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Accurate modeling of transition metal-containing compounds is of great interest due to their wide-ranging and significant applications. These systems present several challenges from an electronic structure perspective, including significant multi-reference characters and many chemically relevant orbitals. A further complication arises from the so-called double d-shell effect, which is known to cause a myriad of issues in the treatment of first-row transition metals with both single- and multi-reference methods. While this effect has been well documented for several decades, a comprehensive understanding of its consequences and underlying causes is still evolving. Here, we characterize the second d-shell effect by analyzing the information entropy of correlated wavefunctions in a periodic series of 3d and 4d transition metal molecular hydrides and oxides. These quantum information techniques provide unique insight into the nuanced electronic structure of these species and are powerful tools for the study of weak and strong correlations in the transition metal d manifold. [ABSTRACT FROM AUTHOR]

The response of a biological network to ligand binding is of crucial importance for regulatory control in various cellular biophysical processes that is achieved with information transmission through the different ligand-bound states of such networks. In this work, we address a vital issue regarding the link between the information content of such network states and the experimentally measurable binding statistics. Several fundamental networks of cooperative ligand binding, with the bound states being adjacent in time only and in both space and time, are considered for this purpose using the chemical master equation approach. To express the binding characteristics in the language of information, a quantity denoted as differential information index is employed based on the Shannon information. The index, determined for the whole network, follows a linear relationship with (logarithmic) ligand concentration with a slope equal to the size of the system. On the other hand, the variation of Shannon information associated with the individual network states and the logarithmic sensitivity of its slope are shown to have generic forms related to the average binding number and variance, respectively, the latter yielding the Hill slope, the phenomenological measure of cooperativity. Furthermore, the variation of Shannon information entropy, the average of Shannon information, is also shown to be related to the average binding. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the relationship between local wages and the internal structure of the regional knowledge base. The purpose is to assess if the workers' compensations are related to the peculiarities of the knowledge base of the regions in which they supply their labor services. The test of this hypothesis is based on the assessment of the impact of related vis-à-vis unrelated knowledge variety on cross-regional wage differentials. The empirical analysis is carried out by exploiting patent data and a unique employer–employee administrative dataset. First, using OECD-PATREG data on patent filing, we build information entropy indexes proxying the variety of NUTS 3 regions' knowledge bases, and the decomposition in the related and unrelated component. Second, we assess the impact of these indexes on wages based on administrative data from the Italian National Institute of Social Security. Our results suggest that workers employed in regions with a heterogenous knowledge structure earn positive wage premia, while related variety has a negative effect on compensation levels. [ABSTRACT FROM AUTHOR]

There is a fundamental limit to what is knowable about atomic and molecular scale systems. This fuzziness is not always due to the act of measurement. Other contributing factors include system parameter uncertainty, functional uncertainty that originates from input functions, and sensors noises to mention a few. This indeterminism has led to major challenges in the development of accurate control methods for atomic scale systems. To address the probabilistic and uncertain nature of these systems, this work proposes a novel control framework that considers the representation of the quantum system states and the quantification of its physical properties following a probabilistic approach. Our framework is fully probabilistic. It uses the Shannon relative entropy from information theory to design optimal randomised controllers that can achieve a desired outcome of an atomic scale system. Two experiments are carried out to illustrate the applicability and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

To solve the security problem of facial image transmission and storage, we propose a facial image encryption algorithm based on an improved 4-D hyperchaotic system. First, a new 4-D hyperchaotic system is obtained by improving the Chen chaotic system, and then the facial region in the image is recognized and encrypted via the Haar cascade face detection technique. The encryption process is divided into two stages: the scrambling phase uses circular layered and bit-plane splicing V-selective scrambling to alter pixel values, and the diffusion phase employs bit-level DNA cross-encoding and arithmetic operations. These techniques increase the complexity and randomness of encryption, increasing its resistance to brute-force attack. Finally, the encrypted facial region is combined with the non-face region. The experimental results show that the number of pixel change rates and the unified average change intensity of the encrypted image are near ideal, with an information entropy of 7.9976 bits. Security analysis confirms that the algorithm can withstand various attacks. [ABSTRACT FROM AUTHOR]

In linguistics and natural language processing, measuring text features is crucial for representing and revealing the properties of texts in terms of topics, genres, sentiment and more. Current methods predominantly rely on the frequency of linguistic units and rarely consider the syntagmatic properties of these units, which can reflect deeper linguistic characteristics (e.g. syntax and pragmatics). This paper proposes a general method for calculating the sequence complexity of text. By combining the features of word length and word frequency, two specific implementations of this method are provided. Using these two formulas, word sequence complexity of text based on the Brown Corpus and the Gutenberg Corpus is calculated. The results show that word sequence complexity of text indicates a characteristic of gradual stabilization. By comparing random texts with natural texts, it is found that word sequence complexity is influenced by syntactic rules rather than sentence order, and natural texts tend to alternate between words of varying lengths and frequencies. Classification experiment results indicate that the proposed word sequence complexity outperforms commonly used quantitative indicators for representing the genre of text, such as TTR, information entropy, Zipf's law parameters and motifs. [ABSTRACT FROM AUTHOR]

Under the background of big data, the evaluation of college students' satisfaction with online ideological and political education (IAPE) is primarily achieved through data mining and analysis techniques, allowing for a more comprehensive and accurate reflection of students' attitudes and feedback. By collecting data through online surveys, social media interactions, and other channels, educators can adjust the content and methods of teaching in real-time to better meet students' needs and improve the effectiveness and satisfaction of IAPE. The satisfaction evaluation of college students' online IAPE in the context of big data is a multiattribute group decision-making (MAGDM) problem. Recently, VIKOR method have been applied to address MAGDM challenges. Single-valued neutrosophic sets (SVNSs) are employed as a tool to represent uncertain data in the satisfaction evaluation of college students' online IAPE within the big data context. In this paper, we propose the single-valued neutrosophic number VIKOR (SVNN-VIKOR) method to solve MAGDM problems under SVNSs. Finally, a numerical case study is presented to validate the effectiveness of the proposed method in evaluating the satisfaction of college students' online IAPE in the context of big data. [ABSTRACT FROM AUTHOR]

Most existing standard learning methods suffer from poor performance in high-dimensional imbalanced learning with class overlapping. To tackle this problem, we propose a novel oversampling algorithm that aims to generate a robust ensemble of manifold dimensionality reduction, grid clustering, and information entropy criteria. Instead of simply balancing positive and negative numbers, the algorithm considers the difference in information entropy for interclass, which first reduces the dimensionality by manifold reduction, and then group data utilize grid clustering. Subsequently, calculate the oversampling weight of each group by information entropy and find seed samples based on entropy and neighborhood. Finally, SMOTE based on Beta distribution combined with standard classifiers achieve the rapid and precise classification for high-dimensional imbalanced datasets with class overlapping. Extensive experimental results on 20 real-world imbalanced datasets and compared with eight popular oversampling algorithms show that our proposed algorithm, while achieving good performance in terms of F-measure, G-mean, and AUPRC, can lead to robust performance under high-dimensional and overlapping. It is worth noting that our algorithm substantially reduces the number of synthetic samples against the quantity-balanced oversampling algorithms, and significantly reduces the generation of class overlapping. [ABSTRACT FROM AUTHOR]

Secure multi-party computation has seen substantial performance improvements in recent years and is being increasingly used in commercial products. While a significant amount of work was dedicated to improving its efficiency under standard security models, the threat models do not account for information leakage from the output of secure function evaluation. Quantifying information disclosure about private inputs from observing the function outcome is the subject of this work. Motivated by the City of Boston gender pay gap studies, in this work, we focus on the computation of the average of salaries and quantify information disclosure about private inputs of one or more participants (the target) to an adversary via information-theoretic techniques. We study a number of distributions including log-normal, which is typically used for modeling salaries. We consequently evaluate information disclosure after repeated evaluation of the average function on overlapping inputs, as was done in the Boston gender pay study that ran multiple times, and provide recommendations for using the sum and average functions in secure computation applications. Our goal is to develop mechanisms that lower information disclosure about participants' inputs to a desired level and provide guidelines for setting up real-world secure evaluation of this function. [ABSTRACT FROM AUTHOR]

We re‐examine the concept of involuntary and frictional unemployment and the neutrality of money in a statistical equilibrium model of the labor market in which boundedly rational workers' and employers' interactions have a non‐zero impact on wages. From this perspective both the degree of involuntary unemployment and the neutrality of money depend on the adjustment of both expectations of the average level of wages and prices and the further adjustment of anticipations of the scale of fluctuations in prices and wage offers. Shocks to the economy can produce new long‐run equilibrium levels of unemployment and short‐run increases in involuntary unemployment arising from unevenness in the adjustment of expectations. [ABSTRACT FROM AUTHOR]

The problem of imbalanced regression is widely prevalent in various intelligent manufacturing systems, significantly constraining the industrial application of machine learning models. Existing research has overlooked the impact of redundant data and has lost valuable information within unlabeled data, therefore, the effectiveness of the models is limited. To this end, we propose a novel model framework (sNN-ST, similarity-based nearest neighbor and Self-Training fusion) to address imbalanced regression in industrial big data. This approach comprises two main steps: first, we identify and remove redundant samples by analyzing the redundancy relationships among samples. Then, we perform pseudo-labeling on unlabeled data, selectively incorporating reliable and non-redundant samples into the labeled dataset. We validate the proposed method on two imbalanced regression datasets. Removing redundant data and effectively utilizing unlabeled data optimize the dataset's distribution and enhance its information entropy. Consequently, the processed dataset significantly improves the overall model performance. We used this model to conduct a Multi-Parameter Global Relative Sensitivity Analysis within a production system. This analysis optimized existing process parameters and improved product quality consistency. This research presents a promising approach to addressing imbalanced regression problems. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks and Transformers have good feature extraction but struggle in no-reference image quality assessment (NR - IQA) with real-world distortions. Vision Transformers, with self-attention, capture global and multi-scale features well. Local information entropy quantifies image region complexity, aligning with human perception. We propose IE - ViT, an NR - IQA model. It uses ViT for feature extraction, a dual-branch for multi-scale assessment, local information entropy for better human-perception simulation, and a global correlation loss. Experiments show it outperforms others on multiple datasets, with high scores on KADID - 10K. [ABSTRACT FROM AUTHOR]

This study explored the relationship between brain and skin activities by investigating the contained information in electroencephalogram (EEG) and galvanic skin response (GSR) signals. Eleven subjects (4M, 7F, 18–22 years) were monitored while listening to various genres of music (relaxing, pop, and rock), while their EEG and GSR were recorded, and then we computed the Shannon entropy of recorded signals. The results revealed that rock music had the most pronounced impact on altering the information content of the signals compared to other musical genres. Additionally, a strong correlation (0.8926) was detected among the changes in contained information in both signals. This indicates a significant correlation between skin and brain activities. Importantly, this analytical approach shows potential for studying the correlation between brain activity and other bodily organs in diverse scenarios, providing insights into physiological responses under various conditions. [ABSTRACT FROM AUTHOR]

Images are important information carriers in our lives, and images should be secure when transmitted and stored. Image encryption algorithms based on chaos theory emerge in endlessly. Based on previous various chaotic image fast encryption algorithms, this paper proposes a color image sector fast encryption algorithm based on one-dimensional composite sinusoidal chaotic mapping. The main purpose of this algorithm is to improve the encryption and decryption speed of color images and improve the efficiency of image encryption in the big data era. First, four basic chaos maps are combined in pairs and added with sine operations. Six one-dimensional composite sinusoidal chaos maps (CSCM) were obtained. Secondly, select the two best chaotic mappings LCS and SCS. The randomness of these two chaotic mappings was verified through Lyapunov index and NIST SP 800–22 randomness tests. Thirdly, the encryption process is carried out according to the shape of a traditional Chinese fan, and the diffusion and scrambling of each pixel of the image are performed in parallel. This greatly improves encryption speed. When diffusing, changing the value of one pixel can affect the values of multiple subsequent pixels. When scrambling, each pixel changes position with the three pixels before it according to the chaotic sequence. Finally, through many experiments, it is proved that the image encryption algorithm not only greatly improves the encryption and decryption speed, but also improves various indexes. The key space reached 2192, the average information entropy was 7.9994, the average NPCR was 99.6172, and the average UACI was 33.4646. The algorithm can also resist some common attacks and accidents, such as exhaustion attack, differential attack, noise attack, information loss and so on. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, the application of interactive mobile technology in the field of education has become increasingly widespread. In particular, optimizing the teacher-student interaction has become a key factor in enhancing teaching effectiveness in language education. Under traditional language teaching models, teacher-student interaction tends to be unidirectional, making it difficult to fully stimulate students' interest and engagement. The introduction of interactive mobile technology has made the teaching process more flexible and diverse, providing new forms and approaches for teacher-student interaction. However, how to scientifically assess and effectively optimize teacher-student interaction remains a critical issue in current educational research. Existing studies mainly focus on technological applications and lack an in-depth exploration of the essence of interaction, particularly in terms of the quantitative analysis of interaction intensity and evolution. This study aims to explore the application and optimization strategies of interactive mobile technology in teacher-student interactive language teaching. The study includes four main components: first, analyzing the evolution of teacher-student interaction in language teaching; second, calculating the intensity of teacher-student interaction based on information entropy; third, constructing and calculating a model for the evolution of interaction intensity; and fourth, proposing language teaching optimization strategies based on the evolution of teacher-student interaction intensity. This study, through theoretical modeling and empirical analysis, seeks to provide scientific evidence and practical guidance for improving the quality of teacher-student interaction and optimizing language teaching. [ABSTRACT FROM AUTHOR]

This paper skillfully incorporates the memristor model into a chaotic system, creating a two-dimensional (2D) hyperchaotic map. The system's exceptional chaotic performance is verified through methods such as phase diagrams, bifurcation diagrams, and Lyapunov exponential spectrum. Additionally, a universal framework corresponding to the chaotic system is proposed. To enhance encryption security, the pixel values of the image are preprocessed, and a hash function is used to generate a hash value, which is then incorporated into the secret keys generation process. Existing algorithms typically encrypt the three channels of a color image separately or perform encryption only at the pixel level, resulting in certain limitations in encryption effectiveness. To address this, this paper proposes a novel encryption algorithm based on 2D hyperchaotic maps that extends from single-channel encryption to multi-channel encryption (SEME-TDHM). The SEME-TDHM algorithm combines single-channel and multi-channel random scrambling, followed by local cross-diffusion of pixel values across different planes. By integrating both pixel-level and bit-level diffusion, the randomness of the image information distribution is significantly increased. Finally, the diffusion matrix is decomposed and restored to generate the encrypted color image. Simulation results and comparative analyses demonstrate that the SEME-TDHM algorithm outperforms existing algorithms in terms of encryption effectiveness. The encrypted image maintains a stable information entropy around 7.999, with average NPCR and UACI values close to the ideal benchmarks of 99.6169% and 33.4623%, respectively, further affirming its outstanding encryption effectiveness. Additionally, the histogram of the encrypted image shows a uniform distribution, and the correlation coefficient is nearly zero. These findings indicate that the SEME-TDHM algorithm successfully encrypts color images, providing strong security and practical utility. [ABSTRACT FROM AUTHOR]

Low-spatial-resolution measurements from contact sensors and excessive measurement noise have impeded the implementation of vibration-based damage detection. To tackle these challenges, we propose a novel vision-based damage detection method combining multi-scale signal analysis theory and data fusion algorithm. For high-spatial-resolution vibration measurements, phase-based optical flow estimation algorithm is adopted to deploy virtual sensors on the structure, yielding reliable mode shapes. We then introduce the concept of entropy into damage detection. A novel damage index, defined in Gaussian multi-scale space and named multi-scale local information entropy (MS-LIE), is proposed. The MS-LIE integrates the multi-scale analysis component and the entropy analysis component, addressing both the issue of detection sensitivity and noise immunity, thereby showcasing enhanced performance. Moreover, a data fusion technique for multi-scale damage information is developed to further mitigate the noise-induced uncertainty and pinpoint damage locations. A series of numerical and experimental scenarios are designed to validate the method, and the results indicate that the proposed method accurately detects single and multiple damages in noisy environments, obviating the need for baseline data as a reference. [ABSTRACT FROM AUTHOR]

Joint roughness of rock joints is one of the key factors influencing the mechanical properties of rock joints. The influence of joint roughness on the mechanical properties of rock mass is usually quantitatively characterized by joint roughness coefficient (JRC). The scale effect and the anisotropy of joint roughness are its important characteristics. The lack of a suitable heterogeneity measure for the scale effect and anisotropy of joint roughness makes it difficult to analyze its heterogeneity quantitatively. To solve this issue, this paper proposes the uniformity measure (normalized information entropy measure) and the nonuniformity measure (normalized information enthalpy measure) of a multivalued sequence based on the Shannon/probability entropy notion. Then, a heterogeneity measure of joint roughness is proposed, which realizes the quantitative characterization of the heterogeneity of the scale effect and anisotropy of joint roughness. Actual rock joint cases were used to collect JRC data with 10 sample sizes from 10 to 100 cm and 24 measurement directions from 0° to 345° and to calculate the heterogeneity measures of the joint roughness scale effect in 24 directions and joint roughness anisotropy in 10 sizes. The analysis results show the rationality and efficiency of the proposed measure. The proposed measure provides researchers and engineers with a reliable heterogeneity analysis tool for joint roughness scale effect and anisotropy. [ABSTRACT FROM AUTHOR]

Centrifugal compressors play a crucial role in energy and power systems, and compressor surge significantly impact their safe operation. Monitoring typical surge inception characteristics in a compressor can effectively serve as an early warning indicator, thereby enhancing safety. This paper presents the results of an acoustic experiment conducted on a centrifugal compressor setup. Four microphones were positioned in front of the compressor inlet to record the noise emitted as the compressor entered its surge region from a near-surge state. An entropy-based method is proposed to extract surge inception from acoustic signals by evaluating the informational complexity. Four types of entropy, including approximate entropy, sampling entropy, fuzzy entropy, and information entropy, were calculated from the acoustic signals. Their time-domain curves exhibit a clear increasing trend prior to surge inception. Further analysis reveals differing growth rates of the entropy curves among the four microphones, confirming the impact of microphone position on the capture of flow information and, consequently, on surge warning accuracy. Eventually, an entropy-weight method is proposed to eliminate the effects of positions and enhance surge warning capabilities by accounting for multiple acoustic signals. [ABSTRACT FROM AUTHOR]

It is often assumed that the maximum number of independent states a black hole may contain is N B H = e S B H , where S B H = A / 4 is the Bekenstein–Hawking entropy and A is the horizon area in Planck units. I present a simple and straightforward argument showing that the number of states that can be distinguished by local observers inside the hole must be greater than this number. [ABSTRACT FROM AUTHOR]

With the increasing importance of securing images during network transmission, this paper introduces a novel image encryption algorithm that integrates a 3D chaotic system with V-shaped scrambling techniques. The proposed method begins by constructing a unique 3D chaotic system to generate chaotic sequences for encryption. These sequences determine a random starting point for V-shaped scrambling, which facilitates the transformation of image pixels into quaternary numbers. Subsequently, four innovative bit-level scrambling strategies are employed to enhance encryption strength. To further improve randomness, DNA encoding is applied to both the image and chaotic sequences, with chaotic sequences directing crossover and DNA operations. Ciphertext feedback is then utilized to propagate changes across the image, ensuring increased complexity and security. Extensive simulation experiments validate the algorithm's robust encryption performance for grayscale images, yielding uniformly distributed histograms, near-zero correlation values, and an information entropy value of 7.9975, approaching the ideal threshold. The algorithm also features a large key space, providing robust protection against brute force attacks while effectively resisting statistical, differential, noise, and cropping attacks. These results affirm the algorithm's reliability and security for image communication and transmission. [ABSTRACT FROM AUTHOR]

Transient protection has the advantage of ultra-high-speed action, but traditional transient protection is susceptible to the influence of two fault condition attributes, namely, transition resistance and initial angle of fault, and there are the problems of insufficient sensitivity and insufficient reliability under weak faults. To this end, the propagation characteristics of high-frequency components of transient voltage in bus and line systems are explored, and a new method of unit protection based on the entropy difference in transient voltage information is proposed. In order to solve the problem of single-ended transient protection not being able to reliably distinguish line faults from bus faults and adjacent line first-end faults, the difference between the entropy of line voltage and the entropy of bus voltage was introduced as a fault characteristic. Aimed at the susceptibility of transient protection to the influence of fault condition attributes, composite fault characteristics containing fault attribute information were obtained by integrating fault characteristics with fault condition attributes to overcome the adverse influence of fault condition attributes on transient protection and improve the reliability of the protection. The algorithm solved 38.9% of the original cross-data, 36.1% of the false actions, and 6.1% of the rejected actions. Finally, the accuracy and reliability of the proposed algorithm were verified by extensive ATP-Draw simulation tests. [ABSTRACT FROM AUTHOR]

A quantitative expression for the value of information within the framework of information theory and of the maximal entropy formulation is discussed. We examine both a local, differential measure and an integral, global measure for the value of the change in information when additional input is provided. The differential measure is a potential and as such carries a physical dimension. The integral value has the dimension of information. The differential measure can be used, for example, to discuss how the value of information changes with time or with other parameters of the problem. [ABSTRACT FROM AUTHOR]

Most network attacks occur at the application layer, where many application layer protocols exist. These protocols have different structures and functionalities, posing feature extraction challenges and resulting in low identification accuracy. This significantly affects application layer protocol recognition, analysis, and detection. We propose a data protocol identification method based on a Residual Network (ResNet) to address this issue. The method involves the following steps: (1) utilizing a delimiter determination algorithm based on information entropy proposed in this paper to determine an optimal set of delimiters; (2) segmenting the original data using the optimal set of delimiters and constructing a feature data block frequency table based on the frequency of segmented data blocks; (3) employing a composite-feature-based RGB image generation algorithm proposed in this paper to generate feature images by combining feature data blocks and original data; and (4) training the ResNet model with the generated feature images to automatically learn protocol features and achieve classification recognition of application layer protocols. Experimental results demonstrate that this method achieves over 98% accuracy, precision, recall, and F1 score across these four metrics. [ABSTRACT FROM AUTHOR]

Knowledge graphs, as an important research direction in artificial intelligence, have been widely applied in many fields and tasks. The relations in knowledge graphs have explicit semantics and play a crucial role in knowledge completion and reasoning. Correctly measuring the inferential value of relations and identifying important relations in a knowledge graph can effectively improve the effectiveness of knowledge graphs in reasoning tasks. However, the existing methods primarily consider the connectivity and structural characteristics of relations, but neglect the semantics and the mutual influence of relations in reasoning tasks. This leads to truly valuable relations being difficult to fully utilize in long-chain reasoning. To address this problem, this work, inspired by information entropy and uncertainty-measurement methods in knowledge bases, proposes a method called Relation Importance Measurement based on Information Entropy (RIMIE) to measure the inferential value of relations in knowledge graphs. RIMIE considers the semantics of relations and the role of relations in reasoning. Specifically, based on the values of relations in logical chains, RIMIE partitions the logical sample set into multiple equivalence classes, and generates a knowledge structure for each relation. Correspondingly, to effectively measure the inferential values of relations in knowledge graphs, the concept of relation entropy is proposed, and it is calculated according to the knowledge structures. Finally, to objectively assess the effectiveness of RIMIE, a group of experiments are conducted, which compare the influences of the relations selected according to RIMIE and other patterns on the triple classifications by knowledge graph representation learning. The experimental results confirm what is claimed above. [ABSTRACT FROM AUTHOR]

The pseudo-random number generator (PRNG) based on chaos has been widely used in the fields of digital communication, cryptography and computer simulation. In this paper, we study a new PRNG based on spatial surface chaotic system (SSCS), which is constructed based on coupling mapping lattice (CML) and one-dimensional Logistic chaotic map. To verify the performance of this generator, we study its nonlinear dynamic properties, including the Lyapunov exponent, ergodicity and bifurcation phenomena. Moreover, we analyze the cryptographic properties such as key space, key sensitivity, correlation, histogram and information entropy, while performing NIST SP800-22 test and TestU01 test for the randomness of this system. Theoretical analysis and numerical simulation results show that the PRNG proposed in this paper has good complexity, ergodicity, sensitivity and randomness. Moreover, the key space can increase dynamically with the increase of encrypted data, especially suitable for the protection of big data such as multimedia, which is more advantageous than most existing chaotic mapping. The results of this paper will provide a new idea for the study of chaotic cryptography, and motivate the study of new discrete chaotic models with desirable statistical properties. [ABSTRACT FROM AUTHOR]

In the field of Internet, an image is of great significance to information transmission. Meanwhile, how to ensure and improve its security has become the focus of international research. We combine DNA codec with quantum Arnold transform (QArT) to propose a new double encryption algorithm for quantum color images to improve the security and robustness of image encryption. First, we utilize the biological characteristics of DNA codecs to perform encoding and decoding operations on pixel color information in quantum color images, and achieve pixel-level diffusion. Second, we use QArT to scramble the position information of quantum images and use the operated image as the key matrix for quantum XOR operations. All quantum operations in this paper are reversible, so the decryption operation of the ciphertext image can be realized by the reverse operation of the encryption process. We conduct simulation experiments on encryption and decryption using three color images of "Monkey", "Flower", and "House". The experimental results show that the peak value and correlation of the encrypted images on the histogram have good similarity, and the average normalized pixel change rate (NPCR) of RGB three-channel is 99.61%, the average uniform average change intensity (UACI) is 33.41%, and the average information entropy is about 7.9992. In addition, the robustness of the proposed algorithm is verified by the simulation of noise interference in the actual scenario. [ABSTRACT FROM AUTHOR]

The aim of low-light image enhancement is to optimize images captured in low-light environments by improving their brightness and contrast. Currently, deep learning has become the main method in the field of low-light image enhancement, necessitating a review of deep learning-based methods. First, this paper classified traditional methods of low-light image enhancement and analyzed and summarized their advantages and disadvantages. Then, this paper focused on deep learning-based methods, classified them into supervised and unsupervised categories, and summarized their respective advantages and disadvantages. This paper also summarized the loss functions applied in deep learning approaches. Next, this paper briefly summarized the commonly used datasets and evaluation metrics, using information entropy to quantitatively compare traditional methods, and employing peak signal-to-noise ratio and structural similarity to objectively evaluate deep learning-based methods. Finally, this paper summarized the shortcomings of current methods and prospect future research directions. [ABSTRACT FROM AUTHOR]

Aiming at the obvious trend of the contours on the high-altitude geomagnetic map, this paper presents a method of high-altitude geomagnetic matching area selection combining geomagnetic information entropy and geomagnetic direction entropy based on the study of geomagnetic information entropy and geomagnetic direction entropy. The method first uses geomagnetic information entropy to select the region with rich geomagnetic information, and then determines the direction of the flight trajectory according to geomagnetic direction entropy to obtain the optimal matching trajectory. Finally, the method compares and analyzes the matching localization results of three flight trajectories in different directions on the geomagnetic map at an altitude of 30,000 m by using semi-physical simulation. The experimental results show that the flight navigation error along the trajectory with small information entropy is small, and its positioning error is 12.7% of the trajectory positioning error along the maximum information entropy direction. Selecting the flight trajectory according to the value of the geomagnetic direction entropy can greatly improve the precision and reliability of the geomagnetic matching localization. The method in this paper can provide a basis for path planning of the geomagnetic matching navigation. [ABSTRACT FROM AUTHOR]

The green innovation performance (GIP) evaluation helps to identify strengths and weaknesses in regional innovation systems and has been crucial for policymakers in developing appropriate regional policies. Recent methodology has focused on establishing an indicator framework and calculating composite scores. It is noteworthy that the non-linear relationship between evaluation scores and indicators is rarely considered. In view of this, an evaluation model was proposed in the study which combines support vector machine (SVM) and chaotic grey wolf algorithm (CGWO). Sixteen indicators from the indicator system of European Innovation Scoreboard were retained for the GIP evaluation with an initial screening of indicators using the information entropy method. Then, four different types of optimization algorithms were used to optimize the SVM to generate non-linear predictions and GIP scores. The applicability of the model was verified for the GIP evaluation of China's provinces. According to the training and test results, the SVM-CGWO model achieved significantly better performance than the other three algorithms, which has important benefits in improving the uniformity of the wolf distribution and the traversal of the wolf pack, together with enhancing operation speed and accuracy. It helps users to rank and benchmark regional GIP at the provincial level, taking into account performance improvement and accuracy of dimensions, as well as reliability issues. [ABSTRACT FROM AUTHOR]

With the development of anti-virus technology, malicious documents have gradually become the main pathway of Advanced Persistent Threat (APT) attacks, therefore, the development of effective malicious document classifiers has become particularly urgent. Currently, detection methods based on document structure and behavioral features encounter challenges in feature engineering, these methods not only have limited accuracy, but also consume large resources, and usually can only detect documents in specific formats, which lacks versatility and adaptability. To address such problems, this paper proposes a novel malicious document detection method-visualizing documents as GGE images (Grayscale, Grayscale matrix, Entropy). The GGE method visualizes the original byte sequence of the malicious document as a grayscale image, the information entropy sequence of the document as an entropy image, and at the same time, the grayscale level co-occurrence matrix and the texture and spatial information stored in it are converted into grayscale matrix image, and fuses the three types of images to get the GGE color image. The Convolutional Block Attention Module-EfficientNet-B0 (CBAM-EfficientNet-B0) model is then used for classification, combining transfer learning and applying the pre-trained model on the ImageNet dataset to the feature extraction process of GGE images. As shown in the experimental results, the GGE method has superior performance compared with other methods, which is suitable for detecting malicious documents in different formats, and achieves an accuracy of 99.44% and 97.39% on Portable Document Format (PDF) and office datasets, respectively, and consumes less time during the detection process, which can be effectively applied to the task of detecting malicious documents in real-time. [ABSTRACT FROM AUTHOR]

Aiming at the difficulty of extracting vibration data under actual working conditions of rolling bearings, this paper proposes a bearing reliability evaluation method based on generative adversarial network sample enhancement and maximum entropy method under the condition of few samples. Based on generative adversarial network, data sample enhancement under few samples is carried out, and the reliability analysis model is established by using the maximum entropy principle and Poisson process. The reliability is evaluated according to the reliability variation frequency, variation speed and variation acceleration. The analysis results show that with the gradual increase of running time, the reliability variation frequency shows a nonlinear growth trend, which can be roughly divided into the initial running-in stage, the stable running-in stage and the intense running-in stage. The reliability variation speed is then used to distinguish the specific starting time of the three stages, and finally the preliminary relationship between the reliability variation acceleration and the remaining life is obtained. The experimental results of the XJTU-SY dataset show that compared with the existing reliability evaluation model, the proposed model has the advantages of less samples, no need for preprocessing and higher accuracy. The proposed model has made a beneficial supplement to the existing reliability analysis methods. [ABSTRACT FROM AUTHOR]

A key tenet of the Transactional Interpretation of Quantum Mechanics is the idea that photon absorption localizes the absorbing material system. In doing so, it measures the location of the absorber and hence reduces information entropy which in turn needs to be balanced by appropriate entropy production, if there is a link between information entropy and thermodynamic entropy. Based on a critical analysis of the physics of information erasure, we clarify the link between information and thermodynamic entropy and develop a rigorous model of entropy production in photon-absorption processes. Links to the interpretation of quantum probabilities and to Maxwell's demon are made. [ABSTRACT FROM AUTHOR]

Background: Existing software for comparison of species delimitation models do not provide a (true) metric or distance functions between species delimitation models, nor a way to compare these models in terms of relative clustering differences along a lattice of partitions. Results: Piikun is a Python package for analyzing and visualizing species delimitation models in an information theoretic framework that, in addition to classic measures of information such as the entropy and mutual information [1], provides for the calculation of the Variation of Information (VI) criterion [2], a true metric or distance function for species delimitation models that is aligned with the lattice of partitions. Conclusions: Piikun is available under the MIT license from its public repository (https://github.com/jeetsukumaran/piikun), and can be installed locally using the Python package manager 'pip'. [ABSTRACT FROM AUTHOR]

In order to solve the problem that images are easy to leak and tamper during transmission and enhance the safety of image encryption, an image encryption algorithm based on deoxyribonucleic acid (DNA) coding and hyperchaotic Lorenz system is proposed. First, the hyperchaotic Lorenz system is proposed to replace the traditional logistic mapping algorithm so that the mapping range of the chaotic sequence reaches surjection, and the hyperchaotic Lorenz system is discretized using the fourth‐order Runge–Kutta algorithm. The generated chaotic sequence is encoded according to DNA coding rules, and the encoded image is encrypted and scrambled by using the index of chaotic sequence. Finally, DNA algorithm rules are used for diffusion processing to obtain the ciphertext image. Key space analysis, histogram analysis, correlation analysis of adjacent pixels, and information entropy analysis of the algorithm results are performed. Meanwhile, the mean square error (MSE) of pepper and salt noise density is analyzed by the peak signal‐to‐noise ratio (PSNR). It shows that the algorithm can resist various external attacks and has high security. [ABSTRACT FROM AUTHOR]

The Shannon information entropy for scandium fluoride (ScF) and hydrogen $ ({\rm H}_2) $ (H 2) molecules in the presence of magnetic and Aharanov–Bohm (AB) fields with modified Kratzer plus screened Coulomb potential in the position and momentum coordinate is investigated. The wavefunction and probability density are obtained by solving the two-dimensional (2D) Schrödinger wave equation using the Nikiforov–Uvarov functional analysis method and then applied to calculate the Shannon entropy. The graphical behaviour of the 2D wavefunction and probability density is showcased. Our results depict the effects of the external fields on Shannon entropy for ScF and $ ({\rm H}_2) $ (H 2) , the negative values of the position Shannon entropies obtained imply higher localisation of the particles and molecules indicating more stability of the system. Another interesting part of this work is that Bialynicki–Birula–Mycielski inequality is tested and proven bounded for both diatomic molecules. A few to mention, this research will enhance the apprehension of the dynamics of quantum molecules and systems subjected to external fields, quantum information signal processing and communication. [ABSTRACT FROM AUTHOR]

In a recent development, a new measure of uncertainty, termed extropy, was introduced as the dual complement of Shannon entropy. This study proposes a modified extropy measure called sup-extropy. The proposed uncertainty measure is shown to be minimal value when the data follow a uniform distribution. Various fundamental properties of this measure are thoroughly examined. The joint and conditional versions of this measure are also presented. To illustrate its practical implications, highlights into its application in letter-catching experiments and information fusion analysis are provided. [ABSTRACT FROM AUTHOR]

The mountainous region, which holds a diverse ecosystem providing services of global importance, is fragile owing to the varied topography and continuous human pressure. The risk from natural hazards in mountainous regions is substantial posing significant challenges and causing causalities. Northwestern Himalayas being located in the junction of the Eurasian and Indo-Australian plates are delicate and face the brunt of vagaries from multiple hazards as well as anthropogenic pressure. Against this backdrop, the present study investigates multi-hazard risk assessment integrating both multi-hazard susceptibility and social vulnerability to look into possible high-risk-prone areas. The study makes use of frequency ratio in an ensemble with the Shannon entropy method to accomplish multi-hazard susceptibility (earthquake, floods, landslide, drought and forest fire), and for social vulnerability (a total of 13 indicators and 33 variables considered), Shannon information entropy model is utilized. The findings indicate that 58.40% area is highly and very highly susceptible to multi-hazard spanning across central, southeastern, northeastern and northwestern parts of the study area, whereas 40.59% area is highly vulnerable to multi-hazard covering western, northwestern, southwestern and southeastern parts of the study area. The results for multi-hazard risk indicated that southwestern, western and southeastern parts are highly risk prone areas accounting for 36.27% of the total geographical area. The study will be beneficial for the stakeholders in developing and putting into practice efficient strategies for disaster risk reduction. The study also recommends local adaptation strategies like Dhajji Diwari and surface excavation for more awareness and effective risk management. [ABSTRACT FROM AUTHOR]

Online imbalanced learning is an emerging topic that combines the challenges of class imbalance and concept drift. However, current works account for issues of class imbalance and concept drift. And only few works have considered these issues simultaneously. To this end, this paper proposes an entropy-based dynamic ensemble classification algorithm (EDAC) to consider data streams with class imbalance and concept drift simultaneously. First, to address the problem of imbalanced learning in training data chunks arriving at different times, EDAC adopts an entropy-based balanced strategy. It divides the data chunks into multiple balanced sample pairs based on the differences in the information entropy between classes in the sample data chunk. Additionally, we propose a density-based sampling method to improve the accuracy of classifying minority class samples into high quality samples and common samples via the density of similar samples. In this manner high quality and common samples are randomly selected for training the classifier. Finally, to solve the issue of concept drift, EDAC designs and implements an ensemble classifier that uses a self-feedback strategy to determine the initial weight of the classifier by adjusting the weight of the sub-classifier according to the performance on the arrived data chunks. The experimental results demonstrate that EDAC outperforms five state-of-the-art algorithms considering four synthetic and one real-world data streams. [ABSTRACT FROM AUTHOR]

Network traffic anomaly detection is a critical issue in network security. Existing Abnormal traffic detection methods rely on statistical-based or anomaly-based approaches, and these detection methods all require a full understanding of traffic characteristics and attack patterns. Information entropy has been widely studied in abnormal traffic detection because it can describe the distribution characteristics of network traffic. However, this method makes it difficult to cope with the timing and variability of network traffic. To address these challenges, this paper proposes a network traffic anomaly detection method based on Renyi entropy. Simultaneously, we introduce a fixed time window and utilize an improved EWMA model within this window to dynamically set thresholds for anomaly detection. Experimental results show that the method proposed in this paper is superior to popular abnormal traffic detection methods in terms of effectiveness and efficiency, it is better adapted to the dynamic changes of network traffic and provides a more reliable solution for anomaly detection. [ABSTRACT FROM AUTHOR]

Hydrogen fuel cells often encounter unstable performance and anomalies during operation, highlighting the need for continuous condition monitoring and iterative design improvements. Efficient prediction models are essential for assessing the performance of deployed cells, providing valuable feedback for enhancing designs and developing new products. Machine learning (ML) algorithms are widely used to build such models. However, the vast operational parameter data and hyperparameters without clear physical significance hinder training efficiency and prediction accuracy. Nowadays, artificial intelligence-generated content (AIGC), such as ChatGPT, offers novel solutions to these challenges. This study introduces AIGC-based knowledge queries into ML hyperparameter design for hydrogen fuel cell performance prediction. Firstly, the complex nonlinear relationship between state parameters is accurately measured by integrated information theory with copula entropy to identify interpretable key features. Then, an improved XGBoost model for performance prediction is established by Bayesian optimisation with AIGC to achieve the fast optimisation design of hyperparameters. The method is validated using actual industrial data including 950,000 state parameters and performance indicators, achieving a 53.95% efficiency improvement over conventional Bayesian optimisation while ensuring accuracy. This study provides technical support for the design and monitoring of hydrogen fuel cells and a feasible idea for the industrial application of AIGC. [ABSTRACT FROM AUTHOR]

The uncertainties of marine environments lastingly challenge navigation and safety of sea transportation. Therefore, the article tackles the extraction, assessment, and analysis as well as the perceptive presentations of probabilistic uncertainties of the random wind waves ocean-wide. The link of the probabilistic uncertainties and statistical variabilities is accomplished in the article by using the reported Global Wave Statistics of coherent and controlled wind wave data visually observed from ships in normal service. The probabilistic uncertainty is defined in the information theory most coherently with the human experience of randomness by the information entropy. The article reveals expressions, tables, graphs, and charts of information entropy which objectively express the uncertainties of observed wind wave directions, heights, and periods in all principal ocean areas. The combinations of areal entropy provide uncertainties of wider ocean zones, sectors, and shipping routes for the assessment of all-around exposures of ships and other objects in service at seas to random wind wave effects appropriately to sea-men's experience of randomness. [ABSTRACT FROM AUTHOR]

Statistical counting ad infinitum is the holographic observable to a statistical dynamics with finite states under independent and identically distributed N sampling. Entropy provides the infinitesimal probability for an observed empirical frequency ν ^ with respect to a probability prior p , when ν ^ ≠ p as N → ∞ . Following Callen's postulate and through Legendre–Fenchel transform, without help from mechanics, we show that an internal energy u emerges; it provides a linear representation of real-valued observables with full or partial information. Gibbs' fundamental thermodynamic relation and theory of ensembles follow mathematically. u is to ν ^ what chemical potential μ is to particle number N in Gibbs' chemical thermodynamics, what β = T − 1 is to internal energy U in classical thermodynamics, and what ω is to t in Fourier analysis. [ABSTRACT FROM AUTHOR]

In the field of complex network analysis, accurately identifying key nodes is crucial for understanding and controlling information propagation. Although several local centrality methods have been proposed, their accuracy may be compromised if interactions between nodes and their neighbors are not fully considered. To address this issue, this paper proposes a key node identification method based on multilayer neighbor node gravity and information entropy (MNNGE). The method works as follows: First, the relative gravity of the nodes is calculated based on their weights. Second, the direct gravity of the nodes is calculated by considering the attributes of neighboring nodes, thus capturing interactions within local triangular structures. Finally, the centrality of the nodes is obtained by aggregating the relative and direct gravity of multilayer neighbor nodes using information entropy. To validate the effectiveness of the MNNGE method, we conducted experiments on various real-world network datasets, using evaluation metrics such as the susceptible-infected-recovered (SIR) model, Kendall τ correlation coefficient, Jaccard similarity coefficient, monotonicity, and complementary cumulative distribution function. Our results demonstrate that MNNGE can identify key nodes more accurately than other methods, without requiring parameter settings, and is suitable for large-scale complex networks. [ABSTRACT FROM AUTHOR]

In this work, we have studied the thermodynamic properties of the Van der Waals black hole in the framework of the relativistic Kaniadakis entropy. We have shown that the black hole properties, such as the mass and temperature, differ from those obtained by using the the Boltzmann–Gibbs approach. Moreover, the deformation κ -parameter changes the behavior of the Gibbs free energy via introduced thermodynamic instabilities, whereas the emission rate is influenced by κ only at low frequencies. Nonetheless, the pressure–volume ( P (V) ) characteristics are found independent of κ and the entropy form, unlike in other anti-de Sitter (AdS) black hole models. In summary, the presented findings partially support the previous arguments of Gohar and Salzano that, under certain circumstances, all entropic models are equivalent and indistinguishable. [ABSTRACT FROM AUTHOR]

In recent years, some researchers have combined deep learning methods such as semantic segmentation with a visual SLAM to improve the performance of classical visual SLAM. However, the above method introduces the uncertainty of the neural network model. To solve the above problems, an improved feature selection method based on information entropy and feature semantic uncertainty is proposed in this paper. The former is used to obtain fewer and higher quality feature points, while the latter is used to correct the uncertainty of the network in feature selection. At the same time, in the initial stage of feature point selection, this paper first filters and eliminates the absolute dynamic object feature points in the a priori information provided by the feature point semantic label. Secondly, the potential static objects can be detected combined with the principle of epipolar geometric constraints. Finally, the semantic uncertainty of features is corrected according to the semantic context. Experiments on the KITTI odometer data set show that compared with SIVO, the translation error is reduced by 12.63% and the rotation error is reduced by 22.09%, indicating that our method has better tracking performance than the baseline method. [ABSTRACT FROM AUTHOR]

The ultimate goal of the QBIT theory is to provide a scientific solution to the problem of consciousness. The theory assumes that qualia (plural for quale) are real physical entities. Each quale is a physical system consisting of qubits bonded together by quantum entanglement. The qubits of a quale are so intimately bonded together that they collectively form a unified whole that is more than (and different from) the sum of its parts. A quale is a highly organized, coherent system. Organization and coherence are manifestations of information. The more the amount of information in a system, the more organized, integrated, and coherent the system is. That is why the QBIT theory suggests that qualia are maximally entangled, maximally coherent systems containing high amounts of information, and extremely low amounts of entropy or uncertainty. [ABSTRACT FROM AUTHOR]

Facing the challenges posed by increasingly complex, dynamic, and unforeseen requirements, the design process is grappling with the critical issue of ensuring sustained product satisfaction amid changing demands. This paper introduces an approach for evaluating design adaptability, considering potential future requirements. Entropy serves as a crucial indicator to quantify design effort and the Markov process is employed to simulate potential requirement changes. The information contents of design requirements and design solutions are defined based on information entropy theory, and the design adaptability of a design candidate is evaluated by calculating the extra design effort for satisfying the design requirements, which is the difference in information content between the design candidate and design requirements. Moreover, a simulation method for requirement evolution is proposed, which integrates information entropy theory and the Markov process to accommodate potential future requirements. The general design adaptability of design solutions is then calculated based on conditional entropy, taking into account the evolving design requirements. Finally, the effectiveness of the proposed approach is validated through a case study involving the design and evaluation of a hybrid additive manufacturing device. [ABSTRACT FROM AUTHOR]

Personnel appraisal and promotion are fundamental processes in Human Resource Management (HRM), requiring advanced methodologies that adeptly combine objective data with subjective assessments. This paper introduces ES-MADM II, an enhanced iteration of the Entropy Synergy Multi-Attribute Decision-Making model, designed to strengthen decision-making robustness and stability. The model incorporates key entropy-based indices such as Normalized Mutual Information (NMI), Criteria Effectiveness Score (CES), Conditional Stability Factor (CSF), and the newly introduced Alternatives Distinction Index (ADI). Together, these indices offer a comprehensive framework for assessing not only decision accuracy but also the overall resilience and clarity of the evaluation process. The effectiveness of ES-MADM II is showcased through military HRM case studies, illustrating how the model enhances personnel performance appraisals and promotion decisions by harmonizing subjective judgments with objective metrics. A detailed sensitivity analysis further demonstrates the model's adaptability to variations in input data while preserving decision integrity. ES-MADM II ultimately fosters a more transparent, balanced, and equitable decision-making process, making it an indispensable tool for HR decision makers in complex organizational settings. This refined approach underscores the model's capacity to navigate the complexities of HR evaluations with rigor and fairness. [ABSTRACT FROM AUTHOR]

The validation of autonomous vehicles remains a vexing challenge for the automotive industry's goal of fully autonomous driving. The systematic hierarchization of the test scenarios would provide valuable insights for the development, testing, and verification of autonomous vehicles, enabling nuanced performance evaluations based on scenario complexity. In this paper, an information entropy-based quantification method is proposed to evaluate the complexity of autonomous vehicle validation scenarios. The proposed method addresses the dynamic uncertainties within driving scenarios in a comprehensive way which includes the unpredictability of dynamic agents such as autonomous vehicles, human-driven vehicles, and pedestrians. The numerical complexity calculation of the approach and the ranking of the scenarios are presented through sample scenarios. To automate processes and assist with the calculations, a novel software tool with a user-friendly interface is developed. The performance of the approach is also evaluated through six example driving scenarios, then through extensive simulation using an open-source microscopic traffic simulator. The performance evaluation results confirm the numerical classification and demonstrate the method's adaptability to diverse scenarios with a comparison of complexity calculation ranking to the ratio of collision, near collision, and normal operation tests observed during simulation testing. The proposed quantification method contributes to the improvement of autonomous vehicle validation procedures by addressing the multifaceted nature of scenario complexities. Beyond advancing the field of validation, the approach also aligns with the broad and active drive of the industry for the widespread deployment of fully autonomous driving. [ABSTRACT FROM AUTHOR]