48080 research outputs found
Sort by
Recent progress of sensing and machine learning technologies for process monitoring and defects detection in wire arc additive manufacturing
No full textWire Arc Additive Manufacturing possesses advantages of high deposition rate and low cost compared with other metal additive manufacturing processes. However, potential defects may occur during the process, such as pores, cracks, lack of fusion, inclusions, delamination, and geometrical deviations. These defects are undesirable and have negative effects. To optimize the performance of the as-built components, and to reduce the potential defects, a feasible solution is to conduct in-process sensing and provide feedback to the control system. This article aims to give a comprehensive review of recent progress on sensing technologies, such as optical, acoustic, vision, thermal, and multiple signals-based sensing technologies, and the application of machine learning to enhance the ability to extract the needed feedback from the in-process monitoring raw data. Effective monitoring of different types of defects typically requires different sensing technologies, focus points, and attentions. Multi-sensor-based sensing systems may thus be needed to provide full-scale information. These necessities include the need for in-time data fusion and more complex data processing. This review analyzes recently explored sensing technologies for their principles and remaining challenges to provide directions for future invention, exploration, and investigation
Social determinants of health and emergency department visits among older adults with multimorbidity: insight from 2010 to 2018 National Health Interview Survey
No full textBackground: Multimorbidity is prevalent among older adults and is associated with adverse health outcomes, including high emergency department (ED) utilization. Social determinants of health (SDoH) are associated with many health outcomes, but the association between SDoH and ED visits among older adults with multimorbidity has received limited attention. This study aimed to examine the association between SDoH and ED visits among older adults with multimorbidity. Methods: A cross-sectional analysis was conducted among 28,917 adults aged 50 years and older from the 2010 to 2018 National Health Interview Survey. Multimorbidity was defined as the presence of two or more self-reported diseases among 10 common chronic conditions, including diabetes, hypertension, asthma, stroke, cancer, arthritis, chronic obstructive pulmonary disease, and heart, kidney, and liver diseases. The SDoH assessed included race/ethnicity, education level, poverty income ratio, marital status, employment status, insurance status, region of residence, and having a usual place for medical care. Logistic regression models were used to examine the association between SDoH and one or more ED visits. Results: Participants’ mean (± SD) age was 68.04 (± 10.66) years, and 56.82% were female. After adjusting for age, sex, and the number of chronic conditions in the logistic regression model, high school or less education (adjusted odds ratio [AOR]: 1.10, 95% confidence interval [CI]: 1.02–1.19), poverty income ratio below the federal poverty level (AOR: 1.44, 95% CI: 1.31–1.59), unmarried (AOR: 1.19, 95% CI: 1.11–1.28), unemployed status (AOR: 1.33, 95% CI: 1.23–1.44), and having a usual place for medical care (AOR: 1.46, 95% CI 1.18–1.80) was significantly associated with having one or more ED visits. Non-Hispanic Black individuals had higher odds (AOR: 1.28, 95% CI: 1.19–1.38), while non-Hispanic Asian individuals had lower odds (AOR: 0.71, 95% CI: 0.59–0.86) of one or more ED visits than non-Hispanic White individuals. Conclusion: SDoH factors are associated with ED visits among older adults with multimorbidity. Systematic multidisciplinary team approaches are needed to address social disparities affecting not only multimorbidity prevalence but also health-seeking behaviors and emergent healthcare access
A Sharp Estimate for the Genus of Embedded Surfaces in the 3-Sphere
No full textBy refining the volume estimate of Heintze and Karcher [11], we obtain a sharp pinching estimate for the genus of a surface in S3, which involves an integral of the norm of its traceless second fundamental form. More specifically, we show that if g is the genus of a closed orientable surface Σ in a 3-dimensional orientable Riemannian manifold M whose sectional curvature is bounded below by 1, then 4π2g(Σ)≤22π2-|M|+∫Σf(|A∘|), where A∘ is the traceless second fundamental form and f is an explicit function. As a result, the space of closed orientable embedded minimal surfaces Σ with uniformly bounded ‖A‖L3(Σ) is compact in the Ck topology for any k≥2
Principles for Adapting Assessments of Executive Function across Cultural Contexts
No full textDirect assessments of executive functions (EFs) are increasingly used in research and clinical settings, with a central assumption that they assess “universal” underlying skills. Their use is spreading globally, raising questions about the cultural appropriateness of assessments devised in Western industrialized countries. We selectively reviewed multidisciplinary evidence and theory to identify sets of cultural preferences that may be at odds with the implicit assumptions of EF assessments. These preferences relate to motivation and compliance; cultural expectations for interpersonal engagement; contextualized vs. academic thinking; cultural notions of speed and time; the willingness to be silly, be incorrect, or do the opposite; and subject-matter familiarity. In each case, we discuss how the cultural preference may be incompatible with the assumptions of assessments, and how future research and practice can address the issue. Many of the cultural preferences discussed differ between interdependent and independent cultures and between schooled and unschooled populations. Adapting testing protocols to these cultural preferences in different contexts will be important for expanding our scientific understanding of EF from the narrow slice of the human population that has participated in the research to date
A Study of Water-Based Nanolubricants Using Hexagonal Boron Nitride (hBN)-Based Nanocomposites as Lubricant Additives
No full textAn Rtec ball-on-disk tribometer was used to investigate the tribological performance of the synthesised water-based nanolubricants containing hBN/TiO2 nanocomposite at room temperature. The water-based nanolubricants with different concentrations were prepared by adding glycerol and sodium dodecyl benzene sulfonate (SDBS) under ultrasonication. These as-prepared nanolubricants demonstrated exceptional dispersion stability for 7 days without distinct sedimentation. The results indicate that the water-based nanolubricants with an overall concentration of 1.0 wt% at different ratios (hBN: TiO2 = (1:0), (0.7:0.3), (0.5:0.5), (0.3:0.7), (0:1)) can effectively reduce the coefficient of friction (COF) and the wear of the ball and disk. In particular, the water-based nanolubricant containing 0.5 wt% hBN and 0.5 wt% TiO2 exhibited the best tribological performance, leading to a significant reduction in COF up to 70%, and decreased the wear area of the ball and disk by up to 79.57% and 60.40%, respectively, compared to those obtained using distilled water. The lubrication mechanisms were mainly attributed to the formation of a protective film, and the mending, polishing, rolling, and synergistic effects of the hBN nanosheets and TiO2 nanoparticles
GEMF: a novel geometry-enhanced mid-fusion network for PLA prediction
No full textAccurate prediction of protein–ligand binding affinity (PLA) is important for drug discovery. Recent advances in applying graph neural networks have shown great potential for PLA prediction. However, existing methods usually neglect the geometric information (i.e. bond angles), leading to difficulties in accurately distinguishing different molecular structures. In addition, these methods also pose limitations in representing the binding process of protein–ligand complexes. To address these issues, we propose a novel geometry-enhanced mid-fusion network, named GEMF, to learn comprehensive molecular geometry and interaction patterns. Specifically, the GEMF consists of a graph embedding layer, a message passing phase, and a multi-scale fusion module. GEMF can effectively represent protein–ligand complexes as graphs, with graph embeddings based on physicochemical and geometric properties. Moreover, our dual-stream message passing framework models both covalent and non-covalent interactions. In particular, the edge-update mechanism, which is based on line graphs, can fuse both distance and angle information in the covalent branch. In addition, the communication branch consisting of multiple heterogeneous interaction modules is developed to learn intricate interaction patterns. Finally, we fuse the multi-scale features from the covalent, non-covalent, and heterogeneous interaction branches. The extensive experimental results on several benchmarks demonstrate the superiority of GEMF compared with other state-of-the-art methods
Does presentation size of instructional materials influence the split-attention effect?
No full textThe split-attention effect posits that learning outcomes are negatively impacted when interrelated text and graphics are spatially segregated rather than cohesively integrated. This study explored how the instructional material's presentation size influences the manifestation of the split-attention effect. Based on cognitive load theory and perceptual load theory, we hypothesized that elevated information density in a compact presentation format would attenuate the advantage of integrated text and graphics, thereby diminishing the salience of the split-attention effect relative to a more expansive presentation size. University students (n = 146) studied a split-attention format or integrated format in either large or small presentation size. Results on retention and comprehension tests and extraneous cognitive load ratings revealed no effects of instructional format, presentation size or their interaction. The present results call for a more nuanced understanding of the split-attention effect and suggest additional research to explore its cognitive foundations
Nurses’ Use of mHealth Apps for Chronic Conditions: Cross-Sectional Survey
No full textBackground: Mobile health (mHealth) is increasingly used to support public health practice, as it has positive benefits such as enhancing self-efficacy and facilitating chronic disease management. Yet, relatively few studies have explored the use of mHealth apps among nurses, despite their important role in caring for patients with and at risk of chronic conditions. Objective: The aim of the study is to explore nurses’ use of mHealth apps to support adults with or at risk of chronic conditions and understand the factors that influence technology adoption. Methods: A web-based cross-sectional survey was conducted between September 2022 and January 2023. The survey was shared via social media and professional nursing organizations to Australian nurses caring for adults with or at risk of chronic conditions. Results: A total of 158 responses were included in the analysis. More than two-thirds (n=108, 68.4%) of respondents reported that they personally used at least 1 mHealth app. Over half (n=83, 52.5% to n=108, 68.4%) reported they use mHealth apps at least a few times a month for clinical purposes. Logistic regression demonstrated that performance expectancy (P=.04), facilitating condition (P=.05), and personal use of mHealth apps (P=.05) were significantly associated with mHealth app recommendation. In contrast, effort expectancy (P=.09) and social influence (P=.46) did not have a significant influence on whether respondents recommended mHealth apps to patients. The inability to identify the quality of mHealth apps and the lack of access to mobile devices or internet were the most common barriers to mHealth app recommendation. Conclusions: While nurses use mHealth apps personally, there is potential to increase their clinical application. Given the challenges reported in appraising and assessing mHealth apps, app regulation and upskilling nurses will help to integrate mHealth apps into usual patient care
Olanzapine Modulate Lipid Metabolism and Adipose Tissue Accumulation via Hepatic Muscarinic M3 Receptor-Mediated Alk-Related Signaling
No full textOlanzapine is an atypical antipsychotic drug and a potent muscarinic M3 receptor (M3R) antagonist. Olanzapine has been reported to cause metabolic disorders, including dyslipidemia. Anaplastic lymphoma kinase (Alk), a tyrosine kinase receptor well known in the pathogenesis of cancer, has been recently identified as a key gene in the regulation of thinness via the regulation of adipose tissue lipolysis. This project aimed to investigate whether Olanzapine could modulate the hepatic Alk pathway and lipid metabolism via M3R. Female rats were treated with Olanzapine and/or Cevimeline (an M3R agonist) for 9 weeks. Lipid metabolism and hepatic Alk signaling were analyzed. Nine weeks’ treatment of Olanzapine caused metabolic disturbance including increased body mass index (BMI), fat mass accumulation, and abnormal lipid metabolism. Olanzapine treatment also led to an upregulation of Chrm3, Alk, and its regulator Ptprz1, and a downregulation of Lmo4, a transcriptional repressor of Alk in the liver. Moreover, there were positive correlations between Alk and Chrm3, Alk and Ptprz1, and a negative correlation between Alk and Lmo4. However, cotreatment with Cevimeline significantly reversed the lipid metabolic disturbance and adipose tissue accumulation, as well as the upregulation of the hepatic Alk signaling caused by Olanzapine. This study demonstrates evidence that Olanzapine may cause metabolic disturbance by modulating hepatic Alk signaling via M3R, which provides novel insight for modulating the hepatic Alk signaling and potential interventions for targeting metabolic disorders
Enhancing the reaction kinetics and structural stability of high-voltage LiCoO2via polyanionic species anchoring
No full textIncreasing the charging voltage to 4.6 V directly enhances battery capacity and energy density of LiCoO2 cathodes for lithium-ion batteries. However, issues of the activated harmful phase evolution and surface instability in high-voltage LiCoO2 lead to dramatic battery capacity decay. Herein, polyanionic PO43− species have been successfully anchored at the surface of LiCoO2 materials, achieving superior battery performance. The polyanionic species acting as micro funnels at the material surface, could expand LiCoO2 surface lattice spacing by 10%, contributing to enhanced Li diffusion kinetics and consequent excellent rate performance of 164 mA h g−1 at 20C (1C = 274 mA g−1). Crucially, polyanionic species with high electronegativity could stabilize surface oxygen at high voltage by reducing O 2p and Co 3d orbital hybridization, thus suppressing surface Co migration and harmful H1-3 phase formation and leading to superior cycling stability with 84% capacity retention at 1C after 300 cycles. Furthermore, pouch cells containing modified LiCoO2 and Li metal electrodes deliver an ultra-high energy density of 513 W h kg−1 under high loadings of 32 mg cm−2. This work provides insightful directions for modifying the material surface structure to obtain high-energy-density cathodes with high-rate performance and long service life