Search

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]

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]

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]

As lithium-ion batteries are widely used in electric vehicles, safety accidents caused by battery failures emerge one after another. Nevertheless, failures caused by changes in the internal structure or characteristics of the battery, such as sudden and progressive failures, are still a serious problem for electric vehicles, challenging existing fault diagnosis methods. This paper first performs wavelet packet decomposition on the battery's raw voltage signal to obtain high-quality low-frequency and high-frequency characteristic signal components. Then performs singular value decomposition on the characteristic signal components to extract the corresponding singular value characteristic parameters, and introduces the Manhattan average distance algorithm to battery faults. Diagnosing and locating faulty battery units using the Laida criterion (3-σ criterion) outlier detection method. Finally, actual vehicle data were used to verify the reliability, stability, accuracy of the method, and compared with the traditional Manhattan distance, correlation coefficient, information entropy methods. The method in this paper has good fault detection effects on vehicles with sudden and progressive faults vehicles. [ABSTRACT FROM AUTHOR]

Background: A variant can be pathogenic or benign with relation to a human disease. Current classification categories from benign to pathogenic reflect a probabilistic summary of the current understanding. A primary metric of clinical utility for multiplexed assays of variant effect (MAVE) is the number of variants that can be reclassified from uncertain significance (VUS). However, a gap in this measure of utility is that it underrepresents the information gained from MAVEs. The aim of this study was to develop an improved quantification metric for MAVE utility. We propose adopting an information content approach that includes data that does not reclassify variants will better reflect true information gain. We adopted an information content approach to evaluate the information gain, in bits, for MAVEs of BRCA1, PTEN, and TP53. Here, one bit represents the amount of information required to completely classify a single variant starting from no information. Results: BRCA1 MAVEs produced a total of 831.2 bits of information, 6.58% of the total missense information in BRCA1 and a 22-fold increase over the information that only contributed to VUS reclassification. PTEN MAVEs produced 2059.6 bits of information which represents 32.8% of the total missense information in PTEN and an 85-fold increase over the information that contributed to VUS reclassification. TP53 MAVEs produced 277.8 bits of information which represents 6.22% of the total missense information in TP53 and a 3.5-fold increase over the information that contributed to VUS reclassification. Conclusions: An information content approach will more accurately portray information gained through MAVE mapping efforts than by counting the number of variants reclassified. This information content approach may also help define the impact of guideline changes that modify the information definitions used to classify groups of variants. [ABSTRACT FROM AUTHOR]

This paper presents a novel 4D hyperchaotic system derived from a modified 3D Lorenz chaotic system. A key aspect of this system is the presence of a single equilibrium point, and its stability is carefully analyzed. The dynamic properties, including the Lyapunov exponent spectrum, bifurcation diagram, and chaotic attractors, are investigated using MATLAB simulations. The results reveal that the system displays hyperchaotic behavior across a wide range of the parameter d , with its chaotic attractor transitioning through four states: hyperchaotic, chaotic, periodic, and quasi-periodic, showcasing the system's complex dynamics. Experimental validation using STM32 embedded hardware successfully reproduces these four types of chaotic attractors, confirming the theoretical predictions. The proposed hyperchaotic system is then applied to image encryption, introducing a novel encryption method. The hyperchaotic key sequence generated by this system meets 15 tests of the NIST SP800-22 standard, and further experimental validation with STM32 hardware demonstrates the algorithm's effectiveness, simplicity, non-linearity, and high security. The encrypted images and sequences are rigorously tested key space analysis, histogram similarity analysis, information entropy analysis, statistical attack analysis, differential attack analysis, key sensitivity analysis, and correlation analysis, highlighting the robustness and reliability of the proposed system. This method is versatile and can be extended to various fields, including audio and video encryption, text encryption, IoT security, financial transaction security, and medical data protection. [ABSTRACT FROM AUTHOR]

This research focuses on classifying translated and non-translated Chinese texts by analyzing syntactic rule features, using an integrated approach of machine learning and entropy analysis. The methodology employs information entropy to gauge the complexity of syntactic rules in both text types. The methodology is based on the concept of information entropy, which serves as a quantitative measure for the complexity inherent in syntactic rules as manifested from tree-based annotations. The goal of the study is to explore whether translated Chinese texts demonstrate syntactic characteristics that are significantly different from those of non-translated texts, thereby permitting a reliable classification between the two. To do this, the research calculates information entropy values for syntactic rules in two comparable corpora, one of translated and the other of non-translated Chinese texts. Then, various machine learning models are applied to these entropy metrics to identify any significant differences between the two groups. The results show significant differences in the syntactic structures. Translated texts have a higher degree of entropy, indicating more complex syntactic constructs compared to non-translated texts. These findings contribute to our understanding of the effect of translation on language syntax, with implications for text classification and translation studies. [ABSTRACT FROM AUTHOR]

Increasing advancements in building digitization, smart sensing, and metering technologies have allowed large amounts of timeseries data to be collected for monitoring, analyzing, and controlling building systems. However, due to sensor or communication failures, the data collected are often incomplete and poor in quality. Data imputation approaches to replace the missing values, specifically based on either statistical or predictive models have been widely adopted for multivariate datasets in other domains. It is hence of interest to find an effective way to impute timeseries data collected from a building system. In this paper, we evaluate multiple data imputation approaches using data collected from a medium sized building situated in Stockholm, Sweden and a small commercial building from the ASHRAE RP-1312 research project. Sensors with widely varying characteristics from the case study buildings were selected to evaluate the imputation methods. The imputation accuracy and the impact of each chosen imputation method on information entropy, short-term building forecasting model performance, and fault detection strategy were evaluated. Results demonstrate that incorporating time-lagged cross correlations within a k -nearest neighbor ( k NN) model provide the most accurate imputations without affecting the quality of subsequent data analysis. [ABSTRACT FROM AUTHOR]

Neurovascular coupling (NVC) is the tight relationship between changes in cerebral blood flow and neural activation. NVC can be evaluated non-invasively using transcranial Doppler ultrasound (TCD)-measured changes in brain activation (cerebral blood velocity [CBv]) using different cognitive tasks and stimuli. This study used a novel approach to analyzing CBv changes occurring in response to 20 tasks from the Addenbrooke's Cognitive Examination III in 40 healthy individuals. The novel approach compared various information entropy families (permutation, Tsallis, and Rényi entropy) and statistical complexity measures based on disequilibrium. Using this approach, we found the majority of the attention, visuospatial, and memory tasks from the Addenbrooke's Cognitive Examination III that showed lower statistical complexity values when compared with the resting state. On the entropy-complexity (HC) plane, a receiver operating characteristic curve was used to distinguish between baseline and cognitive tasks using the area under the curve. Best area under the curve values were 0.91 ± 0.04, p =.001, to distinguish between resting and cognitively active states. Our findings show that brain hemodynamic signals captured with TCD can be used to distinguish between resting state (baseline) and cognitive effort (stimulation paradigms) using entropy and statistical complexity as an alternative method to traditional techniques such as coherent averaging of CBv signals. Further work should directly compare these analysis methods to identify the optimal method for analyzing TCD-measured changes in NVC. [ABSTRACT FROM AUTHOR]

Channel pruning can reduce the number of neural network parameters and computational cost by eliminating redundant channels, its main purpose is to adapt to resource constrained devices. Evaluation-based global pruning and network search-based pruning are two common methods of channel pruning. However, the network architecture pruned by the global mask is often not optimal, while the method that directly searches for the optimal architecture will introduce a large number of hyperparameters, which greatly increases the training cost. In this paper, we propose a novel Two-dimensional information Entropy based Channel Pruning method (TECP). The pruning process consists of two steps. First, a global mask pruning scheme is employed to obtained a pre-pruning model. Then, the two-dimensional information entropy is calculated by using feature maps of dense network to adjust the pre-pruning model adaptively to get a compact network. Moreover, the entropy values are used to determine the minimum number of reserved channels per layer based on to avoid the imbalance of network architecture and the layer collapse caused by global pruning. Extensive experiments with a variety of networks on several datasets clearly demonstrate the effectiveness of our proposed TECP method. For example, results show that on CIFAR-10, the compressed model achieves comparable accuracy to the original model, but with a significantly lower number of parameters (44.29% for ResNet-20 and 46.79% for VGG-16). This is beneficial for industrial deployment. And experimental results also show that TECP method obtain the better performance compared with state-of-the-art method. [ABSTRACT FROM AUTHOR]

Increasing advancements in building digitization, smart sensing, and metering technologies have allowed large amounts of timeseries data to be collected for monitoring, analyzing, and controlling building systems. However, due to sensor or communication failures, the data collected are often incomplete and poor in quality. Data imputation approaches to replace the missing values, specifically based on either statistical or predictive models have been widely adopted for multivariate datasets in other domains. It is hence of interest to find an effective way to impute timeseries data collected from a building system. In this paper, we evaluate multiple data imputation approaches using data collected from a medium sized building situated in Stockholm, Sweden and a small commercial building from the ASHRAE RP-1312 research project. Sensors with widely varying characteristics from the case study buildings were selected to evaluate the imputation methods. The imputation accuracy and the impact of each chosen imputation method on information entropy, short-term building forecasting model performance, and fault detection strategy were evaluated. Results demonstrate that incorporating time-lagged cross correlations within a k -nearest neighbor ( k NN) model provide the most accurate imputations without affecting the quality of subsequent data analysis. [ABSTRACT FROM AUTHOR]

Recent work on Vision Transformers (VTs) showed that introducing a local inductive bias in the VT architecture helps reducing the number of samples necessary for training. However, the architecture modifications lead to a loss of generality of the Transformer backbone, partially contradicting the push towards the development of uniform architectures, shared, e.g., by both the Computer Vision and the Natural Language Processing areas. In this work, we propose a different and complementary direction, in which a local bias is introduced using an auxiliary self-supervised task, performed jointly with standard supervised training. Specifically, we exploit the observation that the attention maps of VTs, when trained with self-supervision, can contain a semantic segmentation structure which does not spontaneously emerge when training is supervised. Thus, we explicitly encourage the emergence of this spatial clustering as a form of training regularization. In more detail, we exploit the assumption that, in a given image, objects usually correspond to few connected regions, and we propose a spatial formulation of the information entropy to quantify this object-based inductive bias. By minimizing the proposed spatial entropy, we include an additional self-supervised signal during training. Using extensive experiments, we show that the proposed regularization leads to equivalent or better results than other VT proposals which include a local bias by changing the basic Transformer architecture, and it can drastically boost the VT final accuracy when using small-medium training sets. The code is available at https://github.com/helia95/SAR. [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]

Titanium alloys are widely used in high-end manufacturing fields such as aerospace and nuclear energy due to their excellent mechanical properties and corrosion resistance, but it is prone to cracking under load, seriously affecting its performance and safety. Therefore, studying the crack initiation process of titanium alloys is of great significance for improving their service life and safety. In this study, TC4 titanium alloy material is taken as the research object, and the crack emergence mechanism during its tensile fracture process is investigated by combining tensile test and AE test. The experiments were carried out under displacement-controlled conditions, and crack emergence detection was carried out by two methods: AE characterisation parameters and AE information entropy, and SEM was used to observe and analyse the specimen fracture. The results indicate that the AE characteristic parameters of different notched specimens have the same evolution pattern, and this method can be used to predict crack initiation, but there is a certain degree of uncertainty. Compared with the former, the information entropy theory can more accurately predict crack initiation. Therefore, the analysis of the entropy of AE information can be used as a highly practical real-time condition detection method to provide effective detection means for engineering applications. [ABSTRACT FROM AUTHOR]

To improve the feature extraction performance in vascular medical images, a multi-scale Retinex medical image enhancement algorithm based on a four-step method is proposed. This method is based on the multi-scale Retinex enhancement method, which divides the entire enhancement process into four steps: The first step is to reduce the overall complexity of the enhancement process through block processing; second, perform Retinex enhancement in each sub-block image; the third step is to optimize the connection area of each sub-block; and the fourth step is to highlight detailed features through gradient filtering. During the experiment, three different images were enhanced. The experimental results show that the proposed method can be used in enhancing the vascular features in medical images and be extended to other application fields. This method can effectively suppress the blurring and halo of the original image, while achieving enhancement effects and better protecting detailed features. From the four sets of indicators, the proposed method has shown more significant improvement than both reference methods. Regarding information entropy, the doubles have increased by a maximum of 96.2% and a minimum of 30.3%. [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]

Designing a high-performance neural network is a difficult task. Neural architecture search (NAS) methods aim to solve this process. However, the construction of a high-quality accuracy predictor, which is a key component of NAS, usually requires significant computation. Therefore, zero-shot proxy-based NAS methods have been actively and extensively investigated. In this work, we propose a new efficient zero-shot proxy, Incorporated-Score, to rank deep neural network architectures instead of using an accuracy predictor. The proposed Incorporated-Score proxy is generated by incorporating the zen-score and entropy information of the network, and it does not need to train any network. We then introduce an optimal NAS algorithm called Incorporated-NAS that targets the maximization of the Incorporated-Score of the neural network within the specified inference budgets. The experiments show that the network designed by Incorporated-NAS with Incorporated-Score outperforms the previously proposed Zen-NAS and achieves a new SOTAaccuracy on the CIFAR-10, CIFAR-100, and ImageNet datasets with a lightweight scale. [ABSTRACT FROM AUTHOR]

AbstractAs the aging population issue worsens, people are experiencing increasing life pressures, and the elderly are unable to receive sufficient care, intensifying the demand for home-based intelligent massage healthcare robots. Addressing the challenges faced by users in expressing preferences for specific areas like the back through gestures or voice during the use of massage robots, and the issue of elderly individuals expressing imprecisely, which hinders effective communication of the desired massage target location to the robot, this paper proposes a new human-machine interaction mode for massage robots. The main innovations include: achieving precise control of the massage area through the representation of a virtual human body on the screen; proposing a virtual-to-real mapping algorithm based on human acupoints to address the consistency issue in human virtual-to-real mapping; introducing a multimodal intent understanding algorithm based on dynamic information entropy to address the current limitations of a single interaction mode and low intent comprehension rates in massage robot interactions. Experimental results demonstrate that the proposed multimodal massage localization algorithm achieves good massage intent recognition effects. By combining natural human-machine interaction with intent understanding, it not only accurately captures user massage intentions and assists in completing massage tasks but also reduces user psychological and cognitive loads, leading to more desirable interaction outcomes. [ABSTRACT FROM AUTHOR]

Anurans have several characteristics in their acoustic communication that distinguish them. These characteristics are influenced by body-related, environmental and social variables. However, the complexity of anuran repertoire and the effect of such variables on it are still understudied. Dendropsophus minutus is a Neotropical treefrog that has a wide repertoire composed of three notes, grouped in at least 71 unique combinations. To quantify complexity, we used the entropy measurement, since it measures the randomness of a sequence. Therefore, we could test if repertoire complexity in D. minutus is related with body condition, social context, and environmental variables. We recorded vocalisations of 80 males in three sites and found 44 unique note combinations not described before, increasing the species' repertoire to 115 combinations. We observed that the species has near maximum entropy for note frequency, but a low entropy for note sequencies, showing a clear a bias for some note combinations. We found a positive relationship between repertoire complexity and air temperature. Thus, based on our results, the complexity of this species' repertoire is an additional trait influenced by climate, and should be considered in future studies. [ABSTRACT FROM AUTHOR]

To improve the performance of roller bearing fault diagnosis, this paper proposes an algorithm based on subtraction average-based optimizer (SABO), variational mode decomposition (VMD), and weighted Manhattan-K nearest neighbor (WMH–KNN). Initially, the SABO algorithm uses a composite objective function, including permutation entropy and mutual information entropy, to optimize the input parameters of VMD. Subsequently, the optimized VMD is used to decompose the signal to obtain the optimal decomposition characteristics and the corresponding intrinsic mode function (IMF). Finally, the weighted Manhattan function (WMH) is used to enhance the classification distance of the KNN algorithm, and WMH–KNN is used for fault diagnosis based on the optimized IMF features. The performance of the SABO–VMD and WMH–KNN models is verified through two experimental cases and compared with traditional methods. The results show that the accuracy of motor-bearing fault diagnosis is significantly improved, reaching 97.22% in Dataset 1, 98.33% in Dataset 2, and 99.2% in Dataset 3. Compared with traditional methods, the proposed method significantly reduces the false positive rate. [ABSTRACT FROM AUTHOR]

The precise thermal error prediction of spindle-bearing systems (SBSs) necessitates a comprehensive analysis of information gathered from multi-source sensors. However, limited data availability due to structural constraints poses challenges to fully characterize the system state. In this study, we introduce a data-model hybrid-driven framework based on sensor optimization placement for accurate thermal error prediction of SBSs. Firstly, a thermal hypernetwork method is developed to consider uneven temperature distribution and establish a unified information fusion model for state estimation. Secondly, based on an analysis of the rapidity and robustness, robust geodesic distance-based fuzzy c-medoid clustering with a simulated annealing algorithm (RGDFCMSA) is proposed to optimize sensor placement by minimizing the information entropy of the system. Next, uncertain parameters with estimability are selected based on SIAN and Sobol's sensitivity indicator under optimal sensor placement. Furthermore, a multilayer particle filter (MLPF) is proposed to estimate temperature fields and predict the thermal error of SBSs by fusing information from multiple sources with different fidelity. Finally, experiments under different working conditions are conducted to validate the effectiveness and accuracy of the proposed method. The result indicates that the proposed framework is capable of an accurate estimation of the global temperature field, uncertain thermal parameters and thermal errors. • A thermal hypernetwork method is proposed for unified information fusion. • A network planning algorithm RGDFCMSA is proposed for optimizing sensor layout. • MLPF is proposed for multi-fidelity and multi-physics sensor fusion. • A hybrid-driven framework is proposed for thermal error prediction. [ABSTRACT FROM AUTHOR]

Topography critically affects the occurrence of soil erosion, and computing slope spectrum information entropy (SSIE) allows for the convenient mirroring of the patterns of macroscopic topographic variation. However, whether SSIE can be effectively utilized for the quantitative assessment of soil erosion across various types of water-erosion areas and the specific methodology for its application remain unclear. This study focused on the quantitative relationship between SSIE, the slope length and slope steepness (LS) factor within various types of water-erosion areas across different spatial scales in China using multi-source geographic information data and technical tools such as remote sensing and geographic information systems. The results revealed (1) clear consistency in the spatial patterns of SSIE and the LS factor, which both displayed a distinct three-step distribution pattern from south to north. (2) The power model (Y = A·X^B) demonstrated a superior capacity to explaining the relationship between SSIE and the LS factors compared to the linear or exponential models, as evidenced by a higher coefficient of determination (R2). R2 values of different evaluation units (second-grade water-erosion area, third-grade water-erosion area, 30 km × 30 km grid, and 15 km × 15 km grid) were 0.88, 0.88, 0.81, and 0.79, respectively. (3) Despite a range of variances across various spatial scale evaluation units and different types of water-erosion areas, no significant disparities were evident within the power model. These findings offer a new topographic factor that can be incorporated into models designed for the expedited evaluation of soil erosion rates across water-erosion areas. Information about the proximity of the SSIE to the LS factor is valuable for enhancing the practical utilization of SSIE in the quantitative evaluation of soil erosion. [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]

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 Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays 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.)

To better meet communication needs, 6G proposes Integrated Satellite-Terrestrial Networks. Integrated Sensing and Communication (ISAC) is one of the key technologies of Integrated Satellite-Terrestrial Networks, which can reduce the energy consumption of the system, improve communication efficiency, and increase the utilization rate of spectrum resources. In the existing technology, the Modulated Wideband Converter (MWC) system can provide support for the miniaturization and intelligence of wireless device sensing and communication systems. Therefore, the MWC system can be used as a preliminary application of ISAC technology. However, the reconstruction effect of the conventional MWC system under the influence of noise is not stable. Therefore, we propose a signal processing optimization scheme for the MWC system based on spatial information entropy. First, the subsequent reconstruction algorithm is considered to require the dynamic and flexible processing of the sampled signals to reduce the influence of noise. Second, for the shortcomings of the original Orthogonal Matching Pursuit (OMP) algorithm, the concept of the genetic algorithm is used to optimize the algorithm by constructing the feature factor through spatial information gain and spatial information features. According to the simulation results, compared with the traditional MWC system, the scheme proposed in this paper is improved in all indicators. [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]

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]

In this paper, a new meaningful ciphertext compression encryption scheme is proposed. Firstly, this paper proposes an adaptive sparsification compressed sensing (CS) to get a better reconstructed image by adaptively assigning a more appropriate sparsity threshold to each image. Secondly, considering the noise-like distribution of the measured values, this paper proposes a histogram-shifted least significant bit (LSB) Xor embedding method to reduce the variation of the cover image. The algorithm can also adaptively find the optimal histogram shift parameters based on the distribution of measured values to optimize the ciphertext quality. The peak signal-to-noise ratio of the ciphertext reaches more than 48dB, which is much higher than the existing optimal algorithm. Then, we also proposed a new two-dimensional infinite collapse hyperchaos (2D-IICM), whose two Lyapunov exponents (LE) both reach more than 4 and are in a hyperchaotic state. The sample entropy and information entropy of the sequence can also reach more than 2 and 7.9 respectively, which is 0.1 and 0.3 points higher than other chaos. Good chaos properties provide higher security to the algorithm. This paper uses SHA256 of plaintext to generate a dynamic key, so that the whole encryption system achieves the encryption effect of one-image and one-key, making the entire system highly resistant to attacks. Codes are available at https://github.com/jsw1995/ICMCE [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]

In recent years, memristors have received widespread attention due to their inherent nonlinear characteristics. To this end, a three-dimensional memristor chaotic system based on secondary nonlinear magnetron memristors has been constructed. Through the analysis of phase portraits, 0–1 tests, Poincare sections, bifurcation diagrams, and Lyapunov exponent spectra, it was found that the system exhibits rich dynamical behaviors, including immovable points, periodic orbits, and chaos. The coexistence attractors of the system were studied, and their basins were also plotted. Comparative complexity analysis indicates that the chaotic system possesses high complexity, thus providing higher security for image encryption. The practicality of the chaotic system was verified through circuit simulation and FPGA. Finally, the chaotic system was applied to DNA image encryption. The focus was on analyzing the histogram, adjacent pixel correlation, information entropy, security, and runtime. The analysis results demonstrate that the algorithm has high security and practicality. [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 progression of complex diseases often involves abrupt and non-linear changes characterized by sudden shifts that trigger critical transformations. Identifying these critical states or tipping points is crucial for understanding disease progression and developing effective interventions. To address this challenge, we have developed a model-free method named Network Information Entropy of Edges (NIEE). Leveraging dynamic network biomarkers, sample-specific networks, and information entropy theories, NIEE can detect critical states or tipping points in diverse data types, including bulk, single-sample expression data. By applying NIEE to real disease datasets, we successfully identified critical predisease stages and tipping points before disease onset. Our findings underscore NIEE's potential to enhance comprehension of complex disease development. [ABSTRACT FROM AUTHOR]

As a research hot topic in the field of network security, the implementation of machine learning, such as federated learning, involves information interactions among a large number of distributed network devices. If we regard these distributed network devices and connection relationships as a complex network, we can identify the influential nodes to find the crucial points for optimizing the imbalance of the reliability of devices in federated learning system. This paper will analyze the advantages and disadvantages of existing algorithms for identifying influential nodes in complex networks, and propose a method from the perspective of information dissemination for finding influential nodes based on Kullback–Leibler divergence model within the neighborhood (KLN). Firstly, the KLN algorithm removes a node to simulate the scenario of node failure in the information dissemination process. Secondly, KLN evaluates the loss of information entropy within the neighborhood after node removal by establishing the KL divergence model. Finally, it assesses the damage influence of the removed node by integrating the network attributes and KL divergence model, thus achieving the evaluation of node importance. To validate the performance of KLN, this paper conducts an analysis and comparison of its results with those of 11 other algorithms on 10 networks, using SIR model as a reference. Additionally, a case study was undertaken on a real epidemic propagation network, leading to the proposal of management and control strategies for daily protection based on the influential nodes. The experimental results indicate that KLN effectively evaluates the importance of the removed node using KL model within the neighborhood, and demonstrate better accuracy and applicability across networks of different scales. [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]

Conventional brute-force attacks can now be detected and identified based on statistical analysis of logs and traffic data. However, they fail to detect low-frequency and distributed brute-force attack behaviors. To address different attack methods, new detection techniques have emerged. This study compares various machine learning algorithms and selects two methods, namely the clustering algorithm k-means and bdscan, as well as the decision tree algorithm for data learning. In one approach, normal user login data is integrated with enterprise email log data. The data is first statistically analyzed and filtered, followed by quantifying data characteristics using information entropy. Subsequently, machine learning algorithms are employed for classification, and the results are visualized for display. In another approach, labeled raw data is used to train a model using the decision tree algorithm. By comparing the two analysis results, a more accurate model can be obtained. These analytical methods can help enterprises strengthen email security and defend against low-frequency and distributed brute-force attacks. [ABSTRACT FROM AUTHOR]

Even with the powerful statistical parameters derived from the Extreme Gradient Boost (XGB) algorithm, it would be advantageous to define the predicted accuracy to the level of a specific case, particularly when the model output is used to guide clinical decision-making. The probability density function (PDF) of the derived intracranial pressure predictions enables the computation of a definite integral around a point estimate, representing the event's probability within a range of values. Seven hold-out test cases used for the external validation of an XGB model underwent retinal vascular pulse and intracranial pressure measurement using modified photoplethysmography and lumbar puncture, respectively. The definite integral ±1 cm water from the median (DIICP) demonstrated a negative and highly significant correlation (-0.5213±0.17, p< 0.004) with the absolute difference between the measured and predicted median intracranial pressure (DiffICPmd). The concordance between the arterial and venous probability density functions was estimated using the two-sample Kolmogorov-Smirnov statistic, extending the distribution agreement across all data points. This parameter showed a statistically significant and positive correlation (0.4942±0.18, p< 0.001) with DiffICPmd. Two cautionary subset cases (Case 8 and Case 9), where disagreement was observed between measured and predicted intracranial pressure, were compared to the seven hold-out test cases. Arterial predictions from both cautionary subset cases converged on a uniform distribution in contrast to all other cases where distributions converged on either log-normal or closely related skewed distributions (gamma, logistic, beta). The mean±standard error of the arterial DIICP from cases 8 and 9 (3.83±0.56%) was lower compared to that of the hold-out test cases (14.14±1.07%) the between group difference was statistically significant (p<0.03). Although the sample size in this analysis was limited, these results support a dual and complementary analysis approach from independently derived retinal arterial and venous non-invasive intracranial pressure predictions. Results suggest that plotting the PDF and calculating the lower order moments, arterial DIICP, and the two sample Kolmogorov-Smirnov statistic may provide individualized predictive accuracy parameters. [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]

Gaussian filter (GF) is a commonly used linear filter in signal and image noise reduction. However, its limitation is that it cannot adapt parameters to deal with non-stationary noise that varies over time. To address this problem and improve the filtering effectiveness of GF in the face of non-stationary non-Gaussian (NSNG) noise, this paper proposes a new approach called adaptive Gaussian filter based on improved complete ensemble empirical mode decomposition (ICEEMDAN-AGF). The ICEEMDAN-AGF firstly uses the fusion information of the dispersion entropy (DE) and the power spectral entropy (PSE) to divide the intrinsic mode functions (IMFs) into two groups. One group is called guiding IMFs, which contains the high-frequency components of the NSNG noise, and the other group is called hybrid IMFs, which contains the low-frequency components of the NSNG noise and all the noise-free signals. Next, a method called multi-resolution local similarity (MRLS) is proposed to identify the mixed modes presented in the guiding IMFs. Then, the variance of the guiding IMFs is used to adjust the window width w and kernel parameter σ of GF. Finally, the adaptive Gaussian filter (AGF) obtained above is used to filter the hybrid IMFs. The experiments shows that ICEEMDAN-AGF performs better than other conventional algorithms on known signals. [ABSTRACT FROM AUTHOR]

We propose a new definition of the γ -convex stochastic processes (C SP) using center and radius (CR) order with the notion of interval valued functions (C. R I. V) . By utilizing this definition and Mean-Square Fractional Integrals, we generalize fractional Hermite–Hadamard–Mercer-type inclusions for generalized   C. R I. V versions of convex, tgs-convex, P-convex, exponential-type convex, Godunova–Levin convex, s-convex, Godunova–Levin s-convex, h-convex, n-polynomial convex, and fractional n-polynomial (C SP) . Also, our work uses interesting examples of   C. R I. V (C SP) with Python-programmed graphs to validate our findings using an extension of Mercer's inclusions with applications related to entropy and information theory. [ABSTRACT FROM AUTHOR]

We consider a population of finite size M, where the current number N of entities, N ∈ { 0 , 1 , 2 , ... , M } , determines its states. We geometrically analyze the amounts of information iN and iM−N, carried by the random variables N and (M−N), respectively, and population entropy S = E (i N) = E (i (M − N)). The population is modeled by a family of Birth-Death Processes of size M+1, indexed by the population utilization parameter ρ (i.e., birth/death ratio), which determines its macrostates. Considering the quantities: x = (N − E (N)) & y = (i N (ρ) − S (ρ)) and M x = ((M − N) − E (M − N)) & M y = (i M − N (ρ) − S (ρ)) as vectors, it is shown that the angles: φ = ∠ (x ; y) and ψ = ∠ ( M x ; M y) are supplementary ones, that is φ (ρ) + ψ (ρ) = π , ρ > 0. Expressions for their inner products < x , y > and < M x , M y > , being the covariances of N&iN(ρ) and (M − N) & i M − N (ρ) , respectively, which sum equals zero, with respect to the parameter ρ are also obtained. Further, what is especially important, it is revealed that φ = ψ = π / 2 , that is both inner products equal zero, at the point ρMmax, which is the value of parameter ρ at which the entropy has maximum value. Finally, for an information linear population only, it is shown that N obeys uniform distribution at the point ρM, max, that is that S (ρ M , max ) = ln ⁡ (M + 1). These results are further illustrated on populations described by truncated geometrical, binomial, and truncated Poisson distribution. [ABSTRACT FROM AUTHOR]

A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are converted into the frequency domain coefficient matrices (FDCM) with discrete cosine transform (DCT) operation. After that, a two-dimensional (2D) coupled chaotic system is developed and used to generate one group of embedded matrices and another group of encryption matrices, respectively. The embedded matrices are integrated with the FDCM to fulfill the frequency domain encryption, and then the inverse DCT processing is implemented to recover the spatial domain signal. Eventually, under the function of the encryption matrices and the proposed diagonal scrambling algorithm, the final color ciphertext is obtained. The experimental results show that the proposed method can not only ensure efficient encryption but also satisfy various sizes of image encryption. Besides, it has better performance than other similar techniques in statistical feature analysis, such as key space, key sensitivity, anti-differential attack, information entropy, noise attack, etc. [ABSTRACT FROM AUTHOR]

Objectives: The aim of this study was to apply spatiotemporal analysis of contrast-enhanced ultrasound (CEUS) loops to quantify the enhancement heterogeneity for improving the differentiation between benign and malignant breast lesions. Materials and methods: This retrospective study included 120 women (age range, 18–82 years; mean, 52 years) scheduled for ultrasound-guided biopsy. With the aid of brightness-mode images, the border of each breast lesion was delineated in the CEUS images. Based on visual evaluation and quantitative metrics, the breast lesions were categorized into four grades of different levels of contrast enhancement. Grade-1 (hyper-enhanced) and grade-2 (partly-enhanced) breast lesions were included in the analysis. Four parameters reflecting enhancement heterogeneity were estimated by spatiotemporal analysis of neighboring time-intensity curves (TICs). By setting the threshold on mean parameter, the diagnostic performance of the four parameters for differentiating benign and malignant lesions was evaluated. Results: Sixty-four of the 120 patients were categorized as grade 1 or 2 and used for estimating the four parameters. At the pixel level, mutual information and conditional entropy present significantly different values between the benign and malignant lesions (p < 0.001 in patients of grade 1, p = 0.002 in patients of grade 1 or 2). For the classification of breast lesions, mutual information produces the best diagnostic performance (AUC = 0.893 in patients of grade 1, AUC = 0.848 in patients of grade 1 or 2). Conclusions: The proposed spatiotemporal analysis for assessing the enhancement heterogeneity shows promising results to aid in the diagnosis of breast cancer by CEUS. Clinical relevance statement: The proposed spatiotemporal method can be developed as a standardized software to automatically quantify the enhancement heterogeneity of breast cancer on CEUS, possibly leading to the improved diagnostic accuracy of differentiation between benign and malignant lesions. Key Points: • Advanced spatiotemporal analysis of ultrasound contrast-enhanced loops for aiding the differentiation of malignant or benign breast lesions. • Four parameters reflecting the enhancement heterogeneity were estimated in the hyper- and partly-enhanced breast lesions by analyzing the neighboring pixel-level time-intensity curves. • For the classification of hyper-enhanced breast lesions, mutual information produces the best diagnostic performance (AUC = 0.893). [ABSTRACT FROM AUTHOR]

The influence of the collective and quantum effects on the Shannon information entropy for atomic states in dense nonideal plasma was investigated. The interaction potential, which takes into account the effect of quantum non-locality as well as electronic correlations, was used to solve the Schrödinger equation for the hydrogen atom. It is shown that taking into account ionic screening leads to an increase in entropy, while taking into account only electronic screening does not lead to significant changes. [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]

This paper highlights that metrics from the machine learning field (e.g., entropy and information gain) used to qualify a classifier model can be used to evaluate the effectiveness of separation systems. To evaluate the efficiency of separation systems and their operation units, entropy- and information gain-based metrics were developed. The receiver operating characteristic (ROC) curve is used to determine the optimal cut point in a separation system. The proposed metrics are verified by simulation experiments conducted on the stochastic model of a waste-sorting system. [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]