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This study aims to provide a comprehensive overview of pro-environmental behavior (PEB) research within higher education institutions (HEIs), highlighting current trends and future challenges.

Using 198 journal articles from the Web of Science, the study conducts co-citation, bibliographic coupling and co-word analyses to map influential publications and forecast trends.

The co-citation analysis revealed three distinct clusters: value-driven environmental behavior, intention-based environmental behavior and green organizational practices and employee PEB. The bibliographic coupling and the co-word analysis revealed more nuanced clusters, holistically identifying academic activities towards PEB. The authors conclude that more strategic and PEB-oriented HEI’s actions are crucial due to the social responsibility of the universities for sustainable development.

This paper provides valuable insights into the expanding area of PEB research and climate leadership empowerment within HEIs. The practical implications of this research are significant for HEIs. It guides the creation of effective policies and interventions to foster sustainable behavior and reduce environmental harm. The study shows the development of educational programs and campaigns promoting sustainable practices among individuals and communities, emphasizing the role of HEIs in cultivating a sustainability-conscious generation.

This research is the first to use bibliometric analysis to provide insight into the landscape and forecast the future of customer experience research in the banking sector.

We used bibliographic coupling and co-word analysis to delineate the existing knowledge structure after reviewing 338 articles from the Web of Science database.

The bibliographic coupling analysis revealed five key clusters: customer engagement and experience in digital banking; customer experience and service management; customer experience and market resilience; digital transformation and customer experience; and digital technology and customer experience—each representing a significant strand of current research. In addition, the co-word analysis revealed four emerging themes: customer experience through AI and blockchain, digital evolution in banking, experience-driven ecosystems for customer satisfaction, and trust-based holistic banking experience.

These findings not only sketch an overview of the current research domain but also hint at emerging areas ideal for scholarly investigation. While highlighting the industry’s rapid adaptation to technological advances, this study calls for more integrative research to unravel the complexities of customer experience in the evolving digital banking ecosystem.

This review presents a novel state-of-the-art analysis of customer banking experience research by employing a science mapping via bibliometric analysis to unveil the knowledge and temporal structure.

The purpose of this paper is to experimentally and numerically study the transient hydraulic impact and overall performance during startup accelerating process of mixed-flow pump.

In this study, the impeller rotor vibration characteristics during the starting period under the action of fluid–structure interaction was investigated, which is based on the bidirectional synchronization cooperative solving method for the flow field and impeller structural response of the mixed-flow pump. Experimental transient external characteristic and the transient dimensionless head results were compared with the numerical calculation results, to validate the accuracy of numerical calculation method. Besides, the deformation and dynamic stress distribution of the blade under the stable rotating speed and accelerating condition were studied based on the bidirectional fluid–structure interaction.

The results show that the combined action of complex hydrodynamic environment and impeller centrifugal force in the startup accelerating process makes the deformation and dynamic stress of blade have the rising trend of reciprocating oscillation. At the end of acceleration, the stress and strain appear as transient peak values and the transient effect is nonignorable. The starting acceleration has a great impact on the deformation and dynamic stress of blade, and the maximum deformation near the rim of impeller outlet edge increases 5 per cent above the stable condition. The maximum stress value increases by about 68.7 per cent more than the steady-state condition at the impeller outlet edge near the hub. The quick change of rotating speed makes the vibration problem around the blade tip area more serious, and then it takes the excessive stress concentration and destruction at the blade root.

This study provides basis and reference for the safety operation of pumps during starting period

The purpose of this paper is to develop a fully discrete local discontinuous Galerkin (LDG) finite element method for solving a time‐fractional advection‐diffusion equation.

The method is based on a finite difference scheme in time and local discontinuous Galerkin methods in space.

By choosing the numerical fluxes carefully the authors' scheme is proved to be unconditionally stable and gets L² error estimates of O(h^k+1+(Δt)²+(Δt)^α/2h^k+(1/2)). Finally Numerical examples are performed to illustrate the effectiveness and the accuracy of the method.

The proposed method is different from the traditional LDG method, which discretes an equation in spatial direction and couples an ordinary differential equation (ODE) solver, such as Runger‐Kutta method. This fully discrete scheme is based on a finite difference method in time and local discontinuous Galerkin methods in space. Numerical examples prove that the authors' method is very effective. The present paper is the authors' first step towards an effective approach based on the discontinuous Galerkin method for the solution of fractional‐order problems.

With the rapid advancement in the automotive industry, the friction coefficient (FC), wear rate (WR) and weight loss (WL) have emerged as crucial parameters to measure the performance of automotive braking systems, so the FC, WR and WL of friction material are predicted and analyzed in this work, with an aim of achieving accurate prediction of friction material properties.

Genetic algorithm support vector machine (GA-SVM) model is obtained by applying GA to optimize the SVM in this work, thus establishing a prediction model for friction material properties and achieving the predictive and comparative analysis of friction material properties. The process parameters are analyzed by using response surface methodology (RSM) and GA-RSM to determine them for optimal friction performance.

The results indicate that the GA-SVM prediction model has the smallest error for FC, WR and WL, showing that it owns excellent prediction accuracy. The predicted values obtained by response surface analysis are closed to those of GA-SVM model, providing further evidence of the validity and the rationality of the established prediction model.

The relevant results can serve as a valuable theoretical foundation for the preparation of friction material in engineering practice.

When solving the cogging torque of complex electromagnetic structures, such as consequent pole hybrid excitation synchronous (CPHES) machine, traditional methods have a huge computational complexity. The notable feature of CPHES machine is the symmetric range of field-strengthening and field-weakening, but this type of machine is destined to be equipped with a complex electromagnetic structure. The purpose of this paper is to propose a hybrid analysis method to quickly and accurately solve the cogging torque of complex 3D electromagnetic structure, which is applicable to CPHES machine with different magnetic pole shapings.

In this paper, a hybrid method for calculating the cogging torque of CPHES machine is proposed, which considers three commonly used pole shapings. Firstly, through magnetic field analysis, the complex 3D finite element analysis (FEA) is simplified to 2D field computing. Secondly, the discretization method is used to obtain the distribution of permeance and permeance differential along the circumference of the air-gap, taking into account the effect of slots. Finally, the cogging torque of the whole motor is obtained by using the idea of modular calculation and the symmetry of the rotor structure.

This method is applicable to different pole shapings. The experimental results show that the proposed method is consistent with 3D FEA and experimental measured results, and the average calculation time is reduced from 8 h to 4 min.

This paper proposes a new concept for calculating cogging torque, which is a hybrid calculation of dimension reduction and discretization modules. Based on magnetic field analysis, the 3D problem is simplified into a 2D issue, reducing computational complexity. Based on the symmetry of the machine structure, a modeling method for discretized analytical models is proposed to calculate the cogging torque of the machine.

This paper applies a volume-price probability wave differential equation to propose a conceptual theory and has innovative behavioral interpretations of intraday dynamic market equilibrium price, in which traders' momentum, reversal and interactive behaviors play roles.

The authors select intraday cumulative trading volume distribution over price as revealed preferences. An equilibrium price is a price at which the corresponding cumulative trading volume achieves the maximum value. Based on the existence of the equilibrium in social finance, the authors propose a testable interacting traders' preference hypothesis without imposing the invariance criterion of rational choices. Interactively coherent preferences signify the choices subject to interactive invariance over price.

The authors find that interactive trading choices generate a constant frequency over price and intraday dynamic market equilibrium in a tug-of-war between momentum and reversal traders. The authors explain the market equilibrium through interactive, momentum and reversal traders. The intelligent interactive trading preferences are coherent and account for local dynamic market equilibrium, holistic dynamic market disequilibrium and the nonlinear and non-monotone V-shaped probability of selling over profit (BH curves).

The authors will understand investors' behaviors and dynamic markets through more empirical execution in the future, suggesting a unified theory available in social finance.

The authors can apply the subjects' intelligent behaviors to artificial intelligence (AI), deep learning and financial technology.

Understanding the behavior of interacting individuals or units will help social risk management beyond the frontiers of the financial market, such as governance in an organization, social violence in a country and COVID-19 pandemics worldwide.

It uncovers subjects' intelligent interactively trading behaviors.

This paper aims to study the transient flow characteristics in a mixed-flow pump during the start-up period.

In this study, numerical calculation of the internal flow field in a mixed-flow pump using the sliding mesh method was carried out. The regulation of the pressure, streamline and the relative speed during the start-up period was analyzed.

The trend of the simulated head is consistent with the experimental results, and the calculated head is around 0.3 m higher than the experimental head when the rotation speed reached the stable stage, indicating that the numerical method for the start-up process simulation of the mixed-flow pump has a high accuracy. At the beginning, the velocity inside the impeller changes little along the radius direction and the flow rate increases slowly during the start-up process. As the rotation speed reached the stable stage, the flow inside the impeller became steady, the vortex reduced and transient effects disappeared gradually.

The study results have significant value for revealing the internal unsteady flow characteristics of the mixed-flow pump and providing the reference for the design optimization of the mixed-flow pump.

To modify a conventional evacuated tube, an improved asymmetric U-type evacuated tube (AUET) is proposed. This study aims to investigate the thermal and hydrodynamic performances of a modified tube and determine the optimal structural form.

Based on the variation of fluid proprieties with temperature, the formulated numerical model was validated and then deployed to investigate the natural circulation in the evacuated tubes. A dimensionless number was proposed to quantify the stratification effect. The influence of the degree of asymmetry of U-type evacuated tubes on the flow patterns, mass flow rate, temperature distribution, thermal stratification and energy conversion efficiency was studied.

When the degree of asymmetry is large, a higher velocity and better thermal stratification are achieved, thereby avoiding stagnant water at the bottom of the tubes simultaneously. Compared with the conventional evacuated tube, the improved evacuated tube exhibited a higher thermal efficiency.

The originally proposed AUET was proven to have better performance in avoiding stagnant water, reducing fluid mixing and improving the heat transfer efficiency.

The main goal of the physical education (PE) environment is that each individual trained should achieve self-fulfillment with the large group of students involved with their own efforts. Deep learning is applying transferrable knowledge in new situations to help the students master in tough circumstances. In PE training, injuries occur when working together as a team. Safety measures are taken immediately as an emergency response to reduce the potential risk in students by providing first aid. To provide safety measures for the injured student immediately, the environment is monitored in real-time using a GPS.

Theory of Humanities Education (ToHE) infers that it has less collection of theories and a wide range of applications than the state-of-the-art systems. ToHE allows students to think creatively and play a vital role in one’s health which is a critical aspect in PE. The ToHE theory focuses on two main concepts, i.e. by using a methodological approach to analyse and deep learning to solve the problem. PE motivates college students to follow a healthy and active lifestyle.

The proposed system is deployed in real time for monitoring the student’s performance and provides an emergency response with an accuracy rate of 90%.

The deep learning offers solutions to the injuries by using the deep convolutional neural network to provide interpretability of the consequence by training it with various injuries that occur in the playground and inappropriate use of sports equipment. A case study provided in this paper outlines an emergency response scenario to an injured student in sports training.