bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of rock porosity on the natural frequency of the sample: theoretical model and experiment
Authors: Zhang Zhao , Gu Zewen , Kong Xiangqing , Liu Baoshan , Wang Zidong , Liu Jianlin 
Published: April 2024

Abstract

Not confined to static effects such as permeability, the effect of porosity on the natural frequency of a rock is crucial to explore its dynamic behaviors. In the present work, a cylinder vibration model governed by the Lame-Navier equation is developed to clarify the mechanism of porosity-effect on the natural frequency of a rock. Focusing on the structural difference of the pore, the porosity-effect on the natural frequency for a cylinder model is preliminarily investigated by finite element (FE) simulations, in consideration of ideal straight and conical hole structures. To probe the distribution of real pores, the micro-computed tomography (micro-CT) technique is used to extract the accurate geometry of pores of the digital core, and the results are imported into the FE model for simulation. By introducing the Nur’s model and Krief’s model, the improved cylinder vibration model is able to predict multiple orders of the natural frequency of real rock samples with various porosities, and therefore overcomes the defects of the conventional spring-dashpot model. Verified by the resonant experiment on various rock samples, the results of the FE model and the improved cylinder vibration model show a basically consistent trend, i.e. the natural frequency decreases with the increase of porosity. These findings are beneficial to a wide range of engineering applications such as resonance enhanced drilling (RED) of rocks, high-speed processing of novel porous materials, and oil or gas explorations.
Segmental Vertebral Three-dimensional Motion in Patients with L4 Isthmic Spondylolisthesis under Weight-bearing Conditions
Authors: Xu Hongda , Deng Haitao , Li Mingfan , Wang Tieheng , Zhang Lei , Gou Yuankun , Xu Jianfeng , Qing Peidong , Zhao Shengxin 
Published: April 2024

Abstract

Objective
To investigate in vivo 6-degree-of-freedom (DOF) vertebral motion in patients with isthmic spondylolisthesis (IS) during various functional weight-bearing activities.
Methods
Fifteen asymptomatic volunteers (mean age 54.8 years) and fourteen patients with IS at L4-5 (mean age 53.4 years) were recruited. The positions of the vertebrae (L4-L5) in the supine, standing, flexion-extension, left-right twisting and left-right bending positions were determined using previously described CT-based models and dual fluoroscopic imaging techniques. Local coordinate systems were established at the center of the anterior vertebra of L4 isthmic spondylolisthesis (AIS), the posterior lamina of L4 isthmic spondylolisthesis (PIS) and the center of the L5 vertebra to obtain the 6DOF range of motion (ROM) at L4-L5 and the range of motion (ROM) between the AIS and the PIS.
Results
The translation along the anteroposterior axis at L4-L5 during flexion-extension, left-right bending and left-right twisting was significantly greater than that of the healthy participants. However, the translation along the mediolateral axis at L4-L5 presented paradoxical motion under different positions: the ROM increased in the supine-standing and flexion-extension positions but decreased in the left-right bending and left-right twisting positions. The separation along the anteroposterior axis during flexion was significantly greater than that during standing, on average, reaching more than 1 mm. The separation along the mediolateral axis during standing, flexion and extension was significantly greater than that in the supine position.
Conclusions
This study revealed the occurrence of displacement between the AIS and PIS, primarily in the form of separation during flexion. Symptomatic patients with isthmic spondylolisthesis exhibit intervertebral instability, which might be underestimated by flexion-extension radiographs.
Comparing Neonatal Abstinence Syndrome and its Management in Babies Prenatally Exposed to Opioids and Methamphetamines
Authors: Behmadi Reza , Vahedi Zahra , Marzban Reza , Akhgarjand Camellia , Houjaghani Hirad 
Published: April 2024

Abstract
Background
The increasing prevalence of methamphetamine use during pregnancy has raised concerns about its impact on neonatal outcomes, including neonatal abstinence syndrome (NAS). This retrospective cohort study aimed to examine the severity of NAS and associated clinical outcomes in neonates based on the type of prenatal substance exposure (opioids, methamphetamines, or a combination).
Material and Methods
The study population included 80 term neonates born to mothers with documented substance use, stratified into three cohorts: opioid-only exposure, stimulant-only exposure, and combined opioid and stimulant exposure. Data on birth weight, gestational age, gender, route of delivery, length of hospital stay, and treatment requirements were extracted from medical records.
Results
Significant differences were observed in birth weight, with infants in the opioid-only group being heavier compared to the other two groups. The rates of small for gestational age (SGA) were 0%, 12.5%, and 21.4% in the opioid, methamphetamine, and Opioid + Methamphetamine groups, respectively. However, gestational age and the need for cesarean delivery did not differ significantly across the groups. Regarding treatment requirements, there were no statistically significant differences among the groups in the need for pharmacological or non-pharmacological interventions, nor in the duration of hospitalization.
Conclusions
Contrary to previous findings, this study suggests that neonates prenatally exposed to methamphetamines may exhibit NAS severity and treatment requirements comparable to those exposed to opioids. These findings challenge the long-held notion that opioid withdrawal universally eclipses the severity of methamphetamine withdrawal in newborns. Further research is needed to elucidate the potential mediating variables that may contribute to the observed equivalency in NAS susceptibility across different drug classes.
Ash areas of the Middle Pleistocene at Cova Negra (Xàtiva, Spain): the microstratigraphic sequence of level VIII
Authors: Oltra-Carrió Isabela , Bergadà M. Mercè , Eixea Aleix , Villaverde Valentín 
Published: April 2024

Abstract

The study of the uses of fire and of the domestic activities linked to them is necessary for a bettter understanding of the relations and behaviors of the Neanderthal groups in southern Europe during the Middle Pleistocene. The analysis of the microstratigraphy of level VIII at Cova Negra, dated between 206–255 ka which situates it in the MIS 7 interglacial complex, has enabled us to identify several distinct microfacies which present different plant compositions. Those remains that stand out have a predominance of Gramineae deposited as bedding at the top of the sequence. Between the microfacies of combustion that have been established, coprolites of carnivores have been identified, related to periods of abandonment by the human populations. The record of all these processes in level VIII at Cova Negra leads us to new approaches concerning the vision that we hold of the first Neanderthals in relation to Mediterranean Iberia. Matters such as the preparation of zones for cleaning or for resting in place perhaps formed part of the behavior of these groups of early Neanderthals.
Exploring the Genetic Link Between COVID-19 Severity and Pre-eclampsia: The Role of NTSR1 Gene SNPs and Calcium Ion Transport
Authors: Li Linli , Shen Qi , Zheng Hui , Mo Yanxiang 
Published: April 2024

Abstract
Introduction: The COVID-19 pandemic and pre-eclampsia both present significant health challenges worldwide, with emerging evidence suggesting a genetic interlinkage that could illuminate new therapeutic avenues. This study aims to explore the genetic relationship between the severity of COVID-19 and pre-eclampsia, with a focus on Single Nucleotide Polymorphisms (SNPs) in the NTSR1 gene and mechanisms of calcium ion transport.
Method: We employed a combination of genetic epidemiology and molecular biology techniques, including comprehensive bibliometric analysis and Mendelian Randomization, to assess the correlation between specific SNPs related to the NTSR1 gene and the severity of both COVID-19 and pre-eclampsia.
Result: Our findings reveal a significant genetic correlation between the severity of COVID-19 and pre-eclampsia, underscored by SNPs associated with the NTSR1 gene and calcium ion transport. This correlation suggests a shared genetic foundation that could exacerbate both conditions, providing insights into potential molecular interactions involved.
Conclusion: The identification of shared genetic pathways between COVID-19 and pre-eclampsia highlights the importance of genetic insights in developing targeted therapeutic interventions. These results open up new avenues for the mitigation of these conditions, emphasizing the role of genetic epidemiology in shaping future healthcare strategies.
Application of limit equilibrium and shear strength reduction techniques for stability assessment of upstream and downstream slope cuts- A case study ofKhalid-Dijo dam project, Southern Ethiopia
Authors: Wendim Demeke 
Published: April 2024

Abstract

Dam failure can occur due to various reasons like structural instability, downstream and upstream slopes instability. The downstream and upstream slope cuts of Khalid-Dijo irrigation dam project has assessed by integrating both limit equilibrium and finite element methods. In this research, limit equilibrium methods such as Morgenstern-Price, Spencer and Janbu corrected methods were utilized. Because those methods are satisfies both force and moment equilibriums. In addition, numerical finite element shear strength reduction method was utilized to compare the analysis results with limit equilibrium methods and validate the analysis. Slide v 6.0 software packages were utilized for limit equilibrium analysis and a phase2 v 8.0 was utilized for finite element analysis. In both methods the analysis was carried out at four anticipated natural conditions (i.e., static dry, static saturated, dynamic dry and dynamic saturated conditions). Slope material (i.e. soil and rock) properties are measured from in situ, laboratory tests and used as input parameters for the analysis to obtain factor of safety and critical strength reduction factor. Those are unit weight, cohesion, angle of internal friction, poison’s ratio, dilation angle and young’s modulus. Thus, the analysis shows that the factor of safety values for limit equilibrium methods and the critical strength reduction factor for finite element method are very close each other for the three slope cuts at all anticipated conditions. The lowest factor of safety and critical strength reduction factor is 1.56 and 2.14 respectively. Thus, it can be concluded that Khalid-Dijo irrigation dam project is safe against upstream and downstream slope cuts failure. The study suggested that the average safety factor values of both methods should be used during the design stage to minimize the risk.
Disc degenerative disease in South Africa: A case-control analysis of selected gene variants
Authors: Pearce Keenau , Less Stephanie , Liebenberg Adriaan W. , Benjeddou Mongi 
Published: April 2024

Abstract

Background Disc degenerative disease is a multifactorial disease for which genetics plays an integral role. Several genes, and their variants, associated with the development and progression of disc degenerative disease have been identified. While several studies have investigated these genes in Asian and European populations, no available evidence exists for the South African population. Therefore, this study aimed to investigate these parameters.Methods and results Biological samples were collected in the form of buccal swabs from patients. DNA extraction was carried out according to established methods. All genotyping was performed using the MassARRAY®System IPLEX extension reaction. or associations between variants and the presence of disc degenerative disease, odds ratios, confidence intervals, chi-squared analysis and logistic regression was calculated. This study found statistically significant associations for five of the evaluated single nucleotide polymorphisms (SNPs) with disc degenerative disease, namely IL-1α rs1304037 and rs1800587, GDF-5 rs143383, ADAMTs-5 rs162509, and MMP-3 rs632478.Conclusion To the best of our knowledge, this study represents the first of its kind to investigate the association of gene variants associated with disc degenerative disease within the South African population. This study has shown that 5 of these gene variants were significantly associated with the presence of disc degenerative disease, reflecting their integral roles in development and possible progression of the disease.
A Deep Learning-based Approach for Predictive Evaluation of Microservice Maintainability
Authors: YILMAZ Rahime , KÖSE Abdullah Huzeyfe , BUZLUCA Feza 
Published: April 2024

Abstract

Microservice Architecture (MSA) has emerged as a prominent paradigm in software system design, emphasizing decomposing monolithic applications into independent and modular functional services. This architectural approach provides a number of benefits; however, realizing these benefits requires a robust evaluation strategy focused on assessing the quality of the software system. This study proposes an innovative learning-based approach to evaluate the microservices’ quality, particularly maintainability. It is based on a deep learning technique that predicts the maintainability levels of microservices into three categories: low, medium, and high, with the low category indicating the need for refactoring. The prediction technique asses maintainability by feeding source-code metric values from different open-source microservice projects as inputs and obtaining results directly through transfer learning. The proposed method employed transfer learning and achieved % a 91.83 F1 score on the validated dataset obtained from open-source projects for predicting the need for refactoring services. Additionally, it reached %82 overall accuracy in the three class categorizations, showing notable performance. Considering these findings, it can be stated that the proposed learning-based evaluation is an effective method to assess microservice quality. As a result, this proposed method offers valuable insights for informed decision-making in software development and maintenance processes.
Polyacrylamide gel synthesis of CuO/CuFe2O4 nanoparticles for H2S gas sensing
Authors: Tabrizi S.A. Hassanzadeh 
Published: April 2024

Abstract

In the present work, CuO/CuFe2O4 nanoparticles were synthesized via a polyacrylamide gel. The produced nanocomposites were utilized as a gas sensor for the detection of H2S gas. The nanoparticles were characterized via XRD, FTIR, SEM and TEM techniques. XRD results revealed that the as-prepared product was amorphous and CuO and CuFe2O4 phases were formed after calcination at 800°C. Microstructural studies showed that the nanoparticles have a particle size distribution ranging from 60 to 120 nm. Most of the particles had a spherical morphology. The polyacrylamide network acted as a template for the formation of the nanoparticles. The H2S gas sensing characteristics of the products were studied at different concentrations and operating temperatures. In addition, the effect of humidity on the gas-sensing response was investigated. The prepared CuO/CuFe2O4 sensors can respond up to 25 when exposed to 10 ppm H2S which is higher than the pure CuO or CuFe2O4 sensors. The sensors reached a detection limit of 0.1 ppm and demonstrated clear sensitivity and quick response and recovery behavior toward H2S gas. The CuO/CuFe2O4 heterogeneous nanostructures also showed proper H2S gas response and selectivity in response to interfering gases like NH3, NO2, HCHO and CO. The gas sensing mechanism of the composites was also discussed.
Research on enterprise carbon reduction technology selection strategy under low-carbon city and smart city
Authors: Wan Anwei , Huang Yongchun , Cui Wei 
Published: April 2024

Abstract

Low-carbon cities and smart cities are important policy tools for achieving China's "dual-carbon" goal. It is of great practical significance to explore the choice of carbon emission reduction technologies for enterprises under different pilot policies, in order to save production costs and enhance the effectiveness of enterprise emission reduction. Based on this, the study takes profit maximization as the goal, and includes only cleaner production technologies and end-of-pipe technologies in carbon emission reduction technologies, constructs a production function based on Hicks' neutral technology, and analyses the carbon reduction technology selection strategies of enterprises under the three types of pilot policies. Further, the Hansen threshold panel model is applied to verify the scientific validity of this technology selection mechanism. It is found that the selection of cleaner production technologies and end-of-pipe technologies in different pilot policies is based on the ratio of end-of-pipe technology inputs to outputs. Specifically: (1) If this ratio is at (0, 0.5), in low carbon cities, enterprises choose lower cost end-of-pipe technologies; in smart cities, enterprises lack the willingness to invest in carbon emission reduction technologies; and in dual-pilot cities, enterprises use both end-of-pipe technologies and cleaner production technologies to reduce carbon emissions. (2) If this ratio is at (0.5, 1), enterprises in all three types of pilot cities choose lower-cost source management technologies to reduce carbon. (3) The results of the heterogeneity analysis show that under the constraint of production cost, non-state-owned enterprises, old enterprises and enterprises in high-carbon industries lack the incentive to increase their investment in carbon reduction technologies. Therefore, governments at all levels should guide these three types of enterprises to enter the carbon trading market, and stimulate the initiative of low-carbon technological innovation of these three types of enterprises through policy subsidies, high-tech enterprise incubation, and the promotion of carbon emission reduction source-governance technologies, so as to ultimately achieve the decline of the total amount of carbon emissions at the smallest cost of carbon emission reduction.
Use of Prompt-Based Learning for Code-Mixed and Code-Switched Text Classification
Authors: Udawatta Pasindu , Udayangana Indunil , Gamage Chathulanka , Shekhar Ravi , Ranathunga Surangika 
Published: April 2024

Abstract

Code-mixing and code-switching (CMCS) are prevalent phenomena observed in social media conversations and various other modes of communication. When developing Natural Language Processing (NLP) systems such as sentiment analysers and hate-speech detectors that operate on this social media data, CMCS text poses challenges. Recent studies have demonstrated that prompt-based learning of pre-trained language models (PLMs) outperforms full fine-tuning of PLMs across various NLP tasks. Despite the growing interest in CMCS text classification, the effectiveness of prompt-based learning for the task remains unexplored. Our study endeavours to bridge this gap by examining the impact of prompt-based learning on CMCS text classification. We discern that the performance in CMCS text classification is significantly influenced by the inclusion of multiple scripts and the intensity of code-mixing. In response, we introduce a novel method, Dynamic+AdapterPrompt, which employs distinct models for each script, integrated with adapters. While DynamicPrompt captures the script-specific representation of CMCS text, AdapterPrompt emphasizes capturing the task-oriented functionality. Our experiments span across Sinhala-English, Kannada-English, and Hindi-English datasets, encompassing sentiment classification, hate-speech detection, and humour detection tasks. The outcomes indicate that our proposed method outperforms strong fine-tuning baselines and basic prompting strategies.
Impact of TMZ treatment on the nucleolar features of live glioblastoma cells
Authors: Torgo Daphne , Mattos Camilla B. , Pereira Luiza C. , Lenz Guido , Lenz Luana S. 
Published: April 2024

Abstract

Nucleoli are essential for maintaining cell homeostasis as they regulate the formation of ribosomal subunits, fundamental for protein synthesis. Failure of proper ribosomal biogenesis under favorable conditions or cell stress can create an environment that may favor cancer development. The number and size of nucleoli are commonly altered in almost all cancers and can be considered a prognostic factor due to their influence on cell growth. Despite their recognized morphological significance in tumor progression, the involvement of the nucleolus in the acute tolerance to chemotherapy and the incidence of nucleolar changes after treatment remains unclear. Here, we characterized the nucleolar response of glioblastoma cells to acute temozolomide (TMZ) treatment. The stress induced by TMZ led to notable changes in nucleolar morphology, including increased nucleolar area and number, accompanied by a reduction in circularity. Furthermore, the signals of Ki67 and fibrillarin, proteins associated with cell proliferation and nucleolar activity, were boosted in response to treatment. These responses differed from typical nucleolar stress, here induced by actinomycin-D, suggesting that TMZ triggers an alternative nucleolar response to the stress caused by chemotherapy. These findings highlight a connection between nucleoli and chemotherapy response, offering new insights into the potential mechanisms of tolerance that underlay cancer dynamics.
Improved Reinforcement Learning for Preventing Consumer Food Waste in Volatile Food Markets
Authors: Turgut Ozgu 
Published: April 2024

Abstract
Lack of proper planning is pointed out as one of the main culprits behind the significant consumer-end food waste. This observation can be used to build effective solutions, especially in digitalized societies. This paper proposes a reinforcement learning (RL) based decision support unit (DSU) in order to prevent food waste specifically for household and restaurant kitchens. The proposed RL model is improved with the help of mathematical modeling. The DSU assumes a generic kitchen environment for improving three important aspects of food management: the total shopping budget, the unused amount of ingredients, and the nutrition level offered by the selected menu. The RL-based algorithm is built to generate menu recommendations in a volatile market. All the practical contributions of the proposed planning tool to “monitor”, “prevent” and “reuse” levers of sustainable food strategy are summarized in a generic kitchen example. Results indicate that the proposed DSU can save around 30% from monthly shopping costs concurrently improving unused ingredients and nutritional intake. More insights about industrial processes such as package sizing and integration with “food sharing” services are also highlighted. Offering only one extra package size option for each ingredient in a market with volatile prices can help to diminish unused amounts completely and cut total shopping costs by 68% in a planned kitchen. Furthermore, the contribution of integrating mathematical optimization results into basic reinforcement learning implementation is observed as a 40% more reduction in the unused ingredients amount especially when the volatility of the market increases due to any disruption.