bluuebunny/crossref_metadata_2025_split huggingface dataset:
Molecular dynamics study on dynamic interlayer friction of graphene and its strain effect
Authors: Gan Shuang , Huang Jianzhang , Cai Yi , Liang Yingjing , Liu Yijie 
Published: April 2023

Abstract
The mechanism of dynamic sliding friction between graphene layers and its strain effect is theoretically analyzed in this paper. A friction pair model with an annular graphene as slider is built to eliminate the influence of commensurability and edge effect. The effects of temperature, normal load, sliding velocity, support stiffness and axial strain on the friction between graphene layers are investigated. The coupling effect of temperature and other influencing factors are clarified. The results show that normal load increases the friction force by decreasing layer spacing. The friction is firstly enhanced as the sliding velocity increase and then is reduced by severe interlayer residual deformation and lattice resonance frequency at high sliding velocity. The support stiffness regulates the interlayer friction by affecting the atomic vibration amplitude of the graphene lattice. By mechanism analysis, it is found that by changing the number of atoms in friction region between layers and the frequency of lattice vibration, the strain can effectively regulate the dynamic friction between graphene layers. Our findings reveal the influence mechanism of affecting factor on dynamic friction of graphene and provide a fundamental understanding for the strains engineering of nanoscale friction.
Association Between Air Pollution and Incidence of Psoriasis: A Nationwide Population-based Case-control Study
Authors: Li Chia-Lun , Ma Sheng-Hsiang , Wu Chun-Ying , Lyu Ying-Syuan , Chang Yun-Ting , Ao Chon-Kit , Wu Chen-Yi 
Published: April 2023

Abstract
Background Air pollutants may aggravate psoriasis. However, the association between air pollution and psoriasis incidence remains unclear. We aimed to identify the association between air pollution and psoriasis incidence.Methods This case-control study included patients with psoriasis and age-, sex-, urbanization level-, index date-, and comorbidities-matched controls from the National Health Insurance Research Database in Taiwan from January 1, 2012, to December 31, 2018. Exposure to air pollutants (sulfur dioxide, carbon monoxide, ozone, coarse particulate matter [PM10], fine particulate matter [PM2.5], and nitrogen dioxide [NO2]) was analyzed in the 5 years preceding psoriasis diagnosis and in controls.Results Overall, 45,093 patients with psoriasis and 180,372 controls were included. After adjusting for age, sex, urbanization level, index date, and comorbidities, our conditional logistic models showed that exposure to PM10 and PM2.5 was associated with a slightly increased psoriasis incidence. Estimating the effects of different air pollutants simultaneously, the adjustment model showed that PM10, PM2.5, and NO2 were associated with a slight increase in psoriasis incidence.Conclusion Although exposure to certain PM10, PM2.5, and NO2 levels was significantly associated with incident psoriasis, the increased risks were trivial, with no dose-response relationship.
DSNet: A Dual-Scaled Network for Multivariate Time Series Classification with Learning Temporal Features
Authors: Qu Xin , Yao Zhilin 
Published: April 2023

Abstract
Multivariate time series classification (MTSC) is a widely recognized and important task in machine learning. Current methods often fail to account for the correlation between holistic and local temporal features. In this paper, we propose a novel dual-scaled network called DSNet, which combines holistic and local features and merges temporal information through a dual-scaled gating mechanism. The DSNet model's holistic module introduces the encoder part of the native Transformer, while the local module mainly consists of our proposed residual convolution module (ResBlock) and Squeeze-And-Excite Block to extract local features. Our experimental results show that our proposed method achieves higher accuracy on 12 out of the 20 UEA MTSC archive datasets. Furthermore, our method has a higher average ranking of 1.55 compared to the other 7 state-of-the-art methods.
Sexual dimorphism in mitochondrial dysfunction and diabetes mellitus: evidence from a population-based cohort study
Authors: Wang Shanjie , Guo JunChen , Liu Xiaoxuan , Tian Wei , Zhang Yiyin , Wang Ye , Liu Yige , E Mingyan , Fang Shaohong 
Published: April 2023

Abstract
Background:
Pathophysiological mechanisms underlying sex-based differences in diabetes remain poorly understood. We aimed to investigate the sex-specific associations between mortality risk and methylmalonic acid (MMA) in adults with the presence or absence of type 2 diabetes.
Methods:
This cohort study included 24164 adults (12123 females and 12041 males) from NHANES study during 1999-2014. Both sexes were separately categorized as those with no diabetes, prediabetes, undiagnosed diabetes, and diagnosed diabetes. Serum MMA was measured at baseline by mass-spectrometric detection. Mortality status was ascertained from baseline until December 31, 2015.
Results:
During a median follow-up of 11.1 years, 3375 deaths were documented. Males had a particularly higher mortality than females in adults with diagnosed diabetes (42.14% vs 32.38%, p<0.001). Notably, the sex-specific difference in associations between MMA and mortality was significant only in adults with diagnosed diabetes (p = 0.028). In addition, MMA levels had an insignificant or weak correlation with sex hormone profiles at baseline, regardless of diabetes status and sex.
Conclusions:
Sex difference in mortality risk was especially significant in diagnosed type 2 diabetes. Increasing equivalent exposure of mitochondrial metabolite MMA was associated with a greater excess risk of future mortality in males with diabetes than females.
Remaining useful life prediction method based on gated dilation causal convolution
Authors: He Jing , Sun Wei , Zhang Changfan , Yu Yue 
Published: April 2023

Abstract
Time series modeling is key to predicting the remaining useful life (RUL) of operating equipment. However, the design of traditional convolutional neural networks cannot flexibly adapt to various time window sizes, and when dealing with long sequences, it needs to have a corresponding deep structure, which is prone to the problem of gradient disappearing. These defects make traditional convolutional neural networks have high prediction errors in mechanical RUL prediction, so this paper proposed a new gated dilation causal convolutional (GDCC) network with multi-feature squeeze excitation unit (MSEU) and sinusoidal position coding, and used it for RUL prediction of rolling bearings. Specifically, MSEU adaptively corrects feature responses from both local and global perspectives; Second, sinusoidal position encoding adds a global order relationship to the hidden layer features; Then, build and stack GDCC for further learning of the front layer features; Finally, the RUL of the rolling bearings is predicted by the Relu activation function. The proposed method was evaluated using vibration data from the accelerated degradation test of rolling bearings and the performance of this method was compared with several advanced predictive methods. Experimental results show that the proposed method is effective in improving the prediction accuracy of bearings RUL, and is better than some existing data-driven prediction methods.
A Graph Neural Network Recommendation Model with Knowledge Graph and Attention Mechanism
Authors: Hill Mabeline , Finch Horatio , Phillips Priya , Martin Aisha 
Published: April 2023

Abstract
With the growth of e-commerce and social media platforms, recommendation systems have become crucial for businesses to increase user satisfaction and retention. Collaborative filtering has been the primary recommendation algorithm, but it faces challenges such as the cold start problem and data sparsity. Researchers have tried to introduce supplementary information to complement the data, and Graph Neural Networks (GNNs) have been useful in capturing the relevance of graphs in recommender systems. In addition, traditional GNNs only fuse the first-order neighborhood features of nodes and cannot capture the deeper structural relationships of nodes in the network. Therefore, when the data set is sparse and each node has only a very small number of neighbors, the recommendation quality of traditional GNN-based recommendation algorithms decreases significantly. In this paper, we propose a GNN recommendation model based on user-side information, which can effectively capture the potential interest of users through user relationship graph for recommendation, and fully exploit the large amount of association information among users. The traditional collaborative filtering idea is incorporated with the knowledge graph as the auxiliary information to fully utilize the potential information from both ends to improve the accuracy and diversity of the recommendation model. In addition, the interaction behaviors and opinions in the user-item graph are jointly captured. Different strengths of user-item interactions are considered, which indicate that various interactions contribute differently to user preferences (or item characteristics), and thus attention mechanisms are used to achieve this goal. The algorithm has been verified by public dataset, and it has better recommendation effect than several other classical recommendation algorithms.
Protective Effects of Aureobasidium pullulans Lysate on Human Skin Fibroblasts and HaCaT Cells Damaged by UV Radiation
Authors: Wang Xin , Zhang Yongtao , Wang Dongdong , Su Ning , Yang Li , Fu Hao , Zhang Jiachan , Li Meng , Wang Changtao 
Published: April 2023

Abstract
Aureobasidium pullulans, commonly known as black yeast, can produce extracellular polysaccharides, heavy oil, melanin and other products which are widely used in food, medicine, cosmetics and other fields. In this study, cytotoxicity experiments are carried out to obtain the optimal concentration of A. pullulans lysis products. Enzyme-linked immunosorbent assay (ELISA) and real-time reverse transcription-polymerase chain reaction (qRT-PCR) are used to detect the contents of extracellular matrix components, changes of related gene levels and content of inflammatory factors. The results show that A. pullulanslysis products can significantly reduce the content of reactive oxygen species (ROS), increase the activity of catalase, enhance the ability of peroxidation and weaken the oxidative stress caused by UVA. They can also significantly reduce the gene expression level of intracellular matrix metalloproteinases (MMPs), up-regulate the gene expression of the tissue inhibitor of metalloproteinase (TIMP), improve protein content and its gene expression, slow down cell damage, reduce the content of inflammatory factors and weaken inflammatory response. In addition, A. pullulanslysis products can regulate the Nrf2/Keap1 and MAPK signaling pathways, and regulate the oxidative stress balance and related factor levels. It is concluded that the cytolytic products of A. pullulans exhibits good protective effects by alleviating inflammatory reactions, easing the degradation of extracellular matrix components and reducing oxidative stress in human skin fibroblasts (HSF) and human immortalized keratinocytes (HaCaT) damaged by UV radiation.
Cost analysis of MXene for low-cost production, and pinpointing of its economic footprint
Authors: Zaed Md Abu , Han Tan Kim , Rahman Saidur , Panday Adarsh Kumar , Saleque Ahmed Mortuza 
Published: April 2023

Abstract
MXene, a two-dimensional (2D) carbide, carbonitride, and nitride, was invented in 2011. A certain number of elements in the periodic table have contributed to the synthesis of MXene from the beginning to the present. Most researchers, however, are focused on a particular type of MXene, Ti3C2Tx, although the scientific community seldom considers the synthesis cost of this outstanding and potentially helpful substance. Herein, we explore the cost of MXene by going through each stage of the production process. Instead, the actual cost may vary by a small margin due to differences in the materials and procedures. However, this study provides a clear understanding of the cost, which is governed by the steps directly involved in the synthesis and characterization of MXene. The cost associated with various essential characterization tools like SEM, TEM, UV-Vis, and XRD is necessary to ensure the successful synthesis of MXene. All local expenses are converted into USD, except for the instrumental life cycle analysis (LCA) and infrastructure cost values. The cost of each gram of MXene is predicted to be $12.20. The predicted cost is close to the market price of MXene, proving the accuracy of the cost calculation presented in this research. This work will assist the scientific community in planning and optimizing MXene's synthesis procedures so that the production cost can be potentially reduced if this material is produced on a larger scale.
Spatial and temporal variation of meteorological parameters in the lower Tigris–Euphrates basin, Türkiye: application of non-parametric methods and an innovative trend approach
Authors: Esit Musa , Çelik Recep , Akbas Ergun 
Published: April 2023

AbstractIn this study, Mann–Kendall (MK), Spearman's rho (SR), and innovative trend analysis with significance test (ITA-ST) are performed on about 53 years of meteorological parameters obtained from 23 meteorological stations located in the lower Tigris–Euphrates basin (LTEB), Türkiye. Finally, sequential Mann–Kendall (SMK) and Cusum tests are applied to detect any abrupt changes in annual time series. Results indicate that MK and SR demonstrate a significant trend in seven of the total annual precipitation series, and ITA-ST captures the existence of a significant trend in 21 of the 23 total annual precipitations. Three methods reveal that there is an increasing trend in both the annual mean temperature and the annual total evapotranspiration (EP). MK, SR, and ITA-ST capture a significant decreasing trend in the 10, 8, and 16 of the 23 annual mean relative humidity (RH) series, respectively. According to the findings, ITA-ST is more sensitive than the classical MK and SR methods. Cusum and SMK tests are detected the start of trend year 21.7 and 8.6% of annual total precipitation, 95.65 and 69.56% of annual mean temperature, 47.82 and 17.4% of total mean RH, and 95.65 and 69.56% of annual total EP time series, respectively. The Cusum test is found to be more sensitive than the SMK test.
Navigational Rights in Archipelagic Waters
Authors: Bugajski Dariusz 
Published: April 2023

The United Nations Convention on the Law of the Sea of 1982, through the institutions of the right of innocent passage, the right of transit passage, and the right of archipelagic sea lanes passage, defines the rules for the navigation of foreign-flagged vessels, including warships, in territorial waters (internal waters, territorial sea, and archipelagic waters). The paper analyses the concept of archipelagic waters, the right of innocent passage within Archipelagic Waters, and the right of archipelagic sea lanes passage, respectively. How vital for the global economy and security are archipelagic waters with navigational rights and overflight on them exemplify waters of Indonesia, where nowadays shipping accounts for approximately 50% of total world trade (including Malacca and Singapore Strait, from the Indian Ocean to the Far East), and is rapidly growing.
Biomimetic Synthesis of Biologically Active Natural Products: An Updated
Review
Authors: Shakour Neda , Mohadeszadeh Manijeh , Iranshahi Mehrdad 
Published: April 2023

Background::
Natural products have optical activities with unusual structural characteristics
or specific stereoselectivity, mostly including spiro-ring systems or quaternary carbon atoms.
Expensive and time-consuming methods for natural product purification, especially natural
products with bioactive properties, have encouraged chemists to synthesize those compounds in
laboratories. Due to their significant role in drug discovery and chemical biology, natural products
have become a major area of synthetic organic chemistry. Most medicinal ingredients available
today are healing agents derived from natural resources, such as plants, herbs, and other natural
products.

Methods::
Materials were compiled using the three databases of ScienceDirect, PubMed, and
Google Scholar. For this study, only English-language publications have been evaluated based on
their titles, abstracts, and full texts.

Results::
Developing bioactive compounds and drugs from natural products has remained challenging
despite recent advances. A major challenge is not whether a target can be synthesized but
how to do so efficiently and practically. Nature has the ability to create molecules in a delicate but
effective manner. A convenient method is to imitate the biogenesis of natural products from microbes,
plants, or animals for synthesizing natural products. Inspired by the mechanisms occurring
in the nature, synthetic strategies facilitate laboratory synthesis of natural compounds with complicated
structures.

Conclusion::
In this review, we have elaborated on the recent syntheses of natural products conducted
since 2008 and provided an updated outline of this area of research (Covering 2008-2022)
using bioinspired methods, including Diels-Alder dimerization, photocycloaddition, cyclization,
and oxidative and radical reactions, which will provide an easy access to precursors for biomimetic
reactions. This study presents a unified method for synthesizing bioactive skeletal products.
SVM-Root: Identification of Root-Associated Proteins in Plants by
Employing the Support Vector Machine with Sequence-Derived Features
Authors: Kumar Meher Prabina , Hati Siddhartha , Sahu Tanmaya Kumar , Pradhan Upendra , Gupta Ajit , Rath Surya Narayan 
Published: April 2023

Background:
Root is a desirable trait for modern plant breeding programs, as the roots play a
pivotal role in the growth and development of plants. Therefore, identification of the genes governing
the root traits is an essential research component. With regard to the identification of root-associated
genes/proteins, the existing wet-lab experiments are resource intensive and the gene expression studies
are species-specific. Thus, we proposed a supervised learning-based computational method for the identification
of root-associated proteins.

Method:
The problem was formulated as a binary classification, where the root-associated proteins and
non-root-associated proteins constituted the two classes. Four different machine learning algorithms
such as support vector machine (SVM), extreme gradient boosting, random forest, and adaptive boosting
were employed for the classification of proteins of the two classes. Sequence-derived features such
as AAC, DPC, CTD, PAAC, and ACF were used as input for the learning algorithms.

Results:
The SVM achieved higher accuracy with the 250 selected features of AAC+DPC+CTD than
that of other possible combinations of feature sets and learning algorithms. Specifically, SVM with the
selected features achieved overall accuracies of 0.74, 0.73, and 0.73 when evaluated with single 5-fold
cross-validation (5F-CV), repeated 5F-CV, and independent test set, respectively.

Conclusions:
A web-enabled prediction tool SVM-Root (https://iasri-sg.icar.gov.in/svmroot/) has
been developed for the computational prediction of the root-associated proteins. Being the first of its
kind, the proposed model is believed to supplement the existing experimental methods and high
throughput GWAS and transcriptome studies.
Phyto-Extracts and Silicone Gel (JUMI) in Reduction of Surgical Scar Post-joint Arthroplasty. A Randomized Control Trial
Authors: Sadat-Ali Mir , AlMousa Sulaiman A. , AlTabash Khalid W. , Abotaleb Mohamed M. , Al Anii Fawaz M. , AlOmran Abdallah S. 
Published: April 2023

Background and Objective:
To assess the clinical potency of JUMI Anti-Scar Cream (JASC) in the prevention of excessive scar formation post total hip replacement (THR) and total knee replacement (TKR).

Patients and Methods:
Our study was an open-label, prospective, randomized control to test the efficacy of JASC. After surgery, the same material was used in both groups to close the skin. After 14 days, once the staples were removed, JASC was applied twice a day for 60 days. Patients were seen at 6 and 12 weeks. At 3-month follow-ups, the scars were assessed using the Visual Analogue Scale (VAS) and Vancouver Scar Scale (VSS).

Results:
Forty patients were part of this trial; two patients in the control group were lost during follow-up. The average age-matched in both groups. There were no adverse or untoward effects due to the use of JASC. At 3 months, there were significant differences between the parameters assessed; VAS scores were P<0.0001. VSS parameters such as vascularity, pigmentation, pliability, and scar height were much lower compared to patients who had standard treatment (P<0.0001).

Conclusion:
Our study demonstrated that JASC was effective and successful in suppressing excessive scar formation. We believe that JASC has the potential as a prime anti-scar therapy, and we recommend more multicentric studies.
Clinical Trial Rg. No: This trial was registered with the Sri Lankan Trial Registry vide #2022/021.
Antimicrobial Stewardship Program: Time to be Focused in
Speciality Settings of Leprosy Institute and Related Health Care
Setup
Authors: Thangaraju Pugazhenthan , Velmurugan Hemasri , Venkatesan Sajitha 
Published: April 2023

Abstract:
Leprosy, often known as Hansen's disease is a contagious chronic infectious
disease caused by Mycobacterium leprae (M. leprae). Our methodology is easily repeatable
in tertiary care settings with diagnostic accuracy resources and staff capable of
building a stewardship team. Comprehensive antimicrobial policies and programmes
are required to properly alleviate the initial issue.
Implementation of Ensemble Methods on Classification of CDK2 Inhibitor as Anti-Cancer Agent
Authors: Kurniawan Isman , Anggraini Mela Mai , Aditsania Annisa , Setiawan Erwin Budi 
Published: April 2023

Cancer is known as the second leading cause of death worldwide. About 7-10 million cases of death by cancer occur every year. The recent treatment to heal the cancer is chemotherapy. However, chemotherapy treatment is known to have side effects and cell resistance issues to certain drugs. Therefore, it is required to develop a new drug that can reduce the side effects and provide a better treatment effect. In general, anti-cancer drugs are developed by targeting Cyclin-Dependent Kinase 2 (CDK2) enzyme. Conventional drug design is not effective and efficient for obtaining new drug candidates because of no information about the biological activity before it is synthesized. In this study, we aim to develop a model to predict the activity of CDK2 inhibitors by using ensemble methods, i.e., XGBoost, Random Forest, and AdaBoost. The study was conducted by calculating several fingerprints, i.e., Estate, Extended, Maccs, and Pubchem, as feature variables. Based on the results, we found that Random Forest with Pubchem fingerprint gives the best result with the value of Matthews Correlation Coefficient (MCC) and Area Under the ROC Curve (AUC) values are 0.979 and 0.999, respectively. From this study, we contributed to revealing the potency of the ensemble with fingerprint in bioactivity prediction, especially CDK2 inhibitors as anti-cancer agents.