bluuebunny/crossref_metadata_2025_split huggingface dataset:
Front profile in time backward for the bistable reaction-diffusion equation on metric graphs
Authors: Morita Yoshihisa 
Published: February 2023

Abstract
In the bistable reaction-diffusion equation on a half-line there exists an entire solution exhibiting the front propagation from the infinity of the line.We glue an arbitrary number of half-lines at a single point (junction) to build a metric graph and examine the front propagation on given half-lines (upstream branches). The aim of the article is to show the existence of an entire solution which converges to the profile of the traveling wave with an arbitrarily assigned phase-shift in each upstream branch as t → −∞. The proof is carried out by constructing an appropriate pair of sub-and super-solutions.
AMS 2020 Subject Classification: 35B08, 35B40, 35K57.
The prediction of undiagnosed diabetes using lifestyle and anthropometric measures: Machine learning vs. Traditional statistical methods
Authors: Choi Seong Gyu , Oh Minsuk , Park Dong–Hyuk , Lee Byeongchan , Lee Yong-ho , Jee Sun Ha , Jeon Justin Y 
Published: February 2023

Abstract
Objective
As delayed diagnosis of diabetes increases the risk of irreversible diabetes complications, detecting undiagnosed diabetes using a prediction model could be useful. Recently, machine learning-based disease prediction models have been used; however, the performance of the machine learning-based prediction model and traditional statistics-based prediction models in predicting undiagnosed diabetes has not been compared. Therefore, we developed a machine learning-based undiagnosed diabetes prediction model and compared its prediction performance with that of a traditional statistics-based prediction model.
Methods
We used the 2014–2020 Korean National Health and Nutrition Examination Survey (KNHANES) (N = 32,827). The KNHANES 2014–2018 data were used as training and internal validation sets and the 2019–2020 data as external validation sets. The receiver operating characteristic curve area under the curve (AUC) was used to compare the prediction performance of the machine learning-based and the traditional statistics-based prediction models.
Results
Using sex, age, resting heart rate, and waist circumference as features, the machine learning-based model showed a higher AUC (0.788 vs. 0.740) than that of the traditional statistical-based prediction model. Using sex, age, waist circumference, family history of diabetes, hypertension, alcohol consumption, and smoking status as features, the machine learning-based prediction model showed a higher AUC (0.802 vs. 0.759) than the traditional statistical-based prediction model. The machine learning-based prediction model using features for maximum prediction performance showed a higher AUC (0.819 vs. 0.765) than the traditional statistical-based prediction model.
Conclusions
machine learning-based prediction models using anthropometric and lifestyle measurements showed good performance in predicting undiagnosed diabetes. The machine learning-based prediction model outperformed the traditional statistics-based prediction models.
Molecular docking approaches of biomolecules extracted from red seaweed Kappaphycus alvarezii against hemolysin protein of bioluminescence disease-causing bacteria Vibrio harveyi
Authors: Sivakumar Krishnamoorthy , Kannappan Sudalayandi , Vijayakumar Balakrishnan 
Published: February 2023

Abstract
Molecular docking is suited a major approaches in structural biology and computer-assisted drug design against the protein of disease-causative agents. Marine resources are an unmatched reservoir of several natural biological products, which exhibit structural features that have not been found in a terrestrial organism. Herein, it was performed that molecular docking with various compounds/ ligand molecules identified by Gas Chromatography and Mass Spectrometry (GC-MS) analysis from the marine red seaweed Kappaphycus alvarezii extract against the hemolysin protein of bacterial disease-causing shrimp pathogen, Vibrio harveyi. Further, extracts of K. alvarezii were evaluated against V. harveyi infection during Penaeus monodon larviculture. This study correlates with various virulence factors including the hemolysin protein of V. harveyi. Among the compounds docked, an inhibitory effect was observed based on docking scores and found the highest binding affinity/inhibiting activity in Cyclotetracosane. Extract of K. alvarezii at 200 µg mL− 1 showed reductions in the V. harveyi counts that leads to a 29.70% reduction in the cumulative percentage mortality (CPM) caused by postlarvae and during larviculture. Using statistical analysis, significant differences (p < 0.05) were observed in the growth and virulence factors of V. harveyi during shrimp larviculture trials. While considering these findings, it was determined that K. alvarezii extract can be replaced as an alternative bio-agents by protecting against V. harveyi infections and possibly other aquatic pathogenic bacteria in shrimp farming systems.
Yield and Quality Response of Potato Grown Under Different Drip Irrigation and Nitrogen Levels
Authors: AKKAMIS Mustafa , CALISKAN Sevgi 
Published: February 2023

Abstract
Proper irrigation and fertilization are essential to achieve high tuber yield and quality in potato production. Water and nitrogen (N) use efficiencies are required to be improved due to high costs of these agricultural inputs. This study aimed to optimize the use of water and N in potato to ensure high water and N use efficiencies as well as achieve optimum yield and tuber quality. Potato plants were subjected to three irrigation treatments, i.e., a water application of 100% (S100), 66% (S66) and 33% (S33) of field capacity and six N levels, 0 (N0), 100 (N1), 200 (N2), 300 (N3), 400 (N4) and 500 (N5) kg N ha− 1. Yield, growth parameters and tuber quality were generally more sensitive to irrigation than nitrogen. The highest total tuber yield was obtained under full irrigation with an application of 300 kg N ha− 1. Plants indicated higher values for yield and growth parameters in full irrigation applications, and significantly lower values were obtained at low irrigations. On the contrary, the full irrigation (S100) application caused a decrease in quality characteristics compared to low irrigations (S66 and S33). The WUE was improved by N supply, when N level applied above 300 N ha− 1. In addition, N3-S66 was determined the most suitable application for tuber quality and WUE. These results indicated that potato can be produced with acceptable yields while saving irrigation water and certain nitrogen level.
Dominating Competition Graph and its Application in Social Network
Authors: Maity Ananta , Das Kousik , Samanta Sovan , Mondal Sukumar 
Published: February 2023

Abstract
Domination and competition have been there since the beginning of the Universe. Bigger stars dominate the smaller ones and draw them towards themselves. Domination and competition are such phenomenon which nothing in this universe can stay without. Stronger things and animals dominate the weaker ones. Again, when there are more than one strong dominant, there comes competition among them. To measure domination and competition together, we introduce here a domination competition graph, through which we identify the most competitive and dominating variables. By dominating competition number, we calculate which nodes (when we draw a graph corresponding perception) are more powerful and how much powerful. We have used forest biodiversity network as an example here. In the end, an application of domination competitions to e-commerce industry is illustrated.
Direct synthesis of vanadium pentoxide powder with carbon recombination as aqueous zinc-ion battery cathode
Authors: Cui Jian , Ma Jidong , Yuan Yinghao , Gu Siyong , Zhou Wenjun , Zhang Houan , Cao Zhiqin 
Published: February 2023

Abstract
Advanced energy storage technologies have been prompted by the benefits of safe, high-capacity, and stable cathode materials for aqueous Zn-ion batteries (ZIBs). Vanadium oxide has lately become a popular cathode material thanks to its advantages of a sufficient operating voltage window and a highly reversible redox reaction. However, Vanadium pentoxide has several drawbacks, including poor electrical conductivity and slow ion diffusion. To resolve these problems, orthorhombic single crystal V2O5 sheets were synthesized by a facile solution combustion synthesis method to improve their performance by adding C6H12O6. As cathode materials for the ZIBs, the V2O5/C particles with larger surfaces and higher oxygen defects concentration are obtained with C6H12O6 addition, which increases the dynamic performance of ion diffusion and the activity of the vanadium-based cathode. The synthesized V2O5/C sample exhibited the specific capacitance of 220 mAh g− 1 (V2O5 is 180 mAh g− 1) in the aqueous electrolytes at 4 A g− 1, which exhibited a high specific capacity of 85.5% after 500 cycles with nearly 100% coulomb efficiency, indicating improved specific capacitance and cyclic stability.
Robust Hyperparameter Estimation in Gaussian Process Regression Model
Authors: Algikar Pooja , Mili Lamine 
Published: February 2023

Abstract
A stochastic process mathematically models the random behavior of systems and naturally occurring phenomena. A popular process is a Gaussian process which assumes a multivariate Gaussian distribution for the response variables. The predictive distributions of target variables can be explicitly obtained by using conditioning and inference techniques. The mean function and covariance function of the predictive distributions are parameterized whose estimates can be obtained by maximizing the marginal likelihood of the training responses or their posterior distribution. Unfortunately, these estimates are not robust to outliers that are often present in the observational training data sets. To overcome this problem, we develop a robust data-driven process based on the Schweppe-type Huber-generalized maximum likelihood (SHGM) estimator to bound the influence of all types of outliers on the estimates while exhibiting a high statistical efficiency at the Gaussian and thick-tail distributions. The weights utilized by the SHGM estimator are calculated using projection statistics, which are robust distances of the data points determined by the row vectors of the design matrix. These weights make the estimator robust to both vertical outliers and bad leverage points while not downweighting good leverage points. We show that the proposed robust model is able to handle up to 25 percent of outliers among the data set. The proposed method is illustrated by two real-world examples in which the performance of the proposed method is assessed against the standard Gaussian process regression.
Metaverse for Real Estate Marketing: The Impact of Virtual Reality on Satisfaction, Perceived Enjoyment and Purchase Intention
Authors: Azmi Athira , Ibrahim Rahinah , Ghafar Maszura Abdul , Rashidi Ali 
Published: February 2023

Abstract
Purpose: This paper investigates how the residential real estate sector could take advantage of the virtual world envisioned by the concept of metaverse – a world of interconnected digital spaces through virtual reality that will incorporate businesses, commerce, entertainment, and social networking.
Methods: This study conducted an experiment with 65 potential homebuyers to evaluate responses in the physical and virtual environment. The hypothesized relationships were tested using PLS-SEM statistical analysis method.
Results: Results indicate that the atmosphere, satisfaction, perceived enjoyment and purchase intention is statistically different (p < 0.05) in the physical and the virtual environment. This study found that the atmosphere in virtual environment positively effect satisfaction and perceived enjoyment, which subsequently positively effect purchase intention.
Conclusion: The results presented in this paper supports the future of metaverse with virtual reality in the real estate sector for enhancing human-computer interactions in terms of consumer experience.
Association of Selenium Levels with Neurodegenerative disease: A Systemic Review and Meta-analysis
Authors: Zhou Jiaxin , Zhang Wenfen , Lian Shaoyan , Li Jieying , Nie Jiaying , Huang Ying , Zhao Ke , He Jiang , Liu Chaoqun 
Published: February 2023

Abstract
Background
Neurodegenerative diseases are debilitating and incurable disorders with a rapid-rising prevalence. Selenium, a trace element, works in anti-oxidant and anti-inflammatory aspects through selenoprotein biologically. Nowadays, it is in a great urgency to discover the intrinsic relationship between selenium and neurodegenerative diseases, and find more effective therapeutic strategies to combat these devastating diseases.
Method
Five public databases including PubMed, Web of Science, EMBASE, Cochrane, Clinical Trials were searched in our research. Random model effects were chosen, and Higgins inconsistency analyses (I2), Cochrane’s Q test, Tau2 were calculated to evaluate the heterogeneity.
Result
The association of selenium in neurodegenerative diseases patients including Alzheimer’s disease, Parkinson’s disease, Multiple sclerosis, Amyotrophic lateral sclerosis and Huntington’s disease was studied. The strong relation was only found in AD patients, especially the erythrocytes in sub-group analysis of various tested tissue type.
Conclusion
Generally, this meta-analysis indicated that Alzheimer’s disease is strongly associated with lower selenium concentration, which may provide clinical reference in the future. However, more researches are in urgent need for further study and treatment for neurodegenerative diseases.
Structural and spectral studies of (1,3,5-triazinane-2,4,6-trione) 1,4-diazabicyclo[2.2.2]octane (TTDO)
Authors: A Ponnuvel , S Nivithaa , A Kala , R Ramkumaar G , S Nagaraja K , Chandran Karnan 
Published: February 2023

Abstract
(1,3,5-triazinane-2,4,6-trione) 1,4-diazabicyclo[2.2.2]octane (TTDO) was prepared from 1,4-Diazabicyclo[2.2.2]octane (DABCO) and cyanuric acid (CA) by solvent evaporation method. The TTDO molecule crystallized in the centrosymmetric space group C2/c with a monoclinic crystal system. The structure has a salt bridge between N1(CA)-H1 and N4(DABCO)-H4. The DABCO comes in between two CA moieties linearly disposed on either side of the two nitrogen atoms of DABCO. On sidewise, the CA molecules extend H-bonding showing a supramolecular assembly. Hydrogen bonding as compelled by stereographic projection. The Hirshfeld surface analysis confirms that the crystal is formed by involving intermolecular interactions. FT-IR confirms the presence of N-H, C-H, C=O and CN stretching vibrations of the molecule. The photoluminescence spectra show intense peaks at 532 nm in the green region. TG-DTA analysis confirms that the molecule is stable up to 143°C and loses CA in the first stage and DABCO decomposes subsequently. Antibacterial activity shows that TTDO inhibits Gram-positive (Staphylococcus aureus) and Gram-negative (Salmonella typhi) bacteria.
Research on 3D Simulation Design and Dynamic Virtual Display of Clothing Flexible Body
Authors: Ren Xiangfang , Niu Sijia , Huang Xinyi 
Published: February 2023

Abstract
Garment industry design development problems and visualization mapping analysis software as a tool to analyze the status of domestic and foreign research, followed by the current situation of the front-end and back-end rapid response of the garment industry, the research problem of this topic, through the garment design modeling (fabric physical model and design, human body and fabric collision detection analysis, garment fabric rendering and flexible body 3D garment simulation model) and garment dynamic virtual display Research to build an effective solution to the problem. It is found that the real effect of garment fabric can be truly restored in the design stage, and the development speed can be improved and the labor cost can be reduced, while the cost waste problem of proofing materials can be solved by using dynamic simulation display, and the selection rate and design level of products.
Passive Visual Underwater Surveillance: A Survey
Authors: Subudhi Badri Narayan , Kapoor Meghna , Rout Deepak Kumar , Thangaraj Veerakumar , Jakhetiya Vinit 
Published: February 2023

Abstract
The objective of this article is to provide a detailed review of the state-of-the-art underwater surveillance process and the new trends of the same. Underwater surveillance has recently getting lots of attention because of its potential applications including the security of the coastal border, effective fish farming, deep-sea exploration, preservation of rare aquatic animals, etc. In the case of underwater, the light that is sensed by the camera got degraded due to many underwater phenomena such as haze, low illumination, scattering, absorption, diffraction, and refraction. The actual color of the object, as well as the scene, gets degraded as the light can not travel deep underwater. In a situation, where the scene of view is not clear, it is very difficult to detect the moving objects present in the scene. Further, the identification of the object of concern and tracking of that becomes more challenging. In this survey, we categorize underwater surveillance as a combination of the three blocks: enhancement, object detection, and object tracking. We categorize all these three blocks based on the underwater complexity and motion models considered. In this article, we tried to enumerate the most detailed descriptions of each category. We also discuss the future directions of research in the area of underwater surveillance.
Bidirectional effect of Triphala on modulating Gut-brain axis to improve cognition in the Murine Model of Alzheimer’s disease
Authors: Upadhyay Prabhat , Tyagi Anurag , Agrawal Sakshi , Kumar Anil , Gupta Sarika 
Published: February 2023

Abstract

Background
The emerging role of gut microbiota and their metabolites in the modulation of the gut-brain axis has received much attention as a new hope for the treatment of hard-to-treat chronic neurodegenerative diseases like Alzheimer’s Disease. The naturally occurring polyphenols can restore the gut-brain axis by modulating gut microbiota and brain neurotransmitters. However, the mechanism of action remained unclear. The Indian traditional medicine Triphala, a rich source of polyphenols, has been used on humans based on Prakriti or disease conditions for many years.
Methods:
In this study, the dual mode (morning and evening) action of Triphala was used to provide scientific evidence of its superior preventive and therapeutic efficacy in C57BL/6 and 5xFAD, APP/PS1 transgenic mouse model of Alzheimer’s disease. For behavior analysis, used the Morris water model and Y maze model to assess spatial memory and exploratory behavior. The blood serum and brain lysate were used to evaluate the inflammatory activity and oxidative parameters in the mice. The gut microbiome analysis was done by 16srRNA analysis from mice feces after 60 days of treatment.
Results
We observed that Triphala treatment has significantly improved cognitive function, by modulating the APP pathway, reducing inflammation, oxidative stress, and restoring the gut-brain axis by increasing the gut microbiota phyla of Bacteroides, Proteobacteria, Actinobacteria, etc., involved in maintaining the gut homeostasis.
Conclusions
Our study paved a new path for using dual modes of Triphala one or in combination to treat incurable AD.
COD as a Dynamic Tool for Pretreatment of Sludge Samples in Microplastic Analysis
Authors: Hatinoglu M. Dilara , Simsek Irem , Altuntas Oguzhan , Karakurt Ozan , Sanin F. Dilek 
Published: February 2023

Abstract
Given the society’s continuous reliance on plastic materials, large amounts of micron-sized plastic particles (i.e., microplastics, MPs) reach wastewater treatment plants (WWTPs) every day. Despite their effective removal from influent wastewater, over 90% of MPs in WWTPs are accumulated in sludge. Yet, there is no universally accepted method for quantification and identification of MPs, which beclouds our understanding of the extent of this pollution. Therefore, this study aims to develop a chemical oxygen demand (COD) based repeatable method for MP analysis in sludge, which is a very complex, MPs-laden by product of WWTPs. The developed method is unique in that it removes the organic substances interfering with polymer analysis by monitoring the COD of sludge for the first time in literature. Upon 90% of organic matter removal, MPs are extracted from the medium by a two-step density-based separation, sieved, stained with Nile Red, and counted under Fluorescence Microscope. Moreover, quality assurance and quality control (QA/QC) strategies including blank preparation and spike and recovery test procedures are followed. The developed protocol ensures at least 80% recovery efficiency of variety of MPs from waste activated sludge samples, which meets the recommendations given in the literature.
Towards Real-Time Online Unsupervised Domain Adaptation for Real-World Person Re-identification
Authors: Neff Christopher , Pazho Armin Danesh , Tabkhi Hamed 
Published: February 2023

Abstract
Following the popularity of Unsupervised Domain Adaptation (UDA) in person re-identification, the recently proposed setting of Online Unsupervised Domain Adaptation (OUDA) attempts to bridge the gap towards practical applications by introducing a consideration of streaming data. However, this still falls short of truly representing real-world applications. This paper defines the setting of Real-world Real-time Online Unsupervised Domain Adaptation (R2OUDA) for Person Re-identification. The R2OUDA setting sets the stage for true real-world real-time OUDA, bringing to light four major limitations found in real-world applications that are often neglected in current research: system generated person images, subset distribution selection, time-based data stream segmentation, and a segment-based time constraint. To address all aspects of this new R2OUDA setting, this paper further proposes Real-World Real-Time Online Streaming Mutual Mean-Teaching (R2MMT), a novel multi-camera system for real-world person re-identification. Taking a popular person re-identification dataset, R2MMT was used to construct over 100 data subsets and train more than 3000 models, exploring the breadth of the R2OUDA setting to understand the training time and accuracy trade-offs and limitations for real-world applications.
R2MMT, a real-world system able to respect the strict constraints of the proposed R2OUDA setting, achieves accuracies within 0.1% of comparable OUDA methods that cannot be applied directly to real-world applications.
Fabrication of Al-Zn/SiC Reinforced Metal Matrix Composite via Powder Metallurgy Method and Investigating the Mechanical Properties of Composite
Authors: sonker Puneet Kumar , Singh Thingujam Jackson , yadav Niteesh Pratap 
Published: February 2023

Abstract
In this investigation, Al-Zn/SiC composite with various weight percentages of SiC were manufactured via powder metallurgy (PM) route. Using the powder metallurgy approach, composites are created by changing the amount of Al in the range of 95%, 90%, 85% and SiC in the range of 5%, 10%, 15% by weight respectively. Before making composites, mechanical alloying was done using a ball mill. The ball to powder ratio is 10:1, the milling speed is 150 rpm, the milling time is one hour, and the process control agent is stearic acid, accordingly. The mechanically alloyed powders were compressed in a die with the sample size of 125mm length at a pressure of 300MPa by using the hydraulic press. The compressed samples were sintered in a furnace at a temperature below the melting point of aluminium. To increase the sintering property, samples were kept for two hours in an argon environment before being cooled in a furnace. The hardness, tensile strength, and wear test evaluations and property evaluations are performed on the samples of the manufactured aluminium composite. This study shows that addition of Zn and SiC to Al showed superior properties and better microstructure than pure Aluminium.