Fracture surface contour study is one of the important requirements for characterization and evaluation of the microstructure of rocks.Based on the improved cube covering method and the 3D contour digital reconstructi...Fracture surface contour study is one of the important requirements for characterization and evaluation of the microstructure of rocks.Based on the improved cube covering method and the 3D contour digital reconstruction model,this study proposes a quantitative microstructure characterization method combining the roughness evaluation index and the 3D fractal dimension to study the change rule of the fracture surface morphology after blasting.This method was applied and validated in the study of the fracture microstructure of the rock after blasting.The results show that the fracture morphology characteristics of the 3D contour digital reconstruction model have good correlation with the changes of the blasting action.The undulation rate of the three-dimensional surface profile of the rock is more prone to dramatic rise and dramatic fall morphology.In terms of tilting trend,the tilting direction also shows gradual disorder,with the tilting angle increasing correspondingly.All the roughness evaluation indexes of the rock fissure surface after blasting show a linear and gradually increasing trend as the distance to the bursting center increases;the difference between the two-dimensional roughness evaluation indexes and the three-dimensional ones of the same micro-area rock samples also becomes increasingly larger,among which the three-dimensional fissure roughness coefficient JRC and the surface roughness ratio Rs display better correlation.Compared with the linear fitting formula of the power function relationship,the three-dimensional fractal dimension of the postblast fissure surface is fitted with the values of JRC and Rs,which renders higher correlation coefficients,and the degree of linear fitting of JRC to the three-dimensional fractal dimension is higher.The fractal characteristics of the blast-affected region form a unity with the three-dimensional roughness evaluation of the fissure surface.展开更多
The Quseir Formation consists mainly of dark gray mudstones with a high organic matter content and excellent hydrocarbon-generating potential.The main objectives of this study are to highlight the dominant structural ...The Quseir Formation consists mainly of dark gray mudstones with a high organic matter content and excellent hydrocarbon-generating potential.The main objectives of this study are to highlight the dominant structural elements in the Komombo Basin,Egypt,and evaluate the geochemical characteristics of the Quseir Formation.Depth maps and a 3 D structural model indicate two normal fault trends NW–SE and ENE–WSW.The NW–SE trend is the dominant one that created the primary half-graben system.The depth to the top of the Quseir Formation gradually decreases from the eastern and central parts towards the corners of the basin.The thickness of the Quseir Formation ranges from about 300 to 1000 ft.The 3 D facies model shows that the shale has a large probability distribution in the study area,compared with the sandstone and siltstone.The source rock potential varies between good in the western part to very good in the eastern part of the basin.The organic-rich interval is dominated by gas-prone kerogen type III based on TOC and Rock-Eval.The pyrolysis data vitrinite reflectance(%Ro)(0.5–0.74%)and Tmax values(406–454 C°)suggest a maturity level that ranges from immature to early maturity stage for hydrocarbon generation.展开更多
Aim: Maxillary dental arch widths were evaluated in individuals having unilateral (UCLP) and bilateral (BCLP) cleft lip and palate (CLP) using three-dimensional (3D) digital models. Material and Method: The study had ...Aim: Maxillary dental arch widths were evaluated in individuals having unilateral (UCLP) and bilateral (BCLP) cleft lip and palate (CLP) using three-dimensional (3D) digital models. Material and Method: The study had been conducted on 80 individuals aged between 14 - 17 years having UCLP and BCLP. 40 of the individuals had UCLP, whereas 40 had BCLP. The maxillary dental models taken from patients before the treatment were scanned using Orthomodel Programme (v.1.01, Orthomodel Inc., Istanbul, Turkey) to obtain 3D imagery. Student’s t-test was used in order to assess the data obtained by using SPSS software version 22.0. Results: In BCLP, the average inter-canine distance was 17.44 ± 1.31 mm, the average inter-molar distance was 36.57 ± 1.12 mm, while inter-canine/inter-molar ratio was 0.47. Whereas in UCLP, it was 25.10 ± 0.63 mm, 42.20 ± 0.53 mm and 0.59. The inter-canine distance in UCLP was found to be large enough to be statistically significant (p 0.05), even though there were differences in inter-molar widths. Conclusion: For the stable orthodontic treatment results, one of the most important points is arch form and widths to be coherent with each other. In our study, the increase of inter-canine distance seen in UCLP indicates that in the cleft region, the maxillary arch is inclined over to the back, while the same situation in BCLP suggests that the maxillary segments are collapsed inside. The difference in the arch is highly affected by the primary surgical treatment.展开更多
Objective: To study the evaluation value of three-dimensional finite element model analysis for bone mineral density (BMD) and bone metabolism activity in patients with osteoporosis. Methods: A total of 218 patients w...Objective: To study the evaluation value of three-dimensional finite element model analysis for bone mineral density (BMD) and bone metabolism activity in patients with osteoporosis. Methods: A total of 218 patients who were diagnosed with osteoporosis in the hospital between February 2014 and January 2017 were collected as observation group, and 100 healthy volunteers who received physical examination in the hospital during the same period were selected as normal control group. The femoral head of the two groups was analyzed by three-dimensional finite element model, and the femoral head BMD levels and serum bone metabolism index contents were measured. Pearson test was used to evaluate the evaluation value of femoral head three-dimensional finite element model for osteoporosis. Results: The cancellous bone and cortical bone Von Mises stress value of observation group were lower than those of normal control group, and femoral neck BMD value of observation group was lower than that of normal control group;serum bone metabolism index BGP content was lower than that of normal control group while NBAP, TRACP-5b and CTX-1 contents were higher than those of normal control group. Pearson test showed that the cancellous bone and cortical bone Von Mises stress value of patients with osteoporosis were directly correlated with BMD value and bone metabolism index contents. Conclusion: The three-dimensional finite element model analysis resultsof patients with osteoporosis can objectively reflect the femoral headBMD value and bone metabolism activity, and is a reliable way to evaluate the risk of long-term fractures.展开更多
Cosmetic safety evaluation employs a series of toxicological tests, on both qualitative and quantitative levels, to assess the potential risks for the daily use of selected cosmetic ingredients and final products. Tra...Cosmetic safety evaluation employs a series of toxicological tests, on both qualitative and quantitative levels, to assess the potential risks for the daily use of selected cosmetic ingredients and final products. Traditionally, safety evaluation of cosmetics uses animal tests. With the development of in vitro science and the 3R （Reduction, Replacement and Refinement） principle, three-dimensional reconstructed human epidermis （3D-RHE） models have been developed and widely applied in cosmetic safety evaluation. Reconstructed human skin models possess anatomy and metabolism biology similar to real human tissue. This paper reviews the current application of 3D-RHE models in the safety evaluation of skin irritation, eye irritation, phototoxicity and genotoxicity potential of cosmetic ingredients/formulas. The advantages and disadvantages of using skin models are also discussed, and comments and suggestions are given for its future development.展开更多
Although previous studies have demonstrated that transcranial focused ultrasound stimulation protects the ischemic brain,clear criteria for the stimulation time window and intensity are lacking.Electrical impedance to...Although previous studies have demonstrated that transcranial focused ultrasound stimulation protects the ischemic brain,clear criteria for the stimulation time window and intensity are lacking.Electrical impedance tomography enables real-time monitoring of changes in cerebral blood perfusion within the ischemic brain,but investigating the feasibility of using this method to assess post-stroke rehabilitation in vivo remains critical.In this study,ischemic stroke was induced in rats through middle cerebral artery occlusion surgery.Transcranial focused ultrasound stimulation was used to treat the rat model of ischemia,and electrical impedance tomography was used to measure impedance during both the acute stage of ischemia and the rehabilitation stage following the stimulation.Electrical impedance tomography results indicated that cerebral impedance increased after the onset of ischemia and decreased following transcranial focused ultrasound stimulation.Furthermore,the stimulation promoted motor function recovery,reduced cerebral infarction volume in the rat model of ischemic stroke,and induced the expression of brain-derived neurotrophic factor in the ischemic brain.Our results also revealed a significant correlation between the impedance of the ischemic brain post-intervention and improvements in behavioral scores and infarct volume.This study shows that daily administration of transcranial focused ultrasound stimulation for 20 minutes to the ischemic hemisphere 24 hours after cerebral ischemia enhanced motor recovery in a rat model of ischemia.Additionally,our findings indicate that electrical impedance tomography can serve as a valuable tool for quantitatively evaluating rehabilitation after ischemic stroke in vivo.These findings suggest the feasibility of using impedance data collected via electrical impedance tomography to clinically assess the effects of rehabilitatory interventions for patients with ischemic stroke.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
An acute skin injury model using continuous tape tearing was established,and studies the application of this model in the evaluation of soothing effects through instrument evaluation.30 healthy adult subjects were sel...An acute skin injury model using continuous tape tearing was established,and studies the application of this model in the evaluation of soothing effects through instrument evaluation.30 healthy adult subjects were selected as the research subjects,and an acute skin model was established on the forearm flexion side of the subjects.The skin color a^(*)value and transdermal water loss rate(TEWL)value of the blank and experimental groups were tested using instruments.The results showed that at 15 and 30 minutes after using the sample,the growth values of a^(*)value and TEWL value in the experimental group were significantly lower than those in the blank group(P<0.05),the acute skin lesion model can effectively evaluate the soothing effect of cosmetics.展开更多
In the context of advancing towards dual carbon goals,numerous factories are actively engaging in energy efficiency upgrades and transformations.To accurately pinpoint energy efficiency bottlenecks within factories an...In the context of advancing towards dual carbon goals,numerous factories are actively engaging in energy efficiency upgrades and transformations.To accurately pinpoint energy efficiency bottlenecks within factories and prioritize renovation sequences,it is crucial to conduct comprehensive evaluations of the energy performance across various workshops.Therefore,this paper proposes an evaluation model for workshop energy efficiency based on the drive-state-response(DSR)framework combined with the fuzzy BORDA method.Firstly,an in-depth analysis of the relationships between different energy efficiency indicators was conducted.Based on the DSR model,evaluation criteria were selected from three dimensions-drive factors,state characteristics,and response measures-to establish a robust energy efficiency indicator system.Secondly,three distinct assessment techniques were selected:Grey Relational Analysis(GRA),Entropy Weight Method(EWM),and Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)forming a diversified set of evaluation methods.Subsequently,by introducing the fuzzy BORDA method,a comprehensive energy efficiency evaluation model was developed,aimed at quantitatively ranking the energy performance status of each workshop.Using a real-world factory as a case study,applying our proposed evaluationmodel yielded detailed scores and rankings for each workshop.Furthermore,post hoc testing was performed using the Spearman correlation coefficient,revealing a statistic value of 10.209,which validates the effectiveness and reliability of the proposed evaluation model.This model not only assists in identifying underperforming workshops within the factory but also provides solid data support and a decision-making basis for future energy efficiency optimization strategies.展开更多
With the rapid development of generative artificial intelligence technologies,represented by large language models,university-level computer science education is undergoing a critical transition-from knowledge-based i...With the rapid development of generative artificial intelligence technologies,represented by large language models,university-level computer science education is undergoing a critical transition-from knowledge-based instruction to competency-oriented teaching.A postgraduate student competency evaluation model can serve as a framework to organize and guide both teaching and research activities at the postgraduate level.A number of relevant research efforts have already been conducted in this area.Graduate education plays a vital role not only as a continuation and enhancement of undergraduate education but also as essential preparation for future research endeavors.An analysis of the acceptance of competency evaluation models refers to the assessment of how various stakeholders perceive the importance of different components within the model.Investigating the degree of acceptance among diverse groups-such as current undergraduate students,current postgraduate students,graduates with less than three years of work experience,and those with more than three years of work experience-can offer valuable insights for improving and optimizing postgraduate education and training practices.展开更多
To comprehensively utilize the valuable geological map,exploration profile,borehole,and geochemical logging data and the knowledge on the formation of the Jinshan Ag-Au deposit for forecasting the exploration targets ...To comprehensively utilize the valuable geological map,exploration profile,borehole,and geochemical logging data and the knowledge on the formation of the Jinshan Ag-Au deposit for forecasting the exploration targets of concealed ore bodies,three-dimensional Mineral Prospectivity Modeling(MPM)of the deposit has been conducted using the weights-of-evidence(WofE)method.Conditional independence between evidence layers was tested,and the outline results using the prediction-volume(P-V)and Student's t-statistic methods for delineating favorable mineralization areas from continuous posterior probability map were critically compared.Four exploration targets delineated ultimately by the Student's t-statistic method for the discovery of minable ore bodies in each of the target areas were discussed in detail.The main conclusions include:(1)three-dimensional modeling of a deposit using multi-source reconnaissance data is useful for MPM in interpreting their relationships with known ore bodies;(2)WofE modeling can be used as a straightforward tool for integrating deposit model and reconnaissance data in MPM;(3)the Student's t-statistic method is more applicable in binarizing the continuous prospectivity map for exploration targeting than the PV approach;and(4)two target areas within high potential to find undiscovered ore bodies were diagnosed to guide future near-mine exploration activities of the Jinshan deposit.展开更多
With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Alth...With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.展开更多
The average stiffness performance indices throughout the workspace are commonly used as global stiffness performance indices to evaluate the overall stiffness performance of parallel mechanisms,which involves an analy...The average stiffness performance indices throughout the workspace are commonly used as global stiffness performance indices to evaluate the overall stiffness performance of parallel mechanisms,which involves an analysis of the stiffness performance of numerous discrete points in the workspace.This necessitates time-consuming and inefficient calculation,which is particularly pronounced in the optimization design stage of the mechanism,where the variations in the global stiffness performance indices versus various dimensional and structural parameters need to be analyzed.This paper presents a semi-analytical approach for stiffness modeling of the novel(R(RPS&RP))&2-UPS parallel mechanism(referred to as the Trifree mechanism)and proposes“local”stiffness performance indices as alternatives to global indices.Drawing on the screw theory,the Cartesian stiffness matrix of the Trifree mechanism is formulated explicitly by considering the compliances of all elastic elements and the over-constraint characteristics inherent in the mechanism.Based on the spherical motion pattern of the Trifree mechanism,four special reference configurations are extracted within the workspace.This yields“local”stiffness performance indices capable of accurately evaluating the overall stiffness performance of the mechanism and effectively improving the computational efficiency.The variations in global and“local”stiffness performance indices versus key design parameters are investigated.Furthermore,the proposed indices are applied to the Tricept and Trimule mechanisms.The results demonstrate that the proposed indices exhibit excellent computational accuracy and efficiency in evaluating the overall stiffness performance of these spherical parallel mechanisms.Moreover,the stiffness performance of the novel parallel mechanism investigated in this study closely resembles that of the well-known Tricept and Trimule mechanisms.This research proposes a semi-analytic stiffness model of the Trifree mechanism and“local”stiffness performance indices to evaluate the overall stiffness performance,thereby substantially improving the computational efficiency without sacrificing accuracy.展开更多
Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to en...Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.展开更多
The rapid development of evolutionary deep learning has led to the emergence of various Neural Architecture Search(NAS)algorithms designed to optimize neural network structures.However,these algorithms often face sign...The rapid development of evolutionary deep learning has led to the emergence of various Neural Architecture Search(NAS)algorithms designed to optimize neural network structures.However,these algorithms often face significant computational costs due to the time-consuming process of training neural networks and evaluating their performance.Traditional NAS approaches,which rely on exhaustive evaluations and large training datasets,are inefficient for solving complex image classification tasks within limited time frames.To address these challenges,this paper proposes a novel NAS algorithm that integrates a hierarchical evaluation strategy based on Surrogate models,specifically using supernet to pre-trainweights and randomforests as performance predictors.This hierarchical framework combines rapid Surrogate model evaluations with traditional,precise evaluations to balance the trade-off between performance accuracy and computational efficiency.The algorithm significantly reduces the time required for model evaluation by predicting the fitness of candidate architectures using a random forest Surrogate model,thus alleviating the need for full training cycles for each architecture.The proposed method also incorporates evolutionary operations such as mutation and crossover to refine the search process and improve the accuracy of the resulting architectures.Experimental evaluations on the CIFAR-10 and CIFAR-100 datasets demonstrate that the proposed hierarchical evaluation strategy reduces the search time and costs compared to traditional methods,while achieving comparable or even superior model performance.The results suggest that this approach can efficiently handle resourceconstrained tasks,providing a promising solution for accelerating the NAS process without compromising the quality of the generated architectures.展开更多
Background:Traditional imaging approaches to keratoconus(KCN)have thus far failed to produce a standardized approach for diagnosis.While many diagnostic modalities and metrics exist,none have proven robust enough to b...Background:Traditional imaging approaches to keratoconus(KCN)have thus far failed to produce a standardized approach for diagnosis.While many diagnostic modalities and metrics exist,none have proven robust enough to be considered a gold standard.This study aims to introduce novel metrics to differentiate between KCN and healthy corneas using three-dimensional(3D)measurements of surface area and volume.Methods:This retrospective observational study examined KCN patients along with healthy control patients between the ages of 20 and 79 years old at the University of Maryland,Baltimore.The selected patients underwent a nine-line raster scan anterior segment optical coherence tomography(AS-OCT).ImageJ was used to determine the central 6 mm of each image and each corneal image was then divided into six 1 mm segments.Free-D software was then used to render the nine different images into a 3D model to calculate corneal surface area and volume.A two-tailed Mann-Whitney test was used to assess statistical significance when comparing these subsets.Results:Thirty-three eyes with KCN,along with 33 healthy control,were enrolled.There were statistically significant differences between the healthy and KCN groups in the metric of anterior corneal surface area(13.927 vs.13.991 mm^(2),P=0.046),posterior corneal surface area(14.045 vs.14.173 mm^(2),P<0.001),and volume(8.430 vs.7.773 mm3,P<0.001)within the central 6 mm.Conclusions:3D corneal models derived from AS-OCT can be used to measure anterior corneal surface area,posterior corneal surface area,and corneal volume.All three parameters are statistically different between corneas with KCN and healthy corneas.Further study and application of these parameters may yield new methodologies for the detection of KCN.展开更多
Offline policy evaluation,evaluating and selecting complex policies for decision-making by only using offline datasets is important in reinforcement learning.At present,the model-based offline policy evaluation(MBOPE)...Offline policy evaluation,evaluating and selecting complex policies for decision-making by only using offline datasets is important in reinforcement learning.At present,the model-based offline policy evaluation(MBOPE)is widely welcomed because of its easy to implement and good performance.MBOPE directly approximates the unknown value of a given policy using the Monte Carlo method given the estimated transition and reward functions of the environment.Usually,multiple models are trained,and then one of them is selected to be used.However,a challenge remains in selecting an appropriate model from those trained for further use.The authors first analyse the upper bound of the difference between the approximated value and the unknown true value.Theoretical results show that this difference is related to the trajectories generated by the given policy on the learnt model and the prediction error of the transition and reward functions at these generated data points.Based on the theoretical results,a new criterion is proposed to tell which trained model is better suited for evaluating the given policy.At last,the effectiveness of the proposed criterion is demonstrated on both benchmark and synthetic offline datasets.展开更多
With the continuous development of the nursing discipline,standardized nurse training has always been a crucial link in the development of nursing science and plays an irreplaceable role in talent cultivation.However,...With the continuous development of the nursing discipline,standardized nurse training has always been a crucial link in the development of nursing science and plays an irreplaceable role in talent cultivation.However,in the current standardized training for some nurses,there are problems such as the simplification of nursing skill evaluation models and insufficient post competence of nurses.Therefore,optimizing the training model for nursing talents has become an inevitable measure.The problem-based learning(PBL)method and the Direct Observation of Procedural Skills(DOPS)evaluation model provide new directions and guidance for the development of training.Against this background,this paper explores effective approaches for standardized nurse training,starting from basic concepts and gradually delving into specific practical paths,aiming to improve the quality of talent cultivation and provide valuable references for other researchers.展开更多
The comprehensive status of blast furnaces was one of the most important factors affecting their economy,quality,and longev-ity.The blast furnace comprehensive status had the nature of“black box,”and it was“unpredi...The comprehensive status of blast furnaces was one of the most important factors affecting their economy,quality,and longev-ity.The blast furnace comprehensive status had the nature of“black box,”and it was“unpredictable.”In this study,a blast furnace com-prehensive status score and prediction method based on a cascade system and a combined model were proposed to address this issue.A dual cascade evaluation system was developed by integrating subjective and objective weighting methods.The analytic hierarchy process,coefficient of variation,entropy weight method,and impart combinatorial games were jointly employed to determine the optimal weight distribution across indicators.Categorized statuses(raw material,gas flow,furnace body,furnace cylinder,and iron-slag)were evaluated.Based on the five categories of the status data,the second cascade was applied to upgrade the quantitative evaluation of the comprehens-ive status.The weights of the different categories were 0.22,0.15,0.22,0.21,and 0.20,respectively.According to the data analysis,the results of the comprehensive status score closely matched the on-site production logs.Based on the blast furnace smelting period,the maximal information coefficient method was applied to the 100 parameters that were most relevant to the comprehensive status.A com-bined prediction model for a comprehensive status score was designed using bidirectional long short-term memory(BiLSTM)and categorical boosting(CatBoost).The test results indicated that the combined model reduced the mean absolute error by an average of 0.275 and increased the hit rate by an average of 5.65 percentage points compared to BiLSTM or CatBoost alone.When the er-ror range was±2.5,the combined model predicted a hit rate of 91.66%for the next hour’s comprehensive status score,and its high accur-acy was deemed satisfactory for the field.SHapley Additive exPlanations(SHAP)and regression fitting were applied to analyze the lin-ear quantitative relationship between the key variables and the comprehensive status score.When the furnace bottom center temperature was increased by 10℃,the comprehensive status score increased by 0.44.This method contributes to a more precise management and control of the comprehensive status of the blast furnace on-site.展开更多
基金National Key Research and Development Program of China,Grant/Award Number:2021YFC2902103National Natural Science Foundation of China,Grant/Award Number:51934001Fundamental Research Funds for the Central Universities,Grant/Award Number:2023JCCXLJ02。
文摘Fracture surface contour study is one of the important requirements for characterization and evaluation of the microstructure of rocks.Based on the improved cube covering method and the 3D contour digital reconstruction model,this study proposes a quantitative microstructure characterization method combining the roughness evaluation index and the 3D fractal dimension to study the change rule of the fracture surface morphology after blasting.This method was applied and validated in the study of the fracture microstructure of the rock after blasting.The results show that the fracture morphology characteristics of the 3D contour digital reconstruction model have good correlation with the changes of the blasting action.The undulation rate of the three-dimensional surface profile of the rock is more prone to dramatic rise and dramatic fall morphology.In terms of tilting trend,the tilting direction also shows gradual disorder,with the tilting angle increasing correspondingly.All the roughness evaluation indexes of the rock fissure surface after blasting show a linear and gradually increasing trend as the distance to the bursting center increases;the difference between the two-dimensional roughness evaluation indexes and the three-dimensional ones of the same micro-area rock samples also becomes increasingly larger,among which the three-dimensional fissure roughness coefficient JRC and the surface roughness ratio Rs display better correlation.Compared with the linear fitting formula of the power function relationship,the three-dimensional fractal dimension of the postblast fissure surface is fitted with the values of JRC and Rs,which renders higher correlation coefficients,and the degree of linear fitting of JRC to the three-dimensional fractal dimension is higher.The fractal characteristics of the blast-affected region form a unity with the three-dimensional roughness evaluation of the fissure surface.
文摘The Quseir Formation consists mainly of dark gray mudstones with a high organic matter content and excellent hydrocarbon-generating potential.The main objectives of this study are to highlight the dominant structural elements in the Komombo Basin,Egypt,and evaluate the geochemical characteristics of the Quseir Formation.Depth maps and a 3 D structural model indicate two normal fault trends NW–SE and ENE–WSW.The NW–SE trend is the dominant one that created the primary half-graben system.The depth to the top of the Quseir Formation gradually decreases from the eastern and central parts towards the corners of the basin.The thickness of the Quseir Formation ranges from about 300 to 1000 ft.The 3 D facies model shows that the shale has a large probability distribution in the study area,compared with the sandstone and siltstone.The source rock potential varies between good in the western part to very good in the eastern part of the basin.The organic-rich interval is dominated by gas-prone kerogen type III based on TOC and Rock-Eval.The pyrolysis data vitrinite reflectance(%Ro)(0.5–0.74%)and Tmax values(406–454 C°)suggest a maturity level that ranges from immature to early maturity stage for hydrocarbon generation.
文摘Aim: Maxillary dental arch widths were evaluated in individuals having unilateral (UCLP) and bilateral (BCLP) cleft lip and palate (CLP) using three-dimensional (3D) digital models. Material and Method: The study had been conducted on 80 individuals aged between 14 - 17 years having UCLP and BCLP. 40 of the individuals had UCLP, whereas 40 had BCLP. The maxillary dental models taken from patients before the treatment were scanned using Orthomodel Programme (v.1.01, Orthomodel Inc., Istanbul, Turkey) to obtain 3D imagery. Student’s t-test was used in order to assess the data obtained by using SPSS software version 22.0. Results: In BCLP, the average inter-canine distance was 17.44 ± 1.31 mm, the average inter-molar distance was 36.57 ± 1.12 mm, while inter-canine/inter-molar ratio was 0.47. Whereas in UCLP, it was 25.10 ± 0.63 mm, 42.20 ± 0.53 mm and 0.59. The inter-canine distance in UCLP was found to be large enough to be statistically significant (p 0.05), even though there were differences in inter-molar widths. Conclusion: For the stable orthodontic treatment results, one of the most important points is arch form and widths to be coherent with each other. In our study, the increase of inter-canine distance seen in UCLP indicates that in the cleft region, the maxillary arch is inclined over to the back, while the same situation in BCLP suggests that the maxillary segments are collapsed inside. The difference in the arch is highly affected by the primary surgical treatment.
基金National Science Foundation of China No:81301292.
文摘Objective: To study the evaluation value of three-dimensional finite element model analysis for bone mineral density (BMD) and bone metabolism activity in patients with osteoporosis. Methods: A total of 218 patients who were diagnosed with osteoporosis in the hospital between February 2014 and January 2017 were collected as observation group, and 100 healthy volunteers who received physical examination in the hospital during the same period were selected as normal control group. The femoral head of the two groups was analyzed by three-dimensional finite element model, and the femoral head BMD levels and serum bone metabolism index contents were measured. Pearson test was used to evaluate the evaluation value of femoral head three-dimensional finite element model for osteoporosis. Results: The cancellous bone and cortical bone Von Mises stress value of observation group were lower than those of normal control group, and femoral neck BMD value of observation group was lower than that of normal control group;serum bone metabolism index BGP content was lower than that of normal control group while NBAP, TRACP-5b and CTX-1 contents were higher than those of normal control group. Pearson test showed that the cancellous bone and cortical bone Von Mises stress value of patients with osteoporosis were directly correlated with BMD value and bone metabolism index contents. Conclusion: The three-dimensional finite element model analysis resultsof patients with osteoporosis can objectively reflect the femoral headBMD value and bone metabolism activity, and is a reliable way to evaluate the risk of long-term fractures.
文摘Cosmetic safety evaluation employs a series of toxicological tests, on both qualitative and quantitative levels, to assess the potential risks for the daily use of selected cosmetic ingredients and final products. Traditionally, safety evaluation of cosmetics uses animal tests. With the development of in vitro science and the 3R （Reduction, Replacement and Refinement） principle, three-dimensional reconstructed human epidermis （3D-RHE） models have been developed and widely applied in cosmetic safety evaluation. Reconstructed human skin models possess anatomy and metabolism biology similar to real human tissue. This paper reviews the current application of 3D-RHE models in the safety evaluation of skin irritation, eye irritation, phototoxicity and genotoxicity potential of cosmetic ingredients/formulas. The advantages and disadvantages of using skin models are also discussed, and comments and suggestions are given for its future development.
基金supported by the Fundamental Research Funds for the Central Universities,Nos.G2021KY05107,G2021KY05101the National Natural Science Foundation of China,Nos.32071316,32211530049+1 种基金the Natural Science Foundation of Shaanxi Province,No.2022-JM482the Education and Teaching Reform Funds for the Central Universities,No.23GZ230102(all to LL and HH).
文摘Although previous studies have demonstrated that transcranial focused ultrasound stimulation protects the ischemic brain,clear criteria for the stimulation time window and intensity are lacking.Electrical impedance tomography enables real-time monitoring of changes in cerebral blood perfusion within the ischemic brain,but investigating the feasibility of using this method to assess post-stroke rehabilitation in vivo remains critical.In this study,ischemic stroke was induced in rats through middle cerebral artery occlusion surgery.Transcranial focused ultrasound stimulation was used to treat the rat model of ischemia,and electrical impedance tomography was used to measure impedance during both the acute stage of ischemia and the rehabilitation stage following the stimulation.Electrical impedance tomography results indicated that cerebral impedance increased after the onset of ischemia and decreased following transcranial focused ultrasound stimulation.Furthermore,the stimulation promoted motor function recovery,reduced cerebral infarction volume in the rat model of ischemic stroke,and induced the expression of brain-derived neurotrophic factor in the ischemic brain.Our results also revealed a significant correlation between the impedance of the ischemic brain post-intervention and improvements in behavioral scores and infarct volume.This study shows that daily administration of transcranial focused ultrasound stimulation for 20 minutes to the ischemic hemisphere 24 hours after cerebral ischemia enhanced motor recovery in a rat model of ischemia.Additionally,our findings indicate that electrical impedance tomography can serve as a valuable tool for quantitatively evaluating rehabilitation after ischemic stroke in vivo.These findings suggest the feasibility of using impedance data collected via electrical impedance tomography to clinically assess the effects of rehabilitatory interventions for patients with ischemic stroke.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
文摘An acute skin injury model using continuous tape tearing was established,and studies the application of this model in the evaluation of soothing effects through instrument evaluation.30 healthy adult subjects were selected as the research subjects,and an acute skin model was established on the forearm flexion side of the subjects.The skin color a^(*)value and transdermal water loss rate(TEWL)value of the blank and experimental groups were tested using instruments.The results showed that at 15 and 30 minutes after using the sample,the growth values of a^(*)value and TEWL value in the experimental group were significantly lower than those in the blank group(P<0.05),the acute skin lesion model can effectively evaluate the soothing effect of cosmetics.
基金funded by the National Social Science Fund of China(Grant No.23BGL234).
文摘In the context of advancing towards dual carbon goals,numerous factories are actively engaging in energy efficiency upgrades and transformations.To accurately pinpoint energy efficiency bottlenecks within factories and prioritize renovation sequences,it is crucial to conduct comprehensive evaluations of the energy performance across various workshops.Therefore,this paper proposes an evaluation model for workshop energy efficiency based on the drive-state-response(DSR)framework combined with the fuzzy BORDA method.Firstly,an in-depth analysis of the relationships between different energy efficiency indicators was conducted.Based on the DSR model,evaluation criteria were selected from three dimensions-drive factors,state characteristics,and response measures-to establish a robust energy efficiency indicator system.Secondly,three distinct assessment techniques were selected:Grey Relational Analysis(GRA),Entropy Weight Method(EWM),and Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)forming a diversified set of evaluation methods.Subsequently,by introducing the fuzzy BORDA method,a comprehensive energy efficiency evaluation model was developed,aimed at quantitatively ranking the energy performance status of each workshop.Using a real-world factory as a case study,applying our proposed evaluationmodel yielded detailed scores and rankings for each workshop.Furthermore,post hoc testing was performed using the Spearman correlation coefficient,revealing a statistic value of 10.209,which validates the effectiveness and reliability of the proposed evaluation model.This model not only assists in identifying underperforming workshops within the factory but also provides solid data support and a decision-making basis for future energy efficiency optimization strategies.
文摘With the rapid development of generative artificial intelligence technologies,represented by large language models,university-level computer science education is undergoing a critical transition-from knowledge-based instruction to competency-oriented teaching.A postgraduate student competency evaluation model can serve as a framework to organize and guide both teaching and research activities at the postgraduate level.A number of relevant research efforts have already been conducted in this area.Graduate education plays a vital role not only as a continuation and enhancement of undergraduate education but also as essential preparation for future research endeavors.An analysis of the acceptance of competency evaluation models refers to the assessment of how various stakeholders perceive the importance of different components within the model.Investigating the degree of acceptance among diverse groups-such as current undergraduate students,current postgraduate students,graduates with less than three years of work experience,and those with more than three years of work experience-can offer valuable insights for improving and optimizing postgraduate education and training practices.
基金financially supported by the Ministry of Science and Technology of China(Nos.2022YFF0801201,2021YFC2900300)the National Natural Science Foundation of China(Nos.41872245,U1911202)the Guangdong Basic and Applied Basic Research Foundation(No.2020A1515010666)。
文摘To comprehensively utilize the valuable geological map,exploration profile,borehole,and geochemical logging data and the knowledge on the formation of the Jinshan Ag-Au deposit for forecasting the exploration targets of concealed ore bodies,three-dimensional Mineral Prospectivity Modeling(MPM)of the deposit has been conducted using the weights-of-evidence(WofE)method.Conditional independence between evidence layers was tested,and the outline results using the prediction-volume(P-V)and Student's t-statistic methods for delineating favorable mineralization areas from continuous posterior probability map were critically compared.Four exploration targets delineated ultimately by the Student's t-statistic method for the discovery of minable ore bodies in each of the target areas were discussed in detail.The main conclusions include:(1)three-dimensional modeling of a deposit using multi-source reconnaissance data is useful for MPM in interpreting their relationships with known ore bodies;(2)WofE modeling can be used as a straightforward tool for integrating deposit model and reconnaissance data in MPM;(3)the Student's t-statistic method is more applicable in binarizing the continuous prospectivity map for exploration targeting than the PV approach;and(4)two target areas within high potential to find undiscovered ore bodies were diagnosed to guide future near-mine exploration activities of the Jinshan deposit.
基金funded by the Joint Project of Industry-University-Research of Jiangsu Province(Grant:BY20231146).
文摘With the widespread application of Internet of Things(IoT)technology,the processing of massive realtime streaming data poses significant challenges to the computational and data-processing capabilities of systems.Although distributed streaming data processing frameworks such asApache Flink andApache Spark Streaming provide solutions,meeting stringent response time requirements while ensuring high throughput and resource utilization remains an urgent problem.To address this,the study proposes a formal modeling approach based on Performance Evaluation Process Algebra(PEPA),which abstracts the core components and interactions of cloud-based distributed streaming data processing systems.Additionally,a generic service flow generation algorithmis introduced,enabling the automatic extraction of service flows fromthe PEPAmodel and the computation of key performance metrics,including response time,throughput,and resource utilization.The novelty of this work lies in the integration of PEPA-based formal modeling with the service flow generation algorithm,bridging the gap between formal modeling and practical performance evaluation for IoT systems.Simulation experiments demonstrate that optimizing the execution efficiency of components can significantly improve system performance.For instance,increasing the task execution rate from 10 to 100 improves system performance by 9.53%,while further increasing it to 200 results in a 21.58%improvement.However,diminishing returns are observed when the execution rate reaches 500,with only a 0.42%gain.Similarly,increasing the number of TaskManagers from 10 to 20 improves response time by 18.49%,but the improvement slows to 6.06% when increasing from 20 to 50,highlighting the importance of co-optimizing component efficiency and resource management to achieve substantial performance gains.This study provides a systematic framework for analyzing and optimizing the performance of IoT systems for large-scale real-time streaming data processing.The proposed approach not only identifies performance bottlenecks but also offers insights into improving system efficiency under different configurations and workloads.
基金Supported by National High-quality Development Project of China(Grant No.2340STCZB193).
文摘The average stiffness performance indices throughout the workspace are commonly used as global stiffness performance indices to evaluate the overall stiffness performance of parallel mechanisms,which involves an analysis of the stiffness performance of numerous discrete points in the workspace.This necessitates time-consuming and inefficient calculation,which is particularly pronounced in the optimization design stage of the mechanism,where the variations in the global stiffness performance indices versus various dimensional and structural parameters need to be analyzed.This paper presents a semi-analytical approach for stiffness modeling of the novel(R(RPS&RP))&2-UPS parallel mechanism(referred to as the Trifree mechanism)and proposes“local”stiffness performance indices as alternatives to global indices.Drawing on the screw theory,the Cartesian stiffness matrix of the Trifree mechanism is formulated explicitly by considering the compliances of all elastic elements and the over-constraint characteristics inherent in the mechanism.Based on the spherical motion pattern of the Trifree mechanism,four special reference configurations are extracted within the workspace.This yields“local”stiffness performance indices capable of accurately evaluating the overall stiffness performance of the mechanism and effectively improving the computational efficiency.The variations in global and“local”stiffness performance indices versus key design parameters are investigated.Furthermore,the proposed indices are applied to the Tricept and Trimule mechanisms.The results demonstrate that the proposed indices exhibit excellent computational accuracy and efficiency in evaluating the overall stiffness performance of these spherical parallel mechanisms.Moreover,the stiffness performance of the novel parallel mechanism investigated in this study closely resembles that of the well-known Tricept and Trimule mechanisms.This research proposes a semi-analytic stiffness model of the Trifree mechanism and“local”stiffness performance indices to evaluate the overall stiffness performance,thereby substantially improving the computational efficiency without sacrificing accuracy.
基金supported by the National Natural Science Foundation of China(No.92371206)the Postgraduate Scientific Research Innovation Project of Hunan Province,China(No.CX2023063).
文摘Satellite Component Layout Optimization(SCLO) is crucial in satellite system design.This paper proposes a novel Satellite Three-Dimensional Component Assignment and Layout Optimization(3D-SCALO) problem tailored to engineering requirements, aiming to optimize satellite heat dissipation while considering constraints on static stability, 3D geometric relationships between components, and special component positions. The 3D-SCALO problem is a challenging bilevel combinatorial optimization task, involving the optimization of discrete component assignment variables in the outer layer and continuous component position variables in the inner layer,with both influencing each other. To address this issue, first, a Mixed Integer Programming(MIP) model is proposed, which reformulates the original bilevel problem into a single-level optimization problem, enabling the exploration of a more comprehensive optimization space while avoiding iterative nested optimization. Then, to model the 3D geometric relationships between components within the MIP framework, a linearized 3D Phi-function method is proposed, which handles non-overlapping and safety distance constraints between cuboid components in an explicit and effective way. Subsequently, the Finite-Rectangle Method(FRM) is proposed to manage 3D geometric constraints for complex-shaped components by approximating them with a finite set of cuboids, extending the applicability of the geometric modeling approach. Finally, the feasibility and effectiveness of the proposed MIP model are demonstrated through two numerical examples"and a real-world engineering case, which confirms its suitability for complex-shaped components and real engineering applications.
文摘The rapid development of evolutionary deep learning has led to the emergence of various Neural Architecture Search(NAS)algorithms designed to optimize neural network structures.However,these algorithms often face significant computational costs due to the time-consuming process of training neural networks and evaluating their performance.Traditional NAS approaches,which rely on exhaustive evaluations and large training datasets,are inefficient for solving complex image classification tasks within limited time frames.To address these challenges,this paper proposes a novel NAS algorithm that integrates a hierarchical evaluation strategy based on Surrogate models,specifically using supernet to pre-trainweights and randomforests as performance predictors.This hierarchical framework combines rapid Surrogate model evaluations with traditional,precise evaluations to balance the trade-off between performance accuracy and computational efficiency.The algorithm significantly reduces the time required for model evaluation by predicting the fitness of candidate architectures using a random forest Surrogate model,thus alleviating the need for full training cycles for each architecture.The proposed method also incorporates evolutionary operations such as mutation and crossover to refine the search process and improve the accuracy of the resulting architectures.Experimental evaluations on the CIFAR-10 and CIFAR-100 datasets demonstrate that the proposed hierarchical evaluation strategy reduces the search time and costs compared to traditional methods,while achieving comparable or even superior model performance.The results suggest that this approach can efficiently handle resourceconstrained tasks,providing a promising solution for accelerating the NAS process without compromising the quality of the generated architectures.
文摘Background:Traditional imaging approaches to keratoconus(KCN)have thus far failed to produce a standardized approach for diagnosis.While many diagnostic modalities and metrics exist,none have proven robust enough to be considered a gold standard.This study aims to introduce novel metrics to differentiate between KCN and healthy corneas using three-dimensional(3D)measurements of surface area and volume.Methods:This retrospective observational study examined KCN patients along with healthy control patients between the ages of 20 and 79 years old at the University of Maryland,Baltimore.The selected patients underwent a nine-line raster scan anterior segment optical coherence tomography(AS-OCT).ImageJ was used to determine the central 6 mm of each image and each corneal image was then divided into six 1 mm segments.Free-D software was then used to render the nine different images into a 3D model to calculate corneal surface area and volume.A two-tailed Mann-Whitney test was used to assess statistical significance when comparing these subsets.Results:Thirty-three eyes with KCN,along with 33 healthy control,were enrolled.There were statistically significant differences between the healthy and KCN groups in the metric of anterior corneal surface area(13.927 vs.13.991 mm^(2),P=0.046),posterior corneal surface area(14.045 vs.14.173 mm^(2),P<0.001),and volume(8.430 vs.7.773 mm3,P<0.001)within the central 6 mm.Conclusions:3D corneal models derived from AS-OCT can be used to measure anterior corneal surface area,posterior corneal surface area,and corneal volume.All three parameters are statistically different between corneas with KCN and healthy corneas.Further study and application of these parameters may yield new methodologies for the detection of KCN.
文摘Offline policy evaluation,evaluating and selecting complex policies for decision-making by only using offline datasets is important in reinforcement learning.At present,the model-based offline policy evaluation(MBOPE)is widely welcomed because of its easy to implement and good performance.MBOPE directly approximates the unknown value of a given policy using the Monte Carlo method given the estimated transition and reward functions of the environment.Usually,multiple models are trained,and then one of them is selected to be used.However,a challenge remains in selecting an appropriate model from those trained for further use.The authors first analyse the upper bound of the difference between the approximated value and the unknown true value.Theoretical results show that this difference is related to the trajectories generated by the given policy on the learnt model and the prediction error of the transition and reward functions at these generated data points.Based on the theoretical results,a new criterion is proposed to tell which trained model is better suited for evaluating the given policy.At last,the effectiveness of the proposed criterion is demonstrated on both benchmark and synthetic offline datasets.
文摘With the continuous development of the nursing discipline,standardized nurse training has always been a crucial link in the development of nursing science and plays an irreplaceable role in talent cultivation.However,in the current standardized training for some nurses,there are problems such as the simplification of nursing skill evaluation models and insufficient post competence of nurses.Therefore,optimizing the training model for nursing talents has become an inevitable measure.The problem-based learning(PBL)method and the Direct Observation of Procedural Skills(DOPS)evaluation model provide new directions and guidance for the development of training.Against this background,this paper explores effective approaches for standardized nurse training,starting from basic concepts and gradually delving into specific practical paths,aiming to improve the quality of talent cultivation and provide valuable references for other researchers.
基金supported by the Youth Program of National Natural Science Foundation of China(No.52404343)the General Program of National Natural Science Foundation of China(No.52274326)+2 种基金the Fundamental Research Funds for the Central Universities,China(No.N2425031)the Seventh Batch of Ten Thousand Talents Plan of China(No.ZX20220553)the Liaoning Province Science and Technology Plan Joint Program,China(Key Research and Development Program Project)(No.2023JH2/101800058).
文摘The comprehensive status of blast furnaces was one of the most important factors affecting their economy,quality,and longev-ity.The blast furnace comprehensive status had the nature of“black box,”and it was“unpredictable.”In this study,a blast furnace com-prehensive status score and prediction method based on a cascade system and a combined model were proposed to address this issue.A dual cascade evaluation system was developed by integrating subjective and objective weighting methods.The analytic hierarchy process,coefficient of variation,entropy weight method,and impart combinatorial games were jointly employed to determine the optimal weight distribution across indicators.Categorized statuses(raw material,gas flow,furnace body,furnace cylinder,and iron-slag)were evaluated.Based on the five categories of the status data,the second cascade was applied to upgrade the quantitative evaluation of the comprehens-ive status.The weights of the different categories were 0.22,0.15,0.22,0.21,and 0.20,respectively.According to the data analysis,the results of the comprehensive status score closely matched the on-site production logs.Based on the blast furnace smelting period,the maximal information coefficient method was applied to the 100 parameters that were most relevant to the comprehensive status.A com-bined prediction model for a comprehensive status score was designed using bidirectional long short-term memory(BiLSTM)and categorical boosting(CatBoost).The test results indicated that the combined model reduced the mean absolute error by an average of 0.275 and increased the hit rate by an average of 5.65 percentage points compared to BiLSTM or CatBoost alone.When the er-ror range was±2.5,the combined model predicted a hit rate of 91.66%for the next hour’s comprehensive status score,and its high accur-acy was deemed satisfactory for the field.SHapley Additive exPlanations(SHAP)and regression fitting were applied to analyze the lin-ear quantitative relationship between the key variables and the comprehensive status score.When the furnace bottom center temperature was increased by 10℃,the comprehensive status score increased by 0.44.This method contributes to a more precise management and control of the comprehensive status of the blast furnace on-site.
