m. fahiminia

One of the most crucial steps in ore exploration is the recognition of geological patterns and features. These features contain mineralogy, lithology, alteration, rock texture, etc. This stage has always been associated with many challenges. Among the challenges of this stage, we can mention the time-consuming and costly nature of this stage and the need for high expertise and human resources to recognize these patterns and features. In recent years, deep learning and machine learning have been adopted in earth sciences. In this research, by using the architecture of U-net, ore and waste were separated, and the grade pattern was identified using the core box images. For this purpose, iron minerals were segmented using binary image segmentation, trial-and-error methods were used to optimize the network, and finally, the model's accuracy for identifying ore was 91%. The IoU metric was utilized for further evaluation; this metric is a suitable criterion for the final evaluation of the image segmentation model, which has reached 75% in recognition of iron ores. For the final evaluation of the obtained model, the grade outputs of the model and the XRF analysis results of one core were compared. The network error was evaluated at 9%, which shows the good accuracy of the obtained model according to the real data.

Rural wastewater management is recognized as one of the pillars of sustainable development. Therefore, the purpose of this study was to assess needs and prioritization of establishment of rural wastewater management facilities in order to supply a safe and alternative water source in the provinces of Iran experiencing severe water stress. This is a cross-sectional descriptive study which was carried out in rural areas of the provinces with severe water stress (South Khorasan, Khorasan Razavi, Fars, Isfahan, Yazd, Semnan, Qom, Sistan and Baluchistan) in 2017-2018. In this study, the number of sample rural areas was calculated by Cochran method and the determination of the sample rural area was done by stratified sampling. The data related to the sample rural area was collected using a validated constructor questionnaire. The results of this study showed that the difference among all the studied provinces in terms of their priority in establishing rural waste water management facilities to provide a safe and alternative water source was very low. The provinces of Isfahan, Semnan and Fars were rated at 76 out of 100, while the provinces of South Khorasan and Sistan and Baluchestan scored 71 points. With emphasis on comprehensive decision making criteria based on sustainable development, the results of this study showed that the provinces with severe water stress have a high priority regarding the establishment of rural sewage management projects in order to provide a safe and alternative water sources.