bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deterioration mechanism and health status prediction of hydrocarbon lubricants under high temperature and humid environment
Authors: Su Rui , Cao Wei , Jin Zili , Wang Yifan , Ding Letian , Maqsood Muhammad , Wang Dong 
Published: January 2024

Abstract
In practical engineering applications, high temperatures and water ingress seriously affect the service life of hydrocarbon lubricants. In this study, the deterioration process of hydrocarbon lubricants under high temperatures and humid environments was investigated, and a new health state prediction model was proposed. Simulation of hydrocarbon lubricant Polyalpha-olefin (PAO) molecules using the ReaxFF force field to analyse the high temperature thermal oxidation process of lubricants. The rheological properties of oil-water emulsions were determined by observing the morphology of oil-water two-phase mixtures with different water contents. A multiparameter fusion viscosity prediction model was proposed using a linear model of the viscosity of aqueous fluids as affected by temperature and water content fitted with the Andrade viscosity-temperature equation to predict lubricant viscosity changes under multiple parameters. On-line validation tests were carried out on a compound planetary transmission system, and the surface topographical parameters of the transmission components were further discussed. The experimental results show that the linear correlation with the improved lubricant viscosity prediction model is 0.966, and the surface wear of transmission components is consistent with the trend of lubricant quality change. These findings provide a fundamental basis for the assessment of lubricant service life in high temperatures and humid environments.
Associations of genetically determined circulating proteins with breast cancer risk or survival
Authors: Chen Hanghang , liu qi , Cheng Xufeng 
Published: January 2024

Abstract
Background
There are few large-scale studies that focus on the associations between circulating proteins and breast cancer (BC) risk or survival. This study aimed to evaluate the potential circulating proteins associated with BC risk or survival using the Mendelian randomization (MR) method.
Methods
We collected the protein quantitative trait locus (pQTL) data of 4,907 circulating proteins from the DeCODE study (n = 35,559) as exposures. We gathered the genome wide association study (GWAS) data of BC from BCAC (OncoArray, n = 138,508) and BCAC (iCOGS, n = 76,167). The FinnGen study (n = 224,737) as the outcomes. The BC survival data was obtained from BCAC (OncoArray, n = 91,686). We used two sample MR framework to assess the associations between genetically predictive proteins and BC risk. Besides strict quality control, sensitivity tests and false discovery rate (FDR) or bonferroni correction, we further performed meta-analysis to ensure the robustness of the results.
Results
Four proteins—SIA4B (OR = 0.58, 95% CI (confidence interval): 0.51–0.64), CDH1 (OR = 0.83, 95% CI: 0.77–0.89), ALPI (OR = 0.91, 95% CI: 0.90–0.93) and CCDC134 (OR = 0.84, 95% CI: 0.80–0.88) are associated with reduced BC risk. 57 circulating proteins passed the sensitivity test and causally associated with BC survival.
Conclusions
Genetically predicted four circulating proteins (SIA4B, CDH1, ALPI and, CCDC134) are associated with reduced BC risk. 57 proteins are associated with BC survival. Our analyses from genetics and MR provide insights into the causes of BC and add evidence for reducing the risk of BC.
Genetic structure of interspecific hybridization between the long corolla tube species Isodon longitubus and its short corolla tube congener I. inflexus
Authors: Yamashiro Tadashi , Yamashiro Asuka , Dhozono Ikumi , Maki Masayuki 
Published: January 2024

Abstract

Interspecific hybridization between the short corolla tube species Isodon inflexus and the long corolla tube species I. longitubus was analyzed using genetic and morphometric markers. Bayesian clustering analysis using microsatellites revealed that plants in the contact zone consisted of two I. inflexus individuals, 33 I. longitubus individuals, and 13 hybrid individuals (F2-like and backcrosses to both I. inflexus and I. longitubus). Using the sequences of the psbA-trnH inter genic spacer in chloroplast DNA, three haplotypes were found among pure I. inflexus, while four haplotypes were found among pure I. longitubus. Most hybrid individuals had a haplotype found only in I. inflexus, suggesting that the initial F1 might have been formed by hybridization with I. inflexus as the maternal parent, and that in later hybrid formation, hybrids or I. inflexus tended to serve as the maternal parent. Although strong prezygotic isolation mechanisms contribute to preventing hybridization between the species, human habitat disturbance might have created the contact zone. Although mature hybrids in the contact zone contained equal proportions of backcrosses to both I. inflexus and I. longitubus, seedlings comprised more individuals originating from backcrosses to I. longitubus. The dominance of backcrosses to I. longitubus was probably caused by the quantitative difference between the parental species in the contact zone. In the contact zone, signs of contemporary hybridization in the nuclear genome might have been diluted by repeated backcrossing. The present study could illustrate the process of unidirectional introgression leading to chloroplast capture, a phenomenon frequently observed in Isodon species in Japan.
Resource utilization of waste medicines: a case study of HCQS for oilfield water treatment
Authors: Luo Yi , Xu Zhongying , Wang Yefei , Li Qiongwei , Chen Gang , Tang Ying 
Published: January 2024

Abstract
Since the outbreak of COVID-19 in 2019, the accumulation of related medicines has led to the disposal of waste medicines, while current disposal of medicines waste huge resources and increases carbon emissions as well. The oil field is a special closed-loop system, where the produced water is internally recycled after water treatment without any discharge. Therefore, oilfield water treatment provides an environment where waste medicines do not interact with the biosphere. In this study, hydroxychloroquine sulfate (HCQS) tablets were used as a fungicide and corrosion inhibitor in oilfield water treatment. The weight loss experiment showed that HCQS exhibit high corrosion inhibition efficiency under acidic conditions. And the corrosion inhibition rate decreases with the increase of temperature. At 333 K, with the concentration of 200 mg/L, the corrosion inhibition rate of HCQS reaches the maximum of 90.86%. The adsorption of HCQS on the surface of low carbon steel conforms to the Langmuir adsorption isotherm and belongs to physical adsorption. The thermodynamic simulation indicates that the adsorption of HCQS on steel surface is an exothermic, spontaneous and entropy reduction process. The electrochemical test results show that HCQS is a mixed inhibitor to the anodic reaction. In addition, the bactericidal effect of HCQS was evaluated, and it was found that when the concentration was 1000 mg/L, the bactericidal rate of HCQS to three kinds of oilfield microorganisms was more than 95%. Finally, the cost-benefit analysis was conducted to discuuse the application feasibility. This simple and effective method can be provided to enable resourceful disposal of waste medicines.
Image dehazing in VLSI using advance DL and ML techniques
Authors: Babu M. Suresh , Devi D. Asha , Chowdary Muthyala. V.V.S. , Ananthula Pranaynath Reddy 
Published: January 2024

Abstract
Image dehazing is a crucial aspect of atmospheric conditions, particularly haze, and its impact on various aspects of daily life and technology. Haze can significantly reduce visibility, affecting both human perception and the performance of machine vision systems. Haze reduces the contrast and sharpness of visual scenes, making it challenging for both humans and machines to perceive and interpret the environment accurately. In applications like autonomous driving, where computer vision plays a vital role, haze can pose serious safety concerns. Reduced visibility may lead to difficulties in detecting obstacles, pedestrians, or other vehicles. Haze introduces a loss of image quality, affecting the performance of computer vision algorithms. This degradation is particularly evident in tasks such as object detection, recognition, and tracking. Haze can mislead machine learning models by introducing artifacts and inaccuracies in the training data. This can result in models that are less reliable in real-world scenarios affected by haze. Image dehazing is a crucial pre-processing step in computer vision tasks that involve images captured in hazy conditions. Dehazing algorithms aim to enhance visibility, reduce the impact of haze, and improve the overall quality of the image.
Prefrontal multistimulus integration within a dedicated disambiguation circuit guides interleaving contingency judgment learning
Authors: Korzus Edward , Pastore Justin , Mayer Johannes , Bailey Tyler , III John Speigel , Steinhauser Jordan , Shuler Kylene , Papalexakis Evangelos 
Published: January 2024

Abstract
Understanding how cortical network dynamics support learning is a challenge. This study investigates the role of local neural mechanisms in the prefrontal cortex during contingency judgment learning (CJL). To better understand brain network mechanisms underlying CJL, we introduced ambiguity into associative learning after fear acquisition, inducing a generalized fear response to an ambiguous stimulus sharing nontrivial similarities with the conditioned stimulus. Real-time recordings at single-neuron resolution from the prelimbic (PL) cortex using miniature microscopes revealed distinct PL network dynamics across CJL phases. Fear acquisition triggered PL network reorganization, led by a disambiguation circuit managing spurious and predictive relationships during cue–danger, cue–safety, and cue–neutrality contingencies. Subjects with PL-targeted memory deficiency showed malfunctioning disambiguation circuit function, while naive subjects lacking unconditioned stimulus exposure lacked a CJL-specific disambiguation circuit. This study uncovers that fear conditioning induces prefrontal cortex cognitive map reorganization, and subsequent CJL relies on the disambiguation circuit's ability to learn predictive relationships.
Enhancing Urban Resilience: Managing Flood-Induced Disruptions in Road Networks
Authors: Salvo Giuseppe , Karakikes Ioannis , Papaioannou Georgios , Polydoropoulou Amalia , Sanfilippo Luigi , Brignone Alberto 
Published: January 2024

Abstract

Emergency preparedness is essential for ensuring the safety and protection of the population in the event of a disaster. To develop effective emergency plans, it is imperative to prepare and assess appropriate response scenarios. An event (an accident or a climatic event) that occurred on a primary road artery could lead to partial or total closure of the artery to vehicular traffic for several hours or days, with negative consequences for vehicular circulation along the transport network including the structure itself. Therefore, in addition to reestablishing the functionality of the affected arterial road, reestablishing the functionality of the affected arterial road is highly important for determining possible alternative routes capable of providing an adaptive response to disruption. The aim of this paper is to improve the resilience of road infrastructures and to ensure reliable network availability under adverse conditions, such as extreme weather. The results of a study carried out in the city of Palermo to develop emergency response scenarios for heavy rainfall are presented. A dynamic microsimulation model was developed and implemented using Dynasim to model the main traffic corridors in the at-risk area. The impact of different emergency management strategies on mobility in response to natural disasters was assessed. The results can guide local public safety agencies in implementing effective traffic management strategies in the event of an actual emergency.
The influence of anti-involution training on the critical thinking of young healthcare professionals in dental outpatient clinics-based on machine learning model
Authors: Chen Yuxiang , Zhao Anna , Yang Haoran , Chen Tingting , Rao Xianqi , Zhou Jianzhong , Li Lin , Li Jing , Li Ziliang 
Published: January 2024

Abstract

Background
The relationship between the impact of anti-involution training on critical thinking and its propensity indicators among young healthcare professionals in dental outpatient clinics remains to be determined. Therefore, this study aimed to investigate these associations and develop an interpretable machine learning (ML) model to assess their predictive value in enhancing critical thinking through anti-involution training.
Methods
A cross-sectional survey encompassing 114 participants was conducted. Spearman correlation analysis was utilized to evaluate the association between propensity indicators and the enhancement of critical thinking through anti-involution training. Subsequently, the data underwent normalization utilizing the “MinMaxScaler” technique, while balancing was achieved by applying the synthetic minority oversampling technique (SMOTE). Following this, predictors were identified using the most minor absolute shrinkage and selection operator (LASSO) regression. Next, diverse machine learning algorithms constructed an individual prediction model to enhance critical thinking through anti-involution training. The prediction model's performance was assessed using receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA). The Shapley additive interpretation (SHAP) method was utilized to interpret the ML model.
Results
Truth-seeking, analytical thinking, and inquisitiveness were identified as predictive factors for enhancing critical thinking. A Random Forest algorithm-based model incorporating these variables yielded favorable results: AUC = 0.889 (95% CI: 0.839–0.937), accuracy = 0.850, sensitivity = 0.855, specificity = 0.933.
Conclusion
The inclinations toward truth-seeking, analytical thinking, and inquisitiveness significantly correlate with the effectiveness of anti-involution training in enhancing critical thinking. Our simplified ML-based predictive model allows for preliminary forecasting, enabling early intervention and guidance for learners facing difficulties in improving critical thinking.
Microbiologically produced surfactin as a factor limiting the use of pesticide against cereal pests
Authors: Koim-Puchowska Beata , Lamparski Robert , Dróżdż-Afelt Joanna Maria 
Published: January 2024

Abstract

The use of pesticides in plant protection is vital in order to acquire food, however, it involves contamination of the environment. The purpose of this paper was to verify the use of the surfactant – surfactin, synthesised by the Bacillus subtilis natto BS19 strain as a measure in the biocontrol of cereal pests. The possibility of using trub as a substrate in the biosynthesis of this compound was investigated. The ability of pest feeding, survival outcomes and the number of laid eggs in response to lambda-cyhalothrin and surfactin in different concentrations were assessed. The most promising results were received after applying a solution containing 46 mg/L of surfactin and 75% of the maximum pesticide dose. After applying the product, a reduction of leaf damage was found to be around 59% and 67% regardless of, while after applying only the pesticide solution: 45% and 57% in Oulema melanopus and Oulema gallaeciana respectively. Applying the same variant of product containing surfactin limited the survival outcomes of insects by around 80%, and after applying the pesticide by 60%. Thus, it can be inferred that an addition of a trub-based lipopeptide in an optimal concentration may minimise the used pesticide dose and therefore limit feeding and survival outcomes of Oulema spp., which is lucrative in terms of ecology and economy. Trub may constitute a substrate for the biosynthesis of surfactin of stable surface-active properties over a wide temperature and pH range of the environment
Rural Indian Women Speak: Insights on Maternal Health Literacy and Access Gaps Regarding Obstetric Emergencies
Authors: BM Bhavana , M Yogesh , Padhiyar Nanveet 
Published: January 2024

Abstract
Background: Maternal morbidity and mortality persist due to delays in seeking care for obstetric complications. Deficits in comprehending danger signs contribute to preventing early identification. There is limited research on awareness levels among rural Indian women.
Objectives: The present study aimed to assess the knowledge of obstetric danger signs among pregnant women in rural Gujarat and explore the associated sociodemographic and obstetric history factors. Qualitative methods further elucidated the experiences, attitudes, and perceptions underlying maternal health literacy.
Methods: A mixed-methods study was conducted among 390 antenatal women selected through multistage sampling from 6 community health centres in the Jamnagar district. A structured questionnaire and in-depth interviews were used for data collection. Quantitative data were analysed via regression modelling, and qualitative data were thematically analysed.
Results: Among the 390 patients, the majority correctly identified pregnancy bleeding (73.3%, n=286), leakage of fluid (71%, n=277), and swelling (74.3%, n=290) as danger signs. Approximately 54.8% (n=214) had good knowledge of dangersigns. Knowledge was significantly greater among women aged >25 years (AOR 3.11, 95% CI 2.02-4.7), among literate women (AOR 2.14, 95% CI 1.12-4.08), among those counselled during ANC (AOR 2.2, 95% CI 1.2-8.1) and among those counselled during PNC (AOR 4.4, 95% CI 1.8-10.7). Qualitative findings revealed gaps in recall, poor comprehension of symptoms, and sociocultural barriers to care-seeking.
Conclusion: Despite interfacing with health services, awareness of obstetric danger signs was incomplete among rural antenatal women. Tailored education through enhanced counselling and family-centered care can improve maternal health literacy.
Prediction Model for Lymph Node Metastasis in Papillary Thyroid Carcinoma Based on Electronic Medical Records
Authors: Zhang JingWen , Zhang XiaoWen , Xia ShuJun , Dong YiJie , Zhou Wei , Liu ZhenHua , Zhang Lu , Zhan WeiWei , Sun YuZhong , Zhou JianQiao 
Published: January 2024

Abstract
Purpose
This study aimed to establish a novel machine learning model for predicting lymph node metastasis(LNM)of patients with papillary thyroid carcinoma (PTC) by utilizing personal electronic medical records (EMR) data.
Methods
The study included 5076 PTC patients underwent total thyroidectomy or lobectomy with lymph node dissection. Based on the integrated learning approach, this study designed a predictive model for LNM. The predictive model employs deep neural network (DNN) models to identify features within cases and vectorize clinical data from electronic medical records into feature matrices. Subsequently, a classifier based on machine learning algorithms is designed to analyse the feature matrices for prediction LNM in PTC. To mitigate the risk of overfitting commonly associated with machine learning algorithms processing high-dimensional matrices, multiple DNNS are utilized to distribute the overfitting risk. Five mainstream machine learning algorithms (NB, DT, XGB, GBM, RDF) are tested as classifier algorithms in the predictive model. Model performance is assessed using precision, recall, F1, and AUC.
Results
Among the patients, 2,261 had lymph node metastasis (LNM), with 2,196 displaying central lymph node metastasis (CLNM) and 472 exhibiting lateral cervical lymph node metastasis (LLNM). The RDF model showcased superior predictive performance compared to other models, achieving a testing AUC of 0.98, precision of 0.98, recall of 0.95, and F1 value of 0.97 in predicting LNM. Moreover, it attained an AUC of 0.98, precision of 0.98, recall of 0.94, and an F1 value of 0.96 in predicting CLNM. Regarding the weighting of the feature matrix for various case data types, gender and multi-focus held higher weights, at 1.24 and 1.23 respectively.
Conclusion
The LNM predictive model proposed in this study could be used as a cost-effective tool for predicting LNM in PTC patients, by utilizing easily available personal electronic medical data, which can provide valuable support to surgeons in devising a personalized treatment plan.
Experimental investigation of the microstructural and wear behaviours of silicon carbide and boron nitride-reinforced AZ91D magnesium matrix hybrid composites
Authors: Ramadoss Natarajan , Vijayakumar P. ,   P Praveen Raj3, Shoba M Kantha 
Published: January 2024

Abstract

This study aimed to fabricate hybrid metal matrix composites of AZ91D magnesium reinforced with varying various weight percentages of SiC and constant weight percentages of BN particles through the stir-squeeze casting method. The influence of the particle ratio on the microstructure and wear behaviour of the composites was studied. The dispersion patterns of particles within the matrix and the interactions between the alloy and the particles were thoroughly investigated using a variety of techniques, including optical microscopy, SEM, EPMA, and EDS.XRD analysis of the AZ91D/SiC/BN hybrid composite revealed a significant volume proportion of the strong Mg17Al12 phase. The synthesized magnesium hybrid composites (AZ91D/9%SiC/3%BN) experienced a volume loss reduction of up to 36.16% under a maximum load of 30 N and a maximum speed of 1 m/s when compared with the monolithic material AZ91D. The results of these analyses demonstrated that the resulting composites exhibited an even dispersion of particles, superior grain structure, and strong interfacial bonding between the AZ91 alloy and the reinforcing particles. The newly developed magnesium hybrid composites have better wear performance than monolithic AZ91D alloys. These findings highlight the enhanced wear resistance of the fabricated composites for antiwear applications.
Elemen Pembentuk Sense of Place Taman Nostalgia sebagai Ruang Terbuka Publik Kota Kupang
Authors: Gon Yulia Isadora Alvita , Rayawulam Robertus Mas , Mberu Yuliana Bhara , Lake Reginaldo Christophori , Jeraman Pilipus 
Published: January 2024

Title: Elements Forming the Sense of Place of Taman Nostalgia as a Public Open Space in the City of Kupang

This research aims to identify the elements that form the sense of place in Taman Nostalgia as a green open space in Kupang. Sense of place is a psychological phenomenon that affects users' perceptions and relationships with their environment. Shamai's theory is used as the basis for the conceptual framework, which is then operationalized through interview and questionnaire methods as data collection tools. This study involves active participants, namely visitors to Taman Nostalgia. In-depth interviews are conducted to gain a detailed understanding of individual visitors' experiences with Taman Nostalgia, while the questionnaire is used to obtain broader data from visitors. The research findings indicate that the sense of place in Taman Nostalgia is strengthened by the presence of the Gong Perdamaian Nasional as one of the significant physical elements. Other relevant elements include architectural design, vegetation, and cultural elements, which also contribute to forming the sense of place. The implications of these findings can be used as a basis for the development and planning of public green open spaces, considering the elements that enhance users' emotional relationships with the urban environment.