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The random forest algorithm， known for its robustness against noise and powerful computational capabilities， is widely employed in diagnosing vibration faults in rotating machinery. However， when applied in industrial settings， the algorithm encounters challenges such as limited sample sizes， the inability to integrate prior knowledge， and comparatively lower accuracy. To tackle these issues， a method for diagnosing vibration faults in steam turbines using an improved random forest （IRF） algorithm is proposed. This approach incorporates prior knowledge to optimize decision trees， utilizing analytical hierarchy process （AHP） and information entropy. Genuine operational datasets from the data center of a million-kW thermal power plant are utilized to validate the efficacy and reliability of the proposed method. Computational findings indicate that， in comparison to the traditional random forest algorithm， IRF achieves higher accuracy and a reduced miss rate， with a 33% decrease in the number of decision trees. Moreover， the operational time is slashed to just 11.4% of that taken by the traditional random forest algorithm. These results suggest that IRF holds significant practical value for real-time， precise vibration fault diagnosis in thermal power units.

Unruh effect entropy is estimated for a spherically symmetric source with an exponential energy distribution; angular degrees of freedom are suggested to be equally likely to contribute. Calculations are performed with an assumption about finite energy and multiplicity ranges. The result is represented in the units of Schwarzschild black hole entropy, with the analytical ratio being expressed analytically and generalized to homogeneous distribution over other degrees of freedom.

Abstract Infectious diseases risk is directly related to human life safety. After the COVID-19 pandemic, people have paid unprecedented attention to the risk of infectious diseases. Compared with treatment after the outbreak of the epidemic, identifying the influencing factors of infectious disease risk and quantitatively analyzing and assessing infectious disease risk before the outbreak of the epidemic plays an equally important role. This article focuses on the risk of irregular outbreaks of infectious diseases. On the one hand, a method based on information gain is proposed to calculate the weight of environmental factors directly related to infectious disease risk, to clarify the correlation between environmental factors and infectious disease risk. On the other hand, the risk calculation method based on risk weight number is proposed to calculate the risk level of different infectious diseases under the influence of specific environmental factors. Finally, the effectiveness and feasibility of the proposed method are verified through case analysis and discussion. By comparing it with other risk assessment methods, the advantages and disadvantages of the proposed method are demonstrated.

This paper proposes an adaptive image enhancement method that aims to effectively restore the brightness, detail, and natural color of various low-illumination images. To be specific, the method first constructs the initial dual-channel illumination map of the image. Next, the optimal illumination correction coefficient is calculated by the prior information entropy of the initial illumination map, which helps to correct potentially erroneous illumination estimates. To restore the illumination, gamma correction is used with the optimal illumination correction coefficient. Finally, an improved perfect reflection constraint model is used to restore the color of the image. Both visual analysis and quantitative comparison with state-of-the-art methods demonstrate the effectiveness of the method in terms of brightness adjustment, detail recovery, and color restoration.

Abstract High-definition images can provide valuable reference for research in fields such as medical image analysis and machine vision. Therefore, image feature enhancement is performed to obtain high-quality images. Conventional methods for image feature enhancement extract features only from a single scale, resulting in low image information entropy. To address this issue, a fuzzy image feature enhancement method using mathematical morphology and deep learning (FIFEMD) is proposed. First, a near-infrared transmission light source is used as the main light source to construct a capture platform, and the grayscale values are normalized and filtered. Second, basic mathematical morphology operations are employed to remove noise points in the original image, and a separable residual dense block is used to extract multi-scale features from the image. Finally, the generated image features are fused and reconstructed using the Hessian matrix method to achieve image feature enhancement. Experimental results demonstrate that the images processed using the FIFEMD method have higher information entropy values, with entropy values above 6.75. The processing time is approximately 2.5 s, indicating high efficiency. Therefore, the FIFEMD method can achieve better image feature enhancement.

Quantum thermodynamics aims to extend standard thermodynamics and non-equilibrium statistical physics to systems with sizes well below the thermodynamic limit. It is a rapidly evolving research field that promises to change our understanding of the foundations of physics, while enabling the discovery of novel thermodynamic techniques and applications at the nanoscale. Thermal management has turned into a major obstacle in pushing the limits of conventional digital computers and could also represent a crucial issue for quantum computers. The practical realization of quantum computers with superconducting loops requires working at cryogenic temperatures to eliminate thermal noise, and ion-trap qubits also need low temperatures to minimize collisional noise. In both cases, the sub-nanometric sizes also bring about the thermal broadening of the quantum states; and even room-temperature photonic computers eventually require cryogenic detectors. A number of thermal and thermodynamic questions, therefore, take center stage, such as quantum re-definitions of work and heat, thermalization and randomization of quantum states, the overlap of quantum and thermal fluctuations, and many others, even including a proper definition of temperature for the small open systems constantly out of equilibrium that are the qubits. This overview provides an introductory perspective on a selection of current trends in quantum thermodynamics and their impact on quantum computers and quantum computing, with language that is accessible to postgraduate students and researchers from different fields.

Abstract The topological photonics plays an important role in the fields of fundamental physics and photonic devices. The traditional method of designing topological system is based on the momentum space, which is not a direct and convenient way to grasp the topological properties, especially for the perturbative structures or coupled systems. Here, we propose an interdisciplinary approach to study the topological systems in real space through combining the information entropy and topological photonics. As a proof of concept, the Kagome model has been analyzed with information entropy. We reveal that the bandgap closing does not correspond to the topological edge state disappearing. This method can be used to identify the topological phase conveniently and directly, even the systems with perturbations or couplings. As a promotional validation, Su–Schrieffer–Heeger model and the valley-Hall photonic crystal have also been studied based on the information entropy method. This work provides a method to study topological photonic phase based on information theory, and brings inspiration to analyze the physical properties by taking advantage of interdisciplinarity. Graphical Abstract

Abstract Graph neural networks have received increased attention over the past years due to their promising ability to handle graph-structured data, which can be found in many real-world problems such as recommender systems and drug synthesis. Most existing research focuses on using graph neural networks to solve homophilous problems, and not much attention has been paid to heterophily-type problems. In this paper, we propose a graph network model for graph coloring, which is a class of representative heterophilous problems. Different from the message passing in conventional graph networks, we introduce negative message passing into a physics-inspired graph neural network for more effective information exchange in handling graph coloring problems. Moreover, a new term in the objective function taking into account the information entropy of nodes is suggested to increase the uniformity of the color assignment of each node, giving the neural network more chance to choose suitable colors for each node. Therefore, it could avoid the final solution getting stuck into the local optimum. Experimental studies are carried out to compare the proposed graph model with five state-of-the-art algorithms on ten publicly available graph coloring problems and d-regular graphs with up to $$10^4$$ 10 4 nodes, demonstrating the effectiveness of the proposed graph neural network.

In the occurrence of environmental disasters involving water resources, deploying an emergency monitoring network for assessing water quality is within the first measures to be taken. Emergency networks usually cover a large set of water quality variables and monitoring stations along the watershed. Focusing on variables that represent greater risk to the environment and have less predictable spatial and temporal distribution is a strategy to optimize efforts on monitoring. The goal of this study is to assess the use of Shannon's entropy to identify non-critical water quality variables in an emergency monitoring network implemented in a watershed impacted by the collapse of a mining iron tailing dam, the Doce River watershed (Brazil). Monitoring stations were grouped into water quality subregions through cluster analysis and Shannon's entropy was used to estimate information redundancy of monitored variables. From information redundancy and after checking for compliance with environment normative, non-critical water quality variables were identified. Results indicated that non-critical variables represent 32–50% of the variables monitored. Emergency network managers find in this method a robust tool to improve the network performance. However, special attention should be paid to outliers' presence that can bias analyses based on Shannon's entropy. HIGHLIGHTS Water quality variable selection criteria based on Shannon's entropy have not been explored in emergency monitoring networks.; The selection criterion based on the concept of ‘non-critical variables’ is appropriate for water quality variables.; The set of selected non-critical variables was very expressive and demonstrates the potential of the method to guide adjustments in emergency networks.; Metals and metalloids are predominant in terms of the chemical nature of the main non-critical variables in the Doce River watershed.;

Abstract Earthquake nowcasting has been proposed as a means of tracking the change in large earthquake potential in a seismically active area. The method was developed using observable seismic data, in which probabilities of future large earthquakes can be computed using Receiver Operating Characteristic methods. Furthermore, analysis of the Shannon information content of the earthquake catalogs has been used to show that there is information contained in the catalogs, and that it can vary in time. So an important question remains, where does the information originate? In this paper, we examine this question using stochastic simulations of earthquake catalogs. Our catalog simulations are computed using an Earthquake Rescaled Aftershock Seismicity (“ERAS”) stochastic model. This model is similar in many ways to other stochastic seismicity simulations, but has the advantage that the model has only 2 free parameters to be set, one for the aftershock (Omori‐Utsu) time decay, and one for the aftershock spatial migration away from the epicenter. Generating a simulation catalog and fitting the two parameters to the observed catalog such as California takes only a few minutes of wall clock time. While clustering can arise from random, Poisson statistics, we show that significant information in the simulation catalogs arises from the “non‐Poisson” power‐law aftershock clustering, implying that the practice of de‐clustering observed catalogs may remove information that would otherwise be useful in forecasting and nowcasting. We also show that the nowcasting method provides similar results with the ERAS model as it does with observed seismicity.

Dance education in colleges and universities is the most important means of inheriting dance skills, cultivating dance talents, and promoting the prosperity and development of dance art. In the new era, the country’s emphasis on “aesthetic education” has provided fertile policy soil for the development of dance majors in universities. Based on the spiritual and cultural needs of the people and the development needs of the national dance art, it is of great urgency for colleges and universities to explore the future oriented Chinese dance higher education and dance creation. Dance education and dance creation are closely linked and interdependent. In the process of Dance education, dance creation inspiration is stimulated. Dance creation and innovation inject new soul into Dance education. College Dance education should combine the two organically to promote the high-quality development of Chinese dance art. The classroom teaching quality evaluation in dance aesthetic education is classical multiple-attributes decision-making (MADM). The probabilistic hesitancy fuzzy sets (PHFSs) are used as a tool for characterizing uncertain information during the classroom teaching quality evaluation in dance aesthetic education. In this paper, we extend the classical grey relational analysis (GRA) method to the probabilistic hesitancy fuzzy MADM with unknown weight information. Firstly, the basic concept, comparative formula and Hamming distance of PHFSs are introduced. Then, the information entropy is used to compute the attribute weights based on the expected values and deviation degree. Then, probabilistic hesitancy fuzzy GRA (PHF-GRA) method is built for MADM under PHFSs. Finally, a practical case study for classroom teaching quality evaluation in dance aesthetic education is designed to validate the proposed method and some comparative studies are also designed to verify the applicability.

Predicting the distributions of path flow between origin-destination (OD) pairs in an urban road network is crucial for developing efficient traffic control and management strategies. Here, we use the large-scale taxi GPS trajectory data of San Francisco and Shenzhen to study the predictability of path flow distribution in urban road networks. We develop an approach to project the time-varying path flow distributions into a high-dimensional space. In the high-dimensional space, information entropy is used to measure the predictability of path flow distribution. We find that the distributions of path flow between OD pairs are in general characterized with a high predictability. In addition, we analyze the factors affecting the predictability of path flow distribution. Finally, an n-gram model incorporating high-order gram and low-order gram is proposed to predict the distribution of path flow. A relatively high prediction accuracy is achieved.

The rapid expansion of urban drainage pipe networks, driven by economic development, poses significant challenges for efficient monitoring and management. The complexity and scale of these networks make it difficult to effectively monitor and manage the discharge of urban domestic sewage, rainwater, and industrial effluents, leading to illegal discharges, leakage, environmental pollution, and economic losses. Efficient management relies on a rational layout of drainage pipe network monitoring points. However, existing research on optimal monitoring point layout is limited, primarily relying on manual analysis and fuzzy clustering methods, which are prone to human bias and ineffective monitoring data. To address these limitations, this study proposes a coupled model approach for the automatic optimization of monitoring point placement in drainage pipe networks. The proposed model integrates the information entropy index, Bayesian reasoning, the Monte Carlo method, and the stormwater management model (SWMM) to optimize monitoring point placement objectively and measurably. The information entropy algorithm is utilized to quantify the uncertainty and complexity of the drainage pipe network, facilitating the identification of optimal monitoring point locations. Bayesian reasoning is employed to update probabilities based on observed data, while the Monte Carlo method generates probabilistic distributions for uncertain parameters. The SWMM is utilized to simulate stormwater runoff and pollutant transport within the drainage pipe network. Results indicate that (1) the relative mean error of the parameter inversion simulation results of the pollution source tracking model is linearly fitted with the information entropy. The calculation shows that there is a good positive linear correlation between them, which verifies the feasibility of the information entropy algorithm in the field of monitoring node optimization; (2) the information entropy algorithm can be well applied to the optimal layout of a single monitoring node and multiple monitoring nodes, and it can correspond well to the inversion results of the tracking model parameters; (3) the constructed monitoring point optimization model can well realize the optimal layout of monitoring points of a drainage pipe network. Finally, the pollution source tracking model is used to verify the effectiveness of the optimal layout of monitoring points, and the whole process has less human participation and a high degree of automation. The automated monitoring point optimization layout model proposed in this study has been successfully applied in practical cases, significantly improving the efficiency of urban drainage network monitoring and reducing the degree of manual participation, which has important practical significance for improving the level of urban water environment management.

Identifying the most important nodes in command and control networks, or evaluating the relative importance of one node compared to others, is a key issue in the study of these networks. Various effective models have been proposed to address this. Traditional gravity models, which consider node degree as the sole factor representing mass, are limited in their ability to accurately identify core nodes, often misclassifying nodes with local high clustering characteristics as central nodes. To improve accuracy, this paper proposes the Coulomb Force Model (CFM), which is inspired by the gravity model but incorporates Coulomb’s law.The CFM uses a dynamic radius method to determine the influence range of each node more precisely. The charge quantity, representing node importance, is determined based on node attributes, and the polarity of the charge indicates the influence on neighboring nodes. This approach introduces intrinsic node information to more accurately measure the differences in node influence, thereby enhancing the algorithm’s precision. We validated the effectiveness and accuracy of the proposed algorithm through comparative experiments on multiple datasets, using classic influence propagation models and evaluation metrics. The results demonstrate the improved performance of the CFM in identifying influential nodes in command and control networks.

This study introduces a novel image cryptosystem designed to improve security and reliability within underwater optical wireless communication (UOWC) systems particularly for water bodies that exhibits higher absorption and scattering coefficients. An image encryption scheme, characterized by high security, and consists of a chaotic iteration of dragon fractal shapes (ChDrFr). The system performance is assessed across five distinct water types, each exhibiting unique optical properties. For enhancing the quality of encrypted images received, median and high-pass filters are utilized. To test the ability of this new algorithm to ward off different types of attacks, numerical simulation is applied. Specifically, a correlation coefficient of approximately zero is witnessed between the original images and those generated by encryption. The optimum values for information entropy and unified average changed intensity (UACI) are almost reached for the encrypted images. A comparison is undertaken between different encryption methods and the one proposed in this paper, depending on ChDrFr. Results show that the proposed encryption scheme yields better results and achieves much securer encrypted images, when compared to the images encrypted by other encryption algorithms. Additionally, the encrypted images show enhancement in the underwater transmission distance with 6% for Pure Sea (PS) and Clear Ocean (CL), 4% for Coastal Sea (CS), 0.9% for Harbor I (HI), and 0.8% for Harbor II (HII). Moreover, the utilization of filters results in improvement in the Structured Similarity Index (SSIM), Peak-Signal-to-Noise-Ratio (PSNR), and Signal-to-Noise-Ratio (SNR).

The negative pressure wave (NPW) signal caused by leakage of water supply pipeline contains a lot of noise due to the influence of water flow state and circuit noise, which will adversely affect the accuracy of leakage detection and leak location. To solve this problem, a novel pipeline pressure signal denoising method based on variational mode decomposition (VMD) is proposed in the paper. Firstly, the key parameters which affect the performance of VMD method: IMFs K and penalty factor $\alpha $ are optimized by genetic algorithm (GA) without prior knowledge. At the same time, different parameters are combined to form different reconstructed signals based on the correlation coefficient (CC). Secondly, the optimal combination of parameters is adaptively determined by using a fitness evaluation function based on the information entropy (IE). Thirdly, based on the optimal parameter combination, reconstruct the signal to achieve noise reduction. Finally, the denoised signal will be used for leak detection and leak location calculation. In order to verify the effectiveness of the proposed method, simulation analysis and laboratory experiments are carried out. Simulation analysis results show that the noise suppression effect of our method is better than that of wavelet, empirical mode decomposition (EMD) and VMD. The highest SNR obtained by our method is 16.187. The laboratory experiment results showed that the NPW signal denoised by proposed method had less error and more stable results in location, with an average relative error of 2.97%.

In advance to the present study, the authors introduced a method which makes it possible to calculate the entropy of natural language digital texts, focusing on word-processed texts, presentations, and webpages. This entropy reveals that the more underdeveloped documents are, the more demanding their content-related modification becomes. It was also found that the time and data required to complete a modification task in an erroneous document is several times more than in its correct counterpart. This finding leads to the end-user paradox: the less trained end-users are, the more errors they make, and the modification of their documents requires more resources. To resolve these discrepancies, the present study defines the sustainability rate of natural language digital texts which calculates the losses – the waste of human resources, time, workspace, computers, energy, frustration, working in bees, losing data – generated by negligent text management. Furthermore, we present examples of how manual and enumerated lists behave to modifications in a 213-page long document and conclude from our investigations that while the waste of human and machine resources occurs repeatedly in erroneous documents, the sustainability rate remains low. To prove the necessity of correction, we cleared the sample document, which took approximately 67 hours of two experts of our research group ( $2\times67$ hours). With this method, we found that the correction of errors can be extremely demanding, but uses resources only once, and further modifications in the now correct document need only the content-required amount of time, activities, entropy, and resources, in accordance with the expectations of the person intended to update the document. To correct documents, we present the Error Recognition Model, which is proved effective and efficient in digital education. All our findings indicate that both education and industry should adapt the presented approach (1) to develop students’ and end-users’ computational thinking skills, (2) to manage and take advantage of errors, (3) to recognize connections between the structure of the text and the complex word processing tools, (4) to pay attention to digital sustainability – beyond hardware and software development and recycling – with a focus on the human factor. Recently, the Error Recognition Model is a reactive problem-solving approach, whose effectiveness is justified. However, the near future is to run parallel the reactive and proactive uses of this approach, while if we look far into the future, the proactive use to digital born natural language texts should dominate.

Many correlation analysis methods can capture a wide range of functional types of variables. However, the influence of uncertainty and distribution status in data is not considered, which leads to the neglect of the regularity information between variables, so that the correlation of variables that contain functional relationship but subject to specific distributions cannot be well identified. Therefore, a novel correlation analysis framework for detecting associations between variables with randomness (RVCR-CA) is proposed. The new method calculates the normalized RMSE to evaluate the degree of functional relationship between variables, calculates entropy difference to measure the degree of uncertainty in variables and constructs the copula function to evaluate the degree of dependence on random variables with distributions. Then, the weighted sum method is performed to the above three indicators to obtain the final correlation coefficient R. In the study, which considers the degree of functional relationship between variables, the uncertainty in variables and the degree of dependence on the variables containing distributions, cannot only measure the correlation of functional relationship variables with specific distributions, but also can better evaluate the correlation of variables without clear functional relationships. In experiments on the data with functional relationship between variables that contain specific distributions, UCI data and synthetic data, the results show that the proposed method has more comprehensive evaluation ability and better evaluation effect than the traditional method of correlation analysis.

Academic circles have long ignored the problems of inadequate and unbalanced housing development in medium-sized cities. Based on data from the sixth and seventh national population censuses of Zhaoqing City, this study constructed the resident housing evaluation system focusing on four sub-systems: construction age, building structure, per capita floor area, and housing living facilities. Through global and local spatial autocorrelation analyses, the spatial-temporal differentiation pattern of residents' housing conditions in Zhaoqing City from 2010 to 2020 was described. An OLS regression model was used to identify the influencing factors of the comprehensive score of residents' housing conditions, revealing the driving mechanisms of the evolution of the spatial pattern of housing conditions. The results show that 1) From 2010 to 2020, the spatial difference characteristics of the housing conditions of residents in Zhaoqing City showed obvious changes, with the high score area shifting from southeast to northwest. 2) The housing conditions of residents showed significant spatial agglomeration characteristics. The spatial agglomeration degree of the overall housing condition score did not change much in the past 10 years, while the spatial agglomeration degree of the construction age, building structure, and housing living facilities scores decreased significantly. 3) In 2020, the housing conditions of urban residents and rural residents presented different spatial differentiation patterns. The per capita floor area subsystem of rural housing dominates the spatial differentiation pattern of housing conditions for all residents in the city. 4) Regarding driving mechanisms, early urban forms constitute the historical basis of the spatial pattern of housing conditions in Zhaoqing City. Government policies and planning are the key driving forces shaping the evolution of spatial patterns, and the filtering effect of the housing market further strengthens the established spatial pattern. Based on these findings, the study proposes the following policy implications for Zhaoqing City: 1) Accelerate the upgrading of the industrial structure and new type urbanization construction, and encourage the city's agricultural migrant population and migrant workers to find jobs and settle down in the central city, development zone, and county; 2) Increase the supply of affordable housing with better housing quality in Zhaoqing New District and High-tech Zone to attract and retain highly skilled talents and college graduates; 3) Control the supply scale of residential land in the New District and High-tech Zone, increase the supply scale of industrial land, and expedite the introduction of strategic industries; 4) Conduct micro-transformations of old residential areas in the old urban areas, upgrade infrastructure and indoor living facilities, and improve the quality of the living environment. This study discusses the evolution trend and driving mechanisms of the spatial differentiation of housing conditions in small- and medium-sized cities and offers policy implications to comprehensively improve the housing conditions of Zhaoqing residents based on local conditions. This has significant reference value for promoting the optimization of housing conditions in small- and medium-sized cities and improving the overall living conditions of citizens.

Abstract The application of reinforcement learning (RL) to the field of autonomous robotics has high requirements about sample efficiency, since the agent expends for interaction with the environment. One method for sample efficiency is to extract knowledge from existing samples and used to exploration. Typical RL algorithms achieve exploration using task-specific knowledge or adding exploration noise. These methods are limited to current policy improvement level and lack of long-term planning. We propose a novel active exploration deep RL algorithm for the continuous action space problem named active exploration deep reinforcement learning (AEDRL). Our method uses the Gaussian process to model dynamic model, enabling the probability description of prediction sample. Action selection is formulated as the solution of the optimization problem. Thus, the optimization objective is specifically designed for selecting samples that can minimize the uncertainty of the dynamic model. Active exploration is achieved through long-term optimized action selection. This long-term considered action exploration method is more guidance for learning. Enable intelligent agents to explore more interesting action spaces. The proposed AEDRL algorithm is evaluated on several robotic control task including classic pendulum problem and five complex articulated robots. The AEDRL can learn a controller using fewer episodes and demonstrates performance and sample efficiency.

Abstract Human leukocyte antigen (HLA) is closely involved in regulating the human immune system. Despite great advance in detecting classical HLA Class I binders, there are few methods or toolkits for recognizing non-classical HLA Class I binders. To fill in this gap, we have developed a deep learning-based tool called DeepHLAPred. The DeepHLAPred used electron-ion interaction pseudo potential, integer numerical mapping and accumulated amino acid frequency as initial representation of non-classical HLA binder sequence. The deep learning module was used to further refine high-level representations. The deep learning module comprised two parallel convolutional neural networks, each followed by maximum pooling layer, dropout layer, and bi-directional long short-term memory network. The experimental results showed that the DeepHLAPred reached the state-of-the-art performanceson the cross-validation test and the independent test. The extensive test demonstrated the rationality of the DeepHLAPred. We further analyzed sequence pattern of non-classical HLA class I binders by information entropy. The information entropy of non-classical HLA binder sequence implied sequence pattern to a certain extent. In addition, we have developed a user-friendly webserver for convenient use, which is available at http://www.biolscience.cn/DeepHLApred/ . The tool and the analysis is helpful to detect non-classical HLA Class I binder. The source code and data is available at https://github.com/tangxingyu0/DeepHLApred .

To address the problem of predicting machining quality for critical features in the manufacturing process of mechanical products, a method that combines information entropy and XGBoost (version 2.1.1) hyperparameter optimization is proposed. Initially, machining data of mechanical products are analyzed based on information entropy theory to identify critical quality characteristics. Subsequently, a quality prediction model for these critical features is established using the XGBoost machine learning framework. The model’s hyperparameters are then optimized through Bayesian optimization. This method is applied as a case study to a medium-speed marine diesel engine piston. After the critical quality characteristics in the machining process are identified, the machining quality of these vital characteristics is predicted, and the results are compared with those obtained from a machine learning model without hyperparameter optimization. The findings demonstrate that the proposed method effectively predicts the machining quality of mechanical products.

This study explores the role of information entropy in understanding nuclear density distributions, including both stable configurations and non-traditional structures such as neutron halos and α-clustering. By quantifying the uncertainty and disorder inherent in nucleon distributions in nuclear many-body systems, information entropy provides a macroscopic measure of the physical properties of the system. A more dispersed and disordered density distribution results in a higher value of information entropy. This intrinsic relationship between information entropy and system complexity allows us to quantify uncertainty and disorder in nuclear structures by analyzing various geometric parameters such as nuclear radius, diffuseness, neutron skin, and cluster structural features.

In response to the fairness issue arising from the unequal delay of vehicles in different phases at intersections and considering the actual situation of small and variable delays for vehicles in low-saturation intersection phases, this paper proposes the concept of “sacrificing efficiency for fairness”. Firstly, the universality of unfair delay phenomena at intersection phases is explained, especially at low-saturation intersections where the fluctuation in phase delays is 1.87 times higher than at other intersections. Then, a fairness evaluation index is constructed using information entropy, and the feasibility of the proposed approach is demonstrated. Subsequently, a signal optimization model that balances efficiency and fairness is proposed. Finally, the proposed model is validated through case studies, showing that it not only simultaneously considers efficiency and fairness but also has minimal impact on efficiency. Moreover, the changes to timing schemes in the efficiency model are much smaller compared to the model that only considers fairness. Sensitivity analysis reveals that the model performs better under low-saturation intersection conditions.

To tackle the issue of the traditional intelligent diagnostic algorithm’s insufficient utilization of correlation characteristics within the time series of fault signals and to meet the challenges of accuracy and computational complexity in rotating machinery fault diagnosis, a novel approach based on a recurrence binary plot (RBP) and a lightweight, deep, separable, dilated convolutional neural network (DSD-CNN) is proposed. Firstly, a recursive encoding method is used to convert the fault vibration signals of rotating machinery into two-dimensional texture images, extracting feature information from the internal structure of the fault signals as the input for the model. Subsequently, leveraging the excellent feature extraction capabilities of a lightweight convolutional neural network embedded with attention modules, the fault diagnosis of rotating machinery is carried out. The experimental results using different datasets demonstrate that the proposed model achieves excellent diagnostic accuracy and computational efficiency. Additionally, compared with other representative fault diagnosis methods, this model shows better anti-noise performance under different noise test data, and it provides a reliable and efficient reference solution for rotating machinery fault-classification tasks.

The complementary combination of emphasizing target objects in infrared images and rich texture details in visible images can effectively enhance the information entropy of fused images, thereby providing substantial assistance for downstream composite high-level vision tasks, such as nighttime vehicle intelligent driving. However, mainstream fusion algorithms lack specific research on the contradiction between the low information entropy and high pixel intensity of visible images under harsh light nighttime road environments. As a result, fusion algorithms that perform well in normal conditions can only produce low information entropy fusion images similar to the information distribution of visible images under harsh light interference. In response to these problems, we designed an image fusion network resilient to harsh light environment interference, incorporating entropy and information theory principles to enhance robustness and information retention. Specifically, an edge feature extraction module was designed to extract key edge features of salient targets to optimize fusion information entropy. Additionally, a harsh light environment aware (HLEA) module was proposed to avoid the decrease in fusion image quality caused by the contradiction between low information entropy and high pixel intensity based on the information distribution characteristics of harsh light visible images. Finally, an edge-guided hierarchical fusion (EGHF) module was designed to achieve robust feature fusion, minimizing irrelevant noise entropy and maximizing useful information entropy. Extensive experiments demonstrate that, compared to other advanced algorithms, the method proposed fusion results contain more useful information and have significant advantages in high-level vision tasks under harsh nighttime lighting conditions.

Numerous social media platforms have evolved into fertile grounds for the proliferation of irrational information, expanding the avenues of information dissemination. This paper initially utilized the Weibo and Bilibili social platforms as exemplars and devised a cross-platform two-layer network SEIaIbR-FXYaYbZ dissemination model grounded in classical infectious disease models. Subsequently, this research computed the model equilibrium point, basic reproduction number, and information entropy through dynamic equations. Finally, the model equations were fitted to real cases to determine optimal parameter solutions and conduct simulation analysis. The simulation results reveal that: (i) information entropy values on both platforms are low, with irrational information predominantly influencing public opinion; (ii) concerning various types of information, the augmentation of rational information results in a reduction of irrational information, while the quantity of rational information remains largely unaffected by changes in the quantity of irrational information; (iii) examining different platforms for information dissemination, alterations in the circulation rate and quantity of rational information on the Weibo platform impact the quantity of rational and irrational information on the Bilibili platform, while those changes on the Bilibili platform exert minimal influence on public opinion information on the Weibo platform. The results and corresponding strategies obtained from this study on the cross-platform dissemination of rational and irrational information on Weibo and Bilibili can provide a reference for relevant departments to guide the rational development of online information and enhance the effective management of public opinion in social media platforms.

The entropy produced by Unruh radiation is estimated and compared to the entropy of a Schwarzschild black hole. We simulate a spherical system of mass M by a set of Unruh horizons and estimate the total entropy of the outgoing radiation. Dependence on the mass and spin of the emitted particles is taken into account. The obtained results can be easily extended to any other intrinsic degrees of freedom of outgoing particles. The ratio of Unruh entropy to the Schwarzschild black hole entropy is derived in exact analytical form. For large black holes, this ratio exhibits high susceptibility to quantum numbers, e.g., spin s, of emitted quanta and varies from 0% for s=0 to 19.0% for s=5/2.

The data from new distribution system is increasingly multi-dimensional and massive. When the distribution network participates in virtual grid division, the deep mining of distribution network data is required. In this study, a feature extraction and source–load collaborative analysis method for distribution network is proposed to master the suitability between renewable energy generation and various loads. Firstly, this method realizes raw data preprocessing through anomaly identification and reconstruction. It then uses information entropy to improve traditional piecewise aggregation approximation to reduce data dimension while considering the fluctuations of data. Finally, the method extracts the features of source–load data through spectral clustering and obtains quantitative analysis results of the matching degree between source and load through collaborative analysis. The effectiveness of the proposed method is verified by load power data and photovoltaic output data.

The storage, transmission, and processing of data become significant problems when large digital data files or databases are involved, as in the case of decentralized online global databases such as blockchain. Here, we propose a novel method that allows for the scalability of digital assets, including blockchain databases in the download, validation, and confidentiality processes, by developing a lightweight blockchain technology called Entropic Blockchain. This is a computer-implemented mathematical method by which to generate an information-entropic numerical barcode representation of a digital asset. Using this technique, a 1–2 Mb block of digital data can be represented by a few bytes, significantly reducing the size of a blockchain. The entropic barcode file can be utilized on its own or as an optically machine-readable entropic barcode for secure data transmission, processing, labeling, identification, and one-way encryption, as well as for compression, validation, and digital tamper-proof checks. The mathematics of this process and all the steps involved in its implementation are discussed in detail in this article.

To address the challenges associated with supervising workers who wear safety belts while working at heights, this study proposes a solution involving the utilization of an object detection model to replace manual supervision. A novel object detection model, named ESE-YOLOv8, is introduced. The integration of the Efficient Multi-Scale Attention (EMA) mechanism within this model enhances information entropy through cross-channel interaction and encodes spatial information into the channels, thereby enabling the model to obtain rich and significant information during feature extraction. By employing GSConv to reconstruct the neck into a slim-neck configuration, the computational load of the neck is reduced without the loss of information entropy, allowing the attention mechanism to function more effectively, thereby improving accuracy. During the model training phase, a regression loss function named the Efficient Intersection over Union (EIoU) is employed to further refine the model’s object localization capabilities. Experimental results demonstrate that the ESE-YOLOv8 model achieves an average precision of 92.7% at an IoU threshold of 50% and an average precision of 75.7% within the IoU threshold range of 50% to 95%. These results surpass the performance of the baseline model, the widely utilized YOLOv5 and demonstrate competitiveness among state-of-the-art models. Ablation experiments further confirm the effectiveness of the model’s enhancements.

With the development of positioning technology and the widespread application of mobile positioning terminal devices, the acquisition of trajectory data has become increasingly convenient. Furthermore, mining information related to scenic spots and tourists from trajectory data has also become increasingly convenient. This study used the normalization results of information entropy to evaluate the attraction of scenic spots and the experience index of tourists. Tourists and scenic spots were chosen as the probability variables to calculate information entropy, and the probability values of each variable were calculated according to certain methods. There is a certain competitive relationship between scenic spots of the same type. When the distance between various scenic spots is relatively close (less than 8 km), a strong cooperative relationship can be established. Scenic spots with various levels of attraction can generally be classified as follows: cultural heritage, natural landscape, and leisure and entertainment. Scenic spots with higher attraction are usually those with a higher A-level and convenient transportation. A considerable number of tourists do not choose to visit crowded scenic destinations but choose some spots that they are more interested in according to personal preferences and based on access to free travel.

In a Magnetically Coupled Resonant Wireless Power Transfer (MCR-WPT) system, the magnetic coupling coil is one of the key factors that determines the system’s output power, transmission efficiency, anti-offset capability, and so on. This article proposes a coil parameter optimization method for a wireless power transfer system based on crowding distance division and adaptive genetic operators. Through optimizing the design of decision variables, such as the numbers of transmitting and receiving coil turns, the spacings between transmitting and receiving coil turns, the inner radii of the transmitting and receiving coils, and the vertical distance of the coil, the best transmission performance can be achieved. This study improves the NSGA-II algorithm through proposing a genetic operator algorithm for average crowding and high crowding populations based on adaptive operators, as well as a genetic operator algorithm for low crowding populations based on information entropy. These improved algorithms avoid problems inherent to traditional genetic operators such as fixed genetic proportions, do not easily cause the algorithm to fall into a local optimal solution, and show better convergence in the ZDT1–ZDT3 test functions. The optimization design method in this article is not only independent of commercial software such as ANSYS Maxwell 2021 R1, but can also significantly improve the calculation speed compared with traditional simulation software.

Since the reliability of the avionics module is crucial for aircraft safety, the fault diagnosis and health management of this module are particularly significant. While deep learning-based prognostics and health management (PHM) methods exhibit highly accurate fault diagnosis, they have disadvantages such as inefficient data feature extraction and insufficient generalization capability, as well as a lack of avionics module fault data. Consequently, this study first employs fault injection to simulate various fault types of the avionics module and performs data enhancement to construct the P2020 communications processor fault dataset. Subsequently, a multichannel fault diagnosis method, the Hybrid Attention Adaptive Multi-scale Temporal Convolution Network (HAAMTCN) for the integrated functional circuit module of the avionics module, is proposed, which adaptively constructs the optimal size of the convolutional kernel to efficiently extract features of avionics module fault signals with large information entropy. Further, the combined use of the Interaction Channel Attention (ICA) module and the Hierarchical Block Temporal Attention (HBTA) module results in the HAAMTCN to pay more attention to the critical information in the channel dimension and time step dimension. The experimental results show that the HAAMTCN achieves an accuracy of 99.64% in the avionics module fault classification task which proves our method achieves better performance in comparison with existing methods.

In particle image velocimetry (PIV) experiments, background noise inevitably exists in the particle images when a particle image is being captured or transmitted, which blurs the particle image, reduces the information entropy of the image, and finally makes the obtained flow field inaccurate. Taking a low-quality original particle image as the research object in this research, a frequency domain processing method based on wavelet decomposition and reconstruction was applied to perform particle image pre-processing. Information entropy analysis was used to evaluate the effect of image processing. The results showed that useful high-frequency particle information representing particle image details in the original particle image was effectively extracted and enhanced, and the image background noise was significantly weakened. Then, information entropy analysis of the image revealed that compared with the unprocessed original particle image, the reconstructed particle image contained more effective details of the particles with higher information entropy. Based on reconstructed particle images, a more accurate flow field can be obtained within a lower error range.

With the rapid development of computer technology, communication technology, and control technology, cyber-physical systems (CPSs) have been widely used and developed. However, there are massive information interactions in CPSs, which lead to an increase in the amount of data transmitted over the network. The data communication, once attacked by the network, will seriously affect the security and stability of the system. In this paper, for the data tampering attack existing in the linear system with multiple binary observations, in the case where the estimation algorithm of the defender is unknown, the optimization index is constructed based on information entropy from the attacker’s point of view, and the problem is modeled. For the problem of the multi-parameter optimization with energy constraints, this paper uses particle swarm optimization (PSO) to obtain the optimal data tampering attack solution set, and gives the estimation method of unknown parameters in the case of unknown parameters. To implement the real-time improvement of online implementation, the BP neural network is designed. Finally, the validity of the conclusions is verified through numerical simulation. This means that the attacker can construct effective metrics based on information entropy without the knowledge of the defense’s discrimination algorithm. In addition, the optimal attack strategy implementation based on PSO and BP is also effective.

Most trajectory compression methods primarily focus on geometric similarity between compressed and original trajectories, lacking explainability of compression results due to ignoring semantic information. This paper proposes a spatio-temporal semantic constrained trajectory compression method. It constructs a new trajectory distance measurement model integrating both semantic and spatio-temporal features. This model quantifies semantic features using information entropy and measures spatio-temporal features with synchronous Euclidean distance. The compression principle is to retain feature points with maximum spatio-temporal semantic distance from the original trajectory until the compression rate is satisfied. Experimental results show these methods closely resemble each other in maintaining geometric similarity of trajectories, but our method significantly outperforms DP, TD-TR, and CascadeSync methods in preserving semantic similarity of trajectories. This indicates that our method considers both geometric and semantic features during compression, resulting in the compressed trajectory becoming more interpretable.

Coarse-grained molecular dynamics simulations are employed to investigate the spatiotemporal evolution of vesicles (polymersomes) through the self-assembly of randomly distributed amphiphilic BAB triblock copolymers with hydrophilic A and hydrophobic B blocks in an aqueous solution. The vesiculation pathway consists of several intermediate structures, such as an interconnected network of copolymer aggregates, a cage of cylindrical micelles, and a lamellar cage. The cage-to-vesicle transition occurs at a constant aggregation number and practically eliminates the hydrophobic interfacial area between the B block and solvent. Molecular reorganization underlying the sequence of morphology transitions from a cage-like aggregate to a vesicle is nearly isentropic. The end-to-end distances of isolated copolymer chains in solution and those within a vesicular assembly follow lognormal probability distributions. This can be attributed to the preponderance of folded chain configurations in which the two hydrophobic end groups of a given chain stay close to each other. However, the probability distribution of end-to-end distances is broader for chains within the vesicle as compared with that of a single chain. This is due to the swelling of the folded configurations within the hydrophobic bilayer. Increasing the hydrophobicity of the B block reduces the vesiculation time without qualitatively altering the self-assembly pathway.

Salient object detection (SOD) aims to accurately identify significant geographical objects in remote sensing images (RSI), providing reliable support and guidance for extensive geographical information analyses and decisions. However, SOD in RSI faces numerous challenges, including shadow interference, inter-class feature confusion, as well as unclear target edge contours. Therefore, we designed an effective Global Semantic-aware Aggregation Network (GSANet) to aggregate salient information in RSI. GSANet computes the information entropy of different regions, prioritizing areas with high information entropy as potential target regions, thereby achieving precise localization and semantic understanding of salient objects in remote sensing imagery. Specifically, we proposed a Semantic Detail Embedding Module (SDEM), which explores the potential connections among multi-level features, adaptively fusing shallow texture details with deep semantic features, efficiently aggregating the information entropy of salient regions, enhancing information content of salient targets. Additionally, we proposed a Semantic Perception Fusion Module (SPFM) to analyze map relationships between contextual information and local details, enhancing the perceptual capability for salient objects while suppressing irrelevant information entropy, thereby addressing the semantic dilution issue of salient objects during the up-sampling process. The experimental results on two publicly available datasets, ORSSD and EORSSD, demonstrated the outstanding performance of our method. The method achieved 93.91% Sα, 98.36% Eξ, and 89.37% Fβ on the EORSSD dataset.

Bridges may undergo structural vibration responses when exposed to seismic waves. An analysis of structural vibration characteristics is essential for evaluating the safety and stability of a bridge. In this paper, a signal time-frequency feature extraction method (NTFT-ESVD) integrating standard time-frequency transformation, singular value decomposition, and information entropy is proposed to analyze the vibration characteristics of structures under seismic excitation. First, the experiment simulates the response signal of the structure when exposed to seismic waves. The results of the time-frequency analysis indicate a maximum relative error of only 1% in frequency detection, and the maximum relative errors in amplitude and time parameters are 5.9% and 6%, respectively. These simulation results demonstrate the reliability of the NTFT-ESVD method in extracting the time-frequency characteristics of the signal and its suitability for analyzing the seismic response of the structure. Then, a real seismic wave event of the Su-Tong Yangtze River Bridge during the Hengchun earthquake in Taiwan (2006) is analyzed. The results show that the seismic waves only have a short-term impact on the bridge, with the maximum amplitude of the vibration response no greater than 1 cm, and the maximum vibration frequency no greater than 0.2 Hz in the three-dimensional direction, indicating that the earthquake in Hengchun will not have any serious impact on the stability and security of the Su-Tong Yangtze River Bridge. Additionally, the reliability of determining the arrival time of seismic waves by extracting the time-frequency information from structural vibration response signals is validated by comparing it with results from seismic stations (SSE/WHN/QZN) at similar epicenter distances published by the USGS. The results of the case study show that the combination of dynamic GNSS monitoring technology and time-frequency analysis can be used to analyze the impact of seismic waves on the bridge, which is of great help to the manager in assessing structural seismic damage.

In the present work it is indicated that the entropy of the information is the most appropriate tool to analyze the behavior of the flow of prices in the financial market. For this, the following points are mentioned: general concepts of chaos theory applied to the financial market, concept of dynamic systems applied to the flow of prices, time series of prices and the entropy of information applied to the flow of prices in the financial market.

In order to ensure the accurate application of the data collected by the phasor measurement unit (PMU), it is necessary to eliminate the abnormal data in its measured values. The existing PMU abnormal data identification algorithm has the disadvantages of high algorithm complexity, difficulty in online updating, difficulty in the calibration of multi-source data, and difficulty in application relying on multi-source data. In this paper, an abnormal data identification framework is proposed based on the PMU event data and abnormal data model and the definition of PMU abnormal data identification information entropy. On the basis of the framework, a PMU abnormal data identification algorithm is proposed based on the balanced iterative reducing and clustering using hierarchies (BIRCH) algorithm. The proposed algorithm is implemented, and an algorithm experiment is carried out for the PMU dataset of a substation. The experimental results show that the proposed algorithm has better accuracy and real-time performance than one-class support vector machine (OCSVM) algorithm and gap statistic algorithm.

Effective management of water resources is possible only with an effectively organized monitoring system. After the emergence and development of information theory, the concept of information entropy found its place in the field of the development of water monitoring systems. The purpose of this work is to review research related to the construction of water monitoring systems and networks that applied the entropy theory in the design process. Methodology. Entropy terms used in the construction of water monitoring systems are summarized. Recent applications of the entropy concept for water monitoring system designs classified by precipitation are reviewed; flow and water level; water quality; soil moisture and groundwater. The integrated method of designing multifactorial monitoring systems is also highlighted. Results. The review analyzes studies and their implementation in the design of water monitoring networks based on entropy. The use of various methods of information theory and their adaptation for use in the design of monitoring networks is demonstrated, with the goal of network design methods being the selection of stations that provide the most information for the monitoring network, while being independent of each other. Through extensive testing, information theory has proven to be a reliable tool for evaluating and designing an optimal water monitoring network. Scientific novelty. This review focuses on studies that have applied information theory or information entropy to construct monitoring networks and systems. Information theory was developed by Shannon in the middle of the last century to measure the information content of a data set and was subsequently applied to solving water resources problems. To date, there are no review studies regarding the design of water monitoring networks based on the concept that entropy will be able to characterize the information specific to the monitoring station or monitoring networks. The main goal is to have the maximum amount of information. Practical significance. The optimal design of the monitoring network can be built based on the specified design criteria; however, the practical application of a new optimal monitoring network is rarely evaluated in a hydrological or other model. It is also important to identify the benefits of entropy-based network design to convince decision-makers of the importance of entropy-based approaches. The optimal network can be subjective, based on the choices made during the entropy calculation and the design method chosen, especially when additional objective functions are considered in the design. This applies to the method chosen to construct the optimal monitoring network, whether it is found using an iterative method where one station is added at a time, or a collection of stations that are added simultaneously. Research has also shown that data length, catchment scale, and the order can affect optimal network design. when using discrete entropy, it was shown that the binning method affects the final network design. Therefore, when selecting options based on the intended application of the monitoring network, a clear understanding and further research is needed to provide recommendations specific to water monitoring networks. In particular, more work is needed on the spatial and temporal scaling of the entropy calculation data to provide robust recommendations for decision-makers.

The use of modern video surveillance systems is associated with solving the tasks of monitoring the activities of personnel and compliance with the technological process based on the analysis and processing of large amounts of video data. This leads to an increase in the cost of information storage, the cost of staff time resources to search for key events over long time periods. The problem of increasing the information value of stored data from video surveillance cameras based on frame filtering and entropy estimation is considered. The implementation of algorithms for processing and compressing information aimed at reducing the volume of stored video data is proposed. The use of this implementation contributes to increasing the overall information value, the efficiency of video surveillance systems by optimizing the volume of stored information and increasing the ratio of useful information. To increase the informational value of video data, a method is proposed that includes the use of modern video compression technologies, a frame filtering algorithm, and an evaluation of the processed video by the Shannon entropy metric. The analysis and comparison of existing video data compression algorithms are performed. An experiment was carried out, as a result of which the correlation between high entropy values and the information value of the frame was proved, the frame filtering algorithm was successfully tested, which allowed to increase entropy by 5.4 times and reduce the duration of the video by 8 times. The use of video data compression methods and efficient codecs, for example, H.265/HEVC, reduced the file size by 14.57 times compared to the original one. The approbation of the proposed method is considered when solving problems of filtering, transmitting, and storing of video data to increase the information value of video data, the productivity of the analysis and information retrieval processes by reducing redundant, useless data fragments. The advantage of the presented method is to remove redundant frames based on motion analysis and entropy estimation of video data, a combination of various approaches to reduce the volume of transmitted and stored information. The application of the method will increase the efficiency of data storage in various video surveillance systems (for logistics centers, warehouse complexes, retail premises).

The article substantiates the problem of translators’ interpretations of information entropy cases in time-remote original texts on the example of William Shakespeare’s plays in the process of translators’ creative reproduction of the original works in the target language. The purpose of the article is to identify the factors that determine the difference in translators’ interpretations and their manifestations in chronologically distant Ukrainian retranslations of the 19th–21st centuries. The tasks of the research are the following: to describe the theoretical basis of the research by determining the specifics of translator’s interpretation and the phenomenon of information entropy from the standpoint of translation theory and practice; to show the influence of different extralingual factors on the process and result of Ukrainian retranslations. The research material is taken from Shakespeare’s tragedies “Romeo and Juliet” (1594) and “Hamlet, Prince of Denmark” (1600), which serve as samples of Early Modern English at the turn of the 16th and 17th centuries, and their chronologically distant Ukrainian retranslations of the 19th–21st centuries. The research methodology incorporates not only general scientific methods, as well as empirical methods in order to substantiate the theoretical basis of the research, but also methods of discourse analysis (cultural and historical, comparative-linguistic-cultural), methods of literary analysis (biographical, hermeneutic, interpretive-textual), methods of linguistic analysis (etymological, contextual, descriptive, semantic, and stylistic) and methods of contrastive translation and transformational analysis, which are used to determine the changes in the scope of information expressed by the units of analysis in the timeremote original texts and in chronologically distant Ukrainian retranslations of the 19th – 21st centuries. The article argues that the difference in translators’ interpretations and, consequently, the variety of retranslations are taken into account by different objective and subjective extralingual factors that influence the process and result of retranslations in cases of information entropy. The objective factors are the historically different social and cultural contexts in which the original texts of the 16th–17th centuries and their chronologically distant Ukrainian retranslations of the 19th–21st centuries are created; the literary trends dominating at the corresponding epochs that influence the author’s and translators’ artistic styles. The subjective factors are translators’ creative personalities that reflect their perception of the surrounding world and personal worldviews determined by their different life experience and social background. Objective and subjective factors, which cause different interpretations of information entropy cases in the time-remote original text and presentation of different scope of information in chronologically distant Ukrainian retranslations, are interrelated and interdependent. If all the factors are the same, i.e. the translators belong to one and the same historical period of time, adhere to the same literary trend and have the same cultural and social background, the translators’ interpretations coincide. At least one of the factors being different, the translators’ interpretations of information entropy cases differ which is a prerequisite for the diachronic plurality in translation of time-remote Shakespeare’s tragedies by Ukrainian translators of the 19th–21st centuries.

Abstract Background Motor imagery is a cognitive process of imagining a performance of a motor task without employing the actual movement of muscles. It is often used in rehabilitation and utilized in assistive technologies to control a brain–computer interface (BCI). This paper provides a comparison of different time–frequency representations (TFR) and their Rényi and Shannon entropies for sensorimotor rhythm (SMR) based motor imagery control signals in electroencephalographic (EEG) data. The motor imagery task was guided by visual guidance, visual and vibrotactile (somatosensory) guidance or visual cue only. Results When using TFR-based entropy features as an input for classification of different interaction intentions, higher accuracies were achieved (up to 99.87%) in comparison to regular time-series amplitude features (for which accuracy was up to 85.91%), which is an increase when compared to existing methods. In particular, the highest accuracy was achieved for the classification of the motor imagery versus the baseline (rest state) when using Shannon entropy with Reassigned Pseudo Wigner–Ville time–frequency representation. Conclusions Our findings suggest that the quantity of useful classifiable motor imagery information (entropy output) changes during the period of motor imagery in comparison to baseline period; as a result, there is an increase in the accuracy and F1 score of classification when using entropy features in comparison to the accuracy and the F1 of classification when using amplitude features, hence, it is manifested as an improvement of the ability to detect motor imagery.

The power consumption information collection system encompasses multiple complex technical relationships, along the data flow chain, numerous data conversion links and processing activities, as well as a multitude of threat exposure surfaces, triggering sources, and uncontrollable factors. This paper proposes a complete lifecycle micro-application management system for the power consumption information collection system, aligning with the objectives and construction programs of the project. The life cycle link of collecting, storing, and sending data about electricity use measures the uncertainty of each piece of information in the binary grid protocol by using the Gini index and information entropy. The characteristics of information data are solved using Bayes’ theorem. By analyzing the users’ behavior patterns, we can prevent them from stealing access rights and other behaviors and dispose of security risks in time. In conjunction with case studies, we conduct simulation experiments to evaluate the power consumption information collection system’s security, complexity, and privacy. In the model without privacy protection, the accuracy rate of member inference attacks is about 68%. This paper’s designed system is more resilient to member inference attacks, with an accuracy rate of less than 50%, demonstrating a superior level of privacy protection for electricity consumption data. The system in this paper uses less time than the other three schemes when the number of users exceeds 2200, peaking at about 700 ms when the number of users reaches 4000.

This paper borrows the concept of entropy and refers the information entropy to the computer-aided English translation to build up the information entropy assessment and evaluation model and the information entropy-based English translation optimization model. The information entropy operation formula is used to calculate the fuzziness between the original text and the target translated text, and at the same time, the segmentation function is utilized to find the approximation value of the information entropy value obtained in a sufficiently fine segmentation of the translated text. Obtain the rectangular window function of semantic Gaussian marginalization, project the information entropy data, add the semantic correlation factors of words, identify the features of semantic information, and realize the optimization of English translation. Simulation experiments are carried out to verify the performance of the model. The information entropy evaluation and analysis model in this paper can achieve convergence after 45 iterations. In contrast, the word error rate and the cut-off error rate of the English translation optimization model are both lower than 10%, and the feature recognition and classification are higher than 80%. The fuzzy statements in Chinese writer Lao She’s work “Teahouse” are selected to explore the naturalness of translation. The information entropy value of the translation optimization model in this paper is closer to the original text, and the English translation’s naturalness is better than that of the GLR model.

The literature on rural revitalization serves as both a record and a witness to the development of rural revitalization in China. This paper outlines rural revitalization literature’s communication mode in the new media environment and proposes a model for its We propose IEOC, an important node group mining algorithm based on information entropy and overlap coefficient, to construct the communication influence model. We also introduce information entropy calculation nodes, measure the overlap of influence using overlap coefficients, and propose a floating probability cascade model, TFIC, to construct the communication influence model of rural revitalization literature combined with the model for analyzing the communication influence of rural revitalization literature, it reveals a moderately low total word frequency of high-frequency words (2332). Out of the 12 rural revitalization literature works under study, only Moon Beach has a communication influence index of more than 1. of more than 1. Compared to other mainstream literature, rural revitalization literature has a communication power of only 1785 and 18369, with fewer high-communication works and high-influence contents. The number of high-impact works and contents is relatively small. Overseas dissemination is characterized by a higher number of Chinese editions than foreign language editions, and there are also shortcomings in translation.

Identifying the authenticity of library intelligence has consistently posed a significant challenge in intelligence management. This paper aims to optimize the D-S evidence theory to address the issues of uncertainty, imprecision, and high conflict in library intelligence evidence fusion. To achieve this, DSmT is employed to extend the D-S evidence theory by eliminating the process of assigning conflicting information with normalization coefficients. Then, through the fuzzy information pseudo-feature analysis method, the library intelligent intelligence information entropy feature quantity is extracted, and semantic feature matching is used to construct the library intelligent intelligence true and false information recognition system. Finally, the recognition performance of the system is examined using a dataset to analyze the intelligence authenticity recognition of mis-spliced bacterial colony genomic data from a biological experiment that has been entered into the library intelligence database. Finally, it was found that the average recognition rate of this paper's system in the four classical datasets is 96.57%, which is higher than the 89.61% and 90.02% of the two evidence fusion classification schemes. The results of this paper's algorithm for high-conflict evidence are 0.9506, 0.0558, and 0.0097, reflecting the accuracy of the evidence support results. Finally, 35 colony genome splicing error intelligence were identified, of which 32 were genuine errors, with a precision rate of 91% and a recall rate of 94%. The library intelligence authenticity recognition system designed in this paper has excellent performance and provides an effective path and usable algorithmic model for library intelligence authentication.