Communities on social media display distinct patterns of linguistic expression and behaviour, collectively referred to as practices.These practices can be traced in textual exchanges, and reflect the intentions, knowledge, values, and norms of users and communities.This paper introduces a comprehensive methodological workflow for computational identification of such practices within social media texts.By focusing on supporters of Ukraine during the Russia-Ukraine war in (1) the activist collective NAFO and (2) the Eurovision Twitter community, we present a gold-standard data set capturing their unique practices.Using this corpus, we perform practice prediction experiments with both open-source baseline models and OpenAI's large language models.Our results demonstrate that closed-source models, especially GPT-4, achieve superior performance, particularly with prompts that incorporate salient features of practices, or utilize Chain-of-Thought prompting.This study provides a detailed error analysis and offers valuable insights into improving the precision of practice identification, thereby supporting context-sensitive moderation and advancing the understanding of online community dynamics.1 communities.In

We aim to compute the movement of permanent stranded implant brachytherapy radioactive sources (seeds) in the prostate from the planned seed distribution to the intraoperative fluoroscopic distribution, and then to the postimplant computed tomography (CT) distribution. We present a novel approach to matching the seeds in these distributions to the plan by grouping the seeds into needle tracks. First, we identify the implantation axis using a sample consensus algorithm. Then, we use a network flow algorithm to group seeds into their needle tracks. Finally, we match the needles from the three stages using both their transverse plane location and the number of seeds per needle. We validated our approach on eight clinical prostate brachytherapy cases, having a total of 871 brachytherapy seeds distributed in 193 needles. For the intraoperative and postimplant data, 99.31% and 99.41% of the seeds were correctly assigned, respectively. For both the preplan to fluoroscopic and fluoroscopic to CT registrations, 100% of the needles were correctly matched. We show that there is an average intraoperative seed displacement of 4.94±2.42 mm and a further 2.97±1.81 mm of postimplant movement. This information reveals several directional trends and can be used for quality control, treatment planning, and intraoperative dosimetry that fuses ultrasound and fluoroscopy.

This paper presents a new needle path planning method for the insertion of rigid needles into deformable tissue. The needle insertion point, needle heading, and needle depth are optimized by minimizing the distance between a rigid needle and a number of targets in the tissue. The optimization method is based on iterative simulations performed using a tissue finite element model. At each iteration, the best 3-D line fitted to the displaced targets in the deformed tissue is used as a candidate for a new insertion line. First, this method is implemented in a prostate brachytherapy simulator under different boundary conditions to minimize the targeting error. It is shown that the optimization method converges in a few iterations and decreases the seed misplacement error to less than the needle diameter. Second, the efficacy of the optimization algorithm is verified by optimizing the insertion parameters for a brachytherapy needle before insertion into a prostate tissue phantom. The elastic properties of the phantom and the needle-tissue interaction parameters were identified in an independent experiment. The optimization algorithm is effective in decreasing the targeting error.

This paper presents a new method of optimizing the needle insertion point, heading and depth for needle insertion into deformable tissue. The goal is to minimize the distance between a number of specified targets and the needle. Assuming a rigid needle and a deformable tissue described by a finite element model, an iterative optimization method is proposed that uses the needle insertion simulation. At each iteration, the best fitted 3D line to the targets in the simulated deformed configuration is used as a candidate for the new insertion line in the next iteration. This method has been implemented in a prostate brachytherapy simulator to minimize seed misplacement errors. The targets are designed to lie on a straight line in the undeformed configuration inside the prostate. To increase the accuracy while simulating the prostate rotation, a non-linear model is used. The neo-Hookean material model is exploited to determine the effects of geometric and mechanical nonlinearities and compressibility effects. It is shown that the optimization algorithm converges in few iterations and decreases the targeting error effectively.