonstrate two novel approaches to characterize local environments in different polymorphs of molecular crystals using learning models that employ either flexibly learned or handcrafted molecular representations. In the first case, we follow our earlier work on graph learning in molecular crystals, deploying an atomistic graph convolutional network combined with molecule-wise aggregation to enable per-molecule environmental classification. For the second model, we develop a new set of descriptors based on symmetry functions combined with a point-vector representation of the molecules, encoding information about the positions and relative orientations of the molecule. We demonstrate very high classification accuracy for both approaches on urea and nicotinamide crystal polymorphs and practical applications to the analysis of dynamical trajectory data for nanocrystals and solid-solid interfaces. Both architectures are applicable to a wide range of molecules and diverse topologies, providing an essential step in the exploration of complex condensed matter phenomena.

Ergonomic analysis in nuclear decommissioning: A comprehensive path planning strategy for workers well-being

Recent studies on path planning in nuclear decommissioning focus on efficient algorithms, often overlooking workers’ physical and cognitive well-being. However, there is a need to consider the effect of the paths recommended by algorithms on workers’ health to prevent work-related musculoskeletal disorders. This study introduces a computational dynamic body map tool designed to assess and monitor musculoskeletal disorders (MSDs) among workers in high-risk decommissioning environments. Utilizing color-coded body maps, the study identifies key ergonomic risk factors and their impact on different body parts. The strategy begins by identifying job tasks requiring ergonomic assessment, distilling the tasks into different activities and the time spent on each activity, and using the result to dynamically compute the average pain severity experienced by workers across different body parts. Subsequent body mapping, hazard surveys, and structured interviews are used to rank workplace risks and worker-specific risk factors. The major contribution of this work is the development of a computational dynamic tool to regularly track worker body maps regularly, ensuring optimal performance. To evaluate the proposed strategy, the study utilized data from a 35-response online MSD questionnaire for safety professionals. To demonstrate the methodology across diverse roles, 10 worker examples were analyzed, aligning with 5 key IAEA nuclear decommissioning categories. Analysis of the computational dynamic body map data reveals specific pain distributions among workers. The most severe pain occurs in the lower back (13.08 %), followed by the right wrist/hand (10 %), the right shoulder, the left elbow (8.46 % each), the left shoulder, the upper back (7.69 % each), the neck and the left wrist/hand (6.92 % each). The findings further reveal that the 36–45 years age category experiences the most pain. In addition, awkward posture was found in the study to be the most likely ergonomic risk factor affecting workers. The results show that incorporating ergonomics into path planning aids risk-informed decision-making for ergonomic interventions and continuous monitoring to maintain a safe and healthier workplace. Integrating the tool presented in this work with a conventional path-planning algorithm provides a comprehensive path-planning strategy for nuclear decommissioning.

Adaptive Clinical Trials: Implementing Dynamic Data Analysis in R Shiny

Adaptive Clinical Trials: Implementing Dynamic Data Analysis in R Shiny

An acoustic exploration of sibilant contrasts and sibilant merger in Mixean Basque

Abstract This exploratory study investigates sibilants in Mixean Low Navarrese, an endangered variety of Basque. This variety has been described with ten different contrastive sibilants: /s̻, s̺, ʃ, t͡s̻, t͡s̺, t͡ʃ, z̻, z̺, ʒ, d͡z̺/. The objective of the paper is to (a) provide a detailed description of the acoustics of Mixean sibilants, and (b) elucidate whether ten categories can be proposed based only on acoustical data, or whether fewer categories should be considered. The study is based on free-conversation data of ten subjects (three females, seven males) aged between 80 and 85 years. We analyze metrics reflecting the place of articulation (spectral moments, and especially the center of gravity (CoG)), including also the temporal dynamics of CoG (using the discrete cosine transform of CoG measurements of nine intervals of each phone). We also explore the acoustic correlates of the contrasts between (a) voiced and voiceless sounds and (b) fricative and affricate sounds. The results show that only seven categories can be proposed based on acoustic measurements. The lamino-alveolar series reliably contrasts with the rest, but the distinction does not hold between the apico-alveolar and the postalveolar series. We found minimal differences in the analysis of dynamic data, and none in the static analysis.

An inversion-based clustering approach for complex clusters

BackgroundThe choice of an appropriate similarity measure plays a pivotal role in the effectiveness of clustering algorithms. However, many conventional measures rely solely on feature values to evaluate the similarity between objects to be clustered. Furthermore, the assumption of feature independence, while valid in certain scenarios, does not hold true for all real-world problems. Hence, considering alternative similarity measures that account for inter-dependencies among features can enhance the effectiveness of clustering in various applications.MethodsIn this paper, we present the Inv measure, a novel similarity measure founded on the concept of inversion. The Inv measure considers the significance of features, the values of all object features, and the feature values of other objects, leading to a comprehensive and precise evaluation of similarity. To assess the performance of our proposed clustering approach that incorporates the Inv measure, we evaluate it on simulated data using the adjusted Rand index.ResultsThe simulation results strongly indicate that inversion-based clustering outperforms other methods in scenarios where clusters are complex, i.e., apparently highly overlapped. This showcases the practicality and effectiveness of the proposed approach, making it a valuable choice for applications that involve complex clusters across various domains.ConclusionsThe inversion-based clustering approach may hold significant value in the healthcare industry, offering possible benefits in tasks like hospital ranking, treatment improvement, and high-risk patient identification. In social media analysis, it may prove valuable for trend detection, sentiment analysis, and user profiling. E-commerce may be able to utilize the approach for product recommendation and customer segmentation. The manufacturing sector may benefit from improved quality control, process optimization, and predictive maintenance. Additionally, the approach may be applied to traffic management and fleet optimization in the transportation domain. Its versatility and effectiveness make it a promising solution for diverse fields, providing valuable insights and optimization opportunities for complex and dynamic data analysis tasks.

Research on Inventory Control Method Based on Demand Response of Power Big Data

The supply chain functions as a complex web, interconnecting various stakeholders such as suppliers, manufacturers, wholesalers, retailers, and ultimately, end consumers. Central to effective supply chain management is the meticulous handling of inventory, a critical factor influencing both cost efficiency and service excellence throughout the entire network. The management of inventory within this context extends beyond the confines of individual enterprises, bearing significance across the entirety of the supply chain. Consequently, achieving optimal performance necessitates a cohesive, holistic approach to management, aligning with the overarching objectives of the system. Through dynamic data analysis of multiple types of power materials, a dynamic inventory control model for power materials is constructed to achieve optimal adjustment of inventory management. Ultimately, a multi-granularity inventory control method based on big data analysis of power warehousing is constructed, which effectively improves inventory management efficiency and reduces logistics management costs for enterprises. Through big data analysis of power material warehousing, the characteristics of power material demand are excavated, and a classification method for power material demand is constructed to achieve an overall inventory control strategy for power materials. The implementation results show that the controlled inventory can better meet the changing demand, thereby improving inventory management efficiency. The multi-granularity inventory control method based on big data mining of warehousing combines inventory and multi-objective optimization theories, proves the applicability and feasibility of the proposed method, effectively improves inventory management efficiency, reduces logistics management costs for enterprises, and provides practical guidance and decision-making reference for improving the intensive management level of power production and maintenance materials.