Search Results (20,400)

Misunderstandings in dyadic interactions often persist despite our best efforts, particularly between native and non-native speakers, resembling a broken duet that refuses to harmonise. This paper delves into the computational mechanisms underpinning these misunderstandings through the lens of the broken Lorenz system—a continuous dynamical model. By manipulating a specific parameter regime, we induce bistability within the Lorenz equations, thereby confining trajectories to distinct attractors based on initial conditions. This mirrors the persistence of divergent interpretations that often result in misunderstandings. Our simulations reveal that differing prior beliefs between interlocutors result in misaligned generative models, leading to stable yet divergent states of understanding when exposed to the same percept. Specifically, native speakers equipped with precise (i.e., overconfident) priors expect inputs to align closely with their internal models, thus struggling with unexpected variations. Conversely, non-native speakers with imprecise (i.e., less confident) priors exhibit a greater capacity to adjust and accommodate unforeseen inputs. Our results underscore the important role of generative models in facilitating mutual understanding (i.e., establishing a shared narrative) and highlight the necessity of accounting for multistable dynamics in dyadic interactions. Full article

Mobile Edge Computing (MEC) is crucial for reducing latency by bringing computational resources closer to the network edge, thereby enhancing the quality of services (QoS). However, the broad deployment of cloudlets poses challenges in efficient network slicing, particularly when traffic distribution is uneven. Therefore, these challenges include managing diverse resource requirements across widely distributed cloudlets, minimizing resource conflicts and delays, and maintaining service quality amid fluctuating request rates. Addressing this requires intelligent strategies to predict request types (common or urgent), assess resource needs, and allocate resources efficiently. Emerging technologies like edge computing and 5G with network slicing can handle delay-sensitive IoT requests rapidly, but a robust mechanism for real-time resource and utility optimization remains necessary. To address these challenges, we designed an end-to-end network slicing approach that predicts common and urgent user requests through T distribution. We formulated our problem as a multi-agent Markov decision process (MDP) and introduced a multi-agent soft actor–critic (MAgSAC) algorithm. This algorithm prevents the wastage of scarce resources by intelligently activating and deactivating virtual network function (VNF) instances, thereby balancing the allocation process. Our approach aims to optimize overall utility, balancing trade-offs between revenue, energy consumption costs, and latency. We evaluated our method, MAgSAC, through simulations, comparing it with the following six benchmark schemes: MAA3C, SACT, DDPG, S2Vec, Random, and Greedy. The results demonstrate that our approach, MAgSAC, optimizes utility by 30%, minimizes energy consumption costs by 12.4%, and reduces execution time by 21.7% compared to the closest related multi-agent approach named MAA3C. Full article

An analytical derivation of the conditional moment-generating function (MGF) for a regime-switching nonlinear drift constant elasticity of variance process is established. The proposed model incorporates both regime-switching mechanisms and nonlinear drift components to better capture market phenomena such as volatility smiles and leverage effects. Regime-switching models can match the tendency of financial markets to often change their behavior abruptly and the phenomenon that the new behavior of financial variables often persists for several periods after such a change. Closed-form formulas for the MGF under various conditions, which are then applied for option pricing, are also derived. The efficacy and accuracy of the results are validated through a discrete Markov chain simulation. The results obtained from the proposed formulas completely match with those from MC simulations, while requiring significantly less computational time. Full article

Additive manufacturing (AM) allows the creation of customized designs for various medical devices, such as implants, casts, and splints. Amongst other AM technologies, fused filament fabrication (FFF) facilitates the production of intricate geometries that are often unattainable through conventional methods like subtractive manufacturing. This study aimed to develop a methodology for substituting a pathological talus bone with a personalized one created using additive manufacturing. The process involved generating a numerical parametric solid model of the specific anatomical region using computed tomography (CT) scans of the corresponding healthy organ from the patient. The healthy talus served as a mirrored template to replace the defective one. Structural simulation of the model through finite element analysis (FEA) helped compare and select different materials to identify the most suitable one for the replacement bone. The implant was then produced using FFF technology. The developed procedure yielded commendable results. The models maintained high geometric accuracy, while significantly reducing the computational time. PEEK emerged as the optimal material for bone replacement among the considered options and several specimens of talus were successfully printed. Full article

The cellular space robot (CSR) is a new type of self-reconfigurable robot. It can adapt the variety of on-orbit service tasks with large space spans through multi-level reconfiguration mechanisms. As the CSR has a large configuration space, kinematic solving becomes a key problem affecting on-orbit operation capability, and kinematic automatic solving research must be conducted. In order to solve this problem, firstly, the cellular space robot system capable of realizing multi-level self-reconfiguration is proposed for the demand of space on-orbit service, and the kinematic equations of modules are constructed by considering a single module function using screw theory. Secondly, the kinematics of the cellular space robot are encapsulated and divided into multiple levels, and the multilevel-assembly relationship-description method for robotic systems is proposed based on graph theory. On this basis, the pathway-solving algorithm is proposed to express the robot organization reachability information. Finally, the module–organ–robot multilevel kinematics solving algorithm is proposed in combination with screw theory. In order to verify the effectiveness of the algorithm in this paper, numerical simulation is used to compare with the proposed algorithm. The results show that compared with the traditional algorithm, the method in this paper only needs to update part of the assembly relations after organ migration, which simplifies the kinematic modeling operation and improves the efficiency of kinematic computation. Full article

This study aims to propose a central composite design (CCD) combined with response surface methodology (RSM) to create a statistical experimental design. A new parametric optimization of entropy generation is presented. The flow behavior of magnetohydrodynamic hybrid nanofluid (HNF) flow through two flat contracting expanding plates of channel alongside radiative heat transmission was considered. The lower fixed plate was externally heated whereas the upper porous plate was cooled by injecting a coolant fluid with a uniform velocity inside the channel. The resulting equations were solved by the Homotopic Analysis Method using MATHEMATICA 10 and Minitab 17.1. The design consists of several input factors, namely a magnetic field parameter ($\mathrm{M}$), radiation parameter ($\mathrm{N}$) and group parameter ($\mathrm{B}\mathrm{r}/\mathrm{A}1$). To obtain the values of flow response parameters, numerical experiments were used. Variables, especially the entropy generation ($\mathrm{N}\mathrm{e}$), were considered for each combination of design. The resulting RSM empirical model obtained a high coefficient of determination, reaching 99.97% for the entropy generation number ($\mathrm{N}\mathrm{e}$). These values show an excellent fit of the model to the data. Full article

This study delves into the dynamic behavior of ultra-high-speed rotor systems underpinned by helium hydrostatic gas bearings, with a focus on the impact of rotational velocity on system performance. We have formulated an integrative dynamic model that harmonizes the rotor motion equation with the transient Reynolds equation. This model has been meticulously resolved via the Finite Difference Method (FDM) and the Wilson-Θ technique. Our findings unveil intricate nonlinear dynamics, including 2T-periodic and multi-periodic oscillations, and underscore the pivotal role of first-order temporal fluctuations, which account for over 20% of the transient pressure at rotational speeds exceeding 95.0 krpm. Further, we have executed empirical studies to evaluate the system’s performance in practical settings. It is observed that when the ratio of low-frequency to fundamental frequency approaches 0.3 and the amplitude ratio exceeds 3, the vigilant monitoring of system stability and reliability is imperative. Collective insights from both computational simulations and experimental studies have enriched our understanding of the dynamic attributes of ultra-high-speed rotor systems. These revelations are crucial for the advancement of more efficacious and resilient rotor systems designed for high-speed applications. Full article

The growing interest in renewable energy solutions for sustainable development has significantly advanced the design and analysis of floating offshore wind turbines (FOWTs). Modeling FOWTs presents challenges due to the considerable coupling between the turbine’s aerodynamics and the floating platform’s hydrodynamics. This review paper highlights the critical role of computational fluid dynamics (CFD) in enhancing the design and performance evaluation of FOWTs. It thoroughly evaluates various CFD approaches, including uncoupled, partially coupled, and fully coupled models, to address the intricate interactions between aerodynamics, hydrodynamics, and structural dynamics within FOWTs. Additionally, this paper reviews a range of software tools for FOWT numerical analysis. The research emphasizes the need to focus on the coupled aero-hydro-elastic models of FOWTs, especially in response to expanding rotor diameters. Further research should focus on developing nonlinear eddy viscosity models, refining grid techniques, and enhancing simulations for realistic sea states and wake interactions in floating wind farms. The research aims to familiarize new researchers with essential aspects of CFD simulations for FOWTs and to provide recommendations for addressing challenges. Full article

Glycosaminoglycans (GAGs) play a key role in a variety of biological processes in the extracellular matrix (ECM) via interactions with their protein targets. Due to their high flexibility, periodicity and electrostatics-driven interactions, GAG-containing complexes are very challenging to characterize both experimentally and in silico. In this study, we, for the first time, systematically analyzed the interactions of endostatin, a proteolytic fragment of collagen XVIII known to be anti-angiogenic and anti-tumoral, with heparin (HP) and representative heparan sulfate (HS) oligosaccharides of various lengths, sequences and sulfation patterns. We first used conventional molecular docking and a docking approach based on a repulsive scaling–replica exchange molecular dynamics technique, as well as unbiased molecular dynamic simulations, to obtain dynamically stable GAG binding poses. Then, the corresponding free energies of binding were calculated and the amino acid residues that contribute the most to GAG binding were identified. We also investigated the potential influence of Zn²⁺ on endostatin–HP complexes using computational approaches. These data provide new atomistic details of the molecular mechanism of HP’s binding to endostatin, which will contribute to a better understanding of its interplay with proteoglycans at the cell surface and in the extracellular matrix. Full article

In this study, a static load test of prestressed steel-reinforced concrete simply supported beams was carried out utilizing three test beams to investigate the bond–slip effect between the section steel and concrete in prestressed steel-reinforced concrete beams. Finite element models of three beams considering two different bond–slip constitutive relations and without considering bond–slip performance were developed in ABAQUS. The influence of shear bolt nails on the bond slip between the section steel and concrete was analyzed, and the load–slip curves of the three test beams were also computed. Generally, the results showed that the finite element calculations considering the bond–slip effect are more consistent with the experimental calculations, and the bond–slip constitutive relationship proposed by Yang Yong is more suitable for the numerical simulation of prestressed steel-reinforced concrete beams. When the effective prestress is increased from 222.15 KN to 279.61 KN, the ultimate bearing capacity increases by 14.8%. When the concrete strength is increased from 37.21 MPa to 47.97 MPa, the ultimate bearing capacity increases by 15.2%. When the stirrup ratio is 0.50%, compared with 0.25%, the ultimate bearing capacity increases by 7.8%. When the steel content is 5.41%, compared with 3.37%, the ultimate bearing capacity increases by 9.1%. The results of this study can provide a reference for future research and engineering applications of bond slip between section steel and concrete in prestressed steel-reinforced concrete beams in the future. Full article

To comprehensively understand the explosion risk in underground energy transportation tunnels, this study employed computational fluid dynamics technology and finite element simulation to numerically analyze the potential impact of an accidental explosion for a specific oil and gas pipeline in China and the potential damage risk to nearby buildings. Furthermore, the study investigated the effects of tunnel inner diameter (d = 4.25 m, 6.5 m), tunnel length (L = 4 km, 8 km, 16 km), and soil depth (primarily L_soil = 20 m, 30 m, 40 m) on explosion dynamics and on structural response characteristics. The findings indicated that as the tunnel length and inner diameter increased, the maximum explosion overpressure gradually rose and the peak arrival time was delayed, especially when d = 4.25 m; with the increase in L, the maximum explosion overpressure rapidly increased from 1.03 MPa to 2.12 MPa. However, when d = 6.5 m, the maximum explosion overpressure increased significantly by 72.8% from 1.25 MPa. Evidently, compared to the change in tunnel inner diameter, tunnel length has a more significant effect on the increase in explosion risk. According to the principle of maximum explosion risk, based on the peak explosion overpressure of 2.16 MPa under various conditions and the TNT equivalent calculation formula, the TNT explosion equivalent of a single section of the tunnel was determined to be 1.52 kg. This theoretical result is further supported by the AUTODYN 15.0 software simulation result of 2.39 MPa (error < 10%). As the soil depth increased, the distance between the building and the explosion source also increased. Consequently, the vibration peak acceleration and velocity gradually decreased, and the peak arrival time was delayed. In comparison to a soil depth of 10 m, the vibration acceleration at soil depths of 20 m and 30 m decreased by 81.3% and 91.7%, respectively. When the soil depth was 10 m, the building was at critical risk of vibration damage. Full article

In the context of increasing global energy shortages and climate change, the human living environment, as a crucial component of residents’ daily lives, has garnered growing attention from the academic community. Research on residential environments is vital for promoting the sustainable development of urban construction and constitutes an important aspect of sustainable development studies. This study focuses on the optimization strategy for the outdoor activity space wind environment in the Xihuayuan residential area in Lanzhou city, utilizing spatial syntax analysis and Computational Fluid Dynamics (CFD) simulation technology. Firstly, the outdoor activity space is analyzed for visibility and spatial accessibility using DepthmapX0.6 software. Then, the outdoor wind environment in the residential area is simulated using PHOENICS 2018 software, and the analysis is conducted on outdoor spaces with a poor wind environment in terms of high accessibility. The results indicate that residents’ outdoor comfort in these spaces is poor, highlighting the urgent need for improvement in the wind environment. This research attempts to optimize the wind environment in high-accessibility spaces within the residential area by improving building layout, orientation, and height. The simulation results after optimization demonstrate an increase in the overall average wind speed to 1.44 m/s, with the proportion of spaces with a good wind environment in high-accessibility areas during summer rising from 33.4% to 59.2%. The optimization strategy effectively improves the wind environment in high-accessibility areas of the residential area. Full article

The aerodynamic loads of a sedan experience significant fluctuations when passing by a sand dune at the roadside under crosswinds, which can easily cause yawing and overturning. Computational fluid dynamics (CFD) methods, based on different turbulence modeling approaches, yield different aerodynamic results for sedans. This study aims to investigate the effects of three prevailing turbulence modeling approaches (renormalization group (RNG) k-ε, large eddy simulation (LES), and improved delayed detached eddy simulation (IDDES)) on the aerodynamic characteristics of a sedan passing by a sand dune under crosswinds. The CFD dynamic mesh models are constructed using the “mosaic” mesh technique to account for the dune–air–sedan interaction. The reliability of the CFD prediction method is verified by comparing it with field test results. The predictive capabilities of the three turbulence modeling approaches are compared in terms of aerodynamic loads and flow field characteristics. The simulation of sand particle movement is conducted through the discrete phase model, aiming to assess the impact of wind–sand flow on the aerodynamic properties of sedans. Corresponding results show that the aerodynamic loads predicted by the LES model closely match (within 4.4–7.5%) the corresponding data obtained from field tests. While the IDDES and LES models demonstrate similar abilities in characterizing the wind field details, and their results exhibit maximum differences of 8.3–15.7%. Meanwhile, the maximum difference between the results obtained by the RNG k-ε and LES models ranges from 14.8% to 18.4%, attributed to its inability to capture subtle changes in the vortex structure within the flow field. This work will provide a numerical modeling reference for studies on the wind–sand flow and the aerodynamic characteristics of sedans running through the desert, and it has implications for the safe driving of sedans under extreme conditions. Full article

Traditional methods of hologram generation, such as point-, polygon-, and layer-based physical simulation approaches, suffer from substantial computational overhead and generate low-fidelity holograms. Deep learning-based computer-generated holography demonstrates effective performance in terms of speed and hologram fidelity. There is potential to enhance the network’s capacity for fitting and modeling in the context of computer-generated holography utilizing deep learning methods. Specifically, the ability of the proposed network to simulate Fresnel diffraction based on the provided hologram dataset requires further improvement to meet expectations for high-fidelity holograms. We propose a neural architecture called Holo-U²Net to address the challenge of generating a high-fidelity hologram within an acceptable time frame. Holo-U²Net shows notable performance in hologram evaluation metrics, including an average structural similarity of 0.9988, an average peak signal-to-noise ratio of 46.75 dB, an enhanced correlation coefficient of 0.9996, and a learned perceptual image patch similarity of 0.0008 on the MIT-CGH-4K large-scale hologram dataset. Full article

This paper introduces a robust synchronization method for orthogonal frequency division multiplexing (OFDM) in multi-unmanned aerial vehicle (UAV) communication systems, focusing on minimizing overhead while achieving reliable synchronization. The proposed synchronization scheme enhances both frame efficiency and implementation simplicity. Initially, a high-efficiency frame structure is designed without a guard time interval, utilizing a preamble sequence to simultaneously achieve both symbol synchronization and automatic gain control (AGC) before demodulation. Subsequently, a novel 2-bit non-uniform quantization method for the Zadoff–Chu sequences is developed, enabling the correlation operations in the traditional symbol synchronization algorithm to be implemented via bitwise exclusive OR (XOR) and addition operations. The complexity of hardware implementation and the energy consumption for symbol synchronization can be reduced significantly. Furthermore, the impact of AGC on frequency synchronization performance is examined, and an improved frequency synchronization method based on AGC gain compensation is proposed. Finally, the performance of the proposed method is rigorously analyzed and compared with that of the traditional method through computer simulations, demonstrating the effectiveness and superiority of the proposed approach. Full article