bluuebunny/crossref_metadata_2025_split huggingface dataset:
Impact of bioretention cells in cities with a cold climate: modeling snow management based on a case study
Authors: Gougeon Garance , Bouattour Ons , Formankova Emma , St-Laurent Julien , Doucet Samuel , Dorner Sarah , Lacroix Sandrine , Kuller Martijn , Dagenais Danielle , Bichai Françoise 
Published: May 2023

Abstract

The performance of blue-green infrastructures (BGIs) has been well documented in temperate and subtropical climates, but evidence supporting their application in cold climates, especially during snowmelt, is still scarce. To address this gap, the present study proposes a modeling method for simulating the performance of bioretention cells during snowmelt according to different spatial implementation scenarios. We used the Storm Water Management Model (SWMM) of a catchment in a medium-sized city in Quebec, Canada as a case study. Pollutants commonly found in the snow (TSS, Cr, Pb, Zn, Cl–) were included in the model using event mean concentrations (EMCs) documented in the literature. Bioretention cells performed best on industrial road sites for the entire snowmelt period. Bioretention cell performance was affected by snow management procedures applied to the roads in residential areas. Not modeling the snow cover build-up and meltdown in the simulation led to higher runoff and bioretention cell performance. Modeling results facilitated the identification of bioretention cell sites that efficiently controlled runoff during snowmelt. Such information is needed to support decision planning for BGIs in cities with cold climate.
Hydrologic regionalization of non-stationary intensity–duration–frequency relationships for Indian mainland
Authors: Mohan Meera G , AR Akhilesh , S Adarsh , S Badarinadh , Krishnan Ajeesh , Rajan Anand 
Published: May 2023

Abstract

Intensity–duration–frequency (IDF) curve is one of the important hydrologic tools used for the design of hydraulic infrastructure. The static return period assumption of precipitation extremes is invalid in a changing climate environment, and the underestimation of rainfall intensity may lead to the failure of infrastructure in extreme events. This study first developed the non-stationary (NS) IDF curves for six selected locations in India based on sub-daily station data based on time-dependent estimates of five combinations of Generalized Extreme Value (GEV) distribution parameters. Then, in order to identify the critical regions of rainfall non-stationarity, the IDF curves were developed for 357 grid points over India using the daily gridded data for the period 1951–2016 at 1° × 1° resolution. The comparison of spatial patterns of rainfall intensity estimates under stationary and non-stationary showed that about 23% of grids showed an overestimation of NS rainfall over their stationary counterparts by at least 15%. About 32 grid locations which showed at least 15% overestimation of rainfall under an NS case displayed a significantly increasing rainfall trend. The majority of the grids with larger deviation of non-stationary rainfall estimates over stationary values are located in India's eastern regions and coastal belts.
Digital Assets and the Global Economy: How the Use of Statistical Models Can Help Bitcoin Price Prediction
Authors: Bakumenko L. P. , Vasileva N. S. 
Published: May 2023

The purpose of the study is to analyze the potential of statistical modeling in predicting the prices of the Bitcoin cryptocurrency and its impact on the economy. In the course of the article, answers were received to such questions as: What is the impact of macroeconomic events on the dynamics of the Bitcoin price? How quickly does the cryptocurrency market stabilize after the falls? How effective is statistical modeling to solve the problem of predicting the price of Bitcoin? Which model shows the best results? What measures of regulation and control of the cryptocurrency market are necessary at the stage of its formation in the Russian Federation?Materials and methods. Historical data on average monthly Bitcoin closing prices and macroeconomic events such as the COVID-19 pandemic and the Russian-Ukrainian conflict were collected and analyzed. The paper uses statistical models, including ARIMA and LSTM, to predict future Bitcoin prices based on historical data. The accuracy of the models was calculated based on such indexes as the mean absolute error (MAE) and the mean square error (MSE). Results. Analysis of the impact of macroeconomic events showed that during the crisis, the attractiveness of Bitcoin increased and investors used this asset as a new investment tool. During the analysis of the consequences of the Russian-Ukrainian conflict for the cryptocurrency market, its reaction to geopolitical events was revealed according to the increased liquidity indexes in the market. In the process of modeling the dynamics of the average monthly Bitcoin price, the model with parameters (1, 1, 0) at MAE = 15.03% was recognized as the best ARIMA model. The LSTM neural network model on a similar data set showed a MAE error equal to 2.57%.Conclusion. The analysis shows that itcoin was the most attractive investment tool during the crisis, which led to a sharp increase in its price in 2021. The Russian-Ukrainian conflict has also affected its price, causing a significant decline in 2022. However, statistical modeling methods predict an increase in the price of Bitcoin in the first half of 2023, and governments may consider regulating or controlling its use to reduce risks associated with the cryptocurrency market. The recommended measures are the introduction of regulations, the introduction of transaction taxes, the development of national digital currencies, public education and the prevention of criminal activity.
Heterogeneous Copper-catalyst Anchored on Chitosan Schiff Basemodified
Carbon Nanotubes for Click Reaction
Authors: Kwak Soo-Jung , Kim Seung-Hoi , Shin Ueon Sang 
Published: May 2023

Abstract:
A newly designed heterogeneous copper catalytic platform immobilizing copper (II) salt on
chitosan Schiff base-modified carbon nanotubes was developed (Cu@CS-Py@CNT). The structure of
the catalyst platform was fully characterized by spectroscopic analyses. The catalytic activity of the
Cu@CS-Py@CNT was investigated in a one-pot synthesis of 1,4-disubstituted 1,2,3-triazoles by the
regioselective click reaction using various benzyl halides or their surrogates, terminal alkynes, and sodium
azide in an aqueous environment without using any external Cu-reducing agents.
An Aminoantipyrine-containing Schiff base Fluorescent Chemosensor for
Hg2+-Selective Detection
Authors: He Xuelei , Song Shijie , Li Chengke , Zhang Qiangqiang , Dong Qiang , Li Lin , Qin Yonggang , Zhao Qiang , Wang Jianxiong 
Published: May 2023

Abstract:
Specific recognition at trace quantity levels of metal ions without using the costly analytical
instrument and a tedious sample preparation method is an extensive concern for environmental
monitoring and mitigation. Mercury(II) (Hg2+) has acute toxicity. The development of ion-selective
fluorescence sensors for the selective detection of Hg2+ is an essential task to accomplish. The aim of
this study is to detect Hg2+ in an aqueous medium. A fluorescence sensor (DP) based on Schiff base
was designed and utilized to detect Hg2+. Scanning electron microscope (SEM), Fourier transformed
Infrared (FT-IR), X-ray powder diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) were
used to learn the sensing mechanism between sensor DP and Hg2+ cations. When Hg2+ ions were added
to the sensor DP, it showed a dramatic fluorescent “turn-on” response for Hg2+ in dimethylformamide
(DMF) solution. And the detection limit (LOD) of DP for Hg2+ in aqueous media (1.0 mol L-1)
was 2.23 × 10-8 mol L-1. In summary, an aminoantipyrine-containing Schiff base fluorescent
chemosensor for extraordinary recognition of Mercury(II) was designed and synthesized via a simple
one-step pathway and led to intermolecular self-assembly through π-π stacking interactions. And the
sensor DP could fluorescently “turn on” when the Hg2+ cation was added. The limitation of Hg2+ was
2.23×10-8 M, which indicated that the sensor DP could be useful as a highly selective and sensitive
sensor for detecting Hg2+ ions in an aqueous medium by the strong interaction with DP. Notably, the
sensor DP was used as a fluorescent display material with satisfactory results.
Discovery of Novel Lysine Methyltransferase (SMYD3) Inhibitors by Utilizing 3D-QSAR, Molecular Docking and Molecular Dynamics Simulation
Authors: Shi YuanZe , Chen XiaoDie , Li JiaLi , Yu Na , Wu JinPing , Zhao XueMin , Shu Mao , Lin ZhiHua 
Published: May 2023

Aim:
To investigate novel isoxazole amide SMYD3 inhibitors as adjuvant anticancer agents
for multiple cancers.

Background:
SET and MYND Domain-Containing Protein 3 is a hopeful therapeutic target for breast,
liver, colon, and prostate cancer.

Objective:
Novel SMYD3 inhibitors were predicted by the 3D-QSAR models.

Methods:
In this present work, 3D-QSAR, molecular docking and molecular dynamics (MD) simulations
were performed on a series of isoxazole amides-based SMYD3 inhibitors.

Results:
Molecular docking revealed residues important to protein-compound interactions, indicating that
SMYD3 inhibitors have a strong affinity with and bind to key protein residues such as TYR239,
MET190, LYS297 and VAL368. The molecular docking results were further validated by molecular dynamics
simulations.

Conclusion:
The above information provided significant guidance for the design of novel SMYD3 inhibitors.
Estimation of Human Oral Fraction Dose Absorbed of Simvastatin from Various Formulations using in-situ Single Pass Intestinal Perfusion Method
Authors: Verma Madhu , Nanda Arun , Chauhan Iti , Yasir Mohd. , Majhi Sagarika , Rajkumari  , Sharma Meenakshi 
Published: May 2023

Background:
SIM is a poorly water-soluble drug with dissolution-dependent bioavaila-bility. A solid dispersion and self-emulsifying drug delivery system was developed, optimized, and evaluated to improve its bioavailability. The permeability coefficient in rats was determined using the in-situ single-pass intestinal perfusion (SPIP) technique. Further, the permeability coefficient (Peff, humans) was used to calculate the permeability and fraction of SIM bioavailable to humans which have not yet been reported for these formulations.

Objective:
To estimate and compare various formulations of Simvastatin (SIM) for bioavailable fraction to humans (Fa) as a function of solubility enhancement.

Methods:
In this study, the preparation and evaluation of SIM formulations i.e., Self-emulsifying drug delivery system (SEDDS) and Solid dispersions (SD) are discussed in brief. An uncomplicat-ed, precise, and accurate HPLC method was validated for simultaneous determination of SIM and phenol red as per ICH guidelines. A comparative in-vitro dissolution test, pharmacokinetic studies, and in-situ SPIP technique in rats were carried out amongst optimized formulations of SIM-SD and SIM-SEDDS, SIM suspension (SIM-SUSP), and SIM marketed preparation (SIM-MP).

Results:
The HPLC method was successfully validated. In-vitro dissolution test displays that both the SIM formulations i.e., SIM-SEDDS and SIM-SD shows better dissolution rate than SIM-MP and SIM-SUSP. Pharmacokinetic studies revealed that SIM-SEDDS, SIM-SD, and SIM-MP showed significant differences when compared to SIM-SUSP in terms of Cmax, [AUC] 0-∞, at P ≤ 0.05. The comparison of permeability coefficient between SIM SEDDS vs. SIM MP and SIM SEDDS vs. SIM SD were non-significant. In contrast, SIM- SUSP vs. all other formulations were significantly different at P ≤ 0.05 (employing two-way ANOVA followed by post-Bonferroni Test). Fa for SIM SUSP, an optimized formulation of SIM-SEDDS, SIM-MP, and SIM-SD are 0.353, 0.977, 0.975, and 0.987 respectively. It is revealed that SIM-SEDDS and SIM-SD showed enhanced absorption and the results are confirmed by in-vitro data, pharmacokinetic studies, and In-situ SPIP techniques.

Conclusion:
The permeability prediction method is a rapid and economical method for screening chemical compounds with the least possible utilization of resources. So, its use can be extended in prime and initial screening prototypes for the evaluation of compounds in the early stages of their formulations.
The Role of Different Medicinal Herbs in Treatment of Polycystic Ovary
Syndrome: A Review
Authors: Sharma Aastha , Bansal Keshav , Bajpai Meenakshi 
Published: May 2023

Abstract:
Polycystic ovary syndrome (PCOS/PCOD) indicates a common, multifactorial endocrine condition
marked by polycystic ovaries, chronic anovulation, and hyperandrogenism, resulting in asymmetric
menstrual periods, hirsutism, acne, and infertility. Standard treatment for PCOS is often accompanied by
adverse effects, and is ineffective in certain situations, refutation-based medical guidance accented an
integrative progress. Furthermore, women suffering from PCOS may benefit from alternative treatments.
Herbal treatment includes herbs that have a phytoestrogenic and non-estrogenic nature, including Mentha
spicata, Panax ginseng, Glycyrrhiza glabra, Aloe barbadensis, Cinnamomum zeylanicum, Matricaria
chamomilla, Trigonella foenum-graecum, Linum usitatissimum, and Zingiber officinale, which are effective
and safe. This review focuses on the reproductive endocrine effects of phytomedicine as a choice
treatment so as to PCOD and accompanying oligo/amenorrhoea, acne, hirsutism, and infertility.
MLCEL: Machine Learning and Cost-Effective Localization Algorithms
for WSNs
Authors: Singh Omkar , Kumar Lalit 
Published: May 2023

Introduction::
Wireless communication systems provide an indispensable act in real-life
scenarios and permit an extensive range of services based on the users' location. The forthcoming implementation
of versatile localization networks and the formation of subsequent generation Wireless
Sensor Network (WSN) will permit numerous applications.

Materials and Methods::
In this perspective, localization algorithms have converted into an essential
tool to afford compact implementation for the location-based system to increase accuracy and reduce
computational time, proposing a Machine Learning and Cost-Effective Localization (MLCEL) algorithm.
MLCEL algorithm is assessed with considered localization algorithms called Support Vector
Machine for Regression (SVR), Artificial Neural Network (ANN), and K Nearest Neighbor (KNN).
Numerous outcomes show that the MLCEL algorithm performs better than state art algorithms. The
simulation is implemented in MATLAB version 8.1 for a network size of 100 nodes. Sensor nodes are
positioned in a network area of 100 ×100 m2.

Conclusion and Results Discussion::
The results are assessed on different parameters, and MLCEL
achieves better results in localization error 13% 16%, cumulative probability 19%-21%, root mean
square error 14%-18%, distance error 17%-20%, and computational time 22%-24% than SVR, ANN,
and KNN.
Phytocannabinoids as Potential Multitargeting Neuroprotectants in Alzheimer’s
Disease
Authors: Rathod Sumit S. , Agrawal Yogeeta O. 
Published: May 2023

Abstract:
The Endocannabinoid System (ECS) is a well-studied system that influences a variety
of physiological activities. It is evident that the ECS plays a significant role in metabolic activities
and also has some neuroprotective properties. In this review, we emphasize several plant-derived
cannabinoids such as β-caryophyllene (BCP), Cannabichromene (CBC), Cannabigerol (CBG),
Cannabidiol (CBD), and Cannabinol (CBN), which are known to have distinctive modulation abilities
of ECS. In Alzheimer's disease (AD), the activation of ECS may provide neuroprotection by
modulating certain neuronal circuitry pathways through complex molecular cascades. The present
article also discusses the implications of cannabinoid receptors (CB1 and CB2) as well as cannabinoid
enzymes (FAAH and MAGL) modulators in AD. Specifically, CBR1 or CB2R modulations
result in reduced inflammatory cytokines such as IL-2 and IL-6, as well as a reduction in
microglial activation, which contribute to an inflammatory response in neurons. Furthermore, naturally
occurring cannabinoid metabolic enzymes (FAAH and MAGL) inhibit the NLRP3 inflammasome
complex, which may offer significant neuroprotection. In this review, we explored the
multi-targeted neuroprotective properties of phytocannabinoids and their possible modulations,
which could offer significant benefits in limiting AD.
Signaling Pathways and Molecular Process of Natural Polyphenols in the
Amelioration of Inflammatory Bowel Disease: A Privileged Scaffold in
New Drug Discovery
Authors: Prakash Om , Singh Ruchi , Bajpai Priyanka , Kumari Meera 
Published: May 2023

Abstract:
GIT is seriously affected by inflammatory bowel disease (IBD), which is characterized
by extreme inflammation and an imbalance in a person's healthy life span. The frequency of occurrence
of such chronic diseases as IBD would continue to increase. In the past decade, increasing
attention has been paid to polyphenols from natural sources have been shown to serve as successful
therapeutic agents for altering the signalling pathways linked to IBD and oxidative stress.
We conducted a structured search for peer-reviewed research articles using the various keywords
in bibliographic databases. By using common tools and a deductive qualitative content analysis
technique, the quality of the retrieved papers and the distinctive findings of the articles included in
the study were evaluated. Notably, experimental and clinical evidence has proved that natural polyphenols
could act as a targeted modulator to play a key role in the prevention or treatment of
IBD. Polyphenol phytochemicals have shown noticeable alleviative effects by acting on the
TLR/NLR, and NF-κB signaling pathway in intestinal inflammation. This study examines the potential
of polyphenols for treating IBD, with an emphasis on modulating cellular signalling mechanisms,
regulating the balance of gut microbiota, and restoring the epithelial barrier. The available
evidence concluded that the utilization of polyphenol-rich sources could control inflammation,
mucosal healing, and positive benefits with minimal side effects. Even though additional study is
required in this area, particularly that which focuses on the interactions, connections, and precise
mechanisms of action linking polyphenols and IBD.
Analysis of the meaning of continuity and differentiation: A study on graph problems
Authors: Ikram Muhammad , Muzaini Muhammad 
Published: May 2023

Continuity and differentiability are concepts that students must master to effectively learn and solve problems in a variety of contexts. Thus, this study sought to elicit students' interpretations of the relationship between continuity and differentiability, particularly in graphic problems. Through a qualitative approach, this study involved 195 third-year undergraduate students from various Indonesian universities. Ten of them agreed to an in-depth interview for exploration and clarification. Thematic analysis was conducted to deduce patterns from the responses of participants based on the findings. This study discovered three types of meanings that students construct when they solve problems: 1) physical meaning; 2) analytical meaning, and 3) covariational meaning. The three findings could serve as a conceptual framework for future learning processes that emphasize continuity and differentiability. Additionally, our research revealed that Indonesian undergraduate students are unfamiliar with the graphical problems associated with the two concepts. Thus, future research will focus on developing learning strategies that incorporate a variety of representations to improve students' conceptual understanding of calculus concepts.
Determinant factors of mathematics achievement in online learning during the covid-19 pandemic
Authors: Hamidy Anwaril , Lam Kee-Fui Turner 
Published: May 2023

Online learning has become more popular during the Covid-19 pandemic, including in mathematics. However, many educators have raised concerns whether there are critical factors determining the mathematics achievement during online learning. This study examines online learning readiness (OLR), math anxiety (MA), online platforms used in learning (Zoom Meeting and Google Classroom) and gender as determinants of mathematics achievement students in online learning. The participants in this study were 197 students of a private university in Samarinda, Indonesia. Online learning readiness and math anxiety were gathered by means of questionnaires. The questionnaires were deemed valid ( 0.500) and reliable (OLR = 0.903; MA = 0.888) based on Confirmatory Factor Analysis (CFA) and Cronbach’s Alpha respectively. Other variables function as dummy based on the attributes possessed and learning experienced by the participants. The data were analyzed using standard multiple linear regression and multiple linear regression with dummy variables. The results reveal that online learning readiness, math anxiety, online learning platforms and gender had a significant effect on students' mathematics achievement in online learning. In addition, online learning platforms and gender contribute to the difference effect of online learning readiness and math anxiety to mathematics achievement. The results suggest that educators and educational institutions should consider these important aspects to optimize the advantages and effectiveness of online learning.
Comparison of different application of mathematics online learning media in terms of critical thinking skills and learning interest
Authors: Oktapia Penti , Hidayati Kana 
Published: May 2023

This study aims to determine: (1) whether students' critical thinking skills taking online learning through WhatsApp Group and learning videos on Facebook are higher on average than those who take online learning through WhatsApp Group and Google Meet. It is (2) whether the learning interest of students who take online learning through WhatsApp Group and learning videos on Facebook is higher on average than students who take online learning through WhatsApp Group and Google Meet. This research was a quasi-experimental research with a posttest-only non-equivalent control group design. All eighth-grade students are considered to be the population at the Masjid Syuhada Integrated Islamic Junior High School Yogyakarta in the 2020/2021 academic year. The research sample was students of class VIII A as the experimental class and class VIII C as the control class. The research instrument was in the form of a critical thinking skills test and student learning interest questionnaires. The hypothesis testing technique used is the Independent Sample T-Test. The study's results showed that the critical thinking skills of students who took online learning through WhatsApp Group and learning videos on Facebook was higher on average than those who took online learning through WhatsApp Group and Google Meet. Furthermore, the learning interest of students who took online learning through WhatsApp Groups and learning videos on Facebook could be considered the same as students who took online learning through WhatsApp Groups and Google Meet.