Actual Data Research Articles

Ultra-short-term wind power forecasting based on feature weight analysis and cluster dynamic division

Accurate and reliable ultra-short-term wind power forecasting (WPF) is of great significance to the safe and stable operation of power systems, but the current research is difficult to balance the prediction accuracy, timeliness, and applicability at the same time. Therefore, this paper proposes a ultra-short-term WPF model based on feature weight analysis and cluster dynamic division. The model introduces an analytic hierarchy process and an entropy weight method to analyze the subjective and objective weight of the influencing features of wind power, respectively, then the subjective and objective weight ratio is determined by the quantum particle swarm optimization (QPSO) algorithm to obtain a more reasonable comprehensive weight of each feature. On this basis, it uses the K-Medoids algorithm to dynamically divide the wind power clusters into class regions by cycles. Then, the class region is used as the prediction unit to establish the TCN-BiLSTM model based on temporal convolutional networks (TCN) and bi-directional long short-term memory (BiLSTM) for training and prediction and optimizes the hyper-parameters of the model by the QPSO algorithm. Finally, the regional predictions are summed to obtain the final ultra-short-term power prediction. In addition, in order to verify the performance of the model, the actual operation data of a power field in Xinjiang, China, are selected for the example validation. The results show that the proposed model can ensure the prediction accuracy while minimizing the training time of the model and outperforms other existing methods in terms of prediction accuracy, timeliness, and applicability.

Dynamic Modeling Framework Based on Automatic Identification of Operating Conditions for Sintering Carbon Consumption Prediction

In iron and steel industry, sintering process requires huge carbon consumption. Achieving accurate and dynamic prediction of carbon consumption in this process is of evident significance to protect the environment and raise the economic efficiency of iron and steel industry. This paper develops an original dynamic modeling framework including automatic identification of operating conditions, modeling in different conditions, and dynamic prediction model of sintering carbon consumption. Firstly, an automatic kernel-based fuzzy C-means algorithm is presented for automatic identification of operating conditions. Dynamic relationships between the inputs and output of the model are analyzed by Copula Entropy, and then the relevant production data in each operating condition are determined by just-in-time learning method. Next, broad learning models are established under different operating conditions. Further, dynamic prediction model of sintering carbon consumption is designed, and the prediction error is adopted to quantify performance of the model and as one criteria to determine the update of production database. Finally, results of experiments using actual production data demonstrate the advantage and validity of the developed model compared with some advanced modeling methods. The developed model considers complex characteristics of the sintering process and presents a better dynamic performance and information mining performance.

Calculating Weibull Coefficients Using the Maximum Likelihood Method and Comparing Performance Across Sites

In this study, the compliance of the Weibull Distribution Function (WDF) and actual wind data (WD) from three different locations were investigated. The coefficients of the WDF were calculated using the Maximum Likelihood Method (MLM) in the Adana, Osmaniye, and Hatay regions. The main purpose of this study is to observe the performances of the MLM in determining the coefficients of the WDF in different regions in different years and to examine the success of this method in estimating the mean wind power and speed of the determined regions. The performance of the indicated approach in all three selected locations was evaluated using the Root Mean Square Error (RMSE), Coefficient of Determination (R2), and Mean Percentage Error (MPE). Also wind power densities were estimated for all three regions, which are one of the most essential metrics for estimating a region's wind energy (WE) potential. WDF power densities were estimated and compared to real wind power densities generated from measured WD for three different places. The performance of the method described in this paper was investigated in depth in various places with varying geographic characteristics. In addition, in the same years, the performance of the chosen method was evaluated in detail in three distinct places, and it was seen how geographical factors affected the method's performance.

Applying Hybrid Clustering in Pulsar Candidate Sifting with Multi-modality for FAST Survey

Pulsar search is always the basis of pulsar navigation, gravitational wave detection and other research topics. Currently, the volume of pulsar candidates collected by the Five-hundred-meter Aperture Spherical radio Telescope (FAST) shows an explosive growth rate that has brought challenges for its pulsar candidate filtering system. Particularly, the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification methods. In this study, a multi-modal and semi-supervised learning based on a pulsar candidate sifting algorithm is presented, which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for parallelization. Experiments on both High Time Resolution Universe Survey II (HTRU2) and actual FAST observation data demonstrate that the proposed algorithm could excellently identify pulsars: On HTRU2, the precision and recall rates of its parallel mode reach 0.981 and 0.988 respectively. On FAST data, those of its parallel mode reach 0.891 and 0.961, meanwhile, the running time also significantly decreases with the increment of parallel nodes within limits. Thus, we can conclude that our algorithm could be a feasible idea for large scale pulsar candidate sifting for FAST drift scan observation.

Исследование обводненности добывающих скважин Демьяновского месторождения

The article is devoted to the problem of waterlogging of borehole products during the extraction of hydrocarbon raw materials, which arises for various reasons. This may be due to the geological structure of the field, non-compliance with the operating modes of wells, poor-quality cementing of backwater spaces, etc. Waterlogging leads to undesirable consequences: reduction of oil and gas production and occurrence of emergency situations. The problems of waterlogging of borehole products are solved using various methods: the use of chemical reagents to reduce the viscosity of water, the use of technical solutions for separating oil and water, repair and insulation works, etc. The Demyansk oil field is considered: its geological features, geological exploration, the main indicators of well operation. The analysis of the dynamics of well flooding and a comparison of the design and actual indicators of the development of the Demyanovskoye field as of 01.01.2017 are presented. The actual dynamics of well flooding is also presented, the reasons for the rapid flooding of the production of wells of the Demyanovsky place of birth are considered. As a result of the analysis of the actual data obtained during the operation of the field, it was established that the main reason for the rapid flooding of the reservoir production of the field is the transfer of wells to a mechanized method of operation, which, in turn, leads to an increase in depression on the formation. It is concluded that the forecast indicators of well flooding calculated on the hydrodynamic model differ significantly from the real ones, and attention is also drawn to the need to select the optimal depression when transferring wells to a mechanized production method to prevent rapid product flooding.

A finite failure software reliability model using extended log-logistic distribution

Software reliability engineering has been developed to examine software systems over the years. Non-homogeneous Poisson process (NHPP) models are widely employed for analyzing software reliability data, as they have the ability to describe the expected number of failures over time using the mean value function. In addition, these models have an intensity function representing the failure rate over time. This article proposes a new non-homogeneous Poisson process model based on extended log-logistic (NHPP ELL) distribution with three parameters. The model properties were derived and represented graphically. Furthermore, the maximum likelihood estimation method was utilized to estimate the model parameters, which were computed numerically using the Newton–Raphson method. Three real software reliability datasets were utilized to evaluate the performance of the new NHPP ELL model based on three distinct criteria: the mean square error, the coefficient of determination (R2), the Theil statistic, the predictive-ratio risk, and the predictive power. Moreover, a comparative study was conducted between the proposed model and several well-known NHPP models regarding goodness-of-fit and predictive performance. All the evaluation measures indicate that the proposed model outperforms the other models across all the considered datasets. In addition, the graphical plots depicting the actual data and the prediction results for all the fitted models show that the proposed model performs well in all three considered software reliability datasets compared to other models.

Finite Element Model of Canine-Specific Vertebrae Incorporating Biomechanical Tissue Nonlinearity

As dogs are considered valuable members of many families, ensuring their health and well-being is essential. This study introduces a numerical nonlinear model that explores the complexities of canine vertebrae, with a specific focus on their experimentally observed mechanical properties. The model underwent rigorous testing, and its results were compared with actual data on the compression of canine lumbar vertebrae. The numerical results and experimental data comparison had a 12% RRMSE. This research enhances our understanding of canine bone health and lays the groundwork for future initiatives aimed at treating and mitigating bone-related diseases in dogs.

Life cycle analysis in hazardous waste logistics in Türkiye: a case study

ABSTRACT The collection and transportation activities engaged in waste management (WM) are known to have significant environmental impacts. This study assessed the current environmental situation regarding the use of diesel fuel in hazardous waste (HW) collection and transportation using a LCA methodology. The study utilized the actual transportation data of the HW collection and transportation activities of a WM company, IZAYDAS, for the year 2021. The functional unit (FU) chosen for analysis was the “environmental impact per annual total fuel consumption.” In addition, the study aimed to understand potential changes in environmental impact if electricity were used as a fuel alternative. The findings revealed that in the current scenario with diesel fuel, the Abiotic Depletion Potential (Fossil Fuel) (ADP Fossil) effect category had a dominant effect (3.92E + 06 MJ). The total Global Warming Potential (GWP100a) effect for HW transportation was determined to be 5.28E + 05 kg CO2 eq in 2021. When electricity was considered a fuel alternative, a decrease was observed in the GWP100a and ADP impact categories. At the same time, Human Toxicity (HTP), Acidification (AP), and Eutrophication (EP) showed an increase due to fossil fuel-based energy production. Sensitivity analysis indicated that all effects are lower in countries that depend on clean energy sources for electricity production.

Investigation on the Extent of Retrograde Condensation of Qianshao Gas Condensate Reservoir Using PVT Experiments and Compositional Reservoir Simulation

In the development of the Qianshao (QS) gas condensate reservoir, it is crucial to consider the phenomenon of retrograde condensation. Understanding the condensate saturation distribution with respect to time and space within the reservoir is essential for planning and implementing effective strategies for the future development of the QS gas condensate reservoir. In this paper, various PVT experiments (including reservoir oil recombination, flash separation, constant composition expansion, and constant volume depletion) were conducted to study the PVT properties and phase behavior of QS gas condensate fluid. Based on experimental data, our in-house PVT computation package was used to determine the appropriate EOS model parameters for the QS gas condensate. A four-step reservoir fluid characterization procedure and workflow for gas condensate reservoirs was developed. Furthermore, by analyzing the pressure-temperature phase envelope, the maximum possible condensate saturation in the QS well area was estimated to be around 3%. Numerical reservoir simulation models were developed using both the EOS model and actual reservoir engineering data. These simulation models were specifically designed to replicate the retrograde condensation process that occurs during production, taking into account both vertical and horizontal wells. By simulating the production process, these single-well reservoir simulation models enable us to quantitatively evaluate the condensate saturation and its distribution over space and time within a specific control area around a single well. Reservoir simulation results show that the condensate build-up around vertical and horizontal wells is quite different. For a vertical well, the maximum condensate oil saturation (30%) around the wellbore is located approximately 5 to 6 m from the well’s center. In contrast, the horizontal well model demonstrates a maximum condensate saturation of no more than 1.5%. This information is crucial for making informed decisions regarding the effective development and management of the QS gas condensate reservoir.

Adjustment of a reservoir model by the material balance method in the petroleum experts MBAL program. Setting up the integrated model in GAP

Relevance. Modern approach to development of oil and gas fields. Digitalization and automation of hydrocarbon production allow not only to reducing the response time to possible deviations from the operating level of production, but also quickly making decisions on its further normalization. The use of modern software to create a reservoir model using the material balance method allows you to calculate current and predicted reservoir pressures depending on the amount of selected and injected fluid. With high-quality adaptation, the integration of the reservoir model, wells and collection system, in its turn, allows you to correctly approach the choice of a field development system, as well as correctly assess the possible potential of the field. Forecast calculations performed on an integrated model can form the basis for budget planning, as well as the basis for carrying out geological and technical activities on wells, commissioning new wells, and modernizing equipment and infrastructure. Moreover, group optimization calculation allows us to maximize additional oil production, taking into account all geological and technical constraints. Aim. Formation and analysis of an approach to setting up a reservoir model by adapting parameters using the material balance method; formation of a solution for adapting the integrated model, reconciliation of the results with actual data; carrying out an optimization calculation and obtaining additional oil production. Methods. Adjustment and adaptation of reservoir models using the material balance method; adjustment and adaptation of the integrated model of a field/group of fields; calculation of the forecast for a period of 30/90 days; optimization calculations. Results. The obtained results make it possible to adapt the components of the integrated model with an accuracy of ±5 %, which makes it possible to simulate the operating mode of the field. As a result of the analysis, tuning criteria were identified, and the minimum required set of parameters for a qualitative adaptation of the reservoir model and the collection system was presented. The effectiveness and accuracy of the tool are also proven by comparing actual data on operating modes with synthetic ones. Based on the work done, it can be concluded that the use of the integrated model shows us a high convergence with real data, which allows us to optimally approach the field development mode. Moreover, an optimization calculation was carried out to obtain additional oil production using the proposed solution, and a forecast for oil production for a period of 90 days was calculated, which was later confirmed when compared with actual data.

ANALYSIS OF HUMAN LOWER LIMB DYNAMICS AND GAIT RECONSTRUCTION

In order to achieve gait reconstruction of human posture, a 7-degree-of-freedom model of the human lower limbs is proposed. On the basis of this model, dynamic analysis was conducted and the Lagrange equation of the human lower limb dynamic system was constructed. In the process of solving the kinetic energy–potential energy relationship of the Lagrange equation, the angular solutions of each joint were obtained. Based on actual data, common gait patterns of human lower limbs were presented, and gait reconstruction experiments were conducted. The experimental results show that under the human lower limb model and dynamic analysis method constructed in this paper, the variation curves of the hip joint, knee joint, and ankle joint of the human lower limb have been effectively reconstructed.

Auditor’s response to firm’s environmental violations and engagement in supplemental environmental projects

Purpose In this study, the authors investigate a pressing concern: how auditors react to their clients facing repercussions due to environmental violations. More specifically, this study aims to examine how environmental engagements, which carry potential risks and liabilities, influence auditors’ decision-making and fee structure. Design/methodology/approach This study uses unique, reliable and actual violation data from the United States Environmental Protection Agency (US-EPA) from 2000 to 2015, focusing on clients involved in environmental violations that led to legal prosecution and penalties and those who subsequently engaged in voluntary supplemental environmental projects (SEPs). The authors use the ordinary least squares method to test the authors’ main research question and later use propensity score matching and alternate data source (ASSET4) to check the robustness of the authors’ results. Findings The authors find that firms with environmental violations are more susceptible to auditor resignation. Moreover, the environmental violator firms that maintain their engagement with auditors pay significantly higher audit fees compared to non-environmental violator firms. Furthermore, these environmental violator firms also face extended audit report delays and take longer to appoint a new auditor. Originality/value This study provides an additional consequence of environmental violations, namely, increased chances of auditor resignation and higher audit fees, alongside the penalties imposed by the US-EPA. Moreover, the authors’ findings position environmental violations and participation in SEPs as important factors in auditors’ business risk assessment.

Prediction method for formation pore pressure based on transfer learning

Pore pressure prediction of drilled wells is the recognition of formation information using logging and other actual drilling data after drilling is completed, which is of great significance in improving the mastery of regional formation information and reducing the potential risks of the engineering program of wells to be drilled. Traditional formation pressure prediction methods rely on manual experience and are difficult to cope with complex formations. Existing machine learning-based methods have high accuracy in the prediction of formation pressure for wells with similar data composition in the same block or logging, but have poor accuracy in the prediction of wells with large differences in data from different blocks or logging. In this paper, a transfer learning algorithm is introduced to modify the existing LSTM-based formation pore pressure prediction model and establish a transfer learning-based formation pore pressure prediction method. In addition, we evaluated the prediction performance of machine learning algorithms and transfer learning algorithms under the same/different blocks and the same/different logging data conditions. The results show that, compared with the existing machine learning algorithms, for the same block and the same logging data conditions, transfer learning can improve the prediction accuracy by 0.15%; for the same block and different logging data conditions, transfer learning can improve the prediction accuracy by 0.33%; for the different block and the same logging data conditions, transfer learning can improve the prediction accuracy by 0.19%; for the different block and different logging data conditions, transfer learning can improve the prediction accuracy by 0.11%. Overall, the addition of the transfer learning method can improve the accuracy and generalization ability of the formation pore pressure prediction model based on machine learning to a larger extent.

Analyzing Motorcycle Accident Frequency Using Generalized Poisson Distributions

Motorcycle accidents in East Java are more common than accidents in other modes of transportation. In addition to the many motorcycle users today, human, environmental, and road factors are considered the highest causes of these accidents. The study's goal is to find the best model of the Generalized Poisson Family Distribution (GPR), namely Lagrangian Poisson Regression (LPR) and to construct a model that will quantify the frequency of motorcycle accidents in East Java. Akaike Information Criterion (AIC) and Schwarz Bayesian Criterion (SBC) criteria are the model comparison methods used in this research. The selection was also made based on the model's exponential coefficient with a 95% CI to further deepen the selection results obtained. In addition, the paired samples test was performed to determine the degree of dissimilarity between the outcomes produced by the developed model and the actual data. The best performance model is applied to identify the characteristics or factors highly involved in motorcycle accidents. The research uses secondary data from related agencies, namely the East Java Regional Police, especially the traffic accident unit, and East Java BPS, for 38 cities and districts in 2021. The numerical optimization method used is the iteratively reweighted least squares (IRLS) algorithm, assisted by R Studio software. The study findings show that LPR is the most efficient and exact approach for modeling the frequency of motorcycle accidents. Meanwhile, the percentage of teenagers (X1), the frequency of motorized vehicles (X3), and the average annual rainfall (X5) have a considerable impact on accident occurrence. This research has an important contribution, especially in the field of transportation modeling and designing appropriate strategies to reduce the frequency of motorcycle accidents.

Analysis of local density during football stadium access: Integrating pedestrian flow simulations and empirical data

This study analyzes numerically the access of football fans to a typical football stadium through pedestrian flow simulations. With this aim, we introduce a novel framework to address the difficulty of simulating pedestrian dynamics in highly complex geometries with multiple accesses. The framework consists of a combination of the Social Force Model (SFM) and Computational Fluid Dynamics tools to calculate multiple desired velocity maps. The introduction of this framework allows the predefinition of the desired velocity fields for the complex geometries in both the interior and surrounding area of the stadium. We validate the results of the proposed framework using actual entry-rate data from the turnstiles of three gates for 15 matches provided by the football club. Remarkably, the validation process allows us to describe the temporal evolution of the spatial distribution of pedestrians during their arrival with a single set of parameters valid for all the analyzed matches. Finally, we assess the potential fatality risk based on the local pedestrian density varying the total number of attendees and their arrival rate. The outcome evidences permanent clogs emerge at the entrance in the most extreme cases with high attendance or low standard deviation of the arrival rate, indicating the possibility of fatality occurrence.

Adaptive feature enhancement of modulation spectrum of ship radiation noise

The Detection of Envelope Modulation on Noise (DEMON) has been widely used in passive sonar target identification for determining the operational status and propeller structure characteristics of ships. However, the traditional DEMON spectrum is susceptible to environmental noise, which increases the difficulty of feature extraction. To address this issue, this paper investigates an adaptive feature enhancement method that combines Variational Mode Decomposition (VMD) and Singular Value Decomposition (SVD). The VMD algorithm utilizes variational equations to achieve adaptive modal decomposition of multiple feature components, while SVD utilizes the uncorrelated nature of signal and noise to achieve background noise reduction. Simulation results demonstrate that the feature strength and output signal-to-noise ratio of the VMD-SVD algorithm are higher than those of the traditional DEMON algorithm under both uniform and non-uniform modulation modes. Furthermore, processing of actual data validates the superiority of the VMD-SVD algorithm over the traditional DEMON algorithm, with the feature strength and output signal-to-noise ratio being maximally increased by 3 dB and 3.8 dB, respectively, compared to the traditional algorithm.

From Design to Management: Exploring BIM’s Role across Project Lifecycles, Dimensions, Data, and Uses, with Emphasis on Facility Management

The importance of Building Information Modelling (BIM) in construction and facility operation is unquestionable, but there is a clear discrepancy between the data included in as-built BIM models and the expected use specified by customers. This disparity presents significant obstacles in properly using BIM for facility management and operational operations. The main goal of this research is to suggest inventive and pragmatic approaches that successfully address the discrepancy between the actual BIM model data, with a specific emphasis on COBie dataset, and the intended BIM applications outlined by stakeholders in the Employer’s Information Requirement (EIR) for facility management and operation. The study methodology is based on a comprehensive examination of current literature, demo case studies, as well as standards pertaining to BIM data, COBie.Type, and EIR requirements. The results of this study consist of a collection of standards, procedures, and suggested practices specifically designed to improve the utilization of as-built BIM model data for facility management and operation. These will closely correlate with the BIM applications stated by the client. Furthermore, the project seeks to enhance industry norms and practices, promoting enhanced cooperation and information sharing among stakeholders. This research has also investigated the efficiency of Solibri Model Checker (SMC) to validate the COBie type and component information provided by COBie.

Development of a Mechanism for Assessing Mutual Structural Relations for Import Substitution of High-Tech Transfer in Life Cycle Management of Fundamentally New Products

The emergence of fundamentally new products is conditioned both by the development of techniques and technologies and by the emerging new needs and conditions of economic and social life of society. In this case, the process of achieving product dominance in the market has a progressive cyclical character in the form of a spiral pattern, the movement along which occurs with acceleration. At the current stage of economic development, most states have problems expanding sales of products and capturing new markets. These problems today are described in the works of various scientists; the research of the authors touches upon the issues of economic development. Today’s economic conditions, which are characterized by the application of sanctions pressure on a large scale, do not assess the opposite effect, when the countries that are subjected to this pressure, and the countries that organize this pressure, suffer economic damage. Some suffer damage in the form of reduced ability to produce knowledge-intensive products by providing them with imported materials and components, others who exert this pressure, by reducing the sales of their products, and in this case, it is relevant to build a mechanism for assessing mutual structural links for import substitution of high-tech transfer in the management of the life cycle of radical new products, which in our view is useful for those or other countries. The hypothesis is that the modeling of mutual structural relations of high-tech transfer in the management of the life cycle of radical new products will significantly improve the mechanisms of industrial policy management and national technological security and ensure sustainable economic development. The aim of the study is the task of developing a mechanism of mutual structural links and assessing the synergetic economic effect based on the approach of intersectoral links, interactions and interdependencies. In the course of the research, the following tasks are solved: the necessity to adequately replace high-tech imports within the framework of national technological security is substantiated, for this purpose it is necessary to create unique equipment for the system of RNP production; the assessment of the possibility of realization of such a task is carried out; the assessment of high-tech competencies of science and production, technical and resource readiness (configuration of the RNP system) is necessary, and a complex model for the assessment of structural and mutual linkages in the economy of innovation is developed. The modeling performed by the authors allowed us to assess the structure of domestic high-tech imports and the coefficients of mutual linkage of imports. The practical significance of the study lies in the fact that the conducted research makes it possible to significantly improve the efficiency of management of innovation processes of high-tech transfer to ensure the creation of a system of production of radically new products at the levels of organization, industry or national economy, which will ensure stable economic development. For this purpose, the structure and dynamics of high-tech imports of the Russian Federation have been analyzed and calculated, taking into account critical technologies and industries. The multiplier effects in high-tech industries of the Russian economy taking into account imports were analyzed on the basis of the latest actual detailed data of Rosstat. The estimation of the mutual relationship of imports and application of the developed toolkit for the example “Mechanical equipment, machine tools and other equipment for general or special purposes” is carried out.

Estimation and Optimal Censoring Plan for a New Unit Log-Log Model via Improved Adaptive Progressively Censored Data

To gather enough data from studies that are ongoing for an extended duration, a newly improved adaptive Type-II progressive censoring technique has been offered to get around this difficulty and extend several well-known multi-stage censoring plans. This work, which takes this scheme into account, focuses on some conventional and Bayesian estimation missions for parameter and reliability indicators, where the unit log-log model acts as the base distribution. The point and interval estimations of the various parameters are looked at from a classical standpoint. In addition to the conventional approach, the Bayesian methodology is examined to derive credible intervals beside the Bayesian point by leveraging the squared error loss function and the Markov chain Monte Carlo technique. Under varied settings, a simulation study is carried out to distinguish between the standard and Bayesian estimates. To implement the proposed procedures, two actual data sets are analyzed. Finally, multiple precision standards are considered to pick the optimal progressive censoring scheme.

Application Analysis of Nursing Students' Grades in Course Relevance Based on Association Rule Mining Algorithm Apriori

By analyzing the correlation between courses in students’ grades, we can provide a decision-making basis forthe revision of courses and syllabi, rationally optimize courses, and further improve teaching effects. With the help of IBM SPSS Modeler data mining software, this paper uses Apriori algorithm for association rule mining to conduct an in-depth analysis of the grades of nursing students in Shandong College of Traditional Chinese Medicine, and to explore the correlation between professional basic courses and professional core courses. Lastly, according to the detailed analysis of the mining results, valuable curriculum information will be found from the actual teaching data.