In this research, adult lacewings were reared in the greenhouse on an artificial diet. The larvae were fed by the eggs of Mediterranean flour moth. Sub lethal effects of endosulfan, imidacloprid and indoxacarb were assessed using Demographic Toxicology method on adult lacewings at concentrations of 317, 46 and 9 mg AI/lit (as LC25), respectively. Most of the biological parameters of the lacewing, except mean age of fecundity and gross hatch rate, were significantly affected by the insecticides. The gross fecundity rate in control, imidacloprid, endosulfan and indoxacarb treated insects were 618, 492, 313 and 279 eggs per female; indicated that the parameter was reduced significantly by imidacloprid and endosulfan treatments compared to the control. The stable population parameters were affected by the treatments. The intrinsic rates of increase (rm) values were 0.179, 0.161, 0.157 and 0.136 female offspring per female per day in control, imidacloprid, endosulfan and indoxacarb treated cohorts, respectively. In general, the sublethal toxicity ranking of the treatments was as follows: imidacloprid<endosulfan<indoxacarb. We concluded that the adult lacewing was sensitive to endosulfan and indoxacarb.

Prior reports have demonstrated that there is a rising trend in the number of fire death over the recent decades. This is mainly due to the quality and quantity of the fire gases that is associated with thematerials used for furnishing, decoration and construction. Reports have shown that combustion of different materials, especially the modernsynthetic materials, has released fire gases such as carbon dioxide, carbon monoxide, hydrogen cyanide, organic nitrites, carbonyls, alcohols, and other organic elements. The study of fire gases in fatal casualties and toxicological investigations of acute and chronic poisoning implies a branch of "Toxicology" which is called "Fire Toxicology".

Citrus red mite, Panonychus citri is an important plant pest species in the citrus growing regions of the world. Due to the use of chemical pesticides with a wide range of effects against this pest and other pests in citrus orchards, P. citri has become a serious pest in citrus orchards, including citrus orchards in the north of the country. In this study, the acaricidal and sublethal effects of diflovidazin on different stages of citrus red mite were investigated. The results showed that diflovidazin was highly toxic to the eggs, larvae and deutonymph stages of citrus red mite. The sublethal effects of diflovidazin on the parameters of citrus red mite life table were investigated and the results showed a significant decrease in generation length average, adult longevity, fecundity and fertility period. The net reproduction rates (R0) were 11. 2 and 1. 62 offspring per female mite for control and treatment of diflovidazin, respectively. The intrinsic (r) and finite (λ, ) rates of increase for the control were 0. 133 and 1. 142 per day, respectively, and for the LC 30 treatment was 0. 033 and 1. 034 per day, respectively. In general, the sub-lethal concentration of difluvidazine showed a diminishing effect on all population growth parameters in treated deutonymphs. Overall, the results indicate the lethal and sub-lethal effects of diflovidazin on citrus red mites, and this compound can be recommended as part of a resistance management program to control this mite.

Computational Toxicology is a rapidly growing field that utilizes artificial intelligence (AI) and machine learning (ML) to predict the toxicity of chemical compounds. Computational Toxicology is an important tool for assessing the risks associated with the exposure of finfish and shellfish to environmental contaminants. By providing insights into the behavior and effects of these compounds, computational models can help to inform management decisions and protect the health of aquatic ecosystems and the humans who depend on them for food and recreation. In aqua-Toxicology research, Quantitative Structure-Activity Relationship (QSAR) models are commonly used to establish the relationship between chemical structures and their aquatic toxicity. Various ML algorithms have been developed to construct QSAR models, including Random Forest (RF), Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Bayesian networks (BNs), k-Nearest Neighbor (kNN), Probabilistic Neural Networks (PNNs), Naïve Bayes, and Decision Trees. Deep learning techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), have also been applied in computational Toxicology to improve the accuracy of QSAR predictions. Moreover, data mining graphs, networks and graph kernels have been utilized to extract relevant features from chemical structures and improve predictive capabilities. In conclusion, the application of artificial intelligence and machine learning in the field of computational Toxicology has immense potential to revolutionize aquatic Toxicology research. Through the utilization of advanced algorithms and data analysis techniques, scientists can now better understand and predict the effects of various toxicants on aquatic organisms.