Deep underground laboratories offer advantages for conducting high-precision observations of weak geophysical signals, benefitting from a low background noise level. It is both valuable and feasible to enhance strong, noisy ground electromagnetic (EM) field data using synchronously recorded underground EM signals, which typically exhibit a high signal-to-noise ratio. In this study, we propose an EM field reconstruction method employing a Long Short-Term Memory (LSTM) recurrent neural network with referenced deep underground EM observations. Initially, a deep learning model was developed to capture the time-varying features of underground multi-component EM fields using the LSTM recurrent neural network. Subsequently, this model was applied to process synchronously observed strong, noisy data from other conventional observation systems, such as those at surface, to achieve noise suppression through signal reconstructions. Both theoretical analysis and practical observational data suggest that the proposed method effectively suppresses noise and reconstructs clean EM signals. This method is efficient and time-saving, representing an effective approach to fully utilizing the advantages of deep underground observation data. Furthermore, this method could be extended to the processing and analysis of other geophysical data.

Despite the growing number of published studies on student learning in the laboratory, there is a critical need to improve methodological rigor. Resonating with discussions on research methods, this paper outlines the importance of theory-informed research questions, the minimization of researcher and participant biases, and the use of triangulation and iteration in data collection to establish rigor. An illustrative case is presented within the context of a large interdisciplinary research project aimed at improving laboratory learning at the university level. The project incorporates two research avenues: one focusing on literature and faculty perspectives, and the other on a comprehensive assessment of multidimensional learning in the laboratory. The project employs a mixed methods paradigm and is grounded in a conceptual framework that conceptualizes laboratory work as epistemic practice, requiring a holistic analysis of student learning. The article concludes by discussing the results and implications of the project's findings, which are synthesized to highlight aspects of establishing methodological rigor. The overarching goal is to develop a comprehensive assessment instrument that captures the complexity and richness of the laboratory learning environment. The findings from this research are expected to contribute to the advancement of laboratory education research by providing a model for methodological rigor that can be applied across various scientific and interdisciplinary contexts.

Pigs are used as laboratory animals in preclinical studies on medical devices for cardiac surgery. On the one hand, the anatomy of the cardiovascular system of these animals has been well studied and acknowledged suitable for use and testing of new cardiovascular devices developed for humans. On the other hand, there are no morphometric characteristics of the aortic root and other parts of it in pigs with different weights. This can lead to difficulties in the work of researchers and even result in the death of experimental animals due to the discrepancy in the size of the implantable devices. Thus, such information is essential to improve the efficiency of surgical technologies used in the correction of aortic pathologies in its various sections. The purpose of our research is to study the anatomy of the aorta in mini-pigs, and to assess whether the size, age, and sex of animals affect the size of the main structures in their aorta. In addition, we attempted to compare the results obtained by the method of transesophageal echocardiography (TEE) and angiography. We studied 28 laboratory mini-pigs weighing from 40 to 105 kg, and then we compared our results with other studies and did not find any relationship between the external somatometric characteristics of animals and the size of their aorta. Each animal has an individual anatomy, so they are to be examined in terms of preoperative planning by any available method - echocardiography, angiography or multispiral computed tomography (MRI).

Background/Objectives: Community-acquired pneumonia (CAP) is a common emergency presentation in Australia, with the choice of admitting specialty unit often influenced by pneumonia severity and comorbidities. However, it remains unclear whether there are between-specialty differences in patient characteristics and outcomes. We sought to address this issue by investigating the characteristics and outcomes of CAP patients admitted to General Medicine (GM) versus Respiratory units.Methods: This retrospective observational study utilised data from the two largest metropolitan hospitals in South Australia, encompassing all non-COVID-19-related CAP admissions throughout 2021 to 2023. Hospital length of stay (LOS), in-patient and 30-day mortality, and 30-day readmission rates were assessed by propensity-score-matching (PSM) with using 14 variables.Results: Of the 3,004 cases of non-COVID-19 CAP admitted across the two hospitals during the study period, 2,673 (71.8%) were admitted under GM units and 331 (9.1%) under Respiratory units. GM patients were, on average, a decade older, presented with a significantly higher burden of comorbidities, exhibited a greater prevalence of frailty and had higher pneumonia severity compared to those admitted under a Respiratory unit, (P