bluuebunny/crossref_metadata_2025_split huggingface dataset:
An assessment of the statistical performance of the horizontal point sampling (HPS) in an open coppice forest
Authors: Ramezani Habib , Ramezani Alireza , Nazariani Nastaran , Naghvi Hamed 
Published: February 2024

Abstract
Sampling surveys are broadly employed in forest inventories due to their efficiency in evaluating forest characteristics. However, the selection of an appropriate sampling method is essential. This study introduces a novel application of horizontal point sampling (HPS) in a coppice forest. The study evaluates the statistical characteristics of the horizontal point sampling (HPS) method when used in conjunction with crown relascope (CR). It further examines the variations in the crown basal area factor (CBAF) and the impact of different sample sizes (n). The study also conducts a comparative analysis between horizontal point sampling and fixed-radius (FR) plot sampling methods. This study was conducted in an open coppice forest. To achieve this, a sampling simulation with a large number of repetitions was employed. In HPS, a greater CBAF for a given sample size leads to a higher relative coefficient variation (CV %). Multiple regression model shows that both sample size (n) and CBAF influence CV %. There is an inverse relationship between n and CV%, whereas a positive relationship exists between CBAF and CV%. A combination of HPS and CR is easily applicable in fieldwork. This combined method demonstrates enhanced efficiency when compared to the traditional FR for estimating some forest attributes in CFs.
An Analytical Study of Mass Transport in Viscoelastic Fluid Under the Influence of Gravity Modulation
Authors: Singh Anupama , Kumar Anand 
Published: February 2024

Abstract
An analytical study of mass transfer has been done on a horizontal porous fluid layer. In the consideration of a non-Newtonian viscoelastic fluid, we investigated the influence of gravity modulation. We used weakly nonlinear stability analysis and the perturbation approach to construct the Ginzburg Landau equation, which dictated the Sherwood number. The Ginzburg-Landau equation solution was found using the software MATH-EMATICA. The impact of relaxation and retardation times of viscoelastic fluid on mass transfer has been discussed graphically. We established in this work that the rate of mass transmission can be efficiently regulated by correctly varying the frequency and amplitude of modulation, and that substantial mass diffusion lowers mass transfer. In the present article we discussed the outcomes for both the modulated and unmodulated scenarios. The main discovery is that the changes in gravitational acceleration over time have a considerable impact on convection dynamics because they increase mass transfer and destabilize the system.
Transparency in Decision-making: the Role of Explainable Ai (Xai) in Customer Churn Analysis
Authors: ÖZKURT Cem 
Published: February 2024

Abstract
In many industries, as in the telecommunications sector, identifying the reasons for customer loss is a primary challenge. Predicting which customers will churn or continue their subscriptions is of utmost importance in the telecommunications sector. Machine learning and data science offer numerous solutions to this issue. These proposed solutions hold a significant place in decision-making processes across various sectors. This study aims to predict lost customers and explain the reasons behind it using machine learning algorithms. The dataset used includes Linear Regression, Logistic Regression, Naive Bayes, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Gradient Boosting, XGBoost (eXtreme Gradient Boosting), LightGBM, AdaBoost and CatBoost algorithms to find the best-performing classification model. Performance metrics such as $R^2$ score, Mean Squared Error, Mean Absolute Error, Root Mean Squared Error, and Accuracy are used in this process. Finally, the best prediction model is explained using eXplainable Artificial Intelligence (XAI).
The Association Between Maternal Sleep and Circadian Rhythms During Pregnancy and Infant Sleep and Socioemotional Outcomes
Authors: Hoyniak Caroline P. , Donohue Meghan R. , Luby Joan L. , Barch Deanna M. , Zhao Peinan , Smyser Christopher D. , Warner Barbara , Rogers Cynthia E. , Herzog Erik D. , England Sarah K. 
Published: February 2024

Abstract

Studies have established that maternal sleep and circadian rhythm disturbances during pregnancy are associated with poor prenatal and perinatal outcomes for mothers and offspring. However, little work has explored its effects on infant sleep or socioemotional outcomes. The current study examined the relationship between maternal sleep and circadian rhythm disturbances during pregnancy and infant sleep and socioemotional outcomes in a diverse sample of N=193 mothers and their infants (51% White; 52% Female; Mage= 11.95 months). Maternal sleep and circadian rhythms during pregnancy were assessed using self-reports and actigraphy. Mothers reported on infants’ sleep and socioemotional outcomes when infants were one year old. When controlling for infant sex, age, gestational age at birth, and family income-to-needs, mothers who reported more sleep problems during pregnancy had infants with more sleep disturbances and dysregulation (e.g., increased feeding difficulties, sensory sensitivities) when they were one year old. Moreover, mothers who had later sleep timing (i.e., went to bed and woke up later, measured via actigraphy) during pregnancy had infants with more dysregulation and externalizing problems, and mothers with increased intra-daily variability in rest-activity rhythms (as measured via actigraphy) had infants with more externalizing problems. Findings suggest that maternal sleep and circadian rhythm disturbances during pregnancy may be a risk factor for infant sleep problems and socioemotional difficulties.
Subcellular Systems Observe Onsager Reciprocity
Authors: Horowitz Arie 
Published: February 2024

Abstract

The complexity of prokaryotic and, exceedingly, of eukaryotic cells, challenges the implementation of methodologies to determine causality and quantify intracellular dynamics. Though stochastic thermodynamics can effectively quantify the dynamics of single proteins or complexes, there is no statistical approach that can generalize subcellular microstates to derive the macrostates of a whole cell. Onsager reciprocity, a coupling between thermodynamic flows, has not been used systematically to quantify and establish causality among cellular dynamics. A prototypical thermodynamic profile of the cell is formulated as a model system to enable representation of its dynamics as force-driven thermodynamic flows. The explanative potential of the methodology of thermodynamic reciprocity is validated against molecular mechanisms wherein the coupling between internal fluxes is recognized. Its predictive potential is then tested on mechanisms wherein coupling is unacknowledged, ruling out reciprocity between the contractile ring of the bacterial ortholog of tubulin, which facilitates the separation of dividing Escherichia coli cells, and the bacterial actin ortholog, which polymerizes along the cells' walls. In contrast, reciprocity is identified between the actomyosin ring that facilitates the division of fission yeast cells and treadmilling longitudinal micro-tubules. Directly pertinent to the definition of the cell's prototypical thermodynamic profile, the reciprocity that had been suggested between AMP-regulated kinase (AMPK) and mechanistic target of rapamycin (mTORC1) in the regulation of cell proliferation is confirmed thermodynamically.
CAERS-CF: Enhancing Convolutional Autoencoder Recommendations through Collaborative Filtering
Authors: Ghadami Amirhossein , Tran Thomas 
Published: February 2024

Abstract

Recommendation systems are crucial in boosting companies' revenues by implementing various strategies to engage customers and encourage them to invest in products or services. Businesses constantly desire to enhance these systems through different approaches. One effective method involves using hybrid recommendation systems, known for their ability to create high-performance models. We introduce a hybrid recommendation system that leverages two types of recommendation systems: first, a novel deep learning-based recommendation system that utilizes users' and items' content data, and second, a traditional recommendation system that employs users' past behaviour data. We introduce a novel deep learning-based recommendation system called Convolutional Autoencoder Recommendation System (CAERS). It uses a Convolutional Autoencoder (CAE) to capture high-order meaningful relationships between users' and items' content information and decode them to predict ratings. Subsequently, we design a traditional model-based collaborative filtering recommendation system (CF) that leverages users' past behaviour data, utilizing Singular Value Decomposition (SVD). Finally, in the last step, we combine the two method's predictions with linear regression. We determine the optimal weight for each prediction generated by collaborative filtering and the deep learning-based recommendation system. Our main objective is to introduce a hybrid model called CAERS-CF that leverages the strengths of the two mentioned approaches. For experimental purposes, we utilize two movie datasets to showcase the performance of CAERS-CF. Our model outperforms each constituent model individually and other state-of-the-art deep learning or hybrid models. Specifically, the hybrid CAERS-CF model exhibits improvements across various metrics, highlighting the effectiveness of our approach in enhancing recommendation systems.
Exploring the impact of biosynthetic gene cluster BGC3 on the cariogenic virulence of Streptococcus mutans
Authors: Yang Jingyi , Zhang Yixin , Zhang Yuwei , Chen Ying , Xu Mindi , Wang Dandan , Chen Yihua , Wang Yixiang , Xia Bin 
Published: February 2024

Abstract
This study focused on the biosynthetic gene cluster BGC3 of Streptococcus mutans, a primary pathogen in dental caries, and its effects on the cariogenic virulence of Streptococcus mutans. BGC3 and ∆BGC3 Streptococcus mutans strains were constructed, and their growth curves were evaluated. Further, their acid production capacity was assessed by comparing their pH reduction levels. The survival of bacteria in phosphate citrate buffer solution (pH 3.0) was quantified. The expression levels of virulence genes (atpF, gtfC, gtfD, spaP, vicR, and ftf) were analysed using the reverse transcription-quantitative polymerase chain reaction. Bacterial viability was determined by microscopical examination of live/dead staining. Co-culture experiments were conducted to evaluate bacterial adaptability. The elimination of BGC3 did not significantly impact Streptococcus mutans growth or acid production in biofilms. The ∆BGC3 strain exhibited enhanced acid resistance and higher expression levels of virulence genes. In co-culture, ∆BGC3 exhibited superior bacterial viability. BGC3 reduced the cariogenic virulence of Streptococcus mutans in terms of acid tolerance and the expression of related genes. The knockout strain exhibited a more robust survival capability than the wild-type strain.
Spatial Transcriptomic Alignment, Integration, and de novo 3D Reconstruction by STAIR
Authors: Yu Yuanyuan , Xie Zhi 
Published: February 2024

Abstract

Spatial transcriptomics (ST) has emerged as a transformative approach for comprehending tissue architecture with molecular profiles. However, amalgamating discrete two-dimensional (2D) ST snapshots into a unified 3D atlas remains an outstanding challenge. To this end, we introduce STAIR, an end-to-end solution for ST alignment, integration, and de novo 3D reconstruction. STAIR uses a heterogeneous graph attention network with spot-level and slice-level attention mechanisms to obtain a unified embedding space and guide 3D space reconstruction in an unsupervised manner. We demonstrate STAIR's marked improvements in slice alignment and integration across samples and platforms over the previous methods. Furthermore, STAIR shows first-of-its-kind performance in de novo 3D reconstruction, with demonstrations in mouse hypothalamus preoptic area, mouse brain, and breast tumor tissue, which provides precise delineation of brain regions and reveals tumor progression in 3D space. Additionally, STAIR integrates additional slices into the existing 3D atlas incorporating both molecular features and physical coordinates. STAIR is the first to address the core obstacles limiting 3D positioning and harnessing alignments for atlas construction and assimilation. It lays a computational foundation to construct unified tissue maps and provides novel biological insights from a 3D perspective.
Applying a Digital Twin and wastewater analysis for robust validation of COVID-19 pandemic forecasts: insights from Catalonia
Authors: Fonseca i Casas Pau , Garcia i Subirana Joan , Corominas Lluís , Bosch Lluís Maria 
Published: February 2024

Abstract
Monitoring SARS-CoV-2 spread is challenging due to asymptomatic infections, numerous variants, and population behavior changes from non-pharmaceutical interventions. We developed a Digital Twin model to simulate SARS-CoV-2 evolution in Catalonia. Continuous validation ensures our model's accuracy. Our system uses Catalonia Health Service data to quantify cases, hospitalizations, and healthcare impact. These data may be under-reported due to screening policy changes. To improve our model's reliability, we incorporate data from the Catalan Surveillance Network of SARS-CoV-2 in Sewage (SARSAIGUA). This paper shows how we use sewage data in the Digital Twin validation process to identify discrepancies between model predictions and real-time data. This continuous validation approach enables us to generate long-term forecasts, gain insights into SARS-CoV-2 spread, reassess assumptions, and enhance our understanding of the pandemic's behavior in Catalonia.
P53 Regulation upon Lipid Peroxidation and Ferroptosis for Intervention against Atherogenesis
Authors: Zhao Yuanqin , Deng Wei , Wang Zhaoyue , Wang Yanxia , Zheng Hongyu , Deng Nianhua , Zhou Kun , Ren Zhong , Jiang Zhisheng 
Published: February 2024

Abstract:
Tumor protein 53 (P53), as an intracellular regulator of antioxidant responses,
participates in the expression of antioxidant defense and lipid metabolism as well as the
synthesis of genes in cells. The balance of oxidation and reduction can be disrupted by
many pathological conditions, and the role of the antioxidant system in protecting the
equilibrium state from pathological effects, such as reactive lipids, is crucial. In particular,
the excessive accumulation of lipid peroxidation products is a key factor driving the
occurrence and development of various diseases. Ferroptosis is an iron-dependent, lipid
peroxidation-driven cell death cascade reaction, which has become a key research area
in cardiovascular diseases. Atherosclerosis (AS) is a pathological change caused by lipid
metabolic disorder, inflammatory response, and endothelial cell injury, and is the most
common cause of cardiovascular disease. This review briefly outlines lipid peroxidation
and key components involving ferroptosis cascade reactions, summarizes and emphasizes
the role of P53-related signaling pathways in mediating lipid peroxidation and ferroptosis,
and focuses on the known P53 target genes that regulate these pathways, as
well as explores the possibility of P53 intervention in the treatment of AS by regulating
lipid peroxidation and ferroptosis processes.
Effects of Prenatal Methcathinone Exposure on the Neurological Behavior
of Adult Offspring
Authors: Youyou Zhang , Zhaoyang Li , Chen Li , Shuquan Zhao , Hui Wang 
Published: February 2024

Background:
Our previous research has shown that prenatal methcathinone exposure affects
the neurodevelopment and neurobehavior of adolescent offspring, but the study on whether these
findings continue into adulthood is limited.

Objective:
This study aims to explore the effects of prenatal methcathinone exposure on anxiety-like
behavior, learning and memory abilities, as well as serum 5-hydroxytryptamine and dopamine concentrations
in adult offspring.

Methods:
Pregnant rats were injected daily with methcathinone between the 7th and 20th days of gestation.
The neurobehavioral performance of both male and female adult offspring rats was evaluated by
neurobehavioral tests, including open-field tests, Morris water maze (MWM) tests, and novel object
recognition (NOR) tests. The levels of 5-hydroxytryptamine and dopamine concentration in rat serum
were detected by ELISA.

Results:
Significant differences were found in the length of center distance and time of center duration
in the open-field test, as well as the times of crossing the platform in the MWM test, between the
prenatal methcathinone exposure group and the control group. Results of the NOR test showed that
adult offspring rats exposed to methcathinone need more time to discriminate the novel object.
No gender differences were detected in the neurobehavioral tests. The serum concentrations of
5-hydroxytryptamine and dopamine in rats exposed to methcathinone prenatally were lower than that
in the control group, and the serum dopamine concentration was independent of gender in each group.

Conclusion:
Prenatal methcathinone exposure affects the neurological behavior in adult offspring, and
5-hydroxytryptamine and dopamine might be involved in the process.
Research Progress in Tumor Diagnosis Based on Raman Spectroscopy
Authors: Wang Zhichao , Shi Huanghao , Zhou Litao , Yin Jian , Yin Huancai , Xie Liangxu , Chang Shan 
Published: February 2024

Background:
Cancer is a major disease that threatens human life and health. Raman spectroscopy can provide an effective detection method.

Objective:
The study aimed to introduce the application of Raman spectroscopy to tumor detection. We have introduced the current mainstream Raman spectroscopy technology and related application research.

Methods:
This article has first introduced the grim situation of malignant tumors in the world. The advantages of tumor diagnosis based on Raman spectroscopy have also been analyzed. Secondly, various Raman spectroscopy techniques applied in the medical field are introduced. Several studies on the application of Raman spectroscopy to tumors in different parts of the human body are discussed. Then the advantages of combining deep learning with Raman spectroscopy in the diagnosis of tumors are discussed. Finally, the related problems of tumor diagnosis methods based on Raman spectroscopy are pointed out. This may provide useful clues for future work.

Conclusion:
Raman spectroscopy can be an effective method for diagnosing tumors. Moreover, Raman spectroscopy diagnosis combined with deep learning can provide more convenient and accurate detection results.
Examining the Effect of Thermal Treatment on the Surface Morphology and Structural Properties of Tin Dioxide Thin Films Grown Using an Economical Spray Deposition Method
Authors: Salah Ibrahim , KALELİ Murat , ÇORLU Tuğba 
Published: February 2024

Tin oxide is a versatile material that is frequently used in temperature, gas, and photo-sensing applications. It is well-known for its beneficial physical and chemical properties. This work describes an economical fabrication technique that uses an airbrush to apply SnO2 to a glass substrate in ambient circumstances. Important variables were carefully controlled, such as a constant 30 cm distance from the head of the airbrush to the preheated substrates, a 0.5 ml/minute deposition rate, and a 200 °C deposition temperature. Following that, annealing procedures were conducted at 250 and 500 °C to investigate the effects on optical, morphological, and structural characteristics. X-ray diffraction (XRD) structural investigation revealed a significant increase in crystallinity at higher annealing temperatures, with each thin film consistently displaying the rutile phase (JCPDS No. 01-0657). The produced tin dioxide thin films appear homogeneous in the images taken by the scanning electron microscope (SEM). However, there were visible structural defects. Additionally, an increase in surface roughness with higher annealing temperatures was found by atomic force microscopy (AFM) examinations. Such result holds significant value in fields like gas sensing and photon absorption, where surface properties are critical to overall performance. Finally, extensive investigations combined with the economical fabrication approach present a potential path toward customizing tin oxide thin films for a range of applications. The material's advantage for practical applications is improved by the capacity to modify structural and morphological properties through annealing conditions, demonstrating its potential in emerging fields of technology.
Exploring Journal of Emerging Technologies in Accounting: A Content and Citation Analysis of JETA
Authors: Göktürk Ibrahim Emre , Güvemli Batuhan , Sarısoy Özkan 
Published: February 2024

ABSTRACT
This study presents a comprehensive bibliometric analysis of the Journal of Emerging Technologies in Accounting (JETA) from 2008 to 2022, the period since its indexing in Web of Science, focusing on authorship patterns, dominant topics, and citation trends. The findings highlight a significant concentration of authorship within JETA, suggesting an opportunity for enhancing diversity and introducing fresh perspectives through expanded authorial engagement. Acknowledging JETA’s significant contributions to blockchain, text analysis/NLP, and AI, this study proposes an exploration into broader, interdisciplinary domains to further enrich the journal’s thematic diversity, in alignment with global academic trends. The study further recognizes an opportunity for JETA to bolster its global impact by inviting contributions from underrepresented regions, such as South America, Australia, and Africa. Additionally, the analysis reveals a commendable trend in JETA toward collaborative and potentially interdisciplinary research, implying that fostering such collaborations could yield innovative research methodologies in the field.
Data Availability: The data supporting the findings of this study can be provided upon request by contacting Batuhan Güvemli.
JEL Classifications: M40; M41; M42; M49.
Changes in Analysts’ Accrual-Related Forecast Optimism after the Disappearance of the Accrual Anomaly
Authors: Keskek Sami , Tse Senyo 
Published: February 2024

ABSTRACT
We investigate changes in analysts’ accrual-related forecast optimism after the disappearance of the accrual anomaly. We find that such optimism persists in the no-anomaly period, particularly when analysts’ incentives are aligned with greater optimism (i.e., for firms with high external financing). By contrast, the accrual-related forecast optimism declines when analysts’ incentives for optimism are low and in circumstances where analysts would have been more aware of accruals information after the anomaly became publicized (i.e., when analysts issued a cash flow forecast). These findings suggest that analysts’ incentives can offset forecast improvements that their heightened accrual awareness would otherwise allow. They also highlight the importance of considering both analysts’ incentives and knowledge when interpreting their forecasts.
Data Availability: Data are available from sources identified in the text.
JEL Classifications: G14; G29; M41.