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Numerical weather prediction (NWP) data possess internal inaccuracies, such as low NWP wind speed corresponding to high actual wind power generation. This study is intended to reduce the negative effects of such inaccuracies by proposing a pure data-selection framework (PDF) to choose useful data prior to modeling, thus improving the accuracy of day-ahead wind power forecasting. Briefly, we convert an entire NWP training dataset into many small subsets and then select the best subset combination via a validation set to build a forecasting model. Although a small subset can increase selection flexibility, it can also produce billions of subset combinations, resulting in computational issues. To address this problem, we incorporated metamodeling and optimization steps into PDF. We then proposed a design and analysis of the computer experiments-based metamodeling algorithm and heuristic-exhaustive search optimization algorithm, respectively.Experimental results demonstrate that (1) it is necessary to select data before constructing a forecasting model; (2) using a smaller subset will likely increase selection flexibility, leading to a more accurate forecasting model; (3) PDF can generate a better training dataset than similarity-based data selection methods (e.g., K-means and support vector classification); and (4) choosing data before building a forecasting model produces a more accurate forecasting model compared with using a machine learning method to construct a model directly.

Analyzing the efficiency of cross-region scientific and technological innovation resources allocation in Chengdu-Chongqing economic circle is helpful to understand the achievements in the construction of economic circle and development trend, and it is great significance to important growth pole for promoting the high-quality development of Chengdu-Chongqing economic circle. Applying the method of three-stage DEA, combined with the input-output data of scientific and technological innovation resources in 44 regions of Chengdu-Chongqing economic circle from 2014 to 2019, this paper studies the allocation efficiency and influencing factors of scientific and technological innovation resources in this region during this period. Results indicate that: Firstly, after excluding the influence of external environmental factors, the average allocation efficiency of scientific and technological innovation resources in Chengdu-Chongqing economic circle changes little from year to year and is at a low level. The allocation efficiency composition of each region is generally characterized by high pure technical efficiency and low scale efficiency. Secondly, the improvement of the degree of opening to the outside world and the level of education investment is conducive to the improvement of the allocation efficiency of scientific and technological innovation resources, while the improvement of the level of economic development and the adjustment of industrial structure have not effectively driven the improvement of the allocation efficiency of scientific and technological innovation resources to a certain extent.

Climate change significantly affects the environmental and socioeconomic conditions in northwest China. Here we evaluate the ability of five general circulation models (GCMs) from 6th phase of the Coupled Model Inter-comparison Project (CMIP6) to reproduce regional temperature and precipitation over northwest China from 1961 to 2014, and project the future temperature and precipitation during 2021 to 2100 under SSPs-RCPs (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0 and SSP5-8.5). The results show that the CMIP6 models can simulate temperature better than precipitation. Projections show that the annual mean temperature will further increase under different SSPs-RCPs scenarios in the 21st century. Future climate changes in the near-term (2021–2040), mid-term (2041–2060) and long-term (2081–2100) are analyzed relative to the reference period (1995–2014). In the long term, warming will be significantly higher than the near and mid-terms. In the long term, annual mean temperature will increase by 1.4°C, 1.9°C, 3.3°C, 5.5°C, 2.7°C, 3.8°C and 6.0°C under SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0 and SSP5-8.5, respectively. Spatially, warming in the Junggar Basin will be higher than those in the Tarim Basin. Seasonally, the maximum warming zone will be in the mountainous areas of Tarim Basin during spring and autumn, in the southern basin during winter, and in the east during summer. Precipitation shows an increasing trend under different SSPs-RCPs in the 21st century. In the long term, increase in precipitation will be significantly higher than in the near and mid-terms. Increase in annual precipitation in the long term will be 4.1% under SSP1-1.9, 13.9% under SSP1-2.6, 28.4% under SSP2-4.5, 35.2% under SSP3-7.0, 6.9% under SSP4-3.4, 8.9% under SSP4-6.0, and 27.3% under SSP5-8.5 relative to the reference period of 1995–2014. Spatially, precipitation increase will be higher in the south than the north, especially higher in mountainous regions than the basin under SSP2-4.5, SSP3-7.0, and SSP5-8.5. Seasonally, highest increase can be expected for winter, followed by spring, with significant increase in mountainous regions of southern Tarim Basin. Summer precipitation will reduce in Tian Shan and basins but will significantly increase in the northern margin of the Kunlun Mountain.

Aridity index reflects the exchanges of energy and water between the land surface and the atmosphere, and its variation can be used to forecast drought and flood patterns, which makes it of great significance for agricultural production. The ratio of potential evapotranspiration and precipitation is applied to analyse the spatial and temporal distributions of the aridity index in the Belt and Road region under the 1.5°C and 2.0°C global warming scenarios on the basis of outputs from four downscaled global climate models. The results show that: (1) Under the 1.5°C warming scenario, the area-averaged aridity index will be similar to that in 1986–2005 (around 1.58), but the changes vary spatially. The aridity index will increase by more than 5% in Central-Eastern Europe, north of West Asia, the monsoon region of East Asia and northwest of Southeast Asia, while it is projected to decrease obviously in the southeast of West Asia. Regarding the seasonal scale, spring and winter will be more arid in South Asia, and the monsoon region of East Asia will be slightly drier in summer compared with the reference period. While, West Asia will be wetter in all seasons, except winter. (2) Relative to 1986–2005, both areal averaged annual potential evapotranspiration and precipitation are projected to increase, and the spatial variation of aridity index will become more obvious as well at the 2.0°C warming level. Although the aridity index over the entire region will be maintained at approximately 1.57 as that in 1.5°C, the index in Central- Eastern Europe, north of West Asia and Central Asia will grow rapidly at a rate of more than 20%, while that in West Siberia, northwest of China, the southern part of South Asia and West Asia will show a declining trend. At the seasonal scale, the increase of the aridity index in Central-Eastern Europe, Central Asia, West Asia, South Asia and the northern part of Siberia in winter will be obvious, and the monsoon region in East Asia will be drier in both summer and autumn. (3) Under the scenario of an additional 0.5°C increase in global temperature from 1.5°C to 2.0°C, the aridity index will increase significantly in Central Asia and north of West Asia but decrease in Southeast Asia and Central Siberia. Seasonally, the aridity index in the Belt and Road region will slightly increase in all other seasons except spring. Central Asia will become drier annually at a rate of more than 20%. The aridity index in South Asia will increase in spring and winter, and that in East Asia will increase in autumn and winter. (4) To changes of the aridity index, the attribution of precipitation and potential evapotranspiration will vary regionally. Precipitation will be the major influencing factor over southern West Asia, southern South Asia, Central-Eastern Siberia, the non-monsoon region of East Asia and the border between West Asia and Central Asia, while potential evapotranspiration will exert greater effects over Central-Eastern Europe, West Siberia, Central Asia and the monsoon region of East Asia.

Droughts are major, large‐scale, weather‐driven natural disasters, on the rise in the changing climate. We project changing population exposure to drought in two vulnerable, adjacent, basins of large rivers, the Tarim River Basin (TRB) and the Indus River Basin (IRB), for the future horizon 2021–2065. Drought events are assessed based on the outputs of multiple Global Climate Models, by applying the Standardized Precipitation Evapotranspiration Index (SPEI) and the Intensity‐Area‐Duration method (IAD). Future population exposure to droughts is evaluated by combining the drought events under three RCP scenarios (RCP2.6, RCP4.5, and RCP8.5) with the projected population from Shared Socioeconomic Pathways (SSPs), acknowledging the recent two‐child policy in China. Results show that frequency of drought events in both river basins could increase in the future, while increase in the TRB is stronger than in the IRB. The areal coverage of drought events in both river basins is projected to be greater in 2021–2065 than in 1961–2005. Increase of frequency and areal coverage in both basins is especially strong for the class of extreme drought events. According to the ensemble mean of multi‐GCMs, population exposure to droughts was 25.0% and 20.9% of the total population in the TRB and the IRB, respectively, in 1961–2005, and it is projected to increase by over 60% for the TRB and to increase by over 30% for the IRB, in 2021–2065.

Analyzing the driving factors of regional carbon emissions is important for achieving emissions reduction. Based on the Kaya identity and Logarithmic Mean Divisia Index method, we analyzed the effect of population, economic development, energy intensity, renewable energy penetration, and coefficient on carbon emissions during 1990&ndash, 2016. Afterwards, we analyzed the contribution rate of sectors&rsquo, energy intensity effect and sectors&rsquo, economic structure effect to the entire energy intensity. The results showed that the influencing factors have different effects on carbon emissions under different stages. During 1990&ndash, 2000, economic development and population were the main factors contributing to the increase in carbon emissions, and energy intensity was an important factor to curb the carbon emissions increase. The energy intensity of industry and the economic structure of agriculture were the main factors to promote the decline of entire energy intensity. During 2001&ndash, 2010, economic growth and emission coefficient were the main drivers to escalate the carbon emissions, and energy intensity was the key factor to offset the carbon emissions growth. The economic structure of transportation, and the energy intensity of industry and service were the main factors contributing to the decline of the entire energy intensity. During 2011&ndash, 2016, economic growth and energy intensity were the main drivers of enhancing carbon emissions, while the coefficient was the key factor in curbing the growth of carbon emissions. The industry&rsquo, s economic structure and transportation&rsquo, s energy intensity were the main factors to promote the decline of the entire energy intensity. Finally, the suggestions of emissions reductions are put forward from the aspects of improving energy efficiency, optimizing energy structure and adjusting industrial structure etc.

Two-stage ensemble-based forecasting methods have been studied extensively in the wind power forecasting field. However, deep learning-based wind power forecasting studies have not investigated two aspects. In the first stage, different learning structures considering multiple inputs and multiple outputs have not been discussed. In the second stage, the model extrapolation issue has not been investigated. Therefore, we develop four deep neural networks for the first stage to learn data features considering the input-and-output structure. We then explore the model extrapolation issue in the second stage using different modeling methods. Considering the overfitting issue, we propose a new moving window-based algorithm using a validation set in the first stage to update the training data in both stages with two different moving window processes.Experiments were conducted at three wind farms, and the results demonstrate that the model with single input multiple output structure obtains better forecasting accuracy compared to existing models. In addition, the ridge regression method results in a better ensemble model that can further improve forecasting accuracy compared to existing machine learning methods. Finally, the proposed two-stage forecasting algorithm can generate more accurate and stable results than existing algorithms.

Understanding the patterns of dryness/wetness in the warming context is important for stakeholders to formulate adaptation strategies to minimize the impacts of floods and droughts. Spatial and temporal characteristics of regional temperature, precipitation, potential evapotranspiration (PET) and dryness/wetness variation were investigated for the period from 2015 to 2100 under SSPs-RCPs in Northwest China. Results show that annual mean temperature, precipitation and PET are projected to increase during 2015–2100, with increases higher in the long-term (2081–2100) than the near-term (2021–2040) and mid-term (2041–2060). Increase rate under SSP5–8.5 and SSP3–7.0 will significantly higher than other scenarios. Relative to the reference period (1995–2014), annual SPEI12 is projected to show a decreasing trend during 2015–2100. Trend will decrease highest under SSP5–8.5 scenario whereas the lowest under SSP1–1.9 and SSP1–2.6 scenarios. The state of drought in Northwest China is near normal during the reference period, drought will significantly escalate and moderate and severe drought will dominate the most of Northwest China with the increase of radiative forcing under SSPs-RCPs during 2015–2100. SSP5–8.5 will have the largest drought area than other scenarios. Observation shows Northwest China characterized a warmer and humid trend during 1961–2014, while GCMs projected that Northwest China will experience significant dryness trend during 2015–2100 under SSPs-RCPs, and SSP3–7.0, SSP4–3.4, SSP4–6.0 and SSP5–8.5 scenarios will be much drier than SSP1–1.9, SSP1–2.6 and SSP2–4.5 scenarios.

The realization of carbon emissions peak is important in the energy base area of China for the sustainable development of the socio-economic sector. The STIRPAT model was employed to analyze the elasticity of influencing factors of carbon emissions during 1990&ndash, 2010 in the Xinjiang autonomous region, China. The results display that population growth is the key driving factor for carbon emissions, while energy intensity is the key restraining factor. With 1% change in population, gross domestic product (GDP) per capita, energy intensity, energy structure, urbanization level, and industrial structure, the change in carbon emissions was 0.80%, 0.48%, 0.20%, 0.07%, 0.58%, and 0.47%, respectively. Based on the results from regression analysis, scenario analysis was employed in this study, and it was found that Xinjiang would be difficult to realize carbon emissions peak early around 2030. Under the condition of the medium-high change rates in energy intensity, energy structure, industrial structure, and with the low-medium change rates in population, GDP per capita, and urbanization level, Xinjiang will achieve carbon emissions peak at of 626.21, 636.24, 459.53, and 662.25 million tons in the year of 2030, 2030, 2040, and 2040, respectively. At last, under the background of Chinese carbon emissions peak around 2030, this paper puts forward relevant policies and suggestions to the sustainable socio-economic development for the energy base area, Xinjiang autonomous region.

WSN (wireless sensor network) has been used in a wide range of applications nowadays. Sensor networks may often relay critical data; thus, security must be a high priority. However, due to their limited computational, energy, and storage resources, sensor nodes are vulnerable to attack. So how to protect sensor nodes from attacks without raising computational capability and energy consumption is a worthwhile issue. A WAC (wireless authentication center) with mixed encryption named “MEWAC” is proposed. MEWAC is based on MCU (Microcontroller Unit) and WiFi (Wireless Fidelity) module and uses RSA, AES (Advanced Encryption Standard), and SHA-1 (Secure Hash Algorithm 1) to provide high performance authentication and data encryption services for sensor nodes. The experimental results show that MEWAC has the advantages of low cost, low power consumption, good performance, and stability; moreover, the authentication protocol improves the security of WSN and reduces the overhead in node authentication.

A variety of retinal pathologies use fundus images to do non-invasive diagnosis through the analysis of retinal vasculatures. An Encoder-decoder architecture based on fully convolutional neural network for retinal vessel segmentation in fundus images, termed RetNet, is presented in this paper. RetNet consists of an encoder module as a contracting pathway to extract hierarchical features and a corresponding decoder module as an expansive pathway to reconstruct the full-size input. Particularly, RetNet integrates two different shortcut connections to capture more contextual and semantic information and can output more precise results without any post-processing techniques. The architecture is evaluated on the publicly accessible dataset of Digital Retinal Image for Vessel Extraction (DRIVE). Its comparisons with the ground truth and several state-of-the-art segmentation approaches including unsupervised and supervised methods show that RetNet can achieve strong performance on the limited medical dataset at a faster convergence speed.

Cloud computing is a completely distributed sharing platform, through which users can request personalized cloud services. With the continually expansion of cloud services scale, more and more service providers are offering services with similar functions but different qualities. Users are faced with the problem of selecting the most suitable cloud services from numerous cloud services. In this paper, a trust assessment model based on recommendation and dynamic self-adaptive in cloud service is proposed. In the trust assessment model, trust is divided into direct trust and indirect trust, which can make the evaluation of trust value more accurate. In addition, a recommendation mechanism is proposed in this paper, the third-party nodes make recommendations according to the actual situation, and accept reward or punishment automatically after interacting. Experiment shows that the model can accurately evaluate the trust between nodes, and effectively resist the attack of malicious nodes.

A flexible software LDPC decoder that exploits data parallelism for simultaneous multicode words decoding on the mobile device is proposed in this paper, supported by multithreading on OpenCL based graphics processing units. By dividing the check matrix into several parts to make full use of both the local memory and private memory on GPU and properly modify the code capacity each time, our implementation on a mobile phone shows throughputs above 100 Mbps and delay is less than 1.6 millisecond in decoding, which make high-speed communication like video calling possible. To realize efficient software LDPC decoding on the mobile device, the LDPC decoding feature on communication baseband chip should be replaced to save the cost and make it easier to upgrade decoder to be compatible with a variety of channel access schemes.

In the heterogeneous wireless network environment, uneven traffic distribution among different wireless access networks results in the distinction of user network resource. A load balancing scheme is proposed to balance load of diverse access networks, lower unfairness of user bandwidth and improve efficiency of heterogeneous wireless networks (HWNs) resource by association control mechanism. The optimization goal of association control is to achieve user max-min fairness bandwidth. The fairness bandwidth allocation in HWNs is NP-hard problem so an approximation algorithm of polynomial-time is presented to achieve suboptimal result. Final simulation demonstrates the presented algorithm's performance in user bandwidth fairness and whole system efficiency.

The mobile ad-hoc network (MANET) is developing rapidly, and mobile servers are the trend of future network nodes. But it is still a problem to produce a suitable mobile server in the MANET. The challenges are from power saving, performance, portable requirements and so on. This paper presents a design of mobile server framework. It modularizes the server as replaceable units so that new technology can be easily deployed to fit the MANET environment. Especially, this paper introduces the messenger oriented P2P (MOP2P) framework. The theoretic analysis reveals that MOP2P in the MANET is more efficient than traditional end-to-end communication.

As a common language in the Web sites, JavaScript helps to make various dynamic Web pages. But it?s more difficult to protect JavaScript source codes than to protect Java or C/C++ programs, because JavaScript codes can?t be compiled into byte or binary codes. This paper discusses the obfuscation method to protect source codes of script languages like JavaScript, and provides a algorithm by using the reference design of the polymorphic viruses to make the random encryption. This algorithm named ?parhelion? has been applied to a set of software of Web page encryption, and a Web-based information security system. The paper also gives the examples to show the effect of the protection.