bluuebunny/crossref_metadata_2025_split huggingface dataset:
High Humidity Aggravates Collagen-induced Arthritis in Mice by Increasing the Expression of Splenic Myeloid-derived Suppressor Cells
Authors: Wang Hongli , Wang Mingzhu , Wang Tengyue , Wen Chengping , He Zhixing , Huang Lin 
Published: April 2024

Abstract

Background: Rheumatoid arthritis (RA) is an autoimmune disease characterized by joint inflammation and bone destruction, leading to severe complications. Previous research has suggested that high humidity conditions may exacerbate RA, but the underlying mechanisms remain unclear. Furthermore, there is a lack of evidence linking humidity to the worsening of RA symptoms in animal models.
Methods: We utilized C57BL/6 mice to establish the CIA mouse model. The arthritis status of mice was assessed under two different humidities (50% and 80%). ELISA was used to measure the concentrations of autoantibodies and pro-inflammatory factors. Histopathological changes in spleen tissues were observed through H&E and immunohistochemistry. Mass cytometry was employed to investigate changes in cell types in the spleens of CIA mice under high humidity conditions. Additionally, we evaluated oxidative stress markers associated with MDSCs and conducted mRNA sequencing of sorted MDSCs to investigate their impact on arthritis in CIA mice.
Results: Compared to normal humidity, high humidity exacerbated arthritis incidence in mice, leading to increased arthritis scores,swelling, serum autoantibodies (anti-COII and anti-CCP), and upregulation of pro-inflammatory cytokines. Significant variations were observed in the spleen index under conditions of high humidity, accompanied by noticeable inflammatory alterations. Furthermore, Elevated humidity levels induced a substantial modulation in MDSCs population in the spleen of CIA mice, along with alterations in oxidative stress markers such as heightened serum ROS levels, and increased expression of COX, SOD, and Nrf2 mRNA. Following successful sorting of MDSCs, mRNA sequencing revealed a decrease in the expression of Rap1 signaling pathway under high humidity environment, which may contribute to the increase of MDSCs cells and aggravate the progression of RA disease.
Conclusion: Our comprehensive analysis demonstrates that high humidity is a negative factor in the occurrence and development of RA, potentially mediated by the influence of the Rap1 signaling pathway on MDSCs.
The adaptation of the 6 item Turnover Intention Scale among Hungarian elderly care workers: A validity and reliability study
Authors: Németh Zsanett , Deák Petra , Szűcs Réka , Makai Alexandra , Hock Márta 
Published: April 2024

Abstract

This research examines psychometric characteristics and reliability of the 6-items turnover intention scale (TIS-6) by Bothma and Roodt’s (2013) on a Hungarian sample. The internal validity of the TIS-6 was performed using data of 269 Hungarian elderly care institutions workers. Confirmatory factor analysis was performed to analyse the structural validity. Convergent and discriminant validity were examined with questions on job characteristics and using Maslach Burnout Inventory and Effort-Reward Imbalance Scale. IBM SPSS 28.0 software was used for statistical analysis, results considered significant at p < .001. The internal consistency of the questionnaire's scale proved to be acceptable (\(\text{α}\text{=.826}\)). Convergent validity was confirmed by the relationship between the components of the questionnaire and burnout (rs=.512; p < .001; rs=.419; p < .001) and workplace stress (rs=.565; p < .001; rs=.310; p < .001). There were significant differences between the TIS-6 scores among the groups with different degrees of burnout (p < .001), which indicated adequate discriminant validity of the questionnaire. The structural validity of the questionnaire was acceptable, the questions of the scale fit good. The Hungarian version of the TIS-6 scale is a valid and reliable tool for assessing the turnover intention among elderly care institutions workers in Hungary.
Introducing Quantum Information and Computation to a Broader Audience with MOOCs at OpenHPI
Authors: Hellstern Gerhard , Hettel Jörg , Just Bettina 
Published: April 2024

Abstract

Quantum computing is an exciting field with high disruptive potential, but very difficult to access. For this reason, numerous concepts are being developed worldwide on how quantum computing can be taught. This always raises questions about the didactic concept, the content actually taught, and how to measure the success of the teaching concept.
In 2022 and 2023, the authors gave a total of nine two-week MOOCs (massive open online courses) with different possible learning paths on the Hasso Plattner Institute's OpenHPI platform. The platform's purpose is to make computer science education available to everyone free of charge. The nine quantum courses form a self-contained curriculum. A total of 17157 course attendances have been taken by 7413 natural persons, and the number is still rising.
This paper presents the course concept and then evaluates the anonymized data on the background of the participants, their behavior in the courses, and their learning success.
In the present paper for the first time such a large dataset of MOOC-based quantum computing education is analyzed.
The summarized results are a heterogeneous personal background of the participants biased towards IT professionals, a majority following the didactic recommendations, and a high success rate, which is strongly correlatatd to following the didactic recommendations. The amount of data from such a large group of quantum computing learners offers numerous starting points for further research in the field of quantum computing education.
Discrimination of small sample tea varieties based on convolutional neural network and deep convolutional generative adversarial network enhanced near-infrared diffuse reflectance spectral dataset
Authors: Guo Yulong , Huang Zhengwei , Sheng Yang , Teng Yan , Li Chunyang , Li Chun , Jiang Ling 
Published: April 2024

Abstract

Near-infrared diffuse reflectance spectroscopy is widely recognized as a rapid, non-destructive, and environmentally friendly detection technology. However, in order to ensure the accuracy and stability of the detection model, a large number of sample data is necessary. This paper proposed the rapid and non-destructive detection of small sample tea variety recognition based on the near-infrared diffuse reflectance spectrum data extended by convolutional neural network (CNN) and deep convolutional generative adversarial network (DCGAN). The near-infrared diffuse reflectance spectra of 240 tea samples were obtained by Lambda 950 spectrometer using eight of the most popular tea varieties on the Chinese market. Firstly, the spectral data was enhanced using translation, linear superposition, noise addition, and DCGAN methods, and the quality of the generated spectra was evaluated using the support vector machine (SVM) and gradient boosting decision tree (GBDT) methods. Compared with other methods, the DCGAN has the highest accuracy of 91.75%. Secondly, the optimal number of iterations of DCGAN was confirmed to be 6000 by SVM and GBDT methods. To further augment the precision of identifying small samples of tea, two additional classification models of the Extreme Gradient Boosting (Xgboost) and CNN were applied to the DCGAN. Finally, the results demonstrated that the CNN achieved the highest identification accuracy of 98.68% compared with SVM (90.46%), GBDT (90.42%), and Xgboost (88.83%) with an additional 100 samples and 6000 iterations. Therefore, the combination of deep convolutional generative adversarial network enhanced near-infrared diffuse reflectance spectral dataset and the CNN successfully realizes the identification of small sample tea varieties. The experimental results strongly indicate that this method holds significant potential for practical implementation in the field of small sample tea varieties identification.
Leveraging Common User Clustering for Improved Performance in Cell-Free NOMA Networks
Authors: Mousavi S. Ali , Monemi Mehdi , Mohseni Reza 
Published: April 2024

Abstract

In existing cell-free non-orthogonal multiple access (NOMA) networks, each user equipment (UE) receives desired signals from a group of access points (APs) simultaneously, however, the UE belongs to only a single NOMA cluster. This prevents exploiting the potential benefits of having clusters with common UEs. Previous studies have investigated clusters with common UE in cellular NOMA networks by considering that UEs at the cell borders can communicate with base stations through multiple clusters using coordinate multi-point techniques. However, this approach has not been investigated for cell-free NOMA networks yet.In this paper, we consider the UE clustering in a cell-free NOMA network considering three strategies, including single-UE OMA (CF-OMA), double-UE NOMA with no common UE (CF-NOMA), and double-UE NOMA with common UE (CF-NOMAC) clusters. We analytically prove that the proposed CF-NOMAC clustering improves the sum rate compared to the CF-NOMA, and CF-OMA methods, provided that certain conditions hold. Considering the three characterized UE clustering methods, we formally define UE clustering, AP grouping, and power allocation problem for maximizing the sum rate in cell-free NOMA networks.We decompose the formulated mixed-integer non-linear program (MINLP) problem into subproblems with integers and continuous decision variables. The integer decision variables correspond to UE clustering and AP grouping which are obtained based on the analytically elaborated results, and the continuous ones correspond to the power values that are obtained by converting a non-convex signomial programming problem into geometrical programming using the monomial approximation technique.Analytical results reveal the outperformance of the proposed algorithm due to employing a lower number of orthogonal clusters in the network.
Integrated Geological Study in an Offshore Renewable Energy Test Site: An Example From the Basque Contiental Shelf (Bay of Biscay, Spain)
Authors: Cantero Iván Asensio , Rodríguez-Méndez Lidia , Tubía Néstor Vegas , Iriarte Aitor Aranguren 
Published: April 2024

Abstract

Testing and research centres for offshore renewable energy, exemplified by facilities like BIMEP on the Basque coast of Spain, play a crucial role in driving the energy transition. In this study, we utilize standardized data for this infrastructure, such as high resolution bathymetry or sediment samples, to conduct a comprehensive geological analysis including both sedimentary and rocky seabed. Our approach could serve as a methodological example that offers a cost-effective means to characterize offshore energy sites. We present a geological map that extends the main lithological formations from onshore to the continental shelf and identifies the main structures, including NW-trending folds and fractures predominantly oriented NE-SW. Sedimentary bottoms are analysed through a Seabed sediment map, illustrating a NE-SW oriented banded distribution attending to granulometry. Bedforms are also studied, they are asymmetric and mainly oriented NE-SW. The Seabed sediment map and the bedform analysis reveal the effect of a SE-directed bottom current as the main mechanism controlling sediment mobility. This current matches with the predominant swell from the NW and with the most energetic waves in the area.
A Novel Kernel-Based Machine Learning Approach for Differential-Algebraic Equations
Authors: Taheri Tayebeh , Aghaei Alireza Afzal , Parand Kourosh 
Published: April 2024

Abstract

The recent introduction of the Least-Squares Support Vector Regression (LS-SVR) algorithm for solving differential and integral equations has sparked interest. In this study, we expand the application of this algorithm to address systems of differential-algebraic equations (DAEs). Our work presents a novel approach to solving general DAEs in an operator format by establishing connections between the LS-SVR machine learning model, weighted residual methods, and Legendre orthogonal polynomials. To assess the effectiveness of our proposed method, we conduct simulations involving various DAE scenarios, such as nonlinear systems, fractional-order derivatives, integro-differential, and partial DAEs. Finally, we carry out comparisons between our proposed method and currently established state-of-the-art approaches, demonstrating its reliability and effectiveness.
Metaverse Counseling: Transforming Depression Care with AI Digital Humans in Virtual Realms
Authors: HWANG SUNIK , Seo TaeWoong , Kim YoonSoo , Oh HaYoung 
Published: April 2024

Abstract

In this paper, we present an innovative solution to address the problem of modern depression through a metaverse-based counseling platform. Recognizing that time, space, and social factors make it difficult for individuals to access counseling, the platform leverages NPCs in the metaverse. The platform analyzes user text data to predict suicide risk and emotional state using models built on datasets such as ‘Suicide and Depression Detection’ and various algorithms such as K-nearest neighbors, logistic regression, and SVM. To implement digital human conversations in Unreal Engine, we used several plugins, including speech-to-text, chatbot, and text-to-speech, to process user speech in real time to enable effective counseling responses. Ultimately, the platform aims to provide accessible counseling services and personalized support to depression patients by overcoming temporal and spatial constraints. This metaverse solution is expected to not only break new ground in the treatment of depression, but also contribute significantly to addressing mental health issues, enabling an innovative approach to modern well-being.
Experimental and simulation analysis of flow patterns and energy dissipation through sluice gates in a U-shaped channel
Authors: Wang Jing , Li Songping , Zhang Yisheng 
Published: April 2024

ABSTRACT

The vertical U-shaped gate holds significant potential for widespread application in flow control within U-shaped channels, as it eliminates the necessity for constructing auxiliary hydraulic structures. The boundary conditions associated with the U-shaped gate are complex, offering distinctive hydraulic features. In this study, the hydraulic characteristics of a vertical U-shaped gate have been investigated by model test and numerical simulation on a U-shaped channel under different flow rates, and the hydraulic evolution process was analyzed. The results show that the minimum relative error of discharges is 0.4%, so the numerical simulation can accurately describe the hydraulic performance of the vertical U-shaped gate. The flow generates a contracted cross-section and presents rhomboid water waves with a ‘hump-like’ convex structure after passing the U-shaped gate, accompanied by large kinetic energy dissipation. The gate opening exerts notable influence on the free surface width. The width of the first contraction section increased by 53.88% as the gate opening ranged from 2.5 to 5.5 cm with a flow rate of 8.24 L/s. The power function relationship of upstream flow Froude number, the width of free surface and energy loss is established. The results are helpful for engineering designing and operation management of a U-shaped gate.
Identification of agricultural surface source pollution in plain river network areas based on 3D-EEMs and convolutional neural networks
Authors: Huan Juan , Yuan Jialong , Zhang Hao , Xu Xiangen , Shi Bing , Zheng Yongchun , Li Xincheng , Zhang Chen , Hu Qucheng , Fan Yixiong , Lv Jiapeng , Zhou Liwan 
Published: April 2024

ABSTRACT

Agricultural non-point sources, as major sources of organic pollution, continue to flow into the river network area of the Jiangnan Plain, posing a serious threat to the quality of water bodies, the ecological environment, and human health. Therefore, there is an urgent need for a method that can accurately identify various types of agricultural organic pollution to prevent the water ecosystems in the region from significant organic pollution. In this study, a network model called RA-GoogLeNet is proposed for accurately identifying agricultural organic pollution in the river network area of the Jiangnan Plain. RA-GoogLeNet uses fluorescence spectral data of agricultural non-point source water quality in Changzhou Changdang Lake Basin, based on GoogLeNet architecture, and adds an efficient channel attention (ECA) mechanism to its A-Inception module, which enables the model to automatically learn the importance of independent channel features. ResNet are used to connect each A-Reception module. The experimental results show that RA-GoogLeNet performs well in fluorescence spectral classification of water quality, with an accuracy of 96.3%, which is 1.2% higher than the baseline model, and has good recall and F1 score. This study provides powerful technical support for the traceability of agricultural organic pollution.
Pollutant removal in an experimental bioretention cell situated in a northern Chinese sponge city
Authors: Shi Chunyan , Feng Xia , Sun Weining , Qiu Hong , Liu Gen , Li Siwen , Xie Jing , Wang Pengxuan , Lin Yingzi , Wei Xindong , Xu Tongyu , Gao Weijun 
Published: April 2024

ABSTRACT

To assess the viability and effectiveness of bioretention cell in enhancing rainwater resource utilization within sponge cities, this study employs field monitoring, laboratory testing, and statistical analysis to evaluate the water purification capabilities of bioretention cell. Findings indicate a marked purification impact on surface runoff, with removal efficiencies of 59.81% for suspended solids (SS), 39.01% for chemical oxygen demand (COD), 37.53% for ammonia nitrogen (NH3-N), and 30.49% for total phosphorus (TP). The treated water largely complies with rainwater reuse guidelines and tertiary sewage discharge standards. Notably, while previous research in China has emphasized water volume control in sponge city infrastructures, less attention has been given to the qualitative aspects and field-based evaluations. This research not only fills that gap but also offers valuable insights and practical implications for bioretention cell integration into sponge city development. Moreover, the methodology and outcomes of this study serve as a benchmark for future sponge city project assessments, offering guidance to relevant authorities.
Recent Advancements in Refashioning of NSAIDs and their Derivatives as
Anticancer Candidates
Authors: Kassab Asmaa E. , Gedawy Ehab M. 
Published: April 2024

Abstract::
Inflammation is critical to the formation and development of tumors and is closely associated with
cancer. Therefore, addressing inflammation and the mediators that contribute to the inflammatory process
may be a useful strategy for both cancer prevention and treatment. Tumor predisposition can be attributed to
inflammation. It has been demonstrated that NSAIDs can modify the tumor microenvironment by enhancing
apoptosis and chemosensitivity and reducing cell migration. There has been a recent rise in interest in drug
repositioning or repurposing because the development of innovative medications is expensive, timeconsuming,
and presents a considerable obstacle to drug discovery. Repurposing drugs is crucial for the
quicker and less expensive development of anticancer medicines, according to an increasing amount of research.
This review summarizes the antiproliferative activity of derivatives of NSAIDs such as Diclofenac,
Etodolac, Celecoxib, Ibuprofen, Tolmetin, and Sulindac, published between 2017 and 2023. Their mechanism
of action and structural activity relationships (SARs) were also discussed to set the path for potential future
repositioning of NSAIDs for clinical deployment in the treatment of cancer.
In vitro Modified Release Studies on Melatoninergic Fluorinated Phenylalkylamides:
Circumventing their Lipophilicity for Oral Administration
Authors: Vlachou Marilena , Siamidi Angeliki , Protopapa Chrystalla , Vlachos Michalis , Kloutsou Sophia , Dreliozi Chrysoula-Christina , Papanastasiou Ioannis P. 
Published: April 2024

Introduction:
In an attempt to circumvent the lipophilicity burden for the oral administration of
new potent synthetic melatoninergic fluorine-substituted methoxyphenylalkyl amides, we conducted in vitro
modified release studies using carefully selected matrix tablets’ biopolymeric materials in different ratios.

Method:
In particular, we sought to attain release profiles of these analogues similar to that of the parent compound,
the chronobiotic hormone Melatonin (MLT), and also of the commercially available drug, Circadin®.

Result:
It was found that some of these systems, albeit being more lipophilic than MLT, mimic the in vitro
release patterns of melatonin and Circadin®.

Conclusion:
Moreover, a number of these derivatives were proven suitable for dealing with sleep onset problems,
whilst others for dealing with combined sleep onset/sleep maintenance dysfunctions.
Ramifications of m6A Modification on ncRNAs in Cancer
Authors: Mehmood Rashid 
Published: April 2024

Abstract::
N6-methyladenosine (m6A) is an RNA modification wherein the N6-position of adenosine
is methylated. It is one of the most prevalent internal modifications of RNA and regulates
various aspects of RNA metabolism. M6A is deposited by m6A methyltransferases, removed by
m6A demethylases, and recognized by reader proteins, which modulate splicing, export, translation,
and stability of the modified mRNA. Recent evidence suggests that various classes of non--
coding RNAs (ncRNAs), including microRNAs (miRNAs), circular RNAs (circRNAs), and long
con-coding RNAs (lncRNAs), are also targeted by this modification. Depending on the ncRNA
species, m6A may affect the processing, stability, or localization of these molecules. The m6A--
modified ncRNAs are implicated in a number of diseases, including cancer. In this review, the author
summarizes the role of m6A modification in the regulation and functions of ncRNAs in tumor
development. Moreover, the potential applications in cancer prognosis and therapeutics are discussed.
Comparison of 4 mm-sized and 3 mm-sized Stent Retrievers in Mechanical
Thrombectomy for M2 Occlusion
Authors: Lee Han Gyul , Yi Ho Jun , Shin Dong-Seong , Kim Bum-Tae 
Published: April 2024

Introduction:
A stent retriever (SR) is widely used in mechanical thrombectomy
(MT) for M2 segment occlusion. However, the suitable size of SR in M2 occlusion remains unclear.
Therefore, we aimed to compare 4 mm-sized SR with 3 mm-sized SR in M2 occlusion.

Methods:
Patients who underwent MT with SR for M2 occlusion were dichotomized into 4×20
mm SR and 3×20 mm SR groups. Then, 1:1 propensity score matching was performed. The M2
segment was divided into proximal and distal segments according to the occlusion site. Subgroup
analysis was then performed for each cohort.

Results:
A total of 111 patients were enrolled, with 4×20 mm SR and 3×20 mm SR applied in 72
(64.9%) and 39 (35.1%) cases, respectively. In propensity score matching, mean number of stent
passages for reperfusion was significantly lower in the 4×20 mm SR group than in the 3×20 mm
SR group (1.5 ± 0.8 vs. 2.1 ± 1.1; p = 0.004). First-pass reperfusion (FPR) was more highly
achieved in the 4×20 mm SR group than in the 3×20 mm SR group (52.6% vs. 42.1%; p =
0.007). In both proximal and distal occlusion cohorts, the 4 mm SR group showed lower mean
number of SR passage (p = 0.004 and p = 0.003, respectively) and higher FPR rate than the 3
mm SR group (p = 0.003 and p = 0.007, respectively).

Conclusion:
In MT for M2 occlusion, 4×20 mm SR enables an effective procedure with lesser
SR passage for reperfusion and a higher rate of FPR than 3×20 mm SR.