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The set covering problem (SCP) is a fundamental NP-hard problem in computer science and has a broad range of important real-world applications. In practice, SCP instances transformed from real-world applications would be of large scale, so it is of significant importance to design effective heuristic algorithms, especially local search ones. However, there exist only few research works on developing local search algorithms for solving SCP. In this article, we propose a new local search algorithm for solving SCP, dubbed NuSC. In particular, NuSC introduces a new combined scoring function for subset selection, which combines different subset properties in an effective way and helps NuSC find more optimized solutions. Besides, NuSC incorporates a dynamic weighting scheme for elements, a tabu search strategy, and a novelty selection mechanism to further enhance its practical performance. In order to study the effectiveness and robustness of our proposed NuSC algorithm, we conduct extensive experiments to compare NuSC against many state-of-the-art competitors on various types of SCP instances. Our experimental results demonstrate that NuSC significantly outperforms its competitors on the majority of instances, indicating the superiority of NuSC. Also, our empirical evaluations confirm the effectiveness of each algorithmic technique underlying NuSC.

Various mobile devices have been used to collect, store and transmit tremendous trajectory data, and it is known that raw trajectory data seriously wastes the storage, network bandwidth and computing resource. To attack this issue, one-pass line simplification ($$\textsf {LS} $$) algorithms have been developed, by compressing data points in a trajectory to a set of continuous line segments. However, these algorithms adopt the perpendicular Euclidean distance, and none of them uses the synchronous Euclidean distance ($$\textsf {SED} $$), and cannot support spatiotemporal queries. To do this, we develop two one-pass error bounded trajectory simplification algorithms ($$\textsf {CISED} $$-$$\textsf {S} $$ and $$\textsf {CISED} $$-$$\textsf {W} $$) using $$\textsf {SED} $$, based on a novel spatiotemporal cone intersection technique. Using four real-life trajectory datasets, we experimentally show that our approaches are both efficient and effective. In terms of running time, algorithms $$\textsf {CISED} $$-$$\textsf {S} $$ and $$\textsf {CISED} $$-$$\textsf {W} $$ are on average 3 times faster than $$\textsf {SQUISH} $$-$$\textsf {E} $$ (the fastest existing $$\textsf {LS} $$ algorithm using $$\textsf {SED} $$). In terms of compression ratios, $$\textsf {CISED} $$-$$\textsf {S} $$ is close to and $$\textsf {CISED} $$-$$\textsf {W} $$ is on average $$19.6\%$$ better than $$\textsf {DPSED} $$ (the existing sub-optimal $$\textsf {LS} $$ algorithm using $$\textsf {SED} $$ and having the best compression ratios), and they are $$21.1\%$$ and $$42.4\%$$ better than $$\textsf {SQUISH} $$-$$\textsf {E} $$ on average, respectively.

With the development of smart phones and online to offline, spatial platforms, such as TaskRabbit, are getting famous and popular. Tasks on these platforms have three main characters: they are in real-time dynamic scenario, they are on the road networks, and some of them have multiple skills. However, existing studies do not take into account all these three things simultaneously. Therefore, an important issue of spatial crowdsourcing platforms is to assign workers to tasks according to their skills on road networks in a real-time scenario. In this paper, we first propose a practical problem, called online multi-skilled task assignment on road networks (OMTARN) problem, and prove that the OMTARN problem is NP-Hard and no online algorithms can achieve a constant competitive ratio on this problem. Then, we design a framework using batch-based algorithms, including fixed and dynamic batch-based algorithm, and we show that how the algorithms update the batch. After that, we use the hierarchically separated tree structure to accelerate our algorithms. Finally, we implement all the algorithms of the OMTARN problem and clarify their strengths and weaknesses by testing them on both synthetic and real datasets.

Striking a balance between improved cluster utilization and guaranteed application QoS is a long-standing research problem in cluster resource management. The majority of current solutions require a large number of sandboxed experimentation for different workload combinations and leverage them to predict possible interference for incoming workloads. This results in non-negligible time complexity that severely restricts its applicability to complex workload co-locations. The nature of pure offline profiling may also lead to model aging problem that drastically degrades the model precision. In this paper, we present Perph, a runtime agent on a per node basis, which decouples ML-based performance prediction and resource inference from centralized scheduler. We exploit the sensitivity of long-running applications to multi-resources for establishing a relationship between resource allocation and consequential performance. We use Online Gradient Boost Regression Tree (OGBRT) to enable the continuous model evolution. Once performance degradation is detected, resource inference is conducted to work out a proper slice of resources that will be reallocated to recover the target performance. The integration with Node Manager (NM) of Apache YARN shows that the throughput of Kafka data-streaming application is 2.0x and 1.82x times that of isolation execution schemes in native YARN and pure cgroup cpu subsystem. In TPC-C benchmarking, the throughput can also be improved by 35% and 23% respectively against YARN native and cgroup cpu subsystem.

Balancing resource utilization and application QoS is a long-standing research topic in cluster resource management. Big data YARN clusters need to co-schedule diverse workloads on shared resources including batch processing jobs, streaming jobs, and other long-running applications such as web services, database services, etc. Current resource managers are only responsible for resource allocation among applications/jobs but completely unaware of runtime QoS requirements of interactive and latency-sensitive applications. Prior works to maximize the QoS of monolithic applications ignore inherent dependencies and temporal-spatio performance variability of components, characteristics of distributed applications primarily driven by microservices. In this paper, we present Toposch, a new resource management system to adaptively co-locate batch tasks and microservices by harvesting runtime latency. In particular, Toposch tracks full footprints of every request across microservices over time. A latency graph is periodically generated for identifying victim microservices through an end-to-end latency critical path analysis. We then exploit per-microservice and per-node risk assessment to gauge the visible resources to the capacity scheduler in YARN. Execution of batch tasks are adaptively throttled or delayed, thereby avoiding latency increase due to node over-saturation. TOPOSCH is integrated with YARN and experiments show that the latency of DLRAs can be reduced by up to 39.8% against the default capacity scheduling in YARN.

User interests modeling has been exploited as a critical component to improve the predictive performance of recommender systems. However, with the absence of explicit information to model user interests, most approaches to recommender systems exploit users activities (user generated contents or user ratings) to inference the interest of users. In reality, the relationship among users also serves as a rich source of information of shared interest. To this end, we propose a framework which avoids the sole dependence of user activities to infer user interests and allows the exploitation of the direct relationship between users to propagate user interests to improve system's performance. In this paper, we advocate a novel modeling framework. We construct a probabilistic user interests model and propose a user interests propagation algorithm (UIP), which applies a factor graph based approach to estimate the distribution of the interests of users. Moreover, we incorporate our UIP algorithm with conventional matrix factorization (MF) for recommender systems. Experimental results demonstrate that our proposed approach outperforms previous methods used for recommender systems.

It is a long-standing challenge to achieve a high degree of resource utilization in cluster scheduling. Resource oversubscription has become a common practice in improving resource utilization and cost reduction. However, current centralized approaches to oversubscription suffer from the issue with resource mismatch and fail to take into account other performance requirements, e.g., tail latency. In this article we present ROSE, a new resource management platform capable of conducting performance-aware resource oversubscription. ROSE allows latency-sensitive long-running applications (LRAs) to co-exist with computation-intensive batch jobs. Instead of waiting for resource allocation to be confirmed by the centralized scheduler, job managers in ROSE can independently request to launch speculative tasks within specific machines according to their suitability for oversubscription. Node agents of those machines can however, avoid any excessive resource oversubscription by means of a mechanism for admission control using multi-resource threshold control and performance-aware resource throttle. Experiments show that in case of mixed co-location of batch jobs and latency-sensitive LRAs, the CPU utilization and the disk utilization can reach 56.34 and 43.49 percent, respectively, but the 95th percentile of read latency in YCSB workloads only increases by 5.4 percent against the case of executing the LRAs alone.

Automaton or table-based multi-pattern matching methods have been widely used in cloud services, i.e., virtual Firewall service, virtual IDS service, etc. In cloud, a large scale of patterns in such services are frequently updated causing by users’ joining or quitting and adjustment of security and management policies. Therefore, how to quickly and accurately update the Automaton and Table becomes an important issue. In this paper, we propose Eagle+, an incremental approach for updating the matching Automaton and Table whilst avoiding recalculating the whole patterns after each change. In Eagle+, we attain efficiency by computing only the latest update set of patterns when updating the Automaton and Table. Moreover, Eagle+ achieves accurately local updating based on three atomic operations, adding, updating and deleting, each of which modifies values on classical Aho–Corasick (AC) automaton, Set Backward Oracle Matching (SBOM) automaton and Wu–Manber (WM) table. Compared with existing pattern updating methods, Eagle+ reduces the computation complexity from O ( n 2 ) to O ( n ) . The experimental results show that Eagle+ can save nearly 72%–92% of the time consumption in updating automatons and perform 100X faster in WM table.

The cloud operating system (cloud OS) is used for managing the cloud resources such that they can be used effectively and efficiently. And also it is the duty of cloud OS to provide convenient interface for users and applications. However, these two goals are often conflicting because convenient abstraction usually needs more computing resources. Thus, the cloud OS has its own characteristics of resource management and task scheduling for supporting various kinds of cloud applications. The evolution of cloud OS is in fact driven by these two often conflicting goals and finding the right tradeoff between them makes each phase of the evolution happen. In this paper, we have investigated the ways of cloud OS evolution from three different aspects: enabling technology evolution, OS architecture evolution and cloud ecosystem evolution. We show that finding the appropriate APIs (application programming interfaces) is critical for the next phase of cloud OS evolution. Convenient interfaces need to be provided without scarifying efficiency when APIs are chosen. We present an API-driven cloud OS practice, showing the great capability of APIs for developing a better cloud OS and helping build and run the cloud ecosystem healthily.

Over a decade, cloud and subsequent joint cloud computing has been evolving into one of biggest disruptive technologies in modern digital age. The rapidly maturing cloud service and system management still heavily relies on virtualization which underpins Infrastructure as a Service (IaaS) to offer on-demand and low-cost computing services. Nevertheless datacenters still suffer from low utilization and resource imbalance. IaaS systems and their workloads, as legacy estates, are intricate to be migrated or re-planned, thereby increasing the complexity of utilization improvement. Arguably workload co-location of long-running applications encapsulated in virtual machines and latency-insensitive batch jobs is an alternative to improve overall resource utilization. However, guaranteeing the quality of long-running services is still challenging. In this context, we proposed an isolation-based cluster resource sharing system Shaready to enable workload co-residences. By means of global resource quota configuration and multi-resource isolation, long-running services in virtual machines can be prioritized with maximized resource provisioning. We implemented and validated it based on Openstack and Yarn clusters, and experiments demonstrate that system CPU and memory utilization can be improved by roughly 50% and 16.67% respectively on average with at most 7% performance degradation.

Water samples at both surface and bottom layers were taken from 102 sites in Lake Taihu to study the fluorescent components and spatial patterns of chromophoric dissolved organic matters (CDOM). Three-dimensional excitation-emission matrix data obtained from the samples were analyzed by parallel factor approach in which four humic-like and two protein-like fluorescent components (named C1–C6) were identified. The results showed that fluorescence intensities were higher in the northern and western lake regions, and notable declines of fluorescence maxima (F max) were observed from the northwest to the center and then to the southeast of the lake. Calculated biological index (BIX) values ranged from 0.88 to 1.44 and humification index (HIX) values from 0.64 to 3.37 for all the samples. The spatial variations of BIX and HIX values suggested stronger allochthonous CDOM characteristics in Zhushan Bay and the western area and autochthonous characteristics in the southern and eastern areas. Vertically, the average F max value of the surface samples was about 6 % less than that of the bottom samples, but noticeable variations existed among different sampling sites and components. These distribution characteristics of CDOM were mainly attributed to the spatial heterogeneity of sources and wind-induced transportation process. Interestingly, the C6 component (Exmax/Emmax = 250/455 nm) seemed to be unique in samples from Zhushan Bay and probably resulted from the discharge of the Taige River. Therefore, it could be used as an indicator of point-source discharge and a tracer to study the fate of CDOM in the lake.

Surface structures of one-dimensional CeO2 nanocrystals are tuned with different cationic ions in a facile and mild hydrothermal system with formate and carbonate. The synthetic key is to control the formation of anisotropic CeO2 precursors with the anion structure of formate and carbonate. The diluted concentration of Na+ ions with NH4+ ions can induce the formation of the CeO2 nanotube (NT) precursors, and yet Na+ ions can promote the production of the CeO2 nanorod (NR) precursors. CeO2 nanocrystals are applied in CO oxidation and show a very important effect of their surface structure on their catalytic performance. CeO2 NTs have rich cerium surface with the higher concentration of Ce3+ ions and can accelerate the mobility of more surface oxygen species than CeO2 NRs with difficultly reduced rich oxygen surface. However, after silver loading, CeO2 NRs can show the higher stability of CeO2 particles, more easily activated surface of CeO2 NRs with more Ce3+ ions, the promoted production of metallic Ag nanoparticles and higher mobility of more surface oxygen species on CeO2 surface. Therefore, CeO2 NTs have a higher catalytic activity of CO oxidation and conversely, after loading Ag, Ag/CeO2 NRs show the higher catalytic performance.

A long-standing challenge in cluster scheduling is to achieve a high degree of utilization of heterogeneous resources in a cluster. In practice there exists a substantial disparity between perceived and actual resource utilization. A scheduler might regard a cluster as fully utilized if a large resource request queue is present, but the actual resource utilization of the cluster can be in fact very low. This disparity results in the formation of idle resources, leading to inefficient resource usage and incurring high operational costs and an inability to provision services. In this paper we present a new cluster scheduling system, ROSE, that is based on a multi-layered scheduling architecture with an ability to over-subscribe idle resources to accommodate unfulfilled resource requests. ROSE books idle resources in a speculative manner: instead of waiting for resource allocation to be confirmed by the centralized scheduler, it requests intelligently to launch tasks within machines according to their suitability to oversubscribe resources. A threshold control with timely task rescheduling ensures fully-utilized cluster resources without generating potential task stragglers. Experimental results show that ROSE can almost double the average CPU utilization, from 36.37% to 65.10%, compared with a centralized scheduling scheme, and reduce the workload makespan by 30.11%, with an 8.23% disk utilization improvement over other scheduling strategies.

This article studies discovery of Graph Functional Dependencies (GFDs), a class of functional dependencies defined on graphs. We investigate the fixed-parameter tractability of three fundamental problems related to GFD discovery. We show that the implication and satisfiability problems are fixed-parameter tractable, but the validation problem is co-W[1]-hard in general. We introduce notions of reduced GFDs and their topological support, and formalize the discovery problem for GFDs. We develop algorithms for discovering GFDs and computing their covers. Moreover, we show that GFD discovery is feasible over large-scale graphs, by providing parallel scalable algorithms that guarantee to reduce running time when more processors are used. Using real-life and synthetic data, we experimentally verify the effectiveness and scalability of the algorithms.

Container technology has been prevalent and widely-adopted in production environment considering the huge benefits to application packing, deploying and management. However, the deployment process is relatively slow by using conventional approaches. In large-scale concurrent deployments, resource contentions on the central image repository would aggravate such situation. In fact, it is observable that the image pulling operation is mainly responsible for the degraded performance. To this end, we propose Cider — a novel deployment system to enable rapid container deployment in a high concurrent and scalable manner at scale. Firstly, on-demand image data loading is proposed by altering the local Docker storage of worker nodes into all-nodes-sharing network storage. Also, the local copy-on-write layer for containers can ensure Cider to achieve the scalability whilst improving the cost-effectiveness during the holistic deployment. Experimental results reveal that Cider can shorten the overall deployment time by 85% and 62% on average when deploying one container and 100 concurrent containers respectively.

The incremental problem for a class Q of graph queries aims to compute, given a query Q in 'Q, graph G, output Q(G) and updates Δ G to G as input, changes Δ O to Q(G) such that Q(G ⊕ Δ G) = Q(G) ⊕ Δ O. It is called bounded if its cost can be expressed as a polynomial function in the sizes of Q, Δ G and Δ O. It is to reduce computations on possibly big G to small Δ G and Δ O. No matter how desirable, however, our first results are negative: for common graph queries such as graph traversal, connectivity, keyword search and pattern matching, their incremental problems are unbounded. In light of the negative results, we propose two characterizations for the effectiveness of incremental computation: (a) localizable, if its cost is decided by small neighbors of nodes in Δ G instead of the entire G; and (b) bounded relative to a batch algorithm T, if the cost is determined by the sizes of Δ G and changes to the affected area that is necessarily checked by T. We show that the incremental computations above are either localizable or relatively bounded, by providing corresponding incremental algorithms. That is, we can either reduce the incremental computations on big graphs to small data, or incrementalize batch algorithms by minimizing unnecessary recomputation. Using real-life graphs, we experimentally verify the effectiveness of our algorithms.

This position paper provides an overview of our recent advances in the study of big graphs, from theory to systems to applications. We introduce a theory of bounded evaluability, to query big graphs by accessing a bounded amount of the data. Based on this, we propose a framework to query big graphs with constrained resources. Beyond queries, we propose functional dependencies for graphs, to detect inconsistencies in knowledge bases and catch spams in social networks. As an example application of big graph analyses, we extend association rules from itemsets to graphs for social media marketing. We also identify open problems in connection with querying, cleaning and mining big graphs.

As water resources have been developed, the input of water into wetlands by rivers has decreased. Thus, these wetland ecosystems are seriously threatened. The upstream reservoir must be operated in a manner that provides downstream wetlands with their ecological water requirement. Here, a method for analysing the effects of artificial water replenishment from reservoirs on wetlands is established. A hydrodynamic mathematical model was coupled by a one-dimensional longitudinal and depth-averaged two-dimensional hydrodynamic model to simulate the water replenishment process in wetlands. The results indicate that the overflow of floodwaters into wetlands may not occur if insufficient reservoir water is discharged. In contrast, if too much water is discharged, excess water will reach the wetland and cause a water shortage upstream. Furthermore, the results showed that there is a quantitative relationship between the area of water in a wetland and the flow discharged by the upstream reservoir. This analytical method provides direct data that can be used to ensure that wetlands receive enough water from reservoirs to maintain their ecological functions and to avoid wasting the water resources of upstream reservoirs.

The Resource Description Framework (RDF) andSPARQL query language are gaining increasing popularity andacceptance. The ever-increasing RDF data has reached a billionscale of triples, resulting in the proliferation of distributed RDFstore systems within the Semantic Web community. However, theelasticity and performance issues are still far from settled inface of data volume explosion and workload spike. In addition, providers face great pressures to provision uninterrupted reliablestorage service whilst reducing the operational costs due to avariety of system failures. Therefore, how to efficiently realizesystem fault tolerance remains an intractable problem. In this paper, we introduce ScalaRDF, a distributed and elastic in-memoryRDF triple store to provision a fault-tolerant and scalable RDFstore and query mechanism. Specifically, we describe a consistenthashing protocol that optimizes the RDF data placement, dataoperations (especially for online RDF triple update operations)and achieves an autonomously elastic data re-distribution in theevent of cluster node joining or departing, avoiding the holisticoscillation of data storage. In addition, the data store is ableto realize a rapid and transparent failover through replicationmechanism which stores in-memory data replica in the next hashhop. The experiments demonstrate that query time and updatetime are reduced by 87% and 90% respectively compared to otherapproaches. For an 18G source dataset, the data redistributiontakes at most 60 seconds when system scales out and at most 100seconds for recovery when nodes undergo crash-stop failures.

Software as a Service (SaaS) is a software delivery and business model widely used by Cloud computing. Instead of purchasing and maintaining a software suite permanently, customers only need to lease the software on-demand. The domain of high assurance distributed systems has focused greatly on the areas of fault tolerance and dependability. In a multi-tenant context, it is particularly important to store, manage and provision data services to customers in a highly efficient and dependable manner due to a large number of file operations involved in running such services. It is also desirable to allow a user group to share and cooperate (e.g., co-edit) on some specific data. In this paper we present a dependable data provisioning service in a multitenant Cloud environment. We describe a metadata management approach and leverage multiple replicated metadata caching to shorten the file access time, with the improved efficiency of data sharing. In order to reduce frequent data transmission and data access latency, we introduce a distributed cooperative disk cache mechanism that supports effective cache placement and pull-push cache synchronization. In addition, we use efficient component failover to enhance the service dependability whilst avoiding negative impact from system failures. Our experimental results show that our system can significantly reduce both unused data transmission and response latency. Specifically, over 50% network transmission and operational latency can be saved for random reads while 28.24% network traffic and 25% response latency can be reduced for random write operations. We believe that these findings are demonstrating positive results along the right direction of resolving storage-related challenges in a multitenant Cloud environment.

As the sizes of IT infrastructure continue to grow, cloud computing is a natural extension of virtualisation technologies that enable scalable management of virtual machines over a plethora of physically connected systems. The so-called virtualisation-based cloud computing paradigm offers a practical approach to green IT/clouds, which emphasise the construction and deployment of scalable, energy-efficient network software applications (NetApp) by virtue of improved utilisation of the underlying resources. The latter is typically achieved through increased sharing of hardware and data in a multi-tenant cloud architecture/environment and, as such, accentuates the critical requirement for enhanced security services as an integrated component of the virtual infrastructure management strategy. This paper analyses the key security challenges faced by contemporary green cloud computing environments, and proposes a virtualisation security assurance architecture, CyberGuarder, which is designed to address several key security problems within the 'green' cloud computing context. In particular, CyberGuarder provides three different kinds of services; namely, a virtual machine security service, a virtual network security service and a policy based trust management service. Specifically, the proposed virtual machine security service incorporates a number of new techniques which include (1) a VMM-based integrity measurement approach for NetApp trusted loading, (2) a multi-granularity NetApp isolation mechanism to enable OS user isolation, and (3) a dynamic approach to virtual machine and network isolation for multiple NetApp's based on energy-efficiency and security requirements. Secondly, a virtual network security service has been developed successfully to provide an adaptive virtual security appliance deployment in a NetApp execution environment, whereby traditional security services such as IDS and firewalls can be encapsulated as VM images and deployed over a virtual security network in accordance with the practical configuration of the virtualised infrastructure. Thirdly, a security service providing policy based trust management is proposed to facilitate access control to the resources pool and a trust federation mechanism to support/optimise task privacy and cost requirements across multiple resource pools. Preliminary studies of these services have been carried out on our iVIC platform, with promising results. As part of our ongoing research in large-scale, energy-efficient/green cloud computing, we are currently developing a virtual laboratory for our campus courses using the virtualisation infrastructure of iVIC, which incorporates the important results and experience of CyberGuarder in a practical context.

A muon facility—EMuS (Experimental Muon Source)—at China Spallation Neutron Source (CSNS) has been studied since 2007. CSNS, which is designed to deliver a proton beam power of 100 kW at Phase-I, and will serve multidisciplinary research based on neutron scattering techniques, has just completed construction, and is ready to open to general users from September 2018. As an additional platform to CSNS, EMuS aims to provide different muon beams for multiple applications, among which, magnetism study by μSR techniques is a core part. By using innovative designs, such as a long target in conical shape situating in superconducting capture solenoids and forward collection method, EMuS can provide very intense muon beams with a proton beam of 5 kW and 1.6 GeV, from surface muons, decay muons, and high momentum muons to slow muons. In this article, the design aspects of EMuS, including general design, target station, muon beamlines, and μSR spectrometer, as well as prospects for applications on magnetism studies, will be reviewed.

Nowadays, various promising paradigms of distributed computing over the Internet, such as Grids, P2P and Clouds, have emerged for resource sharing and collaboration. To enable resources sharing and collaboration across different domains in an open computing environment, virtual organizations (VOs) often need to be established dynamically. However, the dynamic and autonomous characteristics of participating domains pose great challenges to the security of virtual organizations. In this paper, we propose a secure collaboration service, called PEACE-VO, for dynamic virtual organizations management. The federation approach based on role mapping has extensively been used to build virtual organizations over multiple domains. However, there is a serious issue of potential policy conflicts with this approach, which brings a security threat to the participating domains. To address this issue, we first depict concepts of implicit conflicts and explicit conflicts that may exist in virtual organization collaboration policies. Then, we propose a fully distributed algorithm to detect potential policy conflicts. With this algorithm participating domains do not have to disclose their full local privacy policies, and is able to withhold malicious internal attacks. Finally, we present the system architecture of PEACE-VO and design two protocols for VO management and authorization. PEACE-VO services and protocols have successfully been implemented in the CROWN test bed. Comprehensive experimental study demonstrates that our approach is scalable and efficient.

Variations in phosphorus (P) speciation were compared for two types of pilot scale wetlands: a blast furnace slag-based constructed wetland (SCW), and a gravel-based constructed wetland (GCW). Synthetic secondary effluent was used as the influent of the wetlands, which contained 1.1 mg/L P with more than 95% present as soluble reactive P (SRP). However, dissolved organic phosphorus (DOP) and particulate phosphorus (PP) emerged in the water along both wetlands. The levels of these three P species varied between the two wetlands. The GCW was more efficient than SCW at removing SRP, but showed a different trend. SRP decreased continually in the SCW, while it increased at the end of the GCW due to biological release. DOP was constant in SCW and GCW, and the mean value was 0.023 and 0.020 mg/L, respectively. The mean values of PP in the GCW ranged between 0.093 and 0.216 mg/L; much higher than the 0.05 ± 0.01 mg/L measured in the SCW. Sequential extractions showed that iron bound PP (Fe-PP), aluminum bound PP (Al-PP), organic PP (Org-PP) and occluded PP (Oc-PP) were the major components of PP at most locations of the GCW. Fe-PP decreased from 0.53 to 0.14 mg/L in the upper layer, with DO steady at about 0.15 mg/L at the bottom. Oc-PP increased at each layer. Al-PP and Org-PP were steady in the first 140 cm of the GCW, but decreased sharply at the end. Considering the variation of SRP, DOP and PP fractionations, it can be seen that PP exchanged intensively with SRP in the GCW, and might act as an intermediate in the P removal process. Part of the SRP was first transformed into PP, and then absorbed by substrates or deposited in the wetlands. P removal was mainly via Ca precipitation in the SCW, but involved multiple mechanisms in the GCW, such as precipitation, adsorption and biological interactions. The multiple P removal mechanisms might be the reason for the low proportion of SRP, and the better P removal efficiency observed in the GCW.

As hydrogen, deuterium and tritium storage materials, a series of investigations of mechanical and thermal properties of titanium hydrides, deuterides and tritides have been performed, however, very limited theoretical studies of thermodynamic properties for them can be found. Based on density-functional theory (DFT) and density-functional perturbation theory (DFPT) we have discussed systematically the hydrogen isotope effects on the thermodynamic properties of TiX2 (X = H, D, and T) system. Our calculations indicate that for evaluating accurately their physical properties at absolute zero temperature, such as the equilibrium lattice constants, bulk modulus, and heat of formation, the zero-point energy correction must be taken into account. By performing the phonon calculation within quasiharmonic approximation (QHA), we obtain their vibrational free energies, vibrational entropies, and temperature dependence of specific heat, thermal expansion, and bulk modulus. Those results demonstrate that comparing with TiH2, TiT2 and TiD2 are more stable and the zero-point effects play an important role in their thermal expansion. The increase in the force constant between Ti and H causes the higher value of specific heat of TiH2 during the phase transition from FCC to FCT. In addition, comparing with available experimental values, we can conclude that QHA is feasible for describing the thermal properties of TiX2. (C) 2006 Elsevier B.V. All rights reserved.

The main goal of our key project, the CROWN Grid, is to empower in-depth integration of resources and the cooperation of researchers nationwide and worldwide. CROWN exploits a service-oriented architecture based on OGSA. In CROWN, remote service deployment is highly desirable. To the best of our knowledge, however, there is no successful solution to ensure the enabling remote and hot service deployment in grid systems. Traditionally, remote deployment is supported in a cold fashion, which results in many disadvantages, such as low efficiency. Moreover, since the deployer and the target container may be in different domains, great security challenges arise when a service is deployed to a remote container. In this paper, we present ROST, an original scheme of Remote and hOt Service deployment with Trustworthiness. By dynamically updating runtime environment configurations, ROST avoids restarting the runtime system during deployment. In addition, we include trust negotiation in ROST, which greatly increases the flexibility and security of the CROWN Grid. ROST has been successfully implemented. We conduct comprehensive experiments with real applications, and the results show that ROST is viable and significantly improves the service efficiency and quality of CROWN. We believe that the wide deployment of ROST would also benefit other grid systems.