Energy Consumption Model of WSN Based on Manifold Learning Algorithm

Energy saving is one of the most important issues in wireless sensor networks. In order to effectively model the energy consumption -in wireless sensor network, a novel model is proposed based on manifold learning algorithm. Firstly, the components of the energy consumption by computational equations are measured, and the objective function is optimized. Secondly, the parameters in computational equations are estimated by manifold learning algorithm. Finally, the simulation experiments on OPNET and MATLAB Simulink are performed to evaluate the key factors influencing the model. The experimental results show that the proposed model had significant advantage in terms of synchronization accuracy and residual energy in comparison with other methods

Solution structure of a Plasmodium falciparum AMA-1/MSP 1 chimeric protein vaccine candidate (PfCP-2.9) for malaria

Background: The Plasmodium falciparum chimeric protein PfCP-2.9 is a promising asexual-stage malaria vaccine evaluated in clinical trials. This chimeric protein consists of two cysteine-rich domains: domain III of the apical membrane antigen 1 (AMA-1 [III]) and the C-terminal region of the merozoite surface protein 1 (MSP1-19). It has been reported that the fusion of these two antigens enhanced their immunogenicity and antibody-mediated inhibition of parasite growth in vitro. Methods: The N-15-labeled and C-13/N-15-labeled PfCP-2.9 was produced in Pichia pastoris for nuclear magnetic resonance (NMR) structure analysis. The chemical shift assignments of PfCP-2.9 were compared with those previously reported for the individual domains (i.e., PfAMA-1(III) or PfMSP 1-19). The two-dimensional spectra and transverse relaxation rates (R-2) of the PfMSP1-19 alone were compared with that of the PfCP-2.9. Results: Confident backbone assignments were obtained for 122 out of 241 residues of PfCP-2.9. The assigned residues in PfCP-2.9 were very similar to those previously reported for the individual domains. The conformation of the PfMSP1-19 in different constructs is essentially the same. Comparison of transverse relaxation rates (R-2) strongly suggests no weak interaction between the domains. Conclusions: These data indicate that the fusion of AMA-1(III) and MSP1-19 as chimeric protein did not change their structures, supporting the use of the chimeric protein as a potential malaria vaccine.Infectious DiseasesParasitologyTropical MedicineSCI(E)5ARTICLEnull

Hypergraph Node Representation Learning with One-Stage Message Passing

Hypergraphs as an expressive and general structure have attracted considerable attention from various research domains. Most existing hypergraph node representation learning techniques are based on graph neural networks, and thus adopt the two-stage message passing paradigm (i.e. node -> hyperedge -> node). This paradigm only focuses on local information propagation and does not effectively take into account global information, resulting in less optimal representations. Our theoretical analysis of representative two-stage message passing methods shows that, mathematically, they model different ways of local message passing through hyperedges, and can be unified into one-stage message passing (i.e. node -> node). However, they still only model local information. Motivated by this theoretical analysis, we propose a novel one-stage message passing paradigm to model both global and local information propagation for hypergraphs. We integrate this paradigm into HGraphormer, a Transformer-based framework for hypergraph node representation learning. HGraphormer injects the hypergraph structure information (local information) into Transformers (global information) by combining the attention matrix and hypergraph Laplacian. Extensive experiments demonstrate that HGraphormer outperforms recent hypergraph learning methods on five representative benchmark datasets on the semi-supervised hypernode classification task, setting new state-of-the-art performance, with accuracy improvements between 2.52% and 6.70%. Our code and datasets are available.Comment: 11 page

Electronic properties and quantum transports in functionalized graphene Sierpinski carpet fractals

Recent progress in controllable functionalization of graphene surfaces enables the experimental realization of complex functionalized graphene nanostructures, such as Sierpinski carpet (SC) fractals. Herein, we model the SC fractals formed by hydrogen and fluorine functionalized patterns on graphene surfaces, namely, H-SC and F-SC, respectively. We then reveal their electronic properties and quantum transport features. From calculated results of the total and local density of state, we find that states in H-SC and F-SC have two characteristics: (i) low-energy states inside about |E/t|<1 (with t as the near-neighbor hopping) are localized inside free graphene regions due to the insulating properties of functionalized graphene regions, and (ii) high-energy states in F-SC have two special energy ranges including -2.3<E/t<-1.9 with localized holes only inside free graphene areas and 3<E/t<3.7 with localized electrons only inside fluorinated graphene areas. The two characteristics are further verified by the real-space distributions of normalized probability density. We analyze the fractal dimension of their quantum conductance spectra and find that conductance fluctuations in these structures follow the Hausdorff dimension. We calculate their optical conductivity and find that several additional conductivity peaks appear in high energy ranges due to the adsorbed H or F atoms

OntoMedRec: Logically-Pretrained Model-Agnostic Ontology Encoders for Medication Recommendation

Most existing medication recommendation models learn representations for medical concepts based on electronic health records (EHRs) and make recommendations with learnt representations. However, most medications appear in the dataset for limited times, resulting in insufficient learning of their representations. Medical ontologies are the hierarchical classification systems for medical terms where similar terms are in the same class on a certain level. In this paper, we propose OntoMedRec, the logically-pretrained and model-agnostic medical Ontology Encoders for Medication Recommendation that addresses data sparsity problem with medical ontologies. We conduct comprehensive experiments on benchmark datasets to evaluate the effectiveness of OntoMedRec, and the result shows the integration of OntoMedRec improves the performance of various models in both the entire EHR datasets and the admissions with few-shot medications. We provide the GitHub repository for the source code on https://anonymous.4open.science/r/OntoMedRec-D12

A Solution to Co-occurrence Bias: Attributes Disentanglement via Mutual Information Minimization for Pedestrian Attribute Recognition

Recent studies on pedestrian attribute recognition progress with either explicit or implicit modeling of the co-occurrence among attributes. Considering that this known a prior is highly variable and unforeseeable regarding the specific scenarios, we show that current methods can actually suffer in generalizing such fitted attributes interdependencies onto scenes or identities off the dataset distribution, resulting in the underlined bias of attributes co-occurrence. To render models robust in realistic scenes, we propose the attributes-disentangled feature learning to ensure the recognition of an attribute not inferring on the existence of others, and which is sequentially formulated as a problem of mutual information minimization. Rooting from it, practical strategies are devised to efficiently decouple attributes, which substantially improve the baseline and establish state-of-the-art performance on realistic datasets like PETAzs and RAPzs. Code is released on https://github.com/SDret/A-Solution-to-Co-occurence-Bias-in-Pedestrian-Attribute-Recognition.Comment: Accepted in IJCAI2