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Phenolic epoxy vinyl ester resin (PEVER) is an advanced resin matrix, which has excellent heat resistance, electrical insulation. However, the brittleness and poor toughness of its curing product limited its application, so this paper aims to modify the PEVER with hyperbranched polyimide (HBPI), so as to enhance the toughness, heat resistance and dielectric properties of PEVER.

Hexamethylene diisocyanate trimer was used as the central reactant. Methyl tetrahydrophthalic anhydride was used as the branching unit, stannous octoate was used as the catalyst and hydroquinone was prepared as the inhibitor. Then, the hyperbranched structure of HBPI was characterized by Fourier transform infrared spectrometer and ¹³C-NMR. Next, PEVER was mixed with different contents of HBPI, and then the authors tested its curing product.

It is found that with the addition of HBPI, the free volume of the system was increased and the content of polar groups was decreased in each unit space, so the dielectric constant (ε) and the dielectric loss (tanδ) were decreased. In addition, PEVER could be well toughened by HBPI and the thermal stability of PEVER was improved.

HBPI has excellent heat resistance. The addition of hyperbranched polymer increases the free volume of the system so it can slow down the transfer of stress and its nearly circular structure can absorb the impact energy from all directions. Moreover, an appropriate amount of free volume can decrease the dielectric constant of PEVER by reducing the content of polar groups.

Epoxy resin (EP) is a thermosetting resin commonly characterized by its inherent brittleness, which limits its widespread application. To overcome this limitation, a novel flexible chain-blocking hyperbranched polyester (HBP) was conceptualized and synthesized to enhance the toughness and dielectric properties of EP.

Using P-toluene sulfonic acid (p-TSA) as the catalyst and Bis(hydroxymethyl)propionic acid (DMPA) as the branching unit with pentaerythritol (PER) as the core, an experimental synthesis was conducted. Subsequently, n-hexanoic acid was introduced separately to produce hyperbranched polyester with n-hexanoic acid capped structures. Microstructural, mechanical, insulating and dielectric analyses of the composite were performed to determine the optimal proportion of HBP.

Recent research has demonstrated that the flexible segments within hyperbranched polyester create an interpenetrating network structure with the molecular chains of epoxy resin, thereby effectively augmenting the toughness of the epoxy resin. Additionally, HBP has reduced the ε and tgδ values of the epoxy-anhydride cured product by decreasing the number of polar groups per unit volume of EP through the introduction of free volumes.

Currently, HBP serves as an innovative toughening strategy and modifier for epoxy resin. The toughening mechanism involves the generation of free volume by HBP, providing space for EP molecules to maneuver under load. Additionally, the free volume contributes to a reduction in the dielectric constant of EP by diminishing the polarizable group content. Simultaneously, the incorporation of HBP features flexible chains grafted onto the epoxy resin.

Epoxy resin (EP) is a kind of thermosetting resin, and its application is usually limited by poor toughness. In this case, a type of new flexible chain blocking hyperbranched polyester (HBP) was designed and synthesized. The purpose of this study is to enhance the toughness and dielectric properties of EP.

P-toluene sulfonic acid was used as the catalyst, with dimethy propionic acid as the branch unit and pentaerythritol as the core in the experiment. Then, n-hexanoic acid and n-caprylic acid were, respectively, put to gain HBP with a n-hexanoic acid and n-caprylic acid capped structure. The microstructure, mechanical properties, insulation properties and dielectric properties of the composite were characterized for the purpose of finding the appropriate proportion of HBP.

HBP enhanced the toughness of epoxy-cured products by interpenetrating polymer network structure between the flexible chain of HBP and the EP molecular chain. Meanwhile, HBP reduced the ε and tgδ of the epoxy anhydride-cured product by reducing the number of polar groups per unit volume of the EP through free volumes.

Yet EP is a kind of thermosetting resin, which is widely used in coating, aerospace, electronics, polymer composites and military fields, but it is usually limited by poor toughness. In a word, it is an urgent priority to develop new EP with better toughness and mechanical properties.

At present, HBP has been applied as a new kind of toughening strategy and as a modifier for EP. According to the toughening mechanism of HBP modified EP, the free volume of HBP creates a space for the EP molecule to move around when loaded. Moreover, the free volume could cause the dielectric constant of EP to diminish by reducing the content of polarizable groups. Meanwhile, the addition of HBP with flexible chains grafted to the EP could develop an interpenetrating network structure, thus further enhancing the toughness of EP

This paper aims to develop a new method of fuselage drag optimization that can obtain results faster than the conventional methods based on full computational fluid dynamics (CFD) calculations and can be used to improve the efficiency of preliminary design.

An efficient method for helicopter fuselage shape optimization based on surrogate-based optimization is presented. Two numerical simulation methods are applied in different stages of optimization according to their relative advantages. The fast panel method is used to calculate the sample data to save calculation time for a large number of sample points. The initial solution is obtained by combining the Kriging surrogate model and the multi-island genetic algorithm. Then, the accuracy of the solution is determined by using the infill criteria based on CFD corrections. A parametric model of the fuselage is established by several characteristic sections and guiding curves.

It is demonstrated that this method can greatly reduce the calculation time while ensuring a high accuracy in the XH-59A helicopter example. The drag coefficient of the optimized fuselage is reduced by 13.3%. Because of the use of different calculation methods for samples, this novel method reduces the total calculation time by almost fourfold compared with full CFD calculations.

To the best of the authors’ knowledge, this is the first study to provide a novel method of fuselage drag optimization by combining different numerical simulation methods. Some suggestions on fuselage shape optimization are given for the XH-59A example.

This study aims to satisfy the thermal management of gallium nitride (GaN) high-electron mobility transistor (HEMT) devices, microchannel-cooling is designed and optimized in this work.

A numerical simulation is performed to analyze the thermal and flow characteristics of microchannels in combination with computational fluid dynamics (CFD) and multi-objective evolutionary algorithm (MOEA) is used to optimize the microchannels parameters. The design variables include width and number of microchannels, and the optimization objectives are to minimize total thermal resistance and pressure drop under constant volumetric flow rate.

In optimization process, a decrease in pressure drop contributes to increase of thermal resistance leading to high junction temperature and vice versa. And the Pareto-optimal front, which is a trade-off curve between optimization objectives, is obtained by MOEA method. Finally, K-means clustering algorithm is carried out on Pareto-optimal front, and three representative points are proposed to verify the accuracy of the model.

Each design variable on the effect of two objectives and distribution of temperature is researched. The relationship between minimum thermal resistance and pressure drop is provided which can give some fundamental direction for microchannels design in GaN HEMT devices cooling.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

This chapter reports a generative AI (GenAI)-supported short-term IELTS writing course for Chinese university students. A six-step human–AI hybrid argumentative writing framework was proposed to guide the course and enhance students’ prompting skills, including the steps of claim, structure, arguments and counterarguments, credibility and reliability, extend arguments, and revisions. An I-A-I (Initial-Assisted-Independent) approach was adopted to facilitate students’ critical thinking skills and the independent analysis of the content generated by GenAI. Ten students participated in the course, submitted their pre- and post-writing samples, and accepted the interviews. T-tests on participants’ writing samples showed that they performed significantly better in IELTS academic writing, proposing main claims, and organising the structure of the essay. The interview data further revealed that the course helped participants structure their essay, provide evidence for arguments, and revise their essay independently. The results underscored two crucial components of AI literacy in using ChatGPT for academic writing, including the prompt skills to ensure the synergistic interactions with GenAI, and the critical thinking skills to critically assess the responses of ChatGPT. In summary, the chapter provides theoretical contributions and pedagogical implications on the application of GenAI for university students to enhance their academic writing abilities and equip them with the literacy of using GenAI for academic writing.

This study focuses on the CFD numerical simulation and analysis of the vortex stacking problem at the top of the impeller of a high-speed fuel pump, mainly using LCS and entropy production theory to visualize the vortex at the top of the impeller as well as quantitatively analyzing the energy loss caused by the vortex at the top of the impeller. By combining the two methods, the two are well verified with each other that the stacking problem of the vortex at the top of the impeller and the location of the energy loss caused by the vortex are consistent with the vortex location. Such a method can reveal the problem of vortex buildup at the top of the lobe well, and provide a novel guidance idea for improving the performance of high-speed fuel pumps.

Based on CFD numerical simulation and analysis, this study mainly uses LCS and entropy production theory to visualize the top vortex of the impeller. Through the combination of the two methods, the accumulation problem of the top vortex of the impeller and the location of the energy loss caused by the vortex can be well revealed.

(1) The CFD numerical simulation analysis of the high-speed fuel pump is carried out, and the test is conducted to verify the numerical simulation results. The inlet and outlet pressure difference? P is used as the validation index, and the error analysis shows that the error between numerical simulation and test results is within 10%, which meets our requirements. Therefore, we carry out the next analysis with the help of CFD numerical simulation. By analyzing the full working condition simulation, its inlet and outlet differential pressure? P and efficiency? Are evaluated. It is found that its differential pressure decreases with the flow rate and its efficiency reaches its maximum at Qv = 9.87 L/s with a maximum efficiency of 78.32%. (2) We used the LCS in the analysis of vortices at the top of the impeller blades of a high-speed fuel pump. One of the metrics used to describe the LCS in fluid dynamics is the FTLE. The high FTLE region represents the region with the highest and fastest particle trajectory stretching velocity in the fluid flow. We performed a cross-sectional analysis of the FTLE field on the different height surfaces of the impeller on 25% Plane, 50% Plane, and 75% Plane, respectively. And a quarter turn of the rotor rotation was analyzed as a cycle divided into 8 moments. It is found that on 25% Plane, the vortex at the top of the lobe is not obvious, but there are high FTLE values on the shroud surface. On 50% Plane, the lobe top vortex is relatively obvious and the number of vortices is three. The vortex pattern remains stable with the rotating motion of the rotor. At 75% Plane, the lobe top vortex is more visible and its number of vortices increases to about 5 and the vortex morphology is relatively stable. The FTLE ridges visualize the vortex profile. This is a good guide for fluid dynamics analysis. (3) At the same time, we use the entropy production theory to quantitatively analyze the energy loss, and define the entropy production rate Ep. Through the entropy production analysis of the impeller shroud surface and the suction surface of the pressure surface of the blades at eight moments, we find that the areas of high energy loss are mainly concentrated in the leading and trailing edges of the blades as well as in the shroud surface close to the leading edge of the blades, and the value of the entropy production rate is up to 106 W/m3/K. The areas of high energy loss in the leading edge of the blades as well as the trailing edge show a curved arc, and the energy loss is decreasing as it moves away from the shroud surface and closer to the hub surface. The high energy loss areas at the leading and trailing edges of the blades are curved, and the energy loss decreases as they move away from the shroud surface and closer to the hub surface. The energy loss at the pressure surface of the blade is relatively small, about 5 × 105 W/m3/K, which is mainly concentrated near the leading edge of the blade near the shroud surface and the trailing edge of the blade near the hub surface. Such energy loss corresponds to the vortex LCS at the top of the impeller, and the two mirror each other.

This study focuses on the CFD numerical simulation and analysis of the vortex stacking problem at the top of the impeller of a high-speed fuel pump, mainly using LCS and entropy production theory to visualize the vortex at the top of the impeller as well as quantitatively analyzing the energy loss caused by the vortex at the top of the impeller. By combining the two methods, the two are well verified with each other that the stacking problem of the vortex at the top of the impeller and the location of the energy loss caused by the vortex are consistent with the vortex location. Such a method can reveal the problem of vortex buildup at the top of the lobe well, and provide a novel guidance idea for improving the performance of high-speed fuel pumps.

This study conducts a comparative analysis of the diversification effects of China's national carbon market (CEA) and the EU ETS Phase IV (EUA) within major commodity markets.

The study employs the TVP-VAR extension of the spillover index framework to scrutinize the information spillovers among the energy, agriculture, metal, and carbon markets. Subsequently, the study explores practical applications of these findings, emphasizing how investors can harness insights from information spillovers to refine their investment strategies.

First, the CEA provide ample opportunities for portfolio diversification between the energy, agriculture, and metal markets, a desirable feature that the EUA does not possess. Second, a portfolio comprising exclusively energy and carbon assets often exhibits the highest Sharpe ratio. Nevertheless, the inclusion of agricultural and metal commodities in a carbon-oriented portfolio may potentially compromise its performance. Finally, our results underscore the pronounced advantage of minimum spillover portfolios; particularly those that designed minimize net pairwise volatility spillover, in the context of China's national carbon market.

This study addresses the previously unexplored intersection of information spillovers and portfolio diversification in major commodity markets, with an emphasis on the role of CEA.

The purpose of this study is to use the corrected stress field theory to derive the shear capacity of geopolymer concrete beams (GPC) and consider the shear-span ratio as a major factor affecting the shear capacity. This research aims to provide guidance for studying the shear capacity of GPC and to observe how the failure modes of beams change with the variation of the shear-span ratio, thereby discovering underlying patterns.

Three test beams with shear span ratios of 1.5, 2.0 and 2.5 are investigated in this paper. For GPC beams with shear-span ratios of 1.5, 2.0 and 2.5, ultimate capacities are 337kN, 235kN and 195kN, respectively. Transitioning from 1.5 to 2.0 results in a 30% decrease in capacity, a reduction of 102kN. Moving from 2.0 to 2.5 sees a 17% decrease, with a loss of 40KN in capacity. A shear capacity formula, derived from modified compression field theory and considering concrete shear strength, stirrups and aggregate interlocking force, was validated through finite element modeling. Additionally, models with shear ratios of 1 and 3 were created to observe crack propagation patterns.

For GPC beams with shear-span ratios of 1.5, 2.0 and 2.5, ultimate capacities of 337KN, 235KN and 195KN are achieved, respectively. A reduction in capacity of 102KN occurs when transitioning from 1.5 to 2.0 and a decrease of 40KN is observed when moving from 2.0 to 2.5. The average test-to-theory ratio, at 1.015 with a variance of 0.001, demonstrates strong agreement. ABAQUS models beams with ratios ranging from 1.0 to 3.0, revealing crack trends indicative of reduced crack angles with higher ratios. The failure mode observed in the models aligns with experimental results.

This article provides a reference for the shear bearing capacity formula of geopolymer reinforced concrete (GRC) beams, addressing the limited research in this area. Additionally, an exponential model incorporating the shear-span ratio as a variable was employed to calculate the shear capacity, based on previous studies. Moreover, the analysis of shear capacity results integrated literature from prior research. By fitting previous experimental data to the proposed formula, the accuracy of this study's derived formula was further validated, with theoretical values aligning well with experimental results. Additionally, guidance is offered for utilizing ABAQUS in simulating the failure process of GRC beams.