A common task in the immunodetection of structurally close compounds is to analyze the selectivity of immune recognition: it is required to understand the regularities of immune recognition and to elucidate the basic structural elements which provide it. Triazines are compounds of particular interest for such a research due to their high variability and the necessity of their monitoring to provide safety of agricultural products and foodstuffs. We evaluated the binding of 20 triazines with polyclonal (pAb) and monoclonal (mAb) antibodies obtained using atrazine as the immunogenic hapten. A total of >3000 descriptors was used in QSAR analysis of binding activities (pIC50). Comparison of the two enzyme immunoassay systems showed that the system with pAb is much easier to describe using 2D QSAR methodology, while the system with mAb can be described using the 3D QSAR COMFA. Thus, for the 3D QSAR model of the polyclonal antibodies, the main statistical parameter q² (‘leave-many-out’) is equal 0.498, and for monoclonal antibodies q² is equal 0.566. Obviously, in the case of pAb, we deal with several targets, while in the case of mAb the target is one, and therefore it is easier to describe it using specific fields of molecular interactions distributed in space.

The integration of deep learning methods, especially Convolutional Neural Networks (CNN), and Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) has been widely deployed in the field of radar signal processing. Nevertheless, these methods are frequently regarded as black-box models due to the limited visual interpretation of their internal feature representation and parameter organization. In this paper, we propose an innovative approach named C-RISE, which builds upon the RISE algorithm to provide a post-hoc interpretation technique for black-box models used in SAR Images Target Recognition. C-RISE generates saliency maps that effectively visualize the significance of each pixel. Our algorithm outperforms RISE by clustering masks that capture similar fusion features into distinct groups, enabling more appropriate weight distribution and increased focus on the target area. Furthermore, we employ Gaussian blur to process the masked area, preserving the original image structure with optimal consistency and integrity. C-RISE has been extensively evaluated through experiments, and the results demonstrate superior performance over other interpretation methods based on perturbation when applied to neural networks for SAR image target recognition. Furthermore, our approach is highly robust and transferable compared to other interpretable algorithms, including white-box methods.

Due to production space and operating environment requirements, mine production equipment often breaks down, which seriously affects the mine’s production schedule. To ensure the smooth completion of the haulage operation plan under abnormal conditions, a model of the haulage equipment rescheduling plan based on the random simulation of equipment breakdowns is established in this paper. The model aims to accomplish both the maximum completion rate of the original mining plan and the minimum fluctuation of the ore grade during the rescheduling period. This model is optimized by improving the wolf colony algorithm and changing the location update formula of the individuals in the wolf colony. Then, the optimal model solution can be used to optimize the rescheduling of the haulage plan by considering equipment breakdowns. The application of the proposed method in an underground mine revealed that the completion rate of the mine’s daily mining plan reached 83.40% without increasing the number of the equipment, while and the ore quality was stable. Moreover, the improved optimization algorithm converged fast and was characterized by high robustness.

The detrital compositions in sandstone are important recorders of sedimentary provenance and transport, both of which have significant implications for the lithology, age and tectonic evolution of provenance. Through detailed identification and analysis, a high content of volcanogenic compositions (27.6%) was discovered in Shanxi formation of Daniudi Gas Field, Ordos Basin, which is much higher than that of the previous study. The volcanogenic compositions include volcanogenic quartz, lava fragments (dominated by various kinds of rhyolite fragments), tuff fragments, and tuffaceous matrix. In addition, at least 2 volcanic ash layers were discovered. Although the metamorphic rocks of the Precambrian succession in the ancient land of Yin Mountain are usually considered the primary source of the Shanxi formation, it is most likely that the volcanogenic compositions were derived from intermediate-acidic volcanic materials that accumulated in the Daqingshan area during the Late Carboniferous to Early Permian. Those volcanogenic compositions are mainly transported by flowing water rather than by air from provenance to basin. And the accumulation of volcanic materials in the Daqingshan area was controlled by volcanic eruptions occurring solely in this area, rather than by the Inner Mongolia Orogenic Belt. Those volcanic eruptions related to the southward subduction of the Paleo-Asian Ocean Plate beneath the northern margin of the North China Plate in Late Carboniferous to Early Permian. The active continental margin related to the subduction of the Paleo-Asian Ocean Plate lasted until at least the Early Permian, and the closure of the Paleo-Asian Ocean must have occurred later than the Early Permian.