Modern programming languages are constantly evolving, introducing new language features and APIs to enhance software development practices. Software developers often face the tedious task of upgrading their codebase to new programming language versions. Recently, large language models (LLMs) have demonstrated potential in automating various code generation and editing tasks, suggesting their applicability in automating code upgrade. However, there exists no benchmark for evaluating the code upgrade ability of LLMs, as distilling code changes related to programming language evolution from real-world software repositories’ commit histories is a complex challenge. In this work, we introduce CoUpJava, the first large-scale dataset for code upgrade, focusing on the code changes related to the evolution of Java. CoUpJava comprises 10,697 code upgrade samples, distilled from the commit histories of 1,379 open-source Java repositories and covering Java versions 7–23. The dataset is divided into two subsets: CoUpJava-FINE, which captures fine-grained method-level refactorings towards new language features; and CoUpJava-COARSE, which includes coarse-grained repository-level changes encompassing new language features, standard library APIs, and build configurations. Our proposed dataset provides high-quality samples by filtering irrelevant and noisy changes and verifying the compilability of upgraded code. Moreover, CoUpJava reveals diversity in code upgrade scenarios, ranging from small, fine-grained refactorings to large-scale repository modifications.

This paper presents a motion planning approach for rigid-soft hybrid robots that incorporates continuous expression and input mapping, aiming to achieve both tracking and collision avoidance. The conventional piecewise constant curvature model utilized in soft robots suffers from issues of discontinuity and singularity. These inherent drawbacks pose significant challenges when attempting to address the nonlinear problems associated with motion planning. To overcome these limitations, we establish a relationship between the length of drive cable and the coordination of robot terminal. Through the application of Taylor transformation, we effectively eliminate the control variation present in the denominator of this expression. Then, the nonlinear model predictive control (NMPC) with this model expression and input mapping is proposed for motion planning. By directly constructing the current input and output of the motion planning based on the combination of previous data and substituting the linearized part of the model with this data, we mitigate the impact of model inaccuracies on the solution process. Both the obstacles and robot are modeled as polyhedra, and the collision problem is reformulated in the form of continuous nonlinear inequalities. These expressions are then incorporated into the NMPC optimization function as collision avoidance constraints. This approach enables the attainment of a larger feasible movement area and more precise tracking results during the motion planning process. Finally, experimental and simulation results conducted on a rigid-soft hybrid robot demonstrate the feasibility and superiority of the proposed method, validating its effectiveness in practical applications.

Abstract Hirsch‐type indices are studied with special attention to the AR2‐index introduced by Jin. The article consists of two parts: a theoretical part and a practical illustration. In the theoretical part, we recall the definition of the AR2‐index and show that an alternative definition, the so‐called AR21, does not have the properties expected for this type of index. A practical example shows the existence of some of these mathematical properties and illustrates the difference between different h‐type indices. Clearly the h‐index itself is the most robust of all. It is shown that excluding so‐called non‐WoS source articles may have a significant influence on the R‐and, especially, the g‐index.

Abstract Intellectual and technological talents and skills are the driving force for scientific and industrial development, especially in our times characterized by a knowledge‐based economy. Major events in society and related political decisions, however, can have a long‐term effect on a country's scientific well‐being. Although the Cultural Revolution took place from 1966 to 1976, its aftermath can still be felt. This is shown by this study of the production and productivity of Chinese scientists as a function of their age. Based on the 1995–2000 data from the Chinese Science Citation database (CSCD), this article investigates the year‐by‐year age distribution of scientific and technological personnel publishing in China. It is shown that the “Talent Fault” originating during the Cultural Revolution still exists, and that a new gap resulting from recent brain drain might be developing. The purpose of this work is to provide necessary information about the current situation and especially the existing problems of the S&T workforce in China.