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In this study, we propose a tetrahedral mesh generation and adaptive refinement method for multi-chamber, multi-facet, multiscale and surface-solid mesh coupling with extremely thin layers, solving the two challenges of mesh generation and refinement in current electromagnetic simulation models.

Utilizing innovative topology transformation techniques, high-precision intersection judgment algorithms and highly reliable boundary recovery algorithms to reduce the number of Steiner locking points. The feasible space for the reposition of Steiner points is determined by using the linear programming. During mesh refinement, an edge-split method based on geometric center and boundary facets node size is devised. Solving the problem of difficult insertion of nodes in narrow geometric spaces, capable of filtering the longest and boundary edges of tetrahedrons, refining the mesh layer by layer through multiple iterations, and achieving collaborative optimization of surface and tetrahedral mesh. Simultaneously, utilizing a surface-facet preserving mesh topology optimization algorithm to improve the fit degree between the mesh and geometry.

Initial mesh generation for electromagnetic models, compared to commercial software, the method proposed in this paper has a higher pass rate and better mesh quality. For the adaptive refinement performance of high-frequency computing, this method can generate an average of 50% fewer meshes compared to commercial software while meeting simulation accuracy.

This paper proposes a complete set of mesh generation and adaptive refinement theories and methods designed for the structural characteristics of electromagnetic simulation models, which meet the needs of real-world industrial applications.

This paper aimed to synthesise some new selenium-containing pyrazole, thiazole and thiophene dyestuffs and investigate their application in dyeing polyester fabrics.

4,4′-Diselanediyl-bis-(2-methoxyaniline) was diazotized by acetic acid/sodium nitrite and coupled with active methylene components such as malononitrile, ethyl cyanoacetate and ethyl acetoacetate, and their corresponding hydrazono products were cyclised with hydrazine or phenyl hydrazine to obtain a series of arylazo-pyrazole dyestuffs. Furthermore, diazo-coupling reactions of the diazotized 4,4′-diselanediyl-bis-(2-methoxyaniline) were developed to obtain a new series of 2-amino-5-arylazo-thiazole- and 4-aryazo-3-hydroxy-2-substituted thiophene dyestuffs.

The structure of the synthesised dyestuffs was established and confirmed based on their elemental analysis and spectral data (infrared, proton nuclear magnetic resonance and mass spectroscopies). The synthesised selenium-containing dyestuffs were applied as disperse dyes on polyester fabrics by high temperature-pressure technique. The dyeing characteristics of these dyestuffs were evaluated, and they were found to exhibit good fastness properties.

A series of symmetrical diselenide dyestuffs containing pyrazole, thiazole or thiophene coupler components were synthesised. The authors studied their application for dyeing polyester fabrics; they are superior in terms of preparation, yield, purity and fastness properties.

The purpose of this paper is to predict the response and perforation of fibre metal laminates (FMLs) subjected to impact by projectiles at different velocities.

A finite element (FE) model is constructed in which recently proposed dynamic constitutive models for fibre reinforced plastic (FRP) laminates and metals are used. Moreover, a recently developed dynamic cohesive element constitutive model is also used to simulate the debonding between FRP laminates and metal sheets. The FE model is first validated against the test data for glass laminate aluminum reinforced epoxy (GLARE) both under dropped object loading and ballistic impact, then used to perform a parametric study on the influence of projectile nose shape on the perforation of FMLs.

It is found that the present model predicts well the response and perforation of GLARE subjected to impact loading in terms of load-time history, load-displacement curve, residual velocity and failure pattern. It is also found that projectile nose shape has a considerable effect on the perforation of GLARE FMLs and that the ballistic limit is the highest for a flat-ended projectile whilst for a conical-nosed missile the resistance to perforation is the least.

Recently developed constitutive models for FRPs and metals, together with cohesive element model which considers strain rate effect, are used in the FE model to predict the behaviour of FMLs struck by projectiles in a wider range of impact velocities; the present model is advantageous over such existing models as Johnson-Cook (JC) + Chang-Chang and JC (+BW) + MAT162 in terms of failure pattern though they produce similar results for residual velocity.

When recommending products to consumers, it is important to be able to accurately predict how consumers will rate them. However, existing collaborative filtering models are difficult to achieve a balance between rating prediction accuracy and complexity. Therefore, the purpose of this paper is to propose an accurate and effective model to predict users’ ratings of products for the accurate recommendation of products to users.

First, we introduce an attention mechanism that dynamically assigns weights to user preferences, highlighting key interaction information and enhancing the model’s understanding of user behavior. Second, a fold embedding strategy is employed to segment user interaction data, increasing the information density of each subset while reducing the complexity of the attention mechanism. Finally, a masking strategy is integrated to mitigate overfitting by concealing portions of user-item interactions, thereby improving the model’s generalization ability.

The experimental results demonstrate that the proposed model significantly minimizes prediction error across five real-world datasets. On average, the evaluation metrics root mean square error (RMSE) and mean absolute error (MAE) are reduced by 9.11 and 13.3%, respectively. Additionally, the Friedman test results confirm that these improvements are statistically significant. Consequently, the proposed model more accurately captures the intrinsic correlation between users and products, leading to a substantial reduction in prediction error.

We propose a novel collaborative filtering model to learn the user-item interaction matrix effectively. Additionally, we introduce a fold embedding strategy to reduce the computational resource consumption of the attention mechanism. Finally, we implement a masking strategy to encourage the model to focus on key features and patterns, thereby mitigating overfitting.

The purpose of this paper is to numerically study the influence of wall conduction on the heat transfer of supercritical n-decane in the active regenerative cooling channels.

A horizontally placed rectangular pipe with a solid zone and another one without a solid zone were used. A drastic variation of thermo-physical properties was emphatically addressed. After the verification of mesh and turbulence models comparing with the experimental results, a mesh number of 4.5 M and the low Reynolds number SST k-ω turbulence model were chosen. The solution of the governing equations and the acquisition of the numerical results were executed by the commercial software FLUENT 2020 R1.

The numerical results indicate that there is a heat transfer deterioration (HTD) potential for the upper wall, lower wall and sidewall with the decrease of mass flux. Due to wall conduction, the distribution of the fluid temperature at spanwise-normal planes becomes uniform and this feature also takes advantage of the relatively uniform transverse velocity. For the streamwise-normal planes, the low fluid temperature appears close to the upper wall at the region near the sidewall and vice versa for the region near the centre. Undoubtedly, the secondary flow at the cross-section plays a crucial role in this process and the relatively cool mainstream is affected by the vortices.

This study warns that the wall conduction must be considered in the practical design and thermal optimization due to the sensibility of thermo-physical properties to the heat flux. The secondary flow caused by the buoyancy force (gravity) plays a significant role in the supercritical heat transfer and mixed convection heat transfer should be further studied.

Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of the existing research in the industry, this paper proposes a case-adaptation optimization algorithm to support the effective application of tacit knowledge resources.

The attribute simplification algorithm based on the forward search strategy in the neighborhood decision information system is implemented to realize the vertical dimensionality reduction of the case base, and the fuzzy C-mean (FCM) clustering algorithm based on the simulated annealing genetic algorithm (SAGA) is implemented to compress the case base horizontally with multiple decision classes. Then, the subspace K-nearest neighbors (KNN) algorithm is used to induce the decision rules for the set of adapted cases to complete the optimization of the adaptation model.

The findings suggest the rapid enrichment of data, information and tacit knowledge in the field of practice has led to low efficiency and low utilization of knowledge dissemination, and this algorithm can effectively alleviate the problems of users falling into “knowledge disorientation” in the era of the knowledge economy.

This study provides a model with case knowledge that meets users’ needs, thereby effectively improving the application of the tacit knowledge in the explicit case base and the problem-solving efficiency of knowledge users.

The adaptation model can serve as a stable and efficient prediction model to make predictions for the effects of the many logistics and e-commerce enterprises' plans.

This study designs a multi-decision class case-adaptation optimization study based on forward attribute selection strategy-neighborhood rough sets (FASS-NRS) and simulated annealing genetic algorithm-fuzzy C-means (SAGA-FCM) for tacit knowledgeable exogenous cases. By effectively organizing and adjusting tacit knowledge resources, knowledge service organizations can maintain their competitive advantages. The algorithm models established in this study develop theoretical directions for a multi-decision class case-adaptation optimization study of tacit knowledge.

Bolted joints are the most common type of mechanical connections, and improving the anti-loosening performance of bolts for the reliable performance of mechanical and building structures is highly significant.

Because of the lack of sufficient theoretical basis for the evaluation and design of anti-loosening bolts, a quantitative evaluation model exhibiting the following two evaluation criteria for anti-loosening bolts is introduced: bolt rotation angular acceleration criterion and critical transverse load criterion. Based on the relationship among bolt tension, transverse load and bolt rotation angular acceleration, a critical transverse load calculation model is put forward, and the mechanism by which the critical transverse load increases with the increase of bolt tension is revealed.

Based on the above model, a new type of anti-loosening bolt is designed, which generates additional bolt tension when the transverse load increases, and then improves the critical transverse load of the bolt. The effectiveness of the new type of anti-loosening bolt is verified by theoretical calculations and experiments.

The proposed model and method set a preliminary theoretical foundation for the evaluation of bolt anti-loosening performance and the design of a new anti-loosening bolt.

The purpose of this study is to optimize and improve conventional welding using EMF assisted technology. Current industrial production has put forward higher requirements for welding technology, so the optimization and improvement of traditional welding methods become urgent needs.

External magnetic field assisted welding is an emerging technology in recent years, acting in a non-contact manner on the welding. The action of electromagnetic forces on the arc plasma leads to significant changes in the arc behavior, which affects the droplet transfer and molten pool formation and ultimately improve the weld seam formation and joint quality.

In this paper, different types of external magnetic fields are analyzed and summarized, which mainly include external transverse magnetic field, external longitudinal magnetic field and external cusp magnetic field. The research progress of welding behavior under the effect of external magnetic field is described, including the effect of external magnetic field on arc morphology, droplet transfer and weld seam formation law.

However, due to the extremely complex physical processes under the action of the external magnetic field, the mechanism of physical fields such as heat, force and electromagnetism in the welding has not been thoroughly analyzed, in-depth theoretical and numerical studies become urgent.

In recent years, to solve the contradiction between energy supply and demand, the Chinese Government has vigorously promoted shale gas development. With the rapid development of the shale gas industry, the environmental impact problems have become increasingly serious. Therefore, it is of great significance to carry out a comprehensive environmental impact assessment of shale gas development. This study aims to provide a theoretical basis for enterprises to make development decisions on shale gas projects by constructing a model of comprehensive environmental impact assessment for shale gas development.

In this paper, the comprehensive environmental impact factors of shale gas development are analyzed from the two aspects of the natural environment and macro environment, and the index system of comprehensive environmental impact assessment for shale gas development including 7 secondary indicators and 24 tertiary indicators is constructed. Owing to the fact that qualitative indicators are difficult to quantify in the evaluation process, the method of intuitionistic fuzzy analytic hierarchy process (IFAHP) is adopted for evaluation. This method (IFAHP) can delicately describe the hesitancy degree of the decision-makers in the process of assigning a weight to the indicators, and make the weight assignment of each index more accurate. Furthermore, this method overcomes the shortcomings of the conventional methods, such as the complexity of calculation and the large amount of calculation.

The evaluation model is applied to a shale gas platform drilling project in Southwest China. Based on the ratings from 13 experts, the comprehensive environmental impact assessment grade of this project is good, indicating that the shale gas development project is feasible. The result is basically in line with the actual situation.

Based on the consideration of the natural environmental impacts of shale gas development, this paper also has considered the macro environmental impact of shale gas development, and has established the index system of comprehensive environmental impact assessment for shale gas development from the two aspects of the natural environment and macro environment. To overcome such difficulties as incomplete evaluation by decision-makers, cumbersome calculation process and a large amount of calculation, this paper has adopted the method of IFAHP to evaluate and has established a comprehensive environmental impact assessment model for shale gas development based on IFAHP.

This paper aims to anticipate the possible development direction of WAAM. For large-scale and complex components, the material loss and cycle time of wire arc additive manufacturing (WAAM) are lower than those of conventional manufacturing. However, the high-precision WAAM currently requires longer cycle times for correcting dimensional errors. Therefore, new technologies need to be developed to achieve high-precision and high-efficiency WAAM.

This paper analyses the innovations in high-precision WAAM in the past five years from a mechanistic point of view.

Controlling heat to improve precision is an effective method. Methods of heat control include reducing the amount of heat entering the deposited interlayer or transferring the accumulated heat out of the interlayer in time. Based on this, an effective and highly precise WAAM is achievable in combination with multi-scale sensors and a complete expert system.

Therefore, a development direction for intelligent WAAM is proposed. Using the optimised process parameters based on machine learning, adjusting the parameters according to the sensors’ in-process feedback, achieving heat control and high precision manufacturing.