bluuebunny/crossref_metadata_2025_split huggingface dataset:
Aharonov-Bohm interference and the evolution of phase jumps in fractional quantum Hall Fabry-Perot interferometers based on bi-layer graphene
Authors: Ronen Yuval , Kim Jehyun , Dev Himanshu , Kumar Ravi , Ilin Alexey , Haug André , Bhardwaj Vishal , Hong Changki , Watanabe Kenji , Taniguchi Takashi , Stern Ady 
Published: February 2024

Abstract

Quasi-particles in fractional quantum Hall states are collective excitations that carry fractional charge and anyonic statistics. While the fractional charge affects semi-classical characteristics such as shot noise and charging energies, the anyonic statistics is most notable in quantum interference phenomena. In this study, we utilize a versatile bilayer graphene-based Fabry-Pérot Interferometer (FPI) that facilitates the study of a broad spectrum of operating regimes, from Coulomb-dominated to Aharonov-Bohm, for both integer and fractional quantum Hall states. Focusing on the ν=1⁄3 fractional quantum Hall state, we study the Aharonov-Bohm interference of quasi-particles when the magnetic flux through an interference loop and the charge density within the loop are independently varied. When their combined variation is such that the Landau filling remains 1/3 we observe pristine Aharonov-Bohm oscillations with a period of three flux quanta, as is expected from the interference of quasi-particles of one-third of the electron charge. When the combined variation is such that it leads to quasi-particles addition or removal from the loop, phase jumps emerge, and alter the phase evolution. Notably, across all cases, the average phase consistently increases by 2π with each addition of one electron to the loop.
Real-Time Measurement and Analysis of Electromagnetic Fields from Some Typical Base Transceiver Stations
Authors: Lal Radhey , Singh Rajiv Kumar 
Published: February 2024

Abstract
In this study, RF electromagnetic field (EMF) levels produced by GSM 900, and GSM 2100 cellular base stations installed in various locations in Sitapur, UP, India, are measured and evaluated. Utilizing a Selective Radiation Meter (SRM-3006) to detect the power density of EMF levels close to cellular base stations is the measurement technique. An acceptable distance from the cellular base station was used to take the measurement. For the research of EMF levels, four different cellular base station locations in Sitapur, UP, India were selected. The measured results are compared to international standards including the Department of Telecommunication (DoT), Government of India (GoI), and International Commission on Non-Ionizing Radiation Protection (ICNIRP) standards. As a consequence of the research, it has been determined that there are no negative effects on human health or the environment in the area chosen for the study because all measured values of power densities fall well below the established set criteria.
Treatment of Iliac Arteriovenous Malformation Associated with Trisomy 21: A Case Report
Authors: Yang Jinming , Zhao Weichao , Liang Liping , Chi Guoqing 
Published: February 2024

Abstract

Both arteriovenous malformation (AVM) and trisomy 21 are rare diseases. Studies have shown that individuals with trisomy 21 may have potential vascular malformations, with the main site of onset being the portal system. This case involved an adult patient with trisomy 21 who presented with pelvic iliac arteriovenous malformations. The patient underwent interventional treatment, and the prognosis was favorable. It is important to enhance the screening for vascular malformations in patients with trisomy 21 syndrome. At the same time, interventional treatment for AVMs is effective and increasingly important for managing AVMs.
Effects of boric acid on proliferation, apoptosis and miRNAs in medullary thyroid cancer cells
Authors: Yıldırım Onurcan , Seçme Mücahit , Dodurga Yavuz , Mete Gülçin Abban , Fenkci Semin Melahat 
Published: February 2024

Abstract

Background: Medullary thyroid cancer (MTC) is a highly aggressive and chemotherapy-resistant cancer that originates from the thyroid's parafollicular C cells. Due to its resistance to conventional treatments, alternative treatments such as boric acid have been explored.Boric acid, a boron-based compound, has demonstrated anticarcinogenic effects, making it a potential candidate for MTC treatment.
Methods: Cell viability was assessed using the 2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-2H-tetrazolium-5-carboxanilide (XTT) assay. Total RNA was extracted using Trizol reagent for gene expression and microRNA (miRNA) analysis via reverse transcription-polymerase chain reaction (RT-PCR). The extent of apoptosis induced by boric acid was determined using the terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay. Colony formation assays were also conducted to evaluate the impact of boric acid on the colony-forming ability of MTC cells.
Results: At 48 hours, The 50% inhibitory concentration (IC50) of boric acid was found to be 35 μM. Treatment with boric acid resulted in significant modulation of apoptosis-related genes and miRNAs, including increased expression of NOXA, apoptotic protease activating factor 1 (APAF-1), Bcl-2-associated X protein (Bax), caspase-3,and caspase-9, which are associated with apoptosis. In contrast, the expression of B-cell lymphoma 2 (Bcl2) , B‐ cell lymphoma‐ extra-large (Bcl-xl) and microRNA-21 (miR-21), which are linked to the aggressiveness of MTC, was significantly reduced. The TUNEL assay indicated a 14% apoptosis rate, and there was a 67.9% reduction in colony formation, as shown by the colony formation assay.
Conclusion: Our study suggests that boric acid may have anticancer activity in MTC by modulating apoptotic pathways. These findings suggest that boric acid could be a potential therapeutic agent for MTC, and possibly for other malignancies with similar pathogenic mechanisms.
Quantitative DCE-MRI for prediction of pathological complete response prior to neoadjuvant systemic therapy
Authors: Wang Xingrui , Xiao Xuehong , Yang Ang , Zeng Shuyan , Chen Wenxi , Chen Yi , Cui Shien , Huang Zhihua , Zeng Yumei , Huang Xiaoxing 
Published: February 2024

Abstract
Purpose
To explore the correlation between quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-derived tumour characteristics prior to neoadjuvant systemic therapy (NST) and pathological complete response (pCR) in patients with breast cancer patients.
Methods
Among 120 randomly selected patients with breast neoplasms, 28 diagnosed with invasive ductal carcinoma underwent NST. All patients underwent at least three MRI examinations: preoperative and before and after NST. Spearman correlation analysis was used to assess the correlation between pCR and Miller–Payne (MP) scores with pharmacokinetic parameters (Ktrans, Kep, Ve, Vp) in the regions of interest (ROI) in the tumour (ROI1), tumoural junction with the normal gland (ROI2), peritumoural region (ROI3), and background parenchymal enhancement; tumour morphological characteristics (type, location, quantity, margin, and maximum diameter); enhancement or shrinkage mode; and residual condition following preoperative MRI.
Results
A positive correlation was observed between pCR and tumour HER2 expression (r = 0.546); and Kep (r = 0.427) and Ve of ROI3 (r = 0.564) (P < 0.05). A negative correlation between pCR, tumour shrinkage pattern (r=-0.506) and residual tumours (r=-0.551) was observed by preoperative MRI (r=-0.551) (P < 0.05). MP associated with progesterone receptor (r=-0.37), HER2 (r = 0.608), and Ki-67 (r = 0.393) expression; tumour shrinkage pattern (r=-0.625); and preoperative MRI residual tumour (r=-0.715) (P < 0.05). Preoperative MRI tumour residual status associated with Ki-67 (r=-0.465) and tumour shrinkage pattern (r = 0.677) (P < 0.05).
Conclusions
A correlation was observed between DCE-MRI of the peritumoural region prior to NST and pCR. Early MRI evaluation of tumour shrinkage patterns following NST and preoperative tumour residual status showed predictive value for pCR and tumour burden.
FS-mutant: A Few-shot Learning Benchmark for Protein Mutants Mining
Authors: Li Mingchen , Yu Huiqun , Fan Guisheng , Zhou Ziyi , Tan Pan , Hong Liang 
Published: February 2024

Abstract

AI-assisted protein engineering means using machine learning models to mine mutants of a protein from its huge mutant library. Existed techniques, such as supervised models and zero-shot models, have received a lot of attention. The supervised approaches require a lot of data for optimal performance while the zero-shot models do not need assay-labeled data but their performance cannot be improved with labeled data increasing. In practice, researchers only have a small number of data, which is more like a few-shot learning problem. However, the few-shot learning within this domain has remained relatively unexplored. Moreover, there exist no standardized datasets and benchmarks that offer comprehensive basis for evaluating few-shot learning methods. In this paper, we present a few-shot learning dataset (FS-mutant) and benchmarking procedure. The dataset contains mutant data from eleven proteins with a variety of functions while the training set size are limited to 20, 40, 80, 160, and 320. In addition, we implement some zero-shot learning, supervised-learning, and meta-learning baselines and evaluate their performance on FS-mutant. We hope that FS-mutant will encourage future work on this extremely challenging domain. Data, code, and scripts can be found at https://github.com/ginnm/fs-mutant.
Sentiment Analyis and Bitcoin Price Prediction
Authors: BAMIDELE TOYOSI 
Published: February 2024

Abstract
The emergence of Bitcoin as a decentralized digital currency has underscored the importance of developing advanced techniques for predicting its price fluctuations. This study evaluates the predictive power of Bitcoin-related Google search volumes and Twitter sentiment analysis within short time frames. By leveraging machine learning algorithms and opinion mining, we identify correlations between online behaviors and Bitcoin price movements. Our methodology encompasses data sourcing, preprocessing, exploratory analysis, feature selection using Correlation Analysis, F-regression, Shapley values, and price prediction with a Long Short-Term Memory (LSTM) model. Findings reveal that Google search data, compared to Twitter sentiment, significantly enhances model accuracy and reduces prediction errors. The study suggests future research to investigate other search engines and online news sentiment, acknowledging limitations in data quality and accessibility of historical Twitter data.
The Impact of Natural Disasters on the Financialization of enterprises: Facilitation or Inhibition
Authors: Tang Rui , Zhao Mengchan , Wu Yigen 
Published: February 2024

Abstract
When faced with the impact of a disaster, do enterprises focus more on industrial development or try to allocate financial assets to smooth out the impact of the disaster on performance? Based on the sample of non-financial listed enterprises in Shanghai and Shenzhen A-shares, it is found that (1) Episodic and compound natural disasters significantly inhibit enterprise financialization (2) Compound disasters leads to an increase in the holding of short-term financial assets. Episodic and compound disasters inhibit the allocation of long-term financial assets. (3) Private enterprises tend to increase holdings of financial assets under compound disaster shocks, and non-high-tech enterprises and enterprises with low financing constraints contract financial asset allocation more significantly under disaster shocks. (4) Enterprises’ self-help behavior and government humanitarian aid during disaster shocks may inhibit their financialisation behavior. The results provide useful guidance for the smooth functioning of the real economy under uncertainty shocks.
Enhancing Low Voltage Ride-Through Resilience in Grid-Connected Doubly-Fed-Induction-Generator with Sliding Mode Controller
Authors: Ananth D. V.N. , Vineela K. S. T. 
Published: February 2024

Abstract
In power systems, grid disturbances are prevalent, causing significant challenges for Doubly-Fed Induction Generators (DFIGs). During these disturbances, the windings of DFIGs are exposed to large offset inrush currents, which can damage the converter switches and capacitors. Additionally, low voltage faults result in a dip in the rotor speed, leading to current injection in the rotor windings at the changed rotor slip frequency during the fault. This behaviour can be detected using stator two-axis instantaneous voltage and flux. In this study, we propose an innovative application of Sliding Mode Control (SMC) in both the rotor-side converter (RSC) and grid-side converter (GSC) control schemes of a DFIG. The primary objective is to effectively control the flux decay and reduce torque pulsations, rotor speed variations, and current deviations through an enhanced SMC approach. The proposed technique compensates and rapidly decomposes the stator dc-offset flux component based on deviations in the speed of the rotor with the reference value and the dc flux. To assess the effectiveness of the proposed approach, we conduct a comparative analysis between the conventional Proportional-Integral (PI) control and the proposed SMC technique under symmetrical grid fault conditions. The results demonstrate the superior performance of the enhanced SMC in mitigating the adverse effects of grid disturbances on DFIGs.
Relationship between Conjunctival Impression Cytology, the Type of Eye Rubbing and the Stages of Keratoconus
Authors: Rodriguez Luis A. , Guerrero Sara Elena , Rodriguez-Gutierrez Luis A. , Porras Dianela , Orellano María Eugenia 
Published: February 2024

Abstract

Purpose To describe the conjunctival impression cytology changes in keratoconus patients with eye rubbing, dry eye and ocular allergy.Methods Descriptive, cross-sectional study. Forty-four eyes of 22 patients with keratoconus, ocular allergy, eye rubbing and dry eye were included. Ocular Surface Disease Index (OSDI) and Ocular Allergy Index (OAI) questionnaires were given to all participants and impression cytology was performed to obtain Nelson’s classification.Results Stage II-IV keratoconus patients had higher grades of squamous metaplasia (p < 0.01979). Eye rubbing with the index and middle finger was related to Nelson grade 3 (p < 0.0032), stage IV keratoconus (p < 0.0007), and a severe OSDI result (p < 0.0035). A severe result in the OAI was associated with stage IV keratoconus (p < 0.0035) and eye rubbing with the knuckles (p < 0.000024).Conclusion Keratoconus is a multifactorial disease where eye rubbing plays an important role in disease progression. Persistent eye rubbing may increase the levels and activity of inflammatory mediators, leading to the development of the disease. Our study suggests that eye rubbing with the knuckles, the index and middle finger can lead to advanced stages of keratoconus. Keratoconus stage IV was associated with a severe OSDI result, and higher degrees of squamous metaplasia (Nelson grade 3).
Effects of forest structure from graph theory connectivity indicators on river water quality in the Caspian Sea Basin
Authors: Masteali Sahar Heidari , Bayat Mahmoud , Khalyani Azad Henareh 
Published: February 2024

Abstract
Landscape structure is one of the most important factors affecting the sources of food and organic matter in rivers. The spatial patterns of landscape patches including dimension variability and gap sizes between patches contribute in the amount and type of materials discharged to waterbodies and watercourses. We studied the effects of forest-patch connectivity and landscape corridors on water quality in the Greater Caspian Sea Basin. We used 10 landscape metrics and 11 water-quality indicators developed from graph theory to examine if contiguous/unfractured landscapes enhance water quality. We used independent calculations of Pearson’s and Spearman’s correlation coefficients to explore the association of forest-patch connectivity and water quality metrics. Stepwise regression was also used to generate allometry-based power, exponential, and logarithmic models. The results of correlation between forest-patch connectivity indicators and water quality parameters showed that several forest-patch metrics indicating connectivity including dLCP (Landscape coincidence probability) and dIIC (Integral Index of Connectivity) had a significant negative correlation with water quality metrics indicating pollution. This means that increasing forest connectivity is associated with improvement in water quality. The modeling results also showed that almost all selected models with acceptable AIC coefficients were nonlinear models. As connectivity of forest patches decreases and more fragmentation occurs in a watershed, the parameters of water pollution increase and the quality of water decreases. Models showed high R2 values for water quality metrics including CO3 (0.82), water discharge (0.73), Ca (0.77), and TDS (Total Dissolve Solids) (0.70).
Spatial Zoning and Comparison of Mesoscale Coastal Waters Schemes
Authors: Chen Zhou , Haifeng Zhang , Zhao Lin , Du Wen , Xu Min 
Published: February 2024

Abstract
Comprehensive zoning of coastal waters based on the ecosystem is the key to the compilation and effective management of coastal waters spatial planning. In this study, a comprehensive zoning scheme was developed based on ecological zoning of the Marxan model and resource use zoning. Resource use zoning was then used as an ecological protection cost to optimize comprehensive zoning. By comparing and evaluating the spatial structure and benefits of comprehensive zoning optimized schemes, it was found to shorten the boundary length of the protection zone, increase connectivity, and reduce fragmentation between the protection zones. This study is significant in enriching and improving the theoretical and technical methodological system of city-level mesoscale coastal waters spatial planning in China, therefore improving the science and evaluability of planning, and providing spatial guidance and decision-making references for ecological environmental protection and the sustainable use of resources in the coastal waters of Nantong.
Optimizing Recommender Model: Integrating Knowledge Graph Information Fusion and Attention Mechanism
Authors: Patel Liam , Chan Ethan , Tremblay Lucien 
Published: February 2024

Abstract
Graphs are versatile for capturing relationships between objects, as seen in social networks, enabling the implementation of algorithms like community discovery and clustering. The growing interest in deep learning within the graph domain has led to the development of various graph neural network algorithms in recent years. These algorithms offer an effective solution to address graph learning challenges by incorporating graph operations into traditional deep learning models and leveraging both graph structure and attribute information to handle the intricacies of graph data. Graph neural network algorithms represent an extension of traditional deep learning methods, like convolution, into the realm of graph data. These algorithms incorporate the concept of data propagation to formulate deep learning approaches specifically designed for graphs. Notably, they have demonstrated success in diverse domains such as social networks, recommendation systems, knowledge graphs, and others. In addressing the aforementioned challenges, this paper introduces a novel approach utilizing a graph neural network. To overcome the lack of temporal information in the recommendation network's neighbor structure, an ordered input-based gated cyclic unit is incorporated for state aggregation and updating. The model leverages the unit's capacity for capturing contextual relationships, thereby enhancing its ability to capture temporal features in the neighbor structure and improve predictions on new datasets. Additionally, the paper places emphasis on the shallow output of the intermediate layer. A multi-headed attention mechanism is employed to integrate information from multiple layers of output, ensuring that the shallow structural features provided by the intermediate layer play a more significant role in the scoring prediction task. This enhancement further refines the application of graph neural networks in recommendation systems.
Distribution of REE between amphibole and pyroxenes in the lithospheric mantle: An assessment from the lattice strain model
Authors: Wang Chunguang , Liang Yan , Xu Wenliang , Sun Chenguang , Shimizu Kei 
Published: February 2024

Abstract
Amphibole and pyroxenes are the main reservoirs of rare earth elements (REEs) in the lithospheric mantle that has been affected by hydrous metasomatism. In this study, we developed semi-empirical models for REE partitioning between orthopyroxene and amphibole and between clinopyroxene and amphibole. These models were formulated on the basis of parameterized lattice strain models of mineral-melt REE partitioning for orthopyroxene, clinopyroxene, and amphibole, and they were calibrated using major element and REE data of amphibole and pyroxenes in natural mantle samples from intraplate settings. The mineral-melt REE partitioning models suggest that amphibole is not in equilibrium with coexisting pyroxenes in the mantle samples and that the amphibole crystallized at a lower temperature than that of the pyroxenes. We estimated the apparent amphibole crystallization temperature using major element compositions of the amphibole and established temperature- and composition-dependent models that can be used to predict apparent pyroxene-amphibole REE partition coefficients for amphibole-bearing peridotite and pyroxenite from intraplate lithospheric mantle. Apparent pyroxene-amphibole REE partition coefficients predicted by the models can be used to infer REE contents of amphibole from REE contents of coexisting pyroxenes. This is especially useful when the grain size of amphibole is too small for trace element analysis.
Lorenz number and transport properties of Fe: Implications to the thermal conductivity at Earth’s core-mantle boundary
Authors: Yin Yuan , Wang Lin , Zhai Shuangmeng , Liu Yun 
Published: February 2024

Abstract
The electrical resistivity (ρ) and thermal conductivity (κ) of the Earth’s core compositions are essential parameters for constraining the core’s thermal state, the inner core age, and the evolutionary history of the geodynamo. However, controversies persist between experimental and computational results regarding the electronic transport properties (ρ and κ) of the Earth’s core. Iron is the major element in the core, and its transport properties under high-pressure and high-temperature conditions are crucial for understanding the core’s thermal state. We measured the ρ values of solid iron using the four-wire van der Pauw method at 300 K and pressures of 3 to 26 GPa within a multi-anvil press. For comparison, we calculated the ρ and κ values of hexagonal close-packed (hcp) iron at 300–4100 K and 22–136 GPa using the first-principles molecular dynamics (FPMD) method. Our calculations generally align with prior studies, indicating that the electrical resistivity of solid hcp iron at Earth’s core-mantle boundary (CMB) conditions is ~76–83 μΩ∙cm. The resistivity of hcp iron changes slightly as it melts from solid to liquid at pressures from 98 to 134 GPa. The effects of temperature and pressure on the Lorenz numbers of solid hcp iron were investigated according to our calculation results and previous studies. Under the CMB’s pressure conditions, the κ of hcp iron initially decreases with increasing temperature and subsequently increases. The electron-electron scattering plays a dominant role at low temperatures and causes the decrease in κ. At high temperatures, the increase of electronic specific heat significantly increases the Lorentz number and κ. Overall, we estimate the κ of solid hcp iron at the CMB’s condition to be 114 ± 6 W/m/K, slightly lower than the room temperature value of 129 ± 9 W/m/K at the same pressure. Our model shows that a 0–525 km thickness of a thermally stratified layer may exist beneath the Earth’s CMB, depending on the core’s heat flow and thermal conductivity.