Gas multisensor devices offer an effective approach to monitor air pollution, which has become a pandemic in many cities, especially because of transport emissions. To be reliable, properly trained ...models need to be developed that combine output from sensors with weather data; however, many factors can affect the accuracy of the models. The main objective of this study was to explore the impact of several input variables in training different air quality indexes using fuzzy logic combined with two metaheuristic optimizations: simulated annealing (SA) and particle swarm optimization (PSO). In this work, the concentrations of NO
and CO were predicted using five resistivities from multisensor devices and three weather variables (temperature, relative humidity, and absolute humidity). In order to validate the results, several measures were calculated, including the correlation coefficient and the mean absolute error. Overall, PSO was found to perform the best. Finally, input resistivities of NO
and nonmetanic hydrocarbons (NMHC) were found to be the most sensitive to predict concentrations of NO
and CO.
The main objective of this study is to evaluate and compare the performance of different machine learning (ML) algorithms, namely, Artificial Neural Network (ANN), Extreme Learning Machine (ELM), and ...Boosting Trees (Boosted) algorithms, considering the influence of various training to testing ratios in predicting the soil shear strength, one of the most critical geotechnical engineering properties in civil engineering design and construction. For this aim, a database of 538 soil samples collected from the Long Phu 1 power plant project, Vietnam, was utilized to generate the datasets for the modeling process. Different ratios (i.e., 10/90, 20/80, 30/70, 40/60, 50/50, 60/40, 70/30, 80/20, and 90/10) were used to divide the datasets into the training and testing datasets for the performance assessment of models. Popular statistical indicators, such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Correlation Coefficient (R), were employed to evaluate the predictive capability of the models under different training and testing ratios. Besides, Monte Carlo simulation was simultaneously carried out to evaluate the performance of the proposed models, taking into account the random sampling effect. The results showed that although all three ML models performed well, the ANN was the most accurate and statistically stable model after 1000 Monte Carlo simulations (Mean R = 0.9348) compared with other models such as Boosted (Mean R = 0.9192) and ELM (Mean R = 0.8703). Investigation on the performance of the models showed that the predictive capability of the ML models was greatly affected by the training/testing ratios, where the 70/30 one presented the best performance of the models. Concisely, the results presented herein showed an effective manner in selecting the appropriate ratios of datasets and the best ML model to predict the soil shear strength accurately, which would be helpful in the design and engineering phases of construction projects.
In this study, chitosan and alginate were selected to prepare alginate/chitosan nanoparticles to load the drug lovastatin by the ionic gelation method. The synthesized nanoparticles loaded with drug ...were characterized by Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), laser scattering and differential scanning calorimetry (DSC) methods. The FTIR spectrum of the alginate/chitosan/lovastatin nanoparticles showed that chitosan and alginate interacted with lovastatin through hydrogen bonding and dipolar-dipolar interactions between the C-O, C=O, and OH groups in lovastatin, the C-O, NH, and OH groups in chitosan and the C-O, C=O, and OH groups in alginate. The laser scattering results and SEM images indicated that the alginate/chitosan/lovastatin nanoparticles have a spherical shape with a particle size in the range of 50-80 nm. The DSC diagrams displayed that the melting temperature of the alginate/chitosan/lovastatin nanoparticles was higher than that of chitosan and lower than that of alginate. This result means that the alginate and chitosan interact together, so that the nanoparticles have a larger crystal degree when compared with alginate and chitosan individually. Investigations of the in vitro lovastatin release from the alginate/chitosan/lovastatin nanoparticles under different conditions, including different alginate/chitosan ratios, different solution pH values and different lovastatin contents, were carried out by ultraviolet-visible spectroscopy. The rate of drug release from the nanoparticles is proportional to the increase in the solution pH and inversely proportional to the content of the loaded lovastatin. The drug release process is divided into two stages: a rapid stage over the first 10 hr, then the release becomes gradual and stable. The Korsmeyer-Peppas model is most suitable for the lovastatin release process from the alginate/chitosan/lovastatin nanoparticles in the first stage, and then the drug release complies with other models depending on solution pH in the slow release stage. In addition, the toxicity of alginate/chitosan/lovastatin (abbreviated ACL) nanoparticles was sufficiently low in mice in the acute toxicity test. The LD
of the drug was higher than 5000 mg/kg, while in the subchronic toxicity test with treatments of 100 mg/kg and 300 mg/kg ACL nanoparticles, there were no abnormal signs, mortality, or toxicity in general to the function or structure of the crucial organs. The results show that the ACL nanoparticles are safe in mice and that these composite nanoparticles might be useful as a new drug carrier.
Machine Learning (ML) has been applied widely in solving a lot of real-world problems. However, this approach is very sensitive to the selection of input variables for modeling and simulation. In ...this study, the main objective is to analyze the sensitivity of an advanced ML method, namely the Extreme Learning Machine (ELM) algorithm under different feature selection scenarios for prediction of shear strength of soil. Feature backward elimination supported by Monte Carlo simulations was applied to evaluate the importance of factors used for the modeling. A database constructed from 538 samples collected from Long Phu 1 power plant project was used for analysis. Well-known statistical indicators, such as the correlation coefficient (R), root mean squared error (RMSE), and mean absolute error (MAE), were utilized to evaluate the performance of the ELM algorithm. In each elimination step, the majority vote based on six elimination indicators was selected to decide the variable to be excluded. A number of 30,000 simulations were conducted to find out the most relevant variables in predicting the shear strength of soil using ELM. The results show that the performance of ELM is good but very different under different combinations of input factors. The moisture content, liquid limit, and plastic limit were found as the most critical variables for the prediction of shear strength of soil using the ML model.
Snakes and their relationships with humans and other primates have attracted broad attention from multiple fields of study, but not, surprisingly, from neuroscience, despite the involvement of the ...visual system and strong behavioral and physiological evidence that humans and other primates can detect snakes faster than innocuous objects. Here, we report the existence of neurons in the primate medial and dorsolateral pulvinar that respond selectively to visual images of snakes. Compared with three other categories of stimuli (monkey faces, monkey hands, and geometrical shapes), snakes elicited the strongest, fastest responses, and the responses were not reduced by low spatial filtering. These findings integrate neuroscience with evolutionary biology, anthropology, psychology, herpetology, and primatology by identifying a neurobiological basis for primates' heightened visual sensitivity to snakes, and adding a crucial component to the growing evolutionary perspective that snakes have long shaped our primate lineage.
In this paper, we study and discuss existence and continuity of solution to complex Hessian equation
(
χ
+
d
d
c
·
)
k
∧
ω
n
-
k
=
c
f
ω
n
on Hermitian manifold
(
X
,
ω
)
, where
χ
is some smooth ...real
(
1
,
1
)
-
form in
X
and Hermitian form
ω
satisfies that at every given point on
X
, there exist a local chart
Ω
and a smooth real-valued function
G
such that
e
G
ω
is a Kähler form on
Ω
.
The fruits of
are commonly used as foods and cooking ingredients in Vietnam, Laos, and the southeast region of China, whilst the leaves are traditionally used for treating diarrhea and rheumatism. ...This study was conducted to investigate the potential use of this plant bark as antioxidants, and α-amylase and α-glucosidase inhibitors. Five different extracts of
bark (TDB) consisting of the extract (TDBS) and factional extracts hexane (TDBH), ethyl acetate (TDBE), butanol (TDBB), and water (TDBW) were evaluated. The TDBS extract contained the highest amount of total phenolic (112.14 mg gallic acid equivalent per g dry weight), while the TDBB extract had the most effective antioxidant capacity compared to other extracts. Its IC
values were 12.33, 47.87, 33.25, and 103.74 µg/mL in 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2'-azino-bis (ABTS), reducing power (RP), and nitric oxide (NO) assays, respectively. Meanwhile, the lipid peroxidation inhibition of the four above extracts was proximate to that of butylated hydroxytoluene (BHT) as a standard antioxidant. The result of porcine pancreatic α-amylase inhibition showed that TDB extracts have promising effects which are in line with the commercial diabetic inhibitor acarbose. Interestingly, the inhibitory ability on α-glucosidase of all the extracts was higher than that of acarbose. Among the extracts, the TDBB extract expressed the strongest activity on the enzymatic reaction (IC
= 18.93 µg/mL) followed by the TDBW extract (IC
= 25.27 µg/mL), TDBS (IC
= 28.17 µg/mL), and TDBE extract (IC
= 141.37 µg/mL). The phytochemical constituents of the TDB extract were identified by gas chromatography⁻mass spectrometry (GC-MS). The principal constituents included nine phenolics, eight terpenoids, two steroids, and five compounds belonging to other chemical classes, which were the first reported in this plant. Among them, the presence of α- and β-amyrins were identified by GC-MS and appeared as the most dominant constituents in TDB extracts (1.52 mg/g). The results of this study revealed that
bark possessed rich phenolics and terpenoids, which might confer on reducing risks from diabetes. A high quantity of α- and β-amyrins highlighted the potentials of anti-inflammatory, anti-ulcer, anti-hyperlipidemic, anti-tumor, and hepatoprotective properties of
bark.
One of the various sorts of damage to asphalt concrete is cracking. Repeated loads, the deterioration or aging of material combinations, or structural factors can contribute to the development of ...cracks. Asphalt concrete's crack resistance is represented by the CT index. 107 CT Index data samples from the University of Transport Technology's lab are measured. These data samples are used to establish a database from which a Machine Learning (ML) model for predicting the CT Index of asphalt concrete can be built. For creating the highest performing machine learning model, three well-known machine learning methods are introduced: Random Forest (RF), K-Nearest Neighbors (KNN), and Multivariable Adaptive Regression Spines (MARS). Monte Carlo simulation is used to verify the accuracy of the ML model, which includes the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2). The RF model is the most effective ML model, according to analysis and evaluation of performance indicators. By SHAPley Additive exPlanations based on RF model, the input Aggregate content passing 4.75 mm sieve (AP4.75) has a significant effect on the variation of CT Index value. In following, the descending order is Reclaimed Asphalt Pavement content (RAP) > Bitumen content (BC) > Flash point (FP) > Softening point > Rejuvenator content (RC) > Aggregate content passing 0.075mm sieve (AP0.075) > Penetration at 25°C (P). The results study contributes to selecting a suitable AI approach to quickly and accurately determine the CT Index of asphalt concrete mixtures.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The cao vit gibbon (Nomascus nasutus) is one of the rarest primates on Earth and now only survives in a single forest patch of less than 5000 ha on the Vietnam-China border. Accurate monitoring of ...the last remaining population is critical to inform ongoing conservation interventions and track conservation success over time. However, traditional methods for monitoring gibbons, involving triangulation of groups from their songs, are inherently subjective and likely subject to considerable measurement errors. To overcome this, we aimed to use 'vocal fingerprinting' to distinguish the different singing males in the population. During the 2021 population survey, we complemented the traditional observations made by survey teams with a concurrent passive acoustic monitoring array. Counts of gibbon group sizes were also assisted with a UAV-mounted thermal camera. After identifying eight family groups in the acoustic data and incorporating long-term data, we estimate that the population was comprised of 74 individuals in 11 family groups, which is 38% smaller than previously thought. We have no evidence that the population has declined-indeed it appears to be growing, with new groups having formed in recent years-and the difference is instead due to double-counting of groups in previous surveys employing the triangulation method. Indeed, using spatially explicit capture-recapture modelling, we uncovered substantial measurement error in the bearings and distances from field teams. We also applied semi- and fully-automatic approaches to clustering the male calls into groups, finding no evidence that we had missed any males with the manual approach. Given the very small size of the population, conservation actions are now even more urgent, in particular habitat restoration to allow the population to expand. Our new population estimate now serves as a more robust basis for informing management actions and tracking conservation success over time.
Although "social isolation" protects the life and health of Vietnamese citizens from the adverse effects of the COVID-19 pandemic, it also triggers massive reductions in the economic activities of ...the country.
our study aimed to identify negative impacts of COVID-19 on occupations of Vietnamese people during the first national lockdown, including the quality and quantity of jobs as well as adverse problems at work due to COVID-19.
A cross-sectional study using web-based platforms was conducted during the first time of social isolation in Vietnam at the beginning of April 2020. We utilized a respondent-driven sampling technique to select 1423 respondents from 63 cities and provinces over Vietnam. Exploratory factor analysis (EFA) was used to define sub-domains of perceived impacts of COVID-19 on occupations.
Approximately two-thirds of respondents reported decreases in their income (61.6%), and 28.2% reported that their income deficit was 40% and above. The percentage of female individuals having decreased revenue due to COVID-19 was higher than that of male respondents (65.2% and 54.7%, respectively). "Worry that colleagues exposed to COVID-19 patients" and "Being alienated because employment-related to COVID-19" accounted for the highest score in each factor. Compared to healthcare workers, being self-employed/unemployed/retired were less likely to suffer from "Increased workload and conflicts due to COVID-19" and "Disclosure and discrimination related to COVID-19 work exposure."
Our study revealed a drastic reduction in both the quality and quantity of working, as well as the increased fear and stigmatization of exposure to COVID-19 at workplaces. Health protection and economic support are immediate targets that should be focused on when implementing policies and regulations.
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