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The purpose of this paper is to investigate the ability of transition metals (TMs) of iron-, nickel- and zinc-doped graphene nanosheet for adsorption of toxic gas of nitric oxide (NO). The results of this paper have provided a favorable understanding of the interaction between TM-doped graphene nanosheet and NO molecule.

A high performance of TM-doped graphene nanosheet as a gas sensor is demonstrated by modeling the material’s transport characteristics by means of the Langmuir adsorption and three-layered ONIOM/ density functional theory method. The Langmuir adsorption model has been done with a three-layered ONIOM using CAM-B3LYP functional and LANL2DZ and 6–311G (d, p) basis sets by Gaussian 16 revision C.01 program towards the formation of of NO→TM(Mn, Co, Cu)-doped on the Gr nanosheet.

The changes of charge density for Langmuir adsorption of NO on Mn-, Co- and Cu-doped graphene nanosheet orderly have been achieved as: ΔQ_Co-doped = +0.309 >> ΔQ_Mn-doped = −0.074 > ΔQ_Cu-doped = −0.051. Therefore, the number of changes of charge density have concluded a more remarkable charge transfer for Mn-doped graphene nanosheet. However, based on nuclear magnetic resonance spectroscopy, the sharp peaks around Cu doped on the surface of graphene nanosheet and C19 close to junction of N2 and Co17 have been observed. In addition, Cu-doped graphene sheet has a large effect on bond orbitals of C8–Cu 17, C15–Cu 17 and C16–Cu17 in the adsorption of NO on the Cu-doped/Gr which has shown the maximum occupancy. The amounts of ΔGads,NO→Mn−Co through IR computations based on polarizability have exhibited that ΔGads,NO→Mn−Co has indicated the most energy gap because of charge density transfer from the nitrogen atom in NO to Mn-doped graphene nanosheet, though ΔG(NO→Cu−C)0> ΔG(NO→Co−C)0>ΔG(NO→Mn−C)0.

This research aims to explore the adsorption of hazardous pollutant gas of “NO” by using carbon nanostructure doped by “TM” of iron, nickel and zinc to evaluate the effectiveness of adsorption parameters of various TM-doped graphene nanosheets.

Acute and chronic obstructive pulmonary disease (COPD) is a common and progressive lung disease that makes breathing difficult over time and can even lead to death. Despite this, there is no definitive treatment for it yet. This study aims to evaluate the studies on single and combined herbal interventions affecting COPD.

In this study, all articles published in English up to 2020 were extracted from the Web of Science (WoS) database and collected using Boolean tools based on keywords, titles and abstracts. Finally, the data required for bibliographic analysis, such as the author(s), publication year, academic journal, institution, country of origin, institution, financial institution and keywords were extracted from the database.

A total of 573 articles were analyzed. The number of papers in the lung disease field showed an upward trend from 1984 to 2021, and there was a surge in paper publications in 2013. China, Korea and Brazil published the highest number of studies on COPD, and Chinese medical universities published the most papers. Three journals that received the highest scores in this study were the Journal of Ethnopharmacology, International Immunopharmacology and Plos One. In the cloud map, expression, activation and expression were the most frequently researched subjects. In the plus and author keywords, acute lung injury was the most commonly used word. Inflammation, expression of various genes, nitric oxide-dependent pathways, NFkappa B, TNFalpha and lipopolysaccharide-dependent pathways were the mechanisms underlying COPD. Scientometric analysis of COPD provides a vision for future research and policymaking.

This study aimed to evaluate the studies on single and combined herbal interventions affecting COPD.

Combustion of fuel with oxidizer inside a combustion chamber of an internal combustion engine forms inevitable oxides of nitrogen (NOx) due to high temperature at different locations of the combustion chamber. This study aims to quantify NOx formed inside the combustion chamber using two fuels, a conventional diesel (n-heptane) and a biodiesel (methyl oleate).

This research uses a computational fluid dynamics simulation of chemically reacting fluid flow to quantify and compare oxides of nitrogen (NOx) in a compression ignition (CI) engine. The study expends species transport model of ANSYS FLUENT. The simulation model has provided the temperature profile inside the combustion chamber, which is subsequently used to calculate NOx using the NOx model. The simulation uses a single component hydrocarbon and oxygenated hydrocarbon to represent fuels; for instance, it uses n-heptane (C₇H₁₆) for diesel and methyl-oleate (C₁₉H₃₆O₂) for biodiesel. A stoichiometric air–fuel mixture is used for both fuels. The simulation runs a single cylinder CI engine of 650 cm³ swept volume with inlet and exhaust valves closed.

The pattern for variation of velocity, an important flow parameter, which affects combustion and subsequently oxides of nitrogen (NOx) formation at different piston locations, is similar for the two fuels. The variations of in-cylinder temperature and NOx formation with crank angles have similar patterns for the fuels, diesel and biodiesel. However, the numerical values of in-cylinder temperature and mass fraction of NOx are different. The volume averaged static peak temperatures are 1,013 K in case of diesel and 1,121 K in case of biodiesel, while the mass averaged mass fractions of NOx are 15 ppm for diesel and 141 ppm for biodiesel. The temperature rise after combustion is more in case of biodiesel, which augments the oxides of nitrogen formation. A new parameter, relative mass fraction of NOx, yields 28% lower value for biodiesel than for diesel.

This work uses a new concept of simulating simple chemical reacting system model to quantify oxides of NOx using single component fuels. Simplification has captured required fluid flow data to analyse NOx emission from CI engine while reducing computational time and expensive experimental tests.

In the developing field of nano-materials synthesis, copper oxide nanoparticles (NPs) are deemed to be one of the most significant transition metal oxides because of their intriguing characteristics. Its synthesis employing green chemistry principles has become a key source for next-generation antibiotics attributed to its features such as environmental friendliness, ease of use and affordability. Because they are more environmentally benign, plants have been employed to create metallic NPs. These plant extracts serve as capping, stabilising or hydrolytic agents and enable a regulated synthesis as well.

Organic chemical solvents are harmful and entail intense conditions during nanoparticle synthesis. The copper oxide NPs (CuO-NPs) synthesised by employing the green chemistry principle showed potential antitumor properties. Green synthesised CuO-NPs are regarded to be a strong contender for applications in the pharmacological, biomedical and environmental fields.

The aim of this study is to evaluate the anticancer potential of CuO-NPs plant extracts to isolate and characterise the active anticancer principles as well as to yield more effective, affordable, and safer cancer therapies.

This review article highlights the copper oxide nanoparticle's biomedical applications such as anticancer, antimicrobial, dental and drug delivery properties, future research perspectives and direction are also discussed.

The purpose of this paper is to systematically review the functionalities, safety regulations and product applications of herb Stevia rebaudiana extract. This plant material is embedded with multiple functionalities such as antioxidant, antidiabetics, anti-inflammation and antimicrobial. The regulations released from global authorities are covered to ensure the safety premise of stevia. Besides, the product applications of the extract of aerial parts of the herb S. rebaudiana helps us to recognize its value from commercial side.

Relevant literatures are selected and obtained from main scientific databases such as Google Scholar, Web of Science, PubMed and trade magazines published between 2000 and 2023. The keywords and their possible combinations such as sweetening, antioxidant, antidiabetics, anti-inflammation, safety and product development were used to ensure the preciseness and completeness of literature searching. Major data such as sweetness, total phenolic content and dose together with latter critical conclusions from searched publications were appropriately used and discussed. In this review, approximately 150 scientific literatures were meticulously ordered and analyzed. In applications, it is the first time that sentiment analysis was used to obtain a market assessment of the stevia-containing products.

This review paper helps rearrange the scientific affairs of those stevia extract’s functions like sweetening, antioxidant, antidiabetics and inflammation. Sweetness indexes of steviol glycosides were summarized together for comparison while various in vitro and in vivo approaches were reviewed to quantify those functions’ capacities and to depict the related mechanism. The regulation of steviol glycoside compounds such as rebaudioside A was established by global authorities such as US Food and Drug Administration and Joint FAO/World Health Organization Expert Committee to ensure the safety endorsement before commercialization. Then, this study discussed about the market performance of stevia ingredients or products with the self-developed data analytics. This study also investigated the product development progress of stevia-containing food products in the categories of beverage, bakery, dairy and confectionery. Those stevia-containing food consumer goods can be acceptable by certain consumers.

This review paper precisely presents the evidential information about the stevia’s multiple functionalities with mechanisms and global regulation milestones. To the best of the authors’ knowledge, it is then the first time to probe the stevia-containing products’ market performance through data analytics.

Proper topic selection is an essential prerequisite for the success of research. To study this, this article proposes an important concerned factor of topic selection-topic popularity, to examine the relationship between topic selection and team performance.

The authors adopt extracted entities on the type of gene/protein, which are used as proxies as topics, to keep track of the development of topic popularity. The decision tree model is used to classify the ascending phase and descending phase of entity popularity based on the temporal trend of entity occurrence frequency. Through comparing various dimensions of team performance – academic performance, research funding, relationship between performance and funding and corresponding author's influence at different phases of topic popularity – the relationship between the selected phase of topic popularity and academic performance of research teams can be explored.

First, topic popularity can impact team performance in the academic productivity and their research work's academic influence. Second, topic popularity can affect the quantity and amount of research funding received by teams. Third, topic popularity can impact the promotion effect of funding on team performance. Fourth, topic popularity can impact the influence of the corresponding author on team performance.

This is a new attempt to conduct team-oriented analysis on the relationship between topic selection and academic performance. Through understanding relationships amongst topic popularity, team performance and research funding, the study would be valuable for researchers and policy makers to conduct reasonable decision making on topic selection.

This research intended the utilization of Javanese turmeric (0.4% w/v) as a kombucha substrate and analysis of its hepatoprotective activity, in comparison against nonfermented Javanese turmeric beverage (JTB) and black tea kombucha.

Forty-two healthy male Balb/c mice (two- to three-week-old, 20–30 g) were divided into six groups with seven replicates each. The treatments were normal diet, normal diet + Javanese turmeric kombucha (JTK), normal diet + diethylnitrosamine (DEN), DEN + JTB, DEN + JTK, DEN + black tea kombucha. Kombuchas and JTB were given at 0.3 mL/20 g BW/d. DEN was induced intraperitoneally at a dose of 100 mg/kg. Observed biomarkers were blood serum glutamate pyruvate transaminase (SGPT) and serum glutamate oxaloacetate transaminase (SGOT) activity, serum malonaldehyde (MDA), as well as liver histology. Data were analyzed using analysis of variance.

Among DEN-induced groups, JTK significantly (p < 0.05) diminished the level of blood SGPT, SGOT and serum MDA. JTK also had lower blood SGPT (8.604 ± 2.195 U/L) and serum MDA levels (2.884 ± 0.083 nM/mL) compared to the normal group (8.604 ± 2.195 U/L and 5.050 ± 0.998 nM/mL, respectively). JTK also produced the least damaged liver-cell numbers.

JTK demonstrated better hepatoprotective activity compared to JTB.

Indoor air quality monitoring is extremely important in urban, industrial areas. Considering the devastating effect of declining quality of air in major part of the countries like India and China, it is highly recommended to monitor the quality of air which can help people with respiratory diseases, children and elderly people to take necessary precautions and stay safe at their homes. The purpose of this study is to detect air quality and perform predictions which could be part of smart home automation with the use of newer technology.

This study proposes an Internet-of-Things (IoT)-based air quality measurement, warning and prediction system for ambient assisted living. The proposed ambient assisted living system consists of low-cost air quality sensors and ESP32 controller with new generation embedded system architecture. It can detect Indoor Air Quality parameters like CO, PM2.5, NO2, O3, NH3, temperature, pressure, humidity, etc. The low cost sensor data are calibrated using machine learning techniques for performance improvement. The system has a novel prediction model, multiheaded convolutional neural networks-gated recurrent unit which can detect next hour pollution concentration. The model uses a transfer learning (TL) approach for prediction when the system is new and less data available for prediction. Any neighboring site data can be used to transfer knowledge for early predictions for the new system. It can have a mobile-based application which can send warning notifications to users if the Indoor Air Quality parameters exceed the specified threshold values. This is all required to take necessary measures against bad air quality.

The IoT-based system has implemented the TL framework, and the results of this study showed that the system works efficiently with performance improvement of 55.42% in RMSE scores for prediction at new target system with insufficient data.

This study demonstrates the implementation of an IoT system which uses low-cost sensors and deep learning model for predicting pollution concentration. The system is tackling the issues of the low-cost sensors for better performance. The novel approach of pretrained models and TL work very well at the new system having data insufficiency issues. This study contributes significantly with the usage of low-cost sensors, open-source advanced technology and performance improvement in prediction ability at new systems. Experimental results and findings are disclosed in this study. This will help install multiple new cost-effective monitoring stations in smart city for pollution forecasting.

Climate change and its imminent threat to human survival adversely impact the agriculture sector. In an impoverished country like The Gambia, economic costs of climate change are colossal. This study aims to establish a computable general equilibrium (CGE) model for The Gambia’s agriculture sector to examine the effects of climate change on crops, livestock and sea-level rise.

This study used a CGE model with other climate change impact models to compute the impacts of climate change on The Gambia’s agriculture sector. The social accounting matrix calibrates the results from the various models, thereby generating the baseline results which exemplify a “steady-state” and policy shock results illustrating the medium- and long-term effects of climate change on the country’s agriculture sector.

The baseline results indicate the status quo showing the neglect of the agriculture sector due to limited investment in the sector. Hence, the sector is the “hardest hit” sector as a result of climate change. When the model factored in climate change in the medium term (2055) and long term (2085), the macroeconomic indicators of gross domestic product, national savings, wages, disposable income and consumer price index deteriorated, elucidating the vulnerability of the economy to climate change. The consumption of groundnuts, cattle and fish will decline by 5%, 5% and 4%, respectively, in the long term. However, the production of all agricultural commodities will decline by an average of 35% for the same period. The results for international trade show that exportation would decline while importation will increase over time. The general price level for agricultural commodities would increase by 3% in 2055 and 5% in 2085. Generally, the results manifest the severity of climate change in the agriculture sector which will have a multiplier effect on the economy. The impact of climate change would result in agriculture and economic decline causing hunger, poverty and human misery.

The caveat of this study revealed the nuances not captured by previous Gambian climate change studies, thus the novelty of the study.

This study aims to investigate the relationship between dietary polyamine levels, metabolic risk parameters and anthropometric measurements in postmenopausal women.

This cross-sectional study was conducted with 562 45–64-year-old postmenopausal women who presented to a Family Health Center. To collect the data, the Descriptive Information Form and Food Frequency Questionnaire were used. In the data analysis, numbers, percentages, mean, standard deviation and multiple linear regression analysis were used.

The multiple linear regression analysis demonstrated that dietary putrescine intake was negatively associated with systolic blood pressure (ß = −0.179, p < 0.001), dietary spermidine intake was positively associated with waist circumference (WC) (ß = 0.142, p = 0.013), systolic blood pressure (ß = 0.188, p = 0.001), diastolic blood pressure (ß = 0.218, p < 0.001), body mass index (BMI) (ß = 0.169, p = 0.003) and waist-to-height ratio (WHtR) (ß = 0.156, p = 0.006), and dietary spermine intake was negatively associated with WC (ß = −0.158, p = 0.003), systolic blood pressure (ß = −0.195, p < 0.001), BMI (ß = −0.107, p = 0.042) and WHtR (ß = −0.138, p = 0.009).

Owing to the study’s cross-sectional nature, the lack of succession in the cause–effect relationship, the use of self-report Food Frequency Questionnaire to determine dietary polyamine intake and the inability to analyze seasonal differences are among the limitations of the study.

In this study, an association was determined between dietary polyamines, metabolic risk parameters and anthropometric measurements. The findings suggest that dietary polyamines in human health should be further investigated owing to the increasing metabolic risk parameters.