Conducting a detailed assessment of solar irradiation at the pedestrian scale on all ground surfaces of a city can assist in identifying cooler routes for pedestrian navigation or preparing the city for potential overheating issues by pinpointing overexposed areas. This article proposes an effective method for conducting such an assessment within a GIS with metric resolution across territories exceeding 100 km². It is based on standard datasets and implements an efficient strategy that relies on separation of variables, domain decomposition, and dimensionality reduction. This strategy involves creating a synthetic representation of the facades of the surrounding buildings (spatial dimension) which accelerates the calculation of shadows based on the sun’s position (temporal dimension). To demonstrate the effectiveness of this method, we applied it to a French city, generating fourteen maps illustrating the solar irradiation of the area for different months of the year or for two given dates with specific weather conditions. The proposed strategy, along with the synthetic representation of building facades, opens up a wide range of possibilities. In addition to synthesizing machine learning labeled datasets, we can also consider calculating solar irradiation with time steps of a few minutes to update weather conditions throughout a journey.

Logging, which aims to determine the position of logging statements, the verbosity levels, and the log messages, is a crucial process for software reliability enhancement. In recent years, numerous automatic logging tools have been designed to assist developers in one of the logging tasks (e.g., providing suggestions on whether to log in try-catch blocks). These tools are useful in certain situations yet cannot provide a comprehensive logging solution in general. Moreover, although recent research has started to explore end-to-end logging, it is still largely constrained by the high cost of fine-tuning, hindering its practical usefulness in software development. To address these problems, this paper proposes UniLog, an automatic logging framework based on the in-context learning (ICL) paradigm of large language models (LLMs). Specifically, UniLog can generate an appropriate logging statement with only a prompt containing five demonstration examples without any model tuning. In addition, UniLog can further enhance its logging ability after warmup with only a few hundred random samples. We evaluated UniLog on a large dataset containing 12,012 code snippets extracted from 1,465 GitHub repositories. The results show that UniLog achieved the state-of-the-art performance in automatic logging: (1) 76.9% accuracy in selecting logging positions, (2) 72.3% accuracy in predicting verbosity levels, and (3) 27.1 BLEU-4 score in generating log messages. Meanwhile, UniLog requires less than 4% of the parameter tuning time needed by fine-tuning the same LLM.

The combination of teleoperating robots and the Internet of Things (IoT) could be employed in many areas, including remote nursing and semi-mechanical control. However, it is known that subjects can quickly encounter physical and mental fatigue, which can potentially lower accuracy in teleoperation. To address this issue, this article presents a closed-loop teleoperation system based on multisensory fusion, visual, and haptic feedback within the IoT framework. Various sensors for electromyography, inertial measurement unit, and mechanical hand control system are deployed to obtain body signals from participants, subsequently processed by artificial intelligence methods. Resistive sensors are installed at the robotic hand side to learn the contact force between the robot hand and the grasped object. To allow the user to understand the contact force levels, a haptic interface is equipped to provide three-level mechanical vibrations. To help users identify and trace different objects, a region convolution neural network for detecting 100 different categories of things is constructed. In addition, the real-time control (delay$\mathbf {< } 110$ms) is offered by a tri-layered IoT architecture. The feasibility of the proposed technique is validated by a grasping task carried out by 20 volunteers. During experiments, it is observed that merely 10.8 s is averagely needed for participants in finishing the task, with a high average success rate of 97%. Besides, NASA-TLX questionnaire and maximum voluntary contraction test report that users suffer light mental and physical fatigue when the proposed technique is used.