bluuebunny/crossref_metadata_2025_split huggingface dataset:
An inertial shrinking projection self-adaptive algorithm for solving split variational inclusion problems and fixed point problems in Banach spaces
Authors: Ngwepe Matlhatsi Dorah , Jolaoso Lateef Olakunle , Aphane Maggie , Adiele Ugochukwu Oliver 
Published: March 2024

Abstract
In this article, we study the split variational inclusion and fixed point problems using Bregman weak relatively nonexpansive mappings in the

p

p

-uniformly convex smooth Banach spaces. We introduce an inertial shrinking projection self-adaptive iterative scheme for the problem and prove a strong convergence theorem for the sequences generated by our iterative scheme under some mild conditions in real

p

p

-uniformly convex smooth Banach spaces. The algorithm is designed to select its step size self-adaptively and does not require the prior estimate of the norm of the bounded linear operator. Finally, we provide some numerical examples to illustrate the performance of our proposed scheme and compare it with other methods in the literature.
microRNAs, oxidative stress, and genotoxicity as the main inducers in the pathobiology of cancer development
Authors: Vahidi Sogand , Agah Shahram , Mirzajani Ebrahim , Asghari Gharakhyli Elahe , Norollahi Seyedeh Elham , Rahbar Taramsari Morteza , Babaei Kosar , Samadani Ali Akbar 
Published: March 2024

Abstract
Cancer is one of the most serious leading causes of death in the world. Many eclectic factors are involved in cancer progression including genetic and epigenetic alongside environmental ones. In this account, the performance and fluctuations of microRNAs are significant in cancer diagnosis and treatment, particularly as diagnostic biomarkers in oncology. So, microRNAs manage and control the gene expression after transcription by mRNA degradation, or also they can inhibit their translation. Conspicuously, these molecular structures take part in controlling the cellular, physiological and pathological functions, which many of them can accomplish as tumor inhibitors or oncogenes. Relatively, Oxidative stress is defined as the inequality between the creation of reactive oxygen species (ROS) and the body’s ability to detoxify the reactive mediators or repair the resulting injury. ROS and microRNAs have been recognized as main cancer promoters and possible treatment targets. Importantly, genotoxicity has been established as the primary reason for many diseases as well as several malignancies. The procedures have no obvious link with mutagenicity and influence the organization, accuracy of the information, or fragmentation of DNA. Conclusively, mutations in these patterns can lead to carcinogenesis. In this review article, we report the impressive and practical roles of microRNAs, oxidative stress, and genotoxicity in the pathobiology of cancer development in conjunction with their importance as reliable cancer biomarkers and their association with circulating miRNA, exosomes and exosomal miRNAs, RNA remodeling, DNA methylation, and other molecular elements in oncology.
The uniqueness of expression for generalized quadratic matrices
Authors: Chen Meixiang , Yang Zhongpeng , Chen Qinghua 
Published: March 2024

Abstract
It is shown that the expression as

A

2

=
α
A
+
β
P

{A}^{2}=\alpha A+\beta P

for generalized quadratic matrices is not unique by numerical examples. Then it is proven that the uniqueness of expression for generalized quadratic matrices is concerned not only with the properties of

A

A

but also with the rank of

P

P

. Furthermore, the sufficient and necessary conditions for the uniqueness of generalized quadratic matrices’expression are obtained. Finally, some related discussions about generalized quadratic matrices are also given.
Lithische Großgeräte („Makrolithen“) in der jüngeren Steinzeit der Sahara
Authors: Gabriel Baldur 
Published: March 2024

Zusammenfassung
Als Komplementärbegriff zu „Mikrolithen“ und „Megalithik“ wird in der Größenklassifikation von prähistorischen Steinartefakten der Terminus „Makrolithen“ diskutiert. Bei einigen solcher Großgeräte im Neolithikum der Sahara ist der Gebrauchswert kaum umstritten (z. B. Handmühlen, Äxte/Beile), andere sind jedoch eher rätselhaft wie die „Fessel-/Fallensteine“ oder die bis zu 1 m langen Steinstangen. Zusätzlich wird nun ein neuer Typ vorgestellt: ein bis fast 30 cm langes, flaches Steingerät, das in der Längsrichtung deutlich gebogen ist und durch diese Eigenschaft an Hacken erinnert.
Adsorptive removal of Cu(II) ions from aqueous solution using Teff (Eragrostis tef) hay based magnetized biocarbon: RSM-GA, ANN based optimization and kinetics aspects
Authors: Sundramurthy Venkatesa Prabhu , Varadharajan Venkatramanan , Wilson Vincent Herald , Jose Swaminathan , Manoharan Subramanian , Alharbi Naiyf S. , Khaled Jamal M. , Kandasamy Bhuvaneswari , Palanisamy Govindasamy 
Published: March 2024

Abstract
Teff (Eragrostis tef) is known as a staple grain crop which grown as edible seeds, remarkably in Ethiopia. However, after removal of its seeds, the hay obtained from this crop, are considered as agro-waste. Such a lignocellulosic agro-waste can be potentially exploited to prepare activated carbon to treat the harmful industrial effluents for detoxification. In this study, Teff hay (TH) was used as a precursor for preparing the activated carbon. The using H2SO4, chemical activation was carried out followed by carbonaceous process was undertaken to prepared activated carbon (AC) by pyrolysis. The prepared AC was modified as magnetized AC and characterized by various characterization methods. Further, as a bio-absorbent, its potency for adsorptive removal of Cu(II) ions was ascertained. Notably, the most studied and important process factors for adsorption, such as, initial concentration of metal ion, adsorbent dosage, pH, and contact time were subjected to optimization using response surface method (RSM). Further, to ensure the statistical optimization of aforementioned factors, the non-statistical techniques, genetic algorithm (GA) and artificial neural network (ANN) tools have been executed. In addition, the use of prepared Teff-hay based magnetized AC for the removal of Cu(II) by adsorption process was evaluated through different isotherms and kinetic approaches.
Pre-acquired functional connectivity predicts choice inconsistency
Authors: Madar Asaf , Kurtz-David Vered , Hakim Adam , Levy Dino J , Tavor Ido 
Published: March 2024

Economic choice theories usually assume that humans maximize utility in their choices. However, studies have shown that humans make inconsistent choices, leading to suboptimal behavior, even without context dependent manipulations. Previous studies showed that activation in value and motor networks are associated with inconsistent choices at the moment of choice. Here we investigated if the neural predispositions, measured before a choice task, can predict choice inconsistency in a later risk choice task. Using functional connectivity (FC) measures from resting-state functional magnetic resonance imaging (rsfMRI), derived before any choice was made, we aimed to predict subjects’ inconsistency levels in a later-performed choice task. We hypothesized that rsfMRI FC measures extracted from value and motor brain areas would predict inconsistency. Forty subjects (21 females) completed a rsfMRI scan before performing a risky-choice task. We compared models that were trained on FC that included only hypothesized value and motor regions with models trained on whole-brain FC. We found that both model types significantly predicted inconsistency levels. Moreover, even the whole-brain models relied mostly on FC between value and motor areas. For external validation, we used a neural network pre-trained on FC matrices of 40,000 subjects and fine-tuned it on our data, and again showed significant predictions. Together, this shows that the tendency for choice inconsistency is predicted by predispositions of the nervous system, and that synchrony between the motor and value networks plays a crucial role in this tendency.Significance StatementHumans often make irrational, or inconsistent, choices in value-based decision-making. Studies have suggested potential sources for this suboptimal behavior in momentary brain activity during choice, emphasizing the role of value and motor areas in the process of inconsistent choice behavior. Here, we show that inconsistency is predicted by neural predispositions, evident even before any choice was made. We predict subjects’ inconsistency levels from brain connectivity measures derived before they perform any choice task and find that inconsistency is predicted by the synchrony between the value and motor brain networks. This suggests choice inconsistency is related to a-priori constraints of the nervous system, and that value and motor areas play a central role in this behavior.
La modelación matemática en el proceso de formación de docentes de matemática de secundaria
Authors: Chavarría-Vásquez Jesennia , Gamboa-Araya Ronny 
Published: March 2024

Introduction. This article discusses aspects related to mathematical modeling and mathematical modeling as activities that should be included in teacher training processes. The purpose is to cultivate skills in future teachers to integrate mathematical modeling and modeling in the teaching and learning process. Purpose. This paper aims to provide arguments supporting the need for institutions in charge of teacher training to incorporate these processes into their curricular structures. It also aims to furnish ideas about some types of activities that the teacher can implement in class. Discussion. The ideas developed here have been built from a literature review related to the subject, as well as from a research process by the authors in 2021. The latter entailed extracting evidence from the proposal to incorporate mathematical modeling into the study curricula of public universities in Costa Rica. This analysis aimed to ascertain whether each course in the description, objectives, contents, and specific indications, among others, made reference to mathematical modeling and if methodological recommendations were provided for its implementation. It is clear that mathematical modeling, as a didactic strategy, holds significant importance in teaching the discipline. Incorporating mathematical modeling processes in the professional training of teachers and academic training of students is relevant for developing specific skills. Conclusions. There is a need to execute actions aimed at teacher training to implement this type of task. The objective is that students recognize the applicability of the subject and that the knowledge of it enables them to understand its context.
Amazing Graze: Shifts in Jellyfish and Clam Distributions During Dry Years in the San Francisco Estuary
Authors:   California Department of Water Resources, Hartman Rosemary , Twardochleb Laura ,   California Department of Water Resources, Burdi Christina ,   California Department of Water Resources, Wells Elizabeth ,   California Department of Water Resources
Published: March 2024

Aquatic invasive species have drastically changed how the San Francisco Estuary functions. During the past 2 decades, the effects of invasive species in the estuary may have increased in response to frequent and severe drought conditions. The invasive overbite clam (Potamocorbula amurensis), and the Asian Clam (Corbicula fluminea) have well documented consequences on the estuarine food web, but their responses to drought are not well understood. Another invasive species, the jellyfish Maeotias marginata, can further affect the food web, but these effects have not been studied. We investigated the population responses of these invasive species to dry years and their potential effects on the pelagic food web using data from the Interagency Ecological Program’s monitoring surveys. We found M. marginata rapidly moves upstream with changing salinities during dry years, though it sees its highest abundance during high-outflow years in Suisun Bay and Suisun Marsh. Grazing rates of M. marginata in the estuary have not been quantified but are potentially high during localized blooms. The two invasive clams overlap in distribution, but have opposite population responses to drought conditions, with increases in P. amurensis densities and decreases in C. fluminea densities in dry years. With increasing P. amurensis densities, the clams’ combined annual filtration rates increase during drier years in the confluence and Suisun Marsh. Like M. marginata, P. amurensis also shifts upstream during droughts, but because adults cannot move immediately with a change in salinity, the population center of distribution shifts upstream the year after a dry year as a result of juvenile recruitment. If multiple dry years occur in a row, and both P. amurensis and M. marginata move upstream together, their effects on the food web could be compounded, and phytoplankton and zooplankton biomass could steeply decline in the confluence, affecting higher trophic levels in the estuary.
The use of microalgae biomass for the production of marketable products. Part 5. Jet fuel from microalgae biomass
Authors: Samoylova Yu. V. , Sorokina K. N. , Parmon V. N. 
Published: March 2024

Boreskov Institute of Catalysis, Novosibirsk, RussiaThe review focuses on jet fuel from microalgae biomass. Modern standards that biojet fuel obtained from microalgae biomass must meet are presented. The main ways of producing jet fuel from microalgae are considered, namely the processes of “oil to fuel”, “gas to fuel” and “carbohydrates to fuel”, as well as the production of this fuel along with other valuable products as a result of complex bioprocessing of biomass. Data on the potential for using biofuels from microalgae biomass in mixtures with traditional petroleum fuels are presented. The prospects for using this alternative fuel in modern aviation are considered.
WITHDRAWN: Application of medical care four-hand operation mode in digital indirect bonding of palisade guide
Authors: Hu Meichun , Cao Yannan , Wu Xiangbing , Jiang Qian , Zhu Fangyong 
Published: March 2024

Abstract

The full text of this preprint has been withdrawn by the authors as it was submitted and made public without the full consent of all the authors. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.
Big Data Framework for Predicting Infectious Diseases to improve Healthcare by Discovering New Symptom Patterns
Authors: mounir amal , Marie Mohamed Ibrahim , Abd-Elhamid Laila 
Published: March 2024

Abstract

Infectious disease control is one of the most thrilling opportunities form using big data, where these streams of novel data can be used to improve timeliness for preventing. Various public and private sector Healthcare providers generate, store, and analyse big data to improve the services they provide. Lately, the COVID-19-new Corona virus outbreak has put human health, life, production, social connections, and international relations in grave danger. Consequently, big data technologies have been crucial in the pandemic response. Infectious disease occurs when a person has a disease by a pathogen from another person. It is a problem that causes harm for both individual and macro scales. In addition, infectious illness patterns are unknown, which complicate the prediction process. This study aims to create big data framework to predict infectious diseases by discovering new symptoms patterns to enhance healthcare infection prevention and control. To achieve this goal, machine learning algorithms K-Nearest Neighbors (K-NN) and Random Forest (RF) were used to clean and maintain big data from December 2019 to June 2020. Additionally, the mining model FP-growth and Park, Chen, and Yu (PCY) of China were applied to discover new symptom rules. The results show that the RF model performs better than K-NN with accuracy rates of 97%, and the PCY model performs better than FP-growth with an accuracy rate of 98%. These results highlight the potential of big data and machine learning in identifying patterns and predicting infectious diseases, which can ultimately improve public health outcomes.
Estimating the Burr type III distribution of three parameters using Hamiltonian Monte Carlo and application to arthritis relief times data
Authors: Kim Alexander 
Published: March 2024

Abstract

Statistical probability distributions hold great significance in practical problems. Often times, real-world probability distributions do not have a closed form and must be estimated through statistical algorithms. The Hamiltonian Monte Carlo (HMC) algorithm, a Markov Chain Monte Carlo (MCMC) method, is one such method to sample from distributions that cannot easily be sampled from by other means. The HMC algorithm is applied to find Bayes estimates of three parameters of the Burr type III distribution, one of several probability distributions in the Burr distribution family used in a variety of modelling problems from household income to flooding risk. Following the simulation, the algorithm is applied to real-world data, a set of arthritis relief times data where 50 arthritis patients received a fixed amount of an analgesic to test the effectiveness of the medication. The results from HMC are compared with results from Metropolis-Hastings, a simpler MCMC method, which show that HMC offers competitive performance even with a fraction of the number of iterations ran by Metropolis-Hastings.
Mathematics subject classification:60J10, 62F15, 62F10, 70H05
Predicting the epidemiological trend of acute hemorrhagic conjunctivitis in China using Bayesian structural time–series model
Authors: Xu Guangcui , Fan Ting , Zhao Yingzheng , Wu Weidong , Wang Yongbin 
Published: March 2024

Abstract

This study aims to understand the epidemic characteristics of acute hemorrhagic conjunctivitis (AHC) in China and to explore the application value of the Bayesian Time Structure Sequence (BSTS) model. The reported data of AHC cases in China were collected from January 2011 to October 2022. R software was used to construct the BSTS and the Differential Autoregressive Integrated Moving Average (ARIMA) models based on the AHC incidence data from January 2011 to December 2021. The prediction effect of both models was compared by using the data from January to October 2022, and finally the incidence of AHC in China from November 2022 to December 2023 was predicted by the BSTS model. The actual value of AHC incidence in July 2022 under the ARIMA model was not within 95% CI of the predicted value, and these under the BSTS model was within 95% CI of the predicted value. 26,474 new AHC cases were predicted using the BSTS model in China from November 2022 to December 2023. The prediction performance of the BSTS model was better than that of the ARIMA model, and it has a had high application value for the prediction of AHC epidemic trends.
Gene selection driven by DNA methylation in relation to lymph node metastasis in prostate cancer and prognosis analysis
Authors: Sun Ji , Wang Xing , Liang Tie-Jun 
Published: March 2024

Abstract
Purpose Lymph node metastasis is an independent prognostic factor for prostate cancer (PCa), and this study aims to explore the intrinsic molecular mechanisms of PCa lymph node metastasis based on epigenetic modifications using bioinformatics.Methods Gene expression data set TCGA PRAD was downloaded from the UCSC Xena database for differential analysis. Differential genes between patients with and without lymph node metastasis were identified and functionally annotated. DNA methylation data from the GSE220910 dataset were used to identify differential methylation sites (DMPs) using the "ChAMP" R package. The correlation between differential gene expression values and methylation probe beta values was calculated and tested for significance. Finally, a prognosis analysis was conducted for the selected genes regulated by DNA methylation.Results We identified 1380 significantly differentially expressed genes (DEGs), including 906 upregulated and 474 downregulated genes. GO analysis revealed that upregulated genes in patients with lymph node metastasis were mainly involved in processes such as cell division and mitosis, while downregulated genes participated in the cellular response to copper and zinc ions. Subsequently, we further selected 81009 differential methylation sites (DMPs), ultimately retaining 263 DEGs associated with 382 DMPs. Correlation analysis showed that LTA, DOK3, TNFRSF25, and CHRM1 had Pearson correlation coefficients of -0.4092, -0.4111, -0.4054, and − 0.4598, respectively (P < 0.05), with their corresponding methylation probes. Survival analysis indicated that high expression of LTA, DOK3, and TNFRSF25 genes was associated with a shortened progression-free interval (PFI) in PCa patients, while CHRM1 showed the opposite trend (P < 0.05).Conclusion Lymph node metastasis in PCa patients is associated with active cell division and suppression of the response to metal ions. We also discovered that LTA, DOK3, TNFRSF25, and CHRM1 are regulated by DNA methylation, and their abnormal expression significantly impacts patient prognosis.
In vitro comparison of the leakage of carbon dioxide and iodine contrast medium in a bleeding model
Authors: Kitamura Ryoichi , Yoshida Kazuhiro , Maruhashi Takaaki , Tamura Satoshi , Kurihara Yutaro , Suzuki Koyo , Asari Yasushi 
Published: March 2024

Abstract

Purpose
This study aimed to evaluate the effect of carbon dioxide (CO₂) on the sensitivity of bleeding detection by comparing the hydrodynamics of CO₂ and iodine contrast medium using a bleeding model.
Materials and methods
We created a bleeding model with large and small wounds in simulated blood vessels. A syringe was connected to the bleeding model and a blood pressure transducer, and the circuit was filled with CO₂ and iodine contrast medium. The piston of the syringe was pressed, and then the flow rate and intravascular pressure of the CO₂ and iodine contrast medium leaking from the bleeding model were measured. The volume, sphere-equivalent diameter, and sphere-equivalent area of the leaked contrast medium were compared.
Results
At a constant flow rate, the intravascular pressure required for the model to leak was lower for the CO₂ than for the iodine contrast medium, and it was also lower for larger wounds. The CO₂ contrast medium leakage volume, equivalent circle diameter, and equivalent circle area were greater than those of the iodine contrast medium, and they were also greater for larger wounds.
Conclusions
In the bleeding model, the CO₂ contrast medium may be more prone to leakage than the iodine contrast medium in large and small wounds. In terms of visibility, the CO₂ contrast medium may be more likely to detect leakages than the iodine contrast medium.