bluuebunny/crossref_metadata_2025_split huggingface dataset:
Large scale Multi-Labeled Aromatic Medcinal Plant Image classification using Deep Learnning
Authors: Asresa Tsega , Tigistu Getahun , Bayih Melaku 
Published: December 2023

Abstract
Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.
Development of SiO2/rGO from Rice Husk for Photocatalysis, Antioxidant, Electrochemical and green sensor detection Studies
Authors: Swetha N , Lakshmi V Venkata , Mylarappa M , Chandruvasan S , Harisha K S 
Published: December 2023

Abstract
This study reports the extraction, characterization and development of reduced graphene oxide (rGO) doped silicon dioxide (SiO2) nanocomposite by simple reflux method. The nanocomposite was confirmed by using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM) and UV-Visible spectroscopy techniques. The photocatalysis of malachite green (MG) was carried out for rGO, SiO2 and SiO2/rGO nanocomposite shows higher MG degradation about 97% compared to rGO, SiO2 and follows 1st order kinetics. The antioxidant action of SiO2/rGO nanocomposite was assessed using DPPH shows a more antioxidant activity (98%) and lower IC50 about 488.35 mg/mL. From electrochemical, the specific capacitance (Csp) value of SiO2/rGO (114 F/g) was exhibits higher compared to rGO (75 F/g) and SiO2 (96 F/g) respectively. The CV and sensor detection of bee pollen and cow urine samples were performed using nickel mesh electrode in 1M KCl in the potential range − 1 to 1 V. The SiO2/rGO was employed to analyze bee pollen and cow urine concentrations and the detection limits were found to be 0.260 mM and 0.413 mM respectively. The prepared electrode plays an important role for improving sensor detection of bee pollen and cow urine samples.
Flow regime identification of air-water two-phase counter-current flow in vertical annulus and eccentricity effect analysis：a machine learning approach
Authors: Cao Feng , Dang Ruirong , Dang Bo , Zheng Huifeng , Ji Anzhao , Chen Zhanjun , Zhao Jiaxuan , Sun Zhimeng 
Published: December 2023

Abstract
Gas-liquid two-phase counter-current flow has been involved in many industrial fields, due to the existence of the inner pipe and the variation of the eccentricity of the inner and outer pipes, the flow regimes are difficult to be visual identification. To solve this problem, a vertical annulus with adjustable eccentricity was designed. Experiments were carried out under different combination of gas-liquid flowrate and eccentricity. The feature vector was formed by PDF and principal components of PSD of two differential pressure signals. The clustering algorithm CFDP based on local density was used to cluster the experimental data, results show that this method can realize the identification of flow regimes without prior information, and the flow regime transition boundary agrees with the experimental results. The influence of pipe eccentricity on the flow regime transition is analyzed. The results show that, in most cases, pipe eccentricity has no obvious influence on the flow regimes. However, in the flow regime transition region, such as the bubbly flow to slug flow transition region, when the eccentricity is at a medium value, that is, e = 0.25,0.5,0.75, the flow is more likely to transit to slug flow, however, when e = 0,1. The flow tends to remain bubbly flow.
A proposed mathematical model to help preoperative planning between RIRS and MiniPerc for renal stones between 10 and 20 mm using Holmium:YAG laser (Cyber Ho): the Stone Management According to Size-Hardness (SMASH) score
Authors: Perri Davide , Mazzoleni Federica , Besana Umberto , Pacchetti Andrea , Morini Elena , Calandriello Mattia , Pastore Antonio Luigi , Romero-Otero Javier , Bruyere Franck , Sighinolfi Maria Chiara , Rocco Bernardo , Micali Salvatore , Gozen Ali Serdar , Liatsikos Evangelos , Roche Jean Baptiste , Bozzini Giorgio 
Published: December 2023

Abstract
Objectives
To evaluate the performance of a mathematical model to drive preoperative planning between RIRS and MiniPerc (MP) for the treatment of renal stones between 10–20 mm.
Methods
Patients with a renal stone between 10–20 mm were enrolled. A mathematical model named Stone Management According to Size-Hardness (SMASH) score was calculated: hounsfield units (HU) x stone maximum size (cm) / 100. Patients were divided into 4 groups: RIRS with score < 15 (Group A), RIRS with score ≥ 15 (Group B), MP with score < 15 (Group C), MP with score ≥ 15 (Group D). Cyber Ho device was always used. Stone free rate (SFR) was assessed after 3 months. Complication rate and need for auxiliary procedures were evaluated.
Results
Between January 2019 and December 2021, 350 patients were enrolled (87, 88, 82 and 93 in Groups A, B, C and D). Mean stone size was 13.1 vs 13.3 mm in Group A vs B (p = 0.18) and 16.2 vs 18.1 mm in Group C vs D (p = 0.12). SFR was 82%, 61%, 75% and 85% for Groups A, B, C and D. SFR was comparable between Groups C and D (p = 0.32) and Groups A and C (p = 0.22). SFR was significantly higher in Group A over B (p = 0.03) and in Group D over B (p = 0.02). Complication rate was 2.2%, 3.4%, 12.1%, 12.9% for Groups A, B, C, D.
Conclusion
RIRS and MP are both safe and effective. The mathematical model with the proposed cut-off allowed a proper allocation of patients between endoscopic and percutaneous approaches.
Registration number of the study: ISRCTN55546280
Enabling cation-exchange-based sorbents for high-capacity and efficient moisture-swing direct air capture of CO2
Authors: Wang Xueru , Deng Liyuan 
Published: December 2023

Abstract
Direct air capture (DAC) of CO2 is recognized to be a necessary complement to large point-source carbon capture due to the urgent need for a deep reduction in carbon emissions. As an energy-efficient regeneration mechanism, moisture-swing DAC (MSDAC) has received considerable attention in recent years. In this study, we extended the limited functional group types of MSDAC sorbent to most of the monovalent and divalent basic ions, and, for the first time, demonstrated experimentally the potential of cation-exchange-resins (CER) as MSDAC sorbents. Cation-exchange-resins loaded with various cations were tested, exhibiting an astounding CO2 capacity of 6.2 mmol/g in a chelating CER, significantly higher than the capacities of most MSDAC sorbents reported thus far. In addition, CERs can circumvent the common problem of hetero-ion contamination in MSDAC when using anion-exchange-resins (AER) as sorbents, thereby reducing the stringent need for high water quality, simplifying the MSDAC process and facilitating large-scale applications.
Spatial-temporal Traffic Imputation with Uncertainty Quantification on Highway with Generative Adversarial Network and Transformer
Authors: Shen Zhou (Eric) 
Published: December 2023

Abstract
The efficient management of traffic flow on highways is crucial for ensuring safety, reducing congestion, and optimizing transportation infrastructure. However, real-world scenarios often involve missing or incomplete traffic data, posing challenges for decision-making in Intelligent Transportation Systems (ITS). In this paper, we present a novel methodology, the GAN-Transformer Imputer (GTI), for spatial-temporal traffic imputation. Our approach integrates Generative Adversarial Networks (GANs) and Transformers to address the limitations of existing methods. The GAN component generates realistic traffic data, while the Transformer captures long-range dependencies and temporal patterns. Through a series of experiments on synthetic and real-world datasets, we demonstrate the superior performance of GTI compared to state-of-the-art methods. GTI consistently outperforms traditional statistical methods, machine learning approaches, and deep learning methodologies in terms of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and F1 Score. The key contributions of this work lie in the seamless integration of GANs and Transformers, providing a robust solution for spatial-temporal traffic imputation. Our methodology, with its adversarial training and uncertainty quantification, holds promise for enhancing decision-making processes in ITS, contributing to the advancement of transportation systems and urban planning.
Analysis of past and projected changes in extreme precipitation indices in some watersheds in Côte d'Ivoire
Authors: Amichiatchi N'da Jocelyne Maryse Christine , Hounkpè Jean , Soro Gneneyougo Emile , Khadijat Ojelabi Oluwatoyin , Larbi Isaac , Limantol Andrew Manoba , Alhassan Abdul-Rauf Malimanga , Bi Tie Albert Goula , Lawin Agnidé Emmanuel 
Published: December 2023

Abstract
The purpose of this study is to analyse trends in annual rainfall extremes over five watersheds within Côte d'Ivoire using observed (1976–2017) and projected (2020–2050) rainfall data from the fourth version of the Rossby Centre regional atmospheric model, RCA4, for the representative concentration pathways RCP 4.5 and RCP 8.5. Four rainfall extreme indices, namely, the consecutive dry days (CDD), maximum annual rainfall (Pmaxan), very wet day (R95p), and maximum 5-day rainfall (Rx5days), were considered for trend analysis by using the non-parametric modified Mann–Kendall test and the distribution mapping bias-correction technique to adjust the simulated regional climate model of the simulated daily precipitation. As a result, it is found that during the period 1976–2017, there was a significant downward trend in the drought-related index (CDD) at the Bagoue, Baya, Agneby, and Lobo watersheds. The Baya and N'zo watersheds also experienced a significant downward trend under the RCP 4.5 and RCP 8.5 scenarios. The flood-related indices (Pmaxan, R95p, and Rx5days) show a clear downward trend in the recorded data for almost all the considered watersheds and generally a significant upward trend for both cases. These findings indicate that the watersheds are vulnerable to climate-induced disasters.
Hydrogeochemical and qualitative assessment of groundwater in a southern Mediterranean region: an example of the Aïn Témouchent area (northwestern Algeria)
Authors: Hallouche Bachir , Hadji Fatiha 
Published: December 2023

Abstract
The present research aims to assess the hydrogeochemical processes, the suitability of groundwater for human consumption, and the influence degree of seawater intrusion of the coastal aquifers of the Aïn Témouchent region (northwestern Algeria). In this context, 29 groundwater samples were taken during April–May 2018 and analyzed for 11 physicochemical parameters. Piper and Chadha's diagrams allowed the identification of Na–Cl dominant hydrogeochemical facies and Ca (Mg)–Cl type facies, indicating the influence of salt water. Simpson's Index results highlighted that groundwater was slightly contaminated by saltwater. Obtained values of the seawater mixing index (SMI) and Cl−/(HCO3−), Na+/Cl− and Ca2+/Mg2+ ratios indicated the majority of the sampling sites are affected by seawater intrusion. The integration of the water quality index (WQI) spatial variation through the geographic information system (GIS) indicated that in the Southern part of the study region, and 55.2% of water samples are suitable as drinking water. Adversely, for the northern part, where groundwater is influenced by seawater intrusion, water sampling points fall into bad and very bad categories. The results of the study show that hydrogeochemical processes are responsible for the salinity of groundwater in the study area, in addition to marine intrusion.
Review on the Artificial Intelligence-based Nanorobotics Targeted Drug
Delivery System for Brain-specific Targeting
Authors: Rai Akriti , Shah Kamal , Dewangan Hitesh Kumar 
Published: December 2023

Abstract:
Contemporary medical research increasingly focuses on the blood-brain barrier (BBB) to maintain
homeostasis in healthy individuals and provide solutions for neurological disorders, including brain cancer.
Specialized in vitro modules replicate the BBB's complex structure and signalling using micro-engineered perfusion
devices and advanced 3D cell cultures, thus advancing the understanding of neuropharmacology. This
research explores nanoparticle-based biomolecular engineering for precise control, targeting, and transport of
theranostic payloads across the BBB using nanorobots. The review summarizes case studies on delivering therapeutics
for brain tumors and neurological disorders, such as Alzheimer's, Parkinson's, and multiple sclerosis.
It also examines the advantages and disadvantages of nano-robotics. In conclusion, integrating machine learning
and AI with robotics aims to develop safe nanorobots capable of interacting with the BBB without adverse
effects. This comprehensive review is valuable for extensive analysis and is of great significance to healthcare
professionals, engineers specializing in robotics, chemists, and bioengineers involved in pharmaceutical development
and neurological research, emphasizing transdisciplinary approaches.
Physical Activity and Screen Time of Children Aged 5-13 Years during the COVID-19 Lockdown in Mongolia: A Cross-sectional Study
Authors: Badamjav Tegshjargal , Dondov Ganchimeg , Batsaikhan Ankhzaya , Batsaikhan Batbold , Lonjid Tulgaa 
Published: December 2023

Background:
A novel coronavirus infection (COVID-19) has posed a significant threat to global health and the economy over the last two years. In December 2019, a novel coronavirus infection began in Wuhan, China, and quickly spread throughout the world. Since January 2020, the Mongolian government has enforced a social distancing policy, and the country has been one of the few where the disease has not spread. In the 143 days since the outbreak, 575 cases have been reported, resulting in three cases of public emergency preparedness and 50 days of lockdown. The new school year began in September 2021, but due to the Covid-19 pandemic, children will be required to study online from home again on 12th November. It was interesting how the pandemic lockdown affected screen time and physical activity in children.

Objective:
We aimed to assess the relationship between physical activity (PA) and screen time (ST) of children aged 5-13 years’ Mongolian residents during the COVID-19 lockdown.

Methods:
The observational cross-sectional study was conducted during the COVID-19 lockdown between 5th Feb and 9th March 2021 which was the second lockdown of Mongolia, and we have studied 1005 children aged 5-13 years from the capital city. The questionnaire was used which was prepared by the study team and the questionnaire included 33 questions to evaluate screen time and physical activity. All parents reported the questionnaire to give some information about their children. We used appropriate statistical methods in SPSS software.

Results:
A total of 1005 children aged 5-13 years from 628 families were involved in the survey which included 51.9% boys and 44.1% girls. The median of PA and ST was 300 minutes [min, max: 0, 1260] and 30 minutes [min, max: 0,180]. The linear regression revealed a weak negative correlation (r2=0.005, p<0.0001) between PA and ST. When ST and PA analyzed in age by age groups, there was a statistically significant negative dynamic of ST mean time and PA mean score (p<0.0001). Older kids have more ST and lower PA during lockdown time. During this lockdown, 60.4% of the parents reported that their children showed a decrease in PA.

Conclusion:
The mean score of physical inactivity has decreased and ST increased dramatically among children 5-13 years as a result of the Covid-19 lockdown.
Annual Performance Evaluation of a Novel Industrial-scale Solar
Dryer for Phosphogypsum Waste
Authors: El-Ghetany H.H. , El Rafie Sh. , El-Awady M.H. , Osman Randa M. , Awad Abouelata Ahmed M. 
Published: December 2023

Introduction:
Solar drying is considered one of the most promising technologies for
drying the phosphogypsum “Calcium Sulfate Dehydrate” material as a clean source of energy
and saving the environment from its negative impact if it is dried in an open landfill.

Method:
The dried phosphogypsum (PG) may be recycled and utilized in many useful applications.
The presented design of the trapezoidal-shaped greenhouse solar dryer is the preferable
solution to perform the drying process in the daytime with solar energy. It may be usable at
night, during cloudy and rainy periods, using evacuated tube solar collectors via sensible heat
solar thermal energy storage. The system can be operated for 24 hours by solar energy. From
theoretical calculations, it was found that the drying time decreased as the gypsum layer in the
drying trays decreased due to the improvement of the heat transfer mechanism. The designed
solar dryer was studied at various loading capacities depending on the gypsum thickness on the
dryer trays. Capacities were 8280 kg, 6624 kg, 4968 kg, 3312 kg, and 1656 kg, corresponding to
gypsum thickness of 5 cm, 4 cm, 3 cm, 2 cm, and 1 cm, respectively. The daily collected
amount of water and the daily moisture content were estimated with different gypsum thicknesses
during all months of the year.

Result:
It was found that the solar dryer with a capacity of 1,656 kg weight was technically and
economically feasible. It was also found that the maximum reduction of moisture content occurred
during the summer season, while the minimum values were achieved during winter.

Conclusion:
With a mass of gypsum equal to 1,656 kg weight, the moisture content of gypsum
started to decrease from its initial value (35%) until it reached the final value (10%) in 4 scenarios:
the first one, 2 days from April to September, 3 days from March to November, more than 3
days in other months while the minimum moisture content, i.e., 20%, was reported in the first
day in June.
Rosmarinic Acid as a Potential Multi-targeted Inhibitor for SAR-CoV-2:
An In silico Virtual Screening Approach
Authors: Kumar Sumit , Kumar Vikash 
Published: December 2023

Background:
Rosmarinic acid, a natural compound found in various plants like rosemary
and lemon balm, may have potential as a multi-targeted inhibitor for SARS-CoV-2, a strain of virus
responsible for COVID-19. SARS-CoV-2, a fusion protein of S1 and S2 subunits, has multiple precursors
angiotensin-converting enzyme2 (ACE2), transmembrane serine protease 2 (TMPRSS2),
papain-like protease (PLpro), and 3-chymotrypsin-like protease (3CLpro). The chemical interaction
of Rosmarinic acid with SARS-CoV-2 is of major interest reported here.

Objective::
The quantitative study of Rosmarinic acid with various precursors of SARS-CoV-2 has
been accounted for in detail. Furthermore, the conformational flexibility of Rosmarinic acid has also
been investigated during the interaction with four different precursors of SARS-CoV-2.

Methods:
This investigation delves deeply into the analysis of various aspects, including geometric
parameters, atomic charge, the energy gap between the highest occupied and lowest unoccupied molecular
orbitals, dipole moments, and the analysis of non-covalent interactions (NCI). Furthermore,
the study incorporates molecular docking techniques in conjunction with thorough quantum chemical
calculations to provide comprehensive insights.

Results:
Rosmarinic acid shows promise as a versatile inhibitor of SARS-CoV-2, the virus responsible
for COVID-19. It can target multiple key precursors of the virus, including TMPRSS2, angiotensin-
converting enzyme2, 3CLpro, and PLpro, found in the fusion protein comprising S1 and S2 subunits.
This study delves into the quantitative analysis of Rosmarinic acid's interactions with these
precursors. Its adaptable structure allows it to engage with them effectively. Various molecular parameters,
including atomic charge, energy gap between molecular orbitals, dipole moment, and noncovalent
interactions, are comprehensively explored.

Conclusion:
Combining molecular docking and quantum mechanics, the findings suggest Rosmarinic
acid's potential as a multi-targeted SARS-CoV-2 inhibitor.
Getting back to the real world
Authors: Zandvliet David Bryan 
Published: December 2023

Today, STEM and/or STEAM frameworks dominate the discourse around science education and what constitutes a ‘scientific’ literacy. While no one definition prevails in the literature, this literacy is often defined in the context of a current national concerns and focuses largely on Eurocentric (western) models of science and/ or scientific knowledge in terms of concepts, models, theories, or principles. As it currently stands, the term STEM is mostly used when addressing educational policy and curriculum choices in schools, aimed at improving competitiveness in science and technology with implications for workforce and economic development (often with some missing voices from women and Indigenous communities). Without an important socio-cultural critique, education of this kind can maintain and promote hegemonic beliefs and values while ignoring collateral problems relating to scientific or technological development: many of which have been linked to social and environmental injustice. In this paper, I offer three perspectives in an effort to decentre the discourse around the STEM movement. Using the overlapping themes of biocultural diversity, two-eyed seeing and guided inquiry, I offer suggestions on how to reframe science education as an interdisciplinary practice centred on student and community needs. In these ways, science education can ‘get back to the real world’ and promote creative approaches to science literacy, problem solving and cultural inquiry.