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Objective To effectively reflect the real-time impact of urban rail transit station staff behavior on evacuation, and to solve the problem that the adoption of predefined static path method for evacuation route planning in VR (virtual reality) environment cannot reveal the dynamic response mechanism of passenger evacuation to station staff immediate behavior, a VR simulation modeling method is proposed to model the intervention of station staff behavior on passenger emergency evacuation path choice. Method The environment modeling method that depicts station physical space as an abstract space recognizable by path algorithm is introduced. The passenger intelligent agent model that can perceive external information and make decisions autonomously is defined, and the exit selection rules based on A* algorithm are formulated. On this basis, a dynamic path planning model is constructed by considering the dynamic change of station staff behavior to the environment and integrating the real-time evacuation route length, the evacuation process time, and the density of pedestrians. A VR simulation system is developed to carry out simulation and analysis using a large-scale comprehensive hub station as example. Result & Conclusion Simulation analysis results show that at the beginning of evacuation simulation, the passenger intelligent agent can formulate global routes based on exist selection. During the process, it can autonomously perform real-time local path planning based on dynamic obstacle state changes caused by station staff, and maintain high responsiveness as the number of passengers in the scenario increases. It indicates that the proposed method can effectively and efficiently capture the dynamic changes in virtual environment and reflect them in the process of passenger evacuation, thereby providing technical support for conducting emergency evacuation VR experiments.

In the development of silicon photonics, the continued downsizing of photonic integrated circuits will further increase the integration density, which augments the functionality of photonic chips. Compared with the traditional design method, inverse design presents a novel approach for achieving compact photonic devices. However, achieving compact, reconfigurable photonic devices with the inverse design that employs the traditional modulation method exemplified by the thermo-optic effect poses a significant challenge due to the weak modulation capability. Low-loss phase change materials (PCMs) exemplified by Sb2Se3 are a promising candidate for solving this problem benefiting from their high refractive index contrast. In this work, we first developed a robust inverse design method to realize reconfigurable silicon and phase-change materials hybrid photonic devices including mode converter and optical switch. The mode converter exhibits a broadband operation of >100 nm. The optical switch shows an extinction ratio of >25 dB and a multilevel switching of 41 (>5 bits) by simply changing the crystallinity of Sb2Se3. Here, we experimentally demonstrated a Sb2Se3/Si hybrid integrated optical switch for the first time, wherein routing can be switched by the phase transition of the whole Sb2Se3. Our work provides an effective solution for the design of photonic devices that is insensitive to fabrication errors, thereby paving the way for high integration density in future photonic chips.

With the development of multimedia technology, more and more colleges and universities apply network multimedia technology in art teaching to achieve the improvement of the art teaching effect. After analyzing the application scenarios of D2D, D2D technology is combined with social networks to create a teaching classroom where teachers and students can collaborate. A communication link is established in the network classroom to facilitate the sharing of art education content. Combine the network terminal caching system to improve the cache hit rate by caching multiple identical contents. Integrate network multimedia technology into art education and examine its contribution to education. The results show that the caching effect of multimedia technology is mainly reflected in four aspects: completeness, clarity, smoothness, and speed. When the number of caches reaches 12, the caching speed of the teaching content fluctuates in the range of 0.75-0.8. When the number of caches is less than 12, the caching fluency of the content is in the range of 0.85-0.9. This study provides a new direction to promote the reform of art education, which is conducive to improving the teaching effectiveness of art education and cultivating art talents.

To address the challenges of slow convergence speed, poor convergence precision, and getting stuck in local optima for unmanned aerial vehicle (UAV) three-dimensional path planning, this paper proposes a path planning method based on an Improved Human Evolution Optimization Algorithm (IHEOA). First, a mathematical model is used to construct a three-dimensional terrain environment, and a multi-constraint path cost model is established, framing path planning as a multidimensional function optimization problem. Second, recognizing the sensitivity of population diversity to Logistic Chaotic Mapping in a traditional Human Evolution Optimization Algorithm (HEOA), an opposition-based learning strategy is employed to uniformly initialize the population distribution, thereby enhancing the algorithm's global optimization capability. Additionally, a guidance factor strategy is introduced into the leader role during the development stage, providing clear directionality for the search process, which increases the probability of selecting optimal paths and accelerates the convergence speed. Furthermore, in the loser update strategy, an adaptive t-distribution perturbation strategy is utilized for its small mutation amplitude, which enhances the local search capability and robustness of the algorithm. Evaluations using 12 standard test functions demonstrate that these improvement strategies effectively enhance convergence precision and algorithm stability, with the IHEOA, which integrates multiple strategies, performing particularly well. Experimental comparative research on three different terrain environments and five traditional algorithms shows that the IHEOA not only exhibits excellent performance in terms of convergence speed and precision but also generates superior paths while demonstrating exceptional global optimization capability and robustness in complex environments. These results validate the significant advantages of the proposed improved algorithm in effectively addressing UAV path planning challenges. [ABSTRACT FROM AUTHOR]

With the increasing number of space debris, the demand for telescopes to observe space debris is also constantly increasing. The telescope observation scheduling problem requires algorithms to schedule telescopes to maximize observation value within the visible time constraints of space debris, especially when dealing with large-scale problems. This paper proposes a practical heuristic algorithm to solve the telescope observation of space debris scheduling problem. In order to accelerate the solving speed of algorithms on large-scale problems, this paper combines the characteristics of the problem and partitions the large-scale problem into multiple sub-problems according to the observation time. In each sub-problem, a coding method based on the priority of the target going into the queue is proposed in combination with the actual observation data, and a decoding method matching the coding method is designed. In the solution process for each sub-problem, an adaptive variable neighborhood search is used to solve the space debris observation plan. When solving all sub-problems is completed, the observation plans obtained on all sub-problems are combined to obtain the observation plan of the original problem. [ABSTRACT FROM AUTHOR]

Evolutionary multitasking optimization (EMTO) is currently one of the hottest research topics that aims to utilize the correlation between tasks to optimize them simultaneously. Although many evolutionary multitask algorithms (EMTAs) based on traditional differential evolution (DE) and the genetic algorithm (GA) have been proposed, there are relatively few EMTAs based on particle swarm optimization (PSO). Compared with DE and GA, PSO has a faster convergence speed, especially during the later state of the evolutionary process. Therefore, this paper proposes a multitask level-based learning swarm optimizer (MTLLSO). In MTLLSO, multiple populations are maintained and each population corresponds to the optimization of one task separately using LLSO, leveraging high-level individuals with better fitness to guide the evolution of low-level individuals with worse fitness. When information transfer occurs, high-level individuals from a source population are used to guide the evolution of low-level individuals in the target population to facilitate the effectiveness of knowledge transfer. In this way, MTLLSO can obtain the satisfying balance between self-evolution and knowledge transfer. We have illustrated the effectiveness of MTLLSO on the CEC2017 benchmark, where MTLLSO significantly outperformed other compared algorithms in most problems. [ABSTRACT FROM AUTHOR]

The Nash social welfare (NSW) problem is relevant not only to the economic domain but also extends its applicability to the field of computer science. However, maximizing Nash social welfare is an APX-hard problem. In this study, we propose two approaches to enhance the maximization of Nash social welfare. First, a general greedy algorithm (GA) capable of addressing the Nash social welfare problem for both agents with identical and differing valuations was presented. It is proven that the proposed algorithm aligns with the previous greedy algorithm when all agents possess identical valuations. Second, an innovative method for solving the Nash social welfare problems using evolutionary algorithms was developed. This approach integrates the Estimation of Distribution Algorithms (EDAs) with neighborhood search techniques to improve the maximization process of Nash social welfare. Finally, the proposed algorithms were implemented across a range of instances with the objective of maximizing Nash social welfare. The experimental results indicate that the approximation solutions derived from the Estimation of Distribution Algorithm outperform those obtained via the greedy algorithm. [ABSTRACT FROM AUTHOR]

The field of evolutionary multitasking optimization (EMTO) has been a highly anticipated research topic in recent years. EMTO aims to utilize evolutionary algorithms to concurrently solve complex problems involving multiple tasks. Despite considerable advancements in this field, numerous evolutionary multitasking algorithms continue to use a single evolutionary search operator (ESO) throughout the evolution process. This strategy struggles to completely adapt to different tasks, consequently hindering the algorithm's performance. To overcome this challenge, this paper proposes multitasking evolutionary algorithms via an adaptive bi-operator strategy (BOMTEA). BOMTEA adopts a bi-operator strategy and adaptively controls the selection probability of each ESO according to its performance, which can determine the most suitable ESO for various tasks. In an experiment, BOMTEA showed outstanding results on two well-known multitasking benchmark tests, CEC17 and CEC22, and significantly outperformed other comparative algorithms. [ABSTRACT FROM AUTHOR]

The set packing problem is a core NP-complete combinatorial optimization problem which aims to find the maximum collection of disjoint sets from a given collection of sets, S, over a ground set, U. Evolutionary algorithms (EAs) have been widely used as general-purpose global optimization methods and have shown promising performance for the set packing problem. While most previous studies are mainly based on experimentation, there is little theoretical investigation available in this area. In this study, we analyze the approximation performance of simplified versions of EAs, specifically the (1+1) EA, for the set packing problem from a theoretical perspective. Our analysis demonstrates that the (1+1) EA can provide an approximation guarantee in solving the k-set packing problem. Additionally, we construct a problem instance and prove that the (1+1) EA beats the local search algorithm on this specific instance. This proof reveals that evolutionary algorithms can have theoretical guarantees for solving NP-hard optimization problems. [ABSTRACT FROM AUTHOR]

In this study, polyimide (PI) and polysulfonamide (PSA) were used as base materials, and polyethylene glycol (PEG) was added to successfully prepare PI/PSA/PEG nanofiber membranes through electrospinning technology. Subsequently, water etching was performed on the membranes, utilizing the water solubility of PEG to form the rough wrinkled structure, further enhancing the surface hydrophobicity. The experimental results showed that under the conditions of a spinning voltage of 10 kV, PI/PSA mass fraction of 15 wt.%, and PEG-to-PI/PSA mass ratio of 1/3, the obtained fiber membranes exhibit a uniform morphology (an average diameter of 0.73 µm) and excellent hydrophobicity (the initial water contact angle (WCA) reaching 130.4°). After PEG water etching, the surface of the PI/PSA/PEG hydrophobic membranes formed the rough wrinkled structure, which not only improved their mechanical properties but also further enhanced their hydrophobicity (the initial WCA increasing to 137.9°). Hence, fiber membranes are expected to have broad application prospects in fields such as waterproofing and moisture permeability. [ABSTRACT FROM AUTHOR]

This study provided a comprehensive understanding of the source process of the 1819 M 7.7 Kachchh Indian earthquake using physics‐based dynamic rupture modeling and strong ground motion simulations. We successfully simulated the spontaneous dynamic rupture along a curved non‐planar fault using the 3‐D curved‐grid finite‐difference method (CGFDM). The estimated earthquake magnitude is around 7.6, consistent with previous estimations. Our simulations accurately replicated macroscopic rupture patterns and surface deformation, showing agreement with observed data along the Allah Bund fault (ABF) with a maximum displacement ∼5.5 m at the Earth's surface. The maximum modeled coseismic slip on the fault was approximately 7.5 m. Notably, the ABF exhibited characteristics of a weak barrier (leaky barrier) at the bending part, allowing the rupture to propagate further. Despite limitations in surface deformation calculations, the modeled values aligned with the trend of surface fault slip, with a slight deviation in the epicenter toward the east compared to earlier studies. We observed a homogeneous principal stress oriented N25°E, consistent with the present day Indian plate motion. The estimated horizontal peak ground velocities (PGVh) and the maximum value of Intensity X+ aligns well with observations. Furthermore, conducting thorough case studies on significant earthquakes and potential seismic scenarios in stable continental regions is crucial. Such studies play a vital role in validating and improving dynamic rupture models. When combined with statistical methods, this research holds great promise for advancing seismic hazard assessments, earthquake engineering, and strategies for disaster management. Plain Language Summary: This paper is centered around the simulation of the dynamic rupture of the 1819 M 7.7 Kachchh earthquake in India. We have successfully replicated the earthquake's behavior using a three‐dimensional simulation method. The study's results demonstrate the significant influence of the local tectonic setting and non‐planar fault structure on earthquake generation and rupture progression. Although slight discrepancies exist between the simulation results and actual observations, the simulations capture significant trends and reproduce macroscopic rupture patterns. The estimated magnitude of the earthquake aligns well with previous studies. The study highlights the role of fault bending and its impact on surface deformation, contributing to a better understanding of seismic hazards and providing insights for seismic hazard assessments and earthquake source characterization. This work is valuable for comprehending earthquake sources, particularly from earthquake perspectives in the SCR region and other areas. Key Points: Dynamic rupture simulations of the 1819 Kachchh M 7.7 earthquake replicate co‐seismic fault slip, enhance our knowledge of the earthquake sourceWeak barrier characteristics observed at the bending part of the fault enabled further rupturing, influenced by SH and the nucleation pointContributes to understanding earthquake hazards, enhancing seismic assessment, engineering, and disaster management strategies [ABSTRACT FROM AUTHOR]