bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multi-omics differences in the bone marrow between essential thrombocythemia and prefibrotic primary myelofibrosis
Authors: Zhang Anqi , Sun Ting , Yu Dandan , Fu Rongfeng , Liu Xiaofan , Xue Feng , Liu Wei , Ju Mankai , Dai Xinyue , Dong Huan , Gu Wenjing , Chen Jia , Chi Ying , Li Huiyuan , Wang Wentian , Yang Renchi , Chen Yunfei , Zhang Lei 
Published: July 2024

AbstractEssential thrombocythemia (ET) and prefibrotic primary myelofibrosis (pre-PMF) are Philadelphia chromosome-negative myeloproliferative neoplasms. These conditions share overlapping clinical presentations; however, their prognoses differ significantly. Current morphological diagnostic methods lack reliability in subtype differentiation, underlining the need for improved diagnostics. The aim of this study was to investigate the multi-omics alterations in bone marrow biopsies of patients with ET and pre-PMF to improve our understanding of the nuanced diagnostic characteristics of both diseases. We performed proteomic analysis with 4D direct data-independent acquisition and microbiome analysis with 2bRAD-M sequencing technology to identify differential protein and microbe levels between untreated patients with ET and pre-PMF. Laboratory and multi-omics differences were observed between ET and pre-PMF, encompassing diverse pathways, such as lipid metabolism and immune response. The pre-PMF group showed an increased neutrophil-to-lymphocyte ratio and decreased high-density lipoprotein and cholesterol levels. Protein analysis revealed significantly higher CXCR2, CXCR4, and MX1 levels in pre-PMF, while APOC3, APOA4, FABP4, C5, and CFB levels were elevated in ET, with diagnostic accuracy indicated by AUC values ranging from 0.786 to 0.881. Microbiome assessment identified increased levels of Mycobacterium, Xanthobacter, and L1I39 in pre-PMF, whereas Sphingomonas, Brevibacillus, and Pseudomonas_E were significantly decreased, with AUCs for these genera ranging from 0.833 to 0.929. Our study provides preliminary insights into the proteomic and microbiome variations in the bone marrow of patients with ET and pre-PMF, identifying specific proteins and bacterial genera that warrant further investigation as potential diagnostic indicators. These observations contribute to our evolving understanding of the multi-omics variations and possible mechanisms underlying ET and pre-PMF.
Deep learning for derivatives pricing: a comparative study of asymptotic and quasi-process corrections
Authors: Funahashi Hideharu 
Published: July 2024

AbstractIn this study, we compare two methods for using neural networks to efficiently learn the price of derivatives. The first method, proposed by Funahashi (Quant Financ 21(4):575–592, 2021a), involves training the neural networks to learn the difference between the derivative price and its asymptotic expansion, rather than learning the derivative price directly. The target derivative price is then obtained by adding the approximate solution with the predicted value of neural networks. This method reduces the required amount of learning data, often by a factor of one hundred to one thousand, compared to the case where the derivative price is directly learned through neural networks. In the second method, established in this paper, the neural networks learn the difference between the derivative price written on the underlying asset price that follows the target complex stochastic process and the derivative price written on the underlying asset price that has a relatively simple stochastic process that has a closed-form solution for the target derivative prices. This method provides an alternative valuation method when no efficient approximate solution for the derivative value is observed and if one can arbitrarily determine the model parameters of the quasi-process that approximates the original process. We also propose a unified method to determine the model parameters of quasi-processes from underlying asset processes. These methods prove valuable in cases where general analytic solutions are absent, as seen in widely used financial models such as the stochastic volatility models. These cases involve time-consuming numerical calculations to generate learning data, highlighting the value of the two methods, which significantly compress calculation times.
Prediction of bank credit worthiness through credit risk analysis: an explainable machine learning study
Authors: Chang Victor , Xu Qianwen Ariel , Akinloye Shola Habib , Benson Vladlena , Hall Karl 
Published: July 2024

AbstractThe control of credit risk is an important topic in the development of supply chain finance. Financial service providers should distinguish between low- and high-quality customers to predict credit risk accurately. Proper management of credit risk exposure contributes to the long-term viability and profitability of banks, systemic stability, and efficient capital allocation in the economy. Moreover, it benefits the development of supply chain finance. Supply chain finance offers convenient loan transactions that benefit all participants, including the buyer, supplier, and bank. However, poor credit risk management in supply chain finance may cause losses for finance providers and hamper the development of supply chain finance. Machine learning algorithms have significantly improved the accuracy of credit risk prediction systems in supply chain finance. However, their lack of interpretability or transparency makes decision-makers skeptical. Therefore, this study aims to improve AI transparency by ranking the importance of features influencing the decisions made by the system. This study identifies two effective algorithms, Random Forest and Gradient Boosting models, for credit risk detection. The factors that influenced the decision of the models to make them transparent are explicitly illustrated. This study also contributes to the literature on explainable credit risk detection for supply chain finance and provides practical implications for financial institutions to inform decision making.
Deep learning for computer vision based activity recognition and fall detection of the elderly: a systematic review
Authors: Gaya-Morey F. Xavier , Manresa-Yee Cristina , Buades-Rubio José M. 
Published: July 2024

AbstractAs the proportion of elderly individuals in developed countries continues to rise globally, addressing their healthcare needs, particularly in preserving their autonomy, is of paramount concern. A growing body of research focuses on Ambient Assisted Living (AAL) systems, aimed at alleviating concerns related to the independent living of the elderly. This systematic review examines the literature pertaining to fall detection and Human Activity Recognition (HAR) for the elderly, two critical tasks for ensuring their safety when living alone. Specifically, this review emphasizes the utilization of Deep Learning (DL) approaches on computer vision data, reflecting current trends in the field. A comprehensive search yielded 2,616 works from five distinct sources, spanning the years 2019 to 2023 (inclusive). From this pool, 151 relevant works were selected for detailed analysis. The review scrutinizes the employed DL models, datasets, and hardware configurations, with particular emphasis on aspects such as privacy preservation and real-world deployment. The main contribution of this study lies in the synthesis of recent advancements in DL-based fall detection and HAR for the elderly, providing insights into the state-of-the-art techniques and identifying areas for further improvement. Given the increasing importance of AAL systems in enhancing the quality of life for the elderly, this review serves as a valuable resource for researchers, practitioners, and policymakers involved in developing and implementing such technologies.Graphical abstract
Electrical excitability of cancer cells—CELEX model updated
Authors: Djamgoz Mustafa B. A. 
Published: July 2024

AbstractThe normal functioning of every cell in the body depends on its bioelectric properties and many diseases are caused by genetic and/or epigenetic dysregulation of the underlying ion channels. Metastasis, the main cause of death from cancer, is a complex multi-stage process in which cells break away from a primary tumour, invade the surrounding tissues, enter the circulation by encountering a blood vessel and spread around the body, ultimately lodging in distant organs and reproliferating to form secondary tumours leading to devastating organ failure. Such cellular behaviours are well known to involve ion channels. The CELEX model offers a novel insight to metastasis where it is the electrical excitation of the cancer cells that is responsible for their aggressive and invasive behaviour. In turn, the hyperexcitability is underpinned by concomitant upregulation of functional voltage-gated sodium channels and downregulation of voltage-gated potassium channels. Here, we update the in vitro and in vivo evidence in favour of the CELEX model for carcinomas. The results are unequivocal for the sodium channel. The potassium channel arm is also broadly supported by existing evidence although these data are complicated by the impact of the channels on the membrane potential and consequent secondary effects. Finally, consistent with the CELEX model, we show (i) that carcinomas are indeed electrically excitable and capable of generating action potentials and (ii) that combination of a sodium channel inhibitor and a potassium channel opener can produce a strong, additive anti-invasive effect. We discuss the possible clinical implications of the CELEX model in managing cancer.
A Piezoelectric Electrospun Nanofiber mat (Kaolin/PVDF) for the Degradation of Methylene Blue in Water
Authors: Sam Simanye , Ojo Babatope O. , Malinga Soraya P. , Mabuba Nonhlangabezo 
Published: July 2024

AbstractOrganic compounds of various classes have been identified as major pollutants commonly detected in several environmental freshwaters. As a potential solution, electrospun mat-like nanofibers comprising polyvinylidene fluoride (PVDF) nanofibers and kaolin clay composite were synthesized for the degradation of methylene blue dye in wastewater using piezocatalytic technique. The kaolin/PVDF composites were characterized with SEM, XRD, FTIR, Chronoamperometry and electrochemical impedance spectroscopy. The poling of the piezoelectric charges of the electrospun mat-like nanofibers, enhanced by the presence of the crystalline nature of the kaolin clay occasioned the generation of reactive species upon exposure to ultrasonic vibration. Moreover, ultrasound waves in the system prevented passivation of the mat’s active surface during piezocatalysis through continuous cleaning action. A degradation efficiency of 100% was obtained from the degradation of methylene blue dye using the synthesized kaolin/PVDF composites during piezocatalysis. This result positions the nanofiber as an effective catalyst in wastewater treatment applications.
Graphical Abstract
Comparison of the Performance of Structural Break Tests in Stationary and Nonstationary Series: A New Bootstrap Algorithm
Authors: Çamalan Özge , Hasdemir Esra , Omay Tolga , Küçüker Mustafa Can 
Published: July 2024

AbstractStructural breaks are considered as permanent changes in the series mainly because of shocks, policy changes, and global crises. Hence, making estimations by ignoring the presence of structural breaks may cause the biased parameter value. In this context, it is vital to identify the presence of the structural breaks and the break dates in the series to prevent misleading results. Accordingly, the first aim of this study is to compare the performance of unit root with structural break tests allowing a single break and multiple structural breaks. For this purpose, firstly, a Monte Carlo simulation study has been conducted through using a generated homoscedastic and stationary series in different sample sizes to evaluate the performances of these tests. As a result of the simulation study, Zivot and Andrews (J Bus Econ Stat 20(1):25–44, 1992) are the best-performing tests in capturing a single break. The most powerful tests for the multiple break setting are those developed by Kapetanios (J Time Ser Anal 26(1):123–133, 2005) and Perron (Palgrave Handb Econom 1:278–352, 2006). A new Bootstrap algorithm has been proposed along with the study’s primary aim. This newly proposed Bootstrap algorithm calculates the optimal number of statistically significant structural breaks under more general assumptions. Therefore, it guarantees finding an accurate number of optimal breaks in real-world data. In the empirical part, structural breaks in the real interest rate data of the US and Australia resulting from policy changes have been examined. The results concluded that the bootstrap sequential break test is the best-performing approach due to the general assumption made to cover real-world data.
The mediating role of mobile learning motivation in the relationship between pre-service teachers’ digital literacy and pre-service teacher identity
Authors: Bülbül Ayşe Eliüşük , Özelçi Serap Yılmaz 
Published: July 2024

Abstract
This study is significant as it seeks to establish the connection between the digital literacy levels of pre-service teachers and their teacher identities while also examining the mediating role of mobile learning in this relationship. Additionally, it explores the impact of pre-service teachers’ digital literacy and learning motivations on their professional success. This study’s findings can potentially transform the way we prepare pre-service teachers, emphasizing the critical role of digital literacy and mobile learning in their professional growth. The research was conducted meticulously using relational scanning, a method that allows for exploring relationships between variables, and Process macro analysis, a statistical technique for examining complex relationships. Data were collected using the Mobile Learning Motivation Scale (MLMS), Early Teacher Identity Scale (ETIM), and Digital Literacy Scale (DLS). Three hundred forty-four pre-service teachers participated in the study, ensuring a robust dataset. The study revealed several key findings. Firstly, a positive correlation exists between pre-service teachers’ digital literacy levels, teacher identities, and mobile learning. Secondly, an increase in digital literacy levels positively impacted teacher identity. Thirdly, mobile learning was identified as a mediator in the relationship between digital literacy and teacher identity. These findings underscore the potential of mobile learning to enhance the development of teacher identity and digital literacy, highlighting the importance of incorporating these skills into the pre-professional training of teachers.
Biotin-modified mesoporous silica nanoparticles as a delivery system for quercetin and gallic acid for bacterial biofilm eradication
Authors: Hua Shu , Wang Yi , Li Yiwei , Liu Siyuan , Wen Juan , Walsh Laurence J. , Xu Chun 
Published: July 2024

AbstractThe effective management of biofilm-associated infections remains a pressing concern in clinical practice, necessitating the development of efficient eradication strategies. This study introduces biotin-modified mesoporous silica nanoparticles (MSN-Biotin) designed to facilitate the delivery of phytochemical agents, including gallic acid and quercetin, for biofilm disruption. We elucidate the synthesis, characterization, and biofilm penetration ability of MSN-Biotin towards gram-negative E. coli biofilm and gram-positive S. mutans biofilms, emphasizing their potential in anti-biofilm drug delivery. Importantly, in vitro experiments demonstrate that MSN-Biotin loaded with phytochemicals achieved significantly better penetration into E. coli and S. mutans biofilms than traditional mesoporous silica nanoparticles and better biofilm eradication ability than free phytochemical molecules. These findings underscore the promise of MSN-Biotin as a potential efficient platform for delivering antimicrobial agents. This study introduces a novel surface modification approach for the development of antibiofilm nanoparticle systems and establishes their significant potential in biofilm infection management.
Microstructural analysis of titanium alloys based on high-temperature phase reconstruction
Authors: Lypchanskyi Oleksandr , Muszka Krzysztof , Wynne Bradley , Kawalko Jakub , Śleboda Tomasz 
Published: July 2024

AbstractThe microstructural evolution of titanium alloys under high-temperature conditions plays a key role in determining their mechanical properties and hot working behavior. This research presents an advanced method for calibrating β phase reconstruction software using in situ testing on Grade 2 titanium, which achieves accurate reconstruction of the parent β phase microstructure. In addition, unique microstructural observations in the forging of Ti-6246 titanium alloy are highlighted, demonstrating the influence of deformation parameters on the resulting β phase grain structures. Using advanced techniques such as electron backscatter diffraction and Burgers orientation relationship-based software, the research elucidates the behavior of these phases under varying thermal and deformation conditions. In Grade 2 titanium, significant grain growth and phase transformation dynamics were observed upon heating beyond the β-transus temperature during in situ calibration of β phase reconstruction software. The analysis demonstrates the effectiveness of the software in precise reconstructing the parent β phase microstructure based on the orientation of the inherited αs phase. Furthermore, the evaluation of hot forming parameters in Ti-6246 alloy shows the influence of deformation temperature and strain rate on the resulting microstructure. Finite element method analysis coupled with dynamic material modeling elucidates the distribution of temperature, strain rate, and effective strain during forging, which aids in the qualitative assessment of hot workability. Microstructural observations in Ti-6246 alloy forging highlight the presence of elongated colonies of αs phase precipitates, indicative of localized strain intensities and deformation temperatures. In addition, EBSD analysis coupled with β phase reconstruction reveals distinct microstructural features in different regions of the forging. In particular, regions subjected to higher strain rates exhibit elongated β phase grains with pronounced disorientation gradients, suggesting intense deformation. Conversely, optimal forging conditions lead to the appearance of unreinforced axisymmetric β phase grains, indicating dynamic recovery processes. Pole figure analysis further emphasizes the Burgers crystallographic relationship between the αs and β phases, confirming that deformation during forging occurs exclusively within the β phase. These results provide valuable insights into the microstructural evolution in titanium alloys under high-temperature conditions, which are essential for optimizing hot working processes and improving mechanical properties.
Graphical abstract
Functional distribution, personal income inequality, and top shares of income: do social classes still matter?
Authors: Giangregorio Luca , Villani Davide 
Published: July 2024

AbstractThis paper aims at providing new evidence about the link between personal and functional distribution and top-shares composition. We apply a novel class scheme based on two key features of contemporary capitalism i.e., individuals/households receiving multiple types of incomes, and the role of managers. The empirical application in Germany, Spain, and Italy over the period 2000–2017 reveals two main results. First, we observe a direct link between personal and functional distributions. A marginal increase in wages received by labourers would reduce inequality, whereas those received by capitalist households would increase it. Second, we find that a significant portion of labour income at the top of the income distribution corresponds to wages received by capitalist households. We conclude that although the linear correspondence between income source and class location is more blurred today than it was 200 years ago, a class divide is still clear and more prominent than what is often indicated in the literature.
A Comprehensive Investigation of Nanocomposite Polymer Flooding at Reservoir Conditions: New Insights into Enhanced Oil Recovery
Authors: Salem Khalaf G. , Salem Adel M. , Tantawy Mahmoud A. , Gawish Ahmed A. , Gomaa Sayed , El-hoshoudy A. N. 
Published: July 2024

AbstractRecently, the polymer-nanoparticle combination has garnered significant interest in enhanced oil recovery (EOR) due to its promising experimental results. However, the previous research was mostly directed at silica, while alumina and zirconia nanoparticles have gotten the least consideration. Unlike previous works, this study aims to investigate the influence of three NPs: Silica (SiO2), Alumina (Al2O3), and Zirconia (ZrO2) on hydrolyzed polyacrylamide (HPAM). To this end, three nanocomposites were formulated: HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2. Rheological evaluations were performed to examine the viscosity degradation of the three nanocomposites and HPAM under reservoir conditions. Furthermore, interfacial tension (IFT) at the oil–water interface and wettability studies were investigated. Moreover, sand-pack flooding was performed to examine the incremental oil recovery. The results revealed that the polymer viscosity was boosted by 110%, 45%, and 12% for HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 respectively under the investigation range of temperature. Moreover, the polymer viscosity was improved by 73%, 48%, and 12% for HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2 respectively under the investigation range of salinity. Nanocomposites are also found to be a remarkable agent for reducing interfacial tension and changing the contact angle. The flooding experiments confirmed that the EOR by HPAM, HPAM-SiO2, HPAM-Al2O3, and HPAM-ZrO2, was 8.6%, 17.4%, 15.3%, and 13.6% of OOIP respectively. Moreover, the results of flooding experiments were well validated and matched by numerical simulation. Such findings of this work afford new insights into EOR and reinforce the promising outlook of such technique at the field scale.
Otherworldly Properties
Authors: Cunningham Alan 
Published: July 2024

AbstractDespite the many differing perspectives possible regarding the concept of a property
right, one central aspect is, arguably, the primal exclusionary impulse and its special
connection to a particular form of subjectivity, especially in terms of how people feel
about space, enclosed space and any subsequent property rules applicable. Such
aspects limit speculative thought concerning the enactment of challenging housing
reforms. This essay therefore asks: Why is exclusion so relevant to spatial ethics, and
is it only a particular form of subjectivity that is involved in the phenomena of
exclusionary impulses? As a response, new speculative thoughts are proposed that
aim at challenging such specialised subjectivity, exclusionary impulses and spatial
engagement: re-sanctification of enclosed space, de-objectification of being and a call
to develop new or alternative subjectivities and modes of somatic thought.