Search

To develop and validate a nomogram to predict distant metastasis-free survival of patients with locoregionally advanced nasopharyngeal carcinoma.We collected the total clinical data of 820 nasopharyngeal carcinoma (NPC) patients, of whom 482 formed the training cohort from one hospital and 328 made up the validation cohort from another hospital. By analyzing the prognosis of all patients after intensity-modulated radiotherapy by univariate and multivariate Cox regression models, a nomogram related to DMFS was created in the training cohort. The discriminatory and calibration power of the nomogram was successively assessed in the training and validation cohorts by the C‑index and calibration curve. The predictive ability for 3‑year DMFS was compared between the nomogram and TNM stage using ROC curves. Patients were divided into different risk groups based on scores calculated from the nomogram.Age, lymph node gross tumor volume (GTVnd), and gross tumor volume of the nasopharynx (GTVnx) were the factors included in the nomogram. The C‑index of the nomogram was 0.721 in the training cohort and 0.750 in the validation cohort. The calibration curves were satisfactory. Patients in the high-risk group were more likely to develop metastases.A nomogram incorporating age, GTVnd, and GTVnx showed good performance for predicting DMFS in patients with locoregionally advanced NPC.

The service provisioning in multi-domain software-defined elastic optical networks (SD-EONs) is an interesting but difficult problem to tackle, because the basic problem of lightpath provisioning, i.e., the routing and spectrum assignment (RSA), is NP-hard, and each domain is owned and operated by a different carrier. Therefore, even though numerous RSA heuristics have been proposed, there does not exist a universal winner that can always achieve the lowest blocking probability in all the scenarios of a multi-domain SD-EON. This motivates us to revisit the inter-domain provisioning problem in this paper by leveraging deep reinforcement learning (DRL). Specifically, we propose DeepCoop, which is an inter-domain service framework that uses multiple cooperative DRL agents to achieve scalable network automation in a multi-domain SD-EON. DeepCoop employs a DRL agent in each domain to optimize intra-domain service provisioning, while a domain-level path computation element (PCE) is introduced to obtain the sequence of the domains to go through for each lightpath request. By sharing a restricted amount of information among each other, the DRL agents can make their decisions distributedly. To ensure scalability and universality, we design the action space of each DRL agent based on well-known RSA heuristics, and architect the agents based on the soft actor-critic (SAC) scenario. We run extensive simulations to evaluate DeepCoop, and the results show that DeepCoop can adapt to the dynamic environment in a multi-domain SD-EON to always select the best RSA heuristic for minimizing blocking probability, and it outperforms the existing algorithms on inter-domain provisioning in various scenarios. Moreover, we verify that the distributed training implemented in DeepCoop ensures its universality and scalability (i.e., its training and operation do not depend on the topology of the SD-EON).

N etwork space mimicry defense constructs heterogeneous redundant configuration of execution pool through software and hardware diversification technology, and forms multi-mode output vector through scheduler. Although it can provide a point to surface fusion defense mechanism independent of traditional security means, the execution scheduler, which plays a key role, can still sniff or guess through retransmission attack because of its single and static algorithm. The basic principle of the scheduling algorithm is detected to increase the chance of mimicry escape. This paper designs a dynamic scheduling mechanism with diversified algorithms, which increases the complexity and dynamics of the scheduling mechanism, and can effectively reduce the risk of the scheduler being sniffed. Experimental results show that, compared with the traditional scheduling mechanism, the proposed diversified and dynamic scheduling scheme can significantly reduce the risk of the scheduler being attacked, and further improve the security of the mimicry defense system.

An adaptive dynamic programming value iteration algorithm is designed to solve nonlinear continuous-time nonzero-sum games in this paper. Since existing studies were developed on policy iteration, the initial condition of control policy requires to be admissible and thus the application has been limited. The proposed algorithm can be started with a more relaxed condition compared with policy iteration based methods, which relaxes the initial condition of the existing works. A simulation example is displayed to show the effectiveness.

As a revolutionary and subversive theory and technology, the mimic defense system based on dynamic heterogeneous redundancy has played an essential role in cyberspace security. Processing equivalent tasks with heterogeneous executors and arbitrating a final output is the basis of the mimic defense theory. It is necessary to guarantee a normalized result under non-disturbing conditions in external communication sessions with random parameter requirements. Otherwise, it is difficult to distinguish whether the system is under attack. This paper proposes a normalized scheme to deal with the random parameter requests from heterogeneous executors based on the multi-queue architecture in response to this problem. Simulation and experimental results show that the proposed random parameter normalization module based on multi-queue architecture can provide uniform random parameters for the mimic defense system’s heterogeneous executors, effectively synchronizing the random parameter requests in disorder.

Intrusion Detection System (IDS) is a critical research field in the age of internet, therefore a significant number of techniques have been employed for IDS. However, because of the inherent drawbacks of IDS datasets, these techniques are not very successful in identifying all types of intrusion. In this paper, we propose an intrusion detection neural network model based on interval type-2 fuzzy c-means clustering (IDNN-IT2FCM) to help IDS improve detection rates. Firstly, we utilize interval type-2 fuzzy c-means clustering (IT2FCM) to cluster the training set into different training subsets, which makes intrusion detection network learn subset more quickly, robustly, and precisely. Secondly, we design two criteria to decide the cluster belongingness of low-frequent attack samples in the training set and pre-classify the samples of the testing set. Finally, all groups of the testing set are classified by the neural network. Compared with other classification approaches, the proposed method can obtain the satisfying results on NSL-KDD dataset.

Code obfuscation is a kind of powerful protection technique for software code. At present, research on obfuscation techniques is mainly focused on analyzing the effect of single obfuscation method, leaving few discussions on cooperative obfuscation of multiple methods. This paper firstly presented a brief introduction of the concept and methods of code obfuscation, then designed and implemented an obfuscator with multiple obfuscation methods. Then, a collaborative obfuscation strategy suitable for multiple obfuscation methods is proposed in detail. Finally, we verified that the obfuscation strategy indeed improves the performance of obfuscation through experiments.

Environments in both biotic and abiotic ecosystems have been affected by the colonization of non-native flora. In this study, we examined the effect of Bidens alba invasion on different land-use types along a coastline in southern China. Bacterial communities in each site were determined using 16S rDNA sequencing, and soil physicochemical properties were analyzed using standard methods. Although our results indicated that B. alba invasion did not have a significant effect on the alpha diversity of bacteria, it caused significant differences in soil bacterial community composition between invaded and uninvaded soil across different land-use types. Beta diversity and several physicochemical properties in forest, orchard and waterfront environments were recorded to be more susceptible to B. alba invasion. A high proportion of the variation of bacterial communities can be explained by a combination of environmental variables, indicating that environmental selection rather than plant invasion is a more effective process in coastal microbial assemblages. By comparing topological roles of shared OTUs among invaded and uninvaded soil, keystone taxa in invaded soil were identified. Acidobacteria was the major phyla involved in the invasive process which could be driven by environmental selection. How key phyla react in our experiment should be verified by further studies.

Massive data is the basis of machine learning, and continuous data protection is an effective means to ensure data integrity and availability. At present, continuous data protection adopts the same backup strategy in different host security environments, ignoring the potential relationship between host security state and data destruction, resulting in a low utilization rate of backup storage space. To make better use of backup storage space and improve data recovery speed, this paper proposes a file-level continuous data protection scheme for enforcing security baseline (SB-CDP). SB-CDP combines a security baseline with continuous data protection to dynamically adjust the backup strategy based on the host security status provided by the security baseline. The experimental results show that, on the one hand, SB-CDP can effectively utilize the backup storage space, on the other hand, it can effectively improve the data recovery speed and reduce the impact on the system performance.

File transfer is based on the reliable TCP protocol. However, when the network is of poor quality, TCP-based transmission will still perform less effectively due to some reasons. Existing approaches mainly optimize file transmission by modifying network strategies or optimize transmission mechanism. But when network conditions cannot be selected or modified, a suitable parameter adjustment method would be needed. The experiments in this paper firstly perform TCP-based file transmission tests on a poor network condition and find two problems. Then, multiple tests were performed on multiple platforms to detect the conditions under which these problems occurred. Next, to address the issue and improve the transmission performance, we propose an intelligent optimization scheme. By adjusting the transmission parameters and adding policies, the scheme equips the intermediate parameters with intelligent self-adaptation capabilities. We also test and evaluate the performance of the intelligent scheme. The result shows that the file transfer under the new scheme not only basically avoids the target problems, but also reduces the practical upload time under poor network condition from the perspective of decreasing the number of retransmissions and reducing the failure rate.

Data is the main driving factor of artificial intelligence represented by machine learning, and how to ensure data security is one of the severe challenges. In many traditional methods, a single snapshot strategy is used to protect data. In order to meet the flexibility of data protection and optimize storage space, this paper presents a new architecture and an implementation in the Linux kernel. The idea is to hook system calls and analyze the relationship between applications and files. By tracking system calls, the system can perceive the file modification and automatically adjust the time interval for generating snapshots. Time granularity changes with the application load to achieve on-demand protection. Extensive experiments have been carried out to show that the scheme can monitor the process of operating files, reduce storage costs and hardly affect the performance of system.

Typhoons are having an increasingly severe and widespread impact on forest ecosystems around the world due to climate change. However, few studies have examined the impacts of strong typhoons on subtropical evergreen broadleaved forests, particularly in rare old-growth forests. Here, using a 20 ha permanent subtropical forest plot in Guangdong Province, southern China, the impact of super Typhoon Mangkhut on secondary and old-growth subtropical evergreen broadleaved forests was quantitatively investigated and compared. The old-growth forest was damaged more than twice as much as the secondary forest, as measured by the proportion of the basal area of damaged trees. Forest structure and composition played a significant role in determining the vulnerability of forests to powerful typhoons. This study advances knowledge about how old-growth forests respond to global climate change.

Community assembly in natural communities is commonly explained by stochastic and niche-based processes such as environmental filtering and biotic interactions. Many studies have inferred the importance of these processes using a trait-based approach, however, there are still unknowns around what factors affect the importance of different assembly processes in natural communities. In this study, the trait dispersion patterns of 134 species were examined across different functional traits, habitat types, ontogenetic stages and spatial scales from a 20-ha Dinghushan Forest Dynamic Plot in China. The results showed that (1) functional traits related to productivity such as specific leaf area and leaf area mainly showed functional clustering, indicating these two functional traits were more affected by environmental filtering. However, trait dispersion patterns depended on more than the ecological significances of functional traits. For example, trait dispersions of leaf dry matter content, leaf thickness and maximum height did not show consistent patterns across habitat types and ontogenetic stages, suggesting more complex mechanisms may operate on these traits, (2) the trait dispersion varied with the habitat types and ontogenetic stages. Specifically, we found that habitat types only affected the strength of trait dispersions for all the five traits, but ontogenetic stages influenced both the strength and direction of trait dispersions, which depended on the traits selected, (3) the relative importance of soil, topography and space to trait dispersion varied with ontogenetic stages. Topography and space were more important for trait dispersion of saplings but soil was more important for trait dispersion of adults, (4) biotic interactions dominated community assembly at smaller spatial scales but environmental filtering dominated community assembly at larger spatial scales. Overall, the results highlight the importance of functional traits, habitat types, ontogenetic stages and spatial scales to community assembly in natural communities.

The disaster relief material reserve center is an important strategy to deal with natural disasters. Aiming at its location problem, this paper first constructs a site selection decision evaluation system from five aspects: nature, transportation, infrastructure, social benefits and economy. Secondly, the fuzzy matter element analysis and gray TOPSIS (Technique for order preference by similarity to ideal solution) method are used to construct the disaster relief material reserve center location measurement model. Finally, an empirical analysis is carried out in Sichuan Province. The results show that the Sichuan-Central region is the best place to build a disaster relief material reserve center in Sichuan Province. This model can not only effectively locate the disaster relief reserve center, but also provide a new idea for solving the problem of multi-dimensional influencing factors.

In an attempt to ensure sustainable water resource development, this paper constructs a comprehensive scientific index evaluation system focused on the macro socio-economic-ecological environment. Inspired by the theory of dissipative structure, the sustainable development system of water resources is regarded as a complex and huge dissipative system. In order to effectively measure the coordinated development status and orderly evolution trend of the system, this paper uses the information entropy method to construct the measurement model of the water resources system and analyze its internal entropy flow changes. The empirical analysis of the water resources in China from 2007 to 2016 found that coordinated water resource subsystem development could achieve sustainable development, and that over the examined period, the sustainable water resource development system in China became more orderly and coordinated, therefore, the sustainable development aim is gradually being achieved.

In view of sustainable development of tourism, this paper firstly constructs a more comprehensive and scientific index system from the economical/societal/resource-related/environmental aspects of tourism and evaluates the sustainable and comprehensive development level of tourism in 11 provinces and cities of the Yangtze River economic zone by using the weighted TOPSIS (The Technique for Order Preference by Similarity to an Ideal Solution ) method; secondly, it analyzes the coupling coordination evolution relationship between tourism and the economy/society/resources/environment in different provinces and cities of the Yangtze river economic zone based on the coupling coordination model from the spatio-temporal dimension; and finally, it predicts the coupling coordination degree of the provinces and cities in the region in the next few years by the grey model (1,1) and puts forward some countermeasures and suggestions. According to the study, this method provides an effective reference to the study on the sustainable development of tourism and is very significant for learning the sustainable development of regional tourism and establishing specific and scientific countermeasures for improvement.

To analyze the gender, age, fracture classification and variation trend of adult intertrochanteric fractures treated in a single-center hospital in ten years.The data of adult (age ≥ 16 years) intertrochanteric fractures admitted to the Third Hospital of Hebei Medical University from January 2003 to December 2012 were collected retrospectively. All the fractures were acute and the pathological or periprosthetic fractures were excluded. The radiography of fracture were classified by same experienced orthopedic resident and verified by two orthopedic deans and one radiologist. The gender, age and fracture classification were analyzed and compared between January 2003 to December 2007 and January 2008 to December 2012.A total of 3 201 cases were collected. The adult intertrochanteric fractures accounted for 2.97% of all adult fractures and 43.76% of adult proximal femoral fractures. Of all fractures 64.98% were elderly ( ≥ 60 years) and 35.02% were middle-aged (16-59 years). In elderly, female were common (57.78%) while in middle-aged were male commonly (79.13%). According to Evans classification, instable fractures were more common (68.92%). According to AO classification, the most common type was A2 (49.67%) and the least was A3 (15.93%). Comparing between January 2003 to December 2012 and January 2008 to December 2012, the proportion of intertrochanteric fracture of adult fracture was decreased by 0.31% (χ² = 9.29, P = 0.002)and the proportion of intertrochanteric fracture of adult proximal femoral fractures was decreased by 3.15% (χ² = 7.35, P = 0.007). The proportion of elderly patients, female and stable fractures was increased by 14.35% (χ² = 71.98, P0.01), 4.04% (χ² = 8.16, P = 0.004) and 5.62% (χ² = 11.7, P = 0.001), respectively. The proportion of AO classification was not significantly verified (χ² = 3.24, P = 0.198).The intertrochanteric fractures are most common in elderly patients, A2-type of AO Classification and stable (Evans III, IV, V) in Evans classification. Compared with the previous five years, the proportions of female, elderly and stable (Evans I, II) fracture increase in last 5 years.