A Framework For Refining Text Classification and Object Recognition from Academic Articles

With the widespread use of the internet, it has become increasingly crucial to extract specific information from vast amounts of academic articles efficiently. Data mining techniques are generally employed to solve this issue. However, data mining for academic articles is challenging since it requires automatically extracting specific patterns in complex and unstructured layout documents. Current data mining methods for academic articles employ rule-based(RB) or machine learning(ML) approaches. However, using rule-based methods incurs a high coding cost for complex typesetting articles. On the other hand, simply using machine learning methods requires annotation work for complex content types within the paper, which can be costly. Furthermore, only using machine learning can lead to cases where patterns easily recognized by rule-based methods are mistakenly extracted. To overcome these issues, from the perspective of analyzing the standard layout and typesetting used in the specified publication, we emphasize implementing specific methods for specific characteristics in academic articles. We have developed a novel Text Block Refinement Framework (TBRF), a machine learning and rule-based scheme hybrid. We used the well-known ACL proceeding articles as experimental data for the validation experiment. The experiment shows that our approach achieved over 95% classification accuracy and 90% detection accuracy for tables and figures.Comment: This paper has been accepted at 'The International Symposium on Innovations in Intelligent Systems and Applications 2023 (INISTA 2023)

Sintering Preparation of 15 wt% Cr Cast Iron as well as Its Mechanical Properties and Impact Abrasive Wear

15 wt% Cr sintered High Chromium Cast Iron (HCCI) with full density was successfully prepared by Super-solidus Liquid Phase Sintering (SPLS) technique, with water atomized 15 wt% Cr high chromium cast iron powder as initial materials. Its densification behavior and microstructure evolution in SPLS process and mechanical properties were investigated systematically. Additionally, the impact abrasive wear resistance under different impact energies were also analyzed and compared with another sintered HCCI with 20 wt% Cr. The results indicated that sintering temperature has a strong influence on the sintered alloy’s density, hardness, impact toughness and bending strength. The M7C3 type (M is Cr and Fe) carbides were obviously coarsened as temperature increased and their rod-shaped branches were fully developed at the same time, thereby resulting in carbide network formation in the matrix. The reasonable sintering temperature range was 1195–1205 °C, and the optimum mechanical properties had the hardness of 63.9 HRC, bending strength of 2112.65 MPa and impact toughness of 7.92 J/cm2. What is more important impact abrasive wear test results indicated 15 wt% Cr sintered HCCI’s wear resistance could be comparable to 20 wt% Cr sintered HCCI under impact energy 1~3 J/cm2, and it is more cost effective

Fish-bone diagram of research issue: Gain a bird's-eye view on a specific research topic

Novice researchers often face difficulties in understanding a multitude of academic papers and grasping the fundamentals of a new research field. To solve such problems, the knowledge graph supporting research survey is gradually being developed. Existing keyword-based knowledge graphs make it difficult for researchers to deeply understand abstract concepts. Meanwhile, novice researchers may find it difficult to use ChatGPT effectively for research surveys due to their limited understanding of the research field. Without the ability to ask proficient questions that align with key concepts, obtaining desired and accurate answers from this large language model (LLM) could be inefficient. This study aims to help novice researchers by providing a fish-bone diagram that includes causal relationships, offering an overview of the research topic. The diagram is constructed using the issue ontology from academic papers, and it offers a broad, highly generalized perspective of the research field, based on relevance and logical factors. Furthermore, we evaluate the strengths and improvable points of the fish-bone diagram derived from this study's development pattern, emphasizing its potential as a viable tool for supporting research survey.Comment: This paper has been accepted by IEEE SMC 202

Long-term care insurance and labor-force participation of adult children: an analysis of substitution and anticipation effects

BackgroundMany informal caregivers at working age and face the dual burden of providing care and working. This study examines how China’s long-term care insurance (LTCI) pilot programs affect the labor-force participation of adult children who may provide informal care to parents.MethodsWe analyze four waves (2011, 2013, 2015, and 2018) of micro panel data from the China Health and Retirement Longitudinal Study and exploit the staggered rollout of LTCI pilots across cities from 2012 to 2017. A difference-in-differences design estimates the causal impact of LTCI implementation on labor-force participation of adult children, with robustness checks and subgroup analyses by gender, age, cohabitation status, and skill level.ResultsImplementation of LTCI significantly increases the likelihood of adult children remaining in the labor force. Mechanism analysis indicates this effect is driven by both reduced caregiving time (substitution effect) and improved expectations of future support (anticipation effect). The positive impact is particularly strong among men, individuals under 45 years old, cohabitation without parents, and lower-skilled workers.ConclusionExpanding LTCI can effectively alleviate the caregiving-employment conflict and enhance labor participation of adult children. To maximize workforce and social welfare benefits, policymakers should expand LTCI coverage, strengthen community care services, and focus support on high-burden caregiver groups

Klotho-derived peptide KP1 ameliorates SARS-CoV-2-associated acute kidney injury

Introduction: The severe cases of COVID-19, a disease caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), often present with acute kidney injury (AKI). Although old age and preexisting medical conditions have been identified as principal risk factors for COVID-19-associated AKI, the molecular basis behind such a connection remains unknown. In this study, we investigated the pathogenic role of Klotho deficiency in COVID-19-associated AKI and explored the therapeutic potential of Klotho-derived peptide 1 (KP1).Methods: We assessed the susceptibility of Klotho deficient Kl/Kl mice to developing AKI after expression of SARS-CoV-2 N protein. The role of KP1 in ameliorating tubular injury was investigated by using cultured proximal tubular cells (HK-2) in vitro and mouse model of ischemia-reperfusion injury (IRI) in vivo.Results: Renal Klotho expression was markedly downregulated in various chronic kidney disease (CKD) models and in aged mice. Compared to wild-type counterparts, mutant KL/KL mice were susceptible to overexpression of SARS-CoV-2 N protein and developed kidney lesions resembling AKI. In vitro, expression of N protein alone induced HK-2 cells to express markers of tubular injury, cellular senescence, apoptosis and epithelial-mesenchymal transition, whereas both KP1 and Klotho abolished these lesions. Furthermore, KP1 mitigated kidney dysfunction, alleviated tubular injury and inhibited apoptosis in AKI model induced by IRI and N protein.Conclusion: These findings suggest that Klotho deficiency is a key determinant of developing COVID-19-associated AKI. As such, KP1, a small peptide recapitulating Klotho function, could be an effective therapeutic for alleviating AKI in COVID-19 patients

STAGER checklist: Standardized testing and assessment guidelines for evaluating generative artificial intelligence reliability

Generative artificial intelligence (AI) holds immense potential for medical applications, but the lack of a comprehensive evaluation framework and methodological deficiencies in existing studies hinder its effective implementation. Standardized assessment guidelines are crucial for ensuring reliable and consistent evaluation of generative AI in healthcare. Our objective is to develop robust, standardized guidelines tailored for evaluating generative AI performance in medical contexts. Through a rigorous literature review utilizing the Web of Sciences, Cochrane Library, PubMed, and Google Scholar, we focused on research testing generative AI capabilities in medicine. Our multidisciplinary team of experts conducted discussion sessions to develop a comprehensive 32‐item checklist. This checklist encompasses critical evaluation aspects of generative AI in medical applications, addressing key dimensions such as question collection, querying methodologies, and assessment techniques. The checklist and its broader assessment framework provide a holistic evaluation of AI systems, delineating a clear pathway from question gathering to result assessment. It guides researchers through potential challenges and pitfalls, enhancing research quality and reporting and aiding the evolution of generative AI in medicine and life sciences. Our framework furnishes a standardized, systematic approach for testing generative AI's applicability in medicine. For a concise checklist, please refer to Table S or visit GenAIMed.org . Highlights This work formulates the standardized testing and assessment guidelines for evaluating generative artificial intelligence (AI) reliability (STAGER) checklist, a 32‐item framework offering standardized assessment guidelines tailored for evaluating generative AI systems in medical and life science contexts. It consists of key aspects, including question collection, querying approaches, and assessment techniques. It enhances research quality and facilitates advances in this emerging field

Graduate recital in voice

The repertoire for my recital includes a range of eras from approximately 1600 to the twentieth century and a range of styles including Baroque opera, German Lieder by Schumann, French Melodies by Fauré and an English song cycle by Benjamin Britten. These four music genres in contrasting styles give listeners different emotions that split up the poetic lyrics and rhythmic changes.California State University, Northridge. Department of Music

Mechanical Properties and Tensile Failure Mechanism of Friction Stir Welded 2219-T6 and 5A06-H112 Joints

Friction stir welding was employed to weld dissimilar 2219/5A06 Al alloys in this work. The influences of alloy positioning on the mechanical properties and fracture behavior of the joints were studied via fracture morphology observation and microstructural analysis. The results show that the difference in the plastic flow and thermal field in the welding process is caused by different basic material configurations, which results in the formation of a free strengthening phase zone and microstructural heterogeneity in the joint. The low-hardness texture component caused by the free strengthening phase zone and microstructural heterogeneity becomes crack initiation, and a crack tends to propagate along the grain boundaries. Finally, when the stronger 2219-T6 alloy was placed on the advancing side, the joints had better tensile properties. The average tensile strengths of the 2A5R and 5A2R joints can reach 79.8% (343 MPa) and 78.4% (337 MPa) of the 2219 base material, respectively

Mechanical Properties and Tensile Failure Mechanism of Friction Stir Welded 2219-T6 and 5A06-H112 Joints

Friction stir welding was employed to weld dissimilar 2219/5A06 Al alloys in this work. The influences of alloy positioning on the mechanical properties and fracture behavior of the joints were studied via fracture morphology observation and microstructural analysis. The results show that the difference in the plastic flow and thermal field in the welding process is caused by different basic material configurations, which results in the formation of a free strengthening phase zone and microstructural heterogeneity in the joint. The low-hardness texture component caused by the free strengthening phase zone and microstructural heterogeneity becomes crack initiation, and a crack tends to propagate along the grain boundaries. Finally, when the stronger 2219-T6 alloy was placed on the advancing side, the joints had better tensile properties. The average tensile strengths of the 2A5R and 5A2R joints can reach 79.8% (343 MPa) and 78.4% (337 MPa) of the 2219 base material, respectively.</jats:p