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Groundwater resources are crucial sources of water supply, and preserving the quality of these resources is an undeniable necessity. On the other hand, the lack of complete time series data in the observation wells is one of the major limitations in the studies of water resources. In the present study, 17 observation wells were selected at the Asadabad plain, and sampled Electrical Conductivity (EC) statistics and information were prepared. Multivariate regression models, ARIMAX and SVM were considered to simulate and fill the missing EC data of Dehnoush and Biaj wells. Six input structures for the models were determined based on the highest correlation coefficient value, which was defined using the multiple time series method at different delays between the EC of Dehnoush and Biaj stations and that of other stations. The results indicated that increasing the number of inputs to the model reduced the error rate and increased the correlation rate in the simulation. These coefficients reached their minimum value in Structure 6 for the RMSE and NRMSE criteria and their maximum value for the Pearson coefficient. Therefore, to fill in the missing data, other stations' data with the highest correlation at different delays were used instead of using the information of adjacent stations without delay. The results revealed that while the SVM and regression models had relatively similar simulation accuracy, the SVM model exhibited higher accuracy compared to the regression and ARIMAX models.

In the field of operations research, vehicle routing problems (VRP) have a great deal of importance because of its applications in transportation of goods and services. Until now many studies have been done about VRP, but there are few studies which have considered environmental perspective towards the problem. Besides, most of the bygone studies usually have considered only one objective which is minimisation of total travelling cost. In this paper, we try to tackle some flaws of the previous works in the proposed models for green routing problems by considering multiple depots, constraints of hard and soft time windows, heterogeneous vehicles, multiple periods and products. Since the aforementioned problem is considered to be NP-hard, metaheuristic algorithms are needed for solving it. Therefore, to tackle NP-hardness of the proposed model a combined algorithm of genetics algorithms (GA) and simulated annealing (SA) algorithms is proposed. Finally, for demonstrating the efficiency of the proposed algorithm, the solutions provided by the algorithm is compared to the solutions obtained from exact solving method in Gams software. The results demonstrate the efficiency of the proposed method.

•The emergence of CAR-T cell therapy with its exciting results attained in patients with relapsed and refractory hematological malignancies is considered as the biggest advance in cellular cancer immunotherapy. However, severe side effects and toxicity stir concerns regarding the safety of CAR-T cell treatments.Most of CAR-T cell therapies are currently autologous small-scale treatments for patients suffering from B cell malignancies due to the safety concerns about the potential development of a GVHD in allogeneic therapies. So, allogeneic therapies have been less effective than autologous ones. High cost and highly variable manufacturing processes are other limitations on the way of CAR-T cell therapy. In contrast to the unprecedented responses achieved through using CD19-CAR-T cells in the treatment of ALL, this type of treatment has not shown the same results in the battle against solid tumors that is partly related to different characteristics and microenvironment of solid tumors that limit the success of CAR-T cell therapies in patients with solid tumors. Immune checkpoint therapies with previously reported reproducible beneficial effects in 20–30% of patients with different incurable cancers have serious side effects and considerable cost of repeated administration. Also, recently most patients have not responded effectively to these therapies. In contrast, CAR-T cell therapy has two characteristics that may compensate for the limitations of immune checkpoint therapies. Firstly, only one administration of the engineered T cells is needed for long lasting effectiveness of the therapy. Secondly, more than 90% of patients suffering from ALL respond to CAR-T cell therapy, a result which is not obtained through administration of the immune checkpoints. Future development of CAR-T cell technology should address concerns related to the safety of the treatment and improve toxicity management. Also, it should extend the application of technology in diseases where its effectiveness has been demonstrated and opt for more targets and cancers.