Computational Mechanics Research Articles

Blockchain-Based Secure and Efficient Secret Image Sharing with Outsourcing Computation in Wireless Networks

Abstract: We present a novel solution, the Blockchain-based Secure and Efficient Secret Image Sharing (BC-SESIS) scheme, designed to enhance the security and efficiency of secret image sharing in wireless networks. Traditional Secret Image Sharing (SIS) methods generate multiple shadow images to distribute a secret image, allowing retrieval with a subset of these shadows. However, existing SIS schemes suffer from vulnerabilities, particularly during communication, where shadow images are susceptible to tampering and corruption, compromising security. Leveraging blockchain technology, BC-SESIS encrypts and stores shadow images securely within the blockchain, mitigating risks of tampering and corruption. A smart contract, empowered with identity authentication, ensures the requisite threshold (k, n) for secret image restoration. To alleviate computational burdens on smart contracts and users, we propose an efficient outsourcing computation mechanism. This method delegates the restoration task to agent miners within the encryption domain, ensuring secure execution. The effectiveness of the BC-SESIS scheme is substantiated through theoretical analysis and extensive experimentation. Results underscore its capacity to uphold communication security while delivering high computational efficiency within wireless networks.

An efficient partitioned framework to couple Arbitrary Lagrangian-Eulerian and meshless vector form intrinsic finite element methods for fluid-structure interaction problems with deformable structures

The Vector Form Intrinsic Finite Element (VFIFE) is an advanced meshless Lagrangian structural solver. This study introduces a partitioned framework to integrate Arbitrary Lagrangian-Eulerian and VFIFE methods into fluid-structure interaction (FSI) problems featured by large structural displacements and deformations. The VFIFE method enables the FSI simulations with deformable structures due to the nature of Lagrangian particles, which is challenging for traditional computational fluid mechanics techniques. In addition, the VFIFE can be effectively equipped with the partitioned coupling schemes because of the versatility of governing equations. In this algorithm system, several matrix problems in finite element methods and mapping and interpolation in the fluid-structure interface can be avoided. The significant potential of this algorithm in tackling complex FSI problems is demonstrated through various benchmarks, encompassing scenarios like Taylor-Green vortex, flow over a rigid cylinder (Re = 40 ∼ 200), forced vibration of a rigid cylinder, vortex-induced vibration (VIV) of an elastically mounted cylinder, and VIV of a rigid cylinder with an attached flexible cantilever beam. Finally, an experiment on flow past a curtain in a water tank is conducted to further evaluate the precision and stability of the current coupling strategy in three dimensions, demonstrating the feasibility of such methods in flexible structures.

Two dimensional fuzzy context-free languages and tiling patterns

Fuzzy context-free languages are powerful compared to fuzzy regular languages as they are generated by fuzzy context-free grammars and fuzzy pushdown automata, which follow an enhanced computational mechanism. A two dimensional language (picture language) is a collection of two dimensional words, which are a rectangular array of symbols made up of finite alphabets. Two dimensional automata can recognize two dimensional languages that could not be recognized by one dimensional automata. In this paper, we introduce two dimensional fuzzy context-free languages generated by the two dimensional fuzzy context-free grammars and accepted by the two dimensional fuzzy pushdown automata in order to deal with the vagueness that arises in two dimensional context-free languages. We can construct a two dimensional fuzzy context free grammar from the given two dimensional fuzzy pushdown automata and vice versa. In addition, we prove that two dimensional fuzzy context-free languages are closed under union, column concatenation, column star, homomorphism, inverse homomorphism, reflection about right-most vertical, reflection about base, conjugation and half-turn and also show that two dimensional fuzzy context-free languages are not closed under matrix homomorphism, quarter-turn and transpose. Further, we have given the applications and the uses of closure properties in the formation of tiling patterns.

An expert system for hybrid edge to cloud computational offloading in heterogeneous MEC–MCC environments

Mobile Cloud Computing (MCC) integrates cloud computing into the mobile environment, effectively tackling performance, environmental, and security constraints. However, the proliferation of clouds and applications introduces complexities in offloading decisions. Mobile Edge Computing (MEC) has emerged as a solution to bolster MCC performance, capitalizing on the proximity to edge devices. However, optimizing computation offloading remains paramount for heterogeneous smart (mobile phones, wearables, and IoT) devices, necessitating efficient utilization of network resources and computational offloading. To address these challenges, this paper introduces NSANNOM (Network and Device Resources Utilization through Smart ANN-based Offloading Mechanism), an expert system designed for optimal computational offloading decision-making and efficient network resource allocation mechanisms. NSANNOM employs Artificial Neural Networks (ANN) for precise decision-making by validating real-world datasets, underscoring ANN’s superiority over existing algorithms, and showcasing enhanced energy savings, cost efficiency, and latency response. Experimental evaluations demonstrate that the proposed ANN model for the offloading decision-making algorithm achieves a training accuracy of 97% and a validation accuracy of 99%. The system consumes minimal energy (10 MJ) for task scheduling and exhibits remarkable accuracy in resource utilization across multiple tasks (10–50) ranging in size (from 1 to 16 GB). Additionally, it minimizes time delays (in milliseconds) during the offloading process.

Internal-interfacial cracking interaction: Combined phase-field and discontinuous Galerkin/cohesive zone modeling

Interaction simulations between internal and interfacial cracks arising in laminated composites have remained a challenging task in the computational mechanics community. Toward this end, this paper proposes a novel explicit approach that combines a length scale-insensitive phase-field model and a discontinuous Galerkin/cohesive zone model to respectively account for brittle or cohesive fracture of bulk materials and interfacial debonding. The presented approach advocates the incorporation of a discontinuous Galerkin term to overcome the artificial compliance problem inherent in existing phase-field methods that normally adopt intrinsic cohesive zone models for interfacial debonding. The capacity of our approach is further exploited by innovatively integrating the approach with an efficient six-node pentahedral solid-shell element, enabling a natural simulation of through-thickness cracks in thin shell structures with high-aspect-ratio elements. Implementation challenge, arising from the stress decomposition of the solid-shell element, of such integration is well addressed. In light of the considerable computational cost required for a phase-field-based method, the proposed approach is mapped to graphics processing units to improve the computational efficiency. Representative numerical examples have demonstrated the capability of the proposed approach in simulating mode-I and mixed-mode cohesive cracks, as well as brittle fractures in solids and shells. It is also shown that the presented approach is capable of effectively addressing the artificial compliance issue and capturing the competitive behavior between internal and interfacial cracks in a physically consistent manner.

Efficient Numerical Solutions for Fuzzy Time Fractional Convection Diffusion Equations Using Two Explicit Finite Difference Methods

In this study, we explore fractional partial differential equations as a more generalized version of classical partial differential equations. These fractional equations have shown promise in providing improved descriptions of certain phenomena under specific circumstances. The main focus of this paper comprises the development, analysis, and application of two explicit finite difference schemes to solve an initial boundary value problem involving a fuzzy time fractional convection–diffusion equation with a fractional order in the range of 0≤ ξ ≤ 1. The uniqueness of this problem lies in its consideration of fuzziness within both the initial and boundary conditions. To handle the uncertainty, we propose a computational mechanism based on the double parametric form of fuzzy numbers, effectively converting the problem from an uncertain format to a crisp one. To assess the stability of our proposed schemes, we employ the von Neumann method and find that they demonstrate unconditional stability. To illustrate the feasibility and practicality of our approach, we apply the developed scheme to a specific example.

Decomposition of Reinforcement Learning Deficits in Disordered Gambling via Drift Diffusion Modeling and Functional Magnetic Resonance Imaging.

Gambling disorder is associated with deficits in reward-based learning, but the underlying computational mechanisms are still poorly understood. Here, we examined this issue using a stationary reinforcement learning task in combination with computational modeling and functional resonance imaging (fMRI) in individuals that regular participate in gambling (n = 23, seven fulfilled one to three DSM 5 criteria for gambling disorder, sixteen fulfilled four or more) and matched controls (n = 23). As predicted, the gambling group exhibited substantially reduced accuracy, whereas overall response times (RTs) were not reliably different between groups. We then used comprehensive modeling using reinforcement learning drift diffusion models (RLDDMs) in combination with hierarchical Bayesian parameter estimation to shed light on the computational underpinnings of this performance deficit. In both groups, an RLDDM in which both non-decision time and decision threshold (boundary separation) changed over the course of the experiment accounted for the data best. The model showed good parameter and model recovery, and posterior predictive checks revealed that, in both groups, the model accurately reproduced the evolution of accuracies and RTs over time. Modeling revealed that, compared to controls, the learning impairment in the gambling group was linked to a more rapid reduction in decision thresholds over time, and a reduced impact of value-differences on the drift rate. The gambling group also showed shorter non-decision times. FMRI analyses replicated effects of prediction error coding in the ventral striatum and value coding in the ventro-medial prefrontal cortex, but there was no credible evidence for group differences in these effects. Taken together, our findings show that reinforcement learning impairments in disordered gambling are linked to both maladaptive decision threshold adjustments and a reduced consideration of option values in the choice process.

Gemini versus ChatGPT: applications, performance, architecture, capabilities, and implementation

This research paper presents an in-depth comparative examination of Gemini and ChatGPT, two prominent conversational AI models, exploring their respective applications, performance metrics, architectural variances, and overall capabilities. As conversational AI becomes increasingly prevalent across industries, comprehending the nuances of these models becomes pivotal for effective deployment. The paper initiates by outlining the wide array of applications for both Gemini and ChatGPT, spanning industries such as customer service, construction, finance, education, healthcare, and entertainment. It analyzes how each model addresses specific use cases, emphasizing their flexibility and potential impact across different sectors. Following this, the study assesses the performance of Gemini and ChatGPT through both empirical benchmarks and real-world deployment scenarios. Key metrics, including response coherence, accuracy, latency, and scalability, are scrutinized to gauge the models' ability to generate contextually appropriate and coherent responses in conversational contexts. Moreover, the paper elucidates the architectural distinctions between Gemini and ChatGPT, covering variances in training methodologies, model architectures, and underlying technologies. Understanding these architectural nuances provides deeper insights into the computational mechanisms underpinning each model's performance. Lastly, the paper explores the capabilities of Gemini and ChatGPT in handling complex linguistic phenomena, deciphering user intents, and sustaining engaging dialogues over prolonged interactions. This discussion encompasses language generation, sentiment analysis, context retention, and ethical considerations, shedding light on the potential of these models to facilitate meaningful human-computer interactions. Through this thorough comparative analysis, the research contributes to the ongoing conversation surrounding conversational AI systems. It offers valuable insights into the strengths and limitations of Gemini and ChatGPT, empowering stakeholders to make informed decisions regarding their optimal utilization across diverse applications.

Simple and deep graph attention networks

Graph Attention Networks (GATs) and Graph Convolutional Neural Networks (GCNs) are two state-of-the-art architectures in Graph Neural Networks (GNNs). It is well known that both models suffer from performance degradation when more GNN layers are stacked, and many works have been devoted to address this problem. We notice that main research efforts in the line focus on the GCN models, and their techniques cannot well fit the GAT models due to the inherent difference between these two architectures. In GAT, the attention mechanism is limited as it ignores the overwhelming propagation from certain nodes as the number of layers increases. To sufficiently utilize the expressive power of GAT, we propose a new version of GAT named Layer-wise Self-adaptive GAT (LSGAT), which can effectively alleviate the oversmoothing issue in deep GAT and is strictly more expressive than GAT. We redesign the attention coefficients computation mechanism adaptively adjusted by layer depth, which considers both immediate neighboring and non-adjacent nodes from a global view. The experimental evaluation confirms that LSGAT consistently achieves better results on node classification tasks over relevant counterparts.

Using Computational Phenotyping to Identify Divergent Strategies for Effort Allocation Across the Psychosis Spectrum.

Disturbances in effort-cost decision-making have been highlighted as a potential transdiagnostic process underpinning negative symptoms in individuals with schizophrenia. However, recent studies using computational phenotyping show that individuals employ a range of strategies to allocate effort, and use of different strategies is associated with unique clinical and cognitive characteristics. Building on prior work in schizophrenia, this study evaluated whether effort allocation strategies differed in individuals with distinct psychotic disorders. We applied computational modeling to effort-cost decision-making data obtained from individuals with psychotic disorders (n = 190) who performed the Effort Expenditure for Rewards Task. The sample included 91 individuals with schizophrenia/schizoaffective disorder, 90 individuals with psychotic bipolar disorder, and 52 controls. Different effort allocation strategies were observed both across and within different disorders. Relative to individuals with psychotic bipolar disorder, a greater proportion of individuals with schizophrenia/schizoaffective disorder did not use reward value or probability information to guide effort allocation. Furthermore, across disorders, different effort allocation strategies were associated with specific clinical and cognitive features. Those who did not use reward value or probability information to guide effort allocation had more severe positive and negative symptoms, and poorer cognitive and community functioning. In contrast, those who only used reward value information showed a trend toward more severe positive symptoms. These findings indicate that similar deficits in effort-cost decision-making may arise from different computational mechanisms across the psychosis spectrum.

A physics-informed GAN framework based on model-free data-driven computational mechanics

Model-free data-driven computational mechanics, first proposed by Kirchdoerfer and Ortiz, replace phenomenological models with numerical simulations based on sample datasets in strain–stress space. In this study, we integrate this paradigm within physics-informed generative adversarial networks (GANs). We enhance the conventional physics-informed neural network framework by implementing the principles of data-driven computational mechanics into GANs. Specifically, the generator is informed by physical constraints, while the discriminator utilizes the closest strain–stress data to discern the authenticity of the generator’s output. This combined approach presents a new formalism to harness data-driven mechanics and deep learning to simulate and predict mechanical behaviors.

A novel conceptual design approach for autonomous underwater helicopter based on multidisciplinary collaborative optimization

Autonomous underwater helicopters (AUHs) are complex electromechanical systems consisting of multiple interconnected sub-disciplines, posing a significant challenge for traditional sequential design strategies in managing their interactions. To address this challenge, a multidisciplinary collaborative optimization (MCO) strategy is proposed, integrating structural, hydrodynamic, and energy disciplines to enhance AUH cruising range. Additionally, a novel approximation model is introduced, combining the Logistic-Tent chaos sparrow search algorithm (LTC-SSA) with the backpropagation neural network (BPNN) algorithm. This method aims to replace costly computational fluid dynamics (CFD) and structural mechanics calculations, thus reducing AUH optimization costs. The laminate design of the composite pressure hull follows classic lamination theory (CLT) and the Tsai-Wu failure criterion. Results demonstrate that the multidisciplinary co-design strategy significantly reduces optimization time costs of AUH, from several months to several days. Furthermore, the proposed LTC-SSA-BPNN algorithm accurately evaluates sailing resistance and Tsai-Wu failure index, with maximum errors of 1.64% and 9.32% respectively. Additionally, the composite pressure shell achieves a 30.4% weight reduction and a 4.1% reduction in sailing resistance while maintaining structural stability. Moreover, the maximum cruising range increases by 34.4%. This strategy offers theoretical support for AUH conceptual design and promises performance enhancement in practical engineering applications.

Taylor Polynomials in a High Arithmetic Precision as Universal Approximators

Function approximation is a fundamental process in a variety of problems in computational mechanics, structural engineering, as well as other domains that require the precise approximation of a phenomenon with an analytic function. This work demonstrates a unified approach to these techniques, utilizing partial sums of the Taylor series in a high arithmetic precision. In particular, the proposed approach is capable of interpolation, extrapolation, numerical differentiation, numerical integration, solution of ordinary and partial differential equations, and system identification. The method employs Taylor polynomials and hundreds of digits in the computations to obtain precise results. Interestingly, some well-known problems are found to arise in the calculation accuracy and not methodological inefficiencies, as would be expected. In particular, the approximation errors are precisely predictable, the Runge phenomenon is eliminated, and the extrapolation extent may a priory be anticipated. The attained polynomials offer a precise representation of the unknown system as well as its radius of convergence, which provides a rigorous estimation of the prediction ability. The approximation errors are comprehensively analyzed for a variety of calculation digits and test problems and can be reproduced by the provided computer code.

Review of multi‐scale mechanical behavior research on composite solid propellants based on data‐driven approach

AbstractComposite solid propellant is a kind of viscoelastic composite with high filling ratio and multi‐scale composition characteristics, and its macroscopic mechanical properties strongly depend on the microstructure of the propellant materia. However, with the increasing complexity of composition, structure and properties of composite solid propellants, the traditional research paradigm based on experimental observation, theoretical modeling and numerical simulation has encountered new scientific challenges and technical bottlenecks in the mechanical behavior analysis, charge design and manufacturing of composite solid propellants. Among them, the problems such as insufficient experimental observation, lack of theoretical model, limited numerical analysis and difficult verification of results restrict the development of composite solid propellants in future‐oriented engineering applications to a certain extent. The data‐driven computational mechanics method can directly establish complex relationships between variables from high‐dimensional and high‐throughput data, which can capture trends that are difficult to be found by traditional mechanics research methods, and has inherent advantages in the analysis, prediction and optimization of complex systems. This paper mainly reviews and evaluates the research of neural network based modeling, model‐free data‐driven calculation and data‐driven multi‐scale calculation, which provides the correct direction for the subsequent research of multi‐scale mechanical behavior of composite solid propellants based on data‐driven.

Hybrid methods for solving structural geometric nonlinear dynamic equations: Implementation of fifth-order iterative procedures within composite time integration methods

This paper studies a class of hybrid methods that implement multi-point iterative procedures as the nonlinear solver within optimized composite implicit methods. These multi-point methods are employed to enhance convergence order and accuracy without requiring higher-order derivatives for solving structural geometric nonlinear dynamic equations. The promising approach provides an opportunity to delve deeper into its implications and explore the potential applications of multi-point techniques and composite methods across the analysis of dynamical systems. This study involves the utilization of two-point and five-point iterative methods with fifth-order convergence within three-step and four-step optimized composite implicit time integration methods. Moreover, a comprehensive hybrid scheme is introduced by integrating a family of optimized composite implicit methods with single-point and multi-point iterative methods. Then, the performance of the hybrid scheme is analyzed through structural examples, considering factors such as time step size, tolerance threshold, and the degrees of freedom of structures. The findings and numerical results illustrate that multi-point methods outperform the single-point method in terms of the number of iterations. Simultaneously, the four-step time integration method exhibits superior performance compared to the three-step time integration method, albeit with a marginal difference. It is observed that the hybrid method, consisting of the four-step time integration method and the five-point iterative method, performs better than other methods in terms of the number of iterations and errors for solving geometric nonlinear dynamic equations, especially in large structures. Thus, it stands out as a favorable choice for practical analyses and a possible candidate for generalization to other types of nonlinear problems in computational mechanics.

Simulation for Endovascular Treatment

With the advent of high-resolution imaging and advancements in computational fluid dynamics(CFD)and computational structural mechanics(CSM)analyses, clinical simulation of endovascular intervention has gradually become feasible. Virtual stents have become indispensable for coil embolization. For braided stents, such as those with low-profile visualized intraluminal support and flow diverters, predicting postplacement elongation and contraction is challenging; however, software development has enabled more precise treatment planning. Additionally, simulations utilizing three-dimensional(3D)printer models can enable realistic simulations of procedures such as intracranial stents and Woven EndoBridge placement. This approach is beneficial for shunt disorders such as arteriovenous malformations and dural arteriovenous fistulas, offering 3D visualization of shunt access routes and intuitive treatment strategy planning, even for beginners. Furthermore, it can be applied to procedures such as open surgical clipping and nidus resection, aiding in the selection of suitable clips and considerations for ideal resection based on nidus curvature. Simulations using CFD, CSM, and 3D printers are crucial for training surgeons and handling new devices. Harnessing medicine-engineering synergy is essential, and regulatory approval(insurance coverage)and appropriate commercialization of simulations are paramount for the future.

Optimum active power tracking based control of brushless doubly-fed reluctance generator tied to renewable microgrid

Among all the existing wind energy generators (WEG), a brushless doubly-fed reluctance generator (BDFRG) is the better option in terms of maintenance cost and constructional simplicity. For more adaptability, the efficiency of the BDFRG can be improved through proper control mechanisms. Therefore, this paper presents an optimum active power tracking-based control technique for BDFRG for minimum losses. Copper loss of the machine is considered to be the objective function for the proposed optimization technique. However, one issue found to be the major in all the existing controllers is the accuracy of sensor less control of the BDFRG. Therefore, this paper proposes an accurate model reference adaptive system (MRAS) based sensor less mechanism for computation of BDFRG secondary winding flux position. The no sensitivity of the proposed technique to the machine parameter variation and hence the accuracy, catch-on-fly, and non-inclusion of integrator and differentiators and hence less burden on the processor makes the proposed scheme robust. The control scheme is practically implemented with a 2.5 kW BDFRG using MATLAB/Simulink platform.

Efficient Spiking Neural Networks With Radix Encoding.

Spiking neural networks (SNNs) have advantages in latency and energy efficiency over traditional artificial neural networks (ANNs) due to their event-driven computation mechanism and the replacement of energy-consuming weight multiplication with addition. However, to achieve high accuracy, it usually requires long spike trains to ensure accuracy, usually more than 1000 time steps. This offsets the computation efficiency brought by SNNs because a longer spike train means a larger number of operations and larger latency. In this article, we propose a radix-encoded SNN, which has ultrashort spike trains. Specifically, it is able to use less than six time steps to achieve even higher accuracy than its traditional counterpart. We also develop a method to fit our radix encoding technique into the ANN-to-SNN conversion approach so that we can train radix-encoded SNNs more efficiently on mature platforms and hardware. Experiments show that our radix encoding can achieve 25× improvement in latency and 1.7% improvement in accuracy compared to the state-of-the-art method using the VGG-16 network on the CIFAR-10 dataset.

Implementation of the preCICE coupling interface for AC2/ATHLET

Abstract The design of new nuclear reactor types as well as the analysis of certain phenomena in existing reactors require to consider different physical models and three-dimensional phenomena like the effect of turbulence in fluids and three-dimensional (3D) heat conduction in complex structures. One-dimensional (1D) lumped parameter system codes used to simulate transients in nuclear reactors lack high-resolution models. Special codes like computational fluid dynamics (CFD) or computational structural mechanics codes (CSM) can simulate those, but they require significantly more computational resources. Therefore, different coupling methods have been developed to limit the use of computationally expensive codes to those parts of a plant where they are really needed and couple them with systems codes, which simulate the rest. The library preCICE enables simultaneous coupling of multiple simulation programs, e.g., the 3D CFD code OpenFOAM and the 3D CSM code CalculiX. preCICE coupling interfaces were developed for the system code ATHLET. We focused on fluid-fluid couplings and conjugate heat-transfer couplings. Coupled simulations were performed for an experiment at the test facility TALL for the transition from forced to natural circulation coupling ATHLET with OpenFOAM, and a generic building condenser geometry coupling the three codes ATHLET, CalculiX, and OpenFOAM. The correct integral transfer of the quantities at the coupling interfaces was verified. However, it was found that coupled quantities at the interfaces need to be converted from 0D to 2D and backwards considering the underlying physics, which goes beyond the pure numerical considerations of the preCICE library.

The social transmission of empathy relies on observational reinforcement learning

Theories of moral development propose that empathy is transmitted across individuals. However, the mechanisms through which empathy is socially transmitted remain unclear. Here, we combine computational learning models and functional MRI to investigate whether, and if so, how empathic and non-empathic responses observed in others affect the empathy of female observers. The results of three independent studies showed that watching empathic or non-empathic responses generates a learning signal that respectively increases or decreases empathy ratings of the observer. A fourth study revealed that the learning-related transmission of empathy is stronger when observing human rather than computer demonstrators. Finally, we show that the social transmission of empathy alters empathy-related responses in the anterior insula, i.e., the same region that correlated with empathy baseline ratings, as well as its functional connectivity with the temporoparietal junction. Together, our findings provide a computational and neural mechanism for the social transmission of empathy that accounts for changes in individual empathic responses in empathic and non-empathic social environments.