Abstract. The dust phenomenon is one of the main environmental problems that it reversely affects human health and economical and social activities. In the present research, a …

researches suggest the developmen t and evaluation of a machine vision system to estimate the d ust concentration. Due to the importance of the determination …

Oxygen concentrators have integrated flow and pressure sensors. These are essential for detecting patient inhalations and maintaining optimal pressures for oxygen generation. …

Purpose: Online dose verification based on proton-induced positron emitters requires high accuracy in the assignment of elemental composition (e.g., C and O). We developed a machine learning framework for deriving oxygen and carbon concentration based on dual-energy CT (DECT). Methods: Digital phantoms at the head site were constructed based …

Due to the importance of the determination of dust concentration and the development of simple, fast, and cheap method in this regard, the purpose of the present research was …

In the present study, three types of artificial intelligence techniques, least square support vector machine (LSSVM), multivariate adaptive regression splines (MARS) and M5 model tree (M5T) are applied for modeling daily dissolved oxygen (DO) concentration using several water quality variables as inputs. The DO concentration and water quality …

MAC-awake is defined as the anesthetic concentration needed to suppress a voluntary response to verbal command (i.e., eye-opening) in 50% of patients. [5] It is generally one-third of MAC for the …

Cet article présente l'étude de deux machines synchrones à aimants permanents (MSAPs), à concentration de flux et à encochage fractionnaire pour une application de véhicule hybride.

The dust phenomenon is one of the main environmental problems that it reversely affects human health and economical and social activities. In the present research, a novel …

2.4 Machine Learning In Machine learning, concentration inequalities are profoundly used in analyzing different aspects of learning algorithms. For example, • Multi-Armed bandits problem: We use concentration inequalities to analyze algorithms such as UCB algorithms, Thompson Sampling for their regret, a measure on performance of a MAB ...

Prepare to harness technology in a world where intelligent machines are expanding what's possible. Don't just keep up with technological trends—stay ahead of them. MBA x Artificial Intelligence …

localized effect of each stress concentration separately. 2. Compute the actual stress in the shoulder by taking into account the stress concentration caused by a fillet radius in a rectangular bar in tension. 3. Compute the actual stress in the region immediately adjacent to the hole by applying the stress-concentration factor associated for a ...

The concentration of the dyestuff was calculated from the absorbance values measured at 540 and 592 nm for the red and blue, respectively. The statistical analysis of the data suggested that ...

Semantic Scholar extracted view of "Modelling daily dissolved oxygen concentration using least square support vector machine, multivariate adaptive regression splines and M5 model tree" by S. Heddam et al. ... 2012; AbstractThe aim of this study is to examine the accuracy of two different artificial neural network ...

In this modelling study, we implemented deep ensemble machine learning (DEML) to estimate global daily ambient PM 2·5 concentrations at 0·1° × 0·1° spatial resolution between Jan 1, 2000, and Dec 31, 2019. In the DEML framework, ground-based PM 2·5 measurements from 5446 monitoring stations in 65 countries worldwide were combined …

Thus, the objective of this study was to use machine learning-based models to predict the surface sediment concentration (SSC) in the Doce river basin. The cross-sectional averages of measurements ...

The concentration of chlorophyll-a (Chl-a) is an integrative bio-indicator of aquatic ecosystems and a direct indicator that evaluates the ecological status of water bodies. In this study, we focused on predicting the Chl-a concentration in seawater using machine learning (after replacing missing values). To replace the missing values among …

Modeling DO with machine learning is advantageous to scientists, researchers, and decision-makers because the models can require low data inputs, such as a few samples and fewer predictor ...

In the present research, a novel algorithm has been developed based on image processing to estimate dust concentration. An experimental setup was implemented to create airborne dust with different concentration values from 0 to 2750 µg.m-3. The images of the different dust concentration values were acquired and analyzed by …

The study area includes the Mazandaran plain with an area of ten thousand km 2 in the north of Iran. This plain includes the southern coasts of the Caspian Sea located between 50°34′ to 54°10′ eastern longitude and 35°47′ to 37° northern latitude (Fig. 1).The mean annual precipitation of the study area varies from 1300 mm in the west of the plain …

With the rapid development of the economy, surface water pollution is still serious. Nitrite nitrogen can reflect the degree of surface water pollution. Ultraviolet–visible spectroscopy has the advantages of environmental friendliness, easy operation and real-time online in-situ detection, and has a good application prospect in the detection of …

2− and S contents, and Machine Learning (ML) algorithms such as the Random Forest (RF), Support Vector Machine (SVM), and Articial Neural Network (ANN) models were used to predict the soil HM quantities transferred from Sidi-Driss mine drainage to surrounding soils. The results showed that the HM concentrations

However, DOC concentration cannot be directly measured in situ, and discrete sample collection and analysis becomes expensive as temporal resolution increases. To surmount this problem, an option is to predict site-specific DOC concentration with linear modeling and optical data predictors collected from high-cost, …

Concentration inequalities deal with deviations of functions of independent random variables from their expectation. In the last decade new tools have been introduced …

Overall, the results suggest that the ELM is more effective than the MLPNN and MLR for modelling DO concentration in river ecosystems. In this paper, several extreme learning machine (ELM) models, including standard extreme learning machine with sigmoid activation function (S-ELM), extreme learning machine with radial basis …

4.3. À l'aide des plaques quart d'onde, visualiser les isochro-matiques. Étalonner les couleurs à l'aide de la contrainte, sup-posée uniaxiale, dans la zone « loin du trou ». On ...

People typically spend most of their time indoors. It is of importance to establish prediction models to estimate PM 2.5 concentration in indoor environments (e.g., residential s) to allow accurate assessments of exposure in epidemiological studies. This study aimed to develop models to predict PM 2.5 concentration in …