“…This study stands as the first to predict genotoxicity using neural networks, incorporating lipid peroxidation and oxidative stress parameters based on molibden application. Likewise, existing literature indicates that artificial and deep neural networks have demonstrated suitability as models for handling oxidative stress, lipid peroxidation, and antioxidant enzymes 70 , as well as environmental pollution data 71 , 72 . Figure 9 Comparison of means of actual and predicted data of dose-related molibden genotoxicity.…”
In this study, the multifaceted toxicity induced by high doses of the essential trace element molybdenum in Allium cepa L. was investigated. Germination, root elongation, weight gain, mitotic index (MI), micronucleus (MN), chromosomal abnormalities (CAs), Comet assay, malondialdehyde (MDA), proline, superoxide dismutase (SOD), catalase (CAT) and anatomical parameters were used as biomarkers of toxicity. In addition, detailed correlation and PCA analyzes were performed for all parameters discussed. On the other hand, this study focused on the development of a two hidden layer deep neural network (DNN) using Matlab. Four experimental groups were designed: control group bulbs were germinated in tap water and application group bulbs were germinated with 1000, 2000 and 4000 mg/L doses of molybdenum for 72 h. After germination, root tips were collected and prepared for analysis. As a result, molybdenum exposure caused a dose-dependent decrease (p < 0.05) in the investigated physiological parameter values, and an increase (p < 0.05) in the cytogenetic (except MI) and biochemical parameter values. Molybdenum exposure induced different types of CAs and various anatomical damages in root meristem cells. Comet assay results showed that the severity of DNA damage increased depending on the increasing molybdenum dose. Detailed correlation and PCA analysis results determined significant positive and negative interactions between the investigated parameters and confirmed the relationships of these parameters with molybdenum doses. It has been found that the DNN model is in close agreement with the actual data showing the accuracy of the predictions. MAE, MAPE, RMSE and R2 were used to evaluate the effectiveness of the DNN model. Collective analysis of these metrics showed that the DNN model performed well. As a result, it has been determined once again that high doses of molybdenum cause multiple toxicity in A. cepa and the Allium test is a reliable universal test for determining this toxicity. Therefore, periodic measurement of molybdenum levels in agricultural soils should be the first priority in preventing molybdenum toxicity.
“…This study stands as the first to predict genotoxicity using neural networks, incorporating lipid peroxidation and oxidative stress parameters based on molibden application. Likewise, existing literature indicates that artificial and deep neural networks have demonstrated suitability as models for handling oxidative stress, lipid peroxidation, and antioxidant enzymes 70 , as well as environmental pollution data 71 , 72 . Figure 9 Comparison of means of actual and predicted data of dose-related molibden genotoxicity.…”
In this study, the multifaceted toxicity induced by high doses of the essential trace element molybdenum in Allium cepa L. was investigated. Germination, root elongation, weight gain, mitotic index (MI), micronucleus (MN), chromosomal abnormalities (CAs), Comet assay, malondialdehyde (MDA), proline, superoxide dismutase (SOD), catalase (CAT) and anatomical parameters were used as biomarkers of toxicity. In addition, detailed correlation and PCA analyzes were performed for all parameters discussed. On the other hand, this study focused on the development of a two hidden layer deep neural network (DNN) using Matlab. Four experimental groups were designed: control group bulbs were germinated in tap water and application group bulbs were germinated with 1000, 2000 and 4000 mg/L doses of molybdenum for 72 h. After germination, root tips were collected and prepared for analysis. As a result, molybdenum exposure caused a dose-dependent decrease (p < 0.05) in the investigated physiological parameter values, and an increase (p < 0.05) in the cytogenetic (except MI) and biochemical parameter values. Molybdenum exposure induced different types of CAs and various anatomical damages in root meristem cells. Comet assay results showed that the severity of DNA damage increased depending on the increasing molybdenum dose. Detailed correlation and PCA analysis results determined significant positive and negative interactions between the investigated parameters and confirmed the relationships of these parameters with molybdenum doses. It has been found that the DNN model is in close agreement with the actual data showing the accuracy of the predictions. MAE, MAPE, RMSE and R2 were used to evaluate the effectiveness of the DNN model. Collective analysis of these metrics showed that the DNN model performed well. As a result, it has been determined once again that high doses of molybdenum cause multiple toxicity in A. cepa and the Allium test is a reliable universal test for determining this toxicity. Therefore, periodic measurement of molybdenum levels in agricultural soils should be the first priority in preventing molybdenum toxicity.
“…However, it did not attempt to improve the implementation of MCDA in supporting soil remediation or suggest the optimal criteria. Finally, relevant papers (e.g., [93,94]) published after the completion of the database coding were not included.…”
For the successful remediation of polluted sites, priority setting among the possible remediation technologies is of major importance. The related decisions are typically conditioned by a limited set of alternative remediation techniques and multiple, often contradicting criteria. These characteristics make the decision problem suitable for applying a formalised discrete multi-criteria decision analysis (MCDA). This paper reports on the outcome of a systematic review of articles published between 1995 and 2020 encompassing 43 MCDA applications to support the selection of the remediation technique for polluted soils. It focuses on the comparison between implementations of the MCDA methodology. The review identifies four gaps where progress can be made to mobilise the full strength of the MCDA methodology to support the remediation of polluted soils: (i) early stakeholder engagement, (ii) inclusion of social criteria, (iii) an informed choice of the weighting and aggregation method, (iv) and sensitivity analysis.
“…The fourth group of articles highlights the importance of digital tools used for fertilizer application during the period from 2018 to 2023, listed in Table 4, with their main findings described below: Mobile applications and artificial intelligence (AI) technologies are revolutionizing fertilizer dosing in agriculture by providing intuitive and customizable interfaces that adapt to various agroecosystems [48,49]. This knowledge can be used to develop a recommendation system based on machine learning approaches [50]. These tools not only facilitate data management and acquisition but also improve resource use efficiency and reduce environmental pollution [51].…”
Information and communication technology (ICT) in developing countries is a key element for growth and economic development. This work conducted an evaluation regarding the use of ICT to reduce the socioeconomic gaps of rural populations and promote its inclusion in development plans, considering its use to guarantee a sustainable development model. For this, a systematic review of 280 articles was carried out using the Scopus, Latindex, Scielo, Dialnet, Redalyc, and Google Scholar databases during the period from 2018 to 2023, of which 40 articles were selected that address the use of ICTs and the agricultural digitalization for the management of soil, water, and the application of fertilizers and agrochemicals, which guarantee sustainable agricultural development. The results show that there are numerous digital tools available based on artificial intelligence (AI), machine learning (ML), drones, apps, and the Internet of Things, which aid in soil and water management and make use of agrochemicals and water, thus improving efficiency and reducing pollution problems. However, there is a large gap at the international level in acquiring state-of-the-art technological equipment that takes advantage of the potential that exists in terms of new technologies and their efficient use. Much of the research on the use of ICTs in the agricultural field comes from countries with medium or high levels of technological development, especially from Asia, Europe, or North America. As a result, Latin America lags behind in this regard.
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