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The accurate prediction of radio wave propagation along highways plays an important role in the design of high‐speed highway communication systems. The open‐confined mixed space is a connection zone between the open space and confined tunnel along the highway. A hybrid propagation model is presented based on ray‐tracing (RT) and waveguide theory, for the efficient modelling of open‐confined mixed space. The proposed model employs RT to predict radio waves emitted by sources with arbitrary radiation patterns in open space sections, and utilises the waveguide theory approach to accelerate the prediction process in the confined tunnel sections. Both two‐ray and six‐ray RT models are included to take into account the suburban and urban environments. Numerical results are compared with simulated results generated by RT as well as experimental measurements. The validity and usefulness of the proposed model are demonstrated in an actual open‐confined mixed space environment. [ABSTRACT FROM AUTHOR]

Timing Engineering Change Order (ECO) is time-consuming in IC design, requiring multiple rounds of timing analysis. Compared to traditional methods for accelerating timing analysis, which focus on a specific design, timing ECO requires higher accuracy and generalization because the design changes considerably after ECO. Additionally, there are challenges with slow acquisition of data for large designs and insufficient data for small designs. To solve these problems, we propose TSTL-GNN, a novel approach using two-stage transfer learning based on graph structures. Significantly, considering that delay calculation relies on transition time, we divide our model into two stages: the first stage predicts transition time, and the second stage predicts delay. Moreover, we employ transfer learning to transfer the model's parameters and features from the first stage to the second due to the similar calculation formula for delay and transition time. Experiments show that our method has good accuracy on open-source and industrial applications with an average R 2 s c o r e / M A E of 0.9952/13.36, and performs well with data-deficient designs. Compared to previous work, our model reduce prediction errors by 37.1 ps on the modified paths, which are changed by 24.27% on average after ECO. The stable R 2 score also confirms the generalization of our model. In terms of time cost, our model achieved results for path delays consuming up to 80 times less time compared to open-source tool. [ABSTRACT FROM AUTHOR]

In this paper, an inverse problem of determining a source in a time-fractional diffusion equation is investigated. A Fourier extension scheme is used to approximate the solution to avoid the impact on smoothness caused by directly using singular system eigenfunctions for approximation. A modified implicit iteration method is proposed as a regularization technique to stabilize the solution process. The convergence rates are derived when a discrepancy principle serves as the principle for choosing the regularization parameters. Numerical tests are provided to further verify the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Urban areas contribute to over 80% of carbon dioxide emissions, and considerable efforts are being undertaken to characterize spatiotemporal variations of CO2 (carbon dioxide) at a city, regional, and national level, aiming at providing pipelines for carbon mission reduction. The complex underlying surface composition of urban areas makes process-based and physiology-based models inadequate for simulating carbon flux in this context. In this study, long short-term memory (LSTM), support vector machine (SVM), random forest (RF), and artificial neural network (ANN) were employed to develop and investigate their viability in estimating carbon flux at the ecosystem level. All the data used in our study were derived from the long-term chronosequence observations collected from the flux towers within urban complex underlying surface, along with meteorological reanalysis datasets. To assess the generalization ability of these models, the following statistical metrics were utilized: coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). Our analysis revealed that the RF model performed the best in simulating carbon flux over long time series, with the highest R2 values reaching up to 0.852, and exhibiting the smallest RMSE and MAE values at 0.293 μmol·m−2·s−1 and 0.157 μmol·m−2·s−1. As a result, the RF model was chosen for simulating carbon flux at spatial scale and assessing the impact of urban impervious surfaces in the simulation. The results showed that the RF model performs well in simulating carbon flux at the spatial scale. The input of impervious surface area index can improve the performance of the RF model in simulating carbon flux, with R2 values of 84.46% (with the impervious surface area index in) and 83.74% (without the impervious surface area index in). Furthermore, the carbon flux in Fengxian District, Shanghai, exhibited significant spatial heterogeneity: the CO2 flux in the western part of Fengxian District was less than in the eastern part, and the CO2 flux gradually increased from the west to the east. In addition, we creatively introduced the diurnal impervious surface area index based on the Kljun model, and clarified the influence of impervious surface on the spatiotemporal simulation of CO2 flux over the complex urban underlying surface. Based on these findings, we conclude that the RF models can be effectively applied for estimating carbon flux on the complex underlying urban surface. The results of our study reduce the uncertainty in modeling carbon cycling in terrestrial ecosystems, and make the variety of models for the carbon cycling of terrestrial ecosystems more diverse. [ABSTRACT FROM AUTHOR]

Climate change mitigation is the prime priority of businesses in the modern world, making them inclined to opt for clean energy acceptance. From the end-user perspective, acceptance of novel technologies is inevitably vital in promoting such technologies, and certain factors could play an influential role in shaping the acceptance behavior of businesses in embracing those technologies. Against this background, the purpose of this study is to determine the factors that influence the acceptance of solar panel systems (SPS) by small and medium businesses (SMBs) in Pakistan. A questionnaire survey was conducted in seven districts of Pakistan’s Punjab province from December 2021 to February 2022. The data were collected using a Five-point Likert scale. Based on the questionnaire survey, the variance-based structural equation model technique is utilized. The outcomes are: 1) size of the business, ease of using SPS, and credibility of SPS are the top three driving forces. 2) The cost of SPS, and the pressure of competitors are the bottlenecks to the acceptance of SPS, while the absence of technical skills is a neutral factor. 3) According to the effect size and path coefficients, the cost of SPS, and the size of the business are found to be the most significant factors, while the absence of technical skills is found to have less significance. As it is found that economic incentives majorly affect the acceptance of SPS thus, it is concluded that lowering the cost of SPS will increase its acceptance. Based on empirical results, it is suggested to promote public awareness about the environmentally-friendly nature of solar power systems to improve its acceptability at the individual as well as firm levels.