Service-oriented wireless sensor networks and an energy-aware mesh routing algorithm

Service-oriented wireless sensor networks (WSNs) are being paid more and more attention because service computing can hide complexity of WSNs and enables simple and transparent access to individual sensor nodes. Existing WSNs mainly use IEEE 802.15.4 as their communication specification, however, this protocol suite cannot support IP-based routing and service-oriented access because it only specifies a set of physical- and MAC-layer protocols. For inosculating WSNs with IP networks, IEEE proposed a 6LoWPAN (IPv6 over LoW Power wireless Area Networks) as the adaptation layer between IP and MAC layers. However, it is still a challenging task how to discover and manage sensor resources, guarantee the security of WSNs and route messages over resource-restricted sensor nodes. This paper is set to address such three key issues. Firstly, we propose a service-oriented WSN architectural model based on 6LoWPAN and design a lightweight service middleware SOWAM (service-oriented WSN architecture middleware), where each sensor node provides a collection of services and is managed by our SOWAM. Secondly, we develop a security mechanism for the authentication and secure connection among users and sensor nodes. Finally, we propose an energyaware mesh routing protocol (EAMR) for message transmission in a WSN with multiple mobile sinks, aiming at prolonging the lifetime of WSNs as long as possible. In our EAMR, sensor nodes with the residual energy lower than a threshold do not forward messages for other nodes until the threshold is leveled down. As a result, the energy consumption is evened over sensor nodes significantly. The experimental results demonstrate the feasibility of our service-oriented approach and lightweight middleware SOWAM, as well as the effectiveness of our routing algorithm EAMR.<br /

A Shadow-Like Task Migration Model Based on Context Semantics for Mobile and Pervasive Environments

Pervasive computing is a user-centric mobile computing paradigm, in which tasks should be migrated over different platforms in a shadow-like way when users move around. In this paper, we propose a context-sensitive task migration model that recovers program states and rebinds resources for task migrations based on context semantics through inserting resource description and state description sections in source programs. Based on our model, we design and develop a task migration framework xMozart which extends the Mozart platform in terms of context awareness. Our approach can recover task states and rebind resources in the context-aware way, as well as support multi-modality I/O interactions. The extensive experiments demonstrate that our approach can migrate tasks by resuming them from the last broken points like shadows moving along with the users

An Adaptive Context-Aware Transaction Model for Mobile and Ubiquitous Computing

Transaction management for mobile and ubiquitous computing (MUC)aims at providing mobile users with reliable and transparent services anytime anywhere. Traditional mobile transaction models built on client-proxy-server architecture cannot make this vision a reality because (1) in these models, base stations (proxy) are the prerequisite for mobile hosts (client) to connect with databases (server), and 2)few models consider context-based transaction management. In this paper, we propose a new network architecture for MUC transactions, with the goal that people can get online network access and transaction even while moving around; and design a context-aware transaction model and a context-driven coordination algorithm adaptive to dynamically changing MUC transaction context. The simulation results have demonstrated that our model and algorithm can significantly improve the successful ratio of MUC transactions

Approximation by Jackson-type operator on the sphere

This paper discusses the approximation by a Jackson-type operator on the sphere. By using a spherical translation operator, a modulus of smoothness of high order, which is used to bound the rate of approximation of the Jackson-type operator, is introduced. Furthermore, the method of multipliers is applied to characterize the saturation order and saturation class of the operator. In particular, the function of saturation class is expressed by an apparent formula. The results obtained in this paper contain the corresponding ones of the Jackson operator

Using EVT for Geological Anomaly Design and Its Application in Identifying Anomalies in Mining Areas

A geological anomaly is the basis of mineral deposit prediction. Through the study of the knowledge and characteristics of geological anomalies, the category of extreme value theory (EVT) to which a geological anomaly belongs can be determined. Associating the principle of the EVT and ensuring the methods of the shape parameter and scale parameter for the generalized Pareto distribution (GPD), the methods to select the threshold of the GPD can be studied. This paper designs a new algorithm called the EVT model of geological anomaly. These study data on Cu and Au originate from 26 exploration lines of the Jiguanzui Cu-Au mining area in Hubei, China. The proposed EVT model of the geological anomaly is applied to identify anomalies in the Jiguanzui Cu-Au mining area. The results show that the model can effectively identify the geological anomaly region of Cu and Au. The anomaly region of Cu and Au is consistent with the range of ore bodies of actual engineering exploration. Therefore, the EVT model of the geological anomaly can effectively identify anomalies, and it has a high indicating function with respect to ore prospecting

Modeling Multi-aspect Preferences and Intents for Multi-behavioral Sequential Recommendation

Multi-behavioral sequential recommendation has recently attracted increasing attention. However, existing methods suffer from two major limitations. Firstly, user preferences and intents can be described in fine-grained detail from multiple perspectives; yet, these methods fail to capture their multi-aspect nature. Secondly, user behaviors may contain noises, and most existing methods could not effectively deal with noises. In this paper, we present an attentive recurrent model with multiple projections to capture Multi-Aspect preferences and INTents (MAINT in short). To extract multi-aspect preferences from target behaviors, we propose a multi-aspect projection mechanism for generating multiple preference representations from multiple aspects. To extract multi-aspect intents from multi-typed behaviors, we propose a behavior-enhanced LSTM and a multi-aspect refinement attention mechanism. The attention mechanism can filter out noises and generate multiple intent representations from different aspects. To adaptively fuse user preferences and intents, we propose a multi-aspect gated fusion mechanism. Extensive experiments conducted on real-world datasets have demonstrated the effectiveness of our model

Graphene-wrapped reversible reaction for advanced hydrogen storage

Here, we report the fabrication of a graphene-wrapped nanostructured reactive hydride composite, i.e., 2LiBH4-MgH2, made by adopting graphene-supported MgH2 nanoparticles (NPs) as the nanoreactor and heterogeneous nucleation sites. The porous structure, uniform distribution of MgH2 NPs, and the steric confinement by flexible graphene induced a homogeneous distribution of 2LiBH4-MgH2 nanocomposite on graphene with extremely high loading capacity (80 wt%) and energy density. The well-defined structural features, including even distribution, uniform particle size, excellent thermal stability, and robust architecture endow this composite with significant improvements in its hydrogen storage performance. For instance, at a temperature as low as 350 °C, a reversible storage capacity of up to 8.9 wt% H2, without degradation after 25 complete cycles, was achieved for the 2LiBH4-MgH2 anchored on graphene. The design of this three-dimensional architecture can offer a new concept for obtaining high performance materials in the energy storage field

Na2Ru1−xMnxO3 as the cathode for sodium-ion batteries

Sodium-ion batteries (SIBs) have attracted a surge of attention as a potential alternative for replacing lithium-ion batteries (LIBs). However, the current cathodes of SIBs suffer from problems of limited capacity, capacity decay, inferior cycling performance and structural instability. Na2RuO3 is known for its high capacity including both cationic redox and anionic redox processes. Here, we show a general method for improving the sodium storage performance of Na2RuO3via Mn doping. A series of Na2Ru1−xMnxO3 are explored through X-ray diffraction (XRD), galvanostatic charge–discharge testing, electrochemical impedance spectroscopy (EIS) measurements and so on. The results exhibit that a suitable Mn doping (x = 0.1) enhances the kinetics and structural stability of the electrode, accounting for a superior electrochemical performance. Our findings provide a simple method to develop advanced cathodes for SIBs with a long lifespan and large capacity

Ferroptosis-Related Gene-Based Prognostic Model and Immune Infiltration in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common tumors in the urinary system. Ferroptosis plays a vital role in ccRCC development and progression. We did an update of ferroptosis-related multigene expression signature for individualized prognosis prediction in patients with ccRCC. Differentially expressed ferroptosis-related genes in ccRCC and normal samples were screened using The Cancer Genome Atlas. Univariate and multivariate Cox regression analyses and machine learning methods were employed to identify optimal prognosis-related genes. CARS1, CD44, FANCD2, HMGCR, NCOA4, SLC7A11, and ACACA were selected to establish a prognostic risk score model. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that these genes were mainly enriched in immune-related pathways; single-sample Gene Set Enrichment Analysis revealed several immune cells potentially related to ferroptosis. Kaplan–Meier survival analysis demonstrated that patients with high-risk scores had significantly poor overall survival (log-rank P = 7.815 × 10–11). The ferroptosis signature was identified as an independent prognostic factor. Finally, a prognostic nomogram, including the ferroptosis signature, age, histological grade, and stage status, was constructed. Analysis of The Cancer Genome Atlas-based calibration plots, C-index, and decision curve indicated the excellent predictive performance of the nomogram. The ferroptosis-related seven-gene risk score model is useful as a prognostic biomarker and suggests therapeutic targets for ccRCC. The prognostic nomogram may assist in individualized survival prediction and improve treatment strategies