Search Results (1,873)

This paper focuses on a typical pressure-controlled slide valve, utilizing momentum analysis and computational fluid dynamics to simulate and analyze the asymmetry of steady flow force curves under bidirectional throttling patterns. The entropy production theory is employed to reveal the causes of nonlinearity in the steady flow force of an inlet throttling slide valve. Based on flow field analysis, a flow force compensation scheme is proposed by adding a guiding shoulder and matching it with a suitably sized inner annular cavity. The study reveals that fluid momentum at the non-throttling valve port is the primary cause of the bidirectional throttling flow force difference, and under large-opening inlet throttling conditions, it may reverse the direction of the flow force. Vortex separation caused by turbulent pulsations is one of the intrinsic reasons for the nonlinearity of steady flow force. Full article

The head, efficiency, and cavitation characteristics of centrifugal pumps are highly dependent on the velocity field in front of the impeller inlet. In multistage pumps, the velocity field in front of the second and each subsequent stage is determined by the shape (design) of the diffuser return guide vanes. This current work presents the results obtained by performing a numerical study using ANSYS CFX 14.0 to determine the impact of the shape (design) of diffuser return guide vanes on the head and coefficient of efficiency of one stage of a multistage centrifugal pump. Three RGVs with different Outlet angles are studied: ${\alpha }_6$ —original RGV with ${\alpha }_6=90\mathrm{deg}$, RGV1 with ${\alpha }_6=110\mathrm{deg}$ and RGV2 with ${\alpha }_6=128\mathrm{deg}$. The results obtained after performing CFD modeling indicate that with one of the studied RGVs, the pump stage head increases by nearly 20%, while the hydraulic coefficient of efficiency remains almost constant. Applying entropy production theory is used to determine the impact of the various components of entropy production on the total head loss in the studied pump stage. The impact of the Outlet angle of the RGV on the velocity field of the flow in front of the next impeller (stage) as well as the RGV head is also analyzed. The numerical results of the original RGV are compared with the experimental data obtained from large-scale studies of pumps performed at the Laboratory of Hydraulic Machines of the University “Angel Kanchev” of Ruse, Bulgaria. When using the modified RGVs, the head curve of the original pump can be obtained by operating at a lower speed or with a smaller impeller diameter. This may lead to an overall increase in the energy efficiency of the machine, which could be explored as a future task. Full article

Optimizing control rate parameters is one of the key technologies in motor control systems. To address the issues of weak robustness and slow response speed in traditional adaptive control strategies, an adaptive control system based on sliding mode control is proposed to enhance the overall performance of permanent magnet synchronous motors. The Non-dominated Sorting Genetic Algorithm II and Multi-objective Particle Swarm Optimization are employed to effectively optimize control parameters, thereby mitigating motor torque and speed overshoot. A Partial Sample Shannon Entropy Evaluation method, leveraging entropy theory in conjunction with the Z-score method, is introduced to facilitate the feedback regulation of the optimization process by assessing motor output torque. Simulation results confirm that the proposed control strategy, in combination with the optimized control rate parameters, leads to substantial improvements in motor performance. Compared to traditional adaptive control strategies, the proposed approach improves the motor’s steady-state response speed by 42% and reduces rotor error during system fluctuations by 23%, significantly enhancing the motor’s response speed and robustness. Following parameter optimization, speed and torque overshoot are reduced by 38% and 10%, respectively, resulting in a significant improvement in the stability and precision of the motor control system. Full article

The study and improvement of the corrosion resistance of materials used in concentrated solar power plants is a permanent field of research. This involves determining their chemical stability when in contact with heat transfer fluids, such as molten nitrate salts. Various studies indicate an improvement in the corrosion resistance of iron-based alloys with the incorporation of elements that show high reactivity and solubility in molten nitrate salts, such as Cr and Mo. This study analyzes the kinetic and thermodynamic aspects of the beginning of the corrosion process of ferritic steels immersed in Solar Salt at 400, 500, and 600 °C. The analysis of the kinetic data using the Arrhenius equation and the Transition State Theory shows that an increase in the Cr/Mo ratio reduces the activation energy, the standard formation enthalpy, and the standard formation entropy. This indicates that its incorporation favors the degradation of steel; however, the results show a reduction in the corrosion rate. This effect is possible due to a synergistic effect by the formation of insoluble Fe-oxide layers that favor the formation of a Cr oxide layer at the Fe-oxide-metal interface, which limits the subsequent oxidation of Fe. Full article

Enhancing urban ecological resilience (UER) is important in promoting sustainable urban development, and developing new quality productivity (NQP) is an intrinsic requirement to promote industrial change and high-quality development. The coordinated development of UER and NQP can help realize the green transformation and upgrading of various industries. This study considered 30 provinces in China as research objects, quantified their UER from nature, economy, and society, and explored the essential connotation of NQP under the guidance of Marx’s productivity theory. The entropy weight-CRITIC method and TOPSIS model were used to comprehensively measure the development levels of the UER and NQP from 2011 to 2022, and their coupling coordination degree (CCD) of UER and NQP was measured by combining the coupling coordination degree model. Consequently, the Global Moran’s I index and Geographical and Temporal Weighted Regression (GTWR) model were used to explore the effects of different influencing factors on the CCD from the spatiotemporal variability perspective. The results indicated the following: (1) UER and NQP improved during the study period but with large differences between the regions. (2) The overall CCD evolved from a mild imbalance to primary coordination. The average CCD values ranged from low to high in the northeastern, western, central, and eastern regions. (3) The GTWR results showed that the levels of economic development, urbanization rate, and technological innovation contributed positively to the CCD, with the urbanization rate having the strongest positive effect. Foreign investment, environmental regulations, and industrial structure generally negatively inhibit the CCD. Full article

External mine fires are known for their unpredictability, rapid spread, and difficulty in terms of extinguishment, often resulting in severe casualties and property damage when not managed swiftly. This study examines the progression of coal mine fire incidents through scenario deduction and presents an emergency decision-making model based on precursor scenario analysis. We classify precursor elements according to the causes of coal mine fires, organizing scenario elements into states, precursors, and emergency activities using knowledge meta-theory. A dynamic Bayesian network forms the core of the decision-making model, enabling calculation of scenario node probabilities and the development of expert-driven response strategies for critical scenarios. Additionally, we design a comprehensive evaluation index system, utilizing multi-attribute decision-making to establish decision matrices and attribute weights. An improved entropy-weighting TOPSIS method is used to select the optimal emergency decision scheme. The model’s effectiveness is demonstrated through a case study of the “9–27” fire incident at the Chongqing Songzao Coal Mine, where findings affirm the model’s practicality and accuracy in supporting timely, effective emergency responses to external coal mine fires. Full article

In previous work, we investigated thermodynamic processes in systems at the mesoscopic level where traditional thermodynamic descriptions (macroscopic or microscopic) may not be fully adequate. The key result is that entropy in such systems does not change continuously, as in macroscopic systems, but rather in discrete steps characterized by the quantization constant $\beta$. This quantization reflects the underlying discrete nature of the collision process in low-dimensional systems and the essential role played by thermodynamic fluctuations at this scale. Thermodynamic variables conjugate to the forces, along with Glansdorff–Prigogine’s dissipative variable can be discretized, enabling a mesoscopic-scale formulation of canonical commutation rules (CCRs). In this framework, measurements correspond to determining the eigenvalues of operators associated with key thermodynamic quantities. This work investigates the quantization parameter $\beta$ in the CCRs using a nano-gas model analyzed through classical statistical physics. Our findings suggest that $\beta$ is not an unknown fundamental constant. Instead, it emerges as the minimum achievable value derived from optimizing the uncertainty relation within the framework of our model. The expression for $\beta$ is determined in terms of the ratio $\chi$, which provides a dimensionless number that reflects the relative scales of volume and mass between entities at the Bohr (atomic level) and the molecular scales. This latter parameter quantifies the relative influence of quantum effects versus classical dynamics in a given scattering process. Full article

In response to the widespread issue of fake comments on e-commerce platforms, this study aims to analyze and propose a blockchain-based solution to incentivize authentic user feedback and reduce the prevalence of fraudulent reviews. Specifically, this paper constructs a tripartite evolutionary game model between sellers, buyers, and e-commerce platforms to study the real comment mechanism of blockchain. The strategy evolution under different incentive factors is simulated using replication dynamic equation analysis and Matlab software simulation. The study found that introducing smart contracts and “tokens” for incentives not only increased incentives for real comments but also reduced the negative experiences caused by “speculative” sellers, thereby influencing buyers to opt for authentic reviews. By structuring interactions through blockchain, the mechanism helped lower informational entropy thus reducing disorder and unpredictability in buyer and seller behavior and contributing to system stability. Further, by increasing penalties for dishonest behavior under the “credit on the chain” system, the platform lowered entropy in the system by promoting trust and reducing fraudulent activities. The real comment mechanism based on blockchain proposed in this paper can effectively enhance the order and transparency within the comment ecosystem. These findings contribute to theory and practice by providing strategic insights for e-commerce platforms to encourage genuine feedback, reduce informational entropy, and mitigate fake comments, ultimately fostering a more reliable online marketplace. Full article

Despite the huge number of studies of the three-body problem in physics and mathematics, the study of this problem remains relevant due to both its wide practical application and taking into account its fundamental importance for the theory of dynamical systems. In addition, one often has to answer the cognitive question: is irreversibility fundamental for the description of the classical world? To answer this question, we considered a reference classical dynamical system, the general three-body problem, formulating it in conformal Euclidean space and rigorously proving its equivalence to the Newtonian three-body problem. It has been proven that a curved configuration space with a local coordinate system reveals new hidden symmetries of the internal motion of a dynamical system, which makes it possible to reduce the problem to a sixth-order system instead of the eighth order. An important consequence of the developed representation is that the chronologizing parameter of the motion of a system of bodies, which we call internal time, differs significantly from ordinary time in its properties. In particular, it more accurately describes the irreversible nature of multichannel scattering in a three-body system and other chaotic properties of a dynamical system. The paper derives an equation describing the evolution of the flow of geodesic trajectories, with the help of which the entropy of the system is constructed. New criteria for assessing the complexity of a low-dimensional dynamical system and the dimension of stochastic fractal structures arising in three-dimensional space are obtained. An effective mathematical algorithm is developed for the numerical simulation of the general three-body problem, which is traditionally a difficult-to-solve system of stiff ordinary differential equations. Full article

This study introduces an integrative framework for association mining within the Chinese battery electric vehicle market, aiming to reveal key user experience (UX) factors and their interrelationships through multidimensional analysis. Utilizing latent Dirichlet allocation (LDA), the study discerned primary themes from user-generated content (UGC). The entropy weight method categorized level 2 factors, while domain-adaptive sentiment analysis quantified emotional responses to BEV user experience dimensions, highlighting significant sentiment disparities among competitors. Co-occurrence network analysis deepened insights into the emotional fabric of UX by exploring tertiary factor associations. Theoretically, this study advances a novel framework informed by Norman’s UX theory, integrating analytical techniques to capture the complexity of UX. Practically, it delivers strategic guidance for BEV manufacturers by analyzing emotional polarities and attribute associations, guiding product innovation and responding to market dynamics. The empirical evidence corroborates the framework’s efficacy in revealing the emotional associations within BEVUX factors, offering valuable implications for both theoretical development and practical application. Full article

We introduce derangetropy, which is a novel functional measure designed to characterize the dynamics of information within probability distributions. Unlike scalar measures such as Shannon entropy, derangetropy offers a functional representation that captures the dispersion of information across the entire support of a distribution. By incorporating self-referential and periodic properties, it provides insights into information dynamics governed by differential equations and equilibrium states. Through combinatorial justifications and empirical analysis, we demonstrate the utility of derangetropy in depicting distribution behavior and evolution, providing a new tool for analyzing complex and hierarchical systems in information theory. Full article

In this paper, a problem of random disturbance attenuation capabilities for linear time-invariant continuous systems, affected by random disturbances with bounded $\sigma$-entropy, is studied. The $\sigma$-entropy norm defines a performance index of the system on the set of the aforementioned input signals. Two problems are considered. The first is a state-space $\sigma$-entropy analysis of linear systems, and the second is an optimal control design using the $\sigma$-entropy norm as an optimization objective. The state-space solution to the $\sigma$-entropy analysis problem is derived from the representation of the $\sigma$-entropy norm in the frequency domain. The formulae of the $\sigma$-entropy norm computation in the state space are presented in the form of coupled matrix equations: one algebraic Riccati equation, one nonlinear equation over log determinant function, and two Lyapunov equations. Optimal control law is obtained using game theory and a saddle-point condition of optimality. The optimal state-feedback control, which minimizes the $\sigma$-entropy norm of the closed-loop system, is found from the solution of two algebraic Riccati equations, one Lyapunov equation, and the log determinant equation. Full article

The seismic design philosophy based on resilience represents the latest development in structural seismic design theory and is currently the most advanced concept in the field of seismic design research. A building site serves as the foundation and environment for structures, and evaluating its seismic resilience is a crucial aspect of designing and assessing buildings’ seismic resilience. To meet the needs of evaluating building seismic resilience, the concept of seismic resilience for building sites is introduced in this paper. This evaluation is approached from three dimensions: seismic action, site seismic capacity, and site recovery capability. An indicator system for evaluating the seismic resilience of building sites is constructed using 23 key indicators that reflect the site’s seismic resilience. A seismic resilience evaluation model for building sites is established based on the TOPSIS model and the entropy method. The feasibility and rationality of the proposed evaluation method are demonstrated through application examples. The research findings in this paper provide a valuable reference for advancing the evaluation of both building seismic resilience and site seismic resilience. Full article

In recent studies, it has been shown that fluorescence lifetime imaging (FLIM) may reveal intracellular structural details in unstained cytological preparations that are not revealed by standard staining procedures. The aim of our investigation was to examine whether FLIM images could reveal areas suggestive of polymerization in red blood cells (RBCs) of sickle cell disease (SCD) patients. We examined label-free blood films using auto-fluorescence FLIM images of 45 SCD patients and compared the results with those of 27 control persons without hematological disease. All control RBCs revealed homogeneous cytoplasm without any foci. Rounded non-sickled RBCs in SCD showed between zero and three small intensively fluorescent dots with higher lifetime values. In sickled RBCs, we found additionally larger irregularly shaped intensively fluorescent areas with increased FLIM values. These areas were interpreted as equivalent to polymerized hemoglobin. The rounded, non-sickled RBCs of SCD patients with homogeneous cytoplasm were not different from those of the erythrocytes of control patients in light microscopy. Yet, variables from the local binary pattern-transformed matrix of the FLIM values per pixel showed significant differences between non-sickled RBCs and those of control cells. In a linear discriminant analysis, using local binary pattern-transformed texture features (mean and entropy) of the erythrocyte cytoplasm of normal appearing cells, the final model could distinguish between SCD patients and control persons with an accuracy of 84.7% of the patients. When the classification was based on the examination of a single rounded erythrocyte, an accuracy of 68.5% was achieved. Employing the Linear Discriminant Analysis classifier method for machine learning, the accuracy was 68.1%. We believe that our study shows that FLIM is able to disclose the topography of the intracellular polymerization process of hemoglobin in sickle cell disease and that the images are compatible with the theory of the two-step nucleation. Furthermore, we think that the presented technique may be an interesting tool for the investigation of therapeutic inhibition of polymerization. Full article

Neuronal spike patterns are the fundamental units of neural communication in the brain, which is still not fully understood. Entropy measures offer a quantitative framework to assess the variability and information content of these spike patterns. By quantifying the uncertainty and informational content of neuronal patterns, entropy measures provide insights into neural coding strategies, synaptic plasticity, network dynamics, and cognitive processes. Here, we review basic entropy metrics and then we provide examples of recent advancements in using entropy as a tool to improve our understanding of neuronal processing. It focuses especially on studies on critical dynamics in neural networks and the relation of entropy to predictive coding and cortical communication. We highlight the necessity of expanding entropy measures from single neurons to encompass multi-neuronal activity patterns, as cortical circuits communicate through coordinated spatiotemporal activity patterns, called neuronal packets. We discuss how the sequential and partially stereotypical nature of neuronal packets influences the entropy of cortical communication. Stereotypy reduces entropy by enhancing reliability and predictability in neural signaling, while variability within packets increases entropy, allowing for greater information capacity. This balance between stereotypy and variability supports both robustness and flexibility in cortical information processing. We also review challenges in applying entropy to analyze such spatiotemporal neuronal spike patterns, notably, the “curse of dimensionality” in estimating entropy for high-dimensional neuronal data. Finally, we discuss strategies to overcome these challenges, including dimensionality reduction techniques, advanced entropy estimators, sparse coding schemes, and the integration of machine learning approaches. Thus, this work summarizes the most recent developments on how entropy measures contribute to our understanding of principles underlying neural coding. Full article