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The purpose of this study is to adapt the incompressible smoothed particle hydrodynamics (ISPH) method with artificial intelligence to manage the physical problem of double diffusion inside a porous L-shaped cavity including two fins.

The ISPH method solves the nondimensional governing equations of a physical model. The ISPH simulations are attained at different Frank–Kamenetskii number, Darcy number, coupled Soret/Dufour numbers, coupled Cattaneo–Christov heat/mass fluxes, thermal radiation parameter and nanoparticle parameter. An artificial neural network (ANN) is developed using a total of 243 data sets. The data set is optimized as 171 of the data sets were used for training the model, 36 for validation and 36 for the testing phase. The network model was trained using the Levenberg–Marquardt training algorithm.

The resulting simulations show how thermal radiation declines the temperature distribution and changes the contour of a heat capacity ratio. The temperature distribution is improved, and the velocity field is decreased by 36.77% when the coupled heat Cattaneo–Christov heat/mass fluxes are increased from 0 to 0.8. The temperature distribution is supported, and the concentration distribution is declined by an increase in Soret–Dufour numbers. A rise in Soret–Dufour numbers corresponds to a decreasing velocity field. The Frank–Kamenetskii number is useful for enhancing the velocity field and temperature distribution. A reduction in Darcy number causes a high porous struggle, which reduces nanofluid velocity and improves temperature and concentration distribution. An increase in nanoparticle concentration causes a high fluid suspension viscosity, which reduces the suspension’s velocity. With the help of the ANN, the obtained model accurately predicts the values of the Nusselt and Sherwood numbers.

A novel integration between the ISPH method and the ANN is adapted to handle the heat and mass transfer within a new L-shaped geometry with fins in the presence of several physical effects.

The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

The Chinese housing market has witnessed rapid growth during the past decade and the significance of housing price forecasting has undoubtedly elevated, becoming an important issue to investors and policymakers. This study aims to examine neural networks (NNs) for office property price index forecasting from 10 major Chinese cities for July 2005–April 2021.

The authors aim at building simple and accurate NNs to contribute to pure technical forecasts of the Chinese office property market. To facilitate the analysis, the authors explore different model settings over algorithms, delays, hidden neurons and data-spitting ratios.

The authors reach a simple NN with three delays and three hidden neurons, which leads to stable performance of about 1.45% average relative root mean square error across the 10 cities for the training, validation and testing phases.

The results could be used on a standalone basis or combined with fundamental forecasts to form perspectives of office property price trends and conduct policy analysis.

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.

A shared cognitive schema is the fundamental source of tacit understanding within a team. This study aims to address how such a shared cognitive schema emerges and evolves in an interdisciplinary research team.

This study uses an exploratory single case study to analyze the emergence and evolution of a shared cognitive schema in an interdisciplinary research team systematically. The authors spent more than two years collecting data from the IAM team via semistructured interviews, archival data and observation. Subsequently, a framework for the resulting mechanism model was developed by analyzing the data using a three-step process.

This study shows that as the interdisciplinary research team develops, the shared cognitive schema passes through three stages: overlapping cognitive schema, complementary cognitive schema and synergetic cognitive schema. The mechanisms of overlap, complement and synergy play important roles. The convergent roles of partner-based recruiting, knowledge categorization and following the existing institution facilitate the overlapping of knowledge structures. Complementary cognitive schema sharing is facilitated by interdisciplinary member selection, knowledge stock expansion and the effects of accomplished mentors. The synergetic behaviors of group voice, interactive cognition and adaptive learning facilitate synergetic cognitive schema sharing.

This study is the first to discuss the emergence and evolution of a shared cognitive schema at the microlevel of knowledge structure and belief structure. It offers a new theoretical perspective on the development rules of scientific research teams and provides practical enlightenment regarding the establishment and operation of interdisciplinary research teams.

This paper aims to realize an in-situ quality inspection system rapidly for new injection molding (IM) tasks via transfer learning (TL) approach and automation technology.

The proposed in-situ quality inspection system consists of an injection machine, USB camera, programmable logic controller and personal computer, interconnected via OPC or USB communication interfaces. This configuration enables seamless automation of the IM process, real-time quality inspection and automated decision-making. In addition, a MobileNet-based deep learning (DL) model is proposed for quality inspection of injection parts, fine-tuned using the TL approach.

Using the TL approach, the MobileNet-based DL model demonstrates exceptional performance, achieving validation accuracy of 99.1% with the utilization of merely 50 images per category. Its detection speed and accuracy surpass those of DenseNet121-based, VGG16-based, ResNet50-based and Xception-based convolutional neural networks. Further evaluation using a random data set of 120 images, as assessed through the confusion matrix, attests to an accuracy rate of 96.67%.

The proposed MobileNet-based DL model achieves higher accuracy with less resource consumption using the TL approach. It is integrated with automation technologies to build the in-situ quality inspection system of injection parts, which improves the cost-efficiency by facilitating the acquisition and labeling of task-specific images, enabling automatic defect detection and decision-making online, thus holding profound significance for the IM industry and its pursuit of enhanced quality inspection measures.

This study looks to understand the opposing forces that would influence continuance intention. This is significant as users will take into account the positive and negative use experiences in determining their continuance intention. Therefore, this study looks to highlight the opposing forces of users’ continuance intention by proposing the Expectation-Confirmation-Resistance Model (ECRM).

Through an online survey, 411 responses were obtained from mobile payment users. Subsequently, a hybrid approach comprised of the Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) was utilized to analyze the data.

The results revealed that all hypotheses proposed in the ECRM are supported. More precisely, the facilitating and inhibiting variables were found to significantly affect continuance intention. In addition, the ECRM was revealed to possess superior explanatory power over the original model in predicting continuance intention.

This study successfully developed and validated the ECRM which captures both facilitators and inhibitors of continuance intention. Besides, the relevance and significance of users’ innovative resistance to continuance intention have been highlighted. Following this, effective business and research strategies can be developed by taking into account the opposing forces that affect users’ continuance intention.

Polyurethane concrete (PUC), as a new type of steel bridge deck paving material, the bond-slip pattern at the interface with the steel plate is not yet clear. In this study, the mechanical properties of the PUC and steel plate interface under the coupled action of temperature, normal force and tangential force were explored through shear tests and numerical simulations. An analytical model for bond-slip at the PUC/steel plate interface and a predictive model for the shear strength of the PUC/steel plate interface were developed.

The new shear test device designed in this paper overcomes the defect that the traditional oblique shear test cannot test the interface shear performance under the condition of fixed normal force. The universal testing machine (UTM) test machine was used to adjust the test temperature conditions. Combined with the results of the bond-slip test, the finite element simulation of the interface is completed by using the COHENSIVE unit to analyze the local stress distribution characteristics of the interface. The use of variance-based uncertainty analysis guaranteed the validity of the simulation.

The shear strength (τ_f) at the PUC-plate interface was negatively correlated with temperature while it was positively correlated with normal stress. The effect of temperature on the shear properties was more significant than that of normal stress. The slip corresponding to the maximum shear (D₁) positively correlates with both temperature and normal stress. The interfacial shear ductility improves with increasing temperature.

Based on the PUC bond-slip measured curves, the relationship between bond stress and slip at different stages was analyzed, and the bond-slip analytical model at different stages was established; the model was defined by key parameters such as elastic ultimate shear stress τ₀, peak stress τ_f and interface fracture energy G_f.

For some years now, Cameroon has seen a significant increase in its electricity demand, and this need is bound to grow within the next few years owing to the current economic growth and the ambitious projects underway. Therefore, one of the state's priorities is the mastery of electricity demand. In order to get there, it would be helpful to have reliable forecasting tools. This study proposes a novel version of the discrete grey multivariate convolution model (ODGMC(1,N)).

Specifically, a linear corrective term is added to its structure, parameterisation is done in a way that is consistent to the modelling procedure and the cumulated forecasting function of ODGMC(1,N) is obtained through an iterative technique.

Results show that ODGMC(1,N) is more stable and can extract the relationships between the system's input variables. To demonstrate and validate the superiority of ODGMC(1,N), a practical example drawn from the projection of electricity demand in Cameroon till 2030 is used. The findings reveal that the proposed model has a higher prediction precision, with 1.74% mean absolute percentage error and 132.16 root mean square error.

These interesting results are due to (1) the stability of ODGMC(1,N) resulting from a good adequacy between parameters estimation and their implementation, (2) the addition of a term that takes into account the linear impact of time t on the model's performance and (3) the removal of irrelevant information from input data by wavelet transform filtration. Thus, the suggested ODGMC is a robust predictive and monitoring tool for tracking the evolution of electricity needs.

The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China, which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.

The paper describes the key technologies that are involved in the development of the system, such as P-wave identification and earthquake early-warning, multi-source seismic information fusion and earthquake emergency treatment technologies. The paper also presents the test results of the system, which show that it has complete functions and its major performance indicators meet the design requirements.

The study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety. The key technical indicators of the system have excellent performance: The first report time of the P-wave is less than three seconds. From the first arrival of P-wave to the beginning of train braking, the total delay of onboard emergency treatment is 3.63 seconds under 95% probability. The average total delay for power failures triggered by substations is 3.3 seconds.

The paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions. It also contributes to the earthquake prevention and disaster reduction efforts.