With the emergence and development of unmanned aerial vehicles (UAVs), different sensors have become more miniaturized and intelligent. UAVs equipped with various sensors are now an important approach for acquiring spatial data. Many advantages, such as low cost, short revisiting cycle, flexibility and high precision, have made UAVs powerful tools in geological, agricultural, ecological and forestry growth monitoring, as well as evaluation. Now, UAVs are a hotspot in scientific research. Their application in mining areas (MA), although still in its infancy, is developing rapidly in terms of speed, scale and service scope. This research examines aspects such as UAV platforms, different sensors and their application fields, as well as reviewing the advances of scientific research in MA at the present time. By combining current research and the functions of multiple sensors, an application framework for UAV monitoring in MA is constructed. Finally, the challenge and prospects for the development of UAVs and sensors are also considered. This research hopes to provide a technical reference, expanding the knowledge and recognition of UAV monitoring in MA, as well as an assessment of applications in mining, reclamation and environment.

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The combined action of a low dose of an NMDAR antagonist (MK-801) and GLT-1 activation by ceftriaxone effectively changed different phases of CPP behavior.

Many probability techniques have been proposed, but the inspection intervals for composite structures have not been comprehensively addressed. The present study focuses on the most frequent damage type, dent, and uses probabilistic approaches to analyze statistical characteristics of the damage based on maintenance data from a Chinese airline. Dent sizes are considered from three dimensions: the damage diameter, the damage depth, and the diameter/depth ratio. The life-cycle strength of a composite structure is obtained by Monte Carlo simulation, and the probability of failure is quantified corresponding to different inspection intervals. Maintenance cost is introduced, as another criterion, to optimize inspection intervals from both safety and economy. This method enables the dent damage to be assessed quantitatively, which can facilitate engineers from airlines or manufacturers to evaluate the damage tolerance of the composite structure and adjust their related inspection schedules flexibly.

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Background: Nowadays, computer technology is getting popular for clinical aided diagnosis, especially in the direction of medical images. It makes physician diagnosis of lung nodules more efficient by providing them with reliable and accurate segmentation.Methods: A region growing based semi-automated pulmonary nodule segmentation algorithm (ReGANS) was developed with three improvements: an automatic threshold calculation method, a lesion area preprojection method, and an optimized region growing method. The algorithm can quickly and accurately segment a whole lung nodule in a set of computed tomography (CT) images based on an initial manual point. Results:The average time taken for ReGANS to segment 1 pulmonary nodule was 0.83s, and the probability rand index (PRI), global consistency error (GCE), and variation of information (VoI) from a comparison between the algorithm and the radiologist's 2 manual results were 0.93, 0.06, and 0.3 for the boundary range (BR), and 0.86, 0.06, 0.3 for the precise range (PR). The number of images covered by one pulmonary nodule in a CT image set was also evaluated to compare the segmentation algorithm with the radiologist's results, with an error rate of 15%. At the same time, the results were verified in multiple data sets to validate the robustness.Conclusions: Compared with other algorithms, ReGANS can segment the lung nodule image region more quickly and more precisely. The experimental results show that ReGANS can assist medical imaging diagnosis and has good clinical application value. It also provides a faster and more convenient method for pre-data preparation of intelligent algorithms.