Seed germination prediction of Salvia limbata under ecological stresses in protected areas: an artificial intelligence modeling approach

Indoor sulfur dioxide prediction through air quality modeling and assessment of sulfur dioxide and nitrogen dioxide levels in industrial and non-industrial areas

In this study, the levels of sulfur dioxide (SO2) and nitrogen dioxide (NO2) were measured indoors and outdoors using passive samplers in Tymar village (20 homes), an industrial area, and Haji Wsu (15 homes), a non-industrial region, in the summer and the winter seasons. In comparison to Haji Wsu village, the results showed that Tymar village had higher and more significant mean SO2 and NO2 concentrations indoors and outdoors throughout both the summer and winter seasons. The mean outdoor concentration of SO2 was the highest in summer, while the mean indoor NO2 concentration was the highest in winter in both areas. The ratio of NO2 indoors to outdoors was larger than one throughout the winter at both sites. Additionally, the performance of machine learning (ML) approaches: multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) were compared in predicting indoor SO2 concentrations in both the industrial and non-industrial areas. Factor analysis (FA) was conducted on different indoor and outdoor meteorological and air quality parameters, and the resulting factors were employed as inputs to train the models. Cross-validation was applied to ensure reliable and robust model evaluation. RF showed the best predictive ability in the prediction of indoor SO2 for the training set (RMSE = 2.108, MAE = 1.780, and R2 = 0.956) and for the unseen test set (RMSE = 4.469, MAE = 3.728, and R2 = 0.779) values compared to other studied models. As a result, it was observed that the RF model could successfully approach the nonlinear relationship between indoor SO2 and input parameters and provide valuable insights to reduce exposure to this harmful pollutant.

Bayesian modeling for pro-environmental behavior data: sorting and selecting relevant variables

Pro-environmental behaviors towards climate change can be measured and evaluated in different fields. Typically, surveys are the standard tool for extracting personal information regarding this phenomenon. However, statistical modeling for these surveys is not straightforward, as the response variable is often not explicit. Hence, we propose a set of methodological procedures to deal with pro-environmental behavior data. First, validity evidence through a factorial analysis. Second, indexes are created from factor scores, where one of the latent factors summarizes a target variable. Third, a Beta regression is used to model the index of interest. Fourth, the inferential process is performed from a Bayesian perspective, in which posterior probabilities are used to sort and select the relevant variables. Finally, suitable models are obtained, and conclusions can be drawn from them. As a motivation, we used data from two Chilean surveys to illustrate our methodology as well as interpret and discuss the results.

Optimal Query Expansion Based on Hybrid Group Mean Enhanced Chimp Optimization Using Iterative Deep Learning

The internet is surrounded by uncertain information which necessitates the usage of natural language processing and soft computing techniques to extract the relevant documents. The relevant results are retrieved using the query expansion technique which is mainly formulated using the machine learning or deep learning concepts in the existing literature. This paper presents a hybrid group mean-based optimizer-enhanced chimp optimization (GMBO-ECO) algorithm for pseudo-relevance-based query expansion, whereby the actual queries are expanded with their related keywords. The hybrid GMBO-ECO algorithm mainly expands the query based on the terms that have a strong interrelationship with the actual query. To generate the word embeddings, a Word2Vec paradigm is used which learns the word association from large text corpora. The useful context in the text is identified using the improved iterative deep learning framework which determines the user’s intent for the current web search. This step reduces the mismatch of the words and improves the performance of query retrieval. The weak terms are eliminated and the candidate query terms for optimal query expansion are improved via an Okapi measure and cosine similarity techniques. The proposed methodology has been compared to the state-of-the-art methods with and without a query expansion approach. Moreover, the proposed optimal query expansion technique has shown a substantial improvement in terms of a normalized discounted cumulative gain of 0.87, a mean average precision of 0.35, and a mean reciprocal rank of 0.95. The experimental results show the efficiency of the proposed methodology in retrieving the appropriate response for information retrieval. The most common applications for the proposed method are search engines.

Sedative-hypnotic, anxiolytic and possible side effects of Salvia limbata C. A. Mey. Extracts and the effects of phenological stage and altitude on the rosmarinic acid content

ETHNOPHARMACOLOGICAL RELEVANCE

Salvia limbata C. A. Mey. (Persian name: Maryam Goli-e-labeh dar) has been used for treating central nervous disorders such as insomnia, anxiety and depression in Persian traditional medicine. S. limbata is known for its pharmacological activities which could be at least in a part, upon the presence of rosmarinic acid (RA). However, the sedative-hypnotic effect, anxiolytic activity, possible side effects, and the mechanism of action of S. limbata extract has not yet been examined.

AIM OF THE STUDY

In the current study the sedative-hypnotic effect, anxiolytic activity, possible side effects, and the mechanism of action of S. limbata extracts were evaluated. Besides, the effects of altitude and phenological stage on the RA content of S. limbata were investigated.

MATERIALS AND METHODS

Sedative-hypnotic and anxiolytic effects were evaluated through the pentobarbital induced loss of righting reflex test and open field test, respectively. Flumazenil was used to reveal the mechanism of action. Possible side effects were investigated in the passive avoidance and grip strength tests. Besides, the effects of altitude and phenological stage (vegetative, flowering, and seed setting) on the RA content of S. limbata were evaluated using reversed-phase high-performance liquid chromatography (RP-HPLC).

RESULTS

Following behavioral tests, sedative-hypnotic and anxiolytic effects were observed. Since the observed effects were reversed by flumazenil and no side effect on the memory and muscle strength was reported, modulation of the α1-containing GABA-A receptors could be proposed as one of the involved mechanisms. According to the RP-HPLC analysis, harvesting S. limbata in the vegetative stage at the altitude of 2500 m led to the highest content of RA (8.67 ± 0.13 mg/g dry matter). Among different extract of the plant samples collected in the vegetative stage at the altitude of 2500 m, the hydroalcoholic extract showed the highest rosmarinic acid content.

CONCLUSION

The obtained results help to find the optimum situation to gain the highest content of RA as well as the pharmacological activity that could be economically important for the pharmaceutical industries.

A comparison of artificial intelligence techniques for predicting hyperforin content in Hypericum perforatum L. in different ecological habitats

Hyperforin, a major bioactive constituent of Hypericum concentration, is impacted by various phenological phases and soil characteristics. We aimed to design a model predicting hyperforin content in Hypericum perforatum based on different ecological and phenological conditions. We employed artificial intelligence modeling techniques including multilayer perceptron (MLP), radial basis function (RBF), and support vector machine (SVM) to examine the factors critical in predicting hyperforin content. We found that the MLP model (R 2 = .9) is the most suitable and precise model compared with RBF (R 2 = .81) and SVM (R 2 = .74) in predicting hyperforin in H. perforatum based on ecological conditions, plant growth, and soil features. Moreover, phenological stages, organic carbon, altitude, and total N are detected in sensitivity analysis as the main factors that have a considerable impact on hyperforin content. We also report that the developed graphical user interface would be adaptable for key stakeholders including producers, manufacturers, analytical laboratory managers, and pharmacognosists.

A method for aesthetic quality modelling of the form of plants and water in the urban parks landscapes: An artificial neural network approach

This work presents a simplified method for the application of the Multi-Layer Perceptron (MLP) model that aims to predict the aesthetic quality of the landscape designed by water and plants in different forms and volume. The MLP was prepared by (Rosenblat) in the field of computer science, followed by the application of a MLP in landscape aesthetic quality prediction proposed by (Jahani). In the method of this research, the structure of MLP was structured for aesthetic quality prediction of plants and water in urban park landscapes. The accuracies of designed MLP structures were tested to achieve the most accurate one in aesthetic quality prediction. This method creates an environmental decision support system tool for landscape designers, and it is a platform to predict the quality of environment. In practice, the designed environmental decision support system tool is applied by landscape managers to predict the aesthetic quality of landscape in designing new urban parks.•Applies Multi-Layer Perceptron method in landscape assessment.•Accurate MATLAB extension for landscape aesthetic evaluation.•Defined criteria for aesthetic value of landscape.

Changes in the essential oil content and composition of Salvia limbata C.A. Mey at different growth stages and altitudes

Salvia limbata is of great importance to the pharmaceutical industry owing to its various biological effects. Therefore, it is important to investigate the main factors that affect its essential oil composition. Although some investigations have been performed with regard to the phytochemistry of S. limbata, this study investigates, for the first time, the effect of growth stage and altitude on the content and chemical composition of essential oil extracted from S. limbata. For this purpose, the essential oil was extracted from 45 air-dried samples by hydrodistillation and analyzed by GC-MS and GC-flame methods. The highest content of essential oil was obtained from aerial parts in the vegetative stage at an altitude of 1500 m (0.86% v/w). Our findings show that the vegetative stage at 1500 m is the optimal harvest time to extract the highest content of oil while the highest content of monoterpenes (including α-pinene and β-pinene) could be obtained in the same phenological stage at 2000 m. By contrast, the content of sesquiterpenes increased to the highest values in the ripening stage at 1500 and 2500 m. The results of this study help to find the optimal conditions to obtain the highest content of S. limbata essential oil, but additional studies are warranted.

Prediction of hypericin content in Hypericum perforatum L. in different ecological habitat using artificial neural networks

BACKGROUND

Hypericum is an important genus in the family Hypericaceae, which includes 484 species. This genus has been grown in temperate regions and used for treating wounds, eczema and burns. The aim of this study was to predict the content of hypericin in Hypericum perforatum in varied ecological and phenological conditions of habitat using artificial neural network techniques [MLP (Multi-Layer Perceptron), RBF (Radial Basis Function) and SVM (Support Vector Machine)].

RESULTS

According to the results, the MLP model (R² = 0.87) had an advantage over RBF (R² = 0.8) and SVM (R² = 0.54) models and it was relatively accurate in predicting hypericin content in H. perforatum based on the ecological conditions of site including soil types, its characteristics and plant phenological stages of habitat. The results of sensitivity analysis revealed that phenological stages, hill aspects, total nitrogen, altitude and organic carbon are the most influential factors that have an integral effect on the content of hypericin.

CONCLUSIONS

The designed graphical user interface will help pharmacognosist, manufacturers and producers of medicinal plants and so on to run the MLP model on new data to easily discover the content of hypericin in H. perforatum by entering ecological conditions of site, soil characteristics and plant phenological stages.

Artificial intelligence accuracy assessment in NO2 concentration forecasting of metropolises air

Air quality has been the main concern worldwide and Nitrous oxide (NO2) is one of the pollutants that have a significant effect on human health and environment. This study was conducted to compare the regression analysis and neural network model for predicting NO2 pollutants in the air of Tehran metropolis. Data has been collected during a year in the urban area of Tehran and was analyzed using multi-linear regression (MLR) and multilayer perceptron (MLP) neural networks. Meteorological parameters, urban traffic data, urban green space information, and time parameters are applied as input to forecast the daily concentration of NO2 in the air. The results demonstrate that artificial neural network modeling (R2 = 0.89, RMSE = 0.32) results in more accurate predictions than MLR analysis (R2 = 0.81, RMSE = 13.151). According to the result of sensitivity analysis of the model, the value of park area, the average of green space area and one-day time delay are the crucial parameters influencing NO2 concentration of air. Artificial neural network models could be a powerful, effective and suitable tool for analysis and modeling complex and non-linear relation of environmental variables such as ability in forecasting air pollution. Green spaces establishment has a significant role in NO2 reduction even more than traffic volume.

Modeling of trees failure under windstorm in harvested Hyrcanian forests using machine learning techniques

In managed forests, windstorm disturbances reduce the yield of timber by imposing the costs of unscheduled clear-cutting or thinning operations. Hyrcanian forests are affected by permanent winds, with more than 100 km/h which cause damage forest trees and in result of the tree harvesting and gap creation in forest stands, many trees failure accidents happen annually. Using machine learning approaches, we aimed to compare the multi-layer perceptron (MLP) neural network, radial basis function neural network (RBFNN) and support vector machine (SVM) models for identifying susceptible trees in windstorm disturbances. Therefore, we recorded 15 variables in 600 sample plots which are divided into two categories: 1. Stand variables and 2.Tree variables. We developed the tree failure model (TFM) by artificial intelligence techniques such as MLP, RBFNN, and SVM. The MLP model represents the highest accuracy of target trees classification in training (100%), test (93.3%) and all data sets (97.7%). The values of the mean of trees height, tree crown diameter, target tree height are prioritized respectively as the most significant inputs which influence tree susceptibility in windstorm disturbances. The results of MLP modeling defined TFMmlp as a comparative impact assessment model in susceptible tree identification in Hyrcanian forests where the tree failure is in result of the susceptibility of remained trees after wood harvesting. The TFMmlp is applicable in Hyrcanian forest management planning for wood harvesting to decrease the rate of tree failure after wood harvesting and a tree cutting plan could be modified based on designed environmental decision support system tool to reduce the risk of trees failure in wind circulations.