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The premise of this study is that education is an intentional attempt to discover pupils' potential. Students receive learning outcomes for every course they have taken at the conclusion of each semester, along with learning successes or the Cumulative Achievement Index. Using the K-Means Clustering approach is one way to categorize pupils based on their cumulative achievement index. This study uses a combination of quantitative and qualitative methodologies, which is known as a mix-method study. A sequential explanatory design is used in this investigation. This study's findings include the application of the Data Mining Algorithm to classify students' learning outcomes in the Informatics Vocational Education Study Program according to socioeconomic and social factors, particularly if they major in computer education. Because of this, students require computers and other learning aids in order to facilitate learning in the classroom and to avoid any problems when the lecturer assigns homework.

In order to solve the problem that random selection of clustering number k can easily lead to instability and low computational efficiency of k-means clustering algorithm in the zoning calculation of wind load on the surface of long-span roof structures, an improved Canopy-k-means clustering algorithm was proposed. Firstly, the Canopy algorithm was introduced, and the selection of its initial threshold and clustering center was improved to reduce the blindness of the initial value selection so as to improve the reliability of the results of wind load zoning. Secondly, the improved Canopy algorithm was used to preprocess the wind load dataset to determine the cluster number k quickly and accurately. Thirdly, the improved Canopy algorithm was combined with k-means to achieve the accurate identification of the optimal classification number k, so that the improved Canopy-k-means clustering algorithm can get the zoning results quickly and accurately when the wind load on the surface of long-span roof structure was divided. Finally, taking a long-span cylindrical roof dry coal shed structure as an example, based on the test results of the surface wind load data obtained from the wind tunnel test, the improved Canopy-k-means clustering algorithm was used to calculate the surface wind load. The results show that by using the improved Canopy-k-means clustering algorithm, the wind loads on the surface of long-span roofs at 0 °, 50 °and 90 °wind angles are divided into three different zones, and the corresponding SD values are 2.36,3.51 and 2.52,respectively, which are significantly lower than those obtained by the traditional k-means clustering algorithm, and the intra-class compactness and inter-class dispersion are obviously improved. Therefore, the improved Canopy-k-means clustering algorithm can obtain the optimal zoning results quickly and accurately, and has good engineering application value for wind load zoning on the surface of long-span roofs.

Copyright of Advanced Textile Technology is the property of Zhejiang Sci-Tech University Magazines and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Hebei University of Science & Technology is the property of Hebei University of Science & Technology, Journal of Hebei University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Vigorously developing flexible resources in power systems will be the key to building a new power system and realizing energy transformation. The investment construction cost and operation cost of various flexible resources are different, and the adjustment ability is different in different timescales. Therefore, the optimization of complementary allocation of various resources needs to take into account the economy and adjustment ability of different resources. In this paper, the global K-means load clustering model is proposed and the 365-day net load is reduced to eight typical daily net loads by clustering. Secondly, a two-level optimization model of flexible resource complementary allocation considering wind power and photovoltaic consumption is constructed. The flexible resources involved include the flexible transformation of thermal power, hydropower, pumped storage, energy storage, and demand response. The upper-layer model optimizes the capacity allocation of various flexible resources with the minimum investment and construction cost as the goal and the lower layer optimizes the operating output of various units with the minimum operating cost as the goal. The results of the example analysis show that the flexible capacity of thermal power units has nothing to do with the abandonment rate of renewable energy. As the abandonment rate of renewable energy decreases, the optimal capacity of pumped storage, electrochemical energy storage, and hydropower units increases. When the power-abandonment rate of renewable energy is 5%, the optimal allocation capacity of thermal power flexibility transformation, pumped storage, electrochemical energy storage, hydropower unit, and adjustable load in Province A is 5313, 17 090, 5830, 72 113, and 4250 MW, respectively. Under the condition that the renewable-energy abandonment rate is 0, 5%, and 10% respectively, the configured capacity of pumped storage is 20 000, 17 090, and 14 847 MW, respectively. [ABSTRACT FROM AUTHOR]

The duration of a residential building project will directly influence its successful implementation; hence, it is essential to estimate a reasonable timeframe. In this study, a genetic algorithm (GA) was employed to optimize and refine the weights and thresholds of a back propagation (BP) neural network, thereby creating a GA‐BP neural network model. A dataset comprising 111 instances of residential building durations was gathered, segmented into 90 training sets and 21 test sets. The model was validated and assessed through root mean square error (RMSE), correlation coefficient (R), and average error rate, demonstrating that the GA‐BP neural network model is effective in predicting the duration of residential buildings. To enhance the predictive accuracy of the GA‐BP neural network model, this research utilized an artificial bee colony (ABC)‐improved K‐means clustering algorithm to categorize 111 experimental datasets and 33 new datasets. The results indicated that the ABC‐K‐means‐GA‐BP model exhibited robust generalization capabilities and high predictive accuracy, with the fitness function showing optimal performance after 10, 15, and 35 generations, and the best validation performances recorded as 0.0019156, 0.00035905, and 0.0036914. This validates that the proposed ABC‐K‐means‐GA‐BP neural network model significantly aids in forecasting the construction period of residential buildings, which holds substantial practical value for enhancing construction efficiency. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Zhejiang University (Science Edition) is the property of Journal of Zhejiang University (Science Edition) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The adaptive façades serve as the interface between the indoor and outdoor energy of the building. Adaptive façade optimization design can improve daylighting performance, the thermal environment, view performance, and solar energy utilization efficiency, thus reducing building energy consumption. However, traditional design frameworks often neglect the influence of building envelope performance characteristics on adaptive façade optimization design. This paper aims to reveal the potential functional relationship between building façade performance characteristics and adaptive façade design. It proposes an adaptive façade optimization design framework based on building envelope performance characteristics. The method was then applied to a typical office building in northern China. This framework utilizes a K-means clustering algorithm to analyze building envelope performance characteristics, establish a link to adaptive façade design, and use the optimization algorithm and machine learning to make multi-objective optimization predictions. Finally, Pearson's correlation analysis and visual decision tools were employed to explore the optimization potential of adaptive façades concerning indoor daylighting performance, view performance, and solar energy utilization. The results showed that the optimized adaptive façade design enhances useful daylight illuminance (UDI) by 0.52%, quality of view (QV) by 5.36%, and beneficial solar radiation energy (BSR) by 14.93% compared to traditional blinds. In addition, each office unit can generate 309.94 KWh of photovoltaic power per year using photovoltaic shading systems. The framework provides new perspectives and methods for adaptive façade optimization design, which helps to achieve multiple performance objectives for buildings. [ABSTRACT FROM AUTHOR]

The malfunction variables of power stations are related to the areas of weather, physical structure, control, and load behavior. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behavior into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by long-short-term memory and gated recurrent unit algorithms are used to perform the short-term estimation. The environment, the operation, and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a dataset. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, to address the gap in knowledge for any future power grid estimated failures, the achieved results in this paper form good basis for a testbed to estimate any grid future failures. [ABSTRACT FROM AUTHOR]

With the development of big data technology, the field of monitoring and improving teaching quality in universities has ushered in new opportunities and challenges. Big data technology enables the capture and analysis of massive amounts of data generated during the teaching process, providing the possibility for a deeper understanding of teaching activities. However, how to extract useful information from these vast amounts of data and transform it into strategies for teaching improvement is a challenge. The research aims to propose a teaching quality monitoring and improvement method based on big data technology, which combines K-means clustering algorithm and association rule mining algorithm to improve the accuracy of teaching monitoring and the effectiveness of teaching improvement. In order to cope with these challenges, the study proposes a research method of big data technology based on joint K-mean clustering algorithm and association rule mining algorithm. The study first analyzes the teaching quality monitoring and evaluation indexes using the K-mean algorithm. Then the association rule mining algorithm is utilized to mine the data in the teaching quality monitoring indicators with association rules on the basis of the obtained cluster analysis. Finally, on the basis of association rule mining, the study constructs the assessment model of teaching quality monitoring indicators by utilizing the fused method. The outcomes revealed that the average of data analysis accuracy and the average of recall rate of the modeling method were 93.79 % and 91.95 %, respectively. Meanwhile, the evaluation time of the modeling method in the process of teaching quality monitoring data processing was 17.3 s, and the evaluation precision was 93.15 % respectively. Additionally, the process's overall confidence and enhancement are 95.01 % and 86.73 %, respectively, and the modeling method's performance is compared to other approaches. This shown that the approach may significantly boost the precision and effectiveness of monitoring the quality of instruction, as well as offer strong backing for the enhancement of instruction in higher education institutions.

Abstract The evaluation of financial sharing centres in enterprises typically relies on outdated financial data, lacks comprehensive assessment, and presents risks such as employee misconduct. To address these challenges, we propose a risk prediction model for enterprise financial sharing operations based on the K-means clustering algorithm for performance evaluation and the C4.5 algorithm for managing employee risks. Our approach enhances the accuracy and objectivity of performance evaluation while improving the efficiency of personnel risk management. Results indicate that the K-means algorithm classifies employee performance into five levels, facilitating comprehensive performance evaluation. Furthermore, through risk management optimisation, accuracy and recall rates increase to 0.905 and 0.890, respectively. The proposed risk prediction model achieves high accuracy rates of 90.5% and 92.4% in the training and test sets, respectively. Practical application of our methodology and model in A Group's financial sharing centre demonstrates their effectiveness and potential for enhancing the operation and management of enterprise financial sharing centres.

The advancement of 3D printing technology has enabled the fabrication of intricate structures, yet the complexity of the print head's motion path significantly hampers production efficiency. Addressing the challenges posed by the dataset of section points in 3D-printed workpieces, this study introduces an innovative ant colony optimization algorithm tailored to enhance the print head's trajectory. By framing the optimization of the motion path as a Traveling Salesman Problem (TSP), the research employs a custom-designed K-means clustering algorithm to categorize the dataset into distinct clusters. This clustering algorithm partitions each printing point into different subsets based on density, optimizes these subsets through improved K-means clustering computations, and then aggregates the results to classify the entire dataset. Subsequently, the ant colony algorithm arranges the printing sequence of these clusters based on the cluster centers, followed by computing the shortest path within each cluster. To form a cohesive motion trajectory, the nearest nodes between adjacent clusters are linked, culminating in a globally optimal solution. Comparative experiments repeatedly demonstrate significant enhancements in the print head's motion path, leading to marked improvements in printing efficiency. [ABSTRACT FROM AUTHOR]

Sand porosity is an important compactness parameter that influences the mechanical properties of sand. In order to evaluate the temporal variation in sand porosity, a new method of sand porosity evaluation based on the statistics of target sand particles (which refers to particles within a specific particle size range) is presented. The relationship between sand porosity and the number of target sand particles at the soil surface considering observation depth is derived theoretically, and it is concluded that there is an inverse relationship between the two. Digital image processing and the k-means clustering method were used to distinguish particles in digital images where particles may mask each other, and a criterion for determining the number of particles was proposed, that is, the criterion of min(Dao). The execution process was implemented by self-written codes using Python (2021.3). An experiment on a simple case of Go pieces and sand samples of different porosities was conducted. The results show that the sum of the squared error (SSE) in the k-means method can converge with a small number of iterations. Furthermore, there is a minimum value between the parameter Dao and the set value of a single-particle pixel, and the pixel corresponding to this value is a reasonable value of a single-particle pixel, that is, the min(Dao) criterion is proposed. The k-means method combined with the min(Dao) criterion can analyze the number of particles in different particle size ranges with occlusion between particles. The test results of sand samples with different densities show that the method is reasonable. [ABSTRACT FROM AUTHOR]

Risk budgeting is one of the recent and successful strategies for asset portfolio selection. In this strategy, risk budgets are associated with assets, and the amount of investment is adjusted so that the contribution of each asset to the portfolio risk is proportional to its risk budget. To the best of our knowledge, no specific method has been presented in the literature to systematically determine the value of risk budgets. To fill this research gap, in this article, we consider the risk budgets as decision variables and present a bi-level programming model where the upper level decides the risk budgets and the lower level determines the risk budgeting portfolio. Three approaches are introduced to solve the model. The first is a single-level reformulation of the bi-level model, the second is a novel gradient-based algorithm, and the third is the particle swarm optimization algorithm. Moreover, the k-means clustering method is utilized to determine the assets involved in the portfolio. Computational results over real-world datasets demonstrate the significance of the bi-level model. In addition, the results confirm the proficiency of our gradient-based algorithm from both solution quality and running time. [ABSTRACT FROM AUTHOR]

Copyright of Light Industry Machinery is the property of Light Industry Machinery Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the task unloading mode in cloud computing environment, the task unloading problem for IoT devices is studied. Through theoretical analysis, we can know that in the task unloading problem, it is usually contradictory to improve the utilization of cloud resources and reduce the task delay. In order to solve this problem, a task unloading scheme for Internet of things devices using deep reinforcement learning algorithm is proposed. The deep reinforcement learning algorithm is used to model the task unloading problem. The return value with weight is introduced into the algorithm, and the utilization rate of cloud resources and the delay of unloading task are weighed by adjusting the return value of the weight. First of all, the improved k-means clustering algorithm with weighted density is used to cluster the physical machines. The physical machines of each cluster have similar bandwidth and task waiting time. Then, deep reinforcement learning is used to select the best physical machine cluster from the current unloading tasks. Finally, the improved PSO algorithm is used to select the optimal physical machine from the optimal cluster, and Pareto is used to improve the convergence speed. Experimental results show that compared with the traditional method, the proposed algorithm has a good performance, and can achieve the goal of increasing the utilization of physical machine resources and reducing task delay. [ABSTRACT FROM AUTHOR]

Urban rail transit network planning is a complex issue that involves multiple considerations, including traffic flow, passenger demand, geographical environment, economic costs, environmental protection. To address the pressing issues of inadequate station coverage, protracted planning timelines, and suboptimal passenger satisfaction in conventional urban rail transit network planning methodologies, the paper delves into the application of a Deep Dual Q Network in urban rail transit network planning. By initially analyzing the distribution density of Points of Interest (POI), the study employs the K-means clustering algorithm to meticulously select optimal locations for urban rail transit stations. Subsequently, a tailored urban rail transit network planning model is constructed, incorporating the site selection outcomes and delineating its pertinent constraints. Leveraging the prowess of a Deep dual Q Network, the model is efficiently solved, yielding an optimized urban rail transit network planning scheme. Experimental validation reveals remarkable outcomes, with a maximum station coverage rate of 91.9%, a minimized planning duration of 23.6 minutes, and a pinnacle passenger satisfaction rating of 98.1%, underscoring the practical efficacy and significance of the proposed approach. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shanghai Polytechnic University is the property of Journal of Shanghai Polytechnic University Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the rapid development of internet technology, knowledge acquisition has become more convenient and efficient in network operations. University libraries serve as important resources for readers to acquire knowledge, and online resources and services in libraries have become the main direction for readers to acquire knowledge at present. Research the use of binary K-means clustering algorithm and library reader portrait technology to optimize the design of the reader portrait module and construct a multidimensional and multi perspective reader feature system. Reuse Spark programming language and support vector machine to perform computational processing on reader profile data to ensure accurate segmentation of the dataset. Finally, three datasets were used to test the accuracy and efficiency of the algorithm. The experimental comparison shows that the mining and precision segmentation of parallel SVM on the dataset are 93.20%, 85.16%, and 79.35% on the sample set, respectively, in order to optimize the mining performance of the data. The MD multi view binary K-means algorithm has a total Mahalanobis distance of 3.543, 5.268, and 22.385 on the sample dataset, respectively, to demonstrate its superiority in clustering performance. Therefore, the multi view binary K-means algorithm based on Mahalanobis distance has high advantages in reader portrait technology design, and provides technical support and theoretical reference for library reader portrait technology. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

INTRODUCTION: The article discusses the key steps in digital visual design reengineering, with a special emphasis on the importance of information decoding and feature extraction for flat cultural heritage. These processes not only minimize damage to the aesthetic heritage itself but also feature high quality, efficiency, and recyclability. OBJECTIVES: The aim of the article is to explore the issues of gene extraction methods in digital visual design reengineering, proposing a visual gene extraction method through an improved K-means clustering algorithm. METHODS: A visual gene extraction method based on an improved K-means clustering algorithm is proposed. Initially analyzing the digital visual design reengineering process, combined with a color extraction method using the improved JSO algorithm-based K-means clustering algorithm, a gene extraction and clustering method for digital visual design reengineering is proposed and validated through experiments. .ASA-RESULT: The results show that the proposed method improves the accuracy, robustness, and real-time performance of clustering. Through comparative analysis with Dunhuang murals, the effectiveness of the color extraction method based on the K-means-JSO algorithm in the application of digital visual design reengineering is verified. The method based on the K-means-GWO algorithm performs best in terms of average clustering time and standard deviation. The optimization curve of color extraction based on the K-means-JSO algorithm converges faster and with better accuracy compared to the K-means-ABC, K-means-GWO, K-means-DE, K-means-CMAES, and K-means-WWCD algorithms. CONCLUSION: The color extraction method of the K-means clustering algorithm improved by the JSO algorithm proposed in this paper solves the problems of insufficient standardization in feature selection, lack of generalization ability, and inefficiency in visual gene extraction methods. [ABSTRACT FROM AUTHOR]

We propose a new unsupervised learning method for clustering a large number of time series based on a latent factor structure. Each cluster is characterized by its own cluster-specific factors in addition to some common factors which impact on all the time series concerned. Our setting also offers the flexibility that some time series may not belong to any clusters. The consistency with explicit convergence rates is established for the estimation of the common factors, the cluster-specific factors, and the latent clusters. Numerical illustration with both simulated data as well as a real data example is also reported. As a spin-off, the proposed new approach also advances significantly the statistical inference for the factor model of Lam and Yao. for this article are available online. [ABSTRACT FROM AUTHOR]

The paranasal sinuses, a bilaterally symmetrical system of eight air-filled cavities, represent one of the most complex parts of the equine body. This study aimed to extract morphometric measures from computed tomography (CT) images of the equine head and to implement a clustering analysis for the computer-aided identification of age-related variations. Heads of 18 cadaver horses, aged 2–25 years, were CT-imaged and segmented to extract their volume, surface area, and relative density from the frontal sinus (FS), dorsal conchal sinus (DCS), ventral conchal sinus (VCS), rostral maxillary sinus (RMS), caudal maxillary sinus (CMS), sphenoid sinus (SS), palatine sinus (PS), and middle conchal sinus (MCS). Data were grouped into young, middle-aged, and old horse groups and clustered using the K-means clustering algorithm. Morphometric measurements varied according to the sinus position and age of the horses but not the body side. The volume and surface area of the VCS, RMS, and CMS increased with the age of the horses. With accuracy values of 0.72 for RMS, 0.67 for CMS, and 0.31 for VCS, the possibility of the age-related clustering of CT-based 3D images of equine paranasal sinuses was confirmed for RMS and CMS but disproved for VCS. [ABSTRACT FROM AUTHOR]

The analysis of microstates in EEG signals is a crucial technique for understanding the spatiotemporal dynamics of brain electrical activity. Traditional methods such as Atomic Agglomerative Hierarchical Clustering (AAHC), K-means clustering, Principal Component Analysis (PCA), and Independent Component Analysis (ICA) are limited by a fixed number of microstate maps and insufficient capability in cross-task feature extraction. Tackling these limitations, this study introduces a Global Map Dissimilarity (GMD)-driven density canopy K-means clustering algorithm. This innovative approach autonomously determines the optimal number of EEG microstate topographies and employs Gaussian kernel density estimation alongside the GMD index for dynamic modeling of EEG data. Utilizing this advanced algorithm, the study analyzes the Motor Imagery (MI) dataset from the GigaScience database, GigaDB. The findings reveal six distinct microstates during actual right-hand movement and five microstates across other task conditions, with microstate C showing superior performance in all task states. During imagined movement, microstate A was significantly enhanced. Comparison with existing algorithms indicates a significant improvement in clustering performance by the refined method, with an average Calinski-Harabasz Index (CHI) of 35517.29 and a Davis-Bouldin Index (DBI) average of 2.57. Furthermore, an information theoretical analysis of the microstate sequences suggests that imagined movement exhibits higher complexity and disorder than actual movement. By utilizing the extracted microstate sequence parameters as features, the improved algorithm achieved a classification accuracy of 98.41% in EEG signal categorization for motor imagery. A performance of 78.183% accuracy was achieved in a four-class motor imagery task on the BCI-IV-2a dataset. These results demonstrate the potential of the advanced algorithm in microstate analysis, offering a more effective tool for a deeper understanding of the spatiotemporal features of EEG signals. [ABSTRACT FROM AUTHOR]

A large number of digital painting image resources cannot be directly converted into electronic form due to their differences in painting techniques and poor preservation of paintings. Moreover, the difficulty of extracting classification features can also lead to the consumption of human time and misclassification problems. The aim of this research is to address the challenges of converting various digital painting image resources into electronic form and the difficulties of accurately extracting classification features. The goal is to improve the usefulness and accuracy of painting image classification. Converting various digital painting image resources directly into electronic format and accurately extracting classification features are challenging due to differences in painting techniques and painting preservation, as well as the complexity of accurately extracting classification features. Overcoming these adjustments and improving the classification of painting features with the help of artificial intelligence (AI) techniques is crucial. The existing classification methods have good applications in different fields. But their research on painting classification is relatively limited. In order to better manage the painting system, advanced intelligent algorithms need to be introduced for corresponding work, such as feature recognition, image classification, etc. Through these studies, unlabeled classification of massive painting images can be carried out, while guiding future research directions. This study proposes an image classification model based on AI stroke features, which utilizes edge detection and grayscale image feature extraction to extract stroke features; and the convolutional neural network (CNN) and support vector machine are introduced into image classification, and an improved LeNet-5 CNN is proposed to achieve comprehensive assurance of image feature extraction. Considering the diversity of painting image features, the study combines color features with stroke features, and uses weighted K-means clustering algorithm to extract sample features. The experiment illustrates that the K-CNN hybrid model proposed in the study achieved an accuracy of 94.37% in extracting image information, which is higher than 78.24, 85.69, and 86.78% of C4.5, K-Nearest Neighbor (KNN), and Bi directional Long short-term Memory (BiLSTM) algorithms. In terms of image classification information recognition, the algorithms with better performance from good to poor are: the mixed model > BiLSTM > KNN > C4.5 model, with corresponding accuracy values of 0.938, 0.897, 0.872, and 0.851, respectively. And the number of fluctuation nodes in the mixed model is relatively small. And the sample search time is significantly shorter than other comparison algorithms, with a maximum recognition accuracy of 92.64% for the style, content, color, texture, and direction features of the image, which can effectively recognize the contrast and discrimination of the image. This method effectively provides a new technical means and research direction for digitizing image information.

INTRODUCTION: Timely and effective early warning of new media events not only provides academic value to the study of new media events, but also can play a positive role in promoting the resolution of public opinion. OBJECTIVES: Aiming at the current research on early warning of new media events, there are problems such as the theoretical research is not in-depth and the early warning model is not comprehensive. METHOD: In this paper, K-means and seasonal optimization algorithm are used to construct new media event early warning method. Firstly, by analyzing the construction process of new media event early warning system, extracting text feature vector and carrying out text feature dimensionality reduction; then, combining with the random forest algorithm, the new media event early warning method based on intelligent optimization algorithm optimizing K-means clustering algorithm is proposed; finally, the validity and superiority of the proposed method is verified through the analysis of simulation experiments. RESULTS: The method developed in this paper improves the accuracy, time performance of new media event warning techniques. CONCLUSION:Addresses the lack of comprehensiveness of current approaches to early warning of new media events. [ABSTRACT FROM AUTHOR]

The uncertainty and sudden nature of public health emergencies make efficient and accurate emergency material allocation plans particularly important. A site selection model of daily necessities distribution transfer station under the background of public health emergencies was constructed. Considering the occurrence of special events and updating priority coefficients and time window calculations, a multi-objective delivery path planning model was established using transportation distance, penalty cost for violating vehicle capacity and time window constraints as the optimization goals. The site selection problem was solved by the K-means clustering algorithm and the optimal delivery path was obtained using a hybrid genetic algorithm combining genetic algorithm and large neighborhood search algorithm. Finally, the example of material distribution in the Chaoyang District of Changchun was used to empirical analysis. The results indicate that 200 neighborhoods can be clustered into 60 material demand sites for distribution. It requires 26 vehicles to transport the materials from the distribution center to the demand sites and four types of optimal material distribution schemes are obtained, which provides a new idea for solving the emergency material allocation under public health emergencies. [ABSTRACT FROM AUTHOR]

In the iron and steel industry, evaluating the energy utilization efficiency (EUE) and determining the optimal energy matching mode play an important role in addressing increasing energy depletion and environmental problems. Electric Arc Furnace (EAF) steelmaking is a typical short crude steel production route, which is characterized by an energy-intensive fast smelting rhythm and diversified raw charge structure. In this paper, the energy model of the EAF steelmaking process is established to conduct an energy analysis and EUE evaluation. An association rule mining (ARM) strategy for guiding the EAF production process based on data cleaning, feature selection, and an association rule (AR) algorithm was proposed, and the effectiveness of this strategy was verified. The unsupervised algorithm Auto-Encoder (AE) was adopted to detect and eliminate abnormal data, complete data cleaning, and ensure data quality and accuracy. The AE model performs best when the number of nodes in the hidden layer is 18. The feature selection determines 10 factors such as the hot metal (HM) ratio and HM temperature as important data features to simplify the model structure. According to different ratios and temperatures of the HM, combined with k-means clustering and an AR algorithm, the optimal operation process for the EUE in the EAF steelmaking under different smelting modes is proposed. The results indicated that under the conditions of a low HM ratio and low HM temperature, the EUE is best when the power consumption in the second stage ranges between 4853 kWh and 7520 kWh, the oxygen consumption in the second stage ranges between 1816 m3 and 1961 m3, and the natural gas consumption ranges between 156 m3 and 196 m3. Conversely, under the conditions of a high HM ratio and high HM temperature, the EUE tends to decrease, and the EUE is best when the furnace wall oxygen consumption ranges between 4732 m3 and 5670 m3, and the oxygen consumption in the second stage ranges between 1561 m3 and 1871 m3. By comparison, under different smelting modes, the smelting scheme obtained by the ARM has an obvious effect on the improvement of the EUE. With a high EUE, the improvement of the A2B1 smelting mode is the most obvious, from 24.7% to 53%. This study is expected to provide technical ideas for energy conservation and emission reduction in the EAF steelmaking process in the future. [ABSTRACT FROM AUTHOR]