The one-dimensional Hubbard model is studied at and close to half-filling using a generalization of Handscomb’s quantum Monte Carlo method. Results for spin-correlation functions and susceptibilities are presented for systems of up to 128 sites. The spin-correlation function at low temperature is well described by a recently introduced formula relating the correlation function of a finite periodic system to the corresponding T=0 correlation function of the infinite system. For the T→0 divergence of the q=2${\mathit{k}}_{\mathit{F}}$ spin susceptibility, our results are consistent with the form χ(2${\mathit{k}}_{\mathit{F}}$)∼‖ln(T)${\mathrm{\Vert }}^{\mathrm{\sigma }}$ ${\mathit{T}}_{\mathrm{\rho }}^{\mathit{K}}$-1, with σ=1/2.

To investigate the critical yet often overlooked challenge of documentation of hypersensitivity reactions to contrast media. Inaccurate documentation not only informs future imaging events but also impacts other areas in medicine. This study applies Bronfenbrenner's adapted socio-ecological model to examine the literature related to contrast hypersensitivity documentation, highlighting systemic issues that perpetuate suboptimal practice. A scoping review was conducted following the Arksey and O'Malley framework with adaptations for a socio-ecological lens. A search was conducted across Ovid MEDLINE, SCOPUS, ProQuest and CINAHL Plus (2000-present), identifying 673 articles. Seven studies met the inclusion criteria and were analysed using NVIVO software to identify key themes across Bronfenbrenner's system levels of micro, meso, exo, macro and chrono. Findings revealed inconsistencies in documentation practices across all levels of healthcare. At the micro level, patient-practitioner communication is often hindered by the patient's inability to provide an accurate history. At the meso and macro levels, fragmented systems, lack of interoperability and inconsistent terminology contribute to the problem. At the chrono level, the impact of the evolution of documentation practices, standards, and policies over time have influenced current practices. This includes perpetuating a cycle of inaccurate information being utilised in clinical decision making, compromising patient outcomes and increasing healthcare costs. This review is the first to highlight the systemic nature of contrast hypersensitivity documentation challenges using the socio-ecological lens. All medical and allied professionals who document and communicate medical histories can break this self-perpetuating cycle with simple actions such as accurately recording of contrast agent name, details of the reaction, severity, treatment and timelines. Further research is required to develop strategies to drive systemic change, ultimately to benefit patient safety.

The integration of artificial intelligence (AI) into medical radiation science (MRS) education offers significant potential to enhance student training and bridge gaps in traditional pedagogical methods. This technical note describes the development of "Bill," an AI-driven Clinical Scenario Coach, designed as a prototype to simulate realistic clinical challenges for undergraduate radiography students. Bill utilizes OpenAI's GPT-4o model to create structured, interactive learning environments aligned with the Medical Radiation Practice Board of Australia (MRPBA) professional standards. Bill was developed as a collaborative effort between academics and clinical practitioners. Scenarios were designed to simulate tasks ranging from basic technical skills to advanced clinical decision-making in radiography. The prototype incorporates real-time feedback and reflective prompts to align with evidence-based educational practices. The development process focused on ensuring authenticity, relevance, and compliance with MRS-specific standards while optimizing the functionality of the AI framework. The prototype demonstrated the feasibility of using AI to simulate clinical scenarios in radiography education. Scenarios included tasks such as performing mobile X-rays on critically ill patients and managing fluoroscopy near-miss incidents. Bill provided structured, adaptive guidance and feedback tailored to the complexity of each scenario. The framework was found to effectively simulate real-world challenges, offering a dynamic environment for prototype testing and future refinements. The development of Bill marks a step forward in exploring AI applications within MRS education. The prototype successfully illustrates the potential for AI to augment traditional teaching methods and create engaging, adaptive learning environments. While still in its experimental phase, Bill provides a replicable model for AI-driven clinical coaching, with potential applications across various healthcare disciplines.

Background: Standardised breast positioning and optimal compression are critical components of effective breast cancer screening. This scoping review aims to report the current landscape of automated software tools developed for image quality assessment and mammographic technique evaluation, and to examine their reported impact. Methods: A scoping review was undertaken across PubMed (MEDLINE), Scopus, and Emcare. Eligible studies were published between January 2014 and March 2025 and investigated the use of automated software or artificial intelligence-based tools to assess image quality, breast positioning, or compression in mammography or digital breast tomosynthesis. Results: Automated software was predominantly utilised in high-resource settings, where it provided benchmarked feedback, reduced the subjectivity inherent in traditional visual grading systems, and supported radiographer learning and skill development with measurable improvements. However, radiographer training in these systems, the impact of software on clinical workflow, and barriers to implementation, particularly in low-resource settings, were insufficiently addressed in the literature. Furthermore, no studies reported on the relationship between software-generated metrics and breast cancer screening outcomes. Conclusions: Automated software for image quality evaluation represents a significant advancement in breast screening, illustrating the potential of technology to strengthen the screening-to-treatment continuum in breast cancer care. Nonetheless, widespread adoption requires evidence that these tools directly contribute to improved cancer detection outcomes to justify their uptake.

The Alice Springs sculptor Patricia Elvins created a number of busts of Indigenous Australian men, women, and children, which were distributed as casts for the gift and souvenir market. Produced between the early-1960s and the early-1990s, these varnished casts exist with four different artists’ signatures, representing collaboration with different production potters who produced the casts. Macroscopic analysis shows significant differences in weight between casts of the same bust. CT scanning was carried out to understand the make-up of these casts and to illuminate differences in production techniques. The scanning revealed that all figurines were cast, but that casting techniques varied not only between production potters but also among figurines of the same potter. It revealed differences in the densities of the casting material, both between and within specimens, suggesting that production was not standardized but occurred in smaller batches, possibly on demand of low-volume sales stock. The study has shown the potential of non-destructive CT scanning to go beyond this and serve as a tool to examine the casting process itself as well as to contribute to an understanding of the nature of the plasters used.