In this paper, we investigate code-integrated reasoning (CIR), where models generate code when necessary and integrate feedback by executing it through a code interpreter. To acquire this capability, models must learn when and how to use external code tools effectively, which is supported by tool-augmented reinforcement learning (RL). Despite its benefits, tool-augmented RL can still suffer from potential instability in the learning dynamics. In light of this challenge, we present a systematic approach ETIR (Effective TIR) to improving the training effectiveness and stability of tool-augmented RL for code-integrated reasoning. Specifically, we develop enhanced training strategies that balance exploration and stability, progressively building tool-use capabilities while improving reasoning performance. Through extensive experiments on five mainstream mathematical reasoning benchmarks, our model demonstrates significant performance improvements over multiple competitive baselines. Furthermore, we conduct an in-depth analysis of the mechanism of code-integrated reasoning, revealing several key insights, such as the extension of model’s capability boundaries and the simultaneous improvement of reasoning efficiency through code integration. These findings underscore the potential of code-integrated reasoning as a scalable paradigm for advancing robust and efficient language model reasoning.

Line-of-sight (LoS) probability modeling plays an essential role in the reliability determination of millimeter wave (mmWave) communication systems. However, since most LoS probability models do not consider altitude information of communication terminals, they cannot be directly applied to air-to-ground (A2G) communication scenarios. In this paper, we propose a new multi-height LoS probability model for mmWave unmanned aerial vehicle (UAV) communication scenarios. A machine learning (ML)-based parameter estimation method is also developed, which trains the data from the constructed virtual urban scenes. We first propose a LoS/none-LoS (NLoS) identification method to recognize the LoS path and calculate the LoS probability. Then, we construct a two-layer single-input multiple-output back propagation neural network (BPNN) which trains the relationship between the model parameters and the altitude of UAVs. Simulation results show that the proposed LoS probability model has a good consistency with the ray tracing (RT) simulation data and the currently existing models when altitudes of UAVs are low. As the altitude increases, our model is still applicable and can achieve an excellent agreement with the RT data.

Line-of-sight (LoS) probability prediction is critical to the performance optimization of wireless communication systems. However, it is challenging to predict the LoS probability of air-to-ground (A2G) communication scenarios, because the altitude of unmanned aerial vehicles (UAVs) or other aircraft varies from dozens of meters to several kilometers. This paper presents an altitude-dependent empirical LoS probability model for A2G scenarios. Before estimating the model parameters, we design a K -nearest neighbor (KNN) based strategy to classify LoS and non-LoS (NLoS) paths. Then, a two-layer back propagation neural network (BPNN) based parameter estimation method is developed to build the relationship between every model parameter and the UAV altitude. Simulation results show that the results obtained using our proposed model has good consistency with the ray tracing (RT) data, the measurement data, and the results obtained using the standard models. Our model can also provide wider applicable altitudes than other LoS probability models, and thus can be applied to different altitudes under various A2G scenarios.

A general empirical path loss (PL) model for air-to-ground (A2G) millimeter-wave (mmWave) channels is proposed in this paper. Different from existing PL models, the new model takes the height factor of unmanned aerial vehicles (UAVs) into account, and divides the propagation conditions into three cases (i.e., line-of-sight, reflection, and diffraction). A map-based deterministic PL prediction algorithm based on the ray-tracing (RT) technique is developed, and is used to generate numerous PL data for different cases. By fitting and analyzing the PL data under different scenarios and UAV heights, altitude-dependent model parameters are provided. Simulation results show that the proposed model can be effectively used to predict PL values for both low- and high-altitude cases. The prediction results of the proposed model better match the RT-based calculation results than those of the Third Generation Partnership Project (3GPP) model and the close-in model. The standard deviation of the PL is also much smaller. Moreover, the new model is flexible and can be extended to other A2G scenarios (not included in this paper) by adjusting the parameters according to the simulation or measurement data.

In this paper, a practical model for non-stationary Vehicle-to-Vehicle (V2V) multiple-input multiple-output (MIMO) channels is proposed. The new model considers more accurate output phase of Doppler frequency and is simplified by the Taylor series expansions. It is also suitable for generating the V2V channel coefficient with arbitrary velocities and trajectories of the mobile transmitter (MT) and mobile receiver (MR). Meanwhile, the channel parameters of path delay and power are investigated and analyzed. The closed-form expressions of statistical properties, i.e., temporal autocorrelation function (TACF) and spatial cross-correlation function (SCCF) are also derived with the angle of arrival (AoA) and angle of departure (AoD) obeying the Von Mises (VM) distribution. In addition, the good agreements between the theoretical, simulated and measured results validate the correctness and usefulness of the proposed model.

Summary Performance appraisal is a daily, homework, fair, and open appraisal of each hospital, which can mobilize the enthusiasm and creativity of the employees to the greatest extent so that both the company and the employees can achieve development. At present, the ascendant information revolution has become an important factor in promoting the development of the times. The use of advanced information technology to develop advanced information systems to conduct fair and open assessments of the ever‐increasing number of company employees has become the trend of the times. This article makes a historical review of the development and function of the performance appraisal system, and fully analyzes the new features and new functions of the performance appraisal system in the new period, and details the solution. This system develops and uses .NET development platform and SQL Server 2005 database. Using a distributed approach, the system's development costs have reduced, and the system's maintainability and scalability are improved. This paper studies the establishment of a performance‐based distribution model based on diagnostic relevance grouping theory (DRGs) in public hospitals. Based on the DRGs as a theoretical basis, after a long period of historical data collection, a theoretical model was established, repeated regression calculations, and focused evaluations. A performance‐distribution scheme centered on workload has established to reflect the factors such as the labor risk, technical difficulty, and labor cost of the attending doctors in relation to different patient conditions. The formulation and implementation of the new performance plan embodies the input of labor value and conforms to the spirit of the new medical reform.