Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

Economic load dispatch (ELD) is crucial for power system operation, balancing electricity supply and demand while minimising costs. The goal of ELD is to minimise the overall cost of generating electricity and meet operational constraints. These constraints make the system complex. The complexity increases with the dimensionality of the problem. To tackle the complexities of the problem, a hybrid optimisation technique is implemented. This paper introduces an optimised opposition slime mould algorithm to solve this problem. The proposed algorithm uses slime reproductive behaviour and an opposition learning strategy to avoid exploitation and balance exploration. The accessibility of the proposed algorithm is calculated by the fuel cost and the compact solution. The performance and effectiveness are verified through benchmark functions, the CEC 14 functions and real-world load dispatch problems. The applied algorithm has yielded better results on ELD problems, benchmark functions and CEC14 functions than popular algorithms.

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

The modal analysis of fuel pipelines was done by experimental modal vibration measurement. The physical and calculated models of the fuel manifold with various branches were established to examine the effects of branch numbers on manifold system structural parameters. The results show that the natural frequency and frequency calculated by the finite element method are in good agreement with the experimental results. The fuel manifold systems' frequencies increase stepwise. The first third-order frequencies are highest for the fuel manifold with 12 branches, and the later fourth-order ones are highest for the 8 branches manifold system. Due to the symmetrical structure of the pipe, the fuel manifold system has repeated modes. The first and third modes are characterised as bending vibrations. The second mode is a torsional vibration. The fuel manifold resonance frequency is in the low-frequency region; the radial frequency response value is lower than the axial one.

In this paper, continuum damage mechanics (CDM) and the 3D Hashin criterion are implemented to simulate the formation and evolution of the intra-laminar damage.The cohesive layer was simulated by the cohesive zone model (CZM).The finite element method (FEM) results are consistent with the experimental results, which validate the effectiveness of the FEM.Then, different types of adhesively bonded single-lap joints (SLJs) are analysed.The results suggest that the 0 and 90 plies can improve the failure loads of SLJs.The 45 plies are beneficial for alleviating the damage to the adhesive layer.The increase in overlap width has less influence on the damage location and damage modes.Matrix damage and delamination, with less fibre damage, are the main damage modes of SLJs with lower adherend thickness.With the thickness of the adherend increasing, matrix damage and delamination take up an increasingly smaller proportion.

Computer-aided diagnosis using deep learning approaches has made tremendous improvements in medical imaging for automatically detecting tumor area, tumor type, and grading of the tumor. These advancements are limited due to the fact that (1) medical images are often less in quantity, leading to overfitting, and (2) significant inter-class similarity and intra-class variation between the images. The main aim of the study is to develop a deep learning base model (Zhang et al., 2018; 2019; Krizhevsky et al., 2012) as a backbone for the automatic grading of glioma tumors. The synergic deep learning (SDL) architecture enables two pre-trained models to learn from each other mutually and allows them to perform better than vanilla pre-trained models. Our study uses T1-weighted sagittal tumor magnetic resonance imaging (MRI) slices from the REMBRANDT (Scarpace et al., 2019) dataset. The proposed architecture achieves an accuracy of 98.36%, showing that the model achieves excellent performance metrics on a small dataset.