ALBERT

Beschreibung: Based on the results of remote sensing data interpretation, this paper aims to simulate and predict the mountain permafrost distribution changes affected by the mean decadal air temperature (MDAT), from the 1990s to the 2040s, in the Qilian Mountains. A bench-mark map is visually interpreted to acquire a mountain permafrost distribution from the 1990s, based on remote sensing images. Through comparison and estimation, a logistical regression model (LRM) is constructed using the bench-mark map, topographic and land coverage factors and MDAT data from the 1990s. MDAT data from the 2010s to the 2040s are predicted according to survey data from meteorological stations. Using the LRM, MDAT data and the factors, the probabilities (p) of decadal mountain permafrost distribution from the 1990s to the 2040s are simulated and predicted. According to the p value, the permafrost distribution statuses are classified as ‘permafrost probable’ (p 〉 0.7), ‘permafrost possible’ (0.7 ≥ p ≥ 0.3) and ‘permafrost improbable’ (p 〈 0.3). From the 1990s to the 2040s, the ‘permafrost probable’ type mainly degrades to that of ‘permafrost possible’, with the total area degenerating from 73.5 × 103 km2 to 66.5 × 103 km2. The ‘permafrost possible’ type mainly degrades to that of ‘permafrost impossible’, with a degradation area of 6.5 × 103 km2, which accounts for 21.3% of the total area. Meanwhile, the accuracy of the simulation results can reach about 90%, which was determined by the validation of the simulation results for the 1990s, 2000s and 2010s based on remote sensing data interpretation results. This research provides a way of understanding the mountain permafrost distribution changes affected by the rising air temperature rising over a long time, and can be used in studies of other mountains with similar topographic and climatic conditions.

Beschreibung: The efficient and safe extraction of ultra-thick coal seam in the Xinjiang East Junggar Basin has been a major focus in the future of mining in China. This paper systematically studied the overburden strata fracturing process and the structure evolution characteristics based on a typical ultra-thick coal seam condition in Xinjiang, using both physical and numerical modeling studies. The interactions between shields and the roof strata were also examined, from the perspective of ground support. The results indicated that roof structure was mainly in the form of voussoir beam at the early mining stage, where overburden stability was affected by the rock mass properties and mining parameters. The support load mainly included top coal and immediate roof gravity load and the load caused by main roof rotary consolidation. As a result of mining disturbance and strata movement, the overlying strata re-fractured in the later mining stage. The roof structure changed from beam to arch gradually and propagates upwards with the increase of multi-layer mining times. The support load was mainly the gravity load of the friable rock mass within compression arch. The results will provide a guideline for the improvement of roof stability under similar mining conditions in Xinjiang.

Beschreibung: Localization technologies play an important role in disaster management and emergence response. In areas where the environment does not change much after an accident or in the case of dangerous areas monitoring, indoor fingerprint-based localization can be used. In such scenarios, a positioning system needs to have both a high accuracy and a rapid response. However, these two requirements are usually conflicting since a fingerprint-based indoor localization system with high accuracy usually has complex algorithms and needs to process a large amount of data, and therefore has a slow response. This problem becomes even worse when both the size of monitoring area and the number of reference nodes increase. To address this challenging problem, this paper proposes a two-level positioning algorithm in order to improve both the accuracy and the response time. In the off-line stage, a fingerprint database is divided into several sub databases by using an affinity propagation clustering (APC) algorithm based on Shepard similarity. The online stage has two steps: (1) a coarse positioning algorithm is adopted to find the most similar sub database by matching the cluster center with the fingerprint of the node tested, which will narrow the search space and consequently save time; (2) in the sub database area, a support vector regression (SVR) algorithm with its parameters being optimized by particle swarm optimization (PSO) is used for fine positioning, thus improving the online positioning accuracy. Both experiment results and actual implementations proved that the proposed two-level localization method is more suitable than other methods in term of algorithm complexity, storage requirements and localization accuracy in dangerous area monitoring.

Beschreibung: Regional ecosystem health is the basis for regular regional exploration, ecological protection, and sustainable development. This study explored ecosystem health at the southern end of the Hu Line (Sichuan and Yunnan provinces) using the pressure–state–response model and examined the spatial evolution of ecosystem health. The proportion of unhealthy and morbid cities decreased from 45.9% in 2000 to 35.1% in 2016. The imbalance of ecosystem health among cities has gradually increased since 2006, but more high-quality cities have emerged (Z of Moran’s Index 〈 1.96, p 〉 0.05). Overall, the regional ecosystem on the southeast side of the Hu Line was healthier than that on the northwest side. Differences in ecosystem health on both sides of the Hu Line showed decreasing trends over time except for the pressure score. The spatial pattern of ecosystem health moved along the Hu Line because the pressure and state scores of ecosystems were mainly determined by the natural environmental conditions. Based on the county-level assessment, the grade of imbalance within cities was divided, and those that were lagging were identified. To correct regional imbalances, a comprehensive and proactive policy framework for a smart development model was put forward in Sichuan and Yunnan.

Beschreibung: The scientific and efficient mining of ultra-thick coal seam in Xinjiang, China is faced with the problems of low exploration level and lack of theoretical research on underground mining. This paper studied occurrence characteristic of ultra-thick coal seams in Xinjiang, using field investigation and drilling exploration. Based on the variation law of support load under different roof bearing structure form and development height in multi-layer mining, classification method and mining technology selection of ultra-thick coal seam were put forward. The results indicate that: (1) The ultra-thick coal seams in Xinjiang have a distribution characteristic of more north and less south, more east and less west, mainly concentrate in East Junggar and Turpan-Harmi coalfields. The form of the ultra-thick coal seam has the remarkable characteristic of coal seams merging and bifurcating. (2) The mechanical model of the relationship between the support and surrounding rock under different roof bearing structures is established. At the early stage of multi-layer mining, the support load includes the load caused by rotary subsidence of the blocks that formed the near-stope roof bearing structure and the gravity load of rock blocks under roof bearing structure. At the later stage, the support load is mainly gravity load of loose blocks below the far-stope roof bearing structure. (3) According the roof bearing structure form, ultra-thick coal seam can be divided into three types: no stable bearing structure, (higher) beam bearing structure and arch bearing structure. In order to ensure the stability of near-stope roof bearing structure, backfill mining, longwall mining, and longwall mining early and backfill mining later should be adopted in three types ultra-thick coal seams mining respectively.