To meet the high-precision positioning requirements for hybrid machining units, this article presents a geometric error modeling and source error identification methodology for a serial–parallel hybrid kinematic machining unit (HKMU) with five axis. A minimal kinematic error modeling of the serial–parallel HKMU is established with screw-based method after elimination of redundant errors. A set of composite error indices is formulated to describe the terminal accuracy distribution characteristics in a quantitative manner. A modified projection method is proposed to determine the actual compensable and noncompensable source errors of the HKMU by identifying such transformable source errors. Based on this, the error compensation and comparison analysis are carried out on the exemplary HKMU to numerically verify the effectiveness of the proposed modified projection method. The geometric error evaluations reveal that the parallel module has a larger impacts on the terminal accuracy of the platform of the HKMU than the serial module. The error compensation results manifest that the modified projection method can find additional compensable source errors and significantly reduce the average and maximum values of geometric errors of the HKMU. Hence, the proposed methodology can be applied to improve the accuracy of kinematic calibration of the compensable source errors and can reduce the difficulty and workload of tolerance design for noncompensable source errors of such serial–parallel hybrid mechanism.

On January 30, 2022, Northern Ireland observed the 50th anniversary of Bloody Sunday. On that day in 1972, the British Army opened fire on a group of unarmed protesters in Derry, killing 13 and wounding an additional 15. Bloody Sunday was a pivotal moment during the 30 years of conflict in Northern Ireland known as the Troubles, a day widely considered a ‘watershed in British-Irish history’. And while 50 years have passed since this dark day, Bloody Sunday remains vivid in the collective memory of the small country. Considering the cultural and social significance of Bloody Sunday, I sought to answer a simple yet deceptively complicated question: does this still matter? In pursuing this answer, I aimed to understand how journalists and news outlets chose to mark and remember the anniversary in their January and February 2022 coverage. First, I present an overview of Bloody Sunday and its historical role as a catalyst for the three decades of the Troubles. Then, I review relevant memory studies literature in order to understand the role that commemorative news media play in the process of remembering in conflict and post-conflict environments. I then introduce my three research questions and methods before finally discussing the results of my analysis. I found that Bloody Sunday continues to be invoked against British colonialism, that key details of the day remain contested even now, and that the press presented Bloody Sunday as part of a globalised narrative of war-time atrocities.

Precise and efficient grasping detection is vital for robotic arms to execute stable grasping tasks in industrial and household applications. However, existing methods fail to consider refining different scale features and detecting critical regions, resulting in coarse grasping rectangles. To address these issues, we propose a real-time coarse and fine granularity residual attention (CFRA) grasping detection network. First, to enable the network to detect different sizes of objects, we extract and fuse the coarse and fine granularity features. Then, we refine these fused features by introducing a feature refinement module, which enables the network to distinguish between object and background features effectively. Finally, we introduce a residual attention module that handles different shapes of objects adaptively, achieving refined grasping detection. We complete training and testing on both Cornell and Jacquard datasets, achieving detection accuracy of 98.7% and 94.2%, respectively. Moreover, the grasping success rate on the real-world UR3e robot achieves 98%. These results demonstrate the effectiveness and superiority of CFRA.