Improving visualisation and prediction performance of supervised self-organising map by modified contradiction resolution

We have previously proposed a new type of information-theoretic method where a neuron is evaluated by itself (self-evaluation) and by its surrounding neurons (outer-evaluation). If contradiction between different types of evaluation exists, it is reduced as much as possible. In the present paper, we try to separate self- and outer-evaluation more explicitly and introduce the importance of neurons. First, we separate self- and outer-evaluation to enhance the characteristics shared by the two types of evaluation. Second, we introduce the importance of neurons in evaluation. By using a limited number of important neurons in evaluation, we expect the main characteristics in input patterns to emerge. We applied this contradiction resolution to two types of data, namely, the Senate data and the Euro-yen exchange rates. In both data sets, experimental results confirmed that improved prediction performance was obtained. Prediction performance was better than that obtained by the conventional self-organising map (SOM) and radial basis function networks. In addition, final representations obtained by contradiction resolution were easier to interpret than those given by the conventional SOM. Experimental results confirmed that improved interpretation and visualisation were accompanied by improved prediction performance.     

提升“思想道德修养与法律基础”课堂魅力的方法探析

"思想道德修养与法律基础"课程是高校思想政治理论课的重要组成部分。把基础课课堂魅力作为研究内容,通过课堂观察法和自身授课经验总结法,对影响基础课课堂魅力的因素进行分析,发现教师自身和教学方式是主要影响因素。提升基础课教师自身魅力和教学方式魅力可有效解决基础课课堂实效性差等问题,为顺利实现教学目标,有效发挥思想政治教育作用奠定基础。     

MOOC Learner’s Final Grade Prediction Based on an Improved Random Forests Method

Massive Open Online Course (MOOC) has become a popular way of online learning used across the world by millions of people. Meanwhile, a vast amount of information has been collected from the MOOC learners and institutions. Based on the educational data, a lot of researches have been investigated for the prediction of the MOOC learner’s final grade. However, there are still two problems in this research field. The first problem is how to select the most proper features to improve the prediction accuracy, and the second problem is how to use or modify the data mining algorithms for a better analysis of the MOOC data. In order to solve these two problems, an improved random forests method is proposed in this paper. First, a hybrid indicator is defined to measure the importance of the features, and a rule is further established for the feature selection; then, a Clustering-Synthetic Minority Over-sampling Technique (SMOTE) is embedded into the traditional random forests algorithm to solve the class imbalance problem. In experiment part, we verify the performance of the proposed method by using the Canvas Network Person-Course (CNPC) dataset. Furthermore, four well-known prediction methods have been applied for comparison, where the superiority of our method has been proved.     

Research on image segmentation of Miao costume patterns based on the RSKP-UNet model北大核心CSCD

The Miao nationality is the sixth largest ethnic group in China which has a history of thousands of years. It has created a unique material culture and spiritual culture in its development process and the Miao costume is a highly condensed collection of the material and spiritual culture of the Miao nationality. As one of the unique symbols of Miao culture the Miao costume has profound cultural heritage and cultural connotations. The patterns of the Miao costume are particularly eye-catching as they not only symbolize the wisdom of the Miao people in thousands of years of production and life but also symbolize the pursuit of the good spirit of the Miao people. However under the impact of modern pop culture and foreign culture these cultural symbols are gradually disappearing. In order to protect and inherit them the Miao costume pattern segmentation has become the most important work. However the Miao costume pattern segmentation is quite difficult. At present there are few studies on the extraction classification identification and preservation of the features of Miao costume pattern segmentation. With the excellent segmentation performance of the U-Net model and the advantages of easy deployment the paper improves the basic structure of the U-Net model and proposes a Miao costume pattern segmentation algorithm based on the RSKP-UNet Residual Selective-Kernel Parallel U-Net model. The algorithm adds Residual modules in the encoder part of the U-Net model to improve the feature extraction capability of the model and embeds the SKNet modules and ParNet modules in the decoder part to enhance the feature expression capability of the model. The paper uses evaluation metrics to measure the segmentation performance of the model and compares it with four segmentation models based on deep learning. The paper not only combines the current research focus-deep learning and attention mechanism but also introduces the Lovász-hinge loss function to effectively solve the problem of class imbalance in the Miao costume patterns. The RSKP-UNet model is better than other models in various segmentation indicators. Compared with the benchmark model U-Net the Dice coefficient IoU precision recall and accuracy are improved by 6. 98% 11. 07% 2. 89% 6. 75% and 3. 92% . The segmentation algorithm proposed in this paper realizes the extraction of the element content of the Miao costume patterns through image segmentation of Miao costume patterns which can be used to build the Miao costume pattern database in this way thus helping designers relevant researchers and organizations to provide research foundation and completing the protection and inheritance of the Miao costume culture. The paper also provides some reference for the segmentation research of other minority costume patterns. © 2022 Authors. All rights reserved.     

Distribution of Organic Carbon in the Berkeley Pit Lake, Butte, Montana

Abstract.  The concentration of dissolved organic carbon (DOC) in the Berkeley pit lake water ranges from 2 to 4 mg/L, and is comparable to that of its inflow waters. On the dates sampled, the DOC concentrations decreased towards the surface of the lake, in a manner similar to the concentration of dissolved Fe. This may reflect adsorption of DOC onto newly formed ferric precipitates in the epilimnion of the lake. The total organic carbon (TOC) content of the lake sediment is 0.20 to 0.33%, and is on the low end of TOC in natural aquatic sediments. In contrast, the DOC concentrations of sediment pore waters are unusually high, ranging from 50 to 380 mg/L, and are much higher than DOC values of pore waters from typical marine or lacustrine sediments. The high DOC concentrations are explained by release of adsorbed organic carbon from ferric precipitates as they age and recrystallize, coupled with the relative scarcity of heterotrophic bacteria in the acidic and heavy metal-rich waters that would otherwise consume DOC through reduction of sulfate.     

Heavy Metals in Stream Sediments from the Coquimbo region (Chile): Effects of Sustained Mining and Natural Processes in a Semi-arid Andean Basin

Abstract.   Active sediments from the Elqui River in Chile were sampled 4 times at 10 sites during 2000. Concentrations of Ag, Ba, Cd, Co, Cr, Hg, Mn, Mo, Ni, Pb, Sr, Ti, V, Al, Ca, Fe, K, Mg, Na, P, and S were normal. Zinc levels were clearly high, and those of Cu (hundred to thousands ppm) and As (tens to hundreds ppm) were highly anomalous. Dissolved Cu (0.1-12.7 ppm) and Zn (0.2-2.2 ppm) levels were also very high. The anomalies of the upper tributaries are due to the El Indio–Tambo Au-Cu-As district and large hydrothermal alteration zones at altitudes between 3500–4500 m. Lower on the river, old and active tailing waste deposits and on-going mining operations in the Talcuna Cu (Pb) district are responsible. Partially eroded tailing deposits in the alluvial plain of the Elqui River and its tributaries, and especially in the El Indio-Tambo district, after mine closure in 2000, warrant special attention.     

Underground Mine Water for Heating and Cooling using Geothermal Heat Pump Systems

Abstract  In many regions of the world, flooded mines are a potentially cost-effective option for heating and cooling using geothermal heat pump systems. For example, a single coal seam in Pennsylvania, West Virginia, and Ohio contains 5.1 x 10¹² L of water. The growing volume of water discharging from this one coal seam totals 380,000 L/min, which could theoretically heat and cool 20,000 homes. Using the water stored in the mines would conservatively extend this option to an order of magnitude more sites. Based on current energy prices, geothermal heat pump systems using mine water could reduce annual costs for heating by 67% and cooling by 50% over conventional methods (natural gas or heating oil and standard air conditioning).     

Using a Water Balance to Determine the Source of Water in the Flooding Underground Mine Workings of Butte

Abstract.  Nearly 10,000 miles (16,000 km) of underground mine workings began flooding on April 22, 1982 when the large pumps used to dewater the mines of Butte, Montana were shut off. In the first few months, water levels in the workings rose hundreds of meters. Flooding continues to this day at a slower rate, nearly 25 years later. An early evaluation of the water chemistry in the flooding mines suggested that the initially poor water quality was the result of flushing of a reservoir of stored acidity and metals. However, a detailed water balance for the Berkeley pit, underground workings, and associated mining features suggests an alternative explanation. During the early period of mine flooding, acidic surface water from the deactivated heap leach operations and nearby acid rock drainage were routed into the empty Berkeley Pit, and thence drained downward and outward into the underground mine workings, causing widespread degradation of water quality in the underlying workings. After 21 months, the hydraulic gradients in the system reversed, causing a change in the direction of ground water flow and a gradual improvement in water quality of the mine shafts.