Scholarworks
ScholarWorks is an open access repository for the capture of the intellectual work of Montana State University (MSU) in support of its teaching, research and service missions. MSU ScholarWorks is a central point of discovery for accessing, collecting, sharing, preserving, and distributing knowledge to the Montana State University community and the world.
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Casual language and statistics instruction: evidence from a randomized experiment
(International Association for Statistical Education, 2024-08) Hill, Jennifer; Perrett, George; Hancock, Stacey; Win, Le; Bergner, Yoav
Most current statistics courses include some instruction relevant to causal inference. Whether this instruction is incorporated as material on randomized experiments or as an interpretation of associations measured by correlation or regression coefficients, the way in which this material is presented may have important implications for understanding causal inference fundamentals. Although the connection between study design and the ability to infer causality is often described well, the link between the language used to describe study results and causal attribution typically is not well defined. The current study investigates this relationship experimentally using a sample of students in a statistics course at a large western university in the United States. It also provides (non-experimental) evidence about the association between statistics instruction and the ability to understand appropriate causal attribution. The results from our experimental vignette study suggest that the wording of study findings impacts causal attribution by the reader, and, perhaps more surprisingly, that this variation in level of causal attribution across different wording conditions seems to pale in comparison to the variation across study contexts. More research, however, is needed to better understand how to tailor statistics instruction to make students sufficiently wary of unwarranted causal interpretation.
Hybrid Radial-Axial Flow for Enhanced Thermal Performance in Packed Bed Energy Storage
(Wiley, 2024-10) Al-Azawii, Mohammad M. S.; Anderson, Ryan
In this work, a hybrid radial-axial (HRA) system is used to store thermal energy in a packed bed. The heat transfer fluid (HTF) is delivered via a perforated radial pipe placed at the center of the packed bed along the axial length. Hot fluid flows from the center toward the wall through the holes (like other radial systems), but then leaves via the traditional axial flow exit, creating the HRA flow configuration. A computational fluid dynamics (CFD) model is used to analyze the thermal performance of the packed bed during the charging process utilizing the new HRA system. Alumina beads of 6 mm were filler materials and air was HTF with inlet temperature of 75°C for proof of concept. The present paper focuses on two aims: (1) utilizing CFD models to analyze flow and temperature profiles in the packed bed; (2) comparing the model results to experimental results published in a previous HRA flow study and to traditional axial flow. Two HRA configurations were considered based on previous experimental designs, one with uniform holes in the central pipe (R1) and one with gradients in the hole sizes to promote even flow from the central pipe into the bed (R2). The numerical results agree with the experimental results in both cases. The HRA system performance depends on the flow profile created by the hole designs, and it can perform better than the axial flow depending on the design of the radial pipe. Design R2, which promotes even flow from the central pipe into the bed, has higher charging efficiency than standard axial flow methods. For HRA design R2 at 0.0048 m3/s (7 SCFM, standard cubic feet per minute), numerical results for charging efficiency were 75.5% versus 73.8% for traditional axial flow. For HRA design R2 at 0.0061 m3/s (9 SCFM), numerical charging efficiency was 80.5% versus 78.1% for traditional axial flow. These results are consistent with experimental data.
A Review on Recent Progress of Biodegradable Magnetic Microrobots for Targeted Therapeutic Delivery: Materials, Structure Designs, and Fabrication Methods
(ASME International, 2024-08) Cao, Yang; Nunez Michel, Karen; Alimardani, Farzam; Wang, Yi
Targeted therapeutic delivery employs various technologies to enable precise delivery of therapeutic agents (drugs or cells) to specific areas within the human body. Compared with traditional drug administration routes, targeted therapeutic delivery has higher efficacy and reduced medication dosage and side effects. Soft microscale robotics have demonstrated great potential to precisely deliver drugs to the targeted region for performing designated therapeutic tasks. Microrobots can be actuated by various stimuli, such as heat, light, chemicals, acoustic waves, electric fields, and magnetic fields. Magnetic manipulation is well-suited for biomedical applications, as magnetic fields can safely permeate through organisms in a wide range of frequencies and amplitudes. Therefore, magnetic actuation is one of the most investigated and promising approaches for driving microrobots for targeted therapeutic delivery applications. To realize safe and minimally invasive therapies, biocompatibility and biodegradability are essential for these microrobots, which eliminate any post-treatment endoscopic or surgical removals. In this review, recent research efforts in the area of biodegradable magnetic microrobots used for targeted therapeutic delivery are summarized in terms of their materials, structure designs, and fabrication methods. In the end, remaining challenges and future prospects are discussed.
MetaCDP: Metamorphic Testing for Quality Assurance of Containerized Data Pipelines
(IEEE, 2024-06) ur Rehman, Faqeer; Umbreen, Sidrah; Rehman, Mudasser
In the ever-evolving world of technology, companies are investing heavily in building and deploying state-of-the-art Machine Learning (ML) based systems. However, such systems heavily rely on the availability of high-quality data, which is often prepared/generated by the Extract Transform Load (ETL) data pipelines; thus, they are critical components of an end-to-end ML system. A low-performing model (trained on buggy data) running in a production environment can cause both financial and reputational losses for the organization. Therefore, it is of paramount significance to perform the quality assurance of underlying data pipelines from multiple perspectives. However, the computational complexity, continuous change in data, and the integration of multiple components make it challenging to test them effectively, ultimately causing such solutions to suffer from the Oracle problem. In this research paper, we propose MetaCDP, a Metamorphic Testing approach that can be used by both researchers and practitioners for quality assurance of modern Containerized Data Pipelines. We propose 10 Metamorphic Relations (MRs) that target the robustness and correctness of the data pipeline under test, which plays a crucial role in providing high-quality data for developing a clustering-based anomaly detection model. To show the applicability of the proposed approach, we tested a data pipeline (from the E-commerce domain) and uncovered several erroneous behaviors. We also present the nature of issues identified by the proposed MRs, which can better help/guide software engineers and researchers to use best coding practices for maintaining and improving the quality of their data pipelines.
Osteoarthritis Year in Review 2024: Molecular biomarkers of osteoarthritis
(Elsevier BV, 2024-10) Welhaven, Hope D.; Welfley, Avery H.; June, Ronald K.
Objective. To provide a comprehensive and insightful summary of studies on molecular biomarkers at the gene, protein, and metabolite levels across different sample types and joints affected by osteoarthritis (OA). Methods. A literature search using the PubMed database for publications on OA biomarkers published between April 1, 2023 and April 30, 2024 was performed. Publications were then screened, examined at length, and summarized in a narrative review. Results. Out of the 364 papers initially identified, 44 publications met inclusion criteria, were relevant to OA, and were further examined for data extraction and discussion. These studies included 1 genomic analysis, 22 on protein markers, 6 on metabolite markers, 9 on inflammatory mediators, and 6 integrating multiple molecular levels. Conclusions. Significant advancements have been made in identifying molecular biomarkers for OA, encompassing various joints, sample types, and molecular levels. Despite this progress, gaps remain, particularly in the need for validation, larger sample sizes, the integration of more clinical data, and consideration of covariates. For early detection and improved treatment of OA, continued efforts in biomarker identification are needed. This effort should seek to identify effective biomarkers that advance early detection, support prevention, evaluate interventions, and improve patient outcomes.