Respect for patient autonomy is paramount in resolving ethical tensions in end-of-life care. The concept of relational autonomy has contributed to this debate; however, scholars often use this concept in a fragmented manner. This leads to partial answers on ascertaining patients' true wishes, meaningfully engaging patients' significant others, balancing interests among patients and significant others, and determining clinicians' obligations to change patients' unconventional convictions to enhance patient autonomy. A satisfactory solution based on relational autonomy must incorporate patients' competence (apart from decisional capacity), authenticity (their true desires or beliefs) and the involvement level of their significant others. To that end, we argue that John Christman's procedural approach to relational autonomy provides critical insights, such as the diachronic or socio-historical personhood, sustained critical reflection and his recent explication of the nature of asymmetrical relationships and helpful interlocutors. This study reviews Christman's account, proposes minor modifications and advocates for an integrated three-dimensional model for medical decision-making. Clarifying the relationship among the three elements promotes an ethical framework with a coherent understanding of relational autonomy. This model not only provides a descriptive and normative framework for end-of-life care practice but also reconsiders the nature of the clinician-patient relationship and its normative implications. We further present a case study to illustrate the merits of our proposed model. Altogether, our proposal will help navigate complex medical decision-making, foster trust and negotiate shared values between patients and their significant others, particularly in end-of-life care.
Generative Artificial Intelligence (GenAI) functions not merely as a tool but an active collaborator in human knowledge construction; however, the Human-GenAI interaction dynamics is still underexplored. This study investigates Human-GenAI interaction profiles, the network interactivity and profile differences within a statistics learning community, as well as the underlying mechanisms linking Human-GenAI interaction to learning performance. We designed the Human-GenAI Inquiry and Problem-Solving Scaffold to foster shared agency between twentyeight graduate students and GenAI across seven homework assignments in a sixteen-week advanced statistics course. Analytical approaches included k-modes clustering, social network analysis, and Partial Least Squares Structural Equation Modeling, complemented by case studies of interaction profiles. Three distinct Human-GenAI interaction profiles were identified: HumanGenAI collaborators, Peer collaborators with GenAI assistance, and Individual learners with late GenAI adoption. The network interactivity becomes cohesive with GenAI occupying the central hub role within the learning community. The models then demonstrate unique pathways through which Human-GenAI interaction influences learning performance, via degree centrality (number of direct connections) and peer nomination as helpers. The case studies highlight GenAI's capability to augment human roles, encouraging deeper inquiry, expanding the depth of peer discussion, or promoting the exploration of diverse problem-solving strategies. These findings add value to theory and practice by providing empirical evidence for the framework of a scaffolded Human-GenAI shared agency, offering pedagogical implications to foster active student participation and cultivate learner agency within the symbiotic Human-GenAI partnership.
This study explores the optimization of transmission strategies focusing on energy efficiency within a network comprising ground-based beyond-diagonal reconfigurable intelligent surfaces (BD-RIS), a.k.a RIS 2.0, and multiple unmanned aerial vehicles (UAVs). The motivation behind this work stems from the critical need to enhance energy efficiency in next-generation wireless networks, where the integration of UAVs and RIS technologies presents both opportunities and challenges. Specifically, while UAVs offer flexible deployment and improved coverage, their limited battery life and the complex interference environment in multi-user networks necessitate innovative solutions for sustainable operation. Each UAV is designed to serve its corresponding user group, with each group utilizing unique subcarriers to maintain orthogonality and employing a rate-splitting multiple access (RSMA) strategy within each group. The primary objectives of this work are to optimize: 1) the allocation of BD-RIS elements to groups, 2) the phase rotations of BD-RIS, 3) the common rate allocation in RSMA, 4) UAV trajectories, and 5) the design of precoders. To achieve these objectives, we formulate an optimization problem under the framework of mixed-integer nonlinear programming (MINLP), with a focus on maximizing energy efficiency. Our proposed solution combines generalized Benders decomposition (GBD), a manifold-based algorithm, and successive convex approximation (SCA). GBD decomposes the MINLP into primal and master sub-problems, which are iteratively solved. To efficiently address variable coupling in the primal problem, we adopt a block coordinate descent (BCD) method and employ the Riemannian conjugate gradient (RCG) technique for phase rotation. SCA addresses the remaining challenges in the primal problem, while a two-stage approach simplifies the optimization process. Simulations confirm the significant energy efficiency improvements achieved by the proposed method.
The hot deformation behavior of Al0.3Co1.5CrFeNi1.5Ti0.2 high-entropy alloy was studied at temperatures and strain rates varying from 923 to 1373 K and 10(-3) to 1 s(- 1), respectively. A constitutive equation was formulated to characterize material flow within these conditions and predict its behavior under similar or extended conditions. Stress exponent and deformation activation energy were determined to be 4.44 and similar to 490 kJ/mol, respectively, suggesting that the material undergoes high-temperature deformation primarily controlled by dislocation climb. Moreover, at 923 K, the power law breakdown was observed, with no flow softening occurring up to a true strain of 0.5. The processing map identified optimal deformation conditions, achieving highest efficiency (similar to 34 %), at strain rates of 0.03 - 1 s(- 1), and temperatures > 1300 K. Instability domain (strain rate range: 0.01 - 1 s(-1), temperatures: 923-1165 K), was characterized by presence of voids and cracks. Further, transmission electron microscopy study highlighted the dynamic development of L1(2), B2 and sigma phases at different temperatures and identified their correlation with instability domains in the processing map. The predominant deformation mechanisms were identified as dislocation climb and discontinuous dynamic recrystallization (DDRX). Electron back scattered diffraction microstructural investigations confirmed the occurrence of DDRX.
The rapid development of generative artificial intelligence has created new opportunities for innovation in teacher education, dramatically changing how pre-service teachers acquire new skills and knowledge. In the information age, pre-service teachers' information technology-enhanced instructional design competence has received widespread attention. This study introduced the generative artificial intelligence represented by ChatGPT to innovate the traditional teaching mode of pre-service education. Learners use ChatGPT to automatically generate courseware and revise courseware according to the framework provided by concept maps. To promote the depth of thinking and motivation of pre-service teachers in the revision process, we proposed an integrated approach with the peer assessment and ChatGPT. The researchers conducted a quasi-experimental study at a university to investigate the effectiveness of this learning strategy. One class of pre-service teachers (N = 46) was the experimental group using peer assessment-based concept mapping-supported ChatGPT generated content (PA-CGPT), and the other class (N = 45) was the control group using conventional concept mapping-supported ChatGPT generated content (C-CGPT). The study suggests that students in the experimental group exhibited significant improvements in practical skills, learning motivation, higher-order thinking tendencies, satisfaction, and reduced cognitive load. Furthermore, in-depth interviews were conducted with both groups of students to examine their learning outcomes and discuss implications for future research.
