Search Results (3,666)

With the increasing connectivity and automation on the Internet of Vehicles, safety, security, and privacy have become stringent challenges. In the last decade, several cryptography-based protocols have been proposed as intuitive solutions to protect vehicles from information leakage and intrusions. Before generating the encryption keys, a random number generator (RNG) plays an important component in cybersecurity. Several deep learning-based RNGs have been deployed to train the initial value and generate pseudo-random numbers. However, interference from actual unpredictable driving environments renders the system unreliable for its low-randomness outputs. Furthermore, dynamics in the training process make these methods subject to training instability and pattern collapse by overfitting. In this paper, we propose an Effective Pseudo-Random Number Generator (EPRNG) which exploits a deep convolution generative adversarial network (DCGAN)-based approach using our processed vehicle datasets and entropy-driven stopping method-based training processes for the generation of pseudo-random numbers. Our model starts from the vehicle data source to stitch images and add noise to enhance the entropy of the images and then inputs them into our network. In addition, we design an entropy-driven stopping method that enables our model training to stop at the optimal epoch so as to prevent overfitting. The results of the evaluation indicate that our entropy-driven stopping method can effectively generate pseudo-random numbers in a DCGAN. Our numerical experiments on famous test suites (NIST, ENT) demonstrate the effectiveness of the developed approach in high-quality random number generation for the IoV. Furthermore, the PRNGs are successfully applied to image encryption, and the performance metrics of the encryption are close to ideal values. Full article

Classic formulae for entropy and cross-entropy contain operations ${\displaystyle \frac{x}{0}}$ and ${log}_{2}x$ that are not defined on all inputs. This can lead to calculations with problematic subexpressions such as $0{log}_{2}0$ and uncertainties in large scale calculations; partiality also introduces complications in logical analysis. Instead of adding conventions or splitting formulae into cases, we create a new algebra of real numbers with two symbols $\pm \infty$ for signed infinite values and a symbol named ⊥ for the undefined. In this resulting arithmetic, entropy, cross-entropy, Kullback–Leibler divergence, and Shannon divergence can be expressed without concerning any further conventions. The algebra may form a basis for probability theory more generally. Full article

Graph anomaly detection is crucial in many high-impact applications across diverse fields. In anomaly detection tasks, collecting plenty of annotated data tends to be costly and laborious. As a result, few-shot learning has been explored to address the issue by requiring only a few labeled samples to achieve good performance. However, conventional few-shot models may not fully exploit the information within auxiliary sets, leading to suboptimal performance. To tackle these limitations, we propose a dual-level knowledge distillation-based approach for graph anomaly detection, DualKD, which leverages two distinct distillation losses to improve generalization capabilities. In our approach, we initially train a teacher model to generate prediction distributions as soft labels, capturing the entropy of uncertainty in the data. These soft labels are then employed to construct the corresponding loss for training a student model, which can capture more detailed node features. In addition, we introduce two representation distillation losses—short and long representation distillation—to effectively transfer knowledge from the auxiliary set to the target set. Comprehensive experiments conducted on four datasets verify that DualKD remarkably outperforms the advanced baselines, highlighting its effectiveness in enhancing identification performance. Full article

This paper presents the results of an optimization analysis of two types of thermal fluid channels. The selected geometries were evaluated according to the criterion of the Entropy Generation Minimization method as suggested by Adrian Bejan, with reference to a smooth pipe of the same diameter. The aim of this research was to assess the effectiveness of two channels that intensify heat transfer in different ways: with an insert (disrupting the flow in the pipe core) and with internal fins (disrupting the flow at the pipe wall), and to compare the results using the same criterion: the EGM method. The tested insert consisted of spaced streamline-shaped flow turbulizing the elements fixed in the axis of the pipe and spaced at equal distances from each other. The second channel was formed by making a right-angled triangle (rib profile) on the deformation of the pipe wall perimeter. Using computer modeling, the effect of the two geometric parameters of the above-mentioned channels on the flux of entropy generated was studied. These are (a) the diameter of the disturbing element (“droplet”) and the distance between these elements for a channel with a turbulent insert, and (b) the height of the rib and the longitudinal distance between them for a finned channel. The novelty resulting from the research is the discovery that the turbulization of the flow in the pipe wall boundary layer generates significantly less irreversible entropy than the disturbance of the flow in the pipe axis by the insert. Full article

In contrast to previous research that has primarily focused on distance synchronization of states in chaotic systems, shape synchronization emphasizes the geometric shape of the attractors of two chaotic systems. Diverging from the existing work on shape synchronization, this paper introduces the application of adaptive control methods to achieve asymptotic shape synchronization for the first time. By designing an adaptive controller using the proposed adaptive rule, the response system under control is able to attain asymptotic synchronization with the drive system. This method is capable of achieving synchronization for models with parameters requiring estimation in both the drive and response systems. The control approach remains effective even in the presence of uncertainties in model parameters. The paper presents relevant theorems and proofs, and simulation results demonstrate the effectiveness of adaptive asymptotic shape synchronization. Due to the pseudo-random nature of chaotic systems and their extreme sensitivity to initial conditions, which make them suitable for information encryption, a novel channel-integrated image encryption scheme is proposed. This scheme leverages the shape synchronization method to generate pseudo-random sequences, which are then used for shuffling, scrambling, and diffusion processes. Simulation experiments demonstrate that the proposed encryption algorithm achieves exceptional performance in terms of correlation metrics and entropy, with a competitive value of 7.9971. Robustness is further validated through key space analysis, yielding a value of ${10}^{210}\times 2^{512}$, as well as visual tests, including center and edge cropping. The results confirm the effectiveness of adaptive asymptotic shape synchronization in the context of image encryption. Full article

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. Full article

This study estimates regional economic resilience in China from 2000 to 2022, focusing on economic resistance resilience, recovery resilience, and reorientation resilience. The entropy method, kernel density estimation, and spatial Durbin model are applied to examine the spatiotemporal evolution and influencing factors. The results show significant spatial clustering, with stronger resilience in the east and weaker resilience in the west. While economic resilience has generally improved, regional disparities persist. Key factors such as human capital, urban hospitals, financial development, market consumption, and environmental quality have a positive effect on resilience, with spatial spillover effects. However, human capital and urban hospitals also show a negative indirect impact on surrounding regions. The influence of these factors varies across regions and periods, indicating strong spatiotemporal heterogeneity. Full article

During pump operation, the pump-turbine system experiences unstable fluctuations in outlet pressure, which induces turbulence and additional energy losses. Understanding the impact of outlet pressure variations on the internal flow field is crucial for the further development of turbine units. This study employs numerical methods to systematically analyze the effects of outlet pressure changes on flow characteristics and energy loss. The results show that a decrease in outlet pressure to P0.9BEP significantly increases entropy production in the double-row stay guide vane region, primarily due to flow separation and vortex formation. In the flow passage, sealing gap, and tailpipe regions, entropy production is mainly driven by wall effects, while secondary flows influence the spiral case. The vortex distribution in the double-row stay guide vane is complex, with different variation trends observed in the active and fixed guide vane regions. Outlet pressure changes affect the interaction between the flow passage blades and the fluid, leading to localized flow separation and directly impacting energy loss in downstream components. Additionally, the rate of change in outlet pressure significantly influences vortex generation and dissipation. This research provides new theoretical insights and research directions for performance optimization and energy loss control in pump-turbine systems. Full article

In many soil processes, including solute and gas dynamics, the architecture of intra-aggregate pores is a crucial component. Soil management practices and wetting-drying (W-D) cycles, the latter having a significant impact on pore aggregation, are two key factors that shape pore structure. This study examines the effects of W-D cycles on the architecture of intra-aggregate pores under three different soil management systems: no-tillage (NT), minimum tillage (MT), and conventional tillage (CT). The soil samples were subjected to 0 and 12 W-D cycles, and the resulting pore structures were scanned using X-ray micro-computed tomography, generating reconstructed 3D volumetric data. The data analyses were conducted in terms of multifractal spectra, normalized Shannon entropy, lacunarity, porosity, anisotropy, connectivity, and tortuosity. The multifractal parameters of capacity, correlation, and information dimensions showed mean values of approximately 2.77, 2.75, and 2.75 when considering the different management practices and W-D cycles; 3D lacunarity decreased mainly for the smallest boxes between 0 and 12 W-D cycles for CT and NT, with the opposite behavior for MT. The normalized 3D Shannon entropy showed differences of less than 2% before and after the W-D cycles for MT and NT, with differences of 5% for CT. The imaged porosity showed reductions of approximately 50% after 12 W-D cycles for CT and NT. Generally, the largest pores (>0.1 mm³) contributed the most to porosity for all management practices before and after W-D cycles. Anisotropy increased by 9% and 2% for MT and CT after the cycles and decreased by 23% for NT. Pore connectivity showed a downward trend after 12 W-D cycles for CT and NT. Regarding the pore shape, the greatest contribution to porosity and number of pores was due to triaxial-shaped pores for both 0 and 12 W-D cycles for all management practices. The results demonstrate that, within the resolution limits of the microtomography analysis, pore architecture remained resilient to changes, despite some observable trends in specific parameters. Full article

Vopson and Lepadatu recently proposed the Second Law of Infodynamics. The law states that while the total entropy increases, information entropy declines over time. They state that the law has applications over a wide range of disciplines, but they leave many key questions unanswered. This article analyzes and reformulates the law based on thermocontextual interpretation (TCI). The TCI generalizes Hamiltonian mechanics by defining states and transitions thermocontextually with respect to an ambient-temperature reference state. The TCI partitions energy into exergy, which can do work on the ambient surroundings, and entropic energy with zero work potential. The TCI is further generalized here to account for a reference observer’s actual knowledge. This enables partitioning exergy into accessible exergy, which is known and accessible for use, and configurational energy, which is knowable but unknown and inaccessible. The TCI is firmly based on empirically validated postulates. The Second Law of thermodynamics and its information-based analog, MaxEnt, are logically derived corollaries. Another corollary is a reformulated Second Law of Infodynamics. It states that an external agent seeks to increase its access to exergy by narrowing its information gap with a potential exergy source. The principle is key to the origin of self-replicating chemicals and life. Full article

As China’s economy accelerates its transition toward high-quality development, various regions are actively tapping into their distinctive resources to unlock economic potential and exploring pathways to achieve high-quality collaborative development with neighboring areas. This study endeavors to provide both theoretical insights and practical recommendations for the actual development of Anhui Province and its adjacent regions, through an in-depth analysis of their collaborative pursuit of high-quality growth. Employing the entropy weight method and the coupled coordination degree model, this research rigorously evaluates the extent of coordinated high-quality development between Anhui Province and its neighboring provinces, and offers effective planning suggestions grounded in the evaluation results. The findings reveal that (1) coastal cities generally demonstrate a higher level of comprehensive development compared to those located further inland; (2) cities with superior comprehensive development also tend to excel in economic growth, scientific and technological innovation, ecological advancement, and coupling coordination; (3) nevertheless, a higher level of comprehensive development does not necessarily imply better social service provision. Full article

Physically unclonable functions (PUFs) represent emerging cryptographic primitives that exploit the uncertainty of the CMOS manufacturing process as an entropy source for generating unique, random and stable keys. These devices can be potentially used in a wide variety of applications ranging from secret key generation, anti-counterfeiting, and low-cost authentications to advanced protocols such as oblivious transfer and key exchange. Unfortunately, guaranteeing adequate PUF stability is still challenging, thus often requiring post-silicon stability enhancement techniques. The latter help to contrast the raw sensitivity to on-chip noise and variations in the environmental conditions (i.e., voltage and temperature variations), but their area and energy costs are not always feasible for IoT devices that operate with constrained budgets. This pushes the demand for ever more stable, area- and energy-efficient solutions at design time. This review aims to provide an overview of several weak PUF solutions implemented in CMOS technology, discussing their performance and suitability for being employed in security applications. Full article

In this paper, we propose a method to enhance the performance of a random number generator (RNG) that exploits ring oscillators (ROs). Our approach employs additional phase detectors to extract more entropy; thus, RNG uses fewer resources to produce bit sequences that pass all statistical tests proposed by National Institute of Standards and Technology (NIST). Generating a specified number of bits is on-demand, eliminating the need for continuous RNG operation. This feature enhances the security of the produced sequences, as eavesdroppers are unable to observe the continuous random bit generation process, such as through monitoring power lines. Furthermore, our research demonstrates that the proposed RNG’s perfect properties remain unaffected by the manufacturer of the field-programmable gate arrays (FPGAs) used for implementation. This independence ensures the RNG’s reliability and consistency across various FPGA manufacturers. Additionally, we highlight that the tests recommended by the NIST may prove insufficient in assessing the randomness of the output bit streams produced by RO-based RNGs. Full article

Fuzzy semantics have a wide range of applications in life, and especially when expressing people’s evaluation information, it is more specific. As people increasingly prefer to express their personal opinions through media platforms, the opinions of the general public have become an indispensable reference. However, information asymmetry can have a significant impact on the rationality of decision-making. Based on the above considerations, this paper extends bidirectional projection to probabilistic linguistic term sets to preserve the completeness of information as much as possible. The large-scale group decision-making problem under the probabilistic linguistic environment is extended to limited interval values, and a new group decision-making method named IVBPRT-ELECTRE III algorithm (ELECTRE III based on bidirectional projection and regret theory under limited interval-valued probabilistic linguistic term set) is proposed. The method is an extended $\mathrm{E}\mathrm{L}\mathrm{E}\mathrm{C}\mathrm{T}\mathrm{R}\mathrm{E} \mathrm{I}\mathrm{I}\mathrm{I}$ method based on limited interval-valued probabilistic linguistic term set (l-IVPLTS) bidirectional projection by regret theory approach. Firstly, this involves mining the online text comment information on social media about an emergency and considering the effect of the number of fans, determining the attributes and their initial weights for judging the strengths and weaknesses of the emergency management alternative using the TF-IDF and the $\mathrm{W}\mathrm{o}\mathrm{r}\mathrm{d}2\mathrm{v}\mathrm{e}\mathrm{c}$ technology, and using the entropy value to adjust the initial weight of attributes, not only considering the real opinions of the public, but also combining with the views of experts, making the decision-making alternative selection more scientific and reasonable. Secondly, this paper fills the gap of bidirectional projection under l-IVPLTS environment; then, combining l-IVPLTS bidirectional projection and regret theory to determine the objective weights of experts, combines the differences in individual expertise of experts to obtain the comprehensive weights of experts, and uses the extended $\mathrm{E}\mathrm{L}\mathrm{E}\mathrm{C}\mathrm{T}\mathrm{R}\mathrm{E} \mathrm{I}\mathrm{I}\mathrm{I}$ method to rank the alternatives. Finally, the feasibility and validity of the provided method is verified through the Yanjiao explosion incident as a case. Full article

Filters are essential components for maintaining the stability of drip irrigation systems, effectively reducing the risk of clogging. However, when applied to slurry drip irrigation systems, the complexity of slurry water quality makes it unclear how different filter types and their combinations affect the hydraulic performance of the system. This study provides a comprehensive performance evaluation of two common filter types and their combinations, considering various flow rates and biogas slurry-to-water ratios under drip irrigation conditions. The results revealed the following key findings: (1) In the application of biogas slurry drip irrigation, an increase in the concentration or flow rate of the slurry significantly affects the hydraulic performance of the filter, increasing the risk of clogging and shortening the operational lifespan. Notably, the impact of changes in slurry concentration on the hydraulic performance of the filter is much greater than that of the flow rate. Compared to mesh filters, disk filters offer better hydraulic performance, with the contaminant capacity of disk filters being approximately three times that of mesh filters. (2) In biogas slurry drip irrigation, the filter combination generally outperforms single filters in terms of hydraulic performance and contaminant removal capacity. Due to the unique nature of the water source in biogas slurry, a selection process for filter combinations was conducted. It was found that when a disk filter is used as the pre-filter and a mesh filter as the post-filter, the overall rate of head loss change is the smallest, and the clogging uniformity is the least. (3) In the entropy weight-TOPSIS comprehensive evaluation, the filter’s operating time and contaminant capacity are key factors affecting its overall performance. From the perspective of improving the operational stability of the biogas slurry drip irrigation system, it is recommended to use a disk filter + mesh filter combination. This study conducts practical measurements on the hydraulic performance, contaminant removal capacity, filtration accuracy, and other indicators of commonly used mesh and disk filters, aiming to provide useful references for the practical application of biogas slurry drip irrigation filters. Full article