Motion and constraint identification are the fundamental issue throughout the development of parallel mechanisms. Aiming at meaningful result with heuristic and visualizable process, this paper proposes a machine learning-based method for motions and constraints modeling and further develops the automatic software for mobility analysis. As a preliminary, topology of parallel mechanism is characterized by recognizable symbols and mapped to the motion of component limb through programming algorithm. A predictive model for motion and constraint with their nature meanings is constructed based on neural network. An increase in accuracy is obtained by the novel loss function, which combines the errors of network and physical equation. Based on the predictive model, an automatic framework for mobility analysis of parallel mechanisms is constructed. A software is developed with WebGL interface, providing the result of mobility analysis as well as the visualizing process particularly. Finally, five typical parallel mechanisms are taken as examples to verify the approach and its software. The method facilitates to attain motion/constraint and mobility of parallel mechanisms with both numerical and geometric features.

This article offers three musings on Sakiru Adebayo’s Continuous Pasts: Frictions of Memory in Postcolonial Africa, focusing specifically on the challenges and prospects of centering African histories, cultures, and epistemologies in mainstream memory studies. Through a reading of Continuous Pasts, the article contests the marginality of African and Afrodiasporic memory cultures in memory studies, and makes a case for the affordances of “ancestral memory” in articulating a uniquely African and global Black diasporic memory practice.

In parts of southern and western Asia, as elsewhere, the cannon once served as one of the most dramatic tools in the inventories of state executioners. The practice of ‘blowing from a gun’, by which the condemned was bound to the front of a cannon and quite literally blown to pieces, was most infamously employed in British India and the Princely States, and the vast majority of English-language scholarship focuses on these regions. However, blowing from guns was commonplace in several other contemporary states, and the British use of the practice has rarely been situated in this context. The tactic was considered especially useful in Persia and Afghanistan, where weak governance, rebellion, and rampant banditry all threatened the legitimacy of the nascent state in the nineteenth and early twentieth centuries. This article presents a history of the practice of execution by cannon in southern and western Asia, positioning it within the existing literature on public executions in the context of military and civilian justice. In doing so, the article seeks to situate the British use of the tactic within a broader regional practice, arguing that, whilst the British—following the Mughal tradition—used execution by cannon primarily in maintaining military discipline, states such as Persia and Afghanistan instead employed the practice largely in the civilian context. This article also provides a brief technical review of the practice, drawing upon numerous primary sources to examine execution by cannon within the Mughal empire, British India, Persia, and Afghanistan.

The early applications of Visual Simultaneous Localization and Mapping (VSLAM) technology were primarily focused on static environments, relying on the static nature of the environment for map construction and localization. However, in practical applications, we often encounter various dynamic environments, such as city streets, where moving objects are present. These dynamic objects can make it challenging for robots to accurately understand their own position. This paper proposes a real-time localization and mapping method tailored for dynamic environments to effectively deal with the interference of moving objects in such settings. Firstly, depth images are clustered, and they are subdivided into sub-point clouds to obtain clearer local information. Secondly, when processing regular frames, we fully exploit the structural invariance of static sub-point clouds and their relative relationships. Among these, the concept of the sub-point cloud is introduced as novel idea in this paper. By utilizing the results computed based on sub-poses, we can effectively quantify the disparities between regular frames and reference frames. This enables us to accurately detect dynamic areas within the regular frames. Furthermore, by refining the dynamic areas of keyframes using historical observation data, the robustness of the system is further enhanced. We conducted comprehensive experimental evaluations on challenging dynamic sequences from the TUM dataset and compared our approach with state-of-the-art dynamic VSLAM systems. The experimental results demonstrate that our method significantly enhances the accuracy and robustness of pose estimation. Additionally, we validated the effectiveness of the system in dynamic environments through real-world scenario tests.

Afin d'encourager la réflexion sur l'impact de la résistance interne dans la théorie du mouvement au XIVe siècle, je propose d'examiner ici l’évolution du concept chez l'universitaire parisien Nicole Oresme. Dans sa Physique, le penseur présente une position qui ne paraît pas tout à fait aboutie et qui soulève quelques questions en lien avec les qualités motrices des différents éléments constituant les mobiles. Cette situation devient d'autant plus évidente lorsque sa position change, plus tard, dans ses questions sur le traité Du ciel. Oresme semble alors encore moins enclin à accepter l'idée d'une résistance interne dans le mouvement.

This Editorial explores organizational travel risk management and advocates for a comprehensive approach to fortify health security for travelers, emphasizing proactive risk management, robust assessments, and strategic planning. Leveraging insights from very important persons (VIP) protocols, organizations can enhance duty of care and ensure personnel safety amidst global travel complexities.

Large language models (LLMs) offer new research possibilities for social scientists, but their potential as “synthetic data” is still largely unknown. In this paper, we investigate how accurately the popular LLM ChatGPT can recover public opinion, prompting the LLM to adopt different “personas” and then provide feeling thermometer scores for 11 sociopolitical groups. The average scores generated by ChatGPT correspond closely to the averages in our baseline survey, the 2016–2020 American National Election Study (ANES). Nevertheless, sampling by ChatGPT is not reliable for statistical inference: there is less variation in responses than in the real surveys, and regression coefficients often differ significantly from equivalent estimates obtained using ANES data. We also document how the distribution of synthetic responses varies with minor changes in prompt wording, and we show how the same prompt yields significantly different results over a 3-month period. Altogether, our findings raise serious concerns about the quality, reliability, and reproducibility of synthetic survey data generated by LLMs.

Contrast, adversative and corrective can all be represented by er in Classical Chinese, but they are lexicalized respectively by er, danshi and ershi in Modern Chinese. The two lexicalization systems suggest that the opposition relations have commonalities as well as differences. In the framework of relevance theory and ‘three domains’, this study argues that the three opposition relations are in different cognitive domains, at different representational levels, and trigger different inferences, which accounts for their diverse lexicalizations in Modern Chinese. The opposition relations also have cognitive or metaphorical connections with each other, which justifies their unified actualization in Classical Chinese. The pragmatics-cognitive framework could also account for interlinguistic data.