Modeling the severity of accidents based on the most effective variables accounts for developing a high-precision model presenting the possibility of occurrence of each category of future accidents, and it could be utilized to prioritize the corrective measures for authorities. The purpose of this study is to identify the variables affecting the severity of the injury, fatal, and property damage only (PDO) accidents in Rasht city by collecting information on urban accidents from March 2019 to March 2020. In this regard, the multiple logistic regression and the pattern recognition type of artificial neural network (ANN) as a machine learning solution are used to recognize the most influential variables on the severity of accidents and the superior approach for accident prediction. Results show that the multiple logistic regression in the forward stepwise method has R² of 0.854 and an accuracy prediction power of 89.17. It turns out that the accidents occurred between 18 and 24 and KIA Pride vehicle has the highest effect on increasing the severity of accidents, respectively. The most important result of the logit model accentuates the role of environmental variables, including poor lighting conditions alongside unfavorable weather and the dominant role of unsafe and poor quality of vehicles on increasing the severity of accidents. In addition, the machine learning model performs significantly better and has higher prediction accuracy (98.9) than the logit model. In addition, the ANN models greater power to predict and estimate future accidents is confirmed through performance and sensitivity analysis.

With growing urbanization and ongoing development activities, the consumption of heavy metals has been increasing globally. Although heavy metals are vital for the survival of living beings, they can become hazardous when they surpass the permissible limit. The effect of heavy metals varies from normal to acute depending on the individual, so it is necessary to treat the heavy metals before releasing them into the environment. Various conventional treatment technologies have been used based on physical, chemical, and biological methods. However, due to technical and economic constraints and poor sustainability towards the environment, the use of these technologies has been limited. Microalgal-based heavy metal removal has been explored for the past few decades and has been seen as an effective, environment-friendly, and inexpensive method compared to conventional treatment technology. Cyanidiales that belong to red algae have the potential for remediation of heavy metals as they can withstand and tolerate extreme stresses of heat, acid salts, and heavy metals. Cyanidiales are the only photosynthetic organisms that can survive and thrive in acidic mine drainage, where heavy metal contamination is often prevalent. This review focuses on the algal species belonging to three genera of Cyanidiales: Cyanidioschyzon, Cyanidium, and Galdieria. Papers published after 2015 were considered in order to examine these species’ efficiency in heavy metal removal. The result is summarized as maximum removal efficiency at the optimum experimental conditions and based on the parameters affecting the metal ion removal efficiency. This study finds that pH, initial metal concentration, initial algal biomass concentration, algal strains, and growth temperature are the major parameters that affect the heavy metal removal efficiency of Cyanidiales.

This study compares the effects of different hydrated lime contents and curing periods in peat stabilization using unconfined compressive strength (UCS) and consolidated-undrained triaxial (CU) tests which constitutes the first comprehensive experimental study on peat in Iran. Since it includes a novelty comparison between these tests along with providing experimental data for both test types. For this purpose, lime contents of 3, 6, 9, 12 and 15% were used with different curing periods of 7, 14, 28 and 90 days. The results obtained in these tests were then compared. In order to compare, for triaxial tests, a novelty value of equivalent unconfined strength, which is the strength in case of hypothetical zero confining pressure in a triaxial test, is introduced and calculated and then compared with UCS values. Results indicate that the equivalent unconfined strengths of CU tests are always lower than those of the UCS test which can be attributed to pore water pressure generation in CU tests which can decrease the equivalent unconfined strength of soil. Moreover, while the undrained cohesive strength is half of the UCS value, the undrained cohesive strength is 0.35 times the equivalent unconfined strength for peat. Such comparison between UCS test, which is quite common and easily conductible, and triaxial test, which provides the most comprehensive data in soil mechanics, could lead to credible results which can be widely applicable in forest areas and regions with much vegetation which is the case in northern parts of Iran. Moreover, based on the comparison, the optimum lime content for peat stabilization in this study was obtained between nine and twelve percent.