Transparent Computing (TC) is becoming a promising paradigm in network computing era. Although many researchers believe that TC model has a high requirement for the communication bandwidth, there is ...no research on the communication bandwidth boundary or resource allocation, which impedes the development of TC. This paper focuses on studying an efficient transparent computing resource allocation model in an economic view. First, under the quality of experiments (QoE) ensured, the utility function of clients and transparent computing providers (TCPs) is constructed. After that, the demand boundary of communication bandwidth is analyzed under the ideal transparent computing model. Based on the above analyses, a resource allocation scheme based on double-sided combinational auctions (DCA) is proposed so that the resource can be shared by both the service side and the client side with the welfare of the whole society being maximized. Afterward, the results scheduled in different experimental scenarios are given, which verifies the effectiveness of the proposed strategy. Overall, this work provides an effective resource allocation model for optimizing the performance of TC.
The oceans cover more than 71% of the Earth's surface and have a surging amount of data. It is of great significance to seek energy-effective and ultrareliable communication and transmission ...mechanism for effectively gathering abundant maritime data. In this article, we propose an autonomous underwater vehicle (AUV)-assisted data gathering scheme based on clustering and matrix completion (ACMC) to improve the data gathering efficiency in the underwater wireless sensor network (UWSN). Specifically, we first improve the <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula>-means algorithm by adopting the Elbow method to determine the optimal <inline-formula> <tex-math notation="LaTeX">K </tex-math></inline-formula> and setting a distance threshold to select the separate initial cluster centers. Then, we introduce a two-phase AUV trajectory optimization mechanism to effectively reduce the trajectory length of the AUV. In the first phase, the optimized trajectory of the AUV is planned by adopting the greedy algorithm. In the second phase, the ordinary nodes close to the AUV trajectory are selected as secondary cluster heads to share the workload of cluster heads. Finally, we present an in-cluster data collection mechanism based on matrix completion. An extensive experiment validates the effectiveness of our proposed scheme in terms of energy and data collection delay.
Peer-to-Peer (P2P) network can be a promising big data application platform. However, efficient data collection in network still faces a huge challenge in security. In this paper, we utilize the idea ...of machine learning to select trusted data reporter to collect data. The data collection optimization is translated into how to maximize data coverage and minimize the cost under given budget in malicious network, which has rarely been considered in previous studies. Then a Trust-based Minimum Cost Quality Aware (TMCQA) data collection scheme is proposed to perform data collection optimization. The data collection in TMCQA scheme has the following innovations. (1) A trust evaluation mechanism utilizing the idea of machine learning is established to evaluate the trust of the data reporter. Besides, different from the previous strategy, data reporter is taken as the basic unit of data collection instead of data sample which makes the TMCQA scheme is more practical in the P2P network.(2) An optimized data reporter selection strategy is proposed to select optimized reporters based on the three key evaluation indices to improve data collection performance, which are: (a) The trust value of the data reporter. The more credible the reporter, the higher the quality of the collected data. (b) The data coverage ratio for covering the interesting area of the sensing task. (c) The cost of data collection. The data reporter used to have lower cost will be selected to reduce cost. Finally, we verify the validity of the TMCQA scheme proposed in this paper through various experiments. Comparing to Contribution-based with Trust Value Scheme (CNTVS), Random Data Reporter Selection Scheme (RDRSS), and No Time Decay Scheme (TNTDS), with the same budget, the QoS can be improved by 32.21%, 49.39%, 23.68% respectively. The performance of the TMCQA scheme in a malicious P2P network is significantly better than previous strategies.
Accumulating evidence displays that an abnormal deposition of amyloid beta-peptide (Aβ) is the primary cause of the pathogenesis of Alzheimer's disease (AD). And therefore the elimination of Aβ is ...regarded as an important strategy for AD treatment. The discovery of drug candidates using culture neuronal cells against Aβ peptide toxicity is believed to be an effective approach to develop drug for the treatment of AD patients. We have previously showed that artemisinin, a FDA-approved anti-malaria drug, has neuroprotective effects recently. In the present study, we aimed to investigate the effects and potential mechanism of artemisinin in protecting neuronal PC12 cells from toxicity of β amyloid peptide. Our studies revealed that artemisinin, in clinical relevant concentration, protected and rescued PC12 cells from Aβ25-35-induced cell death. Further study showed that artemisinin significantly ameliorated cell death due to Aβ25-35 insult by restoring abnormal changes in nuclear morphology, lactate dehydrogenase, intracellular ROS, mitochondrial membrane potential and activity of apoptotic caspase. Western blotting analysis demonstrated that artemisinin activated extracellular regulated kinase ERK1/2 but not Akt survival signaling. Consistent with the role of ERK1/2, preincubation of cells with ERK1/2 pathway inhibitor PD98059 blocked the effect of artemisinin while PI3K inhibitor LY294002 has no effect. Moreover, Aβ1-42 also caused cells death of PC12 cells while artemisinin suppressed Aβ1-42 cytotoxicity in PC12 cells. Taken together, these results, at the first time, suggest that artemisinin is a potential protectant against β amyloid insult through activation of the ERK1/2 pathway. Our finding provides a potential application of artemisinin in prevention and treatment of AD.
A hydrometallurgical process for the recovery of cobalt oxalate from spent lithium-ion batteries (LIBs) has been developed using alkali leaching followed by reductive acid leaching, solvent ...extraction and chemical deposition of cobalt oxalate. The active cathode powder material was first leached with 5 wt.% NaOH solution for the selective removal of aluminum; and the residues were further leached with 4 M H
2SO
4
+
10% v/v H
2O
2 solution. The leaching efficiency of cobalt was 95% and lithium was 96% under optimum conditions of liquid/solid ratio 10:1, leaching time 120
min and a temperature 85
°C. The impurity ions of Fe(III), Cu(II), Mn(II) in the leach liquor were precipitated by adjusting the pH value. Cobalt(II) was then extracted selectively from the purified aqueous phase with saponified P507 (2-ethylhexyl phosphonic acid mono-2-ethylhexyl ester) and chemically deposited as oxalate from the strip liquor with a yield of ~
93% and purity >
99.9%. This process is simple, environmentally friendly and adequate for the recovery of valuable metals from spent LIBs.
► The parameters which affect the oxidative precipitation of manganese have been discussed. ► Separation of cobalt, nickel and lithium from leach liquor by P507. ► Determination optimum separation and stripping conditions for cobalt, nickel and lithium. ► The process is flexible enough to dispose the spent LIBs employing different cathode materials especially Co-Ni-Mn based cathode materials.
Chiral nanoporous nanoarchitectures exhibit potential applications in various fields including chiral separation, sensing and catalysis. Two-dimensional (2D) supramolecular chemistry offers novel ...methods to build chiral nanoporous networks from achiral molecules. Herein, we report a series of chiral nanoporous networks built by an achiral precursor molecule via a stepwise annealing strategy on Ag(100). The nanoporous network morphologies and structural details are characterized by high-resolution scanning tunneling microscopy (STM). It is revealed that all vertices within networks are chiral. These chiral vertices are either dimeric, trimeric, or tetrameric. The connection of these chiral vertices gives rise to diverse chiral nanopores with varying shapes and sizes. A strict chirality correlation between nanopores and their vertices is determined. Specifically, enantiomeric vertices of one-pair nanopore enantiomers are always in identical type but opposite handedness. This work serves as a model to investigate the influence of vertex chirality on nanopore chirality of a supramolecular matrix. The attained chiral nanopores could potentially be utilized as templates for surface reaction, chiral recognition, etc. The mirror-symmetric silver adatoms clusters dictated by chiral nanopore are discerned.
For the cloud computing based on software-defined networks (SDNs), a larger amount of data is collected to cloud for analysis, which will cause the larger amount of redundancy data and longer service ...response time due to the capacity-limited Internet. To solve this problem, a novel service orchestration and data aggregation framework (SODA) is proposed, which can orchestrate data as services and aggregate data packets to reduce data redundancy and service response delay. In SODA, the network is divided into three layers. 1) Data centers layer (DCL). Data centers (DCs) release software with a specific function to all devices in the network, devices orchestrate data as services and aggregate data packets using software to reduce service response delay. 2) Middle routing layer (MRL). The routing path of data packets in this layer is adjusted according to the correlation of data packets and routing distance. The correlation of data packets is higher and routing distance is short, the probability that data packets are transmitted along the same routing path is higher to reduce redundancy data. 3) Vehicle network layer (VNL). Mobile vehicles are used to transmit data packets and services among devices. A series of experiments and simulation is conducted. The results illustrate that the proposed scheme has better performance compared with the traditional scheme.
Environmental exploration is one of the common tasks in the robotic domain which is also known as foraging. In comparison with the typical foraging tasks, our work focuses on the Multi-Robot Task ...Allocation (MRTA) problem in the exploration and destruction domain, where a team of robots is required to cooperatively search for targets hidden in the environment and attempt to destroy them. As usual, robots have the prior knowledge about the suspicious locations they need to explore but they don’t know the distribution of interested targets. So the destruction task is dynamically generated along with the execution of exploration task. Each robot has different strike ability and each target has uncertain anti-strike ability, which means either the robot or target is likely to be damaged in the destruction task according to that whose ability is higher. The above setting significantly increases the complexity of exploration and destruction problem. The auction-based approach, vacancy chain approach and a deep Q-learning approach based on strategy-level selection are employed in this paper to deal with this problem. A new simulation system based on Robot Operating System and Gazebo is specially built for this MRTA problem. Subsequently, extensive simulation results are provided to show that all proposed approaches are able to solve the MRTA problem in exploration and destruction domain. In addition, experimental results are further analyzed to show that each method has its own advantages and disadvantages.
The target encirclement control of multi-robot systems via deep reinforcement learning has been investigated in this paper. Inspired by the encirclement behavior of dolphins to entrap the fishes, the ...encirclement control is mainly to enforce the robots to achieve a capturing formation pattern around a target, and can be widely applied in many areas such as coverage, patrolling, escorting, etc. Different from traditional methods, we propose a deep reinforcement learning framework for multi-robot target encirclement formation control, combining the advantages of the deep neural network and deterministic policy gradient algorithm, which is free from the complicated work of building the control model and designing the control law. Our method provides a distributed control architecture for each robot in continuous action space, relying only on local teammate information. Besides, the behavioral output at each time step is determined by its own independent network. In addition, both the robots and the moving target can be trained simultaneously. In that way, both cooperation and competition can be contained, and the results validate the effectiveness of the proposed algorithm.
Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent ...accessibility and contact number are essential for deriving restrains in modeling protein folding and protein 3D structure. Thus, accurately predicting these features is a critical step for 3D protein structure building.
In this study, we present DeepSacon, a computational method that can effectively predict protein solvent accessibility and contact number by using a deep neural network, which is built based on stacked autoencoder and a dropout method. The results demonstrate that our proposed DeepSacon achieves a significant improvement in the prediction quality compared with the state-of-the-art methods. We obtain 0.70 three-state accuracy for solvent accessibility, 0.33 15-state accuracy and 0.74 Pearson Correlation Coefficient (PCC) for the contact number on the 5729 monomeric soluble globular protein dataset. We also evaluate the performance on the CASP11 benchmark dataset, DeepSacon achieves 0.68 three-state accuracy and 0.69 PCC for solvent accessibility and contact number, respectively.
We have shown that DeepSacon can reliably predict solvent accessibility and contact number with stacked sparse autoencoder and a dropout approach.
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