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Through caching popular contents at the network edge, wireless edge caching can greatly reduce both the content request latency at mobile devices and the traffic burden at the core network. However, popularity-based caching strategies are vulnerable to Cache Pollution Attacks (CPAs) due to the weak security protection at both edge nodes and mobile devices. In CPAs, through initiating a large number of requests for unpopular contents, malicious users can pollute the edge caching space and degrade the caching efficiency. This paper firstly integrates the dynamic nature of content request and mobile devices into the edge caching framework, and introduces an eavesdropping-based CPA strategy. Then, an edge caching mechanism, which contains a Request Pattern Change-based Cache Pollution Detection (RPC2PD) algorithm and an Attack-aware Cache Defense (ACD) algorithm, is proposed to defend against CPAs. Simulation results show that the proposed mechanism could effectively suppress the effects of CPAs on the caching performance and improve the cache hit ratio.

Reasonable nitrogen (N) and potassium (K) application rates can effectively improve fertilizer use efficiency, rice yield and quality. A two-year field experiment was conducted with combined application of three N rates (135, 180, and 225 kg ha-1, denoted as N1-N3) and four K rates (0, 90, 135, and 180 kg ha-1, denoted as K0-K3) using super indica hybrid rice cultivar Yixiangyou (YXY) 2115 to explore the effects of co-application of N and K on rice growth and development. The results indicated that the combined application of N and K had significantly interactive effects on dry matter (DM) accumulation, nutrients absorption, N harvest index (NHI), K harvest index (KHI), spikelets per panicle and most rice quality indexes. The highest total DM accumulation (17998.17-19432.47 kg ha-1) at maturity stage was obtained under N3K2. The effect of co-application of N and K on nutrients absorption and utilization varied between the two years and within each year. The highest total N and K accumulations at maturity stage were observed under N3K1 and N3K2, respectively, while the highest N recovery efficiency (NRE) and K recovery efficiency (KRE) were observed under N1K3. High expression levels of N and K metabolism-related genes in rice grains were observed under N3K2 or N3K3, consistent with N and K uptake. Co-application of N and K increased rice yield significantly and the highest yield (6745.02-7010.27 kg ha-1) was obtained under N2K2. As more N was gradually applied, rice appearance quality improved but milling, cooking and eating quality decreased. Although appropriate application of K could improve rice milling, cooking and eating quality, it reduced appearance quality. The optimal milling, cooking and eating quality were obtained under N1K2, while the best appearance quality was obtained under N3K0. Overall, a combination of 135-210 kg ha-1 N and 115-137 kg ha-1 K application rates was recommended for achieving relatively higher yield and better quality in rice production.

Lipid content has an important effect on rice eating quality, but the effects of fertilizer application rate on the lipid synthesis and eating quality of rice are not well understood. Potassium (K) has a strong influence on rice quality and the requirement for K fertilizer in rice is greater than for nitrogen (N) and phosphorus (P) fertilizers. To investigate the effects of K fertilizer on the lipid synthesis and eating quality of rice, we used Nanjing 9108 (NJ9108, japonica) and IR72 (indica) rice as experimental materials and four K levels: K0 (0 kg ha–1), K1 (90 kg ha–1), K2 (135 kg ha–1) and K3 (180 kg ha–1). The results showed that the lipid content, free fatty acid (FFA) content, unsaturated fatty acid (UFA) content, malonyl-CoA (MCA) content, phosphatidic acid (PA) content, lipid synthesis-related enzyme activities and eating quality first increased and then decreased with increasing K in both cultivars. The maximum values were obtained under K2. However, the saturated fatty acid (SFA) content showed the opposite trend. No significant differences were found in pyruvate (PYR) content among the K treatments. The protein and oxaloacetic acid (OAA) contents and phosphoenolpyruvate carboxylase (PEPCase) activity of NJ9108 first decreased and then increased with increasing K, and the minimum values were obtained under K2; while IR72 showed the opposite trend and the maximum values were obtained under K1. Overall, increasing K optimized the fatty acid components and increased the lipid content and eating quality of rice by enhancing lipid synthesis-related enzyme activities and regulating substrate competition for lipid and protein synthesis. The optimal K application rate for lipid synthesis, eating quality and grain yield was 135 kg ha–1 for both cultivars.

The wide deployment of the Internet of Things (IoT) necessitates new machine learning (ML) methods and distributed computing paradigms to enable various ML-based IoT applications to effectively process huge amounts of data [...]

Gastric ulcer (GU) is one of the most prevalent digestive diseases that seriously affects people’s health. Previous studies have demonstrated the anti-GU effect of Ruda-6 (RD-6), a classic formulae of traditional Mongolian medicine. However, the underlying mechanism of RD-6 against GU remains elusive. Thus, we conducted an integrative approach of network analysis, RNA-seq, and in vivo validation experiment to elucidate the therapeutic mechanisms of RD-6 in preventing GU. A network analysis was performed to predict the potential targets of RD-6. Rats were pretreated with RD-6 at different doses for 21 days, followed by GU induction with indomethacin injection. The ulcer index and inhibition rates were calculated, and the levels of inflammatory related factors were determined by ELISA. The gastroprotective mechanism of RD-6 against ulceration was verified by RNA-seq and the key pathway was detected by in vivo validation. As the network analysis predicted, RD-6 exerts anti-GU effects by regulating 75 targets and 160 signaling pathways. Animal experiment results suggested that pretreatment with RD-6 significantly ameliorated the gastric mucosal injury and inflammatory response, as evidenced by a reduced ulcer index, decreased interleukin (IL)-1β, IL-6, and IL-17 levels, and increased prostaglandin E2 (PGE2) levels in the GU model rats induced by indomethacin. RNA-seq data identified four potential hub genes that were primarily involved in the IL-17 signaling pathway. Furthermore, in vivo validation experiment showed that RD-6 inhibited the IL-17 signaling pathway by down-regulating the expression of IL17RA, proto-oncogene C-Fos (FOS), IL1B and prostaglandin-endoperoxide synthase 2 (PTGS2). Taken together, the present study provides evidence that RD-6 could effectively protect against indomethacin-induced GU, which might be attributed to suppressed inflammation. The IL-17 signaling pathway may be one of the crucial mechanisms that mediates the effect of RD-6.

The advanced services provided by vehicle ad hoc networks (VANETs) often require vehicles to process complex computing tasks that may not be completed by individual vehicles within a required delay limit. Offloading tasks to road side units (RSUs) is a typical approach to enhancing service performance in VANET; however, RSUs may not always have sufficient resources for handling all task offloading requests. With the increasing amount of computing capacities available on vehicles, offloading tasks to other vehicles offers a promising alternative to RSU-based task offloading. However, vehicle-to-vehicle task offloading in VANET faces some new challenges that have not been fully addressed, among which is the degraded delay performance caused by vehicle mobility. In order to solve this problem, we propose an Online Pre-filtering Task Offloading System (OPTOS) that is able to mitigate the impact of vehicle mobility on task offloading performance. OPTOS comprises a process that selects candidate vehicles for hosting offloaded tasks and an HGSA algorithm that assigns tasks to vehicles for minimizing task completion delay while balancing utilization of computing capacities on different vehicles. We have conducted extensive experiments using a real-world dataset for evaluating the performance of the proposed OPTOS. Obtained results indicate that OPTOS is effective for reducing task completion delay and increasing task success rate in various VANET scenarios with different levels of vehicle mobility.

Privacy protection problem is one of the most concerning issues related to Location-Based Services (LBS) in our daily life. Privacy protection of LBS often requires anonymizing customer's trajectory data. Currently available methods for trajectory anonymity often assume an entire trajectory as one anonymous unit, which may lead to low anonymity efficiency due to the massive amount of trajectory data, especially for customers travel through a long road. Considering people's routine activities, the starting and ending locations of a trip often uncover the user's request intent, which may lead to exposure of user privacy. In order to address the problem of inefficient trajectory anonymity, we propose a location privacy protection method that is based on the initial and final trajectory segmentation (IFTS) in this paper. In the IFTS method, the road network structure is first transformed into an edge cluster model based on its location type. Then, the user trajectory is divided to segments according to the temporal sequence of the ingress and egress nodes. The initial and final trajectory segments are identified and divided into equivalence classes, which then are used for constructing a trajectory graph and the corresponding k-anonymous set. Our experimental results show that the proposed method can reduce the anonymous area, improve anonymity efficiency, and enhance trajectory data utilization compared to the existing methods for trajectory anonymity.

Euphorbiaceae, a family of plants mainly grown in the tropics and subtropics, is also widely distributed all over the world and is well known for being rich in rubber, oil, medicinal materials, starch, wood and other economically important plant products. Glutathione S-transferases (GSTs) constitute a family of proteins encoded by a large supergene family and are widely expressed in animals, bacteria, fungi and plants, but with few reports of them in Euphorbiaceae plants. These proteins participate in and regulate the detoxification and oxidative stress response of heterogeneous organisms, resistance to stress, growth and development, signal transduction and other related processes. In this study, we identified and analyzed the whole genomes of four species of Euphorbiaceae, namely Ricinus communis, Jatropha curcas, Hevea brasiliensis, and Manihot esculenta, which have high economic and practical value. A total of 244 GST genes were identified. Based on their sequence characteristics and conserved domain types, the GST supergene family in Euphorbiaceae was classified into 10 subfamilies. The GST supergene families of Euphorbiaceae and Arabidopsis have been found to be highly conserved in evolution, and tandem repeats and translocations in these genes have made the greatest contributions to gene amplification here and have experienced strong purification selection. An evolutionary analysis showed that Euphorbiaceae GST genes have also evolved into new subtribes (GSTO, EF1BG, MAPEG), which may play a specific role in Euphorbiaceae. An analysis of expression patterns of the GST supergene family in Euphorbiaceae revealed the functions of these GSTs in different tissues, including resistance to stress and participation in herbicide detoxification. In addition, an interaction analysis was performed to determine the GST gene regulatory mechanism. The results of this study have laid a foundation for further analysis of the functions of the GST supergene family in Euphorbiaceae, especially in stress and herbicide detoxification. The results have also provided new ideas for the study of the regulatory mechanism of the GST supergene family, and have provided a reference for follow-up genetics and breeding work.

Federated learning (FL) and split learning (SL) are two emerging collaborative learning methods that may greatly facilitate ubiquitous intelligence in the Internet of Things (IoT). Federated learning enables machine learning (ML) models locally trained using private data to be aggregated into a global model. Split learning allows different portions of an ML model to be collaboratively trained on different workers in a learning framework. Federated learning and split learning, each have unique advantages and respective limitations, may complement each other toward ubiquitous intelligence in IoT. Therefore, the combination of federated learning and split learning recently became an active research area attracting extensive interest. In this article, we review the latest developments in federated learning and split learning and present a survey on the state-of-the-art technologies for combining these two learning methods in an edge computing-based IoT environment. We also identify some open problems and discuss possible directions for future research in this area with the hope of arousing the research community’s interest in this emerging field.

Low-gluten rice cultivar D105 was grown in the field under five nitrogen (N) treatments (N0: 0, N90: 90, N135: 135, N180: 180, and N225: 225 kg·hm−2) to investigate the effect of N application rate on the yield and the resulting dynamic changes in protein content, grain processing quality, and relative content of each component protein. The results indicated that the number of effective panicles, seed setting rate, the number of solid grains, dry matter, leaf nitrate reductase and glutamine synthetase activities, and yield increased with N application rate ranging from 0 to 180 kg·hm−2. However, the seed setting rate and the number of solid grains decreased under N225 treatment, leading to a decline in yield. At maturity, 35 days after flowering, no significant differences between albumin and gliadin in the rice grain were found among the N treatments, while globulin and gluten differed among treatments, indicating that the effect of N application rate on the former was slightly the opposite to that observed on the latter. Further, the N application rates did not change the proportions of component proteins relative to the total protein content in the grain. Processing and taste qualities of D105 low-gluten rice were optimal in the N135 and N180 treatments, and the overall rice quality decreased under the N225 treatment. Therefore, the optimal N application rate for yield and processing quality of D105 low-gluten rice is N180: 180 kg·hm−2.

With rapid advancement of Cloud computing and networking technologies, a wide spectrum of Cloud services have been developed by various providers and utilized by numerous organizations as indispensable ingredients of their information systems. Cloud service performance has a significant impact on performance of the future information infrastructure. Thorough evaluation on Cloud service performance is crucial and beneficial to both service providers and consumers; thus forming an active research area. Some key technologies for Cloud computing, such as virtualization and the Service-Oriented Architecture (SOA), bring in special challenges to service performance evaluation. A tremendous amount of effort has been put by the research community to address these challenges and exciting progress has been made. Among the work on Cloud performance analysis, evaluation approaches developed with a system modeling perspective play an important role. However, related works have been reported in different sections of the literature; thus lacking a big picture that shows the latest status of this area. The objectives of this article is to present a survey that reflects the state of the art of Cloud service performance evaluation from the system modeling perspective. This articles also examines open issues and challenges to the surveyed evaluation approaches and identifies possible opportunities for future research in this important field.

Objective To investigate the effect of low-frequency repetitive transcranial magnetic stimulation (rTMS) combined with task-oriented training on the recovery of upper limb motor function of stroke patients. Methods A total of 42 patients with hemiplegia after stroke were randomly divided into control group (N = 20) and treatment group (N = 22). Control group received routine rehabilitation training and task-oriented training, and treatment group received low-frequency (1 Hz) rTMS over the contralesional cortex addition to routine rehabilitation and task-oriented training. Fugl-Meyer Assessment Scale for Upper Extremity (FMA-UE) and Wolf Motor Function Test (WMFT) were used to evaluate upper limb motor function of all patients before treatment, after 4-week treatment and 3 months after treatment. The latency and central motor conduction time (CMCT) of motor-evoked potential (MEP) in the contralesional cortex were recorded and analyzed. Results Compared with control group, FMA-UE score (P = 0.006) and WMFT score (P = 0.024) were significantly increased in treatment group. There was significant difference in FMA-AUE score (P = 0.000) and WMFT score (P = 0.000) at different time points. Compared with before treatment, FMA-UE score (P = 0.000, for all) and WMFT score (P = 0.000, for all) of patients in both groups were all significantly increased after 4-week treatment and 3 months after treatment. Besides, FMA-UE score (P = 0.000, for all) and WMFT score (P = 0.000, for all) 3 months after treatment were higher than those after 4-week treatment. There was no statistically significant difference between 2 groups on the latency (P = 0.979) and CMCT (P = 0.807) of MEP before and after treatment, and so was the difference on the latency (P = 0.085) and CMCT (P = 0.507) of MEP in the contralesional cortex at different time points (before treatment, after 4-week treatment and 3 months after treatment). Conclusions Low-frequency rTMS over the contralesional cortex combined with task-oriented training could greatly promote upper limb motor function of patients after stroke, and should be recommended to clinical application. DOI: 10.3969/j.issn.1672-6731.2017.04.004

Cloud-native network design, which leverages network virtualization and softwarization together with the service-oriented architectural principle, is transforming communication networks to a versatile platform for converged network-cloud/edge service provisioning. Intelligent and autonomous management is one of the most challenging issues in cloud-native future networks, and a wide range of machine learning (ML)-based technologies have been proposed for addressing different aspects of the management challenge. It becomes critical that the various management technologies are applied on the foundation of a consistent architectural framework with a holistic vision. This calls for standardization of new management architecture that supports seamless the integration of diverse ML-based technologies in cloud-native future networks. The goal of this paper is to provide a big picture of the recent developments of architectural frameworks for intelligent and autonomous management for future networks. The paper surveys the latest progress in the standardization of network management architectures including works by 3GPP, ETSI, and ITU-Tand analyzes how cloud-native network design may facilitate the architecture development for addressing management challenges. Open issues related to intelligent and autonomous management in cloud-native future networks are also discussed in this paper to identify some possible directions for future research and development.

The crucial role of networking in Cloud computing calls for a holistic vision of both networking and computing systems that leads to composite network–compute service provisioning. Software-Defined Network (SDN) is a fundamental advancement in networking that enables network programmability. SDN and software-defined compute/storage systems form a Software-Defined Cloud Environment (SDCE) that may greatly facilitate composite network–compute service provisioning to Cloud users. Therefore, networking and computing systems need to be modeled and analyzed as composite service provisioning systems in order to obtain thorough understanding about service performance in SDCEs. In this paper, a novel approach for modeling composite network–compute service capabilities and a technique for evaluating composite network–compute service performance are developed. The analytic method proposed in this paper is general and agnostic to service implementation technologies; thus is applicable to a wide variety of network–compute services in SDCEs. The results obtained in this paper provide useful guidelines for federated control and management of networking and computing resources to achieve Cloud service performance guarantees.

Data fusion in the Internet of Things (IoT) environment demands collecting and processing a wide variety of data with mixed time characteristics, both real-time and non-real-time data. Most of the previous research on data fusion was about the data processing aspect; however, successful data transmission is a prerequisite for high-performance data fusion in IoT. On the other hand, research on data transmissions in IoT mainly focuses on networking without sufficiently considering the special requirements of the upper-layer applications, such as the data fusion process, that are consuming the transmitted data. In this paper, we tackle the problem of data transmission for data fusion in an IoT environment by proposing a distributed scheduling mechanism VD-CSMA in wireless sensor networks, which considers the values for data fusion, as well as the delay constraints of packets when determining their priority levels for transmission. Simulation results have shown that VD-CSMA may enhance both throughput and delay performance of data transmission as compared to the typical scheduling schemes used for data fusion in IoT.

Service Economics has been successfully proposed and implemented for promoting the macro service market especially in Global value chains which however don’t provide guidance to and integrate directly with the IT side implementation to achieve coherent information coordination and robust adaptability which indicts maximization of business profitability. Therefore there leaves a gap of the refinement from the strategic business planning to ubiquitous services implementation in the API (Application Program Interface) economy. Recently Gio Wiederhold attributed this gap to the knowledge difference between economists and the IT professionals and proposed that IT professionals should act to fill the gap since they have irreplaceable data, information and knowledge concerning the specific implementation. As an instance of this guidance in the practice of Value Driven Design, we proposed a systemic formalization from the value calculation to the design quality measurement which binds the modification and change on the design artifacts with the business value strategy through a framework of managed quality properties in a service design process covering data, information and knowledge assets.

Wireless Sensor Networks (WSNs) and Cloud computing are two emerging technologies, respectively, in the networking and computing fields. Sensor Cloud integrates WSNs and Clouds into an extended form of cloud infrastructure that manages sensors scattered throughout WSNs. Networking plays a crucial role in Cloud computing and becomes a particularly challenging issue in sensor Clouds due to resource limitation of WSNs as the underlying network of the Cloud infrastructure. Therefore, data transmission from sensors to servers in sensor Clouds must be carefully controlled to avoid network congestion. In this paper, we study Random Early Detection-Based (RED-based) congestion control for data transmission in sensor Clouds. Specifically we propose an improved RED (IRED) algorithm that meets sensor Cloud networking requirements better and develop a queueing model coupled with analysis technique to evaluate the performance of the proposed congestion control scheme. We also conduct extensive simulations to evaluate performance of IRED compared with the regular RED algorithm. Our analysis and simulation results demonstrate that the proposed IRED congestion control achieves comparable delay performance and preferable throughput performance in comparison with the standard RED for data transmission in sensor Clouds. The easily implemented control mechanism and low complexity of our proposed algorithm make it more suitable for sensor Clouds.