The vertical influence of temperature and precipitation on snow cover variability in the Central Tianshan Mountains, Northwest China

Seasonal snow cover in mountainous regions will affect local climate and hydrology. In this study, we assessed the role of altitude in determining the relative importance of temperature and precipitation in snow cover variability in the Central Tianshan Mountains. The results show that: (1) in the study area, temperature has a greater influence on snow cover than precipitation during most of the time period studied and in most altitudes. (2) In the high‐elevation area, there is a threshold altitude of 3900±400 m, below which temperature is negatively while precipitation is positively correlated to snow cover, above which the situation is the opposite. Besides, this threshold altitude decreases from snow accumulated period to snow stable period and then increases from snowmelt period to snow‐free period. (3) Below 2000 m, there is another threshold altitude of 1400±100 m during the snow stable period, below (above) which precipitation (temperature) is the main driver of snow cover

THE LONG-TERM HYDROLOGICAL IMPACT ASSESSMENT OF LAND USE AND LAND COVER CHANGES USING L-THIA MODEL IN THE QINHUAI RIVER WATERSHED OF JIANGSU PROVINCE, CHINA

ABSTRACT This study examined the effects of land use and land cover changes due to urbanization on the annual direct runoff of the Qinhuai River Watershed in Jiangsu Province, China. Landsat Thematic Mapper (TM) images from 1988, 1994, 2006 The results also indicate that the annual direct runoff depth is highly correlated with the percentage of impervious surface area. When impervious surface area is less than 9.0%, the annual direct runoff depth will increase linearly with impervious surface area (R 2 =0.97); however, when impervious surface area is greater than 9.0%, the annual direct runoff depth will also increase linearly with impervious surface area (R 2 = 1.00) but at much lower rate

Network Situation Assessment Method Based on Improved BP Neural Network

Although a software defined network (SDN) realizes the flexible configuration and centralized control of network resources, there are potential security risks and challenges. Network security situation awareness (NSSA) technology associates and integrates multi-source heterogeneous information to analyze the impact of the information on the whole network, and network security situation assessment can grasp the network security situation information in real time. However, the existing situation assessment methods have low assessment accuracy, and most of the studies focus on traditional networks, while there are few situation assessment studies in the SDN environment. In this paper, by summarizing the important index parameters of SDN, a network security situation assessment model based on the improved back propagation (BP) neural network (based on the cuckoo search algorithm) is proposed, and the step factor of the cuckoo search algorithm (CS) was improved to improve the search accuracy. The model maps the situation elements to the layers of the neural network, and optimizes the weights and thresholds of the BP neural network through the cuckoo search algorithm to obtain the global optimal solution; it finally realizes the purpose of situation assessment and the comprehensive rating of the SDN environment. In this paper, the evaluation model was verified on the network set up in Mininet. The experimental results show that the situation assessment curve of this model is closer to the real situation value, and the accuracy rate is 97.61%, with good situation assessment results

In-plane shear experimental method and mechanical behavior of ceramic matrix mini-composites

In-plane shear mechanical experiments of ceramic matrix mini-composites (CMMC) were realized in this paper by overcoming the difficulties of material preparation, specimen design, gripping and loading, deformation measurement, etc. The in-plane shear stress-strain responses of different matrix volume fractions were obtained based on the method. The stress-strain response of CMMC was strongly non-linear, and its elastic modulus and strength were positively correlated to the matrix volume fraction. The main factors affecting the digital image correlation (DIC) based micro-region shear deformation measurement are analyzed quantitatively, and the corresponding solutions are discussed. The DIC calculation time model is established, and the accurate estimation of DIC processing time is realized. The shear strain field evolution clearly captured the matrix crack initiation and propagation

A robust Multi-Band Water Index (MBWI) for automated extraction of surface water from Landsat 8 OLI imagery

Surface water is vital resources for terrestrial life, while the rapid development of urbanization results in diverse changes in sizes, amounts, and quality of surface water. To accurately extract surface water from remote sensing imagery is very important for water environment conservations and water resource management. In this study, a new Multi-Band Water Index (MBWI) for Landsat 8 Operational Land Imager (OLI) images is proposed by maximizing the spectral difference between water and non-water surfaces using pure pixels. Based on the MBWI map, the K-means cluster method is applied to automatically extract surface water. The performance of MBWI is validated and compared with six widely used water indices in 29 sites of China. Results show that our proposed MBWI performs best with the highest accuracy in 26 out of the 29 test sites. Compared with other water indices, the MBWI results in lower mean water total errors by a range of 9.31%-25.99%, and higher mean overall accuracies and kappa coefficients by 0.87%-3.73% and 0.06-0.18, respectively. It is also demonstrated for MBWI in terms of robustly discriminating surface water from confused backgrounds that are usually sources of surface water extraction errors, e.g., mountainous shadows and dark built-up areas. In addition, the new index is validated to be able to mitigate the seasonal and daily influences resulting from the variations of the solar condition. MBWI holds the potential to be a useful surface water extraction technology for water resource studies and applications

Woven Fabric Triboelectric Nanogenerator for Biomotion Energy Harvesting and as Self-Powered Gait-Recognizing Socks

In recent years, rapid advancements have developed in multifunctional and wearable electronics, which call for more lightweight, flexible energy sources. However, traditional disposable batteries and rechargeable batteries are not very suitable because of their bulky appearance, limited capacity, low flexibility, and environmental pollution problem. Here, by applying a mature manufacturing technology that has existed in the textile field for a long time, a woven fabric triboelectric nanogenerator (WF-TENG) with a thinner structure that can be mass-fabricated with low cost, perfect stability, and high flexibility is designed and reported. Due to the good intrinsic quality of TENGs, the maximum voltage of this WF-TENG can easily reach 250 V under a pressure of 3.5 kPa and a tapping frequency of 0.33 Hz. Because of the stable plain-woven structure, the output voltage can remain relatively stable even after the WF-TENG has been working for about 5 h continuously, clearly demonstrating its robustness and practical value. Moreover, good sensitivity endows this WF-TENG with the capability of being applied as self-powered sensors, such as a self-powered smart real-time gait-recognizing sock. This WF-TENG shows us a simple and effective method to fabricate a wearable textile product with functional ability, which is very meaningful for future research