Mineral exploration modeling and singularity analysis for geological feature recognition and mineral potential mapping in southeastern Yunnan mineral district, China

Nowadays, with the development in construction of geo-exploratory datasets and data processing techniques, mineral exploration modeling for recognition of mineralization associated geological features and mapping of mineral potentials become more dependent on GIS-based analysis and geo-information from multi-source datasets. Geological, geochemical and geophysical data as three main sources of geo-information in support of mineral exploration have long been employed in many researches. Spatial distributions of geological bodies or controlling factors associated with mineralization were frequently interpreted from these datasets. However, former characterizations of the controlling factors were simply focused on their location information; concerns on spatial variations of their geological signatures and controlling effects on mineralization were not sufficiently emphasized. Therefore, through a series of newly developed GIS-based manipulations, current study intends to demonstrate a comprehensive mineral exploration modeling process for more explicit recognition of controlling factors and their interactions on mineralization and delineation of hydrothermal mineral potentials in southeastern Yunnan mineral district, China. The hydrothermal mineralization as a nonlinear geo-process is accompanied with anomalous energy release and material accumulation in a narrow spatial-temporal interval. Simultaneously, it is a cascade process associated with various geological activities (e.g., magmatism, tectonism, etc.). Knowledge of these associated geo-activities is consequently beneficial to the exploration of hydrothermal mineralization. As the key point of this study, the singularity index mapping method in the context of fractal/multifractal efficient in separating geo-anomalies from both strong and weak background is applied to characterize variations of geological signatures of three controlling factors (i.e., granitic intrusions, faults and the Gejiu formation). With the guidance of multidisciplinary approaches, these geo-information derived from multi-source datasets is further integrated to produce the potential map. In comparison with traditionally used methods, the newly depicted predictor maps enhance weak geo-anomalies hidden within a strong variance of background. In addition, three geo-information integration methods including RGB composition, the principal component analysis and the weights of evidence method are implemented. By the weights of evidence method, the qualitatively and quantitatively interpretable result possessing advantages of the other two methods, simultaneously, is accepted as the final result of currently proposed mineral exploration modeling. Summarized experiences through this study will not only support future exploration in the study area, but also benefit the work in other areas

Is there an association between mild cognitive impairment and dietary pattern in chinese elderly? Results from a cross-sectional population study

<p>Abstract</p> <p>Background</p> <p>Diet has an impact on cognitive function in most prior studies but its association with Mild Cognitive Impairment (MCI) in Chinese nonagenarians and centenarians has not been explored.</p> <p>Methods</p> <p>870 elder dujiangyan residents aged 90 years or more in 2005 census were investigated at community halls or at home. They underwent the Mini-Mental State Examination (MMSE) for assessment of cognitive function and replied to our questionnaire comprised of 12 food items and other risk factors. MCI was defined by two steps: first, subjects with post-stroke disease, Alzheimer's disease or Parkinson's disease and MMSE< 18 were excluded; and then subjects were categorized as MCI (MMSE scores between 19 and 24) and normal (MMSE scores between 25 and 30). Logistic regression models were used to analyze the association between diet and the prevalence of MCI. The model was adjusted for gender, ages, systolic blood pressure, diastolic blood pressure, body mass index, fasting plasma glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol and low-density lipoprotein cholesterol, smoking habits, alcohol and tea consumption, educational levels and exercise in baseline dietary assessment.</p> <p>Results</p> <p>364 elderly finally included, 108 (38.71%) men and 171 (61.29%) women of whom were classified as MCI. A significant correlation between MCI and normal in legume was observed (OR, 0.84; 95%CI, 0.72-0.97), and also in animal oil (any oil that obtained from animal substances) (OR, 0.93; 95%CI, 0.88-0.98). There was no statistical difference of other food items between normal and MCI.</p> <p>Conclusions</p> <p>Among Chinese nonagenarians and centenarians, we found there were significant associations between inadequate intake of legume and animal oil and the prevalence of MCI. No significant correlation between other food items and the prevalence of MCI were demonstrated in this study.</p

The Research of Product Graphical Information Sharing Technology of Virtual Manufacturing Enterprise in E-Commerce Environment

This paper has built a product model by UML and corresponding Product Schema. Then we have illuminated transmit mechanism of the product information by a dumbbell XML document. At last, we have pointed out the direction of the research. This research will provide a significative explore to the product data interchange between the members of virtual manufacturing enterprise in e-commerce environmen

A Chalcogenide Multimode Interferometric Temperature Sensor Operating at a Wavelength of 2 μm

This paper investigated the fabrication of a singlemode-multimode-singlemode fiber structure based on a chalcogenide (As2S3 and AsxS1−x) multimode fiber sandwiched between two standard silica singlemode fibers using a commercial fiber fusion splicer. The temperature dependence of this hybrid fiber structure was investigated and a first proof of concept showed that the hybrid SMS fiber structure has an average experimental temperature sensitivity of circa 84.38 pm/°C over a temperature range of 20 °C∼100°C at the wavelength range around 2 μm. The measured results show a general agreement with numerical simulations based on a guided-mode propagation analysis method. Our result provides a potential platform for the development of compact, high-optical-quality, and robust sensing devices operating at the midinfrared wavelength range

Strain Sensor Based on Grourd-Shaped Single-mode-multimode-single-mode Hybrid Optical Fibre Structure

A fibre-optic strain sensor based on a gourd-shaped joint multimode fibre (MMF) sandwiched between two single-mode fibres (SMFs) is described both theoretically and experimentally. The cladding layers of the two MMFs are reshaped to form a hemisphere using an electrical arc method and spliced together, yielding the required gourd shape. The gourd-shaped section forms a Fabry-Perot cavity between the ends of two adjacent but noncontacting multimode fibres’ core. The effectiveness of the multimode interference based on the Fabry-Perot interferometer (FPI) formed within the multimode inter-fibre section is greatly improved resulting in an experimentally determined strain sensitivity of −2.60 pm/με over the range 0—1000 με. The sensing characteristics for temperature and humidity of this optical fibre strain sensor are also investigated

NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition

Neural networks have shown great potential in accelerating the solution of partial differential equations (PDEs). Recently, there has been a growing interest in introducing physics constraints into training neural PDE solvers to reduce the use of costly data and improve the generalization ability. However, these physics constraints, based on certain finite dimensional approximations over the function space, must resolve the smallest scaled physics to ensure the accuracy and stability of the simulation, resulting in high computational costs from large input, output, and neural networks. This paper proposes a general acceleration methodology called NeuralStagger by spatially and temporally decomposing the original learning tasks into several coarser-resolution subtasks. We define a coarse-resolution neural solver for each subtask, which requires fewer computational resources, and jointly train them with the vanilla physics-constrained loss by simply arranging their outputs to reconstruct the original solution. Due to the perfect parallelism between them, the solution is achieved as fast as a coarse-resolution neural solver. In addition, the trained solvers bring the flexibility of simulating with multiple levels of resolution. We demonstrate the successful application of NeuralStagger on 2D and 3D fluid dynamics simulations, which leads to an additional $10\sim100\times$ speed-up. Moreover, the experiment also shows that the learned model could be well used for optimal control.Comment: ICML 2023 accepte

Research and Design of VR Based Unmanned Aerial Vehicle Model and Database

In response to the application of drones in real life, drones are more susceptible to interference and influence from various external factors such as weather, site, airspace, etc. during flight operations or related tasks. Not only can they not guarantee the completion of expected goals or tasks, but they are also prone to problems such as falling, collision, or accidental injury caused by the unstable state of drones. The drone flight simulation, virtual training, and drone database system developed based on VR technology has improved the safety, diversity, and instability of drones in practical applications, and reduced the interference of external adverse factors on drone flight. A comprehensive drone model database system has been established. This provides effective guarantees for the application and implementation of drones in various fields