rss_2.0Sciendo RSS Feed for Life Scienceshttps://www.sciendo.com/subject/LFhttps://www.sciendo.comhttps://www.sciendo.com/subjectImages/Life_Sciences.jpg700700https://sciendo.com/article/10.2478/rtuect-2023-0064<abstract> <title style='display:none'>Abstract</title> <p>Cement materials have been commonly used in the building and construction industries. However, the process of cement manufacture has long been connected with high consumption of energy and adverse environmental impacts. In this study, in order to produce innovative green cement material that consumes lower energy, resources and is more eco-friendly, industrial waste by-product fly ash cenosphere (FAC) has been utilized as lightweight aggregate to replace cement by 73.3 %. Most research regarding lightweight cement materials with FAC has mainly paid attention to the influence of FAC and the reinforcement via fibre materials, but very few studies have been devoted to the incorporation of nano additives. Therefore, 0.05 %, 0.15 %, 0.45 % of carbon nanotubes (CNTs) and 0.2 %, and 1.0 % of nano silica (NS) were used to modify lightweight cement composite (LWCC). Experiments including flexural strength test, compressive strength test, and thermogravimetric analysis were performed to evaluate the mechanical behaviours and the hydration process of the produced LWCC. Based on the experimental outcomes, incorporating CNTs and NS can effectively contribute to enhancing both flexural and compressive strength, and facilitate cement hydration reaction.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/rtuect-2023-00642023-12-07T00:00:00.000+00:00https://sciendo.com/article/10.2478/rtuect-2023-0063<abstract> <title style='display:none'>Abstract</title> <p>Rising energy demands and the environmental impact of fossil fuel combustion have promoted a growing interest in alternative fuel sources. Biobutanol is a promising biofuel that can be used as a partial or complete substitute for petrol in unmodified internal combustion engines. It can be produced through a microbiological process called ABE fermentation. Currently, its production is uncompetitive in the market, but researchers are still working on solutions to improve the technology. This paper used a multi-criteria decision analysis method to evaluate different alternatives for biobutanol production: microorganism strain, agro-industrial waste substrate as process feedstock, bioreactor type and extraction method. It was determined that <italic>C. beijerinckii</italic> and <italic>C. saccharoperbutylacetonicum</italic> have great potential for being used for efficient biobutanol production. Cheese whey is a promising residue for being used in the fermentation medium. Other residues evaluated in the paper gained similar results as being “close to ideal”. Fed-batch with immobilized cells was chosen as the most promising fermentation method. It showed the greatest prospects as an optimal way to produce butanol. And, finally, adsorption and liquid-liquid extraction methods were identified as the most promising for ABE product extraction in comparison to others. Identified combinations of optimal solutions for microorganisms, fermentation methods, substrates and extraction techniques should be further evaluated in the laboratory setting.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/rtuect-2023-00632023-12-07T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01265<abstract> <title style='display:none'>Abstract</title> <p>The coupling and coupling coordination degree model is used in this paper to classify the coupling coordination degree level of industry and regional economic development. According to the correlation between the fiscal, taxation, and financial system and regional economic development, the coupling driving mechanism algorithm is selected to analyze the degree of assistance of the fiscal, taxation, and financial systems to the coordinated development of the regional economy. Taking the analysis of the Chongqing tourism industry and regional economic development level as an example, the coupling degree relationship between fiscal, tax and financial policies on regional economic development is analyzed according to the coupling coordination degree. The degree of financialization in the northern coastal economic zone is the highest at 4.4885, and the degree of financial deepening in the southern coastal integrated economic zone is 3.7621 lower than that in the northern region, but the coefficient of its β₁′ is 0.0207, which indicates that it has a positive regulating effect on the development of the regional economy.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.012652023-11-27T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01169<abstract> <title style='display:none'>Abstract</title> <p>In this paper, firstly, the relevant parameters of influent C/N quantification were selected, and then, on the basis of the relevant parameters, the apparent shear force formula was deduced, and the apparent shear force was used to reflect the hydraulic condition of the reactor. Next, the experimental device, operation method, influent water quality and analysis method were determined, and the influences of influent C/N on the sludge granulation process were derived from the influences of group I C/N ratio on anaerobic granular sludge and group II C/N ratio on anaerobic granular sludge. Then, the return sludge from the Guangzhou sewage treatment plant in Guangdong Province was selected as the experimental seed sludge, and the methanol concentration of 0.13g/L (COD=200mg/L) was used as the experimental water quality, and the influences of influent C/N on nutrient N₂O denitrification were illustrated by the experimental analyses. The results showed that the NO₂⁻ − N accumulation rate was maintained above 81% in all five water intake modes, and when the ratio of water intake (1:1) and (2:1), a higher concentration of NO₂⁻ − N was accumulated in the first nitrification stage of 26.5 mg/L and 24.9 mg/L, respectively, which could give full play to the advantages of nitrifying bacteria to achieve nitrification rapidly and also reduce the production of nutrient salt N₂O. This study combines macro-experiment and micro-analysis to provide a theoretical basis for granular sludge technology.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.011692023-11-18T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01417<abstract> <title style='display:none'>Abstract</title> <p>This paper utilizes web crawler in network text mining technology to obtain the Jiangsu garden review data on several OTAs and tourism UGC platforms, such as Baidu Tourism, Ctrip.com, and Ma’s Nest. The data preprocessing work such as text de-weighting, word frequency classification, and parameter solving is carried out on the document dataset by using the lexical algorithm, and the preprocessed text data is subjected to TF-IDF keyword extraction and LDA topic extraction. Starting from the characteristics of the cultural lineage, the intrinsic attraction elements affecting the commercial transformation of tourist attractions are explored, and taking the planning of the industrial, cultural tourism town in Ezhou as an example, the process of planning and designing from the establishment of tourism planning concepts, and thus the completion of the visual IP image, is elaborated. The tourism image perception of the four major gardens in Jiangsu is examined from both cognitive and emotional perspectives using the results of TF-IDF and LDA extraction. The results show that the trend of the frequency of each fraction of the emotion of the Humble Administrator’s Garden, Liouyuan Garden, Lion Grove, and Canglang Pavilion scenic spots is roughly the same, and the mean value of emotion is 0.758, 0.851, 0.828, and 0.845, respectively, reflecting the tourists’ average emotional perception of the individual scenic spots. This study provides new methods and ideas for promoting the integrated development of culture, commerce and tourism in small and medium-sized cities so that the culture and tourism industry can be developed sustainably.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.014172023-12-09T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01154<abstract> <title style='display:none'>Abstract</title> <p>The digitization of intangible cultural heritage provides the basis for its protection and inheritance. In this paper, we extracted the classes and interrelationships corresponding to each element for the content of Yi Intangible Cultural Heritage, constructed the data model and accomplished the relationship abstraction of different classes. Based on the structure of different data classes, the correlation between spatial change and non-heritage status is explored. The data model is proposed to cluster non-heritage data based on SWC-WMD distance, which improves the similarity calculation based on WMD distance. The association analysis of non-heritage item classes was performed using Yi folk dance as an example, and the overall non-heritage data was analyzed by clustering. In Yi folk dance, the association formed a network correlation graph with N2, N3, and N12 as the three main centers, in which the dance class items associated with N2, N3, and N12 were 9, 9, and 8, respectively. Mining out the relationships among NH items helps to better the overall NH protection.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.011542023-11-15T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01329<abstract> <title style='display:none'>Abstract</title> <p>Once the failure of rotating machinery occurs, it may cause the whole system to paralyze and cause great economic losses, or it may cause casualties. Therefore, the prediction of the remaining life of bearings is of great significance. The purpose of this paper is to analyze the approximate modeling technology and develop a framework for combined approximate modeling technology. A multi-strategy radial-based approximate model optimization model is proposed based on the limitations of radial-based approximate model technology. Utilizing the weight coefficient solving technique, the variable confidence RBF model, i.e., RBF-LSTM model, is established. Propose the remaining methods for life prediction using the bearing life prediction process. The RBF-LSTM combined approximation model is used to construct the evaluation index for rolling bearing remaining life prediction. Using the empirical analysis method, the optimization effects of different models and the accuracy of bearing remaining life prediction are analyzed, respectively. Experiments show that the data range of the RBF-LSTM combined approximation model is between [23,52], the overall fluctuation range of the data is not large, and the time taken is only 31 s. After 230 calculations, the model optimization effect is better. In the remaining life validation, the starting values of 132h and 148h are less different from real life, only 1.53h and 1.3h, respectively, and the model prediction accuracy is high.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.013292023-12-02T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01426<abstract> <title style='display:none'>Abstract</title> <p>This paper analyzes the fission information dissemination mode from the digital information media mode of the film and television media industry. Using the correlation algorithm to analyze the influence of TV drama ratings and broadcasting accounted for and selecting cluster analysis to explore the relationship between TV drama broadcasting and TV type and rating. Take the ratings as the dependent variable, set the independent variables, establish the multivariate statistical model, and use SPSS software to calculate factor analysis of TV drama ratings. By combining user opinions, optimize the heterogeneous graph neural network film and television communication model based on attribute information. Test the MAE value and effect of the propagation algorithm proposed in this paper using the real Movies Lens dataset. When N=5, the recall, precision and <italic>F</italic>₁ of this paper’s algorithm are 0.295, 0.751, and 0.425, respectively. The difference of the three metrics with the resource diffusion algorithm based on the three-step graph is 0.25, 0.634, and 0.36. When N=50, the difference of the three metrics between this paper’s algorithm and the social diffusion algorithm based on labels is 0.197, 0.071, and 0.101.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.014262023-12-09T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01416<abstract> <title style='display:none'>Abstract</title> <p>In this paper, continuous wavelet transform and discrete wavelet transform are used to detect transient anomalies entrained in normal information and to demonstrate their components. Multi-scale analysis of wavelet transform, Haar wavelet basis and multi-scale edge detection algorithms are utilized to determine the modal extreme points and identify the edge points for faster and more accurate extraction of edge features of the image. In order to further validate the applicability and feasibility of wavelet transform for printing images and to determine the quality inspection criteria based on ink penetration depth and image phase anisotropy, MATLAB software is utilized to perform simulation tests. The results show that the wavelet transform can remove the noise generated by uneven illumination and printing background during the printing process and can detect the edges of the printing image with an error accuracy of ±0.063mm and meet the error correction accuracy of &lt;0.4mm as required by the printing standard. The experiments verify the feasibility of the wavelet transform, which can characterize the depth of penetration of the printing ink and the image anisotropy and provides a theoretical basis for improving the quality of printing. The experiment confirms that wavelet transform can be used to measure printing ink penetration depth and image anisotropy, giving a theoretical basis for improving printing quality.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.014162023-12-09T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01419<abstract> <title style='display:none'>Abstract</title> <p>This paper takes garden landscape as the research object, discusses the application of digital media art in assisting garden landscape design, and obtains the optimization probability of various types of garden landscape types in landscape spatial layout through patch generation using the change simulation (PLUS) model. And after using the multi-scale Retinex algorithm to make image enhancement, a fusion of single-scale enhancement results in achieving the initial optimization of the image, after a dynamic interception and stretching operation to restore the enhancement effect to achieve the optimization of the landscape image. Finally, a group of landscape design images are selected as experimental objects to test the effectiveness of digital media art-assisted landscape design. The results show that with the assistance of digital media art, the modulus of the change distance of neighboring units is between 2.21 and 10.89, and the relative change rate takes the value between 0.45 and 5.21. The method is capable of balancing the ups and downs and repetitive rhythms in the design, ensuring that the landscape has good brightness and visual effects.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.014192023-12-09T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01424<abstract> <title style='display:none'>Abstract</title> <p>In this paper, the image processing capability of the CNN algorithm under the chaotic recursive diagonal model is explored from two aspects of medical image fusion and compression. By analyzing the structure of the chaotic recursive diagonal model, it is possible to combine it with a neural network. A convolutional neural network is used to automatically extract the focusing features of an image and output the probability of a pixel focusing. Combining the convolutional layer to extract image features with the activation function to nonlinearly map the feature map to achieve the effect of image fusion. Focusing on the exploration of the CNN algorithm for image fusion in image compression application processes. The results show that in the image fusion experiments, the CNN algorithm for image fusion data MI mean value is 6.1051, variance is 0.4418. QY mean value is 0.9859. The variance value is 0.0014. Compared to other algorithms, CNN in the image fusion effect has the effect of better distinguishing the edge details and making the appropriate decision. The CNN algorithm of the compression time is shorter. The time used in the compression of the X-chest image is 2.75s, which is 0.42 less than other algorithms. This study provides a new research perspective for medical image processing and is beneficial to improving the efficiency of medical image processing.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.014242023-12-09T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01425<abstract> <title style='display:none'>Abstract</title> <p>This paper first analyzes the mechanism of transmission line dancing and constructs the mathematical model of transmission line dancing and the parameters of transmission line dancing. Then, a transmission line dancing monitoring and warning system is designed by integrating multiple inertial sensors, and the tower monitoring main splitter and wireless inertial monitoring and warning unit are designed, respectively. Then, the transmission line dancing trajectory was denoised using the wavelet threshold method, and the two-way inequality was determined by the attitude decomposition algorithm so as to design the transmission line dancing trajectory parameter identification algorithm. Finally, the designed system is tested experimentally, and the monitoring performance of the dance monitoring trajectory system is analyzed using collected data. The results show that the angular error of the sensor’s pitch and roll attitudes is within 0.5°, the angular error of the heading angle is within 1°, and the acceleration of the smoothed signal is in the range of -0.2/g~0.2/g. The relative error of amplitude recognition is up to 2.6 cm, and the frequency recognition basically agrees with the actual movement frequency, which is 0.21 Hz, and the error of the recognized frequency is within 0.03 Hz. Hz.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.014252023-12-09T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01423<abstract> <title style='display:none'>Abstract</title> <p>This paper diagnoses the transmission line dancing situation based on the wide-area traveling wave information transmission and dancing mechanism. The characteristics of the wide-area initial traveling wave propagation are analyzed, and the traveling wave information of transmission line dancing is analyzed using wavelet transform. Measure the voltage traveling wave energy distribution for online monitoring and diagnosis of transmission lines. To study the dancing amplitude of transmission lines, a finite element analysis model is created. The detuned pendulum anti-dancing device is designed, the detuned pendulum dynamics equation is constructed, and the critical wind speed leading to transmission line dancing is investigated by the theoretical equation method and the stability theory method. Through the empirical analysis method, the transmission line dance monitoring and the anti-dance effect are analyzed. The experiments show that when the transmission line dances at a slower speed, the online monitoring method based on wide-area information monitors the motion of the target spacer bar between two neighboring frames between [1,3], and the processing speed is 138.2 frames per second faster than the other techniques, which is successful in tracking the dancing target of the transmission line. In the anti-dance test, before the anti-dancer was added, the transmission line amplitude reached 12,12m/s from the beginning at a wind speed of 18m/s and 14m/s to provoke dance. After the installation of the anti-dancer, the amplitude is maintained between [0,1] in most cases, and the anti-dancer has a good anti-dance effect.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.014232023-12-09T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01420<abstract> <title style='display:none'>Abstract</title> <p>In this paper, the digital twin platform for the indoor environment is constructed by combining digital twin technology and modern indoor environment elements to innovate the modern indoor environment design method. The digital twin platform is designed for modern indoor environments in two aspects: data acquisition and 3D model visualization for indoor environments. The indoor environment data are collected, cleaned and quasi-exchanged using sensors, the collected multi-source heterogeneous data of the indoor environment are fused by the time alignment method, and the 3D model of the indoor environment is driven by the design of the 3D model’s operations of translation, rotation and scaling. On this basis, the performance of the indoor environment digital twin platform is analyzed, and the data acquisition method and driving effects of the 3D model are explored. The results show that the data transmission measurement delay is within 20ms, the display delay is within 70ms, the transmission frames per second are basically stabilized at about 200FPS, 100FPS, 60FPS, the accuracy reaches 0.9 in the case of multiple data acquisition, and the fusion speed is about 3.4m/s, and the success rate of the driving operation of the overall three-dimensional model of the indoor environment is all greater than 0.96.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.014202023-12-09T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01414<abstract> <title style='display:none'>Abstract</title> <p>In this paper, from the MC optimization oriented to customer demand, we use big data technology to optimize the model, and with the help of the fuzzy cluster analysis method, we convert the variable types of customer demand indexes into different clustering effects. Fuzzy cluster analysis is used to establish the mapping relationship between customer demand, functional requirements of the product, and design parameters. Use the idea of customer demand analysis and transformation and the module division method to build the framework system of product configuration design and complete the construction of a customer demand-oriented product configuration visualization platform. By dividing different customer requirements, the best classification of customer requirements is obtained, and the technical optimization design of washing machine products is taken as an example to analyze the practicability of the platform constructed in this paper. Among the 12 technical characteristics of the washing machine, the importance of <italic>EG</italic>₁₁ is 0.1395, the importance of <italic>EG</italic>₁ is 0.1116, and the importance of <italic>EG</italic>₅ is 0.1017, which indicates that customers are most concerned about the energy-saving function of the product, and thus the enterprise should design the product based on the customer needs to satisfy the customer’s demands.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.014142023-12-09T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01413<abstract> <title style='display:none'>Abstract</title> <p>This paper utilizes the SOA framework to realize the system structure design, combined with the total flow of the claim processing, the design of the acceptance and audit processing, responsibility determination and reminder management of 4 modules, constituting the customer claim management system as a whole. The improved LEACH algorithm is utilized to control the network topology, and the cluster tree combined with data fusion is used to reduce the energy loss of the nodes in the network. Based on the weighted queuing scheduling algorithm, the feedback processing module is designed to determine the grouping rules and calculation methods, and the decision tree algorithm is used to build the demand management prediction model. The accuracy of the prediction of the decision tree algorithm is verified to realize the case information management statistics. Analyze the utility of the improved LEACH algorithm and test the system’s performance. The experimental results show that the improved LEACH algorithm is more balanced in the range of [0,100] dead nodes, and the energy consumption is well balanced. The total delay is 16.13-time slots, which is the lowest delay among the four algorithms. The node death time is 1045 rounds, having the longest network life cycle. The system performance is tested using IO and memory-intensive loads. Before writing a file of 16GB, the system time responsiveness stays around 80%, and the system CPU utilization and disk IO utilization increase as the file is written. The system performance is good.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.014132023-12-09T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01421<abstract> <title style='display:none'>Abstract</title> <p>In this paper, a two-dimensional panel data model of economic policy uncertainty is investigated based on the individual fixed effects of panel quantile regression, and a nonparametric panel model with individual fixed effects is established. The unfolding of nonparametric penalized spline and the introduction of Bayesian in stratified quantile are utilized to construct regression models applicable to accounting robustness, respectively. In the empirical study, the economic policy uncertainty index, accounting robustness and commercial credit supply are measured respectively. The annual data of China’s Shenzhen and Shanghai A-share listed companies during the period from 2012 to 2021 were selected as the research basis, and Bayesian quantile regression was made on the basis of correlation analysis. The coefficient of commercial credit supply is found to be -0.0821, and the variable RD1 is negatively correlated with economic policy uncertainty. This regression result confirms hypothesis H1 of this paper, suggesting that private firms invest less in innovation when economic policy uncertainty is higher. In the test of economic policy uncertainty by type, the regression coefficients of RD2, EPU, and SIZE are negative, respectively -0.0368, −0.2124, and -0.1458, which indicates that fiscal policy, monetary policy, and exchange rate and capital account policy uncertainty are negatively correlated with the supply of business credit to enterprises. Based on this correlation, this study provides guidance for the development of business credit for enterprises.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.014212023-12-09T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01418<abstract> <title style='display:none'>Abstract</title> <p>The technology can fully explore the user’s consumption behavior habits and help the e-commerce platform formulate more precise marketing strategies in a targeted manner. This paper firstly analyzes the optimization of marketing strategy based on the 3R marketing theory, gives the design process of the precise marketing strategy of an e-commerce platform, and analyzes the personalized service based on consumer classification. Secondly, for the shortcomings of the KNN algorithm in the process of accurate classification, the Gaussian function is introduced to weight the optimization of the algorithm, which further realizes the construction of the G-KNN algorithm. Finally, the testing and application analysis of the algorithm model was carried out using the actual user consumption data of the e-commerce platform. The results show that the classification accuracy of the G-KNN algorithm has been maintained at about 95% when the K value exceeds 800, and the F1 composite value of this paper’s algorithm fluctuates around 56% when the K value exceeds 1000. On the e-commerce platform, except for the electrical appliances category classification test, the fit and accuracy of other categories basically match. Using the KNN algorithm incorporating the Gaussian function can effectively realize the accurate classification of user characteristics on the e-commerce platform and provide data support for the e-commerce platform to formulate accurate marketing strategies based on consumer preferences.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.014182023-12-09T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01422<abstract> <title style='display:none'>Abstract</title> <p>This paper is based on the use of recurrent neural networks and LSTM deep neural networks to obtain the financial risk prediction feature sequence in the context of big data. The financial risk prediction feature sequence is used as the input value of the input gate of the LSTM deep neural network model after data filtering, normalization and loss function optimization, and then the financial risk prediction for the output gate of the LSTM deep neural network model. Considering the availability of data, small and medium-sized enterprises listed in A-share companies in the Wind database are selected as sample enterprises, and evaluation indexes are constructed and detected at the same time so as to complete the experimental design of enterprise financial risk prediction in the context of big data. The prediction of enterprise financial risk is empirically analyzed using simulation analysis and statistical analysis. The results show that in the model performance analysis, the average value of ten years of data, the highest value is still the result obtained by LSTM training, 0.761, compared with other models of LSTM deep neural network in static financial risk prediction in the overall best performance. In the case study of Yibai Pharmaceutical, the minimum value of the rate of return, return on total assets, and return on assets were -10.02%, 2.56%, -20.72%, which reflects the fact that the private enterprises still have large profitability space to be mined. This study helps investors or financial institutions such as funds to find out the possible financial risk crisis of listed companies as early as possible to avoid the parties from incurring large financial losses.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.014222023-12-09T00:00:00.000+00:00https://sciendo.com/article/10.2478/amns.2023.2.01415<abstract> <title style='display:none'>Abstract</title> <p>This paper focuses on the use of feature extraction techniques as well as parameter estimation to analyze the immediate pressing tactics in soccer games. The motion target detection method is used to capture the movements of the soccer player. By setting the rotation angle of the point cloud, the soccer movement action is represented in the form of a coordinate system. By combining the inter-frame difference method and setting the motion image threshold, the motion target can be obtained. Utilize Hu moments to extract the features of soccer motion. Combine the center of mass and velocity of soccer motion to reduce the error rate of motion feature extraction. Pairwise quaternions are utilized to represent soccer motion parameters to improve motion estimation. The results show that the soccer team has the greatest success rate of practicing immediate pressing tactics in 3s-4s, and the success rate of applying immediate pressing tactics after 4s is significantly lower. Team C has the highest success rate of huddling with defensive immediate pressing tactics, which reaches 56.1%. The success rate of huddling is closest to that of team A and team B, which are 43.54% and 43.97%, respectively.</p> </abstract>ARTICLEtruehttps://sciendo.com/article/10.2478/amns.2023.2.014152023-12-09T00:00:00.000+00:00en-us-1