bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Feature Engineering-Driven Approach to Detecting Fraud in Ethereum Transactions with Ensemble Models
Authors: Chithanuru Vasavi , Ramaiah Mangayarkarasi 
Published: October 2024

Abstract

The Ethereum blockchain, known for its decentralized and open-source nature, has revolutionized online transactions. However, vulnerabilities within Ethereum's architecture have resulted in several security breaches. By analyzing transaction data, malicious activities can be detected, helping to mitigate cyber threats such as phishing, Ponzi schemes, and eclipse, Sybil, and DDoS attacks. Machine learning-based anomaly detection has proven effective across various fields, and this paper introduces a fine-tuned ensemble machine learning model to detect fraudulent transactions on the Ethereum platform. To optimize the identification of fraudulent transactions, XGBoost and Random Forest algorithms are utilized to extract critical features from the transaction dataset. Additionally, data resampling techniques are applied to counteract overfitting. The proposed framework operates in two phases: the first phase assesses the effectiveness of different machine learning models, while the second phase develops an ensemble model based on these findings. The framework's performance is benchmarked against the baseline models and state-of-the-art methods, demonstrating superior results. Testing reveals that the ensemble model achieves an accuracy of 99.4% and a Matthews’s correlation coefficient of 94.9%.
Operando condition modeling CO2 electrocatalytic reduction on Ni-N-C single atom catalysts
Authors: Ahlquist Mårten , Zhang Guozhen , Wu Fan , Han Yulan , Duan Zhi-Yao , Hu Peijun , Ye Ke 
Published: October 2024

Abstract

The performance of electrocatalysts is shaped not solely by the inherent structures of active sites but also by factors like the charge accumulation on the electrode surface and the electric double-layer (EDL) structure formed at the interface between the electrode and electrolyte. At present, accurately modeling the electrochemical reactions occurring at EDL is a great challenge, mainly because the dynamic chemical change cannot be adequately captured by the commonly used static configurations. Ab initio molecular dynamics (AIMD) simulations face difficulty sampling across both time and space scales. Here, utilizing Ni-N-C/G catalysis CO2 reduction to CO as a framework to examine, we explored the catalytic process affected by EDL and accumulated electron on electrode under operando conditions by integrating grand canonical density functional theory (GC-DFT) calculations with classical molecular dynamic (MD) simulations. The findings suggest that the negative charge accumulating on the cathode material plays a crucial role in facilitating the adsorption and activation of CO2. Additionally, incorporating two intermediates, *COO + OH- and *CO + OH-, can significantly enhance the accuracy of the free energy profile.
Moreover, the EDL can not only enhance the adsorption of CO2 and promote the cleavage of the C-OH (in the *COOH intermediate) but also inhibit the desorption of CO to some extent. Compared to the promotional effect induced by cations coordinating with intermediates, the primary promoting factor lies in the electric field (EF around 85%) distributed around the intermediates. With the inclusion of the EDL correction, our calculations align well with experimental observations, showing that for the CO2 reduction to CO on the Ni-N-C site, as the applied potential becomes increasingly negative, the rate-determining step shifts from *COOH formation to CO desorption at -0.60 VRHE. Our work not only explains a long-standing puzzle for an important catalyst but also highlights the crucial roles of EDL effects, which provide guidance on investigating electrochemical reactions without compromising the complexity of the electrode environment.
An Agent-based Modeling Framework for Learning Progression Research in Middle School Mathematics Curriculum
Authors: Jiang Zhenfeng , Karim Aidah Abdul , Khalid Fariza , Qu Hongchun 
Published: October 2024

Abstract

Large-scale experimental studies on Learning Progression (LP) in middle school mathematics face challenges, such as resource limitations and ethical considerations. This study introduces a simulation-based framework for LP exploration, centered on the Multi-Agent-Based Student Cognitive Development (MAB-SCD) model. The MAB-SCD model, built using Agent-Based Modeling (ABM), integrates student learning processes and cognitive development into coherent learning trajectories. It was conceptualized around the LP construction process and key instructional activities in middle school mathematics, using the BDI cognitive framework for design and implementation. A systematic verification process was conducted to ensure its suitability for LP research. Global sensitivity analysis revealed complex parameter interactions, providing insights into model dynamics and enabling simulation optimization to more accurately represent student learning experiences. Historical data were used for parameter tuning and validation, ensuring the alignment between model outputs and real-world observations. Calibration and validation results confirmed the model’s effectiveness in reflecting students' progress and cognitive development. Additionally, the model's validity was demonstrated in a typical LP research task, showing effective integration of cognitive processes with learning trajectories. Positioned at the intersection of cognitive architecture and educational theory, these findings offer actionable insights for educators and researchers. By promoting the use of computational simulations, this study enhances the understanding of mathematics learning progressions across large student populations over extended periods.
Exploring the spatial and temporal dimension of yellow stem borer in coastal rice ecosystem for enhanced management using geostatistical analysis
Authors: Bapatla Kiran Gandhi , Ponnada Uday Babu , Naik Chinna Babu , Yeddula Srujana , CS Shyam , Bandaru Gayatri , Karnena Koteswararao , Panda Bipin Bihari 
Published: October 2024

Abstract

Rice (Oryza sativa L.), a staple food crop in tropical and subtropical regions, is highly susceptible to damage from the yellow stem borer (YSB), Scirpophaga incertulas (Walker), causing significant economic losses. This study investigates the spatial and temporal dynamics of YSB populations and associated damage in a micro-scale rice landscape in coastal Andhra Pradesh, Southeast India. A total of 5,408 YSB males were captured using pheromone traps, and significant variations in YSB populations and damage were observed across different years and months (P < 0.05). Peaks in YSB activity occurred during the tillering stage in August and from flowering to the grain-filling stage in November. Surface air temperature, humidity and rainfall showed significant positive correlations with YSB populations and damage, while remotely sensed data, including LST and NDVI, had negative associations. Geostatistical analyses revealed a strong spatial aggregation of YSB populations and damage, particularly in August and November, with spherical variogram models providing the best fit. The average spatial dependency of YSB population and damage in rice was estimated to 584.1 m and 673.33 m, respectively. Additionally, spatial autocorrelation (Moran’s I) confirmed significant clustering of YSB populations, while Getis-Ord Gi* analysis identified hotspots in southeastern locations of study area, indicating areas of higher infestation. These findings suggest that incorporating spatial data into pest management strategies can enhance the precision of insecticide applications, reduce environmental impacts and improve the overall efficiency of YSB control. This study provides valuable insights for developing more effective integrated pest management (IPM) strategies for rice cultivation.
A high-resolution model of gene expression during Gossypium hirsutum (cotton) fiber development
Authors: Grover Corrinne E , Jareczek Josef J , Swaminathan Sivakumar , Lee Youngwoo , Howell Alexander H , Rani Heena , II Mark A Arick , Leach Alexis G , Miller Emma R , Yang Pengcheng , Hu Guanjing , Xiong Xianpeng , Mallery Eileen L , Peterson Daniel G , Xie Jun , Haigler Candace H , Zabotina Olga A , Szymanski Daniel B , Wendel Jonathan F 
Published: October 2024

Abstract

Background
Cotton fiber development relies on complex and intricate biological processes to transform newly differentiated fiber initials into the mature, extravagantly elongated cellulosic cells that are the foundation of this economically important cash crop. Here we extend previous research into cotton fiber development by employing controlled conditions to minimize variability and utilizing time-series sampling and analyses to capture daily transcriptomic changes from early elongation through the early stages of secondary wall synthesis (6 to 24 days post anthesis; DPA).
Results
A majority of genes are expressed in fiber, largely partitioned into two major coexpression modules that represent genes whose expression generally increases or decreases during development. Differential gene expression reveals a massive transcriptomic shift between 16 and 17 DPA, corresponding to the onset of the transition phase that leads to secondary wall synthesis. Subtle gene expression changes are captured by the daily sampling, which are discussed in the context of fiber development. Coexpression and gene regulatory networks are constructed and associated with phenotypic aspects of fiber development, including turgor and cellulose production. Key genes are considered in the broader context of plant secondary wall synthesis, noting their known and putative roles in cotton fiber development.
Conclusions
The analyses presented here highlight the importance of fine-scale temporal sampling on understanding developmental processes and offer insight into genes and regulatory networks that may be important in conferring the unique fiber phenotype.
Pregnancy and Early Life Gut Microbiome: Influencing Factors and Health Implications
Authors: Sinha Trishla , Brushett Siobhan , Fernández-Pato Asier , Garmaeva Sanzhima , Andreu-Sánchez Sergio , Spreckels Johanne , Mallon Cyrus , Kuzub Nataliia , Gois Milla Brandao , Kruk Marloes , Jankipersadsing Soesma , Dekens Jackie , Gacesa Ranko , Bang Corinna , Perenboom Corine , Franke Andre , Tytgat Hanne , Mottaz Sara Colombo , Peters Lilian , de Jonge Ank , Verkade Henkjan , Swertz Morris , Wijmenga Cisca , Kuipers Folkert , Scherjon Sicco , Sikkema Jan , Sprikkelman Aline , de Kroon Marlou , Prins Jelmer , Gordijn Sanne , Koppelman Gerard , Reijneveld Sijmen , study Lifelines NEXT cohort , Fu Jingyuan , Yassour Moran , Kurilshikov Alexander , Zhernakova Alexandra 
Published: October 2024

Abstract

The gut microbiomes during pregnancy and infancy play a critical role in maternal and infant health, yet our understanding of what determines their temporal dynamics, interactions, and health impacts remains poor. In this study, we profiled 4,577 longitudinal fecal samples from 714 mother-infant pairs in the Dutch birth cohort LLNEXT, from 12 weeks of pregnancy to one year postpartum. Hospital records and questionnaires were longitudinally evaluated to assess 474 clinical, dietary, and exposure variables, and 24 human milk oligosaccharides were measured in breast milk. We observe that the maternal gut microbiome undergoes subtle changes during pregnancy and postpartum and is influenced by dietary preferences, infections, and pre-pregnancy smoking history. We also observe evidence for substantial mother-to-infant gut microbial strain transmission, with many species exhibiting time-dependent strain-sharing, where higher maternal abundances of specific species increased the likelihood of strain transmission. Notably, we find that maternal gut microbial diversity before birth is the strongest predictor of infant eczema, surpassing family history as a predictive factor. The primary factors shaping the infant gut microbiome are mode of delivery and infant feeding with additional factors like maternal pre-pregnancy smoking, and parity associated with individual species. Of 585 vaginally delivered babies, 155 were born at home, but home delivery showed only a moderate association with microbiome composition, similar to other birth parameters like duration of active pushing and ruptured membranes. While the infant microbiome at 2 weeks was often largely dominated by single bacteria, its composition did not predict later microbiome or health outcomes (up to 12 months of age), suggesting that early-life gut colonizers are not deterministic for later-life microbial communities and health. Overall, we uncover patterns of microbial dynamics during pregnancy, transmission, and early-life microbiome development, providing insights and data for future microbiome-targeted therapies.
A Bdh2-driven Lysosome to Mitochondria Iron Trafficking Controls Ferroptosis in Melanoma
Authors: Agostinis Patrizia , Rizzollo Francesca , Escamilla-Ayala Abril , Fattorelli Nicola , Lysiak Natalia , More Sanket , Barazzuol Lucia , Haute Chris Van den , Asselberghs Joris Van , Nittner David , Coene Jonathan , Venkataramani Vivek , Michalke Bernhard , Gaillet Christine , Cañeque Tatiana , Davidson Irwin , Verhelst Steven , Vangheluwe Peter , Calì Tito , Marine Jean Christophe , Rodriguez Raphaël , Bonnereau Julie 
Published: October 2024

Abstract

Iron sustains cancer cell phenotypic and metabolic plasticity, yet it also sensitizes the mesenchymal/drug-tolerant persister phenotype to ferroptosis. This posits that iron compartmentalization must be tightly regulated. However, the molecular machinery governing organelle Fe2+ compartmentalization remains elusive. Here, we identified Bdh2, the mammalian homolog of the bacterial EntA, as a key effector of inter-organelle Fe2+ redistribution and ferroptosis vulnerability during melanoma transition from a melanocytic (MEL) to a mesenchymal-like (MES) phenotype. In metabolically proficient MEL cells, Bdh2 localizes at the mitochondria-lysosome contacts to generate the siderophore 2,5-dihydroxybenzoic acid (2,5-DHBA) that ferries iron into the mitochondria. Fe2+ transfer by Bdh2 endorses OXPHOS and ATP production, utilized by V-ATPase for lysosomal acidification and MLC maintenance. Loss of Bdh2 expression alters lysosomal pH and MLC tethering dynamics causing lysosomal iron sequestration, which primes MES cells for ferroptosis. Rescuing Bdh2 expression, or supplementing 2,5-DHBA, rectifies lysosomal pH and MLCs, protecting MES cells from ferroptosis and enhancing their ability to metastasize through the bloodstream. Thus, we unveiled a Bdh2-dependent evolutionary-conserved mechanism that orchestrates inter-organelle Fe2+ transfer, linking metabolic regulation of lysosomal pH to the ferroptosis vulnerability of the MES/drug-tolerant persister cells.
Design and implementation of CSVCO for PLL applications
Authors: K Annamma 
Published: October 2024

Abstract

This paper depicts the design and performance analysis of Current Starved Voltage Controlled Oscillator (CSVCO), With different low leakage power techniques. This low power techniques of CSVCO is implemented in Phase Locked Loop by achiving the various field of applications, which are suitable to show efficient result of PLL for fast locking system, such as Frequency synthesizer, Multiplier and Frequency control, Tracking generators, Clock generation and recovery system etc. The proposed work sleep stack technique CSVCO which consumes low power, Less Area, Low Phase Noise, Less Delay, Also achieving higher frequency tuning range. Low power consumption gives significantly higher efficiency of PLL. This CSVCO simulation work is performed about the parameters of the phase noise is -63.8 dBc/Hz with supply voltage 0.45V and frequency of 2.759GHz. Power (mw) 0.002559, Delay(µs) 0.0006544. Complete work done by using cadence virtuoso 45NM CMOS technology.
Dynamic Context Integration in Large Language Models Using a Novel Progressive Layering Framework
Authors: Lapov Viktor , Laurent Nicholas , Araya Lawrence , Ortiz Gabriel , Albrecht Samuel 
Published: October 2024

Abstract

A paradigm shift in language generation technologies has prompted a need for more adaptable model architectures that maintain thematic continuity and efficiently manage computational resources. The Progressive Layering Framework (PLF), introduced in this study, addresses these requirements through a novel, context-sensitive layer configuration that selectively engages layers based on contextual relevance, reducing response latency and preserving thematic integrity across extended interactions. By dynamically allocating memory and prioritizing response coherence, the PLF enhances language model performance by optimizing resource allocation while maintaining contextual alignment, particularly in multi-turn conversational settings. Comprehensive experimental evaluation reveals that the PLF framework not only improves response coherence and computational efficiency but also significantly reduces error rates and context decay, even in scenarios involving high-contextual shifts. Observed gains in thematic retention and adaptive layer utilization suggest that the PLF holds substantial implications for advancing language model architectures, particularly in domains that require sustained adaptability across varied conversational lengths and complexities. These findings demonstrate the PLF's potential to enable more resource-efficient and contextually aware applications, setting a foundation for future advancements in adaptable language generation technologies.
Introducing Dynamic Entropy Layer Profiling: A Novel Approach for Ransomware Detection through Behavioral Feature Analysis
Authors: Tarness Simon , Bennett Matthew , Halloway Frederic , Abramson Lucas , Hathaway Michael 
Published: October 2024

Abstract

The persistent evolution of cyber threats necessitates innovative detection frameworks capable of addressing complex ransomware behaviors that evade traditional security systems. Dynamic Entropy Layer Profiling (DELP) introduces a behavior-based approach centered on entropy profiling across multiple file system layers, capturing distinctive shifts in data structure indicative of ransomware encryption. Through real-time entropy analysis, DELP accurately identifies ransomware activity by observing abrupt entropy increases associated with malicious encryption, distinguishing these anomalies from benign operations that also involve high-entropy processes. DELP’s layered structure and adaptive thresholding significantly reduce false positives and negatives, a common limitation in static and heuristic-based systems when handling polymorphic and obfuscated ransomware strains. Extensive testing demonstrated DELP’s resilience and efficiency, maintaining high detection accuracy across diverse ransomware samples while preserving computational efficiency in real-time scenarios. This approach not only enhances specificity in differentiating ransomware from legitimate encryption activities but also illustrates the potential of entropy-based behavioral analysis in elevating ransomware defenses. The findings indicate that DELP’s novel entropy profiling framework provides a valuable addition to current ransomware detection methodologies, offering robustness and precision in countering advanced ransomware attacks.
Multiple Pulmonary Sclerosing Haemangiomas with a Cavity: A Case Report and Review of the Literature
Authors: Li Yan , Zhang Fangbiao , Wu Zhijun , Wu Yan 
Published: October 2024

Objective:
Pulmonary sclerosing haemangioma (PSH) is a relatively uncommon benign neoplasm that is often asymptomatic and predominantly affects young
and middle-aged females. PSH often appears as a single nodule, whereas multiple lesions with a cavity are relatively rare and easily misdiagnosed

Case Presentation:
In our study, we report a patient with separated nodules in the same lobe with a cavity and clinical manifestations of cough and sputum with a
radiographic presentation similar to that of tuberculosis. The patient underwent percutaneous lung biopsy and thoracoscopic partial
pneumonectomy and was diagnosed with multiple PSHs.

Conclusion:
We report a rare case of multiple PSHs that were treated with a thoracoscopic partial resection of the left upper lobe. Postoperative pathology
confirmed multiple PSHs. Due to the rarity of PSH, it is easily misdiagnosed in clinical practice as lung cancer, tuberculosis, or other diseases. The
final diagnosis depends on the pathology, and surgery is considered to be an appropriate treatment that leads to a good prognosis.
Research Trends on Metabolic Syndrome in Digital Health Care Using Topic Modeling: Systematic Search of Abstracts
Authors: Lee Kiseong , Chung Yoongi , Kim Ji-Su 
Published: October 2024

Background
Metabolic syndrome (MetS) is a prevalent health condition that affects 20%-40% of the global population. Lifestyle modification is essential for the prevention and management of MetS. Digital health care, which incorporates technologies like wearable devices, mobile apps, and telemedicine, is increasingly becoming integral to health care systems. By analyzing existing research trends in the application of digital health care for MetS management, this study identifies gaps in current knowledge and suggests avenues for future research.

Objective
This study aimed to identify core keywords, topics, and research trends concerning the use of digital health care in the management of MetS.

Methods
A systematic search of abstracts from peer-reviewed papers was conducted across 6 academic databases. Following eligibility screening, 162 abstracts were selected for further analysis. The methodological approach included text preprocessing, text network analysis, and topic modeling using the BERTopic algorithm.

Results
Analysis of the 162 selected abstracts yielded a keyword network comprising 1047 nodes and 34,377 edges. The top 5 core keywords were identified as “MetS,” “use,” “patient,” “health,” and “intervention.” We identified 12 unique topics, with topic 1 focusing on the use of telehealth for self-management of diabetes. The diversity of the 12 topics reflected various aspects of digital health care, including telehealth for diabetes management, electronic health records for MetS complications, and wearable devices for monitoring metabolic status. Research trends showed an expanding field of precision medicine driven by the demand for tailored interventions and the significant impact of the COVID-19 pandemic.

Conclusions
By analyzing past research trends and extracting data from scholarly databases, this study has provided valuable insights that can guide future investigations in the field of digital health care and MetS management.
Penanda Interogativa Ka pada Iklan Layanan Masyarakat Jepang: Studi Kasus pada Iklan Produksi AC Japan
Authors: Putri Riyanda Daniati , Firmansyah Dian Bayu , Hartati Hartati 
Published: October 2024

This research discusses the forms and functions of interrogative marker ka and defines its use in Japanese public service advertisements, which AC Japan distributes. The method that is used in this research is a descriptive qualitative study. The research data is sentences with interrogative marker ka, which is found in AC Japan advertisements distributed on Japanese television in the last five years (2017-2021). The theory of forms and functions of interrogative marker ka by Yokoyama (2013) was used in this research. The watch-note technique was used in this research to collect data that would be needed. The data were analyzed with formalist and functionalist approaches. After that, the data would be analyzed by aspects of speech that affect the forms and functions of interrogative marker ka. The result showed that the interrogative marker ka in AC Japan advertisements has a function to state common questions, rhetorical questions, conjectural questions, polar imperatives, and embedded questions. From these several functions, rhetorical questions are more widely used because advertising is an indirect communication medium, which still has to pay attention to the emphasis on the message to be conveyed.