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In this paper, a decentralized consensus algorithm for a robotic swarm is presented, which enable agents to escape collisions and avoid obstacles collectively. To achieve consensus, we have used neighboring clusters and extreme function for data dissemination and consensus in agents to avoid obstacles respectively. The main challenge is to make fast and accurate collective decision by improving data propagation in presence of Byzantine agents. To improve data dissemination in local interactions of agents in decentralized fashion, an iterative rumor-based data propagation model is proposed. Due to presence of Byzantine robots, the LCP and the W-MSR algorithm cannot achieve consensus for obstacle avoidance in artificial swarms. We establish the Expectation-based Extreme Value (EEV) algorithm using the local expectation and the extreme function to solve these problems. The experiments conducted in simulations demonstrate that the rumor spreading method has better results than the Peer-to-Peer method in randomly connected swarm signaling network (SSN) with complex environmental circumstance, the EEV algorithm is more effective than the LCP and the W-MSR for the swarm navigation and consensus in agent on large obstacles / environmental features. Furthermore, in presence of malicious / hacked agents in a swarm it is very difficult to reach consensus. The result show that proposed algorithm can handle the Byzantine agents effectively.

The reservoir of the M oilfield in Tarim Basin is an unconventional fracture-cave carbonate rock, encompassing various reservoir types like fractured, fracture-cave, and cave, exhibiting significant spatial heterogeneity. Despite the limited pore space in fractures, they can serve as seepage pathways, complicating the connectivity between reservoirs. High-precision fracture prediction is critical for the effective development of these reservoirs. The conventional post-stack seismic attribute-based approach, however, is limited in its ability to detect small-scale fractures. To address this limitation, a novel pre-stack fracture prediction method based on azimuthal Young's modulus ellipse fitting is introduced. Offset Vector Tile (OVT) gather is utilized, providing comprehensive information on azimuth and offset. Through analyzing azimuthal anisotropies, such as travel time, amplitude, and elastic parameters, smaller-scale fractures can be detected. First, the original OVT gather data are preprocessed to enhance the signal-to-noise ratio. Subsequently, these data are partially stacked based on different azimuths and offsets. On this basis, pre-stack inversion is carried out for each azimuth to obtain the Young's modulus in each direction, and, finally, the ellipse fitting algorithm is used to obtain the orientation of the long axis of the ellipse and the ellipticity, indicating the fracture orientation and density, respectively. The fracture prediction results are consistent with the geological structural features and fault development patterns of the block, demonstrating good agreement with the imaging logging interpretations. Furthermore, the results align with the production dynamics observed in the production wells within the block. This alignment confirms the high accuracy of the method and underscores its significance in providing a robust foundation for reservoir connectivity studies and well deployment decisions in this region. [ABSTRACT FROM AUTHOR]

It is currently a challenge to accurately predict the deformation and fracture behavior of metal parts in automobile crashes. Many studies have shown that the deformation and fracture behavior of materials are significantly affected by the stress state during automobile crashes with complex stress state characteristics. In order to further promote the application of die-cast magnesium alloys in automobiles, it is particularly important to study the material deformation and fracture behavior of die-cast magnesium alloys. In this paper, the mechanical properties of the AM60B die-cast magnesium alloy sheet under four stress states (shear, tension, R10 notch tension, and cupping) were designed and tested. Based on the von Mises isotropic constitutive model and Swift weighted Hockett–Sherby hardening model, the plastic constitutive model of die-cast magnesium alloy was established. Based on the plastic model and the fracture model (JC, MMC, and DIEM) considering the influence of three stress states, the deformation and fracture behavior of the AM60B die-cast magnesium alloy front-end members in three-point bending were predicted by experiments and finite element simulation. The experimental results show that the deformation mode and loading–displacement curve trend of the AM60B die-cast magnesium alloy front members are the same, the crack initiation point and crack initiation time are the same, and the crack shape is similar. The results show that the complex stress state constitutive model parameters and the DIEM fracture model obtained in this paper can accurately predict the deformation and fracture failure behavior of the AM60B die-cast magnesium alloy sheet. [ABSTRACT FROM AUTHOR]

Introduction: Chronic Ankle Instability (CAI) is a musculoskeletal condition that evolves from acute ankle sprains, and its underlying mechanisms have yet to reach a consensus. Mounting evidence suggests that neuroplastic changes in the brain following ankle injuries play a pivotal role in the development of CAI. Balance deficits are a significant risk factor associated with CAI, yet there is a scarcity of evidence regarding the sensorimotor cortical plasticity related to balance control in affected individuals. This study aims to evaluate the differences in cortical activity and balance abilities between patients with CAI and uninjured individuals during a single-leg stance, as well as the correlation between these factors, in order to elucidate the neurophysiological alterations in balance control among patients with CAI., Methods: The study enrolled 24 patients with CAI and 24 uninjured participants. During single-leg stance, cortical activity was measured using a functional near-infrared spectroscopy (fNIRS) system, which included assessments of the pre-motor cortex (PMC), supplementary motor area (SMA), primary motor cortex (M1), and primary somatosensory cortex (S1). Concurrently, balance parameters were tested utilizing a three-dimensional force platform., Results: Independent sample t- tests revealed that, compared with the uninjured individuals, the patients with CAI exhibited a significant increase in the changes of oxyhemoglobin concentration (ΔHbO) during single-leg stance within the left S1 at Channel 5 ( t = 2.101, p = 0.041, Cohen's d = 0.607), left M1 at Channel 6 ( t = 2.363, p = 0.022, Cohen's d = 0.682), right M1 at Channel 15 ( t = 2.273, p = 0.029, Cohen's d = 0.656), and right PMC/SMA at Channel 11 ( t = 2.467, p = 0.018, Cohen's d = 0.712). Additionally, the center of pressure root mean square (COP-RMS) in the mediolateral (ML) direction was significantly greater ( t = 2.630, p = 0.012, Cohen's d = 0.759) in the patients with CAI. Furthermore, a moderate positive correlation was found between ML direction COP-RMS and ΔHbO2 in the M1 ( r = 0.436; p = 0.033) and PMC/SMA ( r = 0.488, p = 0.016), as well as between anteroposterior (AP) direction COP-RMS and ΔHbO in the M1 ( r = 0.483, p = 0.017)., Conclusion: Patients with CAI demonstrate increased cortical activation in the bilateral M1, ipsilateral PMC/SMA, and contralateral S1. This suggests that patients with CAI may require additional brain resources to maintain balance during single-leg stance, representing a compensatory mechanism to uphold task performance amidst diminished lateral balance ability in the ankle joint., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Liu, Yang, Song, Yang and Chen.)