This study aims to discover attractors for fractals by using generalized F-contractive iterated function system, which falls within a distinct category of mappings defined on \(S_b\)-metric spaces. In particular, we investigate how these systems, when subjected to specific F-contractive conditions, can lead to the identification of a unique attractor. We achieve a diverse range of outcomes for iterated

Surrogate-assisted evolutionary algorithms (SAEAs) have shown great potential to solve computationally expensive optimization problems (EOPs). Their two key components, i.e., the optimizer and the surrogate model, both need to select solutions to promote further evolution and to update the model, respectively. However, the corresponding selection strategies are designed independently and mainly consider

With the widespread application of large language models (LLMs) in natural language processing (NLP), hallucinations have become a significant impediment to their effective use of LLMs in industry applications. To address this challenge, we integrate existing hallucination detection and mitigation methods into a unified hallucination detection and mitigation framework. The framework consists of four

Drone-assisted ship supply has recently garnered widespread attention for its faster, cheaper, and greener advantages, reshaping shore-to-vessel deliveries and expected to become fundamental to future maritime logistics. Facing challenges like time-dependent locations and coordination, we introduce a novel path planning problem for supply ship-drone delivery, in which drones launch from the supply

Auditing is facilitated by audit knowledge graphs, while the biggest challenge in constructing an audit knowledge graph is entity alignment. Entity alignment involves linking entity pairs with the same real-world identity and aims to integrate heterogeneous knowledge across different knowledge graphs. However, most existing works do not effectively combine both attribute and relation representations

Wireless mesh network (WMN) technology enhances wireless communication coverage and increases end-to-end (E2E) delay. The delay in WMNs is influenced by various mesh topologies, which are shaped by transmission power factors. Due to the dynamic nature of WMNs, conventional offline topology optimization methods are ineffective. This paper presents a reinforcement learning (RL)-based dynamic collaborative

High-performance computing (HPC) systems often suffer from interference caused by multiple applications accessing a shared parallel file system, which can negatively impact compute performance. One solution to this problem is to add new tiers to the HPC storage hierarchy that can absorb I/O bursts and support moving data between tiers based on its hotness. Ad-hoc file systems serve as an intermediate

The neural activity patterns of localized brain regions are crucial for recognizing brain intentions. However, existing electroencephalogram (EEG) decoding models, especially those based on deep learning, predominantly focus on global spatial features, neglecting valuable local information, potentially leading to suboptimal performance. Therefore, this study proposed a dynamic hierarchical convolutional

Many previous works on wearable soft exosuits have primarily focused on assisting human motion, while overlooking safety concerns during movement. This article introduces a novel single-motor, altering bi-directional transfer soft exosuit based on impedance optimization and adaptive neural control, which provides assistance to the lower limbs using Bowden cables. This innovative soft exosuit integrates

Due to their unique structure and powerful capability to handle uncertainty and partial reliability of information, Z-numbers have achieved significant success in various fields. Zadeh previously asserted that a Z-number can be regarded as a summary of probability distributions. Researchers have proposed various methods for determining the underlying probability distributions from a given Z-number

In this article, we address the improved error-constrained control problem for unknown, strongly interconnected time-delay nonlinear systems with input saturation and conflicted output constraints. The further challenge we face is that the presence of discontinuous reference signals poses greater difficulties for control design. To tackle these issues, a mechanism for generating smooth, safe reference

Network slicing allows multiplexing virtualized networks, called slices, over a single physical network infrastructure. Research has extensively focused on the placement of virtual functions and the links that compose each network slice. On the other hand, performance greatly depends on how many resources are allocated to virtual nodes and links, after they are placed. This aspect has been mostly neglected

In smart cities, the Cloud of Health Things (CoHT) enhances service delivery and optimizes task scheduling and allocation. As CoHT systems proliferate and offer a range of services with varying Quality of Service (QoS) demands, servers face the challenge of efficiently distributing limited virtual machines across internet-based applications. This can strain performance, particularly for latency-sensitive

The purpose of this study is to inspect the combined effect of reinforcement shape and packing on the macroscopic behaviour of particulate composites. The introduced micromechanical approach modifies the Morphologically Representative Pattern scheme with the Replacement Mori–Tanaka Model. The statistical volume elements have randomly placed inclusions with a selected shape. Four shapes of inhomogeneities

Finite element analysis of the fractional viscoelastic model of asphalt concrete (AC) is typically performed on small specimens, with algorithms that suffer from high memory consumption and low computational efficiency, limiting their application to large-scale structures. This paper proposes a memory-saving algorithm with variable increment size for fractional viscoelastic models of AC in finite element

Aiming at the influence of factors such as the special optical characteristics of water bodies on the perceptual quality of generated images, this paper proposes the DifSG2-CCL model for reducing the special optical characteristics of water bodies and the DPL-SG2 model for introducing perceptual loss. Combining the ideas of cyclic consistency and style migration, this paper builds the Underwater Cycle

Decomposition is an effective and popular strategy used by evolutionary algorithms to solve multiobjective optimization problems (MOPs). It can reduce the difficulty of directly solving MOPs, increase the diversity of the obtained solutions, and facilitate parallel computing. However, with the increase of the number of decision variables, the performance of multiobjective evolutionary algorithms (MOEAs)

Wireless sensor networks (WSNs) face challenges in achieving robust target coverage and connectivity, particularly when varying priorities for targets are modeled with Q-Coverage and Q-Connectivity constraints. However, existing studies often neglect minimizing the number of nodes under these constraints in 3D environments or focus on sensor-to-sensor connections, which are less suitable for target-oriented

Large Language Models (LLMs) have emerged as powerful tools for various natural language processing tasks such as multi-agent chatbots, but their computational complexity and resource requirements pose significant challenges for real-time chatbot applications. Caching strategies can alleviate these challenges by reducing redundant computations and improving response times. In this paper, we propose

The traditional reconstruction methods for numerical rock models, such as simulated annealing reconstruction method, have disadvantages, such as unclear details of the generated structure and the need of prior functions. Therefore, this paper attempts to introduce GANs-based techniques to reconstruct numerical porous rock models. The introduction of GANs-based techniques can solve the problem of requiring

In this study, the complete hydrostatic tangent stiffness for geometrically nonlinear beam finite element model is introduced. Based on consistent global hydrostatic restoring stiffness analysis, the complete form of hydrostatic tangent stiffness is developed. In addition, a surface integration parameterization method for shear-deformable beams is discussed with classifications of two special types

Path planning poses a complex optimization challenge essential for the safe operation and successful mission execution of unmanned aerial vehicles (UAVs). Developing objectives, constraints, and decision-making processes for three-dimensional path planning involving multiple UAVs presents substantial challenges within the multi-objective optimization community. Traditional modeling approaches primarily

Knowledge tracing aims to predict how learners will perform in future exercises on related concepts and to track changes in their knowledge state. Existing models have not fully considered the physical and mental fatigue that occurs in learners during prolonged learning tasks, which leads to reduced problem-solving ability and affects their learning efficiency and performance. This article proposes

With the rapid development of cloud storage, a growing number of data owners prefer to outsource their large-scale data to the remote cloud data centers, thus effectively avoiding the heavy burden of storing the massive data by themselves in local. Due to the promising market prospect, plenty of companies invest cloud storage and offer data storage services, which equipped with different access speed

When addressing constrained multi-objective optimization problems, the presence of complex constraints often results in a non-connected feasible region, segmenting the Pareto front into multiple discrete segments. This fragmentation can significantly limit population diversity. To tackle this issue, we have designed two mechanisms aimed at preserving population diversity and have developed a constrained

Multi-criteria group decision making (MCGDM) is the important part in decision-making process, which has been used in many industries. Coordinating differing opinions and ultimately reaching group consensus in a group decision-making process has become an important area of research. This paper uses probabilistic uncertain linguistic term sets (PULTSs) to express the uncertainty of evaluation information

Sentiment analysis (SA) is a vital research direction in natural language processing (NLP). Compared with the widely-concerned explicit sentiment analysis, implicit sentiment analysis (ISA) is more challenging and rarely studied due to the lack of sentiment words. However, existing implicit sentiment analysis methods are hard to identify implicit sentiment without the support of commonsense and contextual

Few would argue that maximising the performance of the many flow networks (FNs) that operate for the benefit of our society and the economy is anything but essential. Through seeking to mitigate the risks posed by different asset failure modes, maintenance is critical to minimising disruptions and maximising resilience. Repairable flow network (RFN) optimisation and reliability centred maintenance

Federated Semi-Supervised Learning (FSSL) aims to train models based on federated learning using a small amount of labeled data and a large amount of unlabeled data. The limited labeled data and the issue of non-independent and identically distributed (non-IID) data are the major challenges faced by FSSL. Most of the previous methods use traditional fixed thresholds to filter out high-confidence samples

In the realm of point cloud object detection, classification tasks emphasize extracting common features to enhance generalization, often at the expense of individual-specific features. This limitation becomes particularly evident when handling intricate datasets like KITTI. Traditional models struggle to adequately capture individual-specific features, resulting in a scattered distribution of samples

Digital medical imaging, particularly pathology images, is essential for cancer diagnosis but faces challenges in direct model training due to its super-resolution nature. Although weakly supervised learning has reduced the need for manual annotations, many multiple instance learning (MIL) methods struggle to effectively capture crucial spatial relationships in histopathological images. Existing methods