bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Educational Study of Electromagnetic Phenomena on Ferromagnetic Structure Using a Software Environment
Authors: Oyman Serteller Necibe Fusun 
Published: December 2023

AbstractThe solution to electromagnetic problems relies on complex numerical differential equations that are difficult to understand. Therefore, it is crucial to analyze them step by step and visualize them in a computer environment, even by developing a program. This approach enhances understanding of the subject and facilitates further research. In this study, an electromagnetic phenomenon is examined on a ferromagnetic structure using the Maxwell 2D static-elliptical partial differential equation within a developed software environment. The paper covers all aspects, ranging from the mathematical model and analysis solution to the fundamental operating parameters and characteristics, with a focus on educational purposes. The modeling and software development have been executed in a user-friendly manner, enabling students to apply various numerical techniques to practical problems while maintaining control over the entire modeling process. Through this study, it is anticipated that the transition from theory to practice can be facilitated more smoothly and directly. The theoretical analysis has been conducted using Mathematica software, and a comparison with finite element magnetic method simulation on a typical magnetic structure has been presented to illustrate the analogy. A comprehensive analysis of the Maxwell static-elliptical electromagnetic equation, based on a 2D numerical solution within a specific domain, has been performed in a detailed and unambiguous manner, utilizing different equation forms, boundary conditions, and ferromagnetic materials, all for educational purposes.
Modelling on assessment of flood risk susceptibility at the Jia Bharali River basin in Eastern Himalayas by integrating multicollinearity tests and geospatial techniques
Authors: Debnath Jatan , Sahariah Dhrubojyoti , Nath Nityaranjan , Saikia Anup , Lahon Durlov , Islam Md. Nazrul , Hashimoto Shizuka , Meraj Gowhar , Kumar Pankaj , Singh Suraj Kumar , Kanga Shruti , Chand Kesar 
Published: December 2023

AbstractClimate change and anthropogenic factors have exacerbated flood risks in many regions across the globe, including the Himalayan foothill region in India. The Jia Bharali River basin, situated in this vulnerable area, frequently experiences high-magnitude floods, causing significant damage to the environment and local communities. Developing accurate and reliable flood susceptibility models is crucial for effective flood prevention, management, and adaptation strategies. In this study, we aimed to generate a comprehensive flood susceptibility zone model for the Jia Bharali catchment by integrating statistical methods with expert knowledge-based mathematical models. We applied four distinct models, including the Frequency Ratio model, Fuzzy Logic (FL) model, Multi-criteria Decision Making based Analytical Hierarchy Process model, and Fuzzy Analytical Hierarchy Process model, to evaluate the flood susceptibility of the basin. The results revealed that approximately one-third of the Jia Bharali basin area fell within moderate to very high flood-prone zones. In contrast, over 50% of the area was classified as low to very low flood-prone zones. The applied models demonstrated strong performance, with ROC-AUC scores exceeding 70% and MAE, MSE, and RMSE scores below 30%. FL and AHP were recommended for application among the models in areas with similar physiographic characteristics due to their exceptional performance and the training datasets. This study offers crucial insights for policymakers, regional administrative authorities, environmentalists, and engineers working in the Himalayan foothill region. By providing a robust flood susceptibility model, the research enhances flood prevention efforts and management, thereby serving as a vital climate change adaptation strategy for the Jia Bharali River basin and similar regions. The findings also have significant implications for disaster risk reduction and sustainable development in vulnerable areas, contributing to the global efforts towards achieving the United Nations' Sustainable Development Goals.
Monitoring of Insulated Rail Joints Based on Gap Value Measurement
Authors: La Placa Aldo , Freddi Francesco , Giuliani Felice 
Published: December 2023

AbstractThe monitoring of an IRJ would allow targeted maintenance to be carried out, reducing the problems caused by its potential failures. The authors present the results of a test field investigation, that involved the installation of seven longitudinal displacement sensors that continuously record the gap value of insulated rail joints (IRJs). The monitoring of an IRJ would allow targeted maintenance to be carried out, reducing the problems caused by its potential failures. The studied monitoring system was installed in a station of the suburban railway line within the metropolitan city of Bologna (Italy). Analysis of low-and high-frequency recordings was performed. In particular, low-frequency acquisition was used to fit a statistical predictive model that detects a deviation from a standard behaviour and may evidence anomalies. For the high-frequency acquisitions (registered during train passage) some representative quantities, that can provide macroscopic indicators of loss of joint stiffness, were computed. Although gap measurement alone is not exhaustive for identifying all possible failure scenarios, the data acquired by these monitoring devices can represent a possible immediate monitoring solution, based on already available instrumentation, to provide user-friendly predictive analysis systems aiming at improving the railway maintenance.
Survey on Aculops lycopersici and operational factors potentially affecting successful pest management among 50 tomato producers in Germany
Authors: Pfaff Alexander , Böckmann Elias 
Published: December 2023

AbstractAculops lycopersici(Acari: Eriophyoidea) is a pest in tomato cultivation worldwide. In recent years, the number of reports ofA. lycopersiciinfestations in tomato have increased in Germany. In the first half of 2019, a survey of 50 tomato producing farms was conducted to assess the occurrence ofA. lycopersiciand the impact this pest has on tomato cultivation in Germany. The participating farms represented ~ 3.5% of the 1448 farms in Germany with protected tomato production in 2019. Total tomato production area considered in the survey was 131.8 ha which corresponds to ~ 34% of the 385.63 ha of protected tomato production area in Germany in this year.A. lycopersicipresence was reported by 33 of the 50 surveyed farms, within the last 5 years. Amongst these 50 participants it was the pest with the highest relative importance in terms of plant protection effort exerted.A. lycopersicioccurrence was reported more frequently from production systems with a higher intensification. For instance, heating in cold months and a larger production area were considered intensification factors in this study. However, due to autocorrelation between intensification factors it was not possible to link increased occurrence to specific factors. As the intensification factors favouringA. lycopersicioccurrence are more prevalent in integrated production, those farms facedA. lycopersicioccurrence more often than the organic growers in this study. Plant protection strategies often combine broad treatments of sulphur with local abamectin treatments, removal of infested plant material and the introduction of natural enemies.
Largest nearest-neighbour link and connectivity threshold in a polytopal random sample
Authors: Penrose Mathew D. , Yang Xiaochuan , Higgs Frankie 
Published: December 2023

AbstractLet $$X_1,X_2, \ldots $$

X
1

,

X
2

,
…

be independent identically distributed random points in a convex polytopal domain $$A \subset \mathbb {R}^d$$

A
⊂

R

d

. Define the largest nearest-neighbour link$$L_n$$

L
n

to be the smallest r such that every point of $$\mathscr {X}_n:=\{X_1,\ldots ,X_n\}$$

X
n

:
=

{

X
1

,
…
,

X
n

}

has another such point within distance r. We obtain a strong law of large numbers for $$L_n$$

L
n

in the large-n limit. A related threshold, the connectivity threshold$$M_n$$

M
n

, is the smallest r such that the random geometric graph $$G(\mathscr {X}_n, r)$$

G
(

X
n

,
r
)

is connected (so $$L_n \le M_n$$

L
n

≤

M
n

). We show that as $$n \rightarrow \infty $$

n
→
∞

, almost surely $$nL_n^d/\log n$$

n

L
n
d

/
log
n

tends to a limit that depends on the geometry of A, and $$nM_n^d/\log n$$

n

M
n
d

/
log
n

tends to the same limit. We derive these results via asymptotic lower bounds for $$L_n$$

L
n

and upper bounds for $$M_n$$

M
n

that are applicable in a larger class of metric spaces satisfying certain regularity conditions.
Hybrid Neural Network Models for Detecting Fake News Articles
Authors: Khalil Ashwaq , Jarrah Moath , Aldwairi Monther 
Published: December 2023

AbstractThe prevalence of world-wide access to the Internet has come at a cost. A lot of misleading information is posted on public news websites and social media. Many news writers and organizations manipulate their posted data to propagate false information that target different societies and in different languages. Accurate and timely detection of false news is made possible in large part using machine learning-based technologies. This paper targets the problem of detecting fake news in Arabic language using machine learning models. A hybrid model of two deep neural networks is used to classify Arabic news articles in order to detect fake articles. The two types of neural networks are convolutional and bi-directional long-short term memory. Robust features are extracted using two different word vectors and a complex model of a convolutional neural network. Moreover, a set of auxiliary output layers are used to enhance the model accuracy. Multi-class classification is achieved via modifying the primary output layer. Results show an accuracy of 88% and 78% for binary classification and multi-class classification, respectively.
A Generalized Aerosol Algorithm for Multi‐Spectral Satellite Measurement With Physics‐Informed Deep Learning Method
Authors: Jiang Jianfang , Tao Minghui , Xu Xiaoguang , Jiang Zhe , Man Wenjing , Wang Jun , Wang Lunche , Wang Yi , Zheng Yalin , Tao Jinhua , Chen Liangfu 
Published: December 2023

AbstractThe multi‐spectral satellite sensors such as MODIS have a large swath, high spatial resolution, and well onboard calibration, enabling aerosol retrievals with daily global coverage. Despite numerous available bands, MODIS aerosol algorithms over land typically only utilize measurements from 2 to 3 spectral wavelengths to retrieve Aerosol Optical Depth (AOD) based on prescribed aerosol models. To make full use of multi‐spectral measurements and prior information, we developed an aerosol algorithm based on physics‐informed deep learning (PDL) approach. With physical constraint from radiative transfer simulation, PDL can construct model functions between the whole spectral measurements and each retrieved aerosol parameter separately. AERONET validations in eastern China show that MODIS PDL algorithm can accurately retrieve AOD and fine AOD (R = 0.936) at 1 km resolution and has reliable performance in coarse AOD as well as notable sensitivity to aerosol absorption. The flexible and efficient PDL method provides a generalized algorithm for common multi‐spectral satellite measurements.
The Thermal Structure of Central Te Riu a Māui/Zealandia Continent as Determined From the Depth to Base of Magnetic Sources
Authors: Miller C. A. , Kirkby A. , Mortimer N. , Aden F. , Barretto J. , Black J. , Chambefort I. , Stagpoole V. 
Published: December 2023

AbstractTo investigate the thermal structure of central Te Riu a Māui/Zealandia continent we estimate the depth to base of magnetic sources (DBMS), using an updated compilation of magnetic data. We calculate thousands of power spectral density estimates and solve an analytic solution for the fractal distribution of magnetization using a Bayesian method constrained by a sediment thickness model. Our DBMS results are broadly similar to a global model yet show complex relationships with independent models of crust and elastic thicknesses, and depth to base of seismicity (D90). Across central Zealandia, DBMS occurs above, coincident with, and below the Moho reflecting that factors additional to temperature influence DBMS. Away from subduction zones crustal thickness likely modulates DBMS while plate margins show varying influence dependent on margin characteristics. In the Taupō Volcanic Zone we interpret DBMS to reflect Curie point temperature at an average depth of 9–11 km. Error analysis shows that the choice of prior influences the posterior standard deviation and can result in counter intuitive error versus DBMS relationships. We use the DBMS as a basal temperature isotherm in heat flow models to compare with heat flow derived from borehole temperature measurements. Using the standard Curie point temperature of 580°C we find that DBMS derived heat flow models over estimate heat flow in areas underlain by plutonic rocks. We propose this reflects a higher titanomagnetite content in these rocks, although detailed studies of the magnetic mineralogy of Zealandia's plutonic rocks are required.
Tracking Mesoscale Convective Systems in IMERG and Regional Variability of Their Properties in the Tropics
Authors: Rajagopal Manikandan , Russell James , Skok Gregor , Zipser Edward 
Published: December 2023

AbstractMesoscale convective systems (MCSs) constitute only a fraction of convective systems in the tropics but significantly impact tropical weather and global climate. Early studies used satellite infrared (IR) data to track MCSs and study their properties but only for a short period due to computing limitations. Though valuable, the IR brightness temperature (Tb)‐derived precipitation properties have biases. The recent availability of Integrated Multi‐satEllite Retrieval for Global (IMERG) precipitation mission rainfall data of lesser bias than IR Tb‐based rainfall and access to high‐performance computers motivated us to track MCSs over global tropics for 10 years, using the Forward in Time (FiT) algorithm. Though IMERG is advantageous, it poses challenges to tracking MCSs due to convective systems connected by light rain areas and resolution differences between contributing passive microwave sensors. The precipitation field is smoothed and normalized to overcome these problems; then, the FiT algorithm identifies MCSs and tracks them. Our results show that MCSs contribute ∼70% of annual precipitation, though they are only ∼7% of all tracked systems. MCSs occur more often over the Amazon basin and Maritime Continent than in central Africa, known for high thunderstorm frequency, highlighting the contrasting convective regimes. The large, long‐lived, and intensely precipitating MCSs occur more often over the ocean than land, except for the Amazon basin. Fast‐moving MCSs often occur over West Africa, the Amazon basin, and the western Pacific, whereas slow‐moving MCSs are common over Colombia and the Maritime Continent.
A resource of induced pluripotent stem cell (iPSC) lines including clinical, genomic, and cellular data from genetically isolated families with mood and psychotic disorders
Authors: Detera-Wadleigh Sevilla D. , Kassem Layla , Besancon Emily , Lopes Fabiana , Akula Nirmala , Sung Heejong , Blattner Meghan , Sheridan Laura , Lacbawan Ley Nadine , Garcia Joshua , Gordovez Francis , Hosey Katherine , Donner Cassandra , Salvini Claudio , Schulze Thomas , Chen David T. W. , England Bryce , Cross Joanna , Jiang Xueying , Corona Winston , Russ Jill , Mallon Barbara , Dutra Amalia , Pak Evgenia , Steiner Joe , Malik Nasir , de Guzman Theresa , Horato Natia , Mallmann Mariana B. , Mendes Victoria , Dűck Amanda L. , Nardi Antonio E. , McMahon Francis J. 
Published: December 2023

AbstractGenome-wide (GWAS) and copy number variant (CNV) association studies have reproducibly identified numerous risk alleles associated with bipolar disorder (BD), major depressive disorder (MDD), and schizophrenia (SCZ), but biological characterization of these alleles lags gene discovery, owing to the inaccessibility of live human brain cells and inadequate animal models for human psychiatric conditions. Human-derived induced pluripotent stem cells (iPSCs) provide a renewable cellular reagent that can be differentiated into living, disease-relevant cells and 3D brain organoids carrying the full complement of genetic variants present in the donor germline. Experimental studies of iPSC-derived cells allow functional characterization of risk alleles, establishment of causal relationships between genes and neurobiology, and screening for novel therapeutics. Here we report the creation and availability of an iPSC resource comprising clinical, genomic, and cellular data obtained from genetically isolated families with BD and related conditions. Results from the first 324 study participants, 61 of whom have validated pluripotent clones, show enrichment of rare single nucleotide variants and CNVs overlapping many known risk genes and pathogenic CNVs. This growing iPSC resource is available to scientists pursuing functional genomic studies of BD and related conditions.
Amyloid precursor protein facilitates SARS-CoV-2 virus entry into cells and enhances amyloid-β-associated pathology in APP/PS1 mouse model of Alzheimer’s disease
Authors: Chen Jiang , Chen Junsheng , Lei Zhifeng , Zhang Fengning , Zeng Ling-Hui , Wu Ximei , Li Song , Tan Jun 
Published: December 2023

AbstractAlthough there are indications of a trend towards less severe acute respiratory symptoms and a decline in overall lethality from the novel Coronavirus Disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), more and more attention has been paid to the long COVID, including the increased risk of Alzheimer’s disease (AD) in COVID-19 patients. In this study, we aim to investigate the involvement of N-terminal amyloid precursor protein (APP) in SARS-CoV-2-induced amyloid-β (Aβ) pathology. Utilizing both in vitro and in vivo methodologies, we first investigated the interaction between the spike protein of SARS-CoV-2 and N-terminal APP via LSPR and CoIP assays. The in vitro impacts of APP overexpression on virus infection were further evaluated in HEK293T/ACE2 cells, SH-SY5Y cells, and Vero cells. We also analyzed the pseudovirus infection in vivo in a mouse model overexpressing human wild-type APP. Finally, we evaluated the impact of APP on pseudovirus infection within human brain organoids and assessed the chronic effects of pseudovirus infection on Aβ levels. We reported here for the first time that APP, the precursor of the Aβ of AD, interacts with the Spike protein of SARS-CoV-2. Moreover, both in vivo and in vitro data further indicated that APP promotes the cellular entry of the virus, and exacerbates Aβ-associated pathology in the APP/PS1 mouse model of AD, which can be ameliorated by N-terminal APP blockage. Our findings provide experimental evidence to interpret APP-related mechanisms underlying AD-like neuropathology in COVID-19 patients and may pave the way to help inform risk management and therapeutic strategies against diseases accordingly.
Peripheral monocytes and neutrophils promote photoreceptor cell death in an experimental retinal detachment model
Authors: Maidana Daniel E. , Gonzalez-Buendia Lucia , Pastor-Puente Sara , Naqvi Afsar , Paschalis Eleftherios , Kazlauskas Andrius , Miller Joan W. , Vavvas Demetrios G. 
Published: December 2023

AbstractPhotoreceptor cell death and immune cell infiltration are two major events that contribute to retinal degeneration. However, the relationship between these two events has not been well delineated, primarily because of an inadequate understanding of the immunological processes involved in photoreceptor degeneration, especially that of peripheral leukocytes that infiltrate the subretinal space and retinal tissues. In this work, we characterized the role of leukocyte infiltration within the detached retina. We observed that CD45+CD11b+Ly6G+neutrophils and CD45+CD11b+Ly6G−Ly6C+monocytes are the predominant peripheral immune cell populations that infiltrate the retinal and subretinal space after detachment. Selective depletion of monocytes or neutrophils using cell-specific targeting is neuroprotective for photoreceptors. These results indicate that peripheral innate immune cells contribute to photoreceptor degeneration, and targeting these immune cell populations could be therapeutic during retinal detachment.
The duality of CXCR3 in glioblastoma: unveiling autocrine and paracrine mechanisms for novel therapeutic approaches
Authors: Chan Travis Yui Hei , Wong Jenny Sum Yee , Kiang Karrie Mei-Yee , Sun Cherry Won Yuet , Leung Gilberto Ka-Kit 
Published: December 2023

AbstractGlioblastoma (GBM) is a highly aggressive brain tumor associated with limited therapeutic options and a poor prognosis. CXCR3, a chemokine receptor, serves dual autocrine–paracrine functions in cancer. Despite gaps in our understanding of the functional role of the CXCR3 receptor in GBM, it has been shown to hold promise as a therapeutic target for the treatment of GBM. Existing clinical therapeutics and vaccines targeting CXCR3 ligand expression associated with the CXCR3 axes have also shown anti-tumorigenic effects in GBM. This review summarizes existing evidence on the oncogenic function of CXCR3 and its ligands CXCL9, CXCL10, and CXCL11, in GBM, and examines the controversies concerning the immunomodulatory functions of the CXCR3 receptor, including immune T cell recruitment, polarization, and positioning. The mechanisms underlying monotherpies and combination therapies targeting the CXCR3 pathways are discussed. A better understanding of the CXCR3 axes may lead to the development of strategies for overcoming the limitations of existing immunotherapies for GBM.
Upregulation of FAM83F by c-Myc promotes cervical cancer growth and aerobic glycolysis via Wnt/β-catenin signaling activation
Authors: Zhang Changlin , Liu Lixiang , Li Weizhao , Li Mengxiong , Zhang Xunzhi , Zhang Chi , Yang Huan , Xie Jiayuan , Pan Wei , Guo Xue , She Peng , Zhong Li , Li Tian 
Published: December 2023

AbstractCervical cancer (CC) seriously affects women’s health. Therefore, elucidation of the exact mechanisms and identification of novel therapeutic targets are urgently needed. In this study, we identified FAM83F, which was highly expressed in CC cells and tissues, as a potential target. Our clinical data revealed that FAM83F protein expression was markedly elevated in CC tissues and was positively correlated with poor prognosis. Moreover, we observed that FAM83F knockdown significantly inhibited cell proliferation, induced apoptosis, and suppressed glycolysis in CC cells, while its overexpression displayed opposite effects. Mechanistically, FAM83F regulated CC cell growth and glycolysis by the modulation of Wnt/β-catenin pathway. The enhancing effects of FAM83F overexpression on CC cell proliferation and glycolysis could be impaired by the Wnt/β-catenin inhibitor XAV939. Moreover, we found that c-Myc bound to the FAM83F promoter and activated the transcription of FAM83F. Notably, knockdown of FAM83F impaired the enhancement of cell proliferation and glycolysis induced by ectopic c-Myc. Consistent with in vitro findings, results from a xenograft mouse model confirmed the promoting role of FAM83F. In summary, our study demonstrated that FAM83F promoted CC growth and glycolysis through regulating the Wnt/β-catenin pathway, suggesting that FAM83F may be a potential molecular target for CC treatment.