Enhancing the resolution of Magnetic Resonance Imaging (MRI) through super-resolution (SR) reconstruction is crucial for boosting diagnostic precision. However, current SR methods primarily rely on single LR images or multi-contrast features, limiting detail restoration. Inspired by video frame interpolation, this work utilizes the spatiotemporal correlations between adjacent slices to reformulate the SR task of anisotropic 3D-MRI image into the generation of new high-resolution (HR) slices between adjacent 2D slices. The generated SR slices are subsequently combined with the HR adjacent slices to create a new HR 3D-MRI image. We propose a innovative network architecture termed DGWMSR, comprising a backbone network and a feature supplement module (FSM). The backbone's core innovations include the displacement former block (DFB) module, which independently extracts structural and displacement features, and the mask-displacement vector network (MDVNet) which combines with Warp mechanism to facilitate edge pixel detailing. The DFB integrates the inter-slice attention (ISA) mechanism into the Transformer, effectively minimizing the mutual interference between the two types of features and mitigating volume effects during reconstruction. Additionally, the FSM module combines self-attention with feed-forward neural network, which emphasizes critical details derived from the backbone architecture. Experimental results demonstrate the DGWMSR network outperforms current MRI SR methods on Kirby21, ANVIL-adult, and MSSEG datasets.

Accurate medical image segmentation is crucial for clinical applications but often faces inherent challenges such as ambiguous lesion boundaries, complex internal textures, and noise interference. Existing deep learning models, while proficient at capturing global semantic information, have limited capability in perceiving fine-grained structural details. To overcome these limitations, we propose MultiSG-Net, a novel segmentation framework that explicitly constructs and integrates multi-source priors to improve structural detail perception. Specifically, we introduce local entropy as a measure of uncertainty and combine it with Sobel gradients and local standard deviation as representations of structural saliency. The framework comprises three key components: a Combined Feature Extractor (CFE) for integrating and generating structural prior maps to characterize texture complexity, edges, and heterogeneity; a lightweight Structural Prior Encoder (SPE) that efficiently encodes the prior maps into a multi-scale feature pyramid; and a Adaptive Prior-Guided Fusion (APGF) that adaptively incorporates structural priors into semantic features across different encoder stages. Extensive experiments on four public datasets demonstrate that MultiSG-Net outperforms previous methods on multiple metrics, significantly improving boundary delineation and detail preservation.

The slime mold algorithm (SMA) is a metaheuristic optimization algorithm that simulates the foraging behavior of slime molds. Compared to other optimization algorithms, SMA has fewer parameters, faster convergence speed, and stronger optimization capabilities. However, the standard SMA uses two randomly selected individuals to guide the search direction of the population, which results in excessive randomness during the search process. This can lead to the loss of valuable information and waste computational resources. To overcome these limitations, this study proposes an enhanced slime mold algorithm (S2SMA) based on a spiral sensing search mechanism. The main contributions of this study are as follows: Firstly, a fitness–distance balanced oscillation search mechanism is introduced to solve the issue of lack of guidance in the individual oscillatory search phase in the original SMA, thus enhancing the global exploration ability of the algorithm. Secondly, the spiral sensing search mechanism is introduced, reshaping the random redistribution behavior in SMA. This aims to fully utilize the effective information in the existing population, improve search efficiency, and enhance population diversity. Finally, the computational logic of SMA is restructured based on the existing parameters, improving the algorithm’s performance while avoiding additional computational overhead. To validate the effectiveness of the proposed S2SMA, experiments were conducted on 71 test instances from the IEEE CEC2017 and IEEE CEC2021 benchmark sets, as well as three engineering problems. The algorithm was compared with classical algorithms, high‐performance algorithms, and advanced SMA variants. Experimental results show that S2SMA outperforms the classical algorithms, high‐performance algorithms, and other SMA variants in terms of both performance and robustness, demonstrating its potential application in engineering optimization.

With an increasing number of sensor devices connected to industrial systems, the efficient and reliable aggregation of sensor data has become a key topic in Industrial Internet of Things (IIoT). Computing First Networks (CFN) are emerging as a promising technology for aggregating vast quantities of IIoT data. However, existing CFN data collection frameworks are usually centralized, which overly rely on third-party trusted authorities and fail to fully schedule and utilize limited computing resources. More critically, that is prone to trust and security issues. In this paper, considering the heterogeneity and data security in complex industrial scenarios, we propose a blockchain-based and multi-edge CFN collaborative IIoT data hierarchical collection framework (ME-CIDC) to collect massive IIoT data securely and efficiently. In ME-CIDC, a blockchain-driven resource allocation algorithm is proposed for inter-domain CFN, which achieves distributed and efficient task scheduling and data collection by constructing multiple blockchains. A self-incentive mechanism is designed to encourage inter-domain nodes to contribute resources and support the operation of the inter-domain CFN. We also propose an efficient double-layered data aggregation algorithm, which distributes computational tasks across two layers to ensure the efficient collection and aggregation of IIoT data. Extensive simulation and numerical results demonstrate the effectiveness of our proposed scheme.

Beetle Antennae Search algorithm is a kind of intelligent optimization algorithms, which has the advantages of few parameters and simplicity. However, due to its inherent limitations, BAS has poor performance in complex optimization problems. The existing improvements of BAS are mainly based on the utilization of multiple beetles or combining BAS with other algorithms. The present study improves BAS from its origin and keeps the simplicity of the algorithm. First, an adaptive step size reduction method is used to increase the usability of the algorithm, which is based on an accurate factor and curvilinearly reduces the step size; second, the calculated information of fitness functions during each iteration are fully utilized with a contemporary optimal update strategy to promote the optimization processes; third, the theoretical analysis of the multi-directional sensing method is conducted and utilized to further improve the efficiency of the algorithm. Finally, the proposed Enhanced Beetle Antennae Search algorithm is compared with many other algorithms based on unbiased test functions. The test functions are unbiased when their solution space does not contain simple patterns, which may be used to facilitate the searching processes. As a result, EBAS outperformed BAS with at least 1 orders of magnitude difference. The performance of EBAS was even better than several state-of-the-art swarm-based algorithms, such as Slime Mold Algorithm and Grey Wolf Optimization, with similar running times. In addition, a WSN coverage optimization problem is tested to demonstrate the applicability of EBAS on real-world optimizations.

Most of the traditional line loss calculation methods are based on a period of time for line loss calculation, that is, the load current, voltage and other factors are calculated in an average manner over a period of time. In the selection of period, since the variation characteristics of the load itself are not taken into consideration, it lacks scientific basis. In this paper, the time-oriented process method is used to describe the change of load, and using hybrid clustering to divide time period, so the irregular load is divided into linear regular load. Finally, the line loss calculation is performed for each time period, which increases the theoretical accuracy of the line loss calculation and reduces the number of calculation.

Through the statistics of dry core reactor accidents in recent years, the effect of temperature on the encapsulated insulating epoxy resin is an important reason for the safe operation of the encapsulation. However, the existing infrared temperature detecting means cannot detect the state change of the early insulating material in time. In this paper, the member function is determined by using the relationship between the actual engineering background and the distribution of member functions. The concept of fuzzy reliability is introduced in the encapsulation temperature of the reactor, and the temperature distribution function of the encapsulation is determined. The temperature variable T of the seal is related to the membership function of the fuzzy event that the reactor encloses its properties normally, and finally the reliability of the encapsulation for the temperature factor is obtained. The reliability of the encapsulation for temperature is more accurately determined by the method of using fuzzy sets.