DAFT-Net: Dual Attention and Fast Tongue Contour Extraction Using Enhanced U-Net Architecture.

In most silent speech research, continuously observing tongue movements is crucial, thus requiring the use of ultrasound to extract tongue contours. Precisely and in real-time extracting ultrasonic tongue contours presents a major challenge. To tackle this challenge, the novel end-to-end lightweight network DAFT-Net is introduced for ultrasonic tongue contour extraction. Integrating the Convolutional Block Attention Module (CBAM) and Attention Gate (AG) module with entropy-based optimization strategies, DAFT-Net establishes a comprehensive attention mechanism with dual functionality. This innovative approach enhances feature representation by replacing traditional skip connection architecture, thus leveraging entropy and information-theoretic measures to ensure efficient and precise feature selection. Additionally, the U-Net's encoder and decoder layers have been streamlined to reduce computational demands. This process is further supported by information theory, thus guiding the reduction without compromising the network's ability to capture and utilize critical information. Ablation studies confirm the efficacy of the integrated attention module and its components. The comparative analysis of the NS, TGU, and TIMIT datasets shows that DAFT-Net efficiently extracts relevant features, and it significantly reduces extraction time. These findings demonstrate the practical advantages of applying entropy and information theory principles. This approach improves the performance of medical image segmentation networks, thus paving the way for real-world applications.

Entropy generation for exact irreversibility analysis in the MHD channel flow of Williamson fluid with combined convective-radiative boundary conditions.

The scrutinization of entropy optimization in the various flow mechanisms of non-Newtonian fluids with heat transfer has been incredibly enhanced. Through the investigation of irreversibility sources in the steady flow of a non-Newtonian Willaimson fluid, an analysis of entropy generation is carried out in this current work. The current study has an essential aspect of investigating the heat transfer mechanism with flow phenomenon by considering convective-radiative boundary conditions. A horizontal MHD channel is assumed with two parallel plates to develop a mathematical model for the flow phenomenon by considering the variable viscosity of the fluid. The contribution of physical impacts of thermal radiation, Joule heating, and viscous dissipation is interpolated in the constitutive energy equation. The complete flow of the current analysis is established in the form of ordinary differential equations which further take the form of the dimensionless system through the contribution of the similarity variables. A graphical scrutinization of the physical features of the flow phenomenon in relation to the pertinent parameters is proposed. This study reveals that the higher magnitude of radiation parameter and Brinkman number dominates the system's entropy. Moreover, the temperature distribution experiences an increasing mechanism with improved conduction-radiation parameter at the lower plate.

Non-similar solution development for entropy optimized flow of Jeffrey liquid.

Mixed convection in dissipative entropy optimized stagnation point flow of nanomaterial towards stretching Riga sheet is addressed. Brownian and thermophoresis diffusions for nanomaterial are accounted. Constitutive relations for Jeffrey material are utilized. Non-similar solutions for the governing differential systems are developed. OHAM is employed for the convergent series solutions development. Outcomes of pertinent variables on flow quantities of interest are graphically organized. Finally the concluding remarks are arranged.

Entropy Optimization, Generalized Logarithms, and Duality Relations.

Several generalizations or extensions of the Boltzmann-Gibbs thermostatistics, based on non-standard entropies, have been the focus of considerable research activity in recent years. Among these, the power-law, non-additive entropies Sq≡k1-∑ipiqq-1(q∈R;S1=SBG≡-k∑ipilnpi) have harvested the largest number of successful applications. The specific structural features of the Sq thermostatistics, therefore, are worthy of close scrutiny. In the present work, we analyze one of these features, according to which the q-logarithm function lnqx≡x1-q-11-q(ln1x=lnx) associated with the Sq entropy is linked, via a duality relation, to the q-exponential function characterizing the maximum-entropy probability distributions. We enquire into which entropic functionals lead to this or similar structures, and investigate the corresponding duality relations.

Modeling urban encroachment on ecological land using cellular automata and cross-entropy optimization rules.

Rapid urban expansion often leads to substantial encroachment on ecological lands and destruction of natural environments. We developed a new cellular automata model (named CACEO) that uses cross-entropy optimization (CEO) to reproduce and project urban expansion into coastal areas and to assess urban encroachment on ecological lands. The CEO algorithm automatically searches for the near-optimal CA parameters and is capable of objectively parameterizing CA models to predict multi-objective scenarios. We calibrated CACEO by simulating urban expansion at Wenzhou from 1995 to 2005, validated the model from 2005 to 2015 using real data, and then predicted urban expansion for 2025 and 2035. End-state overall accuracies were 93.8% for 2005 and 94.4% for 2015, while figure-of-merit metrics were 27.9% for 2005 and 19.1% for 2015. We predicted four different scenarios to year 2025 and 2035: (1) a business-as-usual (BAU)-scenario using benchmark settings; (2) a District-scenario based on a district-oriented urban development strategy; (3) a Road-scenario based on a road network-oriented urban development strategy; and (4) a Coast-scenario based on a coast-oriented urban development strategy. Each scenario predicts a substantially different pattern of urban encroachment on ecological land and significant loss of farmland, forest, wetland and grassland. These scenarios should be useful in adjusting urban development strategies at Wenzhou and elsewhere.

High-Resolution ISAR Imaging with Modified Joint Range Spatial-Variant Autofocus and Azimuth Scaling.

Well-focused and accurately scaled high-resolution inverse synthetic aperture radar (ISAR) images provide a sound basis for feature extraction and target recognition. This paper proposes a novel high-resolution ISAR imaging algorithm, namely modified joint range spatial-variant autofocus and azimuth scaling algorithm (MJAAS). After motion compensation, the shift of the equivalent rotational center (ERC) of the target destroys the linear relationship between the azimuth chirp rates (ACR) of echo signals and the range coordinates of scattering points, thereby leading to the failure of azimuth scaling. Accordingly, a new joint equivalent rotational center position and effective rotational velocity (JERCP-ERV) signal model is established, serving as the basis of MJAAS. By recourse to the Davidon-Fletcher-Powell (DFP) algorithm, MJAAS can jointly estimate the ERCP and ERV by solving a minimum entropy optimization problem, so as to simultaneously achieve accurate azimuth scaling and range spatial-variant autofocus, which further improves the image focusing performance. MJAAS is not restricted by the modes of motion errors (coherent or non-coherent) and the motion compensation methods, so it can be widely applied to real data with the advantages of strong practicality and high accuracy. Extensive experimental results based on both simulated and real data are provided to corroborate the effectiveness of the proposed algorithm.

Transport of hybrid type nanomaterials in peristaltic activity of viscous fluid considering nonlinear radiation, entropy optimization and slip effects.

BACKGROUND: In last few decades, a new class of working materials which comprises from two solid materials dispersed in a continuous phase liquid was established and deeply scrutinized. These materials are called hybrid nanomaterials. This research article aims to investigate entropy optimization in hybrid nanomaterial flow through a rotating peristaltic channel walls. Flow behavior is analyzed between the channels which is caused by propagation of sinusoidal waves. Viscosity of fluid is considered variable instead of constant characteristics. Fluid saturates through porous attributes of channel walls. Nonliear radiative flux and convective condition are considered. Slip conditions are imposed at the boundary of walls. METHODS: Built-in-Shooting technique is employed to obtain the numerical outcomes for the considered flow problem. RESULTS: Impacts of sundry variables on the entropy, temperature and velocity are scrutinized through different graphs. Numerical result presents that the axial velocity escalates with the inclusion of hybrid nanomaterial. The temperature of fluid enhances through higher estimations of hybrid nanoparticles. CONCLUSIONS: Here the flow behavior is discussed between the channels which are caused by propagation of sinusoidal waves with speed c. Entropy generation rate is minimum for variable viscosity and maximum for hybrid nanoparticles. Hybrid nanoparticles increase the temperature of fluid. Bejan number presents the similar impact for variable viscosity and thermal slip parameters. Temperature field decays through higher values of Brinkman number.

Assembly Reactions of Hepatitis B Capsid Protein into Capsid Nanoparticles Follow a Narrow Path through a Complex Reaction Landscape.

For many viruses, capsids (biological nanoparticles) assemble to protect genetic material and dissociate to release their cargo. To understand these contradictory properties, we analyzed capsid assembly for hepatitis B virus; an endemic pathogen with an icosahedral, 120-homodimer capsid. We used solution X-ray scattering to examine trapped and equilibrated assembly reactions. To fit experimental results, we generated a library of distinct intermediates, selected by umbrella sampling of Monte Carlo simulations. The number of possible capsid intermediates is immense, ∼1030, yet assembly reactions are rapid and completed with high fidelity. If the huge number of possible intermediates were actually present, maximum entropy analysis shows that assembly reactions would be blocked by an entropic barrier, resulting in incomplete nanoparticles. When an energetic term was applied to select the stable species that dominated the reaction mixture, we found only a few hundred intermediates, mapping out a narrow path through the immense reaction landscape. This is a solution to a viral application of the Levinthal paradox. With the correct energetic term, the match between predicted intermediates and scattering data was striking. The grand canonical free energy landscape for assembly, calibrated by our experimental results, supports a detailed analysis of this complex reaction. There is a narrow range of energies that supports on-path assembly. If association energy is too weak or too strong, progressively more intermediates will be entropically blocked, spilling into paths leading to dissociation or trapped incomplete nanoparticles, respectively. These results are relevant to many viruses and provide a basis for simplifying assembly models and identifying new targets for antiviral intervention. They provide a basis for understanding and designing biological and abiological self-assembly reactions.

Research on Fault Diagnosis of Gearbox with Improved Variational Mode Decomposition.

Variational Mode Decomposition (VMD) can decompose signals into multiple intrinsic mode functions (IMFs). In recent years, VMD has been widely used in fault diagnosis. However, it requires a preset number of decomposition layers K and is sensitive to background noise. Therefore, in order to determine K adaptively, Permutation Entroy Optimization (PEO) is proposed in this paper. This algorithm can adaptively determine the optimal number of decomposition layers K according to the characteristics of the signal to be decomposed. At the same time, in order to solve the sensitivity of VMD to noise, this paper proposes a Modified VMD (MVMD) based on the idea of Noise Aided Data Analysis (NADA). The algorithm first adds the positive and negative white noise to the original signal, and then uses the VMD to decompose it. After repeated cycles, the noise in the original signal will be offset to each other. Then each layer of IMF is integrated with each layer, and the signal is reconstructed according to the results of the integrated mean. MVMD is used for the final decomposition of the reconstructed signal. The algorithm is used to deal with the simulation signals and measured signals of gearbox with multiple fault characteristics. Compared with the decomposition results of EEMD and VMD, it shows that the algorithm can not only improve the signal to noise ratio (SNR) of the signal effectively, but can also extract the multiple fault features of the gear box in the strong noise environment. The effectiveness of this method is verified.