bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Relationship Between Clinical Education Status and Academic Motivation of Clinical-Stage Medical Students at Gonabad University of Medical Sciences
Authors: H Fallah , R Ershad Sarabi , M Salajegheh , M Basiri moghadam , M Hajipour 
Published: September 2024

Introduction: Motivation in education and learning are significant challenges in clinical education. This study aimed to determine the relationship between clinical education status and academic motivation among clinical-stage medical students Gonabad University of Medical Sciences.
Method: This cross-sectional study was conducted with 198 medical interns and residents at Gonabad University of Medical Sciences during the academic year 2021-2022 using a census method. Data collection tools included the Clinical Education Status Questionnaire and the Harter Academic Motivation Questionnaire. Descriptive statistics (mean and standard deviation) were used to describe the participants' status, and data were analyzed using independent t-tests, Pearson, and Spearman correlation tests.
Results: The mean score for clinical education status among interns was 51.93 ± 16.56, while for residents, it was 12.93 ± 08.12. The mean score for academic motivation among interns was 102.07 ± 11.30, and for residents, it was 100.07 ± 11.54. Overall, the clinical education status averaged 93.31, indicating a relatively moderate level and Academic motivation averaged 101.05 ± 11.44, indicating a high level, with a significant correlation observed between clinical education status and academic motivation (r = 0.454).
Conclusion: Given the weakness in the clinical education status, as well as the direct relationship between clinical education status and academic motivation, it is recommended that university officials, particularly in the educational and clinical departments, focus on special attention and planning to improve the clinical education status at the university.
A Cross-Sectional Study on Knowledge and Use of Contraception in Damoh District, Madhya Pradesh, India
Authors: Mishra Rahul , Patidar Hemant 
Published: September 2024

Ensuring universal access to sexual and reproductive health (SRH) is one of the 17 Sustainable Development Goals (SDGs) to be achieved by 2030, adopted by the United Nations. Women’s improved reproductive health is positively associated with their knowledge and adoption of contraceptives. Contraception is crucial in determining the family size, birth interval, and women's health and falls under SDG3 and SDG5. This study aimed to highlight the knowledge and use of contraceptives in the Damoh district located in Madhya Pradesh, India. We utilised primary data collected through a personal survey using a structured Interview Schedule from eight sampling units in the district. For this, we employed descriptive statistics and regression techniques for the analysis. Results reveal that a substantial share of women had knowledge of at least a single method of contraception. However, female sterilisation and pills were the most commonly known methods for women. Nonetheless, the knowledge of contraception was not found to be reflected in the adoption as nearly one-third of all women were not using any method of contraception. Further, the level of adoption of different contraceptive methods varied across socio-demographic backgrounds. Therefore, emphasis on information, education and communication (IEC) initiatives may lead to an increase in the adoption of modern contraceptives.
Impulsive Buying Tendencies and Personality: Cognitive and Affective Aspects
Authors: Qureshi Fayyaz Hussain , Sokic Katarina , Khawaja Sarwar 
Published: September 2024

Impulsive buying is a growing problem of modern society with significant financial and psychological consequences, and it is necessary to examine the mechanisms underlying it. Understanding the mechanisms driving this behaviour is crucial. Impulsive buying consists of two key aspects: a cognitive aspect, characterised by a lack of planning and reflection, and an affective aspect, involving emotional responses that can occur before, during, or after a purchase. This study aimed to explore the relationship between personality traits, as measured by the HEXACO model, and tendencies toward impulsive buying. Specifically, it examined how the HEXACO traits differentially predict the cognitive and affective components of impulsive buying tendencies.
Exploring the Relationship Between Electrical Characteristics and Changes in Chemical Composition and Structure of OSG Low-k Films under Thermal Annealing
Authors: Gerelt-Od Mungunsuvd , Kolesnikova Tatiana G. , Mokrushev Pavel A. , Vishnevskiy Alexey S. , Vorotilov Konstantin A. , Gismatulin Andrei A. , Gritsenko Vladimir A. , Baklanov Mikhail R. 
Published: September 2024

The paper is dedicated to studying the influence of annealing temperature on the chemical, structural and electrophysical properties of low-k films made from porous organosilicate glasses containing terminal methyl groups. The films were deposited using centrifugation, followed by drying at 200 °C and annealing at temperatures ranging from 350 °C to 900 °C. Changes in refractive index, porosity, pore size, chemical composition, and electrical properties as a function of annealing temperature are evaluated. The results of JV measurements indicate that the methyl-modified film, which was annealed at 450 °С, demonstrates minimal leakage currents. This phenomenon can be explained by the almost complete removal of surfactant residues and non-condensed silanols, as well as the minimal thermal degradation of methyl groups. The results of the study on leakage current models in methyl-modified films, obtained at various annealing temperatures, lead us to conclude that the predominant mechanism of charge carrier transfer is Poole–Frenkel emission.
Optimizing Injection Well Trajectory to Maximize CO2 Storage Security and Minimize Geomechanical Risk
Authors: Li Yunan , ONeal Ryan , Whitezell Marc , Kovscek Anthony R. 
Published: September 2024

SummaryThe work demonstrates an optimal well design for a potential geological carbon storage (GCS) project in Kern County, California (USA). Carbon dioxide (CO2) plume shape, size, and pressure response history in the subsurface are outcomes. We created a toolbox (pyCCUS) to standardize the well design optimization process and it is applicable to different carbon storage assets. This toolbox is helpful to maximize storage security and minimize geomechanical risk. The numerical model of the storage formation features two-way coupled transport and geomechanical deformation. It honors a predefined injection scheme with injection rates that ramp up and then decline for a total of 12.3 MtCO2 injection in 18 years. The peak injection rate is greater than 1 MtCO2/yr, whereas the post-injection monitoring period is 100 years. We propose to develop a long, deviated injection well to best address the injectivity and plume migration challenges for this complex, heterogeneous, dipping formation. The chosen well trajectory improves injectivity while minimizing formation pressure buildup. The well design optimization successfully reduces the pressure buildup by 54% over the base design while only increasing the areal extent of the plume by 21%. We quantify the CO2 plume shape and size at the land surface. The plume grows rapidly during injection, but it increases only slightly after shut-in due to slow updip migration driven by buoyancy. The plume becomes stationary within the post-injection monitoring period. The optimal injector design balances the optimization goals of CO2 plume size, pressure increase, and pressure buildup at geological faults. The optimal injection well design is robust under uncertainties from injection schemes and geological model realizations. Rock deformation due to the pressure buildup is also computed. The model estimates 2.1 cm of uplift that occurs during the year of the peak annual injection rate. Land surface uplift strongly correlates with the subsurface pressure response.
Multisource Modeling Method for Petroleum Production Prediction Models: A Case Study of CO2-Flooding Performance
Authors: Dong Yukun , Jin Jianxiang , Zhang Jiyuan , Wu Kuankuan , Liu Shuaiwei , Bai Junkai 
Published: September 2024

Summary
Accurate prediction of oil production is crucial for formulating oilfield development strategies. With the rapid development of artificial intelligence, research on utilizing deep learning to construct oil production prediction models has been growing, which has partially compensated for the low computational efficiency of numerical simulators. Although the well-trained source domain model maintains high prediction accuracy on target blocks with similar production conditions, the prediction accuracy of the model declines in scenarios where substantial disparities exist between the production conditions of the target block and the source domain. This discrepancy makes the prediction results unreliable and causes a domain shift issue. We propose a multisource model fine-tuning approach, which leverages a limited amount of target domain data to fine-tune the existing source domain model, enabling it to rapidly converge in the target domain while maintaining superior prediction performance. Based on a heterogeneous low-permeability CO2-flooding reservoir development model, we established a series of source domain data sets, encompassing numerous types of well patterns and permeability fields, and specifically prepared various target domain data sets to verify the effectiveness of the model fine-tuning. Experimental outcomes demonstrate that our proposed model fine-tuning approach facilitates the rapid convergence of the existing model on target domain data. Following testing, the fine-tuned model, which attained a prediction accuracy exceeding 97% in the target domain, significantly improved upon the accuracy compared with the unfine-tuned model. The time required is significantly lower than retraining a new model, and it significantly reduces the need for data in the target domain. This provides support for the rapid generation of new prediction models using existing source domain models and limited target domain data.
Antagonistic action of Siah2 and Pard3/JamC to promote germinal zone exit of differentiated cerebellar granule neurons by modulating Ntn1 signaling via Dcc
Authors: Laumonnerie Christophe , Shamambo Maleelo , Stabley Daniel R. , Lewis Tommy L. , Trivedi Niraj , Howell Danielle , Solecki David J 
Published: September 2024

Abstract

Exiting a germinal zone (GZ) initiates a cascade of events that promote neuronal maturation and circuit assembly. Developing neurons and their progenitors must interpret various niche signals— such as morphogens, guidance molecules, extracellular matrix components, and adhesive cues—to navigate this region. How differentiating neurons integrate and adapt to multiple cellextrinsic niche cues with their cell-intrinsic machinery in exiting a GZ is unknown. We establish cooperation between cell polarity-regulated adhesion and Netrin-1 (Ntn-1) signaling comprises a coincidence detection circuit repelling maturing neurons from their GZ. In this circuit, the Partitioning defective 3 (Pard3) polarity protein and Junctional adhesion molecule-C (JamC) adhesion protein promote, while the Seven in absentia 2 (Siah2) ubiquitin ligase inhibits, Deleted in colorectal cancer (Dcc) receptor surface recruitment to gate differentiation linked repulsion to GZ Ntn-1. These results demonstrate cell polarity as a central integrator of adhesive- and guidance cues cooperating to spur GZ exit.
Ursolic acid synergistically enhances gemcitabine-induced apoptosis in bladder cancer via the PI3K/AKT and JNK signaling pathways
Authors: Huang Xiaolong , Sun Yan , Zhu Junlong , Tong Hang , Wen Peng , He Weiyang 
Published: September 2024

Abstract

Ursolic acid (UA) is a natural compound that exists in a number of Chinese medicinal herbs, which has been demonstrated to enhance the efficacy of chemotherapy in multiple types of cancer. The present study aimed to observe whether UA enhances the antitumor effects of gemcitabine (GEM) in human bladder cancer (BCa) cell lines, and to investigate the possible underlying mechanisms. The human BCa cell lines, T24 and 5637, were treated with GEM and/or UA in vitro. Cell viability was measured by the Cell Counting Kit-8 assay. Apoptosis was detected using Hoechst 33258 staining, western blot analysis and flow cytometry. The expression levels of signaling pathway-related proteins were detected using western blot analysis. UA and GEM synergistically inhibited the proliferation of human BCa cells. Compared with GEM treatment alone, the combination of GEM and UA led to enhanced the antitumor effects, which were associated with the induction of apoptosis. The PI3K/AKT and JNK signaling pathways were involved in human BCa cells treated with GEM and UA. Both the AKT activator, SC79, and the JNK inhibitor, SP600125, reduced the expression of cleaved PARP and cleaved caspase-3. On the whole, the results of the present study demonstrate that UA enhances GEM-induced apoptosis by inactivating the PI3K/AKT signaling pathway and activating the JNK signaling pathway in human BCa cells.
Assessment of Climate Change Risks for Plant Species in South Korea: Sensitivity, Vulnerability, and Adaptation Strategies.
Authors: Hong Seungbum , LEE KYUNGEUN , Oh Jieun , Cha Jaegyu 
Published: September 2024

Abstract

This study presents a climate change risk assessment for plant species in South Korea, incorporating temperature rise projections under the four Shared Socioeconomic Pathways (SSP) scenarios from the IPCC’s Sixth Assessment Report (AR6). The assessment methodology evaluated species sensitivity to temperature changes and the vulnerability to losing suitable habitats by 2100, translating these factors into species extirpation risk. The sensitivity analysis classified the plant species into four grades based on statistical correlations between their geographical distributions and temperature distributions. Subsequently, the vulnerability analysis determined how rapidly the current suitable temperature zones for each species will deviate from future climate conditions by comparing current temperature ranges with projected future temperatures in 20-year intervals under four SSP scenarios. Based on these analyses, species were classified according to risk assessment grades. This assessment indicated a significant increase in the species extirpation rate, particularly under pessimistic scenarios like SSP5-8.5. By 2100, over 1,000 species could face extirpation, representing over 40% of the total evaluated species in this study. This study proposed adaptive strategies for species conservation, prioritizing urgent measures for high-risk species. This approach amins to inform effective policy responses to mitigate the impacts of climate change on South Korea’s plant biodiversity.
Amorphous silica reduces N2O emissions from arable land at the field plot scale
Authors: Hoffmann Mathias , Diaz Oscar Rodrigo Monzon , Zentgraf Isabel , Al-Hamwi Wael , Dubbert Maren , Stein Mathias , Holz Maire , Schaller Jörg 
Published: September 2024

Abstract

Increasing greenhouse gas emissions pose a strong threat due to accelerating global warming. N2O emissions are highly important in this regard as N2O is a very powerful greenhouse gas. Agriculture is the main human-induced source for N2O emissions contributing roughly 60% to total N2O emissions. Soil amorphous silica (ASi) contents are reduced in arable soils due to yearly exports by crop harvest as most crops are silicon accumulator plants. Most recently it has been shown that ASi is increasing water and nutrient availability in soils. Both factors are known to directly and indirectly affect N2O emissions from agroecosystems. In this study we conducted a field plot trial on arable soil depleted in ASi and fertilized this soil its pre-agricultural ASi level. Our data clearly shows that increasing soil ASi to a pre-agricultural level decreased seasonal N2O emissions by ~ 30%. This reduction of N2O emissions due to ASi might be of global relevance as agricultural practice has reduced the ASi content in agricultural soils. If future studies confirm the effect of ASi on N2O emissions, the soil ASi depletion by agricultural practice in the last decades may have led to a substantial increases of N2O emissions.
Thermal modeling of tool-work interface during polymer's friction welding process by mechanical, pneumatic and electrical sources
Authors: mohamed serier , Albaijan Ibrahim , Alazzawi Sheymaa , Arshad Noor Siddiquee , Abdalrhman Milad 
Published: September 2024

Abstract

The High-density polyethylene (HDPE) has become an essential material, widely used in daily life, especially in water transportation networks. What enhances the use of this material is its adaptability to many manufacturing processes, especially with contemporary friction stir welding technology. At present, many researches have studied the welding of joints of polyethylene parts using mechanical energy, that is, in the same way as welding aluminum parts. This study aims to add advantages to welding this material by proving the existence of other energies for polyethylene, such as pneumatic and electrical energy (with an aluminum welding tool). It relies on easy-to-move equipment, especially in difficult cases of treating cracked pipes. The FSW process parameters: rotation speed, advance speed, and tool inclination angle were varied while using the three power sources. The maximum temperature was monitored. The thermal efficiency of the three energies was verified using the experimental design method. Tensile testing was also used to examine the resistance of the weld area.
Intelligent prediction of internal corrosion rate in submarine oil and gas pipelines based on KPCA-ISSA-MKSVR combined strategy
Authors: Jiang Hong-ye , Lei Peng-zheng , li You-lv , Chi Ming-hua , Xu Tao-long , Liao Yi 
Published: September 2024

Abstract

Given the increasingly prominent problem of pipeline internal corrosion leakage caused by the long-term service of submarine oil and gas pipelines, this paper proposes a KPCA-ISSA-MKSVR combined prediction model that can be used to predict the internal corrosion rate. The model uses kernel principal component analysis (KPCA) to reduce the dimension of corrosion influencing factors. Based on the sparrow search algorithm (SSA), the quality of the initial population is optimized by introducing the Tent chaotic map, and the balance between global exploration and local development is achieved by adjusting the periodic nonlinear adaptive convergence factor. The introduction of polynomial mutation disturbance further increases the diversity of the population and avoids the local optimal trap. Combining the advantages of mixed kernel support vector regression (MKSVR) that can integrate multiple kernel functions solves the problem that the traditional SVR model has a strong dependence on kernel functions and can better capture the diversity and complexity of sample data. Furthermore, taking two sets of actual corrosion data as examples, the accuracy of the combination strategy proposed in this study and its superiority over other models are explored. In Example 1, the KPCA-ISSA-MKSVR model reduced the MAE to 0.015, RMSE to 0.018, MAPE to 0.546, and achieved an R² of 0.991. In Example 2, the MAE decreased to 0.013, RMSE to 0.017, MAPE to 0.493, and R²reached 0.994.The KPCA-ISSA-MKSVR combination strategy proposed in this study is accurate and superior in predicting the internal corrosion rate, which can provide strong technical engineering support for the maintenance and safety management of submarine oil and gas pipelines.
Aβ1-42 promotes microglial activation and apoptosis in the progression of AD by binding to TLR4
Authors: Dou Rui-xia , Gu Cheng , Liang Yun-hua , Zhang Lu-lu , Zhang Yi 
Published: September 2024

Abstract

Alzheimer's disease (AD) is one of the most common age-related neurodegenerative diseases and the most devastating form of senile dementia. It has a complex mechanism and no effective treatment. Exploring the pathogenesis of AD and providing ideas for treatment can effectively improve the prognosis of AD. Microglia were incubated with β-amyloid protein 1–42 (Aβ1–42) to construct an AD cell model. After microglia were activated, cell morphology changed, the expression level of inflammatory factors increased, cell apoptosis was promoted, and the expression of Tau protein and related proteins increased. By up-regulating and down-regulating Toll-like receptor 4 (TLR4), the cells were divided into Lv-NC group, Lv-TLR4 group, Sh-NC group, and Sh-TLR4 group. The expression of inflammatory factors was detected again. It was found that compared with the Lv-NC group, the expression of various inflammatory factors in the Lv-TLR4 group decreased, cell apoptosis was inhibited, and the expression of Tau protein and related proteins decreased. Compared with the Sh-NC group, the expression of inflammatory factors in the Sh-TLR4 group increased, cell apoptosis was promoted, and the expression of Tau protein and related proteins increased. These results indicate that Aβ1–42 may promote microglial activation and apoptosis by binding to TLR4. Reducing the expression of TLR4 can reduce the occurrence of inflammatory response in AD cells and slow down cell apoptosis. Therefore, TLR4 is expected to become a new target for the prevention and treatment of AD.
Analysis of canopy light utilization efficiency in high-yielding rapeseed varieties
Authors: Xiao Xiaolu , Duan Bo , Huang Fangyuan , Zhi Ximin , Jiang Zhan , Ma Ni 
Published: September 2024

Abstract

The photosynthetic mechanism responsible for the differences in yield between different rapeseed varieties remains unclear, and there have been no consensus and definite conclusions about the relationship between photosynthesis and yield. Representation of the whole plant by measuring the photosynthetic performance at a single site may lead to biased results. In this study, we comprehensively analyzed the main photosynthetic organs of four high-yielding rapeseed varieties at the seedling, bud, flowering, and podding stages. The canopy photosynthetic parameters were derived by measuring the photosynthetic area, net photosynthetic rate, and chlorophyll content, and canopy photosynthetic capacity was used to evaluate the light utilization efficiency of different rapeseed varieties to establish the relationship between canopy photosynthetic traits and yield. The results showed that there were significant differences in photosynthetic traits among different parts of rapeseed plants. The photosynthetic trait parameters of the whole plant differed significantly when represented by leaves at different positions among different varieties, and different rapeseed varieties exhibited significantly different sensitivity to light intensity. The whole-plant study showed that the canopy photosynthetic capacity was the highest and second highest at the seedling and bud stage, respectively, both of which were closely and positively correlated with rapeseed yield, and ZY501 had higher canopy photosynthetic capacity than other varieties at these two stages due to its larger canopy photosynthetic area. Canopy chlorophyll content was also positively correlated with canopy photosynthetic capacity. These results indicated that investigation of photosynthetic characteristics at single sites in rapeseed might lead to biased results of photosynthetic capacity in different varieties, and provided a new evaluation index for studying the light utilization efficiency of rapeseed. Our results also clarified that canopy photosynthetic area has significantly greater contribution to canopy photosynthetic capacity than canopy photosynthetic efficiency, and provided a theoretical basis for investigating the photosynthesis mechanism underlying high crop yield.