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In order to solve the diamond roller dressing wear situation is difficult to accurately online judgment problem. A set of built-in acoustic emission on-line monitoring diamond roller wear device was designed and developed. Based on Shannon information entropy theory and wavelet packet model, a method of acoustic emission signal processing for diamond wheel dressing was proposed. The method enabled the information entropy of wavelet packet coefficients to be calculated for each layer, and the optimal number of wavelet packet decomposition layers could be determined according to the variation rule of information entropy. Then, the wavelet packet decomposition feature parameter was downsized using principal component analysis, and the feature parameter that best characterizes the wear of diamond rollers was extracted. Finally, PSO-SVM (particle swarm optimization algorithm support vector machine) based on this feature parameter was established. The wear state of diamond rollers was monitored experimentally. The results show that the PSO-SVM model has the highest classification accuracy, with an average correct rate of more than 95.24%, and the effectiveness of PSO-SVM is verified by a large number of experiments. [ABSTRACT FROM AUTHOR]

Due to the initial value sensitivity and high pseudo-randomness of the chaotic system. Chaotic systems are often applied as a Pseudo-Random Number Generator (PRNG) in related fields. Especially in cryptography based on chaos, the Pseudo-Random Numbers (PRNs) produced by PRNG are often used as an interference in the design of encryption algorithm. However, due to lower chaos performance of PRNG such as chaotic output is discontinuous and lower complexity, it may bring the negative impact to its application in related field. In this study, a novel model of 2D sine–cosine-logistic coupling (2D-SCLC) hyperchaotic map is proposed. In comparison with several existing chaos map, the proposed system has better chaotic performance. It can effectively improve the security performance of encryption algorithm, if the proposed chaos map is applied in the design of image encryption algorithm. Further, the PRNs generated by 2D-SCLC is applied to the new image encryption algorithms. By using the security test tools, we can obtain the values of NPCR and UACI have reach 99.61% and 33.44%, they are close to the ideal value. In particular, the information entropy is 7.9996. The security test shows that the algorithms has higher security. This research provides a theoretical guidance for related fields based on chaotic cryptography. [ABSTRACT FROM AUTHOR]

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

With the ever-increasing intertwining of social networks and daily existence, the accumulation of personal privacy information is steadily mounting. However, the exposure of such data could lead to disastrous consequences. Current graph data protection algorithms lack sufficient research on the characteristics of social users, while simultaneously incurring significant time and space overhead. Additionally, the strategies lack adaptability in incorporating noise, often resulting in a subsequent decrease in data availability. To address these issues, a novel approach called the Information Entropy-driven Adaptive Differential Privacy Protection Scheme (IEA-DP) is presented for the release of social data in this study. The proposed solution initially designs the InfomapMerge algorithm to divide the data into communities based on the characteristics of social networks, thereby enabling parallel processing, and mitigating the time and space overhead. Subsequently, the Adaptive Edge Modification Algorithm (AEMA) is proposed to optimize the noise addition process by adaptively adding noise based on the score of node importance. This effectively reduces the amount of added noise, increasing data availability. Finally, the experimental results conducted on six public datasets demonstrate that the IEA-DP scheme strikes a desirable balance between data availability and privacy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) is a physiological signal directly generated by the central nervous system. Brain rhythm is closely related to a person's emotional state and is widely used for EEG emotion recognition. In previous studies, the rhythm specificity between different brain channels was seldom explored. In this paper, the rhythm specificity of brain channels is studied to improve the accuracy of EEG emotion recognition. Variational mode decomposition is used to decompose rhythm signals and enhance features, and two kinds of information entropy, i.e., differential entropy (DE) and dispersion entropy (DispEn) are extracted. The rhythm being used to get the best result of single channel emotion recognition is selected as the representative rhythm, and the remove one method is employed to obtain rhythm information entropy feature. In the experiment, the DEAP database was used for EEG emotion recognition in valence-arousal space. The results showed that the best result of rhythm DE feature classification in the valence dimension is 77.04%, and the best result of rhythm DispEn feature classification in the arousal dimension is 79.25%. [ABSTRACT FROM AUTHOR]

With the development of point cloud-based telemetry technology in recent years, the point cloud data of large field scenes acquired by various sensors have been applied to farmland boundary division, crop growth monitor, area surveying, etc. However, the large field point cloud will cost huge amounts of computational resources in the following transmission, storage and processing, which make it more important to simplify the field point cloud appropriately. In light of limitation of existing algorithms in point cloud simplification of large field scenes, we propose a novel feature-preserved simplification algorithm for large field point cloud data. By introducing the average local entropy as the threshold for area division, our algorithm effectively solves the problem of fuzzy boundary division, as well as preserving the field features and reducing the simplification errors. In view of the problem that the evaluation of current simplification algorithm is mainly focused on qualitative assessment, a quantitative evaluation index for the point cloud simplification is proposed by employing the point-average local entropy, which takes both model retention and simplification efficiency into account. Finally, comparable experiments are performed on four sets of point clouds. The results show that, compared with the statistics of six typical algorithms, the proposed algorithm increases the local entropy by 0.029%, 0.146% and 0.088% on our datasets, and increases 0.029% on the public dataset. The method accurately evaluates the simplification effect. Additionally, the surface area change rate is also used to further evaluate the performance of proposed algorithm, and the quantitative evaluation index is lower than others, which verify the advantages of proposed algorithm in feature protection and large field simplification. [ABSTRACT FROM AUTHOR]

Complexity is an important quantification of uncertain operation in assembly lines and the key source of invisible uncertainty problems in smart manufacturing. The purpose of this paper is to propose a complexity measurement approach to assess the complexity of assembly lines integrating humans, machines and configurations. First, the complexity models of the three states of the operation related to humans and machines are built based on information entropy and the operation time model. Then, an operational complexity model is built at the station level; it is constructed with a single station, parallel stations and sublines based on Kolmogorov entropy. The model quantitatively describes the cumulative complexity along with the material flow. Furthermore, the complexity model of the overall system is given, and the Lempel–Ziv algorithm is applied to measure the complexity flow along with the stations. The complexity equilibrium index is derived to quantify the balancing degree among the stations. The model incorporates uncertain operation into system modeling to quantify the influence of uncertainties on the state of the assembly line. An engine assembly line is used to validate that the approach can measure the complexity from operation to station to system. [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The available studies on vulnerability evaluation of Supply Chain Networks (SCNs) oversimplify the availability of entities during disruptive events by classifying them into only two categories of "operational" and "not operational." Yet, the literature has remained relatively silent on vulnerability analysis of SCNs in the case of capacity reduction. To sidestep this shortcoming, this paper develops an entropy-based method to quantify the vulnerability of SCNs to capacity reduction. This is achieved by measuring the flexibility inherent within the layout of SCNs, both at local and global levels. By performing simulation and sensitivity analyses, this paper predicts the impact of capacity reductions on the performance of SCNs. The results of our study favor the fact that incorporating more backups and increasing the capacity of various entities in the network reduces vulnerability and enhances the robustness of SCNs. The findings of this research may assist operations and supply chain managers in identifying the sensitive areas of SCNs, in turn facilitating the development of an effective risk management strategy to respond to disruptive events. [ABSTRACT FROM AUTHOR]

Information fusion enables the integration and transformation of complimentary data from different sources, providing a unified representation for centralized knowledge discovery, which can contribute to effective decision-making, classification, prediction, and more. Multi-source interval-valued data, represented in the form of intervals to capture uncertainty phenomenons, is a common type of symbolic data that finds extensive applications in the real world. This paper aims to investigate the effective fusion of multi-source interval-valued data and to design dynamic updating algorithms for the situations involving multiple dimensions. The objective is to enhance the efficiency of fusion processes. Firstly, this paper use the Kullback–Leibler divergence to measure the dissimilarity between interval distributions, and construct fuzzy similarity relation. Furthermore, we define a fuzzy information granule structure of interval-valued. Secondly, the concept of fuzzy similarity relations is utilized to construct fuzzy decision-making for objects. Subsequently, based on the aforementioned fuzzy information granule structure and fuzzy decision-making, we propose a novel measure called fuzzy approximate conditional entropy and design a corresponding entropy fusion model. Finally, we discuss various scenarios where dynamic changes occur simultaneously in the attributes and information sources of dynamic multi-source interval-valued data. We design corresponding dynamic update algorithms for these situations. Numerical experiments are conducted on nine UCI datasets to validate our proposed fusion method. The experimental results indicate that our fusion approach exhibits improved classification performance compared to the common fusion methods. The designed dynamic update algorithms are also capable of reducing computation time and enhancing fusion efficiency. [ABSTRACT FROM AUTHOR]

AbstractHospitals are complex organizations which provide a wide array of health care services. This complexity creates challenges for stakeholders who wish to use financial accounting statements to make inferences about the productive choices made by a hospital’s management. These challenges are especially salient when using data reported at the department (or cost center) level, or where the provision of care is coordinated across hospital departments. This study applies information entropy-based comparability analysis techniques to overall and department-level hospital financial data to identify hospital peer groups. Hospitals peer groups not only exhibit similar financial positions overall, but are also likely to exhibit operational similarities at the department level. Data for this analysis are drawn from the financial statements of Washington State critical access hospitals in the fiscal year 2019. The medical laboratory and pharmacy departments were specifically assessed because their services impact or support virtually every other revenue-producing department in the hospital. Findings suggest both departments significantly impact the formation of peer groups, with the pharmacy department contributing the largest impact. [ABSTRACT FROM AUTHOR]

Quantum thermodynamics aims to extend standard thermodynamics and nonequilibrium 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 roomtemperature 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 FROM AUTHOR]

In this paper, we study the formation of Shannon information entropy in spin–orbit coupled (SOC) spin-1 antiferromagnetic dipolar Bose–Einstein condensates with external magnetic field. Our results show that, in the absence of magnetic field and with an increase in dipole–dipole interaction (DDI), information entropy in position space Sr and momentum space Sk remains almost unchanged and increases, respectively. Meanwhile, the order parameter δ decreases, which implies that the system develops toward a disorder state. With the increase of SOC strength, Sr, Sk and δ show similar dynamics behavior. Whereas, in the presence of magnetic field, Sk and δ are localized in the small scope by increasing the dipole and SOC strength. In addition, the value of Sr is nearly the same in this process. These results embody that the introduction of external magnetic field suppresses the role of SOC and DDI, and impedes the condensates towards disorder state. At last, the influence of geometric structure and atom number on information entropy is investigated. It is seen that a narrower trap and fewer atom number make Sr decrease and Sk increase. [ABSTRACT FROM AUTHOR]

Information entropy is widely used in discrete mathematical chemistry as the descriptor to quantify the structural complexity of molecules. This review covers opportunities to use the information entropy parameters to evaluate changes in the complexity of molecules in both simple and multi-step chemical reactions. [ABSTRACT FROM AUTHOR]

Local planning is a critical and difficult task for intelligent vehicles in dynamic transportation environments. In this paper, a new method Suppress Q Deep Q Network (SQDQN) combining traditional deep reinforcement learning Deep Q Network (DQN) with information entropy is proposed for local planning in automatic driving. In the proposed approach, local planning strategy in complex traffic environment established by the actor–critic network based on DQN, the method adopts the way of execution action-evaluation action-update network to explore the optimal local planning strategy. Proposed strategy does not rely on accurate modeling of the scene, so it is suitable for complex and changeable traffic scenes. At the same time, evaluate the update process and determine the update range by using information entropy to solve a common problem in the network that over expectation of actions damage the performance of strategies. Use this approach to improve strategic performance. The trained local planning strategy is evaluated in three simulation scenarios: overtaking, following, driving in hazardous situations. The results illustrate the advantages of the proposed SQDQN method in solving local planning problem. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to determine the appropriate DEM resolution for geomorphic hydrology research in a small watershed of the Loess Plateau, explore the applicability of scale effect curve and information entropy method, and provide a new perspective and approach for determining the appropriate resolution. [Methods] Taking Yanhe River Basin in the loess hilly region as the research area, ArcGIS was used to resample the original resolution DEM, obtain multiple groups of DEM with different resolutions, extract the terrain attribute values at corresponding scales, and draw two different scale effect curves (‘average slope-resolution ’and‘ local variance mean-resolution ’curves). Information entropy and mutual information entropy are introduced to distinguish suitable DEM resolution based on actual terrain. [Results] (1) In the Yanhe River Basin with complex terrain, the results obtained by different determination methods are different, while the results of local variance mean, information entropy and mutual information entropy are similar. (2) The results of determining the appropriate resolution by different terrain attributes are different. The appropriate resolution obtained by profile curvature and plane curvature is similar (the local mean variance is 15～20 m, and the information entropy and mutual information entropy are both 15 m), and the difference of the appropriate resolution obtained by slope is greater than that obtained by curvature (the local mean variance is 20～35 m). The results of information entropy and mutual information entropy are both 20 m. [Conclusion] Both local variance mean and information entropy method are suitable for DEM resolution determination in this area, and the appropriate DEM resolution in Yanhe River Basin is 15～20 m. [ABSTRACT FROM AUTHOR]

In a recent paper (Found Phys 54:14, 2024), Carcassi, Oldofredi and Aidala concluded that the ψ -ontic models defined by Harrigan and Spekkens cannot be consistent with quantum mechanics, since the information entropy of a mixture of non-orthogonal states are different in these two theories according to their information theoretic analysis. In this paper, I argue that this no-go theorem for ψ -ontic models is false by explaining the physical origin of the von Neumann entropy in quantum mechanics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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

The 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.

Smartphones are a vast category of consumer electronic products. Design For Disassembly (DFD) is a methodology that can effectively reduce the disassembly, recycling, and maintenance costs of smartphones. However, due to the diverse constraints posed by the connections among smartphone components, the variability of disassembling tools makes it challenging to objectively characterize the disassemblability of the smartphone. Therefore, information entropy is introduced to characterize the complex state of the system. Disassemblability can be expressed by calculating the operating time of the disassembling tool through information entropy. After multiplying by a constrained quantity factor, the Improved Disassembling Tool Entropy (IDTE) responds to changes in disassemblability as the structural level changes. According to the structure and recycling direction, the disassembling level of smartphones can be divided into module level, part level, and hybrid part-module level. Based on the Maynard Operational Sequencing Technique (MOST), the basic unit of operating time of the disassembling tool is calculated. For the hybrid selective Disassembly Sequence Planning (DSP) of the part and module levels, the Improved Double Genetic Algorithm (IDGA) model is established to compute the optimal disassembly sequence corresponding to each program level. This model calculates the IDTE corresponding to the optimal disassembly sequences at each disassembling level. The validity of IDTE was verified by a coupled comparison test between IDTE and theoretical disassembly time. Finally, an analysis of the disassemblability variations was conducted for two different models of smartphones based on their structure. [ABSTRACT FROM AUTHOR]

Class imbalance and class overlap create difficulties in the training phase of the standard machine learning algorithm. Its performance is not well in minority classes, especially when there is a high class imbalance and significant class overlap. Recently it has been observed by researchers that, the joint effects of class overlap and imbalance are more harmful as compared to their direct impact. To handle these problems, many methods have been proposed by researchers in past years that can be broadly categorized as data‐level, algorithm‐level, ensemble learning, and hybrid methods. Existing data‐level methods often suffer from problems like information loss and overfitting. To overcome these problems, we introduce a novel entropy‐based hybrid sampling (EHS) method to handle class overlap in highly imbalanced datasets. The EHS eliminates less informative majority instances from the overlap region during the undersampling phase and regenerates high informative synthetic minority instances in the oversampling phase near the borderline. The proposed EHS achieved significant improvement in F1‐score, G‐mean, and AUC performance metrics value by DT, NB, and SVM classifiers as compared to well‐established state‐of‐the‐art methods. Classifiers performances are tested on 28 datasets with extreme ranges in imbalance and overlap. [ABSTRACT FROM AUTHOR]

In the analysis of a train–bridge coupled system, fuzzy uncertainty is a factor that must be considered in the prediction of coupled vibration response, but it has not been considered so far. In this work, the concept of information entropy is used to unify the fuzzy uncertainty and random variables into the train–bridge coupled system, and the fuzzy random train–bridge coupled system is established. The fuzzy dynamic response of trains and bridges with fuzzy parameters of the bridge structures and the mass of the carriage were studied, and the mean and variance of the response quantities were calculated using the new point estimation method (NPEM). The combined effect of the fuzziness is considered and the fuzzy value of the system dynamics is obtained. The feasibility of applying this method to train–bridge problems was verified. The calculation results indicated that the maximum amplitude of the fuzzy vertical displacement of the bridge exceeded the conventional vertical displacement by 25.57%, and the maximum amplitude of the fuzzy vertical acceleration of the train exceeded the conventional vertical acceleration by 23.42%. Obviously, in this case, the traditional deterministic calculation method cannot comprehensively and accurately analyze the dynamic response of the train–bridge system. The method in this paper can provide theoretical guidance for evaluating the safety of bridge structures and running safety research in the future. [ABSTRACT FROM AUTHOR]

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.

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. [ABSTRACT FROM AUTHOR]

The use of computer vision relies heavily on the accuracy of the images obtained by the sensors. However, the frequent occurrence of haze, stemming from severe environmental problems, significantly hampers the acquisition of high-precision images, consequently impacting the computer vision industry. Therefore, image defogging becomes an imperative task. Currently, the image defogging process combines physical models with image enhancement techniques, often relying on filtering methods. The selected filter should usually satisfy the ability to preserve the original image and enhance image details. In this regard, the homomorphic filter has excellent performance and is frequently preferred. In the process of homomorphic filtering, four parameters are involved: high-frequency gain( γ H ), low-frequency gain( γ L ), cutoff frequency (D0), and slope sharpening control parameter (C). Notably, determining parameters D0 and C are usually determined by empirical formulation, making it difficult to achieve optimal timeliness and quality in image defogging. Therefore, this paper proposes an optimal selection method for key parameters in homomorphic filtering based on information entropy. This method aims to establish an information entropy-based model, referred to as C-D0 information entropy model, that captures the relationship between slope sharpening and cutoff frequency parameters. This model utilizes a priori image information and employs the least squares method to swiftly determine the optimized parameters C and D0. By utilizing these optimized parameters, the information entropy of the defogged image can be maintained at a high level. Experimental results show that the proposed method enables rapid determination of parameter values, significantly enhancing the timeliness of homomorphic filtering. The parameters quickly selected by this model can result in a 92.12% improvement in the information entropy value of the defogged image compared to the original image. The average improvement rate of information entropy reaches 80.62%. Furthermore, the optimized image exhibits a substantial reduction in the fog effect, leading to a significant improvement in the defogging outcome. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

As a new form of tourism, forest health has gradually attracted the attention of scholars and the public, becoming a new focus of research. There have been many qualitative studies on the product forms and development models of forest health tourism, but there is still a gap in the evaluation indicators and development systems for the development potential of forest health tourism. Using the research results of forest health tourism at home and abroad for reference, we will build an evaluation system for the development potential of forest health tourism, providing new research perspectives and operational practical support for the development of forest health tourism. The development potential evaluation of forest health tourism is a classical MAGDM problems. Recently, the TODIM and VIKOR technique has been employed to cope with MAGDM issues. The single-valued neutrosophic sets (SVNSs) are employed as a tool for characterizing uncertain information during the development potential evaluation of forest health tourism. In this paper, the single-valued neutrosophic number TODIM–VIKOR (SVNN–TODIM–VIKOR) technique is built to solve the MAGDM under SVNSs. In the end, a numerical case study for development potential evaluation of forest health tourism is given to validate the proposed technique. The prime contribution of this paper is managed: (1) the information entropy based on score values are constructed to obtain weight values under SVNSs; (2) an integrated SVNN–TODIM–VIKOR technique is constructed to address the MAGDM issue; and (3) an illustrative example for development potential evaluation of forest health tourism has accomplished to verify the SVNN–TODIM–VIKOR technique. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The use of image fusion technology in the area of information processing is continuing to advance in depth thanks to ongoing hardware advancements and related research. An enhanced convolutional neural network approach is developed to fuse visible and infrared images, and image pre-processing is carried out utilising an image alignment method with edge detection in order to gain more accurate and trustworthy image information. The performance of the fast wavelet decomposition, convolutional neural network, and modified convolutional neural network techniques is compared and examined using four objective assessment criteria. The experimental findings demonstrated that the picture alignment was successful with an offset error of fewer than 3 pixels in the horizontal direction and an angle error of less than 0.3∘ in both directions. The revised convolutional neural network method increased the information entropy, mean gradient, standard deviation, and edge detection information by an average of 46.13%, 39.40%, 19.91%, and 3.72%. The runtime of the modified approach was lowered by 19.42% when compared to the convolutional neural network method, which enhanced the algorithm's performance and boosted the effectiveness of picture fusion. The image fusion accuracy reached 98.61%, indicating that the method has better fusion performance and is of practical value for improving image fusion quality. [ABSTRACT FROM AUTHOR]

The present study proposes a new fuzzy finite element method for dynamic multibody interaction with consideration for structural damage. Here, fuzzy parameters are equivalently transformed into stochastic parameters using information entropy, and the fuzzy response of the structure is obtained by fuzzy calculation combined with the new point estimation method. Numerical examples are used to illustrate the accuracy and efficiency of the presented methods, and scanning method simulations are implemented to validate the computational results. Considering that the damage degree of the pier is uncertain, namely fuzzy uncertainty, stiffness reduction is used to simulate the damage of the pier. The fuzzy dynamic response of the train–bridge system is investigated when the pier structure and the mass of the train are fuzzy parameters. The response of the train–bridge interaction considering damage far exceeds that obtained from conventional deterministic parameter calculations. To ensure running safety, studying the response of the vehicle-system coupled vibration with fuzzy parameters is of great significance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The maximum information entropy production model (MaxEnt), the relative humidity at equilibrium approach (ETRHEQ), and the Surface Flux Equilibrium model (SFE) are three recently developed models to estimate evapotranspiration. Although the connection between ETRHEQ and SFE is evident, no attempts have been made to investigate the congruence, distinctions, or potential complementarity between the two models and MaxEnt. Our mathematical analysis demonstrates that minimizing the vertical variance of RH in ETRHEQ is equivalent to minimizing the dissipation function of energy fluxes in MaxEnt, under the assumption of the same eddy diffusivity of heat and water vapor and with a specific expression for the ratio between the thermal inertia terms for H and LE. The connection between ETRHEQ, SFE, and MaxEnt is independent of Monin‐Obukhov similarity theory (MOST)'s extremum solution, and MOST's extreme solution can be viewed as equivalent to introducing a constant correction factor to account for atmospheric stability. While ETRHEQ and MaxEnt can be united within a single hydrometeorological framework, they diverge in their approaches to modeling evapotranspiration, particularly in how they address the roles of vegetation and land surface heterogeneity. More importantly, the unified framework suggests that turbulence fluxes within the atmospheric boundary layer adhere to the principles of maximum information entropy production. The way in which dissipation, along with its associated entropy production, is established using information entropy theory deviates from traditional thermodynamic entropy formulations. Exploring the connection between thermodynamic and information entropy and developing proper formulations of dissipation for energy fluxes presents an appealing avenue for prospective research. Plain Language Summary: This paper seeks to establish a common theory for explaining the effectiveness of the maximum information entropy production model (MaxEnt), the relative humidity at equilibrium approach (ETRHEQ), and the Surface Flux Equilibrium model (SFE) in estimating evapotranspiration over a wide range of conditions. It uncovers that under reasonable assumptions, ETRHEQ's method in terms of minimizing the vertical variance in relative humidity is equivalent to MaxEnt's approach to minimizing dissipation. The united theory suggests that the movement of air and energy in the lower atmosphere follows the principles of maximum information entropy production, a concept that is a bit different from traditional ideas of entropy in thermodynamics. Looking into how thermodynamic entropy and information entropy are connected and figuring out the right way to calculate dissipation and entropy production for energy flux could lead to new insights in atmospheric science and land‐atmosphere interactions. Key Points: The study presented a pure theorical analysis to unifying MaxEnt, ETRHEQ, and SFE within the same hydrometeorological frameworkMinimizing the vertical variance of RH in ETRHEQ is equivalent to minimizing the dissipation function of energy fluxes in MaxEntThe connection between MaxEnt, ETRHEQ, and SFE is independent of Monin‐Obukhov similarity theory (MOST)'s extremum solution [ABSTRACT FROM AUTHOR]

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One of the current research gaps in multi-scale data analysis is studying information systems characterized by attributes with interval sets as attribute values and multiple scales. To address this gap, we first introduce the concepts of a multi-scale interval-set information system (MISIS) and a multi-scale interval-set decision table (MISDT). We then define the similarity relation between objects in an MISIS and the corresponding rough approximations. We further propose the positive region optimal scale, the modified conditional entropy optimal scale, and the positive complementary conditional entropy optimal scale in an MISDT. We examine the relationships among these optimal scales in consistent and inconsistent MISDTs and show that the positive region optimal scale and the modified conditional entropy optimal scale are equivalent in a consistent MISDT, while in an inconsistent MISDT, the positive region optimal scale is the same as the positive complementary conditional entropy optimal scale, and the modified conditional entropy optimal scale is not greater than the positive complementary conditional entropy optimal scale. Based on the optimal scale, we also develop attribute reduction approaches in MISDTs. Finally, through experimental analysis of data on the UCI dataset, we verify the effectiveness and reasonableness of our proposed methods. [ABSTRACT FROM AUTHOR]

Awarding a contract to a bidder who has unbalanced the bid may cost more money to the owner. An owner who determines that a bid has been unbalanced has the legal right to reject the bid. Although several models have been developed to detect unbalanced bids, some researchers still assert that the number of cases in which owners mistakenly identify balanced bids as unbalanced is too great to ignore. One of the reasons of this failure is the use of an inappropriate benchmark against which the offered unit prices are compared. In this paper it is argued that the use of the geometric mean of the unit prices provided by the bidders as a benchmark helps to obtain robust solutions and an advanced decision-making model is proposed in which the Information Entropy and VIKOR methods are used to detect unbalanced bids. The results obtained from an example project drawn from the literature demonstrate how owners may be misled if they use an inappropriate benchmark in detecting unbalanced bids. This study contributes to the body of knowledge by highlighting the negative impacts of using flawed benchmarks in this process, an area of research that is mostly ignored in the unbalanced bidding literature. [ABSTRACT FROM AUTHOR]

Rivers are the primary source of potable water and other residential uses. Since wastewater from the nearby industries and urban areas degrades the quality of the rivers, it is essential to monitor their water quality and quantity continuously. This study determines the pollution level in a river in accordance with drinking water standards. The entropy-weighted water quality index (EWQI) indicates the level of river pollution, and the principal component analysis (PCA) and Pearson correlation matrix were used to identify pollution sources. Twenty parameters were considered for the study, which includes alkalinity, biochemical oxygen demand (BOD), calcium (Ca+2), cadmium (Cd), chloride (Cl-), copper (Cu), dissolved oxygen (DO), electrical conductivity (EC), fluoride (F-), hardness, iron (Fe), lead (Pb), magnesium (Mg+2), manganese (Mn), nitrate (NO3-), pH, potassium (K+), sodium (Na+), sulphate (SO42-), total dissolved solids (TDS) and zinc (Zn). The EWQI varied between 68.93 (good) to 259.91 (extremely poor). Post-monsoon EWQI values are higher due to the fact that monsoon-filled sewers continue to drain into the river even after the rainy or monsoon season has ended. Winter, pre-monsoon, and monsoon all produced five components (PCs) that explained 79%, 74%, and 80% of the overall variance, respectively and post-monsoon produced 6 PCs that explained 83% of the overall variance. PCA and Pearson correlation matrix indicate pollution from runoff sources during the pre-monsoon and monsoon season along with the pollution sources existing in the monsoon and post-monsoon season, domestic and metal sources. [ABSTRACT FROM AUTHOR]

The paper is devoted to the study of information characteristics which play an important role in solving problems of detection of weak signals and their classification. We formulate and prove lemmas on estimating upper and lower boundaries in statistical and spectral complexity diagrams for different signal-noise mixtures. The obtained theoretical results are verified by numerical experiments, which have confirmed the efficiency of the theoretical estimates. We establish some important laws of the behavior of noise-like and weak signals and the possibilities of their detection in white and blue noise conditions, formulate and prove the corresponding lemmas. Also, we obtain estimates on conditions for the possibility of classifying weak signals by means of information characteristics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this study, the authors combine the acoustic emission (AE) technique with the Brazilian splitting test and the angled die shear test to monitor the development of cracks in red sandstone specimens under compression–tension and compression–shear conditions to identify the mechanism of propagation of tensile and shear failures during the process of damage to rock. We use this to propose a method to analyze the comprehensive index of fracture-induced AEs of red sandstone based on information entropy. A comprehensive index to characterize the tensile and shear failures of rock was obtained by quantifying the entropy weights of the characteristic parameters of AE and their laws of evolution were analyzed. Areas for the classification of different cracking mechanisms of rock were established based on the Box–Cox transform, and the fractal features of crack development under compression–tension and compression–shear conditions were analyzed by using the Grassberger and Procaccia (G–P) algorithm. The results showed differences in the entropy of the characteristic parameters of red sandstone in cases of tensile and shear damage, with the rise time and energy recording the maximum entropies, respectively, and the amplitude yielding the minimum entropy. The comprehensive evaluation indices for tensile failure ξ t and shear failure ξ τ confirmed the existence of at least two mechanisms of deformation-induced damage—tensile fracture and shear slip—in the process of damage to the rock. The curve of evolution of the fractal dimensions D t and D τ of compression–tension and compression–shear, respectively, followed the law of continuously decreasing dimensions, and their turning points reflected the change in crack development in the rock. [ABSTRACT FROM AUTHOR]

With the increasing use of digital images, it is essential to protect confidential image data from unauthorized access. In view of the security problems of digital image in the fields of communication, storage and transmission, this study proposes a digital envelope technology encryption scheme with high security and high speed based on the advantages of symmetric algorithm model and asymmetric algorithm model. This method is based on AES(Advanced Encryption Standard) and ECC(Elliptic Curve Cryptography), and optimized ECC hardware architecture is used for symmetric key sharing to enhance the security of the key. The traditional AES is optimized by adding pseudo- random numbers, using column shift instead of column obfuscation, and three dimensional Sbox box to maintain the Shannon diffusion and obfuscation principle while reducing the time complexity. This study the digital image encryption simulation and performance test of AES algorithm are carried out on FPGA (Field Programmable Gate Array). The test results show that the proposed encryption scheme has the advantages of rapidity, high security and effectiveness, and can better achieve image encryption. [ABSTRACT FROM AUTHOR]

Forced-choice (FC) measures have been widely used in many personality or attitude tests as an alternative to rating scales, which employ comparative rather than absolute judgments. Several response biases, such as social desirability, response styles, and acquiescence bias, can be reduced effectively. Another type of data linked with comparative judgments is response time (RT), which contains potential information concerning respondents' decision-making process. It would be challenging but exciting to combine RT into FC measures better to reveal respondents' behaviors or preferences in personality measurement. Given this situation, this study aims to propose a new item response theory (IRT) model that incorporates RT into FC measures to improve personality assessment. Simulation studies show that the proposed model can effectively improve the estimation accuracy of personality traits with the ancillary information contained in RT. Also, an application on a real data set reveals that the proposed model estimates similar but different parameter values compared with the conventional Thurstonian IRT model. The RT information can explain these differences. [ABSTRACT FROM AUTHOR]