bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of the impact of isothermal hardening on the mechanical properties and abrasion resistance of welded joints on Hardox Extreme steel
Authors: Zemlik Martyna , Konat Łukasz , Białobrzeska Beata , Skoczylas Paweł , Jamroziak Krzysztof 
Published: November 2024

Abstract

The object of the research reported in this study was a welded joint of Hardox Extreme steel, made using submerged arc welding (SAW) and subjected to thermal treatments involving isothermal hardening in various temperature-time variants. This treatment serves as an alternative to conventional hardening, enabling the achievement of high mechanical indices in selected cases due to the formation of fine-lath martensite or lower bainite microstructures. Heat-treated joints were analyzed macro- and microstructurally using stereoscopic, light (LM), and scanning electron microscopy (SEM). The study also determined selected mechanical properties, such as hardness, tensile strength, relative elongation, and reduction of area at break, as well as impact toughness at ambient and reduced temperatures. A separate section was dedicated to characterizing abrasion resistance in the presence of loose abrasive, along with determining the relationship between this parameter and the identified mechanical characteristics. Based on the analysis of the microphotographic images obtained, the main wear mechanisms were also characterized. The analysis of the results allowed the conclusion that in the case of isothermal hardening, the factor determining the obtained microstructural and mechanical properties is the temperature of the performed thermal operations. Furthermore, after the conducted thermal treatments, the parameters characterizing the ductility of the welded joint improved by several percent compared to the state immediately after welding. Therefore, the main goal of the technological operations conducted on welded joints of high-strength steels can be defined as improving ductility, which is justified in applications considering alternatives even to structural steels.
Non-Linear Dynamics in ECG: A Novel Approach for Robust Classification of Cardiovascular Disorders
Authors: Bhattacharya Debanjali , Behera Suraj Kumar , Aithal Ninad , Sinha Neelam 
Published: November 2024

Abstract

Multi-channel ECG-based cardiac disorders detection has an impact on cardiac care and treatment. Limitations of existing methods included variation in ECG waveforms due to the location of electrodes, high non-linearity in the signal, and amplitude measurement in millivolts. The present study reports a nonlinear analysis-based methodology that utilizes Recurrence plot visualization. The patterned occurrence of well-defined structures, such as the QRS complex, can be exploited effectively using Recurrence plots. This Recurrence-based method is applied to the publicly available Physikalisch-Technische Bundesanstalt dataset from PhysioNet database, where we studied four classes of different cardiac disorders (Myocardial infarction, Bundle branch blocks, Cardiomyopathy, and Dysrhythmia) and healthy controls, achieving an impressive classification accuracy of 100%. Additionally, t-SNE plot visualizations of the latent space embeddings derived from Recurrence plots and Recurrence Quantification Analysis features reveal a clear demarcation between the considered cardiac disorders and healthy individuals, demonstrating the potential of this approach.
Modelling phosphorus inputs and dynamics in a large tropical semiarid basin
Authors: Neto Iran Eduardo Lima 
Published: November 2024

Abstract

This study proposed a simple process-based model to predict total phosphorus (TP) inputs and dynamics in a large tropical semiarid basin in Brazil (19,015 km²), where the Castanhão dam, the largest non-hydropower reservoir in Latin America, is located. The model solved on a monthly basis the water balance and TP dynamics along the river system, considering complete mixing at the cross-section, steady-state regime and a piston-type transport with first-order decay. Both primary and secondary data were used to validate the model, with deviations of up to about 20%. A linear increase in the flow rate along the river was observed, indicating that transmission gains were dominant. TP concentration increased with river flow rate and a consistent decay along the system was observed. TP from point-sources (PS) was assumed constant. TP from non-point sources (NPS) decreased with time (R² = 0.90) due to the continuous wash-off process. NPS was the dominant load, decreasing respectively from 99 to 61% of the total load from high to low flows. TP load varied from 1-141 ton/month, with an average of 34 ton/month. This resulted in an average TP yield of 21.2 kg km-2 year-1, which is significantly lower than the national average due to the peculiarities of the region: lower precipitation, intermittent rivers and high-density reservoir network. All the simulated TP values were above the mandatory limit of 0.05 mg/L. PS reduction was only effective for relatively low flows, while NPS reduction was the most relevant remediation measure for both high and low flows. The impact of climate change on TP concentration also yielded TP > 0.05 mg/L for all the projected scenarios, with the input loads to the Castanhão reservoir spanning from 23 to 266 ton/month. The proposed model can also be applied to other regions with different hydroclimatic conditions and land uses.
Dislocations govern noble metal exsolution in perovskite oxide
Authors: Kim Hyunjung , Choi Sungwook , Guha Puspendu , Kim Hayoung , Kim Jaeseung , Kim Sungwon , Harder Ross , Cha Wonsuk , Suh Hoyoung , Ryu Jinseok , Yang Sungeun , Ji Ho-Il , Kwon Deok-Hwang 
Published: November 2024

Abstract

Self-assembled metal nanoparticles exsolved from host oxides have gained prominence in catalysis and electrochemistry owing to their exceptional activity and stability1–3. Understanding the relation between dopant transport and exsolution is important, as the transport mechanism of dopants toward the surface of the host oxide directly influences exsolution sites, density, and dispersion, ultimately determining catalytic functionality4–6. However, the pathways for dopant transport and their interaction with internal defects during exsolution remain unclear because of the complexity of defects hidden in bulk. Here, we reveal the exsolution pathway mediated by dislocation evolution within a host oxide perovskite. By employing in situ Bragg coherent X-ray diffraction imaging and transmission electron microscopy, we show that dislocations nucleate in the bulk interior and propagate to the surface during the reduction of Ru-doped in BaCe0.85Y0.1Ru0.05O3-δ. Moreover, we verify that the Ru dopant is specifically correlated with the formation of mixed dislocations, which then provide transport pathways for the exsolution of Ru nanoparticles. These findings advance our understanding of dislocation dynamics and support the development of exsolved metal nanoparticles for next-generation catalysts7,8.
Impact of Frailty and Clonal Hematopoiesis on Cardiovascular Outcomes in Elderly Patients with Renal Artery Stenosis Undergoing Stenting
Authors: Li Peng , Wang Yiyang , Wang Yang , Ai Hu , Li Yongjun , Ren Junhong 
Published: November 2024

Abstract

Background:
Frailty and clonal hematopoiesis of indeterminate potential (CHIP) have emerged as crucial predictors of adverse cardiovascular outcomes in older adults. However, their combined impact on major adverse cardiovascular events (MACE) in patients with severe atherosclerotic renal artery stenosis (ARAS) remains unclear.
Methods:
We conducted a prospective cohort study involving 175 patients aged 60 years and older with severe ARAS (luminal stenosis ≥ 70%) who underwent renal artery stenting at Beijing Hospital between January 2019 and December 2022. Frailty was assessed using the Fried phenotype, categorizing patients into robust, prefrail, and frail subgroups. CHIP status was determined through targeted gene sequencing of peripheral blood, stratifying patients into No CHIP (VAF < 2%), Small CHIP (VAF 2%-<10%), and Large CHIP (VAF ≥ 10%) subgroups. All patients were systematically followed up until June 30, 2024. The primary outcome was the incidence of MACE, which was a composite of renal function deterioration (RFD), initiation of renal replacement therapy, renal artery revascularization, nonfatal myocardial infarction, hospitalization for heart failure, nonfatal stroke, and cardiorenovascular death. We employed Cox proportional hazards models, Kaplan-Meier survival analysis, and heatmaps to explore the combined impact of frailty and CHIP on MACE risk.
Results:
The mean age of the patients was 68.3 years. Of the cohort, 64.6% had no CHIP, 26.8% had Small CHIP, and 8.6% had Large CHIP. Frail patients showed a higher prevalence of CHIP, particularly in the Small (34.7%) and Large (10.2%) CHIP categories. During a median follow-up of 32 months, 54 MACE occurred. Kaplan-Meier survival curve revealed that frailty was associated with a higher incidence of MACE (35.7% in frail vs. 29.5% in prefrail vs. 24.6% in robust, P = 0.045) and RFD (16.3% in frail vs. 11.5% in prefrail vs. 7.7% in robust, P = 0.034). Patients with Large CHIP experienced significantly higher rates of MACE (60.0% vs. 36.2% in Small CHIP vs. 24.8% in No CHIP, P = 0.004) and RFD (26.7% vs. 14.9% in prefrail vs. 8.0% in robust, P = 0.019). Findings for RFD appeared to be consistent with those for MACE. Frailty and CHIP status showed independent contribution to overall risk. The greatest spread for MACE and RFD risk was obtained in models that incorporated frail and Large CHIP.
Conclusion:
Frailty and CHIP, independently and jointly, contribute to a significantly higher risk of MACE and RFD in elderly patients with severe ARAS undergoing stenting. These findings highlight the necessity for integrated risk stratification and targeted management strategies in this high-risk population.
Challenges in reducing high grand multiparity rates in Ethiopia: EDHS data 2000-2019 toward Sustainable Development Goals 2030: Using multilevel model approach
Authors: Dibaba Diriba , Charkos Tesfaye Getachow 
Published: November 2024

Abstract

Background
One of the Sustainable Development Goals (2030) focuses on reducing the total fertility rate. Reducing grand multiparity in Ethiopia remains a challenge. Understanding the underlying factors that contribute to this issue is crucial for explaining why grand multiparity remains prevalent despite various health interventions and socio-economic progress.
Methods
A community-based cross-sectional study was conducted using data from the Ethiopian Demographic and Health Survey 2000–2019. Multilevel multivariable logistic regression analysis was employed to model the hierarchical data. The final findings were presented as adjusted odds ratios (AOR) with 95% confidence intervals (CI). A p-value < 0.05 was considered statistically significant.
Result
The trend analysis of grand multiparity over 19 years in Ethiopia shows no significant change (linear trend = 1.23, p = 0.27). The prevalence of grand multiparity slightly decreased from 72% in the 2000 EDHS to 66.3% (95% CI: 65.7% − 66.96%) according to the mini EDHS 2019 data. Among individual-level variables, the following were significantly associated with grand multiparity: wealth index, currently married, maternal education, non-family planning, short-acting family planning users, age at first birth < 18 years, and short birth intervals. Among community-level variables, being a rural resident was significantly associated with grand multiparity.
Conclusion
A 19-year trend analysis in Ethiopia shows no significant change in grand multiparity rates, with a slight decrease from 72% in 2000 to 66.3% in 2019. Significant factors associated with grand multiparity include wealth index, marital status, educational level, family planning utilization, age at first birth, birth interval, and place of residence.
Training Physicians to Recognize and Diagnose Rare Diseases Using an Online Serious Game
Authors: Spans An-Sofie , Buffel Connor , Claes Kathleen J , Dierickx Daan , Lambert Catherine , Luypaert Michelle , Morelle Johann , Maertens Chantal 
Published: November 2024

Abstract

Background
Increasing physicians’ awareness and knowledge of rare diseases may positively impact their ability to recognize and diagnose these conditions timely. Here, we developed a novel evidence-based online serious game about the differential diagnosis of thrombotic microangiopathies, which encompass a group of life-threatening conditions for which early recognition is critical for appropriate management.
Methods
An online serious game was developed in which participants were given a fixed amount of time to diagnose three virtual patient cases with rare diseases. Two versions were created, a 45-minute version (three cases) and a 15-min version (one case). A cross-sectional, observational study of the serious game was conducted online from October 2022 to April 2024. Participants included physicians and medical students who accessed the online serious game. The main outcomes included participant accessibility, satisfaction, and perceived change in knowledge of thrombotic microangiopathies.
Results
Participants who accessed the serious game were from 45 countries (n = 926) and chiefly from Europe (87%) followed by the U.S. & Canada (6%). Participants’ medical experience ranged from medical students or graduates (32%) to physicians with ≥ 15 years of experience (17%). At least 8 medical specialties accessed the serious game. Out of 926 participants, 716 tried to diagnose a patient case and 597 completed the serious game. Ninety-three percent (179/193) of participants answering a post-game question reported an improved or substantially improved knowledge of these rare diseases. The mean likelihood of recommending the serious game to a colleague was 8.1/10.
Conclusions
In this study, an online evidence-based serious game was widely accessed and improved the knowledge of a diverse group of physicians. Online serious games offer potential to train physicians to recognize rare diseases. Additional evidence from other diseases could further support and expand the use of this medium.
Clinical trial number:
Not applicable
Trends in maternal mortality and stillbirths by county in health facility data, Kenya, 2011-2022
Authors: Muthee Rose , Mutua Martin , Kiarie Helen , Kagiri Hannah , Serem Edward , Muchemi Simon , Wabwire Scola , Boerma Ties 
Published: November 2024

Abstract

Background: Reports on maternal deaths and stillbirths in health facilities are a critical but underutilized source of information to monitor the quality of care. In addition, with increasing coverage of deliveries by health facilities, such data can provide population estimates of maternal mortality and stillbirth rates. Data quality concerns, however, have often deterred use of facility data.
Methods: Data from the routine monthly reporting system in Kenya were used to assess levels and trends in maternal mortality and stillbirth rates in 47 counties from 2011-2022. Data quality was assessed using multiple methods, including consistency of annual reporting of live births, stillbirths and maternal deaths by counties, plausibility of the ratio of reported stillbirths to maternal death, the county institutional mortality in comparison to delivery coverage, socioeconomic development and health system characteristics and the plausibility of estimated population levels of mortality.
Results: Institutional live birth coverage increased to 87.8% in 2021-2022, ranging from 49% to 99% in counties. Kenya and 39 of its 47 counties experienced a decline in institutional maternal mortality ratio and stillbirth rate during the study period 2011-2022. The maternal mortality decline stagnated from 2018 and was 99 maternal deaths per 100,000 live births in 2022. Consistency of reported data by counties was good over time but several indicators suggest that maternal death reporting was incomplete and more so in less-developed counties. Estimates of the population maternal mortality ratio were much lower than global estimates or census results, while the stillbirth rates were consistent.
Conclusion: The health facility data on maternal death and stillbirths are an important data source for monitoring national and subnational institutional maternal mortality and stillbirth rates and can also inform population estimates. Systematic sustained assessment of reporting completeness will be critical to achieve the full potential of facility data-derived mortality monitoring.
The Role of Format Familiarity and Semantic Transparency in Chinese Reading: Evidence from Eye Movements
Authors: Chen Mingjing 
Published: November 2024

Abstract

The present study examined the role of format familiarity and semantic transparency in Chinese reading, which could reflect the segmented strategy and word-processing characteristics in Chinese reading. Forty undergraduates read Chinese in familiar and unfamiliar formats, containing the target words divided into semantically transparent and semantically ambiguous words. We used the EyeLink 1000 to measure readers’ eye movement index, which can reflect reading processing characteristics. There are several key findings: (1) the familiarity with the text format affected the Chinese reading performance. Chinese readers read shorter fixation times, higher skipping rates, and closer to the word center under left-to-right format; (2) semantic transparency affects segmented strategy and lexical processing in Chinese reading. Chinese readers read shorter fixation times, higher skipping rates, and closer to the word center under semantically transparent words; (3) among the early eye-tracking indexes, the interaction was significant. The left-to-right transparency effect was significant, whereas the right-to-left effect was not. The semantic transparency effect is an essential indicator of vocabulary recognition. The early significant interaction and the late insignificant interaction reflect the separation of Chinese word segmentation and word recognition.
Association Analysis of Maize Stem Vascular Bundle Micro-characteristics with Yield Components Based on Micro-CT and Identification of Related Genes
Authors: Wang Yanru , Zhang Ying , Huang Guanmin , Wang Jinglu , Lv Lujia , Zhao Shuaihao , Lu Xianju , Zhang Minggang , Guo Minkun , Zhang Changyu , Men Qingmei , Guo Xinyu , Zhao Chunjiang 
Published: November 2024

Abstract

Background
The distribution pattern of vascular bundles and microstructure characteristics significantly impact crop yield. Previous studies have primarily focused on investigating the micro-phenotypic characteristics and genetic regulation of individual internode, neglecting the exploration of the relationship between different internodes.
Results
This study, for the first time, comprehensively analyzed multi-scale phenotypic information of stem cross-sections, zones, and vascular bundles in three different internodes (basal third internode, BTI; ear internode, EI; highest internode, HI) of 268 inbred maize lines using Micro-CT scanning. Key findings revealed that BTI and EI exhibited more stable microscopic characteristics than HI. Cross-section and inner zone traits correlated with kernel number per row (RW_N). Inbred lines with thicker BTI and EI stems, larger inner zones, and sparsely distributed but larger vascular bundles demonstrated better yield components, including higher RW_N, kernel weight per ear (ER_KW), and grain weight per ear (ER_GW). Genome-wide association analysis identified 51, 37, and 12 candidate genes in BTI, EI, and HI, respectively, encoding metabolic enzymes, structural proteins, and components involved in plant signal transduction and stress response. Notably, the candidate gene Zm00001d035089, identified in both BTI and EI, is hypothesized to regulate radial growth of maize stalks and influence the anatomical structure of stalk tissues and vascular bundles.
Conclusions
This study provides an important theoretical basis and genetic resources for accurately identifying different internode phenotypes of maize stalks, potentially advancing the selection of high-yielding, high-quality maize varieties.
BioKMS: A Biomedical and Space Science Knowledge Mining System Based on Knowledge Graph and Link Prediction Model
Authors: Wen Yanzhong , Han Yuexia , Sun Jianfei 
Published: November 2024

Abstract

Background: In recent years, Artificial Intelligence (AI) technology, represented by deep learning, has flourished, widely empowering various scientific research. At present, it is worth-noting that biological science and computer technology are developing rapidly and massive biomedical data are accumulating, accompanied by the development of advanced AI algorithms and nascent AI-enabled biomedical research opportunities. Researchers learn to combine AI with real biomedical scenarios, expecting to improve the efficacy of research. However, it is a daunting task for most biomedical researchers, who might lack the knowledge of computer science, to use AI to assist work. Therefore, an open tool integrating advanced AI methods is needed to liberate biomedical researchers from “daily chores” and assist scientific research progress. It is desirable that researchers with little knowledge of AI may still make good use of AI technology to explore scientific clues and promote scientific research.
Results: Here, armed with the AI technology, we develop Biomedical Knowledge Mining System (BioKMS), a web-based biomedical knowledge mining system encompassing both biomedical and space science knowledge, which demonstrates the knowledge graph and link prediction model. More specifically, the knowledge mining refers to the use of effective methods to mine the internal relationships contained in a large number of data, providing scientific clues for biomedical studies. On the one hand, BioKMS enables researchers to explore the links between domain entity and obtain the visual presentation of the knowledge graph, intuitively capturing the domain-related information; On the other hand, researchers can carry out the link prediction with the encapsulated SOTA deep learning model, which predict the potential association, further revealing potential research objects and assisting decision-making by AI. In addition, in order to improve the timeliness and stability of the data, BioKMS provides flexible data feedback interface that researchers can submit new data, thus cementing the optimization of the subsequent algorithm model.
Conclusions: In this study, we proposed a novel system to help biomedical scientists use AI to predict and discover potential biological mechanisms. In addition to serving the traditional ground biomedical research, the system integrates the knowledge graph and deep learning model of space life science field to assist the space biomedical research. The BioKMS can be accessed via this URL: http://8.130.71.195:5000/.