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The present paper addresses a novel two-echelon multi-product Location-Allocation-Routing problem (LARP). It also considers the integration of issues such as disruption, environmental pollution, and energy-efficient vehicles as currently critical issues in a Supply Chain Network (SCN) that includes production plants, central warehouses, and retailers. The aim of this study is to minimize the total cost, which involves costs related to the establishment, shipment processes, environmental pollution, travelling, vehicle usage, and fuel consumption, in a way to cover the total demand of retailers. The problem is NP-hard; thus, to solve it approximately, we developed Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithms. The numerical analysis showed that the proposed algorithms yielded high-quality results in a short computational time where the average gaps of GWO and PSO against CPLEX are 0.78% and 0.9%, respectively. Then, a case study of a dairy factory in Iran is conducted to evaluate the applicability of the proposed methodology and find the optimal policy. Finally, a set of sensitivity analyses is carried out to suggest managerial insights and decision aids. [ABSTRACT FROM AUTHOR]

Since Kermack and McKendrick have introduced their famous epidemiological SIR model in 1927, mathematical epidemiology has grown as an interdisciplinary research discipline including knowledge from biology, computer science, or mathematics. Due to current threatening epidemics such as COVID-19, this interest is continuously rising. As our main goal, we establish an implicit time-discrete SIR (susceptible people–infectious people–recovered people) model. For this purpose, we first introduce its continuous variant with time-varying transmission and recovery rates and, as our first contribution, discuss thoroughly its properties. With respect to these results, we develop different possible time-discrete SIR models, we derive our implicit time-discrete SIR model in contrast to many other works which mainly investigate explicit time-discrete schemes and, as our main contribution, show unique solvability and further desirable properties compared to its continuous version. We thoroughly show that many of the desired properties of the time-continuous case are still valid in the time-discrete implicit case. Especially, we prove an upper error bound for our time-discrete implicit numerical scheme. Finally, we apply our proposed time-discrete SIR model to currently available data regarding the spread of COVID-19 in Germany and Iran. [ABSTRACT FROM AUTHOR]

Conflict of interests is a situation when someone is in need of other people's trust on one the hand and has personal or general interests on the other hand, resulting in conflict with the given responsibility. In this research work, an attempt was made to find the relation between declarations of conflict of interests and reporting positive outcomes in the dental journals in Iran (2000–2016). In this analytical/cross-sectional study, first Health and Biomedical Information was searched and all the Persian and English dental journals published in Iran were collected. Then, all the papers published in the journals from December 2000 to December 2016 were collected and categorized in terms of the year of publication, author or authors' affiliations, Persian and English journal, type of the substance or the drug used (including the manufacturing company), declarations of conflict of interests and the positive or negative conclusion of the report. Data were analyzed with the Fisher's exact test and Chi squared test, using the program SPSS 18. In numerical analysis, the significance was set at P < 0.05. Seventeen dental journals in Persian and English were analyzed: 10 in English and 7 in Persian. Reviewing these studies showed that of 1021 articles in Persian, in 128 cases there was no mention of a declaration of conflict of interests and in 11 cases, the COI had been stated. In addition, from 1220 articles in English, in 825 cases there was no mention of declarations of conflict of interests and in 45 cases, the declarations of COI had been mentioned. There was no significant relation between the COI and 'no' COI and the reporting of positive outcomes in papers in Iranian dental journals in terms of the journal type, year of publication and the journals' guarantee form (P = 0.25, P = 0.41 and P = 0.09). A total of 83% of studies with declarations of COI had one positive outcome, with a significant relationship in this field; however, in 73% of studies with no COI, there was one positive outcome, too. In general, the society expects that doctors would not consider any incentives except the health of the patients in the efforts made by them. The severity of the consequences of COI is of higher value when the patients' health is endangered due to it. In addition, COI might change the attitude and approach of other doctors and peers. [ABSTRACT FROM AUTHOR]

The application of numerical weather prediction (NWP) products is increasing dramatically. Existing reports indicate that ensemble predictions have better skill than deterministic forecasts. In this study, numerical ensemble precipitation forecasts in the TIGGE database were evaluated using deterministic, dichotomous (yes/no), and probabilistic techniques over Iran for the period 2008-16. Thirteen rain gauges spread over eight homogeneous precipitation regimes were selected for evaluation. The Inverse Distance Weighting and Kriging methods were adopted for interpolation of the prediction values, downscaled to the stations at lead times of one to three days. To enhance the forecast quality, NWP values were post-processed via Bayesian Model Averaging. The results showed that ECMWF had better scores than other products. However, products of all centers underestimated precipitation in high precipitation regions while overestimating precipitation in other regions. This points to a systematic bias in forecasts and demands application of bias correction techniques. Based on dichotomous evaluation, NCEP did better at most stations, although all centers overpredicted the number of precipitation events. Compared to those of ECMWF and NCEP, UKMO yielded higher scores in mountainous regions, but performed poorly at other selected stations. Furthermore, the evaluations showed that all centers had better skill in wet than in dry seasons. The quality of post-processed predictions was better than those of the raw predictions. In conclusion, the accuracy of the NWP predictions made by the selected centers could be classified as medium over Iran, while post-processing of predictions is recommended to improve the quality. [ABSTRACT FROM AUTHOR]

The expansion of the Neyriz marble mine into deeper levels caused an unexpected failure particularly in the toes of lower benches. This phenomenon can impact the overall stability of the quarry and results in undesirable environmental and technical consequences. To understand the failure mechanism, a comprehensive study including-laboratory testing, in situ field testing and theoretical analyses are carried out. The theory of the brittle failure which was mainly developed based on the experiences gained during excavation in granite rocks is adopted and augmented in this study to explain the governing mechanism of failure. Mechanical properties of the marble are determined using conventional rock mechanics tests, and the in situ stress field was evaluated using a modified under coring test. Analyzing the laboratory and field data with the available empirical criteria for brittle failure shows that the level of stress in the lower bench is high enough to initiate the brittle failure. Finally, constitutive models developed for this failure mode are adopted in conjunction with numerical modeling to investigate the observed failure in the quarry. Two modeling strategies, based on elastic and elastic-plastic analyses, are considered. Comparing the predicted failure surface with the observed failure profile, it can be concluded that the brittle failure criteria can very well capture the failure mechanism in this marble quarry. This shows that the criteria proposed to describe spalling failure around underground excavation in granite can be effectively employed for assessing the brittle failure in deep open cast and quarry mines in good quality rocks such as marble. [ABSTRACT FROM AUTHOR]

In order to prevent problems like casualties, delays in tunneling procedure, and increasing tunneling costs, predicting groundwater inflow into tunnels and designing an efficient drainage system are necessary. Several analytical solutions exist in the literature, but they cannot accurately estimate groundwater inflow due to simplifying assumptions. Numerical methods, however, are increasingly used to predict groundwater inflow with higher accuracy. This study is aimed at predicting the steady state groundwater ingress into Tabriz line 2 metro tunnel (TML2: a shallow urban tunnel) by considering model dimensions as critical factors in the numerical analysis process. A 2-D numerical finite element analysis of steady state groundwater inflow was performed along the tunnel based on geological and geotechnical investigations. Water inflow into TML2 was then estimated based on optimum model dimensions. The total amount of groundwater inflow into TML2 was estimated to be 7013.6 l/min. The maximum cumulative water inflow into the tunnel was also predicted to occur between 15.55 and 16.25 km in the tunnel with a 4754 l/min inflow rate, which demands an effective drainage program. Finally, for validation of the methodology groundwater inflow values obtained through 2D finite element analysis were compared to those calculated with a well-known inflow evaluation method (Raymer solution). [ABSTRACT FROM AUTHOR]

In this paper, a recursive training procedure with forgetting factor is proposed for on-line calibration of temporal neural networks. The forgetting factor discounts old measurements through an on-line model calibration. The forgetting factor approach enables the recursive algorithm to reduce the effect of the older error data by multiplying the error data by a discounting factor. The proposed procedure is used to calibrate a temporal neural network for reservoir inflow modeling. The mean monthly inflow of the Karoon-III reservoir dam in the south-western part of Iran is used to test the performance of the proposed approach. An autoregressive moving average (ARMA) model is also applied to the same data. The temporal neural network, which is trained with the proposed approach, has shown a significant improvement in the forecast accuracy in comparison with the network trained by the conventional method. It is also demonstrated that the neural network trained with forgetting factor results in better forecasts compared to the statistical ARMA model, which has been calibrated through this approach. [ABSTRACT FROM AUTHOR]